diff --git "a/20230446_matlab.jsonl" "b/20230446_matlab.jsonl"
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+{"plateform": "github", "repo_name": "CankayaUniversity/ceng-407-408-2017-2018-project-blood-vessel-segmentation-master", "name": "segmentation.m", "ext": ".m", "path": "ceng-407-408-2017-2018-project-blood-vessel-segmentation-master/Project/segmentation.m", "size": 695, "source_encoding": "utf_8", "md5": "8468fbee34ea6573785311ec6c4a67c8", "text": "% Segmentation function.\r\nfunction [ves] = segmentation(path)\r\n% Read image.\r\nim=imread(path);\r\n% Image enhancement & gray scale of a green channel image.\r\nimage = imageEnhancement(im);\r\n% Load network.\r\nload net;\r\n[m,n] = size(image);\r\nves=uint8(zeros(size(image)));\r\n% Classification.\r\nfor i = 1:1:m-(9-1)\r\n    for j = 1:1:n-(9-1)\r\n        patch=image(i:i+(9-1),j:j+(9-1));\r\n        if isBlackSpot(patch)==0\r\n        x=net.classify(patch);\r\n            if x=='Positive' % if positive, binarize for the center pixel.\r\n          \r\n                ves((i+(i+(9-1)))/2,(j+(j+(9-1)))/2)=255;\r\n                \r\n            end\r\n        end\r\n    end\r\nend\r\n\r\n% Final image 'ves' constructed.\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "UGM-Geofisika/Dispersion_Inversion-master", "name": "main.m", "ext": ".m", "path": "Dispersion_Inversion-master/main.m", "size": 35750, "source_encoding": "utf_8", "md5": "d9dada994aca8aadc9c712bc8898076f", "text": "function varargout = main(varargin)\n% MAIN MATLAB code for main.fig\n%      MAIN, by itself, creates a new MAIN or raises the existing\n%      singleton*.\n%\n%      H = MAIN returns the handle to a new MAIN or the handle to\n%      the existing singleton*.\n%\n%      MAIN('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in MAIN.M with the given input arguments.\n%\n%      MAIN('Property','Value',...) creates a new MAIN or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before main_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to main_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n%\n% Author: Pablo Pizarro @ppizarror.com, 2017.\n%\n% This program is free software; you can redistribute it and/or\n% modify it under the terms of the GNU General Public License\n% as published by the Free Software Foundation; either version 2\n% of the License, or (at your option) any later version.\n%\n% This program is distributed in the hope that it will be useful,\n% but WITHOUT ANY WARRANTY; without even the implied warranty of\n% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n% GNU General Public License for more details.\n%\n% You should have received a copy of the GNU General Public License\n% along with this program; if not, write to the Free Software\n% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.\n%\n% Last Modified by GUIDE v2.5 09-Feb-2017 16:09:04\n%\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @main_OpeningFcn, ...\n                   'gui_OutputFcn',  @main_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before main is made visible.\nfunction main_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to main (see VARARGIN)\n\n% Choose default command line output for main\nhandles.output = hObject;\n\n% Add bin, and gui to path from folder (recursive)\nPATH_DEPTH = 3;\n\nfor i=1:PATH_DEPTH\n\n    try\n        path_str = '';\n        if i~=1\n            for j=1:i-1\n                path_str = strcat(path_str, '../');\n            end\n        end\n        path_bin = cd(cd(strcat(path_str, 'bin')));\n        path_gui = cd(cd(strcat(path_str, 'gui')));\n        addpath(path_bin);\n        addpath(path_gui);\n        break\n        \n    catch Exception\n        \n        % Folders could not be found\n        if i==PATH_DEPTH\n            fprintf(getReport(Exception));\n            error('Error while setting software path.');\n        end\n        \n    end\nend\n\n% Center window\nmovegui(gcf, 'center');\n\n% Import configurations\nconfig;\n\n% Load language strings\nlang = load_lang(lang_id);\n\n% Disable Excel warning\nwarning('off', 'MATLAB:xlswrite:AddSheet');\n\n% Set gui-app config\nsetappdata(handles.root, 'lang', lang);\nsetappdata(handles.root, 'gui_sound', gui_sound_enabled);\nsetappdata(handles.root, 'delete_entry_if_invalid', delete_entry_if_invalid);\n\n% Set inversion config\nsetappdata(handles.root, 'cgf_sigma', inv_sigma);\nsetappdata(handles.root, 'cgf_mu', inv_mu);\nsetappdata(handles.root, 'cgf_maxiter', inv_maxiter);\nsetappdata(handles.root, 'cgf_tolvs', inv_tol_vs);\n\n% Set plot configuration - style\nsetappdata(handles.root, 'plt_disp_labl_fontsize', plt_dispersion_label_fontsize);\nsetappdata(handles.root, 'plt_dispersion_style', plt_dispersion_style);\nsetappdata(handles.root, 'sol_plot_disp_fontsize', solution_plt_dispersion_fontsize);\nsetappdata(handles.root, 'sol_plot_disp_style_exp', solution_plt_dispersion_experimental_style);\nsetappdata(handles.root, 'sol_plot_disp_style_sol', solution_plt_dispersion_solution_style);\nsetappdata(handles.root, 'plt_dispersion_showlegend', plt_dispersion_show_legend);\nsetappdata(handles.root, 'plt_dispersion_solution_showlegend', solution_plt_dispersion_show_legend);\nsetappdata(handles.root, 'sol_plot_shear_showlegend', solution_plt_shear_show_legend);\nsetappdata(handles.root, 'sol_plot_shear_fontsize', solution_plt_shear_fontsize);\nsetappdata(handles.root, 'dispersion_iteration_style', dispersion_iteration_style);\nsetappdata(handles.root, 'dispersion_iteration_fontsize', dispersion_iteration_fontsize);\nsetappdata(handles.root, 'dispersion_iteration_color', dispersion_iteration_color);\nsetappdata(handles.root, 'dispersion_iteration_random_color', dispersion_iteration_random_color);\nsetappdata(handles.root, 'dispersion_iteration_show_legend', dispersion_iteration_show_legend);\nsetappdata(handles.root, 'solution_plt_shear_curve_style', solution_plt_shear_curve_style);\nsetappdata(handles.root, 'dispersion_iteration_linewidth', dispersion_iteration_linewidth);\nsetappdata(handles.root, 'plt_dispersion_linewidth', plt_dispersion_linewidth);\nsetappdata(handles.root, 'solution_plt_dispersion_experimental_linewidth', ...\n    solution_plt_dispersion_experimental_linewidth);\nsetappdata(handles.root, 'solution_plt_dispersion_linewidth', ...\n    solution_plt_dispersion_linewidth);\nsetappdata(handles.root, 'solution_plot_shear_linewidth', ...\n    solution_plot_shear_linewidth);\nsetappdata(handles.root, 'solution_shear_comparision_fontsize', ...\n    solution_shear_comparision_fontsize);\nsetappdata(handles.root, 'solution_shear_comparision_shear_curve_linewidth', ...\n    solution_shear_comparision_shear_curve_linewidth);\nsetappdata(handles.root, 'solution_shear_comparision_iguess_curve_linewidth', ...\n    solution_shear_comparision_iguess_curve_linewidth);\nsetappdata(handles.root, 'solution_shear_comparision_shear_curve_style', ...\n    solution_shear_comparision_shear_curve_style);\nsetappdata(handles.root, 'solution_shear_comparision_iguess_curve_style', ...\n    solution_shear_comparision_iguess_curve_style);\nsetappdata(handles.root, 'solution_plt_shear_comparision_legend', ...\n    solution_plt_shear_comparision_legend);\n\n% Set solution configuration\nsetappdata(handles.root, 'show_dispersion_comparision', show_dispersion_comparision);\nsetappdata(handles.root, 'show_shear_velocity_plot', show_shear_velocity_plot);\nsetappdata(handles.root, 'show_dispersion_iterations', show_dispersion_iterations);\nsetappdata(handles.root, 'show_shear_velocity_comparision', show_shear_velocity_comparision);\n\n% Set GUI Strings from lang\nset_gui_lang(handles, lang);\n\n% Set new file\nnew_file(handles, lang, false);\nsetappdata(handles.root, 'last_opened_folder', '');\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes main wait for user response (see UIRESUME)\n% uiwait(handles.root);\n\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = main_OutputFcn(hObject, eventdata, handles)  %#ok<*INUSL>\n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --------------------------------------------------------------------\nfunction menu_file_Callback(hObject, eventdata, handles) %#ok<*DEFNU,*INUSD>\n% hObject    handle to menu_file (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes during object creation, after setting all properties.\nfunction initial_solution_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to initial_solution (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n\n% --------------------------------------------------------------------\nfunction table_menu_Callback(hObject, eventdata, handles)\n% hObject    handle to table_menu (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction table_add_row_Callback(hObject, eventdata, handles)\n% hObject    handle to table_add_row (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nadd_row(handles);\n\n\n% --- Executes when entered data in editable cell(s) in initial_solution.\nfunction initial_solution_CellEditCallback(hObject, eventdata, handles)\n% hObject    handle to initial_solution (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.TABLE)\n%\tIndices: row and column indices of the cell(s) edited\n%\tPreviousData: previous data for the cell(s) edited\n%\tEditData: string(s) entered by the user\n%\tNewData: EditData or its converted form set on the Data property. Empty if Data was not changed\n%\tError: error string when failed to convert EditData to appropriate value for Data\n% handles    structure with handles and user data (see GUIDATA)\nreplace_nan_initbl(handles, getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction delete_last_row_Callback(hObject, eventdata, handles)\n% hObject    handle to delete_last_row (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\ndelete_last_row(handles, getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction table_import_from_excel_Callback(hObject, eventdata, handles)\n% hObject    handle to table_import_from_excel (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nimport_initbl_excel(handles, getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction menu_edition_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_edition (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction menu_edition_cleantable_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_edition_cleantable (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclear_initialtable(handles, getappdata(handles.root, 'lang'), true);\n\n\n% --------------------------------------------------------------------\nfunction menu_file_new_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_file_new (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nnew_file(handles, getappdata(handles.root, 'lang'), true);\n\n\n% --------------------------------------------------------------------\nfunction menu_file_load_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_file_load (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nload_project(handles, getappdata(handles.root, 'lang'));\n\n\n% --- Executes during object creation, after setting all properties.\nfunction initial_solution_table_title_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to initial_solution_table_title (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n\n% --------------------------------------------------------------------\nfunction menu_help_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_help (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction menu_view_help_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_view_help (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nmanual;\n\n\n% --------------------------------------------------------------------\nfunction menu_about_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_about (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nabout(getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction menu_file_save_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_file_save (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nsave_project(handles, getappdata(handles.root, 'lang'), false);\n\n\n% --------------------------------------------------------------------\nfunction menu_file_save_as_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_file_save_as (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nsave_project(handles, getappdata(handles.root, 'lang'), true);\n\n\n% --------------------------------------------------------------------\nfunction menu_file_close_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_file_close (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\ndoclose = close_app(handles, getappdata(handles.root, 'lang'));\nif doclose\n    close all;\nend\n\n\n% --- Executes on button press in btn_opendispersion.\nfunction btn_opendispersion_Callback(hObject, eventdata, handles)\n% hObject    handle to btn_opendispersion (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nload_dispersion_file(handles, getappdata(handles.root, 'lang'));\n\n% --------------------------------------------------------------------\nfunction panel_dispersion_file_ButtonDownFcn(hObject, eventdata, handles)\n% hObject    handle to panel_dispersion_file (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on selection change in unit_h.\nfunction unit_h_Callback(hObject, eventdata, handles)\n% hObject    handle to unit_h (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns unit_h contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from unit_h\n\n\n% --- Executes during object creation, after setting all properties.\nfunction unit_h_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to unit_h (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in unit_vsvp.\nfunction unit_vsvp_Callback(hObject, eventdata, handles)\n% hObject    handle to unit_vsvp (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns unit_vsvp contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from unit_vsvp\n\n\n% --- Executes during object creation, after setting all properties.\nfunction unit_vsvp_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to unit_vsvp (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in unit_vr.\nfunction unit_vr_Callback(hObject, eventdata, handles)\n% hObject    handle to unit_vr (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns unit_vr contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from unit_vr\n\n\n% --- Executes during object creation, after setting all properties.\nfunction unit_vr_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to unit_vr (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in unit_rho.\nfunction unit_rho_Callback(hObject, eventdata, handles)\n% hObject    handle to unit_rho (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns unit_rho contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from unit_rho\n\n\n% --- Executes during object creation, after setting all properties.\nfunction unit_rho_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to unit_rho (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on mouse press over axes background.\nfunction plt_dispersion_file_ButtonDownFcn(hObject, eventdata, handles)\n% hObject    handle to plt_dispersion_file (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction disp_plt_viewlarger_Callback(hObject, eventdata, handles)\n% hObject    handle to disp_plt_viewlarger (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nplot_large_dispcurv(handles, getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction dispersion_curve_menu_Callback(hObject, eventdata, handles)\n% hObject    handle to dispersion_curve_menu (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on button press in start_button.\nfunction start_button_Callback(hObject, eventdata, handles)\n% hObject    handle to start_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nstart_inversion(handles, hObject, getappdata(handles.root, 'lang'));\n\n\nfunction param_inv_sigma_Callback(hObject, eventdata, handles)\n% hObject    handle to param_inv_sigma (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_inv_sigma as text\n%        str2double(get(hObject,'String')) returns contents of param_inv_sigma as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_inv_sigma_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_sigma (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\nfunction param_inv_mu_Callback(hObject, eventdata, handles)\n% hObject    handle to param_inv_mu (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_inv_mu as text\n%        str2double(get(hObject,'String')) returns contents of param_inv_mu as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_inv_mu_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_mu (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\nfunction param_maxiter_Callback(hObject, eventdata, handles)\n% hObject    handle to param_maxiter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_maxiter as text\n%        str2double(get(hObject,'String')) returns contents of param_maxiter as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_maxiter_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_maxiter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\nfunction param_tolvs_Callback(hObject, eventdata, handles)\n% hObject    handle to param_tolvs (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_tolvs as text\n%        str2double(get(hObject,'String')) returns contents of param_tolvs as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_tolvs_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_tolvs (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in view_sol_plot.\nfunction view_sol_plot_Callback(hObject, eventdata, handles)\n% hObject    handle to view_sol_plot (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nshow_plots(handles, getappdata(handles.root, 'lang'));\n\n\n% --- Executes on button press in export_results.\nfunction export_results_Callback(hObject, eventdata, handles)\n% hObject    handle to export_results (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nexport_results(handles, hObject, getappdata(handles.root, 'lang'));\n\n\n% --- Executes on key press with focus on param_inv_sigma and none of its controls.\nfunction param_inv_sigma_KeyPressFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_sigma (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.UICONTROL)\n%\tKey: name of the key that was pressed, in lower case\n%\tCharacter: character interpretation of the key(s) that was pressed\n%\tModifier: name(s) of the modifier key(s) (i.e., control, shift) pressed\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- If Enable == 'on', executes on mouse press in 5 pixel border.\n% --- Otherwise, executes on mouse press in 5 pixel border or over param_inv_sigma.\nfunction param_inv_sigma_ButtonDownFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_sigma (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% check_inv_parameters(handles, getappdata(handles.root, 'lang'), true);\n\n\n% --- If Enable == 'on', executes on mouse press in 5 pixel border.\n% --- Otherwise, executes on mouse press in 5 pixel border or over param_inv_mu.\nfunction param_inv_mu_ButtonDownFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_mu (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on key press with focus on param_inv_mu and none of its controls.\nfunction param_inv_mu_KeyPressFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_mu (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.UICONTROL)\n%\tKey: name of the key that was pressed, in lower case\n%\tCharacter: character interpretation of the key(s) that was pressed\n%\tModifier: name(s) of the modifier key(s) (i.e., control, shift) pressed\n% handles    structure with handles and user data (see GUIDATA)\n% check_inv_parameters(handles, getappdata(handles.root, 'lang'), true);\n\n\n% --- If Enable == 'on', executes on mouse press in 5 pixel border.\n% --- Otherwise, executes on mouse press in 5 pixel border or over param_maxiter.\nfunction param_maxiter_ButtonDownFcn(hObject, eventdata, handles)\n% hObject    handle to param_maxiter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on key press with focus on param_maxiter and none of its controls.\nfunction param_maxiter_KeyPressFcn(hObject, eventdata, handles)\n% hObject    handle to param_maxiter (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.UICONTROL)\n%\tKey: name of the key that was pressed, in lower case\n%\tCharacter: character interpretation of the key(s) that was pressed\n%\tModifier: name(s) of the modifier key(s) (i.e., control, shift) pressed\n% handles    structure with handles and user data (see GUIDATA)\n% check_inv_parameters(handles, getappdata(handles.root, 'lang'), true);\n\n\n% --- If Enable == 'on', executes on mouse press in 5 pixel border.\n% --- Otherwise, executes on mouse press in 5 pixel border or over param_tolvs.\nfunction param_tolvs_ButtonDownFcn(hObject, eventdata, handles)\n% hObject    handle to param_tolvs (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on key press with focus on param_tolvs and none of its controls.\nfunction param_tolvs_KeyPressFcn(hObject, eventdata, handles)\n% hObject    handle to param_tolvs (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.UICONTROL)\n%\tKey: name of the key that was pressed, in lower case\n%\tCharacter: character interpretation of the key(s) that was pressed\n%\tModifier: name(s) of the modifier key(s) (i.e., control, shift) pressed\n% handles    structure with handles and user data (see GUIDATA)\n% check_inv_parameters(handles, getappdata(handles.root, 'lang'), true);\n\n\n% --- If Enable == 'on', executes on mouse press in 5 pixel border.\n% --- Otherwise, executes on mouse press in 5 pixel border or over btn_opendispersion.\nfunction btn_opendispersion_ButtonDownFcn(hObject, eventdata, handles)\n% hObject    handle to btn_opendispersion (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on key press with focus on btn_opendispersion and none of its controls.\nfunction btn_opendispersion_KeyPressFcn(hObject, eventdata, handles)\n% hObject    handle to btn_opendispersion (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.UICONTROL)\n%\tKey: name of the key that was pressed, in lower case\n%\tCharacter: character interpretation of the key(s) that was pressed\n%\tModifier: name(s) of the modifier key(s) (i.e., control, shift) pressed\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes when selected cell(s) is changed in initial_solution.\nfunction initial_solution_CellSelectionCallback(hObject, eventdata, handles)\n% hObject    handle to initial_solution (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.TABLE)\n%\tIndices: row and column indices of the cell(s) currently selecteds\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction menu_import_table_from_excel_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_import_table_from_excel (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nimport_initbl_excel(handles, getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction menu_add_row_table_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_add_row_table (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nadd_row(handles);\n\n\n% --------------------------------------------------------------------\nfunction menu_delete_row_table_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_delete_row_table (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\ndelete_last_row(handles, getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction menu_edit_import_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_edit_import (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction menu_import_dispersion_file_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_import_dispersion_file (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nload_dispersion_file(handles, getappdata(handles.root, 'lang'));\n\n\n% --------------------------------------------------------------------\nfunction menu_clean_initial_invparam_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_clean_initial_invparam (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclear_invparam(handles);\n\n\n% --------------------------------------------------------------------\nfunction menu_preferences_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_preferences (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction menu_cfg_app_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_cfg_app (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get lang variable\nlang = getappdata(handles.root, 'lang');\n\n% Check if gui dir exist, if not a message error is displayed\nif exist('gui', 'dir')\n    cfg_app(lang, 'main', handles);\nelse\n    disp_error(handles, lang, 128);\nend\n\n\n% --------------------------------------------------------------------\nfunction menu_cfg_plot_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_cfg_plot (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --------------------------------------------------------------------\nfunction menu_cfg_inversion_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_cfg_inversion (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get lang variable\nlang = getappdata(handles.root, 'lang');\n\n% Check if gui dir exist, if not a message error is displayed\nif exist('gui', 'dir')\n    cfg_inv(lang, 'main', handles.root);\nelse\n    disp_error(handles, lang, 128);\nend\n\n\n% --------------------------------------------------------------------\nfunction menu_cfg_solution_Callback(hObject, eventdata, handles)\n% hObject    handle to menu_cfg_solution (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get lang variable\nlang = getappdata(handles.root, 'lang');\n\n% Check if gui dir exist, if not a message error is displayed\nif exist('gui', 'dir')\n    cfg_sol(lang, 'main', handles.root);\nelse\n    disp_error(handles, lang, 128);\nend\n\n\n% --------------------------------------------------------------------\nfunction dispersion_plt_viewtable_Callback(hObject, eventdata, handles)\n% hObject    handle to dispersion_plt_viewtable (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get lang variable\nlang = getappdata(handles.root, 'lang');\n\nif getappdata(handles.root, 'dispersion_ok')\n    view_disp_table(lang, 'main', handles.root);\nend\n"}
+{"plateform": "github", "repo_name": "UGM-Geofisika/Dispersion_Inversion-master", "name": "cfg_sol.m", "ext": ".m", "path": "Dispersion_Inversion-master/gui/cfg_sol.m", "size": 9758, "source_encoding": "utf_8", "md5": "c33866390ebc4a79a24b23945faa1e47", "text": "function varargout = cfg_sol(varargin)\n% ROOT MATLAB code for root.fig\n%      ROOT, by itself, creates a new ROOT or raises the existing\n%      singleton*.\n%\n%      H = ROOT returns the handle to a new ROOT or the handle to\n%      the existing singleton*.\n%\n%      ROOT('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in ROOT.M with the given input arguments.\n%\n%      ROOT('Property','Value',...) creates a new ROOT or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before cfg_sol_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to cfg_sol_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help root\n\n% Last Modified by GUIDE v2.5 27-Jan-2017 20:27:57\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @cfg_sol_OpeningFcn, ...\n                   'gui_OutputFcn',  @cfg_sol_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before root is made visible.\nfunction cfg_sol_OpeningFcn(hObject, eventdata, handles, varargin) %#ok<*INUSL>\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to root (see VARARGIN)\n\n% Check if application is opened from main\nif ~ (length(varargin)==3 && strcmp(varargin{2}, 'main'))\n    close;\nend\n\n% Set main variables\nlang = varargin{1}; %#ok<*NASGU>\nsetappdata(handles.root, 'lang', lang);\nsetappdata(handles.root, 'main_handles', varargin{3});\n\n% Set app strings\nset(handles.root, 'Name', lang{129});\nset_lang_string(handles.btn_save, lang{20}, 'string');\nset_lang_string(handles.btn_close, lang{147}, 'string');\nset_lang_string(handles.text_comparision, lang{132}, 'string');\nset_lang_string(handles.text_shear, lang{133}, 'string');\nset_lang_string(handles.text_iteration, lang{134}, 'string');\nset_lang_string(handles.text_shear_comparision, lang{140}, 'string');\n\n% Import config\nconfig_solution;\nconfig_app;\n\n% Set config values\nif show_dispersion_comparision\n    set(handles.cfg_comparision, 'Value', 1.0);\nend\nif show_shear_velocity_plot\n    set(handles.cfg_shear, 'Value', 1.0);\nend\nif show_dispersion_iterations\n    set(handles.cfg_iteration, 'Value', 1.0);\nend\nif show_shear_velocity_comparision\n    set(handles.cfg_comparision_shear, 'Value', 1.0);\nend\n\n% Save configs\nsetappdata(handles.root, 'gui_sound', gui_sound_enabled);\n\n% Center window\nmovegui(gcf, 'center');\n\n% Choose default command line output for root\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes root wait for user response (see UIRESUME)\n% uiwait(handles.root);\n\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = cfg_sol_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on selection change in conf_lang.\nfunction conf_lang_Callback(hObject, eventdata, handles)\n% hObject    handle to conf_lang (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns conf_lang contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from conf_lang\n\n\n% --- Executes during object creation, after setting all properties.\nfunction conf_lang_CreateFcn(hObject, eventdata, handles) %#ok<*INUSD,*DEFNU>\n% hObject    handle to conf_lang (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in btn_save.\nfunction btn_save_Callback(hObject, eventdata, handles)\n% hObject    handle to btn_save (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get lang\nlang = getappdata(handles.root, 'lang');\n\n% Get original handles\nroot_handles = getappdata(handles.root, 'main_handles');\n\n% Import config\nconfig_solution;\n\n% Get configs\ncomparision = get(handles.cfg_comparision, 'Value');\nshear = get(handles.cfg_shear, 'Value');\niteration = get(handles.cfg_iteration, 'Value');\nshear_comparision = get(handles.cfg_comparision_shear, 'Value');\n\n% Check if something changed\nif (comparision == show_dispersion_comparision) && (shear == show_shear_velocity_plot) && ...\n        (show_dispersion_iterations == iteration) && (shear_comparision == show_shear_velocity_comparision)\nelse\n    \n    % Set config strings\n    if comparision\n        str_comparision = 'show_dispersion_comparision = true;';\n    else\n        str_comparision = 'show_dispersion_comparision = false;';\n    end\n    if shear\n        str_shear = 'show_shear_velocity_plot = true;';\n    else\n        str_shear = 'show_shear_velocity_plot = false;';\n    end\n    if iteration\n        str_iteration = 'show_dispersion_iterations = true;';\n    else\n        str_iteration = 'show_dispersion_iterations = false;';\n    end\n    if shear_comparision\n        str_shear_comparision = 'show_shear_velocity_comparision = true;';\n    else\n        str_shear_comparision = 'show_shear_velocity_comparision = false;';\n    end\n\n    % Save config file\n    conf_file = fopen('gui/config_solution.m', 'wt');\n    write_conf_header(conf_file, ' SOLUTION CONFIGURATION', ' Configures solution behaviour.');\n    fprintf(conf_file, '%s\\n', '% Show Calculated vs Experimental dispersion curve');\n    fprintf(conf_file, '%s\\n\\n', str_comparision);\n    fprintf(conf_file, '%s\\n', '% Show Shear velocity on depth plot');\n    fprintf(conf_file, '%s\\n\\n', str_shear);\n    fprintf(conf_file, '%s\\n', '% Show Calculated dispersion - iteration changes');\n    fprintf(conf_file, '%s\\n\\n', str_iteration);\n    fprintf(conf_file, '%s\\n', '% Show shear velocity comparision');\n    fprintf(conf_file, '%s\\n\\n', str_shear_comparision);\n    fclose(conf_file);\n   \n    % Set changes\n    setappdata(root_handles, 'show_dispersion_comparision', comparision);\n    setappdata(root_handles, 'show_shear_velocity_plot', shear);\n    setappdata(root_handles, 'show_dispersion_iterations', iteration);\n    setappdata(root_handles, 'show_shear_velocity_comparision', shear_comparision);\n    \nend\nclose;\n\n\n% --- Executes on button press in btn_close.\nfunction btn_close_Callback(hObject, eventdata, handles)\n% hObject    handle to btn_close (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose;\n\n\n% --- Executes on button press in cfg_shear.\nfunction cfg_shear_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_shear (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_shear\n\n\n% --- Executes on button press in cfg_delete_inv_entry.\nfunction cfg_delete_inv_entry_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_delete_inv_entry (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_delete_inv_entry\n\n\n% --- Executes on button press in cfg_comparision.\nfunction cfg_comparision_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_comparision (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_comparision\n\n\n% --- Executes on button press in cfg_iteration.\nfunction cfg_iteration_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_iteration (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_iteration\n\n\n% --- Executes on button press in cfg_comparision_shear.\nfunction cfg_comparision_shear_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_comparision_shear (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_comparision_shear\n\n"}
+{"plateform": "github", "repo_name": "UGM-Geofisika/Dispersion_Inversion-master", "name": "view_disp_table.m", "ext": ".m", "path": "Dispersion_Inversion-master/gui/view_disp_table.m", "size": 3934, "source_encoding": "utf_8", "md5": "d795f65e03e1977c37b975f14f367207", "text": "function varargout = view_disp_table(varargin)\n% VIEW_DISPERSION_TABLE MATLAB code for view_disp_table.fig\n%      VIEW_DISPERSION_TABLE, by itself, creates a new VIEW_DISPERSION_TABLE or raises the existing\n%      singleton*.\n%\n%      H = VIEW_DISPERSION_TABLE returns the handle to a new VIEW_DISPERSION_TABLE or the handle to\n%      the existing singleton*.\n%\n%      VIEW_DISPERSION_TABLE('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in VIEW_DISPERSION_TABLE.M with the given input arguments.\n%\n%      VIEW_DISPERSION_TABLE('Property','Value',...) creates a new VIEW_DISPERSION_TABLE or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before view_dispersion_table_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to view_dispersion_table_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help view_dispersion_table\n\n% Last Modified by GUIDE v2.5 09-Feb-2017 16:24:00\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name', mfilename, ...\n    'gui_Singleton', gui_Singleton, ...\n    'gui_OpeningFcn', @view_dispersion_table_OpeningFcn, ...\n    'gui_OutputFcn', @view_dispersion_table_OutputFcn, ...\n    'gui_LayoutFcn', [], ...\n    'gui_Callback', []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before view_dispersion_table is made visible.\nfunction view_dispersion_table_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to view_dispersion_table (see VARARGIN)\n\n% Choose default command line output for view_dispersion_table\nhandles.output = hObject;\n\n% Check if application is opened from main\nif ~(length(varargin) == 3 && strcmp(varargin{2}, 'main'))\n    close;\nend\n\n% Set main variables\nlang = varargin{1}; %#ok<*NASGU>\nsetappdata(handles.root, 'lang', lang);\nsetappdata(handles.root, 'main_handles', varargin{3});\n\n% Set app lang\nset(handles.root, 'Name', lang{146});\n\n% Set table\nfreq = getappdata(varargin{3}, 'disp_freq');\nvrexp = getappdata(varargin{3}, 'disp_vrexp');\n[nCols, ~] = size(vrexp);\n\n% If data exists\nif nCols ~= 0\n    % Create new cell structure\n    tabl = cell(nCols, 2);\n    tabl_nom = cell(nCols, 1);\n    \n    % Copy data to new cell structures\n    for i = 1:nCols\n        tabl{i, 1} = freq(i);\n        tabl{i, 2} = vrexp(i);\n        tabl_nom{i} = strcat('f', num2str(i));\n    end\n    \n    % Store data to table object\n    set(handles.table, 'Data', tabl);\n    set(handles.table, 'RowName', tabl_nom);\nelse\n    close;\nend\n\n% Center window\nmovegui(gcf, 'center');\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes view_dispersion_table wait for user response (see UIRESUME)\n% uiwait(handles.root);\n\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = view_dispersion_table_OutputFcn(hObject, eventdata, handles) %#ok<*INUSL>\n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n"}
+{"plateform": "github", "repo_name": "UGM-Geofisika/Dispersion_Inversion-master", "name": "manual.m", "ext": ".m", "path": "Dispersion_Inversion-master/gui/manual.m", "size": 2812, "source_encoding": "utf_8", "md5": "547b164df037a6ccb4ebc0699cc92fd3", "text": "function varargout = manual(varargin)\n% MANUAL MATLAB code for manual.fig\n%      MANUAL, by itself, creates a new MANUAL or raises the existing\n%      singleton*.\n%\n%      H = MANUAL returns the handle to a new MANUAL or the handle to\n%      the existing singleton*.\n%\n%      MANUAL('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in MANUAL.M with the given input arguments.\n%\n%      MANUAL('Property','Value',...) creates a new MANUAL or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before manual_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to manual_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help manual\n\n% Last Modified by GUIDE v2.5 26-Jan-2017 09:57:58\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @manual_OpeningFcn, ...\n                   'gui_OutputFcn',  @manual_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before manual is made visible.\nfunction manual_OpeningFcn(hObject, eventdata, handles, varargin) %#ok<*INUSL>\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to manual (see VARARGIN)\n\n% Choose default command line output for manual\nhandles.output = hObject;\n\n% Center window\nmovegui(gcf, 'center');\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes manual wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\n\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = manual_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n"}
+{"plateform": "github", "repo_name": "UGM-Geofisika/Dispersion_Inversion-master", "name": "cfg_inv.m", "ext": ".m", "path": "Dispersion_Inversion-master/gui/cfg_inv.m", "size": 12746, "source_encoding": "utf_8", "md5": "5d47aac8bf783746377d2552555b7bec", "text": "function varargout = cfg_inv(varargin)\n% ROOT MATLAB code for root.fig\n%      ROOT, by itself, creates a new ROOT or raises the existing\n%      singleton*.\n%\n%      H = ROOT returns the handle to a new ROOT or the handle to\n%      the existing singleton*.\n%\n%      ROOT('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in ROOT.M with the given input arguments.\n%\n%      ROOT('Property','Value',...) creates a new ROOT or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before cfg_inv_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to cfg_inv_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help root\n\n% Last Modified by GUIDE v2.5 27-Jan-2017 19:50:35\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @cfg_inv_OpeningFcn, ...\n                   'gui_OutputFcn',  @cfg_inv_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before root is made visible.\nfunction cfg_inv_OpeningFcn(hObject, eventdata, handles, varargin) %#ok<*INUSL>\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to root (see VARARGIN)\n\n% Check if application is opened from main\nif ~ (length(varargin)==3 && strcmp(varargin{2}, 'main'))\n    close;\nend\n\n% Set main variables\nlang = varargin{1}; %#ok<*NASGU>\nsetappdata(handles.root, 'lang', lang);\nsetappdata(handles.root, 'main_handles', varargin{3});\n\n% Set app strings\nset(handles.root, 'Name', lang{130});\nset(handles.panel_initialparams, 'Title', lang{135});\nset_lang_string(handles.btn_save, lang{20}, 'string');\nset_lang_string(handles.btn_close, lang{147}, 'string');\nset(handles.text_mu, 'String', lang{136});\nset(handles.text_sigma, 'String', lang{137});\nset(handles.text_maxiter, 'String', lang{138});\nset(handles.text_tolvs, 'String', lang{139});\n\n% Import config\nconfig_app;\nconfig_inverse;\n\n% Save configs\nsetappdata(handles.root, 'delete_entry_if_invalid', delete_entry_if_invalid);\nsetappdata(handles.root, 'gui_sound', gui_sound_enabled);\n\n% Set initial configuration\nset(handles.param_inv_mu, 'String', inv_mu);\nset(handles.param_inv_sigma, 'String', inv_sigma);\nset(handles.param_maxiter, 'String', inv_maxiter);\nset(handles.param_tolvs, 'String', inv_tol_vs);\n\n% Center window\nmovegui(gcf, 'center');\n\n% Choose default command line output for root\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes root wait for user response (see UIRESUME)\n% uiwait(handles.root);\n\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = cfg_inv_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on selection change in conf_lang.\nfunction conf_lang_Callback(hObject, eventdata, handles)\n% hObject    handle to conf_lang (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns conf_lang contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from conf_lang\n\n\n% --- Executes during object creation, after setting all properties.\nfunction conf_lang_CreateFcn(hObject, eventdata, handles) %#ok<*INUSD,*DEFNU>\n% hObject    handle to conf_lang (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in btn_save.\nfunction btn_save_Callback(hObject, eventdata, handles)\n% hObject    handle to btn_save (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get lang\nlang = getappdata(handles.root, 'lang');\n\n% Get original handles\nroot_handles = getappdata(handles.root, 'main_handles');\n\n% Import config\nconfig_inverse;\n\n% Check entry status\nstatus = check_inv_parameters(handles, lang, true);\nif status\n    sigma = get(handles.param_inv_sigma, 'string');\n    mu = get(handles.param_inv_mu, 'string');\n    maxiter = get(handles.param_maxiter, 'string');\n    tol_vs = get(handles.param_tolvs, 'string');\nelse\n    return\nend\n\n% Check if something changed\nif (str2double(sigma) == inv_sigma) && (str2double(maxiter) == inv_maxiter) && ...\n        (str2double(mu) == inv_mu) && (inv_tol_vs == str2double(tol_vs))\nelse\n    \n    % Create strings\n    str_maxiter = sprintf('inv_maxiter = %s;', maxiter);\n    str_mu = sprintf('inv_mu = %s;', mu);\n    str_sigma = sprintf('inv_sigma = %s;', sigma);\n    str_tolvs = sprintf('inv_tol_vs = %s;', tol_vs);\n\n    % Save config file\n    conf_file = fopen('gui/config_inverse.m', 'wt');\n    write_conf_header(conf_file, ' INVERSE MATLAB CONFIGURATION', ' Set configurations used by mat_inverse libraries.');\n    fprintf(conf_file, '%s\\n', '% Maximum number of iterations, used by mat_inverse');\n    fprintf(conf_file, '%s\\n\\n', str_maxiter);\n    fprintf(conf_file, '%s\\n', '% Mu coefficient, mat_inverse');\n    fprintf(conf_file, '%s\\n\\n', str_mu);\n    fprintf(conf_file, '%s\\n', '% Vs tolerance error, mat_inverse');\n    fprintf(conf_file, '%s\\n\\n', str_tolvs);\n    fprintf(conf_file, '%s\\n', '% Sigma, mat_inverse');\n    fprintf(conf_file, '%s\\n\\n', str_sigma);\n    fclose(conf_file);\n    \n    % Set inversion config\n    sigma = str2double(sigma);\n    mu = str2double(mu);\n    maxiter = str2double(maxiter);\n    tol_vs = str2double(tol_vs);\n    setappdata(root_handles, 'cgf_sigma', sigma);\n    setappdata(root_handles, 'cgf_mu', mu);\n    setappdata(root_handles, 'cgf_maxiter', maxiter);\n    setappdata(root_handles, 'cgf_tolvs', tol_vs);\n    \nend\nclose;\n\n\n% --- Executes on button press in btn_close.\nfunction btn_close_Callback(hObject, eventdata, handles)\n% hObject    handle to btn_close (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose;\n\n\n% --- Executes on button press in cfg_shear.\nfunction cfg_shear_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_shear (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_shear\n\n\n% --- Executes on button press in cfg_delete_inv_entry.\nfunction cfg_delete_inv_entry_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_delete_inv_entry (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_delete_inv_entry\n\n\n% --- Executes on button press in cfg_comparision.\nfunction cfg_comparision_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_comparision (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_comparision\n\n\n% --- Executes on button press in cfg_iteration.\nfunction cfg_iteration_Callback(hObject, eventdata, handles)\n% hObject    handle to cfg_iteration (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of cfg_iteration\n\n\nfunction param_inv_mu_Callback(hObject, eventdata, handles)\n% hObject    handle to param_inv_mu (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_inv_mu as text\n%        str2double(get(hObject,'String')) returns contents of param_inv_mu as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_inv_mu_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_mu (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\nfunction param_inv_sigma_Callback(hObject, eventdata, handles)\n% hObject    handle to param_inv_sigma (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_inv_sigma as text\n%        str2double(get(hObject,'String')) returns contents of param_inv_sigma as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_inv_sigma_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_inv_sigma (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\nfunction param_maxiter_Callback(hObject, eventdata, handles)\n% hObject    handle to param_maxiter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_maxiter as text\n%        str2double(get(hObject,'String')) returns contents of param_maxiter as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_maxiter_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_maxiter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\nfunction param_tolvs_Callback(hObject, eventdata, handles)\n% hObject    handle to param_tolvs (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of param_tolvs as text\n%        str2double(get(hObject,'String')) returns contents of param_tolvs as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction param_tolvs_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to param_tolvs (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n"}
+{"plateform": "github", "repo_name": "UGM-Geofisika/Dispersion_Inversion-master", "name": "mat_inverse.m", "ext": ".m", "path": "Dispersion_Inversion-master/bin/mat_inverse.m", "size": 2755, "source_encoding": "utf_8", "md5": "ecba5e9d7c128ae9a9c8c0df5527cdcd", "text": "function [niter, vr_iter, vp_iter, vs_iter, dns_iter] = mat_inverse(freq, vr_exp, ...\n    sigma, thk, vp, vs, dns, maxiter, mu, tol_vs, gui, object, msg)\n% input:\n%   1. dispersion curve\n%       freq, vr_exp, sigma\n%   2. initial model\n%       thk, vp, vs, dns\n%   3. parameters control the inversion\n%       maxiter, mu, tol_vs (change in vs)\n%   4. GUI parameters\n%       gui (true/false), object, label, msg (message to show, as %d/%d)\n% Modification history:\n% 01/19/2017: Pablo Pizarro (UChile)\n%               (1) Deleted files related to Love wave\n% 01/20/2017: Pablo Pizarro (UChile)\n%               (1) Add GUI support\n%               (2) Iteration number is displayed on GUI\n%               (3) Some statuses are displayed on GUI\n\n% Number of layers\nnl = length(thk);\n\n% Weight matrix\nw = diag(1./sigma);\n\n% Second derivative\ndelta = curv(nl, nl+1);\nL = delta;\nrms = zeros(maxiter, 1);\n\n% Initialize intial guess\nm0 = vs;\nvp0 = vp;\nvs0 = vs;\ndns0 = dns;\n\n% Initialize interation variables\nvp_iter = zeros(nl+1, maxiter);\nvs_iter = zeros(nl+1, maxiter);\ndns_iter = zeros(nl+1, maxiter);\nvr_iter = zeros(length(freq), maxiter);\n\n% Check if gui parameters is defined\nif ~exist('gui', 'var')\n    gui = false;\nend\n\nfor i = 1:maxiter\n    \n    % If GUI\n    if gui\n        pause(0.01);\n        set(object, 'string', sprintf(msg, i, maxiter));\n    end\n    \n    % Calculate theoretical phase velocity and partial derivatives\n    % warning: presently the code only handle 1 type of dispersion!\n    [vr, dvrvs, ~] = mat_disperse(thk, dns0, vp0, vs0, freq);\n    jac = real(squeeze(dvrvs)); % jac = [real(squeeze(dvrvs)) real(squeeze(dvrrho))];\n    \n    % Calculate the rms error\n    error = w * (vr - vr_exp);\n    rms(i) = sqrt(mean(error .^ 2));\n    \n    % Least square inversion\n    wjac = w * jac;\n    \n    b = w * (vr_exp - vr + jac * m0);\n    m1 = (wjac' * wjac + mu ^ 2 * (L' * L)) \\ (wjac' * b);\n    \n    % Evaluate new model\n    vs1 = m1(1:nl+1);\n    vp1 = vp;\n    dns1 = dns; % dns1 = m1(nl+2:nl+2+nl);\n    vr = mat_disperse(thk, dns1, vp1, vs1, freq);\n    error1 = w * (vr - vr_exp);\n    rms1 = sqrt(mean(error1 .^ 2));\n    \n    % Store the models\n    vp_iter(:, i) = vp1;\n    vs_iter(:, i) = vs1;\n    dns_iter(:, i) = dns1;\n    vr_iter(:, i) = vr(:);\n    \n    % Check for convergence, only check vs ?\n    diff_vs = vs1 - vs0;\n    rms_vs_change = sqrt(mean(diff_vs .^ 2));\n    \n    if rms_vs_change < tol_vs || rms1 <= 1\n        break\n    end\n    \n    m0 = m1;\n    dns0 = dns1;\n    vp0 = vp1;\n    vs0 = vs1;\nend\nniter = i;\nend\n\n% Curvature matrix for regularization\nfunction delta = curv(m, ~)\ndelta = diag(ones(1, m), 0) + diag(-2*ones(1, m-1), 1) + diag(ones(1, m-2), 2);\ntmp = zeros(m, 1);\ntmp(m) = - 1;\ntmp(m - 1) = 1;\ndelta = [delta, tmp];\nend"}
+{"plateform": "github", "repo_name": "foss-for-synopsys-dwc-arc-processors/synopsys-caffe-main", "name": "classification_demo.m", "ext": ".m", "path": "synopsys-caffe-main/matlab/demo/classification_demo.m", "size": 5466, "source_encoding": "utf_8", "md5": "45745fb7cfe37ef723c307dfa06f1b97", "text": "function [scores, maxlabel] = classification_demo(im, use_gpu)\n% [scores, maxlabel] = classification_demo(im, use_gpu)\n%\n% Image classification demo using BVLC CaffeNet.\n%\n% IMPORTANT: before you run this demo, you should download BVLC CaffeNet\n% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)\n%\n% ****************************************************************************\n% For detailed documentation and usage on Caffe's Matlab interface, please\n% refer to the Caffe Interface Tutorial at\n% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab\n% ****************************************************************************\n%\n% input\n%   im       color image as uint8 HxWx3\n%   use_gpu  1 to use the GPU, 0 to use the CPU\n%\n% output\n%   scores   1000-dimensional ILSVRC score vector\n%   maxlabel the label of the highest score\n%\n% You may need to do the following before you start matlab:\n%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64\n%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6\n% Or the equivalent based on where things are installed on your system\n% and what versions are installed.\n%\n% Usage:\n%  im = imread('../../examples/images/cat.jpg');\n%  scores = classification_demo(im, 1);\n%  [score, class] = max(scores);\n% Five things to be aware of:\n%   caffe uses row-major order\n%   matlab uses column-major order\n%   caffe uses BGR color channel order\n%   matlab uses RGB color channel order\n%   images need to have the data mean subtracted\n\n% Data coming in from matlab needs to be in the order\n%   [width, height, channels, images]\n% where width is the fastest dimension.\n% Here is the rough matlab code for putting image data into the correct\n% format in W x H x C with BGR channels:\n%   % permute channels from RGB to BGR\n%   im_data = im(:, :, [3, 2, 1]);\n%   % flip width and height to make width the fastest dimension\n%   im_data = permute(im_data, [2, 1, 3]);\n%   % convert from uint8 to single\n%   im_data = single(im_data);\n%   % reshape to a fixed size (e.g., 227x227).\n%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');\n%   % subtract mean_data (already in W x H x C with BGR channels)\n%   im_data = im_data - mean_data;\n\n% If you have multiple images, cat them with cat(4, ...)\n\n% Add caffe/matlab to your Matlab search PATH in order to use matcaffe\nif exist('../+caffe', 'dir')\n  addpath('..');\nelse\n  error('Please run this demo from caffe/matlab/demo');\nend\n\n% Set caffe mode\nif exist('use_gpu', 'var') && use_gpu\n  caffe.set_mode_gpu();\n  gpu_id = 0;  % we will use the first gpu in this demo\n  caffe.set_device(gpu_id);\nelse\n  caffe.set_mode_cpu();\nend\n\n% Initialize the network using BVLC CaffeNet for image classification\n% Weights (parameter) file needs to be downloaded from Model Zoo.\nmodel_dir = '../../models/bvlc_reference_caffenet/';\nnet_model = [model_dir 'deploy.prototxt'];\nnet_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];\nphase = 'test'; % run with phase test (so that dropout isn't applied)\nif ~exist(net_weights, 'file')\n  error('Please download CaffeNet from Model Zoo before you run this demo');\nend\n\n% Initialize a network\nnet = caffe.Net(net_model, net_weights, phase);\n\nif nargin < 1\n  % For demo purposes we will use the cat image\n  fprintf('using caffe/examples/images/cat.jpg as input image\\n');\n  im = imread('../../examples/images/cat.jpg');\nend\n\n% prepare oversampled input\n% input_data is Height x Width x Channel x Num\ntic;\ninput_data = {prepare_image(im)};\ntoc;\n\n% do forward pass to get scores\n% scores are now Channels x Num, where Channels == 1000\ntic;\n% The net forward function. It takes in a cell array of N-D arrays\n% (where N == 4 here) containing data of input blob(s) and outputs a cell\n% array containing data from output blob(s)\nscores = net.forward(input_data);\ntoc;\n\nscores = scores{1};\nscores = mean(scores, 2);  % take average scores over 10 crops\n\n[~, maxlabel] = max(scores);\n\n% call caffe.reset_all() to reset caffe\ncaffe.reset_all();\n\n% ------------------------------------------------------------------------\nfunction crops_data = prepare_image(im)\n% ------------------------------------------------------------------------\n% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that\n% is already in W x H x C with BGR channels\nd = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');\nmean_data = d.mean_data;\nIMAGE_DIM = 256;\nCROPPED_DIM = 227;\n\n% Convert an image returned by Matlab's imread to im_data in caffe's data\n% format: W x H x C with BGR channels\nim_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR\nim_data = permute(im_data, [2, 1, 3]);  % flip width and height\nim_data = single(im_data);  % convert from uint8 to single\nim_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data\nim_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)\n\n% oversample (4 corners, center, and their x-axis flips)\ncrops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');\nindices = [0 IMAGE_DIM-CROPPED_DIM] + 1;\nn = 1;\nfor i = indices\n  for j = indices\n    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);\n    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);\n    n = n + 1;\n  end\nend\ncenter = floor(indices(2) / 2) + 1;\ncrops_data(:,:,:,5) = ...\n  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);\ncrops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);\n"}
+{"plateform": "github", "repo_name": "foss-for-synopsys-dwc-arc-processors/synopsys-caffe-main", "name": "MyVOCevalseg.m", "ext": ".m", "path": "synopsys-caffe-main/matlab/my_script/MyVOCevalseg.m", "size": 4471, "source_encoding": "utf_8", "md5": "f0b406d4e609f1cc3d3694948aeceb67", "text": "%VOCEVALSEG Evaluates a set of segmentation results.\n% VOCEVALSEG(VOCopts,ID); prints out the per class and overall\n% segmentation accuracies. Accuracies are given using the intersection/union \n% metric:\n%   true positives / (true positives + false positives + false negatives) \n%\n% [ACCURACIES,AVACC,CONF] = VOCEVALSEG(VOCopts,ID) returns the per class\n% percentage ACCURACIES, the average accuracy AVACC and the confusion\n% matrix CONF.\n%\n% [ACCURACIES,AVACC,CONF,RAWCOUNTS] = VOCEVALSEG(VOCopts,ID) also returns\n% the unnormalised confusion matrix, which contains raw pixel counts.\nfunction [accuracies,avacc,conf,rawcounts] = MyVOCevalseg(VOCopts,id)\n\n% image test set\n[gtids,t]=textread(sprintf(VOCopts.seg.imgsetpath,VOCopts.testset),'%s %d');\n\n% number of labels = number of classes plus one for the background\nnum = VOCopts.nclasses+1; \nconfcounts = zeros(num);\ncount=0;\n\nnum_missing_img = 0;\n\ntic;\nfor i=1:length(gtids)\n    % display progress\n    if toc>1\n        fprintf('test confusion: %d/%d\\n',i,length(gtids));\n        drawnow;\n        tic;\n    end\n        \n    imname = gtids{i};\n    \n    % ground truth label file\n    gtfile = sprintf(VOCopts.seg.clsimgpath,imname);\n    [gtim,map] = imread(gtfile);    \n    gtim = double(gtim);\n    \n    % results file\n    resfile = sprintf(VOCopts.seg.clsrespath,id,VOCopts.testset,imname);\n    try\n      [resim,map] = imread(resfile);\n    catch err\n      num_missing_img = num_missing_img + 1;\n      %fprintf(1, 'Fail to read %s\\n', resfile);\n      continue;\n    end\n\n    resim = double(resim);\n    \n    % Check validity of results image\n    maxlabel = max(resim(:));\n    if (maxlabel>VOCopts.nclasses), \n        error('Results image ''%s'' has out of range value %d (the value should be <= %d)',imname,maxlabel,VOCopts.nclasses);\n    end\n\n    szgtim = size(gtim); szresim = size(resim);\n    if any(szgtim~=szresim)\n        error('Results image ''%s'' is the wrong size, was %d x %d, should be %d x %d.',imname,szresim(1),szresim(2),szgtim(1),szgtim(2));\n    end\n    \n    %pixel locations to include in computation\n    locs = gtim<255;\n    \n    % joint histogram\n    sumim = 1+gtim+resim*num; \n    hs = histc(sumim(locs),1:num*num); \n    count = count + numel(find(locs));\n    confcounts(:) = confcounts(:) + hs(:);\nend\n\nif (num_missing_img > 0)\n  fprintf(1, 'WARNING: There are %d missing results!\\n', num_missing_img);\nend\n\n% confusion matrix - first index is true label, second is inferred label\n%conf = zeros(num);\nconf = 100*confcounts./repmat(1E-20+sum(confcounts,2),[1 size(confcounts,2)]);\nrawcounts = confcounts;\n\n% Pixel Accuracy\noverall_acc = 100*sum(diag(confcounts)) / sum(confcounts(:));\nfprintf('Percentage of pixels correctly labelled overall: %6.3f%%\\n',overall_acc);\n\n% Class Accuracy\nclass_acc = zeros(1, num);\nclass_count = 0;\nfprintf('Accuracy for each class (pixel accuracy)\\n');\nfor i = 1 : num\n    denom = sum(confcounts(i, :));\n    if (denom == 0)\n        denom = 1;\n    end\n    class_acc(i) = 100 * confcounts(i, i) / denom; \n    if i == 1\n      clname = 'background';\n    else\n      clname = VOCopts.classes{i-1};\n    end\n    \n    if ~strcmp(clname, 'void')\n        class_count = class_count + 1;\n        fprintf('  %14s: %6.3f%%\\n', clname, class_acc(i));\n    end\nend\nfprintf('-------------------------\\n');\navg_class_acc = sum(class_acc) / class_count;\nfprintf('Mean Class Accuracy: %6.3f%%\\n', avg_class_acc);\n\n% Pixel IOU\naccuracies = zeros(VOCopts.nclasses,1);\nfprintf('Accuracy for each class (intersection/union measure)\\n');\n\nreal_class_count = 0;\n\nfor j=1:num\n   \n   gtj=sum(confcounts(j,:));\n   resj=sum(confcounts(:,j));\n   gtjresj=confcounts(j,j);\n   % The accuracy is: true positive / (true positive + false positive + false negative) \n   % which is equivalent to the following percentage:\n   denom = (gtj+resj-gtjresj);\n\n   if denom == 0\n     denom = 1;\n   end\n   \n   accuracies(j)=100*gtjresj/denom;\n   \n   clname = 'background';\n   if (j>1), clname = VOCopts.classes{j-1};end;\n   \n   if ~strcmp(clname, 'void')\n       real_class_count = real_class_count + 1;\n   else\n       if denom ~= 1\n           fprintf(1, 'WARNING: this void class has denom = %d\\n', denom);\n       end\n   end\n   \n   if ~strcmp(clname, 'void')\n       fprintf('  %14s: %6.3f%%\\n',clname,accuracies(j));\n   end\nend\n\n%accuracies = accuracies(1:end);\n%avacc = mean(accuracies);\navacc = sum(accuracies) / real_class_count;\n\nfprintf('-------------------------\\n');\nfprintf('Average accuracy: %6.3f%%\\n',avacc);\n"}
+{"plateform": "github", "repo_name": "foss-for-synopsys-dwc-arc-processors/synopsys-caffe-main", "name": "MyVOCevalsegBoundary.m", "ext": ".m", "path": "synopsys-caffe-main/matlab/my_script/MyVOCevalsegBoundary.m", "size": 4279, "source_encoding": "utf_8", "md5": "704c57ab30eda1a0f001187608d3c786", "text": "%VOCEVALSEG Evaluates a set of segmentation results.\n% VOCEVALSEG(VOCopts,ID); prints out the per class and overall\n% segmentation accuracies. Accuracies are given using the intersection/union \n% metric:\n%   true positives / (true positives + false positives + false negatives) \n%\n% [ACCURACIES,AVACC,CONF] = VOCEVALSEG(VOCopts,ID) returns the per class\n% percentage ACCURACIES, the average accuracy AVACC and the confusion\n% matrix CONF.\n%\n% [ACCURACIES,AVACC,CONF,RAWCOUNTS] = VOCEVALSEG(VOCopts,ID) also returns\n% the unnormalised confusion matrix, which contains raw pixel counts.\nfunction [accuracies,avacc,conf,rawcounts, overall_acc, avg_class_acc] = MyVOCevalsegBoundary(VOCopts, id, w)\n\n% get structural element\nst_w = 2*w + 1;\nse = strel('square', st_w);\n\n% image test set\nfn = sprintf(VOCopts.seg.imgsetpath,VOCopts.testset);\nfid = fopen(fn, 'r');\ngtids = textscan(fid, '%s');\ngtids = gtids{1};\nfclose(fid);\n%[gtids,t]=textread(sprintf(VOCopts.seg.imgsetpath,VOCopts.testset),'%s %d');\n\n% number of labels = number of classes plus one for the background\nnum = VOCopts.nclasses+1; \nconfcounts = zeros(num);\ncount=0;\ntic;\nfor i=1:length(gtids)\n    % display progress\n    if toc>1\n        fprintf('test confusion: %d/%d\\n',i,length(gtids));\n        drawnow;\n        tic;\n    end\n        \n    imname = gtids{i};\n    \n    % ground truth label file\n    gtfile = sprintf(VOCopts.seg.clsimgpath,imname);\n    [gtim,map] = imread(gtfile);    \n    gtim = double(gtim);\n    \n    % results file\n    resfile = sprintf(VOCopts.seg.clsrespath,id,VOCopts.testset,imname);\n    try\n      [resim,map] = imread(resfile);\n    catch err\n      fprintf(1, 'Fail to read %s\\n', resfile);\n      continue;\n    end\n\n    resim = double(resim);\n    \n    % Check validity of results image\n    maxlabel = max(resim(:));\n    if (maxlabel>VOCopts.nclasses), \n        error('Results image ''%s'' has out of range value %d (the value should be <= %d)',imname,maxlabel,VOCopts.nclasses);\n    end\n\n    szgtim = size(gtim); szresim = size(resim);\n    if any(szgtim~=szresim)\n        error('Results image ''%s'' is the wrong size, was %d x %d, should be %d x %d.',imname,szresim(1),szresim(2),szgtim(1),szgtim(2));\n    end\n    \n    % dilate gt\n    binary_gt = gtim == 255;\n    dilate_gt = imdilate(binary_gt, se);\n    target_gt = dilate_gt & (gtim~=255);\n    \n    %pixel locations to include in computation\n    locs = target_gt;\n    %locs = gtim<255;\n    \n    % joint histogram\n    sumim = 1+gtim+resim*num; \n    hs = histc(sumim(locs),1:num*num); \n    count = count + numel(find(locs));\n    confcounts(:) = confcounts(:) + hs(:);\nend\n\n% confusion matrix - first index is true label, second is inferred label\n%conf = zeros(num);\nconf = 100*confcounts./repmat(1E-20+sum(confcounts,2),[1 size(confcounts,2)]);\nrawcounts = confcounts;\n\n% Pixel Accuracy\noverall_acc = 100*sum(diag(confcounts)) / sum(confcounts(:));\nfprintf('Percentage of pixels correctly labelled overall: %6.3f%%\\n',overall_acc);\n\n% Class Accuracy\nclass_acc = zeros(1, num);\nclass_count = 0;\nfprintf('Accuracy for each class (pixel accuracy)\\n');\nfor i = 1 : num\n    denom = sum(confcounts(i, :));\n    if (denom == 0)\n        denom = 1;\n    else\n        class_count = class_count + 1;\n    end\n    class_acc(i) = 100 * confcounts(i, i) / denom; \n    if i == 1\n      clname = 'background';\n    else\n      clname = VOCopts.classes{i-1};\n    end\n    fprintf('  %14s: %6.3f%%\\n', clname, class_acc(i));\nend\nfprintf('-------------------------\\n');\navg_class_acc = sum(class_acc) / class_count;\nfprintf('Mean Class Accuracy: %6.3f%%\\n', avg_class_acc);\n\n% Pixel IOU\naccuracies = zeros(VOCopts.nclasses,1);\nfprintf('Accuracy for each class (intersection/union measure)\\n');\nfor j=1:num\n   \n   gtj=sum(confcounts(j,:));\n   resj=sum(confcounts(:,j));\n   gtjresj=confcounts(j,j);\n   % The accuracy is: true positive / (true positive + false positive + false negative) \n   % which is equivalent to the following percentage:\n   accuracies(j)=100*gtjresj/(gtj+resj-gtjresj);   \n   \n   clname = 'background';\n   if (j>1), clname = VOCopts.classes{j-1};end;\n   fprintf('  %14s: %6.3f%%\\n',clname,accuracies(j));\nend\naccuracies = accuracies(1:end);\navacc = mean(accuracies);\nfprintf('-------------------------\\n');\nfprintf('Average accuracy: %6.3f%%\\n',avacc);\n"}
+{"plateform": "github", "repo_name": "raalf/VAP3-master", "name": "fcnXML2STRUCT.m", "ext": ".m", "path": "VAP3-master/fcnXML2STRUCT.m", "size": 6958, "source_encoding": "utf_8", "md5": "f865267aab457943222a8412bb26b6a7", "text": "function [ s ] = fcnXML2STRUCT( file )\r\n%Convert xml file into a MATLAB structure\r\n% [ s ] = xml2struct( file )\r\n%\r\n% A file containing:\r\n% <XMLname attrib1=\"Some value\">\r\n%   <Element>Some text</Element>\r\n%   <DifferentElement attrib2=\"2\">Some more text</Element>\r\n%   <DifferentElement attrib3=\"2\" attrib4=\"1\">Even more text</DifferentElement>\r\n% </XMLname>\r\n%\r\n% Will produce:\r\n% s.XMLname.Attributes.attrib1 = \"Some value\";\r\n% s.XMLname.Element.Text = \"Some text\";\r\n% s.XMLname.DifferentElement{1}.Attributes.attrib2 = \"2\";\r\n% s.XMLname.DifferentElement{1}.Text = \"Some more text\";\r\n% s.XMLname.DifferentElement{2}.Attributes.attrib3 = \"2\";\r\n% s.XMLname.DifferentElement{2}.Attributes.attrib4 = \"1\";\r\n% s.XMLname.DifferentElement{2}.Text = \"Even more text\";\r\n%\r\n% Please note that the following characters are substituted\r\n% '-' by '_dash_', ':' by '_colon_' and '.' by '_dot_'\r\n%\r\n% Written by W. Falkena, ASTI, TUDelft, 21-08-2010\r\n% Attribute parsing speed increased by 40% by A. Wanner, 14-6-2011\r\n% Added CDATA support by I. Smirnov, 20-3-2012\r\n%\r\n% Modified by X. Mo, University of Wisconsin, 12-5-2012\r\n\r\n    if (nargin < 1)\r\n        clc;\r\n        help xml2struct\r\n        return\r\n    end\r\n    \r\n    if isa(file, 'org.apache.xerces.dom.DeferredDocumentImpl') || isa(file, 'org.apache.xerces.dom.DeferredElementImpl')\r\n        % input is a java xml object\r\n        xDoc = file;\r\n    else\r\n        %check for existance\r\n        if (exist(file,'file') == 0)\r\n            %Perhaps the xml extension was omitted from the file name. Add the\r\n            %extension and try again.\r\n            if (isempty(strfind(file,'.xml')))\r\n                file = [file '.xml'];\r\n            end\r\n            \r\n            if (exist(file,'file') == 0)\r\n                error(['The file ' file ' could not be found']);\r\n            end\r\n        end\r\n        %read the xml file\r\n        xDoc = xmlread(file);\r\n    end\r\n    \r\n    %parse xDoc into a MATLAB structure\r\n    s = parseChildNodes(xDoc);\r\n    \r\nend\r\n\r\n% ----- Subfunction parseChildNodes -----\r\nfunction [children,ptext,textflag] = parseChildNodes(theNode)\r\n    % Recurse over node children.\r\n    children = struct;\r\n    ptext = struct; textflag = 'Text';\r\n    if hasChildNodes(theNode)\r\n        childNodes = getChildNodes(theNode);\r\n        numChildNodes = getLength(childNodes);\r\n\r\n        for count = 1:numChildNodes\r\n            theChild = item(childNodes,count-1);\r\n            [text,name,attr,childs,textflag] = getNodeData(theChild);\r\n            \r\n            if (~strcmp(name,'#text') && ~strcmp(name,'#comment') && ~strcmp(name,'#cdata_dash_section'))\r\n                %XML allows the same elements to be defined multiple times,\r\n                %put each in a different cell\r\n                if (isfield(children,name))\r\n                    if (~iscell(children.(name)))\r\n                        %put existsing element into cell format\r\n                        children.(name) = {children.(name)};\r\n                    end\r\n                    index = length(children.(name))+1;\r\n                    %add new element\r\n                    children.(name){index} = childs;\r\n                    if(~isempty(fieldnames(text)))\r\n                        children.(name){index} = text; \r\n                    end\r\n                    if(~isempty(attr)) \r\n                        children.(name){index}.('Attributes') = attr; \r\n                    end\r\n                else\r\n                    %add previously unknown (new) element to the structure\r\n                    children.(name) = childs;\r\n                    if(~isempty(text) && ~isempty(fieldnames(text)))\r\n                        children.(name) = text; \r\n                    end\r\n                    if(~isempty(attr)) \r\n                        children.(name).('Attributes') = attr; \r\n                    end\r\n                end\r\n            else\r\n                ptextflag = 'Text';\r\n                if (strcmp(name, '#cdata_dash_section'))\r\n                    ptextflag = 'CDATA';\r\n                elseif (strcmp(name, '#comment'))\r\n                    ptextflag = 'Comment';\r\n                end\r\n                \r\n                %this is the text in an element (i.e., the parentNode) \r\n                if (~isempty(regexprep(text.(textflag),'[\\s]*','')))\r\n                    if (~isfield(ptext,ptextflag) || isempty(ptext.(ptextflag)))\r\n                        ptext.(ptextflag) = text.(textflag);\r\n                    else\r\n                        %what to do when element data is as follows:\r\n                        %<element>Text <!--Comment--> More text</element>\r\n                        \r\n                        %put the text in different cells:\r\n                        % if (~iscell(ptext)) ptext = {ptext}; end\r\n                        % ptext{length(ptext)+1} = text;\r\n                        \r\n                        %just append the text\r\n                        ptext.(ptextflag) = [ptext.(ptextflag) text.(textflag)];\r\n                    end\r\n                end\r\n            end\r\n            \r\n        end\r\n    end\r\nend\r\n\r\n% ----- Subfunction getNodeData -----\r\nfunction [text,name,attr,childs,textflag] = getNodeData(theNode)\r\n    % Create structure of node info.\r\n    \r\n    %make sure name is allowed as structure name\r\n    name = toCharArray(getNodeName(theNode))';\r\n    name = strrep(name, '-', '_dash_');\r\n    name = strrep(name, ':', '_colon_');\r\n    name = strrep(name, '.', '_dot_');\r\n\r\n    attr = parseAttributes(theNode);\r\n    if (isempty(fieldnames(attr))) \r\n        attr = []; \r\n    end\r\n    \r\n    %parse child nodes\r\n    [childs,text,textflag] = parseChildNodes(theNode);\r\n    \r\n    if (isempty(fieldnames(childs)) && isempty(fieldnames(text)))\r\n        %get the data of any childless nodes\r\n        % faster than if any(strcmp(methods(theNode), 'getData'))\r\n        % no need to try-catch (?)\r\n        % faster than text = char(getData(theNode));\r\n        text.(textflag) = toCharArray(getTextContent(theNode))';\r\n    end\r\n    \r\nend\r\n\r\n% ----- Subfunction parseAttributes -----\r\nfunction attributes = parseAttributes(theNode)\r\n    % Create attributes structure.\r\n\r\n    attributes = struct;\r\n    if hasAttributes(theNode)\r\n       theAttributes = getAttributes(theNode);\r\n       numAttributes = getLength(theAttributes);\r\n\r\n       for count = 1:numAttributes\r\n            %attrib = item(theAttributes,count-1);\r\n            %attr_name = regexprep(char(getName(attrib)),'[-:.]','_');\r\n            %attributes.(attr_name) = char(getValue(attrib));\r\n\r\n            %Suggestion of Adrian Wanner\r\n            str = toCharArray(toString(item(theAttributes,count-1)))';\r\n            k = strfind(str,'='); \r\n            attr_name = str(1:(k(1)-1));\r\n            attr_name = strrep(attr_name, '-', '_dash_');\r\n            attr_name = strrep(attr_name, ':', '_colon_');\r\n            attr_name = strrep(attr_name, '.', '_dot_');\r\n            attributes.(attr_name) = str((k(1)+2):(end-1));\r\n       end\r\n    end\r\nend"}
+{"plateform": "github", "repo_name": "raalf/VAP3-master", "name": "fcnPLOTCIRC.m", "ext": ".m", "path": "VAP3-master/fcnPLOTCIRC.m", "size": 2440, "source_encoding": "utf_8", "md5": "e988613a8ca8bc1d1f6be5ec87a03277", "text": "function [] = fcnPLOTCIRC(valNELE, matDVE, matVLST, matCENTER, vecDVEROLL, vecDVEPITCH, vecDVEYAW, matCOEFF, ppa)\r\n\r\nfor i = 1:valNELE\r\n    corners = fcnGLOBSTAR(matVLST(matDVE(i,:),:) - matCENTER(i,:), repmat(vecDVEROLL(i),4,1), repmat(vecDVEPITCH(i),4,1), repmat(vecDVEYAW(i),4,1));\r\n    points = polygrid(corners(:,1), corners(:,2), ppa);\r\n    \r\n    len = size(points,1);\r\n%     vort_p = fcnSTARGLOB([points(:,1) points(:,2) zeros(len,1)], repmat(vecDVEROLL(i),len,1), repmat(vecDVEPITCH(i),len,1), repmat(vecDVEYAW(i),len,1)) + matCENTER(i,:);\r\n%     vort = fcnSTARGLOB([(2.*matCOEFF(i,4).*points(:,1) + matCOEFF(i,5)) (2.*matCOEFF(i,1).*points(:,2) + matCOEFF(i,2)) zeros(len,1)], repmat(vecDVEROLL(i),len,1), repmat(vecDVEPITCH(i),len,1), repmat(vecDVEYAW(i),len,1));\r\n%     vort = fcnSTARGLOB([zeros(len,1) (2.*matCOEFF(i,1).*points(:,2) + matCOEFF(i,2)) zeros(len,1)], repmat(vecDVEROLL(i),len,1), repmat(vecDVEPITCH(i),len,1), repmat(vecDVEYAW(i),len,1));\r\n\r\n%     % points(:,2) is eta in local, points(:,1) is xsi\r\n%     circ = matCOEFF(i,1).*points(:,2).^2 + matCOEFF(i,2).*points(:,2) + matCOEFF(i,3) + matCOEFF(i,4).*points(:,1).^2 + matCOEFF(i,5).*points(:,1) + matCOEFF(i,6);\r\n    circ = matCOEFF(i,3).*points(:,2).^2 + matCOEFF(i,2).*points(:,2) + matCOEFF(i,1);\r\n \r\n    len = size(circ,1);\r\n    tri = delaunay(points(:,1), points(:,2));\r\n    \r\n    circ_glob = fcnSTARGLOB([points circ], repmat(vecDVEROLL(i),len,1), repmat(vecDVEPITCH(i),len,1), repmat(vecDVEYAW(i),len,1));\r\n    circ_glob = circ_glob + matCENTER(i,:);\r\n    hold on\r\n    trisurf(tri, circ_glob(:,1), circ_glob(:,2), circ_glob(:,3),'edgealpha',0,'facealpha',0.8);\r\n%     quiver3(vort_p(:,1), vort_p(:,2), vort_p(:,3), vort(:,1), vort(:,2), vort(:,3))\r\n    hold off\r\nend\r\n\r\n\r\nfunction [inPoints] = polygrid( xv, yv, N)\r\n\r\n%Find the bounding rectangle\r\n\tlower_x = min(xv);\r\n\thigher_x = max(xv);\r\n\r\n\tlower_y = min(yv);\r\n\thigher_y = max(yv);\r\n%Create a grid of points within the bounding rectangle\r\n\n\tinc_x = (higher_x - lower_x)/N;\r\n\tinc_y = (higher_y - lower_y)/N;\r\n\n\t\r\n\tinterval_x = lower_x:inc_x:higher_x;\r\n\tinterval_y = lower_y:inc_y:higher_y;\r\n\t[bigGridX, bigGridY] = meshgrid(interval_x, interval_y);\r\n\t\r\n%Filter grid to get only points in polygon\r\n\t[in,on] = inpolygon(bigGridX(:), bigGridY(:), xv, yv);\r\n    in = in | on;\r\n    \r\n%Return the co-ordinates of the points that are in the polygon\r\n\tinPoints = [bigGridX(in), bigGridY(in)];\r\n\r\nend\r\n\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "mrelnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/mrelnet.m", "size": 2585, "source_encoding": "utf_8", "md5": "2f946c92aba76c5b8ef746baed5e437c", "text": "function fit = mrelnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...\n    jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,jsd,intr,maxit,family)\n\nnr = size(y, 2);\n\nwym = wtmean(y, weights);\nnulldev = sum(wtmean(bsxfun(@minus,y,wym).^2,weights)*sum(weights));\n\nif isempty(offset)\n    offset = y * 0;\n    is_offset = false;\nelse\n    if (size(offset) ~= size(y))\n        error('Offset must match dimension of y');\n    end\n    is_offset = true;\nend\n\nif is_sparse\n    task = 30;\n    [lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,irs,pcs,jsd);\nelse\n    task = 31;\n    [lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,jsd);\nend\n\nif (jerr ~= 0)\n    errmsg = err(jerr,maxit,nx,family);\n    if (errmsg.fatal)\n        error(errmsg.msg);\n    else\n        warning(errmsg.msg);\n    end\nend\n\nninmax = max(nin);\nlam = alm;\nif (ulam == 0.0)\n    lam = fix_lam(lam); % first lambda is infinity; changed to entry point\nend\n\nif (nr > 1)\n    beta_list = {};\n%     a0 = a0 - repmat(mean(a0), nr, 1);  do not center for mrelnet!\n    dfmat=a0;\n    dd=[nvars, lmu];\n    if ninmax > 0\n        ca = reshape(ca, nx, nr, lmu);\n        ca = ca(1:ninmax,:,:);\n        ja = ia(1:ninmax);\n        [ja1,oja] = sort(ja);\n        df = any(abs(ca) > 0, 2);\n        df = sum(df, 1);\n        df = df(:);\n        for k=1:nr\n            ca1 = reshape(ca(:,k,:), ninmax, lmu);\n            cak = ca1(oja,:);\n            dfmat(k,:) = sum(abs(cak) > 0, 1);\n            beta = zeros(nvars, lmu);\n            beta(ja1,:) = cak;\n            beta_list{k} = beta;\n        end\n    else\n        for k = 1:nr\n            dfmat(k,:) = zeros(1,lmu);\n            beta_list{k} = zeros(nvars, lmu);\n        end\n        df = zeros(1,lmu);\n    end\n    fit.beta = beta_list;\n    fit.dfmat = dfmat;\n    \n    \nelse\n    \n    dd=[nvars, lmu];\n    if ninmax > 0\n        ca = ca(1:ninmax,:);\n        df = sum(abs(ca) > 0, 1);\n        ja = ia(1:ninmax);\n        [ja1,oja] = sort(ja);\n        beta = zeros(nvars, lmu);\n        beta (ja1, :) = ca(oja,:);\n    else\n        beta = zeros(nvars,lmu);\n        df = zeros(1,lmu);\n    end\n    \n    fit.beta = beta;\n    \n    \nend\n\nfit.a0 = a0;\nfit.dev = rsq;\nfit.nulldev = nulldev;\nfit.df = df';\nfit.lambda = lam;\nfit.npasses = nlp;\nfit.jerr = jerr;\nfit.dim = dd;\nfit.offset = is_offset;\nfit.class = 'mrelnet';\n\n\nfunction new_lam = fix_lam(lam)\n\nnew_lam = lam;\nif (length(lam) > 2)\n    llam=log(lam);\n    new_lam(1)=exp(2*llam(2)-llam(3));\nend\n\n"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "coxnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/coxnet.m", "size": 1563, "source_encoding": "utf_8", "md5": "895747785766daa2c82975f3d019dd18", "text": "function fit = coxnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...\n    jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,maxit,family)\n\n% Internal glmnet function. See also glmnet, cvglmnet.\n\n%time --- column 1\n%status --- column 2\n\nty = y(:,1);\ntevent = y(:,2);\nif (any(ty <= 0))\n    error('negative event times encountered;  not permitted for Cox family');\nend\nif isempty(offset)\n    offset = ty * 0;\n    is_offset = false;\nelse\n    is_offset = true;\nend\n\nif (is_sparse)\n    error('Cox model not implemented for sparse x in glmnet');\nelse\n    task = 41;\n    [lmu,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,ty,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,tevent,cl,maxit,offset);\n\nend\n\nif (jerr ~= 0)\n    errmsg = err(jerr,maxit,nx,family);\n    if (errmsg.fatal)\n        error(errmsg.msg);\n    else\n        warning(errmsg.msg);\n    end\nend\n\nninmax = max(nin);\nlam = alm;\nif (ulam == 0.0)\n    lam = fix_lam(lam); % first lambda is infinity; changed to entry point\nend\n\ndd=[nvars, lmu];\nif ninmax > 0\n    ca = ca(1:ninmax,:);\n    df = sum(abs(ca) > 0, 1);\n    ja = ia(1:ninmax);\n    [ja1,oja] = sort(ja);\n    beta = zeros(nvars, lmu);\n    beta (ja1,:) = ca(oja,:);\nelse\n    beta = zeros(nvars,lmu);\n    df = zeros(1,lmu);\nend\n\nfit.beta = beta;\nfit.dev = dev;\nfit.nulldev = dev0;\nfit.df = df';\nfit.lambda = lam;\nfit.npasses = nlp;\nfit.jerr = jerr;\nfit.dim = dd;\nfit.offset = is_offset;\nfit.class = 'coxnet';\n\n\nfunction new_lam = fix_lam(lam)\n\nnew_lam = lam;\nif (length(lam) > 2)\n    llam=log(lam);\n    new_lam(1)=exp(2*llam(2)-llam(3));\nend\n\n"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "err.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/err.m", "size": 3764, "source_encoding": "utf_8", "md5": "eeab9274fcf914b604c55eb4ec2a867a", "text": "function output = err(n,maxit,pmax,family)\n\nif n==0\n    output.n=0;\n    output.fatal=false;\n    output.msg='';\n   \nelse\n    switch family\n        case 'gaussian'\n            output = err_elnet(n,maxit,pmax);\n        case 'binomial'\n            output = err_lognet(n,maxit,pmax);\n        case 'multinomial'\n            output = err_lognet(n,maxit,pmax);\n        case 'poisson'\n            output = err_fishnet(n,maxit,pmax);\n        case 'cox'\n            output = err_coxnet(n,maxit,pmax);\n        case 'mrelnet'\n            output = err_mrelnet(n,maxit,pmax);\n    end\n    output.msg = sprintf('from glmnet Fortran code (error code %d); %s', n, output.msg);\nend\n\n\n%------------------------------------------------------------------\n% End private function err\n%------------------------------------------------------------------\n\nfunction output = err_elnet(n,maxit,pmax)\n\nif (n > 0)  %fatal error\n    if (n < 7777)\n        msg = 'Memory allocation error; contact package maintainer';\n    elseif (n == 7777)\n        msg = 'All used predictors have zero variance';\n    elseif (n == 10000)\n        msg = 'All penalty factors are <= 0';\n    else\n        msg = 'Unknown error';\n    end\n    output.n = n;\n    output.fatal = true;\n    output.msg = msg;\nelseif (n < 0)  %non-fatal error\n    if (n > -10000)\n        msg = sprintf('Convergence for %dth lambda value not reached after maxit=%d iterations; solutions for larger lambdas returned',-n,maxit);\n    end\n    if (n < -10000)\n        msg = sprintf('Number of nonzero coefficients along the path exceeds pmax=%d at %dth lambda value; solutions for larger lambdas returned',pmax,-n-10000);\n    end\n    output.n = n;\n    output.fatal = false;\n    output.msg = msg;\nend\n\n\nfunction output = err_lognet(n,maxit,pmax)\n\noutput = err_elnet(n,maxit,pmax);\nif (n < -20000)\n    output.msg = sprintf('Max(p(1-p),1.0e-6 at %dth value of lambda; solutions for larger values of lambda returned',-n-20000);\nend\nif ~strcmp(output.msg, 'Unknown error')\n    return;\nend\nif (8000 < n) && (n < 9000)\n    msg = sprintf('Null probability for class%d< 1.0e-5', n-8000);\nelseif (9000 < n) && (n < 10000)\n    msg = sprintf('Null probability for class%d> 1.0 - 1.0e-5',n-9000);\nelse\n    msg = 'Unknown error';\nend\noutput.n = n;\noutput.fatal = true;\noutput.msg = msg;\n\n\nfunction output = err_fishnet(n,maxit,pmax)\n\noutput = err_elnet(n,maxit,pmax);\nif ~strcmp(output.msg, 'Unknown error')\n    return;\nend\nif (n == 8888)\n    msg = 'Negative response values - should be counts';\nelseif (n == 9999)\n    msg = 'No positive observation weights';\nelse\n    msg = 'Unknown error';\nend\noutput.n = n;\noutput.fatal = true;\noutput.msg = msg;\n\n\nfunction output = err_coxnet(n,maxit,pmax)\n\nif (n > 0)  %fatal error\n    output = err_elnet(n,maxit,pmax);\n    if ~strcmp(msg, 'Unknown error')\n        return;\n    end\n    if (n == 8888)\n        msg = 'All observations censored - cannot proceed';\n    elseif (n == 9999)\n        msg = 'No positive observation weights';\n    elseif (n == 20000) || (n == 30000)\n        msg = 'Inititialization numerical error; probably too many censored observations';\n    else\n        msg = 'Unknown error';\n    end\n    output.n = n;\n    output.fatal = true;\n    output.msg = msg;\nelseif (n < 0)\n    if (n <= -30000)\n        msg = sprintf('Numerical error at %dth lambda value; solutions for larger values of lambda returned',-n-30000);\n        output.n = n;\n        output.fatal = false;\n        output.msg = msg;\n    else\n        output = err_elnet(n,maxit,pmax);\n    end\nend\n\n\nfunction output = err_mrelnet(n,maxit,pmax)\n\nif (n == 90000)\n    msg = 'Newton stepping for bounded multivariate response did not converge';\n    output.n = n;\n    output.fatal = false;\n    output.msg = msg;\nelse\n    output = err_elnet(n,maxit,pmax);\nend"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "glmnetPlot.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/glmnetPlot.m", "size": 7609, "source_encoding": "utf_8", "md5": "12fcbd8fb5cafce7d419403fcfab5649", "text": "function glmnetPlot( x, xvar, label, type, varargin )\n\n%--------------------------------------------------------------------------\n% glmnetPlot.m: plot coefficients from a \"glmnet\" object\n%--------------------------------------------------------------------------\n%\n% DESCRIPTION:\n%    Produces a coefficient profile plot fo the coefficient paths for a\n%    fitted \"glmnet\" object.\n%\n% USAGE:\n%    glmnetPlot(fit);\n%    glmnetPlot(fit, xvar);\n%    glmnetPlot(fit, xvar, label);\n%    glmnetPlot(fit, xvar, label, type);\n%    glmnetPlot(fit, xvar, label, type, ...);\n%    (Use empty matrix [] to apply the default value, eg. glmnetPlot(fit,\n%    [], [], type).)\n%\n% INPUT ARGUMENTS:\n% x           fitted \"glmnet\" model.\n% xvar        What is on the X-axis. 'norm' plots against the L1-norm of\n%             the coefficients, 'lambda' against the log-lambda sequence,\n%             and 'dev' against the percent deviance explained.\n% label       If true, label the curves with variable sequence numbers.\n% type        If type='2norm' then a single curve per variable, else\n%             if type='coef', a coefficient plot per response.\n% varargin    Other graphical parameters to plot.\n%\n% DETAILS:\n%    A coefficient profile plot is produced. If x is a multinomial model, a\n%    coefficient plot is produced for each class.\n%\n% LICENSE: GPL-2\n%\n% DATE: 30 Aug 2013\n%\n% AUTHORS:\n%    Algorithm was designed by Jerome Friedman, Trevor Hastie and Rob Tibshirani\n%    Fortran code was written by Jerome Friedman\n%    R wrapper (from which the MATLAB wrapper was adapted) was written by Trevor Hasite\n%    The original MATLAB wrapper was written by Hui Jiang (14 Jul 2009),\n%    and was updated and maintained by Junyang Qian (30 Aug 2013) junyangq@stanford.edu,\n%    Department of Statistics, Stanford University, Stanford, California, USA.\n%\n% REFERENCES:\n%    Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, \n%    http://www.jstatsoft.org/v33/i01/\n%    Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010\n%    \n%    Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent,\n%    http://www.jstatsoft.org/v39/i05/\n%    Journal of Statistical Software, Vol. 39(5) 1-13\n%\n%    Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2010) Strong Rules for Discarding Predictors in Lasso-type Problems,\n%    http://www-stat.stanford.edu/~tibs/ftp/strong.pdf\n%    Stanford Statistics Technical Report\n%\n% SEE ALSO:\n%    glmnet, glmnetSet, glmnetPrint, glmnetPredict and glmnetCoef.\n%\n% EXAMPLES:\n%    x=randn(100,20);\n%    y=randn(100,1);\n%    g2=randsample(2,100,true);\n%    g4=randsample(4,100,true);\n%    fit1=glmnet(x,y);\n%    glmnetPlot(fit1);\n%    glmnetPlot(fit1, 'lambda', true);\n%    fit3=glmnet(x,g4,'multinomial');\n%    glmnetPlot(fit3);\n%\n% DEVELOPMENT: \n%    14 Jul 2009: Original version of glmnet.m written.\n%    30 Aug 2013: Updated glmnet.m with more options and more models\n%                 (multi-response Gaussian, cox and Poisson models) supported.\n\nif nargin < 2 || isempty(xvar)\n    xvar = 'norm';\nend\n\nif nargin < 3 || isempty(label)\n    label = false;\nend\n\nif nargin < 4 || isempty(type)\n    type = 'coef';\nend\n\nxvarbase = {'norm','lambda','dev'};\nxvarind = find(strncmp(xvar,xvarbase,length(xvar)),1);\nif isempty(xvarind)\n    error('xvar should be one of ''norm'', ''lambda'', ''dev''');\nelse\n    xvar = xvarbase{xvarind};\nend\n\ntypebase = {'coef','2norm'};\ntypeind = find(strncmp(type,typebase,length(type)),1);\nif isempty(typeind)\n    error('type should be one of ''coef'', ''2norm''');\nelse\n    type = typebase{typeind};\nend\n\nif any(strcmp(x.class,{'elnet','lognet','coxnet','fishnet'}))\n    plotCoef(x.beta,[],x.lambda,x.df,x.dev,label,xvar,'','Coefficients',varargin{:});\nend\n\nif strcmp(x.class,'multnet') || strcmp(x.class,'mrelnet')\n    beta = x.beta;\n    if strcmp(xvar,'norm')\n        norm = 0;\n        nzbeta = beta;\n        for i=1:length(beta)\n            which = nonzeroCoef(beta{i});\n            nzbeta{i} = beta{i}(which,:);\n            norm = norm + sum(abs(nzbeta{i}),1);\n        end\n    else\n        norm = 0;\n    end\n    \n    if strcmp(type,'coef')\n        ncl = size(x.dfmat,1);\n        if strcmp(x.class,'multnet')\n            for i = 1:ncl\n                plotCoef(beta{i},norm,x.lambda,x.dfmat(i,:),x.dev,label,xvar,'',sprintf('Coefficients: Class %d', i),varargin{:});\n            end\n        else\n            for i = 1:ncl\n                plotCoef(beta{i},norm,x.lambda,x.dfmat(i,:),x.dev,label,xvar,'',sprintf('Coefficients: Response %d', i),varargin{:});\n            end\n        end\n    else\n        dfseq = round(mean(x.dfmat,1));\n        coefnorm = beta{1}*0;\n        for i=1:length(beta)\n            coefnorm = coefnorm + abs(beta{i}).^2;\n        end\n        coefnorm = sqrt(coefnorm);\n        if strcmp(x.class,'multnet')\n            plotCoef(coefnorm,norm,x.lambda,dfseq,x.dev,label,xvar,'',sprintf('Coefficient 2Norms'),varargin{:});\n        else\n            plotCoef(coefnorm,norm,x.lambda,x.dfmat(1,:),x.dev,label,xvar,'',sprintf('Coefficient 2Norms'),varargin{:});\n        end\n        \n    end\nend\n\n%----------------------------------------------------------------\n% End function glmnetPlot\n%----------------------------------------------------------------\n\nfunction plotCoef(beta,norm,lambda,df,dev,label,xvar,xlab,ylab,varargin)\n\nwhich = nonzeroCoef(beta);\nidwhich = find(which);  %row indices\nnwhich = length(idwhich);\nif nwhich == 0\n    error('No plot produced since all coefficients zero')\nend\nif nwhich == 1\n    warning('1 or less nonzero coefficients; glmnet plot is not meaningful');\nend\n\nbeta = beta(which,:);\nif strcmp(xvar, 'norm')\n    if isempty(norm)\n        index = sum(abs(beta),1);\n    else\n        index = norm;\n    end\n    iname = 'L1 Norm';\nelseif strcmp(xvar, 'lambda')\n    index=log(lambda);\n    iname='Log Lambda';\nelseif strcmp(xvar, 'dev')\n    index=dev;\n    iname='Fraction Deviance Explained';\nend\n\nif isempty(xlab)\n    xlab = iname;\nend\n\n%Prepare for the figure (especially for the df labels)\n\nclf;\n\nbeta = transpose(beta);\nplot(index,beta,varargin{:});\n\naxes1 = gca;\naxes;\naxes2 = gca;\n\nxlim1 = get(axes1,'XLim');\nylim1 = get(axes1,'YLim');\n\n%idxrange = range(index);\n%atdf = linspace(min(index)+idxrange/12, max(index)-idxrange/12, 6);\natdf = get(axes1,'XTick');\nindat = ones(size(atdf));\nif (index(end) >= index(1))\n    for j = length(index):-1:1\n        indat(atdf <= index(j)) = j;\n    end\nelse\n    for j = 1:length(index)\n        indat(atdf <= index(j)) = j;\n    end\nend\nprettydf = df(indat);\nprettydf(end) = df(end);\n\nset(axes1,'box','off','XAxisLocation','bottom','YAxisLocation','left');\nset(axes2,'XAxisLocation','top','YAxisLocation','right',...\n    'XLim',[min(index),max(index)],'XTick',atdf,'XTickLabel',prettydf,...\n    'YTick',[],'YTickLabel',[],'TickDir','out');\nxlabel(axes2,'Degrees of Freedom')\naxes(axes1);\n\nline(xlim1,[ylim1(2),ylim1(2)],'Color','k');\nline([xlim1(2),xlim1(2)],ylim1,'Color','k');\n\nxlabel(xlab);\nylabel(ylab);\n\n\nif (label)\n    xpos = max(index);\n    adjpos = 2;\n    if strcmp(xvar,'lambda')\n        xpos = min(index);\n        adjpos = 1;\n    end\n    bsize = size(beta);\n    for i = 1: bsize(2)\n        text(1/2*xpos+1/2*xlim1(adjpos),beta(bsize(1),i),num2str(idwhich(i)));\n    end\nend\n\nlinkaxes([axes1 axes2],'x');\n\n%----------------------------------------------------------------\n% End private function plotCoef\n%----------------------------------------------------------------\n"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "glmnetPredict.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/glmnetPredict.m", "size": 16486, "source_encoding": "utf_8", "md5": "58bccedbf1d9b31e6c7b0e05d2058f3d", "text": "function result = glmnetPredict(object, newx, s, type, exact, offset)\n\n%--------------------------------------------------------------------------\n% glmnetPredict.m: make predictions from a \"glmnet\" object.\n%--------------------------------------------------------------------------\n%\n% DESCRIPTION:\n%    Similar to other predict methods, this functions predicts fitted\n%    values, logits, coefficients and more from a fitted \"glmnet\" object.\n%\n% USAGE:\n%    glmnetPredict(object, newx, s, type, exact, offset)\n%\n%    Fewer input arguments(more often) are allowed in the call, but must\n%    come in the order listed above. To set default values on the way, use\n%    empty matrix []. \n%    For example, pred=glmnetPredict(fit,[],[],'coefficients').\n%   \n%    To make EXACT prediction, the input arguments originally passed to \n%    \"glmnet\" MUST be VARIABLES (instead of expressions, or fields\n%    extracted from some struct objects). Alternatively, users should\n%    manually revise the \"call\" field in \"object\" (expressions or variable\n%    names) to match the original call to glmnet in the parent environment.\n%\n% INPUT ARGUMENTS:\n% object      Fitted \"glmnet\" model object.\n% s           Value(s) of the penalty parameter lambda at which predictions\n%             are required. Default is the entire sequence used to create\n%             the model.\n% newx        Matrix of new values for x at which predictions are to be\n%             made. Must be a matrix; can be sparse. This argument is not \n%             used for type='coefficients' or type='nonzero'.\n% type        Type of prediction required. Type 'link' gives the linear\n%             predictors for 'binomial', 'multinomial', 'poisson' or 'cox'\n%             models; for 'gaussian' models it gives the fitted values.\n%             Type 'response' gives the fitted probabilities for 'binomial'\n%             or 'multinomial', fitted mean for 'poisson' and the fitted\n%             relative-risk for 'cox'; for 'gaussian' type 'response' is\n%             equivalent to type 'link'. Type 'coefficients' computes the\n%             coefficients at the requested values for s. Note that for\n%             'binomial' models, results are returned only for the class\n%             corresponding to the second level of the factor response.\n%             Type 'class' applies only to 'binomial' or 'multinomial'\n%             models, and produces the class label corresponding to the\n%             maximum probability. Type 'nonzero' returns a matrix of\n%             logical values with each column for each value of s, \n%             indicating if the corresponding coefficient is nonzero or not.\n% exact       If exact=true, and predictions are to made at values of s not\n%             included in the original fit, these values of s are merged\n%             with object.lambda, and the model is refit before predictions\n%             are made. If exact=false (default), then the predict function\n%             uses linear interpolation to make predictions for values of s\n%             that do not coincide with those used in the fitting\n%             algorithm. Note that exact=true is fragile when used inside a\n%             nested sequence of function calls. glmnetPredict() needs to\n%             update the model, and expects the data used to create it in\n%             the parent environment.\n% offset      If an offset is used in the fit, then one must be supplied\n%             for making predictions (except for type='coefficients' or\n%             type='nonzero')\n% \n% DETAILS:\n%    The shape of the objects returned are different for \"multinomial\"\n%    objects. glmnetCoef(fit, ...) is equivalent to \n%    glmnetPredict(fit,[],[],'coefficients\").\n%\n% LICENSE: GPL-2\n%\n% DATE: 30 Aug 2013\n%\n% AUTHORS:\n%    Algorithm was designed by Jerome Friedman, Trevor Hastie and Rob Tibshirani\n%    Fortran code was written by Jerome Friedman\n%    R wrapper (from which the MATLAB wrapper was adapted) was written by Trevor Hasite\n%    The original MATLAB wrapper was written by Hui Jiang (14 Jul 2009),\n%    and was updated and maintained by Junyang Qian (30 Aug 2013) junyangq@stanford.edu,\n%    Department of Statistics, Stanford University, Stanford, California, USA.\n%\n% REFERENCES:\n%    Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, \n%    http://www.jstatsoft.org/v33/i01/\n%    Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010\n%    \n%    Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent,\n%    http://www.jstatsoft.org/v39/i05/\n%    Journal of Statistical Software, Vol. 39(5) 1-13\n%\n%    Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2010) Strong Rules for Discarding Predictors in Lasso-type Problems,\n%    http://www-stat.stanford.edu/~tibs/ftp/strong.pdf\n%    Stanford Statistics Technical Report\n%\n% SEE ALSO:\n%    glmnet, glmnetPrint, glmnetCoef, and cvglmnet.\n%\n% EXAMPLES:\n%    x=randn(100,20);\n%    y=randn(100,1);\n%    g2=randsample(2,100,true);\n%    g4=randsample(4,100,true);\n%    fit1=glmnet(x,y);\n%    glmnetPredict(fit1,x(1:5,:),[0.01,0.005]') % make predictions\n%    glmnetPredict(fit1,[],[],'coefficients')\n%    fit2=glmnet(x,g2,'binomial');\n%    glmnetPredict(fit2, x(2:5,:),[], 'response')\n%    glmnetPredict(fit2, [], [], 'nonzero')\n%    fit3=glmnet(x,g4,'multinomial');\n%    glmnetPredict(fit3, x(1:3,:), 0.01, 'response')\n%\n% DEVELOPMENT:\n%    14 Jul 2009: Original version of glmnet.m written.\n%    30 Aug 2013: Updated glmnet.m with more options and more models\n%                 (multi-response Gaussian, cox and Poisson models) supported. \n\n% OLDER UPDATES:\n%    20 Oct 2009: Fixed a bug in bionomial response, pointed out by Ramon\n%                 Casanov from Wake Forest University.\n%    26 Jan 2010: Fixed a bug in multinomial link and class, pointed out by\n%                 Peter Rijnbeek from Erasmus University.\n%    23 Jun 2010: Fixed a bug in multinomial with s, pointed out by \n%                 Robert Jacobsen from Aalborg University.\n\nif nargin < 2 || isempty(newx)\n    newx = [];\nend\nif nargin < 3\n    s = [];\nend\nif nargin < 4 || isempty(type)\n    type = 'link';\nend\nif nargin < 5 || isempty(exact)\n    exact = false;\nend\nif nargin < 6\n    offset = [];\nend\n\ntypebase = {'link','response','coefficients','nonzero','class'};\ntypeind = find(strncmp(type,typebase,length(type)),1);\ntype = typebase{typeind};\n\nif isempty(newx)\n    if ~strcmp(type, 'coefficients') && ~strcmp(type, 'nonzero')\n        error('You need to supply a value for ''newx''');\n    end\nend\n\n%exact case: need to execute statements back in the parent environment\nif (exact && ~isempty(s))\n    which = ismember(s,object.lambda);\n    if ~all(which)\n        lambda = unique([object.lambda;reshape(s,length(s),1)]);\n        %-----create a new variable in the parent environment\n        vname = 'newlam';\n        expr = sprintf('any(strcmp(''%s'', who))',vname);\n        newname = vname;\n        i = 0;\n        while (evalin('caller',expr))\n            i = i + 1;\n            newname = [vname,num2str(i)];\n            expr = sprintf('any(strcmp(who,''%s''))',newname);\n        end\n        parlam = newname;\n        %-----\n        assignin('caller', parlam, lambda);\n        \n        vname = 'temp_opt';\n        expr = sprintf('any(strcmp(''%s'', who))',vname);\n        newname = vname;\n        i = 0;\n        while (evalin('caller',expr))\n            i = i + 1;\n            newname = [vname,num2str(i)];\n            expr = sprintf('any(strcmp(who,''%s''))',newname);\n        end\n        paropt = newname;\n        \n        if strcmp('[]',object.call{3})\n            famcall = object.call{3};\n        else\n            famcall = sprintf('''%s''',object.call{3});\n        end\n        \n        if ~strcmp('[]', object.call{4})\n            evalin('caller', strcat(paropt,'=',object.call{4},';'));\n            evalin('caller', strcat(paropt,'.lambda = ',parlam,';'));\n            newcall = sprintf('glmnet(%s, %s, %s, %s)', ...\n                object.call{1}, object.call{2}, famcall, paropt);\n            object = evalin('caller', newcall);\n        else\n            evalin('caller', strcat(paropt,'.lambda = ',parlam,';'));\n            newcall = sprintf('glmnet(%s, %s, %s, %s)', ...\n                object.call{1}, object.call{2}, famcall, paropt);\n            object = evalin('caller', newcall);\n        end\n        evalin('caller', sprintf('clearvars %s %s;',parlam,paropt));\n    end\nend\n\nif strcmp(object.class,'elnet')\n    a0 = transpose(object.a0);\n    nbeta=[a0; object.beta];\n    \n    if (~isempty(s))\n        lambda=object.lambda;\n        lamlist=lambda_interp(lambda,s);\n        nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));\n    end\n    \n    if strcmp(type, 'coefficients')\n        result = nbeta;\n        return;\n    end\n    if strcmp(type, 'nonzero')\n        result = nonzeroCoef(nbeta(2:size(nbeta,1),:), true);\n        return;\n    end\n    \n    result = [ones(size(newx,1),1), newx] * nbeta;\n    if (object.offset)\n        if isempty(offset)\n            error('No offset provided for prediction, yet used in fit of glmnet');\n        end\n        if (size(offset,2)==2)\n            offset = offset(:,2);\n        end\n        result = result + repmat(offset, 1, size(result, 2));\n    end\nend\n\nif strcmp(object.class,'fishnet')\n     a0 = transpose(object.a0);\n    nbeta=[a0; object.beta];\n    \n    if (~isempty(s))\n        lambda=object.lambda;\n        lamlist=lambda_interp(lambda,s);\n        nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));\n    end\n    \n    if strcmp(type, 'coefficients')\n        result = nbeta;\n        return;\n    end\n    if strcmp(type, 'nonzero')\n        result = nonzeroCoef(nbeta(2:size(nbeta,1),:), true);\n        return;\n    end\n    \n    result = [ones(size(newx,1),1), newx] * nbeta;\n    if (object.offset)\n        if isempty(offset)\n            error('No offset provided for prediction, yet used in fit of glmnet');\n        end\n        if (size(offset,2) == 2)\n            offset = offset(:, 2);\n        end\n        result = result + repmat(offset, 1, size(result,2));\n    end\n    \n    if strcmp(type, 'response')\n        result = exp(result);\n    end\nend   \n\nif strcmp(object.class, 'lognet')\n    a0 = object.a0;\n    nbeta=[a0; object.beta];\n    \n    if (~isempty(s))\n        lambda=object.lambda;\n        lamlist=lambda_interp(lambda,s);\n        nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));\n    end\n    \n    if strcmp(type, 'coefficients')\n        result = nbeta;\n        return;\n    end\n    if strcmp(type, 'nonzero')\n        result = nonzeroCoef(nbeta(2:size(nbeta,1),:), true);\n        return;\n    end\n    \n    result = [ones(size(newx,1),1), newx] * nbeta;\n    if (object.offset)\n        if isempty(offset)\n            error('No offset provided for prediction, yet used in fit of glmnet');\n        end\n        if (size(offset,2)==2)\n            offset = offset(:,2);\n        end\n        result = result + repmat(offset, 1, size(result, 2));\n    end\n    switch type\n        case 'response'\n            pp = exp(-result);\n            result = 1./ (1+pp);\n        case 'class'\n            result = (result > 0) * 2 + (result <= 0) * 1;\n            result = object.label(result);\n    end\nend\n\nif strcmp(object.class, 'multnet') || strcmp(object.class,'mrelnet')\n    if strcmp(object.class,'mrelnet')\n        if strcmp(type, 'response')\n            type = 'link';\n        end\n        object.grouped = true;\n    end\n    \n    a0=object.a0;\n    nbeta=object.beta;\n    nclass=size(a0,1);\n    nlambda=length(s);\n    \n    if (~isempty(s))\n        lambda=object.lambda;\n        lamlist=lambda_interp(lambda,s);\n        for i=1:nclass\n            kbeta=[a0(i,:); nbeta{i}];\n            kbeta=kbeta(:,lamlist.left).*repmat(lamlist.frac',size(kbeta,1),1)+kbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(kbeta,1),1));\n            nbeta{i}=kbeta;\n        end\n    else\n        for i=1:nclass\n            nbeta{i} = [a0(i,:);nbeta{i}];\n        end\n        nlambda = length(object.lambda);\n    end\n    if strcmp(type, 'coefficients')\n        result = nbeta;\n        return;\n    end\n    if strcmp(type, 'nonzero')\n        if (object.grouped)\n            result{1} = nonzeroCoef(nbeta{1}(2:size(nbeta{1},1),:),true);\n        else\n            for i=1:nclass\n                result{i}=nonzeroCoef(nbeta{i}(2:size(nbeta{i},1),:),true);\n            end\n        end\n        return;\n    end\n    npred=size(newx,1);\n    dp = zeros(nclass,nlambda,npred);\n    for i=1:nclass\n        fitk = [ones(size(newx,1),1), newx] * nbeta{i};\n        dp(i,:,:)=dp(i,:,:)+reshape(transpose(fitk),1,nlambda,npred);\n    end\n    if (object.offset)\n        if (isempty(offset))\n            error('No offset provided for prediction, yet used in fit of glmnet');\n        end\n        if (size(offset,2) ~= nclass)\n            error('Offset should be dimension%dx%d',npred,nclass)\n        end\n        toff = transpose(offset);\n        for i = 1:nlambda\n            dp(:,i,:) = dp(:,i,:) + toff;\n        end\n    end\n    switch type\n        case 'response'\n            pp = exp(dp);\n            psum = sum(pp,1);\n            result = permute(pp./repmat(psum,nclass,1),[3,1,2]);\n        case 'link'\n            result=permute(dp,[3,1,2]);\n        case 'class'\n            dp=permute(dp,[3,1,2]);\n            result = [];\n            for i=1:size(dp,3)\n                result = [result, object.label(softmax(dp(:,:,i)))];\n            end\n    end\n    \nend\n\nif strcmp(object.class,'coxnet')\n    nbeta = object.beta;\n    if (~isempty(s))\n        lambda=object.lambda;\n        lamlist=lambda_interp(lambda,s);\n        nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));\n    end\n    if strcmp(type, 'coefficients')\n        result = nbeta;\n        return;\n    end\n    if strcmp(type, 'nonzero')\n        result = nonzeroCoef(nbeta, true);\n        return;\n    end\n    result = newx * nbeta;\n    if (object.offset)\n        if isempty(offset)\n            error('No offset provided for prediction, yet used in fit of glmnet');\n        end\n        result = result + repmat(offset, 1, size(result, 2));\n    end\n    \n    if strcmp(type, 'response')\n        result = exp(result);\n    end\nend\n        \n%-------------------------------------------------------------\n% End private function glmnetPredict\n%-------------------------------------------------------------\n\n\nfunction result = lambda_interp(lambda,s)\n% lambda is the index sequence that is produced by the model\n% s is the new vector at which evaluations are required.\n% the value is a vector of left and right indices, and a vector of fractions.\n% the new values are interpolated bewteen the two using the fraction\n% Note: lambda decreases. you take:\n% sfrac*left+(1-sfrac*right)\n\nif length(lambda)==1 % degenerate case of only one lambda\n    nums=length(s);\n    left=ones(nums,1);\n    right=left;\n    sfrac=ones(nums,1);\nelse\n    s(s > max(lambda)) = max(lambda);\n    s(s < min(lambda)) = min(lambda);\n    k=length(lambda);\n    sfrac =(lambda(1)-s)/(lambda(1) - lambda(k));\n    lambda = (lambda(1) - lambda)/(lambda(1) - lambda(k));\n    coord = interp1(lambda, 1:length(lambda), sfrac);\n    left = floor(coord);\n    right = ceil(coord);\n    sfrac=(sfrac-lambda(right))./(lambda(left) - lambda(right));\n    sfrac(left==right)=1;\nend\nresult.left = left;\nresult.right = right;\nresult.frac = sfrac;\n\n%-------------------------------------------------------------\n% End private function lambda_interp\n%-------------------------------------------------------------\n\nfunction result = softmax(x, gap)\nif nargin < 2\n    gap = false;\nend\nd = size(x);\nmaxdist = x(:, 1);\npclass = repmat(1, d(1), 1);\nfor i =2:d(2)\n    l = x(:, i) > maxdist;\n    pclass(l) = i;\n    maxdist(l) = x(l, i);\nend\nif gap\n    x = abs(maxdist - x);\n    x(1:d(1), pclass) = x * repmat(1, d(2));\n    gaps = pmin(x);\nend\nif gap\n    result = {pclass, gaps};\nelse\n    result = pclass;\nend\n\n%-------------------------------------------------------------\n% End private function softmax\n%-------------------------------------------------------------"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "fishnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/fishnet.m", "size": 1567, "source_encoding": "utf_8", "md5": "a38133450377adad1e403b48017b5d9e", "text": "function fit = fishnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...\n    jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,family)\n\nif any(y < 0)\n    error('negative responses encountered;  not permitted for Poisson family');\nend\n\nif isempty(offset)\n    offset = y * 0;\n    is_offset = false;\nelse\n    is_offset = true;\nend\n\nif (is_sparse)\n    task = 50;\n    [lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,offset,irs,pcs);\nelse\n    task = 51;\n    [lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,offset);\nend\n\nif (jerr ~= 0)\n    errmsg = err(jerr,maxit,nx,family);\n    if (errmsg.fatal)\n        error(errmsg.msg);\n    else\n        warning(errmsg.msg);\n    end\nend\n\nninmax = max(nin);\nlam = alm;\nif (ulam == 0.0)\n    lam = fix_lam(lam); % first lambda is infinity; changed to entry point\nend\n\ndd=[nvars, lmu];\nif ninmax > 0\n    ca = ca(1:ninmax,:);\n    df = sum(abs(ca) > 0, 1);\n    ja = ia(1:ninmax);\n    [ja1,oja] = sort(ja);\n    beta = zeros(nvars, lmu);\n    beta (ja1, :) = ca(oja,:);\nelse\n    beta = zeros(nvars,lmu);\n    df = zeros(1,lmu);\nend\n\nfit.a0 = a0;\nfit.beta = beta;\nfit.dev = dev;\nfit.nulldev = dev0;\nfit.df = df';\nfit.lambda = lam;\nfit.npasses = nlp;\nfit.jerr = jerr;\nfit.dim = dd;\nfit.offset = is_offset;\nfit.class = 'fishnet';\n\n\nfunction new_lam = fix_lam(lam)\n\nnew_lam = lam;\nif (length(lam) > 2)\n    llam=log(lam);\n    new_lam(1)=exp(2*llam(2)-llam(3));\nend\n\n    \n    "}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "cvmrelnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/cvmrelnet.m", "size": 2101, "source_encoding": "utf_8", "md5": "d369fd63346be95c8ff0d36501321ea1", "text": "function result = cvmrelnet(object, lambda, x, y, weights, offset, foldid, ...\n    type, grouped, keep)\n\nif nargin < 10 || isempty(keep)\n    keep = false;\nend\n\ntypenames = struct('deviance','Mean-Squared Error','mse','Mean-Squared Error',...\n    'mae','Mean Absolute Error');\n\nif strcmp(type,'default')\n    type = 'mse';\nend\n\nif ~any(strcmp(type,{'mse','mae','deviance'}))\n    warning('Only ''mse'',''deviance'' or ''mae'' available for multiresponse Gaussian models; ''mse'' used');\n    type = 'mse';\nend\n\n[nobs, nc] = size(y);\n\nif ~isempty(offset)\n    y = y - offset;\nend\n\npredmat = NaN(nobs,nc,length(lambda));\nnfolds = max(foldid);\nnlams = nfolds;\n\nfor i = 1:nfolds\n    which = foldid == i;\n    fitobj = object{i};\n    fitobj.offset = false;\n    preds = glmnetPredict(fitobj,x(which,:));\n    nlami = length(object{i}.lambda);\n    predmat(which,:,1:nlami) = preds;\n    nlams(i) = nlami;\nend\nN = nobs - reshape(sum(isnan(predmat(:,1,:)),1),1,[]);\nbigY = repmat(y, [1,1,length(lambda)]);\nswitch type\n    case 'mse'\n        cvraw = squeeze(sum((bigY - predmat).^2, 2));\n    case 'mae'\n        cvraw = squeeze(sum(abs(bigY - predmat), 2));\nend\nif (nobs/nfolds < 3) && grouped\n    warning('Option grouped=false enforced in cv.glmnet, since < 3 observations per fold');\n    grouped = false;\nend\nif (grouped)\n cvob = cvcompute(cvraw, weights, foldid, nlams);\n    cvraw = cvob.cvraw;\n    weights = cvob.weights;\n    N = cvob.N;\nend\n% end\ncvm = wtmean(cvraw,weights);\nsqccv = (bsxfun(@minus,cvraw,cvm)).^2;\ncvsd = sqrt(wtmean(sqccv,weights)./(N-1));\nresult.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);\nif (keep)\n    result.fit_preval = predmat;\nend   \n\n\nfunction result = glmnet_softmax(x)\n\nd = size(x);\nnas = any(isnan(x),2);\nif any(nas)\n    pclass = NaN(d(1),1);\n    if (sum(nas) < d(1))\n        pclass2 = glmnet_softmax(x(~nas,:));\n        pclass(~nas) = pclass2;\n        result = pclass;\n    end\nelse\n    maxdist = x(:,1);\n    pclass = ones(d(1),1);\n    for i = 2:d(2)\n        l = x(:,i)>maxdist;\n        pclass(l) = i;\n        maxdist(l) = x(l,i);\n    end\n    result = pclass;\nend\n\n\n\n"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "cvmultnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/cvmultnet.m", "size": 2980, "source_encoding": "utf_8", "md5": "245d373006161331bd149a8cc0756fcd", "text": "function result = cvmultnet(object, lambda, x, y, weights, offset, foldid, ...\n    type, grouped, keep)\n\nif nargin < 10 || isempty(keep)\n    keep = false;\nend\n\ntypenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error',...\n    'deviance','Multinomial Deviance','class','Misclassification Error');\n\nif strcmp(type,'default')\n    type = 'deviance';\nend\n\nif ~any(strcmp(type,{'mse','mae','deviance','class'}))\n    warning('Only ''deviance'', ''class'', ''mse'' or ''mae''  available for multinomial models; ''deviance'' used');\n    type = 'deviance';\nend\n\nprob_min = 1e-5; prob_max = 1 - prob_min;\nnc = size(y);\nif nc(2) == 1\n    [classes,~,sy] = unique(y);\n    nc = length(classes);\n    indexes = eye(nc);\n    y = indexes(sy,:);\nelse\n    nc = nc(2);\nend\nis_offset = ~isempty(offset);\npredmat = NaN(size(y,1),nc,length(lambda));\nnfolds = max(foldid);\nnlams = zeros(nfolds,1);\n\nfor i = 1:nfolds\n    which = foldid==i;\n    fitobj = object{i};\n    if (is_offset)\n        off_sub = offset(which,:);\n    else\n        off_sub = [];\n    end\n    preds = glmnetPredict(fitobj,x(which,:),[],'response',[],off_sub);\n    nlami = length(object{i}.lambda);\n    predmat(which,:,1:nlami) = preds;\n    nlams(i) = nlami;\nend\n\nywt = sum(y, 2);\ny = y ./ repmat(ywt,1,size(y,2));\nweights = weights .* ywt;\nN = size(y,1) - sum(isnan(predmat(:,1,:)),1);\nbigY = repmat(y, [1,1,length(lambda)]);\nswitch type\n    case 'mse'\n        cvraw = squeeze(sum((bigY - predmat).^2, 2));\n    case 'mae'\n        cvraw = squeeze(sum(abs(bigY - predmat), 2));\n    case 'deviance'\n        predmat = min(max(predmat,prob_min),prob_max);\n        lp = bigY .* log(predmat);\n        ly = bigY .* log(bigY);\n        ly(bigY == 0) = 0;\n        cvraw = squeeze(sum(2 * (ly - lp), 2));\n    case 'class'\n        classid = NaN(size(y,1),length(lambda));\n        for i = 1:length(lambda)\n            classid(:,i) = glmnet_softmax(predmat(:,:,i));\n        end\n        classid = reshape(classid,[],1);\n        yperm = reshape(permute(bigY, [1,3,2]),[],nc);\n        idx = sub2ind(size(yperm), 1:length(classid), classid');\n        cvraw = reshape(1 - yperm(idx), [], length(lambda));\nend\n\nif (grouped)\n    cvob = cvcompute(cvraw, weights, foldid, nlams);\n    cvraw = cvob.cvraw;\n    weights = cvob.weights;\n    N = cvob.N;\nend\n\ncvm = wtmean(cvraw,weights);\nsqccv = (bsxfun(@minus,cvraw,cvm)).^2;\ncvsd = sqrt(wtmean(sqccv,weights)./(N-1));\nresult.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);\nif (keep)\n    result.fit_preval = predmat;\nend\n\n\nfunction result = glmnet_softmax(x)\n\nd = size(x);\nnas = any(isnan(x),2);\nif any(nas)\n    pclass = NaN(d(1),1);\n    if (sum(nas) < d(1))\n        pclass2 = glmnet_softmax(x(~nas,:));\n        pclass(~nas) = pclass2;\n        result = pclass;\n    end\nelse\n    maxdist = x(:,1);\n    pclass = ones(d(1),1);\n    for i = 2:d(2)\n        l = x(:,i)>maxdist;\n        pclass(l) = i;\n        maxdist(l) = x(l,i);\n    end\n    result = pclass;\nend\n        \n        \n\n\n\n    \n\n\n    "}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "lognet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/lognet.m", "size": 4177, "source_encoding": "utf_8", "md5": "c749bb59e945862ae7b1962afc11fbfc", "text": "function fit = lognet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...\n    jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,kopt,family)\n\n[noo,nc] = size(y);\nif noo ~= nobs\n    error('x and y have different number of rows in call to glmnet');\nend\nif nc == 1\n    [classes,~,sy] = unique(y);\n    nc = length(classes);\n    indexes = eye(nc);\n    y = indexes(sy,:);\nelse\n    classes = 1: nc;\nend\n\nif strcmp(family, 'binomial')\n    if nc > 2\n        error ('More than two classes; use multinomial family instead');\n    end\n    nc = 1; % for calling binomial\n    y = y(:,[2,1]);\nend\no = [];\nif ~isempty(weights)\n    % check if any are zero\n    o = weights > 0;\n    if ~all(o) %subset the data\n        y = y(o,:);\n        x = x(o,:);\n        weights = weights(o);\n        nobs = sum(o);\n    else\n        o = [];\n    end\n    [my,ny] = size(y);\n    y = y .* repmat(weights,1,ny);\nend\n\nif isempty(offset)\n    offset = y * 0;\n    is_offset = false;\nelse\n    if ~isempty(o)\n        offset = offset(o,:);\n    end\n    do = size(offset);\n    if (do(1) ~= nobs)\n        error('offset should have the same number of values as observations in binomial/multinomial call to glmnet');\n    end\n    if (nc == 1)\n        if (do(2) == 1)\n            offset = cat(2,offset,-offset);\n        end\n        if (do(2) > 2)\n            error('offset should have 1 or 2 columns in binomial call to glmnet');\n        end\n    end\n    if strcmp(family,'multinomial') && (do(2) ~= nc)\n        error('offset should have same shape as y in multinomial call to glmnet');\n    end\n    is_offset = true;\nend\n\nif (is_sparse)\n    task = 20;\n    [lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,cl,intr,maxit,nc,kopt,offset,irs,pcs);\nelse\n    task = 21;\n    [lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,cl,intr,maxit,nc,kopt,offset);\nend\n\nif (jerr ~= 0)\n    errmsg = err(jerr,maxit,nx,family);\n    if (errmsg.fatal)\n        error(errmsg.msg);\n    else\n        warning(errmsg.msg);\n    end\nend\n\nninmax = max(nin);\nlam = alm;\nif (ulam == 0.0)\n    lam = fix_lam(lam); % first lambda is infinity; changed to entry point\nend\n\nif strcmp(family, 'multinomial')\n    beta_list = {};\n    a0 = a0 - repmat(mean(a0), nc, 1);      %multinomial: center the coefficients\n    dfmat=a0;\n    dd=[nvars, lmu];\n    if ninmax > 0\n        ca = reshape(ca, nx, nc, lmu);\n        ca = ca(1:ninmax,:,:);\n        ja = ia(1:ninmax);\n        [ja1,oja] = sort(ja);\n        df = any(abs(ca) > 0, 2);\n        df = sum(df, 1);\n        df = df(:)';\n        for k=1:nc\n            ca1 = reshape(ca(:,k,:), ninmax, lmu);\n            cak = ca1(oja,:);\n            dfmat(k,:) = sum(abs(cak) > 0, 1);\n            beta = zeros(nvars, lmu);\n            beta(ja1,:) = cak;\n            beta_list{k} = beta;\n        end\n    else\n        for k = 1:nc\n            dfmat(k,:) = zeros(1,lmu);\n            beta_list{k} = zeros(nvars, lmu);\n        end\n        df = zeros(1,lmu);\n    end\n    fit.a0 = a0;\n    fit.label = classes;\n    fit.beta = beta_list;\n    fit.dev = dev;\n    fit.nulldev = dev0;\n    fit.dfmat = dfmat;\n    fit.df = df';\n    fit.lambda = lam;\n    fit.npasses = nlp;\n    fit.jerr = jerr;\n    fit.dim = dd;\n    if (kopt == 2)\n        grouped = true;\n    else\n        grouped = false;\n    end\n    fit.grouped = grouped;\n    fit.offset = is_offset;\n    fit.class = 'multnet';\n    \nelse\n    dd=[nvars, lmu];\n    if ninmax > 0\n        ca = ca(1:ninmax,:);\n        df = sum(abs(ca) > 0, 1);\n        ja = ia(1:ninmax);\n        [ja1,oja] = sort(ja);\n        beta = zeros(nvars, lmu);\n        beta (ja1, :) = ca(oja,:);\n    else\n        beta = zeros(nvars,lmu);\n        df = zeros(1,lmu);\n    end\n    fit.a0 = a0;\n    fit.label = classes;\n    fit.beta = beta; %sign flips make 2 arget class\n    fit.dev = dev;\n    fit.nulldev = dev0;\n    fit.df = df';\n    fit.lambda = lam;\n    fit.npasses = nlp;\n    fit.jerr = jerr;\n    fit.dim = dd;\n    fit.offset = is_offset;\n    fit.class = 'lognet';\nend\n\n\nfunction new_lam = fix_lam(lam)\n\nnew_lam = lam;\nif (length(lam) > 2)\n    llam=log(lam);\n    new_lam(1)=exp(2*llam(2)-llam(3));\nend\n"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "elnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/elnet.m", "size": 1671, "source_encoding": "utf_8", "md5": "5e93be0d78726b154d27eed1f02e36be", "text": "function fit = elnet(x, is_sparse, irs, pcs, y, weights, offset, gtype, ...\n    parm, lempty, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, ...\n    isd, intr, maxit, family)\n\nybar = y' * weights/ sum(weights);\nnulldev = (y' - ybar).^2 * weights;\nka = find(strncmp(gtype,{'covariance','naive'},length(gtype)),1);\nif isempty(ka)\n    error('unrecognized type');\nend\n\nif isempty(offset)\n    offset = y * 0;\n    is_offset = false;\nelse\n    is_offset = true;\nend\n\nif is_sparse\n    task = 10;\n    [lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,ka,cl,intr,maxit,irs,pcs);\nelse\n    task = 11;\n    [lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,ka,cl,intr,maxit);\n\nend\n    \nif (jerr ~= 0)\n    errmsg = err(jerr,maxit,nx,family);\n    if (errmsg.fatal)\n        error(errmsg.msg);\n    else\n        warning(errmsg.msg);\n    end\nend\n\nninmax = max(nin);\nlam = alm;\nif lempty\n    lam = fix_lam(lam); % first lambda is infinity; changed to entry point\nend\n\ndd=[nvars, lmu];\nif ninmax > 0\n    ca = ca(1:ninmax,:);\n    df = sum(abs(ca) > 0, 1);\n    ja = ia(1:ninmax);\n    [ja1,oja] = sort(ja);\n    beta = zeros(nvars, lmu);\n    beta (ja1, :) = ca(oja,:);\nelse\n    beta = zeros(nvars,lmu);\n    df = zeros(1,lmu);\nend\n\nfit.a0 = a0;\nfit.beta = beta;\nfit.dev = rsq;\nfit.nulldev = nulldev;\nfit.df = df';\nfit.lambda = lam;\nfit.npasses = nlp;\nfit.jerr = jerr;\nfit.dim = dd;\nfit.offset = is_offset;\nfit.class = 'elnet';\n\n\nfunction new_lam = fix_lam(lam)\n\nnew_lam = lam;\nif (length(lam) > 2)\n    llam=log(lam);\n    new_lam(1)=exp(2*llam(2)-llam(3));\nend\n"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "cvcoxnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/cvcoxnet.m", "size": 2338, "source_encoding": "utf_8", "md5": "eed15cafcfb188d8f0abde603f2b3ee9", "text": "function result = cvcoxnet(object, lambda, x, y, weights, offset, foldid, ...\n    type, grouped, keep)\n\n% Internal glmnet function. See also cvglmnet.\n\nif nargin < 10 || isempty(keep)\n    keep = false;\nend\n\ntypenames = struct('deviance','Partial Likelihood Deviance');\nif strcmp(type, 'default')\n    type = 'deviance';\nend\nif ~any(strcmp(type, {'deviance'}))\n    warning('Only ''deviance''  available for Cox models; changed to type=''deviance''');\n    type = 'deviance';\nend\n\nif isempty(offset)\n    offset = zeros(size(x,1),1);\nend\nnfolds = max(foldid);\n\nif (length(weights)/nfolds < 10) && ~grouped\n    warning('Option grouped=true enforced for cv.coxnet, since < 3 observations per fold');\n    grouped = true;\nend\ncvraw = NaN(nfolds,length(lambda));\n\nfor i = 1:nfolds\n    which = foldid == i;\n    fitobj = object{i};\n    coefmat = glmnetPredict(fitobj,[],[],'coefficients');\n    if (grouped)\n        plfull = cox_deviance([],y,x,offset,weights,coefmat);\n        plminusk = cox_deviance([],y(~which,:),x(~which,:),offset(~which),...\n            weights(~which),coefmat);\n        cvraw(i,1:length(plfull)) = plfull - plminusk;\n    else\n        plk = cox_deviance([],y(which,:),x(which,:),offset(which),...\n            weights(which),coefmat);\n        cvraw(i,1:length(plk)) = plk;\n    end\nend\nstatus = y(:,2);\nN = nfolds - sum(isnan(cvraw),1);\nweights = accumarray(reshape(foldid,[],1),weights.*status);\ncvraw = bsxfun(@rdivide,cvraw,weights);  %even some weight = 0 does matter because of adjustment in wtmean!\ncvm = wtmean(cvraw,weights);\nsqccv = (bsxfun(@minus,cvraw,cvm)).^2;\ncvsd = sqrt(wtmean(sqccv,weights)./(N-1));\nresult.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);\nif (keep)\n    warning('keep=TRUE not implemented for coxnet');\nend\n\n\nfunction result = cox_deviance(pred, y, x, offset, weights, beta)\n\nty = y(:,1);\ntevent = y(:,2);\nnobs = length(ty); nvars = size(x,2);\nif isempty(weights)\n    weights = ones(nobs,1);\nend\nif isempty(offset)\n    offset = zeros(nobs,1);\nend\nif isempty(beta)\n    beta = []; nvec = 1; nvars = 0;\nelse\n    nvec = size(beta,2);\nend\n\ntask = 42;\n[flog, jerr] = glmnetMex(task,x,ty,tevent,offset,weights,nvec,beta);\n\n\nif (jerr ~= 0)\n    errmsg = err(jerr,0,0,'cox');\n    if (errmsg.fatal)\n        error(errmsg.msg);\n    else\n        warning(errmsg.msg);\n    end\nend\nresult = -2 * flog;"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "cvfishnet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/cvfishnet.m", "size": 1872, "source_encoding": "utf_8", "md5": "d7cb4820ff676ff3db1adcb0418e402f", "text": "function result = cvfishnet(object,lambda,x,y,weights,offset,foldid,type,grouped,keep)\n\n% Internal glmnet function. See also cvglmnet.\n\nif nargin < 10 || isempty(keep)\n    keep = false;\nend\n\ntypenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error','deviance','Poisson Deviance');\nif strcmp(type, 'default')\n    type = 'deviance';\nend\nif ~any(strcmp(type, {'mse','mae','deviance'}))\n    warning('Only ''mse'', ''deviance'' or ''mae''  available for Poisson models; ''deviance'' used');\n    type = 'deviance';\nend\n\nis_offset = ~isempty(offset);\n\npredmat = NaN(length(y),length(lambda));\nnfolds = max(foldid);\nnlams = nfolds;\n\nfor i = 1:nfolds\n    which = foldid == i;\n    fitobj = object{i};\n    if (is_offset)\n        off_sub = offset(which);\n    else\n        off_sub = [];\n    end\n    preds = glmnetPredict(fitobj,x(which,:),[],[],[],off_sub);\n    nlami = length(object{i}.lambda);\n    predmat(which,1:nlami) = preds;\n    nlams(i) = nlami;\nend\n\nN = size(y,1) - sum(isnan(predmat),1);\n\nyy = repmat(y, 1, length(lambda));\nswitch type\n    case 'mse'\n        cvraw = (yy - predmat).^2;\n    case 'mae'\n        cvraw = abs(yy - predmat);\n    case 'deviance'\n        cvraw = devi(yy, predmat);\nend\n\nif (length(y)/nfolds < 3) && grouped\n    warning('Option grouped=false enforced in cv.glmnet, since < 3 observations per fold');\n    grouped = false;\nend\n\nif (grouped)\n    cvob = cvcompute(cvraw,weights,foldid,nlams);\n    cvraw = cvob.cvraw; \n    weights = cvob.weights;\n    N = cvob.N;\nend\n\ncvm = wtmean(cvraw,weights);\nsqccv = (bsxfun(@minus,cvraw,cvm)).^2;\ncvsd = sqrt(wtmean(sqccv,weights)./(N-1));\n\nresult.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);\n\nif (keep)\n    result.fit_preval = predmat;\nend\n\n\nfunction result = devi(yy, eta)\n\ndeveta = yy .* eta - exp(eta);\ndevy = yy .* log(yy) - yy;\ndevy(yy == 0) = 0;\nresult = 2 * (devy - deveta);"}
+{"plateform": "github", "repo_name": "shangjingbo1226/DPPred-master", "name": "cvlognet.m", "ext": ".m", "path": "DPPred-master/glmnet_matlab/cvlognet.m", "size": 4184, "source_encoding": "utf_8", "md5": "265205120a633a060736651a5eee583d", "text": "function result = cvlognet(object, lambda, x, y, weights, offset, foldid, ...\n    type, grouped, keep)\n\nif nargin < 10 || isempty(keep)\n    keep = false;\nend\n\ntypenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error',...\n    'deviance','Binomial Deviance','auc','AUC','class','Misclassification Error');\n\nif strcmp(type,'default')\n    type = 'deviance';\nend\n\nif ~any(strcmp(type,{'mse','mae','deviance','auc','class'}))\n    warning('Only ''deviance'', ''class'', ''auc'', ''mse'' or ''mae''  available for binomial models; ''deviance'' used');\n    type = 'deviance';\nend\n\nprob_min = 1e-5; prob_max = 1 - prob_min;\nnc = size(y);\nif nc(2) == 1\n    [classes,~,sy] = unique(y);\n    nc = length(classes);\n    indexes = eye(nc);\n    y = indexes(sy,:);\nend\nN = size(y,1);\nnfolds = max(foldid);\nif (N/nfolds < 10) && strcmp(type,'auc')\n    warning(strcat('Too few (< 10) observations per fold for type.measure=''auc'' in cv.lognet; ',...\n        'changed to type.measure=''deviance''. Alternatively, use smaller value for nfolds'));\n    type = 'deviance';\nend\nif (N/nfolds < 3) && grouped\n    warning(strcat('Option grouped=FALSE enforced in cv.glmnet, ',...\n        'since < 3 observations per fold'));\n    grouped = false;\nend\nis_offset = ~isempty(offset);\npredmat = NaN(size(y,1),length(lambda));\nnlams = zeros(nfolds,1);\n\nfor i = 1:nfolds\n    which = foldid==i;\n    fitobj = object{i};\n    if (is_offset)\n        off_sub = offset(which,:);\n    else\n        off_sub = [];  %a bit different from that in R\n    end\n    preds = glmnetPredict(fitobj,x(which,:),[],'response',[],off_sub);\n    nlami = length(object{i}.lambda);\n    predmat(which,1:nlami) = preds;\n    nlams(i) = nlami;\nend\n\nif strcmp(type,'auc')\n    cvraw = NaN(nfolds, length(lambda));\n    good = zeros(nfolds, length(lambda));\n    for i = 1:nfolds\n        good(i,1:nlams(i)) = 1;\n        which = foldid == i;\n        for j = 1:nlams(i)\n            cvraw(i,j) = auc_mat(y(which,:), predmat(which,j), weights(which));\n        end\n    end\n    N = sum(good,1);\n    sweights = zeros(nfolds, 1);\n    for i = 1:nfolds\n        sweights(i) = sum(weights(foldid==i));\n    end\n    weights = sweights;\nelse\n    ywt = sum(y, 2);\n    y = y ./ repmat(ywt,1,size(y,2));\n    weights = weights .* ywt;\n    N = size(y,1) - sum(isnan(predmat),1);\n    yy1 = repmat(y(:,1),1,length(lambda));\n    yy2 = repmat(y(:,2),1,length(lambda));\n    switch type\n        case 'mse'\n            cvraw = (yy1 - (1 - predmat)).^2 + (yy2 - (1 - predmat)).^2;\n        case 'mae'\n            cvraw = abs(yy1 - (1 - predmat)) + abs(yy2 - (1 - predmat));\n        case 'deviance'\n            predmat = min(max(predmat,prob_min),prob_max);\n            lp = yy1.*log(1-predmat) + yy2.*log(predmat);\n            ly = log(y);\n            ly(y == 0) = 0;\n            ly = (y.*ly) * [1;1];\n            cvraw = 2 * (repmat(ly,1,length(lambda)) - lp);\n        case 'class'\n            cvraw = yy1.*(predmat > 0.5) + yy2.*(predmat <= 0.5);\n    end\n    if (grouped)\n        cvob = cvcompute(cvraw, weights, foldid, nlams);\n        cvraw = cvob.cvraw;\n        weights = cvob.weights;\n        N = cvob.N;\n    end\nend\ncvm = wtmean(cvraw,weights);\nsqccv = (bsxfun(@minus,cvraw,cvm)).^2;\ncvsd = sqrt(wtmean(sqccv,weights)./(N-1));\nresult.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);\nif (keep)\n    result.fit_preval = predmat;\nend\n\n\nfunction result = auc_mat(y, prob, weights)\n\nif nargin < 3 || isempty(weights)\n    weights = ones(size(y,1),1);\nend\nWeights = bsxfun(@times,weights,y);\nWeights = Weights(:)';\nny = size(y,1);\nY = [zeros(ny,1);ones(ny,1)];\nProb = [prob; prob];\nresult = auc(Y, Prob, Weights);\n\n\nfunction result = auc(y, prob, w)\n\nif isempty(w)\n    mindiff = min(diff(unique(prob)));\n    pert = unifrnd(0,mindiff/3,size(prob));\n    [~,~,rprob] = unique(prob+pert);\n    n1 = sum(y); n0 = length(y) - n1;\n    u = sum(rprob(y == 1)) - n1*(n1+1)/2;\n    result = u / (n1*n0);\nelse\n    [~,op] = sort(prob);\n    y = y(op); w = w(op);\n    cw = cumsum(w);\n    w1 = w(y == 1);\n    cw1 = cumsum(w1);\n    wauc = sum(w1.*(cw(y==1)-cw1));\n    sumw = cw1(length(cw1));\n    sumw = sumw * (cw(length(cw)) - sumw);\n    result = wauc / sumw;\nend\n    \n\n\n    "}
+{"plateform": "github", "repo_name": "FelixWinterstein/FPGA-shared-mem-master", "name": "generate_data_points.m", "ext": ".m", "path": "FPGA-shared-mem-master/examples/filtering_algorithm/host/data/generate_data_points.m", "size": 2172, "source_encoding": "utf_8", "md5": "deb2eed5cac280eaf68b63d34bd507ca", "text": "%**********************************************************************\n% Felix Winterstein, Imperial College London, 2016\n%\n% File: generate_data_points\n%\n% Revision 1.01\n% Additional Comments: distributed under an Apache-2.0 license, see LICENSE\n%\n%**********************************************************************\n\nfunction generate_data_points\n\nclear;\nclc;\n\n\n%% config\nN=2^20;\nD=3;\nK=128;\nKnew =K;\nstd_dev = 0.10;\nM=1;\n\nfractional_bits = 10;\ngbl_seed_offset = 0;\n\nfor file_idx=1:M\n\n    %% generate data points\n    %rng(16221+gbl_seed_offset);\n    rand('seed',16221+gbl_seed_offset+file_idx);\n    centres = 5*(rand(K,D)-0.5);\n\n    points=zeros(N,D);\n\n    for I=1:K\n        for II=1:D\n            %rng(4567+gbl_seed_offset+10*I+II);\n            randn('seed',4567+gbl_seed_offset+10*I+II+file_idx);\n            points((I-1)*N/K+1:N/K*I,II) = centres(I,II)+std_dev*randn(N/K,1);               \n        end\n    end\n\n    tmp=max([abs(min(points(:,1))),abs(max(points(:,1))),abs(min(points(:,2))),abs(max(points(:,2)))]);\n\n    points=points/tmp;\n    centres = centres/tmp;\n\n    points=round(points*2^fractional_bits);\n    centres=round(centres*2^fractional_bits);\n    \n    %% save data points to file\n    tmp_points=reshape(points,D*N,1); % append 2nd dim after 1st dim\n\n    fid=fopen(['./data_points','_N',num2str(N),'_K',num2str(K),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'.mat'],'w');\n    for I=1:D*N\n       fprintf(fid,'%d\\n',tmp_points(I));\n    end\n    fclose(fid);        \n\n    %% generate new random centres (pick K data points randomly) and save to file\n    new_centres = zeros(Knew,D);\n\n    %rng(4567+gbl_seed_offset+10000+II);\n    rand('seed', 4567+gbl_seed_offset+10000+file_idx);\n    new_centres_idx= round(rand(N,1)*N);\n    new_centres_idx = new_centres_idx(1:Knew);\n    %new_centres = points(new_centres_idx,:);\n\n    %tmp_new_centres=reshape(new_centres,D*Knew,1); % append 2nd dim after 1st dim\n\n    fid=fopen(['./initial_centers','_N',num2str(N),'_K',num2str(Knew),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'_',num2str(file_idx),'.mat'],'w');\n\n    for I=1:Knew\n       fprintf(fid,'%d\\n',new_centres_idx(I));\n    end\n    fclose(fid);\nend \n\n\n\n\nend\n"}
+{"plateform": "github", "repo_name": "FelixWinterstein/FPGA-shared-mem-master", "name": "generate_data_points.m", "ext": ".m", "path": "FPGA-shared-mem-master/examples/filtering_algorithm_no_svm/host/data/generate_data_points.m", "size": 2172, "source_encoding": "utf_8", "md5": "68f8ffaf55695f8a76b472d1df424125", "text": "%**********************************************************************\n% Felix Winterstein, Imperial College London, 2016\n%\n% File: generate_data_points\n%\n% Revision 1.01\n% Additional Comments: distributed under an Apache-2.0 license, see LICENSE\n%\n%**********************************************************************\n\nfunction generate_data_points\n\nclear;\nclc;\n\n\n%% config\nN=2^10;\nD=3;\nK=128;\nKnew =K;\nstd_dev = 0.10;\nM=1;\n\nfractional_bits = 10;\ngbl_seed_offset = 0;\n\nfor file_idx=1:M\n\n    %% generate data points\n    %rng(16221+gbl_seed_offset);\n    rand('seed',16221+gbl_seed_offset+file_idx);\n    centres = 5*(rand(K,D)-0.5);\n\n    points=zeros(N,D);\n\n    for I=1:K\n        for II=1:D\n            %rng(4567+gbl_seed_offset+10*I+II);\n            randn('seed',4567+gbl_seed_offset+10*I+II+file_idx);\n            points((I-1)*N/K+1:N/K*I,II) = centres(I,II)+std_dev*randn(N/K,1);               \n        end\n    end\n\n    tmp=max([abs(min(points(:,1))),abs(max(points(:,1))),abs(min(points(:,2))),abs(max(points(:,2)))]);\n\n    points=points/tmp;\n    centres = centres/tmp;\n\n    points=round(points*2^fractional_bits);\n    centres=round(centres*2^fractional_bits);\n    \n    %% save data points to file\n    tmp_points=reshape(points,D*N,1); % append 2nd dim after 1st dim\n\n    fid=fopen(['./data_points','_N',num2str(N),'_K',num2str(K),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'.mat'],'w');\n    for I=1:D*N\n       fprintf(fid,'%d\\n',tmp_points(I));\n    end\n    fclose(fid);        \n\n    %% generate new random centres (pick K data points randomly) and save to file\n    new_centres = zeros(Knew,D);\n\n    %rng(4567+gbl_seed_offset+10000+II);\n    rand('seed', 4567+gbl_seed_offset+10000+file_idx);\n    new_centres_idx= round(rand(N,1)*N);\n    new_centres_idx = new_centres_idx(1:Knew);\n    %new_centres = points(new_centres_idx,:);\n\n    %tmp_new_centres=reshape(new_centres,D*Knew,1); % append 2nd dim after 1st dim\n\n    fid=fopen(['./initial_centers','_N',num2str(N),'_K',num2str(Knew),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'_',num2str(file_idx),'.mat'],'w');\n\n    for I=1:Knew\n       fprintf(fid,'%d\\n',new_centres_idx(I));\n    end\n    fclose(fid);\nend \n\n\n\n\nend\n"}
+{"plateform": "github", "repo_name": "ewine-project/Flexible-GFDM-PHY-master", "name": "read_ini_file.m", "ext": ".m", "path": "Flexible-GFDM-PHY-master/Host/Reader/read_ini_file.m", "size": 2463, "source_encoding": "utf_8", "md5": "e3cf1e3ecd343ec9b56afbd0fa088639", "text": "function [ini_struct] = read_ini_file(filename)\n\n    ini_struct = struct();\n\n    if (~exist('filename', 'var'))\n        filename = '';        \n    end\n\n    if isempty(filename)\n        [filename, pathname] = uigetfile('*.ini', 'Select a ini file');\n        if isequal(filename,0)\n           error('User selected Cancel!')           \n        else\n           disp(['User selected ', fullfile(pathname, filename)])\n           filename = fullfile(pathname, filename);\n        end        \n    end\n\n    fid = fopen(filename);\n    cnt = 0;\n    \n    while (1)\n        \n        tline = fgetl(fid);\n        cnt = cnt + 1;\n        \n        if (~ischar(tline))\n            %End of file\n            break;\n        end\n        \n        if (strfind(tline, '#'))\n            %skip line because of a comment\n            continue;\n        end\n        \n        if (~strfind(tline, '='))\n            %skip line because there is nothing to read\n            continue;\n        end                     \n        \n        %Remove whitespace\n        tline = strrep(tline, ' ', '');\n        \n        %Split line in variable and value\n        strings = strtrim(strsplit(tline,'='));\n        \n        if length(strings) ~=  2\n            %something is wrong with the line\n            disp(['Something is missing in line ' num2str(cnt) ': ' tline]);  \n            continue;            \n        end\n                        \n        %Split if array is present\n        values = strtrim(strsplit(strings{2},';'));        \n        \n        %Is the first character a string?\n        if (isstrprop(values{1,1}, 'alpha'))\n            [zeilen, spalten] = size(values);\n            if (spalten > 1)\n                ini_struct.(strings{1}) = values;\n            else\n                ini_struct.(strings{1}) = values{1};\n            end\n        else                        \n            ini_struct.(strings{1}) = string_to_number(values);           \n        end\n        \n    end\n\n    fclose(fid);\n\nend\n\nfunction number = string_to_number(str)\n\n    number = [];\n    \n    [zeilen, spalten] = size(str);\n\n    for i = 1:spalten\n        \n        value = strtrim(str{:,i});\n        %Convert complex number in a similar writing style\n        value = strrep(value, 'I', 'i');\n        value = strrep(value, 'J', 'j');\n        %+i5 doesnt work it has to be +i*5\n        value = strrep(value, '+i*', '+i');\n        value = strrep(value, '+i', '+i*');\n        \n        number = [number str2double(value)];\n\n    end\n\nend"}
+{"plateform": "github", "repo_name": "ewine-project/Flexible-GFDM-PHY-master", "name": "write_ini_file.m", "ext": ".m", "path": "Flexible-GFDM-PHY-master/Host/Reader/write_ini_file.m", "size": 2907, "source_encoding": "utf_8", "md5": "1db51ea5f88734a1e7ea4742f0afe1f1", "text": "function [file_string] = write_ini_file(filename, ini_struct)\n\n    if (~exist('filename', 'var'))\n        filename = '';        \n    end\n    \n    if (~exist('ini_struct', 'var'))\n        error('No data to be saved!')     \n    end    \n\n    if isempty(filename)\n        [filename, pathname] = uiputfile('*.ini', 'Select a ini file');\n        if isequal(filename,0)\n           error('User selected Cancel!')           \n        else\n           disp(['User selected ', fullfile(pathname, filename)])\n           filename = fullfile(pathname, filename);\n        end        \n    end\n    \n    %This string is then written into the file\n    file_string = {};\n    \n    if (exist(filename, 'file'))        \n        %% Update existing file\n\n        fid = fopen(filename);\n        cnt = 0;        \n        while (1)\n\n            tline = fgetl(fid);\n            cnt = cnt + 1;\n\n            if (~ischar(tline))\n                %End of file\n                break;\n            end\n\n            if (strfind(tline, '#'))\n                file_string = {file_string{:} tline};\n                %skip line because of a comment                \n                continue;\n            end\n\n            if (~strfind(tline, '='))\n                file_string = {file_string{:} tline};\n                %skip line because there is nothing to read\n                continue;\n            end                     \n\n            %Remove whitespace\n            tline = strrep(tline, ' ', '');\n\n            %Split line in variable and value\n            strings = strtrim(strsplit(tline,'='));\n\n            %this variable exist in our struct?\n            if isfield(ini_struct, strings{1})                \n                value = ini_struct.(strings{1});\n                tline = [strings{1} '=' value_to_string(value)];\n            end\n            file_string = {file_string{:} tline};\n        end\n        fclose(fid);    \n        \n    else\n        %% Create a new file\n        fields = fieldnames(ini_struct);\n        for line = 1:numel(fields)\n            value = ini_struct.(fields{line});\n            file_string = {file_string{:} [fields{line} '=' value_to_string(value)]};\n        end\n        \n    end\n    %% (Over-)write string into file\n    fileID = fopen(filename, 'w');\n    [zeilen, spalten] = size(file_string);\n    for i = 1:spalten\n        fprintf(fileID,'%s\\r\\n', file_string{i});\n    end\n    fclose(fileID);\nend\n\nfunction str = value_to_string(value)\n    [zeilen,spalten] = size(value);\n    str = [];\n    if isnumeric(value)\n        for i = 1:numel(value)\n            str = [str num2str(value(i))];\n            if i ~= numel(value)\n                str = [str ';'];\n            end\n        end\n    elseif iscellstr(value)\n        for i = 1:length(value)\n            str = [str value{i}];\n            if i ~= numel(value)\n                str = [str ';'];\n            end            \n        end\n    else\n        str = value;\n    end\nend"}
+{"plateform": "github", "repo_name": "tangzhenyu/Scene-Text-Understanding-master", "name": "predict_depth.m", "ext": ".m", "path": "Scene-Text-Understanding-master/SynthText_Chinese/prep_scripts/predict_depth.m", "size": 3416, "source_encoding": "utf_8", "md5": "f03754d767f958f0bea597bd0c3564cc", "text": "% MATLAB script to regress a depth mask for an image.\n% uses: (1) https://bitbucket.org/fayao/dcnf-fcsp/\n%       (2) vlfeat\n%       (3) matconvnet\n\n% Author: Ankush Gupta\n\nfunction predict_depth()\n    % setup vlfeat\n    \n    %run( '../libs/vlfeat-0.9.18/toolbox/vl_setup');\n    run( '/home/yuz/lijiahui/fayao-dcnf-fcsp/libs/vlfeat-0.9.18/toolbox/vl_setup');\n    % setup matconvnet\n    % dir_matConvNet='../libs/matconvnet/matlab/';\n    dir_matConvNet='/home/yuz/lijiahui/fayao-dcnf-fcsp/libs/matconvnet_20141015/matlab/';\n    addpath(genpath(dir_matConvNet));\n    run([dir_matConvNet 'vl_setupnn.m']);\n\n    opts=[];\n    opts.useGpu=false;\n    opts.inpaint = true;\n    opts.normalize_depth = false; % limit depth to [0,1]\n    %opts.imdir = '/path/to/image/dir';\n    opts.imdir = '/home/yuz/lijiahui/syntheticdata/SynthText/img_dir';\n    %opts.out_h5 = '/path/to/save/output/depth.h5';\n    opts.out_h5 = '/home/yuz/lijiahui/syntheticdata/depth.h5';\n    % these should point to the pre-trained models from:\n    %  https://bitbucket.org/fayao/dcnf-fcsp/\n    opts.model_file.indoor =  '/home/yuz/lijiahui/fayao-dcnf-fcsp/model_trained/model_dcnf-fcsp_NYUD2.mat';\n    opts.model_file.outdoor =  '/home/yuz/lijiahui/fayao-dcnf-fcsp/model_trained/model_dcnf-fcsp_Make3D.mat';\n\n    fprintf('\\nloading trained model...\\n\\n');\n    mdl = load(opts.model_file.indoor);\n    model.indoor = mdl.data_obj;\n    mdl = load(opts.model_file.outdoor);\n    model.outdoor = mdl.data_obj;\n\n    %if gpuDeviceCount==0\n    %    fprintf(' ** No GPU found. Using CPU...\\n');\n    %    opts.useGpu=false;\n    %end\n\n    imnames = dir(fullfile(opts.imdir),'*');\n    imnames = {imnames.name};\n    N = numel(imnames);\n    for i = 1:N\n        fprintf('%d of %d\\n',i,N);\n        imname = imnames{i};\n        imtype = 'outdoor';\n        img = read_img_rgb(fullfile(opts.imdir,imname));\n        if strcmp(imtype, 'outdoor')\n            opts.sp_size=16;\n            opts.max_edge=600;\n        elseif strcmp(imtype, 'indoor')\n            opts.sp_size=20;\n            opts.max_edge=640;\n        end\n        depth = get_depth(img,model.(imtype),opts);\n        save_depth(imname,depth,opts);\n    end\nend\n\nfunction save_depth(imname,depth,opts)\n    dset_name = ['/',imname];\n    h5create(opts.out_h5, dset_name, size(depth), 'Datatype', 'single');\n    h5write(opts.out_h5, dset_name, depth);\nend\n\nfunction depth = get_depth(im_rgb,model,opts)\n    % limit the maximum edge size of the image:\n    if ~isempty(opts.max_edge)\n        sz = size(im_rgb);\n        [~,max_dim] = max(sz(1:2));\n        osz = NaN*ones(1,2);\n        osz(max_dim) = opts.max_edge;\n        im_rgb = imresize(im_rgb, osz);\n    end\n\n    % do super-pixels:\n    fprintf(' > super-pix\\n');\n    supix = gen_supperpixel_info(im_rgb, opts.sp_size);\n    pinfo = gen_feature_info_pairwise(im_rgb, supix);\n\n    % build \"data-set\":\n    ds=[];\n    ds.img_idxes = 1;\n    ds.img_data = im_rgb;\n    ds.sp_info{1} = supix;\n    ds.pws_info = pinfo;\n    ds.sp_num_imgs = supix.sp_num;\n    % run cnn:\n    fprintf(' > CNN\\n');\n    depth = do_model_evaluate(model, ds, opts);\n\n    if opts.inpaint\n        fprintf(' > inpaint\\n');\n        depth = do_inpainting(depth, im_rgb, supix);\n    end\n\n    if opts.normalize_depth\n        d_min = min(depth(:));\n        d_max = max(depth(:));\n        depth = (depth-d_min) / (d_max-d_min);\n        depth(depth<0) = 0;\n        depth(depth>1) = 1;\n    end\nend\n\npredict_depth()\n"}
+{"plateform": "github", "repo_name": "tangzhenyu/Scene-Text-Understanding-master", "name": "classification_demo.m", "ext": ".m", "path": "Scene-Text-Understanding-master/ctpn_crnn_ocr/CTPN/caffe/matlab/demo/classification_demo.m", "size": 5412, "source_encoding": "utf_8", "md5": "8f46deabe6cde287c4759f3bc8b7f819", "text": "function [scores, maxlabel] = classification_demo(im, use_gpu)\n% [scores, maxlabel] = classification_demo(im, use_gpu)\n%\n% Image classification demo using BVLC CaffeNet.\n%\n% IMPORTANT: before you run this demo, you should download BVLC CaffeNet\n% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)\n%\n% ****************************************************************************\n% For detailed documentation and usage on Caffe's Matlab interface, please\n% refer to Caffe Interface Tutorial at\n% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab\n% ****************************************************************************\n%\n% input\n%   im       color image as uint8 HxWx3\n%   use_gpu  1 to use the GPU, 0 to use the CPU\n%\n% output\n%   scores   1000-dimensional ILSVRC score vector\n%   maxlabel the label of the highest score\n%\n% You may need to do the following before you start matlab:\n%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64\n%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6\n% Or the equivalent based on where things are installed on your system\n%\n% Usage:\n%  im = imread('../../examples/images/cat.jpg');\n%  scores = classification_demo(im, 1);\n%  [score, class] = max(scores);\n% Five things to be aware of:\n%   caffe uses row-major order\n%   matlab uses column-major order\n%   caffe uses BGR color channel order\n%   matlab uses RGB color channel order\n%   images need to have the data mean subtracted\n\n% Data coming in from matlab needs to be in the order\n%   [width, height, channels, images]\n% where width is the fastest dimension.\n% Here is the rough matlab for putting image data into the correct\n% format in W x H x C with BGR channels:\n%   % permute channels from RGB to BGR\n%   im_data = im(:, :, [3, 2, 1]);\n%   % flip width and height to make width the fastest dimension\n%   im_data = permute(im_data, [2, 1, 3]);\n%   % convert from uint8 to single\n%   im_data = single(im_data);\n%   % reshape to a fixed size (e.g., 227x227).\n%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');\n%   % subtract mean_data (already in W x H x C with BGR channels)\n%   im_data = im_data - mean_data;\n\n% If you have multiple images, cat them with cat(4, ...)\n\n% Add caffe/matlab to you Matlab search PATH to use matcaffe\nif exist('../+caffe', 'dir')\n  addpath('..');\nelse\n  error('Please run this demo from caffe/matlab/demo');\nend\n\n% Set caffe mode\nif exist('use_gpu', 'var') && use_gpu\n  caffe.set_mode_gpu();\n  gpu_id = 0;  % we will use the first gpu in this demo\n  caffe.set_device(gpu_id);\nelse\n  caffe.set_mode_cpu();\nend\n\n% Initialize the network using BVLC CaffeNet for image classification\n% Weights (parameter) file needs to be downloaded from Model Zoo.\nmodel_dir = '../../models/bvlc_reference_caffenet/';\nnet_model = [model_dir 'deploy.prototxt'];\nnet_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];\nphase = 'test'; % run with phase test (so that dropout isn't applied)\nif ~exist(net_weights, 'file')\n  error('Please download CaffeNet from Model Zoo before you run this demo');\nend\n\n% Initialize a network\nnet = caffe.Net(net_model, net_weights, phase);\n\nif nargin < 1\n  % For demo purposes we will use the cat image\n  fprintf('using caffe/examples/images/cat.jpg as input image\\n');\n  im = imread('../../examples/images/cat.jpg');\nend\n\n% prepare oversampled input\n% input_data is Height x Width x Channel x Num\ntic;\ninput_data = {prepare_image(im)};\ntoc;\n\n% do forward pass to get scores\n% scores are now Channels x Num, where Channels == 1000\ntic;\n% The net forward function. It takes in a cell array of N-D arrays\n% (where N == 4 here) containing data of input blob(s) and outputs a cell\n% array containing data from output blob(s)\nscores = net.forward(input_data);\ntoc;\n\nscores = scores{1};\nscores = mean(scores, 2);  % take average scores over 10 crops\n\n[~, maxlabel] = max(scores);\n\n% call caffe.reset_all() to reset caffe\ncaffe.reset_all();\n\n% ------------------------------------------------------------------------\nfunction crops_data = prepare_image(im)\n% ------------------------------------------------------------------------\n% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that\n% is already in W x H x C with BGR channels\nd = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');\nmean_data = d.mean_data;\nIMAGE_DIM = 256;\nCROPPED_DIM = 227;\n\n% Convert an image returned by Matlab's imread to im_data in caffe's data\n% format: W x H x C with BGR channels\nim_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR\nim_data = permute(im_data, [2, 1, 3]);  % flip width and height\nim_data = single(im_data);  % convert from uint8 to single\nim_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data\nim_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)\n\n% oversample (4 corners, center, and their x-axis flips)\ncrops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');\nindices = [0 IMAGE_DIM-CROPPED_DIM] + 1;\nn = 1;\nfor i = indices\n  for j = indices\n    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);\n    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);\n    n = n + 1;\n  end\nend\ncenter = floor(indices(2) / 2) + 1;\ncrops_data(:,:,:,5) = ...\n  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);\ncrops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);\n"}
+{"plateform": "github", "repo_name": "karthik-kk/Autoware-master", "name": "velCapture.m", "ext": ".m", "path": "Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velCapture.m", "size": 1047, "source_encoding": "utf_8", "md5": "994f8bb886b3e3ebef61b89d3c9c00a9", "text": "% Function to capture catvehicle velocity and plotting live graph\nfunction velCapture(ROS_IP, roboname)\n%If number of argument is not two, flag message and exit.\nif nargin < 2\n    disp('Uage: velocityProfiler(192.168.0.32, catvehicle)');\n    return;\nend\nclose all;\n%rosshutdown;\nmodelname = strcat('/',roboname);\n%Connect to ROS master\nmaster_uri= strcat('http://',ROS_IP);\nmaster_uri = strcat(master_uri,':11311');\n%rosinit(master_uri);\n\n%get handle for /catvehicle/vel topic for subscribing to the data\nspeedsub = rossubscriber(strcat(modelname,'/vel'));\ndt = datestr(now,'mmmm-dd-yyyy-HH-MM-SS');\nsprintf('Velocity capture starts at %s',dt)\n\nt = 0:0.05:50;\noutput = zeros(length(t),1);\nfigure;\ngrid on;\ntitle('Velocity [m/s]');\nfor i = 1:length(t)\n    speedata = receive(speedsub,10);\n    output(i) = speedata.Linear.X;\n \n    plot([max(i-1,1),i], output([max(i-1,1),i]),'b-');\n    hold on;\n    drawnow;    \nend\ndt = datestr(now,'mmmm-dd-yyyy-HH-MM-SS');\nfile = strcat(dt,'.mat');\n    save(file, 'output');\ngrid on;\ntitle('Velocity [m/s]');\nend\n"}
+{"plateform": "github", "repo_name": "karthik-kk/Autoware-master", "name": "profileByMatrix.m", "ext": ".m", "path": "Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/profileByMatrix.m", "size": 1840, "source_encoding": "utf_8", "md5": "f81181e46cb60adc3466c4779083ce0d", "text": "%Implementation of follower algorithm\n\n%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>\n\n%ROS_IP = IP Address of ROS Master\n%lead = name of the model of leader AV Car\n%follower =  name of the model of follower car\n\nfunction profileByMatrix(ROS_IP, roboname, vel_input, time_input, tire_angle)\n\n\n\n%If number of argument is not two, flag message and exit.\nif nargin < 4\n    sprintf('Uage: velocityProfiler(192.168.0.32, catvehicle, velmatfile, timematfile)');\n    return;\nend\n\nif nargin < 5\n    tire_angle = 0.0;\nend\nrosshutdown;\nclose all;\nmodelname = strcat('/',roboname);\n%Connect to ROS master\nmaster_uri= strcat('http://',ROS_IP);\nmaster_uri = strcat(master_uri,':11311');\nrosinit(master_uri);\n\n%get handle for /catvehicle/cmd_vel topic for publishing the data\nvelpub = rospublisher(strcat(modelname,'/cmd_vel'),rostype.geometry_msgs_Twist);\n\n%get handle for /catvehicle/vel topic for subscribing to the data\nspeedsub = rossubscriber(strcat(modelname,'/vel'));\n\nvmat = load(vel_input);\ntmat = load(time_input);\nt = tmat.t;\n%Velocity profile\ninput = vmat.Vel;\n%Velocity profile will be sine\n%input = abs(2*sin(t));\n\n%Variable to store output velocity\noutput = zeros(length(t),1);\n\n%handle for rosmessage object for velpub topic\nvelMsgs = rosmessage(velpub);\nfor i=1:length(t)\n    velMsgs.Linear.X = input(i);\n    velMsgs.Angular.Z = tire_angle;\n    %Publish on the topic /catvehicle/cmd_vel\n    send(velpub, velMsgs);\n    %Read from the topic /catvehicle/speed\n    speedata = receive(speedsub,10);\n    output(i) = speedata.Linear.X;\n    pause(0.1);\n    if i == 3000\n        break;\n    end\nend\n\n%Plot the input and output velocity profile\n[n, p] = size(output);\nT = 1:n;\nplot(T, input');\nhold on;\nplot(T, output);\ntitle('Original Data');\nlegend('Input function', 'Output response');\ngrid on;\n\nsave input.mat input output\n"}
+{"plateform": "github", "repo_name": "karthik-kk/Autoware-master", "name": "velocityProfiler.m", "ext": ".m", "path": "Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velocityProfiler.m", "size": 1758, "source_encoding": "utf_8", "md5": "d12bb47043d421e21fc4fd4fa8ce4b02", "text": "%Matlab scripto to publish velocity on /catvehicle/cmd_vel topic and\n%subscribe to catvehicle/speed topic\n\n%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>\n\n%ROS_IP = IP Address of ROS Master\n%roboname = name of the model\nfunction velocityProfiler(ROS_IP, roboname, tire_angle)\n\n\n%If number of argument is not two, flag message and exit.\nif nargin < 2\n    sprintf('Uage: velocityProfiler(192.168.0.32, catvehicle)');\n    return;\nend\n\nif nargin < 3\n    tire_angle = 0.0;\nend\nrosshutdown;\nclose all;\nmodelname = strcat('/',roboname);\n%Connect to ROS master\nmaster_uri= strcat('http://',ROS_IP);\nmaster_uri = strcat(master_uri,':11311');\nrosinit(master_uri);\n\n%get handle for /catvehicle/cmd_vel topic for publishing the data\nvelpub = rospublisher(strcat(modelname,'/cmd_vel'),rostype.geometry_msgs_Twist);\n\n%get handle for /catvehicle/vel topic for subscribing to the data\nspeedsub = rossubscriber(strcat(modelname,'/vel'));\n\n%Discretize timestamp\nt = 0:0.01:150;\nv1 = 3;\nv2 = 6;\nv3 = 0;\n\n%Velocity profile\ninput = v1.*(t<50) + v2.*(t>=50).*(t<100) + v3.*(t>= 100);\n\n%Velocity profile will be sine\n%input = abs(2*sin(t));\n\n%Variable to store output velocity\noutput = zeros(length(t),1);\n\n%handle for rosmessage object for velpub topic\nvelMsgs = rosmessage(velpub);\nfor i=1:length(t)\n    velMsgs.Linear.X = input(i);\n    velMsgs.Angular.Z = tire_angle;\n    %Publish on the topic /catvehicle/cmd_vel\n    send(velpub, velMsgs);\n    %Read from the topic /catvehicle/speed\n    speedata = receive(speedsub,10);\n    output(i) = speedata.Linear.X;\nend\n\n%Plot the input and output velocity profile\n[n, p] = size(output);\nT = 1:n;\nplot(T, input');\nhold on;\nplot(T, output);\ntitle('Original Data');\nlegend('Input function', 'Output response');\ngrid on;\n"}
+{"plateform": "github", "repo_name": "karthik-kk/Autoware-master", "name": "follower_profile.m", "ext": ".m", "path": "Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/follower_profile.m", "size": 2381, "source_encoding": "utf_8", "md5": "ea7d00e67f5e7709a2cd7287216af4af", "text": "%Implementation of follower algorithm\n\n%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>\n\n%ROS_IP = IP Address of ROS Master\n%lead = name of the model of leader AV Car\n%follower =  name of the model of follower car\n\nfunction follower_profile(ROS_IP, lead, follower)\n\n%If number of argument is not two, flag message and exit.\nif nargin < 2\n    sprintf('Usage: velocityProfiler(192.168.0.32, catvehicle)');\n    return;\nend\n\nrosshutdown;\nclose all;\nmodelname1 = strcat('/',lead);\nmodelname2 = strcat('/',follower);\n%Connect to ROS master\nmaster_uri= strcat('http://',ROS_IP);\nmaster_uri = strcat(master_uri,':11311');\nrosinit(master_uri);\n\n%get handle for cmd_vel topic for publishing the data\nvelpub1 = rospublisher(strcat(modelname1,'/cmd_vel'),rostype.geometry_msgs_Twist);\nvelpub2 = rospublisher(strcat(modelname2,'/cmd_vel'),rostype.geometry_msgs_Twist);\n\n%get handle for speed topic for subscribing to the data\nspeedsub1 = rossubscriber(strcat(modelname1,'/vel'));\nspeedsub2 = rossubscriber(strcat(modelname2,'/vel'));\n\n%get handle for /DistanceEstimator\ndistanceEstimaterSub = rossubscriber('/DistanceEstimator/dist');\n\n%Discretize timestamp\nt = 0:0.05:150;\nv1 = 3;\nv2 = 6;\nv3 = 0;\n\n%Velocity profile\ninput = v1.*(t<50) + v2.*(t>=50).*(t<100) + v3.*(t>= 100);\n\n%Velocity profile will be sine\n%input = abs(2*sin(t));\n\n%Variable to store output velocity\noutput1 = zeros(length(t),1);\noutput2 = zeros(length(t),1);\n\n%handle for rosmessage object for velpub topic\nvelMsgs1 = rosmessage(velpub1);\nvelMsgs2 = rosmessage(velpub2);\nfor i=1:length(t)\n    velMsgs1.Linear.X = input(i);\n    velMsgs1.Angular.Z = 0.0;\n    %Publish on the topic /catvehicle/cmd_vel\n    send(velpub1, velMsgs1);\n    %Read from the topic /catvehicle/speed\n    speedata1 = receive(speedsub1,10);\n    distance = receive(distanceEstimaterSub,10);\n    x = distance.Data;\n    \n    %Follower control rule\n    velMsgs2.Linear.X = (1/30.*x + 2/3).*speedata1.Linear.X;\n    velMsgs2.Angular.Z = 0.0;\n    send(velpub2, velMsgs2);\n    speedata2 = receive(speedsub2,10);\n    output1(i) = speedata1.Linear.X;\n    output2(i) = speedata2.Linear.X;\nend\n\n%Plot the input and output velocity profile\n[n, p] = size(output1);\nT = 1:n;\nplot(T, input');\nhold on;\nplot(T, output1);\nplot(T, output2);\ntitle('Original Data');\nlegend('Input function', 'Output response of lead','Output response of follower');\ngrid on;\n"}
+{"plateform": "github", "repo_name": "karthik-kk/Autoware-master", "name": "plotDisvout.m", "ext": ".m", "path": "Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDisvout.m", "size": 241, "source_encoding": "utf_8", "md5": "241fa676f6444e00a60b8c69a5e19efd", "text": "% Author: Jonathan Sprinkle\n% plots the distance outputs from a data file\n\nfunction plotData( timeseries )\n\n% this timeseries is what we have\nfigure\nhold on\nplot(timeseries.Data);\nplot(timeseries.uVelOut);\nlegend({'Distance','VelOut'});\n\nend"}
+{"plateform": "github", "repo_name": "karthik-kk/Autoware-master", "name": "plotData.m", "ext": ".m", "path": "Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotData.m", "size": 350, "source_encoding": "utf_8", "md5": "17951edcd31fa9c02deeb1c49a2e0d7b", "text": "% Author: Jonathan Sprinkle\n% plots the distance outputs from a data file\n\nfunction plotData( timeseries )\n\n% this timeseries is what we have\nfigure\nhold on\nplot(timeseries.dist);\nplot(timeseries.velConverted);\nplot(timeseries.vdot);\nplot(timeseries.vout);\nplot(timeseries.uTireAngle);\nlegend({'dist','velConverted','vdot','vout','uTireAngle'});\n\nend"}
+{"plateform": "github", "repo_name": "karthik-kk/Autoware-master", "name": "plotDistances.m", "ext": ".m", "path": "Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDistances.m", "size": 288, "source_encoding": "utf_8", "md5": "c0779b1561faff69c733c5ec8a3a9ac7", "text": "% Author: Jonathan Sprinkle\n% plots the distance outputs from a data file\n\nfunction plotDistances\nload distances.mat\n% this timeseries is what we have\nfigure\nhold on\nplot(DistanceEstimator.Data__signal_1_);\nplot(DistanceEstimator.Data__signal_2_);\nlegend({'Distance','Angle (rad)'});\n\nend"}
+{"plateform": "github", "repo_name": "rohinkumarreddy/Iris-Detection-master", "name": "hamdist.m", "ext": ".m", "path": "Iris-Detection-master/hamdist.m", "size": 710, "source_encoding": "utf_8", "md5": "bc94aff9c7917be49e3bb10e78a5e3e4", "text": "\r\nfunction hd = hamdist(template1, template2, scales)\r\n\r\ntemplate1 = logical(template1);\r\n\r\ntemplate2 = logical(template2);\r\n\r\nhd = NaN;\r\n\r\n% shift template left and right, use the lowest Hamming distance\r\nfor shifts=-8:8\r\n    \r\n    template1s = shiftbits(template1, shifts,scales);\r\n    \r\n    totalbits = (size(template1s,1)*size(template1s,2));\r\n    \r\n    C = xor(template1s,template2);\r\n    \r\n    bitsdiff = sum(sum(C==1));\r\n    \r\n    if totalbits == 0\r\n        \r\n        hd = NaN;\r\n        \r\n    else\r\n        \r\n        hd1 = bitsdiff / totalbits;\r\n        \r\n        \r\n        if  hd1 < hd || isnan(hd)\r\n            \r\n            hd = hd1;\r\n            \r\n        end\r\n        \r\n        \r\n    end\r\n    \r\nend"}
+{"plateform": "github", "repo_name": "YasaraPeiris/MNIST_Clusters-master", "name": "predict.m", "ext": ".m", "path": "MNIST_Clusters-master/TestMNIST/MatlabCodes/predict.m", "size": 1720, "source_encoding": "utf_8", "md5": "b06e9988934fa4b78b42512d26371e90", "text": "function predict(layerset, dataSize)\n\nglobal weights numLayers layers;\n\nlayers = [784, layerset, 10];\n[~, numLayers] = size(layers);\n\nimages = loadTrainImages();\nlabels = loadTrainLabels();\n\nselected = find(labels == 5 | labels == 1);\nlabels = labels(selected);\nimages = images(:, selected);\n\n[~, c] = size(images);\ndataSize = min(c, dataSize);\n\niterations = dataSize;\n\ntestLabels = [];\nclusters = [];\n\nresults = cell(numLayers);\n\nloadWeights();\np = 0.3;\n\nunclassified = 0;\n\nfor r = 1 : iterations\n   \n    results{1} = normc(mat2gray(images(:, r)));\n    \n    %results{1} = sigmf(mat2gray(images(:, r)), [5.0 0.5]);\n    \n    for k = 1 : numLayers - 1\n        \n        results{k + 1} = normc(weights{k} * results{k});\n        \n        %results{k + 1} = sigmf(weights{k} * results{k}, [5.0 0.5]);\n        \n    end\n    \n    [m, i] = max(results{numLayers});\n    if(m >= p)\n        testLabels = [testLabels; labels(r)];\n        clusters = [clusters; i];\n    else\n        unclassified = unclassified + 1;\n    end\n\n    %{\n    temp = [temp, [results{numLayers} ; ones(3, 1)]];\n    c = c + 1;\n\n    if mod(c, 100) == 0\n        disp(c);\n        picMap = [picMap ; temp];\n        temp = [];\n        c = 0;\n    end\n    %}    \n    \nend\n\nplotPerformance([1 : iterations]', [], testLabels, clusters, [2, 3]);\n\ndisp(['Unclassified: ', int2str(unclassified), ' out of ', int2str(dataSize)]);\n\n\nfunction loadWeights()\n\nglobal weights layers;\n\nfileName = sprintf('%d_', layers);\nfileName = strcat(fileName(1 : end - 1), '.mat');\nfileName = fullfile(fileparts(which(mfilename)), '..\\WeightDatabase\\Temp', fileName);\n\nif exist(fileName, 'file') == 2\n    load(fileName, 'weights');\nelse\n    disp('Trained network not available');\n    exit;\nend"}
+{"plateform": "github", "repo_name": "YasaraPeiris/MNIST_Clusters-master", "name": "trainModel_yas.m", "ext": ".m", "path": "MNIST_Clusters-master/TestMNIST/MatlabCodes/trainModel_yas.m", "size": 1924, "source_encoding": "utf_8", "md5": "6c4950f15dc3468e591c1a2c5aab6d28", "text": "\nfunction trainModel_yas(layerset, dataSize) % Using Oja's rule\n\ntrainingRatio = 0.8;\np = 0;\n\nimages = loadTrainImages();\nlabels = loadTrainLabels();\n\n\nselected = find(labels == 0 | labels == 1 );\nlabels = labels(selected);\nimages = images(:, selected');\n\n[~, c] = size(images);\n\ndataSize = min(c, dataSize);\niterations = dataSize;\n\nimage_batch = 10;\n\nnewIterations = fix(iterations/image_batch);\ntestLabels = [];\nclusters = [];\n\ntrainingSize = floor(double(dataSize) * trainingRatio)/image_batch;\n\nunclassified = 0;\nnorms = [];\n\nupdateTime = 0.0;\n\nnet = Network_new([784, layerset, 2]);\nnumLayers = net.numLayers;\ntempW = net.feedforwardConnections;\n%tempW = net.lateralConnections;\ntemp = net.feedforwardConnections;\n\nfor r= 1:newIterations\n    images_new = [];\n    for k=1:image_batch\n        \n        image_id = image_batch*(r-1)+k;\n        images_new = [images_new mat2gray(images(:, image_id))];\n           \n    end\n    \n    results = net.getOutput(images_new,r);\n    \n    if(r > trainingSize)\n       \n            for u=1:image_batch\n                \n                image_id = image_batch*(r-1)+u;\n                [m, i] = max(results{numLayers}(:,u));\n                testLabels = [testLabels; labels(image_id)];\n                clusters = [clusters; i];\n                \n            end\n           \n    end\n    \n    time = tic;\n    net.STDP_update(results,r);\n    updateTime = updateTime + toc(time);\n    for u = 1 : image_batch\n        \n        norms = [norms; zeros(1, numLayers - 1)];\n        \n        weights = net.feedforwardConnections;\n        \n        for k = 1 : numLayers - 1\n            \n            norms(end, k) = norm(weights{k}(:,u) - tempW{k}(:,u),'fro') / numel(weights{k}(:,u));\n            tempW{k}(:,u) = weights{k}(:,u);\n            \n        end\n        \n    end\nend\n\n\nplotPerformance([1 : iterations]', norms, testLabels, clusters, [1, 2, 3]);\n\nshowFinalImage(abs(weights{1} - temp{1}));\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "danielemarinazzo/GC_SS_PNAS-master", "name": "InstModelfilter.m", "ext": ".m", "path": "GC_SS_PNAS-master/InstModelfilter.m", "size": 2343, "source_encoding": "utf_8", "md5": "9ec58f552064a96d14d59ba96f833baf", "text": "%% realization of the instantaneous model : U = L*W\r\n%%% OUTPUT\r\n% U: N*M matrix of filtered noises\r\n\r\n% INPUT\r\n% N data length\r\n% C: input covariance matrix (may be interpreted as Su or Sw, see above)\r\n% B0: M*M matrix of instantaneous effects (when relevant)\r\n\r\n% when flag='StrictlyCausal':\r\n% given Su, applies Cholesky decomposition to find L and Sw\r\n% then generates U = L*W, for a realization of gaussian W of variance Sw\r\n\r\n% when flag='ExtendedGauss':\r\n% given Sw and B(0), computes L=[I-B(0)]^(-1)\r\n% then generates U = L*W, for a realization of gaussian W of variance Sw\r\n\r\n% when flag='ExtendedNonGauss':\r\n% given Swand B(0), computes L=[I-B(0)]^(-1)\r\n% then generates U = L*W, for a realization of nongaussian W of variance Sw\r\n\r\n\r\nfunction U=InstModelfilter(N,C,flag,B0)\r\n\r\nerror(nargchk(3,4,nargin));%min and max input arguments\r\n\r\n\r\nM=size(C,1);\r\n\r\n\r\nswitch flag\r\ncase {'StrictlyCausal'} % C is Su\r\n    [L,Sw]=choldiag(C);\r\n    W = randn(M,N); % W independent and gaussian\r\n    for m=1:M % This normalizes W to have the appropriate variance (and zero mean)\r\n        W(m,:)=sqrt(Sw(m,m))*(W(m,:)-mean(W(m,:)))/std(W(m,:));\r\n    end\r\n    U=L*W;\r\n        \r\ncase {'ExtendedGauss'} % C is Sw\r\n    invL=eye(M)-B0;\r\n    if det(invL)==0, error('B0 is not invertible, ill-conditioned problem!'), end;\r\n    L=inv(invL);\r\n    W = randn(M,N); % W independent and gaussian\r\n    for m=1:M % This normalizes W to have the appropriate variance (and zero mean)\r\n        W(m,:)=sqrt(C(m,m))*(W(m,:)-mean(W(m,:)))/std(W(m,:));\r\n    end\r\n    U=L*W;\r\n        \r\ncase {'ExtendedNonGauss'} % C is Sw\r\n    invL=eye(M)-B0;\r\n    if det(invL)==0, error('B0 is not invertible, ill-conditioned problem!'), end;\r\n    L=inv(invL);\r\n    %note: here we generate W independent but non-Gaussian\r\n    % Nonlinearity exponent, selected to lie in [0.5, 0.8] or [1.2, 2.0]. (<1 gives subgaussian, >1 gives supergaussian)\r\n    q = rand(M,1)*1.1+0.5;    \r\n    ind = find(q>0.8);           \r\n    q(ind) = q(ind)+0.4;     \r\n    % This generates the disturbance variables, which are mutually independent, and non-gaussian\r\n    W = randn(M,N);\r\n    W = sign(W).*(abs(W).^(q*ones(1,N)));\r\n    % This normalizes the disturbance variables to have the appropriate scales\r\n    W = W./( (  sqrt(mean((W').^2)') ./ sqrt(diag(C)) )*ones(1,N) );\r\n    U=L*W;        \r\n        \r\nend\r\n"}
+{"plateform": "github", "repo_name": "danielemarinazzo/GC_SS_PNAS-master", "name": "MVARfilter.m", "ext": ".m", "path": "GC_SS_PNAS-master/MVARfilter.m", "size": 614, "source_encoding": "utf_8", "md5": "0290e5ff7f6435096eed5fecafbf75e9", "text": "%% FILTER A VECTOR NOISE WITH A SPECIFIED STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)\r\n\r\n%%% INPUT\r\n% A=[A(1)...A(p)]: M*pM matrix of the MVAR model coefficients (strictly causal model)\r\n% U: M*N matrix of innovations\r\n\r\n%%% OUTPUT\r\n% Y: M*N matrix of simulated time series\r\n\r\nfunction [Y]=MVARfilter(A,U)\r\n\r\nN=length(U);\r\nM=size(A,1);\r\np=size(A,2)/M;\r\n\r\n% Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)\r\nY=zeros(M,N);\r\nfor n=1:N\r\n    for k=1:p\r\n        if n-k<=0, break; end; % if n<=p, stop when k>=n\r\n        Y(:,n)=Y(:,n) + ( A(:,(k-1)*M+(1:M)) * Y(:,n-k) );\r\n    end\r\n    Y(:,n)=Y(:,n)+U(:,n);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "danielemarinazzo/GC_SS_PNAS-master", "name": "varma2iss.m", "ext": ".m", "path": "GC_SS_PNAS-master/varma2iss.m", "size": 1206, "source_encoding": "utf_8", "md5": "200c8d913b3e3fe6f30cf6228c4453c9", "text": "%% VARMA with B0 term to (Innovations form) State Space parameters\r\n% computes innovations form parameters for a state space model from VARMA\r\n% parameters using Aoki's method - this version allows for zero-lag MA coefficients\r\n\r\nfunction [A,C,K,R,lambda0] = varma2iss(Am,Bm,V,B0)\r\n\r\n%   INPUT: VARMA parameters Am, Bm, V=cov(U)\r\n%   OUTPUT: innovations form SS parameters A, C, K, R\r\n\r\n%%%%% internal test\r\n%variables to be passed are Am, Bm, B0, V=Su\r\n% clear; close all; clc;\r\n% Am=[0.9 0 0 0.5; 0 0.6 0.2 0];\r\n% Bm=[0.5 0; 0 0.5]; B0=Bm./5;\r\n% V=eye(2);\r\n%  \r\nM = size(Am,1); %dimension of observed process\r\np=floor(size(Am,2)/M); %number of AR lags\r\nq=floor(size(Bm,2)/M); %number of MA lags\r\n\r\nL=M*(p+q); % dimension of state process (SS order)\r\n\r\nC=[Am Bm];\r\nR=B0*V*B0';\r\n\r\nIp=eye(M*p);\r\nIq=eye(M*q);\r\nA11=[Am;Ip(1:end-M,:)];\r\n\r\nif q==0\r\n    A=A11;\r\n    K=[eye(M); zeros(M*(p-1),M)];\r\nelse\r\n    A12=[Bm;zeros(M*(p-1),M*q)];\r\n    A21=zeros(M*q,M*p);\r\n    A22=[zeros(M,M*q); Iq(1:end-M,:)];\r\n    A=[A11 A12; A21 A22];\r\n\r\n    K=[eye(M); zeros(M*(p-1),M); inv(B0); zeros(M*(q-1),M)];\r\nend\r\n\r\n\r\n% determine the variance of the process lambda0=E[Yn Yn']\r\nO=dlyap(A,K*R*K');\r\nlambda0=C*O*C'+R;\r\n\r\nend\r\n\r\n\r\n"}
+{"plateform": "github", "repo_name": "danielemarinazzo/GC_SS_PNAS-master", "name": "idMVAR.m", "ext": ".m", "path": "GC_SS_PNAS-master/idMVAR.m", "size": 1446, "source_encoding": "utf_8", "md5": "02db6f116c8164266a38371b50da5231", "text": "%% IDENTIFICATION OF STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)\r\n% makes use of autocovariance method (vector least squares)\r\n\r\n%%% input:\r\n% Y, M*N matrix of time series (each time series is in a row)\r\n% p, model order\r\n% Mode, determines estimation algorithm (0:builtin least squares, else other methods [see mvar.m from biosig package])\r\n\r\n%%% output:\r\n% Am=[A(1)...A(p)], M*pM matrix of the estimated MVAR model coefficients\r\n% S, estimated M*M input covariance matrix\r\n% Yp, estimated time series\r\n% Up, estimated residuals\r\n% Z, observation matrix (often optional, useful e.g. for resampling)\r\n\r\n\r\nfunction [Am,S,Yp,Up,Z,Yb]=idMVAR(Y,p,Mode)\r\n\r\n% error(nargchk(1,3,nargin));\r\n% if nargin < 3, Mode=0; end % default use least squares estimate\r\n% if nargin < 2, p=10; end % default model order\r\n\r\n[M,N]=size(Y);\r\n\r\n%% IDENTIFICATION\r\nZ=NaN*ones(p*M,N-p); % observation matrix\r\nfor j=1:p\r\n    for i=1:M\r\n        Z((j-1)*M+i,1:N-p)=Y(i, p+1-j:N-j);\r\n    end\r\nend\r\n\r\nif Mode==0\r\n    Yb=NaN*ones(M,N-p); % Ybar\r\n    for i=1:M\r\n        Yb(i,1:N-p)=Y(i,p+1:N);\r\n    end\r\n    Am=Yb/Z; % least squares!\r\n%     fprintf('using least squares\\n');        \r\nelse\r\n    Am = mvar(Y', p, Mode); % estimates from biosig code\r\n%     fprintf(['using biosig ' int2str(Mode) ' mode\\n']);  \r\nend\r\n\r\nYp=Am*Z; \r\nYp=[NaN*ones(M,p) Yp]; % Vector of predicted data\r\n\r\nUp=Y-Yp; Up=Up(:,p+1:N); % residuals of strictly causal model\r\nS=cov(Up');\r\n\r\n\r\n"}
+{"plateform": "github", "repo_name": "danielemarinazzo/GC_SS_PNAS-master", "name": "block_fdMVAR.m", "ext": ".m", "path": "GC_SS_PNAS-master/block_fdMVAR.m", "size": 5321, "source_encoding": "utf_8", "md5": "c0626e923c90c31b6156e254a5e1785f", "text": "%% FREQUENCY DOMAIN BLOCK MVAR ANALYSIS\r\n% References:\r\n% L.Faes and G. Nollo, \"Measuring Frequency Domain Granger Causality for Multiple Blocks of Interacting Time Series\", Biological Cybernetics 2013. DOI: 10.1007/s00422-013-0547-5\r\n% L.Faes, S. Erla and G. Nollo, \"Block Partial Directed Coherence: a New Tool for the Structural Analysis of Brain Networks\", International Journal of Bioelectromagnetism, Vol. 14, No. 4, pp. 162 - 166, 2012\r\n\r\n%%% inputs: \r\n% Am=[A(1)...A(p)]: Q*pQ matrix of the MVAR model coefficients (strictly causal model)\r\n% Su: Q*Q covariance matrix of the input noises\r\n% Mv: number of series in each block\r\n% N= number of points for calculation of the spectral functions (nfft)\r\n% Fs= sampling frequency\r\n\r\n%%% outputs:\r\n% bDC= block Directed Coherence (Eq. 14a)\r\n% bPDC= block Partial Directed Coherence (Eq. 14b)\r\n% mF= multivariate total causality (Eq. 13a)\r\n% mG= multivariate direct causality (Eq. 13b)\r\n% bS= block spectral density matrix (Eq. 12a)\r\n% bP= inverse block spectral density matrix (Eq. 12b)\r\n% bH= block transfer matrix\r\n% bAf= block spectral coefficient matrix\r\n% f= vector of frequencies\r\n\r\n\r\nfunction [bDC,bPDC,mF,mG,bS,bP,bH,bAf,f] = block_fdMVAR(Am,Su,Mv,N,Fs)\r\n% clear; close all; clc;\r\n% [Bm,B0,Sw]=simuMVARcoeff(1);\r\n% Am=Bm;Su=Sw;\r\n% Mv=[2 1 1]'; % vector of Mi (dimension of each block)\r\n% N=512;\r\n% Fs=1;\r\n% f = (0:N-1)*(Fs/(2*N));\r\n\r\n%%\r\nQ= size(Am,1); % Am has dim Q*pQ\r\nM=length(Mv);\r\np = size(Am,2)/Q; % p is the order of the MVAR model\r\n\r\nif nargin<5, Fs= 1; end;   \r\nif nargin<4, N = 512; end;\r\nif all(size(N)==1),\t %if N is scalar\r\n    f = (0:N-1)*(Fs/(2*N)); % frequency axis\r\nelse            % if N is a vector, we assume that it is the vector of the frequencies\r\n    f = N; N = length(N);\r\nend;\r\n\r\ns = exp(sqrt(-1)*2*pi*f/Fs); % vector of complex exponentials\r\nz = sqrt(-1)*2*pi/Fs;\r\n\r\n\r\n%% Initializations: spectral matrices have M rows, M columns and are calculated at each of the N frequencies\r\nA = [eye(Q) -Am]; % matrix from which M*M blocks are selected to calculate spectral functions\r\ninvSu=inv(Su);\r\n% i-j block of Su and Su^(-1)\r\nbSu=cell(M,M); binvSu=cell(M,M);  \r\nfor i=1:M\r\n    for j=1:M\r\n        i1=sum(Mv(1:i)); i0=i1-Mv(i)+1;\r\n        j1=sum(Mv(1:j)); j0=j1-Mv(j)+1;\r\n        bSu{i,j}=Su(i0:i1,j0:j1);\r\n        binvSu{i,j}=invSu(i0:i1,j0:j1);\r\n    end\r\nend\r\n\r\n% whole QxQ matrices\r\nAf=zeros(Q,Q,N); % Coefficient Matrix in the frequency domain (it is Abar(w))\r\nH=zeros(Q,Q,N); % Transfer Matrix H(w)\r\nS=zeros(Q,Q,N); % Spectral Matrix S(w)\r\nP=zeros(Q,Q,N); % Inverse Spectral Matrix P(w)\r\n% corresponding cell array of matrices (blocks of size Mi x Mj)\r\nbAf=cell(M,M,N);\r\nbH=cell(M,M,N); \r\nbS=cell(M,M,N); \r\nbP=cell(M,M,N);\r\n% causality functions\r\nbDC=zeros(M,M,N); % block directed coherence\r\nbPDC=zeros(M,M,N); % block partial directed coherence\r\nmF=NaN*ones(M,M,N); % multivariate logarithmic causality f\r\nmG=NaN*ones(M,M,N); % multivariate logarithmic direct causality g\r\n\r\n%%% computation of spectral functions\r\nfor n=1:N, % at each frequency  \r\n        %%% Coefficient matrix in the frequency domain\r\n        As = zeros(Q,Q); % matrix As(z)=I-sum(A(k))\r\n        for k = 1:p+1,\r\n            As = As + A(:,k*Q+(1-Q:0))*exp(-z*(k-1)*f(n));  %indicization (:,k*Q+(1-M:0)) extracts the k-th Q*Q block from the matrix B (A(1) is in the second block, and so on)\r\n        end;        \r\n        Af(:,:,n) = As; %%% Coefficient matrix\r\n        H(:,:,n)  = inv(As); %%% Transfer matrix     \r\n        S(:,:,n)  = H(:,:,n)*Su*H(:,:,n)'; %%% Spectral matrix -   ' stands for Hermitian transpose\r\n        P(:,:,n) = inv(S(:,:,n)); %%% Inverse Spectral matrix P(:,:,n) = As'*invSu*As;\r\n        \r\n        %%% extraction of blocks indexes\r\n        for i=1:M\r\n            for j=1:M\r\n                %indexes of the i-j block\r\n                i1=sum(Mv(1:i)); i0=i1-Mv(i)+1;\r\n                j1=sum(Mv(1:j)); j0=j1-Mv(j)+1;\r\n                % i-j block of all matrices of interest\r\n                bAf{i,j,n}=Af(i0:i1,j0:j1,n);\r\n                bH{i,j,n}=H(i0:i1,j0:j1,n);\r\n                bS{i,j,n}=S(i0:i1,j0:j1,n);\r\n                bP{i,j,n}=P(i0:i1,j0:j1,n);               \r\n            end\r\n        end\r\n        \r\n        % computation of causality measures at frequency n\r\n        for i=1:M\r\n            for j=1:M\r\n                                    \r\n%                 if det(bP{j,j,n}) < -0.000001, error('determinante negativo!'); end\r\n%                 if det(bS{i,i,n}) < -0.000001, error('determinante negativo!'); end\r\n%                 if det(bP{j,j,n} - bAf{i,j,n}'*binvSu{i,i}*bAf{i,j,n}) < -0.000001, error('determinante negativo!'); end\r\n%                 if det(bS{i,i,n} - bH{i,j,n}*bSu{j,j}*bH{i,j,n}') < -0.000001, error('determinante negativo!'); end\r\n                \r\n                bDC(i,j,n) = 1 - abs(det(bS{i,i,n} - bH{i,j,n}*bSu{j,j}*bH{i,j,n}')) / abs(det(bS{i,i,n}));\r\n                bPDC(i,j,n) = 1 - abs(det(bP{j,j,n} - bAf{i,j,n}'*binvSu{i,i}*bAf{i,j,n})) / abs(det(bP{j,j,n}));\r\n                if i~=j\r\n                    mF(i,j,n) = log( abs(det(bS{i,i,n})) / abs(det(bS{i,i,n} - bH{i,j,n}*bSu{j,j}*bH{i,j,n}')) );\r\n                    mG(i,j,n) = log( abs(det(bP{j,j,n})) / abs(det(bP{j,j,n} - bAf{i,j,n}'*binvSu{i,i}*bAf{i,j,n})) );\r\n                end\r\n            end\r\n        end      \r\n        \r\nend;\r\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "bipartite_matching.m", "ext": ".m", "path": "netalign-master/experiments/misc/gaimc/bipartite_matching.m", "size": 6580, "source_encoding": "utf_8", "md5": "bd3212ac06f51f9037ca7a7d80b45981", "text": "function [val m1 m2 mi]=bipartite_matching(varargin)\n% BIPARTITE_MATCHING Solve a maximum weight bipartite matching problem\n%\n% [val m1 m2]=bipartite_matching(A) for a rectangular matrix A \n% [val m1 m2 mi]=bipartite_matching(x,ei,ej,n,m) for a matrix stored\n% in triplet format.  This call also returns a matching indicator mi so\n% that val = x'*mi.\n%\n% The maximum weight bipartite matching problem tries to pick out elements\n% from A such that each row and column get only a single non-zero but the\n% sum of all the chosen elements is as large as possible.\n%\n% This function is slightly atypical for a graph library, because it will\n% be primarily used on rectangular inputs.  However, these rectangular\n% inputs model bipartite graphs and we take advantage of that stucture in\n% this code.  The underlying graph adjency matrix is \n%   G = spaugment(A,0); \n% where A is the rectangular input to the bipartite_matching function.\n%\n% Matlab already has the dmperm function that computes a maximum\n% cardinality matching between the rows and the columns.  This function\n% gives us the maximum weight matching instead.  For unweighted graphs, the\n% two functions are equivalent.\n%\n% Note: If ei and ej contain duplicate edges, the results of this function\n% are incorrect.\n%\n% See also DMPERM\n%\n% Example:\n%   A = rand(10,8); % bipartite matching between random data\n%   [val mi mj] = bipartite_matching(A);\n%   val\n\n% David F. Gleich and Ying Wang\n% Copyright, Stanford University, 2008-2009\n% Computational Approaches to Digital Stewardship\n\n% 2008-04-24: Initial coding (copy from Ying Wang matching_sparse_mex.cpp)\n% 2008-11-15: Added triplet input/output\n% 2009-04-30: Modified for gaimc library\n% 2009-05-15: Fixed error with empty inputs and triple added example.\n\n[rp ci ai tripi n m] = bipartite_matching_setup(varargin{:});\n\nif isempty(tripi)\n    error(nargoutchk(0,3,nargout,'struct'));\nelse    \n    error(nargoutchk(0,4,nargout,'struct'));\nend\n\n\nif ~isempty(tripi) && nargout>3\n    [val m1 m2 mi] = bipartite_matching_primal_dual(rp, ci, ai, tripi, n, m);\nelse\n    [val m1 m2] = bipartite_matching_primal_dual(rp, ci, ai, tripi, n, m);\nend\n\nfunction [rp ci ai tripi n m]= bipartite_matching_setup(A,ei,ej,n,m)\n% convert the input\n\nif nargin == 1\n    if isstruct(A)\n        [nzi nzj nzv]=csr_to_sparse(A.rp,A.ci,A.ai);\n    else\n        [nzi nzj nzv]=find(A); \n    end\n    [n m]=size(A);\n    triplet = 0;\nelseif nargin >= 3 && nargin <= 5    \n    nzi = ei;\n    nzj = ej;\n    nzv = A;\n    if ~exist('n','var') || isempty(n), n = max(nzi); end\n    if ~exist('m','var') || isempty(m), m = max(nzj); end\n    triplet = 1;\nelse    \n    error(nargchk(3,5,nargin,'struct'));\nend\nnedges = length(nzi);\n\nrp = ones(n+1,1); % csr matrix with extra edges\nci = zeros(nedges+n,1);\nai = zeros(nedges+n,1);\nif triplet, tripi = zeros(nedges+n,1); % triplet index\nelse tripi = [];\nend\n\n%\n% 1. build csr representation with a set of extra edges from vertex i to\n% vertex m+i\n%\nrp(1)=0;\nfor i=1:nedges\n    rp(nzi(i)+1)=rp(nzi(i)+1)+1;\nend\nrp=cumsum(rp); \nfor i=1:nedges\n    if triplet, tripi(rp(nzi(i))+1)=i; end % triplet index\n    ai(rp(nzi(i))+1)=nzv(i);\n    ci(rp(nzi(i))+1)=nzj(i);\n    rp(nzi(i))=rp(nzi(i))+1;\nend\nfor i=1:n % add the extra edges\n    if triplet, tripi(rp(i)+1)=-1; end % triplet index\n    ai(rp(i)+1)=0;\n    ci(rp(i)+1)=m+i;\n    rp(i)=rp(i)+1;\nend\n% restore the row pointer array\nfor i=n:-1:1\n    rp(i+1)=rp(i);\nend\nrp(1)=0;\nrp=rp+1;\n\n%\n% 1a. check for duplicates in the data\n%\ncolind = false(m+n,1);\nfor i=1:n\n    for rpi=rp(i):rp(i+1)-1\n        if colind(ci(rpi)), error('bipartite_matching:duplicateEdge',...\n            'duplicate edge detected (%i,%i)',i,ci(rpi)); \n        end\n        colind(ci(rpi))=1;\n    end\n    for rpi=rp(i):rp(i+1)-1, colind(ci(rpi))=0; end % reset indicator\nend\n\n\nfunction [val m1 m2 mi]=bipartite_matching_primal_dual(...\n                            rp, ci, ai, tripi, n, m)\n% BIPARTITE_MATCHING_PRIMAL_DUAL                         \n\nalpha=zeros(n,1); % variables used for the primal-dual algorithm\nbeta=zeros(n+m,1);\nqueue=zeros(n,1);\nt=zeros(n+m,1);\nmatch1=zeros(n,1);\nmatch2=zeros(n+m,1);\ntmod = zeros(n+m,1);\nntmod=0;\n\n\n% \n% initialize the primal and dual variables\n%\nfor i=1:n\n    for rpi=rp(i):rp(i+1)-1\n        if ai(rpi) > alpha(i), alpha(i)=ai(rpi); end\n    end\nend\n% dual variables (beta) are initialized to 0 already\n% match1 and match2 are both 0, which indicates no matches\ni=1;\nwhile i<=n\n    % repeat the problem for n stages\n    \n    % clear t(j)\n    for j=1:ntmod, t(tmod(j))=0; end\n    ntmod=0;\n    \n\n    % add i to the stack\n    head=1; tail=1;\n    queue(head)=i; % add i to the head of the queue\n    while head <= tail && match1(i)==0\n        k=queue(head);\n        for rpi=rp(k):rp(k+1)-1\n            j = ci(rpi);\n            if ai(rpi) < alpha(k)+beta(j) - 1e-8, continue; end % skip if tight\n            if t(j)==0,\n                tail=tail+1; queue(tail)=match2(j);\n                t(j)=k;\n                ntmod=ntmod+1; tmod(ntmod)=j;\n                if match2(j)<1,\n                    while j>0, \n                        match2(j)=t(j);\n                        k=t(j);\n                        temp=match1(k);\n                        match1(k)=j;\n                        j=temp;\n                    end\n                    break; % we found an alternating path\n                end\n            end\n        end\n        head=head+1;\n    end\n    \n    if match1(i) < 1, % still not matched, so update primal, dual and repeat\n        theta=inf;\n        for j=1:head-1\n            t1=queue(j);\n            for rpi=rp(t1):rp(t1+1)-1\n                t2=ci(rpi);\n                if t(t2) == 0 && alpha(t1) + beta(t2) - ai(rpi) < theta,\n                    theta = alpha(t1) + beta(t2) - ai(rpi);\n                end\n            end\n        end\n        \n        for j=1:head-1, alpha(queue(j)) = alpha(queue(j)) - theta; end\n        \n        for j=1:ntmod, beta(tmod(j)) = beta(tmod(j)) + theta; end\n            \n        continue;\n    end\n        \n    i=i+1; % increment i\nend\n\nval=0;\nfor i=1:n\n    for rpi=rp(i):rp(i+1)-1\n        if ci(rpi)==match1(i), val=val+ai(rpi); end\n    end\nend\nnoute = 0; % count number of output edges\nfor i=1:n\n    if match1(i)<=m, noute=noute+1; end\nend\nm1=zeros(noute,1); m2=m1; % copy over the 0 array\nnoute=1;\nfor i=1:n\n    if match1(i)<=m, m1(noute)=i; m2(noute)=match1(i);noute=noute+1; end\nend\n\nif nargout>3\n    mi= false(length(tripi)-n,1);\n    for i=1:n\n        for rpi=rp(i):rp(i+1)-1\n            if match1(i)<=m && ci(rpi)==match1(i), mi(tripi(rpi))=1; end\n        end\n    end\nend\n\n\n\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "graph_draw.m", "ext": ".m", "path": "netalign-master/experiments/misc/gaimc/graph_draw.m", "size": 23504, "source_encoding": "utf_8", "md5": "83adf66de4bc94aea62f934d2e1e3da0", "text": "function h = graph_draw(adj, xy, varargin)\n% GRAPH_DRAW Draw a picture of a graph when the coordinates are known\n%\n% graph_draw(A, xy) draws a picture of graph A where node i is placed\n% at x = xy(i,1), y = xy(i,2).  In the drawing, shaded nodes have \n% self loops.\n%\n% Some of the parameters of the drawing are controlled by specifying\n% optional parameters in the call graph_draw(A, xy, key, value).  The keys\n% and default values are \n%      'linestyle'   -  default '-' \n%      'linewidth'   -  default .5\n%      'linecolor'   -  default Black\n%      'fontsize'    -  fontsize for labels, default 8 \n%      'labels'      -  Cell array containing labels <Default : '1':'N'>\n%      'shapes'      -  1 if node is a box, 0 if oval <Default : zeros>\n% \n% h = graph_draw(A,xy,...) returns a handle for each object.  h(i,1) is\n% the text handle for vertex i, and h(i,2) is the circle handle for\n% vertex i.  \n%\n% Originally written by Erik A. Johnson, Ali Taylan Cemgil, and Leon Peskin\n% Modified by David F. Gleich for gaimc package.\n%\n% See also GPLOT\n% \n% Example:\n%   load_gaimc_graph('dfs_example');\n%   graph_draw(A,xy);\n\n\n% 2009-02-26 interface modified by David Gleich <dgleich@stanford.edu> \n%            to remove automatic layout\n% 2009-05-15: Added example\n\n% 24 Feb 2004  cleaned up, optimized and corrected by Leon Peshkin pesha @ ai.mit.edu \n% Apr-2000  draw_graph   Ali Taylan Cemgil   <cemgil@mbfys.kun.nl> \n% 1995-1997 arrow        Erik A. Johnson     <johnsone@uiuc.edu>\n\nlinestyle = '-';       %   --   -. \nlinewidth = .5;        %   2 \nlinecolor = 'Black';   %   Red\nfontsize = 8;\nN = size(adj,1);\ncolor = ones(N, 3);         % colors of elipses around text \nlabels = cellstr(int2str((1:N)'));    %  labels = cellstr(char(zeros(N,1)+double('+')));\nnode_t = zeros(N,1);                  %  \nfor i = 1:2:length(varargin)                  % get optional args\n    switch varargin{i}\n        case 'linestyle', linestyle = varargin{i+1};\n        case 'linewidth', linewidth = varargin{i+1};\n        case 'linecolor', linecolor = varargin{i+1};\n        case 'labels', labels  = varargin{i+1};\n        case 'fontsize',  fontsize = varargin{i+1}; \n        case 'shapes', node_t  = varargin{i+1};  node_t = node_t(:);\n    end\nend\n\nx = xy(:,1);\nx = x - min(x);\ny = xy(:,2);\ny = y - min(y);\n% scale the graph so it's between 0 and 1\nxrange = max(x);\nyrange = max(y);\nscalefactor = max(xrange,yrange);\nx = x/scalefactor;\ny = y/scalefactor;\n\nlp_ndx = find(diag(adj));       %  recover from self-loops = diagonal ones \ncolor(lp_ndx,:) = repmat([.8 .8 .8],length(lp_ndx),1);     %  makes self-looped nodes blue\nadj = adj - diag(diag(adj));    % clean up the diagonal \n\naxis([-0.1 1.1 -0.1 1.1]);\naxis off; \nset(gcf,'Color',[1 1 1]);\nset(gca,'XTick',[], 'YTick',[], 'box','on'); % axis('square');   %colormap(flipud(gray));\n\nidx1 = find(node_t == 0); wd1 = [];   %  Draw  nodes \nif ~isempty(idx1),\n    [h1 wd1] = textoval(x(idx1), y(idx1), labels(idx1), fontsize, color);\nend;\n\nidx2 = find(node_t ~= 0); wd2 = [];\nif ~isempty(idx2),\n    [h2 wd2] = textbox(x(idx2), y(idx2), labels(idx2), color);\nend;\n\nwd = zeros(size(wd1,1) + size(wd2,1),2);\nif ~isempty(idx1), wd(idx1, :) = wd1; end;\nif ~isempty(idx2), wd(idx2, :) = wd2; end;\n\nfor node = 1:N                          %  Draw  edges \n  edges = find(adj(node,:) == 1);\n  for node2 = edges\n    sign = 1;\n    if ((x(node2) - x(node)) == 0)\n\t    if (y(node) > y(node2)), alpha = -pi/2; else alpha = pi/2; end;\n    else\n        alpha = atan((y(node2)-y(node))/(x(node2)-x(node)));\n\t    if (x(node2) <= x(node)), sign = -1; end;\n    end;\n    dy1 = sign.*wd(node,2).*sin(alpha);   dx1 = sign.*wd(node,1).*cos(alpha);\n    dy2 = sign.*wd(node2,2).*sin(alpha);  dx2 = sign.*wd(node2,1).*cos(alpha);    \n    if  (adj(node2,node) == 0)           % if directed edge\n        my_arrow([x(node)+dx1 y(node)+dy1], [x(node2)-dx2 y(node2)-dy2]);\n    else\t   \n        line([x(node)+dx1 x(node2)-dx2], [y(node)+dy1 y(node2)-dy2], ...\n            'Color', linecolor, 'LineStyle', linestyle, 'LineWidth', linewidth);\n        adj(node2,node) = -1;         % Prevent drawing lines twice\n    end;\n  end;\nend;\n\nif nargout > 2\n    h = zeros(length(wd),2);\n    if ~isempty(idx1), h(idx1,:) = h1;   end;\n    if ~isempty(idx2), h(idx2,:) = h2;   end;\nend;\n\nfunction [t, wd] = textoval(x, y, str, fontsize, c)\n%  [t, wd] = textoval(x, y, str, fontsize)    Draws an oval around text objects\n% INPUT:   x, y - Coordinates\n%           str - Strings\n%             c - colors \n% OUTPUT:     t - Object Handles\n%         width - x and y  width of ovals \nif ~isa(str,'cell'), str = cellstr(str); end;\nN = length(str);    \nwd = zeros(N,2);\ntemp = zeros(N,2);\nfor i = 1:N,\n    tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle','FontSize', fontsize);\n    sz = get(tx, 'Extent');\n    wy = sz(4);\n    wx = max(2/3*sz(3), wy); \n    wx = 0.9 * wx;        %  might want to play with this .9 and .5 coefficients \n    wy = 0.5 * wy;\n    ptc = ellipse(x(i), y(i), wx, wy, c(i,:));\n    set(ptc, 'FaceColor', c(i,:));   % 'w'\n    wd(i,:) = [wx wy];\n    delete(tx);\n    tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle', 'FontSize', fontsize);      \n    temp(i,:) = [tx ptc];\nend;\nt = temp; \n\nfunction [p] = ellipse(x, y, rx, ry, c)\n%  [p] = ellipse(x, y, rx, ry)    Draws Ellipse shaped patch objects\n% INPUT:  x,y -  N x 1 vectors of x and y coordinates\n%      Rx, Ry -   Radii\n%           C -  colors \n% OUTPUT:   p -   Handles of Ellipse shaped path objects\n\n  if length(rx)== 1, rx = ones(size(x)).*rx; end;\n  if length(ry)== 1, ry = ones(size(x)).*ry; end;\nN = length(x);\np = zeros(size(x));\nt = 0:pi/30:2*pi;\nfor i = 1:N\n\tpx = rx(i) * cos(t) + x(i);    py = ry(i) * sin(t) + y(i);\n\tp(i) = patch(px, py, c(i,:));\nend;\n\nfunction [h, wd] = textbox(x,y,str,c)\n%  [h, wd] = textbox(x,y,str)    draws a box around the text \n% INPUT:  x, y - Coordinates\n%         str  - Strings\n% OUTPUT:    h - Object Handles\n%           wd - x and y Width of boxes \n\nif ~isa(str,'cell'), str=cellstr(str); end\nN = length(str);\nwd = zeros(N,2);\nh = zeros(N,2);\nfor i = 1:N,\n    tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle');\n    sz = get(tx, 'Extent');\n    wy = 2/3 * sz(4); wyB = y(i) - wy;  wyT = y(i) + wy;\n    wx = max(2/3 * sz(3), wy); wxL = x(i) - wx; wxR = x(i) + wx;\n    ptc = patch([wxL wxR wxR wxL], [wyT wyT wyB wyB], c(i,:)); \n    set(ptc, 'FaceColor', c(i,:));   %  'w' \n    wd(i,:) = [wx wy];\n    delete(tx);\n    tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle');      \n    h(i,:) = [tx ptc];\nend;\n\nfunction [h,yy,zz] = my_arrow(varargin)\n% [h,yy,zz] = my_arrow(varargin)  Draw a line with an arrowhead.\n\n% A lot of the original code is removed and most of the remaining can probably go too\n% since it comes from a general use function only being called inone context. - Leon Peshkin \n% Copyright 1997, Erik A. Johnson <johnsone@uiuc.edu>, 8/14/97\n\nax         = [];       % set values to empty matrices\ndeflen        = 12;  %  16\ndefbaseangle  = 45;  %  90\ndeftipangle   = 16;\ndefwid = 0;  defpage = 0;  defends = 1;\nArrowTag = 'Arrow';  % The 'Tag' we'll put on our arrows\nstart      = varargin{1};    % fill empty arguments\nstop       = varargin{2}; \ncrossdir   = [NaN NaN NaN];   \nlen        = NaN; baseangle  = NaN;  tipangle = NaN;   wid = NaN;              \npage       = 0; ends  = NaN;   \nstart = [start NaN];   stop = [stop NaN];\no         = 1;     % expand single-column arguments\nax        = gca;\n% set up the UserData data (here so not corrupted by log10's and such)\nud = [start stop len baseangle tipangle wid page crossdir ends];\n% Get axes limits, range, min; correct for aspect ratio and log scale\naxm  = zeros(3,1);   axr = axm;   axrev = axm;  ap  = zeros(2,1);\nxyzlog = axm; limmin    = ap;  limrange  = ap;  oldaxlims = zeros(1,7);\noneax = 1;      % all(ax==ax(1));  LPM\nif (oneax),\n\tT = zeros(4,4); invT = zeros(4,4);\nelse\n\tT = zeros(16,1); invT = zeros(16,1); \nend\naxnotdone = 1; % logical(ones(size(ax)));  LPM \nwhile (any(axnotdone))\n\tii = 1;  % LPM min(find(axnotdone));\n\tcurax = ax(ii);\n\tcurpage = page(ii);\n\t% get axes limits and aspect ratio\n\taxl = [get(curax,'XLim'); get(curax,'YLim'); get(curax,'ZLim')];\n\toldaxlims(find(oldaxlims(:,1)==0, 1),:) = [curax reshape(axl',1,6)];\n\t% get axes size in pixels (points)\n\tu = get(curax,'Units');\n\taxposoldunits = get(curax,'Position');\n\treally_curpage = curpage & strcmp(u,'normalized');\n\tif (really_curpage)\n\t\tcurfig = get(curax,'Parent');  \t\tpu = get(curfig,'PaperUnits');\n\t\tset(curfig,'PaperUnits','points');  pp = get(curfig,'PaperPosition');\n\t\tset(curfig,'PaperUnits',pu);         set(curax,'Units','pixels');\n\t\tcurapscreen = get(curax,'Position'); set(curax,'Units','normalized');\n\t\tcurap = pp.*get(curax,'Position');\n    else\n\t\tset(curax,'Units','pixels');\n\t\tcurapscreen = get(curax,'Position');\n\t\tcurap = curapscreen;\n    end\n\tset(curax,'Units',u);      set(curax,'Position',axposoldunits);\n\t% handle non-stretched axes position\n\tstr_stretch = {'DataAspectRatioMode'; 'PlotBoxAspectRatioMode' ; 'CameraViewAngleMode' };\n\tstr_camera  = {'CameraPositionMode'  ; 'CameraTargetMode' ; ...\n\t                'CameraViewAngleMode' ; 'CameraUpVectorMode'};\n\tnotstretched = strcmp(get(curax,str_stretch),'manual');\n\tmanualcamera = strcmp(get(curax,str_camera),'manual');\n\tif ~arrow_WarpToFill(notstretched,manualcamera,curax)\n\t\t% find the true pixel size of the actual axes\n\t\ttexttmp = text(axl(1,[1 2 2 1 1 2 2 1]), ...\n\t\t               axl(2,[1 1 2 2 1 1 2 2]), axl(3,[1 1 1 1 2 2 2 2]),'');\n\t\tset(texttmp,'Units','points');\n\t\ttextpos = get(texttmp,'Position');\n\t\tdelete(texttmp);\n\t\ttextpos = cat(1,textpos{:});\n\t\ttextpos = max(textpos(:,1:2)) - min(textpos(:,1:2));\n\t\t% adjust the axes position\n\t\tif (really_curpage)  \t\t\t% adjust to printed size\n\t\t\ttextpos = textpos * min(curap(3:4)./textpos);\n\t\t\tcurap = [curap(1:2)+(curap(3:4)-textpos)/2 textpos];\n        else                         % adjust for pixel roundoff\n\t\t\ttextpos = textpos * min(curapscreen(3:4)./textpos);\n\t\t\tcurap = [curap(1:2)+(curap(3:4)-textpos)/2 textpos];\n        end\n    end\n\t% adjust limits for log scale on axes\n\tcurxyzlog = [strcmp(get(curax,'XScale'),'log'); ...\n\t             strcmp(get(curax,'YScale'),'log'); strcmp(get(curax,'ZScale'),'log')];\n\tif (any(curxyzlog))\n\t\tii = find([curxyzlog;curxyzlog]);\n\t\tif (any(axl(ii)<=0))\n\t\t\terror([upper(mfilename) ' does not support non-positive limits on log-scaled axes.']);\n        else\n\t\t\taxl(ii) = log10(axl(ii));\n        end\n    end\n\t% correct for 'reverse' direction on axes;\n\tcurreverse = [strcmp(get(curax,'XDir'),'reverse'); ...\n\t              strcmp(get(curax,'YDir'),'reverse'); strcmp(get(curax,'ZDir'),'reverse')];\n\tii = find(curreverse);\n\tif ~isempty(ii)\n\t\taxl(ii,[1 2])=-axl(ii,[2 1]);\n    end\n\t% compute the range of 2-D values\n\tcurT = get(curax,'Xform');\n\tlim = curT*[0 1 0 1 0 1 0 1;0 0 1 1 0 0 1 1;0 0 0 0 1 1 1 1;1 1 1 1 1 1 1 1];\n\tlim = lim(1:2,:)./([1;1]*lim(4,:));\n\tcurlimmin = min(lim,[],2);\n\tcurlimrange = max(lim,[],2) - curlimmin;\n\tcurinvT = inv(curT);\n\tif ~oneax\n        curT = curT.'; curinvT = curinvT.'; curT = curT(:); curinvT = curinvT(:);\n    end\n\t% check which arrows to which cur corresponds\n\tii = find((ax==curax)&(page==curpage));\n\too = ones(1,length(ii)); \taxr(:,ii) = diff(axl,1,2) * oo;\n\taxm(:,ii) = axl(:,1) * oo;  axrev(:,ii) = curreverse  * oo;\n\tap(:,ii)  = curap(3:4)' * oo; xyzlog(:,ii) = curxyzlog   * oo;\n\tlimmin(:,ii) = curlimmin  * oo;  limrange(:,ii) = curlimrange * oo;\n\tif (oneax),\n\t\tT    = curT;  invT = curinvT;\n    else\n\t\tT(:,ii) = curT * oo; invT(:,ii) = curinvT * oo;\n\tend;\n\taxnotdone(ii) = zeros(1,length(ii));\nend;\noldaxlims(oldaxlims(:,1)==0,:) = [];\n\n% correct for log scales\ncurxyzlog = xyzlog.';  ii = find(curxyzlog(:));\nif ~isempty(ii)\n\tstart(ii) = real(log10(start(ii))); stop(ii) = real(log10(stop(ii)));\n\tif (all(imag(crossdir)==0)) % pulled (ii) subscript on crossdir, 12/5/96 eaj\n\t\tcrossdir(ii) = real(log10(crossdir(ii)));\n    end\nend\nii = find(axrev.');    % correct for reverse directions\nif ~isempty(ii)\n\tstart(ii) = -start(ii);  stop(ii) = -stop(ii); crossdir(ii) = -crossdir(ii);\nend\nstart  = start.';  stop  = stop.';   % transpose start/stop values\n% take care of defaults, page was done above\nii = find(isnan(start(:)));  if ~isempty(ii),  start(ii) = axm(ii)+axr(ii)/2;  end;\nii = find(isnan(stop(:)));  if ~isempty(ii),  stop(ii) = axm(ii)+axr(ii)/2;  end;\nii = find(isnan(crossdir(:))); if ~isempty(ii),  crossdir(ii) = zeros(length(ii),1); end;\nii = find(isnan(len));  if ~isempty(ii),  len(ii) = ones(length(ii),1)*deflen; end;\nbaseangle(ii) = ones(length(ii),1)*defbaseangle;  tipangle(ii) = ones(length(ii),1)*deftipangle; \nwid(ii) = ones(length(ii),1) * defwid;   ends(ii) = ones(length(ii),1) * defends;\n% transpose rest of values\nlen  = len.';  baseangle = baseangle.'; tipangle  = tipangle.'; wid = wid.';  \npage = page.'; crossdir  = crossdir.';  ends = ends.'; ax   = ax.';\n\n% for all points with start==stop, start=stop-(verysmallvalue)*(up-direction);\nii = find(all(start==stop));\nif ~isempty(ii)\n\t% find an arrowdir vertical on screen and perpendicular to viewer\n\t%\ttransform to 2-D\n\t\ttmp1 = [(stop(:,ii)-axm(:,ii))./axr(:,ii);ones(1,length(ii))];\n\t\tif (oneax), twoD=T*tmp1;\n        else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,ii).*tmp1;\n\t\t      tmp2=zeros(4,4*length(ii)); tmp2(:)=tmp1(:);\n\t\t      twoD=zeros(4,length(ii)); twoD(:)=sum(tmp2)'; \n        end\n\t\ttwoD=twoD./(ones(4,1)*twoD(4,:));\n\t%\tmove the start point down just slightly\n\t\ttmp1 = twoD + [0;-1/1000;0;0]*(limrange(2,ii)./ap(2,ii));\n\t%\ttransform back to 3-D\n\t\tif (oneax), threeD=invT*tmp1;\n        else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT(:,ii).*tmp1;\n\t\t      tmp2=zeros(4,4*length(ii)); tmp2(:)=tmp1(:);\n\t\t      threeD=zeros(4,length(ii)); threeD(:)=sum(tmp2)'; \n        end\n\t\tstart(:,ii) = (threeD(1:3,:)./(ones(3,1)*threeD(4,:))).*axr(:,ii)+axm(:,ii);\nend;\n% compute along-arrow points\n%\ttransform Start points\n\ttmp1 = [(start-axm)./axr; 1];\n\tif (oneax), X0=T*tmp1;\n    else  tmp1 = [tmp1;tmp1;tmp1;tmp1]; tmp1=T.*tmp1;\n\t      tmp2 = zeros(4,4); tmp2(:)=tmp1(:);\n\t      X0=zeros(4,1); X0(:)=sum(tmp2)'; \n    end\n\tX0=X0./(ones(4,1)*X0(4,:));\n%\ttransform Stop points\n\ttmp1=[(stop-axm)./axr; 1];\n\tif (oneax), Xf=T*tmp1;\n    else  tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T.*tmp1;\n\t      tmp2=zeros(4,4); tmp2(:)=tmp1(:);\n\t      Xf=zeros(4,1); Xf(:)=sum(tmp2)'; \n    end\n\tXf=Xf./(ones(4,1)*Xf(4,:));\n%\tcompute pixel distance between points\n\tD = sqrt(sum(((Xf(1:2,:)-X0(1:2,:)).*(ap./limrange)).^2));\n%\tcompute and modify along-arrow distances\n\tlen1 = len;\n\tlen2 = len - (len.*tan(tipangle/180*pi)-wid/2).*tan((90-baseangle)/180*pi);\n\tslen0 = 0; \tslen1 = len1 .* ((ends==2)|(ends==3));\n\tslen2 = len2 .* ((ends==2)|(ends==3));\n\tlen0 = 0; len1  = len1 .* ((ends==1)|(ends==3));\n\tlen2  = len2 .* ((ends==1)|(ends==3));\n      ii = find((ends==1)&(D<len2));  \t%\tfor no start arrowhead\n\t  if ~isempty(ii),\n\t\t  slen0(ii) = D(ii)-len2(ii);\n\t  end;\n\t  ii = find((ends==2)&(D<slen2));  \t%\tfor no end arrowhead\n\t  if ~isempty(ii),\n\t\t  len0(ii) = D(ii)-slen2(ii);\n\t  end;\n\tlen1  = len1  + len0;    len2 = len2  + len0;\n\tslen1 = slen1 + slen0; \tslen2 = slen2 + slen0;\n \t% note:  the division by D below will probably not be accurate if both\n \t%        of the following are true:\n \t%           1. the ratio of the line length to the arrowhead\n \t%              length is large\n \t%           2. the view is highly perspective.\n%\tcompute stoppoints\n\ttmp1 = X0.*(ones(4,1)*(len0./D))+Xf.*(ones(4,1)*(1-len0./D));\n\tif (oneax), tmp3 = invT*tmp1;\n    else  tmp1 = [tmp1;tmp1;tmp1;tmp1]; tmp1 = invT.*tmp1;\n\t      tmp2 = zeros(4,4); tmp2(:) = tmp1(:);\n\t      tmp3 = zeros(4,1); tmp3(:) = sum(tmp2)'; \n    end\n\tstoppoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;\n%\tcompute tippoints\n\ttmp1=X0.*(ones(4,1)*(len1./D))+Xf.*(ones(4,1)*(1-len1./D));\n\tif (oneax), tmp3=invT*tmp1;\n    else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;\n\t      tmp2=zeros(4,4); tmp2(:)=tmp1(:);\n\t      tmp3=zeros(4,1); tmp3(:)=sum(tmp2)'; \n    end\n\ttippoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;\n%\tcompute basepoints\n\ttmp1=X0.*(ones(4,1)*(len2./D))+Xf.*(ones(4,1)*(1-len2./D));\n\tif (oneax), tmp3=invT*tmp1;\n    else  tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;\n\t      tmp2=zeros(4,4); tmp2(:)=tmp1(:);\n\t      tmp3=zeros(4,1); tmp3(:)=sum(tmp2)'; \n    end\n\tbasepoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;\n%\tcompute startpoints\n\ttmp1=X0.*(ones(4,1)*(1-slen0./D))+Xf.*(ones(4,1)*(slen0./D));\n\tif (oneax), tmp3=invT*tmp1;\n    else  tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;\n\t      tmp2=zeros(4,4); tmp2(:) = tmp1(:);\n\t      tmp3=zeros(4,1); tmp3(:) = sum(tmp2)'; \n    end\n\tstartpoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;\n%\tcompute stippoints\n\ttmp1=X0.*(ones(4,1)*(1-slen1./D))+Xf.*(ones(4,1)*(slen1./D));\n\tif (oneax), tmp3=invT*tmp1;\n    else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1 = invT.*tmp1;\n\t      tmp2=zeros(4,4); tmp2(:)=tmp1(:); \n\t      tmp3=zeros(4,1); tmp3(:)=sum(tmp2)'; \n    end\n\tstippoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;\n%\tcompute sbasepoints\n\ttmp1=X0.*(ones(4,1)*(1-slen2./D))+Xf.*(ones(4,1)*(slen2./D));\n\tif (oneax), tmp3=invT*tmp1;\n    else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;\n\t      tmp2=zeros(4,4); tmp2(:)=tmp1(:);\n\t      tmp3=zeros(4,1); tmp3(:)=sum(tmp2)'; \n    end\n\tsbasepoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;\n\n% compute cross-arrow directions for arrows with NormalDir specified\nif (any(imag(crossdir(:))~=0)),\n\tii = find(any(imag(crossdir)~=0));\n\tcrossdir(:,ii) = cross((stop(:,ii)-start(:,ii))./axr(:,ii), ...\n\t                       imag(crossdir(:,ii))).*axr(:,ii);\nend;\nbasecross  = crossdir + basepoint;  % compute cross-arrow directions\ntipcross   = crossdir + tippoint;  sbasecross = crossdir + sbasepoint;\nstipcross  = crossdir + stippoint;\nii = find(all(crossdir==0)|any(isnan(crossdir)));\nif ~isempty(ii),\n\tnumii = length(ii);\n\t%\ttransform start points\n\t\ttmp1 = [basepoint(:,ii) tippoint(:,ii) sbasepoint(:,ii) stippoint(:,ii)];\n\t\ttmp1 = (tmp1-axm(:,[ii ii ii ii])) ./ axr(:,[ii ii ii ii]);\n\t\ttmp1 = [tmp1; ones(1,4*numii)];\n\t\tif (oneax), X0=T*tmp1;\n        else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,[ii ii ii ii]).*tmp1;\n\t\t      tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:);\n\t\t      X0=zeros(4,4*numii); X0(:)=sum(tmp2)'; \n        end\n\t\tX0=X0./(ones(4,1)*X0(4,:));\n\t%\ttransform stop points\n\t\ttmp1 = [(2*stop(:,ii)-start(:,ii)-axm(:,ii))./axr(:,ii);ones(1,numii)];\n\t\ttmp1 = [tmp1 tmp1 tmp1 tmp1];\n\t\tif (oneax) Xf=T*tmp1;\n        else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,[ii ii ii ii]).*tmp1;\n\t\t      tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:);\n\t\t      Xf=zeros(4,4*numii); Xf(:)=sum(tmp2)'; \n        end\n\t\tXf=Xf./(ones(4,1)*Xf(4,:));\n\t%\tcompute perpendicular directions\n\t\tpixfact = ((limrange(1,ii)./limrange(2,ii)).*(ap(2,ii)./ap(1,ii))).^2;\n\t\tpixfact = [pixfact pixfact pixfact pixfact];\n\t\tpixfact = [pixfact;1./pixfact];\n\t\t[dummyval,jj] = max(abs(Xf(1:2,:)-X0(1:2,:)));\n\t\tjj1 = ((1:4)'*ones(1,length(jj))==ones(4,1)*jj);\n\t\tjj2 = ((1:4)'*ones(1,length(jj))==ones(4,1)*(3-jj));\n\t\tjj3 = jj1(1:2,:);\n\t\tXp = X0;\n\t\tXp(jj2) = X0(jj2) + ones(sum(jj2(:)),1);\n\t\tXp(jj1) = X0(jj1) - (Xf(jj2)-X0(jj2))./(Xf(jj1)-X0(jj1)) .* pixfact(jj3);\n\t%\tinverse transform the cross points\n\t\tif (oneax), Xp=invT*Xp;\n\t\telse, tmp1=[Xp;Xp;Xp;Xp]; tmp1=invT(:,[ii ii ii ii]).*tmp1;\n\t\t      tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:);\n\t\t      Xp=zeros(4,4*numii); Xp(:)=sum(tmp2)'; end;\n\t\tXp=(Xp(1:3,:)./(ones(3,1)*Xp(4,:))).*axr(:,[ii ii ii ii])+axm(:,[ii ii ii ii]);\n\t\tbasecross(:,ii)  = Xp(:,0*numii+(1:numii));\n\t\ttipcross(:,ii)   = Xp(:,1*numii+(1:numii));\n\t\tsbasecross(:,ii) = Xp(:,2*numii+(1:numii));\n\t\tstipcross(:,ii)  = Xp(:,3*numii+(1:numii));\nend;\n\n% compute all points\n%\tcompute start points\n\taxm11 = [axm axm axm axm axm axm axm axm axm axm axm];\n\taxr11 = [axr axr axr axr axr axr axr axr axr axr axr];\n\tst = [stoppoint tippoint basepoint sbasepoint stippoint startpoint stippoint sbasepoint basepoint tippoint stoppoint];\n\ttmp1 = (st - axm11) ./ axr11;\n\ttmp1 = [tmp1; ones(1,size(tmp1,2))];\n\tif (oneax), X0=T*tmp1;\n    else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[T T T T T T T T T T T].*tmp1;\n\t      tmp2=zeros(4,44); tmp2(:)=tmp1(:);\n\t      X0=zeros(4,11); X0(:)=sum(tmp2)'; \n    end\n\tX0=X0./(ones(4,1)*X0(4,:));\n%\tcompute stop points\n\ttmp1 = ([start tipcross basecross sbasecross stipcross stop stipcross sbasecross basecross tipcross start] ...\n\t     - axm11) ./ axr11;\n\ttmp1 = [tmp1; ones(1,size(tmp1,2))];\n\tif (oneax), Xf=T*tmp1;\n    else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[T T T T T T T T T T T].*tmp1;\n\t      tmp2=zeros(4,44); tmp2(:)=tmp1(:);\n\t      Xf=zeros(4,11); Xf(:)=sum(tmp2)'; \n    end\n\tXf=Xf./(ones(4,1)*Xf(4,:));\n%\tcompute lengths\n\tlen0  = len.*((ends==1)|(ends==3)).*tan(tipangle/180*pi);\n\tslen0 = len.*((ends==2)|(ends==3)).*tan(tipangle/180*pi);\n\tle = [0 len0 wid/2 wid/2 slen0 0 -slen0 -wid/2 -wid/2 -len0 0];\n\taprange = ap./limrange;\n\taprange = [aprange aprange aprange aprange aprange aprange aprange aprange aprange aprange aprange];\n\tD = sqrt(sum(((Xf(1:2,:)-X0(1:2,:)).*aprange).^2));\n\tDii=find(D==0); if ~isempty(Dii), D=D+(D==0); le(Dii)=zeros(1,length(Dii)); end; \n\ttmp1 = X0.*(ones(4,1)*(1-le./D)) + Xf.*(ones(4,1)*(le./D));\n%\tinverse transform\n\tif (oneax), tmp3=invT*tmp1;\n    else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[invT invT invT invT invT invT invT invT invT invT invT].*tmp1;\n\t      tmp2=zeros(4,44); tmp2(:)=tmp1(:);\n\t      tmp3=zeros(4,11); tmp3(:)=sum(tmp2)'; \n    end\n\tpts = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)) .* axr11 + axm11;\n% correct for ones where the crossdir was specified\nii = find(~(all(crossdir==0)|any(isnan(crossdir))));\nif ~isempty(ii),\n\tD1 = [pts(:,1+ii)-pts(:,9+ii) pts(:,2+ii)-pts(:,8+ii) ...\n\t      pts(:,3+ii)-pts(:,7+ii) pts(:,4+ii)-pts(:,6+ii) ...\n\t      pts(:,6+ii)-pts(:,4+ii) pts(:,7+ii)-pts(:,3+ii) ...\n\t      pts(:,8+ii)-pts(:,2+ii) pts(:,9+ii)-pts(:,1+ii)]/2;\n\tii = ii'*ones(1,8) + ones(length(ii),1)*[1:4 6:9];   ii = ii(:)';\n\tpts(:,ii) = st(:,ii) + D1;\nend;\n% readjust for reverse directions\niicols = (1:1)';  iicols = iicols(:,ones(1,11));  iicols = iicols(:).';\ntmp1 = axrev(:,iicols);\nii = find(tmp1(:)); if ~isempty(ii), pts(ii)=-pts(ii); end;\n% readjust for log scale on axes\ntmp1 = xyzlog(:,iicols);\nii = find(tmp1(:)); if ~isempty(ii), pts(ii)=10.^pts(ii); end;\n% compute the x,y,z coordinates of the patches;\nii = (0:10)' + ones(11,1);\nii = ii(:)';\nx = zeros(11,1);  y = x;    z = x;\nx(:) = pts(1,ii)';   y(:) = pts(2,ii)';  z(:) = pts(3,ii)';\n           % do the output\n  % % create or modify the patches\nH = 0; \n   % % make or modify the arrows\nif arrow_is2DXY(ax(1)), zz=[]; else zz=z(:,1); end;\nxyz = {'XData',x(:,1),'YData',y(:,1),'ZData',zz,'Tag',ArrowTag};\nH(1) = patch(xyz{:});\n  % % additional properties\nset(H,'Clipping','off');\nset(H,{'UserData'},num2cell(ud,2));\n  % make sure the axis limits did not change\n\nfunction [out,is2D] = arrow_is2DXY(ax)\n% check if axes are 2-D X-Y plots,  may not work for modified camera angles, etc.\n\tout = zeros(size(ax)); % 2-D X-Y plots\n\tis2D = out;            % any 2-D plots\n\tviews = get(ax(:),{'View'});\n\tviews = cat(1,views{:});\n\tout(:) = abs(views(:,2))==90;\n\tis2D(:) = out(:) | all(rem(views',90)==0)';\n\nfunction out = arrow_WarpToFill(notstretched,manualcamera,curax) %#ok<INUSL>\n% check if we are in \"WarpToFill\" mode.\n\tout = strcmp(get(curax,'WarpToFill'),'on');\n\t% 'WarpToFill' is undocumented, so may need to replace this by\n\t% out = ~( any(notstretched) & any(manualcamera) );\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignmr2.m", "ext": ".m", "path": "netalign-master/experiments/crystalize_mr/netalignmr2.m", "size": 6168, "source_encoding": "utf_8", "md5": "4973769c6f25dc607067664b4d9444ae", "text": "function [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,gamma,stepm,rtype,maxiter,verbose)\n% NETALIGNMR Compute the matching relaxation heuristic for network alignment\n%\n% Given a network alignment problem, the matching heuristic solves a\n% sequence of matching problems to generate good upper and lower bounds on\n% the solutions.\n%\n% [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,stepm,rtype,\n%    maxiter,verbose) \n% fully specifies all inputs and outputs.  \n%\n% Input\n% -----\n%   S : the complete set of squares\n%   w : the matching weights for all edges in the link graph L\n%   a : the value of alpha in the netalign objective\n%   b : the value of beta in the netalign objective\n%   li : the start point of each edge in L (a vertex number from graph A)\n%   lj : the end point of each edge in L (a vertex number from graph B)\n%   gamma : the starting step value (default = 0.5)\n%   stepm : number of non-decreasing iterations adjusting the step length\n%     (default = 100)\n%   rtype : the rounding type (default = 1)\n%     rtype = 1 : only consider the current matching\n%     rtype = 2 : try enriching the matching with info from other squares\n%   maxiter : maximum number of iterations to take (default = 1000)\n%   verbose : output verbose information at each iteration (default = true)\n%\n% Output\n% ------\n%   xbest : the best heuristic solution, it may or may not be a matching\n%   status : three values to describe the status of the solution\n%     status(1) = 1 if the problem is solved to optimality, 0 otherwise\n%     status(2) = best lower bound\n%     status(3) = best upper bound\n%   hist : the history of properties of each iteration\n%     hist(k,) = \n%     hist(k,) = best lower bound at iteration k\n%     hist(k,) = best upper bound at iteration k\n%     hist(k,) = current value at iteration k\n%     hist(k,) = matching weight at iteration k\n%     hist(k,) = matching cardinality at iteration k\n%     hist(k,) = overlap at iteration k\n%\n% Example:\n%   load('../data/natalie_graphs');\n%   netalignmr(S,w,0,1,li,lj);\n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2008-2009\n% Computational Approaches to Digital Stewardship\n\n% 2009-06-11: Initial coding (David and Ying)\n% 2009-06-15: Cleanup and optimization (David)\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('stepm','var') || isempty(stepm), stepm=25; end\nif ~exist('rtype','var') || isempty(rtype), rtype=1; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=1000; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \nif ~exist('gamma', 'var') || isempty(gamma), gamma = 0.4; end\n\nm = max(li);\nn = max(lj);\n\n[rp ci ai tripi matn matm] = bipartite_matching_setup(w,li,lj,m,n); \nmperm = tripi(tripi>0); % a permutation for the matching problem\n\nS = double(S);\nU = sparse(size(S,1),size(S,2));\n\nxbest = w;\nxbest(:) = 0;\n\nflower = 0;                       % best lower bound on the solution\nfupper = Inf;                     % best upper bound on the solution\nnext_reduction_iteration = stepm; % reduce the step\ncrystalize_phase = 0;             % a variable to indicate we are in the crystalization phase\ncrystalize_gamma = 1e-20;\ncrystalize_stepm = 5;\n\n\n\nhist = zeros(maxiter,7);\n\nif verbose % print the header\n    fprintf('%5s   %4s   %8s   %7s %7s %7s  %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'norm-u', 'lower','upper', 'cur', 'obj', 'weight', 'card', 'overlap');\nend    \nfor iter = 1:maxiter   \n    [q,SM] = maxrowmatch((b/2)*S + U-U',li,lj,m,n);\n    x = a*w + q;\n    \n    %[val ma mb mi]= bipartite_matching(nw,li,lj,m,n);\n    ai=zeros(length(tripi),1); ai(tripi>0)=x(mperm);\n    [val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);\n    \n    % compute statistics \n    matchval = mi'*w;\n    overlap = mi'*S*mi/2; \n    card = length(ma);\n    f = a*matchval + b*overlap;\n    \n    if val<fupper\n        fupper=val;\n        if ~crystalize_phase\n            next_reduction_iteration = iter+stepm;\n        end\n    end\n    if f>flower\n        flower=f;\n        itermark = '*';\n        xbest = mi;\n    else\n        itermark = ' ';\n    end        \n    \n    if rtype==1\n        % no work\n    elseif rtype==2\n        mw = S*x;\n        mw = a*w + b/2*mw;\n       \n        ai=zeros(length(tripi),1); ai(tripi>0)=mw(mperm);\n        [val ma mb mx] = bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);\n        \n        card = length(ma);\n        matchval = mx'*w;\n        overlap = mx'*S*mx/2;\n        f = a*matchval + b*overlap;\n        \n        if f>flower\n            flower=f;\n            itermark = '**';\n            mi = mx;\n            xbest = mw;\n        end\n    end\n    \n    % report on current iter\n    hist(iter,1:end) = [norm(nonzeros(U),1), flower, fupper, f, matchval, card, overlap];\n    \n    if verbose\n        fprintf('%5s   %4i   %8.1e   %7g %7g %7g  %7g %7g %7i %7i\\n', ...\n            itermark, iter, norm(nonzeros(U),1), ...\n            flower, fupper, val, ...\n            f, matchval, card, overlap);\n    end\n    \n    if iter==next_reduction_iteration\n        gamma = gamma*0.5;\n        if verbose\n            fprintf('%5s   %4s   reducing step to %g\\n', '', '', gamma);\n        end\n        if gamma < 1e-24, break; end\n        if crystalize_phase\n            next_reduction_iteration = iter+crystalize_stepm;\n        else\n            next_reduction_iteration = iter+stepm;\n        end\n    end\n    \n    if (fupper-flower)<1e-2\n        break;\n    end\n    \n    % check if we need to enter the crystalization phase\n    num_crystalize_iters = crystalize_stepm*(log(crystalize_gamma)-log(gamma))/log(0.5);\n    if ~crystalize_phase && num_crystalize_iters > maxiter - iter\n        crystalize_phase = 1;\n        next_reduction_iteration = iter+crystalize_stepm;\n        fprintf('%5s   %4s   switching to crystalization phase\\n', '', '');\n    end\n    \n    U = U - diag(sparse(gamma*mi))*triu(SM) + tril(SM)'*diag(sparse(gamma*mi));\n    \n    U = bound(U, -.5, .5);\nend\nhist = hist(1:iter,:);\nstatus = zeros(1,3);\nstatus(1) = (fupper-flower)<1e-2;\nstatus(2) = flower;\nstatus(3) = fupper;\n\nfunction S=bound(S,a,b)\nS=spfun(@(x) min(max(x,a),b), S);\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignbp_y.m", "ext": ".m", "path": "netalign-master/experiments/old/rounding/netalignbp_y.m", "size": 6447, "source_encoding": "utf_8", "md5": "60b9cf0f094a4f2449195b5ec82a82d9", "text": "function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,maxiter,verbose)\n% NETALIGNBP Solve the network alignment problem with Belief Propagation\n%\n% \n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2007-2008\n% Computational Approaches to Digital Stewardship\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('gamma','var') || isempty(gamma), gamma=0.85; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \n\nnedges = length(li);\nnsquares = nnz(S)/2;\nm = max(li);\nn = max(lj);\n\n% compute a vector that allows us to transpose data between squares.  \n% Recall the BP algorithm requires edge(i,j) to send messages to edge(r,s)\n% when (i,j) and (r,s) form a square.  However, edge(i,j) needs information\n% the information that (r,s) sent it from the previous iteraton.  \n% If we imagine that each non-zero of S has a value, we want to tranpose\n% these values.\n[sui suj] = find(triu(S,1)); % only consider each square once.\nSI = sparse(sui,suj,1:length(sui),size(S,1),size(S,2)); % assign indices \nSI = SI + SI'; % SI now has symmetric indices \n[si sj sind] = find(SI);\nSP = sparse(si,sind,true,size(S,1),nsquares); % each column in SP has 2 nz\n[sij sijrs] = find(SP);\nsind = (1:nnz(SP))';\nspair = sind; spair(1:2:end) = sind(2:2:end); spair(2:2:end) = sind(1:2:end);\n% sij, sijrs maps between rows and squares\n% spair is now an indexing vector that accomplishes what we need\n\n% Initialize the messages\nma = zeros(nedges,1);\nmb = ma;\nms = zeros(nnz(S),1);\n\ndamping = gamma;\ncurdamp = 1;\niter = 1;\nalpha = a;\nbeta = b;\n\n% Initialize history\nhista = zeros(maxiter,4); % history of messages from ei->a vertices\nhistb = zeros(maxiter,4); % history of messages from ei->b vertices\nfbest = 0; fbestiter = 0;\nif verbose % print the header\n    fprintf('%4s   %4s   %7s %7s %7s %7s   %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...\n        'obj_mb', 'wght_mb', 'card_mb', 'over_mb');\nend\n\n% setup the matching problem once\n[rp ci ai tripi matn matm] = bipartite_matching_setup(...\n                                    w,li,lj,m,n);                               \nclear ai;\n\nwhile iter<=maxiter\n    prevma = ma;\n    prevmb = mb;\n    prevms = ms;\n    curdamp = damping*curdamp;\n    \n    omaxb = othermaxplus(2,li,lj,mb,m,n);\n    omaxa = othermaxplus(1,li,lj,ma,m,n);\n    \n    msflip = ms(spair); % swap ij->ijrs to rs->ijrs \n    mymsflip = msflip+beta;\n    mymsflip = min(beta,mymsflip);\n    mymsflip = max(0,mymsflip);\n        \n    sums = accumarray(sij,mymsflip,[nedges 1],@sum,0);\n    \n    ma = alpha*w - omaxb + sums;\n    mb = alpha*w - omaxa + sums;\n    \n    ms = alpha*w(sij)-(omaxb(sij) + omaxa(sij));\n    ms = ms + othersum(sij,sijrs,mymsflip,nedges,nsquares);\n    \n    ma = curdamp*(ma) + (1-curdamp)*(prevma);\n    mb = curdamp*(mb) + (1-curdamp)*(prevmb);\n    ms = curdamp*(ms) + (1-curdamp)*(prevms);\n    \n    % now compute the matchings\n    sums1 = accumarray(sij,mymsflip,[nedges 1],@sum,0);\n    sums2 = accumarray(sij,mymsflip,[nedges 1],@sum,0);\n    hista(iter,:) = round_messages(alpha*w + beta*sums1,S,w,alpha,beta,rp,ci,tripi,matn,matm);\n    histb(iter,:) = round_messages(alpha*w + beta*sums2,S,w,alpha,beta,rp,ci,tripi,matn,matm);\n    if hista(iter,1)>fbest\n        fbestiter=iter; mbest=sums1; fbest=hista(iter,1);\n    end\n    if histb(iter,1)>fbest\n        fbestiter=-iter; mbest=sums2; fbest=histb(iter,1);\n    end\n    \n    if verbose\n        if fbestiter==iter, bestchar='*a'; \n        elseif fbestiter==-iter, bestchar='*b';\n        else bestchar='';\n        end\n        fprintf('%4s   %4i   %7g %7g %7i %7i   %7g %7g %7i %7i\\n', ...\n            bestchar, iter, hista(iter,:), histb(iter,:));\n    end\n    iter=iter+1;\nend\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m)\nai=zeros(length(tripi),1); ai(tripi>0)=messages;\n[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);\nmatchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*mi))/2; \nf = alpha*matchweight + beta*overlap;\ninfo = [f matchweight cardinality overlap];\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction omp=othermaxplus(dim,li,lj,lw,m,n)\n% OTHERMAXPLUS Apply the other-max-plus operator to a sparse matrix\n%\n% The other-max-plus operator applies the max-plus aggegration function\n% over the rows or columns of the matrix, where, for \n% each non-zeros, the non-zero itself cannot be the maximum.  Consequently,\n% Hence, it's the max-plus of all the \"other\" elements in the row or column\n% (which is controlled by dim).\n%\n% omp=othermaxplus(dim,li,lj,lw,m,n) applies the other-max-plus operator\n% to the matrix sparse(li, lj, lw, m, n).  The return value omp gives\n% the other-max-plus value for each non-zero element, e.g. the matrix\n% is sparse(li, lj, omp, m, n).\n\nif dim==1\n    % max-plus over cols\n    i1 = lj;\n    i2 = li;\n    N = n;\nelse\n    % max-plus over rows\n    i1 = li;\n    i2 = lj;\n    N = m;\nend\n\n% the output of the other-max-plus is either the maximum element \n% in the row or column (if the element itself isn't the maximum) or\n% the second largest element in the row or column (if the element itself\n% IS the maximum).\n\ndimmax1 =0*ones(N,1);      % largest value\ndimmax2 = 0*ones(N,1);     % second largest value, \n% this is correct because of the definition of the max-plus function.  \ndimmaxind = zeros(N,1);   % index of largest value\nnedges = length(li);\n\nfor i=1:nedges\n    if lw(i) > dimmax2(i1(i))\n        if lw(i) > dimmax1(i1(i))\n            dimmax2(i1(i)) = dimmax1(i1(i));\n            dimmax1(i1(i)) = lw(i);\n            dimmaxind(i1(i)) = i2(i);\n        else\n            dimmax2(i1(i)) = lw(i);\n        end\n    end \nend\n\nomp = zeros(size(lw));\nfor i=1:nedges\n    if i2(i) == dimmaxind(i1(i))\n        omp(i) = dimmax2(i1(i));\n    else\n        omp(i) = dimmax1(i1(i));\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction os=othersum(si,sj,s,m,n) %#ok<INUSD,INUSL>\n% OTHERSUM Compute the sum of each column of the matrix, without each\n% individual entry.  This corresponds to a matrix where each entry is the\n% sum of each column but with each entry subtracted.  \n\nrowsum=accumarray(si,s,[m,1]);\nos=rowsum(si)-s;    \n\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignbp_yz.m", "ext": ".m", "path": "netalign-master/experiments/old/rounding/netalignbp_yz.m", "size": 6465, "source_encoding": "utf_8", "md5": "3bd7d5f146af758410916577c9f26650", "text": "function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,maxiter,verbose)\n% NETALIGNBP Solve the network alignment problem with Belief Propagation\n%\n% \n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2007-2008\n% Computational Approaches to Digital Stewardship\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('gamma','var') || isempty(gamma), gamma=0.85; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \n\nnedges = length(li);\nnsquares = nnz(S)/2;\nm = max(li);\nn = max(lj);\n\n% compute a vector that allows us to transpose data between squares.  \n% Recall the BP algorithm requires edge(i,j) to send messages to edge(r,s)\n% when (i,j) and (r,s) form a square.  However, edge(i,j) needs information\n% the information that (r,s) sent it from the previous iteraton.  \n% If we imagine that each non-zero of S has a value, we want to tranpose\n% these values.\n[sui suj] = find(triu(S,1)); % only consider each square once.\nSI = sparse(sui,suj,1:length(sui),size(S,1),size(S,2)); % assign indices \nSI = SI + SI'; % SI now has symmetric indices \n[si sj sind] = find(SI);\nSP = sparse(si,sind,true,size(S,1),nsquares); % each column in SP has 2 nz\n[sij sijrs] = find(SP);\nsind = (1:nnz(SP))';\nspair = sind; spair(1:2:end) = sind(2:2:end); spair(2:2:end) = sind(1:2:end);\n% sij, sijrs maps between rows and squares\n% spair is now an indexing vector that accomplishes what we need\n\n% Initialize the messages\nma = zeros(nedges,1);\nmb = ma;\nms = zeros(nnz(S),1);\n\ndamping = gamma;\ncurdamp = 1;\niter = 1;\nalpha = a;\nbeta = b;\n\n% Initialize history\nhista = zeros(maxiter,4); % history of messages from ei->a vertices\nhistb = zeros(maxiter,4); % history of messages from ei->b vertices\nfbest = 0; fbestiter = 0;\nif verbose % print the header\n    fprintf('%4s   %4s   %7s %7s %7s %7s   %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...\n        'obj_mb', 'wght_mb', 'card_mb', 'over_mb');\nend\n\n% setup the matching problem once\n[rp ci ai tripi matn matm] = bipartite_matching_setup(...\n                                    w,li,lj,m,n);                               \nclear ai;\n\nwhile iter<=maxiter\n    prevma = ma;\n    prevmb = mb;\n    prevms = ms;\n    curdamp = damping*curdamp;\n    \n    omaxb = othermaxplus(2,li,lj,mb,m,n);\n    omaxa = othermaxplus(1,li,lj,ma,m,n);\n    \n    msflip = ms(spair); % swap ij->ijrs to rs->ijrs \n    mymsflip = msflip+beta;\n    mymsflip = min(beta,mymsflip);\n    mymsflip = max(0,mymsflip);\n        \n    sums = accumarray(sij,mymsflip,[nedges 1],@sum,0);\n    \n    ma = alpha*w - omaxb + sums;\n    mb = alpha*w - omaxa + sums;\n    \n    ms = alpha*w(sij)-(omaxb(sij) + omaxa(sij));\n    ms = ms + othersum(sij,sijrs,mymsflip,nedges,nsquares);\n    \n    ma = curdamp*(ma) + (1-curdamp)*(prevma);\n    mb = curdamp*(mb) + (1-curdamp)*(prevmb);\n    ms = curdamp*(ms) + (1-curdamp)*(prevms);\n    \n    % now compute the matchings\n    sums1 = accumarray(sij,mymsflip.*ma(sij),[nedges 1],@sum,0);\n    sums2 = accumarray(sij,mymsflip.*ma(sij),[nedges 1],@sum,0);\n    hista(iter,:) = round_messages(alpha*w + beta*sums1,S,w,alpha,beta,rp,ci,tripi,matn,matm);\n    histb(iter,:) = round_messages(alpha*w + beta*sums2,S,w,alpha,beta,rp,ci,tripi,matn,matm);\n    if hista(iter,1)>fbest\n        fbestiter=iter; mbest=sums1; fbest=hista(iter,1);\n    end\n    if histb(iter,1)>fbest\n        fbestiter=-iter; mbest=sums2; fbest=histb(iter,1);\n    end\n    \n    if verbose\n        if fbestiter==iter, bestchar='*a'; \n        elseif fbestiter==-iter, bestchar='*b';\n        else bestchar='';\n        end\n        fprintf('%4s   %4i   %7g %7g %7i %7i   %7g %7g %7i %7i\\n', ...\n            bestchar, iter, hista(iter,:), histb(iter,:));\n    end\n    iter=iter+1;\nend\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m)\nai=zeros(length(tripi),1); ai(tripi>0)=messages;\n[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);\nmatchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*mi))/2; \nf = alpha*matchweight + beta*overlap;\ninfo = [f matchweight cardinality overlap];\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction omp=othermaxplus(dim,li,lj,lw,m,n)\n% OTHERMAXPLUS Apply the other-max-plus operator to a sparse matrix\n%\n% The other-max-plus operator applies the max-plus aggegration function\n% over the rows or columns of the matrix, where, for \n% each non-zeros, the non-zero itself cannot be the maximum.  Consequently,\n% Hence, it's the max-plus of all the \"other\" elements in the row or column\n% (which is controlled by dim).\n%\n% omp=othermaxplus(dim,li,lj,lw,m,n) applies the other-max-plus operator\n% to the matrix sparse(li, lj, lw, m, n).  The return value omp gives\n% the other-max-plus value for each non-zero element, e.g. the matrix\n% is sparse(li, lj, omp, m, n).\n\nif dim==1\n    % max-plus over cols\n    i1 = lj;\n    i2 = li;\n    N = n;\nelse\n    % max-plus over rows\n    i1 = li;\n    i2 = lj;\n    N = m;\nend\n\n% the output of the other-max-plus is either the maximum element \n% in the row or column (if the element itself isn't the maximum) or\n% the second largest element in the row or column (if the element itself\n% IS the maximum).\n\ndimmax1 =0*ones(N,1);      % largest value\ndimmax2 = 0*ones(N,1);     % second largest value, \n% this is correct because of the definition of the max-plus function.  \ndimmaxind = zeros(N,1);   % index of largest value\nnedges = length(li);\n\nfor i=1:nedges\n    if lw(i) > dimmax2(i1(i))\n        if lw(i) > dimmax1(i1(i))\n            dimmax2(i1(i)) = dimmax1(i1(i));\n            dimmax1(i1(i)) = lw(i);\n            dimmaxind(i1(i)) = i2(i);\n        else\n            dimmax2(i1(i)) = lw(i);\n        end\n    end \nend\n\nomp = zeros(size(lw));\nfor i=1:nedges\n    if i2(i) == dimmaxind(i1(i))\n        omp(i) = dimmax2(i1(i));\n    else\n        omp(i) = dimmax1(i1(i));\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction os=othersum(si,sj,s,m,n) %#ok<INUSD,INUSL>\n% OTHERSUM Compute the sum of each column of the matrix, without each\n% individual entry.  This corresponds to a matrix where each entry is the\n% sum of each column but with each entry subtracted.  \n\nrowsum=accumarray(si,s,[m,1]);\nos=rowsum(si)-s;    \n\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignmr.m", "ext": ".m", "path": "netalign-master/matlab/netalignmr.m", "size": 5472, "source_encoding": "utf_8", "md5": "3bba17d17e4d620f961d10dc0048c844", "text": "function [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,gamma,stepm,rtype,maxiter,verbose)\n% NETALIGNMR Compute the matching relaxation heuristic for network alignment\n%\n% Given a network alignment problem, the matching heuristic solves a\n% sequence of matching problems to generate good upper and lower bounds on\n% the solutions.\n%\n% [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,stepm,rtype,\n%    maxiter,verbose) \n% fully specifies all inputs and outputs.  \n%\n% Input\n% -----\n%   S : the complete set of squares\n%   w : the matching weights for all edges in the link graph L\n%   a : the value of alpha in the netalign objective\n%   b : the value of beta in the netalign objective\n%   li : the start point of each edge in L (a vertex number from graph A)\n%   lj : the end point of each edge in L (a vertex number from graph B)\n%   gamma : the starting step value (default = 0.5)\n%   stepm : number of non-decreasing iterations adjusting the step length\n%     (default = 100)\n%   rtype : the rounding type (default = 1)\n%     rtype = 1 : only consider the current matching\n%     rtype = 2 : try enriching the matching with info from other squares\n%   maxiter : maximum number of iterations to take (default = 1000)\n%   verbose : output verbose information at each iteration (default = true)\n%\n% Output\n% ------\n%   xbest : the best heuristic solution, it may or may not be a matching\n%   status : three values to describe the status of the solution\n%     status(1) = 1 if the problem is solved to optimality, 0 otherwise\n%     status(2) = best lower bound\n%     status(3) = best upper bound\n%   hist : the history of properties of each iteration\n%     hist(k,) = \n%     hist(k,) = best lower bound at iteration k\n%     hist(k,) = best upper bound at iteration k\n%     hist(k,) = current value at iteration k\n%     hist(k,) = matching weight at iteration k\n%     hist(k,) = matching cardinality at iteration k\n%     hist(k,) = overlap at iteration k\n%\n% Example:\n%   load('../data/natalie_graphs');\n%   netalignmr(S,w,0,1,li,lj);\n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2008-2009\n% Computational Approaches to Digital Stewardship\n\n% 2009-06-11: Initial coding (David and Ying)\n% 2009-06-15: Cleanup and optimization (David)\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('stepm','var') || isempty(stepm), stepm=25; end\nif ~exist('rtype','var') || isempty(rtype), rtype=1; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=1000; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \nif ~exist('gamma', 'var') || isempty(gamma), gamma = 0.4; end\n\nm = max(li);\nn = max(lj);\n\n[rp ci ai tripi matn matm] = bipartite_matching_setup(w,li,lj,m,n); \nmperm = tripi(tripi>0); % a permutation for the matching problem\n\nS = double(S);\nU = sparse(size(S,1),size(S,2));\n\nxbest = w;\nxbest(:) = 0;\n\nflower = 0;                       % best lower bound on the solution\nfupper = Inf;                     % best upper bound on the solution\nnext_reduction_iteration = stepm; % reduce the step\n\n\nhist = zeros(maxiter,7);\n\nif verbose % print the header\n    fprintf('%5s   %4s   %8s   %7s %7s %7s  %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'norm-u', 'lower','upper', 'cur', 'obj', 'weight', 'card', 'overlap');\nend    \nfor iter = 1:maxiter   \n    [q,SM] = maxrowmatch((b/2)*S + U-U',li,lj,m,n);\n    x = a*w + q;\n    \n    %[val ma mb mi]= bipartite_matching(nw,li,lj,m,n);\n    ai=zeros(length(tripi),1); ai(tripi>0)=x(mperm);\n    [val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);\n    \n    % compute statistics \n    matchval = mi'*w;\n    overlap = mi'*S*mi/2; \n    card = length(ma);\n    f = a*matchval + b*overlap;\n    \n    if val<fupper\n        fupper=val;\n        next_reduction_iteration = iter+stepm;\n    end\n    if f>flower\n        flower=f;\n        itermark = '*';\n        xbest = mi;\n    else\n        itermark = ' ';\n    end        \n    \n    if rtype==1\n        % no work\n    elseif rtype==2\n        mw = S*x;\n        mw = a*w + b/2*mw;\n       \n        ai=zeros(length(tripi),1); ai(tripi>0)=mw(mperm);\n        [val ma mb mx] = bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);\n        \n        card = length(ma);\n        matchval = mx'*w;\n        overlap = mx'*S*mx/2;\n        f = a*matchval + b*overlap;\n        \n        if f>flower\n            flower=f;\n            itermark = '**';\n            mi = mx;\n            xbest = mw;\n        end\n    end\n    \n    % report on current iter\n    hist(iter,1:end) = [norm(nonzeros(U),1), flower, fupper, f, matchval, card, overlap];\n    \n    if verbose\n        fprintf('%5s   %4i   %8.1e   %7g %7g %7g  %7g %7g %7i %7i\\n', ...\n            itermark, iter, norm(nonzeros(U),1), ...\n            flower, fupper, val, ...\n            f, matchval, card, overlap);\n    end\n    \n    if iter==next_reduction_iteration\n        gamma = gamma*0.5;\n        if verbose\n            fprintf('%5s   %4s   reducing step to %g\\n', '', '', gamma);\n        end\n        if gamma < 1e-24, break; end\n        next_reduction_iteration = iter+stepm;\n    end\n    \n    if (fupper-flower)<1e-2\n        break;\n    end\n    \n    U = U - diag(sparse(gamma*mi))*triu(SM) + tril(SM)'*diag(sparse(gamma*mi));\n    \n    U = bound(U, -.5, .5);\nend\nhist = hist(1:iter,:);\nstatus = zeros(1,3);\nstatus(1) = (fupper-flower)<1e-2;\nstatus(2) = flower;\nstatus(3) = fupper;\n\nfunction S=bound(S,a,b)\nS=spfun(@(x) min(max(x,a),b), S);\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignbp.m", "ext": ".m", "path": "netalign-master/matlab/netalignbp.m", "size": 7067, "source_encoding": "utf_8", "md5": "366425d10c4e61e2ec0d1782c9ac8c30", "text": "function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)\n% NETALIGNBP Solve the network alignment problem with Belief Propagation\n%\n% \n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2007-2009\n% Computational Approaches to Digital Stewardship\n\n%  History\n%  2009-06-02: Implemented Mohsen's new updates to get a higher overlap\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('gamma','var') || isempty(gamma), gamma=0.99; end\nif ~exist('dtype', 'var') || isempty(dtype), dtype=2; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \n\nnedges = length(li);\nnsquares = nnz(S)/2;\nm = max(li);\nn = max(lj);\n\n% compute a vector that allows us to transpose data between squares.  \n% Recall the BP algorithm requires edge(i,j) to send messages to edge(r,s)\n% when (i,j) and (r,s) form a square.  However, edge(i,j) needs information\n% the information that (r,s) sent it from the previous iteraton.  \n% If we imagine that each non-zero of S has a value, we want to tranpose\n% these values.\n[sui suj] = find(triu(S,1)); % only consider each square once.\nSI = sparse(sui,suj,1:length(sui),size(S,1),size(S,2)); % assign indices \nSI = SI + SI'; % SI now has symmetric indices \n[si sj sind] = find(SI);\nSP = sparse(si,sind,true,size(S,1),nsquares); % each column in SP has 2 nz\n[sij sijrs] = find(SP);\nsind = (1:nnz(SP))';\nspair = sind; spair(1:2:end) = sind(2:2:end); spair(2:2:end) = sind(1:2:end);\n% sij, sijrs maps between rows and squares\n% spair is now an indexing vector that accomplishes what we need\n\n% Initialize the messages\nma = zeros(nedges,1);\nmb = ma;\nms = zeros(nnz(S),1);\nsums = zeros(nedges,1);\n\ndamping = gamma;\ncurdamp = 1;\niter = 1;\nalpha = a;\nbeta = b;\n\n% Initialize history\nhista = zeros(maxiter,4); % history of messages from ei->a vertices\nhistb = zeros(maxiter,4); % history of messages from ei->b vertices\nfbest = 0; fbestiter = 0;\nif verbose % print the header\n    fprintf('%4s   %4s   %7s %7s %7s %7s   %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...\n        'obj_mb', 'wght_mb', 'card_mb', 'over_mb');\nend\n\n% setup the matching problem once\n[rp ci ai tripi matn matm] = bipartite_matching_setup(...\n                                    w,li,lj,m,n);         \nmperm = tripi(tripi>0); % a permutation for the matching problem\nclear ai;\n\nwhile iter<=maxiter\n    prevma = ma;\n    prevmb = mb;\n    prevms = ms;\n    prevsums = sums;\n    curdamp = damping*curdamp;\n    \n    omaxb = max(othermaxplus(2,li,lj,mb,m,n),0);\n    omaxa = max(othermaxplus(1,li,lj,ma,m,n),0);\n    \n    msflip = ms(spair); % swap ij->ijrs to rs->ijrs \n    mymsflip = msflip+beta;\n    mymsflip = min(beta,mymsflip);\n    mymsflip = max(0,mymsflip);\n        \n    sums = accumarray(sij,mymsflip,[nedges 1],@sum,0);\n    \n    ma = alpha*w - omaxb + sums;\n    mb = alpha*w - omaxa + sums;\n    \n    ms = alpha*w(sij)-(omaxb(sij) + omaxa(sij));\n    ms = ms + othersum(sij,sijrs,mymsflip,nedges,nsquares);\n\n    % Original updates\n    if dtype==1\n        ma = curdamp*(ma) + (1-curdamp)*(prevma);\n        mb = curdamp*(mb) + (1-curdamp)*(prevmb);\n        ms = curdamp*(ms) + (1-curdamp)*(prevms);\n    elseif dtype==2\n        ma = ma + (1-curdamp)*(prevma+prevmb-alpha*w+prevsums);\n        mb = mb + (1-curdamp)*(prevmb+prevma-alpha*w+prevsums);\n        ms = ms + (1-curdamp)*(prevms+prevms(spair)-beta);\n    elseif dtype==3\n        ma = curdamp*ma + (1-curdamp)*(prevma+prevmb-alpha*w+prevsums);\n        mb = curdamp*mb + (1-curdamp)*(prevmb+prevma-alpha*w+prevsums);\n        ms = curdamp*ms + (1-curdamp)*(prevms+prevms(spair)-beta);\n    end\n    \n    % now compute the matchings\n    hista(iter,:) = round_messages(ma,S,w,alpha,beta,rp,ci,tripi,matn,matm,mperm);\n    histb(iter,:) = round_messages(mb,S,w,alpha,beta,rp,ci,tripi,matn,matm,mperm);\n    if hista(iter,1)>fbest\n        fbestiter=iter; mbest=ma; fbest=hista(iter,1);\n    end\n    if histb(iter,1)>fbest\n        fbestiter=-iter; mbest=mb; fbest=histb(iter,1);\n    end\n    \n    if verbose\n        if fbestiter==iter, bestchar='*a'; \n        elseif fbestiter==-iter, bestchar='*b';\n        else bestchar='';\n        end\n        fprintf('%4s   %4i   %7g %7g %7i %7i   %7g %7g %7i %7i\\n', ...\n            bestchar, iter, hista(iter,:), histb(iter,:));\n    end\n    iter=iter+1;\nend\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)\nai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);\n[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);\nmatchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2; \nf = alpha*matchweight + beta*overlap;\ninfo = [f matchweight cardinality overlap];\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction omp=othermaxplus(dim,li,lj,lw,m,n)\n% OTHERMAXPLUS Apply the other-max-plus operator to a sparse matrix\n%\n% The other-max-plus operator applies the max-plus aggegration function\n% over the rows or columns of the matrix, where, for \n% each non-zeros, the non-zero itself cannot be the maximum.  Consequently,\n% Hence, it's the max-plus of all the \"other\" elements in the row or column\n% (which is controlled by dim).\n%\n% omp=othermaxplus(dim,li,lj,lw,m,n) applies the other-max-plus operator\n% to the matrix sparse(li, lj, lw, m, n).  The return value omp gives\n% the other-max-plus value for each non-zero element, e.g. the matrix\n% is sparse(li, lj, omp, m, n).\n\nif dim==1\n    % max-plus over cols\n    i1 = lj;\n    i2 = li;\n    N = n;\nelse\n    % max-plus over rows\n    i1 = li;\n    i2 = lj;\n    N = m;\nend\n\n% the output of the other-max-plus is either the maximum element \n% in the row or column (if the element itself isn't the maximum) or\n% the second largest element in the row or column (if the element itself\n% IS the maximum).\n\ndimmax1 =0*ones(N,1);      % largest value\ndimmax2 = 0*ones(N,1);     % second largest value, \n% this is correct because of the definition of the max-plus function.  \ndimmaxind = zeros(N,1);   % index of largest value\nnedges = length(li);\n\nfor i=1:nedges\n    if lw(i) > dimmax2(i1(i))\n        if lw(i) > dimmax1(i1(i))\n            dimmax2(i1(i)) = dimmax1(i1(i));\n            dimmax1(i1(i)) = lw(i);\n            dimmaxind(i1(i)) = i2(i);\n        else\n            dimmax2(i1(i)) = lw(i);\n        end\n    end \nend\n\nomp = zeros(size(lw));\nfor i=1:nedges\n    if i2(i) == dimmaxind(i1(i))\n        omp(i) = dimmax2(i1(i));\n    else\n        omp(i) = dimmax1(i1(i));\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction os=othersum(si,sj,s,m,n) %#ok<INUSD,INUSL>\n% OTHERSUM Compute the sum of each column of the matrix, without each\n% individual entry.  This corresponds to a matrix where each entry is the\n% sum of each column but with each entry subtracted.  \n\nrowsum=accumarray(si,s,[m,1]);\nos=rowsum(si)-s;    \n\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "evaluate_alignment.m", "ext": ".m", "path": "netalign-master/matlab/evaluate_alignment.m", "size": 4797, "source_encoding": "utf_8", "md5": "11f4dfb2cdec144b885fe302325fedc0", "text": "function [s Mcc]=evaluate_alignment(A,B,mi,li,lj)\n% EVALUATE_ALIGNMENT Report properties of the graph alignment\n%\n% evaluate_alignment(A,B,mi,li,lj) returns properties of an alignment\n% between graphs A and B.\n% evaluate_alignment(A,B,ma,mb) returns properties of an alignment\n% between graphs A and B.\n%\n%   Edges - number of edges (2-cliques) preserved by the mapping\n%   Triangles - number of triangles (3-cliques) preserved by the mapping\n%   Largest Component - largest connected component with the mapping\n%\n\n%  History\n%  2009-07-01: Initial coding\n\nif nargin==4\n    ma = mi;\n    mb = li;\n    % given as pairs\n    X = sparse(ma,mb,1,size(A,1),size(B,1));\nelse\n    % given as an indicator\n    % make sure x is binary\n    if any(logical(mi)~=mi)\n        error('netalign:invalidMatching',...\n            'the matching indicator mi is not binary')\n    end\n    X = sparse(li,lj,mi,size(A,1),size(B,1));\nend\n% make sure x is a matching\ns1 = sum(X,1);\ns2 = sum(X,2);\nif any(s1~=logical(s1)) || any(s2~=logical(s2))\n    error('netalign:invalidMatching',...\n        'the given alignment is not a matching');\nend\n\n[rpA ciA] = sparse_to_csr(A);\n[rpB ciB] = sparse_to_csr(B);\nAs = struct('rp',rpA,'ci',ciA);\nBs = struct('rp',rpB,'ci',ciB);\nnA = length(rpA)-1;\nnB = length(rpB)-1;\n\n[ma mb] = find(X);\n\n% the overlapped graph\n[nedges,OA,OB]=count_overlap(As,Bs,[ma mb]);\nG = [OA X; X' OB];\n[ci,sizes] = scomponents(G);\n\n[ma mb] = find(X);\n\n\n% create the output as a structure\ns = [];\ns.size = length(ma);\n[s.largest_component,max_ci] = max(sizes);\ns.edges = count_overlap(As,Bs,[ma mb]);\ns.triangles = count_triangle_overlap(As,Bs,[ma mb]);\n\ns.largest_component_A = sum(ci(1:nA)==max_ci);\ns.largest_component_B = sum(ci(nA+1:end)==max_ci);\n\nMcc = sparse(find(ci(1:nA)==max_ci), find(ci(nA+1:end)==max_ci), 1, nA, nB);\n% display output unless requested\nif nargout == 0\n    fprintf('%25s  %i\\n', 'Size', s.size);\n    fprintf('%25s  %i\\n', 'Edge Overlap', s.edges);\n    fprintf('%25s  %i\\n', 'Triangle Overlap', s.triangles);\n    fprintf('%25s  %i\\n', 'Largest Component', s.largest_component);\n    fprintf('%25s  %i\\n', 'Largest Component (A)', s.largest_component_A);\n    fprintf('%25s  %i\\n', 'Largest Component (B)', s.largest_component_B);\nend\n    \n\nfunction [ntris]=count_triangle_overlap(A,B,m)\n\nif ~isstruct(A)\n    [rpA ciA] = sparse_to_csr(A);\nelse\n    rpA = A.rp;\n    ciA = A.ci;\nend\n\nif ~isstruct(B)\n    [rpB ciB] = sparse_to_csr(B);\nelse\n    rpB = B.rp;\n    ciB = B.ci;\nend\n\nnA = length(rpA)-1;\nnB = length(rpB)-1;\n\n% index the matches in a to b\nmAB = zeros(nA,1);\nmAB(m(:,1)) = m(:,2);\nmBA = zeros(nB,1);\nmBA(m(:,2)) = m(:,1);\n\nntris = 0;\nnmatches = size(m,1);\n\n\naindex = zeros(nA,1);\nbindex = zeros(nB,1); % bindex is a work vector\n\nfor i=1:nmatches\n    % count the number of triangles from this match\n    va = m(i,1);\n    vb = m(i,2);\n    \n    % index the neighbors in A\n    for riA=rpA(va):rpA(va+1)-1\n        if ciA(riA)==va, continue; end % skip self loops\n        aindex(ciA(riA))=1;\n    end\n    \n    % index the edges in B\n    for riB=rpB(vb):rpB(vb+1)-1\n        if ciB(riB)==vb, continue; end % skip self loops\n        bindex(ciB(riB))=1;\n    end\n    \n    % for all the edges in A, see if there is an edge in B too\n    for riA=rpA(va):rpA(va+1)-1\n        % va2 is the neighbor of va\n        va2 = ciA(riA);\n        \n        % skip self-loops\n        if va2 == va, continue; end \n        \n        va2image = mAB(va2); % get the image\n        if va2image\n            if bindex(va2image)\n                % let's see if we can complete a triangle in A from\n                % this edge\n                for riA2=rpA(va2):rpA(va2+1)-1\n                    va3 = ciA(riA2);\n                    if va3 == va2, continue; end % skip self-loops\n                    if aindex(va3)\n                        % va,va2,va3 is a triangle and va,va2 is in b\n                        % so check if (va,va3) and (va2,va3) are \n                        % preserved by the mapping\n                        \n                        va3image = mAB(va3);\n                        if va3image && bindex(va3image)\n                            % (va,va3) is present, check (va2,va3)\n                            for riB=rpB(va2image):rpB(va2image+1)-1\n                                b3 = ciB(riB);\n                                if mBA(b3)==va3\n                                    ntris = ntris + 1;\n                                end\n                            end\n                        end\n                    end      \n                end\n            end\n        end\n    end\n    \n    % clear the index of edges in A\n    for riA=rpA(va):rpA(va+1)-1\n        aindex(ciA(riA))=0;\n    end\n    \n    % clear the index of edges in B\n    for riB=rpB(vb):rpB(vb+1)-1\n        bindex(ciB(riB))=0;\n    end\nend\nntris = ntris/6;\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalign_lagrange.m", "ext": ".m", "path": "netalign-master/matlab/netalign_lagrange.m", "size": 3194, "source_encoding": "utf_8", "md5": "c002a2f87068662095111cbc700b4541", "text": "function [xbest fupper hist] = netalign_lagrange(S,w,a,b,li,lj,stepm,rtype,maxiter,verbose)\n% NETALIGN_LAGRANGE Solve the network alignment problem\n% with Lagrangean relaxation\n% \n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2008-2009\n% Computational Approaches to Digital Stewardship\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('stepm','var') || isempty(stepm), stepm=100; end\nif ~exist('rtype','var') || isempty(rtype), rtype=1; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=1000; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \n\nm = max(li);\nn = max(lj);\n\n[rp ci ai tripi matn matm] = bipartite_matching_setup(w,li,lj,m,n); \nmperm = tripi(tripi>0); % a permutation for the matching problem\n\nS = triu(S);\nS = double(S);\nU = sparse(size(S,1),size(S,2));\nxbest = w;\nxbest(:) = 0;\n\nflower = 0;\nfupper = Inf;\nnext_reduction_iteration = stepm; % reduce the step\ngamma = 1;\n\nhist = zeros(maxiter,7);\n\nif verbose % print the header\n    fprintf('%5s   %4s   %8s   %7s %7s  %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'norm-u', 'lower','upper', 'obj', 'weight', 'card', 'overlap');\nend    \nfor iter = 1:maxiter\n    nw = a * w + b *  (sum(max(0, .5 * S + U), 2) + sum(max(0, .5 * S - U),1)');\n    %[val ma mb mi]= bipartite_matching(nw,li,lj,m,n);\n    ai=zeros(length(tripi),1); ai(tripi>0)=nw(mperm);\n    [val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);\n    % compute statistics \n    matchval = mi'*w;\n    overlap = mi'*S*mi; % no divide by 2 is okay, because S = triu(S) :-).\n    card = length(ma);\n    f = a*matchval + b*overlap;\n    \n    if val<fupper\n        fupper=val;\n        next_reduction_iteration = iter+stepm;\n    end\n    if f>flower\n        flower=f;\n        itermark = '*';\n        xbest = mi;\n    else\n        itermark = ' ';\n    end        \n    \n    if rtype==1\n        % no work\n    elseif rtype==2\n        mw = S*mi;\n        mw = mw + S'*mi;\n        mw = a*w + b/2*mw;\n       \n        ai=zeros(length(tripi),1); ai(tripi>0)=mw(mperm);\n        [val ma mb mx] = bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);\n        \n        card = length(ma);\n        matchval = mx'*w;\n        overlap = mx'*S*mx;\n        f = a*matchval + b*overlap;\n        \n        if f>flower\n            flower=f;\n            itermark = '**';\n            mi = mx;\n            xbest = mw;\n        end\n    end\n    \n    % report on current iter\n    hist(iter,1:end) = [norm(nonzeros(U),1), flower, fupper, f, matchval, card, overlap];\n    \n    if verbose\n        fprintf('%5s   %4i   %8.1e   %7g %7g  %7g %7g %7i %7i\\n', ...\n            itermark, iter, norm(nonzeros(U),1), ...\n            flower, fupper, ...\n            f, matchval, card, overlap);\n    end\n    \n    if iter==next_reduction_iteration\n        gamma = gamma*0.5;\n        if verbose\n            fprintf('%5s   %4s   reducing step to %g\\n', '', '', gamma);\n        end\n        next_reduction_iteration = iter+stepm;\n    end\n    \n    U = bound(U - (diag(sparse(mi)) * S - S * diag(sparse(mi))) * gamma, -.5, .5);\n    \nend\n\nfunction S=bound(S,a,b)\nS=spfun(@(x) min(max(x,a),b), S);\n\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignbpmr.m", "ext": ".m", "path": "netalign-master/matlab/netalignbpmr.m", "size": 4762, "source_encoding": "utf_8", "md5": "7286539d79a120310bfc2d322d442ccc", "text": "function [mbest hista histb] = netalignbpmr(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)\n% NETALIGNMBP Solve the network alignment problem with Belief Propagation\n%\n% This version of network alignment uses the matrix formulation of the \n% algorithm.\n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2007-2009\n% Computational Approaches to Digital Stewardship\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('gamma','var') || isempty(gamma), gamma=0.99; end\nif ~exist('dtype', 'var') || isempty(dtype), dtype=2; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \n\nnedges = length(li); nsquares = nnz(S)/2; m = max(li); n = max(lj);\n\n% the following is elegant, but inefficient :-(\n%Ar = sparse(li,1:nedges,1,m,nedges); [ari arj arv]=find(Ar'*Ar-speye(nedges));\n%Ac = sparse(lj,1:nedges,1,n,nedges); [aci acj acv]=find(Ac'*Ac-speye(nedges));\n\n% Initialize the messages\ny = zeros(nedges,1); z = y; Sk = 0*S; \nif dtype>1, d = y; end % needed for damping scheme\n% Initialize a few parameters\ndamping = gamma; curdamp = 1; iter = 1; \n\n% Initialize history\nhista = zeros(maxiter,4); % history of messages from ei->a vertices\nhistb = zeros(maxiter,4); % history of messages from ei->b vertices\nfbest = 0; fbestiter = 0;\nif verbose % print the header\n    fprintf('%4s   %4s   %7s %7s %7s %7s   %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...\n        'obj_mb', 'wght_mb', 'card_mb', 'over_mb');\nend\n\n% setup the matching problem once\n[rp ci ai tripi matn matm] = bipartite_matching_setup(...\n                                    w,li,lj,m,n);\nmperm = tripi(tripi>0);\n\nwhile iter<=maxiter\n    curdamp = damping*curdamp;\n    %Sknew = bound(Sk' + b*S,0,b);\n    if dtype>1, dold=d; end\n    [d,SM] = maxrowmatch(Sk' + b*S,li,lj,matn,matm);\n    SM = bound(SM,0,b);\n    \n    ynew = a*w - max(0,implicit_maxprod(m,li,z)) + d;\n    znew = a*w - max(0,implicit_maxprod(n,lj,y)) + d;\n    % NOTE in comparison netalignbp, othersum isn't needed because othersum\n    % of Sknew = d*S - Sknew\n    Skt = diag(sparse(ynew+znew-a*w-d))*S-SM;\n    if dtype==1\n        Sk = curdamp*Skt+ (1-curdamp)*Sk;\n        y = curdamp*ynew+(1-curdamp)*y;\n        z = curdamp*znew+(1-curdamp)*z;\n    elseif dtype==2\n        prev = (y+z-a*w+dold);\n        y = ynew+(1-curdamp)*prev;\n        z = znew+(1-curdamp)*prev;\n        clear prev;\n        Sk = Skt + (1-curdamp)*(Sk+Sk'-b*S);\n    elseif dtype==3\n        prev = (y+z-a*w+dold);\n        y = curdamp*ynew+(1-curdamp)*prev;\n        z = curdamp*znew+(1-curdamp)*prev;\n        clear prev;\n        Sk = curdamp*Skt + (1-curdamp)*(Sk+Sk'-b*S);\n    end\n    hista(iter,:) = round_messages(y,S,w,a,b,rp,ci,tripi,matn,matm,mperm);\n    histb(iter,:) = round_messages(z,S,w,a,b,rp,ci,tripi,matn,matm,mperm);\n    if hista(iter,1)>fbest\n        fbestiter=iter; mbest=y; fbest=hista(iter,1);\n    end\n    if histb(iter,1)>fbest\n        fbestiter=-iter; mbest=z; fbest=histb(iter,1);\n    end\n    \n    if verbose\n        if fbestiter==iter, bestchar='*a'; \n        elseif fbestiter==-iter, bestchar='*b';\n        else bestchar='';\n        end\n        fprintf('%4s   %4i   %7g %7g %7i %7i   %7g %7g %7i %7i\\n', ...\n            bestchar, iter, hista(iter,:), histb(iter,:));\n    end\n    iter=iter+1;\nend\n\nend\n\nfunction S=bound(S,a,b)\n  S=spfun(@(x) min(b,max(x,a)),S); \nend\nfunction y=maxprod(ai,aj,av,m,x) \n  y=-inf*ones(m,1);\n  for i=1:numel(ai), y(ai(i)) = max(y(ai(i)),av(i)*x(aj(i))); end\nend\nfunction y=implicit_maxprod(n,ai,x)\n% implicitly compute \n%   Ai=sparse(ai,1:length(ai),1,n,length(z))\n%   A=Ar'*Ar - speye(length(z))\n%   maxprod(A,x)\n% which has a very nice structure if you look at the actual summation\n% definition.  It is just the maximum value \n  N = length(ai);\n  y=-inf*ones(N,1);\n  max1 = -inf*ones(n,1);\n  max2 = -inf*ones(n,1);\n  max1ind = zeros(n,1);\n  for i=1:N\n    if x(i)>max2(ai(i))\n      if x(i)>max1(ai(i))\n          max2(ai(i)) = max1(ai(i));\n          max1(ai(i)) = x(i);\n          max1ind(ai(i)) = i;\n      else\n          max2(ai(i)) = x(i);\n      end\n    end\n  end\n  for i=1:N\n    if i==max1ind(ai(i))\n      y(i)=max2(ai(i));\n    else\n      y(i)=max1(ai(i));\n    end\n  end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)\nai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);\n[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);\nmatchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2; \nf = alpha*matchweight + beta*overlap;\ninfo = [f matchweight cardinality overlap];\nend\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignscbp.m", "ext": ".m", "path": "netalign-master/matlab/netalignscbp.m", "size": 7240, "source_encoding": "utf_8", "md5": "4d86fc6727c6040b65240c5e0079a468", "text": "function [mbest hista histb Sk] = netalignscbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)\n% NETALIGNMPSC Solve the network alignment problem with message passing\n%\n% This version of network alignment uses the matrix formulation of the \n% algorithm.\n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2010\n% Computational Approaches to Digital Stewardship\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('gamma','var') || isempty(gamma), gamma=0.99; end\nif ~exist('dtype', 'var') || isempty(dtype), dtype=2; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \n\nnedges = length(li); nsquares = nnz(S); m = max(li); n = max(lj);\n\n% Initialize the messages\ny = zeros(nedges,1); \nz = y; % copy z as the same size\nSk = zeros(nsquares, 1);  % messages and their \"reversed/transposed\" copies\nSkt = zeros(nsquares, 1);\nSw = b * ones(nsquares, 1);\nMSc = b * ones(nsquares, 4); MScnew = zeros(nsquares, 4); MP = zeros(nsquares, 4);\nif dtype>1, d = y; end % needed for damping scheme\n% Initialize a few parameters\ndamping = gamma; curdamp = 1; iter = 1; \n\n% Initialize history\nhista = zeros(maxiter,4); % history of messages from ei->a vertices\nhistb = zeros(maxiter,4); % history of messages from ei->b vertices\nfbest = 0; fbestiter = 0;\nif verbose % print the header\nfprintf('%4s   %4s   %7s %7s %7s %7s   %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...\n        'obj_mb', 'wght_mb', 'card_mb', 'over_mb');\nend\n\n% setup the matching problem once\n[rp ci ai tripi matn matm] = bipartite_matching_setup(...\n\t\t\t\t\t\t      w,li,lj,m,n);\nmperm = tripi(tripi>0);\n[rpS ciS] = sparse_to_csr(S);\nriS = zeros(nsquares, 1);\nfor i=1:nedges\nfor j=rpS(i):rpS(i+1)-1\nriS(j) = i;\n    end\nend\n\nStmp = sparse(riS, ciS, [1:nsquares]');\n[ti tj transmap] = find(Stmp);\n\nSC = findSC(S, li, lj);\nr = 1;\n\nwhile iter<=maxiter\n    curdamp = damping*curdamp;\n    if dtype>1, dold=d; end\n\n    x = Sw;\n    % updates for M_{ii'->f_i} and M_{ii'->g_{i'}}, y, z\n    d = zeros(nedges, 1);\n    Sk_tran = Sk(transmap);\n    for i=1:nedges\n        for j=rpS(i):rpS(i+1)-1\n    %            d(i) = d(i) + max(0, b + Sk_tran(j)) - max(0, Sk_tran(j));\nd(i) = d(i) + max(0, (r + 1) * Sw(j) + Sk_tran(j)) - max(0, r * Sw(j) + Sk_tran(j));\n        end\n    end\nynew = a*w - max(0,implicit_maxprod(m,li,z)) + d;\nznew = a*w - max(0,implicit_maxprod(n,lj,y)) + d;\n\n    % updates for M_{ii'->h_{ii'jj'}}, Sk\n    t = 0;\n    for i=1:nedges\n        t = ynew(i) + znew(i) - a*w(i) - d(i);\n        for j=rpS(i):rpS(i+1)-1\n%            Skt(j) = t - max(0, b + Sk_tran(j)) + max(0, Sk_tran(j));\n            Skt(j) = t - max(0, (r + 1) * Sw(j) + Sk_tran(j)) - max(0, r * Sw(j) + Sk_tran(j));\n        end\n    end\n    \n    St = Sk + Sk(transmap) + Sw;\n    \n    %updates for M_{ii'jj'->h_{ii'jj'}}, Sw\n    for i=1:4\n        MP(:,i) = max(0, implicit_maxprod(max(SC(:,i)), SC(:,i), MSc(:, i)));\n    end\n    Sw = -(MP(:,1) + MP(:,2) + MP(:,3) + MP(:,4)) + b;\n    \n    %updates for M_{ii'jj'->d_{ii'j}}, MSc\n    tmp = Sk + Sk(transmap);\n    for i=1:4\n        MSc(:, i) = Sw + MP(:, i) + min(tmp, min(Sk, Sk(transmap)));;\n    end\n    \n    if dtype==1\n        Sk = curdamp*Skt+ (1-curdamp)*Sk;\n        y = curdamp*ynew+(1-curdamp)*y;\n        z = curdamp*znew+(1-curdamp)*z;\n    elseif dtype==2\n        prev = (y+z-a*w+dold);\n        y = ynew+(1-curdamp)*prev;\n        z = znew+(1-curdamp)*prev;\n        clear prev;\n        Sk = Skt + (1-curdamp)*St;\n        Sw = Sw + (1-curdamp)*St;\n        for i=1:4\n            MSc(:, i) = MSc(:, i) + (1-curdamp)*St;\n        end\n    elseif dtype==3\n        prev = (y+z-a*w+dold);\n        y = curdamp*ynew+(1-curdamp)*prev;\n        z = curdamp*znew+(1-curdamp)*prev;\n        clear prev;\n        Sk = curdamp * Skt + (1-curdamp)*St;\n        Sw = curdamp * Sw + (1-curdamp)*St;\n        for i=1:4\n            MSc(:, i) = curdamp * MSc(:, i) + (1-curdamp)*St;\n        end\n    end\n    %tot = roundbyS(Sk, li, lj, riS, ciS, m, n);\n    %tot2 = roundbyyz(S, y, z, li, lj, m, n);\n    iter=iter+1;\n    hista(iter,:) = round_messages(y,S,w,a,b,rp,ci,tripi,matn,matm,mperm);\n    histb(iter,:) = round_messages(z,S,w,a,b,rp,ci,tripi,matn,matm,mperm);\n\n    if hista(iter,1)>fbest\n        fbestiter=iter; mbest=y; fbest=hista(iter,1);\n    end\n    if histb(iter,1)>fbest\n        fbestiter=-iter; mbest=z; fbest=histb(iter,1);\n    end\n    %fbest = max(fbest, tot);\n    %fbest = max(fbest, tot2);\n    \n    if verbose\n        if fbestiter==iter, bestchar='*a'; \n        elseif fbestiter==-iter, bestchar='*b';\n        else bestchar='';\n        end\n        fprintf('%4s   %4i   %7g %7g %7i %7i  %7g %7g %7i %7i\\n', ...\n            bestchar, iter, hista(iter,:), histb(iter,:));\n    end\nend\n\nend\n\nfunction S=bound(S,a,b)\n  S=spfun(@(x) max(b+x,a)-max(x,a),S);\nend\n\nfunction y=maxprod(ai,aj,av,m,x) \n  y=-inf*ones(m,1);\n  for i=1:numel(ai), y(ai(i)) = max(y(ai(i)),av(i)*x(aj(i))); end\nend\nfunction y=implicit_maxprod(n,ai,x)\n% implicitly compute \n%   Ai=sparse(ai,1:length(ai),1,n,length(z))\n     %   A=Ar'*Ar - speye(length(z))\n%   maxprod(A,x)\n% which has a very nice structure if you look at the actual summation\n% definition.  It is just the maximum value \n  N = length(ai);\n  y=-inf*ones(N,1);\n  max1 = -inf*ones(n,1);\n  max2 = -inf*ones(n,1);\n  max1ind = zeros(n,1);\n  for i=1:N\n    if x(i)>max2(ai(i))\n      if x(i)>max1(ai(i))\n          max2(ai(i)) = max1(ai(i));\n          max1(ai(i)) = x(i);\n          max1ind(ai(i)) = i;\n      else\n          max2(ai(i)) = x(i);\n      end\n    end\n  end\n  for i=1:N\n    if i==max1ind(ai(i))\n      y(i)=max2(ai(i));\n    else\n      y(i)=max1(ai(i));\n    end\n  end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)\nai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);\n[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);\nmatchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2; \nf = alpha*matchweight + beta*overlap;\ninfo = [f matchweight cardinality overlap];\nend\n\nfunction tot=roundbyS(Sk, li, lj, ri, ci, m, n)\n    [v ind] = sort(Sk, 'descend');\n    markA = zeros(m, 1);\n    markB = zeros(n, 1);\n    chosen = zeros(size(li, 1), 1);\n    tot = 0;\n    for i=1:size(Sk, 1)\n        v1 = ri(ind(i));\n        v2 = ci(ind(i));\n        if (v1 > v2)\n            continue;\n        end\n        if ((~chosen(v1) && (markA(li(v1)) || markB(lj(v1)))) || (~chosen(v2) && (markA(li(v2)) || markB(lj(v2)))))\n            continue;\n        end\n        tot = tot + 1;\n        chosen(v1) = 1;\n        chosen(v2) = 1;\n        markA(li(v1)) = 1;\n        markA(li(v2)) = 1;\n        markB(lj(v1)) = 1;\n        markB(lj(v2)) = 1;\n    end\nend\n\nfunction tot=roundbyyz(S, y, z, li, lj, m, n)\n    [v ind] = sort(y+z, 'descend');\n    markA = zeros(m, 1);\n    markB = zeros(n, 1);\n    x = zeros(size(y));\n    for i=1:size(ind,1)\n        if (~markA(li(ind(i))) && ~markB(lj(ind(i))))\n            x(ind(i)) = 1;\n            markA(li(ind(i))) = 1;\n            markB(lj(ind(i))) = 1;\n        end\n    end\n    tot = x' * S * x / 2;\nend\n"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "expand_match.m", "ext": ".m", "path": "netalign-master/matlab/expand_match.m", "size": 2500, "source_encoding": "utf_8", "md5": "f69e78b513c268f81a604be27667dd91", "text": "function L = expand_match(A,B,L)\n% EXPAND_MATCH Expand a set of possible matchings with breadth first search\n%\n% L2 = expand_match(A,B,L) returns a new sparse match matrix L2 for the\n% network alignment problem where L2 includes the neighbors of all existing\n% matchings.\n%\n% \n\n% David F. Gleich and Ying Wang\n% Copyright, Stanford University, 2009\n\n% TODO Example\n\n%  History\n%  2009-05-29 Initial coding, based on old c code\n\n\nnA = size(A,1);\n\n\n[rpB ciB vB] = sparse_to_csr(B);\n[lai laj lav] = compute_expanded_match(nA,rpB,ciB,vB,L);\nclear rpB ciB vB\n\nnB = size(B,1);\n[rpA ciA vA] = sparse_to_csr(A);\n[lbj lbi lbv] = compute_expanded_match(nB,rpA,ciA,vA,L');\nclear rpA ciA vA\n\nlai = [lai; lbi];\nlaj = [laj; lbj];\nlav = [lav; lbv];\nclear lbi lbj lbv\n\nL = sparse(lai, laj, lav, nA, nB);\n\n\n\nfunction [l2i l2j l2v]=compute_expanded_match(nA,rpB,ciB,vB,L)\n% just do one step of bfs and expand the current match\n\n% allocate edges\nl2i = zeros(nnz(L),1);\nl2j = l2i;\nl2v = l2i;\ncuredge = 0;\n\n% convert to CSR\n[rpAB ciAB vAB] = sparse_to_csr(L);\nnB = length(rpB)-1;\n% allocate temp arrays\nwork = zeros(nB,1);\nwork_used = zeros(nB,1);\ndwork = zeros(nB,1);\nfor i=1:nA\n    num_adj = 0;\n    for ri=rpAB(i):rpAB(i+1)-1\n        j = ciAB(ri);\n        deg_in_B = rpB(j+1)-rpB(j);\n        for ri2=rpB(j):rpB(j+1)-1\n            k = ciB(ri2);\n            if work(k) == 0\n                dwork(num_adj+1) = vAB(ri)/deg_in_B;\n                work_used(num_adj+1) = k;\n                work(k) = num_adj+1;\n                num_adj = num_adj + 1;\n            else\n                dwork(work(k)) = max( dwork(work(k)), vAB(ri)/deg_in_B );\n            end\n        end\n        % make sure we include j\n        if work(j) == 0\n            dwork(num_adj+1) = vAB(ri);\n            work_used(num_adj+1) = j;\n            work(j) = num_adj+1;\n            num_adj = num_adj + 1;\n        else\n            dwork(work(k)) = max( dwork(work(k)), vAB(ri) );\n        end\n    end\n    % copy the data and reset the arrays\n    % first check if there is enough space\n    while curedge + num_adj > length(l2i)\n        % double the arrays\n        l2i = [l2i; l2i]; l2j = [l2j; l2j]; l2v = [l2v; l2v]; %#ok<AGROW>\n    end\n    for j=1:num_adj\n        curedge = curedge + 1;\n        l2i(curedge) = i;\n        l2j(curedge) = work_used(j);\n        l2v(curedge) = dwork(j);\n        work(work_used(j)) = 0;\n        work_used(j) = 0;\n        dwork(j) = 0;\n    end\nend\n% resize at the end\nl2i = l2i(1:curedge);\nl2j = l2j(1:curedge);\nl2v = l2v(1:curedge);"}
+{"plateform": "github", "repo_name": "bill-codes/netalign-master", "name": "netalignmbp.m", "ext": ".m", "path": "netalign-master/matlab/netalignmbp.m", "size": 4716, "source_encoding": "utf_8", "md5": "e294c5801bc3cd3fa0ee4deed6698ebf", "text": "function [mbest hista histb] = netalignmbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)\n% NETALIGNMBP Solve the network alignment problem with Belief Propagation\n%\n% This version of network alignment uses the matrix formulation of the \n% algorithm.\n\n% David F. Gleich, Ying Wang, and Mohsen Bayati\n% Copyright, Stanford University, 2007-2009\n% Computational Approaches to Digital Stewardship\n\nif ~exist('a','var') || isempty(a), a=1; end\nif ~exist('b','var') || isempty(b), b=1; end\nif ~exist('gamma','var') || isempty(gamma), gamma=0.99; end\nif ~exist('dtype', 'var') || isempty(dtype), dtype=2; end\nif ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end\nif ~exist('verbose', 'var') || isempty(verbose), verbose=1; end \n\nnedges = length(li); nsquares = nnz(S)/2; m = max(li); n = max(lj);\n\n% the following is elegant, but inefficient :-(\n%Ar = sparse(li,1:nedges,1,m,nedges); [ari arj arv]=find(Ar'*Ar-speye(nedges));\n%Ac = sparse(lj,1:nedges,1,n,nedges); [aci acj acv]=find(Ac'*Ac-speye(nedges));\n\n% Initialize the messages\ny = zeros(nedges,1); z = y; Sk = 0*S; \nif dtype>1, d = y; end % needed for damping scheme\n% Initialize a few parameters\ndamping = gamma; curdamp = 1; iter = 1; \n\n% Initialize history\nhista = zeros(maxiter,4); % history of messages from ei->a vertices\nhistb = zeros(maxiter,4); % history of messages from ei->b vertices\nfbest = 0; fbestiter = 0;\nif verbose % print the header\n    fprintf('%4s   %4s   %7s %7s %7s %7s   %7s %7s %7s %7s\\n', ...\n        'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...\n        'obj_mb', 'wght_mb', 'card_mb', 'over_mb');\nend\n\n% setup the matching problem once\n[rp ci ai tripi matn matm] = bipartite_matching_setup(...\n                                    w,li,lj,m,n);\nmperm = tripi(tripi>0);\n\nwhile iter<=maxiter\n    curdamp = damping*curdamp;\n    Sknew = bound(Sk' + b*S,0,b);\n    if dtype>1, dold=d; end\n    d = sum(Sknew,2);\n    \n    ynew = a*w - max(0,implicit_maxprod(n,lj,z)) + d;\n    znew = a*w - max(0,implicit_maxprod(m,li,y)) + d;\n    % NOTE in comparison netalignbp, othersum isn't needed because othersum\n    % of Sknew = d*S - Sknew\n    Skt = diag(sparse(ynew+znew-a*w-d))*S-Sknew;\n    if dtype==1\n        Sk = curdamp*Skt+ (1-curdamp)*Sk;\n        y = curdamp*ynew+(1-curdamp)*y;\n        z = curdamp*znew+(1-curdamp)*z;\n    elseif dtype==2\n        prev = (y+z-a*w+dold);\n        y = ynew+(1-curdamp)*prev;\n        z = znew+(1-curdamp)*prev;\n        clear prev;\n        Sk = Skt + (1-curdamp)*(Sk+Sk'-b*S);\n    elseif dtype==3\n        prev = (y+z-a*w+dold);\n        y = curdamp*ynew+(1-curdamp)*prev;\n        z = curdamp*znew+(1-curdamp)*prev;\n        clear prev;\n        Sk = curdamp*Skt + (1-curdamp)*(Sk+Sk'-b*S);\n    end\n    hista(iter,:) = round_messages(y,S,w,a,b,rp,ci,tripi,matn,matm,mperm);\n    histb(iter,:) = round_messages(z,S,w,a,b,rp,ci,tripi,matn,matm,mperm);\n    if hista(iter,1)>fbest\n        fbestiter=iter; mbest=y; fbest=hista(iter,1);\n    end\n    if histb(iter,1)>fbest\n        fbestiter=-iter; mbest=z; fbest=histb(iter,1);\n    end\n    \n    if verbose\n        if fbestiter==iter, bestchar='*a'; \n        elseif fbestiter==-iter, bestchar='*b';\n        else bestchar='';\n        end\n        fprintf('%4s   %4i   %7g %7g %7i %7i   %7g %7g %7i %7i\\n', ...\n            bestchar, iter, hista(iter,:), histb(iter,:));\n    end\n    iter=iter+1;\nend\n\nend\n\nfunction S=bound(S,a,b)\n  S=spfun(@(x) max(x+b,0) - max(x+a,0),S); \nend\nfunction y=maxprod(ai,aj,av,m,x) \n  y=-inf*ones(m,1);\n  for i=1:numel(ai), y(ai(i)) = max(y(ai(i)),av(i)*x(aj(i))); end\nend\nfunction y=implicit_maxprod(n,ai,x)\n% implicitly compute \n%   Ai=sparse(ai,1:length(ai),1,n,length(z))\n%   A=Ar'*Ar - speye(length(z))\n%   maxprod(A,x)\n% which has a very nice structure if you look at the actual summation\n% definition.  It is just the maximum value \n  N = length(ai);\n  y=-inf*ones(N,1);\n  max1 = -inf*ones(n,1);\n  max2 = -inf*ones(n,1);\n  max1ind = zeros(n,1);\n  for i=1:N\n    if x(i)>max2(ai(i))\n      if x(i)>max1(ai(i))\n          max2(ai(i)) = max1(ai(i));\n          max1(ai(i)) = x(i);\n          max1ind(ai(i)) = i;\n      else\n          max2(ai(i)) = x(i);\n      end\n    end\n  end\n  for i=1:N\n    if i==max1ind(ai(i))\n      y(i)=max2(ai(i));\n    else\n      y(i)=max1(ai(i));\n    end\n  end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)\nai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);\n[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);\nmatchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2; \nf = alpha*matchweight + beta*overlap;\ninfo = [f matchweight cardinality overlap];\nend\n"}
+{"plateform": "github", "repo_name": "snipsco/kaldi-master", "name": "Generate_mcTrainData_cut.m", "ext": ".m", "path": "kaldi-master/egs/reverb/s5/local/Generate_mcTrainData_cut.m", "size": 7311, "source_encoding": "utf_8", "md5": "f59dd892f0f8da04a515a2c58ff50a69", "text": "function Generate_mcTrainData_cut(WSJ_dir_name, save_dir)\n%\n% Input variables:\n%    WSJ_dir_name: string name of user's clean wsjcam0 corpus directory \n%                  (*Directory structure for wsjcam0 corpushas to be kept as it is after obtaining it from LDC. \n%                    Otherwise this script does not work.)\n%\n% This function generates multi-condition traiing data\n% based on the following items:\n%  1. wsjcam0 corpus (distributed from the LDC)\n%  2. room impulse responses (ones under ./RIR/)\n%  3. noise (ones under ./NOISE/).\n% Generated data has the same directory structure as original wsjcam0 corpus. \n%\n\nif nargin<2\n   error('Usage: Generate_mcTrainData(WSJCAM0_data_path, save_dir)  *Note that the input variable WSJCAM0_data_path should indicate the directory name of your clean WSJCAM0 corpus. '); \nend\nif exist([WSJ_dir_name,'/data/'])==0\n   error(['Could not find wsjcam0 corpus : Please confirm if ',WSJ_dir_name,' is a correct path to your clean WSJCAM0 corpus']); \nend\n\nif ~exist('save_dir', 'var')\n    error('You have to set the save_dir variable in the code before running this script!')\nend\n\ndisplay(['Name of directory for original wsjcam0: ',WSJ_dir_name])\ndisplay(['Name of directory to save generated multi-condition training data: ',save_dir])\nunix(['chmod u+x sphere_to_wave.csh']);\nunix(['chmod u+x bin/*']);\n\n% Parameters related to acoustic conditions\nSNRdB=20;\n\n% List of WSJ speech data\nflist1='etc/audio_si_tr.lst';\n\n%\n% List of RIRs\n%\nnum_RIRvar=24;\nRIR_sim1='./RIR/RIR_SmallRoom1_near_AnglA.wav'; \nRIR_sim2='./RIR/RIR_SmallRoom1_near_AnglB.wav'; \nRIR_sim3='./RIR/RIR_SmallRoom1_far_AnglA.wav';  \nRIR_sim4='./RIR/RIR_SmallRoom1_far_AnglB.wav';  \nRIR_sim5='./RIR/RIR_MediumRoom1_near_AnglA.wav';\nRIR_sim6='./RIR/RIR_MediumRoom1_near_AnglB.wav';\nRIR_sim7='./RIR/RIR_MediumRoom1_far_AnglA.wav'; \nRIR_sim8='./RIR/RIR_MediumRoom1_far_AnglB.wav'; \nRIR_sim9='./RIR/RIR_LargeRoom1_near_AnglA.wav'; \nRIR_sim10='./RIR/RIR_LargeRoom1_near_AnglB.wav';\nRIR_sim11='./RIR/RIR_LargeRoom1_far_AnglA.wav'; \nRIR_sim12='./RIR/RIR_LargeRoom1_far_AnglB.wav'; \nRIR_sim13='./RIR/RIR_SmallRoom2_near_AnglA.wav';\nRIR_sim14='./RIR/RIR_SmallRoom2_near_AnglB.wav';\nRIR_sim15='./RIR/RIR_SmallRoom2_far_AnglA.wav'; \nRIR_sim16='./RIR/RIR_SmallRoom2_far_AnglB.wav'; \nRIR_sim17='./RIR/RIR_MediumRoom2_near_AnglA.wav';\nRIR_sim18='./RIR/RIR_MediumRoom2_near_AnglB.wav';\nRIR_sim19='./RIR/RIR_MediumRoom2_far_AnglA.wav'; \nRIR_sim20='./RIR/RIR_MediumRoom2_far_AnglB.wav'; \nRIR_sim21='./RIR/RIR_LargeRoom2_near_AnglA.wav'; \nRIR_sim22='./RIR/RIR_LargeRoom2_near_AnglB.wav'; \nRIR_sim23='./RIR/RIR_LargeRoom2_far_AnglA.wav';  \nRIR_sim24='./RIR/RIR_LargeRoom2_far_AnglB.wav';  \n\n%\n% List of noise\n% \nnum_NOISEvar=6;\nnoise_sim1='./NOISE/Noise_SmallRoom1';\nnoise_sim2='./NOISE/Noise_MediumRoom1';\nnoise_sim3='./NOISE/Noise_LargeRoom1';\nnoise_sim4='./NOISE/Noise_SmallRoom2';\nnoise_sim5='./NOISE/Noise_MediumRoom2';\nnoise_sim6='./NOISE/Noise_LargeRoom2';\n\n%\n% Start generating noisy reverberant data with creating new directories\n%\n\nfcount=1;\nrcount=1;\nncount=1;\n\nif save_dir(end)=='/';\n    save_dir_tr=[save_dir,'data/mc_train/'];\nelse\n    save_dir_tr=[save_dir,'/data/mc_train/'];\nend\nmkdir([save_dir_tr]);\n%mkdir([save_dir,'/taskfiles/'])\n\nmic_idx=['A';'B';'C';'D';'E';'F';'G';'H'];\nprev_fname='dummy';\n\nfor nlist=1:1\n    % Open file list\n    eval(['fid=fopen(flist',num2str(nlist),',''r'');']);\n\n    while 1\n        \n        % Set data file name\n        fname=fgetl(fid);\n        if ~ischar(fname);\n            break;\n        end\n        \n        idx1=find(fname=='/');  \n        \n        % Make directory if there isn't any\n        if ~strcmp(prev_fname,fname(1:idx1(end)))\n            mkdir([save_dir_tr fname(1:idx1(end))])\n        end\n        prev_fname=fname(1:idx1(end));\n       \n        % load (sphere format) speech signal \n        x=read_sphere([WSJ_dir_name,'/data/', fname]);\n        x=x/(2^15);  % conversion from short-int to float\n        \n        % load RIR and noise for \"THIS\" utterance\n        eval(['RIR=wavread(RIR_sim',num2str(rcount),');']);\n        eval(['NOISE=wavread([noise_sim',num2str(ceil(rcount/4)),',''_',num2str(ncount),'.wav'']);']);\n\n        % Generate 8ch noisy reverberant data        \n        y=gen_obs(x,RIR,NOISE,SNRdB);\n\n        % cut to length of original signal\n        y = y(1:size(x,2),:);\n        \n        % rotine to cyclicly switch RIRs and noise, utterance by utterance \n        rcount=rcount+1;\n        if rcount>num_RIRvar;rcount=1;ncount=ncount+1;end\n        if ncount>10;ncount=1;end\n\n        % save the data\n\n        y=y/4; % common normalization to all the data to prevent clipping\n               % denominator was decided experimentally\n\n        for ch=1:8 \n            eval(['wavwrite(y(:,',num2str(ch),'),16000,''',save_dir_tr fname,'_ch',num2str(ch),'.wav'');']);\n        end\n           \n        display(['sentence ',num2str(fcount),' (out of 7861) finished! (Multi-condition training data)'])\n        fcount=fcount+1;\n\n    end\nend\n\n\n%%%%\nfunction [y]=gen_obs(x,RIR,NOISE,SNRdB)\n% function to generate noisy reverberant data\n\nx=x';\n\n% calculate direct+early reflection signal for calculating SNR\n[val,delay]=max(RIR(:,1));\nbefore_impulse=floor(16000*0.001);\nafter_impulse=floor(16000*0.05);\nRIR_direct=RIR(delay-before_impulse:delay+after_impulse,1);\ndirect_signal=fconv(x,RIR_direct);\n\n% obtain reverberant speech\nfor ch=1:8\n    rev_y(:,ch)=fconv(x,RIR(:,ch));\nend\n\n% normalize noise data according to the prefixed SNR value\nNOISE=NOISE(1:size(rev_y,1),:);\nNOISE_ref=NOISE(:,1);\n\niPn = diag(1./mean(NOISE_ref.^2,1));\nPx = diag(mean(direct_signal.^2,1));\nMsnr = sqrt(10^(-SNRdB/10)*iPn*Px);\nscaled_NOISE = NOISE*Msnr;\ny = rev_y + scaled_NOISE;\ny = y(delay:end,:);\n\n\n%%%%\nfunction [y]=fconv(x, h)\n%FCONV Fast Convolution\n%   [y] = FCONV(x, h) convolves x and h, and normalizes the output  \n%         to +-1.\n%\n%      x = input vector\n%      h = input vector\n% \n%      See also CONV\n%\n%   NOTES:\n%\n%   1) I have a short article explaining what a convolution is.  It\n%      is available at http://stevem.us/fconv.html.\n%\n%\n%Version 1.0\n%Coded by: Stephen G. McGovern, 2003-2004.\n%\n%Copyright (c) 2003, Stephen McGovern\n%All rights reserved.\n%\n%THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n%AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n%IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n%ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n%LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n%CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n%SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n%INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n%CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n%ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n%POSSIBILITY OF SUCH DAMAGE.\n\nLy=length(x)+length(h)-1;  % \nLy2=pow2(nextpow2(Ly));    % Find smallest power of 2 that is > Ly\nX=fft(x, Ly2);\t\t   % Fast Fourier transform\nH=fft(h, Ly2);\t           % Fast Fourier transform\nY=X.*H;        \t           % \ny=real(ifft(Y, Ly2));      % Inverse fast Fourier transform\ny=y(1:1:Ly);               % Take just the first N elements\n"}
+{"plateform": "github", "repo_name": "h-delgado/binary-key-diarizer-master", "name": "pdist22.m", "ext": ".m", "path": "binary-key-diarizer-master/matlab/external/pdist22.m", "size": 5461, "source_encoding": "utf_8", "md5": "173103b09eefbe457c081a3d41cddd3d", "text": "% This function belongs to Piotr Dollar's Toolbox\n% http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html\n% Please refer to the above web page for definitions and clarifications\n%\n% Calculates the distance between sets of vectors.\n%\n% Let X be an m-by-p matrix representing m points in p-dimensional space\n% and Y be an n-by-p matrix representing another set of points in the same\n% space. This function computes the m-by-n distance matrix D where D(i,j)\n% is the distance between X(i,:) and Y(j,:).  This function has been\n% optimized where possible, with most of the distance computations\n% requiring few or no loops.\n%\n% The metric can be one of the following:\n%\n% 'euclidean' / 'sqeuclidean':\n%   Euclidean / SQUARED Euclidean distance.  Note that 'sqeuclidean'\n%   is significantly faster.\n%\n% 'chisq'\n%   The chi-squared distance between two vectors is defined as:\n%    d(x,y) = sum( (xi-yi)^2 / (xi+yi) ) / 2;\n%   The chi-squared distance is useful when comparing histograms.\n%\n% 'cosine'\n%   Distance is defined as the cosine of the angle between two vectors.\n%\n% 'emd'\n%   Earth Mover's Distance (EMD) between positive vectors (histograms).\n%   Note for 1D, with all histograms having equal weight, there is a simple\n%   closed form for the calculation of the EMD.  The EMD between histograms\n%   x and y is given by the sum(abs(cdf(x)-cdf(y))), where cdf is the\n%   cumulative distribution function (computed simply by cumsum).\n%\n% 'L1'\n%   The L1 distance between two vectors is defined as:  sum(abs(x-y));\n%\n%\n% USAGE\n%  D = pdist2( X, Y, [metric] )\n%\n% INPUTS\n%  X        - [m x p] matrix of m p-dimensional vectors\n%  Y        - [n x p] matrix of n p-dimensional vectors\n%  metric   - ['sqeuclidean'], 'chisq', 'cosine', 'emd', 'euclidean', 'L1'\n%\n% OUTPUTS\n%  D        - [m x n] distance matrix\n%\n% EXAMPLE\n%  [X,IDX] = demoGenData(100,0,5,4,10,2,0);\n%  D = pdist2( X, X, 'sqeuclidean' );\n%  distMatrixShow( D, IDX );\n%\n% See also PDIST, DISTMATRIXSHOW\n\n% Piotr's Image&Video Toolbox      Version 2.0\n% Copyright (C) 2007 Piotr Dollar.  [pdollar-at-caltech.edu]\n% Please email me if you find bugs, or have suggestions or questions!\n% Licensed under the Lesser GPL [see external/lgpl.txt]\n\nfunction D = pdist2( X, Y, metric )\n\nif( nargin<3 || isempty(metric) ); metric=0; end;\n\nswitch metric\n  case {0,'sqeuclidean'}\n    D = distEucSq( X, Y );\n  case 'euclidean'\n    D = sqrt(distEucSq( X, Y ));\n  case 'L1'    \n    D = distL1( X, Y );\n  case 'cosine'\n    D = distCosine( X, Y );\n  case 'emd'\n    D = distEmd( X, Y );\n  case 'chisq'\n    D = distChiSq( X, Y );\n  otherwise\n    error(['pdist2 - unknown metric: ' metric]);\nend\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction D = distL1( X, Y )\n\nm = size(X,1);  n = size(Y,1);\nmOnes = ones(1,m); D = zeros(m,n);\nfor i=1:n\n  yi = Y(i,:);  yi = yi( mOnes, : );\n  D(:,i) = sum( abs( X-yi),2 );\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction D = distCosine( X, Y )\n\nif( ~isa(X,'double') || ~isa(Y,'double'))\n  error( 'Inputs must be of type double'); end;\n\np=size(X,2);\nXX = sqrt(sum(X.*X,2)); X = X ./ XX(:,ones(1,p));\nYY = sqrt(sum(Y.*Y,2)); Y = Y ./ YY(:,ones(1,p));\nD = 1 - X*Y';\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction D = distEmd( X, Y )\n\nXcdf = cumsum(X,2);\nYcdf = cumsum(Y,2);\n\nm = size(X,1);  n = size(Y,1);\nmOnes = ones(1,m); D = zeros(m,n);\nfor i=1:n\n  ycdf = Ycdf(i,:);\n  ycdfRep = ycdf( mOnes, : );\n  D(:,i) = sum(abs(Xcdf - ycdfRep),2);\nend\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction D = distChiSq( X, Y )\n\n%%% supposedly it's possible to implement this without a loop!\nm = size(X,1);  n = size(Y,1);\nmOnes = ones(1,m); D = zeros(m,n);\nfor i=1:n\n  yi = Y(i,:);  yiRep = yi( mOnes, : );\n  s = yiRep + X;    d = yiRep - X;\n  D(:,i) = sum( d.^2 ./ (s+eps), 2 );\nend\nD = D/2;\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction D = distEucSq( X, Y )\n\n%if( ~isa(X,'double') || ~isa(Y,'double'))\n % error( 'Inputs must be of type double'); end;\nm = size(X,1); n = size(Y,1);\n%Yt = Y';\nXX = sum(X.*X,2);\nYY = sum(Y'.*Y',1);\nD = XX(:,ones(1,n)) + YY(ones(1,m),:) - 2*X*Y';\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% function D = distEucSq( X, Y )\n%%%% code from Charles Elkan with variables renamed\n% m = size(X,1); n = size(Y,1);\n% D = sum(X.^2, 2) * ones(1,n) + ones(m,1) * sum(Y.^2, 2)' - 2.*X*Y';\n\n\n%%% LOOP METHOD - SLOW\n% [m p] = size(X);\n% [n p] = size(Y);\n%\n% D = zeros(m,n);\n% onesM = ones(m,1);\n% for i=1:n\n%   y = Y(i,:);\n%   d = X - y(onesM,:);\n%   D(:,i) = sum( d.*d, 2 );\n% end\n\n\n%%% PARALLEL METHOD THAT IS SUPER SLOW (slower then loop)!\n% % From \"MATLAB array manipulation tips and tricks\" by Peter J. Acklam\n% Xb = permute(X, [1 3 2]);\n% Yb = permute(Y, [3 1 2]);\n% D = sum( (Xb(:,ones(1,n),:) - Yb(ones(1,m),:,:)).^2, 3);\n\n\n%%% USELESS FOR EVEN VERY LARGE ARRAYS X=16000x1000!! and Y=100x1000\n% call recursively to save memory\n% if( (m+n)*p > 10^5 && (m>1 || n>1))\n%   if( m>n )\n%     X1 = X(1:floor(end/2),:);\n%     X2 = X((floor(end/2)+1):end,:);\n%     D1 = distEucSq( X1, Y );\n%     D2 = distEucSq( X2, Y );\n%     D = cat( 1, D1, D2 );\n%   else\n%     Y1 = Y(1:floor(end/2),:);\n%     Y2 = Y((floor(end/2)+1):end,:);\n%     D1 = distEucSq( X, Y1 );\n%     D2 = distEucSq( X, Y2 );\n%     D = cat( 2, D1, D2 );\n%   end\n%   return;\n% end\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_sem_basis.m", "ext": ".m", "path": "WADG_Matlab-master/bern_sem_basis.m", "size": 1476, "source_encoding": "utf_8", "md5": "a00141b6425a6b137bfc093d5ed345aa", "text": "% wedge basis \n% function V = bern_sem_basis(N,r,s,t)\n%     Vtri = bern_tri(N,r(:),s(:));\n% %     [r2D s2D] = Nodes2D(N); [r2D s2D] = xytors(r2D,s2D);\n% %     VWB = Vandermonde2D(N,r2D,s2D); Vtri = Vandermonde2D(N,r(:),s(:))/VWB;\n% r1D = JacobiGL(0,0,N);\n% VSEM = Vandermonde1D(N,r1D(:));\n% V1D = Vandermonde1D(N,t(:))/VSEM; % sem in vertical direction\n% \n% Np = (N+1)*(N+2)/2;\n% \n% sk = 1;\n% for i = 1:N+1\n%     for j = 1:Np\n%         V(:,sk) = Vtri(:,j).*V1D(:,i);\n%         sk = sk + 1;\n%     end\n% end\n\nfunction [V Vr Vs Vt V1 V2 V3] = bern_sem_basis(N,r,s,t)\n\n% [Vtri Vrtri Vstri V1tri V2tri V3tri] = bern_tri(N,r(:),s(:));\n\n[r2D s2D] = Nodes2D(N); [r2D s2D] = xytors(r2D,s2D);\nVWB = Vandermonde2D(N,r2D,s2D);\nVtri = Vandermonde2D(N,r(:),s(:))/VWB;\n[Vrtri Vstri] = GradVandermonde2D(N,r(:),s(:));\nVrtri = Vrtri/VWB; Vstri = Vstri/VWB;\nV1tri = Vrtri*0; V2tri = Vrtri*0; V3tri = Vrtri*0; % dummy arrays\n\nr1D = JacobiGL(0,0,N);\nVSEM = Vandermonde1D(N,r1D(:));\nV1D = Vandermonde1D(N,t(:))/VSEM; % sem in vertical direction\nVt1D = GradVandermonde1D(N,t(:))/VSEM; % sem in vertical direction\n\nNptri = (N+1)*(N+2)/2;\n\nsk = 1;\nfor i = 1:N+1\n    for j = 1:Nptri\n        V(:,sk)  = Vtri(:,j).*V1D(:,i);\n        Vr(:,sk) = Vrtri(:,j).*V1D(:,i);\n        Vs(:,sk) = Vstri(:,j).*V1D(:,i);\n        Vt(:,sk) = Vtri(:,j).*Vt1D(:,i);\n        V1(:,sk) = V1tri(:,j).*V1D(:,i);\n        V2(:,sk) = V2tri(:,j).*V1D(:,i);\n        V3(:,sk) = V3tri(:,j).*V1D(:,i);\n        sk = sk + 1;\n    end\nend"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_basis_1D.m", "ext": ".m", "path": "WADG_Matlab-master/bern_basis_1D.m", "size": 424, "source_encoding": "utf_8", "md5": "d9d840acea7aa008762de1bb0bfe1d93", "text": "function [V Vr] = bern_basis_1D(N,r)\n\nr = (1+r)/2; % convert to unit\nfor i = 0:N\n    V(:,i+1) = bern_1D(N,i,r);\n    Vr(:,i+1) = d_bern(N,i,r)*.5; % change of vars\nend\n\nfunction bi = bern_1D(N,i,r)\n\nbi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);\n\nfunction dbi = d_bern(N,i,r)\n\nif (i==0)\n    dbi = -N*(1-r).^(N-1);\nelseif (i==N)\n    dbi = N*(r.^(N-1));\nelse\n    dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);\nend\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_quad.m", "ext": ".m", "path": "WADG_Matlab-master/bern_quad.m", "size": 689, "source_encoding": "utf_8", "md5": "355d83ac78285e23030c96d0fadf8fcc", "text": "function [V Vr Vs] = bern_quad(N,r,s)\n\nr = (1+r)/2; % convert to unit\ns = (1+s)/2; % convert to unit\n\nV = zeros(length(r),(N+1)^2);\nVr = zeros(length(r),(N+1)^2);\nVs = zeros(length(r),(N+1)^2);\nsk = 1;\nfor j = 0:N\n    for i = 0:N    \n        V(:,sk) = bern_1D(N,i,r).*bern_1D(N,j,s);\n        Vr(:,sk) = .5*d_bern_1D(N,i,r).*bern_1D(N,j,s);\n        Vs(:,sk) = .5*bern_1D(N,i,r).*d_bern_1D(N,j,s);\n        sk = sk + 1;\n    end\nend\n\nfunction bi = bern_1D(N,i,r)\n\nbi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);\n\nfunction dbi = d_bern_1D(N,i,r)\n\nif (i==0)\n    dbi = -N*(1-r).^(N-1);\nelseif (i==N)\n    dbi = N*(r.^(N-1));\nelse\n    dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);\nend\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_basis_tet.m", "ext": ".m", "path": "WADG_Matlab-master/bern_basis_tet.m", "size": 1786, "source_encoding": "utf_8", "md5": "402f402665ab18ed94d19eb13672619c", "text": "% V = VDM.  Vrst = deriv matrices.  V1-4 barycentric derivs.  ids =\n% permutation to match equispaced node ordering on tet. \n\nfunction [V, Vr, Vs, Vt, V1, V2, V3, V4, id] = bern_basis_tet(N,r,s,t)\n\n%[L1 L2 L3 L4] = rsttobary(r,s,t);\n\nL1 = -(1+r+s+t)/2;\nL2 = (1+r)/2;\nL3 = (1+s)/2;\nL4 = (1+t)/2;\ndL1r = -.5; dL2r = .5; dL3r = 0; dL4r = 0;\ndL1s = -.5; dL2s = 0; dL3s = .5; dL4s = 0;\ndL1t = -.5; dL2t = 0; dL3t = 0; dL4t = .5;\n\nsk = 1;\nfor l = 0:N\n    for k = 0:N-l\n        for j = 0:N-k-l;\n            i = N-j-k-l;\n            C=factorial(N)/(factorial(i)*factorial(j)*factorial(k)*factorial(l));\n%             C = 1;\n            V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k).*(L4.^l);\n            \n            dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k).*(L4.^l);\n            dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k).*(L4.^l);\n            dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1)).*(L4.^l);\n            dL4 = C*l*(L1.^(i)).*(L2.^j).*(L3.^k).*(L4.^(l-1));\n            if i==0\n                dL1 = zeros(size(dL1));\n            end\n            if j==0\n                dL2 = zeros(size(dL1));\n            end\n            if k ==0\n                dL3 = zeros(size(dL1));\n            end\n            if l ==0\n                dL4 = zeros(size(dL1));\n            end\n            Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r + dL4.*dL4r;\n            Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s + dL4.*dL4s;\n            Vt(:,sk) = dL1.*dL1t + dL2.*dL2t + dL3.*dL3t + dL4.*dL4t;\n            V1(:,sk) = dL1;\n            V2(:,sk) = dL2;\n            V3(:,sk) = dL3;\n            V4(:,sk) = dL4;\n            sk = sk + 1;\n        end\n    end\nend\n\nfunction bi = bern(N,i,r)\n\nbi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);\n\nfunction dbi = d_bern(N,i,r)\n\ndbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Wave3D.m", "ext": ".m", "path": "WADG_Matlab-master/Wave3D.m", "size": 4005, "source_encoding": "utf_8", "md5": "61a2d10ebd5610a1443301f0f99b2ba0", "text": "clear\n\nGlobals3D;\n\nN = 4;\nFinalTime = 1;\ncfun = @(x,y,z) ones(size(x));\n%cfun = @(x,y,z) 1 + 0.5*sin(pi*x).*sin(pi*y).*sin(pi*z); % smooth velocity\n%cfun = @(x,y,z) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity\n\n%pfun = @(x,y,z,t) cos(pi*x).*cos(pi*y).*cos(pi*z).*cos(sqrt(3)*pi*t);\n\n pfun = @(x,y,z,t)   sin(pi*x).*sin(pi*y).*sin(pi*z).*cos(pi*t);\n ufun = @(x,y,z,t)  -cos(pi*x).*sin(pi*y).*sin(pi*z).*sin(pi*t);\n vfun = @(x,y,z,t)  -sin(pi*x).*cos(pi*y).*sin(pi*z).*sin(pi*t);\n wfun = @(x,y,z,t)  -sin(pi*x).*sin(pi*y).*cos(pi*z).*sin(pi*t);\n %ffun = @(x,y,z,t)  sqrt(3)*pi*(1-1./cfun(x,y,z)).*cos(pi*x).*cos(pi*y).*cos(pi*z).*sin(sqrt(3)*pi*t);\n ffun = @(x,y,z,t)   pi*2*sin(pi*x).*sin(pi*y).*sin(pi*z).*sin(pi*t);\n\n%generate 3D mesh\n[Nv, VX, VY, VZ, K, EToV] = MeshReaderGmsh3D('cube2.msh');\n\nStartUp3D;\n\n\nDr(abs(Dr)<1e-8) = 0; \n\nNq = 2*N+1;\n\n[rq sq tq wq] = tet_cubature(Nq); % integrate u*v*c\n\nVq = Vandermonde3D(N,rq,sq,tq)/V;\n\nxq = Vq*x; yq = Vq*y; zq=Vq*z;\n \nPq=V*V'*Vq'*diag(wq);\n \nCq = cfun(xq,yq,zq);\n\nLift3D(N, r, s, t);\n% %% Adptive m on each element\n% VMq = Vandermonde3D(M,rq,sq,tq);\n% CqM = VMq*VMq'*diag(wq)*Cq;\n\n\n\n%% initial condition\np = pfun(x,y,z,0);\nu = ufun(x,y,z,0);\nv = vfun(x,y,z,0);\nw = wfun(x,y,z,0);\n\n% p = pfun(x,y,z,0);\n% u = zeros(Np,K);\n% v = zeros(Np,K);\n% w = zeros(Np,K);\n\n\n\n%%\ntime = 0;\n\n% Runge-Kutta residual storage\nresu = zeros(Np,K); resv = zeros(Np,K); resw = zeros(Np,K); resp = zeros(Np,K);\n\nCN = (N+1)*(N+2)*(N+3)/6; % trace inequality constant\nCNh = max(CN*max(Fscale(:)));\ndt = 2/CNh;\n\n%dt = 0.00390625; %coincide with the time step in occa\n% outer time step loop\ntstep = 0;\n\n\n\n\nwhile (time<FinalTime)\n    if(time+dt>FinalTime), dt = FinalTime-time; end\n    \n    for INTRK = 1:5\n        \n        timelocal = time + rk4c(INTRK)*dt;\n        \n        f=ffun(x,y,z,timelocal);\n        \n        [rhsp, rhsu, rhsv, rhsw] = acousticsRHS3D_WADG(p,u,v,w,f);\n        \n        % initiate and increment Runge-Kutta residuals\n        resp = rk4a(INTRK)*resp + dt*rhsp;\n        resu = rk4a(INTRK)*resu + dt*rhsu;\n        resv = rk4a(INTRK)*resv + dt*rhsv;\n        resw = rk4a(INTRK)*resw + dt*rhsw;\n                \n        % update fields\n        u = u+rk4b(INTRK)*resu;\n        v = v+rk4b(INTRK)*resv;\n        w = w+rk4b(INTRK)*resw;\n        p = p+rk4b(INTRK)*resp;\n        \n    \n    end\n    \n    time = time+dt; tstep = tstep+1;\n\n    \n     \n     \nend\n\np_exact = pfun(x,y,z,FinalTime);\n\np_exact_quadrature = pfun(xq,yq,zq,FinalTime);\n\np_quadrature = Vq * p;\n\n[d1,d2]=size(p_quadrature);\n\nerror_accumulation = 0;\n\nfor j1=1:d2\n    for j2=1:d1\n        err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);\n        error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);\n    end\nend\n\nerror_l2 = sqrt(error_accumulation)\n%error_fro = norm(p-p_exact,'fro'); \n\n\n\nfunction [rhsp, rhsu, rhsv, rhsw] = acousticsRHS3D_WADG(p,u,v,w,f)\n\nGlobals3D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\ndw = zeros(Nfp*Nfaces,K); dw(:) = w(vmapP)-w(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv + nz.*dw;\n\n% % Impose reflective boundary conditions (p+ = -p-)\n% ndotdU(mapB) = 0;\n% dp(mapB) = -2*p(vmapB);\n\n% Impose Dirichlet BCs\nndotdU(mapB) = 0;\np(vmapB) = 0;\ndp(mapB) = 0;\n\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\nfluxw =  (tau*ndotdU - dp).*nz;\n\npr = Dr*p; ps = Ds*p; pt = Dt*p;\n\ndpdx = rx.*pr + sx.*ps + tx.*pt;\ndpdy = ry.*pr + sy.*ps + ty.*pt;\ndpdz = rz.*pr + sz.*ps + tz.*pt;\n\ndivU = Dr*(u.*rx + v.*ry + w.*rz) + Ds*(u.*sx + v.*sy + w.*sz)+ Dt*(u.*tx + v.*ty + w.*tz);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + f + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\nrhsw =  -dpdz + LIFT*(Fscale.*fluxw)/2.0;\n\nrhsp = Pq*(Cq.*(Vq*rhsp));\n\nreturn;\n\nend\n\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_hex.m", "ext": ".m", "path": "WADG_Matlab-master/bern_hex.m", "size": 930, "source_encoding": "utf_8", "md5": "c1ba4aade744a74da97433f578142f21", "text": "function [V Vr Vs Vt] = bern_hex(N,r,s,t)\n\nr = (1+r)/2; % convert to unit\ns = (1+s)/2; % convert to unit\nt = (1+t)/2; % convert to unit\n\nNp = (N+1)^3;\nV = zeros(length(r),Np);\nVr = zeros(length(r),Np);\nVs = zeros(length(r),Np);\nVt = zeros(length(r),Np);\nsk = 1;\nfor k = 0:N\n    for j = 0:N\n        for i = 0:N\n            \n            V(:,sk) = bern_1D(N,i,r).*bern_1D(N,j,s).*bern_1D(N,k,t);\n            Vr(:,sk) = .5*d_bern_1D(N,i,r).*bern_1D(N,j,s).*bern_1D(N,k,t);\n            Vs(:,sk) = .5*bern_1D(N,i,r).*d_bern_1D(N,j,s).*bern_1D(N,k,t);\n            Vt(:,sk) = .5*bern_1D(N,i,r).*bern_1D(N,j,s).*d_bern_1D(N,k,t);\n            sk = sk + 1;\n        end\n    end\nend\n\nfunction bi = bern_1D(N,i,r)\n\nbi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);\n\nfunction dbi = d_bern_1D(N,i,r)\n\nif (i==0)\n    dbi = -N*(1-r).^(N-1);\nelseif (i==N)\n    dbi = N*(r.^(N-1));\nelse\n    dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);\nend\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_tet.m", "ext": ".m", "path": "WADG_Matlab-master/bern_tet.m", "size": 2132, "source_encoding": "utf_8", "md5": "4dc141800a89b218f1b7dff33be86eef", "text": "% V = VDM.  Vrst = deriv matrices.  V1-4 barycentric derivs.  ids =\n% permutation to match equispaced node ordering on tet. \n\n% function [V Vr Vs Vt V1 V2 V3 V4 id] = bern_basis_tet(N,r,s,t)\n% \n% % use equivalence between W&B and equispaced nodes - get ordering\n% [re se te] = EquiNodes3D(N);\n% Ve = bern_tet(N,re,se,te);\n% for i = 1:size(Ve,2)\n%    [val iid] = max(Ve(:,i)); \n%    id(i) = iid;\n% end\n% \n% [V Vr Vs Vt V1 V2 V3 V4] = bern_tet(N,r,s,t);\n% V  = V(:,id);\n% Vr = Vr(:,id);\n% Vs = Vs(:,id);\n% Vt = Vt(:,id);\n% V1 = V1(:,id);\n% V2 = V2(:,id);\n% V3 = V3(:,id);\n% V4 = V4(:,id);\n\nfunction [V Vr Vs Vt V1 V2 V3 V4] = bern_tet(N,r,s,t)\n\n% [L1 L2 L3 L4] = rsttobary(r,s,t);\nL1 = -(1+r+s+t)/2;\nL2 = (1+r)/2;\nL3 = (1+s)/2;\nL4 = (1+t)/2;\n\ndL1r = -.5; dL2r = .5; dL3r = 0; dL4r = 0;\ndL1s = -.5; dL2s = 0; dL3s = .5; dL4s = 0;\ndL1t = -.5; dL2t = 0; dL3t = 0; dL4t = .5;\n\nsk = 1;\n% for i = 0:N\n%     for j = 0:N-i\n%         for k = 0:N-i-j\n%             l = N-i-j-k;\nfor l = 0:N\n    for k = 0:N-l\n        for j = 0:N-l-k\n            i = N-j-k-l;\n            C=factorial(N)/(factorial(i)*factorial(j)*factorial(k)*factorial(l));\n            V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k).*(L4.^l);\n            \n            dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k).*(L4.^l);\n            dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k).*(L4.^l);\n            dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1)).*(L4.^l);\n            dL4 = C*l*(L1.^(i)).*(L2.^j).*(L3.^k).*(L4.^(l-1));\n            if i==0\n                dL1 = zeros(size(dL1));\n            end\n            if j==0\n                dL2 = zeros(size(dL1));\n            end\n            if k ==0\n                dL3 = zeros(size(dL1));\n            end\n            if l ==0\n                dL4 = zeros(size(dL1));\n            end\n            Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r + dL4.*dL4r;\n            Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s + dL4.*dL4s;\n            Vt(:,sk) = dL1.*dL1t + dL2.*dL2t + dL3.*dL3t + dL4.*dL4t;\n            V1(:,sk) = dL1;\n            V2(:,sk) = dL2;\n            V3(:,sk) = dL3;\n            V4(:,sk) = dL4;\n            sk = sk + 1;\n        end\n    end\nend\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Wave2D_mesh_fullquadrature.m", "ext": ".m", "path": "WADG_Matlab-master/Wave2D_mesh_fullquadrature.m", "size": 4216, "source_encoding": "utf_8", "md5": "8353d0375b964df4092a4909937506a5", "text": "clear\nGlobals2D\n\nkd=[4 8 16 32];\nh=2./kd;\nN = 4;\nM = 1;\n\nfor i = 1:length(kd)\n\nK1D = kd(i);\nFinalTime = 1.0;\n[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);\nStartUp2D;\n\n%% Set up wavespeed function\n%cfun = @(x,y) ones(size(x));\ncfun = @(x,y) 1+ sin(pi*x).*sin(pi*y); % smooth velocity\n%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity\n\n%% generate quadrature points\nNq = 2*N+1;\n[rq sq wq] = Cubature2D(Nq); % integrate u*v*c\nVq = Vandermonde2D(N,rq,sq)/V;\nxq = Vq*x; yq = Vq*y;\n\n%% construct the projection matrix for nodal basis Pq\nPq = V*V'*Vq'*diag(wq);\n\n%% construct matrix Cq\nCq = cfun(xq,yq);\n\n%% construct the projection matrix for cfun to degree M\nVMq = Vandermonde2D(M,rq,sq);\nCqM = VMq*VMq'*diag(wq)*Cq;\n\n%% initial condition\nx0 = 0; y0 = .1;\np = exp(-25*((x-x0).^2 + (y-y0).^2));\nu = zeros(Np, K);\nv=zeros(Np,K);\n\np_M = p;\nu_M = u;\nv_M = v;\n\ntime = 0;\n\n%% Runge-Kutta residual storage\nresu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);\nresu_M = resu; resv_M = resv; resp_M = resp;\n\n%% compute time step size\nCN = (N+1)*(N+2)/2; % trace inequality constant\nCNh = max(CN*max(Fscale(:)));\ndt = 2/CNh;\n\n%% outer time step loop\ntstep = 0;\n\nwhile (time<FinalTime)\n    if(time+dt>FinalTime), dt = FinalTime-time; end\n    \n    for INTRK = 1:5\n        \n        timelocal = time + rk4c(INTRK)*dt;\n        \n        [rhsp, rhsu, rhsv] = acousticsRHS2D_fullWADG(p,u,v);\n        [rhsp_M, rhsu_M, rhsv_M] = acousticsRHS2D_adaptiveWADG(p_M,u_M,v_M);\n        \n        % initiate and increment Runge-Kutta residuals\n        resp = rk4a(INTRK)*resp + dt*rhsp;\n        resu = rk4a(INTRK)*resu + dt*rhsu;\n        resv = rk4a(INTRK)*resv + dt*rhsv;\n        \n        resp_M = rk4a(INTRK)*resp_M + dt*rhsp_M;\n        resu_M = rk4a(INTRK)*resu_M + dt*rhsu_M;\n        resv_M = rk4a(INTRK)*resv_M + dt*rhsv_M;\n        \n        % update fields\n        u = u+rk4b(INTRK)*resu;\n        v = v+rk4b(INTRK)*resv;\n        p = p+rk4b(INTRK)*resp;\n        \n        u_M = u_M + rk4b(INTRK)*resu_M;\n        v_M = v_M + rk4b(INTRK)*resv_M;\n        p_M = p_M + rk4b(INTRK)*resp_M;\n    end\n    \n    % Increment time\n    time = time+dt; tstep = tstep+1;\nend\n\np_quadrature = Vq * p;\np_M_quadrature = Vq * p_M;\n\n[d1,d2]=size(p_quadrature);\nerror_accumulation = 0;\nfor j1=1:d2\n    for j2=1:d1\n        err = p_quadrature(j2,j1)-p_M_quadrature(j2,j1);\n        error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);\n    end\nend\n\nerror_l2(i) = sqrt(error_accumulation);\nerror_fro(i) = norm(p-p_M,'fro'); \nend\n\nfunction [rhsp, rhsu, rhsv] = acousticsRHS2D_adaptiveWADG(p,u,v)\n\nGlobals2D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\nndotdU(mapB) = 0;\ndp(mapB) = -2*p(vmapB);\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\n\nrhsp = Pq*(CqM.*(Vq*rhsp));\nreturn;\nend\n\nfunction [rhsp, rhsu, rhsv] = acousticsRHS2D_fullWADG(p,u,v)\n\nGlobals2D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\nndotdU(mapB) = 0;\ndp(mapB) = -2*p(vmapB);\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\n\nrhsp = Pq*(Cq.*(Vq*rhsp));\nreturn;\nend\n\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Wave2D_k_manufactured.m", "ext": ".m", "path": "WADG_Matlab-master/Wave2D_k_manufactured.m", "size": 3325, "source_encoding": "utf_8", "md5": "0034b8526d028e8da590042ba197b35b", "text": "clear\nGlobals2D\n\nk=[1 4 8 16]\nN = 4;\nM = 1\n\nfor i = 1:length(k)\n\nK1D = 32;\nFinalTime = 1.0;\n[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);\nStartUp2D;\n\n%% Set up wavespeed function\n%cfun = @(x,y) ones(size(x));\ncfun = @(x,y) 1+ 0.5*sin(pi*x).*sin(pi*y); % smooth velocity\n%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity\n\n%% Set periodic manufactured solution\npfun = @(x,y,t) sin(k(i)*pi*x).*sin(k(i)*pi*y).*cos(k(i)*pi*t);\nufun = @(x,y,t) -cos(k(i)*pi*x).*sin(k(i)*pi*y).*sin(k(i)*pi*t);\nvfun = @(x,y,t) -sin(k(i)*pi*x).*cos(k(i)*pi*y).*sin(k(i)*pi*t);\nffun = @(x,y,t)  k(i)*pi*(-1./(cfun(x,y))+2).*sin(k(i)*pi*x).*sin(k(i)*pi*y).*sin(k(i)*pi*t);\n\n%% generate quadrature points\nNq = 2*N+1;\n[rq sq wq] = Cubature2D(Nq); % integrate u*v*c\nVq = Vandermonde2D(N,rq,sq)/V;\nxq = Vq*x; yq = Vq*y;\n\n%% construct the projection matrix for nodal basis Pq\nPq = V*V'*Vq'*diag(wq);\n\n%% construct matrix Cq\nCq = cfun(xq,yq);\n\n%% construct the projection matrix for cfun to degree M\nVMq = Vandermonde2D(M,rq,sq);\nCqM = VMq*VMq'*diag(wq)*Cq;\n\n%% initial condition\np = pfun(x,y,0);\nu = ufun(x,y,0);\nv = vfun(x,y,0);\n\ntime = 0;\n\n%% Runge-Kutta residual storage\nresu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);\n\n%% compute time step size\nCN = (N+1)*(N+2)/2; % trace inequality constant\nCNh = max(CN*max(Fscale(:)));\ndt = 2/CNh;\n\n%% outer time step loop\ntstep = 0;\n\n\nwhile (time<FinalTime)\n    if(time+dt>FinalTime), dt = FinalTime-time; end\n    \n    for INTRK = 1:5\n        \n        timelocal = time + rk4c(INTRK)*dt;\n        \n        f=ffun(x,y,timelocal);\n        \n        [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f);\n        \n        % initiate and increment Runge-Kutta residuals\n        resp = rk4a(INTRK)*resp + dt*rhsp;\n        resu = rk4a(INTRK)*resu + dt*rhsu;\n        resv = rk4a(INTRK)*resv + dt*rhsv;\n        \n        % update fields\n        u = u+rk4b(INTRK)*resu;\n        v = v+rk4b(INTRK)*resv;\n        p = p+rk4b(INTRK)*resp;\n        \n    end\n    \n    % Increment time\n    time = time+dt; tstep = tstep+1;\nend\n\np_exact = pfun(x,y,FinalTime);\np_exact_quadrature = pfun(xq,yq,FinalTime);\n\np_quadrature = Vq * p;\n\n[d1,d2]=size(p_quadrature);\nerror_accumulation = 0;\nfor j1=1:d2\n    for j2=1:d1\n        err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);\n        error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);\n    end\nend\n\nerror_l2(i) = sqrt(error_accumulation);\nerror_fro(i) = norm(p-p_exact,'fro'); \nend\n\n\n\nfunction [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f)\n\nGlobals2D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\nndotdU(mapB) = 0;\ndp(mapB) = -2*p(vmapB);\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + f + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\n\nrhsp = Pq*(CqM.*(Vq*rhsp));\nreturn;\nend\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Wave2D_mesh_manufactured.m", "ext": ".m", "path": "WADG_Matlab-master/Wave2D_mesh_manufactured.m", "size": 3326, "source_encoding": "utf_8", "md5": "322817f72f150c1acafea4eaefbd319b", "text": "clear\nGlobals2D\n\nkd=[64]\nh=2./kd\nN = 4;\nM = 1;\nk = 16\nfor i = 1:length(kd)\n\nK1D = kd(i);\nFinalTime = 1.5;\n[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);\nStartUp2D;\n\n%% Set up wavespeed function\n%cfun = @(x,y) ones(size(x));\ncfun = @(x,y) 1+ 0.5*sin(pi*x).*sin(pi*y); % smooth velocity\n%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity\n\n%% Set periodic manufactured solution\npfun = @(x,y,t)  sin(k*pi*x).*sin(k*pi*y).*cos(k*pi*t);\nufun = @(x,y,t) -cos(k*pi*x).*sin(k*pi*y).*sin(k*pi*t);\nvfun = @(x,y,t) -sin(k*pi*x).*cos(k*pi*y).*sin(k*pi*t);\nffun = @(x,y,t)  k*pi*(-1./(cfun(x,y))+2).*sin(k*pi*x).*sin(k*pi*y).*sin(k*pi*t);\n\n%% generate quadrature points\nNq = 2*N+1;\n[rq sq wq] = Cubature2D(Nq); % integrate u*v*c\nVq = Vandermonde2D(N,rq,sq)/V;\nxq = Vq*x; yq = Vq*y;\n\n%% construct the projection matrix for nodal basis Pq\nPq = V*V'*Vq'*diag(wq);\n\n%% construct matrix Cq\nCq = cfun(xq,yq);\n\n%% construct the projection matrix for cfun to degree M\nVMq = Vandermonde2D(M,rq,sq);\nCqM = VMq*VMq'*diag(wq)*Cq;\n\n%% initial condition\np = pfun(x,y,0);\nu = ufun(x,y,0);\nv = vfun(x,y,0);\n\ntime = 0;\n\n%% Runge-Kutta residual storage\nresu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);\n\n%% compute time step size\nCN = (N+1)*(N+2)/2; % trace inequality constant\nCNh = max(CN*max(Fscale(:)));\ndt = 2/CNh;\n\n%% outer time step loop\ntstep = 0;\n\nwhile (time<FinalTime)\n    if(time+dt>FinalTime), dt = FinalTime-time; end\n    \n    for INTRK = 1:5\n        \n        timelocal = time + rk4c(INTRK)*dt;\n        \n        f=ffun(x,y,timelocal);\n        \n        [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f);\n        \n        % initiate and increment Runge-Kutta residuals\n        resp = rk4a(INTRK)*resp + dt*rhsp;\n        resu = rk4a(INTRK)*resu + dt*rhsu;\n        resv = rk4a(INTRK)*resv + dt*rhsv;\n        \n        % update fields\n        u = u+rk4b(INTRK)*resu;\n        v = v+rk4b(INTRK)*resv;\n        p = p+rk4b(INTRK)*resp;\n       \n        \n        \n        \n    end\n    \n    % Increment time\n    time = time+dt; tstep = tstep+1;\nend\n\np_exact = pfun(x,y,FinalTime);\np_exact_quadrature = pfun(xq,yq,FinalTime);\n\np_quadrature = Vq * p;\n\n[d1,d2]=size(p_quadrature);\nerror_accumulation = 0;\nfor j1=1:d2\n    for j2=1:d1\n        err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);\n        error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);\n    end\nend\n\nerror_l2(i) = sqrt(error_accumulation);\nerror_fro(i) = norm(p-p_exact,'fro'); \nend\n\n\n\nfunction [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f)\n\nGlobals2D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\nndotdU(mapB) = 0;\ndp(mapB) = -2*p(vmapB);\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + f + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\n\nrhsp = Pq*(CqM.*(Vq*rhsp));\nreturn;\nend\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_tri.m", "ext": ".m", "path": "WADG_Matlab-master/bern_tri.m", "size": 1444, "source_encoding": "utf_8", "md5": "3a04a72548c550e48525a3a60987827c", "text": "% function [V Vr Vs V1 V2 V3 id] = bern_tri(N,r,s)\n% \n% % use equivalence between W&B and equispaced nodes - get ordering\n% [re se] = EquiNodes2D(N); [re se] = xytors(re,se);\n% Ve = bern_tri_b(N,re,se);\n% for i = 1:size(Ve,2)\n%    [val iid] = max(Ve(:,i)); \n%    id(i) = iid;\n% %    id(i) = i;\n% end\n% \n% [V Vr Vs V1 V2 V3] = bern_tri_b(N,r,s);\n% V  = V(:,id);\n% Vr = Vr(:,id); Vs = Vs(:,id);\n% V1 = V1(:,id); V2 = V2(:,id); V3 = V3(:,id);\n\nfunction [V Vr Vs VL1 VL2 VL3] = bern_tri(N,r,s)\n\n% barycentric version\nL1 = -(r+s)/2; L2 = (1+r)/2; L3 = (1+s)/2;\ndL1r = -.5; dL2r = .5; dL3r = 0;\ndL1s = -.5; dL2s = 0; dL3s = .5;\n\nsk = 1;\n% for i = 0:N\n%     for j = 0:N-i\n%         k = N-i-j;\nfor k = 0:N\n    for j = 0:N-k\n        i = N-j-k;\n        C=factorial(N)/(factorial(i)*factorial(j)*factorial(k));\n        V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k);\n        \n        dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k);\n        dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k);\n        dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1));\n        if i==0\n            dL1 = zeros(size(dL1));\n        end\n        if j==0\n            dL2 = zeros(size(dL2));\n        end\n        if k == 0\n            dL3 = zeros(size(dL3));\n        end\n        Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r;        \n        Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s;\n        \n        VL1(:,sk) = dL1;\n        VL2(:,sk) = dL2;\n        VL3(:,sk) = dL3;\n        sk = sk + 1;\n    end\nend\n\nreturn\n\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Wave2D_modified.m", "ext": ".m", "path": "WADG_Matlab-master/Wave2D_modified.m", "size": 2725, "source_encoding": "utf_8", "md5": "16165584c54009cc2f3f03cf162a8f73", "text": "Globals2D\n\nN = 4;\nK1D = 16;\nc_flag = 0;\nFinalTime = 0.5;\n%cfun = @(x,y) ones(size(x));\ncfun = @(x,y) 1 + sin(pi*x).*sin(pi*y); % smooth velocity\n%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity\n\n[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);\nStartUp2D;\n\n [rp sp] = EquiNodes2D(50); [rp sp] = xytors(rp,sp);\nVp = Vandermonde2D(N,rp,sp)/V;\nxp = Vp*x; yp = Vp*y;\n\nNq = 2*N+1;\n[rq sq wq] = Cubature2D(Nq); % integrate u*v*c\nVq = Vandermonde2D(N,rq,sq)/V; \nxq = Vq*x; yq = Vq*y;\n\n\n\n\n%construct Pq\nPq=V*V'*Vq'*diag(wq);\n\n%Pq is the projection matrix for Nodal Basis\n\n%construct the matrix C\nCq=cfun(xq,yq);\n\n\n\n\n%% initial condition\n\nx0 = 0; y0 = .1;\np = exp(-25*((x-x0).^2 + (y-y0).^2));\nu = zeros(Np, K);\nv=zeros(Np,K);\n%%\n\ntime = 0;\n\n% Runge-Kutta residual storage\nresu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);\n\n% compute time step size\nCN = (N+1)*(N+2)/2; % trace inequality constant\nCNh = max(CN*max(Fscale(:)));\ndt = 2/CNh;\n\n% outer time step loop\ntstep = 0;\n\nwhile (time<FinalTime)\n    if(time+dt>FinalTime), dt = FinalTime-time; end\n    \n    for INTRK = 1:5\n        \n        timelocal = time + rk4c(INTRK)*dt;\n        \n        [rhsp, rhsu, rhsv] = acousticsRHS2D_WADG(p,u,v);\n        \n        % initiate and increment Runge-Kutta residuals\n        %apply invM*M_c^2\n        resp = rk4a(INTRK)*resp + dt*rhsp;\n        resu = rk4a(INTRK)*resu + dt*rhsu;\n        resv = rk4a(INTRK)*resv + dt*rhsv;\n        \n        % update fields\n        u = u+rk4b(INTRK)*resu;\n        v = v+rk4b(INTRK)*resv;\n        p = p+rk4b(INTRK)*resp;\n        \n    end;\n    \n    if 1 && nargin==0 && mod(tstep,10)==0\n        clf\n        vv = Vp*p;\n        plot3(xp,yp,vv,'.');\n        axis equal\n        axis tight\n        colorbar\n        title(sprintf('time = %f',time))\n        drawnow\n    end\n    \n    % Increment time\n    time = time+dt; tstep = tstep+1;\nend\n\nfunction [rhsp, rhsu, rhsv] = acousticsRHS2D_WADG(p,u,v)\n\nGlobals2D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\nndotdU(mapB) = 0;\ndp(mapB) = -2*p(vmapB);\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\n\nrhsp = Pq*(Cq.*(Vq*rhsp));\nreturn;\nend\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Wave2D_simple.m", "ext": ".m", "path": "WADG_Matlab-master/Wave2D_simple.m", "size": 2633, "source_encoding": "utf_8", "md5": "977017e5edb69468778850473fbb3602", "text": "function Wave2D\n\n\nGlobals2D\n\nN = 4;\nK1D = 16;\nc_flag = 0;\nFinalTime = 3;\n%cfun = @(x,y) ones(size(x));\n cfun = @(x,y)  .5*sin(pi*x).*sin(pi*y); % smooth velocity\n %cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity\n\n[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);\nStartUp2D;\n\n% plotting nodes\n[rp sp] = EquiNodes2D(50); [rp sp] = xytors(rp,sp);\nVp = Vandermonde2D(N,rp,sp)/V;\nxp = Vp*x; yp = Vp*y;\n\nNq = 2*N+1;\n[rq sq wq] = Cubature2D(Nq); % integrate u*v*c\nVq = Vandermonde2D(N,rq,sq)/V;\nxq = Vq*x; yq = Vq*y;\n\n%% initial condition\n\nx0 = 0; y0 = .1;\np = exp(-25*((x-x0).^2 + (y-y0).^2));\nu = zeros(Np, K);\nv= zeros(Np,K);\nvv = Vp*p;\ncolor_line3(xp,yp,vv,vv)\n%%\n\ntime = 0;\n\n% Runge-Kutta residual storage\nresu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);\n\n% compute time step size\nCN = (N+1)*(N+2)/2; % trace inequality constant\nCNh = max(CN*max(Fscale(:)));\ndt = 2/CNh;\n\n% outer time step loop\ntstep = 0;\nfigure\n\n\nwhile (time<FinalTime)\n    if(time+dt>FinalTime), dt = FinalTime-time; end\n    \n    for INTRK = 1:5\n        \n        timelocal = time + rk4c(INTRK)*dt;\n        \n        [rhsp, rhsu, rhsv] = acousticsRHS2D(p,u,v,timelocal);\n        \n        % initiate and increment Runge-Kutta residuals\n        resp = rk4a(INTRK)*resp + dt*rhsp;\n        resu = rk4a(INTRK)*resu + dt*rhsu;\n        resv = rk4a(INTRK)*resv + dt*rhsv;\n        \n        % update fields\n        u = u+rk4b(INTRK)*resu;\n        v = v+rk4b(INTRK)*resv;\n        p = p+rk4b(INTRK)*resp;\n        \n    end;\n    \n    if 1 && nargin==0 && mod(tstep,10)==0\n        clf\n        vv = Vp*p;\n        color_line3(xp,yp,vv,vv,'.');\n        axis equal\n        axis tight\n        colorbar\n        title(sprintf('time = %f',time))\n        drawnow\n    end\n    \n    % Increment time\n    time = time+dt; tstep = tstep+1;\nend\n\n\n\nfunction [rhsp, rhsu, rhsv] = acousticsRHS2D(p,u,v,time)\n\nGlobals2D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\nndotdU(mapB) = 0;\ndp(mapB) = -2*p(vmapB);\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\n\nreturn;\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_pyr.m", "ext": ".m", "path": "WADG_Matlab-master/bern_pyr.m", "size": 1162, "source_encoding": "utf_8", "md5": "e05be17e730cbb437109d06c7b108ec5", "text": "function [V Vr Vs Vt Va Vb Vc] = bern_pyr(N,r,s,t)\n\na = 2*(r+1)./(1-t)-1;\nb = 2*(s+1)./(1-t)-1;\nc = t;\nids = abs(t-1)<1e-8;\na(ids) = -1;\nb(ids) = -1;\n\ndadr = 2./(1-t); \ndbds = 2./(1-t);\ndadt = (1+a)./(1-t);\ndbdt = (1+b)./(1-t);\n\nsk = 1;\nfor k = 0:N\n    for i = 0:N-k        \n        for j = 0:N-k            \n            V(:,sk) = bern(N-k,i,a).*bern(N-k,j,b).*bern(N,k,c);\n            va = .5*d_bern(N-k,i,a).*bern(N-k,j,b).*bern(N,k,c);\n            vb = .5*bern(N-k,i,a).*d_bern(N-k,j,b).*bern(N,k,c);\n            vc = .5*bern(N-k,i,a).*bern(N-k,j,b).*d_bern(N,k,c);\n            Vr(:,sk) = va.*dadr;\n            Vs(:,sk) = vb.*dbds;\n            Vt(:,sk) = va.*dadt + vb.*dbdt + vc;\n            \n            % for testing\n            Va(:,sk) = va; Vb(:,sk) = vb; Vc(:,sk) = vc;            \n            \n            sk = sk + 1;            \n        end\n    end\nend\n\nfunction [bi] = bern(N,i,r)\n\nr = (1+r)/2;\nbi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);\n% bi = (r.^i).*(1-r).^(N-i);\n\nfunction dbi = d_bern(N,i,r)\n\nif (i==0)\n    dbi = -N*(1-r).^(N-1);\nelseif (i==N)\n    dbi = N*(r.^(N-1));\nelse\n    dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);\nend"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "bern_basis_tri.m", "ext": ".m", "path": "WADG_Matlab-master/bern_basis_tri.m", "size": 2146, "source_encoding": "utf_8", "md5": "b5c5ae2fee4e7f3bac98387d91b0b6d1", "text": "function [V Vr Vs V1 V2 V3 id] = bern_basis_tri(N,r,s)\n\n\n[V Vr Vs V1 V2 V3] = bern_tri(N,r,s);\n\nreturn\n% \n% % use equivalence between W&B and equispaced nodes - get ordering\n% [re se] = EquiNodes2D(N); [re se] = xytors(re,se);\n% Ve = bern_tri(N,re,se);\n% for i = 1:size(Ve,2)\n%    [val iid] = max(Ve(:,i)); \n%    id(i) = iid;\n% %    id(i) = i;\n% end\n% \n% [V Vr Vs V1 V2 V3] = bern_tri(N,r,s);\n% V  = V(:,id);\n% Vr = Vr(:,id);\n% Vs = Vs(:,id);\n% V1 = V1(:,id);\n% V2 = V2(:,id);\n% V3 = V3(:,id);\n\nfunction [V Vr Vs VL1 VL2 VL3] = bern_tri(N,r,s)\n\n% barycentric version\nL1 = -(r+s)/2; L2 = (1+r)/2; L3 = (1+s)/2;\ndL1r = -.5; dL2r = .5; dL3r = 0;\ndL1s = -.5; dL2s = 0; dL3s = .5;\n\nsk = 1;\nfor k = 0:N\n    for j = 0:N-k\n        i = N-j-k;\n        C=factorial(N)/(factorial(i)*factorial(j)*factorial(k));\n        V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k);\n        \n        dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k);\n        dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k);\n        dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1));\n        if i==0\n            dL1 = zeros(size(dL1));\n        end\n        if j==0\n            dL2 = zeros(size(dL2));\n        end\n        if k == 0\n            dL3 = zeros(size(dL3));\n        end\n        Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r;        \n        Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s;\n        \n        VL1(:,sk) = dL1;\n        VL2(:,sk) = dL2;\n        VL3(:,sk) = dL3;\n        sk = sk + 1;\n    end\nend\n\nreturn\n\n\n\n% [a b] = rstoab(r,s);\n% a = .5*(a+1); % convert to unit\n% b = .5*(b+1); % convert to unit\n\n% be careful about derivatives at s = 1\na = .5*(r+1)./(1-.5*(s+1));\nb = .5*(s+1);\na(abs(1-s)<1e-8) = 0;\n\ndadr = 1./(1 - s);\ndads = (r+1)./((1-s).^2);\ndbds = .5;\n\nsk = 1;\nfor i = 0:N\n    for j = 0:N-i\n        k = N-i-j;\n        V(:,sk) = bern(N-k,i,a).*bern(N,k,b);\n        Vr(:,sk) = d_bern(N-k,i,a).*bern(N,k,b).*dadr;\n        Vs(:,sk) = d_bern(N-k,i,a).*bern(N,k,b).*dads + bern(N-k,i,a).*d_bern(N,k,b)*dbds;\n        sk = sk + 1;\n    end\nend\n\nfunction bi = bern(N,i,r)\n\nbi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);\n\nfunction dbi = d_bern(N,i,r)\n\ndbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);\n\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Sample2D.m", "ext": ".m", "path": "WADG_Matlab-master/Sample2D.m", "size": 717, "source_encoding": "utf_8", "md5": "be53a9f67b172f80cb774efda6b67d4b", "text": "\r\nfunction [sampleweights,sampletri] = Sample2D(xout, yout)\r\n  \r\n% function [sampleweights,sampletri] = Sample2D(xout, yout)\r\n% purpose: input = coordinates of output data point\r\n%          output = number of containing tri and interpolation weights\r\n% [ only works for straight sided triangles ]\r\n\r\nGlobals2D;\r\n\r\n% find containing tri\r\n[sampletri,tribary] = ...\r\n    tsearchn([VX', VY'], EToV, [xout,yout]);\r\n\r\n% Matlab barycentric coordinates -> biunit triangle coordinates\r\nsout = 2*tribary(:,3)-1; rout = 2*tribary(:,2)-1;\r\n\r\n% build generalized Vandermonde for the sample point\r\nVout = Vandermonde2D(N, rout, sout);\r\n     \r\n% build interpolation matrix for the sample point\r\nsampleweights = Vout*invV;\r\nreturn;\r\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "tet_cubature.m", "ext": ".m", "path": "WADG_Matlab-master/tet_cubature.m", "size": 103616, "source_encoding": "utf_8", "md5": "8f12d404868158cf03a4dc3cae2976bb", "text": "function [r s t w] = tet_cubature(N)\n\n% CubatureData3D_GX.cpp\n% database of precalculated cubature data\n% 2012/05/02\n%---------------------------------------------------------\n%\n%    N     [1     1     1]   [2     2]   [3     3]   [4\n% cubN      1     2     3     4     5     6     7     8\n% Ncub      1     4     6    11    14    23    31    44\n%---------------------------------------------------------\n%\n%    N      4    [5     5]   [6     6]   [7     7]\n% cubN      9    10    11    12    13    14    15\n% Ncub     57    74    95   122   146   177   214\n%---------------------------------------------------------\n\n\n%----------------------\n% N    cubN      Ncub\n%----------------------\n% 1  ( 2, 3)  [  4,  6]\n% 2  ( 4, 5)  [ 11, 14]\n% 3  ( 6, 7)  [ 23, 31]\n% 4  ( 8, 9)  [ 44, 57]\n% 5  (10,11)  [ 74, 95]\n% 6  (12,13)  [122,146]\n% 7  (14,15)  [177,214]\n% 8  (   15)  [    214]\n%----------------------\n\n\n\n% NodeSet 1 1\nTN2012_1 = [ % 1*4\n    0.000000000000000E+00,  0.000000000000000E+00,  0.000000000000000E+00,  0.942809041582063E+00 ];\n\n% NodeSet 2 4\nTN2012_2 = [ % 4*4\n    -.267702858591007E+00,  0.593901700619949E-01,  -.396942694114215E+00,  0.283333858791636E+00,\n    0.151093184153314E+00,  0.435408773247631E+00,  0.215171925430692E+00,  0.262834019890433E+00,\n    -.136769919523739E+00,  -.328034111559041E+00,  0.295084651993713E+00,  0.300844647504794E+00,\n    0.806744930905196E+00,  -.340097731428529E+00,  -.343037895100255E+00,  0.957965153951996E-01 ];\n\n% NodeSet 3 6\nTN2012_3 = [ % 6*4\n    -.168503718027600E+00,  0.191091491627171E+00,  -.389626731458516E+00,  0.124986334616479E+00,\n    0.278379942753442E-01,  -.230493283883966E-01,  0.548135066324183E+00,  0.211580648438021E+00,\n    -.351221617734345E+00,  0.183514402633999E+00,  0.514781533034353E-01,  0.120742171826672E+00,\n    0.430853246354904E+00,  -.247471582318045E+00,  -.168331564100703E+00,  0.237591631621872E+00,\n    -.467676374796738E+00,  -.423525082726438E+00,  -.158697307788931E+00,  0.132581964894968E+00,\n    0.149283125384843E+00,  0.639784768516452E+00,  -.108021925305539E+00,  0.115326290184051E+00 ];\n\n% NodeSet 4 11\nTN2012_4 = [ % 11*4\n    -.145961228097999E-01,  0.242656457919971E-02,  0.709356578010363E+00,  0.100379025241264E+00,\n    -.293724489630999E+00,  0.176439650676461E+00,  0.110886049494113E+00,  0.103929831025446E+00,\n    0.323087833715827E+00,  -.193207041095656E+00,  0.169542639670465E+00,  0.146112883409388E+00,\n    -.881965417939658E-01,  0.231788427010598E-01,  -.201181939132559E+00,  0.182349462340124E+00,\n    0.127020366331471E+00,  0.545141067721522E+00,  0.130950399075932E+00,  0.718068965649525E-01,\n    -.441430768809118E+00,  -.384822563159018E+00,  0.922542967916253E-01,  0.748841921109801E-01,\n    0.393941858963343E+00,  0.206874467063953E+00,  -.311156042619824E+00,  0.654969120044006E-01,\n    -.603446971461421E-01,  -.457373907492708E+00,  -.330221532932238E+00,  0.565055488469881E-01,\n    -.891405983460118E-01,  0.726809265959946E+00,  -.350793173736374E+00,  0.522257744237005E-01,\n    0.654521303360376E+00,  -.397035624387057E+00,  -.297042483395114E+00,  0.521122321989204E-01,\n    -.730764225967786E+00,  -.266942008864898E+00,  -.386103757024185E+00,  0.370062834158988E-01 ];\n\n% NodeSet 5 14\nTN2012_5 = [ % 14*4\n    0.166418072157768E-15,  0.109436232414623E-15,  0.770436782529672E+00,  0.692899055434865E-01,\n    0.408992591748701E+00,  -.236131982942675E+00,  0.333941052787584E+00,  0.401127730719707E-01,\n    0.633846727543000E-16,  0.472263965885350E+00,  0.333941052787583E+00,  0.401127730719707E-01,\n    -.408992591748701E+00,  -.236131982942675E+00,  0.333941052787583E+00,  0.401127730719708E-01,\n    0.372390093103578E-16,  0.354800148930181E-16,  -.298278869430759E+00,  0.106243195244073E+00,\n    -.243543677053202E+00,  0.140610007506098E+00,  0.994262898102530E-01,  0.106243195244073E+00,\n    0.771865760070526E-17,  -.281220015012195E+00,  0.994262898102531E-01,  0.106243195244073E+00,\n    0.243543677053203E+00,  0.140610007506098E+00,  0.994262898102530E-01,  0.106243195244073E+00,\n    0.629058998756435E+00,  -.363187382268184E+00,  -.256812260843224E+00,  0.692899055434864E-01,\n    -.629058998756435E+00,  -.363187382268184E+00,  -.256812260843224E+00,  0.692899055434865E-01,\n    -.140776895379933E-15,  0.726374764536368E+00,  -.256812260843224E+00,  0.692899055434864E-01,\n    0.408992591748701E+00,  0.236131982942675E+00,  -.333941052787584E+00,  0.401127730719707E-01,\n    -.104796521146490E-15,  -.472263965885350E+00,  -.333941052787583E+00,  0.401127730719707E-01,\n    -.408992591748701E+00,  0.236131982942675E+00,  -.333941052787584E+00,  0.401127730719707E-01 ];\n\n% NodeSet 6 23\nTN2012_6 = [ % 23*4\n    0.491994195152311E-02,  -.139223850343092E-01,  0.106622826396772E+01,  0.668997954335606E-02,\n    0.341505806021954E-01,  0.254207383054340E+00,  0.631588696703923E+00,  0.297073565323308E-01,\n    -.456119832861723E+00,  -.311038544744538E+00,  0.230026765510699E+00,  0.228454763572233E-01,\n    0.214449922976949E+00,  -.123490387324972E+00,  0.563235163630283E+00,  0.460940056044558E-01,\n    -.154069305999594E+00,  -.686521447230217E-01,  0.620626489628262E+00,  0.500002972857723E-01,\n    0.306452540322295E+00,  0.181849550192318E+00,  0.125468587644291E+00,  0.407663998761417E-01,\n    -.130588295951477E-01,  -.248889116907340E-01,  -.335770592628266E+00,  0.632962545391904E-01,\n    0.153524468983807E-01,  -.326114310053292E+00,  0.114778898592700E+00,  0.564001074548194E-01,\n    -.156778448777534E+00,  0.252245430100092E+00,  0.214059780070552E+00,  0.590334934291369E-01,\n    -.397361694050676E+00,  -.197715908337850E+00,  -.796336180288233E-01,  0.606546975975660E-01,\n    0.976488461664955E-02,  0.650418429113368E+00,  0.586844628157869E-02,  0.410974214177224E-01,\n    -.522808798464745E+00,  -.663505530412342E-01,  0.193427268845595E-02,  0.212751151391656E-01,\n    0.556560570221033E+00,  -.318669005422011E+00,  0.137974025142484E-01,  0.439472843110177E-01,\n    0.842308197902346E-01,  0.213805982286452E-01,  0.319575612691255E-01,  0.106159736633763E+00,\n    0.217416609116024E+00,  0.459950700499800E+00,  -.302723230375316E+00,  0.508504156781972E-01,\n    0.321215894522565E+00,  -.426005242154505E+00,  -.299485560260426E+00,  0.469223144374328E-01,\n    -.259364807854540E+00,  0.385198021113758E+00,  -.282519709925312E+00,  0.594595362053551E-01,\n    0.563645476720258E+00,  -.111160622782170E+00,  -.306814049530041E+00,  0.444515894220345E-01,\n    -.641094964462573E+00,  -.193431833850761E+00,  -.366047360516159E+00,  0.240457075184084E-01,\n    -.334415109058215E+00,  -.449175869972365E+00,  -.310242329274613E+00,  0.374428103418277E-01,\n    -.189458838689202E-01,  0.901059945993096E+00,  -.347395205043690E+00,  0.150146857671485E-01,\n    -.821465589619935E+00,  -.510986035935518E+00,  -.263564524010136E+00,  0.982721516374585E-02,\n    0.859741547561566E+00,  -.520808514122574E+00,  -.360343258536933E+00,  0.682714132625144E-02 ];\n\n% NodeSet 7 31\nTN2012_7 = [ % 31*4\n    -.308252291356292E+00,  -.197843287967888E+00,  0.655377140534142E+00,  0.726734289368660E-02,\n    0.323006856476517E+00,  -.116660814480029E+00,  0.596321726821282E+00,  0.113172838083258E-01,\n    0.179003094309949E-02,  0.276022304167135E-02,  0.919167034243546E+00,  0.218890177509953E-01,\n    -.174985856285022E+00,  0.149255307127258E+00,  0.516292138739824E+00,  0.211296379289743E-01,\n    0.351581822451223E-01,  0.322257000983196E+00,  0.549210359947022E+00,  0.219589530907310E-01,\n    -.114891280250506E+00,  0.359815024699799E-01,  -.388691765396646E+00,  0.257668903243402E-01,\n    0.108024261958047E+00,  -.212321108274526E+00,  0.513821465706857E+00,  0.271493965375484E-01,\n    0.996088601920952E-01,  0.337060034616579E+00,  -.273250222640217E+00,  0.405324562077599E-01,\n    0.255258965717304E+00,  0.234593138765271E-01,  -.128926642642749E+00,  0.497630316079319E-01,\n    0.301897209396454E+00,  -.279744081272853E+00,  -.336815502815560E+00,  0.394457458861626E-01,\n    -.363799525166287E-01,  0.678415814779263E+00,  0.496515459049994E-01,  0.195275383339862E-01,\n    -.197974843267435E+00,  -.109926012070685E+00,  0.368810136731088E+00,  0.568528141204943E-01,\n    0.170554760858719E+00,  0.585448114971668E-01,  0.324539845033623E+00,  0.628959292907061E-01,\n    0.538271457535773E+00,  -.332620243815606E+00,  0.653099846338948E-01,  0.296146949713317E-01,\n    -.582625595155966E+00,  -.286639428866960E+00,  0.405007368919429E-01,  0.266935521529227E-01,\n    -.663882748425514E-01,  0.339664373274517E+00,  0.433913762115470E-01,  0.629225940565768E-01,\n    0.139486545048336E+00,  -.307850231836867E+00,  -.855418822812698E-02,  0.605178177170064E-01,\n    -.252544821156148E+00,  -.968770785232076E-01,  -.121223434312817E+00,  0.776291699936313E-01,\n    -.336417947935678E+00,  -.422280930562356E+00,  0.224493699213366E-01,  0.196789751488124E-01,\n    0.508817409509134E+00,  -.460796001465295E-01,  -.538915514978018E-01,  0.231576809797211E-01,\n    -.399767677129958E+00,  0.169411793400156E+00,  -.133176911076194E-01,  0.210705295411276E-01,\n    0.223357920899763E+00,  0.507026392231846E+00,  -.399617306009708E-01,  0.209118054635657E-01,\n    -.288768983232798E+00,  -.432041161224173E+00,  -.330437075713885E+00,  0.348234147884134E-01,\n    0.243005680598846E+00,  0.526215548808249E+00,  -.363369754803439E+00,  0.157467976153176E-01,\n    -.240245918466879E+00,  0.491923965978304E+00,  -.318467007553824E+00,  0.318630473755232E-01,\n    -.573712249516894E+00,  -.745318233435191E-01,  -.334800074141430E+00,  0.264563326318226E-01,\n    0.553074058634518E+00,  -.335502653046674E-02,  -.350744403540142E+00,  0.198864503476964E-01,\n    0.763216087154036E+00,  -.434539428837598E+00,  -.312047989157562E+00,  0.194127668598410E-01,\n    0.110832352531628E-02,  0.883581509323175E+00,  -.315533688400763E+00,  0.181045845852361E-01,\n    -.766658972551922E+00,  -.454032742499625E+00,  -.314870223157801E+00,  0.170243759166119E-01,\n    0.331437926843703E+00,  -.541903403372277E+00,  -.310441026876215E+00,  0.117984136552630E-01 ];\n\n% NodeSet 8 44\nTN2012_8 = [ % 44*4\n    -.161177255167053E-01,  0.799860011950070E-03,  0.114075012048065E+01,  0.164537481630632E-02,\n    -.714882723684071E-02,  0.172391459500714E+00,  0.786069607279345E+00,  0.142022580708632E-01,\n    0.458992337208856E+00,  -.174471635165123E+00,  -.399143091233099E+00,  0.931166933355630E-02,\n    -.452099049249418E+00,  -.263294162907140E+00,  0.353609296024044E+00,  0.954963853667850E-02,\n    0.110956035643063E+00,  -.629572037600564E-01,  0.818940414230147E+00,  0.166052583605351E-01,\n    0.182522429364889E+00,  0.167838281859336E+00,  0.476496271636175E+00,  0.147232729381889E-01,\n    -.150748980541997E+00,  -.782694187324931E-01,  0.762897429508123E+00,  0.169600880437266E-01,\n    -.287732473487860E+00,  -.123468697478364E+00,  -.388005832381780E+00,  0.155840364711871E-01,\n    0.368074721237849E+00,  -.216987676633974E+00,  0.399755369527441E+00,  0.212175082715303E-01,\n    0.478815642489051E+00,  -.462943730059424E-01,  0.114465646499509E+00,  0.120673743703809E-01,\n    -.347877936740082E+00,  -.129563033616473E+00,  0.250917196877864E+00,  0.256962000267611E-01,\n    -.354828586873474E-01,  -.226810803130748E+00,  0.417461557834331E+00,  0.304461329840053E-01,\n    0.259165044293404E+00,  0.133007144302729E+00,  -.274777834511803E+00,  0.265737195097187E-01,\n    -.130503737894631E-02,  0.491940911589454E+00,  0.303192932475321E+00,  0.227767050847918E-01,\n    -.349399607082860E-01,  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-.914318710054964E-01,  0.213070712463992E-02,\n    -.278430725475103E+00,  -.463378945653674E+00,  -.914318710054968E-01,  0.213070712464070E-02,\n    0.145805941349524E+00,  -.526334554268467E+00,  -.263961054397257E+00,  0.146222435997457E-02,\n    0.382916124211298E+00,  0.389438926365626E+00,  -.263961054397247E+00,  0.146222435997416E-02,\n    -.528722065560796E+00,  0.136895627902853E+00,  -.263961054397253E+00,  0.146222435997499E-02,\n    -.182315800139201E-13,  -.173834348867150E-14,  -.300394380298934E+00,  0.106049995563824E-01,\n    -.169910697181091E-13,  -.249020633102366E-15,  0.427541621225973E+00,  0.136188175091050E-01,\n    0.156992255283360E-12,  0.102872783354632E-12,  0.145464511562067E+00,  0.887275759574957E-02,\n    0.535998565692471E-13,  0.781044393753285E-13,  -.107279857079785E+00,  0.855159201414087E-02 ];\n\nmaptorst = true;\nswitch N\n    case 1\n        rstw = TN2012_1;\n    case 2\n        rstw = TN2012_2;\n    case 3\n        rstw = TN2012_3;\n    case 4\n        rstw = TN2012_4;\n    case 5\n        rstw = TN2012_5;\n    case 6\n        rstw = TN2012_6;\n    case 7\n        rstw = TN2012_7;\n    case 8\n        rstw = TN2012_8;\n    case 9\n        rstw = TN2012_9;\n    case 10\n        rstw = TN2012_10;\n    case 11\n        rstw = TN2012_11;\n    case 12\n        rstw = TN2012_12;\n    case 13\n        rstw = TN2012_13;\n    case 14\n        rstw = TN2012_14;\n    case 15\n        rstw = TN2012_15;\n    otherwise\n%         error('Quadrature degree too high.')\n        [r s t w] = tet_cubature_TP(ceil(N/2));\n        rstw = [r(:) s(:) t(:) w(:)];        \n        maptorst = false;\nend\n\nr = rstw(:,1); s = rstw(:,2); t = rstw(:,3); \nif maptorst\n    [r s t] = xyztorst(r,s,t);\nend\nw = rstw(:,4); w = (4/3)*w/sum(w);\n\n% integrates order (2*N+1)\nfunction [r s t w] = tet_cubature_TP(N)\n\n[ra wa] = JacobiGQ(0,0,N);\n[rb wb] = JacobiGQ(1,0,N);\n[rc wc] = JacobiGQ(2,0,N);\n\n[a b c] = meshgrid(ra, rb, rc);\n[wa wb wc] = meshgrid(wa,wb,wc);\n\na = a(:); b = b(:); c = c(:);\nwa = wa(:); wb = wb(:); wc = wc(:);\nw = wa.*wb.*wc;\nw = (4/3)*w/sum(w);\n\n[r s t] = tet_abctorst(a,b,c);\n"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "ElasticityAcoustic2D_unified.m", "ext": ".m", "path": "WADG_Matlab-master/ElasticityAcoustic2D_unified.m", "size": 12686, "source_encoding": "utf_8", "md5": "a6984b83845491ed80d1ac4cd5a5ea33", "text": "% function ElasticAcoustic2D_unified\n\nclear all, clear\n% clear -global *\n\nGlobals2D\nglobal tau1 tau2\nglobal ue3 ue4 ue5 ua3\nglobal fa1 fa2 fe1 fe2\n\nK1D = 16;\nN = 4;\nc_flag = 0;\nFinalTime = 2.0;\ntau1 = 1;\ntau2 = 1;\n\n[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);\n\nStartUp2D;\n\n\n\n[rp sp] = EquiNodes2D(15); [rp sp] = xytors(rp,sp);\nVp = Vandermonde2D(N,rp,sp)/V;\nxp = Vp*x; yp = Vp*y;\n\nNq = 2*N;\n[rq sq wq] = Cubature2D(Nq); % integrate u*v*c\nVq = Vandermonde2D(N,rq,sq)/V;\nPq = V*V'*Vq'*diag(wq); % J's cancel out\nMref = inv(V*V');\nxq = Vq*x; yq = Vq*y;\nJq = Vq*J;\n\n% partition mesh: y > 0 = acoustic\nglobal Ka Ke\n% Ke = 1:K; Ka = []; \n% Ka = 1:K; Ke = [];\nKa = find(mean(y) > 0); Ke = find(mean(y) < 0);\n\n%% find dividing boundary\n\nglobal mapAM vmapAM mapAP vmapAP\nglobal mapEM vmapEM mapEP vmapEP\nmapAM = [];    mapAP = [];\nvmapAM = [];    vmapAP = [];\nmapEM = [];    mapEP = [];\nvmapEM = [];    vmapEP = [];\n\nif ~isempty(Ke) && ~isempty(Ka)\n    \n    mapAM = zeros(Nfp,K1D);    mapAP = zeros(Nfp,K1D);\n    vmapAM = zeros(Nfp,K1D);    vmapAP = zeros(Nfp,K1D);\n    mapEM = zeros(Nfp,K1D);    mapEP = zeros(Nfp,K1D);\n    vmapEM = zeros(Nfp,K1D);    vmapEP = zeros(Nfp,K1D);\n    \n    yM = reshape(y(vmapM),Nfp*Nfaces,K);\n    ska = 1; ske = 1;\n    for e = 1:K\n        yf = reshape(yM(:,e),Nfp,Nfaces);\n        for f = 1:Nfaces\n            if norm(yf(:,f))<1e-8\n                ids = (1:Nfp) + (f-1)*Nfp + (e-1)*Nfp*Nfaces;\n                if ismember(e,Ka) % if in acoustic region\n                    mapAM(:,ska) = mapM(ids);\n                    mapAP(:,ska) = mapP(ids);\n                    vmapAM(:,ska) = vmapM(ids);\n                    vmapAP(:,ska) = vmapP(ids);\n                    ska = ska + 1;\n                elseif ismember(e,Ke) % if in elastic region\n                    mapEM(:,ske) = mapM(ids);\n                    mapEP(:,ske) = mapP(ids);\n                    vmapEM(:,ske) = vmapM(ids);\n                    vmapEP(:,ske) = vmapP(ids);\n                    ske = ske + 1;\n                else\n                    keyboard\n                end                \n            end\n        end\n    end\n    \n%     keyboard\nend\n\n\n\n%%\nglobal Nfld mu lambda Vq Pq c2\nNfld = 5; %(u1,u2,sxx,syy,sxy)\n\nmu = ones(size(xq));\nlambda = ones(size(xq));\nc2 = ones(size(xq));\n\n% k = 1;\n% mu = 1 + .5*cos(k*pi*xq).*cos(k*pi*yq);\n% c2 = 1 + .5*cos(k*pi*xq).*cos(k*pi*yq);\n\n% mu = V\\(Pq*mu); \n% c2 = V\\(Pq*c2);\n% mu = repmat(mu(1,:),length(wq),1)/sqrt(2);\n% c2 = repmat(c2(1,:),length(wq),1)/sqrt(2);\n\n% vv = Vp*Pq*c2; color_line3(xp,yp,vv,vv,'.');return\n% keyboard\n\n%% implement exact Scholte wave\nScholte;\n%keyboard\n%% params setup\n\nx0 = 0; \ny0 = .1;\npp = exp(-100^2*((x-x0).^2 + (y-y0).^2));\n\nf0 = 10;\nt0 = 1/f0;\n\n% point source\n%global fsrc\n%fsrc = @(t) (t < t0).*(1-2*(pi*f0*(t-t0))^2)*exp(-(pi*f0*(t-t0)^2)).* (Pq * exp(-100^2*((xq-x0).^2 + (yq-y0).^2)));\n%fsrc = @(t) 0;\n\ny0 = -.25;\n%p = exp(-25^2*((x-x0).^2 + (y-y0).^2));\nu = zeros(Np, K);\n\n%% set initial value using exact solution of scholte wave\nU{1}(:,Ka) = u1a(x(:,Ka),y(:,Ka),0);\nU{1}(:,Ke) = u1e(x(:,Ke),y(:,Ke),0);\n\nU{2}(:,Ka) = u2a(x(:,Ka),y(:,Ka),0);\nU{2}(:,Ke) = u2e(x(:,Ke),y(:,Ke),0);\n\nU{3}(:,Ka) = s1ax(x(:,Ka),y(:,Ka),0);\nU{3}(:,Ke) = s1ex(x(:,Ke),y(:,Ke),0);\n\nU{4}(:,Ka) = s2ay(x(:,Ka),y(:,Ka),0);\nU{4}(:,Ke) = s2ey(x(:,Ke),y(:,Ke),0);\n\nU{5}(:,Ka) = zeros(Np,length(Ka));\nU{5}(:,Ke) = s12exy(x(:,Ke),y(:,Ke),0);\n\n\n% U{1} = u;\n% U{2} = u;\n% U{3} = u;\n% U{4} = u;\n% U{5} = u;\n\n%% test numerical stability\nif Nfld*Np*K < 2500\n        \n    u = zeros(Nfld*Np*K,1);\n    rhs = zeros(Nfld*Np*K,1);\n    A = zeros(Nfld*Np*K);\n    ids = 1:Np*K;\n    for i = 1:Nfld*Np*K\n        u(i) = 1;\n        for fld = 1:Nfld\n            U{fld} = reshape(u(ids + (fld-1)*Np*K),Np,K);\n        end\n        \n%         ue1 = s1ex(x,y,0);\n%         ue2 = s2ey(x,y,0);\n%         ue3 = s12exy(x,y,0);\n%         ua1 = s1ax(x,y,0);\n        \n        rE = ElasRHS2D(U,0);\n        rA = AcousRHS2D(U,0);        \n        \n        u(i) = 0;\n        for fld = 1:Nfld  \n            rU = zeros(Np,K);\n            rU(:,Ke) = rE{fld}(:,Ke);\n            if fld <= 3\n                rU(:,Ka) = rA{fld}(:,Ka);\n            end\n            rhs(ids + (fld-1)*Np*K) = rU;\n        end\n        \n        A(:,i) = rhs(:);\n        if (mod(i,100)==0)\n            disp(sprintf('on col %d out of %d\\n',i,Np*K*Nfld))\n        end\n    end\n    lam = eig(A);\n    \n    plot(lam,'.','markersize',24)\n    hold on\n    title(sprintf('Largest real part = %g\\n',max(real(lam))))\n    axis equal\n    %         drawnow\n    max(abs(lam))\n    \n    keyboard\nend\n%%\n\n\ntime = 0;\n\n% Runge-Kutta residual storage\nfor fld = 1:Nfld\n    \n    res{fld} = zeros(Np,K);\n    %rhs{fld} = zeros(Np,K);\n    \nend\n\n% compute time step size\nCN = (N+1)*(N+2)/3; % guessing...\ndt = 2/(max(2*mu(:)+lambda(:))*CN*max(Fscale(:)));\nNsteps = ceil(FinalTime/dt);\ndt = FinalTime/Nsteps;\n\n% outer time step loop\ntstep = 0;\n\nM = inv(V*V');\nwqJ = diag(wq)*(Vq*J);\n\n% figure\n% colormap(gray)\n% colormap(hot)\n\n\nfor tstep = 1:Nsteps\n        \n    time = tstep*dt;\n    \n    for INTRK = 1:5\n        \n        timeloc = time + rk4c(INTRK)*dt;\n        \n        \n        \n        ue3 = s1ex(x,y,timeloc);\n        ue4 = s2ey(x,y,timeloc);\n        ue5 = s12exy(x,y,timeloc);\n        ua3 = s1ax(x,y,timeloc);\n        \n        %fa1 = f1a(x,y,timeloc);\n        %fa2 = f2a(x,y,timeloc);\n        \n        %fe1 = f1e(x,y,timeloc);\n        %fe2 = f2e(x,y,timeloc);\n        \n        rhsE = ElasRHS2D(U,timeloc);\n        rhsA = AcousRHS2D(U,timeloc);        \n        \n        % initiate and increment Runge-Kutta residuals\n        for fld = 1:Nfld\n            rhs = zeros(Np,K);                    \n            if fld <= 3\n                rhs(:,Ka) = rhsA{fld}(:,Ka);                \n            end            \n            rhs(:,Ke) = rhsE{fld}(:,Ke);\n            res{fld} = rk4a(INTRK)*res{fld} + dt*rhs;\n            U{fld} = U{fld} + rk4b(INTRK)*res{fld};\n        end\n        \n    end\n    \n    if 1 && (mod(tstep,10)==0 || tstep==Nsteps)\n        clf\n        \n        pe = (U{3} + U{4})/2; % average trace(S)\n        % pe = U{3}; % average trace(S)\n        %pa = (U{3} + U{4})/2;\n        pa = U{3}; % average trace(S)\n        p(:,Ke) = pe(:,Ke);\n        p(:,Ka) = pa(:,Ka);\n        vv = Vp*p;\n        color_line3(xp,yp,vv,vv,'.');\n        axis tight\n        title(sprintf('time = %f',time));\n        colorbar;\n        \n        drawnow        \n        \n     p_exact(:,Ka) = s1ax(xq(:,Ka),yq(:,Ka),time);\n    p_exact(:,Ke) = 0.5*s1ex(xq(:,Ke),yq(:,Ke),time)+0.5*s2ey(xq(:,Ke),yq(:,Ke),time);\n\n    p_quadrature = Vq * p;\n\n    [d1,d2]=size(p_quadrature);\n    error_accumulation = 0;\n\nfor j1=1:d2\n    for j2=1:d1\n        err = p_quadrature(j2,j1)-p_exact(j2,j1);\n        error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);\n    end\nend\n\nerror_l2 = sqrt(error_accumulation)\n\n    \n    \n    \n    \n    \n    end\n    \n    if mod(tstep,100)==0\n        disp(sprintf('On timestep %d out of %d\\n',tstep,round(FinalTime/dt)))\n     end\nend\n\n% keyboard\nset(gca,'fontsize',14)\ntitle('')\naxis tight\n\n\np_exact(:,Ka) = s1ax(xq(:,Ka),yq(:,Ka),FinalTime);\np_exact(:,Ke) = 0.5*s1ex(xq(:,Ke),yq(:,Ke),FinalTime)+0.5*s2ey(xq(:,Ke),yq(:,Ke),FinalTime);\n\np_quadrature = Vq * p;\n\n[d1,d2]=size(p_quadrature);\nerror_accumulation = 0;\nfor j1=1:d2\n    for j2=1:d1\n        err = p_quadrature(j2,j1)-p_exact(j2,j1);\n        error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);\n    end\nend\n\nerror_l2 = sqrt(error_accumulation)\n\n\nfunction [rhs] = ElasRHS2D(U,time)\n\n% function [rhsu, rhsv, rhsp] = acousticsRHS2D(u,v,p)\n% Purpose  : Evaluate RHS flux in 2D acoustics TM form\n\nGlobals2D;\n\nglobal Nfld mu lambda Vq Pq tau1 tau2 useWADG\nglobal C11 C12 C13 C22 C23 C33\nglobal ue3 ue4 ue5 ua3\nglobal fa1 fa2 fe1 fe2\n% Define field differences at faces\nfor fld = 1:Nfld\n    u = U{fld};\n    \n    % compute jumps\n    dU{fld} = zeros(Nfp*Nfaces,K);\n    dU{fld}(:) = u(vmapP)-u(vmapM);\n    \n    ur = Dr*u;\n    us = Ds*u;\n    Ux{fld} = rx.*ur + sx.*us;\n    Uy{fld} = ry.*ur + sy.*us;\nend\n\ndivSx = Ux{3} + Uy{5}; % d(Sxx)dx + d(Sxy)dy\ndivSy = Ux{5} + Uy{4}; % d(Sxy)dx + d(Syy)dy\ndu1dx = Ux{1}; % du1dx\ndu2dy = Uy{2}; % du2dy\ndu12dxy = Ux{2} + Uy{1}; % du2dx + du1dy\n\n% velocity fluxes An^T*sigma\nnSx = nx.*dU{3} + ny.*dU{5};\nnSy = nx.*dU{5} + ny.*dU{4};\n\n% stress fluxes An*u\nnUx = dU{1}.*nx;\nnUy = dU{2}.*ny;\nnUxy = dU{2}.*nx + dU{1}.*ny;\n\nopt=1;\nif opt==1 % traction BCs    \n    \n    % set traction equal to exact traction  \n    nSx(mapB) = -2*(nx(mapB).*U{3}(vmapB) + ny(mapB).*U{5}(vmapB)) + 2*(nx(mapB).*ue3(vmapB) + ny(mapB).*ue5(vmapB));\n    nSy(mapB) = -2*(nx(mapB).*U{5}(vmapB) + ny(mapB).*U{4}(vmapB)) + 2*(nx(mapB).*ue5(vmapB) + ny(mapB).*ue4(vmapB));    \n    \n    %nSx(mapB) =  -2*(nx(mapB).*ue1(vmapB) + ny(mapB).*ue3(vmapB));\n    %nSy(mapB) =  -2*(nx(mapB).*ue3(vmapB) + ny(mapB).*ue2(vmapB)); \n    \n    %nSx(mapB) = -2*(nx(mapB).*U{3}(vmapB) + ny(mapB).*U{5}(vmapB)); \n    %nSy(mapB) = -2*(nx(mapB).*U{5}(vmapB) + ny(mapB).*U{4}(vmapB)); \n    \nelseif opt==2 % basic ABCs\n    nSx(mapB) = -(nx(mapB).*U{3}(vmapB) + ny(mapB).*U{5}(vmapB));\n    nSy(mapB) = -(nx(mapB).*U{5}(vmapB) + ny(mapB).*U{4}(vmapB));\n    dU{1}(mapB) = -U{1}(vmapB);\n    dU{2}(mapB) = -U{2}(vmapB);\nelseif opt==3 % zero velocity\n    dU{1}(mapB) = -2*U{1}(vmapB);\n    dU{2}(mapB) = -2*U{2}(vmapB);    \nend\n\n% coupling\nglobal mapEM vmapEM mapEP vmapEP \nnxf = nx(mapEM); nyf = ny(mapEM); \nu = U{1}(vmapEP); \nv = U{2}(vmapEP);\np = U{3}(vmapEP);\n\nSnx = U{3}(vmapEM).*nxf + U{5}(vmapEM).*nyf;\nSny = U{5}(vmapEM).*nxf + U{4}(vmapEM).*nyf;\nUn = u.*nxf + v.*nyf;\ndU1 = (Un.*nxf-U{1}(vmapEM));\ndU2 = (Un.*nyf-U{2}(vmapEM));\n\nUnM = (u-U{1}(vmapEM)).*nxf + (v-U{2}(vmapEM)).*nyf;\ndU1M = UnM.*nxf;\ndU2M = UnM.*nyf;\n\nnSx(mapEM) = (1*p.*nxf - Snx);\nnSy(mapEM) = (1*p.*nyf - Sny);\nnUx(mapEM) = dU1.*nxf;\nnUy(mapEM) = dU2.*nyf;\nnUxy(mapEM) = dU1.*nyf + dU2.*nxf;\n\n% evaluate central fluxes\nfc{1} = nSx;\nfc{2} = nSy;\nfc{3} = nUx;\nfc{4} = nUy;\nfc{5} = nUxy;\n\n% penalization terms - reapply An\nfp{1} = nx.*fc{3} + ny.*fc{5};\nfp{2} = nx.*fc{5} + ny.*fc{4};\n\nfp{1}(mapEM) = dU1M;\nfp{2}(mapEM) = dU2M;\n\nfp{3} = fc{1}.*nx;\nfp{4} = fc{2}.*ny;\nfp{5} = fc{2}.*nx + fc{1}.*ny;\n\nflux = cell(5,1);\nfor fld = 1:2\n    flux{fld} = zeros(Nfp*Nfaces,K);\n    flux{fld}(:) = fc{fld}(:) + tau1.*fp{fld}(:);\nend\nfor fld = 3:5\n    flux{fld} = zeros(Nfp*Nfaces,K);\n    flux{fld}(:) = fc{fld}(:) + tau2.*fp{fld}(:);\nend\n% compute right hand sides of the PDE's\nrr{1} =  divSx  + LIFT*(Fscale.*flux{1})/2.0;\nrr{2} =  divSy  + LIFT*(Fscale.*flux{2})/2.0;\nrr{3} =  du1dx   +  LIFT*(Fscale.*flux{3})/2.0;\nrr{4} =  du2dy   +  LIFT*(Fscale.*flux{4})/2.0;\nrr{5} =  du12dxy +  LIFT*(Fscale.*flux{5})/2.0;\n\nif 0\n    rhs{1} = rr{1};\n    rhs{2} = rr{2};\n    rhs{3} = (2*mu+lambda).*rr{3} + lambda.*rr{4};\n    rhs{4} = lambda.*rr{3} + (2*mu+lambda).*rr{4};\n    rhs{5} = (mu) .* rr{5};\nelse\n    global Pq Vq\n    rhs{1} = rr{1};\n    rhs{2} = rr{2};\n    rhs{3} = Pq*((2*mu+lambda).*(Vq*rr{3}) + lambda.*(Vq*rr{4}));\n    rhs{4} = Pq*(lambda.*(Vq*rr{3}) + (2*mu+lambda).*(Vq*rr{4}));\n    rhs{5} = Pq*(mu .* (Vq*rr{5}));\nend\n\n% global Ka\n% for fld = 1:5\n%     rhs{fld}(:,Ka) = 0;\n% end\n\nend\n\n\nfunction [rhs] = AcousRHS2D(U,time)\n\n% function [rhsu, rhsv, rhsp] = acousticsRHS2D(u,v,p)\n% Purpose  : Evaluate RHS flux in 2D acoustics TM form\n\nGlobals2D;\nglobal ua3\nglobal fa1 fa2 fe1 fe2\nu = U{1};\nv = U{2};\np = U{3}; % should be same as U{4}\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\n%ndotdU(mapB) = 0; % will not affect, naturally zero\n%dp(mapB) = -2*p(vmapB);\ndp(mapB) = -2*p(vmapB) + 2*ua3(vmapB);\n\n% elastic-acoustic coupling\nglobal mapAM vmapAM mapAP vmapAP \nnxf = nx(mapAM); nyf = ny(mapAM); \nv1 = U{1}(vmapAP); v2 = U{2}(vmapAP);\nsxx = U{3}(vmapAP); syy = U{4}(vmapAP); sxy = U{5}(vmapAP);\n\nSnx = sxx.*nxf + sxy.*nyf;\nSny = sxy.*nxf + syy.*nyf;\nnSn = Snx.*nxf + Sny.*nyf;\ndU1 = (v1-u(vmapAM));\ndU2 = (v2-v(vmapAM));\nndotdU(mapAM) = dU1.*nxf + dU2.*nyf;\ndp(mapAM) = nSn-1*p(vmapAM);\n\n\nglobal tau1 tau2;\nfluxp =  tau2*dp + ndotdU;\nfluxu =  (tau1*ndotdU + dp).*nx;\nfluxv =  (tau1*ndotdU + dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhs{1} = dpdx  + LIFT*(Fscale.*fluxu)/2.0;\nrhs{2} = dpdy  + LIFT*(Fscale.*fluxv)/2.0;\nrhs{3} = divU + LIFT*(Fscale.*fluxp)/2.0;\n\nif 0\n\nglobal c2 Pq Vq\n\n%global fsrc \n%rhs{3} = rhs{3} + fsrc(time);\n\nrhs{3} = Pq*(c2.*(Vq*rhs{3}));\n\nend\n\nend"}
+{"plateform": "github", "repo_name": "KGuo26/WADG_Matlab-master", "name": "Wave2D_M_manufactured.m", "ext": ".m", "path": "WADG_Matlab-master/Wave2D_M_manufactured.m", "size": 3244, "source_encoding": "utf_8", "md5": "7f7a6ac983321b0bffeaf09bbd71fa7f", "text": "clear\nGlobals2D\n\nN = 8;\nk=1\n\nfor M = 0:N\n\nK1D = 32;\nFinalTime = 1.0;\n[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);\nStartUp2D;\n\n%% Set up wavespeed function\n%cfun = @(x,y) ones(size(x));\ncfun = @(x,y) 1+ 0.5*sin(k*pi*x).*sin(k*pi*y); % smooth velocity\n%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity\n\n%% Set periodic manufactured solution\npfun = @(x,y,t) sin(pi*x).*sin(pi*y).*cos(pi*t);\nufun = @(x,y,t) -cos(pi*x).*sin(pi*y).*sin(pi*t);\nvfun = @(x,y,t) -sin(pi*x).*cos(pi*y).*sin(pi*t);\nffun = @(x,y,t)  pi*(-1./(cfun(x,y))+2).*sin(pi*x).*sin(pi*y).*sin(pi*t);\n\n%% generate quadrature points\nNq = 2*N+1;\n[rq sq wq] = Cubature2D(Nq); % integrate u*v*c\nVq = Vandermonde2D(N,rq,sq)/V;\nxq = Vq*x; yq = Vq*y;\n\n%% construct the projection matrix for nodal basis Pq\nPq = V*V'*Vq'*diag(wq);\n\n%% construct matrix Cq\nCq = cfun(xq,yq);\n\n%% construct the projection matrix for cfun to degree M\nVMq = Vandermonde2D(M,rq,sq);\nCqM = VMq*VMq'*diag(wq)*Cq;\n\n%% initial condition\np = pfun(x,y,0);\nu = ufun(x,y,0);\nv = vfun(x,y,0);\n\ntime = 0;\n\n%% Runge-Kutta residual storage\nresu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);\n\n%% compute time step size\nCN = (N+1)*(N+2)/2; % trace inequality constant\nCNh = max(CN*max(Fscale(:)));\ndt = 2/CNh;\n\n%% outer time step loop\ntstep = 0;\n\nwhile (time<FinalTime)\n    if(time+dt>FinalTime), dt = FinalTime-time; end\n    \n    for INTRK = 1:5\n        \n        timelocal = time + rk4c(INTRK)*dt;\n        \n        f=ffun(x,y,timelocal);\n        \n        [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f);\n        \n        % initiate and increment Runge-Kutta residuals\n        resp = rk4a(INTRK)*resp + dt*rhsp;\n        resu = rk4a(INTRK)*resu + dt*rhsu;\n        resv = rk4a(INTRK)*resv + dt*rhsv;\n        \n        % update fields\n        u = u+rk4b(INTRK)*resu;\n        v = v+rk4b(INTRK)*resv;\n        p = p+rk4b(INTRK)*resp;\n        \n    end\n    \n    % Increment time\n    time = time+dt; tstep = tstep+1;\nend\n\np_exact = pfun(x,y,FinalTime);\np_exact_quadrature = pfun(xq,yq,FinalTime);\n\np_quadrature = Vq * p;\n\n[d1,d2]=size(p_quadrature);\nerror_accumulation = 0;\nfor j1=1:d2\n    for j2=1:d1\n        err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);\n        error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);\n    end\nend\n\nerror_l2(M+1) = sqrt(error_accumulation);\nerror_fro(M+1) = norm(p-p_exact,'fro'); \n\nend\n\n\nfunction [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f)\n\nGlobals2D;\n\n% Define field differences at faces\ndp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);\ndu = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);\ndv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);\n\n% evaluate upwind fluxes\nndotdU = nx.*du + ny.*dv;\n\n% Impose reflective boundary conditions (p+ = -p-)\nndotdU(mapB) = 0;\ndp(mapB) = -2*p(vmapB);\n\ntau = 1;\nfluxp =  tau*dp - ndotdU;\nfluxu =  (tau*ndotdU - dp).*nx;\nfluxv =  (tau*ndotdU - dp).*ny;\n\npr = Dr*p; ps = Ds*p;\ndpdx = rx.*pr + sx.*ps;\ndpdy = ry.*pr + sy.*ps;\ndivU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);\n\n% compute right hand sides of the PDE's\nrhsp =  -divU + f + LIFT*(Fscale.*fluxp)/2.0;\nrhsu =  -dpdx + LIFT*(Fscale.*fluxu)/2.0;\nrhsv =  -dpdy + LIFT*(Fscale.*fluxv)/2.0;\n\nrhsp = Pq*(CqM.*(Vq*rhsp));\nreturn;\nend\n"}
+{"plateform": "github", "repo_name": "caghangir/MATLAB-Grid-Search-for-Neural-Networks-master", "name": "gridSearchNN.m", "ext": ".m", "path": "MATLAB-Grid-Search-for-Neural-Networks-master/gridSearchNN.m", "size": 2393, "source_encoding": "utf_8", "md5": "40eb11e58ad1cf4dfde388d97884b636", "text": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Grid Search for Matlab                                   %\n%                                                          %\n% Copyright (C) 2017 Cagatay Demirel. All rights reserved. %\n%                    demirelc16@itu.edu.tr                 %\n%                                                          %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% **** Example (How to use function) *******\n%\n% hidlaysize1 = [15 30 70];\n% hidlaysize2 = [10 20 50];\n% trainopt = {'traingd' 'traingda' 'traingdm' 'traingdx'}; \n% maxepoch = [10 20 40 90];\n% transferfunc = {'logsig' 'tansig'};\n% bestparameters = gridSearchNN(x_train',y_train',hidlaysize1,...\n%                               hidlaysize2,trainopt,maxepoch,transferfunc);\n\n\nfunction out = gridSearchNN(trainX,trainY,param1,param2,param3,param4,...\n                                   param5,varargin)\n\nif(nargin > 4)\n[p,q,r,s,t] = ndgrid(param1,param2,1:length(param3),param4,1:length(param5));\npairs = [p(:) q(:) r(:) s(:) t(:)];\n% scoreboard = cell(size(pairs,1),4);\nelse\n[p,q] = meshgrid(param1,param2);\npairs = [p(:) q(:)];\n% scoreboard = cell(size(pairs,1),3);\nend\n\nvalscores = zeros(size(pairs,1),1);\n\nfor i=1:size(pairs,1)\n  setdemorandstream(672880951)\n  net = patternnet([pairs(i,1) pairs(i,2)]);\n  net.trainFcn = param3{pairs(i,3)};\n  net.trainParam.epochs\t= pairs(i,4);\n  net.layers{2}.transferFcn = param5{pairs(i,5)};\n  net.divideParam.trainRatio = 0.9;\n  net.divideParam.valRatio = 0.1;\n  net.divideParam.testRatio = 0;  \n  \n   vals = crossval(@(XTRAIN, YTRAIN, XTEST, YTEST)NNtrain(XTRAIN, YTRAIN, XTEST, YTEST, net),...\n                     trainX, trainY);\n   valscores(i) = mean(vals);  \n\n%   net = train(net,trainX,trainY);\n%   y_pred = net(valX);    \n%   [~,indicesReal] = max(valY, [], 1); %336x1 matrix\n%   [~, indicesPredicted] = max(y_pred,[],1);\n%   valscores(i) = mean(double(indicesPredicted == indicesReal)); \n\nend\n\n [~,ind] = max(valscores);\n out = {pairs(ind,1) pairs(ind,2) param3{pairs(ind,3)} ...\n        pairs(ind,4) param5{pairs(ind,5)}};\n\n\nend\n\nfunction testval = NNtrain(XTRAIN, YTRAIN, XTEST, YTEST, net)\n\n    net = train(net, XTRAIN', YTRAIN');\n    y_pred = net(XTEST');\n    [~,indicesReal] = max(YTEST',[],1);\n    [~,indicesPredicted] = max(y_pred,[],1);\n     testval = mean(double(indicesPredicted == indicesReal));      \nend\n"}
+{"plateform": "github", "repo_name": "mrmushfiq/qalma-master", "name": "qalma.m", "ext": ".m", "path": "qalma-master/qalma.m", "size": 5230, "source_encoding": "utf_8", "md5": "0e9226a0f71ab8eb22b7073be7ef050e", "text": "function varargout = qalma(varargin)\n% QALMA MATLAB code for qalma.fig\n%      QALMA, by itself, creates a new QALMA or raises the existing\n%      singleton*.\n%\n%      H = QALMA returns the handle to a new QALMA or the handle to\n%      the existing singleton*.\n%\n%      QALMA('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in QALMA.M with the given input arguments.\n%\n%      QALMA('Property','Value',...) creates a new QALMA or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before qalma_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to qalma_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help qalma\n\n% Last Modified by GUIDE v2.5 08-Jun-2017 23:41:29\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @qalma_OpeningFcn, ...\n                   'gui_OutputFcn',  @qalma_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before qalma is made visible.\nfunction qalma_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to qalma (see VARARGIN)\n\n% Choose default command line output for qalma\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes qalma wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\n%set(handles.date_txt, 'String', date);\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = qalma_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on button press in ss_button.\nfunction ss_button_Callback(hObject, eventdata, handles)\n% hObject    handle to ss_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(qalma);\nrun('star_shot');\n\n\n% --- Executes on button press in pf_button.\nfunction pf_button_Callback(hObject, eventdata, handles)\n% hObject    handle to pf_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(qalma);\n%run('./picket_panda8/picket_panda.m');\nrun('picket_panda');\n\n% --- Executes on button press in wl_button.\nfunction wl_button_Callback(hObject, eventdata, handles)\n% hObject    handle to wl_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(qalma);\nrun('wl');\n\n\n% --- Executes on button press in lr_button.\nfunction lr_button_Callback(hObject, eventdata, handles)\n% hObject    handle to lr_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(qalma);\nrun('ci');\n\n\n% --- Executes on button press in lf_button.\nfunction lf_button_Callback(hObject, eventdata, handles)\n% hObject    handle to lf_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(qalma);\nrun('dynalog');\n\n\n% --- Executes on button press in rotate_button.\nfunction rotate_button_Callback(hObject, eventdata, handles)\n% hObject    handle to rotate_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on button press in crop_button.\nfunction crop_button_Callback(hObject, eventdata, handles)\n% hObject    handle to crop_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on button press in invert_button.\nfunction invert_button_Callback(hObject, eventdata, handles)\n% hObject    handle to invert_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n"}
+{"plateform": "github", "repo_name": "mrmushfiq/qalma-master", "name": "star_shot.m", "ext": ".m", "path": "qalma-master/star_shot/star_shot.m", "size": 26383, "source_encoding": "utf_8", "md5": "2b44cdb1fdf5db0023e49a8971a36020", "text": "% M. Mushfiqur Rahman\n% Florida Atlantic University\n% August, 2017\n\nfunction varargout = star_shot(varargin)\n% STAR_SHOT MATLAB code for star_shot.fig\n%      STAR_SHOT, by itself, creates a new STAR_SHOT or raises the existing\n%      singleton*.\n%\n%      H = STAR_SHOT returns the handle to a new STAR_SHOT or the handle to\n%      the existing singleton*.\n%\n%      STAR_SHOT('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in STAR_SHOT.M with the given input arguments.\n%\n%      STAR_SHOT('Property','Value',...) creates a new STAR_SHOT or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before star_shot_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to star_shot_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help star_shot\n\n% Last Modified by GUIDE v2.5 25-Aug-2017 22:35:28\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @star_shot_OpeningFcn, ...\n                   'gui_OutputFcn',  @star_shot_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before star_shot is made visible.\nfunction star_shot_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to star_shot (see VARARGIN)\n\n% Choose default command line output for star_shot\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes star_shot wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\nset(handles.axes1, 'box','off','XTickLabel',[],'xtick',[],'YTickLabel',[],'ytick',[]);\n%set(handles.num_arms_pop, 'String',{4:24});\nset(handles.axes2, 'visible', 'off');\nset(handles.axes3, 'visible', 'off');\nset(handles.radius_name, 'visible', 'off');\nset(handles.radius_txt, 'visible', 'off');\nset(handles.comment_txt, 'visible', 'on');\nset(handles.report_button, 'visible', 'on');\nset(handles.mm_txt, 'visible', 'off');\n\nset(handles.uitable1, 'visible', 'off');\nset(handles.uitable1, 'Data', []);\nset(handles.uitable1, 'ColumnName', {'Image','Gantry','Couch', 'Collimator', 'Radius(mm)','Comment'});\n\nset(handles.wiener_check, 'Value', 1);\nset(handles.w1_pop, 'String',{1:10}, 'Value', 8);\nset(handles.w2_pop, 'String',{1:10}, 'Value', 8);\nset(handles.avg_txt, 'String', '2');\nset(handles.crop_check, 'Value', 1);\nset(handles.resolution_txt, 'String', '0.39');\nset(handles.mag_factor_txt, 'String', '1'); \n\nset(handles.analyze_button, 'Enable', 'off');\nset(handles.report_button, 'Enable', 'off');\nset(handles.add_button, 'Enable', 'off');\nwarning('off','all')\n\n\nglobal analyzer;\nanalyzer = 0; \n\nglobal c1;\nglobal c2;\nglobal c3; \nc1 = get(handles.gantry_check, 'Value');\nc2 = get(handles.couch_check, 'Value');\nc3 = get(handles.collimator_check, 'Value');\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = star_shot_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on button press in browse_button.\nfunction browse_button_Callback(hObject, eventdata, handles)\n% hObject    handle to browse_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img;\nglobal file_name;\n\n[f p] = uigetfile(...    \n    {'*.jpg; *.JPG; *.jpeg; *.JPEG; *.img; *.IMG; *.tif;*.png; *.TIF; *.tiff, *.TIFF','Supported Files (*.jpg,*.img,*.tiff,*.png)'; ...\n    '*.jpg','jpg Files (*.jpg)';...\n    '*.JPG','JPG Files (*.JPG)';...\n    '*.jpeg','jpeg Files (*.jpeg)';...\n    '*.JPEG','JPEG Files (*.JPEG)';...\n    '*.img','img Files (*.img)';...\n    '*.IMG','IMG Files (*.IMG)';...\n    '*.tif','tif Files (*.tif)';...\n    '*.TIF','TIF Files (*.TIF)';...\n    '*.tiff','tiff Files (*.tiff)';...\n    '*.TIFF','TIFF Files (*.TIFF)'},...    \n    'MultiSelect', 'off');\n\ntry    \n    img_d = imread([p f]);\n    img=im2double(img_d);\n    img=uint8(255*mat2gray(img));\n    imshow(img,'Parent',handles.axes1);\n    img = img_d;\n    file_name = f; \n    set(handles.analyze_button, 'Enable', 'on');\n    set(handles.report_button, 'Enable', 'on');\n    set(handles.add_button, 'Enable', 'on');\n\ncatch\n    h = msgbox('Please upload an image');\nend\n\n% --- Executes on button press in analyze_button.\nfunction analyze_button_Callback(hObject, eventdata, handles)\n% hObject    handle to analyze_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nset(handles.analyze_button, 'Enable', 'off');\nglobal img;\nglobal file_name; \nglobal analyzer;\n\n\nanalyzer = analyzer + 1; \n%starF(handles,img);\n\n[radii,pass] = starF(handles,img);\n\nif pass == 1\n\n    set(handles.browse_button, 'Visible', 'off'); \n    setpixelposition(handles.uitable1,[65, 10, 490, 127]);\n    set(handles.uitable1, 'visible', 'on');\n\n    global c1;\n    global c2;\n    global c3;\n\n\n    c1 = get(handles.gantry_check, 'Value');\n    c2 = get(handles.couch_check, 'Value');\n    c3 = get(handles.collimator_check, 'Value');\n\n    D = get(handles.uitable1, 'Data');\n\n    [r,c] = size(D);\n    D{r+1,1} = file_name; \n\n    if c1==1\n        D{r+1,2} = '   X   ';\n    elseif c2 == 1\n        D{r+1,3} = '   X   ';\n    elseif c3 == 1\n        D{r+1,4} = '   X   '; \n    else\n        h = msgbox({'Check at least one of the three components'});\n        pause(2)\n        delete(h);\n    end \n\n    D{r+1,5} = get(handles.radius_txt, 'String');\n%     if pass == 1\n%         D{r+1,5} = get(handles.radius_txt, 'String'); \n%     else\n%         D{r+1,5} = '-';\n%     end\n\n    D{r+1,6} = get(handles.comment_txt, 'String');\n\n    set(handles.uitable1, 'Data', D);\n    set(handles.row_pop, 'String', {1:r+1});\nend\n\n% --- Executes on selection change in num_arms_pop.\nfunction num_arms_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to num_arms_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns num_arms_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from num_arms_pop\n\n\n% --- Executes during object creation, after setting all properties.\nfunction num_arms_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to num_arms_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction radius_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to radius_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of radius_txt as text\n%        str2double(get(hObject,'String')) returns contents of radius_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction radius_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to radius_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction comment_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of comment_txt as text\n%        str2double(get(hObject,'String')) returns contents of comment_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction comment_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in report_button.\nfunction report_button_Callback(hObject, eventdata, handles)\n% hObject    handle to report_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\ntc = get(handles.comment_txt, 'String'); \ntc = strcat('Comment:', tc);\nresult = get(handles.radius_txt, 'String');\nresult = strcat('Radius: ',result); \nD = get(handles.uitable1, 'Data');\n\n\nf2=figure\nt = uitable(f2,'Data',D,'Position',[20 60 500 300]);\nt.ColumnName = {'Image','Gantry','Couch', 'Collimator', 'Radius(mm)','Comment'}\n\nsaveas(f2,'Star_Report.pdf');\n\n\n% --- Executes on button press in add_button.\nfunction add_button_Callback(hObject, eventdata, handles)\n% hObject    handle to add_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img;\nglobal file_name;\n%set(handles.axes2, 'visible', 'off');\nset(handles.axes2, 'Units', 'pixels', 'Position', [300, 300, 10, 10]);\n\n[f p] = uigetfile(...    \n    {'*.jpg; *.JPG; *.jpeg; *.JPEG; *.img; *.IMG; *.tif;*.png; *.TIF; *.tiff, *.TIFF','Supported Files (*.jpg,*.img,*.tiff,*.png)'; ...\n    '*.jpg','jpg Files (*.jpg)';...\n    '*.JPG','JPG Files (*.JPG)';...\n    '*.jpeg','jpeg Files (*.jpeg)';...\n    '*.JPEG','JPEG Files (*.JPEG)';...\n    '*.img','img Files (*.img)';...\n    '*.IMG','IMG Files (*.IMG)';...\n    '*.tif','tif Files (*.tif)';...\n    '*.TIF','TIF Files (*.TIF)';...\n    '*.tiff','tiff Files (*.tiff)';...\n    '*.TIFF','TIFF Files (*.TIFF)'},...    \n    'MultiSelect', 'off');\n\ntry\n    img_d = imread([p f]);\n    img=im2double(img_d);\n    img=uint8(255*mat2gray(img));\n    set(handles.axes1, 'Units', 'pixels', 'Position', [37, 99, 544, 397]);\n    imshow(img,'Parent',handles.axes1);\n\n    setpixelposition(handles.uitable1,[65, 10, 490, 70]);\n\n    img = img_d;\n    file_name = f; \n    set(handles.analyze_button, 'Enable', 'on');\n\ncatch\n    h = msgbox('Please upload an image');\nend\n\n%imshow(img,'Parent',handles.axes1);\n\n% --- Executes on button press in gantry_check.\nfunction gantry_check_Callback(hObject, eventdata, handles)\n% hObject    handle to gantry_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of gantry_check\nset(handles.couch_check,'Value',0); \nset(handles.collimator_check,'Value',0); \n\n\n% --- Executes on button press in couch_check.\nfunction couch_check_Callback(hObject, eventdata, handles)\n% hObject    handle to couch_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of couch_check\nset(handles.gantry_check,'Value',0); \nset(handles.collimator_check,'Value',0); \n\n% --- Executes on button press in collimator_check.\nfunction collimator_check_Callback(hObject, eventdata, handles)\n% hObject    handle to collimator_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of collimator_check\nset(handles.couch_check,'Value',0); \nset(handles.gantry_check,'Value',0); \n\n\n% --- Executes on button press in delete_row_button.\nfunction delete_row_button_Callback(hObject, eventdata, handles)\n% hObject    handle to delete_row_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on selection change in row_pop.\nfunction row_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to row_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns row_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from row_pop\n\n\n% --- Executes during object creation, after setting all properties.\nfunction row_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to row_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in manual_check.\nfunction manual_check_Callback(hObject, eventdata, handles)\n% hObject    handle to manual_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of manual_check\n\n\n\nfunction rotate_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to rotate_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of rotate_txt as text\n%        str2double(get(hObject,'String')) returns contents of rotate_txt as a double\n% global img;\n% angle = str2num(get(handles.rotate_txt,'String'));\n% \n% if get(handles.manual_check, 'Value') == 1\n%     img = imrotate(img, angle);\n%     imshow(img,'Parent',handles.axes2);\n% else\n%     h = msgbox({'Please Activate Manual mode'});\n%     pause(1)\n%     delete(h);\n% end\n\n% --- Executes during object creation, after setting all properties.\nfunction rotate_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to rotate_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in center_check.\nfunction center_check_Callback(hObject, eventdata, handles)\n% hObject    handle to center_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of center_check\n\n\n% --- Executes on button press in invert_check.\nfunction invert_check_Callback(hObject, eventdata, handles)\n% hObject    handle to invert_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of invert_check\nglobal img;\nv1 = get(handles.manual_check, 'Value');\nv2 = get(handles.invert_check, 'Value');\nif v1 == 1 & v2 == 1\n    img = imcomplement(img);\n    imshow(img,'Parent',handles.axes1);\nend\nset(handles.invert_check, 'Value',0);\n\n% --- Executes on button press in analyze_button.\nfunction pushbutton8_Callback(hObject, eventdata, handles)\n% hObject    handle to analyze_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n\nfunction avg_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to avg_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of avg_txt as text\n%        str2double(get(hObject,'String')) returns contents of avg_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction avg_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to avg_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in w1_pop.\nfunction w1_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to w1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns w1_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from w1_pop\n\n\n% --- Executes during object creation, after setting all properties.\nfunction w1_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to w1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in w2_pop.\nfunction w2_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to w2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns w2_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from w2_pop\n\n\n% --- Executes during object creation, after setting all properties.\nfunction w2_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to w2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in delete_button.\nfunction delete_button_Callback(hObject, eventdata, handles)\n% hObject    handle to delete_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nD = get(handles.uitable1, 'Data');\n\n[r,c] = size(D);\nrow = get(handles.row_pop, 'Value');\n\nif r>0\n   D(row,:) = [];\nend\n\nset(handles.uitable1, 'Data', D);\n\nfunction edit9_Callback(hObject, eventdata, handles)\n% hObject    handle to edit9 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of edit9 as text\n%        str2double(get(hObject,'String')) returns contents of edit9 as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction edit9_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to edit9 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\n% --- Executes on button press in report_button.\nfunction pushbutton12_Callback(hObject, eventdata, handles)\n% hObject    handle to report_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on button press in wiener_check.\nfunction wiener_check_Callback(hObject, eventdata, handles)\n% hObject    handle to wiener_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of wiener_check\n\n\n\nfunction edit10_Callback(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of comment_txt as text\n%        str2double(get(hObject,'String')) returns contents of comment_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction edit10_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction mag_factor_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to mag_factor_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of mag_factor_txt as text\n%        str2double(get(hObject,'String')) returns contents of mag_factor_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction mag_factor_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to mag_factor_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction resolution_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to resolution_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of resolution_txt as text\n%        str2double(get(hObject,'String')) returns contents of resolution_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction resolution_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to resolution_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in crop_check.\nfunction crop_check_Callback(hObject, eventdata, handles)\n% hObject    handle to crop_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of crop_check\n\n\n% --- Executes on button press in home_button.\nfunction home_button_Callback(hObject, eventdata, handles)\n% hObject    handle to home_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(star_shot);\nrun('qalma');\n\n\n% --- Executes when user attempts to close figure1.\nfunction figure1_CloseRequestFcn(hObject, eventdata, handles)\n% hObject    handle to figure1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: delete(hObject) closes the figure\ndelete(hObject);\n\nif exist('starshot.png', 'file')==2\n  delete('starshot.png');\nend\n\nif exist('starshot_zoomed.png', 'file')==2\n  delete('starshot_zoomed.png');\nend\nclearvars;"}
+{"plateform": "github", "repo_name": "mrmushfiq/qalma-master", "name": "picket_panda.m", "ext": ".m", "path": "qalma-master/picket_fence/picket_panda.m", "size": 26770, "source_encoding": "utf_8", "md5": "d8e6b4b5b54d088f497dc1f0bef488f7", "text": "% M. Mushfiqur Rahman\n% Florida Atlantic University\n% August, 2017\n\nfunction varargout = picket_panda(varargin)\n% PICKET_PANDA MATLAB code for picket_panda.fig\n%      PICKET_PANDA, by itself, creates a new PICKET_PANDA or raises the existing\n%      singleton*.\n%\n%      H = PICKET_PANDA returns the handle to a new PICKET_PANDA or the handle to\n%      the existing singleton*.\n%\n%      PICKET_PANDA('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in PICKET_PANDA.M with the given input arguments.\n%\n%      PICKET_PANDA('Property','Value',...) creates a new PICKET_PANDA or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before picket_panda_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to picket_panda_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help picket_panda\n\n% Last Modified by GUIDE v2.5 23-Aug-2017 18:14:46\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @picket_panda_OpeningFcn, ...\n                   'gui_OutputFcn',  @picket_panda_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before picket_panda is made visible.\nfunction picket_panda_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to picket_panda (see VARARGIN)\n\n% Choose default command line output for picket_panda\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n% UIWAIT makes picket_panda wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\n\nglobal count; \ncount = 0; % go button click counter\nglobal mag_count; %mag button click counter\nmag_count = 0; \n\nset(handles.axes2, 'visible', 'off', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n\nset(handles.axes1, 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\nset(handles.data_transfer_min, 'Data',[]);\n\nset(handles.smoothing_param,'string','3');\nset(handles.picket_num_pop, 'String',{1:10});\nset(handles.level_txt, 'String', '26');\n\nset(handles.width_left_txt, 'String', '37');\nset(handles.width_right_txt, 'String', '37');\nset(handles.w1_pop, 'String', {1:10});\nset(handles.w2_pop, 'String', {1:10});\n% set(handles.w1_pop, 'Value', 3);\n% set(handles.w2_pop, 'Value', 2);\nset(handles.w1_pop, 'Value', 5);\nset(handles.w2_pop, 'Value', 5);\n\nset(handles.rotate_pop, 'String', [0,90,180,270,360]);\nset(handles.rotate_check, 'Value', 1);\nset(handles.rotate_pop, 'Value', 1);\nset(handles.profile_check, 'Value', 0);\nset(handles.wiener_check, 'Value',1);\nset(handles.mag_txt, 'String', '1.5');\nset(handles.res_txt, 'String', '0.781');\n\nset(handles.go_button, 'Enable', 'off');\nset(handles.recalculate_button, 'Enable', 'off');\nset(handles.plus_button, 'Enable', 'off');\nset(handles.minus_button, 'Enable', 'off');\nset(handles.column_mlc, 'Enable', 'off');\nset(handles.select_line, 'Enable', 'off');\nset(handles.report_button, 'Enable', 'off');\nset(handles.rotate_pop, 'Enable', 'off');\nset(handles.magnify_button, 'Enable', 'off');\nset(handles.clear, 'Enable', 'off');\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = picket_panda_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on button press in pushbutton1.\nfunction pushbutton1_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n%......\n%dicomF(handles);\n\nglobal img;\n[f p] = uigetfile({'*.dcm','DICOM Files'});\n\ntry\n    img_d = dicomread([p f]);\n    img=im2double(img_d);\n    img=uint8(255*mat2gray(img));\n    %img = imrotate(img, 90);\n\n    imshow(img,'Parent',handles.axes1);\n    h = msgbox({'Please select the number of pickets and select the pickets'});\n    pause(2)\n    delete(h);\n    set(handles.go_button, 'Enable', 'on');\n    set(handles.rotate_pop, 'Enable', 'on');\n    set(handles.magnify_button, 'Enable', 'on');\n    set(handles.clear, 'Enable', 'on');\ncatch\n    h = msgbox('Please upload an image');\nend\n\n\n\n% --- Executes when entered data in editable cell(s) in data_transfer_min.\nfunction data_transfer_min_CellEditCallback(hObject, eventdata, handles)\n% hObject    handle to data_transfer_min (see GCBO)\n% eventdata  structure with the following fields (see MATLAB.UI.CONTROL.TABLE)\n%\tIndices: row and column indices of the cell(s) edited\n%\tPreviousData: previous data for the cell(s) edited\n%\tEditData: string(s) entered by the user\n%\tNewData: EditData or its converted form set on the Data property. Empty if Data was not changed\n%\tError: error string when failed to convert EditData to appropriate value for Data\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on selection change in select_line.\nfunction select_line_Callback(hObject, eventdata, handles)\n% hObject    handle to select_line (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns select_line contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from select_line\nset(handles.plus_button, 'Enable', 'on');\nset(handles.minus_button, 'Enable', 'on');\n\n% --- Executes during object creation, after setting all properties.\nfunction select_line_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to select_line (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in plus_button.\nfunction plus_button_Callback(hObject, eventdata, handles)\n% hObject    handle to plus_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal img;\nmoveF(handles,img,-1);  %sign = -1 , one pixel movement\n%magnifier('aacircle', 20, 'current_positions.png');\nset(handles.recalculate_button, 'Enable', 'on');\n\n\n% --- Executes on button press in minus_button.\nfunction minus_button_Callback(hObject, eventdata, handles)\n% hObject    handle to minus_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img;\nmoveF(handles,img,+1);\nset(handles.recalculate_button, 'Enable', 'on');\n%sign = +1 , one pixel movement\n%magnifier('aacircle', 20, 'current_positions.png');\n\n\n% --- Executes on button press in clear.\nfunction clear_Callback(hObject, eventdata, handles)\n% hObject    handle to clear (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img;\nimshow(img);\nh = msgbox({'Please select the number of pickets (or keep as it is) and select the pickets'});\npause(2)\ndelete(h);\nset(handles.go_button, 'Enable', 'on');\nset(handles.data_transfer_min, 'Data',[]);\nset(handles.column_mlc, 'Enable', 'off');\nset(handles.select_line, 'Enable', 'off');\n\n\n\n% --- Executes on selection change in column_mlc.\nfunction column_mlc_Callback(hObject, eventdata, handles)\n% hObject    handle to column_mlc (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% Hints: contents = cellstr(get(hObject,'String')) returns column_mlc contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from column_mlc\nset(handles.select_line, 'Enable', 'on');\n\nglobal img;\n\nmin_data = get(handles.data_transfer_min, 'Data');\n[r,c]= size(min_data);\n\ncolumn = get(handles.column_mlc, 'Value');\nmin = min_data(:,column); %just getting the column required\n\nmy_line = get(handles.select_line,'Value');\nxy = get(handles.xy_data,'Data');\nx=xy(:,1);\n\nlevel = str2num(get(handles.level_txt, 'String'));\n\n%for i=my_line:length(min)-1\n% min(i) = min(i) + 1;\n%end\n\n\nfor i=1:length(min)-1\n    leafpos(i) = (min(i) + min(i+1))/2; \nend\n\nimshow(img);\n\nmin_s = min;\nhold on\np = column;\ncol = num2str(p);\ntext([round(x(p))+level],[0],col, 'Color','black')\n\nfor j=1:length(leafpos)\n%   line([round(x(1))-30 round(x(1))-20],[leafpos(j) leafpos(j)],'Color','r')\n    name = num2str(j);\n    \n    text([round(x(p))],[leafpos(j)],name, 'Color','yellow')\n    \n    line([round(x(p))+level],[leafpos(j)],'Color','r','Marker','*', 'MarkerSize',3)\nend\n\nhold off\n\nhold on\n\nfor j=1:length(min_s)\n    line([round(x(p))+level-4 round(x(p))+level+11],[min_s(j) min_s(j)],'Color','g')\nend\n\n\nhold off\n\n\n\n% --- Executes during object creation, after setting all properties.\nfunction column_mlc_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to column_mlc (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in magnify_button.\nfunction magnify_button_Callback(hObject, eventdata, handles)\n% hObject    handle to magnify_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n%im= getimage(handles.axes1);\n%magnifier('aacircle', 20, 'current_positions.png');\nglobal mag_count;\nmag_count = mag_count + 1;\n\nwhile mag_count > 0\n    if mod(mag_count,2) == 0    \n        set(handles.axes2, 'visible', 'off');\n        set(get(handles.axes2,'children'),'visible','off');\n        %delete(handles.axes2);\n        axes(handles.axes1);\n        %ginput(0);\n        break;\n    else\n        %[x,y] = ginput; \n\n        [x,y] = ginput(1); \n        set(handles.axes2, 'visible', 'on');\n        frame = getframe(handles.axes1);\n        im = frame2im(frame);\n        [r,c] = size(im);\n        %magnifier('aacircle', 20, im);\n\n        imshow(im,'Parent',handles.axes2);\n\n        axes(handles.axes2);\n        zoom(3);\n        axis([double(x+10) double(x + 110) double(y-40) double(y)]);\n        set(handles.axes2, 'Units', 'pixels', 'Position', [x-3, r-y, 200, 140],...\n            'box','off','XTickLabel',[],'xtick',[],'YTickLabel',[],'ytick',[]);\n         \n\n    end\nend\n %mag_counter =0;\n% --- Executes on button press in recalculate_button.\nfunction recalculate_button_Callback(hObject, eventdata, handles)\n% hObject    handle to recalculate_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nset(handles.recalculate_button, 'Enable', 'off');\nset(handles.plus_button, 'Enable', 'off');\nset(handles.minus_button, 'Enable', 'off');\nset(handles.select_line, 'Enable', 'on');\n\nglobal img;\nrecalcF(handles,img);\n\n\n% --- Executes on button press in report_button.\nfunction report_button_Callback(hObject, eventdata, handles)\n% hObject    handle to report_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nf2 = figure\nf2.Position =  [100, 100, 740, 900];\n[snap,map1] = imread('current_positions.png');\n[gaps,map2] = imread('edge_gaps.png');\ntitle('Report');\nsubplot(8,10,1:30), imshow(snap,[]); axis equal;\nsubplot(8,10,31:80),imshow(gaps,[]); axis equal;\nsaveas(f2,'Picket_Report.pdf');\n \n\n\n\nfunction smoothing_param_Callback(hObject, eventdata, handles)\n% hObject    handle to smoothing_param (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of smoothing_param as text\n%        str2double(get(hObject,'String')) returns contents of smoothing_param as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction smoothing_param_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to smoothing_param (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in picket_num_pop.\nfunction picket_num_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to picket_num_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns picket_num_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from picket_num_pop\nv=get(handles.picket_num_pop, 'Value');\nset(handles.column_mlc, 'String', {1:v});\n\n% --- Executes during object creation, after setting all properties.\nfunction picket_num_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to picket_num_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in go_button.\nfunction go_button_Callback(hObject, eventdata, handles)\n% hObject    handle to go_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nset(handles.go_button, 'Enable', 'off');\nset(handles.column_mlc, 'Enable', 'on');\nset(handles.report_button, 'Enable', 'on');\n\nglobal img;\ndicomF2(handles,img);\n\nmin_data = get(handles.data_transfer_min, 'Data');\n\n[r,c]= size(min_data);\npickets = get(handles.picket_num_pop,'Value');\nset(handles.column_mlc, 'String',{1:pickets});\nset(handles.select_line, 'String',{1:r});\n\nglobal count;\ncount = count +1;\n\nfunction width_right_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to width_right_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of width_right_txt as text\n%        str2double(get(hObject,'String')) returns contents of width_right_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction width_right_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to width_right_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction width_left_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to width_left_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of width_left_txt as text\n%        str2double(get(hObject,'String')) returns contents of width_left_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction width_left_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to width_left_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in w2_pop.\nfunction w2_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to w2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns w2_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from w2_pop\n\n\n% --- Executes during object creation, after setting all properties.\nfunction w2_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to w2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in w1_pop.\nfunction w1_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to w1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns w1_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from w1_pop\n\n\n% --- Executes during object creation, after setting all properties.\nfunction w1_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to w1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in rotate_check.\nfunction rotate_check_Callback(hObject, eventdata, handles)\n% hObject    handle to rotate_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of rotate_check\n\n\n% --- Executes on selection change in rotate_pop.\nfunction rotate_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to rotate_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns rotate_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from rotate_pop\n\nglobal img;\n\nv = get(handles.rotate_check, 'Value');\nif v ==1\n    g = get(handles.rotate_pop, 'Value');\n    if g == 2\n        img = imrotate(img, 90);\n    elseif g == 3\n        img = imrotate(img, 180);\n    elseif g == 4\n        img = imrotate(img, 270);\n    elseif g == 5\n        img = imrotate(img, 360);\n    end\nend\nimshow(img,'Parent',handles.axes1);\n\n% --- Executes during object creation, after setting all properties.\nfunction rotate_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to rotate_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in profile_check.\nfunction profile_check_Callback(hObject, eventdata, handles)\n% hObject    handle to profile_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of profile_check\nglobal count;\nif count > 0 & get(handles.profile_check,'Value')== 1\n    openfig('f_x.fig');\nend\n\n\n% --- Executes on button press in wiener_check.\nfunction wiener_check_Callback(hObject, eventdata, handles)\n% hObject    handle to wiener_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of wiener_check\n\n\n% --- Executes on button press in home_button.\nfunction home_button_Callback(hObject, eventdata, handles)\n% hObject    handle to home_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(picket_panda);\n%run('../qalma.m');\nrun('qalma')\n\n\n\nfunction mag_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to mag_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of mag_txt as text\n%        str2double(get(hObject,'String')) returns contents of mag_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction mag_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to mag_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction res_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to res_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of res_txt as text\n%        str2double(get(hObject,'String')) returns contents of res_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction res_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to res_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction level_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to level_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of level_txt as text\n%        str2double(get(hObject,'String')) returns contents of level_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction level_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to level_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in profile_v_check.\nfunction profile_v_check_Callback(hObject, eventdata, handles)\n% hObject    handle to profile_v_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of profile_v_check\nglobal count; \nif count > 0 & get(handles.profile_v_check,'Value')== 1\n    if exist('f_y.fig', 'file')==2\n      openfig('f_y.fig');\n    end\nend\n\n\n% --- Executes when user attempts to close figure1.\nfunction figure1_CloseRequestFcn(hObject, eventdata, handles)\n% hObject    handle to figure1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: delete(hObject) closes the figure\ndelete(hObject);\n\nif exist('f_x.fig', 'file')==2\n  delete('f_x.fig');\nend\n\nif exist('f_y.fig', 'file')==2\n  delete('f_y.fig');\nend\n\nif exist('f_x_s.fig', 'file')==2\n  delete('f_x_s.fig');\nend\n\nif exist('f_x.fig', 'file')==2\n  delete('f_x.fig');\nend\n\nif exist('edge_gaps.png', 'file')==2\n  delete('edge_gaps.png');\nend\n\nif exist('current_positions.png', 'file')==2\n  delete('current_positions.png');\nend\n\nclearvars; "}
+{"plateform": "github", "repo_name": "mrmushfiq/qalma-master", "name": "dynalog.m", "ext": ".m", "path": "qalma-master/dynalog/dynalog.m", "size": 7639, "source_encoding": "utf_8", "md5": "5c135192ef465a4baa5b642159850bd1", "text": "% M. Mushfiqur Rahman\n% Florida Atlantic University\n% August, 2017\n\n\nfunction varargout = dynalog(varargin)\n% DYNALOG MATLAB code for dynalog.fig\n%      DYNALOG, by itself, creates a new DYNALOG or raises the existing\n%      singleton*.\n%\n%      H = DYNALOG returns the handle to a new DYNALOG or the handle to\n%      the existing singleton*.\n%\n%      DYNALOG('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in DYNALOG.M with the given input arguments.\n%\n%      DYNALOG('Property','Value',...) creates a new DYNALOG or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before dynalog_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to dynalog_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help dynalog\n\n% Last Modified by GUIDE v2.5 25-Aug-2017 22:37:45\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @dynalog_OpeningFcn, ...\n                   'gui_OutputFcn',  @dynalog_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before dynalog is made visible.\nfunction dynalog_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to dynalog (see VARARGIN)\n\n% Choose default command line output for dynalog\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes dynalog wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\nset(handles.axes1,'visible','off');\nset(handles.report_button,'visible','off');\nset(handles.comment_txt,'visible','off');\nset(handles.mag_panel, 'visible', 'off');\n\nset(handles.axes2,'visible','off');\nset(handles.axes3,'visible','off');\nset(handles.axes4,'visible','off');\nset(handles.mag_txt, 'String', '1');\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = dynalog_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on button press in a_button.\nfunction a_button_Callback(hObject, eventdata, handles)\n% hObject    handle to a_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal f_a;\nglobal p_a;\n[f_a p_a] = uigetfile(...    \n    {'*.dlg'},...    \n    'MultiSelect', 'off');\nset(handles.file_a_txt, 'String', f_a);\n\n% --- Executes on button press in b_button.\nfunction b_button_Callback(hObject, eventdata, handles)\n% hObject    handle to b_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal f_b;\nglobal p_b; \n[f_b p_b] = uigetfile(...    \n    {'*.dlg'},...    \n    'MultiSelect', 'off');\nset(handles.file_b_txt, 'String', f_b);\n\n\n% --- Executes on button press in dynalyze_button.\nfunction dynalyze_button_Callback(hObject, eventdata, handles)\n% hObject    handle to dynalyze_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal f_a;\nglobal p_a;\nglobal f_b;\nglobal p_b;\ndynalogF(handles, f_a, f_b, p_a, p_b);\n\n\n% --- Executes on button press in report_button.\nfunction report_button_Callback(hObject, eventdata, handles)\n% hObject    handle to report_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\ntc = get(handles.comment_txt, 'String'); \ntc = strcat('Comment: ', tc);\n\nf2 = figure('Position', [100, 100, 724, 700]);\n[picket,map1] = imread('picket_dyn.png');\ntitle('Dynalog Report');\n\nh1=subplot(4,4,1:12); \nimshow(picket,[]);\naxis equal;\nh2=subplot(4,4,[14,15]);\ntext(0.3,1,tc); axis off;\n\nsaveas(f2,'Dynalog_Report.pdf');\n\n\n\nfunction comment_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of comment_txt as text\n%        str2double(get(hObject,'String')) returns contents of comment_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction comment_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in home_button.\nfunction home_button_Callback(hObject, eventdata, handles)\n% hObject    handle to home_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(dynalog);\nrun('qalma');\n\n\n\nfunction mag_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to mag_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of mag_txt as text\n%        str2double(get(hObject,'String')) returns contents of mag_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction mag_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to mag_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes when user attempts to close figure1.\nfunction figure1_CloseRequestFcn(hObject, eventdata, handles)\n% hObject    handle to figure1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: delete(hObject) closes the figure\ndelete(hObject);\nif exist('picket_dyn.png', 'file')==2\n  delete('picket_dyn.png');\nend\nclearvars;"}
+{"plateform": "github", "repo_name": "mrmushfiq/qalma-master", "name": "wl.m", "ext": ".m", "path": "qalma-master/winston_lutz/wl.m", "size": 21479, "source_encoding": "utf_8", "md5": "5c9533af6cf6ffa9e93af23d9a8cf14a", "text": "% M. Mushfiqur Rahman\n% Florida Atlantic University\n% August, 2017\n\nfunction varargout = wl(varargin)\n% WL MATLAB code for wl.fig\n%      WL, by itself, creates a new WL or raises the existing\n%      singleton*.\n%\n%      H = WL returns the handle to a new WL or the handle to\n%      the existing singleton*.\n%\n%      WL('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in WL.M with the given input arguments.\n%\n%      WL('Property','Value',...) creates a new WL or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before wl_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to wl_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help wl\n\n% Last Modified by GUIDE v2.5 25-Aug-2017 22:33:26\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @wl_OpeningFcn, ...\n                   'gui_OutputFcn',  @wl_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before wl is made visible.\nfunction wl_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to wl (see VARARGIN)\n\n% Choose default command line output for wl\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes wl wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\nglobal mag_count;\nmag_count = 0; \n\nset(handles.radiation_slider, 'Min',0,'Max',100,'Value', 14);\nset(handles.ball_slider,'Min',0,'Max',100,'Value', 13);\nset(handles.ball_txt, 'String', 13);\nset(handles.radiation_txt, 'String', 14);\n\nset(handles.axes1, 'Visible', 'off');\nset(handles.axes2, 'Visible', 'off');\nset(handles.axes3, 'Visible', 'off');\n\n\nset(handles.radiation_slider, 'Visible', 'off');\nset(handles.radiation_txt, 'Visible', 'off');\nset(handles.ball_slider, 'Visible', 'off');\nset(handles.ball_txt, 'Visible', 'off');\nset(handles.text7, 'Visible', 'off');\n\nset(handles.original_label, 'Visible', 'off');\nset(handles.filter1_label, 'Visible', 'off');\nset(handles.filter2_label, 'Visible', 'off');\nset(handles.analyze_button, 'Visible', 'on');\n\nset(handles.distance_label, 'Visible', 'off');\nset(handles.distance_txt, 'Visible', 'off');\n\nset(handles.report_button, 'Visible', 'on');\n\nset(handles.uitable1, 'visible', 'off');\nset(handles.uitable1, 'Data', []);\nset(handles.uitable1, 'ColumnName', {'Image', 'Distance(mm)', 'Comment'});\n\nset(handles.mag_txt, 'String', '1');\nset(handles.res_txt, 'String', '0.34');\n\nset(handles.zoom_pop, 'String', {1:10}, 'Value', 6);\nset(handles.analyze_button, 'Enable', 'off');\nset(handles.report_button, 'Enable', 'off');\nset(handles.zoom_pop, 'Enable', 'off');\n\n\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = wl_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on slider movement.\nfunction ball_slider_Callback(hObject, eventdata, handles)\n% hObject    handle to ball_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\nglobal img;\nglobal img_url; \n\nv = get(handles.ball_slider, 'Value');\nset(handles.ball_txt, 'String', num2str(v));\nwlF_loading(handles,img_url); \n\n\n\n% --- Executes during object creation, after setting all properties.\nfunction ball_slider_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to ball_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: slider controls usually have a light gray background.\nif isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor',[.9 .9 .9]);\nend\n\n\n\nfunction ball_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to ball_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of ball_txt as text\n%        str2double(get(hObject,'String')) returns contents of ball_txt as a double\nglobal img;\nglobal img_url; \n\ns= get(handles.ball_txt, 'String');\nset(handles.ball_slider, 'Value', str2num(s));\nwlF_loading(handles,img_url); \n\n\n% --- Executes during object creation, after setting all properties.\nfunction ball_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to ball_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on slider movement.\nfunction radiation_slider_Callback(hObject, eventdata, handles)\n% hObject    handle to radiation_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\nglobal img;\nglobal img_url; \nv = get(handles.radiation_slider, 'Value');\nset(handles.radiation_txt, 'String', num2str(v));\nwlF_loading(handles,img_url); \n\n\n\n% --- Executes during object creation, after setting all properties.\nfunction radiation_slider_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to radiation_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n\n% Hint: slider controls usually have a light gray background.\nif isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor',[.9 .9 .9]);\nend\n\n\n\nfunction radiation_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to radiation_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of radiation_txt as text\n%        str2double(get(hObject,'String')) returns contents of radiation_txt as a double\nglobal img;\nglobal img_url; \n\ns = get(handles.radiation_txt, 'String');\nset(handles.radiation_slider, 'Value', str2num(s));\nwlF_loading(handles,img_url); \n\n\n% --- Executes during object creation, after setting all properties.\nfunction radiation_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to radiation_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in load_button.\nfunction load_button_Callback(hObject, eventdata, handles)\n% hObject    handle to load_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img; \nglobal file_name; \nglobal img_url; \n[f p] = uigetfile(...    \n    {'*.dcm','Supported Files (*.dcm)'; ...\n },...    \n    'MultiSelect', 'off');\n\ntry\n    %img_d = imread([p f]);\n    img_d = dicomread([p f]);\n    img=im2double(img_d);\n    img=uint8(255*mat2gray(img));\n\n    file_name = f;\n\n    set(handles.axes1, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n    set(handles.axes2, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n    set(handles.axes3, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n\n    set(handles.radiation_slider, 'Visible', 'on');\n    set(handles.radiation_txt, 'Visible', 'on');\n    set(handles.ball_slider, 'Visible', 'on');\n    set(handles.ball_txt, 'Visible', 'on');\n\n    set(handles.original_label, 'Visible', 'on');\n    set(handles.filter1_label, 'Visible', 'on');\n    set(handles.filter2_label, 'Visible', 'on');\n    set(handles.analyze_button, 'Visible', 'on');\n\n    set(handles.distance_label, 'Visible', 'on');\n    set(handles.distance_txt, 'Visible', 'on');\n\n    imshow(img,'Parent',handles.axes1);\n    img_url = strcat(p,f);\n    wlF_loading(handles,img_url); \n    set(handles.analyze_button, 'Enable', 'on');\n    set(handles.zoom_pop, 'Enable', 'on');\n\ncatch\n    h = msgbox('Please upload an image');\nend\n\n\n% --- Executes on button press in analyze_button.\nfunction analyze_button_Callback(hObject, eventdata, handles)\n% hObject    handle to analyze_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img;\nglobal file_name;\nglobal img_url; \nset(handles.report_button, 'Visible', 'on');\nwlF(handles,img_url); \nset(handles.uitable1, 'visible', 'on');\nset(handles.load_button, 'Visible', 'off'); \nset(handles.text7, 'Visible', 'on');\nset(handles.report_button, 'Enable', 'on');\n\n\nD = get(handles.uitable1, 'Data');\n\n[r,c] = size(D);\nD{r+1,1} = file_name; \nD{r+1,2} = get(handles.distance_txt, 'String'); \nD{r+1,3} = get(handles.comment_txt, 'String');\n\nset(handles.uitable1, 'Data', D);\nset(handles.row_pop, 'String', {1:r+1});\n\n% --- Executes on button press in instructions_button.\nfunction instructions_button_Callback(hObject, eventdata, handles)\n% hObject    handle to instructions_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on button press in report_button.\nfunction report_button_Callback(hObject, eventdata, handles)\n% hObject    handle to report_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nframe = getframe(handles.axes1);\nimf = frame2im(frame);\n%imwrite(imf, 'wl_result.png');\ntc = get(handles.comment_txt, 'String'); \ntc = strcat('Comment : ', tc);\ndist = get(handles.distance_txt, 'String');\ndist = strcat('Distance between the centers : ', dist);\ntexts = {dist,' ', tc};\n\nD = get(handles.uitable1, 'Data');\n\n\nf1=figure\nt = uitable(f1,'Data',D,'Position',[20 60 400 300]);\nt.ColumnName = {'Image','Distance(mm)','Comment'};\n\n\nsaveas(f1, 'wl_report.pdf')\n\n\nfunction comment_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of comment_txt as text\n%        str2double(get(hObject,'String')) returns contents of comment_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction comment_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in add_image.\nfunction add_image_Callback(hObject, eventdata, handles)\n% hObject    handle to add_image (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img; \nglobal file_name;\nglobal img_url; \n\n[f p] = uigetfile(...    \n    {'*.dcm','Supported Files (*.dcm)'; ...\n },...    \n    'MultiSelect', 'off');\n\n\n% [f p] = uigetfile(...    \n%     {'*.jpg; *.JPG; *.jpeg; *.JPEG; *.img; *.IMG; *.tif;*.png; .dcm;*.TIF; *.tiff, *.TIFF','Supported Files (*.dcm,*.jpg,*.img,*.tiff,*.png)'; ...\n%     '*.jpg','jpg Files (*.jpg)';...\n%     '*.JPG','JPG Files (*.JPG)';...\n%     '*.jpeg','jpeg Files (*.jpeg)';...\n%     '*.JPEG','JPEG Files (*.JPEG)';...\n%     '*.img','img Files (*.img)';...\n%     '*.IMG','IMG Files (*.IMG)';...\n%     '*.tif','tif Files (*.tif)';...\n%     '*.TIF','TIF Files (*.TIF)';...\n%     '*.tiff','tiff Files (*.tiff)';...\n%     '*.TIFF','TIFF Files (*.TIFF)'},...    \n%     'MultiSelect', 'off');\n\ntry\n    %img_d = imread([p f]);\n    img_d = dicomread([p f]);\n    img=im2double(img_d);\n    img=uint8(255*mat2gray(img));\n    file_name = f; \n\n    set(handles.axes1, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n    set(handles.axes2, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n    set(handles.axes3, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n\n    set(handles.radiation_slider, 'Visible', 'on');\n    set(handles.radiation_txt, 'Visible', 'on');\n    set(handles.ball_slider, 'Visible', 'on');\n    set(handles.ball_txt, 'Visible', 'on');\n\n    set(handles.original_label, 'Visible', 'on');\n    set(handles.filter1_label, 'Visible', 'on');\n    set(handles.filter2_label, 'Visible', 'on');\n    set(handles.analyze_button, 'Visible', 'on');\n\n    set(handles.distance_label, 'Visible', 'on');\n    set(handles.distance_txt, 'Visible', 'on');\n\n    imshow(img,'Parent',handles.axes1);\n    img_url = strcat(p,f);\n    wlF_loading(handles,img_url); \n    set(handles.analyze_button, 'Enable', 'on');\n    set(handles.zoom_pop, 'Enable', 'on');\n\n\ncatch\n    h = msgbox('Please upload an image');\nend\n\n\n\nfunction res_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to res_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of res_txt as text\n%        str2double(get(hObject,'String')) returns contents of res_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction res_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to res_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction mag_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to mag_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of mag_txt as text\n%        str2double(get(hObject,'String')) returns contents of mag_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction mag_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to mag_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction edit8_Callback(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of comment_txt as text\n%        str2double(get(hObject,'String')) returns contents of comment_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction edit8_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in home_button.\nfunction home_button_Callback(hObject, eventdata, handles)\n% hObject    handle to home_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(wl);\nrun('qalma');\n\n\n% --- Executes on selection change in zoom_pop.\nfunction zoom_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to zoom_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns zoom_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from zoom_pop\nglobal img;\nglobal img_url; \n\nwlF_loading(handles,img_url); \n\n\n% --- Executes during object creation, after setting all properties.\nfunction zoom_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to zoom_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in pushbutton15.\nfunction pushbutton15_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton15 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n% --- Executes on button press in delete_button.\nfunction delete_button_Callback(hObject, eventdata, handles)\n% hObject    handle to delete_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nD = get(handles.uitable1, 'Data');\n\n[r,c] = size(D);\nrow = get(handles.row_pop, 'Value');\n\nif r>0\n   D(row,:) = [];\nend\n\nset(handles.uitable1, 'Data', D);\n\n\n% --- Executes on selection change in row_pop.\nfunction row_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to row_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns row_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from row_pop\n\n\n% --- Executes during object creation, after setting all properties.\nfunction row_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to row_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes when user attempts to close figure1.\nfunction figure1_CloseRequestFcn(hObject, eventdata, handles)\n% hObject    handle to figure1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: delete(hObject) closes the figure\ndelete(hObject);\nclearvars;"}
+{"plateform": "github", "repo_name": "mrmushfiq/qalma-master", "name": "ci.m", "ext": ".m", "path": "qalma-master/ci/ci.m", "size": 25849, "source_encoding": "utf_8", "md5": "bda1d740da1c89b97e876fc2f9ffe66e", "text": "% M. Mushfiqur Rahman\n% Florida Atlantic University\n% August, 2017\n\nfunction varargout = ci(varargin)\n% CI MATLAB code for ci.fig\n%      CI, by itself, creates a new CI or raises the existing\n%      singleton*.\n%\n%      H = CI returns the handle to a new CI or the handle to\n%      the existing singleton*.\n%\n%      CI('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in CI.M with the given input arguments.\n%\n%      CI('Property','Value',...) creates a new CI or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before ci_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to ci_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help ci\n\n% Last Modified by GUIDE v2.5 31-Aug-2017 18:50:29\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @ci_OpeningFcn, ...\n                   'gui_OutputFcn',  @ci_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before ci is made visible.\nfunction ci_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to ci (see VARARGIN)\n\n% Choose default command line output for ci\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes ci wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\nset(handles.bbs_txt, 'Visible', 'off');\n\nsetpixelposition(handles.radius1_pop,[3, 10, 70, 40]);\nsetpixelposition(handles.radius2_pop,[87, 10, 70, 40]);\n\nsetpixelposition(handles.w_1_pop,[79, 5, 70, 40]);\nsetpixelposition(handles.w_2_pop,[153, 5, 70, 40]);\n\nset(handles.axes1,... \n        'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);\n    \nset(handles.wiener_check,'Value', 1);\nset(handles.dark_check,'Value', 1); \nset(handles.w_1_pop, 'string', {1:10}, 'Value', 5);\nset(handles.w_2_pop, 'string', {1:10}, 'Value', 5);\n\nset(handles.radius1_pop, 'string', {1:20}, 'Value', 1, 'Visible' , 'on');\nset(handles.radius2_pop, 'string', {1:20}, 'Value', 3,  'Visible' , 'on');\n\nset(handles.sensitivity_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.94);\nset(handles.threshold_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.14);\n\nset(handles.sensitivity_txt, 'String', '0.94');\nset(handles.threshold_txt, 'String', '0.14');\n\nset(handles.contrast_low_slider,'Min', 0, 'Max', 1, 'SliderStep', [0.01, 0.1], 'Value', 0);\nset(handles.contrast_high_slider, 'Min', 0, 'Max', 1,'SliderStep', [0.01, 0.1], 'Value', 0.8);\n\nset(handles.contrast_low_txt, 'String', '0');\nset(handles.contrast_high_txt, 'String', '0.8');\n\nset(handles.contrast_low_txt, 'Enable', 'off');\nset(handles.contrast_high_txt, 'Enable', 'off');\nset(handles.contrast_low_slider, 'Enable', 'off');\nset(handles.contrast_high_slider, 'Enable', 'off');\nset(handles.sensitivity_txt, 'Enable', 'off');\nset(handles.threshold_txt, 'Enable', 'off');\nset(handles.sensitivity_slider, 'Enable', 'off');\nset(handles.threshold_slider, 'Enable', 'off');\nset(handles.radius1_pop, 'Enable', 'off');\nset(handles.radius2_pop, 'Enable', 'off');\nset(handles.dark_check,'Enable', 'off'); \nset(handles.bright_check,'Enable', 'off'); \nset(handles.analyze_button, 'Enable', 'off');\nset(handles.add_button, 'Enable', 'off');\nset(handles.wiener_check, 'Enable','off');\nset(handles.w_1_pop, 'Enable','off');\nset(handles.w_2_pop, 'Enable','off');\nset(handles.manual_check, 'Enable','off');\nset(handles.reset_button, 'Enable', 'off');\n\nset(handles.text5, 'Visible', 'off');\nset(handles.dice_txt, 'Visible', 'off'); \n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = ci_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n% --- Executes on button press in load_button.\nfunction load_button_Callback(hObject, eventdata, handles)\n% hObject    handle to load_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img;\nglobal img_url;\n[f p] = uigetfile({'*.dcm','DICOM Files'});\n\ntry\n    img_d = dicomread([p f]);\n    img=im2double(img_d);\n    img=uint8(255*mat2gray(img));\n\n    imshow(img,'Parent',handles.axes1);\n    img_url = strcat(p,f);\n\n    ci_loadingF(handles,img_url);\n    \n    set(handles.contrast_low_txt, 'Enable', 'on');\n    set(handles.contrast_high_txt, 'Enable', 'on');\n    set(handles.contrast_low_slider, 'Enable', 'on');\n    set(handles.contrast_high_slider, 'Enable', 'on');\n    set(handles.sensitivity_txt, 'Enable', 'on');\n    set(handles.threshold_txt, 'Enable', 'on');\n    set(handles.sensitivity_slider, 'Enable', 'on');\n    set(handles.threshold_slider, 'Enable', 'on');\n    set(handles.radius1_pop, 'Enable', 'on');\n    set(handles.radius2_pop, 'Enable', 'on');\n    set(handles.dark_check,'Enable', 'on'); \n    set(handles.bright_check,'Enable', 'on'); \n    set(handles.analyze_button, 'Enable', 'on');\n    set(handles.wiener_check, 'Enable','on');\n    set(handles.w_1_pop, 'Enable','on');\n    set(handles.w_2_pop, 'Enable','on');\n    set(handles.manual_check, 'Enable','on');\n    set(handles.reset_button, 'Enable', 'on');\n    set(handles.text5, 'Visible', 'off');\n    set(handles.dice_txt, 'Visible', 'off'); \n\ncatch\n    h = msgbox('Please upload an image');\nend\n\n    \n% --- Executes on button press in analyze_button.\nfunction analyze_button_Callback(hObject, eventdata, handles)\n% hObject    handle to analyze_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nset(handles.analyze_button, 'Enable', 'off');\nglobal img_url;\nciF(handles,img_url); \nset(handles.load_button, 'Visible', 'off');\nset(handles.add_button, 'Enable', 'on');\nset(handles.text5, 'Visible', 'on');\nset(handles.dice_txt, 'Visible', 'on'); \n\n\n% --- Executes on button press in add_button.\nfunction add_button_Callback(hObject, eventdata, handles)\n% hObject    handle to add_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal img;\nglobal img_url;\n[f p] = uigetfile({'*.dcm','DICOM Files'});\n\ntry\n    img_d = dicomread([p f]);\n    img=im2double(img_d);\n    img=uint8(255*mat2gray(img));\n\n    imshow(img,'Parent',handles.axes1);\n    img_url = strcat(p,f);\n\n    ci_loadingF(handles,img_url);\n    set(handles.analyze_button, 'Enable', 'on');\n    set(handles.text5, 'Visible', 'off');\n    set(handles.dice_txt, 'Visible', 'off'); \n\n    \ncatch\n    h = msgbox('Please upload an image');\nend\n\n\n% --- Executes on selection change in radius2_pop.\nfunction radius2_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to radius2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns radius2_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from radius2_pop\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction radius2_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to radius2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in radius1_pop.\nfunction radius1_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to radius1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns radius1_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from radius1_pop\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction radius1_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to radius1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n\nfunction comment_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of comment_txt as text\n%        str2double(get(hObject,'String')) returns contents of comment_txt as a double\n\n\n% --- Executes during object creation, after setting all properties.\nfunction comment_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to comment_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on slider movement.\nfunction contrast_high_slider_Callback(hObject, eventdata, handles)\n% hObject    handle to contrast_high_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\na = get(handles.contrast_high_slider, 'Value');\nset(handles.contrast_high_txt, 'String', num2str(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction contrast_high_slider_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to contrast_high_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: slider controls usually have a light gray background.\nif isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor',[.9 .9 .9]);\nend\n\n\n\nfunction contrast_high_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to contrast_high_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of contrast_high_txt as text\n%        str2double(get(hObject,'String')) returns contents of contrast_high_txt as a double\na = get(handles.contrast_high_txt, 'String');\nset(handles.contrast_high_slider, 'Value', str2num(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction contrast_high_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to contrast_high_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in wiener_check.\nfunction wiener_check_Callback(hObject, eventdata, handles)\n% hObject    handle to wiener_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of wiener_check\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes on selection change in w_1_pop.\nfunction w_1_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to w_1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns w_1_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from w_1_pop\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction w_1_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to w_1_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on selection change in w_2_pop.\nfunction w_2_pop_Callback(hObject, eventdata, handles)\n% hObject    handle to w_2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: contents = cellstr(get(hObject,'String')) returns w_2_pop contents as cell array\n%        contents{get(hObject,'Value')} returns selected item from w_2_pop\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction w_2_pop_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to w_2_pop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: popupmenu controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on slider movement.\nfunction threshold_slider_Callback(hObject, eventdata, handles)\n% hObject    handle to threshold_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\na = get(handles.threshold_slider, 'Value');\nset(handles.threshold_txt, 'String', num2str(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction threshold_slider_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to threshold_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: slider controls usually have a light gray background.\nif isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor',[.9 .9 .9]);\nend\n\n\n\nfunction threshold_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to threshold_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of threshold_txt as text\n%        str2double(get(hObject,'String')) returns contents of threshold_txt as a double\na = get(handles.threshold_txt, 'String');\nset(handles.threshold_slider, 'Value', str2num(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction threshold_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to threshold_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on slider movement.\nfunction sensitivity_slider_Callback(hObject, eventdata, handles)\n% hObject    handle to sensitivity_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\na = get(handles.sensitivity_slider, 'Value');\nset(handles.sensitivity_txt, 'String', num2str(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction sensitivity_slider_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to sensitivity_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: slider controls usually have a light gray background.\nif isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor',[.9 .9 .9]);\nend\n\n\n\nfunction sensitivity_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to sensitivity_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of sensitivity_txt as text\n%        str2double(get(hObject,'String')) returns contents of sensitivity_txt as a double\na = get(handles.sensitivity_txt,'String');\nset(handles.sensitivity_slider, 'Value', str2num(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction sensitivity_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to sensitivity_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in dark_check.\nfunction dark_check_Callback(hObject, eventdata, handles)\n% hObject    handle to dark_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of dark_check\nset(handles.bright_check, 'Value', 0);\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes on button press in bright_check.\nfunction bright_check_Callback(hObject, eventdata, handles)\n% hObject    handle to bright_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of bright_check\nset(handles.dark_check, 'Value', 0);\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes on slider movement.\nfunction contrast_low_slider_Callback(hObject, eventdata, handles)\n% hObject    handle to contrast_low_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\na = get(handles.contrast_low_slider, 'Value');\nset(handles.contrast_low_txt, 'String', num2str(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction contrast_low_slider_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to contrast_low_slider (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: slider controls usually have a light gray background.\nif isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor',[.9 .9 .9]);\nend\n\n\n\nfunction contrast_low_txt_Callback(hObject, eventdata, handles)\n% hObject    handle to contrast_low_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of contrast_low_txt as text\n%        str2double(get(hObject,'String')) returns contents of contrast_low_txt as a double\na = get(handles.contrast_low_txt, 'String');\nset(handles.contrast_low_slider, 'Value', str2num(a));\n\nglobal img_url;\nci_loadingF(handles,img_url);\n\n% --- Executes during object creation, after setting all properties.\nfunction contrast_low_txt_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to contrast_low_txt (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in home_button.\nfunction home_button_Callback(hObject, eventdata, handles)\n% hObject    handle to home_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nclose(ci);\nrun('qalma');\n\n\n% --- Executes on button press in manual_check.\nfunction manual_check_Callback(hObject, eventdata, handles)\n% hObject    handle to manual_check (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: get(hObject,'Value') returns toggle state of manual_check\nglobal img_url;\n\nif get(handles.manual_check, 'Value') == 1\n    h = msgbox('Please click on the BBs after clicking the analyze button');\n    try\n        ci_loadingF(handles,img_url);\n    catch\n        h1 = msgbox('Please load an image first');\n    end\nelse\n    try\n        ci_loadingF(handles,img_url);\n    catch\n        h1 = msgbox('Please load an image first');\n    end \nend\n\n\n% --- Executes when user attempts to close figure1.\nfunction figure1_CloseRequestFcn(hObject, eventdata, handles)\n% hObject    handle to figure1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hint: delete(hObject) closes the figure\ndelete(hObject);\nclearvars;\n\n\n% --- Executes on button press in reset_button.\nfunction reset_button_Callback(hObject, eventdata, handles)\n% hObject    handle to reset_button (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nset(handles.wiener_check,'Value', 1);\nset(handles.dark_check,'Value', 1); \nset(handles.w_1_pop, 'string', {1:10}, 'Value', 5);\nset(handles.w_2_pop, 'string', {1:10}, 'Value', 5);\n\nset(handles.radius1_pop, 'string', {1:20}, 'Value', 1, 'Visible' , 'on');\nset(handles.radius2_pop, 'string', {1:20}, 'Value', 3,  'Visible' , 'on');\n\nset(handles.sensitivity_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.94);\nset(handles.threshold_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.14);\n\nset(handles.sensitivity_txt, 'String', '0.94');\nset(handles.threshold_txt, 'String', '0.14');\n\nset(handles.contrast_low_slider,'Min', 0, 'Max', 1, 'SliderStep', [0.01, 0.1], 'Value', 0);\nset(handles.contrast_high_slider, 'Min', 0, 'Max', 1,'SliderStep', [0.01, 0.1], 'Value', 0.8);\n\nset(handles.contrast_low_txt, 'String', '0');\nset(handles.contrast_high_txt, 'String', '0.8');\n\nset(handles.text5, 'Visible', 'off');\nset(handles.dice_txt, 'Visible', 'off'); \n\nglobal img_url;\nci_loadingF(handles,img_url);\n"}
+{"plateform": "github", "repo_name": "andyzeng/arc-robot-vision-master", "name": "fill_depth_cross_bf.m", "ext": ".m", "path": "arc-robot-vision-master/suction-based-grasping/external/bxf/fill_depth_cross_bf.m", "size": 1990, "source_encoding": "utf_8", "md5": "b7e5bbcb1bedcb7426978f7df1777af9", "text": "% In-paints the depth image using a cross-bilateral filter. The operation \n% is implemented via several filterings at various scales. The number of\n% scales is determined by the number of spacial and range sigmas provided.\n% 3 spacial/range sigmas translated into filtering at 3 scales.\n%\n% Args:\n%   imgRgb - the RGB image, a uint8 HxWx3 matrix\n%   imgDepthAbs - the absolute depth map, a HxW double matrix whose values\n%                 indicate depth in meters.\n%   spaceSigmas - (optional) sigmas for the spacial gaussian term.\n%   rangeSigmas - (optional) sigmas for the intensity gaussian term.\n%\n% Returns:\n%    imgDepthAbs - the inpainted depth image.\nfunction imgDepthAbs = fill_depth_cross_bf(imgRgb, imgDepthAbs, ...\n    spaceSigmas, rangeSigmas)\n  \n  error(nargchk(2,4,nargin));\n  assert(isa(imgRgb, 'uint8'), 'imgRgb must be uint8');\n  assert(isa(imgDepthAbs, 'double'), 'imgDepthAbs must be a double');\n\n  if nargin < 3\n     %spaceSigmas = [ 12];\n     spaceSigmas = [12 5 8];\n  end\n  if nargin < 4\n    % rangeSigmas = [0.2];\n    rangeSigmas = [0.2 0.08 0.02];\n  end\n  \n  assert(numel(spaceSigmas) == numel(rangeSigmas));\n  assert(isa(rangeSigmas, 'double'));\n  assert(isa(spaceSigmas, 'double'));\n  \n  % Create the 'noise' image and get the maximum observed depth.\n  imgIsNoise = imgDepthAbs == 0 | imgDepthAbs == 10;\n  maxDepthObs = max(imgDepthAbs(~imgIsNoise));\n  \n  % If depth map is empty, exit function\n  if isempty(maxDepthObs)\n    return; \n  end\n  \n  % Convert the depth image to uint8.\n  imgDepth = imgDepthAbs ./ maxDepthObs;\n  imgDepth(imgDepth > 1) = 1;\n  imgDepth = uint8(imgDepth * 255);\n  \n  % Run the cross-bilateral filter.\n  if ispc\n    imgDepthAbs = mex_cbf_windows(imgDepth, rgb2gray(imgRgb), imgIsNoise, spaceSigmas(:), rangeSigmas(:));\n  else\n    imgDepthAbs = mex_cbf(imgDepth, rgb2gray(imgRgb), imgIsNoise, spaceSigmas(:), rangeSigmas(:));\n  end\n  \n  % Convert back to absolute depth (meters).\n  imgDepthAbs = im2double(imgDepthAbs) .* maxDepthObs;\nend"}
+{"plateform": "github", "repo_name": "andyzeng/arc-robot-vision-master", "name": "sub2ind2d.m", "ext": ".m", "path": "arc-robot-vision-master/parallel-jaw-grasping/baseline/sub2ind2d.m", "size": 135, "source_encoding": "utf_8", "md5": "4970286e0c7d89b91364ca0d54668cff", "text": "% A faster version of sub2ind for 2D case\nfunction linIndex = sub2ind2d(sz, rowSub, colSub)\n  linIndex = (colSub-1) * sz(1) + rowSub;\n\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "addSubFoldersToPath.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/addSubFoldersToPath.m", "size": 792, "source_encoding": "utf_8", "md5": "1e9a817e1ab0ef5e56a1b36ec9f9397c", "text": "function addSubFoldersToPath()\n\npathOfThisFunction = mfilename('fullpath');\n[currentpath, ~, ~]= fileparts(pathOfThisFunction);\nallSubpaths = strsplit( genpath(currentpath) ,':');\nblacklist={'.git','.ignore','.graffle','.'}; % '.' is any hidden folder\n\npathsToAdd={};\nfor n=1:numel(allSubpaths)\n\tif shouldAddThisPath(allSubpaths{1,n},blacklist)\n\t\tpathsToAdd{end+1} = allSubpaths{n};\n\tend\nend\n\ndisp('Temporarily adding toolbox subdirecties to the path: ')\nfprintf('\\t%s\\n',pathsToAdd{:})\naddpath( strjoin(pathsToAdd, ':') )\nend\n\nfunction addThisPath = shouldAddThisPath(path,blacklist)\naddThisPath = true;\nfor ignoreStr = blacklist\n\tif isStringMatch(path,ignoreStr{1})\n\t\taddThisPath=false;\n\tend\nend\nend\n\nfunction matchFound = isStringMatch(str,pattern)\nmatchFound = ~strfind(str,pattern);\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "checkGitHubDependencies.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/checkGitHubDependencies.m", "size": 2973, "source_encoding": "utf_8", "md5": "30b69fd5ee94882628374c7dbfc419ea", "text": "function checkGitHubDependencies(dependencies)\n% This function takes a cell array of url's to hithub repositories, loop through\n% them and ensure they exist on the path, or clone them to your local machine.\n%\n% Example input:\n%\n% dependencies={...\n% \t'https://github.com/drbenvincent/mcmc-utils-matlab',...\n% \t'https://github.com/altmany/export_fig'};\n\nassert(iscellstr(dependencies),'Input to function should be a cell array of url''s to github repositories')\n\n% ensure dependencies is a row\nif iscolumn(dependencies)\n\tdependencies = dependencies';\nend\nassert(isrow(dependencies))\n\nfor url=dependencies\n\tprocessDependency(url{:});\nend\n\nend\n\n\nfunction processDependency(url)\ndisplayDependencyToCommandWindow(url);\nrepoName = getRepoNameFromUrl(url);\nif ~isRepoFolderOnPath(repoName)\n\ttargetPath = fullfile(defineInstallPath(),repoName);\n\ttargetPath = removeTrailingColon(targetPath);\n\tcloneGitHubRepo(url, repoName, targetPath);\nelse\n\tupdateGitHubRepo(defineInstallPath(),repoName);\nend\nend\n\nfunction displayDependencyToCommandWindow(url)\ndisp( makeHyperlink(url, makeWeblinkCode(url)) )\nend\n\nfunction repoName = getRepoNameFromUrl(url)\n[~,repoName] = fileparts(url);\nend\n\nfunction installPath = defineInstallPath()\n% installPath will be the Matlab userpath (eg /Users/Username/Documents/MATLAB)\nif isempty(userpath)\n\tuserpath('reset')\nend\ninstallPath = userpath;\n% Fix the trailing \":\" which only sometimes appears (or \";\" on PC)\ninstallPath = removeTrailingColon(installPath);\nend\n\nfunction str = removeTrailingColon(str)\nif str(end)==systemDelimiter()\n\tstr(end)='';\nend\nend\n\nfunction onPath = isRepoFolderOnPath(repoName)\n\tonPath = exist(repoName,'dir')==7;\nend\n\nfunction cloneGitHubRepo(repoAddress, repoName, installPath)\n% ensure the folder exists\n%targetPath = removeTrailingColon(fullfile(defineInstallPath(),repoName));\nensureFolderExists(installPath);\naddpath(installPath);\n% do the cloning\noriginalPath = cd;\ntry\n\tcd(defineInstallPath())\n\tcommand = sprintf('git clone %s.git', repoAddress);\n\t[status, cmdout] = system(command);\ncatch ME\n\trethrow(ME)\nend\ncd(originalPath)\nend\n\nfunction updateGitHubRepo(installPath,repoName)\noriginalPath = cd;\ntry\n\tcd(fullfile(installPath,repoName))\n\t[status, cmdout] = system('git pull');\ncatch ME\n\trethrow(ME)\n\t%warning('Unable to update GitHub repository')\nend\ncd(originalPath)\nend\n\nfunction weblinkCode = makeWeblinkCode(url)\nassert(ischar(url))\nweblinkCode = sprintf('web(''%s'')', url);\nend\n\nfunction hyperlink = makeHyperlink(text, codeToRun)\nassert(ischar(text))\nassert(ischar(codeToRun))\ncodeToRun = ['matlab: ' codeToRun];\nhyperlink = sprintf('<a href=\"%s\">%s</a>', codeToRun, text);\nend\n\nfunction delimiter = systemDelimiter()\nif ismac\n\t\tdelimiter = ':';\n\telseif ispc\n\t\tdelimiter = ';';\nend\nend\n\n% TODO: Work out how to make this work in Matlab\n% function results = exectuteFunctionInPathProvided(func, targetPath)\n%     originalPath = cd;\n%     cd(targetPath)\n%     results = func();\n%     cd(originalPath)\n% end\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "pdftops.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/pdftops.m", "size": 6161, "source_encoding": "utf_8", "md5": "5edac4bbbdae30223cb246a4ec7313d6", "text": "function varargout = pdftops(cmd)\r\n%PDFTOPS  Calls a local pdftops executable with the input command\r\n%\r\n% Example:\r\n%   [status result] = pdftops(cmd)\r\n%\r\n% Attempts to locate a pdftops executable, finally asking the user to\r\n% specify the directory pdftops was installed into. The resulting path is\r\n% stored for future reference.\r\n% \r\n% Once found, the executable is called with the input command string.\r\n%\r\n% This function requires that you have pdftops (from the Xpdf package)\r\n% installed on your system. You can download this from:\r\n% http://www.foolabs.com/xpdf\r\n%\r\n% IN:\r\n%   cmd - Command string to be passed into pdftops (e.g. '-help').\r\n%\r\n% OUT:\r\n%   status - 0 iff command ran without problem.\r\n%   result - Output from pdftops.\r\n\r\n% Copyright: Oliver Woodford, 2009-2010\r\n\r\n% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS.\r\n% Thanks to Christoph Hertel for pointing out a bug in check_xpdf_path under linux.\r\n% 23/01/2014 - Add full path to pdftops.txt in warning.\r\n% 27/05/2015 - Fixed alert in case of missing pdftops; fixed code indentation\r\n% 02/05/2016 - Added possible error explanation suggested by Michael Pacer (issue #137)\r\n% 02/05/2016 - Search additional possible paths suggested by Jonas Stein (issue #147)\r\n% 03/05/2016 - Display the specific error message if pdftops fails for some reason (issue #148)\r\n\r\n    % Call pdftops\r\n    [varargout{1:nargout}] = system([xpdf_command(xpdf_path()) cmd]);\r\nend\r\n\r\nfunction path_ = xpdf_path\r\n    % Return a valid path\r\n    % Start with the currently set path\r\n    path_ = user_string('pdftops');\r\n    % Check the path works\r\n    if check_xpdf_path(path_)\r\n        return\r\n    end\r\n    % Check whether the binary is on the path\r\n    if ispc\r\n        bin = 'pdftops.exe';\r\n    else\r\n        bin = 'pdftops';\r\n    end\r\n    if check_store_xpdf_path(bin)\r\n        path_ = bin;\r\n        return\r\n    end\r\n    % Search the obvious places\r\n    if ispc\r\n        paths = {'C:\\Program Files\\xpdf\\pdftops.exe', 'C:\\Program Files (x86)\\xpdf\\pdftops.exe'};\r\n    else\r\n        paths = {'/usr/bin/pdftops', '/usr/local/bin/pdftops'};\r\n    end\r\n    for a = 1:numel(paths)\r\n        path_ = paths{a};\r\n        if check_store_xpdf_path(path_)\r\n            return\r\n        end\r\n    end\r\n\r\n    % Ask the user to enter the path\r\n    errMsg1 = 'Pdftops not found. Please locate the program, or install xpdf-tools from ';\r\n    url1 = 'http://foolabs.com/xpdf';\r\n    fprintf(2, '%s\\n', [errMsg1 '<a href=\"matlab:web(''-browser'',''' url1 ''');\">' url1 '</a>']);\r\n    errMsg1 = [errMsg1 url1];\r\n    %if strncmp(computer,'MAC',3) % Is a Mac\r\n    %    % Give separate warning as the MacOS uigetdir dialogue box doesn't have a title\r\n    %    uiwait(warndlg(errMsg1))\r\n    %end\r\n\r\n    % Provide an alternative possible explanation as per issue #137\r\n    errMsg2 = 'If you have pdftops installed, perhaps Matlab is shaddowing it as described in ';\r\n    url2 = 'https://github.com/altmany/export_fig/issues/137';\r\n    fprintf(2, '%s\\n', [errMsg2 '<a href=\"matlab:web(''-browser'',''' url2 ''');\">issue #137</a>']);\r\n    errMsg2 = [errMsg2 url1];\r\n\r\n    state = 0;\r\n    while 1\r\n        if state\r\n            option1 = 'Install pdftops';\r\n        else\r\n            option1 = 'Issue #137';\r\n        end\r\n        answer = questdlg({errMsg1,'',errMsg2},'Pdftops error',option1,'Locate pdftops','Cancel','Cancel');\r\n        drawnow;  % prevent a Matlab hang: http://undocumentedmatlab.com/blog/solving-a-matlab-hang-problem\r\n        switch answer\r\n            case 'Install pdftops'\r\n                web('-browser',url1);\r\n            case 'Issue #137'\r\n                web('-browser',url2);\r\n                state = 1;\r\n            case 'Locate pdftops'\r\n                base = uigetdir('/', errMsg1);\r\n                if isequal(base, 0)\r\n                    % User hit cancel or closed window\r\n                    break\r\n                end\r\n                base = [base filesep]; %#ok<AGROW>\r\n                bin_dir = {'', ['bin' filesep], ['lib' filesep]};\r\n                for a = 1:numel(bin_dir)\r\n                    path_ = [base bin_dir{a} bin];\r\n                    if exist(path_, 'file') == 2\r\n                        break\r\n                    end\r\n                end\r\n                if check_store_xpdf_path(path_)\r\n                    return\r\n                end\r\n\r\n            otherwise  % User hit Cancel or closed window\r\n                break\r\n        end\r\n    end\r\n    error('pdftops executable not found.');\r\nend\r\n\r\nfunction good = check_store_xpdf_path(path_)\r\n    % Check the path is valid\r\n    good = check_xpdf_path(path_);\r\n    if ~good\r\n        return\r\n    end\r\n    % Update the current default path to the path found\r\n    if ~user_string('pdftops', path_)\r\n        warning('Path to pdftops executable could not be saved. Enter it manually in %s.', fullfile(fileparts(which('user_string.m')), '.ignore', 'pdftops.txt'));\r\n        return\r\n    end\r\nend\r\n\r\nfunction good = check_xpdf_path(path_)\r\n    % Check the path is valid\r\n    [good, message] = system([xpdf_command(path_) '-h']); %#ok<ASGLU>\r\n    % system returns good = 1 even when the command runs\r\n    % Look for something distinct in the help text\r\n    good = ~isempty(strfind(message, 'PostScript'));\r\n\r\n    % Display the error message if the pdftops executable exists but fails for some reason\r\n    if ~good && exist(path_,'file')  % file exists but generates an error\r\n        fprintf('Error running %s:\\n', path_);\r\n        fprintf(2,'%s\\n\\n',message);\r\n    end\r\nend\r\n\r\nfunction cmd = xpdf_command(path_)\r\n    % Initialize any required system calls before calling ghostscript\r\n    % TODO: in Unix/Mac, find a way to determine whether to use \"export\" (bash) or \"setenv\" (csh/tcsh)\r\n    shell_cmd = '';\r\n    if isunix\r\n        % Avoids an error on Linux with outdated MATLAB lib files\r\n        % R20XXa/bin/glnxa64/libtiff.so.X\r\n        % R20XXa/sys/os/glnxa64/libstdc++.so.X\r\n        shell_cmd = 'export LD_LIBRARY_PATH=\"\"; ';\r\n    end\r\n    if ismac\r\n        shell_cmd = 'export DYLD_LIBRARY_PATH=\"\"; ';\r\n    end\r\n    % Construct the command string\r\n    cmd = sprintf('%s\"%s\" ', shell_cmd, path_);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "crop_borders.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/crop_borders.m", "size": 5133, "source_encoding": "utf_8", "md5": "b744bf935914cfa6d9ff82140b48291e", "text": "function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts)\r\n%CROP_BORDERS Crop the borders of an image or stack of images\r\n%\r\n%   [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])\r\n%\r\n%IN:\r\n%   A - HxWxCxN stack of images.\r\n%   bcol - Cx1 background colour vector.\r\n%   padding - scalar indicating how much padding to have in relation to\r\n%             the cropped-image-size (0<=padding<=1). Default: 0\r\n%   crop_amounts - 4-element vector of crop amounts: [top,right,bottom,left]\r\n%             where NaN/Inf indicate auto-cropping, 0 means no cropping,\r\n%             and any other value mean cropping in pixel amounts.\r\n%\r\n%OUT:\r\n%   B - JxKxCxN cropped stack of images.\r\n%   vA     - coordinates in A that contain the cropped image\r\n%   vB     - coordinates in B where the cropped version of A is placed\r\n%   bb_rel - relative bounding box (used for eps-cropping)\r\n\r\n%{\r\n% 06/03/15: Improved image cropping thanks to Oscar Hartogensis\r\n% 08/06/15: Fixed issue #76: case of transparent figure bgcolor\r\n% 21/02/16: Enabled specifying non-automated crop amounts\r\n% 04/04/16: Fix per Luiz Carvalho for old Matlab releases\r\n% 23/10/16: Fixed issue #175: there used to be a 1px minimal padding in case of crop, now removed\r\n%}\r\n\r\n    if nargin < 3\r\n        padding = 0;\r\n    end\r\n    if nargin < 4\r\n        crop_amounts = nan(1,4);  % =auto-cropping\r\n    end\r\n    crop_amounts(end+1:4) = NaN;  % fill missing values with NaN\r\n\r\n    [h, w, c, n] = size(A);\r\n    if isempty(bcol)  % case of transparent bgcolor\r\n        bcol = A(ceil(end/2),1,:,1);\r\n    end\r\n    if isscalar(bcol)\r\n        bcol = bcol(ones(c, 1));\r\n    end\r\n\r\n    % Crop margin from left\r\n    if ~isfinite(crop_amounts(4))\r\n        bail = false;\r\n        for l = 1:w\r\n            for a = 1:c\r\n                if ~all(col(A(:,l,a,:)) == bcol(a))\r\n                    bail = true;\r\n                    break;\r\n                end\r\n            end\r\n            if bail\r\n                break;\r\n            end\r\n        end\r\n    else\r\n        l = 1 + abs(crop_amounts(4));\r\n    end\r\n\r\n    % Crop margin from right\r\n    if ~isfinite(crop_amounts(2))\r\n        bcol = A(ceil(end/2),w,:,1);\r\n        bail = false;\r\n        for r = w:-1:l\r\n            for a = 1:c\r\n                if ~all(col(A(:,r,a,:)) == bcol(a))\r\n                    bail = true;\r\n                    break;\r\n                end\r\n            end\r\n            if bail\r\n                break;\r\n            end\r\n        end\r\n    else\r\n        r = w - abs(crop_amounts(2));\r\n    end\r\n\r\n    % Crop margin from top\r\n    if ~isfinite(crop_amounts(1))\r\n        bcol = A(1,ceil(end/2),:,1);\r\n        bail = false;\r\n        for t = 1:h\r\n            for a = 1:c\r\n                if ~all(col(A(t,:,a,:)) == bcol(a))\r\n                    bail = true;\r\n                    break;\r\n                end\r\n            end\r\n            if bail\r\n                break;\r\n            end\r\n        end\r\n    else\r\n        t = 1 + abs(crop_amounts(1));\r\n    end\r\n\r\n    % Crop margin from bottom\r\n    bcol = A(h,ceil(end/2),:,1);\r\n    if ~isfinite(crop_amounts(3))\r\n        bail = false;\r\n        for b = h:-1:t\r\n            for a = 1:c\r\n                if ~all(col(A(b,:,a,:)) == bcol(a))\r\n                    bail = true;\r\n                    break;\r\n                end\r\n            end\r\n            if bail\r\n                break;\r\n            end\r\n        end\r\n    else\r\n        b = h - abs(crop_amounts(3));\r\n    end\r\n\r\n    if padding == 0  % no padding\r\n        % Issue #175: there used to be a 1px minimal padding in case of crop, now removed\r\n        %{\r\n        if ~isequal([t b l r], [1 h 1 w]) % Check if we're actually croppping\r\n            padding = 1; % Leave one boundary pixel to avoid bleeding on resize\r\n            bcol(:) = nan;  % make the 1px padding transparent\r\n        end\r\n        %}\r\n    elseif abs(padding) < 1  % pad value is a relative fraction of image size\r\n        padding = sign(padding)*round(mean([b-t r-l])*abs(padding)); % ADJUST PADDING\r\n    else  % pad value is in units of 1/72\" points\r\n        padding = round(padding);  % fix cases of non-integer pad value\r\n    end\r\n\r\n    if padding > 0  % extra padding\r\n        % Create an empty image, containing the background color, that has the\r\n        % cropped image size plus the padded border\r\n        B = repmat(bcol,[(b-t)+1+padding*2,(r-l)+1+padding*2,1,n]);  % Fix per Luiz Carvalho\r\n        % vA - coordinates in A that contain the cropped image\r\n        vA = [t b l r];\r\n        % vB - coordinates in B where the cropped version of A will be placed\r\n        vB = [padding+1, (b-t)+1+padding, padding+1, (r-l)+1+padding];\r\n        % Place the original image in the empty image\r\n        B(vB(1):vB(2), vB(3):vB(4), :, :) = A(vA(1):vA(2), vA(3):vA(4), :, :);\r\n        A = B;\r\n    else  % extra cropping\r\n        vA = [t-padding b+padding l-padding r+padding];\r\n        A = A(vA(1):vA(2), vA(3):vA(4), :, :);\r\n        vB = [NaN NaN NaN NaN];\r\n    end\r\n\r\n    % For EPS cropping, determine the relative BoundingBox - bb_rel\r\n    bb_rel = [l-1 h-b-1 r+1 h-t+1]./[w h w h];\r\nend\r\n\r\nfunction A = col(A)\r\n    A = A(:);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "isolate_axes.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/isolate_axes.m", "size": 4851, "source_encoding": "utf_8", "md5": "611d9727e84ad6ba76dcb3543434d0ce", "text": "function fh = isolate_axes(ah, vis)\r\n%ISOLATE_AXES Isolate the specified axes in a figure on their own\r\n%\r\n% Examples:\r\n%   fh = isolate_axes(ah)\r\n%   fh = isolate_axes(ah, vis)\r\n%\r\n% This function will create a new figure containing the axes/uipanels\r\n% specified, and also their associated legends and colorbars. The objects\r\n% specified must all be in the same figure, but they will generally only be\r\n% a subset of the objects in the figure.\r\n%\r\n% IN:\r\n%    ah - An array of axes and uipanel handles, which must come from the\r\n%         same figure.\r\n%    vis - A boolean indicating whether the new figure should be visible.\r\n%          Default: false.\r\n%\r\n% OUT:\r\n%    fh - The handle of the created figure.\r\n\r\n% Copyright (C) Oliver Woodford 2011-2013\r\n\r\n% Thank you to Rosella Blatt for reporting a bug to do with axes in GUIs\r\n% 16/03/12: Moved copyfig to its own function. Thanks to Bob Fratantonio\r\n%           for pointing out that the function is also used in export_fig.m\r\n% 12/12/12: Add support for isolating uipanels. Thanks to michael for suggesting it\r\n% 08/10/13: Bug fix to allchildren suggested by Will Grant (many thanks!)\r\n% 05/12/13: Bug fix to axes having different units. Thanks to Remington Reid for reporting\r\n% 21/04/15: Bug fix for exporting uipanels with legend/colorbar on HG1 (reported by Alvaro\r\n%           on FEX page as a comment on 24-Apr-2014); standardized indentation & help section\r\n% 22/04/15: Bug fix: legends and colorbars were not exported when exporting axes handle in HG2\r\n\r\n    % Make sure we have an array of handles\r\n    if ~all(ishandle(ah))\r\n        error('ah must be an array of handles');\r\n    end\r\n    % Check that the handles are all for axes or uipanels, and are all in the same figure\r\n    fh = ancestor(ah(1), 'figure');\r\n    nAx = numel(ah);\r\n    for a = 1:nAx\r\n        if ~ismember(get(ah(a), 'Type'), {'axes', 'uipanel'})\r\n            error('All handles must be axes or uipanel handles.');\r\n        end\r\n        if ~isequal(ancestor(ah(a), 'figure'), fh)\r\n            error('Axes must all come from the same figure.');\r\n        end\r\n    end\r\n    % Tag the objects so we can find them in the copy\r\n    old_tag = get(ah, 'Tag');\r\n    if nAx == 1\r\n        old_tag = {old_tag};\r\n    end\r\n    set(ah, 'Tag', 'ObjectToCopy');\r\n    % Create a new figure exactly the same as the old one\r\n    fh = copyfig(fh); %copyobj(fh, 0);\r\n    if nargin < 2 || ~vis\r\n        set(fh, 'Visible', 'off');\r\n    end\r\n    % Reset the object tags\r\n    for a = 1:nAx\r\n        set(ah(a), 'Tag', old_tag{a});\r\n    end\r\n    % Find the objects to save\r\n    ah = findall(fh, 'Tag', 'ObjectToCopy');\r\n    if numel(ah) ~= nAx\r\n        close(fh);\r\n        error('Incorrect number of objects found.');\r\n    end\r\n    % Set the axes tags to what they should be\r\n    for a = 1:nAx\r\n        set(ah(a), 'Tag', old_tag{a});\r\n    end\r\n    % Keep any legends and colorbars which overlap the subplots\r\n    % Note: in HG1 these are axes objects; in HG2 they are separate objects, therefore we\r\n    %       don't test for the type, only the tag (hopefully nobody but Matlab uses them!)\r\n    lh = findall(fh, 'Tag', 'legend', '-or', 'Tag', 'Colorbar');\r\n    nLeg = numel(lh);\r\n    if nLeg > 0\r\n        set([ah(:); lh(:)], 'Units', 'normalized');\r\n        try\r\n            ax_pos = get(ah, 'OuterPosition'); % axes and figures have the OuterPosition property\r\n        catch\r\n            ax_pos = get(ah, 'Position'); % uipanels only have Position, not OuterPosition\r\n        end\r\n        if nAx > 1\r\n            ax_pos = cell2mat(ax_pos(:));\r\n        end\r\n        ax_pos(:,3:4) = ax_pos(:,3:4) + ax_pos(:,1:2);\r\n        try\r\n            leg_pos = get(lh, 'OuterPosition');\r\n        catch\r\n            leg_pos = get(lh, 'Position');  % No OuterPosition in HG2, only in HG1\r\n        end\r\n        if nLeg > 1;\r\n            leg_pos = cell2mat(leg_pos);\r\n        end\r\n        leg_pos(:,3:4) = leg_pos(:,3:4) + leg_pos(:,1:2);\r\n        ax_pos = shiftdim(ax_pos, -1);\r\n        % Overlap test\r\n        M = bsxfun(@lt, leg_pos(:,1), ax_pos(:,:,3)) & ...\r\n            bsxfun(@lt, leg_pos(:,2), ax_pos(:,:,4)) & ...\r\n            bsxfun(@gt, leg_pos(:,3), ax_pos(:,:,1)) & ...\r\n            bsxfun(@gt, leg_pos(:,4), ax_pos(:,:,2));\r\n        ah = [ah; lh(any(M, 2))];\r\n    end\r\n    % Get all the objects in the figure\r\n    axs = findall(fh);\r\n    % Delete everything except for the input objects and associated items\r\n    delete(axs(~ismember(axs, [ah; allchildren(ah); allancestors(ah)])));\r\nend\r\n\r\nfunction ah = allchildren(ah)\r\n    ah = findall(ah);\r\n    if iscell(ah)\r\n        ah = cell2mat(ah);\r\n    end\r\n    ah = ah(:);\r\nend\r\n\r\nfunction ph = allancestors(ah)\r\n    ph = [];\r\n    for a = 1:numel(ah)\r\n        h = get(ah(a), 'parent');\r\n        while h ~= 0\r\n            ph = [ph; h];\r\n            h = get(h, 'parent');\r\n        end\r\n    end\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "im2gif.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/im2gif.m", "size": 6234, "source_encoding": "utf_8", "md5": "8ee74d7d94e524410788276aa41dd5f1", "text": "%IM2GIF Convert a multiframe image to an animated GIF file\r\n%\r\n% Examples:\r\n%   im2gif infile\r\n%   im2gif infile outfile\r\n%   im2gif(A, outfile)\r\n%   im2gif(..., '-nocrop')\r\n%   im2gif(..., '-nodither')\r\n%   im2gif(..., '-ncolors', n)\r\n%   im2gif(..., '-loops', n)\r\n%   im2gif(..., '-delay', n) \r\n%   \r\n% This function converts a multiframe image to an animated GIF.\r\n%\r\n% To create an animation from a series of figures, export to a multiframe\r\n% TIFF file using export_fig, then convert to a GIF, as follows:\r\n%\r\n%    for a = 2 .^ (3:6)\r\n%       peaks(a);\r\n%       export_fig test.tif -nocrop -append\r\n%    end\r\n%    im2gif('test.tif', '-delay', 0.5);\r\n%\r\n%IN:\r\n%   infile - string containing the name of the input image.\r\n%   outfile - string containing the name of the output image (must have the\r\n%             .gif extension). Default: infile, with .gif extension.\r\n%   A - HxWxCxN array of input images, stacked along fourth dimension, to\r\n%       be converted to gif.\r\n%   -nocrop - option indicating that the borders of the output are not to\r\n%             be cropped.\r\n%   -nodither - option indicating that dithering is not to be used when\r\n%               converting the image.\r\n%   -ncolors - option pair, the value of which indicates the maximum number\r\n%              of colors the GIF can have. This can also be a quantization\r\n%              tolerance, between 0 and 1. Default/maximum: 256.\r\n%   -loops - option pair, the value of which gives the number of times the\r\n%            animation is to be looped. Default: 65535.\r\n%   -delay - option pair, the value of which gives the time, in seconds,\r\n%            between frames. Default: 1/15.\r\n\r\n% Copyright (C) Oliver Woodford 2011\r\n\r\nfunction im2gif(A, varargin)\r\n\r\n% Parse the input arguments\r\n[A, options] = parse_args(A, varargin{:});\r\n\r\nif options.crop ~= 0\r\n    % Crop\r\n    A = crop_borders(A, A(ceil(end/2),1,:,1));\r\nend\r\n\r\n% Convert to indexed image\r\n[h, w, c, n] = size(A);\r\nA = reshape(permute(A, [1 2 4 3]), h, w*n, c);\r\nmap = unique(reshape(A, h*w*n, c), 'rows');\r\nif size(map, 1) > 256\r\n    dither_str = {'dither', 'nodither'};\r\n    dither_str = dither_str{1+(options.dither==0)};\r\n    if options.ncolors <= 1\r\n        [B, map] = rgb2ind(A, options.ncolors, dither_str);\r\n        if size(map, 1) > 256\r\n            [B, map] = rgb2ind(A, 256, dither_str);\r\n        end\r\n    else\r\n        [B, map] = rgb2ind(A, min(round(options.ncolors), 256), dither_str);\r\n    end\r\nelse\r\n    if max(map(:)) > 1\r\n        map = double(map) / 255;\r\n        A = double(A) / 255;\r\n    end\r\n    B = rgb2ind(im2double(A), map);\r\nend\r\nB = reshape(B, h, w, 1, n);\r\n\r\n% Bug fix to rgb2ind\r\nmap(B(1)+1,:) = im2double(A(1,1,:));\r\n\r\n% Save as a gif\r\nimwrite(B, map, options.outfile, 'LoopCount', round(options.loops(1)), 'DelayTime', options.delay);\r\nend\r\n\r\n%% Parse the input arguments\r\nfunction [A, options] = parse_args(A, varargin)\r\n% Set the defaults\r\noptions = struct('outfile', '', ...\r\n                 'dither', true, ...\r\n                 'crop', true, ...\r\n                 'ncolors', 256, ...\r\n                 'loops', 65535, ...\r\n                 'delay', 1/15);\r\n\r\n% Go through the arguments\r\na = 0;\r\nn = numel(varargin);\r\nwhile a < n\r\n    a = a + 1;\r\n    if ischar(varargin{a}) && ~isempty(varargin{a})\r\n        if varargin{a}(1) == '-'\r\n            opt = lower(varargin{a}(2:end));\r\n            switch opt\r\n                case 'nocrop'\r\n                    options.crop = false;\r\n                case 'nodither'\r\n                    options.dither = false;\r\n                otherwise\r\n                    if ~isfield(options, opt)\r\n                        error('Option %s not recognized', varargin{a});\r\n                    end\r\n                    a = a + 1;\r\n                    if ischar(varargin{a}) && ~ischar(options.(opt))\r\n                        options.(opt) = str2double(varargin{a});\r\n                    else\r\n                        options.(opt) = varargin{a};\r\n                    end\r\n            end\r\n        else\r\n            options.outfile = varargin{a};\r\n        end\r\n    end\r\nend\r\n\r\nif isempty(options.outfile)\r\n    if ~ischar(A)\r\n        error('No output filename given.');\r\n    end\r\n    % Generate the output filename from the input filename\r\n    [path, outfile] = fileparts(A);\r\n    options.outfile = fullfile(path, [outfile '.gif']);\r\nend\r\n\r\nif ischar(A)\r\n    % Read in the image\r\n    A = imread_rgb(A);\r\nend\r\nend\r\n\r\n%% Read image to uint8 rgb array\r\nfunction [A, alpha] = imread_rgb(name)\r\n% Get file info\r\ninfo = imfinfo(name);\r\n% Special case formats\r\nswitch lower(info(1).Format)\r\n    case 'gif'\r\n        [A, map] = imread(name, 'frames', 'all');\r\n        if ~isempty(map)\r\n            map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\r\n            A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\r\n            A = permute(A, [1 2 5 4 3]);\r\n        end\r\n    case {'tif', 'tiff'}\r\n        A = cell(numel(info), 1);\r\n        for a = 1:numel(A)\r\n            [A{a}, map] = imread(name, 'Index', a, 'Info', info);\r\n            if ~isempty(map)\r\n                map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\r\n                A{a} = reshape(map(uint32(A{a})+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\r\n            end\r\n            if size(A{a}, 3) == 4\r\n                % TIFF in CMYK colourspace - convert to RGB\r\n                if isfloat(A{a})\r\n                    A{a} = A{a} * 255;\r\n                else\r\n                    A{a} = single(A{a});\r\n                end\r\n                A{a} = 255 - A{a};\r\n                A{a}(:,:,4) = A{a}(:,:,4) / 255;\r\n                A{a} = uint8(A(:,:,1:3) .* A{a}(:,:,[4 4 4]));\r\n            end\r\n        end\r\n        A = cat(4, A{:});\r\n    otherwise\r\n        [A, map, alpha] = imread(name);\r\n        A = A(:,:,:,1); % Keep only first frame of multi-frame files\r\n        if ~isempty(map)\r\n            map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\r\n            A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\r\n        elseif size(A, 3) == 4\r\n            % Assume 4th channel is an alpha matte\r\n            alpha = A(:,:,4);\r\n            A = A(:,:,1:3);\r\n        end\r\nend\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "read_write_entire_textfile.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/read_write_entire_textfile.m", "size": 961, "source_encoding": "utf_8", "md5": "775aa1f538c76516c7fb406a4f129320", "text": "%READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory\r\n%\r\n% Read or write an entire text file to/from memory, without leaving the\r\n% file open if an error occurs.\r\n%\r\n% Reading:\r\n%   fstrm = read_write_entire_textfile(fname)\r\n% Writing:\r\n%   read_write_entire_textfile(fname, fstrm)\r\n%\r\n%IN:\r\n%   fname - Pathname of text file to be read in.\r\n%   fstrm - String to be written to the file, including carriage returns.\r\n%\r\n%OUT:\r\n%   fstrm - String read from the file. If an fstrm input is given the\r\n%           output is the same as that input. \r\n\r\nfunction fstrm = read_write_entire_textfile(fname, fstrm)\r\nmodes = {'rt', 'wt'};\r\nwriting = nargin > 1;\r\nfh = fopen(fname, modes{1+writing});\r\nif fh == -1\r\n    error('Unable to open file %s.', fname);\r\nend\r\ntry\r\n    if writing\r\n        fwrite(fh, fstrm, 'char*1');\r\n    else\r\n        fstrm = fread(fh, '*char')';\r\n    end\r\ncatch ex\r\n    fclose(fh);\r\n    rethrow(ex);\r\nend\r\nfclose(fh);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "pdf2eps.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/pdf2eps.m", "size": 1522, "source_encoding": "utf_8", "md5": "4c8f0603619234278ed413670d24bdb6", "text": "%PDF2EPS  Convert a pdf file to eps format using pdftops\r\n%\r\n% Examples:\r\n%   pdf2eps source dest\r\n%\r\n% This function converts a pdf file to eps format.\r\n%\r\n% This function requires that you have pdftops, from the Xpdf suite of\r\n% functions, installed on your system. This can be downloaded from:\r\n% http://www.foolabs.com/xpdf  \r\n%\r\n%IN:\r\n%   source - filename of the source pdf file to convert. The filename is\r\n%            assumed to already have the extension \".pdf\".\r\n%   dest - filename of the destination eps file. The filename is assumed to\r\n%          already have the extension \".eps\".\r\n\r\n% Copyright (C) Oliver Woodford 2009-2010\r\n\r\n% Thanks to Aldebaro Klautau for reporting a bug when saving to\r\n% non-existant directories.\r\n\r\nfunction pdf2eps(source, dest)\r\n% Construct the options string for pdftops\r\noptions = ['-q -paper match -eps -level2 \"' source '\" \"' dest '\"'];\r\n% Convert to eps using pdftops\r\n[status, message] = pdftops(options);\r\n% Check for error\r\nif status\r\n    % Report error\r\n    if isempty(message)\r\n        error('Unable to generate eps. Check destination directory is writable.');\r\n    else\r\n        error(message);\r\n    end\r\nend\r\n% Fix the DSC error created by pdftops\r\nfid = fopen(dest, 'r+');\r\nif fid == -1\r\n    % Cannot open the file\r\n    return\r\nend\r\nfgetl(fid); % Get the first line\r\nstr = fgetl(fid); % Get the second line\r\nif strcmp(str(1:min(13, end)), '% Produced by')\r\n    fseek(fid, -numel(str)-1, 'cof');\r\n    fwrite(fid, '%'); % Turn ' ' into '%'\r\nend\r\nfclose(fid);\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "print2array.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/print2array.m", "size": 10376, "source_encoding": "utf_8", "md5": "a2022c32ae3efa6007a326692227bd39", "text": "function [A, bcol] = print2array(fig, res, renderer, gs_options)\r\n%PRINT2ARRAY  Exports a figure to an image array\r\n%\r\n% Examples:\r\n%   A = print2array\r\n%   A = print2array(figure_handle)\r\n%   A = print2array(figure_handle, resolution)\r\n%   A = print2array(figure_handle, resolution, renderer)\r\n%   A = print2array(figure_handle, resolution, renderer, gs_options)\r\n%   [A bcol] = print2array(...)\r\n%\r\n% This function outputs a bitmap image of the given figure, at the desired\r\n% resolution.\r\n%\r\n% If renderer is '-painters' then ghostcript needs to be installed. This\r\n% can be downloaded from: http://www.ghostscript.com\r\n%\r\n% IN:\r\n%   figure_handle - The handle of the figure to be exported. Default: gcf.\r\n%   resolution - Resolution of the output, as a factor of screen\r\n%                resolution. Default: 1.\r\n%   renderer - string containing the renderer paramater to be passed to\r\n%              print. Default: '-opengl'.\r\n%   gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If\r\n%                multiple options are needed, enclose in call array: {'-a','-b'}\r\n%\r\n% OUT:\r\n%   A - MxNx3 uint8 image of the figure.\r\n%   bcol - 1x3 uint8 vector of the background color\r\n\r\n% Copyright (C) Oliver Woodford 2008-2014, Yair Altman 2015-\r\n%{\r\n% 05/09/11: Set EraseModes to normal when using opengl or zbuffer\r\n%           renderers. Thanks to Pawel Kocieniewski for reporting the issue.\r\n% 21/09/11: Bug fix: unit8 -> uint8! Thanks to Tobias Lamour for reporting it.\r\n% 14/11/11: Bug fix: stop using hardcopy(), as it interfered with figure size\r\n%           and erasemode settings. Makes it a bit slower, but more reliable.\r\n%           Thanks to Phil Trinh and Meelis Lootus for reporting the issues.\r\n% 09/12/11: Pass font path to ghostscript.\r\n% 27/01/12: Bug fix affecting painters rendering tall figures. Thanks to\r\n%           Ken Campbell for reporting it.\r\n% 03/04/12: Bug fix to median input. Thanks to Andy Matthews for reporting it.\r\n% 26/10/12: Set PaperOrientation to portrait. Thanks to Michael Watts for\r\n%           reporting the issue.\r\n% 26/02/15: If temp dir is not writable, use the current folder for temp\r\n%           EPS/TIF files (Javier Paredes)\r\n% 27/02/15: Display suggested workarounds to internal print() error (issue #16)\r\n% 28/02/15: Enable users to specify optional ghostscript options (issue #36)\r\n% 10/03/15: Fixed minor warning reported by Paul Soderlind; fixed code indentation\r\n% 28/05/15: Fixed issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() func)\r\n% 07/07/15: Fixed issue #83: use numeric handles in HG1\r\n% 11/12/16: Fixed cropping issue reported by Harry D.\r\n%}\r\n\r\n    % Generate default input arguments, if needed\r\n    if nargin < 2\r\n        res = 1;\r\n        if nargin < 1\r\n            fig = gcf;\r\n        end\r\n    end\r\n    % Warn if output is large\r\n    old_mode = get(fig, 'Units');\r\n    set(fig, 'Units', 'pixels');\r\n    px = get(fig, 'Position');\r\n    set(fig, 'Units', old_mode);\r\n    npx = prod(px(3:4)*res)/1e6;\r\n    if npx > 30\r\n        % 30M pixels or larger!\r\n        warning('MATLAB:LargeImage', 'print2array generating a %.1fM pixel image. This could be slow and might also cause memory problems.', npx);\r\n    end\r\n    % Retrieve the background colour\r\n    bcol = get(fig, 'Color');\r\n    % Set the resolution parameter\r\n    res_str = ['-r' num2str(ceil(get(0, 'ScreenPixelsPerInch')*res))];\r\n    % Generate temporary file name\r\n    tmp_nam = [tempname '.tif'];\r\n    try\r\n        % Ensure that the temp dir is writable (Javier Paredes 26/2/15)\r\n        fid = fopen(tmp_nam,'w');\r\n        fwrite(fid,1);\r\n        fclose(fid);\r\n        delete(tmp_nam);  % cleanup\r\n        isTempDirOk = true;\r\n    catch\r\n        % Temp dir is not writable, so use the current folder\r\n        [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>\r\n        fpath = pwd;\r\n        tmp_nam = fullfile(fpath,[fname fext]);\r\n        isTempDirOk = false;\r\n    end\r\n    % Enable users to specify optional ghostscript options (issue #36)\r\n    if nargin > 3 && ~isempty(gs_options)\r\n        if iscell(gs_options)\r\n            gs_options = sprintf(' %s',gs_options{:});\r\n        elseif ~ischar(gs_options)\r\n            error('gs_options input argument must be a string or cell-array of strings');\r\n        else\r\n            gs_options = [' ' gs_options];\r\n        end\r\n    else\r\n        gs_options = '';\r\n    end\r\n    if nargin > 2 && strcmp(renderer, '-painters')\r\n        % First try to print directly to tif file\r\n        try\r\n            % Print the file into a temporary TIF file and read it into array A\r\n            [A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam);\r\n            if err, rethrow(ex); end\r\n        catch  % error - try to print to EPS and then using Ghostscript to TIF\r\n            % Print to eps file\r\n            if isTempDirOk\r\n                tmp_eps = [tempname '.eps'];\r\n            else\r\n                tmp_eps = fullfile(fpath,[fname '.eps']);\r\n            end\r\n            print2eps(tmp_eps, fig, 0, renderer, '-loose');\r\n            try\r\n                % Initialize the command to export to tiff using ghostscript\r\n                cmd_str = ['-dEPSCrop -q -dNOPAUSE -dBATCH ' res_str ' -sDEVICE=tiff24nc'];\r\n                % Set the font path\r\n                fp = font_path();\r\n                if ~isempty(fp)\r\n                    cmd_str = [cmd_str ' -sFONTPATH=\"' fp '\"'];\r\n                end\r\n                % Add the filenames\r\n                cmd_str = [cmd_str ' -sOutputFile=\"' tmp_nam '\" \"' tmp_eps '\"' gs_options];\r\n                % Execute the ghostscript command\r\n                ghostscript(cmd_str);\r\n            catch me\r\n                % Delete the intermediate file\r\n                delete(tmp_eps);\r\n                rethrow(me);\r\n            end\r\n            % Delete the intermediate file\r\n            delete(tmp_eps);\r\n            % Read in the generated bitmap\r\n            A = imread(tmp_nam);\r\n            % Delete the temporary bitmap file\r\n            delete(tmp_nam);\r\n        end\r\n        % Set border pixels to the correct colour\r\n        if isequal(bcol, 'none')\r\n            bcol = [];\r\n        elseif isequal(bcol, [1 1 1])\r\n            bcol = uint8([255 255 255]);\r\n        else\r\n            for l = 1:size(A, 2)\r\n                if ~all(reshape(A(:,l,:) == 255, [], 1))\r\n                    break;\r\n                end\r\n            end\r\n            for r = size(A, 2):-1:l\r\n                if ~all(reshape(A(:,r,:) == 255, [], 1))\r\n                    break;\r\n                end\r\n            end\r\n            for t = 1:size(A, 1)\r\n                if ~all(reshape(A(t,:,:) == 255, [], 1))\r\n                    break;\r\n                end\r\n            end\r\n            for b = size(A, 1):-1:t\r\n                if ~all(reshape(A(b,:,:) == 255, [], 1))\r\n                    break;\r\n                end\r\n            end\r\n            bcol = uint8(median(single([reshape(A(:,[l r],:), [], size(A, 3)); reshape(A([t b],:,:), [], size(A, 3))]), 1));\r\n            for c = 1:size(A, 3)\r\n                A(:,[1:l-1, r+1:end],c) = bcol(c);\r\n                A([1:t-1, b+1:end],:,c) = bcol(c);\r\n            end\r\n        end\r\n    else\r\n        if nargin < 3\r\n            renderer = '-opengl';\r\n        end\r\n        % Print the file into a temporary TIF file and read it into array A\r\n        [A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam);\r\n        % Throw any error that occurred\r\n        if err\r\n            % Display suggested workarounds to internal print() error (issue #16)\r\n            fprintf(2, 'An error occured with Matlab''s builtin print function.\\nTry setting the figure Renderer to ''painters'' or use opengl(''software'').\\n\\n');\r\n            rethrow(ex);\r\n        end\r\n        % Set the background color\r\n        if isequal(bcol, 'none')\r\n            bcol = [];\r\n        else\r\n            bcol = bcol * 255;\r\n            if isequal(bcol, round(bcol))\r\n                bcol = uint8(bcol);\r\n            else\r\n                bcol = squeeze(A(1,1,:));\r\n            end\r\n        end\r\n    end\r\n    % Check the output size is correct\r\n    if isequal(res, round(res))\r\n        px = round([px([4 3])*res 3]);  % round() to avoid an indexing warning below\r\n        if ~isequal(size(A), px)\r\n            % Correct the output size\r\n            A = A(1:min(end,px(1)),1:min(end,px(2)),:);\r\n        end\r\n    end\r\nend\r\n\r\n% Function to create a TIF image of the figure and read it into an array\r\nfunction [A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam)\r\n    err = false;\r\n    ex = [];\r\n    % Temporarily set the paper size\r\n    old_pos_mode    = get(fig, 'PaperPositionMode');\r\n    old_orientation = get(fig, 'PaperOrientation');\r\n    set(fig, 'PaperPositionMode','auto', 'PaperOrientation','portrait');\r\n    try\r\n        % Workaround for issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() function)\r\n        fp = [];  % in case we get an error below\r\n        fp = findall(fig, 'Type','patch', 'LineWidth',0.75);\r\n        set(fp, 'LineWidth',0.5);\r\n        % Fix issue #83: use numeric handles in HG1\r\n        if ~using_hg2(fig),  fig = double(fig);  end\r\n        % Print to tiff file\r\n        print(fig, renderer, res_str, '-dtiff', tmp_nam);\r\n        % Read in the printed file\r\n        A = imread(tmp_nam);\r\n        % Delete the temporary file\r\n        delete(tmp_nam);\r\n    catch ex\r\n        err = true;\r\n    end\r\n    set(fp, 'LineWidth',0.75);  % restore original figure appearance\r\n    % Reset the paper size\r\n    set(fig, 'PaperPositionMode',old_pos_mode, 'PaperOrientation',old_orientation);\r\nend\r\n\r\n% Function to return (and create, where necessary) the font path\r\nfunction fp = font_path()\r\n    fp = user_string('gs_font_path');\r\n    if ~isempty(fp)\r\n        return\r\n    end\r\n    % Create the path\r\n    % Start with the default path\r\n    fp = getenv('GS_FONTPATH');\r\n    % Add on the typical directories for a given OS\r\n    if ispc\r\n        if ~isempty(fp)\r\n            fp = [fp ';'];\r\n        end\r\n        fp = [fp getenv('WINDIR') filesep 'Fonts'];\r\n    else\r\n        if ~isempty(fp)\r\n            fp = [fp ':'];\r\n        end\r\n        fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];\r\n    end\r\n    user_string('gs_font_path', fp);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "append_pdfs.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/append_pdfs.m", "size": 2759, "source_encoding": "utf_8", "md5": "9b52be41aff48bea6f27992396900640", "text": "%APPEND_PDFS Appends/concatenates multiple PDF files\r\n%\r\n% Example:\r\n%   append_pdfs(output, input1, input2, ...)\r\n%   append_pdfs(output, input_list{:})\r\n%   append_pdfs test.pdf temp1.pdf temp2.pdf\r\n%\r\n% This function appends multiple PDF files to an existing PDF file, or\r\n% concatenates them into a PDF file if the output file doesn't yet exist.\r\n%\r\n% This function requires that you have ghostscript installed on your\r\n% system. Ghostscript can be downloaded from: http://www.ghostscript.com\r\n%\r\n% IN:\r\n%    output - string of output file name (including the extension, .pdf).\r\n%             If it exists it is appended to; if not, it is created.\r\n%    input1 - string of an input file name (including the extension, .pdf).\r\n%             All input files are appended in order.\r\n%    input_list - cell array list of input file name strings. All input\r\n%                 files are appended in order.\r\n\r\n% Copyright: Oliver Woodford, 2011\r\n\r\n% Thanks to Reinhard Knoll for pointing out that appending multiple pdfs in\r\n% one go is much faster than appending them one at a time.\r\n\r\n% Thanks to Michael Teo for reporting the issue of a too long command line.\r\n% Issue resolved on 5/5/2011, by passing gs a command file.\r\n\r\n% Thanks to Martin Wittmann for pointing out the quality issue when\r\n% appending multiple bitmaps.\r\n% Issue resolved (to best of my ability) 1/6/2011, using the prepress\r\n% setting\r\n\r\n% 26/02/15: If temp dir is not writable, use the output folder for temp\r\n%           files when appending (Javier Paredes); sanity check of inputs\r\n\r\nfunction append_pdfs(varargin)\r\n\r\nif nargin < 2,  return;  end  % sanity check\r\n\r\n% Are we appending or creating a new file\r\nappend = exist(varargin{1}, 'file') == 2;\r\noutput = [tempname '.pdf'];\r\ntry\r\n    % Ensure that the temp dir is writable (Javier Paredes 26/2/15)\r\n    fid = fopen(output,'w');\r\n    fwrite(fid,1);\r\n    fclose(fid);\r\n    delete(output);\r\n    isTempDirOk = true;\r\ncatch\r\n    % Temp dir is not writable, so use the output folder\r\n    [dummy,fname,fext] = fileparts(output); %#ok<ASGLU>\r\n    fpath = fileparts(varargin{1});\r\n    output = fullfile(fpath,[fname fext]);\r\n    isTempDirOk = false;\r\nend\r\nif ~append\r\n    output = varargin{1};\r\n    varargin = varargin(2:end);\r\nend\r\n% Create the command file\r\nif isTempDirOk\r\n    cmdfile = [tempname '.txt'];\r\nelse\r\n    cmdfile = fullfile(fpath,[fname '.txt']);\r\nend\r\nfh = fopen(cmdfile, 'w');\r\nfprintf(fh, '-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile=\"%s\" -f', output);\r\nfprintf(fh, ' \"%s\"', varargin{:});\r\nfclose(fh);\r\n% Call ghostscript\r\nghostscript(['@\"' cmdfile '\"']);\r\n% Delete the command file\r\ndelete(cmdfile);\r\n% Rename the file if needed\r\nif append\r\n    movefile(output, varargin{1});\r\nend\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "using_hg2.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/using_hg2.m", "size": 1100, "source_encoding": "utf_8", "md5": "47ca10d86740c27b9f6b397373ae16cd", "text": "%USING_HG2 Determine if the HG2 graphics engine is used\r\n%\r\n%   tf = using_hg2(fig)\r\n%\r\n%IN:\r\n%   fig - handle to the figure in question.\r\n%\r\n%OUT:\r\n%   tf - boolean indicating whether the HG2 graphics engine is being used\r\n%        (true) or not (false).\r\n\r\n% 19/06/2015 - Suppress warning in R2015b; cache result for improved performance\r\n% 06/06/2016 - Fixed issue #156 (bad return value in R2016b)\r\n\r\nfunction tf = using_hg2(fig)\r\n    persistent tf_cached\r\n    if isempty(tf_cached)\r\n        try\r\n            if nargin < 1,  fig = figure('visible','off');  end\r\n            oldWarn = warning('off','MATLAB:graphicsversion:GraphicsVersionRemoval');\r\n            try\r\n                % This generates a [supressed] warning in R2015b:\r\n                tf = ~graphicsversion(fig, 'handlegraphics');\r\n            catch\r\n                tf = ~verLessThan('matlab','8.4');  % =R2014b\r\n            end\r\n            warning(oldWarn);\r\n        catch\r\n            tf = false;\r\n        end\r\n        if nargin < 1,  delete(fig);  end\r\n        tf_cached = tf;\r\n    else\r\n        tf = tf_cached;\r\n    end\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "eps2pdf.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/eps2pdf.m", "size": 8793, "source_encoding": "utf_8", "md5": "474e976cf6454d5d7850baf14494fedf", "text": "function eps2pdf(source, dest, crop, append, gray, quality, gs_options)\r\n%EPS2PDF  Convert an eps file to pdf format using ghostscript\r\n%\r\n% Examples:\r\n%   eps2pdf source dest\r\n%   eps2pdf(source, dest, crop)\r\n%   eps2pdf(source, dest, crop, append)\r\n%   eps2pdf(source, dest, crop, append, gray)\r\n%   eps2pdf(source, dest, crop, append, gray, quality)\r\n%   eps2pdf(source, dest, crop, append, gray, quality, gs_options)\r\n%\r\n% This function converts an eps file to pdf format. The output can be\r\n% optionally cropped and also converted to grayscale. If the output pdf\r\n% file already exists then the eps file can optionally be appended as a new\r\n% page on the end of the eps file. The level of bitmap compression can also\r\n% optionally be set.\r\n%\r\n% This function requires that you have ghostscript installed on your\r\n% system. Ghostscript can be downloaded from: http://www.ghostscript.com\r\n%\r\n% Inputs:\r\n%   source  - filename of the source eps file to convert. The filename is\r\n%             assumed to already have the extension \".eps\".\r\n%   dest    - filename of the destination pdf file. The filename is assumed\r\n%             to already have the extension \".pdf\".\r\n%   crop    - boolean indicating whether to crop the borders off the pdf.\r\n%             Default: true.\r\n%   append  - boolean indicating whether the eps should be appended to the\r\n%             end of the pdf as a new page (if the pdf exists already).\r\n%             Default: false.\r\n%   gray    - boolean indicating whether the output pdf should be grayscale\r\n%             or not. Default: false.\r\n%   quality - scalar indicating the level of image bitmap quality to\r\n%             output. A larger value gives a higher quality. quality > 100\r\n%             gives lossless output. Default: ghostscript prepress default.\r\n%   gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If\r\n%                multiple options are needed, enclose in call array: {'-a','-b'}\r\n\r\n% Copyright (C) Oliver Woodford 2009-2014, Yair Altman 2015-\r\n\r\n% Suggestion of appending pdf files provided by Matt C at:\r\n% http://www.mathworks.com/matlabcentral/fileexchange/23629\r\n\r\n% Thank you to Fabio Viola for pointing out compression artifacts, leading\r\n% to the quality setting.\r\n% Thank you to Scott for pointing out the subsampling of very small images,\r\n% which was fixed for lossless compression settings.\r\n\r\n% 9/12/2011 Pass font path to ghostscript.\r\n% 26/02/15: If temp dir is not writable, use the dest folder for temp\r\n%           destination files (Javier Paredes)\r\n% 28/02/15: Enable users to specify optional ghostscript options (issue #36)\r\n% 01/03/15: Upon GS error, retry without the -sFONTPATH= option (this might solve\r\n%           some /findfont errors according to James Rankin, FEX Comment 23/01/15)\r\n% 23/06/15: Added extra debug info in case of ghostscript error; code indentation\r\n% 04/10/15: Suggest a workaround for issue #41 (missing font path; thanks Mariia Fedotenkova)\r\n% 22/02/16: Bug fix from latest release of this file (workaround for issue #41)\r\n% 20/03/17: Added informational message in case of GS croak (issue #186)\r\n\r\n    % Intialise the options string for ghostscript\r\n    options = ['-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile=\"' dest '\"'];\r\n    % Set crop option\r\n    if nargin < 3 || crop\r\n        options = [options ' -dEPSCrop'];\r\n    end\r\n    % Set the font path\r\n    fp = font_path();\r\n    if ~isempty(fp)\r\n        options = [options ' -sFONTPATH=\"' fp '\"'];\r\n    end\r\n    % Set the grayscale option\r\n    if nargin > 4 && gray\r\n        options = [options ' -sColorConversionStrategy=Gray -dProcessColorModel=/DeviceGray'];\r\n    end\r\n    % Set the bitmap quality\r\n    if nargin > 5 && ~isempty(quality)\r\n        options = [options ' -dAutoFilterColorImages=false -dAutoFilterGrayImages=false'];\r\n        if quality > 100\r\n            options = [options ' -dColorImageFilter=/FlateEncode -dGrayImageFilter=/FlateEncode -c \".setpdfwrite << /ColorImageDownsampleThreshold 10 /GrayImageDownsampleThreshold 10 >> setdistillerparams\"'];\r\n        else\r\n            options = [options ' -dColorImageFilter=/DCTEncode -dGrayImageFilter=/DCTEncode'];\r\n            v = 1 + (quality < 80);\r\n            quality = 1 - quality / 100;\r\n            s = sprintf('<< /QFactor %.2f /Blend 1 /HSample [%d 1 1 %d] /VSample [%d 1 1 %d] >>', quality, v, v, v, v);\r\n            options = sprintf('%s -c \".setpdfwrite << /ColorImageDict %s /GrayImageDict %s >> setdistillerparams\"', options, s, s);\r\n        end\r\n    end\r\n    % Enable users to specify optional ghostscript options (issue #36)\r\n    if nargin > 6 && ~isempty(gs_options)\r\n        if iscell(gs_options)\r\n            gs_options = sprintf(' %s',gs_options{:});\r\n        elseif ~ischar(gs_options)\r\n            error('gs_options input argument must be a string or cell-array of strings');\r\n        else\r\n            gs_options = [' ' gs_options];\r\n        end\r\n        options = [options gs_options];\r\n    end\r\n    % Check if the output file exists\r\n    if nargin > 3 && append && exist(dest, 'file') == 2\r\n        % File exists - append current figure to the end\r\n        tmp_nam = tempname;\r\n        try\r\n            % Ensure that the temp dir is writable (Javier Paredes 26/2/15)\r\n            fid = fopen(tmp_nam,'w');\r\n            fwrite(fid,1);\r\n            fclose(fid);\r\n            delete(tmp_nam);\r\n        catch\r\n            % Temp dir is not writable, so use the dest folder\r\n            [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>\r\n            fpath = fileparts(dest);\r\n            tmp_nam = fullfile(fpath,[fname fext]);\r\n        end\r\n        % Copy the file\r\n        copyfile(dest, tmp_nam);\r\n        % Add the output file names\r\n        options = [options ' -f \"' tmp_nam '\" \"' source '\"'];\r\n        try\r\n            % Convert to pdf using ghostscript\r\n            [status, message] = ghostscript(options);\r\n        catch me\r\n            % Delete the intermediate file\r\n            delete(tmp_nam);\r\n            rethrow(me);\r\n        end\r\n        % Delete the intermediate file\r\n        delete(tmp_nam);\r\n    else\r\n        % File doesn't exist or should be over-written\r\n        % Add the output file names\r\n        options = [options ' -f \"' source '\"'];\r\n        % Convert to pdf using ghostscript\r\n        [status, message] = ghostscript(options);\r\n    end\r\n    % Check for error\r\n    if status\r\n        % Retry without the -sFONTPATH= option (this might solve some GS\r\n        % /findfont errors according to James Rankin, FEX Comment 23/01/15)\r\n        orig_options = options;\r\n        if ~isempty(fp)\r\n            options = regexprep(options, ' -sFONTPATH=[^ ]+ ',' ');\r\n            status = ghostscript(options);\r\n            if ~status, return; end  % hurray! (no error)\r\n        end\r\n        % Report error\r\n        if isempty(message)\r\n            error('Unable to generate pdf. Check destination directory is writable.');\r\n        elseif ~isempty(strfind(message,'/typecheck in /findfont'))\r\n            % Suggest a workaround for issue #41 (missing font path)\r\n            font_name = strtrim(regexprep(message,'.*Operand stack:\\s*(.*)\\s*Execution.*','$1'));\r\n            fprintf(2, 'Ghostscript error: could not find the following font(s): %s\\n', font_name);\r\n            fpath = fileparts(mfilename('fullpath'));\r\n            gs_fonts_file = fullfile(fpath, '.ignore', 'gs_font_path.txt');\r\n            fprintf(2, '  try to add the font''s folder to your %s file\\n\\n', gs_fonts_file);\r\n            error('export_fig error');\r\n        else\r\n            fprintf(2, '\\nGhostscript error: perhaps %s is open by another application\\n', dest);\r\n            if ~isempty(gs_options)\r\n                fprintf(2, '  or maybe the%s option(s) are not accepted by your GS version\\n', gs_options);\r\n            end\r\n            fprintf(2, '  or maybe you have another gs executable in your system''s path\\n');\r\n            fprintf(2, 'Ghostscript options: %s\\n\\n', orig_options);\r\n            error(message);\r\n        end\r\n    end\r\nend\r\n\r\n% Function to return (and create, where necessary) the font path\r\nfunction fp = font_path()\r\n    fp = user_string('gs_font_path');\r\n    if ~isempty(fp)\r\n        return\r\n    end\r\n    % Create the path\r\n    % Start with the default path\r\n    fp = getenv('GS_FONTPATH');\r\n    % Add on the typical directories for a given OS\r\n    if ispc\r\n        if ~isempty(fp)\r\n            fp = [fp ';'];\r\n        end\r\n        fp = [fp getenv('WINDIR') filesep 'Fonts'];\r\n    else\r\n        if ~isempty(fp)\r\n            fp = [fp ':'];\r\n        end\r\n        fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];\r\n    end\r\n    user_string('gs_font_path', fp);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "export_fig.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/export_fig.m", "size": 64681, "source_encoding": "utf_8", "md5": "6eb52ba116cd2632d3e98cfafa45bca1", "text": "function [imageData, alpha] = export_fig(varargin)\r\n%EXPORT_FIG  Exports figures in a publication-quality format\r\n%\r\n% Examples:\r\n%   imageData = export_fig\r\n%   [imageData, alpha] = export_fig\r\n%   export_fig filename\r\n%   export_fig filename -format1 -format2\r\n%   export_fig ... -nocrop\r\n%   export_fig ... -c[<val>,<val>,<val>,<val>]\r\n%   export_fig ... -transparent\r\n%   export_fig ... -native\r\n%   export_fig ... -m<val>\r\n%   export_fig ... -r<val>\r\n%   export_fig ... -a<val>\r\n%   export_fig ... -q<val>\r\n%   export_fig ... -p<val>\r\n%   export_fig ... -d<gs_option>\r\n%   export_fig ... -depsc\r\n%   export_fig ... -<renderer>\r\n%   export_fig ... -<colorspace>\r\n%   export_fig ... -append\r\n%   export_fig ... -bookmark\r\n%   export_fig ... -clipboard\r\n%   export_fig ... -update\r\n%   export_fig ... -nofontswap\r\n%   export_fig ... -font_space <char>\r\n%   export_fig ... -linecaps\r\n%   export_fig ... -noinvert\r\n%   export_fig(..., handle)\r\n%\r\n% This function saves a figure or single axes to one or more vector and/or\r\n% bitmap file formats, and/or outputs a rasterized version to the workspace,\r\n% with the following properties:\r\n%   - Figure/axes reproduced as it appears on screen\r\n%   - Cropped borders (optional)\r\n%   - Embedded fonts (vector formats)\r\n%   - Improved line and grid line styles\r\n%   - Anti-aliased graphics (bitmap formats)\r\n%   - Render images at native resolution (optional for bitmap formats)\r\n%   - Transparent background supported (pdf, eps, png, tiff)\r\n%   - Semi-transparent patch objects supported (png, tiff)\r\n%   - RGB, CMYK or grayscale output (CMYK only with pdf, eps, tiff)\r\n%   - Variable image compression, including lossless (pdf, eps, jpg)\r\n%   - Optional rounded line-caps (pdf, eps)\r\n%   - Optionally append to file (pdf, tiff)\r\n%   - Vector formats: pdf, eps\r\n%   - Bitmap formats: png, tiff, jpg, bmp, export to workspace\r\n%\r\n% This function is especially suited to exporting figures for use in\r\n% publications and presentations, because of the high quality and\r\n% portability of media produced.\r\n%\r\n% Note that the background color and figure dimensions are reproduced\r\n% (the latter approximately, and ignoring cropping & magnification) in the\r\n% output file. For transparent background (and semi-transparent patch\r\n% objects), use the -transparent option or set the figure 'Color' property\r\n% to 'none'. To make axes transparent set the axes 'Color' property to\r\n% 'none'. PDF, EPS, TIF & PNG are the only formats that support a transparent\r\n% background; only TIF & PNG formats support transparency of patch objects.\r\n%\r\n% The choice of renderer (opengl, zbuffer or painters) has a large impact\r\n% on the quality of output. The default value (opengl for bitmaps, painters\r\n% for vector formats) generally gives good results, but if you aren't\r\n% satisfied then try another renderer.  Notes: 1) For vector formats (EPS,\r\n% PDF), only painters generates vector graphics. 2) For bitmaps, only\r\n% opengl can render transparent patch objects correctly. 3) For bitmaps,\r\n% only painters will correctly scale line dash and dot lengths when\r\n% magnifying or anti-aliasing. 4) Fonts may be substitued with Courier when\r\n% using painters.\r\n%\r\n% When exporting to vector format (PDF & EPS) and bitmap format using the\r\n% painters renderer, this function requires that ghostscript is installed\r\n% on your system. You can download this from:\r\n%   http://www.ghostscript.com\r\n% When exporting to eps it additionally requires pdftops, from the Xpdf\r\n% suite of functions. You can download this from:\r\n%   http://www.foolabs.com/xpdf\r\n%\r\n% Inputs:\r\n%   filename - string containing the name (optionally including full or\r\n%              relative path) of the file the figure is to be saved as. If\r\n%              a path is not specified, the figure is saved in the current\r\n%              directory. If no name and no output arguments are specified,\r\n%              the default name, 'export_fig_out', is used. If neither a\r\n%              file extension nor a format are specified, a \".png\" is added\r\n%              and the figure saved in that format.\r\n%   -format1, -format2, etc. - strings containing the extensions of the\r\n%                              file formats the figure is to be saved as.\r\n%                              Valid options are: '-pdf', '-eps', '-png',\r\n%                              '-tif', '-jpg' and '-bmp'. All combinations\r\n%                              of formats are valid.\r\n%   -nocrop - option indicating that the borders of the output are not to\r\n%             be cropped.\r\n%   -c[<val>,<val>,<val>,<val>] - option indicating crop amounts. Must be\r\n%             a 4-element vector of numeric values: [top,right,bottom,left]\r\n%             where NaN/Inf indicate auto-cropping, 0 means no cropping,\r\n%             and any other value mean cropping in pixel amounts.\r\n%   -transparent - option indicating that the figure background is to be\r\n%                  made transparent (png, pdf, tif and eps output only).\r\n%   -m<val> - option where val indicates the factor to magnify the\r\n%             on-screen figure pixel dimensions by when generating bitmap\r\n%             outputs (does not affect vector formats). Default: '-m1'.\r\n%   -r<val> - option val indicates the resolution (in pixels per inch) to\r\n%             export bitmap and vector outputs at, keeping the dimensions\r\n%             of the on-screen figure. Default: '-r864' (for vector output\r\n%             only). Note that the -m option overides the -r option for\r\n%             bitmap outputs only.\r\n%   -native - option indicating that the output resolution (when outputting\r\n%             a bitmap format) should be such that the vertical resolution\r\n%             of the first suitable image found in the figure is at the\r\n%             native resolution of that image. To specify a particular\r\n%             image to use, give it the tag 'export_fig_native'. Notes:\r\n%             This overrides any value set with the -m and -r options. It\r\n%             also assumes that the image is displayed front-to-parallel\r\n%             with the screen. The output resolution is approximate and\r\n%             should not be relied upon. Anti-aliasing can have adverse\r\n%             effects on image quality (disable with the -a1 option).\r\n%   -a1, -a2, -a3, -a4 - option indicating the amount of anti-aliasing to\r\n%                        use for bitmap outputs. '-a1' means no anti-\r\n%                        aliasing; '-a4' is the maximum amount (default).\r\n%   -<renderer> - option to force a particular renderer (painters, opengl or\r\n%                 zbuffer). Default value: opengl for bitmap formats or\r\n%                 figures with patches and/or transparent annotations;\r\n%                 painters for vector formats without patches/transparencies.\r\n%   -<colorspace> - option indicating which colorspace color figures should\r\n%                   be saved in: RGB (default), CMYK or gray. CMYK is only\r\n%                   supported in pdf, eps and tiff output.\r\n%   -q<val> - option to vary bitmap image quality (in pdf, eps and jpg\r\n%             files only).  Larger val, in the range 0-100, gives higher\r\n%             quality/lower compression. val > 100 gives lossless\r\n%             compression. Default: '-q95' for jpg, ghostscript prepress\r\n%             default for pdf & eps. Note: lossless compression can\r\n%             sometimes give a smaller file size than the default lossy\r\n%             compression, depending on the type of images.\r\n%   -p<val> - option to pad a border of width val to exported files, where\r\n%             val is either a relative size with respect to cropped image\r\n%             size (i.e. p=0.01 adds a 1% border). For EPS & PDF formats,\r\n%             val can also be integer in units of 1/72\" points (abs(val)>1).\r\n%             val can be positive (padding) or negative (extra cropping).\r\n%             If used, the -nocrop flag will be ignored, i.e. the image will\r\n%             always be cropped and then padded. Default: 0 (i.e. no padding).\r\n%   -append - option indicating that if the file (pdfs only) already\r\n%             exists, the figure is to be appended as a new page, instead\r\n%             of being overwritten (default).\r\n%   -bookmark - option to indicate that a bookmark with the name of the\r\n%               figure is to be created in the output file (pdf only).\r\n%   -clipboard - option to save output as an image on the system clipboard.\r\n%                Note: background transparency is not preserved in clipboard\r\n%   -d<gs_option> - option to indicate a ghostscript setting. For example,\r\n%                   -dMaxBitmap=0 or -dNoOutputFonts (Ghostscript 9.15+).\r\n%   -depsc -  option to use EPS level-3 rather than the default level-2 print\r\n%             device. This solves some bugs with Matlab's default -depsc2 device\r\n%             such as discolored subplot lines on images (vector formats only).\r\n%   -update - option to download and install the latest version of export_fig\r\n%   -nofontswap - option to avoid font swapping. Font swapping is automatically\r\n%             done in vector formats (only): 11 standard Matlab fonts are\r\n%             replaced by the original figure fonts. This option prevents this.\r\n%   -font_space <char> - option to set a spacer character for font-names that\r\n%             contain spaces, used by EPS/PDF. Default: ''\r\n%   -linecaps - option to create rounded line-caps (vector formats only).\r\n%   -noinvert - option to avoid setting figure's InvertHardcopy property to\r\n%             'off' during output (this solves some problems of empty outputs).\r\n%   handle -  The handle of the figure, axes or uipanels (can be an array of\r\n%             handles, but the objects must be in the same figure) to be\r\n%             saved. Default: gcf.\r\n%\r\n% Outputs:\r\n%   imageData - MxNxC uint8 image array of the exported image.\r\n%   alpha     - MxN single array of alphamatte values in the range [0,1],\r\n%               for the case when the background is transparent.\r\n%\r\n%   Some helpful examples and tips can be found at:\r\n%      https://github.com/altmany/export_fig\r\n%\r\n%   See also PRINT, SAVEAS, ScreenCapture (on the Matlab File Exchange)\r\n\r\n%{\r\n% Copyright (C) Oliver Woodford 2008-2014, Yair Altman 2015-\r\n\r\n% The idea of using ghostscript is inspired by Peder Axensten's SAVEFIG\r\n% (fex id: 10889) which is itself inspired by EPS2PDF (fex id: 5782).\r\n% The idea for using pdftops came from the MATLAB newsgroup (id: 168171).\r\n% The idea of editing the EPS file to change line styles comes from Jiro\r\n% Doke's FIXPSLINESTYLE (fex id: 17928).\r\n% The idea of changing dash length with line width came from comments on\r\n% fex id: 5743, but the implementation is mine :)\r\n% The idea of anti-aliasing bitmaps came from Anders Brun's MYAA (fex id:\r\n% 20979).\r\n% The idea of appending figures in pdfs came from Matt C in comments on the\r\n% FEX (id: 23629)\r\n\r\n% Thanks to Roland Martin for pointing out the colour MATLAB\r\n% bug/feature with colorbar axes and transparent backgrounds.\r\n% Thanks also to Andrew Matthews for describing a bug to do with the figure\r\n% size changing in -nodisplay mode. I couldn't reproduce it, but included a\r\n% fix anyway.\r\n% Thanks to Tammy Threadgill for reporting a bug where an axes is not\r\n% isolated from gui objects.\r\n%}\r\n%{\r\n% 23/02/12: Ensure that axes limits don't change during printing\r\n% 14/03/12: Fix bug in fixing the axes limits (thanks to Tobias Lamour for reporting it).\r\n% 02/05/12: Incorporate patch of Petr Nechaev (many thanks), enabling bookmarking of figures in pdf files.\r\n% 09/05/12: Incorporate patch of Arcelia Arrieta (many thanks), to keep tick marks fixed.\r\n% 12/12/12: Add support for isolating uipanels. Thanks to michael for suggesting it.\r\n% 25/09/13: Add support for changing resolution in vector formats. Thanks to Jan Jaap Meijer for suggesting it.\r\n% 07/05/14: Add support for '~' at start of path. Thanks to Sally Warner for suggesting it.\r\n% 24/02/15: Fix Matlab R2014b bug (issue #34): plot markers are not displayed when ZLimMode='manual'\r\n% 25/02/15: Fix issue #4 (using HG2 on R2014a and earlier)\r\n% 25/02/15: Fix issue #21 (bold TeX axes labels/titles in R2014b)\r\n% 26/02/15: If temp dir is not writable, use the user-specified folder for temporary EPS/PDF files (Javier Paredes)\r\n% 27/02/15: Modified repository URL from github.com/ojwoodford to /altmany\r\n%           Indented main function\r\n%           Added top-level try-catch block to display useful workarounds\r\n% 28/02/15: Enable users to specify optional ghostscript options (issue #36)\r\n% 06/03/15: Improved image padding & cropping thanks to Oscar Hartogensis\r\n% 26/03/15: Fixed issue #49 (bug with transparent grayscale images); fixed out-of-memory issue\r\n% 26/03/15: Fixed issue #42: non-normalized annotations on HG1\r\n% 26/03/15: Fixed issue #46: Ghostscript crash if figure units <> pixels\r\n% 27/03/15: Fixed issue #39: bad export of transparent annotations/patches\r\n% 28/03/15: Fixed issue #50: error on some Matlab versions with the fix for issue #42\r\n% 29/03/15: Fixed issue #33: bugs in Matlab's print() function with -cmyk\r\n% 29/03/15: Improved processing of input args (accept space between param name & value, related to issue #51)\r\n% 30/03/15: When exporting *.fig files, then saveas *.fig if figure is open, otherwise export the specified fig file\r\n% 30/03/15: Fixed edge case bug introduced yesterday (commit #ae1755bd2e11dc4e99b95a7681f6e211b3fa9358)\r\n% 09/04/15: Consolidated header comment sections; initialize output vars only if requested (nargout>0)\r\n% 14/04/15: Workaround for issue #45: lines in image subplots are exported in invalid color\r\n% 15/04/15: Fixed edge-case in parsing input parameters; fixed help section to show the -depsc option (issue #45)\r\n% 21/04/15: Bug fix: Ghostscript croaks on % chars in output PDF file (reported by Sven on FEX page, 15-Jul-2014)\r\n% 22/04/15: Bug fix: Pdftops croaks on relative paths (reported by Tintin Milou on FEX page, 19-Jan-2015)\r\n% 04/05/15: Merged fix #63 (Kevin Mattheus Moerman): prevent tick-label changes during export\r\n% 07/05/15: Partial fix for issue #65: PDF export used painters rather than opengl renderer (thanks Nguyenr)\r\n% 08/05/15: Fixed issue #65: bad PDF append since commit #e9f3cdf 21/04/15 (thanks Robert Nguyen)\r\n% 12/05/15: Fixed issue #67: exponent labels cropped in export, since fix #63 (04/05/15)\r\n% 28/05/15: Fixed issue #69: set non-bold label font only if the string contains symbols (\\beta etc.), followup to issue #21\r\n% 29/05/15: Added informative error message in case user requested SVG output (issue #72)\r\n% 09/06/15: Fixed issue #58: -transparent removed anti-aliasing when exporting to PNG\r\n% 19/06/15: Added -update option to download and install the latest version of export_fig\r\n% 07/07/15: Added -nofontswap option to avoid font-swapping in EPS/PDF\r\n% 16/07/15: Fixed problem with anti-aliasing on old Matlab releases\r\n% 11/09/15: Fixed issue #103: magnification must never become negative; also fixed reported error msg in parsing input params\r\n% 26/09/15: Alert if trying to export transparent patches/areas to non-PNG outputs (issue #108)\r\n% 04/10/15: Do not suggest workarounds for certain errors that have already been handled previously\r\n% 01/11/15: Fixed issue #112: use same renderer in print2eps as export_fig (thanks to Jesús Pestana Puerta)\r\n% 10/11/15: Custom GS installation webpage for MacOS. Thanks to Andy Hueni via FEX\r\n% 19/11/15: Fixed clipboard export in R2015b (thanks to Dan K via FEX)\r\n% 21/02/16: Added -c option for indicating specific crop amounts (idea by Cedric Noordam on FEX)\r\n% 08/05/16: Added message about possible error reason when groot.Units~=pixels (issue #149)\r\n% 17/05/16: Fixed case of image YData containing more than 2 elements (issue #151)\r\n% 08/08/16: Enabled exporting transparency to TIF, in addition to PNG/PDF (issue #168)\r\n% 11/12/16: Added alert in case of error creating output PDF/EPS file (issue #179)\r\n% 13/12/16: Minor fix to the commit for issue #179 from 2 days ago\r\n% 22/03/17: Fixed issue #187: only set manual ticks when no exponent is present\r\n% 09/04/17: Added -linecaps option (idea by Baron Finer, issue #192)\r\n% 15/09/17: Fixed issue #205: incorrect tick-labels when Ticks number don't match the TickLabels number\r\n% 15/09/17: Fixed issue #210: initialize alpha map to ones instead of zeros when -transparent is not used\r\n% 18/09/17: Added -font_space option to replace font-name spaces in EPS/PDF (workaround for issue #194)\r\n% 18/09/17: Added -noinvert option to solve some export problems with some graphic cards (workaround for issue #197)\r\n%}\r\n\r\n    if nargout\r\n        [imageData, alpha] = deal([]);\r\n    end\r\n    hadError = false;\r\n    displaySuggestedWorkarounds = true;\r\n\r\n    % Ensure the figure is rendered correctly _now_ so that properties like axes limits are up-to-date\r\n    drawnow;\r\n    pause(0.05);  % this solves timing issues with Java Swing's EDT (http://undocumentedmatlab.com/blog/solving-a-matlab-hang-problem)\r\n\r\n    % Parse the input arguments\r\n    fig = get(0, 'CurrentFigure');\r\n    [fig, options] = parse_args(nargout, fig, varargin{:});\r\n\r\n    % Ensure that we have a figure handle\r\n    if isequal(fig,-1)\r\n        return;  % silent bail-out\r\n    elseif isempty(fig)\r\n        error('No figure found');\r\n    end\r\n\r\n    % Isolate the subplot, if it is one\r\n    cls = all(ismember(get(fig, 'Type'), {'axes', 'uipanel'}));\r\n    if cls\r\n        % Given handles of one or more axes, so isolate them from the rest\r\n        fig = isolate_axes(fig);\r\n    else\r\n        % Check we have a figure\r\n        if ~isequal(get(fig, 'Type'), 'figure')\r\n            error('Handle must be that of a figure, axes or uipanel');\r\n        end\r\n        % Get the old InvertHardcopy mode\r\n        old_mode = get(fig, 'InvertHardcopy');\r\n    end\r\n\r\n    % Hack the font units where necessary (due to a font rendering bug in print?).\r\n    % This may not work perfectly in all cases.\r\n    % Also it can change the figure layout if reverted, so use a copy.\r\n    magnify = options.magnify * options.aa_factor;\r\n    if isbitmap(options) && magnify ~= 1\r\n        fontu = findall(fig, 'FontUnits', 'normalized');\r\n        if ~isempty(fontu)\r\n            % Some normalized font units found\r\n            if ~cls\r\n                fig = copyfig(fig);\r\n                set(fig, 'Visible', 'off');\r\n                fontu = findall(fig, 'FontUnits', 'normalized');\r\n                cls = true;\r\n            end\r\n            set(fontu, 'FontUnits', 'points');\r\n        end\r\n    end\r\n\r\n    try\r\n        % MATLAB \"feature\": axes limits and tick marks can change when printing\r\n        Hlims = findall(fig, 'Type', 'axes');\r\n        if ~cls\r\n            % Record the old axes limit and tick modes\r\n            Xlims = make_cell(get(Hlims, 'XLimMode'));\r\n            Ylims = make_cell(get(Hlims, 'YLimMode'));\r\n            Zlims = make_cell(get(Hlims, 'ZLimMode'));\r\n            Xtick = make_cell(get(Hlims, 'XTickMode'));\r\n            Ytick = make_cell(get(Hlims, 'YTickMode'));\r\n            Ztick = make_cell(get(Hlims, 'ZTickMode'));\r\n            Xlabel = make_cell(get(Hlims, 'XTickLabelMode')); \r\n            Ylabel = make_cell(get(Hlims, 'YTickLabelMode')); \r\n            Zlabel = make_cell(get(Hlims, 'ZTickLabelMode')); \r\n        end\r\n\r\n        % Set all axes limit and tick modes to manual, so the limits and ticks can't change\r\n        % Fix Matlab R2014b bug (issue #34): plot markers are not displayed when ZLimMode='manual'\r\n        set(Hlims, 'XLimMode', 'manual', 'YLimMode', 'manual');\r\n        set_tick_mode(Hlims, 'X');\r\n        set_tick_mode(Hlims, 'Y');\r\n        if ~using_hg2(fig)\r\n            set(Hlims,'ZLimMode', 'manual');\r\n            set_tick_mode(Hlims, 'Z');\r\n        end\r\n    catch\r\n        % ignore - fix issue #4 (using HG2 on R2014a and earlier)\r\n    end\r\n\r\n    % Fix issue #21 (bold TeX axes labels/titles in R2014b when exporting to EPS/PDF)\r\n    try\r\n        if using_hg2(fig) && isvector(options)\r\n            % Set the FontWeight of axes labels/titles to 'normal'\r\n            % Fix issue #69: set non-bold font only if the string contains symbols (\\beta etc.)\r\n            texLabels = findall(fig, 'type','text', 'FontWeight','bold');\r\n            symbolIdx = ~cellfun('isempty',strfind({texLabels.String},'\\'));\r\n            set(texLabels(symbolIdx), 'FontWeight','normal');\r\n        end\r\n    catch\r\n        % ignore\r\n    end\r\n\r\n    % Fix issue #42: non-normalized annotations on HG1 (internal Matlab bug)\r\n    annotationHandles = [];\r\n    try\r\n        if ~using_hg2(fig)\r\n            annotationHandles = findall(fig,'Type','hggroup','-and','-property','Units','-and','-not','Units','norm');\r\n            try  % suggested by Jesús Pestana Puerta (jespestana) 30/9/2015\r\n                originalUnits = get(annotationHandles,'Units');\r\n                set(annotationHandles,'Units','norm');\r\n            catch\r\n            end\r\n        end\r\n    catch\r\n        % should never happen, but ignore in any case - issue #50\r\n    end\r\n\r\n    % Fix issue #46: Ghostscript crash if figure units <> pixels\r\n    oldFigUnits = get(fig,'Units');\r\n    set(fig,'Units','pixels');\r\n\r\n    % Set to print exactly what is there\r\n    if options.invert_hardcopy\r\n        set(fig, 'InvertHardcopy', 'off');\r\n    end\r\n    % Set the renderer\r\n    switch options.renderer\r\n        case 1\r\n            renderer = '-opengl';\r\n        case 2\r\n            renderer = '-zbuffer';\r\n        case 3\r\n            renderer = '-painters';\r\n        otherwise\r\n            renderer = '-opengl'; % Default for bitmaps\r\n    end\r\n\r\n    % Handle transparent patches\r\n    hasTransparency = ~isempty(findall(fig,'-property','FaceAlpha','-and','-not','FaceAlpha',1));\r\n    hasPatches      = ~isempty(findall(fig,'type','patch'));\r\n    if hasTransparency\r\n        % Alert if trying to export transparent patches/areas to non-supported outputs (issue #108)\r\n        % http://www.mathworks.com/matlabcentral/answers/265265-can-export_fig-or-else-draw-vector-graphics-with-transparent-surfaces\r\n        % TODO - use transparency when exporting to PDF by not passing via print2eps\r\n        msg = 'export_fig currently supports transparent patches/areas only in PNG output. ';\r\n        if options.pdf\r\n            warning('export_fig:transparency', '%s\\nTo export transparent patches/areas to PDF, use the print command:\\n print(gcf, ''-dpdf'', ''%s.pdf'');', msg, options.name);\r\n        elseif ~options.png && ~options.tif  % issue #168\r\n            warning('export_fig:transparency', '%s\\nTo export the transparency correctly, try using the ScreenCapture utility on the Matlab File Exchange: http://bit.ly/1QFrBip', msg);\r\n        end\r\n    end\r\n\r\n    try\r\n        % Do the bitmap formats first\r\n        if isbitmap(options)\r\n            if abs(options.bb_padding) > 1\r\n                displaySuggestedWorkarounds = false;\r\n                error('For bitmap output (png,jpg,tif,bmp) the padding value (-p) must be between -1<p<1')\r\n            end\r\n            % Get the background colour\r\n            if options.transparent && (options.png || options.alpha)\r\n                % Get out an alpha channel\r\n                % MATLAB \"feature\": black colorbar axes can change to white and vice versa!\r\n                hCB = findall(fig, 'Type','axes', 'Tag','Colorbar');\r\n                if isempty(hCB)\r\n                    yCol = [];\r\n                    xCol = [];\r\n                else\r\n                    yCol = get(hCB, 'YColor');\r\n                    xCol = get(hCB, 'XColor');\r\n                    if iscell(yCol)\r\n                        yCol = cell2mat(yCol);\r\n                        xCol = cell2mat(xCol);\r\n                    end\r\n                    yCol = sum(yCol, 2);\r\n                    xCol = sum(xCol, 2);\r\n                end\r\n                % MATLAB \"feature\": apparently figure size can change when changing\r\n                % colour in -nodisplay mode\r\n                pos = get(fig, 'Position');\r\n                % Set the background colour to black, and set size in case it was\r\n                % changed internally\r\n                tcol = get(fig, 'Color');\r\n                set(fig, 'Color', 'k', 'Position', pos);\r\n                % Correct the colorbar axes colours\r\n                set(hCB(yCol==0), 'YColor', [0 0 0]);\r\n                set(hCB(xCol==0), 'XColor', [0 0 0]);\r\n\r\n                % The following code might cause out-of-memory errors\r\n                try\r\n                    % Print large version to array\r\n                    B = print2array(fig, magnify, renderer);\r\n                    % Downscale the image\r\n                    B = downsize(single(B), options.aa_factor);\r\n                catch\r\n                    % This is more conservative in memory, but kills transparency (issue #58)\r\n                    B = single(print2array(fig, magnify/options.aa_factor, renderer));\r\n                end\r\n\r\n                % Set background to white (and set size)\r\n                set(fig, 'Color', 'w', 'Position', pos);\r\n                % Correct the colorbar axes colours\r\n                set(hCB(yCol==3), 'YColor', [1 1 1]);\r\n                set(hCB(xCol==3), 'XColor', [1 1 1]);\r\n\r\n                % The following code might cause out-of-memory errors\r\n                try\r\n                    % Print large version to array\r\n                    A = print2array(fig, magnify, renderer);\r\n                    % Downscale the image\r\n                    A = downsize(single(A), options.aa_factor);\r\n                catch\r\n                    % This is more conservative in memory, but kills transparency (issue #58)\r\n                    A = single(print2array(fig, magnify/options.aa_factor, renderer));\r\n                end\r\n\r\n                % Set the background colour (and size) back to normal\r\n                set(fig, 'Color', tcol, 'Position', pos);\r\n                % Compute the alpha map\r\n                alpha = round(sum(B - A, 3)) / (255 * 3) + 1;\r\n                A = alpha;\r\n                A(A==0) = 1;\r\n                A = B ./ A(:,:,[1 1 1]);\r\n                clear B\r\n                % Convert to greyscale\r\n                if options.colourspace == 2\r\n                    A = rgb2grey(A);\r\n                end\r\n                A = uint8(A);\r\n                % Crop the background\r\n                if options.crop\r\n                    %[alpha, v] = crop_borders(alpha, 0, 1, options.crop_amounts);\r\n                    %A = A(v(1):v(2),v(3):v(4),:);\r\n                    [alpha, vA, vB] = crop_borders(alpha, 0, options.bb_padding, options.crop_amounts);\r\n                    if ~any(isnan(vB)) % positive padding\r\n                        B = repmat(uint8(zeros(1,1,size(A,3))),size(alpha));\r\n                        B(vB(1):vB(2), vB(3):vB(4), :) = A(vA(1):vA(2), vA(3):vA(4), :); % ADDED BY OH\r\n                        A = B;\r\n                    else  % negative padding\r\n                        A = A(vA(1):vA(2), vA(3):vA(4), :);\r\n                    end\r\n                end\r\n                if options.png\r\n                    % Compute the resolution\r\n                    res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3;\r\n                    % Save the png\r\n                    imwrite(A, [options.name '.png'], 'Alpha', double(alpha), 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res);\r\n                    % Clear the png bit\r\n                    options.png = false;\r\n                end\r\n                % Return only one channel for greyscale\r\n                if isbitmap(options)\r\n                    A = check_greyscale(A);\r\n                end\r\n                if options.alpha\r\n                    % Store the image\r\n                    imageData = A;\r\n                    % Clear the alpha bit\r\n                    options.alpha = false;\r\n                end\r\n                % Get the non-alpha image\r\n                if isbitmap(options)\r\n                    alph = alpha(:,:,ones(1, size(A, 3)));\r\n                    A = uint8(single(A) .* alph + 255 * (1 - alph));\r\n                    clear alph\r\n                end\r\n                if options.im\r\n                    % Store the new image\r\n                    imageData = A;\r\n                end\r\n            else\r\n                % Print large version to array\r\n                if options.transparent\r\n                    % MATLAB \"feature\": apparently figure size can change when changing\r\n                    % colour in -nodisplay mode\r\n                    pos = get(fig, 'Position');\r\n                    tcol = get(fig, 'Color');\r\n                    set(fig, 'Color', 'w', 'Position', pos);\r\n                    A = print2array(fig, magnify, renderer);\r\n                    set(fig, 'Color', tcol, 'Position', pos);\r\n                    tcol = 255;\r\n                else\r\n                    [A, tcol] = print2array(fig, magnify, renderer);\r\n                end\r\n                % Crop the background\r\n                if options.crop\r\n                    A = crop_borders(A, tcol, options.bb_padding, options.crop_amounts);\r\n                end\r\n                % Downscale the image\r\n                A = downsize(A, options.aa_factor);\r\n                if options.colourspace == 2\r\n                    % Convert to greyscale\r\n                    A = rgb2grey(A);\r\n                else\r\n                    % Return only one channel for greyscale\r\n                    A = check_greyscale(A);\r\n                end\r\n                % Outputs\r\n                if options.im\r\n                    imageData = A;\r\n                end\r\n                if options.alpha\r\n                    imageData = A;\r\n                    alpha = ones(size(A, 1), size(A, 2), 'single');\r\n                end\r\n            end\r\n            % Save the images\r\n            if options.png\r\n                res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3;\r\n                imwrite(A, [options.name '.png'], 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res);\r\n            end\r\n            if options.bmp\r\n                imwrite(A, [options.name '.bmp']);\r\n            end\r\n            % Save jpeg with given quality\r\n            if options.jpg\r\n                quality = options.quality;\r\n                if isempty(quality)\r\n                    quality = 95;\r\n                end\r\n                if quality > 100\r\n                    imwrite(A, [options.name '.jpg'], 'Mode', 'lossless');\r\n                else\r\n                    imwrite(A, [options.name '.jpg'], 'Quality', quality);\r\n                end\r\n            end\r\n            % Save tif images in cmyk if wanted (and possible)\r\n            if options.tif\r\n                if options.colourspace == 1 && size(A, 3) == 3\r\n                    A = double(255 - A);\r\n                    K = min(A, [], 3);\r\n                    K_ = 255 ./ max(255 - K, 1);\r\n                    C = (A(:,:,1) - K) .* K_;\r\n                    M = (A(:,:,2) - K) .* K_;\r\n                    Y = (A(:,:,3) - K) .* K_;\r\n                    A = uint8(cat(3, C, M, Y, K));\r\n                    clear C M Y K K_\r\n                end\r\n                append_mode = {'overwrite', 'append'};\r\n                imwrite(A, [options.name '.tif'], 'Resolution', options.magnify*get(0, 'ScreenPixelsPerInch'), 'WriteMode', append_mode{options.append+1});\r\n            end\r\n        end\r\n\r\n        % Now do the vector formats\r\n        if isvector(options)\r\n            % Set the default renderer to painters\r\n            if ~options.renderer\r\n                if hasTransparency || hasPatches\r\n                    % This is *MUCH* slower, but more accurate for patches and transparent annotations (issue #39)\r\n                    renderer = '-opengl';\r\n                else\r\n                    renderer = '-painters';\r\n                end\r\n            end\r\n            options.rendererStr = renderer;  % fix for issue #112\r\n            % Generate some filenames\r\n            tmp_nam = [tempname '.eps'];\r\n            try\r\n                % Ensure that the temp dir is writable (Javier Paredes 30/1/15)\r\n                fid = fopen(tmp_nam,'w');\r\n                fwrite(fid,1);\r\n                fclose(fid);\r\n                delete(tmp_nam);\r\n                isTempDirOk = true;\r\n            catch\r\n                % Temp dir is not writable, so use the user-specified folder\r\n                [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>\r\n                fpath = fileparts(options.name);\r\n                tmp_nam = fullfile(fpath,[fname fext]);\r\n                isTempDirOk = false;\r\n            end\r\n            if isTempDirOk\r\n                pdf_nam_tmp = [tempname '.pdf'];\r\n            else\r\n                pdf_nam_tmp = fullfile(fpath,[fname '.pdf']);\r\n            end\r\n            if options.pdf\r\n                pdf_nam = [options.name '.pdf'];\r\n                try copyfile(pdf_nam, pdf_nam_tmp, 'f'); catch, end  % fix for issue #65\r\n            else\r\n                pdf_nam = pdf_nam_tmp;\r\n            end\r\n            % Generate the options for print\r\n            p2eArgs = {renderer, sprintf('-r%d', options.resolution)};\r\n            if options.colourspace == 1  % CMYK\r\n                % Issue #33: due to internal bugs in Matlab's print() function, we can't use its -cmyk option\r\n                %p2eArgs{end+1} = '-cmyk';\r\n            end\r\n            if ~options.crop\r\n                % Issue #56: due to internal bugs in Matlab's print() function, we can't use its internal cropping mechanism,\r\n                % therefore we always use '-loose' (in print2eps.m) and do our own cropping (in crop_borders)\r\n                %p2eArgs{end+1} = '-loose';\r\n            end\r\n            if any(strcmpi(varargin,'-depsc'))\r\n                % Issue #45: lines in image subplots are exported in invalid color.\r\n                % The workaround is to use the -depsc parameter instead of the default -depsc2\r\n                p2eArgs{end+1} = '-depsc';\r\n            end\r\n            try\r\n                % Generate an eps\r\n                print2eps(tmp_nam, fig, options, p2eArgs{:});\r\n                % Remove the background, if desired\r\n                if options.transparent && ~isequal(get(fig, 'Color'), 'none')\r\n                    eps_remove_background(tmp_nam, 1 + using_hg2(fig));\r\n                end\r\n                % Fix colorspace to CMYK, if requested (workaround for issue #33)\r\n                if options.colourspace == 1  % CMYK\r\n                    % Issue #33: due to internal bugs in Matlab's print() function, we can't use its -cmyk option\r\n                    change_rgb_to_cmyk(tmp_nam);\r\n                end\r\n                % Add a bookmark to the PDF if desired\r\n                if options.bookmark\r\n                    fig_nam = get(fig, 'Name');\r\n                    if isempty(fig_nam)\r\n                        warning('export_fig:EmptyBookmark', 'Bookmark requested for figure with no name. Bookmark will be empty.');\r\n                    end\r\n                    add_bookmark(tmp_nam, fig_nam);\r\n                end\r\n                % Generate a pdf\r\n                eps2pdf(tmp_nam, pdf_nam_tmp, 1, options.append, options.colourspace==2, options.quality, options.gs_options);\r\n                % Ghostscript croaks on % chars in the output PDF file, so use tempname and then rename the file\r\n                try\r\n                    % Rename the file (except if it is already the same)\r\n                    % Abbie K's comment on the commit for issue #179 (#commitcomment-20173476)\r\n                    if ~isequal(pdf_nam_tmp, pdf_nam)\r\n                        movefile(pdf_nam_tmp, pdf_nam, 'f');\r\n                    end\r\n                catch\r\n                    % Alert in case of error creating output PDF/EPS file (issue #179)\r\n                    if exist(pdf_nam_tmp, 'file')\r\n                        error(['Could not create ' pdf_nam ' - perhaps the folder does not exist, or you do not have write permissions']);\r\n                    else\r\n                        error('Could not generate the intermediary EPS file.');\r\n                    end\r\n                end\r\n            catch ex\r\n                % Delete the eps\r\n                delete(tmp_nam);\r\n                rethrow(ex);\r\n            end\r\n            % Delete the eps\r\n            delete(tmp_nam);\r\n            if options.eps || options.linecaps\r\n                try\r\n                    % Generate an eps from the pdf\r\n                    % since pdftops can't handle relative paths (e.g., '..\\'), use a temp file\r\n                    eps_nam_tmp = strrep(pdf_nam_tmp,'.pdf','.eps');\r\n                    pdf2eps(pdf_nam, eps_nam_tmp);\r\n\r\n                    % Issue #192: enable rounded line-caps\r\n                    if options.linecaps\r\n                        fstrm = read_write_entire_textfile(eps_nam_tmp);\r\n                        fstrm = regexprep(fstrm, '[02] J', '1 J');\r\n                        read_write_entire_textfile(eps_nam_tmp, fstrm);\r\n                        if options.pdf\r\n                            eps2pdf(eps_nam_tmp, pdf_nam, 1, options.append, options.colourspace==2, options.quality, options.gs_options);\r\n                        end\r\n                    end\r\n\r\n                    if options.eps\r\n                        movefile(eps_nam_tmp, [options.name '.eps'], 'f');\r\n                    else  % if options.pdf\r\n                        try delete(eps_nam_tmp); catch, end\r\n                    end\r\n                catch ex\r\n                    if ~options.pdf\r\n                        % Delete the pdf\r\n                        delete(pdf_nam);\r\n                    end\r\n                    try delete(eps_nam_tmp); catch, end\r\n                    rethrow(ex);\r\n                end\r\n                if ~options.pdf\r\n                    % Delete the pdf\r\n                    delete(pdf_nam);\r\n                end\r\n            end\r\n        end\r\n\r\n        % Revert the figure or close it (if requested)\r\n        if cls || options.closeFig\r\n            % Close the created figure\r\n            close(fig);\r\n        else\r\n            % Reset the hardcopy mode\r\n            set(fig, 'InvertHardcopy', old_mode);\r\n            % Reset the axes limit and tick modes\r\n            for a = 1:numel(Hlims)\r\n                try\r\n                    set(Hlims(a), 'XLimMode', Xlims{a}, 'YLimMode', Ylims{a}, 'ZLimMode', Zlims{a},... \r\n                                  'XTickMode', Xtick{a}, 'YTickMode', Ytick{a}, 'ZTickMode', Ztick{a},...\r\n                                  'XTickLabelMode', Xlabel{a}, 'YTickLabelMode', Ylabel{a}, 'ZTickLabelMode', Zlabel{a}); \r\n                catch\r\n                    % ignore - fix issue #4 (using HG2 on R2014a and earlier)\r\n                end\r\n            end\r\n            % Revert the tex-labels font weights\r\n            try set(texLabels, 'FontWeight','bold'); catch, end\r\n            % Revert annotation units\r\n            for handleIdx = 1 : numel(annotationHandles)\r\n                try\r\n                    oldUnits = originalUnits{handleIdx};\r\n                catch\r\n                    oldUnits = originalUnits;\r\n                end\r\n                try set(annotationHandles(handleIdx),'Units',oldUnits); catch, end\r\n            end\r\n            % Revert figure units\r\n            set(fig,'Units',oldFigUnits);\r\n        end\r\n\r\n        % Output to clipboard (if requested)\r\n        if options.clipboard\r\n            % Delete the output file if unchanged from the default name ('export_fig_out.png')\r\n            if strcmpi(options.name,'export_fig_out')\r\n                try\r\n                    fileInfo = dir('export_fig_out.png');\r\n                    if ~isempty(fileInfo)\r\n                        timediff = now - fileInfo.datenum;\r\n                        ONE_SEC = 1/24/60/60;\r\n                        if timediff < ONE_SEC\r\n                            delete('export_fig_out.png');\r\n                        end\r\n                    end\r\n                catch\r\n                    % never mind...\r\n                end\r\n            end\r\n\r\n            % Save the image in the system clipboard\r\n            % credit: Jiro Doke's IMCLIPBOARD: http://www.mathworks.com/matlabcentral/fileexchange/28708-imclipboard\r\n            try\r\n                error(javachk('awt', 'export_fig -clipboard output'));\r\n            catch\r\n                warning('export_fig -clipboard output failed: requires Java to work');\r\n                return;\r\n            end\r\n            try\r\n                % Import necessary Java classes\r\n                import java.awt.Toolkit\r\n                import java.awt.image.BufferedImage\r\n                import java.awt.datatransfer.DataFlavor\r\n\r\n                % Get System Clipboard object (java.awt.Toolkit)\r\n                cb = Toolkit.getDefaultToolkit.getSystemClipboard();\r\n\r\n                % Add java class (ImageSelection) to the path\r\n                if ~exist('ImageSelection', 'class')\r\n                    javaaddpath(fileparts(which(mfilename)), '-end');\r\n                end\r\n\r\n                % Get image size\r\n                ht = size(imageData, 1);\r\n                wd = size(imageData, 2);\r\n\r\n                % Convert to Blue-Green-Red format\r\n                try\r\n                    imageData2 = imageData(:, :, [3 2 1]);\r\n                catch\r\n                    % Probably gray-scaled image (2D, without the 3rd [RGB] dimension)\r\n                    imageData2 = imageData(:, :, [1 1 1]);\r\n                end\r\n\r\n                % Convert to 3xWxH format\r\n                imageData2 = permute(imageData2, [3, 2, 1]);\r\n\r\n                % Append Alpha data (unused - transparency is not supported in clipboard copy)\r\n                alphaData2 = uint8(permute(255*alpha,[3,2,1])); %=255*ones(1,wd,ht,'uint8')\r\n                imageData2 = cat(1, imageData2, alphaData2);\r\n\r\n                % Create image buffer\r\n                imBuffer = BufferedImage(wd, ht, BufferedImage.TYPE_INT_RGB);\r\n                imBuffer.setRGB(0, 0, wd, ht, typecast(imageData2(:), 'int32'), 0, wd);\r\n\r\n                % Create ImageSelection object from the image buffer\r\n                imSelection = ImageSelection(imBuffer);\r\n\r\n                % Set clipboard content to the image\r\n                cb.setContents(imSelection, []);\r\n            catch\r\n                warning('export_fig -clipboard output failed: %s', lasterr); %#ok<LERR>\r\n            end\r\n        end\r\n\r\n        % Don't output the data to console unless requested\r\n        if ~nargout\r\n            clear imageData alpha\r\n        end\r\n    catch err\r\n        % Display possible workarounds before the error message\r\n        if displaySuggestedWorkarounds && ~strcmpi(err.message,'export_fig error')\r\n            if ~hadError,  fprintf(2, 'export_fig error. ');  end\r\n            fprintf(2, 'Please ensure:\\n');\r\n            fprintf(2, '  that you are using the <a href=\"https://github.com/altmany/export_fig/archive/master.zip\">latest version</a> of export_fig\\n');\r\n            if ismac\r\n                fprintf(2, '  and that you have <a href=\"http://pages.uoregon.edu/koch\">Ghostscript</a> installed\\n');\r\n            else\r\n                fprintf(2, '  and that you have <a href=\"http://www.ghostscript.com\">Ghostscript</a> installed\\n');\r\n            end\r\n            try\r\n                if options.eps\r\n                    fprintf(2, '  and that you have <a href=\"http://www.foolabs.com/xpdf\">pdftops</a> installed\\n');\r\n                end\r\n            catch\r\n                % ignore - probably an error in parse_args\r\n            end\r\n            fprintf(2, '  and that you do not have <a href=\"matlab:which export_fig -all\">multiple versions</a> of export_fig installed by mistake\\n');\r\n            fprintf(2, '  and that you did not made a mistake in the <a href=\"matlab:help export_fig\">expected input arguments</a>\\n');\r\n            try\r\n                % Alert per issue #149\r\n                if ~strncmpi(get(0,'Units'),'pixel',5)\r\n                    fprintf(2, '  or try to set groot''s Units property back to its default value of ''pixels'' (<a href=\"matlab:web(''https://github.com/altmany/export_fig/issues/149'',''-browser'');\">details</a>)\\n');\r\n                end\r\n            catch\r\n                % ignore - maybe an old MAtlab release\r\n            end\r\n            fprintf(2, '\\nIf the problem persists, then please <a href=\"https://github.com/altmany/export_fig/issues\">report a new issue</a>.\\n\\n');\r\n        end\r\n        rethrow(err)\r\n    end\r\nend\r\n\r\nfunction options = default_options()\r\n    % Default options used by export_fig\r\n    options = struct(...\r\n        'name',         'export_fig_out', ...\r\n        'crop',         true, ...\r\n        'crop_amounts', nan(1,4), ...  % auto-crop all 4 image sides\r\n        'transparent',  false, ...\r\n        'renderer',     0, ...         % 0: default, 1: OpenGL, 2: ZBuffer, 3: Painters\r\n        'pdf',          false, ...\r\n        'eps',          false, ...\r\n        'png',          false, ...\r\n        'tif',          false, ...\r\n        'jpg',          false, ...\r\n        'bmp',          false, ...\r\n        'clipboard',    false, ...\r\n        'colourspace',  0, ...         % 0: RGB/gray, 1: CMYK, 2: gray\r\n        'append',       false, ...\r\n        'im',           false, ...\r\n        'alpha',        false, ...\r\n        'aa_factor',    0, ...\r\n        'bb_padding',   0, ...\r\n        'magnify',      [], ...\r\n        'resolution',   [], ...\r\n        'bookmark',     false, ...\r\n        'closeFig',     false, ...\r\n        'quality',      [], ...\r\n        'update',       false, ...\r\n        'fontswap',     true, ...\r\n        'font_space',   '', ...\r\n        'linecaps',     false, ...\r\n        'invert_hardcopy', true, ...\r\n        'gs_options',   {{}});\r\nend\r\n\r\nfunction [fig, options] = parse_args(nout, fig, varargin)\r\n    % Parse the input arguments\r\n\r\n    % Set the defaults\r\n    native = false; % Set resolution to native of an image\r\n    options = default_options();\r\n    options.im =    (nout == 1);  % user requested imageData output\r\n    options.alpha = (nout == 2);  % user requested alpha output\r\n\r\n    % Go through the other arguments\r\n    skipNext = false;\r\n    for a = 1:nargin-2\r\n        if skipNext\r\n            skipNext = false;\r\n            continue;\r\n        end\r\n        if all(ishandle(varargin{a}))\r\n            fig = varargin{a};\r\n        elseif ischar(varargin{a}) && ~isempty(varargin{a})\r\n            if varargin{a}(1) == '-'\r\n                switch lower(varargin{a}(2:end))\r\n                    case 'nocrop'\r\n                        options.crop = false;\r\n                        options.crop_amounts = [0,0,0,0];\r\n                    case {'trans', 'transparent'}\r\n                        options.transparent = true;\r\n                    case 'opengl'\r\n                        options.renderer = 1;\r\n                    case 'zbuffer'\r\n                        options.renderer = 2;\r\n                    case 'painters'\r\n                        options.renderer = 3;\r\n                    case 'pdf'\r\n                        options.pdf = true;\r\n                    case 'eps'\r\n                        options.eps = true;\r\n                    case 'png'\r\n                        options.png = true;\r\n                    case {'tif', 'tiff'}\r\n                        options.tif = true;\r\n                    case {'jpg', 'jpeg'}\r\n                        options.jpg = true;\r\n                    case 'bmp'\r\n                        options.bmp = true;\r\n                    case 'rgb'\r\n                        options.colourspace = 0;\r\n                    case 'cmyk'\r\n                        options.colourspace = 1;\r\n                    case {'gray', 'grey'}\r\n                        options.colourspace = 2;\r\n                    case {'a1', 'a2', 'a3', 'a4'}\r\n                        options.aa_factor = str2double(varargin{a}(3));\r\n                    case 'append'\r\n                        options.append = true;\r\n                    case 'bookmark'\r\n                        options.bookmark = true;\r\n                    case 'native'\r\n                        native = true;\r\n                    case 'clipboard'\r\n                        options.clipboard = true;\r\n                        options.im = true;\r\n                        options.alpha = true;\r\n                    case 'svg'\r\n                        msg = ['SVG output is not supported by export_fig. Use one of the following alternatives:\\n' ...\r\n                               '  1. saveas(gcf,''filename.svg'')\\n' ...\r\n                               '  2. plot2svg utility: http://github.com/jschwizer99/plot2svg\\n' ...\r\n                               '  3. export_fig to EPS/PDF, then convert to SVG using generic (non-Matlab) tools\\n'];\r\n                        error(sprintf(msg)); %#ok<SPERR>\r\n                    case 'update'\r\n                        % Download the latest version of export_fig into the export_fig folder\r\n                        try\r\n                            zipFileName = 'https://github.com/altmany/export_fig/archive/master.zip';\r\n                            folderName = fileparts(which(mfilename('fullpath')));\r\n                            targetFileName = fullfile(folderName, datestr(now,'yyyy-mm-dd.zip'));\r\n                            urlwrite(zipFileName,targetFileName);\r\n                        catch\r\n                            error('Could not download %s into %s\\n',zipFileName,targetFileName);\r\n                        end\r\n\r\n                        % Unzip the downloaded zip file in the export_fig folder\r\n                        try\r\n                            unzip(targetFileName,folderName);\r\n                        catch\r\n                            error('Could not unzip %s\\n',targetFileName);\r\n                        end\r\n                    case 'nofontswap'\r\n                        options.fontswap = false;\r\n                    case 'font_space'\r\n                        options.font_space = varargin{a+1};\r\n                        skipNext = true;\r\n                    case 'linecaps'\r\n                        options.linecaps = true;\r\n                    case 'noinvert'\r\n                        options.invert_hardcopy = false;\r\n                    otherwise\r\n                        try\r\n                            wasError = false;\r\n                            if strcmpi(varargin{a}(1:2),'-d')\r\n                                varargin{a}(2) = 'd';  % ensure lowercase 'd'\r\n                                options.gs_options{end+1} = varargin{a};\r\n                            elseif strcmpi(varargin{a}(1:2),'-c')\r\n                                if numel(varargin{a})==2\r\n                                    skipNext = true;\r\n                                    vals = str2num(varargin{a+1}); %#ok<ST2NM>\r\n                                else\r\n                                    vals = str2num(varargin{a}(3:end)); %#ok<ST2NM>\r\n                                end\r\n                                if numel(vals)~=4\r\n                                    wasError = true;\r\n                                    error('option -c cannot be parsed: must be a 4-element numeric vector');\r\n                                end\r\n                                options.crop_amounts = vals;\r\n                                options.crop = true;\r\n                            else  % scalar parameter value\r\n                                val = str2double(regexp(varargin{a}, '(?<=-(m|M|r|R|q|Q|p|P))-?\\d*.?\\d+', 'match'));\r\n                                if isempty(val) || isnan(val)\r\n                                    % Issue #51: improved processing of input args (accept space between param name & value)\r\n                                    val = str2double(varargin{a+1});\r\n                                    if isscalar(val) && ~isnan(val)\r\n                                        skipNext = true;\r\n                                    end\r\n                                end\r\n                                if ~isscalar(val) || isnan(val)\r\n                                    wasError = true;\r\n                                    error('option %s is not recognised or cannot be parsed', varargin{a});\r\n                                end\r\n                                switch lower(varargin{a}(2))\r\n                                    case 'm'\r\n                                        % Magnification may never be negative\r\n                                        if val <= 0\r\n                                            wasError = true;\r\n                                            error('Bad magnification value: %g (must be positive)', val);\r\n                                        end\r\n                                        options.magnify = val;\r\n                                    case 'r'\r\n                                        options.resolution = val;\r\n                                    case 'q'\r\n                                        options.quality = max(val, 0);\r\n                                    case 'p'\r\n                                        options.bb_padding = val;\r\n                                end\r\n                            end\r\n                        catch err\r\n                            % We might have reached here by raising an intentional error\r\n                            if wasError  % intentional raise\r\n                                rethrow(err)\r\n                            else  % unintentional\r\n                                error(['Unrecognized export_fig input option: ''' varargin{a} '''']);\r\n                            end\r\n                        end\r\n                end\r\n            else\r\n                [p, options.name, ext] = fileparts(varargin{a});\r\n                if ~isempty(p)\r\n                    options.name = [p filesep options.name];\r\n                end\r\n                switch lower(ext)\r\n                    case {'.tif', '.tiff'}\r\n                        options.tif = true;\r\n                    case {'.jpg', '.jpeg'}\r\n                        options.jpg = true;\r\n                    case '.png'\r\n                        options.png = true;\r\n                    case '.bmp'\r\n                        options.bmp = true;\r\n                    case '.eps'\r\n                        options.eps = true;\r\n                    case '.pdf'\r\n                        options.pdf = true;\r\n                    case '.fig'\r\n                        % If no open figure, then load the specified .fig file and continue\r\n                        if isempty(fig)\r\n                            fig = openfig(varargin{a},'invisible');\r\n                            varargin{a} = fig;\r\n                            options.closeFig = true;\r\n                        else\r\n                            % save the current figure as the specified .fig file and exit\r\n                            saveas(fig(1),varargin{a});\r\n                            fig = -1;\r\n                            return\r\n                        end\r\n                    case '.svg'\r\n                        msg = ['SVG output is not supported by export_fig. Use one of the following alternatives:\\n' ...\r\n                               '  1. saveas(gcf,''filename.svg'')\\n' ...\r\n                               '  2. plot2svg utility: http://github.com/jschwizer99/plot2svg\\n' ...\r\n                               '  3. export_fig to EPS/PDF, then convert to SVG using generic (non-Matlab) tools\\n'];\r\n                        error(sprintf(msg)); %#ok<SPERR>\r\n                    otherwise\r\n                        options.name = varargin{a};\r\n                end\r\n            end\r\n        end\r\n    end\r\n\r\n    % Quick bail-out if no figure found\r\n    if isempty(fig),  return;  end\r\n\r\n    % Do border padding with repsect to a cropped image\r\n    if options.bb_padding\r\n        options.crop = true;\r\n    end\r\n\r\n    % Set default anti-aliasing now we know the renderer\r\n    if options.aa_factor == 0\r\n        try isAA = strcmp(get(ancestor(fig, 'figure'), 'GraphicsSmoothing'), 'on'); catch, isAA = false; end\r\n        options.aa_factor = 1 + 2 * (~(using_hg2(fig) && isAA) | (options.renderer == 3));\r\n    end\r\n\r\n    % Convert user dir '~' to full path\r\n    if numel(options.name) > 2 && options.name(1) == '~' && (options.name(2) == '/' || options.name(2) == '\\')\r\n        options.name = fullfile(char(java.lang.System.getProperty('user.home')), options.name(2:end));\r\n    end\r\n\r\n    % Compute the magnification and resolution\r\n    if isempty(options.magnify)\r\n        if isempty(options.resolution)\r\n            options.magnify = 1;\r\n            options.resolution = 864;\r\n        else\r\n            options.magnify = options.resolution ./ get(0, 'ScreenPixelsPerInch');\r\n        end\r\n    elseif isempty(options.resolution)\r\n        options.resolution = 864;\r\n    end\r\n\r\n    % Set the default format\r\n    if ~isvector(options) && ~isbitmap(options)\r\n        options.png = true;\r\n    end\r\n\r\n    % Check whether transparent background is wanted (old way)\r\n    if isequal(get(ancestor(fig(1), 'figure'), 'Color'), 'none')\r\n        options.transparent = true;\r\n    end\r\n\r\n    % If requested, set the resolution to the native vertical resolution of the\r\n    % first suitable image found\r\n    if native && isbitmap(options)\r\n        % Find a suitable image\r\n        list = findall(fig, 'Type','image', 'Tag','export_fig_native');\r\n        if isempty(list)\r\n            list = findall(fig, 'Type','image', 'Visible','on');\r\n        end\r\n        for hIm = list(:)'\r\n            % Check height is >= 2\r\n            height = size(get(hIm, 'CData'), 1);\r\n            if height < 2\r\n                continue\r\n            end\r\n            % Account for the image filling only part of the axes, or vice versa\r\n            yl = get(hIm, 'YData');\r\n            if isscalar(yl)\r\n                yl = [yl(1)-0.5 yl(1)+height+0.5];\r\n            else\r\n                yl = [min(yl), max(yl)];  % fix issue #151 (case of yl containing more than 2 elements)\r\n                if ~diff(yl)\r\n                    continue\r\n                end\r\n                yl = yl + [-0.5 0.5] * (diff(yl) / (height - 1));\r\n            end\r\n            hAx = get(hIm, 'Parent');\r\n            yl2 = get(hAx, 'YLim');\r\n            % Find the pixel height of the axes\r\n            oldUnits = get(hAx, 'Units');\r\n            set(hAx, 'Units', 'pixels');\r\n            pos = get(hAx, 'Position');\r\n            set(hAx, 'Units', oldUnits);\r\n            if ~pos(4)\r\n                continue\r\n            end\r\n            % Found a suitable image\r\n            % Account for stretch-to-fill being disabled\r\n            pbar = get(hAx, 'PlotBoxAspectRatio');\r\n            pos = min(pos(4), pbar(2)*pos(3)/pbar(1));\r\n            % Set the magnification to give native resolution\r\n            options.magnify = abs((height * diff(yl2)) / (pos * diff(yl)));  % magnification must never be negative: issue #103\r\n            break\r\n        end\r\n    end\r\nend\r\n\r\nfunction A = downsize(A, factor)\r\n    % Downsample an image\r\n    if factor == 1\r\n        % Nothing to do\r\n        return\r\n    end\r\n    try\r\n        % Faster, but requires image processing toolbox\r\n        A = imresize(A, 1/factor, 'bilinear');\r\n    catch\r\n        % No image processing toolbox - resize manually\r\n        % Lowpass filter - use Gaussian as is separable, so faster\r\n        % Compute the 1d Gaussian filter\r\n        filt = (-factor-1:factor+1) / (factor * 0.6);\r\n        filt = exp(-filt .* filt);\r\n        % Normalize the filter\r\n        filt = single(filt / sum(filt));\r\n        % Filter the image\r\n        padding = floor(numel(filt) / 2);\r\n        for a = 1:size(A, 3)\r\n            A(:,:,a) = conv2(filt, filt', single(A([ones(1, padding) 1:end repmat(end, 1, padding)],[ones(1, padding) 1:end repmat(end, 1, padding)],a)), 'valid');\r\n        end\r\n        % Subsample\r\n        A = A(1+floor(mod(end-1, factor)/2):factor:end,1+floor(mod(end-1, factor)/2):factor:end,:);\r\n    end\r\nend\r\n\r\nfunction A = rgb2grey(A)\r\n    A = cast(reshape(reshape(single(A), [], 3) * single([0.299; 0.587; 0.114]), size(A, 1), size(A, 2)), class(A)); % #ok<ZEROLIKE>\r\nend\r\n\r\nfunction A = check_greyscale(A)\r\n    % Check if the image is greyscale\r\n    if size(A, 3) == 3 && ...\r\n            all(reshape(A(:,:,1) == A(:,:,2), [], 1)) && ...\r\n            all(reshape(A(:,:,2) == A(:,:,3), [], 1))\r\n        A = A(:,:,1); % Save only one channel for 8-bit output\r\n    end\r\nend\r\n\r\nfunction eps_remove_background(fname, count)\r\n    % Remove the background of an eps file\r\n    % Open the file\r\n    fh = fopen(fname, 'r+');\r\n    if fh == -1\r\n        error('Not able to open file %s.', fname);\r\n    end\r\n    % Read the file line by line\r\n    while count\r\n        % Get the next line\r\n        l = fgets(fh);\r\n        if isequal(l, -1)\r\n            break; % Quit, no rectangle found\r\n        end\r\n        % Check if the line contains the background rectangle\r\n        if isequal(regexp(l, ' *0 +0 +\\d+ +\\d+ +r[fe] *[\\n\\r]+', 'start'), 1)\r\n            % Set the line to whitespace and quit\r\n            l(1:regexp(l, '[\\n\\r]', 'start', 'once')-1) = ' ';\r\n            fseek(fh, -numel(l), 0);\r\n            fprintf(fh, l);\r\n            % Reduce the count\r\n            count = count - 1;\r\n        end\r\n    end\r\n    % Close the file\r\n    fclose(fh);\r\nend\r\n\r\nfunction b = isvector(options)\r\n    b = options.pdf || options.eps;\r\nend\r\n\r\nfunction b = isbitmap(options)\r\n    b = options.png || options.tif || options.jpg || options.bmp || options.im || options.alpha;\r\nend\r\n\r\n% Helper function\r\nfunction A = make_cell(A)\r\n    if ~iscell(A)\r\n        A = {A};\r\n    end\r\nend\r\n\r\nfunction add_bookmark(fname, bookmark_text)\r\n    % Adds a bookmark to the temporary EPS file after %%EndPageSetup\r\n    % Read in the file\r\n    fh = fopen(fname, 'r');\r\n    if fh == -1\r\n        error('File %s not found.', fname);\r\n    end\r\n    try\r\n        fstrm = fread(fh, '*char')';\r\n    catch ex\r\n        fclose(fh);\r\n        rethrow(ex);\r\n    end\r\n    fclose(fh);\r\n\r\n    % Include standard pdfmark prolog to maximize compatibility\r\n    fstrm = strrep(fstrm, '%%BeginProlog', sprintf('%%%%BeginProlog\\n/pdfmark where {pop} {userdict /pdfmark /cleartomark load put} ifelse'));\r\n    % Add page bookmark\r\n    fstrm = strrep(fstrm, '%%EndPageSetup', sprintf('%%%%EndPageSetup\\n[ /Title (%s) /OUT pdfmark',bookmark_text));\r\n\r\n    % Write out the updated file\r\n    fh = fopen(fname, 'w');\r\n    if fh == -1\r\n        error('Unable to open %s for writing.', fname);\r\n    end\r\n    try\r\n        fwrite(fh, fstrm, 'char*1');\r\n    catch ex\r\n        fclose(fh);\r\n        rethrow(ex);\r\n    end\r\n    fclose(fh);\r\nend\r\n\r\nfunction set_tick_mode(Hlims, ax)\r\n    % Set the tick mode of linear axes to manual\r\n    % Leave log axes alone as these are tricky\r\n    M = get(Hlims, [ax 'Scale']);\r\n    if ~iscell(M)\r\n        M = {M};\r\n    end\r\n    %idx = cellfun(@(c) strcmp(c, 'linear'), M);\r\n    idx = find(strcmp(M,'linear'));\r\n    %set(Hlims(idx), [ax 'TickMode'], 'manual');  % issue #187\r\n    %set(Hlims(idx), [ax 'TickLabelMode'], 'manual');  % this hides exponent label in HG2!\r\n    for idx2 = 1 : numel(idx)\r\n        try\r\n            % Fix for issue #187 - only set manual ticks when no exponent is present\r\n            hAxes = Hlims(idx(idx2));\r\n            props = {[ax 'TickMode'],'manual', [ax 'TickLabelMode'],'manual'};\r\n            if isempty(strtrim(hAxes.([ax 'Ruler']).SecondaryLabel.String))\r\n                % Fix for issue #205 - only set manual ticks when the Ticks number match the TickLabels number\r\n                if numel(hAxes.([ax 'Tick'])) == numel(hAxes.([ax 'TickLabel']))\r\n                    set(hAxes, props{:});  % no exponent and matching ticks, so update both ticks and tick labels to manual\r\n                end\r\n            end\r\n        catch  % probably HG1\r\n            set(hAxes, props{:});  % revert back to old behavior\r\n        end\r\n    end\r\nend\r\n\r\nfunction change_rgb_to_cmyk(fname)  % convert RGB => CMYK within an EPS file\r\n    % Do post-processing on the eps file\r\n    try\r\n        % Read the EPS file into memory\r\n        fstrm = read_write_entire_textfile(fname);\r\n\r\n        % Replace all gray-scale colors\r\n        fstrm = regexprep(fstrm, '\\n([\\d.]+) +GC\\n', '\\n0 0 0 ${num2str(1-str2num($1))} CC\\n');\r\n        \r\n        % Replace all RGB colors\r\n        fstrm = regexprep(fstrm, '\\n[0.]+ +[0.]+ +[0.]+ +RC\\n', '\\n0 0 0 1 CC\\n');  % pure black\r\n        fstrm = regexprep(fstrm, '\\n([\\d.]+) +([\\d.]+) +([\\d.]+) +RC\\n', '\\n${sprintf(''%.4g '',[1-[str2num($1),str2num($2),str2num($3)]/max([str2num($1),str2num($2),str2num($3)]),1-max([str2num($1),str2num($2),str2num($3)])])} CC\\n');\r\n\r\n        % Overwrite the file with the modified contents\r\n        read_write_entire_textfile(fname, fstrm);\r\n    catch\r\n        % never mind - leave as is...\r\n    end\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "ghostscript.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/ghostscript.m", "size": 7902, "source_encoding": "utf_8", "md5": "ff62a40d651197dbea5d3c39998b3bad", "text": "function varargout = ghostscript(cmd)\r\n%GHOSTSCRIPT  Calls a local GhostScript executable with the input command\r\n%\r\n% Example:\r\n%   [status result] = ghostscript(cmd)\r\n%\r\n% Attempts to locate a ghostscript executable, finally asking the user to\r\n% specify the directory ghostcript was installed into. The resulting path\r\n% is stored for future reference.\r\n% \r\n% Once found, the executable is called with the input command string.\r\n%\r\n% This function requires that you have Ghostscript installed on your\r\n% system. You can download this from: http://www.ghostscript.com\r\n%\r\n% IN:\r\n%   cmd - Command string to be passed into ghostscript.\r\n%\r\n% OUT:\r\n%   status - 0 iff command ran without problem.\r\n%   result - Output from ghostscript.\r\n\r\n% Copyright: Oliver Woodford, 2009-2015, Yair Altman 2015-\r\n%{\r\n% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS.\r\n% Thanks to Nathan Childress for the fix to default location on 64-bit Windows systems.\r\n% 27/04/11 - Find 64-bit Ghostscript on Windows. Thanks to Paul Durack and\r\n%            Shaun Kline for pointing out the issue\r\n% 04/05/11 - Thanks to David Chorlian for pointing out an alternative\r\n%            location for gs on linux.\r\n% 12/12/12 - Add extra executable name on Windows. Thanks to Ratish\r\n%            Punnoose for highlighting the issue.\r\n% 28/06/13 - Fix error using GS 9.07 in Linux. Many thanks to Jannick\r\n%            Steinbring for proposing the fix.\r\n% 24/10/13 - Fix error using GS 9.07 in Linux. Many thanks to Johannes \r\n%            for the fix.\r\n% 23/01/14 - Add full path to ghostscript.txt in warning. Thanks to Koen\r\n%            Vermeer for raising the issue.\r\n% 27/02/15 - If Ghostscript croaks, display suggested workarounds\r\n% 30/03/15 - Improved performance by caching status of GS path check, if ok\r\n% 14/05/15 - Clarified warning message in case GS path could not be saved\r\n% 29/05/15 - Avoid cryptic error in case the ghostscipt path cannot be saved (issue #74)\r\n% 10/11/15 - Custom GS installation webpage for MacOS. Thanks to Andy Hueni via FEX\r\n%}\r\n\r\n    try\r\n        % Call ghostscript\r\n        [varargout{1:nargout}] = system([gs_command(gs_path()) cmd]);\r\n    catch err\r\n        % Display possible workarounds for Ghostscript croaks\r\n        url1 = 'https://github.com/altmany/export_fig/issues/12#issuecomment-61467998';  % issue #12\r\n        url2 = 'https://github.com/altmany/export_fig/issues/20#issuecomment-63826270';  % issue #20\r\n        hg2_str = ''; if using_hg2, hg2_str = ' or Matlab R2014a'; end\r\n        fprintf(2, 'Ghostscript error. Rolling back to GS 9.10%s may possibly solve this:\\n * <a href=\"%s\">%s</a> ',hg2_str,url1,url1);\r\n        if using_hg2\r\n            fprintf(2, '(GS 9.10)\\n * <a href=\"%s\">%s</a> (R2014a)',url2,url2);\r\n        end\r\n        fprintf('\\n\\n');\r\n        if ismac || isunix\r\n            url3 = 'https://github.com/altmany/export_fig/issues/27';  % issue #27\r\n            fprintf(2, 'Alternatively, this may possibly be due to a font path issue:\\n * <a href=\"%s\">%s</a>\\n\\n',url3,url3);\r\n            % issue #20\r\n            fpath = which(mfilename);\r\n            if isempty(fpath), fpath = [mfilename('fullpath') '.m']; end\r\n            fprintf(2, 'Alternatively, if you are using csh, modify shell_cmd from \"export...\" to \"setenv ...\"\\nat the bottom of <a href=\"matlab:opentoline(''%s'',174)\">%s</a>\\n\\n',fpath,fpath);\r\n        end\r\n        rethrow(err);\r\n    end\r\nend\r\n\r\nfunction path_ = gs_path\r\n    % Return a valid path\r\n    % Start with the currently set path\r\n    path_ = user_string('ghostscript');\r\n    % Check the path works\r\n    if check_gs_path(path_)\r\n        return\r\n    end\r\n    % Check whether the binary is on the path\r\n    if ispc\r\n        bin = {'gswin32c.exe', 'gswin64c.exe', 'gs'};\r\n    else\r\n        bin = {'gs'};\r\n    end\r\n    for a = 1:numel(bin)\r\n        path_ = bin{a};\r\n        if check_store_gs_path(path_)\r\n            return\r\n        end\r\n    end\r\n    % Search the obvious places\r\n    if ispc\r\n        default_location = 'C:\\Program Files\\gs\\';\r\n        dir_list = dir(default_location);\r\n        if isempty(dir_list)\r\n            default_location = 'C:\\Program Files (x86)\\gs\\'; % Possible location on 64-bit systems\r\n            dir_list = dir(default_location);\r\n        end\r\n        executable = {'\\bin\\gswin32c.exe', '\\bin\\gswin64c.exe'};\r\n        ver_num = 0;\r\n        % If there are multiple versions, use the newest\r\n        for a = 1:numel(dir_list)\r\n            ver_num2 = sscanf(dir_list(a).name, 'gs%g');\r\n            if ~isempty(ver_num2) && ver_num2 > ver_num\r\n                for b = 1:numel(executable)\r\n                    path2 = [default_location dir_list(a).name executable{b}];\r\n                    if exist(path2, 'file') == 2\r\n                        path_ = path2;\r\n                        ver_num = ver_num2;\r\n                    end\r\n                end\r\n            end\r\n        end\r\n        if check_store_gs_path(path_)\r\n            return\r\n        end\r\n    else\r\n        executable = {'/usr/bin/gs', '/usr/local/bin/gs'};\r\n        for a = 1:numel(executable)\r\n            path_ = executable{a};\r\n            if check_store_gs_path(path_)\r\n                return\r\n            end\r\n        end\r\n    end\r\n    % Ask the user to enter the path\r\n    while true\r\n        if strncmp(computer, 'MAC', 3) % Is a Mac\r\n            % Give separate warning as the uigetdir dialogue box doesn't have a\r\n            % title\r\n            uiwait(warndlg('Ghostscript not found. Please locate the program.'))\r\n        end\r\n        base = uigetdir('/', 'Ghostcript not found. Please locate the program.');\r\n        if isequal(base, 0)\r\n            % User hit cancel or closed window\r\n            break;\r\n        end\r\n        base = [base filesep]; %#ok<AGROW>\r\n        bin_dir = {'', ['bin' filesep], ['lib' filesep]};\r\n        for a = 1:numel(bin_dir)\r\n            for b = 1:numel(bin)\r\n                path_ = [base bin_dir{a} bin{b}];\r\n                if exist(path_, 'file') == 2\r\n                    if check_store_gs_path(path_)\r\n                        return\r\n                    end\r\n                end\r\n            end\r\n        end\r\n    end\r\n    if ismac\r\n        error('Ghostscript not found. Have you installed it (http://pages.uoregon.edu/koch)?');\r\n    else\r\n        error('Ghostscript not found. Have you installed it from www.ghostscript.com?');\r\n    end\r\nend\r\n\r\nfunction good = check_store_gs_path(path_)\r\n    % Check the path is valid\r\n    good = check_gs_path(path_);\r\n    if ~good\r\n        return\r\n    end\r\n    % Update the current default path to the path found\r\n    if ~user_string('ghostscript', path_)\r\n        filename = fullfile(fileparts(which('user_string.m')), '.ignore', 'ghostscript.txt');\r\n        warning('Path to ghostscript installation could not be saved in %s (perhaps a permissions issue). You can manually create this file and set its contents to %s, to improve performance in future invocations (this warning is safe to ignore).', filename, path_);\r\n        return\r\n    end\r\nend\r\n\r\nfunction good = check_gs_path(path_)\r\n    persistent isOk\r\n    if isempty(path_)\r\n        isOk = false;\r\n    elseif ~isequal(isOk,true)\r\n        % Check whether the path is valid\r\n        [status, message] = system([gs_command(path_) '-h']); %#ok<ASGLU>\r\n        isOk = status == 0;\r\n    end\r\n    good = isOk;\r\nend\r\n\r\nfunction cmd = gs_command(path_)\r\n    % Initialize any required system calls before calling ghostscript\r\n    % TODO: in Unix/Mac, find a way to determine whether to use \"export\" (bash) or \"setenv\" (csh/tcsh)\r\n    shell_cmd = '';\r\n    if isunix\r\n        shell_cmd = 'export LD_LIBRARY_PATH=\"\"; '; % Avoids an error on Linux with GS 9.07\r\n    end\r\n    if ismac\r\n        shell_cmd = 'export DYLD_LIBRARY_PATH=\"\"; ';  % Avoids an error on Mac with GS 9.07\r\n    end\r\n    % Construct the command string\r\n    cmd = sprintf('%s\"%s\" ', shell_cmd, path_);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "fix_lines.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/fix_lines.m", "size": 6441, "source_encoding": "utf_8", "md5": "ffda929ebad8144b1e72d528fa5d9460", "text": "%FIX_LINES  Improves the line style of eps files generated by print\r\n%\r\n% Examples:\r\n%   fix_lines fname\r\n%   fix_lines fname fname2\r\n%   fstrm_out = fixlines(fstrm_in)\r\n%\r\n% This function improves the style of lines in eps files generated by\r\n% MATLAB's print function, making them more similar to those seen on\r\n% screen. Grid lines are also changed from a dashed style to a dotted\r\n% style, for greater differentiation from dashed lines.\r\n% \r\n% The function also places embedded fonts after the postscript header, in\r\n% versions of MATLAB which place the fonts first (R2006b and earlier), in\r\n% order to allow programs such as Ghostscript to find the bounding box\r\n% information.\r\n%\r\n%IN:\r\n%   fname - Name or path of source eps file.\r\n%   fname2 - Name or path of destination eps file. Default: same as fname.\r\n%   fstrm_in - File contents of a MATLAB-generated eps file.\r\n%\r\n%OUT:\r\n%   fstrm_out - Contents of the eps file with line styles fixed.\r\n\r\n% Copyright: (C) Oliver Woodford, 2008-2014\r\n\r\n% The idea of editing the EPS file to change line styles comes from Jiro\r\n% Doke's FIXPSLINESTYLE (fex id: 17928)\r\n% The idea of changing dash length with line width came from comments on\r\n% fex id: 5743, but the implementation is mine :)\r\n\r\n% Thank you to Sylvain Favrot for bringing the embedded font/bounding box\r\n% interaction in older versions of MATLAB to my attention.\r\n% Thank you to D Ko for bringing an error with eps files with tiff previews\r\n% to my attention.\r\n% Thank you to Laurence K for suggesting the check to see if the file was\r\n% opened.\r\n\r\n% 01/03/15: Issue #20: warn users if using this function in HG2 (R2014b+)\r\n% 27/03/15: Fixed out of memory issue with enormous EPS files (generated by print() with OpenGL renderer), related to issue #39\r\n\r\nfunction fstrm = fix_lines(fstrm, fname2)\r\n\r\n% Issue #20: warn users if using this function in HG2 (R2014b+)\r\nif using_hg2\r\n    warning('export_fig:hg2','The fix_lines function should not be used in this Matlab version.');\r\nend\r\n    \r\nif nargout == 0 || nargin > 1\r\n    if nargin < 2\r\n        % Overwrite the input file\r\n        fname2 = fstrm;\r\n    end\r\n    % Read in the file\r\n    fstrm = read_write_entire_textfile(fstrm);\r\nend\r\n\r\n% Move any embedded fonts after the postscript header\r\nif strcmp(fstrm(1:15), '%!PS-AdobeFont-')\r\n    % Find the start and end of the header\r\n    ind = regexp(fstrm, '[\\n\\r]%!PS-Adobe-');\r\n    [ind2, ind2] = regexp(fstrm, '[\\n\\r]%%EndComments[\\n\\r]+');\r\n    % Put the header first\r\n    if ~isempty(ind) && ~isempty(ind2) && ind(1) < ind2(1)\r\n        fstrm = fstrm([ind(1)+1:ind2(1) 1:ind(1) ind2(1)+1:end]);\r\n    end\r\nend\r\n\r\n% Make sure all line width commands come before the line style definitions,\r\n% so that dash lengths can be based on the correct widths\r\n% Find all line style sections\r\nind = [regexp(fstrm, '[\\n\\r]SO[\\n\\r]'),... % This needs to be here even though it doesn't have dots/dashes!\r\n       regexp(fstrm, '[\\n\\r]DO[\\n\\r]'),...\r\n       regexp(fstrm, '[\\n\\r]DA[\\n\\r]'),...\r\n       regexp(fstrm, '[\\n\\r]DD[\\n\\r]')];\r\nind = sort(ind);\r\n% Find line width commands\r\n[ind2, ind3] = regexp(fstrm, '[\\n\\r]\\d* w[\\n\\r]');\r\n% Go through each line style section and swap with any line width commands\r\n% near by\r\nb = 1;\r\nm = numel(ind);\r\nn = numel(ind2);\r\nfor a = 1:m\r\n    % Go forwards width commands until we pass the current line style\r\n    while b <= n && ind2(b) < ind(a)\r\n        b = b + 1;\r\n    end\r\n    if b > n\r\n        % No more width commands\r\n        break;\r\n    end\r\n    % Check we haven't gone past another line style (including SO!)\r\n    if a < m && ind2(b) > ind(a+1)\r\n        continue;\r\n    end\r\n    % Are the commands close enough to be confident we can swap them?\r\n    if (ind2(b) - ind(a)) > 8\r\n        continue;\r\n    end\r\n    % Move the line style command below the line width command\r\n    fstrm(ind(a)+1:ind3(b)) = [fstrm(ind(a)+4:ind3(b)) fstrm(ind(a)+1:ind(a)+3)];\r\n    b = b + 1;\r\nend\r\n\r\n% Find any grid line definitions and change to GR format\r\n% Find the DO sections again as they may have moved\r\nind = int32(regexp(fstrm, '[\\n\\r]DO[\\n\\r]'));\r\nif ~isempty(ind)\r\n    % Find all occurrences of what are believed to be axes and grid lines\r\n    ind2 = int32(regexp(fstrm, '[\\n\\r] *\\d* *\\d* *mt *\\d* *\\d* *L[\\n\\r]'));\r\n    if ~isempty(ind2)\r\n        % Now see which DO sections come just before axes and grid lines\r\n        ind2 = repmat(ind2', [1 numel(ind)]) - repmat(ind, [numel(ind2) 1]);\r\n        ind2 = any(ind2 > 0 & ind2 < 12); % 12 chars seems about right\r\n        ind = ind(ind2);\r\n        % Change any regions we believe to be grid lines to GR\r\n        fstrm(ind+1) = 'G';\r\n        fstrm(ind+2) = 'R';\r\n    end\r\nend\r\n\r\n% Define the new styles, including the new GR format\r\n% Dot and dash lengths have two parts: a constant amount plus a line width\r\n% variable amount. The constant amount comes after dpi2point, and the\r\n% variable amount comes after currentlinewidth. If you want to change\r\n% dot/dash lengths for a one particular line style only, edit the numbers\r\n% in the /DO (dotted lines), /DA (dashed lines), /DD (dot dash lines) and\r\n% /GR (grid lines) lines for the style you want to change.\r\nnew_style = {'/dom { dpi2point 1 currentlinewidth 0.08 mul add mul mul } bdef',... % Dot length macro based on line width\r\n             '/dam { dpi2point 2 currentlinewidth 0.04 mul add mul mul } bdef',... % Dash length macro based on line width\r\n             '/SO { [] 0 setdash 0 setlinecap } bdef',... % Solid lines\r\n             '/DO { [1 dom 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dotted lines\r\n             '/DA { [4 dam 1.5 dam] 0 setdash 0 setlinecap } bdef',... % Dashed lines\r\n             '/DD { [1 dom 1.2 dom 4 dam 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dot dash lines\r\n             '/GR { [0 dpi2point mul 4 dpi2point mul] 0 setdash 1 setlinecap } bdef'}; % Grid lines - dot spacing remains constant\r\n\r\n% Construct the output\r\n% This is the original (memory-intensive) code:\r\n%first_sec = strfind(fstrm, '% line types:'); % Isolate line style definition section\r\n%[second_sec, remaining] = strtok(fstrm(first_sec+1:end), '/');\r\n%[remaining, remaining] = strtok(remaining, '%');\r\n%fstrm = [fstrm(1:first_sec) second_sec sprintf('%s\\r', new_style{:}) remaining];\r\nfstrm = regexprep(fstrm,'(% line types:.+?)/.+?%',['$1',sprintf('%s\\r',new_style{:}),'%']);\r\n\r\n% Write the output file\r\nif nargout == 0 || nargin > 1\r\n    read_write_entire_textfile(fname2, fstrm);\r\nend\r\nend\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "PosteriorPrediction1D.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/PosteriorPrediction1D.m", "size": 7063, "source_encoding": "utf_8", "md5": "3b31fa324f33b1e3af91b9ef1a04d68e", "text": "classdef PosteriorPrediction1D < handle\n\t%% PosteriorPrediction1D\n\n\tproperties\n\t\tvariableNames\n\t\tfh\n\t\txInterp\n\t\t%samples\n\t\tY\n\t\tnSamples\n\t\tsamples\n\t\tciType\n\t\tnExamples\n\t\tpointEstimateType\n\t\tpointEstimate\n\t\tshouldPlotData\n\t\txData, yData\n\t\tciWidth\n\t\th % a structure containing handles to figure and plot objects\n\tend\n\n\tproperties(Access = protected)\n\tend\n\n\n\tmethods\n\n\t\t% Class constructor\n\t\tfunction obj=PosteriorPrediction1D(fh, varargin)\n\n\t\t\tp = inputParser;\n\t\t\tp.FunctionName = mfilename;\n\t\t\tp.addRequired('fh',@(x) isa(x,'function_handle'));\n\t\t\tp.addParameter('xInterp',[],@isvector);\n\t\t\tp.addParameter('samples',[],@ismatrix);\n\t\t\tp.addParameter('variableNames',{},@iscellstr);\n\t\t\tp.addParameter('ciType','examples',@(x)any(strcmp(x,{'examples','range','probMass'})));\n\t\t\tp.addParameter('nExamples',100,@isscalar);\n\t\t\tp.addParameter('ciWidth',0.95,@isscalar);\n\t\t\tp.addParameter('pointEstimateType','mean',@isstr);\n\t\t\tp.addParameter('shouldPlotData',true,@islogical);\n\t\t\tp.addParameter('xData',[],@isvector);\n\t\t\tp.addParameter('yData',[],@isvector);\n\t\t\tp.addParameter('pointEstimate',[],@isvector);% if we have precomputed point estimate due to numerical problems (eg with log transformed data etc).\n\t\t\tp.parse(fh, varargin{:});\n\t\t\t% add p.Results fields into obj\n\t\t\tfields = fieldnames(p.Results);\n\t\t\tfor n=1:numel(fields)\n\t\t\t\tobj.(fields{n}) = p.Results.(fields{n});\n\t\t\tend\n\n\t\t\tobj.nSamples= size(obj.samples,1);\n\n\t\t\t% predefine handles for point estimates\n\t\t\tobj.h.hPointEst=[];\n\n\t\t\tif isempty(p.Results.pointEstimate)\n\t\t\t\ttemp = mcmc.UnivariateDistribution(obj.samples,...\n\t\t\t\t\t'shouldPlot',false,...\n\t\t\t\t\t'pointEstimateType',p.Results.pointEstimateType);\n\t\t\t\tobj.pointEstimate = temp.(p.Results.pointEstimateType);\n\t\t\tend\n\n\t\t\t% High-level plotting commands\n\t\t\tif p.Results.shouldPlotData\n\t\t\t\tswitch obj.ciType\n\t\t\t\t\tcase{'examples'}\n\t\t\t\t\t\tobj.plotExamples();\n\t\t\t\t\tcase{'range'}\n\t\t\t\t\t\tobj.plotCI();\n\t\t\t\t\tcase{'probMass'}\n\t\t\t\t\t\tobj.plotProbMass();\n\t\t\t\tend\n\t\t\t\taxis tight\n\t\t\t\tobj.plotPointEstimate();\n\t\t\t\tobj.plotData();\n\t\t\tend\n\t\tend\n\n\t\tfunction evaluateFunction(obj,ExamplesToPlot)\n\t\t\t% Evaluate the 1D function for the x-values specified and for\n\t\t\t% each of the MCMC samples. This will result in a matrix with:\n\t\t\t% rows = number of x-axis values\n\t\t\t% cols = number of MCMC samples\n\t\t\t\n            %fprintf('Evaluating the function over %d MCMC samples...\\n', numel(ExamplesToPlot));\n\n\t\t\tif isempty(ExamplesToPlot)\n\t\t\t\tExamplesToPlot = [1:obj.nSamples];\n\t\t\tend\n\n\t\t\ttry\n\t\t\t\t% If the function handle can deal with vectorised inputs\n\t\t\t\t% then this should compute much faster\n\t\t\t\t%obj.Y = obj.fh(obj.xInterp, obj.samples(:,:))';\n\t\t\t\tobj.Y = obj.fh(obj.xInterp, obj.samples(ExamplesToPlot,:))';\n\t\t\tcatch\n\t\t\t\t% but if that fails, fall back on looping through mcmc\n\t\t\t\t% samples\n\t\t\t\twarning('*** SLOWNESS WARNING ***')\n\t\t\t\twarning('Recommend writing your function in a way that can handle vectorised inputs')\n\t\t\t\tY = zeros(numel(obj.xInterp),numel(ExamplesToPlot));\n\t\t\t\tfor s=1:numel(ExamplesToPlot)\n\t\t\t\t\tobj.Y(:,s) = obj.fh(obj.xInterp, obj.samples(ExamplesToPlot(s),:));\n\t\t\t\tend\n\t\t\tend\n\n\t\tend\n\n\n\t\tfunction obj = plotPointEstimate(obj)\n\t\t\t% Plot a single curve with the single set of parameters. These\n\t\t\t% might correspond to the mode of the MCMC parameters, for\n\t\t\t% example.\n\t\t\t% Calculate the y-values\n\t\t\tYpointEstimate = obj.fh(obj.xInterp, obj.pointEstimate);\n\t\t\t% plot the point estimate\n\t\t\thold on\n\t\t\thPointEst = plot(obj.xInterp, YpointEstimate,'k-', 'LineWidth', 3);\n\t\t\t% concatenate handle onto a list, so that we can plot multiple\n\t\t\t% point estimates and have handles to each\n\t\t\tif numel(obj.h.hPointEst)==0\n\t\t\t\tobj.h.hPointEst = hPointEst;\n\t\t\telse\n\t\t\t\tobj.h.hPointEst = [obj.h.hPointEst hPointEst];\n\t\t\tend\n\t\tend\n\n\n\t\tfunction obj = plotExamples(obj)\n\t\t\t% Plots a random set of example functions, each one corresponds\n\t\t\t% to a particular MCMC sample.\n\t\t\t% If we've asked for more examples, than MCMC samples, then\n\t\t\t% just plot all.\n\t\t\tif obj.nExamples > obj.nSamples\n\t\t\t\tobj.nExamples = obj.nSamples;\n\t\t\tend\n\t\t\t% shuffle the deck and pick the top nExamples\n\t\t\tshuffledExamples = randperm(obj.nSamples);\n\t\t\tExamplesToPlot = shuffledExamples([1:obj.nExamples]);\n\t\t\t% Evaluate the function just for these examples\n\t\t\tobj.evaluateFunction(ExamplesToPlot);\n            try\n                hExamples = plot(obj.xInterp, obj.Y,'-',...\n                    'Color',[0.5 0.5 0.5 0.1]);\n            catch % backward compatability\n                hExamples = plot(obj.xInterp, obj.Y,'-',...\n                    'Color',[0.5 0.5 0.5]);\n            end\n\n% \t\t\thExamples = plot(obj.xInterp, obj.Y(:,ExamplesToPlot),'k-');\n\t\t\tobj.h.Axis\t\t= gca;\n\t\t\tobj.h.hExamples = hExamples;\n\t\t\tformatAxes(obj)\n\t\tend\n\n\t\tfunction obj = plotCI(obj)\n\t\t\tobj.evaluateFunction([1:obj.nSamples]);\n\t\t\t% Plots shaded 95%\n\t\t\tvals = [(1-0.95)/2 1-((1-0.95)/2)].*100;\n\t\t\tCI = prctile(obj.Y',vals);\n\t\t\t% draw the shaded error bar zone\n\t\t\tx = [obj.xInterp,fliplr(obj.xInterp)];\n\t\t\ty = [CI(2,:),fliplr(CI(1,:))];\n\t\t\thCI =patch(x,y,[0.8 0.8 0.8]);\n\t\t\thCI.EdgeColor='none';\n\t\t\t% save handle to CI\n\t\t\tobj.h.hCI = hCI;\n\t\t\tformatAxes(obj)\n\t\tend\n\n\t\tfunction obj = plotProbMass(obj)\n\t\t\t% Plots the posterior predictive distribution in the form of a\n\t\t\t% 2D probability mass function.\n\t\t\tobj.evaluateFunction([1:obj.nSamples]);\n\t\t\tyi = linspace( min(obj.Y(:)), max(obj.Y(:)), 100);\n\t\t\t[PM]=calcProbabilityMass(obj,yi);\n\t\t\thProbMass=imagesc(obj.xInterp, yi, PM);\n\t\t\taxis xy\n\t\t\t% save handle to prob mass\n\t\t\tobj.h.hExamples = hProbMass;\n\t\t\tformatAxes(obj)\n\t\tend\n\n\t\tfunction obj = plotData(obj)\n\t\t\thold on\n\t\t\tif isempty(obj.xData), return, end\n\t\t\tif isempty(obj.yData), return, end\n\t\t\tif ~obj.shouldPlotData, return, end\n\t\t\thData=plot(obj.xData,obj.yData,...\n\t\t\t\t'o',...\n\t\t\t\t'MarkerSize',8,...\n\t\t\t\t'MarkerEdgeColor','k',...\n\t\t\t\t'MarkerFaceColor','w');\n\t\t\t% save handle to data points\n\t\t\tobj.h.hData = hData;\n\t\t\tformatAxes(obj)\n\t\tend\n\n\tend\n\nend\n\n\n\n\n\n\n\n%% Private functions\n\nfunction [PM]=calcProbabilityMass(obj,yi)\n% The matrix obj.Y has a number of rows equal to the number of x-axis\n% values that we are evaluating the function over, and has a number of\n% columns equal to the number of MCMC samples provided. What we do here is\n% to loop over the x-values and convert the set of samples into a\n% probability mass function by use of the hist function. We do this for the\n% y values specified in the vector yi.\ndisplay('Calculating probability mass...')\nobj.evaluateFunction([1:obj.nSamples]);\n% preallocate\nPM = zeros(size(obj.Y,1), numel(yi));\n% loop over x-values, calculating posterior mass for the corresponding\n% samples\nfor x=1:size(obj.Y,1)\n\tPM(x,:) = hist( obj.Y(x,:) , yi );\n\t% scale so the max of each column (x-value) is equal to 1\n\tPM(x,:) =PM(x,:) / max(PM(x,:));\nend\nPM=PM'; % transpose\nend\n\n\nfunction formatAxes(obj)\nif ~isempty(obj.xData)\n\txlim([min(obj.xData) max(obj.xData)])\nend\n% axes on top layer\nah=gca; ah.Layer='top';\naxis square\nbox off\nif ~isempty(obj.variableNames)\n\txlabel(obj.variableNames{1},'Interpreter','latex')\n\tylabel(obj.variableNames{2},'Interpreter','latex')\nend\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "kde.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/+kde/kde.m", "size": 5689, "source_encoding": "utf_8", "md5": "910728965b89850c5c417eef4698a074", "text": "function [bandwidth,density,xmesh,cdf]=kde(data,n,tstar_max,MIN,MAX)\n% Tom Edit: added a tstar_max to limit the bandwidth if desired.  It is a\n% scaled value, default = inf;\n% \n% Reliable and extremely fast kernel density estimator for one-dimensional data;\n%        Gaussian kernel is assumed and the bandwidth is chosen automatically;\n%        Unlike many other implementations, this one is immune to problems\n%        caused by multimodal densities with widely separated modes (see example). The\n%        estimation does not deteriorate for multimodal densities, because we never assume\n%        a parametric model for the data.\n% INPUTS:\n%     data    - a vector of data from which the density estimate is constructed;\n%          n  - the number of mesh points used in the uniform discretization of the\n%               interval [MIN, MAX]; n has to be a power of two; if n is not a power of two, then\n%               n is rounded up to the next power of two, i.e., n is set to n=2^ceil(log2(n));\n%               the default value of n is n=2^12;\n%   MIN, MAX  - defines the interval [MIN,MAX] on which the density estimate is constructed;\n%               the default values of MIN and MAX are:\n%               MIN=min(data)-Range/10 and MAX=max(data)+Range/10, where Range=max(data)-min(data);\n% OUTPUTS:\n%   bandwidth - the optimal bandwidth (Gaussian kernel assumed);\n%     density - column vector of length 'n' with the values of the density\n%               estimate at the grid points;\n%     xmesh   - the grid over which the density estimate is computed;\n%             - If no output is requested, then the code automatically plots a graph of\n%               the density estimate.\n%        cdf  - column vector of length 'n' with the values of the cdf\n%  Reference:\n% Kernel density estimation via diffusion\n% Z. I. Botev, J. F. Grotowski, and D. P. Kroese (2010)\n% Annals of Statistics, Volume 38, Number 5, pages 2916-2957.\n\n%\n%  Example:\n%           data=[randn(100,1);randn(100,1)*2+35 ;randn(100,1)+55];\n%              kde(data,2^14,min(data)-5,max(data)+5);\n\nif ~exist('tstar_max','var')\n    tstar_max = inf;\nend\n\ndata=data(:); %make data a column vector\nif nargin<2 % if n is not supplied switch to the default\n    n=2^14;\nend\nn=2^ceil(log2(n)); % round up n to the next power of 2;\nif nargin<5 %define the default  interval [MIN,MAX]\n    minimum=min(data); maximum=max(data);\n    Range=maximum-minimum;\n    MIN=minimum-Range/2; MAX=maximum+Range/2;\nend\n% set up the grid over which the density estimate is computed;\nR=MAX-MIN; dx=R/(n-1); xmesh=MIN+[0:dx:R]; N=length(unique(data));\n%bin the data uniformly using the grid defined above;\ninitial_data=histc(data,xmesh)/N;  initial_data=initial_data/sum(initial_data);\na=dct1d(initial_data); % discrete cosine transform of initial data\n% now compute the optimal bandwidth^2 using the referenced method\nI=[1:n-1]'.^2; a2=(a(2:end)/2).^2;\n% use  fzero to solve the equation t=zeta*gamma^[5](t)\nt_star=root(@(t)fixed_point(t,N,I,a2),N);\nt_star = min(t_star,tstar_max);\n% smooth the discrete cosine transform of initial data using t_star\na_t=a.*exp(-[0:n-1]'.^2*pi^2*t_star/2);\n% now apply the inverse discrete cosine transform\nif (nargout>1)|(nargout==0)\n    density=idct1d(a_t)/R;\nend\n% take the rescaling of the data into account\nbandwidth=sqrt(t_star)*R;\ndensity(density<0)=eps; % remove negatives due to round-off error\nif nargout==0\n    figure(1), plot(xmesh,density)\nend\n% for cdf estimation\nif nargout>3\n    f=2*pi^2*sum(I.*a2.*exp(-I*pi^2*t_star));\n    t_cdf=(sqrt(pi)*f*N)^(-2/3);\n    % now get values of cdf on grid points using IDCT and cumsum function\n    a_cdf=a.*exp(-[0:n-1]'.^2*pi^2*t_cdf/2);\n    cdf=cumsum(idct1d(a_cdf))*(dx/R);\n    % take the rescaling into account if the bandwidth value is required\n    bandwidth_cdf=sqrt(t_cdf)*R;\nend\n\nend\n%################################################################\nfunction  out=fixed_point(t,N,I,a2)\n% this implements the function t-zeta*gamma^[l](t)\nl=7;\nf=2*pi^(2*l)*sum(I.^l.*a2.*exp(-I*pi^2*t));\nfor s=l-1:-1:2\n    K0=prod([1:2:2*s-1])/sqrt(2*pi);  const=(1+(1/2)^(s+1/2))/3;\n    time=(2*const*K0/N/f)^(2/(3+2*s));\n    f=2*pi^(2*s)*sum(I.^s.*a2.*exp(-I*pi^2*time));\nend\nout=t-(2*N*sqrt(pi)*f)^(-2/5);\nend\n\n\n\n%##############################################################\nfunction out = idct1d(data)\n\n% computes the inverse discrete cosine transform\n[nrows,ncols]=size(data);\n% Compute weights\nweights = nrows*exp(i*(0:nrows-1)*pi/(2*nrows)).';\n% Compute x tilde using equation (5.93) in Jain\ndata = real(ifft(weights.*data));\n% Re-order elements of each column according to equations (5.93) and\n% (5.94) in Jain\nout = zeros(nrows,1);\nout(1:2:nrows) = data(1:nrows/2);\nout(2:2:nrows) = data(nrows:-1:nrows/2+1);\n%   Reference:\n%      A. K. Jain, \"Fundamentals of Digital Image\n%      Processing\", pp. 150-153.\nend\n%##############################################################\n\nfunction data=dct1d(data)\n% computes the discrete cosine transform of the column vector data\n[nrows,ncols]= size(data);\n% Compute weights to multiply DFT coefficients\nweight = [1;2*(exp(-i*(1:nrows-1)*pi/(2*nrows))).'];\n% Re-order the elements of the columns of x\ndata = [ data(1:2:end,:); data(end:-2:2,:) ];\n% Multiply FFT by weights:\ndata= real(weight.* fft(data));\nend\n\nfunction t=root(f,N)\n% try to find smallest root whenever there is more than one\nN=50*(N<=50)+1050*(N>=1050)+N*((N<1050)&(N>50));\ntol=10^-12+0.01*(N-50)/1000;\nflag=0;\nwhile flag==0\n    try\n        t=fzero(f,[0,tol]);\n        flag=1;\n    catch\n        tol=min(tol*2,.1); % double search interval\n    end\n    if tol==.1 % if all else fails\n        t=fminbnd(@(x)abs(f(x)),0,.1); flag=1;\n    end\nend\nend\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "kde2d.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/+kde2d/kde2d.m", "size": 7506, "source_encoding": "utf_8", "md5": "6d82435d2728e267990a5041d6b289b2", "text": "function [bandwidth,density,X,Y]=kde2d(data,n,MIN_XY,MAX_XY)\r\n% fast and accurate state-of-the-art\r\n% bivariate kernel density estimator\r\n% with diagonal bandwidth matrix.\r\n% The kernel is assumed to be Gaussian.\r\n% The two bandwidth parameters are\r\n% chosen optimally without ever\r\n% using/assuming a parametric model for the data or any \"rules of thumb\".\r\n% Unlike many other procedures, this one\r\n% is immune to accuracy failures in the estimation of\r\n% multimodal densities with widely separated modes (see examples).\r\n% INPUTS: data - an N by 2 array with continuous data\r\n%            n - size of the n by n grid over which the density is computed\r\n%                n has to be a power of 2, otherwise n=2^ceil(log2(n));\r\n%                the default value is 2^8;\r\n% MIN_XY,MAX_XY- limits of the bounding box over which the density is computed;\r\n%                the format is:\r\n%                MIN_XY=[lower_Xlim,lower_Ylim]\r\n%                MAX_XY=[upper_Xlim,upper_Ylim].\r\n%                The dafault limits are computed as:\r\n%                MAX=max(data,[],1); MIN=min(data,[],1); Range=MAX-MIN;\r\n%                MAX_XY=MAX+Range/4; MIN_XY=MIN-Range/4;\r\n% OUTPUT: bandwidth - a row vector with the two optimal\r\n%                     bandwidths for a bivaroate Gaussian kernel;\r\n%                     the format is:\r\n%                     bandwidth=[bandwidth_X, bandwidth_Y];\r\n%          density  - an n by n matrix containing the density values over the n by n grid;\r\n%                     density is not computed unless the function is asked for such an output;\r\n%              X,Y  - the meshgrid over which the variable \"density\" has been computed;\r\n%                     the intended usage is as follows:\r\n%                     surf(X,Y,density)\r\n% Example (simple Gaussian mixture)\r\n% clear all\r\n%   % generate a Gaussian mixture with distant modes\r\n%   data=[randn(500,2);\r\n%       randn(500,1)+3.5, randn(500,1);];\r\n%   % call the routine\r\n%     [bandwidth,density,X,Y]=kde2d(data);\r\n%   % plot the data and the density estimate\r\n%     contour3(X,Y,density,50), hold on\r\n%     plot(data(:,1),data(:,2),'r.','MarkerSize',5)\r\n%\r\n% Example (Gaussian mixture with distant modes):\r\n%\r\n% clear all\r\n%  % generate a Gaussian mixture with distant modes\r\n%  data=[randn(100,1), randn(100,1)/4;\r\n%      randn(100,1)+18, randn(100,1);\r\n%      randn(100,1)+15, randn(100,1)/2-18;];\r\n%  % call the routine\r\n%    [bandwidth,density,X,Y]=kde2d(data);\r\n%  % plot the data and the density estimate\r\n%  surf(X,Y,density,'LineStyle','none'), view([0,60])\r\n%  colormap hot, hold on, alpha(.8)\r\n%  set(gca, 'color', 'blue');\r\n%  plot(data(:,1),data(:,2),'w.','MarkerSize',5)\r\n%\r\n% Example (Sinusoidal density):\r\n%\r\n% clear all\r\n%   X=rand(1000,1); Y=sin(X*10*pi)+randn(size(X))/3; data=[X,Y];\r\n%  % apply routine\r\n%  [bandwidth,density,X,Y]=kde2d(data);\r\n%  % plot the data and the density estimate\r\n%  surf(X,Y,density,'LineStyle','none'), view([0,70])\r\n%  colormap hot, hold on, alpha(.8)\r\n%  set(gca, 'color', 'blue');\r\n%  plot(data(:,1),data(:,2),'w.','MarkerSize',5)\r\n%\r\n% Notes: If you have a more accurate density estimator \r\n%        (as measured by which routine attains the smallest \r\n%         L_2 distance between the estimate and the true density) or you have \r\n%        problems running this code, please email me at botev@maths.uq.edu.au \r\n\r\n\r\n%  Reference: Z. I. Botev, J. F. Grotowski and D. P. Kroese\r\n%             \"KERNEL DENSITY ESTIMATION VIA DIFFUSION\" ,Submitted to the\r\n%             Annals of Statistics, 2009\r\nglobal N A2 I\r\nif nargin<2\r\n    n=2^8;\r\nend\r\nn=2^ceil(log2(n)); % round up n to the next power of 2;\r\nN=size(data,1);\r\nif nargin<3\r\n    MAX=max(data,[],1); MIN=min(data,[],1); Range=MAX-MIN;\r\n    MAX_XY=MAX+Range/4; MIN_XY=MIN-Range/4;\r\nend\r\nscaling=MAX_XY-MIN_XY;\r\nif N<=size(data,2)\r\n    error('data has to be an N by 2 array where each row represents a two dimensional observation')\r\nend\r\ntransformed_data=(data-repmat(MIN_XY,N,1))./repmat(scaling,N,1);\r\n%bin the data uniformly using regular grid;\r\ninitial_data=ndhist(transformed_data,n);\r\n% discrete cosine transform of initial data\r\na= dct2d(initial_data);\r\n% now compute the optimal bandwidth^2\r\n  I=(0:n-1).^2; A2=a.^2;\r\n\r\n t_star=fzero( @(t)(t-evolve(t)),[0,0.1]);\r\n\r\np_02=func([0,2],t_star);p_20=func([2,0],t_star); p_11=func([1,1],t_star);\r\nt_y=(p_02^(3/4)/(4*pi*N*p_20^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);\r\nt_x=(p_20^(3/4)/(4*pi*N*p_02^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);\r\n% smooth the discrete cosine transform of initial data using t_star\r\na_t=exp(-(0:n-1)'.^2*pi^2*t_x/2)*exp(-(0:n-1).^2*pi^2*t_y/2).*a; \r\n% now apply the inverse discrete cosine transform\r\nif nargout>1\r\n    density=idct2d(a_t)*(numel(a_t)/prod(scaling));\r\n    [X,Y]=meshgrid(MIN_XY(1):scaling(1)/(n-1):MAX_XY(1),MIN_XY(2):scaling(2)/(n-1):MAX_XY(2));\r\nend\r\nbandwidth=sqrt([t_x,t_y]).*scaling; \r\nend\r\n%#######################################\r\nfunction  [out,time]=evolve(t)\r\nglobal N\r\nSum_func = func([0,2],t) + func([2,0],t) + 2*func([1,1],t);\r\ntime=(2*pi*N*Sum_func)^(-1/3);\r\nout=(t-time)/time;\r\nend\r\n%#######################################\r\nfunction out=func(s,t)\r\nglobal N\r\nif sum(s)<=4\r\n    Sum_func=func([s(1)+1,s(2)],t)+func([s(1),s(2)+1],t); const=(1+1/2^(sum(s)+1))/3;\r\n    time=(-2*const*K(s(1))*K(s(2))/N/Sum_func)^(1/(2+sum(s)));\r\n    out=psi(s,time);\r\nelse\r\n    out=psi(s,t);\r\nend\r\n\r\nend\r\n%#######################################\r\nfunction out=psi(s,Time)\r\nglobal I A2\r\n% s is a vector\r\nw=exp(-I*pi^2*Time).*[1,.5*ones(1,length(I)-1)];\r\nwx=w.*(I.^s(1));\r\nwy=w.*(I.^s(2));\r\nout=(-1)^sum(s)*(wy*A2*wx')*pi^(2*sum(s));\r\nend\r\n%#######################################\r\nfunction out=K(s)\r\nout=(-1)^s*prod((1:2:2*s-1))/sqrt(2*pi);\r\nend\r\n%#######################################\r\nfunction data=dct2d(data)\r\n% computes the 2 dimensional discrete cosine transform of data\r\n% data is an nd cube\r\n[nrows,ncols]= size(data);\r\nif nrows~=ncols\r\n    error('data is not a square array!')\r\nend\r\n% Compute weights to multiply DFT coefficients\r\nw = [1;2*(exp(-i*(1:nrows-1)*pi/(2*nrows))).'];\r\nweight=w(:,ones(1,ncols));\r\ndata=dct1d(dct1d(data)')';\r\n    function transform1d=dct1d(x)\r\n\r\n        % Re-order the elements of the columns of x\r\n        x = [ x(1:2:end,:); x(end:-2:2,:) ];\r\n\r\n        % Multiply FFT by weights:\r\n        transform1d = real(weight.* fft(x));\r\n    end\r\nend\r\n%#######################################\r\nfunction data = idct2d(data)\r\n% computes the 2 dimensional inverse discrete cosine transform\r\n[nrows,ncols]=size(data);\r\n% Compute wieghts\r\nw = exp(i*(0:nrows-1)*pi/(2*nrows)).';\r\nweights=w(:,ones(1,ncols));\r\ndata=idct1d(idct1d(data)');\r\n    function out=idct1d(x)\r\n        y = real(ifft(weights.*x));\r\n        out = zeros(nrows,ncols);\r\n        out(1:2:nrows,:) = y(1:nrows/2,:);\r\n        out(2:2:nrows,:) = y(nrows:-1:nrows/2+1,:);\r\n    end\r\nend\r\n%#######################################\r\nfunction binned_data=ndhist(data,M)\r\n% this function computes the histogram\r\n% of an n-dimensional data set;\r\n% 'data' is nrows by n columns\r\n% M is the number of bins used in each dimension\r\n% so that 'binned_data' is a hypercube with\r\n% size length equal to M;\r\n[nrows,ncols]=size(data);\r\nbins=zeros(nrows,ncols);\r\nfor i=1:ncols\r\n    [dum,bins(:,i)] = histc(data(:,i),[0:1/M:1],1);\r\n    bins(:,i) = min(bins(:,i),M);\r\nend\r\n% Combine the  vectors of 1D bin counts into a grid of nD bin\r\n% counts.\r\nbinned_data = accumarray(bins(all(bins>0,2),:),1/nrows,M(ones(1,ncols)));\r\nend\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "my_shaded_errorbar_zone_UL.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-toolbox/utils-plotting/my_shaded_errorbar_zone_UL.m", "size": 815, "source_encoding": "utf_8", "md5": "5455a1384ba84136387029c983c8a775", "text": "function [h]=my_shaded_errorbar_zone_UL(x,upper,lower,col)\r\n% Plots a shaded region of error\r\n%\r\n% my_shaded_errorbar_zone_UL([-10:0.1:10],[-10:0.1:10]+1,[-10:0.1:10]-1,[0.7 0.7 0.7])\r\n%\r\n% eg, my_shaded_errorbar_zone([-10:0.1:10],x,abs(randn(size(x)))+2,[0 0 1])\r\n%\r\n%handle=patch([x max(x)-x+min(x)],[y+e flipud(y'-e')'],[0.8 0.8 0.8]);\r\n%handle=patch([x max(x)-x+min(x)],[upper flipud(lower')'],[0.8 0.8 0.8]);\r\n%\r\n%\r\n% written by: Benjamin T Vincent\r\n\r\nh = holdDecorator( plotErrorBarZone(x, upper, lower, col) );\r\n\r\nend\r\n\r\nfunction h = plotErrorBarZone(x, upper, lower, col)\r\n% draw the shaded error bar zone\r\nx = [x, fliplr(x)];\r\ny = [upper, fliplr(lower)];\r\nh = patch(x, y, [0.8 0.8 0.8]);\r\n% formatting\r\nuistack(h,'bottom')\r\nset(h,'EdgeColor','none')\r\nset(h,'FaceColor',col)\r\nset(h,'FaceAlpha',0.5)\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "plotUtilityFunction.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-experiments/@Model/plotUtilityFunction.m", "size": 2211, "source_encoding": "utf_8", "md5": "e70f242efd3d46c6ec678cceec49d126", "text": "function plotUtilityFunction(obj, thetaStruct, varargin)\n\np = inputParser;\np.FunctionName = mfilename;\np.addRequired('thetaStruct',@isstruct_or_table);\n% p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));\np.addParameter('data',[],@istable);\np.addParameter('pointEstimateType','mean',@isstr);\n% p.addParameter('maxDelay', [], @isscalar);\np.addParameter('utility_func_function_handle','', @(x) isa(x,'function_handle'))\np.parse(thetaStruct, varargin{:});\ndata = p.Results.data;\n\nplotCurve(data, thetaStruct, p.Results.utility_func_function_handle, p)\n%plotData(data);\nformatAxes(data, p);\nend\n\n\nfunction plotCurve(data, thetaStruct, utility_func_function_handle, p)\n\n\n% create set of rewards to calculate & plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nxVals = linspace(-100, 100, 100);\n\n% evaluate and plot just the first N particles\nN = 200;\nfnames = fieldnames(thetaStruct);\nfor n=1:numel(fnames)\n    thetaStruct.(fnames{n}) = thetaStruct.(fnames{n})([1:N]);\nend\n\n% calculate discount fraction for the given theta samples ~~~~~~~~~~~~~~\nprospect.reward = xVals;\n% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nutilityOfReward = utility_func_function_handle(prospect, thetaStruct);\n\nplot(xVals, utilityOfReward,...\n    'Color',[0 0.4 1 0.1])\n\nhold on\n\n%% plot posterior median as black line\nfor n=1:numel(fnames)\n    thetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));\nend\nprospect.reward = xVals;\nutilityOfReward = utility_func_function_handle(prospect, thetaStruct);\nplot(xVals, utilityOfReward,...\n    'Color', 'k',...\n    'LineWidth', 2)\n\nend\n\n\nfunction formatAxes(data, p)\n%opts = calc_opts(data, p);\nxlabel('$R$', 'interpreter','Latex')\nylabel('$u(R)$', 'interpreter','Latex')\n\nset(gca,'XAxisLocation','origin',...\n    'YAxisLocation','origin',...\n    'box', 'off')\n% box off\n% a = get(gca,'YLim');\n% if opts.maxX>0\n% \txlim( [0 opts.maxX] )\n% end\n% ylim([0 min([a(2),10]) ])\ndrawnow\nend\n\n% function opts = calc_opts(data, p)\n% if ~isempty(data)\n% \topts.maxlogB\t= max( abs(data.R_B) );\n% \topts.maxX\t\t= max( data.D_B ) *1.1;\n% else\n% \topts.maxlogB\t= 1000;\n% \topts.maxX\t\t= 20;\n% end\n% \n% if ~isempty(p.Results.maxDelay)\n% \topts.maxX = p.Results.maxDelay;\n% end\n% end\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "plotProbFunction.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-experiments/@Model/plotProbFunction.m", "size": 4833, "source_encoding": "utf_8", "md5": "2fd2b28515f7226b9bda9d45b14e68a6", "text": "function plotProbFunction(obj, thetaStruct, varargin)\n\np = inputParser;\np.FunctionName = mfilename;\np.addRequired('thetaStruct',@isstruct_or_table);\np.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));\np.addParameter('data',[],@istable);\np.addParameter('pointEstimateType','mean',@isstr);\np.addParameter('discounting_function_handle','', @(x) isa(x,'function_handle'))\np.parse(thetaStruct, varargin{:});\ndata = p.Results.data;\n\nplotCurve(data, thetaStruct, p.Results.discounting_function_handle, p)\nplotData(data);\nformatAxes(data);\nend\n\n\nfunction plotCurve(data, thetaStruct, discounting_function_handle, p)\n\nswitch p.Results.xScale\n\tcase{'linear'}\n\t\t\n\t\txProbVector = linspace(10^-4, 1, 1000);\n\t\txOddsVector = prob2oddsagainst(xProbVector);\n\t\t\n% \t\t% create set of probs to calculate & plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n% \t\tif isempty(data)\n% \t\t\txOddsVector = linspace(0,100,1000);\n% \t\telse\n% \t\t\txOddsVector = linspace(0,100,1000);\n% %             % zoom to data (only useful with the odds discounting type plot)\n% % \t\t\txOddsVector = xOddsVector( xOddsVector < max(prob2oddsagainst(data.P_B)));\n% \t\tend\n% \t\t% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n        \n        %% Plot calibration line for h = 1 = risk neutral\n        risk_neutral = 1 ./ (1+1.*xOddsVector); % hyperbolic discounting function\n\t\t\n\t\t% converting x axis from odds to probability makes it like the\n\t\t% traditional \"probability weighting plot\", as opposed to the\n\t\t% \"discounting of odds plot\"\n        plot(oddsagainst2prob(xOddsVector), risk_neutral, '--',...\n            'Color', [0.7 0.7 0.7],...\n            'LineWidth', 4)\n        hold on\n        \n        % evaluate and plot just the first N particles\n        N = 200;\n        fnames = fieldnames(thetaStruct);\n\t\tfor n=1:numel(fnames)\n\t\t\tthetaStruct.(fnames{n}) = thetaStruct.(fnames{n})([1:N]);\n\t\tend\n\t\t\n\t\t% calculate discount fraction for the given theta samples ~~~~~~~~~~~~~~\n\t\tprospect.prob = xProbVector;\n\t\t% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tdF = discounting_function_handle(prospect, thetaStruct);\n\t\t\n\t\tplot(oddsagainst2prob(xOddsVector), dF,...\n\t\t\t'Color',[0 0.4 1 0.1])\n        hold on\n        \n        %% plot posterior median as black line\n        for n=1:numel(fnames)\n            thetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));\n        end\n        prospect.prob = xProbVector;\n        utilityOfReward = discounting_function_handle(prospect, thetaStruct);\n        plot(oddsagainst2prob(xOddsVector), utilityOfReward,...\n            'Color', 'k',...\n            'LineWidth', 2)\n\t\t\n\tcase{'log'}\n\t\terror('not yet implemented plotting discount fractions with log x-axis')\nend\nend\n\n\nfunction plotData(data)\nif isempty(data)\n\treturn\nend\n[x, y, markerCol, markerSize] = convertDataIntoMarkers(data);\nplotMarkers(oddsagainst2prob(x), y, markerCol, markerSize)\nend\n\n% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nfunction [x, y, markerCol, markerSize] = convertDataIntoMarkers(data)\n% find unique experimental designs\nuniqueDesigns = [abs(data.R_A), abs(data.R_B), data.P_A, data.P_B, data.D_A, data.D_B];\n[C, ia, ic] = unique(uniqueDesigns, 'rows');\n%loop over unique designs (ic)\nfor n=1:max(ic)\n\t% binary set of which trials this design was used on\n\tmyset = ic==n;\n\t% Size = number of times this design has been run\n\tF(n) = sum(myset);\n\t% Colour = proportion of times that participant chose immediate\n\t% for that design\n\tmarkerCol(n) = sum(data.R(myset)==0) ./ F(n);\n\t\n\tmarkerSize(n) = F(n);\n\t\n\tx(n) = prob2oddsagainst(data.P_B( ia(n) ));\n\ty(n) = abs(data.R_A( ia(n) )) ./ abs(data.R_B( ia(n) ));\nend\nend\n% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nfunction plotMarkers(x, y, markerCol, markerSize)\nhold on\nfor i=1:max(numel(x))\n\th = plot(x(i), y(i),'o');\n\th.Color='k';\n\th.MarkerFaceColor=[1 1 1] .* (1-markerCol(i));\n\th.MarkerSize = markerSize(i)+4;\n\thold on\nend\nend\n\n% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nfunction formatAxes(data)\nopts = calc_opts(data);\nxlabel('odds against Prospect B', 'interpreter','Latex')\nxlabel('objective probability, $P$', 'interpreter','Latex')\nylabel('$\\pi(P)$', 'interpreter','Latex')\nbox off\n% a = get(gca,'YLim');\n% if opts.maxX > 0\n% \txlim( [0 opts.maxX*1.1] )\n% else\n%     x=get(gca,'XLim');\n%     xlim([0 a(2)])\n% end\nxlim([0 1])\nylim([0 1])\n\n%% Add descriptive helper text\naddTextToFigure('TL', 'risk seeking domain', 10)\naddTextToFigure('BR', 'risk avoidant domain', 10)\n\ndrawnow\nend\n\nfunction opts = calc_opts(data)\nif ~isempty(data)\n\topts.maxlogB\t= max( abs(data.R_B) );\n\topts.maxX\t\t= max( prob2oddsagainst(data.P_B) );\nelse\n\topts.maxlogB\t= 1000;\n\topts.maxX\t\t= 365;\nend\nend\n% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "plotDiscountFunction.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-experiments/@Model/plotDiscountFunction.m", "size": 4074, "source_encoding": "UNKNOWN", "md5": "cd3e7d94e8978e564eef501175a3a9c6", "text": "function plotDiscountFunction(obj, thetaStruct, varargin)\n\np = inputParser;\np.FunctionName = mfilename;\np.addRequired('thetaStruct',@isstruct_or_table);\np.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));\np.addParameter('data',[],@istable);\np.addParameter('pointEstimateType','mean',@isstr);\np.addParameter('maxDelay', [], @isscalar);\np.addParameter('discounting_function_handle','', @(x) isa(x,'function_handle'))\np.parse(thetaStruct, varargin{:});\ndata = p.Results.data;\n\nplotCurve(data, thetaStruct, p.Results.discounting_function_handle, p)\nplotData(data);\nformatAxes(data, p);\nend\n\n\nfunction plotCurve(data, thetaStruct, discounting_function_handle, p)\n\nswitch p.Results.xScale\n\tcase{'linear'}\n\n\t\t% create set of delays to calculate & plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tif ~isempty(p.Results.maxDelay)\n\t\t% if isempty(data)\n\t\t\txVals = logspace(-3,3,1000);\n\t\t\txVals = xVals(xVals<p.Results.maxDelay);\n\t\telse\n\t\t\tmax_delay_of_data = max([ data.D_A; data.D_B]);\n\t\t\txVals = logspace(-3,4,1000);\n\t\t\txVals = xVals(xVals<max_delay_of_data);\n\t\tend\n\t\t% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\t\t% evaluate and plot just the first N particles\n\t\tN = 200;\n\t\tfnames = fieldnames(thetaStruct);\n\t\tfor n=1:numel(fnames)\n\t\t\tthetaStruct.(fnames{n}) = thetaStruct.(fnames{n})([1:N]);\n\t\tend\n\n\t\t% calculate discount fraction for the given theta samples ~~~~~~~~~~~~~~\n\t\tprospect.delay = xVals;\n\t\t% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tdF = discounting_function_handle(prospect, thetaStruct);\n\n\t\tplot(xVals, dF,...\n\t\t\t'Color',[0 0.4 1 0.1])\n        hold on\n        \n        %% plot posterior median as black line\n        for n=1:numel(fnames)\n            thetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));\n        end\n        prospect.reward = xVals;\n        utilityOfReward = discounting_function_handle(prospect, thetaStruct);\n        plot(xVals, utilityOfReward,...\n            'Color', 'k',...\n            'LineWidth', 2)\n\n\n\tcase{'log'}\n\t\terror('not yet implemented plotting discount fractions with log x-axis')\nend\nend\n\n\nfunction plotData(data)\nif isempty(data)\n\treturn\nend\n[x, y, markerCol, markerSize] = convertDataIntoMarkers(data);\nplotMarkers(x, y, markerCol, markerSize)\nend\n\nfunction [x, y, markerCol, markerSize] = convertDataIntoMarkers(data)\n% find unique experimental designs\nuniqueDesigns = [abs(data.R_A), abs(data.R_B), data.D_A, data.D_B, data.D_A, data.D_B];\n[C, ia, ic] = unique(uniqueDesigns, 'rows');\n%loop over unique designs (ic)\nfor n=1:max(ic)\n\t% binary set of which trials this design was used on\n\tmyset = ic==n;\n\t% Size = number of times this design has been run\n\tF(n) = sum(myset);\n\t% Colour = proportion of times that participant chose immediate\n\t% for that design\n\tmarkerCol(n) = sum(data.R(myset)==0) ./ F(n);\n\n\tmarkerSize(n) = F(n);\n\n\tx(n) = data.D_B( ia(n) ); % delay to get �R_B\n\ty(n) = abs(data.R_A( ia(n) )) ./ abs(data.R_B( ia(n) ));\nend\nend\n\nfunction plotMarkers(x, y, markerCol, markerSize)\nhold on\nfor i=1:max(numel(x))\n\th = plot(x(i), y(i),'o');\n\th.Color='k';\n\th.MarkerFaceColor=[1 1 1] .* (1-markerCol(i));\n\th.MarkerSize = markerSize(i)+4;\n\thold on\nend\nend\n\n% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nfunction formatAxes(data, p)\nopts = calc_opts(data, p);\nxlabel('delay, $D^b$', 'interpreter','Latex')\nylabel('$d(D)$', 'interpreter','Latex')\nbox off\na = get(gca,'YLim');\nif opts.maxX>0\n\txlim( [0 opts.maxX] )\nelse\n    x=get(gca,'XLim');\n    xlim([0 a(2)])\nend\nylim([0 min(10, max([a(2),1])) ])\n\n%% Add descriptive helper text\naddTextToFigure('TR', 'choose immediate', 10, 'Color', [0.7 0.7 0.7])\naddTextToFigure('BL', ' choose delayed', 10, 'Color', [0.7 0.7 0.7])\n\ndrawnow\nend\n\nfunction opts = calc_opts(data, p)\nif ~isempty(data)\n\topts.maxlogB\t= max( abs(data.R_B) );\n\topts.maxX\t\t= max( data.D_B ) *1.1;\nelse\n\topts.maxlogB\t= 1000;\n\topts.maxX\t\t= 20;\nend\n\nif ~isempty(p.Results.maxDelay)\n\topts.maxX = p.Results.maxDelay;\nend\nend\n% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "plotDiscountSurface.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-experiments/@Model_hyperbolic1_time_and_prob/plotDiscountSurface.m", "size": 4143, "source_encoding": "UNKNOWN", "md5": "09dea0179f9b5215e875502ae0b37f08", "text": "function plotDiscountSurface(obj, thetaStruct, varargin)\n% plots prob and time discount surface\n\np = inputParser;\np.FunctionName = mfilename;\np.addRequired('thetaStruct',@isstruct);\np.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));\np.addParameter('data',[],@isstruct_or_table)\np.addParameter('pointEstimateType','mean',@isstr);\np.addParameter('prob_discounting_function_handle','', @(x) isa(x,'function_handle'))\np.addParameter('time_discounting_function_handle','', @(x) isa(x,'function_handle'))\np.parse(thetaStruct, varargin{:});\ndata = p.Results.data;\n\nplotSurface(data, thetaStruct, p.Results.prob_discounting_function_handle, p.Results.time_discounting_function_handle, p)\nplotData(data)\nformatAxes(data);\ntitle({'P_A chance of �R_A in D_A days','P_B chance of �R_B in D_B days'})\nend\n\n\nfunction plotSurface(data, thetaStruct, probDiscountingFunctionFH, timeDiscountingFunctionFH, p)\n\n% create set of delays to calculate & plot\nN_DELAYS = 10;\nN_ODDS = 12;\nif isempty(data)\n\tdelays = linspace(0,365,N_DELAYS);\nelse\n\tmax_delay_of_data = max([ data.D_A; data.D_B]);\n\tdelays = linspace(0, max_delay_of_data, N_DELAYS);\nend\n\nif isempty(data)\n\todds = linspace(0, 20, N_ODDS);\nelse\n\todds = linspace(0, max(prob2oddsagainst(data.P_B)), N_ODDS);\nend\n\n% Evaluate only the posterior median\nfnames = fieldnames(thetaStruct);\nfor n=1:numel(fnames)\n\tthetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));\nend\n\nwarning('CREATE THIS NESTED PARAMETER STRUCTURE AUTOMATICALLY')\nnestedParamStruct.prob.h = thetaStruct.h;\nnestedParamStruct.delay.logk = thetaStruct.logk;\n\n%opts = calc_opts(data);\n\n% create grid of values\n[odds_grid, delays_grid] = meshgrid(odds,delays); % create x,y (b,d) grid values\n\nwarning('this is duplication of model.calcPresentSubjectiveValue()')\nprospect.reward = [];\nprospect.delay = delays_grid(:);\nprospect.prob = oddsagainst2prob(odds_grid(:));\nV = ...\n\tprobDiscountingFunctionFH(prospect, nestedParamStruct.prob) .* ...\n\ttimeDiscountingFunctionFH(prospect, nestedParamStruct.delay);\nV = reshape(V, size(odds_grid));\n\n\n\n%% PLOT\nhmesh = mesh(odds_grid, delays_grid, V);\n% shading\nhmesh.FaceColor\t\t='w';\nhmesh.FaceAlpha\t\t=0.7;\n% edges\nhmesh.MeshStyle\t\t='both';\nhmesh.EdgeColor\t\t='k';\nhmesh.EdgeAlpha\t\t=1;\n\n% plot isolines\nhold on\n[c,h] = contour3(odds_grid, delays_grid, V, [0.2:0.2:0.8]);\nh.LineColor = 'k';\nh.LineWidth = 4;\nend\n\nfunction plotData(data)\nif isempty(data)\n\treturn\nend\n[x,y,z,markerCol,markerSize] = convertDataIntoMarkers(data);\nplotMarkers(x, y, z, markerCol, markerSize)\nend\n\nfunction [x,y,z,markerCol,markerSize] = convertDataIntoMarkers(data)\n% find unique experimental designs\nuniqueDelays = [abs(data.R_A), abs(data.R_B), data.D_A, data.D_B, data.D_A, data.D_B];\n[C, ia, ic] = unique(uniqueDelays,'rows');\n% loop over unique designs (ic)\nfor n=1:max(ic)\n\t% binary set of which trials this design was used on\n\tmyset=ic==n;\n\t% markerSize = number of times this design has been run\n\tmarkerSize(n) = sum(myset);\n\t% Colour = proportion of times participant chose immediate for that design\n\tmarkerCol(n) = sum(data.R(myset)==0) ./ markerSize(n);\n\t\n\tx(n) = prob2oddsagainst( data.P_B( ia(n) ) ); % odds against\n\ty(n) = data.D_B( ia(n) ); % delay\n\tz(n) = abs(data.R_A(ia(n))) ./ abs(data.R_B( ia(n)));\nend\nend\n\nfunction plotMarkers(x, y, z, markerCol, markerSize)\nhold on\nfor i=1:numel(x)\n\th = stem3(x(i), y(i), z(i));\n\th.Color='k';\n\th.MarkerFaceColor=[1 1 1] .* (1-markerCol(i));\n\th.MarkerSize = markerSize(i)+4;\n\thold on\nend\nend\n\nfunction formatAxes(data)\n%opts = calc_opts(data);\nxlabel('Odds against, $\\frac{1-P^B}{P^B}$', 'interpreter','latex')\nylabel('delay $D^B$', 'interpreter','latex')\nzlabel('discount factor (and $\\frac{R_A}{R_B}$)', 'interpreter','latex')\n\nview([90+45, 20])\naxis vis3d\naxis tight\naxis square\nzlim([0 1])\ncamproj('perspective')\nset(gca,'ZTick',[0:0.2:1])\nend\n\nfunction opts = calc_opts(data)\nif ~isempty(data)\n\topts.maxlogB\t= max( abs(data.R_B) );\n\topts.maxD\t\t= max( data.D_B );\nelse\n\topts.maxlogB\t= 1000;\n\topts.maxD\t\t= 365;\nend\n\n% what does this even do?\nopts.nIndifferenceLines = 10;\npow=1; while opts.maxlogB > 10^pow; pow=pow+1; end\nopts.pow = pow;\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "plotDiscountSurface.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-experiments/@Model_hyperbolic1ME_time/plotDiscountSurface.m", "size": 3941, "source_encoding": "UNKNOWN", "md5": "5e5f8fc832f6a6154c0225d60dd272f9", "text": "function plotDiscountSurface(obj, thetaStruct, varargin)\n\np = inputParser;\np.FunctionName = mfilename;\np.addRequired('thetaStruct',@isstruct);\np.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));\np.addParameter('data',[],@isstruct_or_table)\np.addParameter('pointEstimateType','mean',@isstr);\np.addParameter('discounting_function_handle','', @(x) isa(x,'function_handle'))\np.parse(thetaStruct, varargin{:});\ndata = p.Results.data;\n\nplotSurface(data, thetaStruct, p.Results.discounting_function_handle, p)\nplotData(data)\nformatAxes(data);\nend\n\n\nfunction plotSurface(data, thetaStruct, discounting_function_handle, p)\n\n% create set of delays to calculate & plot\nN_DELAYS = 10;\nif isempty(data)\n\tdelays = linspace(0,365,N_DELAYS);\nelse\n\tmax_delay_of_data = max([ data.D_A; data.D_B]);\n\tdelays = linspace(0, max_delay_of_data, N_DELAYS);\nend\n\nopts = calc_opts(data);\n\n%% x-axis = b\nN_REWARDS = 10;\nlogbvec = log(logspace(1, opts.pow, N_REWARDS));\n\n% %% y-axis = d\n% dvec = linspace(0, opts.maxD, 15);\n\n%% z-axis (AB)\n[logB,D] = meshgrid(logbvec,delays); % create x,y (b,d) grid values\n\n% -------------------------------------------------------------------------\nwarning('Stop doing this kludge and do it properly (see below)')\nm = median(thetaStruct.m);\nc = median(thetaStruct.c);\nk\t\t= exp(m .* logB + c); % magnitude effect\nAB\t\t= 1 ./ (1 + k.*D); % hyperbolic discount function\n% DO IT PROPERLY, LIKE BELOW ----------------------------------------------\n% delays = D;\n% reward = exp(logB);\n% prospect.reward = reward';\n% prospect.delay = delays';\n% pointEst.m = median(thetaStruct.m);\n% pointEst.c = median(thetaStruct.c);\n% AB = discounting_function_handle(prospect, pointEst);\n% -------------------------------------------------------------------------\n\n%% PLOT\nR_B = exp(logB);\nhmesh = mesh(R_B, D, AB);\n% shading\nhmesh.FaceColor\t\t='w';\nhmesh.FaceAlpha\t\t=0.7;\n% edges\nhmesh.MeshStyle\t\t='both';\nhmesh.EdgeColor\t\t='k';\nhmesh.EdgeAlpha\t\t=1;\n\n% plot isolines\nhold on\n[c,h] = contour3(R_B, D, AB, [0.2:0.2:0.8]);\nh.LineColor = 'k';\nh.LineWidth = 4;\n\nend\n\nfunction plotData(data)\nif isempty(data)\n\treturn\nend\n[x,y,z,markerCol,markerSize] = convertDataIntoMarkers(data);\nplotMarkers(x, y, z, markerCol, markerSize)\nend\n\nfunction [x,y,z,markerCol,markerSize] = convertDataIntoMarkers(data)\n% find unique experimental designs\nD=[abs(data.R_A), abs(data.R_B), data.D_A, data.D_B];\n[C, ia, ic] = unique(D,'rows');\n% loop over unique designs (ic)\nfor n=1:max(ic)\n\t% binary set of which trials this design was used on\n\tmyset=ic==n;\n\t% markerSize = number of times this design has been run\n\tmarkerSize(n) = sum(myset);\n\t% Colour = proportion of times participant chose immediate for that design\n\tmarkerCol(n) = sum(data.R(myset)==0) ./ markerSize(n);\n\n\tx(n) = abs(data.R_B( ia(n) )); % �R_B\n\ty(n) = data.D_B( ia(n) ); % delay to get �R_B\n\t%z(n) = abs(data.R_A_over_R_B( ia(n) ).*data.R_B( ia(n) )) ./ abs(data.R_B( ia(n) ));\n\tz(n) = abs(data.R_A(ia(n))) ./ abs(data.R_B( ia(n)));\nend\nend\n\nfunction plotMarkers(x, y, z, markerCol, markerSize)\nhold on\nfor i=1:numel(x)\n\th = stem3(x(i), y(i), z(i));\n\th.Color='k';\n\th.MarkerFaceColor=[1 1 1] .* (1-markerCol(i));\n\th.MarkerSize = markerSize(i)+4;\n\thold on\nend\nend\n\nfunction formatAxes(data)\nxlabel('$|R^L|$', 'interpreter','latex')\nylabel('delay $D^b$', 'interpreter','latex')\nzlabel('discount factor (and $\\frac{R_A}{R_B}$)', 'interpreter','latex')\n\nopts = calc_opts(data);\nview([90+45, 20])\naxis vis3d\naxis tight\naxis square\nzlim([0 1])\nset(gca,...\n\t'XDir','reverse',...\n\t'XScale','log',...\n\t'XTick',logspace(1,opts.pow,opts.pow-1+1))\nset(gca,'ZTick',[0:0.2:1])\ncamproj('perspective')\nend\n\nfunction opts = calc_opts(data)\nif ~isempty(data)\n\topts.maxlogB\t= max( abs(data.R_B) );\n\topts.maxD\t\t= max( data.D_B );\nelse\n\topts.maxlogB\t= 1000;\n\topts.maxD\t\t= 365;\nend\n\n% what does this even do?\nopts.nIndifferenceLines = 10;\npow=1; while opts.maxlogB > 10^pow; pow=pow+1; end\nopts.pow = pow;\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "generate_designs.m", "ext": ".m", "path": "darc-experiments-matlab-master/darc-experiments/response_error_types/@ChoiceFuncPsychometric/generate_designs.m", "size": 9159, "source_encoding": "utf_8", "md5": "76956e5e60901d47f17673051e41dc9a", "text": "function designs_allowed = generate_designs(obj, previous_designs, responses, thetas)\n%generate_designs\n%\n% Create a matrix of possible designs. These will be considered by our\n% optimization procedure.\n%\n% Inputs\n%\n% Outputs\n% designs_allowed: A matrix of designs. Each column is one component of the\n%\t\t\t\tdesign space. Each row is one design.\n%\n% Tom Rainforth 01/10/16\n\n%% Setup options and variables\n\nfree_design_fields = obj.design_variables(~obj.is_design_variable_fixed());\nfree_design_vals = struct;\nfor n=1:numel(free_design_fields)\n    free_design_vals.(free_design_fields{n}) = obj.(free_design_fields{n});\nend\n\nfixed_design_fields = obj.design_variables(obj.is_design_variable_fixed());\nfixed_design_vals = struct;\nfor n=1:numel(fixed_design_fields)\n    fixed_design_vals.(fixed_design_fields{n}) = obj.(fixed_design_fields{n});\nend\n\n% Load previous designs into the workspace\nobj.unpackDesigns(previous_designs);\n\nn_d = size(previous_designs,1);\nmod_h = mod(n_d,1/obj.heuristic_rate);\nb_use_heuristic = ~isnan(mod_h) && mod_h<0.9999; % Numerical stability\nif b_use_heuristic\n    strategy = obj.heuristic_strategy;\nelse\n    strategy = 'no_heuristic';\nend\n\n%% Read in designs\n\nif strcmpi(strategy,'random_no_replacement')\n    % This is the old strategy that uses the heuristic order and then\n    % randomly chooses all but the number.  For this not all designs are\n    % evaluated\n    \n    free_params = obj.params(~obj.is_theta_fixed());\n    free_params = setdiff(free_params,{'alpha','epsilon'}); % Ignore alpha and epsilon\n    n_design_allowed = numel(free_params);\n    n_designs_to_set = numel(free_design_fields)-n_design_allowed;\n    \n    heuristic_counter = 1;\n    \n    for n=1:numel(obj.heuristic_order)\n        if heuristic_counter > n_designs_to_set\n            break\n        end\n        if any(strcmp(obj.heuristic_order{n},free_design_fields))\n            eval(['heuristic_value = random_no_replacement(obj,' obj.heuristic_order{n} ',''' obj.heuristic_order{n} ''');']);\n            free_design_vals = rmfield(free_design_vals,obj.heuristic_order{n});\n            fixed_design_vals.(obj.heuristic_order{n}) = heuristic_value;\n            heuristic_counter = heuristic_counter+1;\n        end\n    end\n    designs_allowed = gen_designs(obj,free_design_vals,fixed_design_vals);\nelse\n    % Other strategies start by laying out all the designs\n    \n    % First generate all the possible designs\n    designs_allowed = gen_designs(obj,free_design_vals,fixed_design_vals);\nend\n\n%% Eliminate designs already tried of with no chance of being helpful\n\n% Eliminate designs already tried\nif ~isempty(previous_designs)\n    designs_allowed = setdiff(designs_allowed,previous_designs,'rows');\nend\n\n% Eliminate designs whose response is effectively known using the point\n% estimate\n% Now make a point estimate for theta and use it to calculate the\n% sooner and later subjective values for all of these possible\n% designs\ntheta = point_estimate_theta(obj,thetas,'mean');\nalpha = [];\nobj.unpackTheta(theta);\n[VA, VB] = obj.subjective_values(theta,designs_allowed);\nVsum = VA+VB;\nVdiff = VB-VA;\n\n% Eliminate Vsum points where the response is effectively certain\n% such that these are clearly poor designs\np_raw = normcdf(Vdiff/alpha); % Prob response ignoring epsilon\nb_extreme = p_raw<0.005 | p_raw>0.995;\nn_not_extreme = sum(~b_extreme);\nif n_not_extreme<10\n    % Cop out as we don't have a reasonable number of sensible\n    % designs left, take the ten smallest differences (with some\n    % noise to split ties randomly)\n    [~,is] = sort(abs(Vdiff)+(1e-10)*rand(size(Vdiff)));\n    n_take = min(10,numel(is));\n    designs_allowed = designs_allowed(is(1:n_take),:);\n    return\nend\nVsum = Vsum(~b_extreme);\np_raw = p_raw(~b_extreme);\ndesigns_allowed = designs_allowed(~b_extreme,:);\n\n%% Do further design elimination heuristics\n\nif any(strcmpi(strategy,{'no_heuristic','random_no_replacement'}))\n    \n    % For no_heuristic heuristic and random_no_replacement we are now done\n    \nelseif strcmpi(strategy,'subjective_value_spreading')\n    \n    \n    if size(previous_designs,1)<4 % Old was 2\n        % Not enough previous designs.  Let the experimental design\n        % method do its magic\n        return\n    end\n    \n    % Use a kernel density estimator to get the distribution of\n    % Vsum and evaluate at the Vsum points.\n    [VSp,VLp] = obj.subjective_values(theta,previous_designs);\n    Vpsum = VSp+VLp;\n    Vpdiff = VLp-VSp;\n    p_raw_p = normcdf(Vpdiff/alpha);\n    b_p_extreme = p_raw_p<0.005 | p_raw_p>0.995;\n    if sum(~b_p_extreme) > 3 % Old was 1\n        % We don't care about even spacing with what turned out to\n        % be useless questions.  We want an even spacing of the\n        % pertinent ones.  Therefore we only look at distance to\n        % helpful questions for choosing were to go.\n        Vpsum = Vpsum(~b_p_extreme);\n        Vpdiff = Vpdiff(~b_p_extreme);\n    else\n        % There are 3 or less useful questions remain so again don't\n        % care about even space.  Let the optimizer do its magic\n        return\n    end\n    % Find the point in Vsum space that is further from previous\n    % values using\n    hard_coded_scale = 0.1;\n    Vden = kernel_dist(Vsum,Vpsum,hard_coded_scale*(max(Vsum)-min(Vsum)));\n    [~,imin] = min(Vden);\n    \n    % Now only look at points that are close to this in Vsum space.\n    % What we will do is to chop up the p_raw space (i.e. bin the\n    % output probabilities, ignoring epsilon) and then choose the\n    % closest sample to Vsum(imin) in each bin.  This gives a good\n    % spread of probabilities while maintaining points close the target\n    % Vsum.\n    VsumDiff = Vsum-Vsum(imin);\n    % Partitions are uneven as more likely to want to be near the\n    % middle, for now will be even though\n    bin_pos = 0:(1/obj.n_design_opt):1;\n    %bin_pos = betacdf(0:(1/obj.n_design_opt):1,0.5,0.5); % Alternative\n    %for uneven\n    [~,i_bin] = histc(p_raw,bin_pos);\n    % Sort first by bin then the absolute difference to target point.\n    [i_p,i_s] = sortrows([i_bin,abs(VsumDiff)]);\n    % Take the first of each type\n    i_take = [1;1+find(diff(i_p(:,1))~=0)];\n    prob_diffs_take = i_p(i_take,2)/(max(Vsum)-min(Vsum));\n    % We want to eliminate any differences that are too high without\n    % removing all of them\n    hard_closeness_coded_threshold = 0.4/sqrt(size(previous_designs,1));\n    b_too_far = prob_diffs_take>hard_closeness_coded_threshold;\n    i_take = i_take(~b_too_far);\n    designs_allowed = designs_allowed(i_s(i_take),:);\n    \n    % To see whats going on set below to true\n    b_debug_plot = false;\n    \n    if b_debug_plot && size(previous_designs,1)>2 && size(previous_designs,1)>5 && mod(size(previous_designs,1),5)==0\n        % First lets look at Vsum vs p_raw for the candidates (blue),\n        % previous designs (red), and designes selected to be allowed\n        % (green).\n        figure;\n        plot(VA+VB,normcdf((VB-VA)/alpha),'x');\n        hold on;\n        plot([Vsum(imin),Vsum(imin)],[0,1],'--g','LineWidth',2);\n        plot(Vpsum,normcdf(Vpdiff/alpha),'rx','MarkerSize',6,'LineWidth',4);\n        [VSdebug,VLdebug] = obj.subjective_values(theta,designs_allowed);\n        plot(VSdebug+VLdebug,normcdf((VLdebug-VSdebug)/alpha),'gx','MarkerSize',6,'LineWidth',4);\n        % Now lets look at the density of previously chosen Vrank's\n        % along with their positions and the new allowed positions.\n        figure;\n        plot(Vsum,Vden,'x');\n        hold on;\n        plot(Vpsum,zeros(size(Vpsum)),'rx','MarkerSize',6,'LineWidth',4);\n        plot(VSdebug+VLdebug,zeros(size(VSdebug)),'gx','MarkerSize',6,'LineWidth',4);\n        % Pause to let us look\n        keyboard;\n    end\n    \nend\n\nend\n\nfunction designs_allowed = gen_designs(obj,free_design_vals,fixed_design_vals)\n\nfree_design_fields = fields(free_design_vals);\n% Generates variables in this file for the previous design variables\n\ndesign_vars = fixed_design_vals;\nfor m=1:numel(free_design_fields)\n    design_vars.(free_design_fields{m}) = free_design_vals.(free_design_fields{m});\nend\n\nnd_grid_string = '[';\nfor m=1:numel(obj.design_variables)\n    nd_grid_string = [nd_grid_string, obj.design_variables{m} ','];\nend\nnd_grid_string = [nd_grid_string(1:end-1) '] = ndgrid('];\nfor m=1:numel(obj.design_variables)\n    nd_grid_string = [nd_grid_string, 'design_vars.' obj.design_variables{m} ','];\nend\nnd_grid_string = [nd_grid_string(1:end-1) ');'];\neval(nd_grid_string);\n\ndesigns_allowed = [];\nfor m=1:numel(fields(design_vars))\n    eval(['designs_allowed = [designs_allowed,' obj.design_variables{m} '(:)];']);\nend\n\nend\n\nfunction v = random_no_replacement(obj,previous_vals,var_name)\nallowed_vals = obj.(var_name);\nleft_vals = setdiff(allowed_vals,previous_vals);\nif isempty(left_vals)\n    [~,i_int] = unique(previous_vals);\n    i_left = setdiff(1:numel(previous_vals),i_int);\n    twice_vals = previous_vals(i_left);\n    if isempty(twice_vals)\n        left_vals = previous_vals;\n    else\n        v = random_no_replacement(obj,twice_vals,var_name);\n        return\n    end\nend\nv = datasample(left_vals,1);\n\nend\n\nfunction d = kernel_dist(V1,V2,scale)\n\nd = mean(exp(-(bsxfun(@minus,V1,V2').^2)/scale^2),2);\n\nend"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "SCRIPT_logk_comparison_of_methods.m", "ext": ".m", "path": "darc-experiments-matlab-master/generate_figs_for_paper/SCRIPT_logk_comparison_of_methods.m", "size": 3145, "source_encoding": "utf_8", "md5": "e64012ec647f029f09c5a77568cecc0c", "text": "function SCRIPT_logk_comparison_of_methods()\n\naddpath('darc-experiments')\n\n\n%% Sort figure and subpanel arrangement\nfh = figure(56); clf, drawnow\nset(fh, 'WindowStyle','normal')\nnrows = 4;\nsubplot_handles = layout([1,2,3,4; 5,6,7,8; 9,10,11,12]');\ndrawnow\n\n\n%% Load data for the 3 example participants\nexamples = makeExamples();\nassert(numel(examples)==3, 'expecting 3 examples')\n\n\n%% Iterate over the 3 examples, plotting as we go\nfor n=1:numel(examples)\n\tfprintf('%d of %d\\n',n, numel(examples))\n\tind = nrows*(n-1)+1;\n\tprocess_this_example( examples(n), subplot_handles([ind:ind+(nrows-1)]))\nend\n\n\n% NOTE (x,y) position is in DATA units\n%% Add column titles (example name)\ntop_plots = subplot_handles([1, 5, 9]);\nfor n=1:numel(top_plots)\n\tsubplot(top_plots(n))\n\th = text(365/2, 1.25, examples(n).title);\n\th.HorizontalAlignment = 'center';\n\th.FontWeight = 'bold';\n\th.FontSize = 16;\nend\n\n\n%% Add row titles\nrow_title_labels = {'Kirby (2009)',...\n\t'Koffarnus & Bickel (2014)',...\n\t'Frye et al (2016)',...\n\t'our approach'};\ntop_plots = subplot_handles([1, 2, 3, 4]);\nfor n=1:numel(top_plots)\n\tsubplot(top_plots(n))\n\th = text(-100, 0.5, row_title_labels{n});\n\th.Rotation = 90;\n\th.HorizontalAlignment = 'center';\n\th.FontWeight = 'bold';\n\th.FontSize = 16;\nend\n\n% save data\nsave('figs/saved_data_logk_comparison_of_models')\n\n%% Export\nsetAllSubplotOptions(gcf, {'LineWidth', 1.5, 'FontSize',12})\nset(subplot_handles, 'PlotBoxAspectRatio',[1.5 1 1])\nset(gcf,'Position',[10 10 1200 1300])\nensureFolderExists('figs')\nsavefig('figs/logk_comparison_of_models')\n%export_fig('figs/logk_comparison_of_models', '-pdf')\nexport_fig('figs/logk_comparison_of_models', '-png', '-m6');\n\nend\n\n\n\nfunction process_this_example(example, subplot_handles)\n%  Run models\ntrue_logk = example.true_theta;\n% [kirby_Model_to_plot, ktheta, kdata, ~,...\n% \tadaptive_Model_to_plot, adaptive_theta, adaptive_data, ~]...\n% \t= runKirbyAndAdaptive(true_logk);\n\ntrials = 27;\nMAX_DELAY = 365;\n\n%% Kirby example\n[model, theta, data, ~] = runKirby(true_logk, 27);\nsubplot(subplot_handles(1))\nmodel.plotDiscountFunction(theta(:,1),...\n\t'data', data,...\n\t'discounting_function_handle', @model.delayDiscountingFunction, ...\n\t'maxDelay', MAX_DELAY);\ndrawnow\n\n%% KoffarnusAndBickel\n[model, theta, data, ~] = runKoffarnusAndBickel(true_logk, 5);\nsubplot(subplot_handles(2))\nmodel.plotDiscountFunction(theta(:,1),...\n\t'data', data,...\n\t'discounting_function_handle', @model.delayDiscountingFunction, ...\n\t'maxDelay', MAX_DELAY);\ndrawnow\n\n%% Fry Et al\n[model, theta, data, ~] = runFryeEtAl(true_logk, 5*5);\nsubplot(subplot_handles(3))\nmodel.plotDiscountFunction(theta(:,1),...\n\t'data', data,...\n\t'discounting_function_handle', @model.delayDiscountingFunction, ...\n\t'maxDelay', MAX_DELAY);\ndrawnow\n\n%% Our method\n% override default delays with this\n%tempD_B = default_D_B(); D_B = tempD_B(tempD_B<=190);\nD_B = [1:7:MAX_DELAY];\n\n[model, theta, data, ~] = runAdaptiveLogK(true_logk, 30,...\n\t'D_B', D_B);\nsubplot(subplot_handles(4))\nmodel.plotDiscountFunction(theta(:,1),...\n\t'data', data,...\n\t'discounting_function_handle', @model.delayDiscountingFunction, ...\n\t'maxDelay', MAX_DELAY);\ndrawnow\n\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "SCRIPT_logk_param_recovery_role_of_prior.m", "ext": ".m", "path": "darc-experiments-matlab-master/generate_figs_for_paper/SCRIPT_logk_param_recovery_role_of_prior.m", "size": 1364, "source_encoding": "utf_8", "md5": "8356fade05ebefc58e78eb445bb1ed3a", "text": "function SCRIPT_logk_param_recovery_role_of_prior()\n\n%% Setup\naddpath('darc-experiments')\nsave_path = fullfile(pwd,'data');\nlogk_list = [-8:0.05:-1];\n\n\n%% Run parameter sweeps\nresult_adaptive2\t\t= parameter_sweep(logk_list, @param_recovery_adaptive_logk, 2);\nresult_adaptive4\t\t= parameter_sweep(logk_list, @param_recovery_adaptive_logk, 4);\nresult_adaptive8\t\t= parameter_sweep(logk_list, @param_recovery_adaptive_logk, 8);\nresult_adaptive16\t\t= parameter_sweep(logk_list, @param_recovery_adaptive_logk, 16);\n\n\n%% PLOTTING\nfigure_handle = figure(3); clf\nset(figure_handle,'WindowStyle', 'Normal')\n\nsubplot(2,2,1)\nresult_adaptive2.plot_param_recovery(), title('2 trials')\naxis([-8.2 -0.8 -8 0])\n\nsubplot(2,2,2)\nresult_adaptive4.plot_param_recovery(), title('4 trials')\naxis([-8.2 -0.8 -8 0])\n\nsubplot(2,2,3)\nresult_adaptive8.plot_param_recovery(), title('8 trials')\naxis([-8.2 -0.8 -8 0])\n\nsubplot(2,2,4)\nresult_adaptive16.plot_param_recovery(), title('16 trials')\naxis([-8.2 -0.8 -8 0])\n\n\n%% Export\nsetAllSubplotOptions(gcf, {'LineWidth', 2, 'FontSize', 16})\nset(figure_handle, 'Position',[10 0 800 800])\nensureFolderExists('figs')\nexport_fig('figs/logk_param_recovery_role_of_prior.pdf', '-pdf')\nbeep\nend\n\n\nfunction logk_theta_record = param_recovery_adaptive_logk(true_logk, trials)\n[model, theta, data, logk_theta_record] = runAdaptiveLogK(true_logk, trials);\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "SCRIPT_logk_param_recovery.m", "ext": ".m", "path": "darc-experiments-matlab-master/generate_figs_for_paper/SCRIPT_logk_param_recovery.m", "size": 1837, "source_encoding": "utf_8", "md5": "8cd207bb03305b7bd749d5315336357b", "text": "function SCRIPT_logk_param_recovery()\n\n%% Setup\naddpath('darc-experiments')\nsave_path = fullfile(pwd,'data');\nlogk_list = [-8:0.05:-1];\n\n\n%% Run parameter sweeps\nresult_kirby\t\t\t= parameter_sweep(logk_list, @param_recovery_kirby_logk, 27);\nresult_koffarnus\t\t= parameter_sweep(logk_list, @param_recovery_koffarnus, 5);\nresult_fry\t\t\t\t= parameter_sweep(logk_list, @param_recovery_frye, 5*4);\nresult_adaptive20\t\t= parameter_sweep(logk_list, @param_recovery_adaptive_logk, 20);\n\n\n%% PLOTTING\nfigure_handle = figure(3); clf\nset(figure_handle,'WindowStyle', 'Normal')\n\nsubplot(2,2,1)\nresult_kirby.plot_param_recovery(), title('Kirby (2009), 27 trials')\naxis([-8.2 -0.8 -8 0])\n\nsubplot(2,2,2)\nresult_koffarnus.plot_param_recovery(), title('Koffarnus & Bickel (2014), 5 trials')\naxis([-8.2 -0.8 -8 0])\n\nsubplot(2,2,3)\nresult_fry.plot_param_recovery(), title('Frye et al (2016), 20 trials')\naxis([-8.2 -0.8 -8 0])\n\nsubplot(2,2,4)\nresult_adaptive20.plot_param_recovery(), title('Our approach, 20 trials')\naxis([-8.2 -0.8 -8 0])\n\n\n%% Export\nsetAllSubplotOptions(gcf, {'LineWidth', 2, 'FontSize', 16})\nset(figure_handle, 'Position',[10 0 800 800])\nensureFolderExists('figs')\nexport_fig('figs/logk_param_recovery', '-pdf')\nbeep\nend\n\n\nfunction logk_theta_record = param_recovery_kirby_logk(true_logk, trials)\n[model, theta, data, logk_theta_record] = runKirby(true_logk, trials);\nend\n\nfunction logk_theta_record = param_recovery_frye(true_logk, trials)\n[model, theta, data, logk_theta_record] = runFryeEtAl(true_logk, trials);\nend\n\nfunction logk_theta_record = param_recovery_adaptive_logk(true_logk, trials)\n[model, theta, data, logk_theta_record] = runAdaptiveLogK(true_logk, trials);\nend\n\nfunction logk_theta_record = param_recovery_koffarnus(true_logk, trials)\n[model, theta, data, logk_theta_record] = runKoffarnusAndBickel(true_logk, trials);\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "SCRIPT_models_parameter_recovery.m", "ext": ".m", "path": "darc-experiments-matlab-master/generate_figs_for_paper/SCRIPT_models_parameter_recovery.m", "size": 4615, "source_encoding": "utf_8", "md5": "1626fe0b1ac43a7f3a36ffd0fca2874a", "text": "function SCRIPT_models_parameter_recovery()\n\n\n%% Setup\naddpath('darc-experiments')\ntrials = 30; %30\n\nK = 51; % 51\n\n% CALCULATIONS: Do parameter recovery for all models ===========================\n\n%% Hyperbolic discounting of time (with magnitude effect)\ntrue_m_vec = linspace(-1, -0.5, K);\ntrue_c_vec = linspace(-0.5, -2.5, K);\n[m_array, c_array] = do_param_recovery_me(trials, true_m_vec, true_c_vec);\n\n\n%% Hyperbolic discounting of odds against\ntrue_h_vec = logspace(-1,1,K);\nh_array = do_param_recovery_h(trials, true_h_vec);\n\n\n%% Hyperbolic discounting of time AND odds against\ntrue_logk_vec = linspace(-8,-1,K);\ntrue_h_vec = logspace(-1,1,K);\n[TO_logk_array, TO_h_array] = do_param_recovery_time_and_odds(trials, true_logk_vec, true_h_vec);\n\nsave 'other_models_parameter_recovery.mat'\nbeep\n\n\n%% PLOTTING\nfigure_handle = figure(3); clf\nset(figure_handle, 'WindowStyle', 'Normal')\n\n[figure_handle, h_row_labels, h_col_labels, h_main] = ...\nmake_subplot_grid({ {'time discounting', '(with magnitude effect)'},...\n\t{'probability', 'discounting'},...\n\t{'time and probabilty', 'discounting'}},...\n\t{'',''});\n\nsubplot(h_main(1, 1)), m_array.plot_param_recovery(), title('m'), setTickIntervals(0.25, 0.25)\nsubplot(h_main(1, 2)), c_array.plot_param_recovery(), title('c'), setTickIntervals(0.5, 0.5)\n\nsubplot(h_main(2, 1)), h_array.plot_param_recovery(), title('h'), %setTickIntervals(2,2)\nset(gca,'XScale','log', 'YScale','log')\naxis([10^-1 10^1 10^-1 10^1])\nset(gca,'XTickLabel',{0.1, 1, 10},...\n    'YTickLabel',{0.1, 1, 10})\ndelete(h_main(2, 2))\n\nsubplot(h_main(3, 1)), TO_logk_array.plot_param_recovery(), title('log(k)'), setTickIntervals(2,2)\naxis([min(true_logk_vec) max(true_logk_vec) min(true_logk_vec) max(true_logk_vec)])\nsubplot(h_main(3, 2)), TO_h_array.plot_param_recovery(), title('h'), %setTickIntervals(2,2)\nset(gca,'XScale','log', 'YScale','log')\naxis([10^-1 10^1 10^-1 10^1])\nset(gca,'XTickLabel',{0.1, 1, 10},...\n    'YTickLabel',{0.1, 1, 10})\n\n%% Export\nfigure_handle.Units = 'pixels';\nset(figure_handle,'Position',[10 10 600 1000])\nensureFolderExists('figs')\nsavefig('figs/multiple_Model_param_recovery')\nexport_fig('figs/multiple_Model_param_recovery', '-pdf')\nbeep\nend\n\n\n\nfunction [m_array, c_array] = do_param_recovery_me(trials, true_m_vec, true_c_vec)\ndisplay('Hyperbolic discounting of time (with magnitude effect)')\n% Define parameters\n\n% Alpha and Epsilon are treated as fixed parameters\n[alpha, epsilon] = common_parameters();\n\nm_array = [];\nc_array = [];\nparfor n=1:numel(true_m_vec)\n\tfprintf('%d of %d\\n',n, numel(true_m_vec))\n\ttrue_m = true_m_vec(n);\n\ttrue_c = true_c_vec(n);\n\n\tmodel = Model_hyperbolic1ME_time(...\n\t\t'epsilon', epsilon);\n\n\texpt = Experiment(model,...\n\t\t'agent', 'simulated_agent',...\n\t\t'trials', trials,...\n\t\t'true_theta', struct('m', true_m, 'c', true_c, 'alpha', alpha),...\n\t\t'plotting','none');\n\n\texpt = expt.runTrials();\n\n\tm\t= expt.get_specific_theta_record_parameter('m');\n\tm_array = [m_array m];\n\n\tc\t= expt.get_specific_theta_record_parameter('c');\n\tc_array = [c_array c];\n\nend\nend\n\n\nfunction h_array = do_param_recovery_h(trials, true_h_vec)\ndisplay('Hyperbolic discounting of log odds')\n\n% Alpha and Epsilon are treated as fixed parameters\n[alpha, epsilon] = common_parameters();\n\nh_array = [];\nparfor n=1:numel(true_h_vec)\n\tfprintf('%d of %d\\n',n, numel(true_h_vec))\n\ttrue_h = true_h_vec(n);\n\n\tmodel = Model_hyperbolic1_prob(...\n\t\t'epsilon', epsilon,... % fixed value\n\t\t'R_B', 100);\n\n\texpt = Experiment(...\n\t\tmodel,...\n\t\t'agent', 'simulated_agent',...\n\t\t'trials', trials,...\n\t\t'true_theta', struct('h', true_h, 'alpha', alpha),...\n\t\t'plotting','none');\n\n\texpt = expt.runTrials();\n\n\th\t= expt.get_specific_theta_record_parameter('h');\n\th_array = [h_array h];\nend\n\n\nend\n\n\n\nfunction [logk_array, h_array] = do_param_recovery_time_and_odds(trials, true_logk_vec, true_h_vec);\ndisplay('Hyperbolic discounting of time AND log odds against')\n\n% Alpha and Epsilon are treated as fixed parameters\n[alpha, epsilon] = common_parameters();\n\nlogk_array = [];\nh_array = [];\nparfor n=1:numel(true_logk_vec)\n\tfprintf('%d of %d\\n',n, numel(true_logk_vec))\n\ttrue_logk = true_logk_vec(n);\n\ttrue_h = true_h_vec(n);\n\n\tmodel = Model_hyperbolic1_time_and_prob(...\n\t\t'epsilon', epsilon);\n\n\texpt = Experiment(...\n\t\tmodel,...\n\t\t'agent', 'simulated_agent',...\n\t\t'trials', trials,...\n\t\t'true_theta', struct('logk', true_logk, 'h', true_h, 'alpha', alpha),...\n\t\t'plotting','none');\n\n\texpt = expt.runTrials();\n\n\tlogk\t= expt.get_specific_theta_record_parameter('logk');\n\tlogk_array = [logk_array logk];\n\n\th\t= expt.get_specific_theta_record_parameter('h');\n\th_array = [h_array h];\n\nend\nend\n"}
+{"plateform": "github", "repo_name": "drbenvincent/darc-experiments-matlab-master", "name": "fig_darc_schematic.m", "ext": ".m", "path": "darc-experiments-matlab-master/generate_figs_for_paper/fig_darc_schematic.m", "size": 3127, "source_encoding": "utf_8", "md5": "419ae13f8cc195dcb598b39ba7512225", "text": "% fig_darc_schematic\n% Create the basic structure of the figure to demonstrate the approaches:\n% - Expected Utility Theory\n% - Prospect Theory\n% - Discounting\n\nf = figure(1);\nclf\nh = layout([1, 2, 3; 4 5 6; 7 8 9]);\n\n\nreward = linspace(-10,10,100);\nprobability = linspace(0,1,1000);\ndelays = linspace(0,365, 3650);\nodds = (1-probability)./probability;\n\n\n%% EUT\n\n% linear utility\nsubplot(h(1))\n\np = plot(reward, reward, 'k-','Linewidth',2);\n\nh(1).XAxisLocation = 'origin';\nh(1).YAxisLocation = 'origin';\nxlabel('$R$', 'interpreter', 'latex')\nylabel('$u(R)$', 'interpreter', 'latex')\naxis equal\naxis square\nbox off\n\n\n% linear probability\nsubplot(h(2))\n\np = plot(probability, probability, 'k-','Linewidth',2);\nxlabel('$P$', 'interpreter', 'latex')\nylabel('$\\pi (P)$', 'interpreter', 'latex')\naxis equal\nbox off\naxis([0 1 0 1])\n\n\n% exponential discounting\nsubplot(h(3))\nplot(delays, exp(-0.005.*delays), 'k-','Linewidth',2);\nxlabel('$D$', 'interpreter', 'latex')\nylabel('$d(D)$', 'interpreter', 'latex')\nxlim([0, 365])\nbox off\naxis square\n\n\n\n\n\n%% prospect theory\n\n% value function\nsubplot(h(4))\n\nalpha = 0.6;\nbeta = 0.6;\nloss = 1.5;\np = plot(reward, pt_util_function(reward, alpha, beta, loss),...\n    'k-','Linewidth',2);\nxlabel('$R$', 'interpreter', 'latex')\nylabel('$u(R)$', 'interpreter', 'latex')\naxis equal\naxis square\nbox off\nh(4).XAxisLocation = 'origin';\nh(4).YAxisLocation = 'origin';\n\n% prospect theory weighting function\n% linear probability\nsubplot(h(5))\n\ndelta = 0.6;\ngamma = 0.4;\nwf = @(p) (delta.*p.^gamma) ./ ((delta.*p.^gamma) + (1-p).^gamma);\n\np = plot(probability, wf(probability), 'k-','Linewidth',2);\nhold on\nplot([0 1],[0 1], 'k-')\nxlabel('$P$', 'interpreter', 'latex')\nylabel('$\\pi (P)$', 'interpreter', 'latex')\naxis equal\naxis square\nbox off\naxis([0 1 0 1])\n\n\n\n% no time discounting\nsubplot(h(6))\nplot(delays, ones(size(delays)), 'k-','Linewidth',2);\nxlabel('$D$', 'interpreter', 'latex')\nylabel('$d(D)$', 'interpreter', 'latex')\nxlim([0, 365])\nylim([0 1.1])\nbox off\naxis square\n\n\n%% Discounting approaches\n\n% linear utility function\nsubplot(h(7))\n\np = plot(reward, reward, 'k-','Linewidth',2);\nxlabel('$R$', 'interpreter', 'latex')\nylabel('$u(R)$', 'interpreter', 'latex')\naxis equal\naxis square\nbox off\nh(6).XAxisLocation = 'origin';\nh(6).YAxisLocation = 'origin';\n\n% hyperbolic discounting of odds\nsubplot(h(8))\nplot(odds, 1./(1+1.*odds), 'k-','Linewidth',2);\nxlabel('$ odds = \\frac{1-P}{P}$', 'interpreter', 'latex')\nylabel('$ \\pi(\\frac{1-P}{P}) $', 'interpreter', 'latex')\nxlim([0, 10])\naxis square\nbox off\naddTextToFigure('BL',' risk averse', 12)\naddTextToFigure('TR','risk seeking', 12)\n\n% hyperbolic discounting of delay\nsubplot(h(9))\nplot(delays, 1./(1+exp(-3).*delays), 'k-','Linewidth',2);\nxlabel('$D$', 'interpreter', 'latex')\nylabel('$d(D)$', 'interpreter', 'latex')\nxlim([0, 365])\naxis square\nbox off\n\n\n%% Export\nset(gcf,'Position',[10 10 900 700])\nsavefig('figs/darc_schematic_raw')\nexport_fig('figs/darc_schematic_raw', '-pdf')\n\n\nfunction u = pt_util_function(reward, alpha, beta, loss)\nu(reward>=0)    = reward(reward>=0).^alpha;\nu(reward<0)     = -loss.*((-reward(reward<0)).^beta);\nend\n"}
+{"plateform": "github", "repo_name": "ajit2704/underwater-image-enhancement-master", "name": "vanherk.m", "ext": ".m", "path": "underwater-image-enhancement-master/mat/vanherk.m", "size": 4841, "source_encoding": "utf_8", "md5": "5b0cf60c12e2432af9a978d4bad7ff3b", "text": "function Y = vanherk(X,N,TYPE,varargin)\r\n%  VANHERK    Fast max/min 1D filter\r\n%\r\n%    Y = VANHERK(X,N,TYPE) performs the 1D max/min filtering of the row\r\n%    vector X using a N-length filter.\r\n%    The filtering type is defined by TYPE = 'max' or 'min'. This function\r\n%    uses the van Herk algorithm for min/max filters that demands only 3\r\n%    min/max calculations per element, independently of the filter size.\r\n%\r\n%    If X is a 2D matrix, each row will be filtered separately.\r\n%    \r\n%    Y = VANHERK(...,'col') performs the filtering on the columns of X.\r\n%    \r\n%    Y = VANHERK(...,'shape') returns the subset of the filtering specified\r\n%    by 'shape' :\r\n%        'full'  - Returns the full filtering result,\r\n%        'same'  - (default) Returns the central filter area that is the\r\n%                   same size as X,\r\n%        'valid' - Returns only the area where no filter elements are outside\r\n%                  the image.\r\n%\r\n%    X can be uint8 or double. If X is uint8 the processing is quite faster, so\r\n%    dont't use X as double, unless it is really necessary.\r\n%\r\n\r\n% Initialization\r\n[direc, shape] = parse_inputs(varargin{:});\r\nif strcmp(direc,'col')\r\n   X = X';\r\nend\r\nif strcmp(TYPE,'max')\r\n   maxfilt = 1;\r\nelseif strcmp(TYPE,'min')\r\n   maxfilt = 0;\r\nelse\r\n   error([ 'TYPE must be ' char(39) 'max' char(39) ' or ' char(39) 'min' char(39) '.'])\r\nend\r\n\r\n% Correcting X size\r\nfixsize = 0;\r\naddel = 0;\r\nif mod(size(X,2),N) ~= 0\r\n   fixsize = 1;\r\n   addel = N-mod(size(X,2),N);\r\n   if maxfilt\r\n      f = [ X zeros(size(X,1), addel) ];\r\n   else\r\n      f = [X repmat(X(:,end),1,addel)];\r\n   end\r\nelse\r\n   f = X;\r\nend\r\nlf = size(f,2);\r\nlx = size(X,2);\r\nclear X\r\n\r\n% Declaring aux. mat.\r\ng = f;\r\nh = g;\r\n\r\n% Filling g & h (aux. mat.)\r\nig = 1:N:size(f,2);\r\nih = ig + N - 1;\r\n\r\ng(:,ig) = f(:,ig);\r\nh(:,ih) = f(:,ih);\r\n\r\nif maxfilt\r\n   for i = 2 : N\r\n      igold = ig;\r\n      ihold = ih;\r\n      \r\n      ig = ig + 1;\r\n      ih = ih - 1;\r\n      \r\n      g(:,ig) = max(f(:,ig),g(:,igold));\r\n      h(:,ih) = max(f(:,ih),h(:,ihold));\r\n   end\r\nelse\r\n   for i = 2 : N\r\n      igold = ig;\r\n      ihold = ih;\r\n      \r\n      ig = ig + 1;\r\n      ih = ih - 1;\r\n      \r\n      g(:,ig) = min(f(:,ig),g(:,igold));\r\n      h(:,ih) = min(f(:,ih),h(:,ihold));\r\n   end\r\nend\r\nclear f\r\n\r\n% Comparing g & h\r\nif strcmp(shape,'full')\r\n   ig = [ N : 1 : lf ];\r\n   ih = [ 1 : 1 : lf-N+1 ];\r\n   if fixsize\r\n      if maxfilt\r\n         Y = [ g(:,1:N-1)  max(g(:,ig), h(:,ih))  h(:,end-N+2:end-addel) ];\r\n      else\r\n         Y = [ g(:,1:N-1)  min(g(:,ig), h(:,ih))  h(:,end-N+2:end-addel) ];\r\n      end\r\n   else\r\n      if maxfilt\r\n         Y = [ g(:,1:N-1)  max(g(:,ig), h(:,ih))  h(:,end-N+2:end) ];\r\n      else\r\n         Y = [ g(:,1:N-1)  min(g(:,ig), h(:,ih))  h(:,end-N+2:end) ];\r\n      end\r\n   end\r\n   \r\nelseif strcmp(shape,'same')\r\n   if fixsize\r\n      if addel > (N-1)/2\r\n         disp('hoi')\r\n         ig = [ N : 1 : lf - addel + floor((N-1)/2) ];\r\n         ih = [ 1 : 1 : lf-N+1 - addel + floor((N-1)/2)];\r\n         if maxfilt\r\n            Y = [ g(:,1+ceil((N-1)/2):N-1)  max(g(:,ig), h(:,ih)) ];\r\n         else\r\n            Y = [ g(:,1+ceil((N-1)/2):N-1)  min(g(:,ig), h(:,ih)) ];\r\n         end\r\n      else   \r\n         ig = [ N : 1 : lf ];\r\n         ih = [ 1 : 1 : lf-N+1 ];\r\n         if maxfilt\r\n            Y = [ g(:,1+ceil((N-1)/2):N-1)  max(g(:,ig), h(:,ih))  h(:,lf-N+2:lf-N+1+floor((N-1)/2)-addel) ];\r\n         else\r\n            Y = [ g(:,1+ceil((N-1)/2):N-1)  min(g(:,ig), h(:,ih))  h(:,lf-N+2:lf-N+1+floor((N-1)/2)-addel) ];\r\n         end\r\n      end            \r\n   else % not fixsize (addel=0, lf=lx) \r\n      ig = [ N : 1 : lx ];\r\n      ih = [ 1 : 1 : lx-N+1 ];\r\n      if maxfilt\r\n         Y = [  g(:,N-ceil((N-1)/2):N-1) max( g(:,ig), h(:,ih) )  h(:,lx-N+2:lx-N+1+floor((N-1)/2)) ];\r\n      else\r\n         Y = [  g(:,N-ceil((N-1)/2):N-1) min( g(:,ig), h(:,ih) )  h(:,lx-N+2:lx-N+1+floor((N-1)/2)) ];\r\n      end\r\n   end      \r\n   \r\nelseif strcmp(shape,'valid')\r\n   ig = [ N : 1 : lx];\r\n   ih = [ 1 : 1: lx-N+1];\r\n   if maxfilt\r\n      Y = [ max( g(:,ig), h(:,ih) ) ];\r\n   else\r\n      Y = [ min( g(:,ig), h(:,ih) ) ];\r\n   end\r\nend\r\n\r\nif strcmp(direc,'col')\r\n   Y = Y';\r\nend\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction [direc, shape] = parse_inputs(varargin)\r\ndirec = 'lin';\r\nshape = 'same';\r\nflag = [0 0]; % [dir shape]\r\n\r\nfor i = 1 : nargin\r\n   t = varargin{i};\r\n   if strcmp(t,'col') & flag(1) == 0\r\n      direc = 'col';\r\n      flag(1) = 1;\r\n   elseif strcmp(t,'full') & flag(2) == 0\r\n      shape = 'full';\r\n      flag(2) = 1;\r\n   elseif strcmp(t,'same') & flag(2) == 0\r\n      shape = 'same';\r\n      flag(2) = 1;\r\n   elseif strcmp(t,'valid') & flag(2) == 0\r\n      shape = 'valid';\r\n      flag(2) = 1;\r\n   else\r\n      error(['Too many / Unkown parameter : ' t ])\r\n   end\r\nend"}
+{"plateform": "github", "repo_name": "ajit2704/underwater-image-enhancement-master", "name": "maxfilt2.m", "ext": ".m", "path": "underwater-image-enhancement-master/mat/maxfilt2.m", "size": 1849, "source_encoding": "utf_8", "md5": "45cc67fb2afee0dc77cfc7798629574f", "text": "function Y = maxfilt2(X,varargin)\r\n%  MAXFILT2    Two-dimensional max filter\r\n%\r\n%     Y = MAXFILT2(X,[M N]) performs two-dimensional maximum\r\n%     filtering on the image X using an M-by-N window. The result\r\n%     Y contains the maximun value in the M-by-N neighborhood around\r\n%     each pixel in the original image. \r\n%     This function uses the van Herk algorithm for max filters.\r\n%\r\n%     Y = MAXFILT2(X,M) is the same as Y = MAXFILT2(X,[M M])\r\n%\r\n%     Y = MAXFILT2(X) uses a 3-by-3 neighborhood.\r\n%\r\n%     Y = MAXFILT2(..., 'shape') returns a subsection of the 2D\r\n%     filtering specified by 'shape' :\r\n%        'full'  - Returns the full filtering result,\r\n%        'same'  - (default) Returns the central filter area that is the\r\n%                   same size as X,\r\n%        'valid' - Returns only the area where no filter elements are outside\r\n%                  the image.\r\n%\r\n%     See also : MINFILT2, VANHERK\r\n%\r\n\r\n% Initialization\r\n[S, shape] = parse_inputs(varargin{:});\r\n\r\n% filtering\r\nY = vanherk(X,S(1),'max',shape);\r\nY = vanherk(Y,S(2),'max','col',shape);\r\n\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction [S, shape] = parse_inputs(varargin)\r\nshape = 'same';\r\nflag = [0 0]; % size shape\r\n\r\nfor i = 1 : nargin\r\n   t = varargin{i};\r\n   if strcmp(t,'full') & flag(2) == 0\r\n      shape = 'full';\r\n      flag(2) = 1;\r\n   elseif strcmp(t,'same') & flag(2) == 0\r\n      shape = 'same';\r\n      flag(2) = 1;\r\n   elseif strcmp(t,'valid') & flag(2) == 0\r\n      shape = 'valid';\r\n      flag(2) = 1;\r\n   elseif flag(1) == 0\r\n      S = t;\r\n      flag(1) = 1;\r\n   else\r\n      error(['Too many / Unkown parameter : ' t ])\r\n   end\r\nend\r\n\r\nif flag(1) == 0\r\n   S = [3 3];\r\nend\r\nif length(S) == 1;\r\n   S(2) = S(1);\r\nend\r\nif length(S) ~= 2\r\n   error('Wrong window size parameter.')\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "ajit2704/underwater-image-enhancement-master", "name": "bilateralFilter.m", "ext": ".m", "path": "underwater-image-enhancement-master/mat/bilateralFilter.m", "size": 6703, "source_encoding": "utf_8", "md5": "a76d6aab0840ad8b7e6e749526f36660", "text": "%\n% output = bilateralFilter( data, edge, ...\n%                          edgeMin, edgeMax, ...\n%                          sigmaSpatial, sigmaRange, ...\n%                          samplingSpatial, samplingRange )\n%\n% Bilateral and Cross-Bilateral Filter using the Bilateral Grid.\n%\n% Bilaterally filters the image 'data' using the edges in the image 'edge'.\n% If 'data' == 'edge', then it the standard bilateral filter.\n% Otherwise, it is the 'cross' or 'joint' bilateral filter.\n% For convenience, you can also pass in [] for 'edge' for the normal\n% bilateral filter.\n%\n% Note that for the cross bilateral filter, data does not need to be\n% defined everywhere.  Undefined values can be set to 'NaN'.  However, edge\n% *does* need to be defined everywhere.\n%\n% data and edge should be of the greyscale, double-precision floating point\n% matrices of the same size (i.e. they should be [ height x width ])\n%\n% data is the only required argument\n%\n% edgeMin and edgeMax specifies the min and max values of 'edge' (or 'data'\n% for the normal bilateral filter) and is useful when the input is in a\n% range that's not between 0 and 1.  For instance, if you are filtering the\n% L channel of an image that ranges between 0 and 100, set edgeMin to 0 and\n% edgeMax to 100.\n% \n% edgeMin defaults to min( edge( : ) ) and edgeMax defaults to max( edge(:)).\n% This is probably *not* what you want, since the input may not span the\n% entire range.\n%\n% sigmaSpatial and sigmaRange specifies the standard deviation of the space\n% and range gaussians, respectively.\n% sigmaSpatial defaults to min( width, height ) / 16\n% sigmaRange defaults to ( edgeMax - edgeMin ) / 10.\n%\n% samplingSpatial and samplingRange specifies the amount of downsampling\n% used for the approximation.  Higher values use less memory but are also\n% less accurate.  The default and recommended values are:\n% \n% samplingSpatial = sigmaSpatial\n% samplingRange = sigmaRange\n%\n\nfunction output = bilateralFilter( data, edge, edgeMin, edgeMax,...\n    sigmaSpatial, sigmaRange, samplingSpatial, samplingRange )\n\nif( ndims( data ) > 2 ),\n    error( 'data must be a greyscale image with size [ height, width ]' );\nend\n\nif( ~isa( data, 'double' ) ),\n    error( 'data must be of class \"double\"' );\nend\n\nif ~exist( 'edge', 'var' ),\n    edge = data;\nelseif isempty( edge ),\n    edge = data;\nend\n\nif( ndims( edge ) > 2 ),\n    error( 'edge must be a greyscale image with size [ height, width ]' );\nend\n\nif( ~isa( edge, 'double' ) ),\n    error( 'edge must be of class \"double\"' );\nend\n\ninputHeight = size( data, 1 );\ninputWidth = size( data, 2 );\n\nif ~exist( 'edgeMin', 'var' ),\n    edgeMin = min( edge( : ) );\n    %warning( 'edgeMin not set!  Defaulting to: %f\\n', edgeMin );\nend\n\nif ~exist( 'edgeMax', 'var' ),\n    edgeMax = max( edge( : ) );\n    %warning( 'edgeMax not set!  Defaulting to: %f\\n', edgeMax );\nend\n\nedgeDelta = edgeMax - edgeMin;\n\nif ~exist( 'sigmaSpatial', 'var' ),\n    sigmaSpatial = min( inputWidth, inputHeight ) / 16;\n    %fprintf( 'Using default sigmaSpatial of: %f\\n', sigmaSpatial );\nend\n\nif ~exist( 'sigmaRange', 'var' ),\n    sigmaRange = 0.1 * edgeDelta;\n    %fprintf( 'Using default sigmaRange of: %f\\n', sigmaRange );\nend\n\nif ~exist( 'samplingSpatial', 'var' ),\n    samplingSpatial = sigmaSpatial;\nend\n\nif ~exist( 'samplingRange', 'var' ),\n    samplingRange = sigmaRange;\nend\n\nif size( data ) ~= size( edge ),\n    error( 'data and edge must be of the same size' );\nend\n\n% parameters\nderivedSigmaSpatial = sigmaSpatial / samplingSpatial;\nderivedSigmaRange = sigmaRange / samplingRange;\n\npaddingXY = floor( 2 * derivedSigmaSpatial ) + 1;\npaddingZ = floor( 2 * derivedSigmaRange ) + 1;\n\n% allocate 3D grid\ndownsampledWidth = floor( ( inputWidth - 1 ) / samplingSpatial )...\n    + 1 + 2 * paddingXY;\ndownsampledHeight = floor( ( inputHeight - 1 ) / samplingSpatial )...\n    + 1 + 2 * paddingXY;\ndownsampledDepth = floor( edgeDelta / samplingRange ) + 1 + 2 * paddingZ;\n\ngridData = zeros( downsampledHeight, downsampledWidth, downsampledDepth );\ngridWeights = zeros( downsampledHeight, downsampledWidth, downsampledDepth );\n\n% compute downsampled indices\n[ jj, ii ] = meshgrid( 0 : inputWidth - 1, 0 : inputHeight - 1 );\n\n% ii =\n% 0 0 0 0 0\n% 1 1 1 1 1\n% 2 2 2 2 2\n\n% jj =\n% 0 1 2 3 4\n% 0 1 2 3 4\n% 0 1 2 3 4\n\n% so when iterating over ii( k ), jj( k )\n% get: ( 0, 0 ), ( 1, 0 ), ( 2, 0 ), ... (down columns first)\n\ndi = round( ii / samplingSpatial ) + paddingXY + 1;\ndj = round( jj / samplingSpatial ) + paddingXY + 1;\ndz = round( ( edge - edgeMin ) / samplingRange ) + paddingZ + 1;\n\n% perform scatter (there's probably a faster way than this)\n% normally would do downsampledWeights( di, dj, dk ) = 1, but we have to\n% perform a summation to do box downsampling\nfor k = 1 : numel( dz ),\n       \n    dataZ = data( k ); % traverses the image column wise, same as di( k )\n    if ~isnan( dataZ  ),\n        \n        dik = di( k );\n        djk = dj( k );\n        dzk = dz( k );\n\n        gridData( dik, djk, dzk ) = gridData( dik, djk, dzk ) + dataZ;\n        gridWeights( dik, djk, dzk ) = gridWeights( dik, djk, dzk ) + 1;\n        \n    end\nend\n\n% make gaussian kernel\nkernelWidth = 2 * derivedSigmaSpatial + 1;\nkernelHeight = kernelWidth;\nkernelDepth = 2 * derivedSigmaRange + 1;\n\nhalfKernelWidth = floor( kernelWidth / 2 );\nhalfKernelHeight = floor( kernelHeight / 2 );\nhalfKernelDepth = floor( kernelDepth / 2 );\n\n[gridX, gridY, gridZ] = meshgrid( 0 : kernelWidth - 1,...\n    0 : kernelHeight - 1, 0 : kernelDepth - 1 );\ngridX = gridX - halfKernelWidth;\ngridY = gridY - halfKernelHeight;\ngridZ = gridZ - halfKernelDepth;\ngridRSquared = ( gridX .* gridX + gridY .* gridY ) /...\n    ( derivedSigmaSpatial * derivedSigmaSpatial ) +...\n    ( gridZ .* gridZ ) / ( derivedSigmaRange * derivedSigmaRange );\nkernel = exp( -0.5 * gridRSquared );\n\n% convolve\nblurredGridData = convn( gridData, kernel, 'same' );\nblurredGridWeights = convn( gridWeights, kernel, 'same' );\n\n% divide\n% avoid divide by 0, won't read there anyway\nblurredGridWeights( blurredGridWeights == 0 ) = -2; \nnormalizedBlurredGrid = blurredGridData ./ blurredGridWeights;\n% put 0s where it's undefined\nnormalizedBlurredGrid( blurredGridWeights < -1 ) = 0; \n\n% for debugging\n% blurredGridWeights( blurredGridWeights < -1 ) = 0; % put zeros back\n\n% upsample\n% meshgrid does x, then y, so output arguments need to be reversed\n[ jj, ii ] = meshgrid( 0 : inputWidth - 1, 0 : inputHeight - 1 ); \n% no rounding\ndi = ( ii / samplingSpatial ) + paddingXY + 1;\ndj = ( jj / samplingSpatial ) + paddingXY + 1;\ndz = ( edge - edgeMin ) / samplingRange + paddingZ + 1;\n\n% interpn takes rows, then cols, etc\n% i.e. size(v,1), then size(v,2), ...\noutput = interpn( normalizedBlurredGrid, di, dj, dz );"}
+{"plateform": "github", "repo_name": "ajit2704/underwater-image-enhancement-master", "name": "autolevel.m", "ext": ".m", "path": "underwater-image-enhancement-master/mat/autolevel.m", "size": 2027, "source_encoding": "utf_8", "md5": "42c51c0ef7df19d0766b3376c3b1500b", "text": "function imDst = autolevel(varargin)\n[I,lowCut,highCut] =parse_inputs(varargin{:});\n[hei,wid,~] = size(I);\n\nPixelAmount = wid * hei;\nif size(I,3)==3\n\n   [HistRed,~]  = imhist(I(:,:,1));\n   [HistGreen,~] = imhist(I(:,:,2));\n   [HistBlue,~] = imhist(I(:,:,3));\n\n   CumRed = cumsum(HistRed);\n   CumGreen = cumsum(HistGreen);\n   CumBlue = cumsum(HistBlue);\n\n   minR =find(CumRed>=PixelAmount*lowCut,1,'first');\n   minG = find(CumGreen>=PixelAmount*lowCut,1,'first');\n   minB =find(CumBlue>=PixelAmount*lowCut,1,'first');\n\n   maxR =find(CumRed>=PixelAmount*(1-highCut),1,'first');\n   maxG =find(CumGreen>=PixelAmount*(1-highCut),1,'first');\n   maxB = find(CumBlue>=PixelAmount*(1-highCut),1,'first');\n\n   RedMap = linearmap(minR,maxR);\n   GreenMap = linearmap(minG,maxG);\n   BlueMap = linearmap(minB,maxB);\n\n   imDst = zeros(hei,wid,3,'uint8');\n   imDst(:,:,1) = RedMap (I(:,:,1)+1);\n   imDst(:,:,2) = GreenMap(I(:,:,2)+1);\n   imDst(:,:,3) = BlueMap(I(:,:,3)+1);\n\nelse\n   HistGray = imhist(I(:,:));\n   CumGray = cumsum(HistRed);\n   minGray =find(CumGray>=PixelAmount*lowCut,1,'first');\n   maxGray =find(CumGray>=PixelAmount*(1-highCut),1,'first');\n   GrayMap = linearmap(minGray,maxGray);\n\n   imDst = zeros(hei,wid,'uint8');\n   imDst(:,:) = GrayMap (I(:,:)+1); \nend\n\n%--------------------------------------------------------------------\nfunction map = linearmap(low,high)\nmap = [0:1:255];\nfor i=0:255\n   if(i<low)\n       map(i+1) = 0;\n   elseif (i>high)\n       map(i+1) = 255;\n   else\n       map(i+1) =uint8((i-low)/(high-low)*255);\n   end\nend\n\n\n%-------------------------------------------------------------------\nfunction [I,lowCut,highCut] = parse_inputs(varargin)\nnarginchk(1,3)\nI = varargin{1};\nvalidateattributes(I,{'double','logical','uint8','uint16','int16','single'},{},...\n             mfilename,'Image',1);\n\nif nargin == 1\n   lowCut = 0.005;\n   highCut = 0.005;\nelseif nargin == 3\n   lowCut = varargin{2};\n   highCut = varargin{3};\nelse\n   error(message('images:im2double:invalidIndexedImage','single, or logical.'));\nend"}
+{"plateform": "github", "repo_name": "ajit2704/underwater-image-enhancement-master", "name": "saliency_detection.m", "ext": ".m", "path": "underwater-image-enhancement-master/mat/saliency_detection.m", "size": 2484, "source_encoding": "utf_8", "md5": "d13d25afa6342e87bb05929451469b52", "text": "%---------------------------------------------------------\n% Copyright (c) 2009 Radhakrishna Achanta [EPFL]\n% Contact: firstname.lastname@epfl.ch\n%---------------------------------------------------------\n% Citation:\n% @InProceedings{LCAV-CONF-2009-012,\n%    author      = {Achanta, Radhakrishna and Hemami, Sheila and Estrada,\n%                  Francisco and S?strunk, Sabine},\n%    booktitle   = {{IEEE} {I}nternational {C}onference on {C}omputer\n%                  {V}ision and {P}attern {R}ecognition},\n%    year        = 2009\n% }\n%---------------------------------------------------------\n% Please note that the saliency maps generated using this\n% code may be slightly different from those of the paper.\n% This seems to be because the RGB to Lab conversion is\n% different from the one used for the results in the C++ code.\n% The C++ code is available on the same page as this matlab\n% code (http://ivrg.epfl.ch/supplementary_material/RK_CVPR09/index.html)\n% One should preferably use the C++ as reference and use\n% this matlab implementation mostly as proof of concept\n% demo code.\n%---------------------------------------------------------\nfunction sm = saliency_detection(img)\n%\n%---------------------------------------------------------\n% Read image and blur it with a 3x3 or 5x5 Gaussian filter\n%---------------------------------------------------------\n%img = imread('input_image.jpg');%Provide input image path\ngfrgb = imfilter(img, fspecial('gaussian', 3, 3), 'symmetric', 'conv');\n%---------------------------------------------------------\n% Perform sRGB to CIE Lab color space conversion (using D65)\n%---------------------------------------------------------\n%cform = makecform('srgb2lab', 'whitepoint', whitepoint('d65'));\ncform = makecform('srgb2lab');\nlab = applycform(gfrgb,cform);\n%---------------------------------------------------------\n% Compute Lab average values (note that in the paper this\n% average is found from the unblurred original image, but\n% the results are quite similar)\n%---------------------------------------------------------\nl = double(lab(:,:,1)); lm = mean(mean(l));\na = double(lab(:,:,2)); am = mean(mean(a));\nb = double(lab(:,:,3)); bm = mean(mean(b));\n%---------------------------------------------------------\n% Finally compute the saliency map and display it.\n%---------------------------------------------------------\nsm = (l-lm).^2 + (a-am).^2 + (b-bm).^2;\n%imshow(sm,[]);\n%---------------------------------------------------------"}
+{"plateform": "github", "repo_name": "ajit2704/underwater-image-enhancement-master", "name": "white_balance.m", "ext": ".m", "path": "underwater-image-enhancement-master/mat/white_balance.m", "size": 2152, "source_encoding": "utf_8", "md5": "86ed46f043a7b9801c456cde675ebdda", "text": "%my own white-balance function, created by Qu Jingwei\r\nfunction new_image = white_balance3(src_image)\r\n[height,width,dim] = size(src_image);\r\ntemp = zeros(height,width);\r\n%transform the RGB color space to YCbCr color space \r\nycbcr_image = rgb2ycbcr(src_image);\r\nY = ycbcr_image(:,:,1);\r\nCb = ycbcr_image(:,:,2);\r\nCr = ycbcr_image(:,:,3);\r\n%calculate the average value of Cb,Cr\r\nCb_ave = mean(mean(Cb));\r\nCr_ave = mean(mean(Cr));\r\n%calculate the mean square error of Cb, Cr\r\nDb = sum(sum(abs(Cb-Cb_ave))) / (height*width);\r\nDr = sum(sum(abs(Cr-Cr_ave))) / (height*width);\r\n%find the candidate reference white point\r\n%if meeting the following requriments\r\n%then the point is a candidate reference white point\r\ntemp1 = abs(Cb - (Cb_ave + Db * sign(Cb_ave)));\r\ntemp2 = abs(Cb - (1.5 * Cr_ave + Dr * sign(Cr_ave)));\r\nidx_1 = find(temp1<1.5*Db);\r\nidx_2 = find(temp2<1.5*Dr);\r\nidx = intersect(idx_1,idx_2);\r\npoint = Y(idx);\r\ntemp(idx) = Y(idx);\r\ncount = length(point);\r\ncount = count - 1;\r\n%sort the candidate reference white point set with descend value of Y\r\ntemp_point = sort(point,'descend');\r\n%get the 10% points of the candidate reference white point set, which is\r\n%closer to the white region, as the reference white point set\r\nn = round(count/10);\r\nwhite_point(1:n) = temp_point(1:n);\r\ntemp_min = min(white_point);\r\nidx0 = find(temp<temp_min);\r\ntemp(idx0) = 0;\r\nidx1 = find(temp>=temp_min);\r\ntemp(idx1) = 1;\r\n%get the reference white points' R,G,B\r\nwhite_R = double(src_image(:,:,1)).*temp;\r\nwhite_G = double(src_image(:,:,2)).*temp;\r\nwhite_B = double(src_image(:,:,3)).*temp;\r\n%get the averange value of the reference white points' R,G,B\r\nwhite_R_ave = mean(mean(white_R));\r\nwhite_G_ave = mean(mean(white_G));\r\nwhite_B_ave = mean(mean(white_B));\r\n%the maximum Y value of the source image\r\nYmax = double(max(max(Y))) / 15;\r\n%calculate the white-balance gain\r\nR_gain = Ymax / white_R_ave;\r\nG_gain = Ymax / white_G_ave;\r\nB_gain = Ymax / white_B_ave;\r\n%white-balance correction\r\nnew_image(:,:,1) = R_gain * src_image(:,:,1);\r\nnew_image(:,:,2) = G_gain * src_image(:,:,2);\r\nnew_image(:,:,3) = B_gain * src_image(:,:,3);\r\nnew_image = uint8(new_image);\r\nend"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "svrlsmgui.m", "ext": ".m", "path": "svrlsmgui-master/svrlsmgui.m", "size": 62702, "source_encoding": "utf_8", "md5": "32070ab04e38ec6e5a30f181f8bf30c6", "text": "function varargout = svrlsmgui(varargin)\n% SVRLSMGUI MATLAB code for svrlsmgui.fig\n%      SVRLSMGUI, by itself, creates a new SVRLSMGUI or raises the existing\n%      singleton*.\n%\n%      H = SVRLSMGUI returns the handle to a new SVRLSMGUI or the handle to\n%      the existing singleton*.\n%\n%      SVRLSMGUI('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in SVRLSMGUI.M with the given input arguments.\n%\n%      SVRLSMGUI('Property','Value',...) creates a new SVRLSMGUI or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before svrlsmgui_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to svrlsmgui_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help svrlsmgui\n\n% Last Modified by GUIDE v2.5 04-Nov-2019 12:48:03\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename,'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @svrlsmgui_OpeningFcn, 'gui_OutputFcn',  @svrlsmgui_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , 'gui_Callback',   []);\n\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1}); \nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:}); \nend\n\n% End initialization code - DO NOT EDIT\n\n% --- Executes just before svrlsmgui is made visible.\nfunction svrlsmgui_OpeningFcn(hObject, eventdata, handles, varargin)\n    handles.output = hObject; % Choose default command line output for svrlsmgui\n    \n    % Do we need to add the functions subdirectory to the path?\n    pathCell = regexp(path, pathsep, 'split');\n    myPath = fileparts(mfilename('fullpath'));\n    if ~any(strcmp(myPath,pathCell))\n        addpath(myPath)\n    end\n    \n    functionsPath = fullfile(myPath,'functions');\n    if ~any(strcmp(functionsPath,pathCell))\n        addpath(functionsPath)\n    end\n    \n\n    handles = ConfigureSVRLSMGUIOptions(handles);\n    handles.details = CheckIfNecessaryFilesAreInstalled(handles);\n    \n    if handles.details.stats_toolbox && handles.details.spm && handles.details.libsvm\n        handles = UpdateProgress(handles,'All necessary functions are available...',1);\n        handles.parameters = GetDefaultParameters(handles);\n        handles = PopulateGUIFromParameters(handles);\n    elseif ~handles.details.spm \n        handles = UpdateProgress(handles,'SPM12 functions not available. Download and/or add SPM12 to the MATLAB path and relaunch the SVRLSMGUI.',1);\n        handles = DisableAll(handles);\n    elseif ~handles.details.stats_toolbox && ~handles.details.libsvm\n        handles = UpdateProgress(handles,'No SVR algorithm available. Install Statistics Toolbox in MATLAB or compile and install libSVM and relaunch the GUI.',1);\n        handles = DisableAll(handles);\n    elseif ~handles.details.stats_toolbox && handles.details.libsvm % yes stats toolbox, no libsvm\n        handles = UpdateProgress(handles,'Only libSVM is available to compute images. MATLAB''s SVM will not be available.',1);\n        handles.parameters = GetDefaultParameters(handles);\n        handles = PopulateGUIFromParameters(handles);\n    elseif handles.details.stats_toolbox && ~handles.details.libsvm % no stats toolbox, yes libsvm\n        handles = UpdateProgress(handles,'Only MATLAB''s Stats Toolbox is available to compute images. libSVM will not be available.',1);\n        handles.parameters = GetDefaultParameters(handles);\n        handles = PopulateGUIFromParameters(handles);\n    end\n\n    handles.parameters.parallelize = handles.details.can_parallelize; % override default\n\n    % 0.02 - trying to clean it up to run on a variety of systems - 4/24/17\n    % 0.03 - first version to be used by other individuals officially - 5/1/17\n    % 0.04 - 5/2/17\n    % 0.05 - 7/26/17 - moved to the linux machine, working on improving stability and fixing bugs\n    % 0.06 - August 2017 - added parallelization, continued development with paper\n    % 0.07 - September 2017 - summary output, fixed bug in two tail thresholding, public release...\n    % 0.08 - added diagnostic plot for behavioral nuisance model in summary\n    %        output file (corrplot); added custom support vector scaling\n    %        other than max of the map when backprojecting the analysis\n    %        hyperplane\n    % 0.10 - January 2018 - fixing reported bugs\n    % 0.15 - massive code refactoring and implementation of CFWER\n    \n    handles.parameters.gui_version = 0.15; % version of the the gui\n    guidata(hObject, handles); % Update handles structure\n\nfunction handles = DisableAll(handles)\n    set(get(handles.analysispreferencespanel,'children'),'enable','off')\n    set(get(handles.covariatespanel,'children'),'enable','off')\n    set(get(handles.permutationtestingpanel,'children'),'enable','off')\n    set([handles.viewresultsbutton handles.interrupt_button handles.runanalysisbutton],'Enable','off') % handles.cancelanalysisbutton \n    set(handles.optionsmenu,'enable','off') % since viewing this menu references parameters that may not be loaded.  \n    msgbox('One or more necessary component is missing from MATLAB''s path. Address the message in the SVRLSMgui window and restart this gui.')\n\nfunction handles = UpdateCurrentAnalysis(handles,hObject)\n    changemade = true; % default\n\nswitch get(gcbo,'tag') % use gcbo to see what the cbo is and determine what field it goes to -- and to validate\n    case 'no_map_crossvalidation' % turns off crossvalidation...\n        handles.parameters.crossval.do_crossval = false;\n    case 'kfold_map_crossvalidation'\n        if handles.parameters.useLibSVM\n            changemade=false;\n            msgbox('Map crossvalidation not currently supported for libSVM - please switch implementation to MATLAB and try again.')\n            return\n        end\n        answer = inputdlg(sprintf('Enter the numbers of folds for crossvalidation.'), ...\n             'Number of folds', 1,{num2str(handles.parameters.crossval.nfolds)});\n         if isempty(answer), return; end % cancel pressed\n         str = str2num(answer{1});\n         if isempty(str) || str <= 0 || ~isint(str)\n             changemade=false;\n             warndlg('Input must be a positive integer.');\n         else % update the parameter value.\n            handles.parameters.crossval.do_crossval = true;\n            handles.parameters.crossval.method = 'kfold';\n            handles.parameters.crossval.nfolds = str;\n         end\n    case 'hyperparm_quality_report_options'\n        set(handles.hyperparm_qual_n_folds,'Label',['Folds: ' num2str(handles.parameters.hyperparameter_quality.report.nfolds)])\n        set(handles.repro_index_subset_percentage,'Label',['Subset %: ' num2str(handles.parameters.hyperparameter_quality.report.repro_ind_subset_pct)])\n        set(handles.hyperparm_qual_n_replications,'Label',['Replications: ' num2str(handles.parameters.hyperparameter_quality.report.n_replications)])\n    case 'hyperparm_qual_n_folds'\n        answer = inputdlg(sprintf('Enter the number of folds for hyperparameter quality testing:'), ...\n            'Number of folds', 1,{num2str(handles.parameters.hyperparameter_quality.report.nfolds)});\n        if isempty(answer), return; end % cancel pressed\n        str = str2num(answer{1});\n        if isempty(str) || str <= 1 || ~isint(str)\n            changemade=false;\n            warndlg('Input must be a positive integer greater than 1.');\n        else % update the parameter value.\n            handles.parameters.hyperparameter_quality.report.nfolds = str;\n        end\n    case 'repro_index_subset_percentage'\n        answer = inputdlg('Enter the % of sample to use for computing w-map reproducibility index [0-1]:', ...\n            'Sample percent', 1,{num2str(handles.parameters.hyperparameter_quality.report.repro_ind_subset_pct)});\n        if isempty(answer), return; end % cancel pressed\n        str = str2num(answer{1});\n        if isempty(str) || str <= 0 || str >= 1 % ~isint(str)\n            changemade=false;\n            warndlg('Input must be a value between 0 and 1 (i.e. 0 and 100%).');\n        else % update the parameter value.\n            handles.parameters.hyperparameter_quality.report.repro_ind_subset_pct = str;\n        end\n    case 'hyperparm_qual_n_replications'\n        answer = inputdlg(sprintf('Enter the number of replications for hyperparameter quality testing:'), ...\n            'Number of replications', 1,{num2str(handles.parameters.hyperparameter_quality.report.n_replications)});\n        if isempty(answer), return; end % cancel pressed\n        str = str2num(answer{1});\n        if isempty(str) || str <= 1 || ~isint(str)\n            changemade=false;\n            warndlg('Input must be a positive integer greater than 1.');\n        else % update the parameter value.\n            handles.parameters.hyperparameter_quality.report.n_replications = str;\n        end\n    case 'image_data_options_parent_menu'\n        set(handles.do_binarize_data_menu_option,'Checked',myif(handles.parameters.imagedata.do_binarize,'on','off'))\n    case 'do_binarize_data_menu_option'\n        handles.parameters.imagedata.do_binarize = ~handles.parameters.imagedata.do_binarize;\n    case 'set_resolution_parent_menu_option'\n        set(handles.manual_analysis_resolution_menu,'Label',['Manual: ' num2str(handles.parameters.imagedata.resample_to) ' mm'], ...\n            'checked',myif(handles.parameters.imagedata.do_resample,'on','off'));\n        set(handles.do_not_resample_images_menu,'Checked',myif(handles.parameters.imagedata.do_resample,'off','on'));\n        %set(handles.manual_analysis_resolution_menu,'Enable','on') % Until we finish implementation.\n    case 'do_not_resample_images_menu'\n        handles.parameters.imagedata.do_resample = false; % turn resampling off.\n    case 'manual_analysis_resolution_menu'\n        answer = inputdlg(sprintf('Enter the size in mm for voxels to be resampled to.'), ...\n            'Resample size (mm)', 1,{num2str(handles.parameters.imagedata.resample_to)});\n        if isempty(answer), return; end % cancel pressed\n        str = str2num(answer{1});\n        if isempty(str) || str <= 0 || ~isint(str)\n            changemade=false;\n            warndlg('Input must be a positive integer in millimeters.');\n        else % update the parameter value.\n            handles.parameters.imagedata.do_resample = true;\n            handles.parameters.imagedata.resample_to = str;\n        end\n    case 'open_lesion_folder_button'\n        OpenDirectoryInNativeWindow(handles.parameters.lesion_img_folder)\n    case 'open_score_file_button'\n        openFileInSystemViewer(handles.parameters.score_file)\n    case 'open_output_folder_button'\n        OpenDirectoryInNativeWindow(handles.parameters.analysis_out_path)\n    case 'ica_lesion_decompose_option'\n        handles.parameters.beta.do_ica_on_lesiondata = ~handles.parameters.beta.do_ica_on_lesiondata;\n    case 'requirements_menu'\n    case 'search_strategy_options'\n        set(handles.optimization_iterations_menu_option,'Label',['Iterations: ' num2str(handles.parameters.optimization.iterations)])\n        set(handles.griddivs_optimization_menu_option,'Label',['Grid Divs: ' num2str(handles.parameters.optimization.grid_divisions)])\n        return\n\n    case 'summary_prediction_menu'\n        handles.parameters.summary.predictions = ~handles.parameters.summary.predictions;\n    case 'lsm_method_parent_menu'\n        set(get(handles.lsm_method_parent_menu,'children'),'checked','off')\n        if handles.parameters.method.mass_univariate\n            set(handles.mass_univariate_menu_option,'checked','on')\n            set(handles.svr_parent_menu,'enable','off')\n        else\n            set(handles.multivariate_lsm_option,'checked','on')\n            set(handles.svr_parent_menu,'enable','on')\n        end\n        \n        if handles.details.libsvm || handles.details.stats_toolbox\n            set(handles.multivariate_lsm_option,'enable','on')\n        else\n            handles.parameters.method.mass_univariate = true; % at least...\n            set(handles.multivariate_lsm_option,'enable','off')            \n        end\n    case 'mass_univariate_menu_option'\n        handles.parameters.method.mass_univariate = true;\n    case 'multivariate_lsm_option'\n        handles.parameters.method.mass_univariate = false;\n    case 'svr_parent_menu'\n    case 'optimization_is_verbose_menu'\n        handles.parameters.optimization.verbose_during_optimization = ~handles.parameters.optimization.verbose_during_optimization;\n    case 'summary_lesionoverlap'\n        handles.parameters.summary.lesion_overlap = ~handles.parameters.summary.lesion_overlap;\n    case 'summary_paramoptimization'\n        handles.parameters.summary.hyperparameter_optimization_record = ~handles.parameters.summary.hyperparameter_optimization_record;\n    case 'summary_create_summary'\n        handles.parameters.do_make_summary = ~handles.parameters.do_make_summary;\n    case 'summary_narrative_summary'\n        handles.parameters.summary.narrative = ~handles.parameters.summary.narrative;\n    case 'summary_svrbetamap'\n        handles.parameters.summary.beta_map = ~handles.parameters.summary.beta_map;\n    case 'summary_voxelwise_thresholded'\n        handles.parameters.summary.voxelwise_thresholded = ~handles.parameters.summary.voxelwise_thresholded;\n    case 'summary_clusterwise_thresholded'\n        handles.parameters.summary.clusterwise_thresholded = ~handles.parameters.summary.clusterwise_thresholded;\n    case 'summary_cfwerdiagnostics'\n        handles.parameters.summary.cfwer_diagnostics = ~handles.parameters.summary.cfwer_diagnostics;\n    case 'model_variablediagnostics'\n        handles.parameters.summary.variable_diagnostics = ~handles.parameters.summary.variable_diagnostics;\n    case 'summary_clusterstability'\n        handles.parameters.summary.cluster_stability = ~handles.parameters.summary.cluster_stability;\n    case 'summary_parameterassessment'\n        handles.parameters.summary.parameter_assessment = ~handles.parameters.summary.parameter_assessment;\n    case 'do_use_cache_menu'\n        handles.parameters.do_use_cache_when_available = ~handles.parameters.do_use_cache_when_available;\n    case 'crossval_menu_option_none'\n        handles.parameters.optimization.crossval.do_crossval = false; % disable crossvalidation.\n    case 'crossval_menu_option_kfold'\n        msg = sprintf('Enter the number of folds for cross-validation (default = %d):',handles.parameters.optimization.crossval.nfolds_default);\n        answer = inputdlg(msg, ...\n            'Number of folds', 1,{num2str(handles.parameters.optimization.crossval.nfolds)});\n        warning('bug here - if you click cancel it causes an error - in future, embed the str<=0 and isint within the isempty... and do that with other str2num(answer{1})''s as well')\n        if isempty(answer), return; end % cancel pressed\n        str = str2num(answer{1});\n        if isempty(str) || str <= 0 || ~isint(str)\n            changemade=false;\n            warndlg('Input must be a positive integer.');\n        else % update the parameter value.\n            handles.parameters.optimization.crossval.do_crossval = true; % enable crossvalidation.\n            handles.parameters.optimization.crossval.nfolds = str;\n        end\n        \n    case 'do_repartition_menu_option' % flip this choice.\n        handles.parameters.optimization.crossval.repartition = ~handles.parameters.optimization.crossval.repartition;\n    case 'standardize_menu' % set the value to use if not optimization\n        vals = {'true','false'};\n        msg = sprintf('Standardize value (default = %s):',myif(handles.parameters.svr_defaults.standardize,'true','false'));\n        s = listdlg('PromptString',msg,'SelectionMode','single', ...\n            'ListString',vals,'InitialValue',myif(handles.parameters.standardize,1,2), ...\n            'Name','Standardize Parameter','ListSize',[250 80]);\n       \n        if isempty(s)\n            changemade=false; % cancelled...\n        else\n            handles.parameters.standardize = str2num(vals{s}); % myif(v==1,true,false); % new value.\n        end\n        \n    case 'epsilon_menu' % set the value to use if not optimization\n        msg = sprintf('Enter new value for epsilon (default = %0.2f):',handles.parameters.svr_defaults.epsilon);\n        % add min and max range - dev1\n        answer = inputdlg(msg,'Epsilon Parameter',1,{num2str(handles.parameters.epsilon)});\n        if isempty(answer), return; end % cancel pressed\n        numval = str2num(answer{1});\n        if isnumeric(numval) && ~isempty(numval)\n            handles.parameters.epsilon = numval;\n        end\n\n    % Whether to allow optimization of given hyperparameter\n    case 'do_optimize_cost_menu'\n        if ~handles.parameters.optimization.params_to_optimize.cost % then enabling it will prompt for new values...\n            minmsg = sprintf('Minimum (default = %0.3f):',handles.parameters.optimization.params_to_optimize.cost_range_default(1));\n            maxmsg = sprintf('Maximum (default = %0.2f):',handles.parameters.optimization.params_to_optimize.cost_range_default(2));\n            msg = {minmsg,maxmsg};\n            defaultans = {num2str(handles.parameters.optimization.params_to_optimize.cost_range(1)),num2str(handles.parameters.optimization.params_to_optimize.cost_range(2))};\n            answer = inputdlg(msg,'Cost Range',1,defaultans);\n            if isempty(answer), return; end % cancel pressed\n            if any(cellfun(@isempty,answer)), warndlg('Invalid Cost range.'); return; end\n            minval = str2num(answer{1}); maxval = str2num(answer{2});\n            if ~all([isnumeric(minval) isnumeric(maxval)]), warndlg('Cost range values must be numbers.'); return; end\n            if ~all([minval maxval] > 0), warndlg('Cost range values must be positive.'); return; end\n            handles.parameters.optimization.params_to_optimize.cost_range = sort([minval maxval]);\n        end\n        \n        handles.parameters.optimization.params_to_optimize.cost = ~handles.parameters.optimization.params_to_optimize.cost;\n    case 'do_optimize_gamma_menu'\n        if ~handles.parameters.optimization.params_to_optimize.sigma % then enabling it will prompt for new values...\n            useLibSVM = handles.parameters.useLibSVM; % for convenience.\n            parmname = myif(useLibSVM,'Gamma','Sigma'); % depending on what algorithm the user is using.\n            defaultmin = handles.parameters.optimization.params_to_optimize.sigma_range_default(1);\n            defaultmax = handles.parameters.optimization.params_to_optimize.sigma_range_default(2);\n            minmsg = sprintf('Minimum (default = %0.3f):',myif(useLibSVM,sigma2gamma(defaultmin),defaultmin)); % convert to gamma if necessary\n            maxmsg = sprintf('Maximum (default = %0.2f):',myif(useLibSVM,sigma2gamma(defaultmax),defaultmax)); % convert to gamma if necessary\n            msg = {minmsg,maxmsg};\n            oldmin = handles.parameters.optimization.params_to_optimize.sigma_range(1);\n            oldmax = handles.parameters.optimization.params_to_optimize.sigma_range(2);\n            defaultans = {num2str(myif(useLibSVM,sigma2gamma(oldmin),oldmin)),num2str(myif(useLibSVM,sigma2gamma(oldmax),oldmax))}; % convert to gamma if necessary\n            answer = inputdlg(msg,[parmname ' Range'],1,defaultans);  % display as gamma if necessary\n            if isempty(answer), return; end % cancel pressed\n            if any(cellfun(@isempty,answer)), warndlg(['Invalid ' parmname ' range.']); return; end\n            minval = str2num(answer{1}); maxval = str2num(answer{2});\n            if ~all([isnumeric(minval) isnumeric(maxval)]), warndlg([parmname ' range values must be numbers.']); return; end % display as gamma if necessary\n            if ~all([minval maxval] > 0), warndlg([parmname ' range values must be positive.']); return; end % display as gamma if necessary\n            handles.parameters.optimization.params_to_optimize.sigma_range = sort([myif(useLibSVM,sigma2gamma(minval),minval) myif(useLibSVM,sigma2gamma(maxval),maxval)]); % convert gamma back to sigma if necessary.\n        end\n\n        handles.parameters.optimization.params_to_optimize.sigma = ~handles.parameters.optimization.params_to_optimize.sigma;\n    case 'do_optimize_epsilon_menu'\n        if ~handles.parameters.optimization.params_to_optimize.epsilon % then enabling it will prompt for new values...\n            minmsg = sprintf('Minimum (default = %0.3f):',handles.parameters.optimization.params_to_optimize.epsilon_range_default(1));\n            maxmsg = sprintf('Maximum (default = %0.2f):',handles.parameters.optimization.params_to_optimize.epsilon_range_default(2));\n            msg = {minmsg,maxmsg};\n            defaultans = {num2str(handles.parameters.optimization.params_to_optimize.epsilon_range(1)),num2str(handles.parameters.optimization.params_to_optimize.epsilon_range(2))};\n            answer = inputdlg(msg,'Epsilon Range',1,defaultans);\n            if isempty(answer), return; end % cancel pressed\n            if any(cellfun(@isempty,answer)), warndlg('Invalid Epsilon range.'); return; end\n            minval = str2num(answer{1}); maxval = str2num(answer{2});\n            if ~all([isnumeric(minval) isnumeric(maxval)]), warndlg('Epsilon range values must be numbers.'); return; end\n            if ~all([minval maxval] > 0), warndlg('Epsilon range values must be positive.'); return; end\n            handles.parameters.optimization.params_to_optimize.epsilon_range = sort([minval maxval]);\n        end\n        handles.parameters.optimization.params_to_optimize.epsilon = ~handles.parameters.optimization.params_to_optimize.epsilon;\n    case 'do_optimize_standardize_menu'\n        handles.parameters.optimization.params_to_optimize.standardize = ~handles.parameters.optimization.params_to_optimize.standardize;\n\n    % Optimization misc choices\n    case 'optimization_iterations_menu_option'\n        answer = inputdlg('Enter a new number of iterations:','Number of optimization iterations',1,{num2str(handles.parameters.optimization.iterations)});\n        if isempty(answer), return; end % cancel pressed\n        str = str2num(answer{1});\n        if isempty(str) || str <= 0 || ~isint(str)\n            changemade=false;\n            warndlg('Input must be a positive integer.');\n        else % update the parameter value.\n            handles.parameters.optimization.iterations = str;\n        end\n    case 'griddivs_optimization_menu_option'\n        answer = inputdlg('Enter a new number of grid divisions:','Number of optimization grid divisions',1,{num2str(handles.parameters.optimization.grid_divisions)});\n        if isempty(answer), return; end % cancel pressed\n        str = str2num(answer{1});\n        if isempty(str) || str <= 0 || ~isint(str)\n            changemade=false;\n            warndlg('Input must be a positive integer.');\n        else % update the parameter value.\n            handles.parameters.optimization.grid_divisions = str;\n        end    \n        \n    % Optimization search strategy choice\n    case 'random_search_menu_option'\n        handles.parameters.optimization.search_strategy = 'Random Search';\n    case 'bayes_optimization_menu_choice'\n        handles.parameters.optimization.search_strategy = 'Bayes Optimization';\n    case 'gridsearch_option'\n        handles.parameters.optimization.search_strategy = 'Grid Search';        \n\n    % Optimization objective function choice\n    case 'predictbehavior_optimize_menu_choice' % bayes opt predict behavior\n        handles.parameters.optimization.objective_function = 'Predict Behavior';\n    case 'correlation_optimize_menu_choice' % maximum correlation w behavior\n        handles.parameters.optimization.objective_function = 'Maximum Correlation';\n    case 'resubloss_optimize_menu_choice'\n        handles.parameters.optimization.objective_function = 'Resubstitution Loss';\n    % Whether or not to optimize hyperparameters\n    case 'no_optimize_menu_choice'\n        handles.parameters.optimization.do_optimize = false;\n    case 'current_optimization_menu_option'\n        handles.parameters.optimization.do_optimize = true; % will use whatever setting is configured.\n    \n\t% Which SVR algorithm to use\n    case 'use_lib_svm'\n        handles.parameters.useLibSVM = 1;\n        handles.parameters.crossval.do_crossval = false; % not currently supported for libsvm...\n    case 'use_matlab_svr'\n        handles.parameters.useLibSVM = 0;        \n    case 'save_pre_thresh'\n        handles.parameters.SavePreThresholdedPermutations = ~handles.parameters.SavePreThresholdedPermutations;\n    case 'retain_big_binary_file'\n        handles.parameters.SavePermutationData = ~handles.parameters.SavePermutationData;\n    case 'save_post_vox_thresh'\n        handles.parameters.SavePostVoxelwiseThresholdedPermutations = ~handles.parameters.SavePostVoxelwiseThresholdedPermutations;\n    case 'save_post_clusterwise_thresholded'\n        handles.parameters.SavePostClusterwiseThresholdedPermutations= ~handles.parameters.SavePostClusterwiseThresholdedPermutations;\n    case 'save_unthresholded_pmaps_cfwer'\n        handles.parameters.SaveNullPMapsPreThresholding = ~handles.parameters.SaveNullPMapsPreThresholding;\n    case 'save_thresholded_pmaps_cfwer'\n        handles.parameters.SaveNullPMapsPostThresholding = ~handles.parameters.SaveNullPMapsPostThresholding;\n    case 'retain_big_binary_pval_file'\n        handles.parameters.SavePermutationPData = ~handles.parameters.SavePermutationPData;\n    case 'parallelizemenu'\n        handles.parameters.parallelize = ~handles.parameters.parallelize;\n    case 'applycovariatestobehaviorcheckbox'\n        handles.parameters.apply_covariates_to_behavior = get(gcbo,'value');\n    case 'applycovariatestolesioncheckbox'\n        handles.parameters.apply_covariates_to_lesion = get(gcbo,'value');\n    case 'lesionvolumecorrectiondropdown'\n        contents = get(handles.lesionvolumecorrectiondropdown,'string');\n        newval = contents{get(handles.lesionvolumecorrectiondropdown,'value')};\n        handles.parameters.lesionvolcorrection = newval;\n    case 'hypodirectiondropdown'\n        contents = get(handles.hypodirectiondropdown,'string');\n        newval = contents{get(handles.hypodirectiondropdown,'value')};\n        if find(strcmp(newval,handles.options.hypodirection)) == 3 % Disable two-tails from the GUI\n            warndlg('Two-tailed hypothesis tests are not available in this version of SVRLSMGUI.')\n            changemade = false;\n        else\n            handles.parameters.tails = newval;\n        end\n    case 'addcovariate'\n        % first, what are we trying to add?\n        contents = get(handles.potentialcovariateslist,'String'); \n        newcovariate = contents{get(handles.potentialcovariateslist,'Value')};\n        if strcmp(newcovariate,handles.parameters.score_name) % check if it's our main score name...\n            warndlg('This variable is already chosen as the main outcome in the analysis. If you''d like to add it as a covariate, remove it from \"Score Name.\"')\n            changemade = false;\n        elseif any(strcmp(newcovariate,handles.parameters.control_variable_names)) % only if it's not on our list of covariates already.\n            warndlg('This variable is already on the list of covariates. You may not add it twice.')\n            changemade = false;\n        else % it's new\n            handles.parameters.control_variable_names{end+1} = newcovariate;\n        end\n    case 'removecovariate'\n       contents = get(handles.potentialcovariateslist,'String'); % what are we trying to remove? \n       newcovariate = contents{get(handles.potentialcovariateslist,'Value')};\n       if any(strcmp(newcovariate,handles.parameters.control_variable_names)) % only if it IS on our list!\n           index_to_remove = strcmp(newcovariate,handles.parameters.control_variable_names);\n           handles.parameters.control_variable_names(index_to_remove) = [];\n       else\n            changemade = false;\n       end\n    case 'chooselesionfolderbutton'\n        folder_name = uigetdir(handles.parameters.lesion_img_folder,'Choose a folder containing lesion files for this analysis.');\n        if folder_name % if folder_name == 0 then cancel was clicked.\n            [~,attribs] = fileattrib(folder_name); % we need read access from here.\n            if attribs.UserRead\n                handles.parameters.lesion_img_folder = folder_name;\n            else\n                warndlg('You do not have read access to the directory you selected for the lesion files. Adjust the permissions and try again.')\n                changemade = false;                \n            end\n        else\n            changemade = false;\n        end\n    case 'choosescorefilebutton'\n        [FileName,PathName] = uigetfile(fullfile(fileparts(handles.parameters.score_file),'*.csv'),'Select a file with behavioral scores.');\n        if FileName\n            scorefile_name =  fullfile(PathName,FileName);\n            handles.parameters.score_file = scorefile_name;\n            handles.parameters.control_variable_names = {}; % also clear covariates...  \n        else % cancel was clicked.\n            changemade = false;\n        end\n    case 'chooseoutputfolderbutton'\n        folder_name = uigetdir(handles.parameters.analysis_out_path,'Choose a folder in which to save this analysis.');\n        if folder_name\n            [~,attribs] = fileattrib(folder_name); % we need read/write access from here.\n            if attribs.UserRead && attribs.UserWrite\n                handles.parameters.analysis_out_path = folder_name;\n            else\n                warndlg('You do not have read and write access to the directory you selected to save your output. Adjust the permissions and try again.')\n                changemade = false;                                \n            end\n        else\n            changemade = false;\n        end\n    case 'scorenamepopupmenu' % User has changed the one_score in question...\n        contents = get(gcbo,'string');\n        newval = contents{get(gcbo,'value')};\n        if any(strcmp(newval,handles.parameters.control_variable_names))\n            warndlg('This variable is already chosen as a covariate. If you''d like to use it as the outcome of interest, remove it as a covariate.')\n            changemade = false;\n        else\n            handles.parameters.score_name = newval;\n        end\n    case 'analysisnameeditbox'\n        handles.parameters.analysis_name = get(gcbo,'string');\n    case 'lesionthresholdeditbox'\n        str = str2num(get(gcbo,'string')); %TO ADD: also make sure this doesn''t exceed the number of lesions available in the data\n        if isempty(str) || ~isint(str) \n            warndlg('Input must be a positive integer.');\n            changemade = false;\n        else % update the parameter value.\n            handles.parameters.lesion_thresh = str;\n        end\n    case 'computebetamapcheckbox'\n        handles.parameters.beta_map  = get(gcbo,'value');\n    case 'computesensitivitymapcheckbox'\n        handles.parameters.sensitivity_map = get(gcbo,'value');\n    case 'cluster_voxelwise_p_editbox' \n        str = str2num(get(gcbo,'string'));\n        if isempty(str) || str <= 0 || str >= 1 \n            changemade = false;\n            warndlg('Input must be a number between 0 and 1.');\n        else % update the parameter value.\n            handles.parameters.voxelwise_p = str;\n        end\n    case 'clusterwisepeditbox'\n        str = str2num(get(gcbo,'string'));\n        if isempty(str) || str <= 0 || str >= 1\n            warndlg('Input must be a number between 0 and 1.');\n            changemade = false;\n        else % update the parameter value.\n            handles.parameters.clusterwise_p = str;\n        end\n    case 'sv_scaling_95th_percentile'\n        handles.parameters.svscaling = 95;\n    case 'sv_scaling_99th_percentile'\n        handles.parameters.svscaling = 99;\n    case 'maxsvscaling'\n        handles.parameters.svscaling = 100; % default\n    case 'npermutationseditbox' % This is voxelwise permutations\n        str = str2num(get(gcbo,'string'));\n        if isempty(str) || str<=0 || ~isint(str)\n            changemade = false;\n            warndlg('Input must be a positive integer.');\n        else % update the parameter value.\n            handles.parameters.PermNumVoxelwise = str;\n        end\n    case 'permutationtestingcheckbox' % enable/disable permutation testing.\n        handles.parameters.DoPerformPermutationTesting = get(gcbo,'value');\n    case 'do_cfwer_checkbox'\n        handles.parameters.do_CFWER = get(gcbo,'value');\n    case 'cfwer_v_value_editbox'\n        str = str2num(get(gcbo,'string'));\n        if isempty(str) || str <= 0 || ~isint(str)\n            changemade=false;\n            warndlg('Input must be a positive integer.');\n        else % update the parameter value.\n            handles.parameters.cfwer_v_value = str; % note that this is cubic millimeters and will later be converted into # voxels (in read_lesioned_images)\n        end\n    case 'cfwer_p_value_editbox'\n        str = str2num(get(gcbo,'string'));\n        if isempty(str) || str<=0 || str >= 1 % not a valid p value...\n            changemade=false;\n            warndlg('Input must be a positive number less than 1.');\n        else % update the parameter value.\n            handles.parameters.cfwer_p_value = str;\n        end\n     case 'analysismask_menu_option_parent'\n         if strcmp(handles.parameters.analysis_mask_file,'')\n             maskstring = 'Select mask...';\n         else\n             maskstring = handles.parameters.analysis_mask_file;\n         end\n         handles.datamask_to_use_menu_option.Text = maskstring;\n         \n         if handles.parameters.use_analysis_mask % then populate the menu item and set the checkbox...\n             handles.do_not_apply_datamask_menu_option.Checked = false;\n             handles.datamask_to_use_menu_option.Checked = true;\n         else % don't use mask.\n             handles.do_not_apply_datamask_menu_option.Checked = true;\n             handles.datamask_to_use_menu_option.Checked = false;\n         end\n         \n    case 'do_not_apply_datamask_menu_option'\n         handles.parameters.use_analysis_mask = false;\n         changemade = true;\n    case 'datamask_to_use_menu_option'\n        disp('a')\n        handles.parameters.use_analysis_mask = true;\n        [FileName,PathName] = uigetfile('*.nii','Select a mask file within which to run the analysis.');\n        filepath = fullfile(PathName,FileName);\n        if ~exist(filepath,'file'), return; end\n        handles.parameters.analysis_mask_file = filepath; \n        changemade = true;\n    otherwise\n        warndlg(['Unknown callback object ' get(gcbo,'tag') ' - has someone modified the code?'])\nend\n\n    if changemade % then set to not saved...\n        handles.parameters.is_saved = 0;\n    end\n\n    if ~handles.parameters.is_saved % then the analysis configuration was modified, so it hasn't been completed in its current state.\n        handles.parameters.analysis_is_completed = 0; % set \"is completed\" to 0 (in its current state) so the user can click run button, and cannot click show output button.\n        handles = PopulateGUIFromParameters(handles);\n    end\n\n    guidata(hObject, handles); % Update handles structure\n    UpdateTitleBar(handles); % update title bar to show if we have any changes made.\n\nfunction doignore = IgnoreUnsavedChanges(handles)\n    if ~isfield(handles,'parameters') % something bad probably happened with gui initiation.\n    \tdoignore = 1; \n    elseif isfield(handles.parameters,'is_saved') && ~handles.parameters.is_saved % then prompt if the user wants to continue or cancel.\n        choice = questdlg('If you continue you will lose unsaved changes to this analysis configuration.', 'Unsaved Changes', 'Continue Anyway','Cancel','Cancel');\n        switch choice\n            case 'Continue Anyway', doignore = 1;\n            case 'Cancel', doignore = 0;\n        end\n    else % don't hang \n        doignore = 1;\n    end\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = svrlsmgui_OutputFcn(hObject, eventdata, handles) \nvarargout{1} = handles.output;\n\nfunction newmenu_Callback(hObject, eventdata, handles) %#ok<*DEFNU>\n    if IgnoreUnsavedChanges(handles)\n        handles.parameters = GetDefaultParameters(handles);\n        handles = PopulateGUIFromParameters(handles);\n    end\n\nfunction openmenu_Callback(hObject, eventdata, handles)\n    if IgnoreUnsavedChanges(handles)\n        [FileName,PathName] = uigetfile('*.mat','Select an SVRLSMGUI parameters file.');\n        filepath = fullfile(PathName,FileName);\n        if ~exist(filepath,'file'), return; end\n        handles = LoadParametersFromSVRLSMFile(handles,hObject,filepath);\n    end\n\nfunction closemenu_Callback(hObject, eventdata, handles)\n    if IgnoreUnsavedChanges(handles)\n        delete(gcf)\n    end\n\nfunction savemenu_Callback(hObject, eventdata, handles)\n    if exist(handles.parameters.parameter_file_name,'file')\n        handles = SaveSVRLSMGUIFile(handles,hObject); % do the actual save.\n    else\n        saveasmenu_Callback(hObject, eventdata, handles)\n    end\n   \nfunction saveasmenu_Callback(hObject, eventdata, handles)\n    if exist(handles.parameters.parameter_file_name,'file')\n        defaultsavename = handles.parameters.parameter_file_name; % fileparts(handles.parameters.parameter_file_name);\n    else\n        defaultsavename = fullfile(pwd,'Unnamed.mat');\n    end\n    [file,path] = uiputfile('*.mat','Save SVRLSM GUI parameter file as...',defaultsavename);\n    if file == 0 % then cancel was pressed\n        return;\n    end\n\n    handles.parameters.parameter_file_name = fullfile(path,file);\n    handles = SaveSVRLSMGUIFile(handles,hObject); % do the actual save.\n\nfunction quitmenu_Callback(hObject, eventdata, handles)\n    close(gcf) % to trigger close request fcn which handles unsaved changes...\n    \nfunction onlinehelpmenu_Callback(hObject, eventdata, handles)\nweb('https://github.com/atdemarco/svrlsmgui/wiki')\n\nfunction figure1_CreateFcn(hObject, eventdata, handles)\n\nfunction aboutmenu_Callback(hObject, eventdata, handles)\n    helpstr = ['SVRLSM GUI ' num2str(handles.parameters.gui_version) ', Andrew DeMarco 2017-2018, based on Zhang et al. (2014)'];\n    helpdlg(helpstr,'About');\n\nfunction runanalysisbutton_Callback(hObject, eventdata, handles)\n    [success,handles] = RunAnalysis(hObject,eventdata,handles); % now returns handles 10/26/17\n\n    set(handles.runanalysisbutton,'Enable','on') %set(handles.cancelanalysisbutton,'visible','off')\n\n    % Re-enable interface...\n    set(get(handles.permutationtestingpanel,'children'),'enable','on')\n    set(get(handles.analysispreferencespanel,'children'),'enable','on')\n    set(get(handles.covariatespanel,'children'),'enable','on')\n    \n    switch success\n        case 1 % success\n            handles.parameters.analysis_is_completed = 1; % Completed...\n            handles = UpdateProgress(handles,'Analysis has completed successfully.',1);\n        case 0 % failure\n            handles.parameters.analysis_is_completed = 2; % Error...\n            handles = UpdateProgress(handles,'Analysis encountered an error and did not complete...',1);\n            set(handles.interrupt_button,'enable','off') % disable.\n            rethrow(handles.error)\n        case 2 % interrupted\n            handles.parameters.analysis_is_completed = 2; % Error...\n            handles = UpdateProgress(handles,'Analysis was interrupted by user...',1);            \n    end\n    \n    guidata(hObject, handles); % Update handles structure\n    handles = PopulateGUIFromParameters(handles); % refresh gui so we can enable/disable control variable as necessary.\n\n% Select in the dropdown list the selected item.\nfunction realcovariateslistbox_Callback(hObject, eventdata, handles)\n    if isempty(get(hObject,'String')), return; end % hack for error\n    contents = cellstr(get(hObject,'String'));\n    val = contents{get(hObject,'Value')};\n    dropdownoptions = get(handles.potentialcovariateslist,'string');\n    set(handles.potentialcovariateslist,'value',find(strcmp(val,dropdownoptions)))\n\nfunction optionsmenu_Callback(hObject, eventdata, handles)\n    yn = {'off','on'};\n    set(handles.parallelizemenu,'Checked',yn{1+handles.parameters.parallelize}) % is parallelization selected by user?\n    set(handles.parallelizemenu,'Enable',yn{1+handles.details.can_parallelize}) % can we parallelize on this platform?\n\nfunction SVscalingmenu_Callback(hObject, eventdata, handles)\n    children = get(handles.SVscalingmenu,'children');\n    set(children,'Checked','off')\n\n    switch handles.parameters.svscaling\n        case 100 % these are ordered \"backward\" seeming, so 3rd is top in list\n            set(children(3),'Checked','on')\n        case 99\n            set(children(2),'Checked','on')\n        case 95\n            set(children(1),'Checked','on')\n    end\n\nfunction save_perm_data_Callback(hObject, eventdata, handles) % update the subitems with checkboxes\n    yn = {'off','on'};\n    set(handles.save_post_clusterwise_thresholded,'Checked',yn{1+handles.parameters.SavePostClusterwiseThresholdedPermutations})\n    set(handles.save_post_vox_thresh,'Checked',yn{1+handles.parameters.SavePostVoxelwiseThresholdedPermutations})\n    set(handles.save_pre_thresh,'Checked',yn{1+handles.parameters.SavePreThresholdedPermutations})\n    % the cfwer files...\n    set(handles.save_unthresholded_pmaps_cfwer,'Checked',yn{1+handles.parameters.SaveNullPMapsPreThresholding})\n    set(handles.save_thresholded_pmaps_cfwer,'Checked',yn{1+handles.parameters.SaveNullPMapsPostThresholding})\n\nfunction svrmenu_Callback(hObject, eventdata, handles)\nif handles.parameters.useLibSVM\n    set(handles.use_lib_svm,'checked','on')\n    set(handles.use_matlab_svr,'checked','off')\nelse\n    set(handles.use_lib_svm,'checked','off')\n    set(handles.use_matlab_svr,'checked','on')\nend\nif handles.details.stats_toolbox\n    set(handles.use_matlab_svr,'enable','on')\nelse\n    set(handles.use_matlab_svr,'enable','off')\nend\nif handles.details.libsvm\n    set(handles.use_lib_svm,'enable','on')\nelse\n    set(handles.use_lib_svm,'enable','off')\nend\n\nfunction cost_menu_Callback(hObject, eventdata, handles)\n    msg = sprintf('Enter new parameter value for Cost/BoxConstraint (default = %0.1f)',handles.parameters.svr_defaults.cost);\n    answer = inputdlg(msg,'Cost Parameter',1,{num2str(handles.parameters.cost)});\n    if isempty(answer), return; end % cancel pressed\n    numval = str2num(answer{1}); %#ok<*ST2NM>\n    if isnumeric(numval) && ~isempty(numval)\n        handles.parameters.cost = numval;\n        handles.parameters.is_saved = 0;\n        guidata(hObject, handles);\n        handles = PopulateGUIFromParameters(handles);\n    end\n   \nfunction gamma_menu_Callback(hObject, eventdata, handles)\n    useLibSVM = handles.parameters.useLibSVM; % for convenience.\n    parmname = myif(useLibSVM,'Gamma','Sigma'); % depending on what algorithm the user is using.\n    defaultval = handles.parameters.svr_defaults.sigma; \n    msg = sprintf('Enter new parameter value for %s (default = %0.1f)',parmname,myif(useLibSVM,sigma2gamma(defaultval),defaultval)); % convert to gamma if necessary\n    oldval = handles.parameters.sigma;\n    answer = inputdlg(msg,[parmname ' Parameter'],1,{num2str(myif(useLibSVM,sigma2gamma(oldval),oldval))}); % convert to gamma if necessary\n    if isempty(answer), return; end % cancel pressed\n    numval = str2num(answer{1});\n    if isnumeric(numval) && ~isempty(numval)\n        handles.parameters.sigma = myif(useLibSVM,gamma2sigma(numval),numval); % store as sigma, so convert input gamma to sigma if necessary...\n        handles.parameters.is_saved = 0;\n        guidata(hObject, handles);\n        handles = PopulateGUIFromParameters(handles);\n    end\n    \nfunction open_batch_job_Callback(hObject, eventdata, handles)\n    folder_name = uigetdir(pwd,'Choose a folder containing .mat config files of your analyses.');\n    if ~folder_name, return; end % if folder_name == 0 then cancel was clicked.\n    files = dir(fullfile(folder_name,'*.mat'));\n    fname = {files.name};\n    [s,v] = listdlg('PromptString','Choose the analyses to run:','SelectionMode','multi','ListString',fname);\n    if ~v, return; end % cancelled..\n    for f = 1:numel(s)\n        curs=s(f);\n        curfname = fname{curs};\n        curfile = fullfile(folder_name,curfname);\n%         try % so one or more can fail without stopping them all.\n            handles = UpdateProgress(handles,['Batch: Starting file ' curfname '...'],1);\n            [success,handles] = RunAnalysisNoGUI(curfile,handles); % second parm, handles, allows access to gui elements, etc.\n            handles = UpdateProgress(handles,['Batch: Finished file ' curfname '.'],1);\n            \n            switch success\n                case 1 % success\n                    handles.parameters.analysis_is_completed = 1; % Completed...\n                    handles = UpdateProgress(handles,['Batch: Finished file successfully ' curfname '.'],1);\n                case 0 % failure\n                    handles.parameters.analysis_is_completed = 2; % Error...\n                    %handles = UpdateProgress(handles,'Analysis encountered an error and did not complete...',1);\n                    handles = UpdateProgress(handles,['Batch: Analysis encountered an error and did not complete: ' curfname '.'],1);\n                    if isfield(handles,'interrupt_button')\n                        set(handles.interrupt_button,'enable','off') % disable.\n                    end\n                    rethrow(handles.error)\n            end\n\n%         catch\n%             disp('return from open_batch_job in svrlsmgui() with error')\n%             msg = ['A batch job specified by file ' fname{curs} ' encountered an error and was aborted.'];\n%             warning(msg)\n%         end\n    end\n    \n    handles = UpdateProgress(handles,'Batch: All batch jobs done.',1);            \n\nfunction figure1_CloseRequestFcn(hObject, eventdata, handles)\n    if IgnoreUnsavedChanges(handles), delete(hObject); end\n\nfunction checkforupdates_Callback(hObject, eventdata, handles)\n    update_available = check_for_updates;\n    if ~update_available\n        msgbox('There are no updates available.')\n        return\n    end\n    choice = questdlg('New updates are available, would you like to download them?','Update SVRLSMGUI','Not now','Update now','Update now');\n    if strcmp(choice,'Not now'), return, end\n    get_new_version(handles)\n\n%% callbacks -- replace these in the future.\nfunction controlvariablepopupmenu_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction computebetamapcheckbox_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\nfunction computesensitivitymapcheckbox_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\nfunction analysisnameeditbox_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction permutation_unthresholded_checkbox_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\nfunction permutation_voxelwise_checkbox_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\nfunction permutation_largest_cluster_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\nfunction progresslistbox_Callback(hObject, eventdata, handles)\nfunction maxsvscaling_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\nfunction potentialcovariateslist_Callback(hObject, eventdata, handles)\n\nfunction viewresultsbutton_Callback(hObject, eventdata, handles)\n    LaunchResultsDirectory(hObject,eventdata,handles);\nfunction npermutationsclustereditbox_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction parallelizemenu_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\nfunction sv_scaling_99th_percentile_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\nfunction sv_scaling_95th_percentile_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\nfunction debug_menu_Callback(hObject, eventdata, handles)\n\nfunction use_lib_svm_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\nfunction use_matlab_svr_Callback(hObject, eventdata, handles)\nhandles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction interrupt_button_Callback(hObject, eventdata, handles) % attempt to interrupt an ongoing analysis\n    set(hObject,'enable','off') % so user doesn't click a bunch...\n%     poolobj = gcp('nocreate'); % < this doesn't stop, it just relaunches the parpool...\n%     delete(poolobj); % try to stop what's happening if there's parallelization happening\n    set(gcf,'userdata','cancel')\n    guidata(hObject, handles); % Update handles structure so it saves...\n\nfunction no_optimize_menu_choice_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction correlation_optimize_menu_choice_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n    \nfunction resubloss_optimize_menu_choice_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction predictbehavior_optimize_menu_choice_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction optimize_menu_Callback(hObject, eventdata, handles)\n    set(get(hObject,'Children'),'Checked','off') % uncheck all child menus\n    cur_optim_string = CurrentOptimString(handles.parameters);\n    set(handles.current_optimization_menu_option,'Label',cur_optim_string)\n    opts = [handles.parameters_to_optimize_menu handles.search_strategy_menu_option handles.objective_function_menu_option handles.crossvalidation_parent_menu handles.optimization_is_verbose_menu];\n    if ~handles.parameters.optimization.do_optimize % then no optimization...\n        set(handles.no_optimize_menu_choice,'Checked','on')\n        set(opts,'enable','off') % Visual cue that these don't apply when optimization is off\n    else \n        set(handles.current_optimization_menu_option,'Checked','on')\n        set(opts,'enable','on') % Visual cue that these don't apply when optimization is off\n    end\n     if handles.parameters.optimization.verbose_during_optimization \n         set(handles.optimization_is_verbose_menu,'Checked','on')\n     end\n\nfunction current_optimization_menu_option_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction parameters_menu_Callback(hObject, eventdata, handles)\n    do_opt = handles.parameters.optimization.do_optimize; % for convenience\n\n    if do_opt && handles.parameters.optimization.params_to_optimize.cost\n        label = ['Cost: optimize (' num2str(handles.parameters.optimization.params_to_optimize.cost_range(1)) ' - ' num2str(handles.parameters.optimization.params_to_optimize.cost_range(2)) ')'];\n        set(handles.cost_menu,'label',label,'enable','off');\n    else\n        set(handles.cost_menu,'label',['Cost: ' num2str(handles.parameters.cost) myif(handles.parameters.svr_defaults.cost == handles.parameters.cost,' (default)','')],'enable','on');\n    end\n\n    useLibSVM = handles.parameters.useLibSVM; % for convenience.\n    parmname = myif(useLibSVM,'Gamma','Sigma'); % depending on what algorithm the user is using.\n    if do_opt && handles.parameters.optimization.params_to_optimize.sigma\n        oldmin = handles.parameters.optimization.params_to_optimize.sigma_range(1);\n        oldmax = handles.parameters.optimization.params_to_optimize.sigma_range(2);\n        label = [parmname ': optimize (' num2str(myif(useLibSVM,sigma2gamma(oldmin),oldmin)) ' - ' num2str(myif(useLibSVM,sigma2gamma(oldmax),oldmax)) ')'];\n        set(handles.gamma_menu,'label',label,'enable','off');\n    else \n        cursigma = handles.parameters.sigma; % Display as gamma if use libSVM\n        cursigma = myif(useLibSVM,sigma2gamma(cursigma),cursigma); % Display as gamma if use libSVM\n        set(handles.gamma_menu,'label',[parmname ': ' num2str(cursigma) myif(handles.parameters.svr_defaults.sigma == handles.parameters.sigma,' (default)','')],'enable','on'); % Display as gamma if use libSVM\n    end\n    \n    if do_opt && handles.parameters.optimization.params_to_optimize.epsilon\n        label = ['Epsilon: optimize (' num2str(handles.parameters.optimization.params_to_optimize.epsilon_range(1)) ' - ' num2str(handles.parameters.optimization.params_to_optimize.epsilon_range(2)) ')'];\n        set(handles.epsilon_menu,'label',label,'enable','off');\n    else\n        set(handles.epsilon_menu,'label',['Epsilon: ' num2str(handles.parameters.epsilon) myif(handles.parameters.svr_defaults.epsilon == handles.parameters.epsilon,' (default)','')],'enable','on');       \n    end\n\n    if do_opt && handles.parameters.optimization.params_to_optimize.standardize\n        set(handles.standardize_menu,'label','Standardize: optimize (yes/no)','enable','off');\n    else\n        set(handles.standardize_menu,'label',['Standardize: ' myif(handles.parameters.standardize,'true','false') myif(handles.parameters.svr_defaults.standardize == handles.parameters.standardize,' (default)','')],'enable','on');\n    end\n    \nfunction epsilon_menu_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n    \nfunction standardize_menu_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n    \n%% Objective function...\nfunction objective_function_menu_option_Callback(hObject, eventdata, handles)\n    set(get(hObject,'Children'),'Checked','off') % uncheck all child menus\n    set([handles.predictbehavior_optimize_menu_choice handles.correlation_optimize_menu_choice],'enable','off')\n    switch handles.parameters.optimization.objective_function\n        case 'Predict Behavior'\n            set(handles.predictbehavior_optimize_menu_choice,'Checked','on')\n        case 'Maximum Correlation'\n            set(handles.correlation_optimize_menu_choice,'Checked','on')\n        case 'Resubstitution Loss'\n            set(handles.resubloss_optimize_menu_choice,'Checked','on')\n        otherwise\n            error('Unknown optimization objective function.')\n    end\n    \n%% Search strategy ...\nfunction search_strategy_menu_option_Callback(hObject, eventdata, handles)\n    set(get(hObject,'Children'),'Checked','off') % uncheck all child menus\n    switch handles.parameters.optimization.search_strategy\n        case 'Bayes Optimization'\n            set(handles.bayes_optimization_menu_choice,'Checked','on')\n        case 'Grid Search'\n            set(handles.gridsearch_option,'Checked','on')\n        case 'Random Search'\n            set(handles.random_search_menu_option,'Checked','on')\n        otherwise\n            error('Unknown optimization objective function.')\n    end\n\nfunction parameters_to_optimize_menu_Callback(hObject, eventdata, handles)\n    set(get(hObject,'children'),'checked','off')\n    \n    if handles.parameters.optimization.params_to_optimize.cost\n        label = ['Cost (' num2str(handles.parameters.optimization.params_to_optimize.cost_range(1)) ' - ' num2str(handles.parameters.optimization.params_to_optimize.cost_range(2)) ')'];\n        set(handles.do_optimize_cost_menu,'label',label,'checked','on')\n    end\n    \n    useLibSVM = handles.parameters.useLibSVM; % for convenience.\n    parmname = myif(useLibSVM,'Gamma','Sigma'); % depending on what algorithm the user is using, show gamma vs sigma...\n    curmin = handles.parameters.optimization.params_to_optimize.sigma_range(1); % convert to sigma if necessary...\n    curmax = handles.parameters.optimization.params_to_optimize.sigma_range(2); % convert to sigma if necessary...\n    label = [parmname ' (' num2str(myif(useLibSVM,sigma2gamma(curmin),curmin)) ' - ' num2str(myif(useLibSVM,sigma2gamma(curmax),curmax)) ')'];  % convert to sigma if necessary...\n    set(handles.do_optimize_gamma_menu,'label',label,'checked',myif(handles.parameters.optimization.params_to_optimize.sigma,'on','off'))\n    \n    if handles.parameters.optimization.params_to_optimize.epsilon\n        label = ['Epsilon (' num2str(handles.parameters.optimization.params_to_optimize.epsilon_range(1)) ' - ' num2str(handles.parameters.optimization.params_to_optimize.epsilon_range(2)) ')'];\n        set(handles.do_optimize_epsilon_menu,'label',label,'checked','on')\n    end\n    \n    if handles.parameters.optimization.params_to_optimize.standardize\n        label = 'Standardize (yes/no)';\n        set(handles.do_optimize_standardize_menu,'label',label,'checked','on')\n    end\n    \n    %set(handles.do_optimize_epsilon_menu,'enable','on')\n     \n    %dev1\n    %set(handles.do_optimize_standardize_menu,'enable','on') % there's a problem passing 'Standardize' hyperopt range to bayesopt... so don't allow optimization of it.\n        \n    %set(get(hObject,'children'),'enable','on')\n\nfunction crossvalidation_parent_menu_Callback(hObject, eventdata, handles)\n    set(get(hObject,'children'),'checked','off')\n    set(handles.crossval_menu_option_kfold,'label',['K-Fold: ' num2str(handles.parameters.optimization.crossval.nfolds) ' folds' myif(handles.parameters.optimization.crossval.nfolds == handles.parameters.optimization.crossval.nfolds_default,' (default)','')])\n    if ~handles.parameters.optimization.crossval.do_crossval\n        set(handles.crossval_menu_option_none,'checked','on')\n        set(handles.do_repartition_menu_option,'enable','off')\n    else\n        if handles.parameters.optimization.crossval.repartition \n            set(handles.do_repartition_menu_option,'checked','on','enable','on')\n        end\n        if strcmp(handles.parameters.optimization.crossval.method,'kfold')\n            set(handles.crossval_menu_option_kfold,'checked','on')\n        else\n            error('Unknown crossvalidation option string.')\n        end\n    end\n    \nfunction parent_cache_menu_Callback(hObject, eventdata, handles)\n    yn = {'off','on'};\n    set(handles.do_use_cache_menu,'Checked',yn{1+handles.parameters.do_use_cache_when_available})\n    set(handles.retain_big_binary_file,'Checked',yn{1+handles.parameters.SavePermutationData})\n    set(handles.retain_big_binary_pval_file,'Checked',yn{1+handles.parameters.SavePermutationPData})\n\n\nfunction output_summary_menu_Callback(hObject, eventdata, handles)\n    yn = {'off','on'};\n    set(handles.summary_create_summary,'checked',yn{1+handles.parameters.do_make_summary});\n    set(handles.summary_narrative_summary,'checked',yn{1+handles.parameters.summary.narrative});\n    set(handles.summary_svrbetamap,'checked',yn{1+handles.parameters.summary.beta_map});\n    set(handles.summary_voxelwise_thresholded,'checked',yn{1+handles.parameters.summary.voxelwise_thresholded});\n    set(handles.summary_clusterwise_thresholded,'checked',yn{1+handles.parameters.summary.clusterwise_thresholded});\n    set(handles.summary_cfwerdiagnostics,'checked',yn{1+handles.parameters.summary.cfwer_diagnostics});\n    set(handles.model_variablediagnostics,'checked',yn{1+handles.parameters.summary.variable_diagnostics});\n    set(handles.summary_clusterstability,'checked',yn{1+handles.parameters.summary.cluster_stability});\n    set(handles.summary_parameterassessment,'checked',yn{1+handles.parameters.summary.parameter_assessment});\n    set(handles.summary_paramoptimization,'checked',yn{1+handles.parameters.summary.hyperparameter_optimization_record});\n    set(handles.summary_lesionoverlap,'checked',yn{1+handles.parameters.summary.lesion_overlap});\n    set(handles.summary_prediction_menu,'checked',yn{1+handles.parameters.summary.predictions});\n    if handles.parameters.do_make_summary\n        set(get(hObject,'children'),'enable','on')\n    else\n        set(get(hObject,'children'),'enable','off')\n        set(handles.summary_create_summary,'enable','on')\n    end\n    disabled_objs = [handles.summary_paramoptimization handles.summary_prediction_menu];\n    set(disabled_objs,'checked','off','enable','off') % since this is disabled in this first release \n    \n\nfunction requirements_menu_Callback(hObject, eventdata, handles)\n%set(get(handles.requirements_menu,'children'),'checked',false)\nset(handles.spm12_installed_menu,'checked',myif(handles.details.spm,'on','off')) % this will not specifically detect spm12 though!\nset(handles.libsvm_installed_menu,'checked',myif(handles.details.libsvm,'on','off'))\nset(handles.parcomp_toolbox_installed_menu,'checked',myif(handles.details.can_parallelize,'on','off'))\nset(handles.stats_toolbox_installed_menu,'checked',myif(handles.details.stats_toolbox,'on','off'))\nset(handles.matlab_version_installed_menu,'checked','on') % what's the requirement?\nset(get(handles.requirements_menu,'children'),'enable','off')\n\nfunction beta_options_menu_Callback(hObject, eventdata, handles)\nset(handles.ica_lesion_decompose_option,'checked',myif(handles.parameters.beta.do_ica_on_lesiondata,'on','off'))\n\nfunction crossvalidate_map_parent_menu_Callback(hObject, eventdata, handles)\n    set(get(hObject,'children'),'checked','off')\n%     parameters.crossval.do_crossval = false; % by default do not do crossvalidated output...\n%     parameters.crossval.method = 'kfold';\n%     parameters.crossval.nfolds = 5;\n%     parameters.crossval.nfolds_default = parameters.crossval.nfolds;\n    set(handles.kfold_map_crossvalidation,'label',['K-Fold: ' num2str(handles.parameters.crossval.nfolds) ' folds' myif(handles.parameters.optimization.crossval.nfolds == handles.parameters.crossval.nfolds_default,' (default)','')])\n    if ~handles.parameters.crossval.do_crossval\n        set(handles.no_map_crossvalidation,'checked','on')\n        %set(handles.do_repartition_menu_option,'enable','off')\n    else\n%         if handles.parameters.optimization.crossval.repartition \n%             set(handles.do_repartition_menu_option,'checked','on','enable','on')\n%         end\n        if strcmp(handles.parameters.crossval.method,'kfold')\n            set(handles.kfold_map_crossvalidation,'checked','on')\n        else\n            error('Unknown crossvalidation option string.')\n        end\n    end\n\nfunction no_map_crossvalidation_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n\nfunction kfold_map_crossvalidation_Callback(hObject, eventdata, handles)\n    handles = UpdateCurrentAnalysis(handles,hObject);\n"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "lm2table.m", "ext": ".m", "path": "svrlsmgui-master/functions/lm2table.m", "size": 6711, "source_encoding": "utf_8", "md5": "91866ec8af0be9264d12a99b61a74344", "text": "function html = lm2table(mdl,caption)\n\n%% Pretty css ...\n% table { \n%   color: #333;\n%   font-family: Helvetica, Arial, sans-serif;\n%   width: 640px;\n%   /* Table reset stuff */\n%   border-collapse: collapse; border-spacing: 0; \n% }\n% \t\t\n% td, th {  border: 0 none; height: 30px; }\n% \t\t\t\n% th {\n%   /* Gradient Background */\n% \tbackground: linear-gradient(#333 0%,#444 100%);\n% \tcolor: #FFF; font-weight: bold;\n% \theight: 40px;\n% }\n% \t\t\n% td { background: #FAFAFA; text-align: center; }\n% \n% /* Zebra Stripe Rows */\n% \t\t\n% tr:nth-child(even) td { background: #EEE; } \n% tr:nth-child(odd) td { background: #FDFDFD; }\n% \n% /* First-child blank cells! */\n% tr td:first-child, tr th:first-child { \n% \tbackground: none; \n% \tfont-style: italic;\n% \tfont-weight: bold;\n% \tfont-size: 14px;\n% \ttext-align: right;\n% \tpadding-right: 10px;\n% \twidth: 80px; \n% }\n% \n% /* Add border-radius to specific cells! */\n% tr:first-child th:nth-child(2) { \n%   border-radius: 5px 0 0 0; \n% } \n% \n% tr:first-child th:last-child { \n%   border-radius: 0 5px 0 0; \n% }\n% \n% <style>\n% table, th, td {\n%     border: 1px solid black;\n% }\n% </style>\n\n% f=figure;a=axes(f);\n% mdl.plot('parent',a)\n\n% can we pass a model title in the model object?\n% the goal here is to imitate something like Mdl.display in html format...\n\nhtml = createheaders({'Term','Estimate','SE','T Stat','P Value'});\nhtml = add_coefficient_data(html,mdl);\nhtml = add_gen_lm_info(html,mdl);\n\nhtml = wrap_w_table_html(html,caption);% finish this off...\n\nfunction rout = nice_r(rho) \n    rout = strrep(sprintf('r = %.2f', rho),'0.','.'); % enforce no leading unnecessary 0 \n    \nfunction rout = nice_r_nolet(rho) \n    rout = strrep(sprintf('%.2f', rho),'0.','.'); % enforce no leading unnecessary 0 \n\nfunction pout= nice_p(pval) \n    pout= myif(pval < .001,'P < .001',strrep(sprintf('P = %.2f', pval),' 0.',' .')); % enforce no leading unnecessary 0 \n\nfunction pout= nice_p_nolet(pval) % ie no p = or p < ...\n    pout= strrep(myif(pval < .001,'<.001',sprintf('%.3f', pval)),'0.','.'); % enforce no leading unnecessary 0 \n\nfunction html = add_gen_lm_info(html,mdl)\n    % Add in the num obs, err df, rmse, rsquared, adj rsquared, f--stat ...\n\n    [fstat_vs_constant_pval,fstat_vs_constant_fval] = coefTest(mdl);\n    [dw_P,dw_DW] = mdl.dwtest;\n    \n    % new rows to add in this cell array...\n    rows = {add_col(5,'',sprintf('Number of observation: %d, Error degrees freedom: %d',mdl.NumObservations,mdl.DFE)), ...\n            add_col(5,'',sprintf('Root Mean Square Error: %0.2f',mdl.RMSE)), ...\n            add_col(5,'',sprintf('R&#178;: %s, Adjusted R&#178;: %s',nice_r_nolet(mdl.Rsquared.Ordinary),nice_r_nolet(mdl.Rsquared.Adjusted))), ...\n            add_col(5,'',sprintf('F-statistic vs. constant model: %0.2f, %s',fstat_vs_constant_fval,nice_p(fstat_vs_constant_pval))), ...\n            add_col(5,'',sprintf('Durbin-Watson test for corr. resids, DW = %0.2f, %s',dw_DW,nice_p(dw_P)))};\n    \n    for r = 1 : numel(rows) % go through and append each...\n        newrow = rows{r};\n        %newrow = ['<font size=\"9\">' newrow '</font>'];\n        html = [html wrap_w_row_html(newrow)]; % add the row wrapper.\n    end\n    \n    %     Add in the durbin watson test - info from http://www.statisticshowto.com/durbin-watson-test-coefficient/\n    %     The Hypotheses for the Durbin Watson test are:\n    %     H0 = no first order autocorrelation.\n    %     H1 = first order correlation exists.\n    %     (For a first order correlation, the lag is one time unit).\n    %\n    %     Assumptions are:\n    %     That the errors are normally distributed with a mean of 0.\n    %     The errors are stationary.\n    % \n    % The Durbin Watson test reports a test statistic, with a value from 0 to 4, where:\n    % \n    % 2 is no autocorrelation.\n    % 0 to <2 is positive autocorrelation (common in time series data).\n    % >2 to 4 is negative autocorrelation (less common in time series data).\n\n\nfunction html = add_coefficient_data(html,mdl)\n    ncoeffs = numel(mdl.CoefficientNames);\n    for c = 1 : ncoeffs\n        newrow = [];\n        newrow = add_col(1,newrow,mdl.CoefficientNames{c}); % Coefficient name\n        newrow = add_col(1,newrow,mdl.Coefficients.Estimate(c)); % Estimate\n        newrow = add_col(1,newrow,mdl.Coefficients.SE(c)); % SE\n        newrow = add_col(1,newrow,mdl.Coefficients.tStat(c)); % tStat\n        newrow = add_col(1,newrow,nice_p_nolet(mdl.Coefficients.pValue(c))); % pValue\n        html = [html wrap_w_row_html(newrow)]; % add the row wrapper.\n    end\n\nfunction rowdat = add_col(span,rowdat,newval)\n    if span > 1 % e.g., %colspan=\"2\"\n        spanstring = [' colspan=\"' num2str(span) '\"'];\n    else\n        spanstring ='';\n    end\n    \n    switch class(newval)\n        case 'double'\n            htmladd = sprintf('<td%s>%0.3f</td>',spanstring,newval); % insert span here as necessary\n        case 'char'\n            htmladd = sprintf('<td%s>%s</td>',spanstring,newval); % insert span here as necessary\n        otherwise\n            error('unknown data class')\n    end\n    rowdat = [rowdat htmladd]; % concat and return\n\n%    {'(Intercept)'}    {'x1'}    {'x2'}    {'x3'}    {'x4'}    {'x5'}\n%mdl.anova\n%             SumSq     DF    MeanSq      F         pValue  \n%              ______    __    ______    ______    __________\n% \n%     x1       116.77     1    116.77    108.57    2.3674e-17\n%     x2       490.59     1    490.59    456.14    7.7393e-38\n%     x3       728.86     1    728.86    677.67    9.3215e-45\n%     x4         1189     1      1189    1105.5    9.0239e-54\n%     x5       1832.2     1    1832.2    1703.5    4.9317e-62\n%     Error     101.1    94    1.0755                        \n\n% addTerms               dwtest                 plotEffects            random                 \n% anova                  feval                  plotInteraction        removeTerms            \n% coefCI                 plot                   plotPartialDependence  step                   \n% coefTest               plotAdded              plotResiduals          \n% compact                plotAdjustedResponse   plotSlice              \n% disp                   plotDiagnostics        predict                \n\nfunction html = createheaders(headers)\n    html = [];\n    for h = 1 : numel(headers)\n        curheader = headers{h}; % {'Coefficient','Estimate','SE','tStat','pValue'};\n        html = [html '<th>' curheader '</th>'];\n    end\n    html = wrap_w_row_html(html);\n\nfunction wrapped = wrap_w_row_html(html)\n    wrapped = [newline '<tr>' newline html newline '</tr>'];\n\nfunction wrapped = wrap_w_table_html(html,caption)\n    cap = [newline '<caption>' caption '</caption>' newline];\n    wrapped = ['<center><table style=\"width:80%\">' cap html '</table></center>'];"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "step2_parallel.m", "ext": ".m", "path": "svrlsmgui-master/functions/step2_parallel.m", "size": 7925, "source_encoding": "utf_8", "md5": "122e2f4b9e8fd73099f44977fe49aaaa", "text": "function [parameters,variables,thresholds] = step2_parallel(handles,parameters,variables,thresholds,all_perm_data)\n    handles = UpdateProgress(handles,'Sorting null betas for each lesioned voxel in the dataset (parallelized).',1);\n    L = length(variables.m_idx);\n    tail = parameters.tailshort; % so not a broadcast variable.\n\n    ori_beta_vals = variables.ori_beta_vals; % for parfor...\n    do_CFWER = parameters.do_CFWER; % for parfor...\n    total_cols =  length(variables.m_idx); % note this must be m_idx since the data in our giant file is stored in frames of length m_idx not length l_idx.\n    nperms = parameters.PermNumVoxelwise;\n    outpath = variables.output_folder.clusterwise;\n\n     pos_thresh_index = thresholds.pos_thresh_index;\n     neg_thresh_index = thresholds.neg_thresh_index;\n     onetail_thresh_index = thresholds.onetail_cutoff_index; % for use with compare_real_beta() \n    \n    % two_tailed_thresh_index = thresholds.two_tailed_thresh_index;\n    % two_tailed_thresh_index_neg = thresholds.two_tailed_thresh_index_neg;\n\n    %% Begin parfeval code \n    batch_job_size = 500; % this is going to be optimal for different systems/#cores/jobs\n    njobs = ceil(total_cols/batch_job_size); % gotta round up to capture all indices\n\n    %% Schedule the jobs...\n    p = gcp(); % get current parallel pool\n    for j = 1 : njobs\n        this_job_start_index = ((j-1)*batch_job_size) + 1;\n        this_job_end_index = min(this_job_start_index + batch_job_size-1,total_cols); % need min so we don't go past valid indices\n        job_indices = this_job_start_index:this_job_end_index;\n        f(j) = parfeval(p,@parallel_step2_batch_fcn,2,job_indices,all_perm_data,total_cols,tail,ori_beta_vals,onetail_thresh_index,L,nperms,do_CFWER,outpath,neg_thresh_index,pos_thresh_index);\n    end\n    \n    alphas = cell(1,njobs); %reserve space - note we want to accumulate in a row here\n    betamapcutoff = cell(1,njobs); %reserve space - note we want to accumulate in a row here\n    \n    %% Monitor job progress...\n    msg = 'Sorting null betas for each lesioned voxel in the dataset (parallelized).';\n    svrlsm_waitbar(parameters.waitbar,0,msg) % update waitbar progress...\n    for j = 1 : njobs\n        check_for_interrupt(parameters) % allow user to interrupt\n        [idx, jobalphas,jobbetamapcutoffs] = fetchNext(f);\n        alphas{idx} = jobalphas; % combine these cells afterward\n        betamapcutoff{idx} = jobbetamapcutoffs; % combine these cells afterward\n        svrlsm_waitbar(parameters.waitbar,j/njobs) % update waitbar progress...\n    end\n\n    alphas = cell2mat(alphas); % combine afterward\n    betamapcutoff = cell2mat(betamapcutoff); % combine afterward\n\n    %% Now compute beta cutoff values and a pvalue map for the observed betas.\n    % ...we do this now since we can't index into fields of the 'thresholds'variable in a parfor loop\n    switch tail\n        case 'pos' % pos - high scores bad\n            thresholds.one_tail_pos_alphas = alphas;\n            thresholds.pos_beta_map_cutoff = betamapcutoff;\n        case 'neg' % neg - high scores good\n            thresholds.one_tail_neg_alphas = alphas;\n            thresholds.neg_beta_map_cutoff = betamapcutoff;\n%         case 'two' % two-tailed\n%             thresholds.two_tailed_beta_map_cutoff_pos =two_tailed_beta_map_cutoff_pos;\n%             thresholds.two_tailed_beta_map_cutoff_neg =two_tailed_beta_map_cutoff_neg;\n%             thresholds.twotails_alphas = twotails_alphas;\n    end\n    \n    %% Now, since we're parallelized, like in step 1, get all those individual files into one big file that we can memmap\n    if do_CFWER    \n        svrlsm_waitbar(parameters.waitbar,0,'Consolidating null p-map files...');\n        parameters.outfname_big_p = fullfile(variables.output_folder.clusterwise,['pmu_p_maps_N_' num2str(total_cols) '.bin']);\n        fileID = fopen(parameters.outfname_big_p,'w');\n        % this data is output such that each numel(variables.m_idx) contiguous sequential values refer to a voxel across all permutations:  [P1V1, P2V1, P3V1, P4V1, ..., npermutations]\n        for col = 1 : total_cols % can't parallelize this since we need order to be right.\n            if ~mod(500,col) % to reduce num of calls...\n                check_for_interrupt(parameters)\n                svrlsm_waitbar(parameters.waitbar,col/total_cols);\n            end\n            curpermfilepath = fullfile(outpath,['pmu_p_map_' num2str(col) '_of_' num2str(total_cols) '.bin']);\n            cur_perm_data = memmapfile(curpermfilepath,'Format','single');\n            fwrite(fileID, cur_perm_data.Data,'single');\n            clear cur_perm_data; % remove memmap from memory.\n            delete(curpermfilepath); % delete it since we don't want the data hanging around...\n        end\n        fclose(fileID); % close big file\n        svrlsm_waitbar(parameters.waitbar,0,'');\n    end\n    \n%% For each voxel, calculate the beta cutoff and p-value based on our permutation data - also, convert to pvalue volumes if CFWER is requested.\nfunction [alphas,betamapcutoffs] = parallel_step2_batch_fcn(this_job_cols,all_perm_data,total_cols,tail,ori_beta_vals,onetail_thresh_index,L,nperms,do_CFWER,outpath,neg_thresh_index,pos_thresh_index)\n    alphas = nan(1,numel(this_job_cols)); % pre-allocate spac\n    betamapcutoffs = nan(1,numel(this_job_cols)); % pre-allocate spac\n    for jobcolind = 1:numel(this_job_cols) % this is for each voxel in the brain, cutting across permutations\n        col = this_job_cols(jobcolind);\n        curcol = extractSlice(all_perm_data,col,L);\n        observed_beta = ori_beta_vals(col); % original observed beta value.\n        curcol_sorted = sort(curcol); % Smallest values at the left/top\n        \n        %% Calculate P values for the single *observed beta map* relative to the null permutation beta volumes.\n        switch tail\n             case {'pos','neg'}\n                 [alphas(jobcolind),betamapcutoffs(jobcolind)] = compare_real_beta(observed_beta,curcol,tail,onetail_thresh_index); % we can do both tails without the switch as long as it's one-tailed.\n%             case 'pos' % high scores bad\n%                 % 'ascend' is the default assort behavior.\n%                 alphas(jobcolind) = sum(observed_beta < curcol_sorted)/nperms;\n%                 %[alphas(jobcolind),betamapcutoffs(jobcolind)] = compare_real_beta(observed_beta,curcol,tail,onetail_thresh_index);\n%                 betamapcutoffs(jobcolind) = curcol_sorted(pos_thresh_index); % so the 9500th at p of 0.05 on 10,000 permutations\n%             case 'neg' % high scores good\n%                 alphas(jobcolind) = sum(observed_beta > curcol_sorted)/nperms;\n%                 %[alphas(jobcolind),betamapcutoffs(jobcolind)] = compare_real_beta(observed_beta,curcol,tail,onetail_thresh_index);\n%                 betamapcutoffs(jobcolind) = curcol_sorted(neg_thresh_index); % so the 500th at p of 0.05 on 10,000 permutations\n            case 'two' % two-tailed...\n                warning('Check that these tails are right after code refactor') % ad 2/14/18\n% %                 two_tailed_beta_map_cutoff_pos(col) = curcol_sorted(two_tailed_thresh_index); % 250...\n% %                 two_tailed_beta_map_cutoff_neg(col) = curcol_sorted(two_tailed_thresh_index_neg); % 9750...\n% %                 twotails_alphas(col) = sum(abs(observed_beta) > abs(curcol_sorted))/numel(curcol_sorted); % percent of values observed_beta is greater than.\n        end\n       \n        % Save this permutation as p values.\n        if do_CFWER % then we need to make a billion files and combine them like in parallelized step 1...\n            p_vec = betas2pvals(curcol,tail); % each of these files corresponds to the p values for all the permutations of a single voxel\n            fileID = fopen(fullfile(outpath,['pmu_p_map_' num2str(col) '_of_' num2str(total_cols) '.bin']),'w');\n            fwrite(fileID, p_vec,'single');\n            fclose(fileID);\n        end\n    end"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "generic_hyperopts.m", "ext": ".m", "path": "svrlsmgui-master/functions/generic_hyperopts.m", "size": 4224, "source_encoding": "utf_8", "md5": "cb65bf7a69325a21821ec0c7ce414ec8", "text": "function results = generic_hyperopts(parameters,variables)\n    % This attempts to optimize using matlab's default hyperparameter optimization options ... \n    % because it doesn't use bayesopt, it cannot update the progress bar (not output function)\n    \n   % For clarity...\n   lesiondata = variables.lesion_dat;\n   behavdata = variables.one_score;\n\n   %% Configure hyperparam options\n   params = hyperparameters('fitrsvm',lesiondata,behavdata); \n   \n   % Turn optimize off for all hyperparams by default\n   for N = 1:numel(params)\n       params(N).Optimize = false;\n   end\n   \n   % Then turn them on as necessary, and set their ranges from the user analysis config\n   if parameters.optimization.params_to_optimize.sigma\n       params(strcmp({params.Name},'KernelScale')).Optimize = true;\n       params(strcmp({params.Name},'KernelScale')).Range = [parameters.optimization.params_to_optimize.sigma_range];\n   end\n   \n   if parameters.optimization.params_to_optimize.cost\n       params(strcmp({params.Name},'BoxConstraint')).Optimize = true;\n       params(strcmp({params.Name},'BoxConstraint')).Range = [parameters.optimization.params_to_optimize.cost_range];\n   end\n   \n   if parameters.optimization.params_to_optimize.epsilon\n       params(strcmp({params.Name},'Epsilon')).Optimize = true;\n       params(strcmp({params.Name},'Epsilon')).Range = [parameters.optimization.params_to_optimize.epsilon_range];\n   end\n   \n   if parameters.optimization.params_to_optimize.standardize\n       params(strcmp({params.Name},'Standardize')).Optimize = true;\n   end\n   \n   optimizeropts = resolveoptimizeropts(parameters);\n   \n   hyperoptoptions = struct('AcquisitionFunctionName','expected-improvement-plus', optimizeropts{:});\n%        'Optimizer', resolveoptimizer(parameters)\n%        'MaxObjectiveEvaluations',parameters.optimization.iterations, ...\n%        'UseParallel',parameters.parallelize, ...\n%        'Repartition',parameters.optimization.crossval.repartition, ...\n%        'KFold',parameters.optimization.crossval.nfolds, ... %'PlotFcn',[], ...\n%        'Verbose',myif(parameters.optimization.verbose_during_optimization,2,0));\n   \n       % 'OutputFcn',@optim_outputfun); % verbose is either 0 or 2...\n   \n%         disp('OptimizeHyperparameters')\n%         params\n%         disp('HyperparameterOptimizationOptions')\n%         hyperoptoptions\n   Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf', 'OptimizeHyperparameters',params, 'HyperparameterOptimizationOptions', hyperoptoptions);\n\n   results = Mdl.HyperparameterOptimizationResults;\n%    \n%     assignin('base','results',results)\n%     assignin('base','Mdl',Mdl)\n%    \n%    \nfunction optimizeropts = resolveoptimizeropts(parameters)\n\n    switch parameters.optimization.search_strategy \n        case 'Bayes Optimization'\n            optimchoice = 'bayesopt';\n        case 'Grid Search' \n            optimchoice = 'gridsearch';\n        case 'Random Search'\n            optimchoice = 'randomsearch';\n    end\n    \n%     parameters.optimization.crossval.do_crossval = true\n%     warning('forcing repartinioning on for testing')\n    \n    repartitionopt = myif(parameters.optimization.crossval.do_crossval, ...\n        {'Repartition',parameters.optimization.crossval.repartition},{});\n    \n    itersornumdivsstr = myif(strcmp(optimchoice,'gridsearch'),'NumGridDivisions','MaxObjectiveEvaluations'); % do we need grid divs or do we need max objective evaluations...?\n    itersornumdivs = myif(strcmp(optimchoice,'gridsearch'),parameters.optimization.grid_divisions,parameters.optimization.iterations);\n    parameters.optimization.crossval.do_crossval = true;\n    nfolds = myif(parameters.optimization.crossval.do_crossval,parameters.optimization.crossval.nfolds,1);\n    if nfolds == 1  %  we'll throw an error... switch back to 5.\n        nfolds = 5;\n        warning('Crossvalidation set to on for hyperparam opt.')\n    end\n    \n    optimizeropts = {'Optimizer', optimchoice, ...\n        itersornumdivsstr,itersornumdivs, ...\n       'UseParallel',parameters.parallelize, ...\n        repartitionopt{:}, ...\n       'KFold', nfolds, ...\n       'Verbose',myif(parameters.optimization.verbose_during_optimization,2,0), ...\n       'ShowPlots',false};\n\n   %optimizeropts{:}"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "svrlsm_prepare_ica.m", "ext": ".m", "path": "svrlsmgui-master/functions/svrlsm_prepare_ica.m", "size": 7989, "source_encoding": "utf_8", "md5": "1731883379f898e0e583d4ca6325a72e", "text": "function [parameters,variables] = svrlsm_prepare_ica(parameters,variables)\n% We'll decompose the lesion data into ICs... and use percent damage to those ROIs as the lesion data values...\n\n % Reread in each subject in the analysis, the mask, and make concatenated ICA file for fsl\nfor ni= 1 : variables.SubNum % numel(variables.SubjectID)\n    fname = [variables.SubjectID{ni}, '.nii'];\n    fullfname = fullfile(parameters.lesion_img_folder, fname);\n    svrlsm_waitbar(parameters.waitbar,ni / length(variables.SubjectID),sprintf('Rereading lesion file %s...',fname));\n    %vo = spm_vol(fullfname); % The true voxel intensities of the jth image are given by: val*V.pinfo(1,j) + V.pinfo(2,j)\n    %tmp = spm_read_vols(vo);\n    [hdr,tmp]=read_nifti(fullfname); % cause i dunn how to output 4d files from the spm's header.\n    tmp(isnan(tmp)) = 0; % Denan the image.\n    tmp = tmp > 0;  % Binarize\n    Ldat(:,:,:,ni) = uint8(tmp);\n    check_for_interrupt(parameters)\nend\n\n% Make the ica output directory..\nmkdir(variables.output_folder.ica)\n\n% Write a 3D mask of ANY lesions!\nfname = fullfile(variables.output_folder.ica,'3DAnyLesionMask.nii');\nwrite_nifti(hdr,double(sum(Ldat,4)>0),fname); % double so it's not booleans.\nvariables.files_created.ica_3D_any_lesion_mask = fname; % so we know we made this file.\n\n% Write out the 4D concatted lesions we'll need for FSL to run melodic...\nhdr.dim(1) = 4;\nhdr.dim(5) = variables.SubNum;\nfname = fullfile(variables.output_folder.ica,'4DConcattedLesionMasks.nii');\nwrite_nifti(hdr,Ldat,fname);\nvariables.files_created.ica_4d_all_lesions = fname; % so we know we made this file.\n\nvariables.output_folder.ica_icadata = fullfile(variables.output_folder.ica,'data.ica');\n\nsetup_ica % add the right stuff to the path...\n\nwd=pwd;\n\ncd(variables.output_folder.ica)\n\n[~,cattedfroot]=fileparts(variables.files_created.ica_4d_all_lesions); % ica_concat_file); % -d or --dim should be the number of components, but adding it to the end of the call just causes it to fail and come back...\n[~,outmaskfroot]=fileparts(variables.files_created.ica_3D_any_lesion_mask);\n\n%% Compute the ICA decomposition...!\nncomps = 35; % variables.SubNum - 5;  % cause nsubs is too many, and nsubs-1 is too many too. will a different \"method\" allow more components to be deflated?\nunix(sprintf(['melodic -i \"' cattedfroot '\" -v -d ' num2str(ncomps) ' -m \"' outmaskfroot '\" -a concat --Oall --nobet --report']));\n\n% Write out the component overlaps in 3D...\n[max_prob_ic_map_inds,max_prob_ic_map_vals] = write_max_ics(variables.output_folder.ica);\nvariables.files_created.ica_max_prob_ic_map_inds = max_prob_ic_map_inds;\nvariables.files_created.ica_max_prob_ic_map_vals = max_prob_ic_map_vals;\n\ncd(wd); % go back to wd...\n\n% now build our ica components - ica has already been run at this point.\n%[~,cattedfroot]=fileparts(ica_concat_file);\n%icadir = [cattedfroot '.ica'];\n\nbuildComponentType = 2;\ndisp(['Building ICA component type ' num2str(buildComponentType) '.'])\nswitch buildComponentType\n    case 1\n        %% read in the component loadings for each subject\n%         reports = dir(fullfile(ica_path,icadir,'report','t*.txt'));\n%         componentData=[];\n%         for c = 1 : numel(reports)\n%             tmp =readtable(fullfile(reports(c).folder,reports(c).name));\n%             componentData(:,c) = tmp.Var1; % first column...\n%         end\n    case 2\n        [~,alllesiondata] = read_nifti(variables.files_created.ica_4d_all_lesions); % ica_concat_file); % check for percent overlap with this...\n        \n        %% or write_max_ics() output ...\n        %[~,componentimg]=read_nifti(fullfile(pwd,icadir,'stats','maxprob_ic_map_inds.nii'));\n        %[~,componentimg]=read_nifti('maxprob_ic_map_inds.nii');\n        \n        [~,componentimg]=read_nifti(variables.files_created.ica_max_prob_ic_map_inds); % 'maxprob_ic_map_inds.nii');\n        \n        for s = 1:size(alllesiondata,4)\n            curlesion = alllesiondata(:,:,:,s);\n            for component = 1 : max(componentimg(:))\n                curcomponentmask = componentimg==component;\n                curcomponent_nvox = sum(curcomponentmask(:));\n                curlesion_overlap_w_curcomp = curlesion&curcomponentmask;\n                n_curlesion_overlap_w_curcomp = sum(curlesion_overlap_w_curcomp(:));\n                tmp = n_curlesion_overlap_w_curcomp / curcomponent_nvox;\n%                 if isnan(tmp)\n%                     variables.SubjectID{s}\n%                     component\n%                     n_curlesion_overlap_w_curcomp \n%                     curcomponent_nvox\n%                     disp('---')\n%                 end\n                componentData(s,component) = tmp; %#ok<AGROW>\n            end\n        end\n    case 3 % multiply out the probabilities...\n%         [~,alllesiondata] = read_nifti(ica_concat_file); % check for percent overlap with this...\n%         componentData=[];\n%         reports = dir(fullfile(ica_path,icadir,'report','t*.txt'));\n%         ncomps = numel(reports);\n%         for c = 1 : ncomps\n%             [~,curcomponent] = read_nifti(fullfile(ica_path,icadir,'stats',['probmap_' num2str(c) '.nii']));\n%             curcomponent_maxprob = sum(curcomponent(:)); % let the probabilities sum up without binarizing.\n% \n%             for s = 1 : size(alllesiondata,4)\n%                 curlesion = alllesiondata(:,:,:,s);\n%                 multoutprob = curcomponent(curlesion>0); % just add up the probability values...\n%                 componentData(s,c) = 100 * (sum(multoutprob(:)) / curcomponent_maxprob); % percent damage to this component prob map....\n%             end\n%         end\nend\n\n    % now that we've collected our component data, replace the lesion data matrix...\n    variables.lesion_dat_voxelwise = variables.lesion_dat;\n    \n    variables.lesion_dat = componentData; % these are percent damage per component!\n    variables.l_idx_voxelwise = variables.l_idx;\n    variables.m_idx_voxelwise = variables.m_idx;\n    \n    variables.l_idx = variables.l_idx_voxelwise(1:ncomps); % We'll put these back later!\n    variables.m_idx = variables.l_idx_voxelwise(1:ncomps); % We'll put these back later!\n    \n    %assignin('base','variables',variables)\n    %assignin('base','parameters',parameters)\n\n    % size(componentData)\n    % tmp = readtable(lesion_text_file);\n    % data.lesion_vol = tmp.lesion_vol;\n    \nfunction [max_prob_ic_map_inds,max_prob_ic_map_vals] = write_max_ics(ica_path)\n    disp('Writing max IC maps')\n    %wd = '/home/crl/Documents/Projects/svrica/concat_lesions_N=48.ica/stats';\n    \n    wd = fullfile(ica_path,'4DConcattedLesionMasks.ica','stats');\n    cd(wd); % gotta go there cause the spaces and stuff in the folder names...\n    \n    files=dir('prob*.nii');\n    \n    if numel(files) < 10 % probably need to unzip them...\n        unix('gunzip *.nii.gz')\n        files=dir('prob*.nii');\n    end\n    \n    for f = 1 : numel(files)\n        curf = fullfile(files(f).folder,['probmap_' num2str(f) '.nii']);\n        [hdr,img]=read_nifti(curf);\n        allimgs(:,:,:,f) = img; %#ok<AGROW>\n    end\n    \n    outimg = zeros(size(img));\n    outimg2 = zeros(size(img));\n    \n    for x = 1 : size(allimgs,1)\n        for y = 1 : size(allimgs,2)\n            for z = 1 : size(allimgs,3)\n                curvec = squeeze(allimgs(x,y,z,:));\n                if any(curvec)\n                    [val,ind] = max(curvec);\n                    outimg(x,y,z) = ind;\n                    outimg2(x,y,z) = val;\n                end\n            end\n        end\n    end\n    max_prob_ic_map_inds = fullfile(ica_path,'maxprob_ic_map_inds.nii');\n    write_nifti(hdr,outimg,max_prob_ic_map_inds)\n    max_prob_ic_map_vals = fullfile(ica_path,'maxprob_ic_map_vals.nii');\n    write_nifti(hdr,outimg2,max_prob_ic_map_vals)\n\nfunction setup_ica\n    setenv('PATH', [getenv('PATH') ':/usr/share/fsl/5.0/bin:/usr/lib/fsl/5.0']);\n    setenv('LD_LIBRARY_PATH',[getenv('LD_LIBRARY_PATH') ':/usr/lib/fsl/5.0/'])\n    setenv('FSLOUTPUTTYPE','NIFTI_GZ')\n    setenv('FSLDIR','/usr/share/fsl/5.0')    "}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "step1_notparallel_old.m", "ext": ".m", "path": "svrlsmgui-master/functions/step1_notparallel_old.m", "size": 5199, "source_encoding": "utf_8", "md5": "ab0fe65d23076e5b40c6aac94ecc66fd", "text": "function [handles,parameters] = step1_notparallel(handles,parameters,variables)\n    % This is where we'll save our GBs of permutation data output...\n    parameters.outfname_big = fullfile(variables.output_folder.clusterwise,['pmu_beta_maps_N_' num2str(parameters.PermNumVoxelwise) '.bin']);\n    \n    %% Try to use cache to skip this step by relying on cached permutation data\n    if can_skip_generating_beta_perms(parameters,variables)\n        warndlg('Relying on cached data not fully supported/reliable yet.')\n        error('This is not supported')\n        handles = UpdateProgress(handles,'Using cached beta map permutation data...',1);\n        return\n    else\n        handles = UpdateProgress(handles,'Computing beta map permutations (not parallelized)...',1);\n        svrlsm_waitbar(parameters.waitbar,0,'Computing beta permutations...');\n    end\n    \n    %% If we got here then we need to generate the permutation data\n    \n    fileID = fopen(parameters.outfname_big,'w');\n    \n    for PermIdx=1:parameters.PermNumVoxelwise\n        check_for_interrupt(parameters)\n\n        % random permute subjects order for this permutation.\n        trial_score = variables.one_score(randperm(length(variables.one_score)));\n\n        % Which package to use to compute SVM solution\n        if parameters.useLibSVM % then use libSVM ...\n%             box = myif(parameters.optimization.do_optimize & parameters.optimization.params_to_optimize.cost, parameters.optimization.best.cost, parameters.cost);\n%             gamma = myif(parameters.optimization.do_optimize & parameters.optimization.params_to_optimize.sigma, sigma2gamma(parameters.optimization.best.sigma), sigma2gamma(parameters.sigma)); % now derive from sigma...\n%             epsilon = myif(parameters.optimization.do_optimize & parameters.optimization.params_to_optimize.epsilon, parameters.optimization.best.epsilon, parameters.epsilon);\n%             libsvmstring = get_libsvm_spec(box,gamma,epsilon);\n            hyperparms = hyperparmstruct(parameters);\n            libsvmstring = get_libsvm_spec(hyperparms.cost,hyperparms.gamma,hyperparms.epsilon); % Standardization is already applied.\n            m = svmtrain(trial_score,sparse(variables.lesion_dat),libsvmstring); %#ok<SVMTRAIN>\n        else % otherwise use MATLAB...\n            if PermIdx == 1\n                variables.orig_one_score = variables.one_score;  % store this.\n            end\n            \n            variables.one_score = trial_score; % this is so we can use the same ComputeMatlabSVRLSM function :)\n            [m,~,~] = ComputeMatlabSVRLSM(parameters,variables); % ComputeMatlabSVRLSM will utilize our optimized parameters if available...\n            \n            if PermIdx == parameters.PermNumVoxelwise % put it back after we've done all permutations...\n                variables.one_score = variables.orig_one_score; % restore this.\n            end\n        end\n\n        % Compute the beta map\n%         if parameters.useLibSVM\n%             alpha = m.sv_coef';\n%             SVs = m.SVs;\n%         else % MATLAB's version.\n%             alpha = m.Alpha';\n%             SVs = m.SupportVectors;\n%         end\n%         \n        % Compute the beta map\n        alpha = m.(myif(parameters.useLibSVM,'sv_coef','Alpha'))'; % note dynamic field reference\n        SVs = m.(myif(parameters.useLibSVM,'SVs','SupportVectors')); % note dynamic field reference\n        \n        pmu_beta_map = variables.beta_scale * alpha * SVs;\n\n        tmp_map = zeros(variables.vo.dim(1:3)); % make a zeros template....        \n        tmp_map(variables.l_idx) = pmu_beta_map;\n        pmu_beta_map = tmp_map(variables.m_idx).';\n\n        % Save this permutation to our single giant file.\n        fwrite(fileID, pmu_beta_map,'single'); \n\n        if ~mod(PermIdx,20) % update every 20 indices...                \n            % Display progress.\n%             elapsed_time = toc;\n%             remain_time = round(elapsed_time * (parameters.PermNumVoxelwise - PermIdx)/(PermIdx));\n%             remain_time_h = floor(remain_time/3600);\n%             remain_time_m = floor((remain_time - remain_time_h*3600)/60);\n%             remain_time_s = floor(remain_time - remain_time_h*3600 - remain_time_m*60);\n%             prompt_str = sprintf('Permutation %d/%d: Est. remaining time: %dh %dh %ds', PermIdx, parameters.PermNumVoxelwise, remain_time_h, remain_time_m,remain_time_s);\n            prompt_str = get_step1_prog_string(PermIdx,parameters);\n            svrlsm_waitbar(parameters.waitbar,PermIdx/parameters.PermNumVoxelwise,prompt_str);\n        end\n    end\n    svrlsm_waitbar(parameters.waitbar,0,''); % clear\n    fclose(fileID); % close the pmu data output file.\n\nfunction prompt_str = get_step1_prog_string(PermIdx,parameters)\n    elapsed_time = toc;\n    remain_time = round(elapsed_time * (parameters.PermNumVoxelwise - PermIdx)/(PermIdx));\n    remain_time_h = floor(remain_time/3600);\n    remain_time_m = floor((remain_time - remain_time_h*3600)/60);\n    remain_time_s = floor(remain_time - remain_time_h*3600 - remain_time_m*60);\n    prompt_str = sprintf('Permutation %d/%d: Est. remaining time: %dh %dh %ds', PermIdx, parameters.PermNumVoxelwise, remain_time_h, remain_time_m,remain_time_s);"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "step1_notparallel.m", "ext": ".m", "path": "svrlsmgui-master/functions/step1_notparallel.m", "size": 5174, "source_encoding": "utf_8", "md5": "f2d672f41a6a70b48e3d7ed78b5b812a", "text": "function [handles,parameters] = step1_notparallel(handles,parameters,variables)\n    % This is where we'll save our GBs of permutation data output...\n    parameters.outfname_big = fullfile(variables.output_folder.clusterwise,['pmu_beta_maps_N_' num2str(parameters.PermNumVoxelwise) '.bin']);\n    \n    %% Try to use cache to skip this step by relying on cached permutation data\n    if can_skip_generating_beta_perms(parameters,variables)\n        warndlg('Relying on cached data not fully supported/reliable yet. Aborting.')\n        error('This is not supported')\n        handles = UpdateProgress(handles,'Using cached beta map permutation data...',1);\n        return\n    else\n        handles = UpdateProgress(handles,'Computing beta map permutations (not parallelized)...',1);\n        svrlsm_waitbar(parameters.waitbar,0,['Computing beta permutations (' myif(parameters.method.mass_univariate,'mass univariate','multivariate') ')...']);\n    end\n    \n    %% If we got here then we need to generate the permutation data\n    fileID = fopen(parameters.outfname_big,'w');\n    \n    for PermIdx=1:parameters.PermNumVoxelwise\n        % Random permute subjects order for this permutation.\n        trial_score = variables.one_score(randperm(length(variables.one_score)));\n        if parameters.method.mass_univariate % Estimate via mass-univariate\n            pmu_beta_map = nan(size(variables.lesion_dat,2),1); % reserve space\n            for vox = 1 : size(variables.lesion_dat,2)\n                 [Q, R] = qr(trial_score, 0); % use the householder transformations to compute the qr factorization of an n by p matrix x.\n                 y = double(variables.lesion_dat(:,vox));% / 10000; % why divide by 10,000?\n                 %betas(vox) = R \\ (Q' * y);  % equivalent to fitlm's output: lm.Coefficients.Estimate\n                 pmu_beta_map(vox) = R \\ (Q' * y);  % equivalent to fitlm's output: lm.Coefficients.Estimate\n            end\n        else % Estimate via multivariate svr\n            % Which package to use to compute SVR solution\n            if parameters.useLibSVM % then use libSVM\n                hyperparms = hyperparmstruct(parameters);\n                libsvmstring = get_libsvm_spec(hyperparms.cost,hyperparms.gamma,hyperparms.epsilon); % Standardization is already applied.\n                m = svmtrain(trial_score,sparse(variables.lesion_dat),libsvmstring); %#ok<SVMTRAIN>\n            else % use MATLAB\n                if PermIdx == 1, variables.orig_one_score = variables.one_score; end % store this\n                variables.one_score = trial_score; % this is so we can use the same ComputeMatlabSVRLSM function :)\n                [m,w,~] = ComputeMatlabSVRLSM(parameters,variables); % ComputeMatlabSVRLSM will utilize our optimized parameters if available...\n                if PermIdx == parameters.PermNumVoxelwise, variables.one_score = variables.orig_one_score; end % restore this once we're done all our permutations\n            end\n\n            % Compute the beta map here (but only if we didn't already compute it already by necessity via crossvalidation)\n            if ~parameters.crossval.do_crossval % conditional added to support crossvalidated betamap option in June 2019\n                alpha = m.(myif(parameters.useLibSVM,'sv_coef','Alpha'))'; % note dynamic field reference\n                SVs = m.(myif(parameters.useLibSVM,'SVs','SupportVectors')); % note dynamic field reference\n                pmu_beta_map = variables.beta_scale * alpha * SVs;\n            else % use pre-computed (and averaged) beta map(s) -- this should only be available with svr in matlab specifically (not mass univariate, and not libsvm right now)\n                pmu_beta_map = w; % here contains an average of the crossvalidated fold models' beta values, so we don't have to scale or do anything here, it's already all done in the ComputeMatlabSVRLSM() function\n            end\n        end\n\n        tmp_map = zeros(variables.vo.dim(1:3)); % make a zeros template....        \n        tmp_map(variables.l_idx) = pmu_beta_map;\n        pmu_beta_map = tmp_map(variables.m_idx).';\n\n        % Save this permutation to our single giant file.\n        fwrite(fileID, pmu_beta_map,'single'); \n\n        check_for_interrupt(parameters)\n        if ~mod(PermIdx,20) % update every 20 indices - Display progress.\n            prompt_str = get_step1_prog_string(PermIdx,parameters);\n            svrlsm_waitbar(parameters.waitbar,PermIdx/parameters.PermNumVoxelwise,prompt_str);\n        end\n    end\n    svrlsm_waitbar(parameters.waitbar,0,''); % clear\n    fclose(fileID); % close the pmu data output file.\n\n% The string we print saying the progress.\nfunction prompt_str = get_step1_prog_string(PermIdx,parameters)\n    elapsed_time = toc;\n    remain_time = round(elapsed_time * (parameters.PermNumVoxelwise - PermIdx)/(PermIdx));\n    remain_time_h = floor(remain_time/3600);\n    remain_time_m = floor((remain_time - remain_time_h*3600)/60);\n    remain_time_s = floor(remain_time - remain_time_h*3600 - remain_time_m*60);\n    prompt_str = sprintf('Permutation %d/%d: Est. remaining time: %dh %dm %ds', PermIdx, parameters.PermNumVoxelwise, remain_time_h, remain_time_m,remain_time_s);"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "build_and_write_pmaps.m", "ext": ".m", "path": "svrlsmgui-master/functions/build_and_write_pmaps.m", "size": 10054, "source_encoding": "utf_8", "md5": "a98d12f50beab6567543d822657e3661", "text": "function [thresholded,variables] = build_and_write_pmaps(options,parameters,variables,thresholds)\n    switch parameters.tailshort % parameters.tails\n        case 'pos' % options.hypodirection{1} % One-tailed positive tail... high scores BAD\n            [thresholded,variables] = write_p_maps_pos_tail(parameters,variables,thresholds);\n        case 'neg' % options.hypodirection{2} % One-tailed negative tail... high scores GOOD\n            [thresholded,variables] = write_p_maps_neg_tail(parameters,variables,thresholds);\n        case 'two' % options.hypodirection{3} % Both tails..\n            [thresholded,variables] = write_p_maps_two_tailed(parameters,variables,thresholds);\n    end\n\nfunction [thresholded,variables] = write_p_maps_pos_tail(parameters,variables,thresholds)\n    if parameters.do_CFWER % note that the parameters struct isn't returned by this function so nothing changes outside this function scope\n        parameters.voxelwise_p = variables.cfwerinfo.cfwer_single_pval_answer;\n    end\n\n    write_z = true;\n    \n    %% Create and write the un-inverted versions...\n    thresholded.thresholded_pos = zeros(variables.vo.dim(1:3)); % make a zeros template....\n    thresholded.thresholded_pos(variables.m_idx) = thresholds.one_tail_pos_alphas;\n    \n    % Write unthresholded P-map for the positive tail\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P map.nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_pos);\n    variables.files_created.unthresholded_pmap = variables.vo.fname;\n\n    % % Calculate z map\n    %zmap = p2z(thresholded.thresholded_pos); % note the call to p2z() here\n    \n    % calculate z map\n    zmap = zeros(variables.vo.dim(1:3));\n    \n    if write_z \n        zmap(variables.m_idx) = p2z(thresholds.one_tail_pos_alphas); % so we don't try to convert 0 values in the rest of the brain volume...\n\n        % write out unthresholded positive Z map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded Z map.nii');\n        svrlsmgui_write_vol(variables.vo, zmap);\n        variables.files_created.unthresholded_zmap = variables.vo.fname;\n    end\n    \n    % Now write out the thresholded P-map for the positive tail\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Voxelwise thresholded P map.nii');\n    zero_these_vox = thresholded.thresholded_pos > parameters.voxelwise_p;\n    thresholded.thresholded_pos(zero_these_vox) = 0; % zero out voxels whose values are greater than p\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_pos);\n    variables.files_created.thresholded_pmap = variables.vo.fname;\n    \n    if write_z \n        % write out thresholded positive Z map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Voxelwise thresholded Z map.nii');\n        zmap(zero_these_vox) = 0; % apply the mask we calculated like 20 lines ago\n        svrlsmgui_write_vol(variables.vo, zmap);\n        variables.files_created.thresholded_zmap = variables.vo.fname;\n    end\n    \n    %% Create and write the inverted versions.\n    thresholded.thresholded_pos = zeros(variables.vo.dim(1:3)); % make a zeros template....\n    thresholded.thresholded_pos(variables.m_idx) = 1 - thresholds.one_tail_pos_alphas;\n\n    % Write unthresholded P-map for the positive tail\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P map (inv).nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_pos);\n    variables.files_created.unthresholded_pmap_inv = variables.vo.fname;\n\n    % Now write out the thresholded P-map for the positive tail\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Voxelwise thresholded P map (inv).nii');\n    thresholded.thresholded_pos(thresholded.thresholded_pos < (1-parameters.voxelwise_p)) = 0; % zero out sub-threshold p value voxels (note the 1-p)\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_pos);\n    variables.files_created.thresholded_pmap_inv = variables.vo.fname;\n\n\nfunction [thresholded,variables] = write_p_maps_neg_tail(parameters,variables,thresholds)\n    if parameters.do_CFWER % note that the parameters struct isn't returned by this function so nothing changes outside this function scope\n        parameters.voxelwise_p = variables.cfwerinfo.cfwer_single_pval_answer;\n    end\n    \n    write_z = true;\n    \n    %% Create and write the non-inverted versions.\n    thresholded.thresholded_neg = zeros(variables.vo.dim(1:3)); % make a zeros template....        \n    thresholded.thresholded_neg(variables.m_idx) = thresholds.one_tail_neg_alphas;\n    \n    % write out unthresholded negative p map\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P map.nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_neg);\n    variables.files_created.unthresholded_pmap = variables.vo.fname;\n\n    % calculate z map\n    zmap = zeros(variables.vo.dim(1:3));\n    \n    if write_z \n        zmap(variables.m_idx) = p2z(thresholds.one_tail_neg_alphas); % so we don't try to convert 0 values in the rest of the brain volume...\n\n        % write out unthresholded negative Z map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded Z map.nii');\n        svrlsmgui_write_vol(variables.vo, zmap);\n        variables.files_created.unthresholded_zmap = variables.vo.fname;\n    end\n    \n    % write out thresholded negative p map\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Voxelwise thresholded P map.nii');\n    zero_these_vox = thresholded.thresholded_neg > parameters.voxelwise_p;\n    thresholded.thresholded_neg(zero_these_vox) = 0; % zero out voxels whose values are greater than p\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_neg);\n    variables.files_created.thresholded_pmap = variables.vo.fname;\n\n    if write_z \n        % write out thresholded negative Z map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Voxelwise thresholded Z map.nii');\n        zmap(zero_these_vox) = 0; % apply the mask we calculated like 20 lines ago\n        svrlsmgui_write_vol(variables.vo, zmap);\n        variables.files_created.thresholded_zmap = variables.vo.fname;\n    end\n    \n    %% Create and write the inverted versions (P maps, not Z maps)\n    thresholded.thresholded_neg = zeros(variables.vo.dim(1:3)); % make a zeros template....        \n    thresholded.thresholded_neg(variables.m_idx) = 1 - thresholds.one_tail_neg_alphas;\n    \n    % write out unthresholded negative p map\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P map (inv).nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_neg);\n    variables.files_created.unthresholded_pmap_inv = variables.vo.fname;\n    \n    % write out thresholded negative p map\n    thresholded.thresholded_neg(thresholded.thresholded_neg < (1-parameters.voxelwise_p)) = 0; % zero out subthreshold p value voxels (note 1-p)\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Voxelwise thresholded P map (inv).nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_neg);\n    variables.files_created.thresholded_pmap_inv = variables.vo.fname;\n\nfunction [thresholded,variables] = write_p_maps_two_tailed(parameters,variables,thresholds)\n    error('There should be an abs() around here but there isn''t, make sure this works right, especially with CFWER')\n    if parameters.do_CFWER % note that the parameters struct isn't returned by this function so nothing changes outside this function scope\n        parameters.voxelwise_p = variables.cfwerinfo.cfwer_single_pval_answer.onetail.pos;\n    end\n\n    thresholded.thresholded_twotails = zeros(variables.vo.dim(1:3)); % make a zeros template....        \n    if parameters.invert_p_map_flag % it's already inverted...\n        thresholded.thresholded_twotails(variables.m_idx) = thresholds.twotails_alphas;\n        % write out unthresholded p map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values (inv).nii');\n        svrlsmgui_write_vol(variables.vo, thresholded.thresholded_twotails);\n        % variables.files_created.unthresholded_pmap{1} = variables.vo.fname;\n        % variables.files_created.unthresholded_pmap{2} = variables.vo.fname;\n        \n        % write out thresholded p map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values (inv).nii');\n        thresholded.thresholded_twotails(thresholded.thresholded_twotails < (1-(parameters.voxelwise_p/2))) = 0; % zero out subthreshold p value voxels (note 1-p)\n        svrlsmgui_write_vol(variables.vo, thresholded.thresholded_twotails);\n        % variables.files_created.unthresholded_pmap{1} = variables.vo.fname;\n        % variables.files_created.unthresholded_pmap{2} = variables.vo.fname;\n        \n        warning('add writing z map thresholded and unthresholded here')\n    else\n        thresholded.thresholded_twotails(variables.m_idx) = 1 - thresholds.twotails_alphas;\n        % write out unthresholded p map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values.nii');\n        svrlsmgui_write_vol(variables.vo, thresholded.thresholded_twotails);\n        % variables.files_created.unthresholded_pmap{1} = variables.vo.fname;\n        % variables.files_created.unthresholded_pmap{2} = variables.vo.fname;\n\n        warning('add writing z map thresholded and unthresholded here')\n        \n        % write out thresholded p map\n        variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values (inv).nii');\n        thresholded.thresholded_twotails(thresholded.thresholded_twotails > (parameters.voxelwise_p/2)) = 0; % zero out supra-alpha p value voxels\n        svrlsmgui_write_vol(variables.vo, thresholded.thresholded_twotails);\n        % variables.files_created.unthresholded_pmap{1} = variables.vo.fname;\n        % variables.files_created.unthresholded_pmap{2} = variables.vo.fname;\n    end"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "svrlsm_bayesopt.m", "ext": ".m", "path": "svrlsmgui-master/functions/svrlsm_bayesopt.m", "size": 15549, "source_encoding": "utf_8", "md5": "2215a0e43cc0ed243fb6b154de1f8378", "text": "function results = svrlsm_bayesopt(parameters,variables)\n    % For clarity...\n    lesiondata = variables.lesion_dat;\n    behavdata = variables.one_score;\n\n   %% Which parameters to optimize and ranges to optim over\n   params = hyperparameters('fitrsvm',lesiondata,behavdata);\n   standrange = params(strcmp('Standardize',{params.Name})).Range;\n   \n   sigma = optimizableVariable('sigma',parameters.optimization.params_to_optimize.sigma_range,'Transform','log','optimize',parameters.optimization.params_to_optimize.sigma); % gamma ~ sigma...\n   box = optimizableVariable('box',parameters.optimization.params_to_optimize.cost_range,'Transform','log','optimize',parameters.optimization.params_to_optimize.cost); % cost  == boxconstraint\n   epsilon = optimizableVariable('epsilon',parameters.optimization.params_to_optimize.epsilon_range,'Transform','none','optimize',parameters.optimization.params_to_optimize.epsilon);\n   %standardize = optimizableVariable('standardize',standrange,'Transform','none','Type','categorical','optimize',parameters.optimization.params_to_optimize.standardize);\n   %standrange = [0 1]; % [true false]; % {categorical(true), categorical(false)}; % ?!\n   standardize = optimizableVariable('standardize',standrange,'Transform','none','Type','categorical','optimize',parameters.optimization.params_to_optimize.standardize);\n   svrlsm_waitbar(parameters.waitbar,0,['Hyperparameter optimization (bayesopt: ' parameters.optimization.objective_function ')...'])\n   \n     % Has the user requested cross-validation or not?\n%      if parameters.optimization.crossval.do_crossval\n%          % Switch definition of anonymous function for objective function for optimization\n%          switch parameters.optimization.objective_function\n%              case 'Predict Behavior'\n%                  minfn = @(x) predict_behavior_w_crossval(x,lesiondata,behavdata,parameters);\n%              case 'Maximum Correlation'\n%                  minfn = @(x) max_correlation_w_crossval(x,lesiondata,behavdata,parameters);\n%              case 'Resubstitution Loss'\n%                  minfn = @(x) resubstitution_loss_w_crossval(x,lesiondata,behavdata,parameters);\n%              otherwise\n%                  error(['Unknown objective function string: ' parameters.optimization.objective_function])\n%          end\n% \n%          % Start with this partition -- it will be replaced each call to the objective function if Repartition is 'true'\n%          parameters.initialPartition = cvpartition(behavdata,'k',parameters.optimization.crossval.nfolds);\n% \n%          %results = bayesopt(minfn,[sigma,box], ...\n%          results = bayesopt(minfn,[gamma,box,epsilon,standardize], ...\n%              'IsObjectiveDeterministic',false,'AcquisitionFunctionName','expected-improvement-plus', ...\n%              'MaxObjectiveEvaluations',parameters.optimization.iterations, 'UseParallel',parameters.parallelize, ...\n%              'PlotFcn',[],'Verbose',0);\n% \n%      else % No cross-validation was requested, so run these without cross-validation...\n%          \n%          % Switch definition of anonymous function for objective function for optimization\n%          switch parameters.optimization.objective_function\n%              case 'Predict Behavior'\n%                  minfn = @(x) predict_behavior(x,lesiondata,behavdata,parameters);\n%                 %minfn = @(x)sqrt(sum((behavdata - resubPredict(fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',x.box,'KernelScale',x.sigma,'Epsilon',parameters.epsilon,'Standardize',false,'true')))).^2);\n%              case 'Maximum Correlation'\n%                  minfn = @(x) max_correlation(x,lesiondata,behavdata,parameters);\n%              case 'Resubstitution Loss'\n%                  minfn = @(x) resubstitution_loss(x,lesiondata,behavdata,parameters);\n%              otherwise \n%                  error(['Unknown objective function string: ' parameters.optimization.objective_function])\n%          end\n%          \n%         results = bayesopt(minfn,[sigma,box], ... % ,epsilon,standardize], ... % the parameters we're going to try to optimize\n%             'IsObjectiveDeterministic',true,'AcquisitionFunctionName','expected-improvement-plus', ...\n%             'MaxObjectiveEvaluations',parameters.optimization.iterations, 'UseParallel',parameters.parallelize, ...\n%             'PlotFcn',[],'Verbose',0,'OutputFcn',@optim_outputfun);\n% \n%      end\n\n%%\n\n%     % Start with this partition -- it will be replaced each call to the objective function if Repartition is 'true'\n    parameters.initialPartition = cvpartition(behavdata,'k',parameters.optimization.crossval.nfolds);\n    \n    % assignin('base','lesiondata',lesiondata)\n    % error('s')\n    \n    minfn = @(x)combined_bayesopt_objective_functions3(x,sparse(lesiondata),behavdata,parameters);\n\n    results = bayesopt(minfn,[sigma box epsilon standardize], ... \n        'IsObjectiveDeterministic',false, ... % parameters.optimization.crossval.do_crossval, ...\n        'AcquisitionFunctionName','expected-improvement-plus', ... % 'ExplorationRatio', 0.7, ... % 0.5 default\n        'MaxObjectiveEvaluations',parameters.optimization.iterations, ...\n        'UseParallel',parameters.parallelize, ...\n        'NumCoupledConstraints', 1, 'AreCoupledConstraintsDeterministic', false, ... % ~parameters.optimization.crossval.do_crossval, ... % if crossval, then it's not deterministic.\n        'PlotFcn',[],'OutputFcn',@optim_outputfun, ...\n        'Verbose',myif(parameters.optimization.verbose_during_optimization,2,0));  % verbose is either 0 or 2...\n\n    svrlsm_waitbar(parameters.waitbar,0,''); % clear this.\n\n% %% Objective function: Maximum correlation - no cross-validation\n% function objective = max_correlation(x,lesiondata,behavdata,parameters)\n%     svrtype = myif(parameters.useLibSVM,'libSVM','MATLAB');\n%     switch svrtype\n%         case 'libSVM'\n%             error('transform gamma to sigma here?')\n%             m = svmtrain(behavdata,sparse(lesiondata),get_libsvm_spec(x.cost,x.gamma,parameters.epsilon));% note get_libsvm_spec() function\n%             predicted = svmpredict(behavdata, sparse(lesiondata), m, '-q');\n%         case 'MATLAB' % since we take care of standardization in preprocessing\n%             Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',x.box,'KernelScale',x.sigma,'Epsilon',parameters.epsilon,'Standardize',false);\n%             predicted = predict(Mdl,lesiondata);\n%     end\n%     corrvals = corrcoef(predicted,behavdata); % compute the correlation...\n%     objective = -1 * corrvals(2,1); % grab the r. --- and multiply by negative one since we want to maximize the value...\n% \n% %% Objective function: Predict behavior - no cross-validation\nfunction objective = predict_behavior(x,lesiondata,behavdata,parameters)\n%     svrtype = myif(parameters.useLibSVM,'libSVM','MATLAB');\n%      switch svrtype\n%          case 'libSVM'\n%              error('transform gamma to sigma here?')\n%               m = svmtrain(behavdata,sparse(lesiondata),get_libsvm_spec(x.cost,x.gamma,parameters.epsilon));% note get_libsvm_spec() function\n%               predicted = svmpredict(behavdata, sparse(lesiondata), m, '-q');\n%          case 'MATLAB' % since we take care of standardization in preprocessing\n%             touse = get_cur_optim_iter_parms(x,parameters); % returns constants for parms we're not optimizing on\n%            Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',touse.cost,'KernelScale',touse.sigma,'Standardize',touse.standardize,'Epsilon',touse.epsilon);\n             Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',x.cost,'KernelScale',x.sigma,'Standardize',false,'Epsilon',parameters.epsilon);\n            predicted = predict(Mdl,lesiondata);\n%      end\n    difference =  behavdata(:) - predicted(:);\n    mean_abs_diff = mean(abs(difference));\n    objective = mean_abs_diff;  % we want to minimize this value.\n%     \n% %% Objective function: Resubstitution loss - no cross-validation\n% function objective = resubstitution_loss(x,lesiondata,behavdata,parameters)\n% \n%      touse = get_cur_optim_iter_parms(x,parameters); % returns constants for parms we're not optimizing on\n%      Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',touse.cost,'KernelScale',touse.gamma,'Standardize',touse.standardize,'Epsilon',touse.epsilon);\n% %\t Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',x.box,'KernelScale',x.sigma,'Epsilon',parameters.epsilon,'Standardize',false);\n%      objective = resubLoss(Mdl);\n% \n% %% Objective function: Resubstitution loss - with K-fold cross-validation\n% function objective = resubstitution_loss_w_crossval(x,lesiondata,behavdata,parameters)\n%      if parameters.optimization.crossval.repartition\n%          curPartition = cvpartition(behavdata,'k',parameters.optimization.crossval.nfolds);\n%      else\n%          curPartition = parameters.initialPartition;\n%      end\n%      \n%      touse = get_cur_optim_iter_parms(x,parameters); % returns constants for parms we're not optimizing on\n%      Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',touse.cost,'KernelScale',touse.gamma,'Standardize',touse.standardize,'Epsilon',touse.epsilon,'CVPartition',curPartition);\n%               \n% %     Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',x.box,'KernelScale',x.sigma,'Standardize',false,'Epsilon',parameters.epsilon,'CVPartition', curPartition);\n% %        myif(parameters.optimization.crossval.repartition,partition,partition.repartition)); \n%     \n%      objective = kfoldLoss(Mdl);\n% \n% %% Objective function: Predict behavior - with K-fold cross-validation\n% function objective = predict_behavior_w_crossval(x,lesiondata,behavdata,parameters)\n% %     svrtype = myif(parameters.useLibSVM,'libSVM','MATLAB');\n% %      switch svrtype\n% %          case 'libSVM'\n% %              error('transform gamma to sigma here?')\n% %               m = svmtrain(behavdata,sparse(lesiondata),get_libsvm_spec(x.cost,x.gamma,parameters.epsilon));% note get_libsvm_spec() function\n% %               predicted = svmpredict(behavdata, sparse(lesiondata), m, '-q');\n% %          case 'MATLAB' % since we take care of standardization in preprocessing\n%              \n%               if parameters.optimization.crossval.repartition\n%                   curPartition = cvpartition(behavdata,'k',parameters.optimization.crossval.nfolds);\n%               else\n%                   curPartition = parameters.initialPartition;\n%               end\n%               \n%               touse = get_cur_optim_iter_parms(x,parameters); % returns constants for parms we're not optimizing on\n%               Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',touse.cost,'KernelScale',touse.gamma,'Standardize',touse.standardize,'Epsilon',touse.epsilon,'CVPartition',curPartition);\n%               predicted = kfoldPredict(Mdl);\n% %      end\n%     difference =  behavdata(:) - predicted(:);\n%     mean_abs_diff = mean(abs(difference));\n%     objective = mean_abs_diff;  % we want to minimize this value.\n% \n% \n% %% Objective function: Maximum correlation - with K-fold cross-validation\n% function objective = max_correlation_w_crossval(x,lesiondata,behavdata,parameters)\n% %     svrtype = myif(parameters.useLibSVM,'libSVM','MATLAB');\n% %     switch svrtype\n% %         case 'libSVM'\n% %             error('transform gamma to sigma here?')\n% %             m = svmtrain(behavdata,sparse(lesiondata),get_libsvm_spec(x.cost,x.gamma,parameters.epsilon));% note get_libsvm_spec() function\n% %             predicted = svmpredict(behavdata, sparse(lesiondata), m, '-q');\n% %         case 'MATLAB' % since we take care of standardization in preprocessing\n%              if parameters.optimization.crossval.repartition\n%                  curPartition = cvpartition(behavdata,'k',parameters.optimization.crossval.nfolds);\n%              else\n%                  curPartition = parameters.initialPartition.repartition;\n%              end\n%              touse = get_cur_optim_iter_parms(x,parameters); % returns constants for parms we're not optimizing on\n%              Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',touse.cost,'KernelScale',touse.gamma,'Standardize',touse.standardize,'Epsilon',touse.epsilon,'CVPartition',curPartition);\n%              \n%             predicted = kfoldPredict(Mdl);\n%     %end\n%     corrvals = corrcoef(predicted,behavdata); % compute the correlation...\n%     objective = -1 * corrvals(2,1); % grab the r. --- and multiply by negative one since we want to maximize the value...\n% \n\n% function objective = combined_bayesopt_objective_functions(x,lesiondata,behavdata,parameters)\n% \n%     % hyperparameter values to use for this iteration\n%     touse = get_cur_optim_iter_parms(x,parameters); % returns constants for parms we're not optimizing on\n%     \n%     % add in cross-validation parameters dynamically as necessary...\n%     crossvalparms = {}; % default\n%     if parameters.optimization.crossval.do_crossval\n%         if parameters.optimization.crossval.repartition\n%             curPartition = cvpartition(behavdata,'k',parameters.optimization.crossval.nfolds);\n%         else\n%             curPartition = parameters.initialPartition.repartition;\n%         end\n%         crossvalparms ={'CVPartition',curPartition};\n%     end\n%     \n%     Mdl = fitrsvm(lesiondata,behavdata,'KernelFunction','rbf','BoxConstraint',touse.cost,'KernelScale',touse.sigma,'Standardize',touse.standardize,'Epsilon',touse.epsilon,crossvalparms{:}); % 'CVPartition',curPartition);\n%     \n%     switch parameters.optimization.objective_function\n%         case 'Predict Behavior' % embedded conditional of whether we predict or kfoldpredict\n%             if parameters.optimization.crossval.do_crossval\n%                 predicted = kfoldPredict(Mdl);\n%             else\n%                 predicted = predict(Mdl,lesiondata);\n%             end\n%             \n%             difference =  behavdata(:) - predicted(:);\n%             mean_abs_diff = mean(abs(difference));\n%             objective = mean_abs_diff;  % we want to minimize this value.\n% \n%         case 'Maximum Correlation' % embedded conditional of whether we predict or kfoldpredict\n%             if parameters.optimization.crossval.do_crossval\n%                 predicted = kfoldPredict(Mdl);\n%             else\n%                 predicted = predict(Mdl,lesiondata);\n%             end\n%             \n%             corrvals = corrcoef(predicted,behavdata); % compute the correlation...\n%             objective = -1 * corrvals(2,1); % grab the r. --- and multiply by negative one since we want to maximize the value...\n%             \n%         case 'Resubstitution Loss'\n%             if parameters.optimization.crossval.do_crossval\n%                 objective = kfoldLoss(Mdl);\n%             else\n%                 objective = resubLoss(Mdl);\n%             end\n%         otherwise\n%             error(['Unknown objective function string: ' parameters.optimization.objective_function])\n%     end\n%                  \n% function touse = get_cur_optim_iter_parms(x,parameters)\n%     parms = {'cost','sigma','epsilon','standardize'};\n%     for f = parms\n%         if isfield(x,f{1}) % then we're asked to optimize it so return:\n%             touse.(f{1}) = x.(f{1}); % the dynamic value for each iteration\n%         else\n%             touse.(f{1}) = parameters.(f{1}); % or the constant supplied by the user\n%         end\n%     end"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "svrlsm_waitbar.m", "ext": ".m", "path": "svrlsmgui-master/functions/svrlsm_waitbar.m", "size": 726, "source_encoding": "utf_8", "md5": "5732f213c7d380dbecbb6ffa4179a9d8", "text": "function svrlsm_waitbar(waitbar_handles,newval,message)\n    if isempty(waitbar_handles)\n        return\n    end\n%     if numel(waitbar_handles) ~= 2 % < caused by multiple svrlsmgui's open at once\n%         error('Too many handles in input array')\n%     end\n    set_progress_percent(waitbar_handles(1),newval)\n    if nargin > 2\n        set_progress_text(waitbar_handles(2),message)\n    end\n    drawnow\n\n% update percent progress\nfunction set_progress_percent(rectangle_handle,newval)\n    pos = get(rectangle_handle,'position');\n    pos(3) = 100*newval; %update the width parameter....\n    set(rectangle_handle,'position',pos)\n\n% update message\nfunction set_progress_text(txt_handle,message)\n    set(txt_handle,'string',message)"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "step1_parallel.m", "ext": ".m", "path": "svrlsmgui-master/functions/step1_parallel.m", "size": 8340, "source_encoding": "utf_8", "md5": "9ce154f0ddd002dfff3717ec0effd46d", "text": "function [handles,parameters] = step1_parallel(handles,parameters,variables)\n    % This is where we'll save our GBs of permutation data output...\n    parameters.outfname_big = fullfile(variables.output_folder.clusterwise,['pmu_beta_maps_N_' num2str(parameters.PermNumVoxelwise) '.bin']);\n\n    %% Try to use cache to skip this step by relying on cached permutation data\n    if can_skip_generating_beta_perms(parameters,variables)\n        error('caching disabled, cause it''s not completely supported')\n        handles = UpdateProgress(handles,'Using cached beta map permutation data...',1); return\n    else\n        handles = UpdateProgress(handles,'Computing beta map permutations (parallelized)...',1);\n        svrlsm_waitbar(parameters.waitbar,0,'Computing beta permutations (parallelized)...');\n    end\n    \n    %% If we got here then we need to generate the permutation data\n    \n    %% Cute way to create the permuted behavioral data... each column is one permutation.\n    permdata = variables.one_score(cell2mat(cellfun(@(x) randperm(x)',repmat({numel(variables.one_score)},1,parameters.PermNumVoxelwise),'uni',false)));\n    outpath = variables.output_folder.clusterwise;\n    totalperms = parameters.PermNumVoxelwise;\n    hyperparms = hyperparmstruct(parameters);\n    \n    %% Figure out what parameters we'll be using:\n    if parameters.useLibSVM\n        parameters.step1.libsvmstring = get_libsvm_spec(hyperparms.cost,hyperparms.gamma,hyperparms.epsilon); % Standardization is already applied.\n        tmp.libsvmstring = parameters.step1.libsvmstring;\n    else % use matlab's -- note the cell array we're creating - it's fancy since later we'll parameters.step1.matlab_svr_parms{:}\n        parameters.step1.matlab_svr_parms = [{'BoxConstraint', hyperparms.cost, ...\n            'KernelScale', hyperparms.sigma, ...\n            'Standardize', hyperparms.standardize, ...\n            'Epsilon', hyperparms.epsilon} ...\n            myif(parameters.crossval.do_crossval,{'KFold',parameters.crossval.nfolds},[])]; % this is for the crossvalidation option...\n        tmp.matlab_svr_parms = parameters.step1.matlab_svr_parms;\n        tmp.do_crossval = parameters.crossval.do_crossval; % also save this here too.\n    end\n\n    %% parfeval code\n    batch_job_size = 100; % this is going to be optimal for different systems/#cores/jobs - set this through gui?\n    nperms = parameters.PermNumVoxelwise;\n    njobs = ceil(nperms/batch_job_size); % gotta round up to capture all indices\n\n    %% to reduce overhead transfering data to workers...\n    tmp.dims = variables.vo.dim(1:3);\n    tmp.lesiondata = sparse(variables.lesion_dat); % full() it on the other end - does that save time with transfer to worker overhead?!\n    tmp.outpath = variables.output_folder.clusterwise;\n    \n    if ~parameters.method.mass_univariate % then we need to save beta_scale as well...\n        tmp.beta_scale = variables.beta_scale;\n    end\n    \n    tmp.m_idx = variables.m_idx;\n    tmp.l_idx = variables.l_idx;\n    tmp.totalperms = totalperms;\n    tmp.permdata = permdata;\n    tmp.useLibSVM = parameters.useLibSVM;\n    tmp.use_mass_univariate = parameters.method.mass_univariate;\n\n    %% Schedule the jobs...\n    p = gcp(); % get current parallel pool\n    for j = 1 : njobs\n        this_job_start_index = ((j-1)*batch_job_size) + 1;\n        this_job_end_index = min(this_job_start_index + batch_job_size-1,nperms); % need min so we don't go past valid indices\n        this_job_perm_indices = this_job_start_index:this_job_end_index;\n        tmp.this_job_perm_indices = this_job_perm_indices; % update for each set of jobs...\n        f(j) = parfeval(p,@parallel_step1_batch_fcn_lessoverhead,0,tmp);\n    end\n    \n    %% Monitor job progress and allow user to bail, hopefully...\n    for j = 1 : njobs\n        check_for_interrupt(parameters) % allow user to interrupt\n        idx = fetchNext(f);\n        svrlsm_waitbar(parameters.waitbar,j/njobs) % update waitbar progress...\n    end\n    \n    %% Now assemble all those individual files from each parfored permutation into one big file that we can memmap\n    handles = UpdateProgress(handles,'Consolidating beta map permutation data...',1);\n    svrlsm_waitbar(parameters.waitbar,0,'Consolidating beta map permutation data...');\n    fileID = fopen(parameters.outfname_big,'w');\n    for PermIdx=1:parameters.PermNumVoxelwise\n        if ~mod(PermIdx,100)\n            svrlsm_waitbar(parameters.waitbar,PermIdx/parameters.PermNumVoxelwise); % update user on progress\n            check_for_interrupt(parameters)\n        end \n        curpermfilepath = fullfile(outpath,['pmu_beta_map_' num2str(PermIdx) '_of_' num2str(totalperms) '.bin']);\n        cur_perm_data = memmapfile(curpermfilepath,'Format','single');\n        fwrite(fileID, cur_perm_data.Data,'single');\n        clear cur_perm_data; % remove memmap from memory.\n        delete(curpermfilepath); % delete it since we don't want the data hanging around...\n    end\n    svrlsm_waitbar(parameters.waitbar,0,''); % reset.\n    fclose(fileID); % close big file\n\nfunction parallel_step1_batch_fcn_lessoverhead(tmp)\n    if ~tmp.useLibSVM || tmp.use_mass_univariate, tmp.lesiondata = full(tmp.lesiondata); end % we transfer it as sparse... so we need to full() it for all non-libSVM methods\n    \n    for PermIdx = tmp.this_job_perm_indices % each loop iteration will compute one whole-brain permutation result (regardless of LSM method)\n        trial_score = tmp.permdata(:,PermIdx); % extract the row of permuted data.\n        if tmp.use_mass_univariate % solve whole-brain permutation PermIdx on a voxel-by-voxel basis.\n            pmu_beta_map = nan(size(tmp.lesiondata,2),1); % reserve space -- are these dims right?\n            for vox = 1 : size(tmp.lesiondata,2)\n                 [Q, R] = qr(trial_score, 0); % use the householder transformations to compute the qr factorization of an n by p matrix x.\n                 y = double(tmp.lesiondata(:,vox));% / 10000; % why divide by 10,000? %betas(vox) = R \\ (Q' * y);  % equivalent to fitlm's output: lm.Coefficients.Estimate\n                 pmu_beta_map(vox) = R \\ (Q' * y);  % equivalent to fitlm's output: lm.Coefficients.Estimate\n            end\n        else % use an SVR method\n            if tmp.useLibSVM\n                m = svmtrain(trial_score,tmp.lesiondata,tmp.libsvmstring); %#ok<SVMTRAIN> % alpha = m.sv_coef'; % SVs = m.SVs;\n            else % use matlab's...\n                m = fitrsvm(tmp.lesiondata,trial_score,'KernelFunction','rbf', tmp.matlab_svr_parms{:});\n            end\n            \n            if ~tmp.do_crossval % then compute the beta map as usual...\n                pmu_beta_map = tmp.beta_scale * m.(myif(tmp.useLibSVM,'sv_coef','Alpha'))' * m.(myif(tmp.useLibSVM,'SVs','SupportVectors'));\n            else % we don't need to do any computations since w should already contain a scaled/averaged model\n                ws = []; % we'll accumulate in here\n                for mm = 1 : numel(m.Trained)\n                    curMdl = m.Trained{mm};\n                    w = curMdl.Alpha.'*curMdl.SupportVectors;\n                    beta_scale = 10/max(abs(w));\n                    w = w.'*beta_scale;\n                    ws(1:numel(w),mm) = w; % accumulate here...\n                end\n                w = mean(ws,2);\n                pmu_beta_map = w; % here contains an average of the crossvalidated fold models' beta values\n            end\n        end\n        \n        tmp_map = zeros(tmp.dims); % make a zeros template....        \n        tmp_map(tmp.l_idx) = pmu_beta_map; % return the lesion data to their lidx indices...\n        pmu_beta_map = tmp_map(tmp.m_idx).'; % extract only the midx indices, since these are the only voxels that will be output in the results -- midx contains only voxels that exceed the lesion threshold\n\n        % Save this permutation (PermIdx)....\n        fileID = fopen(fullfile(tmp.outpath,['pmu_beta_map_' num2str(PermIdx) '_of_' num2str(tmp.totalperms) '.bin']),'w');\n        fwrite(fileID, pmu_beta_map,'single');\n        fclose(fileID);\n    end\n    \nfunction [Bouts,lesiondataout] = parallel_step1_batch_muvlsm(lesiondata,modelspec)\n    for vox = 1 : size(lesiondata,2) % run of the mill loop now...\n        [B,~,resids] = regress(lesiondata(:,vox),modelspec);\n        lesiondataout(:,vox) = resids + repmat(B(1),size(resids));\n        Bouts(:,vox) = B;\n    end\n"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "build_and_write_beta_cutoffs.m", "ext": ".m", "path": "svrlsmgui-master/functions/build_and_write_beta_cutoffs.m", "size": 3366, "source_encoding": "utf_8", "md5": "f724334d6a434ee45dc9f8b9b64c223f", "text": "function [thresholded,variables] = build_and_write_beta_cutoffs(options,parameters,variables,thresholds,thresholded)\n    switch parameters.tailshort % parameters.tails\n        case 'pos' % One-tailed positive tail... high scores bad \n            [thresholded,variables] = write_beta_cutoff_pos_tail(variables,thresholds,thresholded);\n        case 'neg' % One-tailed negative tail... high scores good\n            [thresholded,variables] = write_beta_cutoff_neg_tail(variables,thresholds,thresholded);\n        case 'two' % Both tails..\n            warning('not enabled at the moment...')\n            [thresholded,variables] = write_beta_cutoff_two_tailed(variables,thresholds,thresholded);\n    end\n\nfunction [thresholded,variables] = write_beta_cutoff_pos_tail(variables,thresholds,thresholded)\n    % Now write out beta cutoff map.\n    thresholded.thresholded_pos = zeros(variables.vo.dim(1:3)); % make a zeros template....\n    thresholded.thresholded_pos(variables.m_idx) = thresholds.pos_beta_map_cutoff; % put the 95th percentil beta values back into the lesion indices in a full volume\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (positive tail).nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_pos);\n    variables.files_created.betamask = variables.vo.fname;\n\nfunction [thresholded,variables] = write_beta_cutoff_neg_tail(variables,thresholds,thresholded)\n    % Now beta cutoff map for one-taled negative tail...\n    thresholded.thresholded_neg = zeros(variables.vo.dim(1:3)); % make a zeros template....        \n    thresholded.thresholded_neg(variables.m_idx) = thresholds.neg_beta_map_cutoff; % put the 5th percentil beta values back into the lesion indices in a full volume\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (negative tail).nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_neg);\n    variables.files_created.betamask = variables.vo.fname;\n\nfunction [thresholded,variables] = write_beta_cutoff_two_tailed(variables,thresholds,thresholded)\n    warning('make sure these tails are right after code refactor') % ad 2/14/18\n    % Two-tailed upper tail\n    thresholded.thresholded_twotail_upper = zeros(variables.vo.dim(1:3)); % make a zeros template....        \n    thresholded.thresholded_twotail_upper(variables.m_idx) = thresholds.two_tailed_beta_map_cutoff_pos; % put the 2.5th percentil beta values back into the lesion indices in a full volume\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (two tail, upper).nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_twotail_upper);\n    variables.files_created.betamask{1} = variables.vo.fname;\n    \n    % Two-tailed lower tail\n    thresholded.thresholded_twotail_lower =  zeros(variables.vo.dim(1:3)); % make a zeros template....        \n    thresholded.thresholded_twotail_lower(variables.m_idx) = thresholds.two_tailed_beta_map_cutoff_neg; % put the 2.5th percentil beta values back into the lesion indices in a full volume\n    variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (two tail, lower).nii');\n    svrlsmgui_write_vol(variables.vo, thresholded.thresholded_twotail_lower);\n    variables.files_created.betamask{2} = variables.vo.fname; % note second cell index"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "svrinteract.m", "ext": ".m", "path": "svrlsmgui-master/functions/svrinteract.m", "size": 10295, "source_encoding": "utf_8", "md5": "844fdc5833c36244c16ecf3dc90d0faf", "text": "function varargout = svrinteract(varargin)\n% SVRINTERACT MATLAB code for svrinteract.fig\n%      SVRINTERACT, by itself, creates a new SVRINTERACT or raises the existing\n%      singleton*.\n%\n%      H = SVRINTERACT returns the handle to a new SVRINTERACT or the handle to\n%      the existing singleton*.\n%\n%      SVRINTERACT('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in SVRINTERACT.M with the given input arguments.\n%\n%      SVRINTERACT('Property','Value',...) creates a new SVRINTERACT or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before svrinteract_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to svrinteract_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help svrinteract\n\n% Last Modified by GUIDE v2.5 19-Mar-2018 12:55:56\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @svrinteract_OpeningFcn, ...\n                   'gui_OutputFcn',  @svrinteract_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before svrinteract is made visible.\nfunction svrinteract_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to svrinteract (see VARARGIN)\n\n% Choose default command line output for svrinteract\nhandles.output = hObject;\nhandles.variables = varargin{1};\nhandles.opts.stand = false;\nhandles.opts.ep = .1;\nhandles.opts.box = 30;\nhandles.opts.ks = 2;\nhandles.opts.nfolds = 1; % nfolds = 1 is no crossval\nhandles.opts.zslice = 25;\n\ntmp = 10 .* randn(handles.variables.vo.dim(1:3));\n\nif (numel(handles.variables.vo.dim) > 3) && (handles.variables.vo.dim(4) > 1) % then it's 4D % handles.variables.vo.dim(4) > 1 % then it's 4d...\n    handles.ndims = 4;\nelse\n    handles.ndims = 3;\nend\n\nif handles.ndims == 4\n    handles.I = imagesc(tmp(:,:,20),'parent',handles.axes1,[-50 50]);\nelse % it's 3d...\n    handles.I = imagesc(tmp(:,:,20),'parent',handles.axes1,[-10 10]);\nend\n\nhandles.montage_zslices = 5:10:(size(tmp,3)-1);\n\nif handles.ndims == 4\n    montage(tmp, 'Indices',handles.montage_zslices,'DisplayRange', [-50 50],'parent',handles.axes1);\nelse\n    montage(tmp, 'Indices',handles.montage_zslices,'DisplayRange', [-10 10],'parent',handles.axes1);\nend\n colormap jet;\n colorbar(handles.axes1)\n\nrealdata=handles.variables.one_score;\nhandles.real = plot(1:numel(handles.variables.one_score),realdata,'sg-','parent',handles.axes2);\nhold on;\nset(gca,'ylim',[min(realdata)-.1*min(realdata) max(realdata)+.1*max(realdata)])\nhold on;\nhandles.pred = plot(1:numel(handles.variables.one_score),zeros(1,numel(handles.variables.one_score)),'rx-','parent',handles.axes2);\n\n% Update handles structure\nguidata(hObject, handles);\n\npaint_current(hObject,handles)\n\n% UIWAIT makes svrinteract wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\n\nfunction paint_current(hObject,handles)\n    set(handles.standardize_toggle,'value',handles.opts.stand)\n    set(handles.epsilon_editbox,'string',num2str(handles.opts.ep)); \n    set(handles.cost_editbox,'string',num2str(handles.opts.box)); \n    set(handles.kenelscale_editbox,'string',num2str(handles.opts.ks)); \n    set(handles.zslice,'string',num2str(handles.opts.zslice)); \n    set(handles.crossval_nfolds_editbox,'string',num2str(handles.opts.nfolds)); \n    \n    if handles.ndims == 4 % then use linear kernel\n    Mdl = fitrsvm(handles.variables.lesion_dat,handles.variables.one_score,'KernelFunction','linear',... % 'rbf', ...\n        'KernelScale',handles.opts.ks,'BoxConstraint',handles.opts.box,'Standardize', ...\n        handles.opts.stand,'Epsilon',handles.opts.ep);\n    else % rbf\n    Mdl = fitrsvm(handles.variables.lesion_dat,handles.variables.one_score,'KernelFunction','rbf', ...\n        'KernelScale',handles.opts.ks,'BoxConstraint',handles.opts.box,'Standardize', ...\n        handles.opts.stand,'Epsilon',handles.opts.ep);\n    end\n    w = Mdl.Alpha.'*Mdl.SupportVectors;\n    handles.variables.beta_scale = 10/max(abs(w));\n    \n    if handles.ndims == 4\n        disp('4D data input...')\n        tmp = zeros(handles.variables.vo.dim(1:4)); % THIS IS DIFFERENT FOR 4D DATA\n        beta_map = tmp;\n        tmp(handles.variables.l_idx) = w'*handles.variables.beta_scale; % return all lesion data to its l_idx indices.\n        beta_map(handles.variables.m_idx) = tmp(handles.variables.m_idx); % m_idx -> m_idx\n        \n        beta_map = sum(beta_map,4); % THIS IS DIFFERENT FOR 4D DATA\n        beta_map_bin = beta_map~=0;\n\n        relslices = find(squeeze(sum(squeeze(sum(beta_map_bin,1)),1)));\n        handles.montage_zslices = min(relslices):2:max(relslices);\n        montage(beta_map,'indices',handles.montage_zslices, 'DisplayRange', [-20 20],'parent',handles.axes1);\n    else\n        disp('3D data input...')\n        tmp = zeros(handles.variables.vo.dim(1:3));\n        beta_map = tmp;\n\n        tmp(handles.variables.l_idx) = w'*handles.variables.beta_scale; % return all lesion data to its l_idx indices.\n        beta_map(handles.variables.m_idx) = tmp(handles.variables.m_idx); % m_idx -> m_idx\n\n        %set(handles.I,'cdata',beta_map(:,:,handles.opts.zslice));\n        %montage(beta_map, 'Indices',handles.montage_zslices,'DisplayRange', [-10 10],'parent',handles.axes1);\n\n        %handles.montage_zslices = 5:10:(size(tmp,3)-1);\n        %handles.variables.lesion_dat\n\n        beta_map_bin = beta_map~=0;\n\n        relslices = find(squeeze(sum(squeeze(sum(beta_map_bin,1)),1)));\n        handles.montage_zslices = min(relslices):2:max(relslices);\n        montage(beta_map,'indices',handles.montage_zslices, 'DisplayRange', [-10 10],'parent',handles.axes1);\n\n    end\n\n\n    colormap jet;\n    colorbar(handles.axes1)\n\n    disp(['Proportion is support vectors = ' num2str(sum(Mdl.IsSupportVector)/numel(Mdl.IsSupportVector))])\n\n    if handles.opts.nfolds == 1 % no crossval\n        predicted = Mdl.predict(handles.variables.lesion_dat);\n    else\n        XVMdl = crossval(Mdl,'KFold',handles.opts.nfolds);\n        predicted = XVMdl.kfoldPredict;        \n    end\n\n\n    set(handles.pred,'ydata',predicted)\n    hold on;\n    set(handles.real,'ydata',handles.variables.one_score)\n%     \n%     realdata=handles.variables.one_score;\n%     handles.real = plot(1:numel(handles.variables.one_score),realdata,'sg-','parent',handles.axes2);\n%     hold on;\n%     set(handles.axes2,'ylim',[min(realdata)-.1*min(realdata) max(realdata)+.1*max(realdata)])\n%    preddata = Mdl.predict(handles.variables.lesion_dat);\n%     handles.pred = plot(1:numel(handles.variables.one_score),preddata,'rx-','parent',handles.axes2);\n    guidata(hObject, handles);\n\n\nfunction varargout = svrinteract_OutputFcn(hObject, eventdata, handles) \nvarargout{1} = handles.output;\n\nfunction epsilon_editbox_Callback(hObject, eventdata, handles)\n    handles.opts.ep = str2double(get(hObject,'String'));\n    paint_current(hObject,handles)\n\n% --- Executes during object creation, after setting all properties.\nfunction epsilon_editbox_CreateFcn(hObject, eventdata, handles)\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\nfunction cost_editbox_Callback(hObject, eventdata, handles)\n    handles.opts.box = str2double(get(hObject,'String'));\n    paint_current(hObject,handles)\n\n\n% --- Executes during object creation, after setting all properties.\nfunction cost_editbox_CreateFcn(hObject, eventdata, handles)\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\nfunction kenelscale_editbox_Callback(hObject, eventdata, handles)\n    handles.opts.ks = str2double(get(hObject,'String'));\n    paint_current(hObject,handles)\n\n\n% --- Executes during object creation, after setting all properties.\nfunction kenelscale_editbox_CreateFcn(hObject, eventdata, handles)\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n% --- Executes on button press in standardize_toggle.\nfunction standardize_toggle_Callback(hObject, eventdata, handles)\n    handles.opts.stand = ~handles.opts.stand;\n    paint_current(hObject,handles)\n\n\n% --- Executes on button press in pushbutton2refre.\nfunction pushbutton2refre_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton2refre (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n\n\nfunction zslice_Callback(hObject, eventdata, handles)\n    handles.opts.zslice = str2double(get(hObject,'String'));\n    paint_current(hObject,handles)\n\n\n\n% --- Executes during object creation, after setting all properties.\nfunction zslice_CreateFcn(hObject, eventdata, handles)\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\nfunction crossval_nfolds_editbox_Callback(hObject, eventdata, handles)\n    handles.opts.nfolds = str2double(get(hObject,'String'));\n    paint_current(hObject,handles)\n\n% --- Executes during object creation, after setting all properties.\nfunction crossval_nfolds_editbox_CreateFcn(hObject, eventdata, handles)\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "optimalParameterReport.m", "ext": ".m", "path": "svrlsmgui-master/functions/optimalParameterReport.m", "size": 10418, "source_encoding": "utf_8", "md5": "f029005bdb9ec73ef901438e0e2fcdf8", "text": "function variables = optimalParameterReport(parameters,variables)\n    mkdir(variables.output_folder.hyperparameterinfo)\n    %variables = optimalParameterReport(parameters,variables);\n    %variables.files_created.cfwerinfo = fullcfwerout;\n\n    % based on Zhang et al (2014)\n    % returns results for two measures of hyper parameter optimality:\n\n    % 1. reproducibility index and\n    % 2. prediction accuracy\n    % 3. mean absolute difference...\n\n    [hyperparameter_quality.pred_accuracy, ...\n     hyperparameter_quality.mean_abs_diff] = computePredictionAccuracy(parameters,variables);\n \n    hyperparameter_quality.repro_index = computeReproducibilityIndex(parameters,variables);\n    hyperparameter_quality.behavioral_predictions = computerBehavioralPrediction(parameters,variables); % this will be used for our WritePredictBehaviorReport in summary output.\n    \n    %% Save the results so we can use them later\n    fname = 'hyperparameter_quality.mat';\n    fpath = fullfile(variables.output_folder.hyperparameterinfo,fname);   \n    save(fpath,'hyperparameter_quality','-v7.3') % if it's bigger than 2 GB we need v7.3 anyway...\n    variables.files_created.hyperparameter_quality = fpath;\n    variables.hyperparameter_quality = hyperparameter_quality;\n    \nfunction behavioral_predictions = computerBehavioralPrediction(parameters,variables)\n    behavdata = variables.one_score; % for clarity extract these.\n    lesiondata = variables.lesion_dat;\n\n    %nfolds = 5; % Zhang et al., 2014\n    nfolds = parameters.hyperparameter_quality.report.nfolds;\n\n    hyperparms = hyperparmstruct(parameters);\n    \n    svrlsm_waitbar(parameters.waitbar,0,'Hyperparameter optimization: Storing behavioral predictions.');\n    if parameters.useLibSVM\n        libsvmstring = get_libsvm_spec(hyperparms.cost,hyperparms.gamma,hyperparms.epsilon); % we may need to re-apply standardization...?\n        crossvalinds = crossvalind('KFold', variables.SubNum, nfolds);\n        lesiondata=sparse(lesiondata);\n        m = svmtrain(behavdata, lesiondata, libsvmstring); %#ok<SVMTRAIN> -- use all data\n        behavioral_predictions.Mdl = m;\n        for curfold=1:nfolds\n            infold = crossvalinds == curfold;\n            outoffold = crossvalinds ~= curfold;\n            m = svmtrain(behavdata(outoffold), lesiondata(outoffold,:), libsvmstring); %#ok<SVMTRAIN>\n            behavioral_predictions.XVMdl_predicted(infold) = svmpredict(behavdata(infold), lesiondata(infold,:), m, '-q'); % predict OOF observations based on cur fold data.\n        end\n        behavioral_predictions.XVMdl = [];\n        \n    else % Run with MATLAB machinery.\n        Mdl = fitrsvm(lesiondata,behavdata,'ObservationsIn','rows','KernelFunction','rbf', 'KernelScale',hyperparms.sigma,'BoxConstraint',hyperparms.cost,'Standardize',hyperparms.standardize,'Epsilon',hyperparms.epsilon);\n        XVMdl = crossval(Mdl,'KFold',nfolds); % this is a 5-fold\n\n        behavioral_predictions.Mdl = Mdl;\n        behavioral_predictions.XVMdl = XVMdl;\n        behavioral_predictions.XVMdl_predicted = kfoldPredict(XVMdl);\n    end\n    svrlsm_waitbar(parameters.waitbar,0,'');\n    \n% 1. reproducibility index\n%    - solve the SVRLSM N times for a random subset of 80% of subjects (Zhang et al used 85 of 106 pts for each rerun)\n%    - compute pairwise correlations between all the resulting SVR-B maps\n%    - mean of the correlation coefficients is the  REPRODUCIBILITY INDEX\n\nfunction repro_index = computeReproducibilityIndex(parameters,variables)\n    % For clarity extract these.\n    behavdata = variables.one_score;\n    lesiondata = variables.lesion_dat;\n    \n    % restore to 40 crossval steps once we introduce parallelization -- maybe make it user-configurable\n    %N_subsets_to_perform = 2; % 10; % Zhang et al., 2015\n    %subset_include_percentage = .8; % Zhang et al., 2015\n    subset_include_percentage = parameters.hyperparameter_quality.report.repro_ind_subset_pct;\n    N_subsets_to_perform = parameters.hyperparameter_quality.report.n_replications;\n\n    nsubs = numel(behavdata);\n    nholdin = round(subset_include_percentage*nsubs); % how many subjects should be in each subset?\n\n    hyperparms = hyperparmstruct(parameters);\n    \n    N_subset_results = cell(1,N_subsets_to_perform); % save the correlation coefficients in this vector, then average them\n    percent_obs_are_SVs = nan(1,N_subsets_to_perform); % count the number of SVs...\n    \n    svrlsm_waitbar(parameters.waitbar,0,'Hyperparameter optimization: Measuring reproducibility index.');\n    for N = 1 : N_subsets_to_perform\n        svrlsm_waitbar(parameters.waitbar,N/N_subsets_to_perform);\n        includesubs = randperm(nsubs,nholdin); % each column contains a unique permutation\n        \n        if parameters.useLibSVM % do libsvm...\n            libsvmstring = get_libsvm_spec(hyperparms.cost,hyperparms.gamma,hyperparms.epsilon); % we may need to re-apply standardization...?\n            lesiondata=sparse(lesiondata);\n            m = svmtrain(behavdata(includesubs), lesiondata(includesubs,:), libsvmstring); %#ok<SVMTRAIN>\n            percent_obs_are_SVs(N) = m.totalSV / variables.SubNum; % should we instead use: sum(m.sv_coef == parameters.cost)) % (this is the number of bounded SVs)\n            w = m.sv_coef'*m.SVs;\n        else % do with MATLAB\n            Mdl = fitrsvm(lesiondata(includesubs,:),behavdata(includesubs,:),'ObservationsIn','rows','KernelFunction','rbf', 'KernelScale',hyperparms.sigma,'BoxConstraint',hyperparms.cost,'Standardize',hyperparms.standardize,'Epsilon',hyperparms.epsilon);\n            percent_obs_are_SVs(N) = sum(Mdl.IsSupportVector) / numel(Mdl.IsSupportVector);\n            w = Mdl.Alpha.'*Mdl.SupportVectors;\n        end\n        \n        if N == 1  % compute initial beta_scale to reuse...\n            beta_scale = 10 / prctile(abs(w),parameters.svscaling); % parameters.svscaling is e.g, 100 or 99 or 95\n        end\n        \n        w = w'*beta_scale; % the beta here is kind of irrelevant because it just scales the data, and correlation is insensitive to scaling...\n        N_subset_results{N} = w; % save the w-map...\n    end\n    \n    % Now for each pair of w maps in N_subset_results{:}, compute a correlation coefficient...\n    C = nchoosek(1:numel(N_subset_results),2); % rows of pairwise indices to correlate...\n    repro_index_correlation_results = nan(size(C,1),1); % reseve space...\n    for row = 1 : size(C,1)\n        corrvals = corrcoef(N_subset_results{C(row,1)},N_subset_results{C(row,2)}); % compute the correlation...\n        r = corrvals(2,1); % grab the r\n        repro_index_correlation_results(row) = r; % store.\n    end\n    \n    repro_index.data = repro_index_correlation_results;\n    repro_index.mean = mean(repro_index_correlation_results);\n    repro_index.std = std(repro_index_correlation_results);\n    repro_index.percent_obs_are_SVs = percent_obs_are_SVs; % number of support vectors...\n    \n% 2. prediction accuracy\n%    - mean correlation coefficient between predixcted scores and testing scores with 40 5-fold cross-validations.\nfunction [pred_accuracy,mean_abs_diff] = computePredictionAccuracy(parameters,variables)\n    behavdata = variables.one_score; % for clarity extract these.\n    lesiondata = variables.lesion_dat;\n    \n    % restore to 40 crossval steps once we introduce parallelization -- maybe make it user-configurable\n    %N_crossvals_to_perform = 2; % 10; % Zhang et al., 2015\n    % nfolds = 5; % Zhang et al., 2014\n\n    nfolds  = parameters.hyperparameter_quality.report.nfolds;\n    N_crossvals_to_perform = parameters.hyperparameter_quality.report.n_replications;\n    \n    hyperparms = hyperparmstruct(parameters);\n\n    N_crossval_correl_results = nan(1,N_crossvals_to_perform); % save the correlation coefficients in this vector, then average them\n    N_crossval_MAD_results = nan(1,N_crossvals_to_perform); % average mean absolute difference between predicted and real.\n    percent_obs_are_SVs = nan(1,N_crossvals_to_perform); % count the number of SVs...\n    \n    if parameters.useLibSVM % try to save time by only doing this once...\n        lesiondata=sparse(lesiondata);\n    end\n    \n    svrlsm_waitbar(parameters.waitbar,0,'Hyperparameter optimization: Measuring prediction accuracy.');\n    \n    predicted = nan(variables.SubNum,1); % reserve space.\n    for N = 1 : N_crossvals_to_perform\n        svrlsm_waitbar(parameters.waitbar,N/N_crossvals_to_perform);\n        if parameters.useLibSVM\n            libsvmstring = get_libsvm_spec(hyperparms.cost,hyperparms.gamma,hyperparms.epsilon); % we may need to re-apply standardization...?\n            crossvalinds = crossvalind('KFold', variables.SubNum, nfolds);\n            for curfold=1:nfolds\n                infold = crossvalinds == curfold;\n                outoffold = crossvalinds ~= curfold;\n                m = svmtrain(behavdata(outoffold), lesiondata(outoffold,:), libsvmstring); %#ok<SVMTRAIN>\n                predicted(infold) = svmpredict(behavdata(infold), lesiondata(infold,:), m, '-q'); % predict OOF observations based on cur fold data.\n            end\n        else\n            Mdl = fitrsvm(lesiondata,behavdata,'ObservationsIn','rows','KernelFunction','rbf', 'KernelScale',hyperparms.sigma,'BoxConstraint',hyperparms.cost,'Standardize',hyperparms.standardize,'Epsilon',hyperparms.epsilon);\n            percent_obs_are_SVs(N) = sum(Mdl.IsSupportVector) / numel(Mdl.IsSupportVector);\n            XVMdl = crossval(Mdl,'Kfold',nfolds); % this is a 5-fold\n            predicted = kfoldPredict(XVMdl);\n        end\n        corrvals = corrcoef(predicted,behavdata); % compute the correlation...\n        r = corrvals(2,1); % grab the r\n        N_crossval_correl_results(N) = r; % save.\n        N_crossval_MAD_results(N) = mean(abs(predicted-behavdata));\n    end\n    svrlsm_waitbar(parameters.waitbar,0,'');\n    pred_accuracy.data = N_crossval_correl_results; % can plot a distribution if we like...\n    pred_accuracy.mean = mean(N_crossval_correl_results);\n    pred_accuracy.std = std(N_crossval_correl_results);\n    pred_accuracy.percent_obs_are_SVs = percent_obs_are_SVs; % number of support vectors...\n    \n    mean_abs_diff.data = N_crossval_MAD_results; % can plot a distribution if we like... redundant w pred_accuracy\n    mean_abs_diff.mean = mean(N_crossval_MAD_results);\n    mean_abs_diff.std = std(N_crossval_MAD_results);\n    mean_abs_diff.percent_obs_are_SVs = percent_obs_are_SVs; % number of support vectors... redundant w pred_accuracy"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "continuize_lesions.m", "ext": ".m", "path": "svrlsmgui-master/functions/continuize_lesions.m", "size": 3445, "source_encoding": "utf_8", "md5": "4c8d28c69af12f9066e66e03d26ae518", "text": "function variables = continuize_lesions(variables,parameters)\n% 8/7/17 - added support for parallelization\n% 8/7/17 - fixed bug - intercept term and adding its beta back into residuals \n% 11/16/17 - added check for user interrupt \n% 2/19/18 - added in-gui waitbar in parallelized and non-parallelized loop \n%           waitbar for the par loop involved replacing the pre-existing parfor code\n%           with 'parfeval' which was introduced in MATLAB 2013b\n\nlesiondataout = nan(size(variables.lesion_dat));\nmodelcols = variables.lesion_nuisance_model;\nlesiondata = variables.lesion_dat;\nconst = ones(size(modelcols,1),1);\nmodelspec = [const modelcols];\nBouts = nan(size(modelspec,2),size(variables.lesion_dat,2));\n\nif parameters.parallelize\n    svrlsm_waitbar(parameters.waitbar,0,'Running lesion nuisance model (parallelized)...')\n    batch_job_size = 2500; % this is going to be optimal for different systems/#cores\n    nvox = size(lesiondata,2);\n    njobs = ceil(nvox/batch_job_size); % gotta round up to capture all indices\n\n    p = gcp(); % get current parallel pool\n    for j = 1 : njobs\n        this_job_start_index = ((j-1)*batch_job_size) + 1;\n        this_job_end_index = min(this_job_start_index + batch_job_size-1,nvox); % need min so we don't go past valid indices\n        job_indices = this_job_start_index:this_job_end_index;\n        f(j) = parfeval(p, @parallel_lesion_batch_fcn, 2,lesiondata(:,job_indices),modelspec); % 2 outputs\n    end\n    \n    Bouts = cell(1,njobs); %reserve space - note we want to accumulate in a row here\n    lesiondataout = cell(1,njobs); %reserve space - note we want to accumulate in a row here\n\n    for j = 1 : njobs\n        check_for_interrupt(parameters) % allow user to interrupt\n        [idx, value1,value2] = fetchNext(f);\n        Bouts{idx} = value1; % combine these cells afterward\n        lesiondataout{idx} = value2; % combine these cells afterward\n        svrlsm_waitbar(parameters.waitbar,j/njobs) % update waitbar progress...\n    end\n    \n    Bouts = cell2mat(Bouts); % combine afterward\n    lesiondataout = cell2mat(lesiondataout); % combine afterward\n\nelse % not parallelized.\n    \n    svrlsm_waitbar(parameters.waitbar,0,'Running lesion nuisance model...')\n    for vox = 1 : size(variables.lesion_dat,2) \n        if ~mod(vox,100), svrlsm_waitbar(parameters.waitbar,vox/size(variables.lesion_dat,2)); end \n        check_for_interrupt(parameters) % allow user to interrupt\n        [B,~,resids] = regress(lesiondata(:,vox),modelspec); \n        lesiondataout(:,vox) = resids + repmat(B(1),size(resids));\n        Bouts(:,vox) = B;\n    end\nend\n\nsvrlsm_waitbar(parameters.waitbar,0,'') % clear the waitbar\n\nvariables.lesion_nuisance_model_betas = Bouts;\nvariables.lesion_dat2 = lesiondataout;\n\n% Parallel batch helper function for parallelizing the lesion nuisance model\nfunction [Bouts,lesiondataout] = parallel_lesion_batch_fcn(lesiondata,modelspec)\n    for vox = 1 : size(lesiondata,2) % run of the mill loop now...\n        [B,~,resids] = regress(lesiondata(:,vox),modelspec);\n        lesiondataout(:,vox) = resids + repmat(B(1),size(resids));\n        Bouts(:,vox) = B;\n    end\n    \n% old parallelized version without batch:\n%     parfor vox = 1 : size(variables.lesion_dat,2)\n%         check_for_interrupt(parameters)\n%         [B,~,resids] = regress(lesiondata(:,vox),modelspec);\n%         lesiondataout(:,vox) = resids + repmat(B(1),size(resids));\n%         Bouts(:,vox) = B;\n%     end\n"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "write_nifti_hdr.m", "ext": ".m", "path": "svrlsmgui-master/functions/nifti/write_nifti_hdr.m", "size": 2762, "source_encoding": "utf_8", "md5": "82987cccd27d7f60b004bdbec61b6d24", "text": "function write_nifti_hdr(h, fname)\r\n\r\n% WRITE_NIFTI_HDR  Write NIFTI header\r\n%\r\n% WRITE_NIFTI_HDR(H, FNAME) writes the header structure H into the file\r\n% FNAME. The validity of H is not checked.\r\n\r\n[pathstr, basename, ext] = fileparts(fname);\r\nif strcmp(ext, '.img')\r\n  fname = fullfile(pathstr, [basename '.hdr']);\r\nend\r\n\r\nif strcmp(ext, '.nii') && exist(fname, 'file')\r\n  fid = fopen(fname, 'r+');\r\nelse\r\n  fid = fopen(fname, 'w');\r\nend\r\n\r\nfwrite(fid, h.sizeof_hdr(1), 'int32');\r\nfwrite(fid, padchar(h.data_type, 10), 'char');\r\nfwrite(fid, padchar(h.db_name, 18), 'char');\r\nfwrite(fid, h.extents(1), 'int32');\r\nfwrite(fid, h.session_error(1), 'int16');\r\nfwrite(fid, h.regular(1), 'char');\r\nfwrite(fid, h.dim_info(1), 'char');\r\n\r\nfwrite(fid, h.dim(1:8), 'int16');\r\nfwrite(fid, h.intent_p1(1), 'float32');\r\nfwrite(fid, h.intent_p2(1), 'float32');\r\nfwrite(fid, h.intent_p3(1), 'float32');\r\nfwrite(fid, h.intent_code(1), 'int16');\r\nfwrite(fid, h.datatype(1), 'int16');\r\nfwrite(fid, h.bitpix(1), 'int16');\r\nfwrite(fid, h.slice_start(1), 'int16');\r\nfwrite(fid, h.pixdim(1:8), 'float32');\r\nfwrite(fid, h.vox_offset(1), 'float32');\r\nfwrite(fid, h.scl_slope(1), 'float32');\r\nfwrite(fid, h.scl_inter(1), 'float32');\r\nfwrite(fid, h.slice_end(1), 'int16');\r\nfwrite(fid, h.slice_code(1), 'uchar');\r\nfwrite(fid, h.xyzt_units(1), 'uchar');\r\nfwrite(fid, h.cal_max(1), 'float32');\r\nfwrite(fid, h.cal_min(1), 'float32');\r\nfwrite(fid, h.slice_duration(1), 'float32');\r\nfwrite(fid, h.toffset(1), 'float32');\r\nfwrite(fid, h.glmax(1), 'int32');\r\nfwrite(fid, h.glmin(1), 'int32');\r\n\r\nfwrite(fid, padchar(h.descrip, 80), 'char');\r\nfwrite(fid, padchar(h.aux_file, 24), 'char');\r\nfwrite(fid, h.qform_code(1), 'int16');\r\nfwrite(fid, h.sform_code(1), 'int16');\r\nfwrite(fid, h.quatern_b(1), 'float32');\r\nfwrite(fid, h.quatern_c(1), 'float32');\r\nfwrite(fid, h.quatern_d(1), 'float32');\r\nfwrite(fid, h.qoffset_x(1), 'float32');\r\nfwrite(fid, h.qoffset_y(1), 'float32');\r\nfwrite(fid, h.qoffset_z(1), 'float32');\r\nfwrite(fid, h.srow_x(1:4), 'float32');\r\nfwrite(fid, h.srow_y(1:4), 'float32');\r\nfwrite(fid, h.srow_z(1:4), 'float32');\r\nfwrite(fid, padchar(h.intent_name, 16), 'char');\r\nfwrite(fid, padchar(h.magic, 4), 'char');\r\n\r\nif h.qform_code == 0 && h.sform_code == 0 && isfield(h, 'originator') && ...\r\n        all(h.originator(1:3) >= 1) && all(h.originator(1:3) <= h.dim(2:4))\r\n    warning('Writing ANALYZE originator over the top of NIFTI orientation fields');\r\n    fseek(fid, 253, 'bof');\r\n    fwrite(fid, h.originator, 'int16')'; % over the top of qform_code and the following few fields\r\nend\r\n\r\nfclose(fid);\r\n\r\n\r\nfunction outstr = padchar(instr, n)\r\n\r\nif length(instr) <= n\r\n    outstr = char(zeros(1, n));\r\n    outstr(1:length(instr)) = instr;\r\nelse\r\n    outstr = instr(1:n);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "analyze_to_nifti.m", "ext": ".m", "path": "svrlsmgui-master/functions/nifti/analyze_to_nifti.m", "size": 1235, "source_encoding": "utf_8", "md5": "18757b17671315605f0fda9ab06b7129", "text": "function analyze_to_nifti(fname)\r\n\r\n% ANALYZE_TO_NIFTI(FNAME)\r\n%\r\n% Converts the ANALYZE image FNAME to .nii (one file) format. If no FNAME\r\n% is provided, converts every .img file in the current directory.\r\n%\r\n% The header information is set up based on what SPM5 does if you reslice.\r\n% The origin is set to the center.\r\n\r\n\r\nif nargin == 0\r\n  d = dir('*.img');\r\n  if length(d) >= 1\r\n    for i = 1:length(d)\r\n      fprintf('%s\\n', d(i).name);\r\n      do(d(i).name);\r\n    end\r\n  end\r\nelse\r\n  do(fname);\r\nend\r\n\r\n\r\nfunction do(fname)\r\n\r\n[hdr, img] = read_nifti(fname);\r\n\r\nnhdr = make_nifti_hdr(hdr.datatype, hdr.dim(2:4), abs(hdr.pixdim(2:4)));\r\n\r\nx = (nhdr.dim(2) / 2 - 0.5) * nhdr.pixdim(2);\r\ny = (nhdr.dim(3) / 2 - 0.5) * nhdr.pixdim(3);\r\nz = (nhdr.dim(4) / 2 - 0.5) * nhdr.pixdim(4);\r\n\r\nnhdr.pixdim(1) = -1;\r\nnhdr.vox_offset = 352;\r\nnhdr.qform_code = 2;\r\nnhdr.sform_code = 2;\r\nnhdr.quatern_b = 0;\r\nnhdr.quatern_c = 1;\r\nnhdr.quatern_d = 0;\r\nnhdr.qoffset_x = x;\r\nnhdr.qoffset_y = -y;\r\nnhdr.qoffset_z = -z;\r\nnhdr.srow_x = [-nhdr.pixdim(2) 0 0 x];\r\nnhdr.srow_y = [0 nhdr.pixdim(3) 0 -y];\r\nnhdr.srow_z = [0 0 nhdr.pixdim(4) -z];\r\nnhdr.magic = 'n+1 ';\r\nnhdr.magic(4) = 0;\r\n\r\nfname((end - 2):end) = 'nii';\r\n\r\nwrite_nifti(nhdr, img, fname);\r\n"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "nifti2jpg.m", "ext": ".m", "path": "svrlsmgui-master/functions/nifti/nifti2jpg.m", "size": 1178, "source_encoding": "utf_8", "md5": "534ca9fc2dd45fab241045c7a7d29e53", "text": "function nifti2jpg(fname, skip, flip)\r\n\r\nif nargin < 2\r\n    skip = 1;\r\nend\r\nif nargin < 3\r\n    flip = [0 0 1; 1 0 1]; % works for NIC T1s after dicom2analyze\r\nend\r\n\r\n[hdr, img] = read_nifti(fname);\r\n\r\nvals = img(:);\r\nvals = sort(vals(1:10:end));\r\nanatmin = vals(round(length(vals) * 10 / 100));\r\nanatmax = vals(round(length(vals) * 95 / 100));\r\n\r\nfor o = 1:3\r\n    for i = 1:skip:hdr.dim(o + 1)\r\n        if o == 1\r\n            slice = squeeze(img(i, :, :));\r\n        elseif o == 2\r\n            slice = squeeze(img(:, i, :));\r\n        else\r\n            slice = squeeze(img(:, :, i));\r\n        end\r\n        if flip(1, o)\r\n            slice = slice';\r\n        end\r\n        if flip(2, o)\r\n            slice = flipud(slice);\r\n        end\r\n        slice = scaleimg(slice, anatmin, anatmax, 0.15, 1);\r\n        imwrite(slice, sprintf('%d_%03d.jpg', o, i), 'JPEG', 'Quality', 50);\r\n    end\r\nend\r\n\r\nfunction sd = scaleimg(img, imin, imax, omin, omax)\r\n\r\n% scales an image such that values between imin and imax now lie between\r\n% omin and omax. values above and below are clipped.\r\n\r\nsd = omin + (img - imin) / (imax - imin) * (omax - omin);\r\nsd(sd < omin) = omin;\r\nsd(sd > omax) = omax;\r\n"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "run_beta_PMU_old.m", "ext": ".m", "path": "svrlsmgui-master/functions/unused/run_beta_PMU_old.m", "size": 24927, "source_encoding": "utf_8", "md5": "245c8f57bb62a9aebd3d04d065c89441", "text": "function variables = run_beta_PMU(parameters, variables, cmd, beta_map,handles)\n    options = handles.options;\n    zerostemplate = zeros(variables.vo.dim(1:3)); % make a zeros template....\n    \n    ori_beta_val = beta_map(variables.m_idx).'; % Original observed beta values.\n    tic;\n    \n    if parameters.parallelize % try to parfor it...\n        handles = UpdateProgress(handles,'Computing beta map permutations (parallelized)...',1);\n        sparseLesionData = sparse(variables.lesion_dat);\n        lidx = variables.l_idx;\n        midx = variables.m_idx;\n        betascale = variables.beta_scale;\n        \n        % create permutations beforehand.\n        permdata = nan(numel(variables.one_score),parameters.PermNumVoxelwise); % each COL will be a permutation.\n        npermels = size(permdata,1);\n        for r = 1 : size(permdata,2) % each col...\n            permdata(:,r) = variables.one_score(randperm(npermels));\n        end\n        \n        outpath = variables.output_folder.clusterwise;\n        totalperms = parameters.PermNumVoxelwise;\n        uselibsvm = parameters.useLibSVM;\n        parfor PermIdx=1:parameters.PermNumVoxelwise\n            check_for_interrupt(parameters)\n            trial_score = permdata(:,PermIdx); % extract the row of permuted data.\n            if uselibsvm\n                m = svmtrain(trial_score,sparseLesionData,cmd); %#ok<SVMTRAIN>\n                alpha = m.sv_coef';\n                SVs = m.SVs;\n            else\n                [m,~,~] = ComputeMatlabSVRLSM(parameters,variables);\n                alpha = m.Alpha'; \n                SVs = m.SupportVectors;\n            end\n            \n            pmu_beta_map = betascale * alpha * SVs;\n            tmp_map = zerostemplate; % zeros(nx, ny, nz);\n            tmp_map(lidx) = pmu_beta_map;\n            pmu_beta_map = tmp_map(midx).';\n            \n            % Save this permutation....\n            fileID = fopen(fullfile(outpath,['pmu_beta_map_' num2str(PermIdx) '_of_' num2str(totalperms) '.bin']),'w');\n            fwrite(fileID, pmu_beta_map,'single');\n            fclose(fileID);\n        end\n        \n        % now get all those individual files into one big file that we can memmap to.\n        outfname_big = fullfile(variables.output_folder.clusterwise,['pmu_beta_maps_N_' num2str(parameters.PermNumVoxelwise) '.bin']);\n        fileID = fopen(outfname_big,'w');\n        \n        for PermIdx=1:parameters.PermNumVoxelwise\n            check_for_interrupt(parameters)\n            curpermfilepath = fullfile(outpath,['pmu_beta_map_' num2str(PermIdx) '_of_' num2str(totalperms) '.bin']);\n            cur_perm_data = memmapfile(curpermfilepath,'Format','single');\n            fwrite(fileID, cur_perm_data.Data,'single');\n            clear cur_perm_data; % remove memmap from memory.\n            delete(curpermfilepath); % delete it since we don't want the data hanging around...\n        end\n        fclose(fileID); % close big file\n        \n    else\n        handles = UpdateProgress(handles,'Computing beta map permutations (not parallelized)...',1);\n        % This is where we'll save our GBs of permutation data output...\n        outfname_big = fullfile(variables.output_folder.clusterwise,['pmu_beta_maps_N_' num2str(parameters.PermNumVoxelwise) '.bin']);\n        fileID = fopen(outfname_big,'w');\n\n        h = waitbar(0,'Computing beta permutations...','Tag','WB');\n        for PermIdx=1:parameters.PermNumVoxelwise\n            check_for_interrupt(parameters)\n\n            % random permute subjects order\n            loc = randperm(length(variables.one_score));\n            trial_score = variables.one_score(loc);\n            \n            % Which package to use to compute SVM solution\n            if parameters.useLibSVM\n                m = svmtrain(trial_score,sparse(variables.lesion_dat),cmd); %#ok<SVMTRAIN>\n            else\n                if PermIdx == 1 \n                    variables.orig_one_score = variables.one_score;\n                end\n                \n                variables.one_score = trial_score;\n                \n                [m,~,~] = ComputeMatlabSVRLSM(parameters,variables);\n                \n                if PermIdx == parameters.PermNumVoxelwise % put it back after we've done all permutations...\n                    variables.one_score = variables.orig_one_score;\n                end\n            end\n            \n            % compute the beta map\n            if parameters.useLibSVM\n                alpha = m.sv_coef';\n                SVs = m.SVs;\n            else % MATLAB's version.\n                alpha = m.Alpha'; \n                SVs = m.SupportVectors;\n            end\n            pmu_beta_map = variables.beta_scale * alpha*SVs;\n            \n            \n            tmp_map = zerostemplate;\n            tmp_map(variables.l_idx) = pmu_beta_map;\n            pmu_beta_map = tmp_map(variables.m_idx).';\n            \n            % Save this permutation....\n            fwrite(fileID, pmu_beta_map,'single');\n            \n            % Display progress.\n            elapsed_time = toc;\n            remain_time = round(elapsed_time * (parameters.PermNumVoxelwise - PermIdx)/(PermIdx));\n            remain_time_h = floor(remain_time/3600);\n            remain_time_m = floor((remain_time - remain_time_h*3600)/60);\n            remain_time_s = floor(remain_time - remain_time_h*3600 - remain_time_m*60);\n            prompt_str = sprintf(['Permutation ', num2str(PermIdx), '/', num2str(parameters.PermNumVoxelwise), ': Est. remaining time: ', num2str(remain_time_h), ' h ', num2str(remain_time_m), ' m ' num2str(remain_time_s), 's\\n']);\n            waitbar(PermIdx/parameters.PermNumVoxelwise,h,prompt_str) % show progress.\n            \n        end\n        \n        close(h) % close the waitbar...\n        fclose(fileID); % close the pmu data output file.\n    end\n    \n    % Read in gigantic memory mapped file... no matter whether we parallelized or not.\n    all_perm_data = memmapfile(outfname_big,'Format','single'); % does the single precision hurt the analysis?\n\n    %% FWE cluster correction based on permutation analysis\n    voxelwise_p_value = parameters.voxelwise_p;\n    pos_thresh_index = median([1 round((1-voxelwise_p_value) * parameters.PermNumVoxelwise) parameters.PermNumVoxelwise]); % row 9500 in 10000 permutations.\n    pos_beta_map_cutoff = nan(1,length(variables.m_idx));\n    one_tail_pos_alphas = nan(1,length(variables.m_idx));\n\n    neg_thresh_index = median([1 round(voxelwise_p_value * parameters.PermNumVoxelwise) parameters.PermNumVoxelwise]); % so row 500 in 10000 permutations\n    neg_beta_map_cutoff = nan(1,length(variables.m_idx));\n    one_tail_neg_alphas = nan(1,length(variables.m_idx));\n\n    two_tailed_thresh_index_neg = median([1 round(((voxelwise_p_value/2)) * parameters.PermNumVoxelwise) parameters.PermNumVoxelwise]); % row 250 in 10000 permutations.\n    two_tailed_thresh_index = median([1 round((1-(voxelwise_p_value/2)) * parameters.PermNumVoxelwise) parameters.PermNumVoxelwise]); % row 9750 in 10000 permutations.\n    two_tailed_beta_map_cutoff_pos = nan(1,length(variables.m_idx));\n    two_tailed_beta_map_cutoff_neg = nan(1,length(variables.m_idx));\n    twotails_alphas = nan(1,length(variables.m_idx));\n\nif parameters.parallelize % try to parfor it...\n    handles = UpdateProgress(handles,'Sorting null betas for each lesioned voxel in the dataset (parallelized).',1);\n    L = length(variables.m_idx);\n    tails = parameters.tails; % so not a broadcast variable.\n    Opt1 = options.hypodirection{1};\n    Opt2 = options.hypodirection{2};\n    Opt3 = options.hypodirection{3};\n    parfor col = 1 : length(variables.m_idx) \n        check_for_interrupt(parameters)\n        curcol = extractSlice(all_perm_data,col,L); % note this is a function at the bottom of this file..\n        observed_beta = ori_beta_val(col); % original observed beta value.\n        curcol_sorted = sort(curcol); % smallest values at the top..\n        switch tails\n            case Opt1\n                one_tail_pos_alphas(col) = sum(observed_beta > curcol_sorted)/numel(curcol_sorted); % percent of values observed_beta is greater than.\n                pos_beta_map_cutoff(col) = curcol_sorted(pos_thresh_index); % so the 9500th at p of 0.05 on 10,000 permutations\n            case Opt2\n                one_tail_neg_alphas(col) = sum(observed_beta < curcol_sorted)/numel(curcol_sorted); % percent of values observed_beta is greater than.\n                neg_beta_map_cutoff(col) = curcol_sorted(neg_thresh_index); % so the 500th at p of 0.05 on 10,000 permutations\n            case Opt3\n                two_tailed_beta_map_cutoff_pos(col) = curcol_sorted(two_tailed_thresh_index); % 250...\n                two_tailed_beta_map_cutoff_neg(col) = curcol_sorted(two_tailed_thresh_index_neg); % 9750...\n                twotails_alphas(col) = sum(abs(observed_beta) > abs(curcol_sorted))/numel(curcol_sorted); % percent of values observed_beta is greater than.\n        end\n    end\nelse        \n    handles = UpdateProgress(handles,'Sorting null betas for each lesioned voxel in the dataset (not parallelized).',1);\n    h = waitbar(0,sprintf('Sorting null betas for each lesioned voxel in the dataset (N = %d).\\n',length(variables.m_idx)),'Tag','WB');\n    dataRef = all_perm_data.Data; % will this eliminate some overhead  \n    L = length(variables.m_idx);\n    for col = 1 : length(variables.m_idx) \n        check_for_interrupt(parameters)\n        curcol = dataRef(col:L:end); % index out each column using skips the length of the data...\n        observed_beta = ori_beta_val(col); % original observed beta value.\n        curcol_sorted = sort(curcol); % smallest values at the top..\n        \n        % hist(curcol_sorted)\n%         histogram(curcol_sorted,35)\n%         hold on\n%         title(num2str(col))\n%         pause\n        % fitdists{col} = fitdist(\n        % pd = fitdist(x,'Kernel','Kernel','epanechnikov')\n\n        \n%         p_vec=nan(size(curcol_sorted)); % allocate space\n%         all_ind = 1:numel(curcol_sorted); % we'll reuse this vector\n%          for i = all_ind % for each svr beta value in the vector\n%              ind_to_compare = setdiff(all_ind,i);\n%              p_vec(i) = 1 - mean(curcol_sorted(i) < curcol_sorted(ind_to_compare));\n%          end\n%         disp([num2str(i) ' of ' num2str(numel(p_vec)) ' - observed svrB = ' num2str(observed_beta)])\n%         [numel(unique(curcol_sorted)) numel(unique(p_vec))]\n\n\n        % Compute beta cutoff values and a pvalue map for the observed betas.\n        switch parameters.tails\n            case options.hypodirection{1} % 'one_positive'\n                one_tail_pos_alphas(col) = sum(observed_beta > curcol_sorted)/numel(curcol_sorted); % percent of values observed_beta is greater than.\n                pos_beta_map_cutoff(col) = curcol_sorted(pos_thresh_index); % so the 9500th at p of 0.05 on 10,000 permutations\n            case options.hypodirection{2} %'one_negative'\n                one_tail_neg_alphas(col) = sum(observed_beta < curcol_sorted)/numel(curcol_sorted); % percent of values observed_beta is greater than.\n                neg_beta_map_cutoff(col) = curcol_sorted(neg_thresh_index); % so the 500th at p of 0.05 on 10,000 permutations\n            case options.hypodirection{3} % 'two'\n                two_tailed_beta_map_cutoff_pos(col) = curcol_sorted(two_tailed_thresh_index); % 250...\n                two_tailed_beta_map_cutoff_neg(col) = curcol_sorted(two_tailed_thresh_index_neg); % 9750...\n                twotails_alphas(col) = sum(abs(observed_beta) > abs(curcol_sorted))/numel(curcol_sorted); % percent of values observed_beta is greater than.\n        end        \n        waitbar(col/L,h) % show progress.\n    end\n    close(h)\n\nend\n    %% Construct volumes of the solved alpha values and write them out - and write out beta cutoff maps, too\n    switch parameters.tails\n        case options.hypodirection{1} % 'one_positive' % One-tailed positive tail...\n            thresholded_pos = zerostemplate; \n            if parameters.invert_p_map_flag % it's already inverted\n                thresholded_pos(variables.m_idx) = one_tail_pos_alphas;            \n                % Write unthresholded P-map for the positive tail            \n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values (inv).nii');\n                svrlsmgui_write_vol(variables.vo, thresholded_pos);\n                % Now write out the thresholded P-map for the positive tail            \n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values (inv).nii');\n                thresholded_pos(thresholded_pos < (1-parameters.voxelwise_p)) = 0; % zero out sub-threshold p value voxels (note the 1-p)\n                svrlsmgui_write_vol(variables.vo, thresholded_pos);\n            else\n                thresholded_pos(variables.m_idx) = 1 - one_tail_pos_alphas;\n                % Write unthresholded P-map for the positive tail            \n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values.nii');\n                svrlsmgui_write_vol(variables.vo, thresholded_pos);\n                % Now write out the thresholded P-map for the positive tail    \n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values.nii');\n                thresholded_pos(thresholded_pos > parameters.voxelwise_p) = 0; % zero out voxels whose values are greater than p \n                svrlsmgui_write_vol(variables.vo, thresholded_pos);\n            end\n            \n            % Now write out beta cutoff map.\n            thresholded_pos = zerostemplate; % zeros(nx,ny,nz); % reserve space\n            thresholded_pos(variables.m_idx) = pos_beta_map_cutoff; % put the 95th percentil beta values back into the lesion indices in a full volume\n            variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (positive tail).nii');\n            svrlsmgui_write_vol(variables.vo, thresholded_pos);\n            \n        case options.hypodirection{2} % 'one_negative' % One-tailed negative tail...\n            thresholded_neg = zerostemplate;\n            if parameters.invert_p_map_flag  % it's already inverted...\n                thresholded_neg(variables.m_idx) = one_tail_neg_alphas;\n                % write out unthresholded negative p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values (inv).nii');\n                svrlsmgui_write_vol(variables.vo, thresholded_neg);\n                % write out thresholded negative p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values (inv).nii');\n                thresholded_neg(thresholded_neg < (1-parameters.voxelwise_p)) = 0; % zero out subthreshold p value voxels (note 1-p)\n                svrlsmgui_write_vol(variables.vo, thresholded_neg);\n            else\n                thresholded_neg(variables.m_idx) = 1 - one_tail_neg_alphas;\n                % write out unthresholded negative p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values.nii');\n                svrlsmgui_write_vol(variables.vo, thresholded_neg);\n                % write out thresholded negative p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values.nii');\n                thresholded_neg(thresholded_neg > parameters.voxelwise_p) = 0; % zero out voxels whose values are greater than p \n                svrlsmgui_write_vol(variables.vo, thresholded_neg);\n            end\n            \n            % Now beta cutoff map for one-taled negative tail...\n            thresholded_neg = zerostemplate; % reserve space;\n            thresholded_neg(variables.m_idx) = neg_beta_map_cutoff; % put the 5th percentil beta values back into the lesion indices in a full volume\n            variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (negative tail).nii');\n            svrlsmgui_write_vol(variables.vo, thresholded_neg);\n            \n        case options.hypodirection{3} %'two'% Both tails..\n            thresholded_twotails = zerostemplate;% reserve space;\n            if parameters.invert_p_map_flag % it's already inverted...\n                thresholded_twotails(variables.m_idx) = twotails_alphas;\n                % write out unthresholded p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values (inv).nii');\n                svrlsmgui_write_vol(variables.vo, thresholded_twotails);\n                % write out thresholded p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values (inv).nii');\n                thresholded_twotails(thresholded_twotails < (1-(parameters.voxelwise_p/2))) = 0; % zero out subthreshold p value voxels (note 1-p)\n                svrlsmgui_write_vol(variables.vo, thresholded_twotails);\n            else\n                thresholded_twotails(variables.m_idx) = 1 - twotails_alphas;\n                % write out unthresholded p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Unthresholded P values.nii');\n                svrlsmgui_write_vol(variables.vo, thresholded_twotails);\n                % write out thresholded p map\n                variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Thresholded P values (inv).nii');\n                thresholded_twotails(thresholded_twotails > (parameters.voxelwise_p/2)) = 0; % zero out supra-alpha p value voxels\n                svrlsmgui_write_vol(variables.vo, thresholded_twotails);\n            end\n            \n            % Beta cutoff maps\n            % Two-tailed upper tail\n            thresholded_twotail_upper = zerostemplate; %zeros(nx,ny,nz); % reserve space;\n            thresholded_twotail_upper(variables.m_idx) = two_tailed_beta_map_cutoff_pos; % put the 2.5th percentil beta values back into the lesion indices in a full volume\n            variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (two tail, upper).nii');\n            svrlsmgui_write_vol(variables.vo, thresholded_twotail_upper);\n            \n            % Two-tailed lower tail\n            thresholded_twotail_lower = zerostemplate; % zeros(nx,ny,nz); % reserve space;\n            thresholded_twotail_lower(variables.m_idx) = two_tailed_beta_map_cutoff_neg; % put the 2.5th percentil beta values back into the lesion indices in a full volume\n            variables.vo.fname = fullfile(variables.output_folder.voxelwise,'Beta value cutoff mask (two tail, lower).nii');\n            svrlsmgui_write_vol(variables.vo, thresholded_twotail_lower);\n    end\n     \n    % Now for each permuted beta map, apply the beta mask and determine largest surviving cluster.\n    \n    h = waitbar(0,'Applying voxelwise beta mask to null data and noting largest null clusters...','Tag','WB');\n    \n    % Reconstruct the volumes so we can threshold and examine cluster sizes\n    all_max_cluster_sizes = nan(parameters.PermNumClusterwise,1); % reserve space.\n    \n    if parameters.PermNumClusterwise > parameters.PermNumVoxelwise, error('Cannot sample more cluster permutations than have been generated by the voxelwise permutation procedure.'); end\n    \n    handles = UpdateProgress(handles,'Applying voxelwise beta mask to null data and noting largest null clusters...',1);\n\n    for f = 1 : parameters.PermNumVoxelwise % go through each frame of generated betas in the null data...\n        waitbar(f/parameters.PermNumVoxelwise,h) % show progress.\n        check_for_interrupt(parameters)\n\n        frame_length = length(variables.m_idx);\n        frame_start_index = 1+((f-1)*frame_length); % +1 since not zero indexing\n        frame_end_index = (frame_start_index-1)+frame_length; % -1 so we are not 1 too long.\n        relevant_data_frame = all_perm_data.Data(frame_start_index:frame_end_index); % extract the frame\n        templatevol = zerostemplate; % zeros(nx,ny,nz); % reserve space\n        templatevol(variables.m_idx) = relevant_data_frame; % put the beta values back in indices.\n        \n         if parameters.SavePreThresholdedPermutations % then write out raw voxel NON-thresholded images for this permutation.\n            variables.vo.fname = fullfile(variables.output_folder.clusterwise,['UNthreshed_perm_' num2str(f) '_of_' num2str(parameters.PermNumVoxelwise) '.nii']);\n            svrlsmgui_write_vol(variables.vo, templatevol);\n         end\n        \n        switch parameters.tails\n            case options.hypodirection{1} \n                pos_threshed = templatevol .* (templatevol>=thresholded_pos); % elementwise greater than operator to threshold positive tail of test betas\n            case options.hypodirection{2} \n                neg_threshed = templatevol .* (templatevol<=thresholded_neg); % elementwise less than operator to threshold negative tail of test betas.\n            case options.hypodirection{3} % Now build a two-tailed thresholded version\n            threshmask = and(templatevol > 0,templatevol >= thresholded_twotail_upper) | and(templatevol < 0,templatevol <= thresholded_twotail_lower);\n            twotail_threshed = templatevol .* threshmask; % mask the two-tailed beta mask for this null data...\n        end\n        \n        if parameters.SavePostVoxelwiseThresholdedPermutations % then write out raw voxel thresholded images for this permutation.\n            switch parameters.tails\n                case options.hypodirection{1} % 'one_positive'\n                    variables.vo.fname = fullfile(variables.output_folder.clusterwise,['pos_threshed_perm_' num2str(f) '_of_' num2str(parameters.PermNumVoxelwise) '.nii']);\n                    svrlsmgui_write_vol(variables.vo, pos_threshed);\n                case options.hypodirection{2} %'one_negative'\n                    variables.vo.fname = fullfile(variables.output_folder.clusterwise,['neg_threshed_perm_' num2str(f) '_of_' num2str(parameters.PermNumVoxelwise) '.nii']);\n                    svrlsmgui_write_vol(variables.vo, neg_threshed);\n                case options.hypodirection{3} %'two'\n                    variables.vo.fname = fullfile(variables.output_folder.clusterwise,['twotail_threshed_perm_' num2str(f) '_of_' num2str(parameters.PermNumVoxelwise) '.nii']);\n                    svrlsmgui_write_vol(variables.vo, twotail_threshed);\n            end\n        end\n        \n        if f <= parameters.PermNumClusterwise\n            switch parameters.tails\n                case options.hypodirection{1}\n                    permtype='pos';\n                    thresholded_mask=pos_threshed;\n                case options.hypodirection{2}\n                    permtype='neg';\n                    thresholded_mask=neg_threshed;\n                case options.hypodirection{3}\n                    permtype='twotail';\n                   thresholded_mask=twotail_threshed;\n            end\n\n            testvol_thresholded = thresholded_mask; % now evaluate the surviving voxels for clusters...\n            CC = bwconncomp(testvol_thresholded, 6);\n            largest_cluster_size = max(cellfun(@numel,CC.PixelIdxList(1,:))); % max val for numels in each cluster object found\n            if isempty(largest_cluster_size)\n                largest_cluster_size = 0;\n            else % threshold the volume and write it out.\n                if parameters.SavePostClusterwiseThresholdedPermutations % then save them...\n                    out_map = remove_scatter_clusters(testvol_thresholded, largest_cluster_size-1);\n                    variables.vo.fname = fullfile(variables.output_folder.clusterwise,[permtype '_threshed_perm_' num2str(f) '_of_' num2str(parameters.PermNumClusterwise) '_largest_cluster.nii']);\n                    svrlsmgui_write_vol(variables.vo, out_map);\n                end\n            end\n            all_max_cluster_sizes(f) = largest_cluster_size; % record...\n        end\n    end\n    close(h)\n\n    % Save the resulting cluster lists\n    fname = 'Largest clusters.mat';\n    save(fullfile(variables.output_folder.clusterwise,fname),'all_max_cluster_sizes');\n    \n    handles = UpdateProgress(handles,'Cleaning up null data...',1);\n    \n    % Clean up as necessary\n    if ~parameters.SavePermutationData\n        fclose all;\n        delete(outfname_big); % delete the monster bin file with raw permutation data in it.\n        if exist(outfname_big,'file') % if it still exists...\n            warning('Was not able to delete large binary file with raw permutation data in it. This file can be quite large, so you may want to manually clean up the file and adjust your permissions so that this is not a problem in the future.')\n        end\n    end\n    \n% for parallelization to eliminate large overhead transfering to and from workers\nfunction sliceData = extractSlice(all_perm_data,col,L)\n    sliceData = all_perm_data.Data(col:L:end);"}
+{"plateform": "github", "repo_name": "atdemarco/svrlsmgui-master", "name": "crossval_diagnostics.m", "ext": ".m", "path": "svrlsmgui-master/functions/unused/crossval_diagnostics.m", "size": 3065, "source_encoding": "utf_8", "md5": "02ba5b4ce471ec375db0f8ee55bbdfad", "text": "function varargout = crossval_diagnostics(varargin)\n% CROSSVAL_DIAGNOSTICS MATLAB code for crossval_diagnostics.fig\n%      CROSSVAL_DIAGNOSTICS, by itself, creates a new CROSSVAL_DIAGNOSTICS or raises the existing\n%      singleton*.\n%\n%      H = CROSSVAL_DIAGNOSTICS returns the handle to a new CROSSVAL_DIAGNOSTICS or the handle to\n%      the existing singleton*.\n%\n%      CROSSVAL_DIAGNOSTICS('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in CROSSVAL_DIAGNOSTICS.M with the given input arguments.\n%\n%      CROSSVAL_DIAGNOSTICS('Property','Value',...) creates a new CROSSVAL_DIAGNOSTICS or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before crossval_diagnostics_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to crossval_diagnostics_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help crossval_diagnostics\n\n% Last Modified by GUIDE v2.5 21-Feb-2018 11:09:25\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @crossval_diagnostics_OpeningFcn, ...\n                   'gui_OutputFcn',  @crossval_diagnostics_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before crossval_diagnostics is made visible.\nfunction crossval_diagnostics_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to crossval_diagnostics (see VARARGIN)\n\n% Choose default command line output for crossval_diagnostics\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes crossval_diagnostics wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\n\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = crossval_diagnostics_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "eventDetector.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/eventDetection/eventDetector.m", "size": 15135, "source_encoding": "utf_8", "md5": "64d69083f3f62775510da94cfc8f656b", "text": "function varargout = eventDetector(ROIStats, stim, ROISet, eventDetectMethod, frameRate, bpfilter, psConfig, saveName)\n% event detection function - wrapper to different event detection algorithms\n% input: structure created by GetRoiStats (*_RoiStats)\ndbgLevel = 2;\n% required folders\nfolderList = {'Projects/EventDetect','Projects/TwoPhotonAnalyzer'};\naddFolders2Path(folderList,1);\nmaxRuns = Inf; % for testing\n% maxRuns = 2; % for testing\ndoCaTracesPlot = 1;\ndoPlotEvents = doCaTracesPlot && 1; %%#ok<NASGU>\ndoRasterPlot = 1;\ndoStimEventRasterPlot = 1;\n% doPsStimPlot = 1;\ndoEventCountPlot = 0;\ndoEventDetection = 1;\no('    #eventDetector: method: \"%s\", maxRuns = %d ...', eventDetectMethod, maxRuns, 1, dbgLevel);\n%% Event detection parameters\n[config, V, P] = getDefaultParameters(eventDetectMethod, frameRate); %%#ok<NASGU,ASGLU>\no('    #eventDetector: event detection parameters configured.', 3, dbgLevel);\n%% Event detection\n% event detection on roiStats\n% % last row is neuropil, remove it\n% allDFFData(end, :) = [];\n% init sizes of data set\nnROIs = size(ROIStats, 1);\nnRuns = size(ROIStats, 2);\nnFrames = size(ROIStats{1, 1}, 2);\n% only process requested runs\nif nRuns > maxRuns;\n    nRuns = maxRuns;\n    ROIStats(:, maxRuns + 1 : end) = [];\nend\n\no('    #eventDetector: variables initialized.', 3, dbgLevel);\no('    #eventDetector: starting event detection ... ', 2, dbgLevel);\neventDetectStartTime = tic;\neventData = cell(size(ROIStats));\nresiduals = cell(size(ROIStats));\nmodels = cell(size(ROIStats));\neventMatAllRuns = zeros(nROIs, nRuns * size(ROIStats{1, 1}, 2));\nstimVectorAllRuns = zeros(1, nRuns * size(ROIStats{1, 1}, 2));\n% outerDffData = ROIStats;\nnRunsWithError = 0;\nnTotEvents = 0;\n% nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\n% for iRun = 1 : nRuns;\nnRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\nfor iRun = 7;\n    try\n%         ROIStats = outerDffData;\n        startIndex = (iRun - 1) * nFrames + 1;\n        endIndex = iRun * nFrames;\n        eventDetect = config.EventDetect;\n        o('      #eventDetector: run %d/%d, %d rois...', iRun, nRuns, nROIs, 4, dbgLevel);\n        if size(stim{iRun}, 2) ~= endIndex - startIndex + 1;\n        o('      #eventDetector: run %d/%d, %d rois: skip for bad size.', iRun, nRuns, nROIs, 1, dbgLevel);\n            continue;\n        end;\n        stimVectorAllRuns(startIndex : endIndex) = stim{iRun};\n        eventDetect.rate = frameRate;\n%         eventCounts = zeros(1, nROIs);\n%         residuals = zeros(1, nROIs);\n        currentEventMat = zeros(nROIs, nFrames);\n        \n        if doEventDetection;\n%             parfor iROI = 1 : nROIs;\n%             for iROI = 1 : nROIs;\n            for iROI = 1 : 3;\n                o('        #eventDetector: run %d/%d - roi %d/%d ...', iRun, nRuns, iROI, nROIs, 5, dbgLevel);\n                caTrace = ROIStats{iROI, iRun};\n%                 caTrace = mpi_BandPassFilterTimeSeries(caTrace, 1 / frameRate, bpfilter.low, bpfilter.high); %#ok<PFBNS>\n                if isempty(caTrace);\n                    warning('eventDetector:caTraceEmpty', 'caTrace is empty!');\n                    continue;\n                elseif isnan(caTrace);\n%                     warning('eventDetector:caTraceNaN', 'caTrace is NaN!');\n                    eventData{iROI, iRun} = nan(1, nFrames);\n                    continue;\n                end;\n                switch lower(eventDetectMethod)\n                    case 'fast_oopsi'\n                        oopsiOut = fast_oopsi(caTrace, V, P);\n                        oopsiOut(oopsiOut < config.EventDetect.oopsi_thr) = 0; %#ok<PFBNS>\n                        currentEventMat(iROI, :) = oopsiOut';\n                        eventData{iROI, iRun} = oopsiOut';\n                    case 'peeling'\n%                         eventOut = doPeeling(eventDetect, caTrace);\n                        [~, ~, peelRes] = Peeling(caTrace, frameRate);\n                        residuals{iROI, iRun} = peelRes.peel;\n                        models{iROI, iRun} = peelRes.model;\n%                         eventCounts(iROI) = sum(peelRes.spiketrain); %%#ok<PFOUS>\n%                         residualVector(iROI) = sum(abs(eventOut.data.peel)) / length(eventOut.data.peel);\n                        currentEventMat(iROI, :) = peelRes.spiketrain;\n                        eventData{iROI, iRun} = peelRes.spiketrain;\n                end;\n                nEventsFound = sum(currentEventMat(iROI, :));\n                nTotEvents = nTotEvents + nEventsFound;\n                o('        #eventDetector: run %d/%d - roi %d/%d done, %d event(s) found.', ...\n                    iRun, nRuns, iROI, nROIs, nEventsFound, 4, dbgLevel);\n            end ;\n        end;\n        eventMatAllRuns(:, startIndex : endIndex) = currentEventMat;\n        o('      #eventDetector: run %d/%d done.', iRun, nRuns, 3, dbgLevel);\n    catch err;\n        nRunsWithError = nRunsWithError + 1; %#ok<NASGU>\n        o('      #eventDetector: problem in run %d/%d.', iRun, nRuns, 2, dbgLevel);\n        rethrow(err);\n    end;\nend;\neventDetectEndTime = toc(eventDetectStartTime);\n% PSTraceRoi = PsPlotAnalysisCellArray(ROIStats, stim, psConfig);\no('    #eventDetector: event detection done for %d runs (%d error(s), %d total events, %.2f sec).', ...\n    nRuns, nRunsWithError, nTotEvents, eventDetectEndTime, 2, dbgLevel);\nif ~isempty(eventMatAllRuns) && nTotEvents > 0;\n    o('    #eventDetector: extracting peri-stimulus events...', 3, dbgLevel);\n    PSEventRoi = PsPlotAnalysis(eventMatAllRuns, stimVectorAllRuns, psConfig);\n    config.PsPlotEventRoi = PSEventRoi;\n    o('    #eventDetector: extracting peri-stimulus events done.', 2, dbgLevel);\nelse\n    o('    #eventDetector: no events found (%d).', nTotEvents, 1, dbgLevel);\nend\n\no('    #eventDetector: moving to plotting section ...', 3, dbgLevel);\n%% Calcium DFF / DRR Plot - with events\nif doCaTracesPlot; % only plot if required\n    o('    #eventDetector: plotting the calcium DFF (or DRR) Plot with events...', 2, dbgLevel); %#ok<*UNRCH>\n    % go through each run\n%     nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\n%     for iRun = 1 : nRuns;\n    nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\n    for iRun = 7;\n        o('      #eventDetector: plotting run %d/%d ...', iRun, nRuns, 4, dbgLevel);\n        fig = plotROICaTracesWithEvent(iRun, saveName, ... % run number and figure name\n            cell2mat(ROIStats(:, iRun)), ...    % calcium traces as a nROI-by-nFrames matrix\n            cell2mat(eventData(:, iRun)), ...   % detected events as a nROI-by-nFrames matrix\n            stim{iRun}, ...                     % stimulus as a nFrames long vector\n            ROISet, ...                         % name and coordinates of the rois\n            frameRate, ...                      % frame rate\n            bpfilter, ...                       % band-pass filter settings\n            eventDetectMethod, ...              % used detection method\n            doPlotEvents);                      % tells whether to plot the events or not\n        \n        fig = plotROICaTracesWithEvent(iRun, saveName, ... % run number and figure name\n            cell2mat(residuals(:, iRun)), ...    % calcium traces as a nROI-by-nFrames matrix\n            cell2mat(eventData(:, iRun)), ...   % detected events as a nROI-by-nFrames matrix\n            stim{iRun}, ...                     % stimulus as a nFrames long vector\n            ROISet, ...                         % name and coordinates of the rois\n            frameRate, ...                      % frame rate\n            [], ...                             % band-pass filter settings\n            eventDetectMethod, ...              % used detection method\n            doPlotEvents);                      % tells whether to plot the events or not\n        \n        fig = plotROICaTracesWithEvent(iRun, saveName, ... % run number and figure name\n            cell2mat(models(:, iRun)), ...    % calcium traces as a nROI-by-nFrames matrix\n            cell2mat(eventData(:, iRun)), ...   % detected events as a nROI-by-nFrames matrix\n            stim{iRun}, ...                     % stimulus as a nFrames long vector\n            ROISet, ...                         % name and coordinates of the rois\n            frameRate, ...                      % frame rate\n            [], ...                             % band-pass filter settings\n            eventDetectMethod, ...              % used detection method\n            doPlotEvents);                      % tells whether to plot the events or not\n        \n        o('      #eventDetector: plotting run %d/%d done, saving...', iRun, nRuns, 4, dbgLevel);\n%         set(fig, 'WindowStyle', 'docked');\n        if doCaTracesPlot > 1;\n            if doPlotEvents;\n                saveName = sprintf('%s_ROICaTracesWithEvents_run%02d_%s', saveName, iRun, ...\n                    eventDetectMethod);\n            else\n                saveName = sprintf('%s_ROICaTracesWithoutEvents_run%02d_%s', saveName, iRun, ...\n                    eventDetectMethod);\n            end;\n            saveas(fig, saveName);\n            saveas(fig, [saveName '.png']);\n            close(fig);\n        end;\n        o('      #eventDetector: plotting & saving run %d/%d done.', iRun, nRuns, 3, dbgLevel);\n    end\n    o('    #eventDetector: plotting %d run(s) done.', nRuns, 2, dbgLevel);\nend;\n%% Event count plot\nif doEventCountPlot;\n    o('    #eventDetector: plotting the event counts for ROI image...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotCountROIMap( ...\n        eventCounts, ...                                % event counts for each ROI\n        256, 256, ... % dimensions of the frame\n        ROISet(1 : end - 1, 2));           % the positions of the ROI\n    %% TODO HARD CODED IMAGE DIMS\n    if doEventCountPlot > 1;\n        set(fig, 'WindowStyle', 'docked');\n        saveas(fig, sprintf('%s_ROIEventCount_%s', saveName, eventDetectMethod));\n        saveas(fig, sprintf('%s_ROIEventCount_%s.png', saveName, eventDetectMethod));\n        close(fig);\n    end;\n    o('    #eventDetector: plotting the event counts for ROI image done.', 3, dbgLevel); %#ok<*UNRCH>\nend;\n%% Population stimulus event raster plot\nif doStimEventRasterPlot;\n    o('    #eventDetector: plotting the peri-stimulus event raster plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotStimEventRaster( ...\n        'PopRasterSinglePlot',  ...             % title of the plot\n        PSEventRoi, ...                     % event counts around the stimulus\n        stim{1}, ...                   % stimuli for extracting their 'name'\n        psConfig.base, ...       % number of frames looked before the stimulus\n        psConfig.evoked, ...     % number of frames looked after the stimulus\n        frameRate);                 % frame rate\n%     set(fig, 'WindowStyle', 'docked');\n    if doStimEventRasterPlot > 1;\n        saveas(fig, sprintf('%s_EventStimRaster_%s', saveName, eventDetectMethod));\n        saveas(fig, sprintf('%s_EventStimRaster_%s.png', saveName, eventDetectMethod));\n        close(fig);\n    end;\n    o('    #eventDetector: plotting the peri-stimulus event raster plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n% %% Population stimulus plot\n% if doPsStimPlot;\n%     o('    #eventDetector: plotting the peri-stimulus plot ...', 2, dbgLevel); %#ok<*UNRCH>\n%     fig = plotPSStimPlot( ...\n%         'PopPeriStimPlot',  ...                 % title of the plot\n%         PSTraceRoi, ...                         % all traces around the stimulus\n%         stim{1}, ...                   % stimuli for extracting their 'name'\n%         psConfig.base, ...       % number of frames looked before the stimulus\n%         psConfig.evoked, ...     % number of frames looked after the stimulus\n%         frameRate);                 % frame rate\n%     if doPsStimPlot > 1;\n% %         set(fig, 'WindowStyle', 'docked');\n%         saveas(fig, sprintf('%s_PSStimAverage_%s', roiStats.saveName, eventDetectMethod));\n%         saveas(fig, sprintf('%s_PSStimAverage_%s.png', roiStats.saveName, eventDetectMethod));\n%         close(fig);\n%     end;\n%     o('    #eventDetector: plotting the peri-stimulus plot done.', 2, dbgLevel); %#ok<*UNRCH>\n% end;\n%% Population raster plot\nif doRasterPlot;\n    o('    #eventDetector: plotting the population raster plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    cellIDaxes = ROISet(:, 1);\n    switch lower(eventDetectMethod)\n        case {'peeling', 'fast_oopsi'};\n            titleStr = 'PopRaster';\n            [fig, ~] = PsPlot2Raster(PSEventRoi, frameRate, ...\n                psConfig.base + 1, cellIDaxes(1 : length(cellIDaxes) - 1), 1, 0);\n            set(fig, 'Name', titleStr, 'NumberTitle', 'off');\n%             set(fig, 'WindowStyle', 'docked');\n        if doRasterPlot > 1;\n            saveas(fig,sprintf('%s_EventRasterByRoi_%s', saveName, eventDetectMethod));\n            saveas(fig,sprintf('%s_EventRasterByRoi_%s.png', saveName, eventDetectMethod));\n            close(fig);\n        end;\n    end\n    o('    #eventDetector: plotting the population raster plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n%% end\nvarargout{1} = config;\nend\n\nfunction [config, V, P] = getDefaultParameters(eventDetectMethod, frameRate)\n    amp = 10;\n    tau = 2;\n    onsettau = 0.01;\n    switch lower(eventDetectMethod);\n        case 'fast_oopsi';\n            config.EventDetect.amp = amp;\n            config.EventDetect.tau = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.doPlot = 0; % should be switched off\n            config.EventDetect.lam = 0.2; % firing rate(ish)\n            config.EventDetect.base_frames = 10;\n            config.EventDetect.oopsi_thr = 0.3;\n            config.EventDetect.integral_thr = 5;\n            config.EventDetect.filter = [7 2];\n            config.EventDetect.minGof = 0.5;\n            P.lam = config.EventDetect.lam;\n            % P.gam = (1-(1/freq_ca)) / ca_tau;\n            V.dt = 1/frameRate;\n            V.est_gam = 1; % estimate decay time parameter (does not work)\n            V.est_sig = 1; % estimate baseline noise SD\n            V.est_lam = 1; % estimate firing rate\n            V.est_a = 0; % estimate spatial filter\n            V.est_b = 0; % estimate background fluo.\n            V.fast_thr = 1;\n            V.fast_iter_max = 3;\n        case 'peeling';\n            V = [];\n            P = [];\n            config.EventDetect.optimizeSpikeTimes = 0;\n            config.EventDetect.schmittHi = [1.75 0 3];\n            config.EventDetect.schmittLo = [-1 -3 0];\n            config.EventDetect.schmittMinDur = [0.3 0.05 3];\n            config.EventDetect.A1 = amp;\n            config.EventDetect.tau1 = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.optimMethod = 'none';\n            config.EventDetect.minPercentChange = 0.1;\n            config.EventDetect.maxIter = 20;\n            config.EventDetect.plotFinal = 0;\n        case 'none';\n            config = [];\n            V = [];\n            P = [];\n            warning('Nothing to do here! Exit ...')\n            return;\n    end;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "eventDetector_old.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/eventDetection/eventDetector_old.m", "size": 12802, "source_encoding": "utf_8", "md5": "ad7f17e5b4904c061333c7f354dac6c0", "text": "function varargout = eventDetector(roiStats, eventDetectMethod)\n% event detection function - wrapper to different event detection algorithms\n% input: structure created by GetRoiStats (*_RoiStats)\ndbgLevel = 2;\n% required folders\nfolderList = {'Projects/EventDetect','Projects/TwoPhotonAnalyzer'};\naddFolders2Path(folderList,1);\nmaxRuns = Inf; % for testing\n% maxRuns = 2; % for testing\ndoCaTracesPlot = 1;\ndoPlotEvents = doCaTracesPlot && 0; %%#ok<NASGU>\ndoRasterPlot = 0;\ndoStimEventRasterPlot = 0;\ndoPsStimPlot = 0;\ndoEventCountPlot = 0;\ndoEventDetection = 0;\no('    #eventDetector: method: \"%s\", maxRuns = %d ...', eventDetectMethod, maxRuns, 1, dbgLevel);\n%% Event detection parameters\n[config, V, P] = getDefaultParameters(eventDetectMethod, roiStats.frameRate{1}); %%#ok<NASGU,ASGLU>\no('    #eventDetector: event detection parameters configured.', 3, dbgLevel);\n%% Event detection\n% event detection on roiStats\nallDFFData = roiStats.dataRoi;\n% last row is neuropil, remove it\nallDFFData(end, :) = [];\n% init sizes of data set\nnROIs = size(allDFFData, 1);\nnRuns = size(allDFFData, 2);\nnFrames = size(allDFFData{1, 1}, 2);\n% only process requested runs\nif nRuns > maxRuns;\n    nRuns = maxRuns;\n    allDFFData(:, maxRuns + 1 : end) = [];\nend\n\no('    #eventDetector: variables initialized.', 3, dbgLevel);\no('    #eventDetector: starting event detection ... ', 2, dbgLevel);\neventDetectStartTime = tic;\neventData = cell(size(allDFFData));\neventMatAllRuns = zeros(nROIs, nRuns * size(allDFFData{1, 1}, 2));\nStimVectorAllRuns = zeros(1, nRuns * size(allDFFData{1, 1}, 2));\nouterDffData = allDFFData;\nnRunsWithError = 0;\nnTotEvents = 0;\n% nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\nfor iRun = 1 : nRuns;\n    try\n        allDFFData = outerDffData;\n        startIndex = (iRun - 1) * nFrames + 1;\n        endIndex = iRun * nFrames;\n        eventDetect = config.EventDetect;\n        o('      #eventDetector: run %d/%d, %d rois...', iRun, nRuns, nROIs, 4, dbgLevel);\n        if size(roiStats.stim{iRun}, 2) ~= startIndex - endIndex + 1;\n        o('      #eventDetector: run %d/%d, %d rois: skip for bad size.', iRun, nRuns, nROIs, 1, dbgLevel);\n            continue;\n        end;\n        StimVectorAllRuns(startIndex : endIndex) = roiStats.stim{iRun};\n        eventDetect.rate = roiStats.frameRate{iRun};\n        eventCounts = zeros(1, nROIs);\n%         residualVector = zeros(1,nROIs);\n        currentEventMat = zeros(nROIs, nFrames);\n        \n        if doEventDetection;\n            parfor iRoi = 1 : nROIs;\n                o('        #eventDetector: run %d/%d - roi %d/%d ...', iRun, nRuns, iRoi, nROIs, 5, dbgLevel);\n                dff = allDFFData{iRoi, iRun};\n                if isempty(dff);\n                    warning('eventDetector:dffEmpty', 'dff is empty!');\n                    continue;\n                end;\n                switch lower(eventDetectMethod)\n                    case 'fast_oopsi'\n                        oopsiOut = fast_oopsi(dff, V, P);\n                        oopsiOut(oopsiOut < config.EventDetect.oopsi_thr) = 0; %#ok<PFBNS>\n                        currentEventMat(iRoi, :) = oopsiOut';\n                        eventData{iRoi, iRun} = oopsiOut';\n                    case 'peeling'\n                        eventOut = doPeeling(eventDetect,dff);\n    %                     residual{iRoi,iRun} = eventOut.data.peel;\n    %                     model{iRoi,iRun} = eventOut.data.model;\n                        eventCounts(iRoi) = sum(eventOut.data.spiketrain); %%#ok<PFOUS>\n    %                     residualVector(iRoi) = sum(abs(eventOut.data.peel)) / length(eventOut.data.peel);\n                        currentEventMat(iRoi, :) = eventOut.data.spiketrain;\n                        eventData{iRoi, iRun} = eventOut.data.spiketrain;\n                end;\n                nEventsFound = sum(currentEventMat(iRoi, :));\n                nTotEvents = nTotEvents + nEventsFound;\n                o('        #eventDetector: run %d/%d - roi %d/%d done, %d event(s) found.', ...\n                    iRun, nRuns, iRoi, nROIs, nEventsFound, 4, dbgLevel);\n            end ;\n        end;\n        eventMatAllRuns(:, startIndex : endIndex) = currentEventMat;\n        o('      #eventDetector: run %d/%d done.', iRun, nRuns, 3, dbgLevel);\n    catch err;\n        nRunsWithError = nRunsWithError + 1; %#ok<NASGU>\n        o('      #eventDetector: problem in run %d/%d.', iRun, nRuns, 2, dbgLevel);\n        rethrow(err);\n    end;\nend;\neventDetectEndTime = toc(eventDetectStartTime);\nPSTraceRoi = PsPlotAnalysis(cell2mat(allDFFData(:, :)), StimVectorAllRuns, roiStats.psConfig);\no('    #eventDetector: event detection done for %d runs (%d error(s), %d total events, %.2f sec).', ...\n    nRuns, nRunsWithError, nTotEvents, eventDetectEndTime, 2, dbgLevel);\nif ~isempty(eventMatAllRuns) && nTotEvents > 0;\n    o('    #eventDetector: extracting peri-stimulus events...', 3, dbgLevel);\n    PSEventRoi = PsPlotAnalysis(eventMatAllRuns, StimVectorAllRuns, roiStats.psConfig);\n    config.PsPlotEventRoi = PSEventRoi;\n    o('    #eventDetector: extracting peri-stimulus events done.', 2, dbgLevel);\nelse\n    o('    #eventDetector: no events found (%d).', nTotEvents, 1, dbgLevel);\nend\n\no('    #eventDetector: moving to plotting section ...', 3, dbgLevel);\n%% Calcium DFF / DRR Plot - with events\nif doCaTracesPlot; % only plot if required\n    o('    #eventDetector: plotting the calcium DFF (or DRR) Plot with events...', 2, dbgLevel); %#ok<*UNRCH>\n    % go through each run\n%     nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\n    for iRun = 1 : nRuns;\n        o('      #eventDetector: plotting run %d/%d ...', iRun, nRuns, 4, dbgLevel);\n        fig = plotROICaTraces(iRun, roiStats.saveName, ... % run number and figure name\n            cell2mat(allDFFData(:, iRun)), ...  % calcium traces as a nROI-by-nFrames matrix\n            cell2mat(eventData(:, iRun)), ...   % detected events as a nROI-by-nFrames matrix\n            roiStats.stim{iRun}, ...            % stimulus as a nFrames long vector\n            roiStats.ROISet, ...                % name and coordinates of the rois\n            roiStats.frameRate{1}, ...          % frame rate\n            eventDetectMethod, ...              % used detection method\n            doPlotEvents);                      % tells whether to plot the events or not\n        o('      #eventDetector: plotting run %d/%d done, saving...', iRun, nRuns, 4, dbgLevel);\n        set(fig, 'WindowStyle', 'docked');\n        if doPlotEvents;\n            saveName = sprintf('%s_ROICaTracesWithEvents_run%02d_%s', roiStats.saveName, iRun, ...\n                eventDetectMethod);\n        else\n            saveName = sprintf('%s_ROICaTracesWithoutEvents_run%02d_%s', roiStats.saveName, iRun, ...\n                eventDetectMethod);\n        end;\n        saveas(fig, saveName);\n        saveas(fig, [saveName '.png']);\n        close(fig);\n        o('      #eventDetector: plotting & saving run %d/%d done.', iRun, nRuns, 3, dbgLevel);\n    end\n    o('    #eventDetector: plotting %d run(s) done.', nRuns, 2, dbgLevel);\nend;\n%% Event count plot\nif doEventCountPlot;\n    o('    #eventDetector: plotting the event counts for ROI image...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotCountROIMap( ...\n        eventCounts, ...                                % event counts for each ROI\n        roiStats.img_dims(1), roiStats.img_dims(2), ... % dimensions of the frame\n        roiStats.ROISet(1 : end - 1, 2));           % the positions of the ROI\n    \n    set(fig, 'WindowStyle', 'docked');\n    saveas(fig, sprintf('%s_ROIEventCount_%s', roiStats.saveName, eventDetectMethod));\n    saveas(fig, sprintf('%s_ROIEventCount_%s.png', roiStats.saveName, eventDetectMethod));\n    close(fig);\n    o('    #eventDetector: plotting the event counts for ROI image done.', 3, dbgLevel); %#ok<*UNRCH>\nend;\n%% Population stimulus event raster plot\nif doStimEventRasterPlot;\n    o('    #eventDetector: plotting the peri-stimulus event raster plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotStimEventRaster( ...\n        'PopRasterSinglePlot',  ...             % title of the plot\n        PSEventRoi, ...                     % event counts around the stimulus\n        roiStats.stim{1}, ...                   % stimuli for extracting their 'name'\n        roiStats.psConfig.baseFrames, ...       % number of frames looked before the stimulus\n        roiStats.psConfig.evokedFrames, ...     % number of frames looked after the stimulus\n        roiStats.frameRate{1});                 % frame rate\n    set(fig, 'WindowStyle', 'docked');\n    saveas(fig, sprintf('%s_EventStimRaster_%s', roiStats.saveName, eventDetectMethod));\n    saveas(fig, sprintf('%s_EventStimRaster_%s.png', roiStats.saveName, eventDetectMethod));\n    close(fig);\n    o('    #eventDetector: plotting the peri-stimulus event raster plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n%% Population stimulus plot\nif doPsStimPlot;\n    o('    #eventDetector: plotting the peri-stimulus plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotPSStimPlot( ...\n        'PopPeriStimPlot',  ...                 % title of the plot\n        PSTraceRoi, ...                         % all traces around the stimulus\n        roiStats.stim{1}, ...                   % stimuli for extracting their 'name'\n        roiStats.psConfig.baseFrames, ...       % number of frames looked before the stimulus\n        roiStats.psConfig.evokedFrames, ...     % number of frames looked after the stimulus\n        roiStats.frameRate{1});                 % frame rate\n    set(fig, 'WindowStyle', 'docked');\n    saveas(fig, sprintf('%s_PSStimAverage_%s', roiStats.saveName, eventDetectMethod));\n    saveas(fig, sprintf('%s_PSStimAverage_%s.png', roiStats.saveName, eventDetectMethod));\n    close(fig);\n    o('    #eventDetector: plotting the peri-stimulus plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n%% Population raster plot\nif doRasterPlot;\n    o('    #eventDetector: plotting the population raster plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    roiSet = roiStats.ROISet;\n    cellIDaxes = roiSet(:, 1);\n    switch lower(eventDetectMethod)\n        case {'peeling', 'fast_oopsi'};\n            titleStr = 'PopRaster';\n            [fig, ~] = PsPlot2Raster(PSEventRoi, roiStats.frameRate{1}, ...\n                roiStats.psConfig.baseFrames + 1, cellIDaxes(1 : length(cellIDaxes) - 1), 1, 0);\n            set(fig, 'Name', titleStr, 'NumberTitle', 'off');\n            set(fig, 'WindowStyle', 'docked');\n            saveas(fig,sprintf('%s_EventRasterByRoi_%s', roiStats.saveName, eventDetectMethod));\n            saveas(fig,sprintf('%s_EventRasterByRoi_%s.png', roiStats.saveName, eventDetectMethod));\n            close(fig);\n    end\n    o('    #eventDetector: plotting the population raster plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n%% end\nvarargout{1} = config;\nend\n\nfunction [config, V, P] = getDefaultParameters(eventDetectMethod, frameRate)\n    amp = 10;\n    tau = 2;\n    onsettau = 0.01;\n    switch lower(eventDetectMethod);\n        case 'fast_oopsi';\n            config.EventDetect.amp = amp;\n            config.EventDetect.tau = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.doPlot = 0; % should be switched off\n            config.EventDetect.lam = 0.2; % firing rate(ish)\n            config.EventDetect.base_frames = 10;\n            config.EventDetect.oopsi_thr = 0.3;\n            config.EventDetect.integral_thr = 5;\n            config.EventDetect.filter = [7 2];\n            config.EventDetect.minGof = 0.5;\n            P.lam = config.EventDetect.lam;\n            % P.gam = (1-(1/freq_ca)) / ca_tau;\n            V.dt = 1/frameRate;\n            V.est_gam = 1; % estimate decay time parameter (does not work)\n            V.est_sig = 1; % estimate baseline noise SD\n            V.est_lam = 1; % estimate firing rate\n            V.est_a = 0; % estimate spatial filter\n            V.est_b = 0; % estimate background fluo.\n            V.fast_thr = 1;\n            V.fast_iter_max = 3;\n        case 'peeling';\n            V = [];\n            P = [];\n            config.EventDetect.optimizeSpikeTimes = 0;\n            config.EventDetect.schmittHi = [1.75 0 3];\n            config.EventDetect.schmittLo = [-1 -3 0];\n            config.EventDetect.schmittMinDur = [0.3 0.05 3];\n            config.EventDetect.A1 = amp;\n            config.EventDetect.tau1 = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.optimMethod = 'none';\n            config.EventDetect.minPercentChange = 0.1;\n            config.EventDetect.maxIter = 20;\n            config.EventDetect.plotFinal = 0;\n        case 'none';\n            config = [];\n            V = [];\n            P = [];\n            warning('Nothing to do here! Exit ...')\n            return;\n    end;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "PeelingOptimizeSpikeTimesSaturation.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/eventDetection/newPeeling/PeelingOptimizeSpikeTimesSaturation.m", "size": 4465, "source_encoding": "utf_8", "md5": "82892eced9f5fb70e0d8ad3f544df164", "text": "function [spkTout,output] = PeelingOptimizeSpikeTimesSaturation(dff,spkTin,lowerT,upperT,...\n    ca_amp,ca_gamma,ca_onsettau,ca_rest, ca_kappas, kd, conc, dffmax, frameRate, dur, optimMethod,maxIter,doPlot)\n% optimization of spike times found by Peeling algorithm\n% minimize the sum of the residual squared\n% while several optimization algorithms are implemented (see below), we have only used pattern\n% search. Other algorithms are only provided for convenience and are not tested sufficiently.\n%\n% Henry Luetcke (hluetck@gmail.com)\n% Brain Research Institut\n% University of Zurich\n% Switzerland\n\nspkTout = spkTin;\nt = (1:numel(dff))./frameRate;\n\nca = spkTimes2FreeCalcium(spkTin,ca_amp,ca_gamma,ca_onsettau,ca_rest, ca_kappas,...\n                                    kd, conc,frameRate,dur);\nmodeltmp = Calcium2Fluor(ca,ca_rest,kd,dffmax);\nmodel = modeltmp(1:length(dff));\n\nif doPlot\n    figure('Name','Before Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\nresidual = dff - model;\nresInit = sum(residual.^2);\n\n% start optimization\nx0 = spkTin;\nlbound = spkTin - lowerT;\nlbound(lbound<0) = 0;\nubound = spkTin + upperT;\nubound(ubound>max(t)) = max(t);\n\nlbound = zeros(size(spkTin));\nubound = repmat(max(t),size(spkTin));\n\nopt_args.dff = dff;\nopt_args.ca_rest = ca_rest;\nopt_args.ca_amp = ca_amp;\nopt_args.ca_gamma = ca_gamma;\nopt_args.ca_onsettau = ca_onsettau;\nopt_args.ca_kappas = ca_kappas;\nopt_args.kd = kd;\nopt_args.conc = conc;\nopt_args.dffmax = dffmax;\nopt_args.frameRate = frameRate;\nopt_args.dur = dur;\n\noptimClock = tic;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        options = saoptimset;\n    case 'pattern search'\n        options = psoptimset;\n    case 'genetic'\n        options = gaoptimset;\n    otherwise\n        error('Optimization method %s not supported.',optimMethod)\nend\n\n% options for optimization algorithms\n% not all options are used for all algorithms\noptions.Display = 'off';\noptions.MaxIter = maxIter;\noptions.MaxIter = Inf;\noptions.UseParallel = 'always';\noptions.ObjectiveLimit = 0;\noptions.TimeLimit = 10; % in s / default is Inf\n\n% experimental\noptions.MeshAccelerator = 'on'; % off by default\noptions.TolFun = 1e-9; % default is 1e-6\noptions.TolMesh = 1e-9; % default is 1e-6\noptions.TolX = 1e-9; % default is 1e-6\n% options.MaxFunEvals = numel(spkTin)*100; % default is 2000*numberOfVariables\n% options.MaxFunEvals = 20000;\n\noptions.Display = 'none';\n% options.Display = 'final';\n\n% options.PlotFcns = {@psplotbestf @psplotbestx};\n% options.OutputFcns = @psoutputfcn_peel;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        [x, fval , exitFlag, output] = simulannealbnd(...\n            @(x) objectiveFunc(x,opt_args),x0,lbound,ubound,options);\n    case 'pattern search'\n        [x, fval , exitFlag, output] = patternsearch(...\n            @(x) objectiveFunc(x,opt_args),x0,[],[],[],[],lbound,...\n            ubound,[],options);\n    case 'genetic'\n        [x, fval , exitFlag, output] = ga(...\n            @(x) objectiveFunc(x,opt_args),numel(x0),[],[],[],[],lbound,...\n            ubound,[],options);\nend\n\nif fval < resInit\n    spkTout = x;\nelse\n    disp('Optimization did not improve residual. Keeping input spike times.')\nend\n\nif doPlot\n    fprintf('Optimization time (%s): %1.2f s\\n',optimMethod,toc(optimClock))\n    fprintf('Final squared residual: %1.2f (Change: %1.2f)\\n',fval,resInit-fval);\n    spkVector = zeros(1,numel(t));\n    for i = 1:numel(spkTout)\n        [~,idx] = min(abs(spkTout(i)-t));\n        spkVector(idx) = spkVector(idx)+1;\n    end\n    model = conv(spkVector,modelTransient);\n    model = model(1:length(t));\n    figure('Name','After Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\n\nfunction residual = objectiveFunc(spkTin,opt_args)\n\ndff = opt_args.dff;\nca_rest = opt_args.ca_rest;\nca_amp = opt_args.ca_amp;\nca_gamma = opt_args.ca_gamma;\nca_onsettau = opt_args.ca_onsettau;\nca_kappas = opt_args.ca_kappas;\nkd = opt_args.kd;\nconc = opt_args.conc;\ndffmax = opt_args.dffmax;\nframeRate = opt_args.frameRate;\ndur = opt_args.dur;\n\nca = spkTimes2FreeCalcium(sort(spkTin),ca_amp,ca_gamma,ca_onsettau,ca_rest, ca_kappas,...\n                                    kd, conc,frameRate,dur);\nmodeltmp = Calcium2Fluor(ca,ca_rest,kd,dffmax);\nmodel = modeltmp(1:length(dff));\n\nresidual = dff-model;\nresidual = sum(residual.^2);\n\n\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "PeelingOptimizeSpikeTimes.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/eventDetection/newPeeling/PeelingOptimizeSpikeTimes.m", "size": 4159, "source_encoding": "utf_8", "md5": "61e419a2e0808657c62b39f38bc0fb60", "text": "function [spkTout,output] = PeelingOptimizeSpikeTimes(dff,spkTin,lowerT,upperT,...\n    rate,tauOn,A1,tau1,optimMethod,maxIter,doPlot)\n% optimization of spike times found by Peeling algorithm\n% minimize the sum of the residual squared\n% while several optimization algorithms are implemented (see below), we have only used pattern\n% search. Other algorithms are only provided for convenience and are not tested sufficiently.\n%\n% Henry Luetcke (hluetck@gmail.com)\n% Brain Research Institut\n% University of Zurich\n% Switzerland\n\nt = (1:numel(dff))./rate;\n\nmodelTransient = modelCalciumTransient(t,t(1),tauOn,A1,tau1);\nmodelTransient = modelTransient';\n\nspkTout = spkTin;\n\nspkVector = zeros(1,numel(t));\nfor i = 1:numel(spkTin)\n    [~,idx] = min(abs(spkTin(i)-t));\n    spkVector(idx) = spkVector(idx)+1;\nend\nmodel = conv(spkVector,modelTransient);\nmodel = model(1:length(t));\n\nif doPlot\n    figure('Name','Before Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\nresidual = dff - model;\n\nresInit = sum(residual.^2);\n\n% start optimization\nx0 = spkTin;\nlbound = spkTin - lowerT;\nlbound(lbound<0) = 0;\nubound = spkTin + upperT;\nubound(ubound>max(t)) = max(t);\n\nlbound = zeros(size(spkTin));\nubound = repmat(max(t),size(spkTin));\n\nopt_args.dff = dff;\nopt_args.rate = rate;\nopt_args.tauOn = tauOn;\nopt_args.A1 = A1;\nopt_args.tau1 = tau1;\noptimClock = tic;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        options = saoptimset;\n    case 'pattern search'\n        options = psoptimset;\n    case 'genetic'\n        options = gaoptimset;\n    otherwise\n        error('Optimization method %s not supported.',optimMethod)\nend\n\n% options for optimization algorithms\n% not all options are used for all algorithms\noptions.Display = 'off';\noptions.MaxIter = maxIter;\noptions.MaxIter = Inf;\noptions.UseParallel = 'always';\noptions.ObjectiveLimit = 0;\n% options.TimeLimit = 10; % in s / default is Inf\n\n% experimental\noptions.MeshAccelerator = 'on'; % off by default\noptions.TolFun = 1e-9; % default is 1e-6\noptions.TolMesh = 1e-9; % default is 1e-6\noptions.TolX = 1e-9; % default is 1e-6\n% options.MaxFunEvals = numel(spkTin)*100; % default is 2000*numberOfVariables\n% options.MaxFunEvals = 20000;\n\noptions.Display = 'none';\n% options.Display = 'final';\n\n% options.PlotFcns = {@psplotbestf @psplotbestx};\n% options.OutputFcns = @psoutputfcn_peel;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        [x, fval , exitFlag, output] = simulannealbnd(...\n            @(x) objectiveFunc(x,opt_args),x0,lbound,ubound,options);\n    case 'pattern search'\n        [x, fval , exitFlag, output] = patternsearch(...\n            @(x) objectiveFunc(x,opt_args),x0,[],[],[],[],lbound,...\n            ubound,[],options);\n    case 'genetic'\n        [x, fval , exitFlag, output] = ga(...\n            @(x) objectiveFunc(x,opt_args),numel(x0),[],[],[],[],lbound,...\n            ubound,[],options);\nend\n\nif fval < resInit\n    spkTout = x;\nelse\n    disp('Optimization did not improve residual. Keeping input spike times.')\nend\n\nif doPlot\n    fprintf('Optimization time (%s): %1.2f s\\n',optimMethod,toc(optimClock))\n    fprintf('Final squared residual: %1.2f (Change: %1.2f)\\n',fval,resInit-fval);\n    spkVector = zeros(1,numel(t));\n    for i = 1:numel(spkTout)\n        [~,idx] = min(abs(spkTout(i)-t));\n        spkVector(idx) = spkVector(idx)+1;\n    end\n    model = conv(spkVector,modelTransient);\n    model = model(1:length(t));\n    figure('Name','After Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\n\nfunction residual = objectiveFunc(spkTin,opt_args)\n\ndff = opt_args.dff;\nrate = opt_args.rate;\ntauOn = opt_args.tauOn;\nA1 = opt_args.A1;\ntau1 = opt_args.tau1;\nt = (1:numel(dff))./rate;\nmodelTransient = spkTimes2Calcium(0,tauOn,A1,tau1,0,0,rate,max(t));\nspkVector = zeros(1,numel(t));\nfor i = 1:numel(spkTin)\n    [~,idx] = min(abs(spkTin(i)-t));\n    spkVector(idx) = spkVector(idx)+1;\nend\nmodel = conv(spkVector,modelTransient);\nmodel = model(1:length(t));\nresidual = dff-model;\nresidual = sum(residual.^2);\n\n\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "Peeling.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/eventDetection/newPeeling/Peeling.m", "size": 9350, "source_encoding": "utf_8", "md5": "feca5c14c59423537078bfaf885a57e7", "text": "function [ca_p, peel_p, data] = Peeling(dff, rate, varargin)\n% this is the main routine of the peeling algorithm\n%\n% Peeling algorithm was developed by Fritjof Helmchen\n% Brain Research Institute\n% University of Zurich\n% Switzerland\n%\n% Matlab implementation and spike timing optimization by Henry Luetcke & Fritjof Helmchen\n% Brain Research Institute\n% University of Zurich\n% Switzerland\n%\n% Please cite:\n% Grewe BF, Langer D, Kasper H, Kampa BM, Helmchen F. High-speed in vivo calcium imaging\n% reveals neuronal network activity with near-millisecond precision.\n% Nat Methods. 2010 May;7(5):399-405. \n\nmaxRate_peel = Inf;\n\nif rate > maxRate_peel\n    peel_rate = maxRate_peel;\n    fit_rate = rate;\n    x = 1/rate:1/rate:numel(dff)/rate;\n    xi = 1/peel_rate:1/peel_rate:max(x);\n    peel_dff = interp1(x,dff,xi);\nelse\n    peel_rate = rate;\n    fit_rate = rate;\n    peel_dff = dff;\nend\n\n[ca_p,exp_p,peel_p, data] = InitPeeling(peel_dff, peel_rate);\n\nif nargin > 2\n    for n = 1:numel(varargin)\n        S = varargin{n};\n        if n == 1\n            ca_p = overrideFieldValues(ca_p,S);\n        elseif n == 2\n            exp_p = overrideFieldValues(exp_p,S);\n        elseif n == 3\n            peel_p = overrideFieldValues(peel_p,S);\n        end\n    end\nend\n\ndata.model = 0;\ndata.freecamodel = ca_p.ca_rest;\ndata.spikes = zeros(1,1000);\ndata.numspikes = 0;\n\ndata.peel = data.dff;\n\nwsiz = round(peel_p.slidwinsiz*exp_p.acqrate);\ncheckwsiz = round(peel_p.negintwin*exp_p.acqrate);\n\npeel_p.smttmindurFrames = ceil(peel_p.smttmindur*exp_p.acqrate);\npeel_p.smttlowMinEvents = 1;\n\nnexttim = 1/exp_p.acqrate;\n\n[ca_p, peel_p, data] = FindNextEvent(ca_p, exp_p, peel_p, data, nexttim);\nif (peel_p.evtfound == 1)\n    data.numspikes = data.numspikes + 1;\n    data.spikes(data.numspikes) = peel_p.nextevt;\n    [ca_p, exp_p, data] = SingleFluorTransient(ca_p, exp_p, data, peel_p.spk_recmode, peel_p.nextevt);\n    data.model = data.model + data.singleTransient;\nend\n\nmaxiter = 999999;\niter = 0;\nnexttimMem = Inf;\nnexttimCounter = 0;\ntimeStepForward = 2./exp_p.acqrate;\nwhile (peel_p.evtfound == 1)\n    % check integral after subtracting Ca transient\n    if (peel_p.spk_recmode == 'linDFF')\n    elseif (peel_p.spk_recmode == 'satDFF')\n       ca_p.onsetposition = peel_p.nextevt;\n       ca_p = IntegralofCaTransient(ca_p, peel_p, exp_p, data);\n    end\n    \n    dummy = data.peel - data.singleTransient;\n    [~,startIdx] = min(abs(data.tim-data.spikes(data.numspikes)));\n    [~,stopIdx] = min(abs(data.tim-(data.spikes(data.numspikes)+...\n        peel_p.intcheckwin)));\n    if startIdx < stopIdx\n        currentTim = data.tim(startIdx:stopIdx);\n        currentPeel = dummy(startIdx:stopIdx);\n        currentIntegral = trapz(currentTim,currentPeel);\n    else\n        % if this is true, startIdx is the last data point and we should\n        % not accept it as a spike\n        currentIntegral = ca_p.negintegral*peel_p.negintacc;\n    end\n    if currentIntegral > (ca_p.negintegral*peel_p.negintacc)\n        data.peel = data.peel - data.singleTransient;\n        nexttim = data.spikes(data.numspikes) - peel_p.stepback;\n        if (nexttim < 0)\n            nexttim = 1/exp_p.acqrate;\n        end\n    else\n        data.spikes(data.numspikes) = [];\n        data.numspikes = data.numspikes-1;\n        data.model = data.model - data.singleTransient;\n        nexttim = peel_p.nextevt + timeStepForward;\n    end\n    \n    peel_p.evtaccepted = 0;\n    \n    [ca_p, peel_p, data] = FindNextEvent(ca_p, exp_p, peel_p, data, nexttim);\n    \n    if peel_p.evtfound\n            data.numspikes = data.numspikes + 1;\n            data.spikes(data.numspikes) = peel_p.nextevt;\n            [ca_p, exp_p, data] = SingleFluorTransient(ca_p, exp_p, data, peel_p.spk_recmode, peel_p.nextevt);\n            data.model = data.model + data.singleTransient;\n    else\n        break\n    end\n    \n    iter = iter + 1;\n    \n    if nexttim == nexttimMem\n        nexttimCounter = nexttimCounter + 1;\n    else\n       nexttimMem = nexttim;\n       nexttimCounter = 0;\n    end\n    %%\n    \n    if nexttimCounter > 50\n       nexttim = nexttim + timeStepForward;\n    end\n    \n    if (iter > maxiter)\n%         warning('Reached maxiter (%1.0f). nexttim=%1.2f. Timeout!',maxiter,nexttim);\n%         save\n%         error('Covergence failed!')\n        break\n    end\nend\n\nif length(data.spikes) > data.numspikes\n    data.spikes(data.numspikes+1:end) = [];\nend\n\n% go back to original frame rate\nif rate > maxRate_peel\n    spikes = data.spikes;\n    [ca_p,exp_p,peel_p, data] = InitPeeling(dff, fit_rate);\n    if nargin > 2\n        for n = 1:numel(varargin)\n            S = varargin{n};\n            if n == 1\n                ca_p = overrideFieldValues(ca_p,S);\n            elseif n == 2\n                exp_p = overrideFieldValues(exp_p,S);\n            elseif n == 3\n                peel_p = overrideFieldValues(peel_p,S);\n            end\n        end\n    end\n    data.spikes = spikes;\nend\n\n% optimization of reconstructed spike times to improve timing\noptMethod = 'pattern search';\noptMaxIter = 100000;\n%lowerT = 1; % relative to x0\n%upperT = 1; % relative to x0\nlowerT = 0.1; % relative to x0\nupperT = 0.1; % relative to x0\n\nif numel(data.spikes) && peel_p.optimizeSpikeTimes\n    if (peel_p.spk_recmode == 'linDFF')\n        spikes = PeelingOptimizeSpikeTimes(data.dff,data.spikes,lowerT,upperT,...\n                    exp_p.acqrate,ca_p.onsettau,ca_p.amp1,ca_p.tau1,optMethod,optMaxIter,0);\n    elseif (peel_p.spk_recmode == 'satDFF')\n        spikes = PeelingOptimizeSpikeTimesSaturation(data.dff,data.spikes,lowerT,upperT,...\n                    ca_p.ca_amp,ca_p.ca_gamma,ca_p.ca_onsettau,ca_p.ca_rest,ca_p.ca_kappas, exp_p.kd,...\n                    exp_p.conc,exp_p.dffmax, exp_p.acqrate, length(data.dff)./exp_p.acqrate, optMethod,optMaxIter,0);\n    else\n        error('Undefined mode');\n    end\n    data.spikes = sort(spikes);\nend\n\n% fit onset to improve timing accuracy\nif peel_p.fitonset\n    onsetfittype = fittype('modelCalciumTransient(t,onsettime,onsettau,amp1,tau1)',...\n        'independent','t','coefficients',{'onsettime','onsettau','amp1'},...\n        'problem',{'tau1'});\n    \n    wleft = round(peel_p.fitwinleft*exp_p.acqrate);     % left window for onset fit\n    wright = round(peel_p.fitwinright*exp_p.acqrate);    % right window for onset fit\n    for i = 1:numel(data.spikes)\n        [~,idx] = min(abs(data.spikes(i)-data.tim));\n        if (idx-wleft) < 1\n            currentwin = data.dff(1:idx+wright);\n            currenttim = data.tim(1:idx+wright);\n        elseif (idx+wright) > numel(data.dff)\n            currentwin = data.dff(idx-wleft:numel(data.dff));\n            currenttim = data.tim(idx-wleft:numel(data.dff));\n            currentwin = currentwin - mean(data.dff(idx-wleft:idx));\n        else\n            currentwin = data.dff(idx-wleft:idx+wright);\n            currenttim = data.tim(idx-wleft:idx+wright);\n            currentwin = currentwin - mean(data.dff(idx-wleft:idx));\n        end\n        lowerBounds = [currenttim(1) 0.1*ca_p.onsettau 0.5*ca_p.amp1];\n        upperBounds = [currenttim(end) 5*ca_p.onsettau 10*ca_p.amp1];\n        startPoint = [data.spikes(i) ca_p.onsettau ca_p.amp1];\n        problemParams = {ca_p.tau1};\n        \n        fOptions = fitoptions('Method','NonLinearLeastSquares','Lower',...\n            lowerBounds,...\n            'Upper',upperBounds,'StartPoint',startPoint);\n        [fitonset,gof] = fit(currenttim',currentwin',onsetfittype,...\n            'problem',problemParams,fOptions);\n        \n        if gof.rsquare < 0.95\n%                         fprintf('\\nBad onset fit (t=%1.3f, r^2=%1.3f)\\n',...\n%                             data.spikes(i),gof.rsquare);\n        else\n            %             fprintf('\\nGood onset fit (r^2=%1.3f)\\n',gof.rsquare);\n            data.spikes(i) = fitonset.onsettime;\n        end\n    end\nend\n\n% loop to create spike train vector from spike times\ndata.spiketrain = zeros(1,numel(data.tim));\nfor i = 1:numel(data.spikes)\n    [~,idx] = min(abs(data.spikes(i)-data.tim));\n    data.spiketrain(idx) = data.spiketrain(idx)+1;\nend\n\n% re-derive model and residuals after optimization\n\nif (peel_p.spk_recmode == 'linDFF')\n    modelTransient = spkTimes2Calcium(0,ca_p.onsettau,ca_p.amp1,ca_p.tau1,...\n                                     ca_p.amp2,ca_p.tau2,exp_p.acqrate,max(data.tim));\n    data.model = conv(data.spiketrain,modelTransient);\n    data.model = data.model(1:length(data.tim));\nelseif (peel_p.spk_recmode == 'satDFF')\n    modeltmp = spkTimes2FreeCalcium(data.spikes,ca_p.ca_amp,ca_p.ca_gamma,ca_p.ca_onsettau,ca_p.ca_rest, ca_p.ca_kappas,...\n                                    exp_p.kd, exp_p.conc,exp_p.acqrate,max(data.tim));\n    data.model = Calcium2Fluor(modeltmp,ca_p.ca_rest,exp_p.kd, exp_p.dffmax);\nend\n\ndata.peel = data.dff - data.model;\n\n% plotting parameter\nif isfield(peel_p,'doPlot')\n    if peel_p.doPlot\n        doPlot = 1;\n    else\n        doPlot = 0;\n    end\nelse\n    doPlot = 0;\nend\nif doPlot % plots at interpolation rate\n    figure; plot(data.tim,data.peel); hold all\n    plot(data.tim,data.dff); hold all\n    plot(data.tim,data.spiketrain,'LineWidth',2)\n    legend({'Residual','Calcium','UPAPs'}) % unverified putative action potential\nend\n\nend\n\nfunction Sout = overrideFieldValues(Sout,Sin)\nfieldIDs = fieldnames(Sin);\nfor n = 1:numel(fieldIDs)\n    Sout.(fieldIDs{n}) = Sin.(fieldIDs{n});\nend\n\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "CalciumDecay.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/eventDetection/newPeeling/CalciumDecay.m", "size": 1116, "source_encoding": "utf_8", "md5": "67138b4224a9d6e8edefa5aa8b387b8e", "text": "function [t,X] = CalciumDecay(p_gamma,p_carest,p_cacurrent,p_kappas,p_kd,p_conc,tspan)\n% Uses ODE45 to solve Single-compartment model differential equation \n% \n% Fritjof Helmchen (helmchen@hifo.uzh.ch)\n% Brain Research Institute,University of Zurich, Switzerland\n% created: 7.10.2013, last update: 25.10.2013 fh\n\noptions=odeset('RelTol',1e-6);                          % set an error\nXo = p_cacurrent;                                       % initial conditions\nmypar = [p_gamma,p_carest,p_kappas,p_kd,p_conc];        % parameters\n[t,X] = ode45(@Relax2CaRest,tspan,Xo,options, mypar);   % call the solver, tspan should contain time vector with more than two elements\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nfunction [dx_dt]= Relax2CaRest(t,x,pp)\n% differential equation describing the decay of calcium conc level to resting level in the presence\n% of indicator dye with variable buffering capacity.\n% paramters pp: 1 - gamma, 2 - ca_rest, 3 - kappaS, 4 - kd, 5 - indicator total concentration (all conc in nM) \ndx_dt =  -pp(1)* (x - pp(2))/(1 + pp(3) + pp(4)*pp(5)/(x + pp(4))^2);\nreturn\nend\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "comparingReferences.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/misc/comparingReferences.m", "size": 2914, "source_encoding": "utf_8", "md5": "fd9fe5ba3dd91c9f7ff76ab741405cee", "text": "function comparingReferences\n\nload('refImg_fromMapping');\nrefImgMapping = refImg;\nload('refImg');\nload('ROIs');\n\nfigure('NumberTitle', 'off', 'Name', 'Reference compare', 'Position', [30, 80, 1850, 910], 'Color', 'white');\n\nmakeSubplot(1, refImg, 'Reference from session', ROIs, [-0.05, 1.05]); %#ok<*USENS,*NODEF>\nmakeSubplot(2, refImgMapping, 'Reference from mapping (ROIs)', ROIs, [-0.05, 1.05]);\n\ncorrCoeffs = corrcoef(refImg, refImgMapping, 'rows', 'pairwise');\nmakeSubplot(3, refImgMapping - refImg, sprintf('Ref_{session} - Ref_{mapping}, corr: %.3f', corrCoeffs(2, 1)), ROIs, [-0.5 0.2]);\n\n% register reference onto local session reference\nCP = fix(256 / 2); % center point\nTTP = fix(2 * 256 / 3); % two third point\nrefPoints = [CP TTP CP TTP TTP CP TTP CP CP CP CP CP];\n[~, targPoints, srcPoints] = turboReg(refImgMapping, refImg, 'rigidBody', 3, refPoints, 0);\n% get the transformation matrix\ntForm = cp2tform(squeeze(srcPoints), squeeze(targPoints), 'affine'); %#ok<DCPTF>\n% get the transformed frame\nrefImgMappingReg = imtransform(refImgMapping, tForm, 'XData', [1, 256], 'YData', [1, 256], 'FillValues', NaN); %#ok<DIMTRNS>\n\n% shift ROIs\nROIsShift = ROIs;\ntranslationShifts = squeeze(srcPoints(:, 1, :) - targPoints(:, 1, :));\nfor iROI = 1 : size(ROIs, 1);\n    % adjust mask\n    ROIsShift{iROI, 3} = imtransform(ROIs{iROI, 3}, tForm, 'XData', [1, 256], 'YData', [1, 256], 'FillValues', NaN) == 1; %#ok<DIMTRNS>\n    % adjust coordinates\n    ROIsShift{iROI, 2} = round(ROIsShift{iROI, 2} + repmat(translationShifts', size(ROIsShift{iROI, 2}, 1), 1));\nend;\n\n\ncorrCoeffs = corrcoef(refImg, refImgMappingReg, 'rows', 'pairwise');\nmakeSubplot(4, refImgMappingReg - refImg, sprintf('Ref_{mapReg} - Ref_{session}, corr: %.3f', corrCoeffs(2, 1)), ROIsShift, [-0.5 0.2]);\n\ncorrCoeffs = corrcoef(refImgMapping, refImgMappingReg, 'rows', 'pairwise');\nmakeSubplot(5, refImgMappingReg - refImgMapping, sprintf('Ref_{mapReg} - Ref_{mapping}, corr: %.3f', corrCoeffs(2, 1)), ROIsShift, [-0.5 0.2]);\n\nmakeSubplot(6, refImgMappingReg, 'Reference from mapping registered', ROIsShift, [-0.05, 1.05]); %#ok<*NODEF>\n\nROIsOri = ROIs; %#ok<NASGU>\nROIs = ROIsShift; %#ok<NASGU>\nsave('ROIs_registered', 'ROIs');\nclear ROIs iROI CP TTP corrCoeffs;\n\nsave('registration');\nexport_fig('registration.fig', gcf);\nexport_fig('registration.png', '-r300', gcf);\n\nend\n\nfunction makeSubplot(iSubPlot, data, titleStr, ROIs, cLim)\n\n    subplot(2, 3, iSubPlot);\n    imagesc(1 : 256, 1 : 256, data);\n    title(titleStr);\n    colormap('gray');\n    set(gca, 'CLim', cLim);\n    % axis('square');\n    hold('on');\n    ROIColors = [0 1 1; 1 0 1; 0 1 0; 1 1 0; 1 0 0; 0 0 1];\n    for iROI = 1 : size(ROIs, 1);\n        contour(ROIs{iROI, 3}, 'Color', ROIColors(iROI, :), 'LineStyle', '-');\n    end;\n    legend(ROIs(:, 1));\n    hold('off');\n\nend\n\n% export_fig('analysis/ref_withROIs.png', '-r300', gcf);\n% export_fig('analysis/ref_withROIs.fig', gcf);"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "plotWideFieldMaps_old.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/misc/plotWideFieldMaps_old.m", "size": 5024, "source_encoding": "utf_8", "md5": "df2406424fd659898e4fbbbb5e365bf9", "text": "% plot Wide-Field maps\n\nsessDirs = dir();\nsessDirs(arrayfun(@(i) isempty(regexp(sessDirs(i).name, '^session\\d\\d_\\d+$', 'once')), 1 : numel(sessDirs))) = [];\n\nsessDirs = sessDirs([1, 2, 4 6]);\n\nif ~exist('sessMat', 'var');\n    sessMat = struct();\n    sessMat(numel(sessDirs)) = struct();\nend;\n\ntrialTypesRegexp = { 'hit', 'CR', 'quiet', 'moveDur', 'moveBef', ...\n    'hit_AND_moveDur', 'hit_AND_quiet', 'hit_AND_moveBef', 'hit_AND_[quiet|moveBef]', ...\n    'CR_AND_moveDur', 'CR_AND_quiet', 'CR_AND_moveBef', 'CR_AND_[quiet|moveBef]' };\ntrialTypeSaveName = { 'hit', 'CR', 'strict_quiet', 'move', 'early_move', ...\n    'strict_quiet_hit', 'move_hit', 'early_move_hit', 'quiet_hit', ...\n    'strict_quiet_CR', 'move_CR', 'early_move_CR', 'quiet_CR' };\n\n%% delay\ntimePeriod = 'delay';\nframesToAvg = 104 : 140;\ncLim = [-0.005, 0.02];\n\nfigure('NumberTitle', 'off', 'Name', timePeriod, 'Position', [67, 432, 1803, 505]);\n    \niPlot = 1;\nfor iSess = 1 : numel(sessDirs);\n    if ~isfield(sessMat(iSess), 'hit') || isempty(sessMat(iSess).hit);\n        hitAvgMat = load([sessDirs(iSess).name, '/Matt_files/cond_hit_average.mat']);\n        sessMat(iSess).hit = hitAvgMat;\n    end;\n    subplot(2, numel(sessDirs), iPlot);\n    imagesc(1 : 256, 1 : 256, smoothn(nanmean(sessMat(iSess).hit.tr_ave(:, :, framesToAvg), 3) - 1, [5 5], 'Gauss'));\n    set(gca, 'CLim', cLim, 'XLim', [1 256], 'YLim', [1 256], 'XTick', [], 'YTick', []);\n    title(sprintf('Hit - %s', sessDirs(iSess).name), 'Interpreter', 'none');\n    colorbar();\n    colormap(gca, 'mapgeog');\n    iPlot = iPlot + 1;\nend;\n\nfor iSess = 1 : numel(sessDirs);\n    if ~isfield(sessMat(iSess), 'CR') || isempty(sessMat(iSess).CR);\n        CRAvgMat = load([sessDirs(iSess).name, '/Matt_files/cond_CR_average.mat']);\n        sessMat(iSess).CR = CRAvgMat;\n    end;\n    subplot(2, numel(sessDirs), iPlot);\n    imagesc(1 : 256, 1 : 256, smoothn(nanmean(sessMat(iSess).CR.tr_ave(:, :, framesToAvg), 3) - 1, [5 5], 'Gauss'));\n    set(gca, 'CLim', cLim, 'XLim', [1 256], 'YLim', [1 256], 'XTick', [], 'YTick', []);\n    title(sprintf('CR - %s', sessDirs(iSess).name), 'Interpreter', 'none');\n    colorbar();\n    colormap(gca, 'mapgeog');\n    iPlot = iPlot + 1;\nend;\n\n%% sensation\ntimePeriod = 'sensation';\nframesToAvg = 60 : 100;\ncLim = [-0.005, 0.02];\n\nfigure('NumberTitle', 'off', 'Name', timePeriod, 'Position', [67, 432, 1803, 505]);\niPlot = 1;\nfor iSess = 1 : numel(sessDirs);\n    if ~isfield(sessMat(iSess), 'hit') || isempty(sessMat(iSess).hit);\n        hitAvgMat = load([sessDirs(iSess).name, '/Matt_files/cond_hit_average.mat']);\n        sessMat(iSess).hit = hitAvgMat;\n    end;\n    subplot(2, numel(sessDirs), iPlot);\n    imagesc(1 : 256, 1 : 256, smoothn(nanmean(hitAvgMat.tr_ave(:, :, framesToAvg), 3) - 1, [5 5], 'Gauss'));\n    set(gca, 'CLim', cLim, 'XLim', [1 256], 'YLim', [1 256], 'XTick', [], 'YTick', []);\n    title(sprintf('Hit - %s', sessDirs(iSess).name), 'Interpreter', 'none');\n    colorbar();\n    colormap(gca, 'mapgeog');\n    iPlot = iPlot + 1;\nend;\n\nfor iSess = 1 : numel(sessDirs);\n    if ~isfield(sessMat(iSess), 'CR') || isempty(sessMat(iSess).CR);\n        CRAvgMat = load([sessDirs(iSess).name, '/Matt_files/cond_CR_average.mat']);\n        sessMat(iSess).CR = CRAvgMat;\n    end;\n    subplot(2, numel(sessDirs), iPlot);\n    imagesc(1 : 256, 1 : 256, smoothn(nanmean(CRAvgMat.tr_ave(:, :, framesToAvg), 3) - 1, [5 5], 'Gauss'));\n    set(gca, 'CLim', cLim, 'XLim', [1 256], 'YLim', [1 256], 'XTick', [], 'YTick', []);\n    title(sprintf('CR - %s', sessDirs(iSess).name), 'Interpreter', 'none');\n    colorbar();\n    colormap(gca, 'mapgeog');\n    iPlot = iPlot + 1;\nend;\n\n%{\n\nfunction doPlot(timePeriod, framesToAvg, cLim);\n\nfigure('NumberTitle', 'off', 'Name', timePeriod, 'Position', [67, 432, 1803, 505]);\niPlot = 1;\nfor iSess = 1 : numel(sessDirs);\n    if ~isfield(sessMat(iSess), 'hit') || isempty(sessMat(iSess).hit);\n        hitAvgMat = load([sessDirs(iSess).name, '/Matt_files/cond_hit_average.mat']);\n        sessMat(iSess).hit = hitAvgMat;\n    end;\n    subplot(2, numel(sessDirs), iPlot);\n    imagesc(1 : 256, 1 : 256, smoothn(nanmean(hitAvgMat.tr_ave(:, :, framesToAvg), 3) - 1, [5 5], 'Gauss'));\n    set(gca, 'CLim', cLim, 'XLim', [1 256], 'YLim', [1 256], 'XTick', [], 'YTick', []);\n    title(sprintf('Hit - %s', sessDirs(iSess).name), 'Interpreter', 'none');\n    colorbar();\n    colormap(gca, 'mapgeog');\n    iPlot = iPlot + 1;\nend;\n\nfor iSess = 1 : numel(sessDirs);\n    if ~isfield(sessMat(iSess), 'CR') || isempty(sessMat(iSess).CR);\n        CRAvgMat = load([sessDirs(iSess).name, '/Matt_files/cond_CR_average.mat']);\n        sessMat(iSess).CR = CRAvgMat;\n    end;\n    subplot(2, numel(sessDirs), iPlot);\n    imagesc(1 : 256, 1 : 256, smoothn(nanmean(CRAvgMat.tr_ave(:, :, framesToAvg), 3) - 1, [5 5], 'Gauss'));\n    set(gca, 'CLim', cLim, 'XLim', [1 256], 'YLim', [1 256], 'XTick', [], 'YTick', []);\n    title(sprintf('CR - %s', sessDirs(iSess).name), 'Interpreter', 'none');\n    colorbar();\n    colormap(gca, 'mapgeog');\n    iPlot = iPlot + 1;\nend;\n\n%}"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "changing_frame_0_balazs.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/misc/changing_frame_0_balazs.m", "size": 6227, "source_encoding": "utf_8", "md5": "4da913c737f7e68564f0e3529a5fd541", "text": "function changing_frame_0_balazs()\n\nfr0 = [];\n\nload('stimStartFrames');\nload('trials_ind');\nload('norm_frame');\nload('rois_OCIA_old');\nload('ROIs_registered');\n\nfr2=57:58;\nfr_dev2 = nan(size(fr_dev)); %#ok<*NODEF>\nstimFrames = 59:68;\nfixedStartFrame = max(stimStartFrame);\n% trialTypes = { 'hit', 'CR' };\ntrialTypes = { 'hit' };\n\nnMaxTrials = 1;\n\n% exclTrials = { [4 5 7 8 11 12 15 19 22 27 28 29], [] };\nexclTrials = { [], [] };\n\ndoSinglePlots = true;\ndoAvgPlot = false;\n\navgYLims = [-0.01 0.01];\ntrialYLims = [-0.05 0.05]; %#ok<*NASGU>\n\navgCLim = [-0.001 0.001];\ntrialCLim = [-0.005 0.01]; %#ok<*NASGU>\n\n\n% ROINames = { 'V1', 'M2', 'A1', 'S1FL' };\n% ROIColors = { 'b', 'k', 'g', 'r' };\n% ROINames = { 'V1', 'A1', 'S1FL' };\n% ROIColors = { 'b', 'g', 'c' };\nROINames = {  };\nROIColors = {  };\n\n\n\nROI2Inds = 1 : 6;\nROIColors2 = [0 1 1; 1 0 1; 0 1 0; 1 1 0; 1 0 0; 0 0 1];\n% ROI2Inds = [6, 3, 1];\n% ROIColors2 = [0 0 1; 0 1 0; 0 1 1;];\n\n\nROIs = ROIs(ROI2Inds, :);\n\n\n%% HIT trials\nfor iType = 1 : numel(trialTypes);\n    \n    listTrials = dir(['cond_' trialTypes{iType} '_trial*']);\n    cond_avg = zeros(256, 256, 240);\n    cond_avg_0 = zeros(256, 256, 240);\n    cond_avgNoAlign = zeros(256, 256, 240);\n    cond_avgNoAlign_0 = zeros(256, 256, 240);\n    nTrials = 0;\n    trialInd = eval(['tr_', trialTypes{iType}]);\n\n    for iTrial = 1 : min(nMaxTrials, size(listTrials, 1));\n\n        if ismember(iTrial, exclTrials{iType}); continue; end;\n\n        trialPath = sprintf('cond_%s_trial%d.mat', trialTypes{iType}, iTrial);\n        if ~exist(trialPath, 'file'); continue; end;\n        \n        fprintf('Loading trial %s %03d ...\\n', trialTypes{iType}, iTrial);\n        load(trialPath);\n\n        % reset the baseline\n        tr = tr .* repmat(fr_dev(:, :, trialInd(iTrial)), [1, 1, size(tr, 3)]);\n        trNoAlign = tr;\n\n        % re-align frames\n        stimStartFrameTrial = stimStartFrame(trialInd(iTrial));\n        nFramesDiff = fixedStartFrame - stimStartFrameTrial;\n        if nFramesDiff > 0;\n            tr = cat(3, nan(size(tr, 1), size(tr, 2), nFramesDiff), tr(:, :, 1 : (end - nFramesDiff)));\n        elseif nFramesDiff < 0;\n            tr = cat(3, tr(:, :, (nFramesDiff + 1) : end), nan(size(tr, 1), size(tr, 2), nFramesDiff));\n        end;\n\n        % re-normalize with baseline\n        tr0 = tr;\n        tr0 = tr0 ./ repmat(fr_dev(:, :, trialInd(iTrial)), [1 1 size(tr0, 3)]);\n        fr_dev2(:, :, trialInd(iTrial)) = nanmean(tr(:, :, fr2), 3);\n        tr = tr ./ repmat(fr_dev2(:, :, trialInd(iTrial)), [1 1 size(tr, 3)]);\n        % re-normalize with baseline\n        trNoAlign0 = trNoAlign;\n        trNoAlign0 = trNoAlign0 ./ repmat(fr_dev(:, :, trialInd(iTrial)), [1 1 size(trNoAlign0, 3)]);\n        fr_dev2(:, :, trialInd(iTrial)) = nanmean(trNoAlign(:, :, fr2), 3);\n        trNoAlign = trNoAlign ./ repmat(fr_dev2(:, :, trialInd(iTrial)), [1 1 size(trNoAlign, 3)]);\n\n        % add average\n        cond_avg = cond_avg + tr;\n        cond_avg_0 = cond_avg_0 + tr0;\n        cond_avgNoAlign = cond_avgNoAlign + tr;\n        cond_avgNoAlign_0 = cond_avgNoAlign_0 + tr0;\n        nTrials = nTrials + 1;\n\n        % plot\n        if doSinglePlots;\n            createPlot(sprintf('%s %03d - trial %03d', trialTypes{iType}, iTrial, trialInd(iTrial)), ...\n                tr0, tr, trNoAlign0, trNoAlign, fr0, fr2, stimFrames, roi_V1, roi_M2, roi_A1, roi_S1FL, ROINames, ...\n                ROIColors, ROIColors2, ROIs, trialCLim, trialYLims); %#ok<*UNRCH>\n\n        end;\n\n    end\n\n    % compute average\n    cond_avg = cond_avg ./ nTrials;\n    cond_avg_0 = cond_avg_0 ./ nTrials;\n    cond_avgNoAlign = cond_avgNoAlign ./ nTrials;\n    cond_avgNoAlign_0 = cond_avgNoAlign_0 ./ nTrials;\n\n    % plot\n    if doAvgPlot;\n\n        createPlot(sprintf('%s average - %03d trial(s)', trialTypes{iType}, nTrials), ...\n            cond_avg_0, cond_avg, cond_avgNoAlign, cond_avgNoAlign_0, fr0, fr2, stimFrames, ...\n            roi_V1, roi_M2, roi_A1, roi_S1FL, ROINames, ROIColors, ROIColors2, ROIs, avgCLim, avgYLims);\n\n    end;\n\nend;\n\nend\n\n\nfunction createPlot(figTitle, dataFr0, dataFr2, dataNoAlignFr0, dataNoAlignFr2, fr0, fr2, ...\n    stimFrames, roi_V1, roi_M2, roi_A1, roi_S1FL, ROINames, ROIColors, ROIColors2, ROIs, cLim, yLims) %#ok<INUSL>\n\nsubPlotTitles = { sprintf('Sound aligned, norm. %02d:%02d', fr0(1), fr0(end)), ...\nsprintf('Sound aligned, norm. %02d:%02d, stim. %02d:%02d', fr0([1 end]), stimFrames([1 end])), ...\nsprintf('Trig. aligned, norm %02d:%02d', fr2([1 end])), ...\nsprintf('Trig. aligned, norm. %02d:%02d, stim. %02d:%02d', fr2([1 end]), stimFrames([1 end])) };\n\nlegendParams = { [ROINames ROIs(:, 1)'], 'Location', 'NorthOutside', 'Orientation', 'Horizontal', 'FontSize', 6 };\n        \nfigure('Name', figTitle, 'NumberTitle', 'off', 'Position', [30 100 1850 970]);\n\ndataToPlot = { dataFr0, dataFr2, dataNoAlignFr0, dataNoAlignFr2 };\n\niSubPlot = 1;\nfor iData = 1 : 4;\n    \n    subplot(2, 4, iSubPlot);\n    title(subPlotTitles{1});\n    hold on;\n    linTr = reshape(dataToPlot{iData}, size(dataToPlot{iData}, 2) * size(dataToPlot{iData}, 2), size(dataToPlot{iData}, 3));\n    for iROI = 1 : numel(ROINames);\n        plot(squeeze(nanmean(linTr(eval(['roi_' ROINames{iROI}]), :), 1)) - 1, ['--', ROIColors{iROI}]);\n    end;\n    for iROI = 1 : size(ROIs, 1);\n        trace = nanmean(GetRoiTimeseries(dataFr2, ROIs{iROI, 3})) - 1;\n        plot(trace, 'Color', ROIColors2(iROI, :), 'LineStyle', '-');\n    end;\n    plot(repmat(stimFrames(1), 1, 2), yLims, ':k');\n    plot(repmat(stimFrames(end), 1, 2), yLims, ':k');\n    set(gca, 'YLim', yLims);\n    hold off;\n    legend(legendParams{:});\n    iSubPlot = iSubPlot + 1;\n\n    subplot(2, 4, iSubPlot);\n    title(subPlotTitles{2});\n    imagesc(1 : 256, 1 : 256, smoothn(nanmean(dataToPlot{iData}(:, :, stimFrames) - 1, 3), [5 5], 'Gauss'), cLim);\n    colorbar();\n    axis('square');\n    colormap(mapgeog);\n    hold on;\n    for iROI = 1 : numel(ROINames);\n        h = zeros(256 * 256, 1);\n        h(eval(['roi_' ROINames{iROI}])) = 1;\n        contour(reshape(h, 256, 256), ROIColors{iROI});\n    end;\n    for iROI = 1 : size(ROIs, 1);\n        contour(ROIs{iROI, 3}, 'Color', ROIColors2(iROI, :), 'LineStyle', '-');\n    end;\n    hold off;\n    iSubPlot = iSubPlot + 1;\n    \nend;\n\nend\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "BEGetTrialInfo.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/BEGetTrialInfo.m", "size": 2254, "source_encoding": "utf_8", "md5": "ecb6e0a8914941c49d705be87c4ca6df", "text": "%% #BEGetTrialInfo\nfunction trialInfo = BEGetTrialInfo(this, iTrial)\n\n% default is empty\nfreq = ''; spotIndex = ''; isTargetOrNTones = ''; resp = ''; respTime = ''; corr = ''; rew = '';\n\nif iTrial > 0 && iTrial <= this.be.config.training.nTrials && isfield(this.be, 'stims');\n    \n    if isfield(this.be, 'spotMatrix') && ~isempty(this.be.spotMatrix);\n        spotIndex = this.be.spotMatrix(iTrial);\n    end;\n    \n    freq = round(this.be.config.tone.freqs(this.be.stims(iTrial)) / 1000);\n    \n    % no goStim = no behavior\n    if ~isempty(this.be.config.tone.goStim);\n        \n        % oddball discrimination\n        if isfield(this.be.config.tone, 'oddProba') && this.be.config.tone.oddProba > 0;\n            \n            isTargetOrNTones = double(this.be.stims(iTrial) ~= this.be.odds(iTrial) && this.be.config.tone.goStim);\n            \n        % frequency/cloud of tone discrimination\n        else\n            \n            isTargetOrNTones = double(ismember(this.be.stims(iTrial), this.be.config.tone.goStim));\n            \n        end;\n    \n    elseif strcmp(this.be.taskType, 'cotOdd') && numel(this.be.nTones) > 1;\n        \n        % fill with number of tones\n        isTargetOrNTones = this.be.nTones(iTrial);\n        \n    end;\n\n    if ~isempty(this.be.config.tone.goStim) && isfield(this.be, 'resps') && ~isnan(this.be.resps(iTrial));\n        resp = this.be.resps(iTrial);\n        if ~isnan(this.be.respDelays(iTrial));\n            respTime = sprintf('%.2f', this.be.respDelays(iTrial));\n        else\n            respTime = '   -  ';\n        end;\n        corr = double((isTargetOrNTones && resp) || (~isTargetOrNTones && ~resp));\n        if corr; corr = ' T '; else corr = ' F '; end;\n        if ~isnan(this.be.giveRewards(iTrial)) && this.be.giveRewards(iTrial);\n            rew = ' T ';\n        else\n            rew = ' F';\n        end;\n        if this.be.resps(iTrial); resp = ' T '; else resp = ' F'; end;\n        if this.be.autoRewardGiven(iTrial) && strcmp(corr, ' T '); corr = ' F*';  end;\n    end;\n    if isTargetOrNTones; isTargetOrNTones = ' T '; else isTargetOrNTones = ' F'; end;\n    \n% no trial number\nelse\n    \n    iTrial = '';\n    \nend;\n\ntrialInfo = {iTrial, spotIndex, freq, isTargetOrNTones, resp, respTime, corr, rew};\n\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DWMatchBehavTrialsToImagingData.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/DWMatchBehavTrialsToImagingData.m", "size": 6399, "source_encoding": "utf_8", "md5": "afd7fe9d63c0d81ca6439a845346cc6f", "text": "function DWMatchBehavTrialsToImagingData(this)\n% DWMatchBehavTrialsToImagingData - [no description]\n%\n%       DWMatchBehavTrialsToImagingData(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n% match the behavior trials and the data files\n\n% update the wait bar\nDWWaitBar(this, 0);\n    \n% get the behavior rows with no animal ID and figure it out from the data structure\nbehavRows = DWFilterTable(this, 'animal !~= \\w+ AND rowType = Behavior data');\nfor iBehavRow = 1 : size(behavRows, 1);\n    % get the DataWatcher table's row index for this row\n    iDWRow = str2double(get(this, iBehavRow, 'rowNum', behavRows));\n    % load the row\n    DWLoadRow(this, iDWRow, 'full');\n    % get the behavior data\n    behavData = getData(this, iDWRow, 'behav', 'data');\n    % set the new animal ID\n    set(this, iDWRow, 'animal', regexprep(['mou_bl_', behavData.animalID], 'mou_bl_mou_bl', 'mou_bl'));\nend;\n\n% get the list of all animals\nuniqueAnimals = get(this, 'animal');\nif ~iscell(uniqueAnimals) && ischar(uniqueAnimals) && ~isempty(uniqueAnimals); uniqueAnimals = { uniqueAnimals }; end;\nuniqueAnimals(cellfun(@isempty, uniqueAnimals)) = { '' };\nuniqueAnimals = unique(uniqueAnimals);\n\n% get the list of all days\nuniqueDays = get(this, 'day');\nif ~iscell(uniqueDays) && ischar(uniqueDays) && ~isempty(uniqueDays); uniqueDays = { uniqueDays }; end;\nuniqueDays(cellfun(@isempty, uniqueDays)) = [];\nuniqueDays = unique(uniqueDays);\n\n% get the selected animal IDs\nselectedAnimalIDs = this.dw.animalIDs(get(this.GUI.handles.dw.filt.animalID, 'Value'));\n% if the dash '-' is selected, select all IDs\nif numel(selectedAnimalIDs) == 1 && strcmp(selectedAnimalIDs{1}, '-');\n    selectedAnimalIDs = uniqueAnimals;\nend;\n\n% get the selected day IDs\nselectedDayIDs = this.dw.dayIDs(get(this.GUI.handles.dw.filt.dayID, 'Value'));\n% if the dash '-' is selected, select all IDs\nif numel(selectedDayIDs) == 1 && strcmp(selectedDayIDs{1}, '-');\n    selectedDayIDs = uniqueDays;\nend;\n\n% cell array storing all the informations to process each session\nallSessionInfos = cell(1000, 6);\n\n% first get all the information for each sessions to process\n% go through each animal\nfor iAnim = 1 : numel(uniqueAnimals);    \n    animalID = uniqueAnimals{iAnim}; % get the current animal\n\n    % skip irrelevant animal IDs\n    if ~ismember(animalID, selectedAnimalIDs); continue; end;\n    \n    % go through each day\n    for iDay = 1 : numel(uniqueDays);    \n        dayID = uniqueDays{iDay}; % get the current day\n\n        % skip irrelevant days\n        if ~ismember(dayID, selectedDayIDs); continue; end;\n\n        % empty spot filters\n        spotID = '';\n        spotFilter = '';\n\n        % create animal filter\n        if isempty(animalID);\n            animalFilter = 'animal !~= \\w AND ';\n        else\n            animalFilter = sprintf('animal = %s AND ', animalID);\n        end;\n\n        % use different filters for different locations\n        locFilter = {\n            '', '';\n        };\n        if ismember('loc', this.dw.tableIDs);\n            locFilter = {\n                '',         ' AND loc !~= \\w+'; \n                'local',    ' AND loc = local';\n                'remote',   ' AND loc = remote';\n            }; \n        end;\n        \n        % go through each location filter\n        for iLocFilter = 1 : size(locFilter, 1);\n            \n            % get the imaging rows indexes\n            imagingRows = DWFilterTable(this, ...\n                sprintf('%s%sday = %s AND rowType = Imaging data AND runType !~= \\\\w+%s', ...\n                animalFilter, spotFilter, dayID, locFilter{iLocFilter, 2}));\n            imagingRowIndexes = str2double(get(this, 'all', 'rowNum', imagingRows));\n\n            % if no imaging data, skip\n            if isempty(imagingRowIndexes) || any(isnan(imagingRowIndexes));\n                continue;\n            % if only one row found, label it as session 1\n            elseif numel(imagingRowIndexes) == 1;\n                sessIDs = 1;            \n            % if several rows, cluster them by session using time\n            else\n                sessIDs = clusterRowsBySession(this, imagingRowIndexes);\n            end;\n\n            % go through session by session\n            for iSess = 1 : size(unique(sessIDs), 1);\n                % get the indexes of this session\n                sessRowIndexes = imagingRowIndexes(sessIDs == iSess);\n                % store the data to process\n                allSessionInfos(end + 1, :) = { animalID, dayID, spotID, iSess, sessRowIndexes, ...\n                    locFilter{iLocFilter, 2} }; %#ok<AGROW>\n            end;\n            \n        end; % end of location filter\n    end; % end of day loop\nend; % end of animal loop\n\n% remove empty lines\nallSessionInfos(cellfun(@isempty, allSessionInfos(:, 1)), :) = [];\nnTotSessions = size(allSessionInfos, 1);\n\n% match all sessions\nfor iTotSess = 1 : nTotSessions;\n    \n    % match the behavior trials using the stored informations\n    DWMatchBehavTrialsToImagingDataForSession(this, allSessionInfos{iTotSess, :});\n    \n    % update the wait bar\n    DWWaitBar(this, 99 * (iTotSess / nTotSessions));\n    \nend;\n\n% % remove raw behavior data for the current session\n% behavRows = DWFilterTable(this, 'rowType = Behavior data');\n% DWFlushData(this, str2double(get(this, 'all', 'rowNum', behavRows)), false, 'behav');\n\n% final update of the wait bar\nDWWaitBar(this, 100);\n\nend\n\n%% - #clusterRowsBySession\nfunction sessIDs = clusterRowsBySession(this, rowNums)\n\n% do not process if not at least 2 rows\nif numel(rowNums) < 2;\n    sessIDs = repmat('1', numel(rowNums), 1);\n    return;\nend;\n    \n% separate rows into morning and afternoon sessions\nnUnknRows = size(rowNums, 1);\ndateNums = zeros(nUnknRows, 1);\nfor iUnknRow = 1 : nUnknRows;\n    dateAndTime = get(this, rowNums(iUnknRow), { 'day', 'time' });\n    dateNums(iUnknRow) = datenum(sprintf('%s__%s', dateAndTime{:}), 'yyyy_mm_dd__HH_MM_SS');\nend;\nsessIDs = clusterdata(dateNums, 'maxclust', 2);\nnearbySessDiffInHours = (dn2unix(dateNums(find(sessIDs == sessIDs(end), 1, 'first'))) ...\n    - dn2unix(dateNums(find(sessIDs == sessIDs(1), 1, 'last')))) / 1000 / 60 / 60;\n% if sessions are too close, it means that it was a single session with a missing trial/interruption\nif nearbySessDiffInHours < 3; % minimum 3 hours between sessions\n    sessIDs = clusterdata(dateNums, 'maxclust', 1);\nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DWMatchBehavTrialsToImagingDataForSession.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/DWMatchBehavTrialsToImagingDataForSession.m", "size": 16055, "source_encoding": "utf_8", "md5": "3a28c46a1d44236c27a5d27a8ab6b9fb", "text": "%% - #DWMatchBehavTrialsToImagingDataForSession\nfunction DWMatchBehavTrialsToImagingDataForSession(this, animalID, dayID, spotID, iSess, rowIndexes, locFilter)\n\n% get the number of imaging trials found\nnTrialsFound = size(rowIndexes, 1);\n\n% abort if no trials\nif ~nTrialsFound; return; end;\n\n% find the right behavior out file : get the behavior rows\nbehavRows = DWFilterTable(this, sprintf('animal = %s AND day = %s AND rowType = Behavior data%s', animalID, dayID, ...\n    locFilter));\nnBehavs = size(behavRows, 1); % count them\n\n% go through all behavior files by comparing behavior trial and data file times\nallBehavUNIXTimes = []; % behaviorally recorded times\nallBehavBelongInds = []; % behavior file IDs (B01, B02, etc.)\n% extract all behavior times from the behavior files\nfor iBehav = 1 : nBehavs;\n    \n    % get the DataWatcher table's row index for this row\n    iDWRow = str2double(get(this, iBehav, 'rowNum', behavRows));\n    \n    % make sure behavior data is loaded\n    DWLoadRow(this, iDWRow, 'full');\n    % get the output structure\n    behavData = getData(this, iDWRow, 'behav', 'data');\n    \n    % skip empty structures\n    if isempty(behavData); continue; end;\n    \n    % find the end timing name (backward compatibility)\n    fieldName = 'endImagExp';\n    if ~isfield(behavData.times, fieldName);\n        fieldName = 'endImagExpect';\n    end;\n    if ~isfield(behavData.times, fieldName);\n        fieldName = 'imgStopExp';\n    end;\n    \n    if ~isfield(behavData.times, fieldName);\n        showWarning(this, 'OCIA:DW:DWMatchBehavTrialsToImagingDataForSession:NoEndImagingFieldName', ...\n            sprintf('Cannot find an imaging end time field for row %03d: %s.', iDWRow, DWGetRowID(this, iDWRow)));\n        continue;\n    end;\n    \n    % extract the behavior UNIX times\n    behavUNIXTimes = (behavData.times.(fieldName) + behavData.times.start) * 1000;\n    behavUNIXTimes(behavUNIXTimes == 0) = [];\n    % concatenate the times\n    allBehavUNIXTimes = [allBehavUNIXTimes behavUNIXTimes]; %#ok<AGROW>\n    allBehavBelongInds = [allBehavBelongInds repmat(iBehav, 1, size(behavUNIXTimes, 2))]; %#ok<AGROW>\n    \nend;\n\n% remove NaN trials because they were not recorded\nallBehavBelongInds(isnan(allBehavUNIXTimes)) = [];\nallBehavUNIXTimes(isnan(allBehavUNIXTimes)) = [];\n\n% use a time threshold to check for time mismatchs\ntimeThresh = this.dw.trialMatchingTimeDifferenceThreshold;\n\n% minimum time differences between behavior timestamps and data file timestamps\n[allMinTDiffs, allMinTInds] = compareImgFileTimeWithBehavTimes(this, allBehavUNIXTimes, rowIndexes, timeThresh);\n\n% abort if no rows to process\nif isempty(allMinTDiffs) || isempty(allMinTInds); return; end;\n\n% best fitting behavior file are the ones with less than 3 hours \niBestBehav = unique(allBehavBelongInds(allMinTDiffs < 3 * 60 * 60));\nnMatchBehavs = size(iBestBehav, 2);\niBestBehavMask = ismember(allBehavBelongInds, iBestBehav);\n\n% abort if no behavior match found\nif isempty(iBestBehav); return; end;\n\n% extract the adjusted time differences\nminTDiff = abs(allMinTDiffs(iBestBehavMask) - nanmedian(allMinTDiffs(iBestBehavMask)));\n\n% calculate the number of expected trials\nnTrialsExp = 0;\nfor iBehav = 1 : nMatchBehavs;\n    % get the DataWatcher table's row index for this row\n    iDWRow = str2double(get(this, iBestBehav(iBehav), 'rowNum', behavRows));\n    % get the output structure\n    behavData = getData(this, iDWRow, 'behav', 'data');\n    % count the number of trials that are expected\n    nTrialsExp = nTrialsExp + find(~isnan(behavData.times.end) & ~isnan(behavData.resps), 1, 'last');\nend;\n\n% calculate the difference in trial numbers\ndiffTrials = nTrialsExp - nTrialsFound;\n\n% get whether the difference is in missing or extra trials\nmissExtraInd = []; % indexes of rows that are missing or extra\nmissExtraLabel = ''; % label specifying whether the trials are missing or extra\n\n% no trial number difference and no timing mismatch => no missmatch\nif diffTrials == 0 && ~any(minTDiff > timeThresh);\n    % nothing to do\n    \n% missing/extra trials\nelseif diffTrials ~= 0;\n    \n    % difference is positive => missing trials\n    if diffTrials > 0;\n        missExtraLabel = 'missing'; % label the mismatch\n        % loop to find all missing trials using a stepwise decreasing time threshold\n        while size(missExtraInd, 1) < diffTrials;\n            missExtraInd = find(minTDiff > timeThresh); % get the missing trials\n            timeThresh = timeThresh - 100; % update the threshold\n        end;\n        \n        % get the most different missing trials\n        [~, missExtraIndOrdered] = sort(minTDiff(missExtraInd)); % sort by most different timing\n        missExtraInd = sort(missExtraInd(missExtraIndOrdered(end - diffTrials + 1 : end)));\n        \n%         % get the first missing trials\n%         missExtraInd = sort(missExtraInd(1 : abs(diffTrials)));\n        \n    % difference is negative => extra trials\n    else\n        missExtraLabel = 'extra'; % label the mismatch\n        diffTrials = - diffTrials; % invert the sign\n        % find the extra trials\n        missExtraInd = find(~ismember(1 : nTrialsExp, allMinTInds(iBestBehavMask)), diffTrials);\n        % if no extra trials found, try to find them using setdiff\n        if isempty(missExtraInd); missExtraInd = setdiff(1 : nTrialsFound, allMinTInds); end;\n        % only take the required number of different trials\n        missExtraInd = sort(missExtraInd(1 : abs(diffTrials)));\n    end;\n\n    % create a list of mismatched indexes\n    missList = regexprep(sprintf('%02d ', missExtraInd), ' $', '');\n    % if the list is too long, make it shorter: find the space characters\n    spaceCharStart = find(missList == ' ', 3, 'first');\n    % find the second and before last space character\n    if isempty(spaceCharStart);     spaceCharStart = min(8, round(numel(missList) * 0.5));\n    else                            spaceCharStart = spaceCharStart(end);\n    end;\n    spaceCharEnd = find(missList == ' ', 3, 'last');\n    if isempty(spaceCharEnd);       spaceCharEnd = max(numel(missList) - 8, round(numel(missList) * 0.5));\n    else                            spaceCharEnd = spaceCharEnd(1);\n    end;\n    if size(missList, 2) > 30; missList = [missList(1 : spaceCharStart) ' ... ' missList(spaceCharEnd : end)]; end;\n    % show the warning\n    showWarning(this, 'OCIA:DWMatchBehavTrialsToImagingDataForSession:MissingTrials', ...\n        sprintf('Found %d %s trial(s) ( %s ) for %s %s %s session %d.', diffTrials, missExtraLabel, ...\n        missList, animalID, dayID, spotID, iSess));\nend;\n\n% label the stimuli\nif strcmp(behavData.taskType, 'cotOdd');\n    stimLabels = { 'lowStd ', 'highStd' };\nelse\n    stimLabels = { 'low', 'high' };\nend;\n\n% create the annotations for the imaging rows\nbehavIDs = cell(nTrialsExp, 1);\nrunTypes = cell(nTrialsExp, 1);\ntrialNums = cell(nTrialsExp, 1);\nspotIDs = cell(nTrialsExp, 1);\nbehavInfo = cell(nTrialsExp, 1);\n\n% initialize the current behavior data to use for annotation\niBehav = 1;\niDWRowBehav = str2double(get(this, iBestBehav(iBehav), 'rowNum', behavRows)); % get the DW table's row index\nbehavID = DWGetRowID(this, iDWRowBehav); % get the behavior row's ID\nbehavData = getData(this, iDWRowBehav, 'behav', 'data'); % get the behavior data\ncurrMaxTrial = find(~isnan(behavData.times.end) & ~isnan(behavData.resps), 1, 'last'); % get the number of trials for this behavior\n\n% loop through all trials\niTrial = 1;\nfor iTotTrial = 1 : nTrialsExp;    \n    % annotate this trial using the behavior's informations\n    behavIDs{iTotTrial} = behavID;\n    runTypes{iTotTrial} = 'Trial';\n    trialNums{iTotTrial} = sprintf('%02d', iTrial);\n    spotIDs{iTotTrial} = sprintf('spot%02d', behavData.spotMatrix(iTrial));\n\n    % gather some information about the behavior\n    behavInfo{iTotTrial} = sprintf('[ %s', stimLabels{behavData.stims(iTrial)});\n    \n    % response type\n    if ~isnan(behavData.respTypes(iTrial));        \n        % target\n        if ismember(behavData.respTypes(iTrial), [1 4]);\n            behavInfo{iTotTrial} = sprintf('%s / targ', behavInfo{iTotTrial});            \n        % distractor\n        elseif ismember(behavData.respTypes(iTrial), [2 3]);\n            behavInfo{iTotTrial} = sprintf('%s / distr', behavInfo{iTotTrial});\n        end;        \n        % correct\n        if ismember(behavData.respTypes(iTrial), [1 2]);\n            behavInfo{iTotTrial} = sprintf('%s / corr', behavInfo{iTotTrial});            \n        % wrong\n        elseif ismember(behavData.respTypes(iTrial), [3 4]);\n            behavInfo{iTotTrial} = sprintf('%s / false', behavInfo{iTotTrial});\n        end;        \n    end;\n    \n    % cloud of tones oddball case\n    if strcmp(behavData.taskType, 'cotOdd') && numel(behavData.nTones) >= iTrial;\n        behavInfo{iTotTrial} = sprintf('%s / %02d tones', behavInfo{iTotTrial}, behavData.nTones(iTrial));\n    end;\n    \n    % close the information section\n    behavInfo{iTotTrial} = sprintf('%s ]', behavInfo{iTotTrial});\n\n    % update the trial count\n    iTrial = iTrial + 1;\n    \n    % if we reach the start of a new behavior file\n    if iTrial > currMaxTrial && iBehav < nMatchBehavs;\n        \n        % update the current behavior data to use for annotation\n        iTrial = 1;\n        iBehav = iBehav + 1;\n        iDWRowBehav = str2double(get(this, iBestBehav(iBehav), 'rowNum', behavRows)); % get the DW table's row index\n        behavID = DWGetRowID(this, iDWRowBehav); % get the behavior row's ID\n        behavData = getData(this, iDWRowBehav, 'behav', 'data'); % get the behavior data\n        currMaxTrial = find(~isnan(behavData.times.end) & ~isnan(behavData.resps), 1, 'last'); % get the number of trials for this behavior\n    end;\nend;\n\n% re-order the rows according to the minimum time indexes\nrowIndexes = rowIndexes(allMinTInds(iBestBehavMask));\n\n% remove the annotations for the missing trials\nif strcmp(missExtraLabel, 'missing');\n    \n    behavIDs(missExtraInd) = [];\n    runTypes(missExtraInd) = [];\n    trialNums(missExtraInd) = [];\n    spotIDs(missExtraInd) = [];\n    behavInfo(missExtraInd) = [];    \n    rowIndexes(missExtraInd) = [];\n    \nend;\n\n% update in the table\nset(this, rowIndexes, 'behav', behavIDs);\nset(this, rowIndexes, 'runType', runTypes);\nset(this, rowIndexes, 'runNum', trialNums);\nset(this, rowIndexes, 'spot', spotIDs);\nset(this, rowIndexes, 'comments', behavInfo);\n\n%% extract behavior data to imaging rows and get spot ID for behavior rows\n% go through each behavior file and try to get a spot ID annotation for them\nfor iBehav = 1 : nBehavs;\n    % get the DataWatcher table's row index for this row\n    iDWRowBehav = str2double(get(this, iBehav, 'rowNum', behavRows)); % get the DW table's row index\n    behavID = DWGetRowID(this, iDWRowBehav); % get the behavior row's ID\n    \n    % get the imaging trial rows that have a matching behavior ID\n    trialRows = DWFilterTable(this, sprintf('rowType = Imaging data AND behav = %s AND runNum ~= \\\\d+', behavID)); \n    \n    % skip the process if no imaging rows have this behavior ID\n    if isempty(trialRows); continue; end;\n    \n    % get the behavior data\n    behavDataForRow = getData(this, iDWRowBehav, 'behav', 'data');\n    \n    % go through each trial\n    for iTrialLoop = 1 : size(trialRows, 1);\n        iTrial = str2double(get(this, iTrialLoop, 'runNum', trialRows)); % get the trial number\n        iDWRowTrial = str2double(get(this, iTrialLoop, 'rowNum', trialRows)); % get the row's index\n        % extract the behavior data for this trial and store it in the imaging row's data\n        setData(this, iDWRowTrial, 'behavExtr', 'data', DWExtractBehavDataForImagingRow(this, iTrial, behavDataForRow));\n        setData(this, iDWRowTrial, 'behavExtr', 'loadStatus', 'full');\n    end;\n    \n    % if the behavior file does not have a spot annotation\n    if isempty(get(this, iDWRowBehav, 'spot'));\n        % create a spot annotation using the one from the frist trial\n        set(this, iDWRowBehav, 'spot', get(this, iTrialLoop, 'spot', trialRows));                \n    end;\n    \nend;\n\nend\n\n%% - #compareImgFileTimeWithBehavTimes\nfunction [minTDiffs, minTInds] = compareImgFileTimeWithBehavTimes(this, behavUNIXTimes, DWTableRowInds, timeDiffThresh)\n\n% do not process if no rows\nDWTableRowInds(isnan(DWTableRowInds)) = []; % remove nans\nif isempty(DWTableRowInds);\n    minTDiffs = [];\n    minTInds = [];\n    return;\nend;\n\nnTrialsExp = size(behavUNIXTimes, 2);\nnTrialsFound = size(DWTableRowInds, 1);\nfileTimes = arrayfun(@(i) dn2unix(datenum(sprintf('%s__%s', get(this, DWTableRowInds(i), 'day'), ...\n    get(this, DWTableRowInds(i), 'time')), 'yyyy_mm_dd__HH_MM_SS')), 1 : nTrialsFound);\n% nFrames = arrayfun(@(i) str2double(regexprep(get(this, DWTableRowInds(i), 'dim'), '^\\d+x\\d+x', '')), 1 : nTrialsFound);\n% fileTimes = fileTimes(nFrames >= this.an.img.funcMovieNFramesLimit);\nminTDiffs = zeros(nTrialsExp, 1);\nminTInds = zeros(nTrialsExp, 1);\nfor iTrial = 1 : nTrialsExp;\n    tDiffs = abs(behavUNIXTimes(iTrial) - fileTimes);\n    [minTDiffs(iTrial), minTInds(iTrial)] = min(abs(tDiffs));\nend;\n\n%% show debug plot if required\nif get(this.GUI.handles.dw.SLROpts.procDataShowDebug, 'Value');\n    figure('Name', 'Time comparison for trial matching', 'NumberTitle', 'off', 'WindowStyle', 'docked');\n    \n    % match lines\n    lineHandles = plot([fileTimes(minTInds); behavUNIXTimes(1 : nTrialsExp)], ...\n        [ones(1, nTrialsExp); 2 * ones(1, nTrialsExp)], 'green');\n    hold on;\n    \n    % imaging files scatter\n    filePointHandles = scatter(fileTimes, ones(1, numel(fileTimes)), 10, 's', 'blue', 'fill');\n    fileTrialList = regexp(regexprep(sprintf('%d,', 1 : numel(fileTimes)), ',$', ''), ',', 'split');\n    behavTrialList = regexp(regexprep(sprintf('%d,', 1 : numel(behavUNIXTimes)), ',$', ''), ',', 'split');\n    text(fileTimes, ones(1, numel(fileTimes)) - 0.05, fileTrialList, 'FontSize', 5, ...\n        'HorizontalAlignment', 'center');\n    \n    % extract which are the deviant times\n    deviantTimes = find(minTDiffs > timeDiffThresh);\n\n    % time differences\n    scaledTDiffs = linScale([0; timeDiffThresh; minTDiffs; max(minTDiffs)], 0, 0.5);\n    timeDiffHandle = plot(behavUNIXTimes, scaledTDiffs(3 : end - 1) + 1.25, 'k', 'LineWidth', 1.5);\n    plot([behavUNIXTimes(1) - 10E5 behavUNIXTimes(end) + 10E5], repmat(scaledTDiffs(1), 1, 2) + 1.25, ':k', 'LineWidth', 0.5);\n    plot([behavUNIXTimes(1) - 10E5 behavUNIXTimes(end) + 10E5], repmat(scaledTDiffs(end), 1, 2) + 1.25, ':k', 'LineWidth', 0.5);\n    text(behavUNIXTimes(1) - 10E5, scaledTDiffs(end) + 1.25, sprintf('%.1f', max(minTDiffs)), 'FontSize', 5, ...\n        'HorizontalAlignment', 'right');\n    text(behavUNIXTimes(1) - 10E5, scaledTDiffs(1) + 1.25, '0.0', 'FontSize', 5, 'HorizontalAlignment', 'right');\n    % threshold\n    plot([behavUNIXTimes(1) - 10E5 behavUNIXTimes(end) + 10E5], repmat(scaledTDiffs(2), 1, 2) + 1.25, ':r', 'LineWidth', 0.5);\n    text(behavUNIXTimes(1) - 10E5, scaledTDiffs(2) + 1.25, sprintf('%.1f', timeDiffThresh), 'FontSize', 5, ...\n        'HorizontalAlignment', 'right', 'Color', 'red');\n    if ~isempty(deviantTimes);\n        deviantTimesHandle = scatter(behavUNIXTimes(deviantTimes), scaledTDiffs(2 + deviantTimes) + 1.25, 65, 'red', ...\n            'filled');\n    else\n        deviantTimesHandle = [];\n    end;\n    \n    \n    % behavior trials scatter\n    behavPointHandles = scatter(behavUNIXTimes, 2 * ones(1, numel(behavUNIXTimes)), 10, 's', 'red', 'fill');\n    text(behavUNIXTimes, 2 * ones(1, numel(behavUNIXTimes)) + 0.05, behavTrialList, 'FontSize', 5, ...\n        'HorizontalAlignment', 'center');\n    \n    ylim([0.8 2.1]);\n    if ~isempty(deviantTimesHandle);\n        legend([filePointHandles, behavPointHandles, lineHandles(1), timeDiffHandle], ...\n            { 'imaging files', 'behavior times', 'potential matches', 'time differences' });\n    else\n        legend([filePointHandles, behavPointHandles, lineHandles(1), timeDiffHandle, deviantTimesHandle], ...\n            { 'imaging files', 'behavior times', 'potential matches', 'time differences', 'deviant time points' });\n    end;\n    hold off;\n    \nend;\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "INRunExp_standard.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/INRunExp_standard.m", "size": 6976, "source_encoding": "utf_8", "md5": "0ca07cd497925df810b83f5c082ea448", "text": "function INRunExp_standard(this)\n% INRunExp_standard - [no description]\n%\n%       INRunExp_standard(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\no('#%s ...', mfilename, 3, this.verb);\n\n%% init\n% extract the parameters structure\nparams = this.in.standard;\ncomParams = this.in.common;\n\n% timing reference\nt0 = nowUNIX();\nthis.in.expStartTime = t0;\nthis.in.timestamp = datestr(unix2dn(t0), 'HHMMSS');\n\n% update experiment counter\nthis.in.common.expNumber = this.in.common.expNumber + 1;\n\n% stores the baseline 1 frames for each run\nthis.in.data.base1Frames = cell(params.nRuns, 1);\n% stores the baseline 2 frames for each run\nthis.in.data.base2Frames = cell(params.nRuns, 1);\n% stores the stimulus frames for each run\nthis.in.data.stimFrames = cell(params.nRuns, 1);\n\n% stores the DFF average frame for each run\nthis.in.data.baseDFFAvg = cell(params.nRuns, 1);\nthis.in.data.stimDFFAvg = cell(params.nRuns, 1);\n\n% set the include states\nthis.in.data.includeInAvg = ones(params.nRuns, 1);\n\n% initialize stimulation\nINInitStim(this);\n\n%% init camera\n% flush the previous data\nstop(this.in.camH);\nflushdata(this.in.camH);\n% switch to non-stop collection mode and set logging to memory\ntriggerconfig(this.in.camH, 'manual');\nset(this.in.camH, 'FramesPerTrigger', Inf, 'LoggingMode', 'memory');\n% start camera but it waits for trigger\nstart(this.in.camH);\n\n%% starting delay\nshowMessage(this, sprintf('%s | Intrinsic: starting delay (%.1f sec) ...', INGetTime(this), comParams.startDelay), ...\n    'yellow');\npause(comParams.startDelay);\nif ~this.in.expRunning; INEndExp(this); return; end;\n\n%% run loop\n% loop over all runs\nfor iRun = 1 : params.nRuns;\n\n    % create the title string\n    titleString = sprintf(' | Intrinsic: Run %02d/%02d:', iRun, params.nRuns);\n\n    % collect the baseline frames\n    showMessage(this, sprintf('%s%s baseline 1 frames collection ...', INGetTime(this), titleString), 'yellow');\n    this.in.data.base1Frames{iRun} = collectData(this, params.baselineAvgDur);\n    if ~this.in.expRunning; INEndExp(this); return; end;\n\n    % wait until stimulus time\n    showMessage(this, sprintf('%s%s waiting for second baseline ...', INGetTime(this), titleString), 'yellow');\n    pause(params.baselineToStimDelay);\n    if ~this.in.expRunning; INEndExp(this); return; end;\n\n    % collect the baseline frames\n    showMessage(this, sprintf('%s%s baseline 2 frames collection ...', INGetTime(this), titleString), 'yellow');\n    this.in.data.base2Frames{iRun} = collectData(this, params.baselineAvgDur);\n    % calculate DFF of baseline data\n    this.in.data.baseDFFAvg{iRun} = getDFFAvg(this, this.in.data.base1Frames{iRun}, this.in.data.base2Frames{iRun});\n    % display data\n    INUpdateGUI(this);\n    if ~this.in.expRunning; INEndExp(this); return; end;\n\n    % wait until stimulus time\n    showMessage(this, sprintf('%s%s waiting for stimulus time ...', INGetTime(this), titleString), 'yellow');\n    pause(params.baselineToStimDelay);\n    if ~this.in.expRunning; INEndExp(this); return; end;\n\n    % stimulus\n    showMessage(this, sprintf('%s%s stimulus ...', INGetTime(this), titleString), 'yellow');\n    % play sound using TDT\n    if strcmp(comParams.stimMode, 'TDT');\n        % use the software trigger to launch the sound\n        this.in.RP.SoftTrg(1);\n        % wait for it to finish\n        pause(params.stdStimDur);\n        \n    % play sound without TDT\n    elseif strcmp(comParams.stimMode, 'soundCard');\n        % blocking so that the program waits for the stimlus to finish\n        this.in.audioplayer.playblocking();\n        \n    % send out a digital trigger\n    elseif strcmp(comParams.stimMode, 'trigOut');\n        outputSingleScan(this.in.daq.sessHandle, 1); % TTL high\n        outputSingleScan(this.in.daq.sessHandle, 0); % back to TTL low\n        % wait for it to finish\n        pause(params.stdStimDur);\n        \n    end;\n    if ~this.in.expRunning; INEndExp(this); return; end;\n\n    % wait until stimulus data collection time\n    showMessage(this, sprintf('%s%s waiting for stimulus collection time ...', INGetTime(this), titleString), 'yellow');\n    pause(params.stimToStimAvgDelay);\n    if ~this.in.expRunning; INEndExp(this); return; end;\n\n    % collect the stimulus frames\n    showMessage(this, sprintf('%s%s stimulus frames collection ...', INGetTime(this), titleString), 'yellow');\n    this.in.data.stimFrames{iRun} = collectData(this, params.stimAvgDur);\n    % calculate DFF of stimulus data\n    this.in.data.stimDFFAvg{iRun} = getDFFAvg(this, this.in.data.base1Frames{iRun}, this.in.data.stimFrames{iRun});\n    % display data\n    INUpdateGUI(this);\n    if ~this.in.expRunning; INEndExp(this); return; end;\n    \n    % wait the end period (recovery), unless it is the last run\n    if iRun ~= params.nRuns;\n        showMessage(this, sprintf('%s%s waiting for recovery period ...', INGetTime(this), titleString), 'yellow');\n        pause(params.waitPeriod);\n    end;\n    if ~this.in.expRunning; INEndExp(this); return; end;\n\nend;\n\n% clean up and free ressources\nINCleanUp(this);\n\n%% spatial downsampling loop\nshowMessage(this, sprintf('%s | Intrinsic: spatial down-sampling ...', INGetTime(this)));\n% loop over all runs\nfor iRun = 1 : params.nRuns;\n    this.in.data.base1Frames{iRun} = INSpatialDownSample(this, this.in.data.base1Frames{iRun});\n    this.in.data.base2Frames{iRun} = INSpatialDownSample(this, this.in.data.base2Frames{iRun});\n    this.in.data.stimFrames{iRun} = INSpatialDownSample(this, this.in.data.stimFrames{iRun});\nend;\nshowMessage(this, sprintf('%s | Intrinsic: spatial down-sampling done.', INGetTime(this)));\n\n% set flags, update counter and update GUI\nthis.in.expRunning = false;\nset(this.GUI.handles.in.paramPanElems.expNumber, 'String', sprintf('%d', this.in.common.expNumber)); % update in GUI\nset(this.GUI.handles.in.runExpBut, 'BackgroundColor', 'red', 'Value', 0);\nshowMessage(this, sprintf('%s | Intrinsic: experiment number %02d done !', INGetTime(this), this.in.common.expNumber));\n\nend\n\nfunction frames = collectData(this, collectDur)\n\n% collection start time\nt0 = nowUNIXSec();\n\n% start collecting\ntrigger(this.in.camH);\n\n% start waiting loop\nwhile nowUNIXSec() - t0 < collectDur;\n    pause(0.01); % avoid full-speed looping\nend;\n\n% stop and collect frames\nstop(this.in.camH);\nframes = getdata(this.in.camH);\no(['#%s: collected ' regexprep(repmat('%02dx', 1, ndims(frames)), 'x$', '') ' frame(s) ...'], ...\n    mfilename(), size(frames), 4, this.verb);\n\npause(0.02);\n\n% flush data\nflushdata(this.in.camH);\n% restart camera\nstart(this.in.camH);\n\nend\n\n% extracts the DFF average of the frames as (F2 - F1) / F1\nfunction DFFAvg = getDFFAvg(this, frames1, frames2)\n    % average frames1 over time\n    frames1 = INSpatialDownSample(this, nanmean(squeeze(frames1), 3));    \n    % average frames2 over time\n    frames2 = INSpatialDownSample(this, nanmean(squeeze(frames2), 3));\n    \n    %% calculate DFF\n    DFFAvg = (frames2 - frames1) ./ frames1;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "INSavePlot.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/INSavePlot.m", "size": 3579, "source_encoding": "utf_8", "md5": "4a457e0f84d96fe70d00758e8266075b", "text": "function INSavePlot(this, savePath, plotToSave, varargin)\n% INSavePlot - [no description]\n%\n%       INSavePlot(this, savePath, plotToSave, varargin)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% if no save path has been provided, create a dialog to request one\nif isempty(savePath);\n    % create the folder if it does not exist yet\n    if exist(this.path.OCIASave, 'dir') ~= 7; mkdir(this.path.OCIASave); end;\n    % create the dialog\n    [saveName, savePath] = uiputfile('*.*', 'Select a path where to save the plot', this.path.OCIASave);\n    if ischar(saveName)\n        savePath = [savePath saveName];\n        saveName = regexprep(saveName, '(\\.\\w{3})?$', '');\n    else % otherwise abort the saving\n        return;\n    end;\nelse\n    saveName = savePath;\nend;\n% clean up path and show message\nsavePath = strrep(savePath, '\\', '/');\nshowMessage(this, sprintf('Saving intrinsic plot to \"%s\" ...', savePath), 'yellow');\nsaveTic = tic; % for performance timing purposes\n\nfigName = saveName;\nsaveFig = figure('Name', figName, 'NumberTitle', 'off', 'Color', 'white', 'Units', 'pixels', ...\n    'Position', get(this.GUI.figH, 'Position'), 'Visible', 'off', 'MenuBar', 'none', 'Toolbar', 'none');\n\nanAxe = this.GUI.in.fouSubAxeHands.(plotToSave);\ninPanelChild = get(get(anAxe, 'Parent'), 'Children');\n% gather everything that doesn't belong to the Intrinsic panel's GUI (buttons)\ninPanelChild(~cellfun(@isempty, regexp(get(inPanelChild, 'Tag'), '^IN')) & inPanelChild ~= anAxe) = [];\n\n% if the panel has no child or only the Intrinsic's axe but it's hidden, then do not save anything\nif isempty(inPanelChild) || (numel(inPanelChild) == 1 && inPanelChild(1) == anAxe && strcmp(get(anAxe, 'Visible'), 'off'));\n    showWarning(this, 'OCIA:INSavePlot:NothingToSave', ...\n        sprintf('Cannot save analyser plot \"%s\" to \"%s\" because there is no plot to save.', figName, savePath));\n    return;\nend;\n\n% copy objects one by one starting from the last one\nfor iObj = fliplr(1 : numel(inPanelChild));\n    removeCallbacks(inPanelChild(iObj));\n    copyobj(inPanelChild(iObj), saveFig);\nend;\n\n% copy the colormap\nset(saveFig, 'Colormap', get(this.GUI.figH, 'Colormap'));\n\nsaveFolder = regexprep(savePath, '/[\\w\\.]+$', '');\nif exist(saveFolder, 'dir') ~= 7; mkdir(saveFolder); end;\n\n% get extension\next = regexprep(regexp(savePath, '\\.(\\w+)$', 'match'), '^\\.', '');\n% if extension is supported by the export_fig function, use that\nif ~isempty(ext) && ~strcmp(ext, 'fig') && ~isempty(regexp(ext, 'png|pdf|jpg|eps|tif|bmp', 'once'));\n    if numel(varargin) > 0 && isnumeric(varargin{1});\n        resolution = sprintf('-r%d', varargin{1});\n    else\n    \tresolution = '-r150';\n    end;\n    if numel(varargin) > 1 && strcmpi(varargin{2}, 'noCrop');\n        export_fig(savePath, ['-' ext], resolution, '-nocrop', saveFig);\n    else\n        export_fig(savePath, ['-' ext], resolution, saveFig);\n    end;\n    \n% otherwise save as figure\nelse                      \n    savePath = [regexprep(savePath, ['\\.' ext '$'], ''), '.fig'];\n    set(saveFig, 'Visible', 'on');\n    saveas(saveFig, savePath);\n    \nend;\nclose(saveFig);\n\nshowMessage(this, sprintf('Saving analyser plot to \"%s\" done (%.3f sec).', savePath, toc(saveTic)));\n    \nend\n\nfunction removeCallbacks(elem)\n    if isprop(elem, 'Callback'); set(elem, 'Callback', []); end;\n    if isprop(elem, 'ButtonDownFcn'); set(elem, 'ButtonDownFcn', []); end;\n    childElems = get(elem, 'Children');\n    for iElem = 1 : numel(childElems);\n        removeCallbacks(childElems(iElem));\n    end;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "JTUpdateVirtualJoints.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/JTUpdateVirtualJoints.m", "size": 5706, "source_encoding": "utf_8", "md5": "72e85fec6b3a1f27b93113ed88dc6252", "text": "function JTUpdateVirtualJoints(this, iFrame, iJointType)\r\n% JTUpdateVirtualJoints - [no description]\r\n%\r\n%       JTUpdateVirtualJoints(this, iFrame, iJointType)\r\n%\r\n% [No description]\r\n%\r\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\r\n\r\n% show debug plots of virtual joints\r\nshowDebugPlot = 0;\n\n% get the joints' x coordinate\r\njoints = squeeze(this.jt.joints(:, iFrame, 1, iJointType));\n\r\n% loop through the joint handles\r\nfor iJoint = 1 : this.jt.nJoints;\n    \r\n    % if a joint coordinate is empty and it is a virtual joint and the previous and next coordinates are not empty,\r\n    %   get the virtual joint's coordinates\r\n    if joints(iJoint) == 0 && this.jt.jointConfig{iJoint, 2} ...\r\n            && joints(iJoint - 1) ~= 0 && joints(iJoint + 1) ~= 0;\n\r\n        jointCoords = squeeze(this.jt.joints(:, iFrame, :, iJointType));\n        p1 = jointCoords(iJoint - 1, :); % coordinates of the joint before\r\n        p2 = jointCoords(iJoint + 1, :); % coordinates of the joint after\r\n        r1 = this.jt.jointConfig{iJoint, 4}(1); % distance with the joint before\r\n        r2 = this.jt.jointConfig{iJoint, 4}(2); % distance with the joint after\r\n\r\n        o(['#JTUpdateVirtualJoints: trying to predict virtual joint %d with: p1: [%.1f,%.1f], p2: [%.1f,%.1f], ', ...\r\n            'd1: %.1f, d2: %.1f ...'], iJoint, p1, p2, r1, r2, 4, this.verb);\n\n        % iteratively try to find the interesection point with increasing distances\r\n        increaseStepR1 = r1 * 0.05; increaseStepR2 = r2 * 0.05; iLoop = 1; nMaxLoop = 10;\n        intersects = {}; % default is empty\r\n        while isempty(intersects) && iLoop < nMaxLoop;\n            \r\n            % try to get the joints\r\n            [cx1, cy1, cx2, cy2, intersects] = calcVirtJoint(p1(1), p1(2), p2(1), p2(2), r1, r2); %#ok<ASGLU>\r\n            \r\n            % if there is going to be another loop, increase distances\r\n            if isempty(intersects) && iLoop < nMaxLoop;\n                r1 = r1 + increaseStepR1;\n                r2 = r2 + increaseStepR2;\n                iLoop = iLoop + 1;\n            end;\n            \r\n        end; % end while loop\r\n        \r\n        % show debug informations\r\n        if showDebugPlot;\n            \r\n            axeH = this.GUI.handles.jt.axe; %#ok<UNRCH>\r\n            hold(axeH, 'on');\n            \r\n            % show the intersection circles\r\n            commonArgs = {'FaceColor', 'none', 'EdgeColor', [0.3 1 0], 'Curvature', [1, 1]};\n            rectangle('Parent', axeH, 'Position', [a - r1 b - r1 2 * r1 2 * r1], commonArgs{:});\n            rectangle('Parent', axeH, 'Position', [c - r2 d - r2 2 * r2 2 * r2], commonArgs{:});\n            \r\n            % show the possible points\r\n            r = 4;\n            commonArgs = {'FaceColor', [0 0 1], 'EdgeColor', [0 0 1], 'Curvature', [1, 1]};\n            rectangle('Parent', axeH, 'Position', [cx1 - r/2 cy1 - r/2 r r], commonArgs{:});\n            rectangle('Parent', axeH, 'Position', [cx2 - r/2 cy2 - r/2 r r], commonArgs{:});\n            \r\n            hold(axeH, 'off');\n            \r\n        end;\n        \r\n        if isempty(intersects);\n            o('#JTUpdateVirtualJoints: virtual joint %d could not be placed (iLoop: %d).', iJoint, iLoop, 2, this.verb);\n            showWarning(this, 'OCIA:JT:JTUpdateVirtualJoints:VirtualJointImpossible', ...\r\n                sprintf(['Impossible to get a virtual joint for joint %d with the distances: ', ...\r\n                '%.1f and %.1f and coordinates: [%.1f,%.1f] and [%.1f,%.1f]!'], iJoint, r1, r2, p1, p2));\n            % clear the joint coordinates\r\n            this.jt.joints(iJoint, iFrame, :, iJointType) = [0 0];\n        else\r\n            o('#JTUpdateVirtualJoints: virtual joint %d is either at: c1 [%.1f,%.1f] or c2 [%.1f,%.1f] (iLoop: %d).', ...\r\n                iJoint, intersects{1}, intersects{2}, iLoop, 3, this.verb);\n            % save the joint coordinates\r\n            this.jt.joints(iJoint, iFrame, :, iJointType) = intersects{1}; % hard coded, always the first intersect\r\n        end;\n\n    end; % end of virtual joint if condition\r\n    \r\nend; % end of joint looping\r\n\r\nend\r\n\r\n% little functino to find the virtual joint by calculating the intersecitno of two circles\r\nfunction [cx1, cy1, cx2, cy2, intersects] = calcVirtJoint(a, b, c, d, r1, r2)\r\n\r\n    cx1 = NaN; cy1 = NaN; cx2 = NaN; cy2 = NaN; intersects = {};\n\n    % Finding the coordinates c (c1,c2) of the virtual joint is basically finding the intersection of two \r\n    % circles centered on p1 (a,b) and p2 (c,d) with respective radius r1 and r2:\r\n    %\r\n    %   (x - a)^2 + (y - b)^2 = r1^2    AND     (x - c)^2 + (y - d)^2 = r2^2\r\n    %\r\n    % First express the distance D between the two circles :\r\n    D = sqrt((c - a)^2 + (d - b)^2);\n\n    % Inter sections only happen if:\r\n    %\r\n    %   r1 + r2 > D                     AND     D > |r1 - r2|\r\n    %\r\n\r\n    % check for intersection\r\n    circlesIntersect = r1 + r2 >= D && D > abs(r1 - r2);\n    if ~circlesIntersect;\n        return;\n    end;\n\n    % To get the coordinates, first express gamma :            \r\n    gamma = 0.25 * sqrt((D + r1 + r2) * (D + r1 - r2) * (D - r1 + r2) * (-D + r1 + r2));\n\n    % Then the coordinates can be calculated with :\r\n    cx1 = ((a + c) / 2) + (((c - a) * (r1^2 - r2^2)) / (2 * D^2)) + ((2 * gamma * (b - d)) / D^2);\n    cx2 = ((a + c) / 2) + (((c - a) * (r1^2 - r2^2)) / (2 * D^2)) - ((2 * gamma * (b - d)) / D^2);\n    cy1 = ((b + d) / 2) + (((d - b) * (r1^2 - r2^2)) / (2 * D^2)) - ((2 * gamma * (a - c)) / D^2);\n    cy2 = ((b + d) / 2) + (((d - b) * (r1^2 - r2^2)) / (2 * D^2)) + ((2 * gamma * (a - c)) / D^2);\n\n    % The intersection points are then:\r\n    intersects = {[cx1, cy1]; [cx2, cy2]};\n\nend\r\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "BELightPulse.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/BELightPulse.m", "size": 1073, "source_encoding": "utf_8", "md5": "7d7e8d6d47649bbc740a57b9bccf2f5e", "text": "function BELightPulse(this, pulseDur, IPI, nPulses)\n% BELightPulse - [no description]\n%\n%       BELightPulse(this, pulseDur, IPI, nPulses)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\nimagTTLState = get(this.GUI.handles.be.imagTTL, 'Value');\no('#%s(): pulseDur %.3f, IPI %.3f, nPulses %d.', mfilename, pulseDur, IPI, nPulses, 2, this.verb);\n\nif this.be.hw.connected && isfield(this.be.hw, 'anOut');\n    start(timer('Name', 'PulseTimer', 'TimerFcn', { @doPulse, this.be, pulseDur, imagTTLState }, ...\n        'Period', pulseDur + IPI, 'TasksToExecute', nPulses, 'ExecutionMode', 'fixedRate'));\nelse\n    showWarning(this, 'OCIA:Behavior:LightHardwareDisconnected', 'Hardware is disconnected.');\n    set(this.GUI.handles.be.light, 'BackgroundColor', 'red', 'Value', 0);\nend;\n\n\nend\n\nfunction doPulse(~, ~, be, pulseDur, imagTTLState)\n\nbe.hw.anOut.outputSingleScan([imagTTLState * 5, 1 * be.params.maxLight]);\npauseTicToc(pulseDur);\nbe.hw.anOut.outputSingleScan([imagTTLState * 5, 0 * be.params.maxLight]);\n    \nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "INTestStim.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/INTestStim.m", "size": 3097, "source_encoding": "utf_8", "md5": "d3c965f0467534cb336f38b928666bc2", "text": "\nfunction INTestStim(this, ~, ~)\n% INTestStim - [no description]\n%\n%       INTestStim(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% update button\nset(this.GUI.handles.in.testStimBut, 'Value', 1, 'Enable', 'off', 'BackgroundColor', 'yellow');\n% disable run exp button\nset(this.GUI.handles.in.runExpBut, 'Enable', 'off');\n\n% extract the parameters structure\ncomParams = this.in.common;\n\ntry\n    \n    % test stimulus differently depending on the stimulus mode\n    switch comParams.stimMode;\n    \n        % play through the sound card\n        case 'soundCard';\n\n            % get the sound stimulus of the current setting\n            [soundToPlay, sampFreq] = INGetSoundStim(this);\n            if isempty(soundToPlay); return; end;\n            % use MATLAB sound function\n            this.in.audioplayer = audioplayer(soundToPlay, sampFreq);\n            this.in.audioplayer.playblocking();\n            \n        % play by software triggering the TDT\n        case { 'TDT', 'trigIn' };\n\n            % get the sound stimulus of the current setting\n            [soundToPlay, sampFreq] = INGetSoundStim(this);\n            if isempty(soundToPlay); return; end;\n            % 1 loop for standard mode and nSweeps for fourier mode\n            nLoops = iff(strcmp(this.in.expMode, 'standard'), 1, this.in.fourier.nSweeps);\n            % calculate wait time\n            waitTime = 1.1 * numel(soundToPlay) * nLoops / sampFreq;\n            % load stimulus\n            this.in.RP = playTDTSound(soundToPlay, 0, this.GUI.figH, nLoops);\n            % launch stimulus with software trigger\n            this.in.RP.SoftTrg(1);\n            \n            % wait for sound to finish\n            startWait = tic;\n            while ~isempty(this.in.RP) && toc(startWait) < waitTime;\n                pause(0.01);\n            end;\n            \n        % send trigger out\n        case 'trigOut';\n            % show message\n            showMessage(this, 'Intrinsic: sending out TTL ...', 'yellow');\n            % suppress silly warning about on-demand channels\n            warning('off', 'daq:Session:onDemandOnlyChannelsAdded');\n            % create NI session with a digital channel\n            this.in.daq.sessHandle = daq.createSession(this.in.daq.vendorName);\n            addDigitalChannel(this.in.daq.sessHandle, this.in.daq.deviceID, this.in.daq.trigOutPort, 'OutputOnly');\n            % restore silly warning\n            warning('on', 'daq:Session:onDemandOnlyChannelsAdded');\n            % init to TTL low\n            outputSingleScan(this.in.daq.sessHandle, 0);\n            % send trigger\n            outputSingleScan(this.in.daq.sessHandle, 1); % TTL high\n            outputSingleScan(this.in.daq.sessHandle, 0); % back to TTL low\n            \n    end;\n\n% if something failed, capture and abort but still clean up\ncatch err;\n    \n    showWarning(this, 'OCIA:INTestStim:TestFailed', ...\n        sprintf('Intrinsic: error during testing of stimulation (\"%s\"): %s.', err.identifier, err.message));\n    \nend;\n\n% release resources\nINCleanUp(this);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "ANSavePlot.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/ANSavePlot.m", "size": 6041, "source_encoding": "utf_8", "md5": "3d1614ebf1f33d5130d6d4f323ecb622", "text": "function ANSavePlot(this, savePath, varargin)\n% ANSavePlot - [no description]\n%\n%       ANSavePlot(this, savePath, varargin)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% if no save path has been provided, create a dialog to request one\nif isempty(savePath);\n    % create the folder if it does not exist yet\n    if exist(this.path.OCIASave, 'dir') ~= 7; mkdir(this.path.OCIASave); end;\n    % create the dialog\n    [saveName, savePath] = uiputfile('*.*', 'Select a path where to save the plot', this.path.OCIASave);\n    if ischar(saveName)\n        savePath = [savePath saveName];\n        saveName = regexprep(saveName, '(\\.\\w{3})?$', '');\n    else % otherwise abort the saving\n        return;\n    end;\nelse\n    saveName = savePath;\nend;\n% clean up path and show message\nsavePath = strrep(savePath, '\\', '/');\nshowMessage(this, sprintf('Saving analyser plot to \"%s\" ...', savePath), 'yellow');\nsaveTic = tic; % for performance timing purposes\n\nfigName = saveName;\nif numel(varargin) > 0 && ~isempty(varargin{1}); figName = varargin{1}; end;\nsaveFig = figure('Name', figName, 'NumberTitle', 'off', 'Color', 'white', 'Units', 'pixels', ...\n    'Position', get(this.GUI.figH, 'Position'), 'Visible', 'off', 'MenuBar', 'none', 'Toolbar', 'none');\n\nanAxe = this.GUI.handles.an.axe;\nanPanelChild = get(this.GUI.handles.panels.AnalyserPanel, 'Children');\n% gather everything that doesn't belong to the Analyser panel's GUI (buttons)\nanPanelChild(~cellfun(@isempty, regexp(get(anPanelChild, 'Tag'), '^AN')) & anPanelChild ~= anAxe) = [];\n\n% if the panel has no child or only the Analyser's axe but it's hidden, then do not save anything\nif isempty(anPanelChild) || (numel(anPanelChild) == 1 && anPanelChild(1) == anAxe && strcmp(get(anAxe, 'Visible'), 'off'));\n    % get the empty or hidden plot reason\n    statusTextReason = get(this.GUI.handles.an.message, 'String');\n    if isempty(statusTextReason); statusTextReason = 'unknown'; end;\n    statusTextReason(1) = lower(statusTextReason(1));\n    statusTextReason = regexprep(statusTextReason, '\\.$', '');\n    % show warning\n    showWarning(this, 'OCIA:ANSavePlot:NothingToSave', ...\n        sprintf('Cannot save analyser plot \"%s\" to \"%s\" because there is no plot to save. Possible cause: %s.', ...\n        figName, savePath, statusTextReason));\n    return;\nend;\n\nif ~verLessThan('matlab', '8.4.0');\n    \n    % remove colorbar if a legend is present because of weird saving system\n    if any(arrayfun(@(i)isa(i, 'matlab.graphics.illustration.ColorBar'), anPanelChild)) ...\n        && any(arrayfun(@(i)isa(i, 'matlab.graphics.illustration.Legend'), anPanelChild));\n        anPanelChild(arrayfun(@(i)isa(i, 'matlab.graphics.illustration.ColorBar'), anPanelChild)) = [];\n    end;\n    \n    % copy objects one by one starting from the last one\n    for iObj = fliplr(1 : numel(anPanelChild));\n        % do not copy axes if next one is a colorbar\n        if isa(anPanelChild(iObj), 'matlab.graphics.axis.Axes') && iObj > 1 ...\n                && (isa(anPanelChild(iObj - 1), 'matlab.graphics.illustration.ColorBar') ...\n                ||  isa(anPanelChild(iObj - 1), 'matlab.graphics.illustration.Legend'));\n            continue;\n        % copy colorbar AND its axes\n        elseif isa(anPanelChild(iObj), 'matlab.graphics.illustration.ColorBar') && iObj < numel(anPanelChild) ...\n            && isa(anPanelChild(iObj + 1), 'matlab.graphics.axis.Axes');\n            removeCallbacks(anPanelChild(iObj));\n            removeCallbacks(anPanelChild(iObj + 1));\n            copyobj([anPanelChild(iObj), anPanelChild(iObj + 1)], saveFig);\n        % copy legend AND its axes\n        elseif isa(anPanelChild(iObj), 'matlab.graphics.illustration.Legend') && iObj < numel(anPanelChild) ...\n            && isa(anPanelChild(iObj + 1), 'matlab.graphics.axis.Axes');\n            removeCallbacks(anPanelChild(iObj));\n            removeCallbacks(anPanelChild(iObj + 1));\n            copyobj([anPanelChild(iObj), anPanelChild(iObj + 1)], saveFig);\n        else\n            % do not copy invisible stuff\n            if strcmp(get(anPanelChild(iObj), 'Visible'), 'off'); continue; end;\n            removeCallbacks(anPanelChild(iObj));\n            copyobj(anPanelChild(iObj), saveFig);\n        end;\n    end;\nelse\n    % copy objects one by one starting from the last one\n    for iObj = fliplr(1 : numel(anPanelChild));\n        removeCallbacks(anPanelChild(iObj));\n        copyobj(anPanelChild(iObj), saveFig);\n    end;\nend;\n\n% copy the colormap\nset(saveFig, 'Colormap', get(this.GUI.figH, 'Colormap'));\n\nsaveFolder = regexprep(savePath, '/[\\w\\.]+$', '');\nif exist(saveFolder, 'dir') ~= 7; mkdir(saveFolder); end;\n\n% get extension\next = regexprep(regexp(savePath, '\\.(\\w+)$', 'match'), '^\\.', '');\n% if extension is supported by the export_fig function, use that\nif ~isempty(ext) && ~strcmp(ext, 'fig') && ~isempty(regexp(ext, 'png|pdf|jpg|eps|tif|bmp', 'once'));\n    if numel(varargin) > 1 && isnumeric(varargin{2});\n        resolution = sprintf('-r%d', varargin{2});\n    else\n    \tresolution = this.an.plotSaveResolution;\n    end;\n    warning('off', 'MATLAB:LargeImage');\n    if numel(varargin) > 2 && strcmpi(varargin{3}, 'noCrop');\n        export_fig(savePath, ['-' ext], resolution, '-nocrop', saveFig);\n    else\n        export_fig(savePath, ['-' ext], resolution, saveFig);\n    end;\n    warning('on', 'MATLAB:LargeImage');\n    \n% otherwise save as figure\nelse                      \n    savePath = [regexprep(savePath, ['\\.' ext '$'], ''), '.fig'];\n    set(saveFig, 'Visible', 'on');\n    saveas(saveFig, savePath);\n    \nend;\nclose(saveFig);\n\nshowMessage(this, sprintf('Saving analyser plot to \"%s\" done (%.3f sec).', savePath, toc(saveTic)));\n    \nend\n\nfunction removeCallbacks(elem)\n    if isprop(elem, 'Callback'); set(elem, 'Callback', []); end;\n    if isprop(elem, 'ButtonDownFcn'); set(elem, 'ButtonDownFcn', []); end;\n    childElems = get(elem, 'Children');\n    for iElem = 1 : numel(childElems);\n        removeCallbacks(childElems(iElem));\n    end;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DIUpdateGUI.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/DIUpdateGUI.m", "size": 4060, "source_encoding": "utf_8", "md5": "7053fe17121fcccefd4ec7791bf84fb4", "text": "%% #DIUpdateGUI\nfunction DIUpdateGUI(this, ~, ~)\n\no('#%s() ...', mfilename, 3, this.verb);\n\ntry % capture display errors\n\n\n%% camera plot\ntry\n    currFrame = peekdata(this.GUI.di.camHandle, 1);\n    flushdata(this.GUI.di.camHandle);\n    currFrame(:, :, 2) = currFrame(:, :, 1);\n    currFrame(:, :, 3) = currFrame(:, :, 1);\n%     imagesc(currFrame, 'Parent', this.GUI.handles.di.camAxe);\n    cameAxeChild = get(this.GUI.handles.di.camAxe, 'Child');\n    if isempty(cameAxeChild);\n        cameAxeChild = imagesc(zeros(576, 720, 3), 'Parent', this.GUI.handles.di.camAxe);\n        axis(this.GUI.handles.di.camAxe, 'equal');\n    end;\n    set(cameAxeChild, 'CData', currFrame);\ncatch\n    imagesc(zeros(576, 720, 3), 'Parent', this.GUI.handles.di.camAxe);\nend;\n\n\n%% activity plot\nactiW = 180; actiH = 180;\n\nif this.GUI.di.activityRunning && ~isempty(this.GUI.di.actiMovies);    \n    % get the current frame\n    activityFrame = this.GUI.di.actiMovies{this.GUI.di.actiMovieIndex}(:, :, :, this.GUI.di.actiMovieFrame);\n    \n    % update the frame\n    this.GUI.di.actiMovieFrame = this.GUI.di.actiMovieFrame + 3;\n    \n    if this.GUI.di.actiMovieFrame > size(this.GUI.di.actiMovies{1}, 4); this.GUI.di.actiMovieFrame = 1; end;\nelse\n    activityFrame = zeros(actiH, actiW, 3);\nend;\n\n\n% plot the frame\nactiAxeChild = get(this.GUI.handles.di.actiAxe, 'Child');\nif isempty(actiAxeChild);\n    actiAxeChild = imagesc(activityFrame, 'Parent', this.GUI.handles.di.actiAxe);\n    axis(this.GUI.handles.di.actiAxe, 'equal');\nend;\nset(actiAxeChild, 'CData', activityFrame);\n\n% calculate zoom factors\nactiWZoom = actiW / this.GUI.di.zoomLevel; actiHZoom = actiH / this.GUI.di.zoomLevel;\nactiMargX = (actiW - actiWZoom) / 2; actiMargY = (actiH - actiHZoom) / 2;\nxLims = [0, actiW - actiMargX]; yLims = [0, actiH - actiMargY];\nset(this.GUI.handles.di.actiAxe, 'XLim', xLims, 'YLim', yLims);\n\n%% reponse axe\n% create the response rate rectangle\ndelete(get(this.GUI.handles.di.respRateAxe, 'Child'));\nhold(this.GUI.handles.di.respRateAxe, 'on');\nrectangle('Parent', this.GUI.handles.di.respRateAxe, 'FaceColor', 'blue', 'EdgeColor', 'black', 'Position', [0 0 min(abs(this.di.nResps + 0.1), 10) 1]);\n% add the threshold\nplot(this.GUI.handles.di.respRateAxe, ones(2, 1) * this.di.respRateThresh, [0 1], 'Color', 'red', 'LineWidth', 8);\nhold(this.GUI.handles.di.respRateAxe, 'off');\nset(this.GUI.handles.di.respRateAxe, 'XLim', [0, 10], 'YLim', [0 1], 'XTick', [], 'YTick', []);\n\n% apply the decay\nthis.di.nResps = min(max((this.di.nResps - this.di.nRespsDecay) * 0.99, 0), 10 + 0.5);\n\n%% time axe\n% create the time rectangle\nif this.di.waitingForResp;\n    \n    delete(get(this.GUI.handles.di.respTimeAxe, 'Child'));\n    hold(this.GUI.handles.di.respTimeAxe, 'on');\n    remTime = min(max(this.di.waitingTime - (nowUNIX - this.di.waitingStartTime), 0) + 0.001, this.di.waitingTime);\n    rectangle('Parent', this.GUI.handles.di.respTimeAxe, 'FaceColor', 'green', 'EdgeColor', 'black', 'Position', [0 0 remTime 1]);\n    hold(this.GUI.handles.di.respTimeAxe, 'off');\n    set(this.GUI.handles.di.respTimeAxe, 'XLim', [0, this.di.waitingTime], 'YLim', [0 1], 'XTick', [], 'YTick', []);\n\n    set(this.GUI.handles.di.panels.time, 'Title', sprintf('Response time - %.1f sec', remTime / 1000));\n\nend;\n\n\n% check if a response was given\nif this.di.waitingForResp && this.di.nResps > this.di.respRateThresh;\n    this.di.resp = true;\n    this.di.waitingForResp = false;\n    stimNum = this.di.stimMatrix(this.di.iTrial, this.di.iStimMat);\n    isTarget = stimNum == this.di.targetStim;\n    showMessage(this, sprintf('RESPONSE !! Trial %d: stimulus %d, target: %d, correct: %d...', this.di.iTrial, stimNum, isTarget, isTarget), 'yellow');\nend;\n\nif this.di.lockMouse;\n    r = java.awt.Robot();\n    lockCoords = this.GUI.pos(1:2) + this.GUI.pos(3:4) - [2 2];\n    r.mouseMove(lockCoords(1), lockCoords(2));   \nend;\n\n\ncatch err;\n    errStack = getStackText(err);\n    showWarning(this, 'OCIA:DIUpdateGUI', sprintf('Problem in the discriminator GUI update function: \"%s\".', err.message));\n    o(errStack, 0, 0);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DWFilterTable.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/DWFilterTable.m", "size": 9418, "source_encoding": "utf_8", "md5": "0487d32d14dbf96aa9d050a83844249d", "text": "function [filtTable, filtTableIndexes] = DWFilterTable(this, filtText, tableToUse, tIDs)\n% DWFilterTable - Filter the DataWatcher table and return the rows (and row indexes)\n%\n%       [filtTable, filtTableIndexes] = DWFilterTable(this, filtText)\n%       [filtTable, filtTableIndexes] = DWFilterTable(this, filtText, tableToUse)\n%\n% Filters the rows of the 'tableToUse' (or the DataWatcher's table if none provided) based on the column names \n%   using the filternig string 'filtText'. The filtering string can be anything like: 'rowType = notebook' or \n%   'rowType ~= imgData' for regexp match or 'spot != spot01' for *not* matching or\n%   'spot = spot01 AND day = 2014_02_08' or 'rowType = imgData OR animal = mouse1', etc.\n%   Sub-column names are also possible like 'data.rawImg.loadStatus = full' to get all the rows where the\n%   data is fully loaded.\n\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n    \n    % if no table was provided, use the default table which is the DataWatcher's table\n    if ~exist('tableToUse', 'var') || isempty(tableToUse);\n        tableToUse = this.dw.table;\n    end;\n    % if no table IDs were provided, use the DataWatcher table's IDs\n    if ~exist('tIDs', 'var') || isempty(tIDs);\n        tIDs = this.dw.tableIDs;\n    end;\n\n    % separate the filter into parts at the join operators\n    [filtParts, ~, ~, ~, ops] = regexp(filtText, '\\s(OR|AND)\\s', 'split');\n    \n    % holder for the filtered table indexes\n    filtTableIndexes = [];\n    \n    % go through each filter pair\n    for iPart = 1 : numel(filtParts);\n    \n        % match the filter string for: colName (column name), eq (equality), val (value)\n        hit = regexp(filtParts{iPart}, '^(?<colName>[\\w\\.\\*]+)\\s+(?<eq>[!~]{0,2}\\=)\\s+(?<val>.+)$', 'names');\n        \n        % cannot match the pair, skip it with warning\n        if isempty(hit);\n            showWarning(this, 'OCIA:DW:DWFilterTable:BadRegexp', ...\n                sprintf('Cannot processed regular expression part: %s. Skipping it.', filtParts{iPart}));\n            continue;\n        end;\n        \n        % get the equality function\n        switch hit.eq;\n            % basic string comparison\n            case '='; eqfun = @strcmp;\n            % basic string comparison with negation\n            case '!='; eqfun = @(varargin)~strcmp(varargin{:});\n            % regular expression comparison\n            case '~='; eqfun = @(varargin)~isempty(regexp(varargin{1}, varargin{2}, 'once'));\n            % regular expression comparison with negation\n            case '!~='; eqfun = @(varargin)isempty(regexp(varargin{1}, varargin{2}, 'once'));\n            % unknown comparison sign\n            otherwise\n                showWarning(this, 'OCIA:DW:DWFilterTable:UnkownVar', ...\n                sprintf('Unknown comparison sign requested for filtering: %s', hit.eq));\n            continue;\n        end;\n        \n        % extract the value and the filtering column's name\n        val = hit.val;\n        colName = hit.colName;\n        \n        % if the column name does not contain a sub-column\n        if isempty(regexp(colName, '^[\\w\\*]+\\.[\\w\\*]+', 'once'));\n            \n            % if the requested filtering column is not part of the table's column list and is not the rowID\n            if ~ismember(colName, tIDs) && ~strcmp(colName, 'rowID');\n                % show a warning and skip\n                showWarning(this, 'OCIA:DW:DWFilterTable:UnkownColName', ...\n                    sprintf('Unknown column name requested for filtering: %s', colName));\n                continue;\n            end;\n\n            % special case for the row IDs\n            if strcmp(colName, 'rowID');\n                values = DWGetRowID(this, 1 : size(tableToUse, 1), tableToUse, tIDs); \n                \n            % otherwise get all the values from the table's column\n            else\n                values = get(this, 'all', colName, tableToUse, tIDs);            \n            end;\n            \n            % make sure values are cell\n            if ~iscell(values); values = { values }; end;\n            \n            % make sure no cell is empty\n            values(cellfun(@isempty, values)) = { '' };\n            \n        % if the variable name has a sub-variable name (like \"data.rawImg\"), extract the values differently\n        else\n            values = getSubColumnValues(this, tIDs, tableToUse, colName);\n        end;\n        \n        % check if all values are strings and abort with a warning if it is not the case\n        isAllCell = all(cellfun(@ischar, values));        \n        if ~isAllCell; \n            showWarning(this, 'OCIA:DW:DWFilterTable:NotCharCellFilter', 'Requested filtering on a non-string column. Aborting.');\n            return;\n        end;\n        \n        % remove the delete tag\n        values = regexprep(values, ['^' this.GUI.dw.deleteTag], '');\n        \n        % get the rows where the requested values match (or not) the table's values\n        tempTableIndexes = find(cellfun(@(valueTable) eqfun(valueTable, val), values));\n        \n        % if this is the first filter pair, use if as starting filtered table\n        if iPart == 1;\n            filtTableIndexes = tempTableIndexes;\n            \n        % if the previous filter was an 'AND' operator, only get the overlapping indexes\n        elseif strcmp(ops{iPart - 1}, ' AND ');\n            % only get the overlap of them\n            filtTableIndexes(~ismember(filtTableIndexes, tempTableIndexes), :) = []; %#ok<AGROW>\n            \n        % if the previous filter was an 'OR' operator, get the unique concatenated indexes\n        elseif strcmp(ops{iPart - 1}, ' OR ');\n            % get the unique concatenated table\n            filtTableIndexes = unique(vertcat(filtTableIndexes, tempTableIndexes), 'rows');\n        end;\n        \n    end; % end of filter pairs looping\n    \n    % create the filtered table using the filtered indexes\n    filtTable = tableToUse(filtTableIndexes, :);    \n\nend\n\n% extract the values of a non-character column\nfunction newValues = getSubColumnValues(this, tIDs, tableToUse, colName)\n\n% get the column name \"parts\"\ncolNameParts = regexp(colName, '\\.', 'split');\n\n% get the values for the column name\nvalues = get(this, 'all', colNameParts{1}, tableToUse, tIDs);\n% make sure values are cell\nif ~iscell(values); values = { values }; end;\n% make sure no empty cells exist by filling them with empty structures\nvalues(cellfun(@isempty, values)) = { struct() };\n% create a new values cell-array which will have the extract sub-column values\nnewValues = cell(numel(values), 1);\n\n% go through each row of the table and asses whether the sub-column matches\nfor iRow = 1 : numel(values);\n    \n    % get a row validity tag\n    isRowValid = true;\n    % get the remaining column parts\n    localColumnParts = colNameParts(2 : end);\n    % get the current structure\n    currentValue = values{iRow};\n    \n    % recursively get the right field from the current structure\n    while ~isempty(localColumnParts);  \n        \n        % if the current value is still a structure and it has the right sub-field\n        if isstruct(currentValue) && isfield(currentValue, localColumnParts{1});\n            % get the \"next\" sub-structure and move forward in the list of the column parts\n            currentValue = currentValue.(localColumnParts{1});\n            localColumnParts(1) = [];\n        \n        % if the current value is still a structure with at least one field and a numbered field was required\n        elseif isstruct(currentValue) && numel(fieldnames(currentValue)) ...\n                && ~isempty(regexp(localColumnParts{1}, '^\\s*\\d+\\s*$', 'once'));\n            fNames = fieldnames(currentValue); % get the field names\n            iField = str2double(localColumnParts{1}); % get the sub-field number\n            % if the required number is not a number or exceeds the limit, abort\n            if isnan(iField) || iField < 1 || iField > numel(fNames); \n                % mark this row as non-valid and abort\n                isRowValid = false;\n                break;   \n            end;\n            % otherwise get the \"next\" sub-structure by getting the right sub-field using the number given as input and\n            %   move forward in the list of the column parts\n            currentValue = currentValue.(fNames{iField});\n            localColumnParts(1) = [];\n        \n        % if the current value is still a structure with at least one field and any field was required\n        elseif isstruct(currentValue) && numel(fieldnames(currentValue)) && strcmp(localColumnParts{1}, '*');\n            % get the \"next\" sub-structure and move forward in the list of the column parts \n            fNames = fieldnames(currentValue); % get the field names           \n            currentValue = currentValue.(fNames{1});\n            localColumnParts(1) = [];\n            \n        % if the current value is still a structure but it does *not* have the right sub-field, abort\n        elseif isstruct(currentValue);\n            % mark this row as non-valid and abort\n            isRowValid = false;\n            break;            \n        end;\n    end;\n    \n    % if the current row is already flagged as non-valid, do not continue\n    if ~isRowValid;\n        % fill the row with an empty string\n        newValues{iRow} = '';\n        continue;\n    end;\n    \n    % store the extracted value\n    newValues{iRow} = currentValue;\n    \nend;\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIACreateParamPanelControls.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/OCIACreateParamPanelControls.m", "size": 26865, "source_encoding": "utf_8", "md5": "96f2eed824df54d1d3e64fbb0b6a310d", "text": "\nfunction OCIACreateParamPanelControls(this, modeID, varargin)\n% OCIACreateParamPanelControls - Creates a parameter pannel menu\n%\n%       OCIACreateParamPanelControls(this, modeID, optionalHandleOfNavButtons)\n%\n% Creates a parameter panel menu from a parameter panel configuration (\"this.GUI.(modeID).paramPanConfig\") and a\n%   data structure (\"this.(modeID).(categ).(id)\"). If \"optionalHandleOfNavButtons\" exists and is a handle to the\n%   parameter panel navigation buttons, the panel is not re-created but just updated to display the right page\n%   (\"this.GUI.(modeID).paramPage\").\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% abort if no configuration provided\nif isempty(this.GUI.(modeID).paramPanConfig); return; end;\n\n%% init\nfontSizeAdjust = 1;\nlabelFontSizeAdjust = 1;\nswitch modeID;\n    \n    % Analyser mode\n    case 'an';\n        \n        % define the updating function\n        updateFunction = @ANUpdatePlot;\n        % define the max number of rows and the number of items to place\n        nMaxRows = 5; nMinRows = 5; nMaxCols = 2; nMinCols = 2; pad = 0.02; inPad = 0.01;\n%         nMaxRows = 4; nMinRows = 4; nMaxCols = 4; nMinCols = 3; pad = 0.02; inPad = 0.01;\n%         fontSizeAdjust = 3.0;\n        \n    % Intrinsic mode\n    case 'in';\n    \n        % define the updating function\n        updateFunction = @INUpdateParams;\n        % define the max number of rows and the number of items to place\n        nMaxRows = 10; nMinRows = 8; nMaxCols = 4; nMinCols = 3; pad = 0.025; inPad = 0.02;\n        \n    % Behavior mode\n    case 'be';\n    \n        % define the updating function\n        updateFunction = @BEUpdateParams;\n        % define the max number of rows and the number of items to place\n        nMaxRows = 15; nMinRows = 15; nMaxCols = 2; nMinCols = 2; pad = 0.02; inPad = 0.015;\n        \n        fontSizeAdjust = 0.5;\n        \n    % TrialView mode\n    case 'tv';\n    \n        % define the updating function\n        updateFunction = @TVUpdateParams;\n        % define the max number of rows and the number of items to place\n        nMaxRows = 10; nMinRows = 10; nMaxCols = 2; nMinCols = 2; pad = 0.02; inPad = 0.015;\n        \n        labelFontSizeAdjust = 1.1;\n        fontSizeAdjust = 0.7;\n        \n    otherwise\n        return;\nend;\n\n%% init the parameter pannel dimensions\n% common inputs\ncommons = { 'Parent', this.GUI.handles.(modeID).paramPan, 'Units', 'normalized', 'Enable', 'off' };\nUICommons = struct();\ncallBackFcn = { 'Callback', @(h, e) updateFunction(this, h, e) };\nUICommons.text = [ commons, { 'Background', 'white', 'Style', 'edit' }, callBackFcn ];\nUICommons.dropdown = [ commons, { 'Background', 'white', 'Style', 'popupmenu' }, callBackFcn ];\nUICommons.list = [ commons, { 'Background', 'white', 'Style', 'list', 'Min', 0, 'Max', 2 }, callBackFcn ];\nUICommons.button = [ commons, { 'Style', 'pushbutton' } ];\nUICommons.slider = [ commons, { 'Style', 'slider' } ];\nUICommons.lab = [ commons, { 'Background', 'white', 'Style', 'text' } ];\nUICommons.paramNav = [ commons, { 'Style', 'pushbutton', 'Enable', 'off', 'FontSize', ...\n    round(this.GUI.pos(4) / 55 * fontSizeAdjust), 'Callback', @(h, e)OCIACreateParamPanelControls(this, modeID, h) } ];\n\n% define the max number of rows and the number of items to place\nnUICtrl = size(this.GUI.(modeID).paramPanConfig, 1);\nUISizes = this.GUI.(modeID).paramPanConfig{:, 5};\nnUICtrlRows = sum(UISizes(:, 1));\n\n% calculate the number of acutal columns and rows to use\nnRows = min(max(ceil(nUICtrlRows / nMaxCols), nMinRows), nMaxRows);\nnCols = min(max(ceil((nUICtrlRows / nRows)), nMinCols), nMaxCols) + 0.15;\nelemW = (1 - (nCols + 1) * pad) / nCols; % width depends on the number of columns\nelemH = (1 - (nRows + 1) * pad) / nRows; % height depends on the number of rows\niRow = 0; iCol = 0; %#ok<NASGU>, init the row and column indexes\n\n% part of the width that is allocated for the label if it is on the side\nlabelWidthRatio = 0.5;\n% height that is allocated for the label if it is on the top\nlabelHeight = 0.5;\n\n% if call was made by one of the navigate buttons\nif ~isempty(varargin) && (varargin{1} == this.GUI.handles.(modeID).nextParams...\n        || varargin{1} == this.GUI.handles.(modeID).prevParams);\n    % get all UI elements except the arrows\n    UIElems = this.GUI.handles.(modeID).paramPanElems;\n    % compute the minimum and maximum positions of the UIControls to desactivate the next/prev buttons\n    minPos = Inf; maxPos = -Inf;\n    % get the move direction\n    direction = iff(varargin{1} == this.GUI.handles.(modeID).prevParams, 1, -1);\n    % get the IDs and go through each elements\n    UIIDs = fieldnames(UIElems);\n    for iElem = 1 : numel(UIIDs);\n        % get the position of this element \n        pos = get(this.GUI.handles.(modeID).paramPanElems.(UIIDs{iElem}), 'Position');\n        % update the position\n        pos = pos + [nMaxCols * (elemW + pad) 0 0 0] .* direction;\n        % move the element\n        set(this.GUI.handles.(modeID).paramPanElems.(UIIDs{iElem}), 'Position', pos);\n        % if the control is outside the visible area, hide them\n        isOut = pos(1) > 0 && (pos(1) + pos(3)) < 1;\n        set(this.GUI.handles.(modeID).paramPanElems.(UIIDs{iElem}), 'Visible', iff(isOut, 'on', 'off'));\n        minPos = min(minPos, pos(1));\n        maxPos = max(maxPos, pos(1) + pos(3));\n    end;\n    % enable/disable the navigating buttons\n    set(this.GUI.handles.(modeID).prevParams, 'Enable', iff(minPos > 0, 'off', 'on'));\n    set(this.GUI.handles.(modeID).nextParams, 'Enable', iff(maxPos < 1, 'off', 'on'));\n\n    % update the page number\n    this.GUI.(modeID).paramPage = this.GUI.(modeID).paramPage + direction;\n    return;\nend;\n\n%% clear the parameters area\nparamPanChildren = get(this.GUI.handles.(modeID).paramPan, 'Children');\n% do not remove the navigating buttons\nif isfield(this.GUI.handles.(modeID), 'nextParams');\n    paramPanChildren(paramPanChildren == this.GUI.handles.(modeID).nextParams) = [];\n    paramPanChildren(paramPanChildren == this.GUI.handles.(modeID).prevParams) = [];\nend;\ndelete(paramPanChildren);\nthis.GUI.handles.(modeID).paramPanElems = struct();\n\n%% create the next previous settings buttons\n% calculate X and Y base positions\nprevButPosX = 0.25 * pad;\nprevButPosY = pad;\nthis.GUI.handles.(modeID).prevParams = uicontrol(UICommons.paramNav{:}, 'String', '<', ...\n    'Tag', sprintf('%sParamPrevParams', upper(modeID)), ...\n    'Position', [prevButPosX prevButPosY elemW * 0.1 (elemH + pad) * nRows - pad], ...\n    'ToolTipString', 'Previous parameter controls');\niRow = iRow + nRows;\niCol = 0.05 + pad;\nnextButPosX = (nCols - 0.1) * (pad + elemW) + 0.75 * pad;\nthis.GUI.handles.(modeID).nextParams = uicontrol(UICommons.paramNav{:}, 'String', '>', ...\n    'Tag', sprintf('%sParamNextParams', upper(modeID)), ...\n    'Position', [nextButPosX prevButPosY elemW * 0.1 (elemH + pad) * nRows], ...\n    'ToolTipString', 'Next parameter controls');\n    \n%% create the UI elements with their labels\n% go through all elements, create them and place them\nfor iUICtrl = 1 : nUICtrl;\n    \n    % get the variables of the current control element\n    rowParams = table2cell(this.GUI.(modeID).paramPanConfig(iUICtrl, :));\n    [categ, id, UIType, valueType, UISize, isLabelAbove, label, tooltip] = rowParams{:};\n    \n    % adjust element height for multiple rows\n    elemHLocal = (1 - (nRows - UISize(1) + 2) * pad) / nRows; % height depends on the number of rows\n    \n    % adjacent buttons\n    if strcmp(UIType, 'button') && UISize(2) > 0 && iUICtrl > 1;\n        rowParamsBef = table2cell(this.GUI.(modeID).paramPanConfig(iUICtrl - 1, :));\n        [~, ~, UITypeBef, ~, UISizeBef] = rowParamsBef{:};\n        % if this is the second button element that is not full width and total width is not exceeding 1\n        if strcmp(UITypeBef, 'button') && UISizeBef(2) > 0 && UISize(2) + UISizeBef(2) <= 1;\n            % put this button at the same row\n            iRow = iRow - 1;\n        end;\n    end;\n        \n    % update row/column index\n    iRow = iRow + UISize(1);\n    % if max number of rows is reached, go to the next column update rows/columns\n    if iRow > nRows;\n        iRow = UISize(1);\n        iCol = iCol + 1;\n    end;\n\n    % calculate X and Y base positions\n    elemPosX = (iCol - 1) * (pad + elemW) + pad;\n    if UISize(1) == nRows;\n        elemPosY = 1 - iRow * elemHLocal - pad;\n    elseif UISize(1) > 1;\n        elemPosY = 1 - iRow * (elemHLocal + pad * (UISize(1) / nRows)) - pad;\n    else\n        elemPosY = 1 - iRow * (elemHLocal + pad) + pad;\n    end;\n    \n    % font sizes\n    ctrlElemFontSize = round(this.GUI.pos(4) / (105 - 3 * nRows)) * fontSizeAdjust;\n    labFontSize = round(this.GUI.pos(4) / (115 - 3 * nRows + 0.1 * numel(label))) * fontSizeAdjust * labelFontSizeAdjust;\n    \n    % calculate positions, depending on whether the label is above or not:\n    % if label is above\n    if isLabelAbove;\n        % position and size for label\n        labElemX = elemPosX;\n%         labElemY = elemPosY + (UISize(1)) * elemHLocal + (UISize(1) - 1) * pad;\n        labElemY = elemPosY + (UISize(1) - labelHeight) * elemHLocal + 0.5 * pad;\n        labElemW = elemW;\n        labelemHLocal = labelHeight * elemHLocal;\n        % position and size for GUI element\n        ctrlElemX = elemPosX;\n        ctrlElemW = elemW;\n        ctrlElemY = elemPosY;\n        ctrlelemHLocal = (UISize(1) - labelHeight) * elemHLocal + pad;\n        labFontSize = round(this.GUI.pos(4) / (125 - 3 * nRows + 0.1 * numel(label))) * fontSizeAdjust * labelFontSizeAdjust;\n        \n    % if label is not above\n    else\n        % position and size for label\n        labElemX = elemPosX;\n        labElemY = elemPosY + (UISize(1) * 0.5 - 0.25) * elemHLocal;\n        labElemW = elemW * (labelWidthRatio - inPad);\n        labelemHLocal = elemHLocal * 0.5;\n        % position and size for GUI element\n        ctrlElemX = elemPosX + elemW * (labelWidthRatio + inPad);\n        ctrlElemW = elemW * (1 - labelWidthRatio - inPad);\n        ctrlElemY = elemPosY;\n        ctrlelemHLocal = UISize(1) * elemHLocal + (UISize(1) - 1) * pad;\n        \n    end;\n    \n    % reduce a bit the size of the dropdown menus\n    if strcmp(UIType, 'dropdown');\n        labFontSize = round(labFontSize * 0.8);\n        ctrlElemFontSize = round(ctrlElemFontSize * 0.8);\n        labElemY = elemPosY + (UISize(1) * 0.5 - 0.1) * elemHLocal;\n        \n    % no label for buttons\n    elseif strcmp(UIType, 'button');\n        ctrlElemX = ctrlElemX - labElemW;\n        ctrlElemW = ctrlElemW + labElemW;\n        labElemW = 0;\n        \n        % special case of two buttons next to each other\n        if UISize(2) > 0;\n            ctrlElemW = ctrlElemW * UISize(2);\n            \n            % adjacent buttons\n            if iUICtrl > 1;\n                rowParamsBef = table2cell(this.GUI.(modeID).paramPanConfig(iUICtrl - 1, :));\n                [~, ~, UITypeBef, ~, UISizeBef] = rowParamsBef{:};\n                % if this is the second button element that is not full width and total width is not exceeding 1\n                if strcmp(UITypeBef, 'button') && UISizeBef(2) > 0 && UISize(2) + UISizeBef(2) <= 1;\n                    % put this button to the right\n                    ctrlElemX = ctrlElemX + ctrlElemW;\n                end;\n            end;\n        end;\n        \n    % process differently big lists\n    elseif strcmp(UIType, 'list') && UISize(1) == nRows && isLabelAbove;\n        labelHeightLocal = 0.8;\n        % position and size for label\n        labElemY = elemPosY + (UISize(1) - labelHeightLocal * 0.9) * elemHLocal + 0.5 * pad;\n        labelemHLocal = labelHeightLocal * elemHLocal;\n        % position and size for GUI element\n        ctrlelemHLocal = (UISize(1) - labelHeightLocal) * elemHLocal + pad;\n        labFontSize = round(this.GUI.pos(4) / (125 - 3 * nRows + 0.1 * numel(label))) * 1.5;\n        \n        \n    % process differently big lists\n    elseif strcmp(UIType, 'list') && UISize(1) > 1 && isLabelAbove;\n        labelHeightLocal = 0.8;\n        labElemY = elemPosY + (UISize(1) - labelHeightLocal * 0.5) * elemHLocal + 0.5 * pad;\n        labFontSize = round(this.GUI.pos(4) / (125 - 3 * nRows + 0.1 * numel(label))) * 1.5;\n                \n    end;\n    \n    % create the label\n    this.GUI.handles.(modeID).paramPanElems.([id '_label']) = uicontrol(UICommons.lab{:}, 'String', label, ...\n        'Position', [labElemX labElemY labElemW labelemHLocal], 'ToolTipString', tooltip, ... 'BackgroundColor', 'red', ...\n        'Tag', sprintf('%sParam%s', upper(modeID), id), 'FontSize', labFontSize);\n    \n        \n    % get category parts\n    categParts = regexp(categ, '\\.', 'split');\n    if numel(categParts) > 1;\n        categ = categParts{1};\n        subCateg = categParts{2};\n    else\n        subCateg = [];\n    end;\n    % if the field does not exist, create it\n    if (~isempty(subCateg) && ~isfield(this.(modeID).(categ).(subCateg), id)) ...\n            || (isempty(subCateg) && ~isfield(this.(modeID).(categ), id));\n        this.(modeID).(categ).(id) = '';\n    end;\n    \n    callbackFcn = [];\n            \n    % process the different UI types\n    switch UIType;\n        \n        % text controls\n        case 'text';       \n            % get the value from the storage variable\n            if isempty(subCateg);\n                storedValue = this.(modeID).(categ).(id);\n            else\n                storedValue = this.(modeID).(categ).(subCateg).(id);\n            end;\n            % if the value is not text and its an array, display as an array between brackets\n            if numel(storedValue) > 1 && ~strcmp(valueType, 'text') && ~strcmp(valueType, 'cellArray');                \n                % add semi colon at each line end\n                stringValue = [ num2str(storedValue), repmat(';', size(storedValue, 1), 1)];\n                % reshape as a single line\n                stringValue = reshape(stringValue', 1, numel(stringValue));\n                % replace all spaces by single space\n                stringValue = regexprep(stringValue, '\\s+', ' ');\n                % remove all starting spaces\n                stringValue = regexprep(stringValue, '^\\s', '');\n                stringValue = regexprep(stringValue, '; ', ';');\n                % remove all empty spaces and replace them by commas\n                stringValue = ['[', regexprep(stringValue, '\\s+', ','), ']'];\n                % clean up: remove last semi colon\n                stringValue = regexprep(stringValue, ';\\]$', ']');\n                % clean up: remove starting comas\n                stringValue = regexprep(stringValue, '[\\[;]$', ']');\n                % clean up: add space after each colon or semi colon\n                stringValue = regexprep(stringValue, '([,;])', '$1 ');\n                \n            % if the value is a text, keep it as a string so do nothing\n            elseif numel(storedValue) > 1 && strcmp(valueType, 'text');  \n                \n                stringValue = storedValue;\n                \n            % if the value is a text, keep it as a cell array string so do nothing\n            elseif iscell(storedValue) && strcmp(valueType, 'cellArray');\n                \n                % process each cell and transform it into a string\n                for iCell = 1 : numel(storedValue);\n                    \n                    % if cell is already a string, skip\n                    if ischar(storedValue{iCell});\n                        continue;\n                        \n                    % if cell is an array, transform it into a string\n                    elseif isnumeric(storedValue{iCell});\n                        % add semi colon at each line end\n                        stringValue = [ num2str(storedValue{iCell}), repmat(';', size(storedValue{iCell}, 1), 1)];\n                        % reshape as a single line\n                        stringValue = reshape(stringValue', 1, numel(stringValue));\n                        % replace all spaces by single space\n                        stringValue = regexprep(stringValue, '\\s+', ' ');\n                        % remove all starting spaces\n                        stringValue = regexprep(stringValue, '^\\s', '');\n                        stringValue = regexprep(stringValue, '; ', ';');\n                        % remove all empty spaces and replace them by commas\n                        stringValue = ['[', regexprep(stringValue, '\\s+', ','), ']'];\n                        % clean up: remove last semi colon\n                        stringValue = regexprep(stringValue, ';\\]$', ']');\n                        % clean up: remove starting comas\n                        stringValue = regexprep(stringValue, '[\\[;]$', ']');\n                        % clean up: add space after each colon or semi colon\n                        storedValue{iCell} = regexprep(stringValue, '([,;])', '$1 ');\n                        \n                    % if just an empty cell\n                    elseif ~isempty(storedValue{iCell});\n                        storedValue{iCell} = '';\n                        \n                    % otherwise if not just and empty, abort conversion\n                    else\n                        storedValue{iCell} = 'unknownDataType';\n                        showWarning(this, 'OCIA:OCIACreateParamPanelControls:UnkownDataTypeInCellArray', sprintf( ...\n                            ['An unknown data type has been found in the cell array of the parameter control ', ...\n                            '\"%s\" of the mode \"%s\", class: %s! Skipping.'], id, modeID, class(storedValue)));\n                        \n                    end;\n                end;\n                \n                % non-empty cell array\n                if ~isempty(storedValue) && numel(storedValue) >= 1 && ~isempty(storedValue{1});\n                    % add a semi colon after each last cell\n                    storedValue(:, end) = arrayfun(@(iCell) [storedValue{iCell, end}, ';'], 1 : size(storedValue, 1), ...\n                        'UniformOutput', false);\n                    % rearrange cell-array\n                    storedValue = storedValue';\n                    % add a coma after each cell\n                    storedValue = arrayfun(@(iCell) [storedValue{iCell}, ','], 1 : numel(storedValue), ...\n                        'UniformOutput', false);\n                    % remove the useless commas\n                    storedValue = regexprep(storedValue, ';,$', ';');\n                    % create the string\n                    stringValue = ['{ ', regexprep(sprintf('%s ', storedValue{:}), ' $', '') ' }'];\n                    \n                % cell-array is empty\n                else\n                    stringValue = '';\n                end;\n            % otherwise the value is just a number\n            else\n                stringValue = num2str(storedValue);\n            end;\n            % leave value empty\n            value = [];\n            \n            % substitue pi\n            stringValue = regexprep(stringValue, '3\\.14', 'pi');\n            stringValue = regexprep(stringValue, '6\\.28', '2pi');\n            \n        % drop down menu elements  \n        case 'dropdown';\n            % if the value from the storage variable is empty\n            if (isempty(subCateg) && isempty(this.(modeID).(categ).(id))) ...\n                    || (~isempty(subCateg) && isempty(this.(modeID).(categ).(subCateg).(id)));\n                value = 1; % select the first item   \n                \n            % otherwise if it is a boolean drop-down and there is a value in the storage variable\n            elseif numel(valueType) == 2 && all(ismember(valueType, { 'true', 'false' }));\n                % get the values to select\n                if isempty(subCateg);\n                    valueString = iff(this.(modeID).(categ).(id), 'true', 'false');\n                else\n                    valueString = iff(this.(modeID).(categ).(subCateg).(id), 'true', 'false');\n                end;\n                value = find(strcmp(valueType, valueString));\n                \n            % otherwise if there is a value in the storage variable\n            else\n                % get the values to select\n                if isempty(subCateg);\n                    value = find(strcmp(valueType, this.(modeID).(categ).(id)));\n                else\n                    value = find(strcmp(valueType, this.(modeID).(categ).(subCateg).(id)));\n                end;\n            end;\n            % use a string the cell-array from the configuration\n            stringValue = valueType;\n            \n        % list elements  \n        case 'list';\n            % get the stored variable\n            if isempty(subCateg);\n                storedVariable = this.(modeID).(categ).(id);\n            else\n                storedVariable = this.(modeID).(categ).(subCateg).(id);\n            end;\n            % if the value from the storage variable is empty\n            if isempty(storedVariable);\n                value = []; % select nothing                \n            % otherwise if there is a value in the storage variable\n            else\n                % make sure the stored variable is a cell\n                if ~iscell(storedVariable); storedVariable = { storedVariable }; end;\n                % make sure the valueType is a cell\n                if ~iscell(valueType); valueType = { valueType }; end;\n                % make sure the valueType is a cell\n                if ~isempty(valueType) && numel(valueType) == 1 && iscell(valueType{1});\n                    valueType = valueType{1};\n                end;\n                % get the values to select\n                value = find(arrayfun(@(i)ismember(valueType{i}, storedVariable), 1 : numel(valueType)));\n            end;\n            % use a string the cell-array from the configuration\n            stringValue = valueType;\n            \n        % buttons\n        case 'button';\n            stringValue = label;\n            if iscell(valueType) && ~isempty(valueType) && isa(valueType{1}, 'function_handle');\n                callbackFcn = valueType{1};\n            elseif ~isempty(valueType) && isa(valueType, 'function_handle');\n                callbackFcn = valueType;\n            end;\n            \n        % sliders\n        case 'slider';\n            stringValue = label;\n            if isempty(subCateg);\n                value = this.(modeID).(categ).(id);\n            else\n                value = this.(modeID).(categ).(subCateg).(id);\n            end;\n            if iscell(valueType) && ~isempty(valueType) && isa(valueType{1}, 'function_handle');\n                callbackFcn = valueType{1};\n\n            elseif ~isempty(valueType) && isa(valueType(1), 'function_handle');\n                callbackFcn = valueType;\n            end;\n            \n    end; % end of GUI type switch\n    \n    \n    % reduce a bit the size of the lists if there are a lot elements inside\n    if strcmp(UIType, 'list') && ~strcmp(id, 'fileList');\n        nElems = numel(stringValue);\n        ctrlElemFontSize = min(max(round(ctrlElemFontSize * 1.2 - 0.07 * nElems), 6), 25);\n    end;\n    \n%     % extract cell in cell\n%     if iscell(stringValue) && ~isempty(stringValue) && numel(stringValue) == 1 && iscell(stringValue{1});\n%         stringValue = stringValue{1};\n%     end;\n            \n    % create the GUI element\n    this.GUI.handles.(modeID).paramPanElems.(id) = uicontrol(UICommons.(UIType){:}, ...\n        'String', stringValue, 'Value', value, 'Tag', sprintf('%sParam%s', upper(modeID), id), ...\n        'Position', [ctrlElemX ctrlElemY ctrlElemW ctrlelemHLocal], 'ToolTipString', tooltip, ...\n        'FontSize', ctrlElemFontSize);\n\n    % set a minimum and a maximum\n    if strcmp(UIType, 'slider');\n        set(this.GUI.handles.(modeID).paramPanElems.(id), 'Min', valueType{2}, 'Max', valueType{3}, 'SliderStep', ...\n            [valueType{4}, valueType{5}]);\n    end;\n    \n    % pushbuttons should not be presset\n    if strcmp(UIType, 'button');\n        set(this.GUI.handles.(modeID).paramPanElems.(id), 'Value', 0);\n    end;\n    \n    if ~isempty(callbackFcn);\n        % pass input arguments\n        if strcmp(UIType, 'button') && numel(valueType) > 1;\n            set(this.GUI.handles.(modeID).paramPanElems.(id), 'Callback', @(h, e) callbackFcn(this, valueType{2 : end}));\n        else\n            set(this.GUI.handles.(modeID).paramPanElems.(id), 'Callback', @(h, e) callbackFcn(this));\n        end;\n        % add a callback for the frame setter\n        if strcmp(UIType, 'slider');\n            jObj = findjobj(this.GUI.handles.(modeID).paramPanElems.(id));\n            set(jObj, 'AdjustmentValueChangedCallback', @(h, e) callbackFcn(this));\n        end;\n    end;\n\n    % if the controls are outside the visible area, hide them\n    if iCol > nCols;\n        set(this.GUI.handles.(modeID).paramPanElems.(id), 'Visible', 'off');\n        set(this.GUI.handles.(modeID).paramPanElems.([id '_label']), 'Visible', 'off');\n        % enable the navigating buttons\n        set(this.GUI.handles.(modeID).nextParams, 'Enable', 'on');\n    end;\n    \nend;\n\n% refresh the GUI\ndrawnow();\n    \n% go through all elements, to update those that need a Java-based update\nfor iUICtrl = 1 : nUICtrl;    \n    % get the variables of the current control element\n    rowParams = table2cell(this.GUI.(modeID).paramPanConfig(iUICtrl, :));\n    [~, id, UIType, valueType, UISize] = rowParams{:};\n    % if the element is a list and the UISize setting requires some Java-based updating of the uicontrol\n    if strcmp(UIType, 'list') && UISize(2) > 0;\n        % launch a timer to update the control\n        start(timer('StartDelay', 0.15, 'TimerFcn', @(~, ~) javaUpdateUIControl( ...\n            this.GUI.handles.(modeID).paramPanElems.(id), round(numel(valueType) / UISize(2)))));\n    end;\nend;\n\n% only show the parameter panel if there are some controls to display\nset(this.GUI.handles.(modeID).paramPan, 'Visible', iff(nUICtrl > 0, 'on', 'off'));\n\n% change the parameter page\nif this.GUI.(modeID).paramPage ~= 1;\n    % reset to page 1\n    this.GUI.(modeID).paramPage = 1;\n    % update the pages\n    for iPage = 1 : this.GUI.(modeID).paramPage;\n        OCIACreateParamPanelControls(this, modeID, this.GUI.handles.(modeID).nextParams);\n    end;\nend;\n\nend\n\n% little function to update an uicontrol\nfunction javaUpdateUIControl(handle, nRows)\n    try       \n        visState = get(handle, 'Visible'); % get visibility state\n        pos = get(handle, 'Position'); % get position\n        % make visible\n        set(handle, 'Position', pos + iff(strcmp(visState, 'off'), [10E5 10E5 0 0], [0 0 0 0]), 'Visible', 'on');\n        jObj = findjobj(handle);\n        jListbox = jObj.getViewport().getView();\n        jListbox.setLayoutOrientation(jListbox.HORIZONTAL_WRAP);\n        jListbox.setVisibleRowCount(nRows);\n        set(handle, 'Visible', visState, 'Position', pos);\n    catch err; %#ok<NASGU>        \n        % Nobody cares if this fails. At least not me >.<\n    end;\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DIStartStopCamera.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/DIStartStopCamera.m", "size": 1247, "source_encoding": "utf_8", "md5": "72f49cb06536a9aff1927ea2ee8e228b", "text": "%% #DIStartStopCamera\nfunction DIStartStopCamera(this, commandString)\n\no('#%s() ...', mfilename, 3, this.verb);\n\ntry % capture display errors\n\nif strcmp(commandString, 'toggle') && strcmp(get(this.GUI.di.camHandle, 'Running'), 'on');\n    commandString = 'stop';\nelseif strcmp(commandString, 'toggle') && strcmp(get(this.GUI.di.camHandle, 'Running'), 'off');\n    commandString = 'start';    \nend;\n\nif strcmp(commandString, 'start');\n    \n    % make sure camera is stopped\n    stop(this.GUI.di.camHandle);\n    \n    % add disk and memory logging options\n    set(this.GUI.di.camHandle, 'LoggingMode', 'disk&memory');\n    if exist(this.path.discrDataSave, 'dir') ~= 7; mkdir(this.path.discrDataSave); end;\n    videoWriterHandle = VideoWriter(sprintf('%s%s.mp4', this.path.discrDataSave, datestr(now, 'yyyymmdd_HHMMSS')), 'MPEG-4');\n    this.GUI.di.camHandle.DiskLogger = videoWriterHandle;\n\n    % start the recording\n    start(this.GUI.di.camHandle);\n    \nelseif strcmp(commandString, 'stop');\n    \n    stop(this.GUI.di.camHandle);\n    \nend;\n\ncatch err;\n    errStack = getStackText(err);\n    showWarning(this, 'OCIA:DIStartStopCamera', sprintf('Problem in the Discriminator camera start/stop function: \"%s\".', err.message));\n    o(errStack, 0, 0);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "INRunExp_trigInTTLOut.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/INRunExp_trigInTTLOut.m", "size": 5723, "source_encoding": "utf_8", "md5": "e2655bca7a3289d93c5abc75499bcb6b", "text": "function INRunExp_trigInTTLOut(this)\n% INRunExp_trigInTTLOut - [no description]\n%\n%       INRunExp_trigInTTLOut(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\nparams = this.in.(this.in.expMode);\nif this.in.TTLOutStr.isWorking; return; end;\n\nif ~this.in.expRunning;\n    exitTriggerMode(this, struct('identifier', 'OCIA:INRunExp_trigInTTLOut:Aborted', ...\n        'message', 'Error during stimulation (\"OCIA:INRunExp_trigInTTLOut:Aborted\"): aborted.'));\nend;\n\n% showMessage(this, sprintf('%s | Intrinsic: trigger time !', INGetTime(this)));\nthis.in.TTLOutStr.isWorking = true;\n\ntry    \n\n    this.in.TTLOutStr.trigTime = nowUNIXSec();\n    iTrial = this.in.TTLOutStr.iTrial;\n    stimNum = this.in.TTLOutStr.stimVect(iTrial);\n    stimID = this.in.TTLOutStr.stimNames{iTrial};\n    if isnumeric(stimID); stimID = sprintf('%d', stimID); end;\n    showMessage(this, sprintf('%s | Intrinsic: trial %d / %d, current stimulus: %d (%s) ...', ...\n        INGetTime(this), iTrial, this.in.TTLOutStr.nTrials, stimNum, stimID));\n\n    % stimulation depends on current trial type\n    switch stimID;\n        \n        case 'auditory';\n%             % launch stimulus with software trigger\n%             this.in.RP.SoftTrg(1);\n            \n        case 'visual';\n            INSendTTLOutTrigger(this, [5 0], 1, [0.1, 0], this.in.trial.BLDur + this.in.TTLOutStr.fixedTrigDelay);\n            \n        case 'somatosensory';\n            INSendTTLOutTrigger(this, [0 2], 2, [0.025, 0.025], this.in.trial.BLDur + this.in.TTLOutStr.fixedTrigDelay);\n            \n        case 'blank';\n            % do nothing\n        \n        otherwise;\n            % if it is a frequency\n            if regexp(stimID, '\\d+');\n                % launch stimulus with software trigger\n                this.in.RP.SoftTrg(1);\n                \n            else\n                exitTriggerMode(this, struct('identifier', 'OCIA:INRunExp_trigInTTLOut:UnknownStim', ...\n                    'message', 'Error during stimulation (\"OCIA:INRunExp_trigInTTLOut:UnknownStim\"): unknown stimulus.'));\n            end;\n    end;\n        \n    this.in.TTLOutStr.iTrial = this.in.TTLOutStr.iTrial + 1;\n    \n    isMultiSensory = numel(params.stimIDs) == 4 ...\n        && all(strcmp(params.stimIDs, { 'auditory', 'visual', 'somatosensory', 'blank' }));\n    \n    % if number of trials is reached\n    if this.in.TTLOutStr.iTrial > this.in.TTLOutStr.nTrials;\n        pauseTicToc(1);\n        exitTriggerMode(this, []);\n        return;\n    \n    % multi-sensory\n    elseif isMultiSensory;\n        % wait and prepare next stimulus\n        pause(1.1 * (this.in.trial.BLDur + this.in.trial.triStimDur));\n        iNextTrial = this.in.TTLOutStr.iTrial;\n        stimNumNextTrial = this.in.TTLOutStr.stimVect(iNextTrial);\n        stimIDNextTrial = this.in.TTLOutStr.stimNames{iNextTrial};\n        if isnumeric(stimIDNextTrial); stimIDNextTrial = sprintf('%d', stimIDNextTrial); end;\n        \n        % next stim is auditory\n        if strcmp(stimIDNextTrial, 'auditory');\n            showMessage(this, sprintf('%s | Intrinsic: preparing next trial as auditory %d / %d, %d (%s) ...', ...\n                INGetTime(this), iNextTrial, this.in.TTLOutStr.nTrials, stimNumNextTrial, stimIDNextTrial));\n            % load sound stimulus\n            this.in.RP = playTDTSound(this.in.TTLOutStr.soundsToPlay .* this.in.TTLOutStr.amplif, 0, this.GUI.figH, 0);\n        else\n            showMessage(this, sprintf('%s | Intrinsic: preparing next trial as no-sound %d / %d, %d (%s) ...', ...\n                INGetTime(this), iNextTrial, this.in.TTLOutStr.nTrials, stimNumNextTrial, stimIDNextTrial));\n            % load empty stimulus\n            this.in.RP = playTDTSound([0 0], 0, this.GUI.figH, 0);\n        end;\n        \n    % if not in multi-sensory mode  \n    elseif ~isMultiSensory;\n        % wait and prepare next stimulus\n        pause(1.1 * (this.in.trial.BLDur + this.in.trial.triStimDur));\n        iNextTrial = this.in.TTLOutStr.iTrial;\n        stimNumNextTrial = this.in.TTLOutStr.stimVect(iNextTrial);\n        stimIDNextTrial = this.in.TTLOutStr.stimNames{iNextTrial};\n        if isnumeric(stimIDNextTrial); stimIDNextTrial = sprintf('%d', stimIDNextTrial); end;\n        showMessage(this, sprintf('%s | Intrinsic: preparing next trial with freq %d / %d, %d (%s) ...', ...\n            INGetTime(this), iNextTrial, this.in.TTLOutStr.nTrials, stimNumNextTrial, stimIDNextTrial));\n        % prepare next stimulus\n        currentSoundToPlay = this.in.TTLOutStr.soundsToPlay(this.in.TTLOutStr.stimVect(this.in.TTLOutStr.iTrial), :);\n        this.in.RP = playTDTSound(currentSoundToPlay .* this.in.TTLOutStr.amplif, 0, this.GUI.figH, 0);\n        \n    end;\n    \n% if something failed\ncatch err;\n    exitTriggerMode(this, err);\n    \nend;\n\nthis.in.TTLOutStr.isWorking = false;\n\nend\n\nfunction exitTriggerMode(this, err)\n\nwarning('off', 'MATLAB:callback:error');\n\n% stop data aquisition\nstop(this.in.daq.sessHandle{1});\n\n% stop timer\nif isfield(this.in.TTLOutStr, 'timer');\n    stop(this.in.TTLOutStr.timer);\nend;\nthis.in.TTLOutStr.isWorking = false;\n\nif ~isempty(err);\n    showWarning(this, 'OCIA:INRunExp_trigInTTLOut:StimFailed', ...\n        sprintf('Error during stimulation (\"%s\"): %s.', err.identifier, err.message));\n    \nend;\n\n% release resources\nINCleanUp(this);\n\n% set flags, update counter and update GUI\nthis.in.expRunning = false;\nset(this.GUI.handles.in.runExpBut, 'BackgroundColor', 'red', 'Value', 0);\n\nif isempty(err);\n    showMessage(this, sprintf('%s | Intrinsic: finished.', INGetTime(this)));\nelse\n    showMessage(this, sprintf('%s | Intrinsic: aborted.', INGetTime(this)));\nend;\n\nwarning('on', 'MATLAB:callback:error');\n    \nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "BERunTrial.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/BERunTrial.m", "size": 41549, "source_encoding": "utf_8", "md5": "0645e93632a4428e9495406823adfe13", "text": "function BERunTrial(this)\n% BERunTrial - [no description]\n%\n%       BERunTrial(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% /!\\ NOTE: all times are in seconds (with decimals though) since trial start or trial init\n\n% init some variables\ntrainConf = this.be.config.training;\ntoneConf = this.be.config.tone;\nparams = this.be.params;\nBETimes = this.be.times;\niTrial = this.be.iTrial;\ntPhase = this.be.trialPhase;\nnTrials = this.be.config.training.nTrials;\n\n% check if experiment is paused/stopped\nif ~this.be.isRunning;\n    return;\nend;\n\n\n%% PHASE SWITCH\n% what we have to do depends on which phase of the trial we are\nswitch tPhase;\n    \n    %% initialize trial\n    % this is not the start of the trial yet\n    case 'init';\n        \n        % clear previous data\n        BEClearData(this);\n        \n        % get init time\n        BETimes.init(iTrial) = roundn(nowUNIXSec(), -3);\n        o('Times:init: %.3f', BETimes.init(iTrial), 2, this.verb);                \n\n        o('==================================================================', 1, this.verb);\n        showMessage(this, sprintf('%s | Trial %03d/%03d - start ... ', datestr(now(), this.be.logDateFormat), ...\n            iTrial, trainConf.nTrials), 'yellow');\n        o('%s | start ...', datestr(now(), this.be.logDateFormat), 2, this.verb);   \n\n        % move to next phase\n        this.be.trialPhase = 'moveSpot';\n        \n    %% move spots\n    case 'moveSpot';\n        \n        o('%s | moveSpot ...', datestr(now(), this.be.logDateFormat), 2, this.verb);   \n        % if spot matrix is not empty, move spots\n        if isfield(this.be, 'spotMatrix') && ~isempty(this.be.spotMatrix);\n            \n            % get the current spot for this trial\n            currentSpot = this.be.spotMatrix(iTrial);\n            \n            % record time\n            BETimes.ETLTrigStart(iTrial) = getTSinceInit();\n            o('Times:ETLTrigStart: %.3f', BETimes.ETLTrigStart(iTrial), 3, this.verb);\n                \n            % select right spot and move there\n            BEETLTableAction(this, [], [], 'select', currentSpot);\n            BEETLTableAction(this, [], [], 'go');  \n            \n            % record time\n            BETimes.ETLTrigEnd(iTrial) = getTSinceInit();\n            o('Times:ETLTrigEnd: %.3f', BETimes.ETLTrigEnd(iTrial), 3, this.verb);\n            \n        end;\n        \n        % move to next phase\n        this.be.trialPhase = 'vidRec';\n        \n    %% start video recording\n    case 'vidRec';\n\n        o('%s | vidRec ...', datestr(now(), this.be.logDateFormat), 2, this.verb);  \n        % if video recording is enabled\n        if isfield(this.GUI.handles.be, 'vidRecEnableOn') && get(this.GUI.handles.be.vidRecEnableOn, 'Value') ...\n                && (~isempty(regexp(this.be.phase, '^(LWP|QW)', 'once')) && iTrial == 1);\n            \n            % triggering not done yet\n            if isnan(BETimes.vidStartTCPTrig(iTrial));\n            \n                showMessage(this, sprintf('%s | Trial %03d/%03d - video start trigger ... ', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');\n\n                % record trigger time\n                BETimes.vidStartTCPTrig(iTrial) = getTSinceInit();\n                o('Times:vidStartTCPTrig: %.3f', BETimes.vidStartTCPTrig(iTrial), 3, this.verb);\n\n                % send trigger and message for the behavior movie recording on the remote computer using a timer\n                dateString = datestr(now, 'yyyy_mm_dd');\n                trialName = sprintf('%s_%s_trial%02d.avi', regexprep(dateString, '_', ''), ...\n                    datestr(now, 'HHMMSS'), iTrial);\n                set(this.GUI.be.vidTrigTimer, 'UserData', trialName);\n                start(this.GUI.be.vidTrigTimer);\n            \n            % triggering done and wait time finished\n            elseif getTSinceInit() > BETimes.vidStartTCPTrig(iTrial) + params.vidRecDelay(1);\n\n                % move to next phase\n                this.be.trialPhase = 'loadSound';\n            \n            % trigger sent but wait time not yet finished\n            else\n                \n                o('#%s(): waiting for video start trigger ...', mfilename(), 3, this.verb);\n                \n            end;\n            \n        % no video triggering\n        else\n            \n            % move to next phase\n            this.be.trialPhase = 'loadSound';\n\n        end;\n        \n    %% load sound\n    case 'loadSound';\n\n        o('%s | loadSound ...', datestr(now(), this.be.logDateFormat), 2, this.verb);  \n        showMessage(this, sprintf('%s | Trial %03d/%03d - loading sound ... ', datestr(now(), this.be.logDateFormat), ...\n            iTrial, trainConf.nTrials), 'yellow');\n        \n        % load sound into the TDT if required\n        if this.be.useTDT;\n            \n            TDTLoadTic = tic;\n            BETimes.soundLoadStart(iTrial) = getTSinceInit();\n            % attenuation = randi(4, 1) - 1; % randomized 0 - 3 dB SPL attenuation\n            attenuation = 0;\n            this.be.TDTRP = playTDTSound(this.be.toneArray{iTrial}, attenuation, this.GUI.figH, 1);\n            BETimes.soundLoadEnd(iTrial) = getTSinceInit();\n            o('#%s(): TDT loaded, attenuation: %d dB SPL (%.3f)', mfilename(), attenuation, toc(TDTLoadTic), ...\n                2, this.verb);\n%             showMessage(this, sprintf('%s | Trial %03d/%03d - TDT loaded ... ', ...\n%                 datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');\n            \n        % initialize a standard audio player\n        else\n            \n            BETimes.soundLoadStart(iTrial) = getTSinceInit();\n            this.be.audioplayer = audioplayer(this.be.toneArray{iTrial}, toneConf.samplingFreq);\n            BETimes.soundLoadEnd(iTrial) = getTSinceInit();\n            o('#%s(): normal audioplayer loaded', mfilename(), 2, this.verb);\n            \n        end;\n        \n        % move to next phase\n        this.be.trialPhase = 'start';\n\n    %% start the trial (random start delay)\n    case 'start';        \n        \n        % random start delay waiting not started yet\n        if isnan(BETimes.startDelay(iTrial));\n            \n            % calculate a random delay for starting the sounds\n            randomStartDelay = trainConf.startDelay + (rand - 0.5) * 2 * trainConf.startDelayRand;\n            BETimes.startDelay(iTrial) = randomStartDelay;\n            o('Times:startDelay: %.3f', BETimes.startDelay(iTrial), 3, this.verb);\n            showMessage(this, sprintf('%s | Trial %03d/%03d - start delay: %.3f sec. ', ...\n                datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials, BETimes.startDelay(iTrial)), ...\n                'yellow');              \n                \n            % mark trial as started\n            BETimes.start(iTrial) = BETimes.init(iTrial) + size(this.be.anInData.piezo, 1) / this.be.hw.anInSampRate;\n            o('Times:start: %.3f', BETimes.start(iTrial), 2, this.verb);   \n\n            % start the imaging before the random start delay\n            BEImagingTTL(this, 1);\n            BETimes.imgStart(iTrial) = getTSinceStart();\n            o('Times:imgStart: %.3f', BETimes.imgStart(iTrial), 2, this.verb);\n            \n        % random start delay not finished and one second since imaging start\n        elseif getTSinceStart() <= BETimes.startDelay(iTrial) && isnan(BETimes.trialStartCue(iTrial)) ...\n                && getTSinceStart() > BETimes.imgStart(iTrial) + 1;\n        \n            BETimes.trialStartCue(iTrial) = getTSinceStart();\n            % trial start cue\n            o('#%s(): trial start cue at %.3f ...', mfilename(), getTSinceStart(), 2, this.verb);\n            pulseParams = num2cell(trainConf.trialStartLightCueParams);\n            BELightPulse(this, pulseParams{:});\n            \n        % random start delay waiting finished\n        elseif getTSinceStart() > BETimes.startDelay(iTrial);\n            \n            % move to next phase\n            this.be.trialPhase = 'playSound';\n\n        % trigger sent but wait time not yet finished\n        else\n            \n            o('#%s(): waiting for start delay ...', mfilename(), 3, this.verb);\n            \n        end;\n\n    %% play sound\n    case 'playSound';\n    \n        % play sound using TDT\n        if this.be.useTDT;\n            \n            this.be.TDTRP.SoftTrg(1);\n            BETimes.sound(iTrial) = getTSinceStart();\n            o('Times:sound: %.3f', BETimes.sound(iTrial), 2, this.verb);\n        \n        % play sound without TDT\n        else\n            \n            % play sound using standard audio player\n            this.be.audioplayer.play();\n            BETimes.sound(iTrial) = getTSinceStart();\n            o('Times:sound: %.3f', BETimes.sound(iTrial), 2, this.verb);\n            \n        end;\n        \n        % move to next phase\n        this.be.trialPhase = 'initWaitResp';\n        \n    %% initialize waiting response period\n    case 'initWaitResp';\n    \n        % waiting starts now\n        BETimes.respWaitStart(iTrial) = getTSinceStart();\n        \n        % random delay for the minimum response time (response window start)\n        randomRespMinDelay = (rand - 0.5) * 2 * trainConf.minRespRand;\n        o('randomRespMinDelay: %.3f', randomRespMinDelay, 3, this.verb);\n        showMessage(this, sprintf('%s | Trial %03d/%03d - resp. window delay: %.3f sec (%.3f + %.3f).', ...\n            datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials, trainConf.minRespTime ...\n            + randomRespMinDelay, trainConf.minRespTime, randomRespMinDelay), 'yellow');  \n        \n        % init the minimum (start) and maximum (end) times for the response window\n        BETimes.respMin(iTrial) = BETimes.respWaitStart(iTrial) + trainConf.minRespTime + randomRespMinDelay;\n        BETimes.respMax(iTrial) = BETimes.respWaitStart(iTrial) + trainConf.maxRespTime + randomRespMinDelay;\n        \n        % initialize the time for the light cue to start and stop\n        BETimes.lightIn(iTrial) = BETimes.respWaitStart(iTrial) + trainConf.lightInTime + randomRespMinDelay;\n        BETimes.lightOut(iTrial) = BETimes.lightIn(iTrial) + trainConf.lightDur;\n        \n        % print out all this stuff\n        o('Times:respWaitStart: %.3f', BETimes.respWaitStart(iTrial), 2, this.verb);\n        o('Times:respMin: %.3f', BETimes.respMin(iTrial), 2, this.verb);\n        o('Times:lightIn: %.3f', BETimes.lightIn(iTrial), 3, this.verb);\n        o('Times:lightOut: %.3f', BETimes.lightIn(iTrial), 3, this.verb);\n        o('Times:respMax: %.3f', BETimes.respMax(iTrial), 3, this.verb);\n\n        % display message appropriate message depending on whether the animal has actually to respond or not:\n        %   presence of a GO stimulus means a response is expected\n        if ~isempty(toneConf.goStim);\n            \n            showMessage(this, sprintf('%s | Trial %03d/%03d - waiting for response ... ', ...\n                datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');\n            \n            % do not set a time to wait for the sound to finish\n            BETimes.soundDur(iTrial) = 0;\n            \n        % no go stim means response is not expected\n        else\n            \n            % calculate sound duration in seconds using number of samples and sampling frequency of the TDT\n            soundDur = numel(this.be.toneArray{iTrial}) / toneConf.samplingFreq;\n            \n            showMessage(this, sprintf('%s | Trial %03d/%03d - playing sound (%.2f sec duration) ... ', ...\n                datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials, soundDur), 'yellow');\n            \n            % set a time to wait for the sound to finish\n            BETimes.soundDur(iTrial) = soundDur;\n            \n        end;\n        \n        o('Times:soundDur: %.3f', BETimes.soundDur(iTrial), 3, this.verb);\n        o('Times:sound+soundDur: %.3f', BETimes.sound(iTrial) + BETimes.soundDur(iTrial), 3, this.verb);\n        \n        % move to next phase\n        this.be.trialPhase = 'waitResp';\n\n        \n    %% wait for response\n    case 'waitResp';\n        \n        % if this is the first time we are waiting for response\n        if isnan(BETimes.respWaitRealStart(iTrial));\n        \n            % real waiting starts now\n            BETimes.respWaitRealStart(iTrial) = getTSinceStart();\n\n            % init some variables for this trial\n            this.be.lightCueGiven(iTrial) = 0;\n            this.be.autoRewardGiven(iTrial) = 0;\n            this.be.autoRewardModes{iTrial} = params.autoRewardMode;\n            \n            % check if auto-reward should be given because of misses\n            if isfield(trainConf, 'NMissToGiveAutoRewardAfter') && ~isinf(trainConf.NMissToGiveAutoRewardAfter);\n                lastHit = find(this.be.respTypes == 1, 1, 'last');\n                if isempty(lastHit);\n                    lastHit = 1;\n                end;\n                nMisses = numel(this.be.respTypes((lastHit + 1) : (iTrial - 1)) == 4);\n                if nMisses >= trainConf.NMissToGiveAutoRewardAfter;\n                    showMessage(this, sprintf(...\n                        '%s | Trial %03d/%03d - auto-reward because of %d miss ...', ...\n                        datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials, nMisses), 'yellow');\n                    this.be.autoRewardModes{iTrial} = 'EarlyOn';\n                end;\n            end;\n            \n            % determine whether earlies are allowed or not\n            if trainConf.allowEarlyLicks >= 1;\n                this.be.earlyAllowed(iTrial) = 1;\n                showMessage(this, sprintf('%s | Trial %03d/%03d - earlies allowed by config.', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');\n\n            elseif trainConf.allowEarlyLicks <= 0;\n                this.be.earlyAllowed(iTrial) = 0;\n                showMessage(this, sprintf('%s | Trial %03d/%03d - earlies *not* allowed by config.', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');\n\n            % probabilistic\n            else\n                randNum = rand();\n                this.be.earlyAllowed(iTrial) = randNum <= trainConf.allowEarlyLicks;\n                showMessage(this, sprintf( ...\n                    '%s | Trial %03d/%03d - earlies given by probability of %.2f: rand = %.2f => %sallowed.', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials, ...\n                    trainConf.allowEarlyLicks, randNum, iff(this.be.earlyAllowed(iTrial), '', '*not* ')), 'yellow');\n            end;\n            \n        end;\n        \n        % if we are still within the waiting window\n        if getTSinceStart() < (BETimes.respMax(iTrial) + BETimes.soundDur(iTrial));\n\n            o('#%s: response waiting loop ... %.3f', mfilename(), getTSinceStart(), 4, this.verb);\n\n            %%  - wait for response: light on\n            % only give reward cue if there is a reward, if light cue has not yet been given and \"lightIn\" is passed\n            if params.rewDur > 0 && isnan(BETimes.lightCueOn(iTrial)) && getTSinceStart() > BETimes.lightIn(iTrial);\n\n                % if light is not yet on, turn it on\n                if isnan(BETimes.lightCueOn(iTrial));\n                    \n                    BETimes.lightCueOn(iTrial) = getTSinceStart();\n                    this.be.lightCueGiven(iTrial) = 1;\n                    o('#%s(): light cue on (%.3f) ...', mfilename(), BETimes.lightCueOn(iTrial), 3, this.verb);\n                    pulseParams = num2cell(trainConf.rewardPeriodLightCueParams);\n                    BELightPulse(this, pulseParams{:});\n\n                % light cue on but light must still be on\n                else\n                    \n                    o('#%s(): lightCueOn: %d, tDiff: %.3f, timeSinceStart: %.3f ...', mfilename(), lightCueOn, ...\n                        (BETimes.lightCueOn(iTrial) + lightOnDur) - getTSinceStart(), ...\n                        getTSinceStart(), 2, this.verb);  \n                    \n                end;\n                \n            end;\n\n            %%  - wait for response: light off\n            % turn light off if needed\n            if ~isnan(BETimes.lightCueOn(iTrial)) && isnan(BETimes.lightCueOff(iTrial)) && getTSinceStart() > BETimes.lightOut(iTrial);\n\n                BELight(this, 0);\n                BETimes.lightCueOff(iTrial) = getTSinceStart();\n                o('#%s(): light cue off (%.3f) ...', mfilename(), BETimes.lightCueOff(iTrial), 3, this.verb);\n                \n            end;\n\n            %%  - wait for response: 'EarlyOn' reward\n            % in 'EarlyOn' mode, reward happens at some fraction of time of the response window\n            respWindowDur = BETimes.respMax(iTrial) - BETimes.respMin(iTrial);\n            EORespTime = BETimes.respMin(iTrial) + params.autoRewardEarlyOnTimeFraction * respWindowDur;\n            isEarlyOn = strcmp(this.be.autoRewardModes{iTrial}, 'EarlyOn') && (isempty(toneConf.goStim) ...\n                || ismember(this.be.stims(iTrial), toneConf.goStim));\n            \n            % if 'EarlyOn' mode is on and reward has not yet been given and 'EarlyOn' time is passed and trial is target\n            if params.rewDur > 0 && isEarlyOn && ~this.be.autoRewardGiven(iTrial) && getTSinceStart() > EORespTime;\n                \n                this.be.autoRewardGiven(iTrial) = 1; \n                \n                BETimes.rewTime(iTrial) = getTSinceStart();\n                o('Times:rewTime: %.3f', BETimes.rewTime(iTrial), 3, this.verb);\n                \n                BEGiveReward(this);\n                this.be.giveRewards(iTrial) = 1;\n                showMessage(this, sprintf('%s | Trial %03d/%03d - ''EarlyOn'' reward !', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');\n                \n                % calc reward collection time\n                BETimes.rewCollStart(iTrial) = BETimes.rewTime(iTrial);\n                o('Times:rewCollStart: %.3f', BETimes.rewCollStart(iTrial), 3, this.verb);\n                BETimes.rewCollEnd(iTrial) = BETimes.rewTime(iTrial) + trainConf.rewCollTime;\n                o('Times:rewCollEnd: %.3f', BETimes.rewCollEnd(iTrial), 3, this.verb);\n                \n            end;\n            \n            %%  - wait for response: sound detection\n            % only search for sound if not already found and if required\n            if isnan(BETimes.realSound(iTrial)) && this.be.params.adjustForDelayWithSound;\n                \n                % get microphone data\n                micr = linScale(abs(this.be.anInData.micr))';\n                % get the number of samples\n                nSamples = size(micr, 2);\n                % get a range for the begining of the signal\n                begRange = round(nSamples * 0.01 : nSamples * 0.1);\n                % get thresholds factor\n                soundThreshFactor = 30;\n                % get a threshold for the sound onset\n                soundYThresh = soundThreshFactor * std(micr(begRange));\n                % get the samples that exceeds the threshold, adding the first sample to catch the\n                %   start of the first sound\n                upSamples = [0 find(micr > soundYThresh)];\n                % get the derivative of the upSamples, drops in the sample indexes indicate interruption of upSamples,\n                %   which means that there is a sound start\n                upSamplesDiff = diff(upSamples);\n                % use the ISI to find peaks. If no ISI, use 0.5 second\n                ISI = 0.5;\n                % difference between detected upSample derivative's peaks must be at least half of the ISI\n                minISI = ISI * 0.5 * this.be.hw.anInSampRate;\n                % get the index of the peaks where the derivative exceeds the ISI threshold and increment\n                %   by one to get the sound start index\n                soundStartIndex = upSamples(find(upSamplesDiff >= minISI) + 1);\n                %{\n                % debug plot\n                plot((1 : nSamples) / this.be.hw.anInSampRate, micr, 'Color', 'green');\n                xLims = get(gca, 'XLim');\n                hold on;\n                plot(xLims, repmat(soundYThresh, 1, 2), 'Color', 'red', 'LineStyle', ':');\n                title(sprintf('soundYThresh: %.6f, soundStartIndex: %d\\nsoundStartTime: %.2f', soundYThresh, ...\n                    soundStartIndex, soundStartIndex / this.be.hw.anInSampRate));\n                hold off;\n                %}\n                % if some start index found\n                if ~isempty(soundStartIndex);\n                    % only keep the first sound start\n                    if numel(soundStartIndex > 1); soundStartIndex = soundStartIndex(1); end;\n                    % get the sound start time\n                    BETimes.realSound(iTrial) = (soundStartIndex / this.be.hw.anInSampRate) ...\n                        - getTSinceInit() + getTSinceStart();\n                    o('Times:realSound: %.3f sec ', BETimes.realSound(iTrial), 3, this.verb);\n                    \n                    % calculate delay with actual sound\n                    manualOffset = 0.15;\n                    BETimes.soundDelay(iTrial) = BETimes.realSound(iTrial) - BETimes.sound(iTrial) - manualOffset;\n                    o('Times:soundDelay: %.3f sec ', BETimes.soundDelay(iTrial), 3, this.verb);\n            \n                    showMessage(this, sprintf('%s | Trial %03d/%03d - soundDelay: %.3f sec. ', ...\n                        datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials, ...\n                        BETimes.soundDelay(iTrial)), 'yellow');\n                    \n                    % remove delay data\n                    BEClearData(this, round(BETimes.soundDelay(iTrial) * this.be.hw.anInSampRate));\n                end;\n                \n            end;\n            \n            %%  - wait for response: response detection\n            % no response by default\n            this.be.resps(iTrial) = 0;\n            \n            if ~isempty(toneConf.goStim);\n                % get the number of samples that we have\n                if isfield(this.be.anInData, 'piezo')\n                    nSamples = size(this.be.procAnInData.piezo, 1);\n                else\n                    nSamples = 0;\n                end;\n\n                % calculate the number of samples between init and start\n                nSamplesOffset = round((BETimes.start(iTrial) - BETimes.init(iTrial)) * this.be.hw.anInSampRate);\n                % get the indexes of the samples that are within the response window\n                startEarlyRespIndex = round((BETimes.respWaitStart(iTrial)) * this.be.hw.anInSampRate) + nSamplesOffset;\n                startRespIndex = round(BETimes.respMin(iTrial) * this.be.hw.anInSampRate) + nSamplesOffset;\n                endRespIndex = min(round(BETimes.respMax(iTrial) * this.be.hw.anInSampRate) + nSamplesOffset, nSamples);\n                \n                % only check for responses if we have enought samples\n                if startEarlyRespIndex <= nSamples;\n\n                    % earlies not allowed and detection *before* response time\n                    if ~this.be.earlyAllowed(iTrial) && getTSinceStart() < BETimes.respMin(iTrial);\n\n                        % get the piezo lick sensor data\n                        piezoData = this.be.procAnInData.piezo;\n                        % response is true if any value is above the threshold\n                        this.be.resps(iTrial) = sum(piezoData(startEarlyRespIndex : end) > params.piezoThresh) >= 1;\n                        % if lick detected, mark it as early\n                        if this.be.resps(iTrial);\n                            this.be.respTypes(iTrial) = 5;\n                        end;\n                        \n                    % earlies allowed or not and detection is *after* response time\n                    elseif getTSinceStart() >= BETimes.respMin(iTrial);\n\n                        % get the piezo lick sensor data\n                        piezoData = this.be.procAnInData.piezo;\n                        % response is true if any value is above the threshold\n                        this.be.resps(iTrial) = sum(piezoData(startRespIndex : endRespIndex) > params.piezoThresh) >= 1;\n                        \n                    % no response detected yet\n                    else\n\n                        this.be.resps(iTrial) = 0;\n\n                    end;\n                    \n%                     % plot things for debug purposes\n%                     if any(piezoData > params.piezoThresh);\n%                         this.be.isRunning = 0;\n%                         stop(this.GUI.be.updateTimer);\n%                         figure();\n%                         t = (1 : nSamples) / this.be.hw.anInSampRate;\n%                         plot(t, linScale(piezoData));\n%                         hold on;\n%                         line([startEarlyRespIndex, startEarlyRespIndex] / this.be.hw.anInSampRate, [0 1], 'Color', 'r');\n%                         line([startRespIndex, startRespIndex] / this.be.hw.anInSampRate, [0 1], 'Color', 'g');\n%                         line([endRespIndex, endRespIndex] / this.be.hw.anInSampRate, [0 1], 'Color', 'm');\n%                         o('plot done', 0, 0);\n%                     end;\n\n                % if not enought sample yet, no response can be detected\n                else\n\n                    this.be.resps(iTrial) = 0;\n\n                end;\n            \n                % if there was a response within the response window\n                if this.be.resps(iTrial);\n\n                    % mark down response time and delay compared to response window start\n                    BETimes.respTime(iTrial) = getTSinceStart(); % artifical delay correction\n                    o('Times:resp: %.3f', BETimes.respTime(iTrial), 3, this.verb);  \n                    this.be.respDelays(iTrial) = BETimes.respTime(iTrial) - BETimes.respMin(iTrial);\n                    o('Times:respDelays: %.3f', this.be.respDelays(iTrial), 3, this.verb);\n\n                    if this.be.respTypes(iTrial) == 5;\n                        lickType = 'EARLY';\n                        \n                    % if not 'EarlyOn' mode or the auto-reward has not been given yet, it is a \"true\" lick\n                    elseif ~isEarlyOn || ~this.be.autoRewardGiven(iTrial);\n                        lickType = 'LICK';\n\n                    % if in 'EarlyOn' mode and the auto-reward has been given, it is a \"induced\" (post-reward) lick\n                    else\n                        lickType = 'INDUCED LICK';\n\n                    end;\n\n                    % show the message with the lick type and the delay\n                    showMessage(this, sprintf('%s | Trial %03d/%03d - %s! (%.3f sec)', datestr(now(), ...\n                        this.be.logDateFormat), iTrial, nTrials, lickType, this.be.respDelays(iTrial)), 'yellow');  \n\n                    % move to next phase\n                    this.be.trialPhase = 'processDecision';\n\n                end;\n            end;\n\n        % if we are not anymore in the waiting window\n        else\n            \n            o('#%s(): end of response wait ... (%.3f)', mfilename(), getTSinceStart(), 3, this.verb);\n            \n            % move to next phase\n            this.be.trialPhase = 'processDecision';\n            \n        end;\n\n        \n    %% process the decision\n    case 'processDecision';\n\n        % if it was turned on and not yet off, make sure light is off\n        if ~isnan(BETimes.lightCueOn(iTrial)) && isnan(BETimes.lightCueOff(iTrial)) ...\n                && getTSinceStart() > BETimes.lightOut(iTrial);\n            BELight(this, 0);\n            BETimes.lightCueOff(iTrial) = getTSinceStart();\n            o('#%s(): light cue off (late) (%.3f) ...', mfilename(), BETimes.lightCueOff(iTrial), 2, this.verb);\n        end;\n        \n        % if no response set yet, then set it to 0 (no response)\n        if isnan(this.be.resps(iTrial)); this.be.resps(iTrial) = 0; end;\n\n        % if goStim is empty, it means no behavior decision had to be made (no response)\n        if ~isempty(toneConf.goStim);\n            \n            % get whether this is the target stimulus\n            isTargetStim = ismember(this.be.stims(iTrial), toneConf.goStim);\n\n            % only create a response type if it is not already set to \"early\"\n            if this.be.respTypes(iTrial) == 5;\n                \n                % set outcome variables\n                this.be.punishTimeOuts(iTrial) = 1;\n                this.be.giveRewards(iTrial) = 0;\n\n            % response and it was a target: hit (correct response) (respType = 1)\n            elseif this.be.resps(iTrial) && isTargetStim;\n                showMessage(this, sprintf('%s | Trial %03d/%03d - HIT! (%.3f sec)', datestr(now(), ...\n                    this.be.logDateFormat), iTrial, nTrials, this.be.respDelays(iTrial)), 'green');\n                \n                % set outcome variables\n                this.be.respTypes(iTrial) = 1;\n                this.be.punishTimeOuts(iTrial) = 0;\n                this.be.giveRewards(iTrial) = 1;\n\n            % NO response and it was NOT a target: correct rejection (respType = 2)\n             elseif ~this.be.resps(iTrial) && ~isTargetStim;\n                showMessage(this, sprintf('%s | Trial %03d/%03d - CORRECT REJECT!', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, nTrials), 'green');\n                \n                % set outcome variables\n                this.be.respTypes(iTrial) = 2;\n                this.be.punishTimeOuts(iTrial) = 0;\n                this.be.giveRewards(iTrial) = 0;\n                \n            % response and it was NOT a target: false alarm (respType = 3)\n            elseif this.be.resps(iTrial) && ~isTargetStim;\n                showMessage(this, sprintf('%s | Trial %03d/%03d - FALSE ALARM! (%.3f sec)', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, nTrials, ...\n                    this.be.respDelays(iTrial)), 'red');\n                \n                % set outcome variables\n                this.be.respTypes(iTrial) = 3;\n                this.be.punishTimeOuts(iTrial) = 1;\n                this.be.giveRewards(iTrial) = 0;\n                \n            % NO response and it was a target: miss (respType = 4)\n            elseif ~this.be.resps(iTrial) && isTargetStim;\n                showMessage(this, sprintf('%s | Trial %03d/%03d - MISS!', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, nTrials), 'red');\n                \n                % set outcome variables\n                this.be.respTypes(iTrial) = 4;\n                this.be.punishTimeOuts(iTrial) = 0;\n                this.be.giveRewards(iTrial) = 0;\n                \n            end;\n\n            % process the auto-reward modes for reward outcomes\n            switch this.be.autoRewardModes{iTrial};\n                % give reward only if target stim (or no targets) AND not early lick\n                case 'EarlyOn';         this.be.giveRewards(iTrial) ...\n                    = (isempty(toneConf.goStim) || ismember(this.be.stims(iTrial), toneConf.goStim)) ...\n                    && this.be.respTypes(iTrial) ~= 5;\n                \n                % give reward only if target stim (or no targets)\n                case 'On';              this.be.giveRewards(iTrial) ...\n                    = (isempty(toneConf.goStim) || ismember(this.be.stims(iTrial), toneConf.goStim)) ...\n                    && this.be.respTypes(iTrial) ~= 5;\n                \n                % never give reward\n                case 'Off';             this.be.giveRewards(iTrial) = 0;\n                    \n                % do nothing, rewarding depends on mouse's response\n                case 'Auto';\n                otherwise;\n                    showWarning(this, 'OCIA:RunTrial:UnknownAutoRewardMode', ...\n                        sprintf('Unknown auto-reward mode: %s. Using default \"auto\" (reward: %d).', ...\n                        this.be.autoRewardModes{iTrial}, this.be.giveReards(iTrial)));\n            end;\n            \n            % move to next phase\n            this.be.trialPhase = 'reward';\n           \n        % no behavior decision (no response), skip to final wait time\n        else\n            \n            % move to next phase\n            this.be.trialPhase = 'finalWait';\n            \n        end;\n        \n    %% reward\n    case 'reward';\n\n        if ~this.be.giveRewards(iTrial);\n            \n            % move to next phase\n            this.be.trialPhase = 'finalWait';\n        \n        % if reward was not already given for this phase\n        elseif isnan(BETimes.rewCollStart(iTrial)) && this.be.giveRewards(iTrial);\n            \n            % only give reward if deserved and if not already given by auto-reward\n            if this.be.giveRewards(iTrial) && ~this.be.autoRewardGiven(iTrial) && isnan(BETimes.rewTime(iTrial));\n                BETimes.rewTime(iTrial) = getTSinceStart();\n                o('Times:rewTime: %.3f', BETimes.rewTime(iTrial), 3, this.verb);\n                showMessage(this, sprintf('%s | Trial %03d/%03d - reward !', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');  \n                BEGiveReward(this);\n            end;\n                \n            % give time to collect it\n            BETimes.rewCollStart(iTrial) = BETimes.rewTime(iTrial);\n            o('Times:rewCollStart: %.3f', BETimes.rewCollStart(iTrial), 3, this.verb);\n            BETimes.rewCollEnd(iTrial) = BETimes.rewTime(iTrial) + trainConf.rewCollTime;\n            o('Times:rewCollEnd: %.3f', BETimes.rewCollEnd(iTrial), 3, this.verb);\n                \n            % prolongate light until the end of the collection period if needed\n            BETimes.lightOut(iTrial) = max(BETimes.rewCollEnd(iTrial), BETimes.lightOut(iTrial));\n            o('Times:lightOut: %.3f (prolongated (2))', BETimes.lightOut(iTrial), 3, this.verb);\n            \n           \n        % if reward was already given for this phase and reward collection is done but spout is still in\n        elseif ~isnan(BETimes.rewCollStart(iTrial)) && this.be.giveRewards(iTrial) ...\n                && getTSinceStart() > BETimes.rewCollEnd(iTrial) && isnan(BETimes.spoutOut(iTrial));\n            \n            % calculate spout out time\n            BETimes.spoutOut(iTrial) = getTSinceStart() + params.spoutDelay(2);\n            \n        % if reward was already given for this phase and reward collection is done and spout should be out\n        elseif ~isnan(BETimes.rewCollStart(iTrial)) && this.be.giveRewards(iTrial) ...\n                && getTSinceStart() > BETimes.rewCollEnd(iTrial) && ~isnan(BETimes.spoutOut(iTrial)) ;\n                \n            % remove spout\n%             BESpoutPos(this, 0);\n\n            % move to next phase\n            this.be.trialPhase = 'finalWait';\n            \n        % spout time not yet defined means it is still reward collection\n        elseif isnan(BETimes.spoutOut(iTrial));\n\n            o('#%s(): waiting for reward collection ...', mfilename(), 3, this.verb); \n            \n        % spout time defined means it is still spout delay wait\n        else\n            \n            o('#%s(): waiting for spout delay ...', 3, this.verb);\n            \n        end;\n        \n        \n        % if it was turned on and not yet off, make sure light is off\n        if ~isnan(BETimes.lightCueOn(iTrial)) && isnan(BETimes.lightCueOff(iTrial)) ...\n                && getTSinceStart() > BETimes.lightOut(iTrial);\n            BELight(this, 0);\n            BETimes.lightCueOff(iTrial) = getTSinceStart();\n            o('#%s(): light cue off (late (2)) (%.3f) ...', mfilename(), BETimes.lightCueOff(iTrial), 2, this.verb);\n        end;\n        \n    %% final end wait (punishment & others)\n    case 'finalWait';\n\n        \n        % no punishmend time yet\n        if isnan(BETimes.endPunish(iTrial));\n            \n            % if no punish setting is not set yet, set it to 0 (no punish)\n            if isnan(this.be.punishTimeOuts(iTrial)); this.be.punishTimeOuts(iTrial) = 0; end;\n            \n            % calculate the punishment delay\n            punishDelay = iff(this.be.punishTimeOuts(iTrial), trainConf.timeoutPunish, 0);\n            \n            % play punishment sound\n            if punishDelay > 0;\n                o('Playing punishment sound ...', 3, this.verb);\n                nLoops = round(min(trainConf.endDelay + punishDelay, 3) / 0.5);\n                this.be.TDTRP.Halt();\n                this.be.TDTRP = playTDTSound(this.be.punishSound, 0, this.GUI.figH, nLoops);\n                this.be.TDTRP.SoftTrg(1);\n            end;\n\n            % calculate the end of punishment time\n            BETimes.endPunish(iTrial) = getTSinceStart() + trainConf.endDelay + punishDelay;\n            o('Times:endPunish: %.3f', BETimes.endPunish(iTrial), 3, this.verb);\n\n            % calculate the expected end of the imaging\n            BETimes.imgStopExp(iTrial) = BETimes.endPunish(iTrial) - punishDelay - params.imgEndStopTime;\n            o('Times:imgStopExp: %.3f', BETimes.imgStopExp(iTrial), 3, this.verb);\n        \n        end;\n        \n        % set a minimum trial duration\n        minTrialDuration = 13;\n        if (getTSinceStart() - BETimes.imgStopExp(iTrial)) < minTrialDuration;\n            BETimes.imgStopExp(iTrial) = minTrialDuration;\n        end;\n\n        % if imaging still runing and time to stop it has passed\n        if getTSinceStart() > BETimes.imgStopExp(iTrial);\n            \n            % stop imaging\n            if isnan(BETimes.imgStopObs(iTrial));\n                \n                % stop imaging\n                BEImagingTTL(this, 0);\n                BETimes.imgStopObs(iTrial) = getTSinceStart();\n                o('Times:imgStopObs: %.3f', BETimes.imgStopObs(iTrial), 3, this.verb);\n                \n            end;\n            \n            % if time to finish trial has passed\n            if getTSinceStart() > BETimes.endPunish(iTrial);\n                \n                showMessage(this, sprintf('%s | Trial %03d/%03d - done. ', datestr(now(), this.be.logDateFormat), ...\n                    iTrial, trainConf.nTrials), 'green');\n            \n                % move out from the trial running\n                this.be.trialPhase = 'stopVidRec';\n            \n            % trial still runing but stop time is not yet arrived\n            else\n\n                o('#%s(): waiting for trial stop ...', mfilename(), 4, this.verb);\n\n            end;\n            \n        % imaging is still runing but stop time is not yet arrived\n        else\n            \n            o('#%s(): waiting for imaging stop ...', mfilename(), 4, this.verb);\n            \n        end;\n        \n        % if it was turned on and not yet off, make sure light is off\n        if ~isnan(BETimes.lightCueOn(iTrial)) && isnan(BETimes.lightCueOff(iTrial)) ...\n                && getTSinceStart() > BETimes.lightOut(iTrial);\n            BELight(this, 0);\n            BETimes.lightCueOff(iTrial) = getTSinceStart();\n            o('#%s(): light cue off (late (3)) (%.3f) ...', mfilename(), BETimes.lightCueOff(iTrial), 2, this.verb);\n        end;\n        \n    %% stop video recording\n    case 'stopVidRec';\n\n        % if video recording is enabled\n        if isfield(this.GUI.handles.be, 'vidRecEnableOn') && get(this.GUI.handles.be.vidRecEnableOn, 'Value') ...\n                && (~isempty(regexp(this.be.phase, '^(LWP|QW)', 'once')) && iTrial == trainConf.nTrials);\n            \n            % triggering not done yet\n            if isnan(BETimes.vidEndTCPTrig(iTrial));\n            \n                showMessage(this, sprintf('%s | Trial %03d/%03d - video stop trigger ... ', ...\n                    datestr(now(), this.be.logDateFormat), iTrial, trainConf.nTrials), 'yellow');\n\n                % record trigger time\n                BETimes.vidEndTCPTrig(iTrial) = getTSinceStart();\n                o('Times:vidEndTCPTrig: %.3f', BETimes.vidEndTCPTrig(iTrial), 3, this.verb);\n                \n                % send trigger to stop the behavior movie recording using a timer\n                set(this.GUI.be.vidTrigTimer, 'UserData', 'stop');\n                start(this.GUI.be.vidTrigTimer);\n            \n            % triggering done and wait time finished\n            elseif getTSinceStart() > BETimes.vidEndTCPTrig(iTrial) + params.vidRecDelay(2);\n\n                % move to next phase\n                this.be.trialPhase = 'finished';\n            \n            % trigger sent but wait time not yet finished\n            else\n                \n                o('#%s(): waiting for video stop trigger ...', mfilename(), 3, this.verb);\n                \n            end;\n            \n        % no video triggering\n        else\n            \n                % move to next phase\n                this.be.trialPhase = 'finished';\n\n        end;\n\n    %% paused\n    case 'paused';\n    \n        % do nothing\n        \n    %% unknown phase\n    otherwise;\n        \n        showMessage(this, sprintf('%s | Trial %03d/%03d: unknown phase \"%s\" ... ', datestr(now(), ...\n            this.be.logDateFormat), iTrial, trainConf.nTrials, this.be.trialPhase), 'yellow');\n\nend;\n\n% store back the times\nthis.be.times = BETimes;\n\nfunction t = getTSinceStart()\n    t = roundn(nowUNIXSec() - BETimes.start(iTrial), -3);\nend\n\nfunction t = getTSinceInit()\n    t = roundn(nowUNIXSec() - BETimes.init(iTrial), -3);\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DWMatchROISetsToData.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/DWMatchROISetsToData.m", "size": 7461, "source_encoding": "utf_8", "md5": "7149efd47267f932563694835593decb", "text": "%% - #DWMatchROISetsToData\nfunction DWMatchROISetsToData(this)\n\n% update the wait bar\nDWWaitBar(this, 0);\n\n% get the list of all animals\nuniqueAnimals = get(this, 'animal');\nif ~isempty(uniqueAnimals) && ~iscell(uniqueAnimals); uniqueAnimals = { uniqueAnimals }; end;\nif isempty(uniqueAnimals); uniqueAnimals = { '' }; end;\nuniqueAnimals(cellfun(@isempty, uniqueAnimals)) = [];\nuniqueAnimals = unique(uniqueAnimals);\nnAnim = numel(uniqueAnimals);\n\n% get the list of all days\nuniqueDays = get(this, 'day');\nif ~isempty(uniqueDays) && ~iscell(uniqueDays); uniqueDays = { uniqueDays }; end;\nif isempty(uniqueDays); return; end;\nuniqueDays(cellfun(@isempty, uniqueDays)) = [];\nuniqueDays = unique(uniqueDays);\nnDays = numel(uniqueDays);\n\n% get the selected animal IDs\nselectedAnimalIDs = this.dw.animalIDs(get(this.GUI.handles.dw.filt.animalID, 'Value'));\n% if the dash '-' is selected, select all IDs\nif numel(selectedAnimalIDs) == 1 && strcmp(selectedAnimalIDs{1}, '-');\n    selectedAnimalIDs = uniqueAnimals;\nend;\n\n% get the selected day IDs\nselectedDayIDs = this.dw.dayIDs(get(this.GUI.handles.dw.filt.dayID, 'Value'));\n% if the dash '-' is selected, select all IDs\nif numel(selectedDayIDs) == 1 && strcmp(selectedDayIDs{1}, '-');\n    selectedDayIDs = uniqueDays;\nend;\n\n% go through each animal\nfor iAnim = 1 : nAnim;    \n    animalID = uniqueAnimals{iAnim}; % get the current animal\n\n    % skip irrelevant animal IDs\n    if ~ismember(animalID, selectedAnimalIDs);\n        % update wait bar\n        DWWaitBar(this, 100 * (iAnim / nAnim));\n        continue;\n    end;\n    \n    % go through each day\n    for iDay = 1 : nDays;    \n        dayID = uniqueDays{iDay}; % get the current day\n\n        % skip irrelevant days\n        if ~ismember(dayID, selectedDayIDs);\n            % update wait bar\n            DWWaitBar(this, 100 * ((iDay / (nDays * nAnim)) + (iAnim - 1) / nAnim));\n            continue;\n        end;\n        \n        % get the ROISet rows\n        ROISetRows = DWFilterTable(this, sprintf('animal = %s AND day = %s AND rowType = ROISet', animalID, dayID));\n        nROISets = size(ROISetRows, 1); % count them\n        % if no ROISets have been found, try without animal ID filtering\n        if nROISets == 0;\n            ROISetRows = DWFilterTable(this, sprintf('animal !~= \\\\w+ AND day = %s AND rowType = ROISet', dayID));\n            nROISets = size(ROISetRows, 1); % count them\n        end;\n        % get the imaging rows\n        imTypeFilter = iff(ismember(this.dw.tableIDs, 'imType'), ' AND imType = movie', '');\n        imagingRows = DWFilterTable(this, sprintf('animal = %s AND day = %s AND rowType = Imaging data%s', ...\n            animalID, dayID, imTypeFilter));\n        nImagingRows = size(imagingRows, 1); % count them\n\n        % go through each ROISet\n        for iROISet = 1 : nROISets;  \n\n            % get the DataWatcher table's row index for this ROISet row\n            iDWRowROISet = str2double(get(this, iROISet, 'rowNum', ROISetRows));\n            % get the ROISet's row ID\n            ROISetRowID = DWGetRowID(this, iDWRowROISet);\n            % make sure the ROISet is loaded\n            DWLoadRow(this, iDWRowROISet, 'full');\n            % extract the data\n            ROISetData = getData(this, iDWRowROISet, 'ROISets', 'data');\n            % abort if the data cannot be found\n            if isempty(ROISetData);\n                showWarning(this, 'OCIA:DWMatchROISetsToData:ROISetDataNotFound', ...\n                    sprintf('Could not find ROISet data for %s (row %03d). Skipping it.', ROISetRowID, iDWRowROISet));                \n                % update wait bar\n                DWWaitBar(this, 100 * ((iROISet / (nROISets * nDays * nAnim)) + ((iDay - 1) / (nDays * nAnim)) + (iAnim - 1) / nAnim));\n                continue;\n            end;\n            % get the ROISet's \"runsValidity\", which tells which trials/runs are valid for this ROISet\n            runsValidity = ROISetData.runsValidity;\n            if ~iscell(runsValidity) && ~isempty(runsValidity); runsValidity = { runsValidity }; end;\n            % if the runsValidity are in the old format 'YYYY_MM_DD__hh_mm_ss', convert them to the new 'YYYYMMDD_hhmmss' format\n            if ~isempty(runsValidity) && ~isempty(regexp(runsValidity{1}, '\\d{4}_\\d{2}_\\d{2}__\\d{2}_\\d{2}_\\d{2}', 'once'));\n                runsValidity = cellfun(@(id)id([1 : 4, 6 : 7, 9 : 10, 12 : 14, 16 : 17, 19 : 20]), runsValidity, ...\n                    'UniformOutput', false);\n            end;\n\n            % if no imaging rows, try to figure out the spot identity of this ROISet\n            if nImagingRows <= 0;\n\n                % get the ROISet's path\n                ROISetPath = get(this, iDWRowROISet, 'path');\n                % go through each possible spot\n                for iSpot = 1 : 10;\n                    % get the spot folder's path\n                    spotPath = regexprep(regexprep(ROISetPath, '/[^/]+$', '/'), 'ROISets', sprintf('spot%02d', iSpot));\n\n                    % go through each possible row\n                    for iRun = 1 : numel(runsValidity);\n                        % get the spot folder's path\n                        dataPath = sprintf('%s%s_%s_%s__%s_%s_%sh/', spotPath, runsValidity{iRun}(1:4), runsValidity{iRun}(5:6), ...\n                            runsValidity{iRun}(7:8), runsValidity{iRun}(10:11), runsValidity{iRun}(12:13), runsValidity{iRun}(14:15));\n\n                        % check if the data exists\n                        if exist(dataPath, 'dir');\n                            set(this, iDWRowROISet, 'spot', sprintf('spot%02d', iSpot));    \n                            break;\n                        end;\n                    end;\n\n                    % if spot was found, interrupt search\n                    if ~isempty(get(this, iDWRowROISet, 'spot')); break; \n                    end;\n                end;\n                continue;\n            end;\n\n            % go through each imaging row\n            for iImagingRow = 1 : nImagingRows;\n\n                % get the DataWatcher table's row index for this imaging row\n                iDWRowImaging = str2double(get(this, iImagingRow, 'rowNum', imagingRows));\n                % get the row ID for the current imaging row\n                imagingRowID = DWGetRowID(this, iDWRowImaging);\n\n                % if there is a match compare the runs validity of the ROISet with this row's ID\n                if any(strcmp(runsValidity, imagingRowID));            \n                    % label the current imaging row as belonging to the current ROISet\n                    set(this, iDWRowImaging, 'ROISet', ROISetRowID);\n                    % if the ROISet does not have a spot ID specified, add the one from the imaging row\n                    if isempty(get(this, iDWRowROISet, 'spot'));\n                        set(this, iDWRowROISet, 'spot', get(this, iDWRowImaging, 'spot'));              \n                    end;\n                    % if there is a GUI and the ROISet's reference image is from the current imaging row\n                    if isGUI(this) && strcmp(imagingRowID, ROISetRowID);\n                        set(this, iDWRowImaging, 'ROISet', sprintf('<html><font color=\"blue\">%s</font>', ROISetRowID));\n                    end;\n                end;\n            end;\n\n            % update wait bar\n            DWWaitBar(this, 100 * ((iROISet / (nROISets * nDays * nAnim)) + ((iDay - 1) / (nDays * nAnim)) + (iAnim - 1) / nAnim));\n\n        end;        \n    end;    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/OCIA.m", "size": 77388, "source_encoding": "utf_8", "md5": "77043c2f7b13e21ffd1f9a11c36961a1", "text": "classdef OCIA < handle\r\n% OCIA - Online Calcium Imaging Assistant\r\n%\r\n%       this = OCIA()\r\n%       this = OCIA(configName)\r\n%       this = OCIA(configName, DWFilt)\r\n%\r\n% Returns an OCIA object 'this', using the configuration file specified by 'configName' using the syntax\r\n%   \"OCIA_config_[configName].m\". If no configuration is specified, \"OCIA_config_default.m\" is used. Starting filters\r\n%   for the DataWatcher can be specified using the 'DWFilt', either as a string ('all', etc.) or cell-array of strings\r\n%   ( {'animalID1', '2014_10_30', 'spot03', ... } ). The file 'OCIA_config_generic' contains all the parameters that\r\n%   can be changed in a custom config file.\r\n\r\n%       %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n%       % Originally created on           08 / 10 / 2013 %\r\n%       % Modified to v2.0 on             20 / 12 / 2013 %\r\n%       % Modified to v3.0 on             21 / 02 / 2014 %\r\n%       % Modified to v4.0 on             19 / 06 / 2014 %\r\n%       % Modified to v5.0 on             15 / 09 / 2014 %\r\n%       %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n%\r\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\r\n\r\n\r\n%% TODO list\r\n%{\r\n\r\n- General\r\n -- add the possibility to have a type of folder in multiple locations (e.g. parent of imgData could be spot OR day)\r\n -- frame jitter, motion correction and motion detection should be independent of a ROISet\r\n -- OCIA_config_default should not initiate the default config itself but call an initiator function \"this =\r\n    initDefault(this)\" or something otherwise the names are confusing\r\n -- add icons to buttons for easier recognition\r\n -- pre-processing GUI: uipanel with options for nFrameSkip(default=0)/fShift/fJitt/moCorr/moDet\r\n    options as checkboxes, and a \"pre-process rows\" button (ANPreProcRows). DWLoad('prev') should do none\r\n    of these corrections, except frameShift and frameSkip.\r\n    Add a display fig checkbox linked to the doPlots of the pre-processing functions.\r\n -- save load modularity:\r\n  --- make the GUI dynamic for the saving options, based on the data's field and configuration\r\n  --- adapt the saving code so that it saves the right things at the right place\r\n  --- fix the save/load/reset options\r\n -- check that flushdata works properly\r\n\r\n- DataWatcher\r\n -- add a \"delete rows\" function (hides the unwanted rows from the DataWatcher), also useful for the imagingWatcher mode\r\n -- intrinsic imaging: access binary file content, show vessels tif files,\r\n    eventually overlap them with expression image using stiching algorithm\r\n -- behavior movie: read the video with 'video2mat.m' and eventually match frames to trials\r\n -- small thumbnail of the currently selected row(s) also for other data types (behavior, intrinsic, behavMovie)\r\n  --- requires frame grouping (? la imageJ ...)\r\n -- only load imaging data once from the DataWatcher's table and then re-use it for analysis and ROI drawing\r\n -- enable the \"real\" analysis pipeline support : load imaging data files (.bin)(HDF5?)\r\n -- implement GUI items/config file for analysis parameters\r\nNO -- add a load metadata button to avoid re-processing all folders just for the metadata\r\nNO -- make the loaded rows' background green to show the data is loaded\r\nNO -- store where each behavior/data file was found and dont reload it if not necessary\r\n\r\n- ImagingWatcher\r\n -- create a new imaging assistant mode or extend the data-watcher to help with online analysis of the imaging\r\n  --- could be on a different computer/matlab session for lower CPU on imaging computer and\r\n      eventually parallel computing for faster loading/processing\r\n -- features:\r\n  --- loading last recorded data\r\n  --- \"quick\" ROIDrawing (semi-auto)\r\n  --- \"quick\" DRR extract\r\n  --- bleaching quantifier\r\n  --- previous days data and metadata (laser, depth, location, surface image, etc.)\r\n\r\n- ROIDrawer\r\n -- modularity in ROISet saving location\r\n -- bug: remove old position callback in RDRenameROI\r\n -- channel selection for ROI drawing, gray scale image of one channel\r\n -- move all ROIs with small arrow GUI pushbuttons <- ^ ->\r\n -- implement an semi-automatic segmentation tool based on cell centers and ring/filled cell body detection\r\n -- cell identity labeling tool\r\n\r\n- Analyser\r\n -- implement a feature of analysing whole day/whole mice with analysRow button and watchType of only day or only animal\r\n -- resizing of the whole panel bug, probably when saving (or plotting?) plots\r\n -- enable the \"real\" analysis pipeline support (load imaging data files (HDF5?))\r\n -- check for colorbar plot saving (resizing issue)\r\n -- check for time-consuming sem calculation problem\r\n -- add plots for several mice\r\n -- implement a bleaching quantifier over multiple runs\r\n -- fix the colorbar saving problem\r\n -- fix the colormap problem\r\n -- fix the analyser plotting ROISet mismatch/non-unique problem\r\n -- when loading data in 'full' mode that was already loaded in 'prev', skip re-loading of 'prev' frames\r\n\r\n- Behavior\r\n -- check sound amplitude (SPL) *randomization*\r\n -- fix licking detection or switch to lick rate\r\n -- implement *alternative* detection system: lick rate\r\n -- introduce a configuration editor or GUI items for mainConf.mat elements\r\n -- save threshold and rewDur for each trial\r\n -- perf analysis: exclude first 3-10 trials + non-responsive regions\r\n -- auto-connect behavior box with COM port communication\r\n\r\n- JointTracker\r\n -- add coordinates display on mouse move\r\n -- load X frames until memory is full and then just load frames on the fly while changing frames\r\n -- when doing the sliding average, remove the existing frames and store them in the temp and then put it back\r\n -- re-process on the fly the subsequent joints when placing a joint (when placing wrist, re-find the MCP)\r\n -- crop function\r\n -- image pre-processing not everywhere but defined by ROI\r\n -- iJoint and iJointType from selection listbox should not be single values but must be arrays (multiple joint\r\n    manipulation)\r\n -- prediction reaffinement\r\n  --- define an \"area mask\" for each joint (or each group of joints), using imfreehand, to constraint their position\r\n  --- use angles and joint distances to constraint the position of the joints\r\n  --- use field flow technique to predict where the joint will move\r\n  --- use correlation dip to change bounding box size (with a minimal box size setting)\r\n -- improve debug plot display so that one can actually see what is going on (nJoints x nProcessSteps image with labeld axes)\r\n -- create a progress bar to show how far we are and how is each frame annotated (manual, semi-auto validated, semi-auto not yet checked, etc.)\r\n -- correlation frame-to-frame or frame-wise only on the bounding boxes\r\n -- base frames pre-processing for better computer and/or manual annotation\r\n  --- subtract sliding window avg image\r\n  --- flatfield\r\n  --- contrast enhancement\r\n -- base frames pre-processing might also only be applied to parts of the frame (like the sliding average only on the\r\n    lower part of the frame)\r\n -- post-hoc evaluation method to get which joints are misplaced, based on:\r\n  --- interpolation of the data points from next and previous joint coordinate ('outlier' detection)\r\n  --- skelton distances\r\n  --- angle-change vs frame-to-frame correlation\r\n -- post-hoc refinement of the match using smaller bounding box\r\n -- computer vision algorithms / ideas\r\n \r\n%}\r\n\r\n%% properties\r\nproperties\r\n\r\n    % verbosity: number telling how much output should be printed out. The higher the more verbose.\r\n    verb = 2;\r\n    % general parameters: version number, start function name, modes, etc.\r\n    main = struct( ...\r\n...     software version\r\n        'version',                  '5.1.10', ...\r\n...     start function name\r\n        'startFunctionName',        'default', ...\r\n...     \"modules\" of the software that need to be included in the current instance\r\n        'modes',                    {{ ...\r\n                                        % mode name         short name\r\n                                        'DataWatcher',      'dw';\r\n                                        'ROIDrawer',        'rd';\r\n                                        'Analyser',         'an';\r\n                                        'Behavior',         'be';\r\n                                        'Intrinsic',        'in';\r\n                                        'JointTracker',     'jt';\r\n                                        'TrialView',        'tv';\r\n                                    }}, ...\r\n...     data types/modes that need to be included in the current instance\r\n        'dataModes',                {{ 'img', 'behav' }}, ...\r\n...     defines whether to show or hide the stack trace upon throwing a warning with the \"#showWarning\" method\r\n        'showWarningStackTraces',   false ...\r\n    );\r\n    \r\n    % paths used by the OCIA\r\n    path = struct();\r\n    \r\n    % GUI related elements\r\n    GUI = struct( 'noGUI', false );\r\n    \r\n    % - Data storage mode\r\n    data = struct();\r\n    \r\n    % - DataWatcher mode\r\n    dw = struct();\r\n    % - ROIDrawer mode\r\n    rd = struct();\r\n    % - Analyser mode\r\n    an = struct();\r\n    % - Behavior mode\r\n    be = struct();\r\n    % - Intrinsic mode\r\n    in = struct();\r\n    % - JointTracker mode\r\n    jt = struct();\r\n    % - Discriminator mode\r\n    di = struct();\r\n    % - Discriminator mode\r\n    tv = struct();\r\n\r\nend\r\n\r\n%% methods - public\r\nmethods\r\n\r\n%% - #OCIA - constructor\r\nfunction this = OCIA(varargin)\r\n% OCIA - Constructor\r\n%\r\n%       this = OCIA()\r\n%       this = OCIA(configName)\r\n%       this = OCIA(configName, DWFilt)\r\n%       this = OCIA(configName, DWFilt, startFunctionName)\r\n%\r\n% Returns an OCIA object 'this', using the configuration file specified by 'configName' using the syntax\r\n%   \"OCIA_config_[configName].m\". If no configuration is specified, \"OCIA_config_default\" is used. Starting filters\r\n%   for the DataWatcher can be specified using the 'DWFilt', either as a string ('all', etc.) or cell-array of strings\r\n%   ( {'animalID1', '2014_10_30', 'spot03', ... } ). The file 'OCIA_config_generic' contains all the parameters that\r\n%   can be changed in a custom config file. Optional 'startFunctionName' string can be provided.\r\n\r\n% Order of function calls for the creation of the object:\r\n%   - parsing inputs ('configName' and 'DWFilt')\r\n%   - call of the config specified by 'configName' (or 'default' if none provided)\r\n%       - first, the \"modes\" that need to be included in the OCIA should be selected in the \"this.main.modes\", as a\r\n%           N_MODES x 2 cell-array of strings, with first column being the mode's name ('DataWatcher') and the second\r\n%           column the short name ('dw'). This needs to be done before the call to the 'OCIA_config_default' because\r\n%           that function initializes the different modes.\r\n%       - the \"data modes\" that need to be included in the OCIA should also be selected in the \"this.main.dataModes\",\r\n%           as a cell-array of strings before the call to the 'OCIA_config_default' for the same reasons as above.\r\n%       - then, 'OCIA_config_default.m' is called. It initiates the OCIA object ('this') with default configuration\r\n%           values to start OCIA:\r\n%           - path (local, raw, ...)\r\n%           - modes (in case not initiated or dataWatcher mode not included)\r\n%           - GUI variables (window position, etc.)\r\n%           - initiate the different modes using their custom configuration functions: \r\n%               'OCIA_modeConfig_[modeName].m'. These will initiate each mode with the default values for the\r\n%               variables needed to run those modes. For example in DataWatcher mode, the table's display, etc.\r\n%           - initiate the different data modes using their custom configuration functions:\r\n%               'OCIA_dataConfig_[dataModeName].m'. These will initiate the 'this.main.dataConfig' cell-array which\r\n%               describes what kind of data types need to be used/stored. These custom configuration function\r\n%               can also eventually initiate some variables in the Analyser mode ('this.an.img' or 'this.an.be')\r\n%               for the analysis of those data types (for example 'this.an.img.defaultFrameRate').\r\n%   - initialization of the drop-down IDs of the DataWatcher to an empty dash ('-')\r\n%   - Java path update for Java classes\r\n%   - creation of the window\r\n%       - creation of each panel using the custom 'OCIA_createWindow_[modeName].m' functions\r\n%       - update of drop-down filters IDs based on the GUI values, which are initialized based on the 'DWFilt' input\r\n%           ('OCIA_createWindow_dataWatcher.m' calls 'DWUpdateFiltersAndWatchTypes' to do this)\r\n%   - window is made visible ('OCIA.show')\r\n%   - custom start function is called 'OCIA_startFunction_[functionName].m' based on the config file's setting stored\r\n%       under 'this.main.startFunctionName' \r\n    \r\n    o('#%s(): constructor ...', mfilename, 4, this.verb);\r\n    o('Launching OCIA v%s ...', this.main.version, 0, this.verb);\r\n    \r\n    %% -- #OCIA: parse inputs and load config file\r\n    o('#%s(): parsing inputs ...', mfilename, 4, this.verb);\r\n    % prepare the input parser object with the requested inputs\r\n    IP = inputParser;\r\n    addOptional(IP,         'configName',         'default',               @ischar);\r\n    addOptional(IP,         'DWFilt',             'empty',                 @(x)isempty(x) || ischar(x) || iscell(x));\r\n    addOptional(IP,         'startFunctionName',  'empty',                 @(x)ischar(x));\r\n    parse(IP, varargin{:});\r\n    \r\n    % get the config function's name and call it\r\n    configName = IP.Results.configName;\r\n    o('#%s(): using config file \"%s\" ...', mfilename, configName, 4, this.verb);\r\n    [~, thisNew] = OCIAGetCallCustomFile(this, 'config', configName, 1, { this }, 1);\r\n    % abort if no \"this\" anymore\r\n    if isempty(thisNew); return; end;\r\n    % otherwise go on\r\n    this = thisNew;\r\n    \r\n    % if DataWatcher filtering was provided as input, overwrite config's parameter with input parameter\r\n    if ~strcmp(IP.Results.DWFilt, 'empty') && ~isempty(IP.Results.DWFilt);\r\n        this.GUI.dw.DWFilt = IP.Results.DWFilt;\r\n    end;\r\n    o('#%s(): filtering elements: \" %s\" ...', mfilename, sprintf('%s - ', this.GUI.dw.DWFilt{:}), 4, this.verb);\r\n    \r\n    % if alternate start function name was provided\r\n    if ~strcmp(IP.Results.startFunctionName, 'empty');\r\n        this.main.startFunctionName = IP.Results.startFunctionName;\r\n    end;\r\n    o('#%s(): filtering elements: \" %s\" ...', mfilename, sprintf('%s - ', this.GUI.dw.DWFilt{:}), 4, this.verb);\r\n    %% -- #OCIA: update the drop-down filters and table IDs\r\n    % define the \"IDs\" field for each drop-down DataWatcher filter element\r\n    dropDownFiltersIDs = this.GUI.dw.filtElems{strcmp(this.GUI.dw.filtElems.GUIType, 'dropdown'), 'id'};\r\n    for iFilter = 1 : numel(dropDownFiltersIDs);\r\n        % create a list with only a dash element, which corresponds to no filtering\r\n        this.dw.([dropDownFiltersIDs{iFilter} 'IDs']) = {'-'};\r\n    end;\r\n\r\n    % fetch IDs\r\n    this.dw.tableIDs = this.GUI.dw.tableDisplay.id;\r\n    % init table to the right size\r\n    this.dw.table = cell(300, numel(this.dw.tableIDs));\r\n    \r\n    % create order column if not yet present\r\n    if ~ismember('order', this.GUI.dw.tableDisplay.Properties.VariableNames)\r\n        this.GUI.dw.tableDisplay.order = (1 : size(this.GUI.dw.tableDisplay, 1))';\r\n    end;\r\n    \r\n    %% -- #OCIA: turn warnings off\r\n    warning('off', 'images:imshow:magnificationMustBeFitForDockedFigure');\r\n    warning('off', 'MATLAB:hg:gltexture:TextureDataTooLargeForDevice');\r\n    warning('off', 'YMA:FindJObj:invisibleHandle');\r\n    warning('off', 'MATLAB:JavaEDTAutoDelegation');\r\n    \r\n    %% --#OCIA: clean up paths\r\n    pathFields = fieldnames(this.path);\r\n    for iField = 1 : numel(pathFields);\r\n        fieldName = pathFields{iField};\r\n        this.path.(fieldName) = regexprep([regexprep(this.path.(fieldName), '\\\\', '/'), '/'], '//$', '/');\r\n        this.path.(fieldName) = regexprep(this.path.(fieldName), '(\\.\\w+)/', '$1');\r\n    end;\r\n    \r\n    %% -- #OCIA: add java folders\r\n    OCIAPath = regexprep(which('OCIA'), '\\\\', '/');\r\n    OCIAPath = regexprep(OCIAPath, '/@OCIA/OCIA.m$', '');\r\n    javaaddpath([OCIAPath '/java/ij.jar']);\r\n    javaaddpath([OCIAPath '/java/TurboRegJava']);\r\n    o('#%s(): added Java path at \"%s\" ...', mfilename, OCIAPath, 4, this.verb);\r\n      \r\n    %% -- #OCIA: create the window and show it\r\n    o('Creating window ...', 0, this.verb);\r\n    OCIACreateWindow(this); % create the GUI window\r\n    pause(0.5);\r\n    show(this); % make the GUI window visible\r\n\r\n    %% -- #OCIA: process start function\r\n    % process the start function\r\n    showMessage(this, sprintf('Initializing using start function \"%s\" ...', this.main.startFunctionName), 'yellow');\r\n    OCIAGetCallCustomFile(this, 'startFunction', this.main.startFunctionName, 1, { this }, 1); % use default if none provided\r\n\r\nend\r\n\r\n\r\n%% - #delete (destructor)\r\nfunction delete(this)\r\n    \r\n    o('#delete()', 4, this.verb);\r\n    \r\n    delete(this.GUI.figH);\r\n    \r\nend\r\n\r\n%% - GUI methods\r\n%% -- #show\r\nfunction show(this)\r\n% show - Show the window\r\n%\r\n%       show(this)\r\n%\r\n% Makes the OCIA window visible. \r\n    \r\n    % do nothing if there is no GUI\r\n    if ~isGUI(this); return; end;\r\n    \r\n    o('#show()', 4, this.verb);\r\n    \r\n    showTic = tic; % for performance timing purposes\r\n    set(this.GUI.figH, 'Visible', 'on');\r\n    pause(0.1);\r\n    o('#show(): done (%.4f sec)', toc(showTic), 4, this.verb);\r\n        \r\nend;\r\n\r\n%% -- #hide\r\nfunction hide(this)\r\n% hide - Hides the window\r\n%\r\n%       hide(this)\r\n%\r\n% Makes the OCIA window invisible. \r\n\r\n    % do nothing if there is no GUI\r\n    if ~isGUI(this); return; end;\r\n    \r\n    o('#hide()', 4, this.verb);\r\n    \r\n    set(this.GUI.figH, 'Visible', 'off');\r\n    \r\nend;\r\n\r\n%% -- #printWindowPosition\r\nfunction printWindowPosition(this)\r\n% printWindowPosition - Prints out the window's position\r\n%\r\n%       printWindowPosition(this)\r\n%\r\n% Prints out the window's current position.\r\n\r\n   showMessage(this, 'Printing current window''s position:');\r\n   showMessage(this, sprintf('this.GUI.pos = [%s];', regexprep(sprintf('%.0f, ', get(this.GUI.figH, 'Position')), ', $', '')));\r\n    \r\nend;\r\n\r\n%% -- #isGUI\r\nfunction isGUIBool = isGUI(this)\r\n% isGUI - GUI-mode check\r\n%\r\n%       isGUIBool = isGUI(this)\r\n%\r\n% Returns the logical 'isGUIBool' telling whether the current instance of the OCIA ('this') is running in a \r\n%   windowed mode or not.\r\n\r\n    % if not in no-GUI mode and either in deployed mode or not in a matlab worker (parallel computing)\r\n    isGUIBool = ~this.GUI.noGUI && (isdeployed || isempty(javachk('desktop')));\r\n        \r\nend;\r\n\r\n%% -- #showMessage\r\nfunction showMessage(this, messageTxt, bgColor)\r\n% showMessage - Display a message\r\n%\r\n%       showMessage(this, messageTxt, bgColor)\r\n%\r\n% Displays the message 'messageTxt' (char) in the log bar and in the command window. If the variable 'bgColor' is\r\n%   specified, the background of the log bar is set to the color specified either as a color string (\"red\", \"blue\",\r\n%   etc.) or as an array of 3 values between 0.0 and 1.0 (RGB). Otherwise the default color (green) is used.\r\n\r\n    % print out the message in the command window\r\n    o(regexprep(messageTxt, '%', '%%'), 1, this.verb);\r\n    \r\n    % do nothing if there is no GUI\r\n    if ~isGUI(this); return; end;\r\n    \r\n    % if variable color not specified or neither a string nor a 3-elements RGB array\r\n    if ~exist('bgColor', 'var') || (~ischar(bgColor) && numel(bgColor) > 3);\r\n        % use the default green color\r\n        bgColor = 'green';\r\n    end;\r\n    \r\n    % display the message and set the background\r\n    set(this.GUI.handles.logBar, 'String', messageTxt, 'Background', bgColor);\r\n    \r\nend;\r\n\r\n%% -- #showWarning\r\nfunction showWarning(this, warnID, warningText, bgColor)\r\n% showMessage - Display a warning\r\n%\r\n%       showWarning(this, warnID, warningText, bgColor)\r\n%\r\n% Displays the warning 'warningText' (char) with the ID 'warningID' (char) in the log bar and in the command window.\r\n%   If the variable 'bgColor' is specified, the background of the log bar is set to the color specified either as a\r\n%   color string (\"red\", \"blue\", etc.) or as an array of 3 values between 0.0 and 1.0 (RGB). Otherwise the default\r\n%   color (yellow) is used.\r\n    \r\n    \r\n    % if variable color not specified or neither a string nor a 3-elements RGB array\r\n    if ~exist('bgColor', 'var') || (~ischar(bgColor) && numel(bgColor) > 3);\r\n        % use the default green color\r\n        bgColor = 'yellow';\r\n    end;\r\n    \r\n    % show the warning but without the stack trace\r\n    if ~this.main.showWarningStackTraces;\r\n        warning off backtrace;\r\n    end;\r\n    warning(warnID, [strrep(warningText, '\\', '\\\\') ' (' warnID ')']);\r\n    if ~this.main.showWarningStackTraces;\r\n        warning on backtrace;\r\n    end;\r\n    \r\n    % do nothing if there is no GUI or if warning is disabled\r\n    warnState = warning('query', warnID);\r\n    if ~isfield(this.GUI, 'noGUI') || ~isfield(this.GUI, 'figH') || ~isfield(this.GUI, 'handles') ...\r\n            || ~isfield(this.GUI.handles, 'logBar') || ~isGUI(this) || strcmp(warnState.state, 'off'); return; end;\r\n    \r\n    % split the warning text at end-of-line characters\r\n    warningTextSplit = regexp(warningText, '\\n', 'split');\r\n    % only display the first line of the warning text and set the background\r\n    set(this.GUI.handles.logBar, 'String', warningTextSplit{1}, 'Background', bgColor);    \r\n    \r\nend;\r\n\r\n%% -- #getJTable\r\nfunction jTable = getJTable(this, hTable)\r\n% getJTable - Get a Java table\r\n%\r\n%       jTable = getJTable(this, hTable)\r\n%\r\n% Returns the Java object 'jTable' associated with the uitable specified by 'hTable'. 'hTable' can be either a\r\n%   string ('DWTable', etc.) or as a uitable handle.\r\n\r\n    jTable = []; % return empty in case there is a problem\r\n    tableName = '';\r\n    \r\n    % if handle is actually a string, get the appropriate uitable handle first\r\n    if ischar(hTable);\r\n        \r\n        % store the table's name\r\n        tableName = hTable;\r\n        \r\n        % check if the table was not already stored in memory\r\n        if isfield(this.GUI, 'jTables') && isfield(this.GUI.jTables, tableName);\r\n            jTable = this.GUI.jTables.(tableName);\r\n            return;\r\n        end;\r\n        \r\n        % get the appropriate handle for the table\r\n        switch tableName;\r\n            case 'DWTable';\r\n                hTable = this.GUI.handles.dw.table;\r\n            case 'BEConfTable';\r\n                hTable = this.GUI.handles.be.confTable;\r\n            case 'BEETLTable';\r\n                hTable = this.GUI.handles.be.ETLTable;\r\n            case 'BEExpTable';\r\n                hTable = this.GUI.handles.be.expTable;\r\n            otherwise;\r\n                showWarning(this, 'OCIA:getJTable:UnknownTable', sprintf('Cannot find JTable for \"%s\".', hTable));\r\n                return;\r\n        end;\r\n    end;\r\n    \r\n    % sometimes the Java-object fetching fails on first attempt, so try a second time\r\n    try\r\n        % get the actual java table underlying the requested uitable\r\n        jTable = findjobj(hTable); jTable = jTable.getComponents();\r\n        jTable = jTable(1); jTable = jTable.getComponents();\r\n        jTable = jTable(1);\r\n        \r\n    % try a second time\r\n    catch e; %#ok<NASGU>\r\n        pause(0.5);\r\n        % get the actual java table underlying the requested uitable\r\n        jTable = findjobj(hTable); jTable = jTable.getComponents();\r\n        jTable = jTable(1); jTable = jTable.getComponents();\r\n        jTable = jTable(1);\r\n    end;\r\n    \r\n    % if the table was acced by a name, store the jtable\r\n    if ~isempty(tableName);\r\n        this.GUI.jTables.(tableName) = jTable;\r\n    end;\r\nend;\r\n\r\n\r\n%% -- #getData\r\nfunction data = getData(this, varargin)\r\n% getData - Get data or load status from the DataWatcher's table\r\n%\r\n%       data = getData(this, rows, dataType)\r\n%       data = getData(this, rows, dataType, subFieldName)\r\n%\r\n% Returns the requested data type from the DataWatcher's table data. \"rows\" should be a double or an array of double and\r\n%   \"columns\" a string or a cell-array of string, \"dataType\" a string. \"subFieldName\" should be a string that specifies\r\n%   which sub-field ('data', 'loadStatus', 'procState') should be returned.\r\n\r\n% by default or in case of error, return nothing\r\ndata = [];\r\n\r\n% all columns for specified row(s)\r\nif numel(varargin) == 2 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && ischar(varargin{2});\r\n    rows = varargin{1};\r\n    dataType = varargin{2};\r\n    subFieldName = [];\r\n    \r\n% specified rows and specified column(s)\r\nelseif numel(varargin) == 3 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && ischar(varargin{2}) && ischar(varargin{3});\r\n    rows = varargin{1};\r\n    dataType = varargin{2};\r\n    subFieldName = varargin{3};\r\n    \r\n% otherwise: bad input arguments\r\nelse\r\n    rows = [];\r\n    dataType = [];\r\n    subFieldName = [];\r\nend;\r\n\r\n% if all rows are required to be selected using the 'all' command\r\nif ischar(rows) && strcmp(rows, 'all');\r\n    rows = 1 : size(this.dw.table, 1);\r\nend;\r\n\r\n% if the parameters where not all provided or could not be figured out, abort with warning\r\nif isempty(rows) || isempty(dataType);\r\n    showWarning(this, 'OCIA:getData:BadInputArguments', 'Bad input arguments for function getData, please read the help.');   \r\n    return;\r\nend;\r\n\r\n% get the data structure\r\ndataStruct = getR(this, rows, 'data');\r\n\r\n% if no data structure found, return nothing\r\nif isempty(dataStruct);\r\n    return;\r\nend;\r\n\r\n% allocate the data as a cell array\r\ndata = cell(size(dataStruct));\r\n% go through each fetched sub-structure\r\nfor iStruct = 1 : numel(dataStruct); \r\n    % if the dataType exists\r\n    if isfield(dataStruct{iStruct}, dataType);\r\n        % if no sub-field name provided, return the data structures themselves\r\n        if isempty(subFieldName);\r\n            data{iStruct} = dataStruct{iStruct}.(dataType);\r\n        % if the sub-field name is required and exists, return it\r\n        elseif isfield(dataStruct{iStruct}.(dataType), subFieldName);\r\n            data{iStruct} = dataStruct{iStruct}.(dataType).(subFieldName);\r\n        end;\r\n    end;    \r\nend;\r\n\r\n% do not return a single empty cell, return an empty array instead\r\nif iscell(data) && numel(data) == 1;\r\n    data = data{1};\r\nend;\r\n\r\nend\r\n\r\n\r\n%% -- #setData\r\nfunction data = setData(this, varargin)\r\n% getData - Get data or load status from the DataWatcher's table\r\n%\r\n%       setData(this, rows, dataType, data)\r\n%       setData(this, rows, dataType, subFieldName, data)\r\n%\r\n% Stores the specified data in the DataWatcher's table specified data type. \"rows\" should be a double or an array of double and\r\n%   \"columns\" a string or a cell-array of string, \"dataType\" a string. \"subFieldName\" should be a string that specifies\r\n%   which sub-field ('data', 'loadStatus', 'procState') should be returned. \"data\" is the data to store, can be a cell\r\n%   array.\r\n\r\n% all columns for specified row(s)\r\nif numel(varargin) == 3 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && ischar(varargin{2});\r\n    rows = varargin{1};\r\n    dataType = varargin{2};\r\n    subFieldName = [];\r\n    data = varargin{3};\r\n    \r\n% specified rows and specified column(s)\r\nelseif numel(varargin) == 4 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && ischar(varargin{2}) && ischar(varargin{3});\r\n    rows = varargin{1};\r\n    dataType = varargin{2};\r\n    subFieldName = varargin{3};\r\n    data = varargin{4};\r\n    \r\n% otherwise: bad input arguments\r\nelse\r\n    rows = [];\r\n    dataType = [];\r\n    subFieldName = [];\r\n    data = [];\r\nend;\r\n\r\n% if all rows are required to be selected using the 'all' command\r\nif ischar(rows) && strcmp(rows, 'all');\r\n    rows = 1 : size(tableToUse, 1);\r\nend;\r\n\r\n% if the parameters where not all provided or could not be figured out, abort with warning\r\nif isempty(rows) || isempty(dataType);\r\n    showWarning(this, 'OCIA:setData:BadInputArguments', 'Bad input arguments for function setData, please read the help.');   \r\n    return;\r\nend;\r\n\r\n% make sure the data's format is a cell-array\r\nif ~iscell(data) || numel(rows) == 1;\r\n    data = { data };\r\nend;\r\n\r\n% if the number of rows is too big, abort with warning\r\nif numel(rows) ~= numel(data);\r\n    showWarning(this, 'OCIA:setData:NumberOfRowsMismatch', ...\r\n        sprintf('Number of rows specified (%02d) is not equal to the size of the input data (%02d).', ...\r\n            numel(rows), numel(data)));   \r\n    return;\r\nend;\r\n\r\n% go through each row\r\nfor iRow = 1 : numel(rows); \r\n    % get the data for this row\r\n    dataStruct = get(this, rows(iRow), 'data');\r\n    % if the dataType exists\r\n    if isfield(dataStruct, dataType);\r\n        % if no sub-field name provided, store the whole data data type\r\n        if isempty(subFieldName);\r\n            dataStruct.(dataType) = data{iRow};\r\n        % otherwise just set the field\r\n        else\r\n            dataStruct.(dataType).(subFieldName) = data{iRow};\r\n        end;\r\n    end; \r\n    % set the data for this row\r\n    set(this, rows(iRow), 'data', dataStruct);   \r\nend;\r\n\r\nend\r\n\r\n\r\n%% -- #get\r\nfunction values = get(this, varargin)\r\n% get - Get values from table\r\n%\r\n%       values = get(this, rows)\r\n%       values = get(this, columns)\r\n%       values = get(this, rows, columns)\r\n%       values = get(this, rows, columns, tableToUse)\r\n%       values = get(this, rows, columns, tableToUse, tableIDs)\r\n%\r\n% Returns the requested rows/columns from the DataWatcher's table. \"rows\" should be a double or an array of double and\r\n%   \"columns\" a string or a cell-array of string. Optionnally, an alternative table \"tableToUse\" can be provided with\r\n%   its own columns names \"tableIDs\".\r\n\r\n% get the raw values\r\nvalues = getR(this, varargin{:});\r\n\r\n% do some post-process tasks if there are some cells\r\nif ~isempty(values);\r\n    \r\n    % filter for the delete tag\r\n    charCells = cellfun(@ischar, values);\r\n    values(charCells) = regexprep(values(charCells), ['^' this.GUI.dw.deleteTag], '');\r\n    values(charCells) = regexprep(values(charCells), '^<html>(<[^>]+>)?(<[^>]+>)?([^<]+)(<[^>]+>)*', '$3');\r\n\r\n    % do not return a single cell, return the value it contains instead\r\n    if iscell(values) && numel(values) == 1;\r\n        values = values{1};\r\n    end;\r\n\r\nend;\r\n\r\nend\r\n\r\n%% -- #getR\r\nfunction values = getR(this, varargin)\r\n% getR - Get raw values from table (no post-processing)\r\n%\r\n%       values = getR(this, rows)\r\n%       values = getR(this, columns)\r\n%       values = getR(this, rows, columns)\r\n%       values = getR(this, rows, columns, tableToUse)\r\n%       values = getR(this, rows, columns, tableToUse, tableIDs)\r\n%\r\n% Returns the requested rows/columns from the DataWatcher's table without any further modification (delete tag removal).\r\n%   \"rows\" should be a double or an array of double and \"columns\" a string or a cell-array of string. Optionnally, \r\n%   an alternative table \"tableToUse\" can be provided with its own columns names \"tableIDs\".\r\n\r\n% get the default table and its IDs\r\ntableToUse = this.dw.table;\r\ntIDs = this.dw.tableIDs;\r\n\r\n% by default or in case of error, return nothing\r\nvalues = [];\r\n\r\n% all columns for specified row(s)\r\nif numel(varargin) == 1 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all')));\r\n    rows = varargin{1};\r\n    columns = tIDs;\r\n    \r\n% all rows for specified column(s)\r\nelseif numel(varargin) == 1 && (ischar(varargin{1}) || iscell(varargin{1}));\r\n    rows = 'all';\r\n    columns = varargin{1};\r\n    \r\n% specified rows and specified column(s)\r\nelseif numel(varargin) == 2 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && (ischar(varargin{2}) || iscell(varargin{2}));\r\n    rows = varargin{1};\r\n    columns = varargin{2};\r\n    \r\n% specified rows and specified column(s) with a custom table\r\nelseif numel(varargin) == 3 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && (ischar(varargin{2}) || iscell(varargin{2})) ...\r\n        && iscell(varargin{3});\r\n    rows = varargin{1};\r\n    columns = varargin{2};\r\n    tableToUse = varargin{3};\r\n    \r\n% specified rows and specified column(s) with a custom table and table IDs\r\nelseif numel(varargin) == 4 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && (ischar(varargin{2}) || iscell(varargin{2})) ...\r\n        && iscell(varargin{3}) && iscell(varargin{4});\r\n    rows = varargin{1};\r\n    columns = varargin{2};\r\n    tableToUse = varargin{3};\r\n    tIDs = varargin{4};\r\n    \r\n% otherwise: bad input arguments\r\nelse\r\n    rows = [];\r\n    columns = [];  \r\nend;\r\n\r\n% if all rows are required to be selected using the 'all' command\r\nif ischar(rows) && strcmp(rows, 'all');\r\n    rows = 1 : size(tableToUse, 1);\r\nend;\r\n\r\n% make sure the column name(s)'format is a cell-array\r\nif ischar(columns) && ~isempty(columns);\r\n    columns = { columns };\r\nend;\r\n\r\n% if no rows selected, abort\r\nif isempty(rows);\r\n    return;\r\nend;\r\n\r\n% if the parameters where not all provided or could not be figured out, abort with warning\r\nif isempty(columns) || isempty(tableToUse) || isempty(tIDs);\r\n    showWarning(this, 'OCIA:getR:BadInputArguments', 'Bad input arguments for function get, please read the help.');   \r\n    return;\r\nend;\r\n\r\n% if the number of rows is too big, abort with warning\r\nif numel(rows) > size(tableToUse, 1);\r\n    showWarning(this, 'OCIA:getR:NumberOfRowsExceeded', ...\r\n        sprintf('Number of rows specified (%02d) exceeds table''s dimensions (%02d).', numel(rows), size(tableToUse, 1)));   \r\n    return;\r\nend;\r\n\r\n% if all is good, then fetch the values:\r\n% get the indexes of the columns that are in the IDs, in the same order as requested\r\n[~, b] = ismember(columns(ismember(columns, tIDs)), tIDs);\r\n% fetch the values\r\nvalues = tableToUse(rows, b);\r\n\r\n% post-process the column names\r\nfor iCol = 1 : numel(columns);\r\n    switch columns{iCol};\r\n        case 'rowID';\r\n            rowID = DWGetRowID(this, rows);\r\n            if ~iscell(rowID); rowID = { rowID }; end;\r\n            values(:, iCol) = rowID;\r\n        case 'rowTypeID';\r\n            rowTypeID = DWGetRowTypeID(this, rows);\r\n            if ~iscell(rowTypeID); rowTypeID = { rowTypeID }; end;\r\n            values(:, iCol) = rowTypeID;\r\n        otherwise;\r\n            % do nothing\r\n    end;\r\nend;\r\n\r\nend\r\n\r\n%% -- #set\r\nfunction varargout = set(this, varargin)\r\n% set - set values in table\r\n%\r\n%      set(this, rows, values)\r\n%      set(this, columns, values)\r\n%      set(this, rows, columns, values)\r\n%      tableToUse = set(this, rows, columns, values, tableToUse)\r\n%      tableToUse = set(this, rows, columns, values, tableToUse, tableIDs)\r\n%\r\n% Stores the specified values in the rows/columns from the DataWatcher's table. \"rows\" should be a double or an \r\n%   array of double and \"columns\" a string or a cell-array of string. Optionnally, an alternative table \"tableToUse\" \r\n%   can be provided with its own columns names \"tableIDs\", in which case the new table is returned.\r\n\r\n% by default, no output\r\nvarargout = {};\r\n\r\n% get the default table and its IDs\r\ntableToUse = this.dw.table;\r\ntIDs = this.dw.tableIDs;\r\n% set the flag specifying if the table used was from input, by default \"false\"\r\ntableFromInput = false;\r\n\r\n% all columns for specified row(s)\r\nif numel(varargin) == 2 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all')));\r\n    rows = varargin{1};\r\n    columns = tIDs;\r\n    values = varargin{2};\r\n    \r\n% all rows for specified column(s)\r\nelseif numel(varargin) == 2 && (ischar(varargin{1}) || iscell(varargin{1}));\r\n    rows = 'all';\r\n    columns = varargin{1};\r\n    values = varargin{2};\r\n    \r\n% specified rows and specified column(s)\r\nelseif numel(varargin) == 3 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && (ischar(varargin{2}) || iscell(varargin{2}));\r\n    rows = varargin{1};\r\n    columns = varargin{2};\r\n    values = varargin{3};\r\n    \r\n% specified rows and specified column(s) with a custom table\r\nelseif numel(varargin) == 4 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && (ischar(varargin{2}) || iscell(varargin{2})) ...\r\n        && iscell(varargin{4});\r\n    rows = varargin{1};\r\n    columns = varargin{2};\r\n    values = varargin{3};\r\n    tableToUse = varargin{4};\r\n    % set the flag specifying that the table used was from input\r\n    tableFromInput = true;\r\n    \r\n% specified rows and specified column(s) with a custom table and table IDs\r\nelseif numel(varargin) == 5 && (isnumeric(varargin{1}) || (ischar(varargin{1}) && strcmp(varargin{1}, 'all'))) ...\r\n        && (ischar(varargin{2}) || iscell(varargin{2})) ...\r\n        && iscell(varargin{4}) && iscell(varargin{5});\r\n    rows = varargin{1};\r\n    columns = varargin{2};\r\n    values = varargin{3};\r\n    tableToUse = varargin{4};\r\n    tIDs = varargin{5};\r\n    % set the flag specifying that the table used was from input\r\n    tableFromInput = true;\r\n    \r\n% otherwise: bad input arguments\r\nelse\r\n    columns = []; \r\nend;\r\n\r\n% if all rows are required to be selected using the 'all' command\r\nif ischar(rows) && strcmp(rows, 'all');\r\n    rows = 1 : size(tableToUse, 1);\r\nend;\r\n\r\n% make sure the column name(s)'format is a cell-array\r\nif ischar(columns) && ~isempty(columns);\r\n    columns = { columns };\r\nend;\r\n\r\n% if the parameters where not all provided or could not be figured out, abort with warning\r\nif isempty(rows) || isempty(columns) || isempty(tableToUse) || isempty(tIDs);\r\n    showWarning(this, 'OCIA:set:BadInputArguments', 'Bad input arguments for function set, please read the help.');   \r\n    return;\r\nend;\r\n\r\n% if the number of rows is too big, abort with warning\r\nif numel(rows) > size(tableToUse, 1);\r\n    showWarning(this, 'OCIA:set:NumberOfRowsExceeded', ...\r\n        sprintf('Number of rows specified (%02d) exceeds table''s dimensions (%02d).', numel(rows), size(tableToUse, 1)));   \r\n    return;\r\nend;\r\n\r\n% make sure the values' format is a cell-array\r\nif ~iscell(values);\r\n    values = { values };\r\nend;\r\n\r\n% if the size of the values does not match exactly the size of what needs to be replaced, try to extend the values\r\ntableToReplace = tableToUse(rows, ismember(tIDs, columns));\r\nif ~all(size(values) == size(tableToReplace));\r\n    \r\n    % if inputs are just transposed, transposed them back\r\n    if all(size(values') == size(tableToReplace));\r\n        values = values';\r\n    \r\n    % if the values are a vector and can be expanded on one dimension to fit the right size\r\n    elseif size(values, 1) == 1 && size(values, 2) == size(tableToReplace, 2);\r\n        values = repmat(values, size(tableToReplace, 1), 1);\r\n    % if the values are a vector and can be expanded on one dimension to fit the right size\r\n    elseif size(values, 1) == 1 && size(values, 2) == size(tableToReplace, 1);\r\n        values = repmat(values', 1, size(tableToReplace, 2));\r\n    % if the values are a vector and can be expanded on one dimension to fit the right size\r\n    elseif size(values, 2) == 1 && size(values, 1) == size(tableToReplace, 1);\r\n        values = repmat(values, 1, size(tableToReplace, 1));\r\n    % if the values are a vector and can be expanded on one dimension to fit the right size\r\n    elseif size(values, 2) == 1 && size(values, 1) == size(tableToReplace, 2);\r\n        values = repmat(values', size(tableToReplace, 1), 1);\r\n    % if the values are a vector and can be expanded on one dimension to fit the right size\r\n    elseif size(values, 2) == 1 && size(values, 1) == 1;\r\n        values = repmat(values', size(tableToReplace, 1), size(tableToReplace, 2));\r\n    % if nothing can be done, abort\r\n    else\r\n        showWarning(this, 'OCIA:set:BadSizeInputValues', ...\r\n            sprintf('Input values have bad size: provided size: %d x %d, required size: %d x %d.', ...\r\n                size(values), size(tableToReplace))); \r\n        return;\r\n    end;\r\nend;\r\n\r\n% set the values\r\ntableToUse(rows, ismember(tIDs, columns)) = values;\r\n\r\n% if the table comes from input arguments, return it\r\nif tableFromInput;\r\n    varargout = { tableToUse };\r\n% otherwise, update the data watcher table\r\nelse\r\n    this.dw.table = tableToUse;\r\nend;\r\n\r\nend\r\n\r\n%% - #event handlers\r\n%% -- #keyPressed\r\nfunction keyPressed(this, ~, e)\r\n    \r\n% get the current mode\r\ncurrentMode = this.main.modes{get(this.GUI.handles.changeMode, 'Value'), 1};\r\n% get whether a mode change was requested\r\nisChangeMode = 0;\r\n% if ismember('shift', e.Modifier);\r\n%     switch e.Key;\r\n%         case {'1', '2', '3', '4', '5', '6', '7', '8', '9'};\r\n%             if str2double(e.Key) <= size(this.main.modes, 1);\r\n%                 OCIAChangeMode(this, this.main.modes{str2double(e.Key), 1});\r\n%             end;\r\n%             isChangeMode = 1;\r\n%     end;\r\n% end;\r\n\r\nif ~isChangeMode;\r\n    switch currentMode\r\n        \r\n        %% --- #keyPressed : ROIDrawer\r\n        case 'TrialView';\r\n            % get current object\r\n            currObj = get(this.GUI.figH, 'CurrentObject');\r\n            \r\n            % check for key presses not while inside a text uicontrol element\r\n            if ~isa(currObj, 'matlab.ui.control.UIControl') || ~ismember(get(currObj, 'Style'), { 'edit' });\r\n                switch e.Key;\r\n\r\n                    % print help for shortcuts\r\n                    case 'h';\r\n                        msgCellArray = { ...\r\n                            'This is the \\bfhelp\\rm for the \\bfshortcuts\\rm of the TrialView mode of OCIA.', ...\r\n                            'Following shortcuts can be used:', ...\r\n                            '\\bf               [H]                       \\rmdisplay this help', ...\r\n                            '\\bf               [up/down arrows]          \\rmmove up/down in the file selection', ...\r\n                            '\\bf               [left/right arrows]       \\rmmove 1 frame  backward or forward', ...\r\n                            '\\bf   [SHIFT]  +  [left/right arrows]       \\rmmove 3 frames backward or forward', ...\r\n                            '\\bf               [space]                   \\rmadd a movement point', ...\r\n                            '\\bf               [R]                       \\rmreset GUI', ...\r\n                            '\\bf    [CTRL]  +  [R]                       \\rmreset movement points for current trial', ...\r\n                            '\\bf   [SHIFT]  +  [R]                       \\rmreset movement points for all trials', ...\r\n                            '\\bf               [L]                       \\rmload current row/trial', ...\r\n                            '\\bf    [CTRL]  +  [L]                       \\rmload parameters and move vectors', ...\r\n                            '\\bf   [SHIFT]  +  [L]                       \\rmload ROIs', ...\r\n                            '\\bf    [CTRL]  +  [S]                       \\rmsave parameters and move vectors', ...\r\n                            '\\bf   [SHIFT]  +  [S]                       \\rmsave ROIs', ...\r\n                        }';\r\n                        % display a message box\r\n                        h = msgbox( msgCellArray, 'Shortcut help for TrialView mode');\r\n                        \r\n                        % make sure the box is big enough\r\n                        boxPos = get(h, 'Position');\r\n                        set(h, 'Position', boxPos + [-200, -75, 400, 150]);\r\n                        % make font bigger\r\n                        hChild = get(h, 'Children');\r\n                        set(hChild(2), 'Units', 'Normalized', 'Position', [0, 0, 1, 1]);\r\n                        hText = get(hChild(2), 'Children');\r\n                        set(hText, 'FontSize', 12, 'Units', 'Normalized', 'Position', [0.01, 0.98, 0], ...\r\n                            'FontName', 'Courier', 'VerticalAlignment', 'top', 'String', msgCellArray, ...\r\n                            'Interpreter', 'tex');\r\n\r\n                    % left/right arrow keys => move by one frame back or forward\r\n                    case { 'leftarrow', 'rightarrow' };\r\n                        % adjust frame\r\n                        this.tv.iFrame = this.tv.iFrame + iff(ismember('shift', e.Modifier), 3, 1) ...\r\n                            * iff(strcmp(e.Key, 'leftarrow'), -1, 1);\r\n                        % keep frame between boundaries\r\n                        this.tv.iFrame = min(max(round(this.tv.iFrame), 1), this.tv.params.WFDataSize(3));\r\n                        % update GUI\r\n                        OCIA_trialview_changeFrame(this);\r\n\r\n                    % up/down arrow keys => move within the file selection list\r\n                    case { 'uparrow', 'downarrow' };\r\n                        if isfield(this.GUI.handles.tv, 'paramPanElems') ...\r\n                                && isfield(this.GUI.handles.tv.paramPanElems, 'fileList');\r\n                            % get selected element and size\r\n                            selElemIndex = get(this.GUI.handles.tv.paramPanElems.fileList, 'Value');\r\n                            nElems = numel(get(this.GUI.handles.tv.paramPanElems.fileList, 'String'));\r\n                            % adjust selection\r\n                            selElemIndex = selElemIndex + iff(strcmp(e.Key, 'uparrow'), -1, 1);\r\n                            % keep index between boundaries\r\n                            selElemIndex = min(max(selElemIndex, 1), nElems);\r\n                            % update GUI\r\n                            set(this.GUI.handles.tv.paramPanElems.fileList, 'Value', selElemIndex);\r\n                            % make sure parameters are updated\r\n                            TVUpdateParams(this);\r\n                        end;\r\n                        \r\n                    % add a movement point\r\n                    case 'space';\r\n                        OCIA_trialview_addMovePoint(this);\r\n                        \r\n                    % resets\r\n                    case 'r';\r\n                        % reset GUI\r\n                        if isempty(e.Modifier);\r\n                            OCIA_startFunction_trialView(this);\r\n                            \r\n                        % reset move points\r\n                        elseif ~isempty(e.Modifier) && all(strcmp(e.Modifier, 'control'));\r\n                            OCIA_trialview_resetMovePoints(this);\r\n                            \r\n                        % reset all move points\r\n                        elseif ~isempty(e.Modifier) && all(strcmp(e.Modifier, 'shift'));\r\n                            OCIA_trialview_resetMovePoints(this, 'all');\r\n                            \r\n                        end;\r\n                        \r\n                    % load\r\n                    case 'l';\r\n                        % load row\r\n                        if isempty(e.Modifier);\r\n                            OCIA_trialview_loadWideFieldData(this);\r\n                            \r\n                        % load params\r\n                        elseif ~isempty(e.Modifier) && all(strcmp(e.Modifier, 'control'));\r\n                            OCIA_trialview_loadParams(this);\r\n                            \r\n                        % load ROIs\r\n                        elseif ~isempty(e.Modifier) && all(strcmp(e.Modifier, 'shift'));\r\n                            OCIA_trialview_loadROIs(this);\r\n                            \r\n                        end;\r\n                        \r\n                    % save\r\n                    case 's';\r\n                        \r\n                        % save params\r\n                        if all(strcmp(e.Modifier, 'control'));\r\n                            OCIA_trialview_saveParams(this);\r\n                            \r\n                        % save ROIs\r\n                        elseif all(strcmp(e.Modifier, 'shift'));\r\n                            OCIA_trialview_saveROIs(this);\r\n                            \r\n                        end;\r\n\r\n                end; % switch end\r\n            end; % end of check for key presses not while inside a text uicontrol element\r\n        \r\n        %% --- #keyPressed : ROIDrawer\r\n        case 'ROIDrawer';\r\n            switch e.Key;\r\n                \r\n                % arrow keys\r\n                case { 'uparrow', 'downarrow', 'leftarrow', 'rightarrow' };\r\n                    \r\n                    % move ROIs\r\n                    if ~ismember('control', e.Modifier) && ~ismember('shift', e.Modifier) ...\r\n                            && ~ismember('alt', e.Modifier);\r\n                        RDMoveROIs(this, strrep(e.Key, 'arrow', ''), this.rd.moveROIsStep);\r\n                        \r\n                    % rotate ROIs\r\n                    elseif ismember('control', e.Modifier) && strcmp(e.Key, 'leftarrow');\r\n                        RDRotateROIs(this, - this.rd.rotateROIsStep);\r\n                        \r\n                    % rotate ROIs\r\n                    elseif ismember('control', e.Modifier) && strcmp(e.Key, 'rightarrow');\r\n                        RDRotateROIs(this, this.rd.rotateROIsStep);\r\n                        \r\n                    % scale ROIs\r\n                    elseif ismember('shift', e.Modifier) && strcmp(e.Key, 'leftarrow');\r\n                        RDScaleROIs(this, this.rd.scaleROIsStep, 0);\r\n                        \r\n                    % scale ROIs\r\n                    elseif ismember('shift', e.Modifier) && strcmp(e.Key, 'rightarrow');\r\n                        RDScaleROIs(this, - this.rd.scaleROIsStep, 0);\r\n                        \r\n                    % scale ROIs\r\n                    elseif ismember('shift', e.Modifier) && strcmp(e.Key, 'uparrow');\r\n                        RDScaleROIs(this, 0, this.rd.scaleROIsStep);\r\n                        \r\n                    % scale ROIs\r\n                    elseif ismember('shift', e.Modifier) && strcmp(e.Key, 'downarrow');\r\n                        RDScaleROIs(this, 0, - this.rd.scaleROIsStep);\r\n                    \r\n                    % change ROI\r\n                    elseif ismember('alt', e.Modifier) && strcmp(e.Key, 'downarrow');\r\n                        currROI = get(this.GUI.handles.rd.selROIsList, 'Value');\r\n                        if isempty(currROI); currROI = 1; end;\r\n                        if currROI(1) < numel(get(this.GUI.handles.rd.selROIsList, 'String'));\r\n                            set(this.GUI.handles.rd.selROIsList, 'Value', currROI(1) + 1);\r\n                        end;\r\n                        RDSelROI(this);\r\n                       \r\n                    % change ROI \r\n                    elseif ismember('alt', e.Modifier) && strcmp(e.Key, 'uparrow');\r\n                        currROI = get(this.GUI.handles.rd.selROIsList, 'Value');\r\n                        if isempty(currROI);\r\n                            currROI = numel(get(this.GUI.handles.rd.selROIsList, 'String'));\r\n                        end;\r\n                        if currROI(1) > 1;\r\n                            set(this.GUI.handles.rd.selROIsList, 'Value', currROI(1) - 1);\r\n                        end;\r\n                        RDSelROI(this);\r\n                    end;\r\n                    \r\n                % toggle zoom\r\n                case 'z';\r\n                    RDActivateZoom(this, ~get(this.GUI.handles.rd.zTool, 'Value'));\r\n                    \r\n                % new ROI\r\n                case 'n';\r\n                    RDDrawNewROI(this, [], []);\r\n                    \r\n                    \r\n                % compare ROISets\r\n                case 'c';\r\n                    \r\n                    % invert target and reference\r\n                    if ismember('control', e.Modifier);\r\n                        selRef = get(this.GUI.handles.rd.refROISetASetter, 'Value');\r\n                        selTarg = get(this.GUI.handles.rd.refROISetBSetter, 'Value');\r\n                        set(this.GUI.handles.rd.refROISetASetter, 'Value', selTarg(1));\r\n                        set(this.GUI.handles.rd.refROISetBSetter, 'Value', selRef(1));\r\n                        RDCompareROIs(this, 'IDs');\r\n                    \r\n                    % ?\r\n                    elseif ismember('alt', e.Modifier);\r\n                        compareState = get(this.GUI.handles.rd.refROISet, 'Value');\r\n                        set(this.GUI.handles.rd.refROISet, 'Value', ~compareState);\r\n                        RDCompareROIs(this, 'IDs');\r\n                        \r\n                    % ?\r\n                    else\r\n                        RDCompareROIs(this, 'IDs');\r\n                    end;\r\n                    \r\n                case 'l';\r\n                    % load ROIs\r\n                    if ismember('control', e.Modifier);\r\n                        RDSaveROIs(this);\r\n                        \r\n                    % select last ROIs\r\n                    else\r\n                        set(this.GUI.handles.rd.selROIsList, 'Value',  numel(get(this.GUI.handles.rd.selROIsList, 'String')));\r\n                        RDSelROI(this);\r\n                    end;\r\n                      \r\n                case 'r';\r\n                    % reload ROIs  \r\n                    if ismember('shift', e.Modifier);\r\n                        \r\n                        spotIDs = get(this, 'all', 'spot');\r\n                        spotIDs(cellfun(@isempty, spotIDs)) = [];\r\n                        uniqueSpotIDs = unique([ '-'; spotIDs]);\r\n                        \r\n                        selectedSpotIDs = get(this, this.dw.selectedTableRows, 'spot');\r\n                        selectedSpotIDs(cellfun(@isempty, selectedSpotIDs)) = [];\r\n                        uniqueSelectedSpotIDs = unique(selectedSpotIDs);\r\n                        \r\n                        selRef = get(this.GUI.handles.rd.refROISetASetter, 'Value');\r\n                        selTarg = get(this.GUI.handles.rd.refROISetBSetter, 'Value');\r\n                        DWProcessWatchFolder(this);\r\n                        DWExtractNotebookInfo(this);\r\n                        pause(0.01);\r\n                        set(this.GUI.handles.dw.filt.spotID, 'String', uniqueSpotIDs, ...\r\n                            'Value', find(strcmp(uniqueSelectedSpotIDs{1}, uniqueSpotIDs)));\r\n                        DWFilterSelectTable(this, 'new');\r\n                        pause(0.01);\r\n                        OCIA_dataWatcherProcess_drawROIs(this);\r\n                        set(this.GUI.handles.rd.tableList, 'Value', 1 : numel(this.rd.selectedTableRows));\r\n                        RDChangeRow(this, this.GUI.handles.rd.tableList);\r\n                        set(this.GUI.handles.rd.refROISetASetter, 'Value', selRef);\r\n                        set(this.GUI.handles.rd.refROISetBSetter, 'Value', selTarg);\r\n                        set(this.GUI.handles.rd.refROISet, 'Value', 1);\r\n                        RDCompareROIs(this);\r\n                        RDLoadROIs(this);\r\n                        \r\n                    % rename ROIs\r\n                    elseif ismember('control', e.Modifier);\r\n                        RDRenameROI(this);\r\n                        \r\n                        \r\n                    % rename ROIs box\r\n                    else\r\n                        set(this.GUI.handles.rd.ROIName, 'String', '');\r\n                        uicontrol(this.GUI.handles.rd.ROIName);\r\n                    end;\r\n                \r\n                case 'space';\r\n\r\n                % delete selected ROIs\r\n                case { 'd', 'delete' };\r\n                    RDDeleteROI(this);\r\n                % toggle ROI display\r\n                case 'i';\r\n                    RDShowHideROIs(this, 'IDs');\r\n                    \r\n                % toggle ROI display\r\n                case 'o';\r\n                   RDShowHideROIs(this, 'ROIs');\r\n                \r\n                case 's';\r\n                    % save ROIs\r\n                    if ismember('control', e.Modifier);\r\n                        RDSaveROIs(this);\r\n                    \r\n                    % select ROIs box\r\n                    else\r\n                        set(this.GUI.handles.rd.selROISetter, 'String', '');\r\n                        uicontrol(this.GUI.handles.rd.selROISetter);\r\n                    end;\r\n                       \r\n                case 'a';\r\n                    % select all ROIs \r\n                    if ismember('control', e.Modifier);\r\n                        set(this.GUI.handles.rd.selROIsList, 'Value', ...\r\n                            1 : numel(get(this.GUI.handles.rd.selROIsList, 'String')));\r\n                        RDSelROI(this);\r\n                    \r\n                    % image adjustement\r\n                    else\r\n                        set(this.GUI.handles.rd.imAdj, 'Value', ~get(this.GUI.handles.rd.imAdj, 'Value'));\r\n                        RDUpdateImage(this, this.GUI.handles.rd.imAdj);\r\n                    end;\r\n                    \r\n                % pseudo flat field\r\n                case 'p';\r\n                    set(this.GUI.handles.rd.pseudFF, 'Value', ~get(this.GUI.handles.rd.pseudFF, 'Value'));\r\n                    RDUpdateImage(this, this.GUI.handles.rd.pseudFF);\r\n                    \r\n                    \r\n                otherwise;\r\n%                     showWarning(this, 'OCIA:keyPressed:UnknownKey', sprintf('Unknown key (%s) pressed.', e.Key));\r\n            end;\r\n            \r\n        %% --- #keyPressed : Analyser\r\n        case 'Analyser';\r\n            switch e.Key;\r\n                case {'leftarrow', 'rightarrow'};\r\n                    if ismember('control', e.Modifier);\r\n                        ANSelPlot(this, strrep(e.Key, 'arrow', ''));\r\n                    end;\r\n                case {'uparrow', 'downarrow'};\r\n                    if ismember('control', e.Modifier);\r\n                        ANSelRuns(this, strrep(e.Key, 'arrow', ''));\r\n                    end;\r\n                case 'z';\r\n                    if ismember('control', e.Modifier);\r\n                        ANActivateZoom(this, ~get(this.GUI.handles.an.zTool, 'Value'));\r\n                    end;\r\n                case 'd';\r\n                    if ismember('control', e.Modifier);\r\n                        ANActivateDataCursor(this, ~get(this.GUI.handles.an.cTool, 'Value'));\r\n                    end;\r\n                case 's';\r\n                    % save plot\r\n                    if ismember('control', e.Modifier);\r\n                        ANSavePlot(this, []);\r\n                    end;\r\n                otherwise;\r\n%                         showWarning(this, 'OCIA:keyPressed:UnknownKey', sprintf('Unknown key (%s) pressed.', e.Key));\r\n            end;\r\n            \r\n        %% --- #keyPressed : JointTracker\r\n        case 'JointTracker';\r\n            % get current object\r\n            currObj = get(this.GUI.figH, 'CurrentObject');\r\n            \r\n            % check for key presses not while inside a text uicontrol element\r\n            if ~isa(currObj, 'matlab.ui.control.UIControl') || ~ismember(get(currObj, 'Style'), { 'edit' });\r\n                switch e.Key;\r\n\r\n                    % print help for shortcuts\r\n                    case 'h';\r\n                        msgCellArray = { ...\r\n                            'This is the \\bfhelp\\rm for the \\bfshortcuts\\rm of the JointTracker mode of OCIA.', ...\r\n                            'Following shortcuts can be used:', ...\r\n                            '\\bf               [H]                       \\rmDisplay this help', ...\r\n                            '-------------------------------------------------------------------------------------------', ...\r\n                            '\\bf             [A / D]                     \\rmPrevious / next frame', ...\r\n                            '\\bf     [left / right arrows]               \\rmPrevious / next  frame', ...\r\n                            '\\bf   [SHIFT]  +  [A / D]                   \\rmFirst / last frame', ...\r\n                            '\\bf   [SHIFT]  + [left / right arrows]      \\rmFirst / last frame', ...\r\n                            '-------------------------------------------------------------------------------------------', ...\r\n                            '\\bf               [C]                       \\rmChange the cursor display (dot <-> pointer)', ...\r\n                            '-------------------------------------------------------------------------------------------', ...\r\n                            '\\bf           [W / S]                       \\rmChange joint type (manual <-> auto)', ...\r\n                            '\\bf       [up / down arrow]                 \\rmChange joint type (manual <-> auto)', ...\r\n                            '-------------------------------------------------------------------------------------------', ...\r\n                            '\\bf               [F]                       \\rmStart / stop auto-track', ...\r\n                            '\\bf               [V]                       \\rmStart / stop manual-track', ...\r\n                            '\\bf               [M]                       \\rmStart / stop manual-track', ...\r\n                        }';\r\n                        % display a message box\r\n                        h = msgbox(msgCellArray);\r\n                        \r\n                        % make sure the box is big enough\r\n                        boxPos = get(h, 'Position');\r\n                        set(h, 'Position', boxPos + [-200, -75, 400, 150]);\r\n                        % make font bigger\r\n                        hChild = get(h, 'Children');\r\n                        set(hChild(2), 'Units', 'Normalized', 'Position', [0, 0, 1, 1]);\r\n                        hText = get(hChild(2), 'Children');\r\n                        set(hText, 'FontSize', 12, 'Units', 'Normalized', 'Position', [0.01, 0.98, 0], ...\r\n                            'FontName', 'Courier', 'VerticalAlignment', 'top', 'String', msgCellArray, ...\r\n                            'Interpreter', 'tex');\r\n                case 'c';\r\n                    JTSwapCursor(this);\r\n                case { 'a', 'leftarrow' };\r\n                    if ismember('shift', e.Modifier);\r\n                        set(this.GUI.handles.jt.frameSetter, 'Value', 1);\r\n                    else\r\n                        set(this.GUI.handles.jt.frameSetter, 'Value', max(this.GUI.jt.iFrame - 1, 1));\r\n                    end;\r\n                case { 'd', 'rightarrow' };\r\n                    if ismember('shift', e.Modifier);\r\n                        if get(this.GUI.handles.jt.autoTrack, 'Value');\r\n                            JTProcess(this, 'all');\r\n                        else\r\n                            set(this.GUI.handles.jt.frameSetter, 'Value', this.jt.nFrames);\r\n                        end;\r\n                    else\r\n                        set(this.GUI.handles.jt.frameSetter, 'Value', min(this.GUI.jt.iFrame + 1, this.jt.nFrames));\r\n                    end;\r\n                    \r\n                case {'w', 's', 'uparrow', 'downarrow', };\r\n                    addValue = 1; if strcmp(e.Key, 's') || strcmp(e.Key, 'downarrow'); addValue = -1; end;\r\n                    newValue = min(max(this.GUI.jt.iJointType + addValue, 1), this.jt.nJointTypes);\r\n                    if newValue ~= get(this.GUI.handles.jt.jointTypeSelSetter, 'Value');\r\n                        set(this.GUI.handles.jt.jointTypeSelSetter, 'Value', newValue);\r\n                        JTChangeJointOrJointType(this, this.GUI.handles.jt.jointTypeSelSetter);\r\n                    end;\r\n                case 'f';\r\n                    set(this.GUI.handles.jt.autoTrack, 'Value', ~get(this.GUI.handles.jt.autoTrack, 'Value'));\r\n                    JTProcess(this, 'autoTrackChanged');\r\n                case {'m', 'v'};\r\n                    set(this.GUI.handles.jt.manuTrack, 'Value', ~get(this.GUI.handles.jt.manuTrack, 'Value'));\r\n                    JTManualTrackStart(this);\r\n                case 'space';\r\n%                     set(this.GUI.handles.jt.viewOpts.preProc, 'Value', ...\r\n%                         ~get(this.GUI.handles.jt.viewOpts.preProc, 'Value'));\r\n%                     JTUpdateGUI(this, this.GUI.handles.jt.viewOpts.preProc);\r\n                otherwise;\r\n%                     showWarning(this, 'OCIA:keyPressed:UnknownKey', sprintf('Unknown key (%s) pressed.', e.Key));\r\n                end;\r\n            end; % end of check for key presses not while inside a text uicontrol element\r\n            \r\n        %% --- #keyPressed : Discriminator\r\n        case 'Discriminator';\r\n            switch e.Key;\r\n                \r\n                % adjust response rate threshold\r\n                case 'leftarrow';\r\n                    this.di.respRateThresh = max(min(this.di.respRateThresh - 0.5, 9.5), 1);\r\n                    showMessage(this, sprintf('Reponse threshold: %.1f.', this.di.respRateThresh), 'yellow');\r\n                \r\n                % adjust response rate threshold   \r\n                case 'rightarrow';\r\n                    this.di.respRateThresh = max(min(this.di.respRateThresh + 0.5, 9.5), 1);\r\n                    showMessage(this, sprintf('Reponse threshold: %.1f.', this.di.respRateThresh), 'yellow');\r\n                    \r\n                % zoom level of activity\r\n                case 'uparrow';\r\n                    this.GUI.di.zoomLevel = max(min(this.GUI.di.zoomLevel + this.GUI.di.zoomLevel * 0.1, 10), 1);\r\n                    showMessage(this, sprintf('Zoom level: %.1f.', this.GUI.di.zoomLevel), 'yellow');\r\n                    \r\n                % zoom level of activity\r\n                case 'downarrow';\r\n                    this.GUI.di.zoomLevel = max(min(this.GUI.di.zoomLevel - this.GUI.di.zoomLevel * 0.1, 10), 1);\r\n                    showMessage(this, sprintf('Zoom level: %.1f.', this.GUI.di.zoomLevel), 'yellow');\r\n                    \r\n                % start/stop camera\r\n                case 'c';\r\n                    DIStartStopCamera(this, 'toggle');\r\n                    showMessage(this, sprintf('Camera running: %s', get(this.GUI.di.camHandle, 'Running')), 'yellow');\r\n                    \r\n                % start/stop activity\r\n                case 'a';\r\n                    this.GUI.di.activityRunning = ~this.GUI.di.activityRunning;\r\n                    if this.GUI.di.activityRunning;\r\n                        this.GUI.di.actiMovieIndex = this.GUI.di.actiMovieIndex + 1;\r\n                        if this.GUI.di.actiMovieIndex > numel(this.GUI.di.actiMovies); this.GUI.di.actiMovieIndex = 1; end;\r\n                    end;\r\n                    showMessage(this, sprintf('Activity movie: %s', iff(this.GUI.di.activityRunning, 'on', 'off')), 'yellow');\r\n                   \r\n                % lock mouse\r\n                case 'l';\r\n                    this.di.lockMouse = ~this.di.lockMouse;\r\n                    showMessage(this, sprintf('Locking mouse: %s', iff(this.di.lockMouse, 'on', 'off')), 'yellow');\r\n                    \r\n                % trial phases:\r\n                % reset\r\n                case '0';\r\n                    this.di.iTrial = 0;\r\n                    this.di.iStimMat = randi(10);\r\n                    this.di.targetStim = randi(2);\r\n                    showMessage(this, sprintf('Reset trials, iTrial: %d, iStimMat: %d, target stimulus: %d.', ...\r\n                        this.di.iTrial, this.di.iStimMat, this.di.targetStim), 'yellow');\r\n                % new trial\r\n                case '1';\r\n                    set(this.GUI.handles.di.messBox, 'String', 'Trial start ...', 'Background', 'yellow');\r\n                    set(this.GUI.handles.di.messBoxBack, 'Background', 'yellow');\r\n                    this.di.iTrial = this.di.iTrial + 1;\r\n                    showMessage(this, sprintf('Current trial: %d.', this.di.iTrial), 'yellow');\r\n                % present stimulus\r\n                case '2';\r\n                    set(this.GUI.handles.di.messBox, 'String', 'Stimulus ...', 'Background', 'yellow');\r\n                    set(this.GUI.handles.di.messBoxBack, 'Background', 'yellow');\r\n                    stimNum = this.di.stimMatrix(this.di.iTrial, this.di.iStimMat);\r\n                    isTarget = stimNum == this.di.targetStim;\r\n                    showMessage(this, sprintf('Stimulus index for trial %d: %d, target: %d.', this.di.iTrial, stimNum, isTarget), 'yellow');\r\n                % get decision\r\n                case '3';\r\n                    this.di.waitingForResp = true;\r\n                    this.di.waitingStartTime = nowUNIX;\r\n                    this.di.resp = false;\r\n                    set(this.GUI.handles.di.messBox, 'String', 'Decision ...', 'Background', 'yellow');\r\n                    set(this.GUI.handles.di.messBoxBack, 'Background', 'yellow');\r\n                    stimNum = this.di.stimMatrix(this.di.iTrial, this.di.iStimMat);\r\n                    isTarget = stimNum == this.di.targetStim;\r\n                    showMessage(this, sprintf('Trial %d: stimulus %d, target: %d - waiting for decision ...', this.di.iTrial, stimNum, isTarget), 'yellow');\r\n                % reward\r\n                case '4';\r\n                    this.di.waitingForResp = false;\r\n                    set(this.GUI.handles.di.messBox, 'String', 'Reward !', 'Background', 'green');\r\n                    set(this.GUI.handles.di.messBoxBack, 'Background', 'green');\r\n                    showMessage(this, 'Reward', 'yellow');\r\n                % punishment\r\n                case '5';\r\n                    this.di.waitingForResp = false;\r\n                    set(this.GUI.handles.di.messBox, 'String', 'Punishment !', 'Background', 'red');\r\n                    set(this.GUI.handles.di.messBoxBack, 'Background', 'red');\r\n                    showMessage(this, 'Punishment', 'yellow');\r\n                    \r\n            end;\r\n    end;\r\nend;\r\n\r\nend;\r\n\r\n%% -- #mouseDown\r\nfunction mouseDown(this, ~, ~)\r\n\r\n    currentMode = this.main.modes{get(this.GUI.handles.changeMode, 'Value'), 1};\r\n    \r\n    switch currentMode;\r\n        \r\n        case 'TrialView';\r\n            \r\n            % get selection type (mouse button)\r\n            selType = get(this.GUI.figH, 'SelectionType');\r\n            % get clicked object\r\n            clickedObj = get(this.GUI.figH, 'CurrentObject');\r\n            if isa(clickedObj, 'matlab.graphics.axis.Axes') || ...\r\n                        (~isa(get(clickedObj, 'Parent'), 'matlab.graphics.primitive.Group') ...\r\n                        && (isa(clickedObj, 'matlab.graphics.chart.primitive.Line') ...\r\n                        || isa(clickedObj, 'matlab.graphics.primitive.Patch') ...\r\n                        || isa(clickedObj, 'matlab.graphics.primitive.Text')));\r\n                    \r\n                    % left click\r\n                    if strcmp(selType, 'normal');\r\n                        this.GUI.tv.mouseDownOnAxe = true;\r\n                    end;\r\n                        \r\n            end;\r\n        \r\n        case 'JointTracker';\r\n            \r\n            % make sure the click is within the axe image and not during a ROI drawing\r\n            pos = get(this.GUI.handles.jt.axe, 'CurrentPoint');\r\n            pos = pos(1, 1 : 2);\r\n            XLim = get(this.GUI.handles.jt.axe, 'XLim');\r\n            YLim = get(this.GUI.handles.jt.axe, 'YLim');\r\n            if this.GUI.jt.selectingROI || any(pos < 0) || pos(1) > XLim(2) || pos(2) > YLim(2);\r\n                return;\r\n            end;\r\n            \r\n            if isempty(this.GUI.jt.placeJointIndex) && isempty(this.GUI.jt.moveJointIndex);\r\n                selectionType = get(this.GUI.figH, 'SelectionType');\r\n                JTJointClickStart(this, strcmp(selectionType, 'extend'));\r\n            end;\r\n    end;\r\n\r\nend;\r\n\r\n%% -- #mouseUp\r\nfunction mouseUp(this, h, e)\r\n\r\n    currentMode = this.main.modes{get(this.GUI.handles.changeMode, 'Value'), 1};\r\n    \r\n    switch currentMode;\r\n        \r\n        case 'TrialView';\r\n            \r\n            % get selection type (mouse button)\r\n            selType = get(this.GUI.figH, 'SelectionType');\r\n            % get clicked object\r\n            clickedObj = get(this.GUI.figH, 'CurrentObject');\r\n            if isa(clickedObj, 'matlab.graphics.axis.Axes') || ...\r\n                    (~isa(get(clickedObj, 'Parent'), 'matlab.graphics.primitive.Group') ...\r\n                    && (isa(clickedObj, 'matlab.graphics.chart.primitive.Line') ...\r\n                    || isa(clickedObj, 'matlab.graphics.primitive.Patch') ...\r\n                    || isa(clickedObj, 'matlab.graphics.primitive.Text')));\r\n                \r\n                % left click\r\n                if strcmp(selType, 'normal');\r\n                    this.GUI.tv.mouseDownOnAxe = false;\r\n                    \r\n                % right click\r\n                elseif strcmp(selType, 'alt');\r\n                    OCIA_trialview_addMovePoint(this);\r\n\r\n                end;\r\n                \r\n            elseif isa(clickedObj, 'matlab.graphics.primitive.Image') && clickedObj == this.GUI.handles.tv.wf.img ...\r\n                    && ~this.GUI.tv.mouseDownOnWFImg;\r\n                OCIA_trialview_drawROI(this);\r\n                \r\n            elseif isa(clickedObj, 'matlab.graphics.primitive.Image') && clickedObj == this.GUI.handles.tv.behav.img ...\r\n                    && ~this.GUI.tv.mouseDownOnWFImg;\r\n                OCIA_trialview_drawROI(this, this.GUI.handles.tv.behav.axe);\r\n                \r\n            end;\r\n        \r\n        case 'ROIDrawer';\r\n            \r\n            RDUpdateGUI(this, h, e);\r\n        \r\n        case 'Behavior';\r\n            \r\n            BEChangePiezoThresh(this, 'mouseAdjust', []);\r\n            \r\n        case 'JointTracker';\r\n            \r\n            % make sure the click is within the axe image and not during a ROI drawing\r\n            pos = get(this.GUI.handles.jt.axe, 'CurrentPoint');\r\n            pos = pos(1, 1 : 2);\r\n            XLim = get(this.GUI.handles.jt.axe, 'XLim');\r\n            YLim = get(this.GUI.handles.jt.axe, 'YLim');\r\n            if this.GUI.jt.selectingROI || any(pos < 0) || pos(1) > XLim(2) || pos(2) > YLim(2);\r\n                return;\r\n            end;\r\n            \r\n            % process the click event\r\n            JTImClick(this, h, e);\r\n            \r\n            % reset the joint tracking/moving settings\r\n            this.GUI.jt.placeJointIndex = [];\r\n            this.GUI.jt.moveJointIndex = [];\r\n            this.GUI.jt.startFrame = [];\r\n            this.GUI.jt.endFrame = [];\r\n            this.GUI.jt.startTime = [];\r\n\r\n            % remove manual tracking\r\n            set(this.GUI.handles.jt.manuTrack, 'Value', 0);\r\n        \r\n        case 'Discriminator';\r\n            \r\n            this.di.nResps = this.di.nResps + 0.75;\r\n    end;\r\n\r\nend;\r\n\r\n%% -- #mouseMoved\r\nfunction mouseMoved(this, ~, e)\r\n\r\n    currentMode = this.main.modes{get(this.GUI.handles.changeMode, 'Value'), 1};\r\n    \r\n    switch currentMode;\r\n        \r\n        case 'TrialView';\r\n            \r\n            if this.GUI.tv.mouseDownOnAxe;\r\n                % get clicked object\r\n                clickedObj = get(this.GUI.figH, 'CurrentObject');\r\n                if isa(clickedObj, 'matlab.graphics.axis.Axes') || ...\r\n                        (~isa(get(clickedObj, 'Parent'), 'matlab.graphics.primitive.Group') ...\r\n                        && (isa(clickedObj, 'matlab.graphics.chart.primitive.Line') ...\r\n                        || isa(clickedObj, 'matlab.graphics.primitive.Patch') ...\r\n                        || isa(clickedObj, 'matlab.graphics.primitive.Text')));\r\n                    OCIA_trialview_changeFrame(this, clickedObj, e);\r\n                end;\r\n            end;\r\n        \r\n        case 'JointTracker';\r\n            \r\n            coords = get(this.GUI.handles.jt.axe, 'CurrentPoint');\r\n            coords = round(coords(1, 1 : 2));\r\n            if all(coords > 0) && coords(1) < size(this.GUI.jt.img, 2) && coords(2) < size(this.GUI.jt.img, 1);\r\n                this.GUI.jt.mouseCoords = coords;\r\n                % update the frame label\r\n                currTimeTotSec = this.GUI.jt.iFrame / this.jt.frameRate;\r\n                currTimeMin = floor(currTimeTotSec / 60);\r\n                currTimeSec = floor(currTimeTotSec - currTimeMin * 60);\r\n                currTimeMSec = floor((currTimeTotSec - currTimeMin * 60 - currTimeSec) * 1000);\r\n                set(this.GUI.handles.jt.frameLabel, 'String', sprintf('F  %03d\\nT  %02d:%02d.%03d\\nM %04d %04d', ...\r\n                    this.GUI.jt.iFrame, currTimeMin, currTimeSec, currTimeMSec, coords));\r\n            end;\r\n    end;\r\n\r\nend;\r\n\r\n%% -- #windowResized\r\nfunction windowResized(this, ~, ~)\r\n    \r\n    % if no GUI, skip the resizing\r\n    if ~isGUI(this); return; end;\r\n\r\n    % store old position\r\n    oldPos = this.GUI.pos;    \r\n    % update to new position\r\n    this.GUI.pos = get(this.GUI.figH, 'Position');    \r\n    % calculate ratios\r\n    widthRatio = oldPos(3) / this.GUI.pos(3);\r\n%     heightRatio = oldPos(4) / this.GUI.pos(4);\r\n    \r\n    % update the font size and the columns widths of the DataWatcher's table\r\n    columnWidths = get(this.GUI.handles.dw.table, 'ColumnWidth');\r\n    columnWidths = num2cell(cellfun(@(w)round(w / widthRatio), columnWidths));\r\n    set(this.GUI.handles.dw.table, 'ColumnWidth', columnWidths, 'FontSize', max(min(this.GUI.pos(3) / 170, 22), 8));\r\n    \r\nend;\r\n\r\n\r\n\r\n\r\n\r\nend  % end methods\r\n\r\n\r\nend\r\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "RDSelROI.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/@OCIA/RDSelROI.m", "size": 3644, "source_encoding": "utf_8", "md5": "0361eb322847a59f38e2c2b6c30702ff", "text": "%%  #OCIA:RD:RDSelROI\nfunction RDSelROI(this, varargin)\n\no('#RDSelROI()', 4, this.verb);\n\nh = []; % no handle by default\n% get the handle if there is any\nif nargin > 1; h = varargin{1}; end;\n\n% if change was requested by a number, overwrite the selection\nif ~isempty(h) && isnumeric(h) && ~ishandle(h);\n    selROIs = h;\n% if change was requested by a string or cell-array of strings, overwrite the selection\nelseif ~isempty(h) && (ischar(h) || iscellstr(h));\n    if ischar(h); h = { h }; end;\n    if strcmp(get(this.GUI.figH, 'SelectionType'), 'extend');\n        selROIs = [str2double(h), RDGetSelectedROIs(this, this.GUI.handles.rd.selROIsList)];\n    else\n        selROIs = str2double(h);\n    end;\n    selROIs(isnan(selROIs)) = [];\n% if a clearing of the selection was requested, empty selection    \nelseif ~isempty(h) && h == this.GUI.handles.rd.selROISetterClear;\n    selROIs = [];\n% if selection was requested by the edit field\nelseif ~isempty(h) && ((h == this.GUI.handles.rd.selROI) || (h == this.GUI.handles.rd.selROISetter));\n    selROIs = RDGetSelectedROIs(this, h);\n% otherwise use selection with the list\nelse\n    selROIs = RDGetSelectedROIs(this, this.GUI.handles.rd.selROIsList);\nend;\n\no('#RDSelROI(): h: %d, selectedROIs: %s .', h, num2str(selROIs), 3, this.verb);\n\n% update the color of the ROIs\nfor iROI = 1 : this.rd.nROIs;\n    if ismember(iROI, selROIs);\n        this.rd.ROIs{iROI, 1}.setColor('red');\n        if ishandle(this.rd.ROIs{iROI, 5}) && strcmp(get(this.rd.ROIs{iROI, 1}, 'Visible'), 'off');\n            set(this.rd.ROIs{iROI, 5}, 'Color', 'blue');\n        end;\n    else\n       this.rd.ROIs{iROI, 1}.setColor('blue');\n        if ishandle(this.rd.ROIs{iROI, 5}) && strcmp(get(this.rd.ROIs{iROI, 1}, 'Visible'), 'off');\n            set(this.rd.ROIs{iROI, 5}, 'Color', 'red');\n        end;\n    end;\nend;\n\n% if no ROIs, no selection text\nif isempty(selROIs);\n    selROIsText = '';\n% otherwise create the selection text\nelse\n    % start with the first number\n    selROIsText = sprintf('%s', this.rd.ROIs{selROIs(1), 2});\n    inRange = false; % determines if we are currently in a range display (1 : ...)\n    for iSel = 2 : numel(selROIs); % go through all numbers starting from the second\n        \n        % get the ROI numbers\n        currROI = this.rd.ROIs{selROIs(iSel), 2};\n        prevROI = this.rd.ROIs{selROIs(iSel - 1), 2};\n        \n        % if we are not in range and next number is just previous + 1, then start a range display\n        if str2double(currROI) - 1 == str2double(prevROI) && ~inRange;\n            selROIsText = sprintf('%s:', selROIsText);\n            inRange = true;\n        % if we are in range and next number is just previous + 1, do not display number and continue range\n        elseif str2double(currROI) - 1 == str2double(prevROI) && inRange;\n            % skip number\n        % if next number is not just previous + 1, then eventually finish range display and display number\n        else\n        \t% if we were in range, finish range display of last number\n            if inRange;\n                selROIsText = sprintf('%s%s', selROIsText, prevROI);\n            end\n            % display next number\n            selROIsText = sprintf('%s,%s', selROIsText, currROI);\n            inRange = false;\n        end;\n    end;\n    \n    % if we are still in range display, it means last number could not be displayed, so display it\n    if inRange;\n        selROIsText = sprintf('%s%s', selROIsText, currROI);\n    end;\n    \n    \nend;\n\n% re-update the selection\nset(this.GUI.handles.rd.selROISetter, 'String', strrep(selROIsText, '00', ''));\nset(this.GUI.handles.rd.selROIsList, 'Value', selROIs);\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_genStimVect_fromAnalogInWideField.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/genStimVect/OCIA_genStimVect_fromAnalogInWideField.m", "size": 15569, "source_encoding": "utf_8", "md5": "db650f76208ac994600bd0dc7f54800a", "text": "%% #OCIA:AN:OCIA_genStimVect_fromAnalogInWideField\nfunction [isValid, unvalidReason] = OCIA_genStimVect_fromAnalogInWideField(this, iDWRow, varargin)\n\n% get whether to do plots or not\nif nargin > 2;      doPlotsTrig = varargin{1}; doPlotsMicr = varargin{1}; doPlotsSummary = varargin{1}; %#ok<NASGU>\nelseif nargin > 3;  doPlotsTrig = varargin{1}; doPlotsMicr = varargin{2}; doPlotsSummary = 0; %#ok<NASGU>\nelse                doPlotsTrig = 0; doPlotsMicr = 0; doPlotsSummary = 0; %#ok<NASGU>\nend;\n\nrowID = DWGetRowID(this, iDWRow); % get the row ID \nisValid = true; % by default, the row is valid\nunvalidReason = ''; % by default no reason\n\no('#%s(): row num: %d ...', mfilename, iDWRow, 3, this.verb);\n\n% get the behavior data for this row\nbehavData = getData(this, iDWRow, 'wfTrBehav', 'data');\n\n% if no behavior data is found, abort\nif isempty(behavData);\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('cannot find associated behavior data (behavior data unavailable in row %03d)', iDWRow);\n    return; % abort processing of this row\nend;\n\niTrial = str2double(get(this, iDWRow, 'runNum')); % get the trial number\n% if no behavior row found using the behavior ID, abort\nif isempty(iTrial) || isnan(iTrial) || iTrial <= 0;\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('bad trial number (\"%s\" => %d\")', get(this, iDWRow, 'runNum'), iTrial);\n    return; % abort processing of this row\nend;\n\n% fix missing fields\nif isfield(behavData, 'nTones');\n    % if more than one element for nTones, there must be one element per trial\n    if numel(behavData.nTones) > 1; nTones = behavData.nTones(iTrial);\n    % otherwise there is only one constant number of tones\n    else                            nTones = behavData.nTones;\n    end;\n% if no field, assume there is only one tone\nelse                                nTones = 1;\nend;\n\n%% init the stim vector\nimgDim = str2dim(get(this, iDWRow, 'dim'));\n% compensate for the skipped frames\nif numel(imgDim) < 3;   nFramesImg = 0;\nelse                    nFramesImg = imgDim(3);\nend;\n% stimulus vector is all zeros except where there are stimulus starts (sound, lick, spout, etc.)\nstimVect = zeros(1, nFramesImg);\n% string storing the stimulus types for this row\nstimTypes = '';\n% cell-array storing the relevant time points for the imaging\nstimTimeFrames = {};\n% initialize variables\nimgFrameRate = 20;\n% calculate the number of bits to encode \nnMaxStimTimes = 10; nBitsToUseForStimTimes = ceil(log2(nMaxStimTimes));\n% assign bits to difference encodings\niBitTime = 1 : nBitsToUseForStimTimes; iBitCloud = iBitTime(end) + (1 : 2); iBitTarg = iBitCloud(end) + (1 : 2);\niBitResp = iBitTarg(end) + (1 : 2); iBitCorr = iBitResp(end) + (1 : 2);\n\n%% delay analysis (trig)\ntrigData = behavData.analogInData(strcmp(behavData.analogInNames, 'trig'), :); % extract the triger's trace\nnormThresh = 3 * std(trigData(1 : 100)); % take the first frames for normalization threshold\ntrigData(abs(trigData) < normThresh) = 0; % normalize to remove the noise of parts when there is no trigger\ntrigTop = find(trigData > 0); % find all the peaks\n\n% get the trigger delay\nanInSampRate = behavData.analogInSampRate;\ntrigInd = trigTop(1);\ntrigDelay = trigInd / anInSampRate;\n\n% store the extracted number: analog input sampling rate, trigger delay\nbehavData.behavSampRate = anInSampRate;\nbehavData.trigDelay = trigDelay;\n\n% if doPlotsTrig > 0; % if requested, plot a figure illustrating the extraction procedure\n%     figure('Name', sprintf('%s_trig', rowID), 'WindowStyle', 'docked', 'NumberTitle', 'off');\n%     plot(trigData, 'k');\n%     hold on;\n%     scatter(trigTop(1), trigData(trigTop(1)), 200, 'b');\n%     title(sprintf('trigDelay: %.3f', trigDelay));\n% end;\n\n%% sound start (stimulus time) analysis (micr)\n\n% get the number of samples\nnSamples = size(behavData.analogInData, 2);\n\n% extract the stimulus sound time and duration from the recorded sound\nmicr = linScale(abs(behavData.analogInData(strcmp(behavData.analogInNames, 'micr'), :)));\n% get a range for the begining of the signal\nbegRange = round(nSamples * 0.01 : nSamples * 0.1);\n% incrementally search for the right threshold\nnSoundsDiff = 1; soundYThresh = 0; soundThreshFactor = 5; soundThreshFactorStep = 5; nLoops = 0; %#ok<NASGU>\nwhile nSoundsDiff && soundThreshFactor < 55;\n    % get a threshold for the sound onset\n    soundThreshFactor = soundThreshFactor + soundThreshFactorStep;\n    soundYThresh = soundThreshFactor * std(micr(begRange));\n    % get the samples that exceeds the threshold, adding the first sample to catch the start of the first sound\n    upSamples = [0 find(micr > soundYThresh)];\n    % get the derivative of the upSamples, drops in the sample indexes indicate interruption of upSamples,\n    %   which means that there is a sound start\n    upSamplesDiff = diff(upSamples);\n    % use the ISI to find peaks. If no ISI, use 0.5 second\n    ISI = 0.5;\n    if isfield(behavData, 'ISI') && behavData.ISI > 0;\n        ISI = behavData.ISI;\n    end;\n    % difference between detected upSample derivative's peaks must be at least half of the ISI\n    minISI = ISI * 0.5 * anInSampRate;\n    % get the index of the peaks where the derivative exceeds the ISI threshold and increment by one to get\n    %   the sound start index\n    soundStartInds = upSamples(find(upSamplesDiff >= minISI) + 1);\n\n    if ~isempty(soundStartInds);\n        % calculate the sound start time\n        soundStartTimes = soundStartInds / anInSampRate;\n    else\n        soundStartTimes = [];\n    end;\n\n    % check whether there is a big frame difference between the imaging and the behavior recording\n    nSoundsDiff = abs(nTones - numel(soundStartTimes));\n    nLoops = nLoops + 1;\n\nend;\n\n% if there is a mismatch in the number of sounds found and more sounds were found, show a warning\nif nSoundsDiff && ~isempty(soundStartTimes) && numel(soundStartTimes) >= nTones;\n    showWarning(this, sprintf('OCIA:%s:MissingStim', mfilename()), ...\n        sprintf(['Problem with number of stimuli found for %s (%d)! Number of stim detected in recorded ', ...\n        'data: %d, expected number of stim: %d. Taking only the first %d stim(s).'], rowID, iDWRow, ...\n        numel(soundStartTimes), nTones, nTones));\n    soundStartTimes = soundStartTimes(1 : nTones);\n    doPlotsMicr = doPlotsMicr + 1; %#ok<NASGU>\n\n% if there is a mismatch in the number of sounds found and less sounds were found, show a warning\nelseif nSoundsDiff;\n    showWarning(this, sprintf('OCIA:%s:MissingStim', mfilename()), ...\n        sprintf(['Problem with number of stimuli found for %s (%d)! Number of stim detected in recorded ', ...\n        'data: %d, expected number of stim: %d.'], rowID, iDWRow, numel(soundStartTimes), nTones));\n    doPlotsMicr = doPlotsMicr + 1; %#ok<NASGU>\nend;\n\n% get the stimulus time, including the imaging start delay\nsoundStartTimesImgReference = soundStartTimes - trigDelay;\nsoundStartIndexesImgReference = round(soundStartTimesImgReference * imgFrameRate); % get the stimulus index\n% remove stim start times that are too early\nif any(soundStartIndexesImgReference < 0);\n    nRemStims = sum(soundStartIndexesImgReference < 0);\n    soundStartIndexesImgReference(soundStartIndexesImgReference <= 0) = [];\n    showWarning(this, sprintf('OCIA:%s:EarlyStim', mfilename()), ...\n        sprintf('Removed %d early stimuli found for %s (%d)!', nRemStims, rowID, iDWRow));\nend;\n\n% store the starting time\nbehavData.soundStartTime = soundStartTimes;\n\n% encode the sound end stimulus\nsoundEndTimesImgReference = soundStartTimesImgReference + behavData.stimDur;\nsoundEndIndexesImgReference = round(soundEndTimesImgReference * imgFrameRate); % get the stimulus index\n\n\n% if doPlotsMicr > 0; % if requested, plot a figure illustrating the extraction procedure\n%     figure('Name', sprintf('%s_micr', rowID), 'WindowStyle', 'docked', 'NumberTitle', 'off');\n%     rectangle('Position', [begRange(1) 0 begRange(end) - begRange(1) soundYThresh * 1.1], ...\n%         'FaceColor', [0.8 1 0.8], 'EdgeColor', [0.8 1 0.8]);\n%     hold on;\n%     plot(micr, 'k');\n%     yLims = get(gca, 'YLim'); xLims = get(gca, 'XLim');\n%     plot(upSamples(1 : end - 1) - 0.5, (upSamplesDiff / max(upSamplesDiff)) * max(micr) * 0.9, 'r');\n%     plot(repmat(soundStartInds, 2, 1), repmat(yLims, numel(soundStartInds), 1)', 'r:');\n%     plot(xLims, repmat(soundYThresh, 2, 1), 'g:');\n%     title(sprintf('stimYThresh: %.5f, stimStartTimes: %s', soundYThresh, sprintf(' %.2fs', soundStartTimes)));\n% end;\n\n%% other stim times based one behavior recording\n% get the light time, including the imaging start delay\nif isfield(behavData, 'trialStartCue') && ~isnan(behavData.trialStartCue);\n    trialStartCueImgReference = behavData.soundStartTime - trigDelay + (behavData.trialStartCue - behavData.soundTime);\n    stimStartIndexesImgReference = round(trialStartCueImgReference * imgFrameRate); % get the stimulus index\n    if stimStartIndexesImgReference <= nFramesImg;\n        stimTimeFrames{end + 1} = stimStartIndexesImgReference;\n    end;\nend;\n\n% add the sound stimulus frames\nstimTimeFrames{end + 1} = soundStartIndexesImgReference;\nstimTimeFrames{end + 1} = soundEndIndexesImgReference;\n\n% REMOVED BECAUSE NOT ACCURATE\n% get the response time, including the imaging start delay\n% if isfield(behavData, 'respTime') && ~isnan(behavData.respTime);\n%     respTimeImgReference = behavData.soundStartTime - trigDelay + (behavData.respTime - behavData.soundTime);\n%     stimStartIndexesImgReference = round(respTimeImgReference * imgFrameRate); % get the stimulus index\n%     if stimStartIndexesImgReference <= nFramesImg;\n%         stimTimeFrames{end + 1} = stimStartIndexesImgReference;\n%     end;\n% end;\n\n% get the response window light cue time, including the imaging start delay\nif isfield(behavData, 'lightTime') && ~isnan(behavData.lightTime);\n    lightCueTimeImgReference = behavData.soundStartTime - trigDelay + (behavData.lightTime - behavData.soundTime);\n    stimStartIndexesImgReference = round(lightCueTimeImgReference * imgFrameRate); % get the stimulus index\n    if stimStartIndexesImgReference <= nFramesImg;\n        stimTimeFrames{end + 1} = stimStartIndexesImgReference;\n    end;\nend;\n\n%% encode the stimuli\nif ~isempty(stimTimeFrames);\n    \n    % go through each stim time\n    for iStimTime = 1 : numel(stimTimeFrames);\n\n        % encode the stimulus time: get the bit code for each stimulus time\n        bitCode = bitget(1, 1 : nBitsToUseForStimTimes);\n        % encode the bitCode into the stimulus vector\n        for iBitLoop = 1 : nBitsToUseForStimTimes;\n            % annotate with the stimuli with the current bit iteratively\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitTime(iBitLoop), bitCode(iBitLoop));\n            stimTypes = sprintf('%s,%s', stimTypes, 'time');\n        end;\n\n        % annotate the stimulus time with the cloud type using the next bit\n        soundType = double(behavData.stim == 1);\n        soundTypeBitCode = bitget(1 + soundType, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n            iBitCloud(iBitLoop), soundTypeBitCode(iBitLoop));\n            stimTypes = sprintf('%s,%s', stimTypes, 'freq');\n        end;\n        % annotate the stimulus time with the target/non-target using the next bit\n        isTarget = double(~isempty(behavData.target) && behavData.target == 1);\n        isTargetBitCode = bitget(1 + isTarget, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitTarg(iBitLoop), isTargetBitCode(iBitLoop));\n            stimTypes = sprintf('%s,%s', stimTypes, 'targ');\n        end;\n        % annotate the stimulus time with the response / non response using the next bit\n        isResp = double(~isempty(behavData.resp) && behavData.resp == 1);\n        isRespBitCode = bitget(1 + isResp, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitResp(iBitLoop), isRespBitCode(iBitLoop));\n            stimTypes = sprintf('%s,%s', stimTypes, 'resp');\n        end;\n        % annotate the stimulus time with the correct / false using the next bit\n        isCorrect = double(~xor(isTarget, isResp));\n        isCorrectBitCode = bitget(1 + isCorrect, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitCorr(iBitLoop), isCorrectBitCode(iBitLoop));\n            stimTypes = sprintf('%s,%s', stimTypes, 'corr');\n        end;\n        % annotate the stimulus time with the auto / normal using the next bit\n        isAuto = double(~isempty(behavData.autoReward) && behavData.autoReward == 1);\n        isAutoBitCode = bitget(1 + isAuto, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitCorr(iBitLoop), isAutoBitCode(iBitLoop));\n            stimTypes = sprintf('%s,%s', stimTypes, 'auto');\n        end;\n    end;\n        \n    % clean up the stimTypes string\n    stimTypes = regexprep(regexprep(stimTypes, '^,', ''), ',$', '');\n\n    % store the created stimulus vector and the different stimulus types encoding\n    setData(this, iDWRow, 'stim', 'data', stimVect);\n    setData(this, iDWRow, 'stim', 'loadStatus', 'full');\n    setData(this, iDWRow, 'stim', 'stimTypes', stimTypes);\n        \nend;\n\n% store back the data\nsetData(this, iDWRow, 'wfTrBehav', 'data', behavData);\n\n%% summary plot\nif doPlotsSummary > 0; % if requested, plot a figure illustrating the extraction procedure\n    figure('Name', sprintf('%s_summary', rowID), 'WindowStyle', 'docked', 'NumberTitle', 'off');\n    hold on;\n    yLims = [- 0.3, 1.2];\n    % trigger\n    plot((1 : numel(trigData)) / anInSampRate - trigDelay, linScale(trigData, [-1, 1]), 'k');\n    plot(repmat(trigTop(1) / anInSampRate - trigDelay, 2, 1), yLims, 'k:');\n    \n    % microphone\n    plot((1 : numel(micr)) / anInSampRate - trigDelay, micr, 'g');\n    plot(repmat(soundStartTimesImgReference, 2, 1), yLims, 'g:');\n    plot(repmat(soundEndTimesImgReference, 2, 1), yLims, 'g:');\n    \n    % piezo\n    lickData = abs(behavData.analogInData(strcmp(behavData.analogInNames, 'piezo'), :)) * 15;\n    plot((1 : numel(lickData)) / anInSampRate - trigDelay, lickData, 'r');\n    plot([1, numel(lickData)] / anInSampRate - trigDelay, repmat(behavData.piezoThresh, 2, 1) * 15, 'r:');\n    if ~exist('respTimeImgReference', 'var'); respTimeImgReference = NaN; end;\n    plot(repmat(respTimeImgReference, 2, 1), yLims, 'r:');\n    \n    % light\n    plot(repmat(trialStartCueImgReference, 2, 1), yLims, 'b:');\n    if ~exist('lightCueTimeImgReference', 'var'); lightCueTimeImgReference = NaN; end;\n    plot(repmat(lightCueTimeImgReference, 2, 1), yLims, 'b:');\n    \n    hold off;\n    ylim(yLims);\n    title( { sprintf('trigDelay: %.3f, trialStartCue: %.3f, soundStart: %.3f, soundEnd: %.3f', ...\n                trigDelay, trialStartCueImgReference, soundStartTimesImgReference, soundEndTimesImgReference), ...\n        sprintf('lightCue: %.3f, respTime: %.3f, respDelay: %.3f (actual: %.3f)', lightCueTimeImgReference, ...\n        respTimeImgReference, behavData.respDelay, respTimeImgReference - lightCueTimeImgReference)});\nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_genStimVect_fromBehavTextFile.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/genStimVect/OCIA_genStimVect_fromBehavTextFile.m", "size": 5478, "source_encoding": "utf_8", "md5": "1379c5e1886d23dbf086cae8b1e60820", "text": "%% #OCIA:AN:OCIA_genStimVect_fromBehavTextFile\nfunction [isValid, unvalidReason] = OCIA_genStimVect_fromBehavTextFile(this, iDWRow, varargin)\n\n% get whether to do plots or not\nif nargin > 2;      doDebugPlots = varargin{1}; \nelse                doDebugPlots = 0;\nend;\n\nrowID = DWGetRowID(this, iDWRow); % get the row ID \nisValid = true; % by default, the row is valid\nunvalidReason = ''; % by default no reason\n\no('#%s(): row num: %d ...', mfilename, iDWRow, 3, this.verb);\n\n%% init the stim vector\n% get the number of skipped frames\nnSkippedFrames = this.an.skipFrame.nFramesBegin + this.an.skipFrame.nFramesEnd;\nimgDim = str2dim(get(this, iDWRow, 'dim'));\n% compensate for the skipped frames\nif numel(imgDim) < 3;   nFramesImg = 0;\nelse                    nFramesImg = imgDim(3) - nSkippedFrames;\nend;\n% stimulus vector is all zeros except where there are stimulus starts (sound, lick, spout, etc.)\nstimVect = zeros(1, nFramesImg);\n% string storing the stimulus types for this row\nstimTypes = '';\n% start bit encoding with bit 1\niBit = 1;\n\n% store temporarly this empty stimulus vector (in case things get stuck later on)\nsetData(this, iDWRow, 'stim', 'data', stimVect);\nsetData(this, iDWRow, 'stim', 'loadStatus', 'partial');\nsetData(this, iDWRow, 'stim', 'stimTypes', regexprep(stimTypes, '^,', ''));\n\n% if no imaging frames, abort\nif ~nFramesImg;\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('no imaging data for row %s %03d (frame number = 0)', rowID, iDWRow);\n    return; % abort processing of this row\nend;\n\n% get the behavior for this row\nbehavData = getData(this, iDWRow, 'behavExtr', 'data');\n\n% get all possible textures and sort them\nrunTypes = get(this, 'all', 'runType');\nrunTypes(cellfun(@isempty, runTypes)) = [];\ntextureIDs = unique(runTypes);\ntextureRoughness = str2double(regexprep(textureIDs, '^P', ''));\n[~, sortIndex] = sort(textureRoughness);\ntextureIDs = textureIDs(sortIndex);\n\n% get frame rate\nframeRate = this.an.img.defaultFrameRate;\n\n% get the texture index\ntextureIndex = find(strcmp(textureIDs, regexprep(behavData.stimulus, 'Texture \\d ', '')));\n\n% get the decision\nif strcmp(behavData.decision , 'Go');\n    decisionIndex = 1;\nelseif strcmp(behavData.decision, 'No Response');\n    decisionIndex = 2;\nelseif strcmp(behavData.decision, 'Inappropriate Response');\n    decisionIndex = 3;\nelseif strcmp(behavData.decision, 'No Go');\n    decisionIndex = 4;\nend;\n\n% get texture's stimulus time and frame number\nstimStartTimeTexture = behavData.stimulus_time / 1000;\nstimStartFrameTexture = round(stimStartTimeTexture * frameRate);\n% adjust for skipped frames\nstimStartFrameTexture = stimStartFrameTexture - this.an.skipFrame.nFramesBegin;\n\n% get texture's stimulus time and frame number\nstimStartTimeLicking = behavData.reward_time / 1000;\nstimStartFrameLicking = round(stimStartTimeLicking * frameRate);\n% adjust for skipped frames\nstimStartFrameLicking = stimStartFrameLicking - this.an.skipFrame.nFramesBegin;\n\n% if no frame found, abort\nif isnan(stimStartFrameTexture);\n    stimStartFrameTexture = [];\nend;\n% if no frame found, abort\nif isnan(stimStartFrameLicking);\n    stimStartFrameLicking = [];\nend;\n\n% calculate the number of bits required for encoding 8 states (8 textures)\nnMaxStims = 8; nBitsToUse = ceil(log2(nMaxStims));\n% get the bit code for each stimulus number\nbitCode = zeros(nBitsToUse, 1);\nbitCode(:, 1) = bitget(textureIndex, 1 : nBitsToUse);\n% encode the bitCode into the stimulus vector\nfor iBitLoop = 1 : nBitsToUse;\n    % annotate with the stimuli with the current bit iteratively\n    stimVect(stimStartFrameTexture) = bitset(stimVect(stimStartFrameTexture), iBit, bitCode(iBitLoop, :));\n    stimTypes = sprintf('%s,%s', stimTypes, 'text_textType');\n    iBit = iBit + 1;\nend;\n% encode the bitCode into the stimulus vector\nfor iBitLoop = 1 : nBitsToUse;\n    % annotate with the stimuli with the current bit iteratively\n    stimVect(stimStartFrameLicking) = bitset(stimVect(stimStartFrameLicking), iBit, bitCode(iBitLoop, :));\n    stimTypes = sprintf('%s,%s', stimTypes, 'lick_textType');\n    iBit = iBit + 1;\nend;\n\n\n% calculate the number of bits required for encoding 8 states (8 outcomes)\nnMaxStims = 8; nBitsToUse = ceil(log2(nMaxStims));\n% get the bit code for each stimulus number\nbitCode = zeros(nBitsToUse, 1);\nbitCode(:, 1) = bitget(decisionIndex, 1 : nBitsToUse);\n% encode the bitCode into the stimulus vector\nfor iBitLoop = 1 : nBitsToUse;\n    % annotate with the stimuli with the current bit iteratively\n    stimVect(stimStartFrameTexture) = bitset(stimVect(stimStartFrameTexture), iBit, bitCode(iBitLoop, :));\n    stimTypes = sprintf('%s,%s', stimTypes, 'text_decision');\n    iBit = iBit + 1;\nend;\n% encode the bitCode into the stimulus vector\nfor iBitLoop = 1 : nBitsToUse;\n    % annotate with the stimuli with the current bit iteratively\n    stimVect(stimStartFrameLicking) = bitset(stimVect(stimStartFrameLicking), iBit, bitCode(iBitLoop, :));\n    stimTypes = sprintf('%s,%s', stimTypes, 'lick_decision');\n    iBit = iBit + 1;\nend;\n\n\n%% saving the stimulus vector\n% clean up the stimTypes string\nstimTypes = regexprep(regexprep(stimTypes, '^,', ''), ',$', '');\n\n% store the created stimulus vector and the different stimulus types encoding\nsetData(this, iDWRow, 'stim', 'data', stimVect);\nsetData(this, iDWRow, 'stim', 'loadStatus', 'full');\nsetData(this, iDWRow, 'stim', 'stimTypes', regexprep(stimTypes, '^,', ''));\n\n\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_genStimVect_noStim.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/genStimVect/OCIA_genStimVect_noStim.m", "size": 882, "source_encoding": "utf_8", "md5": "03e130ba77be5a2d529963b4cd2fe64c", "text": "%% #OCIA:AN:OCIA_genStimVect_fromMicrAnalogIn\nfunction [isValid, unvalidReason] = OCIA_genStimVect_noStim(this, iDWRow, varargin)\n\nisValid = true; % by default, the row is valid\nunvalidReason = ''; % by default no reason\n\n%% init the stim vector\n% get the number of skipped frames\nnSkippedFrames = this.an.skipFrame.nFramesBegin + this.an.skipFrame.nFramesEnd;\nimgDim = str2dim(get(this, iDWRow, 'dim'));\n% compensate for the skipped frames\nif numel(imgDim) < 3;   nFramesImg = 0;\nelse                    nFramesImg = imgDim(3) - nSkippedFrames;\nend;\n% stimulus vector is all zeros except where there are stimulus starts (sound, lick, spout, etc.)\nstimVect = zeros(1, nFramesImg);\n\n% store the extracted items: stimulus vector\nsetData(this, iDWRow, 'stim', 'data', stimVect);\nsetData(this, iDWRow, 'stim', 'loadStatus', 'full');\nsetData(this, iDWRow, 'stim', 'stimTypes', '');\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_genStimVect_fromInputArgument.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/genStimVect/OCIA_genStimVect_fromInputArgument.m", "size": 2411, "source_encoding": "utf_8", "md5": "8cb502bb2a0dfe425818d1461d8d8ff5", "text": "%% #OCIA:AN:OCIA_genStimVect_fromInputArgument\nfunction [isValid, unvalidReason] = OCIA_genStimVect_fromInputArgument(this, iDWRow, stimVectCellArray, ...\n    stimTypesCellArray, nMaxStimTypesCellArray)\n\nisValid = true; % by default, the row is valid\nunvalidReason = ''; % by default no reason\n\no('#%s(): row num: %d ...', mfilename, iDWRow, 3, this.verb);\n\n%% creating the stim vector\n% start bit encoding with bit 1\niBit = 0;\n\n% initiate final stim vector\nfinalStimVect = zeros(size(stimVectCellArray{1}));\nfinalStimTypes = '';\n\n% encode each stimulus type one after the other\nfor iStimVect = 1 : numel(stimVectCellArray);\n    \n    currStimVect = stimVectCellArray{iStimVect};\n    stimIndices = find(currStimVect > 0);\n    stimValues = currStimVect(stimIndices);\n    \n    % calculate the number of bits to encode \n    nMaxStimTypes = nMaxStimTypesCellArray{iStimVect};\n    nBitsToUseForStimTimes = max(ceil(log2(nMaxStimTypes)), 1);\n    % assign bits to difference encodings\n    iBitCurrStimVect = iBit + (1 : nBitsToUseForStimTimes);\n    iBit = iBit + nBitsToUseForStimTimes;\n    \n    % encode the stimulus time: get the bit code for each stimulus time\n    for iInd = 1 : numel(stimIndices);\n        bitCode = bitget(stimValues(iInd), 1 : nBitsToUseForStimTimes);\n        % encode the bitCode into the stimulus vector\n        for iBitLoop = 1 : nBitsToUseForStimTimes;\n            % annotate with the stimuli with the current bit iteratively\n            finalStimVect(stimIndices(iInd)) = bitset(finalStimVect(stimIndices(iInd)), iBitCurrStimVect(iBitLoop), ...\n                bitCode(iBitLoop));\n        end;\n    end;\n    \n    % encode the stimulus type\n    for iBitLoop = 1 : nBitsToUseForStimTimes;\n        finalStimTypes = sprintf('%s,%s', finalStimTypes, stimTypesCellArray{iStimVect});\n    end;\n\n    % store temporarly this empty stimulus vector (in case things get stuck later on)\n    setData(this, iDWRow, 'stim', 'data', finalStimVect);\n    setData(this, iDWRow, 'stim', 'loadStatus', 'partial');\n    setData(this, iDWRow, 'stim', 'stimTypes', regexprep(finalStimTypes, '^,', ''));\n    \nend;\n\n%% saving the stimulus vector    \n% store the created stimulus vector and the different stimulus types encoding\nsetData(this, iDWRow, 'stim', 'data', finalStimVect);\nsetData(this, iDWRow, 'stim', 'loadStatus', 'full');\nsetData(this, iDWRow, 'stim', 'stimTypes', regexprep(finalStimTypes, '^,', ''));\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_genStimVect_fromMicrAnalogIn.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/genStimVect/OCIA_genStimVect_fromMicrAnalogIn.m", "size": 25627, "source_encoding": "utf_8", "md5": "8fb42ab887a6a62a42ac00cb7eb1782b", "text": "%% #OCIA:AN:OCIA_genStimVect_fromMicrAnalogIn\nfunction [isValid, unvalidReason] = OCIA_genStimVect_fromMicrAnalogIn(this, iDWRow, varargin)\n\n% get whether to do plots or not\nif nargin > 2;      doPlotsYscan = varargin{1}; doPlotsMicr = varargin{1};\nelseif nargin > 3;  doPlotsYscan = varargin{1}; doPlotsMicr = varargin{2};\nelse                doPlotsYscan = 0; doPlotsMicr = 0;\nend;\n\nrowID = DWGetRowID(this, iDWRow); % get the row ID \nisValid = true; % by default, the row is valid\nunvalidReason = ''; % by default no reason\n\no('#%s(): row num: %d ...', mfilename, iDWRow, 3, this.verb);\n\n% get the behavior data for this row\nbehavData = getData(this, iDWRow, 'behavExtr', 'data');\n\n% if no behavior data is found, abort\nif isempty(behavData);\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('cannot find associated behavior data (behavior data unavailable in row %03d)', iDWRow);\n    return; % abort processing of this row\nend;\n\niTrial = str2double(get(this, iDWRow, 'runNum')); % get the trial number\n% if no behavior row found using the behavior ID, abort\nif isempty(iTrial) || isnan(iTrial) || iTrial <= 0;\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('bad trial number (\"%s\" => %d\")', get(this, iDWRow, 'runNum'), iTrial);\n    return; % abort processing of this row\nend;\n\n% fix missing fields\nif isfield(behavData, 'nTones');\n    % if more than one element for nTones, there must be one element per trial\n    if numel(behavData.nTones) > 1; nTones = behavData.nTones(iTrial);\n    % otherwise there is only one constant number of tones\n    else                            nTones = behavData.nTones;\n    end;\n% if no field, assume there is only one tone\nelse                                nTones = 1;\nend;\n\n%% init the stim vector\n% get the number of skipped frames\nnSkippedFrames = this.an.skipFrame.nFramesBegin + this.an.skipFrame.nFramesEnd;\nimgDim = str2dim(get(this, iDWRow, 'dim'));\n% compensate for the skipped frames\nif numel(imgDim) < 3;   nFramesImg = 0;\nelse                    nFramesImg = imgDim(3) - nSkippedFrames;\nend;\n% stimulus vector is all zeros except where there are stimulus starts (sound, lick, spout, etc.)\nstimVect = zeros(1, nFramesImg);\n% string storing the stimulus types for this row\nstimTypes = '';\n% initialize variables\nimgDelay = NaN;\ntrueFrameRate = NaN;\n% calculate the number of bits to encode \nnMaxStimTimes = 10; nBitsToUseForStimTimes = ceil(log2(nMaxStimTimes));\n% assign bits to difference encodings\niBitTime = 1 : nBitsToUseForStimTimes; iBitCloud = iBitTime(end) + (1 : 2); iBitTarg = iBitCloud(end) + (1 : 2);\niBitResp = iBitTarg(end) + (1 : 2); iBitCorr = iBitResp(end) + (1 : 2);\n% other random bit\niBit = 1;\n\n%% - #OCIA:AN:OCIA_genStimVect_fromMicrAnalogIn : delay and true frame rate analysis (yscan)\nif ismember('yscan', behavData.analogInNames);\n    % extract the number of frames from the microscope's y scanner's position\n    yscan = behavData.analogInData(strcmp(behavData.analogInNames, 'yscan'), :); % extract the y scanner's trace\n    normThresh = 3 * std(yscan(1 : 1000)); % take the first 1000 frames for normalization threshold\n    yscan(abs(yscan) < normThresh) = 0; % normalize to remove the noise of parts when scanner is not moving\n    yscanTopPrctile = prctile(yscan, 80); % set a threshold at the 80th percentile\n    yscanTop = find(yscan > yscanTopPrctile); % find all the peaks\n    yscanTopDiff = diff(yscanTop); % get the differential of the peaks to find the real peak\n    yscanTopDiffPeaks = [find(yscanTopDiff > 1) size(yscanTop, 2)]; % get the peaks\n    nFramesBehav = size(yscanTopDiffPeaks, 2); % get the number of frames found using the recording of the y scanner\n\n    % get the middle part of the frames (20%-80%) to exclude starting and ending artifacts\n    middleFrames = round(nFramesBehav * 0.2) : round(nFramesBehav * 0.8);\n    % calculate the true frame rate (based on the y position of the scanner)\n    if isfield(behavData, 'analogInSampRate'); % if there is a field containing the analog input sampling rate\n        anInSampRate = behavData.analogInSampRate;\n        % calculate the true frame rate using the inter-peak interval\n        trueFrameRate = anInSampRate / mean(diff(yscanTop(yscanTopDiffPeaks(middleFrames))));\n        \n    % no field containing the analog input rate, figure it out (backward compatibility)\n    else\n        % try to find the sampling rate from the two possible values\n        unknownBehavRate = [3000 3333];\n        % two possible frame rates\n        trueFrameRateOptions = unknownBehavRate / mean(diff(yscanTop(yscanTopDiffPeaks(middleFrames))));\n        % find the closest frame rate to the expected frame rate\n        [~, closestBehavRateInd] = min(abs(trueFrameRateOptions - 77.67));\n        % get analog input sampling rate and the true frame rate corresponding to this closest frame rate\n        anInSampRate = unknownBehavRate(closestBehavRateInd);\n        trueFrameRate = trueFrameRateOptions(closestBehavRateInd);\n        % show a warning for this assumption\n        showWarning(this, 'OCIA:OCIA_genStimVect_fromMicrAnalogIn:NoAnInSampRate', ...\n            sprintf(['Missing analog in sample rate for %s (%d)! Assuming it was %d Hz (=> true frame rate: ', ...\n            '%.2f Hz).'], rowID, iDWRow, anInSampRate, trueFrameRate));\n    end;\n\n    % get the imaging delay by substract the time of a single frame from the first y scanner peak\n    frameLength = round(anInSampRate / trueFrameRate);\n    firstFrameEndInd = yscanTop(yscanTopDiffPeaks(1));\n    firstFrameStartInd = firstFrameEndInd - frameLength;\n    imgDelay = firstFrameStartInd / anInSampRate;\n\n    % store the extracted number: analog input sampling rate, imaging delay and true frame rate\n    \n    behavData.behavSampRate = anInSampRate;\n    behavData.imgDelay = imgDelay;\n    behavData.imgFrameRate = trueFrameRate;\n\n    % check whether there is a big frame difference between the imaging and the behavior recording\n    nFrameDiff = nFramesBehav - nFramesImg;\n    % if there is some imaging data and difference is too big (more than 1 second), abort\n    if nFramesImg && nFrameDiff > 1 * trueFrameRate;\n        showWarning(this, 'OCIA:genStimVect:fromMicrAnalogIn:missingFrames', sprintf(['Missing frames in the ', ...\n            'imaging data: frames from behavior = %d, from imaging = %d (~= %.1f ms difference). Going on ...'], ...\n            nFramesBehav, nFramesImg, 1000 * nFrameDiff / trueFrameRate));\n        % change the display color\n        frameDisplayColor = 'orange';\n        \n    elseif nFramesImg && nFrameDiff < 0;\n        isValid = false; % set the validity flag to false\n        % store the reason why this row was not valid\n        unvalidReason = sprintf(['frame number mismatch (frames from behavior: %d, from imaging: %d ', ...\n            '(~= %.1f ms difference).'], nFramesBehav, nFramesImg, 1000 * nFrameDiff / trueFrameRate);        \n        % change the display color\n        frameDisplayColor = 'red';\n    \n    % no frame mismatch\n    else         \n        % change the display color\n        frameDisplayColor = 'green';\n    end;\n        \n    % add the number of frames from the behavior in the comments\n    comments = getR(this, iDWRow, 'comments');\n    if iscell(comments); comments = comments{1}; end;\n    % clean the HTML away\n    comments = regexprep(comments, '^<html>', ''); % remove HTML tag\n    comments = regexprep(comments, '<font[^>]+>', ''); % remove font tags\n    comments = regexprep(comments, '</font>', ''); % remove font tags\n    % remove the previous frame number tag\n    comments = regexprep(comments, '(, )?\\d+ frames \\(behav\\.\\)', '');\n    % add the HTML for a colored frame number comment\n    comments = sprintf('<html><font color=\"black\">%s%s</font><font color=\"%s\">%d</font><font color=\"black\"> frames (behav.)</font>', comments, ...\n        iff(isempty(comments), '', ', '), frameDisplayColor, nFramesBehav);\n    % put back the comments in the table and update the display\n    set(this, iDWRow, 'comments', comments);\n    DWUpdateColumnsDisplay(this, iDWRow, { 'comments' }, false);\n    \n    % if row is not valid, abort processing of this row\n    if ~isValid; return; end;\n\n    if doPlotsYscan > 0; % if requested, plot a figure illustrating the extraction procedure\n        figure('Name', sprintf('%s_yscan', rowID), 'WindowStyle', 'docked', 'NumberTitle', 'off');\n        plot(yscan, 'k');\n        title(sprintf('nFramesBehav: %d, nFramesImag: %d', nFramesBehav, nFramesImg));\n        hold on;\n        yLims = get(gca, 'YLim');\n        plot(yscanTop, yscan(yscanTop), 'g');\n        plot(yscanTop(1 : end - 1) + 0.5, yscanTopDiff / 100, 'r');\n        plot(repmat(firstFrameStartInd, 2, 1), yLims, 'r:');\n        scatter(yscanTop(yscanTopDiffPeaks), repmat(0.8, nFramesBehav, 1), 'b*');\n    end;\n\nend; % end check yscan exists\n\n%% - #OCIA:AN:OCIA_genStimVect_fromMicrAnalogIn : sound start (stimulus time) analysis (micr)\nif ismember('micr', behavData.analogInNames);\n\n    % get the number of samples\n    nSamples = size(behavData.analogInData, 2);\n    \n    % extract the stimulus sound time and duration from the recorded sound\n    micr = linScale(abs(behavData.analogInData(strcmp(behavData.analogInNames, 'micr'), :))); % extract the microphone's trace\n    % get a range for the begining of the signal\n    begRange = round(nSamples * 0.01 : nSamples * 0.1);\n    % incrementally search for the right threshold\n    nSoundsDiff = 1; soundYThresh = 0; soundThreshFactor = 5; soundThreshFactorStep = 5; nLoops = 0;\n    while nSoundsDiff && soundThreshFactor < 55;\n        % get a threshold for the sound onset\n        soundThreshFactor = soundThreshFactor + soundThreshFactorStep;\n        soundYThresh = soundThreshFactor * std(micr(begRange));\n        % get the samples that exceeds the threshold, adding the first sample to catch the start of the first sound\n        upSamples = [0 find(micr > soundYThresh)];\n        % get the derivative of the upSamples, drops in the sample indexes indicate interruption of upSamples,\n        %   which means that there is a sound start\n        upSamplesDiff = diff(upSamples);\n        % use the ISI to find peaks. If no ISI, use 0.5 second\n        ISI = 0.5;\n        if isfield(behavData, 'ISI') && behavData.ISI > 0;\n            ISI = behavData.ISI;\n        end;\n        % difference between detected upSample derivative's peaks must be at least half of the ISI\n        minISI = ISI * 0.5 * anInSampRate;\n        % get the index of the peaks where the derivative exceeds the ISI threshold and increment by one to get\n        %   the sound start index\n        soundStartInds = upSamples(find(upSamplesDiff >= minISI) + 1);\n\n        if ~isempty(soundStartInds);\n            % calculate the sound start time\n            soundStartTimes = soundStartInds / anInSampRate;\n        else\n            soundStartTimes = [];\n        end;\n\n        % check whether there is a big frame difference between the imaging and the behavior recording\n        nSoundsDiff = abs(nTones - numel(soundStartTimes));\n        nLoops = nLoops + 1;\n\n    end;\n\n    % if there is a mismatch in the number of sounds found and more sounds were found, show a warning\n    if nSoundsDiff && ~isempty(soundStartTimes) && numel(soundStartTimes) >= nTones;\n        showWarning(this, 'OCIA:OCIA_genStimVect_fromMicrAnalogIn:MissingStim', ...\n            sprintf(['Problem with number of stimuli found for %s (%d)! Number of stim detected in recorded ', ...\n            'data: %d, expected number of stim: %d. Taking only the first %d stim(s).'], rowID, iDWRow, ...\n            numel(soundStartTimes), nTones, nTones));\n        soundStartTimes = soundStartTimes(1 : nTones);\n        doPlotsMicr = doPlotsMicr + 1;\n        \n    % if there is a mismatch in the number of sounds found and less sounds were found, show a warning\n    elseif nSoundsDiff;\n        showWarning(this, 'OCIA:OCIA_genStimVect_fromMicrAnalogIn:MissingStim', ...\n            sprintf(['Problem with number of stimuli found for %s (%d)! Number of stim detected in recorded ', ...\n            'data: %d, expected number of stim: %d.'], rowID, iDWRow, numel(soundStartTimes), nTones));\n        doPlotsMicr = doPlotsMicr + 1;\n    end;\n\n    % if there is some imaging data and some sound start, create the simulus vector\n    if nFramesImg && ~isempty(soundStartTimes);\n        % get the stimulus time, including the imaging start delay\n        soundStartTimesImgReference = soundStartTimes - imgDelay;\n        stimStartIndexesImgReference = round(soundStartTimesImgReference * trueFrameRate); % get the stimulus index\n        % remove stim start times that are too early\n        if any(stimStartIndexesImgReference < 0);\n            nRemStims = sum(stimStartIndexesImgReference < 0);\n            stimStartIndexesImgReference(stimStartIndexesImgReference <= 0) = [];\n            showWarning(this, 'OCIA:OCIA_genStimVect_fromMicrAnalogIn:EarlyStim', ...\n                sprintf('Removed %d early stimuli found for %s (%d)!', nRemStims, rowID, iDWRow));\n        end;\n        \n        %% annotate stimulus frames        \n        % if this is an cloud of tone discrimination task\n        if strcmp(behavData.taskType, 'cotDiscr');\n            \n            % encode the sound start stimulus\n            stimTimeFrames = { stimStartIndexesImgReference };\n            \n            % go through each stim time\n            for iStimTime = 1 : numel(stimTimeFrames);\n                \n                % encode the stimulus time: get the bit code for each stimulus time\n                bitCode = bitget(1, 1 : nBitsToUseForStimTimes);\n                % encode the bitCode into the stimulus vector\n                for iBitLoop = 1 : nBitsToUseForStimTimes;\n                    % annotate with the stimuli with the current bit iteratively\n                    stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                        iBitTime(iBitLoop), bitCode(iBitLoop));\n                    stimTypes = sprintf('%s,%s', stimTypes, 'time');\n                end;\n                \n                % annotate the stimulus time with the cloud type using the next bit\n                soundType = double(behavData.stim == 1);\n                soundTypeBitCode = bitget(1 + soundType, 1 : 2);\n                for iBitLoop = 1 : 2;\n                    stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                    iBitCloud(iBitLoop), soundTypeBitCode(iBitLoop));\n                    stimTypes = sprintf('%s,%s', stimTypes, 'cloud');\n                end;\n                % annotate the stimulus time with the target/non-target using the next bit\n                isTarget = double(~isempty(behavData.target) && behavData.target == 1);\n                isTargetBitCode = bitget(1 + isTarget, 1 : 2);\n                for iBitLoop = 1 : 2;\n                    stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                        iBitTarg(iBitLoop), isTargetBitCode(iBitLoop));\n                    stimTypes = sprintf('%s,%s', stimTypes, 'targ');\n                end;\n                % annotate the stimulus time with the response / non response using the next bit\n                isResp = double(~isempty(behavData.resp) && behavData.resp == 1);\n                isRespBitCode = bitget(1 + isResp, 1 : 2);\n                for iBitLoop = 1 : 2;\n                    stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                        iBitResp(iBitLoop), isRespBitCode(iBitLoop));\n                    stimTypes = sprintf('%s,%s', stimTypes, 'resp');\n                end;\n                % annotate the stimulus time with the correct / false using the next bit\n                isCorrect = double(~xor(isTarget, isResp));\n                isCorrectBitCode = bitget(1 + isCorrect, 1 : 2);\n                for iBitLoop = 1 : 2;\n                    stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                        iBitCorr(iBitLoop), isCorrectBitCode(iBitLoop));\n                    stimTypes = sprintf('%s,%s', stimTypes, 'corr');\n                end;\n            end;\n\n        % if this is an oddball experiment with the right number of sounds\n        elseif strcmp(behavData.taskType, 'cotOdd') && isfield(behavData, 'oddPos') && numel(stimStartIndexesImgReference) >= behavData.oddPos;\n            \n            % annotate with 1 on the first bit to mark it as stimulus frame\n            stimVect(stimStartIndexesImgReference) = bitset(stimVect(stimStartIndexesImgReference), iBit, 1);\n            stimTypes = sprintf('%s,%s', stimTypes, 'stim');\n            iBit = iBit + 1;\n            \n            % calculate the number of bits required for encoding 10 states (10 stimuli)\n            nStims = numel(stimStartIndexesImgReference); nMaxStims = 10; nBitsToUse = ceil(log2(nMaxStims));\n            % get the bit code for each stimulus number\n            bitCode = zeros(nBitsToUse, nStims);\n            for iStim = 1 : nStims;\n                bitCode(:, iStim) = bitget(iStim, 1 : nBitsToUse);\n            end;\n            % encode the bitCode into the stimulus vector\n            for iBitLoop = 1 : nBitsToUse;\n                % annotate with the stimuli with the current bit iteratively\n                stimVect(stimStartIndexesImgReference) = bitset(stimVect(stimStartIndexesImgReference), iBit, bitCode(iBitLoop, :));\n                stimTypes = sprintf('%s,%s', stimTypes, 'stimNum');\n                iBit = iBit + 1;\n            end;\n            \n            % annotate with the sound type depending on the standard/odd using the next bit\n            soundType = behavData.stim == 1;\n            stimVect(stimStartIndexesImgReference) = bitset(stimVect(stimStartIndexesImgReference), iBit, soundType);\n            stimVect(stimStartIndexesImgReference(behavData.oddPos)) = bitset(stimVect(stimStartIndexesImgReference(behavData.oddPos)), iBit, ~soundType);\n            stimTypes = sprintf('%s,%s', stimTypes, 'soundType');\n            iBit = iBit + 1;\n            \n            % calculate the number of bits required for encoding 3 states (first, pre-odd, odd)\n            nStims = numel(stimStartIndexesImgReference); nMaxStims = 3; nBitsToUse = ceil(log2(nMaxStims));\n            % get the bit code for each stimulus number\n            bitCode = zeros(nBitsToUse, nStims);\n            for iStim = 1 : nStims;\n                % get the stimulus state to encode for this stimulus\n                if iStim == 1;                          stimState = 1;\n                elseif iStim == behavData.oddPos - 1;   stimState = 2; \n                elseif iStim == behavData.oddPos;       stimState = 3;\n                else                                    stimState = 0;\n                end;\n                bitCode(:, iStim) = bitget(stimState, 1 : nBitsToUse);\n            end;\n            % encode the bitCode into the stimulus vector\n            for iBitLoop = 1 : nBitsToUse;\n                % annotate with the stimuli with the current bit iteratively\n                stimVect(stimStartIndexesImgReference) = bitset(stimVect(stimStartIndexesImgReference), iBit, bitCode(iBitLoop, :));\n                stimTypes = sprintf('%s,%s', stimTypes, 'oddball');\n                iBit = iBit + 1;\n            end;\n            \n        end;\n    end;\n        \n    % clean up the stimTypes string\n    stimTypes = regexprep(regexprep(stimTypes, '^,', ''), ',$', '');\n\n    % store the created stimulus vector and the different stimulus types encoding\n    behavData.soundStartTime = soundStartTimes;\n    setData(this, iDWRow, 'stim', 'data', stimVect);\n    setData(this, iDWRow, 'stim', 'loadStatus', 'full');\n    setData(this, iDWRow, 'stim', 'stimTypes', stimTypes);\n\n    if doPlotsMicr > 0; % if requested, plot a figure illustrating the extraction procedure\n        figure('Name', sprintf('%s_micr', rowID), 'WindowStyle', 'docked', 'NumberTitle', 'off');\n        rectangle('Position', [begRange(1) 0 begRange(end) - begRange(1) soundYThresh * 1.1], ...\n            'FaceColor', [0.8 1 0.8], 'EdgeColor', [0.8 1 0.8]);\n        hold on;\n        plot(micr, 'k');\n        yLims = get(gca, 'YLim'); xLims = get(gca, 'XLim');\n        plot(upSamples(1 : end - 1) - 0.5, (upSamplesDiff / max(upSamplesDiff)) * max(micr) * 0.9, 'r');\n        plot(repmat(soundStartInds, 2, 1), repmat(yLims, numel(soundStartInds), 1)', 'r:');\n        plot(xLims, repmat(soundYThresh, 2, 1), 'g:');\n        title(sprintf('stimYThresh: %.5f, stimStartTimes: %s', soundYThresh, sprintf(' %.2fs', soundStartTimes)));\n    end;\n\nend; % end check micr exists\n\n%% - #OCIA:AN:OCIA_genStimVect_fromMicrAnalogIn : light cue and response time\nif strcmp(behavData.taskType, 'cotDiscr') && ~isnan(imgDelay) && ~isnan(trueFrameRate);\n    \n    % encode the stimuli\n    stimTimeFrames = { };\n    \n    % get the light time, including the imaging start delay\n    if isfield(behavData, 'lightTime') && ~isnan(behavData.lightTime);\n        lightTimeImgReference = behavData.soundStartTime - imgDelay + (behavData.lightTime - behavData.soundTime);\n        stimStartIndexesImgReference = round(lightTimeImgReference * trueFrameRate); % get the stimulus index\n        if stimStartIndexesImgReference <= nFramesImg;\n            stimTimeFrames{end + 1} = stimStartIndexesImgReference;\n        end;\n    end;\n        \n    % get the response time, including the imaging start delay\n    if isfield(behavData, 'respTime') && ~isnan(behavData.respTime);\n        respTimeImgReference = behavData.soundStartTime - imgDelay + (behavData.respTime - behavData.soundTime);\n        stimStartIndexesImgReference = round(respTimeImgReference * trueFrameRate); % get the stimulus index\n        if stimStartIndexesImgReference <= nFramesImg;\n            stimTimeFrames{end + 1} = stimStartIndexesImgReference;\n        end;\n    end;\n        \n    % get the light off time, including the imaging start delay\n    if isfield(behavData, 'lightOffTime') && ~isnan(behavData.lightOffTime);\n        lightOffTimeImgReference = behavData.soundStartTime - imgDelay + (behavData.lightOffTime - behavData.soundTime);\n        stimStartIndexesImgReference = round(lightOffTimeImgReference * trueFrameRate); % get the stimulus index\n        if stimStartIndexesImgReference <= nFramesImg;\n            stimTimeFrames{end + 1} = stimStartIndexesImgReference;\n        end;\n    end;\n    \n    % go through each stim time\n    for iStimTime = 1 : numel(stimTimeFrames);\n\n        % encode the stimulus time: get the bit code for each stimulus time\n        bitCode = bitget(1 + iStimTime, 1 : nBitsToUseForStimTimes);\n        % encode the bitCode into the stimulus vector\n        for iBitLoop = 1 : nBitsToUseForStimTimes;\n            % annotate with the stimuli with the current bit iteratively\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), iBitTime(iBitLoop), ...\n                bitCode(iBitLoop));\n        end;\n\n        % annotate the stimulus time with the cloud type using the next bit\n        soundType = double(behavData.stim == 1);\n        soundTypeBitCode = bitget(1 + soundType, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n            iBitCloud(iBitLoop), soundTypeBitCode(iBitLoop));\n        end;\n        % annotate the stimulus time with the target/non-target using the next bit\n        isTarget = double(~isempty(behavData.target) && behavData.target == 1);\n        isTargetBitCode = bitget(1 + isTarget, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitTarg(iBitLoop), isTargetBitCode(iBitLoop));\n        end;\n        % annotate the stimulus time with the response / non response using the next bit\n        isResp = double(~isempty(behavData.resp) && behavData.resp == 1);\n        isRespBitCode = bitget(1 + isResp, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitResp(iBitLoop), isRespBitCode(iBitLoop));\n        end;\n        % annotate the stimulus time with the correct / false using the next bit\n        isCorrect = double(~xor(isTarget, isResp));\n        isCorrectBitCode = bitget(1 + isCorrect, 1 : 2);\n        for iBitLoop = 1 : 2;\n            stimVect(stimTimeFrames{iStimTime}) = bitset(stimVect(stimTimeFrames{iStimTime}), ...\n                iBitCorr(iBitLoop), isCorrectBitCode(iBitLoop));\n        end;\n    end;\n    \n    % if some stimulus was found\n    if ~isempty(stimTypes);\n        \n        % clean up the stimTypes string\n        stimTypes = regexprep(regexprep(stimTypes, '^,', ''), ',$', '');\n\n        % store the created stimulus vector and the different stimulus types encoding\n        setData(this, iDWRow, 'stim', 'data', stimVect);\n        setData(this, iDWRow, 'stim', 'loadStatus', 'full');\n        setData(this, iDWRow, 'stim', 'stimTypes', regexprep(stimTypes, '^,', ''));\n        \n    end;\n    \nend;\n\n% store back the data\nsetData(this, iDWRow, 'behavExtr', 'data', behavData);\n\n% % display message\n% showMessage(this, sprintf(['Extracting behavior data for %s (%d) done (frames behav: %d, ', ...\n%     'frames img: %d, nStims: %d, nLoops: %d, %3.1f sec).'], rowID, iDWRow, nFramesBehav, ...\n%     nFramesImg, numel(stimStartTimes), nLoops, toc(behavExtrTic)));\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_genStimVect_fromWhisker.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/genStimVect/OCIA_genStimVect_fromWhisker.m", "size": 4088, "source_encoding": "utf_8", "md5": "b073f00c4f340f8c1ab5674eb41e5d5d", "text": "%% #OCIA:AN:OCIA_genStimVect_fromWhisker\nfunction [isValid, unvalidReason] = OCIA_genStimVect_fromWhisker(this, iDWRow, varargin)\n\n% get whether to do plots or not\nif nargin > 2;      doDebugPlots = varargin{1}; \nelse                doDebugPlots = 0;\nend;\n\nrowID = DWGetRowID(this, iDWRow); % get the row ID \nisValid = true; % by default, the row is valid\nunvalidReason = ''; % by default no reason\n\no('#%s(): row num: %d ...', mfilename, iDWRow, 3, this.verb);\n\n%% init the stim vector\n% get the number of skipped frames\nnSkippedFrames = this.an.skipFrame.nFramesBegin + this.an.skipFrame.nFramesEnd;\nimgDim = str2dim(get(this, iDWRow, 'dim'));\n% compensate for the skipped frames\nif numel(imgDim) < 3;   nFramesImg = 0;\nelse                    nFramesImg = imgDim(3) - nSkippedFrames;\nend;\n% stimulus vector is all zeros except where there are stimulus starts (sound, lick, spout, etc.)\nstimVect = zeros(1, nFramesImg);\n% string storing the stimulus types for this row\nstimTypes = '';\n% start bit encoding with bit 1\niBit = 1;\n\n% store temporarly this empty stimulus vector (in case things get stuck later on)\nsetData(this, iDWRow, 'stim', 'data', stimVect);\nsetData(this, iDWRow, 'stim', 'loadStatus', 'partial');\nsetData(this, iDWRow, 'stim', 'stimTypes', regexprep(stimTypes, '^,', ''));\n\n% if no imaging frames, abort\nif ~nFramesImg;\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('no imaging data for row %s %03d (frame number = 0)', rowID, iDWRow);\n    return; % abort processing of this row\nend;\n\n% get the (eventually down-sampled) whisker traces matrix for the requested rows\nwhiskTraces = OCIA_analysis_getRawWhiskTracesMatrix(this, iDWRow, true);\n\n% if no whisker data is found, abort\nif isempty(whiskTraces);\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('cannot find whisker data for row %s %03d', rowID, iDWRow);\n    return; % abort processing of this row\nend;\n\n% normalize the traces by their mean\nwhiskTraces = whiskTraces - nanmean(whiskTraces);\n\n\n%% Whisker peak\nminPeakThresh = 10;\nminPeakDist = 5;\n[peakValues, stimPeakFrames] = findpeaks(whiskTraces, 'MinPeakHeight', minPeakThresh, 'MinPeakDistance', 15);\n\n% if there are some stimulus, fill the simulus vector\nif ~isempty(stimPeakFrames);\n    \n    % annotate the whisking peak with 1 on the current bit to mark it as stimulus frame\n    stimVect(stimPeakFrames) = bitset(stimVect(stimPeakFrames), iBit, 1);\n    stimTypes = sprintf('%s,%s', stimTypes, 'whiskPeak');\n    iBit = iBit + 1;\n    \n    % if requested, plot a figure illustrating the extraction procedure\n    if doDebugPlots > 0;\n        figure('Name', sprintf('%s_whiskPeak', rowID), 'WindowStyle', 'docked', 'NumberTitle', 'off');\n        whiskHandle = plot(whiskTraces, 'k');\n        hold('on');\n        hScatt = scatter(stimPeakFrames, peakValues, 100, 'rs', 'fill');\n        yLims = get(gca, 'YLim'); xLims = get(gca, 'XLim');\n        minPeakHeightHandle = plot(xLims, repmat(minPeakThresh, 2, 1), 'g:');\n        minPeakDistHandle = plot([stimPeakFrames; stimPeakFrames], repmat(yLims', 1, numel(stimPeakFrames)), 'b:');\n        plot([stimPeakFrames + minPeakDist; stimPeakFrames + minPeakDist], repmat(yLims', 1, numel(stimPeakFrames)), 'b:');\n        title(sprintf('minPeakThresh: %.1f, minPeakDist: %.1f', minPeakThresh, minPeakDist));\n        legend([whiskHandle, hScatt(1), minPeakHeightHandle(1), minPeakDistHandle(1)], ...\n            'whisker angle', 'peaks', 'minimum peak height threshold', 'minimum inter-peak distance threshold');\n    end;\n    \nend;\n\n%% Other methods ...\n\n\n\n%% saving the stimulus vector\n% clean up the stimTypes string\nstimTypes = regexprep(regexprep(stimTypes, '^,', ''), ',$', '');\n    \n% store the created stimulus vector and the different stimulus types encoding\nsetData(this, iDWRow, 'stim', 'data', stimVect);\nsetData(this, iDWRow, 'stim', 'loadStatus', 'full');\nsetData(this, iDWRow, 'stim', 'stimTypes', regexprep(stimTypes, '^,', ''));\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_startFunction_widefieldCreateAveragesForEachCondition.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/startFunctions/OCIA_startFunction_widefieldCreateAveragesForEachCondition.m", "size": 9795, "source_encoding": "utf_8", "md5": "eef9147a9768002dbc4259deb36d33ff", "text": "function OCIA_startFunction_widefieldCreateAveragesForEachCondition(this)\n% OCIA_startFunction_widefieldCreateAveragesForEachCondition - [no description]\n%\n%       OCIA_startFunction_widefieldCreateAveragesForEachCondition(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n    \n%     iStartID = 37;\n    iStartID = 41;\n    iExcl = []; % iEndID = Inf;\n%     iEndID = 40;\n    iEndID = 43;\n    iTrialStart = 1;\n    iTrialEnd = Inf;\n    fixedStartFrame = 60;\n\n    % define trial types\n    % { '4 kHz', '28 kHz', 'miss', 'FA', 'early', 'auto' } \n    % { 'go', 'nogo', 'miss', 'FA', 'early', 'auto' }\n    trialTypesRegexp = { 'hit', 'CR', 'quiet', 'moveDur', 'moveBef', ...\n        'hit_AND_moveDur', 'hit_AND_quiet', 'hit_AND_moveBef', 'hit_AND_[quiet|moveBef]', ...\n        'CR_AND_moveDur', 'CR_AND_quiet', 'CR_AND_moveBef', 'CR_AND_[quiet|moveBef]' };\n    trialTypeSaveName = { 'hit', 'CR', 'strict_quiet', 'move', 'early_move', ...\n        'move_hit', 'strict_quiet_hit', 'early_move_hit', 'delay_quiet_hit', ...\n        'move_CR', 'strict_quiet_CR', 'early_move_CR', 'delay_quiet_CR' };\n\n    %% get all widefield data\n    OCIAChangeMode(this, 'DataWatcher');\n    \n    % get the DataWatcher's and the Analyser's GUI handles\n    dwh = this.GUI.handles.dw;\n    \n    % set the watch types\n    set(dwh.watchTypes.animal,      'Value', 1);\n    set(dwh.watchTypes.day,         'Value', 1);\n    set(dwh.watchTypes.wfLV,        'Value', 1);\n    set(dwh.watchTypes.wfLVSess,    'Value', 1);\n    set(dwh.watchTypes.wfLVMat,     'Value', 0);\n    set(dwh.watchTypes.wfAn,        'Value', 0);\n    set(dwh.watchTypes.behav,       'Value', 0);\n    % set the filters\n    set(dwh.filt.animalID,          'Value', 1, 'String', { '-' });\n    set(dwh.filt.dayID,             'Value', 1, 'String', { '-' });\n    set(dwh.filt.wfLVSessID,        'Value', 1, 'String', { '-' });\n    set(dwh.filt.rowTypeID,         'Value', 1, 'String', { '-' });\n    set(dwh.filt.dataLoadStatus,    'Value', 0, 'String', '');\n    set(dwh.filt.rowNum,            'Value', 0, 'String', '');\n    set(dwh.filt.runNum,            'Value', 0, 'String', '');\n    set(dwh.filt.all,               'Value', 0, 'String', '');\n    \n    % update the table\n    DWProcessWatchFolder(this);\n    \n    % set the watch types for processing\n    set(dwh.watchTypes.wfLVMat,     'Value', 1);\n    set(dwh.watchTypes.behav,       'Value', 1);\n    \n    % get triplets\n    IDs = get(this, 'all', { 'animal', 'day', 'wfLVSess', 'runNum' }, DWFilterTable(this, 'rowType = WFLV session AND wfLVSess ~= \\d{6}'));\n    \n    %% go through each row\n    for iID = max(iStartID, 1) : min(size(IDs, 1), iEndID);\n        \n        % do not process excluded IDs\n        if ismember(iID, iExcl); continue; end;\n        \n        % get the IDs and set filters\n        [animalID, dayID, sessID, sessNum] = IDs{iID, :};\n        set(dwh.filt.animalID,          'Value', 2, 'String', { '-', animalID });\n        set(dwh.filt.dayID,             'Value', 2, 'String', { '-', dayID });\n        set(dwh.filt.wfLVSessID,        'Value', 2, 'String', { '-', sprintf('session%s_%s', sessNum, sessID) });\n        \n        % update the table\n        DWProcessWatchFolder(this);\n        \n        % get trial rows\n        [~, trialRowInds] = DWFilterTable(this, 'rowType = WF trial');\n        if isempty(trialRowInds);\n           showWarning(this, sprintf('OCIA:%s:NoTrialIndices', mfilename()), sprintf(...\n               'Problem with animal %s, day %s, session %s (%s): no trial index.\\n', ...\n               animalID, dayID, sessID, sessNum));\n           continue;\n        end;\n        avgStruct = struct();\n        trialCountStruct = struct();\n        DWLoadRow(this, trialRowInds(1), 'full');\n        dims = str2dim(get(this, trialRowInds(1), 'dim'));\n        \n        % re-alignment to sound onset required\n        pathToFirstTrial = get(this, trialRowInds(1), 'path');\n        stimStartPath = regexprep(pathToFirstTrial, 'stim_trial1\\.mat', 'stimStartFrames.mat');\n        % stimulus start defining file exists\n        if exist(stimStartPath, 'file');\n            stimStartFramesMat = load(stimStartPath);\n            stimStartFrames = stimStartFramesMat.stimStartFrame;\n\n        else\n            stimStartFrames = [];\n            showWarning(this, sprintf('OCIA:%s:NoStimStartFrames', mfilename()), sprintf(['Problem with animal %s, ', ...\n               'day %s, session %s (%s): cannot find stim start frames mat file at \"%s\". Skipping realigning.\\n'], ...\n               animalID, dayID, sessID, sessNum, stimStartPath));\n        end;\n        \n        % create average for each condition\n        for iRow = max(iTrialStart, 1) : min(numel(trialRowInds), iTrialEnd);\n            iDWRow = trialRowInds(iRow);\n            \n            % extract info and data for this row\n            commentsForRow = get(this, iDWRow, 'comments');\n            iTrial = str2double(get(this, iDWRow, 'runNum'));\n            \n            % check each trial type for a match\n            for iTrialType = 1 : numel(trialTypeSaveName);\n                trialTypeRegexp = trialTypesRegexp{iTrialType};\n                trialType = trialTypeSaveName{iTrialType};\n                % trial is a match for this type\n                if ~isempty(regexp(commentsForRow, trialTypeRegexp, 'once'));\n                    % add to data structure\n                    [avgStruct, trialCountStruct] = addToDataStruct(this, iDWRow, animalID, dayID, sessID, sessNum, ...\n                        stimStartFrames, iTrial, fixedStartFrame, avgStruct, trialType, dims, trialCountStruct);\n                end;\n                \n                % multiple trial types required\n                if ~isempty(regexp(trialTypeRegexp, '_AND_', 'once'));\n                    multiTrialTypes = regexp(trialTypeRegexp, '_AND_', 'split');\n                    matchForEach = cellfun(@(triTy) ~isempty(regexp(commentsForRow, triTy, 'once')), multiTrialTypes);\n                    % all requirements of matching are met\n                    if all(matchForEach);\n                        % add to data structure\n                        [avgStruct, trialCountStruct] = addToDataStruct(this, iDWRow, animalID, dayID, sessID, ...\n                            sessNum, stimStartFrames, iTrial, fixedStartFrame, avgStruct, trialType, ...\n                            dims, trialCountStruct);\n                        \n                    end;\n                end;\n                \n            end;\n            \n            % clean up by erasing data for this row\n            DWFlushData(this, iDWRow, false, 'wfTrIm');\n        end;\n        \n        % save the data for each condition\n        for iTrialType = 1 : numel(trialTypeSaveName);\n            trialType = trialTypeSaveName{iTrialType};\n            % abort if no trials of this type\n            if ~isfield(trialCountStruct, trialType) || trialCountStruct.(trialType) <= 0; continue; end;\n            % divide by the number of trials\n            N = trialCountStruct.(trialType);\n            avgStruct.(trialType).aligned = avgStruct.(trialType).aligned ./ N;\n            avgStruct.(trialType).unalign = avgStruct.(trialType).unalign ./ N;\n            \n            % save as \"tr_ave\" and \"N\" variable\n            tr_ave = avgStruct.(trialType).unalign; %#ok<NASGU>\n            trialPath = get(this, trialRowInds(1), 'path');\n            save(regexprep(trialPath, 'stim_trial1', sprintf('cond_%s_average', trialType)), 'tr_ave', 'N');\n            \n            % save as \"tr_ave\" and \"N\" variable\n            tr_ave = avgStruct.(trialType).aligned; %#ok<NASGU>\n            trialPath = get(this, trialRowInds(1), 'path');\n            save(regexprep(trialPath, 'stim_trial1', sprintf('cond_%s_average_aligned', trialType)), 'tr_ave', 'N');\n        end;\n        \n    end;\n    \nend\n\nfunction [avgStruct, trialCountStruct] = addToDataStruct(this, iDWRow, animalID, dayID, sessID, sessNum, ...\n    stimStartFrames, iTrial, fixedStartFrame, avgStruct, trialType, dims, trialCountStruct)\n\n    % make sur data is loaded\n    DWLoadRow(this, iDWRow, 'full');\n    dataForRow = getData(this, iDWRow, 'wfTrIm', 'data');\n    imgDims = size(dataForRow);\n\n    % re-alignment to sound onset required\n    if ~isempty(stimStartFrames);\n        % calculate frame shift\n        stimStartFrameTrial = stimStartFrames(iTrial);\n        nFramesDiff = fixedStartFrame - stimStartFrameTrial;\n        if abs(nFramesDiff) > 10;\n            showWarning(this, sprintf('OCIA:%s:AlignFrameRemoval', mfilename()), sprintf(['Problem with animal %s, ', ...\n               'day %s, session %s (%s): removing a lot of frames (%d) for realignment ...\\n'], ...\n               animalID, dayID, sessID, sessNum, abs(nFramesDiff)));\n        end;\n        % not enough frame before sound\n        dataForRowUnalign = dataForRow;\n        if nFramesDiff > 0;\n            dataForRow = cat(3, nan([imgDims(1:2), nFramesDiff]), dataForRow(:, :, 1 : (end - nFramesDiff)));\n        % too many frames before sound\n        elseif nFramesDiff < 0;\n            dataForRow = cat(3, dataForRow(:, :, (abs(nFramesDiff) + 1) : end), nan([imgDims(1:2), abs(nFramesDiff)]));\n        end;\n        \n    % otherwise use nans\n    else\n        dataForRowUnalign = nan(dims);\n\n    end;\n\n    % first trial of this type\n    if ~isfield(avgStruct, trialType);\n        avgStruct.(trialType).aligned = zeros(dims);\n        avgStruct.(trialType).unalign = zeros(dims);\n        trialCountStruct.(trialType) = 0;\n    end;\n    \n    % add average values\n    avgStruct.(trialType).aligned = avgStruct.(trialType).aligned + dataForRow;\n    avgStruct.(trialType).unalign = avgStruct.(trialType).unalign + dataForRowUnalign;\n    \n    % add trial count\n    trialCountStruct.(trialType) = trialCountStruct.(trialType) + 1;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_analysis_wideField_drawCropRect.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/analysis/OCIA_analysis_wideField_drawCropRect.m", "size": 820, "source_encoding": "utf_8", "md5": "f4f3787ccee041fd8eb5d85d86bace20", "text": "function OCIA_analysis_wideField_drawCropRect(this, ~, ~)\n% OCIA_analysis_wideField_drawCropRect - [no description]\n%\n%       OCIA_analysis_wideField_drawCropRect(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% remove previous rectangle\naxeChilds = get(this.GUI.handles.an.axe, 'Children');\ndelete(axeChilds(strcmp(get(axeChilds, 'Tag'), 'imrect')));\n\n% draw the ROI\nhROI = imrect(this.GUI.handles.an.axe);\nhROI.addNewPositionCallback(@(h)updateCropRect(this, h));\n\n% get position and store it\npos = roundn(hROI.getPosition(), 1);\nthis.an.wf.cropRect = pos;\n\n% update parameters\nANUpdatePlot(this, 'params');\n\n\nend\n\nfunction updateCropRect(this, newCropRect)\n    this.an.wf.cropRect = roundn(newCropRect, 1);\n    ANUpdatePlot(this, 'params');\nend\n    \n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_analysis_getROIStat.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/analysis/OCIA_analysis_getROIStat.m", "size": 1842, "source_encoding": "utf_8", "md5": "0108c7662da8e7c25a69f40e48834599", "text": "% get the grouping from the different variables\nfunction [ROIStat, description] = OCIA_analysis_getROIStat(ROIStatToCalc, respMethod, PSCaTracesStats, ...\n    stimIDIndexes, stimIDs)\n    \n% get the average responsiveness to all trials\nROIResps = reshape(nanmean(PSCaTracesStats.ROIRespTrial, 2), ...\n    size(PSCaTracesStats.ROIRespTrial, 1), size(PSCaTracesStats.ROIRespTrial, 3));\n        \n% select which ROI statistic to analyse\nswitch ROIStatToCalc;\n    \n    case 'responsiveness';\n        ROIStat = ROIResps(stimIDIndexes, :)';\n        % linearize the matrix\n        ROIStat = ROIStat(:);\n\n        description = sprintf('Responsiveness (%s %%DRR)', respMethod);\n        \n    case 'response time';\n        \n        ROIStat = PSCaTracesStats.ROIRespTime(stimIDIndexes, :)';\n        % linearize the matrix\n        ROIStat = ROIStat(:);\n        description = 'Response time (sec)';\n        \n    case 'SI';\n        \n        % calculate SI\n        ROIStat = (ROIResps(stimIDIndexes(2), :) - ROIResps(stimIDIndexes(1), :)) ...\n            ./ (ROIResps(stimIDIndexes(1), :) + ROIResps(stimIDIndexes(2), :));\n        description = sprintf('SI: %s (neg.) - %s (pos.)', stimIDs{stimIDIndexes});\n        \n    case 'd''';\n        \n        % calculate d''\n        ROIStat = squeeze(...\n            ( ...\n                nanmean(PSCaTracesStats.ROIRespTrial(stimIDIndexes(2), :, :), 2) ...\n                - nanmean(PSCaTracesStats.ROIRespTrial(stimIDIndexes(1), :, :), 2) ...\n            ) ...\n            ./ ...\n            sqrt( ...\n                0.5 * nanstd(PSCaTracesStats.ROIRespTrial(stimIDIndexes(1), :, :), [], 2) .^ 2 ...\n                + 0.5 * nanstd(PSCaTracesStats.ROIRespTrial(stimIDIndexes(2), :, :), [], 2) .^ 2) ...\n            );\n        description = sprintf('d'': %s (neg.) - %s (pos.)', stimIDs{stimIDIndexes});\n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_analysis_getWhiskVectors.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/analysis/OCIA_analysis_getWhiskVectors.m", "size": 1996, "source_encoding": "utf_8", "md5": "a3c25dc726e7ecc35017c2d86ec7ff70", "text": "%% #OCIA:AN:OCIA_analysis_caTraces_whiskvectors\nfunction [WAEnvs, WAAmp, WASetP, WAExpWhisk, WAFovWhisk] = OCIA_analysis_getWhiskVectors(this, ...\n    rawWhiskTraces, whiskFrameRateCellArray)\n\nnRuns = numel(rawWhiskTraces);\n\n% define frequency bands\nExpwhisk_low_frequ = 7;             % Hz  frequ. band for exploratory whisking \nExpwhisk_up_frequ = 12;             % Hz\nFovwhisk_low_frequ = 15;            % Hz  frequ. band for foveal whisking\nFovwhisk_up_frequ = 25;             % Hz\nEnvWinSize = 0.3;                   % window size in seconds for envelope and sliding mean/amp calculation\n\n% loop through each run and calculate various whisking angle (WA) variables\nWAEnvs = cell(size(rawWhiskTraces));        % envelope (max - min)\nWAAmp = cell(size(rawWhiskTraces));         % amplitude (max)\nWASetP = cell(size(rawWhiskTraces));        % set point (mean angle)\nWAExpWhisk = cell(size(rawWhiskTraces));    % exploratory whisking\nWAFovWhisk = cell(size(rawWhiskTraces));    % foveal whisking\n\nparfor iRun = 1 : nRuns;\n    \n    whiskFrameRate = whiskFrameRateCellArray{iRun};\n    \n    % calculate the envelope\n    whiskAngle = rawWhiskTraces{iRun};\n    nWhiskFrames = size(whiskAngle, 2);\n    winSize = round(EnvWinSize * whiskFrameRate);\n    for iFrame = 1 : nWhiskFrames;    \n        r = iFrame - winSize : iFrame + winSize;\n        r(r < 1 | r > nWhiskFrames) = [];\n        WAEnvs{iRun}(iFrame) = max(whiskAngle(r)) - min(whiskAngle(r));     % Whisking envelope\n        WAAmp{iRun}(iFrame)  = max(whiskAngle(r));                          % Whisking amplitude\n        WASetP{iRun}(iFrame) = mean(whiskAngle(r));                         % Whisker set point\n     end;\n    [~, bandpow1, bandpow2] = spectralDensityAnalysis(whiskAngle, 128, 127, 128, whiskFrameRate, ...\n        Expwhisk_low_frequ, Expwhisk_up_frequ, Fovwhisk_low_frequ, Fovwhisk_up_frequ);\n    \n    % ...(whiskAngle, windowsiz, overlap, nfft, ...)\n    WAExpWhisk{iRun} = bandpow1;\n    WAFovWhisk{iRun} = bandpow2;\n    \nend;\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_analysis_getGrouping.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/analysis/OCIA_analysis_getGrouping.m", "size": 5378, "source_encoding": "utf_8", "md5": "71ab6791a9546bb97c7e85f287c452c3", "text": "% get the grouping from the different variables\nfunction [grouping, groupLabels] = OCIA_analysis_getGrouping(this, iDWRows, stimIDIndexes, selDispStimIDs, groupBy, ROINames, ROIPhases)\n\n% create the grouping variable\nswitch groupBy;\n    case 'ROI';\n\n        % remove the ROISet tag\n        cleanROINames = regexprep(ROINames, 'RS\\d+_', '');\n        uniqueCleanROINames = unique(cleanROINames, 'stable');\n        % get the grouping from ROISet number\n        grouping = cellfun(@(ROIName)find(strcmp(ROIName, uniqueCleanROINames)), cleanROINames);\n        % expand the matrix for the stimulus types\n        grouping = repmat(grouping, 1, numel(stimIDIndexes));\n        % linearize the matrix\n        grouping = grouping(:);\n        % use the ROINames as group labels\n        groupLabels = uniqueCleanROINames';\n\n    case 'day';\n\n        % use the \"ROIPhases\" cell-array if provided and no DataWatcher row indexes provided\n        if exist('ROIPhases', 'var') && ~isempty(ROIPhases) && isempty(iDWRows);\n            \n            % remove the ROISet tag\n            uniquePhases = unique(ROIPhases, 'stable');\n            % get the grouping from ROI phase\n            grouping = cellfun(@(ROIPhase)find(strcmp(ROIPhase, uniquePhases)), ROIPhases);\n            % expand the matrix for the stimulus types\n            grouping = repmat(grouping, 1, numel(stimIDIndexes));\n            % linearize the matrix\n            grouping = grouping(:);\n            % use the ROINames as group labels\n            groupLabels = uniquePhases;\n            \n        else\n            \n            % get the grouping from ROISet number\n            grouping = cellfun(@(ROIName)str2double(regexprep(regexp(ROIName, 'RS\\d+_', 'match'), '[^\\d]', '')), ROINames);\n            % get the days from the rows and from the ROISet IDs\n            allDays = regexprep(unique(get(this, iDWRows, 'day')), '_', '');\n            allROISetsDays = regexprep(unique(get(this, iDWRows, 'ROISet')), '_\\d+$', '');\n            % re-map the grouping using the actual unique days\n            for iROISetDay = 1 : numel(allROISetsDays);\n                grouping(grouping == iROISetDay) = find(strcmp(allROISetsDays{iROISetDay}, allDays));    \n            end;\n\n            % expand the matrix for the stimulus types\n            grouping = repmat(grouping, 1, numel(stimIDIndexes));\n            % linearize the matrix\n            grouping = grouping(:);\n\n            % use different labels for the day groups\n%             groupLabels = this.an.img.groupNames(unique(grouping));\n            groupLabels = allROISetsDays(unique(grouping));\n            \n        end;\n\n    case 'stimType';\n\n        % split the stimulus IDs into stimulus ID and PSPerID\n        splitStimIDs = cell(2, numel(selDispStimIDs));\n        for iStimType = 1 : numel(selDispStimIDs);\n            parts = regexp(selDispStimIDs{iStimType}, ' ', 'split');\n            if numel(parts) > 2;\n                partsString = regexprep(sprintf('%s-', parts{1 : end - 1}), '-$', '');\n                parts = [partsString, parts(end)];\n            end;\n            splitStimIDs(:, iStimType) = parts;\n        end;\n        % extract the unique stimuli / PSPerID\n        uniqueStims = unique(splitStimIDs(1, :), 'stable');\n        \n        % create a grouping for each stimulus type\n        grouping = repmat((1 : numel(stimIDIndexes)), numel(ROINames), 1);\n        % linearize the matrix\n        grouping = grouping(:);\n        \n        % re-map the grouping indexes to make it only about the stimulus IDs and not the PSPerID\n        for iStimType = 1 : numel(selDispStimIDs);\n            grouping(grouping == iStimType) = find(strcmp(splitStimIDs(1, iStimType), uniqueStims));\n        end;\n        \n        % use the stimulus IDs as group labels\n        groupLabels = uniqueStims;\n\n    case 'PSPer';\n\n        % split the stimulus IDs into stimulus ID and PSPerID\n        splitStimIDs = cell(2, numel(selDispStimIDs));\n        for iStimType = 1 : numel(selDispStimIDs);\n            parts = regexp(selDispStimIDs{iStimType}, ' ', 'split');\n            if numel(parts) > 2;\n                partsString = regexprep(sprintf('%s-', parts{1 : end - 1}), '-$', '');\n                parts = [partsString, parts(end)];\n            end;\n            splitStimIDs(:, iStimType) = parts;\n        end;\n        % extract the unique stimuli / PSPerID\n        uniquePSPer = unique(splitStimIDs(2, :), 'stable');\n        \n        % create a grouping for each stimulus type\n        grouping = repmat((1 : numel(stimIDIndexes)), numel(ROINames), 1);\n        % linearize the matrix\n        grouping = grouping(:);\n        \n        % re-map the grouping indexes to make it only about the PSPerID and not the stimulus IDs\n        for iStimType = 1 : numel(selDispStimIDs);\n            grouping(grouping == iStimType) = find(strcmp(splitStimIDs(2, iStimType), uniquePSPer));\n        end;\n        \n        % use the stimulus IDs as group labels\n        groupLabels = uniquePSPer;\n\n    case 'stimTypePSPer';\n\n        % create a grouping for each stimulus type\n        grouping = repmat((1 : numel(stimIDIndexes)), numel(ROINames), 1);\n        % linearize the matrix\n        grouping = grouping(:);\n        % use the stimulus IDs as group labels\n        groupLabels = selDispStimIDs';\n\n    case 'none';\n\n        % return empty vectors\n        grouping = [];\n        groupLabels = [];\n\nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_analysis_behav_getBehavVars.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/analysis/OCIA_analysis_behav_getBehavVars.m", "size": 32382, "source_encoding": "utf_8", "md5": "7db800f203bd40f7c6c94b65743f703a", "text": "function [behavVars, rowIDs, colIDs] = OCIA_analysis_behav_getBehavVars(this, allBehavStructs, ...\n    selectedLoadedBehavRows, includeEOTrials)\n% OCIA_analysis_behav_getBehavVars - [no description]\n%\n%       [behavVars, rowIDs, colIDs] = OCIA_analysis_behav_getBehavVars(this, allBehavStructs, ...\n%                                       selectedLoadedBehavRows, includeEOTrials)\n%\n% Extract the behavior variables from the input structures into a cell array.\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n\n% count the number of strutures\nnBehavStructs = numel(allBehavStructs);\n\n% create a configuration cell-array for the different behavior variables with 3 + \"nBehavData\" + 2 columns:\n%   { id, label, grouping options, plotting parameters, data for each structure, concatenated data (one value per trial), \n%       concat. data without repetitions }\nbehavVars = { ...\n... id                          label                   grouping                               groupMethod  plotParams (plot type, unit, color)\n    'behavInd',                 'behav. file num.',     { 'trial'                           },  '',         { 'box',     '',        'black'  };\n    'date',                     'date',                 { 'trial'                           },  '',         { 'box'    , '',        'black'  };\n    'days',                     'days',                 { 'trial'                           },  '',         { 'box'    , '',        'black'  };\n    'time',                     'time',                 {                                   },  '',         { 'scatter', 'hour',    'black'  };\n    'session',                  'session',              { 'trial'                           },  '',         { 'box'    , '',        'black'  };\n    'dateWithSession',          'date (with session)',  {                                   },  '',         { 'scatter', '',        'black'  };\n    'trialRange',               'used trial range',     {                                   },  '',         { 'scatter', '',        'black'  };\n    'nTrials',                  'n. of trials',         {          'run', 'session', 'date' },  'sum',      { 'scaline', 'trial',   'black'  };\n    'resps',                    'response',             { 'trial', 'run', 'session', 'date' },  'mean',     { 'scaline', '  % ',    'black'  };\n    'minRespTime',              'delay',                { 'trial', 'run', 'session', 'date' },  'mean',     { 'scaline', 'sec.',  [.8 .8 .2] };\n    'earliesAllowed',           'allowedEarlies',       {          'run', 'session', 'date' },  'mean',     { 'scaline', '  % ',  [.2 .8 .8] };\n%     'phase',                    'training phase name',  { 'trial'                           },  '',         { 'box'    , '',        'black'  };\n%     'phaseGroup',               'training phase',       { 'trial'                           },  '',         { 'box'    , '',        'black'  };\n    'nogoPerc',                 'NoGo percent',        {          'run', 'session', 'date' },  'mean',     { 'scaline', '  % ',  [.8 .2 .2] };\n    'animalID',                 'animal ID',            { 'trial'                           },  '',         { 'box'    , '',        'black'  };\n    'stimMatrixRandomIndex',    'stim. matrix num.',    {          'run'                    },  'mean',     { 'scatter', '',        'black'  };\n    'respTypes',                'response type',        { 'trial', 'run', 'session', 'date' },  'mean',     { 'scatter', '',        'black'  };\n    'respDelays',               'response delay',       { 'trial', 'run', 'session', 'date' },  'mean',     { 'scaline', 'sec.',    'black'  };\n%     'imagedTrials',             'imaged trials',        { 'trial',                          },  'sum',      { 'box'    , '',        'black'  };\n    'startDelay',               'starting delay',       { 'trial', 'run', 'session', 'date' },  'mean',     { 'scaline', 'sec.',    'black'  };\n%     'spoutIn',                  'spout delay',          { 'trial', 'run', 'session', 'date' },  'mean',     { 'scaline', 'sec.',    'black'  };\n    'lightCueOn',               'light delay',          { 'trial', 'run', 'session', 'date' },  'mean',     { 'scaline', 'sec.',    'black'  };\n    'nRewards',                 'n. of rew. trials',    { 'trial', 'run', 'session', 'date' },  'sum',      { 'scaline', 'trial',   'green'  };\n%     'nRewardsEO',               'n. of EO rew. trials', { 'trial', 'run', 'session', 'date' },  'sum',      { 'scaline', 'trial',   'green'  };\n%     'suppliedWater',            'supplied water',       {                 'session', 'date' },  'max',      { 'scaline', ' ml ',   [0 0.5 1] };\n%     'rewardWater',              'reward water',         {          'run', 'session', 'date' },  'sum',      { 'line'   , ' ml ',    'cyan'   };\n%     'water',                    'total water',          {          'run', 'session', 'date' },  'sum',      { 'line'   , ' ml ',    'blue'   };\n%     'weight',                   'animal weight',        {                            'date' },  '',         { 'scaline', ' g. ',    'black'  };\n    'counts',                   'performance counts',   {                                   },  '',         { ''       , '',        ''       };\n    'hitRate',                  'hit rate',             { 'trial', 'run', 'session', 'date' },  'mean',     { 'linesca', '  % ',    'green'  };\n    'FARate',                   'false alarm rate',     { 'trial', 'run', 'session', 'date' },  'mean',     { 'linesca', '  % ',    'red'    };\n    'earlies',                  'earlies',              { 'trial', 'run', 'session', 'date' },  'mean',     { 'linesca', '  % ',   [0.2 0.5 1] };\n    'dprime',                   'performance (d'')',    { 'trial', 'run', 'session', 'date' },  'max',      { 'linesca',  'd'' ',   'blue'   };\n};\nrowIDs = behavVars(:, 1); % extract the IDs\nbehavColIDs = regexp(regexprep(sprintf('%03d,', 1 : nBehavStructs), ',$', ''), ',', 'split');\ncolIDs = [{ 'id', 'label', 'grouping', 'groupMethod', 'plotParams' }, behavColIDs, { 'allDataRep', 'allData' }];\nnBehavVars = size(behavVars, 1); % count the number of variables (=> rows)\n% extend for each structure and for the concatenated data\nbehavVars(:, end + 1 : end + nBehavStructs + 2) = cell(nBehavVars, nBehavStructs + 2);\n\n% get the mice info file's content as a cell-array with one cell per line\nmiceInfoLines = readMiceInfoFile(this);\n\n% get all the starting time for each structure\nexpStartTimes = zeros(nBehavStructs, 1);\n% go through each behavior structure\nfor iBehav = 1 : nBehavStructs;\n    % get the behavior data structure\n    behavStruct = allBehavStructs{iBehav};\n    % extract the experiment starting time as a \"datenum\"\n    expStartTimes(iBehav) = unix2dn(behavStruct.expStartTime * 1000);\nend;\n\n% create the session indexes by clustering the times\nif nBehavStructs > 1;\n    % get the date \"datenum\"\n    expStartDates = datenum(datestr(expStartTimes, 'yyyy_mm_dd'), 'yyyy_mm_dd');\n    % cluster the time (without the date)\n    expStartTimesNoDate = expStartTimes - expStartDates;\n    sessionIndexes = clusterdata(expStartTimesNoDate, 'maxclust', 2);\n\n    % make sure the session labeled '1' is the one from the morning\n    meanStartTimesForFirstSession = mean(expStartTimesNoDate(sessionIndexes == 1));\n    meanStartTimesForSecondSession = mean(expStartTimesNoDate(sessionIndexes == 2));\n    % if session 1 is later than session 2, swap them\n    if meanStartTimesForFirstSession > meanStartTimesForSecondSession;\n        sessionIndexes(sessionIndexes == 1) = 3;\n        sessionIndexes(sessionIndexes == 2) = 1;\n        sessionIndexes(sessionIndexes == 3) = 2;\n    end;\nelse\n    sessionIndexes = 1;\nend;\n\no('#%s(): gathering info about %d behavior files ...', mfilename(), nBehavStructs, 2, this.verb);\n\n    \n% go through each behavior variable\nfor iVar = 1 : nBehavVars;\n        \n    % get the current behavior variable\n    behavVarID = get(this, iVar, 'id', behavVars, colIDs);\n    \n    % go through each behavior structure\n    for iBehav = 1 : nBehavStructs;\n        \n        % get the behavior index as string\n        iBehavStr = sprintf('%03d', iBehav);\n        % get the behavior data structure\n        behavStruct = allBehavStructs{iBehav};\n        % get the starting time for this structure\n        expStartTime = expStartTimes(iBehav);\n        % get the number of trials for this structure as the last valid trial having a trial ending time\n        nTotTrials = find(~isnan(behavStruct.times.end) & ~isnan(behavStruct.resps), 1, 'last');\n        % get early-on trials\n        EOTrials = ~isnan(behavStruct.autoRewardGiven) & behavStruct.autoRewardGiven > 0 ...\n            & strcmp(behavStruct.autoRewardModes, 'EarlyOn');\n        nEOTrials = nansum(EOTrials);\n        % remove early on auto-reward trials if required\n        if ~includeEOTrials;\n            nTotTrials = nTotTrials - nEOTrials;\n        end;\n        \n        switch behavVarID;\n\n            %% trialRange\n            case 'trialRange';\n\n                % by default, use all trials\n                trialRange = 1 : nTotTrials;\n\n                % if the phase was is not \"quite wakefulness\" (no reward and no go stimulation),\n                %   then try to remove the invalid trials\n                if ~strcmp(behavStruct.phase, 'QW');\n                    \n                    % get the current day and session\n                    currDay = get(this, find(strcmp(rowIDs, 'date')), iBehavStr, behavVars, colIDs);\n                    currSession = get(this, find(strcmp(rowIDs, 'session')), iBehavStr, behavVars, colIDs);\n\n                    % if first structure, consider this as a first of session\n                    if iBehav == 1;\n                        isFirstStructFromSession = true;   \n\n                    % otherwise check the previous structure's day and session\n                    else\n                        % get the day and session from the previous structure\n                        prevDay = get(this, find(strcmp(rowIDs, 'date')), sprintf('%03d', iBehav - 1), behavVars, colIDs);\n                        prevSession = get(this, find(strcmp(rowIDs, 'session')), sprintf('%03d', iBehav - 1), behavVars, colIDs);\n                        % this is the first file from session only if either the day or the session does not match\n                        isFirstStructFromSession = ~strcmp(prevDay, currDay) || prevSession ~= currSession;  \n\n                    end;\n\n                    % if last structure, consider this as an end of session\n                    if iBehav == nBehavStructs;\n                        isLastStructFromSession = true;\n\n                    % otherwise check the previous structure's day and session\n                    else\n                        % get the day and session from the next structure\n                        nextDay = get(this, find(strcmp(rowIDs, 'date')), sprintf('%03d', iBehav + 1), behavVars, colIDs);\n                        nextSession = get(this, find(strcmp(rowIDs, 'session')), sprintf('%03d', iBehav + 1), behavVars, colIDs);\n                        % this is the first file from session only if either the day or the session does not match\n                        isLastStructFromSession = ~strcmp(nextDay, currDay) || nextSession ~= currSession;\n\n                    end;        \n\n                    % if this is the first structure from a session, remove some trials at the begining\n                    if isFirstStructFromSession && ~isempty(this.an.be.nTrialsSkip);\n                        o('%s#: %s is first structure from session (%s, %d).', mfilename(), iBehavStr, currDay, ...\n                            currSession, 4, this.verb);\n                        trialRange(1 : min(this.an.be.nTrialsSkip(1), nTotTrials)) = [];            \n                    end;\n\n                    % if this is the last structure from a session, remove some trials at the end        \n                    if isLastStructFromSession && ~isempty(this.an.be.nTrialsSkip);\n                        o('%s#: %s is last structure from session (%s, %d).', mfilename(), iBehavStr, currDay, ...\n                            currSession, 4, this.verb);\n                        trialRange(max(end - this.an.be.nTrialsSkip(2) + 1, 1) : end) = [];\n\n                    end;\n\n                    % if this is the last structure from a session, remove some trials at the end        \n                    if isLastStructFromSession && ~isempty(this.an.be.nMinRespTrialSkip);                            \n                        % get the response types\n                        respTypes = behavStruct.respTypes(trialRange);\n                        % get the parameters for the ending non-responsive ratio mesure\n                        minNResps = this.an.be.nMinRespTrialSkip(1);\n                        nLastTrial = this.an.be.nMinRespTrialSkip(2);\n                        % if there are enough trials to calculate the non-responsive trials\n                        if numel(respTypes) > nLastTrial;\n                            % counter for the number of trials removed\n                            nTrialsRemoved = 0;\n                            % get the last trials\n                            lastRespTypes = respTypes(end - nLastTrial + 1 : end);\n                            % get how many response there was in these last trials\n                            nResps = sum(lastRespTypes == 1 | lastRespTypes == 3);\n                            % as long as the number of responsive trials is not enough and there are enough trials\n                            %   to calculate the number of responses\n                            while nResps < minNResps && numel(respTypes) > nLastTrial;\n                                % remove the last trial\n                                respTypes(end) = [];\n                                % update the counter\n                                nTrialsRemoved = nTrialsRemoved + 1;\n                                % get the \"new\" last trials\n                                lastRespTypes = respTypes(end - nLastTrial + 1 : end);\n                                % get the \"new\" number of response(s) in the last trials\n                                nResps = sum(lastRespTypes == 1 | lastRespTypes == 3);\n                            end;\n                            % exclude the last trials\n                            trialRange(end - nTrialsRemoved + 1 : end) = [];\n                        end;\n                    end;\n                end; % end of QW phase check\n\n                % if Early-on trials should not be included\n                if ~includeEOTrials;\n                    % remove early on trials\n                    EOTrialIndices = find(EOTrials);\n                    trialRange(ismember(trialRange, EOTrialIndices)) = [];\n                    \n                end;\n                \n                % store the trial range\n                 data = trialRange; \n\n            %% nTrials\n            case 'nTrials';\n\n                % update the actual number of trials\n                trialRange = get(this, find(strcmp(rowIDs, 'trialRange')), iBehavStr, behavVars, colIDs);\n\n                % store the number of trials\n                 data = zeros(1, nTotTrials);\n                 data(trialRange) = 1;\n\n            %% resps, phase, respDelays, respTypes, animalID, stimMatrixRandomIndex\n            case { 'resps', 'phase', 'respDelays', 'respTypes', 'animalID', 'stimMatrixRandomIndex' };\n                % if the behavior variable is stored in the structure's root, extract it from there\n                if isfield(behavStruct, behavVarID);\n                    data = behavStruct.(behavVarID);\n                else\n                    data = NaN;\n                end;  \n\n            %% lightCueOn, spoutIn, startDelay\n            case { 'lightCueOn', 'spoutIn', 'startDelay' };\n                % if the behavior variable is stored in the structure's root, extract it from there\n                if isfield(behavStruct, 'times') && isfield(behavStruct.times, behavVarID);\n                    data = behavStruct.times.(behavVarID);\n                    % unless this variable is the starting delay, subtract the starting delay to have a time in\n                    % seconds from the trial start\n                    if ~strcmp(behavVarID, 'startDelay') && isfield(behavStruct.times, 'startDelay');\n                        data = data - behavStruct.times.startDelay;\n                    end;\n                else\n                    data = NaN;\n                end; \n                \n            %% phaseGroup\n            case 'phaseGroup';\n                phase = get(this, find(strcmp(rowIDs, 'phase')), iBehavStr, behavVars, colIDs);\n                if regexp(phase, '^Q', 'once');\n                    data = 'baseline';\n                elseif regexp(phase, '^[ABL]', 'once');\n                    data = 'shaping';\n                elseif regexp(phase, '^[CDE][A-Z]\\d', 'once');\n                    data = 'discrimination';\n                else\n                    data = '[unknown]';\n                end\n                \n            %% nogoPerc\n            case 'nogoPerc';\n                data = 100 * nanmean(~ismember(behavStruct.stims, behavStruct.config.tone.goStim));\n                                \n            %% behavInd\n            case 'behavInd';\n                % store the behavior index\n                data = iBehav;\n\n            %% date\n            case 'date';\n                % store the date as string\n                data = datestr(expStartTime, 'mm_dd');\n\n            %% days\n            case 'days';\n                % store the days as string of number of days since experiment start\n                firstDateVec = datevec(get(this, find(strcmp(rowIDs, 'date')), '001', behavVars, colIDs), 'mm_dd');\n                expStartVec = datevec(datestr(expStartTime, 'yyyy_mm_dd'), 'yyyy_mm_dd');\n                data = sprintf('%02d', 1 + etime(expStartVec, firstDateVec) / 3600 / 24);\n                \n            %% dateWithSession\n            case 'dateWithSession';\n                % get the session\n                session = get(this, find(strcmp(rowIDs, 'session')), iBehavStr, behavVars, colIDs);\n                % store the date as string\n                data = [datestr(expStartTime, 'mm_dd'), ' ', iff(session == 1, 'am', 'pm')];\n\n            %% time\n            case 'time';\n                % get the starting hour and minute\n                startHour = str2double(datestr(expStartTime, 'HH'));\n                startMinute = str2double(datestr(expStartTime, 'MM'));\n                % store the time as a decimal hour\n                data = startHour + startMinute / 60;\n\n            %% session\n            case 'session';\n                % use the clustered sessions\n                data = sessionIndexes(iBehav);\n\n            %% imagedTrials\n            case 'imagedTrials';\n                % get the behavior ID of this structure\n                behavRowID = get(this, iBehav, 'behav', selectedLoadedBehavRows);\n                % check whether there is ANY imaging row\n                imagingRows = DWFilterTable(this, 'rowType = Imaging data');\n                % some imaging rows are present\n                if ~isempty(imagingRows);\n                    % try to find imaging rows that have the same behavior ID\n                    imagingRows = DWFilterTable(this, sprintf('behav = %s AND rowType = Imaging data', behavRowID));\n                    % get the trial number of these imaging rows\n                    imagedTrialNumbers = str2double(get(this, 'all', 'runNum', imagingRows));\n                    % find and store which trials of the behavior structure have been imaged\n                    data = double(arrayfun(@(i)ismember(i, imagedTrialNumbers), 1 : nTotTrials));\n                    % get the spot number as index for the data\n                    if ~isempty(imagingRows);\n                        data(data > 0) = str2double(regexprep(get(this, 'all', 'spot', imagingRows), 'spot', ''));\n                    end;\n                    \n                % no imaging rows, try to find it by day\n                else\n                    % get the date\n                    dateForRowNoYear = get(this, find(strcmp(rowIDs, 'date')), iBehavStr, behavVars, colIDs);\n                    dateForRow = [datestr(expStartTime, 'yyyy'), '_', dateForRowNoYear];\n                    % find if there are any spot folders for this day\n                    dateSpotPath = sprintf('%smou_bl_%s/%s/spot*', this.path.localData, behavStruct.animalID, dateForRow);\n                    spotFolders = dir(dateSpotPath);\n                    if ~isempty(spotFolders);\n                        data = ones(nTotTrials, 1);\n                    else\n                        data = zeros(nTotTrials, 1);\n                    end;\n                    \n                end;\n                \n            %% nRewards\n            case 'nRewards';\n                % store the rewarded trials\n                data = double(~isnan(behavStruct.times.end) & ~isnan(behavStruct.resps) & behavStruct.respTypes == 1);\n\n            %% nRewardsEO\n            case 'nRewardsEO';\n                % store the EO rewarded trials\n                if includeEOTrials;\n                    data = double(~isnan(behavStruct.times.end) & ~isnan(behavStruct.resps) & behavStruct.respTypes == 1);\n                    data(~EOTrials) = 0;\n                else\n                    data = [];\n                end;\n\n            %% suppliedWater\n            case 'suppliedWater';\n                % if some info was found\n                if ~isempty(miceInfoLines);\n                    % get the current structure's date in the format of the mice info file, with an \"am/pm\" label \n                    %   depending on the session\n                    dateToSearch = ['-' datestr(expStartTime, 'yymmdd') iff(session == 1, 'am', 'pm')];\n                    % get the lines that have these date\n                    lineIndexes = find(cellfun(@(cont)~isempty(regexp(cont, dateToSearch, 'once')), miceInfoLines));\n                    % if no line found, assume no water was supplied\n                    if isempty(lineIndexes);\n                        suppliedWater = 0;\n                    % if a line was found, get the amount of supplied water\n                    else\n                        % get the animal index of this structure\n                        animalIndex = str2double(behavStruct.animalID(end - 1 : end));\n                        % get the line after the date line\n                        suppliedWaterCellIndex = lineIndexes(animalIndex) + 1;\n                        % extract the amount of supplied water\n                        suppliedWater = str2double(regexp(miceInfoLines{suppliedWaterCellIndex}, '(\\d\\.\\d+)', 'match'));\n                    end;\n                    % store the supplied water\n                    data = suppliedWater;\n                % no info, no data\n                else\n                    suppliedWater = 0;\n                    data = NaN;\n                end;\n                \n            %% rewardWater\n            case 'rewardWater';\n                % get the list of rewarded trials as a logical array of length \"nTrials\"\n                isReward = ~isnan(behavStruct.times.end) & ~isnan(behavStruct.resps) & behavStruct.respTypes == 1;\n                % get the amount of water received on trial using the opening-time <=> water amount conversion ( multiply by 3 ):\n                %   0.02s opening = 0.006ul water, 0.03s opening = 0.009 ul water\n                waterReward = isReward * behavStruct.params.rewDur * 0.3;\n                % store the rewarded water\n                data = waterReward;\n\n            %% water\n            case 'water';\n                % store the total water as the sum of the reward water and the supplied water\n                data = waterReward + suppliedWater / numel(waterReward);\n\n            %% weight\n            case 'weight';\n                % if some info was found\n                if ~isempty(miceInfoLines);\n                    % get the current structure's date in the format of the mice info file\n                    dateToSearch = [datestr(expStartTime, 'yymmdd'), '\\s+'];\n                    % get the lines that have these date\n                    lineIndexes = find(cellfun(@(cont)~isempty(regexp(cont, dateToSearch, 'once')), miceInfoLines));\n                    % if no line found, use NaN as weight\n                    if isempty(lineIndexes);\n                        data = NaN;\n                    % if a line was found, get the weight from it\n                    else\n                        % get the line of the weights (if several lines, take the last one)\n                        weightsLine = miceInfoLines{lineIndexes(end)};\n                        % try to extract the weight numbers\n                        weightHits = str2double(regexprep(regexp(weightsLine, '\\s\\d{2}\\s?', 'match'), '\\s', ''));\n                        % if no extraction possible, use NaN as weight\n                        if isempty(weightHits);\n                            data = NaN;\n                        % if a match was found, get the weight\n                        else\n                            % get the animal index of this structure\n                            animalIndex = str2double(behavStruct.animalID(end));\n                            % take the weight of that animal\n                            data = weightHits(animalIndex);\n                        end;\n                    end;\n                % no info, no data\n                else\n                    data = NaN;\n                end;\n\n            %% counts\n            case 'counts';\n                % analyse the response types\n                data = analyseBehavPerf(behavStruct.respTypes, [], [], 0);\n\n            %% hitRate\n            case 'hitRate';\n                % get the analysis of the response types\n                counts = get(this, find(strcmp(rowIDs, 'counts')), iBehavStr, behavVars, colIDs);\n                % store the percent of go trials on target trial\n                data = counts.TGOP;\n\n            %% FARate\n            case 'FARate';\n                % get the analysis of the response types\n                counts = get(this, find(strcmp(rowIDs, 'counts')), iBehavStr, behavVars, colIDs);\n                % store the percent of go trials on non-target trial\n                data = counts.NTGOP;\n\n            %% dprime\n            case 'dprime';\n                % get the analysis of the response types\n                counts = get(this, find(strcmp(rowIDs, 'counts')), iBehavStr, behavVars, colIDs);\n                % store the dprime value for this structure\n                data = counts.DPRIME;\n\n            %% earlies\n            case 'earlies';\n                % get the analysis of the response types\n                counts = get(this, find(strcmp(rowIDs, 'counts')), iBehavStr, behavVars, colIDs);\n                % store the dprime value for this structure\n                data = counts.INVALIDP;\n\n            %% minRespTime\n            case 'minRespTime';\n                data = behavStruct.times.respMin - behavStruct.times.startDelay;\n                \n            %% earliesAllowed\n            case 'earliesAllowed';\n                if isfield(behavStruct.config.training, 'allowEarlyLicks');\n                    data = behavStruct.config.training.allowEarlyLicks * 100;\n                else\n                    data = NaN;\n                end;\n\n            %% OTHERWISE\n            % if variable is not found, skip and show warning\n            otherwise\n                data = [];\n                showWarning(this, 'OCIA:OCIA_analysis_behav_dprime:UnknownBehavVar', ...\n                    sprintf('Unknown behavior variable: \"%s\". Skipping it.', behavVarID));\n\n        end; % end of behavior variable ID switch\n        \n        % if data has nTrials values, apply the trial range filtering\n        if isnumeric(data) && numel(data) > 1 && ~strcmp(behavVarID, 'trialRange');\n            trialRange = get(this, find(strcmp(rowIDs, 'trialRange')), iBehavStr, behavVars, colIDs);\n            try\n                data = data(trialRange);\n            catch\n                o('stop', 0, 0);\n            end;\n        end; \n        \n        % actually store the behavior variable\n        behavVars = set(this, iVar, iBehavStr, data, behavVars, colIDs); \n        \n    end; % end of behavior structures loop\nend; % end of behavior variable loop\n\n%% create the concatenated data for all structures\n% create a concatenated data where each variable has a value for each trial\n% go through each behavior structure\nfor iBehav = 1 : nBehavStructs;\n    \n    % get the behavior index as string\n    iBehavStr = sprintf('%03d', iBehav);\n    % get the number of trials\n    nTrials = sum(get(this, find(strcmp(rowIDs, 'nTrials')), iBehavStr, behavVars, colIDs));\n    \n    % go through each behavior variable\n    for iVar = 1 : nBehavVars;\n        \n        % get the data for this structure and this variable\n        data = get(this, iVar, iBehavStr, behavVars, colIDs);\n        % get all the data for this variable with one value per trial\n        allDataRep = get(this, iVar, 'allDataRep', behavVars, colIDs);\n        % get all the data for this variable without repetition\n        allData = get(this, iVar, 'allData', behavVars, colIDs);\n        \n        % if data is a string, concatenate with a cell array of \"nTrials\" times the string\n        if ischar(data);\n            allDataRep = [ allDataRep repmat( { data }, 1, nTrials) ]; %#ok<AGROW>\n            allData = [ allData { data } ]; %#ok<AGROW>\n            \n        % if data is a numeric of length 1, concatenate with a replicaton of \"nTrials\" times the data\n        elseif isnumeric(data) && numel(data) == 1;\n            allDataRep = [ allDataRep repmat( data, 1, nTrials) ]; %#ok<AGROW>\n            allData = [ allData data ]; %#ok<AGROW>\n            \n        % if data is a numeric of length \"nTrials\", concatenate the data itself\n        elseif isnumeric(data) && size(data, 1) == nTrials;\n            allDataRep = [ allDataRep data' ]; %#ok<AGROW>\n            allData = [ allData data' ]; %#ok<AGROW>\n            \n        % if data is a numeric of length \"nTrials\", concatenate the data itself\n        elseif isnumeric(data) && size(data,2) == nTrials;\n            allDataRep = [ allDataRep data ]; %#ok<AGROW>\n            allData = [ allData data ]; %#ok<AGROW>\n            \n        % otherwise show a warning and use NaNs\n        else\n            allDataRep = [ allDataRep nan(1, nTrials) ]; %#ok<AGROW>\n            allData = [ allData NaN ]; %#ok<AGROW>\n            % if data is not empty, show a warning\n            if ~isempty(data) && ~isstruct(data);\n                showWarning(this, 'OCIA:OCIA_analysis_behav_getBehavVars:BadSizeBehavVar', ...\n                    sprintf('Behavior variable \"%s\" has a bad size in structure %02d: %d x %d (nTrials = %02d). Using NaNs.', ...\n                    behavVars{iVar, 1}, iBehav, size(data), nTrials));\n            end;\n            \n        end;\n        \n        % store back the concatenated data\n        % get all the data for this variable with one value per trial\n        behavVars = set(this, iVar, 'allDataRep', { allDataRep }, behavVars, colIDs);\n        % get all the data for this variable without repetition\n        behavVars = set(this, iVar, 'allData', { allData }, behavVars, colIDs);\n\n    end; % end of behavior variable loop\nend; % end of behavior structures loop\n\nend\n\nfunction miceInfoLines = readMiceInfoFile(this)\n% extract the lines from the mice info file\n\n% open file\nmiceInfoFilePath = [this.path.localData this.an.be.miceInfoFilePath];\nfID = fopen(miceInfoFilePath);\n\nif fID == -1;\n    miceInfoLines = []; % return nothing\n    showWarning(this, 'OCIA:OCIA_analysis_behav_dprime:CannotReadMiceInfoLine', ...\n        sprintf('Could not read the \"MiceInfo\" file at \"%s\". Skipping it.', miceInfoFilePath));\n    return;\nend;\n\n% allocate a cell array for the lines\nmiceInfoLines = cell(1000, 1);\n\n% read all lines\ni = 1;\nmiceInfoLines{i} = fgetl(fID); i = i + 1;\nwhile ischar(miceInfoLines{i - 1});\n    miceInfoLines{i} = fgetl(fID); i = i + 1;\nend;\n\n% close the file\nfclose(fID);\n\n% remove empty or numeric lines\nmiceInfoLines(cellfun(@isempty, miceInfoLines)) = [];\nmiceInfoLines(cellfun(@isnumeric, miceInfoLines)) = [];\n\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_parseNotebookFile_H45Balazs.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/parseNotebook/OCIA_parseNotebookFile_H45Balazs.m", "size": 9607, "source_encoding": "utf_8", "md5": "0c5a917a0ff89436458c3f52e1cb6233", "text": "function notebookInfo = OCIA_parseNotebookFile_H45Balazs(notebookFilePath)\n\n% written by B. Laurenczy - 2013/10/15\n\n%% init variables\nnotebookInfo = cell(1, 14);\n% notebookInfo = cell(1, 10);\niRow = 1;\nskipLineRead = false;\n% regular expression patterns\n% mouseIDPattern = '^mou_[db]l_\\d{6}_\\d{2}$';\nmouseIDPattern = '^\\w+$';\nheaderPattern = '^(?<spotID>sp\\d{2})(?<runType>[^_ ]+)?_?(?<stimNum>\\d+)?(?<comments>[\\w \\/-,\\.]+)?$';\ndateTimePattern = '^(?<date>\\d{4}_\\d{2}_\\d{2})__(?<time>\\d{2}_\\d{2}_\\d{2})h: +?$';\nimageModePattern = ['- ImagingMode: 2PM (?<imType>[^,]+), XY(?<zOrT>[TZ])?=(?<x>\\d+)x(?<y>\\d+)x?(?<zt>\\d+)?' ...\n    '(, frame rate=)?(?<rate>[\\d\\.]+)?( Hz)?(, z-dist=)?(?<zStep>[\\d\\.]+)?( um)?'];\nscanHeadPattern = '- ScanHead: final intensity=(?<laserInt>[\\d\\.]+)%; zoom=(?<zoom>[\\d\\.]+)';\n%% open notebook file\nnbFileID = fopen(notebookFilePath);\n% get the first line to start the reading\nline = fgetl(nbFileID);\nif line == -1;\n    warning('OCIA_parseNotebookFile_H45Balazs:EmptyFile', 'Notebook file at %s is empty! Aborting.', notebookFilePath);\n    return;\nend;\n% if present, extract the mouseID at the first line\nif regexp(line, mouseIDPattern, 'once');\n    mouseID = line; % store the mouseID\n    line = fgetl(nbFileID); % jump to next line\nelse % otherwise mouseID is not present\n    mouseID = '-';\nend;\n%% mainLoop: read all lines one by one\nwhile ischar(line); % if last line was read, next line will be -1\n    \n    %% - empty lines\n    if numel(line) < 4; % skip empty lines\n        line = fgetl(nbFileID); % jump to next line\n        continue;\n    end;\n    \n    % try to match the line with the \"header\" pattern\n    headerHits = regexp(line, headerPattern, 'names');\n    \n    %% - headerPattern match\n    % if a match was found, create a row in the notebookInfo cell-array\n    if ~isempty(headerHits);\n        \n        % store all the extracted informations\n        notebookInfo{iRow, 1} = mouseID;\n        notebookInfo{iRow, 2} = headerHits.spotID;\n        notebookInfo{iRow, 3} = headerHits.runType;\n        % store the stimulus number, but only if it's not empty\n        if ~isempty(headerHits.stimNum); notebookInfo{iRow, 4} = headerHits.stimNum; end;\n        notebookInfo{iRow, 5} = regexprep(headerHits.comments, '^ +', ''); % remove initial space character\n        \n        dateTimeLine = fgetl(nbFileID);\n        if numel(dateTimeLine) < 4;\n            % store some information for the comment line\n            notebookInfo{iRow, 1} = mouseID;\n            notebookInfo{iRow, 2} = 'unknown';\n            notebookInfo{iRow, 3} = 'comment';\n            notebookInfo{iRow, 5} = line; % store the commentaries in the 'comments' column\n        else\n            extractDateTimeImModeScanHeadInfos();\n        end;\n        \n        iRow = iRow + 1; % increment the row counter\n        \n    %% - headerPattern no-match\n    % other non-empty lines could either be \"orphan\" date-time lines that didn't have any \"header\",\n    %   or simple comment lines with no other date-time/imageMode/etc. informations\n    else\n        \n        % check wether it's a date-time line\n        dateTimeLine = line;\n        dateTimeHits = regexp(dateTimeLine, dateTimePattern, 'names');\n        % if a match was found, process the following lines as if there had been a header\n        if ~isempty(dateTimeHits);\n            \n            % store some informations for this row even if it didn't have a header line\n            notebookInfo{iRow, 1} = mouseID;\n            notebookInfo{iRow, 2} = 'unknown';\n            \n            extractDateTimeImModeScanHeadInfos();\n            \n            iRow = iRow + 1; % increment the row counter\n            \n        % otherwise consider it just as a comment\n        else\n            \n            % store some information for the comment line\n            notebookInfo{iRow, 1} = mouseID;\n            notebookInfo{iRow, 2} = 'unknown';\n            notebookInfo{iRow, 3} = 'comment';\n            notebookInfo{iRow, 5} = line; % store the commentaries in the 'comments' column\n            \n            % check wether next line is a date-time line\n            line = fgetl(nbFileID);\n            % empty line, move to next row with the empty-line already read, no skip needed\n            if numel(line) < 4;\n                iRow = iRow + 1; % increment the row counter\n            % non-empty line, check if it's a date-time line or already a new line of something\n            else\n                dateTimeHits = regexp(line, dateTimePattern, 'names');\n                if ~isempty(dateTimeHits); % line was date-time for this comment\n                    dateTimeLine = line;\n                    extractDateTimeImModeScanHeadInfos();\n                    iRow = iRow + 1; % increment the row counter\n                else % more stuff and not date-time on next line, jump to next row without re-reading a line\n                    skipLineRead = true;\n                    iRow = iRow + 1; % increment the row counter\n                end;\n            end;\n        end\n    end;\n    \n    if ~skipLineRead; line = fgetl(nbFileID); end; % get next line\n    skipLineRead = false;\nend;\n%% close the notebook file\nfclose(nbFileID);\n%% check consistency\n% fill in missing run types\nnRows = size(notebookInfo, 1);\nfor iRow = 1 : nRows;\n    \n    % missing runTypes : try to fill with first word from \"comments\"\n    if isempty(notebookInfo{iRow, 3});\n       if ~isempty(notebookInfo{iRow, 5});\n          commentsFirstWord = regexp(notebookInfo{iRow, 5}, '^\\w+', 'match');\n          notebookInfo{iRow, 3} = commentsFirstWord{1};\n          notebookInfo{iRow, 5} = strrep(notebookInfo{iRow, 5}, commentsFirstWord{1}, ' ');\n          notebookInfo{iRow, 5} = regexprep(notebookInfo{iRow, 5}, '^ +', ''); % remove leading spaces\n       else\n%             warning('OCIA_parseNotebookFile_H45Balazs:NoRunType', 'Could not find a runType for row %d (spotID: \"%s\").', ...\n%                 iRow, notebookInfo{iRow, 2});\n       end\n    end\n    \nend;\n%% function: #extractDateTimeImModeScanHeadInfos\nfunction extractDateTimeImModeScanHeadInfos()\n\n% try to match the next line with the \"dateTime\" pattern. The date-time line is already read here.\ndateTimeHits = regexp(dateTimeLine, dateTimePattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(dateTimeHits);\n    notebookInfo{iRow, 6} = dateTimeHits.day;\n    notebookInfo{iRow, 7} = dateTimeHits.time;\nelse % if not matching, show a warning and leave notebookInfo empty\n    warning('OCIA_parseNotebookFile_H45Balazs:DateTimeMatchFailure', ['Could not match date-time line \"%s\" with ' ...\n        'dateTimePattern \"%s\" ! Trying to continue ...'], dateTimeLine, dateTimePattern);\nend;\n% try to match the next line with the \"imageMode\" pattern\nimageModeLine = fgetl(nbFileID);\nimageModeHits = regexp(imageModeLine, imageModePattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(imageModeHits);\n    % store the image type, eventually transforming 'single frame' to 'frame'\n    notebookInfo{iRow, 8} = imageModeHits.imType;\n    notebookInfo{iRow, 8} = strrep(notebookInfo{iRow, 8}, 'single ', '');\n    % if recorded data had a 3rd dimension (either Z or T)\n    if ~isempty(imageModeHits.zOrT) || ~isempty(imageModeHits.zt);\n        % create a 3rd dimension tag depending on the Z/T labeling: either stack or movie\n        if strcmp(imageModeHits.zOrT, 'T');\n            notebookInfo{iRow, 9} = 'movie';\n        elseif strcmp(imageModeHits.zOrT, 'Z');\n            notebookInfo{iRow, 9} = 'stack';\n        else\n            warning('OCIA_parseNotebookFile_H45Balazs:UnknownZorT', ['3rd dimension of file unknown, neither T (time) or ' ...\n                'Z (stack): %s. Continuing ... '], imageModeHits.zOrT);\n        end;\n        % create a dimension tag like '256x256' or '100x100x500' if there is a 3rd dimension\n        if ~isempty(imageModeHits.zt);\n            notebookInfo{iRow, 10} = sprintf('%sx%sx%s', imageModeHits.x, imageModeHits.y, imageModeHits.zt);\n        else\n            warning('OCIA_parseNotebookFile_H45Balazs:ZorTButNoZTValue', ['File has a 3rd dimension (type: %s) but no ' ...\n                'dimension for that.'], imageModeHits.zt);\n        end;\n    % if there is no 3rd dimension, also create a 2D dimension tag like '256x256'\n    else\n        notebookInfo{iRow, 10} = sprintf('%sx%s', imageModeHits.x, imageModeHits.y);\n    end;\n    % store the rate and the z-dist (Z step for stacks in micro-meters) if they are not empty\n    if ~isempty(imageModeHits.rate); notebookInfo{iRow, 11} = imageModeHits.rate; end;\n    if ~isempty(imageModeHits.zStep); notebookInfo{iRow, 12} = imageModeHits.zStep; end;\nelse % if not matching, show a warning and leave notebookInfo empty\n    warning('OCIA_parseNotebookFile_H45Balazs:ImagingModeMatchFailure', ['Could not match imaging mode line \"%s\" with ' ...\n        'imageModePattern \"%s\" ! Trying to continue ...'], imageModeLine, imageModePattern);\nend;\n% try to match the next line with the \"scanHead\" pattern\nscanHeadLine = fgetl(nbFileID);\nscaneHeadHits = regexp(scanHeadLine, scanHeadPattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(dateTimeHits);\n    notebookInfo{iRow, 13} = scaneHeadHits.laserInt;\n    notebookInfo{iRow, 14} = scaneHeadHits.zoom;\nelse % if not matching, show a warning and leave notebookInfo row empty\n    warning('OCIA_parseNotebookFile_H45Balazs:ScanHeadMatchFailure', ['Could not match scan-head line \"%s\" with ' ...\n        'scanHeadPattern \"%s\" ! Trying to continue ...'], scanHeadLine, scanHeadPattern);\nend;\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_parseNotebookFile_H37Alex.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/parseNotebook/OCIA_parseNotebookFile_H37Alex.m", "size": 9570, "source_encoding": "utf_8", "md5": "b92b584eb116531a7c99ab7a809f163f", "text": "function notebookInfo = OCIA_parseNotebookFile_H37Alex(notebookFilePath)\n\n% written by B. Laurenczy - 2013/10/15\n\n%% init variables\nnotebookInfo = cell(1, 14);\n% notebookInfo = cell(1, 10);\niRow = 1;\nskipLineRead = false;\n% regular expression patterns\n% mouseIDPattern = '^mou_[db]l_\\d{6}_\\d{2}$';\nmouseIDPattern = '^\\w+$';\nheaderPattern = '^(?<regionID>R\\dS\\d)(?<runType>, [adc][HL])?(?<comments>[\\w \\/-,\\.\\!]+)?$';\ndateTimePattern = '^(?<date>\\d{4}_\\d{2}_\\d{2})__(?<time>\\d{2}_\\d{2}_\\d{2})h: +?$';\nimageModePattern = ['- ImagingMode: 2PM (?<imType>[^,]+), XY(?<zOrT>[TZ])?=(?<x>\\d+)x(?<y>\\d+)x?(?<zt>\\d+)?' ...\n    '(, frame rate=)?(?<rate>[\\d\\.]+)?( Hz)?(, z-dist=)?(?<zStep>[\\d\\.]+)?( um)?'];\nscanHeadPattern = '- ScanHead: final intensity=(?<laserInt>[\\d\\.]+)%; zoom=(?<zoom>[\\d\\.]+)';\n%% open notebook file\nnbFileID = fopen(notebookFilePath);\n% get the first line to start the reading\nline = fgetl(nbFileID);\nif line == -1;\n    warning('OCIA_parseNotebookFile_H37Alex:EmptyFile', 'Notebook file at %s is empty! Aborting.', notebookFilePath);\n    return;\nend;\n% if present, extract the mouseID at the first line\nif regexp(line, mouseIDPattern, 'once');\n    mouseID = line; % store the mouseID\n    line = fgetl(nbFileID); % jump to next line\nelse % otherwise mouseID is not present\n    mouseID = '-';\nend;\n%% mainLoop: read all lines one by one\nwhile ischar(line); % if last line was read, next line will be -1\n    \n    %% - empty lines\n    if numel(line) < 4; % skip empty lines\n        line = fgetl(nbFileID); % jump to next line\n        continue;\n    end;\n    \n    % try to match the line with the \"header\" pattern\n    headerHits = regexp(line, headerPattern, 'names');\n    \n    %% - headerPattern match\n    % if a match was found, create a row in the notebookInfo cell-array\n    if ~isempty(headerHits);\n        \n        % store all the extracted informations\n        notebookInfo{iRow, 1} = mouseID;\n        notebookInfo{iRow, 2} = lower(headerHits.regionID);\n        notebookInfo{iRow, 3} = regexprep(headerHits.runType, '^[ ,]+', '');\n        notebookInfo{iRow, 5} = regexprep(headerHits.comments, '^[ ,]+', ''); % remove initial space character\n        \n        dateTimeLine = fgetl(nbFileID);\n        if numel(dateTimeLine) < 4;\n            % store some information for the comment line\n            notebookInfo{iRow, 1} = mouseID;\n            notebookInfo{iRow, 2} = 'unknown';\n            notebookInfo{iRow, 3} = 'comment';\n            notebookInfo{iRow, 5} = regexprep(line, '^[ ,]+', ''); % store the commentaries in the 'comments' column\n        else\n            extractDateTimeImModeScanHeadInfos();\n        end;\n        \n        iRow = iRow + 1; % increment the row counter\n        \n    %% - headerPattern no-match\n    % other non-empty lines could either be \"orphan\" date-time lines that didn't have any \"header\",\n    %   or simple comment lines with no other date-time/imageMode/etc. informations\n    else\n        \n        % check wether it's a date-time line\n        dateTimeLine = line;\n        dateTimeHits = regexp(dateTimeLine, dateTimePattern, 'names');\n        % if a match was found, process the following lines as if there had been a header\n        if ~isempty(dateTimeHits);\n            \n            % store some informations for this row even if it didn't have a header line\n            notebookInfo{iRow, 1} = mouseID;\n            notebookInfo{iRow, 2} = 'unknown';\n            \n            extractDateTimeImModeScanHeadInfos();\n            \n            iRow = iRow + 1; % increment the row counter\n            \n        % otherwise consider it just as a comment\n        else\n            \n            % store some information for the comment line\n            notebookInfo{iRow, 1} = mouseID;\n            notebookInfo{iRow, 2} = 'unknown';\n            notebookInfo{iRow, 3} = 'comment';\n            notebookInfo{iRow, 5} = regexprep(line, '^[ ,]+', ''); % store the commentaries in the 'comments' column\n            \n            % check wether next line is a date-time line\n            line = fgetl(nbFileID);\n            % empty line, move to next row with the empty-line already read, no skip needed\n            if numel(line) < 4;\n                iRow = iRow + 1; % increment the row counter\n            % non-empty line, check if it's a date-time line or already a new line of something\n            else\n                dateTimeHits = regexp(line, dateTimePattern, 'names');\n                if ~isempty(dateTimeHits); % line was date-time for this comment\n                    dateTimeLine = line;\n                    extractDateTimeImModeScanHeadInfos();\n                    iRow = iRow + 1; % increment the row counter\n                else % more stuff and not date-time on next line, jump to next row without re-reading a line\n                    skipLineRead = true;\n                    iRow = iRow + 1; % increment the row counter\n                end;\n            end;\n        end\n    end;\n    \n    if ~skipLineRead; line = fgetl(nbFileID); end; % get next line\n    skipLineRead = false;\nend;\n%% close the notebook file\nfclose(nbFileID);\n%% check consistency\n% fill in missing run types\nnRows = size(notebookInfo, 1);\nfor iRow = 1 : nRows;\n    \n    % missing runTypes : try to fill with first word from \"comments\"\n    if isempty(notebookInfo{iRow, 3});\n       if ~isempty(notebookInfo{iRow, 5});\n          commentsFirstWord = regexp(notebookInfo{iRow, 5}, '^\\w+', 'match');\n          if isempty(commentsFirstWord); continue; end;\n          notebookInfo{iRow, 3} = commentsFirstWord{1};\n          notebookInfo{iRow, 5} = strrep(notebookInfo{iRow, 5}, commentsFirstWord{1}, ' ');\n          notebookInfo{iRow, 5} = regexprep(notebookInfo{iRow, 5}, '^ +', ''); % remove leading spaces\n       else\n%             warning('OCIA_parseNotebookFile_H37Alex:NoRunType', 'Could not find a runType for row %d (spotID: \"%s\").', ...\n%                 iRow, notebookInfo{iRow, 2});\n       end\n    end\n    \nend;\n%% function: #extractDateTimeImModeScanHeadInfos\nfunction extractDateTimeImModeScanHeadInfos()\n\n% try to match the next line with the \"dateTime\" pattern. The date-time line is already read here.\ndateTimeHits = regexp(dateTimeLine, dateTimePattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(dateTimeHits);\n    notebookInfo{iRow, 6} = dateTimeHits.day;\n    notebookInfo{iRow, 7} = dateTimeHits.time;\nelse % if not matching, show a warning and leave notebookInfo empty\n    warning('OCIA_parseNotebookFile_H37Alex:DateTimeMatchFailure', ['Could not match date-time line \"%s\" with ' ...\n        'dateTimePattern \"%s\" ! Trying to continue ...'], dateTimeLine, dateTimePattern);\nend;\n% try to match the next line with the \"imageMode\" pattern\nimageModeLine = fgetl(nbFileID);\nimageModeHits = regexp(imageModeLine, imageModePattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(imageModeHits);\n    % store the image type, eventually transforming 'single frame' to 'frame'\n    notebookInfo{iRow, 8} = imageModeHits.imType;\n    notebookInfo{iRow, 8} = strrep(notebookInfo{iRow, 8}, 'single ', '');\n    % if recorded data had a 3rd dimension (either Z or T)\n    if ~isempty(imageModeHits.zOrT) || ~isempty(imageModeHits.zt);\n        % create a 3rd dimension tag depending on the Z/T labeling: either stack or movie\n        if strcmp(imageModeHits.zOrT, 'T');\n            notebookInfo{iRow, 9} = 'movie';\n        elseif strcmp(imageModeHits.zOrT, 'Z');\n            notebookInfo{iRow, 9} = 'stack';\n        else\n            warning('OCIA_parseNotebookFile_H37Alex:UnknownZorT', ['3rd dimension of file unknown, neither T (time) or ' ...\n                'Z (stack): %s. Continuing ... '], imageModeHits.zOrT);\n        end;\n        % create a dimension tag like '256x256' or '100x100x500' if there is a 3rd dimension\n        if ~isempty(imageModeHits.zt);\n            notebookInfo{iRow, 10} = sprintf('%sx%sx%s', imageModeHits.x, imageModeHits.y, imageModeHits.zt);\n        else\n            warning('OCIA_parseNotebookFile_H37Alex:ZorTButNoZTValue', ['File has a 3rd dimension (type: %s) but no ' ...\n                'dimension for that.'], imageModeHits.zt);\n        end;\n    % if there is no 3rd dimension, also create a 2D dimension tag like '256x256'\n    else\n        notebookInfo{iRow, 10} = sprintf('%sx%s', imageModeHits.x, imageModeHits.y);\n    end;\n    % store the rate and the z-dist (Z step for stacks in micro-meters) if they are not empty\n    if ~isempty(imageModeHits.rate); notebookInfo{iRow, 11} = imageModeHits.rate; end;\n    if ~isempty(imageModeHits.zStep); notebookInfo{iRow, 12} = imageModeHits.zStep; end;\nelse % if not matching, show a warning and leave notebookInfo empty\n    warning('OCIA_parseNotebookFile_H37Alex:ImagingModeMatchFailure', ['Could not match imaging mode line \"%s\" with ' ...\n        'imageModePattern \"%s\" ! Trying to continue ...'], imageModeLine, imageModePattern);\nend;\n% try to match the next line with the \"scanHead\" pattern\nscanHeadLine = fgetl(nbFileID);\nscaneHeadHits = regexp(scanHeadLine, scanHeadPattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(dateTimeHits);\n    notebookInfo{iRow, 13} = scaneHeadHits.laserInt;\n    notebookInfo{iRow, 14} = scaneHeadHits.zoom;\nelse % if not matching, show a warning and leave notebookInfo row empty\n    warning('OCIA_parseNotebookFile_H37Alex:ScanHeadMatchFailure', ['Could not match scan-head line \"%s\" with ' ...\n        'scanHeadPattern \"%s\" ! Trying to continue ...'], scanHeadLine, scanHeadPattern);\nend;\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_parseNotebookFile_default.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/parseNotebook/OCIA_parseNotebookFile_default.m", "size": 9500, "source_encoding": "utf_8", "md5": "ed75ced6923a9b1d824192423aad1eb1", "text": "function [infoTable, tIDs] = OCIA_parseNotebookFile_default(notebookFilePath)\n\n% written by B. Laurenczy - 2014/02/11\n\n%% init variables\ninfoTable = cell(1000, 9);\ntIDs = { 'animal', 'spot', 'runType', 'comments', 'day', 'time', 'imType', 'imType2', 'dimNB'};\niRow = 1;\nskipLineRead = false;\n% regular expression patterns\n% mouseIDPattern = '^mou_[db]l_\\d{6}_\\d{2}$';\nmouseIDPattern = '^\\w+$';\nheaderPattern = '^(?<spotID>sp\\d{2})(?<runType>[^_ ]+)?_?(?<stimNum>\\d+)?\\s*(?<comments>.+)?';\ndayTimePattern = '^(?<day>\\d{4}_\\d{2}_\\d{2})__(?<time>\\d{2}_\\d{2}_\\d{2})h: +?$';\nimageModePattern = '- ImagingMode: 2PM (?<imType>[^,]+), XY(?<zOrT>[TZ])?=(?<x>\\d+)x(?<y>\\d+)x?(?<zt>\\d+)?';\n%% open notebook file\nnbFileID = fopen(notebookFilePath);\nif nbFileID == -1;\n    warning('OCIA_parseNotebookFile_default:CannotOpenFile', 'Notebook file at %s cannot be opened! Aborting.', notebookFilePath);\n    return;\nend;\n% get the first line to start the reading\nline = fgetl(nbFileID);\nif line == -1;\n    warning('OCIA_parseNotebookFile_default:EmptyFile', 'Notebook file at %s is empty! Aborting.', notebookFilePath);\n    return;\nend;\n% if present, extract the mouseID at the first line\nif regexp(line, mouseIDPattern, 'once');\n    mouseID = line; % store the mouseID\n    line = fgetl(nbFileID); % jump to next line\nelse % otherwise mouseID is not present\n    mouseID = '-';\nend;\n%% mainLoop: read all lines one by one\nwhile ischar(line); % if last line was read, next line will be -1\n    \n    %% - empty lines\n    if numel(line) < 4; % skip empty lines\n        line = fgetl(nbFileID); % jump to next line\n        continue;\n    end;\n    \n    % try to match the line with the \"header\" pattern\n    headerHits = regexp(line, headerPattern, 'names');\n    \n    %% - headerPattern match\n    % if a match was found, create a row in the notebookInfo cell-array\n    if ~isempty(headerHits);\n        \n        % store all the extracted informations\n        infoTable{iRow, strcmp(tIDs, 'animal')} = mouseID;\n        infoTable{iRow, strcmp(tIDs, 'spot')} = regexprep(headerHits.spotID, 'sp(\\d+)', 'spot$1');\n        infoTable{iRow, strcmp(tIDs, 'runType')} = headerHits.runType;\n        % store the stimulus number, but only if it's not empty\n        if ~isempty(headerHits.stimNum); infoTable{iRow, 4} = headerHits.stimNum; end;\n        infoTable{iRow, strcmp(tIDs, 'comments')} = regexprep(headerHits.comments, '^ +', ''); % remove initial space character\n        \n        dateTimeLine = fgetl(nbFileID);\n        if numel(dateTimeLine) < 4;\n            % store some information for the comment line\n            infoTable{iRow, strcmp(tIDs, 'animal')} = mouseID;\n            infoTable{iRow, strcmp(tIDs, 'spot')} = '';\n            infoTable{iRow, strcmp(tIDs, 'comments')} = 'comment';\n            infoTable{iRow, strcmp(tIDs, 'comments')} = line; % store the commentaries in the 'comments' column\n        else\n            extractDateTimeImModeScanHeadInfos();\n        end;\n        \n        iRow = iRow + 1; % increment the row counter\n        \n    %% - headerPattern no-match\n    % other non-empty lines could either be \"orphan\" date-time lines that didn't have any \"header\",\n    %   or simple comment lines with no other date-time/imageMode/etc. informations\n    else\n        \n        % check wether it's a date-time line\n        dateTimeLine = line;\n        dateTimeHits = regexp(dateTimeLine, dayTimePattern, 'names');\n        % if a match was found, process the following lines as if there had been a header\n        if ~isempty(dateTimeHits);\n            \n            % store some informations for this row even if it didn't have a header line\n            infoTable{iRow, strcmp(tIDs, 'animal')} = mouseID;\n            infoTable{iRow, strcmp(tIDs, 'spot')} = '';\n            \n            extractDateTimeImModeScanHeadInfos();\n            \n            iRow = iRow + 1; % increment the row counter\n            \n        % otherwise consider it just as a comment\n        else\n            \n            % store some information for the comment line\n            infoTable{iRow, strcmp(tIDs, 'animal')} = mouseID;\n            infoTable{iRow, strcmp(tIDs, 'spot')} = '';\n            infoTable{iRow, strcmp(tIDs, 'runType')} = 'comment';\n            infoTable{iRow, strcmp(tIDs, 'comments')} = line; % store the commentaries in the 'comments' column\n            \n            % check wether next line is a date-time line\n            line = fgetl(nbFileID);\n            % empty line, move to next row with the empty-line already read, no skip needed\n            if numel(line) < 4;\n                iRow = iRow + 1; % increment the row counter\n            % non-empty line, check if it's a date-time line or already a new line of something\n            else\n                dateTimeHits = regexp(line, dayTimePattern, 'names');\n                if ~isempty(dateTimeHits); % line was date-time for this comment\n                    dateTimeLine = line;\n                    extractDateTimeImModeScanHeadInfos();\n                    iRow = iRow + 1; % increment the row counter\n                else % more stuff and not date-time on next line, jump to next row without re-reading a line\n                    skipLineRead = true;\n                    iRow = iRow + 1; % increment the row counter\n                end;\n            end;\n        end\n    end;\n    \n    if ~skipLineRead; line = fgetl(nbFileID); end; % get next line\n    skipLineRead = false;\nend;\n\n%% close the notebook file\nfclose(nbFileID);\n\n%% check consistency\n% fill in missing run types\nnRows = size(infoTable, 1);\nfor iRow = 1 : nRows;\n    \n    % missing runTypes : try to fill with first word from \"comments\"\n    if isempty(infoTable{iRow, strcmp(tIDs, 'runType')});\n       if ~isempty(infoTable{iRow, strcmp(tIDs, 'comments')});\n          commentsFirstWord = regexp(infoTable{iRow, strcmp(tIDs, 'comments')}, '^\\w+', 'match');\n          infoTable{iRow, strcmp(tIDs, 'runType')} = commentsFirstWord{1};\n          infoTable{iRow, strcmp(tIDs, 'comments')} = strrep(infoTable{iRow, strcmp(tIDs, 'comments')}, commentsFirstWord{1}, ' ');\n          infoTable{iRow, strcmp(tIDs, 'comments')} = regexprep(infoTable{iRow, strcmp(tIDs, 'comments')}, '^ +', ''); % remove leading spaces\n       else\n%             warning('OCIA_parseNotebookFile_default:NoRunType', 'Could not find a runType for row %d (spotID: \"%s\").', ...\n%                 iRow, notebookInfo{iRow, 2});\n       end\n    end\n    \nend;\n\n%% remove empty lines\ninfoTableCell = infoTable;\nemptyRows = arrayfun(@(iRow)all(cellfun(@isempty, infoTableCell(iRow, :))), 1 : size(infoTableCell, 1));\ninfoTable(emptyRows, :) = [];\n\n\n%% function: #extractDateTimeImModeScanHeadInfos\nfunction extractDateTimeImModeScanHeadInfos()\n\n% try to match the next line with the \"dateTime\" pattern. The date-time line is already read here.\ndateTimeHits = regexp(dateTimeLine, dayTimePattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(dateTimeHits);\n    infoTable{iRow, strcmp(tIDs, 'day')} = dateTimeHits.day;\n    infoTable{iRow, strcmp(tIDs, 'time')} = dateTimeHits.time;\nelse % if not matching, show a warning and leave notebookInfo empty\n    warning('OCIA_parseNotebookFile_default:DateTimeMatchFailure', ['Could not match date-time line \"%s\" with ' ...\n        'dateTimePattern \"%s\" ! Trying to continue ...'], dateTimeLine, dayTimePattern);\nend;\n% try to match the next line with the \"imageMode\" pattern\nimageModeLine = fgetl(nbFileID);\nimageModeHits = regexp(imageModeLine, imageModePattern, 'names');\n% if a match was found, fill the row in the notebookInfo cell-array\nif ~isempty(imageModeHits);\n    \n    % store the image type, eventually transforming 'single frame' to 'frame'\n    infoTable{iRow, strcmp(tIDs, 'imType')} = imageModeHits.imType;\n    infoTable{iRow, strcmp(tIDs, 'imType')} = strrep(infoTable{iRow, strcmp(tIDs, 'imType')}, 'single ', '');\n    % if recorded data had a 3rd dimension (either Z or T)\n    if ~isempty(imageModeHits.zOrT) || ~isempty(imageModeHits.zt);\n        % create a 3rd dimension tag depending on the Z/T labeling: either stack or movie\n        if strcmp(imageModeHits.zOrT, 'T');\n            infoTable{iRow, strcmp(tIDs, 'imType2')} = 'movie';\n        elseif strcmp(imageModeHits.zOrT, 'Z');\n            infoTable{iRow, strcmp(tIDs, 'imType2')} = 'stack';\n        else\n            warning('OCIA_parseNotebookFile_default:UnknownZorT', ['3rd dimension of file unknown, neither T (time) or ' ...\n                'Z (stack): %s. Continuing ... '], imageModeHits.zOrT);\n        end;\n        % create a dimension tag like '256x256' or '100x100x500' if there is a 3rd dimension\n        if ~isempty(imageModeHits.zt);\n            infoTable{iRow, strcmp(tIDs, 'dimNB')} = sprintf('%sx%sx%s', imageModeHits.x, imageModeHits.y, imageModeHits.zt);\n        else\n            warning('OCIA_parseNotebookFile_default:ZorTButNoZTValue', ['File has a 3rd dimension (type: %s) but no ' ...\n                'dimension for that.'], imageModeHits.zt);\n        end;\n    % if there is no 3rd dimension, also create a 2D dimension tag like '256x256'\n    else\n        infoTable{iRow, strcmp(tIDs, 'dimNB')} = sprintf('%sx%s', imageModeHits.x, imageModeHits.y);\n    end;\n    \nelse % if not matching, show a warning and leave notebookInfo empty\n    warning('OCIA_parseNotebookFile_default:ImagingModeMatchFailure', ['Could not match imaging mode line \"%s\" with ' ...\n        'imageModePattern \"%s\" ! Trying to continue ...'], imageModeLine, imageModePattern);\nend;\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataWatcherProcess_fiberMovies.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataWatcherProcess/OCIA_dataWatcherProcess_fiberMovies.m", "size": 3029, "source_encoding": "utf_8", "md5": "998fce6c62245a138a9e85236238e151", "text": "%% #OCIA_dataWatcherProcess_fiberMovies\nfunction OCIA_dataWatcherProcess_fiberMovies(this, ~, ~)\n\n%% initialize movie\n% get the path of the selected movie\nmoviePath = DWGetFullPath(this, this.dw.selectedTableRows(1));\nif regexp(moviePath, '\\.avi$');\n    \n    % get VideoReader object for the selected movie\n    vrHand = VideoReader(moviePath);\n    % Get movie dimensions   \n    %frameWindows = [85:135, 1185:1235, 2285:2335, 3385:4435, 4485:4535, 5585:5635, 6685:6735, 7785:7835, 8885:8935, 9985:1035];\n    \n    %Generate frame Vector\n    frameWindows = 40:210;\n    for k = 1:9\n        frameWindows = [frameWindows (k*1100 +40):(k*1100 +210)]; %#ok<AGROW>\n    end;\n    %Testing frames:\n    %frameWindows = [85:135, 1185:1235];\n    nFrames = size(frameWindows,2);\n    this.jt.nFrames = nFrames; % store the number of frames\n    H = vrHand.Height;\n    W = vrHand.Width;\n\n    %% load the movie\n    loadTic = tic; % for performance timing purposes\n    showMessage(this, 'Loading movie ...', 'yellow');\n    % pre-allocate movie\n    oriFrames = zeros(H, W, nFrames);\n    % load movie frame by frame\n    DWWaitBar(this, 0);\n    for iFrame = 1:nFrames\n        oriFrames(:, :, iFrame) = nanmean(double(read(vrHand, frameWindows(iFrame))), 3);\n        DWWaitBar(this, 100 * (iFrame / nFrames));\n    end\n    % back up the frames\n    this.jt.oriFrames = oriFrames;\n    this.jt.frames = oriFrames;\n    showMessage(this, sprintf('Loading movie done (%3.1f sec).', toc(loadTic)));\n    \nend;\n\n%% set JointTracker settings\nthis.jt.nJoints = size(this.jt.jointConfig, 1);\nthis.jt.joints = zeros(this.jt.nJoints, nFrames, 2, this.jt.nJointTypes);\nthis.GUI.jt.forcedJoints = false(this.jt.nJoints, nFrames, this.jt.nJointTypes);\nthis.GUI.jt.boundBoxPos = zeros(this.jt.nJoints, this.jt.nFrames, this.jt.nJointTypes, 4);\nthis.GUI.jt.jointROIHandles = cell(this.jt.nJoints, 1);\nthis.jt.jointROIMasks = cell(this.jt.nJoints, 1);\n\n%% initialize GUI\n% change mode\nOCIAChangeMode(this, 'JointTracker');\n\n% replace the image by a dummy one by one dark pixel\nthis.GUI.jt.img = zeros(1, 1);\nset(this.GUI.handles.jt.img, 'CData', linScale(this.GUI.jt.img));\nset(this.GUI.handles.jt.axe, 'XLim', [0.5 1.5], 'YLim', [0.5 1.5]);\n\n% adjust the display options setter\nset(this.GUI.handles.jt.viewOpts.boundBoxes, 'Value', 0);\nset(this.GUI.handles.jt.viewOpts.debugPlots, 'Value', 0);\nset(this.GUI.handles.jt.viewOpts.preProc, 'Value', 0);\n\n% adjust the joint and joint type setters\nset(this.GUI.handles.jt.jointSelSetter, 'Value', 1);\nthis.GUI.jt.iJoint = 1;\nset(this.GUI.handles.jt.jointTypeSelSetter, 'Value', 1);\nthis.GUI.jt.iJointType = 1;\n\n% adjust the frame setter\nthis.GUI.jt.iFrame = 1;\nset(this.GUI.handles.jt.frameSetter, 'Enable', 'on', 'Min', 1, 'Max', nFrames, 'Value', 1, ...\n    'SliderStep', [1 / nFrames 3 / nFrames]);\n% update the frame label\nset(this.GUI.handles.jt.frameLabel, 'String', sprintf('Frame %03d', 1));\n\n% update the GUI\nJTUpdateGUI(this, 'all');\n\n% set the focus to the frame setter\nuicontrol(this.GUI.handles.jt.frameSetter);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataWatcherProcess_trackMovies.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataWatcherProcess/OCIA_dataWatcherProcess_trackMovies.m", "size": 8209, "source_encoding": "utf_8", "md5": "fd53cc601f82c5641c2b56e69a35c184", "text": "%% #OCIA_dataWatcherProcess_trackMovies\r\nfunction OCIA_dataWatcherProcess_trackMovies(this, ~, ~)\r\n\r\n% change mode\r\nOCIAChangeMode(this, 'DataWatcher');\r\n\r\n%% initialize movie\r\n% get the path of the selected movie\r\nmoviePath = this.path.moviePath;\r\nmoviePathCropped = regexprep(moviePath, '\\.avi', '_cropped.avi');\r\n\r\nif regexp(moviePath, '\\.avi$');\r\n    \r\n    % get VideoReader object for the selected movie\r\n    vrHand = VideoReader(moviePath);\r\n    % get movie dimensions   \r\n    nFrames = round(vrHand.Duration * vrHand.FrameRate);\r\n    this.jt.frameRate = vrHand.FrameRate;\r\n    this.jt.nFrames = nFrames; % store the number of frames\r\n    showMessage(this, sprintf('Video has %d frames ...', nFrames), 'yellow');\r\n    H = vrHand.Height;\r\n    W = vrHand.Width;\r\n%     H = round(vrHand.Height * 0.1);\r\n%     W = round(vrHand.Width * 0.1);\r\n\r\n    %% crop movie\r\n    % if cropping is requested\r\n    if this.jt.doCroppingStep;\r\n        % if no cropping rectangle is not defined yet\r\n        if isempty(this.jt.cropRect);\r\n            \r\n            % load a frame\r\n            frame = nanmean(double(readFrame(vrHand)), 3);\r\n            % reset time so vrHand is back at the begininning of the movie\r\n            vrHand.CurrentTime = 0;\r\n            % display frame\r\n            figH = figure('NumberTitle', 'off', 'Name', 'Crop movie - [X, Y, W, H]', ...\r\n                'MenuBar', 'none', 'ToolBar', 'none', 'Units', 'Normalized');\r\n            imH = imagesc(frame);\r\n            colormap(gray);\r\n            % select an area\r\n            imRectH = imrect(get(imH, 'Parent'));\r\n            set(figH, 'Name', sprintf('Crop movie - [%d, %d, %d, %d]', round(imRectH.getPosition())));\r\n            fprintf('Cropping rectangle: [%d, %d, %d, %d]\\n', round(imRectH.getPosition()));\r\n            imRectH.addNewPositionCallback(@(pos) {\r\n                set(figH, 'Name', sprintf('Crop movie -  [%d, %d, %d, %d]', round(pos)));\r\n                fprintf('Cropping rectangle: [%d, %d, %d, %d]\\n', round(imRectH.getPosition()));\r\n            });\r\n        \r\n            return;\r\n        \r\n        % if a cropping rectangle is defined but cropped movie alread exists, print warning and go on\r\n        elseif exist(moviePathCropped, 'file');\r\n            showWarning(this, 'OCIA:JTTrackMovies:CroppedMovieAlreadyExists', ...\r\n                sprintf('Cropped movie already exists at \"%s\". Skipping cropping...', moviePathCropped));\r\n            % switch to cropped file\r\n            this.path.moviePath = moviePathCropped;\r\n            this.jt.doCroppingStep = 0;\r\n            OCIA_dataWatcherProcess_trackMovies(this);\r\n            return;\r\n        \r\n        % if a cropping rectangle is defined and file does not exist yet, crop movie\r\n        else\r\n            showMessage(this, 'Cropping movie ...', 'yellow');\r\n            % get VideoWriter object to write cropped movie\r\n            vwHand = VideoWriter(moviePathCropped, 'Uncompressed AVI');\r\n            vwHand.FrameRate = this.jt.frameRate;\r\n            open(vwHand);\r\n            % crop movie\r\n            for iFrame = 1 : nFrames;\r\n                % skip unwanted frames and switch directly to the required time\r\n                if ~isempty(this.jt.timeCrop) && iFrame < this.jt.timeCrop(1);\r\n                    continue;\r\n                end;\r\n                % switch directly to the required time\r\n                if ~isempty(this.jt.timeCrop) && iFrame == this.jt.timeCrop(1);\r\n                    vrHand.CurrentTime = iFrame / this.jt.frameRate;\r\n                end;\r\n                % skip unwanted frames\r\n                if ~isempty(this.jt.timeCrop) && iFrame > this.jt.timeCrop(2);\r\n                    break;\r\n                end;\r\n                frame = readFrame(vrHand);\r\n                writeVideo(vwHand, imcrop(frame, this.jt.cropRect));\r\n                if ~isempty(this.jt.timeCrop);\r\n                    DWWaitBar(this, 100 * ((iFrame - this.jt.timeCrop(1)) / diff(this.jt.timeCrop)));\r\n                else\r\n                    DWWaitBar(this, 100 * (iFrame / nFrames));\r\n                end;\r\n            end\r\n            close(vwHand);\r\n            % switch to cropped file\r\n            this.path.moviePath = moviePathCropped;\r\n            this.jt.doCroppingStep = 0;\r\n            OCIA_dataWatcherProcess_trackMovies(this);\r\n            return;\r\n        end;        \r\n    end;\r\n\r\n    %% load the movie\r\n    loadTic = tic; % for performance timing purposes\r\n    showMessage(this, 'Loading movie (avi)...', 'yellow');\r\n    % pre-allocate movie\r\n    oriFrames = zeros(H, W, nFrames);\r\n    % load movie frame by frame\r\n    DWWaitBar(this, 0);\r\n    pause(0.001); % required to let the GUI update itself\r\n    for iFrame = 1 : nFrames;\r\n        frame = nanmean(double(readFrame(vrHand)), 3);\r\n        o('Loaded frame %d', iFrame, 0, this.verb);\r\n        oriFrames(:, :, iFrame) = imcrop(frame, [0, 0, W, H]);\r\n        DWWaitBar(this, 100 * (iFrame / nFrames));\r\n        pause(0.001); % required to let the GUI update itself\r\n    end\r\n    % back up the frames\r\n    this.jt.oriFrames = oriFrames;\r\n    this.jt.frames = oriFrames;\r\n    showMessage(this, sprintf('Loading movie done (%3.1f sec).', toc(loadTic)));\r\n    \r\nelseif regexp(moviePath, '\\.tif$');\r\n    \r\n    loadTic = tic; % for performance timing purposes\r\n    showMessage(this, 'Loading movie (tiff)...', 'yellow');\r\n    \r\n    % read the movie\r\n    tiffStruct = tiffread2(moviePath, 1, 10000, @(progressFrac)DWWaitBar(this, progressFrac * 100));\r\n    \r\n    % get movie dimensions   \r\n    W = tiffStruct.width;\r\n    H = tiffStruct.height;\r\n    % get the number of frames and store it\r\n    nFrames = size(tiffStruct, 2);\r\n    this.jt.nFrames = nFrames;\r\n    \r\n    % pre-allocate movie and unwrap it frame by frame\r\n    this.jt.oriFrames = zeros(H, W, nFrames);\r\n    for iFrame = 1 : nFrames;\r\n        this.jt.oriFrames(:, :, iFrame) = double(tiffStruct(iFrame).data);\r\n    end\r\n    \r\n    % back up the frames\r\n    this.jt.frames = this.jt.oriFrames;\r\n    showMessage(this, sprintf('Loading movie done (%3.1f sec).', toc(loadTic)));\r\n    \r\nend;\r\n\r\n%% set JointTracker settings\r\nthis.jt.nJoints = size(this.jt.jointConfig, 1);\r\nthis.jt.joints = zeros(this.jt.nJoints, nFrames, 2, this.jt.nJointTypes);\r\nthis.GUI.jt.forcedJoints = false(this.jt.nJoints, nFrames, this.jt.nJointTypes);\r\nthis.GUI.jt.boundBoxPos = zeros(this.jt.nJoints, this.jt.nFrames, this.jt.nJointTypes, 4);\r\nthis.GUI.jt.jointValidity = nan(this.jt.nJoints, this.jt.nFrames);\r\nthis.GUI.jt.jointROIHandles = cell(this.jt.nJoints, 1);\r\nthis.jt.jointROIMasks = cell(this.jt.nJoints, 1);\r\n\r\n%% initialize GUI\r\n% change mode\r\nOCIAChangeMode(this, 'JointTracker');\r\n\r\n% replace the image by a dummy one by one dark pixel\r\nthis.GUI.jt.img = zeros(1, 1);\r\nset(this.GUI.handles.jt.img, 'CData', linScale(this.GUI.jt.img));\r\nset(this.GUI.handles.jt.axe, 'XLim', [0.5 1.5], 'YLim', [0.5 1.5]);\r\n\r\n% adjust the display options setter\r\nset(this.GUI.handles.jt.viewOpts.boundBoxes, 'Value', 0);\r\nset(this.GUI.handles.jt.viewOpts.debugPlots, 'Value', 0);\r\nset(this.GUI.handles.jt.viewOpts.ROIs, 'Value', 0);\r\nset(this.GUI.handles.jt.viewOpts.jointDist, 'Value', 0);\r\nset(this.GUI.handles.jt.viewOpts.validPlots, 'Value', 0);\r\nset(this.GUI.handles.jt.viewOpts.preProc, 'Value', 0);\r\n\r\n% adjust the joint and joint type setters\r\nset(this.GUI.handles.jt.jointSelSetter, 'Value', 1);\r\nthis.GUI.jt.iJoint = 1;\r\nset(this.GUI.handles.jt.jointTypeSelSetter, 'Value', 1);\r\nthis.GUI.jt.iJointType = 1;\r\n\r\n% adjust the frame setter\r\nthis.GUI.jt.iFrame = 1;\r\nset(this.GUI.handles.jt.frameSetter, 'Enable', 'on', 'Min', 1, 'Max', nFrames, 'Value', 1, ...\r\n    'SliderStep', [1 / nFrames 3 / nFrames]);\r\n    \r\n% update the frame label\r\ncurrTimeTotSec = 1 / this.jt.frameRate;\r\ncurrTimeMin = floor(currTimeTotSec / 60);\r\ncurrTimeSec = floor(currTimeTotSec - currTimeMin * 60);\r\ncurrTimeMSec = floor((currTimeTotSec - currTimeMin * 60 - currTimeSec) * 1000);\r\nset(this.GUI.handles.jt.frameLabel, 'String', sprintf('F  %03d\\nT  %02d:%02d.%03d\\nM 0000 0000', ...\r\n    1, currTimeMin, currTimeSec, currTimeMSec));\r\n\r\n% update the GUI\r\nJTUpdateGUI(this, 'all');\r\n\r\n% set the focus to the frame setter\r\nuicontrol(this.GUI.handles.jt.frameSetter);\r\n\r\nend\r\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataWatcherProcess_importIJROIs.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataWatcherProcess/OCIA_dataWatcherProcess_importIJROIs.m", "size": 5124, "source_encoding": "utf_8", "md5": "01dcc93b17c38d8516f53940750a8bb4", "text": "%% #OCIA_dataWatcherProcess_importIJROIs\nfunction OCIA_dataWatcherProcess_importIJROIs(this, ~, ~)\n\nimportIJROIsTic = tic; % for performance timing purposes\n\nshowMessage(this, 'Extracting and importing imageJ ROISet ...');\nDWWaitBar(this, 0);\n\n% get the index(es) of the ImageJ ROISet rows\nijROISetRows = DWFilterTable(this, 'rowType = IJ ROIs');\nfullFolderPath = get(this, 1, 'path', ijROISetRows);\n\n% check what's in the watchfolder and exclude irrelephant files\nfiles = dir(fullFolderPath); % check out the content of the folder\nfiles(arrayfun(@(x)x.isdir, files)) = []; % only keep files\n% get the patterns for this watch type\npatternsCell = this.dw.watchTypes{'ijroiset', 'subFilePatterns'};\npatternsTable = patternsCell{1}; % extract the table from the cell\nijROISetPattern = patternsTable{strcmp(patternsTable.id, 'ijroisetFile'), 'pattern'};\nijROISetPattern = ijROISetPattern{1}; % extract the string from the cell\n% exclude everything that is not an ImageJ ROISet\nfiles(arrayfun(@(x) isempty(regexp(x.name, ijROISetPattern, 'once')), files)) = [];\n\nnFiles = numel(files); % count the number of remaining files\no('#%s(): found %d roiset zip file(s).', mfilename(), nFiles, 3, this.verb);\n\n% loop trough all existing files\nfor iFile = 1 : nFiles;\n    % get the file name    \n    fileName = files(iFile).name;\n    o('  #%s(): processing file \"%s\".', mfilename(), fileName, 5, this.verb);\n    \n    % extract the spot number\n    regexpHit = regexp(fileName, ijROISetPattern, 'names');\n    % if no hit were found\n    if isempty(regexpHit);\n        showWarning(this, 'OCIA:OCIA_dataWatcherProcess_importIJROIs:UnknownSpot', ...\n            sprintf('Could not extract the spot from file name \"%s\" using expression \"%s\". Skipping it.', ...\n            fileName, ijROISetPattern));\n        continue;\n    end;\n    \n    showMessage(this, sprintf('Decoding ImageJ ROISet %02d/%02d ...', iFile, nFiles));\n\n    % extract the ImageJ ROISet\n    ijROISet = ij_roiDecoder(sprintf('%s%s', fullFolderPath, fileName), this.an.img.defaultImDim);\n    nROIs = size(ijROISet, 1); % count the number of ROIs\n    \n    % pre-allocate a cell-array to store the imported/converted ROIs\n    ROIs = cell(nROIs, 4);\n    % pre-allocate a matrix for the ROI mask\n    ROIMask = zeros(size(ijROISet{1, 4}));\n    \n    % go trough each ROI and convert it to the required form\n    for iROI = 1 : nROIs;\n        % create the ROI type\n        ROIs{iROI, 4} = sprintf('im%s', ijROISet{iROI, 2});\n        if this.dw.IJImportKeepROIName; % keep ROI's name but clean it up\n            ROIs{iROI, 2} = regexprep(ijROISet{iROI, 1}, '[^A-Za-z0-9]', '');\n        else % give a new name\n            ROIs{iROI, 2} = sprintf('%03d', iROI); % give ROI a name\n        end;\n        % store the coordinates\n        ROIs{iROI, 3} = ijROISet{iROI, 3};\n        % update the mask\n        ROIMask(ijROISet{iROI, 4}) = iROI;\n    end;\n    \n    %% save using the ROIDrawer's save function\n    % check that the spot name can be recreated\n    if ~isfield(regexpHit, 'spot') && isfield(regexpHit, 'depth');\n        showWarning(this, 'OCIA:OCIA_dataWatcherProcess_importIJROIs:BadSpotHit', ...\n            sprintf('Could not extract the spot from regexp hit with expression \"%s\". Skipping it.', ...\n            ijROISetPattern));\n        continue;        \n    end;\n    \n    % get the DataWatcher indexes of the imaging data rows of that spot: create the spot filter\n    spotID = sprintf('Spot%s_%s', regexpHit.spot, regexpHit.depth);\n    imagingDataRows = DWFilterTable(this, sprintf('rowType = Imaging data AND spot = %s', spotID));\n    nImagingRows = size(imagingDataRows, 1);\n    % check whether we have some imaging rows for this spot\n    if isempty(imagingDataRows);\n        showWarning(this, 'OCIA:OCIA_dataWatcherProcess_importIJROIs:NoImagingData', ...\n            sprintf('Could not find any imaging data for file \"%s\" (spotID: %s). Skipping it.', ...\n            fileName, spotID));\n        continue;\n    end;\n    \n    % set parameters\n    set(this.GUI.handles.rd.refROISet, 'Value', 0); % not ROISet mode\n    set(this.GUI.handles.rd.saveOpts.ROIs, 'Value', 1);\n    set(this.GUI.handles.rd.saveOpts.runsVal, 'Value', 1);\n    set(this.GUI.handles.rd.saveOpts.refIm, 'Value', 1);\n    this.rd.selectedTableRows = str2double(get(this, 'all', 'rowNum', imagingDataRows)); % mark selected rows\n    % put all rows in ROIDrawer's table, first row selected\n    set(this.GUI.handles.rd.tableList, 'String', cell(1, nImagingRows), 'Value', 1);\n    \n    % copy ROIs, mask, etc.\n    this.rd.ROIs = ROIs;\n    this.rd.nROIs = size(ROIs, 1);\n    this.rd.ROIMask = ROIMask;\n    \n    % save everything\n    RDSaveROIs(this);\n    \n    % show message and update wait bar\n    showMessage(this, sprintf('Saving ImageJ ROISet %02/%02d to MAT-format done.', iFile, nFiles));\n    DWWaitBar(this, iFile * 100 / nFiles);\n    pause(0.5);\n    \nend;\n\n% clear ROIDrawer mode and update wait bar\nRDClearROIs(this);\nset(this.GUI.handles.rd.tableList, 'String', {}, 'Value', []);\nDWWaitBar(this, 100);\n\no('#%s(): importing ImageJ ROIs done (%3.1f sec).', mfilename(), toc(importIJROIsTic), 2, this.verb);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataWatcherProcess_onlineAnalysis.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataWatcherProcess/OCIA_dataWatcherProcess_onlineAnalysis.m", "size": 2959, "source_encoding": "utf_8", "md5": "be69fc0cc3a327f294c91a3b3ba66d94", "text": "function OCIA_dataWatcherProcess_onlineAnalysis(this, h, ~)\n% OCIA_dataWatcherProcess_onlineAnalysis - [no description]\n%\n%       OCIA_dataWatcherProcess_onlineAnalysis(this, h, ~)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\nlocalVerb = 2;\n\n% check if the online analysis button is actually a toggle button\nset(this.GUI.handles.dw.onlineAnalysis, 'Style', 'togglebutton', 'BackgroundColor', 'yellow');\npause(0.01);\nisActivateRequest = get(this.GUI.handles.dw.onlineAnalysis, 'Value');\n\n% abort command\nif ischar(h) && strcmp(h, 'abort');\n    isActivateRequest = false;\nend;\n\n% check if the timer is running\nisOnlineAnalysisOnGoing = false;\nif ~isempty(this.GUI.dw.onlineAnalysisTimer) && strcmp(this.GUI.dw.onlineAnalysisTimer.Running, 'on');\n    isOnlineAnalysisOnGoing = true;\nend;\n\n% re-activation of online analysis\nif isOnlineAnalysisOnGoing && isActivateRequest;\n    o('#%s(): request for re-activating online analysis, skipping.', mfilename(), localVerb, this.verb);\n    \n% re-de-activation of online analysis\nelseif ~isOnlineAnalysisOnGoing && ~isActivateRequest;\n    o('#%s(): request for re-de-activating online analysis, skipping.', mfilename(), localVerb, this.verb);    \n    \n% activation of online analysis\nelseif ~isOnlineAnalysisOnGoing && isActivateRequest;\n    o('#%s(): activating online analysis ...', mfilename(), localVerb, this.verb);\n    \n    % stop previous eventual timer\n    stopOATimer(this);\n    % get online analysis function\n    onlineAnalysisFcn = OCIAGetCallCustomFile(this, 'onlineAnalysis', this.dw.onlineAnalysisFunctionName, 0, { this }, 1);\n    % create timer\n    this.GUI.dw.onlineAnalysisTimer = timer('BusyMode', 'drop', 'ExecutionMode', 'fixedSpacing', ...\n        'Name', 'onlineAnalysisTimer', 'Tag', 'DWOnlineAnalysisTimer', 'Period', this.GUI.dw.onlineAnalysisUpdatePeriod, ...\n        'TimerFcn', @(~, ~)onlineAnalysisFcn(this), 'ErrorFcn', @(~, ~)OCIA_dataWatcherProcess_onlineAnalysis(this, 'abort'), ...\n        'StartDelay', 0.2);\n    % start timer\n    start(this.GUI.dw.onlineAnalysisTimer);\n    % show message\n    showMessage(this, 'Started online analysis.');\n    \n% de-activation of online analysis\nelseif isOnlineAnalysisOnGoing && ~isActivateRequest;\n    o('#%s(): de-activating online analysis ...', mfilename(), localVerb, this.verb);    \n    stopOATimer(this);\n    % show message\n    showMessage(this, 'Stopped online analysis.');\n    \nend;\n\n% update GUI\nset(this.GUI.handles.dw.onlineAnalysis, 'BackgroundColor', iff(isActivateRequest, 'green', 'red'), ...\n    'Value', isActivateRequest);\n\no('#%s(): done.', mfilename(), localVerb, this.verb);\n\nend\n\nfunction stopOATimer(this)\n\n     % if there is a timer, stop it and delete it\n    if ~isempty(this.GUI.dw.onlineAnalysisTimer);\n        stop(this.GUI.dw.onlineAnalysisTimer);\n        delete(this.GUI.dw.onlineAnalysisTimer);\n        this.GUI.dw.onlineAnalysisTimer = [];\n    end;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataWatcherProcess_analyseRows_old.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataWatcherProcess/OCIA_dataWatcherProcess_analyseRows_old.m", "size": 3918, "source_encoding": "utf_8", "md5": "528fa9a37feb8773fc07c70a8c900e0e", "text": "%% #OCIA_dataWatcherProcess_analyseRows\nfunction OCIA_dataWatcherProcess_analyseRows_old(this, ~, ~)\n\nanalyseRowsTic = tic; % for performance timing purposes\n\n% store the selected rows so that even changing the selection in the DataWatcher mode does not affect the Analyser\nthis.an.selectedTableRows = this.dw.selectedTableRows;\n\n% if no row selected, abort with a warning\nnRows = numel(this.an.selectedTableRows); % count the number of selected rows\nif nRows == 0;\n    showWarning(this, 'OCIA:OCIA_dataWatcherProcess_analyseRows:NoRows', 'No rows selected.');\n    return;\nend;\n\no('#OCIA_dataWatcherProcess_analyseRows(): selected rows (%d): %s', nRows, ...\n    sprintf('%d ', this.an.selectedTableRows), 2, this.verb);\n    \n% empty/reset the table, the plot list and the ROI list\nset(this.GUI.handles.an.rowList, 'String', [], 'Value', [], 'ListBoxTop', 1);\nset(this.GUI.handles.an.ROIList, 'String', [], 'Value', [], 'ListBoxTop', 1);\n\n%% - #OCIA_dataWatcherProcess_analyseRows : load/pre-process/analyse selected rows\nloadDataTic = tic; % for performance timing purposes\nOCIAGetCallCustomFile(this, 'preprocess', this.dw.preProcessFunctionName, 1, { this }, 1);\no('#OCIA_dataWatcherProcess_analyseRows(): pre-processing data done (%3.1f sec).', toc(loadDataTic), 2, this.verb);\n\n%% - #OCIA_dataWatcherProcess_analyseRows : prepare analyser panel\n% generate labels for the selected rows\nrowLabels = cell(nRows, 1);\ntIDs = this.dw.tableIDs; % get the table IDs\nfor iRow = 1 : nRows;\n    iDWRow = this.an.selectedTableRows(iRow); % get the DataWatcher's table row index\n    rowLabels{iRow} = ''; % empty label is default\n    % create the display using the column names specified in the config\n    colNames = this.GUI.an.DWTableColumnsToUse;\n    for iCol = 1 : numel(colNames);\n        % get the row ID using the dedicated function\n        if strcmp(colNames{iCol}, 'rowID');\n            rowLabels{iRow} = sprintf('%s - %s', rowLabels{iRow}, DWGetRowID(this, iDWRow));\n        % otherwise just fetch the value from the DataWatcher's table\n        else\n            rowLabels{iRow} = sprintf('%s - %s', rowLabels{iRow}, ...\n                this.dw.table{iDWRow, strcmp(tIDs, colNames{iCol})});\n        end;\n    end;\n    rowLabels{iRow} = regexprep(rowLabels{iRow}, '^\\s+-\\s+', ''); % clean up the label\n    rowLabels{iRow} = regexprep(rowLabels{iRow}, '-\\s+-', '-'); % clean up the label\n    rowLabels{iRow} = regexprep(rowLabels{iRow}, '- $', ''); % clean up the label\nend;\n% fill in the listBox items of the analyser panel\nset(this.GUI.handles.an.rowList, 'String', rowLabels, 'Value', [], 'ListBoxTop', 1);\nset(this.GUI.handles.an.plotList, 'Value', [], 'ListBoxTop', 1);\n\n% clear the plot area and show the loading message\nANClearPlot(this);\nANShowHideMessage(this, 1);\nOCIAChangeMode(this, 'Analyser');\n\n% reset the lists\nset(this.GUI.handles.an.plotList, 'Value', [], 'ListBoxTop', 1);\nset(this.GUI.handles.an.rowList, 'Value', [], 'ListBoxTop', 1);\nset(this.GUI.handles.an.ROIList, 'Value', [], 'ListBoxTop', 1);\n\n%% - #OCIA_dataWatcherProcess_analyseRows : plot\nplotDataTic = tic; % for performance timing purposes\ncurrAnalysis = [];\n% select a default first plot to display depending on the data type\nswitch this.dw.table{this.an.selectedTableRows(1), strcmp(tIDs, 'rowType')};\n    case 'imgData';\n        currAnalysis = find(strcmp(this.an.analysisTypes.id, 'caTraces_basic'));\n    case 'behavData';\n        currAnalysis = find(strcmp(this.an.analysisTypes.id, 'behav_dprime'));\nend;\n\n% select the first row and the default plot\nset(this.GUI.handles.an.rowList, 'Value', 1);\nset(this.GUI.handles.an.plotList, 'Value', currAnalysis);\n\n% do the analysis / plot\nANUpdatePlot(this, 'force');\n\no('#OCIA_dataWatcherProcess_analyseRows(): plotting done (%3.1f sec).', toc(plotDataTic), 2, this.verb);\no('#OCIA_dataWatcherProcess_analyseRows(): analyse rows done (%3.1f sec).', toc(analyseRowsTic), 2, this.verb);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataWatcherProcess_saveResults.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataWatcherProcess/OCIA_dataWatcherProcess_saveResults.m", "size": 3035, "source_encoding": "utf_8", "md5": "42d498164768321b83ac3e51df89af8c", "text": "%% #OCIA_dataWatcherProcess_saveResults\nfunction OCIA_dataWatcherProcess_saveResults(this, ~, ~)\n\n% get imaging rows\n% imgRows = DWFindTableRows(this, 'imgData', '', '', '', '', '', '');\nimgRows = this.dw.selectedTableRows;\n\n% loop trough all rows\nfor iRow = 1 : numel(imgRows);\n    \n    % get the run ID\n    rowID = sprintf('%s__%s', this.dw.table{imgRows(iRow), 2 : 3});\n%     rowIDWithUnderScoreX = sprintf('%s_X%s', rowID, this.dw.table{imgRows(iRow), 9});\n    \n    % get the path for this row and modify it\n    fullFolderPath = DWGetFullPath(this, imgRows(iRow));\n    targetDir = regexprep(fullFolderPath, '/$', sprintf('/%sh_MF/', rowID));\n    \n    % get the ROISet for this row\n    ROISet = ANGetROISetForRow(this, imgRows(iRow));\n    \n    % get the data for this row\n    caData = this.data.img.caTraces{imgRows(iRow)};\n    \n    % if there is some data\n    if ~isempty(caData) && ~isempty(ROISet);\n    \n        % create the structure\n        Ca = struct();\n        Ca.roiLabel = ROISet(:, 1);\n    \n        % create the directory if needed\n        if exist(targetDir, 'dir') ~= 7; mkdir(targetDir); end;\n    \n        % load the calcium data in the right format\n        Ca.dRR = cell(numel(Ca.roiLabel), 1);\n        for iROI = 1 : numel(Ca.roiLabel);\n            Ca.dRR{iROI, 1} = repmat(caData(iROI, :), 2, 1);\n        end;\n\n        % save the data\n        save(sprintf('%somlortest_%s.mat', targetDir, rowID), 'Ca');\n        \n        % copy the other files\n        clickJointFolder = regexprep(targetDir, '_MF/$', '_ClickJoint/');\n        clickJointFile = sprintf('%sMotor_Output_Parameters_%sh_Raw', clickJointFolder, this.dw.table{imgRows(iRow), 3});\n        if exist(clickJointFolder, 'dir') && exist(clickJointFile, 'file');\n            \n            clickJointFileTarget = regexprep(clickJointFile, '_ClickJoint/', '_MF/');\n            copyfile(clickJointFile, clickJointFileTarget);\n            \n            FID1 = fopen(clickJointFileTarget);\n            FMOPs = textscan(FID1, '%f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %*[^\\n]', ...\n                'Delimiter', '\\t', 'HeaderLines', 1);\n            fclose(FID1);\n            FMOPs = cell2mat(FMOPs);\n            xlswrite([clickJointFileTarget '.xlsx'], FMOPs);\n            \n            delete(clickJointFileTarget);\n        end;\n        \n        % csv file\n        csvFileName = dir([fullFolderPath '*.csv']);\n        if ~isempty(csvFileName);\n            csvFileSource = [fullFolderPath csvFileName.name];\n            csvFileTarget = [targetDir csvFileName.name];\n            copyfile(csvFileSource, csvFileTarget);\n            \n            FID2 = fopen(csvFileTarget);\n            MVs = textscan(FID2, '%f %f %*[^\\n]', 'Delimiter', ';');\n            fclose(FID2);\n            MVs = cell2mat(MVs);\n            xlswrite(strrep(csvFileTarget, '.csv', '.xlsx'), MVs);\n            \n            delete(csvFileTarget);\n        end;\n    end;\n    \nend;\n\n\no('#OCIA_dataWatcherProcess_saveResults(): saving results done.', 2, this.verb);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_moDet.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_moDet.m", "size": 15192, "source_encoding": "utf_8", "md5": "6feea3c4ee368f580b086b9938bd6a03", "text": "%% #OCIA:OCIA_dataProcess_imgData_moDet\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_moDet(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\nrowProcState = getData(this, iDWRow, 'procImg', 'procState');\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'moDet')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || any(strcmp(rowProcState, 'moDet'));\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n\n% get whether to do plots or not\nif nargin > 3;  iRun = varargin{2};\nelse            iRun = 1;\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% init\nrowID = DWGetRowID(this, iDWRow);\nrowIDTitle = sprintf('Motion detection (Z) for %s (%d)', rowID, iDWRow);\n\n% figCommons = {'NumberTitle', 'off'}; % figure options\nfigCommons = {'NumberTitle', 'off', 'WindowStyle', 'docked'}; % figure options\n\n% get the ROISet for this row and the row of the reference image\n[ROISet, ~, ~, ~, avgImgRefCell] = ANGetROISetForRow(this, iDWRow);\nnROIs = size(ROISet, 1);\n% make sure we have an image and not a cell, use the specified pre-procesing channel if required\nif iscell(avgImgRefCell);   avgImgRef = avgImgRefCell{this.an.img.preProcChan};\nelse                        avgImgRef = avgImgRefCell;\nend;\n\n% if no reference average image, try to fetch it from the ROIDrawer\nif isempty(avgImgRef);\n    currentMode = this.main.modes{get(this.GUI.handles.changeMode, 'Value'), 1};\n    selRDTableRow = get(this.GUI.handles.rd.tableList, 'Value');\n    % if we are in ROIDrawer mode and there is a selected row in the run table\n    if strcmp(currentMode, 'ROIDrawer') && ~isempty(this.rd.selectedTableRows) && ~isempty(selRDTableRow);\n        % get the row index of the DataWatcher's table\n        iDWRefRow = this.rd.selectedTableRows(selRDTableRow(1));\n        % get the reference average image as the mean of pre-processed frames\n        imgDataRefRow = get(this, iDWRefRow, 'data');\n        avgImgRef = nanmean(imgDataRefRow.procImg.data{this.an.img.preProcChan}, 3);\n    end;\nend;\n\n% if no reference average image, abort\nif isempty(avgImgRef);\n    showWarning(this, 'OCIA:OCIA_dataProcess_imgData_moDet:NoReferenceImage', ...\n        sprintf('%s: No reference image found. Aborting.', rowIDTitle));\n    isValid = false;\n    return;\nend;\n\n% if no ROISet found, create fake ROISet\nif ~nROIs;\n    o('%s: no ROISet found, using fake ROIs ...', mfilename(), 2, this.verb);\n    % make sure data is fully loaded\n    DWLoadRow(this, iDWRow, 'full');\n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n    % get the imaging data\n    imgData = get(this, iDWRow, 'data');\n    imgData = imgData.procImg.data;\n    imgDim = size(imgData{this.an.img.preProcChan});\n    % create the fake ROISet\n    [ROISet, nROIs] = createFakeROISet(imgDim(1:2), imgDim(1) * 0.05, 10, 10);\nend;\n\nshowMessage(this, sprintf('%s ...', rowIDTitle), 'yellow');\n\n% make sure the data is fully loaded\nDWLoadRow(this, iDWRow, 'full');\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% prepare reference image and frames\n% get the imaging data\nimgData = get(this, iDWRow, 'data');\nimgData = imgData.procImg.data;\n\n% get the average of all frames of the pre-processing channel\navgImg = nanmean(imgData{this.an.img.chanVect(1)}, 3);\navgImgOri = avgImg; % save a copy of the original\n\n% get the movie of all frames of pre-processing channel\nimgMovie = imgData{this.an.img.chanVect(1)};\nimgMovieOri = imgMovie; % save a copy of the original\nimgDim = size(imgMovie);\n\n%% filter frames if requested\n% if requested, apply a \"small\" filtering on the movie to have enhanced correlations\nif this.an.moDet.useFilt;\n    filtTic = tic; % for performance timing purposes\n    imgMovie = zeros(imgDim);\n    f = fspecial('gaussian', 2, 1);\n    parfor iFrame = 1 : imgDim(3);\n        imgMovie(:, :, iFrame) = imfilter(imgMovieOri(:, :, iFrame), f, 'replicate');\n%         imgMovie(:, :, iFrame) = medfilt2(imgMovieOri(:, :, iFrame), [2 2], 'symmetric');\n    end;\n    \n    % get a filtered version of the reference average\n    avgImgRef = imfilter(avgImgRef, f, 'replicate');\n    o('%s: filtering done (%3.1f sec).', rowIDTitle, toc(filtTic), 2, this.verb);\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% %{\n%% ROI-pooled frame correlation based on a \"bright\" ROISet\npercBoundBox = 0.04; % percent of image around the ROI's bounding box\n% use only the 50% brightest ROIs, but at least 25 ROIs but not more than \"nROIs\" ROIs\nnBrightROIs = min(max(round(nROIs * 0.5), 25), nROIs);\n% get the ROISet of the brightest ROIs\nbrightROISet = getBrightROIs(ROISet, avgImgRef, nBrightROIs, percBoundBox, doPlots - 1);\n\n% calculate and store the correlation of each ROI's image to the average image\nframeCorrsROI = getFrameCorrROI(brightROISet, percBoundBox, avgImgRef, imgMovie);\n% average the correlations obtained from the bright ROIs\nframeCorrs = nanmean(frameCorrsROI);\n%}\n\n%{\n%% Frame-wise correlation\nframeCorrs = getFrameCorr(avgImgRef, imgMovie);\n%}\n\n%% calculate thresholds\nframeCorrMed = nanmedian(frameCorrs); % get the median if the correlations obtained from the bright ROIs\nframeCorrStd = nanstd(frameCorrs); % get the standard deviation of the correlations obtained from the bright ROIs\n\n% get the out of focuse correlation drop tolerance based on the standard deviation\ncorrelationDropTolerance = max(this.an.moDet.threshFactor * frameCorrStd, 0.1);\n% create a threshold under which frames are classified as out of focus and should be excluded\noutOfFocusThresh = frameCorrMed - correlationDropTolerance;\n% create a threshold where the correlation is good enough again that frames can be classified as in focus again\nbackInFocusThresh = frameCorrMed - correlationDropTolerance * 0.25;\no('%s: thresholds: median: %.3f, correlation drop tolerance: %.3f, out of focus: %.3f, back in focus: %.3f.', ...\n    rowIDTitle, frameCorrMed, correlationDropTolerance, outOfFocusThresh, backInFocusThresh, 2, this.verb);\n\n\n%% get frames to exclude\n% get the excluded frames and the \"mask\" corresponding to them\nexclFrameMask = frameCorrs < outOfFocusThresh;\nexclFrames = find(exclFrameMask); % get the excluded frames' indexes\n\n% if removing single frames is allowed, do not exclude no-neighbor frames\nif ~this.an.moDet.removeSingleFrames;\n    % do not exclude frames that have no neighboring excluded frames\n    toRemoveExclFrames = false(1, imgDim(3));\n    nMinNeighbors = 3;\n    for iFrame = 2 : imgDim(3) - 1;\n        if ismember(iFrame, exclFrames);\n            nNeighbors = sum(ismember(iFrame - (nMinNeighbors - 1) : iFrame + (nMinNeighbors - 1), exclFrames));\n            toRemoveExclFrames(iFrame) = nNeighbors < nMinNeighbors;\n        end;\n    end;\n    % remove the frames that had no neighbors\n    exclFrames(ismember(exclFrames, find(toRemoveExclFrames))) = [];\nend;\n\n% build the excluded frame \"mask\"\nexclFrames = sort(exclFrames);\nexclFrameMask = false(1, imgDim(3));\nexclFrameMask(exclFrames) = true;\nexclFrameMaskBeforeExt = exclFrameMask; % get a copy before exclusion extension\n% show message\nplurFrame = ''; if exclFrames; plurFrame = 's'; end; % get the plural mark\no('%s: found %d frame%s to exclude before extension (%.1f%%).', ...\n    rowIDTitle, numel(exclFrames), plurFrame, 100 * numel(exclFrames) / imgDim(3), 2, this.verb);\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% frame exclusion extension to neighbors\n% extend the excluded frames to the neighboring frames so that all motion is removed. The excluded frames are\n%   processed as a queue\nextTic = tic; % for performance timing purposes\nexclFrameQueue = exclFrames; % create the queue\nif doPlots > 2; figure('Name', sprintf('%s: frame correlations', rowIDTitle), figCommons{:}); end;\nwhile ~isempty(exclFrameQueue);\n    iFrame = exclFrameQueue(1); % get the first element\n    elemReplaced = 0; % set a flag to see if element was replaced, otherwise it should be removed\n    \n    % check if the previous frame should be excluded: not first frame & not already in queue or in excluded frames & \n    %   corr. coef. not above back in focus threshold & corr. coef. higher than current frame (going up the slope)\n%     if iFrame > 1 && ~ismember(iFrame - 1, exclFrameQueue) && ~ismember(iFrame - 1, exclFrames) ...\n%             && frameCorrs(iFrame - 1) < backInFocusThresh && frameCorrs(iFrame - 1) > frameCorrs(iFrame);\n    if iFrame > 1 && ~ismember(iFrame - 1, exclFrameQueue) && ~ismember(iFrame - 1, exclFrames) ...\n            && frameCorrs(iFrame - 1) < backInFocusThresh;\n        % add the frame to the excluded frames\n        exclFrames(end + 1) = iFrame - 1; %#ok<AGROW>\n        % add the frame to the queue in place of the currently processed frame\n        exclFrameQueue(1) = iFrame - 1;\n        elemReplaced = true;\n    end;\n    \n    % check if the next frame should be excluded: not last frame & not already in queue or in excluded frames & \n    %   corr. coef. not above back in focus threshold & corr. coef. higher than current frame (going up the slope)\n%     if iFrame < imgDim(3) && ~ismember(iFrame + 1, exclFrameQueue) && ~ismember(iFrame + 1, exclFrames) ...\n%             && frameCorrs(iFrame + 1) < backInFocusThresh && frameCorrs(iFrame + 1) > frameCorrs(iFrame);\n    if iFrame < imgDim(3) && ~ismember(iFrame + 1, exclFrameQueue) && ~ismember(iFrame + 1, exclFrames) ...\n            && frameCorrs(iFrame + 1) < backInFocusThresh;\n        % add the frame to the excluded frames\n        exclFrames(end + 1) = iFrame + 1; %#ok<AGROW>\n        % if element was not already replaced, add the frame to the queue in place of the currently processed frame\n        if ~elemReplaced;\n            exclFrameQueue(1) = iFrame + 1;\n        else % otherwise extend the queue\n            exclFrameQueue(end + 1) = iFrame + 1; %#ok<AGROW>\n        end;\n        elemReplaced = true;\n    end;\n    \n    % if the element was not replaced by another neighbor, remove it from the queue without any replacement\n    if ~elemReplaced; exclFrameQueue(1) = []; end;\n    \nend\n\n% sort the excluded frames and re-create the mask\nexclFrames = sort(exclFrames);\nexclFrameMask = false(1, imgDim(3));\nexclFrameMask(exclFrames) = true;\nplurFrame = ''; if exclFrames; plurFrame = 's'; end; % get the plural mark\no('%s: found %d frame%s to exclude after extension (%.1f%%) (%3.1f sec).', ...\n    rowIDTitle, numel(exclFrames), plurFrame, 100 * numel(exclFrames) / imgDim(3), toc(extTic), 2, this.verb);\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% debug plotting\nif doPlots > 0; % if requested, plot a figure illustrating the motion correction procedure and result\n\n    figure('Name', sprintf('%s: frame correlations', rowIDTitle), figCommons{:});\n    \n    axeHandles = zeros(2, 1);\n    for iSub = 1 : 2;\n        subplot(2, 1, iSub);\n        plot(frameCorrs, 'g');\n        hold on;\n        axeHandles(iSub) = gca;\n        \n        % get the frame correlations and hide those from the excluded frames (before and after extension)\n        exclFrameCorrs = frameCorrs;\n        if iSub == 1;       exclFrameCorrs(~exclFrameMaskBeforeExt) = NaN;\n        elseif iSub == 2;   exclFrameCorrs(~exclFrameMask)          = NaN; end;\n        plot(exclFrameCorrs, 'r');\n        \n        plot([1 imgDim(3)], repmat(frameCorrMed, 1, 2), 'b');\n        plot([1 imgDim(3)], repmat(backInFocusThresh, 1, 2), 'k:');\n        plot([1 imgDim(3)], repmat(outOfFocusThresh, 1, 2), 'r:');\n        \n        ylim([0 1]);\n        legend({'Valid frame corr.', 'Exc. frame corr.', 'Median', 'back-in-focus thres.', ...\n            'out-of-focus thresh.'}, 'FontSize', 8, 'Location', 'SouthEast');\n    end;\n    linkaxes(axeHandles, 'xy');\n\n    % get the average of all valid frames of the pre-processing channel channel\n    avgImgCorr = nanmean(imgData{this.an.img.chanVect(1)}(:, :, ~exclFrameMask), 3);\n    % get the average of all non-valid frames of the pre-processing channel channel\n    avgImgBadFrames = nanmean(imgData{this.an.img.chanVect(1)}(:, :, exclFrameMask), 3);\n\n    figure('Name', sprintf('%s: average', rowIDTitle), figCommons{:});\n    subplot(2, 2, 1); imshow(linScale(avgImgOri)); title('Original');\n    subplot(2, 2, 2); imshow(linScale(avgImgRef)); title('Reference');\n    subplot(2, 2, 3); imshow(linScale(avgImgBadFrames)); title('Bad frames average');\n    subplot(2, 2, 4); imshow(linScale(avgImgCorr)); title('Corrected');\n    \nend; % end of plotting if case\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% create a mask\nif ~isempty(exclFrames); % if there are some excluded frames, add those to the mask\n    \n    % create an exclusion mask as a matrix of nROIs x nFrames (similar to the caTraces mask)\n    exclMask = ones(nROIs, imgDim(3));\n    exclMask(1 : nROIs, exclFrames) = NaN;\n    \n    % store the exclusion mask\n    this.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.exclMask.data = exclMask;\nend;\n\n%{\n%% --- #OCIA:AN:MotionDetection: process and store data with motion detection\nif ~isempty(exclFrames); % if there are some excluded frames, replace them with NaNs\n        % create a corrected data set\n    imgDataCorr = imgData;\n    % go through each channel\n    for iChan = 1 : numel(imgDataCorr);\n        % replace the exlcuded frames with NaNs\n        imgDataChan = imgDataCorr{iChan};\n        % go through each frame and replace it if necessary\n        parfor iFrame = 1 : size(imgDataChan, 3);\n            if exclFrameMask(iFrame);\n                imgDataChan(:, :, iFrame) = nan(size(imgDataChan(:, :, iFrame))); % replace with NaN\n            end;\n        end;\n        imgDataCorr{iChan} = imgDataChan; % copy back the corrected data\n    end;\n\n    imgData = imgDataCorr; % copy back the corrected data\n    setData(this, iDWRow, 'procImg', 'data', imgData); % store the corrected data\nend;\n\n%}\n\n% if a lot of frames where to be excluded, re-run the motion detection\nif numel(exclFrames) > imgDim(3) * 0.08 && iRun < 3;\n    [isValid, unvalidReason] = OCIA_dataProcess_imgData_moDet(this, iDWRow, doPlots, iRun + 1);\n    return;\nend;\n\n% mark row as processed for motion detection\nsetData(this, iDWRow, 'procImg', 'procState', [rowProcState { 'moDet' }]);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_skipFrame.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_skipFrame.m", "size": 1915, "source_encoding": "utf_8", "md5": "e6b8685e6992028e7df3f99b7ba25864", "text": "%% #OCIA:OCIA_dataProcess_imgData_skipFrame\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_skipFrame(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\nrowProcState = getData(this, iDWRow, 'procImg', 'procState');\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'skipFrame')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || any(strcmp(rowProcState, 'skipFrame')) ...\n        || (this.an.skipFrame.nFramesBegin <= 0 && this.an.skipFrame.nFramesEnd <= 0);\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n\n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n    \n% make sure data is fully loaded\nDWLoadRow(this, iDWRow, 'full');\n\n% get the imaging data\nimgData = get(this, iDWRow, 'data');\nimgData = imgData.procImg.data;\n\n% remove the first frame(s) from each channel\nfor iChan = 1 : numel(imgData);\n    imgData{iChan} = imgData{iChan}(:, :, 1 + this.an.skipFrame.nFramesBegin : end - this.an.skipFrame.nFramesEnd);\nend;\n\n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% store the change\nthis.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.procImg.data = imgData;\n\n% mark row as processed for this processing step\nsetData(this, iDWRow, 'procImg', 'procState', [rowProcState { 'skipFrame' }]);\n\n%%  plotting\n% if requested, plot a figure illustrating what has been done\nif doPlots > 0;\n    \n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_moCorr_HMM.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_moCorr_HMM.m", "size": 994, "source_encoding": "utf_8", "md5": "14c90ea89a699cbba11589d9202ad1fd", "text": "%% #OCIA:OCIA_dataProcess_imgData_moCorr_HMM\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_moCorr_HMM(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\nrowProcState = getData(this, iDWRow, 'procImg', 'procState');\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'moCorr')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || any(strcmp(rowProcState, 'moCorr'));\n    return;\nend;\n\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%%\n\n\n% mark row as processed for motion correction\nsetData(this, iDWRow, 'procImg', 'procState', [rowProcState { 'moCorr' }]);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData.m", "size": 4270, "source_encoding": "utf_8", "md5": "079515d1ffc08676c79160f68b6b1a54", "text": "%% #OCIA:OCIA_dataProcess_imgData\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData(this, iDWRow)\n\n% set a flag that tells whether this row is valid for processing or not\nisValid = false;\n% create a string holding the reason why this row was flagged as not valid\nunvalidReason = 'unknown reason';\nrowID = DWGetRowID(this, iDWRow); % get the row ID \n\ndimTag = get(this, iDWRow, 'dim'); % get the dimension tag\nnFrames = str2double(strrep(regexp(dimTag, 'x\\d+$', 'match'), 'x', '')); % get the number of frames\n\n% if the number of frames is smaller than \"funcMovieNFramesLimit\" frames, movie is not a functional movie\nif nFrames <= this.an.img.funcMovieNFramesLimit;\n    isValid = false; % set the validity flag to false\n    % store the reason why this row was not valid\n    unvalidReason = sprintf('not a functional imaging movie (nFrames = %d, limit = %d)', nFrames, ...\n        this.an.img.funcMovieNFramesLimit);\n    return; % abort processing of this row\nend;\n\n% check whether to show the debug plots or not\nshowDebugPlots = get(this.GUI.handles.dw.SLROpts.procDataShowDebug, 'Value');\n\n% if calcium data is not present\nif isempty(getData(this, iDWRow, 'caTraces', 'data'));\n        \n    % get the selected processing steps and this row's processing state\n    selProcSteps = get(this.GUI.handles.dw.procOptsList, 'Value');\n    rowProcState = getData(this, iDWRow, 'procImg', 'procState');\n    \n    % go through each step and execute the associated function\n    nSteps = size(this.an.procOptions.id, 1);\n    for iStep = 1 : nSteps;\n        \n        % check if the processing should be aborted\n        [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason);\n        if doAbort; return; end;\n        \n        % get the processing step's id and label\n        stepID = this.an.procOptions.id{iStep};\n        stepLabel = this.an.procOptions.label{iStep};\n        \n        % if processing step is not selected, skip it\n        if ~ismember(iStep, selProcSteps); continue; end;\n        \n        % if processing step if loading, load the row fully\n        if strcmp(stepID, 'loadData');\n            DWLoadRow(this, iDWRow, 'full');\n            continue;\n        end;\n        \n        % if processing step is already done for the current row, skip it with a message\n        if any(strcmp(rowProcState, stepID));\n            showMessage(this, sprintf('%s for %s (%03d) already done.', stepLabel, rowID, iDWRow));\n            continue;\n        end;\n        \n        % call the custom function for this processing step\n        warning('off', 'OCIA:dataProcess_imgData:FunctionNotFound');\n        [funcHandle, validityCell] = OCIAGetCallCustomFile(this, 'dataProcess_imgData', ...\n            stepID, 1, { this, iDWRow, showDebugPlots }, 0);\n        warning('on', 'OCIA:dataProcess_imgData:FunctionNotFound');\n        \n        % if the function was found (function handle not empty) and the row is valid\n        if ~isempty(funcHandle) && ~isempty(validityCell) && numel(validityCell) >= 2 && validityCell{1};\n            % show step is complete\n            showMessage(this, sprintf('%s for %s (%03d) done.', stepLabel, rowID, iDWRow));\n            \n        % if the function was not found (function handle is empty), show warning and go on\n        elseif isempty(funcHandle);\n            showWarning(this, 'OCIA:dataProcess_imgData:ProcessFunctionNotFound', ...\n                sprintf('%s for %s (%03d): no processing function found (\"%s\"), skipping this step.', ...\n                stepLabel, rowID, iDWRow, stepID));\n            \n        % if the row was flagged as not valid\n        elseif ~isempty(validityCell) && numel(validityCell) >= 2;\n            [isValid, unvalidReason] = validityCell{:};\n            return;\n            \n        % otherwise something else went wrong\n        else\n            isValid = false;\n            unvalidReason = 'unknown error';\n            return;\n            \n        end;\n        \n        % allow time for GUI update\n        if isGUI(this); pause(0.005); end;\n    end;\nend;\n\n% update the row's loading and processing status\nDWGetUpdateDataLoadStatus(this, iDWRow);\n\n% if we managed to come this far, then the row is valid and unvalidity reason is empty\nisValid = true;\nunvalidReason = '';\n\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_moCorr.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_moCorr.m", "size": 1449, "source_encoding": "utf_8", "md5": "e046854327443ac0fc19c58b8212ce05", "text": "%% #OCIA:OCIA_dataProcess_imgData_moCorr\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_moCorr(this, iDWRow, varargin)\n\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, [], []); if doAbort; return; end;\n        \n% call the custom function for the selected type of motion correction (TurboReg, HMM, ...)\nwarning('off', 'OCIA:OCIA_dataProcess_imgData_moCorrFunctionNotFound');\n[funcHandle, validityCell] = OCIAGetCallCustomFile(this, 'dataProcess_imgData_moCorr', ...\n    this.an.moCorr.type, 1, { this, iDWRow, varargin{:} }, 0); %#ok<CCAT>\nwarning('on', 'OCIA:OCIA_dataProcess_imgData_moCorrFunctionNotFound');\n\n% if the function was found (function handle not empty) and the row is valid\nif ~isempty(funcHandle) && ~isempty(validityCell) && numel(validityCell) >= 2 && validityCell{1};\n    [isValid, unvalidReason] = validityCell{:};\n    \n% if the function was not found (function handle is empty), show warning and go on\nelseif isempty(funcHandle);\n    isValid = false;\n    unvalidReason = sprintf('no processing function found for motion correction type \"%s\"', this.an.moCorr.type);\n\n% if the row was flagged as not valid\nelseif ~isempty(validityCell) && numel(validityCell) >= 2;\n    [isValid, unvalidReason] = validityCell{:};\n    return;\n\n% otherwise something else went wrong\nelse\n    isValid = false;\n    unvalidReason = 'unknown error';\n    return;\n\nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_genStimVect.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_genStimVect.m", "size": 1875, "source_encoding": "utf_8", "md5": "6acd477616696fcc37880a714b4fb587", "text": "%% #OCIA:OCIA_dataProcess_imgData_genStimVect\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_genStimVect(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\ncaData = getData(this, iDWRow, 'caTraces', 'data');\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'genStimVect')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || ~isempty(caData);\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% generate the stimulus vector using the custom function, and get out the validity cell-array as output\n[funcHandle, validityCell] = OCIAGetCallCustomFile(this, 'genStimVect', ...\n    this.an.stimulusVectorGeneratingFunctionName, 1, { this, iDWRow, doPlots }, 0);\n\n% if the generate stimulus function was not found (function handle is empty) or if no validity cell is returned\nif isempty(funcHandle) || isempty(validityCell);\n    \n    % create the unvalidity reason\n    isValid = false;\n    unvalidReason = 'function not found';\n    return; % abort processing of this row\n    \n% if there was an error and the validity was returned but is false (first element of the validity cell-array)\nelseif ~isempty(validityCell) && ~validityCell{1};\n    \n    % extract the validity and the unvalidity reason into the output parameters\n    [isValid, unvalidReason] = validityCell{:};\n    return; % abort processing of this row\n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_wfTr_genStimVect.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_wfTr_genStimVect.m", "size": 1781, "source_encoding": "utf_8", "md5": "89ca04b9628644f1348a94b4b2c2f1e0", "text": "%% #OCIA:OCIA_dataProcess_wfTr_genStimVect\nfunction [isValid, unvalidReason] = OCIA_dataProcess_wfTr_genStimVect(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'genStimVect')) || ~strcmp(get(this, iDWRow, 'rowType'), 'WF trial');\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% generate the stimulus vector using the custom function, and get out the validity cell-array as output\n[funcHandle, validityCell] = OCIAGetCallCustomFile(this, 'genStimVect', ...\n    this.an.stimulusVectorGeneratingFunctionName, 1, { this, iDWRow, doPlots }, 0);\n\n% if the generate stimulus function was not found (function handle is empty) or if no validity cell is returned\nif isempty(funcHandle) || isempty(validityCell);\n    \n    % create the unvalidity reason\n    isValid = false;\n    unvalidReason = 'function not found';\n    return; % abort processing of this row\n    \n% if there was an error and the validity was returned but is false (first element of the validity cell-array)\nelseif ~isempty(validityCell) && ~validityCell{1};\n    \n    % extract the validity and the unvalidity reason into the output parameters\n    [isValid, unvalidReason] = validityCell{:};\n    return; % abort processing of this row\n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_behavData.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_behavData.m", "size": 605, "source_encoding": "utf_8", "md5": "9558137229f20b8370a63ae3b653739e", "text": "%% #OCIA:OCIA_dataProcess_behavData\nfunction [isValid, unvalidReason] = OCIA_dataProcess_behavData(this, iDWRow)\n\n% set a flag that tells whether this row is valid for processing or not\nisValid = false; %#ok<NASGU>\n% create a string holding the reason why this row was flagged as not valid\nunvalidReason = 'unknown reason'; %#ok<NASGU>\n\n% load the row\nDWLoadRow(this, iDWRow, 'full');\n\n% update the row's loading and processing status\nDWGetUpdateDataLoadStatus(this, iDWRow);\n\n% if we managed to come this far, then the row is valid and unvalidity reason is empty\nisValid = true;\nunvalidReason = '';\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_fJitt.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_fJitt.m", "size": 13736, "source_encoding": "utf_8", "md5": "9b86b78d25f09c9835279897968bf35c", "text": "%% #OCIA:OCIA_dataProcess_imgData_fJitt\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_fJitt(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\nrowProcState = getData(this, iDWRow, 'procImg', 'procState');\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'fJitt')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || any(strcmp(rowProcState, 'fJitt'));\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% init\nrowID = DWGetRowID(this, iDWRow);\nrowIDTitle = sprintf('Frame jitter correction for %s (%d)', rowID, iDWRow);\nfigCommons = { 'NumberTitle', 'off', 'WindowStyle', 'docked' }; % figure options\n        \n% get the ROISet for this row\nROISet = ANGetROISetForRow(this, iDWRow);\nnROIs = size(ROISet, 1);\n\n% if no ROISet found, create fake ROISet\nif ~nROIs;\n    o('%s: no ROISet found, using fake ROIs ...', mfilename(), 2, this.verb);\n    % make sure data is fully loaded\n    DWLoadRow(this, iDWRow, 'full');\n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n    % get the imaging data\n    imgData = get(this, iDWRow, 'data');\n    imgData = imgData.procImg.data;\n    imgDim = size(imgData{this.an.img.preProcChan});\n    % create the fake ROISet\n    [ROISet, nROIs] = createFakeROISet(imgDim(1:2), imgDim(1) * 0.05, 10, 10);\nend;\n\npercBoundBox = 0.04; % percent of image around the ROI's bounding box\n% range of pixel shift to test\nshifts = -3 : 3;\n% use only the 10% brightest ROIs, but at least 5 ROIs but not more than \"nROIs\" ROIs\nnBrightROIs = min(max(round(nROIs * 0.1), 5), nROIs);\n\n% make sure data is fully loaded\nDWLoadRow(this, iDWRow, 'full');\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% get the imaging data\nimgData = get(this, iDWRow, 'data');\nimgData = imgData.procImg.data;\nimgDim = size(imgData{this.an.img.preProcChan});\n\n% number of frames in a \"chunck\" of data to analyse, about the tenth of the frames but at least one second of data\nnFrameAvg = round(max(ceil(imgDim(3) / 10), this.an.img.defaultFrameRate));\n\n%% - #OCIA:AN:ANFrameJitterCorrection: loop on chuncks of data\nnChuncks = ceil(imgDim(3) / nFrameAvg); % calculate the number of chuncks \nfor iChunck = 1 : nChuncks;\n    \n    % get the frame range for this chunck, removing frames exceeding limits\n    frameRange = ((iChunck - 1) * nFrameAvg + 1) : min(iChunck * nFrameAvg, imgDim(3));\n    \n    % get the average of all frames of the pre-processing channel\n    avgImg = nanmean(imgData{this.an.img.preProcChan}(:, :, frameRange), 3);\n    \n    %% -- #OCIA:AN:ANFrameJitterCorrection: ROI brightness\n    % get the brightness of the ROIs to calculate correlation only on the \"nBrightROIs\" brightest ROIs\n    ROIBrightness = zeros(1, nROIs);\n    parfor iROI = 1 : nROIs;\n        if strcmpi(ROISet{iROI, 1}, 'npil'); continue; end; % exclude neuropil\n        % get the ROI's mask\n        ROIMask = ROISet{iROI, 2};\n        % get the x and y positions of this ROI's pixels\n        ROIBrightness(iROI) = sum(avgImg(ROIMask > 0));\n    end;\n    % sort the brightness\n    [~, ROIBrightnessIndex] = sort(-ROIBrightness);\n    brighROISet = ROISet(ROIBrightnessIndex(1 : nBrightROIs), :);\n        \n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n    %% -- #OCIA:AN:ANFrameJitterCorrection: loop on shifts: fix the jitter by applying a shift to lines\n    nShifts = numel(shifts);\n    shiftImprovs = zeros(nBrightROIs, nShifts);\n    parfor iShift = 1 : nShifts;\n\n        shift = shifts(iShift); % get the current shift to apply\n        avgImgShift = shiftImage(avgImg, imgDim, shift); % shift the image\n\n        %% --- #OCIA:AN:ANFrameJitterCorrection: loop on ROIs\n        for iROI = 1 : nBrightROIs;\n\n            %% ---- #OCIA:AN:ANFrameJitterCorrection: process ROI\n            % get the ROI's mask\n            ROIMask = brighROISet{iROI, 2};\n            % get the x and y positions of this ROI's pixels\n            ROIPixels = unique(find(ROIMask > 0));\n            [yVals, xVals] = ind2sub(imgDim([1, 2]), ROIPixels);\n            \n            % get the bounding box of this ROI\n            ROIXRange = [round(min(xVals) * (1 - percBoundBox)), round(max(xVals) * (1 + percBoundBox))];\n            ROIYRange = [round(min(yVals) * (1 - percBoundBox)), round(max(yVals) * (1 + percBoundBox))];\n            % skip if the ROI with bounding box is not completely in the image \n            if ROIXRange(1) < 1 || ROIXRange(end) > imgDim(2) || ROIYRange(1) < 1 || ROIYRange(end) > imgDim(1);\n                continue;\n            end;\n            % image of the ROI's bounding box\n            ROIAvgImg = avgImg(ROIYRange(1) : ROIYRange(end), ROIXRange(1) : ROIXRange(end));\n\n            % do a line-wise correlation\n            yCorrsROI = zeros(1, ROIYRange(end) - ROIYRange(1));\n            for y = 1 : (ROIYRange(end) - ROIYRange(1));\n                yCorrsROI(y) = corr(ROIAvgImg(y, :)', ROIAvgImg(y + 1, :)', 'rows', 'pairwise'); %#ok<*PFBNS>\n            end;\n            \n            % corrected image of the ROI's bounding box\n            ROIAvgImgShift = avgImgShift(ROIYRange(1) : ROIYRange(end), ROIXRange(1) : ROIXRange(end));\n\n            % do a line-wise correlation\n            yCorrsROICorrected = zeros(1, ROIYRange(end) - ROIYRange(1));\n            for y = 1 : (ROIYRange(end) - ROIYRange(1));\n                yCorrsROICorrected(y) = corr(ROIAvgImgShift(y, :)', ROIAvgImgShift(y + 1, :)', 'rows', 'pairwise');\n            end;\n\n            shiftImprovs(iROI, iShift) = (nanmean(yCorrsROICorrected) / nanmean(yCorrsROI)) * 100 - 100;\n\n            %% ---- #OCIA:AN:FrameJitterCorrection: ROI plotting\n            if doPlots > 2; % if requested, plot a figure illustrating the shift correction procedure and result\n                \n                figure('Name', sprintf('%s: frames %03d-%03d (%d), ROI%d, shift %+d', ...\n                    rowIDTitle, frameRange([1, end]), iChunck, iROI, shift), figCommons{:});\n                subplot(1, 2, 1); imshow(linScale(ROIAvgImg));\n                subplot(1, 2, 2); imshow(linScale(ROIAvgImgShift));\n                % add titles\n                subplot(1, 2, 1); title('Original');\n                subplot(1, 2, 2); title('Corrected');\n\n                figure('Name', sprintf('%s: frames %03d-%03d (%d), shift %+d, corr.: %.3f->%.3f (%+02.1f%%)', ...\n                    rowIDTitle, frameRange([1, end]), iChunck, shift, nanmean(yCorrsROI), ...\n                    nanmean(yCorrsROICorrected), shiftImprovs(iROI, iShift)), figCommons{:});\n                plot(1 : size(yCorrsROI, 2), yCorrsROI);\n                hold on;\n                plot((1 : size(yCorrsROI, 2)) + 0.5, yCorrsROICorrected, 'r');\n                ylim([0.2 1]);\n                legend('Original', 'Corrected');\n\n            end; % end of plotting if case\n\n        end; % end of ROI for loop\n        \n        %% --- #OCIA:AN:FrameJitterCorrection: general plotting\n        if doPlots > 1; % if requested, plot a figure illustrating the shift correction procedure and result\n\n            % original image\n            figure('Name', sprintf('%s: frames %03d-%03d (%d), shift %+d', ...\n                    rowIDTitle, frameRange([1, end]), iChunck, shift), figCommons{:});\n            subplot(1, 2, 1); imshow(linScale(avgImg));\n            subplot(1, 2, 2); imshow(linScale(avgImgShift));\n            % add titles\n            subplot(1, 2, 1); title('Original');\n            subplot(1, 2, 2); title('Corrected');\n            \n        end; % end of plotting if case\n        \n    end; % end of shifts for loop\n        \n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n        \n    % extract the best shift\n    [~, bestShift] = sort(-nanmean(shiftImprovs, 1));\n    bestShift = shifts(bestShift(1));\n    \n    % if some shifting was found, apply it on the frame and calculate correlations\n    if bestShift;\n        avgImgCorr = shiftImage(avgImg, imgDim, bestShift); % shift the image\n\n        % do a line-wise correlation\n        yCorrs = zeros(1, imgDim(1) - 1);\n        yCorrsCorrected = zeros(1, imgDim(1) - 1);\n        parfor y = 1 : (imgDim(1) - 1);\n            yCorrs(y) = corr(avgImg(y, :)', avgImg(y + 1, :)', 'rows', 'pairwise');\n            yCorrsCorrected(y) = corr(avgImgCorr(y, :)', avgImgCorr(y + 1, :)', 'rows', 'pairwise');\n        end;\n\n        bestShiftImprov = (nanmean(yCorrsCorrected) / nanmean(yCorrs)) * 100 - 100;\n        \n        % if improvement is not big enough, do not apply it\n        if bestShiftImprov < 1; % less than 1% improvement\n            bestShift = 0;\n        else\n            o('%s: frames %03d-%03d (%d), best shift %+d, corr.: %.3f->%.3f (%+02.1f%%).', ...\n                rowIDTitle, frameRange([1, end]), iChunck, bestShift, nanmean(yCorrs), nanmean(yCorrsCorrected), ...\n                bestShiftImprov, 2, this.verb);\n        end;\n    end;\n    \n    if ~bestShift; % if no correction, keep original image\n        avgImgCorr = avgImg;\n        bestShiftImprov = 0;\n        % do a line-wise correlation\n        yCorrs = zeros(1, imgDim(1) - 1);\n        parfor y = 1 : (imgDim(1) - 1);\n            yCorrs(y) = corr(avgImg(y, :)', avgImg(y + 1, :)', 'rows', 'pairwise');\n        end;\n        yCorrsCorrected = yCorrs;\n    end;\n        \n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n    \n    %% --- #OCIA:AN:FrameJitterCorrection: general plotting\n    if doPlots > 0; % if requested, plot a figure illustrating the shift correction procedure and result\n\n        % original image\n        figure('Name', sprintf('%s: frames %03d-%03d (%d), best shift %+d', ...\n                rowIDTitle, frameRange([1, end]), iChunck, bestShift), figCommons{:});\n        subplot(1, 2, 1); imshow(linScale(avgImg));\n        subplot(1, 2, 2); imshow(linScale(avgImgCorr));\n%         subplot(1, 2, 1); imagesc(avgImg);\n%         subplot(1, 2, 2); imagesc(avgImgCorrected);\n        subplot(1, 2, 1); title('Original');\n        subplot(1, 2, 2); title('Corrected');\n\n        figure('Name', sprintf('%s: frames %03d-%03d (%d), best shift: %+d, corr.: %.3f->%.3f (%+02.1f%%)', ...\n            rowIDTitle, frameRange([1, end]), iChunck, bestShift, nanmean(yCorrs), nanmean(yCorrsCorrected), ...\n            bestShiftImprov), figCommons{:});\n        plot(1 : size(yCorrs, 2), yCorrs);\n        hold on;\n        plot((1 : size(yCorrsCorrected, 2)) + 0.5, yCorrsCorrected, 'r');\n        ylim([0.2 1]);\n        legend('Original', 'Corrected');\n\n    end; % end of plotting if case\n        \n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n    \n    % if some shifting was applied, shift all frames in the original data\n    if bestShift;\n        \n        % create a corrected data set\n        imgDataCorrected = imgData;\n        % go through each channel\n        for iChan = 1 : this.an.img.nChans;\n            % skip empty channels\n            if isempty(imgDataCorrected{iChan}); continue; end;\n            % get the current data chunck (images) of the current channel\n            imgDataChanAllFrames = imgDataCorrected{iChan};\n            imgDataChan = imgDataChanAllFrames(:, :, frameRange);\n            % go through each frame and correct it\n            parfor iFrame = 1 : size(imgDataChan, 3);\n                imgDataChan(:, :, iFrame) = shiftImage(imgDataChan(:, :, iFrame), imgDim, bestShift); % shift the image\n            end;\n            imgDataChanAllFrames(:, :, frameRange) = imgDataChan; % copy back the corrected data\n            imgDataCorrected{iChan} = imgDataChanAllFrames; % copy back the corrected data\n        end;\n        \n        % check if the processing should be aborted\n        [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n        \n        % store the corrected data\n        this.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.procImg.data = imgDataCorrected;\n        \n    end;\n    \n\nend; % end of chuncks for loop\n\n% mark row as processed for frame jitter correction\nsetData(this, iDWRow, 'procImg', 'procState', [rowProcState { 'fJitt' }]);\n\nend\n\n%% - #OCIA:AN:ANFrameJitterCorrection:shiftImage\nfunction shiftedImage = shiftImage(avgImg, imgDim, shift)\n\nshiftedImage = avgImg; % start with the original image\n\nfor y = 1 : imgDim(1);\n    if mod(y, 2);\n        xIndexes = (1 : imgDim(2)) + shift;\n        xIndexes(xIndexes < 1 | xIndexes > imgDim(2)) = [];\n        if shift > 0;\n            lineValues = [avgImg(y, xIndexes), nan(1, abs(shift))];\n        elseif shift < 0;\n            lineValues = [nan(1, abs(shift)), avgImg(y, xIndexes)];\n        else\n            lineValues = avgImg(y, xIndexes);\n        end;\n        shiftedImage(y, :) = lineValues;\n    end;\nend;\n        \nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_wfTr.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_wfTr.m", "size": 2911, "source_encoding": "utf_8", "md5": "49ad001f1a3801e538d264c2d9197384", "text": "%% #OCIA:OCIA_dataProcess_wfTr\nfunction [isValid, unvalidReason] = OCIA_dataProcess_wfTr(this, iDWRow)\n\n% set a flag that tells whether this row is valid for processing or not\nisValid = false;\n% create a string holding the reason why this row was flagged as not valid\nunvalidReason = 'unknown reason';\nrowID = DWGetRowID(this, iDWRow); % get the row ID \n\n% check whether to show the debug plots or not\nshowDebugPlots = get(this.GUI.handles.dw.SLROpts.procDataShowDebug, 'Value');\n        \n% get the selected processing steps and this row's processing state\nselProcSteps = get(this.GUI.handles.dw.procOptsList, 'Value');\n\n% go through each step and execute the associated function\nnSteps = size(this.an.procOptions.id, 1);\nfor iStep = 1 : nSteps;\n\n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason);\n    if doAbort; return; end;\n\n    % get the processing step's id and label\n    stepID = this.an.procOptions.id{iStep};\n    stepLabel = this.an.procOptions.label{iStep};\n\n    % if processing step is not selected, skip it\n    if ~ismember(iStep, selProcSteps); continue; end;\n\n    % if processing step if loading, load the row fully\n    if strcmp(stepID, 'loadData');\n        DWLoadRow(this, iDWRow, 'full');\n        continue;\n    end;\n\n    % call the custom function for this processing step\n    warning('off', 'OCIA:dataProcess_wfTr:FunctionNotFound');\n    [funcHandle, validityCell] = OCIAGetCallCustomFile(this, 'dataProcess_wfTr', ...\n        stepID, 1, { this, iDWRow, showDebugPlots }, 0);\n    warning('on', 'OCIA:dataProcess_wfTr:FunctionNotFound');\n\n    % if the function was found (function handle not empty) and the row is valid\n    if ~isempty(funcHandle) && ~isempty(validityCell) && numel(validityCell) >= 2 && validityCell{1};\n        % show step is complete\n        showMessage(this, sprintf('%s for %s (%03d) done.', stepLabel, rowID, iDWRow));\n\n    % if the function was not found (function handle is empty), show warning and go on\n    elseif isempty(funcHandle);\n        showWarning(this, 'OCIA:dataProcess_wfTr:ProcessFunctionNotFound', ...\n            sprintf('%s for %s (%03d): no processing function found (\"%s\"), skipping this step.', ...\n            stepLabel, rowID, iDWRow, stepID));\n\n    % if the row was flagged as not valid\n    elseif ~isempty(validityCell) && numel(validityCell) >= 2;\n        [isValid, unvalidReason] = validityCell{:};\n        return;\n\n    % otherwise something else went wrong\n    else\n        isValid = false;\n        unvalidReason = 'unknown error';\n        return;\n\n    end;\n\n    % allow time for GUI update\n    if isGUI(this); pause(0.005); end;\nend;\n\n% update the row's loading and processing status\nDWGetUpdateDataLoadStatus(this, iDWRow);\n\n% if we managed to come this far, then the row is valid and unvalidity reason is empty\nisValid = true;\nunvalidReason = '';\n\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_moCorr_TurboReg.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_moCorr_TurboReg.m", "size": 20402, "source_encoding": "utf_8", "md5": "fd9b7d416c0cd89e92f898625045d53d", "text": "%% #OCIA:OCIA_dataProcess_imgData_moCorr_TurboReg\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_moCorr_TurboReg(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\nrowProcState = getData(this, iDWRow, 'procImg', 'procState');\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'moCorr')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || any(strcmp(rowProcState, 'moCorr'));\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% init\nrowID = DWGetRowID(this, iDWRow);\nrowIDTitle = sprintf('Motion correction (TurboReg) for %s (%d)', rowID, iDWRow);\n\n% get the transformation for the registration\nregTransf = this.an.moCorr.regTransf;\n\n% figCommons = {'NumberTitle', 'off'}; % figure options\nfigCommons = {'NumberTitle', 'off', 'WindowStyle', 'docked'}; % figure options\n\n% get the ROISet for this row and the row of the reference image\n[ROISet, ~, ~, iDWRefRowForROISet, avgImgRefCell] = ANGetROISetForRow(this, iDWRow);\nnROIs = size(ROISet, 1);\n% make sure we have an image and not a cell, use the specified pre-procesing channel if required\nif iscell(avgImgRefCell);   avgImgRef = avgImgRefCell{this.an.img.preProcChan};\nelse                        avgImgRef = avgImgRefCell;\nend;\n\n% if no reference average image, try to fetch it from the ROIDrawer\nif isempty(avgImgRef);\n    currentMode = this.main.modes{get(this.GUI.handles.changeMode, 'Value'), 1};\n    selRDTableRow = get(this.GUI.handles.rd.tableList, 'Value');\n    % if we are in ROIDrawer mode and there is a selected row in the run table\n    if strcmp(currentMode, 'ROIDrawer') && ~isempty(this.rd.selectedTableRows) && ~isempty(selRDTableRow);\n        % get the row index of the DataWatcher's table\n        iDWRefRow = this.rd.selectedTableRows(selRDTableRow(1));\n        % get the reference average image as the mean of pre-processed frames\n        imgDataRefRow = get(this, iDWRefRow, 'data');\n        avgImgRefCell = imgDataRefRow.procImg.data;\n        avgImgRef = nanmean(avgImgRefCell{this.an.img.preProcChan}, 3);\n    end;\nend;\n\n% if no reference average image, try to fetch it from this row's data\nif isempty(avgImgRef);\n    showWarning(this, 'OCIA:OCIA_dataProcess_imgData_moCorr_TurboReg:NoReferenceImage', ...\n        sprintf('%s: No reference image found. Aligning this row to itself ...', rowIDTitle));\n    % get the reference average image as the mean of pre-processed frames\n    imgDataRefRow = get(this, iDWRow, 'data');\n    avgImgRefCell = imgDataRefRow.procImg.data;\n    avgImgRef = nanmean(avgImgRefCell{this.an.img.preProcChan}, 3);\nend;\n\n% if no reference average image, abort\nif isempty(avgImgRef);\n    showWarning(this, 'OCIA:OCIA_dataProcess_imgData_moCorr_TurboReg:NoReferenceImage', ...\n        sprintf('%s: No reference image found. Aborting.', rowIDTitle));\n    isValid = false;\n    return;\nend;\n\n% if no ROIs, create a fake ROISet\nif ~nROIs;\n    % create the fake ROISet\n    imgDim = size(avgImgRef);\n    [ROISet, nROIs] = createFakeROISet(imgDim(1:2), imgDim(1) * 0.05, 20, 20);\nend;\n\n% make sure the data is fully loaded\nDWLoadRow(this, iDWRow, 'full');\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% prepare reference image and frames to register\n% get the imaging data\nimgData = getData(this, iDWRow, 'procImg', 'data');\n\n% get the average of all frames of the pre-processing channel for this row\navgImg = nanmean(imgData{this.an.img.preProcChan}, 3);\n\n% get the movie of all frames of pre-processing channel\nimgMovie = imgData{this.an.img.preProcChan};\nimgDim = size(imgMovie);\n\n% if requested, apply a \"small\" gaussian filtering on the movie to have enhanced registration\nif this.an.moCorr.useFilt;\n    filtTic = tic; % for performance timing purposes    \n    % save the unfiltered movie\n    imgMovieUnFilt = imgMovie;\n    imgMovie = zeros(imgDim);\n    parfor iFrame = 1 : imgDim(3);\n%         imgMovie(:, :, iFrame) = imfilter(imgMovieUnFilt(:, :, iFrame), fspecial('gaussian', 2, 1), 'replicate');\n        imgMovie(:, :, iFrame) = medfilt2(imgMovieUnFilt(:, :, iFrame), [2 2], 'symmetric');\n%         imgMovie(:, :, iFrame) = PseudoFlatfieldCorrect(imgMovieUnFilt(:, :, iFrame));\n    end;\n    o('%s: filtering done (%3.1f sec).', rowIDTitle, toc(filtTic), 2, this.verb);\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% get reference point\npercBoundBox = 0.04; % percent of image around the ROI's bounding box\n\n% if an ROISet was found, take the brightest ROI as a reference point\nif nROIs;\n    \n    % take brightest ROI(s) as reference point. Note that different registration methods need different\n    %   number of reference points\n    if strcmpi(regTransf, 'translation');\n        nBrightROIs = 1;\n    elseif any(strcmpi(regTransf, {'affine', 'rigidBody'}));\n        nBrightROIs = 3;\n    elseif strcmpi(regTransf, 'bilinear');\n        nBrightROIs = 4;\n    end;\n    \n    % get the ROISet of the brightest ROI(s)\n    brightROISet = getBrightROIs(ROISet, avgImgRef, nBrightROIs, percBoundBox, doPlots - 1);\n    % store the coordinates of those ROIs\n    refPpoints = zeros(nBrightROIs, 2);\n    for iBrightROI = 1 : nBrightROIs;\n        % get the indexes of the mask and calculate the center of it\n        [maskYVals, maskXVals] = ind2sub(imgDim([1, 2]), find(brightROISet{iBrightROI, 4}));\n        refPpoints(iBrightROI, :) = round([nanmean(maskXVals), nanmean(maskYVals)]);\n    end;\n    \n    if doPlots > 0;\n        figure('Name', sprintf('%s: landmarks', rowIDTitle), figCommons{:});\n        imshow(linScale(avgImgRef));\n        hold on;\n        scatter(refPpoints(:, 1), refPpoints(:, 2), 'bx');\n        text(refPpoints(:, 1) - imgDim(1) * 0.03, refPpoints(:, 2) - imgDim(2) * 0.03, ...\n            num2cell(1 : nBrightROIs), 'Color', 'red');\n    end;\n    \n    % transform the coordinates into source-target pairs (src1X src1Y targ1X targ1Y src2X src2Y targ2X targ2Y ...)\n    refPointsBlock = zeros(1, numel(refPpoints) * 2);\n    for iBrightROI = 1 : nBrightROIs;\n        refPointsBlock((iBrightROI - 1) * 4 + 1) = refPpoints(iBrightROI, 1);\n        refPointsBlock((iBrightROI - 1) * 4 + 2) = refPpoints(iBrightROI, 2);\n        refPointsBlock((iBrightROI - 1) * 4 + 3) = refPpoints(iBrightROI, 1);\n        refPointsBlock((iBrightROI - 1) * 4 + 4) = refPpoints(iBrightROI, 2);\n    end;\n\n% otherwise leave empty and center of frame will be used\nelse\n    refPointsBlock = [];\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% perform the registration\n% any NaN frame will remain NaN\nshowMessage(this, sprintf('%s: aligning frames ...', rowIDTitle), 'yellow');\npause(0.01);\nregTic = tic; % for performance timing purposes\n[imgMovieReg, targPoints, srcPoints, regTimes] = turboReg(imgMovie, avgImgRef, regTransf, 10, refPointsBlock, doPlots - 1);\nshowMessage(this, sprintf('%s: aligning frames done (%s, %.3f sec, align. time: %.0f +- %.0f msec).', ...\n    rowIDTitle, regTransf, toc(regTic), nanmean(regTimes) * 1000, nanstd(regTimes) * 1000));\n\n% get the non-empty reference points\nnonEmptyPoints = sum(sum(srcPoints, 1), 3) ~= 0;\n% remove the empty reference points\nsrcPoints = srcPoints(:, nonEmptyPoints, :);\ntargPoints = targPoints(:, nonEmptyPoints, :);\n% get the number of points\nnRegPoints = size(srcPoints, 2);\n\n% if there are less than 3 reference points (translation, nRegPoints = 1), applying transformation with\n%   affine transformation type will not work, so create new reference points with same translation\nif nRegPoints == 1 && nROIs;\n    \n    % get a new ROISet of the brightest ROI(s)\n    newBrightROISet = getBrightROIs(ROISet, avgImgRef, 3, percBoundBox, 0);\n    % store the coordinates of those ROIs\n    refPoints = zeros(size(newBrightROISet, 1), 2);\n    for iNewBrightROI = 1 : size(newBrightROISet, 1);\n        % get the indexes of the mask and calculate the center of it\n        [maskYVals, maskXVals] = ind2sub(imgDim([1, 2]), find(newBrightROISet{iNewBrightROI, 4}));\n        refPoints(iNewBrightROI, :) = round([nanmean(maskXVals), nanmean(maskYVals)]);\n    end;\n    \n    % extract the translations\n    translationShifts = squeeze(srcPoints(:, 1, :) - targPoints(:, 1, :));\n    \n    % recreate the source and target points using now 3 points\n    targPoints = zeros(imgDim(3), 3, 2);\n    srcPoints = zeros(imgDim(3), 3, 2);\n    for iPoint = 1 : 3;\n        targPoints(:, iPoint, :) = repmat(refPoints(iPoint, :), imgDim(3), 1);\n        srcPoints(:, iPoint, :) = squeeze(targPoints(:, iPoint, :)) + translationShifts;\n    end;\n    \n    % get the new number of points\n    nRegPoints = size(srcPoints, 2);\n    \nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% apply registration to channels\napplyTic = tic; % for performance timing purposes\n% apply the motion correction to all channels\nimgDataReg = cell(size(imgData)); % create a holder for the data\n% go through each required channel\nfor iChanLoop = 1 : numel(this.an.img.chanVect);\n    \n    % get the index of the current channel and it's imaging data\n    iChan = this.an.img.chanVect(iChanLoop);\n    imgDataChan = imgData{iChan};\n    \n    showMessage(this, sprintf('%s: applying transformation to channel %d ...', rowIDTitle, iChan), 'yellow');\n    \n    % go through each frame and apply the subpixel translation\n    imgMovieTrans = zeros(imgDim);\n    \n    % transform all frames one by one\n    parfor iFrame = 1 : imgDim(3);\n        frame = imgDataChan(:, :, iFrame); % get the frame\n        inPoints = squeeze(srcPoints(iFrame, :, :));\n        basePoints = squeeze(targPoints(iFrame, :, :));\n        % get the transformation matrix\n        tForm = cp2tform(inPoints, basePoints, 'affine'); %#ok<DCPTF>\n        % get the transformed frame\n        tFrame = imtransform(frame, tForm, 'XData', [1, imgDim(2)], 'YData', [1, imgDim(1)], ...\n            'FillValues', NaN); %#ok<DIMTRNS,PFBNS>\n        % store the frame\n        imgMovieTrans(:, :, iFrame) = tFrame;\n    end;\n    \n    % store the corrected frames\n    imgDataReg{iChan} = imgMovieTrans;\n        \n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\nend;\n\nshowMessage(this, sprintf('%s: applying transformation done (%.3f sec).', rowIDTitle, toc(applyTic)));\n\n\n%% quality control & storage\nqcTic = tic; % for performance timing purposes\n% get the reference frame\nif iscell(avgImgRefCell);   avgImgRefQC = avgImgRefCell{this.an.img.chanVect(1)};\nelse                        avgImgRefQC = avgImgRefCell;\nend;\navgImgRefQC = nanmean(avgImgRefQC, 3);\n% get the correlations for the frames before motion correction\nframesBefore = imgData{this.an.img.chanVect(1)};\navgImgBefore = nanmean(framesBefore, 3);\nframeCorrBefore = prctile(getFrameCorr(avgImgRefQC, framesBefore), 1);\nframeCorrToRefBefore = getFrameCorr(avgImgRefQC, avgImgBefore);\n% get the correlations for the frames after motion correction\nframesAfter = imgDataReg{this.an.img.chanVect(1)};\navgImgAfter = nanmean(framesAfter, 3);\nframeCorrAfter = prctile(getFrameCorr(avgImgRefQC, framesAfter), 1);\nframeCorrToRefAfter = getFrameCorr(avgImgRefQC, avgImgAfter);\n% get the thresholds\nmeanFrameCorrDiffThresh = this.an.moCorr.meanFrameCorrDiffThresh;\nframeCorrToRefDiffThresh = this.an.moCorr.frameCorrToRefDiffThresh;\nframeCorrToRefAbsThresh = this.an.moCorr.frameCorrToRefAbsThresh;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% plotting\n\n% get the average of all frames of corrected movie\navgImgRegTurboReg = nanmean(imgMovieReg, 3);\n% get the average of all frames of the pre-processing channel\navgImgReg = nanmean(imgDataReg{this.an.img.preProcChan}, 3);\n% use the functional channel if the alignement has not been applied to the pre-processing channel\nif isempty(avgImgReg);\n    avgImgReg = nanmean(imgDataReg{this.an.img.chanVect(1)}, 3);\nend;\n\n    \n% get the shifts applied\nshifts = srcPoints - targPoints;\nmaxShift = ceil(max(abs(shifts(:)))) + 1;\n\n% get the imaging data, both none-registered and registerd\nimgMovieNoReg = imgData{this.an.img.preProcChan};\nimgMovieWithReg = imgDataReg{this.an.img.preProcChan};\n% use the functional channel if the alignement has not been applied to the pre-processing channel\nif isempty(imgMovieWithReg);\n    imgMovieNoReg = imgData{this.an.img.chanVect(1)};\n    imgMovieWithReg = imgDataReg{this.an.img.chanVect(1)};\n   if iscell(avgImgRefCell);\n        avgImgRef = avgImgRefCell{this.an.img.chanVect(1)};\n    else\n        avgImgRef = mean(imgMovieNoReg, 3);\n    end;\nend;\n\n% get the ROISet of the brightest ROI(s)\nframeCorrBrightROISet = getBrightROIs(ROISet, avgImgRef, nROIs - 1, percBoundBox, 0);\n\n% calculate frame-wise correlation only on brightest ROIs\nframeCorrNoReg = nanmean(getFrameCorrROI(frameCorrBrightROISet, percBoundBox, avgImgRef, imgMovieNoReg), 1);\nframeCorrWithReg = nanmean(getFrameCorrROI(frameCorrBrightROISet, percBoundBox, avgImgRef, imgMovieWithReg), 1);\n\n% if requested, plot a figure illustrating the result of the motion correction\nif doPlots > 0;\n\n    % summary images\n    figure('Name', sprintf('%s: average images before/after correction', rowIDTitle), figCommons{:});\n    subplot(2, 2, 1); imshow(linScale(avgImgRef));          title('Reference');\n    subplot(2, 2, 2); imshow(linScale(avgImg));             title('Original');\n    subplot(2, 2, 3); imshow(linScale(avgImgReg));          title('Transformed');\n    subplot(2, 2, 4); imshow(linScale(avgImgRegTurboReg));  title('Registered');\n    \n    figure('Name', sprintf('%s: mean frame movements and correlation', rowIDTitle), figCommons{:});\n    subplot(2, 1, 1);\n    hold on;\n    plot(1 : imgDim(3), nanmean(shifts(:, :, 1), 2), 'r');\n    plot(1 : imgDim(3), nanmean(shifts(:, :, 2), 2), 'b');\n    legend('XShift', 'YShift');\n    ylim([-maxShift maxShift]); xlim([0 imgDim(3) + 1]);\n    title('Frame movements');\n    xlabel('Frames'); ylabel('Shift [pixel]');\n    subplot(2, 1, 2);\n    hold on;\n    plot(1 : imgDim(3), frameCorrNoReg, 'r');\n    plot(1 : imgDim(3) + 0.3, frameCorrWithReg, 'b');\n    legend('No reg.', sprintf('%s reg.', regTransf));\n    ylim([0 1]); xlim([0 imgDim(3) + 1]);\n    title(sprintf('Frame-wise correlations (using bounding box area of %d ROIs)', size(frameCorrBrightROISet, 1)));\n    xlabel('Frames'); ylabel('Correlation');\n\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% check quality\nif frameCorrAfter - frameCorrBefore < meanFrameCorrDiffThresh ...\n        || frameCorrToRefAfter - frameCorrToRefBefore < frameCorrToRefDiffThresh ...\n        || frameCorrToRefAfter < frameCorrToRefAbsThresh;\n    \n    % if the current row is not the reference row, quality control is failed\n    if iDWRefRowForROISet ~= iDWRow;\n        showWarning(this, 'OCIA:OCIA_dataProcess_imgData_moCorr_TurboReg:QualityControlFail', ...\n            sprintf(['%s: registration quality control failed: mean frame-wise correlation to the reference: ', ...\n            'before: %.4f, after: %.4f, correlation of average image to reference: before: %.4f, after: %.4f.'], ...\n            rowIDTitle, frameCorrBefore, frameCorrAfter, frameCorrToRefBefore, frameCorrToRefAfter));\n        \n        % summary images\n        figH = figure('Name', sprintf('%s: average images before/after correction', rowIDTitle), figCommons{:});\n        colormap('gray');\n        subplot(2, 2, 1); imagesc(linScale(avgImgRef));\n        title('Reference'); set(gca, 'XTick', [], 'YTick', []); axis('square');\n        subplot(2, 2, 2); imagesc(linScale(avgImg));\n        title('Original'); set(gca, 'XTick', [], 'YTick', []); axis('square');\n        subplot(2, 2, 3); imagesc(linScale(avgImgReg));\n        title('Transformed'); set(gca, 'XTick', [], 'YTick', []); axis('square');\n        subplot(2, 2, 4); imagesc(linScale(avgImgRegTurboReg)); \n        title('Registered'); set(gca, 'XTick', [], 'YTick', []); axis('square');\n        \n        if exist('moCorrFailedQC', 'dir') ~= 7; mkdir('moCorrFailedQC'); end;\n        rowInfos = get(this, iDWRow, { 'animal', 'spot', 'day', 'time' });\n        rowInfos = regexprep(regexprep(rowInfos, '_', ''), 'moubl', '');\n        savePath = sprintf('%s/moCorrFailedQC/%s_%s_%s_%s_moCorrFailedQCImages', regexprep(pwd(), '\\', '/'), rowInfos{:});\n        tightfig(figH);\n        set(figH, 'Position', [10 10 1200 1200]);\n        saveas(figH, savePath, 'png');\n        close(figH);\n        \n        figH = figure('Name', sprintf('%s: mean frame movements and correlation', rowIDTitle), figCommons{:});\n        subplot(2, 1, 1);\n        hold on;\n        plot(1 : imgDim(3), nanmean(shifts(:, :, 1), 2), 'r');\n        plot(1 : imgDim(3), nanmean(shifts(:, :, 2), 2), 'b');\n        legend('XShift', 'YShift');\n        ylim([-maxShift maxShift]); xlim([0 imgDim(3) + 1]);\n        title('Frame movements');\n        xlabel('Frames'); ylabel('Shift [pixel]');\n        subplot(2, 1, 2);\n        hold on;\n        plot(1 : imgDim(3), frameCorrNoReg, 'r');\n        plot(1 : imgDim(3) + 0.3, frameCorrWithReg, 'b');\n        legend('No reg.', sprintf('%s reg.', regTransf));\n        ylim([0 1]); xlim([0 imgDim(3) + 1]);\n        title(sprintf('Frame-wise correlations (using bounding box area of %d ROIs)', size(frameCorrBrightROISet, 1)));\n        xlabel('Frames'); ylabel('Correlation');\n        savePath = sprintf('%s/moCorrFailedQC/%s_%s_%s_%s_moCorrFailedQCShifts', regexprep(pwd(), '\\', '/'), rowInfos{:});\n        tightfig(figH);\n        set(figH, 'Position', [10 10 1200 1200]);\n        saveas(figH, savePath, 'png');\n        close(figH);\n        \n        % if the non-corrected should nevertheless be used, return by specifiying the row as valid\n        if this.an.moCorr.useNonCorrectedIfQualityControlFailed;\n            isValid = true;\n            showMessage(this, sprintf('%s: quality control failed, using the non-corrected frames (row is valid).', ...\n                rowIDTitle));\n            return;\n            \n        % if the non-corrected should not be used, return by specifiying the row as not valid\n        else\n            showMessage(this, sprintf('%s: quality control failed, row is not valid.', rowIDTitle));\n            isValid = false;\n        end;\n        \n    % if the current row is the reference row, quality control is not really failed since the correlations can anyway\n    % not improve\n    else\n        showMessage(this, sprintf(['%s: quality control done (mean corr. diff.: %+.4f, corr. avg. diff: %+.4f, ', ...\n            'corr. to ref: %.4f, ref row, %.3f sec).'], rowIDTitle, frameCorrAfter - frameCorrBefore, ...\n            frameCorrToRefAfter - frameCorrToRefBefore, frameCorrToRefAfter, toc(qcTic)));\n    end;\n    \n% quality control passed\nelse\n    showMessage(this, sprintf(['%s: quality control done (mean corr. diff.: %+.4f, corr. avg. diff: %+.4f, ', ...\n        'corr. to ref: %.4f, %.3f sec).'], rowIDTitle, frameCorrAfter - frameCorrBefore, ...\n        frameCorrToRefAfter - frameCorrToRefBefore, frameCorrToRefAfter, toc(qcTic)));\nend;\n\n% store the pre-processed data (only for non-empty channels)\nfor iChan = 1 : numel(imgDataReg);\n    if ~isempty(imgDataReg{iChan});\n        this.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.procImg.data{iChan} = imgDataReg{iChan};\n    end;\nend;\n\n% mark row as processed for motion correction\nsetData(this, iDWRow, 'procImg', 'procState', [rowProcState { 'moCorr' }]);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_fShift.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_fShift.m", "size": 9776, "source_encoding": "utf_8", "md5": "8bfdd6962d12089b5c936fcc3a0f0344", "text": "%% #OCIA:OCIA_dataProcess_imgData_fShift\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_fShift(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's processing state\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\nrowProcState = getData(this, iDWRow, 'procImg', 'procState');\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'fShift')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || any(strcmp(rowProcState, 'fShift'));\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% init\nrowID = DWGetRowID(this, iDWRow);\nrowIDTitle = sprintf('Frame shift correction for %s (%d)', rowID, iDWRow);\nfigCommons = { 'NumberTitle', 'off', 'WindowStyle', 'docked' }; % figure options\n    \n% make sure data is fully loaded\nDWLoadRow(this, iDWRow, 'full');\n\n% get the imaging data\nimgData = get(this, iDWRow, 'data');\nimgData = imgData.procImg.data;\nimgDim = size(imgData{this.an.img.preProcChan});\n% correct for frame shifting artifact by analysing the correlations in the average image of the selected channel\navgImg = nanmean(imgData{this.an.img.preProcChan}, 3);\n\n% range on which the average image should be analysed (exclude side artifacts)\npercExcl = 0.2;\nxRange = round([imgDim(1) * percExcl, imgDim(1) * (1 - percExcl)]);\nyRange = round([imgDim(2) * percExcl, imgDim(2) * (1 - percExcl)]);\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% do a line-wise correlation\nxCorrs = zeros(1, xRange(end) - xRange(1) + 1);\nxIndOffset = xRange(1) - 1; % offset for indexing\nparfor x = xRange(1) : xRange(end);\n    xCorrs(x - xIndOffset) = corr(avgImg(:, x), avgImg(:, x - 1), 'rows', 'pairwise'); %#ok<*PFBNS>\nend;\nyCorrs = zeros(1, yRange(end) - yRange(1) + 1);\nyIndOffset = yRange(1) - 1; % offset for indexing\nparfor y = yRange(1) : yRange(end);\n    yCorrs(y - yIndOffset) = corr(avgImg(y, :)', avgImg(y - 1, :)', 'rows', 'pairwise');\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% get the correlation thresholds using a certain number of times the standard deviation\ncorrTheshFactor = this.an.fShift.corrThreshFactor;\n% calculate correlations for X and Y differently\ncorrThreshX = nanmean(xCorrs) - corrTheshFactor * (max(xCorrs) - min(xCorrs));\ncorrThreshY = nanmean(yCorrs) - corrTheshFactor * (max(yCorrs) - min(yCorrs));\n% make boundaries for correlations\ncorrThreshX = max(min(corrThreshX, this.an.fShift.maxCorrThresh), this.an.fShift.minCorrThresh);\ncorrThreshY = max(min(corrThreshY, this.an.fShift.maxCorrThresh), this.an.fShift.minCorrThresh);\n% set a minimum of correlation difference in order to investigate the frame shift\ncorrDiffThresh = 0.1;\n\n% correct on the X-axis\nif any(xCorrs < corrThreshX);\n    \n    % get the \"derivative\" of the correlation\n    diffXCorrs = abs(diff(xCorrs));\n    \n    if any(diffXCorrs > corrDiffThresh);\n        \n        % get a fresh copy of the data to change\n        imgDataToCorrect = this.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.procImg.data;\n        % get the line where the shift occured\n        [maxXCorrDiff, maxXCorrDiffInd] = max(diffXCorrs);\n        xLineInd = maxXCorrDiffInd + xRange(1) - 1;\n        % reshape the images accordingly\n        imgDataCorr = cellfun(@(imgs) horzcat(imgs(:, xLineInd : end, :), imgs(:, 1 : xLineInd - 1, :)), ...\n            imgDataToCorrect, 'UniformOutput', false);\n        \n        % average image of the selected channel of the corrected data\n        avgImgCorr = nanmean(imgDataCorr{this.an.img.preProcChan}, 3);\n        \n        % see if it actually made it better: re-do a line-wise correlation\n        xCorrs2 = zeros(1, xRange(end) - xRange(1) + 1);\n        parfor x = xRange(1) : xRange(end);\n            xCorrs2(x - xIndOffset) = corr(avgImgCorr(:, x), avgImgCorr(:, x - 1), 'rows', 'pairwise'); %#ok<*PFBNS>\n        end;\n        % get the \"derivative\" of the correlation\n        diffXCorrs2 = abs(diff(xCorrs2));\n        % get the max correlation drop\n        maxXCorrDiff2 = max(diffXCorrs2);\n        % update the thresholds\n        corrThreshX2 = median(xCorrs) - corrTheshFactor * std(xCorrs);\n        \n        % check if the processing should be aborted\n        [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n        % only apply the change if the \"corrected\" image is actually better than the original\n        if ~any(xCorrs2 < corrThreshX2) || ~any(diffXCorrs2 > corrDiffThresh) || maxXCorrDiff2 < maxXCorrDiff;\n            % store the change\n            this.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.procImg.data = imgDataCorr;\n            % display message\n            showMessage(this, sprintf(' - %s: shift correction done on X.', rowIDTitle));\n        else\n            % keep the original image as \"corrected\"\n            avgImgCorr = avgImg;\n        end;\n    end;\n    \nend;\n\n% correct on the Y-axis\nif any(yCorrs < corrThreshY);\n    \n    % get the \"derivative\" of the correlation\n    diffYCorrs = abs(diff(yCorrs));\n    \n    if any(diffYCorrs > corrDiffThresh);\n        % get a fresh copy of the data to change\n        imgDataToCorrect = this.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.procImg.data;\n        % get the line where the shift occured\n        [maxYCorrDiff, maxYCorrDiffInd] = max(diffYCorrs);\n        yLineInd = maxYCorrDiffInd + yRange(1) - 1;\n        % reshape the images accordingly\n        imgDataCorr = cellfun(@(imgs) vertcat(imgs(yLineInd : end, :, :), imgs(1 : yLineInd - 1, :, :)), ...\n            imgDataToCorrect, 'UniformOutput', false);\n        \n        % average image of the selected channel of the corrected data\n        avgImgCorr = nanmean(imgDataCorr{this.an.img.preProcChan}, 3);\n        \n        % see if it actually made it better: re-do a line-wise correlation\n        yCorrs2 = zeros(1, yRange(end) - yRange(1) + 1);\n        parfor y = yRange(1) : yRange(end);\n            yCorrs2(y - yIndOffset) = corr(avgImgCorr(y, :)', avgImgCorr(y - 1, :)', 'rows', 'pairwise');\n        end;\n        % get the \"derivative\" of the correlation\n        diffYCorrs2 = abs(diff(yCorrs2));\n        % get the max correlation drop\n        maxYCorrDiff2 = max(diffYCorrs2);\n        % update the thresholds\n        corrThreshY2 = median(yCorrs) - corrTheshFactor * std(xCorrs);\n        \n        % check if the processing should be aborted\n        [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n        % only apply the change if the \"corrected\" image is actually better than the original\n        if ~any(yCorrs2 < corrThreshY2) || ~any(diffYCorrs2 > corrDiffThresh) || maxYCorrDiff2 < maxYCorrDiff;\n            % store the change\n            this.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.procImg.data = imgDataCorr;\n            % display message\n            showMessage(this, sprintf(' - %s: shift correction done on Y.', rowIDTitle));\n        else\n            % keep the original image as \"corrected\"\n            avgImgCorr = avgImg;\n        end;\n    end;\n    \nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% mark row as processed for frame shift correction\nsetData(this, iDWRow, 'procImg', 'procState', [rowProcState { 'fShift' }]);\n\n%%  plotting\n% if requested, plot a figure illustrating what has been done\nif doPlots > 0;\n    \n    % correlation values\n    figure('Name', sprintf('%s: line-wise correlations', rowIDTitle), figCommons{:});\n    legendTexts = {'Corr. on X', 'Corr. on Y', 'Corr. thresh. X', 'Corr. thresh. Y'};\n    hold(gca, 'on');\n    plot(xRange(1) : xRange(end), xCorrs, 'r');\n    plot(yRange(1) : yRange(end), yCorrs, 'b');\n    xLims = get(gca, 'XLim');\n    plot(xLims(1) : xLims(end), repmat(corrThreshX, 1, xLims(end) - xLims(1) + 1), 'r:');\n    plot(xLims(1) : xLims(end), repmat(corrThreshY, 1, xLims(end) - xLims(1) + 1), 'b:');\n    if exist('diffXCorrs', 'var') || exist('diffYCorrs', 'var');\n        plot(xLims(1) : xLims(end), repmat(corrDiffThresh + 0.2, 1, xLims(end) - xLims(1) + 1), 'g--');\n        legendTexts{end + 1} = 'Corr. thresh. Diff.';\n    end;\n    if exist('diffXCorrs', 'var');\n        plot(0.5 + (xRange(1) : xRange(end) - 1), diffXCorrs + 0.2, 'r.-');\n        legendTexts{end + 1} = 'Diff. X corr.';\n    end;\n    if exist('diffYCorrs', 'var');\n        plot(0.5 + (xRange(1) : xRange(end) - 1), diffYCorrs + 0.2, 'g.-');\n        legendTexts{end + 1} = 'Diff. Y corr.';\n    end;\n    ylim([0 1]);\n    legend(legendTexts);\n    \n    % if there was a correction done\n    if exist('avgImgCorr', 'var');\n        figure('Name', sprintf('%s: average images', rowIDTitle), figCommons{:});\n        subplot(1, 2, 1); imshow(linScale(avgImg));\n        subplot(1, 2, 2); imshow(linScale(avgImgCorr));\n        subplot(1, 2, 1); title('Original');\n        subplot(1, 2, 2); title('Corrected');\n        \n    else\n        % original image\n        figure('Name', sprintf('%s: original average image (no correction done)', rowIDTitle), figCommons{:});\n        imshow(linScale(avgImg));\n    end;\n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataProcess_imgData_extrCaTraces.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/dataProcess/OCIA_dataProcess_imgData_extrCaTraces.m", "size": 6455, "source_encoding": "utf_8", "md5": "9a39a9a8f93fa8489347c130b681db3d", "text": "%% #OCIA:OCIA_dataProcess_imgData_extrCaTraces\nfunction [isValid, unvalidReason] = OCIA_dataProcess_imgData_extrCaTraces(this, iDWRow, varargin)\n\n% by default, row is valid\nisValid = true;\nunvalidReason = '';\n\n% get the selected processing steps and this row's calcium data\nselProcOpts = this.an.procOptions.id(get(this.GUI.handles.dw.procOptsList, 'Value'));\nrawChanData = get(this, iDWRow, 'data'); rawChanData = rawChanData.rawChan.data;\ncaTracesData = get(this, iDWRow, 'data'); caTracesData = caTracesData.caTraces.data;\n% if this processing is not required or if data is not imaging data or if data was already processed, abort\nif ~any(strcmp(selProcOpts, 'extrCaTraces')) || ~strcmp(get(this, iDWRow, 'rowType'), 'Imaging data') ...\n        || (~isempty(caTracesData) && ~isempty(rawChanData));\n    return;\nend;\n\n% get whether to do plots or not\nif nargin > 2;  doPlots = varargin{1};\nelse            doPlots = 0;\nend;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n%% init\nrowID = DWGetRowID(this, iDWRow);\nrowIDTitle = sprintf('Extracting calcium trace for %s (%d)', rowID, iDWRow);\n\n% display message\nshowMessage(this, sprintf('%s ...', rowIDTitle), 'yellow');\n\n% get the ROISet for this row\nROISet = ANGetROISetForRow(this, iDWRow);\nnROIs = size(ROISet, 1); % number of ROIs\n\n% if no ROIs, abort\nif ~nROIs; return; end;\n\n% make sure the data is at least partially loaded\nDWLoadRow(this, iDWRow, 'partial');\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% get the imaging data\nimgData = get(this, iDWRow, 'data');\nimgData = imgData.procImg.data;\n\n% get the first channel to use for the trace extraction from the configuration\nfirstChan = this.an.img.chanVect(1);\nimgDim = size(imgData{firstChan}); % get the image's dimension\n\n% initialize the data set\nthis.data.img.caTraces{iDWRow} = nan(nROIs, size(imgData{firstChan}, 3));\n\n% background substract images for each channel using the first percentile\nfor iChan = 1 : numel(imgData); % go through all channels\n\n    if isempty(imgData{iChan}) || imgDim(1) == 0; continue; end;\n\n    % calculate cutoff on the first 10% frames\n    nFrames = fix(imgDim(3) * 0.1);\n    bgPrctileCutOff = prctile(reshape(imgData{iChan}(:, :, 1 : nFrames), 1, prod(imgDim(1 : 2)) * nFrames), ...\n        this.an.img.bgPrctile);\n    imgData{iChan} = imgData{iChan} - bgPrctileCutOff; % remove cutoff\n    imgData{iChan}(imgData{iChan} < 0) = 0; % flatten so that there are no negative values\n        \n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\nend;\n\n% get the YFP or GFP data\nYFPData = imgData{this.an.img.chanVect(1)};\n\n% use CFP if there is a second channel\nif numel(this.an.img.chanVect) > 1;\n    CFPData = imgData{this.an.img.chanVect(2)};\nelse\n    CFPData = [];\nend;\n\ndownSampFactor = this.an.an.channelDownSampFactor;\nif ~downSampFactor; downSampFactor = 1; end;\ncaTraces = nan(nROIs, floor(size(YFPData, 3) / downSampFactor));\nchanStats = nan(nROIs, floor(size(YFPData, 3) / downSampFactor), numel(this.an.img.chanVect));\ndefaultFrameRate = this.an.img.defaultFrameRate;\n        \n% check if the processing should be aborted\n[doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\n% process each ROI\nfor iROI = 1 : nROIs;\n\n    % extract the time series for each pixel\n    YFPTimeSeries = GetRoiTimeseries(YFPData, ROISet{iROI, 2});\n    % get a threshold for the number of NaN pixels\n    nonNaNPixelNumberThreshold = round(size(YFPTimeSeries, 1) * 0.15);\n    % get how many pixels are NaN for each frame\n    NaNPixelsTimeSeries = sum(~isnan(YFPTimeSeries));\n    % remove bad frames where more than the threshold pixels are NaN, which gives an unreliable average\n    YFPTimeSeries(:, NaNPixelsTimeSeries < nonNaNPixelNumberThreshold) = NaN;    \n    % get the average of all pixels\n    YFP = nanmean(YFPTimeSeries, 1)';\n    \n    % apply the same for the CFP data (second channel) if it exists\n    if ~isempty(CFPData);\n        % extract the time series for each pixel\n        CFPTimeSeries = GetRoiTimeseries(CFPData, ROISet{iROI, 2});\n        % remove bad frames where more than the threshold pixels are NaN, which gives an unreliable average\n        CFPTimeSeries(:, NaNPixelsTimeSeries < nonNaNPixelNumberThreshold) = NaN;    \n        % get the average of all pixels\n        CFP = nanmean(CFPTimeSeries, 1)';\n    else\n        CFP = [];\n    end;\n\n    % apply filter on traces if required\n    if this.an.an.channelSFGiltFrameSize > 1;\n        YFP = sgolayfilt(YFP, 1, this.an.an.channelSFGiltFrameSize);\n        if ~isempty(CFP);\n            CFP = sgolayfilt(CFP, 1, this.an.an.channelSFGiltFrameSize);\n        end;\n    end;\n\n    % apply downsampling on traces if required\n    if downSampFactor > 1;\n        YFP = interp1DS(defaultFrameRate, defaultFrameRate / downSampFactor, YFP);\n        YFP = YFP(1 : size(caTraces, 2));\n        if ~isempty(CFP);\n            CFP = interp1DS(defaultFrameRate, defaultFrameRate / downSampFactor, CFP);\n            CFP = CFP(1 : size(caTraces, 2));\n        end;\n    end;\n\n    % get the raw traces for each channel\n    chanStats(iROI, :, 1) = YFP;\n    if ~isempty(CFP);\n        chanStats(iROI, :, 2) = CFP;\n    end;\n\n    % extract the dFF/dRR from the channels    \n    caTraces(iROI, :) = extractDFFDRR(YFP, CFP, this.an.img.f0method, this.an.img.f0params, ...\n        this.an.img.polyfitCorrect, this.an.img.polyfitFraction, this.an.img.expfitCorrect, ...\n        this.an.img.expfitWindow, defaultFrameRate, sprintf('%s_%03d_ROI%s', rowID, iDWRow, ...\n        ROISet{iROI, 1}), [], doPlots);\n        \n    % check if the processing should be aborted\n    [doAbort, isValid, unvalidReason] = DWCheckProcessAbort(this, isValid, unvalidReason); if doAbort; return; end;\n\nend;\n\n% store the raw traces \nthis.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.rawChan.data = chanStats;\nthis.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.rawChan.loadStatus = 'full';\n\n% store the calcium traces\nthis.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.caTraces.data = caTraces;\nthis.dw.table{iDWRow, strcmp(this.dw.tableIDs, 'data')}.caTraces.loadStatus = 'full';\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_annotateTable_wenrui.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/annotates/OCIA_annotateTable_wenrui.m", "size": 4177, "source_encoding": "utf_8", "md5": "27e9a97bb43ff75be39b4648cc4c8d45", "text": "function OCIA_annotateTable_wenrui(this)\n% OCIA_annotateTable_wenrui - [no description]\n%\n%       OCIA_annotateTable_wenrui(this)\n%\n% [No description]\n%\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% extract the depth from the spot IDs\ndepths = regexprep(this.dw.spotIDs, '^Spot\\d+_(\\d+)$', '$1');\n\n% get the list of spot for each row\nspots = get(this, 'all', 'spot');\nif ~iscell(spots); spots = { spots }; end;\n% remove empty spot labels\nspots(cellfun(@isempty, spots)) = { '' };\n% extract which spot it corresponds to\n[~, spotIndexes] = ismember(spots, this.dw.spotIDs);\n% if no spot ID was found, use the '-' that is in the first position in the 'depths' cell-array\nspotIndexes(spotIndexes == 0) = 1;\n% set the corresponding depth\nset(this, 'all', 'depth', depths(spotIndexes));\n\n% match ROISets to imaging data\nDWMatchROISetsToData(this);\n\n% match whisker data to imaging data\nmatchWhiskerDataToImagingData(this);\n\n% show the table\nDWDisplayTable(this);\n\nend\n\nfunction matchWhiskerDataToImagingData(this)\n% sub-function to load the whisker data and match it to the imaging rows\n\n% get imaging rows\nimagingRows = DWFilterTable(this, 'rowType = Imaging data');\n% abort if no imaging rows\nif isempty(imagingRows); return; end;\n\n% show message and update wait bar\nshowMessage(this, sprintf('Extracting whisker data for %02d imaging row(s) ...', size(imagingRows, 1)), 'yellow');\nDWWaitBar(this, 0);\n\n% go through each row\nfor iRow = 1 : size(imagingRows, 1);\n    \n    % get the DataWatcher's table index for this row\n    iDWRow = str2double(get(this, 1, 'rowNum', imagingRows(iRow, :)));\n    % get the experiment number\n    expNum = str2double(get(this, iDWRow, 'expNum'));\n    % show a warning and skip if a row has an invalid experiment number\n    if isempty(expNum) || isnan(expNum);\n        showWarning(this, 'OCIA:OCIA_annotateTable_wenrui:WhiskerDataUnknownExpNum', ...\n            sprintf('Row %s (%02d) has an invalid experiment number: %s. Skipping it.', ...\n            DWGetRowID(this, iDWRow), iDWRow, expNum));\n        continue;\n    end;\n    % find the whisker data folder's path\n    whiskerDataFolderRow = DWFilterTable(this, sprintf('rowType = Whisker data AND day = %s', get(this, iDWRow, 'day')));\n    whiskerDataFolderPath = get(this, 1, 'path', whiskerDataFolderRow);\n    \n    % get the path to the correct file using the experiment number\n    whiskerDataPath = sprintf('%s/Experiment_%d_Whisker_Tracking.mat', whiskerDataFolderPath, expNum);\n    % show a warning and skip if file cannot be found\n    if ~exist(whiskerDataPath, 'file');\n        showWarning(this, 'OCIA:OCIA_annotateTable_wenrui:FileNotFound', ...\n            sprintf('Whisker data for row %s (%02d) could not be found at \"%s\". Skipping it.', ...\n            DWGetRowID(this, iDWRow), iDWRow, whiskerDataPath));\n        continue;\n    end;\n        \n    % load the whisker data\n    whiskDataMat = load(whiskerDataPath);\n    if isfield(whiskDataMat, 'MovieInfo');\n        % store the whisker angle data and its frame rate\n        dataStruct = struct('angle', whiskDataMat.MovieInfo.AvgWhiskerAngle, ...\n            'frameRate', whiskDataMat.MovieInfo.FramesPerSecond);\n    elseif isfield(whiskDataMat, 'whiskingAll');\n        % store the whisker angle data and its frame rate (hard-coded)\n        dataStruct = struct('angle', whiskDataMat.whiskingAll', 'frameRate', 200);\n    % unknown way of load data: show a warning and skip\n    else\n        showWarning(this, 'OCIA:OCIA_annotateTable_wenrui:UnknownContent', ...\n            sprintf('Whisker data for row %s (%02d) was found at \"%s\" but content is unknown. Skipping it.', ...\n            DWGetRowID(this, iDWRow), iDWRow, whiskerDataPath));\n        continue;\n    end;\n    \n    % load the data and set the load status to full\n    setData(this, iDWRow, 'whisk', 'data', dataStruct);\n    setData(this, iDWRow, 'whisk', 'loadStatus', 'full');\n    \n    % update wait bar\n    DWWaitBar(this, 100 * iRow / size(imagingRows, 1));\n    \nend;\n\n% show message and update wait bar\nshowMessage(this, sprintf('Extracting whisker data for %02d imaging row(s) done.', size(imagingRows, 1)));\nDWWaitBar(this, 100);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataSaveConfig_img.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/others/OCIA_dataSaveConfig_img.m", "size": 2446, "source_encoding": "utf_8", "md5": "157a4a7a2ff2501ff296389c33bb3222", "text": "function dataConf = OCIA_dataConfig_img(this, iDWRow, dataConf)\n% generate a configuration that specifies how the data should be saved as a cell-array with 7 columns:\n%   { data field name, data to save set/get function, sub-cells save name, names of the attributes to save,\n%       attributes to save, display name, data's size, the data save options }\npreProcType = this.data.preProcType{iDWRow};\nif ~isempty(preProcType); preProcType = regexprep(sprintf('%s,', preProcType{:}), ',$', ''); end;\ndataConf = [ dataConf ; { ...\n    'raw',        @setGetData_img,     'chan%02d', { 'rawLoadType' },   @setGetData_img, ...\n                                                                                'raw images',           [],     [];\n    'preProc',    @setGetData_img,     'chan%02d', { 'preProcType' },   @setGetData_img, ...\n                                                                                'pre-processed images', [],     [];\n    'caTraces',   @setGetData_img,     [],         {},                  {},     'calcium traces',       [],     [];\n    'stim',       @setGetData_img,     [],         {},                  {},     'stimulus vector',      [],     [];\n    'exclMask',   @setGetData_img,     [],         {},                  {},     'exclusion mask',       [],     [];\n}];\n\n% small function specifying which fields should be updated\nfunction out = setGetData_img(fieldName, in)\n    switch fieldName;\n        case 'raw';         if isempty(in); in = this.data.raw{iDWRow};                 else this.data.raw{iDWRow} = in;            end;\n        case 'rawLoadType'; if isempty(in); in = this.data.rawLoadType{iDWRow};         else this.data.rawLoadType{iDWRow} = in;    end;\n        case 'preProc';     if isempty(in); in = this.data.preProc{iDWRow};             else this.data.preProc{iDWRow} = in;        end;\n        case 'preProcType'; if isempty(in); in = preProcType;                           else\n            this.data.preProcType{iDWRow} = regexp(in, ',', 'split'); end;\n        case 'caTraces';    if isempty(in); in = this.data.img.caTraces{iDWRow};        else this.data.img.caTraces{iDWRow} = in;   end;\n        case 'stim';        if isempty(in); in = this.data.img.stim{iDWRow};            else this.data.img.stim{iDWRow} = in;       end;\n        case 'exclMask';    if isempty(in); in = this.data.img.exclMask{iDWRow};        else this.data.img.exclMask{iDWRow} = in;   end;\n    end;\n    out = in;\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataSaveConfig_whisk.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/others/OCIA_dataSaveConfig_whisk.m", "size": 1026, "source_encoding": "utf_8", "md5": "fe313ea9c3368232c2956634ebca3dbf", "text": "function dataConf = OCIA_dataConfig_whisk(this, iDWRow, dataConf)\n% generate a configuration that specifies how the data should be saved as a cell-array with 7 columns:\n%   { data field name, data to save set/get function, sub-cells save name, names of the attributes to save, attributes to save,\n%     display name, data's size, the data save options }\ndataConf = [ dataConf; { ...\n    'whisk',      @setGetData_whisk,            [],         {},                 {},   'whisker data',    [],     [];\n}];\n\n% small function specifying which fields should be updated\nfunction out = setGetData_whisk(fieldName, in)\n    switch fieldName;\n        case 'whisk';\n            if isempty(in);\n                in = this.data.whisk(iDWRow);\n            else % load each attribute field by field\n                fNames = fieldnames(in);\n                for iField = 1 : numel(fNames);\n                    this.data.whisk(iDWRow).(fNames{iField}) = in.(fNames{iField});\n                end;\n            end;\n    end;\n    out = in;\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "OCIA_dataSaveConfig_behav.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/others/OCIA_dataSaveConfig_behav.m", "size": 1031, "source_encoding": "utf_8", "md5": "1350a4228175fd1cdd5c933e69c72029", "text": "function dataConf = OCIA_dataSaveConfig_behav(this, iDWRow, dataConf)\n% generate a configuration that specifies how the data should be saved as a cell-array with 7 columns:\n%   { data field name, data to save set/get function, sub-cells save name, names of the attributes to save, attributes to save,\n%     display name, data's size, the data save options }\ndataConf = [ dataConf; { ...\n    'behav',      @setGetData_behav,            [],         {},                 {},   'behavior data',    [],     [];\n}];\n\n% small function specifying which fields should be updated\nfunction out = setGetData_behav(fieldName, in)\n    switch fieldName;\n        case 'behav';\n            if isempty(in);\n                in = this.data.behav(iDWRow);\n            else % load each attribute field by field\n                fNames = fieldnames(in);\n                for iField = 1 : numel(fNames);\n                    this.data.behav(iDWRow).(fNames{iField}) = in.(fNames{iField});\n                end;\n            end;\n    end;\n    out = in;\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DWMatchBehavTrialsToImagingDataShankar.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/OCIA/custom/others/DWMatchBehavTrialsToImagingDataShankar.m", "size": 6333, "source_encoding": "utf_8", "md5": "68a4994114b2d05a5a911a375b5a86b6", "text": "function DWMatchBehavTrialsToImagingDataShankar(this)\n% DWMatchBehavTrialsToImagingDataShankar - [no description]\n%\n%       DWMatchBehavTrialsToImagingDataShankar(this)\n%\n% Match the behavior trials and the data files.\n\n% 2013-2016 - Copyleft and programmed by Balazs Laurenczy (blaurenczy_at_gmail.com)\n\n% update the wait bar\nDWWaitBar(this, 0);\n\n% get the list of all animals\nuniqueAnimals = get(this, 'animal');\nuniqueAnimals(cellfun(@isempty, uniqueAnimals)) = [];\nuniqueAnimals = unique(uniqueAnimals);\n\n% get the list of all days\nuniqueDays = get(this, 'day');\nuniqueDays(cellfun(@isempty, uniqueDays)) = [];\nuniqueDays = unique(uniqueDays);\n    \n% get the list of all spots\nuniqueSpots = get(this, 'spot');\nuniqueSpots(cellfun(@isempty, uniqueSpots)) = [];\nuniqueSpots = unique(uniqueSpots);\n\n% get the selected animal IDs\nselectedAnimalIDs = this.dw.animalIDs(get(this.GUI.handles.dw.filt.animalID, 'Value'));\n% if the dash '-' is selected, select all IDs\nif numel(selectedAnimalIDs) == 1 && strcmp(selectedAnimalIDs{1}, '-');\n    selectedAnimalIDs = uniqueAnimals;\nend;\n\n% get the selected day IDs\nselectedDayIDs = this.dw.dayIDs(get(this.GUI.handles.dw.filt.dayID, 'Value'));\n% if the dash '-' is selected, select all IDs\nif numel(selectedDayIDs) == 1 && strcmp(selectedDayIDs{1}, '-');\n    selectedDayIDs = uniqueDays;\nend;\n\n% cell array storing all the informations to process each session\nallSessionInfos = cell(1000, 5);\n\ncommentRows = DWFilterTable(this, sprintf('rowType = Imaging data AND runType = comment'));\nif ~isempty(commentRows);\n    commentRowIndexes = str2double(get(this, 'all', 'rowNum', commentRows));\n    set(this, commentRowIndexes, 'runType', '');\nend;\n\n% first get all the information for each sessions to process\n% go through each animal\nfor iAnim = 1 : numel(uniqueAnimals);    \n    animalID = uniqueAnimals{iAnim}; % get the current animal\n\n    % skip irrelevant animal IDs\n    if ~ismember(animalID, selectedAnimalIDs); continue; end;\n    \n    % go through each day\n    for iDay = 1 : numel(uniqueDays);    \n        dayID = uniqueDays{iDay}; % get the current day\n\n        % skip irrelevant days\n        if ~ismember(dayID, selectedDayIDs); continue; end;\n    \n        % go through spot by spot\n        for iSpot = 1 : numel(uniqueSpots);\n            spotID = uniqueSpots{iSpot}; % get the current spot\n\n            % get the imaging rows indexes for this day and this spot\n            imagingRows = DWFilterTable(this, ...\n                sprintf('animal = %s AND day = %s AND spot = %s AND rowType = Imaging data AND runType !~= \\\\w+', ...\n                animalID, dayID, spotID));\n            imagingRowIndexes = str2double(get(this, 'all', 'rowNum', imagingRows));\n\n            % if no imaging data, skip\n            if isempty(imagingRowIndexes) || any(isnan(imagingRowIndexes));\n                continue;\n            end;\n            \n            % find behavior row\n            behavRow = DWFilterTable(this, ...\n                sprintf('animal = %s AND day = %s AND spot = %s AND rowType = Behavior data', ...\n                animalID, dayID, spotID));\n            \n            % if no behavior row, continue\n            if isempty(behavRow); \n                continue;\n            end;\n            \n            % get the DW's table row index\n            iDWRowBehav = str2double(get(this, 1, 'rowNum', behavRow));\n            \n            % get behavior data\n            DWLoadRow(this, iDWRowBehav, 'full');\n            behavData = getData(this, iDWRowBehav, 'behavtext', 'data');\n            \n            % check consistency\n            if numel(behavData) ~= numel(imagingRowIndexes);    \n                showWarning(this, 'OCIA:DWMatchBehavTrialsToImagingDataShankar:NotEnoughBehavRows', ...\n                    'Not enough behavior rows !');\n                continue;\n            end;\n\n            % match behavior data with imaging rows\n            for iRow = 1 : numel(imagingRowIndexes);\n                % annotate in table\n                stimID = behavData(iRow).stimulus;\n                set(this, imagingRowIndexes(iRow), 'runType', regexprep(stimID, 'Texture \\d+ ', '')); \n                set(this, imagingRowIndexes(iRow), 'comments', behavData(iRow).decision);  \n                set(this, imagingRowIndexes(iRow), 'runNum', sprintf('%03d', iRow));\n                % store the structure and mark it as loaded\n                setData(this, imagingRowIndexes(iRow), 'behavExtr', 'data', behavData(iRow));\n                setData(this, imagingRowIndexes(iRow), 'behavExtr', 'loadStatus', 'full');\n            end;\n            \n        end; % end of spot loop\n    end; % end of day loop\nend; % end of animal loop\n\n% remove empty lines\nallSessionInfos(cellfun(@isempty, allSessionInfos(:, 1)), :) = [];\nnTotSessions = size(allSessionInfos, 1);\n\n% match all sessions\nfor iTotSess = 1 : nTotSessions;\n    \n    % match the behavior trials using the stored informations\n    DWMatchBehavTrialsToImagingDataForSession(this, allSessionInfos{iTotSess, :});\n    \n    % update the wait bar\n    DWWaitBar(this, 99 * (iTotSess / nTotSessions));\n    \nend;\n    \n% remove raw behavior data for the current session\nbehavRows = DWFilterTable(this, 'rowType = Behavior data');\nDWFlushData(this, str2double(get(this, 'all', 'rowNum', behavRows)), false, 'behav');\n\n% final update of the wait bar\nDWWaitBar(this, 100);\n\nend\n\n%% - #clusterRowsBySession\nfunction sessIDs = clusterRowsBySession(this, rowNums)\n\n% do not process if not at least 2 rows\nif numel(rowNums) < 2;\n    sessIDs = repmat('1', numel(rowNums), 1);\n    return;\nend;\n    \n% separate rows into morning and afternoon sessions\nnUnknRows = size(rowNums, 1);\ndateNums = zeros(nUnknRows, 1);\nfor iUnknRow = 1 : nUnknRows;\n    dateAndTime = get(this, rowNums(iUnknRow), { 'day', 'time' });\n    dateNums(iUnknRow) = datenum(sprintf('%s__%s', dateAndTime{:}), 'yyyy_mm_dd__HH_MM_SS');\nend;\nsessIDs = clusterdata(dateNums, 'maxclust', 2);\nnearbySessDiffInHours = (dn2unix(dateNums(find(sessIDs == sessIDs(end), 1, 'first'))) ...\n    - dn2unix(dateNums(find(sessIDs == sessIDs(1), 1, 'last')))) / 1000 / 60 / 60;\n% if sessions are too close, it means that it was a single session with a missing trial/interruption\nif nearbySessDiffInHours < 3; % minimum 3 hours between sessions\n    sessIDs = clusterdata(dateNums, 'maxclust', 1);\nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "runExperiment.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/caImgExperiment/@CaImgExperiment/runExperiment.m", "size": 16480, "source_encoding": "utf_8", "md5": "3ec18810499954502e465b6c620c9fb9", "text": "function CaImgExp = runExperiment(CaImgExp, nRuns)\n    % runExperiment method for the CaImgExperiment class. Runs the calcium imaging experiment.\n    \n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    % Originally created on           18 / 03 / 2012 %\n    % Written by B. Laurenczy (blaurenczy@gmail.com) %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n    % display check list\n    if CaImgExp.checkAbort(['Let''s go:\\n' ...\n            '  - Check if heating pad is OK ...\\n' ...\n            '  - Check if laser is OK (shutter, wavelength, etc.) ...\\n' ...\n            '  - Check if sound (TDT?) is on, with attenutation OK ...\\n' ...\n            '  - Check if heloscan trigger cable is plugged ...\\n' ...\n            '  - Check if mouse''s breathing (anesthesia) is OK ...\\n']);\n        return;\n    end;\n\n\n    %% BF test\n    if CaImgExp.checkSkip('BFTest');\n        % do the best frequency characterization test\n%         attenuations = zeros(1, nRuns);\n        attenuations = ones(1, nRuns) * 20;\n    %     attenuations = repmat(attenuations, 2); % 10 repetitions\n        CaImgExp = CaImgExp.doBFTest(attenuations);\n    end;\n\n\n    %% CaImg runs\n\n%     if CaImgExp.checkSkip('Odd10%');\n%         if CaImgExp.checkAbort('WARNING! Do not forget to change stimulus duration and set a BF!!'); return; end;\n%         % run the oddball paradigm with 10% deviants\n%         CaImgExp = doOddballParadigm(CaImgExp, 10, 4);\n%         if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n%     end;\n%     \n%     if CaImgExp.checkSkip('OddTrial');\n%         if CaImgExp.checkAbort('WARNING! Do not forget to change stimulus duration!!'); return; end;\n%         % run the oddball trial paradigm with 10% deviants\n%         CaImgExp = doOddballTrialParadigm(CaImgExp, 5);\n%         if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n%     end;\n\n    if CaImgExp.checkSkip('Omi10%');\n        if CaImgExp.checkAbort('WARNING! Do not forget to change stimulus duration!!'); return; end;\n        % run the omission paradigm with 10% omissions\n        CaImgExp = doOmissionParadigm(CaImgExp, 10, 10);\n        if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n    end;\n\n%     % run the oddball paradigm with 50% deviants, this is the equiprobable control\n%     [CaImgExp, abort] = doEquiProbControl(CaImgExp);\n%     if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n%     \n%     % run the oddball paradigm with 30% deviants\n%     [CaImgExp, abort] = doOddballParadigm(CaImgExp, 30, 2);\n%     if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n% \n%     % run the omission paradigm with 10% of deviant sound, this is the deviant alone control\n%     [CaImgExp, abort] = doDevAloneControl(CaImgExp, 10);\n%     if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n% \n%     % run the oddball paradigm with 30% deviants\n%     [CaImgExp, abort] = doOddballParadigm(CaImgExp, 30, 2);\n%     if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n\n%     % run the omission paradigm with 10% omissions\n%     [CaImgExp, abort] = doOmissionParadigm(CaImgExp, 10, 10);\n%     if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n% \n%     % run the omission paradigm with 30% omissions\n%     [CaImgExp, abort] = doOmissionParadigm(CaImgExp, 30, 4);\n%     if CaImgExp.checkAbort('Press [ENTER] to go on to next paradigm.'); return; end;\n\n%     if CaImgExp.checkAbort('Press [ENTER] if you are happy !'); return; end;\n\nend % runExperiment\n\n\n%% doOddballTrialParadigm\n% oddball trial paradigm, F1 or F2 randomly assigned to be standard\n% nRunsPerFreq: number of runs to do for each frequency (F1 and F2)\nfunction [CaImgExp, abort] = doOddballTrialParadigm(CaImgExp, nRunsPerFreq)\n\n    % create a random sequences of 'swap' for alternating F1 and F2, each one 'nRunsPerFreq' times\n    F1F2Sequence = [ones(nRunsPerFreq, 1); -1 * ones(nRunsPerFreq, 1)];\n    % mix the sequence of F1 and F2\n    F1F2Sequence = F1F2Sequence(randperm(size(F1F2Sequence, 1)));\n\n    % check if the sequence is okay: nRunsPerFreq runs of each and no more '1' runs than -1\n    if size(F1F2Sequence, 1) ~= nRunsPerFreq * 2 || sum(F1F2Sequence) ~= 0;\n        warning('CaImgExperiment:doOddballTrialParadigm:BadF1F2MixSeq', ...\n            'F1F2 mixing sequence not the right length or not equilibrated!');\n        % save a backup of the aborted experiment\n        CaImgExp.saveAll(sprintf('backup_n%dOddTrial_%d_abortedF1F2Seq', CaImgExp.nSpots, proba, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2)));\n        abort = 1;\n        return;\n    end;\n\n    fprintf('Sequence of stimulation will be the following:\\n');\n    disp(F1F2Sequence');\n    \n    % calculate recording duration\n    dutyCycle = 500;\n    nTones = 3 + randi(5); % random number of tones between 4 and 8\n    roundTo = 1000;\n    recDur = dutyCycle * nTones + 2000;\n    recDur = recDur + roundTo - mod(recDur, roundTo); % recording duration rounded to 'roundTo' ms\n\n    % loop through all 'swap' combinations and play the oddball paradigm\n    for i = 1:size(F1F2Sequence, 1);\n\n        % initiate the file name\n        fileName = sprintf('sp%02dOddTrialF%d_%d', CaImgExp.nSpots, 1.5 - F1F2Sequence(i) / 2, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2) + 1);\n        abort = CaImgExp.checkAbort(sprintf('Stim name: %s, recording dur.: %d ms', fileName, recDur));\n        if abort; return; end;\n\n        fprintf('Oddball trial, no %d of %d\\n', i, size(F1F2Sequence, 1));\n\n        % run the stimulation\n        % parameters :  attenLevel, randomSeed = 1 (oddball at end), nStimuli, BFreqIndex, freqDev, swap,\n        %               stimDur = 100ms, dutyCycle = 500ms, ephysRate = 20'000Hz;\n%         [CaImgExp, abort] = CaImgExp.runSingleStimulation('ODD_TRIAL', 0, randi(3) - 1, nTones, ...\n        [CaImgExp, abort] = CaImgExp.runSingleStimulation('ODD_TRIAL', 0, 1, nTones, ...\n            CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, 0, 0.5, F1F2Sequence(i), 'useFMSweep', 5000);\n        \n        if abort;\n            CaImgExp.saveAll('backup_abort'); % save a backup of the aborted experiment\n            return;\n        end;\n        CaImgExp.saveAll(fileName); % save a backup\n        CaImgExp.saveAll(); % save the experiment\n    end;\n\nend % doOddballTrialParadigm\n\n\n%% doOddballParadigm\n% [proba]% oddball paradigm, F1 or F2 randomly assigned to be standard\n% nRunsPerFreq: number of runs to do for each frequency (F1 and F2)\nfunction [CaImgExp, abort] = doOddballParadigm(CaImgExp, proba, nRunsPerFreq)\n\n    % create a random sequences of 'swap' for alternating F1 and F2, each 'nRunsPerFreq' times\n    F1F2Sequence = [ones(nRunsPerFreq, 1); -1 * ones(nRunsPerFreq, 1)];\n    % mix the sequence of F1 and F2\n    F1F2Sequence = F1F2Sequence(randperm(size(F1F2Sequence, 1)));\n\n    % check if the sequence is okay: nRunsPerFreq runs of each and no more '1' runs than -1\n    if size(F1F2Sequence, 1) ~= nRunsPerFreq * 2 || sum(F1F2Sequence) ~= 0;\n        warning('CaImgExperiment:doOddballParadigm:BadF1F2MixSeq', ...\n            'F1F2 mixing sequence not the right length or not equilibrated!');\n        % save a backup of the aborted experiment\n        CaImgExp.saveAll(sprintf('backup_n%dOdd%d_%d_abortedF1F2Seq', CaImgExp.nSpots, proba, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2)));\n        abort = 1;\n        return;\n    end;\n\n    fprintf('Sequence of stimulation will be the following:\\n');\n    disp(F1F2Sequence');\n    \n    autoMode = str2double(input('Use auto mode? ', 's'));\n    \n    % calculate recording duration\n    dutyCycle = 1500;\n    nTones = 50;\n    if CaImgExp.debugMode; nTones = 20; end;\n    roundTo = 2000;\n    recDur = dutyCycle * nTones + 1000;\n    recDur = recDur + roundTo - mod(recDur, roundTo); % recording duration rounded to 'roundTo' ms\n\n    % loop through all 'swap' combinations and play the oddball paradigm\n    for i = 1:size(F1F2Sequence, 1);\n\n        % initiate the file name\n        fileName = sprintf('sp%02dOdd%dF%d_%d', CaImgExp.nSpots, proba, 1.5 - F1F2Sequence(i) / 2, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2) + 1);\n        if autoMode;\n            fprintf('Stim name: %s, recording dur.: %d ms\\n', fileName, recDur);\n        else\n            abort = CaImgExp.checkAbort(sprintf('Stim name: %s, recording dur.: %d ms', fileName, recDur));\n            if abort; return; end;\n        end;\n\n        fprintf('Oddball %d%% no %d of %d\\n', proba, i, size(F1F2Sequence, 1));\n\n        % run the stimulation\n        % parameters :  attenLevel, randomSeed (0-4), nStimuli, BFreqIndex, proba, freqDev, swap,\n        %               stimDur = 100ms, dutyCycle = 500ms, ephysRate = 20'000Hz;\n        \n        [CaImgExp, abort] = CaImgExp.runSingleStimulation('SSA_DEV', 0, randi(5) - 1, nTones, ...\n            CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, proba, 0.5, F1F2Sequence(i), ...\n            'useFMSweep', 0, 'dutyCycle', dutyCycle); % pure tones\n%             CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, proba, 0.5, F1F2Sequence(i), ...\n%             'useFMSweep', 15000, 'dutyCycle', dutyCycle); % FMSweep\n%             CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, proba, 0.5, F1F2Sequence(i), ...\n%             'useBLN', 5000, 'dutyCycle', dutyCycle); % Band limited noise\n        \n        if abort;\n            CaImgExp.saveAll('backup_abort'); % save a backup of the aborted experiment\n            return;\n        end;\n        CaImgExp.saveAll(fileName); % save a backup\n        CaImgExp.saveAll(); % save the experiment\n    end;\n\nend % doOddballParadigm\n\n\n%% doEquiProbControl\nfunction [CaImgExp, abort] = doEquiProbControl(CaImgExp)\n\n    % initiate the file name\n        fileName = sprintf('sp%02dOdd%dF1_%d', CaImgExp.nSpots, 50, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2) + 1);\n    abort = CaImgExp.checkAbort(sprintf('Stim name: %s, sweep dur.: %d ms', fileName, 55000));\n    if abort; return; end;\n\n    % equiprob control, F1 is standard, 1 run\n    % parameters :  attenLevel, randomSeed (0-4), nStimuli, BFreqIndex, proba, freqDev, swap,\n    %               stimDur = 100ms, dutyCycle = 500ms, ephysRate = 20'000Hz;\n    if CaImgExp.debugMode;\n        [CaImgExp, abort] = CaImgExp.runSingleOddballParadigm(0, randi(5) - 1, 20, ...\n            CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, 50, 0.5, 1);\n    else\n        [CaImgExp, abort] = CaImgExp.runSingleOddballParadigm(0, randi(5) - 1, 100, ...\n            CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, 50, 0.5, 1);\n    end;\n    if abort;\n        CaImgExp.saveAll('backup_abort'); % save a backup of the aborted experiment\n        return;\n    end;\n    CaImgExp.saveAll(fileName); % save a backup\n    CaImgExp.saveAll(); % save the experiment\n\n\n    % initiate the file name\n        fileName = sprintf('sp%02dOdd%dF2_%d', CaImgExp.nSpots, 50, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2) + 1);\n    abort = CaImgExp.checkAbort(sprintf('Stim name: %s, sweep dur.: %d ms', fileName, 55000));\n    if abort; return; end;\n\n    % equiprob control, F2 is standard, 1 run\n    % parameters :  attenLevel, randomSeed (0-4), nStimuli, BFreqIndex, proba, freqDev, swap,\n    %               stimDur = 100ms, dutyCycle = 500ms, ephysRate = 20'000Hz;\n    if CaImgExp.debugMode;\n        [CaImgExp, abort] = CaImgExp.runSingleStimulation('SSA_DEV', 0, randi(5) - 1, 20, ...\n            CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, 50, 0.5, -1);\n    else\n        [CaImgExp, abort] = CaImgExp.runSingleStimulation('SSA_DEV', 0, randi(5) - 1, 100, ...\n            CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex, 50, 0.5, -1);\n    end;\n    if abort;\n        CaImgExp.saveAll('backup_abort'); % save a backup of the aborted experiment\n        return;\n    end;\n\n    CaImgExp.saveAll(fileName); % save a backup\n    CaImgExp.saveAll(); % save the experiment\n\nend % doEquiProbControl\n\n\n%% doDevAloneControl\nfunction [CaImgExp, abort] = doDevAloneControl(CaImgExp, proba)\n\n    BFreqIndex = CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex;\n\n    if BFreqIndex == 1 || BFreqIndex == 15;\n        warning('CaImgExperiment:doDevAloneControl:BFOutOfRange', ...\n            ['The deviant alone control will not be accurated as F1 or F2' ...\n            ' cannot be played (out of range)']);\n        % HACK the BF so that BF + 1 and BF -1 are in range\n        if BFreqIndex == 1; BFreqIndex = 2;\n        elseif BFreqIndex == 15; BFreqIndex = 14;\n        end\n    end\n\n    % [100 - proba]% omission = proba% deviants alone, F1 or F2 as lonely deviant, 5 runs each\n    % number of runs to do for each frequency (F1 and F2)\n    nRunsPerFreq = 5;\n    % create a random sequences of 'swap' for alternating F1 and F2, each 'nRunsPerFreq' times\n    F1F2Sequence = [ones(nRunsPerFreq, 1); -1 * ones(nRunsPerFreq, 1)];\n    % mix the sequence of F1 and F2\n    F1F2Sequence = F1F2Sequence(randperm(size(F1F2Sequence, 1)));\n\n    % check if the sequence is okay: nRunsPerFreq runs of each and no more '1' runs than -1\n    if size(F1F2Sequence, 1) ~= nRunsPerFreq * 2 || sum(F1F2Sequence) ~= 0;\n        warning('CaImgExperiment:doDevAloneControl:BadF1F2MixSeq', ...\n            'F1F2 mixing sequence not the right length or not equilibrated!');\n        % save a backup of the aborted experiment\n        CaImgExp.saveAll(sprintf('backup_n%dDevAl%d_%d_abortedF1F2Seq', CaImgExp.nSpots, proba, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2)));\n        abort = 1;\n        return;\n    end;\n\n    fprintf('Sequence of stimulation will be the following:\\n');\n    disp(F1F2Sequence');\n\n    % loop through all 'swap' combinations and play the oddball paradigm\n    for i = 1:size(F1F2Sequence, 1);\n\n        % initiate the file name\n        fileName = sprintf('sp%02dDevAl%dF%d_%d', CaImgExp.nSpots, proba, 1.5 - F1F2Sequence(i) / 2, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2) + 1);\n        abort = CaImgExp.checkAbort(sprintf('Stim name: %s, sweep dur.: %d ms', fileName, 55000));\n        if abort; return; end;\n\n        fprintf('DevAl %d%% no %d of %d\\n', proba, i, size(F1F2Sequence, 1));\n\n        % run the stimulation\n        % parameters :  attenLevel, randomSeed (0-4), nStimuli, BFreqIndex, proba, freqDev, swap,\n        %               stimDur = 100ms, dutyCycle = 500ms, ephysRate = 20'000Hz;\n        if CaImgExp.debugMode;\n            [CaImgExp, abort] = CaImgExp.runSingleStimulation('SSA_OMI', 10, randi(5) - 1, 20, ...\n                BFreqIndex + F1F2Sequence(i), proba, 0.5, 1);\n        else\n            [CaImgExp, abort] = CaImgExp.runSingleStimulation('SSA_OMI', 10, randi(5) - 1, 100, ...\n                BFreqIndex + F1F2Sequence(i), proba, 0.5, 1);\n        end;\n        if abort;\n            CaImgExp.saveAll('backup_abort'); % save a backup of the aborted experiment\n            return;\n        end;\n\n        CaImgExp.saveAll(fileName); % save a backup\n        CaImgExp.saveAll(); % save the experiment\n    end;\n\nend % doDevAloneControl\n\n\n%% doOmissionParadigm\nfunction [CaImgExp, abort] = doOmissionParadigm(CaImgExp, proba, nRuns)\n\n    BFreqIndex = CaImgExp.spots{CaImgExp.nSpots}.BFreqIndex;\n\n    % [proba]% omission, best freq is standard, nRuns runs\n    for i = 1:nRuns;\n\n        % initiate the file name\n        fileName = sprintf('sp%02dOmi%d_%d', CaImgExp.nSpots, proba, ...\n            size(CaImgExp.spots{CaImgExp.nSpots}.stims, 2) + 1);\n        abort = CaImgExp.checkAbort(sprintf('Stim name: %s, sweep dur.: %d ms', fileName, 55000));\n        if abort; return; end;\n\n        fprintf('Omi %d%% no %d of %d\\n', proba, i, nRuns);\n\n        % parameters :  attenLevel, randomSeed (0-4), nStimuli, BFreqIndex, proba, freqDev, swap,\n        %               stimDur = 100ms, dutyCycle = 500ms, ephysRate = 20'000Hz;\n        if CaImgExp.debugMode;\n            [CaImgExp, abort] = CaImgExp.runSingleStimulation('SSA_OMI', 10, randi(5) - 1, 20, ...\n                BFreqIndex, proba, 0.5, -1);\n        else\n            [CaImgExp, abort] = CaImgExp.runSingleStimulation('SSA_OMI', 10, randi(5) - 1, 100, ...\n                BFreqIndex, proba, 0.5, -1);\n        end;\n        if abort;\n            CaImgExp.saveAll('backup_abort'); % save a backup of the aborted experiment\n            return;\n        end;\n\n        CaImgExp.saveAll(fileName); % save a backup\n        CaImgExp.saveAll(); % save the experiment\n    end;\n\nend % doOmissionParadigm"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "preprocLickData.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/preprocLickData.m", "size": 3089, "source_encoding": "utf_8", "md5": "3f4c637d9aa39cc0464e87a71cae95d2", "text": "function lickRateUS = preprocLickData(lickDataCell, lickThreshHard, sampRate, doPlots)\n\nlickRateBinSize = 0.05; % in s (e.g. 0.05 for 50 ms bins)\n\nfor n = 1 : numel(lickDataCell);\n    lickV = lickDataCell{n};\n    t = (1 : numel(lickV)) ./ sampRate;\n    \n    try\n        \n        % filter the lick vector\n        lickVfilt = filterLickVector(lickV, doPlots);\n        lickVfilt(lickVfilt < lickThreshHard) = 0;\n\n        % onsets for each 'lick'\n        lickOn = zeros(1, numel(lickV));\n        for m = 2 : numel(lickOn)\n            if lickVfilt(m) && ~lickVfilt(m - 1)\n                lickOn(m) = 1;\n            end;\n        end;\n    \n        % 'lick rate' based on onsets\n        lickTimes = t(lickOn > 0);\n        tt = t(1) : lickRateBinSize : t(end);\n        [lickRate, tt] = instantfr(lickTimes, tt);\n        lickRateUS = interp1(tt, lickRate, t);\n        \n    catch err;\n        \n        o(' Error while pre-processing lick data : %s.', err.identifier, 0, 0);\n        % fill with nans\n        lickRateUS = nan(size(lickV));\n        \n    end;\n    \n    if doPlots;\n        \n        figure('Name', sprintf('Trial %1.0f', n), 'NumberTitle', 'off')\n        plot(t, lickV, 'k', 'linewidth', 2), hold on\n        plot(t, lickVfilt, 'r--', 'linewidth', 1.5)\n        plot(tt, lickRate ./ 1000, 'y--', 'linewidth', 1.5)\n        plot(t, lickRateUS ./ 1000, 'b--', 'linewidth', 1.5)\n        xlabel('Time (s)'), legend({'raw', 'filt', 'rate/1000', 'rateUS/1000'})\n        \n    end;\n    \nend;\n\nend\n\n\nfunction out = filterLickVector(in, doPlots)\n\nin = abs(in);\n\n% lick data histogram\n[count,xout] = hist(in,sqrt(numel(in)));\n\n% optional: plot histogram\nif doPlots\n    figure('Name','Histogram','NumberTitle','off')\n    plot(xout,count,'k'), hold on\nend\n\n% fit double-peaked gaussian to histogram\nfObj = fit(xout',count','gauss2');\n% optional: plot fit\nif doPlots;\n    plot(fObj,'r--')\nend\n\n% pull out peaks and widths from fit\npeaks = [fObj.b1 fObj.b2];\nwidth = [fObj.c1 fObj.c2];\n% sort so that first peak is the more negative one\n[peaks,ix] = sort(peaks);\nwidth = width(ix);\n% width as SD\nwidth = width ./ sqrt(2);\n% probability of each data point based on normal distribution\np = 1-normcdf(in,peaks(2),width(2));\n\n% threshold p-value --> this will be adjusted for the number of data points\npThresh = 0.001;\npThresh = pThresh ./ numel(in);\n\n% threshold\nthresh = in;\nthresh(p>pThresh) = 0;\n\n% binarize\nthresh_bin = thresh;\nthresh_bin(thresh_bin>0) = 1;\n\n% discard segments smaller minSiz\nminSiz = 3;\ncc = bwconncomp(thresh_bin);\nfor n = 1:numel(cc.PixelIdxList)\n    if numel(cc.PixelIdxList{n})<minSiz\n        thresh_bin(cc.PixelIdxList{n}) = 0;\n    end\nend\n\n% join adjacent segments\nminSeparation = 50;\ncc = bwconncomp(thresh_bin);\nif numel(cc.PixelIdxList) > 1\n    for n = 2:numel(cc.PixelIdxList)\n        currentSegment = cc.PixelIdxList{n};\n        previousSegment = cc.PixelIdxList{n-1};\n        if currentSegment(1)-previousSegment(end) <= minSeparation\n            thresh_bin(previousSegment(end):currentSegment(1)) = 1;\n        end\n    end\nend\n\nthresh(~thresh_bin) = 0;\n\nout = thresh;\n\nend\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "eventDetector.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/eventDetection/eventDetector.m", "size": 9591, "source_encoding": "utf_8", "md5": "66349ac972c2fcdf425e9e2ba5603434", "text": "function eventDetection = eventDetector(ROIStats, stim, eventDetectMethod, frameRate, psConfig, saveName)\n    %[eventMatAllRuns, instFiringRateAllRuns, residuals, models] = testingEventDetector(ROIStats, stim, eventDetectMethod, frameRate, psConfig, saveName)\n% event detection function - wrapper to different event detection algorithms\n% input: structure created by GetRoiStats (*_RoiStats)\n%\n%\n% Adapted from eventDetector coded by Balazs Laurency, 2013\n% Modified by A. van der Bourg, 2014\n\n\ndbgLevel = 2;\n\ndoStimEventRasterPlot = 1;\ndoEventDetection = 1;\n\n%% Event detection parameters\n[config, V, P] = getDefaultParameters(eventDetectMethod, frameRate); %%#ok<NASGU,ASGLU>\no('    #eventDetector: loading event detection parameters of default config!', 3, dbgLevel);\n\n%% Event detection\n% event detection on roiStats\n% % last row is neuropil, remove it\n% allDFFData(end, :) = [];\n% init sizes of data set\nnROIs = size(ROIStats, 1);\nnRuns = size(ROIStats, 2);\nnFrames = size(ROIStats{1, 1}, 2);\n\no('    #eventDetector: variables initialized.', 3, dbgLevel);\no('    #eventDetector: starting event detection ... ', 2, dbgLevel);\neventDetectStartTime = tic;\n\neventData = cell(size(ROIStats));\nresiduals = cell(size(ROIStats));\nmodels = cell(size(ROIStats));\neventMatAllRuns = zeros(nROIs, nRuns * size(ROIStats{1, 1}, 2));\ninstFiringRateAllRuns = zeros(nROIs, nRuns* size(ROIStats{1, 1}, 2));\nstimVectorAllRuns = zeros(1, nRuns * size(ROIStats{1, 1}, 2));\n% outerDffData = ROIStats;\nnRunsWithError = 0;\nnTotEvents = 0;\n\nfor iRun = nRuns;\n    try\n        startIndex = (iRun - 1) * nFrames + 1;\n        endIndex = iRun * nFrames;\n\n        eventDetect = config.EventDetect;\n        \n        o('      #eventDetector: run %d/%d, %d rois...', iRun, nRuns, nROIs, 4, dbgLevel);\n        \n        if size(stim{iRun}, 2) ~= endIndex - startIndex + 1;\n            \n        o('      #eventDetector: run %d/%d, %d rois: skip for bad size.', iRun, nRuns, nROIs, 1, dbgLevel);\n            continue;\n        end;\n        stimVectorAllRuns(startIndex : endIndex) = stim{iRun};\n        eventDetect.rate = frameRate;\n        currentEventMat = zeros(nROIs, nFrames);\n        instFiringRate = zeros(nROIs, nFrames);\n        \n        if doEventDetection;\n             %Parallel computing for multiple ROIs\n            parfor iROI = 1 : nROIs;\n                o('        #eventDetector: run %d/%d - roi %d/%d ...', iRun, nRuns, iROI, nROIs, 5, dbgLevel);\n                caTrace = ROIStats{iROI, iRun};\n%                 caTrace = mpi_BandPassFilterTimeSeries(caTrace, 1 / frameRate, bpfilter.low, bpfilter.high); %#ok<PFBNS>\n                if isempty(caTrace);\n                    warning('eventDetector:caTraceEmpty', 'caTrace is empty!');\n                    continue;\n                elseif isnan(caTrace);\n%                     warning('eventDetector:caTraceNaN', 'caTrace is NaN!');\n                    eventData{iROI, iRun} = nan(1, nFrames); %#ok<PFOUS>\n                    continue;\n                end;\n\n                switch lower(eventDetectMethod)\n                    case 'fast_oopsi'\n                        oopsiOut = fast_oopsi(caTrace, V, P);\n                        oopsiOut(oopsiOut < config.EventDetect.oopsi_thr) = 0; %#ok<PFBNS>\n                        currentEventMat(iROI, :) = oopsiOut';\n                        eventData{iROI, iRun} = oopsiOut';\n                    case 'peeling'\n%                         eventOut = doPeeling(eventDetect, caTrace);\n                        [~, ~, peelRes] = Peeling(caTrace, frameRate);\n                        residuals{iROI, iRun} = peelRes.peel;\n                        models{iROI, iRun} = peelRes.model;\n%                         eventCounts(iROI) = sum(peelRes.spiketrain); %%#ok<PFOUS>\n%                         residualVector(iROI) = sum(abs(eventOut.data.peel)) / length(eventOut.data.peel);\n                        currentEventMat(iROI, :) = peelRes.spiketrain;\n                        eventData{iROI, iRun} = peelRes.spiketrain;\n                        %if doExtractInstFiringRate\n                            % smoothing kernel for converting spike trains into inst. firing rate (in Hz);\n                            % sigma in units of frames (default: 1 frames)\n                            \n                            % Smoothed gauss kernel instantaneous firing\n                            % rate extraction\n                            gausskern = normpdf((1:nFrames),round(0.5*nFrames),1);\n                            APdens = conv(peelRes.spiketrain', gausskern);\n                            %Obtain the instantaneous firing rate from the\n                            %smoothed AP-density\n                            instFiringRate(iROI,:) = frameRate*APdens(round(0.5*nFrames):round(0.5*nFrames)+nFrames-1);\n                        %end;\n                end;\n                nEventsFound = sum(currentEventMat(iROI, :));\n                nTotEvents = nTotEvents + nEventsFound;\n                o('        #eventDetector: run %d/%d - roi %d/%d done, %d event(s) found.', ...\n                    iRun, nRuns, iROI, nROIs, nEventsFound, 4, dbgLevel);\n            end ;\n        end;\n        eventMatAllRuns(:, startIndex : endIndex) = currentEventMat;\n        instFiringRateAllRuns(:, startIndex:endIndex) = instFiringRate;\n        o('      #eventDetector: run %d/%d done.', iRun, nRuns, 3, dbgLevel);\n    catch err;\n        nRunsWithError = nRunsWithError + 1; %#ok<NASGU>\n        o('      #eventDetector: problem in run %d/%d.', iRun, nRuns, 2, dbgLevel);\n        rethrow(err);\n    end;\nend;\n\neventDetectEndTime = toc(eventDetectStartTime);\n% PSTraceRoi = PsPlotAnalysisCellArray(ROIStats, stim, psConfig);\n\no('    #eventDetector: event detection done for %d runs (%d error(s), %d total events, %.2f sec).', ...\n    nRuns, nRunsWithError, nTotEvents, eventDetectEndTime, 2, dbgLevel);\n\nif ~isempty(eventMatAllRuns) && nTotEvents > 0;\n    o('    #eventDetector: extracting peri-stimulus events...', 3, dbgLevel);\n    PSEventRoi = PsPlotAnalysis(eventMatAllRuns, stimVectorAllRuns, psConfig);\n    config.PsPlotEventRoi = PSEventRoi;\n    o('    #eventDetector: extracting peri-stimulus events done.', 2, dbgLevel);\nelse\n    o('    #eventDetector: no events found (%d).', nTotEvents, 1, dbgLevel);\nend\n\no('    #eventDetector: moving to plotting section ...', 3, dbgLevel);\n\n\n%% Population stimulus event raster plot\nif doStimEventRasterPlot;\n    o('    #eventDetector: plotting the peri-stimulus event raster plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotStimEventRaster( ...\n        'PopRasterSinglePlot',  ...             % title of the plot\n        PSEventRoi, ...                     % event counts around the stimulus\n        stim{1}, ...                   % stimuli for extracting their 'name'\n        psConfig.base, ...       % number of frames looked before the stimulus\n        psConfig.evoked, ...     % number of frames looked after the stimulus\n        frameRate);                 % frame rate\n%     set(fig, 'WindowStyle', 'docked');\n    if doStimEventRasterPlot > 1;\n        saveas(fig, sprintf('%s_EventStimRaster_%s', saveName, eventDetectMethod));\n        saveas(fig, sprintf('%s_EventStimRaster_%s.png', saveName, eventDetectMethod));\n        close(fig);\n    end;\n    o('    #eventDetector: plotting the peri-stimulus event raster plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n\n\n%% Output all variables\n eventDetection.eventMatAllRuns = eventMatAllRuns;\n eventDetection.instFiringRateAllRuns= instFiringRateAllRuns;\n eventDetection.residuals = residuals;\n eventDetection.models = models;\n%varargout{1} = config;\n\nend\n\n%Default parameters - optimized for OGB?\nfunction [config, V, P] = getDefaultParameters(eventDetectMethod, frameRate)\n    amp = 12;\n    tau = 2;\n    onsettau = 0.01;\n    switch lower(eventDetectMethod);\n        case 'fast_oopsi';\n            config.EventDetect.amp = amp;\n            config.EventDetect.tau = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.doPlot = 0; % should be switched off\n            config.EventDetect.lam = 0.2; % firing rate(ish)\n            config.EventDetect.base_frames = 10;\n            config.EventDetect.oopsi_thr = 0.3;\n            config.EventDetect.integral_thr = 5;\n            config.EventDetect.filter = [7 2];\n            config.EventDetect.minGof = 0.5;\n\n            P.lam = config.EventDetect.lam;\n            % P.gam = (1-(1/freq_ca)) / ca_tau;\n\n            V.dt = 1/frameRate;\n            V.est_gam = 1; % estimate decay time parameter (does not work)\n            V.est_sig = 1; % estimate baseline noise SD\n            V.est_lam = 1; % estimate firing rate\n            V.est_a = 0; % estimate spatial filter\n            V.est_b = 0; % estimate background fluo.\n            V.fast_thr = 1;\n            V.fast_iter_max = 3;\n        case 'peeling';\n            V = [];\n            P = [];\n            config.EventDetect.optimizeSpikeTimes = 0;\n            config.EventDetect.schmittHi = [1.75 0 3];\n            config.EventDetect.schmittLo = [-1 -3 0];\n            config.EventDetect.schmittMinDur = [0.3 0.05 3];\n            config.EventDetect.A1 = amp;\n            config.EventDetect.tau1 = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.optimMethod = 'none';\n            config.EventDetect.minPercentChange = 0.1;\n            config.EventDetect.maxIter = 20;\n            config.EventDetect.plotFinal = 0;\n        case 'none';\n            config = [];\n            V = [];\n            P = [];\n            warning('Nothing to do here! Exit ...')\n            return;\n    end;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "PeelingOptimizeSpikeTimesSaturation.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/eventDetection/PeelingOptimizeSpikeTimesSaturation.m", "size": 4465, "source_encoding": "utf_8", "md5": "82892eced9f5fb70e0d8ad3f544df164", "text": "function [spkTout,output] = PeelingOptimizeSpikeTimesSaturation(dff,spkTin,lowerT,upperT,...\n    ca_amp,ca_gamma,ca_onsettau,ca_rest, ca_kappas, kd, conc, dffmax, frameRate, dur, optimMethod,maxIter,doPlot)\n% optimization of spike times found by Peeling algorithm\n% minimize the sum of the residual squared\n% while several optimization algorithms are implemented (see below), we have only used pattern\n% search. Other algorithms are only provided for convenience and are not tested sufficiently.\n%\n% Henry Luetcke (hluetck@gmail.com)\n% Brain Research Institut\n% University of Zurich\n% Switzerland\n\nspkTout = spkTin;\nt = (1:numel(dff))./frameRate;\n\nca = spkTimes2FreeCalcium(spkTin,ca_amp,ca_gamma,ca_onsettau,ca_rest, ca_kappas,...\n                                    kd, conc,frameRate,dur);\nmodeltmp = Calcium2Fluor(ca,ca_rest,kd,dffmax);\nmodel = modeltmp(1:length(dff));\n\nif doPlot\n    figure('Name','Before Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\nresidual = dff - model;\nresInit = sum(residual.^2);\n\n% start optimization\nx0 = spkTin;\nlbound = spkTin - lowerT;\nlbound(lbound<0) = 0;\nubound = spkTin + upperT;\nubound(ubound>max(t)) = max(t);\n\nlbound = zeros(size(spkTin));\nubound = repmat(max(t),size(spkTin));\n\nopt_args.dff = dff;\nopt_args.ca_rest = ca_rest;\nopt_args.ca_amp = ca_amp;\nopt_args.ca_gamma = ca_gamma;\nopt_args.ca_onsettau = ca_onsettau;\nopt_args.ca_kappas = ca_kappas;\nopt_args.kd = kd;\nopt_args.conc = conc;\nopt_args.dffmax = dffmax;\nopt_args.frameRate = frameRate;\nopt_args.dur = dur;\n\noptimClock = tic;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        options = saoptimset;\n    case 'pattern search'\n        options = psoptimset;\n    case 'genetic'\n        options = gaoptimset;\n    otherwise\n        error('Optimization method %s not supported.',optimMethod)\nend\n\n% options for optimization algorithms\n% not all options are used for all algorithms\noptions.Display = 'off';\noptions.MaxIter = maxIter;\noptions.MaxIter = Inf;\noptions.UseParallel = 'always';\noptions.ObjectiveLimit = 0;\noptions.TimeLimit = 10; % in s / default is Inf\n\n% experimental\noptions.MeshAccelerator = 'on'; % off by default\noptions.TolFun = 1e-9; % default is 1e-6\noptions.TolMesh = 1e-9; % default is 1e-6\noptions.TolX = 1e-9; % default is 1e-6\n% options.MaxFunEvals = numel(spkTin)*100; % default is 2000*numberOfVariables\n% options.MaxFunEvals = 20000;\n\noptions.Display = 'none';\n% options.Display = 'final';\n\n% options.PlotFcns = {@psplotbestf @psplotbestx};\n% options.OutputFcns = @psoutputfcn_peel;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        [x, fval , exitFlag, output] = simulannealbnd(...\n            @(x) objectiveFunc(x,opt_args),x0,lbound,ubound,options);\n    case 'pattern search'\n        [x, fval , exitFlag, output] = patternsearch(...\n            @(x) objectiveFunc(x,opt_args),x0,[],[],[],[],lbound,...\n            ubound,[],options);\n    case 'genetic'\n        [x, fval , exitFlag, output] = ga(...\n            @(x) objectiveFunc(x,opt_args),numel(x0),[],[],[],[],lbound,...\n            ubound,[],options);\nend\n\nif fval < resInit\n    spkTout = x;\nelse\n    disp('Optimization did not improve residual. Keeping input spike times.')\nend\n\nif doPlot\n    fprintf('Optimization time (%s): %1.2f s\\n',optimMethod,toc(optimClock))\n    fprintf('Final squared residual: %1.2f (Change: %1.2f)\\n',fval,resInit-fval);\n    spkVector = zeros(1,numel(t));\n    for i = 1:numel(spkTout)\n        [~,idx] = min(abs(spkTout(i)-t));\n        spkVector(idx) = spkVector(idx)+1;\n    end\n    model = conv(spkVector,modelTransient);\n    model = model(1:length(t));\n    figure('Name','After Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\n\nfunction residual = objectiveFunc(spkTin,opt_args)\n\ndff = opt_args.dff;\nca_rest = opt_args.ca_rest;\nca_amp = opt_args.ca_amp;\nca_gamma = opt_args.ca_gamma;\nca_onsettau = opt_args.ca_onsettau;\nca_kappas = opt_args.ca_kappas;\nkd = opt_args.kd;\nconc = opt_args.conc;\ndffmax = opt_args.dffmax;\nframeRate = opt_args.frameRate;\ndur = opt_args.dur;\n\nca = spkTimes2FreeCalcium(sort(spkTin),ca_amp,ca_gamma,ca_onsettau,ca_rest, ca_kappas,...\n                                    kd, conc,frameRate,dur);\nmodeltmp = Calcium2Fluor(ca,ca_rest,kd,dffmax);\nmodel = modeltmp(1:length(dff));\n\nresidual = dff-model;\nresidual = sum(residual.^2);\n\n\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "PeelingOptimizeSpikeTimes.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/eventDetection/PeelingOptimizeSpikeTimes.m", "size": 4159, "source_encoding": "utf_8", "md5": "61e419a2e0808657c62b39f38bc0fb60", "text": "function [spkTout,output] = PeelingOptimizeSpikeTimes(dff,spkTin,lowerT,upperT,...\n    rate,tauOn,A1,tau1,optimMethod,maxIter,doPlot)\n% optimization of spike times found by Peeling algorithm\n% minimize the sum of the residual squared\n% while several optimization algorithms are implemented (see below), we have only used pattern\n% search. Other algorithms are only provided for convenience and are not tested sufficiently.\n%\n% Henry Luetcke (hluetck@gmail.com)\n% Brain Research Institut\n% University of Zurich\n% Switzerland\n\nt = (1:numel(dff))./rate;\n\nmodelTransient = modelCalciumTransient(t,t(1),tauOn,A1,tau1);\nmodelTransient = modelTransient';\n\nspkTout = spkTin;\n\nspkVector = zeros(1,numel(t));\nfor i = 1:numel(spkTin)\n    [~,idx] = min(abs(spkTin(i)-t));\n    spkVector(idx) = spkVector(idx)+1;\nend\nmodel = conv(spkVector,modelTransient);\nmodel = model(1:length(t));\n\nif doPlot\n    figure('Name','Before Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\nresidual = dff - model;\n\nresInit = sum(residual.^2);\n\n% start optimization\nx0 = spkTin;\nlbound = spkTin - lowerT;\nlbound(lbound<0) = 0;\nubound = spkTin + upperT;\nubound(ubound>max(t)) = max(t);\n\nlbound = zeros(size(spkTin));\nubound = repmat(max(t),size(spkTin));\n\nopt_args.dff = dff;\nopt_args.rate = rate;\nopt_args.tauOn = tauOn;\nopt_args.A1 = A1;\nopt_args.tau1 = tau1;\noptimClock = tic;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        options = saoptimset;\n    case 'pattern search'\n        options = psoptimset;\n    case 'genetic'\n        options = gaoptimset;\n    otherwise\n        error('Optimization method %s not supported.',optimMethod)\nend\n\n% options for optimization algorithms\n% not all options are used for all algorithms\noptions.Display = 'off';\noptions.MaxIter = maxIter;\noptions.MaxIter = Inf;\noptions.UseParallel = 'always';\noptions.ObjectiveLimit = 0;\n% options.TimeLimit = 10; % in s / default is Inf\n\n% experimental\noptions.MeshAccelerator = 'on'; % off by default\noptions.TolFun = 1e-9; % default is 1e-6\noptions.TolMesh = 1e-9; % default is 1e-6\noptions.TolX = 1e-9; % default is 1e-6\n% options.MaxFunEvals = numel(spkTin)*100; % default is 2000*numberOfVariables\n% options.MaxFunEvals = 20000;\n\noptions.Display = 'none';\n% options.Display = 'final';\n\n% options.PlotFcns = {@psplotbestf @psplotbestx};\n% options.OutputFcns = @psoutputfcn_peel;\n\nswitch lower(optimMethod)\n    case 'simulated annealing'\n        [x, fval , exitFlag, output] = simulannealbnd(...\n            @(x) objectiveFunc(x,opt_args),x0,lbound,ubound,options);\n    case 'pattern search'\n        [x, fval , exitFlag, output] = patternsearch(...\n            @(x) objectiveFunc(x,opt_args),x0,[],[],[],[],lbound,...\n            ubound,[],options);\n    case 'genetic'\n        [x, fval , exitFlag, output] = ga(...\n            @(x) objectiveFunc(x,opt_args),numel(x0),[],[],[],[],lbound,...\n            ubound,[],options);\nend\n\nif fval < resInit\n    spkTout = x;\nelse\n    disp('Optimization did not improve residual. Keeping input spike times.')\nend\n\nif doPlot\n    fprintf('Optimization time (%s): %1.2f s\\n',optimMethod,toc(optimClock))\n    fprintf('Final squared residual: %1.2f (Change: %1.2f)\\n',fval,resInit-fval);\n    spkVector = zeros(1,numel(t));\n    for i = 1:numel(spkTout)\n        [~,idx] = min(abs(spkTout(i)-t));\n        spkVector(idx) = spkVector(idx)+1;\n    end\n    model = conv(spkVector,modelTransient);\n    model = model(1:length(t));\n    figure('Name','After Optimization')\n    plot(t,dff,'k'), hold on, plot(t,model,'r'), plot(t,dff-model,'b')\n    legend('DFF','Model','Residual')\nend\n\n\nfunction residual = objectiveFunc(spkTin,opt_args)\n\ndff = opt_args.dff;\nrate = opt_args.rate;\ntauOn = opt_args.tauOn;\nA1 = opt_args.A1;\ntau1 = opt_args.tau1;\nt = (1:numel(dff))./rate;\nmodelTransient = spkTimes2Calcium(0,tauOn,A1,tau1,0,0,rate,max(t));\nspkVector = zeros(1,numel(t));\nfor i = 1:numel(spkTin)\n    [~,idx] = min(abs(spkTin(i)-t));\n    spkVector(idx) = spkVector(idx)+1;\nend\nmodel = conv(spkVector,modelTransient);\nmodel = model(1:length(t));\nresidual = dff-model;\nresidual = sum(residual.^2);\n\n\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "Peeling.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/eventDetection/Peeling.m", "size": 9395, "source_encoding": "utf_8", "md5": "87cff27352caf1bd8056e60c2e4c81cf", "text": "function [ca_p, peel_p, data] = Peeling(dff, rate, varargin)\n% this is the main routine of the peeling algorithm\n%\n% Peeling algorithm was developed by Fritjof Helmchen\n% Brain Research Institute\n% University of Zurich\n% Switzerland\n%\n% Matlab implementation and spike timing optimization by Henry Luetcke & Fritjof Helmchen\n% Brain Research Institute\n% University of Zurich\n% Switzerland\n%\n% Please cite:\n% Grewe BF, Langer D, Kasper H, Kampa BM, Helmchen F. High-speed in vivo calcium imaging\n% reveals neuronal network activity with near-millisecond precision.\n% Nat Methods. 2010 May;7(5):399-405. \n\nmaxRate_peel = Inf;\n\nif rate > maxRate_peel\n    peel_rate = maxRate_peel;\n    fit_rate = rate;\n    x = 1/rate:1/rate:numel(dff)/rate;\n    xi = 1/peel_rate:1/peel_rate:max(x);\n    peel_dff = interp1(x,dff,xi);\nelse\n    peel_rate = rate;\n    fit_rate = rate;\n    peel_dff = dff;\nend\n\n[ca_p,exp_p,peel_p, data] = InitPeeling(peel_dff, peel_rate);\n\nif nargin > 2\n    for n = 1:numel(varargin)\n        S = varargin{n};\n        if n == 1\n            ca_p = overrideFieldValues(ca_p,S);\n        elseif n == 2\n            exp_p = overrideFieldValues(exp_p,S);\n        elseif n == 3\n            peel_p = overrideFieldValues(peel_p,S);\n        end\n    end\nend\n\ndata.model = 0;\ndata.freecamodel = ca_p.ca_rest;\ndata.spikes = zeros(1,1000);\ndata.numspikes = 0;\n\ndata.peel = data.dff;\n\nwsiz = round(peel_p.slidwinsiz*exp_p.acqrate);\ncheckwsiz = round(peel_p.negintwin*exp_p.acqrate);\n\npeel_p.smttmindurFrames = ceil(peel_p.smttmindur*exp_p.acqrate);\npeel_p.smttlowMinEvents = 1;\n\nnexttim = 1/exp_p.acqrate;\n\n[ca_p, peel_p, data] = FindNextEvent(ca_p, exp_p, peel_p, data, nexttim);\nif (peel_p.evtfound == 1)\n    data.numspikes = data.numspikes + 1;\n    data.spikes(data.numspikes) = peel_p.nextevt;\n    [ca_p, exp_p, data] = SingleFluorTransient(ca_p, exp_p, data, peel_p.spk_recmode, peel_p.nextevt);\n    data.model = data.model + data.singleTransient;\nend\n\nmaxiter = 999999;\niter = 0;\nnexttimMem = Inf;\nnexttimCounter = 0;\ntimeStepForward = 2./exp_p.acqrate;\nwhile (peel_p.evtfound == 1)\n    % check integral after subtracting Ca transient\n    if (peel_p.spk_recmode == 'linDFF')\n    elseif (peel_p.spk_recmode == 'satDFF')\n       ca_p.onsetposition = peel_p.nextevt;\n       ca_p = IntegralofCaTransient(ca_p, peel_p, exp_p, data);\n    end\n    \n    dummy = data.peel - data.singleTransient;\n    [~,startIdx] = min(abs(data.tim-data.spikes(data.numspikes)));\n    [~,stopIdx] = min(abs(data.tim-(data.spikes(data.numspikes)+...\n        peel_p.intcheckwin)));\n    if startIdx < stopIdx\n        currentTim = data.tim(startIdx:stopIdx);\n        currentPeel = dummy(startIdx:stopIdx);\n        currentIntegral = trapz(currentTim,currentPeel);\n    else\n        % if this is true, startIdx is the last data point and we should\n        % not accept it as a spike\n        currentIntegral = ca_p.negintegral*peel_p.negintacc;\n    end\n    if currentIntegral > (ca_p.negintegral*peel_p.negintacc)\n        data.peel = data.peel - data.singleTransient;\n        nexttim = data.spikes(data.numspikes) - peel_p.stepback;\n        if (nexttim < 0)\n            nexttim = 1/exp_p.acqrate;\n        end\n    else\n        data.spikes(data.numspikes) = [];\n        data.numspikes = data.numspikes-1;\n        data.model = data.model - data.singleTransient;\n        nexttim = peel_p.nextevt + timeStepForward;\n    end\n    \n    peel_p.evtaccepted = 0;\n    \n    [ca_p, peel_p, data] = FindNextEvent(ca_p, exp_p, peel_p, data, nexttim);\n    \n    if peel_p.evtfound\n            data.numspikes = data.numspikes + 1;\n            data.spikes(data.numspikes) = peel_p.nextevt;\n            [ca_p, exp_p, data] = SingleFluorTransient(ca_p, exp_p, data, peel_p.spk_recmode, peel_p.nextevt);\n            data.model = data.model + data.singleTransient;\n    else\n        break\n    end\n    \n    iter = iter + 1;\n    \n    if nexttim == nexttimMem\n        nexttimCounter = nexttimCounter + 1;\n    else\n       nexttimMem = nexttim;\n       nexttimCounter = 0;\n    end\n    %%\n    \n    if nexttimCounter > 50\n       nexttim = nexttim + timeStepForward;\n    end\n    \n    if (iter > maxiter)\n%         warning('Reached maxiter (%1.0f). nexttim=%1.2f. Timeout!',maxiter,nexttim);\n%         save\n%         error('Covergence failed!')\n        break\n    end\nend\n\nif length(data.spikes) > data.numspikes\n    data.spikes(data.numspikes+1:end) = [];\nend\n\n% go back to original frame rate\nif rate > maxRate_peel\n    spikes = data.spikes;\n    [ca_p,exp_p,peel_p, data] = InitPeeling(dff, fit_rate);\n    if nargin > 2\n        for n = 1:numel(varargin)\n            S = varargin{n};\n            if n == 1\n                ca_p = overrideFieldValues(ca_p,S);\n            elseif n == 2\n                exp_p = overrideFieldValues(exp_p,S);\n            elseif n == 3\n                peel_p = overrideFieldValues(peel_p,S);\n            end\n        end\n    end\n    data.spikes = spikes;\nend\n\n% optimization of reconstructed spike times to improve timing\noptMethod = 'pattern search';\noptMaxIter = 100000;\n%lowerT = 1; % relative to x0\n%upperT = 1; % relative to x0\nlowerT = 0.1; % relative to x0\nupperT = 0.1; % relative to x0\n\nif numel(data.spikes) && peel_p.optimizeSpikeTimes\n    if (peel_p.spk_recmode == 'linDFF')\n        spikes = PeelingOptimizeSpikeTimes(data.dff,data.spikes,lowerT,upperT,...\n                    exp_p.acqrate,ca_p.onsettau,ca_p.amp1,ca_p.tau1,optMethod,optMaxIter,0);\n    elseif (peel_p.spk_recmode == 'satDFF')\n        spikes = PeelingOptimizeSpikeTimesSaturation(data.dff,data.spikes,lowerT,upperT,...\n                    ca_p.ca_amp,ca_p.ca_gamma,ca_p.ca_onsettau,ca_p.ca_rest,ca_p.ca_kappas, exp_p.kd,...\n                    exp_p.conc,exp_p.dffmax, exp_p.acqrate, length(data.dff)./exp_p.acqrate, optMethod,optMaxIter,0);\n    else\n        error('Undefined mode');\n    end\n    data.spikes = sort(spikes);\nend\n\n% fit onset to improve timing accuracy\nif peel_p.fitonset\n    onsetfittype = fittype('modelCalciumTransient(t,onsettime,onsettau,amp1,tau1)',...\n        'independent','t','coefficients',{'onsettime','onsettau','amp1'},...\n        'problem',{'tau1'});\n    \n    wleft = round(peel_p.fitwinleft*exp_p.acqrate);     % left window for onset fit\n    wright = round(peel_p.fitwinright*exp_p.acqrate);    % right window for onset fit\n    for i = 1:numel(data.spikes)\n        [~,idx] = min(abs(data.spikes(i)-data.tim));\n        if (idx-wleft) < 1\n            currentwin = data.dff(1:idx+wright);\n            currenttim = data.tim(1:idx+wright);\n        elseif (idx+wright) > numel(data.dff)\n            currentwin = data.dff(idx-wleft:numel(data.dff));\n            currenttim = data.tim(idx-wleft:numel(data.dff));\n            currentwin = currentwin - mean(data.dff(idx-wleft:idx));\n        else\n            currentwin = data.dff(idx-wleft:idx+wright);\n            currenttim = data.tim(idx-wleft:idx+wright);\n            currentwin = currentwin - mean(data.dff(idx-wleft:idx));\n        end\n        lowerBounds = [currenttim(1) 0.1*ca_p.onsettau 0.5*ca_p.amp1];\n        upperBounds = [currenttim(end) 5*ca_p.onsettau 10*ca_p.amp1];\n        startPoint = [data.spikes(i) ca_p.onsettau ca_p.amp1];\n        problemParams = {ca_p.tau1};\n        \n        fOptions = fitoptions('Method','NonLinearLeastSquares','Lower',...\n            lowerBounds,...\n            'Upper',upperBounds,'StartPoint',startPoint);\n        [fitonset,gof] = fit(currenttim',currentwin',onsetfittype,...\n            'problem',problemParams,fOptions);\n        \n        if gof.rsquare < 0.95\n%                         fprintf('\\nBad onset fit (t=%1.3f, r^2=%1.3f)\\n',...\n%                             data.spikes(i),gof.rsquare);\n        else\n            %             fprintf('\\nGood onset fit (r^2=%1.3f)\\n',gof.rsquare);\n            data.spikes(i) = fitonset.onsettime;\n        end\n    end\nend\n\n% loop to create spike train vector from spike times\ndata.spiketrain = zeros(1,numel(data.tim));\nfor i = 1:numel(data.spikes)\n    [~,idx] = min(abs(data.spikes(i)-data.tim));\n    data.spiketrain(idx) = data.spiketrain(idx)+1;\nend\n\n% re-derive model and residuals after optimization\n\nif (peel_p.spk_recmode == 'linDFF')\n    modelTransient = spkTimes2Calcium(0,ca_p.onsettau,ca_p.amp1,ca_p.tau1,...\n                                     ca_p.amp2,ca_p.tau2,exp_p.acqrate,max(data.tim));\n    data.model = conv(data.spiketrain,modelTransient);\n    data.model = data.model(1:length(data.tim));\nelseif (peel_p.spk_recmode == 'satDFF')\n    modeltmp = spkTimes2FreeCalcium(data.spikes,ca_p.ca_amp,ca_p.ca_gamma,ca_p.ca_onsettau,ca_p.ca_rest, ca_p.ca_kappas,...\n                                    exp_p.kd, exp_p.conc,exp_p.acqrate,max(data.tim));\n    data.model = Calcium2Fluor(modeltmp,ca_p.ca_rest,exp_p.kd, exp_p.dffmax);\nend\n\ndata.peel = data.dff - data.model;\n\n% plotting parameter\nif isfield(peel_p,'doPlot')\n    if peel_p.doPlot\n        doPlot = 1;\n    else\n        doPlot = 0;\n    end\nelse\n    doPlot = 1;\nend\nif doPlot % plots at interpolation rate\n    figure; plot(data.tim,data.peel); hold all\n    plot(data.tim,data.dff); hold all\n    plot(data.tim,data.model); hold all\n    plot(data.tim,data.spiketrain,'-k','LineWidth',2)\n    legend({'Residual','Calcium','UPAPs'}) % unverified putative action potential\nend\n\nend\n\nfunction Sout = overrideFieldValues(Sout,Sin)\nfieldIDs = fieldnames(Sin);\nfor n = 1:numel(fieldIDs)\n    Sout.(fieldIDs{n}) = Sin.(fieldIDs{n});\nend\n\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "eventDetector_OLDBalazs.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/eventDetection/eventDetector_OLDBalazs.m", "size": 15137, "source_encoding": "utf_8", "md5": "826457af3667681b47d959443193ceeb", "text": "function varargout = eventDetector(ROIStats, stim, ROISet, eventDetectMethod, frameRate, bpfilter, psConfig, saveName)\n% event detection function - wrapper to different event detection algorithms\n% input: structure created by GetRoiStats (*_RoiStats)\ndbgLevel = 2;\n% required folders\n%folderList = {'Projects/EventDetect','Projects/TwoPhotonAnalyzer'};\n%addFolders2Path(folderList,1);\nmaxRuns = Inf; % for testing\n% maxRuns = 2; % for testing\ndoCaTracesPlot = 1;\ndoPlotEvents = doCaTracesPlot && 1; %%#ok<NASGU>\ndoRasterPlot = 1;\ndoStimEventRasterPlot = 1;\n% doPsStimPlot = 1;\ndoEventCountPlot = 0;\ndoEventDetection = 1;\no('    #eventDetector: method: \"%s\", maxRuns = %d ...', eventDetectMethod, maxRuns, 1, dbgLevel);\n%% Event detection parameters\n[config, V, P] = getDefaultParameters(eventDetectMethod, frameRate); %%#ok<NASGU,ASGLU>\no('    #eventDetector: event detection parameters configured.', 3, dbgLevel);\n%% Event detection\n% event detection on roiStats\n% % last row is neuropil, remove it\n% allDFFData(end, :) = [];\n% init sizes of data set\nnROIs = size(ROIStats, 1);\nnRuns = size(ROIStats, 2);\nnFrames = size(ROIStats{1, 1}, 2);\n% only process requested runs\nif nRuns > maxRuns;\n    nRuns = maxRuns;\n    ROIStats(:, maxRuns + 1 : end) = [];\nend\n\no('    #eventDetector: variables initialized.', 3, dbgLevel);\no('    #eventDetector: starting event detection ... ', 2, dbgLevel);\neventDetectStartTime = tic;\neventData = cell(size(ROIStats));\nresiduals = cell(size(ROIStats));\nmodels = cell(size(ROIStats));\neventMatAllRuns = zeros(nROIs, nRuns * size(ROIStats{1, 1}, 2));\nstimVectorAllRuns = zeros(1, nRuns * size(ROIStats{1, 1}, 2));\n% outerDffData = ROIStats;\nnRunsWithError = 0;\nnTotEvents = 0;\n% nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\n% for iRun = 1 : nRuns;\nnRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\nfor iRun = 7;\n    try\n%         ROIStats = outerDffData;\n        startIndex = (iRun - 1) * nFrames + 1;\n        endIndex = iRun * nFrames;\n        eventDetect = config.EventDetect;\n        o('      #eventDetector: run %d/%d, %d rois...', iRun, nRuns, nROIs, 4, dbgLevel);\n        if size(stim{iRun}, 2) ~= endIndex - startIndex + 1;\n        o('      #eventDetector: run %d/%d, %d rois: skip for bad size.', iRun, nRuns, nROIs, 1, dbgLevel);\n            continue;\n        end;\n        stimVectorAllRuns(startIndex : endIndex) = stim{iRun};\n        eventDetect.rate = frameRate;\n%         eventCounts = zeros(1, nROIs);\n%         residuals = zeros(1, nROIs);\n        currentEventMat = zeros(nROIs, nFrames);\n        \n        if doEventDetection;\n%             parfor iROI = 1 : nROIs;\n%             for iROI = 1 : nROIs;\n            for iROI = 1 : 3;\n                o('        #eventDetector: run %d/%d - roi %d/%d ...', iRun, nRuns, iROI, nROIs, 5, dbgLevel);\n                caTrace = ROIStats{iROI, iRun};\n%                 caTrace = mpi_BandPassFilterTimeSeries(caTrace, 1 / frameRate, bpfilter.low, bpfilter.high); %#ok<PFBNS>\n                if isempty(caTrace);\n                    warning('eventDetector:caTraceEmpty', 'caTrace is empty!');\n                    continue;\n                elseif isnan(caTrace);\n%                     warning('eventDetector:caTraceNaN', 'caTrace is NaN!');\n                    eventData{iROI, iRun} = nan(1, nFrames);\n                    continue;\n                end;\n                switch lower(eventDetectMethod)\n                    case 'fast_oopsi'\n                        oopsiOut = fast_oopsi(caTrace, V, P);\n                        oopsiOut(oopsiOut < config.EventDetect.oopsi_thr) = 0; %#ok<PFBNS>\n                        currentEventMat(iROI, :) = oopsiOut';\n                        eventData{iROI, iRun} = oopsiOut';\n                    case 'peeling'\n%                         eventOut = doPeeling(eventDetect, caTrace);\n                        [~, ~, peelRes] = Peeling(caTrace, frameRate);\n                        residuals{iROI, iRun} = peelRes.peel;\n                        models{iROI, iRun} = peelRes.model;\n%                         eventCounts(iROI) = sum(peelRes.spiketrain); %%#ok<PFOUS>\n%                         residualVector(iROI) = sum(abs(eventOut.data.peel)) / length(eventOut.data.peel);\n                        currentEventMat(iROI, :) = peelRes.spiketrain;\n                        eventData{iROI, iRun} = peelRes.spiketrain;\n                end;\n                nEventsFound = sum(currentEventMat(iROI, :));\n                nTotEvents = nTotEvents + nEventsFound;\n                o('        #eventDetector: run %d/%d - roi %d/%d done, %d event(s) found.', ...\n                    iRun, nRuns, iROI, nROIs, nEventsFound, 4, dbgLevel);\n            end ;\n        end;\n        eventMatAllRuns(:, startIndex : endIndex) = currentEventMat;\n        o('      #eventDetector: run %d/%d done.', iRun, nRuns, 3, dbgLevel);\n    catch err;\n        nRunsWithError = nRunsWithError + 1; %#ok<NASGU>\n        o('      #eventDetector: problem in run %d/%d.', iRun, nRuns, 2, dbgLevel);\n        rethrow(err);\n    end;\nend;\neventDetectEndTime = toc(eventDetectStartTime);\n% PSTraceRoi = PsPlotAnalysisCellArray(ROIStats, stim, psConfig);\no('    #eventDetector: event detection done for %d runs (%d error(s), %d total events, %.2f sec).', ...\n    nRuns, nRunsWithError, nTotEvents, eventDetectEndTime, 2, dbgLevel);\nif ~isempty(eventMatAllRuns) && nTotEvents > 0;\n    o('    #eventDetector: extracting peri-stimulus events...', 3, dbgLevel);\n    PSEventRoi = PsPlotAnalysis(eventMatAllRuns, stimVectorAllRuns, psConfig);\n    config.PsPlotEventRoi = PSEventRoi;\n    o('    #eventDetector: extracting peri-stimulus events done.', 2, dbgLevel);\nelse\n    o('    #eventDetector: no events found (%d).', nTotEvents, 1, dbgLevel);\nend\n\no('    #eventDetector: moving to plotting section ...', 3, dbgLevel);\n%% Calcium DFF / DRR Plot - with events\nif doCaTracesPlot; % only plot if required\n    o('    #eventDetector: plotting the calcium DFF (or DRR) Plot with events...', 2, dbgLevel); %#ok<*UNRCH>\n    % go through each run\n%     nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\n%     for iRun = 1 : nRuns;\n    nRuns = 1; o('/!\\\\ WARNING: overriding nRuns! nRuns is now = %d.', nRuns, 0, 0);\n    for iRun = 7;\n        o('      #eventDetector: plotting run %d/%d ...', iRun, nRuns, 4, dbgLevel);\n        fig = plotROICaTracesWithEvent(iRun, saveName, ... % run number and figure name\n            cell2mat(ROIStats(:, iRun)), ...    % calcium traces as a nROI-by-nFrames matrix\n            cell2mat(eventData(:, iRun)), ...   % detected events as a nROI-by-nFrames matrix\n            stim{iRun}, ...                     % stimulus as a nFrames long vector\n            ROISet, ...                         % name and coordinates of the rois\n            frameRate, ...                      % frame rate\n            bpfilter, ...                       % band-pass filter settings\n            eventDetectMethod, ...              % used detection method\n            doPlotEvents);                      % tells whether to plot the events or not\n        \n        fig = plotROICaTracesWithEvent(iRun, saveName, ... % run number and figure name\n            cell2mat(residuals(:, iRun)), ...    % calcium traces as a nROI-by-nFrames matrix\n            cell2mat(eventData(:, iRun)), ...   % detected events as a nROI-by-nFrames matrix\n            stim{iRun}, ...                     % stimulus as a nFrames long vector\n            ROISet, ...                         % name and coordinates of the rois\n            frameRate, ...                      % frame rate\n            [], ...                             % band-pass filter settings\n            eventDetectMethod, ...              % used detection method\n            doPlotEvents);                      % tells whether to plot the events or not\n        \n        fig = plotROICaTracesWithEvent(iRun, saveName, ... % run number and figure name\n            cell2mat(models(:, iRun)), ...    % calcium traces as a nROI-by-nFrames matrix\n            cell2mat(eventData(:, iRun)), ...   % detected events as a nROI-by-nFrames matrix\n            stim{iRun}, ...                     % stimulus as a nFrames long vector\n            ROISet, ...                         % name and coordinates of the rois\n            frameRate, ...                      % frame rate\n            [], ...                             % band-pass filter settings\n            eventDetectMethod, ...              % used detection method\n            doPlotEvents);                      % tells whether to plot the events or not\n        \n        o('      #eventDetector: plotting run %d/%d done, saving...', iRun, nRuns, 4, dbgLevel);\n%         set(fig, 'WindowStyle', 'docked');\n        if doCaTracesPlot > 1;\n            if doPlotEvents;\n                saveName = sprintf('%s_ROICaTracesWithEvents_run%02d_%s', saveName, iRun, ...\n                    eventDetectMethod);\n            else\n                saveName = sprintf('%s_ROICaTracesWithoutEvents_run%02d_%s', saveName, iRun, ...\n                    eventDetectMethod);\n            end;\n            saveas(fig, saveName);\n            saveas(fig, [saveName '.png']);\n            close(fig);\n        end;\n        o('      #eventDetector: plotting & saving run %d/%d done.', iRun, nRuns, 3, dbgLevel);\n    end\n    o('    #eventDetector: plotting %d run(s) done.', nRuns, 2, dbgLevel);\nend;\n%% Event count plot\nif doEventCountPlot;\n    o('    #eventDetector: plotting the event counts for ROI image...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotCountROIMap( ...\n        eventCounts, ...                                % event counts for each ROI\n        256, 256, ... % dimensions of the frame\n        ROISet(1 : end - 1, 2));           % the positions of the ROI\n    %% TODO HARD CODED IMAGE DIMS\n    if doEventCountPlot > 1;\n        set(fig, 'WindowStyle', 'docked');\n        saveas(fig, sprintf('%s_ROIEventCount_%s', saveName, eventDetectMethod));\n        saveas(fig, sprintf('%s_ROIEventCount_%s.png', saveName, eventDetectMethod));\n        close(fig);\n    end;\n    o('    #eventDetector: plotting the event counts for ROI image done.', 3, dbgLevel); %#ok<*UNRCH>\nend;\n%% Population stimulus event raster plot\nif doStimEventRasterPlot;\n    o('    #eventDetector: plotting the peri-stimulus event raster plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    fig = plotStimEventRaster( ...\n        'PopRasterSinglePlot',  ...             % title of the plot\n        PSEventRoi, ...                     % event counts around the stimulus\n        stim{1}, ...                   % stimuli for extracting their 'name'\n        psConfig.base, ...       % number of frames looked before the stimulus\n        psConfig.evoked, ...     % number of frames looked after the stimulus\n        frameRate);                 % frame rate\n%     set(fig, 'WindowStyle', 'docked');\n    if doStimEventRasterPlot > 1;\n        saveas(fig, sprintf('%s_EventStimRaster_%s', saveName, eventDetectMethod));\n        saveas(fig, sprintf('%s_EventStimRaster_%s.png', saveName, eventDetectMethod));\n        close(fig);\n    end;\n    o('    #eventDetector: plotting the peri-stimulus event raster plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n% %% Population stimulus plot\n% if doPsStimPlot;\n%     o('    #eventDetector: plotting the peri-stimulus plot ...', 2, dbgLevel); %#ok<*UNRCH>\n%     fig = plotPSStimPlot( ...\n%         'PopPeriStimPlot',  ...                 % title of the plot\n%         PSTraceRoi, ...                         % all traces around the stimulus\n%         stim{1}, ...                   % stimuli for extracting their 'name'\n%         psConfig.base, ...       % number of frames looked before the stimulus\n%         psConfig.evoked, ...     % number of frames looked after the stimulus\n%         frameRate);                 % frame rate\n%     if doPsStimPlot > 1;\n% %         set(fig, 'WindowStyle', 'docked');\n%         saveas(fig, sprintf('%s_PSStimAverage_%s', roiStats.saveName, eventDetectMethod));\n%         saveas(fig, sprintf('%s_PSStimAverage_%s.png', roiStats.saveName, eventDetectMethod));\n%         close(fig);\n%     end;\n%     o('    #eventDetector: plotting the peri-stimulus plot done.', 2, dbgLevel); %#ok<*UNRCH>\n% end;\n%% Population raster plot\nif doRasterPlot;\n    o('    #eventDetector: plotting the population raster plot ...', 2, dbgLevel); %#ok<*UNRCH>\n    cellIDaxes = ROISet(:, 1);\n    switch lower(eventDetectMethod)\n        case {'peeling', 'fast_oopsi'};\n            titleStr = 'PopRaster';\n            [fig, ~] = PsPlot2Raster(PSEventRoi, frameRate, ...\n                psConfig.base + 1, cellIDaxes(1 : length(cellIDaxes) - 1), 1, 0);\n            set(fig, 'Name', titleStr, 'NumberTitle', 'off');\n%             set(fig, 'WindowStyle', 'docked');\n        if doRasterPlot > 1;\n            saveas(fig,sprintf('%s_EventRasterByRoi_%s', saveName, eventDetectMethod));\n            saveas(fig,sprintf('%s_EventRasterByRoi_%s.png', saveName, eventDetectMethod));\n            close(fig);\n        end;\n    end\n    o('    #eventDetector: plotting the population raster plot done.', 2, dbgLevel); %#ok<*UNRCH>\nend;\n%% end\nvarargout{1} = config;\nend\n\nfunction [config, V, P] = getDefaultParameters(eventDetectMethod, frameRate)\n    amp = 10;\n    tau = 2;\n    onsettau = 0.01;\n    switch lower(eventDetectMethod);\n        case 'fast_oopsi';\n            config.EventDetect.amp = amp;\n            config.EventDetect.tau = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.doPlot = 0; % should be switched off\n            config.EventDetect.lam = 0.2; % firing rate(ish)\n            config.EventDetect.base_frames = 10;\n            config.EventDetect.oopsi_thr = 0.3;\n            config.EventDetect.integral_thr = 5;\n            config.EventDetect.filter = [7 2];\n            config.EventDetect.minGof = 0.5;\n            P.lam = config.EventDetect.lam;\n            % P.gam = (1-(1/freq_ca)) / ca_tau;\n            V.dt = 1/frameRate;\n            V.est_gam = 1; % estimate decay time parameter (does not work)\n            V.est_sig = 1; % estimate baseline noise SD\n            V.est_lam = 1; % estimate firing rate\n            V.est_a = 0; % estimate spatial filter\n            V.est_b = 0; % estimate background fluo.\n            V.fast_thr = 1;\n            V.fast_iter_max = 3;\n        case 'peeling';\n            V = [];\n            P = [];\n            config.EventDetect.optimizeSpikeTimes = 0;\n            config.EventDetect.schmittHi = [1.75 0 3];\n            config.EventDetect.schmittLo = [-1 -3 0];\n            config.EventDetect.schmittMinDur = [0.3 0.05 3];\n            config.EventDetect.A1 = amp;\n            config.EventDetect.tau1 = tau;\n            config.EventDetect.onsettau = onsettau;\n            config.EventDetect.optimMethod = 'none';\n            config.EventDetect.minPercentChange = 0.1;\n            config.EventDetect.maxIter = 20;\n            config.EventDetect.plotFinal = 0;\n        case 'none';\n            config = [];\n            V = [];\n            P = [];\n            warning('Nothing to do here! Exit ...')\n            return;\n    end;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "CalciumDecay.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/eventDetection/CalciumDecay.m", "size": 1116, "source_encoding": "utf_8", "md5": "67138b4224a9d6e8edefa5aa8b387b8e", "text": "function [t,X] = CalciumDecay(p_gamma,p_carest,p_cacurrent,p_kappas,p_kd,p_conc,tspan)\n% Uses ODE45 to solve Single-compartment model differential equation \n% \n% Fritjof Helmchen (helmchen@hifo.uzh.ch)\n% Brain Research Institute,University of Zurich, Switzerland\n% created: 7.10.2013, last update: 25.10.2013 fh\n\noptions=odeset('RelTol',1e-6);                          % set an error\nXo = p_cacurrent;                                       % initial conditions\nmypar = [p_gamma,p_carest,p_kappas,p_kd,p_conc];        % parameters\n[t,X] = ode45(@Relax2CaRest,tspan,Xo,options, mypar);   % call the solver, tspan should contain time vector with more than two elements\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nfunction [dx_dt]= Relax2CaRest(t,x,pp)\n% differential equation describing the decay of calcium conc level to resting level in the presence\n% of indicator dye with variable buffering capacity.\n% paramters pp: 1 - gamma, 2 - ca_rest, 3 - kappaS, 4 - kd, 5 - indicator total concentration (all conc in nM) \ndx_dt =  -pp(1)* (x - pp(2))/(1 + pp(3) + pp(4)*pp(5)/(x + pp(4))^2);\nreturn\nend\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "extractPSTrace.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/periStim/extractPSTrace.m", "size": 3041, "source_encoding": "utf_8", "md5": "d3b2e3521d07d12854cf69d00e9ef54d", "text": "function PSCaTraces = extractPSTrace(caTraces, stims, PSFrames, varargin)\n\nif numel(varargin) > 0 && isnumeric(varargin{1}) && numel(varargin{1}) == 1;\n    nMaxStimPerTrial = varargin{1};\nelse\n    nMaxStimPerTrial = 10;\nend;\n\nif numel(varargin) > 1 && islogical(varargin{2}) && varargin{2};\n    useROIParFor = true;\nelse\n    useROIParFor = false;\nend;\n\n% make sure the peri-stimulus frames are specified as a structure\nif isnumeric(PSFrames) && numel(PSFrames) == 2;\n    PSFrames = struct('base', [PSFrames(1) 0], 'evoked', [0 PSFrames(2)]);\n% make sure the peri-stimulus frames are specified as a structure\nelseif isnumeric(PSFrames) && numel(PSFrames) == 4;\n    PSFrames = struct('base', [PSFrames(1) PSFrames(2)], 'evoked', [PSFrames(3) PSFrames(4)]);\nend;\n\n% get all different stims \"indexes\"\nstimTypes = unique(stims(stims ~= 0 & ~isnan(stims)));\n\n% get the size of the dataset\nnTrials = size(caTraces, 1);\nnStims = nTrials * nMaxStimPerTrial; % maximum possible stimulus per trial (to allocate enough space)\nnROIs = size(caTraces, 2);\nnFrames = size(caTraces, 3);\nnStimTypes = numel(stimTypes);\n% get the peri-stimulus frames' range\nPSFramesRange = unique([PSFrames.base(1) : PSFrames.base(2) PSFrames.evoked(1) : PSFrames.evoked(2)]);\nnPeriStimFrames = max(PSFramesRange) - min(PSFramesRange) + 1;\n\n% create a nStimTypes x nStims x nROIs x nPeriStimFrames matrix\nPSCaTraces = nan(nStimTypes, nStims, nROIs, nPeriStimFrames);\n% go through each ROI and concatenate all repetitions:\n% using parallel loop\nif useROIParFor;\n    parfor iROI = 1 : nROIs;\n        % store in the output structure\n        PSCaTraces(:, :, iROI, :) = extractPSTraceForROI(reshape(caTraces(:, iROI, :), nTrials, nFrames), ...\n            stims, nStims, nTrials, nPeriStimFrames, stimTypes, PSFrames);\n    end\n% without parallel loop\nelse\n    for iROI = 1 : nROIs;\n        % store in the output structure\n        PSCaTraces(:, :, iROI, :) = extractPSTraceForROI(reshape(caTraces(:, iROI, :), nTrials, nFrames), ...\n            stims, nStims, nTrials, nPeriStimFrames, stimTypes, PSFrames);\n    end\nend;\n\n% remove empty trials\nemptyTrials = arrayfun(@(iTrial) all(all(all(isnan(PSCaTraces(:, iTrial, :, :))))), 1 : size(PSCaTraces, 2));\nPSCaTraces(:, emptyTrials, :, :) = [];\n\nend\n\nfunction PSCaTrace = extractPSTraceForROI(caTraces, stims, nStims, nTrials, nPeriStimFrames, stimTypes, PSFrames)\n    % collect relevant data, ignoring empty runs\n    concatCaTraces = []; concatStims = [];\n    % go trough each repetition\n    for iPFTrial = 1 : nTrials;\n        if ~isempty(caTraces(iPFTrial, :)); % ignore empty runs\n            concatCaTraces = cat(2, concatCaTraces, caTraces(iPFTrial, :));\n            concatStims = cat(2, concatStims, stims(iPFTrial, :));\n        end;\n    end;\n    % extract the peri-stimulus averages\n    PSCaTrace = extractPSTraceSingleTrace(concatCaTraces, concatStims, stimTypes, PSFrames.base, PSFrames.evoked);\n    % pad with nans\n    PSCaTrace = cat(2, PSCaTrace, nan(numel(stimTypes), nStims - size(PSCaTrace, 2), nPeriStimFrames));\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "PsPlotAnalysisCellArray.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/utils/periStim/PsPlotAnalysisCellArray.m", "size": 4374, "source_encoding": "utf_8", "md5": "5d32da67e5de7c730620b9a5b3198cce", "text": "function PSData = PsPlotAnalysisCellArray(ROIStatsData, stimCell, psFrames)\n% dataCell      ... cell array with roi time-series (nROIs x nReps), each cell is a matrix of 1 x nFrames\n% stimCell      ... cell array of stim vectors (1 x nReps), each cell is a matrix of 1 x nFrames\n% config        ... peri-stimulus config structure with the number of base and evoked frames\n% PSData        ... cell array of stim-locked ROI time-series (nROIs x nStims)\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n% modified by Balazs\n\n\n% get all different stims \"indexes\"\nstimTypes = unique(cell2mat(stimCell));\n% remove the 0 which is the \"no stim\" index\nstimTypes(stimTypes == 0) = [];\n\nnROIs = size(ROIStatsData, 1);\nnReps = size(ROIStatsData, 2);\nnStims = numel(stimTypes);\n% nFrames = size(ROIStatsData{1, 1}, 2);\n\n% create a cell array (nROIs x nStims), each cell is a matrix of nReps x nPeriStimFrames\nPSData = cell(nROIs, nStims);\n% go through each ROI and concatenate all repetitions\nfor iROI = 1 : nROIs;\n    % collect relevant data, ignoring empty runs\n    allTraces = []; allStims = [];\n    % go trough each repetition\n    for iRep = 1 : nReps;\n        if ~isempty(ROIStatsData{iROI, iRep}); % ignore empty runs\n            allTraces = [allTraces, ROIStatsData{iROI, iRep}]; %#ok<AGROW>\n            allStims = [allStims, stimCell{iRep}]; %#ok<AGROW>\n        end;\n    end;\n    % extract the peri-stimulus averages\n    PSData(iROI, :) = doPsPlot(allTraces, allStims, stimTypes, psFrames.base, psFrames.evoked);\nend\n\nend\n\nfunction psData = doPsPlot(data, stimVector, stimTypes, baseFrames, evokedFrames)\npsData = cell(1,length(stimTypes));\nfor n = 1:numel(stimTypes)\n    currentStim = stimTypes(n);\n    currentStimNo = numel(find(stimVector==currentStim));\n    if isempty(currentStimNo)\n       psData{roi,n} = [];\n       continue\n    end\n    for roi = 1:size(data,1)\n        psData{roi,n} = zeros(currentStimNo,...\n            baseFrames+evokedFrames);\n    end\n    currentStimNo = 0;\n    for t = 1:numel(stimVector)\n        if stimVector(t) == currentStim\n            currentStimNo = currentStimNo + 1;\n            nanPad = 0;\n            startFrame = t - baseFrames;\n            if startFrame < 1\n                nanPad = startFrame-1;\n                startFrame = 1;\n            end\n            stopFrame = t + evokedFrames-1;\n            if stopFrame > numel(stimVector)\n                nanPad = stopFrame - numel(stimVector);\n                stopFrame = numel(stimVector);\n            end\n            for roi = 1:size(data,1)\n                currentRoiTrace = data(roi,startFrame:stopFrame);\n                if nanPad < 0\n                    currentRoiTrace = [nan(1,abs(nanPad)) currentRoiTrace];\n                elseif nanPad > 0\n                    currentRoiTrace = [currentRoiTrace nan(1,nanPad)];\n                end\n                psData{roi,n}(currentStimNo,:) = currentRoiTrace;\n            end\n        end\n    end\nend\nend\n\n% stimTypes = unique(stimVector);  % all non-zero elements\n% stimTypes(stimTypes==0) = [];\n% PsPlotData = cell(size(data,1),length(stimTypes));\n% for n = 1:numel(stimTypes)\n%     currentStim = stimTypes(n);\n%     currentStimNo = numel(find(stimVector==currentStim));\n%     for roi = 1:size(data,1)\n%         PsPlotData{roi,n} = zeros(currentStimNo,...\n%             baseFrames+evokedFrames);\n%     end\n%     currentStimNo = 0;\n%     for t = 1:numel(stimVector)\n%         if stimVector(t) == currentStim\n%             currentStimNo = currentStimNo + 1;\n%             nanPad = 0;\n%             startFrame = t - baseFrames;\n%             if startFrame < 1\n%                 nanPad = startFrame-1;\n%                 startFrame = 1;\n%             end\n%             stopFrame = t + evokedFrames-1;\n%             if stopFrame > numel(stimVector)\n%                 nanPad = stopFrame - numel(stimVector);\n%                 stopFrame = numel(stimVector);\n%             end\n%             for roi = 1:size(data,1)\n%                 currentRoiTrace = data(roi,startFrame:stopFrame);\n%                 if nanPad < 0\n%                     currentRoiTrace = [nan(1,abs(nanPad)) currentRoiTrace];\n%                 elseif nanPad > 0\n%                     currentRoiTrace = [currentRoiTrace nan(1,nanPad)];\n%                 end\n%                 PsPlotData{roi,n}(currentStimNo,:) = currentRoiTrace;\n%             end\n%         end\n%     end\n% end"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "GetRoiStatsRaps.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/GetRoiStatsRaps.m", "size": 14428, "source_encoding": "utf_8", "md5": "c3e91f2c80c771821e60bb7a2d4530ef", "text": "function varargout = GetRoiStatsRaps(config)\n% get ROI timecourses from images, with support for muliple ROIs and runs\n% returns structure data, info and stimulus fields\n% data field contains a cell array with ROI timecourses for different cells\n% in rows and different runs in columns:\n% [cell1_run1] [cell1_run2] ... [cell1_runN]\n% [cell2_run1] [cell2_run2] ... [cell2_runN]\n%     ...           ...              ...\n% [cellN_run1] [cellN_run2] ... [cellN_runN]\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nconfig = ParseConfig(config);\nmatfiles = config.matfiles;\n\n% run in parallel (matlabpool should be available)?\nif matlabpool('size')\n   doParallel = 1;\nelse\n    doParallel = 0;\nend\n% doParallel = 0;\n\nfor n = 1:numel(matfiles)\n    if isempty(strfind(matfiles{n},'.mat'))\n        matfiles{n} = [matfiles{n} '.mat'];\n    end\nend\n\nS = load(matfiles{1});\ndata = S.(genvarname(strrep(matfiles{1},'.mat','')));\n\nsamplingRate = data.hdr.rate;\ndelFrame = data.proc.DelFrame;\n% number of points\nfIdx = strfind(matfiles{1},'pts');\nfor i = fIdx-1:-1:1\n    if strcmp(matfiles{1}(i),'_')\n        config.points = str2num(matfiles{1}(i+1:fIdx-1));\n        break\n    end\nend\n\n% experiment specific info\nanimalID = data.hdr.animalID;\nspotID = data.hdr.spotID;\n\nsaveName = sprintf('%s_%s_RoiStats',animalID,spotID);\n\n% print out some experiment details\nfprintf('%s - %s\\n',animalID,spotID);\nfprintf('Total Points: %1.0f Sampling frequency: %1.2f\\n',config.points,samplingRate);\nfprintf('Total cells: %1.0f\\n',config.points/config.pointsPerCell);\nfprintf('Background vector: %s\\n',num2str(config.bgVector));\n\nconfig.totalCells = config.points/config.pointsPerCell;\n\n%% low-pass filter parameters\nswitch config.LowPass.method\n    case 'sg'\n        lpFiltCutoff = config.LowPass.params(1);\n        lpFiltCutoff = round(lpFiltCutoff.*data.hdr.rate);\n        if ~rem(lpFiltCutoff,2)\n            lpFiltCutoff = lpFiltCutoff+1;\n        end\n        config.LowPass.lpFiltCutoff = lpFiltCutoff;\n        config.LowPass.sgolayOrder = config.LowPass.params(2); % filter order\n        clear lpFiltCutoff\nend\n\n%% Setup event detection - Peeling\nconfig.runsNo = numel(matfiles);\nswitch lower(config.EventDetect.method)\n    case 'peeling'\n        % setup generic input structure for doPeeling\n        % required fields should be defined in calling script (see\n        % doPeeling for documentation)\n        eventS = config.EventDetect;\n        eventS.rate = samplingRate;\n    case 'none'\n        % nothing to do here\n    otherwise\n        error('Unknow event detection method: %s',config.EventDetect.method)\nend\n\nout.roiData = cell(1,config.runsNo);\nout.eventData = cell(1,config.runsNo);\nout.modelData = cell(1,config.runsNo);\nout.residualData = cell(1,config.runsNo);\nout.stim = cell(1,config.runsNo);\nout.hdr = cell(1,config.runsNo);\nout.proc = cell(1,config.runsNo);\n\n%% Process runs\nfor currentRun = 1:config.runsNo\n    fprintf('\\nNow processing run %1.0f\\n',currentRun);\n    % for 'drr', work on both channels, for 'dff' work on ch1\n    S = load(matfiles{currentRun});\n    data = S.(genvarname(strrep(matfiles{currentRun},'.mat','')));\n    \n    out.hdr{currentRun} = data.hdr;\n    out.proc{currentRun} = data.proc;\n    \n    rapsData = cell(1,length(config.channelVector));\n    for n = 1:length(config.channelVector)\n        A = data.img_data{config.channelVector(n)};\n        A(:,end+1) = A(:,1); A(:,1) = []; % first column last\n        rapsData{n} = A;\n    end\n    \n    timepoints = size(rapsData{1},1);\n    \n    if isempty(data.stim)\n        if config.ch3Sound\n            [config.stim{currentRun},soundT] = ...\n                img2vector(double(data.img_data{3}),1/config.pixelTime,0);\n        else\n            config.stim{currentRun} = [];\n        end\n        config.stim{currentRun} = removeBlankPixels(config.stim{currentRun});\n        [stimTable,~,toneClean] = AnalyzePureToneVector(config.stim{currentRun},...\n            1/config.pixelTime,0);\n        %     [stimTable,toneClean] =  cleanUpBadSound(config.stim{currentRun},...\n        %         1/config.pixelTime,0);\n        plotSoundVector = 1;\n    else\n        stimTable = data.stim;\n        plotSoundVector = 0;\n    end\n    [stim,stimTime,stimID] = soundTable2StimVector(stimTable,1/config.pixelTime,0);\n    \n    out.stim{currentRun} = stimTable;\n    \n    % average columns for each cell\n    ch1ByCell = zeros(size(rapsData{1},1),...\n        config.points/config.pointsPerCell);\n    ch2ByCell = zeros(size(rapsData{2},1),...\n        config.points/config.pointsPerCell);\n    \n    pos = 1;\n    for n = 1:config.pointsPerCell:size(rapsData{1},2)\n        currentCell = double(rapsData{1}(:,n:n+config.pointsPerCell-1));\n        currentCell = removeBlankPixels(currentCell,1);\n%         currentCell(:,end) = currentCell(:,end) + ...\n%             (mean(mean(currentCell(:,1:config.pointsPerCell-1)))-mean(currentCell(:,end)));\n        ch1ByCell(:,pos) = mean(currentCell,2);\n        currentCell = double(rapsData{2}(:,n:n+config.pointsPerCell-1));\n        currentCell = removeBlankPixels(currentCell,1);\n%         currentCell(:,end) = currentCell(:,end) + ...\n%             (mean(mean(currentCell(:,1:config.pointsPerCell-1)))-mean(currentCell(:,end)));\n        ch2ByCell(:,pos) = mean(currentCell,2);\n        pos = pos + 1;\n    end\n    \n    % subtract background\n    ch1_bg = mean(ch1ByCell(:,config.bgVector),2);\n    ch2_bg = mean(ch2ByCell(:,config.bgVector),2);\n    for n = 1:size(ch1ByCell,2)\n        ch1ByCell(:,n) = ch1ByCell(:,n) - ch1_bg;\n        ch2ByCell(:,n) = ch2ByCell(:,n) - ch2_bg;\n    end\n    disp('Subtracted background');\n    % remove background columns\n    ch1ByCell(:,config.bgVector) = [];\n    ch2ByCell(:,config.bgVector) = [];\n    \n    switch config.statsType\n        case 'drr'\n            ratio = ch1ByCell ./ ch2ByCell;\n        case 'dff'\n            ratio = ch1ByCell;\n    end\n    \n    % DRR\n    DRR = zeros(size(ratio));\n    for n = 1:size(ch1ByCell,2)\n%         r0 = findF0_prctile(ratio(:,n),samplingRate,5,10);\n        % calculate F0 (use quadratic fit method)\n        r0 = CalculateF0(ratio(:,n),samplingRate);\n        DRR(:,n) = ((ratio(:,n) - r0) ./ r0)*100;\n    end\n    fprintf('Calculated relative fluorescence (%s)\\n',...\n        upper(config.statsType));\n    \n    DRRsmooth = zeros(size(DRR));\n    for n = 1:size(DRR,2)\n        DRRsmooth(:,n) = doLowPassFilter(DRR(:,n),config.LowPass);\n    end\n    \n    % notch filter\n    if config.notchF\n        fprintf('Fluorescence notch filter %1.2fHz (Q=%1.1f)\\n',...\n            config.notchF,config.notchQ);\n        for n = 1:size(DRR,2)\n            DRRsmooth(:,n) = notchFilter(DRRsmooth(:,n),samplingRate,...\n                config.notchF,config.notchQ);\n        end\n    end\n    RoiMatAllRuns = DRRsmooth';\n    \n    out.roiData{currentRun} = RoiMatAllRuns;\n    \n    %% Event detection\n    switch lower(config.EventDetect.method)\n        case 'peeling'\n            fprintf('Started Peeling for %s %s run %1.0f (%1.0f cells)\\n',...\n                animalID,spotID,currentRun,size(RoiMatAllRuns,1))\n            t = tic;\n            eventData = zeros(size(RoiMatAllRuns));\n            modelData = zeros(size(RoiMatAllRuns));\n            residualData = zeros(size(RoiMatAllRuns));\n            if doParallel\n                spiketrainCell = cell(1,size(RoiMatAllRuns,1));\n                modelCell = cell(1,size(RoiMatAllRuns,1));\n                peelCell = cell(1,size(RoiMatAllRuns,1));\n                parfor roi2 = 1:size(RoiMatAllRuns,1)\n                    Sin = eventS; Sin.dff = RoiMatAllRuns(roi2,:);\n                    outS = doPeeling(Sin);\n                    spiketrainCell{1,roi2} = outS.data.spiketrain;\n                    modelCell{1,roi2} = outS.data.model;\n                    peelCell{1,roi2} = outS.data.peel;\n                end\n                for roi2 = 1:size(RoiMatAllRuns,1)\n                    eventData(roi2,:) = spiketrainCell{roi2};\n                    modelData(roi2,:) = modelCell{roi2};\n                    residualData(roi2,:) = peelCell{roi2};\n                end\n                clear spiketrainCell modelCell peelCell\n            else\n                for roi2 = 1:size(RoiMatAllRuns,1)\n                    fprintf('.')\n                    eventS.dff = RoiMatAllRuns(roi2,:);\n                    outS = doPeeling(eventS);\n                    eventData(roi2,:) = outS.data.spiketrain;\n                    modelData(roi2,:) = outS.data.model;\n                    residualData(roi2,:) = outS.data.peel;\n                end\n            end\n            t = toc(t);\n            fprintf('\\nFinished Peeling (t=%1.2f s)\\n',t)\n    end\n    \n    out.eventData{currentRun} = eventData;\n    out.modelData{currentRun} = modelData;\n    out.residualData{currentRun} = residualData;\n    \n    % plot all Rois with stim in stacked plot\n    titleStr = (strrep(matfiles{currentRun},'.mat',''));\n    fig = figure('Name',titleStr,'NumberTitle','off'); hold all\n    currentOffset = 0;\n    time = (1:size(RoiMatAllRuns,2))/samplingRate;\n    cellIDaxes = cell(size(RoiMatAllRuns,1),1);\n    meanYpos = zeros(size(RoiMatAllRuns,1),1);\n    \n    for roi = 1:size(RoiMatAllRuns,1)\n        data = RoiMatAllRuns(roi,:);\n        currentSD = std(data);\n        data = data + (min(data)*-1);\n        dataPlot = data + currentOffset;\n        currentOffset = max(dataPlot);\n        cellIDaxes{roi} = sprintf('%1.0f (%1.2f)',roi,currentSD);\n        meanYpos(roi) = mean(dataPlot);\n        switch lower(config.EventDetect.method)\n            case 'fast_oopsi'\n                events = eventData(roi,:)*30;\n                events = events + currentOffset;\n                currentOffset = max(events);\n        end\n    end\n    \n    if config.ch3Sound && plotSoundVector\n        cellIDaxes{roi+1} = 'sound';\n        dataPlot = ScaleToMinMax(config.stim{currentRun},0,50);\n        dataPlot = dataPlot + currentOffset;\n        meanYpos(roi+1) = mean(dataPlot);\n        currentOffset = max(dataPlot);\n    end\n    hold all\n    set(gca,'ylim',[0 currentOffset],'xlim',[min(time) ...\n        max(time)+0.1*max(time)])\n    set(gca,'XTick',[])\n    ax1 = gca;\n    saveStr = titleStr;\n    try\n        titleStr = sprintf('%s\\nStandard: %1.0f kHz Deviant: %1.0f kHz',...\n            titleStr,stimID(1),stimID(2));\n    end\n    title(titleStr,'Interpreter','none')\n    ylabel(sprintf('%% %s',upper(config.statsType)))\n    ax2 = axes('Position',get(gca,'Position'),'YAxisLocation','right');\n    set(ax2,'ylim',[0 currentOffset],'xlim',[min(time) max(time)],...\n        'YTick',meanYpos,'YTickLabel',cellIDaxes)\n    xlabel('Time / s')\n    hold all\n    stimTimes1 = stimTime(stim==1);\n    stimTimes2 = stimTime(stim==2);\n    for n = 1:numel(stimTimes1)\n        plot([stimTimes1(n) stimTimes1(n)],[0 currentOffset],'--','Color',[0.5 0.5 0.5]), hold all\n    end\n    for n = 1:numel(stimTimes2)\n        plot([stimTimes2(n) stimTimes2(n)],[0 currentOffset],'--r'), hold all\n    end\n    \n    hold all\n    currentOffset = 0;\n    for roi = 1:size(RoiMatAllRuns,1)\n        data = RoiMatAllRuns(roi,:);\n        data = data + (min(data)*-1);\n        dataPlot = data + currentOffset;\n        plot(time,dataPlot,'k')\n        currentOffset = max(dataPlot);\n        events = eventData(roi,:);\n        switch lower(config.EventDetect.method)\n            case 'fast_oopsi'\n                events = events * 30;\n                events = events + currentOffset;\n                plot(time,events,'Color',[0.5 0.5 0.5])\n                currentOffset = max(events);\n            case 'peeling'\n                spikeTimes = time(events>0);\n                scatter(spikeTimes,repmat(min(dataPlot),1,numel(spikeTimes)),'.',...\n                    'MarkerEdgeColor',[0.5 0.5 0.5])\n        end\n    end\n    if config.ch3Sound && plotSoundVector\n        dataPlot = ScaleToMinMax(config.stim{currentRun},0,50);\n        dataPlot = dataPlot + currentOffset;\n        plot(soundT,dataPlot,'Color',[0.5 0.5 0.5])\n    end\n    \n    linkaxes([ax1,ax2],'y')\n    \n    saveas(gcf,[saveStr '.fig'])\n    \nend\n\nSaveAndAssignInBase(out,saveName,'SaveOnly')\n\nif nargout\n    varargout{1} = out;\nend\n\n\n%% Function - doLowPassFilter\nfunction v = doLowPassFilter(v,S)\nswitch S.method\n    case 'sg'\n        v = sgolayfilt(v,S.sgolayOrder,S.lpFiltCutoff);\nend\n\nfunction f0_mat = CalculateF0(roi_mat,f)\nt = (1/f:1/f:length(roi_mat)./f)';\np = polyfit(t,roi_mat,2);\nf0_mat = p(1).*t.^2 + p(2).*t + p(3);\n\n%% Function - ParseConfig\nfunction config = ParseConfig(config)\n% check for missing fields in config structure and add them with\n% default values\n\nif ~isfield(config,'matfiles')\n    matFiles = sort(uigetfile(...\n        '*.mat','Select Mat Files','MultiSelect','on'));\n    config.matfiles = strrep(matFiles,'.mat','');\nelse\n    matfiles = config.matfiles;\nend\n\nif ~isfield(config,'saveName')\n    config.saveName = [matfiles{1} '__RoiStats'];\nend\n\nif ~isfield(config,'pointsPerCell')\n    config.pointsPerCell = 9;\nend\n\nif ~isfield(config,'bgVector')\n    config.bgVector = [1 2 3];\nend\n\nif ~isfield(config,'ch3Sound')\n    config.ch3Sound = 1;\nend\n\nif ~isfield(config,'pixelTime')\n    config.pixelTime = 12;\nend\nconfig.pixelTime = config.pixelTime / 1000000;\n\nif ~isfield(config,'statsType')\n    config.statsType = 'dff';\nend\n\nif ~isfield(config,'notchF')\n    config.notchF = 0;\nend\n\nif ~isfield(config,'notchQ')\n    config.notchQ = 1;\nend\n\n% channels for stats calculation\n% DFF is calculated on the channel specified by config.channelVector(1)\n% DRR is calculated on the channels specified by config.channelVector(1)\n% and config.channelVector(2)\nif ~isfield(config,'channelVector')\n    config.channelVector = [1 2];\nend\n\nif strcmpi(config.statsType,'drr') && numel(config.channelVector) < 2\n    error('config.channelVector must specify at least 2 channels for DRR stats!');\nend\n\nif ~isfield(config,'psConfig')\n    config.psConfig = struct;\n    config.psConfig.baseFrames = 5;\n    config.psConfig.evokedFrames = 15;\nend\n\n% event detection\nif ~isfield(config,'EventDetect')\n    config.EventDetect.method = 'none';\nend\n\n% low-pass filter\nif ~isfield(config,'LowPass')\n    config.LowPass.method = 'none';\n    config.LowPass.params = [];\nend\n% LowPass.method may take the following values:\n% 'none' ... no low-pass filtering is applied\n% 'sg' ... savitzky-golay filter, as implemented in ML\n% LowPass.params is a vector with filter parameters, depending on the\n% specific filter (all cutoffs should be defined in s)\n% for SG, params are [lpFilterCutoff filterOrder]\n\n\n\n\n% e.o.f."}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "GetRoiStatsLineScan.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/GetRoiStatsLineScan.m", "size": 10400, "source_encoding": "utf_8", "md5": "14f4dec618b5d5152ce43b50f448cbac", "text": "function varargout = GetRoiStatsLineScan(config)\n% get ROI timecourses from images, with support for muliple ROIs and runs\n% returns structure data, info and stimulus fields\n% data field contains a cell array with ROI timecourses for different cells\n% in rows and different runs in columns:\n% [cell1_run1] [cell1_run2] ... [cell1_runN]\n% [cell2_run1] [cell2_run2] ... [cell2_runN]\n%     ...           ...              ...\n% [cellN_run1] [cellN_run2] ... [cellN_runN]\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nconfig = ParseConfig(config);\nmatfiles = config.matfiles;\n\nfor n = 1:numel(matfiles)\n   if isempty(strfind(matfiles{n},'.mat'))\n       matfiles{n} = [matfiles{n} '.mat'];\n   end\nend\n\nS = load(matfiles{1});\ndata = S.(genvarname(strrep(matfiles{1},'.mat','')));\n\nconfig.imgDim = data.hdr.size(1:2);\nroiSet = data.roi;\ncellNo = size(roiSet,1);\nconfig.runsNo = numel(matfiles);\nconfig.timepoints = data.hdr.size(1);\n\n% config.dataRoi = cell(cellNo,config.runsNo);\n% config.dataRoiNorm = cell(cellNo,config.runsNo);\n% config.dataNpil = cell(cellNo,config.runsNo);\n\n% cMatFigRoi = figure('Name',sprintf('%s %s',config.statsType,'Roi'),...\n%     'NumberTitle','off','color','white');\n% cMatFigRoiNorm = figure('Name',sprintf('%s %s',config.statsType,'RoiNorm'),...\n%     'NumberTitle','off','color','white');\n% cMatFigRoiNpil = figure('Name',sprintf('%s %s',config.statsType,'RoiNpil'),...\n%     'NumberTitle','off','color','white');\n\nRoiMatAllRuns = [];\nRoiMatNormAllRuns = [];\nRoiMatNpilAllRuns = [];\nStimVectorAllRuns = [];\n\n%% Process runs\nfor currentRun = 1:config.runsNo\n    fprintf('\\nNow processing run %1.0f\\n',currentRun);\n    % for 'drr', work on both channels, for 'dff' work on ch1\n    S = load(matfiles{currentRun});\n    data = S.(genvarname(strrep(matfiles{currentRun},'.mat','')));\n    currentImgData = cell(1,length(config.channelVector));\n    currentMeanImgData = cell(1,length(config.channelVector));\n    for n = 1:length(config.channelVector)\n        currentImgData{n} = double(data.img_data{config.channelVector(n)});\n%         currentMeanImgData{n} = mean(currentImgData{n},3);\n    end\n    config.stim{currentRun} = data.stim;\n    config.frameRate{currentRun} = data.hdr.rate;\n    roiSet = data.roi;\n    roiLabel = roiSet(:,1);\n    \n    if isfield(data.hdr,'bits')\n        bitDepth = data.hdr.bits;\n    else\n        bitDepth = 16;\n    end\n    \n    if islogical(roiSet{1,2})\n       roiSet = ij_RoiSetCompression(roiSet,1);\n    end\n    \n    for currentRoi = 1:size(roiSet,1)\n        roi = roiSet{currentRoi,2};\n        roiId = roiSet{currentRoi,1};\n        % we only need the columns for each roi\n        roiSet{currentRoi,2} = unique(roi(:,2));\n    end\n    config.roi = roiSet(:,1:2);\n    \n    stim = config.stim{currentRun};\n    \n    if isempty(stim)\n        stimVectorFrameRate = zeros(1,config.timepoints);\n    else\n        if isstruct(stim)\n            if stim.rate ~= config.frameRate{currentRun}\n                % compute stimVectorFrameRate\n                stimVectorFrameRate = ResampleStimVector(stim.vector,stim.rate,...\n                    config.timepoints,config.frameRate{currentRun});\n            else\n                stimVectorFrameRate = stim.vector;\n            end\n        elseif isvector(stim) % assume stim vector already converted to frames\n            stimVectorFrameRate = stim;\n        end\n    end\n    \n    if numel(stimVectorFrameRate) > config.timepoints\n        stimVectorFrameRate(config.timepoints+1:end) = [];\n    elseif numel(stimVectorFrameRate) < config.timepoints\n        stimVectorFrameRate(end:config.timepoints) = 0;\n    end\n    \n    StimVectorAllRuns = [StimVectorAllRuns stimVectorFrameRate];\n    \n    if config.forceIJf0\n        stimVectorFrameRate = zeros(1,config.timepoints);\n    end\n    \n    %% LowPass filter\n    points = round(config.lowPass*config.frameRate{currentRun});\n    \n    %% Calculate stats\n    for currentRoi = 1:size(config.roi,1)\n        currentColumns = config.roi{currentRoi,2};\n        roiData = cell(1,numel(config.channelVector));\n        for n = 1:numel(config.channelVector)\n            roiData{n} = currentImgData{n}(:,currentColumns);\n            roiData{n} = nanmean(roiData{n},2);\n            roiData{n} = filter(ones(1,points)/points,1,roiData{n});\n            roiData{n}(1:points,:) = [];\n        end\n        switch lower(config.statsType)\n            case 'dff'\n                roiStats = roiData{1};\n                \n            case 'drr'\n                roiStats = roiData{1} ./ roiData{2};\n        end\n        roiStats = roiStats';\n        % calculate F0\n        %         roiF0 = CalculateF0(roiStats,stimVectorFrameRate,config.baseFrames);\n        roiF0 = findF0(roiStats,config.frameRate{currentRun},1,10);\n        % calculate stats\n        roiStats = ((roiStats-roiF0) ./ roiF0) .* 100;\n        roiStats(isinf(roiStats)) = 0;\n        roiStats(isnan(roiStats)) = 0;\n        \n        % save mean stats timecourse\n        config.dataRoi{currentRoi,currentRun} = roiStats;\n        currentRoiMat(currentRoi,:) = roiStats;\n        \n    end\n    RoiMatAllRuns = [RoiMatAllRuns currentRoiMat];\n    \nend\n\n% plot all Rois with stim in stacked plot\ntitleStr = sprintf('%s',config.saveName);\nfig = figure('Name',titleStr,'NumberTitle','off'); hold all\ncurrentOffset = 0;\ntime = (1:size(RoiMatAllRuns,2))/config.frameRate{1};\nfor roi = 1:size(RoiMatAllRuns,1)\n    id = sprintf('%1.0f',roi);\n    data = RoiMatAllRuns(roi,:);\n    data = data + (min(data)*-1);\n    dataPlot = data + currentOffset;\n    plot(time,dataPlot)\n    currentOffset = max(dataPlot);\n    text(max(time),mean(dataPlot),id)\nend\nfor n = 1:numel(StimVectorAllRuns)\n    if StimVectorAllRuns(n) == 1\n        plot([time(n) time(n)],[0 currentOffset],'LineStyle','--',...\n            'Color',[0.8 0.8 0.8])\n    elseif StimVectorAllRuns(n) == 2\n        plot([time(n) time(n)],[0 currentOffset],'LineStyle','--',...\n            'Color',[0.3 0.3 0.3])\n    end\nend\nset(gca,'ylim',[0 currentOffset],'xlim',[min(time) max(time)+0.1*max(time)])\nxlabel('Time / s')\ntitle(titleStr,'Interpreter','none')\nylabel(sprintf('%% %s',upper(config.statsType)))\n\nsaveas(fig,config.saveName)\n\nSaveAndAssignInBase(config,config.saveName)\n\nif nargout\n    varargout{1} = config;\nend\n\n%% Function - pcolorPlot\nfunction pcolorPlot(fig,runs,currentRun,roiMat,stimVector,roiLabel)\nfigure(fig)\nsubplot(runs,1,currentRun)\nroiMat = [ScaleToMinMax(stimVector,0,25);roiMat];\ncolorMat = padarray(roiMat,[1 1]);\npFig = pcolor([-0.5:(size(colorMat,2)-1.5)],...\n    [-0.5:(size(colorMat,1)-1.5)],colorMat);\nset(pFig,'EdgeAlpha',0);\nxAxis = get(gca,'XLim'); yAxis = get(gca,'YLim');\ncaxis([-5 25]);\nset(gca,'XLim',[0.5 xAxis(2)],'YLim',[0.5 yAxis(2)]);\nroiLabel = ['stim'; roiLabel; 'npil'];\nset(gca,'YTick',[1:length(roiLabel)],'XTick',[]);\nset(gca,'YTickLabel',roiLabel,'FontSize',8);\ncolorbar\n\n%% Function - ParseConfig\nfunction config = ParseConfig(config)\n% check for missing fields in config structure and add them with\n% default values\n\nif ~isfield(config,'matfiles')\n    matfiles = uigetfile('*.mat','Select Mat Files','MultiSelect','on');\n    if ~iscell(matfiles)\n        matfiles = {matfiles};\n    end\n    config.matfiles = strrep(matfiles,'.mat','');\nend\n\nif ~isfield(config,'saveName')\n   config.saveName = [config.matfiles{1} '__RoiStats'];\nend\n\n% force f0 calculation as in ImageJ\nif ~isfield(config,'forceIJf0')\n    config.forceIJf0 = 0;\nend\nif isempty(config.forceIJf0)\n    config.forceIJf0 = 0;\nend\n\n% number of initial frames for normalization (forceImageJf0) OR the number\n% of frames before a stimulus used for renormalization\nif ~isfield(config,'baseFrames')\n    config.baseFrames = 10;\nend\nif isempty(config.baseFrames)\n    config.baseFrames = 10;\nend\n\nif ~isfield(config,'statsType')\n    config.statsType = 'dff';\nend\n\n% channels for stats calculation\n% DFF is calculated on the channel specified by config.channelVector(1)\n% DRR is calculated on the channels specified by config.channelVector(1)\n% and config.channelVector(2)\nif ~isfield(config,'channelVector')\n    config.channelVector = [1 2];\nend\n\nif strcmpi(config.statsType,'drr') && numel(config.channelVector) < 2\n    error('config.channelVector must specify at least 2 channels for DRR stats!');\nend\n\nif ~isfield(config,'psConfig')\n    config.psConfig = struct;\n    config.psConfig.baseFrames = 5;\n    config.psConfig.evokedFrames = 15;\nend\n\nif ~isfield(config,'lowPass')\n    config.lowPass = 1/30; % in s\nend\n\n%% Function - CalculateF0\nfunction f0_mat = CalculateF0(roi_mat,stim,base)\nif ~max(stim)\n    f0_mat = roi_mat(:,1:base);\n    f0_mat = nanmean(f0_mat,2);\n    f0_mat = repmat(f0_mat,1,size(roi_mat,2));\n    return\nend\n% binarise stim vector (each stimulus type leads to\n% renormalization)\nstim(stim>0)=1;\n% define frames of stimulus onset\nstim_onset = zeros(size(stim));\nfor n = 2:length(stim)\n    if stim(n) == 1 && stim(n-1) == 0\n        stim_onset(n) = 1;\n    end\nend\n% designate the base frames BEFORE stimulus onset as baseline\nstim_base = zeros(size(stim));\nfor n = base+1:length(stim)\n    if stim_onset(n) == 1\n        stim_base(n-base:n-1) = 1;\n    end\nend\nf0_mat = zeros(size(roi_mat));\nfor n = 1:length(stim)\n    if stim_base(n) == 1\n        stim_base(n:n+base-1) = 0;\n        % find endpoint of current F0 interval (the frame before\n        % next normalization interval start)\n        f0_stop = find(stim_base==1,1,'first')-1;\n        if isempty(f0_stop)\n            f0_stop = length(stim);\n        end\n        for pixel = 1:size(f0_mat,1)\n            current_mean = mean(roi_mat(pixel,n:n+base-1));\n            f0_mat(pixel,n:f0_stop) = current_mean;\n        end\n    end\nend\n% timepoints before first stimulus get F0 value for first\n% pre-stimulus baseline\npre_stim1 = find(f0_mat(1,:)~=0,1,'first');\nfor pixel = 1:size(f0_mat,1)\n    f0_mat(pixel,1:pre_stim1-1) = f0_mat(pixel,pre_stim1);\nend\n\n\n\n%% Function - ResampleStimVector\nfunction stimVectorFrameRate = ResampleStimVector(stim,stimRate,timepoints,frameRate)\nstimVectorFrameRate = zeros(1,timepoints);\nfor n = 1:timepoints\n    startT = (n-1)/frameRate; stopT = n/frameRate;\n    startStim = round(startT*stimRate);\n    if startStim == 0\n        startStim = 1;\n    end\n    stopStim = round(stopT*stimRate);\n    if startStim > length(stim) || stopStim > length(stim)\n        stimVectorFrameRate(n) = 0;\n    else\n        stimVectorFrameRate(n) = round(mean(stim(startStim:stopStim)));\n    end\nend\n\n\n\n\n\n\n\n% e.o.f."}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "ConvertRawData_legacy.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/ConvertRawData_legacy.m", "size": 12315, "source_encoding": "utf_8", "md5": "54a3f3e6134c4f0dd99acd8f124d9fc8", "text": "function varargout = ConvertRawData(varargin)\n% inputs: no need to specify any inputs --> GUI-based file selection\n% optional input arguments, specified as 'property' 'value' pair in\n% arbitrary order, e.g. 'zoom',1.07 OR as a structure with properties as\n% field names and values, e.g. config.zoom = 1.07\n% for a full list of supported arguments see the end of this file\n\n% License: this file may only be used with agreement to the license terms\n% outlined in the accompanying license.txt\n% if you do not agree with these terms, you MUST NOT use this file or any\n% part thereof in any way\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\n% current version: 2010-04-14\n\n%% Main\n\nRawDataDir = getGlobalParameters('RawDataDir');\nif ~RawDataDir\n   error('Raw data directory MUST be specified');\nend\n\nargs = parseInputArgs(varargin);\nif min(size(args.RawFiles)) > 1\n    runs = size(args.RawFiles,1);\nelse\n    runs = length(args.RawFiles);\n    % make sure runs are in columns\n    args.RawFiles = reshape(args.RawFiles,numel(args.RawFiles),1);\nend\n\nfor n = 1:runs\n    % create structure\n    currentS = initStruct(args,n,RawDataDir);\n    \n    % preprocess image data\n    currentS = preprocStruct(args,n,currentS);\n    \n    % save files\n    saveFiles(currentS,args.SaveDir);\n    \n    S{n} = currentS;\nend\n\nif nargout\n   varargout{1} = S;\nend\n\n\n%% Parse inputs\nfunction S = parseInputArgs(inargs)\nif ~isempty(inargs) && isstruct(inargs{1})\n    % convert structure to pseudo-input cellarray\n    inargs = struct2cellArray(inargs{1});\nend\nRawDataDir = getGlobalParameters('RawDataDir');\n% input files\nSpInput = find(strcmpi(inargs, 'RawFiles'));\nif numel(SpInput)\n    RawFiles = inargs{SpInput+1};\n    if ~iscellstr(RawFiles)\n        error('Modifier for raw files property must be a cell string');\n    end\nelse\n    [FileName,PathName,FilterIndex] = uigetfile({'*.fcs';'*.tif';'*.tiff'},...\n        'Select raw file(s) to import','MultiSelect','on');\n    if ~iscellstr(FileName)\n        FileName = {FileName};\n    end\n    if ~FileName{1}; return; end\n    if ~iscellstr(FileName); FileName = cellstr(FileName); end\n    if strfind(PathName,RawDataDir)\n       PathName = strrep(PathName,RawDataDir,'');\n    end\n    for n = 1:length(FileName) % interpret GUI-selected multiple files as different channels\n        RawFiles{1,n} = fullfile(PathName,FileName{n});\n    end\nend\nS.RawFiles = RawFiles;\n% frame rate\nSpInput = find(strcmpi(inargs, 'frame_rate'));\nif numel(SpInput)\n    frame_rate = inargs{SpInput+1};\n    if isscalar(frame_rate)\n        frame_rate = repmat(frame_rate,1,length(RawFiles));\n    else\n        if length(frame_rate) ~= length(RawFiles)\n            error('Frame rate must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    frame_rate = [];\nend\nS.frame_rate = frame_rate;\n% zoom\nSpInput = find(strcmpi(inargs, 'zoom'));\nif numel(SpInput)\n    zoom_factor = inargs{SpInput+1};\n    if isscalar(zoom_factor)\n        zoom_factor = repmat(zoom_factor,1,length(RawFiles));\n    else\n        if length(zoom_factor) ~= length(RawFiles)\n            error('Zoom must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    zoom_factor = [];\nend\nS.zoom_factor = zoom_factor;\n% image type\nSpInput = find(strcmpi(inargs, 'type'));\nif numel(SpInput)\n    img_type = inargs{SpInput+1};\n    if ~iscellstr(img_type); img_type = cellstr(img_type); end\n    if length(img_type) == 1\n        img_type = repmat(img_type,1,length(RawFiles));\n    elseif length(img_type) ~= length(RawFiles)\n        error('Image type must be a cell string of length 1 or input file number');\n    end\nelse\n    img_type = cell(1,length(RawFiles));\nend\nS.img_type = img_type;\n% Roi set\nSpInput = find(strcmpi(inargs, 'RoiSet'));\nif numel(SpInput)\n    roi_set = inargs{SpInput+1};\n    if ~iscellstr(roi_set); roi_set = cellstr(roi_set); end\n    if length(roi_set) == 1\n        roi_set = repmat(roi_set,1,length(RawFiles));\n    elseif length(roi_set) ~= length(RawFiles)\n        error('RoiSet must be a cell string of length 1 or input file number');\n    end\nelse\n    roi_set = cell(1,length(RawFiles));\nend\nS.roi_set = roi_set;\n% Stimulus\nSpInput = find(strcmpi(inargs, 'Stim'));\nif numel(SpInput)\n    stim = inargs{SpInput+1};\n    if ~iscell(stim); stim = {stim}; end\n    if length(stim) == 1\n        stim = repmat(stim,1,length(RawFiles));\n    elseif length(stim) ~= length(RawFiles)\n        error('Stimulation must be a cell array of length 1 or input file number');\n    end\nelse\n    stim = cell(1,length(RawFiles));\nend\nS.stim = stim;\n% Output directory\nSpInput = find(strcmpi(inargs, 'SaveDir'));\nif numel(SpInput)\n    SaveDir = inargs{SpInput+1};\n    if exist(SaveDir,'dir') ~= 7\n       try\n          mkdir(SaveDir);\n       catch\n           error('Failed to create directory %s',SaveDir);\n       end\n    end\nelse\n    SaveDir = pwd;\nend\nS.SaveDir = SaveDir;\n\n% Preprocessing options - delete frame\nSpInput = find(strcmpi(inargs, 'DelFrame'));\nif numel(SpInput)\n    del_frame = inargs{SpInput+1};\n    if isscalar(del_frame)\n        del_frame = repmat(del_frame,1,length(RawFiles));\n    else\n        if length(del_frame) ~= length(RawFiles)\n            error('DelFrame must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    del_frame = [];\nend\nS.del_frame = del_frame;\n% bg. correction\nSpInput = find(strcmpi(inargs, 'BgCorrect'));\nif numel(SpInput)\n    bgCorrect = inargs{SpInput+1};\nelse\n    bgCorrect = [];\nend\nS.bgCorrect = bgCorrect;\n\n%% Structure initialization\nfunction S = initStruct(inargs,n,RawDataDir)\ninfile = inargs.RawFiles(n,:);\n[FilePath,FileName,FileType] = fileparts(infile{1});\n% if the file cannot be found with the absolute path, try the relative path\n% instead\nif exist(infile{1},'file') ~= 2\n    infile_full = fullfile(RawDataDir,FilePath,[FileName FileType]);\n    if exist(infile_full,'file') ~= 2\n       error('Could not find %s on absolute and relative paths',infile_full);\n    end\nelse\n    infile_full = infile;\nend\n\nswitch FileType\n    case '.fcs'\n        img = import_raw(infile_full);\n        S.img_data{1} = uint8(img.ch1/255); % always save 8-bit integers\n        S.img_data{2} = uint8(img.ch2/255);\n        % try to decode parts of header\n        \n        % save bit-depth for later usage\n        S.bits = 8;\n    case '.tif'\n        % TODO: new tif-import in separate file, return data cell array and\n        % header info\n        for n = 1:numel(infile)\n            [FilePath,FileName,FileType] = fileparts(infile{n});\n            if exist(infile{n},'file') ~= 2\n                infile_full = fullfile(RawDataDir,FilePath,[FileName FileType]);\n                if exist(infile_full,'file') ~= 2\n                    error('Could not find %s on absolute and relative paths',infile{n});\n                end\n            else\n                infile_full = infile{n};\n            end\n            img =tiffread2_wrapper(infile_full);\n            S.img_data{n} = img.data;\n            S.hdr.tifHeader{n} = img.header;\n            if exist(strrep(infile_full,'channel00','channel01')) == 2\n                img =tiffread2_wrapper(strrep(infile_full,'channel00','channel01'));\n                S.img_data{n+1} = img.data;\n                S.hdr.tifHeader{n+1} = img.header;\n            end\n            if exist(strrep(infile_full,'channel00','channel02')) == 2\n                img =tiffread2_wrapper(strrep(infile_full,'channel00','channel02'));\n                S.img_data{n+2} = img.data;\n                S.hdr.tifHeader{n+2} = img.header;\n            end\n        end\n        \n    otherwise\n        error('Unrecognized file type %s',FileType);\nend\n% creation date for infile\nif ~iscellstr(infile_full)\n   dummy{1} = infile_full;\n   infile_full = dummy;\nend\nfile_pars = dir(infile_full{1});\nS.hdr.createDate = file_pars.date;\n\n% other header info\nS.hdr.size = size(S.img_data{1});\nif isempty(inargs.frame_rate)\n    S.hdr.rate = [];\nelse\n    S.hdr.rate = inargs.frame_rate(n);\nend\nif isempty(inargs.zoom_factor)\n    S.hdr.zoom = [];\nelse\n    S.hdr.zoom = inargs.zoom_factor(n);\nend\nS.hdr.type = inargs.img_type{n};\nS.hdr.fileorigin = infile{1};\n\n% channel ID\nS.hdr.channelID = cell(1,length(S.img_data));\n\nS.hdr = orderfields(S.hdr);\n\n% RoiSet decoder\nif isempty(inargs.roi_set{n})\n    S.roi = [];\nelse\n   S.roi = ij_roiDecoder(inargs.roi_set{n},[S.hdr.size(1) S.hdr.size(2)]);\nend\n\n% Stimulation\nif isempty(inargs.stim{n})\n    S.stim = [];\nelse\n   S.stim = inargs.stim{n};\n   if length(S.stim) ~= S.hdr.size(3)\n      warning('Stim vector length does not agree with timepoints for run %s',...\n          int2str(n));\n   end\nend\n\n\nS = orderfields(S);\n\n%% Preprocessing\nfunction S = preprocStruct(inargs,n,S)\n% Delete frame\nif isempty(inargs.del_frame)\n   if size(S.img_data{1},3) > 3\n      del_frame = 1;\n   else\n       del_frame = 0;\n   end\nelse\n    del_frame = inargs.del_frame(n);\nend\nfor channel = 1:length(S.img_data)\n    S.img_data{channel} = S.img_data{channel}(:,:,del_frame+1:end);\n    if ~isempty(S.stim) && channel == 1\n       S.stim = S.stim(del_frame+1:end);\n       S.hdr.size(3) = S.hdr.size(3) - del_frame;\n    end\nend\nS.proc.DelFrame = del_frame;\n\n% Bg. correct (on movies only)\nif ~isempty(inargs.bgCorrect) && strcmp(S.hdr.type,'movie')\n    method = inargs.bgCorrect{1};\n    switch method\n        case 'roi' % need to have inargs.bgCorrect{2} in roi set\n            roiID = inargs.bgCorrect{2};\n            if ~isempty(S.roi) && strcmp(S.roi(:,1),roiID)\n                arg2 = bwunpack(S.roi{strcmp(S.roi(:,1),roiID),2});\n            else\n                error('Could not find Roi for bg. correction');\n            end\n        otherwise\n            arg2 = inargs.bgCorrect{2};\n    end\n    for channel = 1:length(S.img_data)\n        S.img_data{channel} = bg_subtract_HL(S.img_data{channel},arg2,...\n            inargs.bgCorrect{3},method);\n    end\nend\nS.proc.bgCorrect = inargs.bgCorrect;\n\nS = orderfields(S);\n\n%% save files\nfunction saveFiles(currentS,SaveDir)\nsaveName = currentS.hdr.fileorigin;\n[PathName,saveName,ext] = fileparts(saveName);\nsaveName_mean = fullfile(SaveDir,['avg_' saveName]);\n% saveName = fullfile(SaveDir,saveName);\n% save mat-format\ncurrentDir = pwd;\ncd(SaveDir)\nSaveAndAssignInBase(currentS,genvarname(saveName),'SaveOnly');\ncd(currentDir)\n\n% save tiff-format (1 file per channel)\nif ~strcmp(ext,'.tif')\n    for n = 1:length(currentS.img_data)\n        chID = sprintf('ch%s',int2str(n));\n        img.(chID) = currentS.img_data{n};\n        if n == 1 % save average image for first channel only\n            mean_img = img.(chID);\n            mean_img = mean(mean_img,3);\n            avg_img.img = mean_img;\n            write_to_tif(avg_img,saveName_mean,8);\n        end\n    end\n    clear currentS\n    write_to_tif(img,saveName,8); % save as 8-bit tiff\nend\n\n%% Documentation\n% 'RawFiles' - a cellstring with relative path to any of the supported input\n% filetypes, supports several files, e.g. batch mode\n% path should be relative to the RawDataDir path specified in the .config\n% located in matlabroot\\work (platform-independent)\n% or full path to files (platform-dependent)\n% for TIF files, mutliple channels are encoded in columns and multiple\n% files in rows, e.g.: \n% {'file1_ch1.tif' 'file1_ch2.tif'; 'file2_ch1.tif' 'file2_ch2.tif'}\n% 'frame_rate' - frame rate of the image acquisition (scalar or vector with\n% 1 value per file)\n% 'zoom' - zoom factor (scalar or vector)\n% 'type' - cellstring with image type descriptions, e.g. 'stack', 'movie',\n% 'frame' or 'linescan'\n% 'RoiSet' - cellstring with full path to IJ RoiSet.zip files for all runs\n% 'SaveDir' - specifies directory for saving converted files (current\n% directory is used if this is not specified)\n% 'Stim' - stimulation info as cell array with one entry per run or one\n% entry for all runs (default: empty)\n% interpretation of the stimulation protocol depends on later routines,\n% here we will only add the provided values to the structure\n\n\n% Preprocessing options\n% 'DelFrame' - number of frames to delete (defaults to 0 for all images\n% with 3 or less frames, otherwise defaults to 1), also apply to stim\n% vector, if any and to size vector\n% 'BgCorrect' - background correction specification as cell array with \n% {'method', param, plotFlag} --> applied with these parameters to all\n% movies\n% see bg_subtract_HL for details\n% \n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "registerImages_MIRT.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/registerImages_MIRT.m", "size": 3661, "source_encoding": "utf_8", "md5": "a9d34819b9b436f37f06ed9e5e7bdb43", "text": "function [tranformMatrix,newImage] = registerImages_MIRT(refim, im, varargin)\n% wrapper for MIRT registration tools\n% For the MIRT info visit the project website at\n% https://sites.google.com/site/myronenko/research/mirt\n\n% it is safer to add MIRT dynamicaly to the path because the toolbox\n% shadows some native ML functions (e.g. nanmean)\n% in the end, folders should be removed from path again\n% balazsMIRTPath = 'C:\\Users\\laurenczy\\Documents\\MIRT';\n% balazsMIRTPath = 'P:\\matlab\\dynamicPath\\MIRT\\';\n% I changed the code so that you can specify config.balazsMIRTPath (line\n% 28)\n\nif nargin > 2\n    if isstruct(varargin{1});\n        config = varargin{1};\n    else\n        config = getDefaultConfig;\n        config.main.single = varargin{1};\n        if nargin > 3;\n            config.main.saveName = varargin{2};\n        end;\n    end;\nelse\n    config = getDefaultConfig;\nend\n\n% validate config\nconfig2 = getDefaultConfig;\nfields = fieldnames(config2);\nfor n = 1:numel(fields)\n    if ~isfield(config,fields{n})\n        config.(fields{n}) = config2.(fields{n});\n    end\nend\n\nif isfield(config, 'balazsMIRTPath')\n    addpath(genpath(config.balazsMIRTPath));\nelse\n    folder2add = addMIRT2path('dynamicPath/MIRT');\nend\n\n% check if transformation matrix has been provided --> just apply transformation\nif isstruct(refim);\n    tranformMatrix = refim;\n    newImage = mirt2D_transform(im, tranformMatrix);\n    if ~isfield(config, 'balazsMIRTPath');\n        rmpath(folder2add);\n    else\n        rmpath(genpath(config.balazsMIRTPath));\n    end;\n    return\nend\n\n% equilibrate brightness ranges for both images\nrefim = linScale(refim);\nim = linScale(im);\ntry\n    [tranformMatrix, newImage] = mirt2D_register(refim,im, config.main, config.optim);\n    close(gcf);\n    if ~isfield(config, 'balazsMIRTPath');\n        rmpath(folder2add);\n    else\n        rmpath(genpath(config.balazsMIRTPath));\n    end;\ncatch e\n    fprintf('Registration failed. Cleaning up path ...\\n')\n    if ~isfield(config, 'balazsMIRTPath');\n        rmpath(folder2add);\n    else\n        rmpath(genpath(config.balazsMIRTPath));\n    end;\n    rethrow(e)\nend\n\n%% Function - getDefaultConfig\nfunction config = getDefaultConfig\n% these are default settings of the most important MIRT parameters\n% they may or may not give good results with a particular image\n % Main settings\nmain.similarity='SSD';  % similarity measure, e.g. SSD, CC, SAD, RC, CD2, MS, MI \nmain.subdivide=3;       % use 3 hierarchical levels\nmain.okno=5;            % mesh window size\nmain.lambda = 0.005;    % transformation regularization weight, 0 for none\nmain.single=1;          % show mesh transformation at every iteration\nmain.saveName = [datestr(now, 'yyyy_mm_dd__HH_MM_SS') '__RegMesh']; % figure save name\n\n% Optimization settings\noptim.maxsteps = 200;   % maximum number of iterations at each hierarchical level\noptim.fundif = 1e-5;    % tolerance (stopping criterion)\noptim.gamma = 1;        % initial optimization step size \noptim.anneal=0.8;       % annealing rate on the optimization step   \n\nconfig.main = main;\nconfig.optim = optim;\n\n%% Function - addMIRT2path\nfunction folder2add = addMIRT2path(basePath)\n% check for env MATLABLOCALPATH\nenvVarName = 'MATLABLOCALPATH'; % the name of the env var\ntry\n    localPath = getenv(envVarName);\ncatch e\n    error('Failed to load environement variable %s',envVarName)\nend\nfolderList = {basePath};\nfolderList = strrep(folderList,'\\',filesep);\nfolderList = strrep(folderList,'/',filesep);\nfolder2add = sprintf('%s%s%s',localPath,filesep,folderList{1});\nif ~exist(folder2add,'dir')\n    error('\\nFolder %s not found.',folder2add)\nend\nfolder2add = genpath(folder2add);\naddpath(folder2add);\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "ConvertRawData.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/ConvertRawData.m", "size": 23529, "source_encoding": "utf_8", "md5": "09b72a006a2b553e3c3efd8e14720406", "text": "function varargout = ConvertRawData(varargin)\n% inputs: no need to specify any inputs --> GUI-based file selection\n% optional input arguments, specified as 'property' 'value' pair in\n% arbitrary order, e.g. 'zoom',1.07 OR as a structure with properties as\n% field names and values, e.g. config.zoom = 1.07\n% for a full list of supported arguments see the end of this file\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\n% current version: 2011-06-05\n% 2011-01: added freeline 2D (focus) support\n% 2011-06: added AOD (J90) support (Raps and frame scans)\n% 2013-10: added LSM support\n\n%% Main\n\ndbgLevel = 0;\n\n% RawDataDir = getGlobalParameters('RawDataDir');\n% if ~RawDataDir\n%    error('Raw data directory MUST be specified');\n% end\n\no('      #ConvertRawData(): parsing inputs...', 2, dbgLevel);\nargs = parseInputArgs(varargin);\nif min(size(args.RawFiles)) > 1\n    nRuns = size(args.RawFiles, 1);\nelse\n    nRuns = length(args.RawFiles);\n    % make sure runs are in columns\n    args.RawFiles = reshape(args.RawFiles, numel(args.RawFiles), 1);\nend\n\no('      #ConvertRawData(): number of runs: %d.', nRuns, 1, dbgLevel);\nstructArray = cell(1, nRuns);\nfor iRun = 1 : nRuns;\n    o('      #ConvertRawData(): run %d of %d...', iRun, nRuns, 2, dbgLevel);\n    % create structure\n    currStruct = initStruct(args, iRun);\n    if ~isstruct(currStruct); % if an error occured\n        continue;\n    end;\n    o('      #ConvertRawData(): run %d/%d: initialized structure.', iRun, nRuns, 3, dbgLevel);\n    \n    % preprocess image data\n    currStruct = preprocStruct(args, iRun, currStruct);\n    o('      #ConvertRawData(): run %d/%d: pre-processed structure.', iRun, nRuns, 3, dbgLevel);\n    \n    % save files\n    saveFiles(currStruct, args.SaveDir, args);\n    % assign in array\n    structArray{iRun} = currStruct;\n    \n    o('      #ConvertRawData(): run %d/%d: saved files and assigned in array.', iRun, nRuns, 3, dbgLevel);\n    \nend\n\nif nargout\n   varargout{1} = structArray;\nend\n\nend\n\n%% Parse inputs\nfunction outStruct = parseInputArgs(inargs)\n\ndbgLevel = 0;\no('        #ConvertRawData.parseInputArgs(): ...', 4, dbgLevel);\nif ~isempty(inargs) && isstruct(inargs{1})\n    % convert structure to pseudo-input cellarray\n    inargs = struct2cellArray(inargs{1});\nend\n% RawDataDir = getGlobalParameters('RawDataDir');\n% input files\nSpInput = find(strcmpi(inargs, 'RawFiles'));\nif numel(SpInput)\n    RawFiles = inargs{SpInput+1};\n    if ~iscellstr(RawFiles);\n        error('Modifier for raw files property must be a cell string');\n    end\nelse\n    [FileName, PathName, ~] = uigetfile({'*.tif';'*.tiff';'*.fcs';'*.fln';'*.lsm'},...\n        'Select raw file(s) to import','MultiSelect','on');\n    if ~iscellstr(FileName)\n        FileName = {FileName};\n    end\n    if ~FileName{1}; return; end\n    if ~iscellstr(FileName); FileName = cellstr(FileName); end\n%     if strfind(PathName,RawDataDir)\n%        PathName = strrep(PathName,RawDataDir,'');\n%     end\n    RawFiles = cell(1,length(FileName));\n    for n = 1:length(FileName) % interpret GUI-selected multiple files as different channels\n        RawFiles{1, n} = fullfile(PathName, FileName{n});\n    end\nend\noutStruct.RawFiles = RawFiles;\no('        #ConvertRawData.parseInputArgs(): RawFiles created (length: %d)...', numel(RawFiles), 5, dbgLevel);\n\n% animal ID\nSpInput = find(strcmpi(inargs, 'animalID'));\nif numel(SpInput)\n    animalID = inargs{SpInput+1};\nelse\n    animalID = '';\nend\noutStruct.animalID = animalID;\no('        #ConvertRawData.parseInputArgs(): animalID = %s...', animalID, 5, dbgLevel);\n\n% spot ID\nSpInput = find(strcmpi(inargs, 'spotID'));\nif numel(SpInput)\n    spotID = inargs{SpInput+1};\nelse\n    spotID = '';\nend\noutStruct.spotID = spotID;\no('        #ConvertRawData.parseInputArgs(): spotID = %s...', spotID, 5, dbgLevel);\n\n% frame rate\nSpInput = find(strcmpi(inargs, 'frame_rate'));\nif numel(SpInput)\n    frame_rate = inargs{SpInput+1};\n    if isscalar(frame_rate)\n        frame_rate = repmat(frame_rate,1,length(RawFiles));\n    else\n        if length(frame_rate) ~= length(RawFiles)\n            error('Frame rate must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    frame_rate = [];\nend\noutStruct.frame_rate = frame_rate;\n\n% zoom\nSpInput = find(strcmpi(inargs, 'zoom'));\nif numel(SpInput)\n    zoom_factor = inargs{SpInput+1};\n    if isscalar(zoom_factor)\n        zoom_factor = repmat(zoom_factor,1,length(RawFiles));\n    else\n        if length(zoom_factor) ~= length(RawFiles)\n            error('Zoom must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    zoom_factor = [];\nend\noutStruct.zoom_factor = zoom_factor;\n\n% laser power\nSpInput = find(strcmpi(inargs, 'laserPower'));\nif numel(SpInput)\n    laserPower = inargs{SpInput+1};\n    if isscalar(laserPower)\n       laserPower = repmat(laserPower,1,length(RawFiles));\n    else\n        if length(laserPower) ~= length(RawFiles)\n            error('Laser power must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    laserPower = [];\nend\noutStruct.laserPower = laserPower;\n\n% image type\nSpInput = find(strcmpi(inargs, 'type'));\nif numel(SpInput)\n    img_type = inargs{SpInput+1};\n    if ~iscellstr(img_type); img_type = cellstr(img_type); end\n    if length(img_type) == 1\n        img_type = repmat(img_type,1,length(RawFiles));\n    elseif length(img_type) ~= length(RawFiles)\n        error('Image type must be a cell string of length 1 or input file number');\n    end\nelse\n    img_type = {'movie'};\n    img_type = repmat(img_type,1,length(RawFiles));\n%     img_type = cell(1,length(RawFiles));\nend\noutStruct.img_type = img_type;\n\n% Roi set\nSpInput = find(strcmpi(inargs, 'RoiSet'));\nif numel(SpInput)\n    roi_set = inargs{SpInput+1};\n    if iscell(roi_set) && size(roi_set, 2) == 2;\n        % all okay, ROISet with 2 columns: names and ROIMasks for each ROI\n    else\n        if ~iscellstr(roi_set); roi_set = cellstr(roi_set); end\n        if length(roi_set) == 1\n            roi_set = repmat(roi_set,1,length(RawFiles));\n        elseif length(roi_set) ~= length(RawFiles)\n            error('RoiSet must be a cell string of length 1 or input file number');\n        end\n    end;\nelse\n    roi_set = cell(1,length(RawFiles));\nend\noutStruct.roi_set = roi_set;\n\n\n% Reference image\nSpInput = find(strcmpi(inargs, 'RefImg'));\nif numel(SpInput)\n    refImg = inargs{SpInput + 1};\n    if ischar(refImg); % okay will be loaded later\n    elseif isnumeric(refImg); % already loaded image\n    else\n        error('refImg must be a file name or a loaded reference image !');\n    end\nelse\n    refImg = '';\nend\noutStruct.refImg = refImg;\n\n% Stimulus\nSpInput = find(strcmpi(inargs, 'Stim'));\nif numel(SpInput)\n    stim = inargs{SpInput+1};\n    if ~iscell(stim); stim = {stim}; end\n    if length(stim) == 1 && length(RawFiles) > 1\n        stim = repmat(stim,1,length(RawFiles));\n    elseif length(stim) ~= length(RawFiles)\n        error('Stimulation must be a cell array of length 1 or input file number');\n    end\nelse\n    stim = cell(1,length(RawFiles));\nend\noutStruct.stim = stim;\n\n% Ephys\nSpInput = find(strcmpi(inargs, 'Ephys'));\nif numel(SpInput)\n    ephys = inargs{SpInput+1};\n    if ~iscell(ephys) || length(ephys) ~= length(RawFiles)\n        error('Ephys must be a cell array of length input file number');\n    end\n    outStruct.ephys = ephys;\nend\n\n% Output directory\nSpInput = find(strcmpi(inargs, 'SaveDir'));\nif numel(SpInput)\n    SaveDir = inargs{SpInput+1};\n    if exist(SaveDir,'dir') ~= 7\n       try\n          mkdir(SaveDir);\n       catch e\n           error('Failed to create directory %s',SaveDir);\n       end\n    end\nelse\n    SaveDir = pwd;\nend\noutStruct.SaveDir = SaveDir;\n\n% color vector\nSpInput = find(strcmpi(inargs, 'ColorVector'));\nif numel(SpInput)\n    outStruct.ColorVector = inargs{SpInput+1};\nelse\n    outStruct.ColorVector = [2 1 0];\nend\n\n% Dimensions - usually not necessary to specify, except for freeline 2d\n% focus in which case dims should be specified as [columns rows]\nSpInput = find(strcmpi(inargs, 'Dims'));\nif numel(SpInput)\n    outStruct.dims = inargs{SpInput+1};\nelse\n    switch outStruct.img_type{1}\n        case 'free2d_focus'\n          error('Must always specify image dimensions for freeline 2D focus.');\n    end\nend\n\n% Position - can be created with makePositionStructure\nSpInput = find(strcmpi(inargs, 'Position'));\nif numel(SpInput)\n    outStruct.position = inargs{SpInput+1};\nelse\n    outStruct.position = makePositionStructure;\nend\n\n% Preprocessing options - delete frame\nSpInput = find(strcmpi(inargs, 'DelFrame'));\nif numel(SpInput)\n    del_frame = inargs{SpInput+1};\n    if isscalar(del_frame)\n        del_frame = repmat(del_frame,1,length(RawFiles));\n    else\n        if length(del_frame) ~= length(RawFiles)\n            error('DelFrame must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    del_frame = [];\nend\noutStruct.del_frame = del_frame;\n% bg. correction\nSpInput = find(strcmpi(inargs, 'BgCorrect'));\nif numel(SpInput)\n    bgCorrect = inargs{SpInput+1};\nelse\n    bgCorrect = [];\nend\noutStruct.bgCorrect = bgCorrect;\n\nend\n\n%% Structure initialization\nfunction S = initStruct(inargs, iRun)\n\ndbgLevel = 0;\no('        #ConvertRawData#initStruct(): run %d...', iRun, 4, dbgLevel);\ninfile = inargs.RawFiles(iRun, :);\n[FilePath,FileName,FileType] = fileparts(infile{1});\n% if the file cannot be found with the absolute path, try the relative path\n% instead\nif exist(infile{1},'file') ~= 2\n    infile_full = fullfile(FilePath,[FileName FileType]);\n    if exist(infile_full,'file') ~= 2\n       warning('initStruct:FileNotFound', 'Could not find %s on absolute and relative paths', infile_full);\n       S = [];\n       return;\n%        error('Could not find %s on absolute and relative paths',infile_full);\n    end\nelse\n    infile_full = infile;\nend\n\nS.hdr.animalID = inargs.animalID;\nS.hdr.spotID = inargs.spotID;\nS.hdr.laserPower = inargs.laserPower;\nS.hdr.refImg = inargs.refImg;\nS.hdr.frame_rate = inargs.frame_rate;\n\nS.hdr.position = inargs.position;\n\nswitch FileType\n    case {'.fcs','.fln'}\n        switch inargs.img_type{iRun}\n            case 'free2d_focus'\n                img = import_raw(infile_full,inargs.dims(1),inargs.dims(2),768);\n                S.img_data{1} = uint8(img.ch1/255); % always save 8-bit integers\n                S.img_data{2} = uint8(img.ch2/255);\n            otherwise\n                img = import_raw(infile_full);\n                S.img_data{1} = uint8(img.ch1/255); % always save 8-bit integers\n                S.img_data{2} = uint8(img.ch2/255);\n                % try to decode parts of header\n        end\n        % save bit-depth for later usage\n        S.hdr.bits = 8;\n    case '.tif'\n        % TODO: new tif-import in separate file, return data cell array and\n        % header info\n        for m = 1:numel(infile)\n            [FilePath,FileName,FileType] = fileparts(infile{m});\n            if exist(infile{m},'file') ~= 2\n                infile_full = fullfile(FilePath,[FileName FileType]);\n                if exist(infile_full,'file') ~= 2\n                    error('Could not find %s on absolute and relative paths',infile{iRun});\n                end\n            else\n                infile_full = infile{iRun};\n            end\n            img =tiffread2_wrapper(infile_full);\n            S.img_data{m} = img.data;\n            S.hdr.tifHeader{m} = img.header;\n            if ~isempty(strfind(infile_full,'channel00'))\n                if exist(strrep(infile_full,'channel00','channel01'), 'file') == 2\n                    img =tiffread2_wrapper(strrep(infile_full,'channel00','channel01'));\n                    S.img_data{m+1} = img.data;\n                    S.hdr.tifHeader{m+1} = img.header;\n                end\n                if exist(strrep(infile_full,'channel00','channel02'), 'file') == 2\n                    img =tiffread2_wrapper(strrep(infile_full,'channel00','channel02'));\n                    S.img_data{m+2} = img.data;\n                    S.hdr.tifHeader{m+2} = img.header;\n                end\n            end\n            clear img\n            % additional files - AOD J90\n            switch inargs.img_type{m}\n                case {'aod_raps','aod_frame'}\n                    dims = size(S.img_data{m});\n                    % split up channels (assume 3 channels are stored)\n                    FramesPerChannel = dims(3) / 3;\n                    currentChannel = 1;\n                    pos = 0;\n                    switch inargs.img_type{m}\n                        case {'aod_raps'}\n                            imgData = {zeros(dims(1)*FramesPerChannel,dims(2))};\n                        otherwise\n                            imgData = {zeros(dims(1),dims(2),FramesPerChannel)};\n                    end\n                    imgData = repmat(imgData,1,3);\n                    for frame = 1:dims(3)\n                        if pos == FramesPerChannel\n                            currentChannel = currentChannel + 1;\n                            pos = 1;\n                        else\n                            pos = pos + 1;\n                        end\n                        switch inargs.img_type{m}\n                            case {'aod_raps'}\n                                imgData{currentChannel}((dims(1)*(pos-1))+1:dims(1)*pos,:) = ...\n                                    S.img_data{m}(:,:,frame);\n                            otherwise\n                                imgData{currentChannel}(:,:,pos) = ...\n                                    S.img_data{m}(:,:,frame);\n                        end\n                    end\n                    S.img_data = imgData;\n                    clear imgData\n                    \n                    if exist(strrep(infile_full,'.tif','_RAData.txt'), 'file') == 2\n                        S.hdr.RAData = importdata(strrep(infile_full,'.tif','_RAData.txt'));\n                    end\n                    if exist(strrep(infile_full,'.tif','_FOV.png'), 'file') == 2\n                        S.hdr.FOV = importdata(strrep(infile_full,'.tif','_FOV.png'));\n                    end\n            end\n        end\n        S.hdr.bits = 16;\n    case '.lsm' % Zeiss LSM file format\n        for m = 1:numel(infile)\n            [FilePath,FileName,FileType] = fileparts(infile{m});\n            if exist(infile{m},'file') ~= 2\n                infile_full = fullfile(FilePath,[FileName FileType]);\n                if exist(infile_full,'file') ~= 2\n                    error('Could not find %s on absolute and relative paths',infile{iRun});\n                end\n            else\n                infile_full = infile{iRun};\n            end\n            img = readLSMfile(infile_full);\n            S.img_data{m} = img.data;\n            S.hdr.tifHeader{m} = img.hdr;\n            % header info\n            S.hdr.bits = img.hdr.scaninf.BITS_PER_SAMPLE;\n            S.hdr.frame_rate = 1./img.hdr.lsminf.TimeInterval;\n            S.hdr.laserPower = img.hdr.scaninf.LASER_POWER;\n            clear img\n        end\n    otherwise\n        error('Unrecognized file type %s',FileType);\nend\n\n% creation date for infile\nif ~iscellstr(infile_full)\n   dummy{1} = infile_full;\n   infile_full = dummy;\nend\nfile_pars = dir(infile_full{1});\nS.hdr.createDate = file_pars.date;\n\n% other header info\nS.hdr.size = size(S.img_data{1});\n% if isempty(inargs.frame_rate)\n%     S.hdr.rate = [];\n%     if isfield(S.hdr,'tifHeader') && isfield(S.hdr.tifHeader{1,1},'info')\n%         if isempty(strcmp(S.hdr.tifHeader{1,1}.info,'Failed to read XML header'))\n%             S.hdr.rate = 1 / str2double(S.hdr.tifHeader{1,1}.info.TimeIncrement);\n%         end\n%     end\n% else\n%     S.hdr.rate = inargs.frame_rate(iRun);\n% end\nif isempty(inargs.zoom_factor)\n    S.hdr.zoom = [];\nelse\n    S.hdr.zoom = inargs.zoom_factor(iRun);\nend\nS.hdr.type = inargs.img_type{iRun};\nS.hdr.fileorigin = infile{1};\n\n% channel ID\nS.hdr.channelID = cell(1,length(S.img_data));\n\nS.hdr = orderfields(S.hdr);\n\n% RoiSet decoder\nif isempty(inargs.roi_set{iRun})\n    S.roi = [];\nelseif exist(inargs.roi_set{iRun}, 'file');\n   S.roi = ij_roiDecoder(inargs.roi_set{iRun},[S.hdr.size(1) S.hdr.size(2)]);\n   S.roi = ij_RoiSetCompression(S.roi,1);\nelseif iscell(inargs.roi_set) && size(inargs.roi_set, 2) == 2;\n    S.roi = inargs.roi_set;\nend\n\n% Stimulation\nif isempty(inargs.stim{iRun})\n    S.stim = [];\nelse\n   S.stim = inargs.stim{iRun};\n   % only display warning if stim is a vector, not a structure.\n   if ~isstruct(S.stim) && length(S.stim) ~= S.hdr.size(3);\n      warning('Stim vector length does not agree with timepoints for run %s',...\n          int2str(iRun));\n   end\nend\n\nif isfield(inargs,'ephys')\n    S.ephys = inargs.ephys{iRun};\nend\n\nS = orderfields(S);\n\nend\n\n%% Preprocessing\nfunction S = preprocStruct(inargs,n,S)\n% Delete frame\nif isempty(inargs.del_frame)\n   if size(S.img_data{1},3) > 3\n      del_frame = 1;\n   else\n       del_frame = 0;\n   end\nelse\n    del_frame = inargs.del_frame(n);\nend\nfor channel = 1:length(S.img_data)\n    switch inargs.img_type{n}\n        case {'free2d_focus','aod_raps'}\n            S.img_data{channel} = S.img_data{channel}(del_frame+1:end,:);\n        otherwise\n            S.img_data{channel} = S.img_data{channel}(:,:,del_frame+1:end);\n    end\nend\n% adjust stimulus protocol\n% if ~isempty(S.stim)\n%     if isstruct(S.stim)\n%         S.stim.vector(1:round(1/S.hdr.rate*del_frame*S.stim.rate)) = [];\n%     elseif isvector(S.stim) % assume vector already converted to frames\n%         S.stim = S.stim(del_frame+1:end);\n%     end\n% end\nswitch inargs.img_type{n}\n    case 'free2d_focus'\n        S.hdr.size(1) = S.hdr.size(1) - del_frame;\n    otherwise\n        if length(S.hdr.size) > 2\n            S.hdr.size(3) = S.hdr.size(3) - del_frame;\n        end\nend\n% adjust ephys\nif isfield(S,'ephys')\n    S.ephys.data(1:round(1/S.hdr.rate*del_frame*S.ephys.rate)) = [];\nend\n\nS.proc.DelFrame = del_frame;\n\n% Bg. correct\nif ~isempty(inargs.bgCorrect)\n    method = inargs.bgCorrect{1};\n    switch method\n        case 'roi' % need to have inargs.bgCorrect{2} in roi set\n            roiID = inargs.bgCorrect{2};\n            if ~isempty(S.roi) && strcmp(S.roi(:,1),roiID)\n                arg2 = bwunpack(S.roi{strcmp(S.roi(:,1),roiID),2});\n            else\n                error('Could not find Roi for bg. correction');\n            end\n        otherwise\n            arg2 = inargs.bgCorrect{2};\n    end\n    for channel = 1:length(S.img_data)\n        S.img_data{channel} = bg_subtract_HL(S.img_data{channel},arg2,...\n            inargs.bgCorrect{3},method);\n    end\nend\nS.proc.bgCorrect = inargs.bgCorrect;\n\nS = orderfields(S);\n\nend\n\n%% save files\nfunction saveFiles(currentS,SaveDir,args)\nsaveName = currentS.hdr.fileorigin;\n[~, saveName, ext] = fileparts(saveName);\nsaveName_mean = fullfile(SaveDir,[saveName '__avg']);\nsaveName = fullfile(SaveDir,saveName);\nsaveName = strrep(saveName,'__channel00','');\n% saveName_mean = strrep(saveName_mean,'__channel00','');\nif currentS.hdr.bits == 8\n    for n = 1:numel(currentS.img_data)\n        currentS.img_data{n} = uint8(currentS.img_data{n});\n    end\nelse\n    for n = 1:numel(currentS.img_data)\n        currentS.img_data{n} = uint16(currentS.img_data{n});\n    end\nend\n\n% save mat-format\nSaveAndAssignInBase(currentS,saveName,'SaveOnly');\n\n% save average images\nif isempty(strfind(args.img_type{1},'aod_raps'))\n    mean_img = cell(1, numel(currentS.img_data));\n    for n = 1:numel(currentS.img_data)\n        chID = sprintf('ch%s',int2str(n));\n        mean_img{n} = currentS.img_data{n};\n        switch args.img_type{1}\n            case 'free2d_focus'\n                mean_img{n} = mean(mean_img{n});\n                mean_img{n} = repmat(mean_img{n},numel(mean_img{n}),1);\n            otherwise\n                mean_img{n} = mean(mean_img{n},3);\n        end\n        if strcmp(ext,'.tif')\n            avg_img.img = mean_img{n};\n            chID = sprintf('channel%02.0f',n-1);\n            currentSaveName_mean = strrep(saveName_mean,'channel00',chID);\n            if isempty(strfind(args.img_type{1},'aod_frame'))\n                write_to_tif(avg_img,currentSaveName_mean);\n            end\n        else\n            avg_img.(chID) = mean_img{n}; % write below\n        end\n    end\nend\n\n% RGB merge (multi-channel only)\nif ~strcmp(args.img_type{1},'aod_raps') && numel(mean_img) > 1\n    if args.ColorVector(1)\n        red = linScale(mean_img{args.ColorVector(1)});\n    else\n        red = zeros(size(mean_img{1}));\n    end\n    \n    if args.ColorVector(2)\n        green = linScale(mean_img{args.ColorVector(2)});\n    else\n        green = zeros(size(mean_img{1}));\n    end\n    \n    if args.ColorVector(3)\n        blue = linScale(mean_img{args.ColorVector(3)});\n    else\n        blue = zeros(size(mean_img{1}));\n    end\n    rgb.data = cat(3,red,green,blue);\n    if isempty(strfind(saveName_mean,'channel00__'))\n        currentSaveName_mean = [saveName_mean '_RGB'];\n    else\n        currentSaveName_mean = strrep(saveName_mean,'channel00__','RGB');\n    end\n    write_to_tif(rgb,currentSaveName_mean)\nend\n\n% save tiff-format (1 file per channel) for fcs files\nif strcmp(strtrim(ext),'.fcs')\n    for n = 1:length(currentS.img_data)\n        chID = sprintf('ch%s',int2str(n));\n        img.(chID) = currentS.img_data{n};\n    end\n    clear currentS\n    write_to_tif(img,saveName,8); % save as 8-bit tiff\n    saveName = [saveName '__avg'];\n    write_to_tif(avg_img,saveName,8);\nend;\n\nend\n\n%% Documentation\n% 'RawFiles' - a cellstring with relative path to any of the supported input\n% filetypes, supports several files, e.g. batch mode\n% path should be relative to the RawDataDir path specified in the .config\n% located in matlabroot\\work (platform-independent)\n% or full path to files (platform-dependent)\n% for TIF files, mutliple channels are encoded in columns and multiple\n% files in rows, e.g.: \n% {'file1_ch1.tif' 'file1_ch2.tif'; 'file2_ch1.tif' 'file2_ch2.tif'}\n% 'frame_rate' - frame rate of the image acquisition (scalar or vector with\n% 1 value per file)\n% 'zoom' - zoom factor (scalar or vector)\n% 'type' - cellstring with image type descriptions, e.g. 'stack', 'movie',\n% 'frame' or 'linescan'\n% 'RoiSet' - cellstring with full path to IJ RoiSet.zip files for all runs\n%            OR ROISet cell array with 2 columns: names and ROIMasks\n% 'SaveDir' - specifies directory for saving converted files (current\n% directory is used if this is not specified)\n% 'Stim' - stimulation info as cell array with one entry per run or one\n% entry for all runs (default: empty)\n% interpretation of the stimulation protocol depends on later routines,\n% here we will only add the provided values to the structure\n% 'animalID'\n% 'spotID'\n% 'zoom'\n% 'laserPower'\n% 'Position' - Position structure with fields of 'units' (e.g.\n% 'pixel','um'), spotCoordinates (1x3 vector), refCoordinates (nx3 matrix),\n% refID (cellstring with ID for n reference coordinates), specifiy NaN for\n% coordinates that are not available - use makePositionStructure to create\n% 'ephys' - ephys as cell array with one entry per run; provide ephys data\n% structure for each run with (at least) two fields:\n% ephys.data ... the recorded data as vector\n% ephys.rate ... the sampling rate for ephys recording\n\n% Preprocessing options\n% 'DelFrame' - number of frames to delete (defaults to 0 for all images\n% with 3 or less frames, otherwise defaults to 1), also apply to stim\n% vector, if any, to size vector and to ephys, if any\n% 'BgCorrect' - background correction specification as cell array with \n% {'method', param, plotFlag} --> applied with these parameters to all\n% movies\n% see bg_subtract_HL for details\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "GetRoiStats.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/GetRoiStats.m", "size": 30729, "source_encoding": "utf_8", "md5": "40f7e00ec7f7217d038af19be260c737", "text": "function varargout = GetRoiStats(config)\n% get ROI timecourses from images, with support for muliple ROIs and runs\n% returns structure data, info and stimulus fields\n% data field contains a cell array with ROI timecourses for different cells\n% in rows and different runs in columns:\n% [cell1_run1] [cell1_run2] ... [cell1_runN]\n% [cell2_run1] [cell2_run2] ... [cell2_runN]\n%     ...           ...              ...\n% [cellN_run1] [cellN_run2] ... [cellN_runN]\n% assume raw data structure specified as mat-files wih fields:\n% img_data ... cell array for image data (each channel in one cell)\n% roi ... imageJ roi set\n% stim ... stimulus vector (at frame rate, i.e. 1 value per frame)\n% hdr ... header information\n% proc ... preprocessing information (e.g. how many frames have been deleted)\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\ndbgLevel = 1;\n\n%% GetRoiStats - Inititialization\n% parse the configuration structure\nconfig = ParseConfig(config);\no('#GetRoiStats(): %d file(s) to process...', numel(config.matFiles), 1, dbgLevel);\n\n% init some parameters\n[config, matFiles] = initParams(config, dbgLevel);\no('#GetRoiStats(): parameters initialized.', 2, dbgLevel);\n\n% init some more parameters\nnRuns = config.nRuns;\nROISet = [];\nimgDims = cell(nRuns, 1);\nconfig.runFileIDs = cell(nRuns, 1);\nmatFileStructs = cell(1, nRuns);\n\n%% GetRoiStats - Create common ROISet\no('#GetRoiStats(): processing %d runs...', config.nRuns, 2, dbgLevel);\nfor iRun = 1 : nRuns;\n    \n    o('  #GetRoiStats(): processing run %d/%d (%s) ...', iRun, nRuns, matFiles{iRun}, 2, dbgLevel);\n    \n    % make sure the mat file exists\n    if ~exist(matFiles{iRun}, 'file');\n        warning('GetRoiStats:FileNotFound', 'Could not find mat-file \"%s\", skipping run %d.', ...\n            matFiles{iRun}, iRun);\n        continue;\n    end;\n    \n    \n    %% GetRoiStats --- Initialize run\n    % load the mat-file for this run and store it (avoids reloading it in the next for-loop)\n    % also assign a \"fileID\" name for that run (date + time withouth '.mat')\n    fileID = strrep(matFiles{iRun}, '.mat', '');\n    matFileWrapper = load(matFiles{iRun});\n    matFileStruct = matFileWrapper.(genvarname(fileID));\n    matFileStruct.hdr.fileID = fileID;\n    config.runFileIDs{iRun} = fileID;\n    matFileStructs{iRun} = matFileStruct;\n    \n    %% GetRoiStats --- Extract ROIs, calculate mean point and merge\n    % load ROIs as a cell-array with ROI IDs and their masks\n    localROISet = matFileStruct.roi;\n    % process each ROI for mean (middle) point calculation\n    for iLocalROI = 1 : size(localROISet, 1);\n        ROIMask = localROISet{iLocalROI, 2};\n        if isa(ROIMask, 'uint32');\n            ROIMask = bwunpack(ROIMask);\n        end;\n        % calculate mean (middle) point's coordinate\n        [ROIYCoords, ROIXCoords] = find(ROIMask == 1);\n        localROISet{iLocalROI, 3} = [mean(ROIYCoords), mean(ROIXCoords)];\n    end;\n    \n    % if no general ROISet is available, use the first one encountered\n    if isempty(ROISet);\n        ROISet = localROISet;\n    % otherwise merge the ROISets\n    else\n        % search through each local ROISet if it exists in the global ROISet\n        for iLocalROI = 1 : size(localROISet, 1);\n            localROIID = localROISet{iLocalROI, 1};\n            \n            % if local (new) ROI doesn't exist in the global ROISet\n            if ~ismember(localROIID, ROISet(:, 1));\n                % add it to the global ROISet\n                ROISet(end + 1, 1 : 3) = localROISet(iLocalROI, :); %#ok<AGROW>\n            end;\n        end;\n    end;\n    \nend;\n\n% add the neuropil as an ROI\n[ROISet, ~] = addNPilROIToROISet(ROISet, 0.15);\n% store the merged ROIs (with neuropil)\nconfig.ROISet = ROISet;\n\n%% GetRoiStats - Process runs\nROIStatsData = cell(size(ROISet, 1), nRuns);\no('#GetRoiStats(): processing %d runs...', config.nRuns, 2, dbgLevel);\nfor iRun = 1 : nRuns;\n    \n    % retrieve the stored matFileStruct\n    matFileStruct = matFileStructs{iRun};\n    fileID = config.runFileIDs{iRun};\n    \n    o('  #GetRoiStats(): processing run %d/%d (%s)...', iRun, nRuns, fileID, 2, dbgLevel);\n    \n    % initialize containers for the imaging data (recorded movies)\n    currImgData = cell(1, length(config.channelVector));\n    currMeanImgData = cell(1, length(config.channelVector));\n    imgDims{iRun} = config.img_dims;\n    \n    o('    #GetRoiStats(): run %d/%d: ROIs extracted.', iRun, nRuns, 4, dbgLevel);\n    \n    %% GetRoiStats --- Image registration + channel switching\n    % check size of movie and ROISet, must be same\n    if any(size(ROISet{1, 2}) ~= matFileStruct.hdr.size(1:2));\n        o('    #GetRoiStats(): run %d/%d: image registration required... (size of ROI != movie).', ...\n            iRun, nRuns, 4, dbgLevel);\n        matFileStruct = doImageReg(matFileStruct, config);\n        imgDims{iRun} = [size(matFileStruct.img_data{1}, 1) size(matFileStruct.img_data{1}, 2)];\n    end;\n    o('    #GetRoiStats(): run %d/%d: image registration done.', iRun, nRuns, 3, dbgLevel);\n    \n    %% GetRoiStats --- Image averaging\n    % image data is already arranged here as specified in channelVector. CH1 and CH2 are always\n    % ordered in the same way from here to the end :\n    % DFF is calculated on channel 1, DRR is ch1 / ch2\n    for iChan = 1 : numel(matFileStruct.img_data)\n        % make sure we have double data type (might be stored as int16 / int8)\n        currImgData{iChan} = double(matFileStruct.img_data{iChan});\n        % also store a mean image for display of Rois\n        currMeanImgData{iChan} = mean(currImgData{iChan}, 3);\n    end;\n    \n    %% GetRoiStats --- PseudoFlatField correct image\n    if config.doFlatfield;\n        % correct the image's intensity using the \"PseudoFlatfieldCorrect\" (gaussian filtering)\n        currSegmImg = PseudoFlatfieldCorrect(currMeanImgData{1});\n        currSegmImg(currSegmImg < 0) = 0;\n    else\n        currSegmImg = currMeanImgData{1};\n    end;\n    \n    o('    #GetRoiStats(): run %d/%d: Pseudo-flat field correction done.', ...\n        iRun, nRuns, 3, dbgLevel);\n    \n    \n    \n    %% GetRoiStats --- Check ROI presence in current run\n    % reload the ROIs\n    localROISet = matFileStruct.roi;\n    % check the presence of each global ROI in the local ROISet\n    ROISet(:, 4) = cellfun(@(ROIID){ismember(ROIID, localROISet(:, 1))}, ROISet(:, 1));\n    \n    %% GetRoiStats --- Plot ROIs on RGB image\n    if config.doSaveROIRGBPlot;\n        \n        o('    #GetRoiStats(): run %d/%d: Plotting ROIs on RGB ...', iRun, nRuns, 3, dbgLevel);\n        ROIRGBHand = doROIRGBPlot(imgDims{iRun}, localROISet, currSegmImg, currMeanImgData, fileID, 0.7);\n        \n        if config.doSaveROIRGBPlot > 1;\n            if exist('ROIRGB', 'dir') ~= 7; mkdir('ROIRGB'); end;\n            saveas(ROIRGBHand, sprintf('ROIRGB/%s__ROIRGB', fileID));\n            saveas(ROIRGBHand, sprintf('ROIRGB/%s__ROIRGB.png', fileID));\n            close(ROIRGBHand);\n        end;\n    end;\n    \n    nFrames = size(currImgData{1}, 3);\n    o('    #GetRoiStats(): run %d/%d: %d frame(s).', iRun, nRuns, nFrames, 3, dbgLevel);\n    \n    %% GetRoiStats --- ROI and neuropil analysis\n    o('    #GetRoiStats(): run %d/%d: ROI analysis...', iRun, nRuns, 3, dbgLevel);\n    \n    % go through each ROI (and neuropil) and extract the ROIStats from them\n    for iROI = 1 : size(ROISet, 1);\n        \n        % if ROI was not present in this run and is not the neuropil, don't process it\n        if ~strcmp('NPil', ROISet{iROI, 1}) && ~ROISet{iROI, 4}; continue; end;\n        \n        ROIMask = ROISet{iROI, 2};\n        \n        % for neuropil, use the ROIMask from the localROISet\n        if strcmp('NPil', ROISet{iROI, 1});\n            [localROISetWithNPil, ~] = addNPilROIToROISet(localROISet, 0.15);\n            ROIMask = localROISetWithNPil{strcmp('NPil', localROISetWithNPil(:, 1)), 2};\n        end;\n            \n        doSaveF0ExtractPlot = config.doSaveF0AndExtractPlot;\n        if iROI ~= 1 && rand(1) > 0.05; % draw the plot with 5% chance\n            doSaveF0ExtractPlot = 0;\n        end;\n        \n        % store roi data\n        ROIStatsData{iROI, iRun} = extractROIStats(currImgData, ROIMask, ...\n            config.statsType, config.LowPass, config.HiPass, config.f0, ...\n            sprintf('%s_F0AndExtract_run%d_ROI%d', config.saveName, iRun, iROI), ...\n            doSaveF0ExtractPlot);\n        \n    end;\n    \n    clear ROIYCoords ROIXCoords fileID;\n    \nend\n\n%% GetRoiStats - End of runs processing\n% copy stuff in the config, assuming things were similar over all runs...\nconfig.img_dims = imgDims(1);\nconfig.ROISet = ROISet;\nconfig.ROIStatsData = ROIStatsData;\n\n% config.freqIDs = 1.0e+03 * [5, 9, 13, 17, 21, 25, 29, 33, 37, 41];\n% config.freqIDs = 4000 * (2 .^ (((1 : 15) - 1) * 0.25));\n% config.plotLimits = [-1 5];\n\n%% GetRoiStats - Config saving\nsaveName = sprintf('%s_ROIStats', config.saveName);\no('    #GetRoiStats(): saving ROIStats under \"%s\"...', saveName, 3, dbgLevel);\nSaveAndAssignInBase(config, saveName);\no('    #GetRoiStats(): ROIStats saving done.', 2, dbgLevel);\n    \nif nargout;\n    varargout{1} = config;\nend;\n\nend\n\n\n%% Function - doImageReg\nfunction matFileStruct = doImageReg(matFileStruct, config)\n% Perform non-linear registration of movie to hign resolution image\n% Note that at this stage, the channel order should NOT yet be adjusted\n% according to config.ChannelVector\n% Registration performs the following steps:\n% 1. Identify reference image channel and average multiple frames (if any)\n% 2. Resize all movie channels to the xy-dims of the reference image -->\n% this is the minimum processing that needs to be done, so that image\n% dimensions are consistent for downstream analysis; if the reference image\n% is for example the average of each movie, then registration could stop\n% at this point\n% 3. Average all frames of movie in registration channel\n% 4. Register movie average to reference image using non-linear image\n% registration tool MIRT\n% 5. Check quality of the registration\n% 6. Apply transformation matrix to all frames of all channels and write\n% the result back into S.img_data\n\ndbgLevel = 3;\n\nif config.reg.doRegistration;\n    o('      #GetRoiStats.doImageReg(): doing image up-sampling AND registration ...', 2, dbgLevel);\nelse\n    o('      #GetRoiStats.doImageReg(): doing image up-sampling only (no registration) ...', 2, dbgLevel);\nend;\n\n% get the reference image using the channels specified in the config\nrefImg = getRefOrRegImage(matFileStruct.hdr.fileID, 'Reference', ...\n    matFileStruct.refImg, config, config.reg.doSaveRefImagePlot, dbgLevel);\nrefDims = size(refImg);\nnChan = numel(matFileStruct.img_data);\nnFrames = size(matFileStruct.img_data{1}, 3);\n\n% if no matlab pool open or in a worker, use standard for loop, other use parfor\nuseParfor = matlabpool('size') && isempty(javachk('desktop'));\n\n% upsample movie to refImg\nupSampleTic = tic; % for performance timing purposes\nfor iChan = 1 : nChan;\n    o('          #Get...ImageReg(): up-sampling channel %d/%d (useParfor = %d) ...', ...\n        iChan, nChan, useParfor, 2, dbgLevel);\n    origCh = matFileStruct.img_data{iChan};\n    upSampImg = zeros(refDims(1), refDims(2), nFrames);\n    if useParfor;\n        parfor iFrame = 1 : nFrames;\n            upSampImg(:, :, iFrame) = imresize(origCh(:, :, iFrame), refDims);\n        end;\n    else\n        for iFrame = 1 : nFrames;\n            upSampImg(:, :, iFrame) = imresize(origCh(:, :, iFrame), refDims);\n        end;\n    end;\n    matFileStruct.img_data{iChan} = upSampImg;\nend;\no('        #Get...ImageReg(): up-sampling done (%.2f seconds).', toc(upSampleTic), 1, dbgLevel);\n\nif ~config.reg.doRegistration; return; end;\n\no('        #Get...ImageReg(): registration required: doRegistration = %d.', ...\n    config.reg.doRegistration, 2, dbgLevel);\n\n% get the registration image using the registration channel(s) specified in the config\nregImg = getRefOrRegImage(matFileStruct.hdr.fileID, 'Registration', ...\n    matFileStruct.img_data, config, config.reg.doSaveRegImagePlot, dbgLevel);\n\nif ~isfield(matFileStruct, 'tform') || isempty(matFileStruct.tform);\n    \n    o('        #Get...ImageReg(): transform matrix (tform) not present. ', 2, dbgLevel);\n    o('        #Get...ImageReg(): running registration to create it ... ', 2, dbgLevel);\n    tFormCreateTic = tic; % for performance timing purposes\n    config.reg.main.single = config.reg.doSaveRegProgressPlot;\n    config.reg.main.saveName = sprintf('%s__RegMesh', matFileStruct.hdr.fileID);\n    % run registration\n    [tform, newImg] = registerImages_MIRT(refImg, regImg, config.reg);\n    \n    % display overlay\n    if config.reg.doSaveRegOverlayPlot;\n        hReg = figure('Name', sprintf('Image Registration Overlay for %s', matFileStruct.hdr.fileID), ...\n            'NumberTitle', 'off');\n        subplot(1, 2, 1);\n        imshowpair(refImg, edge(regImg,'canny'), 'method', 'blend');\n        title('Reference - Input overlay');\n        subplot(1, 2, 2);\n        imshowpair(refImg, edge(newImg,'canny'), 'method', 'blend');\n        title('Reference - Registered overlay');\n        % save if required\n        if config.reg.doSaveRegOverlayPlot > 1;\n            if exist('RegOverlay', 'dir') ~= 7; mkdir('RegOverlay'); end;\n            saveas(hReg, sprintf('RegOverLay/%s__ImgRegOvl', matFileStruct.hdr.fileID));\n            saveas(hReg, sprintf('RegOverLay/%s__ImgRegOvl.png', matFileStruct.hdr.fileID));\n            close(hReg);\n        end;\n    end;\n    \n    matFileStruct.tform = tform;\n    \n    % save tform into original mat-file (image data already resized)\n    S2 = load(matFileStruct.hdr.fileID);\n    data2 = S2.(genvarname(matFileStruct.hdr.fileID));\n    data2.tform = tform;\n    SaveAndAssignInBase(data2,matFileStruct.hdr.fileID,'SaveOnly')\n    clear data2 S2;\n    o('        #Get...ImageReg(): transform matrix (tform) created (%.2f seconds).', ...\n        toc(tFormCreateTic), 2, dbgLevel);\nelse\n    o('        #Get...ImageReg(): transform matrix (tform) already present.', 2, dbgLevel);\n    tform = matFileStruct.tform;\nend;\n\n% apply transformation only on channels required by the channelVector\ntFormApplyTic = tic; % for performance timing purposes\noriImg_data = matFileStruct.img_data;\nnChanForApply = numel(config.channelVector);\nmatFileStruct.img_data = {}; % clear the previous data\nfor iChan = 1 : nChanForApply;\n        \n    tFormApplyChanTic = tic; % for performance timing purposes\n    o('          #Get...ImageReg(): Applying transformation to channel %d (useParfor = %d) ...', ...\n        config.channelVector(iChan), useParfor, 2, dbgLevel);\n    origCh = oriImg_data{config.channelVector(iChan)};\n    newCh = zeros(size(oriImg_data{config.channelVector(iChan)}));\n    if useParfor;\n        parfor iFrame = 1 : nFrames;\n            [~ ,newCh(:, :, iFrame)] = registerImages_MIRT(tform, origCh(:, :, iFrame), config.reg); %#ok<PFBNS>\n        end;\n    else\n        for iFrame = 1 : nFrames;\n            [~, newCh(:, :, iFrame)] = registerImages_MIRT(tform, origCh(:, :, iFrame), config.reg);\n        end;\n    end;\n    o('          #Get...ImageReg(): Applied transf. to chan. %d on %d frame(s) (%.2f seconds).', ...\n        config.channelVector(iChan), nFrames, toc(tFormApplyChanTic), 2, dbgLevel);\n    matFileStruct.img_data{iChan} = newCh;\nend;\no('        #Get...ImageReg(): Applied transf. to %d channel(s) on %d frame(s) (%.2f seconds).', ...\n    nChanForApply, nFrames, toc(tFormApplyTic), 1, dbgLevel);\n\n% plot the applied transformations\nif config.reg.doSaveRegComparisonPlot;\n    figHand = figure('NumberTitle', 'off', 'Name', ...\n        sprintf('Registration comparison for %s', matFileStruct.hdr.fileID));\n    \n    subplot(nChanForApply + 1, 2, 1);\n    imshow(linScale(regImg), []);\n    title('Registration image');\n    subplot(nChanForApply + 1, 2, 2);\n    imshow(linScale(refImg), []);\n    title('Reference image');\n    for iChan = 1 : nChanForApply;\n        % plot the mean image before\n        subplot(nChanForApply + 1, 2, iChan * 2 + 1);\n        imshow(linScale(mean(oriImg_data{iChan}, 3)), []);\n        title(sprintf('Channel %d before transformation', config.channelVector(iChan)));\n        subplot(nChanForApply + 1, 2, iChan * 2 + 2);\n        imshow(linScale(mean(matFileStruct.img_data{iChan}, 3)), []);\n        title(sprintf('Channel %d after transformation', config.channelVector(iChan)));\n    end;\n    suptitle(sprintf('Registration comparison of %s', strrep(matFileStruct.hdr.fileID, '_', '\\_')));\n    \n    % save if required\n    if config.reg.doSaveRegComparisonPlot > 1;\n        if exist('RegComparison', 'dir') ~= 7; mkdir('RegComparison'); end;\n        saveas(figHand, sprintf('RegComparison/%s__RegComp', matFileStruct.hdr.fileID));\n        saveas(figHand, sprintf('RegComparison/%s__RegComp.png', matFileStruct.hdr.fileID));\n        close(figHand);\n    end;\nend;\n\nfor iChan = 1 : nChanForApply;\n    o('        #Get...ImageReg(): Channels numbering change : chan %d / %d is now chan %d / %d.', ...\n        config.channelVector(iChan), nChan, iChan, nChanForApply, 1, dbgLevel);\nend;\n\nend\n\n%% Function - ParseConfig\nfunction config = ParseConfig(config)\n% check for missing fields in config structure and add them with default values\n\nif ~isfield(config, 'matFiles')\n    matFiles = sort(uigetfile('*.mat', 'Select Mat Files', 'MultiSelect', 'on'));\n    config.matFiles = strrep(matFiles, '.mat', '');\nelse\n    matFiles = config.matFiles;\nend\n\nif ~isfield(config,'saveName')\n    config.saveName = [matFiles{1} '__ROIStats'];\nend\n\nif ~isfield(config,'statsType')\n    config.statsType = 'dff';\nend\n\n% BL - 2013-06-27 : added this field because it was missing\nif ~isfield(config,'roi')\n    config.roi = struct;\nend\n\n% channels for stats calculation\n% DFF is calculated on the channel specified by config.channelVector(1)\n% DRR is calculated on the channels specified by config.channelVector(1)\n% and config.channelVector(2)\nif ~isfield(config,'channelVector')\n    config.channelVector = [1 2];\nend\n\nif strcmpi(config.statsType,'drr') && numel(config.channelVector) < 2\n    error('config.channelVector must specify at least 2 channels for DRR stats!');\nend\n\n% if ~isfield(config,'npilCorrect')\n%     config.npilCorrect = 0.2;\n% end\n% if ~isfield(config,'recycleNpilMovie')\n%     config.recycleNpilMovie = 0;\n% end\n\nif ~isfield(config,'doFlatfield')\n    config.doFlatfield = 0;\nend\n\nif ~isfield(config,'doOgbSRsegmentation')\n    config.doOgbSRsegmentation = 0;\nend\nif config.doOgbSRsegmentation\n    if ~isfield(config,'OgbChannel')\n        config.OgbChannel = 1;\n    end\n    if ~isfield(config,'SrChannel')\n        config.SrChannel = 2;\n    end\nend\nif ~isfield(config,'segOptions')\n    config.segOptions = struct;\nend\n\nif ~isfield(config,'psFrames')\n    config.psFrames = struct;\n    config.psFrames.base = 5;\n    config.psFrames.evoked = 15;\nend\n\n% low-pass filter\nif ~isfield(config,'LowPass')\n    config.LowPass.method = 'none';\n    config.LowPass.params = [0.5 1];\nend\n% LowPass.method may take the following values:\n% 'none' ... no low-pass filtering is applied\n% 'sg' ... savitzky-golay filter, as implemented in ML\n% LowPass.params is a vector with filter parameters, depending on the\n% specific filter (all cutoffs should be defined in s)\n% for SG, params are [lpFilterCutoff filterOrder]\n\n\n% high-pass filter\nif ~isfield(config,'HiPass')\n    config.HiPass.method = 'polyFit';\n    config.HiPass.polyOrder = 2;\nend\n\n% f0 calculation\nif ~isfield(config,'f0')\n    config.f0.method = 'median';\nend\n% specifies how f0 should be calculated\n% method can be 'median', 'polyfit', 'percentile'\n% for polyfit and percentile methods, specify the fit degree or percentile\n% cutoff as params; e.g. for quadratic fit:\n% config.f0.method = 'polyfit'\n% config.f0.params = 2\n\n% registration options\nif ~isfield(config,'reg')\n    config.reg.regChannel = 1; % which channel to perform registration on\n    config.reg.doRegistration = 1; % if set to 0, channels will only be resized to reference image\nelse\n    if ~isfield(config.reg,'regChannel')\n        config.reg.regChannel = 1;\n    end\n    if ~isfield(config.reg,'doRegistration')\n        config.reg.doRegistration = 1;\n    end\nend\n\n% adding some more missing fields\n\nif ~isfield(config,'doSaveROIRGBPlot')\n    config.doSaveROIRGBPlot = 1;\nend\n\n\nend\n\n%% Function - initParams\nfunction [config, matFiles] = initParams(config, dbgLevel)\n\n% this is a list of mat-files containing the raw data\n% data will be concatenated so should be from one imaging area and always contain the same number of Rois\nmatFiles = config.matFiles;\nfor iFile = 1 : numel(matFiles);\n    if isempty(strfind(matFiles{iFile}, '.mat'));\n        matFiles{iFile} = [matFiles{iFile} '.mat'];\n    end;\nend;\n\n% load the first mat-file and extract some general parameters\nmatFileWrapper = load(matFiles{1});\nmatFileStruct = matFileWrapper.(genvarname(strrep(matFiles{1},'.mat','')));\n\nconfig.img_dims = matFileStruct.hdr.size(1 : 2); % x and y dimensions of the image\nconfig.ROISet = matFileStruct.roi; % the ROI set\nconfig.nRuns = numel(matFiles); % number of runs\n\n% low-pass filter parameters\nswitch config.LowPass.method;\n    case 'sg';\n        lpFiltCutoff = config.LowPass.params(1);\n        lpFiltCutoff = round(lpFiltCutoff .* matFileStruct.hdr.rate);\n        if ~rem(lpFiltCutoff, 2);\n            lpFiltCutoff = lpFiltCutoff + 1;\n        end;\n        config.LowPass.lpFiltCutoff = lpFiltCutoff;\n        config.LowPass.sgolayOrder = config.LowPass.params(2); % filter order\n        clear lpFiltCutoff\n        o('  #GetRoiStats(): low-pass filtering parameters set with sgolay filter.', 3, dbgLevel);\n    otherwise;\n        o('  #GetRoiStats(): no low-pass filtering.', 4, dbgLevel);\nend\n\n% pre-allocate cell arrays, each cell will contain the imaging data for\n% that ROI during that run. Also pre-allocate for the neuropil\nconfig.ROIStatsData = cell(size(config.ROISet, 1) + 1, config.nRuns);\n\n% initialize parameters for each run\nfor iRun = 1 : config.nRuns;\n    \n    % make sure the mat-file for that run exists\n    if ~exist(matFiles{iRun}, 'file');\n        warning('GetRoiStats:FileNotFound', 'Could not find mat-file \"%s\", skipping it.',  matFiles{iRun});\n        continue;\n    end;\n    \n    % open the mat-file and extract the structure\n    matFileWrapper = load(matFiles{iRun});\n    matFileStruct = matFileWrapper.(genvarname(strrep(matFiles{iRun},'.mat','')));\n    \n    nFrames = matFileStruct.hdr.size(3);\n    % elongate the stim vector if needed, print an error if it's too short\n    if numel(matFileStruct.stim) < nFrames;\n        matFileStruct.stim(end + 1 : nFrames) = 0;\n    elseif numel(matFileStruct.stim) > nFrames;\n        error('GetRoiStats:StimVectorTooLong', 'Stim vector is longer than time-series for \"%s\".', ...\n            matFiles{iRun});\n    end;\n    \n    config.stim{iRun} = matFileStruct.stim; % stim vector for each run\n    config.frameRate{iRun} = matFileStruct.hdr.frame_rate; % support different frame rates per run\nend;\n\nend\n\n%% Function - CalculateF0\n% fit a quadratic function to the timeseries and use it as F0\n% works best for long timeseries with sparse activity\n% function f0_mat = CalculateF0(roi_mat, f)\nfunction f0_mat = CalculateF0(roi_mat, S)\nswitch lower(S.method)\n    case 'median'\n        f0_mat = median(roi_mat);\n    case 'polyfit'\n        t = 1:length(roi_mat);\n        p = polyfit(t',roi_mat',S.params(1));\n        f0_mat = p(1).*t.^2 + p(2).*t + p(3);\n    case 'percentile'\n        f0_mat = prctile(roi_mat,S.params(1));\n    otherwise\n        error('F0 method %s is not supported',S.method)\nend\nend\n\n%% Function - doLowPassFilter\nfunction v = doLowPassFilter(v, S)\nswitch S.method\n    case 'sg'\n        v = sgolayfilt(v,S.sgolayOrder,S.lpFiltCutoff);\nend\nend\n\n%% Function - doHiPassFilter\nfunction v = doHiPassFilter(v, S)\nswitch S.method\n    case 'polyFit'\n        p = polyfit(1:numel(v),v,S.polyOrder);\n        f = polyval(p,1:numel(v));\n        v = v - f;\nend\nend\n\n%% Function - ResampleStimVector\nfunction stimVectorFrameRate = ResampleStimVector(stim,stimRate,nFrames,frameRate) %#ok<DEFNU>\nstimVectorFrameRate = zeros(1,nFrames);\nfor n = 1:nFrames\n    startT = (n-1)/frameRate; stopT = n/frameRate;\n    startStim = round(startT*stimRate);\n    if startStim == 0\n        startStim = 1;\n    end\n    stopStim = round(stopT*stimRate);\n    if startStim > length(stim) || stopStim > length(stim)\n        stimVectorFrameRate(n) = 0;\n    else\n        stimVectorFrameRate(n) = round(mean(stim(startStim:stopStim)));\n    end\nend\n\nend\n\n%% Function - getRefOrRegImage\nfunction img = getRefOrRegImage(fileID, imgType, baseImgs, config, doSavePlot, dbgLevel)\n\nif doSavePlot;\n    % display the reference image\n    figHand = figure('NumberTitle', 'off', 'Name', sprintf('%s Image for %s', imgType, fileID));\nend;\n\n% using only 1 registration channel\nif numel(config.reg.regChannel) == 1;\n    o('          #Get...ImageReg(): Generating \"%s\" frame based on the channel %d.', ...\n        imgType, config.reg.regChannel, 3, dbgLevel);\n    img = baseImgs{config.reg.regChannel};\n\n    % flatten (average) the image;\n    if size(img, 3) > 1; img = mean(img, 3); end;\n    \n    if doSavePlot;\n        % plot the reference image\n        imshow(img, []);\n        title(sprintf('%s image: channel %d of %s', imgType, config.reg.regChannel, ...\n            strrep(fileID, '_', '\\_')));\n    end;\n    \n% if two channels are specified, do oregistration on the difference between the two (regChan1 - regChan2)\nelseif numel(config.reg.regChannel) == 2;\n    o('          #Get...ImageReg(): Generating \"%s\" frame based on the channels %d and %d.', ...\n        imgType, config.reg.regChannel, 3, dbgLevel);\n    regChan1RefImg = linScale(baseImgs{config.reg.regChannel(1)});\n    regChan2RefImg = linScale(baseImgs{config.reg.regChannel(2)});\n    \n    % flatten (average) the image;\n    if size(regChan1RefImg, 3) > 1; regChan1RefImg = mean(regChan1RefImg, 3); end;\n    if size(regChan2RefImg, 3) > 1; regChan2RefImg = mean(regChan2RefImg, 3); end;\n    \n    img = regChan1RefImg - regChan2RefImg;\n    if config.reg.multiRegChannelForbidNegatives;\n        img(img < 0) = 0; % make sure there's no negative\n    end;\n    img = linScale(img);\n    \n    if doSavePlot;\n        % plot the reference image\n        subplot(1, 3, 1); imshow(regChan1RefImg, []);\n        title(sprintf('Channel %d', config.reg.regChannel(1)));\n        subplot(1, 3, 2); imshow(regChan2RefImg, []);\n        title(sprintf('Channel %d', config.reg.regChannel(2)));\n        subplot(1, 3, 3); imshow(img, []);\n        title(sprintf('%s image: channel %d - %d', imgType, config.reg.regChannel));\n        suptitle(sprintf('%s image of %s', imgType, strrep(fileID, '_', '\\_')));\n    end;\n    \nend;\n\n% save if required\nif doSavePlot > 1;\n    if exist(imgType, 'dir') ~= 7; mkdir(imgType); end;\n    saveas(figHand, sprintf('%s/%s__%sImg', imgType, fileID, imgType(1:3)));\n    saveas(figHand, sprintf('%s/%s__%sImg.png', imgType, fileID, imgType(1:3)));\n    close(figHand);\nend;\n\nend\n\n%% Function - doNeuropilInterpolation\nfunction currentNpilData = doNeuropilInterpolation(config, currentImgData) %#ok<DEFNU>\n\nnpilFile = [saveNameBase '__NpilMovies.mat'];\ncurrentNpilData = cell(1,length(config.channelVector));\ncurrentNpilDataSave = cell(1,length(config.channelVector));\nif config.recycleNpilMovie;\n    try\n        a = load(npilFile);\n        currentNpilData = a.(genvarname(strrep(npilFile,'.mat','')));\n    catch e; %#ok<NASGU>\n        for n = 1:length(config.channelVector)\n            currentNpilData{n} = NeuropilInterpolation(...\n                currentImgData{n},roiSetActiveModel,4,1);\n            if bitDepth == 8\n                currentNpilDataSave{n} = uint8(currentNpilData{n});\n            else\n                currentNpilDataSave{n} = uint16(currentNpilData{n});\n            end\n        end\n        SaveAndAssignInBase(currentNpilDataSave,npilFile,'SaveOnly')\n        clear currentNpilDataSave\n    end\nelse\n    for n = 1:length(config.channelVector)\n        currentNpilData{n} = NeuropilInterpolation(...\n            currentImgData{n},roiSetActiveModel,4,1);\n        if bitDepth == 8\n            currentNpilDataSave{n} = uint8(currentNpilData{n});\n        else\n            currentNpilDataSave{n} = uint16(currentNpilData{n});\n        end\n    end\n    SaveAndAssignInBase(currentNpilDataSave,npilFile,'SaveOnly')\n    clear currentNpilDataSave\nend\n\nend\n\n%% Function - extractROIStats\nfunction ROIStatsFiltNorm = extractROIStats(imgData, ROIMask, ...\n    statsType, LowPassFilterConfig, HiPassFilterConfig, f0config, saveName, ...\n    doSavePlot)\n\nROIImgData = cell(1, numel(imgData));\n\n% pull out timeseries from masked pixels and average for each relevant channels\nfor iChan = 1 : numel(imgData);\n    ROIImgData{iChan} = mean(GetRoiTimeseries(imgData{iChan}, ROIMask), 1);\nend\n\nswitch lower(statsType)\n    case 'dff'\n        ROIStatsRaw = ROIImgData{1};\n    case 'drr'\n        ROIStatsRaw = ROIImgData{1} ./ ROIImgData{2};\nend\n\nmeanOfRawTrace = mean(ROIStatsRaw);\n\n% high-pass filter calcium traces\nROIStatsFilt = doHiPassFilter(ROIStatsRaw, HiPassFilterConfig);\n\n% low-pass filter calcium traces\nROIStatsFilt = doLowPassFilter(ROIStatsFilt, LowPassFilterConfig);\n\nROIStatsFiltAdj = ROIStatsFilt + meanOfRawTrace;\n\n% calculate F0\nF0 = CalculateF0(ROIStatsFiltAdj, f0config);\n\n% calculate normalized fluorescence change\nROIStatsFiltNorm = ((ROIStatsFiltAdj - F0) ./ F0) .* 100;\nROIStatsFiltNorm(isinf(ROIStatsFiltNorm)) = 0;\nROIStatsFiltNorm(isnan(ROIStatsFiltNorm)) = 0;\n\n% plotting for debugging only\nif doSavePlot;\n    figF0AndExtract = figure('NumberTitle', 'off', 'Name', ...\n        sprintf('%s_extractROIStats', saveName));\n    \n    nSubPlots = 4;\n    subplot(nSubPlots, 1, 1);\n    plot(ROIStatsRaw, 'r');\n    line([0, numel(ROIStatsRaw)], [meanOfRawTrace, meanOfRawTrace], 'Color', 'black');\n    legend({'Raw', 'mean'});\n    title(sprintf('Raw trace with mean ( = %d)', round(meanOfRawTrace)));\n    \n    subplot(nSubPlots, 1, 2);\n    plot(ROIStatsFilt, 'k');\n    legend({'Filtered'});\n    title('Raw trace filtered');\n    \n    subplot(nSubPlots, 1, 3);\n    plot(ROIStatsFiltAdj, 'g');\n    if numel(F0) == 1;\n        line([0, numel(ROIStatsFiltAdj)], [F0, F0], 'Color', 'black');\n    else\n        hold on;\n        plot(F0, 'k');\n    end;\n    legend({'Adjusted', 'F0'});\n    title('Raw trace filtered and adjusted (with mean) and F0');\n    \n    subplot(nSubPlots, 1, 4);\n    plot(ROIStatsFiltNorm, 'b');\n    legend({'Filtered'});\n    title('DFF/DRR trace');\n    \n    if doSavePlot > 1;\n        if exist('F0AndExtractPlots', 'dir') ~= 7; mkdir('F0AndExtractPlots'); end;\n        saveNameF0AndExtract = ['F0AndExtractPlots/' saveName];\n        saveas(figF0AndExtract, saveNameF0AndExtract);\n        saveas(figF0AndExtract, [saveNameF0AndExtract '.png']);\n        close(figF0AndExtract);\n    end;\nend;\n\nend\n\n% e.o.f.\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "roiStatsSingleDay.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/roiStatsSingleDay.m", "size": 8667, "source_encoding": "utf_8", "md5": "c684320eb5799d0a156ee487373b3ffb", "text": "function configCell = roiStatsSingleDay(spotList, doAddRois, config)\nroiStatsSingleDayTic = tic; % for performance timing purposes\n\n% add folders to path\nfolderList = {'Projects/2PIanalyzer','Projects/CalciumSim',...\n    'Projects/EventDetect'};\naddFolders2Path(folderList,1);\n\ndbgLevel = 1;\no('#roiStatsSingleDay(): %d spot(s) to process, doAddRois = %d ...', ...\n    size(spotList, 1), doAddRois, 1, dbgLevel);\n\n% loads expInfo cell array\nexpInfoMat = load('ExperimentInfo.mat');\nexpInfo = expInfoMat.expInfo;\n\n% build saveName from current directory name (usually the date) and spot name\n[~, baseName, ~] = fileparts(pwd);\n% baseName = ['ROIStats_' baseName];\n\nstartDir = pwd;\n    \n% second loop to handles movies\no('#roiStatsSingleDay(): searching for movies (BF vs Odd10)...', 2, dbgLevel);\nexpList_bfSpotId = cell(size(expInfo,1), 1); % store BF experiment info\nexpList_bfMatFileName = cell(size(expInfo,1), 1); % store BF experiment info\nexpList_odd10SpotId = cell(size(expInfo,1), 1); % store Odd10 experiment info\nexpList_odd10MatFileName = cell(size(expInfo,1), 1); % store Odd10 experiment info\n\nfor iSpot = 1:numel(spotList);\n    \n    % only target spot(s)    \n    if any(config.targetSpots) && ~any(config.targetSpots == iSpot); continue; end;\n    \n    o('  #roiStatsSingleDay(): spot %d/%d ...', iSpot, numel(spotList), 2, dbgLevel);\n    % go through each row (= each recording)\n    for iRun = 1 : size(expInfo, 1);\n\n        % only target run(s)    \n        if any(config.targetRuns) && ~any(config.targetRuns == iRun); continue; end; %%#ok<PFBNS>\n    \n        % process only spots corresponding to the current spot\n        if ~strcmp(spotList{iSpot}, expInfo{iRun, 1}); continue; end; %%#ok<PFBNS>\n        \n        o('    #roiStatsSingleDay(): spot %d/%d - row %d/%d ...', ...\n            iSpot, numel(spotList), iRun, size(expInfo, 1), 3, dbgLevel);\n        \n        % check the image type, must be a movie\n        if strcmp(expInfo{iRun, 4}, 'movie');\n            % check the image name and classify according to either BF or Odd10\n            if ~isempty(strfind(expInfo{iRun, 2}, 'BF_'));\n                o('      #roiStatsSingleDay(): found BF movie: \"%s\".', expInfo{iRun, 3}, 4, dbgLevel);\n                expList_bfSpotId{iRun} = spotList{iSpot};\n                expList_bfMatFileName{iRun} = expInfo{iRun, 3};\n            elseif ~isempty(strfind(expInfo{iRun,2},'Odd10_'));\n                o('      #roiStatsSingleDay(): found Odd10 movie: \"%s\".', expInfo{iRun, 3}, 4, dbgLevel);\n                expList_odd10SpotId{iRun} = spotList{iSpot};\n                expList_odd10MatFileName{iRun} = expInfo{iRun, 3};\n            else\n                continue;\n            end;\n        end;\n    end;\nend;\no('#roiStatsSingleDay(): movies classified.', 2, dbgLevel);\n\no('#roiStatsSingleDay(): Getting ROI stats for all spots...', 1, dbgLevel);\nconfigCell = cell(2, numel(spotList));\nfor iSpot = 1 : numel(spotList);\n    spotTic = tic; % for performance timing purposes\n    \n    % only target spot(s)    \n    if any(config.targetSpots) && ~any(config.targetSpots == iSpot); continue; end;\n    \n    o('  #roiStatsSingleDay(): processing spot %d/%d (\"%s\") at %s...', ...\n        iSpot, numel(spotList), spotList{iSpot}, datestr(clock,21), 2, dbgLevel);\n    \n    % BF\n    config.matFiles = cell(1,1);\n    nMatFilesFound = 0;\n    config.saveName = [baseName '_BF_' spotList{iSpot}];\n    fullPath = sprintf('%s%s%s', startDir, filesep, spotList{iSpot});\n    \n    % search for get the mat files from the BF runs\n    for iRun = 1 : size(expList_bfSpotId,1)\n        % only target run(s)    \n        if any(config.targetRuns) && ~any(config.targetRuns == iRun); continue; end;\n        \n        if isempty(expList_bfSpotId{iRun}); continue; end;\n        if strcmp(expList_bfSpotId{iRun}, spotList{iSpot});\n            o('    #roiStatsSingleDay(): spot %d/%d - BF row %d/%d ...', ...\n                iSpot, numel(spotList), iRun, size(expInfo, 1), 3, dbgLevel);\n            nMatFilesFound = nMatFilesFound + 1;\n            config.matFiles{nMatFilesFound} = expList_bfMatFileName{iRun};\n        end;\n    end;\n    \n    ticStimConv = tic;\n    o('  #roiStatsSingleDay(): stim conversion and ref image adding for %d mat files ...', ...\n        nMatFilesFound, 2, dbgLevel);\n    parfor iMatFile = 1 : nMatFilesFound;\n        doStimConversAndAddRefImage(sprintf('%s%s%s.mat', fullPath, filesep, ...\n            config.matFiles{iMatFile}), 'bf', fullPath, dbgLevel); %#ok<PFBNS>\n    end;\n    o('  #roiStatsSingleDay(): stim conversion and ref image adding for %d mat files done (%3.1f sec)', ...\n        nMatFilesFound, toc(ticStimConv), 2, dbgLevel);\n    \n    if nMatFilesFound > 0;\n        o('  #roiStatsSingleDay(): Getting ROI stats for BF stims (%d movies)...', ...\n            nMatFilesFound, 2, dbgLevel);\n        cd(fullPath);\n        configCell{1,iSpot} = GetRoiStats(config);\n        cd(startDir);\n    end;\n    \n%     % Odd10\n%     config.matFiles = cell(1,1);\n%     nMatFilesFound = 0;\n%     config.saveName = [baseName '_Odd10_' spotList{iSpot}];\n%     for iRun = 1:size(expList_odd10SpotId,1)\n%         if isempty(expList_odd10SpotId{iRun}); continue; end;\n%         if strcmp(expList_odd10SpotId{iRun},spotList{iSpot});\n%             o('    #roiStatsSingleDay(): spot %d/%d - Odd10 row %d/%d ...', ...\n%                 iSpot, numel(spotList), iRun, size(expInfo, 1), 3, dbgLevel);\n%             nMatFilesFound = nMatFilesFound + 1;\n%             config.matFiles{nMatFilesFound} = expList_odd10MatFileName{iRun};\n%             doStimConversion(sprintf('%s%s%s.mat',fullPath,filesep,...\n%                 config.matFiles{nMatFilesFound}),'odd10');\n%         end;\n%     end;\n%     if nMatFilesFound > 0;\n%         o('  #roiStatsSingleDay(): Getting ROI stats for Odd10 stims (%d movies)...', ...\n%             nMatFilesFound, 2, dbgLevel);\n%         cd(fullPath);\n%         configCell{2,iSpot} = GetRoiStats(config); % warning: can contain a parfor\n%         cd(startDir);\n%     end;\n    \n    o('  #roiStatsSingleDay(): finished spot %d/%d (\"%s\") at %s (%.2f seconds).', ...\n        iSpot, numel(spotList), spotList{iSpot}, datestr(clock,21), toc(spotTic), 2, dbgLevel);\nend;\n\no('  #roiStatsSingleDay(): finished getting ROI stats of %d spot(s) at %s (%.2f seconds).', ...\n    numel(spotList), datestr(clock, 21), toc(roiStatsSingleDayTic), 2, dbgLevel);\n    \nend\n\nfunction doStimConversAndAddRefImage(matfile, stimType, fullPath, dbgLevel)\n\no('    #doStimConversAndAddRefImage(): stim conversion and ref image adding for %s, stimType: %s ...', ...\n        matfile, stimType, 3, dbgLevel);\nTDTRate = 97656.25;\n\nmatSaveName = matfile;\nif ~exist(matfile, 'file')\n       warning('doStimConversion:FileNotFound', 'Could not find mat-file \"%s\", skipping it.', matfile);\n    return;\nend;\nvars = whos('-file', matfile);\nmatFileStruct = load(matfile, vars(1).name);\nmatFileStruct = matFileStruct.(vars(1).name);\n\n% add refImage as image if it's only a string\nif isfield(matFileStruct.hdr, 'refImg') && ischar(matFileStruct.hdr.refImg);\n    o('      #doStimConversAndAddRefImage(): %s: found ref image: %s ...', ...\n            matfile, matFileStruct.hdr.refImg, 3, dbgLevel);\n    refImgMatFilePath = sprintf('%s%s%s.mat', fullPath, filesep, matFileStruct.hdr.refImg);\n    refImgVars = whos('-file', refImgMatFilePath);\n    refImgStruct = load(refImgMatFilePath, refImgVars(1).name);\n    refImgData = refImgStruct.(refImgVars(1).name).img_data;\n    matFileStruct.refImg = cell(1, numel(refImgData));\n    for iChan = 1 : numel(refImgData);\n        matFileStruct.refImg{iChan} = mean(refImgData{iChan}, 3);\n    end;\n    o('      #doStimConversAndAddRefImage(): %s: ref image added as image.', matfile, 3, dbgLevel);\nend;\n\nstim = matFileStruct.stim;\nif isnumeric(stim) % already converted\n    o('      #doStimConversAndAddRefImage(): %s: stim is already a numeric.', matfile, 3, dbgLevel);\n   return ;\nend\n\nframe_rate = matFileStruct.hdr.frame_rate;\ndelFrame = matFileStruct.proc.DelFrame;\n\nstimID = stim.freqIdSeries;\ntimepoints = stim.timePoints;\ntimepoints = timepoints ./ TDTRate;\n% convert to frame and offset by delFrame\ntimepoints = round(timepoints .* frame_rate) - delFrame;\nstimVector = zeros(1,size(matFileStruct.img_data{1},3));\nswitch lower(stimType)\n    case 'odd10'\n        stimID(stimID==1) = 2;\n        stimID(stimID==-1) = 1;\nend\nstimVector(timepoints) = stimID;\nmatFileStruct.proc.stim = matFileStruct.stim;\nmatFileStruct.stim = stimVector;\no('      #doStimConversAndAddRefImage(): %s: created new stim vector (%d frame(s) long).', ...\n    matfile, numel(matFileStruct.stim), 3, dbgLevel);\n\nSaveAndAssignInBase(matFileStruct, matSaveName, 'SaveOnly');\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "extractSummaryInfoForSpots.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/extractSummaryInfoForSpots.m", "size": 8614, "source_encoding": "utf_8", "md5": "85d821b91cc6f7df9c0da703f4792edb", "text": "function extractSummaryInfoForSpots(animalID)\n\ndbgLevel = 4;\nextractAllTic = tic; % for performance timing purposes\n%% Get days with spots\nyear = '2013'; % leave this as a string, not a number\nrawDataRootPath = 'W:\\Neurophysiology\\RawData\\Balazs_Laurenczy\\';\nanalysisRootPath = 'W:\\Neurophysiology\\Projects\\Auditory\\Analysis\\AuditoryLearning\\';\n% analysisRootPath = 'D:\\Balazs\\';\n% analysisRootPath = 'C:\\Users\\laurenczy.HIFO\\Documents\\LocalData\\';\nanalysisRootPath = [analysisRootPath year '\\' animalID '\\'];\no('#extractSummaryInfoForSpots(): animalID = %s.', animalID, 1, dbgLevel);\no('#extract...ForSpots(): rawDataRootPath = \"%s\".', rawDataRootPath, 3, dbgLevel);\no('#extract...ForSpots(): analysisRootPath = \"%s\".', analysisRootPath, 3, dbgLevel);\n% get all days with at least 1 \"spot**\" folder inside for this mouse\ndaysWithSpot = getDaysWithSpot(year, rawDataRootPath, animalID, 0);\no('#extract...ForSpots(): Found %d day(s) with spot for animal %s.', ...\n    numel(daysWithSpot), animalID, 2, dbgLevel);\n% restrict the running to specific day(s), spot(s), run(s). Can be set to 0 to run all or\n% can be set to single number: targetDays = 2; or to a list: targetSpots = [2 3 5];\ntargetDays = 0;\ntargetSpots = 0;\nnChans = 3;\ncolorVector = [1 2 0];\n%% Process each day\nfor iDay = 1 : numel(daysWithSpot);\n    dayTic = tic; % for performance timing purposes\n    \n    % only target day(s)\n    if any(targetDays) && ~any(targetDays == iDay); continue; end;\n    \n    %% Experiment info\n    day = daysWithSpot(iDay);\n    notebookFileName = day.notebookFileName;\n    % list of spots with short name in notebook file\n    spotList = day.spotList;\n    currentDate = day.day;\n    \n    o('#extract...ForSpots(): Processing day %s with %d spot(s).', currentDate, size(spotList, 1), 2, dbgLevel);\n    %% Input files\n    fullRawDataPath = [rawDataRootPath year filesep animalID filesep currentDate];\n    stimFile = [fullRawDataPath filesep 'CaImgExp_' currentDate '.mat'];\n    % perform analysis in different directory than the raw data directory\n    analysisPath = [analysisRootPath currentDate];\n    % load CaImgExp\n    CaImgExpMat = load(stimFile);\n    \n    allZipFiles = [];\n    % fetch all zip files\n    for iSpot = 1 : size(spotList, 1);\n        % only target spot(s)\n        if any(targetSpots) && ~any(targetSpots == iSpot); continue; end;\n        analysisSpotPath = [analysisPath, filesep, spotList{iSpot, 1}, filesep];\n        \n        allZipFiles = [allZipFiles dir([analysisSpotPath '*.zip'])]; %#ok<*AGROW>\n    end;\n    \n    refNamesWithRoiSet = cell(numel(allZipFiles), 1);\n    for iStruct = 1 : numel(allZipFiles);\n        refNamesWithRoiSet{iStruct} = regexprep(allZipFiles(iStruct).name, '__RoiSet.zip', '');\n    end;\n    \n    % read the HelioScan notebook text file and generate and \"experiment info\" cell array where\n    % each row is a different recording (reference image or movie)\n    expInfo = prepare4dataImport(notebookFileName, spotList, refNamesWithRoiSet, 1, 1, 1);\n    o('  #extract...ForSpots(): %s: Experiment info generated.', currentDate, 3, dbgLevel);\n    \n    % import each spot\n    for iSpot = 1 : size(spotList, 1);\n        spotTic = tic; % for performance timing purposes\n        \n        % only target spot(s)\n        if any(targetSpots) && ~any(targetSpots == iSpot); continue; end;\n        \n        % load stims\n        iSpotInCaImgExp = str2double(strrep(spotList{iSpot, 2}, 'sp', ''));\n        spotInfo = CaImgExpMat.CaImgExp.spots{iSpotInCaImgExp};\n        % skip if that spot was not created on that day\n        if isempty(spotInfo);\n            o('    #extract...ForSpots(): %s: skipping spot %d as it is empty.', currentDate, iSpot, 2, dbgLevel);\n            continue;\n        end;\n        spotID = spotList{iSpot, 1}; % for example 'spot01'\n        \n        analysisSpotPath = [analysisPath, filesep, spotID];\n        rawSpotPath = [fullRawDataPath, filesep, spotList{iSpot, 1}, filesep];\n        \n        allRefs = expInfo(strcmp(expInfo(:, 1), spotID) & ~cellfun(@isempty, strfind(expInfo(:, 2), 'Ref256x')), :);\n        refsWithROIs = expInfo(strcmp(expInfo(:, 1), spotID) & ~cellfun(@isempty, strfind(expInfo(:, 2), 'Ref256x')) & ...\n            cell2mat(expInfo(:, 8)), :);\n        \n        % read the data from all channels\n        for iRef = 1 : size(allRefs, 1);\n            refName = allRefs{iRef, 3};\n            refFullFileName = sprintf('%s%s%s%s__channel__CHANNUM__.tif', ...\n                    rawSpotPath, refName, filesep, refName);\n            RGBImage = getMergedImage(refFullFileName, nChans, colorVector);\n            rawDataMousePath = [rawDataRootPath year filesep animalID filesep];\n            zoomedOutStr = '';\n            if allRefs{iRef, 9}; zoomedOutStr = 'zoomOut_'; end;\n            saveName = sprintf('%s%s_%s_%s%dum_%04.1fLI_z%03.1f_RGBRef', rawDataMousePath, spotID, day.day, ...\n                zoomedOutStr, spotInfo.depth, allRefs{iRef, 6}, allRefs{iRef, 7});\n            saveName = strrep(saveName, '.', 'p');\n%             size(RGBImage.data);\n            write_to_tif(RGBImage, saveName);\n        end;\n        \n%         % read the data from all channels\n%         for iRef = 1 : size(refsWithROIs, 1);\n%             refName = refsWithROIs{iRef, 3};\n%             refFullFileName = sprintf('%s%s%s%s__channel__CHANNUM__.tif', ...\n%                     rawSpotPath, refName, filesep, refName);\n%             RGBImage = getMergedImage(refFullFileName, nChans, colorVector);\n%             img = RGBImage.data;\n%             \n%             ROISetFileName = '';\n%             for iStruct = 1 : numel(allZipFiles);\n%                 if strfind(allZipFiles(iStruct).name, refName);\n%                     ROISetFileName = allZipFiles(iStruct).name;\n%                     break;\n%                 end;\n%             end;\n%             \n%             imgDims = [size(img, 1) size(img, 2)];\n%             ROISetFullFileName = sprintf('%s%s%s', analysisSpotPath, filesep, ROISetFileName);\n%             ROISet = ij_roiDecoder(ROISetFullFileName, imgDims);\n%             \n%             figHand = doROIRGBPlot(imgDims, ROISet, img, {img(:, :, 2)}, refsWithROIs{iRef, 3}, 0.8);\n%             title(refsWithROIs{iRef, 3}, 'Interpreter', 'none');\n%             rawDataMousePath = [rawDataRootPath year filesep animalID filesep];\n%             saveName = sprintf('%s%s_%s_RGBRef_withROI.png', rawDataMousePath, spotID, day.day);\n%             saveas(figHand, saveName);\n%             close(figHand);\n%         end;\n        \n        % get the surface stack and do the sum of the frames\n        surfStack = expInfo(strcmp(expInfo(:, 1), spotID) & ~cellfun(@isempty, strfind(expInfo(:, 2), 'surface')) & ...\n            ~cellfun(@isempty, strfind(expInfo(:, 4), '3D stack')), :);\n        if ~isempty(surfStack);\n            surfStackName = surfStack{1, 3};\n            surfStackFullFileName = sprintf('%s%s%s%s__channel__CHANNUM__.tif', ...\n                    rawSpotPath, surfStackName, filesep, surfStackName);\n            RGBImage = getMergedImage(surfStackFullFileName, nChans, colorVector);\n            RGBImage.data = sum(RGBImage.data, 3) ./ size(RGBImage.data, 3);\n            RGBImage.data = linScale(PseudoFlatfieldCorrect(RGBImage.data), 0, 2 ^ 16);\n            saveName = sprintf('%s%s_%s_surface', rawDataMousePath, spotID, day.day);\n            write_to_tif(RGBImage, saveName);\n        end;\n        \n        \n        o('    #extract...ForSpots(): %s - %s: written out all references (%.2f seconds).', currentDate, spotID, ...\n            toc(spotTic), 2, dbgLevel);\n    end\n    o('  #extract...ForSpots(): %s: extracted data (%.2f seconds).', currentDate, ...\n        toc(dayTic), 1, dbgLevel);\nend;\no('#extractSummaryInfoForSpots(): extracted all data for \"%s\" (%.2f seconds).', ...\n    animalID, toc(extractAllTic), 1, dbgLevel);\nend\n\nfunction RGBImage = getMergedImage(refFullFileName, nChans, colorVector)\n\nrefImg = cell(nChans, 1);\nfor iChan = 0 : nChans - 1;\n    fileName = strrep(refFullFileName, '__CHANNUM__', sprintf('%02d', iChan));\n    if ~exist(fileName, 'file');\n        refImg{iChan + 1} = zeros(256, 256);\n    else\n        tiffStruct = tiffread2_wrapper(fileName);\n        refImg{iChan + 1} = tiffStruct.data;\n    end;\nend;\n% merge the channels\nRGBImage    = struct();\nred         = zeros(size(refImg{1}));\ngreen       = zeros(size(refImg{1}));\nblue        = zeros(size(refImg{1}));\nif colorVector(1); red = linScale(refImg{colorVector(1)}); end;\nif colorVector(2); green = linScale(refImg{colorVector(2)}); end;\nif colorVector(3); blue = linScale(refImg{colorVector(3)}); end\nRGBImage.data = cat(3, red, green, blue);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "GetRoiStatsFrameJ90.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/ROIStats/GetRoiStatsFrameJ90.m", "size": 19099, "source_encoding": "utf_8", "md5": "018f54858f295d12e1a279c84d8700c4", "text": "function varargout = GetRoiStatsFrameJ90(config)\n% get ROI timecourses from images, with support for muliple ROIs and runs\n% returns structure data, info and stimulus fields\n% data field contains a cell array with ROI timecourses for different cells\n% in rows and different runs in columns:\n% [cell1_run1] [cell1_run2] ... [cell1_runN]\n% [cell2_run1] [cell2_run2] ... [cell2_runN]\n%     ...           ...              ...\n% [cellN_run1] [cellN_run2] ... [cellN_runN]\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nconfig = ParseConfig(config);\nmatfiles = config.matfiles;\n\nfor n = 1:numel(matfiles)\n    if isempty(strfind(matfiles{n},'.mat'))\n        matfiles{n} = [matfiles{n} '.mat'];\n    end\nend\n\nS = load(matfiles{1});\ndata = S.(genvarname(strrep(matfiles{1},'.mat','')));\ndelFrame = data.proc.DelFrame;\n% experiment specific info\nanimalID = data.hdr.animalID;\nspotID = data.hdr.spotID;\n\nconfig.saveName = sprintf('%s_%s',animalID,spotID);\n\nconfig.img_dims = data.hdr.size(1:2);\nroiSet = data.roi;\ncellNo = size(roiSet,1);\nconfig.runsNo = numel(matfiles);\nconfig.timepoints = data.hdr.size(3);\n\n%% low-pass filter parameters\nswitch config.LowPass.method\n    case 'sg'\n        lpFiltCutoff = config.LowPass.params(1);\n        lpFiltCutoff = round(lpFiltCutoff.*data.hdr.rate);\n        if ~rem(lpFiltCutoff,2)\n            lpFiltCutoff = lpFiltCutoff+1;\n        end\n        config.LowPass.lpFiltCutoff = lpFiltCutoff;\n        config.LowPass.sgolayOrder = config.LowPass.params(2); % filter order\n        clear lpFiltCutoff\nend\n\nconfig.dataRoi = cell(cellNo,config.runsNo);\nconfig.dataRoiNorm = cell(cellNo,config.runsNo);\nconfig.dataNpil = cell(cellNo,config.runsNo);\n\n%% Event detection parameters\nif ~strcmp(config.EventDetect.method,'none')\n    config.EventDetect.amp = 2.5;\n    config.EventDetect.tau = 0.6;\n    config.EventDetect.onsettau = 0.01;\n    config.EventDetect.doPlot = 0; % should be switched off\n    switch lower(config.EventDetect.method)\n        case 'fast_oopsi'\n            config.EventDetect.lam = 0.1; % firing rate\n            config.EventDetect.base_frames = 10;\n            config.EventDetect.oopsi_thr = 0.3;\n            config.EventDetect.integral_thr = 5;\n            config.EventDetect.filter = [7 2];\n            config.EventDetect.minGof = 0.5;\n        case 'peeling'\n            config.EventDetect.schmittHi = 1.75;\n            config.EventDetect.schmittLo = -0.75;\n            config.EventDetect.schmittmindur = 0.3;\n    end\nend\n\n% initialize some parameters\nS = load(matfiles{1});\ndata = S.(genvarname(strrep(matfiles{1},'.mat','')));\nconfig.frameRate = data.hdr.rate;\nif isfield(data.hdr,'bits')\n    bitDepth = data.hdr.bits;\nelse\n    bitDepth = 16;\nend\n\nRoiMatAllRuns = [];\n% eventMatAllRuns = [];\n\n%% Process runs\nfor currentRun = 1:config.runsNo\n    fprintf('\\nNow processing run %1.0f\\n',currentRun);\n    \n    S = load(matfiles{currentRun});\n    data = S.(genvarname(strrep(matfiles{currentRun},'.mat','')));\n    currentImgData = cell(1,length(config.channelVector));\n%     currentMeanImgData = cell(1,length(config.channelVector));\n    for n = 1:length(config.channelVector)\n        currentImgData{n} = double(data.img_data{config.channelVector(n)});\n%         currentMeanImgData{n} = mean(currentImgData{n},3);\n    end\n    \n    if config.ch3Sound\n        [config.stim{currentRun},soundT] = ...\n            img2vector(double(data.img_data{3}),1/config.pixelTime,0);\n    else\n        config.stim{currentRun} = [];\n    end\n    config.stim{currentRun} = removeBlankPixels(config.stim{currentRun});\n%     [stimTable,h,toneClean] = AnalyzePureToneVector(config.stim{currentRun},...\n%         1/config.pixelTime,0);\n%     config.stim{currentRun} = soundTable2StimVector(stimTable,1/config.pixelTime,0);\n    \n    roiSet = data.roi;\n    % convert to mask Roi format\n    roiSet = ij_RoiSetCompression(roiSet,2);\n    \n    refImg = data.refImg;\n    \n    roiRgb = zeros(size(refImg,1),size(refImg,2),3);\n    cmap = jet(size(roiSet,1));\n    alpha_mat = ones(size(refImg));\n    roiCounterImg = zeros(size(alpha_mat));\n    roiSetActiveModel = roiSet;\n    saveActiveModel = 0;\n    roiLabel = roiSet(:,1);\n    \n    for currentRoi = 1:size(roiSet,1)\n        switch class(roiSet{currentRoi,2})\n            case 'uint32'\n                roi = bwunpack(roiSet{currentRoi,2});\n            otherwise\n                roi = roiSet{currentRoi,2};\n        end\n        roiId = roiSet{currentRoi,1};\n        if length(find(roi)) == 1\n            % run Active Model segmentation on point Rois\n            [cog_row, cog_col] = ind2sub(size(alpha_mat),find(roi));\n            roi = ...\n                doActiveModelSegment(refImg,[cog_row cog_col],config.segOptions);\n            roiSetActiveModel{currentRoi,2} = roi;\n            saveActiveModel = 1;\n            [y,x] = find(roi==1);\n            roiSetActiveModel{currentRoi,3} = [mean(y),mean(x)];\n        else\n            [y,x] = find(roi==1);\n            roiSet{currentRoi,3} = [mean(y),mean(x)];\n            roiSetActiveModel{currentRoi,3} = [mean(y),mean(x)];\n        end\n        \n        alpha_mat(roi==1) = 0.75;\n        if ~isempty(str2num(roiId))\n            roiCounterImg(roi==1) = str2num(roiId);\n        end\n        [row, col] = ind2sub(size(alpha_mat),find(roi));\n        for idx = 1:length(row)\n            for color = 1:3\n                roiRgb(row(idx),col(idx),color) = ...\n                    cmap(currentRoi,color);\n            end\n        end\n    end\n    config.roi.roiSet = roiSet;\n    if saveActiveModel\n        config.roi.ActiveModel = roiSetActiveModel;\n    end\n    \n    %% Apply registration\n    tform = data.tform;\n    for n = 1:length(config.channelVector)\n        hiResFunc = zeros(size(refImg,1),size(refImg,2),size(currentImgData{n},3));\n        for frame = 1:size(currentImgData{n},3)\n           currentFrame = removeBlankPixels(currentImgData{n}(:,:,frame));\n           if size(currentFrame) ~= size(refImg)\n               currentFrame = imresize(currentFrame,[size(refImg,1) size(refImg,2)]);\n               % imresize performs bicubic interpolation by default\n           end\n           if ~isempty(tform)\n               currentFrame = imtransform(currentFrame,tform,'xdata',...\n                   [1 size(currentFrame,2)],'ydata',[1 size(currentFrame,1)]);\n           end\n           hiResFunc(:,:,frame) = currentFrame;\n        end\n        currentImgData{n} = hiResFunc;\n        clear hiResFunc\n    end\n    \n    %% Plot Rois\n    % display created Rois on mean frame in RGB multi-color\n    h2 = figure('Name',sprintf('ROIs %s',matfiles{currentRun}),'NumberTitle','off');\n    imshow(roiRgb); hold on\n    hRef = imshow(mean(currentImgData{1},3),[],'InitialMagnification','fit');\n    set(hRef,'AlphaData',alpha_mat);\n    s = regionprops(roiCounterImg,roiCounterImg,'MaxIntensity','Extrema');\n    hold(gca, 'on');\n    for k = 1:numel(s)\n        e = s(k).Extrema;\n        val = s(k).MaxIntensity;\n        text(e(1,1), e(1,2)-1, sprintf('%d', val), ...\n            'Parent', gca, ...\n            'Clipping', 'on', ...\n            'Color', 'r', ...\n            'FontWeight', 'bold', ...\n            'Tag', 'CellLabel');\n    end\n    drawnow\n    saveNameBase = strrep(matfiles{currentRun},'.mat','');\n    saveas(h2,[saveNameBase '__RoiRgb.fig']);\n    hold(gca, 'off');\n    \n    \n    %% Neuropil interpolation\n    npilFile = [saveNameBase '__NpilMovies.mat'];\n    currentNpilData = cell(1,length(config.channelVector));\n    currentNpilDataSave = cell(1,length(config.channelVector));\n    if config.recycleNpilMovie\n        try\n            a = load(npilFile);\n            currentNpilData = a.(genvarname(strrep(npilFile,'.mat','')));\n        catch\n            for n = 1:length(config.channelVector)\n                currentNpilData{n} = NeuropilInterpolation(...\n                    currentImgData{n},roiSetActiveModel,4,1);\n                if bitDepth == 8\n                    currentNpilDataSave{n} = uint8(currentNpilData{n});\n                else\n                    currentNpilDataSave{n} = uint16(currentNpilData{n});\n                end\n            end\n            SaveAndAssignInBase(currentNpilDataSave,npilFile,'SaveOnly')\n            clear currentNpilDataSave\n        end\n    else\n        for n = 1:length(config.channelVector)\n            currentNpilData{n} = NeuropilInterpolation(...\n                currentImgData{n},roiSetActiveModel,4,1);\n            if bitDepth == 8\n                currentNpilDataSave{n} = uint8(currentNpilData{n});\n            else\n                currentNpilDataSave{n} = uint16(currentNpilData{n});\n            end\n        end\n        SaveAndAssignInBase(currentNpilDataSave,npilFile,'SaveOnly')\n        clear currentNpilDataSave\n    end\n    \n    \n    %% Neuropil analysis\n    switch lower(config.statsType)\n        case 'dff'\n            npilStats = mean(GetRoiTimeseries(currentNpilData{1},[]),1);\n        case 'drr'\n            npilStats = mean(GetRoiTimeseries(currentNpilData{1},[]),1) ./...\n                mean(GetRoiTimeseries(currentNpilData{2},[]),1);\n    end\n    % calculate F0 (use quadratic fit method)\n    npilF0 = CalculateF0(npilStats,config.frameRate);\n    npilStats = ((npilStats-npilF0) ./ npilF0) .* 100;\n    npilStats(isinf(npilStats)) = 0;\n    npilStats(isnan(npilStats)) = 0;\n    config.frameNpil.data{currentRun} = npilStats;\n    \n    %% ROI analysis\n    currentRoiMat = zeros(size(roiSetActiveModel,1),config.timepoints);\n    for currentRoi = 1:size(roiSetActiveModel,1)\n        roi = roiSetActiveModel{currentRoi,2};\n        roiImgData = cell(1,numel(config.channelVector));\n        roiNpilData = cell(1,numel(config.channelVector));\n        for n = 1:numel(config.channelVector)\n            roiImgData{n} = mean(GetRoiTimeseries(currentImgData{n},roi),1);\n        end\n        \n        switch lower(config.statsType)\n            case 'dff'\n                roiStats = roiImgData{1};\n            case 'drr'\n                roiCh1 = roiImgData{1};\n                roiCh2 = roiImgData{2};\n                roiStats = roiCh1 ./ roiCh2;\n        end\n        \n        % calculate F0 (use quadratic fit method)\n        roiF0 = CalculateF0(roiStats,config.frameRate);\n        \n        % calculate stats\n        roiStats = ((roiStats-roiF0) ./ roiF0) .* 100;\n        roiStats(isinf(roiStats)) = 0;\n        roiStats(isnan(roiStats)) = 0;\n        \n        % event detection on roiStats\n%         if ~strcmp(config.EventDetect.method,'none')\n%             %% Event detection\n%             eventDetect = config.EventDetect;\n%             eventDetect.ca = roiStats; % thus, we use the non-neuropil corrected trace\n%             eventDetect.rate = config.frameRate{currentRun};\n%             eventStruct = CalciumEventDetect(eventDetect);\n%             config.EventDetect.event{currentRoi,currentRun} = eventStruct.data.spike_predict;\n%             oopsi = eventStruct.data.oopsi;\n%             if strcmp(eventDetect.method,'fast_oopsi')\n%                 config.EventDetect.oopsi{currentRoi,currentRun} = eventStruct.data.oopsi;\n%             elseif strcmp(eventDetect.method,'peeling')\n%                 config.EventDetect.residual{currentRoi,currentRun} = eventStruct.data.oopsi;\n%                 config.EventDetect.model{currentRoi,currentRun} = eventStruct.data.model;\n%             end\n%             fprintf('\\n%s Roi %1.0f Run %1.0f');\n%             fprintf('Events: %1.0f\\n',sum(eventStruct.data.spike_predict));\n%             fprintf('Residual per frame: %1.3f\\n',...\n%                 sum(abs(eventStruct.data.oopsi))/length(eventStruct.data.oopsi));\n%             currentEventMat(currentRoi,:) = eventStruct.data.spike_predict;\n%         end\n        \n        % low-pass filter calcium traces\n        roiStats = doLowPassFilter(roiStats,config.LowPass);\n        \n        % save\n        config.dataRoi{currentRoi,currentRun} = roiStats;\n        currentRoiMat(currentRoi,:) = roiStats;\n    end\n    \n    % lowPass filter frame neuropil\n    npilStats = doLowPassFilter(npilStats,config.LowPass);\n    \n    % save frame neuropil in last row\n    currentRoiMat(currentRoi+1,:) = npilStats;\n    \n    RoiMatAllRuns = [RoiMatAllRuns currentRoiMat];\n    \n%     eventMatAllRuns = [eventMatAllRuns currentEventMat];\nend\n\nstimVectorAllRuns = cell2mat(config.stim);\nstimTimeAllRuns = (1:length(stimVectorAllRuns)).*config.pixelTime;\n\n%% Calcium Event Plot\n% plot all Rois with stim in stacked plot\ntitleStr = sprintf('%s',config.saveName);\nfig = figure('Name',titleStr,'NumberTitle','off'); hold all\ncurrentOffset = 0;\ntime = (1:size(RoiMatAllRuns,2))/config.frameRate;\ncellIDaxes = cell(size(RoiMatAllRuns,1),1);\nmeanYpos = zeros(size(RoiMatAllRuns,1),1);\n\nfor roi = 1:size(RoiMatAllRuns,1)\n    if roi == size(RoiMatAllRuns,1)\n        id = 'npil';\n    else\n        id = roiLabel{roi};\n    end\n    data = RoiMatAllRuns(roi,:);\n    data = data + (min(data)*-1);\n    dataPlot = data + currentOffset;\n    currentOffset = max(dataPlot);\n    cellIDaxes{roi} = id;\n    meanYpos(roi) = mean(dataPlot);\nend\nstimVectorPlot = ScaleToMinMax(stimVectorAllRuns,0,max(RoiMatAllRuns(end,:)));\nstimVectorPlot = (stimVectorPlot - min(stimVectorPlot)) + currentOffset;\n\ncurrentOffset = max (stimVectorPlot);\n\nroiLabel{end+1} = 'sound';\n\nset(gca,'ylim',[0 currentOffset],'xlim',[min(time) max(time)+0.1*max(time)])\nset(gca,'XTick',[])\nax1 = gca;\ntitle(titleStr,'Interpreter','none')\nylabel(sprintf('%% %s',upper(config.statsType)))\nax2 = axes('Position',get(gca,'Position'),'YAxisLocation','right');\nset(ax2,'ylim',[0 currentOffset],'xlim',[min(time) max(time)],...\n    'YTick',meanYpos,'YTickLabel',cellIDaxes)\nxlabel('Time / s')\nhold all\ncurrentOffset = 0;\nfor roi = 1:size(RoiMatAllRuns,1)\n    data = RoiMatAllRuns(roi,:);\n    data = data + (min(data)*-1);\n    dataPlot = data + currentOffset;\n    plot(time,dataPlot)\n    currentOffset = max(dataPlot);\n%     if roi == size(RoiMatAllRuns,1)\n%         break\n%     end\n%     eventData = eventMatAllRuns(roi,:);\n%     spkTimes = [];\n%     for m = 1:length(eventData)\n%         if eventData(m)\n%             h_err = errorbar(time(m),min(dataPlot),0,...\n%                 config.EventDetect.amp,'Color','black');\n%             removeErrorBarEnds(h_err)\n%             set(h_err,'LineWidth',2)\n%         end\n%     end\nend\n\nplot(stimTimeAllRuns,stimVectorPlot,'k')\n\n% for n = 1:numel(StimVectorAllRuns)\n%     if StimVectorAllRuns(n) == 1\n%         plot([time(n) time(n)],[0 currentOffset],'LineStyle','--',...\n%             'Color',[0.8 0.8 0.8])\n%     elseif StimVectorAllRuns(n) == 2\n%         plot([time(n) time(n)],[0 currentOffset],'LineStyle','--',...\n%             'Color',[0.3 0.3 0.3])\n%     end\n% end\nlinkaxes([ax1,ax2],'y')\nsaveas(fig,sprintf('%s_RoiCalciumEvents',config.saveName))\n\nmatfileName = sprintf('%s_RoiStats.mat',config.saveName);\nsave(matfileName,'config')\n\nif nargout\n    varargout{1} = config;\nend\n\n%% Function - ParseConfig\nfunction config = ParseConfig(config)\n% check for missing fields in config structure and add them with\n% default values\n\nif ~isfield(config,'matfiles')\n    matFiles = sort(uigetfile(...\n        '*.mat','Select Mat Files','MultiSelect','on'));\n    config.matfiles = strrep(matFiles,'.mat','');\nelse\n    matfiles = config.matfiles;\nend\n\nif ~isfield(config,'saveName')\n    config.saveName = [matfiles{1} '__RoiStats'];\nend\n\n% force f0 calculation as in ImageJ\nif ~isfield(config,'forceIJf0')\n    config.forceIJf0 = 0;\nend\nif isempty(config.forceIJf0)\n    config.forceIJf0 = 0;\nend\n\n% number of initial frames for normalization (forceImageJf0) OR the number\n% of frames before a stimulus used for renormalization\nif ~isfield(config,'baseFrames')\n    config.baseFrames = 10;\nend\nif isempty(config.baseFrames)\n    config.baseFrames = 10;\nend\n\nif ~isfield(config,'statsType')\n    config.statsType = 'dff';\nend\n\n% channels for stats calculation\n% DFF is calculated on the channel specified by config.channelVector(1)\n% DRR is calculated on the channels specified by config.channelVector(1)\n% and config.channelVector(2)\nif ~isfield(config,'channelVector')\n    config.channelVector = [1 2];\nend\n\nif strcmpi(config.statsType,'drr') && numel(config.channelVector) < 2\n    error('config.channelVector must specify at least 2 channels for DRR stats!');\nend\n\nif ~isfield(config,'npilCorrect')\n    config.npilCorrect = 0.2;\nend\nif ~isfield(config,'recycleNpilMovie')\n    config.recycleNpilMovie = 0;\nend\nif ~isfield(config,'doOgbSRsegmentation')\n    config.doOgbSRsegmentation = 0;\nend\nif config.doOgbSRsegmentation\n    if ~isfield(config,'OgbChannel')\n        config.OgbChannel = 1;\n    end\n    if ~isfield(config,'SrChannel')\n        config.SrChannel = 2;\n    end\nend\nif ~isfield(config,'segOptions')\n    config.segOptions = struct;\nend\n\nif ~isfield(config,'psConfig')\n    config.psConfig = struct;\n    config.psConfig.baseFrames = 5;\n    config.psConfig.evokedFrames = 15;\nend\n\n% event detection\nif ~isfield(config,'EventDetect')\n    config.EventDetect.method = 'none';\nend\n\n% low-pass filter (applied to the calcium trace AFTER event detection)\n% also applied to GLM model\nif ~isfield(config,'LowPass')\n    config.LowPass.method = 'none';\n    config.LowPass.params = [];\nend\n% LowPass.method may take the following values:\n% 'none' ... no low-pass filtering is applied\n% 'sg' ... savitzky-golay filter, as implemented in ML\n% LowPass.params is a vector with filter parameters, depending on the\n% specific filter (all cutoffs should be defined in s)\n% for SG, params are [lpFilterCutoff filterOrder]\n\nif ~isfield(config,'ch3Sound')\n    config.ch3Sound = 1;\nend\n\nif ~isfield(config,'pixelTime')\n    config.pixelTime = 12;\nend\nconfig.pixelTime = config.pixelTime / 1000000;\n\n%% Function - CalculateF0\nfunction f0_mat = CalculateF0(roi_mat,f)\nt = 1/f:1/f:length(roi_mat)./f;\np = polyfit(t',roi_mat',2);\nf0_mat = p(1).*t.^2 + p(2).*t + p(3);\n\n%% Function - doLowPassFilter\nfunction v = doLowPassFilter(v,S)\nswitch S.method\n    case 'sg'\n        v = sgolayfilt(v,S.sgolayOrder,S.lpFiltCutoff);\nend\n\n%% Function - doActiveModelSegment\nfunction roi = doActiveModelSegment(img,cog,options)\noptions.cog = cog;\nif ~isfield(options,'cellsize')\n    options.cellsize = 7; end\nif ~isfield(options,'minMaskSize')\n    options.minMaskSize = 20; end\nif ~isfield(options,'snake_iter1')\n    options.snake_iter1 = 100; end\nif ~isfield(options,'snake_iter')\n    options.snake_iter = 10; end\nif ~isfield(options,'alpha')\n    options.alpha = 1; end\nif ~isfield(options,'beta')\n    options.beta = 0; end\nif ~isfield(options,'tau')\n    options.tau = 0.1; end\nif ~isfield(options,'filt_range')\n    options.filt_range = [3 3]; end\nif ~isfield(options,'filt_order')\n    options.filt_order = 1; end\nif ~isfield(options,'canny_lo')\n    options.canny_lo = [0.1 0.05 0.01]; end\nif ~isfield(options,'canny_hi')\n    options.canny_hi = [0.25 0.15 0.05]; end\nif ~isfield(options,'alt_radius')\n    options.alt_radius = 2; end\nif ~isfield(options,'doPlot')\n    options.doPlot = 0; end\nif ~isfield(options,'enlarge')\n    options.enlarge = 0; end\nif ~isfield(options,'erode')\n    options.erode = 0; end\n\ntry\n    roi = ActiveModel_segment(img,options);\ncatch\n    roi = zeros(size(img,1),size(img,2));\n    rethrow(lasterror);\nend\n\n\n\n% e.o.f."}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "henry_analyzeHDF5_singleDay.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/analysisHDF5/henry_analyzeHDF5_singleDay.m", "size": 8844, "source_encoding": "utf_8", "md5": "594a225ffaaef1c7c4dd24f7b2b9e09b", "text": "function S = henry_analyzeHDF5_singleDay(id, doSave, doDecode)\n% id ... date string for the HDF5 file (e.g. 14011001) or Matlab structure from previous run\n\nconfig.doSave = doSave;\nconfig.doDecode = doDecode;\n\nif ~isstruct(id)\n    fileName = sprintf('%s.h5',id);\n    animalID = sprintf('mou_bl_%s_%s',id(1:6),id(7:8));\n    datasetID = ['/' animalID];\n    o('Loading data for %s ...', animalID, 0, 0);\n    % load HDF5 and convert to data structure\n    S = loadDataFromHDF5(fileName,datasetID);\n    o('Loading data for %s done.', animalID, 0, 0);\nelse\n    S = id;\n    animalID = S.animalID;\n    S = rmfield(S,'animalID');\nend\nclear id\n\nfieldsSpots = fieldnames(S);\nfor n = 1:numel(fieldsSpots) % loop over spots\n    spotID = fieldsSpots{n};\n    currentSpot = S.(fieldsSpots{n});\n    fieldsDays = fieldnames(currentSpot);\n    for m = 1:numel(fieldsDays) % loop over days\n        dateID = strrep(fieldsDays{m},'x','');\n        currentS = currentSpot.(fieldsDays{m});\n        ROISet = S.(spotID).(fieldsDays{m}).ROISets;\n        o('Running analysis for %s %s %s.',animalID,spotID,dateID, 0, 0)\n        %{\n        [caTracesMean, trialCount, tPs, perfTrue, perfShuf] = doSingleDayAnalysis(currentS,config);\n        % Save results\n        S.(spotID).(fieldsDays{m}).results.caTracesMean = caTracesMean;\n        S.(spotID).(fieldsDays{m}).results.tPs = tPs;\n        if config.doDecode\n            S.(spotID).(fieldsDays{m}).results.perfTrue = perfTrue;\n            S.(spotID).(fieldsDays{m}).results.perfShuf = perfShuf;\n        end\n        % Average Neuropil signal for target and distractor\n        titleStr = sprintf('%s %s %s Avg Traces',animalID,spotID,dateID);\n        h = figure('Name',titleStr,'NumberTitle','off');\n        splotTiles = subplotTiles(size(caTracesMean{1},1)); splotPos = 1;\n        for roi = 1:size(caTracesMean{1},1)\n            subplot(splotTiles(1),splotTiles(2),splotPos), splotPos = splotPos + 1;\n            plot(tPs,caTracesMean{1}(roi,:),'k'), hold on\n            plot(tPs,caTracesMean{2}(roi,:),'r')\n            if roi == size(caTracesMean{1},1)\n                title('Neuropil')\n                legend({sprintf('Targ (%1.0f trials)',trialCount(1)),...\n                    sprintf('Dist (%1.0f trials)',trialCount(2))})\n                xlabel('Time (s)'), ylabel('DRR (%)')\n            else\n                title( ROISet{1}{roi,1})\n            end\n        end\n        adjustSubplotAxes(h,'x',[min(tPs) max(tPs)],'y',[])\n        \n        % Average Roi traces for target and distractor\n        titleStr = sprintf('%s %s %s Avg Image',animalID,spotID,dateID);\n        h = figure('Name',titleStr,'NumberTitle','off');\n        nRois = size(caTracesMean{1},1)-1;\n        caTracesMeanMat = [caTracesMean{1}(1:nRois,:); caTracesMean{2}(1:nRois,:)];\n        caTracesMeanMat = ScaleToMinMax(caTracesMeanMat,0,1);\n        subplot(1,2,1) % target\n        imagesc(tPs,1:nRois,caTracesMeanMat(1:nRois,:),[0 1])\n        xlabel('Time (s)'), ylabel('Roi')\n        title(sprintf('Target (%1.0f trials)',trialCount(1)))\n        subplot(1,2,2) % distractor\n        imagesc(tPs,1:nRois,caTracesMeanMat(nRois+1:end,:),[0 1])\n        xlabel('Time (s)'), ylabel('Roi')\n        title(sprintf('Distractor (%1.0f trials)',trialCount(2)))\n        colormap('hot'), colorbar\n        %}\n        % NaiveBayes performance\n        if config.doDecode\n            titleStr = sprintf('%s %s %s NaiveBayes',animalID,spotID,dateID);\n            h = figure('Name',titleStr,'NumberTitle','off');\n            shadedErrorBar(tPs,nanmean(perfShuf,1),nanstd(perfShuf,[],1),'k',1); hold on\n            shadedErrorBar(tPs,nanmean(perfTrue,1),nanstd(perfTrue,[],1),'r',1);\n            set(gca,'xlim',[min(tPs) max(tPs)])\n            legend({'Shuf','True'}), xlabel('Time (s)'), ylabel('Classification Perf. +- SD (%)')\n        end\n    end\nend\nS.animalID = animalID;\n\nif config.doSave\n    SaveAndCloseFigures\nend\n\nend\n\n\n%% Function - doSingleDayAnalysis\nfunction [caTracesMean, trialCount, tPs, perfTrue, perfShuf] = doSingleDayAnalysis(S,config)\n\nimgRate = 76.9; % in Hz\npsTimes = [-0.5 2]; % in s\npsTimesIx = round(psTimes.*imgRate);\n\nstim = S.behav.stim;\ntrials = numel(stim);\n\n% figure out target and non-target\ntargetNontarget = [];\nfor n = 1:numel(S.behav.target)\n    if ~S.behav.target{n}\n        continue\n    end\n    if stim{n} == 1\n        targetNontarget = [1 2];\n    else\n        targetNontarget = [2 1];\n    end\n    break\nend\n% fprintf('Target: stim %1.0f\\n',targetNontarget(1))\n% fprintf('Non-Target: stim %1.0f\\n',targetNontarget(2))\n\ncaTracesTrials = cell(1,2);\nfor t = 1:trials\n    caTraces = S.caTraces{t};\n    imgDelay = S.behav.imgDelays{t};\n    stimStartTime = S.behav.stimStartTime{t}-imgDelay;\n    timeCa = (1:size(caTraces,2))./imgRate;\n    stimStartIx = find(timeCa>=stimStartTime,1,'first');\n    psStartIx = stimStartIx + psTimesIx(1);\n    if psStartIx < 1\n        error('psStart too early on trial %1.0f',t)\n    end\n    psStopIx = stimStartIx + psTimesIx(2);\n    if psStopIx > size(caTraces,2)\n%         warning('psStop too late on trial %1.0f. Skipping ...',t)\n        continue;\n    end\n    if t == 1\n        tPs = ((psStartIx:psStopIx)-stimStartIx)./imgRate;\n    end\n    if isempty(caTracesTrials{stim{t}})\n        caTracesTrials{stim{t}} = caTraces(:,psStartIx:psStopIx);\n    else\n        caTracesTrials{stim{t}} = cat(3, caTracesTrials{stim{t}}, caTraces(:,psStartIx:psStopIx));\n    end\nend\n\nstimStartIx = find(tPs>=0,1,'first');\nfor n = 1:numel(caTracesTrials)\n    data = nanmean(caTracesTrials{n},3);\n    % baseline normalization / filter\n    for m = 1:size(data,1)\n        data(m,:) = data(m,:) - nanmean(data(m,1:stimStartIx));\n        data(m,:) = sgolayfilt(data(m,:),3,11);\n    end\n    caTracesMean{1,targetNontarget(n)} = data;\n    trialCount(1,targetNontarget(n)) = size(caTracesTrials{n},3);\n    decodeData{1,targetNontarget(n)} = caTracesTrials{n};\nend\n\nif config.doDecode\n    % time-resolved decoding analysis (target vs. distractor) for neuron population\n    holdOut = 0.2; % fraction of trials to leave out\n    iters = 100; % number of iterations\n    decodeTimepoints = size(data,2); % consider binning to reduce timepoints\n    perfTrue = zeros(iters,decodeTimepoints);\n    perfShuf = zeros(iters,decodeTimepoints);\n    dispPercent = 10;\n    fprintf('NaiveBayes classification: 00%% done')\n    for t = 1:decodeTimepoints\n        popData = []; labels = [];\n        for stim = 1:numel(decodeData)\n            for trial = 1:size(decodeData{stim},3)\n                popData = [popData; decodeData{stim}(1:end-1,t,trial)']; % last row is neuropil\n                labels(end+1,1) = stim;\n            end\n        end\n        perfTrue(1:iters,t) = doClassify(popData,labels,holdOut,iters,0);\n        perfShuf(1:iters,t) = doClassify(popData,labels,holdOut,iters,1);\n        percentDone = round(t/decodeTimepoints*100);\n        if percentDone >= dispPercent\n            fprintf('\\b\\b\\b\\b\\b\\b\\b\\b%1.0f%% done',percentDone)\n            dispPercent = dispPercent + 10;\n        end\n    end\nelse\n    perfTrue = []; perfShuf = [];\nend\nfprintf('\\n\\n')\n\nend\n\n\n%% Function - doClassify\nfunction varargout = doClassify(data,labels,holdOut,iters,shuffled)\nif ~any(data)\n    for n = 1:nargout\n        varargout{n} = NaN;\n    end\n    return\nend\nperf = zeros(iters,1); postpCell = cell(iters,1);\npredictedCell = cell(iters,1); testlabelsCell = cell(iters,1);\n% compute the max. number of unique hold-outs (should be > iters)\nholdOutN = ceil(numel(labels)*holdOut);\nwarning('off','MATLAB:nchoosek:LargeCoefficient')\nmaxHoldOuts = nchoosek(numel(labels),holdOutN);\nwarning('on','MATLAB:nchoosek:LargeCoefficient')\nif maxHoldOuts < iters\n    warning('Max. number of unique hold-outs is %1.0f and iterations are %1.0f.',...\n        maxHoldOuts,iters)\nend\nparfor n = 1:iters\n    if shuffled\n        labels2 = labels(randperm(numel(labels)));\n    else\n        labels2 = labels;\n    end\n    c = cvpartition(labels2,'holdout',holdOut);\n    trainData = data(c.training,:); trainLabels = labels2(c.training);\n    if ~any(trainData)\n        perf(n) = NaN;\n        continue\n    end\n    testData = data(c.test,:); testLabels = labels2(c.test);\n    model = NaiveBayes.fit(trainData,trainLabels);\n    [postP,predicted,logp] = posterior(model,testData);\n    perf(n) = (numel(find(predicted==testLabels))./numel(testLabels)).*100;\n    postpCell{n} = postP;\n    predictedCell{n} = predicted;\n    testlabelsCell{n} = testLabels;\nend\n\nvarargout{1} = perf;\n\nif nargout > 1\n    varargout{2} = predictedCell;\n    varargout{3} = testlabelsCell;\n    varargout{4} = postpCell;\nend\n\nend\n\n\n%% Function - Save and close figures\nfunction SaveAndCloseFigures\nwhile numel(findobj) > 1\n    set(findall(gcf,'Interpreter','Tex'),'Interpreter','None')\n    makePrettyFigure(gcf);\n    saveName = get(gcf,'Name');\n    saveas(gcf,strrep(saveName,' ','-'))\n    close(gcf)\nend\nend\n\n\n\n\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "henry_analyzeHDF5_multiDay.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/analysisHDF5/henry_analyzeHDF5_multiDay.m", "size": 11569, "source_encoding": "utf_8", "md5": "74a90237433029f9bab1d1ffc0de5844", "text": "function S = henry_analyzeHDF5_multiDay(id, config)\n% id ... date string for the HDF5 file (e.g. 14011001) or Matlab structure from previous run\n\n% ADD ANALYSIS OF DECODING WITH LABELING OF WHICH PHASE IT IS ('NAIVE', 'LEARNING', 'EXPERT')\n% SEE IF TRAINING DATA SET ON EXPERT CAN ALSO PREDICT IN LEARNING\n\n% TRY TO PREDICT DPRIME FROM ACTVITY\n\nif ~isstruct(id)\n    fileName = sprintf('%s.h5',id);\n    animalID = sprintf('mou_bl_%s_%s',id(1:6),id(7:8));\n    datasetID = ['/' animalID];\n    % load HDF5 and convert to data structure\n    S = loadDataFromHDF5(fileName,datasetID);\nelse\n    S = id;\nend\nclear id;\n\nfieldsSpots = fieldnames(S);\nspotFound = 0;\nfor n = 1:numel(fieldsSpots) % loop over spots\n    spotID = fieldsSpots{n};\n    if ~strcmp(spotID,config.spotID)\n        continue\n    else\n        spotFound = 1;\n        currentSpot = S.(fieldsSpots{n});\n        break\n    end\nend\nif ~spotFound\n    error('Did not find %s', config.spotID)\nend\n\nfieldsDays = fieldnames(currentSpot);\nif numel(fieldsDays) < 2\n    warning('Need at least 2 days for multi-day analysis. Skipping ...')\n    S = [];\n    return\nend\n\nif config.doCorr\n    titleStr = sprintf('%s %s MultiDay Correlation',animalID,spotID);\n    hCorr = figure('Name',titleStr,'NumberTitle','off');\nend\nif config.doDecode\n    titleStr = sprintf('%s %s MultiDay Decode',animalID,spotID);\n    hDecode = figure('Name',titleStr,'NumberTitle','off');\nend\nsplot_pos = 1;\nfor n = 2:numel(fieldsDays)\n    dateID1 = strrep(fieldsDays{n-1},'x','');\n    dateID2 = strrep(fieldsDays{n},'x','');\n    S_day1 = currentSpot.(fieldsDays{n-1});\n    S_day2 = currentSpot.(fieldsDays{n});\n    % match RoiSets\n    roiID_day1 = S_day1.ROISets.(cell2mat(fieldnames(S_day1.ROISets))).ROISet(:,1);\n    roiID_day2 = S_day2.ROISets(cell2mat(fieldnames(S_day1.ROISets))).ROISet(:,1);\n    matchIx = zeros(1,2); pos = 1;\n    for m1 = 1:numel(roiID_day1)\n        if strcmpi(roiID_day1{m1},'npil')\n            continue\n        end\n        for m2 = 1:numel(roiID_day2)\n            if strcmpi(roiID_day1{m1},roiID_day2{m2})\n                matchIx(pos,1:2) = [m1,m2]; pos = pos + 1;\n            end\n        end\n    end\n    roiID = roiID_day1(matchIx(:,1));\n    fprintf('\\n%s - %s: %1.0f matching Rois\\n',dateID1,dateID2,pos-1)\n    [caTracesMean_day1, caTracesTrials_day1, tPs] = doSingleDayAnalysis(S_day1,config);\n    [caTracesMean_day2, caTracesTrials_day2] = doSingleDayAnalysis(S_day2,config);\n    % Correlation analysis\n    if config.doCorr\n        [corrMatch, corrNonMatchDiffDay, corrNonMatchSameDay] = ...\n            doMultiDayCorrelationAnalysis(caTracesMean_day1,caTracesMean_day2,matchIx);\n        figure(hCorr)\n        % plot target\n        subplot(numel(fieldsDays)-1,2,splot_pos); splot_pos = splot_pos + 1;\n        [f,x] = ecdf(corrNonMatchDiffDay{1});\n        stairs(x,f,'k'), hold on\n        [f,x] = ecdf(corrNonMatchSameDay{1});\n        stairs(x,f,'b'), hold on\n        [f,x] = ecdf(corrMatch{1});\n        stairs(x,f,'r'), hold on\n        legend({'NMatch Diff Day','NMatch Same Day','Match'},'location','northwest')\n        xlabel('r'), ylabel('cumulative F')\n        title(sprintf('%s - %s Target',dateID1,dateID2),'interpreter','none')\n        % plot distractor\n        subplot(numel(fieldsDays)-1,2,splot_pos); splot_pos = splot_pos + 1;\n        [f,x] = ecdf(corrNonMatchDiffDay{2});\n        stairs(x,f,'k'), hold on\n        [f,x] = ecdf(corrNonMatchSameDay{2});\n        stairs(x,f,'b'), hold on\n        [f,x] = ecdf(corrMatch{2});\n        stairs(x,f,'r'), hold on\n        xlabel('r'), ylabel('cumulative F')\n        title(sprintf('%s - %s Distractor',dateID1,dateID2),'interpreter','none')\n    end\n    % Decoding analysis\n    if config.doDecode\n        [perfTrue, perfShuf] = doMultiDayDecode(caTracesTrials_day1,...\n            caTracesTrials_day2,matchIx,tPs,config);\n        roiID{end+1} = 'Pop';\n        figure(hDecode)\n        subplot(numel(fieldsDays)-1,1,n-1)\n        hErr = errorbar(1:numel(roiID),mean(perfShuf,1),std(perfShuf,1),'sk');\n        removeErrorBarEnds(hErr); hold on\n        hErr = errorbar(1:numel(roiID),mean(perfTrue,1),std(perfTrue,1),'or');\n        removeErrorBarEnds(hErr);\n        set(gca,'xlim',[0 numel(roiID)+1],'xtick',1:numel(roiID),'xticklabel',roiID)\n        title(sprintf('%s - %s',dateID1,dateID2),'interpreter','none')\n        ylabel('Perf. (%)')\n        if n == numel(fieldsDays)\n           xlabel('ROIs'), legend({'Shuf','True'})\n        end\n    end\nend\nif config.doCorr\n    adjustSubplotAxes(hCorr,'x',[-1 1]);\nend\nif config.doDecode\n    adjustSubplotAxes(hDecode,'y',[0 100]);\nend\nS.animalID = animalID;\nif config.doSave\n    SaveAndCloseFigures\nend\n\nend\n\n\n%% Function - doMultiDayDecode\nfunction [perfTrue, perfShuf] = ...\n    doMultiDayDecode(trials_day1,trials_day2,matchIx,tPs,config)\n% train classifier on data from day 1 and test on day 2\n\nstimStartIx = find(tPs>=0,1,'first');\ntPsStim = tPs(stimStartIx:end);\ntPsStimI = tPsStim(1):0.1:tPsStim(end); % interpolation\niters = config.iters;\n% single-cell analysis\nperfTrue = zeros(iters,size(matchIx,1));\nperfShuf = zeros(iters,size(matchIx,1));\nfor n1 = 1:size(matchIx,1)\n    roi_day1_targ = squeeze(trials_day1{1}(matchIx(n1,1),:,:))';\n    roi_day1_targ = roi_day1_targ(:,stimStartIx:end);\n    roi_day1_dist = squeeze(trials_day1{2}(matchIx(n1,1),:,:))';\n    roi_day1_dist = roi_day1_dist(:,stimStartIx:end);\n    trainLabels = [ones(size(roi_day1_targ,1),1); repmat(2,size(roi_day1_dist,1),1)];\n    trainData = [roi_day1_targ; roi_day1_dist];\n    roi_day2_targ = squeeze(trials_day2{1}(matchIx(n1,2),:,:))';\n    roi_day2_targ = roi_day2_targ(:,stimStartIx:end);\n    roi_day2_dist = squeeze(trials_day2{2}(matchIx(n1,2),:,:))';\n    roi_day2_dist = roi_day2_dist(:,stimStartIx:end);\n    testLabels = [ones(size(roi_day2_targ,1),1); repmat(2,size(roi_day2_dist,1),1)];\n    testData = [roi_day2_targ; roi_day2_dist];\n    perfTrue(1:iters,n1) = doClassify(trainData,trainLabels,testData,testLabels,0.2,iters,0);\n    perfShuf(1:iters,n1) = doClassify(trainData,trainLabels,testData,testLabels,0.2,iters,1);\n    % interpolation to reduce dimensionality\n    trainDataI = (interp1(tPsStim,trainData',tPsStimI))';\n    testDataI = (interp1(tPsStim,testData',tPsStimI))';\n    if n1 == 1\n        trainDataPop = trainDataI;\n        testDataPop = testDataI;\n    else\n        trainDataPop = [trainDataPop, trainDataI];\n        testDataPop = [testDataPop, testDataI];\n    end\nend\n% population analysis\n% without dimensionality reduction\nperfTruePop = doClassify(trainDataPop,trainLabels,testDataPop,testLabels,0.2,iters,0);\nperfShufPop = doClassify(trainDataPop,trainLabels,testDataPop,testLabels,0.2,iters,1);\n\n% dimensionality reduction (PCA)\n% [pc,score,latent] = princomp([trainDataPop; testDataPop]);\n% varExplained = cumsum(latent)./sum(latent);\n% cutoff = find(varExplained>0.9,1,'first');\n% trainData = score(1:numel(trainLabels),1:cutoff);\n% testData = score(numel(trainLabels)+1:end,1:cutoff);\n% perfTruePop = doClassify(trainData,trainLabels,testData,testLabels,0.2,iters,0);\n% perfShufPop = doClassify(trainData,trainLabels,testData,testLabels,0.2,iters,1);\n\nperfTrue = [perfTrue, perfTruePop'];\nperfShuf = [perfShuf, perfShufPop'];\n\nend\n\n\n%% Function - doClassify\nfunction perf = doClassify(trainData,trainLabels,testData,testLabels,holdOut,iters,shuffled)\n\n% compute the max. number of unique hold-outs (should be > iters)\nholdOutN = ceil(numel(trainLabels)*holdOut);\nwarning('off','MATLAB:nchoosek:LargeCoefficient')\nmaxHoldOuts = nchoosek(numel(trainLabels),holdOutN);\nwarning('on','MATLAB:nchoosek:LargeCoefficient')\nif maxHoldOuts < iters\n    warning('Max. number of unique hold-outs is %1.0f and iterations are %1.0f.',...\n        maxHoldOuts,iters)\nend\nparfor n = 1:iters\n    if shuffled\n        trainLabels2 = trainLabels(randperm(numel(trainLabels)));\n    else\n        trainLabels2 = trainLabels;\n    end\n    c = cvpartition(trainLabels2,'holdout',holdOut);\n    trainData2 = trainData(c.training,:); trainLabels2 = trainLabels2(c.training);\n    model = NaiveBayes.fit(trainData2,trainLabels2);\n    [~,predicted] = posterior(model,testData);\n    perf(n) = (numel(find(predicted==testLabels))./numel(testLabels)).*100;\nend\n\nvarargout{1} = perf;\n\nend\n\n\n%% Function - doMultiDayCorrelationAnalysis\nfunction [corrMatch, corrNonMatchDiffDay, corrNonMatchSameDay] = ...\n    doMultiDayCorrelationAnalysis(ca_day1,ca_day2,matchIx)\n% approach: first correlate time series of different cells from different days to get 'null'\n% distribution of correlations; then correlate for the same cells on different days and compare to\n% 'null' distribution\ncorrMatch = cell(1,numel(ca_day1));\ncorrNonMatchDiffDay = cell(1,numel(ca_day1));\ncorrNonMatchSameDay = cell(1,numel(ca_day1));\nfor n1 = 1:size(matchIx,1)\n    for t = 1:numel(ca_day1)\n        % matching Rois\n        roi_day1 = ca_day1{t}(matchIx(n1,1),:);\n        roi_day2 = ca_day2{t}(matchIx(n1,2),:);\n        corrMatch{t}(end+1) = corr(roi_day1',roi_day2');\n        % non-matching Rois\n        for n2 = 1:size(matchIx,1)\n            if n1 == n2\n                continue\n            end\n            roi_day2 = ca_day2{t}(matchIx(n2,2),:);\n            corrNonMatchDiffDay{t}(end+1) = corr(roi_day1',roi_day2');\n            roi2_day1 = ca_day1{t}(matchIx(n2,1),:);\n            corrNonMatchSameDay{t}(end+1) = corr(roi_day1',roi2_day1');\n        end\n    end\nend\n\nend\n\n\n%% Function - doSingleDayAnalysis\nfunction [caTracesMean, caTracesTrialsOut, tPs] = doSingleDayAnalysis(S,config)\npsTimesIx = round(config.psTimes.*config.imgRate);\nstim = S.behav.stim;\ntrials = min(numel(stim), numel(S.caTraces));\n% figure out target and non-target\ntargetNontarget = [];\nfor n = 1:numel(S.behav.target)\n    if ~S.behav.target{n}\n        continue\n    end\n    if stim{n} == 1\n        targetNontarget = [1 2];\n    else\n        targetNontarget = [2 1];\n    end\n    break\nend\nfprintf('Target: stim %1.0f\\n',targetNontarget(1))\nfprintf('Non-Target: stim %1.0f\\n',targetNontarget(2))\n\ncaTracesTrials = cell(1,2);\ncaTracesTrialsOut = cell(1,2);\nfor t = 1:trials\n    caTraces = S.caTraces{t};\n    imgDelay = S.behav.imgDelays{t};\n    stimStartTime = S.behav.stimStartTime{t}-imgDelay;\n    timeCa = (1:size(caTraces,2))./config.imgRate;\n    stimStartIx = find(timeCa>=stimStartTime,1,'first');\n    psStartIx = stimStartIx + psTimesIx(1);\n    if psStartIx < 1\n        error('psStart too early on trial %1.0f',t)\n    end\n    psStopIx = stimStartIx + psTimesIx(2);\n    if psStopIx > size(caTraces,2)\n%         warning('psStop too late on trial %1.0f. Skipping ...',t)\n        continue\n    end\n    if t == 1\n        tPs = ((psStartIx:psStopIx)-stimStartIx)./config.imgRate;\n    end\n    if isempty(caTracesTrials{stim{t}})\n        caTracesTrials{stim{t}} = caTraces(:,psStartIx:psStopIx);\n    else\n        caTracesTrials{stim{t}} = cat(3, caTracesTrials{stim{t}}, caTraces(:,psStartIx:psStopIx));\n    end\nend\n\nstimStartIx = find(tPs>=0,1,'first');\nfor n = 1:numel(caTracesTrials)\n    data = nanmean(caTracesTrials{n},3);\n    % baseline normalization / filter\n    for m = 1:size(data,1)\n        data(m,:) = data(m,:) - nanmean(data(m,1:stimStartIx));\n        data(m,:) = sgolayfilt(data(m,:),3,11);\n    end\n    caTracesMean{1,targetNontarget(n)} = data;\n    caTracesTrialsOut{1,targetNontarget(n)} = caTracesTrials{n};\nend\n\nend\n\n\n%% Function - Save and close figures\nfunction SaveAndCloseFigures\nwhile numel(findobj) > 1\n    set(findall(gcf,'Interpreter','Tex'),'Interpreter','None')\n    makePrettyFigure(gcf);\n    saveName = get(gcf,'Name');\n    saveas(gcf,strrep(saveName,' ','-'))\n    close(gcf)\nend\nend\n\n\n\n\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "analyseStimulusTuning.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/postAnalysis/analyseStimulusTuning.m", "size": 9135, "source_encoding": "utf_8", "md5": "9c894bb7f270ae312885d479465bfde5", "text": "function multiCompTuningPairs = analyseStimulusTuning(multiComparisonStats, stimIDs, multiCompareThresh, saveName, plotLimits, doSaveTuningPlot, doSavePairedTuningPlot)\n    % By using the results obtained by multiStimComparison this function\n    % plots the significance levels for different comparison metric (every\n    % stimulus compared with all) in a pairwise overview bar plot\n    %\n    % There also exists significance if one stimulus parameter response is significantly\n    % lower than all the others!\n    %\n    % A comparison is significant for the given threshold if the comparsion\n    % intervals do not overlap = variance interval does not contain 0\n    %\n    % doSaveTuningPlot: Saves all bar plots and plots significances (also\n    % generates plot if there is no significance\n    %\n    % doSavePairedTuningPlot: Only plots significant pairs in subplots\n    %\n    % Usage: analyseStimulusTuning(multiComparisonStats, stimIDs, multiCompareThresh, saveName, plotLimits, doSaveTuningPlot, doSavePairedTuningPlot)\n    %\n    %\n    % Author: A. van der Bourg, 2014\n    %\n    \n    % TODO:\n    % * Add axe handle for Balazs\n    \n    \n    %% Init variables\n    nROIs = size(multiComparisonStats, 1);\n    % ROI pairwise significance array\n    multiCompTuningPairs = cell(nROIs,1);\n    \n    \n    %% Find exclusively pairs for groups that are significant\n    for iROI = 1: nROIs\n        % Obtain the multiple comparison stats for a given ROI\n        comparisonMatrix = multiComparisonStats{iROI, 1};\n        significanceIndex = zeros(size(comparisonMatrix, 1), 1);\n        % Find all significant pairs\n        for iPair = 1: size(comparisonMatrix, 1)\n            % Check if variance interval is between zero (if both negative\n            % or if both positive it is significant, otherwise not)!\n            if comparisonMatrix(iPair, 3) < 0\n                if comparisonMatrix(iPair, 5) < 0\n                    significanceIndex(iPair, 1) = 1;\n                else\n                    significanceIndex(iPair, 1) = 0;\n                end;\n            else if comparisonMatrix(iPair, 3) > 0\n                    if comparisonMatrix(iPair, 5) > 0\n                        significanceIndex(iPair, 1) = 1;\n                    else\n                        significanceIndex(iPair, 1) = 0;\n                    end;\n                end;\n            end;\n            % Store the significant pair information\n            %multiCompTuningPairs{iROI, 1} = significanceIndex;\n        end;\n        \n        \n        %% Save significant pairs\n        sigPairs = generateSigPairs(iROI, significanceIndex, multiComparisonStats);\n        multiCompTuningPairs{iROI, 1} = sigPairs;\n        \n        %% Perform plotting for all conditions\n        if doSaveTuningPlot == 1 || doSaveTuningPlot == 2\n            %Make a bar plot\n            figHandle = figure;\n            meanResps = multiComparisonStats{iROI, 2}';\n            errorHandles = meanResps(2,:);\n            meanResps = meanResps(1,:);\n            axeH = bar(meanResps, 'hist');\n            ylabel('Mean Evoked Response');\n            % Ignore too small plotlimits so star significance is\n            % plotted correctly.\n            if max(meanResps(:)) < plotLimits(2)\n                ylim(plotLimits);\n            end;\n            set(gca, 'XTickLabel', stimIDs);\n            set(axeH,'FaceColor',[1,1,1]*0.5,'LineWidth',1)\n            hold on;\n            errorHandle = errorbar(meanResps, errorHandles, '+');\n            set(errorHandle,'color',[1,1,1]*0.52);\n            %Add significance starts with sigstar function (sometimes\n            %messy)\n            if (length(significanceIndex(significanceIndex ==1))) >0\n                hold on;\n                sigPairs = generateSigPairs(iROI, significanceIndex, multiComparisonStats);\n                sigVector = ones(1,size(sigPairs,2)) * multiCompareThresh;\n                sigstar(sigPairs, sigVector);\n                hold off;\n            end;\n            hold off;\n            if doSaveTuningPlot == 2\n                set(gca, 'FontSize', 8);\n                %figHandle\n                saveFigToDir(gcf, [saveName '_StimulusTuning_ROI' num2str(iROI)], 'ROIStimulusTuning', doSaveTuningPlot, 1, 1);\n                close all;\n            end;\n            \n        end;\n        \n        %% This plot only plots significant pairs as indicated in\n        % significanceIndex and generateSigPairs\n        if doSavePairedTuningPlot == 1 || doSavePairedTuningPlot == 2\n            if (length(significanceIndex(significanceIndex ==1))) >0\n                mainHandle = figure;\n                % Pair matrix that is feeded to bar (Mx2 matrix)\n                pairSet = zeros(length(significanceIndex(significanceIndex ==1)), 2);\n                %Get pairs and error handles\n                sigPairs = generateSigPairs(iROI, significanceIndex, multiComparisonStats);\n                sigVector = ones(1,size(sigPairs,2)) * multiCompareThresh;\n                %Obtain mean values\n                meanResps = multiComparisonStats{iROI, 2}';\n                errorHandles = meanResps(2,:);\n                errorHandles = errorHandles(1, 1:2);\n                %Can this be done in one step? Dunno...\n                meanResps = meanResps(1,:);\n                pairIndex = 1;\n                for iPair = sigPairs\n                    pairSet(pairIndex,1) = meanResps(1, iPair{1,1}(1));\n                    pairSet(pairIndex, 2) = meanResps(1, iPair{1,1}(2));\n                    pairIndex = pairIndex + 1;\n                end;\n                %Plot the significant pairs as subplots (only semi-elegant\n                %solution for now to circumvent problems with paired\n                %bar-plots\n                [xIndex, yIndex] = findSubplotConfig(size(sigPairs,2));\n                \n                %Iterate through all subplots\n                for pairIndex = 1:size(sigPairs, 2)\n                    subplot(yIndex, xIndex, pairIndex);\n                    %Hack for subplots\n                    figHandle = gcf;\n                    hold on;\n                    barSet = [pairSet(pairIndex, 1), pairSet(pairIndex, 2)];\n                    axeH = bar(barSet);\n                    ylabel('Mean Evoked Response');\n                    % Ignore too small plotlimits so star significance is\n                    % plotted correctly.\n                    if max(meanResps(:)) < plotLimits(2)\n                        ylim(plotLimits);\n                    end;\n                    errorHandle = errorbar(barSet, errorHandles, '+');\n                    set(errorHandle,'color',[1,1,1]*0.52);\n                    sigstar([1,2], sigVector(1));\n                    %Get pair and label x-axis\n                    stimLabel = [stimIDs(sigPairs{1, pairIndex}(1)), stimIDs(sigPairs{1, pairIndex}(2))];\n                    set(gca, 'XTick', [1,2]);\n                    set(gca, 'XTickLabel', stimLabel);\n                    set(axeH,'FaceColor',[1,1,1]*0.5,'LineWidth',1)\n                    set(gca, 'FontSize', 8);\n                    title('Significant Multiple Comparison Pairs');\n                end;\n                hold off;\n                \n                if doSavePairedTuningPlot == 2\n                    saveFigToDir(mainHandle, [saveName '_PairedStimulusTuning_ROI' num2str(iROI)], 'ROIPairedStimulusTuning', doSavePairedTuningPlot, 1, 1);\n                    close all;\n                end;\n                \n            end;\n        end;\n        \n    end;\nend\n\nfunction pairVectCell = generateSigPairs(roiIndex, significanceIndex, multiComparisonStats)\n    % Function to generate a pair vector cell array that is needed to plot\n    % all significant multi-comparisons using sigstar\n    pairDims = size(significanceIndex, 1);\n    pairVectCell = cell(1, length(significanceIndex(significanceIndex ==1)));\n    %Go through all possibilties and get the significant paired elements\n    pairVectIndex =1;\n    for pairIndex = 1:pairDims\n        if significanceIndex(pairIndex,1) == 1\n            % We obtain the pairs by getting the paired numbers for the\n            % given significanceIndex entry in multiComparisonStats\n            pairVectCell{1, pairVectIndex} = [multiComparisonStats{roiIndex, 1}(pairIndex,1), multiComparisonStats{roiIndex, 1}(pairIndex,2)];\n            pairVectIndex = pairVectIndex +1;\n        end;\n    end\nend\n\nfunction [xIndex, yIndex] = findSubplotConfig(significantPairs)\n    % Function to evaluate which configuration is best to plot subplots\n    \n    % First check odd cases then even cases\n    if mod(significantPairs,2) == 0\n        % An even number with sqrt can configure sqrt(x)*sqrt(x)\n        if mod(sqrt(significantPairs),2) ==0\n            xIndex = sqrt(significantPairs);\n            yIndex = sqrt(significantPairs);\n        else\n            xIndex = significantPairs / 2;\n            yIndex = 2;\n        end;\n        % Odd cases\n    else\n        if sqrt(significantPairs)*sqrt(significantPairs) == significantPairs;\n            xIndex = sqrt(significantPairs);\n            yIndex = sqrt(significantPairs);\n        else\n            xIndex = round(significantPairs/2);\n            yIndex = 2;\n        end;\n    end\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "doROIRGBPlot.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/postAnalysis/plotting/doROIRGBPlot.m", "size": 2018, "source_encoding": "utf_8", "md5": "ee970a4efdf6e6b9c9d7596a4dd7e72a", "text": "%% Function - doROIRGBPlot\nfunction figH = doROIRGBPlot(axeH, imgDims, ROISet, leftImg, meanImage, fileID, alphaVal)\n\n% created by B. Laurenczy - 2013\n\n% 2D matrix for the alpha transparency of each ROI on the final image\nalphaMat = ones(imgDims(1), imgDims(2));\n% 3D matrix for the RGB values of each ROI\nROIRGB = zeros(imgDims(1), imgDims(2), 3);\n% colormap with a different color for each ROI\nCMap = jet(size(ROISet, 1));\n% 2D matrix containing the indexes of the ROIs for the text plotting\nROICounterImg = zeros(size(alphaMat));\n\n% go trough each ROI to fill in the above variables\nfor iROI = 1 : size(ROISet, 1);\n    % extract the ROIMask and the ROI's ID\n    ROIMask = ROISet{iROI, 2};\n    % set (a bit) transparent the regions where this ROI is\n    alphaMat(ROIMask == 1) = alphaVal;\n    ROICounterImg(ROIMask == 1) = iROI;\n    [row, col] = ind2sub(size(alphaMat), find(ROIMask));\n    % assign the color for each pixel\n    for i = 1 : length(row);\n        ROIRGB(row(i), col(i), :) = CMap(iROI, :);\n    end\nend\n\n% display the ROIs on mean frame in RGB multi-color\nif isempty(axeH);\n    figH = figure('Name', sprintf('ROIs for %s', fileID), 'NumberTitle', 'off');\n    if ~isempty(leftImg);\n        subplot(1, 2, 1, 'Parent', figH);\n        imshow(linScale(leftImg), 'Parent', gca);\n        subplot(1, 2, 2, 'Parent', figH);\n        axeH = gca;\n    else\n        axeH = axes('Parent', figH);\n    end;\nelse\n    figH = getParentFigure(axeH);\nend;\nimshow(ROIRGB, 'Parent', axeH);\nhold(axeH, 'on');\nimShowHandle = imshow(meanImage{1}, [], 'InitialMagnification', 'fit', 'Parent', axeH);\nset(imShowHandle, 'AlphaData', alphaMat);\nregionGroups = regionprops(ROICounterImg, ROICounterImg, 'MaxIntensity', 'Extrema');\nhold(axeH, 'on');\n\nfor iGroup = 1 : numel(regionGroups)\n    regionCenter = regionGroups(iGroup).Extrema;\n    text(regionCenter(1,1), regionCenter(1,2)-1, ROISet{iGroup, 1}, 'Parent', axeH, ...\n        'Clipping', 'on', 'Color', 'r', 'FontWeight', 'bold', 'Tag', 'CellLabel');\nend\nhold(axeH, 'off');\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "PlotRoiTimecourse.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/postAnalysis/plotting/PlotRoiTimecourse.m", "size": 7108, "source_encoding": "utf_8", "md5": "5d58dabaf73fc2b72e2636360675e780", "text": "function varargout = PlotRoiTimecourse(varargin)\n% plot timecourses of Rois in RoiSet\n% in1 ... fcs-file / cell-array of tiff-files (max. 2)\n% in2 ... RoiSet\n% returns structure with fields time (adjusted by Roi location), stats type\n% and value and roi ID\n% return time axis in varargout{1}\n% return timeseries as cell in varargout{2}\n% return Roi IDs as cell in varargout{3}\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nfilename = varargin{1};\nroifile = varargin{2};\n\n\n%% Parameters\n% imaging frequency\nfreq = 7.81;\n% no. of slices to delete\ndel_slice = 1;\n% bg. threshold in percentile\nbg_thresh = 1;\n% smoothing span (0 for none)\n% smoothing is performed by local regression using weighted linear least\n% squares and a 2nd degree polynomial model\n% span is provided in percentage of total number of data points (<=1)\nsmooth_span = 0;\n% highpass filter cutoff in Hz (0 for none)\n% this is an experimental highpass filter that works in the fourier domain\n% and attemts to remove frequency components slower than the cutoff\nhp_cutoff = 0;\n\n% run single channel analysis on channel 2\nSwitchChannels = 0;\n\n% type of stats image ('none','dff','drr')\nstats_type = 'drr';\nts.stats_type = stats_type;\nswitch stats_type\n    case 'none'\n        stats_type = 1;\n    case 'drr'\n        stats_type = 2;\n    case 'dff'\n        stats_type = 3;\nend\n% baseline (e.g. no. of baseline frames)\nbase = 10;\n% stimulus description in vector format (0 0 0 1 1 0 0 ...)\n% empty to use base frames for stats calculation\nstim_vector = [];\n\n%% Load data and preprocess\nif ~isnumeric(filename)\n    if ~iscellstr(filename)\n        files{1} = filename;\n    else\n        files = filename;\n    end\n    \n    \n    % figure title\n    fig_title = strrep(files{1},'.fcs','');\n    fig_title = strrep(fig_title,'.tif','');\n    \n    if ~isempty(strfind(files{1},'.fcs'))\n        img = import_raw(filename);\n    else\n        img_data = tif2mat(files{1},'nosave');\n        img.ch1 = img_data.data;\n        if length(files) > 1\n            img_data = tif2mat(files{2},'nosave');\n            img.ch2 = img_data.data;\n            clear img_data\n        end\n    end\nelse\n    fig_title = 'workspace variable';\n    img.ch1 = filename;\nend\npixel_time = (1/freq)/(size(img.ch1,1)*size(img.ch1,2));\n% load roi set\nif ~iscell(roifile)\n    roi_set = ij_roiDecoder(roifile,[size(img.ch1,1) size(img.ch1,2)]);\nelse\n    roi_set = roifile;\n    % format from roi timecourse extractor???\n    if size(roi_set,2) == 3\n        for n = 1:size(roi_set,1)\n           a = zeros(roi_set{n,2});\n           for m = 1:size(roi_set{n,3})\n              a(roi_set{n,3}(m)) = 1;\n           end\n           roi_set{n,4} = bwpack(a);\n        end\n        roi_set(:,2:3) = [];\n    end\nend\n\n% background Rois are named bg.roi\nif sum(strcmp(roi_set(:,1),'bg.roi'))\n    bg_mask = bwunpack(roi_set{strcmp(roi_set(:,1),'bg.roi'),2});\n    roi_set(strcmp(roi_set(:,1),'bg.roi'),:)=[];\nelse\n    bg_mask = [];\nend\n\n% big bad coding practice disregard here\n% switch channels\n% this can be used to do single channel analysis on channel 2\nif SwitchChannels\n    img.ch1 = img.ch2;\n    warning('Switched channels 1 and 2. Now evaluating channel 2.');\nend\n\nif isempty(bg_mask)\n    img.ch1 = double(PreprocImageData(img.ch1,del_slice,bg_thresh));\nelse\n    img.ch1 = double(PreprocImageData(img.ch1,del_slice,bg_mask));\nend\nif stats_type == 2\n    if isempty(bg_mask)\n        img.ch2 = double(PreprocImageData(img.ch2,del_slice,bg_thresh));\n    else\n        img.ch2 = double(PreprocImageData(img.ch2,del_slice,bg_mask));\n    end\nelse\n    if isfield(img,'ch2')\n        img = rmfield(img,'ch2');\n    end\nend\n[dur,time] = gui_CalculateTimeVector(1:size(img.ch1,3),freq,[]);\n\n\nfigure('Name',fig_title,'NumberTitle','off'); hold all\n\nfor n = 1:size(roi_set,1)\n    fprintf('\\nRoi %s\\n',roi_set{n,1});\n    mask = bwunpack(roi_set{n,2});\n    ts.ch1{n} = mean(GetRoiTimeseries(img.ch1,mask),1);\n    if stats_type == 2\n        ts.ch2{n} = mean(GetRoiTimeseries(img.ch2,mask),1);\n        % calculate DRR\n        stats = ts.ch1{n} ./ ts.ch2{n};\n    elseif stats_type == 1 || stats_type == 3\n        stats = ts.ch1{n};\n    end\n    if stats_type == 2 || stats_type == 3\n        % estimate baseline values (according to stimulus protocol)\n        % without stim. protocol, choose first 10 timepoints\n        f0_mat = CalculateF0(stats,stim_vector,base);\n        % calculate stats\n        stats = ((stats-f0_mat) ./ f0_mat).* 100;\n        stats(stats==Inf) = 0;\n        stats(stats==-Inf) = 0;\n        stats(stats==NaN) = 0;\n    end\n    % smoothing\n    if smooth_span\n        stats = smooth(stats,smooth_span,'loess');\n    end\n    % highpass filter\n    if hp_cutoff\n        stats = mpi_BandPassFilterTimeSeries(stats,1/freq,hp_cutoff,freq*2);\n    end\n    ca_shift = mean(find(mask'==1)) * pixel_time;\n    ca_shift = (1/freq) - ca_shift;\n    ts.time{n} = time - ca_shift;\n    fprintf('\\nAdjusted Roi time by %1.2f frames based on mean ROI location\\n',...\n        ca_shift/(1/freq));\n    ts.stats{n} = stats;\n    ts.roi_id{n} = roi_set{n,1};\n    plot(ts.time{n},stats);\n    clear stats\nend\n\nlegend(ts.roi_id);\n\nvarargout{1} = ts;\n\n\n%% Image preprocessing\nfunction img_data = PreprocImageData(img_data,del_slice,bg_thresh)\n% in1 ... raw image matrix\n% in2 ... number of initial slices to delete\n% in3 ... background subtraction threshold (percentile)\nimg_data(:,:,1:del_slice) = [];\nif max(reshape(img_data,1,numel(img_data))) > 256\n    img_data = img_data./255;\nend\nimg_data = uint8(img_data);\nif ~isempty(bg_thresh)\n    if size(bg_thresh,1) > 1\n        img_data = bg_subtract_HL(img_data,bg_thresh,1,'roi');\n    else\n        img_data = bg_subtract_HL(img_data,bg_thresh,1,'percentile');\n    end\nend\nimg_data(img_data<0)=0;\n\nfunction f0_mat = CalculateF0(roi_mat,stim,base)\nif isempty(stim)\n    f0_mat = roi_mat(:,1:base);\n    f0_mat = nanmean(f0_mat,2);\n    f0_mat = repmat(f0_mat,1,size(roi_mat,2));\n    return\nend\n% binarise stim vector (each stimulus type leads to\n% renormalization)\nstim(stim>0)=1;\n% define frames of stimulus onset\nstim_onset = zeros(size(stim));\nfor n = 2:length(stim)\n    if stim(n) == 1 && stim(n-1) == 0\n        stim_onset(n) = 1;\n    end\nend\n% designate the base frames BEFORE stimulus onset as baseline\nstim_base = zeros(size(stim));\nfor n = base+1:length(stim)\n    if stim_onset(n) == 1\n        stim_base(n-base:n-1) = 1;\n    end\nend\nf0_mat = zeros(size(roi_mat));\nfor n = 1:length(stim)\n    if stim_base(n) == 1\n        stim_base(n:n+base-1) = 0;\n        % find endpoint of current F0 interval (the frame before\n        % next normalization interval start)\n        f0_stop = find(stim_base==1,1,'first')-1;\n        if isempty(f0_stop)\n            f0_stop = length(stim);\n        end\n        for pixel = 1:size(f0_mat,1)\n            current_mean = mean(roi_mat(pixel,n:n+base-1));\n            f0_mat(pixel,n:f0_stop) = current_mean;\n        end\n    end\nend\n% timepoints before first stimulus get F0 value for first\n% pre-stimulus baseline\npre_stim1 = find(f0_mat(1,:)~=0,1,'first');\nfor pixel = 1:size(f0_mat,1)\n    f0_mat(pixel,1:pre_stim1-1) = f0_mat(pixel,pre_stim1);\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "doPSAveragePlotForROI.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/postAnalysis/plotting/doPSAveragePlotForROI.m", "size": 1893, "source_encoding": "utf_8", "md5": "2ba38f9d133c145dfca2444bc537918b", "text": "%% Function - doPSAveragePlot\nfunction [fig, fig2] = doPSAveragePlotForROI(iROI, ROIID, PSROIStatsData, uniqueStims,...\n    stimNames, frameRate, baseFrames)\n\nfigName = sprintf('PSPlot %s', ROIID);\nfig = figure('Name', figName, 'NumberTitle', 'off');\nhold all;\n\n% setup subplot\nif numel(uniqueStims) > 3;\n    M = ceil(sqrt(numel(uniqueStims)));\n    N = M;\nelse\n    M = numel(uniqueStims);\n    N = M;\nend;\n\nif N * (M - 1) >= numel(uniqueStims);\n    M = M - 1;\nend;\n\nAllStimsPSTrace = [];\nfor iStim = 1 : size(PSROIStatsData, 2);\n    \n    stimPSTrace = PSROIStatsData{iROI, iStim};\n    AllStimsPSTrace = [AllStimsPSTrace; stimPSTrace]; %#ok<AGROW>\n    \n    subplot(M, N, iStim);\n    PSTime = (1:size(stimPSTrace,2)) ./ frameRate;\n    PSTime = PSTime - (baseFrames ./ frameRate);\n    hErr = errorbar(PSTime, nanmean(stimPSTrace, 1), sem(stimPSTrace, 1), 'k', 'LineWidth', 2);\n    hold on;\n    removeErrorBarEnds(hErr);\n    \n%     if iStim >= (M * N) - N + 1;\n    if iStim >= size(PSROIStatsData, 2) - N + 1;\n        xlabel('Time [s]');\n    else\n        set(gca, 'XTickLabel', []);\n    end;\n    if mod(iStim, N) == 1;\n        ylabel('DFF [%] \\pm SEM');\n    else\n        set(gca, 'YTickLabel', []);\n    end;\n    title(sprintf('%s\\\\_%s', ROIID, sprintf('%3.1fkHz', stimNames(iStim) / 1000)));\nend;\n\nadjustSubplotAxes(fig, 'y', [], 'x', [min(PSTime) max(PSTime)]);\ntightfig;\nhold off;\n\n[psPeak(iROI), i] = max(nanmean(stimPSTrace,1)); %#ok<ASGLU>\npsPeakSD(iROI) = nanstd(stimPSTrace(:, i)); %#ok<NASGU>\n\nfigName = strrep(figName, 'PSPlot', 'PSPlotAllStims');\nfig2 = figure('Name',figName,'NumberTitle','off');\nhold all;\nplot(PSTime, AllStimsPSTrace, 'Color',[0.5 0.5 0.5]);\nhErr = errorbar(PSTime, nanmean(AllStimsPSTrace,1), sem(AllStimsPSTrace,1),...\n    'r','LineWidth',2);\nremoveErrorBarEnds(hErr);\nxlabel('Time [s]');\nylabel('DFF [%] \\pm SEM');\ntitle(sprintf('PSPlot all stims %s', ROIID));\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "glmAnalysis2P.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/glmAnalysis2P.m", "size": 6600, "source_encoding": "utf_8", "md5": "3db4ec3701a3383bb6d15241bffd2e87", "text": "function glmAnalysis2P\n% GLM analysis function for two-photon imaging data\n% input: structure created by GetRoiStats (*_RoiStats)\n\n\n% make GLM design matrix\nif config.doGLMstats\n    onsettau = 0.05;\n    tau = 0.5;\n    tAxis_singleRun = (1:numel(config.stim{1}))./config.frameRate{1};\n    modelTransient = spkTimes2Calcium(0,onsettau,1,tau,0,0,...\n        config.frameRate{1},tAxis_singleRun(end));\n    modelC = cell(1,numel(config.stim));\n    for n = 1:numel(config.stim)\n        currentStim = config.stim{n};\n        uniqueStims = unique(currentStim);\n        uniqueStims(uniqueStims==0) = [];\n        currentModel = [];\n        for m = 1:numel(uniqueStims)\n            stimOn = zeros(size(currentStim));\n            stimOn(currentStim==uniqueStims(m)) = 1;\n            stimOnConv = conv(stimOn,modelTransient);\n            stimOnConv = stimOnConv(1:numel(stimOn));\n            currentModel = [currentModel reshape(stimOnConv,numel(stimOnConv),1)];\n        end\n        % apply low-pass filter to the model\n        modelC{n} = doLowPassFilter(currentModel,config.LowPass);\n    end\n    % concatenate model\n    A = modelC{1};\n    for n = 2:length(modelC)\n        A = [A; modelC{n}];\n    end\n    % ensure column vectors for model\n    if size(A,1) < size(A,2)\n        A = A';\n    end\n    modelC = A;\n    tAxis_runs = (1:size(modelC,1))./config.frameRate{1};\n    % Orthogonalize the model\n    model = Gram_Schmidt_Process(modelC);\nend\n\n% Significance level, corrected for multiple comparisons\n% number of comparisons = cell number + 1 (neuropil)\n% this applies to significance tests of the GLM only\nswitch lower(config.multiCompareProc)\n    case 'none'\n        alphaCorrected = config.alpha;\n    case 'bonf'\n        alphaCorrected = config.alpha ./ (cellNo+1);\n    otherwise\n        error('Multiple comparison procedure %s not implemented',...\n            config.multiCompareProc);\nend\n\n%% GLM analysis\nif config.doGLMstats\n    for currentRoi = 1:size(RoiMatAllRuns,1)\n        config.glmStatsRoi{currentRoi} = ...\n            doGLMfit(model,RoiMatAllRuns(currentRoi,:),alphaCorrected);\n    end\nend\n\n\n\n%% Plot GLM and PS\npsPlotDir = sprintf('PsPlots_%s',config.saveName);\nif exist(psPlotDir) ~= 7\n    mkdir(psPlotDir)\nend\n\nfor currentRoi = 1:size(RoiMatAllRuns,1)\n    if config.doGLMstats\n        if currentRoi == size(RoiMatAllRuns,1)\n            figName = sprintf('GLM fit Npil');\n        else\n            figName = sprintf('GLM fit Roi%s',roiLabel{currentRoi});\n        end\n        fig = figure('Name',figName,'NumberTitle','off'); hold all\n        \n        glmStats = config.glmStatsRoi{currentRoi};\n        plot(tAxis_runs,RoiMatAllRuns(currentRoi,:),'k'), hold on\n        plot(tAxis_runs,model,'r'), hold on\n        hErr = errorbar(tAxis_runs,glmStats.yfit,glmStats.ciL,glmStats.ciU,...\n            'g','LineWidth',1.5);\n        removeErrorBarEnds(hErr)\n        legend({'DRR' 'Stim' 'Model'})\n        set(gca,'xlim',[0 max(tAxis_runs)])\n        xlabel('Time / s')\n        ylabel('DRR / %')\n        titleStr = sprintf('b=%1.2f (p=%1.2f)',glmStats.b(2),glmStats.pBeta(2));\n        title(titleStr)\n    end\n    \n    if currentRoi == size(RoiMatAllRuns,1)\n        figName = sprintf('PsPlot Npil');\n    else\n        figName = sprintf('PsPlot Roi%s',roiLabel{currentRoi});\n    end\n    fig = figure('Name',figName,'NumberTitle','off'); hold all\n    % setup subplot\n    if numel(uniqueStims) > 3\n        subIdx = repmat(ceil(sqrt(numel(uniqueStims))),1,2);\n    elseif numel(uniqueStims) == 3\n        subIdx = [1 3];\n    elseif numel(uniqueStims) == 2\n        subIdx = [1 2];\n    elseif numel(uniqueStims) == 1\n        subIdx = [1 1];\n    end\n    AllStims = [];\n    for currentStim = 1:size(PsPlotDataRoi,2)\n        stimPsPlot = PsPlotDataRoi{currentRoi,currentStim};\n        AllStims = [AllStims;stimPsPlot];\n        subplot(subIdx(1),subIdx(2),currentStim)\n        psTime = (1:size(stimPsPlot,2))./config.frameRate{1};\n        psTime = psTime - (config.psConfig.baseFrames./config.frameRate{1});\n        hErr = errorbar(psTime,nanmean(stimPsPlot,1),sem(stimPsPlot,1),...\n            'k','LineWidth',2); hold on\n        removeErrorBarEnds(hErr);\n        xlabel('Time / s')\n        ylabel('DFF / % +- SEM')\n        if currentRoi == size(RoiMatAllRuns,1)\n            title(sprintf('Peri-stim plot Npil Stim %1.0f',currentStim))\n        else\n            title(sprintf('Peri-stim plot Roi%s Stim %1.0f',...\n                roiLabel{currentRoi},currentStim))\n        end\n    end\n    adjustSubplotAxes(fig,'y',[],'x',[min(psTime) max(psTime)])\n    saveStr = sprintf('%s%s%s',psPlotDir,filesep,strrep(figName,' ',''));\n    saveas(fig,saveStr)\n    close(fig)\n    [psPeak(currentRoi),idx] = max(nanmean(stimPsPlot,1));\n    psPeakSD(currentRoi) = nanstd(stimPsPlot(:,idx));\n    \n    figName = strrep(figName,'PsPlot','PsPlotAllStims');\n    fig = figure('Name',figName,'NumberTitle','off'); hold all\n    plot(psTime,AllStims,'Color',[0.5 0.5 0.5]), hold on \n    hErr = errorbar(psTime,nanmean(AllStims,1),sem(AllStims,1),...\n        'r','LineWidth',2); hold on\n    removeErrorBarEnds(hErr);\n    xlabel('Time / s')\n    ylabel('DFF / % +- SEM')\n    saveStr = sprintf('%s%s%s',psPlotDir,filesep,strrep(figName,' ',''));\n    saveas(fig,saveStr)\n    close(fig)\nend\n\n\n%% Function - ParseConfig\nfunction config = ParseConfig(config)\n% check for missing fields in config structure and add them with\n% default values\n\nif ~isfield(config,'doGLMstats')\n    config.doGLMstats = 1;\nend\n\n% significance level for GLM stats\nif ~isfield(config,'alpha')\n    config.alpha = 0.05;\nend\n\n% multiple comparison correction procedure (correct for the number of cells\n% / neuropil which are statistically tested)\nif ~isfield(config,'multiCompareProc')\n    config.multiCompareProc = 'bonf';\nend\n% these procedures are currently implemented (beta is the corrected sig. level):\n% 'none' ... beta = alpha\n% 'bonf' ... Bonferroni correction, beta = alpha/n\n% possible future options:\n% Sidak procedure\n% FDR procedure\n\n\n\n%% Function - doGLMfit\nfunction glmStats = doGLMfit(model,data,alpha)\n[b,dev,stats] = glmfit(model,data);\n[yfit,ciL,ciU] = glmval(b,model,'identity',stats);\n% important parameters from fit\nglmStats = struct;\nglmStats.b = b;\nglmStats.dfe = stats.dfe; % degrees of freedom\nglmStats.seBeta = stats.se; % SE for beta\nglmStats.tBeta = stats.t; % t = beta ./ sqrt((sum(res.^2)/dfe))\nglmStats.pBeta = stats.p;\nglmStats.res = stats.resid;\nglmStats.yfit = yfit;\nglmStats.ciL = ciL;\nglmStats.ciU = ciU;\nif stats.p(2) <= alpha\n   glmStats.sig = 1; % reject the null hypothesis \nelse\n    glmStats.sig = 0; % accept the null hypothesis\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "analyzeHDF5.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/analyzeHDF5.m", "size": 8349, "source_encoding": "utf_8", "md5": "fb7db0df66962b7b174a9a468b6386ab", "text": "function S = analyzeHDF5(id)\n% id ... date string for the HDF5 file (e.g. 14011001) or Matlab structure from previous run\n\ndoSave = 1;\n\nif ~isstruct(id)\n    fileName = sprintf('%s.h5',id);\n    animalID = sprintf('mou_bl_%s_%s',id(1:6),id(7:8));\n    datasetID = ['/' animalID];\n    % load HDF5 and convert to data structure\n    S = loadDataFromHDF5(fileName,datasetID);\nelse\n    S = id;\n    animalID = S.animalID;\n    S = rmfield(S,'animalID');\nend\nclear id\n\nfieldsSpots = fieldnames(S);\nfor n = 1:numel(fieldsSpots) % loop over spots\n    spotID = fieldsSpots{n};\n    currentSpot = S.(fieldsSpots{n});\n    fieldsDays = fieldnames(currentSpot);\n    \n    for m = 1:numel(fieldsDays) % loop over days\n        dateID = strrep(fieldsDays{m},'x','');\n        currentS = currentSpot.(fieldsDays{m});\n        \n        fprintf('\\n\\nRunning analysis for %s %s %s\\n',animalID,spotID,dateID)\n        [caTracesMean, trialCount, tPs, perfTrue, perfShuf] = doSingleDayAnalysis(currentS);\n        S.(fieldsSpots{n}).(fieldsDays{m}).caTracesMean = caTracesMean;\n        S.(fieldsSpots{n}).(fieldsDays{m}).trialCount = trialCount;\n        S.(fieldsSpots{n}).(fieldsDays{m}).tPs = tPs;\n        S.(fieldsSpots{n}).(fieldsDays{m}).perfTrue = perfTrue;\n        S.(fieldsSpots{n}).(fieldsDays{m}).perfShuf = perfShuf;\n        \n        %{\n        % Average Neuropil signal for target and distractor\n        titleStr = sprintf('%s %s %s Avg Npil',animalID,spotID,dateID);\n        h = figure('Name',titleStr,'NumberTitle','off');\n        plot(tPs,caTracesMean{1}(end,:),'k'), hold on\n        plot(tPs,caTracesMean{2}(end,:),'r')\n        legend({sprintf('Targ (%1.0f trials)',trialCount(1)),...\n            sprintf('Dist (%1.0f trials)',trialCount(2))})\n        \n        % Average ROI traces for target and distractor\n        titleStr = sprintf('%s %s %s Avg ROI',animalID,spotID,dateID);\n        h = figure('Name',titleStr,'NumberTitle','off');\n        nROIs = size(caTracesMean{1},1)-1;\n        caTracesMeanMat = [caTracesMean{1}(1:nROIs,:); caTracesMean{2}(1:nROIs,:)];\n        caTracesMeanMat = ScaleToMinMax(caTracesMeanMat,0,1);\n        subplot(1,2,1) % target\n        imagesc(tPs,1:nROIs,caTracesMeanMat(1:nROIs,:),[0 1])\n        xlabel('Time (s)'), ylabel('ROI')\n        title(sprintf('Target (%1.0f trials)',trialCount(1)))\n        subplot(1,2,2) % distractor\n        imagesc(tPs,1:nROIs,caTracesMeanMat(nROIs+1:end,:),[0 1])\n        xlabel('Time (s)'), ylabel('ROI')\n        title(sprintf('Distractor (%1.0f trials)',trialCount(2)))\n        colormap('hot'), colorbar\n        \n        % NaiveBayes performance\n        titleStr = sprintf('%s %s %s NaiveBayes',animalID,spotID,dateID);\n        h = figure('Name',titleStr,'NumberTitle','off');\n        shadedErrorBar(tPs,nanmean(perfShuf,1),nanstd(perfShuf,[],1),'k',1); hold on\n        shadedErrorBar(tPs,nanmean(perfTrue,1),nanstd(perfTrue,[],1),'r',1);\n        set(gca,'xlim',[min(tPs) max(tPs)])\n        legend({'Shuf','True'}), xlabel('Time (s)'), ylabel('Classification Perf. +- SD (%)')\n        %}\n    end\nend\nS.animalID = animalID;\n\n% if doSave\n%     SaveAndCloseFigures\n% end\n\nend\n\n%% Function - doSingleDayAnalysis\nfunction [caTracesMean, trialCount, tPs, perfTrue, perfShuf] = doSingleDayAnalysis(S)\n\nimgRate = 76.9; % in Hz\npsTimes = [-0.5 2]; % in s\npsTimesIx = round(psTimes.*imgRate);\n\nstim = S.behav.stim;\ntrials = numel(stim);\n\n% figure out target and non-target\ntargetNontarget = [];\nfor n = 1:numel(S.behav.target)\n    if ~S.behav.target{n}\n        continue\n    end\n    if stim{n} == 1\n        targetNontarget = [1 2];\n    else\n        targetNontarget = [2 1];\n    end\n    break\nend\nfprintf('Target: stim %1.0f\\n',targetNontarget(1))\nfprintf('Non-Target: stim %1.0f\\n',targetNontarget(2))\n\ncaTracesTrials = cell(1,2);\nfor t = 1:trials\n    caTraces = S.caTraces{t};\n    imgDelay = S.behav.imgDelays{t};\n    stimStartTime = S.behav.stimStartTime{t}-imgDelay;\n    timeCa = (1:size(caTraces,2))./imgRate;\n    stimStartIx = find(timeCa>=stimStartTime,1,'first');\n    psStartIx = stimStartIx + psTimesIx(1);\n    if psStartIx < 1\n        error('psStart too early on trial %1.0f',t)\n    end\n    psStopIx = stimStartIx + psTimesIx(2);\n    if psStopIx > size(caTraces,2)\n%         warning('psStop too late on trial %1.0f. Skipping ...',t)\n        continue\n    end\n    if t == 1\n        tPs = ((psStartIx:psStopIx)-stimStartIx)./imgRate;\n    end\n    if isempty(caTracesTrials{stim{t}})\n        caTracesTrials{stim{t}} = caTraces(:,psStartIx:psStopIx);\n    else\n        caTracesTrials{stim{t}} = cat(3, caTracesTrials{stim{t}}, caTraces(:,psStartIx:psStopIx));\n    end\nend\n\nstimStartIx = find(tPs>=0,1,'first');\nfor n = 1:numel(caTracesTrials)\n    data = nanmean(caTracesTrials{n},3);\n    % baseline normalization / filter\n    for m = 1:size(data,1)\n        data(m,:) = data(m,:) - nanmean(data(m,1:stimStartIx));\n        data(m,:) = sgolayfilt(data(m,:),3,11);\n    end\n    oriData = caTracesTrials{n};\n    nPSFramesDS = round(size(oriData, 2) / 4) + 1;\n    DSData = nan(size(oriData, 1), nPSFramesDS, size(oriData, 3));\n    for iROI = 1 : size(oriData, 1);\n        for iTrial = 1 : size(oriData, 3);\n            [DSData(iROI, :, iTrial), tPs] = interp1DS(imgRate, imgRate / 4, squeeze(oriData(iROI, :, iTrial)));\n        end;\n    end;\n    caTracesMean{1,targetNontarget(n)} = data;\n    trialCount(1,targetNontarget(n)) = size(caTracesTrials{n},3);\n%     decodeData{1,targetNontarget(n)} = oriData;\n    decodeData{1,targetNontarget(n)} = DSData;\nend\n\n% time-resolved decoding analysis (target vs. distractor) for neuron population\nholdOut = 0.2; % fraction of trials to leave out\n% iters = 50; % number of iterations\niters = 100; % number of iterations\ndecodeTimepoints = size(decodeData{1},2); % consider binning to reduce timepoints\nperfTrue = zeros(iters,decodeTimepoints);\nperfShuf = zeros(iters,decodeTimepoints);\ndispPercent = 10;\nfprintf('NaiveBayes classification: 00%% done')\nfor t = 1:decodeTimepoints\n    popData = []; labels = [];\n    for stim = 1:numel(decodeData)\n        for trial = 1:size(decodeData{stim},3)\n            popData = [popData; decodeData{stim}(1:end-1,t,trial)']; % last row is neuropil\n            labels(end+1,1) = stim;\n        end\n    end\n    perfTrue(1:iters,t) = doClassify(popData,labels,holdOut,iters,0);\n    perfShuf(1:iters,t) = doClassify(popData,labels,holdOut,iters,1);\n    percentDone = round(t/decodeTimepoints*100);\n    if percentDone >= dispPercent\n        fprintf('\\b\\b\\b\\b\\b\\b\\b\\b%1.0f%% done',percentDone)\n        dispPercent = dispPercent + 10;\n    end\nend\nfprintf('\\n\\n')\n\nend\n\n\n%% Function - doClassify\nfunction varargout = doClassify(data,labels,holdOut,iters,shuffled)\nif ~any(data)\n    for n = 1:nargout\n        varargout{n} = NaN;\n    end\n    return\nend\nperf = zeros(iters,1); postpCell = cell(iters,1);\npredictedCell = cell(iters,1); testlabelsCell = cell(iters,1);\n% compute the max. number of unique hold-outs (should be > iters)\nholdOutN = ceil(numel(labels)*holdOut);\nwarning('off','MATLAB:nchoosek:LargeCoefficient')\nmaxHoldOuts = nchoosek(numel(labels),holdOutN);\nwarning('on','MATLAB:nchoosek:LargeCoefficient')\nif maxHoldOuts < iters\n    warning('Max. number of unique hold-outs is %1.0f and iterations are %1.0f.',...\n        maxHoldOuts,iters)\nend\nparfor n = 1:iters\n    if shuffled\n        labels2 = labels(randperm(numel(labels)));\n    else\n        labels2 = labels;\n    end\n    c = cvpartition(labels2,'holdout',holdOut);\n    trainData = data(c.training,:); trainLabels = labels2(c.training);\n    if ~any(trainData)\n        perf(n) = NaN;\n        continue\n    end\n    testData = data(c.test,:); testLabels = labels2(c.test);\n    model = NaiveBayes.fit(trainData,trainLabels);\n    [postP,predicted,logp] = posterior(model,testData);\n    perf(n) = (numel(find(predicted==testLabels))./numel(testLabels)).*100;\n    postpCell{n} = postP;\n    predictedCell{n} = predicted;\n    testlabelsCell{n} = testLabels;\nend\n\nvarargout{1} = perf;\n\nif nargout > 1\n    varargout{2} = predictedCell;\n    varargout{3} = testlabelsCell;\n    varargout{4} = postpCell;\nend\n\nend\n\n\n%% Function - Save and close figures\nfunction SaveAndCloseFigures\nwhile numel(findobj) > 1\n    set(findall(gcf,'Interpreter','Tex'),'Interpreter','None')\n    makePrettyFigure(gcf);\n    saveName = get(gcf,'Name');\n    saveas(gcf,strrep(saveName,' ','-'))\n    close(gcf)\nend\nend\n\n\n\n\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "treeBaggerClassifier.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/regressionClassifier/treeBaggerClassifier.m", "size": 4908, "source_encoding": "utf_8", "md5": "49f4179317c4ba59e28b5720c576ef65", "text": "function [rmse_true,rmse_shuf,pVal] = ...\n    treeBaggerClassifier(neuronData,stimData)\n% neuronData ... trial x neuron cell array of calcium data\n% each calcium timeseries must be column vector\n% stimData ... trial x 1 cell array of stimulus data for each trial\n% stim vector must be at frame rate and column vector\n\nntrees = 32; % number of trees in forest\nbootSamples = 100; % number of bootstrap samples for comparing true and shuffled rmse\ndoPlot = 1; % plot classifier performance?\n\nneurons = size(neuronData,2);\ntrials = size(neuronData,1);\n% trials = 10; % only a subset to save time\n% neurons = 5;\n\nleaveOutTrials = 2; % how many trials to leave out\nmaxCrossVals = 100; % max. number of cross-validations\n\nleaveOut = uniqueCombinations(1:trials,leaveOutTrials);\nif size(leaveOut,1) > maxCrossVals\n    fprintf('\\nRandomly selecting %1.0f out of %1.0f cross-validations!',...\n        maxCrossVals,size(leaveOut,1))\n    idx = randperm(size(leaveOut,1));\n    leaveOut = leaveOut(idx(1:maxCrossVals),:);\nend\n\nsgolayOrder = 1;\nsgolaySpan = 5; % in frames\nframeShift = [-20:2:20];\nframeShift(frameShift==0) = [];\n\n% options for treebagger\nopts = statset('UseParallel','always');\n\nrmse_true = zeros(trials,neurons);\nrmse_shuf = zeros(trials,neurons);\npVal = zeros(1,neurons);\nfor n = 1:neurons\n    fprintf('\\nNeuron %1.0f\\n',n)\n    % classify by leaving out trials and use them for cross-validation\n    for m = 1:size(leaveOut,1)\n        trainCa = []; trainStim = [];\n        testCa = []; testStim = [];\n        for t = 1:trials\n            currentCa = neuronData{t,n};\n            currentStim = stimData{t};\n            currentCa = sgolayfilt(currentCa,sgolayOrder,sgolaySpan);\n            [currentCa,currentStim] = ...\n                shiftFrames(currentCa,currentStim,frameShift);\n            if isempty(find(leaveOut(m,:)==t,1))\n                % training trial\n                trainCa = [trainCa; currentCa];\n                trainStim = [trainStim; currentStim];\n            else\n                % test trial\n                testCa = [testCa; currentCa];\n                testStim = [testStim; currentStim];\n            end\n        end\n        % build tree with training data / true labels\n        rtree = TreeBagger(ntrees,trainCa,trainStim,...\n            'Method','regression','Options',opts);\n        % predict with left out trial\n        stimPredict = predict(rtree,testCa);\n        % compute rmse with stim\n        rmse_true(m,n) = mean(sqrt((stimPredict-testStim).^2));\n        \n        % build tree with training data / shuffled labels\n        trainStim = trainStim(randperm(numel(trainStim)));\n        rtree = TreeBagger(ntrees,trainCa,trainStim,...\n            'Method','regression','Options',opts);\n        % predict with left out trial\n        stimPredict = predict(rtree,testCa);\n        % compute rmse with stim\n        rmse_shuf(m,n) = mean(sqrt((stimPredict-testStim).^2));\n    end\n    % statistical comparison of performance with true and shuffled labels\n    pVal(n) = doStats(rmse_true(:,n),rmse_shuf(:,n),bootSamples);\nend\n\n\n\n\nif doPlot\n    hErr = errorbar([1:neurons],mean(rmse_true),std(rmse_true),'r');\n    removeErrorBarEnds(hErr); hold on\n    hErr = errorbar([1:neurons],mean(rmse_shuf),std(rmse_shuf),'k');\n    removeErrorBarEnds(hErr);\n    set(gca,'xtick',[1:neurons],'xticklabel',[1:neurons])\n    ylabel('RMSE'),xlabel('Neuron')\n    legend({'true labels','shuffled labels'})\nend\n\n%% Function - shift frames to create additional variables\nfunction [Xout,stim] = shiftFrames(X,stim,shift)\nXout = X;\nfor n = 1:numel(shift)\n    Xnew = circshift(X,shift(n));\n    Xout = [Xout Xnew];\nend\nif min(shift) < 0\n    Xout(end+min(shift)+1,:) = [];\n    stim(end+min(shift)+1,:) = [];\nend\nif max(shift) > 0\n   Xout(1:max(shift),:) = [];\n   stim(1:max(shift),:) = [];\nend\n\n%% Function - Statistical comparison of true and shuffled rmse\nfunction p = doStats(a,b,samples)\n% use a bootstrap approach to test if a-b < 0\n% this is a one-tailed test\nd = a-b;\n% mean_boot = bootstrp(samples,@mean,d);\n% sd_boot = bootstrp(samples,@std,d);\n% p = 1 - normcdf(0,mean(mean_boot),mean(sd_boot));\n\n% or a plain t-test\n[~,p] = ttest(a,b,0.05,'left');\n\n\nfunction out=uniqueCombinations(choicevec,choose)\n% from mapping toolbox function COMBNTNS\nchoicevec = choicevec(:);       %  Enforce a column vector\nchoices=length(choicevec);\n%  If the number of choices and the number to choose\n%  are the same, choicevec is the only output.\nif choices==choose(1)\n\tout=choicevec';\n%  If being chosen one at a time, return each element of\n%  choicevec as its own row\nelseif choose(1)==1\n\tout=choicevec;\n%  Otherwise, recur down to the level at which one such\n%  condition is met, and pack up the output as you come out of\n%  recursion.\nelse\n\tout = [];\n\tfor i=1:choices-choose(1)+1\n\t\ttempout=uniqueCombinations(choicevec(i+1:choices),choose(1)-1);\n\t\tout=[out; choicevec(i)*ones(size(tempout,1),1)\ttempout];\n\tend\nend\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "treeBaggerClassifier.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/AwakeWhisk_Tina/treeBaggerClassifier.m", "size": 4923, "source_encoding": "utf_8", "md5": "4869515cb11a96bbbae85e3de0522e59", "text": "function [rmse_true,rmse_shuf,pVal] = ...\n    treeBaggerClassifier(neuronData,stimData)\n% neuronData ... trial x neuron cell array of calcium data\n% each calcium timeseries must be column vector\n% stimData ... trial x 1 cell array of stimulus data for each trial\n% stim vector must be at frame rate and column vector\n\nntrees = 50; % number of trees in forest\ndoPlot = 1; % plot classifier performance?\n\nneurons = size(neuronData,2);\ntrials = size(neuronData,1);\n% trials = 10; % only a subset to save time\n% neurons = 5;\n\nleaveOutTrials = 3; % how many trials to leave out\nmaxCrossVals = 24; % max. number of cross-validations\n\nleaveOut = uniqueCombinations(1:trials,leaveOutTrials);\nleaveOut = leaveOut(randperm(size(leaveOut,1))',:);\nif size(leaveOut,1) > maxCrossVals\n    fprintf('\\nRandomly selecting %1.0f out of %1.0f cross-validations!',...\n        maxCrossVals,size(leaveOut,1))\n    idx = randperm(size(leaveOut,1));\n    leaveOut = leaveOut(idx(1:maxCrossVals),:);\nend\n\nsgolayOrder = 1;\nsgolaySpan = 0; % in frames (0 ... no filter)\nframeShift = [-10:2:10]; %\nframeShift(frameShift==0) = [];\n\n% options for treebagger\nopts = statset('UseParallel','always');\n\nrmse_true = zeros(trials,neurons);\nrmse_shuf = zeros(trials,neurons);\npVal = zeros(1,neurons);\nfor n = 1:neurons\n    fprintf('\\nNeuron %1.0f\\n',n)\n    % classify by leaving out trials and use them for cross-validation\n    for m = 1:size(leaveOut,1)\n        trainCa = []; trainStim = [];\n        testCa = []; testStim = [];\n        for t = 1:trials\n            currentCa = neuronData{t,n};\n            currentStim = stimData{t};\n            if sgolaySpan\n                currentCa = sgolayfilt(currentCa,sgolayOrder,sgolaySpan);\n            end\n            [currentCa,currentStim] = ...\n                shiftFrames(currentCa,currentStim,frameShift);\n            if isempty(find(leaveOut(m,:)==t,1))\n                % training trial\n                trainCa = [trainCa; currentCa];\n                trainStim = [trainStim; currentStim];\n            else\n                % test trial\n                testCa = [testCa; currentCa];\n                testStim = [testStim; currentStim];\n            end\n        end\n        % build tree with training data / true labels\n        rtree = TreeBagger(ntrees,trainCa,trainStim,...\n            'Method','regression','Options',opts);\n        % predict with left out trial\n        stimPredict = predict(rtree,testCa);\n        % compute rmse with stim\n        rmse_true(m,n) = mean(sqrt((stimPredict-testStim).^2));\n        \n        % build tree with training data / shuffled labels\n        trainStim = trainStim(randperm(numel(trainStim)));\n        rtree = TreeBagger(ntrees,trainCa,trainStim,...\n            'Method','regression','Options',opts);\n        % predict with left out trial\n        stimPredict = predict(rtree,testCa);\n        % compute rmse with stim\n        rmse_shuf(m,n) = mean(sqrt((stimPredict-testStim).^2));\n    end\n    % statistical comparison of performance with true and shuffled labels\n    pVal(n) = doStats(rmse_true(:,n),rmse_shuf(:,n));\nend\n\nif doPlot\n    hErr = errorbar([1:neurons],mean(rmse_true),std(rmse_true),'r');\n    removeErrorBarEnds(hErr); hold on\n    hErr = errorbar([1:neurons],mean(rmse_shuf),std(rmse_shuf),'k');\n    removeErrorBarEnds(hErr);\n    set(gca,'xtick',[1:neurons],'xticklabel',[1:neurons])\n    ylabel('RMSE'),xlabel('Neuron')\n    legend({'true labels','shuffled labels'})\nend\n\n%% Function - shift frames to create additional variables\nfunction [Xout,stim] = shiftFrames(X,stim,shift)\nXout = X;\nfor n = 1:numel(shift)\n    Xnew = circshift(X,shift(n));\n    Xout = [Xout Xnew];\nend\nif min(shift) < 0\n    Xout(end+min(shift)+1,:) = [];\n    stim(end+min(shift)+1,:) = [];\nend\nif max(shift) > 0\n   Xout(1:max(shift),:) = [];\n   stim(1:max(shift),:) = [];\nend\n\n%% Function - Statistical comparison of true and shuffled rmse\nfunction p = doStats(a,b)\n% use a bootstrap approach to test if a-b < 0\n% this is a one-tailed test\nd = a-b;\n% mean_boot = bootstrp(samples,@mean,d);\n% sd_boot = bootstrp(samples,@std,d);\n% p = 1 - normcdf(0,mean(mean_boot),mean(sd_boot));\n\n% or a plain t-test\n[~,p] = ttest(a,b,0.05,'left');\n\n\nfunction out=uniqueCombinations(choicevec,choose)\n% stolen from mapping toolbox function COMBNTNS\nchoicevec = choicevec(:);       %  Enforce a column vector\nchoices=length(choicevec);\n%  If the number of choices and the number to choose\n%  are the same, choicevec is the only output.\nif choices==choose(1)\n\tout=choicevec';\n%  If being chosen one at a time, return each element of\n%  choicevec as its own row\nelseif choose(1)==1\n\tout=choicevec;\n%  Otherwise, recur down to the level at which one such\n%  condition is met, and pack up the output as you come out of\n%  recursion.\nelse\n\tout = [];\n\tfor i=1:choices-choose(1)+1\n\t\ttempout=uniqueCombinations(choicevec(i+1:choices),choose(1)-1);\n\t\tout=[out; choicevec(i)*ones(size(tempout,1),1)\ttempout];\n\tend\nend\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "treeBaggerClassifierTrainTest.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/AwakeWhisk_Tina/treeBaggerClassifierTrainTest.m", "size": 7127, "source_encoding": "utf_8", "md5": "02182e3a0bb5570bd5f6b45cac09cf18", "text": "function [mseTrue, mseShuf] = treeBaggerClassifierTrainTest(neuronTrain,neuronTest,stimTrain,stimTest)\n% in this file, train and test data sets are explicitely specified (e.g. for chronic analysis across\n% sessions)\n% neuronTrain ... trial x neuron cell array of training calcium data\n% neuronTest ... trial x neuron cell array of testing calcium data\n% the number of neurons MUST be identical in train and test data\n% stimTrain ... trial x 1 cell array of stimulus data for each trial (same number of trials as in\n% neuronTrain)\n% stimTest ... trial x 1 cell array of stimulus data for each trial (same number of trials as in\n% neuronTest)\n\nmaxNeurons = inf; % save time during debug\n\n% hold-out a certain fraction of data on multiple iterations\nholdOutF = 0.2;\n\niter = 5; % number of iterations with different hold outs --> error bars on predictions\n\n% options for treebagger\nopts = statset('UseParallel','always');\n\nntrees = 30;\nregressionMethod = 'bag'; % 'lsboost' or 'bag'\n\nneurons = size(neuronTrain,2);\n\nif size(neuronTest,2) ~= neurons\n   error('Must have the same number of neurons in train and test data sets!') \nend\n\ntrialsTrain = size(neuronTrain,1);\ntrialsTest = size(neuronTest,1);\n\nif maxNeurons < neurons\n   neurons = maxNeurons;\nend\n\n% concatenate predictors and response data\n% predictors X ... activity of different neurons (or same neuron at different lags)\n% response data Y ... the parameter to be explained by the neural activity (e.g. whisk vector)\nXtrain = []; Xtest = [];\nYtrain = []; Ytest = [];\nfor n = 1:neurons\n    vTrain = []; vTest = [];\n    for t = 1:trialsTrain\n        % TODO: perhaps high-pass filter of predictors and response data\n        if n == 1\n           Ytrain = [Ytrain; stimTrain{t}];\n        end\n        vTrain = [vTrain; neuronTrain{t,n}];\n    end\n    for t = 1:trialsTest\n        if n == 1\n           Ytest = [Ytest; stimTest{t}];\n        end\n        vTest = [vTest; neuronTest{t,n}];\n    end\n    Xtrain = [Xtrain, vTrain];\n    Xtest = [Xtest, vTest];\nend\n\nwarning('off','all')\n\n%% Population prediction\n[mseTruePop,mseShufPop] = doClassify(Xtrain,Xtest,Ytrain,Ytest,ntrees,regressionMethod,holdOutF,opts,iter);\n\n% figure('Name','Population Prediction');\n% hErr = errorbar(nanmean(lossTrue),std(lossTrue),'r');\n% removeErrorBarEnds(hErr); hold on\n% hErr = errorbar(nanmean(lossShuf),std(lossShuf),'k'); removeErrorBarEnds(hErr);\n% xlabel('Number of trees'); ylabel('Test classification MSE +- SD');\n% title(regressionMethod)\n% legend({'True','Shuffled'})\n\n% mseTrue = lossTrue(:,end); mseShuf = lossShuf(:,end);\n% \n% dPrimePop = ((nanmean(mseShuf)-nanmean(mseTrue))) ./ ...\n%         sqrt((nanvar(mseShuf)+nanvar(mseTrue))./2);\n\nplotSingleNeuron = 0;\n%% Single-neuron prediction\ndPrimeSingleCell = zeros(1,neurons);\nfor n = 1:neurons\n    currentXtrain = Xtrain(:,n); currentXtest = Xtest(:,n);\n    [mseTrue{n},mseShuf{n}] = doClassify(currentXtrain,currentXtest,Ytrain,Ytest,ntrees,...\n        regressionMethod,holdOutF,opts,iter);\n%     mseTrue = lossTrue(:,end); mseShuf = lossShuf(:,end);\n%     dPrimeSingleCell(n) = ((nanmean(mseShuf)-nanmean(mseTrue))) ./ ...\n%         sqrt((nanvar(mseShuf)+nanvar(mseTrue))./2);\n%     if plotSingleNeuron\n%         figure('Name',sprintf('Prediction Neuron %1.0f',n));\n%         hErr = errorbar(nanmean(lossTrue),std(lossTrue),'r');\n%         removeErrorBarEnds(hErr); hold on\n%         hErr = errorbar(nanmean(lossShuf),std(lossShuf),'k'); removeErrorBarEnds(hErr);\n%         xlabel('Number of trees'); ylabel('Test classification MSE +- SD');\n%         title(sprintf('Prediction Neuron %1.0f - %s',n,regressionMethod))\n%         legend({'True','Shuffled'})\n%     end\nend\n% dprime = [dPrimeSingleCell, dPrimePop];\n\nmseTrue{n+1} = mseTruePop; mseShuf{n+1} = mseShufPop;\n\nwarning('on','all')\n\nend\n\n\nfunction [lossTrue,lossShuf] = doClassify(Xtrain,Xtest,Ytrain,Ytest,ntrees,method,holdOutF,opts,iter)\n\nfor i = 1:iter\n    \n    % shuffling\n    YtrainShuff = Ytrain(randperm(numel(Ytrain)));\n    YtestShuff = Ytest(randperm(numel(Ytest)));\n    \n    cvpartTrain = cvpartition(Ytrain,'holdout',holdOutF);\n    cvpartTest = cvpartition(Ytest,'holdout',holdOutF);\n    \n    % select data used for training\n    currentXtrain = Xtrain(training(cvpartTrain),:);\n    currentYtrain = Ytrain(training(cvpartTrain),:);\n    \n    % testing is done on the larger fraction of cvpartTest holdouts, i.e. the training fraction\n    currentXtest = Xtest(training(cvpartTest),:);\n    currentYtest = Ytest(training(cvpartTest),:);\n    \n    % select shuffled data\n    currentYtrainShuf = YtrainShuff(training(cvpartTrain),:);\n    currentYtestShuf = YtestShuff(training(cvpartTest),:);\n    \n    switch method\n        case 'bag'\n            modelTrue = TreeBagger(ntrees,currentXtrain,currentYtrain,...\n                'Method','regression','Options',opts);\n            modelShuf = TreeBagger(ntrees,currentXtrain,currentYtrainShuf,...\n                'Method','regression','Options',opts);\n            lossTrue(i,:) = error(modelTrue,currentXtest,currentYtest,'mode','cumulative');\n            lossShuf(i,:) = error(modelShuf,currentXtest,currentYtestShuf,'mode','cumulative');\n        case 'lsboost'\n            modelTrue = fitensemble(currentXtrain,currentYtrain,method,ntrees,'Tree',...\n                'type','regression');\n            modelShuf = fitensemble(currentXtrain,currentYtrainShuf,method,ntrees,'Tree',...\n                'type','regression');\n            lossTrue(i,:) = loss(modelTrue,currentXtest,currentYtest,'mode','cumulative');\n            lossShuf(i,:) = loss(modelShuf,currentXtest,currentYtestShuf,'mode','cumulative');\n    end\nend\nend\n\n\n%% Function - shift frames to create additional variables\nfunction [Xout,stim] = shiftFrames(X,stim,shift)\nXout = X;\nfor n = 1:numel(shift)\n    Xnew = circshift(X,shift(n));\n    Xout = [Xout Xnew];\nend\nif min(shift) < 0\n    Xout(end+min(shift)+1,:) = [];\n    stim(end+min(shift)+1,:) = [];\nend\nif max(shift) > 0\n   Xout(1:max(shift),:) = [];\n   stim(1:max(shift),:) = [];\nend\nend\n\n%% Function - Statistical comparison of true and shuffled rmse\nfunction p = doStats(a,b)\n% use a bootstrap approach to test if a-b < 0\n% this is a one-tailed test\nd = a-b;\n% mean_boot = bootstrp(samples,@mean,d);\n% sd_boot = bootstrp(samples,@std,d);\n% p = 1 - normcdf(0,mean(mean_boot),mean(sd_boot));\n\n% or a plain t-test\n[~,p] = ttest(a,b,0.05,'left');\nend\n\nfunction out=uniqueCombinations(choicevec,choose)\n% stolen from mapping toolbox function COMBNTNS\nchoicevec = choicevec(:);       %  Enforce a column vector\nchoices=length(choicevec);\n%  If the number of choices and the number to choose\n%  are the same, choicevec is the only output.\nif choices==choose(1)\n\tout=choicevec';\n%  If being chosen one at a time, return each element of\n%  choicevec as its own row\nelseif choose(1)==1\n\tout=choicevec;\n%  Otherwise, recur down to the level at which one such\n%  condition is met, and pack up the output as you come out of\n%  recursion.\nelse\n\tout = [];\n\tfor i=1:choices-choose(1)+1\n\t\ttempout=uniqueCombinations(choicevec(i+1:choices),choose(1)-1);\n\t\tout=[out; choicevec(i)*ones(size(tempout,1),1)\ttempout];\n\tend\nend\nend\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "treeBaggerClassifier2.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/AwakeWhisk_Tina/treeBaggerClassifier2.m", "size": 7070, "source_encoding": "utf_8", "md5": "718e6cc19869446a49a17adf092dde6e", "text": "function varargout = treeBaggerClassifier2(neuronData,stimData)\n% neuronData ... trial x neuron cell array of calcium data\n% each calcium timeseries must be column vector\n% stimData ... trial x 1 cell array of stimulus data for each trial\n% stim vector must be at frame rate and column vector\n\nf = 7.81;\n\nmaxTrials = Inf; % only a subset to save time\nmaxNeurons = Inf;\n\n% hold-out a certain fraction of data for testing\nholdOutF = 0.2; % e.g. 80% used for training\n\n% iter = 10; % number of iterations with different hold outs --> error bars on predictions\n\n% options for treebagger\nopts = statset('UseParallel','always');\n\nntrees = 30;\nregressionMethod = 'bag'; % 'lsboost' or 'bag'\n\nneurons = size(neuronData,2);\ntrials = size(neuronData,1);\n\nif maxNeurons < neurons\n   neurons = maxNeurons;\nend\n\nif maxTrials < trials\n   trials = maxTrials;\nend\n\n% concatenate predictors and response data\n% predictors X ... activity of different neurons (or same neuron at different lags)\n% response data Y ... the parameter to be explained by the neural activity (e.g. whisk vector)\nX = []; Y = [];\nfor n = 1:neurons\n    v = [];\n    for t = 1:trials\n        % TODO: perhaps high-pass filter of predictors and response data\n        if n == 1\n            Y = [Y; preprocVector(stimData{t})];\n        end\n        v = [v; preprocVector(neuronData{t,n})];\n    end\n    X = [X, v];\nend\n\nwarning('off','all')\n\n%% Population prediction\n[lossTrue,lossShuf,modelTrue,modelShuf] = doClassify(X,Y,ntrees,regressionMethod,holdOutF,opts);\n\n% predicted responses\nYpredict = oobPredict(modelTrue);\n\n% plot predictions on trial 1 (really should not be included in training)\nneuronData_trial1 = cell2mat(neuronData(1,:));\nfor n = 1:size(neuronData_trial1,2)\n    neuronData_trial1(:,n) = preprocVector(neuronData_trial1(:,n));\nend\nstimData_trial1 = preprocVector(stimData{1});\nstimData_predict_true = predict(modelTrue,neuronData_trial1);\nstimData_predict_shuf = predict(modelShuf,neuronData_trial1);\nt = (1:numel(stimData_trial1))./f;\nfigure\nplot(t,stimData_trial1,'k'), hold on\nplot(t,stimData_predict_true,'r')\nlegend({'Whisk','Predicted'})\nxlabel('Time / s')\n\nwarning('on','all')\n\nvarargout{1} = lossTrue(end);\nvarargout{2} = lossShuf(end);\n\n\n% figure('Name','Population Prediction');\n% hErr = errorbar(nanmean(lossTrue),std(lossTrue),'r');\n% removeErrorBarEnds(hErr); hold on\n% hErr = errorbar(nanmean(lossShuf),std(lossShuf),'k'); removeErrorBarEnds(hErr);\n% xlabel('Number of trees'); ylabel('Test classification MSE +- SD');\n% title(regressionMethod)\n% legend({'True','Shuffled'})\n\n% mseTrue = lossTrue(:,end); mseShuf = lossShuf(:,end);\n% \n% dPrimePop = ((nanmean(mseShuf)-nanmean(mseTrue))) ./ ...\n%         sqrt((nanvar(mseShuf)+nanvar(mseTrue))./2);\n% \n% plotSingleNeuron = 0;\n% %% Single-neuron prediction\n% dPrimeSingleCell = zeros(1,size(X,2));\n% for n = 1:size(X,2)\n%     [lossTrue,lossShuf] = doClassify(X(:,n),Y,ntrees,regressionMethod,holdOutF,opts,iter);\n%     mseTrue = lossTrue(:,end); mseShuf = lossShuf(:,end);\n%     dPrimeSingleCell(n) = ((nanmean(mseShuf)-nanmean(mseTrue))) ./ ...\n%         sqrt((nanvar(mseShuf)+nanvar(mseTrue))./2);\n%     if plotSingleNeuron\n%         figure('Name',sprintf('Prediction Neuron %1.0f',n));\n%         hErr = errorbar(nanmean(lossTrue),std(lossTrue),'r');\n%         removeErrorBarEnds(hErr); hold on\n%         hErr = errorbar(nanmean(lossShuf),std(lossShuf),'k'); removeErrorBarEnds(hErr);\n%         xlabel('Number of trees'); ylabel('Test classification MSE +- SD');\n%         title(sprintf('Prediction Neuron %1.0f - %s',n,regressionMethod))\n%         legend({'True','Shuffled'})\n%     end\n% end\n% dprime = [dPrimeSingleCell, dPrimePop];\n\n\n\nend\n\nfunction v = preprocVector(v)\n\n% high-pass filter\n% p = polyfit([1:numel(v)]',v,2);\n% v = v - polyval(p,[1:numel(v)]');\n\n% low-pass filter\nv = sgolayfilt(v,2,5);\n\nv = ScaleToMinMax(v,0,1);\n\n\nend\n\nfunction [lossTrue,lossShuf,modelTrue,modelShuf] = doClassify(X,Y,ntrees,regressionMethod,holdOutF,opts)\n\n% cvpart = cvpartition(Y,'holdout',holdOutF);\n% Xtrain = X(training(cvpart),:);\n% Ytrain = Y(training(cvpart),:);\n% Xtest = X(test(cvpart),:);\n% Ytest = Y(test(cvpart),:);\n% \nYshuff = Y(randperm(numel(Y)));\n% YtrainShuff = Yshuff(training(cvpart),:);\n% YtestShuff = Yshuff(test(cvpart),:);\n\n% treebagger needs no hold-out (get errors from out-of-bag classifications)\nmodelTrue = TreeBagger(ntrees,X,Y,...\n    'Method','regression','Options',opts,'minleaf',1,'oobpred','on');\nmodelShuf = TreeBagger(ntrees,X,Yshuff,...\n    'Method','regression','Options',opts,'minleaf',1,'oobpred','on');\nlossTrue = error(modelTrue,X,Y,'mode','cumulative');\nlossShuf = error(modelShuf,X,Y,'mode','cumulative');\n\n% switch regressionMethod\n%     case 'bag'\n%         modelTrue = TreeBagger(ntrees,Xtrain,Ytrain,...\n%             'Method','regression','Options',opts,'minleaf',1,'oobpred','on');\n%         modelShuf = TreeBagger(ntrees,Xtrain,YtrainShuff,...\n%             'Method','regression','Options',opts,'minleaf',1,'oobpred','on');\n%         lossTrue = error(modelTrue,Xtest,Ytest,'mode','cumulative');\n%         lossShuf = error(modelShuf,Xtest,YtestShuff,'mode','cumulative');\n%     case 'lsboost'\n%         modelTrue = fitensemble(Xtrain,Ytrain,regressionMethod,ntrees,'Tree',...\n%             'type','regression');\n%         modelShuf = fitensemble(Xtrain,YtrainShuff,regressionMethod,ntrees,'Tree',...\n%             'type','regression');\n%         lossTrue = loss(modelTrue,Xtest,Ytest,'mode','cumulative');\n%         lossShuf = loss(modelShuf,Xtest,YtestShuff,'mode','cumulative');\n% end\n\nend\n\n\n%% Function - shift frames to create additional variables\nfunction [Xout,stim] = shiftFrames(X,stim,shift)\nXout = X;\nfor n = 1:numel(shift)\n    Xnew = circshift(X,shift(n));\n    Xout = [Xout Xnew];\nend\nif min(shift) < 0\n    Xout(end+min(shift)+1,:) = [];\n    stim(end+min(shift)+1,:) = [];\nend\nif max(shift) > 0\n   Xout(1:max(shift),:) = [];\n   stim(1:max(shift),:) = [];\nend\nend\n\n%% Function - Statistical comparison of true and shuffled rmse\nfunction p = doStats(a,b)\n% use a bootstrap approach to test if a-b < 0\n% this is a one-tailed test\nd = a-b;\n% mean_boot = bootstrp(samples,@mean,d);\n% sd_boot = bootstrp(samples,@std,d);\n% p = 1 - normcdf(0,mean(mean_boot),mean(sd_boot));\n\n% or a plain t-test\n[~,p] = ttest(a,b,0.05,'left');\nend\n\nfunction out=uniqueCombinations(choicevec,choose)\n% stolen from mapping toolbox function COMBNTNS\nchoicevec = choicevec(:);       %  Enforce a column vector\nchoices=length(choicevec);\n%  If the number of choices and the number to choose\n%  are the same, choicevec is the only output.\nif choices==choose(1)\n\tout=choicevec';\n%  If being chosen one at a time, return each element of\n%  choicevec as its own row\nelseif choose(1)==1\n\tout=choicevec;\n%  Otherwise, recur down to the level at which one such\n%  condition is met, and pack up the output as you come out of\n%  recursion.\nelse\n\tout = [];\n\tfor i=1:choices-choose(1)+1\n\t\ttempout=uniqueCombinations(choicevec(i+1:choices),choose(1)-1);\n\t\tout=[out; choicevec(i)*ones(size(tempout,1),1)\ttempout];\n\tend\nend\nend\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "importData_textureDisc.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/TextureDisc_Jerry/importData_textureDisc.m", "size": 1991, "source_encoding": "utf_8", "md5": "9965ffb89b724ddee6083c3d90aa97cf", "text": "function Sout = importData_textureDisc(matfile)\n\nload(matfile)\n\nroiLabel = Ca.roiLabel;\n\n% animal / session ID\nid = roiLabel{1,1};\nidx = strfind(id,'-');\nSout.info.animal = id(1:idx(1)-1);\nSout.info.session = str2num(id(idx(1)+1:idx(2)-1));\nSout.info.sample_rate = Ca.sample_rate;\nSout.info.roiLabel = roiLabel;\n\ntrialID = Ca.Trial_Name; % the HS filename for each imaging trial\n\n% build the texture trial vector by searching through {summary} and finding\n% trialID --> code textures 1 - 4 (textures 1, 3, 5, 7)\nSout.info.trialVectors.texture = findTrialTexture(trialID,summary);\n\n% DRR trial x time matrix for each neuron\ntimepoints = size(Ca.dRR{1,1},2);\nfor roi = 1:numel(roiLabel)\n    id = roiLabel{roi,1};\n    idx = strfind(id,'-');\n    id = id(idx(2)+1:end); % for field name\n    drr = zeros(numel(trialID),timepoints);\n    for trial = 1:numel(trialID)\n        if isempty(Ca.dRR{roi,trial})\n            drr(trial,:) = NaN;\n        else\n            drr(trial,:) = Ca.dRR{roi,trial}(1,:);\n        end\n    end\n    Sout.(id).dRR = drr;\nend\n\n\n%% Function - findTrialTexture\nfunction v = findTrialTexture(trialID,summary)\ntrialCol = 6; % the column of summary in which trial IDs are stored\ntextureCol = 4; % the column of summary in which texture is stored\nv = zeros(numel(trialID),1);\nfor n = 1:numel(trialID)\n   id = trialID{n};\n   match = 0;\n   for m = 2:size(summary,1)\n       if ~isnan(summary{m,trialCol}) & strcmp(summary{m,trialCol},id)\n           match = 1;\n           texture = summary{m,textureCol};\n           switch texture(1:9)\n               case 'Texture 1'\n                   v(n) = 1;\n               case 'Texture 3'\n                   v(n) = 2;\n               case 'Texture 5'\n                   v(n) = 3;\n               case 'Texture 7'\n                   v(n) = 4;\n               otherwise\n                   error('Could not match texture %s (trial %s)',texture,id)\n           end\n       end\n   end\n   if ~match\n      error('Could not match trial %s',id) \n   end\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "textureDisc_dimensionReduce.m", "ext": ".m", "path": "OCIA-master/caImgAnalysis/Decoding_2PI/TextureDisc_Jerry/textureDisc_dimensionReduce.m", "size": 1799, "source_encoding": "utf_8", "md5": "6db53e071307051e42d0481f251ad5b6", "text": "function S = textureDisc_dimensionReduce(S)\n% S is a data structure returned by importData_textureDisc\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nanimalID = S.info.animal;\nsessionID = S.info.session;\nrate = S.info.sample_rate;\n\nroiLabel = S.info.roiLabel;\n% roi labels without session ID\nroiLabel = strrep(roiLabel,sprintf('%s-%1.0f-',animalID,sessionID),'');\n\n% treat each Roi separately\nfor roi = 1:numel(roiLabel)\n    roiID = roiLabel{roi};\n    fprintf('Processing %s\\n',roiID);\n    data = S.(roiLabel{roi}).dRR;\n    S.(roiLabel{roi}).reducedGauss1 = fitSingleTrialTransient(data,'gauss1');\nend\n\n\nfunction fitCoef = fitSingleTrialTransient(data,fitLib)\nfitCoef = zeros(size(data,1),4); % 3 coefficients for gaussian + r^2\nt = (1:size(data,2))';\nfObj = cell(1,size(data,1)); gof = cell(1,size(data,1));\nparfor n = 1:size(data,1)\n    y = data(n,:)';\n    if any(isnan(y))\n        fObj{n} = NaN; gof{n} = NaN;\n        continue\n    end\n    opts = fitoptions(fitLib);\n    opts.Lower = [0 0 1];\n    opts.Upper = [max(y) max(t) max(t)];\n    [fObj{n},gof{n}] = fit(t,y,fitLib,opts);\nend\nfor n = 1:size(data,1)\n    try\n        c = coeffvalues(fObj{n});\n        fitCoef(n,1:numel(c)) = c;\n        fitCoef(n,numel(c)+1) = gof{n}.rsquare;\n    catch\n        fitCoef(n,1:size(fitCoef,2)) = NaN;\n    end\n    %     if ~doPlot; continue; end\n    %     figure('Name',id)\n    %     plot(t,y,'k'), hold on\n    %     plot(fObj)\n    %     switch fitLib\n    %         case 'gauss1'\n    %             titleStr = sprintf('a=%1.3f b=%1.3f c=%1.3f r^2=%1.3f',c(1),c(2),c(3),gof.rsquare);\n    %         case 'fourier1'\n    %             titleStr = sprintf('a0=%1.3f a1=%1.3f b1=%1.3f w=%1.3f r^2=%1.3f',...\n    %                 c(1),c(2),c(3),c(4),gof.rsquare);\n    %     end\n    %     title(titleStr)\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "strucdisp.m", "ext": ".m", "path": "OCIA-master/utils/misc/strucdisp.m", "size": 18695, "source_encoding": "utf_8", "md5": "d5a885d0bc1cb649b355669e25a49853", "text": "function strucdisp(Structure, depth, printValues, maxArrayLength, fileName)\n%STRUCDISP  display structure outline\n%\n%   STRUCDISP(STRUC, DEPTH, PRINTVALUES, MAXARRAYLENGTH, FILENAME) displays\n%   the hierarchical outline of a structure and its substructures. \n%\n%   STRUC is a structure datatype with unknown field content. It can be \n%   either a scalar or a vector, but not a matrix. STRUC is the only\n%   mandatory argument in this function. All other arguments are optional.\n%\n%   DEPTH is the number of hierarchical levels of the structure that are\n%   printed. If DEPTH is smaller than zero, all levels are printed. Default\n%   value for DEPTH is -1 (print all levels).\n%\n%   PRINTVALUES is a flag that states if the field values should be printed\n%   as well. The default value is 1 (print values)\n%\n%   MAXARRAYLENGTH is a positive integer, which determines up to which\n%   length or size the values of a vector or matrix are printed. For a\n%   vector holds that if the length of the vector is smaller or equal to\n%   MAXARRAYLENGTH, the values are printed. If the vector is longer than\n%   MAXARRAYLENGTH, then only the size of the vector is printed.\n%   The values of a 2-dimensional (m,n) array are printed if the number of\n%   elements (m x n) is smaller or equal to MAXARRAYLENGTH.\n%   For vectors and arrays, this constraint overrides the PRINTVALUES flag.\n%\n%   FILENAME is the name of the file to which the output should be printed.\n%   if this argument is not defined, the output is printed to the command\n%   window.\n%\n%   Contact author: B. Roossien <roossien@ecn.nl>\n%   (c) ECN 2007-2008\n%\n%   Version 1.3.0\n\n\n%% Creator and Version information\n% Created by B. Roossien <roossien@ecn.nl> 14-12-2006\n%\n% Based on the idea of \n%       M. Jobse - display_structure (Matlab Central FileID 2031)\n%\n% Acknowledgements:\n%       S. Wegerich - printmatrix (Matlab Central FileID 971)\n%\n% Beta tested by: \n%       K. Visscher\n%\n% Feedback provided by:\n%       J. D'Errico\n%       H. Krause\n%       J.K. Kok\n%       J. Kurzmann\n%       K. Visscher\n%\n%\n% (c) ECN 2006-2007\n% www.ecn.nl\n%\n% Last edited on 08-03-2008\n\n\n\n%% Version History\n%\n% 1.3.0 : Bug fixes and added logicals\n% 1.2.3 : Buf fix - Solved multi-line string content bug\n% 1.2.2 : Bug fix - a field being an empty array gave an error\n% 1.2.1 : Bug fix\n% 1.2.0 : Increased readability of code\n%         Makes use of 'structfun' and 'cellfun' to increase speed and \n%         reduce the amount of code\n%         Solved bug with empty fieldname parameter\n% 1.1.2 : Command 'eval' removed with a more simple and efficient solution\n% 1.1.1 : Solved a bug with cell array fields\n% 1.1.0 : Added support for arrayed structures\n%         Added small matrix size printing\n% 1.0.1 : Bug with empty function parameters fixed\n% 1.0.0 : Initial release\n\n\n\n%% Main program\n%%%%% start program %%%%%\n\n    % first argument must be structure\n    if ~isstruct(Structure)\n        error('First input argument must be structure');\n    end\n    \n    % first argument can be a scalar or vector, but not a matrix\n    if ~isvector(Structure)\n        error('First input argument can be a scalar or vector, but not a matrix');\n    end\n    \n    % default value for second argument is -1 (print all levels)\n    if nargin < 2 || isempty(depth)\n        depth = -1;\n    end\n\n    % second argument must be an integer\n    if ~isnumeric(depth)\n        error('Second argument must be an integer');\n    end\n\n    % second argument only works if it is an integer, therefore floor it\n    depth = floor(depth);\n    \n    % default value for third argument is 1\n    if nargin < 3 || isempty(printValues)\n        printValues = 1;\n    end\n\n    % default value for fourth argument is 10\n    if nargin < 4 || isempty(maxArrayLength)\n        maxArrayLength = 10;\n    end\n\n    \n    % start recursive function   \n    listStr = recFieldPrint(Structure, 0, depth, printValues, ... \n                            maxArrayLength);\n\n    \n    % 'listStr' is a cell array containing the output\n    % Now it's time to actually output the data\n    % Default is to output to the command window\n    % However, if the filename argument is defined, output it into a file\n\n    if nargin < 5 || isempty(fileName)\n        \n        % write data to screen\n        for i = 1 : length(listStr)\n            disp(cell2mat(listStr(i, 1)));\n        end\n        \n    else\n        \n        % open file and check for errors\n        fid = fopen(fileName, 'wt');\n        \n        if fid < 0\n            error('Unable to open output file');\n        end\n        \n        % write data to file\n        for i = 1 : length(listStr)\n            fprintf(fid, '%s\\n', cell2mat(listStr(i, 1)));\n        end\n        \n        % close file\n        fclose(fid);\n        \n    end\n    \nend\n\n\n\n\n\n\n%% FUNCTION: recFieldPrint\nfunction listStr = recFieldPrint(Structure, indent, depth, printValues, ...\n                                 maxArrayLength)\n\n\n% Start to initialiase the cell listStr. This cell is used to store all the\n% output, as this is much faster then directly printing it to screen.\n\nlistStr = {};\n\n\n% \"Structure\" can be a scalar or a vector.\n% In case of a vector, this recursive function is recalled for each of\n% the vector elements. But if the values don't have to be printed, only\n% the size of the structure and its fields are printed.\n\nif length(Structure) > 1\n\n    if (printValues == 0)\n\n        varStr = createArraySize(Structure, 'Structure');\n\n        listStr = [{' '}; {['Structure', varStr]}];\n\n        body = recFieldPrint(Structure(1), indent, depth, ...\n                             printValues, maxArrayLength);\n\n        listStr = [listStr; body; {'   O'}];\n\n    else\n\n        for iStruc = 1 : length(Structure)\n\n            listStr = [listStr; {' '}; {sprintf('Structure(%d)', iStruc)}];\n\n            body = recFieldPrint(Structure(iStruc), indent, depth, ...\n                                 printValues, maxArrayLength);\n\n            listStr = [listStr; body; {'   O'}];\n\n        end\n\n    end\n\n    return\n\nend\n\n\n%% Select structure fields\n% The fields of the structure are distinguished between structure and\n% non-structure fields. The structure fields are printed first, by\n% recalling this function recursively.\n\n% First, select all fields.\n\nfields = fieldnames(Structure);\n\n% Next, structfun is used to return an boolean array with information of\n% which fields are of type structure.\n\nisStruct = structfun(@isstruct, Structure);\n\n% Finally, select all the structure fields\n\nstrucFields = fields(isStruct == 1);\n\n\n%% Recursively print structure fields \n% The next step is to select each structure field and handle it\n% accordingly. Each structure can be empty, a scalar, a vector or a matrix.\n% Matrices and long vectors are only printed with their fields and not with\n% their values. Long vectors are defined as vectors with a length larger\n% then the maxArrayLength value. The fields of an empty structure are not\n% printed at all.\n% It is not necessary to look at the length of the vector if the values\n% don't have to be printed, as the fields of a vector or matrix structure\n% are the same for each element.\n\n% First, some indentation calculations are required.\n\nstrIndent = getIndentation(indent + 1);\nlistStr = [listStr; {strIndent}];\n\nstrIndent = getIndentation(indent);\n\n% Next, select each field seperately and handle it accordingly\n\nfor iField = 1 : length(strucFields)\n\n    fieldName = cell2mat(strucFields(iField));\n    Field =  Structure.(fieldName);\n    \n    % Empty structure\n    if isempty(Field)\n\n        strSize = createArraySize(Field, 'Structure');\n\n        line = sprintf('%s   |--- %s :%s', ...\n                       strIndent, fieldName, strSize);\n\n        listStr = [listStr; {line}];\n\n    % Scalar structure\n    elseif isscalar(Field)\n\n        line = sprintf('%s   |--- %s', strIndent, fieldName);\n\n        % Recall this function if the tree depth is not reached yet\n        if (depth < 0) || (indent + 1 < depth)\n            lines = recFieldPrint(Field, indent + 1, depth, ...\n                                  printValues, maxArrayLength);\n\n            listStr = [listStr; {line}; lines; ...\n                       {[strIndent '   |       O']}];\n        else\n            listStr = [listStr; {line}];\n        end\n\n    % Short vector structure of which the values should be printed    \n    elseif (isvector(Field)) &&  ...\n           (printValues > 0) && ...\n           (length(Field) < maxArrayLength) && ...\n           ((depth < 0) || (indent + 1 < depth))\n\n        % Use a for-loop to print all structures in the array\n        for iFieldElement = 1 : length(Field)\n\n            line = sprintf('%s   |--- %s(%g)', ...\n                           strIndent, fieldName, iFieldElement);\n\n            lines = recFieldPrint(field(iFieldElement), indent + 1, ...\n                                 depth, printValues, maxArrayLength);\n\n            listStr = [listStr; {line}; lines; ...\n                       {[strIndent '   |       O']}];\n\n            if iFieldElement ~= length(Field)\n                listStr = [listStr; {[strIndent '   |    ']}];\n            end\n\n        end\n\n    % Structure is a matrix or long vector\n    % No values have to be printed or depth limit is reached\n    else\n\n        varStr = createArraySize(Field, 'Structure');\n\n        line = sprintf('%s   |--- %s :%s', ...\n                       strIndent, fieldName, varStr);\n\n        lines = recFieldPrint(Field(1), indent + 1, depth, ...\n                              0, maxArrayLength);\n\n        listStr = [listStr; {line}; lines; ...\n                   {[strIndent '   |       O']}];\n\n    end\n\n    % Some extra blank lines to increase readability\n    listStr = [listStr; {[strIndent '   |    ']}];\n\nend % End iField for-loop\n\n\n%% Field Filler\n% To properly align the field names, a filler is required. To know how long\n% the filler must be, the length of the longest fieldname must be found.\n% Because 'fields' is a cell array, the function 'cellfun' can be used to\n% extract the lengths of all fields.\nmaxFieldLength = max(cellfun(@length, fields));\n\n%% Print non-structure fields without values\n% Print non-structure fields without the values. This can be done very\n% quick.\nif printValues == 0\n    \n    noStrucFields = fields(isStruct == 0);\n\n    for iField  = 1 : length(noStrucFields)\n\n        Field = cell2mat(noStrucFields(iField));\n\n        filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n\n        listStr = [listStr; {[strIndent '   |' filler ' ' Field]}];\n\n    end\n\n    return\n\nend\n\n\n%% Select non-structure fields (to print with values)\n% Select fields that are not a structure and group them by data type. The\n% following groups are distinguished:\n%   - characters and strings\n%   - numeric arrays\n%   - logical\n%   - empty arrays\n%   - matrices\n%   - numeric scalars\n%   - cell arrays\n%   - other data types\n\n% Character or string (array of characters)\nisChar = structfun(@ischar, Structure);\ncharFields = fields(isChar == 1);\n\n% Numeric fields\nisNumeric = structfun(@isnumeric, Structure);\n\n% Numeric scalars\nisScalar = structfun(@isscalar, Structure);\nisScalar = isScalar .* isNumeric;\nscalarFields = fields(isScalar == 1);\n\n% Numeric vectors (arrays)\nisVector = structfun(@isvector, Structure);\nisVector = isVector .* isNumeric .* not(isScalar);\nvectorFields = fields(isVector == 1);\n\n% Logical fields\nisLogical = structfun(@islogical, Structure);\nlogicalFields = fields(isLogical == 1);\n\n% Empty arrays\nisEmpty = structfun(@isempty, Structure);\nemptyFields = fields(isEmpty == 1);\n\n% Numeric matrix with dimension size 2 or higher\nisMatrix = structfun(@(x) ndims(x) >= 2, Structure);\nisMatrix = isMatrix .* isNumeric .* not(isVector) ...\n                    .* not(isScalar) .* not(isEmpty);\nmatrixFields = fields(isMatrix == 1);\n\n% Cell array\nisCell = structfun(@iscell, Structure);\ncellFields = fields(isCell == 1);\n\n% Datatypes that are not checked for\nisOther = not(isChar + isNumeric + isCell + isStruct + isLogical + isEmpty);\notherFields = fields(isOther == 1);\n\n\n\n%% Print non-structure fields\n% Print all the selected non structure fields\n% - Strings are printed to a certain amount of characters\n% - Vectors are printed as long as they are shorter than maxArrayLength\n% - Matrices are printed if they have less elements than maxArrayLength\n% - The values of cells are not printed\n\n\n% Start with printing strings and characters. To avoid the display screen \n% becoming a mess, the part of the string that is printed is limited to 31 \n% characters. In the future this might become an optional parameter in this\n% function, but for now, it is placed in the code itself.\n% if the string is longer than 31 characters, only the first 31  characters\n% are printed, plus three dots to denote that the string is longer than\n% printed.\n\nmaxStrLength = 31;\n\nfor iField = 1 : length(charFields)\n\n    Field = cell2mat(charFields(iField));\n\n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    if (size(Structure.(Field), 1) > 1) && (size(Structure.(Field), 2) > 1)\n        \n        varStr = createArraySize(Structure.(Field), 'char');\n        \n    elseif length(Field) > maxStrLength\n        \n        varStr = sprintf(' ''%s...''', Structure.(Field(1:maxStrLength)));\n        \n    else\n        \n        varStr = sprintf(' \"%s\"', Structure.(Field));\n        \n    end\n\n    listStr = [listStr; {[strIndent '   |' filler ' ' Field ' :' varStr]}];\nend\n\n\n% Print empty fields\n\nfor iField = 1 : length(emptyFields)\n    \n    \n    Field = cell2mat(emptyFields(iField));\n    \n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    listStr = [listStr; {[strIndent '   |' filler ' ' Field ' : [ ]' ]}];\n\nend\n\n\n% Print logicals. If it is a scalar, print true/false, else print vector\n% information\n\nfor iField = 1 : length(logicalFields)\n    \n    Field = cell2mat(logicalFields(iField));\n    \n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    if isscalar(Structure.(Field))\n        \n        logicalValue = {'False', 'True'};\n        \n        varStr = sprintf(' %s', logicalValue{Structure.(Field) + 1});\n\n    else\n\n        varStr = createArraySize(Structure.(Field), 'Logic array');\n                \n    end\n\n    listStr = [listStr; {[strIndent '   |' filler ' ' Field ' :' varStr]}];\n    \nend\n\n\n% Print numeric scalar field. The %g format is used, so that integers,\n% floats and exponential numbers are printed in their own format.\n\nfor iField = 1 : length(scalarFields)\n    \n    Field = cell2mat(scalarFields(iField));\n    \n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    varStr = sprintf(' %g', Structure.(Field));\n\n    listStr = [listStr; {[strIndent '   |' filler ' ' Field ' :' varStr]}];\n\nend\n\n\n% Print numeric array. If the length of the array is smaller then\n% maxArrayLength, then the values are printed. Else, print the length of\n% the array.\n\nfor iField = 1 : length(vectorFields)\n    \n    Field = cell2mat(vectorFields(iField));\n    \n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    if length(Structure.(Field)) > maxArrayLength\n        \n        varStr = createArraySize(Structure.(Field), 'Array');\n        \n    else\n\n        varStr = sprintf('%g ', Structure.(Field));\n\n        varStr = ['[' varStr(1:length(varStr) - 1) ']'];\n                    \n    end\n    \n    listStr = [listStr; {[strIndent '   |' filler ' ' Field ' : ' varStr]}];\n\nend\n\n\n% Print numeric matrices. If the matrix is two-dimensional and has more\n% than maxArrayLength elements, only its size is printed.\n% If the matrix is 'small', the elements are printed in a matrix structure.\n% The top and the bottom of the matrix is indicated by a horizontal line of\n% dashes. The elements are also lined out by using a fixed format\n% (%#10.2e). Because the name of the matrix is only printed on the first\n% line, the space is occupied by this name must be filled up on the other\n% lines. This is done by defining a 'filler2'.\n% This method was developed by S. Wegerich.\n\nfor iField = 1 : length(matrixFields)\n    \n    Field = cell2mat(matrixFields(iField));\n    \n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    if numel(Structure.(Field)) > maxArrayLength\n        \n        varStr = createArraySize(Structure.(Field), 'Array');\n\n        varCell = {[strIndent '   |' filler ' ' Field ' :' varStr]};\n        \n    else\n\n        matrixSize = size(Structure.(Field));\n        \n        filler2 = char(ones(1, maxFieldLength + 6) * 32);\n\n        dashes = char(ones(1, 12 * matrixSize(2) + 1) * 45);\n\n        varCell = {[strIndent '   |' filler2 dashes]};\n        \n        % first line with field name\n        varStr = sprintf('%#10.2e |', Structure.(Field)(1, :));\n\n        varCell = [varCell; {[strIndent '   |' filler ' ' ...\n                              Field ' : |' varStr]}];\n\n        % second and higher number rows\n        for j = 2 : matrixSize(1)\n\n            varStr = sprintf('%#10.2e |', Structure.(Field)(j, :));\n            \n            varCell = [varCell; {[strIndent '   |' filler2 '|' varStr]}];\n        end\n\n        varCell = [varCell; {[strIndent '   |' filler2 dashes]}];\n                    \n    end\n    \n    listStr = [listStr; varCell];\n\nend\n\n\n% Print cell array information, i.e. the size of the cell array. The\n% content of the cell array is not printed.\n\nfor iField = 1 : length(cellFields)\n\n    Field = cell2mat(cellFields(iField));\n    \n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    varStr = createArraySize(Structure.(Field), 'Cell');\n\n    listStr = [listStr; {[strIndent '   |' filler ' ' Field ' :' varStr]}];\n\nend\n\n\n% Print unknown datatypes. These include objects and user-defined classes\n\nfor iField = 1 : length(otherFields)\n\n    Field = cell2mat(otherFields(iField));\n    \n    filler = char(ones(1, maxFieldLength - length(Field) + 2) * 45);\n    \n    varStr = createArraySize(Structure.(Field), 'Unknown');\n\n    listStr = [listStr; {[strIndent '   |' filler ' ' Field ' :' varStr]}];\n\nend\n\nend\n\n\n\n%% FUNCTION: getIndentation\n% This function creates the hierarchical indentations\n\nfunction str = getIndentation(indent)\n    x = '   |    ';\n    str = '';\n    \n    for i = 1 : indent\n        str = cat(2, str, x);\n    end\nend\n\n\n\n%% FUNCTION: createArraySize\n% This function returns a string with the array size of the input variable\n% like: \"[1x5 Array]\" or \"[2x3x5 Structure]\" where 'Structure' and 'Array'\n% are defined by the type parameter\n\nfunction varStr = createArraySize(varName, type)\n    varSize = size(varName);\n\n    arraySizeStr = sprintf('%gx', varSize);\n    arraySizeStr(length(arraySizeStr)) = [];\n    \n    varStr = [' [' arraySizeStr ' ' type ']'];\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "client.m", "ext": ".m", "path": "OCIA-master/utils/socket_communication/client.m", "size": 1728, "source_encoding": "utf_8", "md5": "c1e65f4b0d1315930638729cb9769c5e", "text": "% CLIENT connect to a server and read a message\n%\n% Usage - message = client(host, port, number_of_retries)\nfunction message = client(host, port, number_of_retries)\n\n    import java.net.Socket\n    import java.io.*\n\n    if (nargin < 3)\n        number_of_retries = 20; % set to -1 for infinite\n    end\n    \n    retry        = 0;\n    input_socket = [];\n    message      = [];\n\n    while true\n\n        retry = retry + 1;\n        if ((number_of_retries > 0) && (retry > number_of_retries))\n            fprintf(1, 'Too many retries\\n');\n            break;\n        end\n        \n        try\n            fprintf(1, 'Retry %d connecting to %s:%d\\n', ...\n                    retry, host, port);\n\n            % throws if unable to connect\n            input_socket = Socket(host, port);\n\n            % get a buffered data input stream from the socket\n            input_stream   = input_socket.getInputStream;\n            d_input_stream = DataInputStream(input_stream);\n\n            fprintf(1, 'Connected to server\\n');\n\n            % read data from the socket - wait a short time first\n            pause(0.5);\n            bytes_available = input_stream.available;\n            fprintf(1, 'Reading %d bytes\\n', bytes_available);\n            \n            message = zeros(1, bytes_available, 'uint8');\n            for i = 1:bytes_available\n                message(i) = d_input_stream.readByte;\n            end\n            \n            message = char(message);\n            \n            % cleanup\n            input_socket.close;\n            break;\n            \n        catch\n            if ~isempty(input_socket)\n                input_socket.close;\n            end\n\n            % pause before retrying\n            pause(1);\n        end\n    end\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "makeMMNexperiment_duration.m", "ext": ".m", "path": "OCIA-master/utils/soundGen/makeMMNexperiment_duration.m", "size": 2993, "source_encoding": "utf_8", "md5": "92bdcf7093f7ef7456e1a169108f5e79", "text": "function makeMMNexperiment_duration(durVector,f,sf)\n% in1 ... duration vector\n% in2 ... pure tone frequency (Hz)\n% in3 ... sampling frequency\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\n% some fixed parameters\nduty = 0.5; % duty cycle in s\nruns = 2; % no. of runs\n% (must be even, half the runs f1 is standard and f2 deviant, other half\n% vv)\nstims = 100; % no. of stims per run\npercS1S2 = [90 10]; % percentages for standard (S1) and deviant (S2)\n\n% check parameters\nif rem(runs,2)\n    error('Number of runs must be even');\nend\nif sum(percS1S2) ~= 100\n    error('Percentages of standard and deviants must sum to 100');\nend\n% --> must be integers wrt. stims/runs\nfor n = 1:length(percS1S2)\n    if rem(stims*(percS1S2(n)/100),1)\n        error('Number of standard and deviants must be integers');\n    end\nend\n\n% runs vector with 1 (f1 f2) and 2 (f2 f1)\nrunsVector = [1 2];\nrunsVector = repmat(runsVector,1,runs/2);\nrunsVector = shuffleVector(runsVector);\n\nstimVectorAll = cell(1,length(runsVector));\ntoneArrayAll = cell(1,length(runsVector));\n% process each run\nfor currentRun = 1:length(runsVector)\n%     if runsVector(currentRun) == 1\n%         currentVector = durVector;\n%     elseif runsVector(currentRun) == 2\n%         currentVector = fliplr(durVector);\n%     end\n    % make stim vector\n    stimsS1 = stims*(percS1S2(1)/100);\n    stimsS2 = stims*(percS1S2(2)/100);\n    stimVector = [repmat(1,1,stimsS1),repmat(2,1,stimsS2)];\n    \n    while true\n        stimVector = shuffleVector(stimVector);\n        % ensure first couple of stims are not 2\n        nonDeviants = 6;\n        if sum(unique(stimVector(1:nonDeviants))) > 1\n            continue\n        end\n        % no adjacent deviants\n        adjacentDeviant = 6;\n        accept = 1;\n        for n = adjacentDeviant+1:length(stimVector)\n            if stimVector(n) == 2\n                if ~isempty(find(stimVector(n-adjacentDeviant:n-1)==2, 1))\n                    accept = 0;\n                end\n            end\n        end\n        if accept\n            break\n        end\n    end\n    % half of the deviants should be 3\n    deviantsIdx = find(stimVector==2);\n    if rem(numel(deviantsIdx),2)\n       error('Number of deviants must be even.') \n    end\n    deviantsIdx = shuffleArray(deviantsIdx);\n    deviantsIdx = deviantsIdx(1:(numel(deviantsIdx)/2));\n    stimVector(deviantsIdx) = 3;\n    \n    currentToneArray = MakePureToneArray_duration(durVector,stimVector,f,sf);\n    SaveAndAssignInBase(stimVector,sprintf('stimVectorRun%02.0f',currentRun),...\n        'AssignOnly');\n    SaveAndAssignInBase(currentToneArray,sprintf('toneArrayRun%02.0f',currentRun),...\n        'AssignOnly');\n    stimVectorAll{currentRun} = stimVector;\n    toneArrayAll{currentRun} = currentToneArray;\nend\nsaveName = ['expInfo' datestr(clock,30) '.mat'];\n% save(saveName,'stimVectorAll','toneArrayAll');\n\nfprintf('\\nFinished\\n');\n\n\nfunction sRand = shuffleVector(s)\nk = randperm(length(s));\nsRand = s;\nfor n = 1:length(k)\n    sRand(n) = s(k(n));\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "makePureTone.m", "ext": ".m", "path": "OCIA-master/utils/soundGen/makePureTone.m", "size": 1803, "source_encoding": "utf_8", "md5": "4775e6038bf3aba14c55af3dbc971624", "text": "function s = makePureTone(freqs,duration,varargin)\n% generate a pure tone\n% in1 ... carrier frequency in Hz\n% in2 ... duration in s\n% in3 ... sampling frequency in Hz {44100}\n% in4 ... rampDur in fraction of total duration {0.05}\n% in5 ... play tone {0}; 0 or 1\n% in6 ... save wav file {[]}; filename of wav file\n% out1 ... the sound vector s or a cell array if more than one frequency was specified\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nif nargin > 2 && ~isempty(varargin{1});\n    sf = varargin{1};\nelse\n    sf = 44100;\nend\n\nif nargin > 3 && ~isempty(varargin{2});\n    rampDur = varargin{2};\nelse\n    rampDur = 0.05; % ramp duration (fraction of total duration, max. 0.5)\nend\n\nif nargin > 4 && ~isempty(varargin{3});\n    doPlay = varargin{3};\nelse\n    doPlay = 0;\nend\n\nif nargin > 5 && ~isempty(varargin{4});\n    saveWav = varargin{4};\nelse\n    saveWav = [];\nend\n\nmodFreq = 0; % modulation frequency\nmodIndex = 1; % modulation index\n\ns = cell(numel(freqs), 1);\n\nfor i = 1:length(freqs)\n    % make the sound\n    s{i} = MakeSound(sf,duration,freqs(i),modFreq,modIndex,rampDur);\n    \n    if doPlay\n        sound(s{i},sf);\n    end\n    if ~isempty(saveWav)\n        wavwrite(s{i},sf,16,saveWav);\n    end\nend\n\nif numel(freqs) == 1;\n    s = s{1};\nend;\n\n\nfunction s = MakeSound(sf,d,f,mf,mi,rd)\n\n% frequency modulated sound\nn = round(sf * d);                    % number of samples\nc = (1:n) / sf;                % carrier data preparation\nc = 2 * pi * f * c;\n% m = (1:n) / sf;                % modulator data preparation\n% m = mi * cos(2 * pi * mf * m); % sinusoidal modulation\nm = 0;\ns = sin(c + m);                % frequency modulation\n\n% ramp\nrd = d .* rd;\nnr = floor(sf * rd);\nr = sin(linspace(0, pi/2, nr));\nr = [r, ones(1, n - nr * 2), fliplr(r)];\n% make ramped sound\ns = s .* r;\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "makeOmittedStimExperimentNoise.m", "ext": ".m", "path": "OCIA-master/utils/soundGen/makeOmittedStimExperimentNoise.m", "size": 3099, "source_encoding": "utf_8", "md5": "f608ebec790c4a07c4e0772436893523", "text": "function makeOmittedStimExperimentNoise(fCellArray,sf)\n% fCellArray ... {[lower1 upper1],[lower2 upper2]}\n% sf ... sampling frequency\n% if f2 = 0, this will create an omitted stimulus paradigm\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\n% some fixed parameters\ndur = 0.2; % tone duration in s\nruns = 10; % no. of runs\n% (must be even, half the runs f1 is standard and f2 deviant, other half\n% vv)\nstims = 100; % no. of stims per run\npercS1S2 = [90 10]; % percentages for standard (S1) and deviant (S2)\n\nstandardBetweenDeviant = 6; % this is the min. number of standard tones \n% between deviants (also the number of standards at start and end of \n% experiment) \n\n% check parameters\nif rem(runs,2)\n    error('Number of runs must be even');\nend\nif sum(percS1S2) ~= 100\n    error('Percentages of standard and deviants must sum to 100');\nend\n% --> must be integers wrt. stims/runs\nfor n = 1:length(percS1S2)\n    if rem(stims*(percS1S2(n)/100),1)\n        error('Number of standard and deviants must be integers');\n    end\nend\n\n% runs vector with 1 (f1 f2) and 2 (f2 f1)\nrunsVector = [1 2];\nrunsVector = repmat(runsVector,1,runs/2);\nrunsVector = shuffleVector(runsVector);\nif ~fCellArray{2}\n   runsVector = ones(size(runsVector));\nend\n\nstimVectorAll = cell(1,length(runsVector));\ntoneArrayAll = cell(1,length(runsVector));\n% process each run\nfor currentRun = 1:length(runsVector)\n    if runsVector(currentRun) == 1\n        currentfCell = fCellArray;\n    elseif runsVector(currentRun) == 2\n        currentfCell = fliplr(fCellArray);\n    end\n    % make stim vector\n    stimsS1 = stims*(percS1S2(1)/100);\n    stimsS2 = stims*(percS1S2(2)/100);\n    stimVector = [repmat(1,1,stimsS1),repmat(2,1,stimsS2)];\n    \n    while true\n        stimVector = shuffleVector(stimVector);\n        \n        % min. number of standards at start and end\n        if sum(unique(stimVector(1:standardBetweenDeviant))) > 1\n            continue\n        end\n        if sum(unique(stimVector(end-standardBetweenDeviant+1:end))) > 1\n            continue\n        end\n        \n        accept = 1;\n        for n = standardBetweenDeviant+1:length(stimVector)\n            if stimVector(n) == 2\n                if ~isempty(find(stimVector(n-standardBetweenDeviant:n-1)==2, 1))\n                    accept = 0;\n                end\n            end\n        end\n        if accept\n            break\n        end\n    end\n    currentToneArray = MakeNoiseToneArray(currentfCell,stimVector,dur,sf);\n%     SaveAndAssignInBase(stimVector,sprintf('stimVectorRun%02.0f',currentRun),...\n%         'AssignOnly');\n%     SaveAndAssignInBase(currentToneArray,sprintf('toneArrayRun%02.0f',currentRun),...\n%         'AssignOnly');\n    stimVectorAll{currentRun} = stimVector;\n    toneArrayAll{currentRun} = currentToneArray;\nend\nsaveName = ['expInfo' datestr(clock,30) '.mat'];\nsave(saveName,'stimVectorAll','toneArrayAll','fCellArray','sf');\nevalStr = sprintf('load %s',saveName);\nevalin('base',evalStr)\n\nfprintf('\\nFinished\\n');\n\n\nfunction sRand = shuffleVector(s)\nk = randperm(length(s));\nsRand = s;\nfor n = 1:length(k)\n    sRand(n) = s(k(n));\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "makeMMNexperiment.m", "ext": ".m", "path": "OCIA-master/utils/soundGen/makeMMNexperiment.m", "size": 3040, "source_encoding": "utf_8", "md5": "1c64717c6c6d44304d01eeced947ad07", "text": "function makeMMNexperiment(fVector,sf)\n% fVector ... [freq1 freq2]\n% sf ... sampling frequency\n% if freq2 = 0, this will create an omitted stimulus paradigm\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\n% some fixed parameters\ndur = 0.2; % tone duration in s\nruns = 10; % no. of runs\n% (must be even, half the runs f1 is standard and f2 deviant, other half\n% vv)\nstims = 50; % no. of stims per run\npercS1S2 = [90 10]; % percentages for standard (S1) and deviant (S2)\n\nstandardBetweenDeviant = 6; % this is the min. number of standard tones \n% between deviants (also the number of standards at start and end of \n% experiment) \n\n% check parameters\nif rem(runs,2)\n    error('Number of runs must be even');\nend\nif sum(percS1S2) ~= 100\n    error('Percentages of standard and deviants must sum to 100');\nend\n% --> must be integers wrt. stims/runs\nfor n = 1:length(percS1S2)\n    if rem(stims*(percS1S2(n)/100),1)\n        error('Number of standard and deviants must be integers');\n    end\nend\n\n% runs vector with 1 (f1 f2) and 2 (f2 f1)\nrunsVector = [1 2];\nrunsVector = repmat(runsVector,1,runs/2);\nrunsVector = shuffleVector(runsVector);\nif ~fVector(2)\n   runsVector = ones(size(runsVector));\nend\n\nstimVectorAll = cell(1,length(runsVector));\ntoneArrayAll = cell(1,length(runsVector));\n% process each run\nfor currentRun = 1:length(runsVector)\n    if runsVector(currentRun) == 1\n        currentfVector = fVector;\n    elseif runsVector(currentRun) == 2\n        currentfVector = fliplr(fVector);\n    end\n    % make stim vector\n    stimsS1 = stims*(percS1S2(1)/100);\n    stimsS2 = stims*(percS1S2(2)/100);\n    stimVector = [repmat(1,1,stimsS1),repmat(2,1,stimsS2)];\n    \n    while true\n        stimVector = shuffleVector(stimVector);\n        \n        % min. number of standards at start and end\n        if sum(unique(stimVector(1:standardBetweenDeviant))) > 1\n            continue\n        end\n        if sum(unique(stimVector(end-standardBetweenDeviant+1:end))) > 1\n            continue\n        end\n        \n        accept = 1;\n        for n = standardBetweenDeviant+1:length(stimVector)\n            if stimVector(n) == 2\n                if ~isempty(find(stimVector(n-standardBetweenDeviant:n-1)==2, 1))\n                    accept = 0;\n                end\n            end\n        end\n        if accept\n            break\n        end\n    end\n    currentToneArray = MakePureToneArray(currentfVector,stimVector,dur,sf);\n%     SaveAndAssignInBase(stimVector,sprintf('stimVectorRun%02.0f',currentRun),...\n%         'AssignOnly');\n%     SaveAndAssignInBase(currentToneArray,sprintf('toneArrayRun%02.0f',currentRun),...\n%         'AssignOnly');\n    stimVectorAll{currentRun} = stimVector;\n    toneArrayAll{currentRun} = currentToneArray;\nend\nsaveName = ['expInfo' datestr(clock,30) '.mat'];\nsave(saveName,'stimVectorAll','toneArrayAll');\nevalStr = sprintf('load %s',saveName);\nevalin('base',evalStr)\n\nfprintf('\\nFinished\\n');\n\n\nfunction sRand = shuffleVector(s)\nk = randperm(length(s));\nsRand = s;\nfor n = 1:length(k)\n    sRand(n) = s(k(n));\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "makeNoiseTone.m", "ext": ".m", "path": "OCIA-master/utils/soundGen/makeNoiseTone.m", "size": 1583, "source_encoding": "utf_8", "md5": "c793c5a273c13f3af3f81b914137b260", "text": "function s = makeNoiseTone(freqs,duration,varargin)\n% generate a pure tone\n% in1 ... band-limiting frequencies in Hz [lower upper]\n% in2 ... duration in s\n% in3 ... sampling frequency in Hz {44100}\n% in4 ... play tone {0}; 0 or 1\n% in5 ... rd\n% out1 ... the sound vector s\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nif nargin > 2\n    sf = varargin{1};\n    if isempty(sf)\n        sf = 44100;\n    end\nelse\n    sf = 44100;\nend\n\nif nargin > 3\n    doPlay = varargin{2};\nelse\n    doPlay = 0;\nend\n\nif nargin > 4\n    doPlay = varargin{2};\n    rd = varargin{3}; % ramp duration (fraction of total duration, max. 0.5)\nelse\n    rd = 0.05; % ramp duration (fraction of total duration, max. 0.5)\nend\n\ns = randn(1,round(sf*duration)); % Noise signal\ns = mpi_BandPassFilterTimeSeries(s,1/sf,freqs(1),freqs(2));\ns = ScaleToMinMax(s,-1,1);\n\n% ramp\nn = round(sf .* duration);\nrd = duration .* rd;\nnr = floor(sf * rd);\nr = sin(linspace(0, pi/2, nr));\nr = [r, ones(1, n - nr * 2), fliplr(r)];\n% make ramped sound\ns = s .* r;\n\nif doPlay\n    sound(s,sf);\nend\n\n\nfunction s = MakeSound(sf,d,f,mf,mi,rd)\n\n% frequency modulated sound\nn = sf * d;                    % number of samples\nc = (1:n) / sf;                % carrier data preparation\nc = 2 * pi * f * c;\n% m = (1:n) / sf;                % modulator data preparation\n% m = mi * cos(2 * pi * mf * m); % sinusoidal modulation\nm = 0;\ns = sin(c + m);                % frequency modulation\n\n% ramp\nrd = d .* rd;\nnr = floor(sf * rd);\nr = sin(linspace(0, pi/2, nr));\nr = [r, ones(1, n - nr * 2), fliplr(r)];\n% make ramped sound\ns = s .* r;\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "xyerrorbar.m", "ext": ".m", "path": "OCIA-master/utils/plotting/xyerrorbar.m", "size": 1618, "source_encoding": "utf_8", "md5": "bbdb1d7c67cfdc53de32740db8e3dbd1", "text": "%   xyerrorbar.m\n%   (c) Nils Sjoberg 07-09-2004 Sweden\n%   xyerrorbar(x,y,errx,erry,s) plots the data in y vs x with errorbars for\n%   both y and x-data. Variables errx and erry are arrays of length=length(x and y) containing the\n%   error in each and every datapoint.\n%   s contains drawing-options for the plot and the options are the same as in\n%   the ordinary plot-command, however the errorbar is plotted in a very\n%   nice red hue!\n%\n%   Here is an example\n%   >figure\n%   >xyerrorbar([1:0.5:5],[sin([1:0.5:5])],[ones(10,1).*0.1],[ones(10,1).*0.2],'g+')\n%   the result shold be a plot of sin(x) vs x with errorbars for both x and\n%   sin(x) -data.\n\nfunction h=xyerrorbar(x,y,errx,erry,s)\nif length(x)~=length(y)\n    disp('x and y must have the same number of elements')\n    return\nend\nif nargin<5\n    s='';\nend\nhold on\nfor k=1:length(x)\n    l1=line([x(k)-errx(k) x(k)+errx(k)],[y(k) y(k)]);\n    set(l1,'color',[0.5 0.5 0.5])\n    set(get(get(l1,'Annotation'),'LegendInformation'),...\n        'IconDisplayStyle','off'); % Exclude from legend\n    %     l2=line([x(k)-errx(k) x(k)-errx(k)],[y(k)-0.1*errx(k) y(k)+0.1*errx(k)]);\n    %     l3=line([x(k)+errx(k) x(k)+errx(k)],[y(k)-0.1*errx(k) y(k)+0.1*errx(k)]);\n    l4=line([x(k) x(k)],[y(k)-erry(k) y(k)+erry(k)]);\n    set(l4,'color',[0.5 0.5 0.5])\n    set(get(get(l4,'Annotation'),'LegendInformation'),...\n        'IconDisplayStyle','off'); % Exclude from legend\n    %     l5=line([x(k)-0.1*errx(k) x(k)+0.1*errx(k)],[y(k)-erry(k) y(k)-erry(k)]);\n    %     l6=line([x(k)-0.1*errx(k) x(k)+0.1*errx(k)],[y(k)+erry(k) y(k)+erry(k)]);\nend\nh = plot(x,y,s);\nhold off\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "displayData.m", "ext": ".m", "path": "OCIA-master/utils/plotting/displayData.m", "size": 9061, "source_encoding": "utf_8", "md5": "8f602663bd107c33523766f80b676ca0", "text": "function varargout = displayData(varargin)\n\n% this file written by Henry Luetcke\n\nif nargin\n   S = varargin{1}; % parameter structure \nelse\n    S = struct;\nend\ntic\nS = parseInputs(S);\n\n%% load data\n[FilePath,FileName,FileType] = fileparts(S.RawFiles{1});\nfullInfile = fullfile(FilePath,[FileName FileType]);\nif exist(fullInfile,'file') ~= 2\n    error('Could not find %s on absolute and relative paths',fullInfile);\nend\nswitch FileType\n    case '.fcs'\n        img = import_raw(fullInfile);\n        S.img_data{1} = uint8(img.ch1/255); % always save 8-bit integers\n        S.img_data{2} = uint8(img.ch2/255);\n        % save bit-depth for later usage\n        S.hdr.bits = 8;\n    case '.tif'\n        % TODO: new tif-import in separate file, return data cell array and\n        % header info\n        img =tiffread2_wrapper(fullInfile);\n        S.img_data{1} = img.data;\n        S.hdr.tifHeader{1} = img.header;\n        disp('Loaded channel 1');\n        if exist(strrep(fullInfile,'channel00','channel01')) == 2\n            img =tiffread2_wrapper(strrep(fullInfile,'channel00','channel01'));\n            S.img_data{2} = img.data;\n            S.hdr.tifHeader{2} = img.header;\n            disp('Loaded channel 2');\n        end\n        if exist(strrep(fullInfile,'channel00','channel02')) == 2\n            img =tiffread2_wrapper(strrep(fullInfile,'channel00','channel02'));\n            S.img_data{3} = img.data;\n            S.hdr.tifHeader{3} = img.header;\n            disp('Loaded channel 3');\n        end\n        S.hdr.bits = 16;\n    otherwise\n        error('Unrecognized file type %s',FileType);\nend\n\n%% Delete frames\nif S.del_frame\n    for n = 1:length(S.img_data)\n        S.img_data{n} = S.img_data{n}(:,:,S.del_frame+1:end);\n    end\nend\nfprintf('Deleted %1.0f frame(s)\\n',S.del_frame);\n\n%% Background correction\nif S.bgCorrect\n    for n = 1:length(S.img_data)\n        S.img_data{n} = bg_subtract_HL(S.img_data{n},S.bgCorrect,0,...\n            'percentile');\n        S.img_data{n}(S.img_data{n}<0) = 0;\n    end\nend\n\n%% Calculate stats\nswitch lower(S.statsType)\n    case 'dff'\n        stats = S.img_data{S.channelVector==1};\n        avgImg = mean(stats,3);\n        intensityCutoff = prctile(avgImg(:),1);\n        maskImg = ones(size(avgImg));\n        maskImg(avgImg<=intensityCutoff) = 0;\n    case 'drr'\n        stats1 = S.img_data{S.channelVector==1};\n        stats2 = S.img_data{S.channelVector==2};\n        avgImg = mean(stats1,3);\n        stats = stats1 ./ stats2; clear stats1 stats2\n        intensityCutoff = prctile(avgImg(:),1);\n        maskImg = ones(size(avgImg));\n        maskImg(avgImg<=intensityCutoff) = 0; % could extend this to include second channel\nend\n% smooth stats image\nparfor n = 1:size(stats,3)\n    stats(:,:,n) = imfilter(stats(:,:,n),fspecial('gaussian',[10 10]));\nend\nfprintf('Smoothed image\\n');\n\n% calculate stats\nfprintf('Calculating %s image\\n',upper(S.statsType));\nstatsImg = zeros(size(stats));\nparfor x1 = 1:size(stats,1)\n%     fprintf('Processing row %1.0f of %1.0f\\n',x1,size(stats,1));\n    currentRow = shiftdim(stats(x1,:,:))';\n    currentRow = doStats(currentRow,maskImg(x1,:),S.frame_rate,5,25);\n    statsImg(x1,:,:) = reshape(currentRow',size(stats,2),size(stats,3));\n%     for x2 = 1:size(stats,2)\n%         if maskImg(x1,x2)\n%             fVector = reshape(stats(x1,x2,:),1,size(stats,3));\n%             % 5 s segements; background at 25th percentile\n%             f0Vector = findF0(fVector,S.frame_rate,5,25);\n%             statsImg(x1,x2,:) = ...\n%                 ((fVector-f0Vector) ./ f0Vector) .* 100;\n%         else\n%             statsImg(x1,x2,:) = NaN;\n%         end\n%     end\nend\nstats = statsImg; clear statsImg\n\nframeNpil = nanmean(GetRoiTimeseries(stats,[]));\n\n%% Average image\nfigure('Name','Average image','NumberTitle','off','Color','white');\nimagesc(avgImg), colormap(gray), colorbar\nset(gca,'xtick',[],'ytick',[])\n\n%% Activity image\nimgActive = FindActiveCells(stats,1);\n% normalize by neuropil skewness\nimgActive = imgActive ./ skewness(frameNpil);\nfigure('Name','Activity image','NumberTitle','off','Color','white');\nimagesc(imgActive), colorbar\nset(gca,'xtick',[],'ytick',[])\n\n%% Frame neuropil plot\nfigure('Name','Frame neuropil','NumberTitle','off','Color','white');\ntime = 1/S.frame_rate:1/S.frame_rate:size(stats,3)/S.frame_rate;\nplot(time,frameNpil)\nxlabel('Time / s')\nylabel(sprintf('%s / %%',S.statsType));\n\ntilefigs\n% toc\n% return\n\nwindowSize = 5;\nfor x1 = 1:size(stats,1)\n    for x2 = 1:size(stats,2)\n        stats(x1,x2,:) = filtfilt(ones(1,windowSize)/windowSize,1,...\n            stats(x1,x2,:));\n    end\nend\n\n%% Activity movie\n% figure('Name','Activity Movie','NumberTitle','off','Color','white');\n% true-color grayscale image\navgMovie = gray2rgb(avgImg,512,'gray');\navgMovie = repmat(avgMovie,[1 1 1 size(stats,3)]);\navgIntensity = zeros(1,3);\ncmap = hot(256);\nstatsScale = [10 30];\nstatsLUT = statsScale(1):(statsScale(2)-statsScale(1))/255:statsScale(2);\ndisp('Making movie...')\nfor n = 1:size(stats,3)\n    fprintf('Frame %1.0f / %1.0f\\n',n,size(stats,3));\n    stats(:,:,n) = imfilter(stats(:,:,n),fspecial('gaussian',[10 10]));\n    currentAvgMovie = avgMovie(:,:,1:3,n);\n    currentStats = stats(:,:,n);\n    currentRGB = zeros(size(stats,1),size(stats,2),3);\n    for x1 = 1:size(currentStats,1)\n        for x2 = 1:size(currentStats,2)\n            if currentStats(x1,x2) > statsScale(1)\n                avgIntensity = reshape(currentAvgMovie(x1,x2,:),1,3);\n                [val,idx] = min(abs(statsLUT-currentStats(x1,x2)));\n                if idx > size(cmap,1)\n                    idx =  size(cmap,1);\n                end\n                currentStatsRGB = cmap(idx,:);\n                currentRGB(x1,x2,:) = nanmean([avgIntensity; currentStatsRGB]);\n            else\n                currentRGB(x1,x2,:) = currentAvgMovie(x1,x2,1:3);\n            end\n        end\n    end\n    avgMovie(:,:,1:3,n) = currentRGB;\nend\navgMovie(avgMovie>1) = 1;\navgMovie(avgMovie<0) = 0;\nimplay(avgMovie,25)\ntoc\n\nfunction statsImg = doStats(stats,maskImg,frameRate,segmentDur,f0cutoff)\nstatsImg = zeros(size(stats));\nfor n = 1:size(stats,2)\n    if ~maskImg(n)\n        statsImg(:,n) = nan(size(stats,1),1);\n    else\n        fVector = stats(:,n)';\n        % 5 s segements; background at 25th percentile\n        f0Vector = findF0(fVector,frameRate,segmentDur,f0cutoff);\n        fVector = ((fVector-f0Vector) ./ f0Vector) .* 100;\n        statsImg(:,n) = fVector';\n    end\nend\n% statsImg(:,~maskImg) = NaN;\n\n%% ParseInputs - function\nfunction S = parseInputs(inargs)\nif ~isempty(inargs) && isstruct(inargs)\n    % convert structure to pseudo-input cellarray\n    inargs = struct2cellArray(inargs);\nend\n\n% input files - usually Gui-select\nSpInput = find(strcmpi(inargs, 'RawFiles'));\nif numel(SpInput)\n    RawFiles = inargs{SpInput+1};\n    if ~iscellstr(RawFiles)\n        error('Modifier for raw files property must be a cell string');\n    end\nelse\n    [FileName,PathName,FilterIndex] = uigetfile({'*.tif';'*.tiff';'*.fcs'},...\n        'Select raw file(s) - only 1 channel','MultiSelect','off');\n    if ~iscellstr(FileName)\n        FileName = {FileName};\n    end\n    if ~FileName{1}; return; end\n    if ~iscellstr(FileName); FileName = cellstr(FileName); end\n%     if strfind(PathName,RawDataDir)\n%        PathName = strrep(PathName,RawDataDir,'');\n%     end\n    for n = 1:length(FileName) % interpret GUI-selected multiple files as different channels\n        RawFiles{1,n} = fullfile(PathName,FileName{n});\n    end\nend\nS.RawFiles = RawFiles;\n\n% frame rate\nSpInput = find(strcmpi(inargs, 'frame_rate'));\nif numel(SpInput)\n    frame_rate = inargs{SpInput+1};\n    if isscalar(frame_rate)\n        frame_rate = repmat(frame_rate,1,length(RawFiles));\n    else\n        if length(frame_rate) ~= length(RawFiles)\n            error('Frame rate must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    frame_rate = 9.16;\nend\nS.frame_rate = frame_rate;\n\n% Preprocessing options - delete frame\nSpInput = find(strcmpi(inargs, 'DelFrame'));\nif numel(SpInput)\n    del_frame = inargs{SpInput+1};\n    if isscalar(del_frame)\n        del_frame = repmat(del_frame,1,length(RawFiles));\n    else\n        if length(del_frame) ~= length(RawFiles)\n            error('DelFrame must be a scalar or vector of length input file number');\n        end\n    end\nelse\n    del_frame = 1;\nend\nS.del_frame = del_frame;\n% bg. correction - only specify cutoff for percentile method here (default\n% 1)\nSpInput = find(strcmpi(inargs, 'BgCorrect'));\nif numel(SpInput)\n    bgCorrect = inargs{SpInput+1};\nelse\n    bgCorrect = 1;\nend\nS.bgCorrect = bgCorrect;\n\nif ~isfield(S,'statsType')\n    S.statsType = 'dff';\nend\n\n% channels for stats calculation\n% DFF is calculated on the channel specified by config.channelVector(1)\n% DRR is calculated on the channels specified by config.channelVector(1)\n% and config.channelVector(2)\nif ~isfield(S,'channelVector')\n    S.channelVector = [1 2];\nend\n\nif strcmpi(S.statsType,'drr') && numel(S.channelVector) < 2\n    error('channelVector must specify at least 2 channels for DRR stats!');\nend\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "patchline.m", "ext": ".m", "path": "OCIA-master/utils/plotting/patchline.m", "size": 3694, "source_encoding": "utf_8", "md5": "7e03ec713dc3b5ed750f273f9a944d50", "text": "function p = patchline(xs,ys,varargin)\n% Plot lines as patches (efficiently)\n%\n% SYNTAX:\n%     patchline(xs,ys)\n%     patchline(xs,ys,zs,...)\n%     patchline(xs,ys,zs,'PropertyName',propertyvalue,...)\n%     p = patchline(...)\n%\n% PROPERTIES: \n%     Accepts all parameter-values accepted by PATCH.\n% \n% DESCRIPTION:\n%     p = patchline(xs,ys,zs,'PropertyName',propertyvalue,...)\n%         Takes a vector of x-values (xs) and a same-sized\n%         vector of y-values (ys). z-values (zs) are\n%         supported, but optional; if specified, zs must\n%         occupy the third input position. Takes all P-V\n%         pairs supported by PATCH. Returns in p the handle\n%         to the resulting patch object.\n%         \n% NOTES:\n%     Note that we are drawing 0-thickness patches here,\n%     represented only by their edges. FACE PROPERTIES WILL\n%     NOT NOTICEABLY AFFECT THESE OBJECTS! (Modify the\n%     properties of the edges instead.)\n%\n%     LINUX (UNIX) USERS: One test-user found that this code\n%     worked well on his Windows machine, but crashed his\n%     Linux box. We traced the problem to an openGL issue;\n%     the problem can be fixed by calling 'opengl software'\n%     in your <http://www.mathworks.com/help/techdoc/ref/startup.html startup.m>.\n%     (That command is valid at startup, but not at runtime,\n%     on a unix machine.)\n%\n% EXAMPLES:\n%%% Example 1:\n%\n% n = 10;\n% xs = rand(n,1);\n% ys = rand(n,1);\n% zs = rand(n,1)*3;\n% plot3(xs,ys,zs,'r.')\n% xlabel('x');ylabel('y');zlabel('z');\n% p  = patchline(xs,ys,zs,'linestyle','--','edgecolor','g',...\n%     'linewidth',3,'edgealpha',0.2);\n%\n%%% Example 2: (Note \"hold on\" not necessary here!)\n%\n% t = 0:pi/64:4*pi;\n% p(1) = patchline(t,sin(t),'edgecolor','b','linewidth',2,'edgealpha',0.5);\n% p(2) = patchline(t,cos(t),'edgecolor','r','linewidth',2,'edgealpha',0.5);\n% l = legend('sine(t)','cosine(t)');\n% tmp = sort(findobj(l,'type','patch'));\n% for ii = 1:numel(tmp)\n%     set(tmp(ii),'facecolor',get(p(ii),'edgecolor'),'facealpha',get(p(ii),'edgealpha'),'edgecolor','none')\n% end\n%\n%%% Example 3 (requires Image Processing Toolbox):\n%%%   (NOTE that this is NOT the same as showing a transparent image on \n%%%         of the existing image. (That functionality is\n%%%         available using showMaskAsOverlay or imoverlay).\n%%%         Instead, patchline plots transparent lines over\n%%%         the image.)\n%\n% img = imread('rice.png');\n% imshow(img)\n% img = imtophat(img,strel('disk',15));\n% grains = im2bw(img,graythresh(img));\n% grains = bwareaopen(grains,10);\n% edges = edge(grains,'canny');\n% boundaries = bwboundaries(edges,'noholes');\n% cmap = jet(numel(boundaries));\n% ind = randperm(numel(boundaries));\n% for ii = 1:numel(boundaries)\n% patchline(boundaries{ii}(:,2),boundaries{ii}(:,1),...\n%     'edgealpha',0.2,'edgecolor',cmap(ind(ii),:),'linewidth',3);\n% end\n%\n% Written by Brett Shoelson, PhD\n% brett.shoelson@mathworks.com\n% 5/31/2012\n% \n% Revisions:\n% 6/26 Improved rice.png example, modified FEX image.\n%\n% Copyright 2012 MathWorks, Inc.\n%\n% See also: patch, line, plot\n\n[zs,PVs] = parseInputs(varargin{:});\nif rem(numel(PVs),2) ~= 0\n    % Odd number of inputs!\n    error('patchline: Parameter-Values must be entered in valid pairs')\nend\n\n% Facecolor = 'k' is (essentially) ignored here, but syntactically necessary\nif isempty(zs)\n    p = patch([xs(:);NaN],[ys(:);NaN],'k');\nelse\n    p = patch([xs(:);NaN],[ys(:);NaN],[zs(:);NaN],'k');\nend\n\n% Apply PV pairs\nfor ii = 1:2:numel(PVs)\n    set(p,PVs{ii},PVs{ii+1})\nend\nif nargout == 0\n    clear p\nend\n\nfunction [zs,PVs] = parseInputs(varargin)\nif isnumeric(varargin{1})\n    zs = varargin{1};\n    PVs = varargin(2:end);\nelse\n    PVs = varargin;\n    zs = [];\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "makePrettyFigure.m", "ext": ".m", "path": "OCIA-master/utils/plotting/makePrettyFigure.m", "size": 1030, "source_encoding": "utf_8", "md5": "4025d23131e4c264865ded9bcedc5c31", "text": "function figOut = makePrettyFigure(varargin)\n% for a given figure, apply some operations to make it look nicer\n\nif nargin == 0;\n    fig = gcf;\nelse\n    fig = varargin{1};\nend;\n\nfigOut = fig;\n\n% a cell array of default axis properties (column 1 is the property name,\n% column 2 is the property value)\ndefaultProps = { ...\n    'box','off'; ...\n    'linewidth',1.5; ...\n    'fontname','arial'; ...\n    'fontsize',14; ...\n    };\n\n% some basic niceties on the figure\nset(fig,'color','white')\n\n% get children (the axes)\nfigHandles = findall(fig);\n\nfor n = 1:numel(figHandles)\n    setDefaultAxesProps(figHandles(n),defaultProps);\nend\n\nset(findall(0,'tag','legend'),'box','on')\n\n\nfunction setDefaultAxesProps(a,defaultProps)\nfor n = 1:size(defaultProps,1)\n    try\n        set(a,defaultProps{n,1},defaultProps{n,2})\n    end\nend\n% children = get(a,'children');\n% for n = 1:numel(children)\n%     for m = 1:size(defaultProps,1)\n%         try\n%             set(children(n),defaultProps{m,1},defaultProps{m,2})\n%         end\n%     end\n% end\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "PsPlot2Raster.m", "ext": ".m", "path": "OCIA-master/utils/plotting/PsPlot2Raster.m", "size": 7798, "source_encoding": "utf_8", "md5": "50a24bc695620bf37df86f36fba74d0b", "text": "function varargout = PsPlot2Raster(varargin)\n% plot different columns (stims) in ps_plot_cell in different subplots\n% in1 ... psPlot cell array (roi x stim), each cell contains trial x\n% timepoints ps-matrix\n% in2 ... sampling frequency\n% in3 ... offset (frame of stimulus presentation)\n% in4 ... optional cellstring with roi names\n% in5 ... PlotType (1 ... trials-by-Rois, 2 ... Rois-by-trials), default 1\n% in6 ... plot PSTH (1 or 0, only takes effect if PlotType is 1 and\n% Roi count is 1); default 0\n% out1 ... figure handle\n% out2 ... PSTH (roi x stim cell array), always calculated trials x rois\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\ninargs = varargin;\n[ps_plot_cell,freq,offset,roiList,plotType,doPSTH] = ParseInputs(inargs{1:length(inargs)});\n\nroiCount = size(ps_plot_cell,1);\nmaxStim = size(ps_plot_cell,2);\n% roiCount = 2;\n% maxStim = size(ps_plot_cell,2);\ncmap = lines(roiCount);\n% or we plot alternately in 1 of 2 colors\n% cmap = [0 0 0; 1 0 0];\n% cmap = repmat(cmap,roiCount/2,1);\n% if size(cmap,1) < roiCount\n%    cmap(roiCount,:) = cmap(1,:);\n% end\n\n% ytick_pos = zeros(1,roiCount);\n\nh_fig = figure('Name','Raster plot','NumberTitle','off');\nhold on\nif plotType == 1\n    \n        psth_cell = doPlot(ps_plot_cell,roiCount,maxStim,freq,offset,cmap,...\n            roiList,doPSTH);\n    \n    if nargout == 2\n        varargout{2} = psth_cell;\n    end\nelseif plotType == 2\n    trialNo = size(ps_plot_cell{1,1},1);\n    psByTrial = cell(trialNo,maxStim);\n    pos = 1;\n    for stim = 1:maxStim\n        for trial = 1:trialNo\n            for roi = 1:roiCount\n                if ~isnan(ps_plot_cell{roi,stim})\n                    psByTrial{trial,stim}(roi,:) = ...\n                        ps_plot_cell{roi,stim}(trial,:);\n                else\n                    psByTrial{trial,stim}(roi,:) = 0;\n                end\n            end\n        end\n    end\n    for n = 1:trialNo\n        trialList{n} = sprintf('%02g',n);\n    end\n    cmap = lines(trialNo);\n    doPlot(psByTrial,trialNo,maxStim,freq,offset,cmap,trialList,doPSTH);\nend\n\nif nargout\n    varargout{1} = h_fig;\nend\n\n%% Plot function\nfunction varargout = doPlot(ps_plot_cell,roiCount,maxStim,freq,offset,...\n    cmap,roiList,doPSTH)\npsth_cell = cell(roiCount,maxStim);\n\n% number of ps-timepoints\n% while true\n    doBreak = 0;\n    for roi = 1:roiCount\n        eventRange = unique(ps_plot_cell{roi,1});\n        if ~isnan(eventRange(1))\n            timepoints = size(ps_plot_cell{roi,1},2);\n            doBreak = 1;\n            break\n        end\n    end\n    if ~doBreak\n       error('Could not determine number of timepoints');\n    end\n%     if doBreak\n%         break\n%     end\n% end\n% sum rasters for all rois for stims\nAllStimSum = zeros(maxStim,timepoints);\ntrials = zeros(1,maxStim);\nfor stim = 1:maxStim\n    currentSum = zeros(1,timepoints);\n    for roi = 1:roiCount\n        currentRoiSum = nansum(ps_plot_cell{roi,stim},1);\n        if isnan(currentRoiSum)\n           continue \n        end\n        if ~currentRoiSum\n           continue \n        end\n        currentSum = nansum([currentSum;currentRoiSum],1);\n        if ~trials(stim)\n           trials(stim) = size(ps_plot_cell{roi,stim},1);\n        end\n    end\n    AllStimSum(stim,:) = currentSum;\nend\nabsoluteYmax = [];\nmaxTrials = max(trials);\n% setup subplot\nif maxStim > 3\n    subIdx = repmat(ceil(sqrt(maxStim)),1,2);\nelse\n    subIdx = [1 maxStim];\nend\nfor stim = 1:maxStim\n    subplot(subIdx(1),subIdx(2),stim); hold on\n    pos = 1;\n    plottedRoiCount = 1; % some Rois are NaN\n    min_time = inf; max_time = -inf;\n    for roi = 1:roiCount\n        posStart = pos;\n        current_raster = ps_plot_cell{roi,stim};\n        if isnan(current_raster)\n            current_raster = zeros(trials(stim),timepoints);\n        end\n%         roiList2{plottedRoiCount} = roiList{roi};\n        plottedRoiCount = plottedRoiCount + 1;\n        timepoints = size(current_raster,2);\n        if min_time > ((1-offset) / freq)-1/freq;\n            min_time = ((1-offset) / freq)-1/freq;\n        end\n        if max_time < ((size(current_raster,2)-offset) / freq)+1/freq;\n            max_time = ((size(current_raster,2)-offset) / freq)+1/freq;\n        end\n        lastTrialEmpty = 0;\n        for current_run = 1:maxTrials\n            if current_run > trials(stim)\n                current_sweep = zeros(1,size(current_raster,2));\n            else\n                current_sweep = current_raster(current_run,:);\n            end\n            spikeTimes = find(current_sweep);\n            if isempty(spikeTimes)\n                lastTrialEmpty = 1;\n            else\n                lastTrialEmpty = 0;\n            end\n            spikeTimes = (spikeTimes-offset) / freq;\n            h_err = errorbar(spikeTimes,repmat(pos,1,length(spikeTimes)),...\n                repmat(1/(freq*2),1,length(spikeTimes)),...\n                repmat(1/(freq*2),1,length(spikeTimes)),'.k'); hold on\n            set(h_err,'Color',cmap(roi,:),'Marker','none');\n            pos = pos + 1;\n        end\n        % if last trial was empty, plot white point at 0,pos-1\n        %                 if lastTrialEmpty\n        %                     herrorbar(0,pos-1,0,0,'.w'); hold on\n        %                 end\n        posStop = pos;\n        ytick_pos(plottedRoiCount) = round(mean([posStart posStop]));\n        ylims = get(gca,'ylim');\n        psth = nansum(current_raster,1);\n        % 0.5 s moving average filter\n%         windowSize = ceil(0.5 * 0.5 * freq);\n%         psth = filtfilt(ones(1,windowSize)/windowSize,1,psth);\n        if doPSTH\n            psth_scaled = linScale(psth,posStart,posStop);\n            psth_time = (min_time+1/freq):1/freq:(max_time-1/freq);\n            plot(psth_time,psth_scaled,':k','LineWidth',1.5)\n        end\n        psth = psth ./ size(current_raster,1);\n        psth_cell{roi,stim} = reshape(psth,1,numel(psth));\n    end\n    % plot the summed PSTH for all Rois for this stimulus\n    % normalize between 0 and pos/10, then offset by pos\n    if stim == 1\n        AllStimSum = linScale(AllStimSum,0,pos/10);\n    end\n    currentSum = AllStimSum(stim,:);\n    currentSum = currentSum + pos;\n%     currentSum = filtfilt(ones(1,windowSize)/windowSize,1,currentSum);\n    currentSum_time = (min_time+1/freq):1/freq:(max_time-1/freq);\n    plot(currentSum_time,currentSum,':k','LineWidth',1.5)\n    ytick_pos(length(ytick_pos)+1) = mean(currentSum);\n    ytick_pos(1) = [];\n    if isempty(absoluteYmax)\n        absoluteYmax = max(currentSum);\n    else\n        currentYmax = max(currentSum);\n        if currentYmax > absoluteYmax\n            absoluteYmax = currentYmax;\n        end\n    end\n    \n    set(gca,'xlim',[min_time max_time])\n    if stim == 1\n        roiList = reshape(roiList,1,numel(roiList));\n        set(gca,'ytick',ytick_pos,'yticklabel',[roiList 'sum'])\n    else\n        set(gca,'ytick',[])\n    end\n    set(gca,'TickLength',[0 0])\n    title(['Stim ' int2str(stim)])\n    axisHandle(stim) = gca;\nend\n\nfor n = 1:length(axisHandle)\n    set(axisHandle(n),'ylim',[0 absoluteYmax]);\nend\n\nif nargout\n    varargout{1} = psth_cell;\nend\n\n% fprintf('\\nPlotted %s total runs from %s Rois\\n',...\n%     int2str(pos-1),int2str(size(ps_plot_cell,1)));\n\n%% Parse inputs\nfunction [ps_plot_cell,freq,offset,roiList,plotType,doPSTH] = ...\n    ParseInputs(varargin)\nerror(nargchk(3,6,nargin))\nps_plot_cell = varargin{1};\nif ~iscell(ps_plot_cell)\n    ps_plot_cell = {ps_plot_cell};\nend\nfreq = varargin{2};\noffset = varargin{3};\n\nroiCount = size(ps_plot_cell,1);\n\nif nargin >= 4 && ~isempty(varargin{4})\n    roiList = varargin{4};\nelse\n    for n = 1:roiCount\n        roiList{n} = sprintf('%02g',n);\n    end\nend\nif nargin >= 5 && ~isempty(varargin{5})\n    plotType = varargin{5};\nelse\n    plotType = 1;\nend\ndoPSTH = 0;\nif nargin >= 6 && plotType == 1 && ~isempty(varargin{6})\n    doPSTH = varargin{6};\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "adjustSubplotAxes.m", "ext": ".m", "path": "OCIA-master/utils/plotting/adjustSubplotAxes.m", "size": 1602, "source_encoding": "utf_8", "md5": "0fd6cc79ba5adfbf3fc307b09b8e87bd", "text": "function adjustSubplotAxes(h,varargin)\n% adjust axis of all subplots in figure h\n% specify axis by string / value combination, e.g.:\n% 'X',[-5 10] sets all x-limits to [-5 10]\n% 'Y',[] sets y-limits to Min / Max\n% unlisted axes are left untouched\n% varargin{1} ... the axis limits to be set (default: Min / Max)\n\ninargs = varargin;\nxLims = NaN;\nyLims = NaN;\nzLims = NaN;\nfor n = 1:numel(inargs)\n    if isstr(inargs{n})\n        switch lower(inargs{n})\n            case 'x'\n                xLims = inargs{n+1};\n            case 'y'\n                yLims = inargs{n+1};\n            case 'z'\n                zLims = inargs{n+1};\n        end\n    end\nend\n\n% get all axes of the figure h\naxisHandles = findobj(h,'Type','Axes');\nlegendHandles = findobj(h,'Tag','legend');\nfor n = 1:numel(legendHandles)\n   props{n} = get(legendHandles(n));\nend\n\n% set axes\nif ~any(isnan(xLims))\n   setLimits('xlim',xLims,axisHandles);\nend\n\nif ~any(isnan(yLims))\n   setLimits('ylim',yLims,axisHandles);\nend\n\nif ~any(isnan(zLims))\n   setLimits('zlim',zLims,axisHandles);\nend\n\n% reset legend props\nfor n = 1:numel(legendHandles)\n    fields = fieldnames(props{n});\n    for m = 1:numel(fields)\n        try\n            set(legendHandles(n),fields{m},props{n}.(fields{m}))\n        end\n    end\nend\n\nfunction setLimits(type,lims,axisHandles)\nif isempty(lims)\n    % find axis limits of all axes\n    lims = [inf -inf];\n    for n = 1:numel(axisHandles)\n        currentLims = get(axisHandles(n),type);\n        lims(1) = min([lims(1) currentLims(1)]);\n        lims(2) = max([lims(2) currentLims(2)]);\n    end\nend\nset(axisHandles,type,lims)\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "groupedErrBar.m", "ext": ".m", "path": "OCIA-master/utils/plotting/groupedErrBar.m", "size": 5106, "source_encoding": "utf_8", "md5": "94caedf9ae477f0d7de99a816ae21638", "text": "function h = groupedErrBar(dataCell,varargin)\n% plot multiple data sets in groups with errorbar and (optionally) individual data points\n% dataCell ... cell array with 1 vector per cell containing n observations\n% cells in the same row will be grouped, cells in different rows will be different groups (similar\n% to bar function)\n% optional input args ({'parameter',value} pairs):\n% 'plotAll' ... plot individual observations in addition to mean / error (0 or 1)\n% 'avg' ... 'mean', 'median'\n% 'var' ... 'sd', 'sem', 'ci' (ci is 95% CI of the mean and should not be used with median)\n% 'separation' ... 0.2 (determines the separation of groups)\n% 'xticklabel' ... {'1','2','3'} (name of each group)\n% 'legend' ... legend entry for each column\n% doStats ... 0 or 1 (default: 0) --> print out stats comparison for each group\n% output: handle vector\n% Example:\n% A = {randn(10,1) randn(8,1) randn(12,1); randn(5,1) randn(9,1) randn(7,1)};\n% groupedErrBar(A,'var','sem','plotall',1,'xticklabel',{'G1','G2'},'legend',{'t1','t2','t3'})\n% this function requires the statistics toolbox\n\n% TODO: further measures of central tendency and variability (e.g. CI); assymetric error bars; stats\n\nif nargin > 1\n    config = parseInputArgs(varargin);\nelse\n    config = parseInputArgs; % defaults\nend\n\ndataSize = size(dataCell);\n% calculate corresponding x-axis\nxCoarse = 1:dataSize(1); % the different groups\nx = [];\nfor n = 1:dataSize(1)\n    x = [x, linspace(xCoarse(n)-config.separation,xCoarse(n)+config.separation,dataSize(2))];\nend\n\n% run ANOVA on entire data set\nif config.doStats\n    anovaData = [];\n    anovaLabels = cell(1,2);\n    for row = 1:dataSize(1)\n        for col = 1:dataSize(2)\n            anovaData = [anovaData; dataCell{row,col}];\n            anovaLabels{1,1} = [anovaLabels{1,1}; repmat(row,numel(dataCell{row,col}),1)];\n            anovaLabels{1,2} = [anovaLabels{1,2}; repmat(col,numel(dataCell{row,col}),1)];\n        end\n    end\n    [~,table] = anovan(anovaData,anovaLabels,'display','off','model','full',...\n        'varnames',{'Row','Column'});\n    fprintf('\\nANOVA table:\\n')\n    disp(table)\nend\n\ncolorMap = lines(dataSize(2)); % different color for each entry per group\n\n% now just go sequentially through the data and plot\nxPos = 0;\nfor row = 1:dataSize(1)\n    for col = 1:dataSize(2)\n        xPos = xPos + 1;\n        data = dataCell{row,col};\n        config.avgColor = colorMap(col,:);\n        if row == dataSize(1)\n            config.setLegend = 1;\n        else\n            config.setLegend = 0;\n        end\n        doPlot(x(xPos),data,config);\n    end\n    if config.doStats\n       anovaData = []; labels = [];\n       for col = 1:dataSize(2)\n           anovaData = [anovaData; dataCell{row,col}];\n           labels = [labels; repmat(col,numel(dataCell{row,col}),1)];\n       end\n       [~,table,stats] = anovan(anovaData,labels,'display','off');\n       if isempty(config.xticklabel)\n           fprintf('\\nStats - %1.0f\\n',row)\n       else\n           fprintf('\\nStats - %s\\n',config.xticklabel{row})\n       end\n       fprintf('F=%1.3f p=%1.5e df=%1.0f\\n',table{2,6},table{2,7},table{3,3})\n    end\nend\n\n% label axis\nif isempty(config.xticklabel)\n    set(gca,'xtick',1:dataSize(1))\nelse\n    set(gca,'xtick',1:dataSize(1),'xticklabel',config.xticklabel)\nend\nset(gca,'xlim',[0 x(end)+x(1)]), ylabel(sprintf('%s +- %s',config.avg,config.var))\nlegend(config.legend)\n\nmakePrettyFigure(gcf);\nend\n\n\nfunction doPlot(x,data,config)\n\nswitch lower(config.avg)\n    case 'mean'\n        avgData = nanmean(data);\n    case 'median'\n        avgData = nanmedian(data);\n    otherwise\n        error('Avg measure %s not supported',config.avg)\nend\n\nswitch lower(config.var)\n    case 'sd'\n        varData = nanstd(data);\n    case 'sem'\n        varData = sem(data);\n    case 'ci'\n        [~,~,varData] = normfit(data);\n    otherwise\n        error('Var measure %s not supported',config.var)\nend\n\nif config.plotAll\n    hScatter = scatter(repmat(x,numel(data),1),data,'Marker','o','MarkerEdgeColor',[0.5 0.5 0.5]); hold on\n    set(get(get(hScatter,'Annotation'),'LegendInformation'),...\n        'IconDisplayStyle','off');\nend\n\nxErr = x - config.separation./10; % slightly offset\nif numel(varData) == 1\n    hErr = errorbar(xErr,avgData,varData,...\n        'Marker','s','Color',config.avgColor,'LineStyle','none');\nelse\n    hErr = errorbar(xErr,avgData,varData(1),varData(2),...\n        'Marker','s','Color',config.avgColor,'LineStyle','none');\nend\nremoveErrorBarEnds(hErr); hold on\nif ~config.setLegend\n    set(get(get(hErr,'Annotation'),'LegendInformation'),...\n        'IconDisplayStyle','off');\nend\n\nend\n\nfunction config = parseInputArgs(varargin)\n\nif nargin\n    inArgs = varargin;\nelse\n    inArgs = {[]};\nend\n\ninArgs = inArgs{1};\nconfig = struct;\nrequiredArgs = {'plotAll','avg','var','separation','xticklabel','legend','doStats'};\ndefaults = {1,'mean','sem',0.2,[],[],0};\nfor n = 1:numel(requiredArgs)\n    if isempty(find(strcmpi(inArgs,requiredArgs{n}), 1))\n        config.(requiredArgs{n}) = defaults{n};\n    else\n        config.(requiredArgs{n}) = inArgs{find(strcmpi(inArgs,requiredArgs{n}),1)+1};\n    end\nend\n\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "nfb_errorbar.m", "ext": ".m", "path": "OCIA-master/utils/plotting/nfb_errorbar.m", "size": 7945, "source_encoding": "utf_8", "md5": "90c8e91005ce42d017454ea42ffb2e73", "text": "% nfb_errorbar is a generic function which produces a bar chart with error\n% bars\n% its main advantage is that it readily plots each bar and errorbar\n% separately, making it easy to change colours, filling etc for each bar\n% individually later on\n%\n% USAGE:\n% [out1] = nfb_errorbar(in1, in2)\n%\n% Comulsory input arguments\n% in1 ... bar matrix (2D)\n% in2 ... errorbar matrix (2D)\n% of course in1 and in2 could also be a simple vector\n% each matrix entry corresponds to 1 bar / errorbar and values in the same\n% row will be grouped (i.e. bars sit closer on the plot than values from\n% different rows)\n% bar and errorbar matrices MUST be of equal size\n%\n% Optional input arguments (in any order)\n% 'Figure', add errorbar to an existing figure (provide handle to figure)\n% 'Spacing' (default: 0.85)\n% 'Color', string specification of valid Matlab colormap (see doc colormap)\n% (default is 'jet')\n% 'Labels', cellstring of row*columns length (one label\n% per bar) or row length (one label per bar group)\n% i.e. 'Labels',{'value1' 'value2' 'value3'}\n% 'Legend', cellstring provides legend tags for bars (must not be longer\n% than number of bars, i.e. rows*columns of input matrices)\n% 'RemoveTee', remove horizontal lines at end of error bars (0 or 1,\n% default 0), requires removeErrorBarEnds function\n% String args: 'Title', 'XLabel', 'YLabel', 'Name' (Figure Name, if\n% specified, also switches NumberTitle off)\n%\n% out1 ... handle to figure\n\n% this file written by Henry Luetcke (hluetck@gwdg.de)\n\n\nfunction [bar_chart] = nfb_errorbar(varargin)\n\nif nargin < 2\n    disp('You MUST provide at least 2 input arguments');\n    disp(' ');\n    help nfb_errorbar\n    return\nend\n\nbars = varargin{1};\nerrs = varargin{2};\nif size(bars) ~= size(errs)\n    error('Bar and errorbar matrices must be of equal size');\nend\n\nif length(size(bars)) > 2 || length(size(errs)) > 2\n    error('Bar and errorbar matrices must be 2D matrices');\nend\n\n% hluetck::12.02.2008\n% Parameter for specifying spacing between bars explicitely\n% check if spacing of bars has been specified explicitely\nspacing = 0.85;\n% tauer::20.02.2008\n% simplified\nSpInput = find(strcmp(varargin, 'Spacing'));\nif numel(SpInput) \n    spacing = varargin{SpInput+1};\nend\n\n% hluetck::03.03.2008\n% Option to add errorbar plot to existing figure\nSpInput = find(strcmp(varargin, 'Figure'));\nif numel(SpInput)\n    bar_chart = varargin{SpInput+1};\nelse\n    bar_chart = figure;\nend\nhold on\n\n% hluetck::12.02.2008\n% Parameter for specifying spacing between bars explicitely\n% first we generate the x values (depending on the number of rows and\n% columns)\n% tauer::20.02.2008\n% simplified\n\ncurrent_x = 1;\nfor n = 1:size(bars,1)\n    for m = 1:size(bars,2)\n        x_vals((n-1)*size(bars,2)+m) = current_x;\n        current_x = current_x + spacing;\n    end\n    current_x = current_x + 0.5;\nend\n\n% rearrange matrices into vectors and plot\nrows = size(bars,1);\ncols = size(bars,2);\nbars = reshape(bars',(size(bars,1)*size(bars,2)),1);\nerrs = reshape(errs',(size(errs,1)*size(errs,2)),1);\n% hluetck::24.02.2008\n% specify bar color according to Matlab color map\nSpInput = find(strcmp(varargin, 'Color'));\nif numel(SpInput) \n    cmap = varargin{SpInput+1};\n    cstring = sprintf('%s(%s)',cmap,int2str(cols));\n    try evalc(cstring);\n    catch\n        fprintf(...\n            '\\n\"%s\" is not a valid Matlab color map. Using ''jet''.\\n',...\n            cmap);\n        cmap = 'jet';\n    end\nelse\n    cmap = 'jet';\nend\ncstring = sprintf('%s(%s)',cmap,int2str(cols));\ngroup_colors = colormap(cstring);\n% hluetck::23.02.2008\n% changed allocation of bar color\n% now cycle colors within bar groups and not between groups\n% (makes more sense if used in combination with legend?)\n% also adjusted color specification (now RGB vector)\n% plot\ncurrent_col = 0;\ncol = 1;\nfor n = 1:length(bars)\n    if current_col == cols\n        col = 1;\n        current_col = 0;\n    end\n    bar(x_vals(n),bars(n),'FaceColor',group_colors(col,:));\n    current_col = current_col + 1;\n    col = col + 1;\nend\n% for n = length(errs):-1:1\n%     errorbar(x_vals(n),bars(n),errs(n),'k','LineWidth',1.5);\n% end\ne_bar = errorbar(x_vals,bars,errs,'k','LineWidth',1.5,'LineStyle','none');\n% parse opional input arguments\nif nargin > 2\n    opt_args = varargin(3:nargin);\n    for n = 1:length(opt_args)\n        if strcmp(opt_args{n},'RemoveTee') == 1\n           removeTee = opt_args{n+1};\n           if removeTee && exist('removeErrorBarEnds','file') == 2\n               removeErrorBarEnds(e_bar);\n           end\n        end\n        % hluetck::23.02.2008\n        % included option to attach only one label per bar group\n        % in combination with legend option, this now allows efficient\n        % labeling of bar charts\n        % attach labels\n        if strcmp(opt_args{n},'Labels') == 1\n            labels = opt_args{n+1};\n            if length(labels) ~= (rows*cols)\n                if length(labels) ~= rows\n                    disp('Incorrect number of labels has been given');\n                    disp(' ');\n                    help nfb_errorbar\n                    return\n                else\n                    % hluetck::23.02.2008\n                    % attach only 1 label per bar group\n                    if rem(cols,2)\n                        center = ceil(cols/2);\n                        x_vals_short = [];\n                        x_vals_pos = 1;\n                        previous_center = 0;\n                        for m = 1:length(x_vals)\n                            if m == (ceil((cols*x_vals_pos)/2)+previous_center)\n                                previous_center = ceil((cols*x_vals_pos)/2);\n                                x_vals_short(x_vals_pos) = x_vals(m);\n                                x_vals_pos = x_vals_pos + 1;\n                            end\n                        end\n                    else\n                        center = cols/2;\n                        center = ceil(cols/2);\n                        x_vals_short = [];\n                        x_vals_pos = 1;\n                        previous_center = 0;\n                        for m = 1:length(x_vals)\n                            if m == (ceil((cols*x_vals_pos)/2)+previous_center)\n                                previous_center = ceil((cols*x_vals_pos)/2);\n                                x_vals_short(x_vals_pos) = x_vals(m)+0.5;\n                                x_vals_pos = x_vals_pos + 1;\n                            end\n                        end\n                    end\n                    set(gca,'XTick',[x_vals_short],'XTickLabel',labels);\n                end\n            else\n                % hluetck::23.02.2008\n                % attach one label for each bar\n                set(gca,'XTick',[x_vals],'XTickLabel',labels);\n            end\n        end\n\n        % hluetck::23.02.2008\n        % modified legend creation (it does actually work now)\n        % create column legend\n        if strcmp(opt_args{n},'Legend') == 1\n            col_legend = opt_args{n+1};\n            if length(col_legend) > cols*rows\n                disp('Incorrect number of legend values has been given');\n                disp(' ');\n                help nfb_errorbar\n                return\n            end\n            legend(col_legend,'Location','Best');\n        end\n\n        % add a title\n        if strcmp(opt_args{n},'Title') == 1\n            plot_title = opt_args{n+1};\n            h_title = title(plot_title);\n            set(h_title,'Interpreter','none');\n        end\n\n        % y-axis label\n        if strcmp(opt_args{n},'YLabel') == 1\n            plot_ylabel = opt_args{n+1};\n            ylabel(plot_ylabel);\n        end\n\n        % x-axis label\n        if strcmp(opt_args{n},'XLabel') == 1\n            plot_xlabel = opt_args{n+1};\n            xlabel(plot_xlabel);\n        end\n\n        % Figure Name\n        if strcmp(opt_args{n},'Name') == 1\n            fig_name = opt_args{n+1};\n            set(gcf,'Name',fig_name,'NumberTitle','off');\n        end\n    end\nend\n\n\n% e.o.f."}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "fig2m.m", "ext": ".m", "path": "OCIA-master/utils/plotting/fig2m.m", "size": 19924, "source_encoding": "utf_8", "md5": "857a1a6daef974fc98a2e735071bb6e8", "text": "function varargout = fig2m(guiName,outputDir,syscolorfig,cb_gen)\n% FIG2M - Generate programmatic GUI M-File from a FIG-File\n%\n% Version : 1.0\n% Created : 10/05/2006\n% Modified: 14/04/2010\n% Author  : Thomas Montagnon (The MathWorks France)\n%\n% >> outputFile = fig2m(guiName,outputDir,syscolorfig,cb_gen);\n%\n%    guiName     -> Name of the Fig-File (absolute or relative path)\n%    outputDir   -> Directory where the generated M-File will be saved\n%    syscolorfig -> Use system default background color (true or false)\n%    cb_gen      -> Generate callbacks (true or false)\n%    outputFile  -> Name of the generated M-File\n%\n% >> fig2m\n%\n%    If you call the function with no input arguments it will ask you for the\n%    parameters.\n\n\n% INPUT ARGUMENTS\n\nif nargin < 4\n  % Generate callbacks\n  answer = questdlg('Do you want to generate the callbacks?','Callbacks','Yes','No','Yes');\n  if strcmp(answer,'Yes')\n    cb_gen = true;\n  else\n    cb_gen = false;\n  end\nend\n\nif nargin < 3\n  % Use system default background color\n  answer = questdlg('Do you want to use the system default background color?','Background color','Yes','No','Yes');\n  if strcmp(answer,'Yes')\n    syscolorfig = true;\n  else\n    syscolorfig = false;\n  end\nend\n\nif nargin == 1\n  \n  % Check if guiName is a directory or a file\n  if exist(guiName,'dir')\n    [guiName,guiPath] = uigetfile(fullfile(guiName,'*.fig'),'Choose a FIG-File');\n    if isequal(guiName,0)\n      return\n    end\n    guiName = fullfile(guiPath,guiName);\n  end\n  \n  % Output directory\n  outputDir = uigetdir(guiPath,'Choose the output directory');\n  if isequal(outputDir,0)\n    return\n  end\n  \nend\n\nif nargin == 0\n  \n  % Fig-File\n  [guiName,guiPath] = uigetfile('*.fig','Choose a FIG-File');\n  if isequal(guiName,0)\n    return\n  end\n  guiName = fullfile(guiPath,guiName);\n  \n  % Output directory\n  outputDir = uigetdir(guiPath,'Choose the output directory');\n  if isequal(outputDir,0)\n    return\n  end\n  \nend\n\n\n\nif ~exist(guiName,'file')\n  error('Bad input argument');\nend\n\n% Get info about the FIG-File\n[guiPath,guiName,guiExt] = fileparts(guiName);\n\n% Stop the execution of the function if the FIG-File doesn't exist or is invalid\nif ~strcmpi(guiExt,'.fig')\n  uiwait(errordlg('Invalid GUI file','Error'));\n  varargout{1} = '';\n  return\nend\nif ~exist(fullfile(guiPath,[guiName,guiExt]),'file')\n  uiwait(errordlg('GUI file not exists','Error'));\n  varargout{1} = '';\n  return\nend\n\n% Output file name\noutputFile = [guiName '_build.m'];\n\n% Name of the output file\noutFile = fullfile(outputDir,outputFile);\n\n% Graphic objects categories\nCategories = {...\n  'FIGURE',...\n  'CONTEXT MENUS',...\n  'MENU ITEMS',...\n  'PANELS',...\n  'AXES',...\n  'LINES',...\n  'SURFACES',...\n  'STATIC TEXTS',...\n  'PUSHBUTTONS',...\n  'TOGGLE BUTTONS',...\n  'RADIO BUTTONS',...\n  'CHECKBOXES',...\n  'EDIT TEXTS',...\n  'LISTBOXES',...\n  'POPUP MENU',...\n  'SLIDERS',...\n  'UITABLE',...\n  'TOOLBAR', ...\n  'PUSH TOOLS', ...\n  'TOGGLE TOOLS'};\n\n% Default Tags\nDefTags = {...\n  'figure',...\n  'menu',...\n  'menuitem',...\n  'panel',...\n  'axes',...\n  'line',...\n  'surf',...\n  'text',...\n  'pushbutton',...\n  'togglebutton',...\n  'radiobutton',...\n  'checkbox',...\n  'edit',...\n  'listbox',...\n  'popupmenu',...\n  'slider',...\n  'uitable',...\n  'uitoolbar', ...\n  'uipushtool', ...\n  'uitoggletool'};\n\n% Callbacks names\ncb_names = { ...\n  'Callback', ...\n  'ButtonDownFcn', ...\n  'CreateFcn', ...\n  'DeleteFcn', ...\n  'KeyPressFcn', ...\n  'KeyReleaseFcn', ...\n  'ResizeFcn', ...\n  'WindowButtonDownFcn', ...\n  'WindowButtonMotionFcn', ...\n  'WindowButtonUpFcn', ...\n  'WindowScrollWheelFcn', ...\n  'SelectionChangeFcn', ...\n  'ClickedCallback', ...\n  'OffCallback', ...\n  'OnCallback', ...\n  'CellEditCallback', ...\n  'CellSelectionCallback'};\n\n% Default properties values\ndprop = default_prop_values();\n\n% Properties for each style of objects\nprop = control_properties();\n\nidList = fieldnames(prop);\n\n% Ouput file header\nheader = sprintf([...\n  'function fig_hdl = %s\\n' ...\n  '%% %s\\n' ...\n  '%%-------------------------------------------------------------------------------\\n' ...\n  '%% File name   : %-30s\\n' ...\n  '%% Generated on: %-30s\\n' ...\n  '%% Description :\\n' ...\n  '%%-------------------------------------------------------------------------------\\n' ...\n  '\\n\\n'], ...\n  outputFile(1:end-2),...\n  upper(outputFile(1:end-2)),...\n  outputFile,...\n  datestr(now));\n\ntry\n\n  % Open the GUI\n  cdir = pwd;\n  cd(guiPath);\n  figFcn = str2func(guiName);\n  figHdl = figFcn();\n  cd(cdir);\n%   figHdl = openfig(fullfile(guiPath,[guiName,'.fig']));\n\npause(.1);\n\n  % Set Window style & CloseRequestFcn for GUI in order to hide the figure\n  set(figHdl,'WindowStyle','normal','CloseRequestFcn','closereq','visible','off');\n  \n  pause(.1);\n\n  % List all the graphic object by category\n  list.Fg = figHdl;\n  list.Cm = sort(findobj(figHdl,'Type', 'uicontextmenu'));\n  list.Mb = sort(findobj(figHdl,'Type', 'uimenu'));\n  list.Pa = sort(findobj(figHdl,'Type', 'uipanel'));\n  list.Ax = sort(findobj(figHdl,'Type', 'axes'));\n  list.Li = sort(findobj(figHdl,'Type', 'line'));\n  list.Sf = sort(findobj(figHdl,'Type', 'surface'));\n  list.Ta = sort(findobj(figHdl,'Type', 'uitable'));\n  list.To = sort(findobj(figHdl,'Type' ,'uitoolbar'));\n  list.Pt = sort(findobj(figHdl,'Type' ,'uipushtool'));\n  list.Tt = sort(findobj(figHdl,'Type' ,'uitoggletool'));\n  list.St = sort(findobj(figHdl,'Style','text'));\n  list.Pb = sort(findobj(figHdl,'Style','pushbutton'));\n  list.Tb = sort(findobj(figHdl,'Style','togglebutton'));\n  list.Rb = sort(findobj(figHdl,'Style','radiobutton'));\n  list.Cb = sort(findobj(figHdl,'Style','checkbox'));\n  list.Ed = sort(findobj(figHdl,'Style','edit'));\n  list.Lb = sort(findobj(figHdl,'Style','listbox'));\n  list.Pu = sort(findobj(figHdl,'Style','popupmenu'));\n  list.Sl = sort(findobj(figHdl,'Style','slider'));\n  \n  % Init callback list\n  if cb_gen\n    cb_list = {};\n  end\n\n  % Start writing the output file\n  str = sprintf('%s',header);\n  \n  % Add command to init the handles structure\n  str = sprintf('%s%% Initialize handles structure\\nhandles = struct();\\n\\n',str);\n  \n  % Creat all controls\n  str = sprintf('%s%% Create all UI controls\\nbuild_gui();\\n\\n',str);\n  \n  % Add command line to assign output variable\n  str = sprintf('%s%% Assign function output\\nfig_hdl = handles.%s;\\n\\n',...\n    str,get(list.Fg,'Tag'));\n  \n  % Create a nested function to build all uicontrols\n  str = sprintf('%s%%%% ---------------------------------------------------------------------------\\n',str);\n  str = sprintf('%s\\tfunction build_gui()\\n%% Creation of all uicontrols\\n\\n',str);\n\n  % Write the generation code for all the objects, grouped by category\n  for indCat=1:length(idList)\n\n    % Handles vector and properties list for the current category\n    hdlsTemp = list.(idList{indCat});\n    propTemp = prop.(idList{indCat});\n\n    % Object creation function depending on the category\n    switch idList{indCat}\n      case 'Fg'\n        ctrlFcn = 'figure';\n      case 'Pa'\n        ctrlFcn = 'uipanel';\n      case 'Ta'\n        ctrlFcn = 'uitable';\n      case 'Mb'\n        ctrlFcn = 'uimenu';\n      case 'Cm'\n        ctrlFcn = 'uicontextmenu';\n      case 'Ax'\n        ctrlFcn = 'axes';\n      case 'Li'\n        ctrlFcn = 'line';\n      case 'Sf'\n        ctrlFcn = 'surf';\n      case 'To'\n        ctrlFcn = 'uitoolbar';\n      case 'Pt'\n        ctrlFcn = 'uipushtool';\n      case 'Tt'\n        ctrlFcn = 'uitoggletool';\n      otherwise\n        ctrlFcn = 'uicontrol';\n    end\n\n    % If there are objects from the current category then write code\n    if ~isempty(hdlsTemp)\n\n      % Category name\n      str = sprintf('%s\\t\\t%% --- %s -------------------------------------\\n',str,Categories{indCat});\n\n      % Init index for empty tags\n      idxTag = 1;\n      \n      % Browse all the object belonging to the current category\n      for indObj=1:length(hdlsTemp)\n\n        % Get property values for the current object\n        listTemp = get(hdlsTemp(indObj));\n        \n        % If tag is empty then create one\n        if isempty(listTemp.Tag)\n          listTemp.Tag = sprintf('%s%u',DefTags{indCat},idxTag);\n          set(hdlsTemp(indObj),'Tag',listTemp.Tag);\n          idxTag = idxTag + 1;\n        end\n\n        % Special treatment for UIButtongroup (UIButtongroup are UIPanel with\n        % the SelectedObject property): Change creation function name\n        if strcmp(idList{indCat},'Pa')\n          if isfield(listTemp,'SelectedObject')\n            ctrlFcn = 'uibuttongroup';\n          end\n        end\n\n        % Start object creation code\n        % (store all the objects handles in a handles structure)\n        str = sprintf('%s\\t\\thandles.%s = %s( ...\\n',str,listTemp.Tag,ctrlFcn);\n\n        % Browse the object properties\n        for indProp=1:length(propTemp)\n\n          % For Parent & UIContextMenu properties, value is an object handle\n          if strcmp(propTemp{indProp},'Parent') || strcmp(propTemp{indProp},'UIContextMenu')\n            if ~isempty(listTemp.(propTemp{indProp}))\n              propVal = sprintf('handles.%s',get(listTemp.(propTemp{indProp}),'Tag'));\n            else\n              propVal = [];\n            end\n          else\n            propVal = listTemp.(propTemp{indProp});\n          end\n          \n          if ~isfield(dprop,propTemp{indProp}) || ~isequal(propVal,dprop.(propTemp{indProp}))\n\n            % Create Property/Value string according to the class of the property\n            % value\n            switch class(listTemp.(propTemp{indProp}))\n\n              case 'char'\n                s = format_char();\n\n              case {'double','single','uint8'}\n                s = format_numeric();\n                \n              case 'cell'\n                s = format_cell();\n                \n              case 'logical'\n                s = format_logical();\n\n            end % end of switch\n\n            % Write the code line 'Property','Value',...\n            str = sprintf('%s\\t\\t\\t\"%s\", %s, ...\\n',str,propTemp{indProp},s);\n\n          end % end isequal\n\n        end % Next property\n        \n        % Callbacks\n        if cb_gen\n          \n          % Extract all property names\n          l = fieldnames(listTemp);\n          \n          % Find defined callback properties\n          [iprop,icb] = find(strcmp(repmat(l,1,length(cb_names)),repmat(cb_names,length(l),1))); %#ok<ASGLU>\n          \n          for indCb=1:length(icb)\n            if ~isempty(listTemp.(cb_names{icb(indCb)}))\n              cb_list{end+1} = [listTemp.Tag '_' cb_names{icb(indCb)}]; %#ok<AGROW>\n              str = sprintf('%s\\t\\t\\t\"%s\", %s, ...\\n',str,cb_names{icb(indCb)},['@' cb_list{end}]);\n            end\n          end\n          \n        end\n\n        % Suppress the 5 last characters (, ...) and finish the creation command\n        str(end-5:end) = '';\n        str = sprintf('%s);\\n\\n',str);\n\n      end % Next object\n\n    end % End if ~isempty(hdlsTemp)\n\n  end % Next object category\n  \n  % Close the build_gui nested function\n  str = sprintf('%s\\n\\tend\\n\\n',str);\n  \n  % Add callback functions\n  if cb_gen\n    for indCb=1:length(cb_list)\n      str = sprintf('%s%%%% ---------------------------------------------------------------------------\\n',str);\n      str = sprintf('%s\\tfunction %s(hObject,evendata) %%#ok<INUSD>\\n\\n',str,cb_list{indCb});\n      str = sprintf('%s\\tend\\n\\n',str);\n    end\n  end\n  \n  % Close main function\n  str = sprintf('%send\\n',str);\n\n  % Close the figure\n  close(figHdl);\n\n  % Write the output file\n  fid = fopen(outFile,'w');\n  fprintf(fid,'%s',str);\n  fclose(fid);\n  \n  % Open the generated M-File in the editor\n  edit(outFile);\n\n  % Return the name of the output file\n  varargout{1} = outFile;\n\ncatch ME\n  varargout{1} = outFile;\n  try %#ok<TRYNC>\n    close(figHdl);\n  end\n  disp(listTemp.Tag);\n  disp(ME.message);\nend\n\n\n  function s = format_cell()\n    \n    % Cell arrays (Create a single string with each cell separated by\n    % a | character)\n    \n    if strcmpi(propTemp{indProp},'ColumnFormat') && all(cellfun(@isempty,propVal))\n      s = ['{' repmat('''char'' ',size(propVal)) '}'];\n      \n    elseif strcmpi(propTemp{indProp},'ColumnFormat')\n      s = sprintf('\"%s\",',propVal{:});\n      s = ['{' strrep(s(1:end-1),'''''','[]') '}']; % Handles the case of automatic column format\n      \n    elseif strcmpi(propTemp{indProp},'ColumnWidth')\n      s = propVal;\n      s(cellfun(@isstr,s))     = cellfun(@(string) ['''' string ''''],s(cellfun(@isstr,s)),'UniformOutput',false);\n      s(cellfun(@isnumeric,s)) = cellfun(@num2str,s(cellfun(@isnumeric,s)),'UniformOutput',false);\n      s = sprintf('%s,',s{:});\n      s = ['{' s(1:end-1) '}'];\n      \n    elseif strcmpi(propTemp{indProp},'Data')\n      ft = struct('char','\"%s\",','logical','logical(%d),','double','%f,');\n      classes = cellfun(@class,propVal(1,:),'UniformOutput',false);\n      s = cellfun(@(s) ft.(s),classes,'UniformOutput',false);\n      fmt = [s{:}];\n      fmt = [fmt(1:end-1) ';'];\n      propVal = propVal';\n      s = sprintf(fmt,propVal{:});\n      s = strrep(s,'logical(0)','false');\n      s = strrep(s,'logical(1)','true');\n      s = ['{' s(1:end-1) '}'];\n      \n    else\n      s = sprintf('\"%s\",',propVal{:});\n      s = ['{' s(1:end-1) '}'];\n    end\n    \n  end\n\n  function s = format_numeric()\n    % Numeric (Convert numerical value into string)\n    \n    \n    if syscolorfig && strcmpi(idList{indCat},'Fg') && strcmpi(propTemp{indProp},'Color')\n      % When using the system default background colors then override the\n      % property value\n      s = 'get(0,''DefaultUicontrolBackgroundColor'')';\n      \n    elseif any(strcmpi(propTemp{indProp},{'BackgroundColor','ForegroundColor','Color'}))\n      % Limit to 3 digits for all colors vectors/matrices\n      s = mat2str(propVal,3);\n      \n    elseif any(strcmpi(propTemp{indProp},{'Parent','UIContextMenu'}))\n      % Simply output the handles.<tag> characters string when the property is\n      % either Parent or UIContextMenu\n      s = sprintf('%s',propVal);\n      \n    else\n      if length(size(propVal)) > 2\n        matLin = propVal(:);\n        s = sprintf(',%u',size(propVal));\n        s = sprintf('reshape(%s%s)',mat2str(matLin),s);\n      else\n        s = mat2str(propVal);\n      end\n      \n    end\n    \n  end\n\n  function s = format_char()\n    % Characters\n    \n    if size(propVal,1) > 1\n      % For character arrays that have more than 1 line\n      s = 'sprintf(''';\n      for j=1:size(propVal,1)\n        s = sprintf('%s\\t\\t%s\\\\n',s,strrep(propVal(j,:),'''',''''''));\n      end\n      s = [s ''')'];\n      \n    elseif ~isempty(findstr(propVal,char(13)))\n      % For character arrays that contain new line character\n      s = sprintf('sprintf(\"%s\")',strrep(strrep(strrep(propVal,char(13),'\\n'),char(10),''),'''',''''''));\n      \n    else\n      % For character arrays that have a single line and no new line\n      s = sprintf('\"%s\"',strrep(propVal,'''',''''''));\n      \n    end\n    \n  end\n\n  function s = format_logical()\n    % Format logical property values\n    \n    trueFalse = {'false,','true,'};\n    s = trueFalse(propVal+1);\n    s = [s{:}];\n    s = ['[' s(1:end-1) ']'];\n    \n  end\n\nend\n\n\n%-------------------------------------------------------------------------------\nfunction cprop = control_properties()\n\n% Common objects properties\nprop.Def   = {'Parent','Tag','UserData','Visible'};\nprop.Font  = {'FontAngle','FontName','FontSize','FontUnits','FontWeight'};\nprop.Color = {'ForegroundColor','BackgroundColor'};\nprop.Pos   = {'Units','Position'};\nprop.Str   = {'String','TooltipString'};\n\n% Properties for each style of objects\ncprop.Fg = [{'Tag'}, prop.Pos, {'Name','MenuBar','NumberTitle','Color','Resize','UIContextMenu'}];\ncprop.Cm = [prop.Def];\ncprop.Mb = [prop.Def, {'Label','Checked','Enable','ForegroundColor'}];\ncprop.Pa = [prop.Def, prop.Pos, prop.Font, prop.Color, {'Title','TitlePosition','BorderType','BorderWidth','HighlightColor','ShadowColor','UIContextMenu'}];\ncprop.Ax = [prop.Def, prop.Pos, {'UIContextMenu'}];\ncprop.Li = {'BeingDeleted','BusyAction','ButtonDownFcn','Color','CreateFcn','DeleteFcn','DisplayName','EraseMode','HandleVisibility','HitTest','Interruptible','LineStyle','LineWidth','Marker','MarkerEdgeColor','MarkerFaceColor','MarkerSize','Parent','Selected','SelectionHighlight','Tag','UIContextMenu','UserData','Visible','XData','YData','ZData'};\ncprop.Sf = {'AlphaData','AlphaDataMapping','CData','CDataMapping','DisplayName','EdgeAlpha','EdgeColor','EraseMode','FaceAlpha','FaceColor','LineStyle','LineWidth','Marker','MarkerEdgeColor','MarkerFaceColor','MarkerSize','MeshStyle','XData','YData','ZData','FaceLighting','EdgeLighting','BackFaceLighting','AmbientStrength','DiffuseStrength','SpecularStrength','SpecularExponent','SpecularColorReflectance','VertexNormals','NormalMode','ButtonDownFcn','Selected','SelectionHighlight','Tag','UIContextMenu','UserData','Visible','Parent','XDataMode','XDataSource','YDataMode','YDataSource','CDataMode','CDataSource','ZDataSource'};\ncprop.St = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','HorizontalAlignment'}];\ncprop.Pb = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu','CData'}];\ncprop.Tb = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu','CData'}];\ncprop.Rb = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu'}];\ncprop.Cb = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu'}];\ncprop.Ed = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu','HorizontalAlignment','Min','Max'}];\ncprop.Lb = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu','Min','Max'}];\ncprop.Pu = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu'}];\ncprop.Sl = [prop.Def, {'Style'}, prop.Pos, prop.Font, prop.Color, prop.Str, {'Enable','UIContextMenu','Min','Max','SliderStep'}];\ncprop.Ta = [prop.Def, prop.Pos, prop.Font, prop.Color, {'ColumnEditable','ColumnFormat','ColumnName','ColumnWidth','Data','Enable','RearrangeableColumns','RowName','RowStriping','TooltipString','UIContextMenu'}];\ncprop.To = [prop.Def];\ncprop.Pt = [prop.Def, {'TooltipString','CData','Enable','Separator'}];\ncprop.Tt = [prop.Def, {'TooltipString','CData','Enable','Separator','State'}];\nend\n\n\n%-------------------------------------------------------------------------------\nfunction dprop = default_prop_values()\n\ndprop = struct( ...\n  'MenuBar'             , 'figure', ...\n  'NumberTitle'         , 'on', ...\n  'Resize'              , 'off', ...\n  'UIContextMenu'       , [], ...\n  'FontAngle'           , 'normal', ...\n  'FontName'            , 'MS Sans Serif', ...\n  'FontSize'            , 8, ...\n  'FontUnits'           , 'points', ...\n  'FontWeight'          , 'normal', ...\n  'ForegroundColor'     , [0 0 0], ...\n  'BackgroundColor'     , get(0,'DefaultUicontrolBackgroundColor'), ...\n  'TitlePosition'       , 'lefttop', ...\n  'BorderType'          , 'etchedin', ...\n  'BorderWidth'         , 1, ...\n  'HighlightColor'      , [1 1 1], ...\n  'ShadowColor'         , [0.5 0.5 0.5], ...\n  'HorizontalAlignment' , 'center', ...\n  'TooltipString'       , '', ...\n  'CData'               , [], ...\n  'Enable'              , 'on', ...\n  'SliderStep'          , [.01 .1], ...\n  'Min'                 , 0, ...\n  'Max'                 , 1, ...\n  'UserData'            , [], ...\n  'BeingDeleted'        , 'off', ...\n  'BusyAction'          , 'queue', ...\n  'ColumnEditable'      , [], ...\n  'ColumnFormat'        , [], ...\n  'ColumnName'          , 'numbered', ...\n  'ColumnWidth'         , 'auto', ...\n  'Data'                , {cell(4,2)}, ...\n  'RearrangeableColumns', 'off', ...\n  'RowName'             , 'numbered', ...\n  'RowStriping'         , 'on', ...\n  'Visible'             , 'on', ...\n  'String'              , '', ...\n  'Separator'           , 'off', ...\n  'State'               , 'off');\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "sigstar.m", "ext": ".m", "path": "OCIA-master/utils/plotting/sigstar.m", "size": 9019, "source_encoding": "utf_8", "md5": "9e111a96ba442eee144d7eaac47163c8", "text": "function varargout=sigstar(groups,stats,nosort,putStarsUnder)\n\n% SIGSTAR Add significance stars to bar charts, boxplots, line charts, etc,\n%\n% H = SIGSTAR(GROUPS,STATS,NSORT)\n%\n% Purpose\n% Add stars and lines highlighting significant differences between pairs of groups. \n% The user specifies the groups and associated p-values. The function handles much of \n% the placement and drawing of the highlighting. Stars are drawn according to:\n%   * represents p<=0.05\n%  ** represents p<=1E-2\n% *** represents p<=1E-3\n%\n%\n% Inputs\n% GROUPS - a cell array defining the pairs of groups to compare. Groups defined \n%          either as pairs of scalars indicating locations along the X axis or as \n%          strings corresponding to X-tick labels. Groups can be a mixture of both \n%          definition types.\n% STATS -  a vector of p-values the same length as GROUPS. If empty or missing it's \n%          assumed to be a vector of 0.05s the same length as GROUPS. Nans are treated\n%          as indicating non-significance.\n% NSORT -  optional, 0 by default. If 1, then significance markers are plotted in \n%          the order found in GROUPS. If 0, then they're sorted by the length of the \n%          bar.\n%\n% Outputs\n% H - optionally return handles for significance highlights. Each row is a different\n%     highlight bar. The first column is the line. The second column is the text (stars).\n%     \n%\n% Examples\n% 1. \n% bar([5,2,1.5])\n% sigstar({[1,2], [1,3]})\n%\n% 2. \n% bar([5,2,1.5])\n% sigstar({[2,3],[1,2], [1,3]},[nan,0.05,0.05])\n%\n% 3. \n% R=randn(30,2);\n% R(:,1)=R(:,1)+3;\n% boxplot(R)\n% set(gca,'XTick',1:2,'XTickLabel',{'A','B'})\n% H=sigstar({{'A','B'}},0.01);\n% ylim([-3,6.5])\n% set(H,'color','r')\n%\n% 4. Note the difference in the order with which we define the groups in the \n%    following two cases. \n% x=[1,2,3,2,1];\n% subplot(1,2,1)\n% bar(x)\n% sigstar({[1,2], [2,3], [4,5]})\n% subplot(1,2,2)\n% bar(x)\n% sigstar({[2,3],[1,2], [4,5]})\n%\n% ALSO SEE: demo_sigstar\n%\n% KNOWN ISSUES:\n% 1. Algorithm for identifying whether significance bar will overlap with \n%    existing plot elements may not work in some cases (see line 277)\n% 2. Bars may not look good on exported graphics with small page sizes.\n%    Simply increasing the width and height of the graph with the \n%    PaperPosition property of the current figure should fix things.\n%\n% Rob Campbell - CSHL 2013\n\n\n\n%Input argument error checking\n\n%If the user entered just one group pair and forgot to wrap it in a cell array \n%then we'll go easy on them and wrap it here rather then generate an error\nif ~iscell(groups) & length(groups)==2\n\tgroups={groups};\nend\n\nif nargin<2 \n\tstats=repmat(0.05,1,length(groups));\nend\nif isempty(stats)\n\tstats=repmat(0.05,1,length(groups));\nend\nif nargin<3\n\tnosort=0;\nend\nif nargin<4\n\tputStarsUnder=0;\nend\n\n\n\n\n%Check the inputs are of the right sort\nif ~iscell(groups)\n\terror('GROUPS must be a cell array')\nend\n\nif ~isvector(stats)\n\terror('STATS must be a vector')\nend\n\nif length(stats)~=length(groups)\n\terror('GROUPS and STATS must be the same length')\nend\n\n\n\n\n\n\n%Each member of the cell array GROUPS may be one of three things:\n%1. A pair of indices.\n%2. A pair of strings (in cell array) referring to X-Tick labels\n%3. A cell array containing one index and one string\n%\n% For our function to run, we will need to convert all of these into pairs of\n% indices. Here we loop through GROUPS and do this. \n\nxlocs=nan(length(groups),2); %matrix that will store the indices \nxtl=get(gca,'XTickLabel');\n\nfor ii=1:length(groups)\n\tgrp=groups{ii};\n\tif isnumeric(grp)\n\t\txlocs(ii,:)=grp; %Just store the indices if they're the right format already\n\n\telseif iscell(grp) %Handle string pairs or string/index pairs\n\n\t\tif isstr(grp{1})\n\t\t\ta=strmatch(grp{1},xtl);\n\t\telseif isnumeric(grp{1})\n\t\t\ta=grp{1};\n\t\tend\n\t\tif isstr(grp{2})\n\t\t\tb=strmatch(grp{2},xtl);\n\t\telseif isnumeric(grp{2})\n\t\t\tb=grp{2};\n\t\tend\n\n\t\txlocs(ii,:)=[a,b];\n\tend\n\n\t%Ensure that the first column is always smaller number than the second\n\txlocs(ii,:)=sort(xlocs(ii,:));\nend\n\n%If there are any NaNs we have messed up. \nif any(isnan(xlocs(:)))\n\terror('Some groups were not found')\nend\n\n\n\n\n\n\n%Optionally sort sig bars from shortest to longest so we plot the shorter ones first\n%in the loop below. Usually this will result in the neatest plot. If we waned to \n%optimise the order the sig bars are plotted to produce the neatest plot, then this \n%is where we'd do it. Not really worth the effort, though, as few plots are complicated\n%enough to need this and the user can define the order very easily at the command line. \nif ~nosort\n\t[~,ind]=sort(xlocs(:,2)-xlocs(:,1),'ascend');\n\txlocs=xlocs(ind,:);groups=groups(ind);\n\tstats=stats(ind);\nend\n\n\n\n%-----------------------------------------------------\n%Add the sig bar lines and asterisks \nholdstate=ishold;\nhold on\n\nH=ones(length(groups),2); %The handles will be stored here\n\ny=ylim;\nyd=myRange(y)*0.05; %separate sig bars vertically by 5% \n\nfor ii=1:length(groups)\n\tthisY=findMinY(xlocs(ii,:), putStarsUnder) + iff(putStarsUnder == 1, -1, 1) * yd;\n\tH(ii,:)=makeBar(xlocs(ii,:),thisY,stats(ii), putStarsUnder);\nend\n%-----------------------------------------------------\n\n\n\n\n%Now we can add the little downward ticks on the ends of each line. We are\n%being extra cautious and leaving this it to the end just in case the y limits\n%of the graph have changed as we add the highlights. The ticks are set as a\n%proportion of the y axis range and we want them all to be the same the same\n%for all bars.\nyd=myRange(ylim)*0.01; %Ticks are 1% of the y axis range\nif putStarsUnder;\n    yd = -yd;\nend;\nfor ii=1:length(groups)\n\ty=get(H(ii,1),'YData');\n\ty(1)=y(1)-yd;\n\ty(4)=y(4)-yd;\n\tset(H(ii,1),'YData',y)\nend\n\n\n\n\n%Be neat and return hold state to whatever it was before we started\nif ~holdstate\n\thold off\nelseif holdstate\n\thold on\nend\n\n\n%Optionally return the handles to the plotted significance bars (first column of H)\n%and asterisks (second column of H).\nif nargout>0\n\tvarargout{1}=H;\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%Internal functions\n\n\nfunction H=makeBar(x,y,p, putStarsUnder)\n%makeBar produces the bar and defines how many asterisks we get for a \n%given p-value\n\n\nif p<=1E-3\n\tstars='***';\nelseif p<=1E-2\n\tstars='**';\nelseif p<=0.05\n\tstars='*';\nelseif isnan(p)\n\tstars='n.s.';\nelse\n\tstars='';\nend\n\t\t\nx=repmat(x,2,1);\ny=repmat(y,4,1);\nH(1)=plot(x(:),y,'-k','LineWidth',1.5);\n%Increase offset between line and text if we will print \"n.s.\"\n%instead of a star. \nif ~isnan(p)\n    offset=0.005;\nelse\n    offset=0.03;\nend\n\nif putStarsUnder;\n    H(2)=text(mean(x(:)),mean(y)-myRange(ylim)*5*offset,stars,...\n        'HorizontalAlignment','Center',...\n        'BackGroundColor','none');\nelse\n    H(2)=text(mean(x(:)),mean(y)+myRange(ylim)*offset,stars,...\n        'HorizontalAlignment','Center',...\n        'BackGroundColor','none');\nend;\n\n\n\n\nfunction Y=findMinY(x, putStarsUnder)\n%Find the minimum y value needed to clear all the plotted data present \n%over a given range of x values. This allows the significance bar to\n%be plotted in the best location. \n%\n\n%First look for patch objects (bars in a bar-chart, most likely)\np=findobj(gca,'Type','Patch');\nxd=get(p,'XData');\nif iscell(xd)\n\txd=groupedBarFix(xd,'x');\nend\n\nxd(xd<x(1))=0;\nxd(xd>x(2))=0;\noverlapping=any(xd,1); %These x locations overlap\n\n%Find the corresponding y values \nclear xd\nyd=get(p,'YData');\nif iscell(yd)\n\tyd=groupedBarFix(yd,'y');\nend\nyd=yd(:,overlapping);\n%So we must have a value of at least Y in order to miss all the \n%plotted bar data:\nif putStarsUnder;\n    Y=min(yd(:));\nelse\n    Y=max(yd(:));\nend;\n\n\n%Now let's check if any other plot elements (such as significance bars we've \n%already added) exist over this range of x values.\n%\n% NOTE! This code doesn't identify all cases where there is overlap. \n%For example, if you have a significance bar going from 3 to 7 along the x\n%axis and you then try to add a new one from 4 to 5, then it won't see the \n%existing one as overlapping. However, I've yet to find this a problem in\n%practice so I'll leave things be. Can easily be fixed if leads to bugs. \n\np=findobj(gca,'Type','Line');\ntmpY=nan(1,length(p));\nfor ii=1:length(p)\n\txd=get(p(ii),'XData');\n\n\txd(xd<x(1))=0;\n\txd(xd>x(2))=0;\n\toverlapping=xd>0; %These x locations overlap\n\n\tif ~any(overlapping)\n\t\tcontinue\n\tend\n\n\tclear xd\n\tyd=get(p(ii),'YData');\n\tyd=yd(overlapping);\n    if putStarsUnder;\n        tmpY(ii)=min(yd);\n    else\n        tmpY(ii)=max(yd);\n    end;\nend\n\nif putStarsUnder;\n    Y=min([Y,tmpY]);\nelse\n    Y=max([Y,tmpY]);\nend;\n\n%The patch coords of grouped error bars aren't a matrix but a cell array. This needs to be\n%converted to a suitable matrix in order for the code to work. This function does that.  \nfunction out=groupedBarFix(in,xy)\n\tout=ones([size(in{1}),length(in)]);\n\tfor ii=1:length(in)\n\t\tout(:,:,ii)=in{ii};\n\tend\n\n\tswitch xy\n\tcase 'x'\n\t\tout=mean(out,3);\n\tcase 'y'\n\t\tout=max(out,[],3);\n    end\n\n%replacement for stats toolbox range function\nfunction rng=myRange(x)\n  rng = max(x) - min(x);\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "barerrorbar.m", "ext": ".m", "path": "OCIA-master/utils/plotting/barerrorbar.m", "size": 6065, "source_encoding": "utf_8", "md5": "d18f6c7caa4b5acbe0a3a5d61dc73f31", "text": "function varargout = barerrorbar(varargin)\n% BARERRORBAR   Create a bar plot with error bars. BARERRORBAR() uses the\n%   MATLAB functions BAR() and ERRORBAR() and changes the 'XData' property\n%   of the errorbar plot so that the error bars are plotted at the center \n%   of each bar. This does not support \"stack\" bar plots.\n%\n% Syntax: varargout = barerrorbar(varargin)\n%\n% Inputs:\n%   Method 1: Cell array method\n%       varargin{1} - A cell array containing all of the inputs to be fed \n%           to bar().\n%       varargin{2} - A cell array containing all of the inputs to be fed\n%           to errorbar().\n%   Method 2: Simple Method\n%       varargin{1} - The data to be plotted by bar().\n%       varargin{2} - The E input to errorbar().\n%\n% Outputs:\n%   varargout{1} - The handle to the bar plot.\n%   varargout{2} - The handle to the errorbar plot.\n%\n% Examples:\n%   x = 0.2*randn(3,4,100) + 1;\n%   xMeans = mean(x,3);\n%   xMeansConf = repmat(2*0.2/10,3,4);\n%   xMeansL = repmat(3*0.2/10,3,4);\n%   xMeansU = repmat(4*0.2/10,3,4);\n%   \n%   figure\n%   barerrorbar(xMeans,xMeansConf);\n% \n%   figure\n%   barerrorbar({3:5,xMeans,'m'}, {repmat((3:5)',1,4),xMeans, xMeansL,xMeansU,'bx'});\n% \n%   figure\n%   barerrorbar({3:5,xMeans,'k'}, {repmat((3:5)',1,4),xMeans, xMeansL,xMeansU,'bx'});\n%   hold on\n%   barerrorbar({7:9,xMeans}, {repmat((7:9)',1,4),xMeans, 2*xMeansL,4*xMeansU,'d','color',[0.7 0.7 0.7]});\n%\n% Other m-files required: none\n% Subfunctions: interpretinputs\n% MAT-files required: none\n%\n% See also: bar.m errorbar.m\n\n% Author: Kenneth D. Morton Jr. and J. Simeon Stohl\n% Revised by: Kenneth D. Morton Jr.\n% Duke University, Department of Electrical and Computer Engineering\n% Email Address: kennethmorton@ieee.org\n% Created: 07-July-2005\n% Last revision: 06-January-2006\n\n% Check if hold is on\nstartedWithHoldOn = ishold;\n\n[data, barInputs, errorbarInputs] = interpinputs(varargin);\n\n% Create the bar plot and keep the handles for later use.\nbarHandles = bar(barInputs{:});\nbarHandlesStruct = get(barHandles);\n\nif ~startedWithHoldOn\n    hold on\nelse\n    % Hold is already on so we need to check the XTick and save them.\n    oldXTicks = get(gca,'XTick');\n    oldXTickLabels = get(gca,'XTickLabel');\nend\n\n% Find out the bar width which is dependent on the number of bar sets and\n% the number of bars in each set.\nbarWidth = barHandlesStruct(1).BarWidth;\n\nerrorbarHandles = errorbar(errorbarInputs{:});\n\n% The crazy stuff to find the bar centers. Some of it is how bar.m does it.\n[nGroups, nBarsPerGroup] = size(data);\n\n% fix bug if only 1 bar per group - B.Laurenczy 2014-03-21\nif nGroups == 1 && nBarsPerGroup > 1;\n    groupMembership = barHandlesStruct(1).XData;\nelse\n    for iBpg = 1:nBarsPerGroup\n       groupMembership(:,iBpg) = barHandlesStruct(iBpg).XData;\n    end\nend;\n\ngroupWidth = min(0.8, nBarsPerGroup/(nBarsPerGroup+1.5));\ngroupLocs = groupMembership(:,1);\ndistanceToNearestGroup = zeros(1,nGroups);\nif nGroups > 1;\n    for iGroup = 1:nGroups\n        distanceToNearestGroup(iGroup) = ...\n            min(abs(groupLocs(iGroup)-...\n            groupLocs(groupLocs~=groupLocs(iGroup))));\n    end\n    groupWidth = groupWidth*min(distanceToNearestGroup);\n    barGap = (nGroups - 1) * groupWidth / (nGroups-1) / nBarsPerGroup;\n    almostCenters  = (0:nBarsPerGroup-1)'.*barGap - 0.5*barGap*nBarsPerGroup;\n    relativeCenters = almostCenters + mean([(1-barWidth)/2.*barGap; (1+barWidth)/2.*barGap]);\n    centers = repmat(relativeCenters',nGroups,1) + groupMembership;\n    % Change the XData of the errorbars to be at our bar centers.\n    for iBpg = 1:nBarsPerGroup\n        set(errorbarHandles(iBpg),'XData',centers(:,iBpg));\n    end    \nend;\n\n% Turn hold off if it wasn't on to begin with\nif ~startedWithHoldOn\n    hold off\nelse\n    % Set the XTick and XTickLabels to inlcude the old and new information.\n    newXTicks = groupMembership(:,1);\n    newXTickLabels = num2str(newXTicks);\n    set(gca,'XTick',sort(unique([oldXTicks newXTicks'])));\n    if ischar(oldXTickLabels)\n        % If this is a string then they are probably default so update with\n        % the new additions.\n        set(gca,'XTickLabel',[oldXTickLabels; newXTickLabels]);\n    end\nend\n\n% Prepare both sets of handles as outputs, if requested.\nif nargout > 0\n    varargout{1} = barHandles;\nend\nif nargout > 1\n    varargout{2} = errorbarHandles;\nend\n\n% End of barerrorbar()\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction [data, barInputs, errorbarInputs] = interpinputs(inCell)\n% Interperate the inputs so that they can be used in barerrorbar(). Make\n% two different possibilities based on the type of inputs.\n%   Method 1: Cell array method.\n%       varargin{1} - A cell array containing all of the inputs to be fed \n%           to bar().\n%       varargin{2} - A cell array containing all of the inputs to be fed\n%           to errorbar().\n%   Method 2: Simple Method\n%       varargin{1} - The data to be plotted by bar().\n%       varargin{2} - The data to be plotted by errorbar().\n\nif iscell(inCell{1})\n    % We have entered Method 1\n    barInputs = inCell{1};\n    if length(barInputs) > 2\n        data = barInputs{2};\n    elseif length(barInputs) < 2\n        data = barInputs{1};\n    elseif length(barInputs) == 2\n        if ischar(barInputs{2})\n            data = barInputs{1};\n        else\n            data = barInputs{2};\n        end\n    end\nelse\n    barInputs = {inCell{1}};\n    data = inCell{1};\n    nRows = size(barInputs{1},1);\n    if nRows == 1\n        barInputs{1} = barInputs{1}';\n    end \nend\n\n%Plot black dot errorbars for the default.\nif iscell(inCell{2})\n    errorbarInputs = inCell{2};\nelse \n    errorbarInputs = {inCell{1}, inCell{2}, 'k.',};\nend\n\nif length(inCell) > 2\n    error(['Too many input arguments.' ...\n        ' Try using the cell array input method.']);\nend\n\n% Search for the 'stack' option in the bar inputs\nfor iBI = 1:length(barInputs)\n    if ischar(barInputs{iBI})\n        if strcmpi(barInputs{iBI},'stack')\n            error('barerrorbar() does not support \"stack\" bar plots.')\n        end\n    end\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "pdftops.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/pdftops.m", "size": 3574, "source_encoding": "utf_8", "md5": "92ff676904575e16046dfff010b4e145", "text": "function varargout = pdftops(cmd)\n%PDFTOPS  Calls a local pdftops executable with the input command\n%\n% Example:\n%   [status result] = pdftops(cmd)\n%\n% Attempts to locate a pdftops executable, finally asking the user to\n% specify the directory pdftops was installed into. The resulting path is\n% stored for future reference.\n% \n% Once found, the executable is called with the input command string.\n%\n% This function requires that you have pdftops (from the Xpdf package)\n% installed on your system. You can download this from:\n% http://www.foolabs.com/xpdf\n%\n% IN:\n%   cmd - Command string to be passed into pdftops.\n%\n% OUT:\n%   status - 0 iff command ran without problem.\n%   result - Output from pdftops.\n\n% Copyright: Oliver Woodford, 2009-2010\n\n% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on\n% Mac OS.\n% Thanks to Christoph Hertel for pointing out a bug in check_xpdf_path\n% under linux.\n% 23/01/2014 - Add full path to pdftops.txt in warning.\n% 27/05/2015 - Fixed alert in case of missing pdftops; fixed code indentation\n\n    % Call pdftops\n    [varargout{1:nargout}] = system(sprintf('\"%s\" %s', xpdf_path, cmd));\nend\n\nfunction path_ = xpdf_path\n    % Return a valid path\n    % Start with the currently set path\n    path_ = user_string('pdftops');\n    % Check the path works\n    if check_xpdf_path(path_)\n        return\n    end\n    % Check whether the binary is on the path\n    if ispc\n        bin = 'pdftops.exe';\n    else\n        bin = 'pdftops';\n    end\n    if check_store_xpdf_path(bin)\n        path_ = bin;\n        return\n    end\n    % Search the obvious places\n    if ispc\n        path_ = 'C:\\Program Files\\xpdf\\pdftops.exe';\n    else\n        path_ = '/usr/local/bin/pdftops';\n    end\n    if check_store_xpdf_path(path_)\n        return\n    end\n    % Ask the user to enter the path\n    while 1\n        errMsg = 'Pdftops not found. Please locate the program, or install xpdf-tools from ';\n        url = 'http://foolabs.com/xpdf';\n        fprintf(2, '%s\\n', [errMsg '<a href=\"matlab:web(''-browser'',''' url ''');\">' url '</a>']);\n        errMsg = [errMsg url]; %#ok<AGROW>\n        if strncmp(computer,'MAC',3) % Is a Mac\n            % Give separate warning as the MacOS uigetdir dialogue box doesn't have a title\n            uiwait(warndlg(errMsg))\n        end\n        base = uigetdir('/', errMsg);\n        if isequal(base, 0)\n            % User hit cancel or closed window\n            break;\n        end\n        base = [base filesep]; %#ok<AGROW>\n        bin_dir = {'', ['bin' filesep], ['lib' filesep]};\n        for a = 1:numel(bin_dir)\n            path_ = [base bin_dir{a} bin];\n            if exist(path_, 'file') == 2\n                break;\n            end\n        end\n        if check_store_xpdf_path(path_)\n            return\n        end\n    end\n    error('pdftops executable not found.');\nend\n\nfunction good = check_store_xpdf_path(path_)\n    % Check the path is valid\n    good = check_xpdf_path(path_);\n    if ~good\n        return\n    end\n    % Update the current default path to the path found\n    if ~user_string('pdftops', path_)\n        warning('Path to pdftops executable could not be saved. Enter it manually in %s.', fullfile(fileparts(which('user_string.m')), '.ignore', 'pdftops.txt'));\n        return\n    end\nend\n\nfunction good = check_xpdf_path(path_)\n    % Check the path is valid\n    [good, message] = system(sprintf('\"%s\" -h', path_)); %#ok<ASGLU>\n    % system returns good = 1 even when the command runs\n    % Look for something distinct in the help text\n    good = ~isempty(strfind(message, 'PostScript'));\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "crop_borders.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/crop_borders.m", "size": 3666, "source_encoding": "utf_8", "md5": "ebb9c61581b6f0d4a2db2fd1d9e30685", "text": "function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding)\n%CROP_BORDERS Crop the borders of an image or stack of images\n%\n%   [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])\n%\n%IN:\n%   A - HxWxCxN stack of images.\n%   bcol - Cx1 background colour vector.\n%   padding - scalar indicating how much padding to have in relation to\n%             the cropped-image-size (0<=padding<=1). Default: 0\n%\n%OUT:\n%   B - JxKxCxN cropped stack of images.\n%   vA     - coordinates in A that contain the cropped image\n%   vB     - coordinates in B where the cropped version of A is placed\n%   bb_rel - relative bounding box (used for eps-cropping)\n\n% 06/03/15: Improved image cropping thanks to Oscar Hartogensis\n% 08/06/15: Fixed issue #76: case of transparent figure bgcolor\n\n    if nargin < 3\n        padding = 0;\n    end\n    [h, w, c, n] = size(A);\n    if isempty(bcol)  % case of transparent bgcolor\n        bcol = A(ceil(end/2),1,:,1);\n    end\n    if isscalar(bcol)\n        bcol = bcol(ones(c, 1));\n    end\n\n    % Crop margin from left\n    bail = false;\n    for l = 1:w\n        for a = 1:c\n            if ~all(col(A(:,l,a,:)) == bcol(a))\n                bail = true;\n                break;\n            end\n        end\n        if bail\n            break;\n        end\n    end\n\n    % Crop margin from right\n    bcol = A(ceil(end/2),w,:,1);\n    bail = false;\n    for r = w:-1:l\n        for a = 1:c\n            if ~all(col(A(:,r,a,:)) == bcol(a))\n                bail = true;\n                break;\n            end\n        end\n        if bail\n            break;\n        end\n    end\n\n    % Crop margin from top\n    bcol = A(1,ceil(end/2),:,1);\n    bail = false;\n    for t = 1:h\n        for a = 1:c\n            if ~all(col(A(t,:,a,:)) == bcol(a))\n                bail = true;\n                break;\n            end\n        end\n        if bail\n            break;\n        end\n    end\n\n    % Crop margin from bottom\n    bcol = A(h,ceil(end/2),:,1);\n    bail = false;\n    for b = h:-1:t\n        for a = 1:c\n            if ~all(col(A(b,:,a,:)) == bcol(a))\n                bail = true;\n                break;\n            end\n        end\n        if bail\n            break;\n        end\n    end\n\n    % Crop the background, leaving one boundary pixel to avoid bleeding on resize\n    %v = [max(t-padding, 1) min(b+padding, h) max(l-padding, 1) min(r+padding, w)];\n    %A = A(v(1):v(2),v(3):v(4),:,:);\n    if padding == 0  % no padding\n        padding = 1;\n    elseif abs(padding) < 1  % pad value is a relative fraction of image size\n        padding = sign(padding)*round(mean([b-t r-l])*abs(padding)); % ADJUST PADDING\n    else  % pad value is in units of 1/72\" points\n        padding = round(padding);  % fix cases of non-integer pad value\n    end\n\n    if padding > 0  % extra padding\n        % Create an empty image, containing the background color, that has the\n        % cropped image size plus the padded border\n        B = repmat(bcol,(b-t)+1+padding*2,(r-l)+1+padding*2);\n        % vA - coordinates in A that contain the cropped image\n        vA = [t b l r];\n        % vB - coordinates in B where the cropped version of A will be placed\n        vB = [padding+1, (b-t)+1+padding, padding+1, (r-l)+1+padding];\n        % Place the original image in the empty image\n        B(vB(1):vB(2), vB(3):vB(4), :) = A(vA(1):vA(2), vA(3):vA(4), :);\n        A = B;\n    else  % extra cropping\n        vA = [t-padding b+padding l-padding r+padding];\n        A = A(vA(1):vA(2), vA(3):vA(4), :);\n        vB = [NaN NaN NaN NaN];\n    end\n\n    % For EPS cropping, determine the relative BoundingBox - bb_rel\n    bb_rel = [l-1 h-b-1 r+1 h-t+1]./[w h w h];\nend\n\nfunction A = col(A)\n    A = A(:);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "isolate_axes.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/isolate_axes.m", "size": 4721, "source_encoding": "utf_8", "md5": "253cd7b7d8fc7cb00d0cc55926f32de5", "text": "function fh = isolate_axes(ah, vis)\n%ISOLATE_AXES Isolate the specified axes in a figure on their own\n%\n% Examples:\n%   fh = isolate_axes(ah)\n%   fh = isolate_axes(ah, vis)\n%\n% This function will create a new figure containing the axes/uipanels\n% specified, and also their associated legends and colorbars. The objects\n% specified must all be in the same figure, but they will generally only be\n% a subset of the objects in the figure.\n%\n% IN:\n%    ah - An array of axes and uipanel handles, which must come from the\n%         same figure.\n%    vis - A boolean indicating whether the new figure should be visible.\n%          Default: false.\n%\n% OUT:\n%    fh - The handle of the created figure.\n\n% Copyright (C) Oliver Woodford 2011-2013\n\n% Thank you to Rosella Blatt for reporting a bug to do with axes in GUIs\n% 16/03/12: Moved copyfig to its own function. Thanks to Bob Fratantonio\n%           for pointing out that the function is also used in export_fig.m\n% 12/12/12: Add support for isolating uipanels. Thanks to michael for suggesting it\n% 08/10/13: Bug fix to allchildren suggested by Will Grant (many thanks!)\n% 05/12/13: Bug fix to axes having different units. Thanks to Remington Reid for reporting\n% 21/04/15: Bug fix for exporting uipanels with legend/colorbar on HG1 (reported by Alvaro\n%           on FEX page as a comment on 24-Apr-2014); standardized indentation & help section\n% 22/04/15: Bug fix: legends and colorbars were not exported when exporting axes handle in HG2\n\n    % Make sure we have an array of handles\n    if ~all(ishandle(ah))\n        error('ah must be an array of handles');\n    end\n    % Check that the handles are all for axes or uipanels, and are all in the same figure\n    fh = ancestor(ah(1), 'figure');\n    nAx = numel(ah);\n    for a = 1:nAx\n        if ~ismember(get(ah(a), 'Type'), {'axes', 'uipanel'})\n            error('All handles must be axes or uipanel handles.');\n        end\n        if ~isequal(ancestor(ah(a), 'figure'), fh)\n            error('Axes must all come from the same figure.');\n        end\n    end\n    % Tag the objects so we can find them in the copy\n    old_tag = get(ah, 'Tag');\n    if nAx == 1\n        old_tag = {old_tag};\n    end\n    set(ah, 'Tag', 'ObjectToCopy');\n    % Create a new figure exactly the same as the old one\n    fh = copyfig(fh); %copyobj(fh, 0);\n    if nargin < 2 || ~vis\n        set(fh, 'Visible', 'off');\n    end\n    % Reset the object tags\n    for a = 1:nAx\n        set(ah(a), 'Tag', old_tag{a});\n    end\n    % Find the objects to save\n    ah = findall(fh, 'Tag', 'ObjectToCopy');\n    if numel(ah) ~= nAx\n        close(fh);\n        error('Incorrect number of objects found.');\n    end\n    % Set the axes tags to what they should be\n    for a = 1:nAx\n        set(ah(a), 'Tag', old_tag{a});\n    end\n    % Keep any legends and colorbars which overlap the subplots\n    % Note: in HG1 these are axes objects; in HG2 they are separate objects, therefore we\n    %       don't test for the type, only the tag (hopefully nobody but Matlab uses them!)\n    lh = findall(fh, 'Tag', 'legend', '-or', 'Tag', 'Colorbar');\n    nLeg = numel(lh);\n    if nLeg > 0\n        set([ah(:); lh(:)], 'Units', 'normalized');\n        try\n            ax_pos = get(ah, 'OuterPosition'); % axes and figures have the OuterPosition property\n        catch\n            ax_pos = get(ah, 'Position'); % uipanels only have Position, not OuterPosition\n        end\n        if nAx > 1\n            ax_pos = cell2mat(ax_pos(:));\n        end\n        ax_pos(:,3:4) = ax_pos(:,3:4) + ax_pos(:,1:2);\n        try\n            leg_pos = get(lh, 'OuterPosition');\n        catch\n            leg_pos = get(lh, 'Position');  % No OuterPosition in HG2, only in HG1\n        end\n        if nLeg > 1;\n            leg_pos = cell2mat(leg_pos);\n        end\n        leg_pos(:,3:4) = leg_pos(:,3:4) + leg_pos(:,1:2);\n        ax_pos = shiftdim(ax_pos, -1);\n        % Overlap test\n        M = bsxfun(@lt, leg_pos(:,1), ax_pos(:,:,3)) & ...\n            bsxfun(@lt, leg_pos(:,2), ax_pos(:,:,4)) & ...\n            bsxfun(@gt, leg_pos(:,3), ax_pos(:,:,1)) & ...\n            bsxfun(@gt, leg_pos(:,4), ax_pos(:,:,2));\n        ah = [ah; lh(any(M, 2))];\n    end\n    % Get all the objects in the figure\n    axs = findall(fh);\n    % Delete everything except for the input objects and associated items\n    delete(axs(~ismember(axs, [ah; allchildren(ah); allancestors(ah)])));\nend\n\nfunction ah = allchildren(ah)\n    ah = findall(ah);\n    if iscell(ah)\n        ah = cell2mat(ah);\n    end\n    ah = ah(:);\nend\n\nfunction ph = allancestors(ah)\n    ph = [];\n    for a = 1:numel(ah)\n        h = get(ah(a), 'parent');\n        while h ~= 0\n            ph = [ph; h];\n            h = get(h, 'parent');\n        end\n    end\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "im2gif.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/im2gif.m", "size": 6048, "source_encoding": "utf_8", "md5": "5a7437140f8d013158a195de1e372737", "text": "%IM2GIF Convert a multiframe image to an animated GIF file\n%\n% Examples:\n%   im2gif infile\n%   im2gif infile outfile\n%   im2gif(A, outfile)\n%   im2gif(..., '-nocrop')\n%   im2gif(..., '-nodither')\n%   im2gif(..., '-ncolors', n)\n%   im2gif(..., '-loops', n)\n%   im2gif(..., '-delay', n) \n%   \n% This function converts a multiframe image to an animated GIF.\n%\n% To create an animation from a series of figures, export to a multiframe\n% TIFF file using export_fig, then convert to a GIF, as follows:\n%\n%    for a = 2 .^ (3:6)\n%       peaks(a);\n%       export_fig test.tif -nocrop -append\n%    end\n%    im2gif('test.tif', '-delay', 0.5);\n%\n%IN:\n%   infile - string containing the name of the input image.\n%   outfile - string containing the name of the output image (must have the\n%             .gif extension). Default: infile, with .gif extension.\n%   A - HxWxCxN array of input images, stacked along fourth dimension, to\n%       be converted to gif.\n%   -nocrop - option indicating that the borders of the output are not to\n%             be cropped.\n%   -nodither - option indicating that dithering is not to be used when\n%               converting the image.\n%   -ncolors - option pair, the value of which indicates the maximum number\n%              of colors the GIF can have. This can also be a quantization\n%              tolerance, between 0 and 1. Default/maximum: 256.\n%   -loops - option pair, the value of which gives the number of times the\n%            animation is to be looped. Default: 65535.\n%   -delay - option pair, the value of which gives the time, in seconds,\n%            between frames. Default: 1/15.\n\n% Copyright (C) Oliver Woodford 2011\n\nfunction im2gif(A, varargin)\n\n% Parse the input arguments\n[A, options] = parse_args(A, varargin{:});\n\nif options.crop ~= 0\n    % Crop\n    A = crop_borders(A, A(ceil(end/2),1,:,1));\nend\n\n% Convert to indexed image\n[h, w, c, n] = size(A);\nA = reshape(permute(A, [1 2 4 3]), h, w*n, c);\nmap = unique(reshape(A, h*w*n, c), 'rows');\nif size(map, 1) > 256\n    dither_str = {'dither', 'nodither'};\n    dither_str = dither_str{1+(options.dither==0)};\n    if options.ncolors <= 1\n        [B, map] = rgb2ind(A, options.ncolors, dither_str);\n        if size(map, 1) > 256\n            [B, map] = rgb2ind(A, 256, dither_str);\n        end\n    else\n        [B, map] = rgb2ind(A, min(round(options.ncolors), 256), dither_str);\n    end\nelse\n    if max(map(:)) > 1\n        map = double(map) / 255;\n        A = double(A) / 255;\n    end\n    B = rgb2ind(im2double(A), map);\nend\nB = reshape(B, h, w, 1, n);\n\n% Bug fix to rgb2ind\nmap(B(1)+1,:) = im2double(A(1,1,:));\n\n% Save as a gif\nimwrite(B, map, options.outfile, 'LoopCount', round(options.loops(1)), 'DelayTime', options.delay);\nend\n\n%% Parse the input arguments\nfunction [A, options] = parse_args(A, varargin)\n% Set the defaults\noptions = struct('outfile', '', ...\n                 'dither', true, ...\n                 'crop', true, ...\n                 'ncolors', 256, ...\n                 'loops', 65535, ...\n                 'delay', 1/15);\n\n% Go through the arguments\na = 0;\nn = numel(varargin);\nwhile a < n\n    a = a + 1;\n    if ischar(varargin{a}) && ~isempty(varargin{a})\n        if varargin{a}(1) == '-'\n            opt = lower(varargin{a}(2:end));\n            switch opt\n                case 'nocrop'\n                    options.crop = false;\n                case 'nodither'\n                    options.dither = false;\n                otherwise\n                    if ~isfield(options, opt)\n                        error('Option %s not recognized', varargin{a});\n                    end\n                    a = a + 1;\n                    if ischar(varargin{a}) && ~ischar(options.(opt))\n                        options.(opt) = str2double(varargin{a});\n                    else\n                        options.(opt) = varargin{a};\n                    end\n            end\n        else\n            options.outfile = varargin{a};\n        end\n    end\nend\n\nif isempty(options.outfile)\n    if ~ischar(A)\n        error('No output filename given.');\n    end\n    % Generate the output filename from the input filename\n    [path, outfile] = fileparts(A);\n    options.outfile = fullfile(path, [outfile '.gif']);\nend\n\nif ischar(A)\n    % Read in the image\n    A = imread_rgb(A);\nend\nend\n\n%% Read image to uint8 rgb array\nfunction [A, alpha] = imread_rgb(name)\n% Get file info\ninfo = imfinfo(name);\n% Special case formats\nswitch lower(info(1).Format)\n    case 'gif'\n        [A, map] = imread(name, 'frames', 'all');\n        if ~isempty(map)\n            map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\n            A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\n            A = permute(A, [1 2 5 4 3]);\n        end\n    case {'tif', 'tiff'}\n        A = cell(numel(info), 1);\n        for a = 1:numel(A)\n            [A{a}, map] = imread(name, 'Index', a, 'Info', info);\n            if ~isempty(map)\n                map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\n                A{a} = reshape(map(uint32(A{a})+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\n            end\n            if size(A{a}, 3) == 4\n                % TIFF in CMYK colourspace - convert to RGB\n                if isfloat(A{a})\n                    A{a} = A{a} * 255;\n                else\n                    A{a} = single(A{a});\n                end\n                A{a} = 255 - A{a};\n                A{a}(:,:,4) = A{a}(:,:,4) / 255;\n                A{a} = uint8(A(:,:,1:3) .* A{a}(:,:,[4 4 4]));\n            end\n        end\n        A = cat(4, A{:});\n    otherwise\n        [A, map, alpha] = imread(name);\n        A = A(:,:,:,1); % Keep only first frame of multi-frame files\n        if ~isempty(map)\n            map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\n            A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\n        elseif size(A, 3) == 4\n            % Assume 4th channel is an alpha matte\n            alpha = A(:,:,4);\n            A = A(:,:,1:3);\n        end\nend\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "read_write_entire_textfile.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/read_write_entire_textfile.m", "size": 924, "source_encoding": "utf_8", "md5": "779e56972f5d9778c40dee98ddbd677e", "text": "%READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory\n%\n% Read or write an entire text file to/from memory, without leaving the\n% file open if an error occurs.\n%\n% Reading:\n%   fstrm = read_write_entire_textfile(fname)\n% Writing:\n%   read_write_entire_textfile(fname, fstrm)\n%\n%IN:\n%   fname - Pathname of text file to be read in.\n%   fstrm - String to be written to the file, including carriage returns.\n%\n%OUT:\n%   fstrm - String read from the file. If an fstrm input is given the\n%           output is the same as that input. \n\nfunction fstrm = read_write_entire_textfile(fname, fstrm)\nmodes = {'rt', 'wt'};\nwriting = nargin > 1;\nfh = fopen(fname, modes{1+writing});\nif fh == -1\n    error('Unable to open file %s.', fname);\nend\ntry\n    if writing\n        fwrite(fh, fstrm, 'char*1');\n    else\n        fstrm = fread(fh, '*char')';\n    end\ncatch ex\n    fclose(fh);\n    rethrow(ex);\nend\nfclose(fh);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "pdf2eps.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/pdf2eps.m", "size": 1471, "source_encoding": "utf_8", "md5": "a1f41f0c7713c73886a2323e53ed982b", "text": "%PDF2EPS  Convert a pdf file to eps format using pdftops\n%\n% Examples:\n%   pdf2eps source dest\n%\n% This function converts a pdf file to eps format.\n%\n% This function requires that you have pdftops, from the Xpdf suite of\n% functions, installed on your system. This can be downloaded from:\n% http://www.foolabs.com/xpdf  \n%\n%IN:\n%   source - filename of the source pdf file to convert. The filename is\n%            assumed to already have the extension \".pdf\".\n%   dest - filename of the destination eps file. The filename is assumed to\n%          already have the extension \".eps\".\n\n% Copyright (C) Oliver Woodford 2009-2010\n\n% Thanks to Aldebaro Klautau for reporting a bug when saving to\n% non-existant directories.\n\nfunction pdf2eps(source, dest)\n% Construct the options string for pdftops\noptions = ['-q -paper match -eps -level2 \"' source '\" \"' dest '\"'];\n% Convert to eps using pdftops\n[status, message] = pdftops(options);\n% Check for error\nif status\n    % Report error\n    if isempty(message)\n        error('Unable to generate eps. Check destination directory is writable.');\n    else\n        error(message);\n    end\nend\n% Fix the DSC error created by pdftops\nfid = fopen(dest, 'r+');\nif fid == -1\n    % Cannot open the file\n    return\nend\nfgetl(fid); % Get the first line\nstr = fgetl(fid); % Get the second line\nif strcmp(str(1:min(13, end)), '% Produced by')\n    fseek(fid, -numel(str)-1, 'cof');\n    fwrite(fid, '%'); % Turn ' ' into '%'\nend\nfclose(fid);\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "print2array.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/print2array.m", "size": 9369, "source_encoding": "utf_8", "md5": "ca18a1e6c5a944b591a0557bd69f1c2c", "text": "function [A, bcol] = print2array(fig, res, renderer, gs_options)\n%PRINT2ARRAY  Exports a figure to an image array\n%\n% Examples:\n%   A = print2array\n%   A = print2array(figure_handle)\n%   A = print2array(figure_handle, resolution)\n%   A = print2array(figure_handle, resolution, renderer)\n%   A = print2array(figure_handle, resolution, renderer, gs_options)\n%   [A bcol] = print2array(...)\n%\n% This function outputs a bitmap image of the given figure, at the desired\n% resolution.\n%\n% If renderer is '-painters' then ghostcript needs to be installed. This\n% can be downloaded from: http://www.ghostscript.com\n%\n% IN:\n%   figure_handle - The handle of the figure to be exported. Default: gcf.\n%   resolution - Resolution of the output, as a factor of screen\n%                resolution. Default: 1.\n%   renderer - string containing the renderer paramater to be passed to\n%              print. Default: '-opengl'.\n%   gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If\n%                multiple options are needed, enclose in call array: {'-a','-b'}\n%\n% OUT:\n%   A - MxNx3 uint8 image of the figure.\n%   bcol - 1x3 uint8 vector of the background color\n\n% Copyright (C) Oliver Woodford 2008-2014, Yair Altman 2015-\n%{\n% 05/09/11: Set EraseModes to normal when using opengl or zbuffer\n%           renderers. Thanks to Pawel Kocieniewski for reporting the issue.\n% 21/09/11: Bug fix: unit8 -> uint8! Thanks to Tobias Lamour for reporting it.\n% 14/11/11: Bug fix: stop using hardcopy(), as it interfered with figure size\n%           and erasemode settings. Makes it a bit slower, but more reliable.\n%           Thanks to Phil Trinh and Meelis Lootus for reporting the issues.\n% 09/12/11: Pass font path to ghostscript.\n% 27/01/12: Bug fix affecting painters rendering tall figures. Thanks to\n%           Ken Campbell for reporting it.\n% 03/04/12: Bug fix to median input. Thanks to Andy Matthews for reporting it.\n% 26/10/12: Set PaperOrientation to portrait. Thanks to Michael Watts for\n%           reporting the issue.\n% 26/02/15: If temp dir is not writable, use the current folder for temp\n%           EPS/TIF files (Javier Paredes)\n% 27/02/15: Display suggested workarounds to internal print() error (issue #16)\n% 28/02/15: Enable users to specify optional ghostscript options (issue #36)\n% 10/03/15: Fixed minor warning reported by Paul Soderlind; fixed code indentation\n% 28/05/15: Fixed issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() func)\n% 07/07/15: Fixed issue #83: use numeric handles in HG1\n%}\n\n    % Generate default input arguments, if needed\n    if nargin < 2\n        res = 1;\n        if nargin < 1\n            fig = gcf;\n        end\n    end\n    % Warn if output is large\n    old_mode = get(fig, 'Units');\n    set(fig, 'Units', 'pixels');\n    px = get(fig, 'Position');\n    set(fig, 'Units', old_mode);\n    npx = prod(px(3:4)*res)/1e6;\n    if npx > 30\n        % 30M pixels or larger!\n        warning('MATLAB:LargeImage', 'print2array generating a %.1fM pixel image. This could be slow and might also cause memory problems.', npx);\n    end\n    % Retrieve the background colour\n    bcol = get(fig, 'Color');\n    % Set the resolution parameter\n    res_str = ['-r' num2str(ceil(get(0, 'ScreenPixelsPerInch')*res))];\n    % Generate temporary file name\n    tmp_nam = [tempname '.tif'];\n    try\n        % Ensure that the temp dir is writable (Javier Paredes 26/2/15)\n        fid = fopen(tmp_nam,'w');\n        fwrite(fid,1);\n        fclose(fid);\n        delete(tmp_nam);  % cleanup\n        isTempDirOk = true;\n    catch\n        % Temp dir is not writable, so use the current folder\n        [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>\n        fpath = pwd;\n        tmp_nam = fullfile(fpath,[fname fext]);\n        isTempDirOk = false;\n    end\n    % Enable users to specify optional ghostscript options (issue #36)\n    if nargin > 3 && ~isempty(gs_options)\n        if iscell(gs_options)\n            gs_options = sprintf(' %s',gs_options{:});\n        elseif ~ischar(gs_options)\n            error('gs_options input argument must be a string or cell-array of strings');\n        else\n            gs_options = [' ' gs_options];\n        end\n    else\n        gs_options = '';\n    end\n    if nargin > 2 && strcmp(renderer, '-painters')\n        % Print to eps file\n        if isTempDirOk\n            tmp_eps = [tempname '.eps'];\n        else\n            tmp_eps = fullfile(fpath,[fname '.eps']);\n        end\n        print2eps(tmp_eps, fig, 0, renderer, '-loose');\n        try\n            % Initialize the command to export to tiff using ghostscript\n            cmd_str = ['-dEPSCrop -q -dNOPAUSE -dBATCH ' res_str ' -sDEVICE=tiff24nc'];\n            % Set the font path\n            fp = font_path();\n            if ~isempty(fp)\n                cmd_str = [cmd_str ' -sFONTPATH=\"' fp '\"'];\n            end\n            % Add the filenames\n            cmd_str = [cmd_str ' -sOutputFile=\"' tmp_nam '\" \"' tmp_eps '\"' gs_options];\n            % Execute the ghostscript command\n            ghostscript(cmd_str);\n        catch me\n            % Delete the intermediate file\n            delete(tmp_eps);\n            rethrow(me);\n        end\n        % Delete the intermediate file\n        delete(tmp_eps);\n        % Read in the generated bitmap\n        A = imread(tmp_nam);\n        % Delete the temporary bitmap file\n        delete(tmp_nam);\n        % Set border pixels to the correct colour\n        if isequal(bcol, 'none')\n            bcol = [];\n        elseif isequal(bcol, [1 1 1])\n            bcol = uint8([255 255 255]);\n        else\n            for l = 1:size(A, 2)\n                if ~all(reshape(A(:,l,:) == 255, [], 1))\n                    break;\n                end\n            end\n            for r = size(A, 2):-1:l\n                if ~all(reshape(A(:,r,:) == 255, [], 1))\n                    break;\n                end\n            end\n            for t = 1:size(A, 1)\n                if ~all(reshape(A(t,:,:) == 255, [], 1))\n                    break;\n                end\n            end\n            for b = size(A, 1):-1:t\n                if ~all(reshape(A(b,:,:) == 255, [], 1))\n                    break;\n                end\n            end\n            bcol = uint8(median(single([reshape(A(:,[l r],:), [], size(A, 3)); reshape(A([t b],:,:), [], size(A, 3))]), 1));\n            for c = 1:size(A, 3)\n                A(:,[1:l-1, r+1:end],c) = bcol(c);\n                A([1:t-1, b+1:end],:,c) = bcol(c);\n            end\n        end\n    else\n        if nargin < 3\n            renderer = '-opengl';\n        end\n        err = false;\n        % Set paper size\n        old_pos_mode = get(fig, 'PaperPositionMode');\n        old_orientation = get(fig, 'PaperOrientation');\n        set(fig, 'PaperPositionMode', 'auto', 'PaperOrientation', 'portrait');\n        try\n            % Workaround for issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() function)\n            fp = [];  % in case we get an error below\n            fp = findall(fig, 'Type','patch', 'LineWidth',0.75);\n            set(fp, 'LineWidth',0.5);\n            % Fix issue #83: use numeric handles in HG1\n            if ~using_hg2(fig),  fig = double(fig);  end\n            % Print to tiff file\n            print(fig, renderer, res_str, '-dtiff', tmp_nam);\n            % Read in the printed file\n            A = imread(tmp_nam);\n            % Delete the temporary file\n            delete(tmp_nam);\n        catch ex\n            err = true;\n        end\n        set(fp, 'LineWidth',0.75);  % restore original figure appearance\n        % Reset paper size\n        set(fig, 'PaperPositionMode', old_pos_mode, 'PaperOrientation', old_orientation);\n        % Throw any error that occurred\n        if err\n            % Display suggested workarounds to internal print() error (issue #16)\n            fprintf(2, 'An error occured with Matlab''s builtin print function.\\nTry setting the figure Renderer to ''painters'' or use opengl(''software'').\\n\\n');\n            rethrow(ex);\n        end\n        % Set the background color\n        if isequal(bcol, 'none')\n            bcol = [];\n        else\n            bcol = bcol * 255;\n            if isequal(bcol, round(bcol))\n                bcol = uint8(bcol);\n            else\n                bcol = squeeze(A(1,1,:));\n            end\n        end\n    end\n    % Check the output size is correct\n    if isequal(res, round(res))\n        px = round([px([4 3])*res 3]);  % round() to avoid an indexing warning below\n        if ~isequal(size(A), px)\n            % Correct the output size\n            A = A(1:min(end,px(1)),1:min(end,px(2)),:);\n        end\n    end\nend\n\n% Function to return (and create, where necessary) the font path\nfunction fp = font_path()\n    fp = user_string('gs_font_path');\n    if ~isempty(fp)\n        return\n    end\n    % Create the path\n    % Start with the default path\n    fp = getenv('GS_FONTPATH');\n    % Add on the typical directories for a given OS\n    if ispc\n        if ~isempty(fp)\n            fp = [fp ';'];\n        end\n        fp = [fp getenv('WINDIR') filesep 'Fonts'];\n    else\n        if ~isempty(fp)\n            fp = [fp ':'];\n        end\n        fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];\n    end\n    user_string('gs_font_path', fp);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "append_pdfs.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/append_pdfs.m", "size": 2678, "source_encoding": "utf_8", "md5": "949c7c4ec3f5af6ff23099f17b1dfd79", "text": "%APPEND_PDFS Appends/concatenates multiple PDF files\n%\n% Example:\n%   append_pdfs(output, input1, input2, ...)\n%   append_pdfs(output, input_list{:})\n%   append_pdfs test.pdf temp1.pdf temp2.pdf\n%\n% This function appends multiple PDF files to an existing PDF file, or\n% concatenates them into a PDF file if the output file doesn't yet exist.\n%\n% This function requires that you have ghostscript installed on your\n% system. Ghostscript can be downloaded from: http://www.ghostscript.com\n%\n% IN:\n%    output - string of output file name (including the extension, .pdf).\n%             If it exists it is appended to; if not, it is created.\n%    input1 - string of an input file name (including the extension, .pdf).\n%             All input files are appended in order.\n%    input_list - cell array list of input file name strings. All input\n%                 files are appended in order.\n\n% Copyright: Oliver Woodford, 2011\n\n% Thanks to Reinhard Knoll for pointing out that appending multiple pdfs in\n% one go is much faster than appending them one at a time.\n\n% Thanks to Michael Teo for reporting the issue of a too long command line.\n% Issue resolved on 5/5/2011, by passing gs a command file.\n\n% Thanks to Martin Wittmann for pointing out the quality issue when\n% appending multiple bitmaps.\n% Issue resolved (to best of my ability) 1/6/2011, using the prepress\n% setting\n\n% 26/02/15: If temp dir is not writable, use the output folder for temp\n%           files when appending (Javier Paredes); sanity check of inputs\n\nfunction append_pdfs(varargin)\n\nif nargin < 2,  return;  end  % sanity check\n\n% Are we appending or creating a new file\nappend = exist(varargin{1}, 'file') == 2;\noutput = [tempname '.pdf'];\ntry\n    % Ensure that the temp dir is writable (Javier Paredes 26/2/15)\n    fid = fopen(output,'w');\n    fwrite(fid,1);\n    fclose(fid);\n    delete(output);\n    isTempDirOk = true;\ncatch\n    % Temp dir is not writable, so use the output folder\n    [dummy,fname,fext] = fileparts(output); %#ok<ASGLU>\n    fpath = fileparts(varargin{1});\n    output = fullfile(fpath,[fname fext]);\n    isTempDirOk = false;\nend\nif ~append\n    output = varargin{1};\n    varargin = varargin(2:end);\nend\n% Create the command file\nif isTempDirOk\n    cmdfile = [tempname '.txt'];\nelse\n    cmdfile = fullfile(fpath,[fname '.txt']);\nend\nfh = fopen(cmdfile, 'w');\nfprintf(fh, '-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile=\"%s\" -f', output);\nfprintf(fh, ' \"%s\"', varargin{:});\nfclose(fh);\n% Call ghostscript\nghostscript(['@\"' cmdfile '\"']);\n% Delete the command file\ndelete(cmdfile);\n% Rename the file if needed\nif append\n    movefile(output, varargin{1});\nend\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "using_hg2.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/using_hg2.m", "size": 1002, "source_encoding": "utf_8", "md5": "b1620dd31f4d0b8acea2723e354a3518", "text": "%USING_HG2 Determine if the HG2 graphics engine is used\n%\n%   tf = using_hg2(fig)\n%\n%IN:\n%   fig - handle to the figure in question.\n%\n%OUT:\n%   tf - boolean indicating whether the HG2 graphics engine is being used\n%        (true) or not (false).\n\n% 19/06/2015 - Suppress warning in R2015b; cache result for improved performance\n\nfunction tf = using_hg2(fig)\n    persistent tf_cached\n    if isempty(tf_cached)\n        try\n            if nargin < 1,  fig = figure('visible','off');  end\n            oldWarn = warning('off','MATLAB:graphicsversion:GraphicsVersionRemoval');\n            try\n                % This generates a [supressed] warning in R2015b:\n                tf = ~graphicsversion(fig, 'handlegraphics');\n            catch\n                tf = verLessThan('matlab','8.4');  % =R2014b\n            end\n            warning(oldWarn);\n        catch\n            tf = false;\n        end\n        if nargin < 1,  delete(fig);  end\n        tf_cached = tf;\n    else\n        tf = tf_cached;\n    end\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "eps2pdf.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/eps2pdf.m", "size": 8355, "source_encoding": "utf_8", "md5": "3b82818a1bc7c7e39c8491ce325b2235", "text": "function eps2pdf(source, dest, crop, append, gray, quality, gs_options)\n%EPS2PDF  Convert an eps file to pdf format using ghostscript\n%\n% Examples:\n%   eps2pdf source dest\n%   eps2pdf(source, dest, crop)\n%   eps2pdf(source, dest, crop, append)\n%   eps2pdf(source, dest, crop, append, gray)\n%   eps2pdf(source, dest, crop, append, gray, quality)\n%   eps2pdf(source, dest, crop, append, gray, quality, gs_options)\n%\n% This function converts an eps file to pdf format. The output can be\n% optionally cropped and also converted to grayscale. If the output pdf\n% file already exists then the eps file can optionally be appended as a new\n% page on the end of the eps file. The level of bitmap compression can also\n% optionally be set.\n%\n% This function requires that you have ghostscript installed on your\n% system. Ghostscript can be downloaded from: http://www.ghostscript.com\n%\n% Inputs:\n%   source  - filename of the source eps file to convert. The filename is\n%             assumed to already have the extension \".eps\".\n%   dest    - filename of the destination pdf file. The filename is assumed\n%             to already have the extension \".pdf\".\n%   crop    - boolean indicating whether to crop the borders off the pdf.\n%             Default: true.\n%   append  - boolean indicating whether the eps should be appended to the\n%             end of the pdf as a new page (if the pdf exists already).\n%             Default: false.\n%   gray    - boolean indicating whether the output pdf should be grayscale\n%             or not. Default: false.\n%   quality - scalar indicating the level of image bitmap quality to\n%             output. A larger value gives a higher quality. quality > 100\n%             gives lossless output. Default: ghostscript prepress default.\n%   gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If\n%                multiple options are needed, enclose in call array: {'-a','-b'}\n\n% Copyright (C) Oliver Woodford 2009-2014, Yair Altman 2015-\n\n% Suggestion of appending pdf files provided by Matt C at:\n% http://www.mathworks.com/matlabcentral/fileexchange/23629\n\n% Thank you to Fabio Viola for pointing out compression artifacts, leading\n% to the quality setting.\n% Thank you to Scott for pointing out the subsampling of very small images,\n% which was fixed for lossless compression settings.\n\n% 9/12/2011 Pass font path to ghostscript.\n% 26/02/15: If temp dir is not writable, use the dest folder for temp\n%           destination files (Javier Paredes)\n% 28/02/15: Enable users to specify optional ghostscript options (issue #36)\n% 01/03/15: Upon GS error, retry without the -sFONTPATH= option (this might solve\n%           some /findfont errors according to James Rankin, FEX Comment 23/01/15)\n% 23/06/15: Added extra debug info in case of ghostscript error; code indentation\n% 04/10/15: Suggest a workaround for issue #41 (missing font path; thanks Mariia Fedotenkova)\n\n    % Intialise the options string for ghostscript\n    options = ['-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile=\"' dest '\"'];\n    % Set crop option\n    if nargin < 3 || crop\n        options = [options ' -dEPSCrop'];\n    end\n    % Set the font path\n    fp = font_path();\n    if ~isempty(fp)\n        options = [options ' -sFONTPATH=\"' fp '\"'];\n    end\n    % Set the grayscale option\n    if nargin > 4 && gray\n        options = [options ' -sColorConversionStrategy=Gray -dProcessColorModel=/DeviceGray'];\n    end\n    % Set the bitmap quality\n    if nargin > 5 && ~isempty(quality)\n        options = [options ' -dAutoFilterColorImages=false -dAutoFilterGrayImages=false'];\n        if quality > 100\n            options = [options ' -dColorImageFilter=/FlateEncode -dGrayImageFilter=/FlateEncode -c \".setpdfwrite << /ColorImageDownsampleThreshold 10 /GrayImageDownsampleThreshold 10 >> setdistillerparams\"'];\n        else\n            options = [options ' -dColorImageFilter=/DCTEncode -dGrayImageFilter=/DCTEncode'];\n            v = 1 + (quality < 80);\n            quality = 1 - quality / 100;\n            s = sprintf('<< /QFactor %.2f /Blend 1 /HSample [%d 1 1 %d] /VSample [%d 1 1 %d] >>', quality, v, v, v, v);\n            options = sprintf('%s -c \".setpdfwrite << /ColorImageDict %s /GrayImageDict %s >> setdistillerparams\"', options, s, s);\n        end\n    end\n    % Enable users to specify optional ghostscript options (issue #36)\n    if nargin > 6 && ~isempty(gs_options)\n        if iscell(gs_options)\n            gs_options = sprintf(' %s',gs_options{:});\n        elseif ~ischar(gs_options)\n            error('gs_options input argument must be a string or cell-array of strings');\n        else\n            gs_options = [' ' gs_options];\n        end\n        options = [options gs_options];\n    end\n    % Check if the output file exists\n    if nargin > 3 && append && exist(dest, 'file') == 2\n        % File exists - append current figure to the end\n        tmp_nam = tempname;\n        try\n            % Ensure that the temp dir is writable (Javier Paredes 26/2/15)\n            fid = fopen(tmp_nam,'w');\n            fwrite(fid,1);\n            fclose(fid);\n            delete(tmp_nam);\n        catch\n            % Temp dir is not writable, so use the dest folder\n            [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>\n            fpath = fileparts(dest);\n            tmp_nam = fullfile(fpath,[fname fext]);\n        end\n        % Copy the file\n        copyfile(dest, tmp_nam);\n        % Add the output file names\n        options = [options ' -f \"' tmp_nam '\" \"' source '\"'];\n        try\n            % Convert to pdf using ghostscript\n            [status, message] = ghostscript(options);\n        catch me\n            % Delete the intermediate file\n            delete(tmp_nam);\n            rethrow(me);\n        end\n        % Delete the intermediate file\n        delete(tmp_nam);\n    else\n        % File doesn't exist or should be over-written\n        % Add the output file names\n        options = [options ' -f \"' source '\"'];\n        % Convert to pdf using ghostscript\n        [status, message] = ghostscript(options);\n    end\n    % Check for error\n    if status\n        % Retry without the -sFONTPATH= option (this might solve some GS\n        % /findfont errors according to James Rankin, FEX Comment 23/01/15)\n        orig_options = options;\n        if ~isempty(fp)\n            options = regexprep(options, ' -sFONTPATH=[^ ]+ ',' ');\n            status = ghostscript(options);\n            if ~status, return; end  % hurray! (no error)\n        end\n        % Report error\n        if isempty(message)\n            error('Unable to generate pdf. Check destination directory is writable.');\n        elseif ~isempty(strfind(message,'/typecheck in /findfont'))\n            % Suggest a workaround for issue #41 (missing font path)\n            font_name = strtrim(regexprep(message,'.*Operand stack:\\s*(.*)\\s*Execution.*','$1'));\n            fprintf(2, 'Ghostscript error: could not find the following font(s): %s\\n', font_name);\n            fpath = fileparts(mfilename(-fullpath'));\n            gs_fonts_file = fullfile(fpath, '.ignore', 'gs_font_path.txt');\n            fprintf(2, '  try to add the font''s folder to your %s file\\n\\n', gs_fonts_file);\n            error('export_fig error');\n        else\n            fprintf(2, '\\nGhostscript error: perhaps %s is open by another application\\n', dest);\n            if ~isempty(gs_options)\n                fprintf(2, '  or maybe the%s option(s) are not accepted by your GS version\\n', gs_options);\n            end\n            fprintf(2, 'Ghostscript options: %s\\n\\n', orig_options);\n            error(message);\n        end\n    end\nend\n\n% Function to return (and create, where necessary) the font path\nfunction fp = font_path()\n    fp = user_string('gs_font_path');\n    if ~isempty(fp)\n        return\n    end\n    % Create the path\n    % Start with the default path\n    fp = getenv('GS_FONTPATH');\n    % Add on the typical directories for a given OS\n    if ispc\n        if ~isempty(fp)\n            fp = [fp ';'];\n        end\n        fp = [fp getenv('WINDIR') filesep 'Fonts'];\n    else\n        if ~isempty(fp)\n            fp = [fp ':'];\n        end\n        fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];\n    end\n    user_string('gs_font_path', fp);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "ghostscript.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/ghostscript.m", "size": 7706, "source_encoding": "utf_8", "md5": "92dbafb8d4fb243cae8716c6ecb0bbe5", "text": "function varargout = ghostscript(cmd)\n%GHOSTSCRIPT  Calls a local GhostScript executable with the input command\n%\n% Example:\n%   [status result] = ghostscript(cmd)\n%\n% Attempts to locate a ghostscript executable, finally asking the user to\n% specify the directory ghostcript was installed into. The resulting path\n% is stored for future reference.\n% \n% Once found, the executable is called with the input command string.\n%\n% This function requires that you have Ghostscript installed on your\n% system. You can download this from: http://www.ghostscript.com\n%\n% IN:\n%   cmd - Command string to be passed into ghostscript.\n%\n% OUT:\n%   status - 0 iff command ran without problem.\n%   result - Output from ghostscript.\n\n% Copyright: Oliver Woodford, 2009-2015, Yair Altman 2015-\n%{\n% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS.\n% Thanks to Nathan Childress for the fix to default location on 64-bit Windows systems.\n% 27/04/11 - Find 64-bit Ghostscript on Windows. Thanks to Paul Durack and\n%            Shaun Kline for pointing out the issue\n% 04/05/11 - Thanks to David Chorlian for pointing out an alternative\n%            location for gs on linux.\n% 12/12/12 - Add extra executable name on Windows. Thanks to Ratish\n%            Punnoose for highlighting the issue.\n% 28/06/13 - Fix error using GS 9.07 in Linux. Many thanks to Jannick\n%            Steinbring for proposing the fix.\n% 24/10/13 - Fix error using GS 9.07 in Linux. Many thanks to Johannes \n%            for the fix.\n% 23/01/14 - Add full path to ghostscript.txt in warning. Thanks to Koen\n%            Vermeer for raising the issue.\n% 27/02/15 - If Ghostscript croaks, display suggested workarounds\n% 30/03/15 - Improved performance by caching status of GS path check, if ok\n% 14/05/15 - Clarified warning message in case GS path could not be saved\n% 29/05/15 - Avoid cryptic error in case the ghostscipt path cannot be saved (issue #74)\n% 10/11/15 - Custom GS installation webpage for MacOS. Thanks to Andy Hueni via FEX\n%}\n\n    try\n        % Call ghostscript\n        [varargout{1:nargout}] = system([gs_command(gs_path()) cmd]);\n    catch err\n        % Display possible workarounds for Ghostscript croaks\n        url1 = 'https://github.com/altmany/export_fig/issues/12#issuecomment-61467998';  % issue #12\n        url2 = 'https://github.com/altmany/export_fig/issues/20#issuecomment-63826270';  % issue #20\n        hg2_str = ''; if using_hg2, hg2_str = ' or Matlab R2014a'; end\n        fprintf(2, 'Ghostscript error. Rolling back to GS 9.10%s may possibly solve this:\\n * <a href=\"%s\">%s</a> ',hg2_str,url1,url1);\n        if using_hg2\n            fprintf(2, '(GS 9.10)\\n * <a href=\"%s\">%s</a> (R2014a)',url2,url2);\n        end\n        fprintf('\\n\\n');\n        if ismac || isunix\n            url3 = 'https://github.com/altmany/export_fig/issues/27';  % issue #27\n            fprintf(2, 'Alternatively, this may possibly be due to a font path issue:\\n * <a href=\"%s\">%s</a>\\n\\n',url3,url3);\n            % issue #20\n            fpath = which(mfilename);\n            if isempty(fpath), fpath = [mfilename('fullpath') '.m']; end\n            fprintf(2, 'Alternatively, if you are using csh, modify shell_cmd from \"export...\" to \"setenv ...\"\\nat the bottom of <a href=\"matlab:opentoline(''%s'',174)\">%s</a>\\n\\n',fpath,fpath);\n        end\n        rethrow(err);\n    end\nend\n\nfunction path_ = gs_path\n    % Return a valid path\n    % Start with the currently set path\n    path_ = user_string('ghostscript');\n    % Check the path works\n    if check_gs_path(path_)\n        return\n    end\n    % Check whether the binary is on the path\n    if ispc\n        bin = {'gswin32c.exe', 'gswin64c.exe', 'gs'};\n    else\n        bin = {'gs'};\n    end\n    for a = 1:numel(bin)\n        path_ = bin{a};\n        if check_store_gs_path(path_)\n            return\n        end\n    end\n    % Search the obvious places\n    if ispc\n        default_location = 'C:\\Program Files\\gs\\';\n        dir_list = dir(default_location);\n        if isempty(dir_list)\n            default_location = 'C:\\Program Files (x86)\\gs\\'; % Possible location on 64-bit systems\n            dir_list = dir(default_location);\n        end\n        executable = {'\\bin\\gswin32c.exe', '\\bin\\gswin64c.exe'};\n        ver_num = 0;\n        % If there are multiple versions, use the newest\n        for a = 1:numel(dir_list)\n            ver_num2 = sscanf(dir_list(a).name, 'gs%g');\n            if ~isempty(ver_num2) && ver_num2 > ver_num\n                for b = 1:numel(executable)\n                    path2 = [default_location dir_list(a).name executable{b}];\n                    if exist(path2, 'file') == 2\n                        path_ = path2;\n                        ver_num = ver_num2;\n                    end\n                end\n            end\n        end\n        if check_store_gs_path(path_)\n            return\n        end\n    else\n        executable = {'/usr/bin/gs', '/usr/local/bin/gs'};\n        for a = 1:numel(executable)\n            path_ = executable{a};\n            if check_store_gs_path(path_)\n                return\n            end\n        end\n    end\n    % Ask the user to enter the path\n    while true\n        if strncmp(computer, 'MAC', 3) % Is a Mac\n            % Give separate warning as the uigetdir dialogue box doesn't have a\n            % title\n            uiwait(warndlg('Ghostscript not found. Please locate the program.'))\n        end\n        base = uigetdir('/', 'Ghostcript not found. Please locate the program.');\n        if isequal(base, 0)\n            % User hit cancel or closed window\n            break;\n        end\n        base = [base filesep]; %#ok<AGROW>\n        bin_dir = {'', ['bin' filesep], ['lib' filesep]};\n        for a = 1:numel(bin_dir)\n            for b = 1:numel(bin)\n                path_ = [base bin_dir{a} bin{b}];\n                if exist(path_, 'file') == 2\n                    if check_store_gs_path(path_)\n                        return\n                    end\n                end\n            end\n        end\n    end\n    if ismac\n        error('Ghostscript not found. Have you installed it (http://pages.uoregon.edu/koch)?');\n    else\n        error('Ghostscript not found. Have you installed it from www.ghostscript.com?');\n    end\nend\n\nfunction good = check_store_gs_path(path_)\n    % Check the path is valid\n    good = check_gs_path(path_);\n    if ~good\n        return\n    end\n    % Update the current default path to the path found\n    if ~user_string('ghostscript', path_)\n        filename = fullfile(fileparts(which('user_string.m')), '.ignore', 'ghostscript.txt');\n        warning('Path to ghostscript installation could not be saved in %s (perhaps a permissions issue). You can manually create this file and set its contents to %s, to improve performance in future invocations (this warning is safe to ignore).', filename, path_);\n        return\n    end\nend\n\nfunction good = check_gs_path(path_)\n    persistent isOk\n    if isempty(path_)\n        isOk = false;\n    elseif ~isequal(isOk,true)\n        % Check whether the path is valid\n        [status, message] = system([gs_command(path_) '-h']); %#ok<ASGLU>\n        isOk = status == 0;\n    end\n    good = isOk;\nend\n\nfunction cmd = gs_command(path_)\n    % Initialize any required system calls before calling ghostscript\n    % TODO: in Unix/Mac, find a way to determine whether to use \"export\" (bash) or \"setenv\" (csh/tcsh)\n    shell_cmd = '';\n    if isunix\n        shell_cmd = 'export LD_LIBRARY_PATH=\"\"; '; % Avoids an error on Linux with GS 9.07\n    end\n    if ismac\n        shell_cmd = 'export DYLD_LIBRARY_PATH=\"\"; ';  % Avoids an error on Mac with GS 9.07\n    end\n    % Construct the command string\n    cmd = sprintf('%s\"%s\" ', shell_cmd, path_);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "fix_lines.m", "ext": ".m", "path": "OCIA-master/utils/plotting/export_fig/fix_lines.m", "size": 6290, "source_encoding": "utf_8", "md5": "8437006b104957762090e3d875688cb6", "text": "%FIX_LINES  Improves the line style of eps files generated by print\n%\n% Examples:\n%   fix_lines fname\n%   fix_lines fname fname2\n%   fstrm_out = fixlines(fstrm_in)\n%\n% This function improves the style of lines in eps files generated by\n% MATLAB's print function, making them more similar to those seen on\n% screen. Grid lines are also changed from a dashed style to a dotted\n% style, for greater differentiation from dashed lines.\n% \n% The function also places embedded fonts after the postscript header, in\n% versions of MATLAB which place the fonts first (R2006b and earlier), in\n% order to allow programs such as Ghostscript to find the bounding box\n% information.\n%\n%IN:\n%   fname - Name or path of source eps file.\n%   fname2 - Name or path of destination eps file. Default: same as fname.\n%   fstrm_in - File contents of a MATLAB-generated eps file.\n%\n%OUT:\n%   fstrm_out - Contents of the eps file with line styles fixed.\n\n% Copyright: (C) Oliver Woodford, 2008-2014\n\n% The idea of editing the EPS file to change line styles comes from Jiro\n% Doke's FIXPSLINESTYLE (fex id: 17928)\n% The idea of changing dash length with line width came from comments on\n% fex id: 5743, but the implementation is mine :)\n\n% Thank you to Sylvain Favrot for bringing the embedded font/bounding box\n% interaction in older versions of MATLAB to my attention.\n% Thank you to D Ko for bringing an error with eps files with tiff previews\n% to my attention.\n% Thank you to Laurence K for suggesting the check to see if the file was\n% opened.\n\n% 01/03/15: Issue #20: warn users if using this function in HG2 (R2014b+)\n% 27/03/15: Fixed out of memory issue with enormous EPS files (generated by print() with OpenGL renderer), related to issue #39\n\nfunction fstrm = fix_lines(fstrm, fname2)\n\n% Issue #20: warn users if using this function in HG2 (R2014b+)\nif using_hg2\n    warning('export_fig:hg2','The fix_lines function should not be used in this Matlab version.');\nend\n    \nif nargout == 0 || nargin > 1\n    if nargin < 2\n        % Overwrite the input file\n        fname2 = fstrm;\n    end\n    % Read in the file\n    fstrm = read_write_entire_textfile(fstrm);\nend\n\n% Move any embedded fonts after the postscript header\nif strcmp(fstrm(1:15), '%!PS-AdobeFont-')\n    % Find the start and end of the header\n    ind = regexp(fstrm, '[\\n\\r]%!PS-Adobe-');\n    [ind2, ind2] = regexp(fstrm, '[\\n\\r]%%EndComments[\\n\\r]+');\n    % Put the header first\n    if ~isempty(ind) && ~isempty(ind2) && ind(1) < ind2(1)\n        fstrm = fstrm([ind(1)+1:ind2(1) 1:ind(1) ind2(1)+1:end]);\n    end\nend\n\n% Make sure all line width commands come before the line style definitions,\n% so that dash lengths can be based on the correct widths\n% Find all line style sections\nind = [regexp(fstrm, '[\\n\\r]SO[\\n\\r]'),... % This needs to be here even though it doesn't have dots/dashes!\n       regexp(fstrm, '[\\n\\r]DO[\\n\\r]'),...\n       regexp(fstrm, '[\\n\\r]DA[\\n\\r]'),...\n       regexp(fstrm, '[\\n\\r]DD[\\n\\r]')];\nind = sort(ind);\n% Find line width commands\n[ind2, ind3] = regexp(fstrm, '[\\n\\r]\\d* w[\\n\\r]');\n% Go through each line style section and swap with any line width commands\n% near by\nb = 1;\nm = numel(ind);\nn = numel(ind2);\nfor a = 1:m\n    % Go forwards width commands until we pass the current line style\n    while b <= n && ind2(b) < ind(a)\n        b = b + 1;\n    end\n    if b > n\n        % No more width commands\n        break;\n    end\n    % Check we haven't gone past another line style (including SO!)\n    if a < m && ind2(b) > ind(a+1)\n        continue;\n    end\n    % Are the commands close enough to be confident we can swap them?\n    if (ind2(b) - ind(a)) > 8\n        continue;\n    end\n    % Move the line style command below the line width command\n    fstrm(ind(a)+1:ind3(b)) = [fstrm(ind(a)+4:ind3(b)) fstrm(ind(a)+1:ind(a)+3)];\n    b = b + 1;\nend\n\n% Find any grid line definitions and change to GR format\n% Find the DO sections again as they may have moved\nind = int32(regexp(fstrm, '[\\n\\r]DO[\\n\\r]'));\nif ~isempty(ind)\n    % Find all occurrences of what are believed to be axes and grid lines\n    ind2 = int32(regexp(fstrm, '[\\n\\r] *\\d* *\\d* *mt *\\d* *\\d* *L[\\n\\r]'));\n    if ~isempty(ind2)\n        % Now see which DO sections come just before axes and grid lines\n        ind2 = repmat(ind2', [1 numel(ind)]) - repmat(ind, [numel(ind2) 1]);\n        ind2 = any(ind2 > 0 & ind2 < 12); % 12 chars seems about right\n        ind = ind(ind2);\n        % Change any regions we believe to be grid lines to GR\n        fstrm(ind+1) = 'G';\n        fstrm(ind+2) = 'R';\n    end\nend\n\n% Define the new styles, including the new GR format\n% Dot and dash lengths have two parts: a constant amount plus a line width\n% variable amount. The constant amount comes after dpi2point, and the\n% variable amount comes after currentlinewidth. If you want to change\n% dot/dash lengths for a one particular line style only, edit the numbers\n% in the /DO (dotted lines), /DA (dashed lines), /DD (dot dash lines) and\n% /GR (grid lines) lines for the style you want to change.\nnew_style = {'/dom { dpi2point 1 currentlinewidth 0.08 mul add mul mul } bdef',... % Dot length macro based on line width\n             '/dam { dpi2point 2 currentlinewidth 0.04 mul add mul mul } bdef',... % Dash length macro based on line width\n             '/SO { [] 0 setdash 0 setlinecap } bdef',... % Solid lines\n             '/DO { [1 dom 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dotted lines\n             '/DA { [4 dam 1.5 dam] 0 setdash 0 setlinecap } bdef',... % Dashed lines\n             '/DD { [1 dom 1.2 dom 4 dam 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dot dash lines\n             '/GR { [0 dpi2point mul 4 dpi2point mul] 0 setdash 1 setlinecap } bdef'}; % Grid lines - dot spacing remains constant\n\n% Construct the output\n% This is the original (memory-intensive) code:\n%first_sec = strfind(fstrm, '% line types:'); % Isolate line style definition section\n%[second_sec, remaining] = strtok(fstrm(first_sec+1:end), '/');\n%[remaining, remaining] = strtok(remaining, '%');\n%fstrm = [fstrm(1:first_sec) second_sec sprintf('%s\\r', new_style{:}) remaining];\nfstrm = regexprep(fstrm,'(% line types:.+?)/.+?%',['$1',sprintf('%s\\r',new_style{:}),'%']);\n\n% Write the output file\nif nargout == 0 || nargin > 1\n    read_write_entire_textfile(fname2, fstrm);\nend\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "notBoxPlot.m", "ext": ".m", "path": "OCIA-master/utils/plotting/notBoxPlot/notBoxPlot.m", "size": 6618, "source_encoding": "utf_8", "md5": "bce98cc92bdab9639cc0a40da6e039e3", "text": "function varargout=notBoxPlot(y,x,jitter,style)\n% notBoxPlot - Doesn't plot box plots!\n%\n% function notBoxPlot(y,x,jitter,style)\n%\n%\n% Purpose\n% An alternative to a box plot, where the focus is on showing raw\n% data. Plots columns of y as different groups located at points\n% along the x axis defined by the optional vector x. Points are\n% layed over a 1.96 SEM (95% confidence interval) in red and a 1 SD\n% in blue. The user has the option of plotting the SEM and SD as a\n% line rather than area. Raw data are jittered along x for clarity. This\n% function is suited to displaying data which are normally distributed.\n% Since, for instance, the SEM is meaningless if the data are bimodally\n% distributed. \n%\n%\n% Inputs\n% y - each column of y is one variable/group. If x is missing or empty\n%     then each column is plotted in a different x position. \n%\n% x - optional, x axis points at which y columns should be\n%     plotted. This allows more than one set of y values to appear\n%     at one x location. Such instances are coloured differently. \n% Note that if x and y are both vectors of the same length this function\n% behaves like boxplot (see Example 5).\n%\n% jitter - how much to jitter the data for visualization\n%          (optional). The width of the boxes are automatically\n%          scaled to the jitter magnitude.\n%\n% style - a string defining plot style of the data.\n%        'patch' [default] - plots SEM and SD as a box using patch\n%                objects. \n%        'line' - create a plot where the SD and SEM are\n%                constructed from lines. \n%        'sdline' - a hybrid of the above, in which only the SD is \n%                replaced with a line.\n%\n%\n% Outputs\n% H - structure of handles for plot objects.\n%\n%\n% Example 1 - simple example\n% clf \n% subplot(2,1,1)  \n% notBoxPlot(randn(20,5));\n% subplot(2,1,2)\n% h=notBoxPlot(randn(10,40));\n% d=[h.data];\n% set(d(1:4:end),'markerfacecolor',[0.4,1,0.4],'color',[0,0.4,0])\n%  \n% Example 2 - overlaying with areas\n% clf  \n% x=[1,2,3,4,5,5];\n% y=randn(20,length(x));\n% y(:,end)=y(:,end)+3;\n% y(:,end-1)=y(:,end-1)-1;\n% notBoxPlot(y,x);\n%\n% Example 3 - lines\n% clf\n% H=notBoxPlot(randn(20,5),[],[],'line');\n% set([H.data],'markersize',10)\n%\n% Example 4 - mix lines and areas [note that the way this function\n% sets the x axis limits can cause problems when combining plots\n% this way]\n%\n% clf\n% h=notBoxPlot(randn(10,1)+4,5,[],'line');\n% set(h.data,'color','m')  \n% h=notBoxPlot(randn(50,10));\n% set(h(5).data,'color','m')\n%  \n% Example 5 - x and y are vectors\n% clf\n% x=[1,1,1,3,2,1,3,3,3,2,2,3,3];\n% y=[7,8,6,1,5,7,2,1,3,4,5,2,4];\n% notBoxPlot(y,x);\n% \n% Note: an alternative to the style used in Example 5 is to call\n% notBoxPlot from a loop in an external function. In this case, the\n% user will have to take care of the x-ticks and axis limits. \n%    \n% Example 6 - replacing the SD with bars\n% clf\n% y=randn(50,1);\n% clf\n% notBoxPlot(y,1,[],'sdline')\n% notBoxPlot(y,2)   \n% xlim([0,3])\n%\n%\n% Rob Campbell - January 2010\n%\n% also see: boxplot\n\n\n    \n    \n% Check input arguments\nerror(nargchk(0,4,nargin))\nif nargin==0\n    help(mfilename)\n    return\nend\n\n\nif isvector(y), y=y(:); end\n\nif nargin<2 || isempty(x)\n    x=1:size(y,2);\nend\n\nif nargin<3 || isempty(jitter)\n    jitter=0.3; %larger value means greater amplitude jitter\nend\n\nif nargin<4\n  style='patch'; %Can also be 'line' or 'sdline'\nend\nstyle=lower(style);\nif jitter==0 && strcmp(style,'patch') \n    warning('A zero value for jitter means no patch object visible')\nend\n\n\nif isvector(y) & isvector(x) & length(x)>1\n    x=x(:);\n   \n    if length(x)~=length(y)\n        error('length(x) should equal length(y)')\n    end\n    \n    u=unique(x);\n    for ii=1:length(u)\n        f=find(x==u(ii));\n        h(ii)=notBoxPlot(y(f),u(ii),jitter,style);\n    end\n\n\n    %Make plot look pretty\n    if length(u)>1\n        xlim([min(u)-1,max(u)+1])\n        set(gca,'XTick',u)\n    end\n    \n    if nargout==1\n        varargout{1}=h;\n    end\n\n    return\n    \nend\n\n\n\n \nif length(x) ~= size(y,2)\n    error('length of x doesn''t match the number of columns in y')\nend\n\n\n\n    \n    \n\n\n%We're going to render points with the same x value in different\n%colors so we loop through all unique x values and do the plotting\n%with nested functions. No clf in order to give the user more\n%flexibility in combining plot elements.\nhold on\n[uX,a,b]=unique(x);\nh=[];\nfor ii=1:length(uX)\n    f=find(b==ii);\n    h=[h,myPlotter(x(f),y(:,f))];\nend\n\nhold off\n\n%Tidy up plot: make it look pretty \nif length(x)>1\n    set(gca,'XTick',unique(x))\n    xlim([min(x)-1,max(x)+1])\nend\n\n\nif nargout==1\n    varargout{1}=h;\nend\n\n\n\n%Nested functions follow\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction h=myPlotter(X,Y)\n\nSEM=SEM_calc(Y); %Supplied external function\nSD=nanstd(Y);  %Requires the stats toolbox \nmu=nanmean(Y); %Requires the stats toolbox \n\n%The plot colors to use for multiple sets of points on the same x\n%location\ncols=hsv(length(X)+1)*0.5;\ncols(1,:)=0;\njitScale=jitter*0.55; %To scale the patch by the width of the jitter\n\nfor k=1:length(X)\n    thisY=Y(:,k);\n    thisY=thisY(~isnan(thisY));\n    thisX=repmat(X(k),1,length(thisY));\n    if strcmp(style,'patch') \n      h(k).sdPtch=patchMaker(SD(k),[0.6,0.6,1]);\n    end\n    \n    if strcmp(style,'patch') || strcmp(style,'sdline')\n      h(k).semPtch=patchMaker(SEM(k),[1,0.6,0.6]);\n      h(k).mu=plot([X(k)-jitScale,X(k)+jitScale],[mu(k),mu(k)],'-r',...\n           'linewidth',2);\n    end\n    \n    %Plot jittered raw data\n    C=cols(k,:);\n    J=(rand(size(thisX))-0.5)*jitter;\n        \n    h(k).data=plot(thisX+J, thisY, 'o', 'color', C,...\n                   'markerfacecolor', C+(1-C)*0.65);\nend\n\nif strcmp(style,'line') | strcmp(style,'sdline')\n  for k=1:length(X)    \n    %Plot SD\n    h(k).sd=plot([X(k),X(k)],[mu(k)-SD(k),mu(k)+SD(k)],...\n                 '-','color',[0.2,0.2,1],'linewidth',2);\n    set(h(k).sd,'ZData',[1,1]*-1)\n  end\nend\n\nif strcmp(style,'line')\n    for k=1:length(X)     \n        %Plot mean and SEM\n        h(k).mu=plot(X(k),mu(k),'o','color','r',...\n            'markerfacecolor','r',...\n            'markersize',10);\n        \n        h(k).sem=plot([X(k),X(k)],[mu(k)-SEM(k),mu(k)+SEM(k)],'-r',...\n            'linewidth',2);\n        h(k).xAxisLocation=x(k);\n    end\nend\n\n\n\n\nfunction ptch=patchMaker(thisInterval,color)\n    l=mu(k)-thisInterval;\n    u=mu(k)+thisInterval;\n    ptch=patch([X(k)-jitScale, X(k)+jitScale, X(k)+jitScale, X(k)-jitScale],...\n           [l,l,u,u], 0);\n    set(ptch,'edgecolor','none','facecolor',color)\nend %function patchMaker\n\n    \n    \nend %function myPlotter\n\n\n\n\n\n\nend %function notBoxPlot\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "importResonanceFolder.m", "ext": ".m", "path": "OCIA-master/utils/importExport/importResonanceFolder.m", "size": 2264, "source_encoding": "utf_8", "md5": "17b8b7e3b8ac097566672757e58f4ca8", "text": "function imgStruct = importResonanceFolder(dataPath) %#ok<STOUT,INUSD>\n\nerror('importResonanceFolder:Deprecated', 'DEPRECATED: use loadData instead');\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Intrinsic image analyser                       %\n% Originally created on           28 / 11 / 2013 %\n% Written by B. Laurenczy (blaurenczy@gmail.com) %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n%% process files of the folder\nfileNames = ls(dataPath); %#ok<UNRCH> % get all file names\nimgStruct = struct('img_data', [], 'hdr', []);\niChan = 1;\nkeepAvgFramesSeparate = 0;\n\nfor iFile = 1 : size(fileNames, 1);\n    fileName = fileNames(iFile, :);\n    \n    if ~isempty(regexp(fileName, '\\.xml', 'once'));\n    \n        imgStruct.hdr.filePath = [dataPath filesep fileName];\n        xml = xmlread(imgStruct.hdr.filePath);\n        dimX = getNodeValue(xml, 'ResolutionX');\n        dimY = getNodeValue(xml, 'ResolutionY');\n        nFrames = getNodeValue(xml, 'Frames');\n        imgStruct.hdr.size = [dimX, dimY, nFrames];\n        imgStruct.hdr.frame_rate = getNodeValue(xml, 'FrameScanRate');\n        imgStruct.hdr.nAveraging = getNodeValue(xml, 'AveragingRepetitions');\n        imgStruct.hdr.zStepSize = getNodeValue(xml, 'StepSize');\n        \n%         fileID = fopen([dataPath filesep fileName]);\n%         data = fread(fileID, 'uint16');\n%         fclose(fileID);\n        \n    elseif ~isempty(regexp(fileName, '\\.bin', 'once'));\n        \n        fileID = fopen([dataPath filesep fileName]);\n        data = fread(fileID, 'uint16');\n        if imgStruct.hdr.nAveraging == 1;\n            imgStruct.img_data{iChan} = reshape(data, imgStruct.hdr.size);\n        elseif imgStruct.hdr.nAveraging > 1;\n            imgStruct.img_data{iChan} = reshape(data, [imgStruct.hdr.size imgStruct.hdr.nAveraging]);\n        end;\n        if imgStruct.hdr.nAveraging > 1 && ~keepAvgFramesSeparate;\n            imgStruct.img_data{iChan} = reshape(mean(imgStruct.img_data{iChan}, 4), imgStruct.hdr.size);\n        end;\n        fclose(fileID);\n        iChan = iChan + 1;\n        \n    end;\n    \nend;\n\n\nfunction value = getNodeValue(xml, tagName)\n    value = str2double(xml.getDocumentElement.getElementsByTagName(tagName).item(0).getChildNodes.item(0).getNodeValue);\nend\n        \nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "ReadEphys.m", "ext": ".m", "path": "OCIA-master/utils/importExport/ReadEphys.m", "size": 1245, "source_encoding": "utf_8", "md5": "07f7cdadb5d8e096e0e9a910fd033ee4", "text": "function [ephys,FrameClock] = ReadEphys(varargin)\n% read binary data files written by LabView E-phys acquisition program in\n% H45\n% requires file name of file to read and number of timepoints\n% returns e-phys trace and frame clock\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\nif nargin == 2\n    filename = varargin{1};\n    timepoints = varargin{2};\nelseif nargin == 1\n    filename = varargin{1};\n    timepoints = getTimePointDialog;\nelse\n    [FileName, PathName] = uigetfile('*.*','Select file for reading');\n    filename = fullfile(PathName,FileName);\n    timepoints = getTimePointDialog;\nend\nfid = fopen(filename);\nif ~timepoints\n    % read till end of file, FrameClock empty\n%     hdr1 = fread(fid,96,'float32','ieee-be');\n    ephys = fread(fid,'float32','ieee-be');\n    FrameClock = [];\nelse\n    hdr1 = fread(fid,96,'float32','ieee-be');\n    ephys = fread(fid,timepoints,'float32','ieee-be');\n    hdr2 = fread(fid,96,'float32','ieee-be');\n    FrameClock = fread(fid,timepoints,'float32','ieee-be');\nend\nfclose(fid);\n\nfunction timepoints = getTimePointDialog\nprompt = {'Timepoints in file:'};\ndlg_title = 'Specify timepoints';\nnum_lines = 1;\nanswer = inputdlg(prompt,dlg_title,num_lines,{''});\ntimepoints = str2double(answer{1});"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "ij_RoiSetSeedPointSegment.m", "ext": ".m", "path": "OCIA-master/utils/importExport/imageJROI/ij_RoiSetSeedPointSegment.m", "size": 4975, "source_encoding": "utf_8", "md5": "8d601794c01445d79037f07f2ec57d22", "text": "    function ij_RoiSetSeedPointSegment(varargin)\n% segment cell Rois from ImageJ single point RoiSet and save as new ImageJ\n% RoiSet\n% in1 ... configuration structure for this program \n% may be called without input arguments --> GUI file selection and default\n% parameters (c.f. below)\n% uses ActiveModel segmentation provided by Parametric Active Model Toolbox\n% http://www.mathworks.ch/matlabcentral/fileexchange/22871\n% (c) Copyright Bing Li 2005 - 2009.\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\n%% Input and default parameters\nif nargin\n    config = varargin{1};\nelse\n    config = struct;\nend\n\nconfig = ParseConfigStruct(config);\n\n%% Do segmentation\nfor n = 1:length(config.roi_files)\n    \n    % support tiff only at this stage\n    img = tif2mat(config.img_files{n},'nosave');\n    img = img.data;\n    img = mean(img,3);\n    \n    roiSet = ij_roiDecoder(config.roi_files{n},size(img));\n    roiSet_seg = roiSet;\n    \n    % run segmentation for each point Roi\n    for roi = 1:size(roiSet,1)\n        point_mask = bwunpack(roiSet{roi,2});\n        [x,y] = ind2sub(size(img),find(point_mask==1));\n        if length(x) > 1\n            warning('Found more than 1 point in Roi %s of RoiSet %s',...\n                roiSet{roi,1},config.roi_files{n});\n        elseif isempty(x) || isempty(y)\n            warning('Found no points in Roi %s of RoiSet %s',...\n                roiSet{roi,1},config.roi_files{n});\n            roiSet_seg{roi} = [];\n            continue\n        end\n        config.cog = [x y];\n        roiSet_seg{roi,2} = ActiveModel_segment(img,config);\n    end\n    % save as new ImageJ RoiSet\n    tmpdir = datestr(clock,30);\n    mkdir(tmpdir);\n    zip_filename = strrep(config.roi_files{n},'.zip','_Cells.zip');\n    start_dir = pwd;\n    cd(tmpdir);\n    for roi = 1:size(roiSet_seg,1)\n       status = ij_roiEncoder(roiSet_seg{roi,2},roiSet{roi,1});\n    end\n    % create neuropil Rois (either none or 4)\n    if config.npil_rois\n        roi_img = zeros(size(img));\n        for roi = 1:size(roiSet_seg,1)\n            roi_img(roiSet_seg{roi,2}==1) = 1;\n        end\n        roi1 = repmat(1,round(size(roi_img,1)/2),round(size(roi_img,2)/2));\n        roi2 = repmat(2,size(roi_img,1)-round(size(roi_img,1)/2),...\n            round(size(roi_img,2)/2));\n        roi3 = repmat(3,round(size(roi_img,1)/2),size(roi_img,2)-...\n            round(size(roi_img,2)/2));\n        roi4 = repmat(4,size(roi_img,1)-round(size(roi_img,1)/2),...\n            size(roi_img,2)-round(size(roi_img,2)/2));\n        npil_rois = [roi1 roi3; roi2 roi4];\n        npil_rois(roi_img~=0) = 0;\n        for npil_no = 1:4\n            roi = zeros(size(roi_img));\n            roi(npil_rois==npil_no&img>20) = 1;\n            status = ij_roiEncoder(roi,sprintf('n%1.0f.roi',npil_no));\n            roiSet_seg{size(roiSet_seg,1)+1,1} = sprintf('n%1.0f.roi',npil_no);\n            roiSet_seg{size(roiSet_seg,1),2} = roi;\n        end\n    end\n    zip(zip_filename,roiSet_seg(:,1));\n    [path, zip_filename] = fileparts(zip_filename);\n    cd(start_dir);\n    rmdir(tmpdir,'s');\n    mat_filename = strrep(zip_filename,'.zip','.mat');\n    roiSet = roiSet_seg;\n    roiSet(:,1) = strrep(roiSet(:,1),'.roi','');\n    save(mat_filename,'roiSet');\nend\n\n\n%% Parse config structure\nfunction config = ParseConfigStruct(config)\n\n% input file(s)\nif ~isfield(config,'roi_files')\n    [config.roi_files,PathName] = uigetfile('*.zip','MultiSelect','on');\n    if isstr(config.roi_files)\n       config.roi_files = {config.roi_files};\n    end\n    for n = 1:length(config.roi_files)\n       config.roi_files{n} = fullfile(PathName,config.roi_files{n});\n    end\nelseif isstr(config.roi_files)\n    config.roi_files = {config.roi_files};\nend\n\nif ~isfield(config,'img_files')\n   [config.img_files,PathName] = uigetfile({'*.tif';'*.tiff'},'MultiSelect','on');\n   if isstr(config.img_files)\n       config.img_files = {config.img_files};\n    end\n    for n = 1:length(config.img_files)\n       config.img_files{n} = fullfile(PathName,config.img_files{n});\n    end\nelseif isstr(config.img_files)\n    config.img_files = {config.img_files};\nend\n\nif length(config.roi_files) ~= length(config.img_files)\n   error('You MUST specify the same number of image and roi files!');\nend\n\n% required field names and their values\nconfig_fields = {'cellsize' 'snake_iter1' 'snake_iter' ...\n    'alpha' 'beta' 'tau' 'filt_range' 'filt_order' 'canny_lo' ...\n    'canny_hi' 'alt_radius' 'npil_rois' 'doPlot' 'verbosity'};\nconfig_vals = {7 100 10 1 0 0.1 [3 3] 1 [0.1 0.05 0.01] ...\n    [0.25 0.15 0.05] 3 0 0 0};\n\nfor n = 1:length(config_fields)\n    if ~isfield(config,config_fields{n})\n        config.(config_fields{n}) = config_vals{n};\n    end\nend\nconfig_savename = ['AMsegment_config.html'];\nprint2html(config,5,config_savename);\nconfig_savename = strrep(config_savename,'.html','.mat');\nsave(config_savename,'config');\nconfig_savename = strrep(config_savename,'.mat','');\nfprintf('\\nCurrent configuration saved in %s\\n',config_savename);\n\n\n\n\n\n\n% e.o.f.\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "loadDataFromHDF5.m", "ext": ".m", "path": "OCIA-master/utils/importExport/HDF5/loadDataFromHDF5.m", "size": 7865, "source_encoding": "utf_8", "md5": "4d3a9dea75580223eebd08fa81693430", "text": "function data = loadDataFromHDF5(filePath, datasetPath)\n% data = loadDataFromHDF5(filePath, datasetPath)\n% Wrapper for the h5readAsStruct function that reads out the content of the HDF5 file as a structure and reshapes it\n% to a more usable format.\n%\n% Written on 2014-04-07 by B. Laurenczy (blaurenczy@gmail.com)\n\n% read the HDF5 file's content into a structure\ndataStruct = h5readAsStruct(filePath, datasetPath);\n\n% reshape it to our need\ndata = reshapeData(dataStruct, filePath, datasetPath);\n\nend\n\n% reshape each field to our need\nfunction dataStruct = reshapeData(dataStruct, filePath, structPath)\n\n    % if not a structure, just return the data\n    if ~isstruct(dataStruct);\n        return;\n    end;\n    \n    % get the fields\n    fNames = fieldnames(dataStruct);\n    \n    % go through each field and process it\n    for iFName = 1 : numel(fNames);\n        fName = fNames{iFName}; % get the field name\n        \n        % different processing for different field names\n        switch fName;\n            \n            % reshape the ROISet into the 2-column cell array with col. 1 being the names and col. 2 beign the masks\n            % also add the reference image and the runs validities as columns in the cell array\n            % final output in the ROISet field will be a cell array with one row for each ROISet and \n            % 4 columns : { ROISet, runsValidity, refImage, ROISet's runID }\n            case 'ROISet';\n                \n                % read as h5read_wrapper would do\n                ROISetStruct = processStruct(filePath, structPath, {});                \n                dataStruct.ROISet = ROISetStruct.ROISet;\n                dataStruct.runsValidity = ROISetStruct.runsValidity;\n                dataStruct.refImage = ROISetStruct.refImage;\n                \n                %{\n                \n                ROISetStruct = dataStruct.ROISet;\n                ROINamesRuns = fieldnames(ROISetStruct.ROINames);\n                \n                dataStruct.ROISet = cell(numel(ROINamesRuns), 4);\n                \n                for iRun = 1 : numel(ROINamesRuns);\n                    \n                    runID = ROINamesRuns{iRun};\n                    ROINames = ROISetStruct.ROINames.(runID);\n                    nROIs = size(ROINames, 1);\n                    masks = ROISetStruct.masks.(runID);\n                    runsValidity = ROISetStruct.runsValidity.(runID);\n                    nRuns = size(runsValidity, 1);\n                    \n                    % cast matrix of logical into cell-array of logical\n                    masks = arrayfun(@(iROI)masks(:, :, iROI), 1 : nROIs, 'UniformOutput', false)';\n                    % cast matrix of doubles into a cell-array of strings ... in a single line! :D Take that clarity !\n                    runsValidity = arrayfun(@(iRun)char(runsValidity(iRun, :)), 1 : nRuns, 'UniformOutput', false)';\n                    % cast matrix of doubles into a cell-array of strings ... in a single line! :D Take that clarity !\n                    ROINames = arrayfun(@(iROI)strtrim(char(ROINames(iROI, :))), 1 : nROIs, 'UniformOutput', false)';\n                    \n                    % create ROISet based on the reshaped data\n                    dataStruct.ROISet{iRun, 1} = [ROINames, masks];\n                    dataStruct.ROISet{iRun, 2} = runsValidity;\n                    dataStruct.ROISet{iRun, 3} = ROISetStruct.refImage.(runID);\n                    dataStruct.ROISet{iRun, 4} = runID;\n                    \n                end;\n                \n                %}\n                \n            % reshape the behavior data from one structure with many fields to a structure with cell-arrays as fields, \n            % with one cell-array per field and an additional field for the runIDs\n            case 'behav';\n                \n                behavStruct = dataStruct.behav;\n                behavStructArray = structfun(@(x)x, behavStruct, 'UniformOutput', false);\n                dataStruct.runIDs = regexprep(fieldnames(behavStruct), '^x', '');\n                behavFields = fieldnames(behavStructArray);\n                dataStruct.behav = struct();\n                c = struct2cell(behavStructArray);\n                for iField = 1 : numel(behavFields);\n                    dataStruct.behav.(behavFields{iField}) = c(iField, :);\n                end;\n                \n            % reshape the structure as cell-arrays (runIDs should be the same as in the 'behav' field)\n            case {'caTraces', 'stim', 'exclMask'};\n                \n                s = dataStruct.(fName);\n                sFNames = fieldnames(s);\n                c = cell(numel(sFNames), 1);\n                for iRun = 1 : numel(sFNames);\n                    c{iRun} = s.(sFNames{iRun});\n                end;\n                dataStruct.(fName) = c;\n                \n            % otherwise go recusrive\n            otherwise\n                \n                structPath = sprintf('%s/%s', structPath, regexprep(fName, '^x', ''));\n                dataStruct.(fName) = reshapeData(dataStruct.(fName), filePath, structPath);\n                \n        end;\n        \n    end;\n    \nend\n\n\n% separate function to process structures. Fields of the structure can be stored either as data \n%   sets or as attributes if they have \"simple\" values (single numbers, single logicals or string)\nfunction data = processStruct(fileName, datasetPath, h5readArgs)\n\n% read the data-set field names of the structure from the attribute as a comma-separated string\ndatasetNames = str2cell(h5readatt(fileName, datasetPath, 'FieldNames_DS'));\n% read the attribute field names of the structure from the attribute as a comma-separated string\nattNames = str2cell(h5readatt(fileName, datasetPath, 'FieldNames_Att'));\n\n% remove the empty cells\nattNames(cellfun(@isempty, attNames)) = [];\ndatasetNames(cellfun(@isempty, datasetNames)) = [];\n    \n% initate the structure\ndata = struct();\n\n% go through each data-set stored field name\nfor iName = 1 : numel(datasetNames);\n    \n    % get the field name\n    name = datasetNames{iName};\n    % create the data set path using that name\n    datasetPathStruct = sprintf('%s/%s', datasetPath, name);\n    % read the data set to extract the value, recursively\n    data.(name) = h5read_wrapper(fileName, datasetPathStruct, h5readArgs);\n    \nend;\n\n% go through each attribute stored field name\nfor iName = 1 : numel(attNames);\n    \n    % get the field name\n    name = attNames{iName};\n    % load the value from the attribute\n    dataVal = h5readatt(fileName, datasetPath, sprintf('%s_Value', name));\n    % load the orginial data type from the attribute\n    dataType = h5readatt(fileName, datasetPath, sprintf('%s_DataType', name));\n    % load the orginial dimension from the attribute (to restore the orientation: 1xN or Nx1)\n    dim = str2dim(h5readatt(fileName, datasetPath, sprintf('%s_Dim', name)));\n    \n    % process the different data types differently\n    switch dataType;\n        \n        % strings (char)\n        case 'char';\n            \n            % store the value as a converted, reshaped char\n            data.(name) = char(reshape(dataVal, dim));\n        \n        % logicals\n        case 'logical';\n            \n            % store the value as a converted, reshaped logical\n            data.(name) = logical(reshape(dataVal, dim));\n        \n        % numeric\n        case {'single', 'double', 'int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32', 'int64', 'uint64'};\n            \n            % store the value as a reshaped numeric\n            data.(name) = reshape(dataVal, dim);\n        \n        % others: unknown, throw a warning\n        otherwise\n            \n            warning('h5read_wrapper:processStruct:UnknownDataTypeValue', ...\n                'Data type \"%s\" of field \"%s\" at data set path \"%s\" is not supported.', dataType, name, datasetPath);\n            \n    end;\n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "h5createIntermediateGroup.m", "ext": ".m", "path": "OCIA-master/utils/importExport/HDF5/h5createIntermediateGroup.m", "size": 1120, "source_encoding": "utf_8", "md5": "5ba2a52ce40089c79f3c43287ca89493", "text": "% creates an intermediate group in the HDF5 file so that group exists whenever an attribute needs to be attached to it\nfunction h5createIntermediateGroup(fileName, groupPath)\n\n% if file does not exist, create a new file using the default properties\nif ~exist(fileName, 'file');\n    fID = H5F.create(fileName, 'H5F_ACC_TRUNC', 'H5P_DEFAULT', 'H5P_DEFAULT');\n% if file exists, open it using the default properties\nelse\n    fID = H5F.open(fileName, 'H5F_ACC_RDWR', 'H5P_DEFAULT');\nend;\n% check if the group does not already exist\ntry\n    % try to fetch the info for this group ...\n    H5G.get_objinfo(fID, groupPath, false);\n    % ... if no error, then group exists so abort\n    return;\ncatch err; %#ok<NASGU>\n    % error mean no group, so one can create it\nend;\n\n% create group creation property list and set it to allow creation of intermediate groups\ngPropList = H5P.create('H5P_LINK_CREATE');\nH5P.set_create_intermediate_group(gPropList, 1);\n% create the group at groupPath\ngID = H5G.create(fID, groupPath, gPropList, 'H5P_DEFAULT', 'H5P_DEFAULT');\n% close all\nH5P.close(gPropList);\nH5G.close(gID);\nH5F.close(fID);\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "h5read_wrapper.m", "ext": ".m", "path": "OCIA-master/utils/importExport/HDF5/h5read_wrapper.m", "size": 9682, "source_encoding": "utf_8", "md5": "1e980dce42de30109767710808bce2c5", "text": "function data = h5read_wrapper(filePath, datasetPath, h5readArgs)\n% data = h5read_wrapper(fileName, datasetPath, h5readArgs)\n% Wrapper for the h5read function that also handles cell-array, structures and struct-arrays.\n% Recursively reads the data from the HDF5 file \"fileName\" from the dataset path \"datasetPath\".\n% Arguments to the h5read function can be provided using the h5readArgs cell-array.\n\n% get the data type of the data from the attribute\ntry\n    dataType = h5readatt(filePath, datasetPath, 'DataType');\ncatch err;\n   error('h5read_wrapper:DataTypeAttributeNotFound', ...\n        'Could not find the attribute \"DataType\" at path \"%s\" in file \"%s\".', datasetPath, filePath);\nend;\n\n% process the different data types in different ways\nswitch dataType;\n    \n    % numeric\n    case {'single', 'double', 'int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32', 'int64', 'uint64'};\n        \n        % read out the data\n        data = h5read(filePath, datasetPath, h5readArgs{:});\n        \n    % logical\n    case 'logical';\n        \n        % read out the data\n        data = h5read(filePath, datasetPath, h5readArgs{:});\n        % convert to logical\n        data = logical(data);\n        \n    % strings (char)\n    case 'char';\n        \n        % read out the data\n        data = h5read(filePath, datasetPath, h5readArgs{:});\n        % convert to char\n        data = char(data);\n    \n    % cell-arrays\n    case 'cellArray';\n        \n        % cell arrays are processed in a separate function\n        data = processCellArray(filePath, datasetPath, h5readArgs);\n    \n    % cell-arrays saved as matrix\n    case 'cellArrayMat';\n        \n        % cell arrays are processed in a separate function\n        data = processCellArrayMat(filePath, datasetPath, h5readArgs);\n        \n    % structures\n    case 'struct';\n        \n        % structures are processed in a separate function\n        data = processStruct(filePath, datasetPath, h5readArgs);\n        \n    % struct-arrays\n    case 'structArray';\n        \n        % struct-arrays are processed in a separate function\n        data = processStructArray(filePath, datasetPath, h5readArgs);\n        \n    % others: categorical (nominal/ordinal), tables and all kinds of weird stuff are not supported\n    otherwise;\n        \n        warning('h5read_wrapper:DataTypeNotSupported', 'Data type \"%s\" is not supported.', dataType);\n        \n        % abort\n        return;\n    \nend;\n\nend\n\n% separate function to process cell-arrays and load each cell's content separately\nfunction data = processCellArray(fileName, datasetPath, h5readArgs)\n\n% read the dimension of the data from the attribute as a \"3x4x5\" string\ndim = str2dim(h5readatt(fileName, datasetPath, 'cellArray_Dim'));\n\n% get the indexes of the non-empty cells\ndatasetPathNonEmptyCell = sprintf('%s/cellNonEmptyIndexes', datasetPath);\nnonEmptyCells = h5read_wrapper(fileName, datasetPathNonEmptyCell, h5readArgs);\n\n% initiate the cell-array\ndata = cell(dim);\n\n% go through each non-empty cell and load it separately\nfor iCell = find(nonEmptyCells);\n    \n    % create the path using a simple \"cell_[CELL_INDEX]\" path\n    datasetPathCell = sprintf('%s/cell_%d', datasetPath, iCell);\n    % if the cell was empty, the data set was not created so skip it and leave it empty\n    if ~h5exists(fileName, datasetPathCell); continue; end;\n    % read the data set for the content of the cell, recursively\n    data{iCell} = h5read_wrapper(fileName, datasetPathCell, h5readArgs);\n    \nend;\n\nend\n\n% separate function to process cell-arrays that are saved as matrix\nfunction data = processCellArrayMat(fileName, datasetPath, h5readArgs)\n\n% read the dimension of the data from the attribute as a \"3x4x5\" string\ndim = str2dim(h5readatt(fileName, datasetPath, 'cellArrayMat_Dim'));\n\n% extract the cell-array's values as a dataset of doubles\ndatasetPathCell = sprintf('%s/cellArrayMat_Val', datasetPath);\nlinDataMatDouble = h5read_wrapper(fileName, datasetPathCell, h5readArgs);\n\n% extract the cell-array's dimensions\ndatasetPathCell = sprintf('%s/cellArrayMat_Dim', datasetPath);\nmatDim = h5read_wrapper(fileName, datasetPathCell, h5readArgs);\n\n% extract the cell-array's data types\ndatasetPathCell = sprintf('%s/cellArrayMat_DataTypes', datasetPath);\ndataTypes = h5read_wrapper(fileName, datasetPathCell, h5readArgs);\n\n% calculate the number of values per cell by calculating the product of the dimensions\nnValuesPerCell = arrayfun(@(x)prod(matDim(x, matDim(x, :) > 0)), 1 : size(matDim, 1));\n\n% empty cells (dim = 0x0x0x...) must have a number of values of 0\nnValuesPerCell(arrayfun(@(x)~any(matDim(x, :) > 0), 1 : size(matDim, 1))) = 0;\n\n% convert back the matrix to a cell array\nlinDataCellDouble = mat2cell(linDataMatDouble, reshape(nValuesPerCell, numel(nValuesPerCell), 1));\nnCells = size(linDataCellDouble, 1);\n\n% create a linearized cell array with empty cells\nlinData = cell(nCells, 1);\n\n% convert back the doubles into the appropriate data type and reshape the cells to their original dimensions:\n% go cell by cell\nfor iCell = 1 : nCells;\n    % if all dimensions are empty, the cell must remain empty\n    if ~any(matDim(iCell, :)); continue; end;\n    % cast the double into the right value\n    cellValue = cast(linDataCellDouble{iCell}', strtrim(dataTypes(iCell, :)));\n    % reshape the cell's content to the original dimension\n    linData{iCell} = reshape(cellValue, matDim(iCell, matDim(iCell, :) > 0));\nend;\n\n% reshape the cell-array to have the original dimension\ndata = reshape(linData, dim);\n\nend\n\n% separate function to process struct-arrays and load each structure separately\nfunction data = processStructArray(fileName, datasetPath, h5readArgs)\n\n% read the dimension of the struct-array from the attribute as a \"3x4x5\" string\ndim = str2dim(h5readatt(fileName, datasetPath, 'struct_Dim'));\n% read the field names of the structure from the attribute as a comma-separated string\nnames = str2cell(h5readatt(fileName, datasetPath, 'FieldNames'));\n\n% remove the empty cells\nnames(cellfun(@isempty, names)) = [];\n    \n% initate the struct-array using the cell2struct function. Each structure already contains the right field names\ndata = cell2struct(cell([numel(names), dim]), names, 1);\n\n% go trough each structure an load it separately\nfor iStruct = 1 : numel(data);\n    \n    % create the path using a simple \"struct_[STRUCT_INDEX]\" path\n    datasetPathStruct = sprintf('%s/struct_%d', datasetPath, iStruct);\n    % if the structure was empty, the data set was not created so skip it and leave it empty\n    if ~h5exists(fileName, datasetPathStruct); continue; end;\n    % read the data set for a single structure, recursively\n    data(iStruct) = h5read_wrapper(fileName, datasetPathStruct, h5readArgs);\nend;\n\nend\n\n% separate function to process structures. Fields of the structure can be stored either as data \n%   sets or as attributes if they have \"simple\" values (single numbers, single logicals or string)\nfunction data = processStruct(fileName, datasetPath, h5readArgs)\n\n% read the data-set field names of the structure from the attribute as a comma-separated string\ndatasetNames = str2cell(h5readatt(fileName, datasetPath, 'FieldNames_DS'));\n% read the attribute field names of the structure from the attribute as a comma-separated string\nattNames = str2cell(h5readatt(fileName, datasetPath, 'FieldNames_Att'));\n\n% remove the empty cells\nattNames(cellfun(@isempty, attNames)) = [];\ndatasetNames(cellfun(@isempty, datasetNames)) = [];\n    \n% initate the structure\ndata = struct();\n\n% go through each data-set stored field name\nfor iName = 1 : numel(datasetNames);\n    \n    % get the field name\n    name = datasetNames{iName};\n    % create the data set path using that name\n    datasetPathStruct = sprintf('%s/%s', datasetPath, name);\n    % read the data set to extract the value, recursively\n    data.(name) = h5read_wrapper(fileName, datasetPathStruct, h5readArgs);\n    \nend;\n\n% go through each attribute stored field name\nfor iName = 1 : numel(attNames);\n    \n    % get the field name\n    name = attNames{iName};\n    % load the value from the attribute\n    dataVal = h5readatt(fileName, datasetPath, sprintf('%s_Value', name));\n    % load the orginial data type from the attribute\n    dataType = h5readatt(fileName, datasetPath, sprintf('%s_DataType', name));\n    % load the orginial dimension from the attribute (to restore the orientation: 1xN or Nx1)\n    dim = str2dim(h5readatt(fileName, datasetPath, sprintf('%s_Dim', name)));\n    \n    % nan-bug in reading dimension\n    if any(isnan(dim));\n        warning('h5read_wrapper:unreadableAttribute', ...\n            'Cannot read attribute \"%s\" because dimension string is (partly) NaN', name);\n        continue;\n    end;\n    \n    % process the different data types differently\n    switch dataType;\n        \n        % strings (char)\n        case 'char';\n            \n            % store the value as a converted, reshaped char\n            data.(name) = char(reshape(dataVal, dim));\n        \n        % logicals\n        case 'logical';\n            \n            % store the value as a converted, reshaped logical\n            data.(name) = logical(reshape(dataVal, dim));\n        \n        % numeric\n        case {'single', 'double', 'int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32', 'int64', 'uint64'};\n            \n            % store the value as a reshaped numeric\n            data.(name) = reshape(dataVal, dim);\n        \n        % others: unknown, throw a warning\n        otherwise\n            \n            warning('h5read_wrapper:processStruct:UnknownDataTypeValue', ...\n                'Data type \"%s\" of field \"%s\" at data set path \"%s\" is not supported.', dataType, name, datasetPath);\n            \n    end;\n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "h5exists.m", "ext": ".m", "path": "OCIA-master/utils/importExport/HDF5/h5exists.m", "size": 1057, "source_encoding": "utf_8", "md5": "1db0c46a2621b643d377b487c7574545", "text": "% delete a group in the HDF5 file\nfunction doesExist = h5exists(fileName, groupPath)\n\n% check if the file exists\nif ~exist(fileName, 'file');\n    doesExist = false;\n    return;\nend;\n\ndoesExist = true;\nfileID = H5F.open(fileName);\npathParts = regexp(groupPath, '/', 'split');\n\niPart = 2;\nwhile iPart < numel(pathParts);\n    \n    partialPartsString = sprintf('/%s', pathParts{2 : iPart});\n    nextPart = pathParts{iPart + 1};\n    try\n        groupID = H5G.open(fileID, partialPartsString);\n    catch err;\n        if strcmp(err.identifier, 'MATLAB:imagesci:hdf5lib:libraryError') && iPart == 2;\n            doesExist = false;\n            break;\n        end;\n        rethrow(err);\n    end;\n    \n    if H5L.exists(groupID, nextPart, 'H5P_DEFAULT');\n%         fprintf('Path \"%s/%s\": link exists\\n', partialPartsString, nextPart);\n        H5G.close(groupID);\n    else\n%         fprintf('Path \"%s/%s\": link does not exist\\n', partialPartsString, nextPart);\n        doesExist = false;\n        break;\n    end;\n    \n    iPart = iPart + 1;\nend;\n\nH5F.close(fileID);\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "loadDataFromHDF5_old.m", "ext": ".m", "path": "OCIA-master/utils/importExport/HDF5/loadDataFromHDF5_old.m", "size": 4559, "source_encoding": "utf_8", "md5": "b3d2b1533fb9d314c711a71eefa2fff5", "text": "function data = loadDataFromHDF5_old(filePath, datasetPath)\n% data = loadDataFromHDF5(filePath, datasetPath)\n% Wrapper for the h5readAsStruct function that reads out the content of the HDF5 file as a structure and reshapes it\n% to a more usable format.\n%\n% Written on 2014-04-07 by B. Laurenczy (blaurenczy@gmail.com)\n\n% read the HDF5 file's content into a structure\ndataStruct = h5readAsStruct(filePath, datasetPath);\n\n% reshape it to our need\ndata = reshapeData(dataStruct);\n\nend\n\n% reshape each field to our need\nfunction dataStruct = reshapeData(dataStruct)\n\n    % if not a structure, just return the data\n    if ~isstruct(dataStruct);\n        return;\n    end;\n    \n    % get the fields\n    fNames = fieldnames(dataStruct);\n    \n    % go through each field and process it\n    for iFName = 1 : numel(fNames);\n        fName = fNames{iFName}; % get the field name\n        \n        % different processing for different field names\n        switch fName;\n            \n            % reshape the ROISet into the 2-column cell array with col. 1 being the names and col. 2 beign the masks\n            % also add the reference image and the runs validities as columns in the cell array\n            % final output in the ROISet field will be a cell array with one row for each ROISet and \n            % 4 columns : { ROISet, runsValidity, refImage, ROISet's runID }\n            case 'ROISet';\n                \n                ROISetStruct = dataStruct.ROISet;\n                ROINamesRuns = fieldnames(ROISetStruct.ROINames);\n                \n                dataStruct.ROISet = cell(numel(ROINamesRuns), 4);\n                \n                for iRun = 1 : numel(ROINamesRuns);\n                    \n                    runID = ROINamesRuns{iRun};\n                    ROINames = ROISetStruct.ROINames.(runID);\n                    nROIs = size(ROINames, 1);\n                    masks = ROISetStruct.masks.(runID);\n                    runsValidity = ROISetStruct.runsValidity.(runID);\n                    nRuns = size(runsValidity, 1);\n                    \n                    % cast matrix of logical into cell-array of logical\n                    masks = arrayfun(@(iROI)masks(:, :, iROI), 1 : nROIs, 'UniformOutput', false)';\n                    % cast matrix of doubles into a cell-array of strings ... in a single line! :D Take that clarity !\n                    runsValidity = arrayfun(@(iRun)char(runsValidity(iRun, :)), 1 : nRuns, 'UniformOutput', false)';\n                    % cast matrix of doubles into a cell-array of strings ... in a single line! :D Take that clarity !\n                    ROINames = arrayfun(@(iROI)strtrim(char(ROINames(iROI, :))), 1 : nROIs, 'UniformOutput', false)';\n                    \n                    % create ROISet based on the reshaped data\n                    dataStruct.ROISet{iRun, 1} = [ROINames, masks];\n                    dataStruct.ROISet{iRun, 2} = runsValidity;\n                    dataStruct.ROISet{iRun, 3} = ROISetStruct.refImage.(runID);\n                    dataStruct.ROISet{iRun, 4} = runID;\n                    \n                end;\n                \n            % reshape the behavior data from one structure with many fields to a structure with cell-arrays as fields, \n            % with one cell-array per field and an additional field for the runIDs\n            case 'behav';\n                \n                behavStruct = dataStruct.behav;\n                behavStructArray = structfun(@(x)x, behavStruct, 'UniformOutput', false);\n                dataStruct.runIDs = regexprep(fieldnames(behavStruct), '^x', '');\n                behavFields = fieldnames(behavStructArray);\n                dataStruct.behav = struct();\n                c = struct2cell(behavStructArray);\n                for iField = 1 : numel(behavFields);\n                    dataStruct.behav.(behavFields{iField}) = c(iField, :);\n                end;\n                \n            % reshape the structure as cell-arrays (runIDs should be the same as in the 'behav' field)\n            case {'caTraces', 'stim', 'exclMask'};\n                \n                s = dataStruct.(fName);\n                sFNames = fieldnames(s);\n                c = cell(numel(sFNames), 1);\n                for iRun = 1 : numel(sFNames);\n                    c{iRun} = s.(sFNames{iRun});\n                end;\n                dataStruct.(fName) = c;\n                \n            % otherwise go recusrive\n            otherwise\n                \n                dataStruct.(fName) = reshapeData(dataStruct.(fName));\n                \n        end;\n        \n    end;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "h5createwrite_wrapper.m", "ext": ".m", "path": "OCIA-master/utils/importExport/HDF5/h5createwrite_wrapper.m", "size": 16544, "source_encoding": "utf_8", "md5": "9489ee1b4d18ac50e7741d3b0080da0f", "text": "function h5createwrite_wrapper(fileName, datasetPath, data, h5createArgs, h5writeArgs)\n% h5createwrite_wrapper(fileName, datasetPath, data, h5createArgs, h5writeArgs)\n% Wrapper for the h5create and h5write functions that also handles cell-array, structures and struct-arrays.\n% Recursively creates and/or writes the data as HDF5 into the file \"fileName\" under the dataset path \"datasetPath\".\n% Arguments to the h5create and h5write functions can be provided using the h5createArgs and h5writeArgs cell-array.\n\n% maximum limit for having a string structure value written out as an attribute\nCHAR_ATT_LIMIT = 200;\n% maximum limit for having a logical structure value written out as an attribute\nLOGICAL_ATT_LIMIT = 10;\n% maximum limit for having a numeric structure value written out as an attribute\nNUMERIC_ATT_LIMIT = 10;\n% maximum number of dimensions that a cell can have and still be converted as a matrix\nCELL_MAX_DIM = 32;\n% minimum number of characters required to know a datatype from the command \"class\"\nDATA_TYPE_DESCR_LEN = 7;\n\n% get the data type of the data and store it before eventual modifications\ndataType = class(data);\noriginalDataType = dataType;\n\n% process the different data types in different ways\nswitch dataType;\n    \n    % numeric\n    case {'single', 'double', 'int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32', 'int64', 'uint64'};\n        \n        % nothing to do, just store as numeric\n        \n    % logical and strings (char)\n    case {'logical', 'char'};\n        \n        % logical and chars should be converted to 8-bit\n        data = uint8(data);\n        % get the new data type\n        dataType = class(data);\n    \n    % cell-arrays\n    case 'cell';\n        \n        % cell arrays are processed in a separate function\n        processCellArray(fileName, datasetPath, data, h5createArgs, h5writeArgs, CELL_MAX_DIM, DATA_TYPE_DESCR_LEN);\n        \n        % stop here since function \n        return;\n    \n    %  structures and struct-arrays\n    case 'struct';\n        \n        % if creation is required, create the group path first, so that attribute saving already have the path ready\n        h5createIntermediateGroup(fileName, datasetPath);\n            \n        % structures and struct-arrays are processed in a separate function\n        processStruct(fileName, datasetPath, data, h5createArgs, h5writeArgs, CHAR_ATT_LIMIT, LOGICAL_ATT_LIMIT, ...\n            NUMERIC_ATT_LIMIT);\n        \n        return;\n        \n    % others: categorical (nominal/ordinal), tables and all kinds of weird stuff are not supported\n    otherwise;\n        \n        warning('h5createwrite_wrapper:DataTypeNotSupported', 'Data type \"%s\" is not supported.', dataType);\n        \n        % abort\n        return;\n    \nend;\n\n% get the dimension of the data\ndim = size(data);\n\n% is data size is not empty, create the data set with the actual data type\nif ~any(dim == 0);\n\n    % if dataset already exists, delete it\n    if h5exists(fileName, datasetPath); h5deleteGroup(fileName, datasetPath); end;\n    h5create(fileName, datasetPath, dim, 'DataType', dataType, h5createArgs{:});\n    \n    % write the data into the data set with the actual data type\n    h5write(fileName, datasetPath, data, h5writeArgs{:});\n\n% is data size has an empty dimension, chunk size should be specified\nelse\n    \n    % if dataset already exists, delete it\n    if h5exists(fileName, datasetPath); h5deleteGroup(fileName, datasetPath); end;\n    \n    % get the chunk size as the size divided by ten\n    chunkSize = fix(dim / 10);\n    % set the dimensions that are 0 to 1\n    chunkSize(chunkSize == 0) = 1;\n    % create the data set with an infinite size and data type 'double' since an empty array is a double\n    h5create(fileName, datasetPath, inf(1 : ndims(dim)), 'ChunkSize', chunkSize, 'DataType', 'double', ...\n        h5createArgs{:});\n    % store the original dimension of the data into an attribute\n    h5writeatt(fileName, datasetPath, 'DataDim', dim2str(dim));\n    \n%     % write an empty cell the data set with the actual data type\n%     h5write(fileName, datasetPath, [], h5writeArgs{:});\n    \nend;\n\n% store the orginal data type into an attribute\nh5writeatt(fileName, datasetPath, 'DataType', originalDataType);\n\nend\n\n% separate function to process cell-arrays and save each cell's content separately\nfunction processCellArray(fileName, datasetPath, data, h5createArgs, h5writeArgs, ...\n    CELL_MAX_DIM, DATA_TYPE_DESCR_LEN)\n\n% linearize the cell-array (e.g. turn a 3 x 4 x 5 cell-array into a 1 x (3*4*5) = 1 * 60 cell-array\nlinData = data(:);\n% get the number of cells\nnCells = size(linData, 1);\n% get a logical mask of the non-empty cells\nnonEmptyCells = ~cellfun(@isempty, linData);\n\n% if the cell-array is empty, make sure the group is created anyway \nif ~any(~cellfun(@isempty, linData));\n    h5createIntermediateGroup(fileName, datasetPath);\nend;\n\n% check whether the cell array can be converted to a matrix:\n% check if the array only contains chars, logicals, numerics or empty cells\nisCharNumLogicOnly = ~any(~cellfun(@(c) isempty(c) || ischar(c) || islogical(c) || isnumeric(c), linData));\n% check if the array only contains empty cells\nisOnlyEmpty = ~any(nonEmptyCells);\n% check if the dimensions of the cells do not exceed CELL_MAX_DIM\nmaxDimExceeded = any(cellfun(@(c) ndims(c) > CELL_MAX_DIM, linData));\n\n% if it is only data types that can be converted into a matrix and maximum number of dimensions is not exceeded and\n%   the cell-array is not empty and has cells, convert the cell array to a matrix\nif isCharNumLogicOnly && ~maxDimExceeded && nCells > 0 && ~isOnlyEmpty;\n    \n    % get the dimensions of the data as nCells x CELL_MAX_DIM matrix\n    matDim = cell2mat(cellfun(@(x) [size(x) zeros(1, CELL_MAX_DIM - numel(size(x)))], linData, 'UniformOutput', false));\n\n    % get only the relevant matrix of dimension, e.g. take out columns with only 0 zero dimension\n    matDim(:, ~any(matDim)) = [];\n\n    % get the data types as nCells x DATA_TYPE_DESCR_LEN char matrix\n    dataTypes = cell2mat(cellfun(@(x) [class(x) repmat(' ', 1, DATA_TYPE_DESCR_LEN - numel(class(x)))], linData, ...\n        'UniformOutput', false));\n    \n    % convert everything to linearized matrix of doubles with dimensions nTotalValues x 1\n    linDataDouble = cell2mat(cellfun(@(c) double(reshape(c, numel(c), 1)), linData, 'UniformOutput', false));\n    \n    % save the cell-array's values as a dataset of doubles\n    datasetPathCell = sprintf('%s/cellArrayMat_Val', datasetPath);\n    h5createwrite_wrapper(fileName, datasetPathCell, linDataDouble, h5createArgs, h5writeArgs);\n    \n    % save the cell-array's dimensions\n    datasetPathCell = sprintf('%s/cellArrayMat_Dim', datasetPath);\n    h5createwrite_wrapper(fileName, datasetPathCell, matDim, h5createArgs, h5writeArgs);\n    \n    % save the cell-array's data types\n    datasetPathCell = sprintf('%s/cellArrayMat_DataTypes', datasetPath);\n    h5createwrite_wrapper(fileName, datasetPathCell, dataTypes, h5createArgs, h5writeArgs);\n    \n    % write some annotation for this data set: get the dimension first\n    dim = size(data);\n    % write in an attribute the data type as being a matrix-cell-array\n    h5writeatt(fileName, datasetPath, 'DataType', 'cellArrayMat');\n    % write in an attribute the dimension of the data as a \"3x4x5\" string\n    h5writeatt(fileName, datasetPath, 'cellArrayMat_Dim', dim2str(dim));\n  \n% if the cell array contains other data types\nelse\n\n    % if cell-array has not only empty cells\n    if ~isOnlyEmpty;\n        % go through each non-empty cell save it separately\n        nonEmptyCellIndexes = find(nonEmptyCells);\n        for iCellLoop = 1 : numel(nonEmptyCellIndexes);\n            % get the cell index\n            iCell = nonEmptyCellIndexes(iCellLoop);            \n            % create the path using a simple \"cell_[CELL_INDEX]\" path\n            datasetPathCell = sprintf('%s/cell_%d', datasetPath, iCell);\n            % only save cell if it is not empty\n            if ~isempty(linData{iCell});\n                % create/write the data set for the content of the cell, recursively\n                h5createwrite_wrapper(fileName, datasetPathCell, linData{iCell}, h5createArgs, h5writeArgs);\n            end;\n\n        end;\n    end;\n\n    % write some annotation for this data set: get the dimension first\n    dim = size(data);\n    % write in an attribute the data type as being a cell-array\n    h5writeatt(fileName, datasetPath, 'DataType', 'cellArray');\n    % write in an attribute the dimension of the data as a \"3x4x5\" string\n    h5writeatt(fileName, datasetPath, 'cellArray_Dim', dim2str(dim));\n    \n    % write out in a dataset the indexes of the non empty cells:\n    % create the path using \"cellNonEmptyIndexes\"\n    datasetPathNonEmptyCell = sprintf('%s/cellNonEmptyIndexes', datasetPath);\n    % create/write the data set for the nonEmptyCellIndexes\n    h5createwrite_wrapper(fileName, datasetPathNonEmptyCell, nonEmptyCells, h5createArgs, h5writeArgs);\n    \nend\n\nend\n\n% separate function to process structures and struct-arrays. Fields of the structure can be save either\n%   as data sets or as attributes if they have \"simple\" values (single number, single logicals or string)\nfunction processStruct(fileName, datasetPath, data, h5createArgs, h5writeArgs, ...\n    CHAR_ATT_LIMIT, LOGICAL_ATT_LIMIT, NUMERIC_ATT_LIMIT)\n\n% CHAR_ATT_LIMIT: maximum limit for having a string structure value written out as an attribute\n% LOGICAL_ATT_LIMIT: maximum limit for having a logical structure value written out as an attribute\n% NUMERIC_ATT_LIMIT: maximum limit for having a numeric structure value written out as an attribute\n\n% if we are dealing with a single structure (not struct-array)\nif numel(data) == 1;\n    \n    % get the field names\n    names = fieldnames(data);\n    % create 2 cell array for the field names:\n    datasetNames = cell(1, numel(names)); % field names with \"complex\" value saved as data set\n    attNames = cell(1, numel(names)); % field names with \"simple\" value saved as attributes\n    \n    % go through each field name\n    for iName = 1 : numel(names);\n        \n        % get the field name and the value\n        name = names{iName};\n        dataVal = data.(name);\n        % get the data type and the dimension of the value\n        dataType = class(dataVal);\n        dim = size(dataVal);\n        \n        % SIMPLE STRING: if it is a char with no 3rd dimension and one of the dimension is 1 and the other\n        %   does not exceed CHAR_ATT_LIMIT, save as attribute\n        if ischar(dataVal) && numel(dim) <= 2 && any(dim == 1) && ~any(dim > CHAR_ATT_LIMIT);\n            % store the field name as being saved as attribute\n            attNames{iName} = name;\n            % save the value as attribute\n            h5writeatt(fileName, datasetPath, sprintf('%s_Value', name), dataVal);\n            % save the orginial data type as attribute\n            h5writeatt(fileName, datasetPath, sprintf('%s_DataType', name), dataType);\n            % save the orginial dimension as attribute (needed to restore the orientation: 1xN or Nx1)\n            h5writeatt(fileName, datasetPath, sprintf('%s_Dim', name), dim2str(dim));\n            \n        % SIMPLE LOGICAL: if it is a logical with no 3rd dimension and one of the dimension is 1 and the other\n        %   does not exceed LOGICAL_ATT_LIMIT, convert to unsigned integer 8-bit and save as attribute\n        elseif islogical(dataVal) && numel(dim) <= 2 && any(dim == 1) && ~any(dim > LOGICAL_ATT_LIMIT);\n            % store the field name as being saved as attribute\n            attNames{iName} = name;\n            % save the value as uint8 converted attribute\n            h5writeatt(fileName, datasetPath, sprintf('%s_Value', name), uint8(dataVal));\n            % save the orginial data type as attribute\n            h5writeatt(fileName, datasetPath, sprintf('%s_DataType', name), dataType);\n            % save the orginial dimension as attribute (needed to restore the orientation: 1xN or Nx1)\n            h5writeatt(fileName, datasetPath, sprintf('%s_Dim', name), dim2str(dim));\n            \n        % SIMPLE NUMERIC: if it is a numeric with no 3rd dimension and one of the dimension is 1 and the other\n        %   does not exceed NUMERIC_ATT_LIMIT, save as attribute\n        elseif isnumeric(dataVal) && numel(dim) <= 2 && any(dim == 1) && ~any(dim > NUMERIC_ATT_LIMIT);\n            % store the field name as being saved as attribute\n            attNames{iName} = name;\n            % save the value as uint8 converted attribute\n            h5writeatt(fileName, datasetPath, sprintf('%s_Value', name), dataVal);\n            % save the orginial data type as attribute\n            h5writeatt(fileName, datasetPath, sprintf('%s_DataType', name), dataType);\n            % save the orginial dimension as attribute (needed to restore the orientation: 1xN or Nx1)\n            h5writeatt(fileName, datasetPath, sprintf('%s_Dim', name), dim2str(dim));\n            \n        % COMPLEX DATA: if it cannot be saved as attribute, save it as data set using recursion\n        else\n            % create the data set path using that name\n            datasetPathStruct = sprintf('%s/%s', datasetPath, name);\n            % store the field name as being saved as data set\n            datasetNames{iName} = name;\n            % create/write the data set for the value of the field, recursively\n            h5createwrite_wrapper(fileName, datasetPathStruct, dataVal, h5createArgs, h5writeArgs);\n            \n        end;\n        \n    end;\n\n    % remove the empty cells\n    attNames(cellfun(@isempty, attNames)) = [];\n    datasetNames(cellfun(@isempty, datasetNames)) = [];\n    \n    datasetNamesString = cell2str(datasetNames);\n    attNamesString = cell2str(attNames);\n    \n    % try to get already existing field names\n    try\n        prevFieldsDS = h5readatt(fileName, datasetPath, 'FieldNames_DS');\n        prevFieldsAtt = h5readatt(fileName, datasetPath, 'FieldNames_Att');    \n        datasetNamesString = regexprep(regexprep([prevFieldsDS, ',', datasetNamesString], ',$', ''), '^,', '');\n        attNamesString = regexprep(regexprep([prevFieldsAtt, ',', attNamesString], ',$', ''), '^,', '');\n    catch err;\n        if ~strcmp(err.identifier, 'MATLAB:imagesci:hdf5lib:libraryError');\n            rethrow(err);\n        end;\n    end;\n    \n    % remove redundant IDs\n    datasetNamesAll = unique(regexp(datasetNamesString, ',', 'split'), 'stable');\n    datasetNamesString = regexprep(sprintf('%s,', datasetNamesAll{:}), ',$', '');\n    attNamesAll = unique(regexp(attNamesString, ',', 'split'), 'stable');\n    attNamesString = regexprep(sprintf('%s,', attNamesAll{:}), ',$', '');\n    \n    % write the data type as attribute for this structure\n    h5writeatt(fileName, datasetPath, 'DataType', 'struct');\n    % write both the attribute-saved and the dataset-saved field names as comma-separated string attributes\n    h5writeatt(fileName, datasetPath, 'FieldNames_DS', datasetNamesString);\n    h5writeatt(fileName, datasetPath, 'FieldNames_Att', attNamesString);\n\n% if we have 0 or more than an element, process as a struct-array\nelse\n    \n    % linearize the struct-array (e.g. turn a 3 x 4 x 5 cell-array into a 1 x (3*4*5) = 1 * 60 cell-array\n    linData = data(:);\n    \n    % go trough each structure an save it separately\n    for iStruct = 1 : numel(linData);\n        % only save non-empty structures\n        if any(~structfun(@isempty, linData(iStruct)))\n            % create the path using a simple \"struct_[STRUCT_INDEX]\" path\n            datasetPathStruct = sprintf('%s/struct_%d', datasetPath, iStruct);\n            % create/write the data set for the single structure, recursively\n            h5createwrite_wrapper(fileName, datasetPathStruct, linData(iStruct), h5createArgs, h5writeArgs);\n        end;\n    end;\n    \n    % write some annotation for this data set: get the dimension and the field names\n    dim = size(data);\n    \n    % if the struct-array is empty, make sure the the group is created anyway\n    if isempty(data);\n        h5createIntermediateGroup(fileName, datasetPath);\n    end;\n    \n    % get the field names of the structure(s)\n    names = fieldnames(data);\n        \n    % write in an attribute the data type as being a struct-array\n    h5writeatt(fileName, datasetPath, 'DataType', 'structArray');\n    % write in an attribute the dimension of the data as a \"3x4x5\" string\n    h5writeatt(fileName, datasetPath, 'struct_Dim', dim2str(dim));\n    % write in an attribute the filed names as a comma-separated string\n    h5writeatt(fileName, datasetPath, 'FieldNames', cell2str(names));\n    \nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "h5deleteGroup.m", "ext": ".m", "path": "OCIA-master/utils/importExport/HDF5/h5deleteGroup.m", "size": 762, "source_encoding": "utf_8", "md5": "8ef76eb11513fb6fff7cba395bdaa02c", "text": "% delete a group in the HDF5 file\nfunction h5deleteGroup(fileName, groupPath)\n\n% if file does not exist, create a new file using the default properties\nif ~exist(fileName, 'file');\n    fID = H5F.create(fileName, 'H5F_ACC_TRUNC', 'H5P_DEFAULT', 'H5P_DEFAULT');\n% if file exists, open it using the default properties\nelse\n    fID = H5F.open(fileName, 'H5F_ACC_RDWR', 'H5P_DEFAULT');\nend;\n% check if the group does not already exist\ntry\n    % try to fetch the info for this group ...\n    H5G.get_objinfo(fID, groupPath, false);\n    % ... if no error, then group exists, so go on ...\ncatch err; %#ok<NASGU>\n    % error means no group, so nothing to do\n    return;\nend;\n\n% delete the object\nH5L.delete(fID, groupPath, 'H5P_DEFAULT');\n\n% close all\nH5F.close(fID);\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "xcorr_mc.m", "ext": ".m", "path": "OCIA-master/utils/stat/xcorr_mc.m", "size": 1122, "source_encoding": "utf_8", "md5": "bb01c55ef67edb3bbe21ccc25ae74ecb", "text": "function [cc,p,cc_mc] = xcorr_mc(x,y,maxlags,n)\n% monte-carlo cross-correlation of x and y\n% Input\n% x ... input vector 1 (will be shuffled)\n% y ... input vector 2 (will NOT be shuffled)\n% lag ... lag for xcorr\n% n ... number of samples for shuffled data\n% Output\n% cc ... the true cross-correlation between x and y\n% p ... probability of correlation for each time point\n% cc_mc ... the matrix of shuffled cross-correlation values\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\n% force columns vectors\nx = reshape(x,numel(x),1);\ny = reshape(y,numel(y),1);\n\n% true cross-correlation\ncc = xcorr(x,y,maxlags,'coeff');\n\n% shuffled cross-correlation\ncc_mc = doShuffledXcorr(x,y,maxlags,n);\n\n% estimate the 2-tailed probability of true cc at each timepoint\np = doStats(cc_mc,cc);\n\nfunction cc_mc = doShuffledXcorr(x,y,maxlags,n)\ncc_mc = zeros(maxlags*2+1,n);\nfor i = 1:n\n    xPerm = x(randperm(numel(x)));\n    cc_mc(:,i) = xcorr(xPerm,y,maxlags,'coeff');\nend\n\nfunction p = doStats(A,c)\np = zeros(size(A,1),1);\nfor n = 1:size(A,1)\n    [mu,sigma] = normfit(A(n,:));\n    p(n) = 1 - (normcdf(abs(c(n)),mu,sigma));\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "sparseness.m", "ext": ".m", "path": "OCIA-master/utils/stat/sparseness.m", "size": 1021, "source_encoding": "utf_8", "md5": "9e7d0a6350274c815488872e8005f081", "text": "function k = sparseness(varargin)\n% calculate sparseness measure k for response vector r (in1) with method\n% (in2)\n% methods:\n% {'kurtosis'}\n% 'treves_rolls'\n% 'willmore_tolhurst'\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\nif nargin == 1\n    r = varargin{1};\n    method = 'kurtosis';\nelseif nargin == 2\n    r = varargin{1};\n    method = varargin{2};\nelse\n    error('Incorrect number of input arguments.');\nend\n\nswitch lower(method)\n    case 'kurtosis'\n        k = doKurtosis(r);\n    case 'treves_rolls'\n        k = doTrevesRolls(r);\n    case 'willmore_tolhurst'\n        k = doWillmoreTolhurst(r);\nend\n\nfunction k = doKurtosis(r)\nmean_r = mean(r);\nsd_r = std(r);\nk = zeros(size(r));\nfor n = 1:length(r)\n    k(n) = ((r(n)-mean_r)/sd_r)^4;\nend\nk = sum(k)/length(r);\nk = k - 3;\n\nfunction k = doTrevesRolls(r)\nk1 = sum(r/length(r)); k1 = k1^2;\nk2 = sum((r.^2)/length(r));\nk = k1 / k2;\n\n\nfunction k = doWillmoreTolhurst(r)\nk1 = sum(abs(r)/length(r)); k1 = k1^2;\nk2 = sum((r.^2)/length(r));\nk = 1 - (k1 / k2);\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "fit_fminsearch.m", "ext": ".m", "path": "OCIA-master/utils/matlabExtend/fit_fminsearch.m", "size": 1098, "source_encoding": "utf_8", "md5": "aba55d992c51d5e7750b6453a19ddde2", "text": "function fit_fminsearch\n\n% simulate a double-peaked Gaussian\na = normrnd(1,0.5,[10000,1]);\na = [a; normrnd(3,0.5,[10000,1])];\n[count,xout] = hist(a,sqrt(numel(a)));\nfigure('Name','Fit fun figure','NumberTitle','off')\nstairs(xout,count,'k-'), hold on\n\n%% fit with curve fitting toolbox\n% fit double-peaked gaussian to histogram\nfObj = fit(xout',count','gauss2');\nplot(fObj,'r--')\n\n%% fit with fminsearch \n% (no curve fitting toolbox required)\n% start points x0 should be chosen as precisely as possible\npars0 = [max(count) 1 1 max(count) 1 1]';\n[x, fval] = fminsearch(@(x) gauss2(x,count,xout),pars0);\n% x contains the optimal 6 parameters for the Gaussian\n% fval is the minimum of the objective function, ie. the smaller the better the fit\n\nfitCurve = x(1).*exp(-((xout-x(2))./x(3)).^2) + ...\n    x(4).*exp(-((xout-x(5))./x(6)).^2);\nplot(xout,fitCurve,'b--')\n\nlegend({'dota','fit','fminsearch'})\n\nend\n\n\n%% Objective function for minimization\nfunction fOut = gauss2(pars,a,x)\nf = pars(1).*exp(-((x-pars(2))./pars(3)).^2) + ...\n    pars(4).*exp(-((x-pars(5))./pars(6)).^2);\nfOut = sum(abs(f-a));\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mydepfun.m", "ext": ".m", "path": "OCIA-master/utils/matlabExtend/mydepfun.m", "size": 3300, "source_encoding": "utf_8", "md5": "4b01dad21c371327373768234c3dc9be", "text": "function filelist = mydepfun(fn,recursive)\n%MYDEPFUN - Variation on depfun which skips toolbox files\n%\n% filelist = mydepfun(fn)\n% filelist = mydepfun(fn,recursive)\n%\n% Returns a list of files which are required by the specified\n% function, omitting any which are inside $matlabroot/toolbox. \n%\n% \"fn\" is a string specifying a filename in any form that can be\n%   identified by the built-in function \"which\".\n% \"recursive\" is a logical scalar; if false, only the files called\n%   directly by the specified function are returned.  If true, *all*\n%   those files are scanned to, and any required by those, and so on.\n%\n% \"filelist\" is a cell array of fully qualified file name strings,\n%   including the specified file.\n%\n% e.g.\n%     filelist = mydepfun('myfunction')\n%     filelist = mydepfun('C:\\files\\myfunction.m',true) \n\n% Copyright 2006-2010 The MathWorks, Inc.\n\nif ~ischar(fn)\n    error('First argument must be a string');\nend\n\nfoundfile = which(fn);\nif isempty(foundfile)\n    error('File not found: %s',fn);\nend\n\n% Scan this file\nfilelist = i_scan(foundfile);\n\n% If \"recursive\" is supplied and true, scan files on which this one depends.\nif nargin>1 && recursive\n    % Create a list of files which we have still to scan.\n    toscan = filelist;\n    toscan = toscan(2:end); % first entry is always the same file again\n    % Now scan files until we have none left to scan\n    while numel(toscan)>0\n        % Scan the first file on the list\n        newlist = i_scan(toscan{1});\n        newlist = newlist(2:end); % first entry is always the same file again\n        toscan(1) = []; % remove the file we've just scanned\n        % Find out which files are not already on the list.  Take advantage of\n        % the fact that \"which\" and \"depfun\" return the correct capitalisation\n        % of file names, even on Windows, making it safe to use \"ismember\"\n        % (which is case-sensitive).\n        reallynew = ~ismember(newlist,filelist);\n        newlist = newlist(reallynew);\n        % If they're not already in the file list, we'll need to scan them too.\n        % (Conversely, if they ARE in the file list, we've either scanned them\n        %  already, or they're currently on the toscan list)\n%         fprintf('%dx%d - %dx%d\\n', size(newlist), size(toscan));\n        toscan2 = toscan;\n        toscan = unique( [ toscan2 ; newlist ] );\n        filelist = unique( [ filelist ; newlist ] );\n        fprintf('filelist size: %d\\n', numel(filelist));\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%\n% Returns the non-toolbox files which the specified one calls.\n% The specified file is always first in the returned list.\nfunction list = i_scan(f)\n\nfunc = i_function_name(f);\n\nlist = matlab.codetools.requiredFilesAndProducts(func, 'toponly')';\n% list = depfun(func,'-toponly','-quiet');\n\ntoolboxroot = fullfile(matlabroot,'toolbox');\n\nintoolbox = strncmpi(list,toolboxroot,numel(toolboxroot));\n\nlist = list(~intoolbox);\n\n%%%%%%%%%%%%%%%%%%%%%%%%\nfunction func = i_function_name(f)\n% Identifies the function name for the specified file,\n% including the class name where appropriate.  Does not\n% work for UDD classes, e.g. @rtw/@rtw\n\n[dirname,funcname] = fileparts(f);\n[ignore,dirname] = fileparts(dirname);\n\nif ~isempty(dirname) && dirname(1)=='@'\n    func = [ dirname '/' funcname ];\nelse\n    func = funcname;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "smoothn.m", "ext": ".m", "path": "OCIA-master/utils/matlabExtend/smoothn.m", "size": 3357, "source_encoding": "utf_8", "md5": "880f4a892aa0f80387fc54388891b162", "text": "function Y = smoothn(X,sz,filt,std)\n\n%SMOOTHN Smooth N-D data\n%   Y = SMOOTHN(X, SIZE) smooths input data X. The smoothed data is\n%       retuirned in Y. SIZE sets the size of the convolution kernel\n%       such that LENGTH(SIZE) = NDIMS(X)\n%\n%   Y = SMOOTHN(X, SIZE, FILTER) Filter can be 'gaussian' or 'box' (default)\n%       and determines the convolution kernel.\n%\n%   Y = SMOOTHN(X, SIZE, FILTER, STD) STD is a vector of standard deviations \n%       one for each dimension, when filter is 'gaussian' (default is 0.65)\n\nif nargin == 2,\n  filt = 'b';\nelseif nargin == 3,\n  std = 0.65;\nelseif nargin>4 || nargin<2\n  error('Wrong number of input arguments.');\nend\n\n% check the correctness of sz\nif ndims(sz) > 2 || min(size(sz)) ~= 1\n  error('SIZE must be a vector');\nelseif length(sz) == 1\n  sz = repmat(sz,ndims(X));\nelseif ndims(X) ~= length(sz)\n  error('SIZE must be a vector of length equal to the dimensionality of X');\nend\n\n% check the correctness of std\nif filt(1) == 'g'\n  if length(std) == 1\n    std = std*ones(ndims(X),1);\n  elseif ndims(X) ~= length(std)\n    error('STD must be a vector of length equal to the dimensionality of X');\n  end\n  std = std(:)';\nend\n\nsz = sz(:)';\n\n% check for appropriate size\npadSize = (sz-1)/2;\nif ~isequal(padSize, floor(padSize)) || any(padSize<0)\n  error('All elements of SIZE must be odd integers >= 1.');\nend\n\n% generate the convolution kernel based on the choice of the filter\nfilt = lower(filt);\nif (filt(1) == 'b')\n  smooth = ones(sz)/prod(sz); % box filter in N-D\nelseif (filt(1) == 'g')\n  smooth = ndgaussian(padSize,std); % a gaussian filter in N-D\nelse\n  error('Unknown filter');\nend\n\n% pad the data\nX = padreplicate(X,padSize);\n\n% perform the convolution\nY = convn(X,smooth,'valid');\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nfunction h = ndgaussian(siz,std)\n\n% Calculate a non-symmetric ND gaussian. Note that STD is scaled to the\n% sizes in SIZ as STD = STD.*SIZ\n\nndim = length(siz);\nsizd = cell(ndim,1);\n\nfor i = 1:ndim\n  sizd{i} = -siz(i):siz(i);\nend\n\ngrid = gridnd(sizd);\nstd = reshape(std.*siz,[ones(1,ndim) ndim]);\nstd(find(siz==0)) = 1; % no smoothing along these dimensions as siz = 0\nstd = repmat(std,2*siz+1);\n\nh = exp(-sum((grid.*grid)./(2*std.*std),ndim+1));\nh = h/sum(h(:));\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nfunction argout = gridnd(argin)\n\n% exactly the same as ndgrid but it accepts only one input argument of \n% type cell and a single output array\n\nnin = length(argin);\nnout = nin;\n\nfor i=nin:-1:1,\n  argin{i} = full(argin{i}); % Make sure everything is full\n  siz(i) = prod(size(argin{i}));\nend\nif length(siz)<nout, siz = [siz ones(1,nout-length(siz))]; end\n\nargout = [];\nfor i=1:nout,\n  x = argin{i}(:); % Extract and reshape as a vector.\n  s = siz; s(i) = []; % Remove i-th dimension\n  x = reshape(x(:,ones(1,prod(s))),[length(x) s]); % Expand x\n  x = permute(x,[2:i 1 i+1:nout]); % Permute to i'th dimension\n  argout = cat(nin+1,argout,x); % Concatenate to the output \nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nfunction b=padreplicate(a, padSize)\n%Pad an array by replicating values.\nnumDims = length(padSize);\nidx = cell(numDims,1);\nfor k = 1:numDims\n  M = size(a,k);\n  onesVector = ones(1,padSize(k));\n  idx{k} = [onesVector 1:M M*onesVector];\nend\n\nb = a(idx{:});\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "DataHash.m", "ext": ".m", "path": "OCIA-master/utils/matlabExtend/DataHash.m", "size": 15429, "source_encoding": "utf_8", "md5": "e725a80cb9180de1eb03e47b850a95dc", "text": "function Hash = DataHash(Data, Opt)\n% DATAHASH - Checksum for Matlab array of any type\n% This function creates a hash value for an input of any type. The type and\n% dimensions of the input are considered as default, such that UINT8([0,0]) and\n% UINT16(0) have different hash values. Nested STRUCTs and CELLs are parsed\n% recursively.\n%\n% Hash = DataHash(Data, Opt)\n% INPUT:\n%   Data: Array of these built-in types:\n%           (U)INT8/16/32/64, SINGLE, DOUBLE, (real or complex)\n%           CHAR, LOGICAL, CELL (nested), STRUCT (scalar or array, nested),\n%           function_handle.\n%   Opt:  Struct to specify the hashing algorithm and the output format.\n%         Opt and all its fields are optional.\n%         Opt.Method: String, known methods for Java 1.6 (Matlab 2009a):\n%              'SHA-1', 'SHA-256', 'SHA-384', 'SHA-512', 'MD2', 'MD5'.\n%            Known methods for Java 1.3 (Matlab 6.5):\n%              'MD5', 'SHA-1'.\n%            Default: 'MD5'.\n%         Opt.Format: String specifying the output format:\n%            'hex', 'HEX':      Lower/uppercase hexadecimal string.\n%            'double', 'uint8': Numerical vector.\n%            'base64':          Base64 encoded string, only printable\n%                               ASCII characters, 33% shorter than 'hex'.\n%            Default: 'hex'.\n%         Opt.Input: Type of the input as string, not case-sensitive:\n%             'array': The contents, type and size of the input [Data] are\n%                      considered  for the creation of the hash. Nested CELLs\n%                      and STRUCT arrays are parsed recursively. Empty arrays of\n%                      different type reply different hashs.\n%             'file':  [Data] is treated as file name and the hash is calculated\n%                      for the files contents.\n%             'bin':   [Data] is a numerical, LOGICAL or CHAR array. Only the\n%                      binary contents of the array is considered, such that\n%                      e.g. empty arrays of different type reply the same hash.\n%             Default: 'array'.\n%\n% OUTPUT:\n%   Hash: String, DOUBLE or UINT8 vector. The length depends on the hashing\n%         method.\n%\n% EXAMPLES:\n% % Default: MD5, hex:\n%   DataHash([])                % 7de5637fd217d0e44e0082f4d79b3e73\n% % MD5, Base64:\n%   Opt.Format = 'base64';\n%   Opt.Method = 'MD5';\n%   DataHash(int32(1:10), Opt)  % bKdecqzUpOrL4oxzk+cfyg\n% % SHA-1, Base64:\n%   S.a = uint8([]);\n%   S.b = {{1:10}, struct('q', uint64(415))};\n%   Opt.Method = 'SHA-1';\n%   DataHash(S, Opt)            % ZMe4eUAp0G9TDrvSW0/Qc0gQ9/A\n% % SHA-1 of binary values:\n%   Opt.Method = 'SHA-1';\n%   Opt.Input  = 'bin';\n%   DataHash(1:8, Opt)          % 826cf9d3a5d74bbe415e97d4cecf03f445f69225\n%\n% NOTE:\n%   Function handles and user-defined objects cannot be converted uniquely:\n%   - The subfunction ConvertFuncHandle uses the built-in function FUNCTIONS,\n%     but the replied struct can depend on the Matlab version.\n%   - It is tried to convert objects to UINT8 streams in the subfunction\n%     ConvertObject. A conversion by STRUCT() might be more appropriate.\n%   Adjust these subfunctions on demand.\n%\n%   MATLAB CHARs have 16 bits! In consequence the string 'hello' is treated as\n%   UINT16('hello') for the binary input method.\n%\n%   DataHash uses James Tursa's smart and fast TYPECASTX, if it is installed:\n%     http://www.mathworks.com/matlabcentral/fileexchange/17476\n%   As fallback the built-in TYPECAST is used automatically, but for large\n%   inputs this can be more than 100 times slower.\n%   For Matlab 6.5 installing typecastx is obligatory to run DataHash.\n%\n% Tested: Matlab 6.5, 7.7, 7.8, 7.13, WinXP/32, Win7/64\n% Author: Jan Simon, Heidelberg, (C) 2011-2012 matlab.THISYEAR(a)nMINUSsimon.de\n%\n% See also: TYPECAST, CAST.\n% FEX:\n% Michael Kleder, \"Compute Hash\", no structs and cells:\n%   http://www.mathworks.com/matlabcentral/fileexchange/8944\n% Tim, \"Serialize/Deserialize\", converts structs and cells to a byte stream:\n%   http://www.mathworks.com/matlabcentral/fileexchange/29457\n% Jan Simon, \"CalcMD5\", MD5 only, faster C-mex, no structs and cells:\n%   http://www.mathworks.com/matlabcentral/fileexchange/25921\n\n% $JRev: R-k V:011 Sum:kZG25iszfKbg Date:28-May-2012 12:48:06 $\n% $License: BSD (use/copy/change/redistribute on own risk, mention the author) $\n% $File: Tools\\GLFile\\DataHash.m $\n% History:\n% 001: 01-May-2011 21:52, First version.\n% 007: 10-Jun-2011 10:38, [Opt.Input], binary data, complex values considered.\n% 011: 26-May-2012 15:57, Fails for binary input and empty data.\n\n% Main function: ===============================================================\n% Java is needed:\nif ~usejava('jvm')\n   error(['JSimon:', mfilename, ':NoJava'], ...\n      '*** %s: Java is required.', mfilename);\nend\n\n% typecastx creates a shared data copy instead of the deep copy as Matlab's\n% TYPECAST - for a [1000x1000] DOUBLE array this is 100 times faster!\npersistent usetypecastx\nif isempty(usetypecastx)\n   usetypecastx = ~isempty(which('typecastx'));  % Run the slow WHICH once only\nend\n\n% Default options: -------------------------------------------------------------\nMethod    = 'MD5';\nOutFormat = 'hex';\nisFile    = false;\nisBin     = false;\n\n% Check number and type of inputs: ---------------------------------------------\nnArg = nargin;\nif nArg == 2\n   if isa(Opt, 'struct') == 0   % Bad type of 2nd input:\n      error(['JSimon:', mfilename, ':BadInput2'], ...\n         '*** %s: 2nd input [Opt] must be a struct.', mfilename);\n   end\n   \n   % Specify hash algorithm:\n   if isfield(Opt, 'Method')\n      Method = upper(Opt.Method);\n   end\n   \n   % Specify output format:\n   if isfield(Opt, 'Format')\n      OutFormat = Opt.Format;\n   end\n   \n   % Check if the Input type is specified - default: 'array':\n   if isfield(Opt, 'Input')\n      if strcmpi(Opt.Input, 'File')\n         isFile = true;\n         if ischar(Data) == 0\n            error(['JSimon:', mfilename, ':CannotOpen'], ...\n               '*** %s: 1st input is not a file name', mfilename);\n         end\n         \n         if exist(Data, 'file') ~= 2\n            error(['JSimon:', mfilename, ':FileNotFound'], ...\n               '*** %s: File not found: %s.', mfilename, Data);\n         end\n         \n      elseif strncmpi(Opt.Input, 'bin', 3)  % Accept 'binary'\n         isBin = true;\n         if (isnumeric(Data) || ischar(Data) || islogical(Data)) == 0\n            error(['JSimon:', mfilename, ':BadDataType'], ...\n               '*** %s: 1st input is not numeric, CHAR or LOGICAL.', mfilename);\n         end\n      end\n   end\n   \nelseif nArg ~= 1  % Bad number of arguments:\n   error(['JSimon:', mfilename, ':BadNInput'], ...\n      '*** %s: 1 or 2 inputs required.', mfilename);\nend\n\n% Create the engine: -----------------------------------------------------------\ntry\n   Engine = java.security.MessageDigest.getInstance(Method);\ncatch\n   error(['JSimon:', mfilename, ':BadInput2'], ...\n      '*** %s: Invalid algorithm: [%s].', mfilename, Method);\nend\n\n% Create the hash value: -------------------------------------------------------\nif isFile\n   % Read the file and calculate the hash:\n   FID = fopen(Data, 'r');\n   if FID < 0\n      error(['JSimon:', mfilename, ':CannotOpen'], ...\n         '*** %s: Cannot open file: %s.', mfilename, Data);\n   end\n   Data = fread(FID, Inf, '*uint8');\n   fclose(FID);\n   \n   Engine.update(Data);\n   if usetypecastx\n      Hash = typecastx(Engine.digest, 'uint8');\n   else\n      Hash = typecast(Engine.digest, 'uint8');\n   end\n\nelseif isBin             % Contents of an elementary array:\n   if isempty(Data)      % Nothing to do, Engine.update fails for empty input!\n      Hash = typecastx(Engine.digest, 'uint8');\n   elseif usetypecastx   % Faster typecastx:\n      if isreal(Data)\n         Engine.update(typecastx(Data(:), 'uint8'));\n      else\n         Engine.update(typecastx(real(Data(:)), 'uint8'));\n         Engine.update(typecastx(imag(Data(:)), 'uint8'));\n      end\n      Hash = typecastx(Engine.digest, 'uint8');\n      \n   else                  % Matlab's TYPECAST is less elegant:\n      if isnumeric(Data)\n         if isreal(Data)\n            Engine.update(typecast(Data(:), 'uint8'));\n         else\n            Engine.update(typecast(real(Data(:)), 'uint8'));\n            Engine.update(typecast(imag(Data(:)), 'uint8'));\n         end\n      elseif islogical(Data)               % TYPECAST cannot handle LOGICAL\n         Engine.update(typecast(uint8(Data(:)), 'uint8'));\n      elseif ischar(Data)                  % TYPECAST cannot handle CHAR\n         Engine.update(typecast(uint16(Data(:)), 'uint8'));\n         Engine.update(typecast(Data(:), 'uint8'));\n      end\n      Hash = typecast(Engine.digest, 'uint8');\n   end\n   \nelseif usetypecastx  % Faster typecastx:\n   Engine = CoreHash_(Data, Engine);\n   Hash   = typecastx(Engine.digest, 'uint8');\n   \nelse                 % Slower built-in TYPECAST:\n   Engine = CoreHash(Data, Engine);\n   Hash   = typecast(Engine.digest, 'uint8');\nend\n\n% Convert hash specific output format: -----------------------------------------\nswitch OutFormat\n   case 'hex'\n      Hash = sprintf('%.2x', double(Hash));\n   case 'HEX'\n      Hash = sprintf('%.2X', double(Hash));\n   case 'double'\n      Hash = double(reshape(Hash, 1, []));\n   case 'uint8'\n      Hash = reshape(Hash, 1, []);\n   case 'base64'\n      Hash = fBase64_enc(double(Hash));\n   otherwise\n      error(['JSimon:', mfilename, ':BadOutFormat'], ...\n         '*** %s: [Opt.Format] must be: HEX, hex, uint8, double, base64.', ...\n         mfilename);\nend\n\n% return;\n\n% ******************************************************************************\nfunction Engine = CoreHash_(Data, Engine)\n% This mothod uses the faster typecastx version.\n\n% Consider the type and dimensions of the array to distinguish arrays with the\n% same data, but different shape: [0 x 0] and [0 x 1], [1,2] and [1;2],\n% DOUBLE(0) and SINGLE([0,0]):\nEngine.update([uint8(class(Data)), typecastx(size(Data), 'uint8')]);\n\nif isstruct(Data)                    % Hash for all array elements and fields:\n   F      = sort(fieldnames(Data));  % Ignore order of fields\n   Engine = CoreHash_(F, Engine);    % Catch the fieldnames\n   \n   for iS = 1:numel(Data)            % Loop over elements of struct array\n      for iField = 1:length(F)       % Loop over fields\n         Engine = CoreHash_(Data(iS).(F{iField}), Engine);\n      end\n   end\n   \nelseif iscell(Data)                  % Get hash for all cell elements:\n   for iS = 1:numel(Data)\n      Engine = CoreHash_(Data{iS}, Engine);\n   end\n      \nelseif isnumeric(Data) || islogical(Data) || ischar(Data)\n   if isempty(Data) == 0\n      if isreal(Data)                % TRUE for LOGICAL and CHAR also:\n         Engine.update(typecastx(Data(:), 'uint8'));\n      else                           % typecastx accepts complex input:\n         Engine.update(typecastx(real(Data(:)), 'uint8'));\n         Engine.update(typecastx(imag(Data(:)), 'uint8'));\n      end\n   end\n   \nelseif isa(Data, 'function_handle')\n   Engine = CoreHash(ConvertFuncHandle(Data), Engine);\n   \nelse  % Most likely this is a user-defined object:\n   try\n      Engine = CoreHash(ConvertObject(Data), Engine);\n   catch\n      warning(['JSimon:', mfilename, ':BadDataType'], ...\n         ['Type of variable not considered: ', class(Data)]);\n   end\nend\n\n% return;\n\n% ******************************************************************************\nfunction Engine = CoreHash(Data, Engine)\n% This methods uses the slower TYPECAST of Matlab\n% See CoreHash_ for comments.\n\nEngine.update([uint8(class(Data)), typecast(size(Data), 'uint8')]);\n\nif isstruct(Data)                    % Hash for all array elements and fields:\n   F      = sort(fieldnames(Data));  % Ignore order of fields\n   Engine = CoreHash(F, Engine);     % Catch the fieldnames\n   for iS = 1:numel(Data)            % Loop over elements of struct array\n      for iField = 1:length(F)       % Loop over fields\n         Engine = CoreHash(Data(iS).(F{iField}), Engine);\n      end\n   end\nelseif iscell(Data)                  % Get hash for all cell elements:\n   for iS = 1:numel(Data)\n      Engine = CoreHash(Data{iS}, Engine);\n   end\nelseif isempty(Data)\nelseif isnumeric(Data)\n   if isreal(Data)\n      Engine.update(typecast(Data(:), 'uint8'));\n   else\n      Engine.update(typecast(real(Data(:)), 'uint8'));\n      Engine.update(typecast(imag(Data(:)), 'uint8'));\n   end\nelseif islogical(Data)               % TYPECAST cannot handle LOGICAL\n   Engine.update(typecast(uint8(Data(:)), 'uint8'));\nelseif ischar(Data)                  % TYPECAST cannot handle CHAR\n   Engine.update(typecast(uint16(Data(:)), 'uint8'));\nelseif isa(Data, 'function_handle')\n   Engine = CoreHash(ConvertFuncHandle(Data), Engine);\nelse  % Most likely a user-defined object:\n   try\n      Engine = CoreHash(ConvertObject(Data), Engine);\n   catch\n      warning(['JSimon:', mfilename, ':BadDataType'], ...\n         ['Type of variable not considered: ', class(Data)]);\n   end\nend\n\n% return;\n\n% ******************************************************************************\nfunction FuncKey = ConvertFuncHandle(FuncH)\n%   The subfunction ConvertFuncHandle converts function_handles to a struct\n%   using the Matlab function FUNCTIONS. The output of this function changes\n%   with the Matlab version, such that DataHash(@sin) replies different hashes\n%   under Matlab 6.5 and 2009a.\n%   An alternative is using the function name and name of the file for\n%   function_handles, but this is not unique for nested or anonymous functions.\n%   If the MATLABROOT is removed from the file's path, at least the hash of\n%   Matlab's toolbox functions is (usually!) not influenced by the version.\n%   Finally I'm in doubt if there is a unique method to hash function handles.\n%   Please adjust the subfunction ConvertFuncHandles to your needs.\n\n% The Matlab version influences the conversion by FUNCTIONS:\n% 1. The format of the struct replied FUNCTIONS is not fixed,\n% 2. The full paths of toolbox function e.g. for @mean differ.\nFuncKey = functions(FuncH);\n\n% ALTERNATIVE: Use name and path. The <matlabroot> part of the toolbox functions\n% is replaced such that the hash for @mean does not depend on the Matlab\n% version.\n% Drawbacks: Anonymous functions, nested functions...\n% funcStruct = functions(FuncH);\n% funcfile   = strrep(funcStruct.file, matlabroot, '<MATLAB>');\n% FuncKey    = uint8([funcStruct.function, ' ', funcfile]);\n\n% Finally I'm afraid there is no unique method to get a hash for a function\n% handle. Please adjust this conversion to your needs.\n\n% return;\n\n% ******************************************************************************\nfunction DataBin = ConvertObject(DataObj)\n% Convert a user-defined object to a binary stream. There cannot be a unique\n% solution, so this part is left for the user...\n\n% Perhaps a direct conversion is implemented:\nDataBin = uint8(DataObj);\n\n% Or perhaps this is better:\n% DataBin = struct(DataObj);\n\n% return;\n\n% ******************************************************************************\nfunction Out = fBase64_enc(In)\n% Encode numeric vector of UINT8 values to base64 string.\n\nPool = [65:90, 97:122, 48:57, 43, 47];  % [0:9, a:z, A:Z, +, /]\nv8   = [128; 64; 32; 16; 8; 4; 2; 1];\nv6   = [32, 16, 8, 4, 2, 1];\n\nIn  = reshape(In, 1, []);\nX   = rem(floor(In(ones(8, 1), :) ./ v8(:, ones(length(In), 1))), 2);\nY   = reshape([X(:); zeros(6 - rem(numel(X), 6), 1)], 6, []);\nOut = char(Pool(1 + v6 * Y));\n\n% return;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "gencode_rvalue.m", "ext": ".m", "path": "OCIA-master/utils/matlabExtend/gencode/gencode_rvalue.m", "size": 4540, "source_encoding": "utf_8", "md5": "461c5aa9248a9962253bec5ce7d36038", "text": "function [str, sts] = gencode_rvalue(item)\n\n% GENCODE_RVALUE  Code for right hand side of MATLAB assignment\n% Generate the right hand side for a valid MATLAB variable\n% assignment. This function is a helper to GENCODE, but can be used on\n% its own to generate code for the following types of variables:\n% * scalar, 1D or 2D numeric, logical or char arrays\n% * scalar or 1D cell arrays, where each item can be one of the supported\n%   array types (i.e. nested cells are allowed)\n%\n% function [str, sts] = gencode_rvalue(item)\n% Input argument:\n%  item - value to generate code for\n% Output arguments:\n%  str - cellstr with generated code, line per line\n%  sts - true, if successful, false if code could not be generated\n%\n% See also GENCODE, GENCODE_SUBSTRUCT, GENCODE_SUBSTRUCTCODE.\n%\n% This code has been developed as part of a batch job configuration\n% system for MATLAB. See  \n%      http://sourceforge.net/projects/matlabbatch\n% for details about the original project.\n%_______________________________________________________________________\n% Copyright (C) 2007 Freiburg Brain Imaging\n\n% Volkmar Glauche\n% $Id: gencode_rvalue.m 410 2009-06-23 11:47:26Z glauche $\n\nrev = '$Rev: 410 $'; %#ok\n\nstr = {};\nsts = true;\nswitch class(item)\n    case 'char'\n        if ndims(item) == 2\n            cstr = {''};\n            % Create cell string, keep white space padding\n            for k = 1:size(item,1)\n                cstr{k} = item(k,:);\n            end\n            str1 = genstrarray(cstr);\n            if numel(str1) == 1\n                % One string, do not print brackets\n                str = str1;\n            else\n                % String array, print brackets and concatenate str1\n                str = [ {'['} str1(:)' {']'} ];\n            end\n        else\n            % not an rvalue\n            sts = false;\n        end\n    case 'cell'\n        if isempty(item)\n            str = {'{}'};\n        elseif ndims(item) == 2 && any(size(item) == 1)\n            str1 = {};\n            for k = 1:numel(item)\n                [str2 sts] = gencode_rvalue(item{k});\n                if ~sts\n                    break;\n                end\n                str1 = [str1(:)' str2(:)'];\n            end\n            if sts\n                if numel(str1) == 1\n                    % One item, print as one line\n                    str{1} = sprintf('{%s}', str1{1});\n                else\n                    % Cell vector, print braces and concatenate str1\n                    if size(item,1) == 1\n                        endstr = {'}'''};\n                    else\n                        endstr = {'}'};\n                    end\n                    str = [{'{'} str1(:)' endstr];\n                end\n            end\n        else\n            sts = false;\n        end\n    case 'function_handle'\n        if isempty(item)\n            str{1} = 'function_handle.empty;';\n        else\n            fstr = func2str(item);\n            % sometimes (e.g. for anonymous functions) '@' is already included\n            % in func2str output\n            if fstr(1) == '@'\n                str{1} = fstr;\n            else\n                str{1} = sprintf('@%s', fstr);\n            end\n        end\n    otherwise\n        if isobject(item) || ~(isnumeric(item) || islogical(item)) || issparse(item) || ndims(item) > 2\n            sts = false;\n        else\n            % treat item as numeric or logical, don't create 'class'(...)\n            % classifier code for double\n            clsitem = class(item);\n            if isempty(item)\n                if strcmp(clsitem, 'double')\n                    str{1} = '[]';\n                else\n                    str{1} = sprintf('%s([])', clsitem);\n                end\n            else\n                % Use mat2str with standard precision 15\n                if any(strcmp(clsitem, {'double', 'logical'}))\n                    sitem = mat2str(item);\n                else\n                    sitem = mat2str(item,'class');\n                end\n                bsz   = max(numel(sitem)+2,100); % bsz needs to be > 100 and larger than string length\n                str1 = textscan(sitem, '%s', 'delimiter',';', 'bufsize',bsz);\n                if numel(str1{1}) > 1\n                    str = str1{1};\n                else\n                    str{1} = str1{1}{1};\n                end\n            end\n        end\nend\nfunction str = genstrarray(stritem)\n% generate a cell string of properly quoted strings suitable for code\n% generation.\nstr = strrep(stritem, '''', '''''');\nfor k = 1:numel(str)\n    str{k} = sprintf('\"%s\"', str{k});\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "gencode.m", "ext": ".m", "path": "OCIA-master/utils/matlabExtend/gencode/gencode.m", "size": 8361, "source_encoding": "utf_8", "md5": "7355f9b9b8500373a07c87da2f3deab7", "text": "function [str, tag, cind] = gencode(item, tag, tagctx)\n\n% GENCODE  Generate code to recreate any MATLAB struct/cell variable.\n% For any MATLAB variable, this function generates a .m file that\n% can be run to recreate it. Classes can implement their class specific\n% equivalent of gencode with the same calling syntax. By default, classes\n% are treated similar to struct variables.\n%\n% [str, tag, cind] = gencode(item, tag, tagctx)\n% Input arguments:\n% item - MATLAB variable to generate code for (the variable itself, not its\n%        name)\n% tag     - optional: name of the variable, i.e. what will be displayed left\n%           of the '=' sign. This can also be a valid struct/cell array\n%           reference, like 'x(2).y'. If not provided, inputname(1) will be\n%           used.\n% tagctx  - optional: variable names not to be used (e.g. keywords,\n%           reserved variables). A cell array of strings.\n% Output arguments:\n% str  - cellstr containing code lines to reproduce the input variable\n% tag  - name of the generated variable (equal to input tag)\n% cind - index into str to the line where the variable assignment is coded\n%        (usually 1st line for non-object variables)\n%\n% See also GENCODE_RVALUE, GENCODE_SUBSTRUCT, GENCODE_SUBSTRUCTCODE.\n%\n% This code has been developed as part of a batch job configuration\n% system for MATLAB. See  \n%      http://sourceforge.net/projects/matlabbatch\n% for details about the original project.\n%_______________________________________________________________________\n% Copyright (C) 2007 Freiburg Brain Imaging\n\n% Volkmar Glauche\n% $Id: gencode.m 410 2009-06-23 11:47:26Z glauche $\n\nrev = '$Rev: 410 $'; %#ok\n\nif nargin < 2\n    tag = inputname(1);\nend;\nif nargin < 3\n    tagctx = {};\nend\nif isempty(tag)\n    tag = genvarname('val', tagctx);\nend;\n% Item count\ncind = 1;\n% try to generate rvalue code\n[rstr sts] = gencode_rvalue(item);\nif sts\n    lvaleq = sprintf('%s = ', tag);\n    if numel(rstr) == 1\n        str{1} = sprintf('%s%s;', lvaleq, rstr{1});\n    else\n        str = cell(size(rstr));\n        indent = {repmat(' ', 1, numel(lvaleq)+1)};\n        str{1} = sprintf('%s%s', lvaleq, rstr{1});\n        str(2:end-1) = strcat(indent, rstr(2:end-1));\n        str{end} = sprintf('%s%s;', indent{1}, rstr{end});\n        if numel(str) > 10\n            % add cell mode comment to structure longer output\n            str = [{'%%'} str(:)' {'%%'}];\n        end\n    end\nelse   \n    switch class(item)\n        case 'char'\n            str = {};\n            szitem = size(item);\n            subs = gensubs('()', {':',':'}, szitem(3:end));\n            for k = 1:numel(subs)\n                substag = gencode_substruct(subs{k}, tag);\n                str1 = gencode(subsref(item, subs{k}), substag{1}, tagctx);\n                str  = [str(:)' str1(:)'];\n            end\n        case 'cell'\n            str = {};\n            szitem = size(item);\n            subs = gensubs('{}', {}, szitem);\n            for k = 1:numel(subs)\n                substag = gencode_substruct(subs{k}, tag);\n                str1 = gencode(subsref(item, subs{k}), substag{1}, tagctx);\n                str  = [str(:)' str1(:)'];\n            end\n        case 'struct'\n            str = gencode_structobj(item, tag, tagctx);\n        otherwise\n            if isobject(item) || ~(isnumeric(item) || islogical(item))\n                % This branch is hit for objects without a gencode method\n                try\n                    % try to generate code in a struct-like fashion\n                    str = gencode_structobj(item, tag, tagctx);\n                catch\n                    % failed - generate a warning in generated code and\n                    % warn directly\n                    str = {sprintf('warning(''%s: No code generated for object of class %s.'')', tag, class(item))};\n                    if any(exist('cfg_message') == 2:6)\n                        cfg_message('matlabbatch:gencode:unknown', ...\n                                    '%s: Code generation for objects of class \"%s\" must be implemented as object method.', tag, class(item));\n                    else\n                        warning('gencode:unknown', ...\n                                '%s: Code generation for objects of class \"%s\" must be implemented as object method.', tag, class(item));\n                    end\n                end\n            elseif issparse(item)\n                % recreate sparse matrix from indices\n                [tmpi tmpj tmps] = find(item);\n                [stri tagi cindi] = gencode(tmpi);\n                [strj tagj cindj] = gencode(tmpj);\n                [strs tags cinds] = gencode(tmps);\n                str = [stri(:)' strj(:)' strs(:)'];\n                cind = cind + cindi + cindj + cinds;\n                str{end+1} = sprintf('%s = sparse(tmpi, tmpj, tmps);', tag);\n            else\n                str = {};\n                szitem = size(item);\n                subs = gensubs('()', {':',':'}, szitem(3:end));\n                for k = 1:numel(subs)\n                    substag = gencode_substruct(subs{k}, tag);\n                    str1 = gencode(subsref(item, subs{k}), substag{1}, tagctx);\n                    str  = [str(:)' str1(:)'];\n                end\n            end\n    end\nend\n\nfunction subs = gensubs(type, initdims, sz)\n% generate a cell array of subscripts into trailing dimensions of\n% n-dimensional arrays. Type is the subscript type (either '()' or '{}'),\n% initdims is a cell array of leading subscripts that will be prepended to\n% the generated subscripts and sz contains the size of the remaining\n% dimensions.\n\n% deal with special case of row vectors - only add one subscript in this\n% case\nif numel(sz) == 2 && sz(1) == 1 && isempty(initdims)\n    ind = 1:sz(2);\nelse\n    % generate index array, rightmost index varying fastest\n    ind = 1:sz(1);\n    for k = 2:numel(sz)\n        ind = [kron(ind, ones(1,sz(k))); kron(ones(1,size(ind,2)), 1:sz(k))];\n    end;\nend;\nsubs = cell(1,size(ind,2));\n% for each column of ind, generate a separate subscript structure\nfor k = 1:size(ind,2)\n    cellind = num2cell(ind(:,k));\n    subs{k} = substruct(type, [initdims(:)' cellind(:)']);\nend;\nfunction str = gencode_structobj(item, tag, tagctx)\n\n% Create code for a struct array. Also used as fallback for object\n% arrays, if the object does not provide its own gencode implementation.\n\ncitem = class(item);\n% try to figure out fields/properties that can be set\nif isobject(item) && exist('metaclass','builtin')\n    mobj = metaclass(item);\n    % Only create code for properties which are\n    % * not dependent or dependent and have a SetMethod\n    % * not constant\n    % * not abstract\n    % * have public SetAccess\n    sel = cellfun(@(cProp)(~cProp.Constant && ...\n        ~cProp.Abstract && ...\n        (~cProp.Dependent || ...\n        (cProp.Dependent && ...\n        ~isempty(cProp.SetMethod))) && ...\n        strcmp(cProp.SetAccess,'public')),mobj.Properties);\n    fn = cellfun(@(cProp)subsref(cProp,substruct('.','Name')),mobj.Properties(sel),'uniformoutput',false);\nelse\n    % best guess\n    fn = fieldnames(item);\nend\nif isempty(fn)\n    if isstruct(item)\n        str{1} = sprintf('%s = struct([]);', tag);\n    else\n        str{1} = sprintf('%s = %s;', tag, citem);\n    end\nelseif isempty(item)\n    if isstruct(item)\n        fn = strcat('''', fn, '''', ', {}');\n        str{1} = sprintf('%s = struct(', tag);\n        for k = 1:numel(fn)-1\n            str{1} = sprintf('%s%s, ', str{1}, fn{k});\n        end\n        str{1} = sprintf('%s%s);', str{1}, fn{end});\n    else\n        str{1} = sprintf('%s = %s.empty;', tag, citem);\n    end        \nelseif numel(item) == 1\n    if isstruct(item)\n        str = {};\n    else\n        str{1} = sprintf('%s = %s;', tag, citem);\n    end\n    for l = 1:numel(fn)\n        str1 = gencode(item.(fn{l}), sprintf('%s.%s', tag, fn{l}), tagctx);\n        str  = [str(:)' str1(:)'];\n    end\nelse\n    str = {};\n    szitem = size(item);\n    subs = gensubs('()', {}, szitem);\n    for k = 1:numel(subs)\n        if ~isstruct(item)\n            str{end+1} = sprintf('%s = %s;', gencode_substruct(subs{k}, tag), citem);\n        end\n        for l = 1:numel(fn)\n            csubs = [subs{k} substruct('.', fn{l})];\n            substag = gencode_substruct(csubs, tag);\n            str1 = gencode(subsref(item, csubs), substag{1}, tagctx);\n            str  = [str(:)' str1(:)'];\n        end\n    end\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "profile_history.m", "ext": ".m", "path": "OCIA-master/utils/matlabExtend/profile_history/profile_history.m", "size": 21451, "source_encoding": "utf_8", "md5": "09cf826e21a726826828395761ce3c13", "text": "function profile_history(profData, initialDetail, varargin)\n% profile_history - display profiling data as a timeline history\n%\n% profile_history analyzes the latest profiling session and displays the\n% function-call timings and durations in a graphical timeline. The function\n% labels and timeline bars are clickable, linking to their respective\n% detailed profiling report (of the builtin Matlab profiler).\n%\n% profile_history(profData) displays the timeline of a specific profiling\n% session, that was previously stored via a profData=profile('info') command.\n%\n% profile_history(initialDetail) or profile_history(profData,initialDetail)\n% displays the timeline showing an initial detail level of a specified number\n% of functions (default initialDetail=15). The detail level can then be\n% changed interactively, using a slider at the bottom of the figure.\n%\n% Sample usage:\n%    profile on; myProgram(); profile_history\n%    profile on; myProgram(); profData=profile('info'); profile_history(profData)\n%    profile_history(20);\n%    profile_history(profData,15);\n% \n% Technical description:\n%    http://UndocumentedMatlab.com/blog/function-call-timeline-profiling\n%\n% Note: \n%    profile_history uses a wrapper function for the built-in profile.m\n%    that is located in the @char subfolder. If for some reason you see\n%    problems when running the profiler, simply delete the @char folder.\n%    In order to be able to use profile_history after you delete the @char\n%    folder, add the -timestamp parameter whenever you profile: \n%           profile on -timestamp;  myProgram();  profile_history\n%    (adding the -timestamp parameter is not needed when the @char exists)\n%\n% Bugs and suggestions:\n%    Please send to Yair Altman (altmany at gmail dot com)\n%\n% See also:\n%    profile, profview\n%\n% Release history:\n%    1.0 2014-06-16: First version posted on <a href=\"http://www.mathworks.com/matlabcentral/fileexchange/authors/27420\">MathWorks File Exchange</a>\n%    1.1 2014-06-17: Fixes for HG2 (R2014b), fix info-box alignment near figure's right edge\n%    1.2 2014-06-17: Fix the previous update...\n%    1.3 2014-06-18: External tick marks; fixed wrapper function edge-case as per user feedback\n%    1.4 2014-06-26: Added initialDetail input arg and interactive detail-control functionality\n%    1.5 2014-06-27: Fixed bug in case of few profiled functions (checkbox didn't work)\n%    1.6 2014-06-29: Fix for R2010a\n\n% License to use and modify this code is granted freely to all interested, as long as the original author is\n% referenced and attributed as such. The original author maintains the right to be solely associated with this work.\n\n% Programmed and Copyright by Yair M. Altman: altmany(at)gmail.com\n% $Revision: 1.6 $  $Date: 2014/06/27 18:50:00 $\n\n    % Check inputs\n    if nargin<1 || isempty(profData)\n        profData = profile('info');\n    end\n    if nargin<2\n        if isstruct(profData)\n            initialDetail = 15;  % =Default value\n        else  % i.e., profData was skipped, only initialDetail was specified\n            initialDetail = profData;\n            profData = profile('info');\n        end\n    end\n\n    % Ensure that there is valid history data to process\n    if isempty(profData) || ~isstruct(profData) || ~isfield(profData,'FunctionHistory')\n        error('YMA:profile_history:noStruct','Invalid profiling data: call %s following profile. For example:\\n   profile on -timestamp; surf(peaks); %s', mfilename, mfilename);\n    elseif isempty(profData.FunctionHistory)\n        error('YMA:profile_history:noData','No profiling data: call %s following profile. For example:\\n   profile on -timestamp; surf(peaks); %s', mfilename, mfilename);\n    elseif size(profData.FunctionHistory,1) < 4\n        error('YMA:profile_history:noTiming','No timing data: call profile with the -timing parameter. For example:\\n   profile on -timestamp; surf(peaks); %s', mfilename);\n    end\n\n    % Parse the profile history data table\n    histData = profData.FunctionHistory;\n    startTime     = histData(3,1) + histData(4,1)/1e6;\n    relativeTimes = histData(3,:) + histData(4,:)/1e6 - startTime;\n\n    % Differentiate between close calls\n    N = numel(relativeTimes);\n    for idx = 2 : N  % TODO - vectorize this loop\n        if relativeTimes(idx) <= relativeTimes(idx-1)\n            relativeTimes(idx) = relativeTimes(idx-1) + 0.05*profData.ClockPrecision;\n        end\n    end\n    %plot(relativeTimes);\n\n    % Start the report figure in invisible mode (for improved performance)\n    handles.hFig = figure('NumberTitle','off', 'Name','Profiling history', 'Visible','off', 'Color','w', 'Toolbar','figure');\n    handles.funcNames = getFunctionNames(profData);\n    numFuncs = numel(handles.funcNames);\n    handles.hAxes = axes('YDir','reverse', 'OuterPosition',[0,0,1,1], 'LooseInset',[0.05,0.15,0.02,0.01], 'FontSize',8, ...\n                         'NextPlot','add', 'YTick',1:numFuncs, 'TickDir','out', 'YTickLabel',handles.funcNames, 'YLim',[1,numFuncs+0.2], ...\n                         'ButtonDownFcn',{@mouseClickedCallbackFcn,handles.hFig});\n    try\n        % HG2\n        set(handles.hAxes,'TickLabelInterpreter','none');\n    catch\n        % HG1\n    end\n    setappdata(handles.hFig,'hAxes',handles.hAxes);\n    setappdata(handles.hFig,'numFuncs',numFuncs);\n    xlabel(handles.hAxes, 'Secs from start');\n\n    if numFuncs > initialDetail\n        visibility = {'visible','off'};\n    else\n        visibility = {};\n    end\n\n    for funcIdx = 1 : numFuncs  % TODO - vectorize this loop\n        % Get this function's entry and exit times\n        entryIdx = (histData(2,:)==funcIdx) & (histData(1,:)==0);\n        exitIdx  = (histData(2,:)==funcIdx) & (histData(1,:)==1);\n        profData.FunctionTable(funcIdx).EntryTimes = relativeTimes(entryIdx);\n        profData.FunctionTable(funcIdx).ExitTimes  = relativeTimes(exitIdx);\n        if sum(entryIdx) > sum(exitIdx)\n            % Function entry was provided without a corresponding function exit\n            try\n                sumDurations = sum(profData.FunctionTable(funcIdx).ExitTimes - profData.FunctionTable(funcIdx).EntryTimes(1:end-1));\n                lastDuration = profData.FunctionTable(funcIdx).TotalTime - sumDurations;\n                profData.FunctionTable(funcIdx).ExitTimes(end+1) = profData.FunctionTable(funcIdx).EntryTimes(end) + lastDuration;\n            catch\n                % something is seriously fishy - bail out...\n            end\n        end\n        profData.FunctionTable(funcIdx).SelfTime = zeros(1,sum(entryIdx));  % will be updated below\n\n        % Plot the function's execution durations\n        xData = [profData.FunctionTable(funcIdx).EntryTimes; ...\n                 profData.FunctionTable(funcIdx).ExitTimes];\n        handles.hPlotLines{funcIdx} = plot(xData, funcIdx(ones(size(xData))), '.-b', 'HitTest','off')';\n    end\n\n    % Display the execution paths\n    execPathX = reshape(relativeTimes(sort([1:N,2:N-1])),2,[]);\n    execPathY = [];\n    ancestors = [];\n    cp = profData.ClockPrecision;\n    for execIdx = 1 : N  % TODO - vectorize this loop\n        % Update the self-time for the function that just switched\n        funcIdx = histData(2,execIdx);\n        if execIdx > 1\n            duration = relativeTimes(execIdx) - relativeTimes(execIdx-1);\n            duration = cp * round(duration/cp);\n            if histData(1,execIdx) == 0\n                if ~isempty(ancestors)\n                    prevFuncIdx = ancestors(end);\n                else\n                    prevFuncIdx = [];\n                end\n            else\n                %prevFuncIdx = histData(2,execIdx-1);\n                prevFuncIdx = funcIdx;\n            end\n            try\n                funcData = profData.FunctionTable(prevFuncIdx);\n                entryIdx = max(find(funcData.EntryTimes <= relativeTimes(execIdx)));  %#ok<MXFND> % I think ,1,'last') is not available in old ML releases, so use max()\n                if isempty(entryIdx),  entryIdx = 1;  end\n                profData.FunctionTable(prevFuncIdx).SelfTime(entryIdx) = funcData.SelfTime(entryIdx) + duration;\n            catch\n                % ignore - probably prevFuncIdx==[]\n            end\n        end\n\n        % Update the execPathY values and ancestors\n        if histData(1,execIdx) == 0\n            % Push the function-call stack downwards\n            execPathY(end+1) = funcIdx; %#ok<*AGROW>\n            ancestors(end+1) = funcIdx;\n        else % histData(1,execIdx) == 1\n            try\n                % Pop the function-call stack upwards\n                execPathY(end+1) = ancestors(end-1);\n                ancestors(end) = [];\n            catch\n                % no parent function, so hide the exec path here\n                execPathY(end+1) = NaN;\n                ancestors = [];\n            end\n        end\n    end\n    execPathY = reshape(execPathY(sort([1:N-1,1:N-1])),2,[]);\n    handles.hExecPaths = plot(execPathX, execPathY, '-r', 'LineWidth',2, 'HitTest','off', visibility{:});\n\n    % Display waterfalls\n    waterfallX = reshape(execPathX(2:end-1),2,[]);\n    waterfallY = reshape(execPathY(2:end-1),2,[]);\n    handles.hWaterfalls = plot(waterfallX, waterfallY, ':r', 'HitTest','off', visibility{:});\n    direction = sign(diff(waterfallY));\n    hUpMarks   = text(waterfallX(1,direction<0), mean(waterfallY(:,direction<0)), '\\^', 'Color','r', 'HitTest','off', visibility{:}, 'FontSize',10, 'HorizontalAlignment','center', 'VerticalAlignment','middle');\n    hDownMarks = text(waterfallX(1,direction>0), mean(waterfallY(:,direction>0)), 'v',  'Color','r', 'HitTest','off', visibility{:}, 'FontSize',8,  'HorizontalAlignment','center', 'VerticalAlignment','cap');\n    handles.hTextLabels = zeros(numel(handles.hWaterfalls),1);\n    handles.hTextLabels(direction<0) = hUpMarks;\n    handles.hTextLabels(direction>0) = hDownMarks;\n\n    % Add functionality checkboxes\n    uicontrol('Style','checkbox', 'String','execution paths', 'value',1, 'Units','Pixels', 'Position',[ 10,5,100,20], 'Background','w', 'Tag','cbExecPath',  'Callback',{@execPathCallbackFcn,  handles, profData});\n    uicontrol('Style','checkbox', 'String','waterfall lines', 'value',1, 'Units','Pixels', 'Position',[110,5,100,20], 'Background','w', 'Tag','cbWaterfall', 'Callback',{@waterfallCallbackFcn, handles, profData});\n    uicontrol('Style','checkbox', 'String','hover info-box',  'value',1, 'Units','Pixels', 'Position',[200,5,100,20], 'Background','w', 'Tag','cbInfoBox',   'Callback',{@infoBoxCallbackFcn,   handles, profData});\n\n    if numFuncs > initialDetail\n        try\n            % Add the detail slider (much nicer-looking than Matlab's ugly builtin \"slider\" =Win95 scrollbar)\n            jSlider = javax.swing.JSlider;\n            try jSlider = javaObjectEDT(jSlider); catch, end  % R2008b and newer\n            tooltipStr = sprintf('%d functions',initialDetail);\n            set(jSlider, 'Minimum',1, 'Maximum',numFuncs, 'Value',initialDetail, 'ToolTipText',tooltipStr); %, 'Background',java.awt.Color.white); %'PaintLabels',false, 'PaintTicks',false, 'Orientation',jSlider.HORIZONTAL)\n            jSlider.setBackground(java.awt.Color.white);  % Fix for R2010a\n            [hjSlider, hContainer] = javacomponent(jSlider, [400,2,100,20]);\n            set(hContainer, 'Tag','jSlider', 'UserData',hjSlider);\n            set(hjSlider, 'StateChangedCallback',{@detailsCallbackFcn,handles,profData});\n\n            % So far so good - add the labels on the left and right of the slider\n            uicontrol('Style','text', 'String','Display detail: 1 ', 'Units','Pixels', 'Position',[300,1,100,20], 'Background','w', 'horizontalAlignment','right');\n            uicontrol('Style','text', 'String',num2str(numFuncs),    'Units','Pixels', 'Position',[500,1, 50,20], 'Background','w', 'horizontalAlignment','left');\n        catch\n            % never mind - ignore...\n        end\n\n        % Finally, update the plot axes\n        updatePlot(handles, profData);\n    end\n\n    % Set the info-box hover callback\n    set(handles.hFig, 'WindowButtonMotionFcn',{@mouseMovedCallbackFcn,profData});\n\n    % Display the figure\n    set(handles.hFig, 'Visible','on');\nend\n\n% Get valid function names\nfunction funcNames = getFunctionNames(profData)\n    % First get all profiled function names\n    funcNames = {profData.FunctionTable.FunctionName};\n\n    % Strip away profiling artifacts\n    while ~isempty(strfind(funcNames{end},'profile'))\n        funcNames(end) = [];\n    end\n    if strcmpi(funcNames{end},'ispc'),       funcNames(end) = [];  end\n    if strcmpi(funcNames{end},'fileparts'),  funcNames(end) = [];  end\nend\n\n% Execution path checkbox callback\nfunction execPathCallbackFcn(cbExecPath, eventData, handles, profData) %#ok<INUSL>\n    updatePlot(handles, profData)\nend\n\n% Waterfall lines checkbox callback\nfunction waterfallCallbackFcn(cbWaterfall, eventData, handles, profData) %#ok<INUSL>\n    updatePlot(handles, profData)\nend\n\n% Hover info-box checkbox callback\nfunction infoBoxCallbackFcn(cbInfoBox, eventData, handles, profData) %#ok<INUSL>\n    if get(cbInfoBox,'Value')\n        set(handles.hFig, 'WindowButtonMotionFcn',{@mouseMovedCallbackFcn,profData});\n    else\n        set(handles.hFig, 'WindowButtonMotionFcn',[]);\n    end\nend\n\n% Display details slider callback\nfunction detailsCallbackFcn(hSlider, eventData, handles, profData) %#ok<INUSL>\n    newValue = round(hSlider.getValue);\n    prevValue = getappdata(handles.hFig,'detailValue');\n    if ~isequal(prevValue, newValue)\n        setappdata(handles.hFig,'detailValue', newValue);\n        hSlider.setToolTipText(sprintf('%d functions',newValue));\n        updatePlot(handles, profData);\n    end\nend\n\n% Update visibility detail\nfunction updatePlot(handles, profData)\n    if ~isempty(getappdata(handles.hFig,'inCallback')),  return;  end\n    setappdata(handles.hFig,'inCallback',true);\n    try\n        hSlider = get(findall(handles.hFig,'Tag','jSlider'), 'UserData');\n        try\n            numFuncsToDisplay = round(hSlider.getValue);\n        catch\n            numFuncsToDisplay = numel(handles.funcNames);  % if no slider is displayed use all funcs\n        end\n        numFuncs = numel(handles.hPlotLines);\n        [sortedTimes, sortedIdx] = sort([profData.FunctionTable(1:numFuncs).TotalTime]); %#ok<ASGLU>\n        idxToDisplay = sortedIdx(end-numFuncsToDisplay+1:end);\n        sortedIdx = sort(idxToDisplay);\n\n        % Main plot lines (solid blue lines)\n        set([handles.hPlotLines{:}], 'Visible','off');\n        set([handles.hPlotLines{idxToDisplay}], 'Visible','on');\n\n        set(handles.hAxes, 'YTickLabel',handles.funcNames(sortedIdx), 'YTick',sortedIdx);\n\n        % Execution paths (solid red lines)\n        cbExecPath = findall(handles.hFig, 'Tag','cbExecPath');\n        processPlotHandles(cbExecPath, handles.hExecPaths, idxToDisplay);\n\n        % Waterfalls (dotted vertical red lines)\n        cbWaterfall = findall(handles.hFig, 'Tag','cbWaterfall');\n        processPlotHandles(cbWaterfall, handles.hWaterfalls, idxToDisplay, handles.hTextLabels);\n    catch err\n        disp(err.message); %debugging breakpoint\n        disp(err.stack(1));\n    end\n    setappdata(handles.hFig,'inCallback',[]);\nend\n\n% Hide/display plot lines having certain Y values\nfunction processPlotHandles(hCheckbox, handlesToProcess, yValuesToDisplay, hTextLabels)\n    if get(hCheckbox,'Value')\n        % Only display plot line having certain Y values\n        %set(handlesToProcess,'visible','on');\n        set(handlesToProcess,'visible','off');\n        yData = get(handlesToProcess, 'YData');\n        yData = [yData{:}];\n        yStart = yData(1:2:end);\n        yEnd   = yData(2:2:end);\n        thisIdxToDisplay = ismember(yStart,yValuesToDisplay) & ismember(yEnd,yValuesToDisplay);\n        set(handlesToProcess(thisIdxToDisplay),'visible','on');\n\n        % Now the corresponding text handles\n        if nargin > 3\n            hTextLabelsToProcess = setdiff(hTextLabels(thisIdxToDisplay,:), 0);\n            set(hTextLabels(hTextLabels~=0), 'visible','off');\n            set(hTextLabelsToProcess, 'visible','on');\n        end\n    else\n        % Hide *all* plot lines and corresponding text handles\n        set(handlesToProcess, 'visible','off');\n        try set(hTextLabels(hTextLabels~=0), 'visible','off'); catch, end\n    end\nend\n\n% Convert clicked position into a function index\nfunction [funcIdx,currentPoint,axesPos,yLim] = getFuncIdx(hFig, hAxes)\n    % Get the clicked position\n    currentPoint = get(hFig,'CurrentPoint');\n    x = currentPoint(1);\n    y = currentPoint(2);\n\n    % Convert clicked position into a function index\n    if nargin<2,  hAxes = getappdata(hFig,'hAxes');  end\n    axesPos = getpixelposition(hAxes);\n    axesHeight = axesPos(4);\n    deltaYFromBottom = y - axesPos(2);\n    deltaYFromTop = axesHeight - deltaYFromBottom;\n    relYFromTop = min(max(deltaYFromTop/axesHeight,0),1);\n    yLim = get(hAxes,'YLim');\n    numFuncs = getappdata(hFig,'numFuncs');\n    funcIdx = min(max(round(yLim(1) + relYFromTop*diff(yLim)), 1), numFuncs);\n\n    % The following is currently unused:\n    if x < axesPos(1)\n        % Clicked outside the axes (on a function label)\n    else\n        % Clicked within the axes\n    end\nend\n\n% Axes mouse-click callback\nfunction mouseClickedCallbackFcn(hAxes, eventData, hFig) %#ok<INUSL>\n    % First get the clicked function index in the profiling table\n    funcIdx = getFuncIdx(hFig, hAxes);\n\n    % Open the detailed profiling report for the clicked function\n    profview(funcIdx);\nend\n\n% Figure mouse-click callback\nfunction mouseMovedCallbackFcn(hFig, eventData, profData) %#ok<INUSL>\n    % First get the hovered function index in the profiling table\n    [funcIdx,currentPoint,axesPos,yLim] = getFuncIdx(hFig);\n\n    % Get the info text-box handle\n    hInfoBox = getappdata(hFig,'hInfoBox');\n    if isempty(hInfoBox)\n        % Prepare the info-box\n        hInfoBox = text(0,0,'', 'EdgeColor',.8*[1,1,1], 'FontSize',7, 'Tag','hInfoBox', 'VerticalAlignment','bottom', 'interpreter','none', 'HitTest','off');\n        setappdata(hFig,'hInfoBox',hInfoBox);\n    end\n    if currentPoint(2)<axesPos(2) || currentPoint(2)>axesPos(2)+axesPos(4) || currentPoint(1)>axesPos(1)+axesPos(3)\n        % Mouse pointer is outside the axes boundaries - hide the info box\n        set(hInfoBox, 'Visible','off');\n    else\n        % Get the hovered position in data units\n        % Note: we could also use the builtin hgconvertunits function\n        axesWidth = axesPos(3);\n        deltaXFromLeft = currentPoint(1) - axesPos(1);\n        relXFromLeft = min(max(deltaXFromLeft/axesWidth,0),1);\n        hAxes = getappdata(hFig,'hAxes');\n        xLim = get(hAxes,'XLim');\n        dataX = xLim(1) + relXFromLeft*diff(xLim);\n        %disp([x axesWidth deltaXFromLeft relXFromLeft xLim dataX])\n\n        % Get the function's timing data\n        functionData = profData.FunctionTable(funcIdx);\n\n        % Find the nearest function entry point\n        entryIdx = max(find(functionData.EntryTimes <= dataX));  %#ok<MXFND> % I think ,1,'last') is not available in old ML releases, so use max()\n        if isempty(entryIdx),  entryIdx = 1;  end\n\n        % Bail out if the entry is not visible\n        if ~ismember(functionData.FunctionName, get(hAxes,'YTickLabel'))\n            set(hInfoBox, 'Visible','off');\n            return;\n        end\n\n        % Get the entry's time \n        entryTime = functionData.EntryTimes(entryIdx);\n        exitTime  = functionData.ExitTimes(entryIdx);\n        totalTime = exitTime - entryTime;\n        selfTime = functionData.SelfTime(entryIdx);\n        childTime = totalTime - selfTime;\n        selfTotalTime  = sum(functionData.SelfTime);\n        totalTotalTime = sum(functionData.TotalTime);\n        childTotalTime = totalTotalTime - selfTotalTime;\n\n        % Assemble all the info into the info-box string\n        %cp = profData.ClockPrecision;\n        str = functionData.FunctionName;\n        numEntries = numel(functionData.EntryTimes);\n        if numEntries > 1\n            str = sprintf('%s\\n  => call #%d of %d', str, entryIdx, numEntries);\n        end\n        %str = sprintf('%s\\n  Start: %+.3f\\n  Self:  %+.3f\\n  Child: %+.3f\\n  Total: %+.3f\\n  End:  %+.3f', ...\n        %              str, entryTime, selfTime, childTime, totalTime, exitTime);\n        str = sprintf('%s\\n  Start: %+.3f\\n  Self:  %+.3f', str, entryTime, selfTime);\n        if numEntries > 1, str = sprintf('%s (of %.3f)', str, selfTotalTime); end\n        str = sprintf('%s\\n  Child: %+.3f', str, childTime);\n        if numEntries > 1, str = sprintf('%s (of %.3f)', str, childTotalTime); end\n        str = sprintf('%s\\n  Total: %+.3f', str, totalTime);\n        if numEntries > 1, str = sprintf('%s (of %.3f)', str, totalTotalTime); end\n        str = sprintf('%s\\n  End:  %+.3f', str, exitTime);\n        set(hInfoBox, 'Visible','on', 'Position',[dataX,funcIdx], 'String',str, ...\n            'VerticalAlignment','top', 'HorizontalAlignment','left');\n\n        % Update the info-box location based on cursor position\n        extent = get(hInfoBox,'Extent');\n        %disp(num2str([extent,yLim,xLim],'%.2f '))\n        if extent(2) - 2*extent(4) < yLim(1)\n            set(hInfoBox, 'VerticalAlignment','top');\n        else\n            set(hInfoBox, 'VerticalAlignment','bottom');\n        end\n        if extent(1) + extent(3) > xLim(2)\n            %set(hInfoBox, 'HorizontalAlignment','right');\n            set(hInfoBox, 'Position',[dataX-extent(3),funcIdx]);\n        else\n            %set(hInfoBox, 'HorizontalAlignment','left');\n        end\n    end\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "parseXML.m", "ext": ".m", "path": "OCIA-master/utils/file/parseXML.m", "size": 2298, "source_encoding": "utf_8", "md5": "2872d6a03a9a0534868ee63adbb29a11", "text": "function theStruct = parseXML(filename)\n% PARSEXML Convert XML file to a MATLAB structure.\ntry\n    tree = xmlread(filename);\ncatch\n    error('Failed to read XML file %s.',filename);\nend\n\n% Recurse over child nodes. This could run into problems\n% with very deeply nested trees.\ntheStruct = parseChildNodes(tree);\nreturn\ntry\n    theStruct = parseChildNodes(tree);\ncatch\n    error('Unable to parse XML file %s.',filename);\n    rethrow(lasterror)\nend\n\n\n% ----- Subfunction PARSECHILDNODES -----\nfunction children = parseChildNodes(theNode)\n% Recurse over node children.\nchildren = [];\nif theNode.hasChildNodes\n    childNodes = theNode.getChildNodes;\n    numChildNodes = childNodes.getLength;\n    allocCell = cell(1, numChildNodes);\n    \n    children = struct(             ...\n        'Name', allocCell, 'Attributes', allocCell,    ...\n        'Data', allocCell, 'Children', allocCell);\n    delIdx = [];\n    for count = 1:numChildNodes\n        theChild = childNodes.item(count-1);\n        switch char(theChild.getNodeName)\n            case '#texttt'\n                delIdx(length(delIdx)+1) = count;\n            otherwise\n                children(count) = makeStructFromNode(theChild);\n        end\n    end\n    children(delIdx) = [];\nend\n\n% ----- Subfunction MAKESTRUCTFROMNODE -----\nfunction nodeStruct = makeStructFromNode(theNode)\n% Create structure of node info.\n\nnodeStruct = struct(                        ...\n    'Name', char(theNode.getNodeName),       ...\n    'Attributes', parseAttributes(theNode),  ...\n    'Data', '',                              ...\n    'Children', parseChildNodes(theNode));\nif any(strcmp(methods(theNode), 'getData'))\n    nodeStruct.Data = char(theNode.getData);\nelse\n    nodeStruct.Data = '';\nend\n\n% ----- Subfunction PARSEATTRIBUTES -----\nfunction attributes = parseAttributes(theNode)\n% Create attributes structure.\n\nattributes = [];\nif theNode.hasAttributes\n    theAttributes = theNode.getAttributes;\n    numAttributes = theAttributes.getLength;\n    allocCell = cell(1, numAttributes);\n    attributes = struct('Name', allocCell, 'Value', ...\n        allocCell);\n    \n    for count = 1:numAttributes\n        attrib = theAttributes.item(count-1);\n        attributes(count).Name = char(attrib.getName);\n        attributes(count).Value = char(attrib.getValue);\n    end\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "depfunFast.m", "ext": ".m", "path": "OCIA-master/utils/file/depfunFast.m", "size": 3341, "source_encoding": "utf_8", "md5": "e1a987535066e28a1e99c5fce821bdda", "text": "function filelist = depfunFast(fn,recursive)\r\n% Variation on the built-in depfun function, which skips toolbox files\r\n%\r\n% filelist = mydepfun(fn)\r\n% filelist = mydepfun(fn,recursive)\r\n%\r\n% Returns a list of files which are required by the specified\r\n% function, omitting any which are inside $matlabroot/toolbox. \r\n%\r\n% \"fn\" is a string specifying a filename in any form that can be\r\n%   identified by the built-in function \"which\".\r\n% \"recursive\" is a logical scalar; if false, only the files called\r\n%   directly by the specified function are returned.  If true, *all*\r\n%   those files are scanned to, and any required by those, and so on.\r\n%\r\n% \"filelist\" is a cell array of fully qualified file name strings,\r\n%   including the specified file.\r\n%\r\n% e.g.\r\n%     filelist = mydepfun('myfunction')\r\n%     filelist = mydepfun('C:\\files\\myfunction.m',true) \r\n%PMTKauthor Malcolm Wood\r\n%PMTKurl http://www.mathworks.com/matlabcentral/fileexchange/10702\r\n\r\n% This file is from matlabtools.googlecode.com\r\n\r\n\r\nif ~ischar(fn)\r\n    error('First argument must be a string');\r\nend\r\n\r\nfoundfile = which(fn);\r\nif isempty(foundfile)\r\n    error('File not found: %s',fn);\r\nend\r\n\r\n% Scan this file\r\nfilelist = i_scan(foundfile);\r\n\r\n% If \"recursive\" is supplied and true, scan files on which this one depends.\r\nif nargin>1 && recursive\r\n    % Create a list of files which we have still to scan.\r\n    toscan = filelist;\r\n    toscan = toscan(2:end); % first entry is always the same file again\r\n    % Now scan files until we have none left to scan\r\n    while numel(toscan)>0\r\n        % Scan the first file on the list\r\n        newlist = i_scan(toscan{1});\r\n        newlist = newlist(2:end); % first entry is always the same file again\r\n        toscan(1) = []; % remove the file we've just scanned\r\n        % Find out which files are not already on the list.  Take advantage of\r\n        % the fact that \"which\" and \"depfun\" return the correct capitalisation\r\n        % of file names, even on Windows, making it safe to use \"ismember\"\r\n        % (which is case-sensitive).\r\n        reallynew = ~ismember(newlist,filelist);\r\n        newlist = newlist(reallynew);\r\n        % If they're not already in the file list, we'll need to scan them too.\r\n        % (Conversely, if they ARE in the file list, we've either scanned them\r\n        %  already, or they're currently on the toscan list)\r\n        toscan = unique( [ toscan ; newlist ] );\r\n        filelist = unique( [ filelist ; newlist ] );\r\n    end\r\nend\r\nend\r\n%%%%%%%%%%%%%%%%%%%%%\r\n% Returns the non-toolbox files which the specified one calls.\r\n% The specified file is always first in the returned list.\r\nfunction list = i_scan(f)\r\n\r\nfunc = i_function_name(f);\r\n\r\nlist = matlab.codetools.requiredFilesAndProducts(func, 'toponly')';\r\nulist = list;\r\n\r\ntoolboxroot = fullfile(matlabroot,'toolbox');\r\n\r\nintoolbox = strncmpi(ulist,toolboxroot,numel(toolboxroot));\r\n\r\nlist = list(~intoolbox);\r\nend\r\n%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction func = i_function_name(f)\r\n% Identifies the function name for the specified file,\r\n% including the class name where appropriate.  Does not\r\n% work for UDD classes, e.g. @rtw/@rtw\r\n\r\n[dirname,funcname,ext] = fileparts(f);\r\n[ignore,dirname] = fileparts(dirname);\r\n\r\nif ~isempty(dirname) && dirname(1)=='@'\r\n    func = [ dirname '/' funcname ];\r\nelse\r\n    func = funcname;\r\nend\r\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "showByteSize.m", "ext": ".m", "path": "OCIA-master/utils/file/showByteSize.m", "size": 991, "source_encoding": "utf_8", "md5": "0a61a86db4a2ae5060ab4797b40d0d11", "text": "function showByteSize(in, fid) %#ok<INUSL>\n% BYTESIZE writes the memory usage of the provide variable to the given file\n% identifier. Output is written to screen if fid is 1, empty or not provided.\n\nif nargin == 1 || isempty(fid)\n    fid = 1;\nend\n\ns = whos('in');\nfprintf(fid,[Bytes2str(s.bytes) '\\n']);\nend\n\nfunction str = Bytes2str(NumBytes)\n% BYTES2STR Private function to take integer bytes and convert it to\n% scale-appropriate size.\n\nscale = floor(log(NumBytes)/log(1024));\nswitch scale\n    case 0\n        str = [sprintf('%.0f',NumBytes) ' b'];\n    case 1\n        str = [sprintf('%.3f',NumBytes/(1024)) ' kb'];\n    case 2\n        str = [sprintf('%.3f',NumBytes/(1024^2)) ' Mb'];\n    case 3\n        str = [sprintf('%.3f',NumBytes/(1024^3)) ' Gb'];\n    case 4\n        str = [sprintf('%.3f',NumBytes/(1024^4)) ' Tb'];\n    case -inf\n        % Size occasionally returned as zero (eg some Java objects).\n        str = 'Not Available';\n    otherwise\n       str = 'Over a petabyte!!!';\nend\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "exportMfiles.m", "ext": ".m", "path": "OCIA-master/utils/file/exportMfiles.m", "size": 3841, "source_encoding": "utf_8", "md5": "367bfa75d006163523b8208a9c631218", "text": "function exportMfiles(funcname,doPcode,doZip,saveDir)\n% find dependencies for function funcname\n% copy all dependencies to saveDir\n% create zip-archive saveDir.zip\n% optionally, pcode\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nif ~iscell(funcname)\n    funcname = {funcname};\nend\n\nif isempty(funcname)\n    error('No function names specified');\nend\n\nif ~iscellstr(funcname)\n    error('Function names must be strings');\nend\n\nif nargin<2\n    zipfilename = fullfile(pwd,[ funcname{1} '.zip' ]);\nend\n\nreq = cell(size(funcname));\nfor i=1:numel(funcname)\n    req{i} = depfunFast(funcname{i},1); % recursive\nend\nreq = vertcat(req{:}); % cell arrays of full file names\nreq = unique(req);\n\n% filename for the first function file\n[~, baseFuncName, ext] = fileparts(funcname{1});\n\n% Find the common root directory\nd = i_root_directory(req);\n% Calculate relative paths for all required files.\nn = numel(d);\nfor i=1:numel(req)\n    % This is the bit that can't be vectorised\n    req{i} = req{i}(n+1:end); % step over last character (which is the separator)\nend\n\nif exist(saveDir, 'dir') ~= 7; mkdir(saveDir); end;\n\n% req is a cell array with all functions, d is the root directory\nfor n = 1:length(req)\n    currentFile = req{n};\n    [~, currentBaseName, ext] = fileparts(currentFile);\n    if ~exist(fullfile(pwd,[currentBaseName ext]),'file')\n        copyfile(fullfile(d,currentFile),'.');\n        doFileMove = 1;\n    else\n        doFileMove = 0;\n    end\n    currentFile = fullfile(pwd,[currentBaseName ext]);\n    if doPcode\n        if exist([saveDir filesep 'protected'],'dir') ~= 7\n            mkdir([saveDir filesep 'protected'])\n        end\n        if strcmp(saveDir, '.'); saveDir = './.'; end;\n        % never pcode the top-most function\n        if strcmp(currentBaseName,baseFuncName)\n            if doFileMove\n                movefile(currentFile,saveDir)\n            else\n                copyfile(currentFile,saveDir)\n            end\n        else\n            pcode(currentFile)\n            delete(currentFile)\n            currentFile = strrep(currentFile,'.m','.p');\n            if doFileMove\n                movefile(currentFile,[saveDir filesep 'protected'])\n            else\n                copyfile(currentFile,[saveDir filesep 'protected'])\n            end\n        end\n    else\n        if doFileMove\n            movefile(currentFile,saveDir)\n        else\n            copyfile(currentFile,saveDir)\n        end\n    end\nend\n\n% zip up saveDir\nif doZip;\n    zip([saveDir '.zip'],saveDir)\nend;\n\n% delete saveDir\n% rmdir(saveDir)\n\n\n%%%%%%%%%%%%%%%%%%%%%\n% Identifies the common root directory of all files in cell array \"req\"\nfunction d = i_root_directory(req)\n\nd = i_parent(req{1});\nfor i=1:numel(req)\n    t = i_parent(req{i});\n    if strncmp(t,d,numel(d))\n        % req{i} is in directory d.  Next file.\n        continue;\n    end\n    % req{i} is not in directory d.  Up one directory.\n    count = 1;\n    while true\n        % Remove trailing separator before calling fileparts.  Add it\n        % again afterwards.\n        tempd = i_parent(d(1:end-1));\n        if strcmp(d,tempd)\n            % fileparts didn't find us a higher directory\n            error('Failed to find common root directory for %s and %s',req{1},req{i});\n        end\n        d = tempd;\n        if strncmp(t,d,numel(d))\n            % req{i} is in directory d.  Next file.\n            break;\n        end\n        % Safety measure for untested platform.\n        count = count+1;\n        if count>1000\n            error('Bug in i_root_directory.');\n        end\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%\nfunction d = i_parent(d)\n% Identifies the parent directory, including a trailing separator\n\n% Include trailing separator in all comparisons so we don't assume that\n% file C:\\tempX\\file.txt is in directory C:\\temp\nd = fileparts(d);\nif d(end)~=filesep\n    d = [d filesep];\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "tiffread2.m", "ext": ".m", "path": "OCIA-master/utils/file/tiffread2.m", "size": 19177, "source_encoding": "utf_8", "md5": "1eab6091d5c0b85e5ab70139f0d580c3", "text": "function [stack, img_read] = tiffread2(filename, img_first, img_last, funUpdateWaitBar)\n% tiffread, version 2.4\n%\n% [stack, nbImages] = tiffread;\n% [stack, nbImages] = tiffread(filename);\n% [stack, nbImages] = tiffread(filename, imageIndex);\n% [stack, nbImages] = tiffread(filename, firstImageIndex, lastImageIndex);\n%\n%\n% 2010-04-30::hluetck\n% modify to also store file description if field info exists\n%            \n%            \n% Reads 8,16,32 bits uncompressed grayscale and (some) color tiff files,\n% as well as stacks or multiple tiff images, for example those produced\n% by metamorph or NIH-image. However, the entire TIFF standard is not\n% supported (but you may extend it).\n%\n% The function can be called with a file name in the current directory,\n% or without argument, in which case it pop up a file openning dialog\n% to allow manual selection of the file.\n% If the stacks contains multiples images, loading can be restricted by\n% specifying the first and last images to read, or just one image to read.\n%\n% at return, nbimages contains the number of images read, and S is a vector\n% containing the different images with some additional informations. The\n% image pixels values are stored in the field .data, for gray level images,\n% or in the fields .red, .green and .blue\n% the pixels values are in the native (integer) format,\n% and must be converted to be used in most matlab functions.\n%\n% Example:\n% im = tiffread('spindle.stk');\n% imshow( double(im(5).data), [] );\n%\n% Francois Nedelec, EMBL, Copyright 1999-2006.\n% rewriten July 7th, 2004 at Woods Hole during the physiology course.\n% last modified April 12, 2006.\n% Contributions:\n%   Kendra Burbank suggested the waitbar\n%   Hidenao Iwai for the code to read floating point images,\n%   Stephen Lang made tiffread more compliant with PlanarConfiguration\n%\n% Please, help us improve this software: send us feedback/bugs/suggestions\n% This software is provided at no cost by a public research institution.\n% However, postcards are always welcome!\n%\n% Francois Nedelec\n% nedelec (at) embl.de\n% Cell Biology and Biophysics, EMBL; Meyerhofstrasse 1; 69117 Heidelberg; Germany\n% http://www.embl.org\n% http://www.cytosim.org\n\n\n\n\n%Optimization: join adjacent TIF strips: this results in faster reads\nconsolidateStrips = 1;\n\n%if there is no argument, we ask the user to choose a file:\nif (nargin == 0)\n    [filename, pathname] = uigetfile('*.tif;*.stk;*.lsm', 'select image file');\n    filename = [ pathname, filename ];\nend\n\nif (nargin<=1);  img_first = 1; img_last = 10000; end\nif (nargin==2);  img_last = img_first;            end\n\n\n% not all valid tiff tags have been included, as they are really a lot...\n% if needed, tags can easily be added to this code\n% See the official list of tags:\n% http://partners.adobe.com/asn/developer/pdfs/tn/TIFF6.pdf\n%\n% the structure IMG is returned to the user, while TIF is not.\n% so tags usefull to the user should be stored as fields in IMG, while\n% those used only internally can be stored in TIF.\n\nglobal TIF;\nTIF = [];\n\n%counters for the number of images read and skipped\nimg_skip  = 0;\nimg_read  = 0;\n\n% set defaults values :\nTIF.SampleFormat     = 1;\nTIF.SamplesPerPixel  = 1;\nTIF.BOS              = 'l';          %byte order string\n\n% if  isempty(findstr(filename,'.'))\n%     filename = [filename,'.tif'];\n% end\n\nTIF.nFrames = numel(imfinfo(filename));\n\nTIF.file = fopen(filename,'r','l');\nif TIF.file == -1\n    filename = strrep(filename, '.tif', '.stk');\n    TIF.file = fopen(filename,'r','l');\n    if TIF.file == -1\n        error(['file <',filename,'> not found.']);\n    end\nend\n\n\n% read header\n% read byte order: II = little endian, MM = big endian\nbyte_order = fread(TIF.file, 2, '*char');\nif ( strcmp(byte_order', 'II') )\n    TIF.BOS = 'l';                                %normal PC format\nelseif ( strcmp(byte_order','MM') )\n    TIF.BOS = 'b';\nelse\n    error('This is not a TIFF file (no MM or II).');\nend\n\n%----- read in a number which identifies file as TIFF format\ntiff_id = fread(TIF.file,1,'uint16', TIF.BOS);\nif (tiff_id ~= 42)\n    error('This is not a TIFF file (missing 42).');\nend\n\n%----- read the byte offset for the first image file directory (IFD)\nifd_pos = fread(TIF.file,1,'uint32', TIF.BOS);\n\nwhile (ifd_pos ~= 0)\n    \n    clear IMG;\n    IMG.filename = fullfile( pwd, filename );\n    % move in the file to the first IFD\n    fseek(TIF.file, ifd_pos, -1);\n    %disp(strcat('reading img at pos :',num2str(ifd_pos)));\n    \n    %read in the number of IFD entries\n    num_entries = fread(TIF.file,1,'uint16', TIF.BOS);\n    %disp(strcat('num_entries =', num2str(num_entries)));\n    \n    %read and process each IFD entry\n    for i = 1:num_entries\n        \n        % save the current position in the file\n        file_pos  = ftell(TIF.file);\n        \n        % read entry tag\n        TIF.entry_tag = fread(TIF.file, 1, 'uint16', TIF.BOS);\n        entry = readIFDentry;\n        %disp(strcat('reading entry <',num2str(TIF.entry_tag),'>'));\n        \n        switch TIF.entry_tag\n            case 254\n                TIF.NewSubfiletype = entry.val;\n            case 256         % image width - number of column\n                IMG.width          = entry.val;\n            case 257         % image height - number of row\n                IMG.height         = entry.val;\n                TIF.ImageLength    = entry.val;\n            case 258         % BitsPerSample per sample\n                TIF.BitsPerSample  = entry.val;\n                TIF.BytesPerSample = TIF.BitsPerSample / 8;\n                IMG.bits           = TIF.BitsPerSample(1);\n                %fprintf(1,'BitsPerSample %i %i %i\\n', entry.val);\n            case 259         % compression\n                if (entry.val ~= 1); error('Compression format not supported.'); end\n            case 262         % photometric interpretation\n                TIF.PhotometricInterpretation = entry.val;\n                if ( TIF.PhotometricInterpretation == 3 )\n                    fprintf(1, 'warning: ignoring the look-up table defined in the TIFF file');\n                end\n            case 269\n                IMG.document_name  = entry.val;\n            case 270         % comment:\n                TIF.info           = entry.val;\n            case 271\n                IMG.make           = entry.val;\n            case 273         % strip offset\n                TIF.StripOffsets   = entry.val;\n                TIF.StripNumber    = entry.cnt;\n                %fprintf(1,'StripNumber = %i, size(StripOffsets) = %i %i\\n', TIF.StripNumber, size(TIF.StripOffsets));\n            case 274\n                % orientation is read, but the matrix is not rotated\n                if 1 < entry.val && entry.val < 9;\n                    TIF.Orientation = entry.val;\n                    keys = { 'TopLeft', 'TopRight', 'BottomRight', 'BottomLeft', 'LeftTop', ...\n                        'RightTop', 'RightBottom', 'LeftBottom' };\n                    TIF.OrientationText = keys{entry.val};\n                end\n            case 277         % sample_per pixel\n                TIF.SamplesPerPixel  = entry.val;\n                %fprintf(1,'Color image: sample_per_pixel=%i\\n',  TIF.SamplesPerPixel);\n            case 278         % rows per strip\n                TIF.RowsPerStrip   = entry.val;\n            case 279         % strip byte counts - number of bytes in each strip after any compressio\n                TIF.StripByteCounts= entry.val;\n            case 282         % X resolution\n                IMG.x_resolution   = entry.val;\n            case 283         % Y resolution\n                IMG.y_resolution   = entry.val;\n            case 284         %planar configuration describe the order of RGB\n                TIF.PlanarConfiguration = entry.val;\n            case 296         % resolution unit\n                IMG.resolution_unit= entry.val;\n            case 305         % software\n                IMG.software       = entry.val;\n            case 306         % datetime\n                IMG.datetime       = entry.val;\n            case 315\n                IMG.artist         = entry.val;\n            case 317        %predictor for compression\n                if (entry.val ~= 1); error('unsuported predictor value'); end\n            case 320         % color map\n                IMG.cmap           = entry.val;\n                IMG.colors         = entry.cnt/3;\n            case 339\n                TIF.SampleFormat   = entry.val;\n            case 33628       %metamorph specific data\n                IMG.MM_private1    = entry.val;\n            case 33629       %this tag identify the image as a Metamorph stack!\n                TIF.MM_stack       = entry.val;\n                TIF.MM_stackCnt    = entry.cnt;\n                if ( img_last > img_first )\n                    if exist('funUpdateWaitBar', 'var') && isa(funUpdateWaitBar, 'function_handle')\n                        funUpdateWaitBar(0);\n                    elseif exist('waitbar_handle', 'var')\n                        waitbar_handle = waitbar(0,'Please wait...','Name',['Reading ' filename]);\n                    end;\n                end\n            case 33630       %metamorph stack data: wavelength\n                TIF.MM_wavelength  = entry.val;\n            case 33631       %metamorph stack data: gain/background?\n                TIF.MM_private2    = entry.val;\n            otherwise\n%                 fprintf(1,'ignored TIFF entry with tag %i (cnt %i)\\n', TIF.entry_tag, entry.cnt);\n        end\n        % move to next IFD entry in the file\n        fseek(TIF.file, file_pos+12,-1);\n    end\n    \n    %Planar configuration is not fully supported\n    %Per tiff spec 6.0 PlanarConfiguration irrelevent if SamplesPerPixel==1\n    %Contributed by Stephen Lang\n    if ((TIF.SamplesPerPixel ~= 1) && (TIF.PlanarConfiguration == 1))\n        error('PlanarConfiguration = %i not supported', TIF.PlanarConfiguration);\n    end\n    \n    %total number of bytes per image:\n    PlaneBytesCnt = IMG.width * IMG.height * TIF.BytesPerSample;\n    \n    if consolidateStrips\n        %Try to consolidate the strips into a single one to speed-up reading:\n        BytesCnt = TIF.StripByteCounts(1);\n        \n        if BytesCnt < PlaneBytesCnt\n            \n            ConsolidateCnt = 1;\n            %Count how many Strip are needed to produce a plane\n            while TIF.StripOffsets(1) + BytesCnt == TIF.StripOffsets(ConsolidateCnt+1)\n                ConsolidateCnt = ConsolidateCnt + 1;\n                BytesCnt = BytesCnt + TIF.StripByteCounts(ConsolidateCnt);\n                if ( BytesCnt >= PlaneBytesCnt ); break; end\n            end\n            \n            %Consolidate the Strips\n            if ( BytesCnt <= PlaneBytesCnt(1) ) && ( ConsolidateCnt > 1 )\n                %fprintf(1,'Consolidating %i stripes out of %i', ConsolidateCnt, TIF.StripNumber);\n                TIF.StripByteCounts = [BytesCnt; TIF.StripByteCounts(ConsolidateCnt+1:TIF.StripNumber ) ];\n                TIF.StripOffsets = TIF.StripOffsets( [1 , ConsolidateCnt+1:TIF.StripNumber] );\n                TIF.StripNumber  = 1 + TIF.StripNumber - ConsolidateCnt;\n            end\n        end\n    end\n    \n    %read the next IFD address:\n    ifd_pos = fread(TIF.file, 1, 'uint32', TIF.BOS);\n    %if (ifd_pos) disp(['next ifd at', num2str(ifd_pos)]); end\n    \n    if isfield( TIF, 'MM_stack' )\n        \n        if ( img_last > TIF.MM_stackCnt )\n            img_last = TIF.MM_stackCnt;\n        end\n        \n        %this loop is to read metamorph stacks:\n        for ii = img_first:img_last\n            \n            TIF.StripCnt = 1;\n            \n            %read the image\n            fileOffset = PlaneBytesCnt * ( ii - 1 );\n            %fileOffset = 0;\n            %fileOffset = ftell(TIF.file) - TIF.StripOffsets(1);\n            \n            if ( TIF.SamplesPerPixel == 1 )\n                IMG.data  = read_plane(fileOffset, IMG.width, IMG.height, 1);\n            else\n                IMG.red   = read_plane(fileOffset, IMG.width, IMG.height, 1);\n                IMG.green = read_plane(fileOffset, IMG.width, IMG.height, 2);\n                IMG.blue  = read_plane(fileOffset, IMG.width, IMG.height, 3);\n            end\n            \n            if exist('funUpdateWaitBar', 'var') && isa(funUpdateWaitBar, 'function_handle')\n                funUpdateWaitBar(img_read/TIF.MM_stackCnt);\n            elseif exist('waitbar_handle', 'var')\n                waitbar( img_read/TIF.MM_stackCnt, waitbar_handle);\n            end;\n            \n            [ IMG.info, IMG.MM_stack, IMG.MM_wavelength, IMG.MM_private2 ] = extractMetamorphData(ii);\n            \n            img_read = img_read + 1;\n            stack( img_read ) = IMG;\n            \n        end\n        break;\n        \n    else\n        \n        %this part to read a normal TIFF stack:\n        \n        if ( img_skip + 1 >= img_first )\n            \n            TIF.StripCnt = 1;\n            %read the image\n            if ( TIF.SamplesPerPixel == 1 )\n                IMG.data  = read_plane(0, IMG.width, IMG.height, 1);\n            else\n                IMG.red   = read_plane(0, IMG.width, IMG.height, 1);\n                IMG.green = read_plane(0, IMG.width, IMG.height, 2);\n                IMG.blue  = read_plane(0, IMG.width, IMG.height, 3);\n            end\n            \n            % 2010-04-30::hluetck\n            % modify to also store file description if field info exists\n            if isfield(TIF,'info')\n                IMG.descriptor = TIF.info;\n            end\n            \n            img_read = img_read + 1;\n            \n            if exist('funUpdateWaitBar', 'var') && isa(funUpdateWaitBar, 'function_handle')\n                funUpdateWaitBar(img_read/TIF.nFrames);\n            elseif exist('waitbar_handle', 'var')\n                waitbar( img_read/TIF.nFrames, waitbar_handle);\n            end;\n            \n            try\n                stack( img_read ) = IMG;\n            catch\n                %stack\n                %IMG\n                error('The file contains dissimilar images: you can only read them one by one');\n            end\n        else\n            img_skip = img_skip + 1;\n        end\n        \n        if ( img_skip + img_read >= img_last )\n            break;\n        end\n    end\nend\n\n\n\n\n%clean-up\nfclose(TIF.file);\nif exist('funUpdateWaitBar', 'var') && isa(funUpdateWaitBar, 'function_handle')\n    funUpdateWaitBar(1);\nelseif exist('waitbar_handle', 'var')\n    delete( waitbar_handle );\n    clear waitbar_handle;\nend\ndrawnow;\n%return empty array if nothing was read\nif ~ exist( 'stack', 'var')\n    stack = [];\nend\nreturn;\n\n\n%============================================================================\n\nfunction plane = read_plane(offset, width, height, planeCnt)\n\nglobal TIF;\n\n%return an empty array if the sample format has zero bits\nif ( TIF.BitsPerSample(planeCnt) == 0 )\n    plane=[];\n    return;\nend\n\n%fprintf(1,'reading plane %i size %i %i\\n', planeCnt, width, height);\n\n%determine the type needed to store the pixel values:\nswitch( TIF.SampleFormat )\n    case 1\n        classname = sprintf('uint%i', TIF.BitsPerSample(planeCnt));\n    case 2\n        classname = sprintf('int%i', TIF.BitsPerSample(planeCnt));\n    case 3\n        if ( TIF.BitsPerSample(planeCnt) == 32 )\n            classname = 'single';\n        else\n            classname = 'double';\n        end\n    otherwise\n        error('unsuported TIFF sample format %i', TIF.SampleFormat);\nend\n\n% Preallocate a matrix to hold the sample data:\nplane = zeros(width, height, classname);\n\n% Read the strips and concatenate them:\nline = 1;\nwhile ( TIF.StripCnt <= TIF.StripNumber )\n    \n    strip = read_strip(offset, width, planeCnt, TIF.StripCnt, classname);\n    TIF.StripCnt = TIF.StripCnt + 1;\n    \n    % copy the strip onto the data\n    plane(:, line:(line+size(strip,2)-1)) = strip;\n    \n    line = line + size(strip,2);\n    if ( line > height )\n        break;\n    end\n    \nend\n\n% Extract valid part of data if needed\nif ~all(size(plane) == [width height]),\n    plane = plane(1:width, 1:height);\n    error('Cropping data: more bytes read than needed...');\nend\n\n% transpose the image (otherwise display is rotated in matlab)\nplane = plane';\n\nreturn;\n\n\n%=================== sub-functions to read a strip ===================\n\nfunction strip = read_strip(offset, width, planeCnt, stripCnt, classname)\n\nglobal TIF;\n\n%fprintf(1,'reading strip at position %i\\n',TIF.StripOffsets(stripCnt) + offset);\nStripLength = TIF.StripByteCounts(stripCnt) ./ TIF.BytesPerSample(planeCnt);\n\n%fprintf(1, 'reading strip %i\\n', stripCnt);\nfseek(TIF.file, TIF.StripOffsets(stripCnt) + offset, 'bof');\nbytes = fread( TIF.file, StripLength, classname, TIF.BOS );\n\nif ( length(bytes) ~= StripLength )\n    error('End of file reached unexpectedly.');\nend\n\nstrip = reshape(bytes, width, StripLength / width);\n\nreturn;\n\n\n%===================sub-functions that reads an IFD entry:===================\n\n\nfunction [nbBytes, matlabType] = convertType(tiffType)\nswitch (tiffType)\n    case 1\n        nbBytes=1;\n        matlabType='uint8';\n    case 2\n        nbBytes=1;\n        matlabType='uchar';\n    case 3\n        nbBytes=2;\n        matlabType='uint16';\n    case 4\n        nbBytes=4;\n        matlabType='uint32';\n    case 5\n        nbBytes=8;\n        matlabType='uint32';\n    case 11\n        nbBytes=4;\n        matlabType='float32';\n    case 12\n        nbBytes=8;\n        matlabType='float64';\n    otherwise\n        error('tiff type %i not supported', tiffType)\nend\nreturn;\n\n%===================sub-functions that reads an IFD entry:===================\n\nfunction  entry = readIFDentry()\n\nglobal TIF;\nentry.tiffType = fread(TIF.file, 1, 'uint16', TIF.BOS);\nentry.cnt      = fread(TIF.file, 1, 'uint32', TIF.BOS);\n%disp(['tiffType =', num2str(entry.tiffType),', cnt = ',num2str(entry.cnt)]);\n\n[ entry.nbBytes, entry.matlabType ] = convertType(entry.tiffType);\n\nif entry.nbBytes * entry.cnt > 4\n    %next field contains an offset:\n    offset = fread(TIF.file, 1, 'uint32', TIF.BOS);\n    %disp(strcat('offset = ', num2str(offset)));\n    fseek(TIF.file, offset, -1);\nend\n\nif TIF.entry_tag == 33629   %special metamorph 'rationals'\n    entry.val = fread(TIF.file, 6*entry.cnt, entry.matlabType, TIF.BOS);\nelse\n    if entry.tiffType == 5\n        entry.val = fread(TIF.file, 2*entry.cnt, entry.matlabType, TIF.BOS);\n    else\n        entry.val = fread(TIF.file, entry.cnt, entry.matlabType, TIF.BOS);\n    end\nend\nif ( entry.tiffType == 2 ); entry.val = char(entry.val'); end\n\nreturn;\n\n\n%==============distribute the metamorph infos to each frame:\nfunction [info, stack, wavelength, private2 ] = extractMetamorphData(imgCnt)\n\nglobal TIF;\n\ninfo = [];\nstack = [];\nwavelength = [];\nprivate2 = [];\n\nif TIF.MM_stackCnt == 1\n    return;\nend\n\nleft  = imgCnt - 1;\n\nif isfield( TIF, 'info' )\n    S = length(TIF.info) / TIF.MM_stackCnt;\n    info = TIF.info(S*left+1:S*left+S);\nend\n\nif isfield( TIF, 'MM_stack' )\n    S = length(TIF.MM_stack) / TIF.MM_stackCnt;\n    stack = TIF.MM_stack(S*left+1:S*left+S);\nend\n\nif isfield( TIF, 'MM_wavelength' )\n    S = length(TIF.MM_wavelength) / TIF.MM_stackCnt;\n    wavelength = TIF.MM_wavelength(S*left+1:S*left+S);\nend\n\nif isfield( TIF, 'MM_private2' )\n    S = length(TIF.MM_private2) / TIF.MM_stackCnt;\n    private2 = TIF.MM_private2(S*left+1:S*left+S);\nend\n\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "spikeTimeAnalyzer.m", "ext": ".m", "path": "OCIA-master/utils/spike/spikeTimeAnalyzer.m", "size": 5911, "source_encoding": "utf_8", "md5": "40a546c18226484564e906b605c8c857", "text": "function varargout = spikeTimeAnalyzer( s, psthT, evokedT, method, doPlot )\n% inputs (all compulsory):\n% s ... cell array of spike times per trial\n% psthTime ... time bins relative to stim onset (=0)\n% evokedT ... time to count as evoked; used for counting evoked spikes; either n x 2 matrix with\n% different tStart and tStop windows or a number; in the latter case evokedT is expanded to [0 evokedT]\n% method ... method for ifr calculation: 'barsP', 'turbotrend', 'turbotrendboot', 'InstantFR', 'none'\n% outputs (all optional):\n% count ... n x 2 matrix with base (column 1) and evoked (column 2) spike counts per trial (n),\n% normalized to the respective time interval\n% ifr ... 4 x t matrix with mean instantaneous firing rate (row 1), SD of IFR (row 2) and lower (row\n% 3) / upper CI (row 4); depending on the method, not all statistics are computed (if not computed,\n% row is NaN)\n% spike raster ... n x t matrix of spike counts per psth bin\n\n% written by Henry Luetcke (hluetck@gmail.com)\n\n%% Parameters\nlambda = 0.5; % lambda parameter for turbotrend\nsamples = 1000; % samples parameter for bootstrap estimation techniques\ndoShadedEbar = 1;\nerrorStats = 'sd'; % 'sd' or 'ci' ('ci' is 95% CI)\n\nrate = 1./(psthT(2)-psthT(1));\nif numel(evokedT) == 1\n   evokedT = [0 evokedT];\nend\n\n%% Assemble raster and count spikes\nspikeRaster = zeros(numel(s),length(psthT));\ncount = zeros(numel(s),size(evokedT,1)+1);\nif doPlot\n   hFig = figure('Name','Raster Plot Spike Times','NumberTitle','off');\n   subplot(2,1,1)\nend\nfor n = 1:length(s)\n    if ~isempty(s{n}) && doPlot\n        plot(s{n},repmat(n,1,numel(s{n})),'o','MarkerEdgeColor','none','MarkerFaceColor','k'), hold on\n    end\n    spikeTimes = s{n};\n    baseSpikes = numel(find(spikeTimes>=psthT(1)&spikeTimes<0));\n    count(n,1) = baseSpikes;\n    for m = 1:size(evokedT,1)\n        evokedSpikes = numel(find(spikeTimes>=evokedT(m,1)&spikeTimes<=evokedT(m,2)));\n        count(n,m+1) = evokedSpikes;\n    end\n    for m = 1:numel(spikeTimes)\n        [~,idx] = min(abs(psthT-spikeTimes(m)));\n        spikeRaster(n,idx) = spikeRaster(n,idx) + 1;\n    end\nend\nif doPlot, set(gca,'xlim',[psthT(1) psthT(end)]), ylabel('Trials'), end\n\n%% Compute IFR\nifr = nan(4,numel(psthT));\nswitch lower(method)\n    case 'barsp'\n        % smooth histogram using bayesian adaptive regression splines\n        bp = defaultParams;\n        bp.use_logspline = 0;\n        f = barsP(sum(spikeRaster,1),[psthT(1) psthT(end)],numel(s),bp);\n        if isfield(f,'mean')\n            ifr = f.mean';\n            lowerConf = ifr-f.confBands(:,1)';\n            upperConf = f.confBands(:,2)'-ifr;\n            ifr(2,:) = f.sd';\n            ifr(3:4,:) = [upperConf; lowerConf];\n            ifr = ifr ./ length(s).*rate; % not sure if this is legitimate (statistically)\n        else\n            ifr(1,:) = sum(spikeRaster,1)./length(s).*rate;\n        end\n    case 'turbotrend'\n        firingRate = sum(spikeRaster,1)./length(s).*rate;\n        [~, ~, yfit] = turbotrend(psthT, firingRate, lambda, numel(psthT));\n        ifr(1,:) = yfit';\n    case 'turbotrendboot'\n         ifr = doTurboTrendBoot(samples,spikeRaster,numel(s),rate,...\n            lambda,psthT);\n    case 'instantfr'\n        ifr = doInstantFR(s,psthT,samples);\n    case 'none'\n        ifr(1,:) = sum(spikeRaster,1)./length(s).*rate;\n    otherwise\n        error('Method %s not implemented (yet)!',method)\nend\n\n%% Plot raster\npsth = sum(spikeRaster,1)./length(s).*rate;\n% sparseness of psth\npsthKurtosis = kurtosis(psth);\nif doPlot\n    subplot(2,1,2)\n    stairs(psthT,psth,'k'), hold on\n    if doShadedEbar\n        switch lower(errorStats)\n            case 'sd'\n                if ~all(isnan(ifr(2,:)))\n                    shadedErrorBar(psthT,ifr(1,:),ifr(2,:),'r',1);\n                    ylabel('Firing rate +- SD')\n                else\n                    plot(psthT,ifr(1,:),'r');\n                    ylabel('Firing rate')\n                end\n            case 'ci'\n                if ~all(isnan(ifr(3,:))) && ~all(isnan(ifr(4,:)))\n                    shadedErrorBar(psthT,ifr(1,:),ifr(3:4,:),'r',1);\n                    ylabel('Firing rate +- 95% CI')\n                else\n                    plot(psthT,ifr(1,:),'r');\n                    ylabel('Firing rate')\n                end\n        end\n    else\n        plot(psthT,ifr(1,:),'r');\n        ylabel('Firing rate')\n    end\n    \n    ylims = get(gca,'ylim');\n    set(gca,'xlim',[psthT(1) psthT(end)])\n    hLegend = legend({sprintf('Observed (k=%1.3f)',psthKurtosis),sprintf('Fit (%s)',method)});\n    set(hLegend,'box','off','location','best')\nend\n\n%% Define outputs\nvarargout{1} = count;\nvarargout{2} = ifr;\nvarargout{3} = spikeRaster;\nvarargout{4} = psthKurtosis;\nend\n\n\n%% Function - doTurboTrendBoot\nfunction ifr = doTurboTrendBoot(samples,spikeCount,trials,rate,lambda,psthT)\nn = numel(psthT);\n% bootstrap indices for row replacement\n[~,ix] = bootstrp(samples,[],zeros(1,trials));\nyFit = zeros(samples,length(psthT));\nfor i = 1:samples\n    count = spikeCount(ix(:,i),:);\n    firingRate = sum(count,1)./trials.*rate;\n    [~, ~, yFit(i,:)] = turbotrend(psthT, firingRate, lambda, n);\nend\nifr = zeros(4,size(yFit,2));\nifr(1,:) = nanmean(yFit,1);\nifr(2,:) = nanstd(yFit,1);\nifr(3:4,:) = bootci(samples,@mean,yFit);\nend\n\n\n%% Function - doInstantFR\nfunction ifr = doInstantFR(s,psthT,samples)\n% try two different approaches to compute the IFR per trial, but number of spikes usually way to low\n% to achieve robust results\n\nifrTrials = zeros(numel(s),numel(psthT));\nfor n = 1:numel(s)\n    %     ifrTrials(n,:) = instantfr(s{n},psthT);\n    try\n        spikeTimes = s{n};\n        spikeTimes = spikeTimes - psthT(1);\n        [tt, rate] = BayesRR(spikeTimes);\n        tt = tt + psthT(1);\n        ifrTrials(n,:) = interp1(tt,rate,psthT);\n    end\nend\n\nifr = zeros(4,length(psthT));\nifr(1,:) = nanmean(ifrTrials,1);\nifr(2,:) = nanstd(ifrTrials,1);\nifr(3:4,:) = bootci(samples,@nanmean,ifrTrials);\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "concatanateSegments.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/concatanateSegments.m", "size": 1464, "source_encoding": "utf_8", "md5": "768b37514426be26a3cc94efe0113aa4", "text": "%dirname='F:\\data tetrodes\\th_8\\th_8_2014_10_28_a';\n\nfunction f= concatanateSegments(dirname)\nlist = dir(dirname);\ni=3;\noldExpName='Experiment';\nwhiskingAll = [];\nmkdir(fullfile(dirname,'whiskByTrial'));\ncount = 0;\nfor i=3:length(list)\n    fname = list(i).name;\n   \n    ind=strfind(fname,'_Whisker_Tracking');\n    if(~isempty(ind))\n\n        expname = fname(1:ind);\n        \n        load(fullfile(dirname,fname));\n        \n        if (strcmp(oldExpName,expname))           \n            whiskingAll = [whiskingAll; MovieInfo.AvgWhiskerAngle'];\n        else\n            if ~isempty(whiskingAll)\n                count = count+1;\n                save(fullfile(dirname,'whiskByTrial',[oldExpName 't' num2str(count) '.mat']),'whiskingAll');\n                whiskingAll = [];\n            end\n          \n            whiskingAll = [whiskingAll; MovieInfo.AvgWhiskerAngle'];\n        end\n     oldExpName = expname; \n    \n    \n    end\nend\n \ncount=count+1;\nsave(fullfile(dirname,'whiskByTrial',[oldExpName 't' num2str(count) '.mat']),'whiskingAll');\n      \n\n%%\nsubdirname=fullfile(dirname,'whiskByTrial');\nsublist = dir(subdirname);\n\nload(fullfile(subdirname,sublist(3).name));\n[nr nc] = size(whiskingAll);\n\nwhiskAngle = nan(nr+5,numel(sublist)-2);\n\nfor i=3:numel(sublist)\n    load(fullfile(subdirname,sublist(i).name));\n    whiskAngle(1:length(whiskingAll),i-2)=whiskingAll;\n\nend\n\nsave(fullfile(dirname, 'whiskAngle.mat'),'whiskAngle');\nxlswrite('whiskAngle',whiskAngle);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_keyboard_shortcuts.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_keyboard_shortcuts.m", "size": 887, "source_encoding": "utf_8", "md5": "80026debb0b351752a721eab3a1cdda9", "text": "% WT_SHOW_KEYBOARD_SHORTCUTS\n% Show keyboard shortcuts.\n\nfunction wt_keyboard_shortcuts\n\ncShortcuts = { ...\n        'Ctr+P                          Open parameters window' ...\n        'Ctr+D                          Dump current view to printer, file or clipboard' ...\n        'Ctr+S                          Save data' ...\n        'Ctr+N                          Load next movie in list' ...\n        'Ctr+H                          Hide/unhide image' ...\n        'Ctr+M                          Play movie from current frame' ...\n        'SPACE                Stop/start tracking' ...\n        'Any number         Go back N frames' ...\n        '0 (zero)                          Go forward one frame' ...\n        'Ctr+G                          Open Go-to-frame dialog' ...\n        'Ctr+E                          Edit notes' ...\n};\n\nmsgbox(cShortcuts, 'WT Keyboard Shortcuts')\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_find_nose.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_find_nose.m", "size": 932, "source_encoding": "utf_8", "md5": "975914d45d66953aedf8d6b290311c4d", "text": "% WT_FIND_NOSE Finds the nose in image, given eye positions.\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nfunction vNose = wt_find_nose(vRightEye, vLeftEye, nAxLen)\n\nnMidPnt = [mean([vRightEye(:,1) vLeftEye(:,1)],2) mean([vRightEye(:,2) vLeftEye(:,2)],2)]; % eye midpoint\nvRrelM = vRightEye - nMidPnt;\nvAngRrelX = rad2deg(atan(vRrelM(:,2)./vRrelM(:,1)));\nvAngNrelX = deg2rad(90 + vAngRrelX);\nvNrelX = [nAxLen*cos(vAngNrelX) nAxLen*sin(vAngNrelX)];\nvNose = nMidPnt + vNrelX;\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_change_whisker_width.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_change_whisker_width.m", "size": 507, "source_encoding": "utf_8", "md5": "42e2a89228861c394eea10f3855e1ff8", "text": "%%%% WT_CHANGE_WHISKER_WIDTH %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Open dialog window and allow user to select width of displayed whiskers\n% in pixels.\n\nfunction wt_change_whisker_width\n\nglobal g_tWT\n\nsAns = inputdlg('Set whisker width in pixels', 'WT');\n\n% Exceptions\nif isempty(sAns), return; end\nif g_tWT.WhiskerWidth < 1, g_tWT.WhiskerWidth = 1; end\nif g_tWT.WhiskerWidth > 50, g_tWT.WhiskerWidth = 50; end\n\ng_tWT.WhiskerWidth = str2num(sAns{1});\n\n% Refresh display\nwt_display_frame\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_reset_all.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_reset_all.m", "size": 1679, "source_encoding": "utf_8", "md5": "1226ca71091a23a6f1f8303cb5f55ec0", "text": "% WT_RESET_ALL\n% Reset all movie and tracking parameters\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nfunction wt_reset_all\n\n% Issue warning\nsAnswer =  questdlg('This action will delete all tracked whiskers, head-movements and associated parameters. Continue?', ...\n        'Title', 'Yes', 'No', 'No');\nif strcmp(sAnswer, 'No'), return; end\n\nglobal g_tWT\n\ng_tWT.MovieInfo.Roi = [1 1 g_tWT.MovieInfo.Width-1 g_tWT.MovieInfo.Height-1];\ng_tWT.MovieInfo.Rot = 0;\n%g_tWT.MovieInfo.ResizeFactor = 1;\ng_tWT.MovieInfo.Flip = [0 0];\ng_tWT.MovieInfo.SplinePoints = [];\ng_tWT.MovieInfo.WhiskerSide = [];\ng_tWT.MovieInfo.Angle = [];\ng_tWT.MovieInfo.PositionOffset = [];\ng_tWT.MovieInfo.Intersect = [];\ng_tWT.MovieInfo.RefLine = [0 1; 0 2];\ng_tWT.MovieInfo.RightEye = [];\ng_tWT.MovieInfo.LeftEye = [];\ng_tWT.MovieInfo.Nose = [];\ng_tWT.MovieInfo.StimulusA = [];\ng_tWT.MovieInfo.StimulusB = [];\ng_tWT.MovieInfo.EyeNoseAxLen = [];\ng_tWT.MovieInfo.ImCropSize = [g_tWT.MovieInfo.Width g_tWT.MovieInfo.Height];\ng_tWT.MovieInfo.Curvature = [];\ng_tWT.MovieInfo.ObjectRadPos = [];\ng_tWT.MovieInfo.WhiskerIdentity = {};\ng_tWT.MovieInfo.LastFrame = [];\ng_tWT.MovieInfo.CalBarLength = [];\ng_tWT.MovieInfo.CalibCoords = [0 0;0 0];\n\nwt_display_frame\n\nreturn"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_clean_splines.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_clean_splines.m", "size": 7905, "source_encoding": "utf_8", "md5": "2e17634243b0ca52ef2135a4c7ca49a8", "text": "% WT_CLEAN_SPLINES\n%\n% Clean splinepoints by removing movements of certain amplitude and\n% duration. Also included is an optional low-pass filter (mainly intended\n% to filter head-movements before tracking whiskers). Note that all\n% changes made are permanent\n%\n% Syntax: wt_clean_splines(W, OPT), where\n%           W is the index of the whisker to be cleaned\n%           OPT is an optional string command only used by the function\n%           internally.\n%\n% If you call the function directly, you never need to give the OPT\n% parameter. This function will only work when the WT GUI is launched, and\n% then only on the currently loaded movie.\n\nfunction wt_clean_splines(w, varargin)\n\nglobal g_tWT\n\npersistent nWd;\npersistent nAmp;\npersistent nLp;\nif nargin == 3, nLp = varargin{2}; % use passed low-filt value when saving changes\nelse, nLp = 0; % default low-pass (0 means no low-pass)\nend\n\nif isempty(nWd)\n    nWd = 2; % default pulse-width\n    nAmp = 2; % default pulse-amplitude\nend\nmOrigSplPoints = [];\n\n% Check if we should execute one of the sub-routines straight away...\nif nargin >= 2\n    switch varargin{1}\n        case 'setwidth'\n            nWd = str2num(char(inputdlg(sprintf('Set maximum jitter-width (in frames). Save changes from the menu after reviewing the changes.\\n'), 'Set width')));\n        case 'setamplitude'\n            nAmp = str2num(char(inputdlg(sprintf('Set maximum jitter-amplitude. Save changes from the menu after reviewing the changes.\\n'), 'Set amplitude')));\n        case 'lowpass'\n            sLowPass = 'Set low-pass frequency. Save changes from menu after reviewing the changes.\\n';\n            nLp = str2num(char(inputdlg(sLowPass, 'Set low-pass frequency')));\n        case 'savechanges'\n            SaveModifiedSplines(w, nWd, nLp, nAmp);\n            return;\n    end\nend\n\ncTitles = [];\nif w == 0\n    % Clean head-movements\n    mOrigSplPoints = [g_tWT.MovieInfo.RightEye g_tWT.MovieInfo.LeftEye];\n    cTitles = [{'Right-eye X'}, {'Right-eye Y'}, {'Left-eye X'}, {'Left-eye Y'}];\nelse\n    mOrigSplPoints = [];\n    \n    for s = 1:size(g_tWT.MovieInfo.SplinePoints, 1)\n        for nDim = 1:2\n            if (s == 1) & (nDim == 1), continue, end\n            vData = reshape(g_tWT.MovieInfo.SplinePoints(s,nDim,:,w), size(g_tWT.MovieInfo.SplinePoints, 3), 1);\n            mOrigSplPoints = [mOrigSplPoints vData];\n            if nDim == 2, sDim = 'Hor';, else sDim = 'Rad';, end;\n            cTitles{size(cTitles,2)+1} = sprintf('Point# %d - %s', s, sDim);\n        end\n    end\nend\n\n% Plot original splines\nPlotModifiedSplines(mOrigSplPoints, cTitles, nWd, nLp, nAmp, w); % start with default pulse-width = 2\n\nreturn;\n\n\n%%%% PLOT_MODIFIED_SPLINES %%%%\nfunction PlotModifiedSplines(mSplPoints, cTitles, nWd, nLp, nAmp, w)\nglobal g_tWT\nfigure(333); clf;\nset(gcf, 'DoubleBuffer', 'on', 'Menu', 'None', 'Name', 'WT Clean', 'NumberTitle', 'off')\n\n% Plot splineas in different sub-panes\nfor s = 1:size(mSplPoints,2)\n    subplot(size(mSplPoints,2), 1, s); hold on\n    vXlim = [0 size(mSplPoints,1)];\n    vYlim = [min(mSplPoints(:,s))-5 max(mSplPoints(:,s))+5];\n\n    % Figure title\n    if s == 1\n        title(sprintf('Original trace is red, new trace in blue. Width=%d, Amplitude=%d Low-pass=%d', nWd, nAmp, nLp))\n    end\n    \n    % Plot original trace\n    vY = mSplPoints(:,s);\n    vY(find(vY == 0)) = NaN;\n    plot(1:size(mSplPoints,1), vY, 'r')\n\n    % Plot cleaned trace\n    mSlpPointsCleaned = CleanTrace(mSplPoints(:,s), nWd, nLp, nAmp);\n    plot(1:size(mSplPoints,1), mSlpPointsCleaned, 'b')\n\n    % Set axes properties\n    set(gca, 'xlim', vXlim, 'ylim', vYlim, 'FontSize', 8, ...\n        'xtick', vXlim(1):250:vXlim(2), ...\n        'ytick', [] )\n    ylabel('Pixel')\n    if s == size(mSplPoints,2), xlabel('Frame#'), end\n    title(cTitles{s})\nend\n\n% Create figure menu\nuimenu(gcf, 'Label', 'Width', 'Callback', [sprintf('wt_clean_splines(%d, ''setwidth'')', w)]);\nuimenu(gcf, 'Label', 'Amplitude', 'Callback', [sprintf('wt_clean_splines(%d, ''setamplitude'')', w)]);\nuimenu(gcf, 'Label', 'Low-pass', 'Callback', [sprintf('wt_clean_splines(%d, ''lowpass'')', w)]);\nuimenu(gcf, 'Label', 'Save changes', 'Callback', [sprintf('wt_clean_splines(%d, ''savechanges'', %d); close(%d); figure(%d)', w, nLp, gcf, findobj('Tag', 'WTMainWindow') )]);\nuimenu(gcf, 'Label', 'Exit', 'Callback', [sprintf('close(%d)', gcf)]);\n\nreturn;\n\n\n%%%% SAVE_MODIFIED_SPLINES %%%%\nfunction SaveModifiedSplines(w, nWd, nLp, nAmp)\nglobal g_tWT\n% Give warning to user\nswitch questdlg('These changes are irreversible! Are you should you want to save these changes?', 'Warning', 'Yes', 'No', 'No')\n    case 'No'\n        return;\n    case 'Yes'\n        if w == 0\n            g_tWT.MovieInfo.RightEye(:,1) = CleanTrace(g_tWT.MovieInfo.RightEye(:,1), nWd, nLp, nAmp);\n            g_tWT.MovieInfo.RightEye(:,2) = CleanTrace(g_tWT.MovieInfo.RightEye(:,2), nWd, nLp, nAmp);\n            g_tWT.MovieInfo.LeftEye(:,1)  = CleanTrace(g_tWT.MovieInfo.LeftEye(:,1), nWd, nLp, nAmp);\n            g_tWT.MovieInfo.LeftEye(:,2)  = CleanTrace(g_tWT.MovieInfo.LeftEye(:,2), nWd, nLp, nAmp);\n            g_tWT.MovieInfo.Nose = wt_find_nose(g_tWT.MovieInfo.RightEye, g_tWT.MovieInfo.LeftEye, g_tWT.MovieInfo.EyeNoseAxLen);\n        else\n            %%% new\n            mOrigSplPoints = [];\n            for s = 1:size(g_tWT.MovieInfo.SplinePoints, 1)\n                for nDim = 1:2\n                    if (s == 1) & (nDim == 1), continue, end\n                    vData = squeeze(g_tWT.MovieInfo.SplinePoints(s,nDim,:,w));\n                    g_tWT.MovieInfo.SplinePoints(s,nDim,:,w) = ...\n                        CleanTrace(vData, nWd, nLp, nAmp);                    \n                end\n            end\n            \n            % Replace NaN's with zeros\n            g_tWT.MovieInfo.SplinePoints(find(isnan(g_tWT.MovieInfo.SplinePoints))) = 0;\n            \n            %%%\n                       \n%            mOrigSplPoints = [ ...\n%                    reshape(g_tWT.MovieInfo.SplinePoints(1,2,:,w), size(g_tWT.MovieInfo.SplinePoints, 3), 1) ...\n%                    (reshape(g_tWT.MovieInfo.SplinePoints(2,2,:,w), size(g_tWT.MovieInfo.SplinePoints, 3), 1)) ...\n%                    (reshape(g_tWT.MovieInfo.SplinePoints(3,2,:,w), size(g_tWT.MovieInfo.SplinePoints, 3), 1)) ...\n%                    (reshape(g_tWT.MovieInfo.SplinePoints(2,1,:,w), size(g_tWT.MovieInfo.SplinePoints, 3), 1)) ];\n%            % Y coordinates\n%            for s = 1:3\n%                mSlpPointsCleaned = CleanTrace(mOrigSplPoints(:,s), nWd, nLp, nAmp);\n%                g_tWT.MovieInfo.SplinePoints(s, 2, : ,w) = mSlpPointsCleaned;\n%            end\n%            % X coordinate for mid spline-point\n%            mSlpPointsCleaned = CleanTrace(mOrigSplPoints(:,4), nWd, nLp, nAmp);\n%            g_tWT.MovieInfo.SplinePoints(2, 1, : ,w) = mSlpPointsCleaned;\n            \n            g_tWT.MovieInfo.Angle(:,w) = zeros(size(g_tWT.MovieInfo.Angle(:,w))); % clear angle\n            \n        end\nend\nwt_set_status(sprintf('Saved trace %d with width = %d and low-pass = %d', w, nWd, nLp, nAmp))\n\nreturn;\n\n\n%%%% CLEAN_TRACE %%%%\nfunction vNew = CleanTrace(vOld, nMaxWidth, nLowPass, nMaxAmp)\nglobal g_tWT\nvNew = vOld;\n% Iterate over range of widths and amplitudes, and clean out detected jitter\nfor w = 1:nMaxWidth\n    for a = 1:nMaxAmp\n        vTempl = [0 ones(1,w) 0] * a;\n        for i = 1:length(vNew)-(w+1)\n            vec = vNew(i:i+length(vTempl)-1)';\n            if sum(abs(vTempl - abs((vec-vec(1))))) == 0\n                vNew(i:i+length(vTempl)-1) = vNew(i);\n            end\n        end\n    end\nend\n% Low-pass filter\nvNew(find(vNew == 0)) = NaN;\nif nLowPass > 0\n    vIn = vNew;\n    [a,b] = butter(3, nLowPass/(g_tWT.MovieInfo.FramesPerSecond/2), 'low');\n    vFiltSrsIndx = find(~isnan(vIn));\n    vFiltSeries = vIn(vFiltSrsIndx);\n    vOut = zeros(size(vIn))*NaN;\n    vOut(vFiltSrsIndx) = filtfilt(a, b, vFiltSeries);\n    vNew = vOut;\nend\n\nreturn;\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_graphs.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_graphs.m", "size": 37590, "source_encoding": "utf_8", "md5": "e8c936d149d617165a414402eb0796d8", "text": "function wt_graphs( varargin )\n% WT_GRAPHS\n% Organize and create graphs\n%\n% Usage:\n%   wt_graphs\n%   wt_graphs(CMD)\n%   wt_graphs(VAR, TGL)\n%\n% Inputs:\n%   CMD     close\n%           refresh\n%   VAR     angle\n%           curvature\n%   TGL     Toggle parameter ON/OFF\n%           \n\nif length(varargin) == 2\n    varargin{1}\n    varargin{2}\nend\n\nglobal g_tWT\n\npersistent p_vRefLine;\npersistent p_vPlotWhichWhiskers;\npersistent p_cActivePlots;\npersistent p_nAngleDelta;\n\n% Determine which plots to show\nif nargin ~= 2\n    % Reset all parameters (e.g. when menu is closed)\n    if nargin == 1\n        switch lower(varargin{1})\n            case 'close'\n                p_vPlotWhichWhiskers = [];\n                p_cActivePlots = [];\n                delete(findobj('Name', 'WT Plots'))\n                return;\n            case 'refresh'\n                p_vRefLine = [];\n                wt_graphs;\n        end\n    end\n    % Activate menu, or create new if it does not exist\n    if findobj('Tag', 'plotprefs'), figure(findobj('Tag', 'plotprefs'));\n    else, CreateMenu; end    \nelse\n    %%% PLOT GRAPHS\n    % If there are two input arguments, it means either that\n    %   1 - a graph should be turned ON/OFF, or\n    %   2 - if the first argument is a number that a whisker should be turned ON/OFF\n    nFS = g_tWT.MovieInfo.FramesPerSecond;\n    if nargin == 2 | nargin == 0\n        sPlotWhat = varargin{1}; % type of plot\n        nShow = varargin{2}; % if plot should be turned 1=ON or 0=OFF\n        if ~isempty(str2num(sPlotWhat))\n            sPlotWhat = str2num(sPlotWhat);\n            if nShow\n                if isempty(p_vPlotWhichWhiskers), p_vPlotWhichWhiskers = sPlotWhat;\n                else p_vPlotWhichWhiskers(end+1) = sPlotWhat; end\n            else\n                if isempty(p_vPlotWhichWhiskers), return; % we shouldn't get here...\n                else % Remove whisker from list\n                    p_vPlotWhichWhiskers = p_vPlotWhichWhiskers(find(p_vPlotWhichWhiskers~=sPlotWhat));\n                end\n            end\n        else\n            % Create list of plots to show\n            if nShow % turn ON\n                if isempty(p_cActivePlots), p_cActivePlots = {sPlotWhat};\n                else p_cActivePlots{end+1} = sPlotWhat; end\n            else % turn OFF\n                if isempty(p_cActivePlots), return; % we shouldn't get here...\n                else % Remove plot from list\n                    vKeep = [];\n                    for p = 1:size(p_cActivePlots,2)\n                        if ~strcmp(char(p_cActivePlots{p}), sPlotWhat)\n                            vKeep = [vKeep p];\n                        end\n                    end\n                    p_cActivePlots = {p_cActivePlots{[vKeep]}};\n                end\n            end\n        end\n    else, wt_error('Mistake in input argument list'); return; end\n\n    % Don't open window if there are no head movements or options selected\n    if islogical(p_vPlotWhichWhiskers~=0) & (isempty(p_vPlotWhichWhiskers) | isempty(p_cActivePlots))\n        return\n    end\n    \n    % Open figure window if not already opened\n    if isempty(findobj('Name', 'WT Plots'))\n        figure;\n        set(gcf, 'NumberTitle', 'off', ...\n            'Name', 'WT Plots', ...\n            'Doublebuffer', 'on', ...\n            'MenuBar', 'figure', ...\n            'Renderer', 'painters', ...\n            'BackingStore', 'on' );\n        CreatePushButtons;\n    else figure(findobj('Name', 'WT Plots')); CreatePushButtons; end\n    \n    % Determine which figure to plot, calculate their midlines and\n    % respective positions in the current subplot.\n    %  - work from bottom of figure and up...\n    % keep structure that contains 1) type of plot, 2) mid-line, 3) height\n    % (2 and 3 is for normalization of the trace and the calibration bars)\n    % We keep track of plot-type by name and number (for sorting)\n    tPlots = struct('order',{{'Triggers', 'Position from object', 'Base Translation', 'Curvature', 'Angle', 'Velocity', 'Acceleration'}});\n    tPlots.proportion = [ ...\n            .1 ...  % triggers\n            .15 ...  % positon from object\n            .15 ...  % Base Translation\n            .15 ...  % curvature\n            .15 ...  % angle\n            .15 ...  % velocity\n            .15 ...  % acceleration\n        ];\n    tPlots.midline = zeros(1, size(tPlots.order, 2));\n    tPlots.height  = zeros(1, size(tPlots.order, 2));\n    % Units of plots and calibration bars\n    tPlots.calib   = [...\n            NaN, ... % ON/OFF\n            2, ...  % Position from object, mm or pix\n            1, ...  % Base Translation, mm or pix\n            NaN, ...  % Curvature, mm or pix\n            5, ...  % Degrees angle\n            500, ...   %  Velocity, deg/sec\n            250 ...   % Acceleration, deg/sec^2\n        ];\n    nCurrHeightUsed = 0; % Available height of subplot already used\n    for p = 1:size(tPlots.order, 2)\n        % Check if plot should be included\n        if sum(strcmp(p_cActivePlots, char(tPlots.order(p)))) >= 1\n            tPlots.midline(p) = nCurrHeightUsed + tPlots.proportion(p)/2;\n            tPlots.height(p)  = tPlots.proportion(p);\n            nCurrHeightUsed = nCurrHeightUsed + tPlots.proportion(p);\n        end\n    end\n\n    % Expand all traces to occupy full height of subplot (ie. normalize to height of 1)    \n    tPlots.midline = tPlots.midline ./ nCurrHeightUsed;\n    tPlots.height  = tPlots.height  ./ nCurrHeightUsed;\n\n    % Number of extra panels (e.g. for triggered averages\n    if sum( [strcmp(p_cActivePlots, 'Avg cycle (trig A)') strcmp(p_cActivePlots, 'Avg cycle (trig B)')])\n        bExtraPanel = 1;\n    else, bExtraPanel = 0; end\n    \n    %  - delete existing traces if reference angle or AngleDelta has\n    %    changed (also happens when user clicks Refresh)\n    if ~isequal(p_vRefLine, g_tWT.MovieInfo.RefLine) | ~isequal(p_nAngleDelta, g_tWT.MovieInfo.AngleDelta)\n        g_tWT.MovieInfo.Angle = [];\n        g_tWT.MovieInfo.Intersect = [];\n        g_tWT.MovieInfo.PositionOffset = [];\n        g_tWT.MovieInfo.Curvature = [];\n        p_vRefLine = g_tWT.MovieInfo.RefLine;\n        p_nAngleDelta = g_tWT.MovieInfo.AngleDelta;\n    end\n    \n    % Iterate over whiskers\n    for w = 1:length(p_vPlotWhichWhiskers)\n        % All plots are normalized according to their proportionate height\n        % to the subplot (see above)\n        if bExtraPanel\n            nFrameStart = 1+5*(w-1);\n            nFrameEnd = nFrameStart+3;\n            hLeftPanel = subplot(length(p_vPlotWhichWhiskers), 4+bExtraPanel, [nFrameStart:nFrameEnd]); hold on\n            hRightPanel = subplot(length(p_vPlotWhichWhiskers), 4+bExtraPanel, nFrameEnd+1); hold on\n            nRightPanelLength = 0;\n        else\n            hLeftPanel = subplot(length(p_vPlotWhichWhiskers), 1, w); hold on\n        end\n        \n        % Plot head movements\n        if p_vPlotWhichWhiskers(w) == 0\n            vN = FilterSeries(g_tWT.MovieInfo.Nose(:,2));\n            [vN, sUnit] = wt_pix_2_mm(vN ./ max(vN));\n            PlotTrace(vN, vN, hLeftPanel, 'nose', 0.5, 'Head Y Pos', nFS)\n            hold on\n            nCurrentFrame = get(g_tWT.Handles.hSlider, 'Value');\n            hFrameMarker = plot([nCurrentFrame nCurrentFrame].*(1000/nFS), [-.5 1.5], 'r:');\n            set(hFrameMarker, 'Tag', 'framemarker')\n            set(hLeftPanel, 'ylim', [0 1], ...\n                'ytick', [], ...\n                'xlim', [1 nMovDur], ...\n                'box', 'on', ...\n                'FontSize', 8, ...\n                'Tag', 'tracesplot', ...\n                'ButtonDownFcn', [sprintf('v=get(gca,''CurrentPoint'');wt_display_frame(round(v(1,1)/%d))', 1000/nFS)] );\n            axes(hLeftPanel);\n            title(sprintf('%s, %d frames/sec, LoPass=%dHz', ....\n                g_tWT.MovieInfo.Filename, ...\n                g_tWT.MovieInfo.FramesPerSecond, ...\n                str2num(get(findobj('tag','lowpass'), 'String')) ));\n            xlabel('Time (ms)');\n            % Create black bar bar to the left of the plot\n            vPosition = get(hLeftPanel, 'Position');\n            hBlob = uicontrol(gcf, 'Style', 'frame', ...\n                'Units', 'Normalized', ...\n                'Position', [vPosition(1)-0.075 vPosition(2) 0.025 vPosition(4)], ...\n                'ForegroundColor', [0 0 0], 'BackgroundColor', [0 0 0] );\n            continue;\n        end\n\n        % Find missing frames\n        vSplFrames = find(sum(sum(g_tWT.MovieInfo.SplinePoints(:,:,:,p_vPlotWhichWhiskers(w)))) > 0); % tracked frames\n        try\n            vAngFrames = find(g_tWT.MovieInfo.Angle(:,p_vPlotWhichWhiskers(w)) ~= 0 ); % frames with computed angle\n            vMissingFrames = setdiff(vSplFrames, vAngFrames); % tracked frames without angle\n        catch, vMissingFrames = vSplFrames; end\n\n        %  - compute\n        if ~isempty(vMissingFrames)\n            mSplinePoints = g_tWT.MovieInfo.SplinePoints(:, :, vMissingFrames, p_vPlotWhichWhiskers(w));\n            % angle\n            [g_tWT.MovieInfo.Angle(vMissingFrames, p_vPlotWhichWhiskers(w)) g_tWT.MovieInfo.Intersect(vMissingFrames,1:2,p_vPlotWhichWhiskers(w))] = ...\n                wt_get_angle(mSplinePoints, g_tWT.MovieInfo.RefLine, g_tWT.MovieInfo.AngleDelta);\n        end\n\n        % Plot angle\n        nAngleIndx = find(strcmp(tPlots.order, 'Angle'));\n        vAngle = g_tWT.MovieInfo.Angle(:, p_vPlotWhichWhiskers(w));\n        vAngleFilt = zeros(size(vAngle))*NaN;\n        vAngle(find(vAngle==0)) = NaN;\n        vAngleFilt = FilterSeries(vAngle, 'angle');\n        if tPlots.height(nAngleIndx) ~= 0\n            [vAngle, nNormFact] = NormalizeToHeight(vAngleFilt, tPlots.midline(nAngleIndx), tPlots.height(nAngleIndx));\n            PlotTrace(vAngle, vAngleFilt, hLeftPanel, 'angle', tPlots.midline(nAngleIndx), 'Angle', nFS);\n            DrawCalibrationBar(nNormFact, tPlots.calib(nAngleIndx), vAngle, sprintf('%.1f deg', tPlots.calib(nAngleIndx)), nFS)\n            PlotMaxDwellTime(vAngleFilt, vAngle, nNormFact, 'deg', nFS);\n            %  - plot average angle\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig A)')))\n                nLen = PlotTraceWithErrorBars(vAngle, vAngleFilt, g_tWT.MovieInfo.StimulusA, hRightPanel, 'angle-average-stimA');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig B)')))\n                nLen = PlotTraceWithErrorBars(vAngle, vAngleFilt, g_tWT.MovieInfo.StimulusB, hRightPanel, 'angle-average-stimB');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n        end\n        \n        % Plot Base Translation (intersect)\n        nIntersectIndx = find(strcmp(tPlots.order, 'Base Translation'));\n        if tPlots.height(nIntersectIndx) ~= 0\n            [vIntersect, sUnit] = wt_pix_2_mm(g_tWT.MovieInfo.Intersect(:, 2, p_vPlotWhichWhiskers(w)));\n            vIntersectFilt = FilterSeries(vIntersect);\n            [vIntersect, nNormFact] = NormalizeToHeight(vIntersectFilt, tPlots.midline(nIntersectIndx), tPlots.height(nIntersectIndx));\n            PlotTrace(vIntersect, vIntersectFilt, hLeftPanel, 'intersect', tPlots.midline(nIntersectIndx), 'Inters', nFS)\n            DrawCalibrationBar(nNormFact, tPlots.calib(nIntersectIndx), vIntersect, sprintf('%d %s', tPlots.calib(nIntersectIndx), sUnit), nFS)\n            PlotMaxDwellTime(vIntersectFilt, vIntersect, nNormFact, sprintf('%s', sUnit), nFS);\n            %  - plot mean intersect\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig A)')))\n                nLen = PlotTraceWithErrorBars(vIntersect, vIntersectFilt, g_tWT.MovieInfo.StimulusA, hRightPanel, 'intersect-average-stimA');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig B)')))\n                nLen = PlotTraceWithErrorBars(vIntersect, vIntersectFilt, g_tWT.MovieInfo.StimulusB, hRightPanel, 'intersect-average-stimB');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n        end\n\n        % Plot whisker's position from object (Y position relative to object)\n        nPosIndx = find(strcmp(tPlots.order, 'Position from object'));\n        bBreak = 0;\n        if tPlots.height(nPosIndx) ~= 0\n            mWhisker = g_tWT.MovieInfo.SplinePoints(:,:,:, p_vPlotWhichWhiskers(w));\n            % Head-movements are tracked\n            try, vObj = g_tWT.MovieInfo.ObjectRadPos(p_vPlotWhichWhiskers(w),:,:);\n            catch\n                warndlg('No object has been marked.');\n                bBreak = 1;\n            end\n            \n            if ~bBreak\n                try\n                    mRightEye = g_tWT.MovieInfo.RightEye;\n                    mLeftEye = g_tWT.MovieInfo.LeftEye;\n                    mNose = g_tWT.MovieInfo.Nose;\n                    nWhiskerSide = g_tWT.MovieInfo.WhiskerSide(p_vPlotWhichWhiskers(w));\n                    vPosFromObj = wt_get_position_offset(mWhisker, ...\n                        vObj, ...\n                        g_tWT.MovieInfo.ImCropSize, ...\n                        g_tWT.MovieInfo.RadExt, ...\n                        g_tWT.MovieInfo.HorExt, ...\n                        mRightEye, ...\n                        mLeftEye, ...\n                        mNose, ...\n                        nWhiskerSide );\n                catch\n                    vPosFromObj = wt_get_position_offset(mWhisker, ...\n                        vObj, ...\n                        g_tWT.MovieInfo.ImCropSize, ...\n                        g_tWT.MovieInfo.RadExt, ...\n                        g_tWT.MovieInfo.HorExt );\n                end\n                g_tWT.MovieInfo.PositionOffset(1:length(vPosFromObj), p_vPlotWhichWhiskers(w)) = vPosFromObj;\n                g_tWT.MovieInfo.PositionOffset(find(g_tWT.MovieInfo.PositionOffset==0)) = NaN;\n\n                [vPosFromObj, sUnit] = wt_pix_2_mm(g_tWT.MovieInfo.PositionOffset(:, p_vPlotWhichWhiskers(w)));\n\n                vPosFromObjFilt = FilterSeries(vPosFromObj);\n                [vPosFromObj, nNormFact] = NormalizeToHeight(vPosFromObjFilt, tPlots.midline(nPosIndx), tPlots.height(nPosIndx));\n                PlotTrace(vPosFromObj, vPosFromObjFilt, hLeftPanel, 'position-offset', tPlots.midline(nPosIndx), 'Pos', nFS)\n                DrawCalibrationBar(nNormFact, tPlots.calib(nPosIndx), vPosFromObj, sprintf('%.f %s', tPlots.calib(nPosIndx), sUnit), nFS)\n                PlotMaxDwellTime(vPosFromObjFilt, vPosFromObj, nNormFact, sprintf('%s', sUnit), nFS, 0);\n\n                %  - plot average position\n                if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig A)')))\n                    nLen = PlotTraceWithErrorBars(vPosFromObj, vPosFromObjFilt, g_tWT.MovieInfo.StimulusA, hRightPanel, 'PositionFromPbject-average-stimA');\n                    nRightPanelLength = max([nRightPanelLength nLen]);\n                end\n                if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig B)')))\n                    nLen = PlotTraceWithErrorBars(vPosFromObj, vPosFromObjFilt, g_tWT.MovieInfo.StimulusB, hRightPanel, 'PositionFromPbject-average-stimB');\n                    nRightPanelLength = max([nRightPanelLength nLen]);\n                end\n            end\n        end\n\n        % Plot velocity\n        nVelocityIndx = find(strcmp(tPlots.order, 'Velocity'));\n        if tPlots.height(nVelocityIndx) ~= 0\n            vVelocityFilt = [NaN; diff(vAngleFilt)*g_tWT.MovieInfo.FramesPerSecond];\n            [vVelocity, nNormFact] = NormalizeToHeight(vVelocityFilt, tPlots.midline(nVelocityIndx), tPlots.height(nVelocityIndx));            \n            PlotTrace(vVelocity, vVelocityFilt, hLeftPanel, 'velocity', tPlots.midline(nVelocityIndx), 'Vel', nFS)\n            DrawCalibrationBar(nNormFact, tPlots.calib(nVelocityIndx), vVelocity, sprintf('%d deg/sec', tPlots.calib(nVelocityIndx)), nFS)\n            PlotMaxDwellTime(vVelocityFilt, vVelocity, nNormFact, '', nFS, 0);\n            % Average velocity across cycles\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig A)')))\n                nLen = PlotTraceWithErrorBars(vVelocity, vVelocityFilt, g_tWT.MovieInfo.StimulusA, hRightPanel, 'velocity-average-stimB');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig B)')))\n                nLen = PlotTraceWithErrorBars(vVelocity, vVelocityFilt, g_tWT.MovieInfo.StimulusB, hRightPanel, 'velocity-average-stimB');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n        end\n        \n        % Plot acceleration\n        nAccelerationIndx = find(strcmp(tPlots.order, 'Acceleration'));\n        if tPlots.height(nAccelerationIndx) ~= 0\n            vAccelerationFilt = [NaN; NaN; diff(diff(vAngleFilt))*g_tWT.MovieInfo.FramesPerSecond];\n            [vAcceleration, nNormFact] = NormalizeToHeight(vAccelerationFilt, tPlots.midline(nAccelerationIndx), tPlots.height(nAccelerationIndx));            \n            PlotTrace(vAcceleration, vAccelerationFilt, hLeftPanel, 'acceleration', tPlots.midline(nAccelerationIndx), 'Acc', nFS)\n            DrawCalibrationBar(nNormFact, tPlots.calib(nAccelerationIndx), vAcceleration, sprintf('%d deg/sec^2', tPlots.calib(nAccelerationIndx)), nFS)\n            PlotMaxDwellTime(vAccelerationFilt, vAcceleration, nNormFact, '', nFS, 0);\n            % Average velocity across cycles\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig A)')))\n                nLen = PlotTraceWithErrorBars(vAcceleration, vAccelerationFilt, g_tWT.MovieInfo.StimulusA, hRightPanel, 'acceleration-average-stimA');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig B)')))\n                nLen = PlotTraceWithErrorBars(vAcceleration, vAccelerationFilt, g_tWT.MovieInfo.StimulusB, hRightPanel, 'acceleration-average-stimB');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n        end \n        \n        % Plot curvature\n        nCurvatureIndx = find(strcmp(tPlots.order, 'Curvature'));\n        if tPlots.height(nCurvatureIndx) ~= 0\n\n            % Find missing frames\n            vSplFrames = find(sum(sum(g_tWT.MovieInfo.SplinePoints(:,:,:,p_vPlotWhichWhiskers(w)))) > 0); % tracked frames\n            try\n                vCurvFrames = find(g_tWT.MovieInfo.Curvature(:,p_vPlotWhichWhiskers(w)) ~= 0 ); % frames with computed curv\n                vMissingFrames = setdiff(vSplFrames, vCurvFrames); % tracked frames without curv\n            catch, vMissingFrames = vSplFrames; end\n\n            if ~isempty(vMissingFrames)\n                mSplinePoints = g_tWT.MovieInfo.SplinePoints(:, :, vMissingFrames, p_vPlotWhichWhiskers(w));\n                if g_tWT.MovieInfo.AngleDelta == 0\n                    g_tWT.MovieInfo.Curvature(vMissingFrames, p_vPlotWhichWhiskers(w)) = wt_get_curv_at_base(mSplinePoints);\n                else\n                    g_tWT.MovieInfo.Curvature(vMissingFrames, p_vPlotWhichWhiskers(w)) = wt_get_curvature(mSplinePoints);\n                end\n                g_tWT.MovieInfo.Curvature([1:vMissingFrames(1) vMissingFrames(end):end], p_vPlotWhichWhiskers(w)) = NaN;\n            end\n\n            \n            %[vCurvature, sUnit] = wt_pix_2_mm(g_tWT.MovieInfo.Curvature(:, p_vPlotWhichWhiskers(w)));\n            vCurvature = g_tWT.MovieInfo.Curvature(:, p_vPlotWhichWhiskers(w));\n            sUnit = 'pix';\n            if isfield(g_tWT.MovieInfo, 'PixelsPerMM')\n                if ~isempty(g_tWT.MovieInfo.PixelsPerMM)\n                    vCurvature = vCurvature .* g_tWT.MovieInfo.PixelsPerMM;\n                    sUnit = 'mm';\n                end\n            end\n            vCurvatureFilt = FilterSeries(vCurvature);\n            [vCurvature, nNormFact] = NormalizeToHeight(vCurvatureFilt, tPlots.midline(nCurvatureIndx), tPlots.height(nCurvatureIndx));\n            PlotTrace(vCurvature, vCurvatureFilt, hLeftPanel, 'curvature', tPlots.midline(nCurvatureIndx), 'Curv', nFS)\n            DrawCalibrationBar(nNormFact, tPlots.calib(nCurvatureIndx), vCurvature, sprintf('%.2f s', tPlots.calib(nCurvatureIndx), sUnit), nFS)\n            PlotMaxDwellTime(vCurvatureFilt, vCurvature, nNormFact, sprintf('%s', sUnit), nFS);\n            %  - plot average curvature\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig A)')))\n                nLen = PlotTraceWithErrorBars(vCurvature, vCurvatureFilt, g_tWT.MovieInfo.StimulusA, hRightPanel, 'curvature-average-stimA');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n            if ~isempty(find(strcmp(p_cActivePlots, 'Avg cycle (trig B)')))\n                nLen = PlotTraceWithErrorBars(vCurvature, vCurvatureFilt, g_tWT.MovieInfo.StimulusB, hRightPanel, 'curvature-average-stimB');\n                nRightPanelLength = max([nRightPanelLength nLen]);\n            end\n        end\n\n        % Plot stimulus\n        nTrigIndx = find(strcmp(tPlots.order, 'Triggers'));\n        if tPlots.height(nTrigIndx) ~= 0\n            vStimA = g_tWT.MovieInfo.StimulusA;\n            vStimB = g_tWT.MovieInfo.StimulusB;\n            vStimA(find(~vStimA)) = NaN;\n            vStimB(find(~vStimB)) = NaN;\n            % Normalize\n            vStimA = vStimA / max(vStimA) * tPlots.height(nTrigIndx);\n            vStimA = vStimA + tPlots.midline(nTrigIndx)-tPlots.height(nTrigIndx)/2;\n            vStimB = vStimB / max(vStimB) * tPlots.height(nTrigIndx);\n            vStimB = vStimB + tPlots.midline(nTrigIndx)-tPlots.height(nTrigIndx)/2;\n            subplot(hLeftPanel); plot((1:length(vStimA) .*(1000/nFS) ), vStimA/3, 'r', 'LineWidth', 2)\n            subplot(hLeftPanel); plot((1:length(vStimA) .*(1000/nFS) ), vStimB/1.5, 'g', 'LineWidth', 2)\n        end\n        hold on\n        nCurrentFrame = get(g_tWT.Handles.hSlider, 'Value');\n        hFrameMarker = plot([nCurrentFrame nCurrentFrame].*(1000/nFS), [-.5 1.5], 'r:');\n        set(hFrameMarker, 'Tag', 'framemarker')\n\n        % Set plot properties\n        nMovDur = g_tWT.MovieInfo.NumFrames * (1000/g_tWT.MovieInfo.FramesPerSecond); % msec\n        set(hLeftPanel, 'ylim', [0 1], ...\n            'ytick', [], ...\n            'xlim', [1 nMovDur], ...\n            'box', 'on', ...\n            'FontSize', 8, ...\n            'Tag', 'tracesplot', ...\n            'ButtonDownFcn', [sprintf('v=get(gca,''CurrentPoint'');wt_display_frame(round(v(1,1)/%d))', 1000/nFS)] );\n        axes(hLeftPanel);\n        title(sprintf('%s, %d frames/sec, LoPass=%dHz', ....\n            g_tWT.MovieInfo.Filename, ...\n            g_tWT.MovieInfo.FramesPerSecond, ...\n            str2num(get(findobj('tag','lowpass'), 'String')) ), 'interpreter','none');\n        xlabel('Time (ms)');\n        if bExtraPanel\n            if nRightPanelLength <= 0, nRightPanelLength = 100; end\n            set(hRightPanel, 'ylim', [0 1], ...\n                'ytick', [], ...\n                'xlim', [1 nRightPanelLength], ...\n                'box', 'on', ...\n                'FontSize', 8 );\n            axes(hRightPanel);\n            title('Cycle average');\n            xlabel('Time (msec)');\n        end\n        % Create colored bar left to plots that indicate whisker identity\n        vPosition = get(hLeftPanel, 'Position');\n        hBlob = uicontrol(gcf, 'Style', 'frame', ...\n            'Units', 'Normalized', ...\n            'Position', [vPosition(1)-0.075 vPosition(2) 0.025 vPosition(4)], ...\n            'ForegroundColor', g_tWT.Colors(p_vPlotWhichWhiskers(w),:), ...\n            'BackgroundColor', g_tWT.Colors(p_vPlotWhichWhiskers(w),:));\n    end    \nend\nreturn;\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction PrintHeader( sHeader , hWindow , nYpos )\nuicontrol(hWindow, 'Style', 'frame', 'Position', [1 nYpos 150 20], ...\n    'BackgroundColor', [.4 .4 .4] );\nuicontrol(hWindow, 'Style', 'text', 'Position', [5 nYpos+2 145 14], ...\n    'String', sHeader, ...\n    'HorizontalAlignment', 'center', ...\n    'foregroundcolor', [.9 .9 .9], ...\n    'BackgroundColor', [.4 .4 .4], ...\n    'FontWeight', 'bold' );\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Normalize time-series to fit within its dedicated vertical space in\n% subplot.\nfunction [vSeriesOut, nNormFact] = NormalizeToHeight(vSeriesIn, nMidline, nHeight);\nif length(find(~isnan(vSeriesIn))) == 1\n    vSeriesOut = vSeriesIn / max(vSeriesIn);\n    nNormFact = max(vSeriesOut) ./ max(vSeriesIn);\nelse\n    vSeriesOut = vSeriesIn - min(vSeriesIn);\n    vSeriesOut = vSeriesOut / max(vSeriesOut) * nHeight;\n    vSeriesOut = vSeriesOut + nMidline - nHeight/2;\n    nHeightOrig = abs(diff([max(vSeriesIn) min(vSeriesIn)]));\n    nHeightNew = abs(diff([max(vSeriesOut) min(vSeriesOut)]));\n    nNormFact = nHeightNew ./ nHeightOrig;\nend\nreturn\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Low-pass filter timeseries according to GUI settings.\nfunction vSeriesOut = FilterSeries(vSeriesIn,varargin)\nglobal g_tWT\nnLowPassFreq = str2num(get(findobj('tag','lowpass'), 'String'));\nif ~isempty(nLowPassFreq) & ~(nLowPassFreq <= 0)\n    [a,b] = butter(3, nLowPassFreq/(g_tWT.MovieInfo.FramesPerSecond/2), 'low');\n    vFiltSrsIndx = find(~isnan(vSeriesIn));\n    vFiltSeries = vSeriesIn(vFiltSrsIndx);\n    vSeriesOut = zeros(size(vSeriesIn))*NaN;\n    vSeriesOut(vFiltSrsIndx) = filtfilt(a, b, vFiltSeries);\nelse, vSeriesOut = vSeriesIn; end\nvSeriesOut(find(vSeriesOut==0)) = NaN;\nreturn;\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Calculate cycle average of time-series\nfunction [vCycleAvg, vCycleError] = GetCycleAverage(vSeriesIn, vStim);\nvOnsets = find(diff(vStim)==1)+1; % find all onset moments\nnMinSize = median(diff(vOnsets)) / 2;  % remove segments with durations smaller than half of the median duration\ntry\n    vOnsets(find(diff(vOnsets)< nMinSize)) = [];\ncatch\n    wt_error('An error occurred when computing average cycles. Do you have fewer than 2 cycles?')\nend\n\n% Drop cycles that overlap with non-tracked frames\nvDropIndx = find(vOnsets >= length(vSeriesIn));\nif ~isempty(vDropIndx), vOnsets((vDropIndx(1)-1):vDropIndx(end)) = []; end\n\n% Drop cycles not in the requested cycle range\nnFromCycle = str2num(get(findobj('Tag', 'fromcycle'), 'string'));\nif nFromCycle < 1, nFromCycle = 1; end\nnToCycle = str2num(get(findobj('Tag', 'tocycle'), 'string'));\nif nToCycle > length(vOnsets), nToCycle = length(vOnsets); end\n\n% Create all segments to use\nmSegments = [];\nnIndx = 1;\nnOnsets = length(vOnsets);\nfor s = 1:nOnsets\n    if s < nFromCycle | s > nToCycle, continue, end\n    if s == nOnsets, vNewSeg = vSeriesIn(vOnsets(s):end);\n    else, vNewSeg = vSeriesIn(vOnsets(s):vOnsets(s+1)-1); end\n    if length(vNewSeg) > size(mSegments,1)\n        % zeropad matrix that holds segments if new segment is longer than\n        % all previous segments\n        padarray(mSegments, length(vNewSeg)-size(mSegments,1), 0, 'post');\n    end\n    if isempty(mSegments), mSegments = vNewSeg;\n    else, mSegments(1:length(vNewSeg),nIndx) = vNewSeg; end\n    nIndx = nIndx + 1;\nend\n% Calculate mean of all segments\nvCycleAvg = mean(mSegments,2);\n% Calculate standard-error of all segments\nvCycleError = std(mSegments, 0, 2) / sqrt(size(mSegments,2));\nreturn\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Draw calibration bar in subplot\nfunction DrawCalibrationBar(nNormFact, nCalibSize, vSeries, sCalibString, nFS)\nglobal g_tWT\nif isempty(vSeries), return; end\nnCalibLineWidth = 6;\nnCalibHeight = nCalibSize * nNormFact;\nnXMax = g_tWT.MovieInfo.NumFrames * (1000/nFS);\nplot([nXMax-nCalibLineWidth nXMax-nCalibLineWidth] .* (1000/nFS), [min(vSeries) min(vSeries)+nCalibHeight], 'k', 'LineWidth', nCalibLineWidth);\nhTxt = text(nXMax+25, min(vSeries)+nCalibHeight/2, sCalibString);\nset(hTxt, 'Rotation', -90, 'HorizontalAlignment', 'center', 'FontSize', 8);\nreturn\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Calculate max dwell-time location in time series\n% ex. PlotMaxDwellTime(vAngleFilt, vAngle, nNormFact, 'deg')\nfunction  nMaxDwellTime = PlotMaxDwellTime(vSeriesOrig, vSeriesPlot, nNormFact, sString, nFS, varargin)\nvBinCenters = min(vSeriesOrig):(max(vSeriesOrig)-min(vSeriesOrig))/100:max(vSeriesOrig);\n\n% If data length == 1\nif length(find(~isnan(vSeriesOrig))) < 2 | isempty(vBinCenters)\n    nMaxDwellTime = 1;\n    return\nend\n\nif ~isempty(varargin) % Plot line through a given value\n    nMaxDwellTime = varargin{1};\nelse\n    [vCount, vDwellTime] = hist(vSeriesOrig, vBinCenters);\n    nMaxDwellTime = vDwellTime(find(vCount==max(vCount)));\n    nMaxDwellTime = nMaxDwellTime(1);\nend\nnY = (nMaxDwellTime*nNormFact) - diff([min(vSeriesPlot) min(vSeriesOrig*nNormFact)]);\n\nhLine = plot([0 length(vSeriesOrig).*(1000/nFS)], [nY nY], 'g:', 'LineWidth', 2);\nif strcmp(sString, 'deg')\n    sLineString = sprintf('Y = %.5f %s)', nMaxDwellTime, sString);\n    hTxt = text(-2, nY, sprintf('%.1f %s', nMaxDwellTime, sString));\nelse\n    sLineString = sprintf('Y = %.5f %s', nMaxDwellTime, sString);\n    hTxt = text(-2, nY, sprintf('%.1f %s', nMaxDwellTime, sString));\nend\nset(hLine, 'buttondownfcn', sprintf('msgbox(''%s'')', sLineString));\nset(hTxt, 'FontSize', 6, 'HorizontalAlignment', 'right');\nreturn\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Create the menu window\nfunction CreateMenu\nglobal g_tWT\n\ncOptions = {'Angle', 'Velocity (dA)', 'Acceleration (ddA)', 'Curvature', 'Base Translation', 'Position from object', 'Triggers', 'Avg cycle (trig A)', 'Avg cycle (trig B)'};\n% Set text parameters\nnFontSize = 8;\nnLinSep = 10;\nnGuiElements = size(cOptions, 2) + size(g_tWT.MovieInfo.SplinePoints, 4) + 3; % options plus filter dialog\nif ~isempty(g_tWT.MovieInfo.EyeNoseAxLen)\n    nGuiElements = nGuiElements + 1;\nend\n% Open figure window\nvScrnSize = get(0, 'ScreenSize');\nnFigHeight = nGuiElements * (nFontSize*2 + nLinSep) + 60;\nnFigWidth = 150;\nvFigPos = [5 vScrnSize(4)-(nFigHeight+21) nFigWidth nFigHeight];\nhCurrWin = figure;\nset(hCurrWin, 'NumberTitle', 'off', ...\n    'Name', 'WT Menu', ...\n    'Position', vFigPos, ...\n    'MenuBar', 'none', ...\n    'Tag', 'plotprefs', ...\n    'CloseRequestFcn', 'wt_graphs(''close''); closereq;' )\n% Place text string and boxes inside window\nnCurrLine = nFigHeight;\nnCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\nPrintHeader('Select plots', hCurrWin, nCurrLine)\nfor o = 1:size(cOptions, 2)\n    nCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\n    uicontrol(hCurrWin, 'Style', 'checkbox', 'Position', [10 nCurrLine 150 20], ...\n        'Callback', 'wt_graphs(get(gcbo,''string''), get(gcbo,''value''))', ...\n        'String', char(cOptions(o)), ...\n        'HorizontalAlignment', 'left', ...\n        'BackgroundColor', [.8 .8 .8] );\nend\n% Filter options\nnCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\nPrintHeader('Low-pass', hCurrWin, nCurrLine)\nnCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\nuicontrol(hCurrWin, 'Style', 'edit', 'Position', [40 nCurrLine 40 20], ...\n    'HorizontalAlignment', 'center', ...\n    'Tag', 'lowpass' );\nuicontrol(hCurrWin, 'Style', 'text', 'Position', [80 nCurrLine-4 30 20], ...\n    'String', 'Hz', ...\n    'BackgroundColor', [.8 .8 .8], ...\n    'HorizontalAlignment', 'center' )\n% Whisker representations\nnCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\nPrintHeader('Select whiskers', hCurrWin, nCurrLine)\nfor w = 1:size(g_tWT.MovieInfo.SplinePoints, 4)\n    if isempty(g_tWT.MovieInfo.SplinePoints), continue, end\n    nCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\n    hBox = uicontrol(hCurrWin, 'Style', 'checkbox', 'Position', [10 nCurrLine 125 20], ...\n        'Callback', 'wt_graphs(get(gcbo, ''Tag''), get(gcbo, ''Value''))', ... % 0=OFF, 1=ON\n        'HorizontalAlignment', 'right', ...\n        'String', wt_whisker_id(w), ...\n        'FontWeight', 'bold', ...\n        'Tag', num2str(w), ...\n        'foregroundColor', 'w', ...\n        'BackgroundColor', g_tWT.Colors(w,:) );\nend\n% Checkbox for plotting head-movements\nif ~isempty(g_tWT.MovieInfo.EyeNoseAxLen)\n    nCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\n    uicontrol(hCurrWin, 'Style', 'checkbox', 'Position', [10 nCurrLine 125 20], ...\n        'Callback', 'wt_graphs(get(gcbo, ''Tag''), get(gcbo, ''Value''))', ... % 0=OFF, 1=ON\n        'String', 'Head', ...\n        'FontWeight', 'bold', ...\n        'Tag', '0', ...\n        'BackgroundColor', [0 0 0], ...\n        'ForegroundColor', [1 1 1] );\nend\n% Refresh pushbutton\nnCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\nuicontrol(hCurrWin, 'Style', 'pushbutton', 'Position', [10 nCurrLine 125 20], ...\n    'Callback', 'wt_graphs(''refresh''); wt_graphs(get(gcbo, ''Tag''), get(gcbo, ''Value''))', ... % 0=OFF, 1=ON\n    'String', 'Refresh', ...\n    'FontWeight', 'bold' );\nreturn;\n\n%%%%% PLOTTRACE %%%%\nfunction PlotTrace(vTrace, vUserData, hAxes, sIdent, vMidline, sString, nFS)\naxes(hAxes)\nvXX = (1:length(vTrace)) .* (1000/nFS);\nhTrace = plot(vXX, vTrace, 'k');\ncUserData = {sIdent, vUserData};\nhTraceMenu = uicontextmenu;\nset(hTrace, 'Tag', 'export-trace', 'UserData', cUserData, 'UIContextMenu', hTraceMenu)\nuimenu(hTraceMenu, 'Label', 'Copy to new figure', 'Callback', 'x=get(gco,''userdata'');figure;plot(x{2})');\nhTxt = text(-10, vMidline, sString);\nset(hTxt, 'Rotation', 90, 'HorizontalAlignment', 'center', 'FontSize', 8);\nreturn;\n\n%%%% PLOTTRACEWITHERRORBARS %%%%\nfunction nLen = PlotTraceWithErrorBars(vTrace, vUserData, vStim, hAxes, sIdent)\n% Compute mean and standard-error\n[vMean, vError] = GetCycleAverage(vTrace, vStim);\nnLen = length(vMean);\n[vUserData, vError2] = GetCycleAverage(vUserData, vStim);\n\nvColor = [.7 .7 .7];\naxes(hAxes)\nvMean = reshape(vMean, length(vMean), 1);\nvError = reshape(vError, length(vError), 1);\nvXt = (0:length(vError)-1)';\nvXb = flipud(vXt);\nvYt = vMean + vError;\nvYb = flipud(vMean - vError);\nfill([vXt;vXb], [vYt;vYb], vColor, 'EdgeColor', vColor)\nhTrace = plot(0:length(vMean)-1,vMean, 'k');\ncUserData = {sIdent, vUserData};\nhTraceMenu = uicontextmenu;\nset(hTrace, 'Tag', 'export-trace', 'UserData', cUserData, 'UIContextMenu', hTraceMenu)\nuimenu(hTraceMenu, 'Label', 'Copy to new figure', 'Callback', 'x=get(gco,''userdata'');figure;plot(x{2})');\nreturn;\n\n%%%% CREATEPUSHBUTTONS %%%%\nfunction CreatePushButtons\n\nhFromField = findobj('Tag', 'fromcycle');\nif isempty(hFromField), nFromCycle = 2;\nelse, nFromCycle = str2num(get(hFromField, 'string')); end\n\nhToField = findobj('Tag', 'tocycle');\nif isempty(hToField), nToCycle = 99;\nelse, nToCycle = str2num(get(hToField, 'string')); end\n\nclf\nuicontrol( 'Position', [5 5 20 20], ...\n    'Style', 'pushbutton', ...\n    'String', 'G', ...\n    'FontSize', 7, 'HorizontalAlignment', 'center', ...\n    'CallBack', @ToggleGrid );\nuicontrol( 'Position', [30 5 20 20], ...\n    'Style', 'pushbutton', ...\n    'String', 'M', ...\n    'FontSize', 7, 'HorizontalAlignment', 'center', ...\n    'CallBack', @ToggleMarkers );\nuicontrol( 'Position', [55 5 20 20], ...\n    'Style', 'pushbutton', ...\n    'String', 'E', ...\n    'FontSize', 7, 'HorizontalAlignment', 'center', ...\n    'CallBack', @ExportData );\nuicontrol( 'Position', [85 5 100 20], ...\n    'Style', 'text', ...\n    'String', 'Average cycles', ...\n    'FontSize', 7, ...\n    'HorizontalAlignment', 'left' );\nuicontrol( 'Position', [185 5 30 20], ...\n    'Style', 'edit', ...\n    'String', nFromCycle, ...\n    'Tag', 'fromcycle', ...\n    'FontSize', 7, 'HorizontalAlignment', 'center' );\nuicontrol( 'Position', [215 5 20 20], ...\n    'Style', 'text', ...\n    'String', 'to', ...\n    'FontSize', 7, 'HorizontalAlignment', 'center' );\nuicontrol( 'Position', [235 5 30 20], ...\n    'Style', 'edit', ...\n    'String', nToCycle, ...\n    'Tag', 'tocycle', ...\n    'FontSize', 7, 'HorizontalAlignment', 'center' );\nuicontrol( 'Position', [265 5 20 20], ...\n    'Style', 'pushbutton', ...\n    'String', '!', ...\n    'FontSize', 7, 'HorizontalAlignment', 'center', ...\n    'Callback', 'wt_graphs(''refresh''); wt_graphs(get(gcbo, ''Tag''), get(gcbo, ''Value''))'); % 0=OFF, 1=ON\ndrawnow;\nreturn;\n\n%%%% TOGGLEGRID %%%%\nfunction ToggleGrid(varargin)\nvhAxes = findobj(gcf, 'Type', 'axes'); % Get handles to all axes children\nfor i = 1:length(vhAxes)\n    if strcmp(get(vhAxes(i), 'xgrid'), 'off')\n        set(vhAxes(i), 'XGrid', 'on');\n    else\n        set(vhAxes(i), 'XGrid', 'off');\n    end\nend\nreturn;\n\n%%%% TOGGLEMARKERS ****\nfunction ToggleMarkers(varargin)\npersistent pMarkerStatus;\nif isempty(pMarkerStatus), pMarkerStatus=0; end\npMarkerStatus = ~pMarkerStatus; % toggle marker-status\nif pMarkerStatus, sMarker='.'; else, sMarker='none'; end\nvhLines = findobj(gcf, 'Type', 'line');\nset(vhLines, 'Marker', sprintf('%s',sMarker));\nreturn;\n\n%%%% EXPORTDATA %%%%\n% Export all traces to either a plain text-file or Excel\nfunction ExportData(varargin)\n% Get object handles for all line-elements that will be exported\nvhTraces = findobj('Tag', 'export-trace');\n% Create container that will hold all the data\ncData = cell({});\ncHeaders = cell({});\nfor h = 1:length(vhTraces)\n    cUserData = get(vhTraces(h), 'Userdata');\n    cData{h} = cUserData{2};\n    cHeaders{h} = cUserData{1};\nend\n% Query whether to export to Excel or text file\nsAns = questdlg('Export to Excel or text file?', 'Export', 'Excel', 'Text file', 'Excel');\nif isempty(sAns), return\nelse\n    if strcmp(sAns, 'Excel')\n        ExportToExcel(cHeaders, cData);\n    else\n        [sFilename, sPathname] = uiputfile('*.txt', 'Save data as');\n        mData = [];\n        for c = 1:size(cData,2)\n            mData(1:length(cData{c}),size(mData,2)+1) = cData{c};\n        end\n        mData = [(0:(size(mData,1))-1)' mData]; % insert framenumber in 1st column\n        dlmwrite([sPathname sFilename], mData, '\\t');\n    end\nend\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_wizard_ctrl.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_wizard_ctrl.m", "size": 10146, "source_encoding": "utf_8", "md5": "875f79eadaf0ebadb41e2143ca2195c3", "text": "function wt_wizard_ctrl(sName, sStateVector, sFuncVector)\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%          The code in this script was contributed by Aharon Sheer.\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nglobal g_tWT\n\npersistent nNextState  % The state that will be done if we press Perform\n\nnStateVectorSize = size(sStateVector);\nnStrings = nStateVectorSize(1);\n\nnFuncVectorSize = size(sFuncVector);\nif ( nStrings ~= nFuncVectorSize(1) )\n    error('Num Functions not same as num States');\nend\n\n%--------------------------------------------------------------------------\n% There are several types of states:\n%   1) the First State.  Here the Wizard can only perform the current\n%   state, or advance to the next state, or exit\n%\n%   2) the Last State.  Here the Wizard can only perform the current state,\n%   or go back to the previous state, or exit, or Finish\n%\n%   3) All intermediate states.  Here the Wizard can perform the current state,\n%   or go back to the previous state, or advance to the next state, or exit\n%\n%   4) Past the Last State.  Here the Wizard can only go back to the previous state, \n%   or exit, or Finish\n%--------------------------------------------------------------------------\nnFirstState = 1;\nnLastState = nStrings;\n\nnExitByX = 0; % When user hits \"X\" in upper right corner\n\n% forever loop\nwhile 1\n    \n%-------------------------------------\n% Are we at First State?\n%-------------------------------------\nif (isempty(nNextState) | (nNextState <= nFirstState) )\n    %-------------------------------------\n    % First State\n    %-------------------------------------\n    % at beginning\n    % action buttons - display only Perform and Exit\n    nPerform = 1;  % no BACK yet\n    nSkip = 2;\n    nExit = 3;\n    nNextState = nFirstState;  % step to first state\n    nSkipState = nNextState + 1;   \n    nAction = menu('Wizard', ...\n                    ['Perform -- ', sStateVector(nNextState, :)], ...\n                    ['Skip to ', sStateVector(nSkipState, :)],     ...\n                    'Exit');\n    if (nAction == nExitByX | nAction == nExit)\n        return;  %  user exited the menu with no choice, or chose Exit\n    elseif (nAction == nSkip)\n        % DO NOT DO THE ACTION\n        % Skip to next action unless already at end\n        if (nNextState < nLastState)\n            nNextState = nNextState + 1;\n        end\n    else\n        % DO THE ACTION\n        success = wt_do_action(nNextState, sFuncVector);\n        if (success)\n            nNextState = nNextState + 1;    % Step to Next State\n        end\n    end % end of processing actions\nelse\n    % no longer at beginning\n    nBackState = nNextState - 1;\n    \n    %-------------------------------------\n    % Are we at Intermediate State?\n    %-------------------------------------\n    if (nNextState < nLastState)\n        %-------------------------------------\n        % Intermediate State\n        %-------------------------------------\n        % display both Back and Perform and Skip\n        nSkipState = nNextState + 1;\n        nBack = 1;   % action buttons\n        nPerform = 2;\n        nSkip = 3;\n        nExit = 4;\n        nAction = menu('Wizard', ...\n                    'Back',                                     ...\n                    ['Perform -- ', sStateVector(nNextState, :)], ...\n                    ['Skip to ', sStateVector(nSkipState, :)],   ...\n                    'Exit');\n        if (nAction == nBack) \n            %return to previous state\n            nNextState = nBackState;\n            % DO NOT DO THE ACTION\n        elseif (nAction == nExitByX | nAction == nExit)\n            return;  %  user exited the menu with no choice, or chose Exit\n        elseif (nAction == nSkip)\n            % DO NOT DO THE ACTION\n            % Skip to next action unless already at end\n            nNextState = nSkipState;\n        else\n            % Perform\n            % DO THE ACTION\n            success = wt_do_action(nNextState, sFuncVector);\n            if (success & (nNextState < nLastState))\n                nNextState = nNextState + 1;    % Step to Next State\n            end\n        end  % end of processing actions\n    \n    %-------------------------------------\n    % Are we at Last State?\n    %-------------------------------------\n    elseif (nNextState == nLastState)\n        %-------------------------------------\n        % Last State\n        %-------------------------------------\n        % display both Back and Perform but not Skip, allow Finish\n        nBack = 1;   % action buttons\n        nPerform = 2;\n        nExit = 3;\n        nFinish = 4;\n        \n        nAction = menu('Wizard', ...\n                    'Back',                                     ...\n                    ['Perform -- ', sStateVector(nNextState, :)], ...\n                    'Exit',                                     ...\n                    'Finish'                                    ...\n                    );\n        if (nAction == nBack) \n            %return to previous state\n            nNextState = nBackState;\n            % DO NOT DO THE ACTION\n        elseif (nAction == nExitByX | nAction == nExit)\n            return;  %  user exited the menu with no choice, or chose Exit\n        elseif (nAction == nFinish)\n            nNextState = nFirstState;  % return to first state\n            return;  % user finished processing \n        else\n            % Perform\n            % DO THE ACTION\n            success = wt_do_action(nNextState, sFuncVector);\n            if (success)\n                nNextState = nNextState + 1;    % Step to State After Last State\n            end\n    \n        end  % end of processing actions\n\n    %-------------------------------------\n    % Are we at state After Last State?\n    %-------------------------------------\n    else (nNextState > nLastState)\n        %-------------------------------------\n        % State After Last State\n        %-------------------------------------\n        % display Back but not Skip or Perform, allow Finish\n        nBack = 1;   % action buttons\n        nFinish = 2;\n        \n        nAction = menu('Wizard', ...\n                    'Back',                                     ...\n                    'Finish'                                    ...\n                    );\n        if (nAction == nBack) \n            %return to previous state\n            nNextState = nBackState;\n            % DO NOT DO THE ACTION\n        elseif (nAction == nExitByX)\n            return;  %  user exited the menu with no choice\n        else (nAction == nFinish)\n            nNextState = nFirstState;  % return to first state\n            return;  % user finished processing \n        end;  % end of processing actions\n        \n    end  % end of all states except First State\n    \nend   %end of all states\n\nend     % end of while forever\n\n\n\n\n% DO THE ACTION \nfunction success = wt_do_action(nNextState, sFuncVector)\nsuccess = 1;    % assume success\nsActionName = deblank(sFuncVector(nNextState, :));\nif (strcmp(sActionName, ''))\n    success = 0;  % no function name\nelse\n    success = feval(sActionName);\nend\n\n\n% go to frame...\nfunction success = wt_wizard_go_to_frame\nsF = inputdlg('Go to frame', 'Go to frame', 1);\nif isempty(sF)\n    % WARNING -- fail if frame NOT found\n    success = 0;  % 'Go to frame' failed \nelse\n    wt_display_frame(str2num(char(sF)));\n    success = 1; % 'Go to Frame' succeeded\nend\n\n\n% 'Tracking head movements'\n% To terminate input press RETURN\nfunction success = wt_wizard_init_head_tracker\nsuccess = 1;\nwt_init_head_tracker;  % Tracking head movements\n\n\n% Cleaning head movements\nfunction success = wt_wizard_clean_head_movements\nsuccess = 1;\nwt_clean_splines(0)  % Cleaning head movements\n\n\n\n% 'Setting Image Region of Interest'      \nfunction success = wt_wizard_select_roi\nsuccess = 1;\nwt_select_roi;  % 'Setting Image Region of Interest'      \n\n\n% Setting Parameters\nfunction success = wt_wizard_tracking_parameters\nsuccess = 1;\nwt_set_parameters;  % 'Setting Parameters'\n\n\n% Marking Whisker\n% To terminate input press RETURN\nfunction success = wt_wizard_mark_whisker\nglobal g_tWT\nsuccess = 1;\nwt_mark_whisker;  % Marking Whisker\n\n% verify that a whisker has been marked\n% find index of last SplinePoint\nif isempty(g_tWT.MovieInfo.SplinePoints)\n    % no SplinePoint defined\n    success = 0;  % failed to define a whisker\nend\n\n\n\n% Set last frame\nfunction success = wt_wizard_set_last_frame\nglobal g_tWT\nsuccess = 1;\nsL = inputdlg('Set last frame to track', 'Set last frame', 1, {num2str(get(g_tWT.Handles.hSlider, 'value'))}  ...\n            );\nif isempty(sL)\n    % WARNING -- fail if last frame NOT found\n    success = 0;  % 'Set Last frame' failed \nelse\n    g_tWT.MovieInfo.LastFrame(1:size(g_tWT.MovieInfo.SplinePoints,4)) = str2num(char(sL));\n    success = 1; % 'Set last Frame' succeeded\nend\n\n\n% Rotate image anti-clockwise\nfunction success = wt_wizard_rotate_anti\nsuccess = 1;\nwt_rotate_frame(-1);\n\n\n% Track stimulus squares\nfunction success = wt_wizard_track_stimulus\nsuccess = 1;\nwt_track_stimulus;\n\n% Calibrate\nfunction success = wt_wizard_calibrate\nsuccess = 1;\nwt_calibration('calibrate');\n\n% 'Set Full Length'\nfunction success = wt_wizard_set_full_length\nglobal g_tWT\nsuccess = 1;\n% find index of last SplinePoint\nif isempty(g_tWT.MovieInfo.SplinePoints)\n    % no SplinePoint defined\n    success = 0;  % do nothing\nelse\n    % Set full length for LAST SplinePoint\n    nIndx = size(g_tWT.MovieInfo.SplinePoints, 4) + 1;\n    wt_mark_whisker('setfulllength', nIndx);\nend\n\n\n% Edit notes\nfunction success = wt_wizard_edit_notes\nglobal g_tWT\nsuccess = 1;\n% If notes DO NOT yet exist for this movie, pop up the Notes window\nif isfield(g_tWT.MovieInfo, 'Notes')\n    if isempty(g_tWT.MovieInfo.Notes), wt_edit_notes, end  % No notes yet, force writing of notes\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_image_preprocess.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_image_preprocess.m", "size": 1643, "source_encoding": "utf_8", "md5": "8206eeba9fb64b5ae81978e901201c64", "text": "function [mImgOut, mImgOutCroppedOnly] = wt_image_preprocess( mImgIn )\n% IM = WT_IMAGE_PREPROCESS(IM)\n% Pre-processes movie frames during tracking:\n%   (1) Crop\n%   (2) Rotate\n%   (3) Invert contrast\n%\n\nglobal g_tWT\nbDebug = g_tWT.VerboseMode;\n\nmImgOut = mImgIn; % assign original image\n\n%%% DEBUG ONLY\nif bDebug % original image (cropped)\n    hFig = findobj('Tag', 'IMAGE_PREPROCESS_WINDOW');\n    if isempty(hFig)\n        hFig = figure;\n        set(hFig, 'Tag', 'IMAGE_PREPROCESS_WINDOW', 'DoubleBuffer', 'on', 'Name', 'WT - IMAGE_PREPROCESS_WINDOW', 'numbertitle', 'off')\n    else, figure(hFig), end\n    subplot(1,3,1); imagesc(mImgIn); colorbar; colormap gray; SetPlotProps\n    title('Original (avg subtr)')\nend\n%%% DEBUG END\n\n% Crop\nif ~(g_tWT.MovieInfo.Roi(3:4)+1 == [g_tWT.MovieInfo.Height g_tWT.MovieInfo.Width])\n    mImgOut = imcrop(mImgOut, g_tWT.MovieInfo.Roi);\nend\n\n% Rotate\nif g_tWT.MovieInfo.Rot ~= 0, mImgOut = imrotate(mImgOut, g_tWT.MovieInfo.Rot, 'bicubic'); end\nif g_tWT.MovieInfo.Flip(1) mImgOut = flipud(mImgOut); end % flip up-down\nif g_tWT.MovieInfo.Flip(2) mImgOut = fliplr(mImgOut); end % flip left-right\nmImgOutCroppedOnly = mImgOut;\n\n% Invert\nif g_tWT.MovieInfo.Invert, mImgOut = (255 - mImgOut) ; end\n\n\n%%% DEBUG ONLY\nif bDebug % original image (cropped)\n    subplot(1,3,2); imagesc(mImgOut); colorbar; colormap gray\n    title('Crop/rotate/invert'); SetPlotProps;\n    subplot(1,3,3); imagesc(mImgOut); colorbar; colormap gray\n    title('Normalized'); SetPlotProps;\n    drawnow\nend\n%%% DEBUG END\n\nreturn\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction SetPlotProps\nset(gca, 'xtick', [], 'ytick', [])\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_set_parameters.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_set_parameters.m", "size": 9886, "source_encoding": "utf_8", "md5": "f5dbb32b1dbc879034399099bdb6a129", "text": "function wt_set_parameters(varargin)\n% wt_set_parameters\n\nglobal g_tWT\n\n% If no movie is loaded, do not open Parameters dialog\nbMovieLoaded = 0;\nif isfield(g_tWT.MovieInfo, 'Filename')\n    if ~isempty(g_tWT.MovieInfo.Filename)\n        bMovieLoaded = 1;\n    end\nend\nif ~bMovieLoaded, return, end\n\n% Execute sub-function\nif nargin > 0\n    switch varargin{1}\n        case 'apply'\n            ApplyParams\n        case 'updatevars'\n            UpdateVars(varargin{2});\n        case 'loadparams'\n            LoadParams;\n        case 'saveparams'\n            SaveParams;\n        case 'savedefaultparams'\n            SaveDefaultParams;\n    end\n    return;\nend\n\nglobal g_hOptWin;\nif ~isempty(g_hOptWin), figure(g_hOptWin), return, end\n\n%%%% Start of field data %%%%\n\n% FORMAT OF PARAMETERS FIELD STRUCTURE\n% {DESCRIPTION, DEFAULT_VALUE, OPTION, TARGET_VARIABLE}\nglobal g_tOptFields; g_tOptFields = [];\nglobal g_cFieldNames; g_cFieldNames = [];\ng_tOptFields.nDisplayTitle   = {'Display', '', 'title', ''};\ng_tOptFields.nRotate         = {'Rotate image (deg)', num2str(g_tWT.MovieInfo.Rot), '', 'g_tWT.MovieInfo.Rot'};\ng_tOptFields.vFlip           = {'Flip image (up-down left-right)', num2str(g_tWT.MovieInfo.Flip), '', 'g_tWT.MovieInfo.Flip'};\ng_tOptFields.vROI            = {'Region of interest [x y width height]', num2str(g_tWT.MovieInfo.Roi), '', 'g_tWT.MovieInfo.Roi'};\ng_tOptFields.nRefresh        = {'Interval between refresh (frames)', num2str(g_tWT.MovieInfo.ScreenRefresh), '', 'g_tWT.MovieInfo.ScreenRefresh'};\ng_tOptFields.nLoadFrames     = {'Pre-load (frames)', num2str(g_tWT.MovieInfo.NoFramesToLoad), '', 'g_tWT.MovieInfo.NoFramesToLoad'};\n\ng_tOptFields.nHeadTitle      = {'Coordinates of reference line', '', 'title', ''};\ng_tOptFields.vHeadA          = {'Point A', num2str(g_tWT.MovieInfo.RefLine(1,:)), '', 'g_tWT.MovieInfo.RefLine(1,:)'};\ng_tOptFields.vHeadB          = {'Point B', num2str(g_tWT.MovieInfo.RefLine(2,:)), '', 'g_tWT.MovieInfo.RefLine(2,:)'};\ng_tOptFields.nFindWhiskTitle = {'Whisker tracking parameters', '', 'title', ''};\ng_tOptFields.nHorJitterSlow  = {'Horizontal jitter [left mid right] SLOW >', num2str(g_tWT.MovieInfo.HorJitterSlow), '', 'g_tWT.MovieInfo.HorJitterSlow'};\ng_tOptFields.nHorJitter      = {'Horizontal jitter [left mid right] FAST >>', num2str(g_tWT.MovieInfo.HorJitter), '', 'g_tWT.MovieInfo.HorJitter'};\ng_tOptFields.nVertJitter     = {'Radial jitter of mid-point', num2str(g_tWT.MovieInfo.RadJitter), '', 'g_tWT.MovieInfo.RadJitter'};\ng_tOptFields.nWhiskerWidth   = {'Whisker filter-width', num2str(g_tWT.MovieInfo.WhiskerWidth), '', 'g_tWT.MovieInfo.WhiskerWidth'};\ng_tOptFields.nFilterLength   = {'Width of local-angle filters', num2str(g_tWT.MovieInfo.FilterLen), '', 'g_tWT.MovieInfo.FilterLen'};\ng_tOptFields.nImageProcTitle = {'Image pre-processing', '', 'title', ''};\n\n% Construct string to display in background frames input field\nvFr = g_tWT.MovieInfo.AverageFrames;\nsStr = sprintf('[%s]', sprintf('%d %d; ', vFr'));\nsStr = sprintf('%s]', sStr(1:findstr('; ]', sStr)-1));\ng_tOptFields.vAvgFrames      = {'Background frames [from to]', sStr, '', 'g_tWT.MovieInfo.AverageFrames'};\ng_tOptFields.nBGFrameLowPass = {'Low-pass background image (pixels)', num2str(g_tWT.MovieInfo.BGFrameLowPass), '', 'g_tWT.MovieInfo.BGFrameLowPass'};\ng_tOptFields.nInvert         = {'Invert contrast', num2str(g_tWT.MovieInfo.Invert), '', 'g_tWT.MovieInfo.Invert'};\n\ng_tOptFields.nPosIntTitle    = {'Position extrapolation', '', 'title', ''};\ng_tOptFields.nPosIntOn       = {'Enable (0/1)', num2str(g_tWT.MovieInfo.UsePosExtrap), '', 'g_tWT.MovieInfo.UsePosExtrap'};\ng_tOptFields.nGaussHw        = {'Scale filter by factor of', num2str(g_tWT.MovieInfo.ExtrapFiltHw), '', 'g_tWT.MovieInfo.ExtrapFiltHw'};\n\ng_tOptFields.nAngleCalc      = {'Angle calculation', '', 'title', ''};\ng_tOptFields.nDelta          = {'Pixels from base', num2str(g_tWT.MovieInfo.AngleDelta), '', 'g_tWT.MovieInfo.AngleDelta'};\ng_tOptFields.nCalibTitle     = {'Calibration', '', 'title', ''};\ng_tOptFields.nCalBarLength   = {'Length of calibration bar (mm)', num2str(g_tWT.MovieInfo.CalBarLength), '', 'g_tWT.MovieInfo.CalBarLength'};\n\nvFr = g_tWT.MovieInfo.CalibCoords;\nsStr = sprintf('[%s]', sprintf('%d %d; ', vFr'));\nsStr = sprintf('%s]', sStr(1:findstr('; ]', sStr)-1));\ng_tOptFields.mCalibCoords    = {'Calibration marks [Xa Ya; Xb Yb]', sStr, '', 'g_tWT.MovieInfo.CalibCoords'};\n\n%%%% End of field data %%%%\n\n% GUI Parameters\nnGuiElements = size(fieldnames(g_tOptFields), 1);\nnFontSize = 8;\nnLinSep = 6;\nvScrnSize = get(0, 'ScreenSize');\nnFigHeight = nGuiElements * (nFontSize*2 + nLinSep) + 50;\nnFigWidth = 500;\nnTxtColRatio = 3/5; % portion of figure width occupied by the text column\nvFigPos = [(vScrnSize(3)/2)-(nFigWidth/2) (vScrnSize(4)/2)-(nFigHeight/2) nFigWidth nFigHeight];\ng_hOptWin = figure;\n\nset(g_hOptWin, 'NumberTitle', 'off', 'Position', vFigPos, 'MenuBar', 'none', 'DeleteFcn', 'clear global g_hOptWin')\n\nnTxtColWidth = nFigWidth*nTxtColRatio; % width of descriptive text column\nnEdtColWidth = nFigWidth-nTxtColWidth; % width of input field column\n\n% Place GUI elements on the figure\nnCurrLine = nFigHeight;\ng_cFieldNames = fieldnames(g_tOptFields);\nhEdt = [];\nfor f = 1:nGuiElements    \n    nCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\n    % Separator if applicable\n    if strcmp('title',  g_tOptFields.(char(g_cFieldNames(f)))(:,3)  )\n        uicontrol(g_hOptWin, 'Style', 'text', 'Position', [0 nCurrLine nTxtColWidth 20], ...\n            'String', sprintf(' %s', char(g_tOptFields.(char(g_cFieldNames(f)))(:,1))), ...\n            'HorizontalAlignment', 'left', ...\n            'FontWeight', 'bold', ...\n            'BackgroundColor', [.8 .8 .8] );\n        continue\n    end\n    \n    % Text\n    uicontrol(g_hOptWin, 'Style', 'text', 'Position', [0 nCurrLine nTxtColWidth 20], ...\n        'String', sprintf('    %s', char(g_tOptFields.(char(g_cFieldNames(f)))(:,1))), ...\n        'HorizontalAlignment', 'left', ...\n        'BackgroundColor', [.8 .8 .8] );\n    % Input field\n    hEdt = uicontrol(g_hOptWin, 'Style', 'edit', 'Position', [nTxtColWidth nCurrLine nEdtColWidth 20], ...\n        'String', g_tOptFields.(char(g_cFieldNames(f)))(:,2), ...\n        'HorizontalAlignment', 'center', 'Tag', char(g_cFieldNames(f)));\n    set(hEdt(end), 'Callback', ...\n        [sprintf('global g_tOptFields; g_tOptFields.(''%s'')(:,2) = get(gco, ''String'');', char(g_cFieldNames(f)))] )\nend\n\n% Load button\nuicontrol(g_hOptWin, 'Style', 'pushbutton', ...\n    'string', 'Load', ...\n    'position', [nFigWidth/2-185 10 50 20], ...\n    'Callback', ['wt_set_parameters(''loadparams'')'])\n\n% Save button\nuicontrol(g_hOptWin, 'Style', 'pushbutton', ...\n    'string', 'Save', ...\n    'position', [nFigWidth/2-125 10 50 20], ...\n    'Callback', ['wt_set_parameters(''saveparams'')'])\n\n% Save As Default button\nuicontrol(g_hOptWin, 'Style', 'pushbutton', ...\n    'string', 'Save as default', ...\n    'position', [nFigWidth/2-65 10 120 20], ...\n    'Callback', ['wt_set_parameters(''savedefaultparams'')'])\n\nset(g_hOptWin, 'userdata', nGuiElements);\n\n% 'Apply' button\nuicontrol(g_hOptWin, 'Style', 'pushbutton', ...\n    'string', 'Apply (B)', ...\n    'position', [nFigWidth/2+65 10 60 20], ...\n    'Callback', @ApplyParams)\n    \n% 'Done' button\nuicontrol(g_hOptWin, 'Style', 'pushbutton', ...\n    'string', 'Done', ...\n    'position', [nFigWidth/2+135 10 50 20], ...\n    'Callback', @DoneParams )\n\nreturn;\n\n%%%% LOAD PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction LoadParams\nglobal g_tOptFields g_hOptWin;\n\n% Load .mat file\n[sFilename, sPathname] = uigetfile('*.mat', 'Select parameters file');\nif sFilename==0\n    return\nend\nload(sprintf('%s%s', sPathname, sFilename));\npause(0.5);\n\n% Update gui\ncFieldnames = fieldnames(g_tOptFields);\nfor e = 1:size(fieldnames(g_tOptFields),1)\n    h = findobj(g_hOptWin, 'Tag', char(cFieldnames(e)));\n    if ~isempty(h)\n        set(h, 'String', g_tOptFields.(char(cFieldnames(e)))(2));\n    end\nend\n\nreturn;\n\n\n%%%% SAVE PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction SaveParams\nglobal g_tOptFields\n\n% Save .mat file\n[sFilename, sPathname] = uiputfile('wt_default_parameters.mat', 'Save parameters as...');\nif sFilename==0\n    return\nend\npause(0.5);\nsave(sprintf('%s%s', sPathname, sFilename), 'g_tOptFields');\n\nreturn;\n\n%%%% SAVE DEFAULT PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction SaveDefaultParams\nglobal g_tOptFields\n\n% Save .m file\nsFilename = 'wt_default_parameters.mat';\nsPathname = 'C:\\';\nsave(sprintf('%s%s', sPathname, sFilename), 'g_tOptFields');\n\nreturn;\n\n%%%% UPDATE VARS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction UpdateVars(nGuiElements)\nglobal g_tWT\nglobal g_cFieldNames;\nglobal g_tOptFields;\n\ng_tWT.FiltVec = [];\n\n% Update global variables with the new\nfor f = 1:nGuiElements\n    cTargetVar = char(g_tOptFields.(char(g_cFieldNames(f)))(:,4));\n    if ~isempty(cTargetVar)\n        eval(sprintf('%s = [%s];', cTargetVar, char(g_tOptFields.(char(g_cFieldNames(f)))(:,2))));\n    end\nend\n\nreturn;\n\n%%%% APPLY Params %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction ApplyParams(varargin)\n\nglobal g_hOptWin;\n\n% Enable Redo on this function\nwt_batch_redo('wt_set_parameters(''apply'')')\n\nnGuiElements = get(g_hOptWin, 'userdata');\nwt_set_parameters('updatevars', nGuiElements);\nwt_calibration('recalculate');\nwt_update_window_title_status;\nwt_display_frame;\n\nfigure(g_hOptWin);\nreturn;\n\n%%%% DONE Params %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction DoneParams(varargin)\nglobal g_hOptWin;\n\nnGuiElements = get(g_hOptWin, 'userdata');\n\nwt_set_parameters('updatevars', nGuiElements);\nwt_calibration('recalculate');\nwt_update_window_title_status;\n\nclose(g_hOptWin);\nclear global g_cFieldNames;\nclear global g_tOptFields;\nwt_display_frame;\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_toggle_imageshow.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_toggle_imageshow.m", "size": 1038, "source_encoding": "utf_8", "md5": "b9f1e7badee6073df78f3a1350acbcfa", "text": "%%%% WT_TOGGLE_IMAGESHOW %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nfunction wt_toggle_imageshow\n\nglobal g_tWT\n\n% Toggle verbose status\ng_tWT.HideImage = ~g_tWT.HideImage;\n\n% Update user-menu\nswitch g_tWT.HideImage\n    case 0\n        sStatus = 'off';        \n    case 1\n        sStatus = 'on';        \n    otherwise\n        wt_error('Verbose mode status is undefined. Click continue to set verbose to OFF.')\n        g_tWT.HideImage = 0;\n        sStatus = 'off';\nend\nset(findobj('Label', 'Hide image'), 'checked', sStatus);\nwt_display_frame\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_select_file.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_select_file.m", "size": 668, "source_encoding": "utf_8", "md5": "09d3aee4c8195babda4f43a86ff08344", "text": "% WT_SELECT_FILE\n\nfunction sMovies = wt_select_file\n\nglobal g_tWT\n\n[sFilename, sFilepath] = uigetfile({'*.avi', 'AVI-files (*.avi)'; '*.mat', 'MAT-files (*.mat)';'*.*', 'All File (*.*)'}, 'Select file');\n\n% Return immeditaley if user cancelled this action\nif ~sFilename return; end\n\n% Build list of files\ng_tWT.Movies = struct([]);\ng_tWT.Movies(1).filename = sprintf('%s%s', sFilepath, sFilename);\n\n% Change current directory to that of the loaded file\ncd(sFilepath);\n\n% Reinitialize the gui menus etc.\nwt_prep_gui;\n\n% Clear ROI and rotation parameters\ng_tWT.MovieInfo.Roi = [];\ng_tWT.MovieInfo.Rot = 0;\n\n% Load movie and display first frame\nwt_load_movie(1);\n\nreturn;"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_rotate_coords.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_rotate_coords.m", "size": 5599, "source_encoding": "utf_8", "md5": "9e4a334eb51adfbc44014d732fd60204", "text": "% WT_ROTATE_COORDS\n%\n% Transform coordinates of objects or whiskers between absolute and\n% relative framework. A note on transformations:\n%   Absolute coordinates are in the framework of the actual image\n%   Relative coordinates are with respect to head position in the image\n%\n% Syntax: wt_rotate_coords(COORDS, DIR, RE, LE, NO, S, F, DEB, RAD, HOR), where\n%           COORDS are pair-wise [X Y] coordinates to be transformed. Any\n%           number of such pairs may be passed arranged along rows.\n%           DIR is the transform to be performed:\n%               'rel2abs' for a relative to absolute transformation\n%               'abs2rel' for an absolute to relative transformation\n%           RY are right-eye coordinates across frames arranged across rows\n%           LY are right-eye coordinates across frames arranged across rows\n%           N are nose coordinates across frames arranged across rows\n%           S is the side on the face of the whisker:\n%               1 = right\n%               2 = left\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nfunction vNewCoords = wt_rotate_coords(vCoords, sDirection, vRightEye, vLeftEye, vNose, nSide, ...\n    vImCropSize, nRadExt, nHorExt)\n\n% Return immediately if object position is not given for current whiskers\nif sum(vCoords) == 0, return; end\n\n% Current frame head coordinates\nmHead = [vLeftEye; vRightEye; vNose];\n\n% Initialize output matrix\nvNewCoords = repmat(NaN, size(vCoords));\n\n% Iterate over pairs of [X Y] coordinates\nfor i = 1:size(vCoords,1)\n    switch sDirection\n        case 'rel2abs'\n            vNewCoords(i,:) = TransformRelToAbs(vCoords(i,:), nSide, mHead, vImCropSize, nRadExt, nHorExt);\n        case 'abs2rel'\n            vNewCoords(i,:) = TransformAbsToRel(vCoords(i,:), nSide, mHead, vImCropSize, nRadExt, nHorExt);\n    end\nend\n\nreturn;\n\n\n%%%% TransformRelToAbs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Transform relative to absolute coordinates\n\nfunction mCoordsNew = TransformRelToAbs(mCoords, nSide, mHead, vImCropSize, nRadExt, nHorExt)\n\n% Relative to absolute transformation:\n%  nA = angle between nose-eye and nose-object axis (in relative framework)\n%  nB = angle of eye relative to nose (in absolute framework)\n%  nC = unknown angle of object relative to nose (in absolute framework)\n%  Solution: nC = nA - nB\n%  From nC we derive the position of the object in the absolute framework.\n\nvNoseAbs = mHead(3,:);\nvNoseRel = [1 vImCropSize(2)-nHorExt+6];\nif nSide == 1, vEyeAbs = [mHead(2,1) mHead(2,2)]; else vEyeAbs = [mHead(1,1) mHead(1,2)]; end\nvObjRel = mCoords - repmat(vNoseRel,size(mCoords,1),1); % note: object relative to nose!\nnA = abs(-deg2rad(90)-(atan(vObjRel(:,2)./vObjRel(:,1))));\nvEyeRel = vEyeAbs - vNoseAbs; % note: eye relative to nose!\nnB = abs(atan(vEyeRel(2)/vEyeRel(1)));\nnC = nA - nB;\n% Now get position of object in absolute framework and we're done...\nnR = sqrt(sum(vObjRel'.^2))';% distance of object from nose\nnXRel = nR .* cos(nC); % object x position relative to nose\nnYRel = nR .* sin(nC); % object y position relative to nose\nif nSide == 1\n    mCoordsNew = [ ...\n            repmat(vNoseAbs(1),size(mCoords,1),1) - nXRel ...\n            repmat(vNoseAbs(2),size(mCoords,1),1) + nYRel\n        ];\nelseif nSide == 2\n    mCoordsNew = repmat(vNoseAbs,size(mCoords,1),1) + [nXRel nYRel];\nend\n\nreturn;\n\n\n%%%% TransformRelToAbs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Transform absolute to relative coordinates\n\nfunction vNewCoords = TransformAbsToRel(vCoords, nSide, mHead, vImCropSize, nRadExt, nHorExt)\n\n% 1. Find angle between nose-eye and nose-object axis\n% 2. Find distance from nose to object\n\n% Object position relative to nose, and angle\nvObjRelNose = [vCoords(1)-mHead(3,1) vCoords(2)-mHead(3,2)];\nnLenNose2Obj = sqrt(sum([vObjRelNose.^2]));\nnNoseObjAng = rad2deg(atan2(vObjRelNose(2),vObjRelNose(1)));\n\n% Eye position relative to nose, and angle\nif nSide == 1, vEyeRelNose = [mHead(2,1)-mHead(3,1) mHead(2,2)-mHead(3,2)];\nelse, vEyeRelNose = [mHead(1,1)-mHead(3,1) mHead(1,2)-mHead(3,2)]; end\nnNoseEyeAng = rad2deg(atan2(vEyeRelNose(2),vEyeRelNose(1)));\n\n% Get new angle of object relative to nose\nif nSide == 2\n    nNewNoseObjAng = nNoseObjAng - (90 - abs(nNoseEyeAng));\nelse\n    nNewNoseObjAng = -180 - (nNoseObjAng - (90 - abs(nNoseEyeAng)));\nend\n\n% Calculate object position relative to nose\nnX = nLenNose2Obj * cos(deg2rad(nNewNoseObjAng));\nnY = nLenNose2Obj * sin(deg2rad(nNewNoseObjAng));\nvRelObjPos = [nX nY];\n\n% Calculate real position in frame\n% IMPORTANT: The code below compensates for the fact that both left and\n% right frame are shown in the WT GUI in the same axis. Therefore, to\n% obtain true relative position for left-side the cut-out section's width\n% (effectively g_tWT.MovieInfo.RadExt) must be subtracted from the object's X\n% coordinate.\nif nSide == 2\n    vNose = [nRadExt+2 vImCropSize(2) - nHorExt+6];\n    vNewCoords = [vNose(1)+vRelObjPos(1) vNose(2)+vRelObjPos(2)];\nelse\n    vNose = [1 vImCropSize(2)-nHorExt+6];\n    vNewCoords = [vNose(1)+vRelObjPos(1) vNose(2)+vRelObjPos(2)];\nend\n\n%figure(444); clf; hold on; grid on\n%plot(vCoords(1),vCoords(2),'r*')\n%plot(mHead(:,1),mHead(:,2),'g*')\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_extrapolate_position.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_extrapolate_position.m", "size": 4880, "source_encoding": "utf_8", "md5": "86fd722243d9611965ccc5bd115194d2", "text": "function mImgFilter = wt_extrapolate_position(mW0, mImg, mWA)\n% WT_EXTRAPOLATE_POSITION\n% Create an image-mask that will 'highlight' the likely position of the\n% whisker in current frame, based on velocity information in previos\n% frames.\n%\n%   1 - Linear interpolation of whisker shape in the X preceeding frames\n%   2 - Select end-points and mid-point (half-way along the interpolated whisker)\n%   3 - Filter trajectory of all three points\n%   4 - Cubic extrapolation of new position\n%   5 - Generate scaled gaussian filter\n%   6 - Show debug window that contains: filtered velocity and extrapolated\n%       position\n\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, 2005\n\nglobal g_tWT\n\nnX = 20; % use 10 frames back\nbDebug = g_tWT.VerboseMode;\n\n% Return immediately if fewer than X frames have already been processed\nif length(find(mWA(2,1,:))) < nX + 1 % check tracked frames in 2nd X value\n    mImgFilter = ones(size(mImg));\n    return;\nend\n\n% Resize whisker-coordinates to fit image\nmW0 = mW0;% * g_tWT.MovieInfo.ResizeFactor;\nmWA = mWA;% * g_tWT.MovieInfo.ResizeFactor;\n\n% Linear extrapolation of whisker shape in X preceeding frames\nmXX = []; mYY = [];\nfor f = size(mWA, 3)-nX:size(mWA, 3)\n    if isempty(find(mWA(:,1,f))), continue, end\n    vX = mWA(find(mWA(:,1,f)),1,f);\n    vY = mWA(find(mWA(:,1,f)),2,f);\n    vXX = [vX(1) vX(1)+(vX(end)-vX(1))/2 vX(end)];\n    mYY = [mYY; spline(vX(find(vX)), vY(find(vX)), vXX)];\n    mXX = [mXX; vXX];\nend\n\n% Filter whisker trajectory\npersistent A B;\nnFilterOrder = 4;\nif isempty(A)\n    nFreq = 1/4;\n    [B,A] = butter(nFilterOrder, nFreq);\nend\nif size(mYY, 1) > 3*nFilterOrder\n    mYY = filtfilt(B, A, mYY);\nend\n\n% Cubic extrapolation of new position\nvNewY = interp1(1:size(mYY,1), mYY, size(mYY,1)+1, 'cubic', 'extrap');\nvNewXX = mXX(end,1):mXX(end,end);\n[vXX, vNewYY] = wt_spline(mXX(end,:), vNewY, mXX(end,1):mXX(end,end));\n[C,IA,IB] = intersect(round(mXX(end,:)), vNewXX);\nvNewX = C;\nvNewY = vNewYY(IB);\n\n% Generate scales gaussian filter centered on whisker location, with spread\n% scaled according to new whisker position limits.\nvJit = g_tWT.MovieInfo.HorJitter;\nvJit = [vJit(1) vJit(1)+vJit(2) vJit(1)+vJit(2)+vJit(3)];\n[vXX, vNewYYUpLim] = wt_spline(vNewX, vNewY+vJit, vNewXX);\n[vXX, vNewYYDownLim] = wt_spline(vNewX, vNewY-vJit, vNewXX);\n\nmImgFilter = zeros(size(mImg));\nnMaxHw = round(max(abs((vNewYY - vNewYYUpLim))) * g_tWT.MovieInfo.ExtrapFiltHw) + 10;\nmImgFilter = wt_zeropad(mImgFilter, nMaxHw, 0); % zeropad image\nnAlpha = 2; % used to characterize the gaussion ('sharpness')\nfor x = 1:length(vNewXX)\n    nHw = abs((vNewYY(x) - vNewYYUpLim(x))) * g_tWT.MovieInfo.ExtrapFiltHw;\n    vFG = gausswin(nHw*2+1, nAlpha)';\n    vYIndx = (round(vNewYY(x)-nHw):round(vNewYY(x)+nHw)) + nMaxHw;\n    vYIndx = vYIndx(find(vYIndx > 0));\n    if length(vYIndx) == 1, break, end\n    vFG = interp1(linspace(vYIndx(1),vYIndx(end),length(vFG)), vFG, vYIndx, 'nearest');\n    if vNewXX(x) == size(mImg, 2), break;\n    else, mImgFilter(vYIndx, vNewXX(x)) = vFG'; end\nend\nmImgFilter = mImgFilter(nMaxHw+1:(size(mImg,1)+nMaxHw), :); % remove zeropad\nmImgFilter = mImgFilter / max(mImgFilter(:)); % normalize to max\n\n% Debug\nif bDebug\n    hFig = findobj('Tag', 'EXTRAPOLATE_POSITION_WINDOW');\n    if isempty(hFig)\n        hFig = figure;\n        set(hFig, 'Tag', 'EXTRAPOLATE_POSITION_WINDOW', 'DoubleBuffer', 'on', 'Name', 'WT - EXTRAPOLATE_POSITION_WINDOW', 'numbertitle', 'off')\n    else, figure(hFig), end\n    subplot(2,1,1);\n    plot(mYY); hold on\n    plot(repmat([size(mYY,1) size(mYY,1)+1],3,1)', [mYY(end,:); vNewY])\n    plot(repmat(size(mYY,1),3,1), mYY(end,:)); grid on\n    title('Extrapolation of new whisker location (Y axis)')\n    subplot(2,2,3); title('Whisker and generated filter')\n    plot(mXX(end,:), mYY(end,:), 'g'); hold on\n    plot(vNewXX, vNewYY, 'b');\n    plot(vNewX, vNewY, 'bo')\n    plot(vNewXX, vNewYYUpLim, 'r')\n    plot(vNewXX, vNewYYDownLim, 'r')\n    grid on\n    title('Whisker and limits')\n    subplot(2,2,4)\n    imagesc(mImgFilter.*mImg); hold on\n    plot(mXX(end,:), mYY(end,:), 'g'); hold on\n    plot(vNewXX, vNewYY, 'b');\n    plot(vNewXX, vNewYYUpLim, 'r')\n    plot(vNewXX, vNewYYDownLim, 'r')    \n    title('Filter * Image')\n    truesize; colormap gray\nend\n\n\nreturn;\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction w = gausswin(N, a)\n\n% Index vector\nk = -(N-1)/2:(N-1)/2;\n\n% Equation 44a from [1]\nw = exp((-1/2)*(a * k/(N/2)).^2)'; \n\nreturn;\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_calibration.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_calibration.m", "size": 4876, "source_encoding": "utf_8", "md5": "a143e384f49f955761952d3ab551c926", "text": "function wt_calibration(sOption)\n% WT_CALIBRATION Calibrate and measure image regions\n% \n%  wt_calibration('calibrate')\n%  wt_calibration('measure')\n%  wt_calibration('recalculate')\n\nglobal g_tWT\n\nswitch lower(sOption)\n    case 'calibrate'\n        Calibrate\n        wt_display_frame\n    case 'calibrate-import-image'\n        CalibrateWithImportedImage\n        wt_display_frame\n    case 'measure'\n        Measure\n    case 'recalculate'\n        RecalculatePixelsPerMM\n    otherwise\n        Calibrate\n        wt_display_frame\nend\nreturn\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction Calibrate\nglobal g_tWT\n% Get distance between locations\nif ~isempty(g_tWT.MovieInfo.EyeNoseAxLen)\n    g_tWT.DisplayMode = 0; % go to absolute view\nend\nwt_display_frame\nhLine = [];\ntry\n    for i = 1:2\n        [vX(i) vY(i)] = ginput(1);  % press of return will provide no input\n        hLine(end+1) = plot(vX(i), vY(i), 'r.');\n    end\ncatch, return, end\nhLine(end+1) = plot(vX, vY, 'r:', 'LineWidth', 1);\nnL = round(sqrt(sum([diff(vX).^2 diff(vY).^2])));\n\n% Ask how much the distance corresponds to in millimeters\n% Control that input is a number\ncAns = [];\nsMsg = 'Enter distance in millimeters corresponding to %d pixels.';\nwhile length(str2num(char(cAns))) ~= 1\n    cAns = inputdlg(sprintf(sMsg, nL), 'WT Calibrate', 1);\n    sMsg = 'Invalid entry (must be a number; do not use comma).\\nEnter distance in millimeters corresponding to %d pixels.';\nend\n\ndelete(hLine)\ng_tWT.MovieInfo.CalibCoords = round([vX' vY']);\ng_tWT.MovieInfo.CalBarLength = str2num(cAns{1});\n\nRecalculatePixelsPerMM;\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction CalibrateWithImportedImage\nglobal g_tWT\n\n[sFilename, sFilepath] = uigetfile({'*.bmp';'*.tiff';'*.gif';'*.jpg';'*.png';'*.*'}, 'Select image file'); % select image file\nif ~sFilename return; end % return if user cancelled dialog\nmImg = imread([sFilepath sFilename]); % Load image\nwt_display_frame; cla % refresh and clear display\nimagesc(wt_image_preprocess(mImg)) % Display image\n\nhLine = [];\ntry\n    for i = 1:2\n        [vX(i) vY(i)] = ginput(1);  % press of return will provide no input\n        hLine(end+1) = plot(vX(i), vY(i), 'r.');\n    end\ncatch, return, end\nhLine(end+1) = plot(vX, vY, 'r:', 'LineWidth', 1);\nnL = round(sqrt(sum([diff(vX).^2 diff(vY).^2])));\n\n% Ask how much the distance corresponds to in millimeters\n% Control that input is a number\ncAns = [];\nsMsg = 'Enter distance in millimeters corresponding to %d pixels.';\nwhile length(str2num(char(cAns))) ~= 1\n    cAns = inputdlg(sprintf(sMsg, nL), 'WT Calibrate', 1);\n    sMsg = 'Invalid entry (must be a number; do not use comma).\\nEnter distance in millimeters corresponding to %d pixels.';\nend\n\ndelete(hLine)\ng_tWT.MovieInfo.CalibCoords = round([vX' vY']);\ng_tWT.MovieInfo.CalBarLength = str2num(cAns{1});\n\nRecalculatePixelsPerMM;\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction Measure\nglobal g_tWT\n\nif ~isfield(g_tWT.MovieInfo, 'PixelsPerMM') % create field if it does not exist\n    g_tWT.MovieInfo.PixelsPerMM = [];\nend\n\nif isempty(g_tWT.MovieInfo.PixelsPerMM) % check image has been calibrated\n    sUnit = 'pix';\n    nPixPerMM = 1;\nelse\n    sUnit = 'mm';\n    nPixPerMM = g_tWT.MovieInfo.PixelsPerMM;\nend\n\nhLin = [];\ntry\n    for i = 1:2\n        [vX(i) vY(i)] = ginput(1);  % press of return will provide no input\n        if isempty(hLin)\n            hLin = plot(vX(i), vY(i), 'rx-');\n        else\n            set(hLin, 'XData', vX, 'YData', vY)\n        end\n    end\ncatch % in case user pressed Return\n    delete(hLin)\nend\nnDistMM = sqrt(diff(vX)^2 + diff(vY)^2) / nPixPerMM;\nhTxt = text(mean(vX), mean(vY), sprintf('%.1f%s', nDistMM, sUnit), ...\n    'Color', 'w', 'BackgroundColor', 'k', 'FontSize', 8, ...\n    'HorizontalAlignment', 'center');\nset([hLin hTxt], 'UserData', {hLin, hTxt}) % store handles\n\nhCntxtMenu = uicontextmenu;\nset([hLin hTxt], 'uicontextmenu',  hCntxtMenu)\nuimenu(hCntxtMenu, 'Label', 'Remove', 'Callback', 'vU=get(gco, ''UserData'');delete(vU{1}),delete(vU{2})')\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction RecalculatePixelsPerMM\nglobal g_tWT\n\nif sum(sum(g_tWT.MovieInfo.CalibCoords)) == 0, return; end\n\nvX = g_tWT.MovieInfo.CalibCoords(:,1);\nvY = g_tWT.MovieInfo.CalibCoords(:,2);\n\n% Store pixels per millimeter\nnL = round(sqrt(sum([diff(vX).^2 diff(vY).^2])));\ng_tWT.MovieInfo.PixelsPerMM = nL / g_tWT.MovieInfo.CalBarLength;\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_get_build_number.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_get_build_number.m", "size": 511, "source_encoding": "utf_8", "md5": "4cd3f5ac2ca28358b6203bad67c583ad", "text": "% wt_get_build_number\nfunction sBuild = wt_get_build_number\n\n% Set paths\nsWT_dir = which('wt');\nsWT_dir = sWT_dir(1:findstr(sWT_dir, 'wt.m')-1);\n\n% Get build number from SVN entries files\nsBuild = 'Unknown';\nif ispc, sSVNPath = [sWT_dir '.svn\\' 'entries'];\nelse, sSVNPath = [sWT_dir '.svn/' 'entries']; end\nif exist(sSVNPath)\n    fid = fopen(sSVNPath);\n    for i = 1:5\n        sLine = fgetl(fid);\n        if strcmp(sLine, 'dir')\n            sBuild = fgetl(fid);\n        end\n    end\n    fclose(fid);\nend\n\nreturn\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_dump_screen.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_dump_screen.m", "size": 1849, "source_encoding": "utf_8", "md5": "375701158967ce6a3ae8cf1288e6e117", "text": "%%%% WT_DUMP_SCREEN *****************************************\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nfunction wt_dump_screen\n\nglobal g_tWT\n\nhFrameWin = findobj('Tag', 'WTMainWindow');\n\nsAnswer = questdlg('Select where to dump the screen', ...\n    'Dump screen', ...\n    'Copy to clipboard', 'Save to disk', 'Send to printer', ...\n    'Copy to clipboard');\n\n% Hide slider and text elements\nset(g_tWT.Handles.hSlider, 'visible', 'off')\nhText = findobj('tag','antpost');\nset(hText, 'visible', 'off')\nset(findobj('tag','gotoframe'), 'visible', 'off')\nset(findobj('tag','movieandframe'), 'visible', 'off')\n\nswitch sAnswer\n    case 'Copy to clipboard' % copy to clipboard\n        print(hFrameWin, '-dbitmap')\n    case 'Save to disk' % save to disk\n        [sFilename sFilepath, nFilterIndx] = uiputfile({'*.tif';'*.eps'}, 'Select output file');\n        if nFilterIndx == 1     % save as TIDD\n            print(hFrameWin, '-dtiff', sprintf('%s%s', sFilepath, sFilename));\n        elseif nFilterIndx == 2 % save as color EPS\n            print(hFrameWin, '-depsc', sprintf('%s%s', sFilepath, sFilename));\n        end\n    case 'Send to printer' % send to printer\n        print(hFrameWin, '-v')\nend\n\n% Turn back on slider\nset(g_tWT.Handles.hSlider, 'visible', 'on')\nset(hText, 'visible', 'on')\nset(findobj('tag','gotoframe'), 'visible', 'on')\nset(findobj('tag','movieandframe'), 'visible', 'on')\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_prep_gui.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_prep_gui.m", "size": 20021, "source_encoding": "utf_8", "md5": "00b3b9f97fec08f4c6ae1e7479c20136", "text": "function wt_prep_gui\n% WT_PREP_GUI\n\nglobal g_tWT\n\nhFrameWin = findobj('Tag', 'WTMainWindow');\nset(hFrameWin, 'color', [.8 .8 .8])\n\nfigure(hFrameWin); clf; g_tWT.Handles.hSlider = [];\nset(hFrameWin, 'NumberTitle', 'off', 'Name', ['WhiskerTracker  (Revision ' wt_get_build_number ')'], ...\n    'resizeFcn', ['wt_resize_slider'])\n\ng_tWT.FrameAx = axes;\nset(g_tWT.FrameAx, 'Visible', 'off', ...\n    'buttondownfcn', ['global g_tWT; g_sLastBtnPress = get(gcf,''selectiontype'')'], ...\n    'DrawMode', 'fast', ...\n    'Position', [.03 .06 .94 .9] );\n\n% Create toolbar\nsPath = which('wt');\nsPath = [sPath(1:end-4) 'icons/'];\nhToolbar = uitoolbar('Parent', g_tWT.WTWindow, 'Tag', 'WT_Toolbar');\n\nmCData = im2double(imread([sPath 'tool_open.png'])); mCData(mCData == 0) = NaN; % open\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'WT_Toolbar_Open', 'TooltipString', 'Open file', 'ClickedCallback', ['wt_select_file']);\n\nmCData = im2double(imread([sPath 'tool_save.png'])); mCData(mCData == 0) = NaN; % save\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'WT_Toolbar_Save', 'TooltipString', 'Save', 'ClickedCallback', ['wt_save_data']);\n\nmCData = im2double(imread([sPath 'tool_zoom_in.png'])); mCData(mCData == 0) = NaN; % zoom in\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'Spiky_WaitbarAction_ZoomOut', 'TooltipString', 'Zoom in', 'ClickedCallback', ['zoom on'], 'separator', 'on');\n\n[mCData, mCM] = imread([sPath 'tools_table.gif']); mCData = ind2rgb(mCData, mCM); mCData(mCData == 1) = NaN; % parameters\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'Spiky_WaitbarAction_EditParameters', 'TooltipString', 'Edit Parameters', 'ClickedCallback', ['wt_set_parameters']);\n\nmCData = im2double(imread([sPath 'tool_rotate_3d.png'])); mCData(mCData == 0) = NaN; % zoom in\nmCData_orig = mCData;\nfor i = 1:3, mCData(:,:,i) = fliplr(mCData(:,:,i)); end\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'Spiky_WaitbarAction_RotateClockwise', 'TooltipString', 'Rotate Clockwise', 'ClickedCallback', ['wt_rotate_frame(1); wt_batch_redo(''wt_rotate_frame(1)'')'], 'separator', 'on');\nuipushtool('Parent', hToolbar, 'cdata', mCData_orig, 'Tag', 'Spiky_WaitbarAction_RotateAntiClockwise', 'TooltipString', 'Rotate Anti-Clockwise', 'ClickedCallback', ['wt_rotate_frame(-1); wt_batch_redo(''wt_rotate_frame(-1)'')']);\n\nmCData = im2double(imread([sPath 'tool_plottools_show.png'])); mCData(mCData == 0) = NaN; % zoom in\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'Spiky_WaitbarAction_PanRight', 'TooltipString', 'Auto-resize window', 'ClickedCallback', ['wt_autosize_window']);\n\n[mCData, mCM] = imread([sPath 'right.gif']); mCData = ind2rgb(mCData, mCM); mCData(mCData == 1) = NaN; % Start tracking\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'Spiky_WaitbarAction_PanRight', 'TooltipString', 'Track SLOW', 'ClickedCallback', ['global g_tWT;g_tWT.StopProc=0;wt_track_auto(''slow'')'], 'separator', 'on');\n\n[mCData, mCM] = imread([sPath 'right_double.gif']); mCData = ind2rgb(mCData, mCM); mCData(mCData == 1) = NaN; % Start tracking\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'Spiky_WaitbarAction_PanRight', 'TooltipString', 'Track FAST', 'ClickedCallback', ['global g_tWT;g_tWT.StopProc=0;wt_track_auto(''fast'')']);\n\nmCData = im2double(imread([sPath 'cancel.png'])); mCData(mCData == 0) = NaN; % zoom in\nuipushtool('Parent', hToolbar, 'cdata', mCData, 'Tag', 'Spiky_WaitbarAction_CancelTracking', 'TooltipString', 'Cancel Tracking', 'ClickedCallback', ['global g_tWT; g_tWT.StopProc = 1;']);\n\n% Menu\nhFile = uimenu(hFrameWin, 'Label', 'File');\nuimenu(hFrameWin, 'Label','Open...', 'Parent', hFile, 'Callback', ['wt_select_file'], 'accelerator', 'O');\nuimenu(hFrameWin, 'Label','Open Directory...', 'Parent', hFile, 'Callback', ['wt_select_directory'], 'accelerator', 'D');\nuimenu(hFrameWin, 'Label','Open Directory Tree...', 'Parent', hFile, 'Callback', ['wt_select_directory_tree'], 'accelerator', 'T');\nuimenu(hFrameWin, 'Label','Open Selection...', 'Parent', hFile, 'Callback', ['wt_select_batchfile']);\n\n% Produce list of loaded movies\nif ~isempty(g_tWT.Movies)\n\n    uimenu(hFrameWin, 'Label','Load Data...', 'Parent', hFile, 'Callback', ['wt_load_data(''deffile'')'], 'Separator', 'on');\n    uimenu(hFrameWin, 'Label','Save', 'Parent', hFile, 'Callback', ['wt_save_data'], 'accelerator', 'S');\n    uimenu(hFrameWin, 'Label','Save As...', 'Parent', hFile, 'Callback', ['wt_save_data(''defpath'')']);\n\n    hMovies = uimenu(hFrameWin, 'Label', 'Movies', 'Parent', hFile, 'Separator', 'on');\n    uimenu(hFrameWin, 'Label','Next Movie', 'Parent', hMovies, 'Callback', ['wt_load_movie'], 'accelerator', 'N');\n    for m = 1:size(g_tWT.Movies,2)\n        % Menu item label; limit to 50 chars long\n        sLabel = sprintf('%s', g_tWT.Movies(m).filename);\n        if length(sLabel) > 50\n            sLabel = ['..' sLabel(end-47:end)];\n        end\n        if m == 1\n            hCurrFile = uimenu(hFrameWin, 'Label', sLabel, 'Parent', hMovies, 'Callback', [sprintf('wt_load_movie(%d)', m)], 'Separator', 'on');\n        else\n            hCurrFile = uimenu(hFrameWin, 'Label', sLabel, 'Parent', hMovies, 'Callback', [sprintf('wt_load_movie(%d)', m)]);\n        end\n        % Tick off to indicate if movie already has an associated .mat file\n        if exist(sprintf('%smat', g_tWT.Movies(m).filename(1:findstr('.avi', g_tWT.Movies(m).filename))), 'file') ...\n                | exist(sprintf('%smat.gz', g_tWT.Movies(m).filename(1:findstr('.avi', g_tWT.Movies(m).filename))), 'file')\n            set(hCurrFile, 'checked', 'on')\n        end\n    end\n\n    % Whisker markers etc\n    hMarkers = uimenu(hFrameWin, 'Label', 'Whiskers');\n    uimenu(hFrameWin, 'Label','New...', 'Parent', hMarkers, 'Callback', ['wt_mark_whisker'], 'accelerator', 'W');\n    uimenu(hFrameWin, 'Label','Paste (B)', 'Parent', hMarkers, 'Callback', ['wt_copy_paste_whisker(''paste'', NaN); wt_batch_redo(''wt_copy_paste_whisker(''''paste'''', NaN)'');'], 'accelerator', 'W');\n    uimenu(hFrameWin, 'Label','Delete...', 'Parent', hMarkers, 'Callback', ['wt_clear_selected_whisker']);\n    uimenu(hFrameWin, 'Label','Delete All (B)', 'Parent', hMarkers, 'Callback', ['wt_clear_whisker(''all''); wt_batch_redo(''wt_clear_whisker(''''all'''')'')']);\n    uimenu(hFrameWin, 'Label','Set Last Frame...', 'Parent', hMarkers, 'Callback', ['wt_set_last_frame'], 'Separator', 'on');\n    uimenu(hFrameWin, 'Label', 'Whisker Display Width...', 'Parent', hMarkers, 'Callback', 'wt_change_whisker_width')\n    uimenu(hFrameWin, 'Label', 'Show Whisker Identities', 'Parent', hMarkers, 'Callback', 'wt_toggle_show_identity')\n\n    uimenu(hFrameWin, 'Label','Track Labels (B)', 'Parent', hMarkers, 'Callback', ['wt_track_whisker_label(0,''continue-all'',0); wt_batch_redo(''wt_track_whisker_label(0,''''continue-all'''',0)'')'], 'Separator', 'on');\n    uimenu(hFrameWin, 'Label','Labelling Filter...', 'Parent', hMarkers, 'Callback', ['global g_tWT; g_tWT.LabelFilter=wt_create_filter(g_tWT.LabelFilter);']);\n    uimenu(hFrameWin, 'Label','Mark Whisker and Label...', 'Parent', hMarkers, 'Callback', ['wt_mark_whisker_and_label;']);\n    uimenu(hFrameWin, 'Label','Clear Labels (B)', 'Parent', hMarkers, 'Callback', ['wt_track_whisker_label(0, ''delete-all''); wt_batch_redo(''wt_track_whisker_label(0,''''delete-all'''');'')']);\n    uimenu(hFrameWin, 'Label','Track Whiskers With Labels (B)', 'Parent', hMarkers, 'Callback', ['wt_track_whiskers_with_labels('''')']);\n    uimenu(hFrameWin, 'Label','Show Label Names', 'Parent', hMarkers, 'Callback', 'wt_toggle_show_label_identity')\n    \n    % Head related menu items\n    hHead = uimenu(hFrameWin, 'Label', 'Head');\n    uimenu(hFrameWin, 'Label','Track Head', 'Parent', hHead, 'Callback', ['wt_init_head_tracker']);\n    uimenu(hFrameWin, 'Label','Set Head Position...', 'Parent', hHead, 'Callback', ['wt_init_head_tracker(''static_head'')']);\n    uimenu(hFrameWin, 'Label','Configure Eye Filter...', 'Parent', hHead, 'Callback', ['global g_tWT; g_tWT.EyeFilter=wt_create_filter(g_tWT.EyeFilter);'], 'separator', 'on');\n    uimenu(hFrameWin, 'Label','Clean Head Movements', 'Parent', hHead, 'Callback', ['wt_clean_splines(0)']);    \n\n    % Image options\n    hImage = uimenu(hFrameWin, 'Label', 'View');\n    uimenu(hFrameWin, 'Label','Select ROI', 'Parent', hImage, 'Callback', ['wt_select_roi']);\n    uimenu(hFrameWin, 'Label','Toggle View Mode', 'Parent', hImage, 'Callback', 'wt_toggle_display_mode');\n    uimenu(hFrameWin, 'Label','Rotate Clockwise (B)', 'Parent', hImage, 'Callback', ['wt_rotate_frame(1); wt_batch_redo(''wt_rotate_frame(1)'')'], 'Separator', 'on', 'accelerator', 'c');\n    uimenu(hFrameWin, 'Label','Rotate Anti-Clockwise (B)', 'Parent', hImage, 'Callback', ['wt_rotate_frame(-1); wt_batch_redo(''wt_rotate_frame(-1)'')'], 'accelerator', 'a');\n    uimenu(hFrameWin, 'Label','Flip Vertical (B)', 'Parent', hImage, 'Callback', ['wt_flip_frame(''updown''); wt_batch_redo(''wt_flip_frame(''''updown'''')'')'], 'separator', 'on');\n    uimenu(hFrameWin, 'Label','Flip Horizontal (B)', 'Parent', hImage, 'Callback', ['wt_flip_frame(''leftright''); wt_batch_redo(''wt_flip_frame(''''leftright'''')'')']);\n    uimenu(hFrameWin, 'Label','Refresh', 'Parent', hImage, 'Callback', ['wt_prep_gui; wt_display_frame'], 'Separator', 'on', 'accelerator', 'R');\n    uimenu(hFrameWin, 'Label','Zoom', 'Parent', hImage, 'Callback', ['zoom on'], 'accelerator', 'z');\n    uimenu(hFrameWin, 'Label','Hide', 'Parent', hImage, 'Callback', ['wt_toggle_imageshow'], 'accelerator', 'H');\n    uimenu(hFrameWin, 'Label','Go To Frame...', 'Parent', hImage, 'Callback', @GoToFrame, 'accelerator', 'G');\n    \n    uimenu(hFrameWin, 'Label','Reset (B)', 'Parent', hImage, 'Callback', ['wt_reset_all; wt_batch_redo(''wt_reset_all'')'], 'separator', 'on');\n\n    % Measure menu\n    hImage = uimenu(hFrameWin, 'Label', 'Measure');\n\n    uimenu(hFrameWin, 'Label','Plot...', 'Parent', hImage, 'Callback', ['wt_graphs']);\n    hCompute = uimenu(hFrameWin, 'Label', 'Compute', 'Parent', hImage);\n    uimenu(hFrameWin, 'Label', 'Angle (B)', 'Parent', hCompute, 'Callback', ['wt_compute_kinematics(''angle'', 0); wt_batch_redo(''wt_compute_kinematics(''''angle'''',0)'');']);\n    uimenu(hFrameWin, 'Label', 'Curvature (B)', 'Parent', hCompute, 'Callback', ['wt_compute_kinematics(''curvature'', 0); wt_batch_redo(''wt_compute_kinematics(''''curvature'''',0)'');']);\n    uimenu(hFrameWin, 'Label', 'All Parameters (B)', 'Parent', hCompute, 'Callback', ['wt_compute_kinematics(''all'', 0); wt_batch_redo(''wt_compute_kinematics(''''all'''',0)'');']);\n    \n    uimenu(hFrameWin, 'Label','Set Reference Angle...', 'Parent', hImage, 'Callback', 'wt_set_reference_line');\n    uimenu(hFrameWin, 'Label','Use Default Reference Angle (B)', 'Parent', hImage, 'Callback', 'wt_set_default_reference_line; wt_batch_redo(''wt_set_default_reference_line'')');\n    uimenu(hFrameWin, 'Label','Calibrate...', 'Parent', hImage, 'Callback', ['wt_calibration(''calibrate'')'], 'Separator', 'on');\n    uimenu(hFrameWin, 'Label','Calibrate With Image...', 'Parent', hImage, 'Callback', ['wt_calibration(''calibrate-import-image'')']);\n    uimenu(hFrameWin, 'Label','Measure...', 'Parent', hImage, 'Callback', ['wt_calibration(''measure'')']);\n    uimenu(hFrameWin, 'Label','Create Outline', 'Parent', hImage, 'Callback', ['wt_create_outline(''add'')'], 'Separator', 'on');\n    uimenu(hFrameWin, 'Label','Paste Outline (B)', 'Parent', hImage, 'Callback', ['wt_create_outline(''paste'', NaN); wt_batch_redo(''wt_create_outline(''''paste'''', NaN)'')']);\n    uimenu(hFrameWin, 'Label','Delete All Outlines (B)', 'Parent', hImage, 'Callback', ['wt_create_outline(''deleteall'', NaN); wt_batch_redo(''wt_create_outline(''''deleteall'''', NaN)'')']);\n    uimenu(hFrameWin, 'Label','Hide Outlines', 'Parent', hImage, 'Callback', ['wt_create_outline(''hide'')']);\n\nend\n\n% Options menu\nhOptions = uimenu(hFrameWin, 'Label', 'Options');\n\nhScripts = uimenu(hFrameWin, 'Label', 'Scripts', 'Parent', hOptions);\nuimenu(hFrameWin, 'Label', 'Run Script... (B)', 'Parent', hScripts, 'Callback', ['wt_run_script']);\nuimenu(hFrameWin, 'Label', 'Run Batch Script...', 'Parent', hScripts, 'Callback', ['wt_run_script(''batch'')']);\nuimenu(hFrameWin, 'Label', 'Get Script Help...', 'Parent', hScripts, 'Callback', ['wt_run_script_help']);\n\n% Get list of existing scripts in ./scripts directory\nsPath = which('wt');\nsPath = checkfilename([sPath(1:end-4) 'scripts\\']);\ntFiles = dir(sPath);\nbFirst = 1;\nfor f = 1:length(tFiles)\n    if ~isempty(strfind(tFiles(f).name, '.m')) & isempty(strfind(tFiles(f).name, '.m~'))\n        sName = strrep(tFiles(f).name(1:end-2), '_', ' ');\n        vIndx = strfind(sName, ' ');\n        sName([1 vIndx+1]) = upper(sName([1 vIndx+1]));\n        if bFirst\n            uimenu(hFrameWin, 'Label', sName, 'Parent', hScripts, 'Callback', [sprintf('wt_run_script(''%s'')', tFiles(f).name)], 'Separator', 'on');\n            bFirst = 0;\n        else\n            uimenu(hFrameWin, 'Label', sName, 'Parent', hScripts, 'Callback', [sprintf('wt_run_script(''%s'')', tFiles(f).name)]);\n        end\n    end\nend\n\nif ~isempty(g_tWT.Movies)\n    uimenu(hFrameWin, 'Label', 'Batch Redo', 'Parent', hOptions, 'Callback', ['wt_batch_redo(''redo'')'], 'accelerator', 'B');\n\n    uimenu(hFrameWin, 'Label', 'Parameters...', 'Parent', hOptions, 'Callback', ['wt_set_parameters'], 'accelerator', 'P');\n    uimenu(hFrameWin, 'Label', 'User Variables (B)...', 'Parent', hOptions, 'Callback', ['wt_user_variables;wt_batch_redo(''wt_user_variables(''''copyfrommem'''')'')'], 'accelerator', 'U');\n    %uimenu(hFrameWin, 'Label','Track Stimulus Triggers', 'Parent', hOptions, 'Callback', ['wt_track_stimulus']);\n\n    uimenu(hFrameWin, 'Label','Signal-to-Noise', 'Parent', hOptions, 'Callback', ['wt_toggle_signal_noise'], 'Separator', 'on');\n    uimenu(hFrameWin, 'Label','Notes...', 'Parent', hOptions, 'Callback', ['wt_edit_notes'], 'accelerator', 'e');\n    uimenu(hFrameWin, 'Label','Debug Window', 'Parent', hOptions, 'Callback', ['wt_toggle_verbose']);\n    uimenu(hFrameWin, 'Label','Compress Datafiles', 'Parent', hOptions, 'Callback', ['wt_toggle_datacompress'], 'Separator', 'on');\n    uimenu(hFrameWin, 'Label','Uncompress Movie', 'Parent', hOptions, 'Callback', ['wt_uncompress_movie']);\n    uimenu(hFrameWin, 'Label','Play Movie', 'Parent', hOptions, 'Callback', ['wt_play_movie'], 'Separator', 'on', 'accelerator', 'M');\n    uimenu(hFrameWin, 'Label','Save Movie', 'Parent', hOptions, 'Callback', ['wt_play_movie(''save'')']);\n    uimenu(hFrameWin, 'Label','Dump Screen...', 'Parent', hOptions, 'Callback', ['wt_dump_screen'], 'Separator', 'on');\n\nend\n\n% Help menu\nhHelp = uimenu(hFrameWin, 'Label', 'Help');\nuimenu(hFrameWin, 'Label','WT Help', 'Parent', hHelp, ...\n    'Callback', ...\n    ['hWordHandle=actxserver(''Word.Application'');set(hWordHandle,''Visible'',1);invoke(get(hWordHandle,''Documents''),''Open'',which(''WT_Help.doc''))']);\nuimenu(hFrameWin, 'Label', 'Keyboard shortcuts', 'Parent', hHelp, 'Callback', ['wt_keyboard_shortcuts']);\nuimenu(hFrameWin, 'Label','About WT', 'Parent', hHelp, 'Callback', ['wt_about_wt'], 'Separator', 'on');\n\nif ~isempty(g_tWT.Movies)\n    % 'Go to frame' push-button\n    g_tWT.Handles.hGoToButton = uicontrol(g_tWT.WTWindow, 'units', 'normalized' ...\n        , 'Position', [.88 .025 .07 .03] ... % fig opos = .05 .05 .9 .9\n        , 'Style', 'edit' ...\n        , 'String', '' ...\n        , 'Tag', 'gotoframe' ...\n        , 'tooltipString', 'Enter value of frame to go to and press Enter' ...\n        , 'Callback', ['wt_display_frame(str2num(get(gco,''string'')))'] );\n    % Fix height in pixels\n    set(g_tWT.Handles.hGoToButton, 'units', 'pixels');\n    vPos = get(g_tWT.Handles.hGoToButton, 'position');\n    set(g_tWT.Handles.hGoToButton, 'Position', [vPos(1) 20 vPos(3) 17]);\n    \n    % 'Go to last tracked frame' push button\n    g_tWT.Handles.hGoToEndButton = uicontrol(g_tWT.WTWindow, 'units', 'normalized' ...\n        , 'Position', [.95 .025 .03 .03] ... % fig opos = .05 .05 .9 .9\n        , 'Style', 'pushbutton' ...\n        , 'String', 'E' ...\n        , 'Tag', 'gotoframe' ...\n        , 'tooltipString', 'Go to last whisker-tracked frame' ...\n        , 'CallBack', @GotoLastFrame );\n    % Fix button height in pixels\n    set(g_tWT.Handles.hGoToEndButton, 'units', 'pixels');\n    vPos = get(g_tWT.Handles.hGoToEndButton, 'position');\n    set(g_tWT.Handles.hGoToEndButton, 'Position', [vPos(1) 20 vPos(3) 17]);\n    \n    % 'Go to first tracked frame' push button\n    g_tWT.Handles.hGoToFirstButton = uicontrol(g_tWT.WTWindow, 'units', 'normalized' ...\n        , 'Position', [.02 .025 .03 .03] ... % fig opos = .05 .05 .9 .9\n        , 'Style', 'pushbutton' ...\n        , 'String', '1' ...\n        , 'Tag', 'gotoframe' ...\n        , 'tooltipString', 'Go to first frame in movie' ...\n        , 'CallBack', @GotoFirstFrame );\n    % Fix button height in pixels\n    set(g_tWT.Handles.hGoToFirstButton, 'units', 'pixels');\n    vPos = get(g_tWT.Handles.hGoToFirstButton, 'position');\n    set(g_tWT.Handles.hGoToFirstButton, 'Position', [vPos(1) 20 vPos(3) 17]);\n\n    % Status text field below slider\n    g_tWT.Handles.hStatusText = uicontrol(g_tWT.WTWindow, 'units', 'normalized' ...\n        , 'Position', [.02 .005 .96 .02] ...\n        , 'Style', 'text' ...\n        , 'fontsize', 8 ...\n        , 'horizontalalignment', 'left' ...\n        , 'String', '' ...\n        , 'Tag', 'statustext' ...\n        , 'FontWeight', 'normal' ...\n        , 'backgroundcolor', get(g_tWT.WTWindow, 'color'));\n    % Fix height in pixels\n    set(g_tWT.Handles.hStatusText, 'units', 'pixels');\n    vPos = get(g_tWT.Handles.hStatusText, 'position');\n    set(g_tWT.Handles.hStatusText, 'Position', [vPos(1) 2 vPos(3) 15]);\nend\n\n% List of previously accessed paths\nif ~isempty(g_tWT.AccessedPaths)\n    for p = 1:length(g_tWT.AccessedPaths)\n        sLabel = sprintf('%d  %s', p, g_tWT.AccessedPaths{p});\n        if length(sLabel) > 40\n            sLabel = ['..' sLabel(end-37:end)];\n        end\n        if p == 1\n            uimenu(hFrameWin, 'Label', sLabel, ...\n                'Parent', hFile, ...\n                'Callback', [sprintf('wt_select_directory(''%s'')', g_tWT.AccessedPaths{p})], ...\n                'Separator', 'on');\n        else\n            uimenu(hFrameWin, 'Label', sLabel, ...\n                'Parent', hFile, ...\n                'Callback', [sprintf('wt_select_directory(''%s'')', g_tWT.AccessedPaths{p})]);\n        end\n    end\nend\n\nuimenu(hFrameWin, 'Label','E&xit WT', 'Parent', hFile, 'Callback', ['wt_exit'], 'separator', 'on', 'accelerator', 'q');\n\nset(hFrameWin, 'keypressfcn', @FigKeyPressExec)\nset(g_tWT.WTWindow, 'visible', 'on')\n\n\nreturn % end of main function\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction FigKeyPressExec( varargin )\n\nglobal g_tWT\n\nsKey = lower(get(gcf, 'currentcharacter'));\n\n% If pressed key is a number, then go that number of frames back from\n% current\nif ~isempty(str2num(sKey))\n    if str2num(sKey) == 0\n        nNewFrame = round(get(g_tWT.Handles.hSlider, 'Value')) + 1;\n    else\n        nNewFrame = round(get(g_tWT.Handles.hSlider, 'Value')) - str2num(sKey);\n    end\n    if nNewFrame < 1 | nNewFrame > g_tWT.MovieInfo.NumFrames\n        beep; return;\n    end\n    wt_display_frame(nNewFrame);\nend\n\nif strcmp(' ', sKey)\n    if isfield(g_tWT, 'StopProc')\n        if g_tWT.StopProc, g_tWT.StopProc = 0; wt_track_auto('bicubic');\n        else, g_tWT.StopProc = 1; end\n    end\nend\n\n\nfunction GoToFrame(varargin)\nglobal g_tWT\nsF = inputdlg('Go to frame', 'Go to frame', 1);\nif isempty(sF), return\nelse, wt_display_frame(str2num(char(sF))); end\nreturn;\n\n\nfunction GotoFirstFrame(varargin)\nglobal g_tWT\nif ~isempty(g_tWT.MovieInfo.Nose)\n    f = find(~isnan(g_tWT.MovieInfo.Nose(:,1)));\nelse, f = 1; end\nwt_display_frame(f(1));\nreturn;\n\n\nfunction GotoLastFrame(varargin)\nglobal g_tWT\nif ~isempty(g_tWT.MovieInfo.SplinePoints)\n    f = size(g_tWT.MovieInfo.SplinePoints, 3);\nelse\n    f = g_tWT.MovieInfo.NumFrames;\nend\nwt_display_frame(f);\nreturn;\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_mark_object.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_mark_object.m", "size": 5048, "source_encoding": "utf_8", "md5": "c852ba81c9b336c1264876140284470d", "text": "function wt_mark_object(nWhisker, sOption, nLocation)\n% WT_MARK_OBJECT\n% Mark object. Accepts whisker ID as its single input parameter.\n% Brings up a cross-hair to mark location in frame where whisker touches\n% object. Only works in conjunction with the WT GUI.\n% Syntax: wt_mark_object(ID)\nglobal g_tWT\n\nnCurrentFrame = round(get(g_tWT.Handles.hSlider, 'Value'));\n\nswitch sOption\n    case 'delete'\n        g_tWT.MovieInfo.ObjectRadPos(nWhisker, :, nLocation) = NaN;\n        delete(findobj('Tag', sprintf('object%d%d', nWhisker, nLocation)))\n    case 'move'\n        g_tWT.MovieInfo.ObjectRadPos(nWhisker, 1:2, nLocation) = GetCoords(nWhisker);\n        %  - replace zeros with NaNs\n        g_tWT.MovieInfo.ObjectRadPos(find(g_tWT.MovieInfo.ObjectRadPos==0)) = NaN;\n    case 'editframe'\n        % Display current frame value and allow changing it\n        cNewFrame = inputdlg('New frame number', 'Edit frame', 1,  ...\n                            {num2str(g_tWT.MovieInfo.ObjectRadPos(nWhisker, 3, nLocation))});\n        if ~isempty(cNewFrame)\n            g_tWT.MovieInfo.ObjectRadPos(nWhisker, 3, nLocation) = str2num(char(cNewFrame));\n        end            \n        %  - replace zeros with NaNs\n        g_tWT.MovieInfo.ObjectRadPos(find(g_tWT.MovieInfo.ObjectRadPos==0)) = NaN;\n    case 'add'\n        % If this is the 1st marked touch location for this whisker, change\n        % the 1st frame for this location to be the 1st tracked frame\n        nF = find(squeeze(g_tWT.MovieInfo.SplinePoints(1,1,:,nWhisker)));\n        if nWhisker > size(g_tWT.MovieInfo.ObjectRadPos,1)\n            nCurrentFrame = nF(1);\n        else\n            % Following lines takes into account that old movies did not\n            % allow marking of more than one object\n            if size(g_tWT.MovieInfo.ObjectRadPos,2) == 3\n                if ~any(g_tWT.MovieInfo.ObjectRadPos(nWhisker,3,:))\n                    nCurrentFrame = nF(1);\n                end\n            elseif size(g_tWT.MovieInfo.ObjectRadPos,2) == 2\n                g_tWT.MovieInfo.ObjectRadPos(nWhisker,3,:) = nF(1); % set frame of previous location to 1st tracked frame\n            end\n        end\n\n        % Check if there already is a location that starts from current frame\n        if size(g_tWT.MovieInfo.ObjectRadPos,2) == 3 & size(g_tWT.MovieInfo.ObjectRadPos,1) >= nWhisker\n            if find(g_tWT.MovieInfo.ObjectRadPos(nWhisker,3,:)==nCurrentFrame)\n                wt_error('There already is a defined location starting in current frame. Move location instead.')\n            end\n        end\n        vAbsCoords = GetCoords(nWhisker);\n\n        % Save in absolute coordinates\n        %  - determine number of locations marked for this whisker\n        if isempty(g_tWT.MovieInfo.ObjectRadPos)\n            nObjectNum = 0;\n        else\n            try\n                nObjectNum = length(find(~isnan(g_tWT.MovieInfo.ObjectRadPos(nWhisker, 1:2, :))))/2;\n            catch, nObjectNum = 0; end\n        end\n        %  - replace zeros with NaNs\n        g_tWT.MovieInfo.ObjectRadPos(find(g_tWT.MovieInfo.ObjectRadPos==0)) = NaN;\n        %  - tidy up order of locations\n        for w = 1:size(g_tWT.MovieInfo.ObjectRadPos,1)\n            vRows = find(~isnan(g_tWT.MovieInfo.ObjectRadPos(w,1,:)));\n            tmp = g_tWT.MovieInfo.ObjectRadPos;\n            tmp(w,:,:) = NaN;\n            tmp(w,:,1:length(vRows)) = g_tWT.MovieInfo.ObjectRadPos(w,:,vRows);\n            g_tWT.MovieInfo.ObjectRadPos = tmp;\n        end\n        %  - allocate new location\n        g_tWT.MovieInfo.ObjectRadPos(nWhisker, 1:3, nObjectNum+1) = [vAbsCoords nCurrentFrame];\n        %  - replace zeros with NaNs\n        g_tWT.MovieInfo.ObjectRadPos(find(g_tWT.MovieInfo.ObjectRadPos==0)) = NaN;\n\n        % Delete whisker-distance-from-object vector\n        g_tWT.MovieInfo.PositionOffset(:,nWhisker) = NaN;\nend\n\n% Refresh frame\nwt_display_frame\n\nreturn;\n\n\n%%%% SUB ROUTINES %%%%\nfunction vAbsCoords = GetCoords(nWhisker)\n\nglobal g_tWT\nnCurrentFrame = round(get(g_tWT.Handles.hSlider, 'Value'));\n\n% Get user input in absolute coordinates\n[nX nY] = ginput(1);\nvAbsCoords = [nX nY];% ./ g_tWT.MovieInfo.ResizeFactor;\n\n\n% If head-position is known for the *current frame* then transform from\n% relative to absolute coordinates.\nif ~isempty(g_tWT.MovieInfo.EyeNoseAxLen) & g_tWT.MovieInfo.Nose(nCurrentFrame, 1) & g_tWT.DisplayMode\n    if ~isempty(g_tWT.MovieInfo.Nose(nCurrentFrame,:)) & ~isnan(prod(g_tWT.MovieInfo.Nose(nCurrentFrame,:)))\n        nWhiskerSide = g_tWT.MovieInfo.WhiskerSide(nWhisker);\n        if nWhiskerSide == 2, vAbsCoords(1) = vAbsCoords(1) - (g_tWT.MovieInfo.ImCropSize(1) + 1); end\n        vAbsCoords = wt_rotate_coords(vAbsCoords, ...\n            'rel2abs', ...\n            g_tWT.MovieInfo.RightEye(nCurrentFrame, :), ...\n            g_tWT.MovieInfo.LeftEye(nCurrentFrame, :), ...\n            g_tWT.MovieInfo.Nose(nCurrentFrame, :), ...\n            nWhiskerSide, ...\n            g_tWT.MovieInfo.ImCropSize, ...\n            g_tWT.MovieInfo.RadExt, ...\n            g_tWT.MovieInfo.HorExt );    \n    end\nend\n\nreturn\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_head_tracker.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_head_tracker.m", "size": 7494, "source_encoding": "utf_8", "md5": "8bb26b2011d840cf56a96a3ba9c0b063", "text": "function bResult = wt_head_tracker(sFileName)\n% WT_HEAD_TRACKER\n% Track rat head movements\n%\n\nwarning off MATLAB:divideByZero\nwarning off MATLAB:mir_warning_variable_used_as_function\n\nglobal g_tWT\nbResult = 1;\nwt_toggle_display_mode(0)\n\ng_tWT.MovieInfo.ImCropSize = round([g_tWT.MovieInfo.RadExt g_tWT.MovieInfo.EyeNoseAxLen+g_tWT.MovieInfo.HorExt*2]);\n\n% Open waitbar dialog\nhWaitbar = waitbar(0, sprintf('Loading buffer (%d frames)...', g_tWT.MovieInfo.NoFramesToLoad), 'CreateCancelBtn', 'delete(gcbf)');\nset(hWaitbar, 'units', get(findobj('tag', 'WTMainWindow'), 'units'))\nvFrPos = get(findobj('tag', 'WTMainWindow'), 'position');\nvWtPos = get(hWaitbar, 'position');\nset(hWaitbar, 'tag', 'auto');\n\n% Add buttons for manual control\nuicontrol(hWaitbar, 'Style', 'pushbutton', 'Position', [25 12 50 23], ...\n    'Callback', 'set(gcf, ''tag'', ''manual_right'')', 'String', 'Right' );\nuicontrol(hWaitbar, 'Style', 'pushbutton', 'Position', [85 12 50 23], ...\n    'Callback', 'set(gcf, ''tag'', ''manual_left'')', 'String', 'Left' );\nuicontrol(hWaitbar, 'Style', 'pushbutton', 'Position', [145 12 50 23], ...\n    'Callback', 'set(gcf, ''tag'', ''manual_both'')', 'String', 'Both' );\nuicontrol(hWaitbar, 'Style', 'pushbutton', 'Position', [205 12 50 23], ...\n    'Callback', 'set(gcf, ''tag'', ''stop'')', 'String', 'Stop' );\ndrawnow\n\nnYlim = g_tWT.MovieInfo.Height;\nnXlim = g_tWT.MovieInfo.Width;\n\nset(g_tWT.Handles.hSlider, 'enable', 'off') % disable slider\n\n% Process frames\nnFirstFrame = find(~isnan(g_tWT.MovieInfo.LeftEye(:,1)))+1;\nnRealFrame = nFirstFrame(1);\nbBreakOuter = 0;\n\n% Outer loop: iterate over frame segments\nwhile nRealFrame <= g_tWT.MovieInfo.NumFrames\n    nRangeStart = nRealFrame;\n    nRangeEnd = nRangeStart + g_tWT.MovieInfo.NoFramesToLoad - 1;\n    nRangeEnd = min([nRangeEnd g_tWT.MovieInfo.NumFrames]);\n    vFrameRange = nRangeStart:nRangeEnd; % range of frames in buffer\n    waitbar(nRealFrame/g_tWT.MovieInfo.NumFrames, hWaitbar, sprintf('Loading buffer (%d frames)...', g_tWT.MovieInfo.NoFramesToLoad))\n    mFrames = wt_load_avi(sFileName, vFrameRange); % load frame buffer\n    \n    % Inner loop: iterate over individual frames in buffer\n    nFrame = 1;\n    bBreakInner = 0;\n    while nFrame <= length(vFrameRange) & nFrame > 0\n        nRealFrame = vFrameRange(nFrame);\n        try\n            waitbar(nRealFrame/g_tWT.MovieInfo.NumFrames, hWaitbar, sprintf('Processing frame %d', nRealFrame))\n        catch, bResult = 0; nBreakOuter = 1; break; end\n\n        if g_tWT.MovieInfo.Invert == 0, nInvert = -1; else, nInvert = 1; end\n\n        % Rotate\n        if g_tWT.MovieInfo.Rot ~= 0, mFrames(:,:,nFrame) = imrotate(mFrames(:,:,nFrame), g_tWT.MovieInfo.Rot); end\n        if g_tWT.MovieInfo.Flip(1) mFrames(:,:,nFrame) = flipud(mFrames(:,:,nFrame)); end % flip up-down\n        if g_tWT.MovieInfo.Flip(2) mFrames(:,:,nFrame) = fliplr(mFrames(:,:,nFrame)); end % flip left-right\n\n        if strcmp(get(hWaitbar, 'tag'), 'auto')\n            % Automatic\n            try\n                g_tWT.MovieInfo.RightEye(nRealFrame, :) = wt_track_spot(double(mFrames(:,:,nFrame)).*nInvert, g_tWT.MovieInfo.RightEye(nRealFrame-1, :), [], g_tWT.EyeFilter.Filter, g_tWT.VerboseMode);\n                g_tWT.MovieInfo.LeftEye(nRealFrame, :) = wt_track_spot(double(mFrames(:,:,nFrame)).*nInvert, g_tWT.MovieInfo.LeftEye(nRealFrame-1, :), [], g_tWT.EyeFilter.Filter, g_tWT.VerboseMode);\n            catch, bBreakOuter = 1; break, end\n            nFrame = nFrame + 1;\n        elseif strcmp(get(hWaitbar, 'tag'), 'stop') % stop tracking at current frame\n            bBreakOuter = 1; break\n        else\n            % Manual (ignore buffer and load frame-by-frame)\n            nFrame = 0;\n            while ~strcmp(get(hWaitbar, 'tag'), 'auto')\n                if nRealFrame < nFirstFrame, nRealFrame = nFirstFrame; end\n\n                % Get automatic suggestions\n                mFrame = wt_load_avi(sFileName, nRealFrame); % load frame buffer\n                g_tWT.MovieInfo.RightEye(nRealFrame, :) = wt_track_spot(double(mFrame).*nInvert, g_tWT.MovieInfo.RightEye(nRealFrame-1, :), [], g_tWT.EyeFilter.Filter, g_tWT.VerboseMode);\n                g_tWT.MovieInfo.LeftEye(nRealFrame, :) = wt_track_spot(double(mFrame).*nInvert, g_tWT.MovieInfo.LeftEye(nRealFrame-1, :), [], g_tWT.EyeFilter.Filter, g_tWT.VerboseMode);\n                g_tWT.MovieInfo.Nose(nRealFrame, :) = wt_find_nose(g_tWT.MovieInfo.RightEye(nRealFrame, :), g_tWT.MovieInfo.LeftEye(nRealFrame, :), g_tWT.MovieInfo.EyeNoseAxLen);\n                wt_display_frame(nRealFrame); % refresh frame\n\n                hLine = line(-10,-10); % indicator line\n                set(hLine, 'linewidth', 5, 'color', [1 0 0])\n\n                % Decide which side(s) to track\n                switch get(hWaitbar, 'tag')\n                    case 'manual_both', cSides = {'Right' 'Left'};\n                    case 'manual_right', cSides = {'Right'};\n                    case 'manual_left', cSides = {'Left'};\n                end\n\n                for sS = cSides\n                    % Update indicator line\n                    if strcmp(sS,'Right'), set(hLine, 'xdata', [1 1], 'ydata', [0 nYlim]);\n                    else, set(hLine, 'xdata', [nXlim nXlim], 'ydata', [0 nYlim]), end\n                    nX = -1; nY = -1;\n                    % Get input\n                    while ~ValidCoord(nX,nY), [nX, nY, nButton] = ginput(1); end\n                    if nButton == 2 | nButton == 3\n                        % Break if middle or right button was pressed\n                        set(hWaitbar, 'tag', 'auto')\n                        figure(hWaitbar)\n                        bBreakInner = 1;\n                        break\n                    elseif nButton > 3\n                        % Change framenumber if a number key was pressed\n                        nRealFrame = nRealFrame - str2num(char(nButton));\n                    else\n                        % Assign coordinate\n                        eval(sprintf('g_tWT.MovieInfo.%sEye(nRealFrame, :) = round([nX nY]);', char(sS)));\n                        if strcmp(sS,cSides{end}), nRealFrame = nRealFrame + 1; end\n                    end\n                end\n                delete(hLine)\n                if bBreakInner\n                    break\n                end\n            end\n        end\n    end % inner loop\n\n    if bBreakOuter, break\n    else % refresh frame\n        g_tWT.MovieInfo.RightEye = g_tWT.MovieInfo.RightEye;\n        g_tWT.MovieInfo.LeftEye = g_tWT.MovieInfo.LeftEye;\n        g_tWT.MovieInfo.Nose = wt_find_nose(g_tWT.MovieInfo.RightEye, g_tWT.MovieInfo.LeftEye, g_tWT.MovieInfo.EyeNoseAxLen);\n        if nFrame\n            wt_display_frame(nRealFrame, mFrames(:,:,nFrame-1));\n        else, wt_display_frame(nRealFrame); end\n        figure(hWaitbar); drawnow\n    end\n\nend % outer loop\ndelete(hWaitbar)\n\ng_tWT.MovieInfo.RightEye = g_tWT.MovieInfo.RightEye;\ng_tWT.MovieInfo.LeftEye = g_tWT.MovieInfo.LeftEye;\ng_tWT.MovieInfo.Nose = wt_find_nose(g_tWT.MovieInfo.RightEye, g_tWT.MovieInfo.LeftEye, g_tWT.MovieInfo.EyeNoseAxLen);\n\nset(g_tWT.Handles.hSlider, 'enable', 'on', 'value', nFirstFrame-1) % enable slider\nwt_toggle_display_mode(1) % refresh frame\nwt_display_frame(nFirstFrame-1)\n\nreturn\n\n%%%%  VALIDCOORD\n%%%%  Check if ginput() coordinates is inside the frame\nfunction nB = ValidCoord(nX,nY)\nglobal g_tWT\nnB = 1;\nvYlim = get(g_tWT.FrameAx, 'ylim');\nvXlim = get(g_tWT.FrameAx, 'xlim');\nif nX < 1 | nX > vXlim(2), nB = 0; end\nif nY < 1 | nY > vYlim(2), nB = 0; end\nreturn\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_plot_signal_noise.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_plot_signal_noise.m", "size": 3785, "source_encoding": "utf_8", "md5": "16ae4d2f5488b3e23f73a272f627ff86", "text": "function bStop = wt_plot_signal_noise(vSR)\n% WT_PLOT_SIGNAL_NOISE\n% Plots the signal-to-noise ratio of the whisker against the background.\n% The ratio itself is computed in the find_next_whisker.dll. This function\n% just accepts the number(s) to plot. Adds a red, horizontal bar for\n% setting the threshold at which tracking should be automatically stopped.\n%\n% Syntax:\n%  wt_plot_signal_noise(N)  : add N to XDATA in SR window\n%  wt_plot_signal_noise([]) : reset SR plot\n\nglobal g_tWT\n\npersistent p_hSRWin\npersistent p_nAutoclear\npersistent p_nClock\npersistent p_vFPS\n\n% Clear tracking speed variable\nif isempty(vSR), p_vFPS = []; end\n\n% Update tracking speed counter (current and average FPS)\nif ~isempty(p_nClock)\n    nElapsedTime = etime(clock, p_nClock);\n    p_nClock = clock;\n    p_vFPS = [p_vFPS length(vSR)/nElapsedTime]; % tracking speed, fps\n    set(findobj('tag','fps'), 'label', sprintf('Tracking speed: %.1f fps', mean(p_vFPS)))\nelse, p_nClock = clock; end\n\nif isempty(p_nAutoclear), p_nAutoclear = 500;\nelse\n    if ~isempty(findobj('tag', 'AutoClearButton'))\n        p_nAutoclear = get(findobj('tag', 'AutoClearButton'), 'UserData');\n    end\nend\n\n% Figure\nif isempty(p_hSRWin) | ~ishandle(p_hSRWin)\n    p_hSRWin = figure;\n    set(p_hSRWin, 'Name', 'S/N  Use mouse to set threshold', ...\n        'MenuBar', 'none', ...\n        'NumberTitle', 'off', ...\n        'Position', [450 370 480 200], ...\n        'CloseRequestFcn', 'wt_toggle_signal_noise;delete(gcbo)', ...\n        'Color', 'k', ...\n        'Tag', 'SignalNoiseFig' )\n    uimenu(gcf, 'Label', 'Clear', 'callback', 'wt_plot_signal_noise([])')\n    uimenu(gcf, 'Label', sprintf('Set autoclear (%d frames)', p_nAutoclear), 'callback', @SetAutoClear, 'Tag', 'AutoClearButton')\n    uimenu(gcf, 'Label', 'Tracking speed: 0 fps', 'Tag', 'fps')\nend\n\n% Axes\nhAxes = findobj('tag', 'sn_plot');\nif isempty(hAxes)\n    hAxes = subplot('Position', [.1 .15 .8 .7]);\n    set(gca, 'FontSize', 7, 'color', 'k', ...\n        'xcolor', 'w', 'ycolor', 'w', ...\n        'ButtonDownFcn', @SetThreshold, ...\n        'tag', 'sn_plot' )\n    ylabel('S/N'); xlabel('Time (frames)')\nend\n\n% Plot\nhLines = get(hAxes(end), 'children'); % there are two line objects: S/N and THRESHOLD\nif isempty(hLines)\n    hLines(1) = line(1,1);\n    hThreshLine = line(0,2);\n    set(hLines(1), 'Tag', 'SR', 'Marker', '.', 'color', [0 .8 0]);\n    set(hThreshLine, 'Tag', 'THRESH', 'color', 'r', 'LineStyle', ':');\nend\n\n% Return if the input is empty\nif isempty(vSR), vSR = 0;\nelse, vSR = [get(findobj('tag','SR'),'ydata') vSR]; end\n\nset(hAxes, 'ylim', [min(vSR)-2 max(vSR)+2])\nset(findobj('tag','SR'), 'ydata', vSR, 'xdata', 0:length(vSR)-1)\nvYdata = get(findobj('tag','THRESH'), 'ydata');\nset(findobj('tag','THRESH'), 'ydata', repmat(vYdata(1), 1, length(vSR)), 'xdata', 0:length(vSR)-1)\n\nhLines = get(hAxes(end), 'children'); % there are two line objects: S/N and THRESHOLD\n\n% Evaluate SR\nvThresh = get(findobj('tag','THRESH'), 'ydata');\nif (vSR(end) <= vThresh(end)) & vSR(end)~=0\n    beep\n    g_tWT.StopProc = 1;\nend\n\n% If length of vSR > p_nAutoclear, clear the display\nif length(vSR) > p_nAutoclear, wt_plot_signal_noise([]), end\n\nreturn\n\n%%% Change SR threshold %%%\nfunction SetThreshold(varargin)\n\nmPnts = get(gca,'CurrentPoint');\nnY = mPnts(1,2);\n\nnXlim = get(gca, 'xlim');\nset(findobj('tag','THRESH'), 'xdata', 0:nXlim(2), ...\n    'ydata', repmat(nY,1,length(0:nXlim(2))) )\n\nreturn\n\n\n%%% Change Autoclear %%%\nfunction SetAutoClear( varargin )\nsAutoClFrames = inputdlg('Number of frames between autoclear', 'Set autoclear', 1);\nif isempty(char(sAutoClFrames)), return, end\nnAutoClFrames = str2num(char(sAutoClFrames));\nset(findobj('tag', 'AutoClearButton'), 'Label', sprintf('Set autoclear (%d frames)', nAutoClFrames), 'UserData', nAutoClFrames);\nreturn\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_clear_selected_whisker.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_clear_selected_whisker.m", "size": 2251, "source_encoding": "utf_8", "md5": "aba3a703c9cf0c5ea4afc2e0ca864686", "text": "function wt_clear_selected_whisker()\n% WT_CLEAR_SELECTED_WHISKER\n% Delete all or parts of tracked data of selected whiskers, or delete all\n% tracked whiskers.\n\nglobal g_tWT\n\ncIdents = {g_tWT.MovieInfo.WhiskerIdentity{:}};\n\nnFontSize = 8;\nnLinSep = 10;\n\nvScrnSize = get(0, 'ScreenSize');\nnFigHeight = (length(cIdents)-1) * (nFontSize*2 + nLinSep) + 60;\nnFigWidth = 150;\nvFigPos = [5 vScrnSize(4)-(nFigHeight+21) nFigWidth nFigHeight];\nhCurrWin = figure;\nset(hCurrWin, 'NumberTitle', 'off', ...\n    'Name', 'Select whisker', ...\n    'Position', vFigPos, ...\n    'Tag', 'ClearSelectedWhiskers', ...\n    'MenuBar', 'none' )\n\nnCurrLine = nFigHeight;\nnCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\n\nfor w = 1:length(cIdents)\n    w_this = w;\n    if w > 10, w_this = w-10; end\n    if w > 20, w_this = w-20; end\n    if w > 30, w_this = w-30; end\n    if w > 40, w_this = w-40; end\n    if isempty(cIdents{w}), cIdents{w}{1} = ''; end\n    hBox = uicontrol(hCurrWin, 'Style', 'checkbox', 'Position', [10 nCurrLine 125 20], ...\n        'Callback', '', ...\n        'HorizontalAlignment', 'right', ...\n        'String', cIdents{w}, ...\n        'FontWeight', 'bold', ...\n        'Tag', sprintf('whisker_%d_%s', w, cIdents{w}{:}), ...\n        'BackgroundColor', g_tWT.Colors(w_this,:) );\n    nCurrLine = nCurrLine - (nFontSize*2 + nLinSep);\nend\n\n% Refresh pushbutton\nuicontrol(hCurrWin, 'Style', 'pushbutton', 'Position', [10 nCurrLine 125 20], ...\n    'Callback', @ClearWhiskers, ...\n    'String', 'Clear whiskers', ...\n    'FontWeight', 'bold' );\n\nuiwait(hCurrWin)\n\nreturn\n\n%%%%%%%%%%%%%% CLEAR WHISKERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction ClearWhiskers(varargin)\n\nglobal g_tWT\nhCurrWin = findobj('Tag', 'ClearSelectedWhiskers');\n\n% Iterate over checkboxes and decide which to delete\nvDelWhiskers = [];\ncIdents = {g_tWT.MovieInfo.WhiskerIdentity{:}};\nfor w = 1:length(cIdents)\n    if isempty(cIdents{w}), cIdents{w}{1} = ''; end\n    hCheckBox = findobj(hCurrWin, 'Tag', sprintf('whisker_%d_%s', w, cIdents{w}{:}));\n    if get(hCheckBox, 'Value')\n        vDelWhiskers(end+1) = w;\n    end\nend\n\n% Clear whiskers\nif ~isempty(vDelWhiskers)\n    wt_clear_whisker(vDelWhiskers)\nend\n\n% Close window\nclose(hCurrWin)\n\n% Re-open window\nwt_clear_selected_whisker\n\nreturn\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_rotate_frame.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_rotate_frame.m", "size": 993, "source_encoding": "utf_8", "md5": "a92c107a47390dbb6d73e084fa620630", "text": "% WT_ROTATE_FRAME\n%\n\n\nfunction wt_rotate_frame( nDir )\n\nglobal g_tWT\n\n% Display warning message\nif ~isfield(g_tWT.MovieInfo, 'SplinePoints'), return, end\n\nif ~isempty(g_tWT.MovieInfo.SplinePoints)\n    sQuestResp = questdlg('Changing the ROI will delete already marked whiskers. Continue?', 'Warning', 'Yes', 'No', 'Yes');\n    if strcmp(sQuestResp, 'No') return; end\nend\n\nnAngles = [0 90 180 270];\nnCurrPos = find(nAngles == g_tWT.MovieInfo.Rot);\n\n% Determine new 'clock' position\nif nDir == 0 % return to default\n    g_tWT.MovieInfo.Rot = 0;\n    g_tWT.MovieInfo.Roi = [1 1 g_tWT.MovieInfo.Width-1 g_tWT.MovieInfo.Height-1];% * g_tWT.MovieInfo.ResizeFactor;\nelse\n    nNewPos = nCurrPos - nDir;\n    switch nNewPos\n        case 0\n            g_tWT.MovieInfo.Rot = nAngles(4);\n        case 5\n            g_tWT.MovieInfo.Rot = nAngles(1);\n        otherwise\n            g_tWT.MovieInfo.Rot = nAngles(nNewPos);\n    end\nend\n\ng_tWT.MovieInfo.SplinePoints = [];\n\n% Refresh frame\nwt_display_frame\n\nreturn"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_flip_frame.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_flip_frame.m", "size": 866, "source_encoding": "utf_8", "md5": "9b7c021b84096aead6b9a000be27738e", "text": "%%%% WT_FLIP_FRAME %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nfunction wt_flip_frame( sDirection )\n\nglobal g_tWT\n\nswitch sDirection\n    case 'updown' % Flip Up-Down\n        g_tWT.MovieInfo.Flip(1) = ~g_tWT.MovieInfo.Flip(1);\n    case 'leftright' % Flip Left-Right\n        g_tWT.MovieInfo.Flip(2) = ~g_tWT.MovieInfo.Flip(2);\nend\n\n% Refresh display\nwt_display_frame"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_create_filter.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_create_filter.m", "size": 6277, "source_encoding": "utf_8", "md5": "38d33983265ffab70c0fc85628361dbe", "text": "function tFilter = wt_create_filter(tDefaultFilter, sOption)\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n\n% Create a mexican hat (DoG) filter interactively\n%\n% Usage:\n% Set filter interactively\n%   tFilter = wt_create_filter(tDefaultFilter)\n%\n% Create filter only (don't open GUI)\n%   tFilter = wt_create_filter(tDefaultFilter, 'create-only')\n%\n\nif exist('sOption')\n    switch lower(sOption)\n        case 'create-only'\n            tFilter = CreateFilter(tDefaultFilter);\n            return\n    end\nend\n\nhWT_win = findobj('Tag', 'WTMainWindow');\nif ~isempty(hWT_win)\n    wt_display_frame; % refresh frame\nend\n\nif ~exist('tDefaultFilter')\n    tDefaultFilter.Size = 10;\n    tDefaultFilter.Sigma_A = 3;\n    tDefaultFilter.Sigma_B = 1;\nend\n\n% Open window\nhFig = figure;\nset(hFig, 'Position', [350 520 520 180], 'MenuBar', 'none', 'NumberTitle', 'off', 'Name' ,'WT - GENERATE FILTER', 'Tag', 'WT_GENERATE_FILTER', 'Resize', 'off')\n\nhSub = subplot('Position', [.65 .1 .35 .8]);\nset(hSub, 'xtick', [], 'ytick', [], 'Color', 'k', 'Tag', 'WT_GENERATE_FILTER_SUBPLOT')\naxis square\n\n% Slide bars\nhSliderAB_size = uicontrol(hFig, 'style', 'slider');\nSetSliderProps(hSliderAB_size, [.15 .85 .4 .1], 1, 101, [.01 .1], tDefaultFilter.Size, 'AB_SIZE', 'Size', @UpdateFilter)\n\nhSliderA_sigma = uicontrol(hFig, 'style', 'slider');\nSetSliderProps(hSliderA_sigma, [.15 .54 .4 .1], 0.001, 10, [.01 .1], tDefaultFilter.Sigma_A, 'A_SIGMA', 'A sigma', @UpdateFilter)\n\nhSliderB_sigma = uicontrol(hFig, 'style', 'slider');\nSetSliderProps(hSliderB_sigma, [.15 .41 .4 .1], 0.001, 10, [.01 .1], tDefaultFilter.Sigma_B, 'B_SIGMA', 'B sigma', @UpdateFilter)\n\nhTxt = uicontrol(gcf, 'style', 'text'); % Text string indicating slider value\nset(hTxt, 'units', 'normalized', 'string', 'Size should greater than the maximal expected frame to frame displacement', 'Position', [.05 .675 .575 .15], 'HorizontalAlignment', 'left')\n\nhTxt = uicontrol(gcf, 'style', 'text'); % Text string indicating slider value\nset(hTxt, 'units', 'normalized', 'string', 'Sigmas should be adjusted so that the object of interest appears dark in the image', 'Position', [.05 .23 .575 .15], 'HorizontalAlignment', 'left')\n\n\nhSaveButton = uicontrol(hFig, 'style', 'pushbutton'); % SAVE button\nset(hSaveButton, 'units', 'normalized', 'Position', [.15 .05 .15 .15], 'string', 'Save', 'Callback', ['uiresume(gcf);'])\nhCancelButton = uicontrol(hFig, 'style', 'pushbutton'); % CANCEL button\nset(hCancelButton, 'units', 'normalized', 'Position', [.4 .05 .15 .15], 'string', 'Cancel', 'Callback', ['close(gcf);'])\n\nTestFilter;\n\nuiwait(hFig)\n\nif ~isempty(findobj('Tag', 'WT_GENERATE_FILTER'))\n    tFilter.Size = round(get(findobj('Tag', 'AB_SIZE'), 'Value'));\n    tFilter.Sigma_A = get(findobj('Tag', 'A_SIGMA'), 'Value');\n    tFilter.Sigma_B = get(findobj('Tag', 'B_SIGMA'), 'Value');\n    tFilter = CreateFilter(tFilter);\n    close(hFig)\nelse\n    tFilter = tDefaultFilter;\nend\n\nwt_display_frame\n\nTestFilter('clear')\n\nreturn\n\n% Create filter\nfunction TestFilter(sOption)\n\nglobal g_tWT\npersistent mImg\n\nif exist('sOption')\n    switch lower(sOption)\n        case 'clear'\n            clear mImg;\n            return\n    end\nend\n\nnSize = round(get(findobj('Tag', 'AB_SIZE'), 'Value'));\nmGaussA = fspecial('gaussian', nSize, get(findobj('Tag', 'A_SIGMA'), 'Value')); % Gaussian A\nmGaussB = fspecial('gaussian', nSize, get(findobj('Tag', 'B_SIGMA'), 'Value')); % Gaussian B\nmFilter = mGaussA - mGaussB; % DoG (Difference of Gaussians)\n\nhSub = findobj('Tag', 'WT_GENERATE_FILTER_SUBPLOT');\naxis(hSub);\nimagesc(mFilter);\ncolormap pink\nset(hSub, 'xtick', [], 'ytick', [], 'Color', 'k', 'Tag', 'WT_GENERATE_FILTER_SUBPLOT')\naxis square\ndrawnow\n\n% Convolve WT window\nhWT_win = findobj('Tag', 'WTMainWindow');\nif ~isempty(hWT_win) % don't convolve if WT window is closed\n    if isempty(mImg)\n        hAx = findobj(hWT_win, 'type', 'axes');\n        mImg = get(findall(hAx, 'type', 'image'), 'cdata');\n    end\n    mImg_conv = conv2(double(mImg), mFilter, 'same'); % convolve\n    wt_display_frame(get(g_tWT.Handles.hSlider, 'value'), mImg_conv); % FIX FRAMENUMBER!!!!!!\nend\n\nfigure(findobj('Tag', 'WT_GENERATE_FILTER'))\n\nreturn\n% end test filter\n\n% Update filter parameters\nfunction UpdateFilter(varargin)\nhAB_SIZE = findobj('Tag', 'AB_SIZE');\nhAB_SIZE_txt = findobj('Tag', 'AB_SIZE-txt');\nset(hAB_SIZE_txt, 'String', round(get(hAB_SIZE, 'Value')));\n\nhA_SIGMA = findobj('Tag', 'A_SIGMA');\nhA_SIGMA_txt = findobj('Tag', 'A_SIGMA-txt');\nset(hA_SIGMA_txt, 'String', get(hA_SIGMA, 'Value'));\n\nhB_SIGMA = findobj('Tag', 'B_SIGMA');\nhB_SIGMA_txt = findobj('Tag', 'B_SIGMA-txt');\nset(hB_SIGMA_txt, 'String', get(hB_SIGMA, 'Value'));\n\nTestFilter;\n\nreturn\n% end update filter\n\n% Set slider properties\nfunction SetSliderProps(hSlider, vPos, nMin, nMax, vStep, nDefault, sTag, sString, sCallback)\nset(hSlider, 'units', 'normalized' ...\n    , 'Position', vPos ...\n    , 'Style', 'slider' ...\n    , 'Tag', sTag ...\n    , 'Min', nMin ...\n    , 'Max', nMax ...\n    , 'SliderStep', vStep ...\n    , 'Value', nDefault ...\n    , 'CallBack', sCallback );\nhTxt = uicontrol(gcf, 'style', 'text'); % Text string indicating slider value\nset(hTxt, 'units', 'normalized', 'string', get(hSlider, 'Value'), 'Position', [vPos(1)+vPos(3) vPos(2) .075 vPos(4)], 'Tag', sprintf('%s-txt', sTag))\n\nhTxt = uicontrol(gcf, 'style', 'text'); % Descriptive text string\nset(hTxt, 'units', 'normalized', 'string', sString, 'Position', [.05 vPos(2), vPos(1)-.05 vPos(4)], 'HorizontalAlignment', 'left', 'FontWeight','bold')\n\nreturn\n% end slider properties\n\n% Create filter\nfunction tFilter = CreateFilter(tFilter)\nmGaussA = fspecial('gaussian', tFilter.Size, tFilter.Sigma_A); % Gaussian A\nmGaussB = fspecial('gaussian', tFilter.Size, tFilter.Sigma_B); % Gaussian B\ntFilter.Filter = mGaussA - mGaussB; % DoG (Difference of Gaussians)\nreturn\n% end create filter\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_crop_behaving_video.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_crop_behaving_video.m", "size": 7146, "source_encoding": "utf_8", "md5": "5176de9198f557e2304c65564f2de0a9", "text": "% WT_CROP_BEHAVING_VIDEO\n% Extract region of interest parallel to whisker-pad.\n% wt_crop_behaving_video(M, C, HOR, RAD, METHOD), where\n%   M is the loaded frame\n%   C is a 3-by-2 vector containing X Y coordinates of right-eye, left-eye\n%     and nose.\n%   HOR is the length of the extracted region (anterior-posterior axis)\n%   RAD is the width of the extracted region (distance from face)\n%   METHOD is the interpolation method used when rotating the extracted\n%     region, either 'linear' (fast) or 'bicubic' (slow, but better).\n%\n\nfunction mCropImg = wt_crop_behaving_video(mImg, mCoords, hor_ext, rad_ext, method);\nglobal g_tWT\n\n% Resize cropping parameters according to the ersize factor\n%hor_ext = hor_ext * g_tWT.MovieInfo.ResizeFactor;\n%rad_ext = rad_ext * g_tWT.MovieInfo.ResizeFactor;\n%mCoords = mCoords * g_tWT.MovieInfo.ResizeFactor;\n\nbDebug = g_tWT.VerboseMode;\nswitch lower(method)\n    case 'bicubic'\n        bBicubic = 1;\n    case 'nearest'\n        bBicubic = 0;\n    otherwise\n        error('Unknown method in wt_crop_behaving_vide')\nend\n\n% Rotate and extract the relevant parts of the image\n% A small note: the routine first defines the vertices of the relevant\n% section in the coordinates of the original frame. It then extracts this\n% part, rotates it and re-crops it so only the defined rectangle is kept.\n% An alternative would be to rotate the entire frame, and then crop. This is\n% intuitively simpler, but because the imrotate(...) function is slow on\n% large matrices (e.g. such as the entire frame), this method takes more\n% than 3 times longer to execute than the first method.\n\n% TODO:\n%   - account for possible errors coordinates are outside the frame\n%   - return both right and left side and the ROI in absolute coordinates\n%   - check consistency of mCoords input matrix\n\nif bDebug\n    hFig = findobj('Tag', 'CROP_FRAME_WINDOW');\n    if isempty(hFig)\n        hFig = figure;\n        set(hFig, 'Tag', 'CROP_FRAME_WINDOW', 'DoubleBuffer', 'on', 'Name', 'WT - CROP_FRAME_WINDOW', 'numbertitle', 'off')\n    else, figure(hFig), end\n    clf;\nend\n\nnPadFact = 4;\n\n% zero-pad image to avoid boundary errors\nmImg = padarray(mImg, [hor_ext*nPadFact rad_ext*nPadFact], mean(mean(mImg(:,:,1))),'both');\n\n% repeat once for right eye and once for left eye\nnew_coords = zeros(4,2);\nfor e = 1:2 % 1=right 2=left\n\n    % angle between eye and nose\n    y = abs(mCoords(e,2) - mCoords(3,2));\n    x = mCoords(e,1) - mCoords(3,1);\n    if x < 0\n        theta = -1*(90 + rad2deg(atan(y/x)));\n    else\n        theta = 90 - rad2deg(atan(y/abs(x)));\n    end\n\n    if (mCoords(3,1)-mCoords(e,1)) < 0\n        ang = pi + atan( (mCoords(3,2)-mCoords(e,2))/(mCoords(3,1)-mCoords(e,1)) ); % ang between points\n    else\n        ang = atan( (mCoords(3,2)-mCoords(e,2))/(mCoords(3,1)-mCoords(e,1)) ); % ang between points\n    end\n\n    eye_nose_zero_intersect = interp1([mCoords(3,2) mCoords(e,2)], [mCoords(3,1) mCoords(e,1)], 0, 'linear', 'extrap');\n    r_nose = sqrt(sum([(mCoords(3,1)-eye_nose_zero_intersect) mCoords(3,2)].^2)) + hor_ext;\n    new_coords(1,:) = [ r_nose*cos(ang)+eye_nose_zero_intersect r_nose*sin(ang) ]; % ant A\n\n    r_eye = sqrt(sum([(mCoords(e,1)-eye_nose_zero_intersect) mCoords(e,2)].^2)) - hor_ext;\n    new_coords(2,:) = [ r_eye*cos(ang)+eye_nose_zero_intersect r_eye*sin(ang) ]; % post A\n\n    adj = [rad_ext*cos(ang-deg2rad(90)) rad_ext*sin(ang-deg2rad(90))];\n    if e == 1 % right eye\n        new_coords(4,:) = new_coords(1,:) - adj; % ant B\n        new_coords(3,:) = new_coords(2,:) - adj; % post B\n    else % left eye\n        new_coords(4,:) = new_coords(1,:) + adj; % ant B\n        new_coords(3,:) = new_coords(2,:) + adj; % post B\n    end\n\n    % transform coordinates to take into account zero-padding\n    new_coords = [new_coords(:,1)+rad_ext*nPadFact new_coords(:,2)+hor_ext*nPadFact];\n\n    % extract the rectangle\n    x = min(new_coords(:,1));\n    y = min(new_coords(:,2));\n    w = max(new_coords(:,1)) - x; %end % use width of 1st frame\n    h = max(new_coords(:,2)) - y; %end % use height of 1st frame\n    try\n        mTemp = mImg(round(y:y+h), round(x:x+w));\n    catch\n        wt_error('Possible error with head-coordinates. Check by changing display mode.')\n    end\n\n    % calculate what the size of the rotate image will be (uncropped)\n    theta_rad = deg2rad(theta);\n    R = [cos(theta_rad) sin(theta_rad); -sin(theta_rad) cos(theta_rad)];\n    half_w = size(mTemp, 1)/2;\n    half_h = size(mTemp, 2)/2;\n    % corners = [up_left; up_right; low_left; low_right]\n    corners(1,:) = [-half_w half_h];\n    corners(2,:) = [half_w half_h];\n    corners(3,:) = [-half_w -half_h];\n    corners(4,:) = [half_w -half_h];\n    new_corners = corners * R;\n    if theta < 0\n        new_size = abs([new_corners(3,1)*2 new_corners(1,2)*2]);\n    else\n        new_size = abs([new_corners(1,1)*2 new_corners(2,2)*2]);\n    end\n    % zeropad image before rotating to take into account loss of pixels\n    % during cropping\n    vPad = ceil((new_size-size(mTemp))/2);\n    % remove negative values\n    vPad(vPad<0) = 0;\n    mTemp = padarray(mTemp, vPad, 'both');\n\n    % rotate rectangle\n    if bBicubic\n        %  - use Matlab method if choosing bicubic interpolation\n        mTemp = imrotate(mTemp, theta, 'bicubic', 'crop');\n    else\n        %  - use C script of choosing nearest interpolation\n        mTemp = rotate_matrix(double(mTemp), theta);\n    end\n\n    % crop again so only the relevant section is included in final image\n    % We have replaced the two lines below with parameters defined earlier\n    % in the global namespace, as we want all frames to have the same size.\n    %new_w = round(rad_ext);\n    %new_h = round(hor_ext*2 + sqrt(sum([mCoords(3,1)-mCoords(e,1) mCoords(3,2)-mCoords(e,2)].^2))); %end\n    new_w = g_tWT.MovieInfo.ImCropSize(1);% * g_tWT.MovieInfo.ResizeFactor;\n    new_h = g_tWT.MovieInfo.ImCropSize(2);% * g_tWT.MovieInfo.ResizeFactor;\n\n    x = round((size(mTemp, 2) - new_w) / 2);\n    y = round((size(mTemp, 1) - new_h) / 2);\n    mCropImg{e} = mTemp(round(y:y+new_h), round(x:x+new_w));\n\n    if bDebug\n        subplot(1,4,e+2)\n        imagesc(mCropImg{e}); SetPlotProps;\n    end\n    \nend\n\n% Plot\nif bDebug\n    subplot(1,4,1:2)\n    imagesc(mImg(hor_ext:size(mImg,1)-hor_ext,rad_ext:size(mImg,2)-rad_ext))\n    title('Original')\n    colormap gray; hold on\n    scatter(mCoords(:,1),mCoords(:,2),'g.') % eyes/nose\n    scatter(new_coords(:,1)-rad_ext, new_coords(:,2)-hor_ext, 'r.') % rect coords\n    plot(new_coords([1 2 3 4 1], 1)-rad_ext, new_coords([1 2 3 4 1], 2)-hor_ext, 'r')\n    SetPlotProps;\n    drawnow\nend\n\n% make sure that both rectangles have the same dimensions before returning\n% them\nmin_height = min([size(mCropImg{1},1) size(mCropImg{2},1)]);\nmin_width  = min([size(mCropImg{1},2) size(mCropImg{2},2)]);\n\n% crop outwards (from whisker roots) and downwards (from neck)\nmCropImg{1} = mCropImg{1}(1:min_height, (size(mCropImg{1},2)+1-min_width):end);\nmCropImg{2} = mCropImg{2}(1:min_height, 1:min_width);\n\n% flip right rectangle horizontally\nmCropImg{1} = fliplr(mCropImg{1});\n\nreturn;\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction SetPlotProps\nset(gca, 'xtick', [], 'ytick', [])"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_subtract_bg_frame.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_subtract_bg_frame.m", "size": 8477, "source_encoding": "utf_8", "md5": "aa677748a07abb05bd8819239e12c399", "text": "% WT_SUBTRACT_BG_FRAME\n% IMG_OUT = wt_subtract_bg_frame(IMG, F), where\n%   IMG is the frame from which to subtract the background frame\n%   F is the current frame number\n%   IMG_OUT is IMG minus the background\n%\n% Subtract background frame from passed image. The passed coordinates are\n% used to select what portion of the frames to subtract, in case the passed\n% image has alredy been cropped. vCoords are in absolute coordinates. The\n% current-frame parameter is used to determine whcih series of frames to\n% subtract from the passed image, in case different background-frames have\n% been calculated (e.g. before object entry and after object entry)\n%\n% The background frame is low-pass filtered if a number has been entered in\n% the 'Low-pass background image (pixels)' field in the parameters GUI. The\n% value entered is used to determine the diameter (in pixels) of a low-pass\n% median filter.\n%\n% IMPORTANT NOTE:\n% If in the matrix where background frame series are specified any row\n% contains two zeros [0 0] then ALL background frames will be subtracted\n% from ALL frames!\n%\n\nfunction mImgOut = wt_subtract_bg_frame(varargin)\n\nglobal g_tWT\npersistent p_tBgFrames\npersistent p_nBGLowPass\n\nif nargin == 1\n    switch varargin{1}\n        case 'reset'\n            p_tBgFrames = struct([]);\n            return;\n    end\nelse\n    mImgIn          = varargin{1};\n    vCoords         = [1 1 g_tWT.MovieInfo.Width-1 g_tWT.MovieInfo.Height-1];% * g_tWT.MovieInfo.ResizeFactor;\n    nCurrentFrame   = varargin{2};\nend\n\nbDebug = g_tWT.VerboseMode;\n\n% Iterate over p_cBgFrames and decide which background-frames to scrap and\n% which to keep. If all are scrapped, calculate all background frames. If\n% all are kept, then don't recalculate any of them. Here we also shuffle\n% the order of the background frames and case some are kept and new ones\n% are inserted. For instance, new background frames will be calculated if\n% the user changes the start- and stop-frame values of the background\n% frames in the GUI.\n\n% Select frame-series to average\nmAvgFrames = g_tWT.MovieInfo.AverageFrames;\n\n% Determine whether to subtract ALL background frames from current frame\nnZeroRow = find(any(~all(mAvgFrames,2)));\nif ~isempty(nZeroRow)\n    bSubtractAll = 1;\n    mAvgFrames(nZeroRow, :) = [];\nelse, bSubtractAll = 0; end\n\n% Exit immediately if background frames should not be used\nif isempty(mAvgFrames)\n    p_tBgFrames = struct([]);\n    mImgOut = double(mImgIn);\n    return;\nend\n\n% If the size of the background-frame smoothing window has changed, scrap\n% all existing background frames and calculate these again (and then smooth\n% with the new window)\nif p_nBGLowPass ~= g_tWT.MovieInfo.BGFrameLowPass\n    p_tBgFrames = struct([]); % clear persistent variable (we concatenate the existing frames below)\nend\np_nBGLowPass = g_tWT.MovieInfo.BGFrameLowPass;\n\n% Select which of the existing background frames to keep\ntTempFrames = struct([]);\nfor bgA = 1:size(p_tBgFrames, 2)\n    for bgB = 1:size(mAvgFrames, 1)\n        if (p_tBgFrames(bgA).from == mAvgFrames(bgB,1)) ...\n                & (p_tBgFrames(bgA).to == mAvgFrames(bgB,2))\n            % Background-frame already exists...\n            mAvgFrames(bgB,:) = [NaN NaN];\n            nNewIndx = size(tTempFrames,2)+1;\n            tTempFrames(nNewIndx).img = p_tBgFrames(bgA).img;\n            tTempFrames(nNewIndx).from = p_tBgFrames(bgA).from;\n            tTempFrames(nNewIndx).to = p_tBgFrames(bgA).to;\n        end\n    end\nend\nmAvgFrames(find(isnan(mAvgFrames))) = [];\n\np_tBgFrames = struct([]); % clear persistent variable (we concatenate the existing frames below)\n\n% Determine how many background frames to calculate.\nif isempty(mAvgFrames), nNoBgFrames = 0;\nelse, nNoBgFrames = size(mAvgFrames, 1); end\n\n% Initialize waitbar\nfor bg = 1:nNoBgFrames\n    if ~exist('hWaitBar'), hWaitBar = waitbar(0, ''); end\n    % Update waitbar\n    waitbar(bg/(nNoBgFrames+1), hWaitBar, sprintf('Calculating background-frame %d of %d', bg, nNoBgFrames))\n\n    nFrom = mAvgFrames(bg,1); % start range\n    nTo = mAvgFrames(bg,2); % end range\n    if nFrom == 0, nFrom = 1, end\n    if nTo == 0, nTo = nFrom + 1; end\n\n    if nFrom > nTo % check consistency in input\n        wt_error('The first background frame has a higher frame-number than the last. Check values in Image->Parameters.')\n    end\n\n    nAviTo = nTo; % limit nTo to max frames\n    if nTo > g_tWT.MovieInfo.NumFrames\n        nAviTo = g_tWT.MovieInfo.NumFrames;\n    end\n    \n    % Load background frames in chunks to avoid memory problems\n    if g_tWT.MovieInfo.NoFramesToLoad >= (nTo - nFrom), vRanges = [nFrom nTo];\n    else, vRanges = nFrom:g_tWT.MovieInfo.NoFramesToLoad:nTo; end\n    mAveragedFrames = [];\n    for r = 2:length(vRanges)\n        vRange = [vRanges(r-1) (vRanges(r)-1)]; % [min max]\n        vFrames = max([1 vRange(1)]):min([vRange(2) g_tWT.MovieInfo.NumFrames]); % all frames to be loaded\n        if isempty(g_tWT.MovieInfo.FilenameUncompressed)\n            mFrames = wt_load_avi(g_tWT.MovieInfo.Filename, vFrames, 'noresize');\n        else\n            mFrames = wt_load_avi(g_tWT.MovieInfo.FilenameUncompressed, vFrames, 'noresize');\n        end\n        \n        try, mFrames = double(mFrames);\n        catch, wt_error('Out of memory. Try to reduce the number of background frames.'), end\n\n        mAveragedFrame = mean(mFrames, 3);\n        mAveragedFrames(:,:,r-1) = mAveragedFrame;\n    end\n    mAveragedFrame = mean(mAveragedFrames,3);\n    \n    % Crop image\n    mAveragedFrame = imcrop(mAveragedFrame, vCoords);%./g_tWT.MovieInfo.ResizeFactor);\n\n    % Smooth background image\n    if isfield(g_tWT.MovieInfo, 'BGFrameLowPass')\n        nLowPass = g_tWT.MovieInfo.BGFrameLowPass;\n        if ~isempty(nLowPass) & (nLowPass > 1)\n            mWin = ones(nLowPass);\n            mAveragedFrame = conv2(mAveragedFrame, mWin, 'same') ./ prod(size(mWin));\n        end\n    end\n    \n    % Store averaged frame in the persistent holder variable\n    p_tBgFrames(bg).img = mAveragedFrame;\n    p_tBgFrames(bg).from = mAvgFrames(bg,1);\n    p_tBgFrames(bg).to = mAvgFrames(bg,2);\n\nend\nif exist('hWaitBar'), close(hWaitBar); end\n\n% Concatenate new background frames with the exisiting ones we stored in the beginning of this function\np_tBgFrames = cat(2, p_tBgFrames, tTempFrames);\n\n% Sort background frames in ascending order by 'from' field\n[vNewOrder vNewIndxOrd] = sortrows(char(p_tBgFrames.from));\np_tBgFrames = p_tBgFrames(vNewIndxOrd);\n\n% Show all background frames in debug mode\nif bDebug\n    hFig = findobj('Tag', 'SUBTRACT_BACKGROUND_WINDOW');\n    if isempty(hFig)\n        hFig = figure;\n        set(hFig, 'Tag', 'SUBTRACT_BACKGROUND_WINDOW', 'DoubleBuffer', 'on', 'Name', 'WT - SUBTRACT_BACKGROUND_WINDOW', 'numbertitle', 'off')\n    else, figure(hFig), end\n    clf\n    for bg = 1:size(p_tBgFrames,2)\n        subplot(2,max([3 size(p_tBgFrames,2)]),bg)\n        imagesc(p_tBgFrames(bg).img); colormap gray;\n        title(sprintf('BG frame %d-%d', p_tBgFrames(bg).from, p_tBgFrames(bg).to))\n    end\nend\n\n\n% Finally, subtract the appropriate background frame from the passed frame\n% (use input parameter nCurrentFrame to determine which background frame to\n% subtract)\nmBgFrame = zeros(size(mImgIn));\nmImgOut = mImgIn;\nif bSubtractAll\n    cAll = {p_tBgFrames(:).img};\n    mAll = cat(3, cAll{:,:,:});\n    mBgFrame = mean(mAll,3);\n    mBgFrameRe = mBgFrame;\n    mImgOut = double(mImgIn) - double(mBgFrameRe);\nelse\n    for bg = 1:size(p_tBgFrames,2)\n        % Subtract background frame is there is a such one for the current frame\n        if (nCurrentFrame >= p_tBgFrames(bg).from) & (nCurrentFrame <= p_tBgFrames(bg).to)\n            % Check images have same dimensions\n            if (size(mImgIn,1) == size(p_tBgFrames(bg).img,1)) & (size(mImgIn,2) == size(p_tBgFrames(bg).img,2))\n                mBgFrame = double(p_tBgFrames(bg).img);\n                mBgFrameRe = mBgFrame;\n                mBgFrameN = mBgFrameRe./max(max(mBgFrameRe));\n                mImgInN = double(mImgIn)./max(max(double(mImgIn)));\n                mImgOut = mImgInN - mBgFrameN;\n            else\n                wt_error('Current frame and its background frame do not have the same dimensions.')\n            end\n        end\n    end\nend\n\n% Plot original, background frame and adjusted image\nif bDebug\n    subplot(2,3,4)\n    imagesc(mImgIn); title('Original')\n    subplot(2,3,5)\n    imagesc(mBgFrame); title('Background')\n    subplot(2,3,6)\n    imagesc(mImgOut); title('Adjusted')\n    colormap gray\n    drawnow\nend\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_adjust_whisker_length.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_adjust_whisker_length.m", "size": 2894, "source_encoding": "utf_8", "md5": "390bc5df4dbfa6f89ebf615bca7636e6", "text": "% WT_ADJUST_WHISKER_LENGTH\n% Adjust whisker length to that of first known frame.\n%\n% Syntax:    [vX, vY] = wt_adjust_whisker_length(I,X,Y), where\n%               I = I'th whisker\n%               X = vector of X splinepoints\n%               Y = vector of Y splinepoints\n%           Output variables:\n%               X = adjusted X splinepoints\n%               Y = adjusted Y splinepoints\n%\n% This function works in conjunction with the WT GUI.\n\nfunction [vX, vY] = wt_adjust_whisker_length(nChWhisker, vX, vY)\n\nglobal g_tWT\n\nvXorig = vX;\nvYorig = vY;\n\n% Make sure no values are equal on the x axis\nwhile 1\n    vIndx = find(~diff(vX));\n    if length(vIndx) == 0, break, end\n    vX(vIndx+1) = vX(vIndx) + 3;\nend\n\n% Length of initially marked whisker\nnKnownFrames = find(g_tWT.MovieInfo.SplinePoints(1, 1, :, nChWhisker));\nvXinit = g_tWT.MovieInfo.SplinePoints(:, 1, nKnownFrames(1), nChWhisker);\nvYinit = g_tWT.MovieInfo.SplinePoints(:, 2, nKnownFrames(1), nChWhisker);\nnWhiskerLength = GetWhiskerLength(vXinit, vYinit);\n\n% Length of new whisker\ntry, nLen = GetWhiskerLength(vX, vY);\ncatch, wt_error('Error when recalculating whisker length'); end\n\nif (length(vX) == 4) & (vX(end)>0 & vY(end)>0), nLastIndx = 4;\nelse, nLastIndx = 3; end\n\nif nLen > nWhiskerLength % new whisker is LONGER than original\n    vXX = min(vX((1:nLastIndx))):max(vX((1:nLastIndx))); % whisker spline in 1st known frame\n    [vXX, vYY] = wt_spline(vX(1:nLastIndx), vY(1:nLastIndx), vXX);\n    vCumLen = cumsum(sqrt(diff(vXX).^2 + diff(vYY).^2));\n    [nDiff, nDiffIndx] = min(abs(vCumLen - nWhiskerLength));\n    vX(nLastIndx) = vXX(nDiffIndx+1);\n    vY(nLastIndx) = vYY(nDiffIndx+1);\nelseif nLen < nWhiskerLength  % new whisker is SHORTER than original\n    % Increase length of new whisker by 1 pix until its same length as original\n    while nLen < nWhiskerLength\n        % Extrapolate Y value at new X position\n        vY(nLastIndx) = interp1(vX(1:nLastIndx), vY(1:nLastIndx), vX(nLastIndx)+1, 'spline', 'extrap');\n        vX(nLastIndx) = vX(nLastIndx)+1;\n        try, nLen = GetWhiskerLength(vX, vY);\n        catch, wt_error('Error when computing whisker length'), end\n    end\nend\n\n% Sort splinepoint in case 2nd and 3rd swapped places (should be in\n% increasing order along X dimension)\nvIndx = find(vX & vY);\nmC = [vX vY];\nmC(vIndx,:) = sortrows(mC(vIndx,:));\nvX = mC(:,1);\nvY = mC(:,2);\n\n% Make sure no values are equal on the x axis\nwhile 1\n    vIndx = find(~diff(vX));\n    if length(vIndx) == 0, break, end\n    vX(vIndx+1) = vX(vIndx) + 3;\nend\n\nreturn\n\n%%%% GET_WHISKER_LENGTH %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction nLen  = GetWhiskerLength(vX, vY)\n% Remove 4th element if present AND zero\nvKeepXIndx = find(vX);\nvX = vX(vKeepXIndx);\nvY = vY(vKeepXIndx);\n\nvXX = min(vX):max(vX); % whisker spline in 1st known frame\n[vXX, vYY] = wt_spline(vX, vY, vXX);\nnLen = sum(sqrt(diff(vXX).^2 + diff(vYY).^2));\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_mark_whisker.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_mark_whisker.m", "size": 9460, "source_encoding": "utf_8", "md5": "4d40afc1c5ad815e172aff1e9cf8b192", "text": "function wt_mark_whisker(varargin)\n% WT_MARK_WHISKER\n% Mark new whisker, or add/remove spline-points that define whiskerpoints.\n% Syntax: wt_mark_whisker(OPT), where\n%           OPT is an optional string that runs one of the following\n%           subroutines:\n%               'new'           - mark a new whisker\n%               'addpoint'      - add splinepoint to existing whisker\n%               'removepoint'   - remove splinepoint from existing whisker\n%               'movebasepoint' - move the whisker base point\n%               'setfulllength' - mark the full length of the whisker\n%               'setlastframe'  - mark the last frame on which to track the\n%                                   whisker\n%\n% The default subroutine is 'new'. For all other subroutines, \n% you must provide a second parameter which is the whisker ID/index\n% to modify.\n\nif nargin >= 1\n    sSub = varargin{1};\n    nW = varargin{2};\nelse, sSub = 'new'; end\n\nswitch sSub\n    case 'new'\n        MarkNewWhisker;\n    case 'addpoint'\n        AddPoint(nW);\n    case 'removepoint'\n        RemovePoint(nW);\n    case 'setlastframe'\n        SetLastFrame(nW);\n    case 'setfulllength'\n        SetFullLength(nW);\n        return\n    case 'movebasepoint'\n        MoveBasePoint(nW);\nend\n\nwt_display_frame % refresh current frame\n\nreturn;\n\n%%%% MARKNEWWHISKER %%%%%%%%%%%%%%%%%%%%%%%\nfunction MarkNewWhisker\nglobal g_tWT\n\nnFirstFrame = round(get(g_tWT.Handles.hSlider, 'Value'));\n\n% If current frame is before head was tracked, move it to 1st frame with\n% head\nif ~isempty(g_tWT.MovieInfo.Nose)\n    vIndx = find(~isnan(g_tWT.MovieInfo.Nose(:,1)));\n    if nFirstFrame < vIndx(1), nFirstFrame = vIndx(1); end\n    wt_display_frame(nFirstFrame);\nend\n\naxes(g_tWT.FrameAx); hold on\nvX = []; vY = [];\nhDots = [];\ntry\n    for i = 1:3\n            [vX(i), vY(i)] = ginput(1);  % user can hit RETURN\n            hDots(end+1) = line(vX(i), vY(i));\n            set(hDots(end), 'color', 'g', 'marker', '.')\n    end\ncatch\n    return  % in case the user did not input data but instead hit RETURN\nend\ndelete(hDots)\n\n% Sort points\nmCoords = round(sortrows([vX' vY']));\n\n% If this is a two-sided frame, detect which side this whisker belongs to\nnCurrentFrame = get(g_tWT.Handles.hSlider, 'Value');\nif ~isempty(g_tWT.MovieInfo.EyeNoseAxLen)\n    if ~isnan(prod(g_tWT.MovieInfo.Nose(nCurrentFrame, :))) % head is known only if nose is known\n        vAxSize = get(g_tWT.FrameAx, 'XLim');\n        % Locate side based on x val of first point\n        if mCoords(1,1) < vAxSize(2)/2\n            % Left part of frame (right side of rat). Don't change mCoords\n            nSide = 1;\n        else\n            % Right part of frame (left side of rat)\n            % Change mCoords to absolute coordinates in right frame part\n            mCoords(:,1) = mCoords(:,1) - vAxSize(2)/2; % only change X\n            nSide = 2;\n        end\n    else, nSide = 1; end\nelse nSide = 1; end\n\n% Save whisker splinepoints\nif isempty(g_tWT.MovieInfo.SplinePoints), nIndx = 1;\nelse, nIndx = size(g_tWT.MovieInfo.SplinePoints, 4) + 1; end\ng_tWT.MovieInfo.SplinePoints(1:3, 1:2, nFirstFrame, nIndx) = mCoords;% ./ g_tWT.MovieInfo.ResizeFactor;\ng_tWT.MovieInfo.Angle(nFirstFrame, nIndx) = 0;\ng_tWT.MovieInfo.Intersect(nFirstFrame, 1:2, nIndx) = [0 0];\ng_tWT.MovieInfo.MidPointConstr(1:2, nIndx) = [0 0]';\ng_tWT.MovieInfo.WhiskerSide(nIndx) = nSide; % 0=left, 1=right\ng_tWT.MovieInfo.LastFrame(nIndx) = g_tWT.MovieInfo.NumFrames;\n\n% Set whisker identity\nwt_set_identity(nIndx);\n\nreturn;\n\n\n%%%% ADDPOINT %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction AddPoint(nW)\nglobal g_tWT\nnPtLim = 4; % max num of allowed splinepoints\nnCurrFrame = round(get(g_tWT.Handles.hSlider, 'Value'));\nmWhisker = g_tWT.MovieInfo.SplinePoints(:,:,nCurrFrame,nW);\nif size(mWhisker,1) >= 4\n    if mWhisker(4,1) ~= 0\n        hWarnDlg = warndlg(sprintf('The maximum number of allowed points (%d) has already been reached.', nPtLim), 'WT Error');\n        waitfor(hWarnDlg); return;\n    else\n        mWhisker = mWhisker(1:3,:);\n    end\nend\n\n% Get new point and check that its within X bounds of existing whisker\nvAxSize = get(g_tWT.FrameAx, 'XLim');\nwhile 1\n    [nX, nY] = ginput(1); % get position of new object\n    if g_tWT.MovieInfo.WhiskerSide(nW) == 2\n        nX = nX - vAxSize(2)/2;\n    end\n    if nX <= max(mWhisker(:,1)) & nX >= min(mWhisker(:,1)), break\n    else\n        sAns = questdlg('The marked point is outside boundaries of current whisker.', ...\n            'Error in input', ...\n            'Try again', 'Cancel', 'Try again' );\n       if strcmp(sAns, 'Cancel'), return; end\n    end\nend\n\nplot(nX + vAxSize(2)/2, nY, 'r.'); % plot new point\nmWhisker = round(sortrows([mWhisker; nX nY]));\nif size(g_tWT.MovieInfo.SplinePoints,1) ~= 4\n    g_tWT.MovieInfo.SplinePoints(4,1:2,:,nW) = 0; % expand all frames with an extra row of values (NaN)\nend\ng_tWT.MovieInfo.SplinePoints(:,:,nCurrFrame,nW) = mWhisker; % save new point\n\nreturn;\n\n%%%% REMOVEPOINT %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction RemovePoint(nW)\nglobal g_tWT\n\nnCurrFrame = get(g_tWT.Handles.hSlider, 'Value');\nmWhisker = g_tWT.MovieInfo.SplinePoints(:,:,nCurrFrame,nW);\nif size(mWhisker,1) >= 4\n    if mWhisker(4,1) == 0\n        hWarnDlg = warndlg('There are no points to delete.', 'WT Error');\n        waitfor(hWarnDlg);\n        return;\n    end\nend\n\n[nX, nY] = ginput(1); % get position of new object\nvAxSize = get(g_tWT.FrameAx, 'XLim');\nif g_tWT.MovieInfo.WhiskerSide(nW) == 2\n    nX = nX - vAxSize(2)/2;\nend\n\n% Find closest point (only 2nd or 3rd)\n[y, nIndx] = min(sqrt(abs(mWhisker(2:3,1)-nX).^2 + abs(mWhisker(2:3,2)-nY).^2));\nnIndx = nIndx + 1;\n% Verify removal of point\nsAns = questdlg(sprintf('You have chosen to delete point number %d from the left. Please confirm.', nIndx), ...\n    'WT Confirm', ...\n    'OK', 'Cancel', 'Cancel');\nif strcmp(sAns, 'Cancel'), return; end\n\n% Remove point\nswitch nIndx\n    case 2 % remove 1st midpoint\n        g_tWT.MovieInfo.SplinePoints(2:3,:,nCurrFrame,nW) = g_tWT.MovieInfo.SplinePoints(3:4,:,nCurrFrame,nW);\n        g_tWT.MovieInfo.SplinePoints(4,:,nCurrFrame,nW) = 0;\n    case 3 % remove 2nd midpoint\n        g_tWT.MovieInfo.SplinePoints(3,:,nCurrFrame,nW) = g_tWT.MovieInfo.SplinePoints(4,:,nCurrFrame,nW);\n        g_tWT.MovieInfo.SplinePoints(4,:,nCurrFrame,nW) = 0;\nend\n\nreturn;\n\n\n%%%% SETLASTFRAME %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction SetLastFrame(nW);\nglobal g_tWT\n\n% default is current frame\nsLastFrame = inputdlg('Set last frame to track', 'Set last frame', 1, {num2str(get(g_tWT.Handles.hSlider, 'value'))});\n\n% Change last frame value unless user hit Cancel or eXit\nif ~isempty(sLastFrame)\n    g_tWT.MovieInfo.LastFrame(nW) = str2num(char(sLastFrame));\nend\n\nreturn;\n\n\n%%%% SETFULLLENGTH %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction SetFullLength(nW);\nglobal g_tWT\n\nif ~isfield(g_tWT.MovieInfo, 'WhiskerLength')\n    g_tWT.MovieInfo.WhiskerLength = zeros(1,size(g_tWT.MovieInfo.SplinePoints,4)).*NaN;\nelseif length(g_tWT.MovieInfo.WhiskerLength) < size(g_tWT.MovieInfo.SplinePoints,4)\n    g_tWT.MovieInfo.WhiskerLength(nW) = NaN;\nend\n\nsCurrLen = num2str(g_tWT.MovieInfo.WhiskerLength(nW));\n\nsAns = questdlg('How do you want to set the full whisker length?', 'WT', 'Measure','Enter value','Cancel','Measure');\nswitch sAns\n    case 'Enter value'\n        sLen = inputdlg('Enter whisker length in millimeters', 'WT', 1, {sCurrLen});\n        if isempty(sLen), return, end\n        nLen = str2num(cell2mat(sLen));\n    case 'Measure'\n        \n        % Check that movie is calibrated\n        if isempty(g_tWT.MovieInfo.CalBarLength) | all(g_tWT.MovieInfo.CalibCoords(:) == 0)\n            wt_error('You must calibrate before marking the full whisker length. Select Image -> Calibrate... from the menu.')\n        end\n        \n        if g_tWT.DisplayMode == 1, wt_toggle_display_mode, end, wt_display_frame % absolute view mode\n        \n        axes(g_tWT.FrameAx); hold on % get points\n        vX = []; vY = [];\n        try\n            for i = 1:3\n                [vX(i), vY(i)] = ginput(1);\n                plot(vX(i), vY(i), 'g.')\n            end\n        catch, return, end\n        mCoords = round(sortrows([vX' vY'])); % sort\n        \n        vXX = mCoords(1,1):mCoords(end,1);\n        [vXX, vYY] = wt_spline(mCoords(:,1), mCoords(:,2), vXX); % spline\n        plot(vXX, vYY, 'w:', 'linewidth', 2) % plot whisker\n        \n        nLen = sum(sqrt(diff(vXX).^2 + diff(vYY).^2)); % whisker length (pixels)\n        nLen = nLen / g_tWT.MovieInfo.PixelsPerMM; % length (mm)\n        msgbox(sprintf('Whisker length is %.1f mm', nLen), 'WT')\n    case 'Cancel', return\nend\ng_tWT.MovieInfo.WhiskerLength(nW) = nLen; % length (mm)\ng_tWT.MovieInfo.WhiskerLength(find(g_tWT.MovieInfo.WhiskerLength == 0)) = NaN;\n\nreturn\n\n%%%% MOVEBASEPOINT %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction MoveBasePoint(nW)\nglobal g_tWT\n\nnCurrentFrame = get(g_tWT.Handles.hSlider, 'Value');\n\n[nX, nY] = ginput(1);\n\n% If this is a two-sided frame, detect which side this whisker belongs to\nnCurrentFrame = get(g_tWT.Handles.hSlider, 'Value');\nif ~isempty(g_tWT.MovieInfo.EyeNoseAxLen) % if head has been tracked...\n    if ~isnan(prod(g_tWT.MovieInfo.Nose(nCurrentFrame, :))) % head is known only if nose is known\n        vAxSize = get(g_tWT.FrameAx, 'XLim');\n        if nX > vAxSize(2)/2 % Right part of frame (left side of rat)\n            nX = nX - vAxSize(2)/2; % change X\n        end\n    end\nend\n\ng_tWT.MovieInfo.SplinePoints(1,1:2,nCurrentFrame,nW) = [round(nX) nY];\nwt_display_frame;\n\nreturn\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_user_variables.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_user_variables.m", "size": 2835, "source_encoding": "utf_8", "md5": "74f02db2f7a53d0fb75ebcb184aaae2d", "text": "function wt_user_variables(varargin)\n% Create and modify user-defined variables\n% The purpose of this function is to provide the user with an easy way to\n% store experiment variables inside the WT .mat file. These user variables\n% can also be variables that are accessed from within scripts.\n%\n% Note that adding or modifying experiment variables can be performed as a\n% batch job.\n\n% Copy user variables from persistent variable instead of GUI/\n% This option is used for batch jobs, i.e. copying the variables of one\n% movies to all other movies in directory/tree\nif ~isempty(varargin)\n    if strcmp(varargin{1}, 'copyfrommem')\n        RetrieveUserVariables();\n        return;\n    end\nend\n\n\nglobal g_tWT;\n\n% Check if variables window is already open. If YES, then retrieve and save\n% variables, and close window\nhFig = findobj('Tag', 'WTUserVariables', 'type', 'figure');\nif ~isempty(hFig)\n    RetrieveUserVariables();\n    return\nend\n\nhFig = figure('closeRequestFcn', 'set(gcbf,''userdata'',''closed'')');\ncColumnNames = {'Variable', 'Value', 'Type'};\ncColumnFormat = {'char', 'char', {'String','Number'}}; \ncColumnEditable =  [true true true];\n\ncData = repmat({''},200,3);\n\nif isfield(g_tWT.MovieInfo, 'tUserVariables')\n    for i = 1:length(g_tWT.MovieInfo.tUserVariables)\n        cData{i, 1} = g_tWT.MovieInfo.tUserVariables(i).sVariable;\n        cData{i, 2} = g_tWT.MovieInfo.tUserVariables(i).sValue;\n        cData{i, 3} = g_tWT.MovieInfo.tUserVariables(i).sType;\n    end\nend\n\nset(hFig, 'color', [.2 .2 .2], 'Name', 'WT User Variables', 'NumberTitle', 'off', 'ToolBar', 'none', ...\n    'menuBar','none', 'Tag', 'WTUserVariables')\nhTable = uitable('Units', 'normalized','Position', [0 0 1 1], 'Data', cData, 'ColumnName', ...\n    cColumnNames, 'ColumnEditable', cColumnEditable, 'ColumnFormat', cColumnFormat, ...\n    'ColumnWidth', {120, 282, 100}, 'Tag', 'WTUserVariablesTable');\n\n% Create callback to this function when window is closed\nset(hFig, 'CloseRequestFcn', 'wt_user_variables')\n\nreturn\n\n\n%%%% Retrieve and save user variables\nfunction RetrieveUserVariables\nglobal g_tWT\npersistent cData\n\n% Get figure handle\nhFig = findobj('Tag', 'WTUserVariables', 'type', 'figure');\n\n% If no window is open, then used persistent variable to fill g_tWT\nif ~isempty(hFig)\n    % Get table handle\n    hTable = findobj('Tag', 'WTUserVariablesTable', 'type', 'uitable');    \n    cData = get(hTable, 'data'); % modified data from table\nend\n\ng_tWT.MovieInfo.tUserVariables = struct([]); % clear variables\nfor c = 1:size(cData, 1)\n    if isempty(cData{c,1}) | isempty(cData{c,2}), continue, end\n    g_tWT.MovieInfo.tUserVariables(end+1).sVariable = cData{c, 1};\n    g_tWT.MovieInfo.tUserVariables(end).sValue = cData{c, 2};\n    g_tWT.MovieInfo.tUserVariables(end).sType = cData{c, 3};\nend\n\n% Delete user variables window\ndelete(hFig)\n\nreturn\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_select_directory_tree.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_select_directory_tree.m", "size": 2153, "source_encoding": "utf_8", "md5": "417e8fdbcd1f4fd60f3f876e7994ed58", "text": "function wt_select_directory_tree\n% WT_SELECT_DIRECTORY_TREE\n% Select all movies recursively from all sub-directories below a selected\n% top-level directory.\n%\n% This function prompts the user to select a top-level directory and\n% generates a list of all AVI movies, with complete paths, contained in the\n% entire directory tree under the selected top-level directory.\n%\n% The list of retrieved movies can be accessed through the menu item Files\n% -> Movies.\n%\n\nglobal g_tWT\n\n% Select top-level directory\nif ~isempty(g_tWT.AccessedPaths), sCurrPath = g_tWT.AccessedPaths{1};\nelse, sCurrPath = '0'; end\nsPath = uigetdir(sCurrPath, 'Select top-level directory of selection');\n\nif ~all(sPath), return; end % cancel if no path was selected\n\n% Iterate recursively over all sub-directories and extract paths of AVI\n% movies\ncSelection = GetFilePaths(sPath, '.avi');\n\n% Display error and abort if no AVIs were found\nif isempty(cSelection)\n    warndlg('No AVI movies were found below the chosen directory', 'No movies found')\n    return\nend\n\n% Reset current selection list\ng_tWT.Movies = struct([]);\n\n% Assign new selection to g_tWT\nfor f = 1:length(cSelection)\n    g_tWT.Movies(f).filename = wt_check_path(cSelection{f});\nend\n\n% Re-initialize GUI\nwt_prep_gui;\n\n% Load and display first movie\nwt_load_movie(1);\n\n\nreturn\n\n\n\n% --- Get paths of videos in a directory and its sub-directories\n% Recursive search for .bin files from a selected directory\n% inputs: sSuffix    Find files this file extension (e.g. '.avi')\n%         sBaseDir   Base directory\nfunction cPaths = GetFilePaths(sBaseDir, sSuffix)\ncPaths = {};\ntDirList = dir(sBaseDir);\nfor t1 = 3:length(tDirList)\n    if tDirList(t1).isdir % depth = 1\n        % recursively call this function\n        sBaseDirRecurs = wt_check_path([sBaseDir '/' tDirList(t1).name]);\n        cPaths2 = GetFilePaths(sBaseDirRecurs, sSuffix);\n        cPaths = {cPaths{:}, cPaths2{:}};\n    else\n        % Add .bin files to paths cell\n        if strcmp(tDirList(t1).name(end-3:end), sSuffix)\n            sFilename = wt_check_path([sBaseDir '/' tDirList(t1).name]);\n            cPaths{end+1} = sFilename;\n        end\n    end\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_set_last_frame.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_set_last_frame.m", "size": 1021, "source_encoding": "utf_8", "md5": "6199a5377c8aeda55264f84ac6b28483", "text": "%%%% WT_SET_LAST_FRAME %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction wt_set_last_frame\n% Set Last Frame for all Whiskers defined so far \n% Must check for Cancel or eXit\n%\n% Whisker Tracker (WT)\n%\n% Authors: Per Magne Knutsen, Dori Derdikman\n%\n% (c) Copyright 2004 Yeda Research and Development Company Ltd.,\n%     Rehovot, Israel\n%\n% This software is protected by copyright and patent law. Any unauthorized\n% use, reproduction or distribution of this software or any part thereof\n% is strictly forbidden. \n%\n% Citation:\n% Knutsen, Derdikman, Ahissar (2004), Tracking whisker and head movements\n% of unrestrained, behaving rodents, J. Neurophys, 2004, IN PRESS\n%\n\nglobal g_tWT\n\n% default is current frame\nL = inputdlg('Set last frame to track', 'Set last frame', 1, {num2str(get(g_tWT.Handles.hSlider, 'value'))} );\n\n% Change last frame value unless user hit Cancel or eXit\nif ~isempty(L)\n    for nW = 1:size(g_tWT.MovieInfo.SplinePoints, 4)\n        g_tWT.MovieInfo.LastFrame(nW) = str2num(char(L));\n    end\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_create_outline.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_create_outline.m", "size": 5411, "source_encoding": "utf_8", "md5": "5fca5ab8f2f3db6a7d388de56b54c24c", "text": "function wt_create_outline(sOption, nOL)\n% WT_CREATE_OUTLINE Mark lines that denote various axis in image\n% This function allows points, lines, polygons etc. to be labelled for\n% later data analysis. WT itself does not use either of these\n%\n%  wt_create_outline('add', OL)\n%  wt_create_outline('draw', OL)\n%  wt_create_outline('copy', OL)\n%  wt_create_outline('paste', OL)\n%       If OL is NaN, a new object will be created\n%  wt_create_outline('delete', OL)\n%  wt_create_outline('name', OL)\n%  wt_create_outline('hide', OL)\n\nglobal g_tWT\n\nswitch lower(sOption)\n    case 'add'\n        set(g_tWT.Handles.hStatusText, 'string', 'LEFT = Add point, MIDDLE = Exit, RIGHT = Undo last');\n        tOutline = AddOutline;\n        set(g_tWT.Handles.hStatusText, 'string', '');\n        if isempty(tOutline.Coords), return, end % return if no coordinates entered\n        tOutline.Name = {''};\n        \n        if ~isfield(g_tWT.MovieInfo, 'Outlines')\n            g_tWT.MovieInfo.Outlines = tOutline;\n        else\n            g_tWT.MovieInfo.Outlines(end+1) = tOutline;\n        end\n        wt_set_identity(length(g_tWT.MovieInfo.Outlines), 'outline')\n        wt_display_frame\n        \n    case 'draw'\n        DrawOutline(nOL)\n    case 'copy'\n        CopyPasteOutline(nOL, 'copy')\n    case 'paste'\n        CopyPasteOutline(nOL, 'paste')\n        wt_display_frame\n    case 'delete' % delete outline\n        g_tWT.MovieInfo.Outlines(nOL) = [];\n        delete(findobj('tag',sprintf('outline-%d', nOL)))\n        wt_display_frame\n    case 'deleteall' % delete ALL outline\n        if isfield(g_tWT.MovieInfo, 'Outlines')\n            g_tWT.MovieInfo = rmfield(g_tWT.MovieInfo, 'Outlines');\n            wt_prep_gui\n            wt_display_frame\n        end\n    case 'name' % rename\n        wt_set_identity(nOL, 'outline')\n    case 'hide' % hide all outlines\n        if ~isfield(g_tWT, 'HideOutlines'), g_tWT.HideOutlines = 0; end\n        g_tWT.HideOutlines = ~g_tWT.HideOutlines;\n        switch g_tWT.HideOutlines % update user-menu\n            case 0, sStatus = 'off';\n            case 1, sStatus = 'on';\n        end\n        set(findobj('Label', 'Hide outlines'), 'checked', sStatus);\n        wt_display_frame\n    otherwise\n        return\nend\n\nreturn\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction DrawOutline(nOL)\nglobal g_tWT\n\nhOL = findobj('Tag', sprintf('outline-%d', nOL));\nvX = g_tWT.MovieInfo.Outlines(nOL).Coords(:,1);\nvY = g_tWT.MovieInfo.Outlines(nOL).Coords(:,2);\nif ~isempty(hOL) % refresh\n    if g_tWT.HideOutlines % hide outlines\n        set(hOL, 'XData', vX, 'YData', vY, 'Visible', 'off')\n    else\n        set(hOL, 'XData', vX, 'YData', vY, 'Visible', 'on')\n    end\nelse % redraw\n    if ~isfield(g_tWT, 'HideOutlines'), g_tWT.HideOutlines = 0; end\n    if g_tWT.HideOutlines % hide outlines\n        hOL = plot(vX, vY, 'wx-', 'Tag', sprintf('outline-%d', nOL), 'Visible', 'off');\n    else\n        hOL = plot(vX, vY, 'wx-', 'Tag', sprintf('outline-%d', nOL), 'Visible', 'on');\n    end\nend\nhMenu = uicontextmenu;\nset(hOL, 'uicontextmenu',  hMenu)\nuimenu(hMenu, 'Label', 'Set name', 'Callback', [sprintf('wt_create_outline(''name'',%d)', nOL)])\nuimenu(hMenu, 'Label', 'Delete', 'Callback', [sprintf('wt_create_outline(''delete'',%d)', nOL)])\nuimenu(hMenu, 'Label', 'Copy', 'Callback', [sprintf('wt_create_outline(''copy'',%d)', nOL)])\nuimenu(hMenu, 'Label', 'Paste', 'Callback', [sprintf('wt_create_outline(''paste'',%d)', nOL)])\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction CopyPasteOutline(nOL, sAction)\nglobal g_tWT\npersistent vCopiedX\npersistent vCopiedY\npersistent sCopiedName\n\nhOL = findobj('Tag', sprintf('outline-%d', nOL));\n\nswitch sAction\n    case 'copy'\n        vCopiedX = g_tWT.MovieInfo.Outlines(nOL).Coords(:,1);\n        vCopiedY = g_tWT.MovieInfo.Outlines(nOL).Coords(:,2);\n        sCopiedName = g_tWT.MovieInfo.Outlines(nOL).Name;\n    case 'paste'\n        if isempty(vCopiedX) | isempty(vCopiedY)\n            uiwait(warndlg('Nothing to paste'))\n            return\n        end\n        if ~isfield(g_tWT.MovieInfo, 'Outlines')\n            g_tWT.MovieInfo.Outlines = struct([]);\n        end\n        if isnan(nOL) % create new outline\n            g_tWT.MovieInfo.Outlines(end+1).Coords = [vCopiedX vCopiedY];\n            g_tWT.MovieInfo.Outlines(end).Name = sCopiedName;\n        else % copy coordinates into currently selected outline\n            g_tWT.MovieInfo.Outlines(nOL).Coords = [vCopiedX vCopiedY];\n        end\nend\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction tOutline = AddOutline()\nvX = [];\nvY = [];\nhOutline = plot(0,0,'wx-'); % outline handle\nwhile 1\n    [nX, nY, nButton] = ginput(1);\n    switch nButton\n        case 1 % left   ADD POINT\n            vX(end+1) = nX;\n            vY(end+1) = nY;\n        case 3 % middle UNDO LAST POINT\n            if ~isempty(vX)\n                vX(end) = [];\n                vY(end) = [];\n            end\n        case 2 % right  FINISHED\n            break;\n    end\n    set(hOutline, 'xdata', vX, 'ydata', vY)\nend\ndelete(hOutline)\ntOutline.Coords = [vX' vY'];\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_copy_paste_whisker.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/wt_copy_paste_whisker.m", "size": 2350, "source_encoding": "utf_8", "md5": "1aa4c51e190eed32144a0a9ad9a72997", "text": "function wt_copy_paste_whisker(sOption, nW)\n% Copy/paste a selected whisker to/from clipoard\n\nswitch sOption\n    case 'copy'\n        CopyPasteWhisker(nW, 'copy');\n    case 'paste'\n        CopyPasteWhisker(nW, 'paste');\n        wt_display_frame;\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction CopyPasteWhisker(nW, sAction)\nglobal g_tWT\n\npersistent sCopiedName\npersistent mCopiedCoords\n\nnCurrFrame = g_tWT.CurrentFrameBuffer.Frame;\n\nswitch sAction\n    case 'copy'\n        % Get current frame\n    \tmCopiedCoords = g_tWT.MovieInfo.SplinePoints(:,:,nCurrFrame,nW); % copy current frame only\n    \tsCopiedName = cell2mat(g_tWT.MovieInfo.WhiskerIdentity{nW});\n        if ~isempty(sCopiedName)\n            wt_set_status(['Whisker ' sCopiedName ' has been copied to the clipboard'])\n        else\n            wt_set_status(['Whisker ' sCopiedName 'has been copied to the clipboard'])\n        end\n    case 'paste'\n        if isempty(mCopiedCoords)\n            wt_set_status('No available whisker in clipboard to paste')\n            return\n        end\n        % New whisker index\n        if isempty(g_tWT.MovieInfo.SplinePoints)\n            nNewW = 1;\n        else\n            nNewW = size(g_tWT.MovieInfo.SplinePoints, 4) + 1;\n        end\n\n        % Save whisker data\n        % Note: Copied whisker is copied into current frame\n        g_tWT.MovieInfo.SplinePoints(:,:,nCurrFrame,nNewW) = mCopiedCoords;\n\n        g_tWT.MovieInfo.Angle(1:nCurrFrame, nNewW) = NaN;\n        g_tWT.MovieInfo.Angle(nCurrFrame, nNewW) = 0;\n\n        g_tWT.MovieInfo.Intersect(1:nCurrFrame, 1:2, nNewW) = [NaN NaN];\n        g_tWT.MovieInfo.Intersect(nCurrFrame, 1:2, nNewW) = [0 0];\n\n        g_tWT.MovieInfo.MidPointConstr(1:2, nNewW) = [0 0]';\n        g_tWT.MovieInfo.WhiskerSide(nNewW) = 1; % 0=left, 1=right\n\n        g_tWT.MovieInfo.LastFrame(nNewW) = g_tWT.MovieInfo.NumFrames;\n        \n        % TODO: Change all zeros to NaNs in SplinePoints!\n        %vIndx = find(g_tWT.MovieInfo.SplinePoints(:,:,:,nNewW) == 0);\n        \n        if isempty(sCopiedName)\n            g_tWT.MovieInfo.WhiskerIdentity{nNewW} = '';\n        else\n            g_tWT.MovieInfo.WhiskerIdentity{nNewW} = mat2cell(sCopiedName);\n        end\n\n        wt_set_status('')\nend\n\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "checkfilename.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/bin/checkfilename.m", "size": 510, "source_encoding": "utf_8", "md5": "6739291d3d5ed243836882af670df101", "text": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Converts between forward and backward slash on Windows and Linux filesystems\nfunction sFilename = CheckFilename(sFilename)\nif ispc\n    sFilename = strrep(sFilename, '/', '\\');\n    sFilename = strrep(sFilename, '\\\\', '\\');\nelseif isunix\n    sFilename = strrep(sFilename, '\\', '/');\n    sFilename = strrep(sFilename, '//', '/');\nend\nreturn\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "wt_create_filters.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/bin/wt_create_filters.m", "size": 690, "source_encoding": "utf_8", "md5": "77a8b948154ca3fc7b9fcc69b7081298", "text": "%%%% WT_CREATE_FILTERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% create a filter for each angle for checking \"whiskeriness\"\n\nfunction filters_vec = wt_create_filters(whisker_width,whisker_length)\n\nflt = zeros(whisker_length);\nstart_ind = ceil(whisker_length/2)-floor(3*whisker_width/2);\nflt(start_ind:start_ind+3*whisker_width-1,:) = ...\n   [0.5 * ones(whisker_width,whisker_length);        ...\n       -ones(whisker_width,whisker_length);             ...\n       0.5 * ones(whisker_width,whisker_length)]        ...\n      /whisker_width/ whisker_length; \n\nmin_ang = -90;\nmax_ang = 90;\n\nfor ang=min_ang:max_ang\n  \n  filters_vec(:,:,ang-min_ang+1) = imrotate(flt,-ang,'bicubic','crop');  \n  \nend\n  "}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "ExportToExcel.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/wt_knutsen/bin/windows/ExportToExcel.m", "size": 1230, "source_encoding": "utf_8", "md5": "9a793be148c08ea1554add206bccaf28", "text": "% ExportToExcel(cHeaders, cData)\n% by Naama Rubin\nfunction ExportToExcel(cHeaders, cData)\n\n% Open new instance of Excel\nExcel = actxserver('Excel.Application');\nset(Excel, 'Visible', 1);\n\n% Create new workbook\nWorkbooks = Excel.Workbooks;\nWorkbook = invoke(Workbooks, 'Add');\nSheets = Excel.ActiveWorkBook.Sheets;\n\n% Sheet columns\ncCols = {'A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z'};\n\n% Make the 1st sheet active\ninvoke(get(Sheets, 'Item', 1), 'Activate');\nActivesheet = Excel.Activesheet;\n\n% Iterate over cells that contain data\nfor c = 1:length(cHeaders)\n    % Insert header\n    Range = get(Activesheet, 'Range', sprintf('%s1', cCols{c}), sprintf('%s1', cCols{c}));\n    set(Range, 'Value', cHeaders{c})\n    % Insert data\n    nLastRow = length(cData{c});\n    Range = get(Activesheet, 'Range', sprintf('%s2', cCols{c}), sprintf('%s%d', cCols{c}, nLastRow));\n    set(Range, 'Value', cData{c})\n    % Remove NaN's in data\n    vNaNIndx = find(isnan(cData{c}));\n    for i = 1:length(vNaNIndx)\n        Range = get(Activesheet, 'Range', sprintf('%s%d', cCols{c}, vNaNIndx(i)+1), sprintf('%s%d', cCols{c}, vNaNIndx(i)+1));\n        set(Range, 'Value', '')\n    end\nend\n\nreturn;\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "concatanateSegmentsOriginalMovie.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/concatanate/concatanateSegmentsOriginalMovie.m", "size": 1415, "source_encoding": "utf_8", "md5": "9c753d4f1fb2ae25de3fc6958222f7a6", "text": "\n\nfunction f= concatanateSegments(dirname)\nlist = dir(dirname);\ni=3;\noldExpName='Experiment';\nwhiskingAll = [];\nmkdir(fullfile(dirname,'whiskByTrial'));\ncount = 0;\nfor i=3:length(list)\n    fname = list(i).name;\n   \n    ind=strfind(fname,'_Whisker_Tracking');\n    if(~isempty(ind))\n\n        expname = fname(1:ind-1);\n        \n        load(fullfile(dirname,fname));\n        \n        if (strcmp(oldExpName,expname))           \n            whiskingAll = [whiskingAll; MovieInfo.AvgWhiskerAngle'];\n        else\n            if ~isempty(whiskingAll)\n                count = count+1;\n                save(fullfile(dirname,'whiskByTrial',[oldExpName 't' num2str(count) '.mat']),'whiskingAll');\n                whiskingAll = [];\n            end\n          \n            whiskingAll = [whiskingAll; MovieInfo.AvgWhiskerAngle'];\n        end\n     oldExpName = expname; \n    \n    \n    end\nend\n \ncount=count+1;\nsave(fullfile(dirname,'whiskByTrial',[oldExpName 't' num2str(count) '.mat']),'whiskingAll');\n      \n\n%%\nsubdirname=fullfile(dirname,'whiskByTrial');\nsublist = dir(subdirname);\n\nload(fullfile(subdirname,sublist(3).name));\n[nr nc] = size(whiskingAll);\n\nwhiskAngle = nan(nr+5,numel(sublist)-2);\n\nfor i=3:numel(sublist)\n    load(fullfile(subdirname,sublist(i).name));\n    whiskAngle(1:length(whiskingAll),i-2)=whiskingAll;\n\nend\n\nsave(fullfile(dirname, 'whiskAngle.mat'),'whiskAngle');\nxlswrite('whiskAngle',whiskAngle);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "concatanateSegments.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/whisker_tracking/concatanate/concatanateSegments.m", "size": 1415, "source_encoding": "utf_8", "md5": "f8c5c57461919a03ee74e82fcc5004ed", "text": "\n\nfunction f= concatanateSegments(dirname)\nlist = dir(dirname);\ni=3;\noldExpName='Experiment';\nwhiskingAll = [];\nmkdir(fullfile(dirname,'whiskByTrial'));\ncount = 0;\nfor i=3:length(list)\n    fname = list(i).name;\n   \n    ind=strfind(fname,'_Whisker_Tracking');\n    if(~isempty(ind))\n\n        expname = fname(1:ind-3);\n        \n        load(fullfile(dirname,fname));\n        \n        if (strcmp(oldExpName,expname))           \n            whiskingAll = [whiskingAll; MovieInfo.AvgWhiskerAngle'];\n        else\n            if ~isempty(whiskingAll)\n                count = count+1;\n                save(fullfile(dirname,'whiskByTrial',[oldExpName 't' num2str(count) '.mat']),'whiskingAll');\n                whiskingAll = [];\n            end\n          \n            whiskingAll = [whiskingAll; MovieInfo.AvgWhiskerAngle'];\n        end\n     oldExpName = expname; \n    \n    \n    end\nend\n \ncount=count+1;\nsave(fullfile(dirname,'whiskByTrial',[oldExpName 't' num2str(count) '.mat']),'whiskingAll');\n      \n\n%%\nsubdirname=fullfile(dirname,'whiskByTrial');\nsublist = dir(subdirname);\n\nload(fullfile(subdirname,sublist(3).name));\n[nr nc] = size(whiskingAll);\n\nwhiskAngle = nan(nr+5,numel(sublist)-2);\n\nfor i=3:numel(sublist)\n    load(fullfile(subdirname,sublist(i).name));\n    whiskAngle(1:length(whiskingAll),i-2)=whiskingAll;\n\nend\n\nsave(fullfile(dirname, 'whiskAngle.mat'),'whiskAngle');\nxlswrite('whiskAngle',whiskAngle);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "makePureTone_new.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/Iwen_auditory_functions/makePureTone_new.m", "size": 1496, "source_encoding": "utf_8", "md5": "680b0d87131bb0a6a09654fa8797c3c8", "text": "function s = makePureTone_new(freqs,duration,varargin)\n% generate a pure tone\n% in1 ... carrier frequency in Hz\n% in2 ... duration in s\n% in3 ... sampling frequency in Hz {44100}\n% in4 ... play tone {0}; 0 or 1\n% in5 ... save wav file {[]}; filename of wav file\n% out1 ... the sound vector s\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n\nif nargin > 2\n    sf = varargin{1};\n    if isempty(sf)\n        sf = 44100;\n    end\nelse\n    sf = 44100;\nend\n\nif nargin > 3\n    doPlay = varargin{2};\nelse\n    doPlay = 0;\nend\n\nif nargin > 4\n    saveWav = varargin{3};\nelse\n    saveWav = [];\nend\n\nmodFreq = 0; % modulation frequency\nmodIndex = 1; % modulation index\nrampDur = 0.1; % ramp duration (fraction of total duration, max. 0.5)\n\nfor i = 1:length(freqs)\n    % make the sound\n    s = MakeSound(sf,duration,freqs(i),modFreq,modIndex,rampDur);\n    \n    if doPlay\n        sound(s,sf);\n    end\n    if ~isempty(saveWav)\n        wavwrite(s,sf,16,saveWav);\n    end\nend\n\n\nfunction s = MakeSound(sf,d,f,mf,mi,rd)\n\n% frequency modulated sound\nn = sf * d;                    % number of samples\nc = (1:n) / sf;                % carrier data preparation\nc = 2 * pi * f * c;\n% m = (1:n) / sf;                % modulator data preparation\n% m = mi * cos(2 * pi * mf * m); % sinusoidal modulation\nm = 0;\ns = sin(c + m);                % frequency modulation\n\n% ramp\nrd = d .* rd;\nnr = floor(sf * rd);\nr = sin(linspace(0, pi/2, nr));\nr = [r, ones(1, n - nr * 2), fliplr(r)];\n% make ramped sound\ns = s .* r;\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "TDMS_dataToGroupChanStruct_v4.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/behavior_functions/import_lick_data/TDMS_dataToGroupChanStruct_v4.m", "size": 2718, "source_encoding": "utf_8", "md5": "0a6dab50f4d81124788a530edfe98382", "text": "function output = TDMS_dataToGroupChanStruct_v4(inputStruct,varargin)\n%TDMS_dataToGroupChanStruct_v4  \n%\n%   NOTE: This reproduces the data structure for the old lab functionality \n%   of the function getStructTDM\n%   \n%   See Also: TDMS_genvarname2, TDMS_readTDMSFile\n\nREPLACE_STR = '_';\nPREPEND_STR = 'p_';\nPREPEND_GRP_STRING = 'g_';\nPREPEND_CHAN_STRING = 'c_';\nALWAYS_PREPEND = false;\nPROP_NAME = 'Props';\n\npropNames    = inputStruct.propNames;\npropValues   = inputStruct.propValues;\ngroupIndices = inputStruct.groupIndices;\ngroupNames   = inputStruct.groupNames;\nchanIndices  = inputStruct.chanIndices;\nchanNames    = inputStruct.chanNames;\nrootIndex    = inputStruct.rootIndex;\ndata         = inputStruct.data;\n\ngroupNames = cellfun(@(x) TDMS_genvarname2(x,REPLACE_STR,PREPEND_GRP_STRING,ALWAYS_PREPEND),groupNames,'UniformOutput',false);\n\n\n\nrootPropStruct = propsToStruct(propNames{rootIndex},propValues{rootIndex},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND);\noutput = struct(PROP_NAME,rootPropStruct);\n\nfor iGroup = 1:length(groupIndices)\n    curGroupIndex = groupIndices(iGroup);\n    curChanIndices = chanIndices{iGroup};\n    curChanNames   = chanNames{iGroup};\n    \n    curChanNames = cellfun(@(x) TDMS_genvarname2(x,REPLACE_STR,PREPEND_CHAN_STRING,ALWAYS_PREPEND),curChanNames,'UniformOutput',false);\n    \n    groupPropStruct = propsToStruct(propNames{curGroupIndex},propValues{curGroupIndex},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND);\n    groupStruct = struct('name',groupNames(iGroup),PROP_NAME,groupPropStruct);\n    for iChan = 1:length(curChanIndices)\n        curChanIndex = curChanIndices(iChan);\n        chanPropStruct = propsToStruct(propNames{curChanIndex},propValues{curChanIndex},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND);\n        chanStruct =  struct('name',curChanNames{iChan},PROP_NAME,chanPropStruct,...\n            'data',[]);\n        chanStruct.data = data{curChanIndex};\n        %NOTE: I had a case statement in case the data type was a string,\n        %which would change the interpretation of the struct input\n        if strcmp(chanStruct.name,'name')\n            error('The variable \"name\" for a channel is off limits, need a different conversion wrapper (probably 2)')\n        end\n        groupStruct.(TDMS_genvarname2(chanStruct.name,...\n            REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)) = chanStruct;\n    end\n    output.(TDMS_genvarname2(groupStruct.name,...\n        REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)) = groupStruct;\nend\nend\n\nfunction propStruct = propsToStruct(names,values,REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)\npropStruct = struct([]);\nfor iProp = 1:length(names)\n   propStruct(1).(TDMS_genvarname2(names{iProp},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)) = values{iProp}; \nend\nend\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "TDMS_dataToGroupChanStruct_v3.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/behavior_functions/import_lick_data/TDMS_dataToGroupChanStruct_v3.m", "size": 2710, "source_encoding": "utf_8", "md5": "1ca966df0513a1925134cd2633570a53", "text": "function output = TDMS_dataToGroupChanStruct_v3(inputStruct,varargin)\n%TDMS_dataToGroupChanStruct_v3  \n%\n%   translates objects AND properties to valid field names\n%\n%   Similar to TDMS_dataToGroupChanStruct_v1, but also translates\n%   properties\n%\n%   output = TDMS_dataToGroupChanStruct_v3(inputStruct)\n%\n%   OPTIONAL INPUTS (property/value pairs)\n%   ============================================\n%   REPLACE_STR = default '_', what to replace invalid characters with\n%   PREPEND_STR = default 'v' (for variable), gets prepended if first\n%               character is invalid (must be alpha, not numeric or _)\n%   ALWAYS_PREPEND = default false, if true always prepends the prepend string,\n%               regardless of whether or not it is needed\n%\n%   \n%\n%   See Also: TDMS_genvarname2\n\nREPLACE_STR = '_';\nPREPEND_STR = 'v';\nALWAYS_PREPEND = false;\n\npropNames    = inputStruct.propNames;\npropValues   = inputStruct.propValues;\ngroupIndices = inputStruct.groupIndices;\ngroupNames   = inputStruct.groupNames;\nchanIndices  = inputStruct.chanIndices;\nchanNames    = inputStruct.chanNames;\nrootIndex    = inputStruct.rootIndex;\ndata         = inputStruct.data;\n\nrootPropStruct = propsToStruct(propNames{rootIndex},propValues{rootIndex},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND);\noutput = struct('props',rootPropStruct);\n\nfor iGroup = 1:length(groupIndices)\n    curGroupIndex = groupIndices(iGroup);\n    curChanIndices = chanIndices{iGroup};\n    curChanNames   = chanNames{iGroup};\n    \n    groupPropStruct = propsToStruct(propNames{curGroupIndex},propValues{curGroupIndex},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND);\n    groupStruct = struct('name',groupNames(iGroup),'props',groupPropStruct);\n    for iChan = 1:length(curChanIndices)\n        curChanIndex = curChanIndices(iChan);\n        chanPropStruct = propsToStruct(propNames{curChanIndex},propValues{curChanIndex},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND);\n        chanStruct =  struct('name',curChanNames{iChan},'props',chanPropStruct,...\n            'data',[]);\n        chanStruct.data = data{curChanIndex};\n        %NOTE: I had a case statement in case the data type was a string,\n        %which would change the interpretation of the struct input\n        groupStruct.(TDMS_genvarname2(chanStruct.name,...\n            REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)) = chanStruct;\n    end\n    output.(TDMS_genvarname2(groupStruct.name,...\n        REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)) = groupStruct;\nend\nend\n\nfunction propStruct = propsToStruct(names,values,REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)\npropStruct = struct([]);\nfor iProp = 1:length(names)\n   propStruct(1).(TDMS_genvarname2(names{iProp},REPLACE_STR,PREPEND_STR,ALWAYS_PREPEND)) = values{iProp}; \nend\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "TDMS_preprocessFile.m", "ext": ".m", "path": "OCIA-master/other/functions_widefield/behavior_functions/import_lick_data/tdmsSubfunctions/TDMS_preprocessFile.m", "size": 24202, "source_encoding": "utf_8", "md5": "9027de756f8016d279addde445319f56", "text": "function metaStruct = TDMS_preprocessFile(fid,tdmsFileName,params)\n%TDMS_preprocessFile  Populates props and gets segment information\n%\n%   NOTE: This file shouldn't be called directly by the user\n%\n%   metaStruct = TDMS_preprocessFile(fid,tdmsFileName,params)\n%\n%   INPUTS\n%   =======================================================================\n%   fid          : file id of opened tdms file, may be null if INDEX_DEBUG is true\n%   tdmsFileName : full path to tdms file OR tdms index file for INDEX_DEBUG\n%   params       : all optional inputs from TDMS_readTDMSFile\n%\n%   OUTPUTS\n%   =======================================================================\n%   metaStruct :\n%       structure with fields:\n%\n%   The following 3 variables are all the same length, and have one index\n%   for each unique object.  Each object may be one of the following types,\n%   file root, group object, or channel object.  Only channel objects may\n%   have data.\n%\n%   rawDataInfo           : (structure array)\n%           NOTE: This holds the most recent info for an object, and can be\n%           changed on a per segment basis, segInfo is used to hold\n%           information that is needed per segement\n%\n%           .lengthOfIndex    - length of raw data index (not used)\n%           .dataType         - enumerated dataType (Labview)\n%           .dimensionData    - currently ignored (should be 1d)\n%           .numberOfValues   - # of values to read each time\n%           .totalSizeBytes   - # of bytes per values\n%           .numberProperties - # of properties defined for the object\n%           .infoSet          - a logical on whether or not this data has\n%                               been set (for error checking)\n%           .propNames        - a cell array for definining property names\n%           .propValues       - cell array for holding property values\n%\n%   numberDataPoints     : an array which specifies the # of data points for\n%                          each object (used for preallocation)\n%   objectNameList       : (cell array of strings) names of all objects in the\n%                          TDMS file\n%\n%   Info for each segment ...\n%\n%   segInfo              : (struct array)\n%           .rawPos - position of raw data for that segment\n%           .kTocNewObjList - flag on whether a new channel list was\n%           created or not\n%           .objOrder - array specifying indices of the objects to assign\n%                       data to, order specifies read order, and values\n%                       are indexed\n%           into the objectNameList & rawDataInfo struct\n%           .nRawObjects - length of objOrder\n%           .nSamplesRead - array, # of samples to read for each object\n%           .isInterleaved - logical, whether that segment uses interleaved\n%                               data or not\n%           .isBigEndian - logical\n%           .nChunks     - # of times to read data using the parameters\n%                           above before moving onto the next segment\n%\n%   See also: TDMS_processLeadIn, TDMS_getDataSize, TDMS_getPropValue\n\n\n%PARAMETERS - passed in from TDMS_readTDMSFile\n%==========================================================================\nUTC_DIFF        = params.UTC_DIFF;\nMAX_NUM_OBJECTS = params.MAX_NUM_OBJECTS;\nMAX_NUM_PROPS   = params.MAX_NUM_PROPS;\nN_SEGS_GUESS    = params.N_SEGS_GUESS;\nN_SEGS_INC      = params.N_SEGS_INC;\nDEBUG           = params.DEBUG;\nDATE_STR_FORMAT = params.DATE_STR_FORMAT;\nUNICODE_FORMAT  = params.STRING_ENCODING;\nUSE_INDEX       = params.USE_INDEX;\nINIT_CHUNK_SIZE = params.INIT_CHUNK_SIZE;\nTDMS_INDEX_EXT  = params.TDMS_INDEX_EXT;\nMACHINE_FORMAT  = params.MACHINE_FORMAT;\nSTRING_ENCODING = params.STRING_ENCODING;\nCURRENT_VERSION = params.CURRENT_VERSION;\nINDEX_DEBUG     = params.INDEX_DEBUG;\n\n%DONT CHANGE THESE\n%==========================================================================\nLEAD_IN_LENGTH = 28; %# of bytes of lead in\n\n%FIGURING OUT WHICH FILE TO READ\n%==========================================================================\n[tdmsPathToFile,tdmsNameOnly] = fileparts(tdmsFileName);\nif isempty(params.META_STRUCT)\n    isIndexFID = false;\n    if USE_INDEX || INDEX_DEBUG\n        if INDEX_DEBUG\n            indexFile = tdmsFileName;\n        else\n            indexFile = fullfile(tdmsPathToFile,[tdmsNameOnly TDMS_INDEX_EXT]);\n        end\n        if exist(indexFile,'file')\n            fid = fopen(indexFile,'r',MACHINE_FORMAT,STRING_ENCODING);\n            isIndexFID = true;\n        else\n            %Just use the tdms file, which we have already tested to exist\n            if INDEX_DEBUG\n                %NOTE: With INDEX_DEBUG we explicitly passed in the index\n                %file to read and parse (generally for debugging purposes\n                %so if it doesn't exist, then we have a problem\n                error('Specified tdms_index file doesn not exist')\n            end\n        end\n    end\nelse\n    %Quits early if meta struct input is valid\n    %======================================================================\n    metaStruct = params.META_STRUCT;\n    if ~isstruct(metaStruct) || ~isfield(metaStruct,'version') || ~isfield(metaStruct,'fileName')\n        error('The META_STRUCT parameter passed should be a structure with fields \"version\" and \"fileName\"')\n    end\n    if metaStruct.version ~= CURRENT_VERSION\n        error('Mismatch in version creation, version of struct %d, please run with latest version: %d')\n    end\n    if ~strcmpi(metaStruct.fileName,tdmsNameOnly)\n        fprintf('Filename from metaStruct: %s\\n',metaStruct.fileName);\n        fprintf('Filename from input: %s\\n',tdmsNameOnly);\n        error('Mismatch in filenames, see printout above')\n    end\n    return\nend\n\nif isIndexFID\n    lastLetter = double('h');  %used for .tdms_index files\nelse\n    lastLetter = double('m');  %used for .tdms files\nend\n\n\n%INITIALIZE OUTPUTS\n%==========================================================================\ncurNumTotalObjects  = 0; %Current # of objects\nnumberDataPoints    = zeros(1,MAX_NUM_OBJECTS);\nobjectNameList      = cell(1,MAX_NUM_OBJECTS);  %names of all objects\n\nrawDataInfo = initRawInfoStruct(MAX_NUM_PROPS,MAX_NUM_OBJECTS);\nsegInfo     = initSegStruct(N_SEGS_GUESS);\n%==========================================================================\n\n%TEMP VARIABLES\n%==========================================================================\nobjectHasRawData    = false(1,MAX_NUM_OBJECTS); %This is needed for adding\n%# of data points together\nranOnce    = false;\nnSegs      = 0;\n\n%Get eof & return to start\nfseek(fid,0,1);\neofPosition = ftell(fid);\nfseek(fid,0,-1);\n\n%START OF READING\n%==========================================================================\ncurPosCounter = 0; %Used to keep track of where in the actual tdms file we \n%would be at various points, this is used for parsing verification as well\n%as assisting with more complicated reads\nwhile ftell(fid) ~= eofPosition\n    \n    nSegs = nSegs + 1;\n    if nSegs > length(segInfo)\n        segInfo = [segInfo initSegStruct(N_SEGS_INC)]; %#ok<AGROW>\n    end\n    \n    if DEBUG\n        disp('------  START OF DEBUG -------')\n        fprintf(2,'CURRENT SEGMENT: %d\\n',nSegs);\n        fprintf(2,'Current file position: %d\\n',curPosCounter);\n    end\n    \n    %LEAD IN HANDLING\n    %======================================================================\n    [flags,info]  = TDMS_processLeadIn(fid,lastLetter);\n    curPosCounter = curPosCounter + LEAD_IN_LENGTH + info.segLength;\n    segInfo(nSegs).rawPos = curPosCounter - info.segLength + info.metaLength;\n    \n    \n    if ~ranOnce\n        %NOTE: This might be false if no channels are defined ...\n        if flags.kTocNewObjList == false\n            error('the kTocNewObjList was false when first run, this is not expected')\n        end\n        ranOnce = true;\n    end\n    %======================================================================\n    \n    \n    %======================================================================\n    %                       META INFORMATION PROCESSING\n    %======================================================================\n    if flags.hasMetaData\n        \n        %Get # of changed objects\n        numNewObjInSeg = fread(fid,1,'uint32');\n        \n        %Reinitialize order list if new\n        if flags.kTocNewObjList\n            curObjListCount      = 0;\n            objOrder             = zeros(1,2*numNewObjInSeg);\n            nValuesRead          = zeros(1,2*numNewObjInSeg);\n            %NOTE: I padded this by doubling the #, we might append extra\n            %channels in subsequent reads, I currently don't resize this\n            %...\n        end\n        \n        for iNewObject = 1:numNewObjInSeg\n            \n            %1) GET OBJECT PATH\n            %-------------------------------\n            objPathLength = fread(fid,1,'uint32');\n            temp          = fread(fid,objPathLength,'*uint8');\n            objName       = native2unicode(temp,UNICODE_FORMAT)'; %#ok<*N2UNI>\n            \n            %POPULATE ALL OBJECT LIST\n            %----------------------------------------\n            objIndex = find(strcmp(objectNameList(1:curNumTotalObjects),objName),1);\n            if isempty(objIndex)\n                curNumTotalObjects  = curNumTotalObjects + 1;\n                if curNumTotalObjects > length(rawDataInfo)\n                    rawDataInfo      = [rawDataInfo      ...\n                        initRawInfoStruct(MAX_NUM_PROPS,MAX_NUM_OBJECTS)]; %#ok<AGROW>\n                    numberDataPoints = [numberDataPoints zeros(1,MAX_NUM_OBJECTS)]; %#ok<AGROW>\n                    objectNameList   = [objectNameList   cell(1,MAX_NUM_OBJECTS)]; %#ok<AGROW>\n                    objectHasRawData = [objectHasRawData false(1,MAX_NUM_OBJECTS)];  %#ok<AGROW>\n                end\n                objIndex            = curNumTotalObjects;\n                objectNameList{curNumTotalObjects} = objName;\n            end\n            \n            %RAW DATA INDEX PROCESSING\n            %---------------------------------------------\n            curPos = ftell(fid);\n            rawDataIndexLength = fread(fid,1,'uint32');\n            rawDataInfo(objIndex).lengthOfIndex = rawDataIndexLength;\n            \n            if DEBUG\n                fprintf(2,'RawDataLength: %d\\n',rawDataIndexLength);\n                fprintf(2,'CurrentPos: %d\\n',curPos);\n            end\n            \n            \n            switch rawDataIndexLength\n                case 0 %Same as previous\n                    if rawDataInfo(objIndex).infoSet == false\n                        error('Channel %s set to use previous rawDataIndex but this channel is new',objName)\n                    end\n                case 2^32-1 %no raw data\n                    rawDataInfo(objIndex).infoSet    = true;\n                    objectHasRawData(objIndex)       = false;\n                otherwise\n                    objectHasRawData(objIndex)    = true;\n                    rawDataInfo(objIndex).infoSet = true;\n                    \n                    %DATA TYPE HANDLING\n                    %------------------------------------------------------\n                    dataType = fread(fid,1,'uint32');\n                    if rawDataInfo(objIndex).infoSet && dataType ~= rawDataInfo(objIndex).dataType && numberDataPoints(objIndex) > 0\n                        error('Raw data type for channel %s has changed from %d to %d',...\n                            objName,rawDataInfo(objIndex).dataType,dataType)\n                    else\n                        rawDataInfo(objIndex).dataType = dataType;\n                    end\n\n                    %DATA SIZE HANDLING\n                    %-----------------------------------------------------\n                    rawDataInfo(objIndex).dimensionData  = fread(fid,1,'uint32');\n                    if rawDataInfo(objIndex).dimensionData ~= 1\n                        error('Code doesn''t yet handle non 1D data')\n                    end\n                    \n                    rawDataInfo(objIndex).numberOfValues = fread(fid,1,'uint64');\n                    \n                    %RawDaqMX\n                    %------------------------------------------------------\n                    %NOTES:\n                    if dataType == 2^32-1\n                        %JIM CODE IN PROGRESS\n                        rawDataInfo(objIndex).isRawDAQmx = true; %We can\n% % %                         %post process this to convert from bits to an\n% % %                         %actual value\n% % %                         \n% % %                         %FORMAT:\n% % %                         %1) -\n% % %                         daqMXVersion = rawDataIndexLength;\n% % %                         if ~ismember(daqMXVersion,[4713 4714])\n% % %                             error('Unexpected version: %d',daqMXVersion)\n% % %                         end\n% % % \n% % % \n% % %                         %2) Let's get the remaining # of bytes\n% % %                         %This seems to be 32 ...\n% % %                         \n% % % % %                         wtf = fread(fid,8,'uint32');\n% % % % %                         disp(wtf)\n% % %                         %4713\n% % %                         %13000012\n% % %                         %15000018\n% % %                         %? -> 8th indicates data type? OR -> bytes per sample\n% % %                         \n% % %                         %4714\n% % %                         %1 0 0 0 256 256 512\n% % % \n% % %                         %3) Update datetype - where the heck is it?\n% % %                         %rawDataInfo(objIndex).dataType = dataType;\n                    else %Non RawDAQmx type\n                        if rawDataInfo(objIndex).dataType == 32\n                            %If string, size is specified by an additional field\n                            rawDataInfo(objIndex).totalSizeBytes = fread(fid,1,'uint64');\n                        else\n                            rawDataInfo(objIndex).totalSizeBytes = ...\n                                rawDataInfo(objIndex).numberOfValues*TDMS_getDataSize(dataType);\n                        end\n                        \n                        %Another chance to check correct reading\n                        if curPos + rawDataIndexLength ~= ftell(fid)\n                            error(['Raw Data Index length was incorrect: %d stated vs %d observed,' ...\n                                'likely indicates bad code or a bad tdms file'],rawDataIndexLength,ftell(fid) - curPos)\n                        end\n                    end\n                    \n                    if DEBUG\n                        fprintf(2,'nSegs: %d\\n',nSegs);\n                        fprintf(2,'objName: %s\\n',objName);\n                    end\n            end\n            \n            \n            %--------------------------------------------------------------\n            %POPULATE ORDER TO RETRIEVE RAW DATA\n            %--------------------------------------------------------------\n            if objectHasRawData(objIndex)\n                appendToList = false;\n                if flags.kTocNewObjList\n                    appendToList = true;\n                else %Only append if not currently specified\n                    I_objOrder = find(objOrder(1:curObjListCount) == objIndex,1);\n                    if isempty(I_objOrder)\n                        appendToList = true;\n                    else\n                        nValuesRead(I_objOrder) = rawDataInfo(objIndex).numberOfValues;\n                    end\n                end\n                \n                %NOTE: No overflow code in place yet, however we do\n                %initialize with twice the # of objects specified to be \n                %in a segement when a new list is created, new segments\n                %might add more objects to the list\n                if appendToList\n                    curObjListCount                 = curObjListCount + 1;\n                    objOrder(curObjListCount)       = objIndex;\n                    nValuesRead(curObjListCount)    = rawDataInfo(objIndex).numberOfValues;\n                end\n            end\n            \n            \n            %PROPERTY HANDLING\n            %--------------------------------------------------------------\n            numberProperties = fread(fid,1,'uint32');\n            %Below is the # of props already assigned to that channel\n            nPropsChan       = rawDataInfo(objIndex).numberProperties;\n            curProps         = rawDataInfo(objIndex).propNames;\n            \n            for iProp = 1:numberProperties\n                propNameLength  = fread(fid,1,'uint32');\n                temp            = fread(fid,propNameLength,'*uint8');\n                propName        = native2unicode(temp,UNICODE_FORMAT)';\n                propDataType    = fread(fid,1,'uint32');\n                \n                propIndex = find(strcmp(curProps(1:nPropsChan),propName),1);\n                if isempty(propIndex)\n                    %Updates needed for new properties\n                    nPropsChan              = nPropsChan + 1;\n                    propIndex               = nPropsChan;\n                    curProps{propIndex}     = propName;\n                    rawDataInfo(objIndex).propNames{propIndex} = propName;\n                end\n               \n                %Update value\n                propValue = TDMS_getPropValue(fid,propDataType,UTC_DIFF,DATE_STR_FORMAT);\n                rawDataInfo(objIndex).propValues{propIndex} = propValue;\n            end\n            rawDataInfo(objIndex).numberProperties = nPropsChan;\n            \n            if DEBUG\n                fprintf(2,'end of index position: %d\\n',ftell(fid));\n            end\n        end\n    end\n    %======================================================================\n    %               END OF META DATA PROCESSING\n    \n    \n    \n    %RAW DATA SAMPLE COUNTING\n    %======================================================================\n    \n    segInfo(nSegs).kTocNewObjList = flags.kTocNewObjList;\n    byteSizeRaw = info.segLength - info.metaLength;\n    if ~flags.hasRawData || byteSizeRaw == 0\n        segInfo(nSegs).nChunks = 0;\n    else\n        segInfo(nSegs).objOrder      = objOrder(1:curObjListCount);\n        segInfo(nSegs).nRawObjects   = curObjListCount;\n        segInfo(nSegs).nSamplesRead  = nValuesRead(1:curObjListCount);\n        segInfo(nSegs).isInterleaved = flags.isInterleaved;\n        segInfo(nSegs).isBigEndian   = flags.isBigEndian;\n        \n        %# OF CHUNK PROCESSING\n        %------------------------------------------------------------------\n        totalBytesPerChunk = sum([rawDataInfo(objOrder(1:curObjListCount)).totalSizeBytes]);\n        nChunks            = byteSizeRaw/totalBytesPerChunk;\n        \n        %Some error checking\n        %------------------------------------------\n        if DEBUG\n            fprintf(2,'nChunks: %d\\n',nChunks);\n            fprintf(2,'nSamplesRead: %s\\n',mat2str(nValuesRead(1:curObjListCount)));\n            fprintf(2,'totalBytesPerChunk: %d\\n',totalBytesPerChunk);\n            fprintf(2,'byteSizeRaw: %d\\n',byteSizeRaw);\n        end\n        \n        if nChunks ~= floor(nChunks)\n            error(['The remaining data doesn''t split evently into' ...\n                ' chunks, estimated # of chunks: %d'],nChunks)\n        end\n        \n        chunkByteOffset = 0;\n        %Increment the number of data points\n        for iObject = 1:curObjListCount\n            \n            curIndex = objOrder(iObject);\n            \n            %This allows us to grow these values if we haven't sufficiently preallocated\n            if rawDataInfo(curIndex).chunkIndex + nChunks > rawDataInfo(curIndex).chunkLength\n                rawDataInfo(curIndex).dataMatrix  = [rawDataInfo(curIndex).dataMatrix; zeros(INIT_CHUNK_SIZE,3)];\n                rawDataInfo(curIndex).chunkLength = rawDataInfo(curIndex).chunkLength + INIT_CHUNK_SIZE;\n            end\n            \n            %DataMatrix:\n            %==============================================================\n            %This information is used for reading parts of an object during\n            %a single read instead of the entire object\n            \n            %Column 1, file position\n            indices = (rawDataInfo(curIndex).chunkIndex+1):(rawDataInfo(curIndex).chunkIndex+nChunks);\n            rawDataInfo(curIndex).dataMatrix(indices,1) = ...\n                segInfo(nSegs).rawPos + chunkByteOffset + (0:totalBytesPerChunk:(nChunks-1)*totalBytesPerChunk);\n            \n            %Column 2, first sample at that position\n            rawDataInfo(curIndex).dataMatrix(indices,2) = ...\n                numberDataPoints(curIndex) + (0:rawDataInfo(curIndex).numberOfValues:(nChunks-1)*rawDataInfo(curIndex).numberOfValues) + 1;\n            \n            %Column 3, segment number\n            rawDataInfo(curIndex).dataMatrix(indices,3) = nSegs;\n            \n            chunkByteOffset                  = chunkByteOffset + rawDataInfo(curIndex).totalSizeBytes;\n            numberDataPoints(curIndex)       = numberDataPoints(curIndex) + rawDataInfo(curIndex).numberOfValues*nChunks;\n            rawDataInfo(curIndex).chunkIndex = rawDataInfo(curIndex).chunkIndex+nChunks;\n        end\n        \n        %nChunksAll = nChunksAll + nChunks;\n        segInfo(nSegs).nChunks = nChunks;\n        \n        %This needs to be handled in some manner for RawDaqMx\n        if ~isIndexFID\n            fseek(fid,byteSizeRaw,'cof');\n        end\n    end\n    \n    %Addition for raw data (needs to be fixed)\n    %In general, the tdms_index requires skipping some meta data\n    %I think this is only needed for raw data (I need to flush this out\n    %better as it will cause an error when reading index files for regular\n    %tdms files)\n%     if isIndexFID\n%         fseek(fid,segInfo(nSegs).rawPos,'bof');\n%     end\nend\n\n\n\n\n\n%Trim output:\n%==========================================================================\nrawDataInfo         = rawDataInfo(1:curNumTotalObjects);\nfor iObject = 1:length(rawDataInfo)\n    nProps = rawDataInfo(iObject).numberProperties;\n    rawDataInfo(iObject).propNames  = rawDataInfo(iObject).propNames(1:nProps);\n    rawDataInfo(iObject).propValues = rawDataInfo(iObject).propValues(1:nProps);\n    \n    %Add extra value for later processing\n    if rawDataInfo(iObject).chunkIndex ~= 0\n        rawDataInfo(iObject).chunkIndex = rawDataInfo(iObject).chunkIndex + 1;\n        rawDataInfo(iObject).dataMatrix(rawDataInfo(iObject).chunkIndex,2) = numberDataPoints(iObject) + 1;\n    end\nend\n\nnumberDataPoints    = numberDataPoints(1:curNumTotalObjects);\nobjectNameList     = objectNameList(1:curNumTotalObjects);\nsegInfo             = segInfo(1:nSegs);\n\nmetaStruct = struct(...\n    'numberDataPoints', numberDataPoints,...\n    'objectNameList',   {objectNameList},...\n    'segInfo',          segInfo,...\n    'rawDataInfo',      rawDataInfo,...\n    'fileName',         tdmsNameOnly,...\n    'version',          CURRENT_VERSION);\n\nif isIndexFID\n    %If using the index file to parse meta, close it\n    fclose(fid);\nend\n\n\nend\n\nfunction segStruct = initSegStruct(nSegs)\n%initSegStruct\n%\n%   segStruct = initSegStruct(nSegs)\n%\nsegStruct = struct(...\n    'rawPos',repmat({0},[1 nSegs]),...\n    'kTocNewObjList',0,...\n    'objOrder',[],...\n    'nRawObjects',0,...\n    'nSamplesRead',[],...\n    'isInterleaved',false,...\n    'isBigEndian',false,...\n    'nChunks',false);\n\nend\n\nfunction rawInfoStruct = initRawInfoStruct(MAX_NUM_PROPS,MAX_NUM_OBJECTS)\n\nrawInfoStruct = struct( ...\n    'isRawDAQmx',       false, ...\n    'lengthOfIndex',    0,...\n    'dataType',         0,...\n    'dimensionData',    0,...\n    'numberOfValues',   0,...\n    'totalSizeBytes',   0,... %Only valid for strings\n    'numberProperties', 0,...\n    'chunkIndex',       0,...\n    ... %Could initialize to zero, this would save space\n    ... %with non-raw data objects\n    'chunkLength',      0,...\n    ... %SEE ALSO: chunk loop above for resizing\n    'dataMatrix',       zeros(0,3),...\n    ...%Position #\n    ...%1st Sample #\n    ...%Seg #\n    'infoSet',          false, ...\n    'propNames',        repmat({cell(1,MAX_NUM_PROPS)},1,MAX_NUM_OBJECTS),...\n    'propValues',       repmat({cell(1,MAX_NUM_PROPS)},1,MAX_NUM_OBJECTS));\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "hanning.m", "ext": ".m", "path": "OCIA-master/other/functions_slovin/spectogram_functions/hanning.m", "size": 292, "source_encoding": "utf_8", "md5": "56fe015f155b73f7eb537f7a3093ead2", "text": "function w = hanning(n)\nif ~rem(n,2)\n   w = .5*(1 - cos(2*pi*(1:n/2)'/(n+1)));\n   w = [w; w(end:-1:1)];\nelse\n   w = .5*(1 - cos(2*pi*(1:(n+1)/2)'/(n+1)));\n   w = [w; w(end-1:-1:1)];\nend\n\nfunction myprintf(verbose, varargin)\n    if strcmpi(verbose, 'on')\n        fprintf(varargin{:});\n    end;"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainer_GUI_export.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer_GUI_export.m", "size": 57797, "source_encoding": "utf_8", "md5": "bb7ef417782c2fbef69065eee4adb42f", "text": "function varargout = mtrainer_GUI_export(varargin)\n\n% TODO:\n%   - stopping the experiment: trial no 100 ? plot ? set buttons to stop ?\n%   - reward anyway system\n%   - forbid disconnecting HW or changing mouseID while experiment is running\n%   - lickThresh calibrator with setter\n%   - online lick ploting\n%   - online performance\n%   - logging into GUI window\n%   - config setter\n\n\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @mtrainer_GUI_export_OpeningFcn, ...\n                   'gui_OutputFcn',  @mtrainer_GUI_export_OutputFcn, ...\n                   'gui_LayoutFcn',  @mtrainer_GUI_export_LayoutFcn, ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1});\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout;\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n% --- Executes just before mtrainer_GUI_export is made visible.\nfunction mtrainer_GUI_export_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to mtrainer_GUI_export (see VARARGIN)\n\n% Choose default command line output for mtrainer_GUI_export\nhandles.output = hObject;\n% Update handles structure\nguidata(hObject, handles);\ninitialize_gui(hObject, handles, false);\nset(hObject, 'DeleteFcn', @onWindowClose);\nset(hObject, 'Position', [100 100 800 620]);\n% --- Outputs from this function are returned to the command line.\nfunction varargout = mtrainer_GUI_export_OutputFcn(hObject, eventdata, handles) %#ok<*INUSL>\n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n% --------------------------------------------------------------------\nfunction initialize_gui(fig_handle, handles, isreset) %#ok<*INUSD>\n% If the metricdata field is present and the start flag is false, it means\n% we are we are just re-initializing a GUI by calling it from the cmd line\n% while it is up. So, bail out as we dont want to start the data.\n\ninitMain(handles);\nupdateGUI();\n% --- Executes on button press in connectHW.\nfunction connectHW_Callback(hObject, eventdata, handles)\n% hObject    handle to connectHWButton_Callback (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif isfield(main.hw, 'connected') && main.hw.connected;\n    disconnectHW();\nelse\n    connectHW();\nend\n\nupdateGUI();\n% --- Executes on button press in start.\nfunction start_Callback(hObject, eventdata, handles)\n% hObject    handle to start (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif main.isRunning;\n    warning('mtrainerGUI:CannotStartAlreadyRunning', ...\n        'Cannot start because experiment is already running and not paused.');\n    updateGUI();\n    return;\nelse\n    startExperiment();\nend\n    \nupdateGUI();\ntry\n    mainLoop();\ncatch e;\n    closeDiaryAndSaveOutput();\n    throw(e);\nend;\n% --- Executes on button press in pause.\nfunction pause_Callback(hObject, eventdata, handles)\n% hObject    handle to pause (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif main.isRunning;\n    if main.isPaused;\n        main.isPaused = false;\n    else\n        main.isPaused = true;\n    end\nelse\n    warning('mtrainerGUI:CannotPauseNotRunning', ...\n        'Cannot pause because experiment is not running.');\nend\n\nupdateGUI();\n\n% --- Executes on button press in stop.\nfunction stop_Callback(hObject, eventdata, handles) %#ok<*DEFNU>\n% hObject    handle to stop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif main.isRunning;\n    main.isRunning = false;\n    closeDiaryAndSaveOutput();\nelse\n    warning('mtrainerGUI:CannotStopNotRunning', 'Cannot stop because experiment is not running.');\nend\n\nupdateGUI();\n% --- Executes on button press in reward.\nfunction reward_Callback(hObject, eventdata, handles)\n% hObject    handle to reward (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\ngiveReward(main.rewardDur, 0);\n% --- Executes on slider movement.\nfunction rewardDurSetter_Callback(hObject, eventdata, handles)\n% hObject    handle to rewardDurSetter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\n\nglobal main;\nmain.rewardDur = get(hObject,'Value');\nset(handles.rewardDur, 'String', sprintf('%04.2f', main.rewardDur));\n% --- Executes on slider movement.\nfunction lickThreshSetter_Callback(hObject, eventdata, handles)\n% hObject    handle to lickThreshSetter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\n\nglobal main;\nmain.lickThresh = get(hObject,'Value');\nset(handles.lickThresh, 'String', sprintf('%04.2f', main.lickThresh));\n% --- Executes when selected cell(s) is changed in config.\nfunction config_CellSelectionCallback(hObject, eventdata, handles)\n% hObject    handle to config (see GCBO)\n% eventdata  structure with the following fields (see UITABLE)\n%\tIndices: row and column indices of the cell(s) currently selecteds\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\no('#config_CellSelectionCallback: selection changed: row %d, col %d.', eventdata.Indices, 3, main.dbgLvl);\nrow = eventdata.Indices(1);\nconfigCell = get(main.handles.config, 'Data');\nmain.mouseId = configCell{row, 1};\nmain.taskType = configCell{row, 2};\nmain.phase = configCell{row, 3};\nmain.configLoaded = false;\no('#config_CellSelectionCallback: mouseId: %s, taskType: %s, phase: %s.', ...\n    main.mouseId, main.taskType, main.phase, 3, main.dbgLvl);\nupdateGUI();\n% --- Executes on button press in loadConfig.\nfunction loadConfig_Callback(hObject, eventdata, handles)\n% hObject    handle to loadConfig (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nloadConfig();\n%% #initMain\nfunction initMain(handles)\n\nglobal main;\nmain = struct();\nmain.dbgLvl = 3;\nmain.debugMode = 0;\nmain.basePath = 'P:/programming/Work/PhD/matlab/behavior/mtrainer/';\n% main.basePath = 'D:/Users/BaL/programming/Work/PhD/matlab/behavior/mtrainer/';\nconfigsPath = sprintf('%sconfigs.mat', main.basePath);\nif ~exist(configsPath, 'file');\n    error('mtrainerGUI:initMain:ConfigsFileNotFound', ...\n        'Cannot find the configs at \"%s\" (file not found).', configsPath);\nend;\nconfigsMat = load(configsPath);\nmain.configs = configsMat.configs;\n[~, computerName] = system('hostname');\nroomMatch = regexp(computerName, '^HIFOWS(?<room>H\\d+)\\-\\d+\\s+$', 'names');\nif main.debugMode;\n    warning('mtrainer_GUI:initMain:DebugMode', 'Debug mode is ON.');\n    roomMatch = struct('room', 'H30');\n    o('#initMain: debug mode => room tag enforced to: \"%s\".', roomMatch.room, 1, main.dbgLvl);\nend;\nif isempty(roomMatch);\n    error('mtrainerGUI:initMain:HardwareLoad:NoRoomTagFound', ...\n        'Cannot load hardware because there''s no room tag in the computer''s name.');\nelseif ~isfield(main.configs.hardware, roomMatch.room);\n    error('mtrainerGUI:initMain:HardwareLoad:UnknownRoom', ...\n        'Cannot load hardware because room \"%s\" is unknown.', roomMatch.room);\nend;\nmain.hw = main.configs.hardware.(roomMatch.room);\nmain.hw.connected = false;\nmain.updateRate = 0.1; % 100ms update rate\n\nmain.isRunning = false;\nmain.isPaused = false;\nmain.isClosed = false;\nmain.configLoaded = false;\nmain.iTrial = 0;\nmain.rewardDur = str2double(get(handles.rewardDur,'String'));\nmain.handles = handles;\nstateData = get(main.handles.state, 'Data');\nstateData(:, 1) = getTrialInfo(0);\nstateData(:, 2) = getTrialInfo(0);\nstateData(:, 3) = getTrialInfo(0);\nset(main.handles.state, 'Data', stateData);\nset(main.handles.config, 'Data', main.configs.animals);\nmain.mouseId = '';\no('Mouse trainer v1.0 initiated.', 1, main.dbgLvl);\nfunction onWindowClose(src, eventData)\n\nglobal main;\nmain.isClosed = true;\n%% #connectHW\nfunction connectHW()\n\nglobal main;\no('Connecting hardware ...', 1, main.dbgLvl);\nset(main.handles.connectHW, 'BackgroundColor', 'yellow');\nif ~main.debugMode;\n    main.hw.lickSensor = daq.createSession(main.hw.adaptorID);\n    main.hw.lickSensor.Rate = main.hw.analogIn_SampleRate;\n    main.hw.lickSensor.addAnalogInputChannel(main.hw.analogIn_Device, main.hw.analogIn_Channel, 'Voltage');\n    main.hw.lickSensor.Channels(1).InputType = main.hw.analogIn_InputType;\n    main.hw.lickSensor.Channels(1).Range = main.hw.analogIn_Range;\n    \n    main.hw.lickSensor.IsNotifyWhenDataAvailableExceedsAuto = true;\n    main.hw.lickSensorListener = main.hw.lickSensor.addlistener('DataAvailable', ...\n        @(src, event) recordAndStop(src, event));\n    main.hw.lickSensor.IsContinuous = true;\n    main.hw.lickSensor.prepare();\nelse\n    o('  \"Connecting\" fake analog input.', 1, main.dbgLvl);\n    main.hw.lickSensor = struct();\n    main.hw.lickSensorListener = struct();\n    main.hw.lickSensor.stop = 1;\n    main.hw.lickSensor.startBackground = 1;\n%     main.hw.lickSensor.IsRunning = @()(round(rand + 0.4));\n%     main.hw.lickSensor.IsRunning = @()(round(rand - 0.4));\n    main.hw.lickSensor.IsRunning = 1;\n    pause(1);\nend;\no('  Analog input (lick sensor and listener): OK.', 1, main.dbgLvl);\nif ~main.debugMode;\n    main.hw.valveControl = daq.createSession(main.hw.adaptorID);\n    main.hw.valveControl.addDigitalChannel(main.hw.digitalOut_Device, main.hw.digitalOut_PortLine, 'OutputOnly');\n    main.hw.valveControl.outputSingleScan(0); % make sure valve is closed\nelse\n    o('  \"Connecting\" fake digital output.', 1, main.dbgLvl);\n    main.hw.valveControl = struct();\n    pause(1);\nend;\no('  Digital output (valve control): OK.', 1, main.dbgLvl);\nmain.hw.connected = true;\no('Hardware connected.', 1, main.dbgLvl);\n%% #disconnectHW\nfunction disconnectHW()\n\nglobal main;\no('Disconnecting hardware ...', 1, main.dbgLvl);\n% analog input\nif ~main.debugMode;\n    if isfield(main.hw, 'lickSensorListener');\n        main.hw = rmfield(main.hw, 'lickSensorListener');\n    end;\n    if isfield(main.hw, 'lickSensor');\n        main.hw.lickSensor.stop();\n        main.hw = rmfield(main.hw, 'lickSensor');\n    end;\n    if isfield(main.hw, 'valveControl');\n    %     main.hw.valveControl.stop();\n        main.hw = rmfield(main.hw, 'valveControl');\n    end;\n    daqreset;\nend;\nmain.hw.connected = false;\no('Hardware disconnected.', 1, main.dbgLvl);\n%% #giveReward\nfunction giveReward(rewDur, rewDelay)\n    \nglobal main;\nif main.hw.connected && isfield(main.hw, 'valveControl');\n    if ~main.debugMode;\n        t_rewardStart = tic;\n        % wait until for start delay\n        while toc(t_rewardStart) < rewDelay; end;\n        t_rewardStart = tic;\n        % open digital out\n%         main.hw.valveControl.outputSingleScan(0);\n        main.hw.valveControl.outputSingleScan(1);\n        % wait until for open duration\n        while toc(t_rewardStart) < rewDur; end;\n        % close digital out\n%         main.hw.valveControl.outputSingleScan(1);\n        main.hw.valveControl.outputSingleScan(0);\n    end;\n    o('Rewarding for %.2f sec with a delay of %.2f.', rewDur, rewDelay, 3, main.dbgLvl);\nelse\n    warning('mtrainerGUI:NoRewardHardwareDisconnected', 'Cannot give reward because hardware is disconnected.');\nend\n\n%% #updateGUI\nfunction updateGUI()\n\nglobal main;\nif main.isClosed; return; end;\nstateData = get(main.handles.state, 'Data');\nhandles = main.handles;\nif main.hw.connected;\n    set(handles.connectHW, 'BackgroundColor', 'green');\n    set(handles.connectHW, 'Value', 1);\nelse\n    set(handles.connectHW, 'BackgroundColor', 'red');\n    set(handles.connectHW, 'Value', 0);\nend;\nif main.isRunning;\n    set(handles.start, 'Value', 1);\n    set(handles.start, 'BackgroundColor', 'green');\n    if main.isPaused;\n        set(handles.pause, 'Value', 1);\n        set(handles.pause, 'BackgroundColor', [1 1 0]);\n    else\n        set(handles.pause, 'Value', 0);\n        set(handles.pause, 'BackgroundColor', 'green');\n    end;\n    \n    if isfield(main, 'lickData');\n        axes(handles.lickSensorAxes);\n        data = abs(main.lickData);\n        nSamples = numel(data);\n        t = (1 : nSamples) / main.hw.analogIn_SampleRate;\n        plot(t, data);\n        line([0, t(end)], [main.lickThresh, main.lickThresh], 'Color', 'r');\n        ylim([0, 1]);\n    end;\n    \n%     stateData(:, 1) = stateData(:, 2);\n    stateData(:, 1) = getTrialInfo(main.iTrial - 1);\n    stateData(:, 2) = getTrialInfo(main.iTrial);\n    if main.iTrial < main.config.training.nTrials;\n        stateData(:, 3) = getTrialInfo(main.iTrial + 1);\n    else\n        stateData(:, 3) = getTrialInfo(0);\n    end;\n    \nelse\n    \n    axes(handles.lickSensorAxes);\n    plot(0, 0);\n    \n    set(handles.start, 'Value', 0);\n    set(handles.start, 'BackgroundColor', 'red');\n    set(handles.pause, 'Value', 0);\n    set(handles.pause, 'BackgroundColor', 'red');\n        \n    if main.configLoaded && main.iTrial == 0;\n        stateData(:, 3) = getTrialInfo(1);\n    else\n        stateData(:, 3) = getTrialInfo(0);\n    end;\nend;\nset(main.handles.state, 'Data', stateData);\nset(main.handles.configLoaded, 'Value', logical(main.configLoaded));\n%% #getTrialInfo\nfunction trialInfo = getTrialInfo(iTrial)\n\nglobal main;\nif iTrial;\n    freq = sprintf('%4.2f', main.config.tone.freqs(main.stims(iTrial)) / 1000);\n    if isfield(main.config.tone, 'oddProba') && main.config.tone.oddProba > 0; % oddball discrimination\n        isTarget = double(main.stims(iTrial) ~= main.odds(iTrial) && main.config.tone.goStim);\n    else % frequency discrimination\n        isTarget = double(main.stims(iTrial) == main.config.tone.goStim);\n    end;\n    if isfield(main, 'resps') && ~isnan(main.resps(iTrial));\n        resp = main.resps(iTrial);\n        respTime = main.respTimes(iTrial);\n        if isnan(respTime); respTime = '-'; end;\n        corr = double((isTarget && resp) || (~isTarget && ~resp));\n        ITL = main.nInTriLick(iTrial);\n    else\n        resp = '-';\n        respTime = '-';\n        corr = '-';\n        ITL = '-';\n    end;\n    trialInfo = {iTrial, freq, isTarget, resp, respTime, corr, ITL};\n    \nelse\n    trialInfo = {'-', '-', '-', '-', '-', '-', '-'};\nend;\n%% #loadConfig\nfunction loadConfig()\n\nglobal main;\no('Loading configuration ...', 2, main.dbgLvl);\nif isempty(main.mouseId);\n    warning('loadConfig:NoMouseIdSelected', 'Cannot load config because no mouseId is selected.');\n    return;\nend;\n% main.doEmptyWater = 0;\n% main.doEmptyWater = 1;\n% if > 0, sets the number of seconds to wait before a new run of mtrainer\n% main.automode = 5;\n\nmain.iTrial = 0;\nmain.lickThresh = 0.14;\n% main.lickThresh = NaN;\nmain.config = main.configs.behavior.(main.taskType).(main.phase);\n%% Setup - stimuli and trials\no('  loadConfig#: setup stimuli and trials... ', 3, main.dbgLvl);\n% set up stimulus vector\nstims = [];\nfor iFreq = 1 : numel(main.config.tone.freqs);\n    stimForFreq = repmat(iFreq, 1, ...\n        round(main.config.tone.stimProba(iFreq) * main.config.training.nTrials));\n    stims = [stims, stimForFreq]; %#ok<AGROW>\nend\n\n% stim vector should not be randomly permuted\nstims = stims(randperm(numel(stims)));\nstims = stims(1 : main.config.training.nTrials);\nclear stim;\nswitch(main.taskType);\n    case {'freqDiscr', 'oddDiscr'};\n        % set up oddball vector by altering stims with a certain probability\n        odds = stims;\n        oddPos = zeros(size(odds));\n        uniqueStims = unique(stims);\n        isOdd = rand(1, numel(stims)) < main.config.tone.oddProba;\n        uniqueOddPos = main.config.tone.nTones - 1 : main.config.tone.nTones; % last 2 tones can be odd\n        for iStim = 1 : numel(isOdd);\n            if isOdd(iStim);\n                otherStims = uniqueStims(uniqueStims ~= stims(iStim));\n                otherStims = otherStims(randperm(numel(otherStims)));\n                mixedOddPos = uniqueOddPos(randperm(numel(uniqueOddPos)));\n                odds(iStim) = otherStims(1);\n                oddPos(iStim) = mixedOddPos(1);\n            end;\n        end;\n        % set up tone array\n        main.toneArray = MakePureMultiToneOddballArray(main.config.tone.freqs, stims, odds, oddPos, ...\n            main.config.tone.nTones, main.config.tone.stimDur, main.config.tone.ISI, ...\n            main.config.tone.samplingFreq);\n        \n    case 'cotDiscr';\n        \n        odds = zeros(size(stims));\n        oddPos = zeros(size(stims));\n        isOdd = zeros(size(stims));\n        \n        atten = 0.5;\n        main.toneArray = MakeCloudOfTonesSound(main.config.tone.uniqueFreqs, main.config.tone.freqIndexes, ...\n            main.config.tone.cloudDispersion, stims, main.config.tone.toneDur, main.config.tone.toneISI, ...\n            main.config.tone.stimDur, atten, main.config.tone.samplingFreq, 0, 0);\n        \n        \nend;\nmain.stims = stims;\nmain.odds = odds;\nmain.oddPos = oddPos;\nmain.isOdd = isOdd;\nmain.configLoaded = true;\nupdateGUI();\no('  loadConfig#: setup stimuli and trials done. ', 2, main.dbgLvl);\no('Loading configuration done.', 2, main.dbgLvl);\n%% #startExperiment\nfunction startExperiment()\n\nglobal main;\nif ~main.configLoaded;\n    warning('startExperiment:configNotLoaded', 'Cannot start experiment because config is not loaded.');\n    return;\nend;\nif isempty(main.mouseId);\n    warning('startExperiment:NoMouseId', 'Cannot start experiment because there is no mouseId.');\n    return;\nend;\nif ~main.hw.connected;\n    warning('startExperiment:HardwareDisconnected', ...\n        'Cannot start experiment because hardware is disconnected.');\n    return;\nend;\no('startExperiment#: initializing experiment ... ', 2, main.dbgLvl);\nmain.isPaused = false;\nmain.isRunning = true;\nmain.iTrial = 1;\n    \nmain.saveTime = datestr(now(), 'yyyy_mm_dd__HH_MM_SS');\n% saveFolder = sprintf('D:/Balazs/2013/mtrainerTests/%s/', ...\n% saveFolder = sprintf('C:/Users/laurenczy.HIFO/Documents/LocalData/2013/mtrainerTests/%s/', ...\nsaveFolder = sprintf('C:/Users/laurenczy/mtrainerTests/%s/', main.mouseId);\n% saveFolder = sprintf('D:/Users/BaL/PhD/mtrainerTests/%s/', main.mouseId);\nmain.savePath = sprintf('%s%s__%s', saveFolder, main.mouseId, main.saveTime);\nmkdir(saveFolder);\nlogfile = sprintf('%s_log.txt', main.savePath);\nmain.logDateFormat = 'yyyy-mm-dd HH:MM:SS.FFF';\n% % create an 'onCleanup' function to handle interruptions by CTRL+C\n% c = onCleanup(@()closeDiaryAndSaveOutput(main.savePath, main.hw.lickSensorListener, ...\n%     main.hw.lickSensor, main.hw.valveControl));\no('  startExperiment#: logFile initialized. Starting diary...', 2, main.dbgLvl);\ndiary(logfile);\n%% Start experiment\no('Experiment start: %s\\n\\n', datestr(now(), main.logDateFormat), 1, main.dbgLvl);\n    \n\n%% Setup - saving settings\nglobal recordedValues;\nrecordedValues = 0;\no('config: %s phase %s (%dF%dT)', main.taskType, main.phase, numel(main.config.tone.freqs), ...\n    main.config.tone.nTones, 0, main.dbgLvl);\no('animal: %s', main.mouseId, 0, main.dbgLvl);\no('savePath: %s', main.savePath, 0, main.dbgLvl);\no('lickThresh: %1.2f, ISI: %1.2f', main.lickThresh, main.config.tone.ISI, 1, main.dbgLvl);\no('rewardCollectionTime: %1.2f, InTriLickTimerDur: %1.2f', main.config.training.rewCollTime, ...\n    main.config.training.InTriLickTimerDur, 1, main.dbgLvl);\nnTrials = main.config.training.nTrials;\nmain.trialStartDelays = zeros(1, nTrials);\nmain.trialStartTimes = zeros(1, nTrials);\nmain.trialEndTimes = zeros(1, nTrials);\nmain.respTimes = nan(1, nTrials);\nmain.nInTriLick = nan(1, nTrials);\nmain.resps = nan(1, nTrials);\nmain.respDelays = nan(1, nTrials);\n% for each trial: 1 = correct detect, 2 = correct reject, 3 = false alarm, 4 = miss, 5 = early\nmain.respTypes = nan(1, nTrials);\nmain.expStartTime = tic;\nmain.isPunish = 0;\n%% #mainLoop\nfunction mainLoop()\n\nglobal main;\ndisplayedPause = 0;\nwhile main.isRunning;\n    \n    o('mainLoop#: iTrial = %d.', main.iTrial, 4, main.dbgLvl);\n    \n    if ~main.isPaused;\n        if displayedPause; o(' --- RESUMED --- ', 1, main.dbgLvl); displayedPause = 0; end;\n        o('mainLoop#: running trial %d.', main.iTrial, 3, main.dbgLvl);\n        runTrial();\n        if main.isRunning;\n            o('Trial %d: pause ended, updating trial number.', main.iTrial, 2, main.dbgLvl);\n            if main.iTrial == main.config.training.nTrials;\n                main.isRunning = false;\n                %% End experiment\n                o('\\nExperiment end: %s', datestr(now(), main.logDateFormat), 1, main.dbgLvl);\n                main.expTotDurTime = toc(main.expStartTime);\n                o('Total time: %02.0f:%02.0f', ...\n                    main.expTotDurTime / 60, mod(main.expTotDurTime, 60), main.dbgLvl);\n                mtrainerAnalyser_performance(main.respTypes, 2, main.savePath, main.nInTriLick);\n                closeDiaryAndSaveOutput();\n            else\n                main.iTrial = main.iTrial + 1;\n                o('Trial %d: updated trial number: %d.', main.iTrial - 1, main.iTrial, 2, main.dbgLvl);\n            end;\n        else\n            o('Trial %d: not running anymore, no update of trial number.', main.iTrial, 2, main.dbgLvl);\n        end;\n        o('mainLoop#: running trial %d done.', main.iTrial, 3, main.dbgLvl);\n        updateGUI();\n    else\n        if ~displayedPause; o(' --- PAUSED --- ', 1, main.dbgLvl); displayedPause = 1; end;\n        o('mainLoop#: waiting for %f sec (trial %d).', main.updateRate, main.iTrial, 3, main.dbgLvl);\n        pause(main.updateRate);\n    end;\n    \nend;\n%% #runTrial\nfunction runTrial()\n\nglobal main recordedValues;\nnTrials = main.config.training.nTrials;\niTrial = main.iTrial;\n% listen for response\nmain.hw.lickSensor.startBackground();\npause(0.01)\ninterTrialLickCount = 0;\nmain.trialStartTimes(iTrial) = tic;\n% introduce a random delay for starting the tone\nmain.trialStartDelays(iTrial) = main.config.training.startDelay + ...\n    (rand(1) - 0.5) * 2 * main.config.training.startDelayRand;\n% add punishement time-out if needed\nif main.isPunish;\n    main.trialStartDelays(iTrial) = main.trialStartDelays(iTrial) + main.config.training.timeoutPunish;\nend;\n%% Experiment - initial wait\nwhile toc(uint64(main.trialStartTimes(iTrial))) < main.trialStartDelays(iTrial);\n%             fprintf('CheckingA: main.hw.lickSensor.IsRunning()? %d\\n', main.hw.lickSensor.IsRunning());\n    % check if experiment is paused/stopped\n    if ~main.isRunning; return; end;\n%     while main.isPaused; pause(main.updateRate); end;\n%     if ~main.isRunning; return; end;\n    % check if no lick has been done\n    if ~main.hw.lickSensor.IsRunning(); % if not running anymore, it means a licking has been done\n        remainingTime = main.trialStartDelays(iTrial) - toc(uint64(main.trialStartTimes(iTrial)));\n        if interTrialLickCount > 0;\n            fprintf('|%.1f', remainingTime);\n        else\n            fprintf('    inter-trial licking: |%.1f', remainingTime);\n        end;\n        % reset the starting delay to main.config.training.InTriLickTimerDur\n        main.trialStartTimes(iTrial) = tic;\n        if main.isPunish;\n            main.trialStartDelays(iTrial) = max(main.config.training.InTriLickTimerDur, remainingTime);\n        else\n            main.trialStartDelays(iTrial) = main.config.training.InTriLickTimerDur;\n        end;\n        interTrialLickCount = interTrialLickCount + 1;\n        pause(1);\n        % restart the listening\n        main.hw.lickSensor.startBackground();\n        pause(0.1);\n%             fprintf('CheckingB: main.hw.lickSensor.IsRunning()? %d\\n', main.hw.lickSensor.IsRunning());\n    end\n    % avoid full-speed looping\n    pause(0.01);\nend;\nif interTrialLickCount;\n    fprintf('|: %d inter-trial lick(s).\\n', interTrialLickCount);\nend\nmain.nInTriLick(iTrial) = interTrialLickCount;\nresp = 0;\nmain.isPunish = 0;\n% check if experiment is paused/stopped\nif ~main.isRunning; return; end;\n% while main.isPaused; pause(main.updateRate); end;\n% if ~main.isRunning; return; end;\n    \nt_trialStart = tic;\nfprintf('Trial %03d/%03d - %s        ... ', iTrial, nTrials, datestr(now(), main.logDateFormat));\n%% Experiment - play sound\nif main.hw.lickSensor.IsRunning();\n    \n    soundToPlay = main.toneArray{iTrial};\n    player = audioplayer(soundToPlay, main.config.tone.samplingFreq);\n    player.play;\n    \nelse\n    fprintf('early (already licking, lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n    main.isPunish = 1;\n    main.respTypes(iTrial) = 5;\n    % analyse every 10 trials\n    if ~rem(iTrial, 10) && iTrial ~= nTrials;\n        mtrainerAnalyser_performance(main.respTypes, 0);\n    end;\n    return;\nend\n\nbreakOut = 0;\n%% Experiment - wait for response\nwhile toc(t_trialStart) < main.config.training.trialDur;\n    \n    % check if experiment is paused/stopped\n    if ~main.isRunning; return; end;\n%     while main.isPaused; pause(main.updateRate); end;\n%     if ~main.isRunning; return; end;\n    \n    if main.hw.lickSensor.IsRunning()\n        % no response detected\n    else\n        % seconds since trial start\n        respTime = toc(t_trialStart);\n        main.respTimes(iTrial) = respTime;\n        % response should be 'minRespTime' after the end of the tone!\n        totStimDur = (main.config.tone.stimDur + main.config.tone.ISI) * main.config.tone.nTones - main.config.tone.ISI;\n        limitRespTime = totStimDur + main.config.training.minRespTime;\n%                 limitRespTime = main.config.training.minRespTime;\n        main.respDelays(iTrial) = limitRespTime - respTime;\n        % early response, count as kind of false alarm\n        if respTime < limitRespTime;\n%                     fprintf('early (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n            fprintf('early (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                respTime, limitRespTime, limitRespTime - respTime);\n            main.isPunish = 1;\n            main.respTypes(iTrial) = 5;\n            breakOut = 1;\n            break;\n        else\n%                     fprintf('Lick at %s\\n', datestr(clock, main.logDateFormat))\n            resp = 1;\n        end;\n        break; % break out of while loop\n    end;\n    pause(0.02); % necessary to allow time for data collection\nend;\nif breakOut;\n    main.trialEndTimes(iTrial) = toc(uint64(main.trialStartTimes(iTrial)));\n    % analyse every 10 trials\n    if ~rem(iTrial, 10) && iTrial ~= nTrials;\n        mtrainerAnalyser_performance(main.respTypes, 0);\n    end;\n    return; % continue for loop\nend;\nif main.hw.lickSensor.IsRunning();\n    main.hw.lickSensor.stop();\nend;\n% check if experiment is paused/stopped\nif ~main.isRunning; return; end;\n% while main.isPaused; pause(main.updateRate); end;\n% if ~main.isRunning; return; end;\n%% Experiment - analyse response\nmain.resps(iTrial) = resp;\nisOddTrial = main.stims(iTrial) ~= main.odds(iTrial);\nif isfield(main.config.tone, 'oddProba') && main.config.tone.oddProba > 0; % oddball discrimination\n    isTargetStim = isOddTrial && main.config.tone.goStim;\nelse % frequency discrimination\n    isTargetStim = main.stims(iTrial) == main.config.tone.goStim;\nend;\nif resp == 1 && isTargetStim;\n    % correct response\n%             fprintf('correct detection (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n    fprintf('correct detection (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n        respTime, limitRespTime, limitRespTime - respTime);\n    main.respTypes(iTrial) = 1;\n    % give reward\n    giveReward(main.config.training.rewDur, main.config.training.rewDelay);\n    % wait to collect the reward\n    pause(main.config.training.rewCollTime);\nelseif resp == 1 && ~isTargetStim;\n    % false alarm\n%             fprintf('false alarm (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n    fprintf('false alarm (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n        respTime, limitRespTime, limitRespTime - respTime);\n    main.respTypes(iTrial) = 3;\n    % punish time-out\n    main.isPunish = 1;\nelseif resp == 0 && isTargetStim;\n    fprintf('miss.\\n');\n    main.respTypes(iTrial) = 4;\n    % punish time-out\n    main.isPunish = 1;\n elseif resp == 0 && ~isTargetStim;\n    fprintf('correct rejection.\\n');\n    main.respTypes(iTrial) = 2;\n    % go to next trial\nend\nmain.trialEndTimes(iTrial) = toc(uint64(main.trialStartTimes(iTrial)));\n% analyse every 10 trials\nif ~rem(iTrial, 10) && iTrial ~= nTrials;\n    mtrainerAnalyser_performance(main.respTypes, 0);\nend;\n%% #recordAndStop\nfunction recordAndStop(src, event)\n\nglobal main recordedValues;\n[recordedValues, main.lickData] = stopWhenExceedThreshold(src, event, main.lickThresh);\nupdateGUI();\n\n%% #closeDiaryAndSaveOutput\nfunction closeDiaryAndSaveOutput()\n    \nglobal main;\no('\\nClosing diary... ', 2, main.dbgLvl);\ndiary off;\no('Closing diary done.', 1, main.dbgLvl);\nout = main;\ntry out.hw = rmfield(out.hw, 'toneArray');          catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'lickSensorListener'); catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'lickSensor');         catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'valveControl');       catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'lickData');           catch e; end; %#ok<NASGU>\n\nif exist('out', 'var');\n    matFileName = sprintf('%s_out.mat', main.savePath);\n    o('Saving output as %s... ', matFileName, 2, main.dbgLvl);\n    save(matFileName, 'out');\n    o('Saving output done.', matFileName, 1, main.dbgLvl);\nelse\n    o('/!\\\\ Output not saved !', 1, main.dbgLvl);\nend;\n% disconnectHW();\n\n% --- Creates and returns a handle to the GUI figure. \nfunction h1 = mtrainer_GUI_export_LayoutFcn(policy)\n% policy - create a new figure or use a singleton. 'new' or 'reuse'.\n\npersistent hsingleton;\nif strcmpi(policy, 'reuse') & ishandle(hsingleton)\n    h1 = hsingleton;\n    return;\nend\nload mtrainer_GUI_export.mat\n\n\nappdata = [];\nappdata.GUIDEOptions = struct(...\n    'active_h', [], ...\n    'taginfo', struct(...\n    'figure', 2, ...\n    'pushbutton', 15, ...\n    'text', 24, ...\n    'edit', 8, ...\n    'frame', 4, ...\n    'radiobutton', 22, ...\n    'uipanel', 15, ...\n    'slider', 5, ...\n    'popupmenu', 4, ...\n    'listbox', 3, ...\n    'togglebutton', 3, ...\n    'checkbox', 4, ...\n    'uitable', 4, ...\n    'axes', 2), ...\n    'override', 1, ...\n    'release', 13, ...\n    'resize', 'none', ...\n    'accessibility', 'callback', ...\n    'mfile', 1, ...\n    'callbacks', 1, ...\n    'singleton', 1, ...\n    'syscolorfig', 1, ...\n    'blocking', 0, ...\n    'lastSavedFile', 'P:\\programming\\Work\\PhD\\matlab\\behavior\\mtrainer_GUI_export.m', ...\n    'lastFilename', 'P:\\programming\\Work\\PhD\\matlab\\behavior\\mtrainer\\mtrainer_GUI.fig');\nappdata.lastValidTag = 'figure1';\nappdata.GUIDELayoutEditor = [];\nappdata.initTags = struct(...\n    'handle', [], ...\n    'tag', 'figure1');\n\nh1 = figure(...\n'PaperUnits',get(0,'defaultfigurePaperUnits'),...\n'Color',[0.941176470588235 0.941176470588235 0.941176470588235],...\n'Colormap',[0 0 0.5625;0 0 0.625;0 0 0.6875;0 0 0.75;0 0 0.8125;0 0 0.875;0 0 0.9375;0 0 1;0 0.0625 1;0 0.125 1;0 0.1875 1;0 0.25 1;0 0.3125 1;0 0.375 1;0 0.4375 1;0 0.5 1;0 0.5625 1;0 0.625 1;0 0.6875 1;0 0.75 1;0 0.8125 1;0 0.875 1;0 0.9375 1;0 1 1;0.0625 1 1;0.125 1 0.9375;0.1875 1 0.875;0.25 1 0.8125;0.3125 1 0.75;0.375 1 0.6875;0.4375 1 0.625;0.5 1 0.5625;0.5625 1 0.5;0.625 1 0.4375;0.6875 1 0.375;0.75 1 0.3125;0.8125 1 0.25;0.875 1 0.1875;0.9375 1 0.125;1 1 0.0625;1 1 0;1 0.9375 0;1 0.875 0;1 0.8125 0;1 0.75 0;1 0.6875 0;1 0.625 0;1 0.5625 0;1 0.5 0;1 0.4375 0;1 0.375 0;1 0.3125 0;1 0.25 0;1 0.1875 0;1 0.125 0;1 0.0625 0;1 0 0;0.9375 0 0;0.875 0 0;0.8125 0 0;0.75 0 0;0.6875 0 0;0.625 0 0;0.5625 0 0],...\n'DockControls','off',...\n'IntegerHandle','off',...\n'InvertHardcopy',get(0,'defaultfigureInvertHardcopy'),...\n'MenuBar','none',...\n'Name','mtrainer_GUI',...\n'NumberTitle','off',...\n'PaperPosition',get(0,'defaultfigurePaperPosition'),...\n'PaperSize',get(0,'defaultfigurePaperSize'),...\n'PaperType',get(0,'defaultfigurePaperType'),...\n'Position',[544 0 801 625],...\n'Resize','off',...\n'HandleVisibility','callback',...\n'UserData',[],...\n'Tag','figure1',...\n'Visible','on',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'hwPanel';\n\nh2 = uipanel(...\n'Parent',h1,...\n'Units','characters',...\n'Title','Hardware',...\n'Tag','hwPanel',...\n'Clipping','on',...\n'Position',[1.8 28.6923076923077 24.2 18.5384615384615],...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'connectHW';\n\nh3 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'BackgroundColor',[1 0 0],...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('connectHW_Callback',hObject,eventdata,guidata(hObject)),...\n'Position',[1.8 15.3846153846154 20 1.76923076923077],...\n'String','CONNECT HW',...\n'Style','togglebutton',...\n'Tag','connectHW',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'reward';\n\nh4 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('reward_Callback',hObject,eventdata,guidata(hObject)),...\n'CData',[],...\n'ListboxTop',0,...\n'Position',[1.8 13.0769230769231 20.2 1.76923076923077],...\n'String','REWARD',...\n'UserData',[],...\n'Tag','reward',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'rewardDurLabel';\n\nh5 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'HorizontalAlignment','left',...\n'Position',[1.8 10 17.2 2.46153846153846],...\n'String',{  'reward dur.'; '[sec]' },...\n'Style','text',...\n'Tag','rewardDurLabel',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'rewardDur';\n\nh6 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'FontSize',10,...\n'Position',[13.8 10.3076923076923 8.2 1.61538461538462],...\n'String','0.3',...\n'Style','text',...\n'Tag','rewardDur',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'rewardDurSetter';\n\nh7 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'BackgroundColor',[0.9 0.9 0.9],...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('rewardDurSetter_Callback',hObject,eventdata,guidata(hObject)),...\n'Max',20,...\n'Min',0.01,...\n'Position',[1.6 8.38461538461538 20.2 1.61538461538462],...\n'String',{  'Slider' },...\n'Style','slider',...\n'SliderStep',[0.01 0],...\n'Value',0.3,...\n'Tag','rewardDurSetter',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'lickThreshLabel';\n\nh8 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'HorizontalAlignment','left',...\n'Position',[2 5.46153846153846 17.2 2.46153846153846],...\n'String',{  'lick threshold'; '[sec]' },...\n'Style','text',...\n'Tag','lickThreshLabel',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'lickThresh';\n\nh9 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'FontSize',10,...\n'Position',[14 5.76923076923077 8.2 1.61538461538462],...\n'String','0.14',...\n'Style','text',...\n'Tag','lickThresh',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'lickThreshSetter';\n\nh10 = uicontrol(...\n'Parent',h2,...\n'Units','characters',...\n'BackgroundColor',[0.9 0.9 0.9],...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('lickThreshSetter_Callback',hObject,eventdata,guidata(hObject)),...\n'Max',0.5,...\n'Min',0.01,...\n'Position',[1.8 3.84615384615385 20.2 1.61538461538462],...\n'String',{  'Slider' },...\n'Style','slider',...\n'SliderStep',[0.01 0],...\n'Value',0.3,...\n'Tag','lickThreshSetter',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'expControlPanel';\n\nh11 = uipanel(...\n'Parent',h1,...\n'Units','characters',...\n'Title','Experiment control',...\n'Tag','expControlPanel',...\n'Clipping','on',...\n'Position',[26.6 43.3076923076923 56.4 3.92307692307692],...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'stop';\n\nh12 = uicontrol(...\n'Parent',h11,...\n'Units','characters',...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('stop_Callback',hObject,eventdata,guidata(hObject)),...\n'CData',[],...\n'ListboxTop',0,...\n'Position',[38 0.538461538461539 16.4 1.76923076923077],...\n'String','STOP',...\n'UserData',[],...\n'Tag','stop',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'start';\n\nh13 = uicontrol(...\n'Parent',h11,...\n'Units','characters',...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('start_Callback',hObject,eventdata,guidata(hObject)),...\n'CData',[],...\n'ListboxTop',0,...\n'Position',[1.8 0.538461538461539 16.2 1.76923076923077],...\n'String','START',...\n'Style','togglebutton',...\n'HitTest','off',...\n'UserData',[],...\n'Tag','start',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'pause';\n\nh14 = uicontrol(...\n'Parent',h11,...\n'Units','characters',...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('pause_Callback',hObject,eventdata,guidata(hObject)),...\n'CData',[],...\n'ListboxTop',0,...\n'Position',[19.8 0.538461538461539 16.2 1.76923076923077],...\n'String','PAUSE',...\n'Style','togglebutton',...\n'UserData',[],...\n'Tag','pause',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'currStatePanel';\n\nh15 = uipanel(...\n'Parent',h1,...\n'Units','characters',...\n'Title','Current state',...\n'Tag','currStatePanel',...\n'Clipping','on',...\n'Position',[27 27.9230769230769 55 14.6923076923077],...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'state';\nappdata.PropertyMetaData = {  {  'DataPropertyDimension' 'DataPropertyConditionedDimension' 'DataPropertySource' 'BackgroundColorPropertyDimension' 'ColumnNameTyped' 'RowNameTyped' } {  [4 2] [7 3] 'DataDefault' [2 3] {  'prev' 'curr' 'next' } {  'iTrial' 'freq' 'targ?' 'resp?' 'respTime' 'corr?' 'ITL' } } };\n\nh16 = uitable(...\n'Parent',h15,...\n'Units','characters',...\n'BackgroundColor',[1 1 1;0.96078431372549 0.96078431372549 0.96078431372549],...\n'ColumnFormat',{  'numeric' 'numeric' 'numeric' },...\n'ColumnEditable',mat{1},...\n'ColumnName',{  'prev'; 'curr'; 'next' },...\n'ColumnWidth',{  53 53 53 },...\n'Data',{  [] [] []; [] [] []; [] [] []; [] [] []; [] [] []; [] [] []; [] [] [] },...\n'FontSize',7,...\n'Position',[1.8 0.692307692307692 50.4 12.4615384615385],...\n'RowName',{  'iTrial'; 'freq'; 'targ?'; 'resp?'; 'respTime'; 'corr?'; 'ITL' },...\n'UserData',[],...\n'Tag','state',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'configPanel';\n\nh17 = uipanel(...\n'Parent',h1,...\n'Units','characters',...\n'Title','Config',...\n'Tag','configPanel',...\n'Clipping','on',...\n'Position',[83.8 14.0769230769231 74.2 33.1538461538462],...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'config';\nappdata.PropertyMetaData = {  {  'DataPropertyDimension' 'DataPropertyConditionedDimension' 'DataPropertySource' 'BackgroundColorPropertyDimension' 'ColumnNameTyped' 'RowNameTyped' } {  [4 2] [4 3] 'DataDefault' [2 3] {  'mouseId' 'taskType' 'phase' } {  blanks(0) blanks(0) blanks(0) blanks(0) } } };\n\nh18 = uitable(...\n'Parent',h17,...\n'Units','characters',...\n'ColumnFormat',{  [] [] [] },...\n'ColumnEditable',mat{2},...\n'ColumnName',{  'mouseId'; 'taskType'; 'phase' },...\n'ColumnWidth',{  85 100 50 },...\n'Data',{  blanks(0) blanks(0) blanks(0); blanks(0) blanks(0) blanks(0); blanks(0) blanks(0) blanks(0); blanks(0) blanks(0) blanks(0) },...\n'Position',[1.4 17.6153846153846 48.6 13.9230769230769],...\n'RowName',blanks(0),...\n'CellSelectionCallback',@(hObject,eventdata)mtrainer_GUI_export('config_CellSelectionCallback',hObject,eventdata,guidata(hObject)),...\n'UserData',[],...\n'Tag','config',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'loadConfig';\n\nh19 = uicontrol(...\n'Parent',h17,...\n'Units','characters',...\n'Callback',@(hObject,eventdata)mtrainer_GUI_export('loadConfig_Callback',hObject,eventdata,guidata(hObject)),...\n'CData',[],...\n'ListboxTop',0,...\n'Position',[51.8 29.7692307692308 16.4 1.76923076923077],...\n'String','LOAD',...\n'UserData',[],...\n'Tag','loadConfig',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'configLoaded';\n\nh20 = uicontrol(...\n'Parent',h17,...\n'Units','characters',...\n'Enable','inactive',...\n'Position',[51.8 27.4615384615385 21.2 1.76923076923077],...\n'String','Config loaded ?',...\n'Style','checkbox',...\n'SelectionHighlight','off',...\n'Tag','configLoaded',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nappdata = [];\nappdata.lastValidTag = 'lickSensorAxes';\n\nh21 = axes(...\n'Parent',h1,...\n'Units','characters',...\n'Position',[5.8 14.8461538461538 74.2 12.4615384615385],...\n'CameraPosition',[0.5 0.5 9.16025403784439],...\n'CameraPositionMode',get(0,'defaultaxesCameraPositionMode'),...\n'Color',get(0,'defaultaxesColor'),...\n'ColorOrder',get(0,'defaultaxesColorOrder'),...\n'LooseInset',[23.218 6.19384615384616 16.967 4.22307692307692],...\n'XColor',get(0,'defaultaxesXColor'),...\n'YColor',get(0,'defaultaxesYColor'),...\n'ZColor',get(0,'defaultaxesZColor'),...\n'Tag','lickSensorAxes',...\n'CreateFcn', {@local_CreateFcn, blanks(0), appdata} );\n\nh22 = get(h21,'title');\n\nset(h22,...\n'Parent',h21,...\n'Units','data',...\n'FontUnits','points',...\n'BackgroundColor','none',...\n'Color',[0 0 0],...\n'DisplayName',blanks(0),...\n'EdgeColor','none',...\n'EraseMode','normal',...\n'DVIMode','auto',...\n'FontAngle','normal',...\n'FontName','Helvetica',...\n'FontSize',10,...\n'FontWeight','normal',...\n'HorizontalAlignment','center',...\n'LineStyle','-',...\n'LineWidth',0.5,...\n'Margin',2,...\n'Position',[0.5 1.04012345679012 1.00005459937205],...\n'Rotation',0,...\n'String',blanks(0),...\n'Interpreter','tex',...\n'VerticalAlignment','bottom',...\n'ButtonDownFcn',[],...\n'CreateFcn', {@local_CreateFcn, [], ''} ,...\n'DeleteFcn',[],...\n'BusyAction','queue',...\n'HandleVisibility','off',...\n'HelpTopicKey',blanks(0),...\n'HitTest','on',...\n'Interruptible','on',...\n'SelectionHighlight','on',...\n'Serializable','on',...\n'Tag',blanks(0),...\n'UserData',[],...\n'Visible','on',...\n'XLimInclude','on',...\n'YLimInclude','on',...\n'ZLimInclude','on',...\n'CLimInclude','on',...\n'ALimInclude','on',...\n'IncludeRenderer','on',...\n'Clipping','off');\n\nh23 = get(h21,'xlabel');\n\nset(h23,...\n'Parent',h21,...\n'Units','data',...\n'FontUnits','points',...\n'BackgroundColor','none',...\n'Color',[0 0 0],...\n'DisplayName',blanks(0),...\n'EdgeColor','none',...\n'EraseMode','normal',...\n'DVIMode','auto',...\n'FontAngle','normal',...\n'FontName','Helvetica',...\n'FontSize',10,...\n'FontWeight','normal',...\n'HorizontalAlignment','center',...\n'LineStyle','-',...\n'LineWidth',0.5,...\n'Margin',2,...\n'Position',[0.497304582210243 -0.145061728395062 1.00005459937205],...\n'Rotation',0,...\n'String',blanks(0),...\n'Interpreter','tex',...\n'VerticalAlignment','cap',...\n'ButtonDownFcn',[],...\n'CreateFcn', {@local_CreateFcn, [], ''} ,...\n'DeleteFcn',[],...\n'BusyAction','queue',...\n'HandleVisibility','off',...\n'HelpTopicKey',blanks(0),...\n'HitTest','on',...\n'Interruptible','on',...\n'SelectionHighlight','on',...\n'Serializable','on',...\n'Tag',blanks(0),...\n'UserData',[],...\n'Visible','on',...\n'XLimInclude','on',...\n'YLimInclude','on',...\n'ZLimInclude','on',...\n'CLimInclude','on',...\n'ALimInclude','on',...\n'IncludeRenderer','on',...\n'Clipping','off');\n\nh24 = get(h21,'ylabel');\n\nset(h24,...\n'Parent',h21,...\n'Units','data',...\n'FontUnits','points',...\n'BackgroundColor','none',...\n'Color',[0 0 0],...\n'DisplayName',blanks(0),...\n'EdgeColor','none',...\n'EraseMode','normal',...\n'DVIMode','auto',...\n'FontAngle','normal',...\n'FontName','Helvetica',...\n'FontSize',10,...\n'FontWeight','normal',...\n'HorizontalAlignment','center',...\n'LineStyle','-',...\n'LineWidth',0.5,...\n'Margin',2,...\n'Position',[-0.0768194070080863 0.490740740740741 1.00005459937205],...\n'Rotation',90,...\n'String',blanks(0),...\n'Interpreter','tex',...\n'VerticalAlignment','bottom',...\n'ButtonDownFcn',[],...\n'CreateFcn', {@local_CreateFcn, [], ''} ,...\n'DeleteFcn',[],...\n'BusyAction','queue',...\n'HandleVisibility','off',...\n'HelpTopicKey',blanks(0),...\n'HitTest','on',...\n'Interruptible','on',...\n'SelectionHighlight','on',...\n'Serializable','on',...\n'Tag',blanks(0),...\n'UserData',[],...\n'Visible','on',...\n'XLimInclude','on',...\n'YLimInclude','on',...\n'ZLimInclude','on',...\n'CLimInclude','on',...\n'ALimInclude','on',...\n'IncludeRenderer','on',...\n'Clipping','off');\n\nh25 = get(h21,'zlabel');\n\nset(h25,...\n'Parent',h21,...\n'Units','data',...\n'FontUnits','points',...\n'BackgroundColor','none',...\n'Color',[0 0 0],...\n'DisplayName',blanks(0),...\n'EdgeColor','none',...\n'EraseMode','normal',...\n'DVIMode','auto',...\n'FontAngle','normal',...\n'FontName','Helvetica',...\n'FontSize',10,...\n'FontWeight','normal',...\n'HorizontalAlignment','right',...\n'LineStyle','-',...\n'LineWidth',0.5,...\n'Margin',2,...\n'Position',[-0.0795148247978437 2.65740740740741 1.00005459937205],...\n'Rotation',0,...\n'String',blanks(0),...\n'Interpreter','tex',...\n'VerticalAlignment','middle',...\n'ButtonDownFcn',[],...\n'CreateFcn', {@local_CreateFcn, [], ''} ,...\n'DeleteFcn',[],...\n'BusyAction','queue',...\n'HandleVisibility','off',...\n'HelpTopicKey',blanks(0),...\n'HitTest','on',...\n'Interruptible','on',...\n'SelectionHighlight','on',...\n'Serializable','on',...\n'Tag',blanks(0),...\n'UserData',[],...\n'Visible','off',...\n'XLimInclude','on',...\n'YLimInclude','on',...\n'ZLimInclude','on',...\n'CLimInclude','on',...\n'ALimInclude','on',...\n'IncludeRenderer','on',...\n'Clipping','off');\n\n\nhsingleton = h1;\n\n\n% --- Set application data first then calling the CreateFcn. \nfunction local_CreateFcn(hObject, eventdata, createfcn, appdata)\n\nif ~isempty(appdata)\n   names = fieldnames(appdata);\n   for i=1:length(names)\n       name = char(names(i));\n       setappdata(hObject, name, getfield(appdata,name));\n   end\nend\n\nif ~isempty(createfcn)\n   if isa(createfcn,'function_handle')\n       createfcn(hObject, eventdata);\n   else\n       eval(createfcn);\n   end\nend\n\n\n% --- Handles default GUIDE GUI creation and callback dispatch\nfunction varargout = gui_mainfcn(gui_State, varargin)\n\ngui_StateFields =  {'gui_Name'\n    'gui_Singleton'\n    'gui_OpeningFcn'\n    'gui_OutputFcn'\n    'gui_LayoutFcn'\n    'gui_Callback'};\ngui_Mfile = '';\nfor i=1:length(gui_StateFields)\n    if ~isfield(gui_State, gui_StateFields{i})\n        error(message('MATLAB:guide:StateFieldNotFound', gui_StateFields{ i }, gui_Mfile));\n    elseif isequal(gui_StateFields{i}, 'gui_Name')\n        gui_Mfile = [gui_State.(gui_StateFields{i}), '.m'];\n    end\nend\n\nnumargin = length(varargin);\nif numargin == 0\n    % MTRAINER_GUI_EXPORT\n    % create the GUI only if we are not in the process of loading it\n    % already\n    gui_Create = true;\nelseif local_isInvokeActiveXCallback(gui_State, varargin{:})\n    % MTRAINER_GUI_EXPORT(ACTIVEX,...)\n    vin{1} = gui_State.gui_Name;\n    vin{2} = [get(varargin{1}.Peer, 'Tag'), '_', varargin{end}];\n    vin{3} = varargin{1};\n    vin{4} = varargin{end-1};\n    vin{5} = guidata(varargin{1}.Peer);\n    feval(vin{:});\n    return;\nelseif local_isInvokeHGCallback(gui_State, varargin{:})\n    % MTRAINER_GUI_EXPORT('CALLBACK',hObject,eventData,handles,...)\n    gui_Create = false;\nelse\n    % MTRAINER_GUI_EXPORT(...)\n    % create the GUI and hand varargin to the openingfcn\n    gui_Create = true;\nend\n\nif ~gui_Create\n    % In design time, we need to mark all components possibly created in\n    % the coming callback evaluation as non-serializable. This way, they\n    % will not be brought into GUIDE and not be saved in the figure file\n    % when running/saving the GUI from GUIDE.\n    designEval = false;\n    if (numargin>1 && ishghandle(varargin{2}))\n        fig = varargin{2};\n        while ~isempty(fig) && ~ishghandle(fig,'figure')\n            fig = get(fig,'parent');\n        end\n        \n        designEval = isappdata(0,'CreatingGUIDEFigure') || isprop(fig,'__GUIDEFigure');\n    end\n        \n    if designEval\n        beforeChildren = findall(fig);\n    end\n    \n    % evaluate the callback now\n    varargin{1} = gui_State.gui_Callback;\n    if nargout\n        [varargout{1:nargout}] = feval(varargin{:});\n    else       \n        feval(varargin{:});\n    end\n    \n    % Set serializable of objects created in the above callback to off in\n    % design time. Need to check whether figure handle is still valid in\n    % case the figure is deleted during the callback dispatching.\n    if designEval && ishghandle(fig)\n        set(setdiff(findall(fig),beforeChildren), 'Serializable','off');\n    end\nelse\n    if gui_State.gui_Singleton\n        gui_SingletonOpt = 'reuse';\n    else\n        gui_SingletonOpt = 'new';\n    end\n\n    % Check user passing 'visible' P/V pair first so that its value can be\n    % used by oepnfig to prevent flickering\n    gui_Visible = 'auto';\n    gui_VisibleInput = '';\n    for index=1:2:length(varargin)\n        if length(varargin) == index || ~ischar(varargin{index})\n            break;\n        end\n\n        % Recognize 'visible' P/V pair\n        len1 = min(length('visible'),length(varargin{index}));\n        len2 = min(length('off'),length(varargin{index+1}));\n        if ischar(varargin{index+1}) && strncmpi(varargin{index},'visible',len1) && len2 > 1\n            if strncmpi(varargin{index+1},'off',len2)\n                gui_Visible = 'invisible';\n                gui_VisibleInput = 'off';\n            elseif strncmpi(varargin{index+1},'on',len2)\n                gui_Visible = 'visible';\n                gui_VisibleInput = 'on';\n            end\n        end\n    end\n    \n    % Open fig file with stored settings.  Note: This executes all component\n    % specific CreateFunctions with an empty HANDLES structure.\n\n    \n    % Do feval on layout code in m-file if it exists\n    gui_Exported = ~isempty(gui_State.gui_LayoutFcn);\n    % this application data is used to indicate the running mode of a GUIDE\n    % GUI to distinguish it from the design mode of the GUI in GUIDE. it is\n    % only used by actxproxy at this time.   \n    setappdata(0,genvarname(['OpenGuiWhenRunning_', gui_State.gui_Name]),1);\n    if gui_Exported\n        gui_hFigure = feval(gui_State.gui_LayoutFcn, gui_SingletonOpt);\n        % make figure invisible here so that the visibility of figure is\n        % consistent in OpeningFcn in the exported GUI case\n        if isempty(gui_VisibleInput)\n            gui_VisibleInput = get(gui_hFigure,'Visible');\n        end\n        set(gui_hFigure,'Visible','off')\n\n        % openfig (called by local_openfig below) does this for guis without\n        % the LayoutFcn. Be sure to do it here so guis show up on screen.\n        movegui(gui_hFigure,'onscreen');\n    else\n        gui_hFigure = local_openfig(gui_State.gui_Name, gui_SingletonOpt, gui_Visible);\n        % If the figure has InGUIInitialization it was not completely created\n        % on the last pass.  Delete this handle and try again.\n        if isappdata(gui_hFigure, 'InGUIInitialization')\n            delete(gui_hFigure);\n            gui_hFigure = local_openfig(gui_State.gui_Name, gui_SingletonOpt, gui_Visible);\n        end\n    end\n    if isappdata(0, genvarname(['OpenGuiWhenRunning_', gui_State.gui_Name]))\n        rmappdata(0,genvarname(['OpenGuiWhenRunning_', gui_State.gui_Name]));\n    end\n\n    % Set flag to indicate starting GUI initialization\n    setappdata(gui_hFigure,'InGUIInitialization',1);\n    % Fetch GUIDE Application options\n    gui_Options = getappdata(gui_hFigure,'GUIDEOptions');\n    % Singleton setting in the GUI M-file takes priority if different\n    gui_Options.singleton = gui_State.gui_Singleton;\n    if ~isappdata(gui_hFigure,'GUIOnScreen')\n        % Adjust background color\n        if gui_Options.syscolorfig\n            set(gui_hFigure,'Color', get(0,'DefaultUicontrolBackgroundColor'));\n        end\n\n        % Generate HANDLES structure and store with GUIDATA. If there is\n        % user set GUI data already, keep that also.\n        data = guidata(gui_hFigure);\n        handles = guihandles(gui_hFigure);\n        if ~isempty(handles)\n            if isempty(data)\n                data = handles;\n            else\n                names = fieldnames(handles);\n                for k=1:length(names)\n                    data.(char(names(k)))=handles.(char(names(k)));\n                end\n            end\n        end\n        guidata(gui_hFigure, data);\n    end\n\n    % Apply input P/V pairs other than 'visible'\n    for index=1:2:length(varargin)\n        if length(varargin) == index || ~ischar(varargin{index})\n            break;\n        end\n\n        len1 = min(length('visible'),length(varargin{index}));\n        if ~strncmpi(varargin{index},'visible',len1)\n            try set(gui_hFigure, varargin{index}, varargin{index+1}), catch break, end\n        end\n    end\n\n    % If handle visibility is set to 'callback', turn it on until finished\n    % with OpeningFcn\n    gui_HandleVisibility = get(gui_hFigure,'HandleVisibility');\n    if strcmp(gui_HandleVisibility, 'callback')\n        set(gui_hFigure,'HandleVisibility', 'on');\n    end\n\n    feval(gui_State.gui_OpeningFcn, gui_hFigure, [], guidata(gui_hFigure), varargin{:});\n    if isscalar(gui_hFigure) && ishghandle(gui_hFigure)\n        % Handle the default callbacks of predefined toolbar tools in this\n        % GUI, if any\n        guidemfile('restoreToolbarToolPredefinedCallback',gui_hFigure);\n        \n        % Update handle visibility\n        set(gui_hFigure,'HandleVisibility', gui_HandleVisibility);\n        % Call openfig again to pick up the saved visibility or apply the\n        % one passed in from the P/V pairs\n        if ~gui_Exported\n            gui_hFigure = local_openfig(gui_State.gui_Name, 'reuse',gui_Visible);\n        elseif ~isempty(gui_VisibleInput)\n            set(gui_hFigure,'Visible',gui_VisibleInput);\n        end\n        if strcmpi(get(gui_hFigure, 'Visible'), 'on')\n            figure(gui_hFigure);\n            \n            if gui_Options.singleton\n                setappdata(gui_hFigure,'GUIOnScreen', 1);\n            end\n        end\n\n        % Done with GUI initialization\n        if isappdata(gui_hFigure,'InGUIInitialization')\n            rmappdata(gui_hFigure,'InGUIInitialization');\n        end\n\n        % If handle visibility is set to 'callback', turn it on until\n        % finished with OutputFcn\n        gui_HandleVisibility = get(gui_hFigure,'HandleVisibility');\n        if strcmp(gui_HandleVisibility, 'callback')\n            set(gui_hFigure,'HandleVisibility', 'on');\n        end\n        gui_Handles = guidata(gui_hFigure);\n    else\n        gui_Handles = [];\n    end\n\n    if nargout\n        [varargout{1:nargout}] = feval(gui_State.gui_OutputFcn, gui_hFigure, [], gui_Handles);\n    else\n        feval(gui_State.gui_OutputFcn, gui_hFigure, [], gui_Handles);\n    end\n\n    if isscalar(gui_hFigure) && ishghandle(gui_hFigure)\n        set(gui_hFigure,'HandleVisibility', gui_HandleVisibility);\n    end\nend\n\nfunction gui_hFigure = local_openfig(name, singleton, visible)\n\n% openfig with three arguments was new from R13. Try to call that first, if\n% failed, try the old openfig.\nif nargin('openfig') == 2\n    % OPENFIG did not accept 3rd input argument until R13,\n    % toggle default figure visible to prevent the figure\n    % from showing up too soon.\n    gui_OldDefaultVisible = get(0,'defaultFigureVisible');\n    set(0,'defaultFigureVisible','off');\n    gui_hFigure = openfig(name, singleton);\n    set(0,'defaultFigureVisible',gui_OldDefaultVisible);\nelse\n    gui_hFigure = openfig(name, singleton, visible);\n    %workaround for CreateFcn not called to create ActiveX\n    if feature('HGUsingMATLABClasses')\n        peers=findobj(findall(allchild(gui_hFigure)),'type','uicontrol','style','text');\n        for i=1:length(peers)\n            if isappdata(peers(i),'Control')\n                actxproxy(peers(i));\n            end            \n        end\n    end\nend\n\nfunction result = local_isInvokeActiveXCallback(gui_State, varargin)\n\ntry\n    result = ispc && iscom(varargin{1}) ...\n             && isequal(varargin{1},gcbo);\ncatch\n    result = false;\nend\n\nfunction result = local_isInvokeHGCallback(gui_State, varargin)\n\ntry\n    fhandle = functions(gui_State.gui_Callback);\n    result = ~isempty(findstr(gui_State.gui_Name,fhandle.file)) || ...\n             (ischar(varargin{1}) ...\n             && isequal(ishghandle(varargin{2}), 1) ...\n             && (~isempty(strfind(varargin{1},[get(varargin{2}, 'Tag'), '_'])) || ...\n                ~isempty(strfind(varargin{1}, '_CreateFcn'))) );\ncatch\n    result = false;\nend\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "daq_sessionInterfaceH45.m", "ext": ".m", "path": "OCIA-master/behavior/daq/daq_sessionInterfaceH45.m", "size": 4666, "source_encoding": "utf_8", "md5": "4bfeda072efe6d1b8ef7c49d06067dcc", "text": "function [data, S, F, T, P] = daq_sessionInterfaceH45(dur, recordRate, condition, doPlot)\n\n% s = daq.createSession('ni');\n% s.Rate = 50;\n% s.DurationInSeconds = 40;\n% s.addAnalogInputChannel('Dev1','ai0', 'Voltage');\n% s.Channels.InputType = 'SingleEnded';\n% s.Channels.Range = [-10 10];\n\nrealTimePlot = 0;\n\nrecordChanNames = {'shutter'};\nrecordChanRanges = {1, 1, 1, 5, 5};\nnChans = numel(recordChanNames);\nrecordChans = cell(nChans, 1);\n\ndaqreset;\nfprintf('Connecting hardware...\\n');\ns = daq.createSession('nidaq');\ns.Rate = recordRate;\nfor iChan = 1 : nChans;\n    recordChans{iChan} = s.addAnalogInputChannel('ExtraChannels', iChan, 'Voltage');\n    recordChans{iChan}.Name = recordChanNames{iChan};\n    recordChans{iChan}.Range = repmat(recordChanRanges{iChan}, 1, 2) .* [-1 1];\n    recordChans{iChan}.InputType = 'SingleEnded';\nend;\n\n% s.addAnalogInputChannel('ExtraChannels', 'ai0', 'Voltage'); % shutter\n% s.addAnalogInputChannel('ExtraChannels', 'ai1', 'Voltage'); % laser power\n% s.addAnalogInputChannel('ExtraChannels', 2, 'Voltage'); % motion\n% s.addAnalogInputChannel('ExtraChannels', 'ai3', 'Voltage'); % microphone\n% s.addAnalogInputChannel('ExtraChannels', 'ai4', 'Voltage'); % resonnance X position\n% s.addAnalogInputChannel('ExtraChannels', 'ai5', 'Voltage');\n% s.addAnalogInputChannel('ExtraChannels', 'ai6', 'Voltage');\n% s.addAnalogInputChannel('ExtraChannels', 'ai7', 'Voltage');\n\n% s.Channels.InputType = 'SingleEnded';\nchanRange = [-5, 5];\n% s.Channels.Range = chanRange;\nfprintf('s1 ok.\\n');\n\n% s2 = daq.createSession(config.hardware.adaptorID);\n% s2.addDigitalChannel(config.hardware.digitalOut_Device, config.hardware.digitalOut_PortLine, 'OutputOnly');\n% fprintf('And s2 ok. Ready to go.\\n');\n\ns.DurationInSeconds = dur;\ndata = [];\n\nif realTimePlot;\n%     'Position', [150, 300, 950, 350], \n    h = figure('Name', 'Recording', 'NumberTitle', 'off', 'WindowStyle', 'docked');\n    plot(0, 0);\n    hold on;\n    ylim(chanRange);\n    xlim([0, s.DurationInSeconds]);\nend;\n\ns.addlistener('DataAvailable', @collectAndPlotData);\n\ns.startForeground();\n\nfunction collectAndPlotData(~, event)\n    data = [data (event.Data - bsxfun(@rdivide, event.Data, mean(event.Data)))'];\n    if realTimePlot;\n        figure(h);\n        t = 1 : size(data, 2);\n        plot(repmat(t ./ recordRate, 1, nChans), data(:, t)');\n    end;\nend\n\n\nif realTimePlot;\n    figure(h);\nelse\n    h = figure('Name', 'Recording', 'NumberTitle', 'off', 'WindowStyle', 'docked');\nend;\n\ncolors = jet(nChans);\nt = 1 : size(data, 2);\nfor iChan = 1 : nChans;\n    subplot(nChans, 1, iChan);\n    plot(t' ./ recordRate, data(iChan, t)', 'Color', colors(iChan, :));\n    ylim([min(data(iChan, :)) max(data(iChan, :))]);\n    ylabel(recordChanNames{iChan});\nend;\n\nN = 8;\n% figure('Name', 'Spectrogram', 'NumberTitle', 'off', 'WindowStyle', 'docked');\n% % spectrogram(data, 512, 480, 512, recordRate);\n% spectrogram(data, 128 * N, 120 * N, 128 * N, recordRate);\n% colorbar;\n\nS = cell(nChans, 1);\nF = cell(nChans, 1);\nT = cell(nChans, 1);\nP = cell(nChans, 1);\n\nfor iChan = 1 : nChans;\n    \n    [S{iChan}, F{iChan}, T{iChan}, P{iChan}] = spectrogram(data(iChan, :), 128 * N, 120 * N, 128 * N, recordRate);\n    \n    if ~doPlot; continue; end;\n\n    %% plotting\n    figure('Name', sprintf('Spectrogram_%s_%s', condition, recordChanNames{iChan}), ...\n        'NumberTitle', 'off', 'WindowStyle', 'docked');\n    realLogS = real(log(S{iChan}));\n\n    subplot(2, 1, 1);\n    imagesc(realLogS');\n    nFreqs = size(realLogS, 1);\n    nSamples = size(realLogS, 2);\n    maxFreq = recordRate * 0.5;\n    maxT = s.DurationInSeconds;\n    freqIDs = (0 : 1000 : round(maxFreq / 1000) * 1000) / 1000;\n    toShowFreqs = round(0 : nFreqs / (maxFreq / 1000) : nFreqs);\n    t = 0 : 1 : s.DurationInSeconds;\n    timeIndex = round(0 : nSamples / (maxT / 1) : nSamples);\n    set(gca, 'XTick', toShowFreqs, 'XTickLabel', freqIDs, 'YTick', timeIndex, 'YTickLabel', t);\n    xlabel('Frequencies [kHz]');\n    ylabel('Time [s]');\n\n    subplot(2, 1, 2);\n    freqDist = sum(realLogS, 2);\n    secDeriv = diff(freqDist, 2);\n    secDerivMinThresh = prctile(secDeriv, 2);\n    peakInd = find(secDeriv < secDerivMinThresh);\n    peakIndSingle = peakInd;\n    peakIndSingle(diff(peakIndSingle) < 15) = [];\n    plot(F{iChan}, freqDist);\n    hold on;\n    % scatter(F(peakIndSingle), freqDist(peakIndSingle) + 300, 'bx');\n    for iPeak = 1 : numel(peakIndSingle);\n        text(F{iChan}(peakIndSingle(iPeak)) + 100, freqDist(peakIndSingle(iPeak)) + 400, ...\n            sprintf('%d Hz', round(F{iChan}(peakIndSingle(iPeak)))), 'FontSize', 8);\n    end;\n\n    suptitle(strrep(sprintf('%s_%s', condition, recordChanNames{iChan}), '_', '\\_'));\nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "daq_sessionInterfaceH30.m", "ext": ".m", "path": "OCIA-master/behavior/daq/daq_sessionInterfaceH30.m", "size": 6252, "source_encoding": "utf_8", "md5": "0ea24a3bcc0b8963af28024c82ae66b6", "text": "function [data, S, F, T, P] = daq_sessionInterfaceH30(dur, recordRate, condition, doPlot)\n\n% s = daq.createSession('ni');\n% s.Rate = 50;\n% s.DurationInSeconds = 40;\n% s.addAnalogInputChannel('Dev1','ai0', 'Voltage');\n% s.Channels.InputType = 'SingleEnded';\n% s.Channels.Range = [-10 10];\n\nrealTimePlot = 0;\n\n% recordChanNames = {'shutter', 'breathing', 'microphone', 'laserPower', 'resXPos'};\n% recordChanNames = {'lickSensor'};\n% recordChanNames = {'lickSensor', 'microphone'};\nrecordChanNames = {'piezo', 'micr', 'yscan'};\n% recordChanRanges = {1, 1, 1, 5, 5};\nnChans = numel(recordChanNames);\nrecordChans = cell(nChans, 1);\n\ndaqreset;\nfprintf('Connecting hardware...\\n');\ns = daq.createSession('ni');\ns.Rate = recordRate;\n% for iChan = 1 : nChans;\n%     recordChans{iChan} = s.addAnalogInputChannel('ExtraChannels', iChan, 'Voltage');\n% %     recordChans{iChan} = s.addAnalogInputChannel('BehaviorBox', iChan, 'Voltage');\n%     recordChans{iChan}.Name = recordChanNames{iChan};\n%     recordChans{iChan}.Range = repmat(recordChanRanges{iChan}, 1, 2) .* [-1 1];\n%     recordChans{iChan}.InputType = 'SingleEnded';\n% end;\n\n% s.addAnalogInputChannel('BehaviorBox', 'ai0', 'Voltage'); % piezo\n% s.addAnalogInputChannel('BehaviorBox', 'ai1', 'Voltage'); % micr\n% s.addAnalogInputChannel('BehaviorBox', 'ai2', 'Voltage'); % yscan\n% s.addAnalogInputChannel('ExtraChannels', 'ai0', 'Voltage'); % shutter\n% s.addAnalogInputChannel('ExtraChannels', 'ai1', 'Voltage'); % laser power\n% s.addAnalogInputChannel('ExtraChannels', 2, 'Voltage'); % motion\n% s.addAnalogInputChannel('ExtraChannels', 'ai3', 'Voltage'); % microphone\n% s.addAnalogInputChannel('ExtraChannels', 'ai4', 'Voltage'); % resonnance X position\n% s.addAnalogInputChannel('ExtraChannels', 'ai5', 'Voltage');\n% s.addAnalogInputChannel('ExtraChannels', 'ai6', 'Voltage');\n% s.addAnalogInputChannel('ExtraChannels', 'ai7', 'Voltage');\ns.addAnalogOutputChannel('ExtraChannels', 'ao0', 'Voltage'); % imaging TTL gate\n\n% s.Channels.InputType = 'SingleEnded';\nchanRange = [-10, 10];\n% s.Channels.Range = chanRange;\nfprintf('Hardware connected.\\n');\n\n% s2 = daq.createSession(config.hardware.adaptorID);\n% s2.addDigitalChannel(config.hardware.digitalOut_Device, config.hardware.digitalOut_PortLine, 'OutputOnly');\n% fprintf('And s2 ok. Ready to go.\\n');\n\ns.DurationInSeconds = dur;\ndata = [];\n\nif realTimePlot;\n%     'Position', [150, 300, 950, 350], \n    h = figure('Name', 'Recording', 'NumberTitle', 'off', 'WindowStyle', 'docked');\n    plot(0, 0);\n    hold on;\n    ylim(chanRange);\n    xlim([0, s.DurationInSeconds]);\nend;\n\ns.addlistener('DataAvailable', @collectAndPlotData);\n\n% gongMat = load('gong.mat');\n% m = load('C:\\Users\\laurenczy\\Desktop\\cloudOfTonesParameters.mat');\n% cloudOfTones = MakeCloudOfTonesSound(m.freqs * 0.5, m.uniqueCloudCenterIndexes, m.cloudDispersion, m.stims, m.toneDur, ...\n%     m.toneISI, m.stimDur, 1, m.sampFreq, 0, 0);\n\n% s.startForeground();\ns.startBackground();\n\n% pause(1);\n% sound(gongMat.y * 0.3);\n% sound(repmat(cloudOfTones{2}, 1, 10), m.sampFreq);\n% pause(0.5);\n% sound(cloudOfTones{2}, m.sampFreq);\n% pause(0.5);\n% sound(cloudOfTones{3}, m.sampFreq);\n% pause(0.5);\n% sound(cloudOfTones{4}, m.sampFreq);\n% pause(0.5);\n% sound(cloudOfTones{5}, m.sampFreq);\n\ns.wait();\n\nfunction collectAndPlotData(~, event)\n    currentData = event.Data;\n    if isempty(data);\n        currentData(fix(0.001 * recordRate) : end, :) = NaN;\n    end;\n    data = [data (currentData - bsxfun(@rdivide, currentData, mean(currentData)))'];\n    if realTimePlot;\n        figure(h);\n        t = 1 : size(data, 2);\n        plot(repmat(t ./ recordRate, 1, nChans), data(:, t)');\n    end;\nend\n\nif realTimePlot;\n    figure(h);\nelse\n    h = figure('Name', 'Recording', 'NumberTitle', 'off', 'WindowStyle', 'docked');\nend;\n\ncolors = lines(nChans);\n% colors = jet(nChans);\nt = 1 : size(data, 2);\nfor iChan = 1 : nChans;\n    subplot(nChans, 1, iChan);\n    dataForChan = data(iChan, :);\n%     normData = linScale(dataForChan - nanmean(dataForChan), -1, 1);\n    normData = dataForChan - nanmean(dataForChan);\n%     normData = dataForChan;\n    plot(t' ./ recordRate, normData', 'Color', colors(iChan, :));\n%     minData = nanmin(normData);\n%     maxData = nanmax(normData);\n%     ylim([min(minData, -1) max(maxData, 1)]);\n    ylabel(recordChanNames{iChan});\nend;\n\nN = 8;\n% figure('Name', 'Spectrogram', 'NumberTitle', 'off', 'WindowStyle', 'docked');\n% % spectrogram(data, 512, 480, 512, recordRate);\n% spectrogram(data, 128 * N, 120 * N, 128 * N, recordRate);\n% colorbar;\n\nS = cell(nChans, 1);\nF = cell(nChans, 1);\nT = cell(nChans, 1);\nP = cell(nChans, 1);\n\nfor iChan = 1 : nChans;\n    \n    [S{iChan}, F{iChan}, T{iChan}, P{iChan}] = spectrogram(data(iChan, :), 128 * N, 120 * N, 128 * N, recordRate);\n    \n    if ~doPlot; continue; end;\n\n    %% plotting\n    figure('Name', sprintf('Spectrogram_%s_%s', condition, recordChanNames{iChan}), ...\n        'NumberTitle', 'off', 'WindowStyle', 'docked');\n    realLogS = real(log(S{iChan}));\n\n    subplot(2, 1, 1);\n    imagesc(realLogS');\n    nFreqs = size(realLogS, 1);\n    nSamples = size(realLogS, 2);\n    maxFreq = recordRate * 0.5;\n    maxT = s.DurationInSeconds;\n    freqIDs = (0 : 1000 : round(maxFreq / 1000) * 1000) / 1000;\n    toShowFreqs = round(0 : nFreqs / (maxFreq / 1000) : nFreqs);\n    t = 0 : 1 : s.DurationInSeconds;\n    timeIndex = round(0 : nSamples / (maxT / 1) : nSamples);\n    set(gca, 'XTick', toShowFreqs, 'XTickLabel', freqIDs, 'YTick', timeIndex, 'YTickLabel', t);\n    xlabel('Frequencies [kHz]');\n    ylabel('Time [s]');\n\n    subplot(2, 1, 2);\n    freqDist = nansum(realLogS, 2);\n    secDeriv = diff(freqDist, 2);\n    secDerivMinThresh = prctile(secDeriv, 2);\n    peakInd = find(secDeriv < secDerivMinThresh);\n    peakIndSingle = peakInd;\n    peakIndSingle(diff(peakIndSingle) < 15) = [];\n    plot(F{iChan}, freqDist);\n    hold on;\n    % scatter(F(peakIndSingle), freqDist(peakIndSingle) + 300, 'bx');\n    for iPeak = 1 : numel(peakIndSingle);\n        text(F{iChan}(peakIndSingle(iPeak)) + 100, freqDist(peakIndSingle(iPeak)) + 400, ...\n            sprintf('%d Hz', round(F{iChan}(peakIndSingle(iPeak)))), 'FontSize', 8);\n    end;\n\n    suptitle(strrep(sprintf('%s_%s', condition, recordChanNames{iChan}), '_', '\\_'));\nend;\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "daq_sessionInterface.m", "ext": ".m", "path": "OCIA-master/behavior/daq/daq_sessionInterface.m", "size": 1475, "source_encoding": "utf_8", "md5": "7db7e719bbb05c2ed3fc8276d2d00939", "text": "function daq_sessionInterface(config)\n\n% s = daq.createSession('ni');\n% s.Rate = 50;\n% s.DurationInSeconds = 40;\n% s.addAnalogInputChannel('Dev1','ai0', 'Voltage');\n% s.Channels.InputType = 'SingleEnded';\n% s.Channels.Range = [-10 10];\n\n% daqreset;\nfprintf('Connecting hardware...\\n');\ns = daq.createSession(config.hardware.adaptorID);\nrecordRate = config.hardware.analogIn_SampleRate;\nrecordRate = 1000;\ns.Rate = recordRate;\ns.addAnalogInputChannel(config.hardware.analogIn_Device, ...\n    config.hardware.analogIn_Channel, 'Voltage');\ns.Channels.InputType = config.hardware.analogIn_InputType;\ns.Channels.Range = config.hardware.analogIn_Range;\nfprintf('s1 ok.\\n');\n\n% s2 = daq.createSession(config.hardware.adaptorID);\n% s2.addDigitalChannel(config.hardware.digitalOut_Device, config.hardware.digitalOut_PortLine, 'OutputOnly');\n% fprintf('And s2 ok. Ready to go.\\n');\n\ns.DurationInSeconds = 10;\n\nglobal data;\ndata = [];\n\nglobal h;\n% 'Position', [150, 300, 950, 350], \nh = figure('Name', 'ContinuousRecording', 'NumberTitle', 'off', ...\n        'MenuBar', 'none', 'ToolBar', 'none', 'WindowStyle', 'docked');\nplot(0, 0);\nhold on;\nxlim([-0.3, s.DurationInSeconds * s.Rate * 1.1]);\n% ylim([-0.3 0.3]);\n\nlh = s.addlistener('DataAvailable', @collectAndPlotData);\n\ns.startForeground();\n\nfunction collectAndPlotData(~, event)\n%     fprintf('#collectData\\n');\n    data = [data (event.Data - mean(event.Data))'];\n    t = 1 : numel(data(data ~= 0));\n    plot(t, data(t));\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "daq_ni_usb6008.m", "ext": ".m", "path": "OCIA-master/behavior/daq/daq_ni_usb6008.m", "size": 2637, "source_encoding": "utf_8", "md5": "8f4ab52789a17dc8daf2ef4e7fcd36ee", "text": "function daq_ni_usb6008\n\nai=analoginput('nidaq','Dev1');\nao=analogoutput('nidaq','Dev1');\ndio=digitalio('nidaq','Dev1');\n\ndisp('done create device')\n\ntestMode = 'analogIn'; % analogOut or analogIn or digital\n\n\nswitch lower(testMode)\n    case 'analogin'\n        %% Collect analog input\n        % analog input channel\n        addchannel(ai, 0);\n        disp('done add channel')\n        % get available properties\n%         get(ai);\n        % set some properties\n        set(ai,'InputType','SingleEnded')\n        % sample rate and recording duration\n        ai.SampleRate = 10000;\n        ai.SamplesPerTrigger = 50000; % can be Inf for continuous acquisition\n        disp('Press any key to start')\n        pause\n        % start acquisition\n        start(ai);\n        % get data\n        data = getdata(ai);\n        plot(data)\n        stop(ai), delete(ai)\n        \n    case 'analogout'\n        %% Write analog output\n        % analog output channel\n        addchannel(ao, 1)\n        % get available properties\n        get(ao);\n        % sample rate and recording duration\n        samplingInterval = 0.005; % in s\n        tMax = 100; n = tMax ./samplingInterval;\n        a = [0 1]; a = repmat(a,1,n./2);\n        startDelay = 0.5; % in s\n        % have to send each sample separately (USB-6008 does not support\n        % putdata / start combination)\n        % use a timer object\n        % create the timer\n        t = timer('period',samplingInterval,'startDelay',startDelay,...\n            'TasksToExecute',numel(a),'ExecutionMode','fixedRate');\n        % callback functions\n        t.StartFcn = @timerStartFcn;\n        t.StopFcn = @timerStopFcn;\n        t.TimerFcn = {@timerFcn,a,ao};\n        % start timer\n        start(t)\n        \n    case 'digital'\n        %% Digital IO\n        % digital io\n        digital_out = addline(dio,0:7,'out');\n        digital_in = addline(dio,8:11,'in');\n        \n        % output loop\n        value = [0 0 0 0 0 0 0 0];\n        for n = 1:50\n            if value(1) == 0 % only change line 1\n                value(1) = 1;\n            else\n                value(1) = 0;\n            end\n            % change digital out\n            putvalue(digital_out,value)\n            % get digital in\n            io1 = getvalue(dio.Line(9));\n            fprintf('\\nDigital in is %1.0f\\n',io1)\n            pause(0.5)\n        end\n        % switch off\n        value = [1 0 0 0 0 0 0 0];\n        putvalue(digital_out,value)\n        \nend\n\n\nfunction timerFcn(t,event,data,ao)\nputsample(ao,data(t.TasksExecuted))\n\nfunction timerStartFcn(t,event)\ndisp('Timer started')\n\nfunction timerStopFcn(t,event)\ndisp('Timer stopped')\ndelete(t)\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "waitForExternalTrigger_H30.m", "ext": ".m", "path": "OCIA-master/behavior/daq/waitForExternalTrigger_H30.m", "size": 1558, "source_encoding": "utf_8", "md5": "080163444a2aff0f5793c00952a3c729", "text": "function status = waitForExternalTrigger_H30(timeout)\n% input argument: timeout in seconds\n% RETURNS:\n%       - status = 0 if waiting timed out\n%       - status = 1 if trigger was successfully received\n%       - status = 2 if could not connect to the nidaq analog input (or other error)\n\n% created on 2014-02-01 by Balazs Laurenczy (blaurenczy@gmail.com)\n\nstatus = 0;\ntry\n    s = daq.createSession('ni');\n    s.Rate = 1000;\n    s.addAnalogInputChannel('ScanOutputCard', 'ai0', 'Voltage');\n    s.Channels.Range = [-1 1];\n    s.Channels.InputType = 'SingleEnded';\n    s.DurationInSeconds = timeout;\n    s.NotifyWhenDataAvailableExceeds = 50;\n    s.addlistener('DataAvailable', @checkTriggerInData);\ncatch err; %#ok<NASGU>\n   warning('externalTrigger_nidaq:ConnectError', 'Could not connect to nidaq analog input!');\n   status = 2;\n   return;\nend;\nthresh = NaN;\nstatus = 0;\nfunction checkTriggerInData(~, event)\n    if isnan(thresh);\n        thresh = max(max(event.Data) * 5, 0.15);\n%         o('Defined thresh: %6.4f, max(data): %6.4f.', thresh, max(event.Data), 0, 0);\n    end;\n%     o('Collected data, thresh: %6.4f, max(data): %6.4f.', thresh, max(event.Data), 0, 0);\n    timePointAboveThresh = find(event.Data > thresh, 1);\n    if ~isempty(timePointAboveThresh);\n%         firstTimePointAboveThresh = timePointAboveThresh(1);\n%         o('Found trigger, firstTimePointAboveThresh: %d/%d.', firstTimePointAboveThresh, numel(event.Data), 0, 0);\n        status = 1;\n        s.stop();\n    end;\nend\n\ntry\n    s.startForeground();\ncatch err; %#ok<NASGU>\nend;\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "externalTrigger_nidaq.m", "ext": ".m", "path": "OCIA-master/behavior/daq/externalTrigger_nidaq.m", "size": 1910, "source_encoding": "utf_8", "md5": "52a0ca32193ecd4ba0079915dae9e268", "text": "function status = waitForExternalTrigger_H30(timeout)\n% input argument: timeout in seconds\n% RETURNS:\n%       - status = 0 if waiting timed out\n%       - status = 1 if trigger was successfully received\n%       - status = 2 if could not connect to the nidaq analog input\n\n% this file written by Henry Luetcke (hluetck@gmail.com)\n% modified on 2013-03-20 by Balazs Laurenczy (blaurenczy@gmail.com)\n% modified more on 2013-12-19 by Balazs Laurenczy (blaurenczy@gmail.com)\n\nstatus = 0;\n% Create an analog input object using Board ID \"Dev1\".\ntry\n    s = daq.createSession('ni');\n    s.Rate = 2000000;\n    s.addAnalogInputChannel('ExtraChannels', 'ai0', 'Voltage');\n    s.Channels.Range = [-1 1];\n    s.Channels.InputType = 'SingleEnded';\n    s.DurationInSeconds = 3;\n    lh = s.addlistener('DataAvailable', @checkTriggerInData);\ncatch err; %#ok<NASGU>\n   warning('externalTrigger_nidaq:ConnectError', 'Could not connect to nidaq analog input!');\n   status = 2;\n   return;\nend;\nthresh = NaN;\nfunction checkTriggerInData(~, event)\n    if isnan(thresh);\n        thresh = max(event.Data) * 5;\n    end;\n    o('Collected data, thresh: %6.4f, max(data): %6.4f.', thresh, max(event.Data), 0, 0);\n    timePointAboveThresh = find(event.Data > thresh);\n    if ~isempty(timePointAboveThresh);\n        firstTimePointAboveThresh = timePointAboveThresh(1);\n        o('Found trigger, firstTimePointAboveThresh: %d/%d.', firstTimePointAboveThresh, numel(event.Data), 0, 0);\n    end;\nend\n\n% set(ai,'TriggerType','HwAnalog');\n% set(s.Channels, 'TriggerType', 'HwDigital');\n% set(s.Channels, 'TriggerCondition', 'PositiveEdge'); % for HelioScan\n% set(s.Channels, 'HwDigitalTriggerSource', 'PFI0');\ns.startForeground();\n% % Wait up to timeout seconds for the acquisition to complete\n% try\n%     wait(ai, timeout);\n%     data = getdata(ai);\n%     status = 1;\n% catch\n%     status = 0;\n% end\n% \n% % Clean up\n% stop(ai);\n% delete(ai);\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "analyseBehavPerf.m", "ext": ".m", "path": "OCIA-master/behavior/perfAnalysis/analyseBehavPerf.m", "size": 5182, "source_encoding": "utf_8", "md5": "6051e144aa04de4837f45fd6cc287d3c", "text": "%% Function - analyseBehavPerf\nfunction counts = analyseBehavPerf(respTypes, binWidthProp, binWidthCum, doBinCout)\n    \n    dbgLvl = 0;\n    o('  #analyzeBehavPerf(): nTrials: %d.', size(respTypes, 2), 1, dbgLvl);\n    \n    % transform the response types into sums\n    respTypeToRespCountTic = tic; % for performance timing purposes\n    respCount = zeros(5, 1);\n    respTypes(isnan(respTypes)) = 6;\n    for i = 1:5; respCount(i) = sum(respTypes == i); end;\n    o('  #analyzeBehavPerf(): respTypeToRespCountTic: %.2f ms...', toc(respTypeToRespCountTic) * 1000, 2, dbgLvl);\n    \n    singleCountsTic = tic; % for performance timing purposes\n    counts = struct();\n    \n    % responseCount columns: correct detect, correct reject, false alarm, miss\n    counts.TOT = sum(respCount);\n    counts.INVALID = respCount(5);\n    counts.INVALIDP = counts.INVALID / counts.TOT * 100; % INVALID response (kind of false alarm)\n    counts.VTOT = counts.TOT - counts.INVALID;\n    counts.VTOTP = counts.VTOT / counts.TOT * 100; % \"valid\" responses (non-INVALID)\n    \n    % all response types with their percent\n    counts.TGO = respCount(1);\n    counts.NTNGO = respCount(2);\n    counts.NTGO = respCount(3);\n    counts.TNGO = respCount(4);\n    \n    % targets and targets in percent\n    counts.T = counts.TGO + counts.TNGO;\n    counts.TP = counts.T / counts.VTOT * 100;\n    % non-targets and non-targets in percent \n    counts.NT = counts.NTNGO + counts.NTGO;\n    counts.NTP = counts.NT / counts.VTOT * 100;\n    % GOs and GOs in percent\n    counts.GO = counts.TGO + counts.NTGO;\n    counts.GOP = counts.GO / counts.VTOT * 100;\n    % NO-GOs and NO-GO in percent \n    counts.NGO = counts.TNGO + counts.NTNGO;\n    counts.NGOP = counts.NGO / counts.VTOT * 100;\n    % corrects and corrects in percent \n    counts.C = counts.TGO + counts.NTNGO;\n    counts.CP = counts.C / counts.VTOT * 100;\n    % falses and falses in percent \n    counts.F = counts.TNGO + counts.NTGO;\n    counts.FP = counts.F / counts.VTOT * 100;\n    % correctness index taking into acount falses\n    counts.CiP = (counts.CP - counts.FP + 100) / 2 ;\n    counts.DPRIME = dprime(counts.TGO ./ counts.T, ...\n        counts.NTGO ./ counts.NT, counts.T, counts.NT);\n\n    % reponse type percents    \n    counts.TGOP = counts.TGO / counts.T * 100;\n    counts.NTNGOP = counts.NTNGO / counts.NT * 100;\n    counts.NTGOP = counts.NTGO / counts.NT * 100;\n    counts.TNGOP = counts.TNGO / counts.T * 100;\n\n    o('  #analyzeBehavPerf(): singleCountsTic: %.2f ms...', toc(singleCountsTic) * 1000, 2, dbgLvl);\n    \n    if ~doBinCout; return; end;\n    \n    binCountsTic = tic; % for performance timing purposes\n    % evolution of indexes over time\n    counts.TGOs = zeros(1, counts.TOT);\n    counts.NTNGOs = zeros(1, counts.TOT);\n    counts.NTGOs = zeros(1, counts.TOT);\n    counts.TNGOs = zeros(1, counts.TOT);\n    counts.INVALIDs = zeros(1, counts.TOT);\n    counts.DPRIMEs = zeros(1, counts.TOT);\n\n    if isempty(binWidthCum);\n        useProp = 1;\n        binWidth = max(fix(counts.TOT * binWidthProp), 1);\n    else\n        useProp = 0;\n        binWidth = binWidthCum;\n    end;\n    if isempty(binWidth);\n        error('mtrainerAnalyzer_getCounts:NoBinWidth', 'No bin width specified');\n    end;\n    \n    for i = 1 : counts.TOT;\n        if binWidth ~= -1;\n            if useProp;\n                iStart = max(i - binWidth, 1);\n                iEnd = min(i + binWidth, counts.TOT);\n            else\n                iStart = ceil(max(i - 0.5 * (binWidth - 1), 1));\n                iEnd = floor(min(i + 0.5 * (binWidth - 1), counts.TOT));\n            end;\n            T = sum(ismember(respTypes(iStart : iEnd), [1 4]));\n            NT = sum(ismember(respTypes(iStart : iEnd), [2 3]));\n            counts.TGOs(i) = (sum(respTypes(iStart : iEnd) == 1) / T) * 100;\n            counts.NTNGOs(i) = (sum(respTypes(iStart : iEnd) == 2) / NT) * 100;\n            counts.NTGOs(i) = (sum(respTypes(iStart : iEnd) == 3) / NT) * 100;\n            counts.TNGOs(i) = (sum(respTypes(iStart : iEnd) == 4) / T) * 100;\n            counts.INVALIDs(i) = (sum(respTypes(iStart : iEnd) == 5) / (iEnd - iStart)) * 100;\n            countsForBin = analyseBehavPerf(respTypes(iStart : iEnd), binWidthProp, binWidthCum, 0);\n            counts.DPRIMEs(i) = dprime(countsForBin.TGO ./ countsForBin.T, ...\n                countsForBin.NTGO ./ countsForBin.NT, countsForBin.T, countsForBin.NT);\n        else\n            counts.TGOs(i) = sum(respTypes(1 : i) == 1) / i * 100;\n            counts.NTNGOs(i) = sum(respTypes(1 : i) == 2) / i * 100;\n            counts.NTGOs(i) = sum(respTypes(1 : i) == 3) / i * 100;\n            counts.TNGOs(i) = sum(respTypes(1 : i) == 4) / i * 100;\n            counts.INVALIDs(i) = sum(respTypes(1 : i) == 5) / i * 100;\n            countsForBin = analyseBehavPerf(respTypes(1 : i), binWidthProp, binWidthCum, 0);\n            counts.DPRIMEs(i) = dprime(countsForBin.TGO ./ countsForBin.T, ...\n                countsForBin.NTGO ./ countsForBin.NT, countsForBin.T, countsForBin.NT);\n        end;\n    end;\n    \n    counts.RESPs = (counts.TGOs + counts.NTGOs) / 2;\n    o('  #analyzeBehavPerf(): binCountsTic: %.2f ms...', toc(binCountsTic) * 1000, 2, dbgLvl);\n    \nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainerAnalyser_pooledPerformance_bins.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainerAnalyser_pooledPerformance_bins.m", "size": 6607, "source_encoding": "utf_8", "md5": "542605197d955dd0ad8e813ec2c311a0", "text": "%% Function - mtrainerAnalyser_pooledPerformance\nfunction figHand = mtrainerAnalyser_pooledPerformance_bins(outAll, titleStr, binWidth)\n\n% dbgLevel = 2;\n\nnTrials = length(outAll.respTypes);\n\n% calculate number of bins, make sure that there is at least one\nnBins = max(round(nTrials / binWidth), 1);\n\nfigName = titleStr;\nfigName = strrep(figName, '\\_', '_');\nfigName = strrep(figName, '''', '');\nfigName = strrep(figName, ' ', '');\nfigHand = figure('NumberTitle', 'off', 'WindowStyle', 'docked', 'Name', figName);\n\nhitRate = zeros(1, nBins);           % correct detection, CR+, hitRate\n% hitRateError = zeros(1, nBins);      % correct detection, CR+, hitRate errors\nfalsePosRate = zeros(1, nBins);      % false alarm, CR-, false positive\n% falsePosRateError = zeros(1, nBins); % false alarm, CR-, false positive errors\ndprimes = zeros(1, nBins);           % d prime\n% dprimesError = zeros(1, nBins);      % d prime errors\n\nuseSEM = 1;\nprevNFreqs = 0;\nprevNTones = 0;\ndprimeMaxVal = 3.5;\ndprimeMinVal = -2;\nchangeOfFreqOrToneNum = zeros(1, nBins);\n\nfor iBin = 1 : nBins;\n    iStart = (iBin - 1) * binWidth + 1; % for binWidth = 10: 1, 11, 21, etc.\n    iEnd = min(iBin * binWidth, nTrials); % for binWidth = 10: 10, 20, 30, etc.\n    countsForBin = mtrainerAnalyzer_getCounts(outAll.respTypes(iStart : iEnd));\n        \n    % extract the current session's number of frequency and number of tones\n    nFreqs = outAll.nFreqs(iStart : iEnd);\n    nTones = outAll.nTones(iStart : iEnd);\n    if ~numel(nFreqs); continue; end;\n    nFreqs = nFreqs(1);\n    nTones = nTones(1);\n    \n    % skip basic sessions\n%     if nFreqs == 1 && nTones == 1;\n%         hitRate(iBin) = NaN;\n%         falsePosRate(iBin) = NaN;\n%         dprimes(iBin) = NaN;\n%         continue;\n%     end;\n    \n    % if there is a change in the number of frequencies or tones, mark it\n    if prevNFreqs ~= 0 && (nFreqs ~= prevNFreqs || nTones ~= prevNTones);\n        changeOfFreqOrToneNum(iBin) = iBin - 1;\n    end;\n        \n    % store for next iteration\n    prevNFreqs = nFreqs;\n    prevNTones = nTones;\n\n    hitRate(iBin) = 100 * countsForBin.TGO / countsForBin.T;\n    falsePosRate(iBin) = 100 * countsForBin.NTGO / countsForBin.NT;\n    if isnan(countsForBin.DPRIME) || isinf(countsForBin.DPRIME);\n        countsForBin.DPRIME = 0;\n    end;\n\n    % restrict the dprime value between +dprimeMaxVal and -dprimeMaxVal\n    countsForBin.DPRIME = min(dprimeMaxVal, countsForBin.DPRIME);\n    countsForBin.DPRIME = max(-dprimeMaxVal, countsForBin.DPRIME);\n\n    dprimes(iBin) = countsForBin.DPRIME;\nend;\n\nTGOsHand = plot(1 : nBins, hitRate, '-sg', 'MarkerFaceColor', 'green');\n% TGOsErrHand = errorbar(1 : nBins, hitRate, hitRateError, '-sg', 'MarkerFaceColor', 'green');\n% removeErrorBarEnds(TGOsErrHand);\n\nhold all;\nhitRateAxes = gca;\nNTNGOsHand = plot((1 : nBins) + 0.15, falsePosRate, '-sr', 'MarkerFaceColor', 'red');\n% NTNGOsErrHand = errorbar((1 : nBins) + 0.15, falsePosRate, falsePosRateError, '-sr', 'MarkerFaceColor', 'red');\n% removeErrorBarEnds(NTNGOsErrHand);\n\nset(hitRateAxes, 'ylim', [0 110], 'YTick', 0 : 10 : 110, 'YTickLabel', 0 : 10 : 110);\n\nif useSEM;\n%     ylabel('% of Trials \\pm SEM'); %%#ok<UNRCH>\n    ylabel('% of trials'); %%#ok<UNRCH>\nelse\n%     ylabel('% of Trials \\pm SD'); %#ok<UNRCH>\n    ylabel('% of trials'); %#ok<UNRCH>\nend;\n\nvertLineHandles = zeros(1, sum(changeOfFreqOrToneNum > 0));\nvertLineTextHandles = zeros(2, sum(changeOfFreqOrToneNum > 0));\niLineHandle = 1;\nfor iChange = 1 : numel(changeOfFreqOrToneNum);\n    if ~changeOfFreqOrToneNum(iChange); continue; end;\n    changeX = changeOfFreqOrToneNum(iChange) - 0.5;\n    \n    iBin = changeOfFreqOrToneNum(iChange);\n    iStart = (iBin - 1) * binWidth + 1; % for binWidth = 10: 1, 11, 21, etc.\n    iEnd = min(iBin * binWidth, nTrials); % for binWidth = 10: 10, 20, 30, etc.\n    \n    nFreqs1 = outAll.nFreqs(iStart : iEnd);\n    nFreqs1 = nFreqs1(1);\n    \n    iBin = changeOfFreqOrToneNum(iChange) + 1;\n    iStart = (iBin - 1) * binWidth + 1; % for binWidth = 10: 1, 11, 21, etc.\n    iEnd = min(iBin * binWidth, nTrials); % for binWidth = 10: 10, 20, 30, etc.\n    \n    nFreqs2 = outAll.nFreqs(iStart : iEnd);\n    nFreqs2 = nFreqs2(1);\n    \n    if mod(iChange, 2) == 0;\n        changeY = 9;\n    else\n        changeY = 12;\n    end;\n    \n    vertLineTextHandles(1, iLineHandle) = text(changeX, changeY, ...\n        sprintf('%d freq \\\\leftarrow ', nFreqs1), 'HorizontalAlignment', 'right');\n    vertLineTextHandles(2, iLineHandle) = text(changeX, changeY, sprintf(' \\\\rightarrow %d freq', nFreqs2));\n    \n    vertLineHandles(iLineHandle) = line([changeX changeX], [0 100], ...\n        'LineStyle', ':', 'Color', 'black');\n    iLineHandle = iLineHandle + 1;\n    \n    \nend;\n\nxlimits = [0 nBins * 1.05];\nxlim(xlimits);\nset(hitRateAxes, 'XTick', [], 'XTickLabel', []);\n\nxTickAxes = axes('Position', get(gca, 'Position'), ...\n    'YAxisLocation', 'right', 'XAxisLocation', 'bottom', 'Color', 'none');\nstep = nBins / (nTrials / 500);\nset(xTickAxes, 'xlim', xlimits, ...\n    'XTick', 1 : step : (nBins + 1), 'XTickLabel', 0 : 500 : ((nBins + 1) * binWidth));\nset(xTickAxes, 'YTick', [], 'YTickLabel', []);\nxlabel('Trials');\n\ndprimeAxes = axes('Position', get(gca, 'Position'), ...\n    'YAxisLocation', 'right', 'XAxisLocation', 'top', 'Color', 'none');\nhold all;\ndprimesHand = plot((1 : nBins) + 0.3, dprimes, '-sb', 'MarkerFaceColor', 'blue');\n% dprimesErrHand = errorbar((1 : nBins) + 0.3, dprimes, dprimesError, '-sb', 'MarkerFaceColor', 'blue');\n% removeErrorBarEnds(dprimesErrHand);\n\nset(dprimeAxes, 'ylim', [dprimeMinVal dprimeMaxVal], 'YTick', dprimeMinVal : 0.5 : dprimeMaxVal, ...\n    'YTickLabel', dprimeMinVal : 0.5 : dprimeMaxVal);\nset(dprimeAxes, 'XTick', [], 'XTickLabel', []);\nxlim(xlimits);\nif useSEM;\n%     ylabel('d'' value \\pm SEM'); %%#ok<UNRCH>\n    ylabel('d'' value'); %%#ok<UNRCH>\nelse\n%     ylabel('d'' value \\pm SD'); %#ok<UNRCH>\n    ylabel('d'' value'); %#ok<UNRCH>\nend;\n\n% legend([TGOsErrHand, NTNGOsErrHand, dprimesErrHand], {'HitRate', 'FalsePos', 'd'''}, ...\n%     'Location', 'NorthWest');\nlegend([TGOsHand, NTNGOsHand, dprimesHand], {'HitRate', 'FalsePos', 'd'''}, ...\n    'Location', 'NorthWest');\n\n% title(sprintf('%s \\\\fontsize{10} [Total number of trials = %.0f, binWidth = %.0f]', ...\n%     titleStr, nTrials, binWidth));\ntitle(titleStr);\n\nhold off;\nmakePrettyFigure(figHand);\nset(xTickAxes, 'FontSize', 9);\nfor iLineHandle = 1 : numel(vertLineHandles);\n    set(vertLineHandles(iLineHandle), 'LineWidth', 0.5);\n    set(vertLineTextHandles(1, iLineHandle), 'FontSize', 10);\n    set(vertLineTextHandles(2, iLineHandle), 'FontSize', 10);\nend;\n    \nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainer.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainer.m", "size": 16753, "source_encoding": "utf_8", "md5": "f5cf1c7c0cf86007548ef75917035e5c", "text": "function mtrainer()\n% main function for mtrainer experiment suite\n% all times are in s!\n\n% written by Henry Luetcke (hluetck@gmail.com)\n% 2013-05-07\n% modified by Balazs Laurenczy (blaurenczy@gmail.com)\n% 2013-05-15\n% modified by Balazs Laurenczy (blaurenczy@gmail.com)\n% 2013-08-08\n\ndoEmptyWater = 0;\n% doEmptyWater = 1;\n\nautomode = 5; % if > 0, sets the number of seconds to wait before a new run of mtrainer\n\nlickThresh = 0.13;\n% lickThresh = NaN;\n\nunsavedOut = struct();\n\n% mouseId = 'testing'; taskType = 'freqDiscrimination'; phase = 'C';\n\n% mouseId = '130801_01'; taskType = 'freqDiscrimination'; phase = 'F';\nmouseId = '130801_03'; taskType = 'freqDiscrimination'; phase = 'F';\n\n% mouseId = '130408_02'; taskType = 'freqDiscrimination'; phase = 'D';\n% mouseId = '130416_01'; taskType = 'oddballDiscrimination'; phase = 'E';\n% mouseId = '130425_01'; taskType = 'freqDiscrimination';  phase = 'B';\n% mouseId = '130425_02'; taskType = 'oddballDiscrimination'; phase = 'C';\n% mouseId = '130528_01'; taskType = 'oddballDiscrimination'; phase = 'B';\n% mouseId = '130528_02'; taskType = 'oddballDiscrimination'; phase = 'B';\n\nbasePath = 'P:\\matlab\\daq\\mtrainer\\configs\\';\nconfigMat = load([basePath taskType filesep taskType '_phase' phase '.mat']);\nconfig = configMat.config;\n\nunsavedOut.mouseId = mouseId;\nunsavedOut.taskType = taskType;\nunsavedOut.phase = phase;\nunsavedOut.basePath = basePath;\nunsavedOut.config = config;\n\n%% Setup - stimuli and trials\nfprintf('Setup stimuli and trials... ');\n% set up stimulus vector\nstims = [];\nfor iFreq = 1 : numel(config.tone.freqs)\n    stimForFreq = repmat(iFreq, 1, round(config.tone.stimProba(iFreq) * config.training.nTrials));\n    stims = [stims, stimForFreq]; %#ok<AGROW>\nend\nstims = stims(randperm(numel(stims)));\nclear stim;\n\n% set up oddball vector by altering stims with a certain probability\nodds = stims;\noddPos = zeros(size(odds));\nuniqueStims = unique(stims);\nisOdd = rand(1, numel(stims)) < config.tone.oddProba;\nuniqueOddPos = config.tone.nTones - 1 : config.tone.nTones; % last 2 tones can be odd\nfor iStim = 1 : numel(isOdd);\n    if isOdd(iStim);\n        otherStims = uniqueStims(uniqueStims ~= stims(iStim));\n        otherStims = otherStims(randperm(numel(otherStims)));\n        mixedOddPos = uniqueOddPos(randperm(numel(uniqueOddPos)));\n        odds(iStim) = otherStims(1);\n        oddPos(iStim) = mixedOddPos(1);\n    end;\nend;\n\n% set up tone array\ntoneArray = MakePureMultiToneOddballArray(config.tone.freqs, stims, odds, oddPos, ...\n    config.tone.nTones, config.tone.toneDur, config.tone.ISI, config.tone.samplingFreq);\n\nunsavedOut.stims = stims;\nunsavedOut.odds = odds;\nunsavedOut.oddPos = oddPos;\nunsavedOut.isOdd = isOdd;\n\nfprintf('done.\\n');\n\n%% Setup - hardware\n% define globals\nglobal recordedValues;\n\n% analog input\ndaqreset;\nfprintf('Connecting hardware...\\n');\ns1 = daq.createSession(config.hardware.adaptorID);\nrecordRate = config.hardware.analogIn_SampleRate;\n% recordRate = 700;\ns1.Rate = recordRate;\ns1.addAnalogInputChannel(config.hardware.analogIn_Device, ...\n    config.hardware.analogIn_Channel, 'Voltage');\ns1.Channels(1).InputType = config.hardware.analogIn_InputType;\ns1.Channels(1).Range = config.hardware.analogIn_Range;\nfprintf('s1 ok.\\n');\n\ns2 = daq.createSession(config.hardware.adaptorID);\ns2.addDigitalChannel(config.hardware.digitalOut_Device, config.hardware.digitalOut_PortLine, 'OutputOnly');\nfprintf('And s2 ok. Ready to go.\\n');\n\nif isnan(lickThresh);\n    % baseline measurement\n    fprintf('Testing analog input for licking threshold (baseline)... ');\n    recordDuration = 2;\n    s1.DurationInSeconds = recordDuration;\n    baseline = s1.startForeground; % this will block the command line\n    meanData = mean(baseline);\n    stdData = std(baseline);\n    lickThresh = 2.8 * stdData;\n    fprintf('done.\\n')\n\n    pause(1);\n\n    % lick measurement\n    fprintf('Testing analog input for licking threshold (licking)... ');\n    s1.DurationInSeconds = recordDuration;\n    lick = s1.startForeground; % this will block the command line\n    fprintf('done.\\n2SD: %1.2f, lick threshold: %1.2f\\n', 2 * stdData, lickThresh);\n\n    % plotting\n    figure('Name', 'LickingThreshold', 'Position', [150, 300, 950, 350], 'NumberTitle', 'off' ...\n... %         , 'MenuBar', 'none', 'ToolBar', 'none');\n    );\n    plot((1:numel(baseline)) / recordRate, baseline - meanData, 'b');\n    hold on;\n    plot((1:numel(lick)) / recordRate, lick - meanData, 'r');\n    line([0, recordDuration], [lickThresh, lickThresh], 'Color', 'green');\n    line([0, recordDuration], [2 * stdData, 2 * stdData], 'Color', 'cyan');\n    line([0, recordDuration], [- 2 * stdData, - 2 * stdData], 'Color', 'cyan');\n    legend({'baseline', 'licking', 'lickTreshold', '\\pm 2SD'});\n    title(sprintf('2SD: %1.2f, lick thresh %1.2f', 2 * stdData, lickThresh));\nelse\n    fprintf('Testing analog input for licking threshold... skipped.\\nLick threshold: %1.2f\\n', ...\n        lickThresh);\nend;\n\nunsavedOut.lickThresh = lickThresh;\n\n% digital out\nfprintf('Testing reward system by opening for %.2fs after a delay of %.2fs... ', ...\n    config.training.rewDur, config.training.rewDelay);\ngiveReward(s2, config.training.rewDur * 3, config.training.rewDelay);\nfprintf('done!\\n')\nif doEmptyWater > 0;\n    doEmptyWater = str2double(input('Empty water ? >0 = yes, <0 = no\\n', 's')); %#ok<UNRCH>\n    while doEmptyWater > 0;\n        fprintf('Emptying water... ');\n        giveReward(s2, doEmptyWater, 0);\n        fprintf(' done.');\n        doEmptyWater = str2double(input('Empty more ? >0 = yes, <0 = no\\n', 's'));\n        if isnan(doEmptyWater); doEmptyWater = 0; end;\n        if ~doEmptyWater; break; end;\n    end;\nend;\n\ns1.IsNotifyWhenDataAvailableExceedsAuto = true;\nlh = s1.addlistener('DataAvailable', @(src, event) recordAndStop(src, event, lickThresh));\ns1.IsContinuous = true;\ns1.prepare;\n\n%% Setup - saving settings\nrecordedValues = 0;\nsaveTime = datestr(now, 'yyyy_mm_dd__HH_MM_SS');\nsaveFolder = sprintf('C:\\\\Users\\\\laurenczy\\\\mtrainerData\\\\%s\\\\', mouseId);\nsavePath = sprintf('%s%s__%s', saveFolder, mouseId, saveTime);\nmkdir(saveFolder);\nlogfile = sprintf('%s_log.txt', savePath);\nlogDateFormat = 'yyyy-mm-dd HH:MM:SS.FFF';\n\n% create an 'onCleanup' function to handle interruptions by CTRL+C\nc = onCleanup(@()closeDiaryAndSaveOutput(savePath, lh, s1, s2));\n\nfprintf('Finished setting up and hardware tests.\\n');\nfprintf('CHECK MOUSE ID!! currently: %s.\\n', mouseId);\nif automode;\n    pause(automode);\nelse\n    fprintf('Then press any key to start!'); %#ok<UNRCH>\n    pause;\nend;\n\nunsavedOut.savePath = savePath;\nunsavedOut.saveTime = saveTime;\n\ndiary(logfile);\n%% Start experiment\nfprintf('Experiment start: %s\\n\\n', datestr(now, logDateFormat));\ntry\n    \n    %% Experiment - initialize variables\n    \n    dbgLevel = 0;\n    out = unsavedOut;\n\n    o('config: %s phase %s (%dF%dT)', out.taskType, out.phase, numel(config.tone.freqs), ...\n        config.tone.nTones, 0, dbgLevel);\n    o('animal: %s', mouseId, 0, dbgLevel);\n    o('savePath: %s', savePath, 0, dbgLevel);\n    o('lickThresh: %1.2f, ISI: %1.2f', lickThresh, config.tone.ISI, 0, 0);\n    o('rewardCollectionTime: %1.2f, InTriLickTimerDur: %1.2f', ...\n        config.training.rewCollTime, ...\n        config.training.InTriLickTimerDur, 0, dbgLevel);\n    \n    nTrials = config.training.nTrials;\n    \n%     totalTrialDur = config.training.trialDur + config.training.startDelay + ...\n%         config.training.rewCollTime;\n    % compute relative start times for each trial\n%     out.trialStarts_sched = 0 : totalTrialDur : (nTrials - 1) * totalTrialDur;\n%     out.trialStarts_obs = zeros(1, nTrials);\n%     out.trialEnds_obs = zeros(1, nTrials);\n\n    out.trialStartDelays = zeros(1, nTrials);\n    out.trialStartTimes = zeros(1, nTrials);\n    out.trialEndTimes = zeros(1, nTrials);\n    out.respTimes = nan(1, nTrials);\n    out.nInTriLick = nan(1, nTrials);\n    out.resps = nan(1, nTrials);\n    out.respDelays = nan(1, nTrials);\n    % for each trial: 1 = correct detect, 2 = correct reject, 3 = false alarm, 4 = miss, 5 = early\n    out.respTypes = nan(1, nTrials);\n    out.expStartTime = tic;\n    isPunish = 0;\n    \n    for iTrial = 1 : nTrials;\n        out.iTrial = iTrial;\n        % listen for response\n        s1.startBackground;\n        pause(0.01)\n        interTrialLickCount = 0;\n        out.trialStartTimes(iTrial) = tic;\n        \n        % introduce a random delay for starting the tone\n        out.trialStartDelays(iTrial) = config.training.startDelay + ...\n            (rand(1) - 0.5) * 2 * config.training.startDelayRand;\n        \n        % add punishement time-out if needed\n        if isPunish;\n            out.trialStartDelays(iTrial) = out.trialStartDelays(iTrial) + config.training.timeoutPunish;\n        end;\n        \n        %% Experiment - initial wait\n        while toc(uint64(out.trialStartTimes(iTrial))) < out.trialStartDelays(iTrial);\n%             fprintf('CheckingA: s1.IsRunning? %d\\n', s1.IsRunning);\n            % check if no lick has been done\n            if ~s1.IsRunning; % if not running anymore, it means a licking has been done\n                remainingTime = out.trialStartDelays(iTrial) - toc(uint64(out.trialStartTimes(iTrial)));\n                if interTrialLickCount > 0;\n                    fprintf('|%.1f', remainingTime);\n                else\n                    fprintf('    inter-trial licking: |%.1f', remainingTime);\n                end;\n                \n                % reset the starting delay to config.training.InTriLickTimerDur\n                out.trialStartTimes(iTrial) = tic;\n                if isPunish;\n                    out.trialStartDelays(iTrial) = max(config.training.InTriLickTimerDur, remainingTime);\n                else\n                    out.trialStartDelays(iTrial) = config.training.InTriLickTimerDur;\n                end;\n                interTrialLickCount = interTrialLickCount + 1;\n                pause(1);\n                % restart the listening\n                s1.startBackground;\n                pause(0.1);\n%             fprintf('CheckingB: s1.IsRunning? %d\\n', s1.IsRunning);\n            end\n            % avoid full-speed looping\n            pause(0.01);\n        end;\n        if interTrialLickCount;\n            fprintf('|: %d inter-trial lick(s).\\n', interTrialLickCount);\n        end\n        out.nInTriLick(iTrial) = interTrialLickCount;\n        resp = 0;\n        isPunish = 0;\n        \n        t_trialStart = tic;\n        fprintf('Trial %03d/%03d - %s        ... ', iTrial, nTrials, datestr(now, logDateFormat));\n        \n        %% Experiment - play sound\n        if s1.IsRunning\n            soundToPlay = toneArray{iTrial};\n            player = audioplayer(soundToPlay, config.tone.samplingFreq); %#ok<TNMLP>\n            player.play;\n        else\n            fprintf('early (already licking, lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n                recordedValues);\n            \n            isPunish = 1;\n            \n            out.respTypes(iTrial) = 5;\n            % analyse every 10 trials\n            if ~rem(iTrial, 10) && iTrial ~= nTrials;\n                mtrainerAnalyser_performance(out.respTypes, 0);\n            end;\n            continue;\n        end\n        breakOut = 0;\n        %% Experiment - wait for response\n        while toc(t_trialStart) < config.training.trialDur;\n            if s1.IsRunning\n                % no response detected\n            else\n                % seconds since trial start\n                respTime = toc(t_trialStart);\n                out.respTimes(iTrial) = respTime;\n                % response should be 'minRespTime' after the end of the tone!\n                totToneDur = (config.tone.toneDur + config.tone.ISI) * config.tone.nTones - config.tone.ISI;\n                limitRespTime = totToneDur + config.training.minRespTime;\n%                 limitRespTime = config.training.minRespTime;\n                out.respDelays(iTrial) = limitRespTime - respTime;\n                % early response, count as kind of false alarm\n                if respTime < limitRespTime;\n%                     fprintf('early (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n%                         recordedValues);\n                    fprintf('early (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                        respTime, limitRespTime, limitRespTime - respTime);\n                    \n                    isPunish = 1;\n                    out.respTypes(iTrial) = 5;\n                    breakOut = 1;\n                    break;\n                else\n%                     fprintf('Lick at %s\\n', datestr(clock, logDateFormat))\n                    resp = 1;\n                end;\n                break; % break out of while loop\n            end;\n            pause(0.02); % necessary to allow time for data collection\n        end;\n        if breakOut;\n            out.trialEndTimes(iTrial) = toc(uint64(out.trialStartTimes(iTrial)));\n            % analyse every 10 trials\n            if ~rem(iTrial, 10) && iTrial ~= nTrials;\n                mtrainerAnalyser_performance(out.respTypes, 0);\n            end;\n            continue;  % continue for loop\n        end;\n        if s1.IsRunning;\n            s1.stop;\n        end;\n        %% Experiment - analyse response\n        out.resps(iTrial) = resp;\n        isOddTrial = stims(iTrial) ~= odds(iTrial);\n        if config.tone.oddProba > 0; % oddball discrimination\n            isTargetStim = isOddTrial && config.tone.goStim;\n        else % frequency discrimination\n            isTargetStim = stims(iTrial) == config.tone.goStim;\n        end;\n        if resp == 1 && isTargetStim;\n            % correct response\n%             fprintf('correct detection (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n%                 recordedValues);\n            fprintf('correct detection (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                respTime, limitRespTime, limitRespTime - respTime);\n            out.respTypes(iTrial) = 1;\n            % give reward\n            giveReward(s2,config.training.rewDur,config.training.rewDelay);\n            % wait to collect the reward\n            pause(config.training.rewCollTime);\n        elseif resp == 1 && ~isTargetStim;\n            % false alarm\n%             fprintf('false alarm (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n%                 recordedValues);\n            fprintf('false alarm (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                respTime, limitRespTime, limitRespTime - respTime);\n            out.respTypes(iTrial) = 3;\n            % punish time-out\n            isPunish = 1;\n        elseif resp == 0 && isTargetStim;\n            fprintf('miss.\\n');\n            out.respTypes(iTrial) = 4;\n            % punish time-out\n            isPunish = 1;\n         elseif resp == 0 && ~isTargetStim;\n            fprintf('correct rejection.\\n');\n            out.respTypes(iTrial) = 2;\n            % go to next trial\n        end\n        out.trialEndTimes(iTrial) = toc(uint64(out.trialStartTimes(iTrial)));\n        \n        % analyse every 10 trials\n        if ~rem(iTrial, 10) && iTrial ~= nTrials;\n            mtrainerAnalyser_performance(out.respTypes, 0);\n        end\n    end\n    \n    %% End experiment\n    fprintf('\\nExperiment end: %s\\n', datestr(now, logDateFormat));\n    out.expTotDurTime = toc(out.expStartTime);\n    fprintf('Total time: %02.0f:%02.0f\\n', out.expTotDurTime / 60, mod(out.expTotDurTime, 60));\n    mtrainerAnalyser_performance(out.respTypes, 2, out.savePath, out.nInTriLick);\n\ncatch err\n    fprintf('\\n\\nAn error occured. Last trial: %1.0f. Cleaning up ...\\n\\n', iTrial)\n    rethrow(err);\nend\n\n%% Function - giveReward\nfunction giveReward(s2, rewDur, rewDelay)\n    \n    t_rewardStart = tic;\n    % wait until for start delay\n    while toc(t_rewardStart) < rewDelay; end;\n    t_rewardStart = tic;\n    % open digital out\n    s2.outputSingleScan(0);\n    % wait until for open duration\n    while toc(t_rewardStart) < rewDur; end;\n    % close digital out\n    s2.outputSingleScan(1);\nend\n\nfunction recordAndStop(src, event, lickThresh)\n    recordedValues = stopWhenExceedThreshold(src, event, lickThresh);\nend\n\nfunction closeDiaryAndSaveOutput(savePath, lh, s1, s2)\n    fprintf('\\nClosing diary... ');\n    diary off;\n    fprintf('done.\\n');\n    \n    if (exist('out', 'var'));\n        matFileName = sprintf('%s_out.mat', savePath);\n        fprintf('Saving output as %s... ', matFileName);\n        save(matFileName, 'out');\n        fprintf('done.\\n');\n    else\n        fprintf('/!\\\\ Output not saved\\n');\n    end;\n    \n    fprintf('Closing channels... ');\n    if exist('lh', 'var'); delete(lh); end;\n    if exist('s1', 'var'); delete(s1); end;\n    if exist('s2', 'var'); delete(s2); end;\n    fprintf('done.\\n');\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainerAnalyser_performance.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainerAnalyser_performance.m", "size": 8824, "source_encoding": "utf_8", "md5": "7c3efc292a98890cdd2df684966b8536", "text": "%% Function - mtrainerAnalyser_performance\nfunction mtrainerAnalyser_performance(varargin)\n\n    if nargin >= 1;\n        % for each trial: 1 = correct detect, 2 = correct reject, 3 = false alarm, 4 = miss, 5 = early\n        respTypes = varargin{1};\n        doSavePlots = 0;\n        savePath = '';\n    end;\n    if nargin >= 2;\n        doSavePlots = varargin{2};\n        savePath = 'perf.png';\n    end;\n    if nargin >= 3;\n        savePath = varargin{3};\n    end;\n    if nargin == 4;\n        nIntrLick = varargin{4};\n    end;\n\n    if nargin < 1 || nargin > 4;\n        error('mtrainerAnalyser_performance:WrongArgumentNumberException', ...\n            ['Number of arguments should be 1 ', ...\n            '(responseTypeVector), 2 (responseTypeVector, doSavePlots) ', ...\n            'or 3 (responseTypeVector, doSavePlots, savePath) (or 4 with nIntrLick)', ...\n            'Please see the help for this function.']);\n    end;\n    \n    counts_all = mtrainerAnalyzer_getCounts(respTypes);\n    \n    % print out a 'nicely' formatted table\n    % 'fprintf' patterns for the content of the headers and the cells\n    n = '3'; % number of digits to display for number\n    np = '3'; % number of digits to display for percents\n    headerPattern = [' (%0' n 'd|%0' np '.f%%) '];\n    cellPattern = ['%0' n 'd (%0' np '.0f%%)  '];\n    \n    fprintf(['Performance: d'' = %.3f, early: %0' n 'd/%0' n 'd (%0' np '.f%%), ' ...\n        'correctness: %0' np '.f%%\\n'], counts_all.DPRIME, counts_all.EARLY, counts_all.TOT, ...\n        counts_all.EARLYP, counts_all.CiP);\n    if ~counts_all.NT; tableContent = cell(3, 2);\n    else tableContent = cell(3, 3); end;\n    % header line\n    tableContent{1, 1} = sprintf([' VALID: %0' n 'd/%0' n 'd (%0' np '.f%%)'], ...\n        counts_all.VTOT, counts_all.TOT, counts_all.VTOTP);\n    tableContent{1, 2} = sprintf(['TARG.',     headerPattern], counts_all.T, counts_all.TP);\n    if counts_all.NT; tableContent{1, 3} = sprintf(['NON-TARG.', headerPattern], ...\n            counts_all.NT, counts_all.NTP); end;\n    % GO line\n    tableContent{2, 1} = sprintf([' GO     ',   headerPattern], counts_all.GO, counts_all.GOP);\n    tableContent{2, 2} = sprintf(cellPattern, counts_all.TGO, counts_all.TGOP);\n    if counts_all.NT; tableContent{2, 3} = sprintf(cellPattern, counts_all.NTGO, counts_all.NTGOP); end;\n    % NO-GO line\n    tableContent{3, 1} = sprintf([' NO-GO  ',   headerPattern], counts_all.NGO, counts_all.NGOP);\n    tableContent{3, 2} = sprintf(cellPattern, counts_all.TNGO, counts_all.TNGOP);\n    if counts_all.NT; tableContent{3, 3} = sprintf(cellPattern, counts_all.NTNGO, counts_all.NTNGOP); end;\n    tprintf(23, tableContent);\n    fprintf('(Percentages of the table are relative to valid trials (non-early))\\n');\n    \n    if doSavePlots < 1;\n        return;\n    end;\n    \n    saveName = strrep(regexp(savePath, '\\\\[\\d_]+$', 'match'), '\\', '');\n    if numel(saveName);\n        saveName = ['Perf - ' saveName{1}];\n    else\n        saveName = 'Perf';\n    end;\n    fprintf('Plotting... (doSavePlots: %d)\\n', doSavePlots);\n    figure('Name', saveName, 'NumberTitle', 'off', 'MenuBar', 'none', 'WindowStyle', 'docked');\n    title(strrep(saveName, '_', '\\_'));\n    % Valid trials\n    subplot(3, 2, 1);\n    bar(1, counts_all.EARLY, 1, 'FaceColor', [0.3 0.3 0.3]);\n    hold on;\n    bar(2, counts_all.VTOT, 1, 'FaceColor', [0 1 0]);\n    text(1.3, - max(ylim) / 10, sprintf('Early (%3.f%%)', counts_all.EARLYP), ...\n        'horizontalalignment', 'right', 'FontSize', 10);\n    text(2.3, - max(ylim) / 10, sprintf('Valid (%3.f%%)', counts_all.VTOTP), ...\n        'horizontalalignment', 'right', 'FontSize', 10);\n    set(gca, 'XTickLabel', '');\n    title(sprintf('Early versus valid trials (%d trials)', counts_all.TOT));\n%     ylim([0 counts_all.TOT * 1.01]);\n    ylim([0 max(counts_all.EARLY, counts_all.VTOT) * 1.2]);\n    ylabel('Number of Trials');\n    % Correct vs false\n    subplot(3, 2, 2);\n    bar(1, counts_all.C, 1, 'FaceColor', [0 1 0]);\n    hold on;\n    bar(2, counts_all.F, 1, 'FaceColor', [1 0 0]);\n    text(1.4, - max(ylim) / 10, sprintf('Correct (%3.f%%)', counts_all.CP), ...\n        'horizontalalignment', 'right', 'FontSize', 10);\n    text(2.4, - max(ylim) / 10, sprintf('Wrong (%3.f%%)', counts_all.FP), ...\n        'horizontalalignment', 'right', 'FontSize', 10);\n    set(gca, 'XTickLabel', '');\n    title(sprintf('Correct versus wrong trials (%d valid trials)', counts_all.VTOT));\n    ylim([0 max(counts_all.C, counts_all.F) * 1.2]);\n%     ylim([0 counts_all.TOT * 1.01]);\n    ylabel('Number of Trials');\n    \n    \n    TGOovTimeP = zeros(1, counts_all.TOT);\n    NTNGOovTimeP = zeros(1, counts_all.TOT);\n    NTGOovTimeP = zeros(1, counts_all.TOT);\n    TNGOovTimeP = zeros(1, counts_all.TOT);\n    EARLYovTimeP = zeros(1, counts_all.TOT);\n    \n%     binWidth = -1; % cumulative\n    binWidth = fix(counts_all.TOT * 0.1); % binning\n%     binWidth = 3; % binning\n    for i = 1 : counts_all.TOT;\n%     for i = 1 : binWidth : counts_all.TOT;\n        if binWidth ~= -1;\n%             iStart = max(i - 0.5 * (binWidth - 1), 1);\n%             iEnd = min(i + 0.5 * (binWidth - 1), counts_all.TOT);\n            iStart = max(i - binWidth, 1);\n            iEnd = min(i + binWidth, counts_all.TOT);\n            TGOovTimeP(i) = (sum(respTypes(iStart : iEnd) == 1) / (iEnd - iStart)) * 100;\n            NTNGOovTimeP(i) = (sum(respTypes(iStart : iEnd) == 2) / (iEnd - iStart)) * 100;\n            NTGOovTimeP(i) = (sum(respTypes(iStart : iEnd) == 3) / (iEnd - iStart)) * 100;\n            TNGOovTimeP(i) = (sum(respTypes(iStart : iEnd) == 4) / (iEnd - iStart)) * 100;\n            EARLYovTimeP(i) = (sum(respTypes(iStart : iEnd) == 5) / (iEnd - iStart)) * 100;\n        else\n            TGOovTimeP(i) = sum(respTypes(1 : i) == 1) / i * 100;\n            NTNGOovTimeP(i) = sum(respTypes(1 : i) == 2) / i * 100;\n            NTGOovTimeP(i) = sum(respTypes(1 : i) == 3) / i * 100;\n            TNGOovTimeP(i) = sum(respTypes(1 : i) == 4) / i * 100;\n            EARLYovTimeP(i) = sum(respTypes(1 : i) == 5) / i * 100;\n        end;\n    end;\n    \n    pause(0.05);\n    subplot(3, 2, 3);\n    percents = [TGOovTimeP; NTNGOovTimeP; NTGOovTimeP; ...\n        TNGOovTimeP; EARLYovTimeP];\n    bar(percents', 1, 'stacked', 'EdgeColor', 'none');\n%     bar(percents', binWidth, 'stacked', 'EdgeColor', 'none');\n    hold off;\n    title('Percent of trials types over time');\n    ylim([0 100]);\n    xlim([1 counts_all.TOT]);\n    ylabel('% Trials');\n    xlabel('Trials');\n%     legHand = legend({'Corr. det.', 'Corr. rej.', 'False alarm', 'Miss', 'Early'}, 'Location', 'EastOutside', ...\n%         'FontSize', 12, 'Orientation', 'vertical');\n    \n    DPRIMEovTimeP = zeros(1, counts_all.TOT);\n    \n%     binWidth = -1; % cumulative\n    binWidth = fix(counts_all.TOT * 0.1); % binning\n%     binWidth = 5; % binning\n    for i = 1 : counts_all.TOT;\n%     for i = 1 : binWidth : counts_all.TOT;\n        if binWidth ~= -1;\n%             iStart = max(i - 0.5 * (binWidth - 1), 1);\n%             iEnd = min(i + 0.5 * (binWidth - 1), counts_all.TOT);\n            iStart = max(i - binWidth, 1);\n            iEnd = min(i + binWidth, counts_all.TOT);\n            counts_till_i = mtrainerAnalyzer_getCounts(respTypes(iStart : iEnd));\n            DPRIMEovTimeP(i) = dprime(counts_till_i.TGO ./ counts_till_i.T, ...\n                counts_till_i.NTGO ./ counts_till_i.NT, counts_till_i.T, counts_till_i.NT);\n        else\n            counts_till_i = mtrainerAnalyzer_getCounts(respTypes(1 : i));\n            DPRIMEovTimeP(i) = dprime(counts_till_i.TGO ./ counts_till_i.T, ...\n                counts_till_i.NTGO ./ counts_till_i.NT, counts_till_i.T, counts_till_i.NT);\n        end;\n    end;\n    subplot(3, 2, 4);\n    plot(1 : counts_all.TOT, DPRIMEovTimeP);\n    title('d'' over time');\n%     ylim([0 100]);\n    xlim([1 counts_all.TOT]);\n    ylabel('d''');\n    xlabel('Trials');\n%     legHand = legend({'Corr. det.', 'Corr. rej.', 'False alarm', 'Miss', 'Early'}, 'Location', 'EastOutside', ...\n%         'FontSize', 12, 'Orientation', 'vertical');\n    \n    subplot(3, 2, 5);\n    plot(1 : counts_all.TOT, TGOovTimeP + NTGOovTimeP + EARLYovTimeP);\n    title('Responsiveness');\n    ylim([0 100]);\n    xlim([1 counts_all.TOT]);\n    ylabel('% of GO');\n    xlabel('Trials');\n    \n    if exist('nIntrLick', 'var');\n        subplot(3, 2, 6);\n        plot(1 : numel(nIntrLick), nIntrLick);\n        title('Number of inter-trial licking');\n%         ylim([0 100]);\n        xlim([1 numel(nIntrLick)]);\n        ylabel('Number of lick');\n        xlabel('Trials');\n    end;\n\n    pause(0.05);\n%     makePrettyFigure(gcf);\n    \n%     set(legHand, 'FontSize', 12);\n    \n    if doSavePlots < 2;\n        return;\n    end;\n    fprintf('Saving... (doSavePlots: %d)\\n', doSavePlots);\n    saveas(gcf, [savePath '_perf.png']);\n    saveas(gcf, [savePath '_perf.fig']);\n    \nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainerAnalyser_loadAllFromFolder.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainerAnalyser_loadAllFromFolder.m", "size": 11698, "source_encoding": "utf_8", "md5": "f65b08bd16909d18e68a76b009bf88ee", "text": "%% Function - mtrainerAnalyser_loadAllFromFolder\nfunction outAll = mtrainerAnalyser_loadAllFromFolder(folderPath, skipIncomplete, doMakeConsistent, ...\n    doRemoveUnresponsive, doSavePlots)\n\n%% Initialize variables\ndbgLevel = 4;\n\n% output structure for all data in the requested folder\noutAll = struct();\n\n% fields that need to be gathered\ntoGatherFields = {\n    'respTypes', 'nInTriLick', 'datesAsNum', 'taskTypes', ...\n    'nFreqs', 'nTones', 'freqs', 'goFreq' ...\n};\n\n% initialize the structure with these fields\nfor iField = 1 : numel(toGatherFields);\n    outAll.(toGatherFields{iField}) = [];\nend;\n\n% remodelling of the saved out structrures\ntoRemoveField = { ...\n    'trialEnds_obs'\n    'trialStarts_obs'\n    'trialStarts_sched'\n    }; %#ok<NASGU>\ntoRenameField = { ... %#ok<NASGU>\n%     'responseTypeVector', 'respTypes';\n%     'responseTimeVector', 'respTimes';\n%     'trial', 'iTrial';\n%     'trialEndVector_observed', 'trialEnds_obs';\n%     'trialStartVector_observed', 'trialStarts_obs';\n%     'trialStartVector_scheduled', 'trialStarts_sched';\n    'trialEndsTimes', 'trialEndTimes';\n    'respDelay', 'respDelays';\n    }; %#ok<NASGU>\nrequiredFields = { ... %#ok<NASGU>\n    'mouseId', 'MOUSE_ID';\n    'taskType', 'TASK_TYPE';\n    'phase', '?';\n    'basePath', '?';\n    'config', struct();\n    'stims', [];\n    'odds', [];\n    'oddPos', [];\n    'isOdd', [];\n    'savePath', '?';\n    'saveTime', 'SAVE_TIME';\n%     'trialStarts_sched', [];\n%     'trialStarts_obs', [];\n%     'trialEnds_obs', [];\n    'respTimes', [];\n    'nInTriLick', [];\n    'resps', [];\n    'respDelays', [];\n    'respTypes', [];\n    'expStartTime', -1;\n    'expTotDurTime', -1;\n    }; %#ok<NASGU>\n\n%% Gather mat-files and loop through them\nmatFiles = dir([folderPath '/*.mat']);\no('#mtrainerAnalyser_loadAllFromFolder: processing %d mat-file(s) in folder \"%s\" ...', ...\n    numel(matFiles), folderPath, 1, dbgLevel);\nnValidMatFiles = 0;\nfor iExp = 1 : numel(matFiles);\n    % get the full path and load the mat-file\n    fullPath = sprintf('%s/%s', folderPath, matFiles(iExp).name);\n    mat = load(fullPath);\n    mouseId = matFiles(iExp).name(1:9); %#ok<NASGU>\n    saveTime = matFiles(iExp).name(12:31); %#ok<NASGU>\n    \n    % check for the presence of the 'out' struct inside, otherwise create it\n    if ~isfield(mat, 'out');\n        out = mat;\n        save(fullPath, 'out');\n    else\n        out = mat.out;\n    end;\n    \n    % extract the task type, the number of tones/frequencies used from the logFile\n    out = parseLogFile(out, fullPath, iExp, dbgLevel);\n    \n    % remodelling of the out.mat file: change names, delete useless fields, etc.\n    if doMakeConsistent;\n        out = makeConsistent(out, fullPath, iExp, toRemoveField, toRenameField, requiredFields, dbgLevel); %#ok<UNRCH>\n    end;\n    \n    % skip problematic vectors\n    if ~isfield(out, 'respTypes');\n        o('  #mtrain...FromFolder: %d: %s has no ''respTypes'' field.', ...\n            iExp, matFiles(iExp).name, 2, dbgLevel);\n        continue;\n    elseif skipIncomplete && any(out.respTypes == 0);\n        o('  #mtrain...FromFolder: %d: %s has an incomplete ''respTypes''.', ...\n            iExp, matFiles(iExp).name, 2, dbgLevel);\n        continue;\n    else\n        o('  #mtrain...FromFolder: %d: %s is valid.', iExp, matFiles(iExp).name, 2, dbgLevel);\n        nValidMatFiles = nValidMatFiles + 1;\n    end;\n    \n    % remodelling of the out.mat file: removing long non responsive parts\n    if doRemoveUnresponsive;\n        outOri = out;\n        [out, unResps] = removeUnresponsive(out, iExp, dbgLevel); %#ok<UNRCH>\n    end;\n    \n    dateAsStr = matFiles(iExp).name(12:31);\n    \n    if doSavePlots;\n        mtrainerAnalyser_performance(outOri.respTypes, 1, ['\\01_' dateAsStr], outOri.nInTriLick);\n        mtrainerAnalyser_performance(out.respTypes, 1, ['\\02_' dateAsStr], out.nInTriLick);\n    end;\n    \n    dateAsNum = datenum(dateAsStr, 'yyyy_mm_dd__HH_MM_SS');\n    \n    nTrials = numel(out.respTypes);\n    \n    outAll.respTypes    = [outAll.respTypes     out.respTypes];\n    outAll.nInTriLick   = [outAll.nInTriLick    out.nInTriLick];\n    \n    outAll.freqs        = [outAll.freqs         out.config.tone.freqs(out.stims)];\n    outAll.datesAsNum   = [outAll.datesAsNum    repmat(dateAsNum, 1, nTrials)];\n    outAll.taskTypes    = [outAll.taskTypes     repmat(sum(out.taskType), 1, nTrials)];\n    outAll.nFreqs       = [outAll.nFreqs        repmat(numel(out.config.tone.freqs), 1, nTrials)];\n    outAll.nTones       = [outAll.nTones        repmat(out.config.tone.nTones, 1, nTrials)];\n    outAll.goFreq       = [outAll.goFreq        repmat(out.config.tone.freqs(out.config.tone.goStim), ...\n                                                    1, nTrials)];\n    \n    if numel(outAll.respTypes) ~= numel(outAll.nInTriLick);\n        o('  #mtrain...FromFolder: %d: %s problem!', iExp, matFiles(iExp).name, 0, dbgLevel);\n    end;\n        \nend;\n\nnDays = numel(unique(cellstr(datestr(outAll.datesAsNum, 'yyyymmdd'))));\no('#mtrain...FromFolder: found %d/%d valid file(s), %d trial(s) in %d unique day(s).', ...\n    nValidMatFiles, numel(matFiles), numel(outAll.respTypes), nDays, 1, dbgLevel);\n\nend\n\nfunction out = parseLogFile(out, fullPath, iExp, dbgLevel)\n\n    % try the taskType first in the out file\n    if isfield(out, 'taskType') && ~strcmp(out.taskType, '?') && ~strcmp(out.taskType, 'UNKNOWN') ...\n            && isfield(out.config, 'tone') && isfield(out.config.tone, 'nTones');\n        taskType = out.taskType;\n        o('  #mtrain...FromFolder: %d: checking for taskType, taskType present: \"%s\"...', ...\n            iExp, taskType, 3, dbgLevel);\n    else\n        taskType = '?';\n        % extract taskType\n        o('  #mtrain...FromFolder: %d: checking for taskType, so far unset (\"%s\")...', ...\n            iExp, taskType, 3, dbgLevel);\n        fid = fopen(strrep(fullPath, '_out.mat', '_log.txt'));\n        line = fgetl(fid);\n        while ischar(line);\n            m = regexp(line, '(?<nFreqs>\\d+)F(?<nTones>\\d+)T', 'names');\n            if ~isempty(m);\n                nFreqs = str2double(m.nFreqs);\n                nTones = str2double(m.nTones);\n                if nFreqs == 1 && nTones > 1;\n                    taskType = 'oddballDiscrimination';\n                elseif nFreqs == 2 && nTones > 1;\n                    taskType = 'oddballDiscrimination';\n                elseif nFreqs > 1 && nTones == 1;\n                    taskType = 'freqDiscrimination';\n                elseif nFreqs == 1 && nTones == 1;\n                    taskType = 'basic';\n                else\n                    taskType = 'UNKNOWN';\n                end;\n                o('  #mtrain...FromFolder: %d: MATCH: nFreqs = %d, nTones = %d, taskType = \"%s\".', ...\n                    iExp, nFreqs, nTones, taskType, 3, dbgLevel);\n                out.taskType = taskType;\n                if ~isfield(out.config, 'tone');\n                    out.config.tone = struct();\n                end;\n                out.config.tone.nTones = nTones;\n                if ~isfield(out.config.tone, 'freqs');\n                    if nFreqs == 1; out.config.tone.freqs = 12000;\n                    elseif nFreqs == 2; out.config.tone.freqs = [7000, 12000];\n                    elseif nFreqs == 4; out.config.tone.freqs = [5000, 7000, 12000, 15000];\n                    else out.config.freqs = repmat(-1, 1, nFreqs);\n                    end;\n                end;\n                save(fullPath, 'out');\n                break;\n            end;\n            line = fgetl(fid);\n        end\n\n        fclose(fid);\n    end;\n    if strcmp(out.taskType, '?');\n        o('  #mtrain...FromFolder: %d: task type not found (\"%s\")...', ...\n            iExp', out.taskType, 1, dbgLevel);\n    end;\nend\n\nfunction out = makeConsistent(out, fullPath, iExp, toRemoveField, toRenameField, requiredFields, dbgLevel)\n    if exist('toRemoveField', 'var');\n        nFields = 0;\n        for iField = 1 : size(toRemoveField, 1);\n            if isfield(out, toRemoveField{iField});\n                out = rmfield(out, toRemoveField{iField});\n                nFields = nFields + 1;\n            end;\n        end;\n        if nFields;\n            save(fullPath, 'out');\n            o('  #mtrain...FromFolder: %d: removed %d field(s).', iExp, nFields, 3, dbgLevel);\n        end;\n    end;\n    if exist('toRenameField', 'var');\n        nFields = 0;\n        for iField = 1 : size(toRenameField, 1);\n            if isfield(out, toRenameField{iField, 1});\n                out.(toRenameField{iField, 2}) = out.(toRenameField{iField, 1});\n                out = rmfield(out, toRenameField{iField, 1});\n                nFields = nFields + 1;\n            end;\n        end;\n        if nFields;\n            o('  #mtrain...FromFolder: %d: renamed %d field(s).', iExp, nFields, 3, dbgLevel);\n            save(fullPath, 'out');\n        end;\n    end;\n    if exist('requiredFields', 'var');\n        nFields = 0;\n        for iField = 1 : size(requiredFields, 1);\n            if ~isfield(out, requiredFields{iField, 1});\n                if exist(requiredFields{iField, 1}, 'var');\n                    eval(['out.(requiredFields{iField, 1}) = ' requiredFields{iField, 1} ';']);\n                else\n                    out.(requiredFields{iField, 1}) = requiredFields{iField, 2};\n                end;\n                nFields = nFields + 1;\n            end;\n        end;\n        if nFields;\n            o('  #mtrain...FromFolder: %d: created %d required fields.', iExp, nFields, 3, dbgLevel);\n            save(fullPath, 'out');\n        end;\n    end;\nend\n\nfunction [out, unResps] = removeUnresponsive(out, iExp, dbgLevel)\n    \n    o('  #mtrain...FromFolder: %d: removing unresponsive trials ...', iExp, 3, dbgLevel);\n    unrespSeriesIndexes = [];\n    unResps = cell(1);\n    minUnrespLength = 15; % minimum number of consecutive unresponsive trials\n    \n    toKeepIndexes = true(size(out.resps));\n    toKeepIndexes(isnan(out.resps)) = false;\n    \n    fieldNames = {\n        'stims', 'odds', 'oddPos', 'isOdd', 'trialStartDelays', 'trialStartTimes', 'trialEndTimes', ...\n        'respTimes', 'nInTriLick', 'resps', 'respDelays', 'respTypes'\n        };\n    \n    % remove NaNs\n    for iField = 1 : numel(fieldNames);\n        if numel(out.(fieldNames{iField})) < max(toKeepIndexes);\n            o('  #mtrain...FromFolder: %d: problem with field \"%s\"...', iExp, fieldNames{iField}, ...\n                3, dbgLevel);\n            continue;\n        end;\n        out.(fieldNames{iField}) = out.(fieldNames{iField})(toKeepIndexes);\n    end;\n    \n    % separate the trials by unresponsive 'bins'/'blobs'\n    CC = bwconncomp(~out.resps);\n    \n    % go through each bin of unresponsive consecutive trials\n    for iBin = 1 : numel(CC.PixelIdxList);\n        indexList = CC.PixelIdxList{iBin}'; % extract the list of indexes\n        \n        % if the bin is long enough and contains at least one 'correct rejection' and one 'miss' ...\n        if numel(indexList) >= minUnrespLength && any(out.respTypes(indexList) == 2) && any(out.respTypes(indexList) == 4);\n            % then we can consider it as unresponsive and gather the indexes\n            unrespSeriesIndexes = [unrespSeriesIndexes indexList]; %#ok<AGROW>\n            unResps{end + 1} = indexList; %#ok<AGROW>\n        end;\n    end;\n    \n    toKeepIndexes = true(size(out.resps));\n    toKeepIndexes(unrespSeriesIndexes) = false;\n    \n    for iField = 1 : numel(fieldNames);\n        if numel(out.(fieldNames{iField})) < max(toKeepIndexes);\n            o('  #mtrain...FromFolder: %d: problem with field \"%s\"...', iExp, fieldNames{iField}, ...\n                3, dbgLevel);\n            continue;\n        end;\n        out.(fieldNames{iField}) = out.(fieldNames{iField})(toKeepIndexes);\n    end;\n    \nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainerAnalyser_pooledPerformance.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainerAnalyser_pooledPerformance.m", "size": 13056, "source_encoding": "utf_8", "md5": "b60d5cf1801f259ba926464fd19f619c", "text": "%% Function - mtrainerAnalyser_pooledPerformance\nfunction mtrainerAnalyser_pooledPerformance(outAll, titleStr)\n\ndbgLevel = 2;\n\nuniqueDays = unique(cellstr(datestr(outAll.datesAsNum, 'yyyymmdd')));\nnDays = numel(uniqueDays);\no('#mtrain...pooledPerf: %d trial(s) in %d day(s).', numel(outAll.respTypes), nDays, 1, dbgLevel);\n\nruns = unique(cellstr(datestr(outAll.datesAsNum, 'yyyymmdd_HHMMSS')));\nnextSessThreshIndiv = 0.08; % pool data with morning and afternoon sessions separately\n% nextSessThreshIndiv = 0.0005; % pool data with each run (100 trials) separately\nnextSessThreshDaily = 0.35; % pool data daily\n\n[~, sessionIndexesIndiv, nTrialsPerSessIndiv] = separateIntoSessions(outAll, runs, ...\n    nextSessThreshIndiv, dbgLevel);\n[runs, sessionIndexesDaily, nTrialsPerSessDaily] = separateIntoSessions(outAll, runs, ...\n    nextSessThreshDaily, dbgLevel); %#ok<ASGLU>\n\nsessionIndexes = sessionIndexesDaily;\nnTrialsPerSess = nTrialsPerSessDaily;\nnSessionIndiv = max(sessionIndexesIndiv);\nnSessionDaily = max(sessionIndexesDaily);\nnSession = nSessionDaily;\n\no('#mtrain...pooledPerf: found %d daily session(s) and %d individual session(s).', ...\n    nSessionDaily, nSessionIndiv, 2, dbgLevel);\no('#mtrain...pooledPerf: mean nTrials per day: %03.0f, per indiv session: %03.0f.', ...\n    mean(nTrialsPerSessDaily), mean(nTrialsPerSessIndiv), 2, dbgLevel);\n\nfigName = titleStr;\nfigName = strrep(figName, '\\_', '_');\nfigName = strrep(figName, '''', '');\nfigName = strrep(figName, ' ', '');\nfigHand = figure('NumberTitle', 'off', 'WindowStyle', 'docked', 'Name', figName);\n\nhitRate = zeros(1, nSession);          % correct detection, CR+, hitRate\nhitRateError = zeros(1, nSession);     % correct detection, CR+, hitRate errors\nfalsePosRate = zeros(1, nSession);     % false alarm, CR-, false positive\nfalsePosRateError = zeros(1, nSession);% false alarm, CR-, false positive errors\ndprimes = zeros(1, nSession);          % d prime\ndprimesError = zeros(1, nSession);     % d prime errors\n\nuseSEM = 1;\niBinAll = 1;\nprevNFreqs = 0;\nprevNTones = 0;\ndprimeMaxVal = 3.5;\ndprimeMinVal = -2;\nchangeOfFreqOrToneNum = zeros(1, nSession);\n\nfor iSess = 1 : nSession;\n    \n    % extract the current session's number of frequency and number of tones\n    nFreqs = outAll.nFreqs(sessionIndexes == iSess);\n    nTones = outAll.nTones(sessionIndexes == iSess);\n    if ~numel(nFreqs); continue; end;\n    nFreqs = nFreqs(1);\n    nTones = nTones(1);\n    \n    % skip basic sessions\n%     if nFreqs == 1 && nTones == 1;\n%         hitRate(iBinAll) = NaN;\n%         falsePosRate(iBinAll) = NaN;\n%         dprimes(iBinAll) = NaN;\n%         iBinAll = iBinAll + 1;\n%         continue;\n%     end;\n    \n    % if there is a change in the number of frequencies or tones, mark it\n    if prevNFreqs ~= 0 && (nFreqs ~= prevNFreqs || nTones ~= prevNTones);\n        changeOfFreqOrToneNum(iSess) = iSess - 1;\n    end;\n        \n    % store for next iteration\n    prevNFreqs = nFreqs;\n    prevNTones = nTones;\n    \n    % extract the respTypes for this session\n    % for each trial, 1 = correct detect, 2 = correct reject, 3 = false alarm, 4 = miss, 5 = early\n    sessionRespTypes = outAll.respTypes(sessionIndexes == iSess);\n    \n    sessInDay = sessionIndexesIndiv(sessionIndexes == iSess);\n    sessStart = min(sessInDay);\n    sessEnd = max(sessInDay);\n    dPrimesIndivSess = zeros(1, sessEnd - sessStart + 1);\n    hitRatesIndivSess = zeros(1, sessEnd - sessStart + 1);\n    falsePosIndivSess = zeros(1, sessEnd - sessStart + 1);\n    for iSessIndiv = sessStart : sessEnd;\n        indivSessionRespTypes = outAll.respTypes(sessionIndexesIndiv == iSessIndiv);\n        countsForIndivSession = mtrainerAnalyzer_getCounts(indivSessionRespTypes);\n        if isnan(countsForIndivSession.DPRIME);\n            countsForIndivSession.DPRIME = 0;\n        end;\n        dPrimesIndivSess(iSessIndiv - sessStart + 1) = countsForIndivSession.DPRIME;\n        hitRatesIndivSess(iSessIndiv - sessStart + 1) = ...\n            100 * countsForIndivSession.TGO / countsForIndivSession.T;\n        falsePosIndivSess(iSessIndiv - sessStart + 1) = ...\n            100 * countsForIndivSession.NTGO / countsForIndivSession.NT;\n    end;\n    \n    if useSEM;\n        dprimesError(iBinAll) = sem(dPrimesIndivSess); %%#ok<UNRCH>\n        hitRateError(iBinAll) = sem(hitRatesIndivSess);\n        falsePosRateError(iBinAll) = sem(falsePosIndivSess);\n    else\n        dprimesError(iBinAll) = std(dPrimesIndivSess); %#ok<UNRCH>\n        hitRateError(iBinAll) = std(hitRatesIndivSess);\n        falsePosRateError(iBinAll) = std(falsePosIndivSess);\n    end;\n    \n    % make an overall counting for the session\n    countsForSession = mtrainerAnalyzer_getCounts(sessionRespTypes);\n    o('  #mtrain...pooledPerf: session %d, taskType: \"%s\".', iSess, ...\n        sprintf('%F%T', nFreqs, nTones), 3, dbgLevel);\n    \n    hitRate(iBinAll) = 100 * countsForSession.TGO / countsForSession.T;\n    falsePosRate(iBinAll) = 100 * countsForSession.NTGO / countsForSession.NT;\n    if isnan(countsForSession.DPRIME);\n        countsForSession.DPRIME = 0;\n    end;\n\n    % restrict the dprime value between dprimeMaxVal and dprimeMinVal\n    countsForSession.DPRIME = min(dprimeMaxVal, countsForSession.DPRIME);\n    countsForSession.DPRIME = max(dprimeMinVal, countsForSession.DPRIME);\n\n    dprimes(iBinAll) = countsForSession.DPRIME;\n    \n    o('  #mtrain...pooledPerf: session %d, HitRate: %04.1f, FalsePosRate = %04.1f, d'' = %.2f.', ...\n        iSess, hitRate(iBinAll), falsePosRate(iBinAll), dprimes(iBinAll), 3, dbgLevel);\n    \n    iBinAll = iBinAll + 1;\n%     \n%     binWidth = 100;\n%     \n%     % calculate number of bins, make sure that there is at least one\n%     nBins = max(round(countsForSession.TOT / binWidth), 1);\n%     for iBin = 1 : nBins;\n%         iStart = (iBin - 1) * binWidth + 1; % for binWidth = 10: 1, 11, 21, etc.\n%         iEnd = min(iBin * binWidth, countsForSession.TOT); % for binWidth = 10: 10, 20, 30, etc.\n%         countsForBin = mtrainerAnalyzer_getCounts(sessionRespTypes(iStart : iEnd));\n%         \n%         if countsForBin.GOP < 50;\n%             trialIndex = find(sessionIndexes == iSess);\n%             trialIndex = trialIndex(1) + iBin * binWidth;\n%             runsForSession = runs(cell2mat(runs(:, 2)) == iSess);\n%             iRun = 1;\n%             while iRun < numel(runsForSession);\n%                 if sum(nTrialsPerSess\n%             end;\n%             o('  #mtrain...pooledPerf: session %d, %s, bin %d (trial %.0f) has a GOP of %.2f.', ...\n%                 iSess, runsForSession{1}, iBin, trialIndex, countsForBin.GOP, 3, dbgLevel);\n%             hitRate(iBinAll) = NaN;\n%             falsePosRate(iBinAll) = NaN;\n%             dprimes(iBinAll) = NaN;\n%             iBinAll = iBinAll + 1;\n%             continue;\n%         end;\n%         \n%         hitRate(iBinAll) = 100 * countsForBin.TGO / countsForBin.T;\n%         falsePosRate(iBinAll) = 100 * countsForBin.NTGO / countsForBin.NT;\n%         if isnan(countsForBin.DPRIME) || isinf(countsForBin.DPRIME);\n%             countsForBin.DPRIME = 0;\n%         end;\n%         \n%         % restrict the dprime value between +dprimeMaxVal and -dprimeMaxVal\n%         countsForBin.DPRIME = min(dprimeMaxVal, countsForBin.DPRIME);\n%         countsForBin.DPRIME = max(-dprimeMaxVal, countsForBin.DPRIME);\n%         \n%         dprimes(iBinAll) = countsForBin.DPRIME;\n%         iBinAll = iBinAll + 1;\n%     end;\nend;\n\nnTotBins = iBinAll - 1;\n\n% TGOsHand = plot(hitRate, '-sg', 'MarkerFaceColor', 'green');\nTGOsErrHand = errorbar(1 : nSession, hitRate, hitRateError, '-sg', 'MarkerFaceColor', 'green');\nremoveErrorBarEnds(TGOsErrHand);\n\nhold all;\nhitRateAxes = gca;\n% NTNGOsHand = plot(falsePosRate, '-sr', 'MarkerFaceColor', 'red');\nNTNGOsErrHand = errorbar((1 : nSession) + 0.15, falsePosRate, falsePosRateError, '-sr', 'MarkerFaceColor', 'red');\nremoveErrorBarEnds(NTNGOsErrHand);\n\nset(hitRateAxes, 'ylim', [0 110], 'YTick', 0 : 10 : 110, 'YTickLabel', 0 : 10 : 110);\n\nif useSEM;\n    ylabel('% of Trials \\pm SEM'); %%#ok<UNRCH>\nelse\n    ylabel('% of Trials \\pm SD'); %#ok<UNRCH>\nend;\n\nvertLineHandles = zeros(1, sum(changeOfFreqOrToneNum > 0));\nvertLineTextHandles = zeros(2, sum(changeOfFreqOrToneNum > 0));\niLineHandle = 1;\nfor iChange = 1 : numel(changeOfFreqOrToneNum);\n    if ~changeOfFreqOrToneNum(iChange); continue; end;\n    changeX = changeOfFreqOrToneNum(iChange) + 0.5;\n    \n    nFreqs1 = outAll.nFreqs(sessionIndexes == changeOfFreqOrToneNum(iChange));\n    nFreqs1 = nFreqs1(1);\n    nFreqs2 = outAll.nFreqs(sessionIndexes ==changeOfFreqOrToneNum(iChange) + 1);\n    nFreqs2 = nFreqs2(1);\n    \n    if mod(iChange, 2) == 0;\n        changeY = 9;\n    else\n        changeY = 12;\n    end;\n    \n    vertLineTextHandles(1, iLineHandle) = text(changeX, changeY, ...\n        sprintf('%d freq \\\\leftarrow ', nFreqs1), 'HorizontalAlignment', 'right');\n    vertLineTextHandles(2, iLineHandle) = text(changeX, changeY, sprintf(' \\\\rightarrow %d freq', nFreqs2));\n    \n    vertLineHandles(iLineHandle) = line([changeX changeX], [0 100], ...\n        'LineStyle', ':', 'Color', 'black');\n    iLineHandle = iLineHandle + 1;\n    \n    \nend;\nxlimits = [0 nTotBins * 1.05];\nxlim(xlimits);\nset(hitRateAxes, 'XTick', [], 'XTickLabel', []);\n\nxTickAxes = axes('Position', get(gca, 'Position'), ...\n    'YAxisLocation', 'right', 'XAxisLocation', 'bottom', 'Color', 'none');\nsessionLabels = cellstr(datestr(outAll.datesAsNum([diff(sessionIndexes) > 0, true]), 'dd/mm'));\nset(xTickAxes, 'xlim', xlimits, ...\n    'XTick', 1 : nSession, 'XTickLabel', sessionLabels(1 : nSession));\n% set(xTickAxes, 'xlim', xlimits, ...\n%     'XTick', 0 : 10 : nTotBins, 'XTickLabel', (0 : 10 : nTotBins) * binWidth);\nset(xTickAxes, 'YTick', [], 'YTickLabel', []);\nxlabel('Sessions');\n\ndprimeAxes = axes('Position', get(gca, 'Position'), ...\n    'YAxisLocation', 'right', 'XAxisLocation', 'top', 'Color', 'none');\nhold all;\n% dprimesHand = plot(dprimes, '-sb', 'MarkerFaceColor', 'blue');\ndprimesErrHand = errorbar((1 : nSession) + 0.3, dprimes, dprimesError, '-sb', 'MarkerFaceColor', 'blue');\nremoveErrorBarEnds(dprimesErrHand);\n\nset(dprimeAxes, 'ylim', [dprimeMinVal dprimeMaxVal], 'YTick', dprimeMinVal : 0.5 : dprimeMaxVal, ...\n    'YTickLabel', dprimeMinVal : 0.5 : dprimeMaxVal);\nset(dprimeAxes, 'XTick', [], 'XTickLabel', []);\nxlim(xlimits);\nif useSEM;\n    ylabel('d'' value \\pm SEM'); %%#ok<UNRCH>\nelse\n    ylabel('d'' value \\pm SD'); %#ok<UNRCH>\nend;\n\nlegend([TGOsErrHand, NTNGOsErrHand, dprimesErrHand], {'HitRate', 'FalsePos', 'd'''}, ...\n    'Location', 'NorthWest');\n% legend([TGOsHand, NTNGOsHand, dprimesHand], {'HitRate', 'FalsePos', 'd'''}, ...\n%     'Location', 'NorthWest');\n\ntitle(sprintf('%s \\\\fontsize{10} [Total number of trials = %.0f, mean trials per session = %.0f]', ...\n    titleStr, sum(nTrialsPerSess), mean(nTrialsPerSess)));\n\nhold off;\nmakePrettyFigure(figHand);\nset(xTickAxes, 'FontSize', 9);\nfor iLineHandle = 1 : numel(vertLineHandles);\n    set(vertLineHandles(iLineHandle), 'LineWidth', 0.5);\n    set(vertLineTextHandles(1, iLineHandle), 'FontSize', 10);\n    set(vertLineTextHandles(2, iLineHandle), 'FontSize', 10);\nend;\n    \nend\n\nfunction [runs, sessionIndexes, nTrialsPerSess] = separateIntoSessions(outAll, runs, nextSessThresh, dbgLevel)\n\nsessionIndexes = zeros(1, numel(outAll.respTypes));\nnTrialsPerSess = zeros(1, 1);\niRun = 1;\niSess = 1;\n\n% loop trough all runs\nwhile iRun <= size(runs, 1);\n    % current run's date as number\n    dateAsNum = datenum(runs{iRun, 1}, 'yyyymmdd_HHMMSS');\n    o('  #mtrain...pooledPerf: run %d, date: %s (%8.3f).', iRun, runs{iRun, 1}, dateAsNum, 3, dbgLevel);\n    \n    % for all runs before the last, check with next run\n    if iRun < size(runs, 1);\n        dateAsNumNext = datenum(runs{iRun + 1, 1}, 'yyyymmdd_HHMMSS');\n    else % for last run, make the difference big enough\n        dateAsNumNext = dateAsNum + 100 * nextSessThresh;\n    end;\n    \n    runs{iRun, 2} = iSess;\n    o('    #mtrain...pooledPerf: run %d''s session: %d, dateAsNumNext: %8.3f, diff: %.2f, thresh: %.2f.', ...\n        iRun, iSess, dateAsNumNext, abs(dateAsNum - dateAsNumNext), nextSessThresh, 3, dbgLevel);\n    \n    % if they are too different, next run is in a new session\n    if abs(dateAsNum - dateAsNumNext) > nextSessThresh;\n        % get all the respTypes for that session (earlier dates that are not yet assigned to a sessions)\n        respTypesForSession = outAll.respTypes(outAll.datesAsNum <= dateAsNum & ~sessionIndexes);\n        nTrials = numel(respTypesForSession);\n        zeroIndex = find(~sessionIndexes); % get indexes where sessionIndex is still 0\n        sessionIndexes(zeroIndex(1) : zeroIndex(1) + nTrials - 1) = ones(1, nTrials) * iSess;\n        nTrialsPerSess(iSess) = nTrials;\n        o('    #mtrain...pooledPerf: run %d is the last of session %d, nTrials: %d, tagging from %d to %d.', ...\n            iRun, iSess, nTrials, zeroIndex(1), zeroIndex(1) + nTrials - 1, 3, dbgLevel);\n        iSess = iSess + 1;\n        iRun = iRun + 1;\n    else % otherwise if they are not too different, go on\n        iRun = iRun + 1;\n    end;\nend;\n\nend"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainerAnalyser_freqAnalysis_bins.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainerAnalyser_freqAnalysis_bins.m", "size": 2389, "source_encoding": "utf_8", "md5": "e6cff6d257bc5bfe9d98519822f2e677", "text": "%% Function - mtrainerAnalyser_pooledPerformance\nfunction figHand = mtrainerAnalyser_freqAnalysis_bins(outAll, titleStr, binWidth)\n\ndbgLevel = 2;\n\nnFreqs = 5;\n\n% for each trial: 1 = correct detect, 2 = correct reject, 3 = false alarm, 4 = miss, 5 = early\nrespTypes = outAll.respTypes(outAll.nFreqs == nFreqs);\nfreqs = outAll.freqs(outAll.nFreqs == nFreqs);\nuniqueFreqs = unique(freqs);\ngoFreq = unique(outAll.goFreq(outAll.nFreqs == nFreqs));\n\nfigHand = 0;\n\nif numel(goFreq) > 1; o('Oh-oh. Problem ! Multiple go freqs', 1, dbgLevel); return; end;\n\nnTrials = length(respTypes);\n\n% calculate number of bins, make sure that there is at least one\nnBins = max(round(nTrials / binWidth), 1);\n\nfigName = titleStr;\nfigName = strrep(figName, '\\_', '_');\nfigName = strrep(figName, '''', '');\nfigName = strrep(figName, ' ', '');\nfigHand = figure('NumberTitle', 'off', 'WindowStyle', 'docked', 'Name', figName);\n\n% errors = nan(nFreqs, nBins);\nerrors = nan(nFreqs - 1, nBins);\n    \nfor iBin = 1 : nBins;\n    iStart = (iBin - 1) * binWidth + 1; % for binWidth = 10: 1, 11, 21, etc.\n    iEnd = min(iBin * binWidth, nTrials); % for binWidth = 10: 10, 20, 30, etc.\n    \n    \n    for iFreq = 1 : nFreqs;\n        \n        respTypesForFreq = respTypes(freqs(iStart : iEnd) == uniqueFreqs(iFreq));\n        o('Bin %d - freq %d, respTypes: %d', iBin, iFreq, numel(respTypesForFreq), 3, dbgLevel);\n        \n        if uniqueFreqs(iFreq) == goFreq;\n            continue;\n%             errors(iFreq, iBin) = 100 * sum(respTypesForFreq == 4) / numel(respTypesForFreq);\n        else\n            errors(iFreq, iBin) = 100 * sum(respTypesForFreq == 3) / numel(respTypesForFreq);\n        end;\n        \n    end;\n    \nend;\n\nerrors = errors(setdiff(1 : nFreqs, find(uniqueFreqs == goFreq)), :);\n\ncolors = lines(nFreqs - 1);\n% colors = lines(nFreqs);\nplotHandles = plot(errors', '-s');\nfor iHandle = 1 : numel(plotHandles);\n    set(plotHandles(iHandle), 'Color', colors(iHandle, :), 'MarkerFaceColor', colors(iHandle, :));\nend;\n\n% legend({'4kHz', '8kHz', '12kHz', '16kHz', '20kHz'});\nlegend({'4kHz', '8kHz', '16kHz', '20kHz'});\nylabel('Errors in %');\n\nstep = nBins / (nTrials / 500);\nset(gca, 'xlim', [0.8 nBins + 0.2], 'XTick', 1 : step : (nBins + 1), ...\n    'XTickLabel', 0 : 500 : ((nBins + 1) * binWidth));\n% set(gca, 'YTick', [], 'YTickLabel', []);\nxlabel('Trials');\ntitle(titleStr);\n\nmakePrettyFigure(figHand);\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainer_oldversion_scheduledStarts.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainer_oldversion_scheduledStarts.m", "size": 16949, "source_encoding": "utf_8", "md5": "cb689fcefe86d96085f1a191049aa7cd", "text": "function mtrainer()\n% main function for mtrainer experiment suite\n% all times are in s!\n\n% written by Henry Luetcke (hluetck@gmail.com)\n% 2013-05-07\n% modified by Balazs Laurenczy (blaurenczy@gmail.com)\n% 2013-05-15\n\ndoEmptyWater = 1;\nautomode = 1;\nunsavedOut = struct();\n\nmouseId = 'testing'; taskType = 'freqDiscrimination'; phase = 'D';\n% mouseId = '130408_02'; taskType = 'freqDiscrimination'; phase = 'D';\n% mouseId = '130416_01'; taskType = 'oddballDiscrimination'; phase = 'E';\n% mouseId = '130425_01'; taskType = 'freqDiscrimination';  phase = 'B';\n% mouseId = '130425_02'; taskType = 'oddballDiscrimination'; phase = 'C';\n% mouseId = '130528_01'; taskType = 'oddballDiscrimination'; phase = 'B';\n% mouseId = '130528_02'; taskType = 'oddballDiscrimination'; phase = 'B';\n\nbasePath = 'P:\\matlab\\daq\\mtrainer\\configs\\';\nconfigMat = load([basePath taskType filesep taskType '_phase' phase '.mat']);\nconfig = configMat.config;\n\nunsavedOut.mouseId = mouseId;\nunsavedOut.taskType = taskType;\nunsavedOut.phase = phase;\nunsavedOut.basePath = basePath;\nunsavedOut.config = config;\n\n%% Setup - stimuli and trials\nfprintf('Setup stimuli and trials... ');\n% set up stimulus vector\nstims = [];\nfor iFreq = 1 : numel(config.tone.freqs)\n    stimForFreq = repmat(iFreq, 1, round(config.tone.stimProba(iFreq) * config.tone.nTrials));\n    stims = [stims, stimForFreq]; %#ok<AGROW>\nend\nstims = stims(randperm(numel(stims)));\nclear stim;\n\n% set up oddball vector by altering stims with a certain probability\nodds = stims;\noddPos = zeros(size(odds));\nuniqueStims = unique(stims);\nisOdd = rand(1, numel(stims)) < config.tone.oddProba;\nuniqueOddPos = config.tone.nTones - 1 : config.tone.nTones; % last 2 tones can be odd\nfor iStim = 1 : numel(isOdd);\n    if isOdd(iStim);\n        otherStims = uniqueStims(uniqueStims ~= stims(iStim));\n        otherStims = otherStims(randperm(numel(otherStims)));\n        mixedOddPos = uniqueOddPos(randperm(numel(uniqueOddPos)));\n        odds(iStim) = otherStims(1);\n        oddPos(iStim) = mixedOddPos(1);\n    end;\nend;\n\n% set up tone array\ntoneArray = MakePureMultiToneOddballArray(config.tone.freqs, stims, odds, oddPos, ...\n    config.tone.nTones, config.tone.toneDur, config.tone.ISI, config.tone.samplingFreq);\n\nunsavedOut.stims = stims;\nunsavedOut.odds = odds;\nunsavedOut.oddPos = oddPos;\nunsavedOut.isOdd = isOdd;\n\nfprintf('done.\\n');\n\n%% Setup - hardware\n% define globals\nglobal recordedValues;\n\n% analog input\ndaqreset;\nfprintf('Connecting hardware...\\n');\ns1 = daq.createSession(config.hardware.adaptorID);\ns1.Rate = config.hardware.analogIn_SampleRate;\ns1.addAnalogInputChannel(config.hardware.analogIn_Device, config.hardware.analogIn_Channel, 'Voltage');\ns1.Channels(1).InputType = config.hardware.analogIn_InputType;\ns1.Channels(1).Range = config.hardware.analogIn_Range;\nfprintf('s1 ok.\\n');\n\ns2 = daq.createSession(config.hardware.adaptorID);\ns2.addDigitalChannel(config.hardware.digitalOut_Device, config.hardware.digitalOut_PortLine, 'OutputOnly');\nfprintf('And s2 ok. Ready to go.\\n');\n\nlickThresh = 0.18;\n% lickThresh = NaN;\n\nif isnan(lickThresh);\n    % baseline measurement\n    fprintf('Testing analog input for licking threshold (baseline)... ');\n    recordDuration = 2;\n    s1.DurationInSeconds = recordDuration;\n    baseline = s1.startForeground; % this will block the command line\n    meanData = mean(baseline);\n    stdData = std(baseline);\n    lickThresh = 2.5 * stdData;\n    fprintf('done.\\n')\n\n    pause(1);\n\n    % lick measurement\n    fprintf('Testing analog input for licking threshold (licking)... ');\n    s1.DurationInSeconds = recordDuration;\n    lick = s1.startForeground; % this will block the command line\n    fprintf('done.\\n2SD: %1.2f, lick threshold: %1.2f\\n', 2 * stdData, lickThresh);\n\n    % plotting\n    figure('Name', 'LickingThreshold', 'Position', [150, 300, 950, 350], 'NumberTitle', 'off', ...\n        'MenuBar', 'none', 'ToolBar', 'none');\n    plot((1:numel(baseline)) / 1000, baseline - meanData, 'b');\n    hold on;\n    plot((1:numel(lick)) / 1000, lick - meanData, 'r');\n    line([0, recordDuration], [lickThresh, lickThresh], 'Color', 'green');\n    line([0, recordDuration], [2 * stdData, 2 * stdData], 'Color', 'cyan');\n    line([0, recordDuration], [- 2 * stdData, - 2 * stdData], 'Color', 'cyan');\n    legend({'baseline', 'licking', 'lickTreshold', '\\pm 2SD'});\nelse\n    fprintf('Testing analog input for licking threshold... skipped.\\nLick threshold: %1.2f\\n', ...\n        lickThresh);\nend;\n\nunsavedOut.lickThresh = lickThresh;\n\n% digital out\nfprintf('Testing reward system by opening for %.2fs after a delay of %.2fs... ', ...\n    config.training.rewDur, config.training.rewDelay);\ngiveReward(s2, config.training.rewDur, config.training.rewDelay);\nfprintf('done!\\n')\nif doEmptyWater;\n    doEmptyWater = str2double(input('Empty water ? 1 = yes, 0 = no\\n', 's')); %#ok<UNRCH>\n    while doEmptyWater;\n        fprintf('Emptying water... ');\n        giveReward(s2, 0.03, 0);\n    %         giveReward(s2, 3, 0);\n        fprintf(' done.');\n        doEmptyWater = str2double(input('Empty more ? 1 = yes, 0 = no\\n', 's'));\n        if ~doEmptyWater; break; end;\n    end;\nend;\n\ns1.IsNotifyWhenDataAvailableExceedsAuto = true;\nlh = s1.addlistener('DataAvailable', @(src, event) recordAndStop(src, event, lickThresh));\ns1.IsContinuous = true;\ns1.prepare;\n\n%% Setup - saving settings\nrecordedValues = 0;\nsaveTime = datestr(now, 'yyyy_mm_dd__HH_MM_SS');\nsaveFolder = sprintf('C:\\\\Users\\\\laurenczy\\\\mtrainerData\\\\%s\\\\', mouseId);\nsavePath = sprintf('%s%s__%s', saveFolder, mouseId, saveTime);\nmkdir(saveFolder);\nlogfile = sprintf('%s_log.txt', savePath);\nlogDateFormat = 'yyyy-mm-dd HH:MM:SS.FFF';\n\n% create an 'onCleanup' function to handle interruptions by CTRL+C\nc = onCleanup(@()closeDiaryAndSaveOutput(savePath, lh, s1, s2));\n\nfprintf('Finished setting up and hardware tests.\\n');\nfprintf('CHECK MOUSE ID!! currently: %s.\\n', mouseId);\nif automode;\n    pause(5);\nelse\n    fprintf('Then press any key to start!'); %#ok<UNRCH>\n    pause;\nend;\n\nunsavedOut.savePath = savePath;\nunsavedOut.saveTime = saveTime;\n\ndiary(logfile);\n%% Start experiment\nfprintf('Experiment start: %s\\n\\n', datestr(now, logDateFormat));\ntry\n    \n    %% Experiment - initialize variables\n    \n    dbgLevel = 0;\n    out = unsavedOut;\n\n    o('config: %s phase %s (%dF%dT)', out.taskType, out.phase, numel(config.tone.freqs), ...\n        config.tone.nTones, 0, dbgLevel);\n    o('animal: %s', mouseId, 0, dbgLevel);\n    o('savePath: %s', savePath, 0, dbgLevel);\n    o('lickThresh: %1.2f, ISI: %1.2f', lickThresh, config.tone.ISI, 0, 0);\n    o('rewardCollectionTime: %1.2f, InTriLickTimerDur: %1.2f', ...\n        config.training.rewCollTime, ...\n        config.training.InTriLickTimerDur, 0, dbgLevel);\n    \n    totalTrialDur = config.training.trialDur + config.training.startDelay + ...\n        config.training.rewCollTime;\n    nTrials = numel(stims);\n    % compute relative start times for each trial\n    out.trialStarts_sched = 0 : totalTrialDur : (nTrials - 1) * totalTrialDur;\n    out.trialStarts_obs = zeros(1, nTrials);\n    out.trialEnds_obs = zeros(1, nTrials);\n    out.respTimes = nan(1, nTrials);\n    out.nInTriLick = zeros(1, nTrials);\n    out.resps = nan(1, nTrials);\n    out.respDelay = nan(1, nTrials);\n    % for each trial: 1 = correct detect, 2 = correct reject, 3 = false alarm, 4 = miss, 5 = early\n    out.respTypes = zeros(1, nTrials);\n    out.expStartTime = tic;\n    \n    for iTrial = 1 : nTrials;\n        out.iTrial = iTrial;\n        % listen for response\n        s1.startBackground;\n        pause(0.01)\n        interTrialLickCount = 0;\n        %% Experiment - initial wait\n        while toc(out.expStartTime) < out.trialStarts_sched(iTrial);\n%             fprintf('CheckingA: s1.IsRunning? %d\\n', s1.IsRunning);\n            % check if no lick has been done\n            if ~s1.IsRunning;\n                remainingTime = out.trialStarts_sched(iTrial) - toc(out.expStartTime);\n                addedDelay = 0;\n                % delay the schedule if needed\n                if (remainingTime < config.training.InTriLickTimerDur);\n                    addedDelay = config.training.InTriLickTimerDur - remainingTime;\n                    out.trialStarts_sched(iTrial:end) = out.trialStarts_sched(iTrial:end) + addedDelay;\n                    % refresh the remaining time\n                    remainingTime = out.trialStarts_sched(iTrial) - toc(out.expStartTime);\n                end;\n                if interTrialLickCount > 0;\n                    fprintf('|%.1f', remainingTime);\n                else\n                    fprintf('    inter-trial licking, delaying for %.1fs: |%.1f', ...\n                        addedDelay, remainingTime);\n                end;\n                interTrialLickCount = interTrialLickCount + 1;\n                pause(1);\n                % restart the listening\n                s1.startBackground;\n                pause(0.1);\n%             fprintf('CheckingB: s1.IsRunning? %d\\n', s1.IsRunning);\n            end\n            % avoid full-speed looping\n            pause(0.01);\n        end\n        if interTrialLickCount;\n            fprintf('|: %d inter-trial lick(s).\\n', interTrialLickCount);\n        end\n        out.nInTriLick(iTrial) = interTrialLickCount;\n        out.trialStarts_obs(iTrial) = toc(out.expStartTime);\n        resp = 0;\n        \n        t_trialStart = tic;\n        fprintf('Trial %03d/%03d - %s        ... ', iTrial, nTrials, datestr(now, logDateFormat));\n        \n        %% Experiment - play sound\n        if s1.IsRunning\n            soundToPlay = toneArray{iTrial};\n            player = audioplayer(soundToPlay, config.tone.samplingFreq); %#ok<TNMLP>\n            player.play;\n        else\n            fprintf('early (already licking, lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n                recordedValues);\n            if iTrial < nTrials\n                out.trialStarts_sched(iTrial+1:end) = ...\n                    out.trialStarts_sched(iTrial+1:end) + config.training.timeoutPunish;\n            end\n            out.respTypes(iTrial) = 5;\n            % analyse every 10 trials\n            if ~rem(iTrial, 10) && iTrial ~= nTrials;\n                mtrainerAnalyser_performance(out.respTypes, 0);\n            end\n            continue\n        end\n        breakOut = 0;\n        %% Experiment - wait for response\n        while toc(t_trialStart) < config.training.trialDur\n            if s1.IsRunning\n                % no response detected\n            else\n                % seconds since trial start\n                respTime = toc(t_trialStart);\n                out.respTimes(iTrial) = respTime;\n                % response should be 'minRespTime' after the end of the tone!\n                totToneDur = (config.tone.toneDur + config.tone.ISI) * config.tone.nTones - config.tone.ISI;\n                limitRespTime = totToneDur + config.training.minRespTime;\n                out.respDelay(iTrial) = limitRespTime - respTime;\n                    % early response, count as kind of false alarm\n                if respTime < limitRespTime;\n%                     fprintf('early (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n%                         recordedValues);\n                    fprintf('early (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                        respTime, limitRespTime, limitRespTime - respTime);\n                    if iTrial < nTrials\n                        out.trialStarts_sched(iTrial+1:end) =  out.trialStarts_sched(iTrial+1:end) ...\n                            + config.training.timeoutPunish;\n                    end;\n                    pause(config.training.timeoutPunish * 0.2);\n                    out.respTypes(iTrial) = 5;\n                    breakOut = 1;\n                    break;\n                else\n%                     fprintf('Lick at %s\\n', datestr(clock, logDateFormat))\n                    resp = 1;\n                end;\n                break; % break out of while loop\n            end;\n            pause(0.02); % necessary to allow time for data collection\n        end;\n        if breakOut;\n            out.trialEnds_obs(iTrial) = toc(out.expStartTime);\n            % analyse every 10 trials\n            if ~rem(iTrial, 10) && iTrial ~= nTrials;\n                mtrainerAnalyser_performance(out.respTypes, 0);\n            end;\n            continue;  % continue for loop\n        end;\n        if s1.IsRunning;\n            s1.stop;\n        end;\n        %% Experiment - analyse response\n        out.resps(iTrial) = resp;\n        isOddTrial = stims(iTrial) ~= odds(iTrial);\n        if config.tone.oddProba > 0; % oddball discrimination\n            isTargetStim = isOddTrial && config.tone.goStim;\n        else % frequency discrimination\n            isTargetStim = stims(iTrial) == config.tone.goStim;\n        end;\n        if resp == 1 && isTargetStim;\n            % correct response\n%             fprintf('correct detection (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n%                 recordedValues);\n            fprintf('correct detection (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                respTime, limitRespTime, limitRespTime - respTime);\n            out.respTypes(iTrial) = 1;\n            % give reward\n            giveReward(s2,config.training.rewDur,config.training.rewDelay);\n            % wait so they can collect the reward\n            pause(config.training.rewCollTime);\n            if iTrial < nTrials;\n                out.trialStarts_sched(iTrial+1:end) = ...\n                    out.trialStarts_sched(iTrial+1:end) ...\n                    + (config.training.rewDur + config.training.rewDelay);\n            end;\n        elseif resp == 1 && ~isTargetStim;\n            % false alarm\n%             fprintf('false alarm (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', ...\n%                 recordedValues);\n            fprintf('false alarm (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                respTime, limitRespTime, limitRespTime - respTime);\n            out.respTypes(iTrial) = 3;\n            if iTrial < nTrials;\n                out.trialStarts_sched(iTrial+1:end) = ...\n                    out.trialStarts_sched(iTrial+1:end) + config.training.timeoutPunish;\n            end\n        elseif resp == 0 && isTargetStim;\n            fprintf('miss.\\n');\n            out.respTypes(iTrial) = 4;\n            if iTrial < nTrials\n                out.trialStarts_sched(iTrial+1:end) = ...\n                    out.trialStarts_sched(iTrial+1:end) + config.training.timeoutPunish;\n            end\n         elseif resp == 0 && ~isTargetStim;\n            fprintf('correct rejection.\\n');\n            out.respTypes(iTrial) = 2;\n            \n            % go to next trial\n            \n            % give reward and wait so they can collect it\n%             giveReward(s2,config.training.rewDur,config.training.rewDelay);\n%             pause(config.training.rewCollTime);\n%             if iTrial < nTrials\n%                 out.trialStarts_sched(iTrial+1:end) = out.trialStarts_sched(iTrial+1:end) + ...\n%                     config.training.rewDur + config.training.rewDelay;\n%             end\n\n        end\n        out.trialEnds_obs(iTrial) = toc(out.expStartTime);\n        \n        % analyse every 10 trials\n        if ~rem(iTrial, 10) && iTrial ~= nTrials;\n            mtrainerAnalyser_performance(out.respTypes, 0);\n        end\n    end\n    \n    %% End experiment\n    fprintf('\\nExperiment end: %s\\n', datestr(now, logDateFormat));\n    out.expTotDurTime = toc(out.expStartTime);\n    fprintf('Total time: %02.0f:%02.0f\\n', out.expTotDurTime / 60, mod(out.expTotDurTime, 60));\n    mtrainerAnalyser_performance(out.respTypes, 2, out.savePath, out.nInTriLick);\n\ncatch err\n    fprintf('\\n\\nAn error occured. Last trial: %1.0f. Cleaning up ...\\n\\n', iTrial)\n    rethrow(err);\nend\n\n%% Function - giveReward\nfunction giveReward(s2, rewDur, rewDelay)\n    \n    t_rewardStart = tic;\n    % wait until for start delay\n    while toc(t_rewardStart) < rewDelay; end;\n    t_rewardStart = tic;\n    % open digital out\n    s2.outputSingleScan(0);\n    % wait until for open duration\n    while toc(t_rewardStart) < rewDur; end;\n    % close digital out\n    s2.outputSingleScan(1);\nend\n\nfunction recordAndStop(src, event, lickThresh)\n    recordedValues = stopWhenExceedThreshold(src, event, lickThresh);\nend\n\nfunction closeDiaryAndSaveOutput(savePath, lh, s1, s2)\n    fprintf('\\nClosing diary... ');\n    diary off;\n    fprintf('done.\\n');\n    \n    if (exist('out', 'var'));\n        matFileName = sprintf('%s_out.mat', savePath);\n        fprintf('Saving output as %s... ', matFileName);\n        save(matFileName, 'out');\n        fprintf('done.\\n');\n    else\n        fprintf('/!\\\\ Output not saved\\n');\n    end;\n    \n    fprintf('Closing channels... ');\n    if exist('lh', 'var'); delete(lh); end;\n    if exist('s1', 'var'); delete(s1); end;\n    if exist('s2', 'var'); delete(s2); end;\n    fprintf('done.\\n');\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainer_oldversion_legacyInterface.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainer_oldversion_legacyInterface.m", "size": 8514, "source_encoding": "utf_8", "md5": "745363c7c815304e2e4265dd8ef8ab38", "text": "function mtrainer(varargin)\n% main function for mtrainer experiment suite\n% all times are in s!\n\n% written by Henry Luetcke (hluetck@gmail.com)\n% 2012-11-08\n\nif ~nargin\n    % gui select mat-file with config structure\n    [FileName,PathName] = uigetfile('*.mat','Select configuration file');\n    S = load(fullfile(PathName,FileName));\n    fields = fieldnames(S);\n    config = S.(fields{1});\nelse\n    config = varargin{1};\nend\n\ntry\n    parseConfig(config);\ncatch\n    rethrow(lasterror)\n    error('Failed to parse config struture')\nend\n\n%% Setup stimuli and trials\n% set up stimVector\nstimVector = [];\nfor n = 1:length(config.tone.fVector)\n    stim = repmat(n,1,round(config.tone.stimProbabilities(n)*config.tone.trials));\n    stimVector = [stimVector, stim];\nend\nstimVector = stimVector(randperm(numel(stimVector)));\n\n% set up tone array\ntoneArray = MakePureToneArray(config.tone.fVector,stimVector,...\n    config.tone.toneDuration,config.tone.sf);\n\n%% Start hardware\n% analog input\ndaqreset\nai=analoginput(config.hardware.adaptorID,config.hardware.analogIn_Device);\naddchannel(ai, config.hardware.analogIn_Channel);\nset(ai,'InputType','SingleEnded')\n% sample rate and recording duration\nai.SampleRate = config.hardware.aiSampleRate;\n\n% digital IO\ndio=digitalio(config.hardware.adaptorID,config.hardware.digitalIO_Device);\ndigitalOut = addline(dio,config.hardware.digitalOut_Channel,'out');\n\n% hardware 'warm-up'\n% analog in\nai.SamplesPerTrigger = 100;\nstart(ai);\n% get data\ndata = getdata(ai);\nfigure, plot(data)\nlickThresh = 10.*std(data) + max(data);\nfprintf('Lick threshold: %1.2f\\n',lickThresh)\nstop(ai), clear data\n% digital out\nputvalue(digitalOut,1) % 1 is light off, 0 is light on (strangely enough)\nfor n = 1:10; putvalue(digitalOut,0), putvalue(digitalOut,1), pause(0.1); end\n\n% some changes to ai\nai.SamplesPerTrigger = Inf;\n% % timer function\n% trial = 1; t_trialStart = clock;\n% ai.TimerFcn = {@daqTimerFunction};\n% ai.TimerPeriod = 0.01;\n% set(ai, 'TriggerRepeat', Inf);\n\nlogfile = sprintf('mtrainerLogfile_%s.txt',datestr(clock,30));\ndiary(logfile)\n\ndisp('Finished set up and hardware tests. Press any key to start!')\npause\n\n%% Start experiment\nfprintf('Experiment start: %s\\n\\n',datestr(now,'dd-mm-yyyy HH:MM:SS.FFF'))\ntry\n    totalTrialDur = config.training.trialDuration(1) + config.training.startDelay;\n    samplesPerTrial = round((totalTrialDur-0.1).*ai.SampleRate); % a bit less to allow for processing time\n    maxTrials = numel(stimVector);\n    maxTrials = 50; % for testing\n    responseCount = zeros(1,4); % correct detect, correct reject, false alarm, miss\n    % compute relative start times for each trial\n    trialStartVector_scheduled = [0:totalTrialDur:(maxTrials-1)*totalTrialDur];\n    trialStartVector_observed = zeros(1,maxTrials);\n    trialEndVector_observed = zeros(1,maxTrials);\n    daqStartTime = cell(1,maxTrials);\n    start(ai);\n    t_expStart = tic;\n    for trial = 1:maxTrials\n        while toc(t_expStart) < trialStartVector_scheduled(trial)\n            % do nothing\n        end\n        trialStartVector_observed(trial) = toc(t_expStart);\n        t_trialStart = tic;\n        fprintf('Trial %1.0f - %s\\n',trial, datestr(now, 'HH:MM:SS.FFF'))\n        \n        % play tone\n        sound(toneArray{trial},config.tone.sf)\n        response = 0;\n        % listen for response\n        while toc(t_trialStart) < config.training.trialDuration(1)\n            size = min([ai.SamplesAvailable 10]);\n            data = peekdata(ai, size);\n            if max(data) > 0.1 % lick detected\n                fprintf('Lick at %s (max = %1.2f)\\n',datestr(clock,'HH:MM:SS.FFF'),max(data))\n                response = 1;\n                if stimVector(trial) == config.tone.goStim\n                    % correct response\n                    fprintf('Correct detection\\n')\n                    responseCount(1) = responseCount(1) + 1;\n                    % give reward\n                    giveReward(digitalOut,config.training.rewardDuration,config.training.rewardDelay);\n                    if trial < maxTrials\n                        trialStartVector_scheduled(trial+1:end) = ...\n                            trialStartVector_scheduled(trial+1:end) + (config.training.rewardDuration+config.training.rewardDelay);\n                    end\n                else\n                    % false alarm\n                    fprintf('False alarm\\n')\n                    responseCount(3) = responseCount(3) + 1;\n                    if trial < maxTrials\n                        trialStartVector_scheduled(trial+1:end) = ...\n                            trialStartVector_scheduled(trial+1:end) + config.training.timeoutPunish;\n                    end\n                end\n                break\n            end\n        end\n        if ~response && stimVector(trial) == config.tone.goStim\n            fprintf('Miss\\n')\n            responseCount(4) = responseCount(4) + 1;\n            if trial < maxTrials\n                trialStartVector_scheduled(trial+1:end) = ...\n                    trialStartVector_scheduled(trial+1:end) + config.training.timeoutPunish;\n            end\n        elseif ~response && stimVector(trial) ~= config.tone.goStim\n            fprintf('Correct rejection\\n')\n            responseCount(2) = responseCount(2) + 1;\n            giveReward(digitalOut,config.training.rewardDuration,config.training.rewardDelay);\n            if trial < maxTrials\n                trialStartVector_scheduled(trial+1:end) = ...\n                    trialStartVector_scheduled(trial+1:end) + (config.training.rewardDuration+config.training.rewardDelay);\n            end\n        end\n        \n        trialEndVector_observed(trial) = toc(t_expStart);\n    end\n    \n    fprintf('\\nExperiment end: %s\\n\\n',datestr(now,'dd-mm-yyyy HH:MM:SS.FFF'))\n    \n    [lickData,lickDataT,daqStartTime] = ...\n        getdata(ai,ai.SamplesAcquired);\n    stop(ai); delete(ai); delete(dio)\n    \n    save('out.mat','lickData','lickDataT','daqStartTime',...\n        'trialStartVector_scheduled','trialStartVector_observed',...\n        'trialEndVector_observed')\n    figure('Name','Trial start difference')\n    plot((trialStartVector_observed-trialStartVector_scheduled).*1000,'k')\n    xlabel('Trial'), ylabel('actual - scheduled / ms')\n    doPerformanceAnalysis(responseCount);\n    diary off\ncatch\n    disp('An error occured. Cleaning up ...')\n    stop(ai), delete(ai), delete(dio), diary off\n    rethrow(lasterror)\nend\n\n\n%% Function - giveReward\nfunction giveReward(digitalOut,rewardDuration,rewardDelay)\nt_rewardStart = tic;\nwhile toc(t_rewardStart) < rewardDelay\n    % do nothing\nend\nt_rewardStart = tic;\n% open digital out\nputvalue(digitalOut,0)\nwhile toc(t_rewardStart) < rewardDuration\n    % do nothing\nend\nputvalue(digitalOut,1)\n\n%% Function - doPerformanceAnalysis\nfunction doPerformanceAnalysis(responseCount)\n% responseCount columns: correct detect, correct reject, false alarm, miss\ntrials = sum(responseCount);\nT = responseCount(1)+responseCount(4); % targets\nN = responseCount(2)+responseCount(3); % non-targets\nfprintf('\\n\\nPerformance:\\n')\nfprintf('Trials: %1.0f\\n',trials)\nfprintf('Correct detections: %1.0f (%1.2f%%)\\n',responseCount(1),responseCount(1)./T.*100)\nfprintf('Correct rejections: %1.0f (%1.2f%%)\\n',responseCount(2),responseCount(2)./N.*100)\nfprintf('False alarms: %1.0f (%1.2f%%)\\n',responseCount(3),responseCount(3)./N.*100)\nfprintf('Miss: %1.0f (%1.2f%%)\\n',responseCount(4),responseCount(4)./T.*100)\nfprintf('Total correct: %1.0f (%1.2f%%)\\n',sum(responseCount(1:2)),sum(responseCount(1:2))./trials.*100)\nfprintf('Total errors: %1.0f (%1.2f%%)\\n',sum(responseCount(3:4)),sum(responseCount(3:4))./trials.*100)\ndPrim = dprime(responseCount(1)./trials,responseCount(3)./trials,T,N);\nfprintf('d'':%1.3f\\n',dPrim)\n\n\n%% Function - parseConfig\nfunction parseConfig(config)\n% list of required hardware fields:\nrequiredFields = {...\n    'adaptorID'\n    'analogIn_Device'\n    'analogIn_Channel'\n    'aiSampleRate'\n    'digitalIO_Device'\n    'digitalOut_Channel'\n    };\naccessFields(config.hardware,requiredFields);\n\n% list of required tone fields:\nrequiredFields = {...\n    'sf'\n    'fVector'\n    'stimProbabilities'\n    'trials'\n    'goStim'\n    'toneDuration'\n    };\naccessFields(config.tone,requiredFields);\n\n% list of required training fields:\nrequiredFields = {...\n    'responseTime'\n    'trialDuration'\n    'startDelay'\n    'rewardDuration'\n    'rewardDelay'\n    'timeoutPunish'\n    };\naccessFields(config.training,requiredFields);\n\nfunction accessFields(S,fields)\nfor n = 1:numel(fields)\n    S.(fields{n});\nend\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "HelmchenLabSoftware/OCIA-master", "name": "mtrainer_GUI.m", "ext": ".m", "path": "OCIA-master/behavior/mtrainer/mtrainer_GUI.m", "size": 34409, "source_encoding": "utf_8", "md5": "3d5a15b6438819e18ebd58da906241e8", "text": "function varargout = mtrainer_GUI(varargin)\n\n% TODO:\n%   - stopping the experiment: trial no 100 ? plot ? set buttons to stop ?\n%   - reward anyway system\n%   - forbid disconnecting HW or changing mouseID while experiment is running\n%   - piezoThresh calibrator with setter\n%   - online lick ploting\n%   - online performance\n%   - logging into GUI window\n%   - config setter\n\n\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @mtrainer_GUI_OpeningFcn, ...\n                   'gui_OutputFcn',  @mtrainer_GUI_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1});\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout;\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n% --- Executes just before mtrainer_GUI is made visible.\nfunction mtrainer_GUI_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to mtrainer_GUI (see VARARGIN)\n\n% Choose default command line output for mtrainer_GUI\nhandles.output = hObject;\n% Update handles structure\nguidata(hObject, handles);\ninitialize_gui(hObject, handles, false);\nset(hObject, 'DeleteFcn', @onWindowClose);\nset(hObject, 'Position', [200 800 800 620]);\n% --- Outputs from this function are returned to the command line.\nfunction varargout = mtrainer_GUI_OutputFcn(hObject, eventdata, handles) %#ok<*INUSL>\n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n% --------------------------------------------------------------------\nfunction initialize_gui(fig_handle, handles, isreset) %#ok<*INUSD>\n% If the metricdata field is present and the start flag is false, it means\n% we are we are just re-initializing a GUI by calling it from the cmd line\n% while it is up. So, bail out as we dont want to start the data.\n\ninitMain(handles);\n% updateGUI();\n% --- Executes on button press in connectHW.\nfunction connectHW_Callback(hObject, eventdata, handles)\n% hObject    handle to connectHWButton_Callback (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif isfield(main.hw, 'connected') && main.hw.connected;\n    disconnectHW();\nelse\n    connectHW();\nend\n\n% updateGUI();\n% --- Executes on button press in start.\nfunction start_Callback(hObject, eventdata, handles)\n% hObject    handle to start (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif main.isRunning;\n    warning('mtrainerGUI:CannotStartAlreadyRunning', ...\n        'Cannot start because experiment is already running and not paused.');\n    updateGUI();\n    return;\nelse\n    startExperiment();\nend\n    \n% updateGUI();\ntry\n    mainLoop();\ncatch e;\n    closeDiaryAndSaveOutput();\n    throw(e);\nend;\n% --- Executes on button press in pause.\nfunction pause_Callback(hObject, eventdata, handles)\n% hObject    handle to pause (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif main.isRunning;\n    if main.isPaused;\n        main.isPaused = false;\n    else\n        main.isPaused = true;\n    end\nelse\n    warning('mtrainerGUI:CannotPauseNotRunning', ...\n        'Cannot pause because experiment is not running.');\nend\n\n% updateGUI();\n\n% --- Executes on button press in stop.\nfunction stop_Callback(hObject, eventdata, handles) %#ok<*DEFNU>\n% hObject    handle to stop (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\nif main.isRunning;\n    main.isRunning = false;\n    closeDiaryAndSaveOutput();\nelse\n    warning('mtrainerGUI:CannotStopNotRunning', 'Cannot stop because experiment is not running.');\nend\n\n% updateGUI();\n% --- Executes on button press in reward.\nfunction reward_Callback(hObject, eventdata, handles)\n% hObject    handle to reward (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\ngiveReward(main.rewardDur, 0);\n% --- Executes on slider movement.\nfunction rewardDurSetter_Callback(hObject, eventdata, handles)\n% hObject    handle to rewardDurSetter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\n\nglobal main;\nmain.rewardDur = get(hObject,'Value');\nset(handles.rewardDur, 'String', sprintf('%04.2f', main.rewardDur));\n% --- Executes on slider movement.\nfunction piezoThreshSetter_Callback(hObject, eventdata, handles)\n% hObject    handle to piezoThreshSetter (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'Value') returns position of slider\n%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider\n\nglobal main;\nmain.piezoThresh = get(hObject, 'Value');\nset(handles.piezoThresh, 'String', sprintf('%04.2f', main.piezoThresh));\n% --- Executes when selected cell(s) is changed in config.\nfunction config_CellSelectionCallback(hObject, eventdata, handles)\n% hObject    handle to config (see GCBO)\n% eventdata  structure with the following fields (see UITABLE)\n%\tIndices: row and column indices of the cell(s) currently selecteds\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal main;\no('#config_CellSelectionCallback: selection changed: row %d, col %d.', eventdata.Indices, 3, main.dbgLvl);\nrow = eventdata.Indices(1);\nconfigCell = get(main.handles.config, 'Data');\nmain.mouseId = configCell{row, 1};\nmain.taskType = configCell{row, 2};\nmain.phase = configCell{row, 3};\nmain.configLoaded = false;\no('#config_CellSelectionCallback: mouseId: %s, taskType: %s, phase: %s.', ...\n    main.mouseId, main.taskType, main.phase, 3, main.dbgLvl);\n% updateGUI();\n% --- Executes on button press in loadConfig.\nfunction loadConfig_Callback(hObject, eventdata, handles)\n% hObject    handle to loadConfig (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nloadConfig();\n%% #initMain\nfunction initMain(handles)\n\nglobal main;\nmain = struct();\nmain.dbgLvl = 3;\nmain.debugMode = 0;\nmain.basePath = 'C:/Users/laurenczy/Documents/programming/Work/PhD/matlab/behavior/mtrainer/';\n% main.basePath = 'D:/Users/BaL/programming/Work/PhD/matlab/behavior/mtrainer/';\nconfigsPath = sprintf('%sconfigs.mat', main.basePath);\nif ~exist(configsPath, 'file');\n    error('mtrainerGUI:initMain:ConfigsFileNotFound', ...\n        'Cannot find the configs at \"%s\" (file not found).', configsPath);\nend;\nconfigsMat = load(configsPath);\nmain.configs = configsMat.configs;\n[~, computerName] = system('hostname');\nroomMatch = regexp(computerName, '^HIFOWS(?<room>H\\d+)\\-\\d+\\s+$', 'names');\nif main.debugMode;\n    warning('mtrainer_GUI:initMain:DebugMode', 'Debug mode is ON.');\n    roomMatch = struct('room', 'H30');\n    o('#initMain: debug mode => room tag enforced to: \"%s\".', roomMatch.room, 1, main.dbgLvl);\nend;\nif isempty(roomMatch);\n    error('mtrainerGUI:initMain:HardwareLoad:NoRoomTagFound', ...\n        'Cannot load hardware because there''s no room tag in the computer''s name.');\nelseif ~isfield(main.configs.hardware, roomMatch.room);\n    error('mtrainerGUI:initMain:HardwareLoad:UnknownRoom', ...\n        'Cannot load hardware because room \"%s\" is unknown.', roomMatch.room);\nend;\nmain.hw = struct();\nmain.hw.conf = main.configs.hardware.(roomMatch.room);\nmain.hw.connected = false;\nmain.hw.analogIns = {'piezo', 'micr', 'yscan'};\nmain.hw.digitalOuts = {'valve'};\nmain.hw.recordDur = 8.1;\nmain.updateRate = 0.1; % 100ms update rate\nmain.piezoThresh = 0.1;\nmain.isRunning = false;\nmain.isPaused = false;\nmain.isClosed = false;\nmain.configLoaded = false;\nmain.iTrial = 0;\nmain.rewardDur = str2double(get(handles.rewardDur,'String'));\nmain.handles = handles;\nstateData = get(main.handles.state, 'Data');\nstateData(:, 1) = getTrialInfo(0);\nstateData(:, 2) = getTrialInfo(0);\nstateData(:, 3) = getTrialInfo(0);\nset(main.handles.state, 'Data', stateData);\nset(main.handles.config, 'Data', main.configs.animals);\nmain.GUIUpdateTimer = timer('TimerFcn', @(~, ~)updateGUI(), 'ExecutionMode', 'fixedRate', ...\n    'Period', main.updateRate * 1, 'StopFcn', @(~, ~)o('GUIUpdateTimer stopped.', 1, main.dbgLvl), ...\n    'ErrorFcn', @(~, ~)o('GUIUpdateTimer stopped with error.', 1, main.dbgLvl));\nstart(main.GUIUpdateTimer);\nmain.mouseId = '';\no('Mouse trainer v1.0 initiated.', 1, main.dbgLvl);\nfunction onWindowClose(src, eventData)\n\nglobal main;\nstop(main.GUIUpdateTimer);\ndelete(main.GUIUpdateTimer);\nmain.isClosed = true;\n%% #connectHW\nfunction connectHW()\n\nglobal main;\no('Connecting hardware ...', 1, main.dbgLvl);\nset(main.handles.connectHW, 'BackgroundColor', 'yellow');\n        \nmain.hw.sampleRate = 0;\nnAnIn = numel(main.hw.analogIns);\n    \nif nAnIn;\n    main.analogInData = struct();\n    main.hw.anIn = daq.createSession(main.hw.conf.adaptorID);\nend;\nfor iAnIn = 1 : nAnIn;\n    \n    anInName = main.hw.analogIns{iAnIn};\n    \n    if ~main.debugMode;\n        \n        main.hw.sampleRate = main.hw.sampleRate + main.hw.conf.(anInName).sampleRate;\n        main.hw.anIn.addAnalogInputChannel(main.hw.conf.(anInName).deviceName, ...\n            main.hw.conf.(anInName).channel, 'Voltage');\n        main.hw.anIn.Channels(iAnIn).Name = anInName;\n        main.hw.anIn.Channels(iAnIn).InputType = main.hw.conf.(anInName).inputType;\n        main.hw.anIn.Channels(iAnIn).Range = main.hw.conf.(anInName).range;\n        main.analogInData.(anInName) = [];\n    else\n        o('  \"Connecting\" fake analog input %d/%d (%s).', iAnIn, nAnIn, anInName, 1, main.dbgLvl);\n        main.hw.(anInName) = struct();\n%         main.hw.([anInName 'Listener']) = struct();\n        main.hw.(anInName).stop = 1;\n        main.hw.(anInName).startBackground = 1;\n    %     main.hw.(analogInName).IsRunning = @()(round(rand + 0.4));\n    %     main.hw.(analogInName).IsRunning = @()(round(rand - 0.4));\n        main.hw.(anInName).IsRunning = 1;\n        pause(1);\n    end;\n    o('  Analog input %d/%d (%s): OK.', iAnIn, nAnIn, anInName, 1, main.dbgLvl);\nend;\nif nAnIn;\n    main.hw.([anInName 'Listener']) = main.hw.anIn.addlistener('DataAvailable', @(src, event) storeData(src, event));\n    main.hw.anIn.IsNotifyWhenDataAvailableExceedsAuto = true;\n    main.hw.anIn.IsContinuous = true;\n    main.hw.sampleRate = main.hw.sampleRate / nAnIn;\n    main.hw.anIn.Rate = main.hw.sampleRate;\n    main.hw.anIn.NotifyWhenDataAvailableExceeds = 200;\n%     main.hw.anIn.Rate = 3000;\n    main.hw.anIn.prepare();\nend;\nfor iDigOut = 1 : numel(main.hw.digitalOuts);\n    digOutName = main.hw.digitalOuts{iDigOut};\n    if ~main.debugMode;\n        main.hw.(digOutName) = daq.createSession(main.hw.conf.adaptorID);\n        main.hw.(digOutName).addDigitalChannel(main.hw.conf.(digOutName).deviceName, ...\n            main.hw.conf.(digOutName).portLine, 'OutputOnly');\n        main.hw.(digOutName).outputSingleScan(0); % make sure valve is closed\n    else\n        o('  \"Connecting\" fake digital output %d (%s).', iDigOut, digOutName, 1, main.dbgLvl);\n        main.hw.valve = struct();\n        pause(1);\n    end;\n    o('  Digital output %d (%s): OK.', iDigOut, digOutName, 1, main.dbgLvl);\nend;\nmain.hw.connected = true;\no('Hardware connected.', 1, main.dbgLvl);\nif ~strcmp(main.GUIUpdateTimer.Running, 'on');\n    start(main.GUIUpdateTimer);\nend;\nmain.hw.anIn.startBackground();\n%% #disconnectHW\nfunction disconnectHW()\n\nglobal main;\no('Disconnecting hardware ...', 1, main.dbgLvl);\nif ~main.debugMode;\n    \n    main.hw.anIn.stop();\n    \n%     % analog inputs\n%     for iAnIn = 1 : numel(main.hw.analogIns);\n%         anInName = main.hw.analogIns{iAnIn};\n%         if isfield(main.hw, ([anInName 'Listener']));\n%             main.hw = rmfield(main.hw, [anInName 'Listener']);\n%         end;\n%         if isfield(main.hw, anInName);\n%             main.hw.(anInName).stop();\n%             main.hw = rmfield(main.hw, anInName);\n%         end;\n%     end;\n%     % digital outputs\n%     for iDigOut = 1 : numel(main.hw.digitalOuts);\n%         digOutName = main.hw.digitalOuts{iDigOut};\n%         if isfield(main.hw, digOutName);\n% %             main.hw.(digOutName).stop();\n%             main.hw = rmfield(main.hw, digOutName);\n%         end;\n%     end;\n    daqreset();\nend;\nmain.hw.connected = false;\no('Hardware disconnected.', 1, main.dbgLvl);\n%% #giveReward\nfunction giveReward(rewDur, rewDelay)\n    \nglobal main;\nif main.hw.connected && isfield(main.hw, 'valve');\n    if ~main.debugMode;\n        t_rewardStart = tic;\n        % wait until for start delay\n        while toc(t_rewardStart) < rewDelay; end;\n        t_rewardStart = tic;\n        % open digital out\n%         main.hw.valve.outputSingleScan(0);\n        main.hw.valve.outputSingleScan(1);\n        % wait until for open duration\n        while toc(t_rewardStart) < rewDur; end;\n        % close digital out\n%         main.hw.valve.outputSingleScan(1);\n        main.hw.valve.outputSingleScan(0);\n    end;\n    o('Rewarding for %.2f sec with a delay of %.2f.', rewDur, rewDelay, 3, main.dbgLvl);\nelse\n    warning('mtrainerGUI:NoRewardHardwareDisconnected', 'Cannot give reward because hardware is disconnected.');\nend\n\n%% #updateGUI\nfunction updateGUI()\n\nglobal main;\n% o('#updateGUI()', 1, main.dbgLvl);\nif main.isClosed; return; end;\nstateData = get(main.handles.state, 'Data');\nhandles = main.handles;\nif main.hw.connected;\n    set(handles.connectHW, 'BackgroundColor', 'green');\n    set(handles.connectHW, 'Value', 1);\nelse\n    set(handles.connectHW, 'BackgroundColor', 'red');\n    set(handles.connectHW, 'Value', 0);\nend;\nif main.isRunning;\n    set(handles.start, 'Value', 1);\n    set(handles.start, 'BackgroundColor', 'green');\n    if main.isPaused;\n        set(handles.pause, 'Value', 1);\n        set(handles.pause, 'BackgroundColor', [1 1 0]);\n    else\n        set(handles.pause, 'Value', 0);\n        set(handles.pause, 'BackgroundColor', 'green');\n    end;\n    \n%     stateData(:, 1) = stateData(:, 2);\n    stateData(:, 1) = getTrialInfo(main.iTrial - 1);\n    stateData(:, 2) = getTrialInfo(main.iTrial);\n    if main.iTrial < main.config.training.nTrials;\n        stateData(:, 3) = getTrialInfo(main.iTrial + 1);\n    else\n        stateData(:, 3) = getTrialInfo(0);\n    end;\n    \nelse\n    \n    set(handles.start, 'Value', 0);\n    set(handles.start, 'BackgroundColor', 'red');\n    set(handles.pause, 'Value', 0);\n    set(handles.pause, 'BackgroundColor', 'red');\n        \n    if main.configLoaded && main.iTrial == 0;\n        stateData(:, 3) = getTrialInfo(1);\n    else\n        stateData(:, 3) = getTrialInfo(0);\n    end;\nend;\ndelete(get(handles.piezoAxes, 'Children')); % delete previous plot\nif isfield(main, 'analogInData');\n    hold(handles.piezoAxes, 'on');\n    if isfield(main.analogInData, 'yscan');\n        nSamples = size(main.analogInData.yscan, 1);\n        if nSamples;\n            t = (1 : nSamples) / main.hw.sampleRate;\n            plot(handles.piezoAxes, t, main.analogInData.yscan * 3, 'b');\n        end;\n    end;\n    if isfield(main.analogInData, 'micr');\n        nSamples = size(main.analogInData.micr, 1);\n        if nSamples;\n            t = (1 : nSamples) / main.hw.sampleRate;\n            plot(handles.piezoAxes, t, main.analogInData.micr * 2, 'g');\n        end;\n    end;\n    if isfield(main.analogInData, 'piezo');\n        nSamples = size(main.analogInData.piezo, 1);\n        if nSamples;\n            t = (1 : nSamples) / main.hw.sampleRate;\n            plot(handles.piezoAxes, t, main.analogInData.piezo * 10, 'r');\n            if isfield(main, 'piezoThresh');\n                plot(handles.piezoAxes, [0, t(end)], [main.piezoThresh, main.piezoThresh] * 10, 'r:');\n            end;\n        end;\n    end;\nend;\nxlim(handles.piezoAxes, [0 main.hw.recordDur]);\nylim(handles.piezoAxes, [-3, 3]);\nset(main.handles.state, 'Data', stateData);\nset(main.handles.configLoaded, 'Value', logical(main.configLoaded));\n%% #getTrialInfo\nfunction trialInfo = getTrialInfo(iTrial)\n\nglobal main;\nif iTrial;\n    freq = sprintf('%4.2f', main.config.tone.freqs(main.stims(iTrial)) / 1000);\n    if isfield(main.config.tone, 'oddProba') && main.config.tone.oddProba > 0; % oddball discrimination\n        isTarget = double(main.stims(iTrial) ~= main.odds(iTrial) && main.config.tone.goStim);\n    else % frequency discrimination\n        isTarget = double(main.stims(iTrial) == main.config.tone.goStim);\n    end;\n    if isfield(main, 'resps') && ~isnan(main.resps(iTrial));\n        resp = main.resps(iTrial);\n        respTime = main.respTimes(iTrial);\n        if isnan(respTime); respTime = '-'; end;\n        corr = double((isTarget && resp) || (~isTarget && ~resp));\n        ITL = main.nInTriLick(iTrial);\n    else\n        resp = '-';\n        respTime = '-';\n        corr = '-';\n        ITL = '-';\n    end;\n    trialInfo = {iTrial, freq, isTarget, resp, respTime, corr, ITL};\n    \nelse\n    trialInfo = {'-', '-', '-', '-', '-', '-', '-'};\nend;\n%% #loadConfig\nfunction loadConfig()\n\nglobal main;\no('Loading configuration ...', 2, main.dbgLvl);\nif isempty(main.mouseId);\n    warning('loadConfig:NoMouseIdSelected', 'Cannot load config because no mouseId is selected.');\n    return;\nend;\n% main.doEmptyWater = 0;\n% main.doEmptyWater = 1;\n% if > 0, sets the number of seconds to wait before a new run of mtrainer\n% main.automode = 5;\n\nmain.iTrial = 0;\n% main.piezoThresh = NaN;\nmain.config = main.configs.behavior.(main.taskType).(main.phase);\n%% Setup - stimuli and trials\no('  loadConfig#: setup stimuli and trials... ', 3, main.dbgLvl);\n% set up stimulus vector\nstims = [];\nfor iFreq = 1 : numel(main.config.tone.freqs);\n    stimForFreq = repmat(iFreq, 1, ...\n        round(main.config.tone.stimProba(iFreq) * main.config.training.nTrials));\n    stims = [stims, stimForFreq]; %#ok<AGROW>\nend\n\n% stim vector should not be randomly permuted\nstims = stims(randperm(numel(stims)));\nstims = stims(1 : main.config.training.nTrials);\nclear stim;\nswitch(main.taskType);\n    case {'freqDiscr', 'oddDiscr'};\n        % set up oddball vector by altering stims with a certain probability\n        odds = stims;\n        oddPos = zeros(size(odds));\n        uniqueStims = unique(stims);\n        isOdd = rand(1, numel(stims)) < main.config.tone.oddProba;\n        uniqueOddPos = main.config.tone.nTones - 1 : main.config.tone.nTones; % last 2 tones can be odd\n        for iStim = 1 : numel(isOdd);\n            if isOdd(iStim);\n                otherStims = uniqueStims(uniqueStims ~= stims(iStim));\n                otherStims = otherStims(randperm(numel(otherStims)));\n                mixedOddPos = uniqueOddPos(randperm(numel(uniqueOddPos)));\n                odds(iStim) = otherStims(1);\n                oddPos(iStim) = mixedOddPos(1);\n            end;\n        end;\n        % set up tone array\n        main.toneArray = MakePureMultiToneOddballArray(main.config.tone.freqs, stims, odds, oddPos, ...\n            main.config.tone.nTones, main.config.tone.stimDur, main.config.tone.ISI, ...\n            main.config.tone.samplingFreq);\n        \n    case 'cotDiscr';\n        \n        odds = zeros(size(stims));\n        oddPos = zeros(size(stims));\n        isOdd = zeros(size(stims));\n        \n        atten = 0.5;\n        main.toneArray = MakeCloudOfTonesSound(main.config.tone.uniqueFreqs, main.config.tone.freqIndexes, ...\n            main.config.tone.cloudDispersion, stims, main.config.tone.toneDur, main.config.tone.toneISI, ...\n            main.config.tone.stimDur, atten, main.config.tone.samplingFreq, 0, 0);\n        \n        \nend;\nmain.stims = stims;\nmain.odds = odds;\nmain.oddPos = oddPos;\nmain.isOdd = isOdd;\nmain.configLoaded = true;\nupdateGUI();\no('  loadConfig#: setup stimuli and trials done. ', 2, main.dbgLvl);\no('Loading configuration done.', 2, main.dbgLvl);\n%% #startExperiment\nfunction startExperiment()\n\nglobal main;\nif ~main.configLoaded;\n    warning('startExperiment:configNotLoaded', 'Cannot start experiment because config is not loaded.');\n    return;\nend;\nif isempty(main.mouseId);\n    warning('startExperiment:NoMouseId', 'Cannot start experiment because there is no mouseId.');\n    return;\nend;\nif ~main.hw.connected;\n    warning('startExperiment:HardwareDisconnected', ...\n        'Cannot start experiment because hardware is disconnected.');\n    return;\nend;\no('startExperiment#: initializing experiment ... ', 2, main.dbgLvl);\nmain.isPaused = false;\nmain.isRunning = true;\nmain.iTrial = 1;\n    \nmain.saveTime = datestr(now(), 'yyyy_mm_dd__HH_MM_SS');\n% saveFolder = sprintf('D:/Balazs/2013/mtrainerTests/%s/', ...\n% saveFolder = sprintf('C:/Users/laurenczy.HIFO/Documents/LocalData/2013/mtrainerTests/%s/', ...\nsaveFolder = sprintf('C:/Users/laurenczy/mtrainerTests/%s/', main.mouseId);\n% saveFolder = sprintf('D:/Users/BaL/PhD/mtrainerTests/%s/', main.mouseId);\nmain.savePath = sprintf('%s%s__%s', saveFolder, main.mouseId, main.saveTime);\nmkdir(saveFolder);\nlogfile = sprintf('%s_log.txt', main.savePath);\nmain.logDateFormat = 'yyyy-mm-dd HH:MM:SS.FFF';\n% % create an 'onCleanup' function to handle interruptions by CTRL+C\n% c = onCleanup(@()closeDiaryAndSaveOutput(main.savePath, main.hw.piezoListener, ...\n%     main.hw.piezo, main.hw.valve));\no('  startExperiment#: logFile initialized. Starting diary...', 2, main.dbgLvl);\ndiary(logfile);\n%% Start experiment\no('Experiment start: %s\\n\\n', datestr(now(), main.logDateFormat), 1, main.dbgLvl);\n    \n\n%% Setup - saving settings\nglobal recordedValues;\nrecordedValues = 0;\no('config: %s phase %s (%dF%dT)', main.taskType, main.phase, numel(main.config.tone.freqs), ...\n    main.config.tone.nTones, 0, main.dbgLvl);\no('animal: %s', main.mouseId, 0, main.dbgLvl);\no('savePath: %s', main.savePath, 0, main.dbgLvl);\no('piezoThresh: %1.2f, ISI: %1.2f', main.piezoThresh, main.config.tone.ISI, 1, main.dbgLvl);\no('rewardCollectionTime: %1.2f, InTriLickTimerDur: %1.2f', main.config.training.rewCollTime, ...\n    main.config.training.InTriLickTimerDur, 1, main.dbgLvl);\nnTrials = main.config.training.nTrials;\nmain.trialStartDelays = zeros(1, nTrials);\nmain.trialStartTimes = zeros(1, nTrials);\nmain.trialEndTimes = zeros(1, nTrials);\nmain.respTimes = nan(1, nTrials);\nmain.nInTriLick = nan(1, nTrials);\nmain.resps = nan(1, nTrials);\nmain.respDelays = nan(1, nTrials);\n% for each trial: 1 = correct detect, 2 = correct reject, 3 = false alarm, 4 = miss, 5 = early\nmain.respTypes = nan(1, nTrials);\nmain.expStartTime = tic;\nmain.isPunish = 0;\n%% #mainLoop\nfunction mainLoop()\n\nglobal main;\ndisplayedPause = 0;\nwhile main.isRunning;\n    \n    o('mainLoop#: iTrial = %d.', main.iTrial, 4, main.dbgLvl);\n    \n    if ~main.isPaused;\n        if displayedPause; o(' --- RESUMED --- ', 1, main.dbgLvl); displayedPause = 0; end;\n        o('mainLoop#: running trial %d.', main.iTrial, 3, main.dbgLvl);\n        runTrial();\n        if main.isRunning;\n%             o('Trial %d: pause ended, updating trial number.', main.iTrial, 2, main.dbgLvl);\n            if main.iTrial == main.config.training.nTrials;\n                main.isRunning = false;\n                %% End experiment\n                o('\\nExperiment end: %s', datestr(now(), main.logDateFormat), 1, main.dbgLvl);\n                main.expTotDurTime = toc(main.expStartTime);\n                o('Total time: %02.0f:%02.0f', ...\n                    main.expTotDurTime / 60, mod(main.expTotDurTime, 60), main.dbgLvl);\n                mtrainerAnalyser_performance(main.respTypes, 2, main.savePath, main.nInTriLick);\n                closeDiaryAndSaveOutput();\n            else\n                main.iTrial = main.iTrial + 1;\n                o('Trial %d: updated trial number: %d.', main.iTrial - 1, main.iTrial, 2, main.dbgLvl);\n            end;\n        else\n            o('Trial %d: not running anymore, no update of trial number.', main.iTrial, 2, main.dbgLvl);\n        end;\n        o('mainLoop#: running trial %d done.', main.iTrial, 3, main.dbgLvl);\n%         updateGUI();\n    else\n        if ~displayedPause; o(' --- PAUSED --- ', 1, main.dbgLvl); displayedPause = 1; end;\n        o('mainLoop#: waiting for %f sec (trial %d).', main.updateRate, main.iTrial, 3, main.dbgLvl);\n        pause(main.updateRate);\n    end;\n    \nend;\n%% #runTrial\nfunction runTrial()\n\nglobal main;\nnTrials = main.config.training.nTrials;\niTrial = main.iTrial;\n% listen for response\n% main.hw.anIn.startBackground();\npause(0.01)\ninterTrialLickCount = 0;\nmain.trialStartTimes(iTrial) = tic;\n% introduce a random delay for starting the tone\nmain.trialStartDelays(iTrial) = main.config.training.startDelay + ...\n    (rand(1) - 0.5) * 2 * main.config.training.startDelayRand;\n% add punishement time-out if needed\nif main.isPunish;\n    main.trialStartDelays(iTrial) = main.trialStartDelays(iTrial) + main.config.training.timeoutPunish;\nend;\n%% Experiment - initial wait\nwhile toc(uint64(main.trialStartTimes(iTrial))) < main.trialStartDelays(iTrial);\n%     fprintf('CheckingA: main.hw.piezo.IsRunning()? %d\\n', main.hw.piezo.IsRunning());\n    % check if experiment is paused/stopped\n    if ~main.isRunning; return; end;\n    while main.isPaused; pause(main.updateRate); end;\n    if ~main.isRunning; return; end;\n%     % check if no lick has been done\n%     if ~main.hw.piezo.IsRunning(); % if not running anymore, it means a licking has been done\n%         remainingTime = main.trialStartDelays(iTrial) - toc(uint64(main.trialStartTimes(iTrial)));\n%         if interTrialLickCount > 0;\n%             fprintf('|%.1f', remainingTime);\n%         else\n%             fprintf('    inter-trial licking: |%.1f', remainingTime);\n%         end;\n% \n%         % reset the starting delay to main.config.training.InTriLickTimerDur\n%         main.trialStartTimes(iTrial) = tic;\n%         if main.isPunish;\n%             main.trialStartDelays(iTrial) = max(main.config.training.InTriLickTimerDur, remainingTime);\n%         else\n%             main.trialStartDelays(iTrial) = main.config.training.InTriLickTimerDur;\n%         end;\n%         interTrialLickCount = interTrialLickCount + 1;\n%         pause(1);\n%         % restart the listening\n% %         main.hw.piezo.startBackground();\n% %         pause(0.1);\n% %             fprintf('CheckingB: main.hw.piezo.IsRunning()? %d\\n', main.hw.piezo.IsRunning());\n%     end\n    % avoid full-speed looping\n    pause(0.1);\n%     updateGUI();\nend;\n% if interTrialLickCount;\n%     fprintf('|: %d inter-trial lick(s).\\n', interTrialLickCount);\n% end\nmain.nInTriLick(iTrial) = interTrialLickCount;\nresp = 0;\nmain.isPunish = 0;\n% check if experiment is paused/stopped\nif ~main.isRunning; return; end;\n% while main.isPaused; pause(main.updateRate); end;\n% if ~main.isRunning; return; end;\n    \nt_trialStart = tic;\nfprintf('Trial %03d/%03d - %s        ... ', iTrial, nTrials, datestr(now(), main.logDateFormat));\n%% Experiment - play sound\n% if main.hw.piezo.IsRunning();\n    \n    soundToPlay = main.toneArray{iTrial};\n    player = audioplayer(soundToPlay, main.config.tone.samplingFreq);\n    player.play;\n    \n% else\n%     fprintf('early (already licking, lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n%     main.isPunish = 1;\n%     main.respTypes(iTrial) = 5;\n%     % analyse every 10 trials\n%     if ~rem(iTrial, 10) && iTrial ~= nTrials;\n%         mtrainerAnalyser_performance(main.respTypes, 0);\n%     end;\n%     return;\n% end\n\nbreakOut = 0;\n%% Experiment - wait for response\nwhile toc(t_trialStart) < main.config.training.trialDur;\n    \n    % check if experiment is paused/stopped\n    if ~main.isRunning; return; end;\n%     while main.isPaused; pause(main.updateRate); end;\n%     if ~main.isRunning; return; end;\n    \n%     if main.hw.piezo.IsRunning();\n    if 1;\n        % no response detected\n    else\n        % seconds since trial start\n        respTime = toc(t_trialStart);\n        main.respTimes(iTrial) = respTime;\n        % response should be 'minRespTime' after the end of the tone!\n        totStimDur = (main.config.tone.stimDur + main.config.tone.ISI) * main.config.tone.nTones - main.config.tone.ISI;\n        limitRespTime = totStimDur + main.config.training.minRespTime;\n%                 limitRespTime = main.config.training.minRespTime;\n        main.respDelays(iTrial) = limitRespTime - respTime;\n        % early response, count as kind of false alarm\n        if respTime < limitRespTime;\n%                     fprintf('early (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n            fprintf('early (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n                respTime, limitRespTime, limitRespTime - respTime);\n            main.isPunish = 1;\n            main.respTypes(iTrial) = 5;\n            breakOut = 1;\n            break;\n        else\n%                     fprintf('Lick at %s\\n', datestr(clock, main.logDateFormat))\n            resp = 1;\n        end;\n        break; % break out of while loop\n    end;\n    pause(0.02); % necessary to allow time for data collection\n%     updateGUI();\nend;\nif breakOut;\n    main.trialEndTimes(iTrial) = toc(uint64(main.trialStartTimes(iTrial)));\n    % analyse every 10 trials\n    if ~rem(iTrial, 10) && iTrial ~= nTrials;\n        mtrainerAnalyser_performance(main.respTypes, 0);\n    end;\n    return; % continue for loop\nend;\n% if main.hw.piezo.IsRunning();\n%     main.hw.piezo.stop();\n% end;\n% check if experiment is paused/stopped\nif ~main.isRunning; return; end;\n% while main.isPaused; pause(main.updateRate); end;\n% if ~main.isRunning; return; end;\n%% Experiment - analyse response\nmain.resps(iTrial) = resp;\nisOddTrial = main.stims(iTrial) ~= main.odds(iTrial);\nif isfield(main.config.tone, 'oddProba') && main.config.tone.oddProba > 0; % oddball discrimination\n    isTargetStim = isOddTrial && main.config.tone.goStim;\nelse % frequency discrimination\n    isTargetStim = main.stims(iTrial) == main.config.tone.goStim;\nend;\nif resp == 1 && isTargetStim;\n    % correct response\n%             fprintf('correct detection (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n    fprintf('correct detection (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n        respTime, limitRespTime, limitRespTime - respTime);\n    main.respTypes(iTrial) = 1;\n    % give reward\n    giveReward(main.config.training.rewDur, main.config.training.rewDelay);\n    % wait to collect the reward\n    pause(main.config.training.rewCollTime);\nelseif resp == 1 && ~isTargetStim;\n    % false alarm\n%             fprintf('false alarm (lick sensor: min/mean/max: %.2f/%.2f/%.2f).\\n', recordedValues);\n    fprintf('false alarm (response time: %.4f, limit response time: %.4f, diff: %.5f).\\n', ...\n        respTime, limitRespTime, limitRespTime - respTime);\n    main.respTypes(iTrial) = 3;\n    % punish time-out\n    main.isPunish = 1;\nelseif resp == 0 && isTargetStim;\n    fprintf('miss.\\n');\n    main.respTypes(iTrial) = 4;\n    % punish time-out\n    main.isPunish = 1;\n elseif resp == 0 && ~isTargetStim;\n    fprintf('correct rejection.\\n');\n    main.respTypes(iTrial) = 2;\n    % go to next trial\nend\nmain.trialEndTimes(iTrial) = toc(uint64(main.trialStartTimes(iTrial)));\n% analyse every 10 trials\nif ~rem(iTrial, 10) && iTrial ~= nTrials;\n    mtrainerAnalyser_performance(main.respTypes, 0);\nend;\n%% #recordAndStop\nfunction recordAndStop(src, event)\n\n% global main recordedValues;\n% [recordedValues, main.recordedData] = stopWhenExceedThreshold(src, event, main.piezoThresh);\n% updateGUI();\n%% #storeData\nfunction storeData(src, event)\n\nglobal main;\nnChans = size(event.Data, 2);\nfor iChan = 1 : nChans;\n    anInName = main.hw.analogIns{iChan};\n    prevData = main.analogInData.(anInName);\n%     currChanData = linScale(event.Data(:, iChan) - mean(event.Data(:, iChan)));\n    currChanData = event.Data(:, iChan) - mean(event.Data(:, iChan));\n%     currChanData = event.Data(:, iChan);\n    allChanData = [prevData; currChanData];\n    if size(allChanData, 1) / main.hw.sampleRate > main.hw.recordDur;\n        main.analogInData.(anInName) = currChanData;\n    else    \n        main.analogInData.(anInName) = allChanData;\n    end;\n    \n%     if (size(allChanData, 1) + nSamples) / main.hw.sampleRate > main.hw.recordDur;\n%         updateGUI();\n%     end;\nend;\n% normAbsData = abs(normData);\n% allData = event.Data;\n% supThreshData = normAbsData(normAbsData > main.piezoThresh);\n% recordedValues = [min(supThreshData), mean(supThreshData), max(supThreshData)];\n% main.recordedData = allData;\n% recordDur\n\n% if any(normAbsData > threshold);\n% end;\n% \n% [recordedValues, main.recordedData] = stopWhenExceedThreshold(src, event, main.piezoThresh);\n% updateGUI();\n\n\n%% #closeDiaryAndSaveOutput\nfunction closeDiaryAndSaveOutput()\n    \nglobal main;\no('\\nClosing diary... ', 2, main.dbgLvl);\ndiary off;\no('Closing diary done.', 1, main.dbgLvl);\nout = main;\ntry out.hw = rmfield(out.hw, 'toneArray');          catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'piezoListener'); catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'piezo');         catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'valve');       catch e; end; %#ok<NASGU>\ntry out.hw = rmfield(out.hw, 'recordedData');           catch e; end; %#ok<NASGU>\n\nif exist('out', 'var');\n    matFileName = sprintf('%s_out.mat', main.savePath);\n    o('Saving output as %s... ', matFileName, 2, main.dbgLvl);\n    save(matFileName, 'out');\n    o('Saving output done.', matFileName, 1, main.dbgLvl);\nelse\n    o('/!\\\\ Output not saved !', 1, main.dbgLvl);\nend;\n% disconnectHW();\n"}
+{"plateform": "github", "repo_name": "PacktPublishing/ROS-Programming-Building-Powerful-Robots-master", "name": "teleop.m", "ext": ".m", "path": "ROS-Programming-Building-Powerful-Robots-master/Module03/Chapter08/Matlab/teleop.m", "size": 10043, "source_encoding": "utf_8", "md5": "7f1f1a8bd40b2cbd4fb318fe3d696aa9", "text": "function varargout = teleop(varargin)\n%Before starting this code, you have to launch any robotic simulation on\n%ROS PC or start a ROS robot\n\n\n\n% For example, you can test it using turtlebot simulation\n% You can launch turtlebot simulation using following command\n% $ roslaunch turtlebot_gazebo turtlebot_world.launch\n% Retreive IP of the PC and put it on the MATLAB app, also note the command\n% velocity\n\n\n% TELEOP MATLAB code for teleop.fig\n%      TELEOP, by itself, creates a new TELEOP or raises the existing\n%      singleton*.\n%\n%      H = TELEOP returns the handle to a new TELEOP or the handle to\n%      the existing singleton*.\n%\n%      TELEOP('CALLBACK',hObject,eventData,handles,...) calls the local\n%      function named CALLBACK in TELEOP.M with the given input arguments.\n%\n%      TELEOP('Property','Value',...) creates a new TELEOP or raises the\n%      existing singleton*.  Starting from the left, property value pairs are\n%      applied to the GUI before teleop_OpeningFcn gets called.  An\n%      unrecognized property name or invalid value makes property application\n%      stop.  All inputs are passed to teleop_OpeningFcn via varargin.\n%\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\n%      instance to run (singleton)\".\n%\n% See also: GUIDE, GUIDATA, GUIHANDLES\n\n% Edit the above text to modify the response to help teleop\n\n% Last Modified by GUIDE v2.5 07-Dec-2016 22:22:30\n\n% Begin initialization code - DO NOT EDIT\ngui_Singleton = 1;\ngui_State = struct('gui_Name',       mfilename, ...\n                   'gui_Singleton',  gui_Singleton, ...\n                   'gui_OpeningFcn', @teleop_OpeningFcn, ...\n                   'gui_OutputFcn',  @teleop_OutputFcn, ...\n                   'gui_LayoutFcn',  [] , ...\n                   'gui_Callback',   []);\nif nargin && ischar(varargin{1})\n    gui_State.gui_Callback = str2func(varargin{1});\nend\n\nif nargout\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\nelse\n    gui_mainfcn(gui_State, varargin{:});\nend\n% End initialization code - DO NOT EDIT\n\n\n% --- Executes just before teleop is made visible.\nfunction teleop_OpeningFcn(hObject, eventdata, handles, varargin)\n% This function has no output args, see OutputFcn.\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n% varargin   command line arguments to teleop (see VARARGIN)\n\n% Choose default command line output for teleop\nhandles.output = hObject;\n\n% Update handles structure\nguidata(hObject, handles);\n\n% UIWAIT makes teleop wait for user response (see UIRESUME)\n% uiwait(handles.figure1);\n\n%Declaring some initial variables globally\nglobal ros_master_ip \nglobal ros_master_port \nglobal teleop_topic_name\n\n%DEFAULT ROS MASTER IP, PORT AND TELEOP TOPIC\nros_master_ip = '192.168.1.102';\nros_master_port = '11311';\nteleop_topic_name = '/cmd_vel_mux/input/teleop';\n\n\n%Initializing robot speed \nglobal left_spinVelocity\nglobal right_spinVelocity\n\nglobal forwardVelocity\nglobal backwardVelocity\n\nleft_spinVelocity = 2;       % Angular velocity (rad/s)\nright_spinVelocity = -2;       % Angular velocity (rad/s)\n\nforwardVelocity = 3;    % Linear velocity (m/s)\nbackwardVelocity = -3; % Linear velocity (reverse) (m/s)\n\n% --- Outputs from this function are returned to the command line.\nfunction varargout = teleop_OutputFcn(hObject, eventdata, handles) \n% varargout  cell array for returning output args (see VARARGOUT);\n% hObject    handle to figure\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Get default command line output from handles structure\nvarargout{1} = handles.output;\n\n\n\n% --- Executes on button press in pushbutton2.\n%Right movement\nfunction pushbutton2_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton2 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\nglobal velmsg\nglobal robot\nglobal right_spinVelocity\nglobal teleop_topic_name\n\n%Assigning data to Twist message \n\nvelmsg.Angular.Z = right_spinVelocity;\nvelmsg.Linear.X = 0;\n\n%Publishing message\n\nsend(robot,velmsg);\nlatchpub = rospublisher(teleop_topic_name, 'IsLatching', true);\n\n\n\n% --- Executes on button press in pushbutton3.\n%Left movement\nfunction pushbutton3_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton3 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal velmsg\nglobal robot\nglobal left_spinVelocity\nglobal teleop_topic_name\n\n\nvelmsg.Angular.Z = left_spinVelocity;\nvelmsg.Linear.X = 0;\n\nsend(robot,velmsg);\nlatchpub = rospublisher(teleop_topic_name, 'IsLatching', true);\n\n\n\n\n% --- Executes on button press in pushbutton4.\n%Forward movement\nfunction pushbutton4_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton4 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal velmsg\nglobal robot\nglobal teleop_topic_name\n\nglobal forwardVelocity\n\nvelmsg.Angular.Z = 0;\nvelmsg.Linear.X = forwardVelocity;\n\nsend(robot,velmsg);\nlatchpub = rospublisher(teleop_topic_name, 'IsLatching', true);\n\n\n\n% --- Executes on button press in pushbutton5.\n%Backward motion\nfunction pushbutton5_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton5 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal velmsg\nglobal robot\nglobal backwardVelocity\nglobal teleop_topic_name\n\n\nvelmsg.Angular.Z = 0;\nvelmsg.Linear.X = backwardVelocity;\n\nsend(robot,velmsg);\nlatchpub = rospublisher(teleop_topic_name, 'IsLatching', true);\n\n\n\n\n% --- Executes on button press in pushbutton6.\n%This button will connect to a ROS network\nfunction pushbutton6_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton6 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nglobal ros_master_ip\nglobal ros_master_port\nglobal teleop_topic_name\nglobal robot\nglobal velmsg\n\nros_master_uri = strcat('http://',ros_master_ip,':',ros_master_port)\nsetenv('ROS_MASTER_URI',ros_master_uri)\n\nrosinit\n\n%Initializing ROS publishers\nrobot = rospublisher(teleop_topic_name,'geometry_msgs/Twist');\nvelmsg = rosmessage(robot);\n\n% --- Executes on button press in pushbutton7.\n%This button disconnect from ROS network\nfunction pushbutton7_Callback(hObject, eventdata, handles)\n% hObject    handle to pushbutton7 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\nrosshutdown\n\n% setting ROS master ip from edit box\n%This function will retreive ROS MASTER IP from editbox\nfunction edit1_Callback(hObject, eventdata, handles)\n% hObject    handle to edit1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of edit1 as text\n%        str2double(get(hObject,'String')) returns contents of edit1 as a double\nglobal ros_master_ip\n\nros_master_ip = get(hObject,'String')\n\n\n% --- Executes during object creation, after setting all properties.\nfunction edit1_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to edit1 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n%Retreiving ROS MASTER PORT from edit box\nfunction edit2_Callback(hObject, eventdata, handles)\n% hObject    handle to edit2 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of edit2 as text\n%        str2double(get(hObject,'String')) returns contents of edit2 as a double\nglobal ros_master_port\n\n\nros_master_port = get(hObject,'String')\n\n% --- Executes during object creation, after setting all properties.\nfunction edit2_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to edit2 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n\n\n%We can retreive teleop topic from this box\n\nfunction edit3_Callback(hObject, eventdata, handles)\n% hObject    handle to edit3 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    structure with handles and user data (see GUIDATA)\n\n% Hints: get(hObject,'String') returns contents of edit3 as text\n%        str2double(get(hObject,'String')) returns contents of edit3 as a double\nglobal teleop_topic_name\n\nteleop_topic_name = get(hObject,'String')\n\n\n% --- Executes during object creation, after setting all properties.\nfunction edit3_CreateFcn(hObject, eventdata, handles)\n% hObject    handle to edit3 (see GCBO)\n% eventdata  reserved - to be defined in a future version of MATLAB\n% handles    empty - handles not created until after all CreateFcns called\n\n% Hint: edit controls usually have a white background on Windows.\n%       See ISPC and COMPUTER.\nif ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))\n    set(hObject,'BackgroundColor','white');\nend\n"}
+{"plateform": "github", "repo_name": "thigiacmaytinh/FaceDetection-master", "name": "modelConvert.m", "ext": ".m", "path": "FaceDetection-master/lib/opencv320/contrib/ximgproc/tutorials/scripts/modelConvert.m", "size": 2117, "source_encoding": "utf_8", "md5": "dd8b0dc376b1da49ced7529a9e3a7723", "text": "function modelConvert(model, outname)\n%% script for converting Piotr's matlab model into YAML format\n\noutfile = fopen(outname, 'w');\n\nfprintf(outfile, '%%YAML:1.0\\n\\n');\n\nfprintf(outfile, ['options:\\n'...\n                  '    numberOfTrees: 8\\n'...\n                  '    numberOfTreesToEvaluate: 4\\n'...\n                  '    selfsimilarityGridSize: 5\\n'...\n                  '    stride: 2\\n'...\n                  '    shrinkNumber: 2\\n'...\n                  '    patchSize: 32\\n'...\n                  '    patchInnerSize: 16\\n'...\n                  '    numberOfGradientOrientations: 4\\n'...\n                  '    gradientSmoothingRadius: 0\\n'...\n                  '    regFeatureSmoothingRadius: 2\\n'...\n                  '    ssFeatureSmoothingRadius: 8\\n'...\n                  '    gradientNormalizationRadius: 4\\n\\n']);\n\nfprintf(outfile, 'childs:\\n');\nprintToYML(outfile, model.child', 0);\n\nfprintf(outfile, 'featureIds:\\n');\nprintToYML(outfile, model.fids', 0);\n\nfprintf(outfile, 'thresholds:\\n');\nprintToYML(outfile, model.thrs', 0);\n\nN = 1000;\nfprintf(outfile, 'edgeBoundaries:\\n');\nprintToYML(outfile, model.eBnds, N);\n\nfprintf(outfile, 'edgeBins:\\n');\nprintToYML(outfile, model.eBins, N);\n\nfclose(outfile);\ngzip(outname);\n\nend\n\nfunction printToYML(outfile, A, N)\n%% append matrix A to outfile as\n%%    - [a11, a12, a13, a14, ..., a1n]\n%%    - [a21, a22, a23, a24, ..., a2n]\n%%    ...\n%%\n%% if size(A, 2) == 1, A is printed by N elemnent per row\n\n    if (length(size(A)) ~= 2)\n        error('printToYML: second-argument matrix should have two dimensions');\n    end\n\n    if (size(A,2) ~= 1)\n        for i=1:size(A,1)\n            fprintf(outfile, '    - [');\n            fprintf(outfile, '%d,', A(i, 1:end-1));\n            fprintf(outfile, '%d]\\n', A(i, end));\n        end\n    else\n        len = length(A);\n        for i=1:ceil(len/N)\n            first = (i-1)*N + 1;\n             last = min(i*N, len) - 1;\n\n            fprintf(outfile, '    - [');\n            fprintf(outfile, '%d,', A(first:last));\n            fprintf(outfile, '%d]\\n', A(last + 1));\n        end\n    end\n    fprintf(outfile, '\\n');\nend"}
+{"plateform": "github", "repo_name": "imistyrain/ssd-master", "name": "classification_demo.m", "ext": ".m", "path": "ssd-master/matlab/demo/classification_demo.m", "size": 5412, "source_encoding": "utf_8", "md5": "8f46deabe6cde287c4759f3bc8b7f819", "text": "function [scores, maxlabel] = classification_demo(im, use_gpu)\n% [scores, maxlabel] = classification_demo(im, use_gpu)\n%\n% Image classification demo using BVLC CaffeNet.\n%\n% IMPORTANT: before you run this demo, you should download BVLC CaffeNet\n% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)\n%\n% ****************************************************************************\n% For detailed documentation and usage on Caffe's Matlab interface, please\n% refer to Caffe Interface Tutorial at\n% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab\n% ****************************************************************************\n%\n% input\n%   im       color image as uint8 HxWx3\n%   use_gpu  1 to use the GPU, 0 to use the CPU\n%\n% output\n%   scores   1000-dimensional ILSVRC score vector\n%   maxlabel the label of the highest score\n%\n% You may need to do the following before you start matlab:\n%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64\n%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6\n% Or the equivalent based on where things are installed on your system\n%\n% Usage:\n%  im = imread('../../examples/images/cat.jpg');\n%  scores = classification_demo(im, 1);\n%  [score, class] = max(scores);\n% Five things to be aware of:\n%   caffe uses row-major order\n%   matlab uses column-major order\n%   caffe uses BGR color channel order\n%   matlab uses RGB color channel order\n%   images need to have the data mean subtracted\n\n% Data coming in from matlab needs to be in the order\n%   [width, height, channels, images]\n% where width is the fastest dimension.\n% Here is the rough matlab for putting image data into the correct\n% format in W x H x C with BGR channels:\n%   % permute channels from RGB to BGR\n%   im_data = im(:, :, [3, 2, 1]);\n%   % flip width and height to make width the fastest dimension\n%   im_data = permute(im_data, [2, 1, 3]);\n%   % convert from uint8 to single\n%   im_data = single(im_data);\n%   % reshape to a fixed size (e.g., 227x227).\n%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');\n%   % subtract mean_data (already in W x H x C with BGR channels)\n%   im_data = im_data - mean_data;\n\n% If you have multiple images, cat them with cat(4, ...)\n\n% Add caffe/matlab to you Matlab search PATH to use matcaffe\nif exist('../+caffe', 'dir')\n  addpath('..');\nelse\n  error('Please run this demo from caffe/matlab/demo');\nend\n\n% Set caffe mode\nif exist('use_gpu', 'var') && use_gpu\n  caffe.set_mode_gpu();\n  gpu_id = 0;  % we will use the first gpu in this demo\n  caffe.set_device(gpu_id);\nelse\n  caffe.set_mode_cpu();\nend\n\n% Initialize the network using BVLC CaffeNet for image classification\n% Weights (parameter) file needs to be downloaded from Model Zoo.\nmodel_dir = '../../models/bvlc_reference_caffenet/';\nnet_model = [model_dir 'deploy.prototxt'];\nnet_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];\nphase = 'test'; % run with phase test (so that dropout isn't applied)\nif ~exist(net_weights, 'file')\n  error('Please download CaffeNet from Model Zoo before you run this demo');\nend\n\n% Initialize a network\nnet = caffe.Net(net_model, net_weights, phase);\n\nif nargin < 1\n  % For demo purposes we will use the cat image\n  fprintf('using caffe/examples/images/cat.jpg as input image\\n');\n  im = imread('../../examples/images/cat.jpg');\nend\n\n% prepare oversampled input\n% input_data is Height x Width x Channel x Num\ntic;\ninput_data = {prepare_image(im)};\ntoc;\n\n% do forward pass to get scores\n% scores are now Channels x Num, where Channels == 1000\ntic;\n% The net forward function. It takes in a cell array of N-D arrays\n% (where N == 4 here) containing data of input blob(s) and outputs a cell\n% array containing data from output blob(s)\nscores = net.forward(input_data);\ntoc;\n\nscores = scores{1};\nscores = mean(scores, 2);  % take average scores over 10 crops\n\n[~, maxlabel] = max(scores);\n\n% call caffe.reset_all() to reset caffe\ncaffe.reset_all();\n\n% ------------------------------------------------------------------------\nfunction crops_data = prepare_image(im)\n% ------------------------------------------------------------------------\n% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that\n% is already in W x H x C with BGR channels\nd = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');\nmean_data = d.mean_data;\nIMAGE_DIM = 256;\nCROPPED_DIM = 227;\n\n% Convert an image returned by Matlab's imread to im_data in caffe's data\n% format: W x H x C with BGR channels\nim_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR\nim_data = permute(im_data, [2, 1, 3]);  % flip width and height\nim_data = single(im_data);  % convert from uint8 to single\nim_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data\nim_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)\n\n% oversample (4 corners, center, and their x-axis flips)\ncrops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');\nindices = [0 IMAGE_DIM-CROPPED_DIM] + 1;\nn = 1;\nfor i = indices\n  for j = indices\n    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);\n    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);\n    n = n + 1;\n  end\nend\ncenter = floor(indices(2) / 2) + 1;\ncrops_data(:,:,:,5) = ...\n  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);\ncrops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);\n"}
+{"plateform": "github", "repo_name": "LidkeLab/matlab-instrument-control-master", "name": "readH5File.m", "ext": ".m", "path": "matlab-instrument-control-master/@MIC_H5/readH5File.m", "size": 12944, "source_encoding": "utf_8", "md5": "672b14572a6fdb018bd3c7db059ca5ae", "text": "function [H5Structure] = readH5File(FilePath, GroupName)\n%Extracts contents of an h5 file into H5Structure.\n% This method will extract the Data and Attributes from a group\n% named GroupName in the .h5 file specified by FilePath.\n% Examples:\n%   H5Structure = readH5File('C:\\file.h5') will extract all\n%       contents of file.h5 and store them in H5Structure.\n%   H5Structure = readH5File('C:\\file.h5', 'Laser647') will extract\n%       contents of the group 'Laser647' from file.h5 given only\n%       the group name.\n%   H5Structure = readH5File('C:\\file.h5', ...\n%       '/Channel01/Zposition001/Laser647') will extract contents\n%       of the group 'Laser647' from file.h5 given a full group\n%       path.\n%\n% INPUTS: \n%   FilePath: String containing the path to the .h5 file of interest.\n%   GroupName: (optional) Name of a specific group in the .h5 file to be\n%              extracted.\n%\n% OUTPUTS:\n%   H5Structure: Structured array containing the information extracted from\n%                the .h5 file at FilePath.\n%\n% REQUIRES:\n%   MATLAB 2016b or later\n%\n% CITATION:\n\n% Created by:\n%   David James Schodt (LidkeLab, 2018)\n\n\n% Ensure that FilePath points to a valid file.\n% NOTE: == 2 means a file indeed exists at FilePath\nif exist(FilePath, 'file') ~= 2\n    error(['File specified by FilePath = ', ...\n        '''%s'' does not exist.'], FilePath)\nend\n\n% If GroupName was not specified, set a flag to indicate we\n% want to extract all contents from the .h5 file.\nif exist('GroupName', 'var')\n    % Groupname was given, don't set the SaveAll flag.\n    SaveAll = 0;\nelse\n    % GroupName was not given, set the SaveAll flag.\n    SaveAll = 1;\nend\n\n% Read in all of the information available about the structure of the h5\n% file using MATLAB's built-in h5info() method.\nCurrentGroups = h5info(FilePath); % named for later convenience\n\n% Determine the .h5 file structure being used (i.e. is each\n% dataset in its own group or does one group contain all of\n% the datasets).\n% NOTE: DataFormat==1 means each dataset is in its own group,\n%       DataFormat==0 means each dataset is contained in one\n%       \"supergroup\" of all datasets.\nDataFormat = contains(CurrentGroups.Groups.Groups.Groups(1).Name, 'Data');\n\n% Search the .h5 file for the desired groups and store their location\n% within the file for later extraction.\nDesiredGroups = []; % groups that we wish to save\nif DataFormat\n    % For .h5 files in the DataFormat, we need to continue searching\n    % through the data groups so that we find all instances of the\n    % requested group.\n    DataGroupsSearched = 0; % boolean\nelse\n    % For .h5 files that do not have the DataFormat, there is only one data\n    % group to search.\n    DataGroupsSearched = 1;\nend\nwhile isempty(DesiredGroups) || ~DataGroupsSearched\n    % If we are extracting all contents in the .h5 file at FilePath, we \n    % don't want to perform the search procedure in this while loop.  Set \n    % DesiredGroups to CurrentGroups (all of the groups found in the .h5 \n    % file) and break out of the loop.\n    if SaveAll\n        DesiredGroups = CurrentGroups;\n        break\n    end\n    \n    % Search the current groups for the group of interest.\n    DesiredGroups = [DesiredGroups, ...\n        findGroupPaths(CurrentGroups, GroupName)];\n    \n    % If using the data group format and we are at the .h5 structure data\n    % group level, we need to search for the group of interest within the\n    % sub-groups of each data group.  Otherwise, we need to move one level\n    % deeper into the structure to continue the search.\n    CurrentGroupNameFormat = extractGroupName(CurrentGroups(1).Name);\n    if contains(CurrentGroupNameFormat, 'Data')\n        % We are at the data group level and must search each data \n        % group individually.\n        for ii = 1:numel(CurrentGroups)\n            DesiredGroups = [DesiredGroups, ...\n                findGroupPaths(CurrentGroups(ii).Groups, GroupName)];\n        end\n        \n        % Set the DataGroupsSearched flag to indicate we've searched all of\n        % the data groups.\n        DataGroupsSearched = 1;\n    else\n        % Move one level deeper into the structure and continue searching\n        % for the group of interest.\n        try\n            % Attempt to move one level deeper into the structure.\n            CurrentGroups = CurrentGroups.Groups;\n        catch\n            % We've reached the end of the structure, there are no more \n            % groups to explore.\n            error(['No group named ''%s'' was found in ', ...\n                'the file specified by FilePath = ''%s'''], ...\n                GroupName, FilePath)\n        end\n    end\nend\n\n% Now that we've found the desired group(s), store the\n% Attributes, Data, and Children in a more useable format.\nfor ii = 1:numel(DesiredGroups)\n    % Set DesiredGroup = DesiredGroups(ii) to reduce indexing\n    % clutter/improve code readability.\n    DesiredGroup = DesiredGroups(ii);\n    \n    % Determine if the current group is the child of a data group (this \n    % will be needed later).\n    DesiredGroupParentName = extractGroupName(DesiredGroup.Name, 2);\n    IsDataGroupChild = DataFormat ...\n        && contains(DesiredGroupParentName, 'Data');\n    \n    % If the desired group has attributes, store them in the\n    % output structure.\n    if ~isempty(DesiredGroup.Attributes)\n        for jj = 1:numel(DesiredGroup.Attributes)\n            % Store each attribute as a field in the output structure \n            % accesible directly by that attributes name.  If the\n            % DesiredGroup is a data group, we'll place the attribute\n            % information one level deeper into the output structure.\n            AttributeName = DesiredGroup.Attributes(jj).Name;\n            if IsDataGroupChild\n                % For children of a datagroup, we need a different path\n                % format within the structure (for consistency with the\n                % structure produced for non-datagroup files).\n                H5Structure(ii).Attributes.(AttributeName) = ...\n                    DesiredGroup.Attributes(jj).Value;\n            else\n                H5Structure.Attributes.(AttributeName) = ...\n                    DesiredGroup.Attributes(jj).Value;\n            end\n        end\n    else\n        % Create an empty field for the Attributes to prevent\n        % issues with functions that may be using this method, proceeding\n        % depending on whether or not we are currently storing a data group\n        % (for files that contain separate groups for each dataset).\n        if IsDataGroupChild\n            % For children of a datagroup, we need a different path\n            % format within the structure (for consistency with the\n            % structure produced for non-datagroup files).\n            H5Structure(ii).Attributes = [];\n        else\n            H5Structure.Attributes = [];\n        end\n    end\n    \n    % If the desired group has data, store the data in the output OldStruct\n    % structure.\n    if ~isempty(DesiredGroup.Datasets)\n        for jj = 1:numel(DesiredGroup.Datasets)\n            % Read the ii-th dataset from the h5 file and store the data \n            % in a field of the output structure, matching the datasets \n            % name in the h5 file to the name of the field in the output \n            % structure. If the DesiredGroup is a data group, we'll place \n            % the dataset information one level deeper into the output \n            % structure.\n            DatasetName = DesiredGroup.Datasets(jj).Name;\n            if IsDataGroupChild\n                % For children of a datagroup, we need a different path\n                % format within the structure (for consistency with the\n                % structure produced for non-datagroup files).\n                H5Structure(ii).Data.(DatasetName) = h5read(...\n                    FilePath, [DesiredGroup.Name, '/', ...\n                    DesiredGroup.Datasets(jj).Name]);\n            else\n                H5Structure.Data.(DatasetName) = ...\n                    h5read(FilePath, [DesiredGroup.Name, '/', ...\n                    DesiredGroup.Datasets(jj).Name]);\n            end\n        end\n    else\n        % Create an empty field for the Data to prevent issues\n        % with functions that may be using this method, proceeding\n        % depending on whether or not we are currently storing a data group\n        % (for files that contain separate groups for each dataset).\n        if IsDataGroupChild\n            % For children of a datagroup, we need a different path\n            % format within the structure (for consistency with the\n            % structure produced for non-datagroup files).\n            H5Structure(ii).Data = [];\n        else\n            H5Structure.Data = [];\n        end\n    end\n    \n    % If the desired group has Children (subgroups), recurse\n    % through those to store their Attributes, Data, and\n    % Children.\n    if ~isempty(DesiredGroup.Groups)\n        % Create a cell array of subgroup names (if subgroups\n        % exist).\n        % NOTE: If you are comparing the output structure to\n        % the Data, Attributes, and Children format used in the\n        % exportState() method, the subgroups are assumed to be\n        % the Children.\n        SubgroupNames = {DesiredGroup.Groups.Name};\n        for jj = 1:numel(SubgroupNames)\n            % Iteratively explore subgroups of the desired\n            % group to store their attributes and data.\n            SubgroupStructure = MIC_H5.readH5File(FilePath, ...\n                SubgroupNames{jj});\n            \n            % Remove the path information from the subgroup name, e.g. \n            % /Channel01/Zposition001 will become Zposition001.\n            SubgroupName = extractGroupName(SubgroupNames{jj});\n\n            % Store the subgroup structure into the output H5Structure.\n            H5Structure.Children.(SubgroupName) = SubgroupStructure;\n        end\n    else\n        % Create an empty field for the Children to prevent\n        % issues with functions that may be using this method, proceeding\n        % depending on whether or not we are currently storing a data group\n        % (for files that contain separate groups for each dataset).\n        if IsDataGroupChild\n            % For children of a datagroup, we need yet another path\n            % format within the structure (for consistency with the\n            % structure produced for non-datagroup files).\n            H5Structure(ii).Children = [];\n        else\n            H5Structure.Children = [];\n        end\n    end\nend\n\nend\n\nfunction [DesiredGroups] = findGroupPaths(CurrentGroups, GroupName)\n% This function will create a list of paths to a group with name GroupName\n% within the set of groups CurrentGroups.\n\n% Iterate through each of the CurrentGroups to search for GroupName.\nDesiredGroups = [];\nCurrentGroupNames = {CurrentGroups.Name};\nfor ii = 1:numel(CurrentGroupNames)\n    % If the GroupName isn't provided as a full path, simplify the current \n    % group name to remove group structure e.g. if\n    % CurrentGroupNames{ii} = '/Channel01/Zposition001', simplify it to \n    % 'ZPosition001' for comparison to GroupName.\n    if GroupName(1) == '/'\n        % If the first character of the input GroupName is '/', assume\n        % that a full path was provided to the desired group.\n        CurrentGroupName = CurrentGroupNames{ii};\n    else\n        % The full path was not given, just a raw group name was given.\n        CurrentGroupName = extractGroupName(CurrentGroupNames{ii});\n    end\n    \n    % Check if the CurrentGroupName matches the desired GroupName, storing\n    % the path to the current group if we have a match.\n    if strcmp(CurrentGroupName, GroupName)\n        DesiredGroups = [DesiredGroups, CurrentGroups(ii)];\n    end\nend\n\nend\n\nfunction [GroupName] = extractGroupName(FullGroupName, PathLength)\n% This function will extract the group name from a full group\n% path within an .h5 file, e.g. input\n% FullGroupName = '/Channel01/Zposition001/Data001'\n% will yield GroupName = 'Data001'.  The optional input PathLength\n% specifies how many levels from the bottom to extract, e.g. in the\n% previous example PathLength would be 1, but if PathLength was 2 we would\n% have GroupName = 'Zposition001'.\n\n% Set default parameters if needed.\nif ~exist('PathLength', 'var')\n    PathLength = 1;\nend\n\n% If the last character of FullGroupName is a '/', we should remove it.\nif (FullGroupName(end) == '/') && (numel(FullGroupName) > 1)\n    FullGroupName = FullGroupName(end-1);\nend\n\n% Find the last PathLength indices within FullGroupName corresponding to\n% the appearance of a '/' character.\nLastSlashIndex = find(FullGroupName=='/', PathLength, 'last');\n\n% Return either the GroupName of interest or the name of the lowest level\n% group in FullGroupName as appropriate.\nif numel(LastSlashIndex) > 1\n    GroupName = FullGroupName(LastSlashIndex(1)+1:LastSlashIndex(2)-1);\nelse\n    GroupName = FullGroupName(LastSlashIndex+1:end);\nend\n\n\nend"}
+{"plateform": "github", "repo_name": "LidkeLab/matlab-instrument-control-master", "name": "gui.m", "ext": ".m", "path": "matlab-instrument-control-master/@MIC_GalvoDigital/gui.m", "size": 7602, "source_encoding": "utf_8", "md5": "8de2934750c217a43375207089f3210f", "text": "% gui: Graphical User Interface to MIC_GalvoDigital\n%\n% Functions: gui2properties, properties2gui, closeFigure, CalAngle,\n%            setAngle_Button, setParameters_Button, ToggleGalvo\n%\n% REQUIREMENTS:\n%    MIC_Abstract.m\n%    MATLAB NI-DAQmx driver installed via the Support Package Installer\n%\n% CITATION: Hanieh Mazloom-Farsibaf, Lidlelab, 2017.\n\nfunction guiFig = gui(obj)\n%gui Graphical User Interface to MIC_LightSource_Abstract\n\n%Prevent opening more than one figure for same instrument\nif ishandle(obj.GuiFigure)\n    guiFig = obj.GuiFigure;\n    figure(obj.GuiFigure);\n    return\nend\n\n%Open figure\nguiFig = figure('NumberTitle','off','Resize','off','Units','pixels','MenuBar','none',...\n    'ToolBar','none','Visible','on', 'Position',[100 300 500 310]);\n\n%Construct the components\nhandles.output = guiFig;\nguidata(guiFig,handles);\n\n\n\n% Scanning Parametes\nrangeAngle = sprintf('Angle [%g, %g] ',-obj.Range,obj.Range);\ntextangle = uicontrol('Style','text','String',rangeAngle,...\n    'Position',[10 250,180,25],'FontSize',10);\ntextScanningParameters = uicontrol('Style','text','String','Scanning Parameters',...\n    'Position',[10 200,180,25],'FontSize',11,'FontWeight','bold');\ntextNoStepsPerScan = uicontrol('Style','text','String','Number of Steps Per Scan',...\n    'Position',[10 160,180,20],'FontSize',10);\ntextNoScan = uicontrol('Style','text','String','Number of Scans',...\n    'Position',[10 120,180,20],'FontSize',10);\ntextStepSize = uicontrol('Style','text','String','Step Size',...\n    'Position',[10 80,180,20],'FontSize',10);\ntextOffset = uicontrol('Style','text','String','Starting Position (offset)',...\n    'Position',[10 40,180,20],'FontSize',10);\n\nhandles.edit_angle = uicontrol('Parent',guiFig, 'Style', 'edit', 'String', ...\n    '','Position', [210 250 80 25],'Tag','EditAngle','Callback',@CalAngle);\nhandles.edit_NoStepsPerScan = uicontrol('Parent',guiFig, 'Style', 'edit', 'String', ...\n    '','Tag','EditNSteps','Position', [210 160 80 25]);\nhandles.edit_NoScan = uicontrol('Parent',guiFig, 'Style', 'edit', 'String', ...\n    '','Tag','EditNScans','Position', [210 120 80 25]);\nhandles.edit_StepSize = uicontrol('Parent',guiFig,'Style', 'edit', 'String', ...\n    '','Tag','EditStepsize','Position', [210 80 80 25]);\nhandles.edit_Offset = uicontrol('Parent',guiFig, 'Style', 'edit', 'String', ...\n    '','Tag','EditOffset','Position', [210 40 80 25]);\n\nhandles.setAngle=uicontrol('Style', 'pushbutton', 'String', 'Set Angle',...\n    'Position', [350 250 80 30], 'Callback', @setAngle_Button);\n\nhandles.setParameters=uicontrol('Style', 'pushbutton', 'String', 'Set Parameters',...\n    'Position', [350 130 110 30], 'Callback', @setParameters_Button);\n\nhandles.Toggle_EnableGalvo=uicontrol('Style','togglebutton',...\n    'String','Disable','Position',[350 50 65,50],...\n    'BackgroundColor',[0.8  0.8  0.8],'Tag','positionToggle','Callback',@ToggleGalvo);\n%\nalign([textNoStepsPerScan,textNoScan,textStepSize,textOffset,textScanningParameters],'Center','None');\nalign([handles.edit_NoStepsPerScan,handles.edit_NoScan,handles.edit_StepSize...\n    ,handles.edit_Offset],'Center','None');\n\nalign([handles.Toggle_EnableGalvo,handles.setParameters,handles.setAngle],'Center','None');\n\n\n% Create a property based on GuiFigure\nobj.GuiFigure = guiFig;\nobj.GuiFigure.Name = obj.InstrumentName;\n\n%Prevent closing after a 'close' or 'close all'\nobj.GuiFigure.HandleVisibility='off';\n\n%Save Propeties upon close\nobj.GuiFigure.CloseRequestFcn = @closeFigure;\n\n%Initialize GUI properties\nproperties2gui();\n\n    function gui2properties()\n        \n    end\n\n    function properties2gui()\n        if ~isempty(obj.N_Step) || ~isempty(obj.N_Scan)|| ~isempty(obj.StepSize)|| ~isempty(obj.Offset)\n            set(handles.edit_NoStepsPerScan,'Value',obj.N_Step)\n            set(handles.edit_NoScan,'Value',obj.N_Scan);\n            set(handles.edit_StepSize,'Value',obj.StepSize);\n            set(handles.edit_Offset,'Value',obj.Offset);\n        end\n        set(handles.Toggle_EnableGalvo,'Value',obj.IsEnable);\n        if ~isempty(obj.Angle)\n            if isempty(obj.Word)\n                obj.angle2word(obj.Angle)\n            end\n            set(handles.edit_angle,'Value',obj.Angle)\n        else\n            obj.Angle=0;\n            set(handles.edit_angle,'Value',obj.Angle)\n            obj.angle2word(obj.Angle)\n        end\n        \n        if obj.IsEnable==1\n            set(handles.Toggle_EnableGalvo,'String','Enable');\n            set(handles.Toggle_EnableGalvo,'BackgroundColor','green');\n        else\n            set(handles.Toggle_EnableGalvo,'String','Disable');\n            set(handles.Toggle_EnableGalvo,'BackgroundColor',[.8 .8 .8]);\n        end\n        \n        \n        \n    end\n\n    function closeFigure(~,~)\n        %        gui2properties();\n        %         obj.clear; Ask Keith\n        delete(obj.GuiFigure);\n        \n    end\n\n%calculate word based on input angle\n    function CalAngle(~,~)\n        Angle=str2double(get(handles.edit_angle,'String'));\n        if isnan(Angle)\n            error('Enter a valid angle')\n        end\n        obj.angle2word(Angle);\n        state=get(handles.Toggle_EnableGalvo,'Value');\n        % turn off the togglebutton if it is already on\n        if state\n            set(handles.Toggle_EnableGalvo,'Value',0);\n            set(handles.Toggle_EnableGalvo,'BackgroundColor',[0.8  0.8  0.8]);%,[0.6 0.8 0])\n            set(handles.Toggle_EnableGalvo,'String','Disable')\n        end\n    end\n%Callback function to set angle\n    function setAngle_Button(~,~)\n        \n        Angle=str2double(get(handles.edit_angle,'String'));\n        if isnan(Angle)\n            error('Enter a valid angle')\n        end\n        \n        obj.setAngle();\n        \n    end\n%Callback function to set Sequence\n    function setParameters_Button(~,~)\n        %take Scanning Parameters\n        N_Step=str2double(get(handles.edit_NoStepsPerScan,'String'));\n        N_Scan=str2double(get(handles.edit_NoScan,'String'));\n        StepSize=str2double(get(handles.edit_StepSize,'String'));\n        Offset=str2double(get(handles.edit_Offset,'String'));\n        \n        if isnan(N_Step) || isnan(N_Scan)|| isnan(StepSize)|| isnan(Offset)\n            error('Choose proper values for Scanning Parameters')\n        end\n        \n        % set Scanning Parameters\n        obj.N_Step=N_Step;\n        obj.N_Scan=N_Scan;\n        obj.StepSize=StepSize;\n        obj.Offset=Offset;\n        \n        %set the sequence of Words to move the Galvo Mirror\n        obj.setSequence();\n    end\n\n%Callback function for toggle button\n    function ToggleGalvo(~,~)\n        state=get(handles.Toggle_EnableGalvo,'Value');\n        %\n        %         N_Step=str2double(get(handles.edit_NoStepsPerScan,'String'));\n        %         N_Scan=str2double(get(handles.edit_NoScan,'String'));\n        %         StepSize=str2double(get(handles.edit_StepSize,'String'));\n        %         Offset=str2double(get(handles.edit_Offset,'String'));\n        %\n        %         if isnan(N_Step) || isnan(N_Scan)|| isnan(StepSize)|| isnan(Offset)\n        %             error('Choose proper values for Scanning Parameters')\n        %         end\n        \n        if state\n            %   obj.Power=str2double(get(handles.SetPower,'String'));\n            set(handles.Toggle_EnableGalvo,'BackgroundColor','green');%,[0.6 0.8 0])\n            set(handles.Toggle_EnableGalvo,'String','Enable')\n            obj.enable();\n        else\n            set(handles.Toggle_EnableGalvo,'BackgroundColor',[0.8  0.8  0.8])\n            set(handles.Toggle_EnableGalvo,'String','Disable')\n            obj.disable();\n        end\n    end\n\nend\n\n"}
+{"plateform": "github", "repo_name": "LidkeLab/matlab-instrument-control-master", "name": "errorcheck.m", "ext": ".m", "path": "matlab-instrument-control-master/@MIC_MCLNanoDrive/errorcheck.m", "size": 3188, "source_encoding": "utf_8", "md5": "2abc63fb57985d22013e75228d9714dc", "text": "function varargout = errorcheck(obj,fname,varargin)\n\n% certain errors throw variable error codes but are specific to axis controls, this is logical branch for that\nif obj.LastError ~= obj.ErrorCode.MCL_SUCCESS\n    displayError(fname,varargin);\nend\n\n% Error check function for processing most of the MCL Nano Drive errors\nswitch obj.LastError\n    case obj.ErrorCode.MCL_SUCCESS\n        % nest if statements for when we call this, not sure what\n        % this needs to be yet\n        switch fname\n            case 'MCL_GetProductInfo'\n                obj.ProductInfo.ADC_resolution=varargin{1}.ADC_resolution;\n                obj.ProductInfo.DAC_resolution=varargin{1}.DAC_resolution;\n                obj.ProductInfo.Product_id=varargin{1}.Product_id;\n                obj.ProductInfo.FirmwareVersion=varargin{1}.FirmwareVersion;\n                obj.ProductInfo.FirmwareProfile=varargin{1}.FirmwareProfile;\n            case 'MCL_SingleWriteN'\n                obj.Position(varargin{2}) = varargin{1};\n\n        end\n        \n    case obj.ErrorCode.MCL_GENERAL_ERROR\n        error('Some Internal Sanity Check Failed');\n    case obj.ErrorCode.MCL_DEV_ERROR\n        error('A problem occurred when transferring data to the Nano-Drive');\n    case obj.ErrorCode.MCL_DEV_NOT_ATTACHED\n        error('The Nano-Drive cannot complete the task because it is not attached.');\n    case obj.ErrorCode.MCL_USAGE_ERROR\n        error('This library function is not supported by this particular Nano-Drive');\n    case obj.ErrorCode.MCL_DEV_NOT_READY\n        error('The Nano-Drive is currently completing or waiting to complete another task');\n    case obj.ErrorCode.MCL_ARGUMENT_ERROR\n        error('An argument is out of range or a required pointer is equal to NULL');\n    case obj.ErrorCode.MCL_INVALID_AXIS\n        varargout{1} = 'Invalid Axis'; % axis returns as invalid\n        warning('The Attempted operation on this axis does not exist in the Nano-Drive');\n    case obj.ErrorCode.MCL_INVALID_HANDLE\n        error('The handle is not valid, or at least not in this instance of the DLL');\n    case obj.ErrorCode.MCL_MISC\n        switch fname\n            case 'MCL_InitHandle'\n                error('MCLNanoDrive:NoHandle','Cannot get a handle to the device. Make sure device is on.  If on, try ''MCLNanoDrive.libreset''');            \n            case 'MCL_CorrectDriverVersion'\n                warning('MCLNanoDriveStage:WrongDLL','DLL in use does not match the current driver version');\n        end\n    otherwise\n        switch fname\n            case 'MCL_GetCalibration'\n                varargout{1} = obj.LastError; % return correct axis values\n                obj.LastError = 0;\n        end\n        \nend\nend\n\n% I had to make a static function because sometimes MCL will throw variable error codes for a single command\nfunction displayError(fname,varargin)\nswitch fname\n    case 'MCL_SingleWriteN'\n        switch varargin{2}\n            case 1\n                dimChar = X;\n            case 2\n                dimChar = Y;\n            case 3\n                dimChar = Z;\n        end\n        disp(obj.LastError);\n        error('MCLNanoDrive:MoveFailed','Could not move to new %s position',dimChar);\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "LidkeLab/matlab-instrument-control-master", "name": "gui.m", "ext": ".m", "path": "matlab-instrument-control-master/@MIC_SPTCollect/gui.m", "size": 23826, "source_encoding": "utf_8", "md5": "efd104ecb8670653062f168d48d398c1", "text": "\nfunction gui(obj)\n% GUI SRcollect Gui for TIRF microscope\n%   Detailed explanation goes here\n\nh = findall(0,'tag','TIRF-SRcollect.gui');\nif ~(isempty(h))\n    figure(h);\n    return;\nend\n%%\nxsz=400;\nysz=1000;\nxst=100;\nyst=50;\npw=.95;\npsep=.001;\nstaticst=10;\neditst=110;\n\nguiFig = figure('Units','pixels','Position',[xst yst xsz ysz],...\n    'MenuBar','none','ToolBar','none','Visible','on',...\n    'NumberTitle','off','UserData',0,'Tag',...\n    'SPTCollect.gui','HandleVisibility','off','name','SPTcollect.gui','CloseRequestFcn',@FigureClose);\n\ndefaultBackground = get(0,'defaultUicontrolBackgroundColor');\nset(guiFig,'Color',defaultBackground);\nhandles.output = guiFig;\nguidata(guiFig,handles);\n\nrefh=1;\n\n% File Panel\nph=0.09;\nphp = ph*ysz;\nhFilePanel = uipanel('Parent',guiFig,'Title','FILE','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\n\nuicontrol('Parent',hFilePanel, 'Style', 'edit', 'String', 'Save Directory:','Enable','off','Position', [staticst php-40 100 20]);\nhandles.Edit_FileDirectory = uicontrol('Parent',hFilePanel, 'Style', 'edit', 'String','Set Auto','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-40 250 20]);\nuicontrol('Parent',hFilePanel, 'Style', 'edit', 'String', 'Base FileName:','Enable','off','Position', [staticst php-65 100 20]);\nhandles.Edit_FileName = uicontrol('Parent',hFilePanel, 'Style', 'edit', 'String','Set Auto','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-65 250 20]);\nuicontrol('Parent',hFilePanel, 'Style', 'edit', 'String','File type:','Enable','off','Position', [staticst php-90 100 20]);\nhandles.saveFileType = uicontrol('Parent',hFilePanel, 'Style', 'popupmenu', 'String',{'.mat','.h5'},'Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-90 250 20],'CallBack',@saveFile);\n\n% Camera Panel\nph=0.17;\nphp = ph*ysz;\nhCameraPanel = uipanel('Parent',guiFig,'Title','CAMERA','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Camera Gain:','Enable','off','Position', [staticst php-40 100 20]);\nhandles.Popup_CameraGain = uicontrol('Parent',hCameraPanel, 'Style', 'popupmenu', 'String',{'Low (Alexa647)','High (FAP/FP)'},'Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-40 250 20],'CallBack',@gain_set);\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Camera ROI:','Enable','off','Position', [staticst php-70 80 20]);\nROIlist={'Full','Left','Right','Left Center','Right Center','Center Horizontally','Center Horizontally Half','128*16 Center','64*32 low right','64*32 low left','64*16 low right'};\nhandles.Popup_CameraROI = uicontrol('Parent',hCameraPanel, 'Style', 'popupmenu', 'String',ROIlist,'Enable','on','BackgroundColor',[1 1 1],'Position', [90 php-70 85 20]);\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','IRCamera ROI:','Enable','off','Position', [staticst+180 php-70 80 20]);\nROIlist_IR={'Full','Center 350' ,'Center 256','Center 128'};\nhandles.Popup_IRCameraROI = uicontrol('Parent',hCameraPanel, 'Style', 'popupmenu', 'String',ROIlist_IR,'Enable','on','BackgroundColor',[1 1 1],'Position', [90+180 php-70 85 20]);\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Exp. Time Focus:','Enable','off','Position', [staticst php-100 100 20]);\nhandles.Edit_CameraExpTimeFocusSet = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','0.01','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-100 50 20]);\n% uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Actual:','Enable','off','Position', [175 php-100 100 20]);\n% handles.Edit_CameraExpTimeFocusActual = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','','Enable','off','Position', [250 php-100 50 20]);\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','IRCamera Zoom:','Enable','off','Position', [staticst+180 php-100 100 20]);\nhandles.Popup_CameraDispZoom_IR = uicontrol('Parent',hCameraPanel, 'Style','popupmenu','String',{'50%','100%','200%','400%','1000%'},'Value',2,'Enable','on','BackgroundColor',[1 1 1],'Position', [280 php-100 50 20],'CallBack',@zoom_set_IR);\n\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Exp. Time Seq.:','Enable','off','Position', [staticst php-130 100 20]);\nhandles.Edit_CameraExpTimeSeqSet = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','0.01','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-130 50 20],'CallBack',@sequence_set);\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Actual:','Enable','off','Position', [175 php-130 100 20]);\nhandles.Edit_CameraExpTimeSeqActual = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String',num2str(obj.CameraObj.SequenceCycleTime),'Enable','off','Position', [250 php-130 50 20]);\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Num Frames:','Enable','off','Position', [staticst php-160 100 20]);\nhandles.Edit_CameraNumFrames = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','2000','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-160 50 20]);\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Zoom:','Enable','off','Position', [175 php-160 100 20]);\nhandles.Popup_CameraDispZoom = uicontrol('Parent',hCameraPanel, 'Style','popupmenu','String',{'50%','100%','200%','400%','1000%'},'Value',4,'Enable','on',...\n    'BackgroundColor',[1 1 1],'Position', [250 php-160 50 20],'CallBack',@zoom_set);\n\n% Registration Panel\nph=0.213;\nphp = ph*ysz;\nhRegPanel = uipanel('Parent',guiFig,'Title','REGISTRATION','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\n\nuicontrol('Parent',hRegPanel, 'Style', 'edit', 'String','Exp. Time Reg.:','Enable','off','Position', [staticst php-40 100 20]);\nhandles.Edit_RegExpTime = uicontrol('Parent',hRegPanel, 'Style', 'edit', ...\n    'String','0.01','Enable','on','BackgroundColor',[1 1 1],...\n    'Position', [editst php-40 50 20]);\nhandles.Button_RegLoadRef=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Load Reference','Enable','on',...\n    'Position', [staticst php-70 100 20],'Callback',@LoadRef);\nhandles.Edit_RegFileName = uicontrol('Parent',hRegPanel, 'Style', 'edit', ...\n    'String','File Name','Enable','on','BackgroundColor',[1 1 1],...\n    'Position', [editst php-70 250 20]);\nhandles.Button_RegAlign=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Align','Enable','on','Position', ...\n    [staticst php-100 80 20],'Callback',@Align);\nhandles.Button_RegShowRef=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton', ...\n    'String','Show Reference','Enable','on','Position', ...\n    [staticst+80 php-100 100 20],'Callback',@ShowRef);\nhandles.Button_RegTakeCurrent=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Take Current','Enable','on','Position', ...\n    [staticst+180 php-40 90 20],'Callback',@TakeCurrent);\nhandles.Button_RegCenterStage=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton', ...\n    'String','Center Stage','Enable','on','Position', ...\n    [staticst+270 php-40 90 20],'Callback',@CenterStage);\nhandles.Button_RegTakeReference=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Take Reference','Enable','on','Position', ...\n    [staticst+180 php-100 90 20],'Callback',@TakeReference);\nhandles.Button_RegSaveReference=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton', ...\n    'String','Save Reference','Enable','on','Position', ...\n    [staticst+270 php-100 90 20],'Callback',@SaveReference);\n\nBGh=100;\nhandles.ButtonGroup_RegCollectType=uibuttongroup('Parent',hRegPanel, 'Position', [.02 (php-130-BGh+30)/php .96 BGh/php]);\nb1=uicontrol('Parent',handles.ButtonGroup_RegCollectType, 'Style', 'radio', 'tag','None','String','No Registration','Enable','on','Position', [staticst BGh-30 250 20]);\nb2=uicontrol('Parent',handles.ButtonGroup_RegCollectType, 'Style', 'radio','tag','Self', 'String','Align to Self (Takes/Saves Reference Image)','Enable','on','Position', [staticst BGh-60 250 20]);\nb3=uicontrol('Parent',handles.ButtonGroup_RegCollectType, 'Style', 'radio','tag','Ref','String','Align to Reference','Enable','on','Position', [staticst BGh-90 250 20]);\n\n\nb4=uicontrol('Parent',handles.ButtonGroup_RegCollectType,'Style','text','String','Reg Camera Type','Position',[staticst+125 BGh-34 140 20]);\nb5 = uicontrol('Parent',handles.ButtonGroup_RegCollectType, 'Style', 'popupmenu', 'String',{'Andor Camera','IRCamera'},'Enable','on','BackgroundColor',[1 1 1],...\n    'Position', [staticst+240 BGh-30 100 20],'CallBack',@RegCamType_set);\n\nb6=uicontrol('Parent',handles.ButtonGroup_RegCollectType,'Style','text','String','Active Stabilization','Position',[staticst+125 BGh-94 210 20]);\nb7=uicontrol('Parent',handles.ButtonGroup_RegCollectType,'Style', 'checkbox', 'Value',0,'Position', [staticst+165 BGh-90 15 20],'Callback',@ActiveStabilization);\n\n% LIGHTSOURCE Panel\nph=0.3;\nhLampPanel = uipanel('Parent',guiFig,'Title','LIGHT SOURCE','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\nuicontrol(hLampPanel,'Style','text','String','On during Focus',...\n    'Position',[-3 240,60,25]);\nuicontrol(hLampPanel,'Style','text','String','On during Aquisition',...\n    'Position',[-3 180,60,28]);\nuicontrol(hLampPanel,'Style','text','String','Low Power',...\n    'Position',[0 120,60,25]);\nuicontrol(hLampPanel,'Style','text','String','High Power',...\n    'Position',[0 70,60,25]);\nuicontrol(hLampPanel,'Style','text','String','[MinPower,MaxPower]',...\n    'Position',[-1 5,60,35]);\n\nh561Panel=uipanel(hLampPanel,'Title','561 nm','TitlePosition','centertop',...\n    'Position',[1/5 1/5 1/5 4/5]);\nhandles.Focus561 = uicontrol(h561Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 185 130 20]);\nhandles.Aquisition561 = uicontrol(h561Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 130 130 20]);\nhandles.LP561 = uicontrol(h561Panel,'Style','edit',...\n                'Position',[21 80 30 20],'Callback',@setLaser561Low);\nhandles.HP561 = uicontrol(h561Panel,'Style','edit',...\n                'Position',[21 25 30 20]);\n% powerString561 = sprintf('[%g, %g] (%s)',obj.Laser561Obj.MinPower,obj.Laser561Obj.MaxPower, obj.Laser561.PowerUnit);\n% uicontrol(hLampPanel,'Style','text','String',powerString561,...\n%     'Position',[75, 5,60,35]);\n\nh638Panel=uipanel(hLampPanel,'Title','638 nm','TitlePosition','centertop',...\n    'Position',[2/5 1/5 1/5 4/5]);\nhandles.Focus638 = uicontrol(h638Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 185 130 20]);\nhandles.Aquisition638 = uicontrol(h638Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 130 130 20]);\nhandles.LP638 = uicontrol(h638Panel,'Style','edit',...\n                'Position',[21 80 30 20],'Callback',@setLaser638Low);\nhandles.HP638 = uicontrol(h638Panel,'Style','edit',...\n                'Position',[21 25 30 20]);\npowerString638 = sprintf('[%g, %g] (%s)',obj.Laser638Obj.MinPower,obj.Laser638Obj.MaxPower, obj.Laser638Obj.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerString638,...\n    'Position',[160 5,60,35]);\n\nhlamp850Panel=uipanel(hLampPanel,'Title','Lamp 850 nm','TitlePosition','centertop',...\n    'Position',[3/5 1/5 1/5 4/5]);\nhandles.Focuslamp850 = uicontrol(hlamp850Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 185 130 20]);\nhandles.Aquisitionlamp850 = uicontrol(hlamp850Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 130 130 20]);\nhandles.LED850lLampPower = uicontrol(hlamp850Panel,'Style','edit',...\n                'Position',[21 80 30 20],'Callback',@setLamp850Power);\npowerString850 = sprintf('[%g, %g] (%s)',obj.Lamp850Obj.MinPower,obj.Lamp850Obj.MaxPower, obj.Lamp850Obj.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerString850,...\n    'Position',[235 5,60,35]);\n\nhX71Panel=uipanel(hLampPanel,'Title','Lamp','TitlePosition','centertop',...\n    'Position',[4/5 1/5 1/5 4/5]);\nhandles.FocusIX71Lamp = uicontrol(hX71Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 185 130 20]);\nhandles.AquisitionIX71Lamp = uicontrol(hX71Panel,'Style','checkbox',...\n                'Value',0,'Position',[29 130 130 20]);\nhandles.IX71LampPower = uicontrol(hX71Panel,'Style','edit',...\n                'Position',[21 80 30 20],'Callback',@setLampPower);\npowerStringLamp = sprintf('[%g, %g] (%s)',obj.LampObj.MinPower,obj.LampObj.MaxPower, obj.LampObj.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerStringLamp,...\n    'Position',[310 5,60,35]);\n\n% CONTROL Panel\nph=0.190;\nhControlPanel = uipanel('Parent',guiFig,'Title','CONTROL','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nphp = ph*ysz;\nuicontrol('Parent',hControlPanel, 'Style', 'edit', 'String','Number of Sequences:','Enable','off','Position', [staticst php-40 150 20]);\nhandles.Edit_ControlNSequence = uicontrol('Parent',hControlPanel, 'Style', 'edit', 'String','20','Enable','on','BackgroundColor',[1 1 1],'Position', [editst+50 php-40 50 20]);\nhandles.Button_ControlFocusLamp=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','Focus Lamp','Enable','on','Position', [staticst php-90 103 35],'BackgroundColor',[1 1 .8],'Callback',@FocusLamp);\nhandles.Button_ControlFocusLaserLow=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','Focus Laser (Low)','Enable','on','Position', [staticst+123 php-90 103 35],'BackgroundColor',[1 .8 .8],'Callback',@FocusLow);\nhandles.Button_ControlFocusLamp850=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','Focus Lamp 850','Enable','on','Position', [staticst+246 php-90 103 35],'BackgroundColor',[1 1 .8],'Callback',@FocusLamp850);\nhandles.Toggle_LaserLamp=uicontrol('Parent',hControlPanel, 'Style', 'toggle', 'String','Lamp','Enable','on','Position', [staticst php-138 103 35],'BackgroundColor',[1 1 .8],'Callback',@ToggleLamp);\nhandles.Button_ControlStart=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','START (SR)','Enable','on','Position', [staticst php-186 103 35],'BackgroundColor',[0 1 0],'Callback',@Start);\nhandles.Button_ControlFocusLaserHigh=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','Focus Laser (High)','Enable','on','Position', [staticst+123 php-138 103 35],'BackgroundColor',[1 0 0],'Callback',@FocusHigh);\nhandles.Button_ControlAbort=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','ABORT','Enable','on','Position', [staticst+123 php-186 103 35],'BackgroundColor',[1 0 1],'Callback',@Abort);\nhandles.Button_ControlStart_SPT=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','START (SPT+SR)','Enable','on','Position', [staticst+246 php-186 103 35],'BackgroundColor',[0 1 0],'Callback',@Start_SPT);\n\n%% Setup GUI Values\n\nproperties2gui();\n\n\n%% Figure Callbacks\n\n    function saveFile(~,~)\n        file_val=get(handles.saveFileType,'value');\n        switch file_val\n            case 1\n                obj.SaveFileType='mat';\n            case 2\n                obj.SaveFileType='h5';\n        end\n    end\n\n    function LoadRef(~,~)\n        obj.loadref();\n        set(handles.Edit_RegFileName,'String',obj.R3DObj.RefImageFile);\n    end\n\n    function Align(~,~)\n        gui2properties();\n        obj.align();\n    end\n\n    function ShowRef(~,~)\n        obj.showref();\n    end\n\n    function TakeCurrent(~,~)\n        gui2properties();\n        obj.takecurrent();\n    end\n\n    function CenterStage(~,~)\n        obj.StageObj.center();\n    end\n\n    function TakeReference(~,~)\n        gui2properties();\n        obj.takeref();\n    end\n\n    function SaveReference(~,~)\n        obj.saveref();\n        properties2gui();\n    end\n\n    function ToggleLamp(~,~)\n        gui2properties();\n        stateLamp1=get(handles.Toggle_LaserLamp,'Value');\n        if stateLamp1\n            obj.LampObj.setPower(obj.LampPower);\n            obj.LampObj.on;\n        else\n            obj.LampObj.off;\n%            pause(obj.LampWait);\n        end\n    end\n\n\n    function FocusLamp(~,~)\n        gui2properties();\n        obj.focusLamp();\n    end\n  function FocusLamp850(~,~)\n        gui2properties();\n        obj.focusLamp850();\n    end\n\n    function FocusLow(~,~)\n        gui2properties();\n        obj.focusLow();\n    end\n\n    function FocusHigh(~,~)\n        gui2properties();\n        obj.focusHigh();\n    end\n\n    function Abort(~,~)\n        obj.AbortNow=1; \n        set(handles.Button_ControlStart, 'String','START (SR)','Enable','on');\n        set(handles.Button_ControlStart_SPT, 'String','START (SPT+SR)','Enable','on');\n    end\n\n    function Start(~,~)\n        gui2properties();\n        [temp status]=obj.CameraObj.call_temperature\n        if status==2\n        error('Camera is cooling down! Please wait for a few mintues!')\n        end \n        set(handles.Button_ControlStart, 'String','Acquiring','Enable','off');\n        obj.sequenceType='SRCollect';\n        obj.StartSequence(handles);\n        set(handles.Button_ControlStart, 'String','START (SR)','Enable','on');\n    end \n\n     function Start_SPT(~,~)\n        gui2properties();\n        [temp status]=obj.CameraObj.call_temperature\n        if status==2\n        error('Camera is cooling down! Please wait for a few mintues!')\n        end \n        set(handles.Button_ControlStart, 'String','Acquiring','Enable','off');\n        obj.sequenceType='Tracking+SRCollect';\n        obj.StartSequence(handles);\n        set(handles.Button_ControlStart, 'String','START (SPT+SR)','Enable','on');\n    end \n    \n    function setLampPower(~,~)\n        gui2properties();\n        obj.setLampPower();  \n    end\n\n    function setLamp850Power(~,~)\n        gui2properties();\n        obj.setLampPower();\n    end\n\n    function setLaser561Low(~,~)\n        obj.Laser561Obj.setPower(str2double(handles.LP561.String));\n    end\n\n    function setLaser638Low(~,~)\n        obj.Laser638Obj.setPower(str2double(handles.LP638.String));\n    end\n\n    function FigureClose(~,~)\n        gui2properties();\n        delete(guiFig);\n    end\n\n    function zoom_set(~,~)\n        zoom_val = get(handles.Popup_CameraDispZoom,'Value');\n        switch zoom_val\n            case 1\n                obj.CameraObj.DisplayZoom = 0.5;\n            case 2\n                obj.CameraObj.DisplayZoom = 1;\n            case 3\n                obj.CameraObj.DisplayZoom = 2;\n            case 4\n                obj.CameraObj.DisplayZoom = 4;\n            case 5\n                obj.CameraObj.DisplayZoom = 10;\n        end\n        \n    end\n\nfunction zoom_set_IR(~,~)\n        zoom_val = get(handles.Popup_CameraDispZoom_IR,'Value');\n        switch zoom_val\n            case 1\n                obj.IRCameraObj.DisplayZoom = 0.5;\n            case 2\n                obj.IRCameraObj.DisplayZoom = 1;\n            case 3\n                obj.IRCameraObj.DisplayZoom = 2;\n            case 4\n                obj.IRCameraObj.DisplayZoom = 4;\n            case 5\n                obj.IRCameraObj.DisplayZoom = 10;\n        end\n        \n    end\n\n    function gain_set(~,~)\n        gain_val=get(handles.Popup_CameraGain,'value');\n        switch gain_val\n            case 1\n                obj.CameraEMGainHigh=100;\n            case 2\n                obj.CameraEMGainHigh=200;\n        end\n    end\n\n    function RegCamType_set(~,~)\n        RegCamType=get(b5,'Value');\n        switch RegCamType\n            case 1\n                obj.RegCamType='Andor Camera';\n            case 2\n                obj.RegCamType='IRCamera';\n        end\n        obj.set_RegCamType();\n    end\n\n    function ActiveStabilization(~,~)\n        obj.ActiveRegCheck=get(b7,'Value');\n    end \n\n    function sequence_set(~,~)\n        gui2properties();\n        obj.CameraObj.ExpTime_Sequence = obj.ExpTime_Sequence_Set;        \n        obj.CameraObj.AcquisitionType = 'sequence';\n        obj.CameraObj.setup_acquisition;\n        set(handles.Edit_CameraExpTimeSeqActual,'String',num2str(obj.CameraObj.SequenceCycleTime));\n    end\n\n    function gui2properties()\n        %Get GUI values and update to object properties\n        obj.SaveDir=get(handles.Edit_FileDirectory,'String');\n        obj.BaseFileName=get(handles.Edit_FileName,'String');\n        obj.CameraGain=get(handles.Popup_CameraGain,'value');\n        obj.CameraROI=get(handles.Popup_CameraROI,'value');\n        obj.IRCameraROI=get(handles.Popup_IRCameraROI,'value');\n        obj.ExpTime_Focus_Set=str2double(get(handles.Edit_CameraExpTimeFocusSet,'string'));\n        obj.ExpTime_Sequence_Set=str2double(get(handles.Edit_CameraExpTimeSeqSet,'string'));\n        obj.NumFrames=str2double(get(handles.Edit_CameraNumFrames,'string'));\n        obj.R3DObj.ExposureTime=str2double(get(handles.Edit_RegExpTime,'string'));\n        obj.R3DObj.RefImageFile=get(handles.Edit_RegFileName,'string');\n        obj.ActiveRegCheck=get(b7,'Value')\n        obj.focus561Flag=get(handles.Focus561,'Value');\n        obj.focus638Flag=get(handles.Focus638,'Value');\n        obj.focusLampFlag=get(handles.FocusIX71Lamp,'Value');\n        obj.focusLamp850Flag=get(handles.Focuslamp850,'Value');\n        obj.Laser561Low=str2double(handles.LP561.String);\n        obj.Laser638Low=str2double(handles.LP638.String);\n        obj.Laser561High=str2double(handles.HP561.String);\n        obj.Laser638High=str2double(handles.HP638.String);\n        obj.LampPower=str2double(handles.IX71LampPower.String);\n        obj.Lamp850Power=str2double(handles.LED850lLampPower.String);\n        obj.Laser561Aq=handles.Aquisition561.Value;\n        obj.Laser638Aq=handles.Aquisition638.Value;\n        obj.LampAq=handles.AquisitionIX71Lamp.Value;\n        obj.Lamp850Aq=handles.Aquisitionlamp850.Value;\n        obj.NumSequences=str2double(get(handles.Edit_ControlNSequence,'string')); \n        obj.RegType=get(get(handles.ButtonGroup_RegCollectType,'SelectedObject'),'Tag'); \n        \n    end\n   \n    function properties2gui()\n        %Set GUI values from object properties\n        set(handles.Edit_FileDirectory,'String',obj.SaveDir);\n        set(handles.Edit_FileName,'String',obj.BaseFileName);\n        set(handles.Popup_CameraGain,'value',obj.CameraGain);\n        set(handles.Popup_CameraROI,'value',obj.CameraROI);\n        set(handles.Popup_IRCameraROI,'value',obj.IRCameraROI);\n        set(handles.Edit_CameraExpTimeFocusSet,'string',num2str(obj.ExpTime_Focus_Set));\n        set(handles.Edit_CameraExpTimeSeqSet,'string',num2str(obj.ExpTime_Sequence_Set));\n        %         set(handles.Edit_RegExpTime,'string',num2str(obj.R3DObj.ExposureTime));\n        set(handles.Edit_CameraNumFrames,'string',num2str(obj.NumFrames));\n        %         set(handles.Edit_RegFileName,'string',obj.R3DObj.RefImageFile);\n        if strcmp(obj.RegCamType,'Andor Camera')\n            v=1;\n        elseif strcmp(obj.RegCamType,'IRCamera')\n            v=2;\n        end\n        set(b5,'value',v);\n        set(b7,'value',obj.ActiveRegCheck);\n        set(handles.LP561,'string',obj.Laser561Low);\n        set(handles.LP638,'string',obj.Laser638Low);\n        set(handles.HP561,'string',obj.Laser561High);\n        set(handles.HP638,'string',obj.Laser638High);\n        set(handles.IX71LampPower,'string',obj.LampPower);\n        set(handles.LED850lLampPower,'string',obj.Lamp850Power);\n        set(handles.Focus561,'value',obj.focus561Flag);\n        set(handles.Focus638,'value',obj.focus638Flag);\n        set(handles.FocusIX71Lamp,'value',obj.focusLampFlag);\n        set(handles.Focuslamp850,'value',obj.focusLamp850Flag);\n        set(handles.Edit_ControlNSequence,'string',num2str(obj.NumSequences));\n        set(handles.Edit_CameraExpTimeSeqActual,'String',num2str(obj.CameraObj.SequenceCycleTime));\n\n        switch obj.RegType\n            case 'None'\n                set(handles.ButtonGroup_RegCollectType,'SelectedObject',b1); \n            case 'Self'\n                set(handles.ButtonGroup_RegCollectType,'SelectedObject',b2); \n            case 'Ref'\n                set(handles.ButtonGroup_RegCollectType,'SelectedObject',b3); \n        end    \n    end\n   \n    \n\n\n\nend\n\n"}
+{"plateform": "github", "repo_name": "LidkeLab/matlab-instrument-control-master", "name": "setCamProperties.m", "ext": ".m", "path": "matlab-instrument-control-master/@MIC_AndorCamera/setCamProperties.m", "size": 5290, "source_encoding": "utf_8", "md5": "00353ba3ce9c36d6a6457121019fb226", "text": "function setCamProperties(obj,CamSetting)\n%SETCAMERAPROPERTIES Summary of this function goes here\n%   in: a duplicate CameraSetting structure to be checked and set\n% this function sets the camera properties on the camera so that what you\n% select on the gui is committed to the API\n\nobj.ReadyForAcq=0;\n%obj.CameraParameters=in;\n\n% ADChannel;\n% Amp;\n% BaselineClamp;\n% Cooler;\n% Fan;\n% HSSpeed;\n% PreAmpGain;\n% Shutter;\n% Trigger;\n% VSAmplitudes;\n% VSSpeed;\n% VerShiftVoltage;\n\nsetCamSDK(obj,CamSetting);\nobj.get_parameters;\nobj.ReadyForAcq=1;\n\nend\n\nfunction setCamSDK(obj,CamSetting)\n% sub function to set the property given the input field, works on case by case basis!\n% all of these are copied and pasted from other legacy code so variables\n% will have to be renamed shortly!\ncamFields = fields(CamSetting);\ndebug = false;\nfor ii=1:length(camFields)\n    switch (camFields{ii})\n        case 'ManualShutter'\n            obj.CameraSetting.ManualShutter.Bit = CamSetting.ManualShutter.Bit;\n            \n        case 'EMGain'\n            obj.LastError = SetEMCCDGain(CamSetting.EMGain.Value);\n            obj.errorcheck('SetEMCCDGain');\n            obj.CameraSetting.EMGain.Value=CamSetting.EMGain.Value;\n            if debug\n                % check low level behavior\n                [obj.LastError,EMGainVal] = GetEMCCDGain;\n                disp(['Check EMGain: set ', num2str(CamSetting.EMGain.Value),' , get ' num2str(EMGainVal)]);\n            end\n        case 'ADChannel'\n            obj.LastError = SetADChannel(CamSetting.ADChannel.Bit);\n            obj.errorcheck('SetADChannel');\n            obj.CameraSetting.ADChannel.Bit=CamSetting.ADChannel.Bit;\n            if debug\n                % check low level behavior\n                disp(['Check ADChannel: set ', CamSetting.ADChannel.Desc]);\n            end\n        case 'Amp'\n            obj.LastError = SetOutputAmplifier(CamSetting.Amp.Bit);\n            obj.errorcheck('SetOutputAmplifier');\n            obj.CameraSetting.Amp.Bit=CamSetting.Amp.Bit;\n            if debug\n                % check low level behavior\n                disp(['Check Amp: set ', CamSetting.Amp.Desc]);\n            end\n        case 'HSSpeed'\n            AmpBit = CamSetting.Amp.Bit;\n            obj.LastError = SetHSSpeed(AmpBit,CamSetting.HSSpeed.Bit);\n            obj.errorcheck('SetHSSpeed');\n            obj.CameraSetting.HSSpeed.Bit=CamSetting.HSSpeed.Bit;\n            if debug\n                % check low level behavior\n                disp(['Check HSSpeed: set ', CamSetting.HSSpeed.Desc]);\n            end\n        case 'BaselineClamp'\n            obj.LastError = SetBaselineClamp(CamSetting.BaselineClamp.Bit);\n            obj.errorcheck('SetBaselineClamp');\n            obj.CameraSetting.BaselineClamp.Bit=CamSetting.BaselineClamp.Bit;\n            if debug\n                % check low level behavior\n            disp(['Check BaselineClamp: set ', CamSetting.BaselineClamp.Desc]);\n            end\n        case 'Cooler'\n            obj.LastError = SetCoolerMode(CamSetting.Cooler.Bit);\n            obj.errorcheck('SetCoolerMode');\n            obj.CameraSetting.Cooler.Bit=CamSetting.Cooler.Bit;\n        case 'Fan'\n            obj.LastError = SetFanMode(CamSetting.Fan.Bit);\n            obj.errorcheck('SetFanMode');\n            obj.CameraSetting.Fan.Bit=CamSetting.Fan.Bit;\n        case 'PreAmpGain'\n            obj.LastError = SetPreAmpGain(CamSetting.PreAmpGain.Bit);\n            obj.errorcheck('SetPreAmpGain');\n            obj.CameraSetting.PreAmpGain.Bit=CamSetting.PreAmpGain.Bit;\n        case 'Shutter'\n%             obj.LastError = SetShutter(value.typ{1},value.mode{1},...\n%                 value.closingtime,value.openingtime);\n%             obj.errorcheck('SetShutter'); \n            % Set Shutter to Auto (1) or Manual (0)\n            obj.CameraSetting.ManualShutter.Bit = CamSetting.ManualShutter.Bit;\n        case 'Trigger'\n            obj.LastError=SetTriggerMode(CamSetting.Trigger.Bit); %   Set trigger mode; 0 for Internal\n            obj.errorcheck('SetTriggerMode');\n            obj.CameraSetting.Trigger.Bit=CamSetting.Trigger.Bit;\n            if debug\n                % check low level behavior\n                disp(['Check Trigger: set ', CamSetting.Trigger.Desc]);\n            end\n        case 'VSAmplitude' % this is the vertical shift voltage!\n            obj.LastError = SetVSAmplitude(CamSetting.VSAmplitude.Bit);\n            obj.errorcheck('SetVSAmplitude');\n            obj.CameraSetting.VSAmplitude.Bit=CamSetting.VSAmplitude.Bit;\n            if debug\n                % check low level behavior\n                disp(['Check VSAmplitude: set ', CamSetting.VSAmplitude.Desc]);\n            end\n        case 'VSSpeed'\n            obj.LastError = SetVSSpeed(CamSetting.VSSpeed.Bit);\n            obj.errorcheck('SetVSSpeed');\n            obj.CameraSetting.VSSpeed.Bit=CamSetting.VSSpeed.Bit;\n            if debug\n                % check low level behavior\n                disp(['Check VSSpeed: set ', CamSetting.VSSpeed.Desc]);\n            end\n        case 'FrameTransferMode'\n            obj.LastError = SetFrameTransferMode(CamSetting.FrameTransferMode.Bit);\n            obj.errorcheck('SetFrameTransferMode');\n            obj.CameraSetting.FrameTransferMode.Bit=CamSetting.FrameTransferMode.Bit;\n    end\nend\n        \nend\n\n"}
+{"plateform": "github", "repo_name": "LidkeLab/matlab-instrument-control-master", "name": "gui.m", "ext": ".m", "path": "matlab-instrument-control-master/@MIC_RB_PSFEng/gui.m", "size": 17913, "source_encoding": "utf_8", "md5": "3ffb4d357fa8f7872686a894b1cd3210", "text": "function gui( obj )\n%GUI Gui for MIC_RB_PSFEng class\n%   Detailed explanation goes here\n\n%Prevent opening more than one figure for same instrument\nif ishandle(obj.GuiFigure)\n    guiFig = obj.GuiFigure;\n    figure(obj.GuiFigure);\n    return\nend\n\n%Open figure\nguiFig = figure('NumberTitle','off','Resize','off','Units','pixels','MenuBar','none',...\n    'ToolBar','none','Visible','on', 'Position',[70 600 450 400]);\n% Create a property based on GuiFigure\nobj.GuiFigure = guiFig;\nobj.GuiFigure.Name = sprintf('%s', obj.InstrumentName);\n%Prevent closing after a 'close' or 'close all'\nobj.GuiFigure.HandleVisibility='off';\n%Save Propeties upon close\nobj.GuiFigure.CloseRequestFcn = @closeFigure;\n%Mouse scroll wheel callback\nobj.GuiFigure.WindowScrollWheelFcn = @wheel;\n\n% Laser control\nminPower=obj.Laser642.MinPower;\nmaxPower=obj.Laser642.MaxPower;\nunitPower=obj.Laser642.PowerUnit;\n\n% File name and save dir\nhandles.FilePanel = uipanel(guiFig,'Title','File','FontSize',8,...\n             'Units','Pixels','Position',[10 280 210 120]);\nuicontrol(handles.FilePanel,'Style','text','String','Save Directory',...\n    'Position',[5,85,130,15],'FontSize',8,'HorizontalAlignment','left'); \nhandles.editSaveDir = uicontrol(handles.FilePanel,'Style','edit',...\n    'Position',[5,65,200,20],'FontSize',8);\nuicontrol(handles.FilePanel,'Style','text','String','File name',...\n    'Position',[5,50,130,15],'FontSize',8,'HorizontalAlignment','left'); \nhandles.editFileName = uicontrol(handles.FilePanel,'Style','edit',...\n    'Position',[5,30,200,20],'FontSize',8);\nuicontrol(handles.FilePanel,'Style','text','String','Save type',...\n    'Position',[5,5,130,15],'FontSize',8,'HorizontalAlignment','left'); \nhandles.editSaveType = uicontrol(handles.FilePanel,'Style','popup','String',...\n    {'mat','h5'},'Position',[75,5,100,20],'FontSize',8);\n\n% laser control\nhandles.LaserPanel = uipanel(guiFig,'Title','Laser','FontSize',8,...\n             'Units','Pixels','Position',[10 210 210 70]);\nuicontrol(handles.LaserPanel,'Style','text','String',...\n    ['Power limits ' num2str(minPower) ' - ' num2str(maxPower) ' ' unitPower],...\n    'Position',[5,35,130,15],'FontSize',8,'HorizontalAlignment','left');\nuicontrol(handles.LaserPanel,'Style','text','String','Set Power',...\n    'Position',[5,10,60,15],'FontSize',8,'HorizontalAlignment','left');\nhandles.SetLaserPower = uicontrol(handles.LaserPanel,'Style','edit',...\n    'Position',[70 10,60,15],'FontSize',8,'Callback',@setPower);\nhandles.Toggle_Laser=uicontrol(handles.LaserPanel,'Style','togglebutton',...\n    'String','Off','Position',[150,10,40,40],...\n    'BackgroundColor',[0.8  0.8  0.8],'Tag','LaserPowerButton','Callback',@toggleLaser);\n\n% Camera control\nhandles.CameraPanel = uipanel(guiFig,'Title','Camera','FontSize',8,...\n             'Units','Pixels','Position',[10 10 210 200]);\nhandles.CamFocus = uicontrol(handles.CameraPanel,'Style','pushbutton',...\n    'String','Focus','Position',[5,5,50,30],'FontSize',8,'Callback',@focusCamera);\nhandles.CamFocusWithI = uicontrol(handles.CameraPanel,'Style','pushbutton',...\n    'String','Focus with Intensity feedback','Position',[60,5,145,30],'FontSize',8,'Callback',@focusCameraWithFeedback);\nuicontrol(handles.CameraPanel,'Style','text','String',...\n    'ROI','Position',[5,165,50,15],'FontSize',8,'HorizontalAlignment','left');\nhandles.CamROIpopup = uicontrol(handles.CameraPanel,'Style','popup','String',...\n    {'center 128','center 256','center 512','center 1024','full'},...\n    'Position', [60 165 130 15],'FontSize',8,'Callback',@setROI);\nuicontrol(handles.CameraPanel,'Style','text','String',...\n    'Zoom','Position',[5,135,50,15],'FontSize',8,'HorizontalAlignment','left');\nhandles.CamZoom_popup = uicontrol(handles.CameraPanel,'Style','popupmenu',...\n    'String',{'50%','100%','200%','400%','1000%'},'Value',4,...\n    'Position', [60 135 130 15],'FontSize',8,'CallBack',@setZoom);\nuicontrol(handles.CameraPanel,'Style','text','String',...\n    'Acquisition time (s)','Position',[5,100,100,15],'FontSize',8,...\n    'HorizontalAlignment','left');\nhandles.CamAcqTime = uicontrol(handles.CameraPanel,'Style','edit',...\n    'Position',[120,100,70,15],'FontSize',8,'Tag','CamAcqEdit',...\n    'Callback',@setAcqTime);\n\n% Piezo control\nhandles.PiezoPanel = uipanel(guiFig,'Title','Piezo','FontSize',8,...\n             'Units','Pixels','Position',[230 300 210 100]);\nminPos=obj.Piezo.MinPosition;\nmaxPos=obj.Piezo.MaxPosition;\nposUnit=obj.Piezo.PositionUnit;\nfineStepFrac = 0.01;\ncoarseStepFrac = 0.1;\nfineStep = (maxPos-minPos)*fineStepFrac;\ncoarseStep = (maxPos-minPos)*coarseStepFrac;\n% slider\nsliderVertPos = 50;\nsliderLeft = 40;\nsliderWidth = 130;\nsliderHeight = 20;\nhandles.sliderPosition=uicontrol(handles.PiezoPanel,'Style','slider','Min',minPos,...\n    'Max',maxPos,'Value',minPos,'SliderStep',[fineStepFrac fineStepFrac],...\n    'Position', [sliderLeft sliderVertPos sliderWidth sliderHeight],...\n    'Tag','positionSlider','Callback',@positionSlider);\nhandles.sliderPosition.KeyPressFcn = @sliderKey;\nuicontrol(handles.PiezoPanel,'Style','text','String',...\n    [num2str(minPos),' ',posUnit],'Position',...\n    [sliderLeft+15 sliderVertPos+25,30,12],'HorizontalAlignment','left',...\n    'FontSize',8);\nuicontrol(handles.PiezoPanel,'Style','text','String',...\n    [num2str(maxPos),' ',posUnit],'Position',...\n    [sliderLeft+80 sliderVertPos+25,40,12],'HorizontalAlignment','left',...\n    'FontSize',8);\nuicontrol(handles.PiezoPanel,'Style','text','String','F','Position',...\n    [sliderLeft sliderVertPos+25,10,12],'HorizontalAlignment','left',...\n    'FontSize',8);\nuicontrol(handles.PiezoPanel,'Style','text','String','F','Position',...\n    [sliderLeft+sliderWidth-10 sliderVertPos+25,10,12],'HorizontalAlignment','left',...\n    'FontSize',8);\n% coarse buttons\njogHeight = sliderVertPos;\nhandles.buttonJogDown = uicontrol(handles.PiezoPanel,'Style','pushbutton','String','<',...\n    'Position',[sliderLeft-25 jogHeight,20,sliderHeight],'FontSize',16,'Callback',@jogDown);\nhandles.buttonJogUp = uicontrol(handles.PiezoPanel,'Style','pushbutton','String','>',...\n    'Position',[sliderLeft+sliderWidth+5 jogHeight,20,sliderHeight],'FontSize',16,'Callback',@jogUp);\nuicontrol(handles.PiezoPanel,'Style','text','String','C','Position',...\n    [sliderLeft-20 sliderVertPos+25,10,12],'HorizontalAlignment','left',...\n    'FontSize',8);\nuicontrol(handles.PiezoPanel,'Style','text','String','C','Position',...\n    [sliderLeft+sliderWidth+10 sliderVertPos+25,10,12],'HorizontalAlignment','left',...\n    'FontSize',8);\n% set step size and position\nuicontrol(handles.PiezoPanel,'Style','text','String','Step size  Fine','Position',...\n    [5 sliderVertPos-20,90,12],'HorizontalAlignment','left',...\n    'FontSize',8);\nhandles.editFineStep = uicontrol(handles.PiezoPanel,'Style','edit',...\n    'Position',[80,sliderVertPos-22,30,15],'FontSize',8,...\n    'String',num2str(fineStep),'Callback',@setFineStepSize);\nuicontrol(handles.PiezoPanel,'Style','text','String','Coarse','Position',...\n    [125 sliderVertPos-20,40,12],'HorizontalAlignment','left',...\n    'FontSize',8);\nhandles.editCoarseStep = uicontrol(handles.PiezoPanel,'Style','edit',...\n    'Position',[170,sliderVertPos-22,30,15],'FontSize',8,...\n    'String',num2str(coarseStep));\nuicontrol(handles.PiezoPanel,'Style','text','String','Set position','Position',...\n    [5 sliderVertPos-45,90,12],'HorizontalAlignment','left',...\n    'FontSize',8);\nhandles.editPosition = uicontrol(handles.PiezoPanel,'Style','edit',...\n    'Position',[80,sliderVertPos-48,60,15],'FontSize',8,'Callback',@setPosition);\n\n% set SLM\nhandles.SLMPanel = uipanel(guiFig,'Title','SLM','FontSize',8,...\n     'Units','Pixels','Position',[230 160 210 140]);\nhandles.calPupilPos = uicontrol(handles.SLMPanel,'Style','pushbutton',...\n    'String','Cal Pupil Position','Position',[5,5,100,30],'FontSize',8,'Callback',@calPupilPos);\nhandles.optimPSF = uicontrol(handles.SLMPanel,'Style','pushbutton',...\n    'String','Optimize PSF','Position',[105,5,100,30],'FontSize',8,'Callback',@optimPupil);\nuicontrol(handles.SLMPanel,'Style','text','String','# zernike coef to optimize',...\n    'Position',[5,40,150,15],'FontSize',8);\nhandles.editNumCoef = uicontrol(handles.SLMPanel,'Style','edit','String','13',...\n    'Position',[160,40,40,15],'FontSize',8);\n    \n% acquire PSF\nhandles.AcqPanel = uipanel(guiFig,'Title','Acquire PSF','FontSize',8,...\n    'Units','Pixels','Position',[230 10 210 150]);\nuicontrol(handles.AcqPanel,'Style','text','String','Z range ([min step max])',...\n    'Position',[5,110,140,15],'FontSize',8,'HorizontalAlignment','left');\nhandles.editZrange = uicontrol(handles.AcqPanel,'Style','edit','String','-1 0.25 1',...\n    'Position',[140,110,60,15],'FontSize',8,'HorizontalAlignment','center');\nuicontrol(handles.AcqPanel,'Style','text','String','Frames per Z position',...\n    'Position',[5,90,140,15],'FontSize',8,'HorizontalAlignment','left');\nhandles.editNumFrames = uicontrol(handles.AcqPanel,'Style','edit','String','100',...\n    'Position',[140,90,60,15],'FontSize',8,'HorizontalAlignment','center');\nhandles.startZstack = uicontrol(handles.AcqPanel,'Style','pushbutton',...\n    'String','Start','Position',[5,5,60,30],'FontSize',8,...\n    'Callback',@startZstack);\n\n% Initialize GUI properties\nproperties2gui();\n% Change some defaults\nhandles.CamROIpopup.Value = 1;\nhandles.CamZoom_popup.Value = 4;\nhandles.CamAcqTime.String = '0.01';\nhandles.SetLaserPower.String = 1;\ngui2properties();\n\n%% callback functions\n\n    function closeFigure(~,~)\n        gui2properties();\n        delete(obj.GuiFigure);\n    end\n\n    function properties2gui()\n        % update gui with object properties\n        \n        % laser\n        handles.SetLaserPower.String = obj.Laser642.Power;\n        set(handles.Toggle_Laser,'Value',obj.Laser642.IsOn);\n        if obj.Laser642.IsOn==1\n            set(handles.Toggle_Laser,'String','On');\n            set(handles.Toggle_Laser,'BackgroundColor','red');\n        else\n            set(handles.Toggle_Laser,'String','Off');\n            set(handles.Toggle_Laser,'BackgroundColor',[.8 .8 .8]);\n        end\n        \n        % camera\n        handles.CamAcqTime.String = obj.Camera.ExpTime_Focus;\n        \n        % piezo\n        if isempty(obj.Piezo.CurrentPosition) || isnan(obj.Piezo.CurrentPosition)\n            obj.Piezo.getPosition;\n        end\n        handles.sliderPosition.Value = obj.Piezo.CurrentPosition;\n        handles.editPosition.String = num2str(obj.Piezo.CurrentPosition);\n    end\n\n    function gui2properties()\n        setPower();\n        setROI();\n        setZoom();\n        setAcqTime();\n        \n    end\n\n    function setPower(~,~)\n        textValue=str2double(get(handles.SetLaserPower,'String'));\n        if textValue > obj.Laser642.MaxPower || isnan(textValue)\n            error('Choose a number for Power between [MinPower,MaxPower]');\n        end\n         obj.Laser642.setPower(textValue);\n    end\n\n    function toggleLaser(~,~)\n        state=get(handles.Toggle_Laser,'Value');\n        if state\n            obj.Laser642.on();\n            set(handles.Toggle_Laser,'BackgroundColor','red');\n            set(handles.Toggle_Laser,'String','On')\n        else\n            set(handles.Toggle_Laser,'BackgroundColor',[0.8  0.8  0.8])\n            set(handles.Toggle_Laser,'String','Off')\n            obj.Laser642.off();\n        end\n    end\n\n    function focusCamera(~,~)\n        obj.Laser642.on();\n        obj.Camera.start_focus;\n        obj.Laser642.off();\n    end\n\nfunction focusCameraWithFeedback(~,~)\n        obj.Laser642.on();\n        obj.Camera.start_focusWithFeedback;\n        obj.Laser642.off();\n    end\n\nfunction setROI(~,~)\n        roi_val = handles.CamROIpopup.Value;\n        switch roi_val\n            % given as [Xstart Xend Ystart Yend]\n            case 1\n                ROI=[961 1088 961 1088];% center 128\n            case 2\n                ROI=[897 1152 897 1152];% center 256\n            case 3\n                ROI=[769 1280 769 1280];% center 512\n            case 4\n                ROI=[513 1536 513 1536]; % center 1024\n            case 5\n                ROI=[1 2048 1 2048]; % full 2048\n        end\n        obj.Camera.ROI = ROI;\n    end\n    \n    function setZoom(~,~)\n        zoom_val = handles.CamZoom_popup.Value;\n        switch zoom_val\n            case 1 % 50%\n                obj.Camera.DisplayZoom = 0.5;\n            case 2 % 100%\n                obj.Camera.DisplayZoom = 1;\n            case 3 % 200%\n                obj.Camera.DisplayZoom = 2;\n            case 4 % 400%\n                obj.Camera.DisplayZoom = 4;\n            case 5 % 1000%\n                obj.Camera.DisplayZoom = 10;\n        end\n    end\n\n    function setAcqTime(~,~)\n        Value=str2double(get(handles.CamAcqTime,'String'));\n        obj.Camera.ExpTime_Capture = Value;\n        obj.Camera.ExpTime_Focus = Value;\n        obj.Camera.ExpTime_Sequence = Value;\n    end\n\n    function positionSlider(~,~)\n        Value=handles.sliderPosition.Value;\n        obj.Piezo.setPosition(Value)\n        handles.editPosition.String = num2str(Value);\n    end\n\n    function jogUp(~,~)\n        stepSize = str2double(handles.editCoarseStep.String);\n        newPos = obj.Piezo.CurrentPosition + stepSize;\n        if newPos > obj.Piezo.MaxPosition\n            warning('Position outside range, moving to maximum position')\n            newPos = obj.Piezo.MaxPosition;\n        end\n        obj.Piezo.setPosition(newPos);\n        properties2gui()\n    end\n\n    function jogDown(~,~)\n        stepSize = str2double(handles.editCoarseStep.String);\n        newPos = obj.Piezo.CurrentPosition - stepSize;\n        if newPos < obj.Piezo.MinPosition\n            warning('Position outside range, moving to minimum position')\n            newPos = obj.Piezo.MinPosition;\n        end\n        obj.Piezo.setPosition(newPos);\n        properties2gui()\n    end\n\n    function setFineStepSize(~,~)\n        stepSize=str2double(handles.editFineStep.String);\n        stepPercent = stepSize/(obj.Piezo.MaxPosition-obj.Piezo.MinPosition);\n        handles.sliderPosition.SliderStep = [stepPercent stepPercent];\n    end\n\n    function setPosition(~,~)\n        Value=str2double(handles.editPosition.String);\n        if Value < obj.Piezo.MinPosition \n            warning('MIC_LinearStage_Abstract:GuiInvPos',...\n                'Invalid Position (%i %s) Position cannot be smaller then %i %s, moving to minimum position',...\n                Value, obj.Piezo.PositionUnit, obj.Piezo.MinPosition, obj.Piezo.PositionUnit);\n            Value = obj.Piezo.MinPosition;\n        end\n        if Value > obj.Piezo.MaxPosition \n            warning('MIC_LinearStage_Abstract:GuiInvPos',...\n                'Invalid Position (%i %s) Position cannot be larger then %i %s, moving to maximum position',...\n                Value, obj.Piezo.PositionUnit, obj.Piezo.MaxPosition, obj.Piezo.PositionUnit);\n            Value = obj.Piezo.MaxPosition;\n        end\n        obj.Piezo.setPosition(Value)\n        handles.sliderPosition.Value = Value;\n    end\n\n    function wheel(~,Event)\n        point = guiFig.CurrentPoint;\n        % check whether mouse is over slider\n        if point(1) < sliderLeft || point(1) > sliderLeft+sliderWidth ...\n                || point(2) < sliderBottom || point(2) > sliderVertPos+sliderHeight\n            return\n        end\n        stepSize = str2double(handles.editCoarseJog.String);\n        step = Event.VerticalScrollCount*-stepSize;\n        newPos = obj.Piezo.CurrentPosition + step;\n        if newPos < obj.Piezo.MinPosition || newPos > obj.Piezo.MaxPosition\n            return\n        end\n        obj.Piezo.setPosition(newPos);\n        properties2gui()\n    end\n\n    function calPupilPos(~,~)\n        obj.calibratePupilPosition();\n    end\n\n    function optimPupil(~,~)\n        NumCoef = str2double(handles.editNumCoef.String);\n        obj.optimizePupil(NumCoef);\n    end\n\n    function startZstack(~,~)\n        % get save info\n        SaveDir = handles.editSaveDir.String;\n        FileName = handles.editFileName.String;\n        SaveType = handles.editSaveType.String{handles.editSaveType.Value};\n        TimeStamp = datestr(clock,'yyyy-mm-dd-HH-MM-SS');\n        % get z range info\n        ZrangeStr = handles.editZrange.String;\n        idx = strfind(ZrangeStr,' ');\n        Zrange = [str2double(ZrangeStr(1:idx(1)-1)),...\n            str2double(ZrangeStr(idx(1)+1:idx(2)-1)),...\n            str2double(ZrangeStr(idx(2)+1:end))];\n        z0=obj.Piezo.CurrentPosition;\n        z = Zrange(1):Zrange(2):Zrange(3);\n        dataset=[];\n        %Setup Camera\n        obj.Camera.SequenceLength=str2double(handles.editNumFrames.String);\n        obj.Camera.AcquisitionType = 'sequence';\n        obj.Camera.setup_acquisition;\n        %Collect\n        obj.Laser642.on();\n        pause(.25);\n        for ii=1:length(z)\n            obj.Piezo.setPosition(z0+z(ii))\n            pause(.1);\n            sequence=obj.Camera.start_sequence();\n            dataset=cat(4,dataset,sequence);\n        end\n        %Turn off Laser and reset Piezo\n        obj.Laser642.off();\n        obj.Piezo.setPosition(z0);\n        % show data\n        dipshow(permute(squeeze(mean(dataset,3)),[2,1,3]));\n        % save data\n        switch SaveType\n            case 'mat'\n                Params.Camera = obj.Camera.exportState();\n                Params.Piezo = obj.Piezo.exportState();\n                Params.Laser642 = obj.Laser642.exportState();\n                Params.SLM = obj.SLM.exportState();\n                Params.Zrange = Zrange;\n                Params.NA=obj.NA;\n                Params.RefrIdx=obj.RefrIdx;\n                Params.Fobjective=obj.Fobjective;\n                Params.SLMangle = obj.SLMangle;\n                Params.PixelSize = obj.PixelSize;\n                Params.ZCoefOptimized = obj.ZCoefOptimized; %#ok<STRNU>\n                save(fullfile(SaveDir,[FileName '#' TimeStamp '.mat']),'dataset','Params');\n            case 'h5'\n                warning('no h5 saving yet')\n        end\n    end\nend\n\n"}
+{"plateform": "github", "repo_name": "LidkeLab/matlab-instrument-control-master", "name": "gui.m", "ext": ".m", "path": "matlab-instrument-control-master/@MIC_TIRF_SRcollect/gui.m", "size": 21009, "source_encoding": "utf_8", "md5": "93650b7a3e977f739c1540ff8e7a8970", "text": "\nfunction gui(obj)\n% GUI SRcollect Gui for TIRF microscope\n%   Detailed explanation goes here\n\nh = findall(0,'tag','TIRF-SRcollect.gui');\nif ~(isempty(h))\n    figure(h);\n    return;\nend\n%%\nxsz=400;\nysz=1060;\nxst=100;\nyst=100;\npw=.95;\npsep=.001;\nstaticst=10;\neditst=110;\n\nguiFig = figure('Units','pixels','Position',[xst yst xsz ysz],...\n    'MenuBar','none','ToolBar','none','Visible','on',...\n    'NumberTitle','off','UserData',0,'Tag',...\n    'TIRF-SRcollect.gui','HandleVisibility','off','name','SRcollect.gui','CloseRequestFcn',@FigureClose);\n\ndefaultBackground = get(0,'defaultUicontrolBackgroundColor');\nset(guiFig,'Color',defaultBackground);\nhandles.output = guiFig;\nguidata(guiFig,handles);\n\nrefh=1;\n\n% File Panel\nph=0.09;\nphp = ph*ysz;\nhFilePanel = uipanel('Parent',guiFig,'Title','FILE','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\n\nuicontrol('Parent',hFilePanel, 'Style', 'edit', 'String', 'Save Directory:','Enable','off','Position', [staticst php-40 100 20]);\nhandles.Edit_FileDirectory = uicontrol('Parent',hFilePanel, 'Style', 'edit', 'String','Set Auto','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-40 250 20]);\nuicontrol('Parent',hFilePanel, 'Style', 'edit', 'String', 'Base FileName:','Enable','off','Position', [staticst php-65 100 20]);\nhandles.Edit_FileName = uicontrol('Parent',hFilePanel, 'Style', 'edit', 'String','Set Auto','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-65 250 20]);\nuicontrol('Parent',hFilePanel, 'Style', 'edit', 'String','File type:','Enable','off','Position', [staticst php-90 100 20]);\nhandles.saveFileType = uicontrol('Parent',hFilePanel, 'Style', 'popupmenu', 'String',{'.mat','.h5'},'Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-90 250 20],'CallBack',@saveFile);\n\n% Camera Panel\nph=0.17;\nphp = ph*ysz;\nhCameraPanel = uipanel('Parent',guiFig,'Title','CAMERA','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Camera Gain:','Enable','off','Position', [staticst php-40 100 20]);\nhandles.Popup_CameraGain = uicontrol('Parent',hCameraPanel, 'Style', 'popupmenu', 'String',{'Low (Alexa647)','High (FAP/FP)'},'Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-40 250 20],'CallBack',@gain_set);\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Camera ROI:','Enable','off','Position', [staticst php-70 100 20]);\nROIlist={'Full','Left','Right','Left Center','Right Center','Center Horizontally','Left Top','Left Bottom','Right Top','Right Bottom','Top','Bottom','Center256'};\nhandles.Popup_CameraROI = uicontrol('Parent',hCameraPanel, 'Style', 'popupmenu', 'String',ROIlist,'Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-70 250 20]);\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Exp. Time Focus:','Enable','off','Position', [staticst php-100 100 20]);\nhandles.Edit_CameraExpTimeFocusSet = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','0.01','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-100 50 20]);\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Actual:','Enable','off','Position', [175 php-100 100 20]);\nhandles.Edit_CameraExpTimeFocusActual = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','','Enable','off','Position', [250 php-100 50 20]);\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Exp. Time Seq.:','Enable','off','Position', [staticst php-130 100 20]);\nhandles.Edit_CameraExpTimeSeqSet = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','0.01','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-130 50 20],'CallBack',@sequence_set);\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Actual:','Enable','off','Position', [175 php-130 100 20]);\nhandles.Edit_CameraExpTimeSeqActual = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String',num2str(obj.CameraObj.SequenceCycleTime),'Enable','off','Position', [250 php-130 50 20]);\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Num Frames:','Enable','off','Position', [staticst php-160 100 20]);\nhandles.Edit_CameraNumFrames = uicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','2000','Enable','on','BackgroundColor',[1 1 1],'Position', [editst php-160 50 20]);\n\nuicontrol('Parent',hCameraPanel, 'Style', 'edit', 'String','Zoom:','Enable','off','Position', [175 php-160 100 20]);\nhandles.Popup_CameraDispZoom = uicontrol('Parent',hCameraPanel, 'Style','popupmenu','String',{'50%','100%','200%','400%','1000%'},'Value',4,'Enable','on','BackgroundColor',[1 1 1],'Position', [250 php-160 50 20],'CallBack',@zoom_set);\n\n% Registration Panel\nph=0.213;\nphp = ph*ysz;\nhRegPanel = uipanel('Parent',guiFig,'Title','REGISTRATION','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\n\nuicontrol('Parent',hRegPanel, 'Style', 'edit', 'String','Exp. Time Reg.:','Enable','off','Position', [staticst php-40 100 20]);\nhandles.Edit_RegExpTime = uicontrol('Parent',hRegPanel, 'Style', 'edit', ...\n    'String','0.01','Enable','on','BackgroundColor',[1 1 1],...\n    'Position', [editst php-40 50 20]);\nhandles.Button_RegLoadRef=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Load Reference','Enable','on',...\n    'Position', [staticst php-70 100 20],'Callback',@LoadRef);\nhandles.Edit_RegFileName = uicontrol('Parent',hRegPanel, 'Style', 'edit', ...\n    'String','File Name','Enable','on','BackgroundColor',[1 1 1],...\n    'Position', [editst php-70 250 20]);\nhandles.Button_RegAlign=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Align','Enable','on','Position', ...\n    [staticst php-100 80 20],'Callback',@Align);\nhandles.Button_RegShowRef=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton', ...\n    'String','Show Reference','Enable','on','Position', ...\n    [staticst+80 php-100 100 20],'Callback',@ShowRef);\nhandles.Button_RegTakeCurrent=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Take Current','Enable','on','Position', ...\n    [staticst+180 php-40 90 20],'Callback',@TakeCurrent);\nhandles.Button_RegCenterStage=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton', ...\n    'String','Center Stage','Enable','on','Position', ...\n    [staticst+270 php-40 90 20],'Callback',@CenterStage);\nhandles.Button_RegTakeReference=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton',...\n    'String','Take Reference','Enable','on','Position', ...\n    [staticst+180 php-100 90 20],'Callback',@TakeReference);\nhandles.Button_RegSaveReference=uicontrol('Parent',hRegPanel, 'Style', 'pushbutton', ...\n    'String','Save Reference','Enable','on','Position', ...\n    [staticst+270 php-100 90 20],'Callback',@SaveReference);\n\n\nBGh=100;\nhandles.ButtonGroup_RegCollectType=uibuttongroup('Parent',hRegPanel, 'Position', [.02 (php-130-BGh+30)/php .96 BGh/php]);\nb1=uicontrol('Parent',handles.ButtonGroup_RegCollectType, 'Style', 'radio', 'tag','None','String','No Registration','Enable','on','Position', [staticst BGh-30 250 20]);\nb2=uicontrol('Parent',handles.ButtonGroup_RegCollectType, 'Style', 'radio','tag','Self', 'String','Align to Self (Takes/Saves Reference Image)','Enable','on','Position', [staticst BGh-60 250 20]);\nb3=uicontrol('Parent',handles.ButtonGroup_RegCollectType, 'Style', 'radio','tag','Ref','String','Align to Reference','Enable','on','Position', [staticst BGh-90 250 20]);\n\n% LIGHTSOURCE Panel\nph=0.3;\nhLampPanel = uipanel('Parent',guiFig,'Title','LIGHT SOURCE','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nrefh=refh-ph-psep;\nuicontrol(hLampPanel,'Style','text','String','On during Focus',...\n    'Position',[-3 240,60,25]);\nuicontrol(hLampPanel,'Style','text','String','On during Aquisition',...\n    'Position',[-3 180,60,28]);\nuicontrol(hLampPanel,'Style','text','String','Low Power',...\n    'Position',[0 120,60,25]);\nuicontrol(hLampPanel,'Style','text','String','High Power',...\n    'Position',[0 70,60,25]);\nuicontrol(hLampPanel,'Style','text','String','[MinPower,MaxPower]',...\n    'Position',[-1 5,60,35]);\n\nh405Panel=uipanel(hLampPanel,'Title','405 nm','Position',[1/6 1/6 1/6 5/6]);\nhandles.Focus405 = uicontrol(h405Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 190 130 20]);\nhandles.Aquisition405 = uicontrol(h405Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 130 130 20]);\nhandles.LP405 = uicontrol(h405Panel,'Style','edit',...\n                'Position',[15 80 30 20],'Callback',@setLaser405Low);\nhandles.HP405 = uicontrol(h405Panel,'Style','edit',...\n                'Position',[15 25 30 20]);\npowerString405 = sprintf('[%g, %g] (%s)',obj.Laser405.MinPower,obj.Laser405.MaxPower, obj.Laser405.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerString405,...\n    'Position',[64, 5,60,35]);\n\nh488Panel=uipanel(hLampPanel,'Title','488 nm','Position',[2/6 1/6 1/6 5/6]);\nhandles.Focus488 = uicontrol(h488Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 190 130 20]);\nhandles.Aquisition488 = uicontrol(h488Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 130 130 20]);\nhandles.LP488 = uicontrol(h488Panel,'Style','edit',...\n                'Position',[15 80 30 20],'Callback',@setLaser488Low);\nhandles.HP488 = uicontrol(h488Panel,'Style','edit',...\n                'Position',[15 25 30 20]);\npowerString488 = sprintf('[%g, %g] (%s)',obj.Laser488.MinPower,obj.Laser488.MaxPower, obj.Laser488.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerString488,...\n    'Position',[125 5,60,35]);\n\nh561Panel=uipanel(hLampPanel,'Title','561 nm','Position',[3/6 1/6 1/6 5/6]);\nhandles.Focus561 = uicontrol(h561Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 190 130 20]);\nhandles.Aquisition561 = uicontrol(h561Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 130 130 20]);\nhandles.LP561 = uicontrol(h561Panel,'Style','edit',...\n                'Position',[15 80 30 20],'Callback',@setLaser561Low);\nhandles.HP561 = uicontrol(h561Panel,'Style','edit',...\n                'Position',[15 25 30 20]);\npowerString561 = sprintf('[%g, %g] (%s)',obj.Laser561.MinPower,obj.Laser561.MaxPower, obj.Laser561.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerString561,...\n    'Position',[190 5,60,35]);\n\nh642Panel=uipanel(hLampPanel,'Title','642 nm','Position',[4/6 1/6 1/6 5/6]);\nhandles.Focus642 = uicontrol(h642Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 190 130 20]);\nhandles.Aquisition642 = uicontrol(h642Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 130 130 20]);\nhandles.LP642 = uicontrol(h642Panel,'Style','edit',...\n                'Position',[15 80 30 20],'Callback',@setLaser642Low);\nhandles.HP642 = uicontrol(h642Panel,'Style','edit',...\n                'Position',[15 25 30 20]);\npowerString642 = sprintf('[%g, %g] (%s)',obj.Laser642.MinPower,obj.Laser642.MaxPower, obj.Laser642.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerString642,...\n    'Position',[250 5,60,35]);\n\nhX71Panel=uipanel(hLampPanel,'Title','Lamp','Position',[5/6 1/6 1/6 5/6]);\nhandles.FocusIX71Lamp = uicontrol(hX71Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 190 130 20]);\nhandles.AquisitionIX71Lamp = uicontrol(hX71Panel,'Style','checkbox',...\n                'Value',0,'Position',[23 130 130 20]);\nhandles.IX71LampPower = uicontrol(hX71Panel,'Style','edit',...\n                'Position',[15 80 30 20],'Callback',@SetLampPower);\npowerStringLamp = sprintf('[%g, %g] (%s)',obj.LampObj.MinPower,obj.LampObj.MaxPower, obj.LampObj.PowerUnit);\nuicontrol(hLampPanel,'Style','text','String',powerStringLamp,...\n    'Position',[315 5,60,35]);\n\n\n% CONTROL Panel\nph=0.190;\nhControlPanel = uipanel('Parent',guiFig,'Title','CONTROL','Position',[(1-pw)/2 refh-ph-psep pw ph]);\nphp = ph*ysz;\nuicontrol('Parent',hControlPanel, 'Style', 'edit', 'String','Number of Sequences:','Enable','off','Position', [staticst php-40 150 20]);\nhandles.Edit_ControlNSequence = uicontrol('Parent',hControlPanel, 'Style', 'edit', 'String','20','Enable','on','BackgroundColor',[1 1 1],'Position', [editst+50 php-40 50 20]);\nhandles.Button_ControlFocusLamp=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','Focus Lamp','Enable','on','Position', [staticst php-90 150 40],'BackgroundColor',[1 1 .8],'Callback',@FocusLamp);\nhandles.Button_ControlFocusLaserLow=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','Focus Laser (Low)','Enable','on','Position', [staticst+200 php-90 150 40],'BackgroundColor',[1 .8 .8],'Callback',@FocusLow);\nhandles.Toggle_LaserLamp=uicontrol('Parent',hControlPanel, 'Style', 'toggle', 'String','Lamp','Enable','on','Position', [staticst php-138 150 40],'BackgroundColor',[1 1 .8],'Callback',@ToggleLamp);\nhandles.Button_ControlStart=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','START','Enable','on','Position', [staticst php-175 150 30],'BackgroundColor',[0 1 0],'Callback',@Start);\nhandles.Button_ControlFocusLaserHigh=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','Focus Laser (High)','Enable','on','Position', [staticst+200 php-138 150 40],'BackgroundColor',[1 0 0],'Callback',@FocusHigh);\nhandles.Button_ControlAbort=uicontrol('Parent',hControlPanel, 'Style', 'pushbutton', 'String','ABORT','Enable','on','Position', [staticst+200 php-175 150 30],'BackgroundColor',[1 0 1],'Callback',@Abort);\n\n%% Setup GUI Values\n\nproperties2gui();\n\n\n%% Figure Callbacks\n\n    function saveFile(~,~)\n        file_val=get(handles.saveFileType,'value');\n        switch file_val\n            case 1\n                obj.SaveFileType='mat';\n            case 2\n                obj.SaveFileType='h5';\n        end\n    end\n\n    function LoadRef(~,~)\n        obj.loadref();\n        set(handles.Edit_RegFileName,'String',obj.R3DObj.RefImageFile);\n    end\n\n    function Align(~,~)\n        gui2properties();\n        obj.align();\n    end\n\n    function ShowRef(~,~)\n        obj.showref();\n    end\n\n    function TakeCurrent(~,~)\n        gui2properties();\n        obj.takecurrent();\n    end\n\n    function CenterStage(~,~)\n        obj.StageObj.center();\n    end\n\n    function TakeReference(~,~)\n        gui2properties();\n        obj.takeref();\n    end\n\n    function SaveReference(~,~)\n        obj.saveref();\n        properties2gui();\n    end\n\n    function ToggleLamp(~,~)\n        gui2properties();\n        stateLamp1=get(handles.Toggle_LaserLamp,'Value');\n        if stateLamp1\n            obj.LampObj.setPower(obj.LampPower);\n            obj.LampObj.on;\n        else\n            obj.LampObj.off;\n%            pause(obj.LampWait);\n        end\n    end\n\n\n    function FocusLamp(~,~)\n        gui2properties();\n        obj.focusLamp();\n    end\n\n    function FocusLow(~,~)\n        gui2properties();\n        obj.focusLow();\n    end\n\n    function FocusHigh(~,~)\n        gui2properties();\n        obj.focusHigh();\n    end\n\n    function Abort(~,~)\n        obj.AbortNow=1; \n        set(handles.Button_ControlStart, 'String','START','Enable','on');\n    end\n\n    function Start(~,~)\n        gui2properties();\n        set(handles.Button_ControlStart, 'String','Acquiring','Enable','off');\n        obj.StartSequence(handles);\n        set(handles.Button_ControlStart, 'String','START','Enable','on');\n    end \n    \n    function SetLampPower(~,~)\n        gui2properties();\n        obj.setLampPower();  \n    end\n    \n    function setLaser405Low(~,~)\n        obj.Laser405.setPower(str2double(handles.LP405.String));\n    end\n\n    function setLaser488Low(~,~)\n        obj.Laser405.setPower(str2double(handles.LP488.String));\n    end\n\n    function setLaser561Low(~,~)\n        obj.Laser405.setPower(str2double(handles.LP561.String));\n    end\n\n    function setLaser642Low(~,~)\n        obj.Laser405.setPower(str2double(handles.LP642.String));\n    end\n\n    function FigureClose(~,~)\n        gui2properties();\n        delete(guiFig);\n    end\n\n    function zoom_set(~,~)\n        zoom_val = get(handles.Popup_CameraDispZoom,'Value');\n        switch zoom_val\n            case 1\n                obj.CameraObj.DisplayZoom = 0.5;\n            case 2\n                obj.CameraObj.DisplayZoom = 1;\n            case 3\n                obj.CameraObj.DisplayZoom = 2;\n            case 4\n                obj.CameraObj.DisplayZoom = 4;\n            case 5\n                obj.CameraObj.DisplayZoom = 10;\n        end\n        \n    end\n\n    function gain_set(~,~)\n        gain_val=get(handles.Popup_CameraGain,'value');\n        switch gain_val\n            case 1\n                obj.CameraEMGainHigh=100;\n            case 2\n                obj.CameraEMGainHigh=200;\n        end\n    end\n\n    function sequence_set(~,~)\n        gui2properties();\n        obj.CameraObj.ExpTime_Sequence = obj.ExpTime_Sequence_Set;        \n        obj.CameraObj.AcquisitionType = 'sequence';\n        obj.CameraObj.setup_acquisition;\n        set(handles.Edit_CameraExpTimeSeqActual,'String',num2str(obj.CameraObj.SequenceCycleTime));\n    end\n\n    function gui2properties()\n        %Get GUI values and update to object properties\n        obj.SaveDir=get(handles.Edit_FileDirectory,'String');\n        obj.BaseFileName=get(handles.Edit_FileName,'String');\n        obj.CameraGain=get(handles.Popup_CameraGain,'value');\n        obj.CameraROI=get(handles.Popup_CameraROI,'value');\n        obj.ExpTime_Focus_Set=str2double(get(handles.Edit_CameraExpTimeFocusSet,'string'));\n        obj.ExpTime_Sequence_Set=str2double(get(handles.Edit_CameraExpTimeSeqSet,'string'));\n        obj.NumFrames=str2double(get(handles.Edit_CameraNumFrames,'string'));\n        obj.R3DObj.ExposureTime=str2double(get(handles.Edit_RegExpTime,'string'));\n        obj.R3DObj.RefImageFile=get(handles.Edit_RegFileName,'string');\n        obj.focus405Flag=get(handles.Focus405,'Value');\n        obj.focus488Flag=get(handles.Focus488,'Value');\n        obj.focus561Flag=get(handles.Focus561,'Value');\n        obj.focus642Flag=get(handles.Focus642,'Value');\n        obj.focusLampFlag=get(handles.FocusIX71Lamp,'Value');\n        obj.Laser405Low=str2double(handles.LP405.String);\n        obj.Laser488Low=str2double(handles.LP488.String);\n        obj.Laser561Low=str2double(handles.LP561.String);\n        obj.Laser642Low=str2double(handles.LP642.String);\n        obj.Laser405High=str2double(handles.HP405.String);\n        obj.Laser488High=str2double(handles.HP488.String);\n        obj.Laser561High=str2double(handles.HP561.String);\n        obj.Laser642High=str2double(handles.HP642.String);\n        obj.LampPower=str2double(handles.IX71LampPower.String);\n        obj.Laser405Aq=handles.Aquisition405.Value;\n        obj.Laser488Aq=handles.Aquisition488.Value;\n        obj.Laser561Aq=handles.Aquisition561.Value;\n        obj.Laser642Aq=handles.Aquisition642.Value;\n        obj.LampAq=handles.AquisitionIX71Lamp.Value;\n        obj.NumSequences=str2double(get(handles.Edit_ControlNSequence,'string')); \n        obj.RegType=get(get(handles.ButtonGroup_RegCollectType,'SelectedObject'),'Tag'); \n        \n    end\n   \n    function properties2gui()\n        %Set GUI values from object properties\n        set(handles.Edit_FileDirectory,'String',obj.SaveDir);\n        set(handles.Edit_FileName,'String',obj.BaseFileName);\n        set(handles.Popup_CameraGain,'value',obj.CameraGain);\n        set(handles.Popup_CameraROI,'value',obj.CameraROI);\n        set(handles.Edit_CameraExpTimeFocusSet,'string',num2str(obj.ExpTime_Focus_Set));\n        set(handles.Edit_CameraExpTimeSeqSet,'string',num2str(obj.ExpTime_Sequence_Set));\n        set(handles.Edit_RegExpTime,'string',num2str(obj.R3DObj.ExposureTime));\n        set(handles.Edit_CameraNumFrames,'string',num2str(obj.NumFrames));\n        set(handles.Edit_RegFileName,'string',obj.R3DObj.RefImageFile);\n        set(handles.LP405,'string',obj.Laser405Low);\n        set(handles.LP488,'string',obj.Laser488Low);\n        set(handles.LP561,'string',obj.Laser561Low);\n        set(handles.LP642,'string',obj.Laser642Low);\n        set(handles.HP405,'string',obj.Laser405High);\n        set(handles.HP488,'string',obj.Laser488High);\n        set(handles.HP561,'string',obj.Laser561High);\n        set(handles.HP642,'string',obj.Laser642High);\n        set(handles.IX71LampPower,'string',obj.LampPower);\n        set(handles.Focus405,'value',obj.focus405Flag);\n        set(handles.Focus488,'value',obj.focus488Flag);\n        set(handles.Focus561,'value',obj.focus561Flag);\n        set(handles.Focus642,'value',obj.focus642Flag);\n        set(handles.FocusIX71Lamp,'value',obj.focusLampFlag);\n        set(handles.Edit_ControlNSequence,'string',num2str(obj.NumSequences));\n        set(handles.Edit_CameraExpTimeSeqActual,'String',num2str(obj.CameraObj.SequenceCycleTime));\n\n        switch obj.RegType\n            case 'None'\n                set(handles.ButtonGroup_RegCollectType,'SelectedObject',b1); \n            case 'Self'\n                set(handles.ButtonGroup_RegCollectType,'SelectedObject',b2); \n            case 'Ref'\n                set(handles.ButtonGroup_RegCollectType,'SelectedObject',b3); \n        end    \n    end\n   \n    \n\n\n\nend\n\n"}
+{"plateform": "github", "repo_name": "vaibhavjoshi16/Matlab-solver-for-adaptive-two-phase-flows-master", "name": "coarsening.m", "ext": ".m", "path": "Matlab-solver-for-adaptive-two-phase-flows-master/coarsening.m", "size": 5050, "source_encoding": "utf_8", "md5": "19e7e1fc6bd780ce0690c5302041ffbf", "text": "function [Sol,eta] = coarsening(Sol,eta,theta)\n% COARSENING coarsens the current mesh\n%\n% USAGE\n%    [Sol,eta] = coarsening(Sol,eta,theta)\n%\n% INPUT \n%      Sol:  current mesh data\n%      eta:  error indicator for each triangle\n%    theta:  parameter in (0,1). \n%            We mark minimal number of triangles M such that\n%               \\sum_{T \\in M} \\eta_T < \\theta*\\sum\\eta_T\n%\n% OUTPUT\n%      Sol:  new mesh data after coarsening\n%      eta:  new error indicator for each triangle\n%\n% NOTE\n%    Only one level coarsening\n%\n% REFERENCE\n%    AFEM@matlab Manual --> Algorithms --> Coarsening     \n%\n\n% L. Chen & C. Zhang 11-15-2006\n% Modified by Vaibhav Joshi, Rajeev K. Jaiman\n\n%--------------------------------------------------------------------------\n% Construct data structure\n%--------------------------------------------------------------------------\nN = size(Sol.node,1); \nNT = size(Sol.elem,1);\ndualEdge = sparse(Sol.elem(:,[1,2,3]),Sol.elem(:,[2,3,1]),[1:NT,1:NT,1:NT]);\nvalence = accumarray(Sol.elem(:),ones(3*NT,1),[N 1]);\neta_p = accumarray(Sol.elem(:),[eta;eta;eta],[N 1]);\n \n%--------------------------------------------------------------------------\n% Mark nodes for coarsen\n%--------------------------------------------------------------------------\ntotal = sum(eta_p); maxeta = max(eta_p);\ncurrent = 0; marker = uint8(zeros(N,1)); \nnewestNode = unique(Sol.elem(:,1)); % newest vertices of all elements\ncoarseNode = find(Sol.type==1); % nodes in the initial triangulation\nnewestNode = setdiff(newestNode,coarseNode);\nisGood = (valence(newestNode) == 2) | (valence(newestNode) == 4);\ngoodNode = newestNode(isGood);\n% A node is called 'good' if it can be removed without breaking the mesh\n% comformity. A node is 'good' if and only if\n%  0. it is not a node of marco elements in the initial triangluation;\n%  1. it is the newest node in each of the elements with it as a node;\n%  2. if it is an interior node, the valence is 4;\n%  3. if it is on the boundary, the valence is 2.\n[temp,ix] = sort(eta_p(goodNode));\nfor i = 1:length(goodNode)\n    p = goodNode(ix(i));\n    if (eta_p(p) > 0.2*maxeta), break; end % only coarsen where error small\n    if (current + eta_p(p) > theta*total), break; end\n    marker(p) = 1;    % mark p to be removed\n    Sol.type(p) = 0; % node p is non active now\n    current = current + eta_p(p);\nend\nif (sum(marker)==0), return; end % no node can be coarsened\n\n%--------------------------------------------------------------------------\n% Coarsen the mesh by removing marked nodes\n%--------------------------------------------------------------------------\nfor t = 1:NT\n    if (Sol.elem(t,1)>0) % otherwise t is already deleted\n        p = Sol.elem(t,1);\n        if (marker(p)==1) % p is a marked good point\n            brother = dualEdge(Sol.elem(t,2),p);\n            Sol.elem(t,1) = Sol.elem(t,2);\n            Sol.elem(t,2) = Sol.elem(t,3);\n            Sol.elem(t,3) = Sol.elem(brother,2);\n            Sol.elem(brother,1) = 0; % brother is going to be deleted\n            eta(t) = eta(t) + eta(brother); % new eta = eta_t + eta_brother\n        end % endif for all markd good nodes\n    end\nend % end of for loop on all elements\n\n%--------------------------------------------------------------------------\n% Clear element and eta arrarys\n%--------------------------------------------------------------------------\nix = (Sol.elem(:,1) == 0); \nSol.elem(ix,:) = []; \neta(ix,:) = [];\n\n%--------------------------------------------------------------------------\n% Update boundary edges\n%--------------------------------------------------------------------------\nmarkedNode = goodNode((marker(goodNode)==1)&(valence(goodNode)==2));\nSol.Dirichlet = updatebd(Sol.Dirichlet, markedNode);\nSol.Neumann   = updatebd(Sol.Neumann, markedNode);\n\n%--------------------------------------------------------------------------\n% End of function COARSENING\n%--------------------------------------------------------------------------\n\n%--------------------------------------------------------------------------\n% Sub functions called by COARSENING\n%--------------------------------------------------------------------------\nfunction bdEdge = updatebd(bdEdge, markedNode)\n% UPDATEDBD coarsen the boundary edges\n%\n% USAGE\n%    bdEdge = updatebd(bdEdge, markedNode)\n%\n% INPUT\n%     bdEdge:  old boundary edges\n% markedNode:  node marked for coarsening\n%\n% OUTPUT\n%     bdEdge:  new boundary edges after coarsening\n%\n\nif (isempty(bdEdge) || isempty(markedNode)), return; end\nNB = size(bdEdge,1); \nneig = sparse(bdEdge,ones(NB,1)*[2,1],[1:NB,1:NB]);\n% skeleton of edges around a node\nfor k = 1: length(markedNode)\n    i = neig(markedNode(k),1);\n    j = neig(markedNode(k),2);\n    % o --- i --- o --- j --- o\n    %             ^\n    %         noActive(k)\n    bdEdge(i,2) = bdEdge(j,2);\n    bdEdge(j,1) = 0;\nend\nix = (bdEdge(:,1)==0); \nbdEdge(ix,:) = [];\n%--------------------------------------------------------------------------\n% End of function UPDATEDBD\n%--------------------------------------------------------------------------\n"}
+{"plateform": "github", "repo_name": "vaibhavjoshi16/Matlab-solver-for-adaptive-two-phase-flows-master", "name": "bisection.m", "ext": ".m", "path": "Matlab-solver-for-adaptive-two-phase-flows-master/bisection.m", "size": 11047, "source_encoding": "utf_8", "md5": "76f20349eeb792a7d8eaac98f42ee2a2", "text": "function [Sol,eta] = bisection(Sol,eta,theta)\n% BISECTION\trefines the triangulation using newest vertex bisection\n%\n% USAGE\n%    [Sol,eta] = bisection(Sol,eta,theta)\n%\n% INPUT \n%      Sol:  current mesh data with all variable data\n%      eta:  error indicator for each triangle\n%    theta:  parameter in (0,1). \n%            We mark minimal number of triangles M such that\n%              \\sum_{T \\in M} \\eta_T > \\theta*\\sum\\eta_T\n%\n% OUTPUT\n%      Sol:  new mesh data with interpolated variable data after refinement\n%      eta:  new error indicator for each triangle\n%\n% REFERENCE\n% \tLong Chen,\n%   Short bisection implementation in MATLAB\n%   Research Notes, 2006 \n%\n\n% L. Chen & C. Zhang 11-12-2006\n%\n% Modified by: Vaibhav Joshi, Rajeev K. Jaiman\n\n%--------------------------------------------------------------------------\n% Construct data structure\n%--------------------------------------------------------------------------\nedge = [Sol.elem(:,[1,2]); Sol.elem(:,[1,3]); Sol.elem(:,[2,3])];\nedge = unique(sort(edge,2),'rows');\n\nN = size(Sol.node,1);\nNT = size(Sol.elem,1); \nNE = size(edge,1);\n\ndualEdge = sparse(Sol.elem(:,[1,2,3]),Sol.elem(:,[2,3,1]),[1:NT,1:NT,1:NT]);\nd2p = sparse(edge(:,[1,2]),edge(:,[2,1]),[1:NE,1:NE]);\n% Detailed explanation can be founded at\n%   Manual --> Data Structure -->  Auxlliary data structure \n\n%--------------------------------------------------------------------------\n% Meomery management for node arrary\n%--------------------------------------------------------------------------\nrecycle = find(Sol.type==0); \nlast = length(recycle);\n% Coarsening can create empty spaces in the node array. We collect those\n% scattered spaces in recycle arrary and 'last' will point out the last\n% empty node index. \n% mesh.type array is used to distinguish the type of nodes:\n%   0: empty nodes (deleted by coarsening);\n%   1: nodes in the initial triangulation or nodes from regular refinement;\n%   2: new added nodes due to refinement;\n%   5: temporay node (will be deleted when bisection finished).\n\n%--------------------------------------------------------------------------\n% Mark triangles according to the error indicator\n%--------------------------------------------------------------------------\ntotal = sum(eta); \ncurrent = 0; \n\n[temp,ix] = sort(-eta); % sort in descent order\nmarker = zeros(NE,1); \n\n% initialize for possible new nodes\nSol.node = [Sol.node; zeros(NE,2)];\nSol.type = [Sol.type; uint8(5*ones(NE,1))];\nSol.u = [Sol.u; zeros(NE,2)];\nSol.uPrev = [Sol.uPrev; zeros(NE,2)];\nSol.uAlpha = [Sol.uAlpha; zeros(NE,2)];\nSol.uDot = [Sol.uDot; zeros(NE,2)];\nSol.uDotPrev = [Sol.uDotPrev; zeros(NE,2)];\nSol.uDotAlpha = [Sol.uDotAlpha; zeros(NE,2)];\nSol.p = [Sol.p; zeros(NE,1)];\nSol.pPrev = [Sol.pPrev; zeros(NE,1)];\nSol.phi = [Sol.phi; zeros(NE,1)];\nSol.phiPrev = [Sol.phiPrev; zeros(NE,1)];\nSol.phiAlpha = [Sol.phiAlpha; zeros(NE,1)];\nSol.phiDot = [Sol.phiDot; zeros(NE,1)];\nSol.phiDotPrev = [Sol.phiDotPrev; zeros(NE,1)];\nSol.phiDotAlpha = [Sol.phiDotAlpha; zeros(NE,1)];\n\nfor t = 1:NT\n    \n    if (current > theta*total), break; end % est on marked elem big enough\n    \n    index = 1; \n    ct = ix(t);\n    \n    while (index==1)\n        \n        base = d2p(Sol.elem(ct,2),Sol.elem(ct,3));\n        if (marker(base)>0) % base is already marked\n            index = 0;\n        else\n            current = current + eta(ct);\n            if (last==0)\n                newNode = N + 1; \n                N = N+1; \n            end\n            if (last>0)\n                newNode = recycle(last); \n                last = last-1; \n            end\n            marker( d2p(Sol.elem(ct,2),Sol.elem(ct,3)) ) = newNode;\n\n            % A new node is added to the mesh. Numerical solution at this\n            % new added node is approximated by linear interpolation. Type\n            % of this node is 2 (generated by newest vertex bisection)\n            Sol.node(newNode,:) = ( Sol.node(Sol.elem(ct,2),:) + ...\n                                     Sol.node(Sol.elem(ct,3),:) )/2;\n            Sol.u(newNode,1) = ( Sol.u(Sol.elem(ct,2),1) + ...\n                                   Sol.u(Sol.elem(ct,3),1) )/2;\n            Sol.uPrev(newNode,1) = ( Sol.uPrev(Sol.elem(ct,2),1) + ...\n                                   Sol.uPrev(Sol.elem(ct,3),1) )/2; \n            Sol.uAlpha(newNode,1) = ( Sol.uAlpha(Sol.elem(ct,2),1) + ...\n                                   Sol.uAlpha(Sol.elem(ct,3),1) )/2;                    \n            Sol.uDot(newNode,1) = ( Sol.uDot(Sol.elem(ct,2),1) + ...\n                                   Sol.uDot(Sol.elem(ct,3),1) )/2;  \n            Sol.uDotPrev(newNode,1) = ( Sol.uDotPrev(Sol.elem(ct,2),1) + ...\n                                   Sol.uDotPrev(Sol.elem(ct,3),1) )/2;  \n            Sol.uDotAlpha(newNode,1) = ( Sol.uDotAlpha(Sol.elem(ct,2),1) + ...\n                                   Sol.uDotAlpha(Sol.elem(ct,3),1) )/2;  \n            Sol.u(newNode,2) = ( Sol.u(Sol.elem(ct,2),2) + ...\n                                   Sol.u(Sol.elem(ct,3),2) )/2;\n            Sol.uPrev(newNode,2) = ( Sol.uPrev(Sol.elem(ct,2),2) + ...\n                                   Sol.uPrev(Sol.elem(ct,3),2) )/2; \n            Sol.uAlpha(newNode,2) = ( Sol.uAlpha(Sol.elem(ct,2),2) + ...\n                                   Sol.uAlpha(Sol.elem(ct,3),2) )/2;                    \n            Sol.uDot(newNode,2) = ( Sol.uDot(Sol.elem(ct,2),2) + ...\n                                   Sol.uDot(Sol.elem(ct,3),2) )/2;  \n            Sol.uDotPrev(newNode,2) = ( Sol.uDotPrev(Sol.elem(ct,2),2) + ...\n                                   Sol.uDotPrev(Sol.elem(ct,3),2) )/2;  \n            Sol.uDotAlpha(newNode,2) = ( Sol.uDotAlpha(Sol.elem(ct,2),2) + ...\n                                   Sol.uDotAlpha(Sol.elem(ct,3),2) )/2; \n            Sol.p(newNode) = ( Sol.p(Sol.elem(ct,2)) + ...\n                                   Sol.p(Sol.elem(ct,3)) )/2;\n            Sol.pPrev(newNode) = ( Sol.pPrev(Sol.elem(ct,2)) + ...\n                                   Sol.pPrev(Sol.elem(ct,3)) )/2; \n            Sol.phi(newNode) = ( Sol.phi(Sol.elem(ct,2)) + ...\n                                   Sol.phi(Sol.elem(ct,3)) )/2;\n            Sol.phiPrev(newNode) = ( Sol.phiPrev(Sol.elem(ct,2)) + ...\n                                   Sol.phiPrev(Sol.elem(ct,3)) )/2; \n            Sol.phiAlpha(newNode) = ( Sol.phiAlpha(Sol.elem(ct,2)) + ...\n                                   Sol.phiAlpha(Sol.elem(ct,3)) )/2; \n            Sol.phiDot(newNode) = ( Sol.phiDot(Sol.elem(ct,2)) + ...\n                                   Sol.phiDot(Sol.elem(ct,3)) )/2;\n            Sol.phiDotPrev(newNode) = ( Sol.phiDotPrev(Sol.elem(ct,2)) + ...\n                                   Sol.phiDotPrev(Sol.elem(ct,3)) )/2; \n            Sol.phiDotAlpha(newNode) = ( Sol.phiDotAlpha(Sol.elem(ct,2)) + ...\n                                   Sol.phiDotAlpha(Sol.elem(ct,3)) )/2;                   \n            Sol.type(newNode) = 2;\n            \n            % Find the element which shares the base edge of the current\n            % element. If it is 0, it means the base of the current element\n            % is on the boundary.\n            ct = dualEdge( Sol.elem(ct,3), Sol.elem(ct,2) );\n            if ( ct == 0 ), index = 0; end \n            % the while will ended if\n            % 1.  ct==0 means we are on the boundary\n            % 2.  base(ct) is already marked\n            \n        end\n    end % end while for recursive marking\nend % end of for loop on all elements\n% Detailed explanation of the algorithm can be found at\n%   Manual --> Algorithms --> Bisection\n\n% delete possible empty entries\nix = (Sol.type == 5); \nSol.node(ix,:) = []; \nSol.type(ix) = [];\nSol.u(ix,:) = [];\nSol.uPrev(ix,:) = [];\nSol.uAlpha(ix,:) = [];\nSol.uDot(ix,:) = [];\nSol.uDotPrev(ix,:) = [];\nSol.uDotAlpha(ix,:) = [];\nSol.p(ix) = [];\nSol.pPrev(ix) = [];\nSol.phi(ix) = [];\nSol.phiPrev(ix) = [];\nSol.phiAlpha(ix) = [];\nSol.phiDot(ix) = [];\nSol.phiDotPrev(ix) = [];\nSol.phiDotAlpha(ix) = [];\n\n%--------------------------------------------------------------------------\n% Refine marked edges for each triangle\n%--------------------------------------------------------------------------\nnumnew = 2*sum(marker~=0); % number of new elements need to be added\nSol.elem = [Sol.elem; zeros(numnew,3)];\neta = [eta; zeros(numnew,1)];\n\ninew = NT + 1; % index for current new added right child\nfor t = 1:NT\n    base = d2p(Sol.elem(t,2),Sol.elem(t,3));\n    if (marker(base)>0)\n        p = [Sol.elem(t,:), marker(base)];\n        \n        % Case 1: divide the current marked triangle\n        Sol.elem(t,:)  = [p(4),p(1),p(2)]; % t is always a left child\n        Sol.elem(inew,:) = [p(4),p(3),p(1)]; % new is a right child\n        eta(t) = eta(t)/2; % update error indicators \n        eta(inew) = eta(t);\n        inew = inew + 1;\n        \n        % Case 2: divide the right child, different, careful!!!\n        right = d2p(p(3),p(1));\n        if (marker(right)>0)\n            Sol.elem(inew-1,:) = [marker(right),p(4),p(3)]; \n            Sol.elem(inew,:)   = [marker(right),p(1),p(4)];\n            eta(inew-1) = eta(inew-1)/2;\n            eta(inew) = eta(inew-1);\n            inew = inew + 1;\n        end\n\n        % Case 3: divide the left child, similar to the case 1. \n        left = d2p(p(1),p(2));\n        if (marker(left)>0)\n            Sol.elem(t,:) = [marker(left),p(4),p(1)]; \n            Sol.elem(inew,:) = [marker(left),p(2),p(4)];  \n            eta(t) = eta(t)/2;\n            eta(inew) = eta(t);\n            inew = inew + 1;\n        end\n\n    end % end of refinement of one element\nend % end of for loop on all elements\n\n% delete possible empty entries\nSol.elem = Sol.elem(1:inew-1,:);\neta = eta(1:inew-1,:);\n\n%--------------------------------------------------------------------------\n% Update boundary edges\n%--------------------------------------------------------------------------\nSol.Dirichlet = updatebd(Sol.Dirichlet,marker,d2p);\nSol.Neumann   = updatebd(Sol.Neumann,marker,d2p);\n\n%--------------------------------------------------------------------------\n% End of function BISECTION\n%--------------------------------------------------------------------------\n\n%--------------------------------------------------------------------------\n% Sub functions called by BISECTION\n%--------------------------------------------------------------------------\n\nfunction bdEdge = updatebd(bdEdge,marker,d2p)\n% UPDATEDBD refine the boundary edges\n%\n% USAGE\n%    bdEdge = updatebd(bdEdge,marker,d2p)\n%\n% INPUT\n%   bdEdge:  set of boundary edges\n%   marker:  new node index for marked edge\n%      d2p:  index mapping from dual edge to primary edge\n% \n% OUTPUT\n%   bdEdge:  set of refined boundary edges\n%\n\nNB = size(bdEdge,1);\nfor k = 1:NB\n     i = bdEdge(k,1); \n     j = bdEdge(k,2);\n     if (marker(d2p(i,j)) >0)\n         bdEdge(k,:) = [i,marker(d2p(i,j))];\n         bdEdge(size(bdEdge,1)+1,:) = [marker(d2p(i,j)),j];\n     end\nend\n%--------------------------------------------------------------------------\n% End of function UPDATEDBD\n%--------------------------------------------------------------------------\n"}
+{"plateform": "github", "repo_name": "zheng-da/incubator-mxnet-master", "name": "callmxnet.m", "ext": ".m", "path": "incubator-mxnet-master/matlab/+mxnet/private/callmxnet.m", "size": 1666, "source_encoding": "utf_8", "md5": "23c3a6783ce55cb2598391d3aa27cf4e", "text": "% Licensed to the Apache Software Foundation (ASF) under one\n% or more contributor license agreements.  See the NOTICE file\n% distributed with this work for additional information\n% regarding copyright ownership.  The ASF licenses this file\n% to you under the Apache License, Version 2.0 (the\n% \"License\"); you may not use this file except in compliance\n% with the License.  You may obtain a copy of the License at\n%\n%   http://www.apache.org/licenses/LICENSE-2.0\n%\n% Unless required by applicable law or agreed to in writing,\n% software distributed under the License is distributed on an\n% \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n% KIND, either express or implied.  See the License for the\n% specific language governing permissions and limitations\n% under the License.\n\nfunction callmxnet(func, varargin)\n%CALLMXNET call mxnet functions\n\nif ~libisloaded('libmxnet')\n  cur_pwd = pwd;\n  mxnet_root = [fileparts(mfilename('fullpath')), '/../../../'];\n  cd(mxnet_root);\n  mxnet_root = pwd;\n  cd(cur_pwd);\n  \n  assert(exist([mxnet_root, '/lib/libmxnet.so'   ], 'file') == 2 || ...\n         exist([mxnet_root, '/lib/libmxnet.dylib'], 'file') == 2 || ...\n         exist([mxnet_root, '/lib/libmxnet.dll'  ], 'file') == 2, ...\n         'you need to build mxnet first');\n  assert(exist([mxnet_root, '/include/mxnet/c_predict_api.h']) == 2, ...\n         'failed to find c_predict_api.h')\n  addpath([mxnet_root, '/lib'])\n  addpath([mxnet_root, '/include/mxnet'])\n\n  [err, warn] = loadlibrary('libmxnet', 'c_predict_api.h');\n  assert(isempty(err));\n  if warn, warn, end\nend\n\nassert(ischar(func))\nret = calllib('libmxnet', func, varargin{:});\nassert(ret == 0)\nend\n"}
+{"plateform": "github", "repo_name": "zheng-da/incubator-mxnet-master", "name": "parse_json.m", "ext": ".m", "path": "incubator-mxnet-master/matlab/+mxnet/private/parse_json.m", "size": 19881, "source_encoding": "utf_8", "md5": "9cd6f452886def83ecf848d78f2e903a", "text": "% Licensed to the Apache Software Foundation (ASF) under one\n% or more contributor license agreements.  See the NOTICE file\n% distributed with this work for additional information\n% regarding copyright ownership.  The ASF licenses this file\n% to you under the Apache License, Version 2.0 (the\n% \"License\"); you may not use this file except in compliance\n% with the License.  You may obtain a copy of the License at\n%\n%   http://www.apache.org/licenses/LICENSE-2.0\n%\n% Unless required by applicable law or agreed to in writing,\n% software distributed under the License is distributed on an\n% \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n% KIND, either express or implied.  See the License for the\n% specific language governing permissions and limitations\n% under the License.\n\nfunction data = parse_json(fname,varargin)\n%PARSE_JSON parse a JSON (JavaScript Object Notation) file or string\n%\n% Based on jsonlab (https://github.com/fangq/jsonlab) created by Qianqian Fang. Jsonlab is lisonced under BSD or GPL v3.\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'^\\s*(?:\\[.+\\])|(?:\\{.+\\})\\s*$','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   try\n       string = fileread(fname);\n   catch\n       try\n           string = urlread(['file://',fname]);\n       catch\n           string = urlread(['file://',fullfile(pwd,fname)]);\n       end\n   end\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            object.(valid_field(str))=val;\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n    if(isstruct(object))\n        object=struct2jdata(object);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=-1;\n    if(isfield(varargin{1},'progressbar_'))\n        pbar=varargin{1}.progressbar_;\n    end\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ismatrix(object))\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n\n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    pos=skip_whitespace(pos,inStr,len);\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        pos=skip_whitespace(pos,inStr,len);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    pos=skip_whitespace(pos,inStr,len);\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction newpos=skip_whitespace(pos,inStr,len)\n    newpos=pos;\n    while newpos <= len && isspace(inStr(newpos))\n        newpos = newpos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str);\n        switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos);\n                keyboard;\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr isoct\n    currstr=inStr(pos:min(pos+30,end));\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n\n    if(isfield(varargin{1},'progressbar_'))\n        waitbar(pos/len,varargin{1}.progressbar_,'loading ...');\n    end\n\n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' )))\n        return;\n    end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos))\n            return;\n        end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end\n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r))\n            e1r=bpos(pos);\n        end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l))\n            e1l=bpos(pos);\n        end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0)\n    error('unmatched \"]\"');\nend\n\nfunction opt=varargin2struct(varargin)\n%\n% opt=varargin2struct('param1',value1,'param2',value2,...)\n%   or\n% opt=varargin2struct(...,optstruct,...)\n%\n% convert a series of input parameters into a structure\n%\n% input:\n%      'param', value: the input parameters should be pairs of a string and a value\n%       optstruct: if a parameter is a struct, the fields will be merged to the output struct\n%\n% output:\n%      opt: a struct where opt.param1=value1, opt.param2=value2 ...\n%\n\nlen=length(varargin);\nopt=struct;\nif(len==0) return; end\ni=1;\nwhile(i<=len)\n    if(isstruct(varargin{i}))\n        opt=mergestruct(opt,varargin{i});\n    elseif(ischar(varargin{i}) && i<len)\n        opt=setfield(opt,lower(varargin{i}),varargin{i+1});\n        i=i+1;\n    else\n        error('input must be in the form of ...,''name'',value,... pairs or structs');\n    end\n    i=i+1;\nend\nfunction val=jsonopt(key,default,varargin)\n%\n% val=jsonopt(key,default,optstruct)\n%\n% setting options based on a struct. The struct can be produced\n% by varargin2struct from a list of 'param','value' pairs\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n%\n% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $\n%\n% input:\n%      key: a string with which one look up a value from a struct\n%      default: if the key does not exist, return default\n%      optstruct: a struct where each sub-field is a key\n%\n% output:\n%      val: if key exists, val=optstruct.key; otherwise val=default\n%\n\nval=default;\nif(nargin<=2) return; end\nopt=varargin{1};\nif(isstruct(opt))\n    if(isfield(opt,key))\n       val=getfield(opt,key);\n    elseif(isfield(opt,lower(key)))\n       val=getfield(opt,lower(key));\n    end\nend\n\nfunction s=mergestruct(s1,s2)\n%\n% s=mergestruct(s1,s2)\n%\n% merge two struct objects into one\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% date: 2012/12/22\n%\n% input:\n%      s1,s2: a struct object, s1 and s2 can not be arrays\n%\n% output:\n%      s: the merged struct object. fields in s1 and s2 will be combined in s.\n\nif(~isstruct(s1) || ~isstruct(s2))\n    error('input parameters contain non-struct');\nend\nif(length(s1)>1 || length(s2)>1)\n    error('can not merge struct arrays');\nend\nfn=fieldnames(s2);\ns=s1;\nfor i=1:length(fn)\n    s=setfield(s,fn{i},getfield(s2,fn{i}));\nend\nfunction newdata=struct2jdata(data,varargin)\n%\n% newdata=struct2jdata(data,opt,...)\n%\n% convert a JData object (in the form of a struct array) into an array\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n%\n% input:\n%      data: a struct array. If data contains JData keywords in the first\n%            level children, these fields are parsed and regrouped into a\n%            data object (arrays, trees, graphs etc) based on JData\n%            specification. The JData keywords are\n%               \"_ArrayType_\", \"_ArraySize_\", \"_ArrayData_\"\n%               \"_ArrayIsSparse_\", \"_ArrayIsComplex_\"\n%      opt: (optional) a list of 'Param',value pairs for additional options\n%           The supported options include\n%               'Recursive', if set to 1, will apply the conversion to\n%                            every child; 0 to disable\n%\n% output:\n%      newdata: the covnerted data if the input data does contain a JData\n%               structure; otherwise, the same as the input.\n%\n% examples:\n%      obj=struct('_ArrayType_','double','_ArraySize_',[2 3],\n%                 '_ArrayIsSparse_',1 ,'_ArrayData_',null);\n%      ubjdata=struct2jdata(obj);\n\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nif(jsonopt('Recursive',0,varargin{:})==1)\n  for i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\n  end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n"}
+{"plateform": "github", "repo_name": "vladostan/pulse-master", "name": "pcamat.m", "ext": ".m", "path": "pulse-master/ComponentAnalysis/FastICA/pcamat.m", "size": 12075, "source_encoding": "utf_8", "md5": "bcb1117d4132558d0d54d8b7b616a902", "text": "function [E, D] = pcamat(vectors, firstEig, lastEig, s_interactive, ...\n    s_verbose);\n%PCAMAT - Calculates the pca for data\n%\n% [E, D] = pcamat(vectors, firstEig, lastEig, ... \n%                 interactive, verbose);\n%\n% Calculates the PCA matrices for given data (row) vectors. Returns\n% the eigenvector (E) and diagonal eigenvalue (D) matrices containing the\n% selected subspaces. Dimensionality reduction is controlled with\n% the parameters 'firstEig' and 'lastEig' - but it can also be done\n% interactively by setting parameter 'interactive' to 'on' or 'gui'.\n%\n% ARGUMENTS\n%\n% vectors       Data in row vectors.\n% firstEig      Index of the largest eigenvalue to keep.\n%               Default is 1.\n% lastEig       Index of the smallest eigenvalue to keep.\n%               Default is equal to dimension of vectors.\n% interactive   Specify eigenvalues to keep interactively. Note that if\n%               you set 'interactive' to 'on' or 'gui' then the values\n%               for 'firstEig' and 'lastEig' will be ignored, but they\n%               still have to be entered. If the value is 'gui' then the\n%               same graphical user interface as in FASTICAG will be\n%               used. Default is 'off'.\n% verbose       Default is 'on'.\n%\n%\n% EXAMPLE\n%       [E, D] = pcamat(vectors);\n%\n% Note \n%       The eigenvalues and eigenvectors returned by PCAMAT are not sorted.\n%\n% This function is needed by FASTICA and FASTICAG\n\n% For historical reasons this version does not sort the eigenvalues or\n% the eigen vectors in any ways. Therefore neither does the FASTICA or\n% FASTICAG. Generally it seams that the components returned from\n% whitening is almost in reversed order. (That means, they usually are,\n% but sometime they are not - depends on the EIG-command of matlab.)\n\n% @(#)$Id: pcamat.m,v 1.5 2003/12/15 18:24:32 jarmo Exp $\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Default values:\nif nargin < 5, s_verbose = 'on'; end\nif nargin < 4, s_interactive = 'off'; end\nif nargin < 3, lastEig = size(vectors, 1); end\nif nargin < 2, firstEig = 1; end\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Check the optional parameters;\nswitch lower(s_verbose)\n case 'on'\n  b_verbose = 1;\n case 'off'\n  b_verbose = 0;\n otherwise\n  error(sprintf('Illegal value [ %s ] for parameter: ''verbose''\\n', s_verbose));\nend\n\nswitch lower(s_interactive)\n case 'on'\n  b_interactive = 1;\n case 'off'\n  b_interactive = 0;\n case 'gui'\n  b_interactive = 2;\n otherwise\n  error(sprintf('Illegal value [ %s ] for parameter: ''interactive''\\n', ...\n\t\ts_interactive));\nend\n\noldDimension = size (vectors, 1);\nif ~(b_interactive)\n  if lastEig < 1 | lastEig > oldDimension\n    error(sprintf('Illegal value [ %d ] for parameter: ''lastEig''\\n', lastEig));\n  end\n  if firstEig < 1 | firstEig > lastEig\n    error(sprintf('Illegal value [ %d ] for parameter: ''firstEig''\\n', firstEig));\n  end\nend\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Calculate PCA\n\n% Calculate the covariance matrix.\nif b_verbose, fprintf ('Calculating covariance...\\n'); end\ncovarianceMatrix = cov(vectors', 1);\n\n% Calculate the eigenvalues and eigenvectors of covariance\n% matrix.\n[E, D] = eig (covarianceMatrix);\n\n% The rank is determined from the eigenvalues - and not directly by\n% using the function rank - because function rank uses svd, which\n% in some cases gives a higher dimensionality than what can be used\n% with eig later on (eig then gives negative eigenvalues).\nrankTolerance = 1e-7;\nmaxLastEig = sum (diag (D) > rankTolerance);\nif maxLastEig == 0,\n  fprintf (['Eigenvalues of the covariance matrix are' ...\n\t    ' all smaller than tolerance [ %g ].\\n' ...\n\t    'Please make sure that your data matrix contains' ...\n\t    ' nonzero values.\\nIf the values are very small,' ...\n\t    ' try rescaling the data matrix.\\n'], rankTolerance);\n  error ('Unable to continue, aborting.');\nend\n\n% Sort the eigenvalues - decending.\neigenvalues = flipud(sort(diag(D)));\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Interactive part - command-line\nif b_interactive == 1\n\n  % Show the eigenvalues to the user\n  hndl_win=figure;\n  bar(eigenvalues);\n  title('Eigenvalues');\n\n  % ask the range from the user...\n  % ... and keep on asking until the range is valid :-)\n  areValuesOK=0;\n  while areValuesOK == 0\n    firstEig = input('The index of the largest eigenvalue to keep? (1) ');\n    lastEig = input(['The index of the smallest eigenvalue to keep? (' ...\n                    int2str(oldDimension) ') ']);\n    % Check the new values...\n    % if they are empty then use default values\n    if isempty(firstEig), firstEig = 1;end\n    if isempty(lastEig), lastEig = oldDimension;end\n    % Check that the entered values are within the range\n    areValuesOK = 1;\n    if lastEig < 1 | lastEig > oldDimension\n      fprintf('Illegal number for the last eigenvalue.\\n');\n      areValuesOK = 0;\n    end\n    if firstEig < 1 | firstEig > lastEig\n      fprintf('Illegal number for the first eigenvalue.\\n');\n      areValuesOK = 0;\n    end\n  end\n  % close the window\n  close(hndl_win);\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Interactive part - GUI\nif b_interactive == 2\n\n  % Show the eigenvalues to the user\n  hndl_win = figure('Color',[0.8 0.8 0.8], ...\n    'PaperType','a4letter', ...\n    'Units', 'normalized', ...\n    'Name', 'FastICA: Reduce dimension', ...\n    'NumberTitle','off', ...\n    'Tag', 'f_eig');\n  h_frame = uicontrol('Parent', hndl_win, ...\n    'BackgroundColor',[0.701961 0.701961 0.701961], ...\n    'Units', 'normalized', ...\n    'Position',[0.13 0.05 0.775 0.17], ...\n    'Style','frame', ...\n    'Tag','f_frame');\n\nb = uicontrol('Parent',hndl_win, ...\n\t'Units','normalized', ...\n\t'BackgroundColor',[0.701961 0.701961 0.701961], ...\n\t'HorizontalAlignment','left', ...\n\t'Position',[0.142415 0.0949436 0.712077 0.108507], ...\n\t'String','Give the indices of the largest and smallest eigenvalues of the covariance matrix to be included in the reduced data.', ...\n\t'Style','text', ...\n\t'Tag','StaticText1');\ne_first = uicontrol('Parent',hndl_win, ...\n\t'Units','normalized', ...\n\t'Callback',[ ...\n          'f=round(str2num(get(gcbo, ''String'')));' ...\n          'if (f < 1), f=1; end;' ...\n          'l=str2num(get(findobj(''Tag'',''e_last''), ''String''));' ...\n          'if (f > l), f=l; end;' ...\n          'set(gcbo, ''String'', int2str(f));' ...\n          ], ...\n\t'BackgroundColor',[1 1 1], ...\n\t'HorizontalAlignment','right', ...\n\t'Position',[0.284831 0.0678168 0.12207 0.0542535], ...\n\t'Style','edit', ...\n        'String', '1', ...\n\t'Tag','e_first');\nb = uicontrol('Parent',hndl_win, ...\n\t'Units','normalized', ...\n\t'BackgroundColor',[0.701961 0.701961 0.701961], ...\n\t'HorizontalAlignment','left', ...\n\t'Position',[0.142415 0.0678168 0.12207 0.0542535], ...\n\t'String','Range from', ...\n\t'Style','text', ...\n\t'Tag','StaticText2');\ne_last = uicontrol('Parent',hndl_win, ...\n\t'Units','normalized', ...\n\t'Callback',[ ...\n          'l=round(str2num(get(gcbo, ''String'')));' ...\n          'lmax = get(gcbo, ''UserData'');' ...\n          'if (l > lmax), l=lmax; fprintf([''The selected value was too large, or the selected eigenvalues were close to zero\\n'']); end;' ...\n          'f=str2num(get(findobj(''Tag'',''e_first''), ''String''));' ...\n          'if (l < f), l=f; end;' ...\n          'set(gcbo, ''String'', int2str(l));' ...\n          ], ...\n\t'BackgroundColor',[1 1 1], ...\n\t'HorizontalAlignment','right', ...\n\t'Position',[0.467936 0.0678168 0.12207 0.0542535], ...\n\t'Style','edit', ...\n        'String', int2str(maxLastEig), ...\n        'UserData', maxLastEig, ...\n\t'Tag','e_last');\n% in the first version oldDimension was used instead of \n% maxLastEig, but since the program would automatically\n% drop the eigenvalues afte maxLastEig...\nb = uicontrol('Parent',hndl_win, ...\n\t'Units','normalized', ...\n\t'BackgroundColor',[0.701961 0.701961 0.701961], ...\n\t'HorizontalAlignment','left', ...\n\t'Position',[0.427246 0.0678168 0.0406901 0.0542535], ...\n\t'String','to', ...\n\t'Style','text', ...\n\t'Tag','StaticText3');\nb = uicontrol('Parent',hndl_win, ...\n\t'Units','normalized', ...\n\t'Callback','uiresume(gcbf)', ...\n\t'Position',[0.630697 0.0678168 0.12207 0.0542535], ...\n\t'String','OK', ...\n\t'Tag','Pushbutton1');\nb = uicontrol('Parent',hndl_win, ...\n\t'Units','normalized', ...\n\t'Callback',[ ...\n          'gui_help(''pcamat'');' ...\n          ], ...\n\t'Position',[0.767008 0.0678168 0.12207 0.0542535], ...\n\t'String','Help', ...\n\t'Tag','Pushbutton2');\n\n  h_axes = axes('Position' ,[0.13 0.3 0.775 0.6]);\n  set(hndl_win, 'currentaxes',h_axes);\n  bar(eigenvalues);\n  title('Eigenvalues');\n\n  uiwait(hndl_win);\n  firstEig = str2num(get(e_first, 'String'));\n  lastEig = str2num(get(e_last, 'String'));\n\n  % close the window\n  close(hndl_win);\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% See if the user has reduced the dimension enought\n\nif lastEig > maxLastEig\n  lastEig = maxLastEig;\n  if b_verbose\n    fprintf('Dimension reduced to %d due to the singularity of covariance matrix\\n',...\n           lastEig-firstEig+1);\n  end\nelse\n  % Reduce the dimensionality of the problem.\n  if b_verbose\n    if oldDimension == (lastEig - firstEig + 1)\n      fprintf ('Dimension not reduced.\\n');\n    else\n      fprintf ('Reducing dimension...\\n');\n    end\n  end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Drop the smaller eigenvalues\nif lastEig < oldDimension\n  lowerLimitValue = (eigenvalues(lastEig) + eigenvalues(lastEig + 1)) / 2;\nelse\n  lowerLimitValue = eigenvalues(oldDimension) - 1;\nend\n\nlowerColumns = diag(D) > lowerLimitValue;\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Drop the larger eigenvalues\nif firstEig > 1\n  higherLimitValue = (eigenvalues(firstEig - 1) + eigenvalues(firstEig)) / 2;\nelse\n  higherLimitValue = eigenvalues(1) + 1;\nend\nhigherColumns = diag(D) < higherLimitValue;\n\n% Combine the results from above\nselectedColumns = lowerColumns & higherColumns;\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% print some info for the user\nif b_verbose\n  fprintf ('Selected [ %d ] dimensions.\\n', sum (selectedColumns));\nend\nif sum (selectedColumns) ~= (lastEig - firstEig + 1),\n  error ('Selected a wrong number of dimensions.');\nend\n\nif b_verbose\n  fprintf ('Smallest remaining (non-zero) eigenvalue [ %g ]\\n', eigenvalues(lastEig));\n  fprintf ('Largest remaining (non-zero) eigenvalue [ %g ]\\n', eigenvalues(firstEig));\n  fprintf ('Sum of removed eigenvalues [ %g ]\\n', sum(diag(D) .* ...\n    (~selectedColumns)));\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Select the colums which correspond to the desired range\n% of eigenvalues.\nE = selcol(E, selectedColumns);\nD = selcol(selcol(D, selectedColumns)', selectedColumns);\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Some more information\nif b_verbose\n  sumAll=sum(eigenvalues);\n  sumUsed=sum(diag(D));\n  retained = (sumUsed / sumAll) * 100;\n  fprintf('[ %g ] %% of (non-zero) eigenvalues retained.\\n', retained);\nend\n\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction newMatrix = selcol(oldMatrix, maskVector);\n\n% newMatrix = selcol(oldMatrix, maskVector);\n%\n% Selects the columns of the matrix that marked by one in the given vector.\n% The maskVector is a column vector.\n\n% 15.3.1998\n\nif size(maskVector, 1) ~= size(oldMatrix, 2),\n  error ('The mask vector and matrix are of uncompatible size.');\nend\n\nnumTaken = 0;\n\nfor i = 1 : size (maskVector, 1),\n  if maskVector(i, 1) == 1,\n    takingMask(1, numTaken + 1) = i;\n    numTaken = numTaken + 1;\n  end\nend\n\nnewMatrix = oldMatrix(:, takingMask);"}
+{"plateform": "github", "repo_name": "vladostan/pulse-master", "name": "icaplot.m", "ext": ".m", "path": "pulse-master/ComponentAnalysis/FastICA/icaplot.m", "size": 13259, "source_encoding": "utf_8", "md5": "dde3e6d852f657a3c1eaacbd03f5dcc7", "text": "function icaplot(mode, varargin);\n%ICAPLOT - plot signals in various ways\n%\n% ICAPLOT is mainly for plottinf and comparing the mixed signals and\n% separated ica-signals.\n%\n% ICAPLOT has many different modes. The first parameter of the function\n% defines the mode. Other parameters and their order depends on the\n% mode. The explanation for the more common parameters is in the end.\n%\n% Classic\n%     icaplot('classic', s1, n1, range, xrange, titlestr)\n%\n%     Plots the signals in the same manner as the FASTICA and FASTICAG\n%     programs do. All the signals are plotted in their own axis.\n%\n% Complot\n%     icaplot('complot', s1, n1, range, xrange, titlestr)\n%\n%     The signals are plotted on the same axis. This is good for\n%     visualization of the shape of the signals. The scale of the signals \n%     has been altered so that they all fit nicely.\n%\n% Histogram\n%     icaplot('histogram', s1, n1, range, bins, style)\n%     \n%     The histogram of the signals is plotted. The number of bins can be\n%     specified with 'bins'-parameter. The style for the histograms can\n%     be either 'bar' (default) of 'line'.\n%\n% Scatter\n%     icaplot('scatter', s1, n1, s2, n2, range, titlestr, s1label,\n%     s2label, markerstr)\n%\n%     A scatterplot is plotted so that the signal 1 is the 'X'-variable\n%     and the signal 2 is the 'Y'-variable. The 'markerstr' can be used\n%     to specify the maker used in the plot. The format for 'markerstr'\n%     is the same as for Matlab's PLOT. \n%\n% Compare\n%     icaplot('compare', s1, n1, s2, n2, range, xrange, titlestr,\n%     s1label, s2label)\n%\n%     This for for comparing two signals. The main used in this context\n%     would probably be to see how well the separated ICA-signals explain \n%     the observed mixed signals. The s2 signals are first scaled with\n%     REGRESS function.\n%\n% Compare - Sum\n%     icaplot('sum', s1, n1, s2, n2, range, xrange, titlestr, s1label,\n%     s2label)\n%\n%     The same as Compare, but this time the signals in s2 (specified by\n%     n2) are summed together.\n%\n% Compare - Sumerror\n%     icaplot('sumerror', s1, n1, s2, n2, range, xrange, titlestr,\n%     s1label, s2label)\n%     \n%     The same as Compare - Sum, but also the 'error' between the signal\n%     1 and the summed IC's is plotted.\n%\n%\n% More common parameters\n%     The signals to be plotted are in matrices s1 and s2. The n1 and n2\n%     are used to tell the index of the signal or signals to be plotted\n%     from s1 or s2. If n1 or n2 has a value of 0, then all the signals\n%     from corresponding matrix will be plotted. The values for n1 and n2 \n%     can also be vectors (like: [1 3 4]) In some casee if there are more\n%     than 1 signal to be plotted from s1 or s2 then the plot will\n%     contain as many subplots as are needed. \n%\n%     The range of the signals to be plotted can be limited with\n%     'range'-parameter. It's value is a vector ( 10000:15000 ). If range \n%     is 0, then the whole range will be plotted.\n%\n%     The 'xrange' is used to specify only the labels used on the\n%     x-axis. The value of 'xrange' is a vector containing the x-values\n%     for the plots or [start end] for begin and end of the range\n%     ( 10000:15000 or [10 15] ). If xrange is 0, then value of range\n%     will be used for x-labels.\n%\n%     You can give a title for the plot with 'titlestr'. Also the\n%     's1label' and 's2label' are used to give more meaningfull label for \n%     the signals.\n%\n%     Lastly, you can omit some of the arguments from the and. You will\n%     have to give values for the signal matrices (s1, s2) and the\n%     indexes (n1, n2)\n\n% @(#)$Id: icaplot.m,v 1.2 2003/04/05 14:23:58 jarmo Exp $\n\nswitch mode\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n  % 'dispsig' is to replace the old DISPSIG\n  % '' & 'classic' are just another names - '' quite short one :-)\n case {'', 'classic', 'dispsig'} \n  % icaplot(mode, s1, n1, range, xrange, titlestr)\n  if length(varargin) < 1, error('Not enough arguments.'); end\n  if length(varargin) < 5, titlestr = '';else titlestr = varargin{5}; end\n  if length(varargin) < 4, xrange = 0;else xrange = varargin{4}; end\n  if length(varargin) < 3, range = 0;else range = varargin{3}; end\n  if length(varargin) < 2, n1 = 0;else n1 = varargin{2}; end\n  s1 = varargin{1};\n  range=chkrange(range, s1);\n  xrange=chkxrange(xrange, range);\n  n1=chkn(n1, s1);\n\n  clf;\n  \n  numSignals = size(n1, 2);\n  for i = 1:numSignals,\n    subplot(numSignals, 1, i);\n    plot(xrange, s1(n1(i), range));\n  end\n  subplot(numSignals,1, 1);\n  if (~isempty(titlestr))\n    title(titlestr);\n  end\n  \n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n case 'complot'\n  % icaplot(mode, s1, n1, range, xrange, titlestr)\n  if length(varargin) < 1, error('Not enough arguments.'); end\n  if length(varargin) < 5, titlestr = '';else titlestr = varargin{5}; end\n  if length(varargin) < 4, xrange = 0;else xrange = varargin{4}; end\n  if length(varargin) < 3, range = 0;else range = varargin{3}; end\n  if length(varargin) < 2, n1 = 0;else n1 = varargin{2}; end\n  s1 = remmean(varargin{1});\n  range=chkrange(range, s1);\n  xrange=chkxrange(xrange, range);\n  n1=chkn(n1, s1);\n  \n  for i = 1:size(n1, 2)\n    S1(i, :) = s1(n1(i), range);\n  end\n  \n  alpha = mean(max(S1')-min(S1'));\n  for i = 1:size(n1,2)\n    S2(i,:) = S1(i,:) - alpha*(i-1)*ones(size(S1(1,:)));\n  end\n  \n  plot(xrange, S2');\n  axis([min(xrange) max(xrange) min(min(S2)) max(max(S2)) ]);\n  \n  set(gca,'YTick',(-size(S1,1)+1)*alpha:alpha:0);\n  set(gca,'YTicklabel',fliplr(n1));\n  \n  if (~isempty(titlestr))\n    title(titlestr);\n  end\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n case 'histogram'\n  % icaplot(mode, s1, n1, range, bins, style)\n  if length(varargin) < 1, error('Not enough arguments.'); end\n  if length(varargin) < 5, style = 'bar';else style = varargin{5}; end\n  if length(varargin) < 4, bins = 10;else bins = varargin{4}; end\n  if length(varargin) < 3, range = 0;else range = varargin{3}; end\n  if length(varargin) < 2, n1 = 0;else n1 = varargin{2}; end\n  s1 = varargin{1};\n  range = chkrange(range, s1);\n  n1 = chkn(n1, s1);\n  \n  numSignals = size(n1, 2);\n  rows = floor(sqrt(numSignals));\n  columns = ceil(sqrt(numSignals));\n  while (rows * columns < numSignals)\n    columns = columns + 1;\n  end\n  \n  switch style\n   case {'', 'bar'}\n    for i = 1:numSignals,\n      subplot(rows, columns, i);\n      hist(s1(n1(i), range), bins);\n      title(int2str(n1(i)));\n      drawnow;\n    end\n    \n   case 'line'\n    for i = 1:numSignals,\n      subplot(rows, columns, i);\n      [Y, X]=hist(s1(n1(i), range), bins);\n      plot(X, Y);\n      title(int2str(n1(i)));\n      drawnow;\n    end\n   otherwise\n    fprintf('Unknown style.\\n')\n  end\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n case 'scatter'\n  % icaplot(mode, s1, n1, s2, n2, range, titlestr, xlabelstr, ylabelstr, markerstr)\n  if length(varargin) < 4, error('Not enough arguments.'); end\n  if length(varargin) < 9, markerstr = '.';else markerstr = varargin{9}; end\n  if length(varargin) < 8, ylabelstr = 'Signal 2';else ylabelstr = varargin{8}; end\n  if length(varargin) < 7, xlabelstr = 'Signal 1';else xlabelstr = varargin{7}; end\n  if length(varargin) < 6, titlestr = '';else titlestr = varargin{6}; end\n  if length(varargin) < 5, range = 0;else range = varargin{5}; end\n  n2 = varargin{4};\n  s2 = varargin{3};\n  n1 = varargin{2};\n  s1 = varargin{1};\n  range = chkrange(range, s1);\n  n1 = chkn(n1, s1);\n  n2 = chkn(n2, s2);\n  \n  rows = size(n1, 2);\n  columns = size(n2, 2);\n  for r = 1:rows\n    for c = 1:columns\n      subplot(rows, columns, (r-1)*columns + c);\n      plot(s1(n1(r), range),s2(n2(c), range),markerstr);\n      if (~isempty(titlestr))\n\ttitle(titlestr);\n      end\n      if (rows*columns == 1)\n\txlabel(xlabelstr);\n\tylabel(ylabelstr);\n      else \n\txlabel([xlabelstr ' (' int2str(n1(r)) ')']);\n\tylabel([ylabelstr ' (' int2str(n2(c)) ')']);\n      end\n      drawnow;\n    end\n  end\n  \n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n case {'compare', 'sum', 'sumerror'}\n  % icaplot(mode, s1, n1, s2, n2, range, xrange, titlestr, s1label, s2label)\n  if length(varargin) < 4, error('Not enough arguments.'); end\n  if length(varargin) < 9, s2label = 'IC';else s2label = varargin{9}; end\n  if length(varargin) < 8, s1label = 'Mix';else s1label = varargin{8}; end\n  if length(varargin) < 7, titlestr = '';else titlestr = varargin{7}; end\n  if length(varargin) < 6, xrange = 0;else xrange = varargin{6}; end\n  if length(varargin) < 5, range = 0;else range = varargin{5}; end\n  s1 = varargin{1};\n  n1 = varargin{2};\n  s2 = varargin{3};\n  n2 = varargin{4};\n  range = chkrange(range, s1);\n  xrange = chkxrange(xrange, range);\n  n1 = chkn(n1, s1);\n  n2 = chkn(n2, s2);\n\n  numSignals = size(n1, 2);\n  if (numSignals > 1)\n    externalLegend = 1;\n  else\n    externalLegend = 0;\n  end\n  \n  rows = floor(sqrt(numSignals+externalLegend));\n  columns = ceil(sqrt(numSignals+externalLegend));\n  while (rows * columns < (numSignals+externalLegend))\n    columns = columns + 1;\n  end\n  \n  clf;\n  \n  for j = 1:numSignals\n    subplot(rows, columns, j);\n    switch mode\n     case 'compare'\n      plotcompare(s1, n1(j), s2,n2, range, xrange);\n      [legendtext,legendstyle]=legendcompare(n1(j),n2,s1label,s2label,externalLegend);\n     case 'sum'\n      plotsum(s1, n1(j), s2,n2, range, xrange);\n      [legendtext,legendstyle]=legendsum(n1(j),n2,s1label,s2label,externalLegend);\n     case 'sumerror'\n      plotsumerror(s1, n1(j), s2,n2, range, xrange);\n      [legendtext,legendstyle]=legendsumerror(n1(j),n2,s1label,s2label,externalLegend);\n    end\n    \n    if externalLegend\n      title([titlestr ' (' s1label  ' ' int2str(n1(j)) ')']);\n    else\n      legend(char(legendtext));\n      if (~isempty(titlestr))\n\ttitle(titlestr);\n      end\n    end\n  end\n  \n  if (externalLegend)\n    subplot(rows, columns, numSignals+1);\n    legendsize = size(legendtext, 2);\n    hold on;\n    for i=1:legendsize\n      plot([0 1],[legendsize-i legendsize-i], char(legendstyle(i)));\n      text(1.5, legendsize-i, char(legendtext(i)));\n    end\n    hold off;\n    axis([0 6 -1 legendsize]);\n    axis off;\n  end\n  \n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction plotcompare(s1, n1, s2, n2, range, xrange);\n  style=getStyles;\n  K = regress(s1(n1,:)',s2');\n  plot(xrange, s1(n1,range), char(style(1)));\n  hold on\n  for i=1:size(n2,2)\n    plotstyle=char(style(i+1));\n    plot(xrange, K(n2(i))*s2(n2(i),range), plotstyle);\n  end\n  hold off\n\nfunction [legendText, legendStyle]=legendcompare(n1, n2, s1l, s2l, externalLegend);\n  style=getStyles;\n  if (externalLegend)\n    legendText(1)={[s1l ' (see the titles)']};\n  else\n    legendText(1)={[s1l ' ', int2str(n1)]};\n  end\n  legendStyle(1)=style(1);\n  for i=1:size(n2, 2)\n    legendText(i+1) = {[s2l ' ' int2str(n2(i))]};\n    legendStyle(i+1) = style(i+1);\n  end\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction plotsum(s1, n1, s2, n2, range, xrange);\n  K = diag(regress(s1(n1,:)',s2'));\n  sigsum = sum(K(:,n2)*s2(n2,:));\n  plot(xrange, s1(n1, range),'k-', ...\n       xrange, sigsum(range), 'b-');\n\nfunction [legendText, legendStyle]=legendsum(n1, n2, s1l, s2l, externalLegend);\n  if (externalLegend)\n    legendText(1)={[s1l ' (see the titles)']};\n  else\n    legendText(1)={[s1l ' ', int2str(n1)]};\n  end\n  legendText(2)={['Sum of ' s2l ': ', int2str(n2)]};\n  legendStyle={'k-';'b-'};\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction plotsumerror(s1, n1, s2, n2, range, xrange);\n  K = diag(regress(s1(n1,:)',s2'));\n  sigsum = sum(K(:,n2)*s2(n2,:));\n  plot(xrange, s1(n1, range),'k-', ...\n       xrange, sigsum(range), 'b-', ...\n       xrange, s1(n1, range)-sigsum(range), 'r-');\n\nfunction [legendText, legendStyle]=legendsumerror(n1, n2, s1l, s2l, externalLegend);\n  if (externalLegend)\n    legendText(1)={[s1l ' (see the titles)']};\n  else\n    legendText(1)={[s1l ' ', int2str(n1)]};\n  end\n  legendText(2)={['Sum of ' s2l ': ', int2str(n2)]};\n  legendText(3)={'\"Error\"'};\n  legendStyle={'k-';'b-';'r-'};\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction style=getStyles;\n  color = {'k','r','g','b','m','c','y'};\n  line = {'-',':','-.','--'};\n  for i = 0:size(line,2)-1\n    for j = 1:size(color, 2)\n      style(j + i*size(color, 2)) = strcat(color(j), line(i+1));\n    end\n  end\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction range=chkrange(r, s)\n  if r == 0\n    range = 1:size(s, 2);\n  else\n    range = r;\n  end\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction xrange=chkxrange(xr,r);\n  if xr == 0\n    xrange = r;\n  elseif size(xr, 2) == 2\n    xrange = xr(1):(xr(2)-xr(1))/(size(r,2)-1):xr(2);\n  elseif size(xr, 2)~=size(r, 2)\n    error('Xrange and range have different sizes.');\n  else\n    xrange = xr;\n  end\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction n=chkn(n,s)\n  if n == 0\n    n = 1:size(s, 1);\n  end"}
+{"plateform": "github", "repo_name": "taojiastanford/Automation-of-Calligraphy-master", "name": "SOMFn.m", "ext": ".m", "path": "Automation-of-Calligraphy-master/TryAutoencoder/SOMFn.m", "size": 842616, "source_encoding": "utf_8", "md5": "2a3d050c1e1d69645129185dadd1a0c7", "text": "function [Y,Xf,Af] = SOMFn(X,~,~)\r\n%MYNEURALNETWORKFUNCTION neural network simulation function.\r\n%\r\n% Generated by Neural Network Toolbox function genFunction, 10-Dec-2016 10:17:26.\r\n%\r\n% [Y] = myNeuralNetworkFunction(X,~,~) takes these arguments:\r\n%\r\n%   X = 1xTS cell, 1 inputs over TS timesteps\r\n%   Each X{1,ts} = 400xQ matrix, input #1 at timestep ts.\r\n%\r\n% and returns:\r\n%   Y = 1xTS cell of 1 outputs over TS timesteps.\r\n%   Each Y{1,ts} = 100xQ matrix, output #1 at timestep ts.\r\n%\r\n% where Q is number of samples (or series) and TS is the number of timesteps.\r\n\r\n%#ok<*RPMT0>\r\n\r\n% ===== NEURAL NETWORK CONSTANTS =====\r\n\r\n% Layer 1\r\nIW1_1 = [0.24178633272214692 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0.02826120245008526 0.0026463412232561488 0.003818015164474465 0.0071468430894091849 0.0021540735727617181 0.0029139816305722706 0.0032960910520710269 0.0042887897369350229 0.0052115196291106156 0.004915156030439816 0.0029002320724279963 0.0081727751205665777 0.0032877758564839425 0.0039811117648029231 0.0032790070962217485 0.0021947354459955514 0.0044918012457234712 0.0044018249033973746 0.0078942907840015095 0.96824154803673734 0.0024033788774603256 0.0040166144686472009 0.0043552850952359003 0.011122640370039684 0.003886306667807368 0.0050716051249257311 0.002908870828654762 0.0023691687493667102 0.0031155535043331014 0.010653639945748064 0.97516746947510313 0.0037104734452139939 0.0027042526313269177 0.66638868480612556 0.73057245739435439 0.0041908393448316563 0.0033814210087192136 0.49203762860310163 0.0070821200001869641 0.60589944394538287 0.49060734822103319 0.0047929020251751449 0.0042590756871934447 0.48884246617032806 0.43441929903270127 0.0032013577633996813 0.76042324733535682 0.01776869333343218 0.0051936190890512716 0.0039639557917086274 0.0041253400720190478 0.2333116271336784 0.0050561447202883605 0.0053865159209109626 0.0054455619175529588 0.0031351909683220015 0.91997454654503374 0.23675231171080791 0.49351421428258779 0.0043144977851573068 0.99054164610809792 0.0052003584854599704 0.0057561495850094371 0.39280615042520789 0.42884670628777888 0.0039356921621649752 0.0050002870900259415 0.90566691974847424 0.0028521064975250612 0.0030793880357663883 0.0025659618217257845 0.0068427764204991191 0.0030318473970846038 0.5717229033440665 0.3912263497080638 0.0046810205001485018 0.0034120111120348974 0.0027165427522983401 0.0023285844819571294 0.0027507098843530956 0.0030118993569119984 0.0026745612064321776 0.0062160697330982577 0.51641109335312607 0.0031153944407362991 0.0048476518117710459 0.0041942693565401269 0.0084586104098554153 0.0037528340451680508 0.0029043817943584542 0.0036237609760038994 0.0024884289262431837 0.0033571436871993651 0.0032232304577212408 0.0033517604082332949 0.90765799677971604 0.13755220769501253 0.0033146716361462727 0.067538502840324893 0.0052393120891527428 0.0030948780679671923 0.0049791359960964727 0.78726467082819618 0.0031834101509731635 0.87884068036782093 0.0031623146548697195 0.0032821015176518684 0.0039413877709007887 0.012108623110771632 0.58083735952249194 0.0037034818592410284 0.0034911131956232144 0.0051542609773960841 0.12535675376033018 0.66242415393531762 0.0034073285195728993 0.004170695475926892 0.0042851015154399333 0.55038553234397236 0.70083995832158796 0.031192447050999602 0.0018470932034749406 0.0030207105005236557 0.53502724619630737 0.3744433762618925 0.0084903000081457906 0.3245068867888018 0.0046420262342272917 0.0069062663430460676 0.0036468156010235273 0.0026145176684455838 0.0051308195890211183 0.0042227815661250506 0.29854613472626207 0.3771885155224321 0.031884324219851656 0.003645090234655196 0.0042493782961550994 0.3145714296815314 0.0067633096224449935 0.015507798141888084 0.01259754851382414 0.0060279647885965477 0.37658731672385903 0.0037377453789527995 0.0031367842245163592 0.86829554367741435 0.29448034691586317 0.01365626621179785 0.0070631321008166967 0.0029862752166248761 0.0029947980094728469 0.0050041919539295576 0.0045606061097177627 0.0021727488630241109];\r\n\r\n% ===== SIMULATION ========\r\n\r\n% Format Input Arguments\r\nisCellX = iscell(X);\r\nif ~isCellX, X = {X}; end;\r\n\r\n% Dimensions\r\nTS = size(X,2); % timesteps\r\n\r\n% Allocate Outputs\r\nY = cell(1,TS);\r\n\r\n% Time loop\r\nfor ts=1:TS\r\n    \r\n    % Input 1\r\n    % no processing\r\n    \r\n    % Layer 1\r\n    z1 = negdist_apply(IW1_1,X{1,ts});\r\n    a1 = compet_apply(z1);\r\n    \r\n    % Output 1\r\n    Y{1,ts} = a1;\r\nend\r\n\r\n% Final Delay States\r\nXf = cell(1,0);\r\nAf = cell(1,0);\r\n\r\n% Format Output Arguments\r\nif ~isCellX, Y = cell2mat(Y); end\r\nend\r\n\r\n% ===== MODULE FUNCTIONS ========\r\n\r\n% Negative Distance Weight Function\r\nfunction z = negdist_apply(w,p,~)\r\n[S,R] = size(w);\r\nQ = size(p,2);\r\nif isa(w,'gpuArray')\r\n    z = iNegDistApplyGPU(w,p,R,S,Q);\r\nelse\r\n    z = iNegDistApplyCPU(w,p,S,Q);\r\nend\r\nend\r\nfunction z = iNegDistApplyCPU(w,p,S,Q)\r\nz = zeros(S,Q);\r\nif (Q<S)\r\n    pt = p';\r\n    for q=1:Q\r\n        z(:,q) = sum(bsxfun(@minus,w,pt(q,:)).^2,2);\r\n    end\r\nelse\r\n    wt = w';\r\n    for i=1:S\r\n        z(i,:) = sum(bsxfun(@minus,wt(:,i),p).^2,1);\r\n    end\r\nend\r\nz = -sqrt(z);\r\nend\r\nfunction z = iNegDistApplyGPU(w,p,R,S,Q)\r\np = reshape(p,1,R,Q);\r\nsd = arrayfun(@iNegDistApplyGPUHelper,w,p);\r\nz = -sqrt(reshape(sum(sd,2),S,Q));\r\nend\r\nfunction sd = iNegDistApplyGPUHelper(w,p)\r\nsd = (w-p) .^ 2;\r\nend\r\n\r\n% Competitive Transfer Function\r\nfunction a = compet_apply(n,~)\r\nif isempty(n)\r\n    a = n;\r\nelse\r\n    [S,Q] = size(n);\r\n    nanInd = any(isnan(n),1);\r\n    a = zeros(S,Q,'like',n);\r\n    [~,maxRows] = max(n,[],1);\r\n    onesInd = maxRows + S*(0:(Q-1));\r\n    a(onesInd) = 1;\r\n    a(:,nanInd) = NaN;\r\nend\r\nend\r\n"}
+{"plateform": "github", "repo_name": "zhangchn/otx-master", "name": "PPCProcessor.m", "ext": ".m", "path": "otx-master/src/source/Processors/PPCProcessor.m", "size": 62175, "source_encoding": "utf_8", "md5": "713d85183996bcd7d3f41e667af270b8", "text": "/*\n    PPCProcessor.m\n\n    A subclass of ExeProcessor that handles PPC-specific issues.\n\n    This file is in the public domain.\n*/\n\n#import <Cocoa/Cocoa.h>\n\n#import \"PPCProcessor.h\"\n#import \"ArchSpecifics.h\"\n#import \"ListUtils.h\"\n#import \"ObjcAccessors.h\"\n#import \"ObjectLoader.h\"\n#import \"Searchers.h\"\n#import \"SyscallStrings.h\"\n#import \"UserDefaultKeys.h\"\n\nextern BOOL gCancel;\n\n@implementation PPCProcessor\n\n//  initWithURL:controller:options:\n// ----------------------------------------------------------------------------\n\n- (id)initWithURL: (NSURL*)inURL\n       controller: (id)inController\n          options: (ProcOptions*)inOptions\n{\n    if ((self = [super initWithURL: inURL\n        controller: inController options: inOptions]))\n    {\n        strncpy(iArchString, \"ppc\", 4);\n\n        iArchSelector               = CPU_TYPE_POWERPC;\n        iFieldWidths.offset         = 8;\n        iFieldWidths.address        = 10;\n        iFieldWidths.instruction    = 10;\n        iFieldWidths.mnemonic       = 9;\n        iFieldWidths.operands       = 17;\n    }\n\n    return self;\n}\n\n//  dealloc\n// ----------------------------------------------------------------------------\n\n- (void)dealloc\n{\n    if (iLocalSelves)\n    {\n        free(iLocalSelves);\n        iLocalSelves    = NULL;\n    }\n\n    if (iLocalVars)\n    {\n        free(iLocalVars);\n        iLocalVars  = NULL;\n    }\n\n}\n\n//  loadDyldDataSection:\n// ----------------------------------------------------------------------------\n\n- (void)loadDyldDataSection: (section*)inSect\n{\n    [super loadDyldDataSection: inSect];\n\n    if (!iAddrDyldStubBindingHelper)\n        return;\n\n    iAddrDyldFuncLookupPointer  = iAddrDyldStubBindingHelper + 24;\n}\n\n//  codeFromLine:\n// ----------------------------------------------------------------------------\n\n- (void)codeFromLine: (Line*)inLine\n{\n    uint32_t  theInstruction  = (iMachHeader.filetype == MH_OBJECT) ?\n        *(uint32_t*)((char*)iMachHeaderPtr + (inLine->info.address + iTextOffset)) :\n        *(uint32_t*)((char*)iMachHeaderPtr + (inLine->info.address - iTextOffset));\n\n    inLine->info.codeLength = 4;\n\n    uint32_t* intPtr = (uint32_t*)&inLine->info.code[0];\n\n    *intPtr = theInstruction;\n}\n\n#pragma mark -\n//  commentForLine:\n// ----------------------------------------------------------------------------\n\n- (void)commentForLine: (Line*)inLine;\n{\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    char*   theDummyPtr = NULL;\n    char*   theSymPtr   = NULL;\n    UInt8   opcode      = PO(theCode);\n    uint32_t  localAddy;\n\n    iLineCommentCString[0]  = 0;\n\n    // Examine the primary opcode to see if we need to look for comments.\n    switch (opcode)\n    {\n        case 0x0a:  // cmpli | cmplwi   UIMM\n        case 0x0b:  // cmpi | cmpwi     SIMM\n        {\n            SInt16  imm = SIMM(theCode);\n\n            // Check for a single printable 7-bit char.\n            if (imm >= 0x20 && imm < 0x7f)\n                snprintf(iLineCommentCString, 4, \"'%c'\", imm);\n\n            break;\n        }\n\n        case 0x11:  // sc\n            [self commentForSystemCall];\n\n            break;\n\n        case 0x10:  // bc, bca, bcl, bcla\n        case 0x12:  // b, ba, bl, bla\n        {\n            // Check for absolute branches to the ObjC runtime page. Similar to\n            // the comm page behavior described at\n            // http://www.opensource.apple.com/darwinsource/10.4.7.ppc/xnu-792.6.76/osfmk/ppc/cpu_capabilities.h\n            // However, the ObjC runtime page is not really a comm page, and it\n            // cannot be accessed by bca and bcla instructions, due to their\n            // 16-bit limitation.\n\n            // Deal with absolute branches.\n            if (AA(theCode))\n            {\n                uint32_t  target  = LI(theCode);\n\n                switch (target)\n                {\n                    case kRTAddress_objc_msgSend:\n                    {\n                        char    tempComment[MAX_COMMENT_LENGTH];\n\n                        strncpy(tempComment, kRTName_objc_msgSend,\n                            strlen(kRTName_objc_msgSend) + 1);\n\n                        if (iOpts.verboseMsgSends)\n                            [self commentForMsgSend:tempComment fromLine:inLine];\n\n                        strncpy(iLineCommentCString, tempComment,\n                            strlen(tempComment) + 1);\n\n                        break;\n                    }\n\n                    case kRTAddress_objc_assign_ivar:\n                    {\n                        char    tempComment[MAX_COMMENT_LENGTH];\n\n                        strncpy(tempComment, kRTName_objc_assign_ivar,\n                            strlen(kRTName_objc_assign_ivar) + 1);\n\n                        // Bail if we don't know about the class.\n                        if (!iCurrentClass)\n                        {\n                            strncpy(iLineCommentCString, tempComment,\n                                strlen(tempComment) + 1);\n                            break;\n                        }\n\n                        if (iRegInfos[5].isValid)\n                        {\n                            objc1_32_ivar   theIvar         = {0};\n                            objc1_32_class  swappedClass    = *(objc1_32_class *)iCurrentClass;\n\n                            #if __LITTLE_ENDIAN__\n                                swap_objc1_32_class(&swappedClass);\n                            #endif\n\n                            if (!iIsInstanceMethod)\n                            {\n                                if (![self getObjc1MetaClass:&swappedClass fromClass:&swappedClass])\n                                    break;\n\n                                #if __LITTLE_ENDIAN__\n                                    swap_objc1_32_class(&swappedClass);\n                                #endif\n                            }\n\n                            if (![self findIvar:&theIvar inClass:&swappedClass withOffset:iRegInfos[5].value])\n                            {\n                                strncpy(iLineCommentCString, tempComment,\n                                    strlen(tempComment) + 1);\n                                break;\n                            }\n\n                            theSymPtr   = [self getPointer:theIvar.ivar_name type:NULL];\n\n                            if (!theSymPtr)\n                            {\n                                strncpy(iLineCommentCString, tempComment,\n                                    strlen(tempComment) + 1);\n                                break;\n                            }\n\n                            if (iOpts.variableTypes)\n                            {\n                                char    theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                                theTypeCString[0]   = 0;\n\n                                [self getDescription: theTypeCString\n                                             forType: [self getPointer:theIvar.ivar_type type:NULL]];\n\n                                snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s (%s)%s\", tempComment, theTypeCString, theSymPtr);\n                            }\n                            else\n                                snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s %s\", tempComment, theSymPtr);\n                        }\n                        else    // !mReginfos[5].isValid\n                            snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", tempComment);\n\n                        break;\n                    }\n\n                    case kRTAddress_objc_assign_global:\n                        strncpy(iLineCommentCString, kRTName_objc_assign_global,\n                            strlen(kRTName_objc_assign_global) + 1);\n                        break;\n\n                    case kRTAddress_objc_assign_strongCast:\n                        strncpy(iLineCommentCString, kRTName_objc_assign_strongCast,\n                            strlen(kRTName_objc_assign_strongCast) + 1);\n                        break;\n\n                    default:\n                        break;\n                }\n            }\n            else    // not an absolute branch\n            {\n                // Insert anonymous label or 'return' if there's not a comment yet.\n                if (iLineCommentCString[0])\n                    break;\n\n                uint32_t  absoluteAddy;\n\n                if (opcode == 0x12)\n                    absoluteAddy =\n                        inLine->info.address + LI(theCode);\n                else\n                    absoluteAddy =\n                        inLine->info.address + BD(theCode);\n\n                FunctionInfo    searchKey   = {absoluteAddy, NULL, 0, 0};\n                FunctionInfo*   funcInfo    = bsearch(&searchKey,\n                    iFuncInfos, iNumFuncInfos, sizeof(FunctionInfo),\n                    (COMPARISON_FUNC_TYPE)Function_Info_Compare);\n\n                if (funcInfo && funcInfo->genericFuncNum != 0)\n                {\n                    snprintf(iLineCommentCString,\n                        ANON_FUNC_BASE_LENGTH + 11, \"%s%d\",\n                        ANON_FUNC_BASE, funcInfo->genericFuncNum);\n                    break;\n                }\n\n// FIXME: mCurrentFuncInfoIndex is -1 here when it should not be\n                funcInfo = &iFuncInfos[iCurrentFuncInfoIndex];\n\n                if (!funcInfo->blocks)\n                    break;\n\n                uint32_t  i;\n\n                for (i = 0; i < funcInfo->numBlocks; i++)\n                {\n                    if (funcInfo->blocks[i].beginAddress != absoluteAddy)\n                        continue;\n\n                    if (funcInfo->blocks[i].isEpilog)\n                        snprintf(iLineCommentCString, 8, \"return;\");\n\n                    break;\n                }\n            }\n\n            break;\n        }\n\n        case 0x13:  // bcctr, bclr, isync\n            if (SO(theCode) != 528) // bcctr\n                break;\n\n            // Print value of ctr, ignoring the low 2 bits.\n            if (iCTR.isValid)\n                snprintf(iLineCommentCString, 10, \"0x%x\",\n                    iCTR.value & ~3);\n\n            break;\n\n        case 0x30:  // lfs      SIMM\n        case 0x34:  // stfs     SIMM\n        case 0x32:  // lfd      SIMM\n        case 0x36:  // stfd     SIMM\n        {\n            if (!iRegInfos[RA(theCode)].isValid || RA(theCode) == 0)\n                break;\n\n            if (iRegInfos[RA(theCode)].classPtr)\n            {   // search instance vars\n                objc_32_class_ptr classPtr = iRegInfos[RA(theCode)].classPtr;\n\n                uint32_t offset = UIMM(theCode);\n                char *typePtr = NULL;\n                if (![self getIvarName:&theSymPtr type:&typePtr withOffset:offset inClass:classPtr])\n                    break;\n\n                if (theSymPtr)\n                {\n                    if (iOpts.variableTypes)\n                    {\n                        char    theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                        theTypeCString[0]   = 0;\n\n                        [self getDescription:theTypeCString forType:typePtr];\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"(%s)%s\", theTypeCString, theSymPtr);\n                    }\n                    else\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                }\n            }\n            else\n            {\n                localAddy   = iRegInfos[RA(theCode)].value + SIMM(theCode);\n                theDummyPtr = [self getPointer:localAddy type:NULL];\n\n                if (!theDummyPtr)\n                    break;\n\n                if (opcode == 0x32 || opcode == 0x36)   // lfd | stfd\n                {\n                    UInt64  theInt64    = *(UInt64*)theDummyPtr;\n\n                    theInt64    = OSSwapBigToHostInt64(theInt64);\n\n                    // dance around printf's type coersion\n                    snprintf(iLineCommentCString,\n                        30, \"%lG\", *(double*)&theInt64);\n                }\n                else    // lfs | stfs\n                {\n                    uint32_t  theInt32    = *(uint32_t*)theDummyPtr;\n\n                    theInt32    = OSSwapBigToHostInt32(theInt32);\n\n                    // dance around printf's type coersion\n                    snprintf(iLineCommentCString,\n                        30, \"%G\", *(float*)&theInt32);\n                }\n            }\n\n            break;\n        }\n\n        case 0x0e:  // li | addi    SIMM\n        case 0x18:  // ori          UIMM\n        case 0x20:  // lwz          SIMM\n        case 0x22:  // lbz          SIMM\n        case 0x24:  // stw          SIMM\n        case 0x26:  // stb          SIMM\n        case 0x28:  // lhz          SIMM\n        case 0x2c:  // sth          SIMM\n        {\n            if (!iRegInfos[RA(theCode)].isValid || RA(theCode) == 0)\n                break;\n\n            if (iRegInfos[RA(theCode)].classPtr)    // relative to a class\n            {   // search instance vars\n                objc_32_class_ptr classPtr = iRegInfos[RA(theCode)].classPtr;\n\n                uint32_t offset = UIMM(theCode);\n                char *typePtr = NULL;\n                if (![self getIvarName:&theSymPtr type:&typePtr withOffset:offset inClass:classPtr])\n                    break;\n\n                if (theSymPtr)\n                {\n                    if (iOpts.variableTypes)\n                    {\n                        char    theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                        theTypeCString[0]   = 0;\n\n                        [self getDescription:theTypeCString forType:typePtr];\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"(%s)%s\", theTypeCString, theSymPtr);\n                    }\n                    else\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                }\n            }\n            else    // absolute address\n            {\n                if (opcode == 0x18) // ori      UIMM\n                    localAddy   = iRegInfos[RA(theCode)].value |\n                        UIMM(theCode);\n                else\n                    localAddy   = iRegInfos[RA(theCode)].value +\n                        SIMM(theCode);\n\n                UInt8   theType = PointerType;\n                uint32_t  theValue;\n\n                theSymPtr   = [self getPointer:localAddy type:&theType];\n\n                if (theSymPtr)\n                {\n                    switch (theType)\n                    {\n                        case DataGenericType:\n                            theValue    = *(uint32_t*)theSymPtr;\n                            theValue    = OSSwapBigToHostInt32(theValue);\n                            theDummyPtr = [self getPointer:theValue type:&theType];\n\n                            switch (theType)\n                            {\n                                case PointerType:\n                                    theSymPtr   = theDummyPtr;\n                                    break;\n\n                                default:\n                                    theSymPtr   = NULL;\n                                    break;\n                            }\n\n                            break;\n\n                        case DataConstType:\n                            theSymPtr   = NULL;\n                            break;\n\n// See http://www.opensource.apple.com/darwinsource/10.4.7.ppc/Csu-58/dyld.s\n// They hardcoded the values, we may as well...\n                        case DYLDType:\n                        {\n                            char*   dyldComment = NULL;\n\n                            theValue    = *(uint32_t*)theSymPtr;\n                            theValue    = OSSwapBigToHostInt32(theValue);\n\n                            switch(theValue)\n                            {\n                                case kDyldAddress_LaSymBindingEntry:\n                                    dyldComment = kDyldName_LaSymBindingEntry;\n                                    break;\n                                case kDyldAddress_FuncLookupPointer:\n                                    dyldComment = kDyldName_FuncLookupPointer;\n                                    break;\n\n                                default:\n                                    break;\n                            }\n\n                            if (dyldComment)\n                                snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", dyldComment);\n\n                            break;\n                        }\n\n                        case PointerType:\n                            break;\n\n                        case CFStringType:\n                        {\n                            cfstring_object theCFString =  *(cfstring_object*)theSymPtr;\n\n                            if (theCFString.oc_string.length == 0)\n                            {\n                                theSymPtr   = NULL;\n                                break;\n                            }\n\n                            theCFString.oc_string.chars = OSSwapBigToHostInt32(theCFString.oc_string.chars);\n                            theSymPtr   = [self getPointer:theCFString.oc_string.chars type:NULL];\n\n                            break;\n                        }\n\n                        case ImpPtrType:\n                        case NLSymType:\n                        {\n                            theValue    = *(uint32_t*)theSymPtr;\n                            theValue    = OSSwapBigToHostInt32(theValue);\n                            theDummyPtr = [self getPointer:theValue type:NULL];\n\n                            if (!theDummyPtr)\n                            {\n                                theSymPtr   = NULL;\n                                break;\n                            }\n\n                            theValue    = *(uint32_t*)(theDummyPtr + 4);\n                            theValue    = OSSwapBigToHostInt32(theValue);\n\n                            if (theValue != typeid_NSString)\n                            {\n                                theValue    = *(uint32_t*)theDummyPtr;\n                                theValue    = OSSwapBigToHostInt32(theValue);\n                                theDummyPtr = [self getPointer:theValue type:NULL];\n\n                                if (!theDummyPtr)\n                                {\n                                    theSymPtr   = NULL;\n                                    break;\n                                }\n                            }\n\n                            cfstring_object theCFString = *(cfstring_object*)theDummyPtr;\n\n                            if (theCFString.oc_string.length == 0)\n                            {\n                                theSymPtr   = NULL;\n                                break;\n                            }\n\n                            theCFString.oc_string.chars = OSSwapBigToHostInt32(theCFString.oc_string.chars);\n                            theSymPtr   = [self getPointer:theCFString.oc_string.chars type:NULL];\n\n                            break;\n                        }\n\n                        case OCGenericType:\n                        case OCStrObjectType:\n                        case OCClassType:\n                        case OCModType:\n                            if (![self getObjc1Description:&theDummyPtr fromObject:theSymPtr type:theType]) \n                                break;\n\n                            if (theDummyPtr)\n                            {\n                                switch (theType)\n                                {\n                                    case OCClassType:\n                                        iRegInfos[RT(theCode)].classPtr =\n                                            (objc_32_class_ptr)theSymPtr;\n                                        break;\n\n                                    default:\n                                        break;\n                                }\n                            }\n\n                            theSymPtr   = theDummyPtr;\n                            break;\n\n                        default:\n                            break;\n                    }\n\n                    if (theSymPtr && !iLineCommentCString[0])\n                    {\n                        if (theType == PStringType)\n                            snprintf(iLineCommentCString, 255,\n                                \"%*s\", theSymPtr[0], theSymPtr + 1);\n                        else\n                            snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                    }\n                }   // if (theSymPtr)\n                else\n                {   // Maybe it's a four-char code...\n                    if ((opcode == 0x0e || opcode == 0x18) &&   // li | addi | ori\n                        localAddy >= 0x20202020 && localAddy < 0x7f7f7f7f)\n                    {\n                        localAddy   = OSSwapBigToHostInt32(localAddy);\n\n                        char*   fcc = (char*)&localAddy;\n\n                        if (fcc[0] >= 0x20 && fcc[0] < 0x7f &&\n                            fcc[1] >= 0x20 && fcc[1] < 0x7f &&\n                            fcc[2] >= 0x20 && fcc[2] < 0x7f &&\n                            fcc[3] >= 0x20 && fcc[3] < 0x7f)\n                            snprintf(iLineCommentCString,\n                                7, \"'%.4s'\", fcc);\n                    }\n                }\n            }   // if !(.classPtr)\n\n            break;\n        }   // case 0x0e...\n\n        default:\n            break;\n    }\n}\n\n//  commentForSystemCall\n// ----------------------------------------------------------------------------\n//  System call number is stored in r0, possible values defined in\n//  <sys/syscall.h>. Call numbers are indices into a lookup table of handler\n//  routines. Args being passed to the looked-up handler start at r3 or r4,\n//  depending on whether it's an indirect SYS_syscall.\n\n- (void)commentForSystemCall\n{\n    if (!iRegInfos[0].isValid ||\n         iRegInfos[0].value > SYS_MAXSYSCALL)\n    {\n        snprintf(iLineCommentCString, 11, \"syscall(?)\");\n        return;\n    }\n\n    BOOL    isIndirect      = (iRegInfos[0].value == SYS_syscall);\n    uint32_t  syscallNumReg   = isIndirect ? 3 : 0;\n    uint32_t  syscallArg1Reg  = isIndirect ? 4 : 3;\n\n    if (!iRegInfos[syscallNumReg].isValid   ||\n        iRegInfos[syscallNumReg].value > SYS_MAXSYSCALL)\n    {\n        snprintf(iLineCommentCString, 11, \"syscall(?)\");\n        return;\n    }\n\n    const char* theSysString    = gSysCalls[iRegInfos[syscallNumReg].value];\n\n    if (!theSysString)\n        return;\n\n    char    theTempComment[50];\n\n    theTempComment[0]   = 0;\n    strncpy(theTempComment, theSysString, strlen(theSysString) + 1);\n\n    // Handle various system calls.\n    switch (iRegInfos[syscallNumReg].value)\n    {\n        case SYS_ptrace:\n            if (iRegInfos[syscallArg1Reg].isValid &&\n                iRegInfos[syscallArg1Reg].value == PT_DENY_ATTACH)\n                snprintf(iLineCommentCString, 40, \"%s(%s)\",\n                    theTempComment, \"PT_DENY_ATTACH\");\n            else\n                snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theTempComment);\n\n            break;\n\n        default:\n            snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theTempComment);\n\n            break;\n    }\n}\n\n//  selectorForMsgSend:fromLine:\n// ----------------------------------------------------------------------------\n\n- (char*)selectorForMsgSend: (char*)outComment\n                   fromLine: (Line*)inLine\n{\n    char* selString = NULL;\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    // Bail if this is not an eligible branch.\n    if (PO(theCode) != 0x12)    // b, bl, ba, bla\n        return NULL;\n\n    // Bail if this is not an objc_msgSend variant.\n    if (memcmp(outComment, \"_objc_msgSend\", 13))\n        return NULL;\n\n    UInt8   sendType        = [self sendTypeFromMsgSend:outComment];\n    uint32_t  selectorRegNum  =\n        (sendType == sendSuper_stret || sendType == send_stret) ? 5 : 4;\n\n    if (!iRegInfos[selectorRegNum].isValid ||\n        !iRegInfos[selectorRegNum].value)\n        return NULL;\n\n    // Get at the selector.\n    UInt8   selType     = PointerType;\n    char*   selPtr      = [self getPointer:iRegInfos[selectorRegNum].value type:&selType];\n\n    switch (selType)\n    {\n        case PointerType:\n            selString   = selPtr;\n\n            break;\n\n        case OCGenericType:\n            if (selPtr)\n            {\n                uint32_t  selPtrValue = *(uint32_t*)selPtr;\n\n                selPtrValue = OSSwapBigToHostInt32(selPtrValue);\n                selString   = [self getPointer:selPtrValue type:NULL];\n            }\n\n            break;\n\n        default:\n            fprintf(stderr, \"otx: [PPCProcessor selectorForMsgSend:fromLine:]: \"\n                \"unsupported selector type: %d at address: 0x%x\\n\",\n                selType, inLine->info.address);\n\n            break;\n    }\n\n    return selString;\n}\n\n//  commentForMsgSend:fromLine:\n// ----------------------------------------------------------------------------\n\n- (void)commentForMsgSend: (char*)ioComment\n                 fromLine: (Line*)inLine\n{\n    char* selString = [self selectorForMsgSend:ioComment fromLine:inLine];\n\n    // Bail if we couldn't find the selector.\n    if (!selString)\n    {\n        iMissedSelectorCount++;\n        return;\n    }\n    \n    iMatchedSelectorCount++;\n\n    UInt8   sendType    = [self sendTypeFromMsgSend:ioComment];\n\n    // Get the address of the class name string, if this a class method.\n    uint32_t  classNameAddy   = 0;\n\n    // If *.classPtr is non-NULL, it's not a name string.\n    if (sendType == sendSuper_stret || sendType == send_stret)\n    {\n        if (iRegInfos[4].isValid && !iRegInfos[4].classPtr)\n            classNameAddy   = iRegInfos[4].value;\n    }\n    else\n    {\n        if (iRegInfos[3].isValid && !iRegInfos[3].classPtr)\n            classNameAddy   = iRegInfos[3].value;\n    }\n\n    char*   className           = NULL;\n    char*   returnTypeString    =\n        (sendType == sendSuper_stret || sendType == send_stret) ?\n        \"(struct)\" : \"\";\n    char    tempComment[MAX_COMMENT_LENGTH];\n\n    tempComment[0]  = 0;\n\n    if (classNameAddy)\n    {\n        // Get at the class name\n        UInt8   classNameType   = PointerType;\n        char*   classNamePtr    = [self getPointer:classNameAddy type:&classNameType];\n\n        switch (classNameType)\n        {\n            // Receiver can be various things in these sections, but we\n            // only want to display class names as receivers.\n            case DataGenericType:\n            case DataConstType:\n            case CFStringType:\n            case ImpPtrType:\n            case OCStrObjectType:\n            case OCModType:\n            case PStringType:\n            case DoubleType:\n                break;\n\n            case NLSymType:\n                if (classNamePtr)\n                {\n                    uint32_t    namePtrValue    = *(uint32_t*)classNamePtr;\n\n                    namePtrValue    = OSSwapBigToHostInt32(namePtrValue);\n                    classNamePtr    = [self getPointer:namePtrValue type:&classNameType];\n\n                    switch (classNameType)\n                    {\n                        case CFStringType:\n                            if (classNamePtr != NULL)\n                            {\n                                cfstring_object classNameCFString = *(cfstring_object*)classNamePtr;\n\n                                namePtrValue    = classNameCFString.oc_string.chars;\n                                namePtrValue    = OSSwapBigToHostInt32(namePtrValue);\n                                classNamePtr    = [self getPointer:namePtrValue type:NULL];\n                                className       = classNamePtr;\n                            }\n\n                            break;\n\n                        // Not sure what these are for, but they're out there.\n                        case NLSymType:\n                        case FloatType:\n                            break;\n\n                        default:\n                            printf(\"otx: [PPCProcessor commentForMsgSend:fromLine:]: \"\n                                \"non-lazy symbol pointer points to unrecognized section: %d\\n\", classNameType);\n                        break;\n                    }\n                }\n\n                break;\n\n            case PointerType:\n                className   = classNamePtr;\n                break;\n\n            case OCGenericType:\n                if (classNamePtr)\n                {\n                    uint32_t  namePtrValue    = *(uint32_t*)classNamePtr;\n\n                    namePtrValue    = OSSwapBigToHostInt32(namePtrValue);\n                    className       = [self getPointer:namePtrValue type:NULL];\n                }\n\n                break;\n\n            case OCClassType:\n                if (classNamePtr)\n                    [self getObjc1Description:&className fromObject:classNamePtr type:OCClassType];\n\n                break;\n\n            default:\n                fprintf(stderr, \"otx: [PPCProcessor commentForMsgSend]: \"\n                    \"unsupported class name type: %d at address: 0x%x\\n\",\n                    classNameType, inLine->info.address);\n\n                break;\n        }\n    }\n\n    if (className)\n    {\n        snprintf(tempComment, MAX_COMMENT_LENGTH,\n            ((sendType == sendSuper || sendType == sendSuper_stret) ?\n            \"+%s[[%s super] %s]\" : \"+%s[%s %s]\"),\n            returnTypeString, className, selString);\n    }\n    else\n    {\n        switch (sendType)\n        {\n            case send:\n            case send_rtp:\n            case send_variadic:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[r3 %s]\", returnTypeString, selString);\n                break;\n\n            case sendSuper:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[[r3 super] %s]\", returnTypeString, selString);\n                break;\n\n            case send_stret:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[r4 %s]\", returnTypeString, selString);\n                break;\n\n            case sendSuper_stret:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[[r4 super] %s]\", returnTypeString, selString);\n                break;\n\n            default:\n                break;\n        }\n    }\n\n    if (tempComment[0])\n        strncpy(ioComment, tempComment, strlen(tempComment) + 1);\n}\n\n//  chooseLine:\n// ----------------------------------------------------------------------------\n\n- (void)chooseLine: (Line**)ioLine\n{\n    if (!(*ioLine) || !(*ioLine)->info.isCode ||\n        !(*ioLine)->alt || !(*ioLine)->alt->chars)\n        return;\n\n    uint32_t theCode = *(uint32_t*)(*ioLine)->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    if (PO(theCode) == 18)  // b, ba, bl, bla\n    {\n        Line* theNewLine  = malloc(sizeof(Line));\n\n        memcpy(theNewLine, (*ioLine)->alt, sizeof(Line));\n        theNewLine->chars   = malloc(theNewLine->length + 1);\n        strncpy(theNewLine->chars, (*ioLine)->alt->chars,\n            theNewLine->length + 1);\n\n        // Swap in the verbose line and free the previous verbose lines.\n        [self deleteLinesBefore:(*ioLine)->alt fromList:&iVerboseLineListHead];\n        [self replaceLine:*ioLine withLine:theNewLine inList:&iPlainLineListHead];\n        *ioLine = theNewLine;\n    }\n}\n\n#pragma mark -\n//  resetRegisters:\n// ----------------------------------------------------------------------------\n\n- (void)resetRegisters: (Line*)inLine\n{\n    if (!inLine)\n    {\n        fprintf(stderr, \"otx: [PPCProcessor resetRegisters]: \"\n            \"tried to reset with NULL inLine\\n\");\n        return;\n    }\n\n    // Setup the registers with default info. r3 is 'self' at the beginning\n    // of any Obj-C method, and r12 holds the address of the 1st instruction\n    // if the function was called indirectly. In the case of direct calls,\n    // r12 will be overwritten before it is used, if it is used at all.\n    [self getObjcClassPtr:&iCurrentClass fromMethod:inLine->info.address];\n    [self getObjc1CatPtr:&iCurrentCat fromMethod:inLine->info.address];\n    memset(iRegInfos, 0, sizeof(GPRegisterInfo) * 32);\n\n    // If we didn't get the class from the method, try to get it from the\n    // category.\n    if (!iCurrentClass && iCurrentCat)\n    {\n        objc1_32_category swappedCat = *iCurrentCat;\n\n        #if __LITTLE_ENDIAN__\n            swap_objc1_32_category(&swappedCat);\n        #endif\n\n        [self getObjcClassPtr:&iCurrentClass fromName:[self getPointer:swappedCat.class_name type:NULL]];\n    }\n\n    iRegInfos[3].classPtr   = iCurrentClass;\n    iRegInfos[3].catPtr     = iCurrentCat;\n    iRegInfos[3].isValid    = YES;\n    iRegInfos[12].value     = iCurrentFuncPtr;\n    iRegInfos[12].isValid   = YES;\n    iLR                     = (GPRegisterInfo){0};\n    iCTR                    = (GPRegisterInfo){0};\n\n    // Try to find out whether this is a class or instance method.\n    MethodInfo* thisMethod  = NULL;\n\n    if ([self getObjcMethod:&thisMethod fromAddress:inLine->info.address])\n        iIsInstanceMethod   = thisMethod->inst;\n\n    if (iLocalSelves)\n    {\n        free(iLocalSelves);\n        iLocalSelves    = NULL;\n        iNumLocalSelves = 0;\n    }\n\n    if (iLocalVars)\n    {\n        free(iLocalVars);\n        iLocalVars      = NULL;\n        iNumLocalVars   = 0;\n    }\n\n    iCurrentFuncInfoIndex++;\n\n    if (iCurrentFuncInfoIndex >= iNumFuncInfos)\n        iCurrentFuncInfoIndex   = -1;\n}\n\n//  updateRegisters:\n// ----------------------------------------------------------------------------\n//  Keep our local copy of the GPRs in sync as much as possible with the\n//  values that the exe will use at runtime. Assign classPtr and catPtr fields\n//  in a register's info if its new value points to a class or category.\n// http://developer.apple.com/documentation/DeveloperTools/Conceptual/LowLevelABI/Articles/32bitPowerPC.html\n// http://developer.apple.com/documentation/DeveloperTools/Conceptual/MachOTopics/Articles/dynamic_code.html\n\n- (void)updateRegisters: (Line*)inLine;\n{\n    if (!inLine)\n    {\n        fprintf(stderr, \"otx: [PPCProcessor updateRegisters]: \"\n            \"tried to update with NULL inLine\\n\");\n        return;\n    }\n\n    uint32_t theNewValue;\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    if (IS_BRANCH_LINK(theCode))\n    {\n        iLR.value   = inLine->info.address + 4;\n        iLR.isValid = YES;\n    }\n\n    switch (PO(theCode))\n    {\n        case 0x07:  // mulli        SIMM\n        {\n            if (!iRegInfos[RA(theCode)].isValid)\n            {\n                iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n                break;\n            }\n\n            UInt64  theProduct  =\n                (SInt32)iRegInfos[RA(theCode)].value * SIMM(theCode);\n\n            iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n            iRegInfos[RT(theCode)].value    = (uint32_t)(theProduct & 0xffffffff);\n            iRegInfos[RT(theCode)].isValid  = YES;\n\n            break;\n        }\n\n        case 0x08:  // subfic       SIMM\n            if (!iRegInfos[RA(theCode)].isValid)\n            {\n                iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n                break;\n            }\n\n            theNewValue = iRegInfos[RA(theCode)].value - SIMM(theCode);\n\n            iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n            iRegInfos[RT(theCode)].value    = theNewValue;\n            iRegInfos[RT(theCode)].isValid  = YES;\n\n            break;\n\n        case 0x0c:  // addic        SIMM\n        case 0x0d:  // addic.       SIMM\n        case 0x0e:  // addi | li    SIMM\n            if (RA(theCode) == 1    &&  // current reg is stack pointer (r1)\n                SIMM(theCode) >= 0)     // we're accessing local vars, not args\n            {\n                BOOL    found = NO;\n                uint32_t  i;\n\n                // Check for copied self pointer. This happens mostly in \"init\"\n                // methods, as in: \"self = [super init]\"\n                if (iLocalSelves)   // self was copied to a local variable\n                {\n                    // If we're accessing a local var copy of self,\n                    // copy that info back to the reg in question.\n                    for (i = 0; i < iNumLocalSelves; i++)\n                    {\n                        if (iLocalSelves[i].offset != UIMM(theCode))\n                            continue;\n\n                        iRegInfos[RT(theCode)] = iLocalSelves[i].regInfo;\n                        found = YES;\n\n                        break;\n                    }\n                }\n\n                if (found)\n                    break;\n\n                // Check for other local variables.\n                if (iLocalVars)\n                {\n                    for (i = 0; i < iNumLocalVars; i++)\n                    {\n                        if (iLocalVars[i].offset != UIMM(theCode))\n                            continue;\n\n                        iRegInfos[RT(theCode)] = iLocalVars[i].regInfo;\n                        found = YES;\n\n                        break;\n                    }\n                }\n\n                if (found)\n                    break;\n            }\n\n            // We didn't find any local variables, try immediates.\n            if (RA(theCode) == 0)   // li\n            {\n                iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = UIMM(theCode);\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }\n            else                    // addi\n            {\n                // Update rD if we know what rA is.\n                if (!iRegInfos[RA(theCode)].isValid)\n                {\n                    iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n                    break;\n                }\n\n                iRegInfos[RT(theCode)].classPtr = NULL;\n                iRegInfos[RT(theCode)].catPtr   = NULL;\n\n                theNewValue = iRegInfos[RA(theCode)].value + SIMM(theCode);\n\n                iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = theNewValue;\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }\n\n            break;\n\n        case 0x0f:  // addis | lis\n            iRegInfos[RT(theCode)].classPtr = NULL;\n            iRegInfos[RT(theCode)].catPtr   = NULL;\n\n            if (RA(theCode) == 0)   // lis\n            {\n                iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = UIMM(theCode) << 16;\n                iRegInfos[RT(theCode)].isValid  = YES;\n                break;\n            }\n\n            // addis\n            if (!iRegInfos[RA(theCode)].isValid)\n            {\n                iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n                break;\n            }\n\n            theNewValue = iRegInfos[RA(theCode)].value +\n                (SIMM(theCode) << 16);\n\n            iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n            iRegInfos[RT(theCode)].value    = theNewValue;\n            iRegInfos[RT(theCode)].isValid  = YES;\n\n            break;\n\n        case 0x10:  // bcl, bcla\n        case 0x13:  // bclrl, bcctrl\n            if (!IS_BRANCH_LINK(theCode))   // fall thru if link\n                break;\n\n        case 0x12:  // b, ba, bl, bla\n        {\n            if (!LK(theCode))   // bl, bla\n                break;\n\n            iRegInfos[3]    = (GPRegisterInfo){0};\n\n            break;\n        }\n        case 0x15:  // rlwinm\n        {\n            if (!iRegInfos[RT(theCode)].isValid)\n            {\n                iRegInfos[RA(theCode)]  = (GPRegisterInfo){0};\n                break;\n            }\n\n            uint32_t  rotatedRT   =\n                rotl(iRegInfos[RT(theCode)].value, RB(theCode));\n            uint32_t  theMask     = 0x0;\n            UInt8   i;\n\n            for (i = MB(theCode); i <= ME(theCode); i++)\n                theMask |= 1 << (31 - i);\n\n            theNewValue = rotatedRT & theMask;\n\n            iRegInfos[RA(theCode)]          = (GPRegisterInfo){0};\n            iRegInfos[RA(theCode)].value    = theNewValue;\n            iRegInfos[RA(theCode)].isValid  = YES;\n\n            break;\n        }\n\n        case 0x18:  // ori\n            if (!iRegInfos[RT(theCode)].isValid)\n            {\n                iRegInfos[RA(theCode)]  = (GPRegisterInfo){0};\n                break;\n            }\n\n            theNewValue =\n                iRegInfos[RT(theCode)].value | (uint32_t)UIMM(theCode);\n\n            iRegInfos[RA(theCode)]          = (GPRegisterInfo){0};\n            iRegInfos[RA(theCode)].value    = theNewValue;\n            iRegInfos[RA(theCode)].isValid  = YES;\n\n            break;\n\n        case 0x1f:  // multiple instructions\n            switch (SO(theCode))\n            {\n                case 23:    // lwzx\n                    iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n                    break;\n\n                case 8:     // subfc\n                case 40:    // subf\n                    if (!iRegInfos[RA(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n                        break;\n                    }\n\n                    // 2's complement subtraction\n                    theNewValue =\n                        (iRegInfos[RA(theCode)].value ^= 0xffffffff) +\n                        iRegInfos[RB(theCode)].value + 1;\n\n                    iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n                    iRegInfos[RT(theCode)].value    = theNewValue;\n                    iRegInfos[RT(theCode)].isValid  = YES;\n\n                    break;\n\n                case 339:   // mfspr\n                    iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n\n                    if (SPR(theCode) == LR  &&  // from LR\n                        iLR.isValid)\n                    {   // Copy LR into rD.\n                        iRegInfos[RT(theCode)].value    = iLR.value;\n                        iRegInfos[RT(theCode)].isValid  = YES;\n                    }\n\n                    break;\n\n                case 444:   // or | or.\n                    if (!iRegInfos[RT(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RA(theCode)]  = (GPRegisterInfo){0};\n                        break;\n                    }\n\n                    theNewValue =\n                        (iRegInfos[RT(theCode)].value |\n                         iRegInfos[RB(theCode)].value);\n\n                    iRegInfos[RA(theCode)]          = (GPRegisterInfo){0};\n                    iRegInfos[RA(theCode)].value    = theNewValue;\n                    iRegInfos[RA(theCode)].isValid  = YES;\n\n                    // If we just copied a register, copy the\n                    // remaining fields.\n                    if (RT(theCode) == RB(theCode))\n                    {\n                        iRegInfos[RA(theCode)].classPtr =\n                            iRegInfos[RB(theCode)].classPtr;\n                        iRegInfos[RA(theCode)].catPtr   =\n                            iRegInfos[RB(theCode)].catPtr;\n                    }\n\n                    break;\n\n                case 467:   // mtspr\n                    if (SPR(theCode) == CTR)    // to CTR\n                    {\n                        if (!iRegInfos[RS(theCode)].isValid)\n                        {\n                            iCTR    = (GPRegisterInfo){0};\n                            break;\n                        }\n\n                        iCTR.value = iRegInfos[RS(theCode)].value;\n                        iCTR.isValid = YES;\n                    }\n\n                    break;\n\n                case 24:    // slw\n                    if (!iRegInfos[RS(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RA(theCode)]  = (GPRegisterInfo){0};\n                        break;\n                    }\n\n                    if (SB(iRegInfos[RB(theCode)].value))\n                    {\n                        theNewValue =\n                            iRegInfos[RS(theCode)].value <<\n                                SV(iRegInfos[RB(theCode)].value);\n                    }\n                    else    // If RB.5 == 0, RA = 0.\n                        theNewValue = 0;\n\n                    iRegInfos[RA(theCode)]          = (GPRegisterInfo){0};\n                    iRegInfos[RA(theCode)].value    = theNewValue;\n                    iRegInfos[RA(theCode)].isValid  = YES;\n\n                    break;\n\n                case 536:   // srw\n                    if (!iRegInfos[RS(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RA(theCode)]  = (GPRegisterInfo){0};\n                        break;\n                    }\n\n                    theNewValue =\n                        iRegInfos[RS(theCode)].value >>\n                            SV(iRegInfos[RB(theCode)].value);\n\n                    iRegInfos[RA(theCode)]          = (GPRegisterInfo){0};\n                    iRegInfos[RA(theCode)].value    = theNewValue;\n                    iRegInfos[RA(theCode)].isValid  = YES;\n\n                    break;\n\n                default:\n                    break;\n            }\n\n            break;\n\n        case 0x20:  // lwz\n        case 0x22:  // lbz\n            if (RA(theCode) == 0)\n            {\n                iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = SIMM(theCode);\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }\n            else if (iRegInfos[RA(theCode)].isValid)\n            {\n                void *tempPtr = [self getPointer:iRegInfos[RA(theCode)].value + SIMM(theCode) type:NULL];\n\n                if (tempPtr)\n                {\n                    iRegInfos[RT(theCode)]          = (GPRegisterInfo){0};\n                    iRegInfos[RT(theCode)].value    = *(uint32_t*)tempPtr;\n                    iRegInfos[RT(theCode)].value    =\n                        OSSwapBigToHostInt32(iRegInfos[RT(theCode)].value);\n                    iRegInfos[RT(theCode)].isValid  = YES;\n                }\n                else\n                    iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n            }\n            else if (iLocalVars)\n            {\n                uint32_t  i;\n\n                for (i = 0; i < iNumLocalVars; i++)\n                {\n                    if (iLocalVars[i].offset == SIMM(theCode))\n                    {\n                        iRegInfos[RT(theCode)]  = iLocalVars[i].regInfo;\n                        break;\n                    }\n                }\n            }\n            else\n                iRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n\n            break;\n\n/*      case 0x22:  // lbz\n            mRegInfos[RT(theCode)]  = (GPRegisterInfo){0};\n\n            if (RA(theCode) == 0)\n            {\n                mRegInfos[RT(theCode)].value    = SIMM(theCode);\n                mRegInfos[RT(theCode)].isValid  = YES;\n            }\n\n            break;*/\n\n        case 0x24:  // stw\n            if (!iRegInfos[RT(theCode)].isValid ||\n                RA(theCode) != 1                ||\n                SIMM(theCode) < 0)\n                break;\n\n            if (iRegInfos[RT(theCode)].classPtr)    // if it's a class\n            {\n                iNumLocalSelves++;\n                iLocalSelves    = realloc(iLocalSelves,\n                    iNumLocalSelves * sizeof(VarInfo));\n                iLocalSelves[iNumLocalSelves - 1]   = (VarInfo)\n                    {iRegInfos[RT(theCode)], UIMM(theCode)};\n            }\n            else\n            {\n                iNumLocalVars++;\n                iLocalVars  = realloc(iLocalVars,\n                    iNumLocalVars * sizeof(VarInfo));\n                iLocalVars[iNumLocalVars - 1]   = (VarInfo)\n                    {iRegInfos[RT(theCode)], UIMM(theCode)};\n            }\n\n            break;\n\n/*      case 0x21:\n        case 0x23:\n        case 0x25:\n        case 0x26:\n        case 0x27:\n        case 0x28:\n        case 0x29:\n        case 0x2a:\n        case 0x2b:\n        case 0x2c:\n        case 0x2d:\n        case 0x2e:\n        case 0x2f:\n            break;*/\n\n        default:\n            break;\n    }\n}\n\n//  restoreRegisters:\n// ----------------------------------------------------------------------------\n\n- (BOOL)restoreRegisters: (Line*)inLine\n{\n    if (!inLine)\n    {\n        fprintf(stderr, \"otx: [PPCProcessor restoreRegisters]: \"\n            \"tried to restore with NULL inLine\\n\");\n        return NO;\n    }\n\n    BOOL needNewLine = NO;\n\n    if (iCurrentFuncInfoIndex < 0)\n        return NO;\n\n    // Search current FunctionInfo for blocks that start at this address.\n    FunctionInfo*   funcInfo    =\n        &iFuncInfos[iCurrentFuncInfoIndex];\n\n    if (!funcInfo->blocks)\n        return NO;\n\n    uint32_t  i;\n\n    for (i = 0; i < funcInfo->numBlocks; i++)\n    {\n        if (funcInfo->blocks[i].beginAddress !=\n            inLine->info.address)\n            continue;\n\n        // Update machine state.\n        MachineState    machState   =\n            funcInfo->blocks[i].state;\n\n        memcpy(iRegInfos, machState.regInfos,\n            sizeof(GPRegisterInfo) * 32);\n        iLR     = machState.regInfos[LRIndex];\n        iCTR    = machState.regInfos[CTRIndex];\n\n        if (machState.localSelves)\n        {\n            if (iLocalSelves)\n                free(iLocalSelves);\n\n            iNumLocalSelves = machState.numLocalSelves;\n            iLocalSelves    = malloc(\n                sizeof(VarInfo) * iNumLocalSelves);\n            memcpy(iLocalSelves, machState.localSelves,\n                sizeof(VarInfo) * iNumLocalSelves);\n        }\n\n        if (machState.localVars)\n        {\n            if (iLocalVars)\n                free(iLocalVars);\n\n            iNumLocalVars   = machState.numLocalVars;\n            iLocalVars      = malloc(\n                sizeof(VarInfo) * iNumLocalVars);\n            memcpy(iLocalVars, machState.localVars,\n                sizeof(VarInfo) * iNumLocalVars);\n        }\n\n        // Optionally add a blank line before this block.\n        if (iOpts.separateLogicalBlocks && inLine->chars[0] != '\\n' &&\n            !inLine->info.isFunction)\n            needNewLine = YES;\n\n        break;\n    }   // for (i = 0...)\n\n    return needNewLine;\n}\n\n//  lineIsFunction:\n// ----------------------------------------------------------------------------\n\n- (BOOL)lineIsFunction: (Line*)inLine\n{\n    if (!inLine)\n        return NO;\n\n    uint32_t  theAddy = inLine->info.address;\n\n    if (theAddy == iAddrDyldStubBindingHelper   ||\n        theAddy == iAddrDyldFuncLookupPointer)\n        return YES;\n\n    MethodInfo* theDummyInfo    = NULL;\n\n    // In Obj-C apps, the majority of funcs will have Obj-C symbols, so check\n    // those first.\n    if ([self findClassMethod:&theDummyInfo byAddress:theAddy])\n        return YES;\n\n    if ([self findCatMethod:&theDummyInfo byAddress:theAddy])\n        return YES;\n\n    // If it's not an Obj-C method, maybe there's an nlist.\n    if ([self findSymbolByAddress:theAddy])\n        return YES;\n\n    // If otool gave us a function name...\n    if (inLine->prev && !inLine->prev->info.isCode)\n        return YES;\n\n    BOOL isFunction = NO;\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    if ((theCode & 0xfc1fffff) == 0x7c0802a6)   // mflr to any reg\n    {   // Allow for late mflr\n        BOOL    foundUB = NO;\n        Line*   thePrevLine = inLine->prev;\n\n        // Walk back to the most recent unconditional branch, looking\n        // for existing symbols.\n        while (!foundUB && thePrevLine)\n        {\n            // Allow for multiple mflr's\n            if (thePrevLine->info.isFunction)\n                return NO;\n\n            theCode = *(uint32_t*)thePrevLine->info.code;\n            theCode = OSSwapBigToHostInt32(theCode);\n\n            if ((theCode & 0xfc0007ff) == 0x7c000008)   // trap\n            {\n                foundUB = YES;\n                continue;\n            }\n\n            UInt8   opcode  = PO(theCode);\n\n            if (opcode == 16 || opcode == 18 || opcode == 19)\n            // bc, bca, bcl, bcla, b, ba, bl, bla, bclr, bclrl and more\n            {\n                if (!IS_BRANCH_CONDITIONAL(theCode) &&\n                    theCode != 0x429f0005 &&    // bcl w/ \"always branch\"\n                    (theCode & 0x48000001) != 0x48000001)    // bl\n                    foundUB = YES;\n            }\n\n            if (!foundUB)\n                thePrevLine = thePrevLine->prev;\n        }\n\n        if (!thePrevLine)\n            return YES;\n\n        thePrevLine = thePrevLine->next;\n\n        // If the code line following the most recent unconditional\n        // branch is not already recognized, flag it now.\n        if (thePrevLine == inLine)\n            isFunction  = YES;\n        else\n        {\n            BOOL foundStart = NO;\n\n            for (; thePrevLine != inLine;\n                thePrevLine = thePrevLine->next)\n            {\n                if (!thePrevLine->info.isCode)\n                    continue;   // not code, keep looking\n                else if (!thePrevLine->info.isFunction)\n                {               // not yet recognized, try it\n                    theCode = *(uint32_t*)thePrevLine->info.code;\n                    theCode = OSSwapBigToHostInt32(theCode);\n\n                    if (theCode == 0x7fe00008   ||  // ignore traps\n                        theCode == 0x60000000   ||  // ignore nops\n                        theCode == 0x00000000)      // ignore .longs\n                        continue;\n                    else\n                    {\n                        thePrevLine->info.isFunction = YES;\n                        foundStart = YES;\n                        break;\n                    }\n                }\n                else    // already recognized, bail\n                {\n                    foundStart = YES;\n                    break;\n                }\n            }\n\n            if (!foundStart)\n                isFunction = YES;\n        }\n    }   // if (theCode == 0x7c0802a6)\n\n    return isFunction;\n}\n\n//  codeIsBlockJump:\n// ----------------------------------------------------------------------------\n\n- (BOOL)codeIsBlockJump: (UInt8*)inCode\n{\n    uint32_t theCode = *(uint32_t*)inCode;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n    return IS_BLOCK_BRANCH(theCode);\n}\n\n//  gatherFuncInfos\n// ----------------------------------------------------------------------------\n\n- (void)gatherFuncInfos\n{\n    Line*           theLine     = iPlainLineListHead;\n    uint32_t          theCode;\n    uint32_t          progCounter = 0;\n\n    // Loop thru lines.\n    while (theLine)\n    {\n        if (!(progCounter % (PROGRESS_FREQ * 5)))\n        {\n            if (gCancel == YES)\n                return;\n\n//            [NSThread sleepForTimeInterval: 0.0];\n        }\n\n        if (!theLine->info.isCode)\n        {\n            theLine = theLine->next;\n            continue;\n        }\n\n        theCode = *(uint32_t*)theLine->info.code;\n        theCode = OSSwapBigToHostInt32(theCode);\n\n        if (theLine->info.isFunction)\n        {\n            iCurrentFuncPtr = theLine->info.address;\n            [self resetRegisters:theLine];\n        }\n        else\n            [self restoreRegisters:theLine];\n\n        [self updateRegisters:theLine];\n\n        // Check if we need to save the machine state.\n        if (IS_BLOCK_BRANCH(theCode) && iCurrentFuncInfoIndex >= 0 &&\n            PO(theCode) != 0x13)    // no new blocks for blr, bctr\n        {\n            uint32_t branchTarget = 0;\n\n            // Retrieve the branch target.\n            if (PO(theCode) == 0x12)    // b\n                branchTarget    = theLine->info.address + LI(theCode);\n            else if (PO(theCode) == 0x10)   // bc\n                branchTarget    = theLine->info.address + BD(theCode);\n\n            // Retrieve current FunctionInfo.\n            FunctionInfo*   funcInfo    =\n                &iFuncInfos[iCurrentFuncInfoIndex];\n\n            // 'currentBlock' will point to either an existing block which\n            // we will update, or a newly allocated block.\n            BlockInfo*  currentBlock    = NULL;\n            Line*       endLine         = NULL;\n            BOOL        isEpilog        = NO;\n            uint32_t      i;\n\n            if (funcInfo->blocks)\n            {   // Blocks exist, find 1st one matching this address.\n                // This is an exhaustive search, but the speed hit should\n                // only be an issue with extremely long functions.\n                for (i = 0; i < funcInfo->numBlocks; i++)\n                {\n                    if (funcInfo->blocks[i].beginAddress == branchTarget)\n                    {\n                        currentBlock = &funcInfo->blocks[i];\n                        break;\n                    }\n                }\n\n                if (currentBlock)\n                {   // Determine if the target block is an epilog.\n                    if (currentBlock->endLine == NULL &&\n                        iOpts.returnStatements)\n                    {\n                        // Find the first line of the target block.\n                        Line    searchKey = {NULL, 0, NULL, NULL, NULL, {branchTarget, {0}, YES, NO}};\n                        Line*   searchKeyPtr = &searchKey;\n                        Line**  beginLine = bsearch(&searchKeyPtr, iLineArray, iNumCodeLines, sizeof(Line*),\n                            (COMPARISON_FUNC_TYPE)Line_Address_Compare);\n\n                        if (beginLine != NULL)\n                        {\n                            // Walk through the block. It's an epilog if it ends\n                            // with 'blr' and contains no 'bl's.\n                            Line*   nextLine    = *beginLine;\n                            BOOL    canBeEpliog = YES;\n                            uint32_t  tempCode;\n\n                            while (nextLine)\n                            {\n                                tempCode = *(uint32_t*)nextLine->info.code;\n                                tempCode = OSSwapBigToHostInt32(tempCode);\n\n                                if (IS_BRANCH_LINK(tempCode))\n                                    canBeEpliog = NO;\n\n                                if (IS_BLOCK_BRANCH(tempCode))\n                                {\n                                    endLine = nextLine;\n\n                                    if (canBeEpliog && IS_BLR(tempCode))\n                                        isEpilog = YES;\n\n                                    break;\n                                }\n\n                                nextLine = nextLine->next;\n                            }\n                        }\n\n//                      currentBlock->endLine   = endLine;\n                    }\n                }\n                else\n                {   // No matching blocks found, so allocate a new one.\n                    funcInfo->numBlocks++;\n                    funcInfo->blocks = realloc(funcInfo->blocks,\n                        sizeof(BlockInfo) * funcInfo->numBlocks);\n                    currentBlock =\n                        &funcInfo->blocks[funcInfo->numBlocks - 1];\n                    *currentBlock = (BlockInfo){0};\n                }\n            }\n            else\n            {   // No existing blocks, allocate one.\n                funcInfo->numBlocks++;\n                funcInfo->blocks    = calloc(1, sizeof(BlockInfo));\n                currentBlock        = funcInfo->blocks;\n            }\n\n            // sanity check\n            if (!currentBlock)\n            {\n                fprintf(stderr, \"otx: [PPCProcessor gatherFuncInfos] \"\n                    \"currentBlock is NULL. Flame the dev.\\n\");\n                return;\n            }\n\n            // Create a new MachineState.\n            GPRegisterInfo* savedRegs   = malloc(\n                sizeof(GPRegisterInfo) * 34);\n\n            memcpy(savedRegs, iRegInfos, sizeof(GPRegisterInfo) * 32);\n            savedRegs[LRIndex]  = iLR;\n            savedRegs[CTRIndex] = iCTR;\n\n            VarInfo*    savedSelves = NULL;\n\n            if (iLocalSelves)\n            {\n                savedSelves = malloc(\n                    sizeof(VarInfo) * iNumLocalSelves);\n                memcpy(savedSelves, iLocalSelves,\n                    sizeof(VarInfo) * iNumLocalSelves);\n            }\n\n            VarInfo*    savedVars   = NULL;\n\n            if (iLocalVars)\n            {\n                savedVars   = malloc(\n                    sizeof(VarInfo) * iNumLocalVars);\n                memcpy(savedVars, iLocalVars,\n                    sizeof(VarInfo) * iNumLocalVars);\n            }\n\n            MachineState    machState   =\n                {savedRegs, savedSelves, iNumLocalSelves,\n                    savedVars, iNumLocalVars};\n\n            // Store the new BlockInfo.\n            BlockInfo   blockInfo   =\n                {branchTarget, endLine, isEpilog, machState};\n\n            memcpy(currentBlock, &blockInfo, sizeof(BlockInfo));\n        }\n\n        theLine = theLine->next;\n        progCounter++;\n    }\n\n    iCurrentFuncInfoIndex   = -1;\n}\n\n#ifdef OTX_DEBUG\n//  printBlocks:\n// ----------------------------------------------------------------------------\n\n- (void)printBlocks: (uint32_t)inFuncIndex;\n{\n    if (!iFuncInfos)\n        return;\n\n    FunctionInfo*   funcInfo    = &iFuncInfos[inFuncIndex];\n\n    if (!funcInfo || !funcInfo->blocks)\n        return;\n\n    uint32_t  i, j;\n\n    fprintf(stderr, \"\\nfunction at 0x%x:\\n\\n\", funcInfo->address);\n    fprintf(stderr, \"%d blocks\\n\", funcInfo->numBlocks);\n\n    for (i = 0; i < funcInfo->numBlocks; i++)\n    {\n        fprintf(stderr, \"\\nblock %d at 0x%x:\\n\\n\", i + 1,\n            funcInfo->blocks[i].beginAddress);\n\n        for (j = 0; j < 32; j++)\n        {\n            if (!funcInfo->blocks[i].state.regInfos[j].isValid)\n                continue;\n\n            fprintf(stderr, \"\\t\\tr%d: 0x%x\\n\", j,\n                funcInfo->blocks[i].state.regInfos[j].value);\n        }\n    }\n}\n#endif  // OTX_DEBUG\n\n@end\n"}
+{"plateform": "github", "repo_name": "zhangchn/otx-master", "name": "Exe32Processor.m", "ext": ".m", "path": "otx-master/src/source/Processors/Exe32Processor.m", "size": 71832, "source_encoding": "utf_8", "md5": "fb7d3166b5926cb8213fe98a8dd69574", "text": "/*\n    Exe32Processor.m\n\n    This file relies upon, and steals code from, the cctools source code\n    available from: http://www.opensource.apple.com/darwinsource/\n\n    This file is in the public domain.\n*/\n\n#import <Cocoa/Cocoa.h>\n\n#import <mach/mach_time.h>\n\n#import \"Exe32Processor.h\"\n#import \"ArchSpecifics.h\"\n#import \"ListUtils.h\"\n#import \"ObjcAccessors.h\"\n#import \"ObjectLoader.h\"\n#import \"Searchers.h\"\n#import \"SysUtils.h\"\n#import \"UserDefaultKeys.h\"\n#import \"X86Processor.h\"\n\n@implementation Exe32Processor\n\n// Exe32Processor is a base class that handles processor-independent issues.\n// PPCProcessor and X86Processor are subclasses that add functionality\n// specific to those CPUs. The AppController class creates a new instance of\n// one of those subclasses for each processing, and deletes the instance as\n// soon as possible. Member variables may or may not be re-initialized before\n// destruction. Do not reuse a single instance of those subclasses for\n// multiple processings.\n\n//  initWithURL:controller:options:\n// ----------------------------------------------------------------------------\n\n- (id)initWithURL: (NSURL*)inURL\n       controller: (id)inController\n          options: (ProcOptions*)inOptions\n{\n    if (!inURL || !inController || !inOptions)\n        return nil;\n\n    if ((self = [super initWithURL: inURL controller: inController\n        options: inOptions]) == nil)\n        return nil;\n\n    return self;\n}\n\n//  dealloc\n// ----------------------------------------------------------------------------\n\n- (void)dealloc\n{\n    if (iFuncSyms)\n    {\n        free(iFuncSyms);\n        iFuncSyms   = NULL;\n    }\n\n    if (iObjcSects)\n    {\n        free(iObjcSects);\n        iObjcSects  = NULL;\n    }\n\n    if (iClassMethodInfos)\n    {\n        free(iClassMethodInfos);\n        iClassMethodInfos   = NULL;\n    }\n\n    if (iCatMethodInfos)\n    {\n        free(iCatMethodInfos);\n        iCatMethodInfos = NULL;\n    }\n\n    if (iClassIvars)\n    {\n        free(iClassIvars);\n        iClassIvars = NULL;\n    }\n\n    if (iLineArray)\n    {\n        free(iLineArray);\n        iLineArray = NULL;\n    }\n\n    [self deleteFuncInfos];\n    [self deleteLinesFromList: iPlainLineListHead];\n    [self deleteLinesFromList: iVerboseLineListHead];\n\n}\n\n//  deleteFuncInfos\n// ----------------------------------------------------------------------------\n\n- (void)deleteFuncInfos\n{\n    if (!iFuncInfos)\n        return;\n\n    uint32_t          i;\n    uint32_t          j;\n    FunctionInfo* funcInfo;\n    BlockInfo*    blockInfo;\n\n    for (i = 0; i < iNumFuncInfos; i++)\n    {\n        funcInfo    = &iFuncInfos[i];\n\n        if (funcInfo->blocks)\n        {\n            for (j = 0; j < funcInfo->numBlocks; j++)\n            {\n                blockInfo   = &funcInfo->blocks[j];\n\n                if (blockInfo->state.regInfos)\n                {\n                    free(blockInfo->state.regInfos);\n                    blockInfo->state.regInfos   = NULL;\n                }\n\n                if (blockInfo->state.localSelves)\n                {\n                    free(blockInfo->state.localSelves);\n                    blockInfo->state.localSelves    = NULL;\n                }\n            }\n\n            free(funcInfo->blocks);\n            funcInfo->blocks    = NULL;\n        }\n    }\n\n    free(iFuncInfos);\n    iFuncInfos  = NULL;\n}\n\n#pragma mark -\n//  processExe:\n// ----------------------------------------------------------------------------\n//  The master processing method, designed to be executed in a separate thread.\n\n- (BOOL)processExe: (NSString*)inOutputFilePath\n{\n    if (!iFileArchMagic)\n    {\n        fprintf(stderr, \"otx: tried to process non-machO file\\n\");\n        return NO;\n    }\n\n    iOutputFilePath = inOutputFilePath;\n    iMachHeaderPtr  = NULL;\n\n    if (![self loadMachHeader])\n    {\n        fprintf(stderr, \"otx: failed to load mach header\\n\");\n        return NO;\n    }\n\n    [self loadLCommands];\n\n    NSMutableDictionary*    progDict    =\n        [[NSMutableDictionary alloc] initWithObjectsAndKeys:\n        [NSNumber numberWithBool: YES], PRNewLineKey,\n        @\"Calling otool\", PRDescriptionKey,\n        nil];\n\n#ifdef OTX_CLI\n    [iController reportProgress: progDict];\n#else\n    [iController performSelectorOnMainThread: @selector(reportProgress:)\n        withObject: progDict waitUntilDone: NO];\n#endif\n\n\n    [self populateLineLists];\n\n    if (gCancel == YES)\n        return NO;\n\n    progDict    = [[NSMutableDictionary alloc] initWithObjectsAndKeys:\n        [NSNumber numberWithBool: YES], PRNewLineKey,\n        @\"Gathering info\", PRDescriptionKey,\n        nil];\n\n#ifdef OTX_CLI\n    [iController reportProgress: progDict];\n#else\n    [iController performSelectorOnMainThread: @selector(reportProgress:)\n        withObject: progDict waitUntilDone: NO];\n#endif\n\n\n    // Gather info about lines while they're virgin.\n    [self gatherLineInfos];\n\n    if (gCancel == YES)\n        return NO;\n\n    // Find functions and allocate funcInfo's.\n    [self findFunctions];\n\n    if (gCancel == YES)\n        return NO;\n\n    // Gather info about logical blocks. The second pass applies info\n    // for backward branches.\n    [self gatherFuncInfos];\n\n    if (gCancel == YES)\n        return NO;\n\n    [self gatherFuncInfos];\n\n    if (gCancel == YES)\n        return NO;\n\n    uint32_t  progCounter = 0;\n    double  progValue   = 0.0;\n\n    progDict    = [[NSMutableDictionary alloc] initWithObjectsAndKeys:\n        [NSNumber numberWithBool: NO], PRIndeterminateKey,\n        [NSNumber numberWithDouble: progValue], PRValueKey,\n        [NSNumber numberWithBool: YES], PRNewLineKey,\n        @\"Generating file\", PRDescriptionKey,\n        nil];\n\n#ifdef OTX_CLI\n    [iController reportProgress: progDict];\n#else\n    [iController performSelectorOnMainThread: @selector(reportProgress:)\n        withObject: progDict waitUntilDone: NO];\n#endif\n\n\n    Line*   theLine = iPlainLineListHead;\n\n    // Loop thru lines.\n    while (theLine)\n    {\n        if (!(progCounter % PROGRESS_FREQ))\n        {\n            if (gCancel == YES)\n                return NO;\n\n            progValue   = (double)progCounter / iNumLines * 100;\n            progDict    = [[NSMutableDictionary alloc] initWithObjectsAndKeys:\n                [NSNumber numberWithDouble: progValue], PRValueKey,\n                nil];\n\n#ifdef OTX_CLI\n            [iController reportProgress: progDict];\n#else\n            [iController performSelectorOnMainThread: @selector(reportProgress:)\n                withObject: progDict waitUntilDone: NO];\n#endif\n\n        }\n\n        if (theLine->info.isCode)\n        {\n            [self processCodeLine:&theLine];\n\n            if (iOpts.entabOutput)\n                [self entabLine:theLine];\n        }\n        else\n            [self processLine:theLine];\n\n        theLine = theLine->next;\n        progCounter++;\n    }\n\n    if (gCancel == YES)\n        return NO;\n\n    progDict    = [[NSMutableDictionary alloc] initWithObjectsAndKeys:\n        [NSNumber numberWithBool: YES], PRIndeterminateKey,\n        [NSNumber numberWithBool: YES], PRNewLineKey,\n        @\"Writing file\", PRDescriptionKey,\n        nil];\n\n#ifdef OTX_CLI\n    [iController reportProgress: progDict];\n#else\n    [iController performSelectorOnMainThread: @selector(reportProgress:)\n        withObject: progDict waitUntilDone: NO];\n#endif\n\n\n    // Create output file.\n    if (![self printLinesFromList: iPlainLineListHead])\n    {\n        return NO;\n    }\n\n    if (iOpts.dataSections)\n    {\n        if (![self printDataSections])\n        {\n            return NO;\n        }\n    }\n\n    progDict = [[NSMutableDictionary alloc] initWithObjectsAndKeys:\n        [NSNumber numberWithBool: YES], PRCompleteKey,\n        nil];\n\n#ifdef OTX_CLI\n    [iController reportProgress: progDict];\n#else\n    [iController performSelectorOnMainThread: @selector(reportProgress:)\n        withObject: progDict waitUntilDone: NO];\n#endif\n\n\n    return YES;\n}\n\n//  populateLineLists\n// ----------------------------------------------------------------------------\n\n- (BOOL)populateLineLists\n{\n    Line*   thePrevVerboseLine  = NULL;\n    Line*   thePrevPlainLine    = NULL;\n\n    // Read __text lines.\n    [self populateLineList: &iVerboseLineListHead verbosely: YES\n        fromSection: \"__text\" afterLine: &thePrevVerboseLine\n        includingPath: YES];\n\n    [self populateLineList: &iPlainLineListHead verbosely: NO\n        fromSection: \"__text\" afterLine: &thePrevPlainLine\n        includingPath: YES];\n\n    // Read __coalesced_text lines.\n    if (iCoalTextSect.size)\n    {\n        [self populateLineList: &iVerboseLineListHead verbosely: YES\n            fromSection: \"__coalesced_text\" afterLine: &thePrevVerboseLine\n            includingPath: NO];\n\n        [self populateLineList: &iPlainLineListHead verbosely: NO\n            fromSection: \"__coalesced_text\" afterLine: &thePrevPlainLine\n            includingPath: NO];\n    }\n\n    // Read __textcoal_nt lines.\n    if (iCoalTextNTSect.size)\n    {\n        [self populateLineList: &iVerboseLineListHead verbosely: YES\n            fromSection: \"__textcoal_nt\" afterLine: &thePrevVerboseLine\n            includingPath: NO];\n\n        [self populateLineList: &iPlainLineListHead verbosely: NO\n            fromSection: \"__textcoal_nt\" afterLine: &thePrevPlainLine\n            includingPath: NO];\n    }\n\n    // Connect the 2 lists.\n    Line*   verboseLine = iVerboseLineListHead;\n    Line*   plainLine   = iPlainLineListHead;\n\n    while (verboseLine && plainLine)\n    {\n        verboseLine->alt    = plainLine;\n        plainLine->alt      = verboseLine;\n\n        verboseLine = verboseLine->next;\n        plainLine   = plainLine->next;\n    }\n\n    // Optionally insert md5.\n    if (iOpts.checksum)\n        [self insertMD5];\n\n    return YES;\n}\n\n//  populateLineList:verbosely:fromSection:afterLine:includingPath:\n// ----------------------------------------------------------------------------\n\n- (BOOL)populateLineList: (Line**)inList\n               verbosely: (BOOL)inVerbose\n             fromSection: (char*)inSectionName\n               afterLine: (Line**)inLine\n           includingPath: (BOOL)inIncludePath\n{\n    char cmdString[MAX_UNIBIN_OTOOL_CMD_SIZE] = \"\";\n    NSString* otoolPath = [self pathForTool: @\"otool\"];\n    NSUInteger otoolPathLength = [otoolPath length];\n    size_t archStringLength = strlen(iArchString);\n\n    // otool freaks out when somebody says -arch and it's not a unibin.\n    if (iExeIsFat)\n    {\n        // Bail if it won't fit.\n        if ((otoolPathLength + archStringLength + 7 /* strlen(\" -arch \") */) >= MAX_UNIBIN_OTOOL_CMD_SIZE)\n            return NO;\n\n        snprintf(cmdString, MAX_UNIBIN_OTOOL_CMD_SIZE,\n            \"%s -arch %s\", [otoolPath UTF8String], iArchString);\n    }\n    else\n    {\n        // Bail if it won't fit.\n        if (otoolPathLength >= MAX_UNIBIN_OTOOL_CMD_SIZE)\n            return NO;\n\n        strncpy(cmdString, [otoolPath UTF8String], otoolPathLength);\n    }\n\n    NSString* oPath = [iOFile path];\n    NSString* otoolString = [NSString stringWithFormat:\n        @\"%s %s -s __TEXT %s \\\"%@\\\"%s\", cmdString,\n        (inVerbose) ? \"-VQ\" : \"-vQ\", inSectionName, oPath,\n        (inIncludePath) ? \"\" : \" | sed '1 d'\"];\n    FILE* otoolPipe = popen(UTF8STRING(otoolString), \"r\");\n\n    if (!otoolPipe)\n    {\n        fprintf(stderr, \"otx: unable to open %s otool pipe\\n\",\n            (inVerbose) ? \"verbose\" : \"plain\");\n        return NO;\n    }\n\n    char theCLine[MAX_LINE_LENGTH];\n\n    while (fgets(theCLine, MAX_LINE_LENGTH, otoolPipe))\n    {\n        // Many thanx to Peter Hosey for the calloc speed test.\n        // http://boredzo.org/blog/archives/2006-11-26/calloc-vs-malloc\n\n        Line*   theNewLine  = calloc(1, sizeof(Line));\n\n        theNewLine->length  = strlen(theCLine);\n        theNewLine->chars   = malloc(theNewLine->length + 1);\n        strncpy(theNewLine->chars, theCLine,\n            theNewLine->length + 1);\n\n        // Add the line to the list.\n        [self insertLine:theNewLine after:*inLine inList:inList];\n\n        *inLine = theNewLine;\n    }\n\n    if (pclose(otoolPipe) == -1)\n    {\n        perror((inVerbose) ? \"otx: unable to close verbose otool pipe\" :\n            \"otx: unable to close plain otool pipe\");\n        return NO;\n    }\n\n    return YES;\n}\n\n#pragma mark -\n//  gatherLineInfos\n// ----------------------------------------------------------------------------\n//  To make life easier as we make changes to the lines, whatever info we need\n//  is harvested early here.\n\n- (void)gatherLineInfos\n{\n    Line*   theLine     = iPlainLineListHead;\n    uint32_t  progCounter = 0;\n\n    while (theLine)\n    {\n        if (!(progCounter % (PROGRESS_FREQ * 5)))\n        {\n            if (gCancel == YES)\n                return;\n\n//            [NSThread sleepForTimeInterval: 0.0];\n        }\n\n        if ([self lineIsCode:theLine->chars])\n        {\n            theLine->info.isCode    = YES;\n            theLine->info.address   = [self addressFromLine:theLine->chars];\n            [self codeFromLine:theLine];  // FIXME: return a value like the cool kids do.\n\n            if (theLine->alt)\n            {\n                theLine->alt->info.isCode = theLine->info.isCode;\n                theLine->alt->info.address = theLine->info.address;\n                theLine->alt->info.codeLength = theLine->info.codeLength;\n                memcpy(theLine->alt->info.code, theLine->info.code,\n                    sizeof(theLine->info.code));\n            }\n\n            [self checkThunk:theLine];\n        }\n        else    // not code...\n        {\n            if (strstr(theLine->chars, \"(__TEXT,__coalesced_text)\"))\n                iEndOfText  = iCoalTextSect.s.addr + iCoalTextSect.s.size;\n            else if (strstr(theLine->chars, \"(__TEXT,__textcoal_nt)\"))\n                iEndOfText  = iCoalTextNTSect.s.addr + iCoalTextNTSect.s.size;\n        }\n\n        theLine = theLine->next;\n        progCounter++;\n        iNumLines++;\n    }\n\n    iEndOfText  = iTextSect.s.addr + iTextSect.s.size;\n}\n\n//  findFunctions\n// ----------------------------------------------------------------------------\n\n- (void)findFunctions\n{\n    // Loop once to flag all funcs.\n    Line*   theLine = iPlainLineListHead;\n\n    while (theLine)\n    {\n        theLine->info.isFunction    = [self lineIsFunction:theLine];\n        \n        if (theLine->alt)\n            theLine->alt->info.isFunction   = theLine->info.isFunction;\n\n        theLine = theLine->next;\n    }\n\n    // Loop again to allocate funcInfo's.\n    theLine = iPlainLineListHead;\n    iLineArray = calloc(iNumLines, sizeof(Line*));\n    iNumCodeLines = 0;\n\n    while (theLine)\n    {\n        if (theLine->info.isFunction)\n        {\n            iNumFuncInfos++;\n            iFuncInfos  = realloc(iFuncInfos,\n                sizeof(FunctionInfo) * iNumFuncInfos);\n\n            uint32_t  genericFuncNum  = 0;\n\n            if (theLine->prev && theLine->prev->info.isCode)\n                genericFuncNum  = ++iCurrentGenericFuncNum;\n\n            iFuncInfos[iNumFuncInfos - 1]   = (FunctionInfo)\n                {theLine->info.address, NULL, 0, genericFuncNum};\n        }\n\n        if (theLine->info.isCode)\n            iLineArray[iNumCodeLines++] = theLine;\n\n        theLine = theLine->next;\n    }\n}\n\n//  processLine:\n// ----------------------------------------------------------------------------\n\n- (void)processLine: (Line*)ioLine;\n{\n    if (!strlen(ioLine->chars))\n        return;\n\n    // otool is inconsistent in printing section headers. Sometimes it\n    // prints \"Contents of (x)\" and sometimes just \"(x)\". We'll take this\n    // opportunity to use the shorter version in all cases.\n    char*   theContentsString       = \"Contents of \";\n    UInt8   theContentsStringLength = strlen(theContentsString);\n    char*   theTextSegString        = \"(__TEXT,__\";\n\n    // Kill the \"Contents of\" if it exists.\n    if (strstr(ioLine->chars, theContentsString))\n    {\n        char    theTempLine[MAX_LINE_LENGTH];\n\n        theTempLine[0]  = '\\n';\n        theTempLine[1]  = 0;\n\n        strncat(theTempLine, &ioLine->chars[theContentsStringLength],\n            strlen(&ioLine->chars[theContentsStringLength]));\n\n        ioLine->length  = strlen(theTempLine);\n        strncpy(ioLine->chars, theTempLine, ioLine->length + 1);\n\n        return;\n    }\n    else if (strstr(ioLine->chars, theTextSegString))\n    {\n        if (strstr(ioLine->chars, \"__coalesced_text)\"))\n        {\n            iEndOfText = iCoalTextSect.s.addr + iCoalTextSect.s.size;\n            iCurrentFunctionStart = ioLine->info.address;\n        }\n        else if (strstr(ioLine->chars, \"__textcoal_nt)\"))\n        {\n            iEndOfText = iCoalTextNTSect.s.addr + iCoalTextNTSect.s.size;\n            iCurrentFunctionStart = ioLine->info.address;\n        }\n\n        char    theTempLine[MAX_LINE_LENGTH];\n\n        theTempLine[0]  = '\\n';\n        theTempLine[1]  = 0;\n\n        strncat(theTempLine, ioLine->chars, strlen(ioLine->chars));\n\n        ioLine->length++;\n        ioLine->chars = (char*)realloc(ioLine->chars, ioLine->length + 1);\n        strncpy(ioLine->chars, theTempLine, ioLine->length + 1);\n\n        return;\n    }\n\n    // If we got here, we have a symbol name.\n    if (iOpts.demangleCppNames)\n    {\n        if (strstr(ioLine->chars, \"__Z\") == ioLine->chars)\n        {\n            char demangledName[MAX_LINE_LENGTH];\n\n            // Line should end with \":\\n\". Terminate over the colon if it exists.\n            if (ioLine->chars[ioLine->length - 2] == ':')\n                ioLine->chars[ioLine->length - 2] = 0;\n            else\n                ioLine->chars[ioLine->length - 1] = 0;\n\n            NSFileHandle* filtWrite = [iCPFiltInputPipe fileHandleForWriting];\n            NSFileHandle* filtRead = [iCPFiltOutputPipe fileHandleForReading];\n            NSMutableData* dataToWrite = [NSMutableData dataWithBytes: ioLine->chars\n                                                               length: strlen(ioLine->chars)];\n\n            [dataToWrite appendBytes: \"\\n\"\n                              length: 1];\n\n            @try {\n                [filtWrite writeData: dataToWrite];\n            }\n            @catch (NSException* e) {\n                NSLog(@\"otx: [Exe32Processor processLine:] failed to write to c++filt: %@\", [e reason]);\n            }\n\n            NSData* readData = nil;\n\n            @try {\n                readData = [filtRead availableData];\n            }\n            @catch (NSException* e) {\n                NSLog(@\"otx: [Exe32Processor processLine:] failed to read from c++filt: %@\", [e reason]);\n            }\n\n            if (readData != nil)\n            {\n                NSUInteger demangledDataLength = [readData length];\n                NSUInteger demangledStringLength = MIN(demangledDataLength, MAX_LINE_LENGTH - 1);\n\n                if (demangledStringLength < demangledDataLength)\n                    NSLog(@\"otx: demangled C++ name is too large. Increase MAX_LINE_LENGTH to %lu.\\n\"\n                           \"Name demangling will now go off the rails.\", (unsigned long)demangledDataLength);\n\n                // Get the string and terminate it.\n                [readData getBytes: demangledName\n                            length: demangledStringLength];\n                demangledName[demangledStringLength] = 0;\n\n                free(ioLine->chars);\n                ioLine->length = demangledStringLength;\n                ioLine->chars = malloc(ioLine->length + 1);\n\n                strncpy(ioLine->chars, demangledName, ioLine->length + 1);\n            }\n            else\n            {\n                NSLog(@\"otx: [Exe32Processor processLine:] got nil data from c++filt\");\n            }\n        }\n    }\n}\n\n//  processCodeLine:\n// ----------------------------------------------------------------------------\n\n- (void)processCodeLine: (Line**)ioLine;\n{\n    if (!ioLine || !(*ioLine) || !((*ioLine)->chars))\n    {\n        fprintf(stderr, \"otx: tried to process NULL code line\\n\");\n        return;\n    }\n\n    [self chooseLine:ioLine];\n\n    size_t  theOrigLength           = (*ioLine)->length;\n    char    localOffsetString[9]    = {0};\n    char    theAddressCString[9]    = {0};\n    char    theMnemonicCString[20]  = {0};\n\n    char    addrSpaces[MAX_FIELD_SPACING];\n    char    instSpaces[MAX_FIELD_SPACING];\n    char    mnemSpaces[MAX_FIELD_SPACING];\n    char    opSpaces[MAX_FIELD_SPACING];\n    char    commentSpaces[MAX_FIELD_SPACING];\n    char    theOrigCommentCString[MAX_COMMENT_LENGTH];\n    char    theCommentCString[MAX_COMMENT_LENGTH];\n\n    theOrigCommentCString[0]    = 0;\n    theCommentCString[0]        = 0;\n\n    // Swap in saved registers if necessary\n    BOOL    needNewLine = [self restoreRegisters:*ioLine];\n\n    iLineOperandsCString[0] = 0;\n\n    char*   origFormatString    = \"%s\\t%s\\t%s%n\";\n    uint32_t  consumedAfterOp     = 0;\n\n    // The address and mnemonic always exist, separated by a tab.\n    sscanf((*ioLine)->chars, origFormatString, theAddressCString,\n        theMnemonicCString, iLineOperandsCString, &consumedAfterOp);\n\n    // If we didn't grab everything up to the newline, there's a comment\n    // remaining. Copy it, starting after the preceding tab.\n    if (consumedAfterOp && consumedAfterOp < theOrigLength - 1)\n    {\n        size_t  origCommentLength   = theOrigLength - consumedAfterOp - 1;\n\n        strncpy(theOrigCommentCString, (*ioLine)->chars + consumedAfterOp + 1,\n            origCommentLength);\n\n        // Add the null terminator.\n        theOrigCommentCString[origCommentLength - 1]    = 0;\n    }\n\n    char theCodeCString[32] = {0};\n    UInt8* inBuffer = (*ioLine)->info.code;\n    SInt16 i;\n\n    char codeFormatString[61] = \"%02x%02x%02x%02x%02x%02x%02x%02x%02x%02x%02x%02x%02x%02x%02x\";\n\n    if ((*ioLine)->info.codeLength > 15)\n    {\n        fprintf(stderr, \"otx: surprisingly long instruction of %u bytes at 0x%08x\\n\", (*ioLine)->info.codeLength, (*ioLine)->info.address);\n        return;\n    }\n\n    codeFormatString[(*ioLine)->info.codeLength * 4] = 0;\n\n    snprintf(theCodeCString, 31, codeFormatString,\n        inBuffer[0], inBuffer[1], inBuffer[2], inBuffer[3],\n        inBuffer[4], inBuffer[5], inBuffer[6], inBuffer[7],\n        inBuffer[8], inBuffer[9], inBuffer[10], inBuffer[11],\n        inBuffer[12], inBuffer[13], inBuffer[14]);\n\n    i = iFieldWidths.instruction - strlen(theCodeCString);\n\n    mnemSpaces[i - 1] = 0;\n\n    for (; i > 1; i--)\n        mnemSpaces[i - 2] = 0x20;\n\n    i = iFieldWidths.mnemonic - strlen(theMnemonicCString);\n\n    opSpaces[i - 1] = 0;\n\n    for (; i > 1; i--)\n        opSpaces[i - 2] = 0x20;\n\n    // Fill up commentSpaces based on operands field width.\n    if (iLineOperandsCString[0] && theOrigCommentCString[0])\n    {\n        i   = iFieldWidths.operands - strlen(iLineOperandsCString);\n\n        commentSpaces[i - 1]    = 0;\n\n        for (; i > 1; i--)\n            commentSpaces[i - 2]    = 0x20;\n    }\n\n    // Remove \"; symbol stub for: \"\n    if (theOrigCommentCString[0])\n    {\n        char*   theSubstring    =\n            strstr(theOrigCommentCString, \"; symbol stub for: \");\n\n        if (theSubstring)\n            snprintf(theCommentCString, MAX_COMMENT_LENGTH, \"%s\", &theOrigCommentCString[19]);\n        else\n            snprintf(theCommentCString, MAX_COMMENT_LENGTH, \"%s\", theOrigCommentCString);\n    }\n\n    BOOL    needFuncName = NO;\n    char    theMethCName[1000];\n\n    theMethCName[0] = 0;\n\n    // Check if this is the beginning of a function.\n    if ((*ioLine)->info.isFunction)\n    {\n        // Squash the new block flag, just in case.\n        iEnteringNewBlock = NO;\n\n        // New function, new local offset count and current func.\n        iCurrentFunctionStart = iCurrentFuncPtr = (*ioLine)->info.address;\n\n        // Try to build the method name.\n        MethodInfo* theSwappedInfoPtr   = NULL;\n        MethodInfo  theSwappedInfo;\n\n        if ([self getObjcMethod:&theSwappedInfoPtr fromAddress:iCurrentFuncPtr])\n        {\n            theSwappedInfo  = *theSwappedInfoPtr;\n\n            char*   className   = NULL;\n            char*   catName     = NULL;\n\n            if (iObjcVersion < 2)\n            {\n                if (iSwapped)\n                {\n                    swap_objc1_32_method(&theSwappedInfo.m);\n                    swap_objc1_32_class(&theSwappedInfo.oc_class);\n                    swap_objc1_32_category(&theSwappedInfo.oc_cat);\n                }\n            \n                if (theSwappedInfo.oc_cat.category_name)\n                {\n                    className   = [self getPointer:theSwappedInfo.oc_cat.class_name type:NULL];\n                    catName     = [self getPointer:theSwappedInfo.oc_cat.category_name type:NULL];\n                }\n                else if (theSwappedInfo.oc_class.name)\n                {\n                    className   = [self getPointer:theSwappedInfo.oc_class.name type:NULL];\n                }\n            \n            }\n            else if (iObjcVersion == 2)\n            {\n                if (iSwapped)\n                {\n                    swap_objc2_32_method(&theSwappedInfo.m2);\n                    swap_objc2_32_class(&theSwappedInfo.oc_class2);\n                }\n\n                if (theSwappedInfo.oc_class2.data)\n                {\n                    objc2_32_class_ro_t* roData = (objc2_32_class_ro_t*)(iObjcConstSect.contents +\n                        (uintptr_t)(theSwappedInfo.oc_class2.data - iObjcConstSect.s.addr));\n\n                    UInt32 name = roData->name;\n\n                    if (iSwapped)\n                        name = OSSwapInt32(name);\n\n                    className = [self getPointer:name type:NULL];\n                }\n            }\n\n            if (className)\n            {\n                char*   selName = [self getPointer:theSwappedInfo.m.method_name type:NULL];\n\n                if (selName)\n                {\n                    if (!theSwappedInfo.m.method_types)\n                        return;\n\n                    char*   methTypes = [self getPointer:theSwappedInfo.m.method_types type:NULL];\n                    char    returnCType[MAX_TYPE_STRING_LENGTH];\n\n                    returnCType[0]  = 0;\n\n                    if (methTypes)\n                    {\n                        [self decodeMethodReturnType: methTypes\n                                output: returnCType];\n                    }\n                    else\n                    {\n                        strncpy(returnCType, \"???\", 4);\n                    }\n\n                    if (catName)\n                    {\n                        char*   methNameFormat  = iOpts.returnTypes ?\n                            \"\\n%1$c(%5$s)[%2$s(%3$s) %4$s]:\\n\" :\n                            \"\\n%c[%s(%s) %s]:\\n\";\n\n                        snprintf(theMethCName, 1000,\n                            methNameFormat,\n                            (theSwappedInfo.inst) ? '-' : '+',\n                            className, catName, selName, returnCType);\n                    }\n                    else\n                    {\n                        char*   methNameFormat  = iOpts.returnTypes ?\n                            \"\\n%1$c(%4$s)[%2$s %3$s]:\\n\" : \"\\n%c[%s %s]:\\n\";\n\n                        snprintf(theMethCName, 1000,\n                            methNameFormat,\n                            (theSwappedInfo.inst) ? '-' : '+',\n                            className, selName, returnCType);\n                    }\n                }\n            }\n        }   // if ([self getObjcMethod:&theSwappedInfoPtr fromAddress:mCurrentFuncPtr])\n\n        // Add or replace the method name if possible, else add '\\n'.\n        if ((*ioLine)->prev && (*ioLine)->prev->info.isCode)    // prev line is code\n        {\n            if (theMethCName[0])\n            {\n                Line*   theNewLine  = malloc(sizeof(Line));\n\n                theNewLine->length  = strlen(theMethCName);\n                theNewLine->chars   = malloc(theNewLine->length + 1);\n\n                strncpy(theNewLine->chars, theMethCName,\n                    theNewLine->length + 1);\n                [self insertLine:theNewLine before:*ioLine inList:&iPlainLineListHead];\n            }\n            else if ((*ioLine)->info.address == iAddrDyldStubBindingHelper)\n            {\n                Line*   theNewLine  = malloc(sizeof(Line));\n                char*   theDyldName = \"\\ndyld_stub_binding_helper:\\n\";\n\n                theNewLine->length  = strlen(theDyldName);\n                theNewLine->chars   = malloc(theNewLine->length + 1);\n\n                strncpy(theNewLine->chars, theDyldName, theNewLine->length + 1);\n                [self insertLine:theNewLine before:*ioLine inList:&iPlainLineListHead];\n            }\n            else if ((*ioLine)->info.address == iAddrDyldFuncLookupPointer)\n            {\n                Line*   theNewLine  = malloc(sizeof(Line));\n                char*   theDyldName = \"\\n__dyld_func_lookup:\\n\";\n\n                theNewLine->length  = strlen(theDyldName);\n                theNewLine->chars   = malloc(theNewLine->length + 1);\n\n                strncpy(theNewLine->chars, theDyldName, theNewLine->length + 1);\n                [self insertLine:theNewLine before:*ioLine inList:&iPlainLineListHead];\n            }\n            else\n                needFuncName = YES;\n        }\n        else    // prev line is not code\n        {\n            if (theMethCName[0])\n            {\n                Line*   theNewLine  = malloc(sizeof(Line));\n\n                theNewLine->length  = strlen(theMethCName);\n                theNewLine->chars   = malloc(theNewLine->length + 1);\n\n                strncpy(theNewLine->chars, theMethCName,\n                    theNewLine->length + 1);\n                [self replaceLine:(*ioLine)->prev withLine:theNewLine inList:&iPlainLineListHead];\n            }\n            else\n            {   // theMethName sux, add '\\n' to otool's method name.\n                char    theNewLine[MAX_LINE_LENGTH];\n\n                if ((*ioLine)->prev->chars[0] != '\\n')\n                {\n                    theNewLine[0]   = '\\n';\n                    theNewLine[1]   = 0;\n                }\n                else\n                    theNewLine[0]   = 0;\n\n                strncat(theNewLine, (*ioLine)->prev->chars,\n                    (*ioLine)->prev->length);\n\n                free((*ioLine)->prev->chars);\n                (*ioLine)->prev->length = strlen(theNewLine);\n                (*ioLine)->prev->chars  = malloc((*ioLine)->prev->length + 1);\n                strncpy((*ioLine)->prev->chars, theNewLine,\n                    (*ioLine)->prev->length + 1);\n            }\n        }\n        \n        [self resetRegisters:*ioLine];\n    }   // if ((*ioLine)->info.isFunction)\n\n    // Find a comment if necessary.\n    if (!theCommentCString[0])\n    {\n        [self commentForLine:*ioLine];\n\n        size_t  origCommentLength   = strlen(iLineCommentCString);\n\n        if (origCommentLength)\n        {\n            char    tempComment[MAX_COMMENT_LENGTH];\n            uint32_t  i, j = 0;\n\n            // Escape newlines, carriage returns and tabs.\n            for (i = 0; i < origCommentLength; i++)\n            {\n                if (iLineCommentCString[i] == '\\n')\n                {\n                    tempComment[j++]    = '\\\\';\n                    tempComment[j++]    = 'n';\n                }\n                else if (iLineCommentCString[i] == '\\r')\n                {\n                    tempComment[j++]    = '\\\\';\n                    tempComment[j++]    = 'r';\n                }\n                else if (iLineCommentCString[i] == '\\t')\n                {\n                    tempComment[j++]    = '\\\\';\n                    tempComment[j++]    = 't';\n                }\n                else\n                    tempComment[j++]    = iLineCommentCString[i];\n            }\n\n            // Add the null terminator.\n            tempComment[j]  = 0;\n\n            if (iLineOperandsCString[0])\n                snprintf(theCommentCString, MAX_COMMENT_LENGTH, \"%s\", tempComment);\n            else\n                snprintf(iLineOperandsCString, MAX_COMMENT_LENGTH, \"%s\", tempComment);\n\n            // Fill up commentSpaces based on operands field width.\n            SInt32  k   = (SInt32)(iFieldWidths.operands - strlen(iLineOperandsCString));\n\n            commentSpaces[k - 1]    = 0;\n\n            for (; k > 1; k--)\n                commentSpaces[k - 2]    = 0x20;\n        }\n    }   // if (!theCommentCString[0])\n    else    // otool gave us a comment.\n    {\n        // Optionally modify otool's comment.\n        if (iOpts.verboseMsgSends)\n            [self commentForMsgSend:theCommentCString fromLine:*ioLine];\n    }\n\n    // Demangle operands if necessary.\n    if (iLineOperandsCString[0] && iOpts.demangleCppNames)\n    {\n        if (strstr(iLineOperandsCString, \"__Z\") == iLineOperandsCString)\n        {\n            char demangledName[MAX_OPERANDS_LENGTH];\n\n            NSFileHandle* filtWrite = [iCPFiltInputPipe fileHandleForWriting];\n            NSFileHandle* filtRead = [iCPFiltOutputPipe fileHandleForReading];\n            NSMutableData* dataToWrite = [NSMutableData dataWithBytes: iLineOperandsCString\n                                                               length: strlen(iLineOperandsCString)];\n\n            [dataToWrite appendBytes: \"\\n\"\n                              length: 1];\n\n            @try {\n                [filtWrite writeData: dataToWrite];\n            }\n            @catch (NSException* e) {\n                NSLog(@\"otx: [Exe32Processor processCodeLine:] failed to write operands to c++filt: %@\", [e reason]);\n            }\n\n            NSData* readData = nil;\n\n            @try {\n                readData = [filtRead availableData];\n            }\n            @catch (NSException* e) {\n                NSLog(@\"otx: [Exe32Processor processCodeLine:] failed to read operands from c++filt: %@\", [e reason]);\n            }\n\n            if (readData != nil)\n            {\n                NSUInteger demangledDataLength = [readData length];\n                NSUInteger demangledStringLength = MIN(demangledDataLength, MAX_OPERANDS_LENGTH - 1);\n\n                if (demangledStringLength < demangledDataLength)\n                    NSLog(@\"otx: demangled C++ name is too large. Increase MAX_OPERANDS_LENGTH to %lu.\\n\"\n                           \"Name demangling will now go off the rails.\", (unsigned long)demangledDataLength);\n\n                [readData getBytes: demangledName\n                            length: demangledStringLength];\n\n                // Remove the trailing newline and terminate.\n                demangledStringLength--;\n                demangledName[demangledStringLength] = 0;\n                strncpy(iLineOperandsCString, demangledName, demangledStringLength + 1);\n            }\n            else\n            {\n                NSLog(@\"otx: [Exe32Processor processCodeLine:] got nil operands data from c++filt\");\n            }\n        }\n    }\n\n    // Demangle comment if necessary.\n    if (theCommentCString[0] && iOpts.demangleCppNames)\n    {\n        if (strstr(theCommentCString, \"__Z\") == theCommentCString)\n        {\n            char demangledName[MAX_COMMENT_LENGTH];\n\n            NSFileHandle* filtWrite = [iCPFiltInputPipe fileHandleForWriting];\n            NSFileHandle* filtRead = [iCPFiltOutputPipe fileHandleForReading];\n            NSMutableData* dataToWrite = [NSMutableData dataWithBytes: theCommentCString\n                                                               length: strlen(theCommentCString)];\n\n            [dataToWrite appendBytes: \"\\n\"\n                              length: 1];\n\n            @try {\n                [filtWrite writeData: dataToWrite];\n            }\n            @catch (NSException* e) {\n                NSLog(@\"otx: [Exe32Processor processCodeLine:] failed to write comment to c++filt: %@\", [e reason]);\n            }\n\n            NSData* readData = nil;\n\n            @try {\n                readData = [filtRead availableData];\n            }\n            @catch (NSException* e) {\n                NSLog(@\"otx: [Exe32Processor processCodeLine:] failed to read comment from c++filt: %@\", [e reason]);\n            }\n\n            if (readData != nil)\n            {\n                NSUInteger demangledDataLength = [readData length];\n                NSUInteger demangledStringLength = MIN(demangledDataLength, MAX_COMMENT_LENGTH - 1);\n\n                if (demangledStringLength < demangledDataLength)\n                    NSLog(@\"otx: demangled C++ name is too large. Increase MAX_COMMENT_LENGTH to %lu.\\n\"\n                           \"Name demangling will now go off the rails.\", (unsigned long)demangledDataLength);\n\n                [readData getBytes: demangledName\n                            length: demangledStringLength];\n\n                // Remove the trailing newline and terminate.\n                demangledStringLength--;\n                demangledName[demangledStringLength] = 0;\n                strncpy(theCommentCString, demangledName, demangledStringLength + 1);\n            }\n            else\n            {\n                NSLog(@\"otx: [Exe32Processor processCodeLine:] got nil comment data from c++filt\");\n            }\n        }\n    }\n\n    // Optionally add local offset.\n    if (iOpts.localOffsets)\n    {\n        // Build a right-aligned string  with a '+' in it.\n        snprintf((char*)&localOffsetString, iFieldWidths.offset,\n            \"%6u\", ((*ioLine)->info.address - iCurrentFunctionStart));\n\n        // Find the space that's followed by a nonspace.\n        // *Reverse count to optimize for short functions.\n        for (i = 0; i < 5; i++)\n        {\n            if (localOffsetString[i] == 0x20 &&\n                localOffsetString[i + 1] != 0x20)\n            {\n                localOffsetString[i] = '+';\n                break;\n            }\n        }\n\n        // Fill up addrSpaces based on offset field width.\n        i   = iFieldWidths.offset - 6;\n\n        addrSpaces[i - 1] = 0;\n\n        for (; i > 1; i--)\n            addrSpaces[i - 2] = 0x20;\n    }\n\n    // Fill up instSpaces based on address field width.\n    i   = iFieldWidths.address - 8;\n\n    instSpaces[i - 1] = 0;\n\n    for (; i > 1; i--)\n        instSpaces[i - 2] = 0x20;\n\n    // Insert a generic function name if needed.\n    if (needFuncName)\n    {\n        FunctionInfo    searchKey   = {(*ioLine)->info.address, NULL, 0, 0};\n        FunctionInfo*   funcInfo    = bsearch(&searchKey,\n            iFuncInfos, iNumFuncInfos, sizeof(FunctionInfo),\n            (COMPARISON_FUNC_TYPE)Function_Info_Compare);\n\n        // sizeof(UINT32_MAX) + '\\n' * 2 + ':' + null term\n        UInt8   maxlength   = ANON_FUNC_BASE_LENGTH + 14;\n        Line*   funcName    = calloc(1, sizeof(Line));\n\n        funcName->chars     = malloc(maxlength);\n\n        // Hack Alert: In the case that we have too few funcInfo's, print\n        // \\nAnon???. Of course, we'll still intermittently crash later, but\n        // when we don't, the output will look pretty.\n        // Replace \"if (funcInfo)\" from rev 319 around this...\n        if (funcInfo)\n            funcName->length    = snprintf(funcName->chars, maxlength,\n                \"\\n%s%d:\\n\", ANON_FUNC_BASE, funcInfo->genericFuncNum);\n        else\n            funcName->length    = snprintf(funcName->chars, maxlength,\n                \"\\n%s???:\\n\", ANON_FUNC_BASE);\n\n        [self insertLine:funcName before:*ioLine inList:&iPlainLineListHead];\n    }\n\n    // Finally, assemble the new string.\n    char    finalFormatCString[MAX_FORMAT_LENGTH];\n    uint32_t  formatMarker    = 0;\n\n    if (needNewLine)\n    {\n        formatMarker++;\n        finalFormatCString[0]   = '\\n';\n        finalFormatCString[1]   = 0;\n    }\n    else\n        finalFormatCString[0]   = 0;\n\n    if (iOpts.localOffsets)\n        formatMarker += snprintf(&finalFormatCString[formatMarker],\n            10, \"%s\", \"%s %s\");\n\n    if (iOpts.showCode)\n        formatMarker += snprintf(&finalFormatCString[formatMarker],\n            10, \"%s\", \"%s %s\");\n\n    if (iLineOperandsCString[0])\n    {\n        if (theCommentCString[0])\n            snprintf(&finalFormatCString[formatMarker],\n                30, \"%s\", \"%s %s%s %s%s %s%s\\n\");\n        else\n            snprintf(&finalFormatCString[formatMarker],\n                30, \"%s\", \"%s %s%s %s%s\\n\");\n    }\n    else\n        snprintf(&finalFormatCString[formatMarker],\n            30, \"%s\", \"%s %s%s\\n\");\n\n    char    theFinalCString[MAX_LINE_LENGTH] = \"\";\n\n    if (iOpts.localOffsets && iOpts.showCode)\n        snprintf(theFinalCString, MAX_LINE_LENGTH,\n            finalFormatCString, localOffsetString,\n            addrSpaces, theAddressCString,\n            instSpaces, theCodeCString,\n            mnemSpaces, theMnemonicCString,\n            opSpaces, iLineOperandsCString,\n            commentSpaces, theCommentCString);\n    else if (iOpts.localOffsets)\n        snprintf(theFinalCString, MAX_LINE_LENGTH,\n            finalFormatCString, localOffsetString,\n            addrSpaces, theAddressCString,\n            instSpaces, theMnemonicCString,\n            opSpaces, iLineOperandsCString,\n            commentSpaces, theCommentCString);\n    else if (iOpts.showCode)\n        snprintf(theFinalCString, MAX_LINE_LENGTH,\n            addrSpaces, theAddressCString,\n            instSpaces, theCodeCString,\n            mnemSpaces, theMnemonicCString,\n            opSpaces, iLineOperandsCString,\n            commentSpaces, theCommentCString);\n    else\n        snprintf(theFinalCString, MAX_LINE_LENGTH,\n            finalFormatCString, theAddressCString,\n            instSpaces, theMnemonicCString,\n            opSpaces, iLineOperandsCString,\n            commentSpaces, theCommentCString);\n\n    free((*ioLine)->chars);\n\n    if (iOpts.separateLogicalBlocks && iEnteringNewBlock &&\n        theFinalCString[0] != '\\n')\n    {\n        (*ioLine)->length   = strlen(theFinalCString) + 1;\n        (*ioLine)->chars    = malloc((*ioLine)->length + 1);\n        (*ioLine)->chars[0] = '\\n';\n        strncpy(&(*ioLine)->chars[1], theFinalCString, (*ioLine)->length);\n    }\n    else\n    {\n        (*ioLine)->length   = strlen(theFinalCString);\n        (*ioLine)->chars    = malloc((*ioLine)->length + 1);\n        strncpy((*ioLine)->chars, theFinalCString, (*ioLine)->length + 1);\n    }\n\n    // The test above can fail even if mEnteringNewBlock was YES, so we\n    // should reset it here instead.\n    iEnteringNewBlock = NO;\n\n    [self updateRegisters:*ioLine];\n    [self postProcessCodeLine:ioLine];\n\n    // Possibly prepend a \\n to the following line.\n    if ([self codeIsBlockJump:(*ioLine)->info.code])\n        iEnteringNewBlock = YES;\n}\n\n//  addressFromLine:\n// ----------------------------------------------------------------------------\n\n- (uint32_t)addressFromLine: (const char*)inLine\n{\n    // sanity check\n    if ((inLine[0] < '0' || inLine[0] > '9') &&\n        (inLine[0] < 'a' || inLine[0] > 'f'))\n        return 0;\n\n    uint32_t  theAddress  = 0;\n\n    sscanf(inLine, \"%08x\", &theAddress);\n    return theAddress;\n}\n\n//  lineIsCode:\n// ----------------------------------------------------------------------------\n//  Line is code if first 8 chars are hex numbers and 9th is tab.\n\n- (BOOL)lineIsCode: (const char*)inLine\n{\n    if (strlen(inLine) < 10)\n        return NO;\n\n    UInt16  i;\n\n    for (i = 0 ; i < 8; i++)\n    {\n        if ((inLine[i] < '0' || inLine[i] > '9') &&\n            (inLine[i] < 'a' || inLine[i] > 'f'))\n            return NO;\n    }\n\n    return (inLine[8] == '\\t');\n}\n\n//  chooseLine:\n// ----------------------------------------------------------------------------\n//  Subclasses may override.\n\n- (void)chooseLine: (Line**)ioLine\n{}\n\n#pragma mark -\n//  printDataSections\n// ----------------------------------------------------------------------------\n//  Append data sections to output file.\n\n- (BOOL)printDataSections\n{\n    FILE*   outFile = NULL;\n\n    if (iOutputFilePath)\n        outFile = fopen(UTF8STRING(iOutputFilePath), \"a\");\n    else\n        outFile = stdout;\n\n    if (!outFile)\n    {\n        perror(\"otx: unable to open output file\");\n        return NO;\n    }\n\n    if (iDataSect.size)\n    {\n        if (fprintf(outFile, \"\\n(__DATA,__data) section\\n\") < 0)\n        {\n            perror(\"otx: unable to write to output file\");\n            return NO;\n        }\n\n        [self printDataSection: &iDataSect toFile: outFile];\n    }\n\n    if (iCoalDataSect.size)\n    {\n        if (fprintf(outFile, \"\\n(__DATA,__coalesced_data) section\\n\") < 0)\n        {\n            perror(\"otx: unable to write to output file\");\n            return NO;\n        }\n\n        [self printDataSection: &iCoalDataSect toFile: outFile];\n    }\n\n    if (iCoalDataNTSect.size)\n    {\n        if (fprintf(outFile, \"\\n(__DATA,__datacoal_nt) section\\n\") < 0)\n        {\n            perror(\"otx: unable to write to output file\");\n            return NO;\n        }\n\n        [self printDataSection: &iCoalDataNTSect toFile: outFile];\n    }\n\n    if (iOutputFilePath)\n    {\n        if (fclose(outFile) != 0)\n        {\n            perror(\"otx: unable to close output file\");\n            return NO;\n        }\n    }\n\n    return YES;\n}\n\n//  printDataSection:toFile:\n// ----------------------------------------------------------------------------\n\n- (void)printDataSection: (section_info*)inSect\n                  toFile: (FILE*)outFile;\n{\n    uint32_t  i, j, k, bytesLeft;\n    uint32_t  theDataSize         = inSect->size;\n    char    theLineCString[70];\n    char*   theMachPtr          = (char*)iMachHeaderPtr;\n\n    theLineCString[0]   = 0;\n\n    for (i = 0; i < theDataSize; i += 16)\n    {\n        bytesLeft   = theDataSize - i;\n\n        if (bytesLeft < 16) // last line\n        {\n            theLineCString[0]   = 0;\n            snprintf(theLineCString,\n                20 ,\"%08x |\", inSect->s.addr + i);\n\n            unsigned char   theHexData[17]      = {0};\n            unsigned char   theASCIIData[17]    = {0};\n\n            memcpy(theHexData,\n                (const void*)(theMachPtr + inSect->s.offset + i), bytesLeft);\n            memcpy(theASCIIData,\n                (const void*)(theMachPtr + inSect->s.offset + i), bytesLeft);\n\n            j   = 10;\n\n            for (k = 0; k < bytesLeft; k++)\n            {\n                if (!(k % 4))\n                    theLineCString[j++] = 0x20;\n\n                snprintf(&theLineCString[j], 4, \"%02x\", theHexData[k]);\n                j += 2;\n\n                if (theASCIIData[k] < 0x20 || theASCIIData[k] == 0x7f)\n                    theASCIIData[k] = '.';\n            }\n\n            // Append spaces.\n            for (; j < 48; j++)\n                theLineCString[j]   = 0x20;\n\n            // Append ASCII chars.\n            snprintf(&theLineCString[j], 70, \"%s\\n\", theASCIIData);\n        }\n        else    // first lines\n        {           \n            uint32_t*         theHexPtr           = (uint32_t*)\n                (theMachPtr + inSect->s.offset + i);\n            unsigned char   theASCIIData[17]    = {0};\n            UInt8           j;\n\n            memcpy(theASCIIData,\n                (const void*)(theMachPtr + inSect->s.offset + i), 16);\n\n            for (j = 0; j < 16; j++)\n                if (theASCIIData[j] < 0x20 || theASCIIData[j] == 0x7f)\n                    theASCIIData[j] = '.';\n\n#if TARGET_RT_LITTLE_ENDIAN\n            theHexPtr[0]    = OSSwapInt32(theHexPtr[0]);\n            theHexPtr[1]    = OSSwapInt32(theHexPtr[1]);\n            theHexPtr[2]    = OSSwapInt32(theHexPtr[2]);\n            theHexPtr[3]    = OSSwapInt32(theHexPtr[3]);\n#endif\n\n            snprintf(theLineCString, sizeof(theLineCString),\n                \"%08x | %08x %08x %08x %08x  %s\\n\",\n                inSect->s.addr + i,\n                theHexPtr[0], theHexPtr[1], theHexPtr[2], theHexPtr[3],\n                theASCIIData);\n        }\n\n        if (fprintf(outFile, \"%s\", theLineCString) < 0)\n        {\n            perror(\"otx: [ExeProcessor printDataSection:toFile:]: \"\n                \"unable to write to output file\");\n            return;\n        }\n    }\n}\n\n#pragma mark -\n//  selectorForMsgSend:fromLine:\n// ----------------------------------------------------------------------------\n//  Subclasses may override.\n\n- (char*)selectorForMsgSend: (char*)outComment\n                   fromLine: (Line*)inLine\n{\n    return NULL;\n}\n\n#pragma mark -\n//  insertMD5\n// ----------------------------------------------------------------------------\n\n- (void)insertMD5\n{\n    NSString* md5String = [self generateMD5String];\n\n    Line* newLine = calloc(1, sizeof(Line));\n    const char* utf8String = [md5String UTF8String];\n\n    newLine->length = [md5String length];\n    newLine->chars = malloc(newLine->length + 1);\n    strncpy(newLine->chars, utf8String, newLine->length + 1);\n\n    [self insertLine:newLine after:iPlainLineListHead inList:&iPlainLineListHead];\n}\n\n#pragma mark -\n//  entabLine:\n// ----------------------------------------------------------------------------\n//  A cheap and fast way to entab a line, assuming it contains no tabs already.\n//  If tabs get added in the future, this WILL break. Single spaces are not\n//  replaced with tabs, even when possible, since it would save no additional\n//  bytes. Comments are not entabbed, as that would remove the user's ability\n//  to search for them in the source code or a hex editor.\n\n- (void)entabLine: (Line*)ioLine;\n{\n    if (!ioLine || !ioLine->chars)\n        return;\n\n    // only need to do this math once...\n    static uint32_t   startOfComment  = 0;\n\n    if (startOfComment == 0)\n    {\n        startOfComment  = iFieldWidths.address + iFieldWidths.instruction +\n            iFieldWidths.mnemonic + iFieldWidths.operands;\n\n        if (iOpts.localOffsets)\n            startOfComment  += iFieldWidths.offset;\n    }\n\n    char    entabbedLine[MAX_LINE_LENGTH];\n    size_t  theOrigLength = ioLine->length;\n\n    // If 1st char is '\\n', skip it.\n    uint32_t  firstChar   = (ioLine->chars[0] == '\\n');\n    uint32_t  i;          // old line marker\n    uint32_t  j   = 0;    // new line marker\n\n    if (firstChar)\n    {\n        j++;\n        entabbedLine[0] = '\\n';\n        entabbedLine[1] = 0;\n    }\n    else\n        entabbedLine[0] = 0;\n\n    // Inspect 4 bytes at a time.\n    for (i = firstChar; i < theOrigLength; i += 4)\n    {\n        // Don't entab comments.\n        if (i >= (startOfComment + firstChar) - 4)\n        {\n            strncpy(&entabbedLine[j], &ioLine->chars[i],\n                (theOrigLength - i) + 1);\n\n            break;\n        }\n\n        // If fewer than 4 bytes remain, adding any tabs is pointless.\n        if (i > theOrigLength - 4)\n        {   // Copy the remainder and split.\n            while (i < theOrigLength)\n                entabbedLine[j++] = ioLine->chars[i++];\n\n            // Add the null terminator.\n            entabbedLine[j] = 0;\n\n            break;\n        }\n\n        // If the 4th char is not a space, the first 3 chars don't matter.\n        if (ioLine->chars[i + 3] == 0x20)   // 4th char is a space...\n        {\n            if (ioLine->chars[i + 2] == 0x20)   // 3rd char is a space...\n            {\n                if (ioLine->chars[i + 1] == 0x20)   // 2nd char is a space...\n                {\n                    if (ioLine->chars[i] == 0x20)   // all 4 chars are spaces!\n                        entabbedLine[j++] = '\\t';   // write a tab and split\n                    else    // only the 1st char is not a space\n                    {       // copy 1st char and tab\n                        entabbedLine[j++] = ioLine->chars[i];\n                        entabbedLine[j++] = '\\t';\n                    }\n                }\n                else    // 2nd char is not a space\n                {       // copy 1st 2 chars and tab\n                    entabbedLine[j++] = ioLine->chars[i];\n                    entabbedLine[j++] = ioLine->chars[i + 1];\n                    entabbedLine[j++] = '\\t';\n                }\n            }\n            else    // 3rd char is not a space\n            {       // copy 1st 3 chars and tab\n                entabbedLine[j++] = ioLine->chars[i];\n                entabbedLine[j++] = ioLine->chars[i + 1];\n                entabbedLine[j++] = ioLine->chars[i + 2];\n                entabbedLine[j++] = '\\t';\n            }\n        }\n        else    // 4th char is not a space\n        {       // copy all 4 chars\n            memcpy(&entabbedLine[j], &ioLine->chars[i], 4);\n            j += 4;\n        }\n\n        // Add the null terminator.\n        entabbedLine[j] = 0;\n    }\n\n    // Replace the old C string with the new one.\n    free(ioLine->chars);\n    ioLine->length  = strlen(entabbedLine);\n    ioLine->chars   = malloc(ioLine->length + 1);\n    strncpy(ioLine->chars, entabbedLine, ioLine->length + 1);\n}\n\n\n- (BOOL)getIvarName:(char **)outName type:(char **)outType withOffset:(uint32_t)offset inClass:(objc_32_class_ptr)classPtr\n{\n    if (iObjcVersion == 1) {\n        objc1_32_ivar  ivar = {0};\n        objc1_32_class cls  = *(objc1_32_class *)classPtr;\n    \n        #if __BIG_ENDIAN__\n            swap_objc1_32_class(&cls);\n        #endif\n\n        if (!iIsInstanceMethod)\n        {\n            if (![self getObjc1MetaClass:&cls fromClass:&cls])\n                return NO;\n        \n            #if __BIG_ENDIAN__\n                swap_objc1_32_class(&cls);\n            #endif\n        }\n\n        if (![self findIvar:&ivar inClass:&cls withOffset:offset])\n            return NO;\n        \n        char *name = [self getPointer:ivar.ivar_name type:NULL];\n        char *type = [self getPointer:ivar.ivar_type type:NULL];\n        \n        if (outName) *outName = name;\n        if (outType) *outType = type;\n        \n        return YES;\n\n    } else if (iObjcVersion == 2) {\n        objc2_32_ivar_t* ivar;\n\n        if ([self findIvar:&ivar inClass2:(objc2_32_class_t *)classPtr withOffset:offset])\n        {\n            char *name = [self getPointer:ivar->name type:NULL];\n            char *type = [self getPointer:ivar->type type:NULL];\n            \n            if (outName) *outName = name;\n            if (outType) *outType = type;\n        }\n        \n        return YES;\n    }\n\n    return NO;\n}\n\n//  getPointer:type:    (was get_pointer)\n// ----------------------------------------------------------------------------\n//  Convert a relative ptr to an absolute ptr. Return which data type is being\n//  referenced in outType.\n\n- (char*)getPointer: (uint32_t)inAddr\n               type: (UInt8*)outType\n{\n    if (inAddr == 0)\n        return NULL;\n\n    if (outType)\n        *outType    = PointerType;\n\n    char*   thePtr  = NULL;\n    uint32_t  i;\n\n            // (__TEXT,__cstring) (char*)\n    if (inAddr >= iCStringSect.s.addr &&\n        inAddr < iCStringSect.s.addr + iCStringSect.size)\n    {\n        thePtr = (iCStringSect.contents + (inAddr - iCStringSect.s.addr));\n\n        // Make sure we're pointing to the beginning of a string,\n        // not somewhere in the middle.\n        if (*(thePtr - 1) != 0 && inAddr != iCStringSect.s.addr)\n            thePtr  = NULL;\n        // Check if this may be a Pascal string. Thanks, Metrowerks.\n        else if (outType && strlen(thePtr) == thePtr[0] + 1)\n            *outType    = PStringType;\n    }\n    else    // (__TEXT,__const) (Str255* sometimes)\n    if (inAddr >= iConstTextSect.s.addr &&\n        inAddr < iConstTextSect.s.addr + iConstTextSect.size)\n    {\n        thePtr  = (iConstTextSect.contents + (inAddr - iConstTextSect.s.addr));\n\n        if (outType && strlen(thePtr) == thePtr[0] + 1)\n            *outType    = PStringType;\n        else\n            thePtr  = NULL;\n    }\n    else    // (__TEXT,__objc_methname) (char*)\n    if (inAddr >= iObjcMethnameSect.s.addr &&\n        inAddr < iObjcMethnameSect.s.addr + iObjcMethnameSect.size)\n    {\n        thePtr  = (iObjcMethnameSect.contents + (inAddr - iObjcMethnameSect.s.addr));\n    }\n    else    // (__TEXT,__objc_methtype) (char*)\n    if (inAddr >= iObjcMethtypeSect.s.addr &&\n        inAddr < iObjcMethtypeSect.s.addr + iObjcMethtypeSect.size)\n    {\n        thePtr  = (iObjcMethtypeSect.contents + (inAddr - iObjcMethtypeSect.s.addr));\n    }\n    else    // (__TEXT,__objc_classname) (char*)\n    if (inAddr >= iObjcClassnameSect.s.addr &&\n        inAddr < iObjcClassnameSect.s.addr + iObjcClassnameSect.size)\n    {\n        thePtr  = (iObjcClassnameSect.contents + (inAddr - iObjcClassnameSect.s.addr));\n    }\n    else    // (__TEXT,__literal4) (float)\n    if (inAddr >= iLit4Sect.s.addr &&\n        inAddr < iLit4Sect.s.addr + iLit4Sect.size)\n    {\n        thePtr  = (char*)(iLit4Sect.contents + (inAddr - iLit4Sect.s.addr));\n\n        if (outType)\n            *outType    = FloatType;\n    }\n    else    // (__TEXT,__literal8) (double)\n    if (inAddr >= iLit8Sect.s.addr &&\n        inAddr < iLit8Sect.s.addr + iLit8Sect.size)\n    {\n        thePtr  = (char*)(iLit8Sect.contents + (inAddr - iLit8Sect.s.addr));\n\n        if (outType)\n            *outType    = DoubleType;\n    }\n\n    if (thePtr)\n        return thePtr;\n\n            // (__OBJC,__cstring_object) (objc_string_object)\n    if (inAddr >= iNSStringSect.s.addr &&\n        inAddr < iNSStringSect.s.addr + iNSStringSect.size)\n    {\n        thePtr  = (char*)(iNSStringSect.contents + (inAddr - iNSStringSect.s.addr));\n\n        if (outType)\n            *outType    = OCStrObjectType;\n    }\n    else    // (__OBJC,__class) (objc_class)\n    if (inAddr >= iClassSect.s.addr &&\n        inAddr < iClassSect.s.addr + iClassSect.size)\n    {\n        thePtr  = (char*)(iClassSect.contents + (inAddr - iClassSect.s.addr));\n\n        if (outType)\n            *outType    = OCClassType;\n    }\n    else    // (__OBJC,__meta_class) (objc_class)\n    if (inAddr >= iMetaClassSect.s.addr &&\n        inAddr < iMetaClassSect.s.addr + iMetaClassSect.size)\n    {\n        thePtr  = (char*)(iMetaClassSect.contents + (inAddr - iMetaClassSect.s.addr));\n\n        if (outType)\n            *outType    = OCClassType;\n    }\n    else    // (__OBJC,__module_info) (objc_module)\n    if (inAddr >= iObjcModSect.s.addr &&\n        inAddr < iObjcModSect.s.addr + iObjcModSect.size)\n    {\n        thePtr  = (char*)(iObjcModSect.contents + (inAddr - iObjcModSect.s.addr));\n\n        if (outType)\n            *outType    = OCModType;\n    }\n\n            //  (__OBJC, ??) (char*)\n            // __message_refs, __class_refs, __instance_vars, __symbols\n    for (i = 0; !thePtr && i < iNumObjcSects; i++)\n    {\n        if (inAddr >= iObjcSects[i].s.addr &&\n            inAddr < iObjcSects[i].s.addr + iObjcSects[i].size)\n        {\n            thePtr  = (char*)(iObjcSects[i].contents + (inAddr - iObjcSects[i].s.addr));\n\n            if (outType)\n                *outType    = OCGenericType;\n        }\n    }\n\n    if (thePtr)\n        return thePtr;\n\n            // (__IMPORT,__pointers) (cf_string_object*)\n    if (inAddr >= iImpPtrSect.s.addr &&\n        inAddr < iImpPtrSect.s.addr + iImpPtrSect.size)\n    {\n        thePtr  = (char*)(iImpPtrSect.contents + (inAddr - iImpPtrSect.s.addr));\n\n        if (outType)\n            *outType    = ImpPtrType;\n    }\n\n    if (thePtr)\n        return thePtr;\n\n            // (__DATA,__data) (char**)\n    if (inAddr >= iDataSect.s.addr &&\n        inAddr < iDataSect.s.addr + iDataSect.size)\n    {\n        thePtr  = (char*)(iDataSect.contents + (inAddr - iDataSect.s.addr));\n\n        UInt8   theType     = DataGenericType;\n        uint32_t  theValue    = *(uint32_t*)thePtr;\n\n        if (iSwapped)\n            theValue    = OSSwapInt32(theValue);\n\n        if (theValue != 0)\n        {\n            theType = PointerType;\n\n            static  uint32_t  recurseCount    = 0;\n\n            while (theType == PointerType)\n            {\n                recurseCount++;\n\n                if (recurseCount > 5)\n                {\n                    theType = DataGenericType;\n                    break;\n                }\n\n                thePtr  = [self getPointer:theValue type:&theType];\n\n                if (!thePtr)\n                {\n                    theType = DataGenericType;\n                    break;\n                }\n\n                theValue    = *(uint32_t*)thePtr;\n            }\n\n            recurseCount    = 0;\n        }\n\n        if (outType)\n            *outType    = theType;\n    }\n    else    // (__DATA,__const) (void*)\n    if (inAddr >= iConstDataSect.s.addr &&\n        inAddr < iConstDataSect.s.addr + iConstDataSect.size)\n    {\n        thePtr  = (char*)(iConstDataSect.contents + (inAddr - iConstDataSect.s.addr));\n\n        if (outType)\n        {\n            uint32_t  theID   = *(uint32_t*)thePtr;\n\n            if (iSwapped)\n                theID   = OSSwapInt32(theID);\n\n            if (theID == typeid_NSString)\n                *outType    = OCStrObjectType;\n            else\n            {\n                theID   = *(uint32_t*)(thePtr + 4);\n\n                if (iSwapped)\n                    theID   = OSSwapInt32(theID);\n\n                if (theID == typeid_NSString)\n                    *outType    = CFStringType;\n                else\n                    *outType    = DataConstType;\n            }\n        }\n    }\n    else    // (__DATA,__cfstring) (cf_string_object*)\n    if (inAddr >= iCFStringSect.s.addr &&\n        inAddr < iCFStringSect.s.addr + iCFStringSect.size)\n    {\n        thePtr  = (char*)(iCFStringSect.contents + (inAddr - iCFStringSect.s.addr));\n\n        if (outType)\n            *outType    = CFStringType;\n    }\n    else    // (__DATA,__objc_classrefs)\n    if (inAddr >= iObjcClassRefsSect.s.addr &&\n        inAddr < iObjcClassRefsSect.s.addr + iObjcClassRefsSect.size)\n    {\n        if (inAddr % 4 == 0)\n        {\n            UInt32 classRef = *(UInt32*)(iObjcClassRefsSect.contents +\n                (inAddr - iObjcClassRefsSect.s.addr));\n\n            if (classRef &&\n                classRef >= iObjcDataSect.s.addr &&\n                classRef < iObjcDataSect.s.addr + iObjcDataSect.s.size)\n            {\n                if (iSwapped)\n                    classRef = OSSwapInt32(classRef);\n\n                objc2_32_class_t swappedClass = *(objc2_32_class_t*)(iObjcDataSect.contents +\n                    (classRef - iObjcDataSect.s.addr));\n\n                if (iSwapped)\n                    swap_objc2_32_class(&swappedClass);\n\n                if (swappedClass.data &&\n                    swappedClass.data >= iObjcConstSect.s.addr &&\n                    swappedClass.data < iObjcConstSect.s.addr + iObjcConstSect.s.size)\n                {\n                    objc2_32_class_ro_t roData = *(objc2_32_class_ro_t*)(iObjcConstSect.contents +\n                        (swappedClass.data - iObjcConstSect.s.addr));\n\n                    if (iSwapped)\n                        roData.name = OSSwapInt32(roData.name);\n\n                    thePtr = [self getPointer:roData.name type:NULL];\n\n                    if (outType)\n                        *outType = OCClassRefType;\n                }\n\n            }\n        }\n    }\n    else    // (__DATA,__objc_msgrefs)\n    if (inAddr >= iObjcMsgRefsSect.s.addr &&\n        inAddr < iObjcMsgRefsSect.s.addr + iObjcMsgRefsSect.size)\n    {\n        objc2_32_message_ref_t ref = *(objc2_32_message_ref_t*)(iObjcMsgRefsSect.contents +\n            (inAddr - iObjcMsgRefsSect.s.addr));\n\n        if (iSwapped)\n            ref.sel = OSSwapInt32(ref.sel);\n\n        thePtr = [self getPointer:ref.sel type:NULL];\n\n        if (outType)\n            *outType = OCMsgRefType;\n    }\n    else    // (__DATA,__objc_catlist)\n    if (inAddr >= iObjcCatListSect.s.addr &&\n        inAddr < iObjcCatListSect.s.addr + iObjcCatListSect.size)\n    {\n    }\n    else    // (__DATA,__objc_superrefs)\n    if (inAddr >= iObjcSuperRefsSect.s.addr &&\n        inAddr < iObjcSuperRefsSect.s.addr + iObjcSuperRefsSect.size)\n    {\n        UInt32 superAddy = *(UInt32*)(iObjcSuperRefsSect.contents +\n            (inAddr - iObjcSuperRefsSect.s.addr));\n\n        if (iSwapped)\n            superAddy = OSSwapInt32(superAddy);\n\n        if (superAddy &&\n            superAddy >= iObjcDataSect.s.addr &&\n            superAddy < iObjcDataSect.s.addr + iObjcDataSect.s.size)\n        {\n            objc2_32_class_t swappedClass = *(objc2_32_class_t*)(iObjcDataSect.contents +\n                (superAddy - iObjcDataSect.s.addr));\n\n            if (iSwapped)\n                swap_objc2_32_class(&swappedClass);\n\n            if (swappedClass.data &&\n                swappedClass.data >= iObjcConstSect.s.addr &&\n                swappedClass.data < iObjcConstSect.s.addr + iObjcConstSect.s.size)\n            {\n                objc2_32_class_ro_t* roPtr = (objc2_32_class_ro_t*)(iObjcConstSect.contents +\n                    (swappedClass.data - iObjcConstSect.s.addr));\n                UInt32 namePtr = roPtr->name;\n\n                if (iSwapped)\n                    namePtr = OSSwapInt32(namePtr);\n\n                if (namePtr != 0)\n                {\n                    thePtr = [self getPointer:namePtr type:NULL];\n\n                    if (outType)\n                        *outType = OCSuperRefType;\n                }\n            }\n        }\n    }\n    else    // (__DATA,__objc_selrefs)\n    if (inAddr >= iObjcSelRefsSect.s.addr &&\n        inAddr < iObjcSelRefsSect.s.addr + iObjcSelRefsSect.size)\n    {\n        UInt32 selAddy = *(UInt32*)(iObjcSelRefsSect.contents +\n            (inAddr - iObjcSelRefsSect.s.addr));\n\n        if (iSwapped)\n            selAddy = OSSwapInt32(selAddy);\n\n        if (selAddy != 0)\n        {\n            thePtr = [self getPointer:selAddy type:NULL];\n\n            if (outType)\n                *outType = OCSelRefType;\n        }\n    }\n    else\n    if ((inAddr >= iObjcProtoRefsSect.s.addr &&    // (__DATA,__objc_protorefs)\n        inAddr < iObjcProtoRefsSect.s.addr + iObjcProtoRefsSect.size) ||\n        (inAddr >= iObjcProtoListSect.s.addr &&    // (__DATA,__objc_protolist)\n        inAddr < iObjcProtoListSect.s.addr + iObjcProtoListSect.size))\n    {\n        UInt32 protoAddy;\n        UInt8 tempType;\n\n        if (inAddr >= iObjcProtoRefsSect.s.addr &&\n            inAddr < iObjcProtoRefsSect.s.addr + iObjcProtoRefsSect.size)\n        {\n            protoAddy = *(UInt32*)(iObjcProtoRefsSect.contents +\n                (inAddr - iObjcProtoRefsSect.s.addr));\n            tempType = OCProtoRefType;\n        }\n        else\n        {\n            protoAddy = *(UInt32*)(iObjcProtoListSect.contents +\n                (inAddr - iObjcProtoListSect.s.addr));\n            tempType = OCProtoListType;\n        }\n\n        if (iSwapped)\n            protoAddy = OSSwapInt32(protoAddy);\n\n        if (protoAddy != 0 &&\n            (protoAddy >= iDataSect.s.addr && protoAddy < (iDataSect.s.addr + iDataSect.size)))\n        {\n            objc2_32_protocol_t* proto = (objc2_32_protocol_t*)(iDataSect.contents +\n                (protoAddy - iDataSect.s.addr));\n            UInt32 protoName = proto->name;\n\n            if (iSwapped)\n                protoName = OSSwapInt32(protoName);\n\n            if (protoName != 0)\n            {\n                thePtr = [self getPointer:protoName type:NULL];\n\n                if (outType)\n                    *outType = tempType;\n            }\n        }\n    }\n    else    // (__DATA,__nl_symbol_ptr) (cf_string_object*)\n    if (inAddr >= iNLSymSect.s.addr &&\n        inAddr < iNLSymSect.s.addr + iNLSymSect.size)\n    {\n        thePtr  = (char*)(iNLSymSect.contents + (inAddr - iNLSymSect.s.addr));\n\n        if (outType)\n            *outType    = NLSymType;\n    }\n    else    // (__DATA,__dyld) (function ptr)\n    if (inAddr >= iDyldSect.s.addr &&\n        inAddr < iDyldSect.s.addr + iDyldSect.size)\n    {\n        thePtr  = (char*)(iDyldSect.contents + (inAddr - iDyldSect.s.addr));\n\n        if (outType)\n            *outType    = DYLDType;\n    }\n\n    // should implement these if they ever contain CFStrings or NSStrings\n/*  else    // (__DATA, __coalesced_data) (?)\n    if (localAddy >= mCoalDataSect.s.addr &&\n        localAddy < mCoalDataSect.s.addr + mCoalDataSect.size)\n    {\n    }\n    else    // (__DATA, __datacoal_nt) (?)\n    if (localAddy >= mCoalDataNTSect.s.addr &&\n        localAddy < mCoalDataNTSect.s.addr + mCoalDataNTSect.size)\n    {\n    }*/\n\n    return thePtr;\n}\n\n#ifdef OTX_DEBUG\n//  printSymbol:\n// ----------------------------------------------------------------------------\n//  Used for symbol debugging.\n\n- (void)printSymbol: (nlist)inSym\n{\n    fprintf(stderr, \"----------------\\n\\n\");\n    fprintf(stderr, \" n_strx = 0x%08x\\n\", inSym.n_un.n_strx);\n    fprintf(stderr, \" n_type = 0x%02x\\n\", inSym.n_type);\n    fprintf(stderr, \" n_sect = 0x%02x\\n\", inSym.n_sect);\n    fprintf(stderr, \" n_desc = 0x%04x\\n\", inSym.n_desc);\n    fprintf(stderr, \"n_value = 0x%08x (%u)\\n\\n\", inSym.n_value, inSym.n_value);\n\n    if ((inSym.n_type & N_STAB) != 0)\n    {   // too complicated, see <mach-o/stab.h>\n        fprintf(stderr, \"STAB symbol\\n\");\n    }\n    else    // not a STAB\n    {\n        if ((inSym.n_type & N_PEXT) != 0)\n            fprintf(stderr, \"Private external symbol\\n\\n\");\n        else if ((inSym.n_type & N_EXT) != 0)\n            fprintf(stderr, \"External symbol\\n\\n\");\n\n        UInt8   theNType    = inSym.n_type & N_TYPE;\n        UInt16  theRefType  = inSym.n_desc & REFERENCE_TYPE;\n\n        fprintf(stderr, \"Symbol name: %s\\n\", (char*)((char *)iMachHeaderPtr + iStringTableOffset + inSym.n_un.n_strx));\n        fprintf(stderr, \"Symbol type: \");\n\n        if (theNType == N_ABS)\n            fprintf(stderr, \"Absolute\\n\");\n        else if (theNType == N_SECT)\n            fprintf(stderr, \"Defined in section %u\\n\", inSym.n_sect);\n        else if (theNType == N_INDR)\n            fprintf(stderr, \"Indirect\\n\");\n        else\n        {\n            if (theNType == N_UNDF)\n                fprintf(stderr, \"Undefined\\n\");\n            else if (theNType == N_PBUD)\n                fprintf(stderr, \"Prebound undefined\\n\");\n\n            switch (theRefType)\n            {\n                case REFERENCE_FLAG_UNDEFINED_NON_LAZY:\n                    fprintf(stderr, \"REFERENCE_FLAG_UNDEFINED_NON_LAZY\\n\");\n                    break;\n                case REFERENCE_FLAG_UNDEFINED_LAZY:\n                    fprintf(stderr, \"REFERENCE_FLAG_UNDEFINED_LAZY\\n\");\n                    break;\n                case REFERENCE_FLAG_DEFINED:\n                    fprintf(stderr, \"REFERENCE_FLAG_DEFINED\\n\");\n                    break;\n                case REFERENCE_FLAG_PRIVATE_DEFINED:\n                    fprintf(stderr, \"REFERENCE_FLAG_PRIVATE_DEFINED\\n\");\n                    break;\n                case REFERENCE_FLAG_PRIVATE_UNDEFINED_NON_LAZY:\n                    fprintf(stderr, \"REFERENCE_FLAG_PRIVATE_UNDEFINED_NON_LAZY\\n\");\n                    break;\n                case REFERENCE_FLAG_PRIVATE_UNDEFINED_LAZY:\n                    fprintf(stderr, \"REFERENCE_FLAG_PRIVATE_UNDEFINED_LAZY\\n\");\n                    break;\n\n                default:\n                    break;\n            }\n        }\n    }\n\n    fprintf(stderr, \"\\n\");\n}\n\n//  printBlocks:\n// ----------------------------------------------------------------------------\n//  Used for block debugging. Subclasses may override.\n\n- (void)printBlocks: (uint32_t)inFuncIndex;\n{\n    if (!iFuncInfos)\n        return;\n\n    FunctionInfo*   funcInfo    = &iFuncInfos[inFuncIndex];\n\n    if (!funcInfo || !funcInfo->blocks)\n        return;\n\n    uint32_t  i;\n\n    fprintf(stderr, \"\\nfunction at 0x%x:\\n\\n\", funcInfo->address);\n    fprintf(stderr, \"%d blocks\\n\", funcInfo->numBlocks);\n\n    for (i = 0; i < funcInfo->numBlocks; i++)\n    {\n        fprintf(stderr, \"\\nblock %d at 0x%x:\\n\\n\", i + 1,\n            funcInfo->blocks[i].beginAddress);\n\n        GPRegisterInfo *regInfo = funcInfo->blocks[i].state.regInfos;\n        \n        if (regInfo[EAX].isValid) fprintf(stderr, \"\\t\\teax: 0x%x\\n\", regInfo[EAX].value);\n        if (regInfo[EBX].isValid) fprintf(stderr, \"\\t\\tebx: 0x%x\\n\", regInfo[EBX].value);\n        if (regInfo[ECX].isValid) fprintf(stderr, \"\\t\\tecx: 0x%x\\n\", regInfo[ECX].value);\n        if (regInfo[EDX].isValid) fprintf(stderr, \"\\t\\tedx: 0x%x\\n\", regInfo[EDX].value);\n        if (regInfo[ESP].isValid) fprintf(stderr, \"\\t\\tesp: 0x%x\\n\", regInfo[ESP].value);\n        if (regInfo[EBP].isValid) fprintf(stderr, \"\\t\\tebp: 0x%x\\n\", regInfo[EBP].value);\n        if (regInfo[ESI].isValid) fprintf(stderr, \"\\t\\tesi: 0x%x\\n\", regInfo[ESI].value);\n        if (regInfo[EDI].isValid) fprintf(stderr, \"\\t\\tedi: 0x%x\\n\", regInfo[EDI].value);\n    }\n}\n#endif  // OTX_DEBUG\n\n@end\n"}
+{"plateform": "github", "repo_name": "zhangchn/otx-master", "name": "PPC64Processor.m", "ext": ".m", "path": "otx-master/src/source/Processors/PPC64Processor.m", "size": 74333, "source_encoding": "utf_8", "md5": "6357b279a717e624564964c7ba8e1495", "text": "/*\n    PPC64Processor.m\n\n    A subclass of Exe64Processor that handles PPC64-specific issues.\n\n    This file is in the public domain.\n*/\n\n#import <Cocoa/Cocoa.h>\n\n#import \"PPC64Processor.h\"\n#import \"PPCProcessor.h\"\n#import \"Arch64Specifics.h\"\n#import \"List64Utils.h\"\n#import \"Objc64Accessors.h\"\n#import \"Object64Loader.h\"\n#import \"Searchers64.h\"\n#import \"SyscallStrings.h\"\n#import \"UserDefaultKeys.h\"\n\n@implementation PPC64Processor\n\n//  initWithURL:controller:options:\n// ----------------------------------------------------------------------------\n\n- (id)initWithURL: (NSURL*)inURL\n       controller: (id)inController\n          options: (ProcOptions*)inOptions\n{\n    if ((self = [super initWithURL: inURL\n        controller: inController options: inOptions]))\n    {\n        strncpy(iArchString, \"ppc64\", 6);\n\n        iArchSelector               = CPU_TYPE_POWERPC64;\n        iFieldWidths.offset         = 8;\n        iFieldWidths.address        = 18;\n        iFieldWidths.instruction    = 10;\n        iFieldWidths.mnemonic       = 9;\n        iFieldWidths.operands       = 17;\n    }\n\n    return self;\n}\n\n//  dealloc\n// ----------------------------------------------------------------------------\n\n- (void)dealloc\n{\n    if (iLocalSelves)\n    {\n        free(iLocalSelves);\n        iLocalSelves    = NULL;\n    }\n\n    if (iLocalVars)\n    {\n        free(iLocalVars);\n        iLocalVars  = NULL;\n    }\n\n}\n\n//  loadDyldDataSection:\n// ----------------------------------------------------------------------------\n\n- (void)loadDyldDataSection: (section_64*)inSect\n{\n    [super loadDyldDataSection: inSect];\n\n    if (!iAddrDyldStubBindingHelper)\n        return;\n\n    iAddrDyldFuncLookupPointer  = iAddrDyldStubBindingHelper + 24;\n}\n\n//  codeFromLine:\n// ----------------------------------------------------------------------------\n\n- (void)codeFromLine: (Line64*)inLine\n{\n    uint32_t  theInstruction  = (iMachHeader.filetype == MH_OBJECT) ?\n        *(uint32_t*)((char*)iMachHeaderPtr + (inLine->info.address + iTextOffset)) :\n        *(uint32_t*)((char*)iMachHeaderPtr + (inLine->info.address - iTextOffset));\n\n    inLine->info.codeLength = 4;\n\n    uint32_t* intPtr = (uint32_t*)&inLine->info.code[0];\n\n    *intPtr = theInstruction;\n}\n\n#pragma mark -\n//  commentForLine:\n// ----------------------------------------------------------------------------\n\n- (void)commentForLine: (Line64*)inLine;\n{\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    char*   theDummyPtr = NULL;\n    char*   theSymPtr   = NULL;\n    UInt8   opcode      = PO(theCode);\n    UInt64  localAddy;\n\n    iLineCommentCString[0]  = 0;\n\n    // Examine the primary opcode to see if we need to look for comments.\n    switch (opcode)\n    {\n        case 0x0a:  // cmpli | cmplwi   UIMM\n        case 0x0b:  // cmpi | cmpwi     SIMM\n        {\n            SInt16  imm = SIMM(theCode);\n\n            // Check for a single printable 7-bit char.\n            if (imm >= 0x20 && imm < 0x7f)\n                snprintf(iLineCommentCString, 4, \"'%c'\", imm);\n\n            break;\n        }\n\n        case 0x11:  // sc\n            [self commentForSystemCall];\n\n            break;\n\n        case 0x10:  // bc, bca, bcl, bcla\n        case 0x12:  // b, ba, bl, bla\n        {\n            // Check for absolute branches to the ObjC runtime page. Similar to\n            // the comm page behavior described at\n            // http://www.opensource.apple.com/darwinsource/10.4.7.ppc/xnu-792.6.76/osfmk/ppc/cpu_capabilities.h\n            // However, the ObjC runtime page is not really a comm page, and it\n            // cannot be accessed by bca and bcla instructions, due to their\n            // 16-bit limitation.\n\n            // Deal with absolute branches.\n            if (AA(theCode))\n            {\n                uint32_t  target  = LI(theCode);\n\n                switch (target)\n                {\n                    case kRTAddress_objc_msgSend:\n                    {\n                        char    tempComment[MAX_COMMENT_LENGTH];\n\n                        strncpy(tempComment, kRTName_objc_msgSend,\n                            strlen(kRTName_objc_msgSend) + 1);\n\n                        if (iOpts.verboseMsgSends)\n                            [self commentForMsgSend:tempComment fromLine:inLine];\n\n                        strncpy(iLineCommentCString, tempComment,\n                            strlen(tempComment) + 1);\n\n                        break;\n                    }\n\n                    case kRTAddress_objc_assign_ivar:\n                    {\n                        char    tempComment[MAX_COMMENT_LENGTH];\n\n                        strncpy(tempComment, kRTName_objc_assign_ivar,\n                            strlen(kRTName_objc_assign_ivar) + 1);\n\n                        // Bail if we don't know about the class.\n                        if (!iCurrentClass)\n                        {\n                            strncpy(iLineCommentCString, tempComment,\n                                strlen(tempComment) + 1);\n                            break;\n                        }\n\n                        if (iRegInfos[5].isValid)\n                        {\n                            objc2_64_ivar_t* theIvar = NULL;\n                            objc2_64_class_t swappedClass = *iCurrentClass;\n\n                            #if __LITTLE_ENDIAN__\n                                swap_objc2_64_class(&swappedClass);\n                            #endif\n\n                            if (!iIsInstanceMethod)\n                            {\n                                if (![self getObjcMetaClass:&swappedClass fromClass:&swappedClass])\n                                    break;\n\n                                #if __LITTLE_ENDIAN__\n                                    swap_objc2_64_class(&swappedClass);\n                                #endif\n                            }\n\n                            if (![self findIvar:&theIvar inClass:&swappedClass withOffset:iRegInfos[5].value])\n                            {\n                                strncpy(iLineCommentCString, tempComment, strlen(tempComment) + 1);\n                                break;\n                            }\n\n                            theSymPtr = [self getPointer:theIvar->name type:NULL];\n\n                            if (!theSymPtr)\n                            {\n                                strncpy(iLineCommentCString, tempComment, strlen(tempComment) + 1);\n                                break;\n                            }\n\n                            if (iOpts.variableTypes)\n                            {\n                                char    theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                                theTypeCString[0]   = 0;\n\n                                [self getDescription:theTypeCString forType:[self getPointer:theIvar->type type:NULL]];\n                                snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s (%s)%s\", tempComment, theTypeCString, theSymPtr);\n                            }\n                            else\n                                snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s %s\", tempComment, theSymPtr);\n                        }\n                        else    // !mReginfos[5].isValid\n                            snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", tempComment);\n\n                        break;\n                    }\n\n                    case kRTAddress_objc_assign_global:\n                        strncpy(iLineCommentCString, kRTName_objc_assign_global,\n                            strlen(kRTName_objc_assign_global) + 1);\n                        break;\n\n                    case kRTAddress_objc_assign_strongCast:\n                        strncpy(iLineCommentCString, kRTName_objc_assign_strongCast,\n                            strlen(kRTName_objc_assign_strongCast) + 1);\n                        break;\n\n                    default:\n                        break;\n                }\n            }\n            else    // not an absolute branch\n            {\n                // Insert anonymous label or 'return' if there's not a comment yet.\n                if (iLineCommentCString[0])\n                    break;\n\n                UInt64  absoluteAddy;\n\n                if (opcode == 0x12)\n                    absoluteAddy =\n                        inLine->info.address + LI(theCode);\n                else\n                    absoluteAddy =\n                        inLine->info.address + BD(theCode);\n\n                Function64Info  searchKey   = {absoluteAddy, NULL, 0, 0};\n                Function64Info* funcInfo    = bsearch(&searchKey,\n                    iFuncInfos, iNumFuncInfos, sizeof(Function64Info),\n                    (COMPARISON_FUNC_TYPE)Function_Info_Compare);\n\n                if (funcInfo && funcInfo->genericFuncNum != 0)\n                {\n                    snprintf(iLineCommentCString,\n                        ANON_FUNC_BASE_LENGTH + 11, \"%s%d\",\n                        ANON_FUNC_BASE, funcInfo->genericFuncNum);\n                    break;\n                }\n\n// FIXME: mCurrentFuncInfoIndex is -1 here when it should not be\n                funcInfo = &iFuncInfos[iCurrentFuncInfoIndex];\n\n                if (!funcInfo->blocks)\n                    break;\n\n                uint32_t  i;\n\n                for (i = 0; i < funcInfo->numBlocks; i++)\n                {\n                    if (funcInfo->blocks[i].beginAddress != absoluteAddy)\n                        continue;\n\n                    if (funcInfo->blocks[i].isEpilog)\n                        snprintf(iLineCommentCString, 8, \"return;\");\n\n                    break;\n                }\n            }\n\n            break;\n        }\n\n        case 0x13:  // bcctr, bclr, isync\n            if (SO(theCode) != 528) // bcctr\n                break;\n\n            if (iRegInfos[4].messageRefSel != NULL)\n            {\n                char* sel = iRegInfos[4].messageRefSel;\n\n                strncpy(iLineOperandsCString, \" \", 2);\n\n                if (iRegInfos[3].className != NULL)\n                    snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"+[%s %s]\", iRegInfos[3].className, sel);\n                else // Instance method?\n                    if (iRegInfos[3].isValid)\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"objc_msgSend(%%r3, %s)\", sel);\n                    else\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"-[%%r3 %s]\", sel);\n            }\n\n            // Print value of ctr, ignoring the low 2 bits.\n//            if (iCTR.isValid)\n//                snprintf(iLineCommentCString, 10, \"0x%x\",\n//                    iCTR.value & ~3);\n\n            break;\n\n        case 0x30:  // lfs      SIMM\n        case 0x34:  // stfs     SIMM\n        case 0x32:  // lfd      SIMM\n        case 0x36:  // stfd     SIMM\n        {\n            if (!iRegInfos[RA(theCode)].isValid || RA(theCode) == 0)\n                break;\n\n            if (iRegInfos[RA(theCode)].classPtr)\n            {   // search instance vars\n                objc2_64_ivar_t* theIvar = NULL;\n                objc2_64_class_t swappedClass = *iRegInfos[RA(theCode)].classPtr;\n\n                #if __LITTLE_ENDIAN__\n                    swap_objc2_64_class(&swappedClass);\n                #endif\n\n                if (!iIsInstanceMethod)\n                {\n                    if (![self getObjcMetaClass:&swappedClass fromClass:&swappedClass])\n                        break;\n\n                    #if __LITTLE_ENDIAN__\n                        swap_objc2_64_class(&swappedClass);\n                    #endif\n                }\n\n                if (![self findIvar:&theIvar inClass:&swappedClass withOffset:UIMM(theCode)])\n                    break;\n\n                theSymPtr = [self getPointer:theIvar->name type:NULL];\n\n                if (theSymPtr)\n                {\n                    if (iOpts.variableTypes)\n                    {\n                        char theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                        theTypeCString[0] = 0;\n\n                        [self getDescription:theTypeCString forType:[self getPointer:theIvar->type type:NULL]];\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"(%s)%s\",\n                            theTypeCString, theSymPtr);\n                    }\n                    else\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                }\n            }\n            else\n            {\n                localAddy   = iRegInfos[RA(theCode)].value + SIMM(theCode);\n                theDummyPtr = [self getPointer:localAddy type:NULL];\n\n                if (!theDummyPtr)\n                    break;\n\n                if (opcode == 0x32 || opcode == 0x36)   // lfd | stfd\n                {\n                    UInt64  theInt64    = *(UInt64*)theDummyPtr;\n\n                    theInt64    = OSSwapBigToHostInt64(theInt64);\n\n                    // dance around printf's type coersion\n                    snprintf(iLineCommentCString,\n                        30, \"%lG\", *(double*)&theInt64);\n                }\n                else    // lfs | stfs\n                {\n                    uint32_t  theInt32    = *(uint32_t*)theDummyPtr;\n\n                    theInt32    = OSSwapBigToHostInt32(theInt32);\n\n                    // dance around printf's type coersion\n                    snprintf(iLineCommentCString,\n                        30, \"%G\", *(float*)&theInt32);\n                }\n            }\n\n            break;\n        }\n\n        case 0x0e:  // li | addi    SIMM\n        case 0x18:  // ori          UIMM\n        case 0x20:  // lwz          SIMM\n        case 0x22:  // lbz          SIMM\n        case 0x24:  // stw          SIMM\n        case 0x26:  // stb          SIMM\n        case 0x28:  // lhz          SIMM\n        case 0x2c:  // sth          SIMM\n        {\n            if (!iRegInfos[RA(theCode)].isValid || RA(theCode) == 0)\n                break;\n\n            if (iRegInfos[RA(theCode)].classPtr)    // relative to a class\n            {   // search instance vars\n                objc2_64_ivar_t* theIvar = NULL;\n                objc2_64_class_t swappedClass = *iRegInfos[RA(theCode)].classPtr;\n\n                if (![self findIvar:&theIvar inClass:&swappedClass withOffset:UIMM(theCode)])\n                    break;\n\n                theSymPtr = [self getPointer:theIvar->name type:NULL];\n\n                if (theSymPtr)\n                {\n                    if (iOpts.variableTypes)\n                    {\n                        char theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                        theTypeCString[0] = 0;\n\n                        [self getDescription:theTypeCString forType:[self getPointer:theIvar->type type:NULL]];\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"(%s)%s\", theTypeCString, theSymPtr);\n                    }\n                    else\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                }\n            }\n            else    // absolute address\n            {\n                if (opcode == 0x18) // ori      UIMM\n                    localAddy   = iRegInfos[RA(theCode)].value | UIMM(theCode);\n                else\n                    localAddy   = iRegInfos[RA(theCode)].value + SIMM(theCode);\n\n                UInt8   theType = PointerType;\n                uint32_t  theValue;\n\n                theSymPtr   = [self getPointer:localAddy type:&theType];\n\n                if (theSymPtr)\n                {\n                    switch (theType)\n                    {\n                        case DataGenericType:\n                            theValue    = *(uint32_t*)theSymPtr;\n                            theValue    = OSSwapBigToHostInt32(theValue);\n                            theDummyPtr = [self getPointer:theValue type:&theType];\n\n                            switch (theType)\n                            {\n                                case PointerType:\n                                    theSymPtr   = theDummyPtr;\n                                    break;\n\n                                default:\n                                    theSymPtr   = NULL;\n                                    break;\n                            }\n\n                            break;\n\n                        case DataConstType:\n                            theSymPtr   = NULL;\n                            break;\n\n// See http://www.opensource.apple.com/darwinsource/10.4.7.ppc/Csu-58/dyld.s\n// They hardcoded the values, we may as well...\n                        case DYLDType:\n                        {\n                            char*   dyldComment = NULL;\n\n                            theValue    = *(uint32_t*)theSymPtr;\n                            theValue    = OSSwapBigToHostInt32(theValue);\n\n                            switch(theValue)\n                            {\n                                case kDyldAddress_LaSymBindingEntry:\n                                    dyldComment = kDyldName_LaSymBindingEntry;\n                                    break;\n                                case kDyldAddress_FuncLookupPointer:\n                                    dyldComment = kDyldName_FuncLookupPointer;\n                                    break;\n\n                                default:\n                                    break;\n                            }\n\n                            if (dyldComment)\n                                strcpy(iLineCommentCString, dyldComment);\n\n                            break;\n                        }\n\n                        case PointerType:\n                            break;\n\n                        case CFStringType:\n                        {\n                            cfstring_object_64 theCFString =  *(cfstring_object_64*)theSymPtr;\n\n                            if (theCFString.oc_string.length == 0)\n                            {\n                                theSymPtr   = NULL;\n                                break;\n                            }\n\n                            theCFString.oc_string.chars =\n                                OSSwapBigToHostInt64(theCFString.oc_string.chars);\n                            theSymPtr   = [self getPointer:theCFString.oc_string.chars type:NULL];\n\n                            break;\n                        }\n\n                        case ImpPtrType:\n                        case NLSymType:\n                        {\n                            theValue    = *(uint32_t*)theSymPtr;\n                            theValue    = OSSwapBigToHostInt32(theValue);\n                            theDummyPtr = [self getPointer:theValue type:NULL];\n\n                            if (!theDummyPtr)\n                            {\n                                theSymPtr   = NULL;\n                                break;\n                            }\n\n                            theValue    = *(uint32_t*)(theDummyPtr + 4);\n                            theValue    = OSSwapBigToHostInt32(theValue);\n\n                            if (theValue != typeid_NSString)\n                            {\n                                theValue    = *(uint32_t*)theDummyPtr;\n                                theValue    = OSSwapBigToHostInt32(theValue);\n                                theDummyPtr = [self getPointer:theValue type:NULL];\n\n                                if (!theDummyPtr)\n                                {\n                                    theSymPtr   = NULL;\n                                    break;\n                                }\n                            }\n\n                            cfstring_object_64 theCFString = *(cfstring_object_64*)theDummyPtr;\n\n                            if (theCFString.oc_string.length == 0)\n                            {\n                                theSymPtr   = NULL;\n                                break;\n                            }\n\n                            theCFString.oc_string.chars =\n                                OSSwapBigToHostInt64(theCFString.oc_string.chars);\n                            theSymPtr = [self getPointer:theCFString.oc_string.chars type:NULL];\n\n                            break;\n                        }\n\n                        case OCGenericType:\n                        case OCStrObjectType:\n                        case OCClassType:\n                        case OCModType:\n                            if (![self getObjcDescription:&theDummyPtr fromObject:theSymPtr type:theType])\n                                break;\n\n                            if (theDummyPtr)\n                            {\n                                switch (theType)\n                                {\n                                    case OCClassType:\n                                        iRegInfos[RT(theCode)].classPtr =\n                                            (objc2_64_class_t*)theSymPtr;\n                                        break;\n\n                                    default:\n                                        break;\n                                }\n                            }\n\n                            theSymPtr   = theDummyPtr;\n                            break;\n\n                        default:\n                            break;\n                    }\n\n                    if (theSymPtr && !iLineCommentCString[0])\n                    {\n                        if (theType == PStringType)\n                            snprintf(iLineCommentCString, 255,\n                                \"%*s\", theSymPtr[0], theSymPtr + 1);\n                        else\n                            snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                    }\n                }   // if (theSymPtr)\n                else\n                {   // Maybe it's a four-char code...\n                    if ((opcode == 0x0e || opcode == 0x18) &&   // li | addi | ori\n                        localAddy >= 0x20202020 && localAddy < 0x7f7f7f7f)\n                    {\n                        localAddy   = OSSwapBigToHostInt64(localAddy);\n\n                        char*   fcc = (char*)&localAddy;\n\n                        if (fcc[0] >= 0x20 && fcc[0] < 0x7f &&\n                            fcc[1] >= 0x20 && fcc[1] < 0x7f &&\n                            fcc[2] >= 0x20 && fcc[2] < 0x7f &&\n                            fcc[3] >= 0x20 && fcc[3] < 0x7f)\n                            snprintf(iLineCommentCString,\n                                7, \"'%.4s'\", fcc);\n                    }\n                }\n            }   // if !(.classPtr)\n\n            break;\n        }   // case 0x0e...\n\n        case 0x3a:  // ld\n        {\n            if (iRegInfos[RA(theCode)].isValid == NO)\n                break;\n\n            UInt64 address = iRegInfos[RA(theCode)].value + DS(theCode);\n            UInt8 type;\n            char* symName = NULL;\n\n            theSymPtr = [self getPointer:address type:&type];\n\n            if (theSymPtr)\n            {\n                switch (type)\n                {\n                    case CFStringType:\n                        [self getObjcDescription:&symName fromObject:theSymPtr type:type];\n\n                        if (symName)\n                            snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", symName);\n\n                        break;\n\n                    case OCClassRefType:\n                    case OCSuperRefType:\n                    case OCProtoRefType:\n                    case OCProtoListType:\n                    case OCMsgRefType:\n                    case OCSelRefType:\n                        snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                        break;\n\n                    default:\n                        break;\n                }\n            }\n            else\n            {\n                if (address >= iDataSect.s.addr && address < (iDataSect.s.addr + iDataSect.size))\n                {\n                    objc2_64_ivar_t* foundIvar = NULL;\n                    objc2_64_ivar_t* maybeIvar = (objc2_64_ivar_t*)((iDataSect.contents) + (address - iDataSect.s.addr));\n\n                    if (maybeIvar->offset != 0)\n                    {\n                        UInt64 offset = OSSwapBigToHostInt64(maybeIvar->offset);\n\n                        if ([self findIvar:&foundIvar inClass:iCurrentClass withOffset:offset])\n                        {\n                            theSymPtr = [self getPointer:foundIvar->name type:NULL];\n\n                            if (theSymPtr)\n                            {\n                                if (iOpts.variableTypes)\n                                {\n                                    char theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                                    theTypeCString[0] = 0;\n\n                                    [self getDescription:theTypeCString forType:[self getPointer:foundIvar->type type:NULL]];\n                                    snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"(%s)%s\", theTypeCString, theSymPtr);\n                                }\n                                else\n                                    snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theSymPtr);\n                            }\n                        }\n                    }\n                }\n            }\n\n            break;\n        }\n\n        default:\n            break;\n    }\n}\n\n//  commentForSystemCall\n// ----------------------------------------------------------------------------\n//  System call number is stored in r0, possible values defined in\n//  <sys/syscall.h>. Call numbers are indices into a lookup table of handler\n//  routines. Args being passed to the looked-up handler start at r3 or r4,\n//  depending on whether it's an indirect SYS_syscall.\n\n- (void)commentForSystemCall\n{\n    if (!iRegInfos[0].isValid ||\n         iRegInfos[0].value > SYS_MAXSYSCALL)\n    {\n        snprintf(iLineCommentCString, 11, \"syscall(?)\");\n        return;\n    }\n\n    BOOL    isIndirect      = (iRegInfos[0].value == SYS_syscall);\n    uint32_t  syscallNumReg   = isIndirect ? 3 : 0;\n    uint32_t  syscallArg1Reg  = isIndirect ? 4 : 3;\n\n    if (!iRegInfos[syscallNumReg].isValid   ||\n        iRegInfos[syscallNumReg].value > SYS_MAXSYSCALL)\n    {\n        snprintf(iLineCommentCString, 11, \"syscall(?)\");\n        return;\n    }\n\n    const char* theSysString    = gSysCalls[iRegInfos[syscallNumReg].value];\n\n    if (!theSysString)\n        return;\n\n    char    theTempComment[50];\n\n    theTempComment[0]   = 0;\n    strncpy(theTempComment, theSysString, strlen(theSysString) + 1);\n\n    // Handle various system calls.\n    switch (iRegInfos[syscallNumReg].value)\n    {\n        case SYS_ptrace:\n            if (iRegInfos[syscallArg1Reg].isValid &&\n                iRegInfos[syscallArg1Reg].value == PT_DENY_ATTACH)\n                snprintf(iLineCommentCString, 40, \"%s(%s)\",\n                    theTempComment, \"PT_DENY_ATTACH\");\n            else\n                snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theTempComment);\n\n            break;\n\n        default:\n            snprintf(iLineCommentCString, MAX_COMMENT_LENGTH, \"%s\", theTempComment);\n\n            break;\n    }\n}\n\n//  selectorForMsgSend:fromLine:\n// ----------------------------------------------------------------------------\n\n- (char*)selectorForMsgSend: (char*)outComment\n                   fromLine: (Line64*)inLine\n{\n    char* selString = NULL;\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    // Bail if this is not an eligible branch.\n    if (PO(theCode) != 0x12)    // b, bl, ba, bla\n        return NULL;\n\n    // Bail if this is not an objc_msgSend variant.\n    if (memcmp(outComment, \"_objc_msgSend\", 13))\n        return NULL;\n\n    UInt8   sendType        = [self sendTypeFromMsgSend:outComment];\n    uint32_t  selectorRegNum  =\n        (sendType == sendSuper_stret || sendType == send_stret) ? 5 : 4;\n\n    if (!iRegInfos[selectorRegNum].isValid ||\n        !iRegInfos[selectorRegNum].value)\n        return NULL;\n\n    // Get at the selector.\n    UInt8   selType     = PointerType;\n    char*   selPtr      = [self getPointer:iRegInfos[selectorRegNum].value type:&selType];\n\n    switch (selType)\n    {\n        case PointerType:\n            selString   = selPtr;\n\n            break;\n\n        case OCGenericType:\n            if (selPtr)\n            {\n                uint32_t  selPtrValue = *(uint32_t*)selPtr;\n\n                selPtrValue = OSSwapBigToHostInt32(selPtrValue);\n                selString   = [self getPointer:selPtrValue type:NULL];\n            }\n\n            break;\n\n        default:\n            fprintf(stderr, \"otx: [PPC64Processor selectorForMsgSend:fromLine:]: \"\n                \"unsupported selector type: %d at address: 0x%llx\\n\",\n                selType, inLine->info.address);\n\n            break;\n    }\n\n    return selString;\n}\n\n//  commentForMsgSend:fromLine:\n// ----------------------------------------------------------------------------\n\n- (void)commentForMsgSend: (char*)ioComment\n                 fromLine: (Line64*)inLine\n{\n    char tempComment[MAX_COMMENT_LENGTH];\n\n    tempComment[0] = 0;\n\n    if (!strncmp(ioComment, \"_objc_msgSend\", 13))\n    {\n    char* selString = [self selectorForMsgSend:ioComment fromLine:inLine];\n    \n    // Bail if we couldn't find the selector.\n    if (!selString)\n    {\n        iMissedSelectorCount++;\n        return;\n    }\n    \n    iMatchedSelectorCount++;\n\n    UInt8 sendType = [self sendTypeFromMsgSend:ioComment];\n\n    // Get the address of the class name string, if this a class method.\n    UInt64 classNameAddy = 0;\n\n    // If *.classPtr is non-NULL, it's not a name string.\n    if (sendType == sendSuper_stret || sendType == send_stret)\n    {\n        if (iRegInfos[4].isValid && !iRegInfos[4].classPtr)\n            classNameAddy = iRegInfos[4].value;\n    }\n    else\n    {\n        if (iRegInfos[3].isValid && !iRegInfos[3].classPtr)\n            classNameAddy = iRegInfos[3].value;\n    }\n\n    char*   className           = NULL;\n    char*   returnTypeString    =\n        (sendType == sendSuper_stret || sendType == send_stret) ?\n        \"(struct)\" : \"\";\n//    char    tempComment[MAX_COMMENT_LENGTH];\n\n//    tempComment[0]  = 0;\n\n    if (classNameAddy)\n    {\n        // Get at the class name\n        UInt8   classNameType   = PointerType;\n        char*   classNamePtr    = [self getPointer:classNameAddy type:&classNameType];\n\n        switch (classNameType)\n        {\n            // Receiver can be a static string or pointer in these sections, but we\n            // only want to display class names as receivers.\n            case DataGenericType:\n            case DataConstType:\n            case CFStringType:\n            case ImpPtrType:\n            case OCStrObjectType:\n                break;\n\n            case NLSymType:\n                if (classNamePtr)\n                {\n                    UInt64    namePtrValue    = *(UInt64*)classNamePtr;\n\n                    namePtrValue    = OSSwapBigToHostInt64(namePtrValue);\n                    classNamePtr    = [self getPointer:namePtrValue type:&classNameType];\n\n                    switch (classNameType)\n                    {\n                        case CFStringType:\n                            if (classNamePtr != NULL)\n                            {\n                                cfstring_object_64 classNameCFString = *(cfstring_object_64*)classNamePtr;\n\n                                namePtrValue    = classNameCFString.oc_string.chars;\n                                namePtrValue    = OSSwapBigToHostInt64(namePtrValue);\n                                classNamePtr    = [self getPointer:namePtrValue type:NULL];\n                                className       = classNamePtr;\n                            }\n\n                            break;\n\n                        // Not sure what these are for, but they're NULL.\n                        case NLSymType:\n                            break;\n\n                        default:\n                            printf(\"otx: [PPC64Processor commentForMsgSend:fromLine:]: \"\n                                \"non-lazy symbol pointer points to unrecognized section: %d\\n\", classNameType);\n                            break;\n                    }\n                }\n\n                break;\n\n            case PointerType:\n                className   = classNamePtr;\n                break;\n\n            case OCGenericType:\n                if (classNamePtr)\n                {\n                    uint32_t  namePtrValue    = *(uint32_t*)classNamePtr;\n\n                    namePtrValue    = OSSwapBigToHostInt32(namePtrValue);\n                    className       = [self getPointer:namePtrValue type:NULL];\n                }\n\n                break;\n\n            case OCClassType:\n                if (classNamePtr)\n                    [self getObjcDescription:&className fromObject:classNamePtr type:OCClassType];\n\n                break;\n\n            default:\n                fprintf(stderr, \"otx: [PPC64Processor commentForMsgSend]: \"\n                    \"unsupported class name type: %d at address: 0x%llx\\n\",\n                    classNameType, inLine->info.address);\n\n                break;\n        }\n    }\n\n    if (className)\n    {\n        snprintf(tempComment, MAX_COMMENT_LENGTH,\n            ((sendType == sendSuper || sendType == sendSuper_stret) ?\n            \"+%s[[%s super] %s]\" : \"+%s[%s %s]\"),\n            returnTypeString, className, selString);\n    }\n    else\n    {\n        switch (sendType)\n        {\n            case send:\n            case send_rtp:\n            case send_variadic:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[r3 %s]\", returnTypeString, selString);\n                break;\n\n            case sendSuper:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[[r3 super] %s]\", returnTypeString, selString);\n                break;\n\n            case send_stret:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[r4 %s]\", returnTypeString, selString);\n                break;\n\n            case sendSuper_stret:\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \"-%s[[r4 super] %s]\", returnTypeString, selString);\n                break;\n\n            default:\n                break;\n        }\n    }\n    }   // if (!strncmp(ioComment, \"_objc_msgSend\", 13))\n    else if (!strncmp(ioComment, \"_objc_assign_ivar\", 17))\n    {\n        if (iCurrentClass && iRegInfos[5].isValid)\n        {\n            char* theSymPtr = NULL;\n            objc2_64_ivar_t* theIvar = NULL;\n            objc2_64_class_t swappedClass = *iCurrentClass;\n\n            if (!iIsInstanceMethod)\n            {\n                if (![self getObjcMetaClass:&swappedClass fromClass:&swappedClass])\n                    return;\n\n                #if __LITTLE_ENDIAN__\n                    swap_objc2_64_class(&swappedClass);\n                #endif\n            }\n\n            if (![self findIvar:&theIvar inClass:&swappedClass withOffset:iRegInfos[5].value])\n                return;\n\n            theSymPtr = [self getPointer:theIvar->name type:NULL];\n\n            if (!theSymPtr)\n                return;\n\n            if (iOpts.variableTypes)\n            {\n                char    theTypeCString[MAX_TYPE_STRING_LENGTH];\n\n                theTypeCString[0]   = 0;\n\n                [self getDescription:theTypeCString forType:[self getPointer:theIvar->type type:NULL]];\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \" (%s)%s\", theTypeCString, theSymPtr);\n            }\n            else\n                snprintf(tempComment, MAX_COMMENT_LENGTH, \" %s\", theSymPtr);\n\n            strncat(ioComment, tempComment, strlen(tempComment));\n        }\n    }\n}\n\n//  chooseLine:\n// ----------------------------------------------------------------------------\n\n- (void)chooseLine: (Line64**)ioLine\n{\n    if (!(*ioLine) || !(*ioLine)->info.isCode ||\n        !(*ioLine)->alt || !(*ioLine)->alt->chars)\n        return;\n\n    uint32_t theCode = *(uint32_t*)(*ioLine)->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    if (PO(theCode) == 18)  // b, ba, bl, bla\n    {\n        Line64* theNewLine  = malloc(sizeof(Line64));\n\n        memcpy(theNewLine, (*ioLine)->alt, sizeof(Line64));\n        theNewLine->chars   = malloc(theNewLine->length + 1);\n        strncpy(theNewLine->chars, (*ioLine)->alt->chars,\n            theNewLine->length + 1);\n\n        // Swap in the verbose line and free the previous verbose lines.\n        [self deleteLinesBefore:(*ioLine)->alt fromList:&iVerboseLineListHead];\n        [self replaceLine:*ioLine withLine:theNewLine inList:&iPlainLineListHead];\n        *ioLine = theNewLine;\n    }\n}\n\n#pragma mark -\n//  resetRegisters:\n// ----------------------------------------------------------------------------\n\n- (void)resetRegisters: (Line64*)inLine\n{\n    if (!inLine)\n    {\n        fprintf(stderr, \"otx: [PPC64Processor resetRegisters]: \"\n            \"tried to reset with NULL inLine\\n\");\n        return;\n    }\n\n    // Setup the registers with default info. r3 is 'self' at the beginning\n    // of any Obj-C method, and r12 holds the address of the 1st instruction\n    // if the function was called indirectly. In the case of direct calls,\n    // r12 will be overwritten before it is used, if it is used at all.\n    [self getObjcClassPtr:&iCurrentClass fromMethod:inLine->info.address];\n//    [self getObjcCatPtr:&iCurrentCat fromMethod:inLine->info.address];\n    memset(iRegInfos, 0, sizeof(GP64RegisterInfo) * 32);\n\n    // If we didn't get the class from the method, try to get it from the\n    // category.\n/*    if (!iCurrentClass && iCurrentCat)\n    {\n        objc_category   swappedCat  = *iCurrentCat;\n\n        #if __LITTLE_ENDIAN__\n            swap_objc_category(&swappedCat);\n        #endif\n\n        [self getObjcClassPtr:&iCurrentClass fromName:[self getPointer:swappedCat.class_name type:NULL]];\n    }*/\n\n    iRegInfos[3].classPtr   = iCurrentClass;\n//    iRegInfos[3].catPtr     = iCurrentCat;\n    iRegInfos[3].isValid    = YES;\n    iRegInfos[12].value     = iCurrentFuncPtr;\n    iRegInfos[12].isValid   = YES;\n    iLR                     = (GP64RegisterInfo){0};\n    iCTR                    = (GP64RegisterInfo){0};\n\n    // Try to find out whether this is a class or instance method.\n    Method64Info* thisMethod  = NULL;\n\n    if ([self getObjcMethod:&thisMethod fromAddress:inLine->info.address])\n        iIsInstanceMethod   = thisMethod->inst;\n\n    if (iLocalSelves)\n    {\n        free(iLocalSelves);\n        iLocalSelves    = NULL;\n        iNumLocalSelves = 0;\n    }\n\n    if (iLocalVars)\n    {\n        free(iLocalVars);\n        iLocalVars      = NULL;\n        iNumLocalVars   = 0;\n    }\n\n    iCurrentFuncInfoIndex++;\n\n    if (iCurrentFuncInfoIndex >= iNumFuncInfos)\n        iCurrentFuncInfoIndex   = -1;\n}\n\n//  updateRegisters:\n// ----------------------------------------------------------------------------\n//  Keep our local copy of the GPRs in sync as much as possible with the\n//  values that the exe will use at runtime. Assign classPtr and catPtr fields\n//  in a register's info if its new value points to a class or category.\n// http://developer.apple.com/documentation/DeveloperTools/Conceptual/LowLevelABI/Articles/32bitPowerPC.html\n// http://developer.apple.com/documentation/DeveloperTools/Conceptual/MachOTopics/Articles/dynamic_code.html\n\n- (void)updateRegisters: (Line64*)inLine;\n{\n    if (!inLine)\n    {\n        fprintf(stderr, \"otx: [PPC64Processor updateRegisters]: \"\n            \"tried to update with NULL inLine\\n\");\n        return;\n    }\n\n    UInt64 theNewValue;\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    if (IS_BRANCH_LINK(theCode))\n    {\n        iLR.value = inLine->info.address + 4;\n        iLR.isValid = YES;\n    }\n\n    switch (PO(theCode))\n    {\n        case 0x07:  // mulli        SIMM\n        {\n            if (!iRegInfos[RA(theCode)].isValid)\n            {\n                iRegInfos[RT(theCode)] = (GP64RegisterInfo){0};\n                break;\n            }\n\n            UInt64 theProduct = (SInt32)iRegInfos[RA(theCode)].value * SIMM(theCode);\n\n            iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n            iRegInfos[RT(theCode)].value    = theProduct & 0xffffffff;\n            iRegInfos[RT(theCode)].isValid  = YES;\n\n            break;\n        }\n\n        case 0x08:  // subfic       SIMM\n            if (!iRegInfos[RA(theCode)].isValid)\n            {\n                iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n                break;\n            }\n\n            theNewValue = iRegInfos[RA(theCode)].value - SIMM(theCode);\n\n            iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n            iRegInfos[RT(theCode)].value    = theNewValue;\n            iRegInfos[RT(theCode)].isValid  = YES;\n\n            break;\n\n        case 0x0c:  // addic        SIMM\n        case 0x0d:  // addic.       SIMM\n        case 0x0e:  // addi | li    SIMM\n            if (RA(theCode) == 1    &&  // current reg is stack pointer (r1)\n                SIMM(theCode) >= 0)     // we're accessing local vars, not args\n            {\n                BOOL    found = NO;\n                uint32_t  i;\n\n                // Check for copied self pointer. This happens mostly in \"init\"\n                // methods, as in: \"self = [super init]\"\n                if (iLocalSelves)   // self was copied to a local variable\n                {\n                    // If we're accessing a local var copy of self,\n                    // copy that info back to the reg in question.\n                    for (i = 0; i < iNumLocalSelves; i++)\n                    {\n                        if (iLocalSelves[i].offset != UIMM(theCode))\n                            continue;\n\n                        iRegInfos[RT(theCode)] = iLocalSelves[i].regInfo;\n                        found = YES;\n\n                        break;\n                    }\n                }\n\n                if (found)\n                    break;\n\n                // Check for other local variables.\n                if (iLocalVars)\n                {\n                    for (i = 0; i < iNumLocalVars; i++)\n                    {\n                        if (iLocalVars[i].offset != UIMM(theCode))\n                            continue;\n\n                        iRegInfos[RT(theCode)] = iLocalVars[i].regInfo;\n                        found = YES;\n\n                        break;\n                    }\n                }\n\n                if (found)\n                    break;\n            }\n\n            // We didn't find any local variables, try immediates.\n            if (RA(theCode) == 0)   // li\n            {\n                iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = UIMM(theCode);\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }\n            else                    // addi\n            {\n                // Update rD if we know what rA is.\n                if (!iRegInfos[RA(theCode)].isValid)\n                {\n                    iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n                    break;\n                }\n\n                iRegInfos[RT(theCode)].classPtr = NULL;\n                iRegInfos[RT(theCode)].className = NULL;\n                iRegInfos[RT(theCode)].messageRefSel = NULL;\n//                iRegInfos[RT(theCode)].catPtr   = NULL;\n\n                theNewValue = iRegInfos[RA(theCode)].value + SIMM(theCode);\n\n                UInt8 type;\n                char* ref = [self getPointer:theNewValue type:&type];\n\n                if (type == OCMsgRefType)\n                    iRegInfos[RT(theCode)].messageRefSel = ref;\n                else if (type == OCClassRefType)\n                    iRegInfos[RT(theCode)].className = ref;\n\n                iRegInfos[RT(theCode)].value    = theNewValue;\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }\n\n            break;\n\n        case 0x0f:  // addis | lis\n            iRegInfos[RT(theCode)].classPtr = NULL;\n//            iRegInfos[RT(theCode)].catPtr   = NULL;\n\n            if (RA(theCode) == 0)   // lis\n            {\n                iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = UIMM(theCode) << 16;\n                iRegInfos[RT(theCode)].isValid  = YES;\n                break;\n            }\n\n            // addis\n            if (!iRegInfos[RA(theCode)].isValid)\n            {\n                iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n                break;\n            }\n\n            theNewValue = iRegInfos[RA(theCode)].value +\n                (SIMM(theCode) << 16);\n\n            iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n            iRegInfos[RT(theCode)].value    = theNewValue;\n            iRegInfos[RT(theCode)].isValid  = YES;\n\n            break;\n\n        case 0x10:  // bcl, bcla\n        case 0x13:  // bclrl, bcctrl\n            if (!IS_BRANCH_LINK(theCode))   // fall thru if link\n                break;\n\n        case 0x12:  // b, ba, bl, bla\n        {\n            if (!LK(theCode))   // bl, bla\n                break;\n\n            iRegInfos[3]    = (GP64RegisterInfo){0};\n\n            break;\n        }\n        case 0x15:  // rlwinm\n        {\n            if (!iRegInfos[RT(theCode)].isValid)\n            {\n                iRegInfos[RA(theCode)]  = (GP64RegisterInfo){0};\n                break;\n            }\n\n            UInt64  rotatedRT   =\n                rotl(iRegInfos[RT(theCode)].value, RB(theCode));\n            uint32_t  theMask     = 0x0;\n            UInt8   i;\n\n            for (i = MB(theCode); i <= ME(theCode); i++)\n                theMask |= 1 << (31 - i);\n\n            theNewValue = rotatedRT & theMask;\n\n            iRegInfos[RA(theCode)]          = (GP64RegisterInfo){0};\n            iRegInfos[RA(theCode)].value    = theNewValue;\n            iRegInfos[RA(theCode)].isValid  = YES;\n\n            break;\n        }\n\n        case 0x18:  // ori\n            if (!iRegInfos[RT(theCode)].isValid)\n            {\n                iRegInfos[RA(theCode)]  = (GP64RegisterInfo){0};\n                break;\n            }\n\n            theNewValue =\n                iRegInfos[RT(theCode)].value | (uint32_t)UIMM(theCode);\n\n            iRegInfos[RA(theCode)]          = (GP64RegisterInfo){0};\n            iRegInfos[RA(theCode)].value    = theNewValue;\n            iRegInfos[RA(theCode)].isValid  = YES;\n\n            break;\n\n        case 0x1e:  // rldicl\n        {\n            if (!iRegInfos[RT(theCode)].isValid)\n            {\n                iRegInfos[RA(theCode)]  = (GP64RegisterInfo){0};\n                break;\n            }\n\n            UInt8 shift = SH(theCode);\n            UInt8 maskBegin = MB64(theCode);\n\n            UInt64  rotatedRT = rotl64(iRegInfos[RT(theCode)].value, shift);\n            UInt64  theMask = (UInt64)-1 >> maskBegin;\n\n            theNewValue = rotatedRT & theMask;\n\n            iRegInfos[RA(theCode)]          = (GP64RegisterInfo){0};\n            iRegInfos[RA(theCode)].value    = theNewValue;\n            iRegInfos[RA(theCode)].isValid  = YES;\n\n            break;\n        }\n\n        case 0x1f:  // multiple instructions\n            switch (SO(theCode))\n            {\n                case 21:    // ldx\n/*                    if ((RA(theCode) != 0 && !iRegInfos[RA(theCode)].isValid) ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RT(theCode)] = (GP64RegisterInfo){0};\n                        break;\n                    }\n\n                    theNewValue = (RA(theCode) == 0) ? 0 :\n                        iRegInfos[RA(theCode)].value + iRegInfos[RB(theCode)].value;*/\n\n                    iRegInfos[RT(theCode)] = (GP64RegisterInfo){0};\n\n                    break;\n                case 23:    // lwzx\n                    iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n                    break;\n\n                case 8:     // subfc\n                case 40:    // subf\n                    if (!iRegInfos[RA(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n                        break;\n                    }\n\n                    // 2's complement subtraction\n                    theNewValue =\n                        (iRegInfos[RA(theCode)].value ^= 0xffffffff) +\n                        iRegInfos[RB(theCode)].value + 1;\n\n                    iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n                    iRegInfos[RT(theCode)].value    = theNewValue;\n                    iRegInfos[RT(theCode)].isValid  = YES;\n\n                    break;\n\n                case 266:   // add\n                    if (iRegInfos[RA(theCode)].isValid && iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RT(theCode)].value =\n                            iRegInfos[RA(theCode)].value + iRegInfos[RB(theCode)].value;\n                        iRegInfos[RT(theCode)].isValid = YES;\n                        iRegInfos[RT(theCode)].classPtr = NULL;\n                        iRegInfos[RT(theCode)].className = NULL;\n                        iRegInfos[RT(theCode)].messageRefSel = NULL;\n                    }\n                    else\n                        iRegInfos[RT(theCode)] = (GP64RegisterInfo){0};\n\n                    break;\n\n                case 339:   // mfspr\n                    iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n\n                    if (SPR(theCode) == LR  &&  // from LR\n                        iLR.isValid)\n                    {   // Copy LR into rD.\n                        iRegInfos[RT(theCode)].value    = iLR.value;\n                        iRegInfos[RT(theCode)].isValid  = YES;\n                    }\n\n                    break;\n\n                case 444:   // or | or.\n                    if (!iRegInfos[RT(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RA(theCode)]  = (GP64RegisterInfo){0};\n                        break;\n                    }\n\n                    theNewValue =\n                        (iRegInfos[RT(theCode)].value |\n                         iRegInfos[RB(theCode)].value);\n\n                    iRegInfos[RA(theCode)]          = (GP64RegisterInfo){0};\n                    iRegInfos[RA(theCode)].value    = theNewValue;\n                    iRegInfos[RA(theCode)].isValid  = YES;\n\n                    // If we just copied a register, copy the\n                    // remaining fields.\n                    if (RT(theCode) == RB(theCode))\n                    {\n                        iRegInfos[RA(theCode)].classPtr =\n                            iRegInfos[RB(theCode)].classPtr;\n                        iRegInfos[RA(theCode)].className =\n                            iRegInfos[RB(theCode)].className;\n                        iRegInfos[RA(theCode)].messageRefSel =\n                            iRegInfos[RB(theCode)].messageRefSel;\n//                        iRegInfos[RA(theCode)].catPtr   =\n//                            iRegInfos[RB(theCode)].catPtr;\n                    }\n\n                    break;\n\n                case 467:   // mtspr\n                    if (SPR(theCode) == CTR)    // to CTR\n                    {\n                        if (!iRegInfos[RS(theCode)].isValid)\n                        {\n                            iCTR = (GP64RegisterInfo){0};\n                            break;\n                        }\n\n                        iCTR.value = iRegInfos[RS(theCode)].value;\n                        iCTR.isValid = YES;\n                    }\n\n                    break;\n\n                case 24:    // slw\n                    if (!iRegInfos[RS(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RA(theCode)] = (GP64RegisterInfo){0};\n                        break;\n                    }\n\n                    if (SB(iRegInfos[RB(theCode)].value))\n                    {\n                        theNewValue =\n                            iRegInfos[RS(theCode)].value <<\n                                SV(iRegInfos[RB(theCode)].value);\n                    }\n                    else    // If RB.5 == 0, RA = 0.\n                        theNewValue = 0;\n\n                    iRegInfos[RA(theCode)]          = (GP64RegisterInfo){0};\n                    iRegInfos[RA(theCode)].value    = theNewValue;\n                    iRegInfos[RA(theCode)].isValid  = YES;\n\n                    break;\n\n                case 536:   // srw\n                    if (!iRegInfos[RS(theCode)].isValid ||\n                        !iRegInfos[RB(theCode)].isValid)\n                    {\n                        iRegInfos[RA(theCode)]  = (GP64RegisterInfo){0};\n                        break;\n                    }\n\n                    theNewValue =\n                        iRegInfos[RS(theCode)].value >>\n                            SV(iRegInfos[RB(theCode)].value);\n\n                    iRegInfos[RA(theCode)]          = (GP64RegisterInfo){0};\n                    iRegInfos[RA(theCode)].value    = theNewValue;\n                    iRegInfos[RA(theCode)].isValid  = YES;\n\n                    break;\n\n                default:\n                    // Ideally, we would zero the destination register here. Sadly, because SO values can vary in their formats, we don't know which register is the destination.\n                    break;\n            }\n\n            break;\n\n        case 0x20:  // lwz\n        case 0x22:  // lbz\n            if (RA(theCode) == 0)\n            {\n                iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = SIMM(theCode);\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }\n            else if (iRegInfos[RA(theCode)].isValid)\n            {\n                void *tempPtr = [self getPointer:iRegInfos[RA(theCode)].value + SIMM(theCode) type:NULL];\n\n                if (tempPtr)\n                {\n                    iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n                    iRegInfos[RT(theCode)].value    = *(uint32_t*)tempPtr;\n                    iRegInfos[RT(theCode)].value    =\n                        OSSwapBigToHostInt64(iRegInfos[RT(theCode)].value);\n                    iRegInfos[RT(theCode)].isValid  = YES;\n                }\n                else\n                    iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n            }\n            else if (iLocalVars)\n            {\n                uint32_t  i;\n\n                for (i = 0; i < iNumLocalVars; i++)\n                {\n                    if (iLocalVars[i].offset == SIMM(theCode))\n                    {\n                        iRegInfos[RT(theCode)]  = iLocalVars[i].regInfo;\n                        break;\n                    }\n                }\n            }\n            else\n                iRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n\n            break;\n\n/*      case 0x22:  // lbz\n            mRegInfos[RT(theCode)]  = (GP64RegisterInfo){0};\n\n            if (RA(theCode) == 0)\n            {\n                mRegInfos[RT(theCode)].value    = SIMM(theCode);\n                mRegInfos[RT(theCode)].isValid  = YES;\n            }\n\n            break;*/\n\n        case 0x24:  // stw\n            if (!iRegInfos[RT(theCode)].isValid ||\n                RA(theCode) != 1                ||\n                SIMM(theCode) < 0)\n                break;\n\n            if (iRegInfos[RT(theCode)].classPtr)    // if it's a class\n            {\n                iNumLocalSelves++;\n                iLocalSelves    = realloc(iLocalSelves,\n                    iNumLocalSelves * sizeof(Var64Info));\n                iLocalSelves[iNumLocalSelves - 1]   = (Var64Info)\n                    {iRegInfos[RT(theCode)], UIMM(theCode)};\n            }\n            else\n            {\n                iNumLocalVars++;\n                iLocalVars  = realloc(iLocalVars,\n                    iNumLocalVars * sizeof(Var64Info));\n                iLocalVars[iNumLocalVars - 1]   = (Var64Info)\n                    {iRegInfos[RT(theCode)], UIMM(theCode)};\n            }\n\n            break;\n\n/*      case 0x21:\n        case 0x23:\n        case 0x25:\n        case 0x26:\n        case 0x27:\n        case 0x28:\n        case 0x29:\n        case 0x2a:\n        case 0x2b:\n        case 0x2c:\n        case 0x2d:\n        case 0x2e:\n        case 0x2f:\n            break;*/\n\n        case 0x3a:  // ld\n        {\n            if (RA(theCode) == 0)\n            {\n                iRegInfos[RT(theCode)]          = (GP64RegisterInfo){0};\n                iRegInfos[RT(theCode)].value    = DS(theCode);\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }\n            else if (iRegInfos[RA(theCode)].className != NULL && DS(theCode) == 0)\n            {\n                iRegInfos[RT(theCode)] = iRegInfos[RA(theCode)];\n            }\n            else if (iRegInfos[RA(theCode)].isValid)\n            {\n                iRegInfos[RT(theCode)].classPtr = NULL;\n                iRegInfos[RT(theCode)].className = NULL;\n                iRegInfos[RT(theCode)].messageRefSel = NULL;\n\n                UInt64 newValue = iRegInfos[RA(theCode)].value + DS(theCode);\n\n                iRegInfos[RT(theCode)].value = newValue;\n                iRegInfos[RT(theCode)].isValid = YES;\n\n                UInt8 type;\n\n                char* ref = [self getPointer:newValue type:&type];\n\n                if (type == OCClassRefType || type == OCSuperRefType)\n                    iRegInfos[RT(theCode)].className = ref;\n            }\n            else if (RA(theCode) == 1 && DS(theCode) >= 0)\n            {\n                BOOL    found = NO;\n                uint32_t  i;\n\n                // Check for copied self pointer. This happens mostly in \"init\"\n                // methods, as in: \"self = [super init]\"\n                if (iLocalSelves)   // self was copied to a local variable\n                {\n                    // If we're accessing a local var copy of self,\n                    // copy that info back to the reg in question.\n                    for (i = 0; i < iNumLocalSelves; i++)\n                    {\n                        if (iLocalSelves[i].offset != UIMM(theCode))\n                            continue;\n\n                        iRegInfos[RT(theCode)] = iLocalSelves[i].regInfo;\n                        found = YES;\n                        break;\n                    }\n                }\n\n                if (found)\n                    break;\n\n                // Check for other local variables.\n                if (iLocalVars)\n                {\n                    for (i = 0; i < iNumLocalVars; i++)\n                    {\n                        if (iLocalVars[i].offset != UIMM(theCode))\n                            continue;\n\n                        iRegInfos[RT(theCode)] = iLocalVars[i].regInfo;\n                        found = YES;\n                        break;\n                    }\n                }\n\n                // We don't know what's being loaded, just zero the receiver.\n                if (found == NO)\n                    iRegInfos[RT(theCode)] = (GP64RegisterInfo){0};\n            }\n            else\n                iRegInfos[RT(theCode)] = (GP64RegisterInfo){0};\n\n/*            else if (iRegInfos[RA(theCode)].isValid)\n            {\n                UInt64 address = iRegInfos[RA(theCode)].value + DS(theCode);\n                UInt8 ptrType = PointerType;\n\n                char* sel = [self getPointer:address type:&ptrType];\n\n                if (sel != NULL && ptrType == OCMsgRefType)\n                    iRegInfos[RT(theCode)].messageRefSel = sel;\n                else\n                    iRegInfos[RT(theCode)].messageRefSel = NULL;\n\n                iRegInfos[RT(theCode)].classPtr = NULL;\n                iRegInfos[RT(theCode)].className = NULL;\n                iRegInfos[RT(theCode)].value    = address;\n                iRegInfos[RT(theCode)].isValid  = YES;\n            }*/\n\n            break;\n        }\n\n        case 0x3e:  // std\n            if (!iRegInfos[RT(theCode)].isValid || RA(theCode) != 1 || DS(theCode) < 0)\n                break;\n\n            if (iRegInfos[RT(theCode)].classPtr)    // if it's a class\n            {\n                iNumLocalSelves++;\n                iLocalSelves = realloc(iLocalSelves, iNumLocalSelves * sizeof(Var64Info));\n                iLocalSelves[iNumLocalSelves - 1] = (Var64Info){iRegInfos[RT(theCode)], (uint32_t)DS(theCode)};\n            }\n            else\n            {\n                iNumLocalVars++;\n                iLocalVars = realloc(iLocalVars, iNumLocalVars * sizeof(Var64Info));\n                iLocalVars[iNumLocalVars - 1] = (Var64Info) {iRegInfos[RT(theCode)], (uint32_t)DS(theCode)};\n            }\n\n            break;\n\n        default:\n            break;\n    }\n}\n\n//  restoreRegisters:\n// ----------------------------------------------------------------------------\n\n- (BOOL)restoreRegisters: (Line64*)inLine\n{\n    if (!inLine)\n    {\n        fprintf(stderr, \"otx: [PPC64Processor restoreRegisters]: \"\n            \"tried to restore with NULL inLine\\n\");\n        return NO;\n    }\n\n    BOOL needNewLine = NO;\n\n    if (iCurrentFuncInfoIndex < 0)\n        return NO;\n\n    // Search current Function64Info for blocks that start at this address.\n    Function64Info* funcInfo    =\n        &iFuncInfos[iCurrentFuncInfoIndex];\n\n    if (!funcInfo->blocks)\n        return NO;\n\n    uint32_t  i;\n\n    for (i = 0; i < funcInfo->numBlocks; i++)\n    {\n        if (funcInfo->blocks[i].beginAddress !=\n            inLine->info.address)\n            continue;\n\n        // Update machine state.\n        Machine64State  machState   =\n            funcInfo->blocks[i].state;\n\n        memcpy(iRegInfos, machState.regInfos,\n            sizeof(GP64RegisterInfo) * 32);\n        iLR     = machState.regInfos[LRIndex];\n        iCTR    = machState.regInfos[CTRIndex];\n\n        if (machState.localSelves)\n        {\n            if (iLocalSelves)\n                free(iLocalSelves);\n\n            iNumLocalSelves = machState.numLocalSelves;\n            iLocalSelves    = malloc(\n                sizeof(Var64Info) * iNumLocalSelves);\n            memcpy(iLocalSelves, machState.localSelves,\n                sizeof(Var64Info) * iNumLocalSelves);\n        }\n\n        if (machState.localVars)\n        {\n            if (iLocalVars)\n                free(iLocalVars);\n\n            iNumLocalVars   = machState.numLocalVars;\n            iLocalVars      = malloc(\n                sizeof(Var64Info) * iNumLocalVars);\n            memcpy(iLocalVars, machState.localVars,\n                sizeof(Var64Info) * iNumLocalVars);\n        }\n\n        // Optionally add a blank line before this block.\n        if (iOpts.separateLogicalBlocks && inLine->chars[0] != '\\n' &&\n            !inLine->info.isFunction)\n            needNewLine = YES;\n\n        break;\n    }   // for (i = 0...)\n\n    return needNewLine;\n}\n\n//  lineIsFunction:\n// ----------------------------------------------------------------------------\n\n- (BOOL)lineIsFunction: (Line64*)inLine\n{\n    if (!inLine)\n        return NO;\n\n    UInt64 theAddy = inLine->info.address;\n\n    if (theAddy == iAddrDyldStubBindingHelper   ||\n        theAddy == iAddrDyldFuncLookupPointer)\n        return YES;\n\n    Method64Info* theDummyInfo    = NULL;\n\n    // In Obj-C apps, the majority of funcs will have Obj-C symbols, so check\n    // those first.\n    if ([self findClassMethod:&theDummyInfo byAddress:theAddy])\n        return YES;\n\n//    if ([self findCatMethod:&theDummyInfo byAddress:theAddy])\n//        return YES;\n\n    // If it's not an Obj-C method, maybe there's an nlist.\n    if ([self findSymbolByAddress:theAddy])\n        return YES;\n\n    // If otool gave us a function name...\n    if (inLine->prev && !inLine->prev->info.isCode)\n        return YES;\n\n    BOOL isFunction = NO;\n    uint32_t theCode = *(uint32_t*)inLine->info.code;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n\n    if ((theCode & 0xfc1fffff) == 0x7c0802a6)   // mflr to any reg\n    {   // Allow for late mflr\n        BOOL    foundUB = NO;\n        Line64* thePrevLine = inLine->prev;\n\n        // Walk back to the most recent unconditional branch, looking\n        // for existing symbols.\n        while (!foundUB && thePrevLine)\n        {\n            // Allow for multiple mflr's\n            if (thePrevLine->info.isFunction)\n                return NO;\n\n            theCode = *(uint32_t*)thePrevLine->info.code;\n            theCode = OSSwapBigToHostInt32(theCode);\n\n            if ((theCode & 0xfc0007ff) == 0x7c000008)   // trap\n            {\n                foundUB = YES;\n                continue;\n            }\n\n            UInt8   opcode  = PO(theCode);\n\n            if (opcode == 16 || opcode == 18 || opcode == 19)\n            // bc, bca, bcl, bcla, b, ba, bl, bla, bclr, bclrl and more\n            {\n                if (!IS_BRANCH_CONDITIONAL(theCode) &&\n                    theCode != 0x429f0005 &&    // bcl w/ \"always branch\"\n                    (theCode & 0x48000001) != 0x48000001)    // bl\n                    foundUB = YES;\n            }\n\n            if (!foundUB)\n                thePrevLine = thePrevLine->prev;\n        }\n\n        if (!thePrevLine)\n            return YES;\n\n        thePrevLine = thePrevLine->next;\n\n        // If the code line following the most recent unconditional\n        // branch is not already recognized, flag it now.\n        if (thePrevLine == inLine)\n            isFunction  = YES;\n        else\n        {\n            BOOL foundStart = NO;\n\n            for (; thePrevLine != inLine;\n                thePrevLine = thePrevLine->next)\n            {\n                if (!thePrevLine->info.isCode)\n                    continue;   // not code, keep looking\n                else if (!thePrevLine->info.isFunction)\n                {               // not yet recognized, try it\n                    theCode = *(uint32_t*)thePrevLine->info.code;\n                    theCode = OSSwapBigToHostInt32(theCode);\n\n                    if (theCode == 0x7fe00008   ||  // ignore traps\n                        theCode == 0x60000000   ||  // ignore nops\n                        theCode == 0x00000000)      // ignore .longs\n                        continue;\n                    else\n                    {\n                        thePrevLine->info.isFunction = YES;\n                        foundStart = YES;\n                        break;\n                    }\n                }\n                else    // already recognized, bail\n                {\n                    foundStart = YES;\n                    break;\n                }\n            }\n\n            if (!foundStart)\n                isFunction = YES;\n        }\n    }   // if (theCode == 0x7c0802a6)\n\n    return isFunction;\n}\n\n//  codeIsBlockJump:\n// ----------------------------------------------------------------------------\n\n- (BOOL)codeIsBlockJump: (UInt8*)inCode\n{\n    uint32_t theCode = *(uint32_t*)inCode;\n\n    theCode = OSSwapBigToHostInt32(theCode);\n    return IS_BLOCK_BRANCH(theCode);\n}\n\n//  gatherFuncInfos\n// ----------------------------------------------------------------------------\n\n- (void)gatherFuncInfos\n{\n    Line64* theLine     = iPlainLineListHead;\n    uint32_t  theCode;\n    uint32_t  progCounter = 0;\n\n    // Loop thru lines.\n    while (theLine)\n    {\n        if (!(progCounter % (PROGRESS_FREQ * 5)))\n        {\n            if (gCancel == YES)\n                return;\n\n//            [NSThread sleepForTimeInterval: 0.0];\n        }\n\n        if (!theLine->info.isCode)\n        {\n            theLine = theLine->next;\n            continue;\n        }\n\n        theCode = *(uint32_t*)theLine->info.code;\n        theCode = OSSwapBigToHostInt32(theCode);\n\n        if (theLine->info.isFunction)\n        {\n            iCurrentFuncPtr = theLine->info.address;\n            [self resetRegisters:theLine];\n        }\n        else\n            [self restoreRegisters:theLine];\n\n        [self updateRegisters:theLine];\n\n        // Check if we need to save the machine state.\n        if (IS_BLOCK_BRANCH(theCode) && iCurrentFuncInfoIndex >= 0 &&\n            PO(theCode) != 0x13)    // no new blocks for blr, bctr\n        {\n            UInt64 branchTarget = 0;\n\n            // Retrieve the branch target.\n            if (PO(theCode) == 0x12)    // b\n                branchTarget = theLine->info.address + LI(theCode);\n            else if (PO(theCode) == 0x10)   // bc\n                branchTarget = theLine->info.address + BD(theCode);\n\n            // Retrieve current Function64Info.\n            Function64Info* funcInfo    =\n                &iFuncInfos[iCurrentFuncInfoIndex];\n\n            // 'currentBlock' will point to either an existing block which\n            // we will update, or a newly allocated block.\n            Block64Info*    currentBlock    = NULL;\n            Line64*         endLine         = NULL;\n            BOOL            isEpilog        = NO;\n            uint32_t          i;\n\n            if (funcInfo->blocks)\n            {   // Blocks exist, find 1st one matching this address.\n                // This is an exhaustive search, but the speed hit should\n                // only be an issue with extremely long functions.\n                for (i = 0; i < funcInfo->numBlocks; i++)\n                {\n                    if (funcInfo->blocks[i].beginAddress == branchTarget)\n                    {\n                        currentBlock = &funcInfo->blocks[i];\n                        break;\n                    }\n                }\n\n                if (currentBlock)\n                {   // Determine if the target block is an epilog.\n                    if (currentBlock->endLine == NULL &&\n                        iOpts.returnStatements)\n                    {\n                        // Find the first line of the target block.\n                        Line64      searchKey = {NULL, 0, NULL, NULL, NULL, {branchTarget, {0}, YES, NO}};\n                        Line64*     searchKeyPtr = &searchKey;\n                        Line64**    beginLine = bsearch(&searchKeyPtr, iLineArray, iNumCodeLines, sizeof(Line64*),\n                            (COMPARISON_FUNC_TYPE)Line_Address_Compare);\n\n                        if (beginLine != NULL)\n                        {\n                            // Walk through the block. It's an epilog if it ends\n                            // with 'blr'.\n                            Line64* nextLine    = *beginLine;\n                            uint32_t  tempCode;\n\n                            while (nextLine)\n                            {\n                                tempCode = *(uint32_t*)nextLine->info.code;\n                                tempCode = OSSwapBigToHostInt32(tempCode);\n\n                                if (IS_BLOCK_BRANCH(tempCode))\n                                {\n                                    endLine = nextLine;\n\n                                    if (IS_BLR(tempCode))\n                                        isEpilog = YES;\n\n                                    break;\n                                }\n\n                                nextLine = nextLine->next;\n                            }\n                        }\n\n//                      currentBlock->endLine   = endLine;\n                    }\n                }\n                else\n                {   // No matching blocks found, so allocate a new one.\n                    funcInfo->numBlocks++;\n                    funcInfo->blocks = realloc(funcInfo->blocks,\n                        sizeof(Block64Info) * funcInfo->numBlocks);\n                    currentBlock =\n                        &funcInfo->blocks[funcInfo->numBlocks - 1];\n                    *currentBlock = (Block64Info){0};\n                }\n            }\n            else\n            {   // No existing blocks, allocate one.\n                funcInfo->numBlocks++;\n                funcInfo->blocks    = calloc(1, sizeof(Block64Info));\n                currentBlock        = funcInfo->blocks;\n            }\n\n            // sanity check\n            if (!currentBlock)\n            {\n                fprintf(stderr, \"otx: [PPC64Processor gatherFuncInfos] \"\n                    \"currentBlock is NULL. Flame the dev.\\n\");\n                return;\n            }\n\n            // Create a new Machine64State.\n            GP64RegisterInfo*   savedRegs   = malloc(\n                sizeof(GP64RegisterInfo) * 34);\n\n            memcpy(savedRegs, iRegInfos, sizeof(GP64RegisterInfo) * 32);\n            savedRegs[LRIndex]  = iLR;\n            savedRegs[CTRIndex] = iCTR;\n\n            Var64Info*  savedSelves = NULL;\n\n            if (iLocalSelves)\n            {\n                savedSelves = malloc(\n                    sizeof(Var64Info) * iNumLocalSelves);\n                memcpy(savedSelves, iLocalSelves,\n                    sizeof(Var64Info) * iNumLocalSelves);\n            }\n\n            Var64Info*  savedVars   = NULL;\n\n            if (iLocalVars)\n            {\n                savedVars   = malloc(\n                    sizeof(Var64Info) * iNumLocalVars);\n                memcpy(savedVars, iLocalVars,\n                    sizeof(Var64Info) * iNumLocalVars);\n            }\n\n            Machine64State  machState   =\n                {savedRegs, savedSelves, iNumLocalSelves,\n                    savedVars, iNumLocalVars};\n\n            // Store the new Block64Info.\n            Block64Info blockInfo   =\n                {branchTarget, endLine, isEpilog, machState};\n\n            memcpy(currentBlock, &blockInfo, sizeof(Block64Info));\n        }\n\n        theLine = theLine->next;\n        progCounter++;\n    }\n\n    iCurrentFuncInfoIndex   = -1;\n}\n\n#ifdef OTX_DEBUG\n//  printBlocks:\n// ----------------------------------------------------------------------------\n\n- (void)printBlocks: (uint32_t)inFuncIndex;\n{\n    if (!iFuncInfos)\n        return;\n\n    Function64Info* funcInfo    = &iFuncInfos[inFuncIndex];\n\n    if (!funcInfo || !funcInfo->blocks)\n        return;\n\n    uint32_t  i, j;\n\n    fprintf(stderr, \"\\nfunction at 0x%llx:\\n\\n\", funcInfo->address);\n    fprintf(stderr, \"%d blocks\\n\", funcInfo->numBlocks);\n\n    for (i = 0; i < funcInfo->numBlocks; i++)\n    {\n        fprintf(stderr, \"\\nblock %d at 0x%llx:\\n\\n\", i + 1,\n            funcInfo->blocks[i].beginAddress);\n\n        for (j = 0; j < 32; j++)\n        {\n            if (!funcInfo->blocks[i].state.regInfos[j].isValid)\n                continue;\n\n            fprintf(stderr, \"\\t\\tr%d: 0x%llx\\n\", j,\n                funcInfo->blocks[i].state.regInfos[j].value);\n        }\n    }\n}\n#endif  // OTX_DEBUG\n\n@end\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "choosePlanes.m", "ext": ".m", "path": "IA-Lab-master/GUI/choosePlanes.m", "size": 1713, "source_encoding": "utf_8", "md5": "8b73f8946c63776dba8f7342c186874b", "text": "function zvalues = choosePlanes(IL,initSelection,parenth)\r\n\r\n% choose selected z planes for modifying the import file.\r\n\r\nzidx = find(strcmpi('zslice',IL.ChoiceStruct.Labels),1,'first');\r\norigzvalues = IL.ChoiceStruct.Choices{zidx};\r\n\r\nif nargin<3 || isempty(parenth)\r\n    parenth = gfigure();\r\nend\r\nset(parenth,'closerequestfcn',@figclosefun)\r\n\r\nvbox = uix.VBox('parent',parenth,'padding',20);\r\nuicontrol('parent',vbox,'style','text','String','Select planes:');\r\nlbox = uicontrol('parent',vbox,'style','listbox','Max',100,'min',0,...\r\n    'string',arrayfun(@num2str,origzvalues,'uni',false),'value',[]);\r\n\r\nif nargin>1 && ~isempty(initSelection)\r\n    selinds = any(bsxfun(@eq,origzvalues(:),initSelection(:)'),2);\r\n    set(lbox,'value',origzvalues(selinds))\r\nend\r\n\r\ntemphbox = uix.HBox('parent',vbox);\r\nuicontrol('parent',temphbox,'style','pushbutton','String','Select all',...\r\n    'callback',{@selectallcallback,lbox});\r\nuix.Empty('parent',temphbox);\r\nuicontrol('parent',temphbox,'style','pushbutton','String','Finish',...\r\n    'callback',@(src,evt)close(parenth));\r\nset(temphbox,'widths',[-1,-2,-2])\r\n\r\nset(vbox,'heights',[-1,-5,-1]);\r\n\r\nuiwait(parenth);\r\n\r\n% get the selected wells, then delete the figure\r\nzinds = get(lbox,'value');\r\n\r\nzvalues = origzvalues(zinds);\r\n\r\ndelete(parenth)\r\n\r\nend\r\n\r\nfunction selectallcallback(src,evt,lbox)\r\n\r\nnumvals = numel(get(lbox,'String')); % need to check this works even in the event of one plane\r\n\r\nset(lbox,'value',1:numvals)\r\n\r\nend\r\n\r\nfunction figclosefun(src,evt)\r\n\r\nif isequal(get(src, 'waitstatus'), 'waiting')\r\n    % The GUI is still in UIWAIT, us UIRESUME\r\n    uiresume(src);\r\nelse\r\n    % The GUI is no longer waiting, just close it\r\n    delete(src);\r\nend\r\n\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "chooseWells.m", "ext": ".m", "path": "IA-Lab-master/GUI/chooseWells.m", "size": 1183, "source_encoding": "utf_8", "md5": "db96ecfe82d88de9511f61be6874106f", "text": "function wellrowcol = chooseWells(IL,initSelection,parenth)\r\n\r\n% find out what choices are available\r\nwellidx = find(strcmpi('well',IL.ChoiceStruct.Labels),1,'first');\r\n\r\n[r,c] = wellstr2rowcol(IL.ChoiceStruct.Choices{wellidx});\r\ndata = NaN*zeros(IL.PlateDimensions([2,1]));\r\ndata(amcSub2Ind(size(data),[r,c])) = 1;\r\n            \r\nif nargin<3 || isempty(parenth)\r\n    parenth = gfigure();\r\nend\r\nset(parenth,'closerequestfcn',@figclosefun)\r\n\r\nif nargin<2 || isempty(initSelection)\r\n    initSelection = zeros(size(data));\r\nelseif size(initSelection,2)==2 && size(data,2)~=2\r\n    temp = initSelection;\r\n    initSelection = zeros(size(data));\r\n    initSelection(amcSub2Ind(size(data),temp)) = 1;\r\nend\r\n\r\nthis.WellSelector = PlateShowSelect(data(:,:,[1,1,1]),parenth,initSelection);\r\n\r\nuiwait(parenth);\r\n\r\n% get the selected wells, then delete the figure\r\nwellinds = this.WellSelector.Status==1;\r\nwellrowcol = findn(wellinds);\r\ndelete(parenth)\r\n\r\nend\r\n\r\n\r\nfunction figclosefun(src,evt)\r\n\r\nif isequal(get(src, 'waitstatus'), 'waiting')\r\n    % The GUI is still in UIWAIT, us UIRESUME\r\n    uiresume(src);\r\nelse\r\n    % The GUI is no longer waiting, just close it\r\n    delete(src);\r\nend\r\n\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "getStringGUI.m", "ext": ".m", "path": "IA-Lab-master/GUI/getStringGUI.m", "size": 1184, "source_encoding": "utf_8", "md5": "5fc2dd0f88ca0bf52baa4d95bb53748c", "text": "function str = getStringGUI(title, msg, varargin)\r\n\r\n    % get the user to enter a string\r\n\r\n    if nargin<2 || isempty(msg)\r\n        msg = 'Enter some text:';\r\n    end\r\n    if nargin<1 || isempty(title)\r\n        title = 'Get text';\r\n    end\r\n\r\n    fig = gfigure('Name',title,'closerequestfcn',@closereqfun,varargin{:});\r\n\r\n    vbox = uix.VBox('parent',fig);\r\n\r\n    texth = uicontrol('style','text','String',msg,'parent',vbox);\r\n\r\n    edith = uicontrol('style','edit','parent',vbox);\r\n\r\n    hbox = uix.HBox('parent',vbox);\r\n    uix.Empty('parent',hbox);\r\n\r\n    okbutton = uicontrol('style','pushbutton','String','OK','parent',hbox,...\r\n        'callback',{@OKcallback,fig});\r\n    \r\n    set(hbox,'widths',[-4,-1])\r\n    set(vbox,'heights',[-2,-2,-1],'spacing',10,'padding',10)\r\n    \r\n    uiwait(fig);\r\n\r\n    str = get(edith,'String');\r\n\r\n    delete(fig);\r\n\r\nend\r\n\r\nfunction OKcallback(src,evt,fig)\r\n    uiresume(fig);\r\nend\r\n\r\nfunction closereqfun(src,evt)\r\n    if isequal(get(src, 'waitstatus'), 'waiting')\r\n        % The GUI is still in UIWAIT, us UIRESUME\r\n        uiresume(src);\r\n    else\r\n        % The GUI is no longer waiting, just close it\r\n        delete(src);\r\n    end\r\n\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "chooseFields.m", "ext": ".m", "path": "IA-Lab-master/GUI/chooseFields.m", "size": 1820, "source_encoding": "utf_8", "md5": "8cff1357e138e3aeb7e124e10002eb05", "text": "function fvalues = chooseFields(IL,initSelection,parenth)\r\n\r\n% choose selected z planes for modifying the import file.\r\n% This could be completely generalised, nothing field or z specific here\r\n% apart from the choice of label\r\n\r\nfidx = find(strcmpi('field',IL.ChoiceStruct.Labels),1,'first');\r\norigfvalues = IL.ChoiceStruct.Choices{fidx};\r\n\r\nif nargin<3 || isempty(parenth)\r\n    parenth = gfigure();\r\nend\r\nset(parenth,'closerequestfcn',@figclosefun)\r\n\r\nvbox = uix.VBox('parent',parenth,'padding',20);\r\nuicontrol('parent',vbox,'style','text','String','Select planes:');\r\nlbox = uicontrol('parent',vbox,'style','listbox','Max',100,'min',0,...\r\n    'string',arrayfun(@num2str,origfvalues,'uni',false),'value',[]);\r\n\r\nif nargin>1 && ~isempty(initSelection)\r\n    selinds = any(bsxfun(@eq,origfvalues(:),initSelection(:)'),2);\r\n    set(lbox,'value',origfvalues(selinds))\r\nend\r\n\r\ntemphbox = uix.HBox('parent',vbox);\r\nuicontrol('parent',temphbox,'style','pushbutton','String','Select all',...\r\n    'callback',{@selectallcallback,lbox});\r\nuix.Empty('parent',temphbox);\r\nuicontrol('parent',temphbox,'style','pushbutton','String','Finish',...\r\n    'callback',@(src,evt)close(parenth));\r\nset(temphbox,'widths',[-1,-2,-2])\r\n\r\nset(vbox,'heights',[-1,-5,-1]);\r\n\r\nuiwait(parenth);\r\n\r\n% get the selected wells, then delete the figure\r\nfinds = get(lbox,'value');\r\n\r\nfvalues = origfvalues(finds);\r\n\r\ndelete(parenth)\r\n\r\nend\r\n\r\nfunction selectallcallback(src,evt,lbox)\r\n\r\nnumvals = numel(get(lbox,'String')); % need to check this works even in the event of one plane\r\n\r\nset(lbox,'value',1:numvals)\r\n\r\nend\r\n\r\nfunction figclosefun(src,evt)\r\n\r\nif isequal(get(src, 'waitstatus'), 'waiting')\r\n    % The GUI is still in UIWAIT, us UIRESUME\r\n    uiresume(src);\r\nelse\r\n    % The GUI is no longer waiting, just close it\r\n    delete(src);\r\nend\r\n\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "prepareForExport.m", "ext": ".m", "path": "IA-Lab-master/ExportClasses/prepareForExport.m", "size": 2849, "source_encoding": "utf_8", "md5": "b51365d0b86be053e25c4b1c4fc72590", "text": "function stats2 = prepareForExport(stats,include,exclude)\r\n\r\n% prepare the statistics structure for export\r\n% \r\n% To export to a csv-type file, each of the fields should contain only\r\n% scalars\r\n% So go through the fields, and if there are non-scalars, replace the field\r\n% with multiple fields, one for each element\r\n\r\nfnames = fieldnames(stats);\r\n\r\nif nargin<3\r\n    if nargin==2 && isstruct(include)\r\n        inames = fieldnames(include);\r\n        \r\n        ind = find(strcmpi('exclude',inames),1,'first');\r\n        if ~isempty(ind)\r\n            exclude = include.(inames{ind});\r\n        else\r\n            exclude = false(numel(fnames),1);\r\n        end\r\n        \r\n        ind = find(strcmpi('include',inames),1,'first');\r\n        if ~isempty(ind)\r\n            include = include.(inames{ind});\r\n        else\r\n            include = true(numel(fnames),1);\r\n        end\r\n    else\r\n        exclude = false(numel(fnames),1);\r\n    end\r\nend\r\n\r\nif nargin<2 || isempty(include)\r\n    include = true(numel(fnames),1);\r\nend\r\n\r\nif iscell(exclude)\r\n    exclude = cellfun(@(x)any(strcmpi(x,exclude)),fnames);\r\nend\r\n\r\nif iscell(include)\r\n    % cell array of field names supplied\r\n%     fstr = include;\r\n    include = cellfun(@(x)any(strcmpi(x,include)),fnames);\r\nend\r\n\r\nmaxSize = zeros(numel(fnames),1);\r\n% skip = false(numel(fnames),1);\r\nskip = exclude(:) | ~include(:);\r\n\r\nfor ii = 1:numel(fnames)\r\n    \r\n    fieldsize = arrayfun(@(x)numel(x.(fnames{ii})),stats);\r\n    \r\n    maxSize(ii) = max(fieldsize);\r\n    maxInd = find(fieldsize==maxSize(ii),1,'first');\r\n    if iscell(stats(maxInd).(fnames{ii}))\r\n        % for a cell array, check that it's strings inside\r\n        if ~ischar(stats(maxInd).(fnames{ii}){1})\r\n            skip(ii) = true;\r\n        end\r\n    elseif ischar(stats(maxInd).(fnames{ii}))\r\n        maxSize(ii) = 1; % strings of any length can be accommodated\r\n    end\r\nend\r\n\r\ntrivial = maxSize<=1 & ~skip;\r\nnonTrivial = maxSize>1 & ~skip;\r\n\r\nfor ii = 1:numel(fnames)\r\n    if trivial(ii)\r\n        % this syntax is awful!\r\n        [stats2(1:numel(stats),1).(fnames{ii})] = stats.(fnames{ii});\r\n    elseif nonTrivial(ii)\r\n        fmt = ['%s_%0.', num2str(ceil(log10(maxSize(ii)))), 'd'];\r\n        for jj = 1:maxSize(ii)\r\n            newname = sprintf(fmt,fnames{ii},jj);\r\n            \r\n            tempval = arrayfun(@(x)subarrayfcn(x,fnames{ii},jj),stats,'uni',false);\r\n            \r\n            [stats2(1:numel(stats),1).(newname)] = tempval{:};\r\n        end\r\n    end\r\nend\r\n\r\nif ~exist('stats2','var')\r\n    stats2 = struct;\r\nend\r\n\r\nend\r\n\r\nfunction val = subarrayfcn(x,fname,ind)\r\n\r\n% this function is used by arrayfun above to extract the ind'th element of\r\n% the field fname of the structure x, or NaN if that element doesn't exist\r\n%\r\n% ie\r\n% x.(fname)(ind)\r\n\r\nif numel(x.(fname))<ind\r\n    val = NaN;\r\nelse\r\n    val = x.(fname)(ind);\r\nend\r\n\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "label2outline.m", "ext": ".m", "path": "IA-Lab-master/Utilities/label2outline.m", "size": 3037, "source_encoding": "utf_8", "md5": "5ecb67916987dd5c80e4bee236a291db", "text": "function bordxy = label2outline(L,skip,interpfact)\r\n\r\n% Currently the interpolation means that there is a bit of a discontinuity\r\n% at the join, this needs tidying up by extending the range of the\r\n% interpolation input\r\n\r\n% no real logic behind choosing these factors, just whatever looks nice and\r\n% retains the detail of the label matrix\r\nif nargin<2 || isempty(skip)\r\n    skip = 6;\r\nend\r\nif nargin<3 || isempty(interpfact)\r\n    interpfact = 2*skip;\r\nend\r\n\r\nstats = labelBorder2D(L);\r\n\r\nbordxy = cell(numel(stats),1);\r\n\r\nfor ii = 1:numel(stats)\r\n    borderskip = stats(ii).border([(1:skip:end-1),1],:); % make sure circular\r\n    if ~isnan(interpfact)\r\n        inds = (-1:(size(borderskip,1)+2))';\r\n        actinds = mod(inds-1,size(borderskip,1))+1;\r\n        bordxy{ii} = interp1(inds,borderskip(actinds,:),...\r\n            linspace(1,size(borderskip,1),interpfact*size(borderskip,1))','spline');\r\n    else\r\n        bordxy{ii} = borderskip;\r\n    end\r\nend\r\n\r\nend\r\n\r\nfunction stats = labelBorder2D(L)\r\n\r\n% return a structure which contains border information for the different\r\n% regions in the label matrix L.\r\n% Absolute means that the centroid isn't subtracted off\r\n\r\n% this approach is predicated on having 2D separation.  This means that 3D\r\n% overlapping objects or objects without a boundary of zeros separating\r\n% them will cause problems\r\n\r\nbw = L>0;\r\n\r\n[B,L2] = bwboundaries(bw,8,'noholes');\r\n\r\nif max(L2(:))==max(L(:))\r\n\r\n    cumL = borderLength(B);\r\n\r\n    stats = regionprops(L2,L,'MeanIntensity');\r\n    for ii = 1:numel(stats)\r\n    %     stats(ii).rawborder = B{ii};\r\n        stats(ii).cumL = cumL{ii};\r\n        stats(ii).border = B{ii};\r\n    end\r\n\r\n    % rearrange based on the mean region intensity, which should always match\r\n    % exactly with the region in the original matrix\r\n    ix = [stats.MeanIntensity];\r\n\r\n    % stats = stats(ix);\r\n    stats(ix) = stats;\r\n    \r\n    stats = rmfield(stats,'MeanIntensity');\r\nelse\r\n    % no clear 2D boundary, so have to do one label at a time\r\n    % This also carries the assumption that each region is contiguous.\r\n    \r\n    for ii = 1:max(L(:))\r\n        \r\n        if nnz(L==ii)>0\r\n            B = bwboundaries(L==ii,8,'noholes');\r\n            if ~iscell(B)\r\n                B = {B}; % easier to do this way round..\r\n            end\r\n\r\n            cumL = borderLength(B);\r\n            \r\n            tstats.cumL = cumL{1};\r\n            tstats.border = B{1};\r\n\r\n\r\n            stats(ii) = tstats;\r\n        else\r\n            tstats.cumL = NaN;\r\n            tstats.border = NaN;\r\n            \r\n            stats(ii) = tstats;\r\n        end\r\n    end\r\n    \r\nend\r\n\r\nend\r\n\r\nfunction cumL = borderLength(B)\r\n\r\n% return the cumulative length along the border pixels contained in B\r\n\r\nif ~iscell(B)\r\n    B = {B};\r\nend\r\n\r\ncumL = cell(size(B));\r\nfor ii = 1:numel(B)\r\n    dd = diff(B{ii},1,1);\r\n    \r\n    sL = sqrt(sum(dd.^2,2));% simple addition may be quicker than calling sum\r\n                            % but MIGHT not alway be in 2D..\r\n    \r\n    cumL{ii} = [0;cumsum(sL)];\r\nend\r\n\r\n\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "expandCellIndices.m", "ext": ".m", "path": "IA-Lab-master/Utilities/expandCellIndices.m", "size": 800, "source_encoding": "utf_8", "md5": "88d51af88990f29b0c4028308028871a", "text": "function expandIdx = expandCellIndices(cellidx)\r\n\r\n% expand the cell indices cellidx into a list of permutations\r\n\r\n% use a recursive function?\r\nexpandIdx = recurseexpand(cellidx{1},cellidx(2:end));\r\n\r\n\r\nend\r\n\r\nfunction expandIdx = recurseexpand(currlist,remcellidx)\r\n    % for every element of currlist, combine with each element of the next cellidx\r\n    % and then call the next level down\r\n    try\r\n    if isempty(remcellidx)\r\n        expandIdx = currlist;\r\n    else\r\n        initlen = size(currlist,1);\r\n\r\n        nextlen = numel(remcellidx{1});\r\n\r\n        newidx = [currlist(ceil((1:(initlen*nextlen))/nextlen),:), repmat(remcellidx{1}(:),[initlen,1])];\r\n        \r\n        expandIdx = recurseexpand(newidx,remcellidx(2:end));\r\n        \r\n    end\r\n    catch ME\r\n        rethrow(ME)\r\n    end\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "matchLabels.m", "ext": ".m", "path": "IA-Lab-master/Utilities/matchLabels.m", "size": 865, "source_encoding": "utf_8", "md5": "c436f156ccd8f0e0b607f5edd2c4847b", "text": "function [Lnew,probvals] = matchLabels(L2,Lseed)\r\n\r\n% adjust the values in L2 so that they match those in the seed region.\r\n% Try not to assume that the seed region will be wholely contained within\r\n% L2, but that there is overlap.\r\n%\r\n% also, optionally check for confusion in which region is which, and flag\r\n% this up\r\n\r\nL3 = L2;\r\nL3(Lseed==0) = 0;\r\n\r\nstats = regionprops(L3,'PixelIdxList');\r\n\r\nidxlist = {stats.PixelIdxList}';\r\n\r\n% need to remove zeros and then check that the variance is zero and find\r\n% the mean value\r\n\r\n% is this any quicker than doing in a loop??\r\n[mu,vv] = cellfun(@(x)meanvarfunction(x,Lseed),idxlist);\r\n\r\nprobvals = find(vv>0);\r\n\r\nmu(probvals) = 0;\r\n\r\nmu = [0;mu(:)];\r\n\r\nLnew = mu(L2+1);\r\n\r\nend\r\n\r\nfunction [mu,vv] = meanvarfunction(idx,L2)\r\n\r\nvals = L2(idx);\r\nvals(vals==0) = [];\r\nmu = mean(vals);\r\nvv = mean(vals.^2) - mu.^2;\r\n\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "az_parseXML_ChannelColour_mes.m", "ext": ".m", "path": "IA-Lab-master/Utilities/az_parseXML_ChannelColour_mes.m", "size": 3396, "source_encoding": "utf_8", "md5": "d6908fa960c4fea024cc188e7a861100", "text": "function o_struct = az_parseXML_ChannelColour_mes(i_fileName)\r\n% \r\n% i_fileName = '\\\\UK-Image-01\\HCB\\Technology Eval\\Automated confocal\\Yokogawa CV7000\\CV7000 Demo Data\\Corrected Images\\Bead 96well_20140827_122725\\AssayPlate_BD_#353219\\JP_60x_beads_96w_4col.mes'\r\n\r\n    try\r\n       tree = xmlread(i_fileName);\r\n    catch\r\n       error('Failed to read XML file %s.',i_fileName);\r\n    end\r\n\r\n    % Recurse over child nodes. This could run into problems \r\n    % with very deeply nested trees.\r\n    try\r\n       xmlStruct = parseChildNodes(tree);\r\n    catch\r\n       error('Unable to parse XML file %s.',i_fileName);\r\n    end\r\n \r\n%%____________________________________________________________\r\n%%\r\n    counter = 0;\r\n\r\n    for i = 1:length(xmlStruct.Children)\r\n        \r\n        if strcmp(xmlStruct.Children(i).Name, 'bts:ChannelList') == true\r\n\r\n            for j = 1:length(xmlStruct.Children(i).Children)\r\n                if strcmp(xmlStruct.Children(i).Children(j).Name, 'bts:Channel') == true\r\n        \r\n                    counter = counter + 1;\r\n                    o_struct(counter).Ch = uint8(str2double(xmlStruct.Children(i).Children(j).Attributes.Ch));\r\n                    o_struct(counter).Colour(1) = hex2dec(xmlStruct.Children(i).Children(j).Attributes.Color(4:5))/255;\r\n                    o_struct(counter).Colour(2) = hex2dec(xmlStruct.Children(i).Children(j).Attributes.Color(6:7))/255;\r\n                    o_struct(counter).Colour(3) = hex2dec(xmlStruct.Children(i).Children(j).Attributes.Color(8:9))/255;\r\n                end;\r\n            end;\r\n            break;\r\n        end;\r\n    end;\r\n    \r\nend\r\n\r\n\r\n% ----- Local function PARSECHILDNODES -----\r\nfunction children = parseChildNodes(theNode)\r\n% Recurse over node children.\r\n    children = [];\r\n    if theNode.hasChildNodes\r\n       childNodes = theNode.getChildNodes;\r\n       numChildNodes = childNodes.getLength;\r\n       allocCell = cell(1, numChildNodes);\r\n\r\n       children = struct(             ...\r\n          'Name', allocCell, ...\r\n          'Attributes', allocCell,    ...\r\n          'Children', allocCell);\r\n\r\n        for count = 1:numChildNodes\r\n            theChild = childNodes.item(count-1);\r\n            children(count) = makeStructFromNode(theChild);\r\n        end\r\n    end\r\nend\r\n\r\n% ----- Local function MAKESTRUCTFROMNODE -----\r\nfunction nodeStruct = makeStructFromNode(theNode)\r\n% Create structure of node info.\r\n\r\n    nodeStruct = struct(                        ...\r\n       'Name', char(theNode.getNodeName),       ...\r\n       'Attributes', parseAttributes(theNode),  ...\r\n       'Children', parseChildNodes(theNode));\r\nend\r\n\r\n% ----- Local function PARSEATTRIBUTES -----\r\nfunction attributes = parseAttributes(theNode)\r\n% Create attributes structure.\r\n\r\n    attributes = [];\r\n    if theNode.hasAttributes\r\n       theAttributes = theNode.getAttributes;\r\n       numAttributes = theAttributes.getLength;\r\n\r\n        %-- Delete the leading 'dts:' if any\r\n        token = 'bts:';\r\n\r\n        for count = 1:numAttributes\r\n            attrib = theAttributes.item(count-1);\r\n\r\n            %-- Delete the leading 'dts:' if any\r\n            aName = char(attrib.getName);\r\n\r\n            [~,endIndex] = regexp(aName,token);\r\n\r\n            if ~isempty(endIndex)\r\n                tempName = aName(endIndex + 1 : end);\r\n                attributes.(tempName) = char(attrib.getValue);\r\n            end\r\n        end;\r\n    end\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "parseChildNodes.m", "ext": ".m", "path": "IA-Lab-master/Utilities/parseChildNodes.m", "size": 1742, "source_encoding": "utf_8", "md5": "aa71c09fe13a05a2507661f5a965daa6", "text": "function children = parseChildNodes(theNode)\r\n% Recurse over node children.\r\n    children = [];\r\n    if theNode.hasChildNodes\r\n       childNodes = theNode.getChildNodes;\r\n       numChildNodes = childNodes.getLength;\r\n       allocCell = cell(1, numChildNodes);\r\n\r\n       children = struct(             ...\r\n          'Name', allocCell, ...\r\n          'Attributes', allocCell,    ...\r\n          'Children', allocCell);\r\n\r\n        for count = 1:numChildNodes\r\n            theChild = childNodes.item(count-1);\r\n            children(count) = makeStructFromNode(theChild);\r\n        end\r\n    end\r\nend\r\n\r\n% ----- Local function MAKESTRUCTFROMNODE -----\r\nfunction nodeStruct = makeStructFromNode(theNode)\r\n% Create structure of node info.\r\n\r\n    nodeStruct = struct(                        ...\r\n       'Name', char(theNode.getNodeName),       ...\r\n       'Attributes', parseAttributes(theNode),  ...\r\n       'Children', parseChildNodes(theNode));\r\nend\r\n\r\n% ----- Local function PARSEATTRIBUTES -----\r\nfunction attributes = parseAttributes(theNode)\r\n% Create attributes structure.\r\n\r\n    attributes = [];\r\n    if theNode.hasAttributes\r\n       theAttributes = theNode.getAttributes;\r\n       numAttributes = theAttributes.getLength;\r\n\r\n        %-- Delete the leading 'dts:' if any\r\n        token = 'bts:';\r\n\r\n        for count = 1:numAttributes\r\n            attrib = theAttributes.item(count-1);\r\n\r\n            %-- Delete the leading 'dts:' if any\r\n            aName = char(attrib.getName);\r\n\r\n            [~,endIndex] = regexp(aName,token);\r\n\r\n            if ~isempty(endIndex)\r\n                tempName = aName(endIndex + 1 : end);\r\n                attributes.(tempName) = char(attrib.getValue);\r\n            end\r\n        end;\r\n    end\r\nend            "}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "savejson.m", "ext": ".m", "path": "IA-Lab-master/Utilities/jsonlab/savejson.m", "size": 19584, "source_encoding": "utf_8", "md5": "116395347173d949bf39b82ac3bdd794", "text": "function json=savejson(rootname,obj,varargin)\r\n%\r\n% json=savejson(rootname,obj,filename)\r\n%    or\r\n% json=savejson(rootname,obj,opt)\r\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\r\n%\r\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\r\n% Object Notation) string\r\n%\r\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\r\n% created on 2011/09/09\r\n%\r\n% $Id$\r\n%\r\n% input:\r\n%      rootname: the name of the root-object, when set to '', the root name\r\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\r\n%        the MATLAB variable name will be used as the root name.\r\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array,\r\n%      class instance).\r\n%      filename: a string for the file name to save the output JSON data.\r\n%      opt: a struct for additional options, ignore to use default values.\r\n%        opt can have the following fields (first in [.|.] is the default)\r\n%\r\n%        opt.FileName [''|string]: a file name to save the output JSON data\r\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\r\n%                         of a 1D/2D array;\r\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\r\n%                         precedent indentation; if 0, no indentation\r\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\r\n%                         array in JSON array format; if sets to 1, an\r\n%                         array will be shown as a struct with fields\r\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\r\n%                         sparse arrays, the non-zero elements will be\r\n%                         saved to _ArrayData_ field in triplet-format i.e.\r\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\r\n%                         with a value of 1; for a complex array, the \r\n%                         _ArrayData_ array will include two columns \r\n%                         (4 for sparse) to record the real and imaginary \r\n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \r\n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\r\n%                         will use true/false rather than 1/0.\r\n%        opt.SingletArray [0|1]: if this is set to 1, arrays with a single\r\n%                         numerical element will be shown without a square\r\n%                         bracket, unless it is the root object; if 0, square\r\n%                         brackets are forced for any numerical arrays.\r\n%        opt.SingletCell  [1|0]: if 1, always enclose a cell with \"[]\" \r\n%                         even it has only one element; if 0, brackets\r\n%                         are ignored when a cell has only 1 element.\r\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\r\n%                         will use the name of the passed obj variable as the \r\n%                         root object name; if obj is an expression and \r\n%                         does not have a name, 'root' will be used; if this \r\n%                         is set to 0 and rootname is empty, the root level \r\n%                         will be merged down to the lower level.\r\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\r\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\r\n%                         and $1 represents the sign. For those who want to use\r\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\r\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\r\n%                         to represent NaN\r\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\r\n%                         for example, if opt.JSONP='foo', the JSON data is\r\n%                         wrapped inside a function call as 'foo(...);'\r\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \r\n%                         back to the string form\r\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\r\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\r\n%\r\n%        opt can be replaced by a list of ('param',value) pairs. The param \r\n%        string is equivallent to a field in opt and is case sensitive.\r\n% output:\r\n%      json: a string in the JSON format (see http://json.org)\r\n%\r\n% examples:\r\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \r\n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\r\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\r\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\r\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\r\n%               'SpecialData',[nan, inf, -inf]);\r\n%      savejson('jmesh',jsonmesh)\r\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\r\n%\r\n% license:\r\n%     BSD or GPL version 3, see LICENSE_{BSD,GPLv3}.txt files for details\r\n%\r\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\r\n%\r\n\r\nif(nargin==1)\r\n   varname=inputname(1);\r\n   obj=rootname;\r\n   if(isempty(varname)) \r\n      varname='root';\r\n   end\r\n   rootname=varname;\r\nelse\r\n   varname=inputname(2);\r\nend\r\nif(length(varargin)==1 && ischar(varargin{1}))\r\n   opt=struct('filename',varargin{1});\r\nelse\r\n   opt=varargin2struct(varargin{:});\r\nend\r\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\r\nif(isfield(opt,'norowbracket'))\r\n    warning('Option ''NoRowBracket'' is depreciated, please use ''SingletArray'' and set its value to not(NoRowBracket)');\r\n    if(~isfield(opt,'singletarray'))\r\n        opt.singletarray=not(opt.norowbracket);\r\n    end\r\nend\r\nrootisarray=0;\r\nrootlevel=1;\r\nforceroot=jsonopt('ForceRootName',0,opt);\r\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || ...\r\n        iscell(obj) || isobject(obj)) && isempty(rootname) && forceroot==0)\r\n    rootisarray=1;\r\n    rootlevel=0;\r\nelse\r\n    if(isempty(rootname))\r\n        rootname=varname;\r\n    end\r\nend\r\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\r\n    rootname='root';\r\nend\r\n\r\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\r\nif(jsonopt('Compact',0,opt)==1)\r\n    whitespaces=struct('tab','','newline','','sep',',');\r\nend\r\nif(~isfield(opt,'whitespaces_'))\r\n    opt.whitespaces_=whitespaces;\r\nend\r\n\r\nnl=whitespaces.newline;\r\n\r\njson=obj2json(rootname,obj,rootlevel,opt);\r\nif(rootisarray)\r\n    json=sprintf('%s%s',json,nl);\r\nelse\r\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\r\nend\r\n\r\njsonp=jsonopt('JSONP','',opt);\r\nif(~isempty(jsonp))\r\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\r\nend\r\n\r\n% save to a file if FileName is set, suggested by Patrick Rapin\r\nfilename=jsonopt('FileName','',opt);\r\nif(~isempty(filename))\r\n    if(jsonopt('SaveBinary',0,opt)==1)\r\n\t    fid = fopen(filename, 'wb');\r\n\t    fwrite(fid,json);\r\n    else\r\n\t    fid = fopen(filename, 'wt');\r\n\t    fwrite(fid,json,'char');\r\n    end\r\n    fclose(fid);\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=obj2json(name,item,level,varargin)\r\n\r\nif(iscell(item))\r\n    txt=cell2json(name,item,level,varargin{:});\r\nelseif(isstruct(item))\r\n    txt=struct2json(name,item,level,varargin{:});\r\nelseif(ischar(item))\r\n    txt=str2json(name,item,level,varargin{:});\r\nelseif(isobject(item)) \r\n    txt=matlabobject2json(name,item,level,varargin{:});\r\nelse\r\n    txt=mat2json(name,item,level,varargin{:});\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=cell2json(name,item,level,varargin)\r\ntxt={};\r\nif(~iscell(item))\r\n        error('input is not a cell');\r\nend\r\n\r\ndim=size(item);\r\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\r\n    item=reshape(item,dim(1),numel(item)/dim(1));\r\n    dim=size(item);\r\nend\r\nlen=numel(item);\r\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\r\npadding0=repmat(ws.tab,1,level);\r\npadding2=repmat(ws.tab,1,level+1);\r\nnl=ws.newline;\r\nbracketlevel=~jsonopt('singletcell',1,varargin{:});\r\nif(len>bracketlevel)\r\n    if(~isempty(name))\r\n        txt={padding0, '\"', checkname(name,varargin{:}),'\": [', nl}; name=''; \r\n    else\r\n        txt={padding0, '[', nl};\r\n    end\r\nelseif(len==0)\r\n    if(~isempty(name))\r\n        txt={padding0, '\"' checkname(name,varargin{:}) '\": []'}; name=''; \r\n    else\r\n        txt={padding0, '[]'};\r\n    end\r\nend\r\nfor i=1:dim(1)\r\n    if(dim(1)>1)\r\n        txt(end+1:end+3)={padding2,'[',nl};\r\n    end\r\n    for j=1:dim(2)\r\n       txt{end+1}=obj2json(name,item{i,j},level+(dim(1)>1)+(len>bracketlevel),varargin{:});\r\n       if(j<dim(2))\r\n           txt(end+1:end+2)={',' nl};\r\n       end\r\n    end\r\n    if(dim(1)>1)\r\n        txt(end+1:end+3)={nl,padding2,']'};\r\n    end\r\n    if(i<dim(1))\r\n        txt(end+1:end+2)={',' nl};\r\n    end\r\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\r\nend\r\nif(len>bracketlevel)\r\n    txt(end+1:end+3)={nl,padding0,']'};\r\nend\r\ntxt = sprintf('%s',txt{:});\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=struct2json(name,item,level,varargin)\r\ntxt={};\r\nif(~isstruct(item))\r\n\terror('input is not a struct');\r\nend\r\ndim=size(item);\r\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\r\n    item=reshape(item,dim(1),numel(item)/dim(1));\r\n    dim=size(item);\r\nend\r\nlen=numel(item);\r\nforcearray= (len>1 || (jsonopt('SingletArray',0,varargin{:})==1 && level>0));\r\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\r\nws=jsonopt('whitespaces_',ws,varargin{:});\r\npadding0=repmat(ws.tab,1,level);\r\npadding2=repmat(ws.tab,1,level+1);\r\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+forcearray);\r\nnl=ws.newline;\r\n\r\nif(isempty(item)) \r\n    if(~isempty(name)) \r\n        txt={padding0, '\"', checkname(name,varargin{:}),'\": []'};\r\n    else\r\n        txt={padding0, '[]'};\r\n    end\r\n    txt = sprintf('%s',txt{:});\r\n    return;\r\nend\r\nif(~isempty(name)) \r\n    if(forcearray)\r\n        txt={padding0, '\"', checkname(name,varargin{:}),'\": [', nl};\r\n    end\r\nelse\r\n    if(forcearray)\r\n        txt={padding0, '[', nl};\r\n    end\r\nend\r\nfor j=1:dim(2)\r\n  if(dim(1)>1)\r\n      txt(end+1:end+3)={padding2,'[',nl};\r\n  end\r\n  for i=1:dim(1)\r\n    names = fieldnames(item(i,j));\r\n    if(~isempty(name) && len==1 && ~forcearray)\r\n        txt(end+1:end+5)={padding1, '\"', checkname(name,varargin{:}),'\": {', nl};\r\n    else\r\n        txt(end+1:end+3)={padding1, '{', nl};\r\n    end\r\n    if(~isempty(names))\r\n      for e=1:length(names)\r\n\t    txt{end+1}=obj2json(names{e},item(i,j).(names{e}),...\r\n             level+(dim(1)>1)+1+forcearray,varargin{:});\r\n        if(e<length(names))\r\n            txt{end+1}=',';\r\n        end\r\n        txt{end+1}=nl;\r\n      end\r\n    end\r\n    txt(end+1:end+2)={padding1,'}'};\r\n    if(i<dim(1))\r\n        txt(end+1:end+2)={',' nl};\r\n    end\r\n  end\r\n  if(dim(1)>1)\r\n      txt(end+1:end+3)={nl,padding2,']'};\r\n  end\r\n  if(j<dim(2))\r\n      txt(end+1:end+2)={',' nl};\r\n  end\r\nend\r\nif(forcearray)\r\n    txt(end+1:end+3)={nl,padding0,']'};\r\nend\r\ntxt = sprintf('%s',txt{:});\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=str2json(name,item,level,varargin)\r\ntxt={};\r\nif(~ischar(item))\r\n        error('input is not a string');\r\nend\r\nitem=reshape(item, max(size(item),[1 0]));\r\nlen=size(item,1);\r\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\r\nws=jsonopt('whitespaces_',ws,varargin{:});\r\npadding1=repmat(ws.tab,1,level);\r\npadding0=repmat(ws.tab,1,level+1);\r\nnl=ws.newline;\r\nsep=ws.sep;\r\n\r\nif(~isempty(name)) \r\n    if(len>1)\r\n        txt={padding1, '\"', checkname(name,varargin{:}),'\": [', nl};\r\n    end\r\nelse\r\n    if(len>1)\r\n        txt={padding1, '[', nl};\r\n    end\r\nend\r\nfor e=1:len\r\n    val=escapejsonstring(item(e,:));\r\n    if(len==1)\r\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\r\n        if(isempty(name))\r\n            obj=['\"',val,'\"'];\r\n        end\r\n        txt(end+1:end+2)={padding1, obj};\r\n    else\r\n        txt(end+1:end+4)={padding0,'\"',val,'\"'};\r\n    end\r\n    if(e==len)\r\n        sep='';\r\n    end\r\n    txt{end+1}=sep;\r\nend\r\nif(len>1)\r\n    txt(end+1:end+3)={nl,padding1,']'};\r\nend\r\ntxt = sprintf('%s',txt{:});\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=mat2json(name,item,level,varargin)\r\nif(~isnumeric(item) && ~islogical(item))\r\n        error('input is not an array');\r\nend\r\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\r\nws=jsonopt('whitespaces_',ws,varargin{:});\r\npadding1=repmat(ws.tab,1,level);\r\npadding0=repmat(ws.tab,1,level+1);\r\nnl=ws.newline;\r\nsep=ws.sep;\r\n\r\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\r\n   (isempty(item) && any(size(item))) ||jsonopt('ArrayToStruct',0,varargin{:}))\r\n    if(isempty(name))\r\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\r\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\r\n    else\r\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\r\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\r\n    end\r\nelse\r\n    if(numel(item)==1 && jsonopt('SingletArray',0,varargin{:})==0 && level>0)\r\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\r\n    else\r\n        numtxt=matdata2json(item,level+1,varargin{:});\r\n    end\r\n    if(isempty(name))\r\n    \ttxt=sprintf('%s%s',padding1,numtxt);\r\n    else\r\n        if(numel(item)==1 && jsonopt('SingletArray',0,varargin{:})==0)\r\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\r\n        else\r\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\r\n        end\r\n    end\r\n    return;\r\nend\r\ndataformat='%s%s%s%s%s';\r\n\r\nif(issparse(item))\r\n    [ix,iy]=find(item);\r\n    data=full(item(find(item)));\r\n    if(~isreal(item))\r\n       data=[real(data(:)),imag(data(:))];\r\n       if(size(item,1)==1)\r\n           % Kludge to have data's 'transposedness' match item's.\r\n           % (Necessary for complex row vector handling below.)\r\n           data=data';\r\n       end\r\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\r\n    end\r\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\r\n    if(size(item,1)==1)\r\n        % Row vector, store only column indices.\r\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\r\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\r\n    elseif(size(item,2)==1)\r\n        % Column vector, store only row indices.\r\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\r\n           matdata2json([ix,data],level+2,varargin{:}), nl);\r\n    else\r\n        % General case, store row and column indices.\r\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\r\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\r\n    end\r\nelse\r\n    if(isreal(item))\r\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\r\n            matdata2json(item(:)',level+2,varargin{:}), nl);\r\n    else\r\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\r\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\r\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\r\n    end\r\nend\r\ntxt=sprintf('%s%s%s',txt,padding1,'}');\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=matlabobject2json(name,item,level,varargin)\r\nif numel(item) == 0 %empty object\r\n    st = struct();\r\nelse\r\n    % \"st = struct(item);\" would produce an inmutable warning, because it\r\n    % make the protected and private properties visible. Instead we get the\r\n    % visible properties\r\n    propertynames = properties(item);\r\n    for p = 1:numel(propertynames)\r\n        for o = numel(item):-1:1 % aray of objects\r\n            st(o).(propertynames{p}) = item(o).(propertynames{p});\r\n        end\r\n    end\r\nend\r\ntxt=struct2json(name,st,level,varargin{:});\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=matdata2json(mat,level,varargin)\r\n\r\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\r\nws=jsonopt('whitespaces_',ws,varargin{:});\r\ntab=ws.tab;\r\nnl=ws.newline;\r\n\r\nif(size(mat,1)==1)\r\n    pre='';\r\n    post='';\r\n    level=level-1;\r\nelse\r\n    pre=sprintf('[%s',nl);\r\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\r\nend\r\n\r\nif(isempty(mat))\r\n    txt='null';\r\n    return;\r\nend\r\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\r\n%if(numel(mat)>1)\r\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\r\n%else\r\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\r\n%end\r\n\r\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\r\n    formatstr=[repmat(tab,1,level) formatstr];\r\nend\r\n\r\ntxt=sprintf(formatstr,mat');\r\ntxt(end-length(nl):end)=[];\r\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\r\n   txt=regexprep(txt,'1','true');\r\n   txt=regexprep(txt,'0','false');\r\nend\r\n%txt=regexprep(mat2str(mat),'\\s+',',');\r\n%txt=regexprep(txt,';',sprintf('],\\n['));\r\n% if(nargin>=2 && size(mat,1)>1)\r\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\r\n% end\r\ntxt=[pre txt post];\r\nif(any(isinf(mat(:))))\r\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\r\nend\r\nif(any(isnan(mat(:))))\r\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction newname=checkname(name,varargin)\r\nisunpack=jsonopt('UnpackHex',1,varargin{:});\r\nnewname=name;\r\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\r\n    return\r\nend\r\nif(isunpack)\r\n    isoct=jsonopt('IsOctave',0,varargin{:});\r\n    if(~isoct)\r\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\r\n    else\r\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\r\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\r\n        if(isempty(pos))\r\n            return;\r\n        end\r\n        str0=name;\r\n        pos0=[0 pend(:)' length(name)];\r\n        newname='';\r\n        for i=1:length(pos)\r\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\r\n        end\r\n        if(pos(end)~=length(name))\r\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\r\n        end\r\n    end\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction newstr=escapejsonstring(str)\r\nnewstr=str;\r\nisoct=exist('OCTAVE_VERSION','builtin');\r\nif(isoct)\r\n   vv=sscanf(OCTAVE_VERSION,'%f');\r\n   if(vv(1)>=3.8)\r\n       isoct=0;\r\n   end\r\nend\r\nif(isoct)\r\n  escapechars={'\\\\','\\\"','\\/','\\a','\\f','\\n','\\r','\\t','\\v'};\r\n  for i=1:length(escapechars);\r\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\r\n  end\r\n  newstr=regexprep(newstr,'\\\\\\\\(u[0-9a-fA-F]{4}[^0-9a-fA-F]*)','\\$1');\r\nelse\r\n  escapechars={'\\\\','\\\"','\\/','\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\r\n  for i=1:length(escapechars);\r\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\r\n  end\r\n  newstr=regexprep(newstr,'\\\\\\\\(u[0-9a-fA-F]{4}[^0-9a-fA-F]*)','\\\\$1');\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "loadjson.m", "ext": ".m", "path": "IA-Lab-master/Utilities/jsonlab/loadjson.m", "size": 16843, "source_encoding": "ibm852", "md5": "76e78ae4dcbb1c593583aa12596c6608", "text": "function data = loadjson(fname,varargin)\r\n%\r\n% data=loadjson(fname,opt)\r\n%    or\r\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\r\n%\r\n% parse a JSON (JavaScript Object Notation) file or string\r\n%\r\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\r\n% created on 2011/09/09, including previous works from \r\n%\r\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\r\n%            created on 2009/11/02\r\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\r\n%            created on  2009/03/22\r\n%         Joel Feenstra:\r\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\r\n%            created on 2008/07/03\r\n%\r\n% $Id$\r\n%\r\n% input:\r\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\r\n%             will be interpreted as a JSON string\r\n%      opt: a struct to store parsing options, opt can be replaced by \r\n%           a list of ('param',value) pairs - the param string is equivallent\r\n%           to a field in opt. opt can have the following \r\n%           fields (first in [.|.] is the default)\r\n%\r\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\r\n%                         for each element of the JSON data, and group \r\n%                         arrays based on the cell2mat rules.\r\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\r\n%                         speed-optimized array parser when loading an \r\n%                         array object. The fast array parser may \r\n%                         collapse block arrays into a single large\r\n%                         array similar to rules defined in cell2mat; 0 to \r\n%                         use a legacy parser; if set to a larger-than-1\r\n%                         value, this option will specify the minimum\r\n%                         dimension to enable the fast array parser. For\r\n%                         example, if the input is a 3D array, setting\r\n%                         FastArrayParser to 1 will return a 3D array;\r\n%                         setting to 2 will return a cell array of 2D\r\n%                         arrays; setting to 3 will return to a 2D cell\r\n%                         array of 1D vectors; setting to 4 will return a\r\n%                         3D cell array.\r\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\r\n%\r\n% output:\r\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\r\n%           and [...] are converted to arrays\r\n%\r\n% examples:\r\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\r\n%      dat=loadjson(['examples' filesep 'example1.json'])\r\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\r\n%\r\n% license:\r\n%     BSD or GPL version 3, see LICENSE_{BSD,GPLv3}.txt files for details \r\n%\r\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\r\n%\r\n\r\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\r\n\r\nif(regexp(fname,'^\\s*(?:\\[.*\\])|(?:\\{.*\\})\\s*$','once'))\r\n   string=fname;\r\nelseif(exist(fname,'file'))\r\n   try\r\n       string = fileread(fname);\r\n   catch\r\n       try\r\n           string = urlread(['file://',fname]);\r\n       catch\r\n           string = urlread(['file://',fullfile(pwd,fname)]);\r\n       end\r\n   end\r\nelse\r\n   error('input file does not exist');\r\nend\r\n\r\npos = 1; len = length(string); inStr = string;\r\nisoct=exist('OCTAVE_VERSION','builtin');\r\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\r\njstr=regexprep(inStr,'\\\\\\\\','  ');\r\nescquote=regexp(jstr,'\\\\\"');\r\narraytoken=sort([arraytoken escquote]);\r\n\r\n% String delimiters and escape chars identified to improve speed:\r\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\r\nindex_esc = 1; len_esc = length(esc);\r\n\r\nopt=varargin2struct(varargin{:});\r\n\r\nif(jsonopt('ShowProgress',0,opt)==1)\r\n    opt.progressbar_=waitbar(0,'loading ...');\r\nend\r\njsoncount=1;\r\nwhile pos <= len\r\n    switch(next_char)\r\n        case '{'\r\n            data{jsoncount} = parse_object(opt);\r\n        case '['\r\n            data{jsoncount} = parse_array(opt);\r\n        otherwise\r\n            error_pos('Outer level structure must be an object or an array');\r\n    end\r\n    jsoncount=jsoncount+1;\r\nend % while\r\n\r\njsoncount=length(data);\r\nif(jsoncount==1 && iscell(data))\r\n    data=data{1};\r\nend\r\n\r\nif(isfield(opt,'progressbar_'))\r\n    close(opt.progressbar_);\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction object = parse_object(varargin)\r\n    parse_char('{');\r\n    object = [];\r\n    if next_char ~= '}'\r\n        while 1\r\n            str = parseStr(varargin{:});\r\n            if isempty(str)\r\n                error_pos('Name of value at position %d cannot be empty');\r\n            end\r\n            parse_char(':');\r\n            val = parse_value(varargin{:});\r\n            object.(valid_field(str))=val;\r\n            if next_char == '}'\r\n                break;\r\n            end\r\n            parse_char(',');\r\n        end\r\n    end\r\n    parse_char('}');\r\n    if(isstruct(object))\r\n        object=struct2jdata(object);\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction object = parse_array(varargin) % JSON array is written in row-major order\r\nglobal pos inStr isoct\r\n    parse_char('[');\r\n    object = cell(0, 1);\r\n    dim2=[];\r\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\r\n    pbar=-1;\r\n    if(isfield(varargin{1},'progressbar_'))\r\n        pbar=varargin{1}.progressbar_;\r\n    end\r\n\r\n    if next_char ~= ']'\r\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\r\n            [endpos, e1l, e1r]=matching_bracket(inStr,pos);\r\n            arraystr=['[' inStr(pos:endpos)];\r\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\r\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\r\n            arraystr(arraystr==sprintf('\\n'))=[];\r\n            arraystr(arraystr==sprintf('\\r'))=[];\r\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\r\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\r\n        \tastr=inStr((e1l+1):(e1r-1));\r\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\r\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\r\n        \tastr(astr==sprintf('\\n'))=[];\r\n        \tastr(astr==sprintf('\\r'))=[];\r\n        \tastr(astr==' ')='';\r\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\r\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\r\n        \tend\r\n            else % array is 1D\r\n        \tastr=arraystr(2:end-1);\r\n        \tastr(astr==' ')='';\r\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\r\n        \tif(nextidx>=length(astr)-1)\r\n                    object=obj;\r\n                    pos=endpos;\r\n                    parse_char(']');\r\n                    return;\r\n        \tend\r\n            end\r\n\r\n            try\r\n              if(~isempty(dim2))\r\n        \tastr=arraystr;\r\n        \tastr(astr=='[')='';\r\n        \tastr(astr==']')='';\r\n        \tastr(astr==' ')='';\r\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\r\n        \tif(nextidx>=length(astr)-1)\r\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\r\n                    pos=endpos;\r\n                    parse_char(']');\r\n                    if(pbar>0)\r\n                        waitbar(pos/length(inStr),pbar,'loading ...');\r\n                    end\r\n                    return;\r\n        \tend\r\n              end\r\n              arraystr=regexprep(arraystr,'\\]\\s*,','];');\r\n            catch\r\n            end\r\n\telse\r\n            arraystr='[';\r\n\tend\r\n        try\r\n           arraystr=regexprep(arraystr,'^\\s*\\[','{','once');\r\n           arraystr=regexprep(arraystr,'\\]\\s*$','}','once');\r\n           if(isoct && regexp(arraystr,'\"','once'))\r\n                error('Octave eval can produce empty cells for JSON-like input');\r\n           end\r\n           object=eval(arraystr);\r\n           pos=endpos;\r\n        catch\r\n         while 1\r\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\r\n            val = parse_value(newopt);\r\n            object{end+1} = val;\r\n            if next_char == ']'\r\n                break;\r\n            end\r\n            parse_char(',');\r\n         end\r\n        end\r\n    end\r\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\r\n      try\r\n        oldobj=object;\r\n        object=cell2mat(object')';\r\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\r\n            object=oldobj;\r\n        elseif(size(object,1)>1 && ismatrix(object))\r\n            object=object';\r\n        end\r\n      catch\r\n      end\r\n    end\r\n    parse_char(']');\r\n    \r\n    if(pbar>0)\r\n        waitbar(pos/length(inStr),pbar,'loading ...');\r\n    end\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction parse_char(c)\r\n    global pos inStr len\r\n    pos=skip_whitespace(pos,inStr,len);\r\n    if pos > len || inStr(pos) ~= c\r\n        error_pos(sprintf('Expected %c at position %%d', c));\r\n    else\r\n        pos = pos + 1;\r\n        pos=skip_whitespace(pos,inStr,len);\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction c = next_char\r\n    global pos inStr len\r\n    pos=skip_whitespace(pos,inStr,len);\r\n    if pos > len\r\n        c = [];\r\n    else\r\n        c = inStr(pos);\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction newpos=skip_whitespace(pos,inStr,len)\r\n    newpos=pos;\r\n    while newpos <= len && isspace(inStr(newpos))\r\n        newpos = newpos + 1;\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction str = parseStr(varargin)\r\n    global pos inStr len  esc index_esc len_esc\r\n % len, ns = length(inStr), keyboard\r\n    if inStr(pos) ~= '\"'\r\n        error_pos('String starting with \" expected at position %d');\r\n    else\r\n        pos = pos + 1;\r\n    end\r\n    str = '';\r\n    while pos <= len\r\n        while index_esc <= len_esc && esc(index_esc) < pos\r\n            index_esc = index_esc + 1;\r\n        end\r\n        if index_esc > len_esc\r\n            str = [str inStr(pos:len)];\r\n            pos = len + 1;\r\n            break;\r\n        else\r\n            str = [str inStr(pos:esc(index_esc)-1)];\r\n            pos = esc(index_esc);\r\n        end\r\n        nstr = length(str);\r\n        switch inStr(pos)\r\n            case '\"'\r\n                pos = pos + 1;\r\n                if(~isempty(str))\r\n                    if(strcmp(str,'_Inf_'))\r\n                        str=Inf;\r\n                    elseif(strcmp(str,'-_Inf_'))\r\n                        str=-Inf;\r\n                    elseif(strcmp(str,'_NaN_'))\r\n                        str=NaN;\r\n                    end\r\n                end\r\n                return;\r\n            case '\\'\r\n                if pos+1 > len\r\n                    error_pos('End of file reached right after escape character');\r\n                end\r\n                pos = pos + 1;\r\n                switch inStr(pos)\r\n                    case {'\"' '\\' '/'}\r\n                        str(nstr+1) = inStr(pos);\r\n                        pos = pos + 1;\r\n                    case {'b' 'f' 'n' 'r' 't'}\r\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\r\n                        pos = pos + 1;\r\n                    case 'u'\r\n                        if pos+4 > len\r\n                            error_pos('End of file reached in escaped unicode character');\r\n                        end\r\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\r\n                        pos = pos + 5;\r\n                end\r\n            otherwise % should never happen\r\n                str(nstr+1) = inStr(pos);\r\n                keyboard;\r\n                pos = pos + 1;\r\n        end\r\n    end\r\n    error_pos('End of file while expecting end of inStr');\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction num = parse_number(varargin)\r\n    global pos inStr isoct\r\n    currstr=inStr(pos:min(pos+30,end));\r\n    if(isoct~=0)\r\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\r\n        [num] = sscanf(currstr, '%f', 1);\r\n        delta=numstr+1;\r\n    else\r\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\r\n        if ~isempty(err)\r\n            error_pos('Error reading number at position %d');\r\n        end\r\n    end\r\n    pos = pos + delta-1;\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction val = parse_value(varargin)\r\n    global pos inStr len\r\n    \r\n    if(isfield(varargin{1},'progressbar_'))\r\n        waitbar(pos/len,varargin{1}.progressbar_,'loading ...');\r\n    end\r\n    \r\n    switch(inStr(pos))\r\n        case '\"'\r\n            val = parseStr(varargin{:});\r\n            return;\r\n        case '['\r\n            val = parse_array(varargin{:});\r\n            return;\r\n        case '{'\r\n            val = parse_object(varargin{:});\r\n            return;\r\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\r\n            val = parse_number(varargin{:});\r\n            return;\r\n        case 't'\r\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\r\n                val = true;\r\n                pos = pos + 4;\r\n                return;\r\n            end\r\n        case 'f'\r\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\r\n                val = false;\r\n                pos = pos + 5;\r\n                return;\r\n            end\r\n        case 'n'\r\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\r\n                val = [];\r\n                pos = pos + 4;\r\n                return;\r\n            end\r\n    end\r\n    error_pos('Value expected at position %d');\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction error_pos(msg)\r\n    global pos inStr len\r\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\r\n    if poShow(3) == poShow(2)\r\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\r\n    end\r\n    msg = [sprintf(msg, pos) ': ' ...\r\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\r\n    error( ['JSONparser:invalidFormat: ' msg] );\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction str = valid_field(str)\r\nglobal isoct\r\n% From MATLAB doc: field names must begin with a letter, which may be\r\n% followed by any combination of letters, digits, and underscores.\r\n% Invalid characters will be converted to underscores, and the prefix\r\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\r\n    pos=regexp(str,'^[^A-Za-z]','once');\r\n    if(~isempty(pos))\r\n        if(~isoct)\r\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\r\n        else\r\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\r\n        end\r\n    end\r\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' )))\r\n        return;\r\n    end\r\n    if(~isoct)\r\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\r\n    else\r\n        pos=regexp(str,'[^0-9A-Za-z_]');\r\n        if(isempty(pos))\r\n            return;\r\n        end\r\n        str0=str;\r\n        pos0=[0 pos(:)' length(str)];\r\n        str='';\r\n        for i=1:length(pos)\r\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\r\n        end\r\n        if(pos(end)~=length(str))\r\n            str=[str str0(pos0(end-1)+1:pos0(end))];\r\n        end\r\n    end\r\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction endpos = matching_quote(str,pos)\r\nlen=length(str);\r\nwhile(pos<len)\r\n    if(str(pos)=='\"')\r\n        if(~(pos>1 && str(pos-1)=='\\'))\r\n            endpos=pos;\r\n            return;\r\n        end        \r\n    end\r\n    pos=pos+1;\r\nend\r\nerror('unmatched quotation mark');\r\n%%-------------------------------------------------------------------------\r\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\r\nglobal arraytoken\r\nlevel=1;\r\nmaxlevel=level;\r\nendpos=0;\r\nbpos=arraytoken(arraytoken>=pos);\r\ntokens=str(bpos);\r\nlen=length(tokens);\r\npos=1;\r\ne1l=[];\r\ne1r=[];\r\nwhile(pos<=len)\r\n    c=tokens(pos);\r\n    if(c==']')\r\n        level=level-1;\r\n        if(isempty(e1r))\r\n            e1r=bpos(pos);\r\n        end\r\n        if(level==0)\r\n            endpos=bpos(pos);\r\n            return\r\n        end\r\n    end\r\n    if(c=='[')\r\n        if(isempty(e1l))\r\n            e1l=bpos(pos);\r\n        end\r\n        level=level+1;\r\n        maxlevel=max(maxlevel,level);\r\n    end\r\n    if(c=='\"')\r\n        pos=matching_quote(tokens,pos+1);\r\n    end\r\n    pos=pos+1;\r\nend\r\nif(endpos==0) \r\n    error('unmatched \"]\"');\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "loadubjson.m", "ext": ".m", "path": "IA-Lab-master/Utilities/jsonlab/loadubjson.m", "size": 13774, "source_encoding": "utf_8", "md5": "cf1ef9319b0f46d709ea706a0048f15d", "text": "function data = loadubjson(fname,varargin)\r\n%\r\n% data=loadubjson(fname,opt)\r\n%    or\r\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\r\n%\r\n% parse a JSON (JavaScript Object Notation) file or string\r\n%\r\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\r\n% created on 2013/08/01\r\n%\r\n% $Id$\r\n%\r\n% input:\r\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\r\n%             will be interpreted as a UBJSON string\r\n%      opt: a struct to store parsing options, opt can be replaced by \r\n%           a list of ('param',value) pairs - the param string is equivallent\r\n%           to a field in opt. opt can have the following \r\n%           fields (first in [.|.] is the default)\r\n%\r\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\r\n%                         for each element of the JSON data, and group \r\n%                         arrays based on the cell2mat rules.\r\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\r\n%                         in the UBJSON input data. B - Big-Endian format for \r\n%                         integers (as required in the UBJSON specification); \r\n%                         L - input integer fields are in Little-Endian order.\r\n%           opt.NameIsString [0|1]: for UBJSON Specification Draft 8 or \r\n%                         earlier versions (JSONLab 1.0 final or earlier), \r\n%                         the \"name\" tag is treated as a string. To load \r\n%                         these UBJSON data, you need to manually set this \r\n%                         flag to 1.\r\n%\r\n% output:\r\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\r\n%           and [...] are converted to arrays\r\n%\r\n% examples:\r\n%      obj=struct('string','value','array',[1 2 3]);\r\n%      ubjdata=saveubjson('obj',obj);\r\n%      dat=loadubjson(ubjdata)\r\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\r\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\r\n%\r\n% license:\r\n%     BSD or GPL version 3, see LICENSE_{BSD,GPLv3}.txt files for details \r\n%\r\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\r\n%\r\n\r\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\r\n\r\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\r\n   string=fname;\r\nelseif(exist(fname,'file'))\r\n   fid = fopen(fname,'rb');\r\n   string = fread(fid,inf,'uint8=>char')';\r\n   fclose(fid);\r\nelse\r\n   error('input file does not exist');\r\nend\r\n\r\npos = 1; len = length(string); inStr = string;\r\nisoct=exist('OCTAVE_VERSION','builtin');\r\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\r\njstr=regexprep(inStr,'\\\\\\\\','  ');\r\nescquote=regexp(jstr,'\\\\\"');\r\narraytoken=sort([arraytoken escquote]);\r\n\r\n% String delimiters and escape chars identified to improve speed:\r\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\r\nindex_esc = 1; len_esc = length(esc);\r\n\r\nopt=varargin2struct(varargin{:});\r\nfileendian=upper(jsonopt('IntEndian','B',opt));\r\n[os,maxelem,systemendian]=computer;\r\n\r\njsoncount=1;\r\nwhile pos <= len\r\n    switch(next_char)\r\n        case '{'\r\n            data{jsoncount} = parse_object(opt);\r\n        case '['\r\n            data{jsoncount} = parse_array(opt);\r\n        otherwise\r\n            error_pos('Outer level structure must be an object or an array');\r\n    end\r\n    jsoncount=jsoncount+1;\r\nend % while\r\n\r\njsoncount=length(data);\r\nif(jsoncount==1 && iscell(data))\r\n    data=data{1};\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction object = parse_object(varargin)\r\n    parse_char('{');\r\n    object = [];\r\n    type='';\r\n    count=-1;\r\n    if(next_char == '$')\r\n        type=inStr(pos+1); % TODO\r\n        pos=pos+2;\r\n    end\r\n    if(next_char == '#')\r\n        pos=pos+1;\r\n        count=double(parse_number());\r\n    end\r\n    if next_char ~= '}'\r\n        num=0;\r\n        while 1\r\n            if(jsonopt('NameIsString',0,varargin{:}))\r\n                str = parseStr(varargin{:});\r\n            else\r\n                str = parse_name(varargin{:});\r\n            end\r\n            if isempty(str)\r\n                error_pos('Name of value at position %d cannot be empty');\r\n            end\r\n            %parse_char(':');\r\n            val = parse_value(varargin{:});\r\n            num=num+1;\r\n            object.(valid_field(str))=val;\r\n            if next_char == '}' || (count>=0 && num>=count)\r\n                break;\r\n            end\r\n            %parse_char(',');\r\n        end\r\n    end\r\n    if(count==-1)\r\n        parse_char('}');\r\n    end\r\n    if(isstruct(object))\r\n        object=struct2jdata(object);\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction [cid,len]=elem_info(type)\r\nid=strfind('iUIlLdD',type);\r\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\r\nbytelen=[1,1,2,4,8,4,8];\r\nif(id>0)\r\n    cid=dataclass{id};\r\n    len=bytelen(id);\r\nelse\r\n    error_pos('unsupported type at position %d');\r\nend\r\n%%-------------------------------------------------------------------------\r\n\r\n\r\nfunction [data, adv]=parse_block(type,count,varargin)\r\nglobal pos inStr isoct fileendian systemendian\r\n[cid,len]=elem_info(type);\r\ndatastr=inStr(pos:pos+len*count-1);\r\nif(isoct)\r\n    newdata=int8(datastr);\r\nelse\r\n    newdata=uint8(datastr);\r\nend\r\nid=strfind('iUIlLdD',type);\r\nif(id<=5 && fileendian~=systemendian)\r\n    newdata=swapbytes(typecast(newdata,cid));\r\nend\r\ndata=typecast(newdata,cid);\r\nadv=double(len*count);\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\n\r\nfunction object = parse_array(varargin) % JSON array is written in row-major order\r\nglobal pos inStr\r\n    parse_char('[');\r\n    object = cell(0, 1);\r\n    dim=[];\r\n    type='';\r\n    count=-1;\r\n    if(next_char == '$')\r\n        type=inStr(pos+1);\r\n        pos=pos+2;\r\n    end\r\n    if(next_char == '#')\r\n        pos=pos+1;\r\n        if(next_char=='[')\r\n            dim=parse_array(varargin{:});\r\n            count=prod(double(dim));\r\n        else\r\n            count=double(parse_number());\r\n        end\r\n    end\r\n    if(~isempty(type))\r\n        if(count>=0)\r\n            [object, adv]=parse_block(type,count,varargin{:});\r\n            if(~isempty(dim))\r\n                object=reshape(object,dim);\r\n            end\r\n            pos=pos+adv;\r\n            return;\r\n        else\r\n            endpos=matching_bracket(inStr,pos);\r\n            [cid,len]=elem_info(type);\r\n            count=(endpos-pos)/len;\r\n            [object, adv]=parse_block(type,count,varargin{:});\r\n            pos=pos+adv;\r\n            parse_char(']');\r\n            return;\r\n        end\r\n    end\r\n    if next_char ~= ']'\r\n         while 1\r\n            val = parse_value(varargin{:});\r\n            object{end+1} = val;\r\n            if next_char == ']'\r\n                break;\r\n            end\r\n            %parse_char(',');\r\n         end\r\n    end\r\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\r\n      try\r\n        oldobj=object;\r\n        object=cell2mat(object')';\r\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\r\n            object=oldobj;\r\n        elseif(size(object,1)>1 && ismatrix(object))\r\n            object=object';\r\n        end\r\n      catch\r\n      end\r\n    end\r\n    if(count==-1)\r\n        parse_char(']');\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction parse_char(c)\r\n    global pos inStr len\r\n    skip_whitespace;\r\n    if pos > len || inStr(pos) ~= c\r\n        error_pos(sprintf('Expected %c at position %%d', c));\r\n    else\r\n        pos = pos + 1;\r\n        skip_whitespace;\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction c = next_char\r\n    global pos inStr len\r\n    skip_whitespace;\r\n    if pos > len\r\n        c = [];\r\n    else\r\n        c = inStr(pos);\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction skip_whitespace\r\n    global pos inStr len\r\n    while pos <= len && isspace(inStr(pos))\r\n        pos = pos + 1;\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction str = parse_name(varargin)\r\n    global pos inStr\r\n    bytelen=double(parse_number());\r\n    if(length(inStr)>=pos+bytelen-1)\r\n        str=inStr(pos:pos+bytelen-1);\r\n        pos=pos+bytelen;\r\n    else\r\n        error_pos('End of file while expecting end of name');\r\n    end\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction str = parseStr(varargin)\r\n    global pos inStr\r\n % len, ns = length(inStr), keyboard\r\n    type=inStr(pos);\r\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\r\n        error_pos('String starting with S expected at position %d');\r\n    else\r\n        pos = pos + 1;\r\n    end\r\n    if(type == 'C')\r\n        str=inStr(pos);\r\n        pos=pos+1;\r\n        return;\r\n    end\r\n    bytelen=double(parse_number());\r\n    if(length(inStr)>=pos+bytelen-1)\r\n        str=inStr(pos:pos+bytelen-1);\r\n        pos=pos+bytelen;\r\n    else\r\n        error_pos('End of file while expecting end of inStr');\r\n    end\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction num = parse_number(varargin)\r\n    global pos inStr isoct fileendian systemendian\r\n    id=strfind('iUIlLdD',inStr(pos));\r\n    if(isempty(id))\r\n        error_pos('expecting a number at position %d');\r\n    end\r\n    type={'int8','uint8','int16','int32','int64','single','double'};\r\n    bytelen=[1,1,2,4,8,4,8];\r\n    datastr=inStr(pos+1:pos+bytelen(id));\r\n    if(isoct)\r\n        newdata=int8(datastr);\r\n    else\r\n        newdata=uint8(datastr);\r\n    end\r\n    if(id<=5 && fileendian~=systemendian)\r\n        newdata=swapbytes(typecast(newdata,type{id}));\r\n    end\r\n    num=typecast(newdata,type{id});\r\n    pos = pos + bytelen(id)+1;\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction val = parse_value(varargin)\r\n    global pos inStr\r\n\r\n    switch(inStr(pos))\r\n        case {'S','C','H'}\r\n            val = parseStr(varargin{:});\r\n            return;\r\n        case '['\r\n            val = parse_array(varargin{:});\r\n            return;\r\n        case '{'\r\n            val = parse_object(varargin{:});\r\n            return;\r\n        case {'i','U','I','l','L','d','D'}\r\n            val = parse_number(varargin{:});\r\n            return;\r\n        case 'T'\r\n            val = true;\r\n            pos = pos + 1;\r\n            return;\r\n        case 'F'\r\n            val = false;\r\n            pos = pos + 1;\r\n            return;\r\n        case {'Z','N'}\r\n            val = [];\r\n            pos = pos + 1;\r\n            return;\r\n    end\r\n    error_pos('Value expected at position %d');\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction error_pos(msg)\r\n    global pos inStr len\r\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\r\n    if poShow(3) == poShow(2)\r\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\r\n    end\r\n    msg = [sprintf(msg, pos) ': ' ...\r\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\r\n    error( ['JSONparser:invalidFormat: ' msg] );\r\n\r\n%%-------------------------------------------------------------------------\r\n\r\nfunction str = valid_field(str)\r\nglobal isoct\r\n% From MATLAB doc: field names must begin with a letter, which may be\r\n% followed by any combination of letters, digits, and underscores.\r\n% Invalid characters will be converted to underscores, and the prefix\r\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\r\n    pos=regexp(str,'^[^A-Za-z]','once');\r\n    if(~isempty(pos))\r\n        if(~isoct)\r\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\r\n        else\r\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\r\n        end\r\n    end\r\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' )))\r\n        return;\r\n    end\r\n    if(~isoct)\r\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\r\n    else\r\n        pos=regexp(str,'[^0-9A-Za-z_]');\r\n        if(isempty(pos))\r\n            return;\r\n        end\r\n        str0=str;\r\n        pos0=[0 pos(:)' length(str)];\r\n        str='';\r\n        for i=1:length(pos)\r\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\r\n        end\r\n        if(pos(end)~=length(str))\r\n            str=[str str0(pos0(end-1)+1:pos0(end))];\r\n        end\r\n    end\r\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction endpos = matching_quote(str,pos)\r\nlen=length(str);\r\nwhile(pos<len)\r\n    if(str(pos)=='\"')\r\n        if(~(pos>1 && str(pos-1)=='\\'))\r\n            endpos=pos;\r\n            return;\r\n        end        \r\n    end\r\n    pos=pos+1;\r\nend\r\nerror('unmatched quotation mark');\r\n%%-------------------------------------------------------------------------\r\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\r\nglobal arraytoken\r\nlevel=1;\r\nmaxlevel=level;\r\nendpos=0;\r\nbpos=arraytoken(arraytoken>=pos);\r\ntokens=str(bpos);\r\nlen=length(tokens);\r\npos=1;\r\ne1l=[];\r\ne1r=[];\r\nwhile(pos<=len)\r\n    c=tokens(pos);\r\n    if(c==']')\r\n        level=level-1;\r\n        if(isempty(e1r))\r\n            e1r=bpos(pos);\r\n        end\r\n        if(level==0)\r\n            endpos=bpos(pos);\r\n            return\r\n        end\r\n    end\r\n    if(c=='[')\r\n        if(isempty(e1l))\r\n            e1l=bpos(pos);\r\n        end\r\n        level=level+1;\r\n        maxlevel=max(maxlevel,level);\r\n    end\r\n    if(c=='\"')\r\n        pos=matching_quote(tokens,pos+1);\r\n    end\r\n    pos=pos+1;\r\nend\r\nif(endpos==0) \r\n    error('unmatched \"]\"');\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "saveubjson.m", "ext": ".m", "path": "IA-Lab-master/Utilities/jsonlab/saveubjson.m", "size": 18302, "source_encoding": "utf_8", "md5": "71eb65f95dee01aa2bf33ff80a5bf80d", "text": "function json=saveubjson(rootname,obj,varargin)\r\n%\r\n% json=saveubjson(rootname,obj,filename)\r\n%    or\r\n% json=saveubjson(rootname,obj,opt)\r\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\r\n%\r\n% convert a MATLAB object (cell, struct or array) into a Universal \r\n% Binary JSON (UBJSON) binary string\r\n%\r\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\r\n% created on 2013/08/17\r\n%\r\n% $Id$\r\n%\r\n% input:\r\n%      rootname: the name of the root-object, when set to '', the root name\r\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\r\n%        the MATLAB variable name will be used as the root name.\r\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array,\r\n%      class instance)\r\n%      filename: a string for the file name to save the output UBJSON data\r\n%      opt: a struct for additional options, ignore to use default values.\r\n%        opt can have the following fields (first in [.|.] is the default)\r\n%\r\n%        opt.FileName [''|string]: a file name to save the output JSON data\r\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\r\n%                         array in JSON array format; if sets to 1, an\r\n%                         array will be shown as a struct with fields\r\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\r\n%                         sparse arrays, the non-zero elements will be\r\n%                         saved to _ArrayData_ field in triplet-format i.e.\r\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\r\n%                         with a value of 1; for a complex array, the \r\n%                         _ArrayData_ array will include two columns \r\n%                         (4 for sparse) to record the real and imaginary \r\n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \r\n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\r\n%                         will use true/false rather than 1/0.\r\n%        opt.SingletArray [0|1]: if this is set to 1, arrays with a single\r\n%                         numerical element will be shown without a square\r\n%                         bracket, unless it is the root object; if 0, square\r\n%                         brackets are forced for any numerical arrays.\r\n%        opt.SingletCell  [1|0]: if 1, always enclose a cell with \"[]\" \r\n%                         even it has only one element; if 0, brackets\r\n%                         are ignored when a cell has only 1 element.\r\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\r\n%                         will use the name of the passed obj variable as the \r\n%                         root object name; if obj is an expression and \r\n%                         does not have a name, 'root' will be used; if this \r\n%                         is set to 0 and rootname is empty, the root level \r\n%                         will be merged down to the lower level.\r\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\r\n%                         for example, if opt.JSON='foo', the JSON data is\r\n%                         wrapped inside a function call as 'foo(...);'\r\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \r\n%                         back to the string form\r\n%\r\n%        opt can be replaced by a list of ('param',value) pairs. The param \r\n%        string is equivallent to a field in opt and is case sensitive.\r\n% output:\r\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\r\n%\r\n% examples:\r\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \r\n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\r\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\r\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\r\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\r\n%               'SpecialData',[nan, inf, -inf]);\r\n%      saveubjson('jsonmesh',jsonmesh)\r\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\r\n%\r\n% license:\r\n%     BSD or GPL version 3, see LICENSE_{BSD,GPLv3}.txt files for details\r\n%\r\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\r\n%\r\n\r\nif(nargin==1)\r\n   varname=inputname(1);\r\n   obj=rootname;\r\n   if(isempty(varname)) \r\n      varname='root';\r\n   end\r\n   rootname=varname;\r\nelse\r\n   varname=inputname(2);\r\nend\r\nif(length(varargin)==1 && ischar(varargin{1}))\r\n   opt=struct('filename',varargin{1});\r\nelse\r\n   opt=varargin2struct(varargin{:});\r\nend\r\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\r\nif(isfield(opt,'norowbracket'))\r\n    warning('Option ''NoRowBracket'' is depreciated, please use ''SingletArray'' and set its value to not(NoRowBracket)');\r\n    if(~isfield(opt,'singletarray'))\r\n        opt.singletarray=not(opt.norowbracket);\r\n    end\r\nend\r\nrootisarray=0;\r\nrootlevel=1;\r\nforceroot=jsonopt('ForceRootName',0,opt);\r\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || ...\r\n        iscell(obj) || isobject(obj)) && isempty(rootname) && forceroot==0)\r\n    rootisarray=1;\r\n    rootlevel=0;\r\nelse\r\n    if(isempty(rootname))\r\n        rootname=varname;\r\n    end\r\nend\r\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\r\n    rootname='root';\r\nend\r\njson=obj2ubjson(rootname,obj,rootlevel,opt);\r\nif(~rootisarray)\r\n    json=['{' json '}'];\r\nend\r\n\r\njsonp=jsonopt('JSONP','',opt);\r\nif(~isempty(jsonp))\r\n    json=[jsonp '(' json ')'];\r\nend\r\n\r\n% save to a file if FileName is set, suggested by Patrick Rapin\r\nfilename=jsonopt('FileName','',opt);\r\nif(~isempty(filename))\r\n    fid = fopen(filename, 'wb');\r\n    fwrite(fid,json);\r\n    fclose(fid);\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=obj2ubjson(name,item,level,varargin)\r\n\r\nif(iscell(item))\r\n    txt=cell2ubjson(name,item,level,varargin{:});\r\nelseif(isstruct(item))\r\n    txt=struct2ubjson(name,item,level,varargin{:});\r\nelseif(ischar(item))\r\n    txt=str2ubjson(name,item,level,varargin{:});\r\nelseif(isobject(item)) \r\n    txt=matlabobject2ubjson(name,item,level,varargin{:});\r\nelse\r\n    txt=mat2ubjson(name,item,level,varargin{:});\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=cell2ubjson(name,item,level,varargin)\r\ntxt='';\r\nif(~iscell(item))\r\n        error('input is not a cell');\r\nend\r\n\r\ndim=size(item);\r\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\r\n    item=reshape(item,dim(1),numel(item)/dim(1));\r\n    dim=size(item);\r\nend\r\nbracketlevel=~jsonopt('singletcell',1,varargin{:});\r\nlen=numel(item); % let's handle 1D cell first\r\nif(len>bracketlevel) \r\n    if(~isempty(name))\r\n        txt=[N_(checkname(name,varargin{:})) '[']; name=''; \r\n    else\r\n        txt='['; \r\n    end\r\nelseif(len==0)\r\n    if(~isempty(name))\r\n        txt=[N_(checkname(name,varargin{:})) 'Z']; name=''; \r\n    else\r\n        txt='Z'; \r\n    end\r\nend\r\nfor j=1:dim(2)\r\n    if(dim(1)>1)\r\n        txt=[txt '['];\r\n    end\r\n    for i=1:dim(1)\r\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>bracketlevel),varargin{:})];\r\n    end\r\n    if(dim(1)>1)\r\n        txt=[txt ']'];\r\n    end\r\nend\r\nif(len>bracketlevel)\r\n    txt=[txt ']'];\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=struct2ubjson(name,item,level,varargin)\r\ntxt='';\r\nif(~isstruct(item))\r\n\terror('input is not a struct');\r\nend\r\ndim=size(item);\r\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\r\n    item=reshape(item,dim(1),numel(item)/dim(1));\r\n    dim=size(item);\r\nend\r\nlen=numel(item);\r\nforcearray= (len>1 || (jsonopt('SingletArray',0,varargin{:})==1 && level>0));\r\n\r\nif(~isempty(name)) \r\n    if(forcearray)\r\n        txt=[N_(checkname(name,varargin{:})) '['];\r\n    end\r\nelse\r\n    if(forcearray)\r\n        txt='[';\r\n    end\r\nend\r\nfor j=1:dim(2)\r\n  if(dim(1)>1)\r\n      txt=[txt '['];\r\n  end\r\n  for i=1:dim(1)\r\n     names = fieldnames(item(i,j));\r\n     if(~isempty(name) && len==1 && ~forcearray)\r\n        txt=[txt N_(checkname(name,varargin{:})) '{']; \r\n     else\r\n        txt=[txt '{']; \r\n     end\r\n     if(~isempty(names))\r\n       for e=1:length(names)\r\n\t     txt=[txt obj2ubjson(names{e},item(i,j).(names{e}),...\r\n             level+(dim(1)>1)+1+forcearray,varargin{:})];\r\n       end\r\n     end\r\n     txt=[txt '}'];\r\n  end\r\n  if(dim(1)>1)\r\n      txt=[txt ']'];\r\n  end\r\nend\r\nif(forcearray)\r\n    txt=[txt ']'];\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=str2ubjson(name,item,level,varargin)\r\ntxt='';\r\nif(~ischar(item))\r\n        error('input is not a string');\r\nend\r\nitem=reshape(item, max(size(item),[1 0]));\r\nlen=size(item,1);\r\n\r\nif(~isempty(name)) \r\n    if(len>1)\r\n        txt=[N_(checkname(name,varargin{:})) '['];\r\n    end\r\nelse\r\n    if(len>1)\r\n        txt='[';\r\n    end\r\nend\r\nfor e=1:len\r\n    val=item(e,:);\r\n    if(len==1)\r\n        obj=[N_(checkname(name,varargin{:})) '' '',S_(val),''];\r\n        if(isempty(name))\r\n            obj=['',S_(val),''];\r\n        end\r\n        txt=[txt,'',obj];\r\n    else\r\n        txt=[txt,'',['',S_(val),'']];\r\n    end\r\nend\r\nif(len>1)\r\n    txt=[txt ']'];\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=mat2ubjson(name,item,level,varargin)\r\nif(~isnumeric(item) && ~islogical(item))\r\n        error('input is not an array');\r\nend\r\n\r\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\r\n   (isempty(item) && any(size(item))) ||jsonopt('ArrayToStruct',0,varargin{:}))\r\n      cid=I_(uint32(max(size(item))));\r\n      if(isempty(name))\r\n    \ttxt=['{' N_('_ArrayType_'),S_(class(item)),N_('_ArraySize_'),I_a(size(item),cid(1)) ];\r\n      else\r\n          if(isempty(item))\r\n              txt=[N_(checkname(name,varargin{:})),'Z'];\r\n              return;\r\n          else\r\n    \t      txt=[N_(checkname(name,varargin{:})),'{',N_('_ArrayType_'),S_(class(item)),N_('_ArraySize_'),I_a(size(item),cid(1))];\r\n          end\r\n      end\r\nelse\r\n    if(isempty(name))\r\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\r\n    else\r\n        if(numel(item)==1 && jsonopt('SingletArray',0,varargin{:})==0)\r\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\r\n           \ttxt=[N_(checkname(name,varargin{:})) numtxt];\r\n        else\r\n    \t    txt=[N_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\r\n        end\r\n    end\r\n    return;\r\nend\r\nif(issparse(item))\r\n    [ix,iy]=find(item);\r\n    data=full(item(find(item)));\r\n    if(~isreal(item))\r\n       data=[real(data(:)),imag(data(:))];\r\n       if(size(item,1)==1)\r\n           % Kludge to have data's 'transposedness' match item's.\r\n           % (Necessary for complex row vector handling below.)\r\n           data=data';\r\n       end\r\n       txt=[txt,N_('_ArrayIsComplex_'),'T'];\r\n    end\r\n    txt=[txt,N_('_ArrayIsSparse_'),'T'];\r\n    if(size(item,1)==1)\r\n        % Row vector, store only column indices.\r\n        txt=[txt,N_('_ArrayData_'),...\r\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\r\n    elseif(size(item,2)==1)\r\n        % Column vector, store only row indices.\r\n        txt=[txt,N_('_ArrayData_'),...\r\n           matdata2ubjson([ix,data],level+2,varargin{:})];\r\n    else\r\n        % General case, store row and column indices.\r\n        txt=[txt,N_('_ArrayData_'),...\r\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\r\n    end\r\nelse\r\n    if(isreal(item))\r\n        txt=[txt,N_('_ArrayData_'),...\r\n            matdata2ubjson(item(:)',level+2,varargin{:})];\r\n    else\r\n        txt=[txt,N_('_ArrayIsComplex_'),'T'];\r\n        txt=[txt,N_('_ArrayData_'),...\r\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\r\n    end\r\nend\r\ntxt=[txt,'}'];\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=matlabobject2ubjson(name,item,level,varargin)\r\nif numel(item) == 0 %empty object\r\n    st = struct();\r\nelse\r\n    % \"st = struct(item);\" would produce an inmutable warning, because it\r\n    % make the protected and private properties visible. Instead we get the\r\n    % visible properties\r\n    propertynames = properties(item);\r\n    for p = 1:numel(propertynames)\r\n        for o = numel(item):-1:1 % aray of objects\r\n            st(o).(propertynames{p}) = item(o).(propertynames{p});\r\n        end\r\n    end\r\nend\r\ntxt=struct2ubjson(name,st,level,varargin{:});\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction txt=matdata2ubjson(mat,level,varargin)\r\nif(isempty(mat))\r\n    txt='Z';\r\n    return;\r\nend\r\ntype='';\r\nhasnegtive=(mat<0);\r\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\r\n    if(isempty(hasnegtive))\r\n       if(max(mat(:))<=2^8)\r\n           type='U';\r\n       end\r\n    end\r\n    if(isempty(type))\r\n        % todo - need to consider negative ones separately\r\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\r\n        if(isempty(id~=0))\r\n            error('high-precision data is not yet supported');\r\n        end\r\n        key='iIlL';\r\n\ttype=key(id~=0);\r\n    end\r\n    txt=[I_a(mat(:),type,size(mat))];\r\nelseif(islogical(mat))\r\n    logicalval='FT';\r\n    if(numel(mat)==1)\r\n        txt=logicalval(mat+1);\r\n    else\r\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\r\n    end\r\nelse\r\n    if(numel(mat)==1)\r\n        txt=['[' D_(mat) ']'];\r\n    else\r\n        txt=D_a(mat(:),'D',size(mat));\r\n    end\r\nend\r\n\r\n%txt=regexprep(mat2str(mat),'\\s+',',');\r\n%txt=regexprep(txt,';',sprintf('],['));\r\n% if(nargin>=2 && size(mat,1)>1)\r\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\r\n% end\r\nif(any(isinf(mat(:))))\r\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\r\nend\r\nif(any(isnan(mat(:))))\r\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\r\nend\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction newname=checkname(name,varargin)\r\nisunpack=jsonopt('UnpackHex',1,varargin{:});\r\nnewname=name;\r\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\r\n    return\r\nend\r\nif(isunpack)\r\n    isoct=jsonopt('IsOctave',0,varargin{:});\r\n    if(~isoct)\r\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\r\n    else\r\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\r\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\r\n        if(isempty(pos))\r\n            return;\r\n        end\r\n        str0=name;\r\n        pos0=[0 pend(:)' length(name)];\r\n        newname='';\r\n        for i=1:length(pos)\r\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\r\n        end\r\n        if(pos(end)~=length(name))\r\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\r\n        end\r\n    end\r\nend\r\n%%-------------------------------------------------------------------------\r\nfunction val=N_(str)\r\nval=[I_(int32(length(str))) str];\r\n%%-------------------------------------------------------------------------\r\nfunction val=S_(str)\r\nif(length(str)==1)\r\n  val=['C' str];\r\nelse\r\n  val=['S' I_(int32(length(str))) str];\r\nend\r\n%%-------------------------------------------------------------------------\r\nfunction val=I_(num)\r\nif(~isinteger(num))\r\n    error('input is not an integer');\r\nend\r\nif(num>=0 && num<255)\r\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\r\n   return;\r\nend\r\nkey='iIlL';\r\ncid={'int8','int16','int32','int64'};\r\nfor i=1:4\r\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\r\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\r\n    return;\r\n  end\r\nend\r\nerror('unsupported integer');\r\n\r\n%%-------------------------------------------------------------------------\r\nfunction val=D_(num)\r\nif(~isfloat(num))\r\n    error('input is not a float');\r\nend\r\n\r\nif(isa(num,'single'))\r\n  val=['d' data2byte(num,'uint8')];\r\nelse\r\n  val=['D' data2byte(num,'uint8')];\r\nend\r\n%%-------------------------------------------------------------------------\r\nfunction data=I_a(num,type,dim,format)\r\nid=find(ismember('iUIlL',type));\r\n\r\nif(id==0)\r\n  error('unsupported integer array');\r\nend\r\n\r\n% based on UBJSON specs, all integer types are stored in big endian format\r\n\r\nif(id==1)\r\n  data=data2byte(swapbytes(int8(num)),'uint8');\r\n  blen=1;\r\nelseif(id==2)\r\n  data=data2byte(swapbytes(uint8(num)),'uint8');\r\n  blen=1;\r\nelseif(id==3)\r\n  data=data2byte(swapbytes(int16(num)),'uint8');\r\n  blen=2;\r\nelseif(id==4)\r\n  data=data2byte(swapbytes(int32(num)),'uint8');\r\n  blen=4;\r\nelseif(id==5)\r\n  data=data2byte(swapbytes(int64(num)),'uint8');\r\n  blen=8;\r\nend\r\n\r\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\r\n  format='opt';\r\nend\r\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\r\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\r\n      cid=I_(uint32(max(dim)));\r\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\r\n  else\r\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\r\n  end\r\n  data=['[' data(:)'];\r\nelse\r\n  data=reshape(data,blen,numel(data)/blen);\r\n  data(2:blen+1,:)=data;\r\n  data(1,:)=type;\r\n  data=data(:)';\r\n  data=['[' data(:)' ']'];\r\nend\r\n%%-------------------------------------------------------------------------\r\nfunction data=D_a(num,type,dim,format)\r\nid=find(ismember('dD',type));\r\n\r\nif(id==0)\r\n  error('unsupported float array');\r\nend\r\n\r\nif(id==1)\r\n  data=data2byte(single(num),'uint8');\r\nelseif(id==2)\r\n  data=data2byte(double(num),'uint8');\r\nend\r\n\r\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\r\n  format='opt';\r\nend\r\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\r\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\r\n      cid=I_(uint32(max(dim)));\r\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\r\n  else\r\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\r\n  end\r\n  data=['[' data];\r\nelse\r\n  data=reshape(data,(id*4),length(data)/(id*4));\r\n  data(2:(id*4+1),:)=data;\r\n  data(1,:)=type;\r\n  data=data(:)';\r\n  data=['[' data(:)' ']'];\r\nend\r\n%%-------------------------------------------------------------------------\r\nfunction bytes=data2byte(varargin)\r\nbytes=typecast(varargin{:});\r\nbytes=bytes(:)';\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "AddTextToImageWithBorder.m", "ext": ".m", "path": "IA-Lab-master/Utilities/textimage/AddTextToImageWithBorder.m", "size": 2034, "source_encoding": "utf_8", "md5": "98e36284eff76a81ec7ab9b7a1986c24", "text": "function Image = AddTextToImageWithBorder(Image,String,Position,Color,Font,FontSize,BorderWidth,BorderColor)\r\n\r\n% Image = AddTextToImage(Image,String,Position,Color,Font,FontSize,BorderWidth,BorderColor)\r\n%\r\n%   Works as AddTextToImage, except also allows for a border of thickness\r\n%   BorderWidth pixels and color given by BorderColor. BorderColor should\r\n%   be a 1- or 3-element vector giving Y or RGB in the range 0 to 1\r\n%   regardless of the image class.\r\n%\r\n%\tIf no border parameters are provided, default is 1 pixel black.\r\n%\r\n% Daniel Warren\r\n% Particle Therapy Cancer Research Institute\r\n% University of Oxford\r\n\r\nif ~exist('BorderWidth','var')\r\n\tBorderWidth = 1;\r\nend\r\nif ~exist('BorderColor','var')\r\n\tBorderColor = 0;\r\nend\r\n\r\nif BorderWidth == 0\r\n\tImage = AddTextToImage(Image,String,Position,Color,Font,FontSize);\r\n\treturn;\r\nend\r\n\r\n% uint8 images go from 0 to 255, whereas double ones go from 0 to 1\r\nif isa(Image, 'uint8')\r\n    ScaleFactor = 255;\r\nelse\r\n    ScaleFactor = 1;\r\nend\r\n\r\n% monochrome images need monochrome text, colour images need colour text\r\nif ndims(Image) == 2 %#ok<ISMAT>\r\n    BorderColor = mean(BorderColor(:));\r\nend\r\nif ndims(Image) == 3 && numel(BorderColor) == 1\r\n    BorderColor = [BorderColor BorderColor BorderColor];\r\nend\r\n\r\nMask = AddTextToImage(false(size(Image(:,:,1))),String,Position,1,Font,FontSize);\r\nConvElement = double(CircleMask(1+2*BorderWidth,1+2*BorderWidth,BorderWidth+0.5,BorderWidth+0.5,0.5+BorderWidth));\r\nOutline = xor(conv2(double(Mask),ConvElement,'same'),Mask);\r\nImage = AddTextToImage(Image,String,Position,Color,Font,FontSize);\r\n\r\nfor i = 1:size(Image,3) \r\ntmp = Image(:,:,i); \r\ntmp(Outline) = ScaleFactor*BorderColor(i); \r\nImage(:,:,i) = tmp; \r\nend\r\n\r\nend\r\n\r\nfunction out = CircleMask(w,h,cx,cy,r)\r\n% function out = CircleMask(w,h,cx,cy,r)\r\n%     Outputs a 1D bitmap of a solid circle of radius r pixels\r\n%     centered at pixel (cx,cy) within an matrix of size w x h\r\n%     pixels.\r\n\r\n[x y] = meshgrid(1:w,1:h);\r\nout = ((x-cx-0.5).^2+(y-cy-0.5).^2) <= r^2;\r\n\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "real2rgb.m", "ext": ".m", "path": "IA-Lab-master/Utilities/sc/real2rgb.m", "size": 4918, "source_encoding": "utf_8", "md5": "e3d94e10c8a2aca6dfbb6e867ed707bc", "text": "function [B lims map] = real2rgb(A, cmap, lims)\r\n%REAL2RGB  Converts a real-valued matrix into a truecolor image\r\n%\r\n% Examples:\r\n%   B = real2rgb(A, cmap);\r\n%   B = real2rgb(A, cmap, lims);\r\n%   [B lims map] = real2rgb(...);\r\n%\r\n% This function converts a real-valued matrix into a truecolor image (i.e.\r\n% double array with values between 0 and 1) using the colormap specified\r\n% (either user-defined or the name of a colormap function). The output\r\n% image is suitable for display using IMAGE or IMSHOW, exporting using\r\n% IMWRITE, texture mapping a surface etc.\r\n%\r\n% Colormaps specified by name, e.g. 'hot', can be reversed ('-hot'), made\r\n% to convert linearly to grayscale when printed on a black & white printer\r\n% ('hot*'), or both ('-hot*').\r\n%\r\n% Value limits and a colormap table can be output, for use generating the\r\n% correct colorbar, e.g.:\r\n%   [B lims map] = real2rgb(peaks(256), '-hot*');\r\n%   hIm = imshow(B);\r\n%   set(gcf, 'Colormap', map);\r\n%   set(gca, 'CLim', lims);\r\n%   set(hIm, 'CDataMapping', 'scaled');\r\n%   colorbar;\r\n%\r\n% IN:\r\n%   A - MxN real matrix.\r\n%   cmap - JxK user-defined colormap, or a string indicating the name\r\n%          of the colormap to be used. K = 3 or 4. If K == 4 then\r\n%          cmap(1:end-1,4) contains the relative widths of the bins between\r\n%          colors. If cmap is a colormap function name then the prefix '-'\r\n%          indicates that the colormap is to be reversed, while the suffix\r\n%          '*' indicates that the colormap bins are to be rescaled so that\r\n%          each bin produces the same change in gray level, such that the\r\n%          colormap converts linearly to grayscale when printed in black\r\n%          and white.\r\n%   lims - 1x2 array of saturation limits to be used on A. Default:\r\n%          [min(A(:)) max(A(:))].\r\n%\r\n% OUT:\r\n%   B - MxNx3 truecolor image.\r\n%   lims - 1x2 array of saturation limits used on A. Same as input lims, if\r\n%          given.\r\n%   map - 256x3 colormap similar to that used to generate B.\r\n\r\n% Copyright: Oliver Woodford, 2009-2010\r\n\r\n% Thank you to Peter Nave for reporting a bug whereby colormaps larger than\r\n% 256 entries long are returned.\r\n\r\n% Don't do much if A is wrong size\r\n[y x c] = size(A);\r\nif c > 1\r\n    error('A can only have 2 dimensions');\r\nend\r\nif y*x*c == 0\r\n    % Create an empty array with the correct dimensions\r\n    B = zeros(y, x, (c~=0)*3);\r\n    return\r\nend\r\n\r\n% Generate the colormap\r\nif ischar(cmap)\r\n    % If map starts with a '-' sign, invert the colormap\r\n    reverseMap = cmap(1) == '-';\r\n    % If the map ends with a '*', attempt to make map convert linearly to\r\n    % grayscale\r\n    grayMap = cmap(end) == '*';\r\n    % Extract the map name\r\n    cmap = lower(cmap(reverseMap+1:end-grayMap));\r\n    % Load the map\r\n    try\r\n        % Check for a concise table first\r\n        map = feval(cmap, Inf);\r\n    catch\r\n        map = [];\r\n    end\r\n    if invalid_map(map)\r\n        try\r\n            % Just load a large table\r\n            map = feval(cmap, 256);\r\n        catch\r\n            error('Colormap ''%s'' not found', cmap);\r\n        end\r\n        if invalid_map(map)\r\n            error('Invalid colormap');\r\n        end\r\n    end\r\n    if reverseMap\r\n        % Reverse the map\r\n        map = map(end:-1:1,:);\r\n        if size(map, 2) == 4\r\n            % Shift up the bin lengths\r\n            map(1:end-1,4) = map(2:end,4);\r\n        end\r\n    end\r\n    if grayMap && size(map, 1) > 2\r\n        % Ensure the map converts linearly to grayscale\r\n        map(1:end-1,4) = abs(diff(map(:,1:3) * [0.299; 0.587; 0.114]));\r\n    end\r\nelse\r\n    % Table-based colormap given\r\n    map = cmap;\r\nend\r\n\r\n% Only work with real doubles\r\nB = reshape(double(real(A)), y*x, c);\r\n\r\n% Compute limits and scaled values\r\nmaxInd = 1 + (size(map, 1) - 2) * (size(map, 2) ~= 4);\r\nif nargin < 3\r\n    lims = [];\r\nend\r\n[B lims] = rescale(B, lims, [0 maxInd]);\r\n\r\n% Compute indices and offsets\r\nif size(map, 2) == 4\r\n    % Non-linear colormap\r\n    bins = map(1:end-1,4);\r\n    cbins = cumsum(bins);\r\n    bins(bins==0) = 1;\r\n    bins = cbins(end) ./ bins;\r\n    cbins = [0; cbins(1:end-1) ./ cbins(end); 1+eps];\r\n    [ind ind] = histc(B, cbins);\r\n    B = (B - cbins(ind)) .* bins(ind);\r\n    clear bins cbins\r\nelse\r\n    % Linear colormap\r\n    ind = min(floor(B), maxInd-1);\r\n    B = B - ind;\r\n    ind = ind + 1;\r\nend\r\n\r\n% Compute the output image\r\nB = B(:,[1 1 1]);\r\nB = map(ind,1:3) .* (1 - B) + map(ind+1,1:3) .* B;\r\nB = min(max(B, 0), 1); % Rounding errors can make values slip outside bounds\r\nB = reshape(B, y, x, 3);\r\n\r\nif nargout > 2 && (size(map, 1) ~= 256 || size(map, 2) == 4)\r\n    % Generate the colormap (for creating a colorbar with)\r\n    map = reshape(real2rgb(0:255, map, [0 255]), 256, 3);\r\nend\r\nreturn\r\n\r\nfunction notmap = invalid_map(map)\r\nnotmap = isempty(map) || ndims(map) ~= 2 || size(map, 1) < 1 || size(map, 2) < 3 || size(map, 2) > 4 || ~all(reshape(map(:,1:3) >= 0 & map(:,1:3) <= 1, [], 1));\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "imsc.m", "ext": ".m", "path": "IA-Lab-master/Utilities/sc/imsc.m", "size": 1299, "source_encoding": "utf_8", "md5": "97cd4a988efe36fe0575539142fc605e", "text": "%IMSC Wrapper function to SC which replicates display behaviour of IMAGESC\r\n%\r\n% Examples:\r\n%   imsc(I, varargin)\r\n%   imsc(x, y, I, varargin)\r\n%   h = imsc(...)\r\n%\r\n% IN:\r\n%    x - 1x2 vector of x-axis bounds. If x(1) > x(2) the image is flipped\r\n%        left-right. Default: [1 size(I, 2)].\r\n%    y - 1x2 vector of y-axis bounds. If y(1) > y(2) the image is flipped\r\n%        up-down. Default: [1 size(I, 1)].\r\n%    I - MxNxC input image.\r\n%    varargin - Extra input parameters passed to SC. See SC's help for more\r\n%               information.\r\n%\r\n% OUT:\r\n%    h - Handle of the image graphics object generated.\r\n%\r\n% See also IMAGESC, SC.\r\n\r\n% Copyright: Oliver Woodford, 2010\r\n\r\nfunction h = imsc(varargin)\r\n\r\n% Check for x, y as first two inputs\r\nif numel(varargin{1}) == 2 && numel(varargin{2}) == 2\r\n    % Render\r\n    [I clim map] = sc(varargin{3:end});\r\n    % Display\r\n    h = image(varargin{1}, varargin{2}, I);\r\nelse\r\n    % Render\r\n    [I clim map] = sc(varargin{:});\r\n    % Display\r\n    h = image(I);\r\nend\r\n% Fix up colormap, if there is one\r\nif ~isempty(clim)\r\n    set(h, 'CDataMapping', 'scaled');\r\n    ha = get(h, 'Parent');\r\n    set(ha, 'CLim', clim);\r\n    set(get(ha, 'Parent'), 'Colormap', map);\r\nend\r\n% Don't display the handle if not requested\r\nif nargout < 1\r\n    clear h\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "rescale.m", "ext": ".m", "path": "IA-Lab-master/Utilities/sc/rescale.m", "size": 1613, "source_encoding": "utf_8", "md5": "427de5e2419533818c74ea0c8fd7f773", "text": "function [B lims] = rescale(A, lims, out_lims)\r\n%RESCALE  Linearly rescale values in an array\r\n%\r\n% Examples:\r\n%   B = rescale(A)\r\n%   B = rescale(A, lims)\r\n%   B = rescale(A, lims, out_lims)\r\n%   [B lims] = rescale(A)\r\n%\r\n% Linearly rescales values in an array, saturating values outside limits.\r\n%\r\n% IN:\r\n%   A - Input array of any size and class.\r\n%   lims - 1x2 array of saturation limits to be used on A. Default:\r\n%          [min(A(:)) max(A(:))].\r\n%   out_lims - 1x2 array of output limits the values in lims are to be\r\n%              rescaled to. Default: [0 1].\r\n%\r\n% OUT:\r\n%   B - size(A) double array.\r\n%   lims - 1x2 array of saturation limits used on A. Equal to the input\r\n%          lims, if given.\r\n\r\n% $Id: rescale.m,v 1.2 2009/04/05 15:09:11 ojw Exp $\r\n% Copyright: Oliver Woodford, 2009\r\n\r\nif nargin < 3\r\n    out_lims = [0 1];\r\nend\r\nif nargin < 2 || isempty(lims)\r\n    M = isfinite(A);\r\n    if ~any(reshape(M, numel(M), 1))\r\n        % All NaNs, Infs or -Infs\r\n        B = double(A > 0);\r\n        lims = [0 1];\r\n    else\r\n        lims = [min(A(M)) max(A(M))];\r\n        B = normalize(A, lims, out_lims);\r\n    end\r\n    clear M\r\nelse\r\n    B = normalize(A, lims, out_lims);\r\n    B = min(max(B, out_lims(1)), out_lims(2));\r\nend\r\nreturn\r\n\r\nfunction B = normalize(A, lims, out_lims)\r\nif lims(2) == lims(1) || out_lims(1) == out_lims(2)\r\n    B = zeros(size(A));\r\nelse\r\n    B = double(A);\r\n    if lims(1)\r\n        B = B - lims(1);\r\n    end\r\n    v = (out_lims(2) - out_lims(1)) / (lims(2) - lims(1));\r\n    if v ~= 1\r\n        B = B * v;\r\n    end\r\nend\r\nif out_lims(1)\r\n    B = B + out_lims(1);\r\nend\r\nreturn"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "imdisp.m", "ext": ".m", "path": "IA-Lab-master/Utilities/sc/imdisp.m", "size": 19654, "source_encoding": "utf_8", "md5": "c97ad19a7be236223dde28b870a9b800", "text": "function hIm = imdisp(I, varargin)\r\n%IMDISP  Display one or more images nicely\r\n%\r\n% Examples:\r\n%   imdisp\r\n%   imdisp(I)\r\n%   imdisp(I, map)\r\n%   imdisp(I, lims)\r\n%   imdisp(I, map, lims)\r\n%   imdisp(..., param1, value1, param2, value2, ...)\r\n%   h = imdisp(...)\r\n%\r\n% This function displays one or more images nicely. Images can be defined\r\n% by arrays or filenames. Multiple images can be input in a cell array or\r\n% stacked along the fourth dimension, and are displayed as a grid of\r\n% subplots (an improvement over MONTAGE). The size of grid is calculated or\r\n% user defined. The figure size is set so that images are magnified by an\r\n% integer value.\r\n%\r\n% If the image grid size is user defined, images not fitting in the grid\r\n% can be scrolled through using the following key presses:\r\n%    Up - Back a row.\r\n%    Down - Forward a row.\r\n%    Left - Back a page (or column if there is only one row).\r\n%    Right - Forward a page (or column if there is only one row).\r\n%    Shift - 2 x speed.\r\n%    Ctrl - 4 x speed.\r\n%    Shift + Ctrl - 8 x speed.\r\n%\r\n% This allows fast scrolling through a movie or image stack, e.g. \r\n%    imdisp(imstack, 'Size', 1)\r\n% The function can be used as a visual DIR, e.g. \r\n%    imdisp()\r\n% to display all images in the current directory on a grid, or \r\n%    imdisp({}, 'Size', 1)\r\n% to scroll through them one at a time.\r\n%\r\n% IN:\r\n%   I - MxNxCxP array of images, or 1xP cell array. C is 1 for indexed\r\n%       images or 3 for RGB images. P is the number of images. If I is a\r\n%       cell array then each cell must contain an image. Images can equally\r\n%       be defined by filenames. If I is an empty cell array then all the\r\n%       images in the current directory are used. Default: {}.\r\n%   map - Kx3 colormap to be used with indexed images. Default: gray(256).\r\n%   lims - [LOW HIGH] display range for indexed images. Default: [min(I(:))\r\n%          max(I(:))].\r\n%   Optional parameters - name, value parameter pairs for the following:\r\n%      'Size' - [H W] size of grid to display image on. If only H is given\r\n%               then W = H. If either H or W is NaN then the number of rows\r\n%               or columns is chosen such that all images fit. If both H\r\n%               and W are NaN or the array is empty then the size of grid\r\n%               is chosen to fit all images in as large as possible.\r\n%               Default: [].\r\n%      'Indices' - 1xL list of indices of images to display. Default: 1:P.\r\n%      'Border' - [TB LR] borders to give each image top and bottom (TB)\r\n%                 and left and right (LR), to space out images. Borders are\r\n%                 normalized to the subplot size, i.e. TB = 0.01 gives a\r\n%                 border 1% of the height of each subplot. If only TB is\r\n%                 given, LR = TB. Default: 0.01.\r\n%      'DisplayRange' - Same as lims input.\r\n%      'Map' - Kx3 colormap or (additionally from above) name of MATLAB\r\n%              colormap, for use with indexed images. Default: gray(256).\r\n%\r\n% OUT:\r\n%   h - HxW array of handles to images.\r\n%\r\n%   See also IMAGE, IMAGESC, IMSHOW, MONTAGE.\r\n\r\n% Copyright: Oliver Woodford 2010\r\n\r\n% Parse inputs\r\n[map layout gap indices lims] = parse_inputs(varargin);\r\n\r\nif nargin == 0 || (iscell(I) && isempty(I))\r\n    % Read in all the images in the directory\r\n    I = get_im_names;\r\n    if isempty(I)\r\n        % No images found\r\n        if nargout > 0\r\n            hIm = [];\r\n        end\r\n        return\r\n    end\r\nend\r\n\r\n% Check if input is filenames\r\nif ischar(I)\r\n    [x y c] = size(I);\r\n    if (x > 1 && y > 1) || c > 1 \r\n        I = num2cell(I, 2);\r\n    else\r\n        I = {I(:)'};\r\n    end\r\nend\r\n\r\n% Get limits, etc.\r\nif isnumeric(I) || islogical(I)\r\n    [y x c n] = size(I);\r\n    if isempty(lims)\r\n        lims = min_max(I);\r\n    elseif isequal(0, lims)\r\n        lims = default_limits(I);\r\n    elseif c == 3\r\n        % Rescale\r\n        if ~isfloat(I)\r\n            I = single(I);\r\n        end\r\n        I = min(max((I - lims(1)) ./ (lims(2) - lims(1)), 0), 1);\r\n    end\r\n    if isfloat(I) && c == 3 && n > 1\r\n        I = uint8(I * 256 - 0.5);\r\n        lims = round(lims * 256 - 0.5);\r\n    end\r\nelseif iscell(I)\r\n    n = numel(I);\r\n    A = I{1};\r\n    if ischar(A)\r\n        % Read in the image (or images for multi-frame files)\r\n        if n == 1\r\n            I = imread_rgb_multi(A);\r\n            if iscell(I)\r\n                n = numel(I);\r\n                A = I{1};\r\n                [y x c] = size(A);\r\n            else\r\n                [y x c n] = size(I);\r\n                A = I;\r\n            end\r\n        else\r\n            A = imread_rgb(A);\r\n            I{1} = A;\r\n            [y x c] = size(A);\r\n        end\r\n    else\r\n        [y x c] = size(A);\r\n    end\r\n    % Assume all images are the same size and type as the first\r\n    if isempty(lims) || isequal(0, lims)\r\n        lims = default_limits(A);\r\n    end\r\nelse\r\n    error('I not of recognized type.');\r\nend\r\n\r\n% Select indexed images\r\nif ~isequal(indices, -1)\r\n    if iscell(I)\r\n        I = I(indices);\r\n        n = numel(I);\r\n    else\r\n        I = I(:,:,:,indices);\r\n        n = size(I, 4);\r\n    end\r\nend\r\n\r\n% Get the current figure\r\nhFig = get(0, 'CurrentFigure');\r\nif isempty(hFig)\r\n    % Create a new figure\r\n    hFig = figure;\r\nend\r\n\r\nif n < 2\r\n    % Set the colormap\r\n    set(hFig, 'Colormap', map);\r\nend\r\n\r\n% Display the image(s)\r\nif n == 0\r\n    hIm = display_image([], gca, [0 1]);\r\n    \r\n    if nargout == 0\r\n        clear hIm % Avoid printing this out\r\n    end\r\n    return\r\nelseif n == 1\r\n    % IMSHOW mode\r\n    % Display the single image\r\n    hAx = gca;\r\n    if iscell(I)\r\n        I = I{1};\r\n    end\r\n    hIm = display_image(I, hAx, lims);\r\n    \r\n    if nargout == 0\r\n        clear hIm % Avoid printing this out\r\n    end\r\n    \r\n    % Only resize image if it is alone in the figure\r\n    if numel(findobj(get(hFig, 'Children'), 'Type', 'axes')) > 1\r\n        return\r\n    end\r\n    % Could still be the first subplot - do another check\r\n    axesPos = get(hAx, 'Position');\r\n    newAxesPos = [gap(1) gap(end) 1-2*gap(1) 1-2*gap(end)];\r\n    if isequal(axesPos, get(hFig, 'DefaultAxesPosition'))\r\n        % Default position => not a subplot\r\n        % Fill the window\r\n        set(hAx, 'Units', 'normalized', 'Position', newAxesPos);\r\n        axesPos = newAxesPos;\r\n    end\r\n    if ~isequal(axesPos, newAxesPos)\r\n        % Figure not alone, so don't resize.\r\n        return\r\n    end\r\n    layout = [1 1];\r\nelse\r\n    % MONTAGE mode\r\n    % Compute a good layout\r\n    layout = choose_layout(n, y, x, layout);\r\n\r\n    % Create a data structure to store the data in\r\n    num = prod(layout);\r\n    state.num = num * ceil(n / num);\r\n    hIm = zeros(layout);\r\n    hAx = zeros(layout);\r\n    \r\n    % Clear the figure\r\n    if n > num\r\n        hFig = clf(hFig, 'reset');\r\n    else\r\n        hFig = clf(hFig);\r\n    end\r\n    \r\n    % Set the colormap\r\n    set(hFig, 'Colormap', map);\r\n\r\n    % Set the first lot of images\r\n    index = mod(0:num-1, state.num) + 1;\r\n    hw = 1 ./ layout;\r\n    gap = gap ./ layout;\r\n    dims = hw - 2 * gap;\r\n    dims = dims([2 1]);\r\n    for a = 1:layout(1)\r\n        for b = 1:layout(2)\r\n            c = index(b + (layout(1) - a) * layout(2));\r\n            if c > n\r\n                A = [];\r\n            elseif iscell(I)\r\n                A = I{c};\r\n                if ischar(A)\r\n                    A = imread_rgb(A);\r\n                    I{c} = A;\r\n                end\r\n            else\r\n                A = I(:,:,:,c);\r\n            end\r\n            hAx(a,b) = axes('Position', [(b-1)*hw(2)+gap(2) (a-1)*hw(1)+gap(1) dims], 'Units', 'normalized');\r\n            hIm(a,b) = display_image(A, hAx(a,b), lims);\r\n        end\r\n    end\r\n    \r\n    % Check if we need to be able to scroll through images\r\n    if n > num\r\n        % Intialize rest of data structure\r\n        state.hIm = hIm;\r\n        state.hAx = hAx;\r\n        state.index = 1;\r\n        state.layout = layout;\r\n        state.n = n;\r\n        state.I = I;\r\n        % Set the callback for image navigation, and save the image data in the figure\r\n        set(hFig, 'KeyPressFcn', @keypress_callback, 'Interruptible', 'off', 'BusyAction', 'cancel', 'UserData', state);\r\n    end\r\n    \r\n    % Flip hIm so it matches the layout\r\n    hIm = hIm(end:-1:1,:);\r\n    \r\n    if nargout == 0\r\n        clear hIm % Avoid printing this out\r\n    end\r\nend\r\n\r\nif strcmp(get(hFig, 'WindowStyle'), 'docked')\r\n    % Figure is docked, so can't resize\r\n    return\r\nend\r\n\r\n% Set the figure size well\r\n% Compute the image size\r\nImSz = layout([2 1]) .* [x y] ./ (1 - 2 * gap([end 1]));\r\n    \r\n% Get the size of the monitor we're on\r\nfigPosCur = get(hFig, 'Position');\r\n% Monitor sizes\r\nMonSz = get(0, 'MonitorPositions');\r\nMonOn = size(MonSz, 1);\r\nif MonOn > 1\r\n    % Make the origin the top left corner of the primary monitor\r\n    correction = 0;\r\n    if ispc\r\n        for a = 1:MonOn\r\n            if isequal(MonSz(a,1:2), [1 1])\r\n                correction = MonSz(a,4);\r\n                break\r\n            end\r\n        end\r\n    end\r\n    % Determine which monitor the centre of the image is on\r\n    figCenter = figPosCur(1:2) + figPosCur(3:4) / 2;\r\n    figCenter = MonSz - repmat(figCenter, [MonOn 2]);\r\n    MonOn = all(sign(figCenter) == repmat([-1 -1 1 1], [MonOn 1]), 2);\r\n    MonOn(1) = MonOn(1) | ~any(MonOn);\r\n    MonSz = MonSz(MonOn,:);\r\n    % Correct the size\r\n    MonSz(3:4) = MonSz(3:4) - MonSz(1:2) + 1;\r\n    % Correct the origin\r\n    if correction\r\n        MonSz(2) = correction - MonSz(4) - MonSz(2) + 2;\r\n    end\r\nend\r\n\r\n% Check if the window is maximized\r\n% This is a hack which may only work on Windows! No matter, though.\r\nif isequal(MonSz([1 3]), figPosCur([1 3]))\r\n    % Leave maximized\r\n    return\r\nend\r\n\r\n% Compute the size to set the window\r\nMaxSz = MonSz(3:4) - [20 120];\r\nRescaleFactor = min(MaxSz ./ ImSz);\r\nif RescaleFactor > 1\r\n    % Integer scale for enlarging, but don't make too big\r\n    MaxSz = min(MaxSz, [1200 800]);\r\n    RescaleFactor = max(floor(min(MaxSz ./ ImSz)), 1);\r\nend\r\nfigPosNew = ceil(ImSz * RescaleFactor);\r\n\r\n% Don't move the figure if the size isn't changing\r\nif isequal(figPosCur(3:4), figPosNew)\r\n    return\r\nend\r\n\r\n% Keep the centre of the figure stationary\r\nfigPosNew = [floor(figPosCur(1:2)+(figPosCur(3:4)-figPosNew)/2) figPosNew];\r\n\r\n% Ensure the figure is in bounds\r\nfigPosNew(1:2) = min(max(figPosNew(1:2), MonSz(1:2)+6), MonSz(1:2)+MonSz(3:4)-[6 101]-figPosNew(3:4));\r\n\r\n% Set the figure size and position\r\nset(hFig, 'Position', figPosNew);\r\nreturn\r\n\r\n%% Keypress callback\r\n% The function which does all the display stuff\r\nfunction keypress_callback(fig, event_data)\r\n% Check what key was pressed and update the image index as necessary\r\nswitch event_data.Character\r\n    case 28 % Left\r\n        up = -1; % Back a page\r\n    case 29 % Right\r\n        up = 1; % Forward a page\r\n    case 30 % Up\r\n        up = -0.1; % Back a row\r\n    case 31 % Down\r\n        up = 0.1; % Forward a row\r\n    otherwise\r\n        % Another key was pressed - ignore it\r\n        return\r\nend\r\n% Use control and shift for faster scrolling\r\nif ~isempty(event_data.Modifier)\r\n    up = up * (2 ^ (strcmpi(event_data.Modifier, {'shift', 'control'}) * [1; 2]));\r\nend\r\n% Get the state data\r\nstate = get(fig, 'UserData');\r\n% Get the current index\r\nindex = state.index;\r\n% Get number of images\r\nn = prod(state.layout);\r\n% Generate 12 valid indices\r\nif abs(up) < 1\r\n    % Increment by row\r\n    index = index + state.layout(2) * (up * 10) - 1;\r\nelse\r\n    if state.layout(1) == 1\r\n        % Increment by column\r\n        index = index + up - 1;\r\n    else\r\n        % Increment by page\r\n        index = index + n * up - 1;\r\n    end\r\nend\r\nindex = mod(index:index+n, state.num) + 1;\r\n% Plot the images\r\nfigure(fig);\r\nfor a = 1:state.layout(1)\r\n    for b = 1:state.layout(2)\r\n        % Get the image\r\n        c = index(b + (state.layout(1) - a) * state.layout(2));\r\n        if c > state.n\r\n            % Set the image data\r\n            set(state.hIm(a,b), 'CData', []);\r\n        elseif iscell(state.I)\r\n            A = state.I{c};\r\n            if ischar(A)\r\n                % Filename - read the image from disk\r\n                A = imread_rgb(A);\r\n                state.I{c} = A;\r\n            end\r\n            % Set the image data\r\n            set(state.hIm(a,b), 'CData', A);\r\n            % Reset the axes limits\r\n            if ~isempty(A)\r\n                set(state.hAx(a,b), 'XLim', [0.5 size(A, 2)+0.5], 'YLim', [0.5 size(A, 1)+0.5]);\r\n            end\r\n        else\r\n            % Set the image data\r\n            set(state.hIm(a,b), 'CData', state.I(:,:,:,c));\r\n        end\r\n    end\r\nend\r\ndrawnow;\r\n% Save the current index\r\nstate.index = index(1);\r\nset(fig, 'UserData', state);\r\nreturn\r\n\r\n%% Display the image\r\nfunction hIm = display_image(A, hAx, lims)\r\nif isempty(A)\r\n    hIm = image(zeros(1, 1, 3));\r\n    set(hIm, 'CData', []);\r\nelse\r\n    hIm = image(A);\r\nend\r\nset(hAx, 'Visible', 'off', 'DataAspectRatio', [1 1 1], 'DrawMode', 'fast', 'CLim', lims);\r\nset(get(hAx, 'XLabel'), 'Visible', 'on');\r\nset(get(hAx, 'YLabel'), 'Visible', 'on');\r\nset(get(hAx, 'Title'), 'Visible', 'on');\r\nset(hIm, 'CDataMapping', 'scaled');\r\nreturn\r\n\r\n%% Choose a good layout for the images\r\nfunction layout = choose_layout(n, y, x, layout)\r\nv = numel(layout);\r\nN = isnan(layout);\r\nif v == 0 || all(N)\r\n    % Compute approximate layout\r\n    sz = get(0, 'ScreenSize');\r\n    sz = sz(3:4) ./ [x y];\r\n    layout = ceil(sz([2 1]) ./ sqrt(prod(sz) / n));\r\n    % Remove superfluous rows or columns\r\n    while 1\r\n        switch ([prod(layout - [1 0]) prod(layout - [0 1])] >= n) * [2; 1]\r\n            case 0\r\n                break;\r\n            case 1\r\n                layout = layout - [0 1];\r\n            case 2\r\n                layout = layout - [1 0];\r\n            case 3\r\n                if min(sz .* (layout - [0 1])) > min(sz .* (layout - [1 0]))\r\n                    layout = layout - [0 1];\r\n                else\r\n                    layout = layout - [1 0];\r\n                end\r\n        end\r\n    end\r\nelseif v == 1\r\n    layout = layout([1 1]);\r\nelseif any(N)\r\n    layout(N) = ceil(n / layout(~N));\r\nend\r\nlayout = reshape(layout, 1, 2);\r\nreturn\r\n\r\n%% Read image to uint8 rgb array\r\nfunction A = imread_rgb(name)\r\ntry\r\n    [A map alpha] = imread(name);\r\ncatch\r\n    % Format not recognized by imread, so create a red cross (along diagonals)\r\n    A = eye(101) | diag(ones(100, 1), 1) | diag(ones(100, 1), -1);\r\n    A = (uint8(1) - uint8(A | flipud(A))) * uint8(255);\r\n    A = cat(3, zeros(size(A), 'uint8')+uint8(255), A, A);\r\n    return\r\nend\r\nA = A(:,:,:,1); % Keep only first frame of multi-frame files\r\nif ~isempty(map)\r\n    map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\r\n    A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\r\nelseif size(A, 3) == 4\r\n    if lower(name(end)) == 'f'\r\n        % TIFF in CMYK colourspace - convert to RGB\r\n        if isfloat(A)\r\n            A = A * 255;\r\n        else\r\n            A = single(A);\r\n        end\r\n        A = 255 - A;\r\n        A(:,:,4) = A(:,:,4) / 255;\r\n        A = uint8(A(:,:,1:3) .* A(:,:,[4 4 4]));\r\n    else\r\n        % Assume 4th channel is an alpha matte\r\n        alpha = A(:,:,4);\r\n        A = A(:,:,1:3);\r\n    end\r\nend\r\nif ~isempty(alpha)\r\n    % Apply transprency over a grey checkerboard pattern\r\n    if isa(alpha, 'uint8')\r\n        alpha = double(alpha) / 255;\r\n    end\r\n    A = double(A) .* alpha(:,:,ones(1, size(A, 3)));\r\n    sqSz = max(size(alpha));\r\n    sqSz = floor(max(log(sqSz / 100), 0) * 10 + 1 + min(sqSz, 100) / 20);\r\n    grid = repmat(85, ceil(size(alpha) / sqSz));\r\n    grid(2:2:end,1:2:end) = 171;\r\n    grid(1:2:end,2:2:end) = 171;\r\n    grid = kron(grid, ones(sqSz));\r\n    alpha = grid(1:size(A, 1),1:size(A, 2)) .* (1 - alpha);\r\n    A = uint8(A + alpha(:,:,ones(1, size(A, 3))));\r\nend\r\nreturn\r\n\r\n%% Read (potentially) multi-frame image to uint8 rgb array\r\nfunction A = imread_rgb_multi(name)\r\ntry\r\n    % Get file info\r\n    info = imfinfo(name);\r\ncatch\r\n    % Revert to standard case\r\n    A = imread_rgb(name);\r\n    return\r\nend\r\nif numel(info) < 2\r\n    % Single image\r\n    A = imread_rgb(name);\r\n    return\r\nelse\r\n    % Multi-frame image\r\n    switch lower(info(1).Format)\r\n        case 'gif'\r\n            [A map] = imread(name, 'frames', 'all');\r\n            if ~isempty(map)\r\n                map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\r\n                A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\r\n                A = permute(A, [1 2 5 4 3]);\r\n            end\r\n        case {'tif', 'tiff'}\r\n            A = cell(numel(info), 1);\r\n            for a = 1:numel(A)\r\n                [A{a} map] = imread(name, 'Index', a, 'Info', info);\r\n                if ~isempty(map)\r\n                    map = uint8(map * 256 - 0.5); % Convert to uint8 for storage\r\n                    A{a} = reshape(map(uint32(A{a})+1,:), [size(A) size(map, 2)]); % Assume indexed from 0\r\n                end\r\n                if size(A{a}, 3) == 4\r\n                    % TIFF in CMYK colourspace - convert to RGB\r\n                    if isfloat(A{a})\r\n                        A{a} = A{a} * 255;\r\n                    else\r\n                        A{a} = single(A{a});\r\n                    end\r\n                    A{a} = 255 - A{a};\r\n                    A{a}(:,:,4) = A{a}(:,:,4) / 255;\r\n                    A{a} = uint8(A(:,:,1:3) .* A{a}(:,:,[4 4 4]));\r\n                end\r\n            end\r\n        otherwise\r\n            % Multi-frame not supported for this format\r\n            A = imread_rgb(name);\r\n            return\r\n    end\r\nend\r\nreturn\r\n\r\n%% Get the names of all images in a directory\r\nfunction L = get_im_names\r\nD = dir;\r\nn = 0;\r\nL = cell(size(D));\r\n% Go through the directory list\r\nfor a = 1:numel(D)\r\n    % Check if file is a supported image type\r\n    if numel(D(a).name) > 4 && ~D(a).isdir && (any(strcmpi(D(a).name(end-3:end), {'.png', '.tif', '.jpg', '.bmp', '.ppm', '.pgm', '.pbm', '.gif', '.ras'})) || any(strcmpi(D(a).name(end-4:end), {'.tiff', '.jpeg'})))\r\n        n = n + 1;\r\n        L{n} = D(a).name;\r\n    end\r\nend\r\nL = L(1:n);\r\nreturn\r\n\r\n%% Parse inputs\r\nfunction [map layout gap indices lims] = parse_inputs(inputs)\r\n\r\n% Set defaults\r\nmap = [];\r\nlayout = [];\r\ngap = 0;\r\nindices = -1;\r\nlims = 0;\r\n\r\n% Check for map and display range\r\nfor b = 1:numel(inputs)\r\n    if ~isnumeric(inputs{b})\r\n        b = b - 1;\r\n        break;\r\n    end\r\n    if size(inputs{b}, 2) == 3\r\n        map = inputs{b};\r\n    elseif numel(inputs{b}) < 3\r\n        lims = inputs{b};\r\n    end\r\nend\r\n\r\n% Go through option pairs\r\nfor a = b+1:2:numel(inputs)\r\n    switch lower(inputs{a})\r\n        case 'map'\r\n            map = inputs{a+1};\r\n            if ischar(map)\r\n                map = feval(map, 256);\r\n            end\r\n        case {'size', 'grid'}\r\n            layout = inputs{a+1};\r\n        case {'gap', 'border'}\r\n            gap = inputs{a+1};\r\n        case 'indices'\r\n            indices = inputs{a+1};\r\n        case {'lims', 'displayrange'}\r\n            lims = inputs{a+1};\r\n        otherwise\r\n            error('Input option %s not recognized', inputs{a});\r\n    end\r\nend\r\n\r\nif isempty(map)\r\n   map = gray(256);\r\nend\r\nreturn\r\n\r\n%% Return default limits for the image type\r\nfunction lims = default_limits(A)\r\nif size(A, 3) == 1\r\n    lims = min_max(A);\r\nelse\r\n    lims = [0 1];\r\n    if ~isfloat(A)\r\n        lims = lims * double(intmax(class(A)));\r\n    end\r\nend\r\nreturn\r\n\r\n%% Return minimum and maximum values\r\nfunction lims = min_max(A)\r\nM = isfinite(A);\r\nif all(M(:))\r\n    lims = double([min(A(:)) max(A(:))]);\r\nelse\r\n    lims = double([min(A(M)) max(A(M))]);\r\nend\r\nif isempty(lims)\r\n    lims = [0 1];\r\nelseif lims(1) == lims(2)\r\n    lims(2) = lims(1) + 1;\r\nend\r\nreturn\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "sc.m", "ext": ".m", "path": "IA-Lab-master/Utilities/sc/sc.m", "size": 31219, "source_encoding": "utf_8", "md5": "f247fe4402993a806f759a89a6ddf8a6", "text": "function [I limits map] = sc(I, varargin)\r\n%SC  Display/output truecolor images with a range of colormaps\r\n%\r\n% Examples:\r\n%   sc(image)\r\n%   sc(image, limits)\r\n%   sc(image, map)\r\n%   sc(image, limits, map)\r\n%   sc(image, map, limits)\r\n%   sc(..., col1, mask1, col2, mask2,...)\r\n%   out = sc(...)\r\n%   [out clim map] = sc(...)\r\n%   sc\r\n%\r\n% Generates a truecolor RGB image based on the input values in 'image' and\r\n% any maximum and minimum limits specified, using the colormap specified.\r\n% The image is displayed on screen if there is no output argument.\r\n% \r\n% SC has these advantages over MATLAB image rendering functions:\r\n%   - images can be displayed or output; makes combining/overlaying images\r\n%     simple.\r\n%   - images are rendered/output in truecolor (RGB [0,1]); no nasty\r\n%     discretization of the input data.\r\n%   - many special, built-in colormaps for viewing various types of data.\r\n%   - linearly interpolates user defined linear and non-linear colormaps.\r\n%   - no border and automatic, integer magnification (unless figure is\r\n%     docked or maximized) for better display.\r\n%   - multiple images can be generated simultaneously.\r\n%\r\n% For a demonstration, simply call SC without any input arguments.\r\n%\r\n% If you don't like the display behaviour of SC, e.g. the fixed aspect\r\n% ratio, no border and axes, then you can replicate the behaviour of\r\n% IMAGESC using the wrapper function IMSC.\r\n%\r\n% IN:\r\n%   image - MxNxCxP or 3xMxNxP image array. MxN are the dimensions of the\r\n%           image(s), C is the number of channels, and P the number of\r\n%           images. If P > 1, images can only be exported, not displayed.\r\n%   limits - [min max] where values in image less than min will be set to\r\n%            min and values greater than max will be set to max.\r\n%   map - Kx3 or Kx4 user defined colormap matrix, where the optional 4th\r\n%         column is the relative distance between colours along the scale,\r\n%         or a string containing the name of the colormap to use to create\r\n%         the output image. Default: 'none', which is RGB for 3-channel\r\n%         images, grayscale otherwise.  Conversion of multi-channel images\r\n%         to intensity for intensity-based colormaps is done using the L2\r\n%         norm. Most MATLAB colormaps are supported. All named colormaps\r\n%         can be reversed by prefixing '-' to the string. This maintains\r\n%         integrity of the colorbar. Special, non-MATLAB colormaps are:\r\n%      'hicontrast' - a high contrast colormap for intensity images that\r\n%                     maintains intensity scale when converted to\r\n%                     grayscale, for example when printing in black &\r\n%                     white.\r\n%      'prob' - first channel is plotted as hue, and the other channels\r\n%               modulate intensity. Useful for laying probabilites over\r\n%               images.\r\n%      'prob_jet' - first channel is plotted as jet colormap, and the other\r\n%                   channels modulate intensity.\r\n%      'diff' - intensity values are marked blue for > 0 and red for < 0.\r\n%               Darker colour means larger absolute value. For multi-\r\n%               channel images, the L2 norm of the other channels sets\r\n%               green level. 3 channel images are converted to YUV and\r\n%               images with more that 3 channels are projected onto the\r\n%               principle components first.\r\n%      'compress' - compress many channels to RGB while maximizing\r\n%                   variance.\r\n%      'contrast' - apply the CONTRAST function to create an approximately\r\n%                   equal intensity distribution.\r\n%      'flow' - display two channels representing a 2d Cartesian vector as\r\n%               hue for angle and intensity for magnitude (darker colour\r\n%               indicates a larger magnitude).\r\n%      'phase' - first channel is intensity, second channel is phase in\r\n%                radians. Darker colour means greater intensity, hue\r\n%                represents phase from 0 to 2 pi.\r\n%      'stereo' - pair of concatenated images used to generate a red/cyan\r\n%                 anaglyph.\r\n%      'stereo_col' - pair of concatenated RGB images used to generate a\r\n%                     colour anaglyph.\r\n%      'segment' - given concatenated image and segmentation (index image),\r\n%                  colour each segment with the mean colour of the image in\r\n%                  that segment.\r\n%      'rand' - gives an index image a random colormap. Useful for viewing\r\n%               segmentations.\r\n%      'rgb2gray' - converts an RGB image to grayscale in the same fashion\r\n%                   as MATLAB's rgb2gray (in the image processing toolbox).\r\n%   col/mask pairs - Pairs of parameters for coloring specific parts of the\r\n%                    image differently. The first (col) parameter can be\r\n%                    a MATLAB color specifier, e.g. 'b' or [0.5 0 1], or\r\n%                    one of the colormaps named above, or an MxNx3 RGB\r\n%                    image. The second (mask) paramater should be an MxN\r\n%                    logical array indicating those pixels (true) whose\r\n%                    color should come from the specified color parameter.\r\n%                    If there is only one col parameter, without a mask\r\n%                    pair, then mask = any(isnan(I, 3)), i.e. the mask is\r\n%                    assumed to indicate the location of NaNs. Note that\r\n%                    col/mask pairs are applied in order, painting over\r\n%                    previous pixel values.\r\n%\r\n% OUT:\r\n%   out - MxNx3xP truecolour (double) RGB image array in range [0, 1].\r\n%   clim - 1x2 vector of limits of the input array mapped to linearly\r\n%           [0, 1] in out. [] if this mapping is not available, e.g. if\r\n%           output image is multi-spectral.\r\n%   map - 256x3 colormap of the output. [] is limits is []. \r\n%\r\n% See also IMAGE, IMAGESC, IMSC, COLORMAP, COLORBAR.\r\n\r\n% Copyright: Oliver Woodford, 2007-2010\r\n\r\n%% Check for arguments\r\nif nargin == 0\r\n    % If there are no input arguments then run the demo\r\n    if nargout > 0\r\n        error('Output expected from no inputs!');\r\n    end\r\n    demo; % Run the demo\r\n    return\r\nend\r\n\r\n%% Size our image(s)\r\n[y x c n] = size(I);\r\nI = reshape(I, y, x, c, n);\r\n\r\n%% Check if image is given with RGB colour along the first dimension\r\nif y == 3 && c > 3\r\n    % Flip colour to 3rd dimension\r\n    I = permute(I, [2 3 1 4]);\r\n    [y x c n] = size(I);\r\nend\r\n\r\n%% Don't do much if I is empty\r\nif isempty(I)\r\n    if nargout == 0\r\n        % Clear the current axes if we were supposed to display the image\r\n        cla; axis off;\r\n    else\r\n        % Create an empty array with the correct dimensions\r\n        I = zeros(y, x, (c~=0)*3, n);\r\n    end\r\n    return\r\nend\r\n\r\n%% Check for multiple images\r\n% If we have a non-singleton 4th dimension we want to display the images in\r\n% a 3x4 grid and use buttons to cycle through them\r\nif n > 1\r\n    % Return transformed images in an YxXx3xN array\r\n    A = zeros(y, x, 3, n);\r\n    for a = 1:n\r\n        A(:,:,:,a) = sc(I(:,:,:,a), varargin{:});\r\n    end\r\n    I = A;\r\n    if nargout == 0\r\n        clear I\r\n        if n > 12\r\n            sz = [3 4];\r\n        else\r\n           sz = []; \r\n        end\r\n        imdisp(A, 'Size', sz, 'Border', 0.01); \r\n    end\r\n    return\r\nend\r\n\r\n%% Parse the input arguments coming after I (1st input)\r\n[map limits mask] = parse_inputs(I, varargin, y, x);\r\n\r\n%% Call the rendering function\r\nif isnumeric(map) && (size(map, 2) == 3 || size(map, 2) == 4)\r\n    % Table-based colormap\r\n    [I limits map] = table_wrapper(I, map, limits);\r\nelseif ischar(map)    \r\n    % Predefined colormap\r\n    [I limits map] = colormap_switch(I, map, limits);\r\nelse\r\n    error('map input invalid.');\r\nend\r\n\r\n%% Update any masked pixels\r\nI = reshape(I, y*x, 3);\r\nfor a = 1:size(mask, 2)\r\n    I(mask{2,a},1) = mask{1,a}(:,1);\r\n    I(mask{2,a},2) = mask{1,a}(:,2);\r\n    I(mask{2,a},3) = mask{1,a}(:,3);\r\nend\r\nI = reshape(I, [y x 3]); % Reshape to correct size\r\n\r\n\r\n%% Only display if the output isn't used\r\nif nargout == 0\r\n    % Display the image\r\n    hIm = imdisp(I, 'Border', 0);\r\n    % Set the colormap (if valid)\r\n    if ~isempty(limits)\r\n        set(gcf, 'Colormap', map);\r\n        set(gca, 'CLim', limits);\r\n        set(hIm, 'CDataMapping', 'scaled');\r\n    end\r\n    % Don't print out the matrix if we've forgotten the \";\"\r\n    clear I\r\nend\r\nreturn\r\n\r\n%% Colormap switch\r\nfunction [I limits map] = colormap_switch(A, cmap, limits)\r\n% Reshape\r\n[y x c] = size(A);\r\nI = full(real(double(reshape(A, y*x, 1, c))));\r\n% If map starts with a '-' sign, invert the colormap\r\nreverseMap = cmap(1) == '-';\r\n% If the map ends with a '*', attempt to make map convert linearly to\r\n% grayscale\r\ngrayMap = cmap(end) == '*';\r\n% Large switch statement for all the colormaps\r\nswitch lower(cmap(1+reverseMap:end-grayMap))\r\n%% Prism\r\n    case 'prism'\r\n        % Similar to the MATLAB internal prism colormap, but only works on\r\n        % index images, assigning each index (or rounded float) to a\r\n        % different colour\r\n        [I limits] = index_im(I);\r\n        % Generate prism colormap\r\n        map = prism(6);\r\n        if reverseMap\r\n            map = map(end:-1:1,:); % Reverse the map\r\n        end\r\n        % Lookup the colours\r\n        I = mod(I, 6) + 1;\r\n        I = map(I,:);\r\n%% Rand\r\n    case 'rand'\r\n        % Assigns a random colour to each index\r\n        [I limits num_vals] = index_im(I);\r\n        % Generate random colormap\r\n        map = rand(num_vals, 3);\r\n        % Lookup the colours\r\n        I = map(I,:);\r\n%% Diff\r\n    case 'diff'\r\n        % Show positive as blue and negative as red, white is 0\r\n        switch c\r\n            case 1\r\n                I(:,2:3) = 0;\r\n            case 2\r\n                % Second channel can only have absolute value\r\n                I(:,3) = abs(I(:,2));\r\n            case 3\r\n                % Diff of RGB images - convert to YUV first\r\n                I = rgb2yuv(I);\r\n                I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(2);\r\n            otherwise\r\n                % Use difference along principle component, and other\r\n                % channels to modulate second channel\r\n                I = calc_prin_comps(I);\r\n                I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(c - 1);\r\n                I(:,4:end) = [];\r\n        end\r\n        % Generate limits\r\n        if isempty(limits)\r\n            limits = [min(I(:,1)) max(I(:,1))];\r\n        end\r\n        limits = max(abs(limits));\r\n        if limits\r\n            % Scale\r\n            if c > 1\r\n                I(:,[1 3]) = I(:,[1 3]) / limits;\r\n            else\r\n                I = I / (limits * 0.5);\r\n            end\r\n        else\r\n            limits = 1;\r\n        end\r\n        % Colour\r\n        M = I(:,1) > 0;\r\n        I(:,2) = -I(:,1) .* ~M;\r\n        I(:,1) = I(:,1) .* M;\r\n        if reverseMap\r\n            % Swap first two channels\r\n            I = I(:,[2 1 3]);\r\n        end\r\n        %I = 1 - I * [1 0.4 1; 0.4 1 1; 1 1 0.4]; % (Green/Red)\r\n        I = 1 - I * [1 1 0.4; 0.4 1 1; 1 0.4 1]; % (Blue/Red)\r\n        I = min(max(I, 0), 1);\r\n        limits = [-limits limits]; % For colorbar\r\n%% Flow\r\n    case 'flow'\r\n        % Calculate amplitude and phase, and use 'phase'\r\n        if c ~= 2\r\n            error('''flow'' requires two channels');\r\n        end\r\n        A = sqrt(sum(I .^ 2, 3));\r\n        if isempty(limits)\r\n            limits = [min(A) max(A)*2];\r\n        else\r\n            limits = [0 max(abs(limits)*sqrt(2))*2];\r\n        end\r\n        I(:,1) = atan2(I(:,2), I(:,1));\r\n        I(:,2) = A;\r\n        if reverseMap\r\n            % Invert the amplitude\r\n            I(:,2) = -I(:,2);\r\n            limits = -limits([2 1]);\r\n        end\r\n        I = phase_helper(I, limits, 2); % Last parameter tunes how saturated colors can get\r\n        % Set NaNs (unknown flow) to 0\r\n        I(isnan(I)) = reverseMap;\r\n        limits = []; % This colormap doesn't have a valid colorbar\r\n%% Phase\r\n    case 'phase'\r\n        % Plot amplitude as intensity and angle as hue\r\n        if c < 2\r\n            error('''phase'' requires two channels');\r\n        end\r\n        if isempty(limits)\r\n            limits = [min(I(:,1)) max(I(:,1))];\r\n        end\r\n        if reverseMap\r\n            % Invert the phase\r\n            I(:,2) = -I(:,2);\r\n        end\r\n        I = I(:,[2 1]);\r\n        if diff(limits)\r\n            I = phase_helper(I, limits, 1.3); % Last parameter tunes how saturated colors can get\r\n        else\r\n            % No intensity - just cycle hsv\r\n            I = real2rgb(mod(I(:,1) / (2 * pi), 1), 'hsv');\r\n        end\r\n        limits = []; % This colormap doesn't have a valid colorbar\r\n%% RGB2Gray\r\n    case {'rgb2gray', 'rgb2grey'}\r\n        % Compress RGB to greyscale\r\n        [I limits] = rgb2gray(I, limits, reverseMap);\r\n%% RGB2YUV\r\n    case 'rgb2yuv'\r\n        % Convert RGB to YUV - not for displaying or saving to disk!\r\n        [I limits] = rgb2yuv(I);\r\n%% YUV2RGB\r\n    case 'yuv2rgb'\r\n        % Convert YUV to RGB - undo conversion of rgb2yuv\r\n        if c ~= 3\r\n            error('''yuv2rgb'' requires a 3 channel image');\r\n        end\r\n        I = I(:,:) * [1 1 1; 0, -0.39465, 2.03211; 1.13983, -0.58060  0];\r\n        I = rescale(I, limits);\r\n        limits = []; % This colormap doesn't have a valid colorbar\r\n%% Prob\r\n    case 'prob'\r\n        % Plot first channel as grey variation of 'bled' and modulate\r\n        % according to other channels\r\n        if c > 1\r\n            A = rgb2gray(I(:,:,2:end), [], false);\r\n            I = I(:,:,1);\r\n        else\r\n            A = 0.5;\r\n        end\r\n        cmap2 = cmap;\r\n        cmap2((1:4)+reverseMap) = 'bled';\r\n        [I limits] = real2rgb(I, cmap2, limits);\r\n        I = rescale(A + I, [-0.1 1.3]);\r\n%% Prob_jet\r\n    case 'prob_jet'\r\n        % Plot first channel as 'jet' and modulate according to other\r\n        % channels\r\n        if c > 1\r\n            A = rgb2gray(I(:,:,2:end), [], false);\r\n            I = I(:,:,1);\r\n        else\r\n            A = 0.5;\r\n        end\r\n        cmap2 = cmap;\r\n        cmap2(reverseMap+(1:5)) = [];\r\n        [I limits] = real2rgb(I, cmap2, limits);\r\n        I = rescale(A + I, [0.2 1.8]);\r\n%% Compress\r\n    case 'compress'\r\n        % Compress to RGB, maximizing variance\r\n        % Determine and scale to limits\r\n        I = rescale(I, limits);\r\n        if reverseMap\r\n            % Invert after everything\r\n            I = 1 - I;\r\n        end\r\n        % Zero mean\r\n        meanCol = mean(I, 1);\r\n        isBsx = exist('bsxfun', 'builtin');\r\n        if isBsx\r\n            I = bsxfun(@minus, I, meanCol);\r\n        else\r\n            I = I - meanCol(ones(x*y, 1, 'uint8'),:);\r\n        end\r\n        % Calculate top 3 principle components\r\n        I = calc_prin_comps(I, 3);\r\n        % Normalize each channel independently\r\n        if isBsx\r\n            I = bsxfun(@minus, I, min(I, [], 1));\r\n            I = bsxfun(@times, I, 1./max(I, [], 1));\r\n        else\r\n            for a = 1:3\r\n                I(:,a) = I(:,a) - min(I(:,a));\r\n                I(:,a) = I(:,a) / max(I(:,a));\r\n            end\r\n        end\r\n        % Put components in order of human eyes' response to channels\r\n        I = I(:,[2 1 3]);\r\n        limits = []; % This colormap doesn't have a valid colorbar\r\n%% Contrast\r\n    case 'contrast'\r\n        % Use MATLAB's CONTRAST function to spread the gray levels evenly\r\n        [I limits] = intensity(I, limits, 0);\r\n        map = contrast(I, 256);\r\n        if reverseMap\r\n            map = map(end:-1:1,:);\r\n        end\r\n        I = real2rgb(I, map, [0 1]);\r\n%% Stereo (anaglyph)\r\n    case 'stereo'\r\n        % Convert 2 colour images to intensity images\r\n        % Show first channel as red and second channel as cyan\r\n        A = rgb2gray(I(:,1:floor(end/2)), limits, false);\r\n        I = rgb2gray(I(:,floor(end/2)+1:end), limits, false);\r\n        if reverseMap\r\n            I(:,2:3) = A(:,1:2); % Make first image cyan\r\n        else\r\n            I(:,1) = A(:,1); % Make first image red\r\n        end\r\n        limits = []; % This colormap doesn't have a valid colorbar\r\n%% Coloured anaglyph\r\n    case 'stereo_col'\r\n        if c ~= 6\r\n            error('''stereo_col'' requires a 6 channel image');\r\n        end\r\n        I = rescale(I, limits);\r\n        % Red channel from one image, green and blue from the other\r\n        if reverseMap\r\n            I(:,1) = I(:,4); % Make second image red\r\n        else\r\n            I(:,2:3) = I(:,5:6); % Make first image red\r\n        end\r\n        I = I(:,1:3);\r\n        limits = []; % This colormap doesn't have a valid colorbar\r\n%% Coloured segments\r\n    case 'segment'\r\n        % Last channel assumed to be an index image. Output each segment as\r\n        % a mean of the other channels in the segment.\r\n        if c < 2\r\n            error('''segment'' requires at least 2 channels');\r\n        end\r\n        [S num_vals num_vals] = index_im(I(:,end));\r\n        % Generate the means\r\n        R = 1 ./ (accumarray(S, 1, [num_vals 1]) + 1e-300);\r\n        for a = c-1:-1:1\r\n            J(:,1,a) = accumarray(S, I(:,a), [num_vals 1]) .* R;\r\n        end\r\n        if c == 4\r\n            J = rescale(J, limits);\r\n            limits = [];\r\n        else\r\n            [J limits map] = table_wrapper(J, 'gray', limits);\r\n        end\r\n        % Generate the image\r\n        I = J(S,:);\r\n%% None\r\n    case 'none'\r\n        % No colormap - just output the image\r\n        if c == 3\r\n            I = rescale(I, limits);\r\n            limits = [];\r\n            if reverseMap\r\n                I = 1 - I;\r\n            end\r\n        else\r\n            cmap((1:4)+reverseMap) = 'gray';\r\n            [I limits map] = table_wrapper(I, cmap, limits);\r\n        end\r\n%% Grey\r\n    case 'grey'\r\n        cmap(3+reverseMap) = 'a';\r\n        [I limits map] = table_wrapper(I, cmap, limits);\r\n%% Colourcube\r\n    case {'colorcube2', 'colourcube2'}\r\n        % Psychedelic colormap inspired by MATLAB's version\r\n        [I limits] = intensity(I, limits, reverseMap); % Intensity map\r\n        step = 4;\r\n        I = I * (step * (1 - eps));\r\n        J = I * step;\r\n        K = floor(J);\r\n        I = cat(3, mod(K, step)/(step-1), J - floor(K), mod(floor(I), step)/(step-1));\r\n%% Functional colormap\r\n    otherwise\r\n        [I limits map] = table_wrapper(I, cmap, limits);\r\nend\r\nI = reshape(I, y, x, 3);\r\n% Fix up the colormap\r\nif nargout > 2\r\n    if isempty(limits)\r\n        map = [];\r\n    elseif ~exist('map', 'var')\r\n        map = (0:255) * ((limits(2) - limits(1)) / 255) + limits(1);\r\n        map = squeeze(colormap_switch(map, cmap, limits));\r\n    end\r\nend\r\nreturn\r\n\r\n%% Parse input variables\r\nfunction [map limits mask] = parse_inputs(I, inputs, y, x)\r\n\r\n% Check the first two arguments for the colormap and limits\r\nninputs = numel(inputs);\r\nmap = 'none';\r\nlimits = [];\r\nmask = 1;\r\nfor a = 1:min(2, ninputs)\r\n    if ischar(inputs{a}) && numel(inputs{a}) > 1\r\n        % Name of colormap\r\n        map = inputs{a};\r\n    elseif isnumeric(inputs{a})\r\n        [p q r] = size(inputs{a});\r\n        if (p * q * r) == 2\r\n            % Limits\r\n            limits = double(inputs{a});\r\n        elseif p > 1 && (q == 3 || q == 4) && r == 1\r\n            % Table-based colormap\r\n            map = inputs{a};\r\n        else\r\n            break;\r\n        end\r\n    else\r\n        break;\r\n    end\r\n    mask = mask + 1;\r\nend\r\n% Check for following inputs\r\nif mask > ninputs\r\n    mask = cell(2, 0);\r\n    return\r\nend\r\n% Following inputs must either be colour/mask pairs, or a colour for NaNs\r\nif ninputs - mask == 0\r\n    mask = cell(2, 1);\r\n    mask{1} = inputs{end};\r\n    mask{2} = ~all(isfinite(I), 3);\r\nelseif mod(ninputs-mask, 2) == 1\r\n    mask = reshape(inputs(mask:end), 2, []);\r\nelse\r\n    error('Error parsing inputs');\r\nend\r\n% Go through pairs and generate\r\nfor a = 1:size(mask, 2)\r\n    % Generate any masks from functions\r\n    if isa(mask{2,a}, 'function_handle')\r\n        mask{2,a} = mask{2,a}(I);\r\n    end\r\n    if ~islogical(mask{2,a})\r\n        error('Mask is not a logical array');\r\n    end\r\n    if ~isequal(size(mask{2,a}), [y x])\r\n        error('Mask does not match image size');\r\n    end\r\n    if ischar(mask{1,a})\r\n        if numel(mask{1,a}) == 1\r\n            % Generate colours from MATLAB colour strings\r\n            mask{1,a} = rem(floor((strfind('kbgcrmyw', mask{1,a}) - 1) * [0.25 0.5 1]), 2);\r\n        else\r\n            % Assume it's a colormap name\r\n            mask{1,a} = sc(I, mask{1,a});\r\n        end\r\n    end\r\n    mask{1,a} = reshape(mask{1,a}, [], 3);\r\n    if size(mask{1,a}, 1) ~= y*x && size(mask{1,a}, 1) ~= 1\r\n        error('Replacement color/image of unexpected dimensions');\r\n    end\r\n    if size(mask{1,a}, 1) ~= 1\r\n        mask{1,a} = mask{1,a}(mask{2,a},:);\r\n    end\r\nend\r\nreturn\r\n\r\n%% RGB2gray\r\nfunction [B limits] = rgb2gray(A, limits, reverseMap)\r\n% Compress RGB to grayscale\r\nc = size(A, 3);\r\nB = A;\r\nif c == 3\r\n    B = reshape(reshape(B, [], 3) * [0.299; 0.587; 0.114], size(B, 1), size(B, 2));\r\nend\r\n[B limits] = intensity(B, limits, reverseMap);\r\n% Replicate channels\r\nB = B(:,:,[1 1 1]);\r\nreturn\r\n        \r\n%% RGB2YUV\r\nfunction [B limits] = rgb2yuv(A)\r\n% Convert RGB to YUV - not for displaying or saving to disk!\r\nc = size(A, 3);\r\nif c ~= 3\r\n    error('rgb2yuv requires a 3 channel image');\r\nend\r\nB = reshape(A, [], c) * [0.299, -0.14713, 0.615; 0.587, -0.28886, -0.51498; 0.114, 0.436, -0.10001];\r\nlimits = []; % This colormap doesn't have a valid colorbar\r\nreturn\r\n\r\n%% Phase helper\r\nfunction I = phase_helper(I, limits, n)\r\nI = squeeze(I);\r\nI(:,1) = mod(I(:,1)/(2*pi), 1);\r\nI(:,2) = I(:,2) - limits(1);\r\nI(:,2) = I(:,2) * (n / (limits(2) - limits(1)));\r\nI(:,3) = n - I(:,2);\r\nI(:,[2 3]) = min(max(I(:,[2 3]), 0), 1);\r\nI = hsv2rgb(reshape(I, [], 1, 3));\r\nreturn\r\n\r\n%% Real2rgb wrapper\r\nfunction [B limits map] = table_wrapper(A, cmap, limits)\r\n% Convert to intensity\r\n[B limits] = intensity(A, limits, 0);\r\n% Convert to colour using the specified colormap\r\n[B map map] = real2rgb(B, cmap, [0 1]);\r\nreturn\r\n\r\n%% Intensity maps\r\nfunction [I limits] = intensity(I, limits, reverseMap)\r\n% Squash to 1d using L2 norm\r\nif size(I, 3) > 1\r\n    I = sqrt(sum(I .^ 2, 3));\r\nend\r\n% Determine and scale to limits\r\n[I limits] = rescale(I, limits);\r\nif limits(1) == limits(2)\r\n    limits(2) = limits(1) + 1;\r\nend\r\nif reverseMap\r\n    % Invert after everything\r\n    I = 1 - I;\r\nend\r\nreturn\r\n\r\n%% Index images\r\nfunction [J limits num_vals] = index_im(I)\r\n% Returns an index image\r\nif size(I, 2) ~= 1\r\n    error('Index maps only work on single channel images');\r\nend\r\nJ = round(I);\r\nrescaled = any(abs(I - J) > 0.01);\r\nif rescaled\r\n    % Appears not to be an index image. Rescale over 256 indices\r\n    m = min(I);\r\n    m = m * (1 - sign(m) * eps);\r\n    I = I - m;\r\n    I = I * (256 / max(I(:)));\r\n    J = ceil(I);\r\n    num_vals = 256;\r\nelseif nargout > 2\r\n    % Output the number of values\r\n    J = J - (min(J) - 1);\r\n    num_vals = max(J);\r\nend\r\n% These colormaps don't have valid colorbars\r\nlimits = [];    \r\nreturn\r\n\r\n%% Calculate principle components\r\nfunction I = calc_prin_comps(I, numComps)\r\nc = size(I, 3);\r\nI = reshape(I, [], c);\r\nif nargin < 2\r\n    numComps = c;\r\nend\r\n% Do SVD\r\n[I S] = svd(I, 0);\r\n% Calculate projection of data onto components\r\nS = diag(S);\r\nS = S(1:numComps)';\r\nif exist('bsxfun', 'builtin')\r\n    I = bsxfun(@times, I(:,1:numComps), S);\r\nelse\r\n    I = I(:,1:numComps) .* S(ones(size(I, 1), 1, 'uint8'),:);\r\nend\r\nreturn\r\n\r\n%% Demo function to show capabilities of sc\r\nfunction demo\r\n%% Demo gray & lack of border\r\nfigure; fig = gcf; Z = peaks(256); sc(Z);\r\ndisplay_text([...\r\n' Lets take a standard, MATLAB, real-valued function:\\n\\n    peaks(256)\\n\\n'...\r\n' Calling:\\n\\n    figure\\n    Z = peaks(256);\\n    sc(Z)\\n\\n'...\r\n' gives (see figure). SC automatically scales intensity to fill the\\n'...\r\n' truecolor range of [0 1].\\n\\n'...\r\n' If your figure isn''t docked, then the image will have no border, and\\n'...\r\n' will be magnified by an integer factor (in this case, 2) so that the\\n'...\r\n' image is a reasonable size.']);\r\n\r\n%% Demo colour image display \r\nfigure(fig); clf;\r\nload mandrill; mandrill = ind2rgb(X, map); sc(mandrill);\r\ndisplay_text([...\r\n' That wasn''t so interesting. The default colormap is ''none'', which\\n'...\r\n' produces RGB images given a 3-channel input image, otherwise it produces\\n'...\r\n' a grayscale image. So calling:\\n\\n    load mandrill\\n'...\r\n'    mandrill = ind2rgb(X, map);\\n    sc(mandrill)\\n\\n gives (see figure).']);\r\n\r\n%% Demo discretization\r\nfigure(fig); clf;\r\nsubplot(121); sc(Z, 'jet'); label(Z, 'sc(Z, ''jet'')');\r\nsubplot(122); imagesc(Z); axis image off; colormap(jet(64)); % Fix the fact we change the default depth\r\nlabel(Z, 'imagesc(Z); axis image off; colormap(''jet'');');\r\ndisplay_text([...\r\n' However, if we want to display intensity images in color we can use any\\n'...\r\n' of the MATLAB colormaps implemented (most of them) to give truecolor\\n'...\r\n' images. For example, to use ''jet'' simply call:\\n\\n'...\r\n'    sc(Z, ''jet'')\\n\\n'...\r\n' The MATLAB alternative, shown on the right, is:\\n\\n'...\r\n'    imagesc(Z)\\n    axis equal off\\n    colormap(jet)\\n\\n'...\r\n' which generates noticeable discretization artifacts.']);\r\n\r\n%% Demo intensity colormaps\r\nfigure(fig); clf;\r\nsubplot(221); sc(Z, 'hsv'); label(Z, 'sc(Z, ''hsv'')');\r\nsubplot(222); sc(Z, 'colorcube'); label(Z, 'sc(Z, ''colorcube'')');\r\nsubplot(223); sc(Z, 'hicontrast'); label(Z, 'sc(Z, ''hicontrast'')');\r\nsubplot(224); sc(Z-round(Z), 'diff'); label(Z, 'sc(Z-round(Z), ''diff'')');\r\ndisplay_text([...\r\n' There are several other intensity colormaps to choose from. Calling:\\n\\n'...\r\n'    help sc\\n\\n'...\r\n' will give you a list of them. Here are several others demonstrated.']);\r\n\r\n%% Demo saturation limits & colormap reversal\r\nfigure(fig); clf;\r\nsubplot(121); sc(Z, [0 max(Z(:))], '-hot'); label(Z, 'sc(Z, [0 max(Z(:))], ''-hot'')');\r\nsubplot(122); sc(mandrill, [-0.5 0.5]); label(mandrill, 'sc(mandrill, [-0.5 0.5])');\r\ndisplay_text([...\r\n' SC can also rescale intensity, given an upper and lower bound provided\\n'...\r\n' by the user, and invert most colormaps simply by prefixing a ''-'' to the\\n'...\r\n' colormap name. For example:\\n\\n'...\r\n'    sc(Z, [0 max(Z(:))], ''-hot'');\\n'...\r\n'    sc(mandrill, [-0.5 0.5]);\\n\\n'...\r\n' Note that the order of the colormap and limit arguments are\\n'...\r\n' interchangable.']);\r\n\r\n%% Demo prob\r\nload gatlin;\r\ngatlin = X;\r\nfigure(fig); clf; im = cat(3, abs(Z)', gatlin(1:256,end-255:end)); sc(im, 'prob');\r\nlabel(im, 'sc(cat(3, prob, gatlin), ''prob'')');\r\ndisplay_text([...\r\n' SC outputs the recolored data as a truecolor RGB image. This makes it\\n'...\r\n' easy to combine colormaps, either arithmetically, or by masking regions.\\n'...\r\n' For example, we could combine an image and a probability map\\n'...\r\n' arithmetically as follows:\\n\\n'...\r\n'    load gatlin\\n'...\r\n'    gatlin = X(1:256,end-255:end);\\n'...\r\n'    prob = abs(Z)'';\\n'...\r\n'    im = sc(prob, ''hsv'') .* sc(prob, ''gray'') + sc(gatlin, ''rgb2gray'');\\n'...\r\n'    sc(im, [-0.1 1.3]);\\n\\n'...\r\n' In fact, that particular colormap has already been implemented in SC.\\n'...\r\n' Simply call:\\n\\n'...\r\n'    sc(cat(3, prob, gatlin), ''prob'');']);\r\n\r\n%% Demo colorbar\r\ncolorbar;\r\ndisplay_text([...\r\n' SC also makes possible the generation of a colorbar in the normal way, \\n'...\r\n' with all the colours and data values correct. Simply call:\\n\\n'...\r\n'    colorbar\\n\\n'...\r\n' The colorbar doesn''t work with all colormaps, but when it does,\\n'...\r\n' inverting the colormap (using ''-map'') maintains the integrity of the\\n'...\r\n' colorbar (i.e. it works correctly) - unlike if you invert the input data.']);\r\n\r\n%% Demo combine by masking\r\nfigure(fig); clf;\r\nsc(Z, [0 max(Z(:))], '-hot', sc(Z-round(Z), 'diff'), Z < 0);\r\ndisplay_text([...\r\n' It''s just as easy to combine generated images by masking too. Here''s an\\n'...\r\n' example:\\n\\n'...\r\n'    im = cat(4, sc(Z, [0 max(Z(:))], ''-hot''), sc(Z-round(Z), ''diff''));\\n'...\r\n'    mask = repmat(Z < 0, [1 1 3]);\\n'...\r\n'    mask = cat(4, mask, ~mask);\\n'...\r\n'    im = sum(im .* mask, 4);\\n'...\r\n'    sc(im)\\n\\n'...\r\n' In fact, SC can also do this for you, by adding image/colormap and mask\\n'...\r\n' pairs to the end of the argument list, as follows:\\n\\n'...\r\n'    sc(Z, [0 max(Z(:))], ''-hot'', sc(Z-round(Z), ''diff''), Z < 0);\\n\\n'...\r\n' A benefit of the latter approach is that you can still display a\\n'...\r\n' colorbar for the first colormap.']);\r\n\r\n%% Demo user defined colormap\r\nfigure(fig); clf; sc(abs(Z), rand(10, 3)); colorbar;\r\ndisplay_text([...\r\n' SC can also use user defined colormaps to display indexed images.\\n'...\r\n' These can be defined as a linear colormap. For example:\\n\\n'...\r\n'    sc(abs(Z), rand(10, 3))\\n    colorbar;\\n\\n'...\r\n' Note that the colormap is automatically linearly interpolated.']);\r\n\r\n%% Demo non-linear user defined colormap\r\nfigure(fig); clf; sc(abs(Z), [rand(10, 3) exp((1:10)/2)']); colorbar;\r\ndisplay_text([...\r\n' Non-linear colormaps can also be defined by the user, by including the\\n'...\r\n' relative distance between the given colormap points on the colormap\\n'...\r\n' scale in the fourth column of the colormap matrix. For example:\\n\\n'...\r\n'    sc(abs(Z), [rand(10, 3) exp((1:10)/2)''])\\n    colorbar;\\n\\n'...\r\n' Note that the colormap is still linearly interpolated between points.']);\r\n\r\n%% Demo compress\r\nload mri;\r\nmri = D;\r\nfigure(fig); clf;\r\nsc(squeeze(mri(:,:,:,1:6)), 'compress');\r\ndisplay_text([...\r\n' SC has many colormaps for displaying multi-dimensional data, including\\n'...\r\n' ''flow'' for optic flow fields, ''phase'' for edge maps with phase, and\\n'...\r\n' ''stereo'' for generating anaglyphs.\\n\\n'...\r\n' For images with more than 3 channels, SC can compress these images to RGB\\n'...\r\n' while maintaining the maximum amount of variance in the data. For\\n'...\r\n' example, this 6 channel image:\\n\\n'...\r\n'    load mri\\n    mri = D;\\n    sc(squeeze(mri(:,:,:,1:6), ''compress'')']);\r\n\r\n%% Demo texture map\r\nfigure(fig); clf;\r\nsurf(Z, sc(Z, 'hicontrast'), 'edgecolor', 'none');\r\ndisplay_text([...\r\n' Further benefits of SC outputting the image as an array (on top of being\\n'...\r\n' able to combine images) are that the image can be saved straight to disk\\n'...\r\n' using imwrite(), or can be used to texture map a surface, thus:\\n\\n'...\r\n'    tex = sc(Z, ''hicontrast'');\\n'...\r\n'    surf(Z, tex, ''edgecolor'', ''none'');']);\r\n\r\n%% Demo multiple images\r\nclose(fig); % Only way to get round loss of focus (bug?)\r\nfigure(fig); clf; sc(mri, 'bone');\r\ndisplay_text([...\r\n' Finally, SC can process multiple images when passed in as a 4d array,\\n'...\r\n' either for export or for display. In the latter case, images are\\n'...\r\n' displayed as a montage of up to 12 images. For example:\\n\\n'...\r\n'    sc(mri, ''bone'')']);\r\n\r\ndisplay_text([...\r\n' Any additional images can then be viewed by scrolling through them using\\n'...\r\n' using the scroll (arrow) keys. Try it and see.']);\r\n\r\nclc; fprintf('End of demo.\\n');\r\nreturn\r\n\r\n%% Some helper functions for the demo\r\nfunction display_text(str)\r\nclc;\r\nfprintf([str '\\n\\n']);\r\nfprintf('Press a key to go on.\\n');\r\nfigure(gcf);\r\nwaitforbuttonpress;\r\nreturn\r\n\r\nfunction label(im, str)\r\ntext(size(im, 2)/2, size(im, 1)+12, str,...\r\n    'Interpreter', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle');\r\nreturn\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "rescale.m", "ext": ".m", "path": "IA-Lab-master/Utilities/sc/private/rescale.m", "size": 1613, "source_encoding": "utf_8", "md5": "427de5e2419533818c74ea0c8fd7f773", "text": "function [B lims] = rescale(A, lims, out_lims)\r\n%RESCALE  Linearly rescale values in an array\r\n%\r\n% Examples:\r\n%   B = rescale(A)\r\n%   B = rescale(A, lims)\r\n%   B = rescale(A, lims, out_lims)\r\n%   [B lims] = rescale(A)\r\n%\r\n% Linearly rescales values in an array, saturating values outside limits.\r\n%\r\n% IN:\r\n%   A - Input array of any size and class.\r\n%   lims - 1x2 array of saturation limits to be used on A. Default:\r\n%          [min(A(:)) max(A(:))].\r\n%   out_lims - 1x2 array of output limits the values in lims are to be\r\n%              rescaled to. Default: [0 1].\r\n%\r\n% OUT:\r\n%   B - size(A) double array.\r\n%   lims - 1x2 array of saturation limits used on A. Equal to the input\r\n%          lims, if given.\r\n\r\n% $Id: rescale.m,v 1.2 2009/04/05 15:09:11 ojw Exp $\r\n% Copyright: Oliver Woodford, 2009\r\n\r\nif nargin < 3\r\n    out_lims = [0 1];\r\nend\r\nif nargin < 2 || isempty(lims)\r\n    M = isfinite(A);\r\n    if ~any(reshape(M, numel(M), 1))\r\n        % All NaNs, Infs or -Infs\r\n        B = double(A > 0);\r\n        lims = [0 1];\r\n    else\r\n        lims = [min(A(M)) max(A(M))];\r\n        B = normalize(A, lims, out_lims);\r\n    end\r\n    clear M\r\nelse\r\n    B = normalize(A, lims, out_lims);\r\n    B = min(max(B, out_lims(1)), out_lims(2));\r\nend\r\nreturn\r\n\r\nfunction B = normalize(A, lims, out_lims)\r\nif lims(2) == lims(1) || out_lims(1) == out_lims(2)\r\n    B = zeros(size(A));\r\nelse\r\n    B = double(A);\r\n    if lims(1)\r\n        B = B - lims(1);\r\n    end\r\n    v = (out_lims(2) - out_lims(1)) / (lims(2) - lims(1));\r\n    if v ~= 1\r\n        B = B * v;\r\n    end\r\nend\r\nif out_lims(1)\r\n    B = B + out_lims(1);\r\nend\r\nreturn"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "sfm_local_chanvese.m", "ext": ".m", "path": "IA-Lab-master/ThirdParty/lanktonchanvese/sfm_local_chanvese.m", "size": 2172, "source_encoding": "utf_8", "md5": "3a6307d657f860fcd0f80de155397eb3", "text": "% [seg Lz] = sfm_local_chanvese(img,mask,iterations,lambda,rad,display)\r\n% \r\n% img - any image (2D or 3D).  color images will be \r\n%       converted to grayscale.\r\n%\r\n% mask - binary image representing initialization.  \r\n%        (1's foreground, 0's background)\r\n% \r\n% iterations - number of iterations to run\r\n%\r\n% lambda - relative weighting of curve smoothness \r\n%          (lambda will ususally be between [0,1])\r\n%\r\n% rad - the radius of the ball used for localization\r\n%\r\n% display - whether or not to display intermediate results\r\n%\r\n% seg - binary map of resulting segmentation \r\n%       (1's foreground, 0's background)\r\n%\r\n% Lz - A list of the indexes of points on the zero level set.\r\n%\r\n% --------------------------------------\r\n% written by Shawn Lankton (4/17/2009) - www.shawnlankton.com\r\n%\r\nfunction [seg Lz] = sfm_local_chanvese(img,mask,iterations,lam,rad,display)\r\n  \r\n  % ensure mex is compiled\r\n  if(isempty(which('sfm_local_chanvese_mex')))\r\n    compile_sfm_local_chanvese\r\n  end\r\n\r\n  % check for color image and convert to grayscale\r\n  if(numel(size(img))==3) %if image is 3D\r\n    if(size(img,3)==3)     %z dim is 3... probably color\r\n      img = rgb2gray(img);%rescale to grayscale\r\n    end\r\n  end\r\n  \r\n  % mex is expecting img to be a double\r\n  img = double(img);      \r\n  mask = double(mask);\r\n  \r\n  if(any(size(img)~=size(mask)))\r\n    error('img and mask must be the same size');\r\n  end\r\n  \r\n  % default value of iterations is 100\r\n  if(~exist('iterations','var')) \r\n    iterations = 100; \r\n  end\r\n  iterations = double(iterations);\r\n\r\n  % default value of lambda is 0.1\r\n  if(~exist('lam','var')) \r\n    lam = 0.1; \r\n  end\r\n  lam = double(lam);\r\n\r\n  % default value of rad is 10\r\n  if(~exist('rad','var')) \r\n    rad = 10; \r\n  end\r\n  rad = double(rad);\r\n\r\n  % default value of display is 1\r\n  if(~exist('display','var')) \r\n    display = 1; \r\n  end\r\n  display = double(display);\r\n\r\n  if(display)\r\n    clf;\r\n    imagesc(img);\r\n  end\r\n  \r\n\r\n  % perform segmentation\r\n  [phi c] = sfm_local_chanvese_mex(img,mask,iterations,display,lam,rad);\r\n  \r\n  % get labelmap from signed distance function\r\n  seg = phi<=0;\r\n  Lz  = uint32(c)+1;\r\n  \r\n  \r\n  \r\n  \r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "fat_contour.m", "ext": ".m", "path": "IA-Lab-master/ThirdParty/lanktonchanvese/fat_contour.m", "size": 571, "source_encoding": "utf_8", "md5": "d41a83dbdb50798bcf825109fc225bb8", "text": "% FAT_CONTOUR draw a easily visible contour\r\nfunction [h1 h2] = fat_contour(phi,dashed,c1)\r\n%Coded by: Shawn Lankton\r\n%Function:  Display a contour\r\n  c2 = 'k';\r\n  if(~exist('c1','var')) c1 = 'r'; end\r\n  if(~exist('dashed','var')) dashed = false; end\r\n  \r\n  t1 = 4;\r\n  t2 = 2;\r\n\r\n  hold on;\r\n  if(dashed)\r\n    h1 = contour(phi,[0 0],c1,'linewidth',t1,'linestyle','--');  \r\n    h2 = contour(phi,[0 0],c2,'linewidth',t2,'linestyle','--');\r\n  else\r\n    h1 = contour(phi,[0 0],c1,'linewidth',t1);  \r\n    h2 = contour(phi,[0 0],c2,'linewidth',t2);\r\n  end\r\n  hold off;\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "AboutWound.m", "ext": ".m", "path": "IA-Lab-master/Examples/Pellet/AboutWound.m", "size": 3125, "source_encoding": "utf_8", "md5": "e11339d283a2565d848bcb30104c1aa5", "text": "function varargout = AboutWound(varargin)\r\n% ABOUTWOUND MATLAB code for AboutWound.fig\r\n%      ABOUTWOUND, by itself, creates a new ABOUTWOUND or raises the existing\r\n%      singleton*.\r\n%\r\n%      H = ABOUTWOUND returns the handle to a new ABOUTWOUND or the handle to\r\n%      the existing singleton*.\r\n%\r\n%      ABOUTWOUND('CALLBACK',hObject,eventData,handles,...) calls the local\r\n%      function named CALLBACK in ABOUTWOUND.M with the given input arguments.\r\n%\r\n%      ABOUTWOUND('Property','Value',...) creates a new ABOUTWOUND or raises the\r\n%      existing singleton*.  Starting from the left, property value pairs are\r\n%      applied to the GUI before AboutWound_OpeningFcn gets called.  An\r\n%      unrecognized property name or invalid value makes property application\r\n%      stop.  All inputs are passed to AboutWound_OpeningFcn via varargin.\r\n%\r\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\r\n%      instance to run (singleton)\".\r\n%\r\n% See also: GUIDE, GUIDATA, GUIHANDLES\r\n\r\n% Edit the above text to modify the response to help AboutWound\r\n\r\n% Last Modified by GUIDE v2.5 12-Dec-2016 10:16:07\r\n\r\n% Begin initialization code - DO NOT EDIT\r\ngui_Singleton = 1;\r\ngui_State = struct('gui_Name',       mfilename, ...\r\n                   'gui_Singleton',  gui_Singleton, ...\r\n                   'gui_OpeningFcn', @AboutWound_OpeningFcn, ...\r\n                   'gui_OutputFcn',  @AboutWound_OutputFcn, ...\r\n                   'gui_LayoutFcn',  [] , ...\r\n                   'gui_Callback',   []);\r\nif nargin && ischar(varargin{1})\r\n    gui_State.gui_Callback = str2func(varargin{1});\r\nend\r\n\r\nif nargout\r\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\r\nelse\r\n    gui_mainfcn(gui_State, varargin{:});\r\nend\r\n% End initialization code - DO NOT EDIT\r\nend\r\n\r\n% --- Executes just before AboutWound is made visible.\r\nfunction AboutWound_OpeningFcn(hObject, eventdata, handles, varargin)\r\n% This function has no output args, see OutputFcn.\r\n% hObject    handle to figure\r\n% eventdata  reserved - to be defined in a future version of MATLAB\r\n% handles    structure with handles and user data (see GUIDATA)\r\n% varargin   command line arguments to AboutWound (see VARARGIN)\r\n\r\n    anImage = imread('pellet.png');\r\n    imshow(anImage, [], 'Parent', handles.axes1, 'InitialMagnification', 'fit');\r\n    \r\n    set(handles.axes1,'xcolor','w','ycolor','w','xtick',[],'ytick',[]);\r\n    set(handles.figure1,'Name','About Pellet Explorer')\r\n    \r\n    % Update handles structure\r\n    guidata(hObject, handles);\r\n    \r\n    % UIWAIT makes YEAbout wait for user response (see UIRESUME)\r\n    % uiwait(handles.figure1);\r\nend\r\n\r\n% --- Outputs from this function are returned to the command line.\r\nfunction varargout = AboutWound_OutputFcn(hObject, eventdata, handles) \r\n% varargout  cell array for returning output args (see VARARGOUT);\r\n% hObject    handle to figure\r\n% eventdata  reserved - to be defined in a future version of MATLAB\r\n% handles    structure with handles and user data (see GUIDATA)\r\n\r\n% Get default command line output from handles structure\r\n    varargout = cell(nargout,1);\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "amcFillNaNs.m", "ext": ".m", "path": "IA-Lab-master/Examples/Spheroid3D/utilities/amcFillNaNs.m", "size": 1083, "source_encoding": "utf_8", "md5": "58043bf2cbc8e1f285c575e09666201e", "text": "function outim = amcFillNaNs(im,blurScale,maxIts,initScale,epsilon)\r\n\r\n% fill in NaNs by iteratively blurring from the edges\r\n\r\nif nargin<2 || isempty(blurScale)\r\n    blurScale = max(1,0.01*min(size(im,1),size(im,2)));\r\nend\r\n\r\nif nargin<3 || isempty(maxIts)\r\n    maxIts = 40;\r\nend\r\n\r\nif nargin<4 || isempty(initScale)\r\n    initScale = 5*blurScale;\r\nend\r\n\r\nif nargin<5 || isempty(epsilon)\r\n    epsilon = 2e-2;\r\nend\r\n\r\nnanreg = isnan(im);\r\n\r\nfim = gaussBlurNaNs(im,initScale,nanreg);\r\nwhile nnz(isnan(fim))>0\r\n    fim = gaussBlurNaNs(fim,initScale);\r\nend\r\n\r\noutim = im;\r\noutim(nanreg) = fim(nanreg);\r\n\r\nfor ii = 1:maxIts\r\n    fim = gaussFiltND(outim,blurScale*[1,1]);\r\n    if max(abs(fim(nanreg)-outim(nanreg))./outim(nanreg))<epsilon\r\n        outim(nanreg) = fim(nanreg);\r\n        break;\r\n    else\r\n        outim(nanreg) = fim(nanreg);\r\n    end\r\nend\r\n\r\nend\r\n\r\nfunction fim = gaussBlurNaNs(im,lscale,bw)\r\n\r\nif nargin<3 || isempty(bw)\r\n    bw = isnan(im);\r\nend\r\n\r\nim(bw) = 0;\r\n\r\nnum = gaussFiltND(im,lscale*[1,1],0);\r\ndenom = gaussFiltND(~bw,lscale*[1,1],0);\r\n\r\nfim = num./denom;\r\n\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "bwdistsc.m", "ext": ".m", "path": "IA-Lab-master/Examples/Spheroid3D/utilities/bwdistsc.m", "size": 18385, "source_encoding": "utf_8", "md5": "a8192c2781c7f03ef9cf7e9f0b91c769", "text": "function D=bwdistsc(bw,aspect)\r\n% D=BWDISTSC(BW,ASPECT)\r\n% BWDISTSC computes Euclidean distance transform of the binary 3D image \r\n% BW. For each pixel in BW, the distance transform assignes a number\r\n% that is the distance between that pixel and the nearest nonzero pixel \r\n% of BW. BW may be a single 2D image, 3D array or a cell array of \r\n% 2D slices. ASPECT is 3-component vector defining aspect ratio in \r\n% the dataset BW. If ASPECT is not specified, isotropic aspect \r\n% ratio [1 1 1] is assumed.\r\n%\r\n% BWDISTSC uses fast optimized scanning algorithm and cell-arrays to \r\n% represent internal data, so that it is less demanding to physical \r\n% memory. In many cases BWDISTSC is actually faster than MATLAB's \r\n% optimized kd-tree algorithm used for Euclidean distance \r\n% transform in 3D. \r\n%\r\n% BWDISTSC tries to use MATLAB bwdist for 2D scans if possible, which \r\n% is significantly faster. Otherwise BWDISTSC uses internal algorithm \r\n% to perform 2D scans.\r\n%\r\n%     Yuriy Mishchenko  JFRC HHMI Chklovskii Lab  JUL 2007\r\n\r\n% This code is free for use or modifications, just please give credit \r\n% where appropriate. And if you modify code or fix bugs, please drop \r\n% me a message at gmyuriy@hotmail.com.\r\n\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n% Scan algorithms below use following Lema:                     %\r\n% LEMA: let F(X,z) be lower envelope of a family of parabola:   %\r\n% F(X,z)=min_{i} [G_i(X)+(z-k_i)^2];                            %\r\n% and let H_k(X,z)=A(X)+(z-k)^2 be a parabola.                  %\r\n% Then for H_k(X,z)==F(X,z) at each X there exist at most       %\r\n% two solutions k1<k2 such that H_k12(X,z)=F(X,z), and          %\r\n% H_k(X,z)<F(X,z) is restricted to at most k1<k2.               %\r\n% Here X is any-dimensional coordinate.                         %\r\n%                                                               %\r\n% Thus, simply scan away from any z such that H_k(X,z)<F(X,z)   %\r\n% in either direction as long as H_k(X,z)<F(X,z) and update     %\r\n% F(X,z). Note that need to properly choose starting point;     %\r\n% starting point is any z such that H_k(X,z)<F(X,z); z==k is    %\r\n% usually, but not always the starting point!!!                 %\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n\r\n% parse inputs\r\nif(nargin<2 || isempty(aspect)) aspect=[1 1 1]; end\r\n\r\n% determine geometry of data\r\nif(iscell(bw)) shape=[size(bw{1}),length(bw)]; else shape=size(bw); end\r\n\r\n% fix to handle 1D & 2D images\r\nif(length(shape)<3) shape(length(shape)+1:3)=1; end\r\nif(length(aspect)<3) aspect(length(aspect)+1:3)=1; end\r\n\r\n% allocate space\r\nD=cell(1,shape(3)); for k=1:shape(3) D{k}=zeros(shape(1:2)); end\r\n\r\n%%%%%%%%%%%%% scan along XY %%%%%%%%%%%%%%%%\r\nfor k=1:shape(3)    \r\n    if(iscell(bw)) bwXY=bw{k}; else bwXY=bw(:,:,k); end\r\n        \r\n    % initialize arrays\r\n    DXY=zeros(shape(1:2));\r\n    D1=zeros(shape(1:2));\r\n    DK=zeros(shape(1:2));\r\n\r\n    % if can, use 2D bwdist from image processing toolbox    \r\n    if(exist('bwdist') && aspect(1)==aspect(2))\r\n        D1=aspect(1)^2*bwdist(bwXY).^2;\r\n    else    % if not, use full XY-scan\r\n        %%%%%%%%%%%%%%% X-SCAN %%%%%%%%%%%%%%%        \r\n        % reference nearest bwXY \"on\"-pixel in x direction downward:\r\n        \r\n        %  scan bottow-up, copy x-reference from previous row unless \r\n        %  there is bwXY \"on\"-pixel in that point in current row\r\n        xlower=repmat(Inf,shape(1:2)); \r\n        \r\n        xlower(1,find(bwXY(1,:)))=1;    % fill in first row\r\n        for i=2:shape(1)\r\n            xlower(i,:)=xlower(i-1,:);  % copy previous row\r\n            xlower(i,find(bwXY(i,:)))=i;% unless there is pixel\r\n        end\r\n        \r\n        % reference nearest bwXY \"on\"-pixel in x direction upward:\r\n        xupper=repmat(Inf,shape(1:2));\r\n        \r\n        xupper(end,find(bwXY(end,:)))=shape(1);\r\n        for i=shape(1)-1:-1:1\r\n            xupper(i,:)=xupper(i+1,:);\r\n            xupper(i,find(bwXY(i,:)))=i;\r\n        end\r\n                \r\n        % find points for which distance needs to be updated\r\n        idx=find(~bwXY); [x,y]=ind2sub(shape(1:2),idx);\r\n        \r\n        % set distance as the shortest to upward or to downward\r\n        DXY(idx)=aspect(1)^2*min((x-xlower(idx)).^2,(x-xupper(idx)).^2);\r\n        \r\n        %%%%%%%%%%%%%%% Y-SCAN %%%%%%%%%%%%%%%\r\n        % this will be the envelop \r\n        % envelop is initialized at Inf to ensure single scan direction, \r\n        % otherwise may end up in infinite loop when trying to find \r\n        % starting point\r\n        D1=repmat(Inf,shape(1:2));\r\n        % these will be the references to parabolas defining the envelop\r\n        DK=repmat(Inf,shape(1:2));\r\n        % starting points\r\n        i0=zeros(shape(1),1);\r\n        % convenience x-coords array \r\n        x=(1:shape(1))'; \r\n        \r\n        for i=1:shape(2)\r\n            % need to select starting point for each X:\r\n            % * starting point should be below current envelop\r\n            % * i0==i is not necessarily a starting point\r\n            % * there is at most one starting point\r\n            % * there may be no starting point\r\n            \r\n            % i0 is the starting points for each X: i0(X) is the first \r\n            % y-index such that parabola from line i is below the envelop\r\n            \r\n            % first guess is the current y-line\r\n            i0(:)=i;\r\n            \r\n            % some auxiliary datasets\r\n            d0=DXY(:,i);\r\n\r\n            % L0 indicates for which X starting point had been fixed\r\n            L0=isinf(d0) | (d0==0);\r\n            \r\n            while(~isempty(find(~L0,1)))\r\n                % reference starting points in DXY\r\n                idx=sub2ind(shape(1:2),x(~L0),i0(~L0));\r\n                \r\n                % reduce out trivial points (DXY==0)\r\n                L=(DXY(idx)==0);\r\n                L0(~L0)=L;\r\n                idx=idx(~L);\r\n                \r\n                if(isempty(idx)) continue; end\r\n                \r\n                % these are current best parabolas for starting points\r\n                ik=DK(idx);\r\n                \r\n                % these are new values from parabola from line #i\r\n                dtmp=d0(~L0)+aspect(2)^2*(i0(~L0)-i).^2;\r\n                \r\n                % these starting points are OK - below the envelop\r\n                L=D1(idx)>dtmp; D1(idx(L))=dtmp(L);\r\n                \r\n                % points which are still above the envelop but ik==i0,\r\n                % will not get any better, so fix them as well\r\n                L=L | (ik==i0(~L0));\r\n                                \r\n                % all other points are not OK, need new starting point:\r\n                % starting point should be at least below parabola \r\n                % beating us at current choice of i0\r\n                \r\n                % solve quadratic equation to find where this happens\r\n                ik=(ik-i); \r\n                di=(D1(idx(~L))-dtmp(~L))./ik(~L)/2/aspect(2)^2;\r\n\r\n                % should select next highest index to the equality\r\n                di=fix(di)+sign(di);\r\n                \r\n                % the new starting points\r\n                idx=find(~L0); \r\n                i0(idx(~L))=i0(idx(~L))+di;\r\n\r\n                % update L0 to indicate which points we've fixed\r\n                L0(~L0)=L; L0(idx(~L))=(di==0);\r\n                \r\n                % points that went out of boundaries can't get better; \r\n                % fix them as well\r\n                idx=idx(~L);\r\n                idx=idx((i0(idx)<1) | (i0(idx)>shape(2))); \r\n                i0(idx)=i;\r\n                L0(idx)=1;                \r\n            end\r\n\r\n            % reduce out trivial points DXY(idx)<DXY(:,i)\r\n            idx=sub2ind(shape(1:2),x,i0);\r\n            L=(DXY(idx)>0) | (i0==i);\r\n            idx=idx(L);\r\n\r\n            % these will keep track along which X should \r\n            % keep updating distances            \r\n            map_lower=L;\r\n            map_upper=L;            \r\n            idx_lower=idx;\r\n            idx_upper=idx;\r\n            \r\n            % set trivial pixels D==0 in line #i:\r\n            % this has to be done b/s we manually discarded them from L0\r\n            D1(d0==0,i)=0;\r\n\r\n            % scan from starting points for each X,i0 in increments of 1\r\n            di=0;       % distance from current y-line\r\n            eols=2;     % end-of-line-scan flag\r\n            totlen=prod(shape(1:2));\r\n            while(eols)\r\n                eols=2;\r\n                di=di+1;\r\n                \r\n                % select X which can be updated for di<0;\r\n                % i.e. X which had been below envelop all way till now\r\n                if(~isempty(idx_lower))\r\n                    % shift y by -1\r\n                    idx_lower=idx_lower-shape(1);\r\n                    \r\n                    % prevent index dropping below 1st\r\n                    L=(idx_lower>=1);\r\n                    map_lower(map_lower)=L;\r\n                    idx_lower=idx_lower(L);\r\n                    \r\n                    if(~isempty(idx_lower))\r\n                        dtmp=d0(map_lower)+...\r\n                            aspect(2)^2*(i0(map_lower)-di-i).^2;\r\n                    \r\n                        % these pixels are to be updated with i0-di\r\n                        L=D1(idx_lower)>dtmp & DXY(idx_lower)>0;\r\n                        map_lower(map_lower)=L;\r\n                        idx_lower=idx_lower(L);\r\n                        D1(idx_lower)=dtmp(L);\r\n                        DK(idx_lower)=i;\r\n                    end\r\n                else    % if this is empty, get ready to quit\r\n                    eols=eols-1;\r\n                end\r\n\r\n                % select X which can be updated for di>0;\r\n                % i.e. X which had been below envelop all way till now\r\n                if(~isempty(idx_upper))\r\n                    % shift y by +1\r\n                    idx_upper=idx_upper+shape(1);\r\n                    \r\n                    % prevent index from going over array limits\r\n                    L=(idx_upper<=totlen);\r\n                    map_upper(map_upper)=L;\r\n                    idx_upper=idx_upper(L);\r\n                    \r\n                    if(~isempty(idx_upper))                        \r\n                        dtmp=d0(map_upper)+...\r\n                            aspect(2)^2*(i0(map_upper)+di-i).^2;\r\n                    \r\n                        % check which pixels are to be updated with i0+di\r\n                        L=D1(idx_upper)>dtmp & DXY(idx_upper)>0;\r\n                        map_upper(map_upper)=L;\r\n                        idx_upper=idx_upper(L);\r\n                        D1(idx_upper)=dtmp(L);\r\n                        DK(idx_upper)=i;\r\n                    end\r\n                else    % if this is empty, get ready to quit\r\n                    eols=eols-1;\r\n                end  \r\n            end\r\n        end\r\n    end\r\n    D{k}=D1; \r\nend\r\n\r\n%%%%%%%%%%%%% scan along Z %%%%%%%%%%%%%%%%\r\n% this will be the envelop: \r\n% envelop has to be initialized at Inf to ensure single direction of scan, \r\n% otherwise may end up in infinite loop when trying to find starting point\r\nD1=cell(size(D));\r\nfor k=1:shape(3) D1{k}=repmat(Inf,shape(1:2)); end\r\n% these will be the references to parabolas defining the envelop\r\nDK=cell(size(D));\r\nfor k=1:shape(3) DK{k}=repmat(Inf,shape(1:2)); end\r\n\r\n% start building the envelope \r\nfor k=1:shape(3)\r\n    % need to select starting point for each X:\r\n    % * starting point should be below current envelop\r\n    % * k0==k is not necessarily a starting point\r\n    % * there may be no starting point\r\n    \r\n    % k0 is the starting points for each XY: k0(XY) is the first\r\n    % z-index such that parabola from line k is below the envelop\r\n\r\n    % initial starting point guess is current slice\r\n    k0=repmat(k,shape(1:2));\r\n    \r\n    % L0 indicates which starting points had been fixed\r\n    L0=isinf(D{k}) | (D{k}==0);\r\n    idxtot=find(~L0);\r\n    \r\n    while(~isempty(idxtot))\r\n        % because of using cells need to explicitly scan in Z\r\n        % to avoid repetitious searches in k0, parse first\r\n        ss=getregions(k0(idxtot));\r\n        sslen=length(ss);\r\n        \r\n        for kk=1:sslen\r\n            % these are starting points @kk which had not been set\r\n            idx=idxtot(ss(kk).PixelIdxList);\r\n            \r\n            % reduce out trivial points (D==0)\r\n            if(kk~=k)\r\n                L=(D{kk}(idx)==0);\r\n                L0(idx)=L;\r\n                idx=idx(~L);\r\n            end\r\n\r\n            if(isempty(idx)) continue; end\r\n            \r\n            % these are currently best parabolas for slice kk\r\n            ik=DK{kk}(idx);            \r\n            \r\n            % these are new values for slice kk from parabola from k\r\n            dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;\r\n            \r\n            % these points are OK - below current envelop\r\n            L=D1{kk}(idx)>dtmp; D1{kk}(idx(L))=dtmp(L);            \r\n            \r\n            % these points are not OK, but since ik==k0\r\n            % can't get any better\r\n            L=L | (ik==kk);            \r\n                \r\n            % all other points are not OK, need new starting point:\r\n            % starting point should be at least below parabola\r\n            % beating us at current choice of k0\r\n            \r\n            % solve quadratic equation to find where this happens\r\n            ik=(ik-k);\r\n            dk=(D1{kk}(idx(~L))-dtmp(~L))./ik(~L)/2/aspect(3)^2;\r\n            dk=fix(dk)+sign(dk);\r\n            k0(idx(~L))=k0(idx(~L))+dk;\r\n    \r\n            % update starting points that had been set\r\n            L0(idx)=L;\r\n            L0(idx(~L))=(dk==0);\r\n    \r\n            % points that went out of boundaries can't get better\r\n            idx=idx(~L);\r\n            idx=idx((k0(idx)<1) | (k0(idx)>shape(3)));\r\n            L0(idx)=1;\r\n            k0(idx)=k;\r\n        end\r\n\r\n        idxtot=find(~L0);\r\n    end\r\n    \r\n    % map_lower/map_upper keeps track of which pixels can be yet updated\r\n    % with new distance, i.e. all such XY that had been below envelop for\r\n    % all dk up to now for dk<0/dk>0 respectively\r\n    map_lower=true(shape(1:2));\r\n    map_upper=true(shape(1:2));\r\n\r\n    % parse different values in k0 to avoid repetitious searching below\r\n    ss=getregions(k0); \r\n    sslen=length(ss);\r\n\r\n    % reduce out trivially faulty starting points\r\n    for kk=1:sslen\r\n        if(kk==k) continue; end\r\n        \r\n        idx=ss(kk).PixelIdxList;\r\n        \r\n        L=D{kk}(idx)>D{k}(idx);\r\n        map_lower(idx)=L;\r\n        map_upper(idx)=L;\r\n    end\r\n    \r\n    % these are maintained to keep fast track of whether maps are empty\r\n    idx_lower=find(map_lower);\r\n    idx_upper=find(map_upper);\r\n    \r\n    % set trivial pixels D==0 in slice k:\r\n    % this has to be done b/s we manually discarded them from L0\r\n    D1{k}(D{k}==0)=0;        \r\n\r\n    % scan away from starting points in increments of 1\r\n    dk=0;       % distance from current xy-slice\r\n    eols=2;     % end-of-scan flag\r\n    while(eols)\r\n        eols=2;\r\n        dk=dk+1;\r\n\r\n        if(~isempty(idx_lower))\r\n            % prevent index from going over the boundaries\r\n            L=(k0(map_lower)-dk>=1);\r\n            map_lower(map_lower)=L;\r\n            % need to explicitly scan in Z because of using cell-arrays\r\n            for kk=1:sslen-dk\r\n                % get all XY such that k0-dk==kk\r\n                idx=ss(kk+dk).PixelIdxList; \r\n                L=map_lower(idx);\r\n                idx=idx(L);                \r\n\r\n                if(~isempty(idx))\r\n                    dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;\r\n                    \r\n                    % these pixels are to be updated with k0-dk\r\n                    L=D1{kk}(idx)>dtmp & D{kk}(idx)>0;\r\n                    map_lower(idx)=L;\r\n                    D1{kk}(idx(L))=dtmp(L);\r\n                    \r\n                    % ridiculously, but this is faster than\r\n                    % direct assignment\r\n                    dtmp=idx(L);\r\n                    dtmp(:)=k;\r\n                    DK{kk}(idx(L))=k;\r\n                end\r\n            end\r\n            idx_lower=idx_lower(map_lower(idx_lower));\r\n        else\r\n            eols=eols-1;\r\n        end\r\n\r\n        if(~isempty(idx_upper))\r\n            % prevent index from going over the boundaries            \r\n            L=(k0(map_upper)+dk<=shape(3));\r\n            map_upper(map_upper)=L;\r\n            % need to explicitly scan in Z because of using cell-arrays\r\n            for kk=dk+1:min(shape(3),sslen+dk)\r\n                % get all XY such that k0+dk==kk\r\n                idx=ss(kk-dk).PixelIdxList;\r\n                L=map_upper(idx);\r\n                idx=idx(L);                \r\n\r\n                if(~isempty(idx))                    \r\n                    dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;\r\n    \r\n                    % these pixels are to be updated with k0+dk\r\n                    L=D1{kk}(idx)>dtmp & D{kk}(idx)>0;\r\n                    map_upper(idx)=L;\r\n                    D1{kk}(idx(L))=dtmp(L);\r\n                    \r\n                    dtmp=idx(L); \r\n                    dtmp(:)=k;\r\n                    DK{kk}(idx(L))=dtmp;\r\n                end\r\n            end\r\n            idx_upper=idx_upper(map_upper(idx_upper));\r\n        else\r\n            eols=eols-1;\r\n        end\r\n    end\r\nend\r\n\r\n% the answer\r\nif(iscell(bw))\r\n    D=cell(size(bw));\r\n    for k=1:shape(3) D{k}=sqrt(D1{k}); end\r\nelse\r\n    D=zeros(shape);\r\n    for k=1:shape(3) D(:,:,k)=sqrt(D1{k}); end\r\nend\r\n\r\n\r\nfunction s=getregions(map)\r\n% this function is replacer for regionprops(map,'PixelIdxList');\r\n% it produces the list of different values along with list of \r\n% indexes of pixels in map with these values; 's' is struct-array \r\n% such that s(i).PixelIdxList contains list of pixels in map \r\n% with value i.\r\n\r\n% enable using regionprops if available, faster on 7.3\r\nfregionprops=1;\r\n\r\n% version control for using regionprops\r\nv=version; v=str2num(v(1:3)); \r\nfregionprops=fregionprops & v>=7.3;\r\n\r\n% in later matlab regionprops is actually faster than this code\r\nif(exist('regionprops') & fregionprops)\r\n    s=regionprops(map,'PixelIdxList');\r\n    return\r\nend\r\n\r\nidx=(1:prod(size(map)))';\r\ndtmp=double(map(:));\r\n\r\n[dtmp,ind]=sort(dtmp); \r\nidx=idx(ind);\r\nind=[0;find([diff(dtmp(:));1])];\r\n\r\ns=[];\r\nfor i=2:length(ind)\r\n    if(dtmp(ind(i)))==0 continue; end\r\n    s(dtmp(ind(i))).PixelIdxList=idx(ind(i-1)+1:ind(i));\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "nuclearPropertiesFunctionTest.m", "ext": ".m", "path": "IA-Lab-master/Tests/nuclearPropertiesFunctionTest.m", "size": 693, "source_encoding": "utf_8", "md5": "ba0a6abfb01ff7b5766b72580e174ee8", "text": "function tests = nuclearPropertiesFunctionTest\r\ntests = functiontests(localfunctions);\r\n\r\nend\r\nfunction setupOnce(testCase)\r\n\r\nparentfolder = fileparts(which('nuclearPropertiesWorkflow.m'));\r\nparser = ParserYokogawa(fullfile(parentfolder,'AssayPlate'));\r\ntestCase.TestData.Workflow = nuclearPropertiesWorkflow('tempoutput',parser);\r\n\r\nend\r\n\r\nfunction testReproducibleStats(testCase)\r\n\r\nimobj = testCase.TestData.Workflow.BatchParser.ParserObj.getAC2DObj('well','E03');\r\ntestCase.verifyTrue(iscell(imobj));\r\ntestCase.verifyEqual(numel(imobj),1,'Should be 1 image object returned');\r\n\r\nimdata = imobj{1}.getDataC2D();\r\n\r\ntestCase.verifyEqual(numel(imdata),3,'Should be 3 channels');\r\n\r\n\r\n\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "YEAbout.m", "ext": ".m", "path": "IA-Lab-master/YokoV2/YEAbout.m", "size": 3650, "source_encoding": "utf_8", "md5": "80021317baece190475f4d64bdddfe24", "text": "function varargout = YEAbout(varargin)\r\n% YEABOUT MATLAB code for YEAbout.fig\r\n%      YEABOUT, by itself, creates a new YEABOUT or raises the existing\r\n%      singleton*.\r\n%\r\n%      H = YEABOUT returns the handle to a new YEABOUT or the handle to\r\n%      the existing singleton*.\r\n%\r\n%      YEABOUT('CALLBACK',hObject,eventData,handles,...) calls the local\r\n%      function named CALLBACK in YEABOUT.M with the given input arguments.\r\n%\r\n%      YEABOUT('Property','Value',...) creates a new YEABOUT or raises the\r\n%      existing singleton*.  Starting from the left, property value pairs are\r\n%      applied to the GUI before YEAbout_OpeningFcn gets called.  An\r\n%      unrecognized property name or invalid value makes property application\r\n%      stop.  All inputs are passed to YEAbout_OpeningFcn via varargin.\r\n%\r\n%      *See GUI Options on GUIDE's Tools menu.  Choose \"GUI allows only one\r\n%      instance to run (singleton)\".\r\n%\r\n% See also: GUIDE, GUIDATA, GUIHANDLES\r\n\r\n% Edit the above text to modify the response to help YEAbout\r\n\r\n% Last Modified by GUIDE v2.5 14-Oct-2016 13:34:02\r\n\r\n% Begin initialization code - DO NOT EDIT\r\ngui_Singleton = 1;\r\ngui_State = struct('gui_Name',       mfilename, ...\r\n                   'gui_Singleton',  gui_Singleton, ...\r\n                   'gui_OpeningFcn', @YEAbout_OpeningFcn, ...\r\n                   'gui_OutputFcn',  @YEAbout_OutputFcn, ...\r\n                   'gui_LayoutFcn',  [] , ...\r\n                   'gui_Callback',   []);\r\nif nargin && ischar(varargin{1})\r\n    gui_State.gui_Callback = str2func(varargin{1});\r\nend\r\n\r\nif nargout\r\n    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});\r\nelse\r\n    gui_mainfcn(gui_State, varargin{:});\r\nend\r\n% End initialization code - DO NOT EDIT\r\nend\r\n\r\n% --- Executes just before YEAbout is made visible.\r\nfunction YEAbout_OpeningFcn(hObject, eventdata, handles, varargin)\r\n% This function has no output args, see OutputFcn.\r\n% hObject    handle to figure\r\n% eventdata  reserved - to be defined in a future version of MATLAB\r\n% handles    structure with handles and user data (see GUIDATA)\r\n% varargin   command line arguments to YEAbout (see VARARGIN)\r\n\r\n    %_______________________________________________\r\n    %   change Yinhai Icon\r\n% %     jFrame = get(hObject,'JavaFrame');\r\n% %     jIcon = javax.swing.ImageIcon('\\\\UK-Image-01\\IAGroup\\SB\\2016\\Yoko\\Source Code\\Artworks\\icon_48.png');\r\n% %     jFrame.setFigureIcon(jIcon);\r\n    % commented this out until any conflict with the licensing agreement is\r\n    % clarified\r\n    \r\n    % this isn't necessary, everything is stored in handles\r\n%     hAxes = findobj('Tag', 'AxesAbout');\r\n\r\n%     anImage = imread('\\\\UK-Image-01\\IAGroup\\SB\\2016\\Yoko\\Source Code\\Artworks\\panel.png');\r\n    anImage = imread('IALab.png');\r\n    imshow(anImage, [], 'Parent', handles.AxesAbout, 'InitialMagnification', 'fit');\r\n    \r\n    set(handles.AxesAbout,'xcolor','w','ycolor','w','xtick',[],'ytick',[]);\r\n    set(handles.figure1,'Name','About IALab')\r\n    \r\n    % Update handles structure\r\n    guidata(hObject, handles);\r\n    \r\n    % UIWAIT makes YEAbout wait for user response (see UIRESUME)\r\n    % uiwait(handles.figure1);\r\n    \r\nend\r\n\r\n% --- Outputs from this function are returned to the command line.\r\nfunction varargout = YEAbout_OutputFcn(hObject, eventdata, handles) \r\n% varargout  cell array for returning output args (see VARARGOUT);\r\n% hObject    handle to figure\r\n% eventdata  reserved - to be defined in a future version of MATLAB\r\n% handles    structure with handles and user data (see GUIDATA)\r\n\r\n    % Get default command line output from handles structure\r\n    varargout = cell(nargout,1);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "gaussFiltND.m", "ext": ".m", "path": "IA-Lab-master/Filters/gaussFiltND.m", "size": 1751, "source_encoding": "utf_8", "md5": "37578851d6366b2c6b5ba79f53e5a72c", "text": "function fim = gaussFiltND(im,frad,boundcon,relsiz)\r\n\r\n% N-dimensional Gaussian filtering\r\n% This works by permuting the image rather than the\r\n% kernel so that the filtering is always done along the first dimension,\r\n% which seems to be faster for some reason.\r\n\r\nif nargin<4 || isempty(relsiz)\r\n    relsiz = 6; % size of the filter relative to the Gaussian radius\r\nend\r\nif nargin<3 || isempty(boundcon)\r\n    boundcon = 'replicate';\r\nend\r\n\r\nif ~isa(im,'double')\r\n\tim = double(im);\r\nend\r\n\r\n% the number of elements of frad explicitly tells us how many dimensions we\r\n% want to filter, so a scalar would mean only the x-dimension is filtered\r\n\r\nsiz = odd(relsiz.*frad,'up'); % element-wise in case we've specified different relative sizes in each dimension..\r\n\r\n% need to work out which dimensions are to be permuted\r\ntotdim = numel(size(im)); % assume we won't be daft enough to use this function for low dimensional images..\r\nndim = numel(frad);\r\n\r\n% allow a filter radius of zero to specify no filtering\r\npvect = 1:totdim;\r\npvect(1:ndim) = [ndim,1:(ndim-1)];\r\n\r\nfim = im;\r\n% work in reverse order\r\nfor ii = ndim:-1:1\r\n    fim = permute(fim,pvect);\r\n    \r\n    if frad(ii)>0\r\n        n = (0.55:0.1:(siz(ii)+0.45))';\r\n        m = (1+siz(ii))/2;\r\n        temp1 = exp(-((n-m).^2)/(2*frad(ii)^2));\r\n        kk = sum(reshape(temp1,[10,numel(temp1)/10]),1)';\r\n        kk = kk/sum(kk);\r\n        \r\n        fim = imfilter(fim,kk,boundcon);\r\n    end\r\nend\r\n\r\nend\r\n\r\nfunction out = odd(x,direction)\r\n\r\nif nargin<2\r\n\tdirection = 'up';\r\nend\r\nif ~ischar(direction)\r\n\tif direction>0\r\n\t\tdirection = 'up';\r\n\telse\r\n\t\tdirection = 'down';\r\n\tend\r\nend\r\n\r\nswitch direction\r\n\tcase 'up'\r\n\t\tout = ceil((x + 1)/2)*2 - 1;\r\n\tcase 'down'\r\n\t\tout = floor((x + 1)/2)*2 - 1;\r\nend\r\n\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "amcBilateral2D.m", "ext": ".m", "path": "IA-Lab-master/Filters/amcBilateral2D.m", "size": 5348, "source_encoding": "utf_8", "md5": "c9c7a1e74c52e78500f4ee6e81555d70", "text": "function fim = amcBilateral2D(im,rdata,sxy,sr,rbins,xydsc)\r\n\r\n% fast bilateral filtering, taking advantage of MATLAB's efficiency of\r\n% array indexing\r\n\r\n% Adam Corrigan\r\n\r\n% if the normalization for r-binning is done only on rg without changing\r\n% im, we don't have to scale then rescale at the end..\r\n\r\n% first thing to do is create a 3D image of im separated by intensity\r\nif nargin<4 || isempty(rbins)\r\n    rbins = 10;\r\nend\r\n\r\nif nargin<5 || isempty(xydsc)\r\n    xydsc = 2; % this should really be related to the size of the xy filter\r\nend\r\n\r\nif isscalar(xydsc)\r\n    xydsc = xydsc*[1,1];\r\nend\r\n\r\nif isempty(rdata)\r\n    minim = double(min(im(:)));\r\n    maxim = double(max(im(:)));\r\n    rg = ceil(rbins*(double(im)-minim)/(maxim-minim));\r\nelse\r\n    minim = double(min(rdata(:)));\r\n    maxim = double(max(rdata(:)));\r\n    rg = ceil(rbins*(double(rdata)-minim)/(maxim-minim));\r\nend\r\nrg(rg==0) = 1;\r\nrg(rg>rbins) = rbins;\r\n\r\n[xx,yy] = amcMakeGrid(im);\r\n\r\ninsiz = [size(im,1),size(im,2),rbins];\r\noutsiz = ceil(insiz./[xydsc,1]);\r\n\r\nif numel(sxy)==1\r\n    sxy = [1,1]*sxy;\r\nend\r\n\r\n% width of the z-kernel\r\nsb = sr*rbins;\r\n\r\n% width of the xy-kernel in downscaled units\r\nscxy = sxy./xydsc;\r\n\r\n% % linind = amcSub2Ind(insiz,[xx(:),yy(:),rg(:)]);\r\nloutind = amcSub2Ind(outsiz,[ceil([xx(:),yy(:)]./(ones(numel(xx),1)*xydsc)),rg(:)]);\r\n\r\n\r\n% % num3 = zeros(size(im,1),size(im,2),rbins);\r\n% % denom3 = zeros(size(num3));\r\n% % \r\n% % for ii = 1:rbins\r\n% %     ntemp = zeros(size(im));\r\n% %     dtemp = zeros(size(im));\r\n% %     ntemp(rg==ii) = im(rg==ii);\r\n% %     dtemp(rg==ii) = 1;\r\n% %     \r\n% %     num3(:,:,ii) = ntemp;\r\n% %     denom3(:,:,ii) = dtemp;\r\n% % end\r\n\r\n% % d3 = zeros(insiz);\r\n% % n3 = zeros(insiz);\r\n% % d3(linind) = 1;\r\n% % n3(linind) = im(:);\r\n\r\n% have to use the 'in' indices here because we may have duplicates in the\r\n% 'out' indices and they need to be summed.\r\n% The two lines below take half the total running time, can we find a\r\n% better way? Perhaps accumarray?\r\n% % scd = blocksum(d3,[xydsc,xydsc,1],1);\r\n% % scn = blocksum(n3,[xydsc,xydsc,1],1);\r\n\r\n\r\n% This can't deal with NaNs..\r\n% scd = reshape(accumarray(loutind,ones(size(loutind)),[prod(outsiz),1]),outsiz);\r\n% scn = reshape(accumarray(loutind,im(:),[prod(outsiz),1]),outsiz);\r\n\r\ninds = ~isnan(im);\r\n\r\nscd = reshape(accumarray(loutind(inds),ones(nnz(inds),1),[prod(outsiz),1]),outsiz);\r\nscn = reshape(accumarray(loutind(inds),im(inds),[prod(outsiz),1]),outsiz);\r\n\r\n% the rest of the running time is the actual filtering, which might be\r\n% improved\r\n% % fi = dog3split2([scxy,sb]);\r\n% % fnum = gauss3filt(scn,fi);\r\n% % fdenom = gauss3filt(scd,fi);\r\nfnum = gaussFiltND(scn,[scxy,sb]);\r\nfdenom = gaussFiltND(scd,[scxy,sb]);\r\n\r\nfim = zeros(insiz(1:2));\r\nfim(inds) = fnum(loutind(inds))./fdenom(loutind(inds));\r\n\r\nend\r\n\r\nfunction [x,y,z] = amcMakeGrid(im)\r\n\r\n% makegrid (because it uses ndgrid) can be very memory intensive (the\r\n% problem seems to be the permute command (line 48 of ndgrid)\r\n\r\n% Do something about this\r\nif numel(im)>3\r\n    ss = size(im);\r\nelse\r\n    ss = im;\r\nend\r\n\r\nif numel(ss)==1\r\n    ss = ss*ones(1,nargout);\r\nend\r\n\r\nif numel(ss)==2 && nargout>2\r\n    ss = [ss,1];\r\nend\r\n\r\nswitch length(ss)\r\n    case 2\r\n%         [x,y] = ndgrid(1:ss(1),1:ss(2));\r\n        x = repmat((1:ss(1))',[1 ss(2)]);\r\n        y = repmat((1:ss(2)),[ss(1) 1]);\r\n        z = ones(size(x));\r\n    case 3\r\n%         [x,y,z] = ndgrid(1:ss(1),1:ss(2),1:ss(3));\r\n        x = repmat((1:ss(1))',[1 ss(2) ss(3)]);\r\n        y = repmat((1:ss(2)),[ss(1) 1 ss(3)]);\r\n        z = repmat(permute((1:ss(3)),[1 3 2]),[ss(1) ss(2) 1]);\r\nend\r\n\r\nend\r\n\r\nfunction fim = gaussFiltND(im,frad,boundcon,relsiz)\r\n\r\n% N-dimensional Gaussian filtering\r\n% This works by permuting the image rather than the\r\n% kernel so that the filtering is always done along the first dimension,\r\n% which seems to be faster for some reason.\r\n\r\nif nargin<4 || isempty(relsiz)\r\n    relsiz = 6; % size of the filter relative to the Gaussian radius\r\nend\r\nif nargin<3 || isempty(boundcon)\r\n    boundcon = 'replicate';\r\nend\r\n\r\nif ~isa(im,'double')\r\n\tim = double(im);\r\nend\r\n\r\n% the number of elements of frad explicitly tells us how many dimensions we\r\n% want to filter, so a scalar would mean only the x-dimension is filtered\r\n\r\nsiz = odd(relsiz.*frad,'up'); % element-wise in case we've specified different relative sizes in each dimension..\r\n\r\n% need to work out which dimensions are to be permuted\r\ntotdim = numel(size(im)); % assume we won't be daft enough to use this function for low dimensional images..\r\nndim = numel(frad);\r\n\r\n% allow a filter radius of zero to specify no filtering\r\npvect = 1:totdim;\r\npvect(1:ndim) = [ndim,1:(ndim-1)];\r\n\r\nfim = im;\r\n% work in reverse order\r\nfor ii = ndim:-1:1\r\n    fim = permute(fim,pvect);\r\n    \r\n    if frad(ii)>0\r\n        n = (0.55:0.1:(siz(ii)+0.45))';\r\n        m = (1+siz(ii))/2;\r\n        temp1 = exp(-((n-m).^2)/(2*frad(ii)^2));\r\n        kk = sum(reshape(temp1,[10,numel(temp1)/10]),1)';\r\n        kk = kk/sum(kk);\r\n        \r\n        fim = imfilter(fim,kk,boundcon);\r\n    end\r\nend\r\n\r\nend\r\n\r\nfunction out = odd(x,direction)\r\n\r\nif nargin<2\r\n\tdirection = 'up';\r\nend\r\nif ~ischar(direction)\r\n\tif direction>0\r\n\t\tdirection = 'up';\r\n\telse\r\n\t\tdirection = 'down';\r\n\tend\r\nend\r\n\r\nswitch direction\r\n\tcase 'up'\r\n\t\tout = ceil((x + 1)/2)*2 - 1;\r\n\tcase 'down'\r\n\t\tout = floor((x + 1)/2)*2 - 1;\r\nend\r\n\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "bwdistsc.m", "ext": ".m", "path": "IA-Lab-master/Filters/bwdistsc.m", "size": 18385, "source_encoding": "utf_8", "md5": "a8192c2781c7f03ef9cf7e9f0b91c769", "text": "function D=bwdistsc(bw,aspect)\r\n% D=BWDISTSC(BW,ASPECT)\r\n% BWDISTSC computes Euclidean distance transform of the binary 3D image \r\n% BW. For each pixel in BW, the distance transform assignes a number\r\n% that is the distance between that pixel and the nearest nonzero pixel \r\n% of BW. BW may be a single 2D image, 3D array or a cell array of \r\n% 2D slices. ASPECT is 3-component vector defining aspect ratio in \r\n% the dataset BW. If ASPECT is not specified, isotropic aspect \r\n% ratio [1 1 1] is assumed.\r\n%\r\n% BWDISTSC uses fast optimized scanning algorithm and cell-arrays to \r\n% represent internal data, so that it is less demanding to physical \r\n% memory. In many cases BWDISTSC is actually faster than MATLAB's \r\n% optimized kd-tree algorithm used for Euclidean distance \r\n% transform in 3D. \r\n%\r\n% BWDISTSC tries to use MATLAB bwdist for 2D scans if possible, which \r\n% is significantly faster. Otherwise BWDISTSC uses internal algorithm \r\n% to perform 2D scans.\r\n%\r\n%     Yuriy Mishchenko  JFRC HHMI Chklovskii Lab  JUL 2007\r\n\r\n% This code is free for use or modifications, just please give credit \r\n% where appropriate. And if you modify code or fix bugs, please drop \r\n% me a message at gmyuriy@hotmail.com.\r\n\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n% Scan algorithms below use following Lema:                     %\r\n% LEMA: let F(X,z) be lower envelope of a family of parabola:   %\r\n% F(X,z)=min_{i} [G_i(X)+(z-k_i)^2];                            %\r\n% and let H_k(X,z)=A(X)+(z-k)^2 be a parabola.                  %\r\n% Then for H_k(X,z)==F(X,z) at each X there exist at most       %\r\n% two solutions k1<k2 such that H_k12(X,z)=F(X,z), and          %\r\n% H_k(X,z)<F(X,z) is restricted to at most k1<k2.               %\r\n% Here X is any-dimensional coordinate.                         %\r\n%                                                               %\r\n% Thus, simply scan away from any z such that H_k(X,z)<F(X,z)   %\r\n% in either direction as long as H_k(X,z)<F(X,z) and update     %\r\n% F(X,z). Note that need to properly choose starting point;     %\r\n% starting point is any z such that H_k(X,z)<F(X,z); z==k is    %\r\n% usually, but not always the starting point!!!                 %\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n\r\n% parse inputs\r\nif(nargin<2 || isempty(aspect)) aspect=[1 1 1]; end\r\n\r\n% determine geometry of data\r\nif(iscell(bw)) shape=[size(bw{1}),length(bw)]; else shape=size(bw); end\r\n\r\n% fix to handle 1D & 2D images\r\nif(length(shape)<3) shape(length(shape)+1:3)=1; end\r\nif(length(aspect)<3) aspect(length(aspect)+1:3)=1; end\r\n\r\n% allocate space\r\nD=cell(1,shape(3)); for k=1:shape(3) D{k}=zeros(shape(1:2)); end\r\n\r\n%%%%%%%%%%%%% scan along XY %%%%%%%%%%%%%%%%\r\nfor k=1:shape(3)    \r\n    if(iscell(bw)) bwXY=bw{k}; else bwXY=bw(:,:,k); end\r\n        \r\n    % initialize arrays\r\n    DXY=zeros(shape(1:2));\r\n    D1=zeros(shape(1:2));\r\n    DK=zeros(shape(1:2));\r\n\r\n    % if can, use 2D bwdist from image processing toolbox    \r\n    if(exist('bwdist') && aspect(1)==aspect(2))\r\n        D1=aspect(1)^2*bwdist(bwXY).^2;\r\n    else    % if not, use full XY-scan\r\n        %%%%%%%%%%%%%%% X-SCAN %%%%%%%%%%%%%%%        \r\n        % reference nearest bwXY \"on\"-pixel in x direction downward:\r\n        \r\n        %  scan bottow-up, copy x-reference from previous row unless \r\n        %  there is bwXY \"on\"-pixel in that point in current row\r\n        xlower=repmat(Inf,shape(1:2)); \r\n        \r\n        xlower(1,find(bwXY(1,:)))=1;    % fill in first row\r\n        for i=2:shape(1)\r\n            xlower(i,:)=xlower(i-1,:);  % copy previous row\r\n            xlower(i,find(bwXY(i,:)))=i;% unless there is pixel\r\n        end\r\n        \r\n        % reference nearest bwXY \"on\"-pixel in x direction upward:\r\n        xupper=repmat(Inf,shape(1:2));\r\n        \r\n        xupper(end,find(bwXY(end,:)))=shape(1);\r\n        for i=shape(1)-1:-1:1\r\n            xupper(i,:)=xupper(i+1,:);\r\n            xupper(i,find(bwXY(i,:)))=i;\r\n        end\r\n                \r\n        % find points for which distance needs to be updated\r\n        idx=find(~bwXY); [x,y]=ind2sub(shape(1:2),idx);\r\n        \r\n        % set distance as the shortest to upward or to downward\r\n        DXY(idx)=aspect(1)^2*min((x-xlower(idx)).^2,(x-xupper(idx)).^2);\r\n        \r\n        %%%%%%%%%%%%%%% Y-SCAN %%%%%%%%%%%%%%%\r\n        % this will be the envelop \r\n        % envelop is initialized at Inf to ensure single scan direction, \r\n        % otherwise may end up in infinite loop when trying to find \r\n        % starting point\r\n        D1=repmat(Inf,shape(1:2));\r\n        % these will be the references to parabolas defining the envelop\r\n        DK=repmat(Inf,shape(1:2));\r\n        % starting points\r\n        i0=zeros(shape(1),1);\r\n        % convenience x-coords array \r\n        x=(1:shape(1))'; \r\n        \r\n        for i=1:shape(2)\r\n            % need to select starting point for each X:\r\n            % * starting point should be below current envelop\r\n            % * i0==i is not necessarily a starting point\r\n            % * there is at most one starting point\r\n            % * there may be no starting point\r\n            \r\n            % i0 is the starting points for each X: i0(X) is the first \r\n            % y-index such that parabola from line i is below the envelop\r\n            \r\n            % first guess is the current y-line\r\n            i0(:)=i;\r\n            \r\n            % some auxiliary datasets\r\n            d0=DXY(:,i);\r\n\r\n            % L0 indicates for which X starting point had been fixed\r\n            L0=isinf(d0) | (d0==0);\r\n            \r\n            while(~isempty(find(~L0,1)))\r\n                % reference starting points in DXY\r\n                idx=sub2ind(shape(1:2),x(~L0),i0(~L0));\r\n                \r\n                % reduce out trivial points (DXY==0)\r\n                L=(DXY(idx)==0);\r\n                L0(~L0)=L;\r\n                idx=idx(~L);\r\n                \r\n                if(isempty(idx)) continue; end\r\n                \r\n                % these are current best parabolas for starting points\r\n                ik=DK(idx);\r\n                \r\n                % these are new values from parabola from line #i\r\n                dtmp=d0(~L0)+aspect(2)^2*(i0(~L0)-i).^2;\r\n                \r\n                % these starting points are OK - below the envelop\r\n                L=D1(idx)>dtmp; D1(idx(L))=dtmp(L);\r\n                \r\n                % points which are still above the envelop but ik==i0,\r\n                % will not get any better, so fix them as well\r\n                L=L | (ik==i0(~L0));\r\n                                \r\n                % all other points are not OK, need new starting point:\r\n                % starting point should be at least below parabola \r\n                % beating us at current choice of i0\r\n                \r\n                % solve quadratic equation to find where this happens\r\n                ik=(ik-i); \r\n                di=(D1(idx(~L))-dtmp(~L))./ik(~L)/2/aspect(2)^2;\r\n\r\n                % should select next highest index to the equality\r\n                di=fix(di)+sign(di);\r\n                \r\n                % the new starting points\r\n                idx=find(~L0); \r\n                i0(idx(~L))=i0(idx(~L))+di;\r\n\r\n                % update L0 to indicate which points we've fixed\r\n                L0(~L0)=L; L0(idx(~L))=(di==0);\r\n                \r\n                % points that went out of boundaries can't get better; \r\n                % fix them as well\r\n                idx=idx(~L);\r\n                idx=idx((i0(idx)<1) | (i0(idx)>shape(2))); \r\n                i0(idx)=i;\r\n                L0(idx)=1;                \r\n            end\r\n\r\n            % reduce out trivial points DXY(idx)<DXY(:,i)\r\n            idx=sub2ind(shape(1:2),x,i0);\r\n            L=(DXY(idx)>0) | (i0==i);\r\n            idx=idx(L);\r\n\r\n            % these will keep track along which X should \r\n            % keep updating distances            \r\n            map_lower=L;\r\n            map_upper=L;            \r\n            idx_lower=idx;\r\n            idx_upper=idx;\r\n            \r\n            % set trivial pixels D==0 in line #i:\r\n            % this has to be done b/s we manually discarded them from L0\r\n            D1(d0==0,i)=0;\r\n\r\n            % scan from starting points for each X,i0 in increments of 1\r\n            di=0;       % distance from current y-line\r\n            eols=2;     % end-of-line-scan flag\r\n            totlen=prod(shape(1:2));\r\n            while(eols)\r\n                eols=2;\r\n                di=di+1;\r\n                \r\n                % select X which can be updated for di<0;\r\n                % i.e. X which had been below envelop all way till now\r\n                if(~isempty(idx_lower))\r\n                    % shift y by -1\r\n                    idx_lower=idx_lower-shape(1);\r\n                    \r\n                    % prevent index dropping below 1st\r\n                    L=(idx_lower>=1);\r\n                    map_lower(map_lower)=L;\r\n                    idx_lower=idx_lower(L);\r\n                    \r\n                    if(~isempty(idx_lower))\r\n                        dtmp=d0(map_lower)+...\r\n                            aspect(2)^2*(i0(map_lower)-di-i).^2;\r\n                    \r\n                        % these pixels are to be updated with i0-di\r\n                        L=D1(idx_lower)>dtmp & DXY(idx_lower)>0;\r\n                        map_lower(map_lower)=L;\r\n                        idx_lower=idx_lower(L);\r\n                        D1(idx_lower)=dtmp(L);\r\n                        DK(idx_lower)=i;\r\n                    end\r\n                else    % if this is empty, get ready to quit\r\n                    eols=eols-1;\r\n                end\r\n\r\n                % select X which can be updated for di>0;\r\n                % i.e. X which had been below envelop all way till now\r\n                if(~isempty(idx_upper))\r\n                    % shift y by +1\r\n                    idx_upper=idx_upper+shape(1);\r\n                    \r\n                    % prevent index from going over array limits\r\n                    L=(idx_upper<=totlen);\r\n                    map_upper(map_upper)=L;\r\n                    idx_upper=idx_upper(L);\r\n                    \r\n                    if(~isempty(idx_upper))                        \r\n                        dtmp=d0(map_upper)+...\r\n                            aspect(2)^2*(i0(map_upper)+di-i).^2;\r\n                    \r\n                        % check which pixels are to be updated with i0+di\r\n                        L=D1(idx_upper)>dtmp & DXY(idx_upper)>0;\r\n                        map_upper(map_upper)=L;\r\n                        idx_upper=idx_upper(L);\r\n                        D1(idx_upper)=dtmp(L);\r\n                        DK(idx_upper)=i;\r\n                    end\r\n                else    % if this is empty, get ready to quit\r\n                    eols=eols-1;\r\n                end  \r\n            end\r\n        end\r\n    end\r\n    D{k}=D1; \r\nend\r\n\r\n%%%%%%%%%%%%% scan along Z %%%%%%%%%%%%%%%%\r\n% this will be the envelop: \r\n% envelop has to be initialized at Inf to ensure single direction of scan, \r\n% otherwise may end up in infinite loop when trying to find starting point\r\nD1=cell(size(D));\r\nfor k=1:shape(3) D1{k}=repmat(Inf,shape(1:2)); end\r\n% these will be the references to parabolas defining the envelop\r\nDK=cell(size(D));\r\nfor k=1:shape(3) DK{k}=repmat(Inf,shape(1:2)); end\r\n\r\n% start building the envelope \r\nfor k=1:shape(3)\r\n    % need to select starting point for each X:\r\n    % * starting point should be below current envelop\r\n    % * k0==k is not necessarily a starting point\r\n    % * there may be no starting point\r\n    \r\n    % k0 is the starting points for each XY: k0(XY) is the first\r\n    % z-index such that parabola from line k is below the envelop\r\n\r\n    % initial starting point guess is current slice\r\n    k0=repmat(k,shape(1:2));\r\n    \r\n    % L0 indicates which starting points had been fixed\r\n    L0=isinf(D{k}) | (D{k}==0);\r\n    idxtot=find(~L0);\r\n    \r\n    while(~isempty(idxtot))\r\n        % because of using cells need to explicitly scan in Z\r\n        % to avoid repetitious searches in k0, parse first\r\n        ss=getregions(k0(idxtot));\r\n        sslen=length(ss);\r\n        \r\n        for kk=1:sslen\r\n            % these are starting points @kk which had not been set\r\n            idx=idxtot(ss(kk).PixelIdxList);\r\n            \r\n            % reduce out trivial points (D==0)\r\n            if(kk~=k)\r\n                L=(D{kk}(idx)==0);\r\n                L0(idx)=L;\r\n                idx=idx(~L);\r\n            end\r\n\r\n            if(isempty(idx)) continue; end\r\n            \r\n            % these are currently best parabolas for slice kk\r\n            ik=DK{kk}(idx);            \r\n            \r\n            % these are new values for slice kk from parabola from k\r\n            dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;\r\n            \r\n            % these points are OK - below current envelop\r\n            L=D1{kk}(idx)>dtmp; D1{kk}(idx(L))=dtmp(L);            \r\n            \r\n            % these points are not OK, but since ik==k0\r\n            % can't get any better\r\n            L=L | (ik==kk);            \r\n                \r\n            % all other points are not OK, need new starting point:\r\n            % starting point should be at least below parabola\r\n            % beating us at current choice of k0\r\n            \r\n            % solve quadratic equation to find where this happens\r\n            ik=(ik-k);\r\n            dk=(D1{kk}(idx(~L))-dtmp(~L))./ik(~L)/2/aspect(3)^2;\r\n            dk=fix(dk)+sign(dk);\r\n            k0(idx(~L))=k0(idx(~L))+dk;\r\n    \r\n            % update starting points that had been set\r\n            L0(idx)=L;\r\n            L0(idx(~L))=(dk==0);\r\n    \r\n            % points that went out of boundaries can't get better\r\n            idx=idx(~L);\r\n            idx=idx((k0(idx)<1) | (k0(idx)>shape(3)));\r\n            L0(idx)=1;\r\n            k0(idx)=k;\r\n        end\r\n\r\n        idxtot=find(~L0);\r\n    end\r\n    \r\n    % map_lower/map_upper keeps track of which pixels can be yet updated\r\n    % with new distance, i.e. all such XY that had been below envelop for\r\n    % all dk up to now for dk<0/dk>0 respectively\r\n    map_lower=true(shape(1:2));\r\n    map_upper=true(shape(1:2));\r\n\r\n    % parse different values in k0 to avoid repetitious searching below\r\n    ss=getregions(k0); \r\n    sslen=length(ss);\r\n\r\n    % reduce out trivially faulty starting points\r\n    for kk=1:sslen\r\n        if(kk==k) continue; end\r\n        \r\n        idx=ss(kk).PixelIdxList;\r\n        \r\n        L=D{kk}(idx)>D{k}(idx);\r\n        map_lower(idx)=L;\r\n        map_upper(idx)=L;\r\n    end\r\n    \r\n    % these are maintained to keep fast track of whether maps are empty\r\n    idx_lower=find(map_lower);\r\n    idx_upper=find(map_upper);\r\n    \r\n    % set trivial pixels D==0 in slice k:\r\n    % this has to be done b/s we manually discarded them from L0\r\n    D1{k}(D{k}==0)=0;        \r\n\r\n    % scan away from starting points in increments of 1\r\n    dk=0;       % distance from current xy-slice\r\n    eols=2;     % end-of-scan flag\r\n    while(eols)\r\n        eols=2;\r\n        dk=dk+1;\r\n\r\n        if(~isempty(idx_lower))\r\n            % prevent index from going over the boundaries\r\n            L=(k0(map_lower)-dk>=1);\r\n            map_lower(map_lower)=L;\r\n            % need to explicitly scan in Z because of using cell-arrays\r\n            for kk=1:sslen-dk\r\n                % get all XY such that k0-dk==kk\r\n                idx=ss(kk+dk).PixelIdxList; \r\n                L=map_lower(idx);\r\n                idx=idx(L);                \r\n\r\n                if(~isempty(idx))\r\n                    dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;\r\n                    \r\n                    % these pixels are to be updated with k0-dk\r\n                    L=D1{kk}(idx)>dtmp & D{kk}(idx)>0;\r\n                    map_lower(idx)=L;\r\n                    D1{kk}(idx(L))=dtmp(L);\r\n                    \r\n                    % ridiculously, but this is faster than\r\n                    % direct assignment\r\n                    dtmp=idx(L);\r\n                    dtmp(:)=k;\r\n                    DK{kk}(idx(L))=k;\r\n                end\r\n            end\r\n            idx_lower=idx_lower(map_lower(idx_lower));\r\n        else\r\n            eols=eols-1;\r\n        end\r\n\r\n        if(~isempty(idx_upper))\r\n            % prevent index from going over the boundaries            \r\n            L=(k0(map_upper)+dk<=shape(3));\r\n            map_upper(map_upper)=L;\r\n            % need to explicitly scan in Z because of using cell-arrays\r\n            for kk=dk+1:min(shape(3),sslen+dk)\r\n                % get all XY such that k0+dk==kk\r\n                idx=ss(kk-dk).PixelIdxList;\r\n                L=map_upper(idx);\r\n                idx=idx(L);                \r\n\r\n                if(~isempty(idx))                    \r\n                    dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;\r\n    \r\n                    % these pixels are to be updated with k0+dk\r\n                    L=D1{kk}(idx)>dtmp & D{kk}(idx)>0;\r\n                    map_upper(idx)=L;\r\n                    D1{kk}(idx(L))=dtmp(L);\r\n                    \r\n                    dtmp=idx(L); \r\n                    dtmp(:)=k;\r\n                    DK{kk}(idx(L))=dtmp;\r\n                end\r\n            end\r\n            idx_upper=idx_upper(map_upper(idx_upper));\r\n        else\r\n            eols=eols-1;\r\n        end\r\n    end\r\nend\r\n\r\n% the answer\r\nif(iscell(bw))\r\n    D=cell(size(bw));\r\n    for k=1:shape(3) D{k}=sqrt(D1{k}); end\r\nelse\r\n    D=zeros(shape);\r\n    for k=1:shape(3) D(:,:,k)=sqrt(D1{k}); end\r\nend\r\n\r\n\r\nfunction s=getregions(map)\r\n% this function is replacer for regionprops(map,'PixelIdxList');\r\n% it produces the list of different values along with list of \r\n% indexes of pixels in map with these values; 's' is struct-array \r\n% such that s(i).PixelIdxList contains list of pixels in map \r\n% with value i.\r\n\r\n% enable using regionprops if available, faster on 7.3\r\nfregionprops=1;\r\n\r\n% version control for using regionprops\r\nv=version; v=str2num(v(1:3)); \r\nfregionprops=fregionprops & v>=7.3;\r\n\r\n% in later matlab regionprops is actually faster than this code\r\nif(exist('regionprops') & fregionprops)\r\n    s=regionprops(map,'PixelIdxList');\r\n    return\r\nend\r\n\r\nidx=(1:prod(size(map)))';\r\ndtmp=double(map(:));\r\n\r\n[dtmp,ind]=sort(dtmp); \r\nidx=idx(ind);\r\nind=[0;find([diff(dtmp(:));1])];\r\n\r\ns=[];\r\nfor i=2:length(ind)\r\n    if(dtmp(ind(i)))==0 continue; end\r\n    s(dtmp(ind(i))).PixelIdxList=idx(ind(i-1)+1:ind(i));\r\nend"}
+{"plateform": "github", "repo_name": "amcorrigan/IA-Lab-master", "name": "gaussFiltOffset.m", "ext": ".m", "path": "IA-Lab-master/Filters/gaussFiltOffset.m", "size": 1775, "source_encoding": "utf_8", "md5": "3c47e3ae95addd318e6d79acb19301e9", "text": "function fim = gaussFiltOffset(im,frad,offset,boundcon,relsiz)\r\n\r\n% N-dimensional Gaussian filtering\r\n% This works by permuting the image rather than the\r\n% kernel so that the filtering is always done along the first dimension,\r\n% which seems to be faster for some reason.\r\n\r\nif nargin<5 || isempty(relsiz)\r\n    relsiz = 8; % size of the filter relative to the Gaussian radius\r\nend\r\nif nargin<4 || isempty(boundcon)\r\n    boundcon = 'replicate';\r\nend\r\n\r\nif ~isa(im,'double')\r\n\tim = double(im);\r\nend\r\n\r\n% the number of elements of frad explicitly tells us how many dimensions we\r\n% want to filter, so a scalar would mean only the x-dimension is filtered\r\n\r\nsiz = odd(relsiz.*frad,'up'); % element-wise in case we've specified different relative sizes in each dimension..\r\n\r\n% need to work out which dimensions are to be permuted\r\ntotdim = numel(size(im)); % assume we won't be daft enough to use this function for low dimensional images..\r\nndim = numel(frad);\r\n\r\n% allow a filter radius of zero to specify no filtering\r\npvect = 1:totdim;\r\npvect(1:ndim) = [ndim,1:(ndim-1)];\r\n\r\nfim = im;\r\n% work in reverse order\r\nfor ii = ndim:-1:1\r\n    fim = permute(fim,pvect);\r\n    \r\n    if frad(ii)>0\r\n        n = (0.55:0.1:(siz(ii)+0.45))';\r\n        m = (1+siz(ii))/2;\r\n        temp1 = exp(-((n-m - offset(ii)).^2)/(2*frad(ii)^2));\r\n        kk = sum(reshape(temp1,[10,numel(temp1)/10]),1)';\r\n        kk = kk/sum(kk);\r\n        \r\n        fim = imfilter(fim,kk,boundcon);\r\n    end\r\nend\r\n\r\nend\r\n\r\nfunction out = odd(x,direction)\r\n\r\nif nargin<2\r\n\tdirection = 'up';\r\nend\r\nif ~ischar(direction)\r\n\tif direction>0\r\n\t\tdirection = 'up';\r\n\telse\r\n\t\tdirection = 'down';\r\n\tend\r\nend\r\n\r\nswitch direction\r\n\tcase 'up'\r\n\t\tout = ceil((x + 1)/2)*2 - 1;\r\n\tcase 'down'\r\n\t\tout = floor((x + 1)/2)*2 - 1;\r\nend\r\n\r\nend"}
+{"plateform": "github", "repo_name": "MelWe/mm-hypothesis-master", "name": "testvariancebaseddimandTVLIDAR.m", "ext": ".m", "path": "mm-hypothesis-master/code/testvariancebaseddimandTVLIDAR.m", "size": 17901, "source_encoding": "utf_8", "md5": "ce6eaeefa9d22ac2c81927608fc1d80d", "text": "function testvariancebaseddimandTVLIDAR( testid )\n%This file illustrates the variance based intrinsic dimension code and\n%total variance functions on the Bridge_87K.txt file\n% This is a slight modification of Linda's code to work on neighborhood \n% created using k nearest neighbors instead of epsilon balls.\n\n\n\nif testid == 8\n    fnamedata = 'Bridge_87K.txt';\n    data = dlmread(fnamedata);\n    x = data(:,1);\n    y = data(:,2);\n    z = data(:,3);\n    xdiam = max(x) - min(x);\n    ydiam = max(y) - min(y);\n    zdiam = max(z) - min(z);\n    maxdiam = sqrt(xdiam^2 + ydiam^2 + zdiam^2);\n   \n    h1 = figure;\n    scatter3(x,y,z);\n    str = sprintf('scatterplot of data for testid %d',testid);\n    title(str)\n    % dirname = '/Users/lindaness/Documents/MATLAB/MSVDLinda/testsLIDAR/';\n    dirname = '/Users/kyacouboudjima/Dropbox (Amherst College)/Research/Computing/LocalCodes/ProjectBased/WiSDM/Current';\n    fnamescatter = [dirname,sprintf('test%d',testid)];\n    saveas(h1,[fnamescatter,'.fig']);\n    saveas(h1,[fnamescatter,'.png']);\n    \n    varthreshhold = .95; K = 3; epszero = 10^(-12); TVfigs = 1;\n    radii = (maxdiam)*2.^(-1.*[4:7])  \n    q = length(radii);\n    fname = [dirname,sprintf('testvarbasedidim testid %d',testid)];\n    idimfigs = 1;\n    [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname );\n    idimsummary\n    display('columheadings: idim,firstscaleindex, count');\n    istatssummary\n    save(fname,'varthreshhold', 'K', 'radii', 'epszero', 'TVfigs', 'fname');\n    save(fname,'data','-append');\n    save(fname, 'idimsummary','istatssummary','idim','firstscaleindex','scaleprob', 'scales','consecutive','dense','SG','indG','SSVs','-append');\n    [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim,SG,radii,q,scales,indG,TVfigs,fname);\n    save(fname,'SSVEnergy','idimSSVEnergy','EidimSSVEnergy','TV','ETV','idimTV','EidimTV','-append');\nend\nend\n\n\nfunction [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname )\n%idim plots are shown if idimfigs == 1\n%variance function pots are shown if TVfigs == 1\n%radii is a decreasing sequence of radiuses determining a sequence of\n%balls around each point. The index of each radius is referred to as the\n%scale.\n[m,n] = size(data); % m data points, n coordinates\nmaxdim = min(m,n);\nq = length(radii);\n%This program looks for points with intrinsic dimensions <= K (with respect\n%to the family of point neighborhoods specified by radii\ncutoff = K*log(K);\n%This function computes the intrinsic dimension idim for each point by\n%a concentration of variance analysis using varthreshhold (e.g. .95)\n%SSVs is an m x maxdim*q matrix of q groups of squared singular values\n%of matrices of points in each ball, the matrices are centered so EV = 0\n%The squared singular values are then the variances of each centered coordinate.\n%PeG is an m x maxdim*q matrix of q groups of discrete probability distriutions\n%for each of the m points (relative variances); SG is an m x q matrix of\n%total variances for each ball; CumG is an m x maxdim*q matrix of q groups\n%of vectors (c1, ..., cn) defining the cumulative distribution for\n%(p1,..,pn) so cj = p1 + ... pj \n% idim(p) = i, i = minimum over the scales of smallestindex j cj >= varthresshold\n%e.g if varthreshhold = .95 ,idim(p) = i if there is a ball (scale) for which\n%the sum of the variances of the first i centered coordinates >= .95\n%scales is an m xq matrix of 0's and 1's. each row (pt) has 1's at the scales \n%i, such that ci >= varthresshold and i = idim(pt) \n%i.e. idim is computed by concentration of variance analysis  and the\n%scales is the set of indices of balls where the variance accumulates to\n%the fastest to varthreshhold\n\n%Two summary matrices are computed: idimsummary and istatssummary\n%rows of idimsummary (idim value, number of points with idim value)\n%i.e. the discrete distribution of idim for the data set\n%rows of istats summary (idim value, first scale index, number of points \n%with the specified idim value and first scale index.\n%i.e. the discrete distribution of idim,firstscale for the data set\n\n%If idimfigs == 1  scatter plots of the data points color-coded by idim\n%are computed\n\n%Different Total Variance functions are computed for each point, including\n%the SSVEnergy function. \n%Summaries of idim statistics are computed; idim and the Total Varianc\n%functions are plotted\n\n\n[ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff );\nM = SSVs; \n[ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero);\n%PrG1599 = PrG(1599,:)\n%SG199 = SG(1599,:)\n%CumG1599 = CumG(1599,:)\ngroupdim = n;\nt = varthreshhold;\n\n[idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG);\n\nu = unique(idim);\nidimsummary = zeros(length(u),2 );\nfor i = 1:length(u)\n    ind = find(idim == u(i));\n    lgth = length(ind);\n    idimsummary(i,:) = [u(i),lgth];\nend\n\n[istats,irhs,ilhs] = unique([idim,firstscaleindex],'rows');\n\n%istatssummary = zeros(length(istats),4);\nistatssummary = zeros(length(istats),3);\nfor i = 1:size(istats,1)\n    %x = abs(istats(i,1:3));\n    x = istats(i,1:2);\n    y = length(find(ilhs == i));\n    istatssummary(i,:) = [x,y];\nend\n\n\n\n\n\n\n\n\n\n\n\n\n\nif idimfigs == 1\n    ind = find(idim > 0);\n    m2 = length(ind);\n    \n    h = figure;\n    a = [1:m2];\n    b = idim(ind,:);\n    scatter(a,b,100);\n    titlestr = sprintf('ordered points with y coordinate = idim');\n    if ~length(fname) == 0\n        fname1 = [fname,'orderedidim','.png'];\n        saveas(h,fname1);\n        fname1 = [fname,'orderedidim','.fig'];\n        saveas(h,fname1);\n    end\n    \n    if n == 3\n        X = data(ind,1); Y = data(ind,2); Z = data(ind,3);\n        idim2 = idim(ind,:);\n        firstscaleindex2 = firstscaleindex(ind,:);\n\n        h = figure;\n        colormap(jet(m2));\n        scatter3(X,Y,Z,10,idim2);\n        titlestr = sprintf('points color-coded by idim');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idim','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idim','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,10,ilhs(ind,:));\n        %scatter3(X,Y,Z,100,firstscaleindex2);\n        titlestr = sprintf('points color-coded by lexicographic ordering of idim,firstscaleindex');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'lexidim','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'lexidim','.fig'];\n            saveas(h,fname1);\n        end\n    end\nend\nend\n\nfunction [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim, SG,radii,q,scales,indG,TVfigs,fname)\n%data is an m xn matrix of m data points with n coordinate\n%SG is an m x q matrix of total variances for data in each of the q balls\n%with radii specified in radius (total variances of the centered data)\n%SG entries for a ball are -1 if the intrinsic dimension was not computed for that ball \n%due to too few points etc\n%scales is an m x q matrix of 0's and 1's specifying the scales at which idim was observed\n%for each data point\n%indG is an m x q matrix of 0's and 1's specifying the scales for which\n%intrinsic dimension was computed \n%A variety of total variance functions are computed including the SSVEnergy\n%function we computed at WiSDM\n%if TVfig == 1, the total variance functions are plotted for the subset of\n%points for which intrinsic dimension was computed.\n\n[m,n] = size(data);\n%SG is an m x q matrix containg the total variances for each ball \n%(or -1's for the ball where SG and hence the intrinsic dimension were\n%not computed\n\n%Compute different Versions of Total Variance Functions\n%TV sum of total variances over the relevant balls\nTV = sum(SG.*indG,2); \nETV = TV./sum(indG,2); %m x 1 average of total variances of relevant balls\n\nidimTV = sum(scales.*SG,2); %sum of scale variances for idim scales \n%total variance for each of these scale is concentrated near idim scales\nEidimTV = idimTV./sum(scales,2); %Expected Value over the scales where \n%local idim = idim for the point\n\nR = repmat(radii.^2,m,1);\nRelSG = SG./R; %RelSG total variances for each ball are normalized by\n%dividing by the square of the radius; functions analagous to above are \n%computed from the relative variances. \nSSVEnergy =sum(RelSG,2) ; %This is the SSVEnergy function computed at WiSDM\nidimSSVEnergy = sum(scales.*RelSG,2);\nEidimSSVEnergy = idimSSVEnergy./sum(scales,2);\n\n\nif TVfigs == 1\n     ind = find(idim > 0);\n     SG = SG(ind,:);\n     m = size(data(ind,:),1);\n    \n    %h = figure;\n    %x = 1:m;\n    %hold on\n    %for i = 1:q\n        %plot(x,SG(:,i));\n    %end\n    %titlestr = sprintf('Total Variance Curves for each scale');\n    %title(titlestr)\n    %hold off\n    \n    if n == 3\n     X = data(ind,1); Y = data(ind,2); Z = data(ind,3);\n     TV = TV(ind,:);\n     idimTV = idimTV(ind,:);\n     EidimTV = EidimTV(ind,:);\n     SSVEnergy = SSVEnergy(ind,:);\n     idimSSVEnergy = idimSSVEnergy(ind,:);\n     EidimSSVEnergy = EidimSSVEnergy(ind,:);\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,TV);\n        titlestr = sprintf('points color-coded by sum of Total Variances over scales where idim was computed');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'TV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'TV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,idimTV);\n        titlestr = sprintf('points color-coded by sum of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimTV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idimTV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,EidimTV);\n        titlestr = sprintf('points color-coded by EV of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'EidimTV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'EidimTV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,SSVEnergy);\n        titlestr = sprintf('SSVEnergy - points color-coded by sum of normalized total variances over scales where idim was computed') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'SSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'SSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,idimSSVEnergy);\n        titlestr = sprintf('idmSSVEnergy - points color-coded by sum of normalized total variances over idim scales') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimSSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idimSSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,EidimSSVEnergy);\n        titlestr = sprintf('EidmSSVEnergy - points color-coded by EV of normalized total variances over idim scales') ;\n        title(titlestr); \n        if ~length(fname) == 0\n            fname1 = [fname,'EidimSSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'EidimSSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n    end\nend\n\nend\n\nfunction [ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff )\n% data is an m x n matrix, whose rows are the data\n%r is a 1 x q list of decreasing radii (e.g 2^0, 2^(-1), 2^(-2), ....);\n%This function computes the squared singular values for each point p and each\n%subset d(p,r) of data points at distance <= r from data point p \n%Results are returned in the m x q*maxdim matrix SSVs, maxdim = min(m,n);\n%For the ith data point p = data(i,:) \n%SSVs for p and d(p,r(j)) are in row i,  columns [1:maxdim] + maxdim*(j-1)\n%SSVs are not computed if |d(p,r)| < cutoff, instead -1's are stored in SSVs \\\n%dense is an m x q matrix of 0's and 1's\n%dense(i,j)  = |d(p,r(j))| < cutoff\n\n[m,n] = size(data);\nq = length(radii);\n%maxdim is the maximum number of non-zero singular values\nmaxdim = min(m,n);\n%dense(i,j) = 1 if |d(p,r)| > cutoff else 0, p = data(i,:)\ndense = ones(m,q);\nSSVs = -1*ones(m,q*maxdim);\nballcount = zeros(m,q);\n\n%for each point \nfor i = 1:m\n    if mod(i,1000) == 0\n        i\n    end\n    p = data(i,:);\n    ballmembership = zeros(m,q);\n    for j = 1:q\n        r = radii(j);\n        [b,B] = ball(data,p,r);\n        ballmembership(:,j) = B;\n        %compute EV of b and translate by -EV so EV(c) = 0;\n        c = center(b);\n        cardc = size(c,1);\n        if cardc > cutoff\n            s = svd(c);\n            sq = s.*s;\n            inds2 = [1:maxdim] + maxdim*(j-1);\n            SSVs(i,inds2) = sq; \n        else\n            dense(i,j) = 0;\n        end      \n    end\n    ballcount(i,:) = sum(ballmembership);\nend\nend\n\nfunction [v,B] = ball( data,p,r) \n%% p=center; r=radius %%\n%This functions finds the data points in the ball of radius r \n%centered at p. \n[m,n] = size(data);\nx = sqrt(sum((data- ones(m,1)*p).^2,2));\nB= x < r*ones(m,1);\nv=data(B,:);\nend\n\nfunction [ centereddata ] = center( data )\n%UNTITLED4 Summary of this function goes here\n%   Detailed explanation goes here\n[m,n] = size(data);\nEV = (1/m)*sum(data);\ncentereddata = data - ones(m,1)*EV;\nend\n\nfunction [ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero)\n%M is an m x q*maxdim matrix\n%dense is an m x q matrix\n%epszero very small and positive and used to test for zero e.g 10^(-12)\n%SG is m x q; PrG and SG are m x q*maxdim; indG is m x q\n%For the jth group of maxdim columns, for each row i, if dense(i,j) == 1 and\n%if the entries are >=0  with positive sum (> epszero) , indG(i,j) = 1 and\n%the rowsum,probability distribution, and cumlative probability distribution\n%of the row are computed and stored in the matrices SG, PrG and CumG.\n%for the other rows in the group indG = 0 and the entries of \n%SG, PrG and CumG are -1's.\n%if M does not have q*maxdim columns PrG,SG,CumG have -1 entries and inG = 0\n\n[m,n] = size(M);\nPrG = -1*ones(m,n);\nCumG = -1*ones(m,n);\nSG = -1*ones(m,q);\nindG = zeros(m,q);\nif n == q*maxdim\n    for j = 1:q\n        inddense = find(dense(:,j) == 1);\n        indj = [1:maxdim] + maxdim*(j-1);\n        [ Pr,S,Cum,ind ] = Probdist(M(inddense,indj),epszero);\n        PrG(inddense,indj) = Pr;\n        SG(inddense,j) = S;\n        CumG(inddense,indj) = Cum;\n        indG(inddense(ind),j) = 1;\n    end\nend\nend\n\n\nfunction [ Pr,S,Cum,ind ] = Probdist(M,epszero)\n%M is a m x n matrix \n%S is an m x 1 matrix; Cum and Pr are m x n matrices. \n%S,Cum,Pr are initialized to -1;\n%for the rows of M which are non-negative with positive sum (> epszero)\n%S is the sum of the row\n%Pr is the discrete probability distribution determined by the row\n%Cum is the cumulative distribution determined by the row\n%Example row = [1,2,3]; S = 6, Pr = [1/6,2/6,3/6], Cum = [1/6,1/2,1]\n%For the other rows of M, the entries of S, Pr, and Cu are -1.\n%ind is the index of the non-negative rows with positive sum\n\n\n[m,n] = size(M);\nS = -1*ones(m,1);\nPr = -1*ones(m,n);\nCum = -1*ones(m,n);\nnonneg = M >= 0;\npossum = sum(M,2);\nN = sum(nonneg,2);\nind = find(N == n & possum > epszero);\nS(ind,:)  = sum(M(ind,:),2);\nk = length(ind);\nif k > 0\n    Tmp = zeros(k,1);\n    for i = 1:n\n        Pr(ind,i) = M(ind,i)./S(ind,1);\n        Tmp = Tmp + Pr(ind,i);\n        Cum(ind,i) = Tmp;\n    end\nend\n\n\n\n\nend\n\nfunction [idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG)\n%CumG is a matrix whose rows are q groups of cumulative distributions\n%of size groupdim\n%t is a threshhold 0 <= t <= 1\n%The function returns column index of the first element in each row\n%which is >= t; if no such index exists, the function returns -1.\n[m,n] = size(CumG);\nif ~(n == q*groupdim)\n    idim = [];\n    firstscaleindex = [];\n    scales = [];\n    idimG = [];\n    mxG = [];\n    scaleprob = [];\n    consecutive = [];\n    display('in idimprob -- CumG does not have the expected number of columns');\nelse\n    idim = -1*ones(m,1);\n    firstscaleindex = -1*ones(m,1);\n    scales = zeros(m,q);\n    idimG = -1*ones(m,q);\n    mxG = -1*ones(m,q);\n    scaleprob = -1*ones(m,1);\n    consecutive = -1*ones(m,1);\n    for j = 1:q\n        ind = groupdim*(j-1) + [1:groupdim];\n        C = CumG(:,ind);\n        D = C >= t;\n        %find first index in each row where C >= t\n        [mx,ind] = max(D,[],2);\n        for i = 1:m\n            if mx(i) == 1\n                idimG(i,j) = ind(i,1);\n                mxG(i,j) = C(i,ind(i));\n            elseif mx(i) == 0\n                idimG(i,j) = -1;\n                mxG(i,j) = -1;\n            end\n        end\n        %col = mxG(:,j)\n    end\nend\n%The intrinsic dimension for a point is the minimum of the intrinsic dimensions for\n%each scale for which the intrinsic dimension is computed. \n%If it is not computed at any scale idim = -1.\nfor i = 1:m\n    ind1 = find(indG(i,:) == 1);\n    if length(ind1) > 0\n        idim(i,1) = min(idimG(i,ind1));\n        indscales = find(idimG(i,:) == idim(i,1));\n        scales(i,indscales) = 1;\n        scaleprob(i,1) = length(indscales)/length(ind1);\n        if indscales == indscales(1) - 1 + [1:length(indscales)]\n            consecutive(i,1) = 1;\n        end\n        firstscaleindex(i,1) = indscales(1);\n    end\nend\nend\n\n"}
+{"plateform": "github", "repo_name": "MelWe/mm-hypothesis-master", "name": "testvariancebaseddimandTV.m", "ext": ".m", "path": "mm-hypothesis-master/code/testvariancebaseddimandTV.m", "size": 18780, "source_encoding": "utf_8", "md5": "83380d1bbcb2228f43ae70132ada3231", "text": " function testvariancebaseddimandTV( testid )\n%This file illustrates the variance based intrinsic dimension code and\n%total variance functions.\n%       a variant of the geodesic minimal spanning tree (GMST) to estimate \n%       the intrinsic dimension and entropy of the manifold on which the \n%       data lie.\n\n\n%   Detailed explanation goes here\nif testid == 1\n    varthreshhold = .95 ;K = 3; rgparam = 5; sampleparam = 800;radii = 2:-.1:.1; epszero = 10^(-12); TVfigs = 1; fname = 'test1';\n    q = length(radii);\n    [ sample,spheresample,linesample,samplesize ] = ExampleUpdated( sampleparam,rgparam);\n    sizeoflinesample = size(linesample)\n    sizeofspheresample = size(spheresample)\n    data = sample; \n    idimfigs = 1;\n    [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname );\n    idimsummary\n    display('columheadings: idim,firstscaleindex, count');\n    istatssummary\n    [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim,SG,radii,q,scales,indG,TVfigs,fname);\n    save(fname,'varthreshhold', 'K', 'rgparam', 'sampleparam', 'radii', 'epszero', 'TVfigs','q', 'fname');\n    save(fname,'sample','spheresample','linesample','samplesize','sizeoflinesample','sizeofspheresample','-append');\n    save(fname, 'idimsummary','istatssummary','idim','firstscaleindex','scaleprob', 'scales','consecutive','dense','SG','indG','SSVs','-append');\n    save(fname,'SSVEnergy','idimSSVEnergy','EidimSSVEnergy','TV','ETV','idimTV','EidimTV','-append');\n\nelseif testid == 2\n    varthreshhold = .95 ;K = 3; rgparam = 5; sampleparam = 800;ind = 0:10; radii = 2:-.1:.1; epszero = 10^(-12); TVfigs = 1; fname = 'test2';\n    q = length(radii);\n    [ sample,spheresample,linesample,samplesize ] = ExampleUpdated( sampleparam,rgparam);\n    sizeoflinesample = size(linesample)\n    sizeofspheresample = size(spheresample)\n    data = sample; \n    idimfigs = 1;\n    [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname );\n    idimsummary\n    display('columheadings: idim,firstscaleindex, count');\n    istatssummary\n    [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim,SG,radii,q,scales,indG,TVfigs,fname);\n    save(fname,'varthreshhold', 'K', 'rgparam', 'sampleparam', 'radii', 'epszero', 'TVfigs', 'fname');\n    save(fname,'sample','spheresample','linesample','samplesize','sizeoflinesample','sizeofspheresample','-append');\n    save(fname, 'idimsummary','istatssummary','idim','firstscaleindex','scaleprob', 'scales','consecutive','dense','SG','indG','SSVs','-append');\n    save(fname,'SSVEnergy','idimSSVEnergy','EidimSSVEnergy','TV','ETV','idimTV','EidimTV','-append');\n\nend\nend\n\nfunction [ sample,spheresample,linesample,samplesize ] = ExampleUpdated( sampleparam,rgparam)\n%This function generates random samples from a figure consisting of a sphere of\n%radius 1/2 and a through the origin\n%  \n%x = rsin(theta)cos(phi)\n%y = rsin(theta)sin(phi)\n%z = rcos(theta)\n%theta in [0,pi]\n%phi in [0,2*pi]\nrng(rgparam);\ntheta  = pi*rand(floor(pi*sampleparam),1);\nphi = 2*pi*rand(floor(pi*sampleparam),1);\nspheresample = [.5*sin(theta).*cos(phi),.5*sin(theta).*sin(phi),.5*cos(theta)];\nsz = floor(.5*sampleparam);\nr = rand(sz,1);\nr = 2*(r - .5*ones(sz,1));\nind = find(r >= .5 | r <= -.5);\nlinesample = zeros(length(ind),3);\nlinesample(:,1) = r(ind);\nsample = [spheresample;linesample];\n%add two points on the intersection of the line and the spere\n%these points should have intrinisic dimension 3\nsample = [sample;.5,0,0;-.5,0,0];\nsamplesize = length(sample);\nx = sample(:,1); y = sample(:,2); z = sample(:,3);\nscatter3(x,y,z)\n\nend\n\nfunction [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname )\n%idim plots are shown if idimfigs == 1\n%variance function pots are shown if TVfigs == 1\n%radii is a decreasing sequence of radiuses determining a sequence of\n%balls around each point. The index of each radius is referred to as the\n%scale.\n[m,n] = size(data); % m data points, n coordinates\nmaxdim = min(m,n);\nq = length(radii);\n%This program looks for points with intrinsic dimensions <= K (with respect\n%to the family of point neighborhoods specified by radii\ncutoff = K*log(K);\n%This function computes the intrinsic dimension idim for each point by\n%a concentration of variance analysis using varthreshhold (e.g. .95)\n%SSVs is an m x maxdim*q matrix of q groups of squared singular values\n%of matrices of points in each ball, the matrices are centered so EV = 0\n%The squared singular values are then the variances of each centered coordinate.\n%PeG is an m x maxdim*q matrix of q groups of discrete probability distriutions\n%for each of the m points (relative variances); SG is an m x q matrix of\n%total variances for each ball; CumG is an m x maxdim*q matrix of q groups\n%of vectors (c1, ..., cn) defining the cumulative distribution for\n%(p1,..,pn) so cj = p1 + ... pj \n% idim(p) = i, i = minimum over the scales of smallestindex j cj >= varthresshold\n%e.g if varthreshhold = .95 ,idim(p) = i if there is a ball (scale) for which\n%the sum of the variances of the first i centered coordinates >= .95\n%scales is an m xq matrix of 0's and 1's. each row (pt) has 1's at the scales \n%i, such that ci >= varthresshold and i = idim(pt) \n%i.e. idim is computed by concentration of variance analysis  and the\n%scales is the set of indices of balls where the variance accumulates to\n%the fastest to varthreshhold\n\n%Two summary matrices are computed: idimsummary and istatssummary\n%rows of idimsummary (idim value, number of points with idim value)\n%i.e. the discrete distribution of idim for the data set\n%rows of istats summary (idim value, first scale index, number of points \n%with the specified idim value and first scale index.\n%i.e. the discrete distribution of idim,firstscale for the data set\n\n%If idimfigs == 1  scatter plots of the data points color-coded by idim\n%are computed\n\n%Different Total Variance functions are computed for each point, including\n%the SSVEnergy function. \n%Summaries of idim statistics are computed; idim and the Total Varianc\n%functions are plotted\n\n\n[ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff );\nM = SSVs; \n[ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero);\n%PrG1599 = PrG(1599,:)\n%SG199 = SG(1599,:)\n%CumG1599 = CumG(1599,:)\ngroupdim = n;\nt = varthreshhold;\n\n[idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG);\n\nu = unique(idim);\nidimsummary = zeros(length(u),2 );\nfor i = 1:length(u)\n    ind = find(idim == u(i));\n    lgth = length(ind);\n    idimsummary(i,:) = [u(i),lgth];\nend\n\n[istats,irhs,ilhs] = unique([idim,firstscaleindex],'rows');\n\n%istatssummary = zeros(length(istats),4);\nistatssummary = zeros(length(istats),3);\nfor i = 1:size(istats,1)\n    %x = abs(istats(i,1:3));\n    x = istats(i,1:2);\n    y = length(find(ilhs == i));\n    istatssummary(i,:) = [x,y];\nend\n\n\n\n\n\n\n\n\n\n\n\n\n\nif idimfigs == 1\n    ind = find(idim > 0);\n    m2 = length(ind);\n    \n    h = figure;\n    a = [1:m2];\n    b = idim(ind,:);\n    scatter(a,b,100);\n    titlestr = sprintf('ordered points with y coordinate = idim');\n    if ~length(fname) == 0\n        fname1 = [fname,'orderedidim','.png'];\n        saveas(h,fname1);\n    end\n    \n    if n == 3\n        X = data(ind,1); Y = data(ind,2); Z = data(ind,3);\n        idim2 = idim(ind,:);\n        firstscaleindex2 = firstscaleindex(ind,:);\n\n        h = figure;\n        colormap(jet(m2));\n        scatter3(X,Y,Z,100,idim2);\n        titlestr = sprintf('points color-coded by idim');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idim','.png'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,100,ilhs(ind,:));\n        %scatter3(X,Y,Z,100,firstscaleindex2);\n        titlestr = sprintf('points color-coded by lexicographic ordering of idim,firstscaleindex');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'lexidim','.png'];\n            saveas(h,fname1);\n        end\n    end\nend\nend\n\nfunction [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim, SG,radii,q,scales,indG,TVfigs,fname)\n%data is an m xn matrix of m data points with n coordinate\n%SG is an m x q matrix of total variances for data in each of the q balls\n%with radii specified in radius (total variances of the centered data)\n%SG entries for a ball are -1 if the intrinsic dimension was not computed for that ball \n%due to too few points etc\n%scales is an m x q matrix of 0's and 1's specifying the scales at which idim was observed\n%for each data point\n%indG is an m x q matrix of 0's and 1's specifying the scales for which\n%intrinsic dimension was computed \n%A variety of total variance functions are computed including the SSVEnergy\n%function we computed at WiSDM\n%if TVfig == 1, the total variance functions are plotted for the subset of\n%points for which intrinsic dimension was computed.\n\n[m,n] = size(data);\n%SG is an m x q matrix containg the total variances for each ball \n%(or -1's for the ball where SG and hence the intrinsic dimension were\n%not computed\n\n%Compute different Versions of Total Variance Functions\n%TV sum of total variances over the relevant balls\nTV = sum(SG.*indG,2); \nETV = TV./sum(indG,2); %m x 1 average of total variances of relevant balls\n\nidimTV = sum(scales.*SG,2); %sum of scale variances for idim scales \n%total variance for each of these scale is concentrated near idim scales\nEidimTV = idimTV./sum(scales,2); %Expected Value over the scales where \n%local idim = idim for the point\n\nR = repmat(radii.^2,m,1);\nRelSG = SG./R; %RelSG total variances for each ball are normalized by\n%dividing by the square of the radius; functions analagous to above are \n%computed from the relative variances. \nSSVEnergy =sum(RelSG,2) ; %This is the SSVEnergy function computed at WiSDM\nidimSSVEnergy = sum(scales.*RelSG,2);\nEidimSSVEnergy = idimSSVEnergy./sum(scales,2);\n\n\nif TVfigs == 1\n     ind = find(idim > 0);\n     SG = SG(ind,:);\n     m = size(data(ind,:),1);\n    \n    %h = figure;\n    %x = 1:m;\n    %hold on\n    %for i = 1:q\n        %plot(x,SG(:,i));\n    %end\n    %titlestr = sprintf('Total Variance Curves for each scale');\n    %title(titlestr)\n    %hold off\n    \n    if n == 3\n     X = data(ind,1); Y = data(ind,2); Z = data(ind,3);   \n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,100,TV);\n        titlestr = sprintf('points color-coded by sum of Total Variances over scales where idim was computed');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'TV','.png'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,100,idimTV);\n        titlestr = sprintf('points color-coded by sum of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimTV','.png'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,100,EidimTV);\n        titlestr = sprintf('points color-coded by EV of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'EidimTV','.png'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,100,SSVEnergy);\n        titlestr = sprintf('SSVEnergy - points color-coded by sum of normalized total variances over scales where idim was computed') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'SSVEnergy','.png'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,100,idimSSVEnergy);\n        titlestr = sprintf('idmSSVEnergy - points color-coded by sum of normalized total variances over idim scales') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimSSVEnergy','.png'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,100,EidimSSVEnergy);\n        titlestr = sprintf('EidmSSVEnergy - points color-coded by EV of normalized total variances over idim scales') ;\n        title(titlestr); \n        if ~length(fname) == 0\n            fname1 = [fname,'EidimSSVEnergy','.png'];\n            saveas(h,fname1);\n        end\n    end\nend\n\nend\n\nfunction [ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff )\n% data is an m x n matrix, whose rows are the data\n%r is a 1 x q list of decreasing radii (e.g 2^0, 2^(-1), 2^(-2), ....);\n%This function computes the squared singular values for each point p and each\n%subset d(p,r) of data points at distance <= r from data point p \n%Results are returned in the m x q*maxdim matrix SSVs, maxdim = min(m,n);\n%For the ith data point p = data(i,:) \n%SSVs for p and d(p,r(j)) are in row i,  columns [1:maxdim] + maxdim*(j-1)\n%SSVs are not computed if |d(p,r)| < cutoff, instead -1's are stored in SSVs \\\n%dense is an m x q matrix of 0's and 1's\n%dense(i,j)  = |d(p,r(j))| < cutoff\n\n[m,n] = size(data);\nq = length(radii);\n%maxdim is the maximum number of non-zero singular values\nmaxdim = min(m,n);\n%dense(i,j) = 1 if |d(p,r)| > cutoff else 0, p = data(i,:)\ndense = ones(m,q);\nSSVs = -1*ones(m,q*maxdim);\nballcount = zeros(m,q);\n\n%for each point \nfor i = 1:m\n    p = data(i,:);\n    ballmembership = zeros(m,q);\n    for j = 1:q\n        r = radii(j);\n        [b,B] = ball(data,p,r);\n        ballmembership(:,j) = B;\n        %compute EV of b and translate by -EV so EV(c) = 0;\n        c = center(b);\n        cardc = size(c,1);\n        if cardc > cutoff\n            s = svd(c);\n            sq = s.*s;\n            inds2 = [1:maxdim] + maxdim*(j-1);\n            SSVs(i,inds2) = sq; \n        else\n            dense(i,j) = 0;\n        end      \n    end\n    ballcount(i,:) = sum(ballmembership);\nend\n\n% size(SSVs)\n% scatter3(data(:,1),data(:,2),data(:,3),100, SSVs)\nend\n\nfunction [v,B] = ball( data,p,r) \n%% p=center; r=radius %%\n%This functions finds the data points in the ball of radius r \n%centered at p. \n[m,n] = size(data);\nx = sqrt(sum((data- ones(m,1)*p).^2,2));\nB= x < r*ones(m,1);\nv=data(B,:);\nend\n\nfunction [ centereddata ] = center( data )\n%UNTITLED4 Summary of this function goes here\n%   Detailed explanation goes here\n[m,n] = size(data);\nEV = (1/m)*sum(data);\ncentereddata = data - ones(m,1)*EV;\nend\n\nfunction [ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero)\n%M is an m x q*maxdim matrix\n%dense is an m x q matrix\n%epszero very small and positive and used to test for zero e.g 10^(-12)\n%SG is m x q; PrG and SG are m x q*maxdim; indG is m x q\n%For the jth group of maxdim columns, for each row i, if dense(i,j) == 1 and\n%if the entries are >=0  with positive sum (> epszero) , indG(i,j) = 1 and\n%the rowsum,probability distribution, and cumlative probability distribution\n%of the row are computed and stored in the matrices SG, PrG and CumG.\n%for the other rows in the group indG = 0 and the entries of \n%SG, PrG and CumG are -1's.\n%if M does not have q*maxdim columns PrG,SG,CumG have -1 entries and inG = 0\n\n[m,n] = size(M);\nPrG = -1*ones(m,n);\nCumG = -1*ones(m,n);\nSG = -1*ones(m,q);\nindG = zeros(m,q);\nif n == q*maxdim\n    for j = 1:q\n        inddense = find(dense(:,j) == 1);\n        indj = [1:maxdim] + maxdim*(j-1);\n        [ Pr,S,Cum,ind ] = Probdist(M(inddense,indj),epszero);\n        PrG(inddense,indj) = Pr;\n        SG(inddense,j) = S;\n        CumG(inddense,indj) = Cum;\n        indG(inddense(ind),j) = 1;\n    end\nend\nend\n\n\nfunction [ Pr,S,Cum,ind ] = Probdist(M,epszero)\n%M is a m x n matrix \n%S is an m x 1 matrix; Cum and Pr are m x n matrices. \n%S,Cum,Pr are initialized to -1;\n%for the rows of M which are non-negative with positive sum (> epszero)\n%S is the sum of the row\n%Pr is the discrete probability distribution determined by the row\n%Cum is the cumulative distribution determined by the row\n%Example row = [1,2,3]; S = 6, Pr = [1/6,2/6,3/6], Cum = [1/6,1/2,1]\n%For the other rows of M, the entries of S, Pr, and Cu are -1.\n%ind is the index of the non-negative rows with positive sum\n\n\n[m,n] = size(M);\nS = -1*ones(m,1);\nPr = -1*ones(m,n);\nCum = -1*ones(m,n);\nnonneg = M >= 0;\npossum = sum(M,2);\nN = sum(nonneg,2);\nind = find(N == n & possum > epszero);\nS(ind,:)  = sum(M(ind,:),2);\nk = length(ind);\nif k > 0\n    Tmp = zeros(k,1);\n    for i = 1:n\n        Pr(ind,i) = M(ind,i)./S(ind,1);\n        Tmp = Tmp + Pr(ind,i);\n        Cum(ind,i) = Tmp;\n    end\nend\n\n\n\n\nend\n\nfunction [idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG)\n%CumG is a matrix whose rows are q groups of cumulative distributions\n%of size groupdim\n%t is a threshhold 0 <= t <= 1\n%The function returns column index of the first element in each row\n%which is >= t; if no such index exists, the function returns -1.\n[m,n] = size(CumG);\nif ~(n == q*groupdim)\n    idim = [];\n    firstscaleindex = [];\n    scales = [];\n    idimG = [];\n    mxG = [];\n    scaleprob = [];\n    consecutive = [];\n    display('in idimprob -- CumG does not have the expected number of columns');\nelse\n    idim = -1*ones(m,1);\n    firstscaleindex = -1*ones(m,1);\n    scales = zeros(m,q);\n    idimG = -1*ones(m,q);\n    mxG = -1*ones(m,q);\n    scaleprob = -1*ones(m,1);\n    consecutive = -1*ones(m,1);\n    for j = 1:q\n        ind = groupdim*(j-1) + [1:groupdim];\n        C = CumG(:,ind);\n        D = C >= t;\n        %find first index in each row where C >= t\n        [mx,ind] = max(D,[],2);\n        for i = 1:m\n            if mx(i) == 1\n                idimG(i,j) = ind(i,1);\n                mxG(i,j) = C(i,ind(i));\n            elseif mx(i) == 0\n                idimG(i,j) = -1;\n                mxG(i,j) = -1;\n            end\n        end\n        %col = mxG(:,j)\n    end\nend\n%The intrinsic dimension for a point is the minimum of the intrinsic dimensions for\n%each scale for which the intrinsic dimension is computed. \n%If it is not computed at any scale idim = -1.\nfor i = 1:m\n    ind1 = find(indG(i,:) == 1);\n    if length(ind1) > 0\n        idim(i,1) = min(idimG(i,ind1));\n        indscales = find(idimG(i,:) == idim(i,1));\n        scales(i,indscales) = 1;\n        scaleprob(i,1) = length(indscales)/length(ind1);\n        if indscales == indscales(1) - 1 + [1:length(indscales)]\n            consecutive(i,1) = 1;\n        end\n        firstscaleindex(i,1) = indscales(1);\n    end\nend\nend\n\n"}
+{"plateform": "github", "repo_name": "MelWe/mm-hypothesis-master", "name": "idimprob.m", "ext": ".m", "path": "mm-hypothesis-master/code/vidim2code/idimprob.m", "size": 1907, "source_encoding": "utf_8", "md5": "ad428ae3d0eed13d1868b9a7f0230b9d", "text": "\nfunction [idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG)\n%CumG is a matrix whose rows are q groups of cumulative distributions\n%of size groupdim\n%t is a threshhold 0 <= t <= 1\n%The function returns column index of the first element in each row\n%which is >= t; if no such index exists, the function returns -1.\n[m,n] = size(CumG);\nif ~(n == q*groupdim)\n    idim = [];\n    firstscaleindex = [];\n    scales = [];\n    idimG = [];\n    mxG = [];\n    scaleprob = [];\n    consecutive = [];\n    display('in idimprob -- CumG does not have the expected number of columns');\nelse\n    idim = -1*ones(m,1);\n    firstscaleindex = -1*ones(m,1);\n    scales = zeros(m,q);\n    idimG = -1*ones(m,q);\n    mxG = -1*ones(m,q);\n    scaleprob = -1*ones(m,1);\n    consecutive = -1*ones(m,1);\n    for j = 1:q\n        ind = groupdim*(j-1) + [1:groupdim];\n        C = CumG(:,ind);\n        D = C >= t;\n        %find first index in each row where C >= t\n        [mx,ind] = max(D,[],2);\n        for i = 1:m\n            if mx(i) == 1\n                idimG(i,j) = ind(i,1);\n                mxG(i,j) = C(i,ind(i));\n            elseif mx(i) == 0\n                idimG(i,j) = -1;\n                mxG(i,j) = -1;\n            end\n        end\n        %col = mxG(:,j)\n    end\nend\n%The intrinsic dimension for a point is the minimum of the intrinsic dimensions for\n%each scale for which the intrinsic dimension is computed. \n%If it is not computed at any scale idim = -1.\nfor i = 1:m\n    ind1 = find(indG(i,:) == 1);\n    if length(ind1) > 0\n        idim(i,1) = min(idimG(i,ind1));\n        indscales = find(idimG(i,:) == idim(i,1));\n        scales(i,indscales) = 1;\n        scaleprob(i,1) = length(indscales)/length(ind1);\n        if indscales == indscales(1) - 1 + [1:length(indscales)]\n            consecutive(i,1) = 1;\n        end\n        firstscaleindex(i,1) = indscales(1);\n    end\nend\nend\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "MelWe/mm-hypothesis-master", "name": "idimprob2.m", "ext": ".m", "path": "mm-hypothesis-master/code/vidim2code/idimprob2.m", "size": 2395, "source_encoding": "utf_8", "md5": "7cc95d6059d6370a0a744fcb71ce9bd5", "text": "\nfunction [idimsig, idimsigstats] = idimprob2( CumG,q,groupdim,t,indG,ballcount)\n%CumG is a matrix whose rows are q groups of cumulative distributions\n%of size groupdim\n%t is a threshhold 0 <= t <= 1\n%The function returns column index of the first element in each row\n%which is >= t; if no such index exists, the function returns -1.\n[m,n] = size(CumG);\nif ~(n == q*groupdim)\n    idimsig = [];\n    firstscaleindex = [];\n    scales = [];\n    idimG = [];\n    mxG = [];\n    scaleprob = [];\n    consecutive = [];\n    display('in idimprob -- CumG does not have the expected number of columns');\nelse\n    idimsig = -1*ones(m,1);\n    firstscaleindex = -1*ones(m,1);\n    scales = zeros(m,q);\n    idimG = -1*ones(m,q);\n    mxG = -1*ones(m,q);\n    scaleprob = -1*ones(m,1);\n    consecutive = -1*ones(m,1);\n    for j = 1:q\n        ind = groupdim*(j-1) + [1:groupdim];\n        C = CumG(:,ind);\n        D = C >= t;\n        %find first index in each row where C >= t\n        [mx,ind] = max(D,[],2);\n        for i = 1:m\n            if mx(i) == 1\n                idimG(i,j) = ind(i,1);\n                mxG(i,j) = C(i,ind(i));\n            elseif mx(i) == 0\n                idimG(i,j) = -1;\n                mxG(i,j) = -1;\n            end\n        end\n        %col = mxG(:,j)\n    end\nend\n[ idimGsig,scalessig,totalsigscales ] = significantidim(idimG,ballcount );\n%The intrinsic dimension for a point is the minimum of the intrinsic dimensions for\n%each scale for which the intrinsic dimension is computed. \n%If it is not computed at any scale idim = -1.\nfor i = 1:m\n    %ind1 = find(indG(i,:) == 1);\n    ind1 = find(idimGsig(i,:)) > 0;\n    if length(ind1) > 0\n        idimsig(i,1) = min(idimGsig(i,ind1));\n        indscales = find(idimGsig(i,:) == idimsig(i,1));\n        scales(i,indscales) = 1;\n        scaleprob(i,1) = length(indscales)/length(ind1);\n        if indscales == indscales(1) - 1 + [1:length(indscales)]\n            consecutive(i,1) = 1;\n        end\n        firstscaleindex(i,1) = indscales(1);\n    end\nend\n\ntotalidimscales = sum(scales,2);\n\nidimsigstats(1).idimsig = idimsig;\nidimsigstats(1).firstscaleindex = firstscaleindex;\nidimsigstats(1).indG = indG;\nidimsigstats(1).scales = scales;\nidimsigstats(1).idimG = idimG;\nidimsigstats(1).scaleprob = scaleprob;\nidimsigstats(1).scales = scales;\nidimsigstats(1).consecutive = consecutive;\nidimsigstats(1).dense = dense;\nidimsigstats(1).SG = SG;\n\n\n\n\nend\n\n"}
+{"plateform": "github", "repo_name": "MelWe/mm-hypothesis-master", "name": "testvariancebaseddimLIDAR.m", "ext": ".m", "path": "mm-hypothesis-master/code/vidimcode/testvariancebaseddimLIDAR.m", "size": 21008, "source_encoding": "utf_8", "md5": "134fa64fe2c0f09fad7d1c4fca69bd79", "text": "function testvariancebaseddimLIDAR( testid )\n%This file illustrates the variance based intrinsic dimension code and\n%total variance functions on the Bridge_87K.txt file\n\n\n\nif testid == 8\n    fnamedata = '/Users/lindaness/Documents/MATLAB/LIDAR2/Bridge_87K.txt';\n    data = dlmread(fnamedata);\n    x = data(:,1);\n    y = data(:,2);\n    z = data(:,3);\n    xdiam = max(x) - min(x);\n    ydiam = max(y) - min(y);\n    zdiam = max(z) - min(z);\n    maxdiam = sqrt(xdiam^2 + ydiam^2 + zdiam^2)\n   \n    h1 = figure;\n    scatter3(x,y,z);\n    str = sprintf('scatterplot of data for testid %d',testid);\n    title(str)\n    dirname = '/Users/lindaness/Documents/MATLAB/MSVDLinda/testsLIDAR/';\n    fnamescatter = [dirname,sprintf('test%d',testid)];\n    saveas(h1,[fnamescatter,'.fig']);\n    saveas(h1,[fnamescatter,'.png']);\n    \n    varthreshhold = .95; K = 3; epszero = 10^(-12); TVfigs = 1;\n    radii = (maxdiam)*2.^(-1.*[4:7])  \n    q = length(radii);\n    fname = [dirname,sprintf('testvarbasedidim testid %d',testid)];\n    idimfigs = 1;\n    [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname );\n    idimsummary\n    display('columheadings: idim,firstscaleindex, count');\n    istatssummary\n    save(fname,'varthreshhold', 'K', 'radii', 'epszero', 'TVfigs', 'fname');\n    save(fname,'data','-append');\n    save(fname, 'idimsummary','istatssummary','idim','firstscaleindex','scaleprob', 'scales','consecutive','dense','SG','indG','SSVs','-append');\n    [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim,SG,radii,q,scales,indG,TVfigs,fname);\n    save(fname,'SSVEnergy','idimSSVEnergy','EidimSSVEnergy','TV','ETV','idimTV','EidimTV','-append');\nelseif testid == 9\n    fnamedata = '/Users/lindaness/Documents/MATLAB/wisdmreproducibility/datasets/Bridge_87K.txt';\n    dirname = '/Users/lindaness/Documents/MATLAB/wisdmreproducibility/results/';\n    fnamescatter = [dirname,sprintf('test%d-data',testid)];\n    fname = [dirname,sprintf('test%d-results',testid)];\n    \n    data = dlmread(fnamedata);\n    x = data(:,1);\n    y = data(:,2);\n    z = data(:,3);\n    xdiam = max(x) - min(x);\n    ydiam = max(y) - min(y);\n    zdiam = max(z) - min(z);\n    maxdiam = sqrt(xdiam^2 + ydiam^2 + zdiam^2)\n   \n    h1 = figure;\n    scatter3(x,y,z);\n    str = sprintf('scatterplot of data for testid %d',testid);\n    title(str)\n    saveas(h1,[fnamescatter,'.fig']);\n    saveas(h1,[fnamescatter,'.png']);\n    \n    varthreshhold = .95; K = 3; epszero = 10^(-12); TVfigs = 1;\n    radii = (maxdiam)*2.^(-1.*[4:7])  \n    q = length(radii);\n    \n    idimfigs = 1;\n    [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname );\n    idimsummary\n    display('columheadings: idim,firstscaleindex, count');\n    istatssummary\n    save(fname,'varthreshhold', 'K', 'radii', 'epszero', 'TVfigs', 'fname');\n    save(fname,'data','-append');\n    save(fname, 'idimsummary','istatssummary','idim','firstscaleindex','scaleprob', 'scales','consecutive','dense','SG','indG','SSVs','-append');\n    [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim,SG,radii,q,scales,indG,TVfigs,fname);\n    save(fname,'SSVEnergy','idimSSVEnergy','EidimSSVEnergy','TV','ETV','idimTV','EidimTV','-append');\nelseif testid == 10\n    fnamedata = '/Users/lindaness/Documents/MATLAB/wisdmreproducibility/datasets/Bridge_87K.txt';\n    dirname = '/Users/lindaness/Documents/MATLAB/wisdmreproducibility/results/';\n    fnamescatter = [dirname,sprintf('test%d-data',testid)];\n    fname = [dirname,sprintf('test%d-vidim-',testid)];\n    \n    data = dlmread(fnamedata);\n    x = data(:,1);\n    y = data(:,2);\n    z = data(:,3);\n    xdiam = max(x) - min(x);\n    ydiam = max(y) - min(y);\n    zdiam = max(z) - min(z);\n    maxdiam = sqrt(xdiam^2 + ydiam^2 + zdiam^2)\n   \n    h1 = figure;\n    scatter3(x,y,z);\n    str = sprintf('scatterplot of data for testid %d',testid);\n    title(str)\n    saveas(h1,[fnamescatter,'.fig']);\n    saveas(h1,[fnamescatter,'.png']);\n    \n    varthreshhold = .95; K = 3; epszero = 10^(-12); TVfigs = 1;\n    radii = (maxdiam)*2.^(-1.*[4:7])  \n    q = length(radii)\n    \n    idimfigs = 1;\n    [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname );\n    idimsummary\n    display('columheadings: idim,firstscaleindex, count');\n    istatssummary\n    save(fname,'varthreshhold', 'K', 'radii', 'epszero', 'TVfigs', 'fname');\n    save(fname,'data','-append');\n    save(fname, 'idimsummary','istatssummary','idim','firstscaleindex','scaleprob', 'scales','consecutive','dense','SG','indG','SSVs','-append');\n    [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim,SG,radii,q,scales,indG,TVfigs,fname);\n    save(fname,'SSVEnergy','idimSSVEnergy','EidimSSVEnergy','TV','ETV','idimTV','EidimTV','-append');\n    \n    \nend\nend\n\n\nfunction [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname )\n%idim plots are shown if idimfigs == 1\n%variance function pots are shown if TVfigs == 1\n%radii is a decreasing sequence of radiuses determining a sequence of\n%balls around each point. The index of each radius is referred to as the\n%scale.\n[m,n] = size(data); % m data points, n coordinates\nmaxdim = min(m,n);\nq = length(radii);\n%This program looks for points with intrinsic dimensions <= K (with respect\n%to the family of point neighborhoods specified by radii\ncutoff = K*log(K);\n%This function computes the intrinsic dimension idim for each point by\n%a concentration of variance analysis using varthreshhold (e.g. .95)\n%SSVs is an m x maxdim*q matrix of q groups of squared singular values\n%of matrices of points in each ball, the matrices are centered so EV = 0\n%The squared singular values are then the variances of each centered coordinate.\n%PeG is an m x maxdim*q matrix of q groups of discrete probability distriutions\n%for each of the m points (relative variances); SG is an m x q matrix of\n%total variances for each ball; CumG is an m x maxdim*q matrix of q groups\n%of vectors (c1, ..., cn) defining the cumulative distribution for\n%(p1,..,pn) so cj = p1 + ... pj \n% idim(p) = i, i = minimum over the scales of smallestindex j cj >= varthresshold\n%e.g if varthreshhold = .95 ,idim(p) = i if there is a ball (scale) for which\n%the sum of the variances of the first i centered coordinates >= .95\n%scales is an m xq matrix of 0's and 1's. each row (pt) has 1's at the scales \n%i, such that ci >= varthresshold and i = idim(pt) \n%i.e. idim is computed by concentration of variance analysis  and the\n%scales is the set of indices of balls where the variance accumulates to\n%the fastest to varthreshhold\n\n%Two summary matrices are computed: idimsummary and istatssummary\n%rows of idimsummary (idim value, number of points with idim value)\n%i.e. the discrete distribution of idim for the data set\n%rows of istats summary (idim value, first scale index, number of points \n%with the specified idim value and first scale index.\n%i.e. the discrete distribution of idim,firstscale for the data set\n\n%If idimfigs == 1  scatter plots of the data points color-coded by idim\n%are computed\n\n%Different Total Variance functions are computed for each point, including\n%the SSVEnergy function. \n%Summaries of idim statistics are computed; idim and the Total Varianc\n%functions are plotted\n\n\n[ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff );\nM = SSVs; \n[ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero);\n%PrG1599 = PrG(1599,:)\n%SG199 = SG(1599,:)\n%CumG1599 = CumG(1599,:)\ngroupdim = n;\nt = varthreshhold;\n\n[idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG);\n\nu = unique(idim);\nidimsummary = zeros(length(u),2 );\nfor i = 1:length(u)\n    ind = find(idim == u(i));\n    lgth = length(ind);\n    idimsummary(i,:) = [u(i),lgth];\nend\n\n[istats,irhs,ilhs] = unique([idim,firstscaleindex],'rows');\n\n%istatssummary = zeros(length(istats),4);\nistatssummary = zeros(length(istats),3);\nfor i = 1:size(istats,1)\n    %x = abs(istats(i,1:3));\n    x = istats(i,1:2);\n    y = length(find(ilhs == i));\n    istatssummary(i,:) = [x,y];\nend\n\n\n\n\n\n\n\n\n\n\n\n\n\nif idimfigs == 1\n    ind = find(idim > 0);\n    m2 = length(ind);\n    \n    h = figure;\n    a = [1:m2];\n    b = idim(ind,:);\n    scatter(a,b,100);\n    titlestr = sprintf('ordered points with y coordinate = idim');\n    if ~length(fname) == 0\n        fname1 = [fname,'orderedidim','.png'];\n        saveas(h,fname1);\n        fname1 = [fname,'orderedidim','.fig'];\n        saveas(h,fname1);\n    end\n    \n    if n == 3\n        X = data(ind,1); Y = data(ind,2); Z = data(ind,3);\n        idim2 = idim(ind,:);\n        firstscaleindex2 = firstscaleindex(ind,:);\n\n        h = figure;\n        colormap(jet(m2));\n        scatter3(X,Y,Z,10,idim2);\n        titlestr = sprintf('points color-coded by idim');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idim','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idim','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,10,ilhs(ind,:));\n        %scatter3(X,Y,Z,100,firstscaleindex2);\n        titlestr = sprintf('points color-coded by lexicographic ordering of idim,firstscaleindex');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'lexidim','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'lexidim','.fig'];\n            saveas(h,fname1);\n        end\n    end\nend\nend\n\nfunction [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim, SG,radii,q,scales,indG,TVfigs,fname)\n%data is an m xn matrix of m data points with n coordinate\n%SG is an m x q matrix of total variances for data in each of the q balls\n%with radii specified in radius (total variances of the centered data)\n%SG entries for a ball are -1 if the intrinsic dimension was not computed for that ball \n%due to too few points etc\n%scales is an m x q matrix of 0's and 1's specifying the scales at which idim was observed\n%for each data point\n%indG is an m x q matrix of 0's and 1's specifying the scales for which\n%intrinsic dimension was computed \n%A variety of total variance functions are computed including the SSVEnergy\n%function we computed at WiSDM\n%if TVfig == 1, the total variance functions are plotted for the subset of\n%points for which intrinsic dimension was computed.\n\n[m,n] = size(data);\n%SG is an m x q matrix containg the total variances for each ball \n%(or -1's for the ball where SG and hence the intrinsic dimension were\n%not computed\n\n%Compute different Versions of Total Variance Functions\n%TV sum of total variances over the relevant balls\nTV = sum(SG.*indG,2); \nETV = TV./sum(indG,2); %m x 1 average of total variances of relevant balls\n\nidimTV = sum(scales.*SG,2); %sum of scale variances for idim scales \n%total variance for each of these scale is concentrated near idim scales\nEidimTV = idimTV./sum(scales,2); %Expected Value over the scales where \n%local idim = idim for the point\n\nR = repmat(radii.^2,m,1);\nRelSG = SG./R; %RelSG total variances for each ball are normalized by\n%dividing by the square of the radius; functions analagous to above are \n%computed from the relative variances. \nSSVEnergy =sum(RelSG,2) ; %This is the SSVEnergy function computed at WiSDM\nidimSSVEnergy = sum(scales.*RelSG,2);\nEidimSSVEnergy = idimSSVEnergy./sum(scales,2);\n\n\nif TVfigs == 1\n     ind = find(idim > 0);\n     SG = SG(ind,:);\n     m = size(data(ind,:),1);\n    \n    %h = figure;\n    %x = 1:m;\n    %hold on\n    %for i = 1:q\n        %plot(x,SG(:,i));\n    %end\n    %titlestr = sprintf('Total Variance Curves for each scale');\n    %title(titlestr)\n    %hold off\n    \n    if n == 3\n     X = data(ind,1); Y = data(ind,2); Z = data(ind,3);\n     TV = TV(ind,:);\n     idimTV = idimTV(ind,:);\n     EidimTV = EidimTV(ind,:);\n     SSVEnergy = SSVEnergy(ind,:);\n     idimSSVEnergy = idimSSVEnergy(ind,:);\n     EidimSSVEnergy = EidimSSVEnergy(ind,:);\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,TV);\n        titlestr = sprintf('points color-coded by sum of Total Variances over scales where idim was computed');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'TV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'TV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,idimTV);\n        titlestr = sprintf('points color-coded by sum of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimTV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idimTV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,EidimTV);\n        titlestr = sprintf('points color-coded by EV of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'EidimTV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'EidimTV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,SSVEnergy);\n        titlestr = sprintf('SSVEnergy - points color-coded by sum of normalized total variances over scales where idim was computed') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'SSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'SSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,idimSSVEnergy);\n        titlestr = sprintf('idmSSVEnergy - points color-coded by sum of normalized total variances over idim scales') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimSSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idimSSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,EidimSSVEnergy);\n        titlestr = sprintf('EidmSSVEnergy - points color-coded by EV of normalized total variances over idim scales') ;\n        title(titlestr); \n        if ~length(fname) == 0\n            fname1 = [fname,'EidimSSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'EidimSSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n    end\nend\n\nend\n\nfunction [ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff )\n% data is an m x n matrix, whose rows are the data\n%r is a 1 x q list of decreasing radii (e.g 2^0, 2^(-1), 2^(-2), ....);\n%This function computes the squared singular values for each point p and each\n%subset d(p,r) of data points at distance <= r from data point p \n%Results are returned in the m x q*maxdim matrix SSVs, maxdim = min(m,n);\n%For the ith data point p = data(i,:) \n%SSVs for p and d(p,r(j)) are in row i,  columns [1:maxdim] + maxdim*(j-1)\n%SSVs are not computed if |d(p,r)| < cutoff, instead -1's are stored in SSVs \\\n%dense is an m x q matrix of 0's and 1's\n%dense(i,j)  = |d(p,r(j))| < cutoff\n\n[m,n] = size(data);\nq = length(radii);\n%maxdim is the maximum number of non-zero singular values\nmaxdim = min(m,n);\n%dense(i,j) = 1 if |d(p,r)| > cutoff else 0, p = data(i,:)\ndense = ones(m,q);\nSSVs = -1*ones(m,q*maxdim);\nballcount = zeros(m,q);\n\n%for each point \nfor i = 1:m\n    if mod(i,1000) == 0\n        i\n    end\n    p = data(i,:);\n    ballmembership = zeros(m,q);\n    for j = 1:q\n        r = radii(j);\n        [b,B] = ball(data,p,r);\n        ballmembership(:,j) = B;\n        %compute EV of b and translate by -EV so EV(c) = 0;\n        c = center(b);\n        cardc = size(c,1);\n        if cardc > cutoff\n            s = svd(c);\n            sq = s.*s;\n            inds2 = [1:maxdim] + maxdim*(j-1);\n            SSVs(i,inds2) = sq; \n        else\n            dense(i,j) = 0;\n        end      \n    end\n    ballcount(i,:) = sum(ballmembership);\nend\nend\n\nfunction [v,B] = ball( data,p,r) \n%% p=center; r=radius %%\n%This functions finds the data points in the ball of radius r \n%centered at p. \n[m,n] = size(data);\nx = sqrt(sum((data- ones(m,1)*p).^2,2));\nB= x < r*ones(m,1);\nv=data(B,:);\nend\n\nfunction [ centereddata ] = center( data )\n%UNTITLED4 Summary of this function goes here\n%   Detailed explanation goes here\n[m,n] = size(data);\nEV = (1/m)*sum(data);\ncentereddata = data - ones(m,1)*EV;\nend\n\nfunction [ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero)\n%M is an m x q*maxdim matrix\n%dense is an m x q matrix\n%epszero very small and positive and used to test for zero e.g 10^(-12)\n%SG is m x q; PrG and SG are m x q*maxdim; indG is m x q\n%For the jth group of maxdim columns, for each row i, if dense(i,j) == 1 and\n%if the entries are >=0  with positive sum (> epszero) , indG(i,j) = 1 and\n%the rowsum,probability distribution, and cumlative probability distribution\n%of the row are computed and stored in the matrices SG, PrG and CumG.\n%for the other rows in the group indG = 0 and the entries of \n%SG, PrG and CumG are -1's.\n%if M does not have q*maxdim columns PrG,SG,CumG have -1 entries and inG = 0\n\n[m,n] = size(M);\nPrG = -1*ones(m,n);\nCumG = -1*ones(m,n);\nSG = -1*ones(m,q);\nindG = zeros(m,q);\nif n == q*maxdim\n    for j = 1:q\n        inddense = find(dense(:,j) == 1);\n        indj = [1:maxdim] + maxdim*(j-1);\n        [ Pr,S,Cum,ind ] = Probdist(M(inddense,indj),epszero);\n        PrG(inddense,indj) = Pr;\n        SG(inddense,j) = S;\n        CumG(inddense,indj) = Cum;\n        indG(inddense(ind),j) = 1;\n    end\nend\nend\n\n\nfunction [ Pr,S,Cum,ind ] = Probdist(M,epszero)\n%M is a m x n matrix \n%S is an m x 1 matrix; Cum and Pr are m x n matrices. \n%S,Cum,Pr are initialized to -1;\n%for the rows of M which are non-negative with positive sum (> epszero)\n%S is the sum of the row\n%Pr is the discrete probability distribution determined by the row\n%Cum is the cumulative distribution determined by the row\n%Example row = [1,2,3]; S = 6, Pr = [1/6,2/6,3/6], Cum = [1/6,1/2,1]\n%For the other rows of M, the entries of S, Pr, and Cu are -1.\n%ind is the index of the non-negative rows with positive sum\n\n\n[m,n] = size(M);\nS = -1*ones(m,1);\nPr = -1*ones(m,n);\nCum = -1*ones(m,n);\nnonneg = M >= 0;\npossum = sum(M,2);\nN = sum(nonneg,2);\nind = find(N == n & possum > epszero);\nS(ind,:)  = sum(M(ind,:),2);\nk = length(ind);\nif k > 0\n    Tmp = zeros(k,1);\n    for i = 1:n\n        Pr(ind,i) = M(ind,i)./S(ind,1);\n        Tmp = Tmp + Pr(ind,i);\n        Cum(ind,i) = Tmp;\n    end\nend\n\n\n\n\nend\n\nfunction [idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG)\n%CumG is a matrix whose rows are q groups of cumulative distributions\n%of size groupdim\n%t is a threshhold 0 <= t <= 1\n%The function returns column index of the first element in each row\n%which is >= t; if no such index exists, the function returns -1.\n[m,n] = size(CumG);\nif ~(n == q*groupdim)\n    idim = [];\n    firstscaleindex = [];\n    scales = [];\n    idimG = [];\n    mxG = [];\n    scaleprob = [];\n    consecutive = [];\n    display('in idimprob -- CumG does not have the expected number of columns');\nelse\n    idim = -1*ones(m,1);\n    firstscaleindex = -1*ones(m,1);\n    scales = zeros(m,q);\n    idimG = -1*ones(m,q);\n    mxG = -1*ones(m,q);\n    scaleprob = -1*ones(m,1);\n    consecutive = -1*ones(m,1);\n    for j = 1:q\n        ind = groupdim*(j-1) + [1:groupdim];\n        C = CumG(:,ind);\n        D = C >= t;\n        %find first index in each row where C >= t\n        [mx,ind] = max(D,[],2);\n        for i = 1:m\n            if mx(i) == 1\n                idimG(i,j) = ind(i,1);\n                mxG(i,j) = C(i,ind(i));\n            elseif mx(i) == 0\n                idimG(i,j) = -1;\n                mxG(i,j) = -1;\n            end\n        end\n        %col = mxG(:,j)\n    end\nend\n%The intrinsic dimension for a point is the minimum of the intrinsic dimensions for\n%each scale for which the intrinsic dimension is computed. \n%If it is not computed at any scale idim = -1.\nfor i = 1:m\n    ind1 = find(indG(i,:) == 1);\n    if length(ind1) > 0\n        idim(i,1) = min(idimG(i,ind1));\n        indscales = find(idimG(i,:) == idim(i,1));\n        scales(i,indscales) = 1;\n        scaleprob(i,1) = length(indscales)/length(ind1);\n        if indscales == indscales(1) - 1 + [1:length(indscales)]\n            consecutive(i,1) = 1;\n        end\n        firstscaleindex(i,1) = indscales(1);\n    end\nend\nend\n\n"}
+{"plateform": "github", "repo_name": "MelWe/mm-hypothesis-master", "name": "computevidim.m", "ext": ".m", "path": "mm-hypothesis-master/code/vidimcode/computevidim.m", "size": 16974, "source_encoding": "utf_8", "md5": "305cfbbbc17b00669d077b04063972bf", "text": "function [data,idim] = computevidim( data,radii,K,varthreshhold,epszero,idimfigs,TVfigs, fname)\n%This computes variance-based intrinsic dimension for the \n%m x n data matrix. (The rows are the data points.)\n%Two summaries idimsummary and istatssummary are displayed (and saved).\n%The results are saved in the file [dirname,fname,'-vidim-']\n\n[ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname );\nidimsummary\ndisplay('columheadings: idim,firstscaleindex, count');\nistatssummary\nsave(fname,'varthreshhold', 'K', 'radii', 'epszero', 'TVfigs', 'fname');\nsave(fname,'data','-append');\nsave(fname, 'idimsummary','istatssummary','idim','firstscaleindex','scaleprob', 'scales','consecutive','dense','SG','indG','SSVs','-append');\nq = length(radii);\n[SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim,SG,radii,q,scales,indG,TVfigs,fname);\nsave(fname,'SSVEnergy','idimSSVEnergy','EidimSSVEnergy','TV','ETV','idimTV','EidimTV','-append');   \nend\n\n\nfunction [ idimsummary,istatssummary,idim,firstscaleindex,scaleprob, scales,consecutive,dense,SG,indG,SSVs] = variancebaseddim( data,radii,K,varthreshhold,epszero,idimfigs,fname )\n%idim plots are shown if idimfigs == 1\n%variance function pots are shown if TVfigs == 1\n%radii is a decreasing sequence of radiuses determining a sequence of\n%balls around each point. The index of each radius is referred to as the\n%scale.\n[m,n] = size(data); % m data points, n coordinates\nmaxdim = min(m,n);\nq = length(radii);\n%This program looks for points with intrinsic dimensions <= K (with respect\n%to the family of point neighborhoods specified by radii\ncutoff = K*log(K);\n%This function computes the intrinsic dimension idim for each point by\n%a concentration of variance analysis using varthreshhold (e.g. .95)\n%SSVs is an m x maxdim*q matrix of q groups of squared singular values\n%of matrices of points in each ball, the matrices are centered so EV = 0\n%The squared singular values are then the variances of each centered coordinate.\n%PeG is an m x maxdim*q matrix of q groups of discrete probability distriutions\n%for each of the m points (relative variances); SG is an m x q matrix of\n%total variances for each ball; CumG is an m x maxdim*q matrix of q groups\n%of vectors (c1, ..., cn) defining the cumulative distribution for\n%(p1,..,pn) so cj = p1 + ... pj \n% idim(p) = i, i = minimum over the scales of smallestindex j cj >= varthresshold\n%e.g if varthreshhold = .95 ,idim(p) = i if there is a ball (scale) for which\n%the sum of the variances of the first i centered coordinates >= .95\n%scales is an m xq matrix of 0's and 1's. each row (pt) has 1's at the scales \n%i, such that ci >= varthresshold and i = idim(pt) \n%i.e. idim is computed by concentration of variance analysis  and the\n%scales is the set of indices of balls where the variance accumulates to\n%the fastest to varthreshhold\n\n%Two summary matrices are computed: idimsummary and istatssummary\n%rows of idimsummary (idim value, number of points with idim value)\n%i.e. the discrete distribution of idim for the data set\n%rows of istats summary (idim value, first scale index, number of points \n%with the specified idim value and first scale index.\n%i.e. the discrete distribution of idim,firstscale for the data set\n\n%If idimfigs == 1  scatter plots of the data points color-coded by idim\n%are computed\n\n%Different Total Variance functions are computed for each point, including\n%the SSVEnergy function. \n%Summaries of idim statistics are computed; idim and the Total Varianc\n%functions are plotted\n\n\n[ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff );\nM = SSVs; \n[ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero);\n%PrG1599 = PrG(1599,:)\n%SG199 = SG(1599,:)\n%CumG1599 = CumG(1599,:)\ngroupdim = n;\nt = varthreshhold;\n\n[idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG);\n\nu = unique(idim);\nidimsummary = zeros(length(u),2 );\nfor i = 1:length(u)\n    ind = find(idim == u(i));\n    lgth = length(ind);\n    idimsummary(i,:) = [u(i),lgth];\nend\n\n[istats,irhs,ilhs] = unique([idim,firstscaleindex],'rows');\n\n%istatssummary = zeros(length(istats),4);\nistatssummary = zeros(length(istats),3);\nfor i = 1:size(istats,1)\n    %x = abs(istats(i,1:3));\n    x = istats(i,1:2);\n    y = length(find(ilhs == i));\n    istatssummary(i,:) = [x,y];\nend\n\n\n\n\n\n\n\n\n\n\n\n\n\nif idimfigs == 1\n    ind = find(idim > 0);\n    m2 = length(ind);\n    \n    h = figure;\n    a = [1:m2];\n    b = idim(ind,:);\n    scatter(a,b,100);\n    titlestr = sprintf('ordered points with y coordinate = idim');\n    if ~length(fname) == 0\n        fname1 = [fname,'orderedidim','.png'];\n        saveas(h,fname1);\n        fname1 = [fname,'orderedidim','.fig'];\n        saveas(h,fname1);\n    end\n    \n    if n == 3\n        X = data(ind,1); Y = data(ind,2); Z = data(ind,3);\n        idim2 = idim(ind,:);\n        firstscaleindex2 = firstscaleindex(ind,:);\n\n        h = figure;\n        colormap(jet(m2));\n        scatter3(X,Y,Z,10,idim2);\n        titlestr = sprintf('points color-coded by idim');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idim','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idim','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(m));\n        scatter3(X,Y,Z,10,ilhs(ind,:));\n        %scatter3(X,Y,Z,100,firstscaleindex2);\n        titlestr = sprintf('points color-coded by lexicographic ordering of idim,firstscaleindex');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'lexidim','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'lexidim','.fig'];\n            saveas(h,fname1);\n        end\n    end\nend\nend\n\nfunction [SSVEnergy,idimSSVEnergy,EidimSSVEnergy,TV,ETV,idimTV,EidimTV ] = TotalVarianceFunctions(data,idim, SG,radii,q,scales,indG,TVfigs,fname)\n%data is an m xn matrix of m data points with n coordinate\n%SG is an m x q matrix of total variances for data in each of the q balls\n%with radii specified in radius (total variances of the centered data)\n%SG entries for a ball are -1 if the intrinsic dimension was not computed for that ball \n%due to too few points etc\n%scales is an m x q matrix of 0's and 1's specifying the scales at which idim was observed\n%for each data point\n%indG is an m x q matrix of 0's and 1's specifying the scales for which\n%intrinsic dimension was computed \n%A variety of total variance functions are computed including the SSVEnergy\n%function we computed at WiSDM\n%if TVfig == 1, the total variance functions are plotted for the subset of\n%points for which intrinsic dimension was computed.\n\n[m,n] = size(data);\n%SG is an m x q matrix containg the total variances for each ball \n%(or -1's for the ball where SG and hence the intrinsic dimension were\n%not computed\n\n%Compute different Versions of Total Variance Functions\n%TV sum of total variances over the relevant balls\nTV = sum(SG.*indG,2); \nETV = TV./sum(indG,2); %m x 1 average of total variances of relevant balls\n\nidimTV = sum(scales.*SG,2); %sum of scale variances for idim scales \n%total variance for each of these scale is concentrated near idim scales\nEidimTV = idimTV./sum(scales,2); %Expected Value over the scales where \n%local idim = idim for the point\n\nR = repmat(radii.^2,m,1);\nRelSG = SG./R; %RelSG total variances for each ball are normalized by\n%dividing by the square of the radius; functions analagous to above are \n%computed from the relative variances. \nSSVEnergy =sum(RelSG,2) ; %This is the SSVEnergy function computed at WiSDM\nidimSSVEnergy = sum(scales.*RelSG,2);\nEidimSSVEnergy = idimSSVEnergy./sum(scales,2);\n\n\nif TVfigs == 1\n     ind = find(idim > 0);\n     SG = SG(ind,:);\n     m = size(data(ind,:),1);\n    \n    %h = figure;\n    %x = 1:m;\n    %hold on\n    %for i = 1:q\n        %plot(x,SG(:,i));\n    %end\n    %titlestr = sprintf('Total Variance Curves for each scale');\n    %title(titlestr)\n    %hold off\n    \n    if n == 3\n     X = data(ind,1); Y = data(ind,2); Z = data(ind,3);\n     TV = TV(ind,:);\n     idimTV = idimTV(ind,:);\n     EidimTV = EidimTV(ind,:);\n     SSVEnergy = SSVEnergy(ind,:);\n     idimSSVEnergy = idimSSVEnergy(ind,:);\n     EidimSSVEnergy = EidimSSVEnergy(ind,:);\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,TV);\n        titlestr = sprintf('points color-coded by sum of Total Variances over scales where idim was computed');\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'TV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'TV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,idimTV);\n        titlestr = sprintf('points color-coded by sum of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimTV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idimTV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,EidimTV);\n        titlestr = sprintf('points color-coded by EV of Total Variance over idim scales for each point') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'EidimTV','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'EidimTV','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,SSVEnergy);\n        titlestr = sprintf('SSVEnergy - points color-coded by sum of normalized total variances over scales where idim was computed') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'SSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'SSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,idimSSVEnergy);\n        titlestr = sprintf('idmSSVEnergy - points color-coded by sum of normalized total variances over idim scales') ;\n        title(titlestr);\n        if ~length(fname) == 0\n            fname1 = [fname,'idimSSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'idimSSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n\n        h = figure;\n        colormap(jet(length(ind)));\n        scatter3(X,Y,Z,100,EidimSSVEnergy);\n        titlestr = sprintf('EidmSSVEnergy - points color-coded by EV of normalized total variances over idim scales') ;\n        title(titlestr); \n        if ~length(fname) == 0\n            fname1 = [fname,'EidimSSVEnergy','.png'];\n            saveas(h,fname1);\n            fname1 = [fname,'EidimSSVEnergy','.fig'];\n            saveas(h,fname1);\n        end\n    end\nend\n\nend\n\nfunction [ SSVs,dense,ballcount ] = ComputeSSVs( data,radii,cutoff )\n% data is an m x n matrix, whose rows are the data\n%r is a 1 x q list of decreasing radii (e.g 2^0, 2^(-1), 2^(-2), ....);\n%This function computes the squared singular values for each point p and each\n%subset d(p,r) of data points at distance <= r from data point p \n%Results are returned in the m x q*maxdim matrix SSVs, maxdim = min(m,n);\n%For the ith data point p = data(i,:) \n%SSVs for p and d(p,r(j)) are in row i,  columns [1:maxdim] + maxdim*(j-1)\n%SSVs are not computed if |d(p,r)| < cutoff, instead -1's are stored in SSVs \\\n%dense is an m x q matrix of 0's and 1's\n%dense(i,j)  = |d(p,r(j))| < cutoff\n\n[m,n] = size(data);\nq = length(radii);\n%maxdim is the maximum number of non-zero singular values\nmaxdim = min(m,n);\n%dense(i,j) = 1 if |d(p,r)| > cutoff else 0, p = data(i,:)\ndense = ones(m,q);\nSSVs = -1*ones(m,q*maxdim);\nballcount = zeros(m,q);\n\n%for each point \nfor i = 1:m\n    if mod(i,1000) == 0\n        i\n    end\n    p = data(i,:);\n    ballmembership = zeros(m,q);\n    for j = 1:q\n        r = radii(j);\n        [b,B] = ball(data,p,r);\n        ballmembership(:,j) = B;\n        %compute EV of b and translate by -EV so EV(c) = 0;\n        c = center(b);\n        cardc = size(c,1);\n        if cardc > cutoff\n            s = svd(c);\n            sq = s.*s;\n            inds2 = [1:maxdim] + maxdim*(j-1);\n            SSVs(i,inds2) = sq; \n        else\n            dense(i,j) = 0;\n        end      \n    end\n    ballcount(i,:) = sum(ballmembership);\nend\nend\n\nfunction [v,B] = ball( data,p,r) \n%% p=center; r=radius %%\n%This functions finds the data points in the ball of radius r \n%centered at p. \n[m,n] = size(data);\nx = sqrt(sum((data- ones(m,1)*p).^2,2));\nB= x < r*ones(m,1);\nv=data(B,:);\nend\n\nfunction [ centereddata ] = center( data )\n%UNTITLED4 Summary of this function goes here\n%   Detailed explanation goes here\n[m,n] = size(data);\nEV = (1/m)*sum(data);\ncentereddata = data - ones(m,1)*EV;\nend\n\nfunction [ PrG,SG,CumG,indG ] = ProbDistGroups(M,q,maxdim,dense,epszero)\n%M is an m x q*maxdim matrix\n%dense is an m x q matrix\n%epszero very small and positive and used to test for zero e.g 10^(-12)\n%SG is m x q; PrG and SG are m x q*maxdim; indG is m x q\n%For the jth group of maxdim columns, for each row i, if dense(i,j) == 1 and\n%if the entries are >=0  with positive sum (> epszero) , indG(i,j) = 1 and\n%the rowsum,probability distribution, and cumlative probability distribution\n%of the row are computed and stored in the matrices SG, PrG and CumG.\n%for the other rows in the group indG = 0 and the entries of \n%SG, PrG and CumG are -1's.\n%if M does not have q*maxdim columns PrG,SG,CumG have -1 entries and inG = 0\n\n[m,n] = size(M);\nPrG = -1*ones(m,n);\nCumG = -1*ones(m,n);\nSG = -1*ones(m,q);\nindG = zeros(m,q);\nif n == q*maxdim\n    for j = 1:q\n        inddense = find(dense(:,j) == 1);\n        indj = [1:maxdim] + maxdim*(j-1);\n        [ Pr,S,Cum,ind ] = Probdist(M(inddense,indj),epszero);\n        PrG(inddense,indj) = Pr;\n        SG(inddense,j) = S;\n        CumG(inddense,indj) = Cum;\n        indG(inddense(ind),j) = 1;\n    end\nend\nend\n\n\nfunction [ Pr,S,Cum,ind ] = Probdist(M,epszero)\n%M is a m x n matrix \n%S is an m x 1 matrix; Cum and Pr are m x n matrices. \n%S,Cum,Pr are initialized to -1;\n%for the rows of M which are non-negative with positive sum (> epszero)\n%S is the sum of the row\n%Pr is the discrete probability distribution determined by the row\n%Cum is the cumulative distribution determined by the row\n%Example row = [1,2,3]; S = 6, Pr = [1/6,2/6,3/6], Cum = [1/6,1/2,1]\n%For the other rows of M, the entries of S, Pr, and Cu are -1.\n%ind is the index of the non-negative rows with positive sum\n\n\n[m,n] = size(M);\nS = -1*ones(m,1);\nPr = -1*ones(m,n);\nCum = -1*ones(m,n);\nnonneg = M >= 0;\npossum = sum(M,2);\nN = sum(nonneg,2);\nind = find(N == n & possum > epszero);\nS(ind,:)  = sum(M(ind,:),2);\nk = length(ind);\nif k > 0\n    Tmp = zeros(k,1);\n    for i = 1:n\n        Pr(ind,i) = M(ind,i)./S(ind,1);\n        Tmp = Tmp + Pr(ind,i);\n        Cum(ind,i) = Tmp;\n    end\nend\n\n\n\n\nend\n\nfunction [idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG)\n%CumG is a matrix whose rows are q groups of cumulative distributions\n%of size groupdim\n%t is a threshhold 0 <= t <= 1\n%The function returns column index of the first element in each row\n%which is >= t; if no such index exists, the function returns -1.\n[m,n] = size(CumG);\nif ~(n == q*groupdim)\n    idim = [];\n    firstscaleindex = [];\n    scales = [];\n    idimG = [];\n    mxG = [];\n    scaleprob = [];\n    consecutive = [];\n    display('in idimprob -- CumG does not have the expected number of columns');\nelse\n    idim = -1*ones(m,1);\n    firstscaleindex = -1*ones(m,1);\n    scales = zeros(m,q);\n    idimG = -1*ones(m,q);\n    mxG = -1*ones(m,q);\n    scaleprob = -1*ones(m,1);\n    consecutive = -1*ones(m,1);\n    for j = 1:q\n        ind = groupdim*(j-1) + [1:groupdim];\n        C = CumG(:,ind);\n        D = C >= t;\n        %find first index in each row where C >= t\n        [mx,ind] = max(D,[],2);\n        for i = 1:m\n            if mx(i) == 1\n                idimG(i,j) = ind(i,1);\n                mxG(i,j) = C(i,ind(i));\n            elseif mx(i) == 0\n                idimG(i,j) = -1;\n                mxG(i,j) = -1;\n            end\n        end\n        %col = mxG(:,j)\n    end\nend\n%The intrinsic dimension for a point is the minimum of the intrinsic dimensions for\n%each scale for which the intrinsic dimension is computed. \n%If it is not computed at any scale idim = -1.\nfor i = 1:m\n    ind1 = find(indG(i,:) == 1);\n    if length(ind1) > 0\n        idim(i,1) = min(idimG(i,ind1));\n        indscales = find(idimG(i,:) == idim(i,1));\n        scales(i,indscales) = 1;\n        scaleprob(i,1) = length(indscales)/length(ind1);\n        if indscales == indscales(1) - 1 + [1:length(indscales)]\n            consecutive(i,1) = 1;\n        end\n        firstscaleindex(i,1) = indscales(1);\n    end\nend\nend\n\n"}
+{"plateform": "github", "repo_name": "MelWe/mm-hypothesis-master", "name": "idimprob.m", "ext": ".m", "path": "mm-hypothesis-master/code/vidimunpackedcode/idimprob.m", "size": 1907, "source_encoding": "utf_8", "md5": "ad428ae3d0eed13d1868b9a7f0230b9d", "text": "\nfunction [idim,firstscaleindex, scales,idimG,mxG,scaleprob,consecutive] = idimprob( CumG,q,groupdim,t,indG)\n%CumG is a matrix whose rows are q groups of cumulative distributions\n%of size groupdim\n%t is a threshhold 0 <= t <= 1\n%The function returns column index of the first element in each row\n%which is >= t; if no such index exists, the function returns -1.\n[m,n] = size(CumG);\nif ~(n == q*groupdim)\n    idim = [];\n    firstscaleindex = [];\n    scales = [];\n    idimG = [];\n    mxG = [];\n    scaleprob = [];\n    consecutive = [];\n    display('in idimprob -- CumG does not have the expected number of columns');\nelse\n    idim = -1*ones(m,1);\n    firstscaleindex = -1*ones(m,1);\n    scales = zeros(m,q);\n    idimG = -1*ones(m,q);\n    mxG = -1*ones(m,q);\n    scaleprob = -1*ones(m,1);\n    consecutive = -1*ones(m,1);\n    for j = 1:q\n        ind = groupdim*(j-1) + [1:groupdim];\n        C = CumG(:,ind);\n        D = C >= t;\n        %find first index in each row where C >= t\n        [mx,ind] = max(D,[],2);\n        for i = 1:m\n            if mx(i) == 1\n                idimG(i,j) = ind(i,1);\n                mxG(i,j) = C(i,ind(i));\n            elseif mx(i) == 0\n                idimG(i,j) = -1;\n                mxG(i,j) = -1;\n            end\n        end\n        %col = mxG(:,j)\n    end\nend\n%The intrinsic dimension for a point is the minimum of the intrinsic dimensions for\n%each scale for which the intrinsic dimension is computed. \n%If it is not computed at any scale idim = -1.\nfor i = 1:m\n    ind1 = find(indG(i,:) == 1);\n    if length(ind1) > 0\n        idim(i,1) = min(idimG(i,ind1));\n        indscales = find(idimG(i,:) == idim(i,1));\n        scales(i,indscales) = 1;\n        scaleprob(i,1) = length(indscales)/length(ind1);\n        if indscales == indscales(1) - 1 + [1:length(indscales)]\n            consecutive(i,1) = 1;\n        end\n        firstscaleindex(i,1) = indscales(1);\n    end\nend\nend\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cvx_version.m", "ext": ".m", "path": "Programs-master/matlab/cvx/cvx_version.m", "size": 14459, "source_encoding": "utf_8", "md5": "64d0f08cf9bf3c75fcad41acaf35a8c0", "text": "function varargout = cvx_version( varargin )\n\n% CVX_VERSION   Returns version and environment information for CVX.\n%\n%    When called with no arguments, CVX_VERSION prints out version and\n%    platform information that is needed when submitting CVX bug reports.\n%\n%    This function is also used internally to return useful variables that\n%    allows CVX to adjust its settings to the current environment.\n\nglobal cvx___\n\nargs = varargin;\ncompile = false;\nquick = nargout > 0;\nif nargin\n    if ~ischar( args{1} ),\n        quick = true;\n    else\n        tt = strcmp( args, '-quick' );\n        quick = any( tt );\n        if quick, args(tt) = []; end\n        tt = strcmp( args, '-compile' );\n        compile = any( tt );\n        if compile, quick = false; args(tt) = []; end\n    end\nend\n\nif isfield( cvx___, 'loaded' ),\n    \n    if quick, return; end\n    fs = cvx___.fs;\n    mpath = cvx___.where;\n    isoctave = cvx___.isoctave;\n    \nelse\n    \n    % Matlab / Octave flag\n    isoctave = exist( 'OCTAVE_VERSION', 'builtin' );\n\n    % File and path separators, MEX extension\n    if isoctave,\n        comp = octave_config_info('canonical_host_type');\n        mext = 'mex';\n        izpc = false;\n        izmac = false;\n        if octave_config_info('mac'),\n            msub = 'mac';\n            izmac = true;\n        elseif octave_config_info('windows'),\n            msub = 'win';\n            izpc = true;\n        elseif octave_config_info('unix') && any(strfind(comp,'linux')),\n            msub = 'lin';\n        else\n            msub = 'unknown';\n        end\n        if ~isempty( msub ),\n            msub = [ 'o_', msub ];\n            if strncmp( comp, 'x86_64', 6 ),\n                msub = [ msub, '64' ];\n            else\n                msub = [ msub, '32' ];\n            end\n        end\n    else\n        comp = computer;\n        izpc = strncmp( comp, 'PC', 2  );\n        izmac = strncmp( comp, 'MAC', 3 );\n        mext = mexext;\n        msub = '';\n    end\n    if izpc,\n        fs = '\\'; \n        fsre = '\\\\';\n        ps = ';'; \n        cs = false;\n    else\n        fs = '/'; \n        fsre = '/';\n        ps = ':';\n        cs = ~izmac;\n    end\n\n    % Install location\n    mpath = mfilename('fullpath');\n    temp = strfind( mpath, fs );\n    mpath = mpath( 1 : temp(end) - 1 );\n\n    % Numeric version\n    nver = version;\n    nver(nver=='.') = ' ';\n    nver = sscanf(nver,'%d');\n    nver = nver(1) + 0.01 * ( nver(2) + 0.01 * nver(3) );\n    \n    if isoctave || ~usejava('jvm'),\n        jver = 0;\n    else\n        jver = char(java.lang.System.getProperty('java.version'));\n        try\n            ndxs = strfind( jver, '.' );\n            jver = str2double( jver(1:ndxs(2)-1) );\n        catch\n            jver = 0;\n        end\n    end\n    \n    cvx___.where = mpath;\n    cvx___.isoctave = isoctave;\n    cvx___.nver = nver;\n    cvx___.jver = jver;\n    cvx___.comp = comp;\n    cvx___.mext = mext;\n    cvx___.msub = msub;\n    cvx___.fs = fs;\n    cvx___.fsre = fsre;\n    cvx___.ps = ps;\n    cvx___.cs = cs;\n    \nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Quick exit for non-verbose output %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif quick,\n    if nargout,\n        varargout = { fs, cvx___.ps, mpath, cvx___.mext };\n    end\n    cvx_load_prefs( false );\n    cvx___.loaded = true;\n    return\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Verbose output (cvx_setup, cvx_version plain) %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\ncvx_ver = '2.1';\ncvx_bld = '1123';\ncvx_bdate = 'Sun Dec 17 18:58:10 2017';\ncvx_bcomm = 'cff5298';\nline = '---------------------------------------------------------------------------';\nfprintf( '\\n%s\\n', line );\nfprintf( 'CVX: Software for Disciplined Convex Programming       (c)2014 CVX Research\\n' );\nfprintf( 'Version %3s, Build %4s (%7s)%42s\\n', cvx_ver, cvx_bld, cvx_bcomm, cvx_bdate );\nfprintf( '%s\\n', line );\nfprintf( 'Installation info:\\n    Path: %s\\n', cvx___.where );\nif isoctave,\n    fprintf( '    GNU Octave %s on %s\\n', version, cvx___.comp );\nelse\n    verd = ver('MATLAB');\n    fprintf( '    MATLAB version: %s %s\\n', verd.Version, verd.Release );\n    if usejava( 'jvm' ),\n        os_name = char(java.lang.System.getProperty('os.name'));\n        os_arch = char(java.lang.System.getProperty('os.arch'));\n        os_version = char(java.lang.System.getProperty('os.version'));\n        java_str = char(java.lang.System.getProperty('java.version'));\n        fprintf('    OS: %s %s version %s\\n', os_name, os_arch, os_version );\n        fprintf('    Java version: %s\\n', java_str );\n    else\n        fprintf( '    Architecture: %s\\n', cvx___.comp );\n        fprintf( '    Java version: disabled\\n' );\n    end\nend\n    \n%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Check for valid version %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nissue = false;\nisoctave = cvx___.isoctave;\nnver = cvx___.nver;\nif isoctave,\n    if nver <= 3.08,\n        fprintf( '%s\\nCVX is not yet supported on Octave.\\n(Please do not waste further time trying: changes to Octave are required.\\nBut they are coming! Stay tuned.)\\n%s\\n', line, line );\n        issue = true;\n    end\nelseif nver < 7.08 && strcmp( cvx___.comp(end-1:end), '64' ),\n    fprintf( '%s\\nCVX requires MATLAB 7.8 or later (7.5 or later on 32-bit platforms).\\n' , line, line );\n    issue = true;\nelseif nver < 7.05,\n    fprintf( '%s\\nCVX requires MATLAB 7.5 or later (7.8 or later on 64-bit platforms).\\n' , line, line );\n    issue = true;\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%\n% Verify file contents %\n%%%%%%%%%%%%%%%%%%%%%%%%\n\nfid = fopen( [ mpath, fs, 'MANIFEST' ], 'r' );\nif fid > 0,\n    fprintf( 'Verfying CVX directory contents:' );\n    manifest = textscan( fid, '%s' );\n    manifest = manifest{1};\n    fclose( fid );\n    newman = get_manifest( mpath, fs );\n    if ~isequal( manifest, newman ),\n        missing = setdiff( manifest, newman );\n        additional = setdiff( newman, manifest );\n        if ~isempty( missing ) || ~isempty( additional ),\n            if fs ~= '/',\n                missing = strrep( missing, '/', fs );\n                additional = strrep( additional, '/', fs );\n            end\n            if ~isempty( missing ),\n                fprintf( '\\n    WARNING: The following files/directories are missing:\\n' );\n                isdir = cellfun(@(x)x(end)==fs,missing);\n                missing_d = missing(isdir);\n                missing_f = missing(~isdir);\n                while ~isempty( missing_d ),\n                    mdir = missing_d{1};\n                    ss = strncmp( missing_d, mdir, length(mdir) );\n                    tt = strncmp( missing_f, mdir, length(mdir) );\n                    fprintf( '        %s%s%s + %d files, %d subdirectories\\n', mpath, fs, mdir, nnz(tt), nnz(ss) - 1 );\n                    missing_d(ss) = [];\n                    missing_f(tt) = [];\n                end\n                for k = 1 : min(length(missing_f),10),\n                    fprintf( '        %s%s%s\\n', mpath, fs, missing_f{k} );\n                end\n                if length(missing_f) > 10,\n                    fprintf( '        (and %d more files)\\n', length(missing_f) - 10 );\n                end\n                fprintf( '    These omissions may prevent CVX from operating properly.\\n'  );\n            end\n            if ~isempty( additional ),\n                if isempty( missing ), fprintf( '\\n' ); end\n                fprintf( '    WARNING: The following extra files/directories were found:\\n' );\n                isdir = cellfun(@(x)x(end)==fs,additional);\n                issedumi = cellfun(@any,regexp( additional, [ '^sedumi.*[.]', mexext, '$' ] ));\n                additional_d = additional(isdir&~issedumi);\n                additional_f = additional(~isdir&~issedumi);\n                additional_s = additional(issedumi);\n                while ~isempty( additional_d ),\n                    mdir = additional_d{1};\n                    ss = strncmp( additional_d, mdir, length(mdir) );\n                    tt = strncmp( additional_f, mdir, length(mdir) );\n                    fprintf( '        %s%s%s + %d files, %d subdirectories\\n', mpath, fs, mdir, nnz(tt), nnz(ss) - 1 );\n                    additional_d(ss) = [];\n                    additional_f(tt) = [];\n                end\n                for k = 1 : min(length(additional_f),10),\n                    fprintf( '        %s%s%s\\n', mpath, fs, additional_f{k} );\n                end\n                if length(additional_f) > 10,\n                    fprintf( '        (and %d more files)\\n', length(additional_f) - 10 );\n                end\n                fprintf( '    These files may alter the behavior of CVX in unsupported ways.\\n' );\n                if ~isempty( additional_s ),\n                    fprintf( '    ERROR: obsolete versions of SeDuMi MEX files were found:\\n' );\n                    for k = 1 : length(additional_s),\n                        fprintf( '        %s%s%s\\n', mpath, fs, additional_f{k} );\n                    end\n                    fprintf( '    These files are now obsolete, and must be removed to ensure\\n' );\n                    fprintf( '    that SeDuMi operates properly and produces sound results.\\n' );\n                    if ~issue,\n                        fprintf( '    Please remove these files and re-run CVX_SETUP.\\n' );\n                        issue = true;\n                    end\n                end\n            end\n        else\n            fprintf( '\\n    No missing files.\\n' );\n        end\n    else\n        fprintf( '\\n    No missing files.\\n' );\n    end\nelse    \n    fprintf( 'Manifest missing; cannot verify file structure.\\n' ) ;\nend\nif ~compile,\n    mexpath = [ mpath, fs, 'lib', fs ];\n    mext = cvx___.mext;\n    if ( ~exist( [ mexpath, 'cvx_eliminate_mex.', mext ], 'file' ) || ...\n         ~exist( [ mexpath, 'cvx_bcompress_mex.', mext ], 'file' ) ) && ~issue,\n        issue = true;\n        if ~isempty( msub ),\n          mexpath = [ mexpath, msub, fs ];\n          issue = ~exist( [ mexpath, 'cvx_eliminate_mex.mex' ], 'file' ) || ...\n                         ~exist( [ mexpath, 'cvx_bcompress_mex.mex' ], 'file' );\n        end\n        if issue,\n          fprintf( '    ERROR: one or more MEX files for this platform are missing.\\n' );\n          fprintf( '    These files end in the suffix \".%s\". CVX will not operate\\n', mext );\n          fprintf( '    without these files. Please visit\\n' );\n          fprintf( '        http://cvxr.com/cvx/download\\n' );\n          fprintf( '    And download a distribution targeted for your platform.\\n' );\n        end\n    end\nend\n\n%%%%%%%%%%%%%%%\n% Preferences %\n%%%%%%%%%%%%%%%\n\ncvx_load_prefs( true );\n    \n%%%%%%%%%%%%%%%%\n% License file %\n%%%%%%%%%%%%%%%%\n\nif isoctave,\n    if ~isempty( cvx___.license ),\n        fprintf( 'CVX Professional is not supported with Octave.\\n' );\n    end\nelseif cvx___.jver < 1.6,\n    fprintf('       WARNING: full support for CVX Professional licenses\\n' );\n    fprintf('       requres Java version 1.6.0 or later. Please upgrade.\\n' );\nelseif exist( 'cvx_license', 'file' ),\n    cvx_license( args{:} );\nend\n\n%%%%%%%%%%%%%%%\n% Wrapping up %\n%%%%%%%%%%%%%%%\n\nif ~issue,\n    cvx___.loaded = true;\nend\nclear fs;\nfprintf( '%s\\n', line );\nif length(dbstack) <= 1,\n    fprintf( '\\n' );\nend\n\n%%%%%%%%%%%%%%%%%%%%%%\n% Preference loading %\n%%%%%%%%%%%%%%%%%%%%%%\n\nfunction cvx_load_prefs( verbose )\n\nglobal cvx___\nfs = cvx___.fs;\nisoctave = cvx___.isoctave;\nerrmsg = '';\nif verbose,\n    fprintf( 'Preferences: ' );\nend\nif isoctave,\n    pfile = [ prefdir, fs, '.cvx_prefs.mat' ];\nelse\n    pfile = [ regexprep( prefdir(1), [ cvx___.fsre, 'R\\d\\d\\d\\d\\w$' ], '' ), fs, 'cvx_prefs.mat' ];\nend\noutp = [];\ntry\n    if exist( pfile, 'file' )\n        outp = load( pfile );\n        pfile2 = pfile;\n    elseif ~isoctave,\n        pfile2 = [ prefdir, fs, 'cvx_prefs.mat' ];\n        if exist( pfile2, 'file' ),\n            outp = load( pfile2 );\n        end\n    end\ncatch errmsg\n    errmsg = cvx_error( errmsg, 67, false, '    ' );\n    errmsg = sprintf( 'CVX encountered the following error attempting to load your preferences:\\n%sPlease attempt to diagnose this error and try again.\\nYou may need to re-run CVX_SETUP as well.\\nIn the meanwhile, preferences will be set to their defaults.\\n', errmsg );\nend\nif ~isempty( outp ),\n    try\n        cvx___.expert = outp.expert;\n        cvx___.precision = outp.precision;\n        cvx___.precflag = outp.precflag;\n        cvx___.rat_growth = outp.rat_growth;\n        cvx___.path = outp.path;\n        cvx___.solvers = outp.solvers;\n        cvx___.license = outp.license;\n    catch\n        outp = [];\n        errmsg = 'Your CVX preferences file seems out of date; default preferences will be used.';\n    end\nend\nif isempty( outp ),\n    cvx___.expert = false;\n    cvx___.precision = [eps^0.5,eps^0.5,eps^0.25];\n    cvx___.precflag = 'default';\n    cvx___.rat_growth = 10;\n    cvx___.path = [];\n    cvx___.solvers = [];\n    cvx___.license = [];\nend\ncvx___.pfile = pfile;\nif verbose,\n    if ~isempty( errmsg ),\n        fprintf( 'error during load:\\n%s', cvx_error( errmsg, 70, false, '   ' ) );\n    elseif isempty( cvx___.path ),\n        fprintf( 'none found; defaults loaded.\\n' );\n    else\n        fprintf( '\\n    Path: %s\\n', pfile2 );\n    end\nelseif ~isempty( errmsg ),\n    warning( 'CVX:BadPrefsLoad', errmsg );\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Recursive manifest building function %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nfunction newman = get_manifest( mpath, fs )\ndirs   = {};\nfiles  = {};\nnfiles = dir( mpath );\nndir   = '';\ndndx   = 0;\npat2   = '^\\.|~$|';\npat    = '^\\.|~$|^cvx_license.[md]at$|^doc$|^examples$';\nwhile true,\n    isdir  = [ nfiles.isdir ];\n    nfiles = { nfiles.name };\n    tt     = cellfun( @isempty, regexp( nfiles, pat ) ); pat = pat2;\n    isdir  = isdir(tt);\n    nfiles = nfiles(tt);\n    ndirs  = nfiles(isdir);\n    if ~isempty(ndirs),\n        dirs = horzcat( dirs, strcat(strcat(ndir,ndirs), fs ) ); %#ok\n    end\n    nfiles = nfiles(~isdir);\n    if ~isempty(nfiles),\n        files = horzcat( files, strcat(ndir,nfiles) ); %#ok\n    end\n    if length( dirs ) == dndx, break; end\n    dndx = dndx + 1;\n    ndir = dirs{dndx};\n    nfiles = dir( [ mpath, fs, ndir ] );\nend\n[tmp,ndxs1] = sort(upper(dirs)); %#ok\n[tmp,ndxs2] = sort(upper(files)); %#ok\nnewman = horzcat( dirs(ndxs1), files(ndxs2) );\nif fs ~= '/',\n    newman = strrep( newman, fs, '/' );\nend\nnewman = newman(:);\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cvx_grbgetkey.m", "ext": ".m", "path": "Programs-master/matlab/cvx/cvx_grbgetkey.m", "size": 19096, "source_encoding": "utf_8", "md5": "080162e4fd27b14ea8387362148db7d1", "text": "function success = cvx_grbgetkey( kcode, overwrite )\n\n% CVX_GRBGETKEY   Retrieves and saves a Gurobi/CVX license.\n%\n% This function is used to install Gurobi license keys for use in CVX. It\n% is called with your Gurobi license code as a string argument; e.g.\n%\n%     cvx_grbgetkey xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx\n%     cvx_grbgetkey( 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx' )\n% \n% If you have not yet obtained a Gurobi license code, please visit the page\n% \n%     <a href=\"matlab: web('http://www.gurobi.com/documentation/5.5/quick-start-guide/node5','-browser');\">http://www.gurobi.com/documentation/5.5/quick-start-guide/node5</a>\n% \n% for information on your various options (trial, academic, commercial).\n% Once you have received notice that your license has been created, visit\n% the Gurobi license page\n% \n%     <a href=\"matlab: web('http://www.gurobi.com/download/licenses/current','-browser');\">http://www.gurobi.com/download/licenses/current</a>\n% \n% to retrieve the 36-character license code.\n%\n% The retrieved Gurobi license will be stored in your MATLAB preferences\n% directory (see the PREFDIR command) on Mac and Linux, and your home \n% directory on Windows. If a license file already exists at this location,\n% and its expiration date has not yet passed, CVX_GRBGETKEY will refuse to\n% retrieve a new license. To override this behavior, call the function\n% with an -OVERWRITE argument:\n%\n%     cvx_grbgetkey xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx -overwrite\n%     cvx_grbgetkey( 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx', '-overwrite' )\n% \n% Note that this utility is meant to retrieve licenses for the version of\n% Gurobi that is bundled with CVX. While it will retrieve full licenses as\n% well, it is strongly recommended that you move such licenses to one of the\n% standard Gurobi locations, discussed here:\n%\n%    <a href=\"matlab: web(''http://www.gurobi.com/documentation/5.5/quick-start-guide/node8'',''-browser'');\">http://www.gurobi.com/documentation/5.5/quick-start-guide/node8</a>\n%\n% Note that in order to use Gurobi with CVX, *both* a Gurobi license and a\n% CVX Professional license are required.\n\nif exist( 'OCTAVE_VERSION', 'builtin' ),\n    error( 'CVX:NoOctave', 'CVX_GRBGETKEY cannot be used with Octave.' );\nend\n\nfs = filesep;\nmpath = mfilename('fullpath');\ntemp = strfind( mpath, fs );\nmpath = mpath( 1 : temp(end) - 1 );\nmext = mexext;\n\nsuccess = true;\nline = '---------------------------------------------------------------------------';\njprintf({\n    ''\n    line\n    'CVX/Gurobi license key installer'\n    line\n});\n\n%%%%%%%%%%%%%%%%%%%%%%%%%\n% Process the arguments %\n%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif nargin == 2 && ischar( kcode ) && size( kcode, 1 ) == 1 && kcode(1) == '-',\n    tmp = kcode;\n    kcode = overwrite;\n    overwrite = tmp;\nend\nemsg = [];\nif nargin < 1,\n    emsg = '';\nelseif isempty( kcode ) || ~ischar( kcode ) || ndims( kcode ) > 2 || size( kcode, 1 ) ~= 1, %#ok\n    emsg = 'Invalid license code: must be a string.';\nelseif ~regexp( kcode, '^[0-9a-f]{8,8}-[0-9a-f]{4,4}-[0-9a-f]{4,4}-[0-9a-f]{4,4}-[0-9a-f]{12,12}$', 'once' )\n    emsg = sprintf( 'Invalid license code: %s', kcode );\nelseif nargin < 2 || isempty( overwrite ),\n    overwrite = false;\nelseif isequal( overwrite, '-overwrite' ),\n    overwrite = true;\nelseif ischar( overwrite ) && size( overwrite, 1 ) == 1,\n    emsg = sprintf( 'Invalid argument: %s', overwrite );\nelse\n    emsg = 'Invalid second argument.';\nend\nif ischar( emsg ),\n    if ~isempty( emsg ),\n        fprintf( '*** %s\\n\\n', emsg );\n    end\n    jprintf({\n        'This function is used to install Gurobi license keys for use in CVX. It'\n        'is called with your license code as an argument; e.g.'\n        ''\n        '    %s xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'\n        '    %s( ''xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'' )'\n        ''\n        'If you have not yet obtained a license code, please visit the page'\n        ''\n        '    <a href=\"matlab: web(''http://www.gurobi.com/documentation/5.5/quick-start-guide/node5'',''-browser'');\">http://www.gurobi.com/documentation/5.5/quick-start-guide/node5</a>'\n        ''\n        'for information on your various options (trial, academic, commercial). Once'\n        'a license has been created, you may retrieve its 36-character code by'\n        'logging into your Gurobi account and visiting the page'\n        ''\n        '    <a href=\"matlab: web(''http://www.gurobi.com/download/licenses/current'',''-browser'');\">http://www.gurobi.com/download/licenses/current</a>'\n        ''\n    }, mfilename, mfilename );\n    success = false;\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Check to see if Gurobi is present %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success,\n    if fs == '\\', fsre = '\\\\'; else fsre = fs; end\n    gname = [ mpath, fs, 'gurobi' ];\n    if ~exist( gname, 'dir' ),\n        jprintf({\n            'This function is meant to be used only with the version of Gurobi that is'\n            'bundled with CVX; but your CVX installation does not include Gurobi.'\n            'To rectify this, you may either download a CVX/Gurobi bundle from'\n            ''\n            '    <a href=\"matlab: web(''http://cvxr.com/cvx/download'',''-browser'');\">http://cvxr.com/cvx/download</a>'\n            ''\n            'or download the full Gurobi package from Gurobi directly:'\n            ''\n            '    <a href=\"matlab: web(''http://www.gurobi.com'',''-browser'');\">http://www.gurobi.com</a>'\n            ''\n            'In either case, you will need both a CVX Professional license and a Gurobi'\n            'license to proceed.'\n            });\n        success = false;\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Ensure that this platform is compatible with the bundled Gurobi solver %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success,\n    gname = [ gname, fs, mext(4:end) ];\n    if ~exist( gname, 'dir' ),\n        mismatch = false;\n        switch mext,\n            case 'mexmaci', pform = '32-bit OSX';\n            case 'glx',     pform = '32-bit Linux';\n            case 'a64',     pform = '64-bit Linux'; mismatch = true;\n            case 'maci64',  pform = '64-bit OSX'; mismatch = true;\n            case 'w32',     pform = '32-bit Windows'; mismatch = true;\n            case 'w64',     pform = '64-bit Windows'; mismatch = true;\n            otherwise,      pform = [];\n        end\n        if mismatch,\n            jprintf({\n                'The %s version of Gurobi is missing, perhaps because you downloaded a CVX'\n                'package for a different MATLAB platform. Please visit'\n                ''\n                '    <a href=\"matlab: web(''http://cvxr.com/cvx/download'',''-browser'');\">http://cvxr.com/cvx/download</a>'\n                ''\n                'and download and install the correct package.'\n                }, pform );\n        elseif isempty( pform ),\n            fprintf( 'CVX/Gurobi is not supported on the %s platform.\\n', computer );\n        else\n            fprintf( 'CVX/Gurobi is not supported on %s. Consider using the 64-bit version of MATLAB.\\n', pform );\n        end\n        success = false;\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Confirm the existence of the grbgetkey utility %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success,\n    gname = [ gname, fs, 'grbgetkey' ];\n\tif fs == '\\',\n\t\tgname = [ gname, '.exe' ];\n\tend\n\tif ~exist( gname, 'file' ),\n\t\tjprintf({\n\t\t\t'Your CVX package is missing the file'\n\t\t\t''\n\t\t\t'    %s'\n\t\t\t''\n\t\t\t'which is necessary to complete this task. Please reinstall CVX.'\n            }, gname );\n        success = false;\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Create the preferences directory, if necessary %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success,\n    fdir = regexprep( prefdir, [ fsre, 'R\\d\\d\\d\\d\\w*$' ], '' );\n    if ~exist( fdir, 'dir' ),\n        [success,msg] = mkdir( fdir );\n        if ~success,\n            jprintf({\n                'This function needs to write to the directory'\n                ''\n                '     %s'\n                ''\n                'which does not exist. An attempt to create it resulted in this error:'\n                ''\n                '    %s'\n                ''\n                'Please rectify this problem and try again.'\n                }, fdir, msg );\n            success = false;\n        end\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Guard against overwriting an existing license %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success,\n    % We actually use the parent directory of 'prefdir' if that directory has\n    % the standard version format: e.g., if prefdir is ~/joe/.matlab/R2012b,\n    % we store our data in ~/joe/.matlab, so that all versions of MATLAB can\n    % see the same CVX data.\n    fname = [ fdir, fs, 'cvx_gurobi.lic' ];\n    if exist( fname, 'file' ),\n        if overwrite,\n            msg = [];\n        else\n            msg = 'This license may or may not be current.';\n            fid = fopen( fname );\n            if fid ~= 0,\n                fstr = fread( fid, Inf, 'uint8=>char' )';\n                fclose( fid );\n                matches = regexp( fstr, 'EXPIRATION=\\d\\d\\d\\d-\\d\\d-\\d\\d', 'once' );\n                if matches && floor(datenum(fstr(matches+11:matches+20),'yyyy-mm-dd'))<floor(datenum(clock))\n                    msg = [];\n                else\n                    msg = 'This license has not yet expired.';\n                end\n            end\n        end\n        if ~isempty( msg )\n            jprintf({\n                'An existing license file has been found at location:'\n                ''\n                '    %s'\n                ''\n                '%s If you wish to overwrite this file,'\n                'please re-run this function with an \"-overwrite\" argument: that is,'\n                ''\n                '   %s %s -overwrite'\n                '   %s( ''%s'', ''-overwrite'' )'\n                ''\n                'Otherwise, please move the existing license and try again.'\n                }, fname, msg, mfilename, kcode, mfilename, kcode );\n            success = false;\n        end\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Create a temporary destination directory %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\ntdir = [];\nif success,\n    for k = 1 : 10,\n        tdir = tempname;\n        if ~exist( tdir, 'file' ), break; end\n    end\n    [success,msg] = mkdir(tdir);\n    if ~success,\n        jprintf({\n            'This function attempted to create the temporary directory'\n            ''\n            '    %s'\n            ''\n            'but the following error occurred:'\n            ''\n            '   %s'\n            ''\n            'Please rectify the problem and try again.';\n        }, tdir, msg );\n        success = false;\n        tdir = [];\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%\n% Download the license %\n%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success\n\tfprintf( 'Contacting the Gurobi Optimization license server...' );\n\t[status,result]=system( sprintf( '%s --path=%s %s', gname, tdir, kcode ) ); %#ok\n\tfprintf( 'done.\\n' );\n    if any( strfind( result, 'Unable to determine hostname' ) ) || any( strfind( result, 'not recognized as belonging to an academic domain' ) ),\n        jprintf({\n            'The attempt to retrieve the license key failed with the following error'\n            'while trying to verify your academic license eligibility:'\n            ''\n            '    %s'\n            'For information about this error, please consult the Gurobi documentation'\n            ''\n            '    <a href=\"matlab: web(''http://www.gurobi.com/documentation/5.5/quick-start-guide/node5'',''-browser'');\">http://www.gurobi.com/documentation/5.5/quick-start-guide/node5</a>'\n            ''\n            'Once you have rectified the error, please try again.'\n        }, regexprep(result,'.*---------------------\\n+(.*?)\\n+$','$1') );\n        success = false;\n    elseif any( strfind( result, 'already issued for host' ) )\n        matches = regexp( fstr, 'already issued for host ''([^'']+)''', 'once' );\n        if ~isempty( matches ),\n            matches = sprintf( ' (%s)', matches{1}{1} );\n        else\n            matches = '';\n        end\n        jprintf({\n            'This license has already been issued for a different host%s.'\n            'Please acquire a new license for this host from Gurobi.'\n            }, matches );\n        success = false;\n    elseif ~any( strfind( result, 'License key saved to file' ) ),\n        jprintf({\n            'The attempt to retrieve the license key failed with the following error:'\n            ''\n            '    %s'\n            'For information about this error, please consult the Gurobi documentation'\n            ''\n            '    <a href=\"matlab: web(''http://www.gurobi.com/documentation/5.5/quick-start-guide/node5'',''-browser'');\">http://www.gurobi.com/documentation/5.5/quick-start-guide/node5</a>'\n            ''\n            'Once you have rectified the error, please try again.'\n        }, regexprep(result,'.*---------------------\\n+(.*?)\\n+$','$1') );\n        fprintf( '%s\\n', line );\n        fprintf( result(1+(result(1)==10):end) );\n        success = false;\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Read the license file to determine its expiration %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success,\n    tname = [ tdir, fs, 'gurobi.lic' ];\n    [fid,msg] = fopen( tname, 'r' );\n    if fid == 0,\n        jprintf({\n            'An unexpected error occured: the Gurobi license file could not be'\n            'opened. The file was expected to be in the temporary location'\n            ''\n            '    %s'\n            ''\n            'but the attempt to read it resulted in the following error:'\n            ''\n            '    %s'\n            ''\n            'Please attempt to rectify the problem and try again; if necessary,'\n            'please contact <a href=\"mailto:cvx@cvxr.com\">CVX support</a>.'\n            }, tname, msg );\n        success = false;\n        tdir = [];\n    else\n        fstr = fread( fid, Inf, 'uint8=>char' )';\n        fclose( fid );\n        matches = regexp( fstr, 'EXPIRATION=\\d\\d\\d\\d-\\d\\d-\\d\\d', 'once' );\n        if matches,\n            if floor(datenum(fstr(matches+11:matches+20),'yyyy-mm-dd'))<floor(datenum(clock))\n                jprintf({\n                    'The license was successfully downloaded, but it expired on %s.'\n                    'Please contact Gurobi for a new license.'\n                    }, fstr(matches+11:matches+20) );\n                success = false;\n            else\n                jprintf({\n                    'Download successful. The license can be found at'\n                    ''\n                    '    %s'\n                    ''\n                    'The license will expire at the end of the day on %s.'\n                }, fname, fstr(matches+11:matches+20) ); \n            end\n        end\n        if success,\n            matches = regexp( fstr, '\\nAPPNAME=CVX\\n', 'once' );\n            if ~any( matches ),\n                [ matches, mends ] = regexp( fstr, '\\nAPPNAME=\\w+', 'start', 'end' );\n                if any( matches ),\n                    jprintf({\n                        line\n                        'ERROR: This license is reserved for the application \"%s\" and will'\n                        '*not* work with CVX. We strongly recommend that you move this license to'\n                        'another location, in accordance with this documentation:'\n                        ''\n                        '    <a href=\"matlab: web(''http://www.gurobi.com/documentation/5.5/quick-start-guide/node8'',''-browser'');\">http://www.gurobi.com/documentation/5.5/quick-start-guide/node8</a>'\n                        ''            \n                        'To use Gurobi with CVX, you must either obtain a full Gurobi license or a'\n                        'CVX specific license.'\n                    }, fstr(matches(1)+9:mends) );\n                    success = false;\n                else\n                    if ispc,\n                        cmd = '   movefile(''%s'',''%sgurobi.lic'')';\n                        fdr = getenv('USERPROFILE');\n                        if fdr(end) ~= '\\', fdr(end+1) = '\\'; end\n                    else\n                        cmd = '   movefile %s %sgurobi.lic';\n                        fdr = '~/';\n                    end\n                    jprintf({\n                        line\n                        'WARNING: This license is not a CVX-specific license. It will still work with'\n                        'CVX; however, if you also wish to use it with other applications, then we'\n                        'strongly recommend that you copy it to a standard Gurobi location. Consult'\n                        'the following Gurobi documentation for more information:'\n                        ''\n                        '    <a href=\"matlab: web(''http://www.gurobi.com/documentation/5.5/quick-start-guide/node8'',''-browser'');\">http://www.gurobi.com/documentation/5.5/quick-start-guide/node8</a>'\n                        ''\n                        'For instance, to move this file to the standard home directory location,'\n                        'copy and paste this command into your MATLAB command line:'\n                        ''\n                        cmd\n                    }, fname, fdr );\n                end\n            end\n        end\n    end\nend\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Move the license to its proper location %\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nif success,\n    [success,msg] = movefile( tname, fname, 'f' );\n    if ~success,\n        jprintf({\n            'The attempt to move the Gurobi license file from its temporary location'\n            ''\n            '    %s'\n            ''\n            'to its final location'\n            ''\n            '    %s'\n            ''\n            'resulted in the following error:'\n            ''\n            '    %s'\n            ''\n            'Please rectify the problem and try again; or move the file manually. Once'\n            'the license is in place, please run CVX_SETUP to configure CVX to use the'\n            'Gurobi solver with the new license.'\n        }, tname, fname, msg );\n        success = false;\n    end\nend\n\n%%%%%%%%%%%%\n% Clean up %\n%%%%%%%%%%%%\n\nif ~isempty(tdir),\n    [success,message] = rmdir( tdir, 's' ); %#ok\nend\n\nif success,\n    jprintf({\n        line\n'Now that the license has been retrieved, please run CVX_SETUP to configure'\n'CVX to use the Gurobi solver with the new license.'\n    });\nend\n\njprintf({\n    line\n    ''\n});\n\nif nargout == 0,\n    clear success\nend\n\nfunction jprintf( strs, varargin )\nstrs = strs(:)';\n[ strs{2,:} ] = deal( '\\n' );\nfprintf( cat(2,strs{:}), varargin{:} );\n\n% Copyright 2014 CVX Research, Inc.\n% This file is governed by the terms of the CVX Professional license;\n% redistribution is *not* permitted. Please see the file LICENSE.txt for\n% full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDNTcorr.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDNTcorr.m", "size": 1001, "source_encoding": "utf_8", "md5": "c42eba1c6bae660b88921b7c8747490e", "text": "%%************************************************************************\n%% HSDNTcorr: corrector step for the NT direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%************************************************************************\n\nfunction [par,dX,dy,dZ,resnrm] = HSDNTcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n    dX,dZ,coeff,L,X,Z)\n\nglobal printlevel\nglobal solve_ok\n%%\n[rhs,EinvRc] = HSDNTrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ);\nm = length(rp); ncolU = size(coeff.mat12,2);\nrhs = [rhs; zeros(m+ncolU-length(rhs),1)];\n%%\nsolve_ok = 1; %#ok\n[xx,resnrm,solve_ok] = HSDbicgstab(coeff,rhs,L,[],[],printlevel);\nif (solve_ok<=0) && (printlevel)\n    fprintf('\\n  warning: iterative solver fails: %3.1f.',solve_ok);\nend\nif (par.printlevel>=3); fprintf(' %2.0f',length(resnrm)-1); end\n%%\n[par,dX,dy,dZ] = HSDNTdirfun(blk,At,par,Rd,EinvRc,xx);\n%%************************************************************************\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDHKMdirfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDHKMdirfun.m", "size": 1551, "source_encoding": "utf_8", "md5": "1034e25e48a42d2fa143f93f47961fe9", "text": "%%*******************************************************************\n%% HSDHKMdirfun: compute (dX,dZ), given dy, for the HKM direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [par,dX,dy,dZ] = HSDHKMdirfun(blk,At,par,Rd,EinvRc,X,xx)\n\nglobal solve_ok\n\ndX = cell(size(blk,1),1); dZ = cell(size(blk,1),1); dy = [];\nif (any(isnan(xx)) || any(isinf(xx)))\n    solve_ok = 0;\n    fprintf('\\n  HSDHKMdirfun: solution contains NaN or inf.');\n    return;\nend\n%%\nm = par.m;\ndy2 = xx(1:m+2);\n%%\nfor p=1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy2));\n        dX{p} = EinvRc{p} - par.dd{p}.*dZ{p};\n    elseif strcmp(pblk{1},'q')\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy2));\n        tmp = par.dd{p}.*dZ{p} ...\n            + qops(pblk,qops(pblk,dZ{p},par.Zinv{p},1),X{p},3) ...\n            + qops(pblk,qops(pblk,dZ{p},X{p},1),par.Zinv{p},3);\n        dX{p} = EinvRc{p} - tmp;\n    elseif strcmp(pblk{1},'s')\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),par.permA(p,:),par.isspAy(p),dy2));\n        tmp = Prod3(pblk,X{p},dZ{p},par.Zinv{p},0);\n        tmp = 0.5*(tmp+tmp');\n        dX{p} = EinvRc{p}-tmp;\n    end\nend\ndy  = dy2(1:m);\npar.dtau = dy2(m+1);\npar.dtheta = dy2(m+2);\npar.dkap = (par.mu./par.tau - par.kap) - par.kap*(par.dtau/par.tau);\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDsqlp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDsqlp.m", "size": 11860, "source_encoding": "utf_8", "md5": "00b8311a8efbee36662ca9288870a1cd", "text": "%%*****************************************************************************\n%% HSDsqlp: solve an semidefinite-quadratic-linear program\n%%   by infeasible path-following method on the homogeneous self-dual model.\n%%\n%%  [obj,X,y,Z,info,runhist] =\n%%      HSDsqlp(blk,At,C,b,OPTIONS,X0,y0,Z0);\n%%\n%%  Input: blk: a cell array describing the block diagonal structure of SQL data.\n%%          At: a cell array with At{p} = [svec(Ap1) ... svec(Apm)]\n%%         b,C: data for the SQL instance.\n%%     OPTIONS: a structure that specifies parameters required in HSDsqlp.m,\n%%              (if it is not given, the default in sqlparameters.m is used).\n%%\n%%  (X0,y0,Z0): an initial iterate (if it is not given, the default is used).\n%%\n%%  Output: obj  = [<C,X> <b,y>].\n%%          (X,y,Z): an approximately optimal solution or a primal or dual\n%%                   infeasibility certificate.\n%%          info.termcode = termination-code\n%%          info.iter     = number of iterations\n%%          info.obj      = [primal-obj, dual-obj]\n%%          info.cputime  = total-time\n%%          info.gap      = gap\n%%          info.pinfeas  = primal_infeas\n%%          info.dinfeas  = dual_infeas\n%%          runhist.pobj    = history of primal objective value.\n%%          runhist.dobj    = history of dual   objective value.\n%%          runhist.gap     = history of <X,Z>.\n%%          runhist.pinfeas = history of primal infeasibility.\n%%          runhist.dinfeas = history of dual   infeasibility.\n%%          runhist.cputime = history of cputime spent.\n%%----------------------------------------------------------------------------\n%%  The OPTIONS structure specifies the required parameters:\n%%      vers  gam  predcorr  expon  gaptol  inftol  steptol\n%%      maxit  printlevel  ...\n%%      (all have default values set in sqlparameters.m).\n%%\n%%*************************************************************************\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction [obj,X,y,Z,info,runhist] = ...\n    HSDsqlp(blk,At,C,b,OPTIONS,X0,y0,Z0,kap0,tau0,theta0)\n\nif (nargin < 5); OPTIONS = []; end\n\nisemptyAtb = 0;\nif isempty(At) && isempty(b);\n    %% Add redundant constraint: <-I,X> <= 0\n    b = 0;\n    At = ops(ops(blk,'identity'),'*',-1);\n    numblk = size(blk,1);\n    blk{numblk+1,1} = 'l'; blk{numblk+1,2} = 1;\n    At{numblk+1,1} = 1; C{numblk+1,1} = 0;\n    isemptyAtb = 1;\nend\n%%\n%%-----------------------------------------\n%% get parameters from the OPTIONS structure.\n%%-----------------------------------------\n%%\n% warning off;\n\nmatlabversion = sscanf(version,'%f');\nif strcmp(computer,'PCWIN64') || strcmp(computer,'GLNXA64')\n    par.computer = 64;\nelse\n    par.computer = 32;\nend\npar.matlabversion = matlabversion(1);\npar.vers        = 0;\npar.predcorr    = 1;\npar.gam         = 0;\npar.expon       = 1;\npar.gaptol      = 1e-8;\npar.inftol      = 1e-8;\npar.steptol     = 1e-6;\npar.maxit       = 100;\npar.printlevel  = 3;\npar.stoplevel   = 1;\npar.scale_data  = 0;\npar.spdensity   = 0.4;\npar.rmdepconstr = 0;\npar.smallblkdim = 50;\npar.schurfun     = cell(size(blk,1),1);\npar.schurfun_par = cell(size(blk,1),1);\n%%\nparbarrier  = cell(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s') || strcmp(pblk{1},'q')\n        parbarrier{p} = zeros(1,length(pblk{2}));\n    elseif strcmp(pblk{1},'l') || strcmp(pblk{1},'u' )\n        parbarrier{p} = zeros(1,sum(pblk{2}));\n    end\nend\nparbarrier_0 = parbarrier;\n%%\nif nargin > 4,\n    if isfield(OPTIONS,'vers');        par.vers     = OPTIONS.vers; end\n    if isfield(OPTIONS,'predcorr');    par.predcorr = OPTIONS.predcorr; end\n    if isfield(OPTIONS,'gam');         par.gam      = OPTIONS.gam; end\n    if isfield(OPTIONS,'expon');       par.expon    = OPTIONS.expon; end\n    if isfield(OPTIONS,'gaptol');      par.gaptol   = OPTIONS.gaptol; end\n    if isfield(OPTIONS,'inftol');      par.inftol   = OPTIONS.inftol; end\n    if isfield(OPTIONS,'steptol');     par.steptol  = OPTIONS.steptol; end\n    if isfield(OPTIONS,'maxit');       par.maxit    = OPTIONS.maxit; end\n    if isfield(OPTIONS,'printlevel');  par.printlevel  = OPTIONS.printlevel; end\n    if isfield(OPTIONS,'stoplevel');   par.stoplevel   = OPTIONS.stoplevel; end\n    if isfield(OPTIONS,'scale_data');  par.scale_data  = OPTIONS.scale_data; end\n    if isfield(OPTIONS,'spdensity');   par.spdensity   = OPTIONS.spdensity; end\n    if isfield(OPTIONS,'rmdepconstr'); par.rmdepconstr = OPTIONS.rmdepconstr; end\n    if isfield(OPTIONS,'smallblkdim'); par.smallblkdim = OPTIONS.smallblkdim; end\n    if isfield(OPTIONS,'parbarrier');\n        parbarrier = OPTIONS.parbarrier;\n        if isempty(parbarrier); parbarrier = parbarrier_0; end\n        if ~iscell(parbarrier);\n            tmp = parbarrier; clear parbarrier; parbarrier{1} = tmp;\n        end\n        if (length(parbarrier) < size(blk,1))\n            len = length(parbarrier);\n            parbarrier(len+1:size(blk,1)) = parbarrier_0(len+1:size(blk,1));\n        end\n    end\n    if isfield(OPTIONS,'schurfun');\n        par.schurfun = OPTIONS.schurfun;\n        if ~isempty(par.schurfun); par.scale_data = 0; end\n    end\n    if isfield(OPTIONS,'schurfun_par'); par.schurfun_par = OPTIONS.schurfun_par; end\n    if isempty(par.schurfun);     par.schurfun = cell(size(blk,1),1); end\n    if isempty(par.schurfun_par); par.schurfun_par = cell(size(blk,1),1); end\nend\nif (size(blk,2) > 2); par.smallblkdim = 0; end\n%%\n%%-----------------------------------------\n%% convert matrices to cell arrays.\n%%-----------------------------------------\n%%\nif ~iscell(At); At = {At}; end;\nif ~iscell(C);  C = {C}; end;\nif all(size(At) == [size(blk,1), length(b)]);\n    convertyes = zeros(size(blk,1),1);\n    for p = 1:size(blk,1)\n        if strcmp(blk{p,1},'s') && all(size(At{p,1}) == sum(blk{p,2}))\n            convertyes(p) = 1;\n        end\n    end\n    if any(convertyes)\n        if (par.printlevel);\n            fprintf('\\n sqlp: converting At into required format');\n        end\n        At = svec(blk,At,ones(size(blk,1),1));\n    end\nend\n%%\n%%-----------------------------------------\n%% validate SQLP data.\n%%-----------------------------------------\n%%\n% tstart = cputime;\n[blk,At,C,b,blkdim,numblk] = validate(blk,At,C,b,par);\n[blk,At,C,b,iscmp] = convertcmpsdp(blk,At,C,b);\nif (iscmp) && (par.printlevel>=2)\n    fprintf('\\n SQLP has complex data');\nend\nif (nargin <= 5) || (isempty(X0) || isempty(y0) || isempty(Z0));\n    if (max([ops(At,'norm'),ops(C,'norm'),norm(b)]) > 1e2)\n        [X0,y0,Z0] = infeaspt(blk,At,C,b,1);\n    else\n        [X0,y0,Z0] = infeaspt(blk,At,C,b,2,1);\n    end\nend\nif ~iscell(X0);  X0 = {X0}; end;\nif ~iscell(Z0);  Z0 = {Z0}; end;\nif (length(y0) ~= length(b))\n    error('HSDsqlp: length of b and y0 not compatible');\nend\n[X0,Z0] = validate_startpoint(blk,X0,Z0,par.spdensity);\n%%\nif (nargin <= 8) || (isempty(kap0) || isempty(tau0) || isempty(theta0))\n    if (max([ops(At,'norm'),ops(C,'norm'),norm(b)]) > 1e6)\n        kap0 = 10*blktrace(blk,X0,Z0);\n    else\n        kap0 = blktrace(blk,X0,Z0);\n    end\n    tau0 = 1; theta0 = 1;\nend\n%%\nif (par.printlevel>=2)\n    fprintf('\\n num. of constraints = %2.0d',length(b));\n    if blkdim(1);\n        fprintf('\\n dim. of sdp    var  = %2.0d,',blkdim(1));\n        fprintf('   num. of sdp  blk  = %2.0d',numblk(1));\n    end\n    if blkdim(2);\n        fprintf('\\n dim. of socp   var  = %2.0d,',blkdim(2));\n        fprintf('   num. of socp blk  = %2.0d',numblk(2));\n    end\n    if blkdim(3); fprintf('\\n dim. of linear var  = %2.0d',blkdim(3)); end\n    if blkdim(4); fprintf('\\n dim. of free   var  = %2.0d',blkdim(4)); end\nend\n%%\n%%-----------------------------------------\n%% detect unrestricted blocks in linear blocks\n%%-----------------------------------------\n%%\nuser_supplied_schurfun = 0;\nfor p = 1:size(blk,1)\n    if ~isempty(par.schurfun{p}); user_supplied_schurfun = 1; end\nend\nif (user_supplied_schurfun == 0)\n    [blk2,At2,C2,ublkinfo,parbarrier2,X02,Z02] = ...\n        detect_ublk(blk,At,C,parbarrier,X0,Z0,par.printlevel);\nelse\n    blk2 = blk; At2 = At; C2 = C;\n    parbarrier2 = parbarrier; X02 = X0; Z02 = Z0;\n    ublkinfo = cell(size(blk2,1),1);\nend\nublksize = blkdim(4);\nfor p = 1:size(ublkinfo,1)\n    ublksize = ublksize + length(ublkinfo{p});\nend\n%%\n%%-----------------------------------------\n%% detect diagonal blocks in semidefinite blocks\n%%-----------------------------------------\n%%\nif (user_supplied_schurfun==0)\n    [blk3,At3,C3,diagblkinfo,diagblkchange,parbarrier3,X03,Z03] = ...\n        detect_lblk(blk2,At2,C2,b,parbarrier2,X02,Z02,par.printlevel); %#ok\nelse\n    blk3 = blk2; At3 = At2; C3 = C2;\n    % parbarrier3 = parbarrier2; \n    X03 = X02; Z03 = Z02;\n    diagblkchange = 0;\n    diagblkinfo = cell(size(blk3,1),1);\nend\n%%\n%%-----------------------------------------\n%% main solver\n%%-----------------------------------------\n%%\n%exist_analytic_term = 0;\n%for p = 1:size(blk3,1);\n%    idx = find(parbarrier3{p} > 0);\n%    if ~isempty(idx); exist_analytic_term = 1; end\n%end\n%%\nif (par.vers == 0);\n    if blkdim(1); par.vers = 1; else par.vers = 2; end\nend\npar.blkdim = blkdim;\npar.ublksize = ublksize;\n[obj,X3,y,Z3,info,runhist] = ...\n    HSDsqlpmain(blk3,At3,C3,b,par,X03,y0,Z03,kap0,tau0,theta0);\n%%\n%%-----------------------------------------\n%% recover semidefinite blocks from linear blocks\n%%-----------------------------------------\n%%\nif any(diagblkchange)\n    X2 = cell(size(blk2,1),1); Z2 = cell(size(blk2,1),1);\n    count = 0;\n    for p = 1:size(blk2,1)\n        pblk = blk2(p,:);\n        n = sum(pblk{2});\n        blkno      = diagblkinfo{p,1};\n        idxdiag    = diagblkinfo{p,2};\n        idxnondiag = diagblkinfo{p,3};\n        if ~isempty(idxdiag)\n            len = length(idxdiag);\n            Xtmp = [idxdiag,idxdiag,X3{end}(count+1:count+len); n, n, 0];\n            Ztmp = [idxdiag,idxdiag,Z3{end}(count+1:count+len); n, n, 0];\n            if ~isempty(idxnondiag)\n                [ii,jj,vv] = find(X3{blkno});\n                Xtmp = [Xtmp; idxnondiag(ii),idxnondiag(jj),vv]; %#ok\n                [ii,jj,vv] = find(Z3{blkno});\n                Ztmp = [Ztmp; idxnondiag(ii),idxnondiag(jj),vv]; %#ok\n            end\n            X2{p} = spconvert(Xtmp);\n            Z2{p} = spconvert(Ztmp);\n            count = count + len;\n        else\n            X2(p) = X3(blkno); Z2(p) = Z3(blkno);\n        end\n    end\nelse\n    X2 = X3; Z2 = Z3;\nend\n%%\n%%-----------------------------------------\n%% recover linear block from unrestricted block\n%%-----------------------------------------\n%%\nnumblk = size(blk,1);\nnumblknew = numblk;\nX = cell(numblk,1); Z = cell(numblk,1);\nfor p = 1:numblk\n    n = blk{p,2};\n    if isempty(ublkinfo{p,1})\n        X{p} = X2{p}; Z{p} = Z2{p};\n    else\n        Xtmp = zeros(n,1); Ztmp = zeros(n,1);\n        Xtmp(ublkinfo{p,1}) = max(0,X2{p});\n        Xtmp(ublkinfo{p,2}) = max(0,-X2{p});\n        Ztmp(ublkinfo{p,1}) = max(0,Z2{p});\n        Ztmp(ublkinfo{p,2}) = max(0,-Z2{p});\n        if ~isempty(ublkinfo{p,3})\n            numblknew = numblknew + 1;\n            Xtmp(ublkinfo{p,3}) = X2{numblknew};\n            Ztmp(ublkinfo{p,3}) = Z2{numblknew};\n        end\n        X{p} = Xtmp; Z{p} = Ztmp;\n    end\nend\n%%\n%%-----------------------------------------\n%% recover complex solution\n%%-----------------------------------------\n%%\nif (iscmp)\n    for p = 1:numblk\n        pblk = blk(p,:);\n        n = sum(pblk{2})/2;\n        if strcmp(pblk{1},'s');\n            X{p} = X{p}(1:n,1:n) + sqrt(-1)*X{p}(n+1:2*n,1:n);\n            Z{p} = Z{p}(1:n,1:n) + sqrt(-1)*Z{p}(n+1:2*n,1:n);\n            X{p} = 0.5*(X{p}+X{p}');\n            Z{p} = 0.5*(Z{p}+Z{p}');\n        end\n    end\nend\nif (isemptyAtb)\n    X = X(1:end-1); Z = Z(1:end-1);\nend\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDsortA.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDsortA.m", "size": 2577, "source_encoding": "utf_8", "md5": "0a74ddbb8a0c79bf22592d780d865e06", "text": "%%*********************************************************************\n%% sortA: sort columns of At{p} in ascending order according to the\n%%        number of nonzero elements.\n%%\n%% [At,C,b,X0,Z0,permA,permZ] = sortA(blk,At,C,b,X0,Z0);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*********************************************************************\n\nfunction [At,C,Cnew,X0,Z0,permA,invpermA,permZ] = HSDsortA(blk,At,C,Cnew,b,X0,Z0)\n\nglobal spdensity smallblkdim\n%%\nnumblk = size(blk,1);\nm = length(b);\nnnzA = zeros(numblk,m);\npermA = kron(ones(numblk,1),1:m);\ninvpermA = kron(ones(numblk,1),1:m);\npermZ = cell(size(blk,1),1);\n%%\nfor p=1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'s') && (max(pblk{2}) > smallblkdim)\n        n22 = sum(pblk{2}.*(pblk{2}+1))/2;\n        m1 = size(At{p,1},2);\n        if (length(pblk{2}) == 1)\n            tmp = abs(C{p}) + abs(Z0{p});\n            if  (~isempty(At{p,1}))\n                tmp = tmp + smat(blk(p,:),abs(At{p,1})*ones(m1,1),1);\n            end\n            if (nnz(tmp) < spdensity*n22);\n                per = symamd(tmp);\n                invper = zeros(n,1); invper(per) = 1:n;\n                permZ{p} = invper;\n                if (~isempty(At{p,1}))\n                    isspAt = issparse(At{p,1});\n                    for k = 1:m1\n                        Ak = smat(pblk,At{p,1}(:,k),1);\n                        At{p,1}(:,k) = svec(pblk,Ak(per,per),isspAt);\n                    end\n                end\n                C{p}  = C{p}(per,per);\n                Z0{p} = Z0{p}(per,per);\n                X0{p} = X0{p}(per,per);\n                Cnew{p} = Cnew{p}(per,per);\n            else\n                per = [];\n            end\n            if (length(pblk) > 2) && (~isempty(per))\n                P = spconvert([(1:n)', per', ones(n,1)]);\n                At{p,2} = P*At{p,2};\n            end\n        end\n        if ~isempty(At{p,1}) && (mexnnz(At{p,1}) < m*n22/2)\n            for k = 1:m1\n                Ak = At{p,1}(:,k);\n                nnzA(p,k) = length(find(abs(Ak) > eps));\n            end\n            [dummy,permAp] = sort(nnzA(p,1:m1)); %#ok\n            At{p,1}  = At{p,1}(:,permAp);\n            permA(p,1:m1) = permAp;\n            invpermA(p,permAp) = 1:m1;\n        end\n    elseif strcmp(pblk{1},'q') || strcmp(pblk{1},'l') || strcmp(pblk{1},'u');\n        if ~issparse(At{p,1});\n            At{p,1} = sparse(At{p,1});\n        end\n    end\nend\n%%*********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDHKMrhsfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDHKMrhsfun.m", "size": 2666, "source_encoding": "utf_8", "md5": "16409ae4672f80ef54a33c31ef30000f", "text": "%%*******************************************************************\n%% HSDHKMrhsfun: compute the right-hand side vector of the\n%%               Schur complement equation for the  HKM direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [rhs,EinvRc,hRd] = HSDHKMrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ)\n\nm = par.m;\nif (nargin > 8)\n    corrector = 1;\nelse\n    corrector = 0;\n    hRd = zeros(m+2,1);\nend\nhEinvRc = zeros(m+2,1);\nEinvRc = cell(size(blk,1),1);\nif length(sigmu)==1; sigmu = sigmu*ones(1,size(blk,1)); end\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'l')\n        if (corrector)\n            Rq = dX{p}.*dZ{p};\n        else\n            Rq = sparse(n,1);\n            tmp  = par.dd{p}.*Rd{p};\n            tmp2 = mexMatvec(At{p},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = sigmu(p)./Z{p}-X{p} -Rq./Z{p};\n        tmp2 = mexMatvec(At{p,1},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'q')\n        if (corrector)\n            ff{p} = qops(pblk,1./par.gamz{p},Z{p},3); %#ok\n            hdx = qops(pblk,par.gamz{p},ff{p},5,dX{p});\n            hdz = qops(pblk,par.gamz{p},ff{p},6,dZ{p});\n            hdxdz = Arrow(pblk,hdx,hdz);\n            Rq = qops(pblk,par.gamz{p},ff{p},6,hdxdz);\n        else\n            Rq  = sparse(n,1);\n            tmp = par.dd{p}.*Rd{p} ...\n                + qops(pblk,qops(pblk,Rd{p},par.Zinv{p},1),X{p},3) ...\n                + qops(pblk,qops(pblk,Rd{p},X{p},1),par.Zinv{p},3);\n            tmp2 = mexMatvec(At{p,1},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = sigmu(p)*par.Zinv{p}-X{p} -Rq;\n        tmp2 = mexMatvec(At{p,1},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'s')\n        if (corrector)\n            Rq = Prod3(pblk,dX{p},dZ{p},par.Zinv{p},0);\n            Rq = 0.5*(Rq+Rq');\n        else\n            Rq = sparse(n,n);\n            tmp = Prod3(pblk,X{p},Rd{p},par.Zinv{p},0,par.nzlistAy{p});\n            EinvRc{p} = 0.5*(tmp+tmp');\n            tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),EinvRc(p));\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = sigmu(p)*par.Zinv{p}-X{p}- Rq;\n        tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),EinvRc(p));\n        hEinvRc = hEinvRc + tmp2;\n    end\nend\n%%\nrhs = rp + hRd - hEinvRc;\nrhs(m+1) = rhs(m+1) + (par.mu/par.tau - par.kap);\nif (corrector)\n    rhs(m+1) = rhs(m+1) - par.dtau*par.dkap/par.tau;\nend\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDsqlpcheckconvg.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDsqlpcheckconvg.m", "size": 6249, "source_encoding": "utf_8", "md5": "a579e4972fd77d5cc3e11b72bf56d3a9", "text": "%%*****************************************************************************\n%% HSDsqlpcheckconvg: check convergence.\n%%\n%% ZpATynorm, AX, normX, normZ are with respect to the\n%% original variables, not the HSD variables.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*****************************************************************************\n\nfunction [param,breakyes,use_olditer,msg] = HSDsqlpcheckconvg(param,runhist)\n\ntermcode    = param.termcode;\niter        = param.iter;\nobj         = param.obj;\nrelgap      = param.relgap;\ngap         = param.gap;\nprim_infeas = param.prim_infeas;\ndual_infeas = param.dual_infeas;\nmu          = param.mu;\nprim_infeas_bad = param.prim_infeas_bad;\ndual_infeas_bad = param.dual_infeas_bad;\nprintlevel  = param.printlevel;\nstoplevel   = param.stoplevel;\ninftol      = param.inftol;\ngaptol      = param.gaptol;\nkap         = param.kap;\ntau         = param.tau;\ntheta       = param.theta;\nbreakyes    = 0;\nuse_olditer = 0;\nmsg         = [];\ninfeas      = max(prim_infeas,dual_infeas);\nprim_infeas_min = min(param.prim_infeas_min, max(prim_infeas,1e-10));\ndual_infeas_min = min(param.dual_infeas_min, max(dual_infeas,1e-10));\n%%\nerr = max(infeas,relgap);\nif (obj(2) > 0); homRd = param.ZpATynorm/obj(2); else homRd = inf; end\nif (obj(1) < 0); homrp = norm(param.AX)/(-obj(1)); else homrp = inf; end\nif (param.normX > 1e15*param.normX0 || param.normZ > 1e15*param.normZ0)\n    termcode = 3;\n    breakyes = 1;\nend\nif (homRd < min(1e-6,1e-2*sqrt(err*inftol)) && tau < 1e-4 ...\n        && prim_infeas > 0.5*runhist.pinfeas(iter)) ...\n        || (homRd < 10*tau && tau < 1e-7)\n    termcode = 1;\n    breakyes = 1;\nend\nif (homrp < min(1e-6,1e-2*sqrt(err*inftol)) && tau < 1e-4 ...\n        && dual_infeas > 0.5*runhist.dinfeas(iter)) ...\n        || (homrp < 10*tau && tau < 1e-7)\n    termcode = 2;\n    breakyes = 1;\nend\nif (err < gaptol)\n    msg = sprintf('Stop: max(relative gap,infeasibilities) < %3.2e',gaptol);\n    if (printlevel); fprintf('\\n  %s',msg); end\n    termcode = 0;\n    breakyes = 1;\nend\nmin_prim_infeas = min(runhist.pinfeas(1:iter));\nprim_infeas_bad = prim_infeas_bad + (prim_infeas > ...\n    max(1e-10,5*min_prim_infeas) && (min_prim_infeas < 1e-2));\nif (mu < 1e-6)\n    idx = max(1,iter-1): iter;\nelseif (mu < 1e-3);\n    idx = max(1,iter-2): iter;\nelse\n    idx = max(1,iter-3): iter;\nend\nidx2 = max(1,iter-4): iter;\ngap_ratio2 = runhist.gap(idx2+1)./runhist.gap(idx2);\ngap_slowrate = min(0.8,max(0.6,2*mean(gap_ratio2)));\ngap_ratio = runhist.gap(idx+1)./runhist.gap(idx);\npstep = runhist.step(iter+1);\nif (infeas < 1e-4 || prim_infeas_bad) && (relgap < 1e-3) ...\n        && (iter > 5) && (prim_infeas > (1-pstep/2)*runhist.pinfeas(iter))\n    gap_slow = all(gap_ratio > gap_slowrate) && (relgap < 1e-3);\n    min_pinfeas = min(runhist.pinfeas);\n    if (relgap < 0.1*infeas) ...\n            && ((runhist.step(iter+1) < 0.5) || (min_pinfeas < min(1e-6,0.1*prim_infeas))) ...\n            && (dual_infeas > 0.9*runhist.dinfeas(iter) || (dual_infeas < 1e-2*gaptol))\n        msg = 'Stop: relative gap < infeasibility';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -1;\n        breakyes = 1;\n    elseif (gap_slow) && (infeas > 0.9*runhist.infeas(iter)) ...\n            && (theta < 1e-8)\n        msg = 'Stop: progress is too slow';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -5;\n        breakyes = 1;\n    end\nelseif (prim_infeas_bad) && (iter >50) && all(gap_ratio > gap_slowrate)\n    msg = 'Stop: progress is bad';\n    if (printlevel); fprintf('\\n  %s',msg); end\n    termcode = -5;\n    breakyes = 1;\nelseif (infeas < 1e-8) && (gap > 1.2*mean(runhist.gap(idx)))\n    msg = 'Stop: progress is bad*';\n    if (printlevel); fprintf('\\n  %s',msg); end\n    termcode = -5;\n    breakyes = 1;\nend\nif (err < 1e-3) && (iter > 10) ...\n        && (runhist.pinfeas(iter+1) > 0.9*runhist.pinfeas(max(1,iter-5))) ...\n        && (runhist.dinfeas(iter+1) > 0.9*runhist.dinfeas(max(1,iter-5))) ...\n        && (runhist.relgap(iter+1)  > 0.1*runhist.relgap(max(1,iter-5)));\n    msg = 'Stop: progress is bad**';\n    if (printlevel); fprintf('\\n  %s',msg); end\n    termcode = -5;\n    breakyes = 1;\nend\nif (infeas > 100*max(1e-12,min(runhist.infeas)) && relgap < 1e-4)\n    msg = 'Stop: infeas has deteriorated too much';\n    if (printlevel); fprintf('\\n  %s, %3.1e',msg,infeas); end\n    use_olditer = 1;\n    termcode = -7;\n    breakyes = 1;\nend\nif (min(runhist.infeas) < 1e-4 || prim_infeas_bad) ...\n        && (max(runhist.infeas) > 1e-4) && (iter > 5)\n    relgap2 = abs(diff(obj))/(1+mean(abs(obj)));\n    if (relgap2 < 1e-3);\n        step_short = all(runhist.step(iter:iter+1) < 0.1) ;\n    elseif (relgap2 < 1)\n        idx = max(1,iter-3): iter+1;\n        step_short = all(runhist.step(idx) < 0.05);\n    else\n        step_short = 0;\n    end\n    if (step_short)\n        msg = 'Stop: steps too short consecutively';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -5;\n        breakyes = 1;\n    end\nend\nif (iter > 3 && iter < 20) && (max(runhist.step(max(1,iter-3):iter+1)) < 1e-3) ...\n        && (infeas > 1) && (min(homrp,homRd) > 1000*inftol)\n    if (stoplevel >= 2)\n        msg = 'Stop: steps too short consecutively';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -5;\n        breakyes = 1;\n    end\nend\nif (pstep < 1e-4) && (err > 1.1*max(runhist.relgap(iter),runhist.infeas(iter)))\n    msg = 'Stop: steps are too short';\n    if (printlevel); fprintf('\\n  %s',msg); end\n    use_olditer = 1;\n    termcode = -5;\n    breakyes = 1;\nend\nif (iter == param.maxit)\n    termcode = -6;\n    msg = 'Stop: maximum number of iterations reached';\n    if (printlevel); fprintf('\\n  %s',msg); end\nend\nif (infeas < 1e-8 && relgap < 1e-10 && kap < 1e-13 && theta < 1e-15)\n    msg = 'Stop: obtained accurate solution';\n    if (printlevel); fprintf('\\n  %s',msg); end\n    termcode = 0;\n    breakyes = 1;\nend\nparam.prim_infeas_bad = prim_infeas_bad;\nparam.prim_infeas_min = prim_infeas_min;\nparam.dual_infeas_bad = dual_infeas_bad;\nparam.dual_infeas_min = dual_infeas_min;\nparam.termcode = termcode;\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDNTdirfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDNTdirfun.m", "size": 1459, "source_encoding": "utf_8", "md5": "a045827a3ca1adcf8806cfd8234ad5e4", "text": "%%*******************************************************************\n%% HSDNTdirfun: compute (dX,dZ), given dy, for the NT direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [par,dX,dy,dZ] = HSDNTdirfun(blk,At,par,Rd,EinvRc,xx)\n\nglobal solve_ok\n\ndX = cell(size(blk,1),1); dZ = cell(size(blk,1),1); dy = [];\nif (any(isnan(xx)) || any(isinf(xx)))\n    solve_ok = 0;\n    fprintf('\\n  HSDNTdirfun: solution contains NaN or inf.');\n    return;\nend\n%%\nm = par.m;\ndy2 = xx(1:m+2);\n%%\nfor p=1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy2));\n        tmp   = par.dd{p}.*dZ{p};\n        dX{p} = EinvRc{p} - tmp;\n    elseif strcmp(pblk{1},'q')\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy2));\n        tmp = par.dd{p}.*dZ{p} + qops(pblk,qops(pblk,dZ{p},par.ee{p},1),par.ee{p},3);\n        dX{p} = EinvRc{p} - tmp;\n    elseif strcmp(pblk{1},'s')\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),par.permA(p,:),par.isspAy(p),dy2));\n        tmp   = Prod3(pblk,par.W{p},dZ{p},par.W{p},1);\n        dX{p} = EinvRc{p}-tmp;\n    end\nend\ndy  = dy2(1:m);\npar.dtau = dy2(m+1);\npar.dtheta = dy2(m+2);\npar.dkap = (par.mu./par.tau - par.kap) - par.kap*(par.dtau/par.tau);\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDNTrhsfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDNTrhsfun.m", "size": 3424, "source_encoding": "utf_8", "md5": "02348c55d691a53b023639b8103757be", "text": "%%*******************************************************************\n%% HSDNTrhsfun: compute the right-hand side vector of the\n%%              Schur complement equation for the NT direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [rhs,EinvRc,hRd] = HSDNTrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ)\nglobal spdensity\n\nm = par.m;\nif (nargin > 8)\n    corrector = 1;\nelse\n    corrector = 0;\n    hRd = zeros(m+2,1);\nend\nhEinvRc = zeros(m+2,1);\nEinvRc  = cell(size(blk,1),1);\nif length(sigmu)==1; sigmu = sigmu*ones(1,size(blk,1)); end\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});  numblk = length(pblk{2});\n    if strcmp(pblk{1},'l')\n        if (corrector)\n            Rq = dX{p}.*dZ{p};\n        else\n            Rq = sparse(n,1);\n            tmp  = par.dd{p}.*Rd{p};\n            tmp2 = mexMatvec(At{p},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = sigmu(p)./Z{p}-X{p} -Rq./Z{p};\n        tmp2 = mexMatvec(At{p},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'q')\n        w = sqrt(par.gamz{p}./par.gamx{p});\n        if (corrector)\n            hdx = qops(pblk,w,par.ff{p},5,dX{p});\n            hdz = qops(pblk,w,par.ff{p},6,dZ{p});\n            hdxdz = Arrow(pblk,hdx,hdz);\n            vv = qops(pblk,w,par.ff{p},5,X{p});\n            Vihdxdz = Arrow(pblk,vv,hdxdz,1);\n            Rq = qops(pblk,w,par.ff{p},6,Vihdxdz);\n        else\n            Rq = sparse(n,1);\n            tmp  = par.dd{p}.*Rd{p} + qops(pblk,qops(pblk,Rd{p},par.ee{p},1),par.ee{p},3);\n            tmp2 = mexMatvec(At{p},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = qops(pblk,-sigmu(p)./(par.gamz{p}.*par.gamz{p}),Z{p},4)-X{p}-Rq;\n        tmp2 = mexMatvec(At{p},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'s')\n        n2 = pblk{2}.*(pblk{2}+1)/2;\n        if (corrector)\n            hdZ = Prod3(pblk,par.G{p},dZ{p},par.G{p}',1);\n            hdX = spdiags(-par.sv{p},0,n,n)-hdZ;\n            tmp = Prod2(pblk,hdX,hdZ,0);\n            tmp = 0.5*(tmp+tmp');\n            if (numblk == 1)\n                d = par.sv{p};\n                e = ones(pblk{2},1);\n                Rq = 2*tmp./(d*e'+e*d');\n                if (nnz(Rq) <= spdensity*n2); Rq = sparse(Rq); end\n            else\n                Rq = sparse(n,n);\n                s = [0, cumsum(pblk{2})];\n                for i = 1:numblk\n                    pos = s(i)+1 : s(i+1);\n                    d = par.sv{p}(pos); e = ones(length(pos),1);\n                    Rq(pos,pos) = 2*tmp(pos,pos)./(d*e' + e*d'); %#ok\n                end\n            end\n        else\n            Rq = sparse(n,n);\n            EinvRc{p} = Prod3(pblk,par.W{p},Rd{p},par.W{p},1,par.nzlistAy{p});\n            tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),EinvRc(p));\n            hRd = hRd + tmp2;\n        end\n        tmp = spdiags(sigmu(p)./par.sv{p} -par.sv{p},0,n,n);\n        EinvRc{p} = Prod3(pblk,par.G{p}',tmp-Rq,par.G{p},1);\n        tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),EinvRc(p));\n        hEinvRc = hEinvRc + tmp2;\n    end\nend\n%%\nrhs = rp + hRd - hEinvRc;\nrhs(m+1) = rhs(m+1) + (par.mu/par.tau - par.kap);\nif (corrector)\n    rhs(m+1) = rhs(m+1) - par.dtau*par.dkap/par.tau;\nend\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDHKMpred.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDHKMpred.m", "size": 2644, "source_encoding": "utf_8", "md5": "81b89c36e0d0bad30836c551264a6a05", "text": "%%*******************************************************************\n%% HSDHKMpred: Compute (dX,dy,dZ) for the H..K..M direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [par,dX,dy,dZ,coeff,L,hRd] = ...\n    HSDHKMpred(blk,At,par,rp,Rd,sigmu,X,Z,invZchol)\n\nglobal schurfun schurfun_par\n%%\n%% compute HKM scaling\n%%\nZinv = cell(size(blk,1),1);   dd = cell(size(blk,1),1);\ngamx = cell(size(blk,1),1);   gamz = cell(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    numblk = length(pblk{2});\n    if strcmp(pblk{1},'l')\n        Zinv{p} = 1./Z{p};\n        dd{p} = X{p}./Z{p};\n    elseif strcmp(pblk{1},'q')\n        gaptmp  = qops(pblk,X{p},Z{p},1);\n        gamz2   = qops(pblk,Z{p},Z{p},2);\n        gamz{p} = sqrt(gamz2);\n        Zinv{p} = qops(pblk,-1./gamz2,Z{p},4);\n        dd{p}   = qops(pblk,gaptmp./gamz2,ones(n,1),4);\n    elseif strcmp(pblk{1},'s')\n        if (numblk == 1)\n            Zinv{p} = Prod2(pblk,full(invZchol{p}),invZchol{p}',1);\n        else\n            Zinv{p} = Prod2(pblk,invZchol{p},invZchol{p}',1);\n        end\n    end\nend\npar.Zinv = Zinv; par.gamx = gamx; par.gamz = gamz; par.dd = dd;\n%%\n%% compute schur matrix\n%%\nm = par.m;\nschur = sparse(m+2,m+2);\nUU = [];  EE = [];\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        [schur,UU,EE] = schurmat_lblk(blk,At,par,schur,UU,EE,p,par.dd);\n    elseif strcmp(pblk{1},'q');\n        [schur,UU,EE] = schurmat_qblk(blk,At,par,schur,UU,EE,p,par.dd,par.Zinv,X);\n    elseif strcmp(pblk{1},'s')\n        if isempty(schurfun{p})\n            schur = schurmat_sblk(blk,At,par,schur,p,X,par.Zinv);\n        elseif ischar(schurfun{p})\n            if ~isempty(par.permZ{p})\n                Zpinv = Zinv{p}(par.permZ{p},par.permZ{p});\n                Xp = X{p}(par.permZ{p},par.permZ{p});\n            else\n                Xp = X{p};\n                Zpinv = Zinv{p};\n            end\n            schurtmp = feval( schurfun{p}, Xp,Zpinv,schurfun_par(p,:));\n            schur = schur + schurtmp;\n        end\n    end\nend\n%%\n%% compute rhs\n%%\n[rhs,EinvRc,hRd] = HSDHKMrhsfun(blk,At,par,X,Z,rp,Rd,sigmu);\n%%\n%% solve linear system\n%%\npar.addschur   = par.kap/par.tau;\nschur(m+1,m+1) = schur(m+1,m+1) + par.kap/par.tau;\nschur(m+2,m+2) = schur(m+2,m+2) + par.addschur;\n[xx,coeff,L] = HSDlinsysolve(par,schur,UU,EE,par.Umat,rhs);\n%%\n%% compute (dX,dZ)\n%%\n[par,dX,dy,dZ] = HSDHKMdirfun(blk,At,par,Rd,EinvRc,X,xx);\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDsqlpmain.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDsqlpmain.m", "size": 28301, "source_encoding": "utf_8", "md5": "df880652075e1e6d94eefd6ddfe38c64", "text": "%%*****************************************************************************\n%% HSDsqlp: solve an semidefinite-quadratic-linear program\n%%   by infeasible path-following method on the homogeneous self-dual model.\n%%\n%%  [obj,X,y,Z,info,runhist] =\n%%      HSDsqlp(blk,At,C,b,OPTIONS,X0,y0,Z0,kap0,tau0,theta0);\n%%\n%%  Input:\n%%  blk    : a cell array describing the block diagonal structure of SQL data.\n%%   At    : a cell array with At{p} = [svec(Ap1) ... svec(Apm)]\n%%  b,C    : data for the SQL instance.\n%%  OPTIONS: a structure that specifies parameters required in HSDsqlp.m,\n%%           (if it is not given, the default in sqlparameters.m is used).\n%%\n%%  (X0,y0,Z0): an initial iterate (if it is not given, the default is used).\n%%  (kap0,tau0,theta0): initial parameters (if not given, the default is used).\n%%\n%%  Output: obj  = [<C,X> <b,y>].\n%%          (X,y,Z): an approximately optimal solution or a primal or dual\n%%                   infeasibility certificate.\n%%          info.termcode = termination-code\n%%          info.iter     = number of iterations\n%%          info.obj      = [primal-obj, dual-obj]\n%%          info.cputime  = total-time\n%%          info.gap      = gap\n%%          info.pinfeas  = primal_infeas\n%%          info.dinfeas  = dual_infeas\n%%          runhist.pobj    = history of primal objective value.\n%%          runhist.dobj    = history of dual   objective value.\n%%          runhist.gap     = history of <X,Z>.\n%%          runhist.pinfeas = history of primal infeasibility.\n%%          runhist.dinfeas = history of dual   infeasibility.\n%%          runhist.cputime = history of cputime spent.\n%%----------------------------------------------------------------------------\n%%  The OPTIONS structure specifies the required parameters:\n%%      vers  gam  predcorr  expon  gaptol  inftol  steptol\n%%      maxit  printlevel  ...\n%%      (all have default values set in sqlparameters.m).\n%%\n%%*************************************************************************\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction [obj,X,y,Z,info,runhist] = ...\n    HSDsqlpmain(blk,At,C,b,par,X0,y0,Z0,kap0,tau0,theta0)\n\n%%\n%%-----------------------------------------\n%% get parameters from the OPTIONS structure.\n%%-----------------------------------------\n%%\nglobal spdensity  smallblkdim  solve_ok use_LU\nglobal schurfun   schurfun_par\n% matlabversion = par.matlabversion;\nisoctave = exist( 'OCTAVE_VERSION', 'builtin' );\nif isoctave,\n  w1 = warning('off','Octave:nearly-singular-matrix');\nelse\n  w1 = warning('off','MATLAB:nearlySingularMatrix');\n  w2 = warning('off','MATLAB:singularMatrix');\nend\nvers          = par.vers;\npredcorr      = par.predcorr;\ngam           = par.gam;\nexpon         = par.expon;\ngaptol        = par.gaptol;\ninftol        = par.inftol;\nsteptol       = par.steptol;\nmaxit         = par.maxit;\nprintlevel    = par.printlevel;\nstoplevel     = par.stoplevel;\n% scale_data    = par.scale_data;\nspdensity     = par.spdensity;\nrmdepconstr   = par.rmdepconstr;\nsmallblkdim   = par.smallblkdim;\nschurfun      = par.schurfun;\nschurfun_par  = par.schurfun_par;\n% ublksize      = par.ublksize;\n%%\ntstart = clock;\nX = X0; y = y0; Z = Z0;\nfor p = 1:size(blk,1)\n    if strcmp(blk{p,1},'u'); Z{p} = zeros(blk{p,2},1); end\nend\n%%\n%%-----------------------------------------\n%% convert unrestricted blk to linear blk.\n%%-----------------------------------------\n%%\nublkidx = zeros(size(blk,1),1);\nCpert = zeros(size(blk,1),1);\nCnew = C;\nperturb_C = 1;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    tmp = max(1,norm(C{p},'fro'))/sqrt(n);\n    if strcmp(pblk{1},'s')\n        if (perturb_C); Cpert(p) = 1e-3*tmp; end\n        Cnew{p} = C{p} + Cpert(p)*speye(n);\n    elseif strcmp(pblk{1},'q')\n        if (perturb_C); Cpert(p) = 0*tmp; end; %% old: 1e-3\n        s = 1+[0, cumsum(pblk{2})];\n        tmp2 = zeros(n,1); len = length(pblk{2});\n        tmp2(s(1:len)) = ones(len,1);\n        Cnew{p} = C{p} + Cpert(p)*tmp2;\n    elseif strcmp(pblk{1},'l')\n        if (perturb_C); Cpert(p) = 1e-4*tmp; end; %% old: 1e-3\n        Cnew{p} = C{p} + Cpert(p)*ones(n,1);\n    elseif strcmp(pblk{1},'u')\n        msg = sprintf('convert ublk to linear blk');\n        if (printlevel); fprintf('\\n *** %s',msg); end\n        ublkidx(p) = 1;\n        n = 2*pblk{2};\n        blk{p,1} = 'l'; blk{p,2} = n;\n        if (perturb_C); Cpert(p) = 1e-2*tmp; end\n        C{p}  = [C{p}; -C{p}];\n        At{p} = [At{p}; -At{p}];\n        Cnew{p} = C{p} + Cpert(p)*ones(n,1);\n        X{p}  = 1+randmat(n,1,0,'u'); \n        Z{p}  = 1+randmat(n,1,0,'u');\n    end\nend\n%%\n%%-----------------------------------------\n%% check if the matrices Ak are\n%% linearly independent.\n%%-----------------------------------------\n%%\nm0 = length(b);\n[At,b,y,indeprows,depconstr,feasible,AAt] = ...\n    checkdepconstr(blk,At,b,y,rmdepconstr);\nif (~feasible)\n    msg = 'SQLP is not feasible';\n    if (printlevel); fprintf('\\n %s',msg); end\n    return;\nend\npar.depconstr = depconstr;\n%%\nnormb = 1+max(abs(b));\nnormC = zeros(length(C),1);\nfor p = 1:length(C); normC(p) = max(max(abs(C{p}))); end\nnormC = 1+max(normC);\nnn = ops(C,'getM');\nm = length(b);\nif (nargin <= 8) || (isempty(kap0) || isempty(tau0) || isempty(theta0))\n    if (max([ops(At,'norm'),ops(C,'norm'),norm(b)]) > 1e6)\n        kap0 = 10*blktrace(blk,X,Z);\n    else\n        kap0 = blktrace(blk,X,Z);\n    end\n    tau0 = 1; theta0 = 1;\nend\nkap = kap0; tau = tau0; theta = theta0;\n%%\nnormX0 = ops(X0,'norm')/tau; normZ0 = ops(Z0,'norm')/tau;\nbbar  = (tau*b-AXfun(blk,At,[],X))/theta;\nZpATy = ops(Z,'+',Atyfun(blk,At,[],[],y));\nCbar = ops(ops(ops(tau,'*',C),'-',ZpATy),'/',theta);\ngbar = (blktrace(blk,C,X)-b'*y+kap)/theta;\nabar = (blktrace(blk,X,Z)+tau*kap)/theta;\nfor p = 1:size(blk,1);\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        At{p} = [At{p}, -svec(pblk,Cnew{p},1), svec(pblk,Cbar{p},1)];\n    else\n        At{p} = [At{p}, -Cnew{p}, Cbar{p}];\n    end\nend\nBmat = [sparse(m,m), -b, bbar; b', 0, gbar; -bbar', -gbar, 0];\nem1 = zeros(m+2,1); em1(m+1) = 1;\nem2 = zeros(m+2,1); em2(m+2) = 1;\npar.Umat = [[b;0;0], [bbar;gbar;0], em1, em2];\npar.m = m;\npar.diagAAt = [full(diag(AAt)); 1; 1];\n%%\n%%-----------------------------------------\n%% find the combined list of non-zero\n%% elements of Aj, j = 1:k, for each k.\n%%-----------------------------------------\n%%\npar.numcolAt = length(b)+2;\n[At,C,Cnew,X,Z,par.permA,par.invpermA,par.permZ] = ...\n    HSDsortA(blk,At,C,Cnew,[b;0;0],X,Z); %#ok\n[par.isspA,par.nzlistA,par.nzlistAsum,par.isspAy,par.nzlistAy] = ...\n    nzlist(blk,At,par);\n%%\n%%-----------------------------------------\n%% initialization\n%%-----------------------------------------\n%%\ny2 = [y; tau; theta];\nAX = AXfun(blk,At,par.permA,X);\nrp = [zeros(m,1); kap; -abar] - AX - Bmat*y2;\n% Rd = ops(Atyfun(blk,At,par.permA,par.isspAy,-y2),'-',Z);\ntrXZ = blktrace(blk,X,Z);\nmu  = (trXZ+kap*tau)/(nn+1);\nobj = [blktrace(blk,C,X), b'*y]/tau;\ngap = trXZ/tau^2;\nrelgap = gap/(1+mean(abs(obj)));\nZpATy = ops(Z,'+',Atyfun(blk,At,par.permA,par.isspAy,[y;0;0]));\nZpATynorm = ops(ZpATy,'norm');\nprim_infeas = norm(b - AX(1:m)/tau)/normb;\ndual_infeas = ops(ops(C,'-',ops(ZpATy,'/',tau)),'norm')/normC;\ninfeas = max(prim_infeas,dual_infeas);\n%%\ntermcode = 0;\npstep = 1; dstep = 1; pred_convg_rate = 1; corr_convg_rate = 1;\nbesttol = max( relgap, infeas );\n% homRd = inf; homrp = inf; dy = zeros(length(b),1);\nmsg = []; msg2 = [];\nrunhist.pobj    = obj(1);\nrunhist.dobj    = obj(2);\nrunhist.gap     = gap;\nrunhist.relgap  = relgap;\nrunhist.pinfeas = prim_infeas;\nrunhist.dinfeas = dual_infeas;\nrunhist.infeas  = infeas;\nrunhist.cputime = etime(clock,tstart);\nrunhist.step    = 0;\nrunhist.kappa   = kap;\nrunhist.tau     = tau;\nrunhist.theta   = theta;\nrunhist.useLU   = 0;\nttime.preproc   = runhist.cputime;\nttime.pred = 0; ttime.pred_pstep = 0; ttime.pred_dstep = 0;\nttime.corr = 0; ttime.corr_pstep = 0; ttime.corr_dstep = 0;\nttime.pchol = 0; ttime.dchol = 0; ttime.misc = 0;\n%%\n%%-----------------------------------------\n%% display parameters, and initial info\n%%-----------------------------------------\n%%\nif (printlevel >= 2)\n    fprintf('\\n********************************************');\n    fprintf('************************************************\\n');\n    fprintf('   SDPT3: homogeneous self-dual path-following algorithms');\n    fprintf('\\n********************************************');\n    fprintf('************************************************\\n');\n    [hh,mm,ss] = mytime(ttime.preproc);\n    if (printlevel>=3)\n        fprintf(' version  predcorr  gam  expon\\n');\n        if (vers == 1); fprintf('   HKM '); elseif (vers == 2); fprintf('    NT '); end\n        fprintf('     %1.0f      %4.3f   %1.0f\\n',predcorr,gam,expon);\n        fprintf('it pstep dstep pinfeas dinfeas  gap')\n        fprintf('     mean(obj)    cputime    kap   tau    theta\\n');\n        fprintf('------------------------------------------------');\n        fprintf('--------------------------------------------\\n');\n        fprintf('%2.0f|%4.3f|%4.3f|%2.1e|%2.1e|',0,0,0,prim_infeas,dual_infeas);\n        fprintf('%2.1e|%- 7.6e| %s:%s:%s|',gap,mean(obj),hh,mm,ss);\n        fprintf('%2.1e|%2.1e|%2.1e|',kap,tau,theta);\n    end\nend\n%%\n%%---------------------------------------------------------------\n%% start main loop\n%%---------------------------------------------------------------\n%%\nEE = ops(blk,'identity');\nnormE = ops(EE,'norm');\nZpertold = 1;\n[Xchol,indef(1)] = blkcholfun(blk,X);\n[Zchol,indef(2)] = blkcholfun(blk,Z);\nif any(indef)\n    msg = 'stop: X, Z are not both positive definite';\n    if (printlevel); fprintf('\\n  %s\\n',msg); end\n    info.termcode = -3;\n    info.msg1 = msg;\n    return;\nend\n%%\nparam.termcode    = termcode;\nparam.iter        = 0;\nparam.normX0      = normX0;\nparam.normZ0      = normZ0;\nparam.m0          = m0;\nparam.indeprows   = indeprows;\nparam.prim_infeas_bad = 0;\nparam.dual_infeas_bad = 0;\nparam.prim_infeas_min = prim_infeas;\nparam.dual_infeas_min = dual_infeas;\nparam.gaptol      = gaptol;\nparam.inftol      = inftol;\nparam.maxit       = maxit;\nparam.printlevel  = printlevel;\nparam.stoplevel   = stoplevel;\nbreakyes = 0;\ndy = zeros(length(b),1); dtau = 0; dtheta = 0;\nXbest = X; ybest = y; Zbest = Z;\n% kapbest = kap; taubest = tau; thetabest = theta;\n%%\nfor iter = 1:maxit;\n    update_iter = 0; pred_slow = 0; corr_slow = 0; % step_short = 0;\n    tstart  = clock;\n    timeold = tstart;\n    par.kap   = kap;\n    par.tau   = tau;\n    par.theta = theta;\n    par.mu   = mu;\n    par.iter = iter;\n    par.y    = y;\n    par.dy2  = [dy; dtau; dtheta];\n    par.rp   = rp;\n    par.ZpATynorm = ZpATynorm;\n    %%\n    %%--------------------------------------------------\n    %% perturb C\n    %%--------------------------------------------------\n    %%\n    if (perturb_C)\n        [At,Cpert] = HSDsqlpCpert(blk,At,par,C,X,Cpert,runhist);\n        maxCpert(iter) = max(Cpert); %#ok\n        %%fprintf(' %2.1e',max(Cpert));\n        if (iter > 10 && norm(diff(maxCpert([iter-3,iter]))) < 1e-13)\n            Cpert = 0.5*Cpert;\n            maxCpert(iter) = max(Cpert); %#ok\n        end\n        AX = AXfun(blk,At,par.permA,X);\n        rp = [zeros(m,1); kap; -abar] - AX - Bmat*y2;\n        Rd = ops(Atyfun(blk,At,par.permA,par.isspAy,-y2),'-',Z);\n    end\n    %%---------------------------------------------------------------\n    %% predictor step.\n    %%---------------------------------------------------------------\n    %%\n    if (predcorr)\n        sigma = 0;\n    else\n        sigma = 1-0.9*min(pstep,dstep);\n        if (iter == 1); sigma = 0.5; end;\n    end\n    sigmu = sigma*mu;\n    \n    invXchol = cell(size(blk,1),1);\n    invZchol = ops(Zchol,'inv');\n    if (vers == 1);\n        [par,dX,dy,dZ,coeff,L,hRd] = ...\n            HSDHKMpred(blk,At,par,rp,Rd,sigmu,X,Z,invZchol);\n    elseif (vers == 2);\n        [par,dX,dy,dZ,coeff,L,hRd] = ...\n            HSDNTpred(blk,At,par,rp,Rd,sigmu,X,Z,Zchol,invZchol);\n    end\n    if (solve_ok <= 0)\n        msg = 'stop: difficulty in computing predictor directions';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        runhist.cputime(iter+1) = etime(clock,tstart);\n        termcode = -4;\n        break;\n    end\n    timenew = clock;\n    ttime.pred = ttime.pred + etime(timenew,timeold); timeold=timenew;\n    %%\n    %%-----------------------------------------\n    %% step-lengths for predictor step\n    %%-----------------------------------------\n    %%\n    if (gam == 0)\n        gamused = 0.9 + 0.09*min(pstep,dstep);\n    else\n        gamused = gam;\n    end\n    kapstep = max( (par.dkap<0)*(-kap/(par.dkap-eps)), (par.dkap>=0)*1e6 );\n    taustep = max( (par.dtau<0)*(-tau/(par.dtau-eps)), (par.dtau>=0)*1e6 );\n    [Xstep,invXchol] = steplength(blk,X,dX,Xchol,invXchol);\n    timenew = clock;\n    ttime.pred_pstep = ttime.pred_pstep + etime(timenew,timeold); timeold=timenew;\n    Zstep = steplength(blk,Z,dZ,Zchol,invZchol);\n    pstep = min(1,gamused*min([Xstep,Zstep,kapstep,taustep]));\n    dstep = pstep;\n    kappred = kap + pstep*par.dkap;\n    taupred = tau + pstep*par.dtau;\n    trXZpred = trXZ + pstep*blktrace(blk,dX,Z) + dstep*blktrace(blk,X,dZ) ...\n        + pstep*dstep*blktrace(blk,dX,dZ);\n    mupred  = (trXZpred + kappred*taupred)/(nn+1);\n    mupredhist(iter) = mupred; %#ok\n    timenew = clock;\n    ttime.pred_dstep = ttime.pred_dstep + etime(timenew,timeold); timeold=timenew;\n    %%\n    %%-----------------------------------------\n    %%  stopping criteria for predictor step.\n    %%-----------------------------------------\n    %%\n    if (min(pstep,dstep) < steptol) && (stoplevel)\n        msg = 'stop: steps in predictor too short';\n        if (printlevel)\n            fprintf('\\n  %s',msg);\n            fprintf(': pstep = %3.2e,  dstep = %3.2e',pstep,dstep);\n        end\n        runhist.cputime(iter+1) = etime(clock,tstart);\n        termcode = -2;\n        breakyes = 1;\n    end\n    if (iter >= 2)\n        idx = max(2,iter-2) : iter;\n        pred_slow = all(mupredhist(idx)./mupredhist(idx-1) > 0.4);\n        idx = max(2,iter-5) : iter;\n        pred_convg_rate = mean(mupredhist(idx)./mupredhist(idx-1));\n        pred_slow = pred_slow + (mupred/mu > 5*pred_convg_rate);\n    end\n    if (~predcorr)\n        if (max(mu,infeas) < 1e-6) && (pred_slow) && (stoplevel)\n            msg = 'stop: lack of progress in predictor';\n            if (printlevel)\n                fprintf('\\n  %s',msg);\n                fprintf(': mupred/mu = %3.2f, pred_convg_rate = %3.2f.',...\n                    mupred/mu,pred_convg_rate);\n            end\n            runhist.cputime(iter+1) = etime(clock,tstart);\n            termcode = -2;\n            breakyes = 1;\n        else\n            update_iter = 1;\n        end\n    end\n    %%\n    %%---------------------------------------------------------------\n    %% corrector step.\n    %%---------------------------------------------------------------\n    %%\n    if (predcorr) && (~breakyes)\n        step_pred = min(pstep,dstep);\n        if (mu > 1e-6)\n            if (step_pred < 1/sqrt(3));\n                expon_used = 1;\n            else\n                expon_used = max(expon,3*step_pred^2);\n            end\n        else\n            expon_used = max(1,min(expon,3*step_pred^2));\n        end\n        sigma = min( 1, (mupred/mu)^expon_used );\n        sigmu = sigma*mu;\n        %%\n        if (vers == 1)\n            [par,dX,dy,dZ] = HSDHKMcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n                dX,dZ,coeff,L,X,Z);\n        elseif (vers == 2)\n            [par,dX,dy,dZ] = HSDNTcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n                dX,dZ,coeff,L,X,Z);\n        end\n        if (solve_ok <= 0)\n            msg = 'stop: difficulty in computing corrector directions';\n            if (printlevel); fprintf('\\n  %s',msg); end\n            runhist.cputime(iter+1) = etime(clock,tstart);\n            termcode = -4;\n            break;\n        end\n        timenew = clock;\n        ttime.corr = ttime.corr + etime(timenew,timeold); timeold=timenew;\n        %%\n        %%-----------------------------------\n        %% step-lengths for corrector step\n        %%-----------------------------------\n        %%\n        if (gam == 0)\n            gamused = 0.9 + 0.09*min(pstep,dstep);\n        else\n            gamused = gam;\n        end\n        kapstep = max( (par.dkap<0)*(-kap/(par.dkap-eps)), (par.dkap>=0)*1e6 );\n        taustep = max( (par.dtau<0)*(-tau/(par.dtau-eps)), (par.dtau>=0)*1e6 );\n        Xstep = steplength(blk,X,dX,Xchol,invXchol);\n        timenew = clock;\n        ttime.corr_pstep = ttime.corr_pstep + etime(timenew,timeold); timeold=timenew;\n        Zstep = steplength(blk,Z,dZ,Zchol,invZchol);\n        timenew = clock;\n        pstep = min(1,gamused*min([Xstep,Zstep,kapstep,taustep]));\n        dstep = pstep;\n        kapcorr = kap + pstep*par.dkap;\n        taucorr = tau + pstep*par.dtau;\n        trXZcorr = trXZ + pstep*blktrace(blk,dX,Z) + dstep*blktrace(blk,X,dZ)...\n            + pstep*dstep*blktrace(blk,dX,dZ);\n        mucorr = (trXZcorr+kapcorr*taucorr)/(nn+1);\n        ttime.corr_dstep = ttime.corr_dstep + etime(timenew,timeold); timeold=timenew;\n        %%\n        %%-----------------------------------------\n        %%  stopping criteria for corrector step\n        %%-----------------------------------------\n        %%\n        if (iter >= 2)\n            idx = max(2,iter-2) : iter;\n            corr_slow = all(runhist.gap(idx)./runhist.gap(idx-1) > 0.8);\n            idx = max(2,iter-5) : iter;\n            corr_convg_rate = mean(runhist.gap(idx)./runhist.gap(idx-1));\n            corr_slow = corr_slow + (mucorr/mu > max(min(1,5*corr_convg_rate),0.8));\n        end\n        if (max(mu,infeas) < 1e-6) && (iter > 10) && (stoplevel) ...\n                && (corr_slow && mucorr/mu > 1.0)\n            msg = 'stop: lack of progress in corrector';\n            if (printlevel)\n                fprintf('\\n  %s',msg);\n                fprintf(': mucorr/mu = %3.2f, corr_convg_rate = %3.2f',...\n                    mucorr/mu,corr_convg_rate);\n            end\n            runhist.cputime(iter+1) = etime(clock,tstart);\n            termcode = -2;\n            breakyes = 1;\n        else\n            update_iter = 1;\n        end\n    end\n    %%\n    %%---------------------------------------------------------------\n    %% udpate iterate\n    %%---------------------------------------------------------------\n    %%\n    indef = [1 1];\n    if (update_iter)\n        for t = 1:5\n            [Xchol,indef(1)] = blkcholfun(blk,ops(X,'+',dX,pstep));\n            timenew = clock;\n            ttime.pchol = ttime.pchol + etime(timenew,timeold); timeold = timenew;\n            if (indef(1)); pstep = 0.8*pstep; else break; end\n        end\n        if (t > 1); pstep = gamused*pstep; end\n        for t = 1:5\n            [Zchol,indef(2)] = blkcholfun(blk,ops(Z,'+',dZ,dstep));\n            timenew = clock;\n            ttime.dchol = ttime.dchol + etime(timenew,timeold); timeold = timenew;\n            if (indef(2)); dstep = 0.8*dstep; else break; end\n        end\n        if (t > 1); dstep = gamused*dstep; end\n        AdX = AXfun(blk,At,par.permA,dX);\n        AXtmp = AX(1:m) + pstep*AdX(1:m); tautmp = par.tau+pstep*par.dtau;\n        prim_infeasnew = norm(b-AXtmp/tautmp)/normb;\n        pinfeas_bad(1) = (prim_infeasnew > max([1e-8,relgap,10*infeas]));\n        pinfeas_bad(2) = (prim_infeasnew > max([1e-4,20*prim_infeas]) ...\n            && (infeas < 1e-2));\n        pinfeas_bad(3) = (max([relgap,dual_infeas]) < 1e-4) ...\n            && (prim_infeasnew > max([2*prim_infeas,10*dual_infeas,1e-7]));\n        if any(indef)\n            msg = 'stop: X, Z not both positive definite';\n            if (printlevel); fprintf('\\n  %s',msg); end\n            termcode = -3;\n            breakyes = 1;\n        elseif any(pinfeas_bad)\n            if (stoplevel) && (max(pstep,dstep)<=1) && (kap < 1e-3) ...\n                    && (prim_infeasnew > dual_infeas);\n                msg = 'stop: primal infeas has deteriorated too much';\n                if (printlevel); fprintf('\\n  %s, %2.1e',msg,prim_infeasnew);\n                    fprintf(' %2.1d,%2.1d,%2.1d',...\n                        pinfeas_bad(1),pinfeas_bad(2),pinfeas_bad(3));\n                end\n                termcode = -7;\n                breakyes = 1;\n            end\n        end\n        if (~breakyes)\n            X = ops(X,'+',dX,pstep);\n            y = y + dstep*dy;  Z = ops(Z,'+',dZ,dstep);\n            theta = max(0, theta + pstep*par.dtheta);\n            kap = kap + pstep*par.dkap;\n            if (tau + pstep*par.dtau > theta); tau = tau + pstep*par.dtau; end\n        end\n    end\n    %%\n    %%--------------------------------------------------\n    %% perturb Z: do this step before checking for break\n    %%--------------------------------------------------\n    perturb_Z = 1;\n    if (~breakyes) && (perturb_Z)\n        trXZtmp = blktrace(blk,X,Z);\n        trXE  = blktrace(blk,X,EE);\n        Zpert = max(1e-12,0.2*min(relgap,prim_infeas)).*normC./normE;\n        Zpert = min(Zpert,0.1*trXZtmp./trXE);\n        Zpert = min([1,Zpert,1.5*Zpertold]);\n        if (infeas < 1e-2)\n            Z = ops(Z,'+',EE,Zpert);\n            [Zchol,indef(2)] = blkcholfun(blk,Z);\n            if any(indef(2))\n                msg = 'stop: Z not positive definite';\n                if (printlevel); fprintf('\\n  %s',msg); end\n                termcode = -3;\n                breakyes = 1;\n            end\n        end\n        Zpertold = Zpert;\n    end\n    %%\n    %%---------------------------------------------------------------\n    %% compute rp, Rd, infeasibities, etc.\n    %%---------------------------------------------------------------\n    %%\n    y2 = [y; tau; theta];\n    AX = AXfun(blk,At,par.permA,X);\n    rp = [zeros(m,1); kap; -abar] - AX - Bmat*y2;\n    % Rd = ops(Atyfun(blk,At,par.permA,par.isspAy,-y2),'-',Z);\n    trXZ = blktrace(blk,X,Z);\n    mu  = (trXZ+kap*tau)/(nn+1);\n    obj = [blktrace(blk,C,X), b'*y]/tau;\n    gap = trXZ/tau^2;\n    relgap = gap/(1+mean(abs(obj)));\n    ZpATy = ops(Z,'+',Atyfun(blk,At,par.permA,par.isspAy,[y;0;0]));\n    ZpATynorm = ops(ZpATy,'norm');\n    prim_infeas = norm(b-AX(1:m)/tau)/normb;\n    dual_infeas = ops(ops(C,'-',ops(ZpATy,'/',tau)),'norm')/normC;\n    infeas = max(prim_infeas,dual_infeas);\n    runhist.pobj(iter+1)    = obj(1);\n    runhist.dobj(iter+1)    = obj(2);\n    runhist.gap(iter+1)     = gap;\n    runhist.relgap(iter+1)  = relgap;\n    runhist.pinfeas(iter+1) = prim_infeas;\n    runhist.dinfeas(iter+1) = dual_infeas;\n    runhist.infeas(iter+1)  = infeas;\n    runhist.cputime(iter+1) = etime(clock,tstart);\n    runhist.step(iter+1)    = min(pstep,dstep);\n    runhist.kappa(iter+1)   = kap;\n    runhist.tau(iter+1)     = tau;\n    runhist.theta(iter+1)   = theta;\n    runhist.useLU(iter+1)   = use_LU;\n    timenew = clock;\n    ttime.misc = ttime.misc + etime(timenew,timeold); % timeold = timenew;\n    [hh,mm,ss] = mytime(sum(runhist.cputime));\n    if (printlevel>=3)\n        fprintf('\\n%2.0f|%4.3f|%4.3f|',iter,pstep,dstep);\n        fprintf('%2.1e|%2.1e|%2.1e|',prim_infeas,dual_infeas,gap);\n        fprintf('%- 7.6e| %s:%s:%s|',mean(obj),hh,mm,ss);\n        fprintf('%2.1e|%2.1e|%2.1e|',kap,tau,theta);\n    end\n    %%\n    %%--------------------------------------------------\n    %% check convergence.\n    %%--------------------------------------------------\n    param.termcode    = termcode;\n    param.kap         = kap;\n    param.tau         = tau;\n    param.theta       = theta;\n    param.iter        = iter;\n    param.obj         = obj;\n    param.gap         = gap;\n    param.relgap      = relgap;\n    param.mu          = mu;\n    param.prim_infeas = prim_infeas;\n    param.dual_infeas = dual_infeas;\n    param.AX          = AX(1:m)/tau;\n    param.ZpATynorm   = ZpATynorm/tau;\n    param.normX       = ops(X,'norm')/tau;\n    param.normZ       = ops(Z,'norm')/tau;\n    if (~breakyes)\n        [param,breakyes,use_olditer,msg] = HSDsqlpcheckconvg(param,runhist);\n        termcode = param.termcode;  %% important\n        if (use_olditer)\n            X = ops(X,'-',dX,pstep);\n            y = y - dstep*dy;\n            Z = ops(Z,'-',dZ,dstep);\n            kap   = kap - pstep*par.dkap;\n            tau   = tau - pstep*par.dtau;\n            theta = theta - pstep*par.dtheta;\n            prim_infeas = runhist.pinfeas(iter);\n            dual_infeas = runhist.dinfeas(iter);\n            gap = runhist.gap(iter); relgap = runhist.relgap(iter);\n            obj = [runhist.pobj(iter), runhist.dobj(iter)];\n        end\n    end\n    %%--------------------------------------------------\n    %% check for break\n    %%--------------------------------------------------\n    newtol = max(relgap,infeas);\n    update_best(iter+1) = ~( newtol >= besttol ); %#ok\n    if update_best(iter+1),\n        Xbest = X; ybest = y; Zbest = Z;\n        besttol = newtol;\n    end\n    if besttol < 1e-4 && ~any(update_best(max(1,iter-1):iter+1))\n        msg = 'lack of progess in infeas';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -9;\n        breakyes = 1;\n    end\n    if besttol < 1e-3 && newtol > 1.2*besttol && theta < 1e-10 && kap < 1e-6\n        msg = 'lack of progress in infeas';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -9;\n        breakyes = 1;\n    end\n    if (breakyes > 0.5); break; end\nend\n%%---------------------------------------------------------------\n%% end of main loop\n%%---------------------------------------------------------------\n%%\nuse_bestiter = 1;\nif (use_bestiter) && (param.termcode <= 0)\n    X = Xbest; y = ybest; Z = Zbest;\n    % kap = kapbest; tau = taubest; theta = thetabest;\n    trXZ = blktrace(blk,X,Z);\n    obj  = [blktrace(blk,C,X), b'*y]/tau;\n    gap  = trXZ/tau^2;\n    relgap = gap/(1+mean(abs(obj)));\n    AX     = AXfun(blk,At,par.permA,X);\n    ZpATy  = ops(Z,'+',Atyfun(blk,At,par.permA,par.isspAy,[y;0;0]));\n    ZpATynorm = ops(ZpATy,'norm');\n    prim_infeas = norm(b-AX(1:m)/tau)/normb;\n    dual_infeas = ops(ops(C,'-',ops(ZpATy,'/',tau)),'norm')/normC;\n    infeas = max(prim_infeas,dual_infeas);\n    runhist.pobj(iter+1)    = obj(1);\n    runhist.dobj(iter+1)    = obj(2);\n    runhist.gap(iter+1)     = gap;\n    runhist.relgap(iter+1)  = relgap;\n    runhist.pinfeas(iter+1) = prim_infeas;\n    runhist.dinfeas(iter+1) = dual_infeas;\n    runhist.infeas(iter+1)  = infeas;\nend\n%%---------------------------------------------------------------\n%% produce infeasibility certificates if appropriate\n%%---------------------------------------------------------------\n%%\nX = ops(X,'/',tau); y = y/tau; Z = ops(Z,'/',tau);\nif (iter >= 1)\n    param.termcode    = termcode;\n    param.obj         = obj;\n    param.relgap      = relgap;\n    param.prim_infeas = prim_infeas;\n    param.dual_infeas = dual_infeas;\n    param.AX          = AX(1:m)/tau;\n    param.ZpATynorm   = ZpATynorm/tau;\n    [X,y,Z,resid,reldist,param,msg2] = ...\n        HSDsqlpmisc(blk,At,C,b,X,y,Z,par.permZ,param);\n    termcode = param.termcode;\nend\n%%\n%%---------------------------------------------------------------\n%% recover unrestricted blk from linear blk\n%%---------------------------------------------------------------\n%%\nfor p = 1:size(blk,1)\n    if (ublkidx(p) == 1)\n        n = blk{p,2}/2;\n        X{p} = X{p}(1:n)-X{p}(n+1:2*n);\n        Z{p} = Z{p}(1:n);\n    end\nend\n%%\n%%---------------------------------------------------------------\n%%  print summary\n%%---------------------------------------------------------------\n%%\ndimacs = [prim_infeas; 0; dual_infeas; 0];\ndimacs = [dimacs; [-diff(obj); gap]/(1+sum(abs(obj)))];\ninfo.dimacs   = dimacs;\ninfo.termcode = termcode;\ninfo.iter     = iter;\ninfo.obj      = obj;\ninfo.gap      = gap;\ninfo.relgap   = relgap;\ninfo.pinfeas  = prim_infeas;\ninfo.dinfeas  = dual_infeas;\ninfo.cputime  = sum(runhist.cputime);\ninfo.time     = ttime;\ninfo.resid    = resid;\ninfo.reldist  = reldist;\ninfo.normX    = ops(X,'norm');\ninfo.normy    = norm(y);\ninfo.normZ    = ops(Z,'norm');\ninfo.normA    = ops(At,'norm');\ninfo.normb    = norm(b);\ninfo.normC    = ops(C,'norm');\ninfo.msg1     = msg;\ninfo.msg2     = msg2;\n%%\nif isoctave,\n  warning(w1.state,w1.identifier);\nelse\n  warning(w2.state,w2.identifier);\n  warning(w1.state,w1.identifier);\nend\nsqlpsummary(info,ttime,[],printlevel);\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDlinsysolve.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDlinsysolve.m", "size": 6495, "source_encoding": "utf_8", "md5": "0644ae75443d221edcf585f5a5736fe5", "text": "%%***************************************************************\n%% linsysolve: solve linear system to get dy, and direction\n%%             corresponding to unrestricted variables.\n%%\n%% [xx,coeff,L,resnrm] = linsysolve(schur,UU,EE,Bmat,rhs);\n%%\n%% child functions: mybicgstable.m\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***************************************************************\n\nfunction [xx,coeff,L,resnrm] = HSDlinsysolve(par,schur,UU,EE,Bmat,rhs)\n\nglobal solve_ok msg\nglobal nnzmat nnzmatold matfct_options matfct_options_old use_LU\n\nspdensity  = par.spdensity;\nprintlevel = par.printlevel;\niter       = par.iter;\n\nm = length(schur);\nif (iter==1); use_LU = 0; matfct_options_old = ''; end %#ok\nif isempty(nnzmatold); nnzmatold = 0; end %#ok\n%%\n%% diagonal perturbation\n%% old: pertdiag = 1e-15*max(1,diagschur);\n%%\ndiagschur = abs(full(diag(schur)));\nconst = 1e-2/max(1,norm(par.dy2));\nalpha = max(1e-14,min(1e-10,const*norm(par.rp))/(1+norm(diagschur.*par.dy2)));\npertdiag = alpha*max(1e-8,diagschur); %% Note: alpha is close to 1e-15.\nmexschurfun(schur,pertdiag);\n%%if (printlevel); fprintf(' %3.1e ',alpha); end\nif (par.depconstr) || (min(diagschur) < min([1e-20*max(diagschur), 1e-4]))\n    lambda = 0.1*min(1e-14,const*norm(par.rp)/(1+norm(par.diagAAt.*par.dy2)));\n    mexschurfun(schur,lambda*par.diagAAt);\n    %%if (printlevel); fprintf('*'); end\nend\nif (max(diagschur)/min(diagschur) > 1e14) && (par.blkdim(2) == 0) ...\n        && (iter > 10)\n    tol = 1e-6;\n    idx = find(diagschur < tol); len = length(idx);\n    pertdiagschur = zeros(m,1);\n    if (len > 0 && len < 5) && (norm(rhs(idx)) < tol)\n        pertdiagschur(idx) = 1*ones(length(idx),1);\n        mexschurfun(schur,pertdiagschur);\n        if (printlevel); fprintf('#'); end\n    end\nend\n%%\n%%\n%%\nUU = [UU, Bmat];\nif ~isempty(EE)\n    len = max(max(EE(:,1)),max(EE(:,2)));\nelse\n    len = 0;\nend\ntmp = [len+1,len+3,-1; len+2,len+4,1; len+3,len+1,1; len+4,len+2,-1;\n    len+2,len+2,par.addschur];  %% this is the -inverse\nEE = [EE; tmp];\nncolU = size(UU,2);\n%%\n%% assemble coefficient matrix\n%%\nif isempty(EE)\n    coeff.mat22 = [];\nelse\n    coeff.mat22 = spconvert(EE);\nend\ncoeff.mat12 = UU;\ncoeff.mat11 = schur; %% important to use perturbed schur matrix\n%%\n%% pad rhs with zero vector\n%% decide which solution methods to use\n%%\nrhs = [rhs; zeros(m+ncolU-length(rhs),1)];\nif (ncolU > 300); use_LU = 1; end\n%%\n%% Cholesky factorization\n%%\nL = []; resnrm = []; xx = inf*ones(m,1);\nif (~use_LU)\n    nnzmat = mexnnz(coeff.mat11);\n    % nnzmatdiff = (nnzmat ~= nnzmatold);\n    solve_ok = 1;  solvesys = 1;\n    if (nnzmat > spdensity*m^2) || (m < 500)\n        matfct_options = 'chol';\n    else\n        matfct_options = 'spchol';\n    end\n    if (printlevel > 2); fprintf(' %s',matfct_options); end\n    L.matdim = length(schur);\n    if strcmp(matfct_options,'chol')\n        if issparse(schur); schur = full(schur); end;\n        if (iter<=5); %%--- to fix strange anonmaly in Matlab\n            mexschurfun(schur,1e-20,2);\n        end\n        L.matfct_options = 'chol';\n        [L.R,indef] = chol(schur);\n        L.perm = 1:m;\n    elseif strcmp(matfct_options,'spchol')\n        if ~issparse(schur); schur = sparse(schur); end;\n        L.matfct_options = 'spchol';\n        [L.R,indef,L.perm] = chol(schur,'vector');\n        L.Rt = L.R';\n    end\n    if (indef)\n        solve_ok = -2; solvesys = 0;\n        msg = 'HSDlinsysolve: Schur complement matrix not positive definite';\n        if (printlevel); fprintf('\\n  %s',msg); end\n    end\n    if (solvesys)\n        if (ncolU)\n            tmp = coeff.mat12'*linsysolvefun(L,coeff.mat12)-coeff.mat22;\n            if issparse(tmp); tmp = full(tmp); end\n            [L.Ml,L.Mu,L.Mp] = lu(tmp);\n            tol = 1e-16;\n            condest = max(abs(diag(L.Mu)))/min(abs(diag(L.Mu)));\n            if any(abs(diag(L.Mu)) < tol) || (condest > 1e50*sqrt(norm(par.diagAAt))); %% old: 1e30\n                solvesys = 0; solve_ok = -4;\n                use_LU = 1;\n                msg = 'SMW too ill-conditioned, switch to LU factor';\n                if (printlevel); fprintf('\\n  %s, %2.1e.',msg,condest); end\n            end\n        end\n        if (solvesys)\n            [xx,resnrm,solve_ok] = HSDbicgstab(coeff,rhs,L,[],[],printlevel);\n            if (solve_ok<=0) && (printlevel)\n                fprintf('\\n  warning: HSDbicgstab fails: %3.1f.',solve_ok);\n            end\n        end\n    end\n    if (solve_ok < 0)\n        if (m < 6000 && strcmp(matfct_options,'chol')) || ...\n                (m < 1e5 && strcmp(matfct_options,'spchol'))\n            use_LU = 1;\n            if (printlevel); fprintf('\\n  switch to LU factor'); end\n        end\n    end\nend\n%%\n%% LU factorization\n%%\nif (use_LU)\n    nnzmat = mexnnz(coeff.mat11)+mexnnz(coeff.mat12);\n    % nnzmatdiff = (nnzmat ~= nnzmatold);\n    solve_ok = 1; %#ok\n    if ~isempty(coeff.mat22)\n        raugmat = [coeff.mat11, coeff.mat12; coeff.mat12', coeff.mat22];\n    else\n        raugmat = coeff.mat11;\n    end\n    if (nnzmat > spdensity*m^2) || (m+ncolU < 500)\n        matfct_options = 'lu';\n    else\n        matfct_options = 'splu';\n    end\n    if (printlevel > 2); fprintf(' %s ',matfct_options); end\n    L.matdim = length(raugmat);\n    if strcmp(matfct_options,'lu')\n        if issparse(raugmat); raugmat = full(raugmat); end\n        L.matfct_options = 'lu';\n        [L.L,L.U,L.p] = lu(raugmat,'vector');\n    elseif strcmp(matfct_options,'splu')\n        if ~issparse(raugmat); raugmat = sparse(raugmat); end\n        L.matfct_options = 'splu';\n        [L.L,L.U,L.p,L.q,L.s] = lu(raugmat,'vector');\n        L.s = full(diag(L.s));\n    elseif strcmp(matfct_options,'ldl')\n        if issparse(raugmat); raugmat = full(raugmat); end\n        L.matfct_options = 'ldl';\n        [L.L,L.D,L.p] = ldl(raugmat,'vector');\n        L.D = sparse(L.D);\n    elseif strcmp(matfct_options,'spldl')\n        if ~issparse(raugmat); raugmat = sparse(raugmat); end\n        L.matfct_options = 'spldl';\n        [L.L,L.D,L.p,L.s] = ldl(raugmat,'vector');\n        L.s  = full(diag(L.s));\n        L.Lt = L.L';\n    end\n    [xx,resnrm,solve_ok] = HSDbicgstab(coeff,rhs,L,[],[],printlevel);\n    if (solve_ok<=0) && (printlevel)\n        fprintf('\\n  warning: HSDbicgstab fails: %3.1f,',solve_ok);\n    end\nend\nif (printlevel>=3); fprintf('%2.0f ',length(resnrm)-1); end\n%%\nnnzmatold = nnzmat; matfct_options_old = matfct_options;\n%%***************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDsqlpmisc.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDsqlpmisc.m", "size": 3299, "source_encoding": "utf_8", "md5": "f36316ddff8099a74241fa1590fc584a", "text": "%%*****************************************************************************\n%% HSDsqlpmisc:\n%% produce infeasibility certificates if appropriate\n%%\n%% Input: X,y,Z are the original variables, not the HSD variables.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004.\n%%*****************************************************************************\n\nfunction [X,y,Z,resid,reldist,param,msg] = HSDsqlpmisc(blk,At,C,b,X,y,Z,permZ,param)\n\nobj         = param.obj;\nrelgap      = param.relgap;\nprim_infeas = param.prim_infeas;\ndual_infeas = param.dual_infeas;\nZpATynorm   = param.ZpATynorm;\ninftol      = param.inftol;\nm0          = param.m0;\nindeprows   = param.indeprows;\ntermcode    = param.termcode;\nAX          = param.AX;\nnormX0      = param.normX0;\nnormZ0      = param.normZ0;\nprintlevel  = param.printlevel;\n%%\nresid = []; reldist = []; msg = [];\nAnorm = ops(At,'norm'); xnorm = ops(X,'norm'); ynorm = norm(y);\n%%\nif (termcode <= 0)\n    %%\n    %% To detect near-infeasibility when the algorithm provides\n    %% a \"better\" certificate of infeasibility than of optimality.\n    %%\n    err = max([prim_infeas,dual_infeas,relgap]);\n    % iflag = 0;\n    if (obj(2) > 0)\n        homRd = ZpATynorm/obj(2);\n        if (homRd < 1e-2*sqrt(err*inftol))\n            % iflag = 1;\n            termcode = 1;\n            param.termcode = 1;\n        end\n    elseif (obj(1) < 0)\n        homrp = norm(AX)/(-obj(1));\n        if (homrp < 1e-2*sqrt(err*inftol))\n            % iflag = 1;\n            termcode = 2;\n            param.termcode = 2;\n        end\n    end\nend\nif (termcode == 1)\n    rby = 1/(b'*y); y = rby*y; Z = ops(Z,'*',rby);\n    resid = ZpATynorm * rby;\n    reldist = ZpATynorm/(Anorm*ynorm);\n    msg = 'Stop: primal problem is suspected of being infeasible';\n    if (printlevel); fprintf('\\n  %s',msg); end\nend\nif (termcode == 2)\n    tCX = blktrace(blk,C,X);\n    X = ops(X,'*',1/(-tCX));\n    resid = norm(AX) /(-tCX);\n    reldist = norm(AX)/(Anorm*xnorm);\n    msg = 'Stop: dual problem is suspected of being infeasible';\n    if (printlevel); fprintf('\\n  %s',msg); end\nend\nif (termcode == 3)\n    maxblowup = max(ops(X,'norm')/normX0,ops(Z,'norm')/normZ0);\n    msg = sprintf('Stop: primal or dual is diverging, %3.1e',maxblowup);\n    if (printlevel); fprintf('\\n  %s',msg); end\nend\n[X,Z] = unperm(blk,permZ,X,Z);\nif ~isempty(indeprows)\n    ytmp = zeros(m0,1);\n    ytmp(indeprows) = y;\n    y = ytmp;\nend\n%%*****************************************************************************\n%% unperm: undo the permutations applied in validate.\n%%\n%% [X,Z,Xiter,Ziter] = unperm(blk,permZ,X,Z,Xiter,Ziter);\n%%\n%% undoes the permutation introduced in validate.\n%% can also be called if Xiter and Ziter have not been set as\n%%\n%% [X,Z] = unperm(blk,permZ,X,Z);\n%%\n%% SDPT3: version 3.0\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last modified: 2 Feb 01\n%%*****************************************************************************\n\nfunction [X,Z] = unperm(blk,permZ,X,Z)\n%%\nfor p = 1:size(blk,1)\n    if (strcmp(blk{p,1},'s') && ~isempty(permZ{p}))\n        per = permZ{p};\n        X{p} = X{p}(per,per);\n        Z{p} = Z{p}(per,per);\n    end\nend\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDbicgstab.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDbicgstab.m", "size": 3084, "source_encoding": "utf_8", "md5": "96ee9f939e0b2527113539aa0b633ffc", "text": "%%*************************************************************************\n%% HSDbicgstab\n%%\n%% [xx,resnrm,flag] = HSDbicgstab(A,b,M1,tol,maxit)\n%%\n%% iterate on  bb - (M1)*AA*x\n%%\n%% r = b-A*xtrue;\n%%\n%%*************************************************************************\n\nfunction [xx,resnrm,flag] = HSDbicgstab(A,b,M1,tol,maxit,printlevel)\n\nN = length(b);\nif (nargin < 6); printlevel = 1; end\nif (nargin < 5) || isempty(maxit); maxit = max(20,length(A.mat22)); end;\nif (nargin < 4) || isempty(tol); tol = 1e-8; end;\ntolb = min(1e-4,tol*norm(b));\nflag = 1;\n\nx = zeros(N,1);\nif isstruct(A); r = b-matvec(A,x); else r = b-mexMatvec(A,x); end;\nerr = norm(r); resnrm(1) = err;  minresnrm = err; xx = x;\n%%if (err < tolb); return; end\n\nomega = 1.0;\nr_tld = r;\n%%\n%%\n%%\nsmtol = 1e-40;\nfor iter = 1:maxit,\n    \n    rho   = (r_tld'*r);\n    if (abs(rho) < smtol)\n        flag = 2;\n        if (printlevel); fprintf('*'); end;\n        break;\n    end\n    if (iter > 1)\n        beta  = (rho/rho_1)* (alp/omega);\n        p = r + beta*(p - omega*v);\n    else\n        p = r;\n    end\n    p_hat = precond(A,M1,p);\n    if isstruct(A); v = matvec(A,p_hat); else v = mexMatvec(A,p_hat); end;\n    alp = rho / (r_tld'*v);\n    s = r - alp*v;\n    %%\n    s_hat = precond(A,M1,s);\n    if isstruct(A); t = matvec(A,s_hat); else t = mexMatvec(A,s_hat); end;\n    omega = (t'*s) / (t'*t);\n    x = x + alp*p_hat + omega*s_hat;\n    r = s - omega*t;\n    rho_1 = rho;\n    %%\n    %% check convergence\n    %%\n    err = norm(r); resnrm(iter+1) = err; %#ok\n    if (err < minresnrm);\n        xx = x; minresnrm = err;\n    end\n    if (err < tolb)\n        break;\n    end\n    if (err > 10*minresnrm)\n        if (printlevel); fprintf('^'); end\n        break;\n    end\n    if (abs(omega) < smtol)\n        flag = 2;\n        if (printlevel); fprintf('*'); end;\n        break;\n    end\nend\n%%\n%%*************************************************************************\n%%*************************************************************************\n%% matvec: matrix-vector multiply.\n%% matrix = [A.mat11, A.mat12; A.mat12', A.mat22]\n%%*************************************************************************\n\nfunction Ax = matvec(A,x)\n\nm = length(A.mat11); m2 = length(x)-m;\nif (m2 > 0)\n    x1 = full(x(1:m));\nelse\n    x1 = full(x);\nend\nAx = mexMatvec(A.mat11,x1);\nif (m2 > 0)\n    x2 = full(x(m+1:m+m2));\n    Ax = Ax + mexMatvec(A.mat12,x2);\n    Ax2 = mexMatvec(A.mat12,x1,1) + mexMatvec(A.mat22,x2);\n    Ax = [Ax; Ax2];\nend\n%%*************************************************************************\n%% precond:\n%%*************************************************************************\n\nfunction Mx = precond(A,L,x)\n\nm = L.matdim; m2 = length(x)-m;\nif (m2 > 0)\n    x1 = full(x(1:m));\nelse\n    x1 = full(x);\nend\nif (m2 > 0)\n    x2 = x(m+1:m+m2);\n    w = linsysolvefun(L,x1);\n    z = mexMatvec(A.mat12,w,1) -x2;\n    z = L.Mu \\ (L.Ml \\ (L.Mp*z));\n    x1 = x1 - mexMatvec(A.mat12,z);\nend\n%%\nMx = linsysolvefun(L,x1);\n%%\nif (m2 > 0)\n    Mx = [Mx; z];\nend\n%%*************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDHKMcorr.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDHKMcorr.m", "size": 985, "source_encoding": "utf_8", "md5": "1e1983a66956f1d3e4e8e279b1dbe2d0", "text": "%%*****************************************************************\n%% HSDHKMcorr: corrector step for the HKM direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*****************************************************************\n\nfunction [par,dX,dy,dZ,resnrm] = HSDHKMcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n    dX,dZ,coeff,L,X,Z)\n\nglobal printlevel\nglobal solve_ok\n%%\n[rhs,EinvRc] = HSDHKMrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ);\nm = length(rp); ncolU = size(coeff.mat12,2);\nrhs = [rhs; zeros(m+ncolU-length(rhs),1)];\n%%\nsolve_ok = 1; %#ok\n[xx,resnrm,solve_ok] = HSDbicgstab(coeff,rhs,L,[],[],printlevel);\nif (solve_ok<=0) && (printlevel)\n    fprintf('\\n  warning: iterative solver fails: %3.1f.',solve_ok);\nend\nif (par.printlevel>=3); fprintf('%2.0f ',length(resnrm)-1); end\n%%\n[par,dX,dy,dZ] = HSDHKMdirfun(blk,At,par,Rd,EinvRc,X,xx);\n%%*****************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDNTpred.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDNTpred.m", "size": 2034, "source_encoding": "utf_8", "md5": "e194daf53d375b24154b1caf94b7646d", "text": "%%**********************************************************************\n%% HSDNTpred: Compute (dX,dy,dZ) for NT direction.\n%%\n%% compute SVD of Xchol*Zchol via eigenvalue decompostion of\n%%     Zchol * X * Zchol' = V * diag(sv2) * V'.\n%% compute W satisfying W*Z*W = X.\n%%     W = G'*G,  where G = diag(sqrt(sv)) * (invZchol*V)'\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************************\n\nfunction [par,dX,dy,dZ,coeff,L,hRd] = ...\n    HSDNTpred(blk,At,par,rp,Rd,sigmu,X,Z,Zchol,invZchol)\n\nglobal schurfun schurfun_par\n%%\n%% compute NT scaling matrix\n%%\n[par.W,par.G,par.sv,par.gamx,par.gamz,par.dd,par.ee,par.ff] = ...\n    NTscaling(blk,X,Z,Zchol,invZchol);\n%%\n%% compute schur matrix\n%%\nm = par.m;\nschur = sparse(m+2,m+2);\nUU = []; EE = [];\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        [schur,UU,EE] = schurmat_lblk(blk,At,par,schur,UU,EE,p,par.dd);\n    elseif strcmp(pblk{1},'q');\n        [schur,UU,EE] = schurmat_qblk(blk,At,par,schur,UU,EE,p,par.dd,par.ee);\n    elseif strcmp(pblk{1},'s')\n        if isempty(schurfun{p})\n            schur = schurmat_sblk(blk,At,par,schur,p,par.W);\n        elseif ischar(schurfun{p})\n            if ~isempty(par.permZ{p})\n                Wp = par.W{p}(par.permZ{p},par.permZ{p});\n            else\n                Wp = par.W{p};\n            end\n            schurtmp = feval(schurfun{p},Wp,Wp,schurfun_par(p,:));\n            schur = schur + schurtmp;\n        end\n    end\nend\n%%\n%% compute rhs\n%%\n[rhs,EinvRc,hRd] = HSDNTrhsfun(blk,At,par,X,Z,rp,Rd,sigmu);\n%%\n%% solve linear system\n%%\npar.addschur   = par.kap/par.tau;\nschur(m+1,m+1) = schur(m+1,m+1) + par.kap/par.tau;\nschur(m+2,m+2) = schur(m+2,m+2) + par.addschur;\n[xx,coeff,L] = HSDlinsysolve(par,schur,UU,EE,par.Umat,rhs);\n%%\n%% compute (dX,dZ)\n%%\n[par,dX,dy,dZ] = HSDNTdirfun(blk,At,par,Rd,EinvRc,xx);\n%%**********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HSDsqlpCpert.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/HSDSolver/HSDsqlpCpert.m", "size": 2263, "source_encoding": "utf_8", "md5": "326ec0065ebb155fab5ee8b4670ec0db", "text": "%%*****************************************************************************\n%% HSDsqlpCpert: perturb C.\n%%\n%%*****************************************************************************\n\nfunction [At,Cpert] = HSDsqlpCpert(blk,At,par,C,X,Cpert,runhist)\n\niter = length(runhist.pinfeas);\nprim_infeas = runhist.pinfeas(iter);\ndual_infeas = runhist.dinfeas(iter);\nrelgap      = runhist.relgap(iter);\ninfeas      = runhist.infeas(iter);\ntheta       = runhist.theta(iter);\n%%\nCpertold = Cpert;\nerr = max(relgap,infeas);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    tmp = max(1,norm(C{p},'fro'))/sqrt(n);\n    if (err < 1e-6)\n        if (norm(X{p},'fro') < 1e2); const=0.2; else const=0.3; end\n        Cpert(p) = max(const*Cpert(p),1e-10*tmp);\n    elseif (err < 1e-2)\n        if (norm(X{p},'fro') < 1e2); const=0.4; else const=0.5; end\n        Cpert(p) = max(const*Cpert(p),1e-8*tmp);\n    else\n        Cpert(p) = max(0.9*Cpert(p),1e-6*tmp);\n    end\n    Cpert = min(Cpert,Cpertold);\n    if (prim_infeas < min([0.1*dual_infeas, 1e-7*runhist.pinfeas(1)])) ...\n            && (iter > 1 && dual_infeas > 0.8*runhist.dinfeas(iter-1) && relgap < 1e-4)\n        Cpert(p) = 0.5*Cpert(p);\n    elseif (dual_infeas < min([0.1*prim_infeas, 1e-7*runhist.dinfeas(1)])) ...\n            && (iter > 1 && prim_infeas > 0.8*runhist.pinfeas(iter-1) && relgap < 1e-4)\n        Cpert(p) = 0.5*Cpert(p);\n    elseif (max(relgap,1e-2*infeas) < 1e-6 && relgap < 0.1*infeas)\n        Cpert(p) = 0.5*Cpert(p);\n    end\n    if (prim_infeas < min([1e-4*dual_infeas,1e-7]) && theta < 1e-6) ...\n            || (prim_infeas < 1e-4 && theta < 1e-10)\n        Cpert(p) = 0.1*Cpert(p);\n    elseif (dual_infeas < min([1e-4*prim_infeas,1e-7]) && theta < 1e-6) ...\n            || (dual_infeas < 1e-4 && theta < 1e-10)\n        Cpert(p) = 0.1*Cpert(p);\n    elseif (iter > 1 && theta > 0.9*runhist.theta(iter-1) && infeas < 1e-3)\n        Cpert(p) = 0.1*Cpert(p);\n    end\n    if strcmp(pblk{1},'s')\n        Cnew = C{p} + Cpert(p)*speye(n);\n        At{p}(:,par.invpermA(p,end-1)) = -svec(pblk,Cnew,1);\n    else\n        Cnew = C{p} + Cpert(p)*ones(n,1);\n        At{p}(:,par.invpermA(p,end-1)) = -Cnew;\n    end\nend\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cheby0.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Examples/cheby0.m", "size": 2576, "source_encoding": "utf_8", "md5": "a31e95ee5e80694cd1c3f2ceb594d369", "text": "%%**********************************************************\n%% cheby0: \n%%\n%%    minimize || p(d) ||_infty \n%%    p = polynomial of degree <= m such that p(0) = 1.\n%% \n%%    Here d = n-vector \n%%----------------------------------------------------------\n%% [blk,Avec,C,b,X0,y0,Z0,objval,p] = cheby0(d,m,solve);\n%%\n%% d = a vector. \n%% m = degree of polynomial. \n%% feas  = 1 if want feasible starting point\n%%       = 0 if otherwise.\n%% solve = 0 if just want initialization\n%%       = 1 if want to solve the problem\n%%\n%% SDPT3: version 3.0 \n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last modified: 2 Feb 01\n%%**********************************************************\n\nfunction [blk,Avec,C,b,X0,y0,Z0,objval,p] = cheby0(d,m,solve);\n\n  if nargin <= 2; solve = 0; end;\n  if (size(d,1) < size(d,2)); d = d.'; end;\n  cmp = 1-isreal(d); \n\n  tstart=cputime; \n  n = length(d);\n  e = ones(n,1);\n  V(1:n,1) = e/norm(e); R(1,1) = 1/norm(e);\n  for i =1:m    \n      v = d.*V(:,i);  \n      for j = 1:i                 %% Arnoldi iterations:\n          H(j,i) = (V(:,j))'*v;   %% constructing upper-Hessenberg matrix.\n          v = v - H(j,i)*V(:,j);  %% orthonormaliztion of Krylov basis.\n      end;\n      H(i+1,i) = norm(v);\n      V(:,i+1) = v/H(i+1,i);\n      R(1:i+1,i+1) = (1/H(i+1,i))*([0; R(1:i,i)] - [R(1:i,1:i)*H(1:i,i); 0]);\n  end   \n  if (cmp)\n     blk{1,1} = 'q'; blk{1,2} = 3*ones(1,n);\n     C = zeros(3*n,1); C(2:3:3*n) = ones(n,1);\n     b = [zeros(2*m,1); -1];\n     Atmp = [];\n     II = [0:3:3*n-3]'; ee = ones(n,1); \n     for k=1:m\n         dVk = d.*V(:,k);\n         Atmp = [Atmp; [2+II, k*ee, real(dVk)]; [3+II, k*ee, imag(dVk)]]; \n         Atmp = [Atmp; [2+II, (m+k)*ee, -imag(dVk)]; [3+II, (m+k)*ee, real(dVk)]]; \n     end\n     Atmp = [Atmp;  [1+II, (2*m+1)*ee, -ones(n,1)]]; \n  else\n     blk{1,1} = 'l'; blk{1,2} = 2*ones(1,n);\n     b = [zeros(m,1); -1];\n     C = [ones(n,1); -ones(n,1)];\n     Atmp = [];\n     II = [1:n]'; ee = ones(n,1); \n     for k=1:m\n         dVk = d.*V(:,k);\n         Atmp = [Atmp; [II, k*ee, dVk]; [n+II, k*ee, -dVk]]; \n     end \n     Atmp = [Atmp; [II, (m+1)*ee, -ee]; [n+II, (m+1)*ee, -ee]];\n  end\n  Avec = spconvert(Atmp);\n  [X0,y0,Z0] = infeaspt(blk,Avec,C,b); \n%% \n  if (solve)\n     [obj,X,y,Z] = sqlp(blk,Avec,C,b,[],X0,y0,Z0);\n     if (cmp)\n        y  = y(1:m) + sqrt(-1)*y(m+1:2*m); \n     else\n        y = y(1:m); \n     end     \n     x1 = R(1:m,1:m)*y(1:m); \n     p = [-x1(m:-1:1); 1];  \n     objval = -mean(obj);\n  else\n     objval = []; p = [];\n  end \n%%**********************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "randmat.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/randmat.m", "size": 782, "source_encoding": "utf_8", "md5": "44e2c609bf458ffd5a37d9f816a0ea1b", "text": "%%******************************************************\n%% randmat: generate an mxn matrix using matlab's\n%%          rand or randn functions using state = k.\n%%\n%%******************************************************\nfunction v = randmat(m,n,k,randtype)\n\ntry\n    s = rng;\n    rng(k);\n    if strcmp(randtype,'n')\n        v = randn(m,n);\n    elseif strcmp(randtype,'u')\n        v = rand(m,n);\n    end\n    rng(s);\ncatch\n    if strcmp(randtype,'n')\n        s = randn('state'); %#ok\n        randn('state',k); %#ok\n        v = randn(m,n);\n        randn('state',s); %#ok\n    elseif strcmp(randtype,'u')\n        s = rand('state'); %#ok\n        rand('state',k); %#ok\n        v = rand(m,n);\n        rand('state',s); %#ok\n    end\nend\n%%******************************************************"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "skron.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/skron.m", "size": 1389, "source_encoding": "utf_8", "md5": "3aba6bed9dc50b45f766b4a8620c4ac3", "text": "%%***********************************************************************\n%% skron:  Find the matrix presentation of\n%%         symmetric kronecker product skron(A,B), where\n%%         A,B are symmetric.\n%%\n%% Important: A,B are assumed to be symmetric.\n%%\n%% K = skron(blk,A,B);\n%%\n%% blk: a cell array specifying the block diagonal structure of A,B.\n%%\n%% (ij)-column of K = 0.5*svec(AUB + BUA),  where\n%%                     U  = xij*(ei*ej' + ej*ei')\n%%                    xij = 1/2       if i=j\n%%                        = 1/sqrt(2) otherwise.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***********************************************************************\n\nfunction K = skron(blk,A,B)\n\nif iscell(A) && ~ iscell(B)\n    error('skron: A,B must be both matrices or both cell arrays')\nend\nif iscell(A)\n    K = cell(size(blk,1),1);\n    for p = 1:size(blk,1)\n        if (norm(A{p}-B{p},'fro') < 1e-13)\n            sym = 1;\n        else\n            sym = 0;\n        end\n        if strcmp(blk{p,1},'s')\n            K{p} = mexskron(blk(p,:),A{p},B{p},sym);\n        end\n    end\nelse\n    if (norm(A-B,'fro') < 1e-13)\n        sym = 1;\n    else\n        sym = 0;\n    end\n    if strcmp(blk{1,1},'s')\n        K = mexskron(blk,A,B,sym);\n    end\nend\n%%***********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "NTcorr.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/NTcorr.m", "size": 1315, "source_encoding": "utf_8", "md5": "458c52ec6bf00d3507df137889a53c7e", "text": "%%************************************************************************\n%% NTcorr: corrector step for the NT direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%************************************************************************\n\nfunction [dX,dy,dZ] = NTcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n    dX,dZ,coeff,L,X,Z)\n\nglobal matfct_options solve_ok\n\nprintlevel = par.printlevel;\n%%\n[rhs,EinvRc] = NTrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ);\nm = length(rp); ncolU = size(coeff.mat12,2);\nrhs = [rhs; zeros(m+ncolU-length(rhs),1)];\n%%\nif strcmp(matfct_options,'chol') || strcmp(matfct_options,'spchol') ...\n        || strcmp(matfct_options,'ldl') || strcmp(matfct_options,'spldl')\n    [xx,resnrm,solve_ok] = symqmr(coeff,rhs,L,[],[],printlevel);\n    if (solve_ok<=0) && (printlevel)\n        fprintf('\\n  warning: symqmr fails: %3.1f.',solve_ok);\n    end\nelse\n    [xx,resnrm,solve_ok] = mybicgstab(coeff,rhs,L,[],[],printlevel);\n    if (solve_ok<=0) && (printlevel)\n        fprintf('\\n  warning: bicgstab fails: %3.1f.',solve_ok);\n    end\nend\nif (printlevel>=3); fprintf('%2.0d ',length(resnrm)-1); end\n%%\n[dX,dy,dZ] = NTdirfun(blk,At,par,Rd,EinvRc,xx,m);\n%%************************************************************************\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HKMcorr.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/HKMcorr.m", "size": 1313, "source_encoding": "utf_8", "md5": "ff69a87fe927bf7f964fd55cbf7ec718", "text": "%%*****************************************************************\n%% HKMcorr: corrector step for the HKM direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*****************************************************************\n\nfunction [dX,dy,dZ,resnrm,EinvRc] = HKMcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n    dX,dZ,coeff,L,X,Z)\n\nglobal matfct_options solve_ok\n\nprintlevel = par.printlevel;\n%%\n[rhs,EinvRc] = HKMrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ);\nm = length(rp); ncolU = size(coeff.mat12,2);\nrhs = [rhs; zeros(m+ncolU-length(rhs),1)];\n%%\nif strcmp(matfct_options,'chol') || strcmp(matfct_options,'spchol') ...\n        || strcmp(matfct_options,'ldl') || strcmp(matfct_options,'spldl')\n    [xx,resnrm,solve_ok] = symqmr(coeff,rhs,L,[],[],printlevel);\n    if (solve_ok<=0) && (printlevel)\n        fprintf('\\n  warning: symqmr fails: %3.1f.',solve_ok);\n    end\nelse\n    [xx,resnrm,solve_ok] = mybicgstab(coeff,rhs,L,[],[],printlevel);\n    if (solve_ok<=0) && (printlevel)\n        fprintf('\\n  warning: bicgstab fails: %3.1f.',solve_ok);\n    end\nend\nif (printlevel>=3); fprintf('%2.0d ',length(resnrm)-1); end\n%%\n[dX,dy,dZ] = HKMdirfun(blk,At,par,Rd,EinvRc,X,xx,m);\n%%*****************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "steplength.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/steplength.m", "size": 5590, "source_encoding": "utf_8", "md5": "2b52f7d5b9712cf17885f9ca3eaeb666", "text": "%%***************************************************************************\n%% steplength: compute xstep such that  X + xstep*dX >= 0.\n%%\n%% [xstep] = steplength(blk,X,dX,Xchol,invXchol);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***************************************************************************\n\nfunction [xstep,invXchol] = steplength(blk,X,dX,Xchol,invXchol)\n\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    numblk = length(pblk{2});\n    pblksize = sum(pblk{2});\n    if nnz(isnan(dX{p})) || nnz(isinf(dX{p}))\n        xstep = 0;\n        break;\n    end\n    if strcmp(pblk{1},'s')\n        if (max(pblk{2}) >= 200)\n            use_lanczos = 1;\n        else\n            use_lanczos = 0;\n        end\n        if (use_lanczos)\n            tol = 1e-3;\n            maxit = max(min(pblksize,30),round(sqrt(pblksize)));\n            [lam,delta] = lanczosfun(Xchol{p},-dX{p},maxit,tol);\n            %%\n            %% Note: lam <= actual largest eigenvalue <= lam + delta.\n            %%\n            d = lam+delta;\n        else\n            if isempty(invXchol{p});\n                invXchol{p} = inv(Xchol{p});\n            end\n            tmp = Prod2(pblk,dX{p},invXchol{p},0);\n            M = Prod2(pblk,invXchol{p}',tmp,1);\n            d = blkeig(pblk,-M);\n        end\n        tmp = max(d) + 1e-15*max(abs(d));\n        if (tmp > 0);\n            xstep(p) = 1/max(tmp); %#ok\n        else\n            xstep(p) = 1e12; %#ok\n        end\n    elseif strcmp(pblk{1},'q')\n        aa = qops(pblk,dX{p},dX{p},2);\n        bb = qops(pblk,dX{p},X{p},2);\n        cc = qops(pblk,X{p},X{p},2);\n        dd = bb.*bb - aa.*cc;\n        tmp = min(aa,bb);\n        idx = dd > 0 & tmp < 0;\n        steptmp = 1e12*ones(numblk,1);\n        if any(idx)\n            steptmp(idx) = -(bb(idx)+sqrt(dd(idx)))./aa(idx);\n        end\n        idx = abs(aa) < eps & bb < 0;\n        if any(idx)\n            steptmp(idx) = -cc(idx)./(2*bb(idx));\n        end\n        %%\n        %% also need first component to be non-negative\n        %%\n        ss = 1 + [0, cumsum(pblk{2})];\n        ss = ss(1:length(pblk{2}));\n        dX0 = dX{p}(ss);\n        X0 = X{p}(ss);\n        idx = dX0 < 0 & X0 > 0;\n        if any(idx)\n            steptmp(idx) = min(steptmp(idx),-X0(idx)./dX0(idx));\n        end\n        xstep(p) = min(steptmp); %#ok\n    elseif strcmp(pblk{1},'l')\n        idx = dX{p} < 0;\n        if any(idx)\n            xstep(p) = min(-X{p}(idx)./dX{p}(idx)); %#ok\n        else\n            xstep(p) = 1e12; %#ok\n        end\n    elseif strcmp(pblk{1},'u')\n        xstep(p) = 1e12; %#ok\n    end\nend\nxstep = min(xstep);\n%%***************************************************************************\n%%***************************************************************************\n%% lanczos: find the largest eigenvalue of\n%%          invXchol'*dX*invXchol via the lanczos iteration.\n%%\n%% [lam,delta] = lanczosfun(Xchol,dX,maxit,tol,v)\n%%\n%% lam:  an estimate of the largest eigenvalue.\n%% lam2: an estimate of the second largest eigenvalue.\n%% res:  residual norm of the largest eigen-pair.\n%% res2: residual norm of the second largest eigen-pair.\n%%***************************************************************************\n\nfunction [lam,delta,res] = lanczosfun(Xchol,dX,maxit,tol,v)\n\nif (norm(dX,'fro') < 1e-13)\n    lam = 0; delta = 0; res = 0;\n    return;\nend\nn = length(dX);\nif (nargin < 5);\n    v = randmat(n,1,0,'n');\nend\nif (nargin < 4); tol = 1e-3; end\nif (nargin < 3); maxit = 30; end\nV = zeros(n,maxit+1); H = zeros(maxit+1,maxit);\nv = v/norm(v);\nV(:,1) = v;\nif issparse(Xchol); Xcholtransp = Xchol'; end\n%%\n%% lanczos iteration.\n%%\nfor k = 1:maxit\n    if issparse(Xchol)\n        w = dX*mextriangsp(Xcholtransp,v,1);\n        w = mextriangsp(Xchol,w,2);\n    else\n        w = dX*mextriang(Xchol,v,1);\n        w = mextriang(Xchol,w,2);\n    end\n    wold = w;\n    if (k > 1);\n        w = w - H(k,k-1)*V(:,k-1);\n    end;\n    alp = w'*V(:,k);\n    w   = w - alp*V(:,k);\n    H(k,k) = alp;\n    %%\n    %% one step of iterative refinement if necessary.\n    %%\n    if (norm(w) <= 0.8*norm(wold));\n        s = (w'*V(:,1:k))';\n        w = w - V(:,1:k)*s;\n        H(1:k,k) = H(1:k,k) + s;\n    end;\n    nrm = norm(w);\n    v = w/nrm;\n    V(:,k+1) = v;\n    H(k+1,k) = nrm;  H(k,k+1) = nrm;\n    %%\n    %% compute ritz pairs and test for convergence\n    %%\n    if (rem(k,5) == 0) || (k == maxit);\n        Hk = H(1:k,1:k); Hk = 0.5*(Hk+Hk');\n        [Y,D] = eig(Hk);\n        eigH  = real(diag(D));\n        [dummy,idx] = sort(eigH); %#ok\n        res_est = abs(H(k+1,k)*Y(k,idx(k)));\n        if (res_est <= 0.1*tol) || (k == maxit);\n            lam = eigH(idx(k));\n            lam2 = eigH(idx(k-1));\n            z  = V(:,1:k)*Y(:,idx(k));\n            z2 = V(:,1:k)*Y(:,idx(k-1));\n            if issparse(Xchol)\n                tmp = dX*mextriangsp(Xcholtransp,z,1);\n                res = norm(mextriangsp(Xchol,tmp,2) -lam*z);\n                tmp = dX*mextriangsp(Xcholtransp,z2,1);\n                res2 = norm(mextriangsp(Xchol,tmp,2) -lam*z2);\n            else\n                tmp = dX*mextriang(Xchol,z,1);\n                res = norm(mextriang(Xchol,tmp,2) -lam*z);\n                tmp = dX*mextriang(Xchol,z2,1);\n                res2 = norm(mextriang(Xchol,tmp,2) -lam*z2);\n            end\n            tmp = lam-lam2 -res2;\n            if (tmp > 0); beta = tmp; else beta = eps; end;\n            delta = min(res,res^2/beta);\n            if (delta <= tol); break; end;\n        end\n    end\nend\n%%***************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "SDPT3data_SEDUMIdata.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/SDPT3data_SEDUMIdata.m", "size": 3843, "source_encoding": "utf_8", "md5": "26106829dfa4c0fbb1ca8c4aa9839fa8", "text": "%%**********************************************************\n%% SDPT3data_SEDUMIdata: convert SQLP data in SDPT3 format to\n%%                       SeDuMi format\n%%\n%% [At,b,c,K] = SDPT3data_SEDUMIdata(blk,AAt,CC,bb);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************\n\nfunction [At,b,c,K] = SDPT3data_SEDUMIdata(blk,AAt,CC,bb)\n\nc = []; At = [];\nb = bb;\nmm = length(bb);\n%%\nif (~iscell(CC))\n    Ctmp = CC; clear CC; CC{1} = Ctmp;\nend\n%%\n%% extract unrestricted blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if (p==1); K.f = []; end\n    if strcmp(pblk{1},'u')\n        K.f = [K.f, pblk{2}];\n        At  = [At; AAt{p}]; %#ok\n        c   = [c; CC{p}]; %#ok\n    end\nend\nK.f = sum(K.f);\n%%\n%% extract linear blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if (p==1); K.l = []; end\n    if strcmp(pblk{1},'l')\n        K.l = [K.l, pblk{2}];\n        At  = [At; AAt{p,1}]; %#ok\n        c   = [c; CC{p,1}]; %#ok\n    end\nend\nK.l = sum(K.l);\n%%\n%% extract second order cone blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if (p==1); K.q = []; end\n    if strcmp(pblk{1},'q')\n        K.q = [K.q, pblk{2}];\n        At  = [At; AAt{p,1}]; %#ok\n        c   = [c; CC{p,1}]; %#ok\n    end\nend\n%%\n%% extract rotated cone blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if (p==1); K.r = []; end\n    if strcmp(pblk{1},'r')\n        K.r = [K.r, pblk{2}];\n        At  = [At; AAt{p,1}]; %#ok\n        c   = [c; CC{p,1}]; %#ok\n    end\nend\n%%\n%% extract semidefinite cone blk\n%%\nfor p = 1:size(blk,1)\n    if (p==1); K.s = []; end\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        K.s = [K.s, pblk{2}];\n        ss = [0,cumsum(pblk{2})];\n        idxstart = [0,cumsum(pblk{2}.*pblk{2})];\n        numblk = length(pblk{2});\n        nnzA = nnz(AAt{p,1});\n        II = zeros(2*nnzA,1);\n        JJ = zeros(2*nnzA,1);\n        VV = zeros(2*nnzA,1);\n        m2 = size(AAt{p,1},2);\n        if (length(pblk) > 2)\n            rr = [0, cumsum(pblk{3})];\n            dd = AAt{p,3};\n            idxD = [0; find(diff(dd(:,1))); size(dd,1)];\n        end\n        count = 0;\n        for k = 1:mm\n            if (k<= m2);\n                Ak = smat(pblk,AAt{p,1}(:,k),1);\n            else\n                idx = rr(k)+1 : rr(k+1);\n                Vk  = AAt{p,2}(:,idx);\n                len = pblk{3}(k);\n                if (size(dd,2) == 4)\n                    idx2 = idxD(k)+1:idxD(k+1);\n                    Dk = spconvert([dd(idx2,2:4); len,len,0]);\n                elseif (size(dd,2) == 1);\n                    Dk = spdiags(dd(idx),0,len,len);\n                end\n                Ak = Vk*Dk*Vk';\n            end\n            for tt = 1:numblk\n                if (numblk > 1)\n                    idx = ss(tt)+1: ss(tt+1);\n                    Aksub = full(Ak(idx,idx));\n                else\n                    Aksub = Ak;\n                end\n                tmp = Aksub(:);\n                nzidx = find(tmp);\n                len = length(nzidx);\n                II(count+1:count+len,1) = idxstart(tt)+nzidx;\n                JJ(count+1:count+len,1) = k*ones(length(nzidx),1);\n                VV(count+1:count+len,1) = tmp(nzidx);\n                count = count + len;\n            end\n        end\n        II = II(1:count);\n        JJ = JJ(1:count);\n        VV = VV(1:count);\n        At = [At; spconvert([II,JJ,VV; sum(pblk{2}.*pblk{2}), mm, 0])]; %#ok\n        Cp = CC{p};\n        ctmp = [];\n        for tt = 1:numblk\n            if (numblk > 1)\n                idx = ss(tt)+1: ss(tt+1);\n                Csub = full(Cp(idx,idx));\n            else\n                Csub = Cp;\n            end\n            ctmp = [ctmp; Csub(:)]; %#ok\n        end\n        c = [c; ctmp]; %#ok\n    end\nend\n%%**********************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "schurmat_sblk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/schurmat_sblk.m", "size": 4549, "source_encoding": "utf_8", "md5": "f992891144a934ab4edffde2a692e435", "text": "%%*******************************************************************\n%% schurmat_sblk: compute Schur complement matrix corresponding to\n%%                SDP blocks.\n%%\n%% symm = 0, HKM\n%%      = 1, NT\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction schur = schurmat_sblk(blk,At,par,schur,p,X,Y)\n\nglobal  nnzschur nzlistschur\n\niter = par.iter;\nsmallblkdim = par.smallblkdim;\n\nif isempty(smallblkdim); smallblkdim = 50; end\nif (nargin == 7); symm = 0; else symm = 1; Y = X; end;\nm = length(schur);\npblk = blk(p,:);\nif (iter == 1)\n    nnzschur(size(blk,1),1) = m*m;\n    nzlistschur = cell(size(blk,1),1);\nend\n%%\nif (max(pblk{2}) > smallblkdim) || (length(pblk{2}) <= 10)\n    %%\n    %% compute schur for matrices that are very sparse.\n    %%\n    m1 = size(At{p,1},2);\n    if issparse(schur); schur = full(schur); end;\n    J = min(m1, find(par.nzlistA{p,1} < inf,1,'last')-1);\n    if (J > 0)\n        if issparse(X{p}) && ~issparse(Y{p}); X{p} = full(X{p}); end\n        if ~issparse(X{p}) && issparse(Y{p}); Y{p} = full(Y{p}); end\n        if (iter <= 3)\n            [nnzschur(p),nzlisttmp] = mexschur(pblk,At{p,1},par.nzlistA{p,1},...\n                par.nzlistA{p,2},par.permA(p,:),Y{p},X{p},J,symm,schur);\n            if (nnzschur(p) == mexnnz(nzlisttmp))\n                nzlistschur{p} = nzlisttmp;\n            else\n                nzlistschur{p} = [];\n            end\n        else\n            if isempty(nzlistschur{p})\n                mexschur(pblk,At{p,1},par.nzlistA{p,1},...\n                    par.nzlistA{p,2},par.permA(p,:),Y{p},X{p},J,symm,schur);\n            else\n                mexschur(pblk,At{p,1},par.nzlistA{p,1},...\n                    par.nzlistA{p,2},par.permA(p,:),Y{p},X{p},J,symm,schur,nzlistschur{p});\n            end\n        end\n    end\n    %%\n    %% compute schur for matrices that are not so sparse or dense.\n    %%\n    if (m1 < m) %% for low rank constraints\n        ss = [0, cumsum(pblk{3})];\n        len = sum(pblk{3});\n        dd = At{p,3};\n        DD = spconvert([dd(:,2:4); len,len,0]);\n        XVD = X{p}*At{p,2}*DD;\n        YVD = Y{p}*At{p,2}*DD;\n    end\n    L = find(par.nzlistAsum{p,1} < inf,1,'last') -1;\n    if (J < L)\n        len = par.nzlistAsum{p,1}(J+1); list = par.nzlistAsum{p,2}(1:len,:);\n    end\n    if (m1 > 0)\n        for k = J+1:m\n            if (k<=m1)\n                isspAk = par.isspA(p,k);\n                Ak = mexsmat(blk,At,isspAk,p,k);\n                if (k <= L)\n                    idx1 = par.nzlistAsum{p,1}(k)+1; idx2 = par.nzlistAsum{p,1}(k+1);\n                    list = [list; par.nzlistAsum{p,2}(idx1:idx2,:)]; %#ok\n                    list = sortrows(list,[2 1]);\n                    tmp = Prod3(pblk,X{p},Ak,Y{p},symm,list);\n                else\n                    tmp = Prod3(pblk,X{p},Ak,Y{p},symm);\n                end\n            else %%--- for low rank constraints\n                idx = ss(k-m1)+1 :ss(k-m1+1);\n                tmp = XVD(:,idx)* (Y{p}*At{p,2}(:,idx))';\n            end\n            if (~symm)\n                tmp = 0.5*(mexsvec(pblk,tmp) + mexsvec(pblk,tmp'));\n            else\n                tmp = mexsvec(pblk,tmp);\n            end\n            permk = par.permA(p,k);\n            idx  = par.permA(p,1:min(k,m1));\n            tmp2 = schur(idx,permk) + mexinprod(blk,At,tmp,min(k,m1),p);\n            schur(idx,permk) = tmp2;\n            schur(permk,idx) = tmp2';\n        end\n    end\n    if (m1 < m) %% for low rank constraints\n        m2 = m - m1;\n        XVtmp = XVD'*At{p,2};\n        YVtmp = At{p,2}'*YVD;\n        for k = 1:m2\n            idx0 = ss(k)+1 : ss(k+1);\n            tmp = XVtmp(:,idx0) .* YVtmp(:,idx0);\n            tmp = tmp*ones(length(idx0),1);\n            tmp3 = schur(m1+1:m1+m2,m1+k) + mexqops(pblk{3},tmp,ones(length(tmp),1),1);\n            schur(m1+1:m1+m2,m1+k) = tmp3;\n        end\n    end\nelse\n    %%--- for SDP block where each sub-block is small dimensional\n    if issparse(X{p}) && ~issparse(Y{p}); Y{p} = sparse(Y{p}); end\n    if ~issparse(X{p}) && issparse(Y{p}); X{p} = sparse(X{p}); end\n    tmp = mexskron(pblk,X{p},Y{p});\n    schurtmp = At{p,1}'*tmp*At{p,1};\n    %% schurtmp = 0.5*(schurtmp + schurtmp');\n    if (norm(par.permA(p,:)-(1:m)) > 0)\n        Perm = spconvert([(1:m)', par.permA(p,:)', ones(m,1)]);\n        schur = schur + Perm'*schurtmp*Perm;\n    else\n        schur = schur + schurtmp;\n    end\nend\n%%*******************************************************************\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "blktrace.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/blktrace.m", "size": 2084, "source_encoding": "utf_8", "md5": "6a5c3d9ff74073a8e864c246727eaa86", "text": "%%**********************************************************************\n%% blktrace: compute <X1,Z1> + ... + <Xp,Zp>\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************************\n\nfunction trXZ = blktrace(blk,X,Z,parbarrier)\n\nif (nargin == 3)\n    trXZ = 0;\n    for p = 1:size(blk,1)\n        pblk = blk(p,:);\n        if strcmp(pblk{1},'s')\n            if (length(pblk{2}) == 1)\n                trXZ = trXZ + sum(sum(X{p}.*Z{p}));\n            else\n                xx = mexsvec(pblk,X{p},0);\n                zz = mexsvec(pblk,Z{p});\n                trXZ = trXZ + xx'*zz;\n            end\n        else\n            trXZ = trXZ + sum(X{p}.*Z{p});\n        end\n    end\nelseif (nargin == 4)\n    trXZ = 0;\n    for p = 1:size(blk,1)\n        pblk = blk(p,:);\n        if (norm(parbarrier{p}) == 0)\n            if strcmp(pblk{1},'s')\n                if (length(pblk{2}) == 1)\n                    trXZ = trXZ + sum(sum(X{p}.*Z{p}));\n                else\n                    xx = mexsvec(pblk,X{p},0);\n                    zz = mexsvec(pblk,Z{p});\n                    trXZ = trXZ + xx'*zz;\n                end\n            else\n                trXZ = trXZ + sum(X{p}.*Z{p});\n            end\n        else\n            idx = find(parbarrier{p} == 0);\n            if ~isempty(idx)\n                if strcmp(pblk{1},'s')\n                    sumXZ = sum(X{p}.*Z{p});\n                    ss = [0,cumsum(pblk{2})];\n                    for k = 1:length(idx)\n                        idxtmp = ss(idx(k))+1:ss(idx(k)+1);\n                        trXZ = trXZ + sum(sumXZ(idxtmp));\n                    end\n                elseif strcmp(pblk{1},'q')\n                    tmp = qops(pblk,X{p},Z{p},1);\n                    trXZ = trXZ + sum(tmp(idx));\n                elseif strcmp(pblk{1},'l')\n                    trXZ = trXZ + sum(X{p}(idx).*Z{p}(idx));\n                end\n            end\n        end\n    end\nend\n%%**********************************************************************\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlpu2lblk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlpu2lblk.m", "size": 3099, "source_encoding": "utf_8", "md5": "1a67e45c349d19614e890521aed11db5", "text": "%%***************************************************************************\n%% sqlpu2lblk: decide whether to convert ublk to lblk\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 10 Jul 2007\n%%***************************************************************************\n\nfunction [blk,At,C,X,Z,ublk2lblk,ublkidx] = sqlpu2lblk(blk,At,C,X,Z,par,convertlen)\n\n%%\nublk2lblk = zeros(size(blk,1),1);\nublkidx = cell(size(blk,1),2);\n\nfor p = 1:size(blk,1)\n    pblk = blk(p,:); n0 = sum(pblk{2});\n    if strcmp(pblk{1},'u') && (pblk{2} > 0)\n        ublk2lblk(p) = 1;\n        if (pblk{2} > convertlen); return; end\n        AAt = At{p}*At{p}';\n        mexschurfun(AAt,1e-15*max(1,diag(AAt)));\n        % indef = 0;\n        [L.R,indef,L.perm] = chol(AAt,'vector');\n        L.d = full(diag(L.R)).^2;\n        if (~indef) && (max(L.d)/min(L.d) < 1e6)\n            ublk2lblk(p) = 0;\n            msg = '*** no conversion for ublk';\n            if (par.printlevel); fprintf(' %s',msg); end\n        else\n            dd(L.perm,1) = abs(L.d); %#ok\n            idxN = find(dd < 1e-11*mean(L.d));\n            idxB = setdiff((1:n0)',idxN);\n            ddB  = dd(idxB);\n            ddN  = dd(idxN);\n            if ~isempty(ddN) && ~isempty(ddB) && (min(ddB)/max(ddN) < 10)\n                idxN = []; idxB = (1:n0)';\n            end\n            ublkidx{p,1} = n0; ublkidx{p,2} = idxN;\n            if ~isempty(idxN)\n                restol = 1e-8;\n                [W,resnorm] = findcoeff(At{p}',idxB,idxN);\n                resnorm(2) = norm(C{p}(idxN) - W'*C{p}(idxB));\n                if (max(resnorm) < restol)\n                    % feasible = 1;\n                    blk{p,2} = length(idxB);\n                    Atmp  = At{p}';\n                    At{p} = Atmp(:,idxB)';\n                    C{p}  = C{p}(idxB);\n                    X{p} = X{p}(idxB); Z{p} = Z{p}(idxB);\n                    msg = 'removed dependent columns in constraint matrix for ublk';\n                    if (par.printlevel); fprintf('\\n %s\\n',msg); end\n                end\n            end\n        end\n    end\nend\n%%***************************************************************************\n%%***************************************************************************\n%% findcoeff:\n%%\n%% [W,resnorm] = findcoeff(A,idXB,idXN);\n%%\n%% idXB = indices of independent columns of A.\n%% idxN = indices of   dependent columns of A.\n%%\n%% AB = A(:,idxB); AN = A(:,idxN) = AB*W\n%%\n%% SDPT3: version 3.0\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last modified: 2 Feb 01\n%%***************************************************************************\n\nfunction [W,resnorm] = findcoeff(A,idxB,idxN)\n\nAB = A(:,idxB);\nAN = A(:,idxN);\nn = size(AB,2);\n%%\n%%-----------------------------------------\n%% find W so that AN = AB*W\n%%-----------------------------------------\n%%\n[L,U,P,Q] = lu(sparse(AB));\nrhs  = P*AN;\nLhat = L(1:n,:);\nW = Q*( U \\ (Lhat \\ rhs(1:n,:)));\nresnorm = norm(AN-AB*W,'fro')/max(1,norm(AN,'fro'));\n%%***************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "Prod3.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/Prod3.m", "size": 1677, "source_encoding": "utf_8", "md5": "2ee9f0d732e5ea1f326bfcf275b0da3c", "text": "%%************************************************************\n%% Prod3: compute the entries of Q = A*B*C specified in\n%%        nzlistQ.\n%%\n%% Q = Prod3(blk,A,B,C,sym,nzlistQ)\n%% Important: (a) A is assumed to be symmetric if nzlistQ\n%%                has 2 columns (since mexProd2nz computes A'*B).\n%%            (b) The 2nd column of nzlistQ must be sorted in\n%%                ascending order.\n%%\n%% (optional) sym = 1, if Q is symmetric.\n%%                = 0, otherwise.\n%% (optional) nzlistQ = list of non-zero elements of Q to be\n%%                      computed.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%************************************************************\n\nfunction Q = Prod3(blk,A,B,C,sym,nzlistQ)\n\nif (nargin<5); sym = 0; end;\ncheckcell = [iscell(A) iscell(B) iscell(C)];\nif (nargin==6)\n    checkcell(1,4) = iscell(nzlistQ);\nelse\n    nzlistQ = inf;\nend\n%%\nif any(checkcell-1)\n    if (size(blk,1) > 1)\n        error('Prod3: blk and A,B,C are not compatible');\n    end\n    if strcmp(blk{1},'s')\n        [len,len2] = size(nzlistQ);\n        if (len == 0); nzlistQ = inf; len2 = 1; end;\n        if (len2 == 1) && (nzlistQ == inf)\n            tmp = Prod2(blk,A,B,0);\n            Q = Prod2(blk,tmp,C,sym);\n        else\n            tmp = Prod2(blk,B,C,0);\n            Q = mexProd2nz(blk,A,tmp,nzlistQ);\n            if sym; Q = 0.5*(Q+Q'); end;\n        end\n    elseif strcmp(blk{1},'q') || strcmp(blk{1},'l') || strcmp(blk{1},'u')\n        Q = A.*B.*C;\n    end\nelse\n    error('Prod3: A,B,C,nzlistQ must all be matrices');\nend\n%%************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlptermcode.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlptermcode.m", "size": 1230, "source_encoding": "utf_8", "md5": "40b494719c3c614b6196dd9846778c4c", "text": "%%*************************************************************************\n%% sqlptermcode.m: explains the termination code in sqlp.m\n%%\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction sqlptermcode\n\nfprintf('\\n   3: norm(X) or norm(Z) diverging');\nfprintf('\\n   2: dual   problem is suspected to be infeasible')\nfprintf('\\n   1: primal problem is suspected to be infeasible')\nfprintf('\\n   0: max(relative gap,infeasibility) < gaptol');\nfprintf('\\n  -1: relative gap < infeasibility');\nfprintf('\\n  -2: lack of progress in predictor or corrector');\nfprintf('\\n  -3: X or Z not positive definite');\nfprintf('\\n  -4: difficulty in computing predictor or corrector direction');\nfprintf('\\n  -5: progress in relative gap or infeasibility is bad');\nfprintf('\\n  -6: maximum number of iterations reached');\nfprintf('\\n  -7: primal infeasibility has deteriorated too much');\nfprintf('\\n  -8: progress in relative gap has deteriorated');\nfprintf('\\n  -9: lack of progress in infeasibility');\nfprintf('\\n')\n%%*************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "AXfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/AXfun.m", "size": 1869, "source_encoding": "utf_8", "md5": "e816cba65d629a72167375f9a7697b2f", "text": "%%*************************************************************************\n%% AXfun: compute AX(k) = <Ak,X>, k = 1:m\n%%\n%%   AX = AXfun(blk,At,permA,X);\n%%\n%% Note: permA may be set to [] if no permutation is neccessary.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction AX = AXfun(blk,At,permA,X)\n\nif isempty(permA); ismtpermA = 1; else ismtpermA = 0; end\n\nfor p = 1:size(blk,1);\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        m1 = size(At{p,1},2);\n        if (p==1)\n            if (length(pblk) > 2); m2 = length(pblk{3}); else m2 = 0; end\n            m = m1 + m2;\n            AX = zeros(m,1);  tmp = zeros(m,1);\n        end\n        if (~isempty(At{p,1}))\n            if (ismtpermA)\n                tmp = (svec(pblk,X{p})'*At{p,1})';\n                %%tmp = mexinprod(blk,At,svec(pblk,X{p}),m1,p);\n            else\n                tmp(permA(p,1:m1),1) = (svec(pblk,X{p})'*At{p,1})';\n                %%tmp(permA(p,1:m1),1) = mexinprod(blk,At,svec(pblk,X{p}),m1,p);\n            end\n        end\n        if (length(pblk) > 2)  %% for low rank constraints\n            m2 = length(pblk{3});\n            dd = At{p,3};\n            len = sum(pblk{3});\n            DD = spconvert([dd(:,2:4); len,len,0]);\n            XVD = X{p}*At{p,2}*DD;\n            if (length(X{p}) > 1)\n                tmp2 = sum(At{p,2}.*XVD)';\n            else\n                tmp2 = (At{p,2}.*XVD)';\n            end\n            tmp(m1+1:m1+m2) = mexqops(pblk{3},tmp2,ones(length(tmp2),1),1);\n        end\n        AX = AX + tmp;\n    else\n        if (p==1); m = size(At{p,1},2); AX = zeros(m,1);  tmp = zeros(m,1); end\n        AX = AX + (X{p}'*At{p,1})';\n    end\nend\n%%*************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "NTpred.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/NTpred.m", "size": 1963, "source_encoding": "utf_8", "md5": "3a2374e8ffa4806b637e50e551b9c17c", "text": "%%**********************************************************************\n%% NTpred: Compute (dX,dy,dZ) for NT direction.\n%%\n%% compute SVD of Xchol*Zchol via eigenvalue decompostion of\n%%     Zchol * X * Zchol' = V * diag(sv2) * V'.\n%% compute W satisfying W*Z*W = X.\n%%     W = G'*G,  where G = diag(sqrt(sv)) * (invZchol*V)'\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************************\n\nfunction [par,dX,dy,dZ,coeff,L,hRd] = ...\n    NTpred(blk,At,par,rp,Rd,sigmu,X,Z,Zchol,invZchol)\n\nglobal schurfun schurfun_par\n%%\n%% compute NT scaling matrix\n%%\n[par.W,par.G,par.sv,par.gamx,par.gamz,par.dd,par.ee,par.ff] = ...\n    NTscaling(blk,X,Z,Zchol,invZchol);\n%%\n%% compute schur matrix\n%%\nm = length(rp);\nschur = sparse(m,m);\nUU = []; EE = []; Afree = [];\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        [schur,UU,EE] = schurmat_lblk(blk,At,par,schur,UU,EE,p,par.dd);\n    elseif strcmp(pblk{1},'q');\n        [schur,UU,EE] = schurmat_qblk(blk,At,par,schur,UU,EE,p,par.dd,par.ee);\n    elseif strcmp(pblk{1},'s')\n        if isempty(schurfun{p})\n            schur = schurmat_sblk(blk,At,par,schur,p,par.W);\n        elseif ischar(schurfun{p})\n            if ~isempty(par.permZ{p})\n                Wp = par.W{p}(par.permZ{p},par.permZ{p});\n            else\n                Wp = par.W{p};\n            end\n            schurtmp = feval(schurfun{p},Wp,Wp,schurfun_par(p,:));\n            schur = schur + schurtmp;\n        end\n    elseif strcmp(pblk{1},'u')\n        Afree = [Afree, At{p}']; %#ok\n    end\nend\n%%\n%% compute rhs\n%%\n[rhs,EinvRc,hRd] = NTrhsfun(blk,At,par,X,Z,rp,Rd,sigmu);\n%%\n%% solve linear system\n%%\n[xx,coeff,L] = linsysolve(par,schur,UU,Afree,EE,rhs);\n%%\n%% compute (dX,dZ)\n%%\n[dX,dy,dZ] = NTdirfun(blk,At,par,Rd,EinvRc,xx,m);\n%%**********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlp.m", "size": 11506, "source_encoding": "utf_8", "md5": "d4acdbc7a5a1c0fd270f5d3de302c545", "text": "%%*****************************************************************************\n%% sqlp: solve an semidefinite-quadratic-linear program\n%%       by infeasible path-following method.\n%%\n%%  [obj,X,y,Z,info,runhist] = sqlp(blk,At,C,b,OPTIONS,X0,y0,Z0);\n%%\n%%  Input: blk: a cell array describing the block diagonal structure of SQL data.\n%%          At: a cell array with At{p} = [svec(Ap1) ... svec(Apm)]\n%%         b,C: data for the SQL instance.\n%%  (X0,y0,Z0): an initial iterate (if it is not given, the default is used).\n%%     OPTIONS: a structure that specifies parameters required in sqlp.m,\n%%              (if it is not given, the default in sqlparameters.m is used).\n%%\n%%  Output: obj  = [<C,X> <b,y>].\n%%          (X,y,Z): an approximately optimal solution or a primal or dual\n%%                   infeasibility certificate.\n%%          info.termcode = termination-code\n%%          info.iter     = number of iterations\n%%          info.obj      = [primal-obj, dual-obj]\n%%          info.cputime  = total-time\n%%          info.gap      = gap\n%%          info.pinfeas  = primal_infeas\n%%          info.dinfeas  = dual_infeas\n%%          runhist.pobj    = history of primal objective value.\n%%          runhist.dobj    = history of dual   objective value.\n%%          runhist.gap     = history of <X,Z>.\n%%          runhist.pinfeas = history of primal infeasibility.\n%%          runhist.dinfeas = history of dual   infeasibility.\n%%          runhist.cputime = history of cputime spent.\n%%----------------------------------------------------------------------------\n%%  The OPTIONS structure specifies the required parameters:\n%%      vers  gam  predcorr  expon  gaptol  inftol  steptol\n%%      maxit  printlevel  scale_data ...\n%%      (all have default values set in sqlparameters.m).\n%%*************************************************************************\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction [obj,X,y,Z,info,runhist] = sqlp(blk,At,C,b,OPTIONS,X0,y0,Z0)\n\nif (nargin < 5); OPTIONS = []; end\n\nisemptyAtb = 0;\nif isempty(At) && isempty(b);\n    %% Add redundant constraint: <-I,X> <= 0\n    b = 0;\n    At = ops(ops(blk,'identity'),'*',-1);\n    numblk = size(blk,1);\n    blk{numblk+1,1} = 'l'; blk{numblk+1,2} = 1;\n    At{numblk+1,1} = 1; C{numblk+1,1} = 0;\n    isemptyAtb = 1;\nend\n%%\n%%-----------------------------------------\n%% get parameters from the OPTIONS structure.\n%%-----------------------------------------\n%%\n\nmatlabversion = sscanf(version,'%f');\nif strcmp(computer,'PCWIN64') || strcmp(computer,'GLNXA64')\n    par.computer = 64;\nelse\n    par.computer = 32;\nend\npar.matlabversion = matlabversion(1);\npar.vers        = 0;\npar.predcorr    = 1;\npar.gam         = 0;\npar.expon       = 1;\npar.gaptol      = 1e-8;\npar.inftol      = 1e-8;\npar.steptol     = 1e-6;\npar.maxit       = 100;\npar.printlevel  = 3;\npar.stoplevel   = 1;\npar.scale_data  = 0;\npar.spdensity   = 0.4;\npar.rmdepconstr = 0;\npar.smallblkdim = 50;\npar.schurfun     = cell(size(blk,1),1);\npar.schurfun_par = cell(size(blk,1),1);\n%%\nparbarrier  = cell(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s') || strcmp(pblk{1},'q')\n        parbarrier{p} = zeros(1,length(pblk{2}));\n    elseif strcmp(pblk{1},'l') || strcmp(pblk{1},'u' )\n        parbarrier{p} = zeros(1,sum(pblk{2}));\n    end\nend\nparbarrier_0 = parbarrier;\n%%\nif nargin > 4,\n    if isfield(OPTIONS,'vers');        par.vers     = OPTIONS.vers; end\n    if isfield(OPTIONS,'predcorr');    par.predcorr = OPTIONS.predcorr; end\n    if isfield(OPTIONS,'gam');         par.gam      = OPTIONS.gam; end\n    if isfield(OPTIONS,'expon');       par.expon    = OPTIONS.expon; end\n    if isfield(OPTIONS,'gaptol');      par.gaptol   = OPTIONS.gaptol; end\n    if isfield(OPTIONS,'inftol');      par.inftol   = OPTIONS.inftol; end\n    if isfield(OPTIONS,'steptol');     par.steptol  = OPTIONS.steptol; end\n    if isfield(OPTIONS,'maxit');       par.maxit    = OPTIONS.maxit; end\n    if isfield(OPTIONS,'printlevel');  par.printlevel  = OPTIONS.printlevel; end\n    if isfield(OPTIONS,'stoplevel');   par.stoplevel   = OPTIONS.stoplevel; end\n    if isfield(OPTIONS,'scale_data');  par.scale_data  = OPTIONS.scale_data; end\n    if isfield(OPTIONS,'spdensity');   par.spdensity   = OPTIONS.spdensity; end\n    if isfield(OPTIONS,'rmdepconstr'); par.rmdepconstr = OPTIONS.rmdepconstr; end\n    if isfield(OPTIONS,'smallblkdim'); par.smallblkdim = OPTIONS.smallblkdim; end\n    if isfield(OPTIONS,'parbarrier');\n        parbarrier = OPTIONS.parbarrier;\n        if isempty(parbarrier); parbarrier = parbarrier_0; end\n        if ~iscell(parbarrier);\n            tmp = parbarrier; clear parbarrier; parbarrier{1} = tmp;\n        end\n        if (length(parbarrier) < size(blk,1))\n            len = length(parbarrier);\n            parbarrier(len+1:size(blk,1)) = parbarrier_0(len+1:size(blk,1));\n        end\n    end\n    if isfield(OPTIONS,'schurfun');\n        par.schurfun = OPTIONS.schurfun;\n        if ~isempty(par.schurfun); par.scale_data = 0; end\n    end\n    if isfield(OPTIONS,'schurfun_par'); par.schurfun_par = OPTIONS.schurfun_par; end\n    if isempty(par.schurfun);     par.schurfun = cell(size(blk,1),1); end\n    if isempty(par.schurfun_par); par.schurfun_par = cell(size(blk,1),1); end\nend\nif (size(blk,2) > 2); par.smallblkdim = 0; end\n%%\n%%-----------------------------------------\n%% convert matrices to cell arrays.\n%%-----------------------------------------\n%%\nif ~iscell(At); At = {At}; end;\nif ~iscell(C);  C = {C}; end;\nif all(size(At) == [size(blk,1), length(b)]);\n    convertyes = zeros(size(blk,1),1);\n    for p = 1:size(blk,1)\n        if strcmp(blk{p,1},'s') && all(size(At{p,1}) == sum(blk{p,2}))\n            convertyes(p) = 1;\n        end\n    end\n    if any(convertyes)\n        if (par.printlevel);\n            fprintf('\\n sqlp: converting At into required format');\n        end\n        At = svec(blk,At,ones(size(blk,1),1));\n    end\nend\n%%\n%%-----------------------------------------\n%% validate SQLP data.\n%%-----------------------------------------\n%%\n% tstart = cputime;\n[blk,At,C,b,blkdim,numblk,parbarrier] = validate(blk,At,C,b,par,parbarrier);\n[blk,At,C,b,iscmp] = convertcmpsdp(blk,At,C,b);\nif (iscmp) && (par.printlevel>=2);\n    fprintf('\\n SQLP has complex data');\nend\nif (nargin <= 5) || (isempty(X0) || isempty(y0) || isempty(Z0));\n    par.startpoint = 1;\n    [X0,y0,Z0] = infeaspt(blk,At,C,b);\nelse\n    par.startpoint = 2;\n    if ~iscell(X0);  X0 = {X0}; end;\n    if ~iscell(Z0);  Z0 = {Z0}; end;\n    y0 = real(y0);\n    if (length(y0) ~= length(b));\n        error('sqlp: length of b and y0 not compatible');\n    end\n    [X0,Z0] = validate_startpoint(blk,X0,Z0,par.spdensity,iscmp);\nend\nif (par.printlevel>=2)\n    fprintf('\\n num. of constraints = %2.0d',length(b));\n    if blkdim(1);\n        fprintf('\\n dim. of sdp    var  = %2.0d,',blkdim(1));\n        fprintf('   num. of sdp  blk  = %2.0d',numblk(1));\n    end\n    if blkdim(2);\n        fprintf('\\n dim. of socp   var  = %2.0d,',blkdim(2));\n        fprintf('   num. of socp blk  = %2.0d',numblk(2));\n    end\n    if blkdim(3); fprintf('\\n dim. of linear var  = %2.0d',blkdim(3)); end\n    if blkdim(4); fprintf('\\n dim. of free   var  = %2.0d',blkdim(4)); end\nend\n%%\n%%-----------------------------------------\n%% detect unrestricted blocks in linear blocks\n%%-----------------------------------------\n%%\nuser_supplied_schurfun = 0;\nfor p = 1:size(blk,1)\n    if ~isempty(par.schurfun{p}); user_supplied_schurfun = 1; end\nend\nif (user_supplied_schurfun == 0)\n    [blk2,At2,C2,ublkinfo,parbarrier2,X02,Z02] = ...\n        detect_ublk(blk,At,C,parbarrier,X0,Z0,par.printlevel);\nelse\n    blk2 = blk; At2 = At; C2 = C;\n    parbarrier2 = parbarrier; X02 = X0; Z02 = Z0;\n    ublkinfo = cell(size(blk2,1),1);\nend\nublksize = blkdim(4);\nfor p = 1:size(ublkinfo,1)\n    ublksize = ublksize + length(ublkinfo{p});\nend\n%%\n%%-----------------------------------------\n%% detect diagonal blocks in semidefinite blocks\n%%-----------------------------------------\n%%\nif (user_supplied_schurfun==0)\n    [blk3,At3,C3,diagblkinfo,diagblkchange,parbarrier3,X03,Z03] = ...\n        detect_lblk(blk2,At2,C2,b,parbarrier2,X02,Z02,par.printlevel);\nelse\n    blk3 = blk2; At3 = At2; C3 = C2;\n    parbarrier3 = parbarrier2; X03 = X02; Z03 = Z02;\n    diagblkchange = 0;\n    diagblkinfo = cell(size(blk3,1),1);\nend\n%%\n%%-----------------------------------------\n%% main solver\n%%-----------------------------------------\n%%\n%    exist_analytic_term = 0;\n%    for p = 1:size(blk3,1);\n%       idx = find(parbarrier3{p} > 0);\n%       if ~isempty(idx); exist_analytic_term = 1; end\n%    end\n%\nif (par.vers == 0);\n    if blkdim(1); par.vers = 1; else par.vers = 2; end\nend\npar.blkdim = blkdim;\npar.ublksize = ublksize;\n[obj,X3,y,Z3,info,runhist] = ...\n    sqlpmain(blk3,At3,C3,b,par,parbarrier3,X03,y0,Z03);\n%%\n%%-----------------------------------------\n%% recover semidefinite blocks from linear blocks\n%%-----------------------------------------\n%%\nif any(diagblkchange)\n    X2 = cell(size(blk2,1),1); Z2 = cell(size(blk2,1),1);\n    count = 0;\n    for p = 1:size(blk2,1)\n        pblk = blk2(p,:);\n        n = sum(pblk{2});\n        blkno      = diagblkinfo{p,1};\n        idxdiag    = diagblkinfo{p,2};\n        idxnondiag = diagblkinfo{p,3};\n        if ~isempty(idxdiag)\n            len = length(idxdiag);\n            Xtmp = [idxdiag,idxdiag,X3{end}(count+1:count+len); n, n, 0];\n            Ztmp = [idxdiag,idxdiag,Z3{end}(count+1:count+len); n, n, 0];\n            if ~isempty(idxnondiag)\n                [ii,jj,vv] = find(X3{blkno});\n                Xtmp = [Xtmp; idxnondiag(ii),idxnondiag(jj),vv]; %#ok\n                [ii,jj,vv] = find(Z3{blkno});\n                Ztmp = [Ztmp; idxnondiag(ii),idxnondiag(jj),vv]; %#ok\n            end\n            X2{p} = spconvert(Xtmp);\n            Z2{p} = spconvert(Ztmp);\n            count = count + len;\n        else\n            X2(p) = X3(blkno); Z2(p) = Z3(blkno);\n        end\n    end\nelse\n    X2 = X3; Z2 = Z3;\nend\n%%\n%%-----------------------------------------\n%% recover linear block from unrestricted block\n%%-----------------------------------------\n%%\nnumblk = size(blk,1);\nnumblknew = numblk;\nX = cell(numblk,1); Z = cell(numblk,1);\nfor p = 1:numblk\n    n = blk{p,2};\n    if isempty(ublkinfo{p,1})\n        X{p} = X2{p}; Z{p} = Z2{p};\n    else\n        Xtmp = zeros(n,1); Ztmp = zeros(n,1);\n        Xtmp(ublkinfo{p,1}) = max(0,X2{p});\n        Xtmp(ublkinfo{p,2}) = max(0,-X2{p});\n        Ztmp(ublkinfo{p,1}) = max(0,Z2{p});\n        Ztmp(ublkinfo{p,2}) = max(0,-Z2{p});\n        if ~isempty(ublkinfo{p,3})\n            numblknew = numblknew + 1;\n            Xtmp(ublkinfo{p,3}) = X2{numblknew};\n            Ztmp(ublkinfo{p,3}) = Z2{numblknew};\n        end\n        X{p} = Xtmp; Z{p} = Ztmp;\n    end\nend\n%%\n%%-----------------------------------------\n%% recover complex solution\n%%-----------------------------------------\n%%\nif (iscmp)\n    for p = 1:numblk\n        pblk = blk(p,:);\n        n = sum(pblk{2})/2;\n        if strcmp(pblk{1},'s');\n            X{p} = X{p}(1:n,1:n) + sqrt(-1)*X{p}(n+1:2*n,1:n);\n            Z{p} = Z{p}(1:n,1:n) + sqrt(-1)*Z{p}(n+1:2*n,1:n);\n            X{p} = 0.5*(X{p}+X{p}');\n            Z{p} = 0.5*(Z{p}+Z{p}');\n        end\n    end\nend\nif (isemptyAtb)\n    X = X(1:end-1); Z = Z(1:end-1);\nend\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "infeaspt.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/infeaspt.m", "size": 5422, "source_encoding": "utf_8", "md5": "ed7ce61d1094307a576ad8ddfd42bd30", "text": "%%********************************************************************\n%% infeaspt: generate an initial point for sdp.m\n%%\n%%  [X0,y0,Z0] = infeaspt(blk,At,C,b,options,scalefac);\n%%\n%%  options = 1  if want X0,Z0 to be scaled identity matrices\n%%          = 2  if want X0,Z0 to be scalefac*(identity matrices).\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%********************************************************************\n\nfunction [X0,y0,Z0] = infeaspt(blk,At,C,b,options,scalefac)\n%%\nif (nargin < 5); options = 1; end;\nif (options == 1); scalefac = []; end;\nif (options == 2) && (nargin < 6); scalefac = 1000; end;\nif (scalefac <= 0); error('scalefac must a positive number'); end;\n%%\nif ~iscell(At); At = {At}; end;\nif ~iscell(C);  C = {C}; end;\nm = length(b);\nif all(size(At) == [size(blk,1) m]);\n    convertyes = zeros(size(blk,1),1);\n    for p = 1:size(blk,1)\n        if strcmp(blk{p,1},'s') && all(size(At{p,1}) == sum(blk{p,2}))\n            convertyes(p) = 1;\n        end\n    end\n    if any(convertyes)\n        At = svec(blk,At,ones(size(blk,1),1));\n    end\nend;\n%%\n%%[blk,At,C,b] = validate(blk,At,C,b);\n%%\nX0 = cell(size(C)); Z0 = cell(size(C));\nm = length(b);\nfor p = 1:size(blk,1);\n    pblk = blk(p,:);\n    blktmp = pblk{2};\n    n = length(C{p});\n    y0 = zeros(m,1);\n    b2 = 1 + abs(b');\n    if (options == 1);\n        if strcmp(pblk{1},'s');\n            normAni = [];\n            X0{p} = sparse(n,n); Z0{p} = sparse(n,n);\n            ss = [0, cumsum(blktmp)];\n            tt = [0, cumsum(blktmp.*(blktmp+1)/2)];\n            for i = 1:length(pblk{2})\n                if ~isempty(At{p,1})\n                    pos = tt(i)+1 : tt(i+1);\n                    Ai = At{p,1}(pos,:);\n                    normAni = 1+sqrt(sum(Ai.*Ai));\n                end\n                if (length(At(p,:)) >= 2) %% for low rank constraints\n                    dd = At{p,3};\n                    qq = [0, cumsum(pblk{3})]; normtmp = ones(1,length(pblk{3}));\n                    idxD = [0; find(diff(dd(:,1))); size(dd,1)];\n                    for k = 1:length(pblk{3})\n                        idx = qq(k)+1 : qq(k+1);\n                        idx2 = idxD(k)+1: idxD(k+1);\n                        Ak = At{p,2}(:,idx);\n                        ii = dd(idx2,2)-qq(k); %% undo cumulative indexing\n                        jj = dd(idx2,3)-qq(k);\n                        len = pblk{3}(k);\n                        Dk = spconvert([ii,jj,dd(idx2,4); len,len,0]);\n                        tmp = Ak'*Ak*Dk;\n                        normtmp(1,k) = 1+sqrt(sum(sum(tmp.*tmp')));\n                    end\n                    normAni = [normAni, normtmp]; %#ok\n                end\n                pos = ss(i)+1 : ss(i+1);  ni = length(pos);\n                tmp = C{p}(pos,pos);\n                normCni = 1+sqrt(sum(sum(tmp.*tmp)));\n                const  = 10; %%--- old: const = 1;\n                constX = max([const,sqrt(ni),ni*(b2./normAni)]);\n                constZ = max([const,sqrt(ni),normAni,normCni]);\n                X0{p}(pos,pos) = constX*spdiags(1+1e-10*randmat(ni,1,0,'u'),0,ni,ni);\n                Z0{p}(pos,pos) = constZ*spdiags(1+1e-10*randmat(ni,1,0,'u'),0,ni,ni);\n            end\n        elseif strcmp(pblk{1},'q');\n            s = 1+[0, cumsum(blktmp)];\n            len = length(blktmp);\n            normC = 1+norm(C{p});\n            normA = 1+sqrt(sum(At{p,1}.*At{p,1}));\n            idenqX = zeros(sum(blktmp),1);\n            idenqZ = zeros(sum(blktmp),1);\n            idenqX(s(1:len)) = max([1,b2./normA])*sqrt(blktmp') ;\n            idenqZ(s(1:len)) = max([sqrt(blktmp); max([normA,normC])*ones(1,len)])';\n            idenqX(s(1:len)) = idenqX(s(1:len)).*(1+1e-10*randmat(len,1,0,'u'));\n            idenqZ(s(1:len)) = idenqZ(s(1:len)).*(1+1e-10*randmat(len,1,0,'u'));\n            X0{p} = idenqX;\n            Z0{p} = idenqZ;\n        elseif strcmp(pblk{1},'l');\n            normC = 1+norm(C{p});\n            normA = 1+sqrt(sum(At{p,1}.*At{p,1}));\n            const = 10; %%--- old: const =1;\n            constX = max([const,sqrt(n),sqrt(n)*b2./normA]);\n            constZ = max([const,sqrt(n),normA,normC]);\n            X0{p} = constX*(1+1e-10*randmat(n,1,0,'u'));\n            Z0{p} = constZ*(1+1e-10*randmat(n,1,0,'u'));\n        elseif strcmp(pblk{1},'u');\n            X0{p} = sparse(n,1);\n            Z0{p} = sparse(n,1);\n        else\n            error(' blk: some fields not specified correctly');\n        end;\n    elseif (options == 2);\n        if strcmp(pblk{1},'s');\n            n = sum(blktmp);\n            X0{p} = scalefac*spdiags(1+1e-10*randmat(n,1,0,'u'),0,n,n);\n            Z0{p} = scalefac*spdiags(1+1e-10*randmat(n,1,0,'u'),0,n,n);\n        elseif strcmp(pblk{1},'q');\n            s = 1+[0, cumsum(blktmp)];\n            len = length(blktmp);\n            idenq = zeros(sum(blktmp),1);\n            idenq(s(1:len)) = 1+1e-10*randmat(len,1,0,'u');\n            X0{p} = scalefac*idenq;\n            Z0{p} = scalefac*idenq;\n        elseif strcmp(pblk{1},'l');\n            X0{p} = scalefac*(1+1e-10*randmat(n,1,0,'u'));\n            Z0{p} = scalefac*(1+1e-10*randmat(n,1,0,'u'));\n        elseif strcmp(pblk{1},'u');\n            X0{p} = sparse(n,1);\n            Z0{p} = sparse(n,1);\n        else\n            error(' blk: some fields not specified correctly');\n        end\n    end\nend\n%%********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "Arrow.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/Arrow.m", "size": 933, "source_encoding": "utf_8", "md5": "4a1803a8a960eba5462879d0ae4cbb78", "text": "%%********************************************************\n%% Arrow:\n%%\n%% Fx = Arrow(pblk,f,x,options);\n%%\n%% if options == 0;\n%%    Fx = Arr(F)*x\n%% if options == 1;\n%%    Fx = Arr(F)^{-1}*x\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%********************************************************\n\nfunction   Fx = Arrow(pblk,f,x,options)\n\nif nargin == 3; options = 0; end;\n\ns = 1 + [0, cumsum(pblk{2})];\nidx1 = s(1:length(pblk{2}));\nif options == 0\n    inprod = mexqops(pblk{2},f,x,1);\n    Fx  = mexqops(pblk{2},f(idx1),x,3) + mexqops(pblk{2},x(idx1),f,3);\n    Fx(idx1) = inprod;\nelse\n    gamf2 = mexqops(pblk{2},f,f,2);\n    gamprod = mexqops(pblk{2},f,x,2);\n    alpha = gamprod./gamf2;\n    Fx = mexqops(pblk{2},1./f(idx1),x,3) - mexqops(pblk{2},alpha./f(idx1),f,3);\n    Fx(idx1) = alpha;\nend\n%%\n%%********************************************************\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "mybicgstab.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/mybicgstab.m", "size": 3536, "source_encoding": "utf_8", "md5": "7ae20f5d14c6533fd9a50b23a1e5c8b3", "text": "%%*************************************************************************\n%% mybicgstab\n%%\n%% [xx,resnrm,flag] = mybicgstab(A,b,M1,tol,maxit)\n%%\n%% iterate on  bb - (M1)*AA*x\n%%\n%% r = b-A*xtrue;\n%%\n%%*************************************************************************\n\nfunction [xx,resnrm,flag] = mybicgstab(A,b,M1,tol,maxit,printlevel)\n\nN = length(b);\nif (nargin < 6); printlevel = 1; end\nif (nargin < 5) || isempty(maxit); maxit = max(30,length(A.mat22)); end;\nif (nargin < 4) || isempty(tol); tol = 1e-10; end;\ntolb = min(1e-4,tol*norm(b));\nflag = 1;\n\nx = zeros(N,1);\nif (norm(x))\n    if isstruct(A); r = b-matvec(A,x); else r = b-mexMatvec(A,x); end;\nelse\n    r =b;\nend\nerr = norm(r); resnrm(1) = err;  minresnrm = err; xx = x;\n%%if (err < 1e-3*tolb); return; end\n\nomega  = 1.0;\nr_tld = r;\n%%\n%%\n%%\nbreakyes = 0;\nsmtol = 1e-40;\nfor iter = 1:maxit,\n    \n    rho   = (r_tld'*r);\n    if (abs(rho) < smtol)\n        flag = 2;\n        if (printlevel); fprintf('*'); end;\n        breakyes = 1;\n        break;\n    end\n    if (iter > 1)\n        beta  = (rho/rho_1)* (alp/omega);\n        p = r + beta*(p - omega*v);\n    else\n        p = r;\n    end\n    p_hat = precond(A,M1,p);\n    if isstruct(A); v = matvec(A,p_hat); else v = mexMatvec(A,p_hat); end;\n    alp = rho / (r_tld'*v);\n    s = r - alp*v;\n    %%\n    s_hat = precond(A,M1,s);\n    if isstruct(A); t = matvec(A,s_hat); else t = mexMatvec(A,s_hat); end;\n    omega = (t'*s) / (t'*t);\n    x = x + alp*p_hat + omega*s_hat;\n    r = s - omega*t;\n    rho_1 = rho;\n    %%\n    %% check convergence\n    %%\n    err = norm(r); resnrm(iter+1) = err; %#ok\n    if (err < minresnrm);\n        xx = x; minresnrm = err;\n    end\n    if (err < tolb)\n        break;\n    end\n    if (err > 10*minresnrm)\n        if (printlevel); fprintf('^'); end\n        breakyes = 2;\n        break;\n    end\n    if (abs(omega) < smtol)\n        flag = 2;\n        if (printlevel); fprintf('*'); end\n        breakyes = 1;\n        break;\n    end\nend\nif (~breakyes) && (printlevel >=3); fprintf(' '); end\n%%\n%%*************************************************************************\n%%*************************************************************************\n%% precond:\n%%*************************************************************************\n\nfunction Mx = precond(A,L,x)\n\nm = L.matdim; m2 = length(x)-m;\nMx = zeros(length(x),1);\n\nfor iter = 1\n    if norm(Mx); r = x - matvec(A,Mx); else r = x; end\n    if (m2 > 0)\n        r1 = full(r(1:m));\n    else\n        r1 = full(r);\n    end\n    if (m2 > 0)\n        r2 = r(m+1:m+m2);\n        w = linsysolvefun(L,r1);\n        z = mexMatvec(A.mat12,w,1) - r2;\n        z = L.Mu \\ (L.Ml \\ (L.Mp*z));\n        r1 = r1 - mexMatvec(A.mat12,z);\n    end\n    d = linsysolvefun(L,r1);\n    if (m2 > 0)\n        d = [d; z]; %#ok\n    end\n    Mx = Mx + d;\nend\n%%*************************************************************************\n%%*************************************************************************\n%% matvec: matrix-vector multiply.\n%% matrix = [A.mat11, A.mat12; A.mat12', A.mat22]\n%%*************************************************************************\n\nfunction Ax = matvec(A,x)\n\nm = length(A.mat11); m2 = length(x)-m;\nif issparse(x); x = full(x); end\nif (m2 > 0)\n    x1 = x(1:m);\nelse\n    x1 = x;\nend\nAx = mexMatvec(A.mat11,x1);\nif (m2 > 0)\n    x2 = x(m+1:m+m2);\n    Ax = Ax + mexMatvec(A.mat12,x2);\n    Ax2 = mexMatvec(A.mat12,x1,1) + mexMatvec(A.mat22,x2);\n    Ax = [full(Ax); full(Ax2)];\nend\n%%*************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "degeneracy.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/degeneracy.m", "size": 5175, "source_encoding": "utf_8", "md5": "52287ec83b17f60c33554d7a25764672", "text": "%%***************************************************************\n%% degeneracy: determine if an SDP problem is non-degenerate.\n%%\n%% [ddx,ddz,B1,B2,sig1,sig12] = degeneracy(blk,At,X,y,Z);\n%%\n%% Assume that strict complementarity holds:\n%% for primal non-degeneracy, we need rank([B1 B2]) = m\n%% for dual non-degeneracy,   we need B1 to have full column rank.\n%%\n%%***************************************************************\n\nfunction [ddx,ddz,XB1,XB2,ZB1,sig1,sig12] = degeneracy(blk,At,X,y,Z)\n\nif ~iscell(X); tmp = X; clear X; X{1} = tmp; end;\nif ~iscell(Z); tmp = Z; clear Z; Z{1} = tmp; end;\n\nm = length(y); XB1 = []; XB2 = []; ZB1 = [];\nnumblk = size(blk,1);\n%%\n%%\n%%\nfor p = 1:size(blk,1)\n    n = length(X{p});\n    if strcmp(blk{p,1},'s')\n        % mu = sum(sum(X{p}.*Z{p}))/n;\n        [Qx,Dx] = eig(full(X{p})); [dx,idx] = sort(diag(Dx));\n        Qx = Qx(:,idx(n:-1:1)); dx = dx(n:-1:1); dx = dx + max(dx)*eps;\n        % Dx = diag(dx);\n        [Qz,Dz] = eig(full(Z{p})); [dz,idx] = sort(diag(Dz));\n        Qz = Qz(:,idx); dz = dz + max(dz)*eps;\n        % Dz = diag(dz);\n        sep_option = 1;\n        if (sep_option==1)\n            tolx(p) = mean(sqrt(dx.*dz)); %#ok\n            tolz(p) = tolx(p); %#ok\n        elseif (sep_option==2)\n            ddtmp = dx./dz;\n            idxtmp = find(ddtmp<1e12);\n            len = max(3,min(idxtmp));\n            ddratio = ddtmp(len:n-1)./ddtmp(len+1:n);\n            [dummy,idxmax] = max(ddratio); %#ok\n            idxmax2 = idxtmp(idxtmp==len+idxmax-1);\n            tmptolx = mean(dx(idxmax2:idxmax2+1));\n            tmptolz = mean(dz(idxmax2:idxmax2+1));\n            tolx(p) = exp(mean(log([tmptolx tmptolz]))); %#ok\n            tolz(p) = tolx(p); %#ok\n        end\n        idxr = find(dx > tolx(p));  rp = length(idxr);\n        idxs = find(dz > tolz(p));  sp = length(idxs);\n        r(p) = rp; s(p) = sp; %#ok\n        prim_rank(p)    = n*(n+1)/2 - (n-rp)*(n-rp+1)/2; %#ok\n        dual_rank(p)   = (n-sp)*(n-sp+1)/2; %#ok\n        strict_comp(p) = (r(p)+s(p) == n); %#ok\n        if (nargout > 2)\n            Q1 = Qx(:,idxr); Q2 = Qx(:,setdiff(1:n,idxr));\n            B11 = zeros(m,rp*(rp+1)/2);\n            B22 = zeros(m,rp*(n-rp));\n            for k = 1:m\n                Ak = smat(blk(p,:),At{p}(:,k));\n                tmp = Q1'*Ak*Q2;\n                B22(k,:) = tmp(:)';\n                tmp = Q1'*Ak*Q1;\n                B11(k,:) = svec(blk(p,:),tmp)';\n            end\n            XB1 = [XB1, B11]; %#ok\n            XB2 = [XB2, sqrt(2)*B22]; %#ok\n            Qz1 = Qz(:,setdiff(1:n,idxs));\n            ZB11 = zeros(m,(n-sp)*(n-sp+1)/2);\n            for k = 1:m\n                Ak = smat(blk(p,:),At{p}(:,k));\n                tmp = Qz1'*Ak*Qz1;\n                ZB11(k,:) = svec(blk(p,:),tmp)';\n            end\n            ZB1 = [ZB1, ZB11]; %#ok\n        end\n    elseif strcmp(blk{p,1},'q')\n        error('qblk is not allowed at the moment.');\n    elseif strcmp(blk{p,1},'l')\n        % mu = X{p}'*Z{p}/n;\n        dx = sort(X{p}); dx = dx(n:-1:1);\n        dz = sort(Z{p});\n        tolx(p) = mean(sqrt(dx.*dz));  %#ok\n        tolz(p) = tolx(p); %#ok\n        idxr = find(dx > tolx(p));  rp = length(idxr);\n        idxs = find(dz > tolz(p));  sp = length(idxs);\n        r(p) = rp; s(p) = sp; %#ok\n        prim_rank(p)    = rp; %#ok\n        dual_rank(p)    = n-sp; %#ok\n        strict_comp(p) = (r(p)+s(p) == n);  %#ok\n        if (nargout > 2)\n            idx = X{p} > tolx(p);\n            XB1 = [XB1, full(At{p}(idx,:))']; %#ok\n            zidx = Z{p} < tolz(p);\n            ZB1 = [ZB1, full(At{p}(zidx,:))']; %#ok\n        end\n    end\n    ddx{p} = dx; ddz{p} = dz; %#ok\n    fprintf('\\n    blkno = %1.0d, tol = %3.1e,%3.1e,  m = %2.0d',...\n        p,tolx(p),tolz(p),m);\n    fprintf('\\n    n= %2.0d,  r= %2.0d, s= %2.0d',n,r(p),s(p));\n    fprintf('\\n    n2-(n-r)2 = %2.0d',prim_rank(p));\n    fprintf('\\n    (n-s)2    = %2.0d',dual_rank(p));\n    fprintf('\\n    complemen = %2.1e  %2.1e\\n',max(dx+dz),min(dx+dz));\n    subplot(121)\n    color = (1-p/numblk)*[0 0 1] + (p/numblk)*[1 0 0];\n    semilogy(dx,'+','color',color); hold on;\n    semilogy(dz,'o','color',color);\n    semilogy([1 n],tolx(p)*[1 1],'color',color);\n    semilogy([1 n],tolz(p)*[1 1],'--','color',color);\n    title('eig(X) and eig(Z)');\n    subplot(122)\n    semilogy(dx+dz,'*','color',color); hold on;\n    title('dx+dz')\nend\n%%\n%%\n%%\nsubplot(121); hold off; subplot(122); hold off;\nprim_non_degen = (sum(prim_rank)  >= m);\ndual_non_degen = (sum(dual_rank) <= m);\nfprintf('\\n    sum(n2-(n-r)2)            = %2.0d (>=m)',sum(prim_rank));\nfprintf('\\n    sum((n-s)2)               = %2.0d (<=m)',sum(dual_rank));\nfprintf('\\n    nec. cond. prim_non_degen = %1d',prim_non_degen);\nfprintf('\\n    nec. cond. dual_non_degen = %1d',dual_non_degen);\nfprintf('\\n    strict_comp               = %1d\\n',all(strict_comp));\nsig1 = svd(ZB1);\nsig12 = svd([XB1, XB2]');\nfprintf('\\n    svd(ZB1):          max, min = %2.1e %2.1e, cond = %2.1e\\n',...\n    max(sig1),min(sig1),max(sig1)/min(sig1));\nfprintf('    svd([XB1, XB2]^T): max, min = %2.1e %2.1e, cond = %2.1e\\n',...\n    max(sig12),min(sig12),max(sig12)/min(sig12));\n%%***************************************************************\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "mytime.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/mytime.m", "size": 557, "source_encoding": "utf_8", "md5": "2d076b8ed1091521bbe986194c6e2b84", "text": "%%*********************************************\n%% mytime:\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*********************************************\n\nfunction [hh,mm,ss] = mytime(t)\n\nt = round(t);\nh = floor(t/3600);\nm = floor(rem(t,3600)/60);\ns = rem(rem(t,60),60);\n\nhh = num2str(h);\nif (h > 0) && (m < 10)\n    mm = ['0',num2str(m)];\nelse\n    mm = num2str(m);\nend\nif (s < 10)\n    ss = ['0',num2str(s)];\nelse\n    ss = num2str(s);\nend\n%%**********************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "validate.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/validate.m", "size": 7427, "source_encoding": "utf_8", "md5": "60774ce6b3bd16bde97e4078d89db39b", "text": "%%***********************************************************************\n%% validate: validate data\n%%\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***********************************************************************\n\nfunction  [blk,At,C,b,dim,nnblk,parbarrier] = ...\n    validate(blk,At,C,b,par,parbarrier)\n\nif (nargin >= 5)\n    spdensity = par.spdensity;\nelse\n    spdensity = 0.4;\nend\n%%\nif ~iscell(blk);\n    error('validate: blk must be a cell array'); end;\nif (size(blk,2) < 2)\n    error('validate: blk must be a cell array with at least 2 columns');\nend\nif ~iscell(At) || ~iscell(C);\n    error('validate: At, C must be cell arrays');\nend\nif (size(At,1) ~= size(blk,1))\n    if (size(At,2) == size(blk,1));\n        At = At';\n    else\n        error('validate: size of At is not compatible with blk');\n    end\nend\nif (size(C,1) ~= size(blk,1))\n    if (size(C,2) == size(blk,1))\n        C = C';\n    else\n        error('validate: size of C is not compatible with blk');\n    end\nend\nif (min(size(b)) > 1); error('validate: b must be a vector'); end\nif (size(b,1) < size(b,2)); b = b'; end\nm = length(b);\n%%\n%%-----------------------------------------\n%% validate blk, At, C\n%%-----------------------------------------\n%%\nfor p = 1:size(blk,1)\n    if (size(blk{p,2},1) > size(blk{p,2},2))\n        blk{p,2} = blk{p,2}';\n    end\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    numblk = length(pblk{2});\n    if strcmp(pblk{1},'s');\n        m1 = size(At{p,1},2);\n        n2 = sum(pblk{2}.*pblk{2});  n22 = sum(pblk{2}.*(pblk{2}+1))/2;\n        ntotal(p) = n22; %#ok\n        if ~all(size(C{p}) == n)\n            error('validate: blk and C are not compatible');\n        end\n        if (norm(C{p}-C{p}',inf) > 1e-13*norm(C{p},inf));\n            error('validate: C is not symmetric');\n        end\n        if all(size(At{p,1})==[m1, n22]) && m1~=n22\n            At{p,1} = At{p,1}';\n        end\n        if (~isempty(At{p,1})) && (size(At{p,1},1) ~= n22)\n            error('validate: blk and At not compatible');\n        end\n        if (nnz(At{p,1}) < spdensity*n22*m1)\n            if ~issparse(At{p,1}); At{p,1} = sparse(At{p,1}); end\n        end\n        if (length(pblk) > 2) %% for low rank constraints\n            len = sum(pblk{3});\n            if (size(pblk{1,3},2) < size(pblk{1,3},1))\n                blk{p,3} = blk{p,3}';\n            end\n            if any(size(At{p,2})- [n,len])\n                error(' low rank structure specified in blk and At not compatible')\n            end\n            if (length(At(p,:)) > 2)  && ~isempty(At{p,3})\n                if all(size(At{p,3},2)-[1,4])\n                    error(' low rank structure in At{p,3} not specified correctly')\n                end\n                if (size(At{p,3},2) == 1)\n                    if (size(At{p,3},1) < size(At{p,3},2)); At{p,3} = At{p,3}'; end\n                    lenn = length(At{p,3});\n                    constrnum = mexexpand(pblk{3},(1:length(pblk{3}))');\n                    At{p,3} = [constrnum, (1:lenn)', (1:lenn)', At{p,3}];\n                elseif (size(At{p,3},2) == 4)\n                    dd = At{p,3};\n                    [dummy,idxsort] = sort(dd(:,1)); %#ok\n                    dd = dd(idxsort,:);\n                    lenn = size(dd,1);\n                    idxD = [0; find(diff(dd(:,1))); lenn];\n                    ii = zeros(lenn,1); jj = zeros(lenn,1);\n                    ss = [0,cumsum(pblk{3})];\n                    for k = 1:length(pblk{3})\n                        idx = idxD(k)+1 : idxD(k+1);\n                        ii(idx) = dd(idx,2)+ss(k); %% convert to cumulative indexing\n                        jj(idx) = dd(idx,3)+ss(k);\n                    end\n                    At{p,3} = [dd(:,1),ii,jj,dd(:,4)];\n                end\n            else\n                constrnum = mexexpand(pblk{3},(1:length(pblk{3}))');\n                At{p,3} = [constrnum, (1:len)', (1:len)', ones(len,1)];\n            end\n        end\n        if (nnz(C{p}) < spdensity*n2) || (numblk > 1);\n            if ~issparse(C{p}); C{p} = sparse(C{p}); end;\n        else\n            if issparse(C{p}); C{p} = full(C{p}); end;\n        end\n    elseif strcmp(pblk{1},'q') || strcmp(pblk{1},'l') || strcmp(pblk{1},'u');\n        ntotal(p) = n; %#ok\n        if (min(size(C{p})) ~= 1 || max(size(C{p})) ~= n);\n            error('validate: blk and C are not compatible');\n        end;\n        if (size(C{p},1) < size(C{p},2)); C{p} = C{p}'; end\n        if all(size(At{p,1}) == [m, n]) && m~=n;\n            At{p,1} = At{p,1}';\n        end\n        if ~all(size(At{p,1}) == [n,m]);\n            error('validate: blk and At not compatible');\n        end\n        if ~issparse(At{p,1});\n            At{p,1} = sparse(At{p,1});\n        end\n        if (nnz(C{p}) < spdensity*n);\n            if ~issparse(C{p}); C{p} = sparse(C{p}); end;\n        else\n            if issparse(C{p}); C{p} = full(C{p}); end;\n        end;\n    else\n        error(' blk: some fields are not specified correctly');\n    end\nend\nif (sum(ntotal) < m)\n    error(' total dimension of C should be > length(b)');\nend\n%%\n%%-----------------------------------------\n%% problem dimension\n%%-----------------------------------------\n%%\ndim = zeros(1,4);\nnnblk = zeros(1,2);\nnn = zeros(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        dim(1) = dim(1) + sum(pblk{2});\n        nnblk(1) = nnblk(1) + length(pblk{2});\n        nn(p) = sum(pblk{2});\n    elseif strcmp(pblk{1},'q')\n        dim(2) = dim(2) + sum(pblk{2});\n        nnblk(2) = nnblk(2) + length(pblk{2});\n        nn(p) = length(pblk{2});\n    elseif strcmp(pblk{1},'l')\n        dim(3) = dim(3) + sum(pblk{2});\n        nn(p) = sum(pblk{2});\n    elseif strcmp(pblk{1},'u')\n        dim(4) = dim(4) + sum(pblk{2});\n        nn(p) = sum(pblk{2});\n    end\nend\n%%\n%%-----------------------------------------\n%% validate parbarrier\n%%-----------------------------------------\n%%\nif (nargin == 6)\n    if ~iscell(parbarrier);\n        if (length(parbarrier) == size(blk,1))\n            tmp = parbarrier;\n            clear parbarrier;\n            parbarrier = cell(size(blk,1),1);\n            for p = 1:size(blk,1)\n                parbarrier{p} = tmp(p);\n            end\n        end\n    end\n    if (size(parbarrier,2) > size(parbarrier,1))\n        parbarrier = parbarrier';\n    end\n    for p = 1:size(blk,1)\n        pblk = blk(p,:);\n        if (size(parbarrier{p},1) > size(parbarrier{p},2))\n            parbarrier{p} = parbarrier{p}';\n        end\n        len = length(parbarrier{p});\n        if strcmp(pblk{1},'s') || strcmp(pblk{1},'q')\n            if (len == 1)\n                parbarrier{p} = parbarrier{p}*ones(1,length(pblk{2}));\n            elseif (len == 0)\n                parbarrier{p} = zeros(1,length(pblk{2}));\n            elseif (len ~= length(pblk{2}))\n                error('blk and parbarrier not compatible');\n            end\n        elseif strcmp(pblk{1},'l')\n            if (len == 1)\n                parbarrier{p} = parbarrier{p}*ones(1,sum(pblk{2}));\n            elseif (len == 0)\n                parbarrier{p} = zeros(1,sum(pblk{2}));\n            elseif (len ~= sum(pblk{2}))\n                error('blk and parbarrier not compatible');\n            end\n        elseif strcmp(pblk{1},'u')\n            parbarrier{p}= zeros(1,sum(pblk{2}));\n        end\n    end\nend\n%%***********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "svec.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/svec.m", "size": 2027, "source_encoding": "utf_8", "md5": "43bbe7c274d2d1bfb1014fc2000f47e3", "text": "%*********************************************************\n%% svec: compute the vector svec(M),\n%%\n%%   x = svec(blk,M,isspx);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************\n\nfunction x = svec(blk,M,isspx)\n\nif iscell(M)\n    if (size(blk,1) ~= size(M,1))\n        error('svec: number of rows in blk and M not equal');\n    end\n    if (nargin == 2)\n        %%if (size(M,2) == 1)\n        %%   isspx = zeros(size(blk,1),1);\n        %%else\n        %%   isspx = ones(size(blk,1),1);\n        %%end\n        isspx = ones(size(blk,1),1);\n    else\n        if (length(isspx) < size(blk,1))\n            isspx = ones(size(blk,1),1);\n        end\n    end\n    x = cell(size(blk,1),1);\n    for p=1:size(blk,1)\n        pblk = blk(p,:);\n        n = sum(pblk{2});  m = size(M,2);\n        if strcmp(pblk{1},'s')\n            n2 = sum(pblk{2}.*(pblk{2}+1))/2;\n            if (isspx(p));\n                x{p} = sparse(n2,m);\n            else\n                x{p} = zeros(n2,m);\n            end\n            numblk = length(pblk{2});\n            if (pblk{2} > 0)\n                for k = 1:m\n                    if (numblk > 1) && ~issparse(M{p,k});\n                        x{p}(:,k) = mexsvec(pblk,sparse(M{p,k}),isspx(p));\n                    else\n                        x{p}(:,k) = mexsvec(pblk,M{p,k},isspx(p));\n                    end\n                end\n            end\n        else\n            if (isspx(p))\n                x{p} = sparse(n,m);\n            else\n                x{p} = zeros(n,m);\n            end\n            for k = 1:m\n                x{p}(:,k) = M{p,k};\n            end\n        end\n    end\nelse\n    if strcmp(blk{1},'s')\n        numblk = length(blk{2});\n        if (numblk > 1) && ~issparse(M);\n            x = mexsvec(blk,sparse(M),1);\n        else\n            x = mexsvec(blk,sparse(M));\n        end\n    else\n        x = M;\n    end\nend\n%%**********************************************************\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "read_sedumi.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/read_sedumi.m", "size": 7265, "source_encoding": "utf_8", "md5": "2035885c053fc9de89cf19fa667603d2", "text": "%%*******************************************************************\n%%  Read in a problem in SeDuMi format.\n%%\n%%  [blk,A,C,b,perm] = read_sedumi(fname,b,c,K)\n%%\n%%  Input: fname.mat = name of the file containing SDP data in\n%%                     SeDuMi format.\n%%\n%% Important note: Sedumi's notation for free variables \"K.f\"\n%%                 is coded in SDPT3 as blk{p,1} = 'u', where\n%%                 \"u\" is used for unrestricted variables.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%******************************************************************\n\nfunction [blk,Avec,C,b,perm] = read_sedumi(fname,b,c,K,smallblkdim)\n\nif (nargin < 5)\n    smallblkdim = 50;\nend\n\nA = 0;\nAt = 0;\nif ischar(fname)\n    %%\n    %%  load the matlab file containing At, c, b, and K\n    %%\n    K.f = []; K.l = []; K.q = [];\n    compressed = 0;\n    if exist([fname,'.mat.gz'],'file');\n        compressed = 1;\n        unix(['gunzip ', fname,'.mat.gz']);\n    elseif exist([fname,'.gz'],'file');\n        compressed = 2;\n        unix(['gunzip ', fname,'.gz']);\n    elseif exist([fname,'.mat.Z'],'file');\n        compressed = 3;\n        unix(['uncompress ', fname,'.mat.Z'],'file');\n    elseif exist([fname,'.Z'],'file');\n        compressed = 4;\n        unix(['uncompress ', fname,'.Z']);\n    end\n    if exist([fname,'.mat'],'file') || exist(fname,'file')\n        eval(['load ', fname]);\n    else\n        fprintf('*** Problem not found, please specify the correct path or problem. \\n');\n        blk = []; Avec = []; C = []; b = [];\n        return;\n    end\n    if (compressed == 1)\n        unix(['gzip ', fname,'.mat']);\n    elseif (compressed == 2)\n        unix(['gzip ', fname]);\n    elseif (compressed == 3)\n        unix(['compress ', fname,'.mat']);\n    elseif (compressed == 4)\n        unix(['compress ', fname]);\n    end\nelseif (nargin < 4)\n    error('read_sedumi: need 4 input ');\nelse\n    A = fname;\nend\n%%\nif exist('c','var')\n    if (size(c,1) == 1), c = c'; end;\nend\nif exist('C','var')\n    c = C; %#ok\n    if (size(c,1) == 1), c = c'; end;\nend\nif (size(b,1) == 1), b = b'; end;\nif (norm(A,'fro') > 0) && (size(A,2) == length(b)); At = A; end\n%%\nif (norm(At,'fro')==0), At = A'; end;\nnn = size(At,1); if (max(size(c)) == 1); c = c*ones(nn,1); end;\nif ~isfield(K,'f'); K.f = 0; end\nif ~isfield(K,'l'); K.l = 0; end\nif ~isfield(K,'q'); K.q = 0; end\nif ~isfield(K,'s'); K.s = 0; end\nif (K.f == 0) || isempty(K.f); K.f = 0; end;\nif (K.l == 0) || isempty(K.l); K.l = 0; end;\nif (sum(K.q) == 0) || isempty(K.q); K.q = 0; end\nif (sum(K.s) == 0) || isempty(K.s); K.s = 0; end\n%%\n%%\n%%\n% m = length(b);\nrowidx = 0;  idxblk = 0;\nif ~(K.f == 0)\n    len = K.f;\n    idxblk = idxblk + 1;\n    blk{idxblk,1} = 'u'; blk{idxblk,2} = K.f;\n    Atmp = At(rowidx+1:rowidx+len,:);\n    Avec{idxblk,1} = Atmp;\n    C{idxblk,1} = c(rowidx+1:rowidx+len);\n    perm{idxblk} = [];\n    rowidx = rowidx + len;\nend\nif ~(K.l == 0)\n    len = K.l;\n    idxblk = idxblk + 1;\n    blk{idxblk,1} = 'l'; blk{idxblk,2} = K.l;\n    Atmp = At(rowidx+1:rowidx+len,:);\n    Avec{idxblk,1} = Atmp;\n    C{idxblk,1} = c(rowidx+1:rowidx+len);\n    perm{idxblk} = [];\n    rowidx = rowidx + len;\nend\nif ~(K.q == 0)\n    len = sum(K.q);\n    idxblk = idxblk + 1;\n    blk{idxblk,1} = 'q';\n    if size(K.q,1) <= size(K.q,2);\n        blk{idxblk,2} = K.q;\n    else\n        blk{idxblk,2} = K.q';\n    end\n    Atmp = At(rowidx+1:rowidx+len,:);\n    Avec{idxblk,1} = Atmp;\n    C{idxblk,1} = c(rowidx+1:rowidx+len);\n    perm{idxblk} = [];\n    rowidx = rowidx + len;\nend\nif ~(K.s == 0)\n    blksize = K.s;\n    if (size(blksize,2) == 1); blksize = blksize'; end\n    blknnz = [0, cumsum(blksize.*blksize)];\n    deblkidx = find(blksize > smallblkdim);\n    if ~isempty(deblkidx)\n        for p = 1:length(deblkidx)\n            idxblk = idxblk + 1;\n            n = blksize(deblkidx(p));\n            pblk{1,1} = 's'; pblk{1,2} = n;\n            blk(idxblk,:) = pblk;\n            Atmp = At(rowidx+blknnz(deblkidx(p))+(1:n*n),:);\n            %%\n            %% column-wise positions of upper triangular part\n            %%\n            tmp = triu(ones(n)); tmp = tmp(:);\n            idxtriu = find(tmp);\n            %%\n            %% row-wise positions of lower triangular part\n            %%\n            tmp = tril(reshape(1:n*n,n,n)); tmp = tmp(:);\n            idxtmp = find(tmp);\n            [dummy,idxsub] = sort(rem(tmp(idxtmp),n)); %#ok\n            idxtril = [idxtmp(idxsub(n+1:end));idxtmp(idxsub(1:n))];\n            %%\n            tmp2 = sqrt(2)*triu(ones(n),1) + speye(n,n);\n            tmp2 = tmp2(:);\n            dd = tmp2(tmp2~=0);\n            n2 = n*(n+1)/2;\n            Atmptriu = Atmp(idxtriu,:); %#ok\n            Atmptril = Atmp(idxtril,:);\n            if (norm(Atmptriu-Atmptril,'fro') > 1e-13)\n                fprintf('\\n warning: constraint matrices not symmetric.');\n                fprintf('\\n          matrices are symmetrized.\\n');\n                Atmptriu = 0.5*(Atmptriu+Atmptril);\n            end\n            Avec{idxblk,1} = spdiags(dd,0,n2,n2)*Atmptriu;\n            Ctmp = c(rowidx+blknnz(deblkidx(p))+(1:n*n));\n            Ctmp = mexmat(pblk,Ctmp,1);\n            C{idxblk,1} = 0.5*(Ctmp+Ctmp'); %#ok\n            perm{idxblk,1} = deblkidx(p);\n        end\n    end\n    spblkidx = find(blksize <= smallblkdim);\n    if ~isempty(spblkidx)\n        cnt = 0;  cnt2 = 0;\n        spblksize = blksize(spblkidx);\n        nn  = sum(spblksize.*spblksize);\n        nn2 = sum(spblksize.*(spblksize+1)/2);\n        pos = zeros(nn,1);\n        dd  = zeros(nn2,1);\n        idxtriu = zeros(nn2,1);\n        idxtril = zeros(nn2,1);\n        for p = 1:length(spblkidx)\n            n = blksize(spblkidx(p));\n            n2 = n*(n+1)/2;\n            pos(cnt+1:cnt+n*n) = rowidx+blknnz(spblkidx(p))+(1:n*n);\n            %%\n            %% column-wise positions of upper triangular part\n            %%\n            tmp  = triu(ones(n)); tmp = tmp(:);\n            idxtriu(cnt2+1:cnt2+n2) = cnt+find(tmp);\n            %%\n            %% row-wise positions of lower triangular part\n            %%\n            tmp = tril(reshape(1:n*n,n,n)); tmp = tmp(:);\n            idxtmp = find(tmp);\n            [dummy,idxsub] = sort(rem(tmp(idxtmp),n)); %#ok\n            idxtril(cnt2+1:cnt2+n2) = cnt+[idxtmp(idxsub(n+1:end));idxtmp(idxsub(1:n))];\n            %%\n            tmp2 = sqrt(2)*triu(ones(n),1) + speye(n,n);\n            tmp2 = tmp2(:);\n            dd(cnt2+1:cnt2+n2) = tmp2(tmp2~=0);\n            cnt  = cnt + n*n;\n            cnt2 = cnt2 + n2;\n        end\n        idxblk = idxblk + 1;\n        blk{idxblk,1} = 's';  blk{idxblk,2} = blksize(spblkidx);\n        Atmp = At(pos,:);\n        Atmptriu = Atmp(idxtriu,:);\n        Atmptril = Atmp(idxtril,:);\n        if (norm(Atmptriu-Atmptril,'fro') > 1e-13)\n            fprintf('\\n warning: constraint matrices not symmetric.');\n            fprintf('\\n          matrices are symmetrized.\\n');\n            Atmptriu = 0.5*(Atmptriu+Atmptril);\n        end\n        Avec{idxblk,1} = spdiags(dd,0,length(dd),length(dd))*Atmptriu;\n        Ctmp = c(pos);\n        Ctmp = mexmat(blk(idxblk,:),Ctmp,1);\n        C{idxblk,1} = 0.5*(Ctmp+Ctmp');\n        perm{idxblk,1} = spblkidx;\n    end\nend\n%%\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "qprod.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/qprod.m", "size": 655, "source_encoding": "utf_8", "md5": "2d6550b139b2dfcf3d134f61deec3686", "text": "%%***************************************************\n%% qprod:\n%%\n%% Input: A = [A1 A2 ... An]\n%%        x = [x1; x2; ...; xn]\n%% Output: [A1*x1 A2*x2 ... An*xn]\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***************************************************\n\nfunction Ax = qprod(pblk,A,x)\n\nif (size(pblk,1) > 1)\n    error('qprod: pblk can only have 1 row');\nend\nif issparse(x); x = full(x); end; %% for spconvert\nn = length(x);\nii = (1:n)';\njj = mexexpand(pblk{2},(1:length(pblk{2}))');\nX = spconvert([ii, jj, x]);\nAx = A*X;\n%%***************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "nzlist.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/nzlist.m", "size": 5014, "source_encoding": "utf_8", "md5": "1225b63fe9e00df09546243f60599dc2", "text": "%%***********************************************************************\n%% nzlist: find the combined list of non-zero elements\n%%         of Aj, j = 1:k, for each k,\n%%         assuming that the Aj's are permuted such that\n%%         A1 has the fewest nonzero elements, followed by A2, and so on.\n%%\n%% [isspA,nzlistA,nzlistAsum,isspAy,nzlistAy] = nzlist(blk,At,par)\n%%\n%%  isspA(p,k) = 1 if Apk is sparse, 0 if it is dense.\n%%  nzlistA = px2 cell array.\n%%            nzlistA{p,1}(k) is the starting row index (in C convention)\n%%            in the 2-column matrix nzlistA{p,2} that\n%%            stores the row and column index of the nonzero elements\n%%            of Apk.\n%%            nzlistA{p,1}(k) = inf if nnz(Apk) exceeds given threshold.\n%%  nzlistAsum = px2 cell array.\n%%            nzlistA{p,1}(k) is the starting row index (in C convention)\n%%            in the 2-column matrix nzlistA{p,2} that\n%%            stores the row and column index of the nonzero elements\n%%            of Apk that are not already present\n%%            in the combined list from Ap1+...Ap,k-1.\n%%  nzlistAy = px1 cell array.\n%%            nzlistAy{p} is a 2-column matrix that stores the\n%%            row and column index of the nonzero elements of\n%%            Ap,1+.... Ap,m.\n%%            nzlistAy{p} = inf  if the number of nonzero elements\n%%            exceeds a given threshold.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***********************************************************************\n\nfunction [isspA,nzlistA,nzlistAsum,isspAy,nzlistAy] = nzlist(blk,At,par)\n\nspdensity   = par.spdensity;\nsmallblkdim = par.smallblkdim;\nm = par.numcolAt;\n%%\nnumblk = size(blk,1);\nisspA = zeros(numblk,m);\nnzlistA = cell(numblk,2);  nzlistAsum = cell(numblk,2);\nisspAy = zeros(numblk,1);  nzlistAy = cell(numblk,1);\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s') && ((max(pblk{2}) > smallblkdim) || (length(pblk{2}) <= 10))\n        numblk = length(pblk{2});\n        n = sum(pblk{2});\n        n2 = sum(pblk{2}.*pblk{2});\n        if (numblk == 1)\n            nztol = spdensity*n;\n            nztol2 = spdensity*n2/2;\n        else\n            nztol = spdensity*n/2;\n            nztol2 = spdensity*n2/4;\n        end\n        nzlist1 = zeros(1,m+1); nzlist2 = [];\n        nzlist3 = zeros(1,m+1); nzlist4 = []; breakyes = zeros(1,2);\n        Asum = sparse(n,n);\n        %%\n        m1 = size(At{p,1},2);\n        for k = 1:m1\n            Ak = mexsmat(blk,At,1,p,k);\n            nnzAk = nnz(Ak);\n            isspA(p,k) = (nnzAk < spdensity*n2) || (numblk > 1);\n            if ~all(breakyes)\n                [I,J] = find(abs(Ak) > 0);\n                %%\n                %% nonzero elements of Ak.\n                %%\n                if (breakyes(1) == 0);\n                    if (nnzAk <= nztol)\n                        idx = find(I<=J);\n                        nzlist1(k+1) = nzlist1(k)+length(idx);\n                        nzlist2 = [nzlist2; [I(idx), J(idx)] ]; %#ok\n                    else\n                        nzlist1(k+1:m+1) = inf*ones(1,m-k+1);\n                        breakyes(1) = 1;\n                    end\n                end\n                %%\n                %% nonzero elements of ||A1||+...+||Ak||.\n                %%\n                if (breakyes(2) == 0)\n                    nztmp = zeros(length(I),1);\n                    for t = 1:length(I);\n                        i=I(t); j=J(t); nztmp(t)=Asum(i,j);\n                    end\n                    %% find new nonzero positions when Ak is added to Asum.\n                    idx = find(nztmp == 0);\n                    nzlist3(k+1) = nzlist3(k) + length(idx);\n                    if (nzlist3(k+1) < nztol2);\n                        nzlist4 = [ nzlist4; [I(idx), J(idx)] ]; %#ok\n                    else\n                        nzlist3(k+1:m+1) = inf*ones(1,m-k+1);\n                        breakyes(2) = 1;\n                    end\n                    Asum = Asum+abs(Ak);\n                end\n            end\n        end\n        if (numblk == 1)\n            isspAy(p,1) = (nzlist1(m+1) < inf) || (nzlist3(m+1) < inf);\n        else\n            isspAy(p,1) = 1;\n        end\n        nzlistA{p,1} = nzlist1;    nzlistA{p,2} = nzlist2;\n        nzlistAsum{p,1} = nzlist3; nzlistAsum{p,2} = nzlist4;\n        %%\n        %% nonzero elements of (A1*y1+...Am*ym).\n        %%\n        if (nzlist3(m+1) < inf);\n            if (length(pblk) > 2)\n                % m2 = length(pblk{3});\n                len = sum(pblk{3});\n                DD = spconvert([At{p,3}(:,2:4); len, len, 0]);\n                Asum = Asum + abs(At{p,2}*DD*At{p,2}');\n            end\n            [I,J] = find(Asum > 0);\n            if (length(I) < nztol2)\n                nzlistAy{p} = [I, J];\n            else\n                nzlistAy{p} = inf;\n            end\n        else\n            nzlistAy{p} = inf;\n        end\n    end\nend\n%%***********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "detect_lblk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/detect_lblk.m", "size": 4228, "source_encoding": "utf_8", "md5": "a69e3486a171d0169d9b62b749ed6cd8", "text": "%%*******************************************************************\n%% detect_lblk: detect diagonal blocks in the SDP data.\n%%\n%% [blk,At,C,diagblkinfo,blockchange,parbarrier,X,Z] = ...\n%%           detect_lblk(blk,At,C,b,parbarrier,X,Z);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%******************************************************************\n\nfunction [blk,At,C,diagblkinfo,blockchange,parbarrier,X,Z] = ...\n    detect_lblk(blk,At,C,b,parbarrier,X,Z,printlevel)\n\nif (nargin < 8); printlevel = 1; end\n%%\n%% Acum = abs(C) + sum_{k=1}^m abs(Ak)\n%% but with diagonal elements removed.\n%%\nblkold = blk;\nm = length(b);\nee = ones(m,1);\nnumdiagelt  = 0;\nnumblknew   = 0;\nblockchange = zeros(size(blk,1),1);\nAcum        = cell(size(blk,1),1);\ndiagblkinfo = cell(size(blk,1),3);\nfor p=1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'s') && (length(pblk{2}) == 1) && (size(At{p,1},2)==m)\n        Acumtmp = smat(blk(p,:),abs(At{p})*ee,1) + abs(C{p});\n        Acum{p} = Acumtmp - spdiags(diag(Acumtmp),0,n,n);\n        rownorm = sqrt(sum(Acum{p}.*Acum{p}))';\n        idxdiag = find(rownorm < 1e-15);\n        if ~isempty(idxdiag)\n            blockchange(p) = 1;\n            numdiagelt = numdiagelt + length(idxdiag);\n            idxnondiag = setdiff((1:n)',idxdiag);\n            diagblkinfo{p,2} = idxdiag;\n            diagblkinfo{p,3} = idxnondiag(:);\n            if ~isempty(idxnondiag)\n                numblknew = numblknew + 1;\n                diagblkinfo{p,1} = numblknew;\n            end\n        else\n            numblknew = numblknew + 1;\n            diagblkinfo{p,1} = numblknew;\n        end\n    else\n        numblknew = numblknew + 1;\n        diagblkinfo{p,1} = numblknew;\n    end\nend\n%%\n%% extract diagonal sub-blocks in nondiagonal blocks\n%% into a single linear block\n%%\nif any(blockchange)\n    numblk = size(blkold,1);\n    idx_keepblk = [];\n    Atmp  = cell(1,m);\n    Adiag = cell(1,m);\n    C(numblk+1,1) = cell(1,1);\n    Cdiag = []; Xdiag = []; Zdiag = [];\n    parbarrierdiag = [];\n    for p = 1:numblk\n        % n = sum(blkold{p,2});\n        if (blockchange(p)==1)\n            idxdiag    = diagblkinfo{p,2};\n            idxnondiag = diagblkinfo{p,3};\n            if ~isempty(idxdiag);\n                blk{p,2} = length(idxnondiag);\n                len = length(idxdiag);\n                for k = 1:m;\n                    Ak = mexsmat(blkold,At,1,p,k);\n                    tmp = diag(Ak);\n                    Atmp{k} = Ak(idxnondiag,idxnondiag);\n                    Adiag{k} = [Adiag{k}; tmp(idxdiag)];\n                end\n                tmp = diag(C{p,1});\n                Cdiag = [Cdiag; tmp(idxdiag)]; %#ok\n                C{p,1} = C{p,1}(idxnondiag,idxnondiag);\n                At(p) = svec(blk(p,:),Atmp,1);\n                if (nargin >= 7)\n                    parbarrierdiag = [parbarrierdiag, parbarrier{p}*ones(1,len)]; %#ok\n                    tmp = diag(X{p,1});\n                    Xdiag = [Xdiag; tmp(idxdiag)]; %#ok\n                    tmp = diag(Z{p,1});\n                    Zdiag = [Zdiag; tmp(idxdiag)]; %#ok\n                    X{p,1} = X{p,1}(idxnondiag,idxnondiag);\n                    Z{p,1} = Z{p,1}(idxnondiag,idxnondiag);\n                end\n            end\n            if ~isempty(idxnondiag)\n                idx_keepblk = [idx_keepblk, p]; %#ok\n            else\n                if (printlevel)\n                    fprintf(' %2.0dth semidefinite block is actually diagonal\\n',p);\n                end\n            end\n        else\n            idx_keepblk = [idx_keepblk, p]; %#ok\n        end\n    end\n    blk{numblk+1,1} = 'l';\n    blk{numblk+1,2} = numdiagelt;\n    C{numblk+1,1}   = Cdiag;\n    At(numblk+1,1) = svec(blk(numblk+1,:),Adiag,1);\n    idx_keepblk = [idx_keepblk, numblk+1];\n    blk = blk(idx_keepblk,:);\n    C   = C(idx_keepblk,:);\n    At  = At(idx_keepblk,:);\n    if (nargin >= 7)\n        parbarrier{numblk+1,1} = parbarrierdiag;\n        X{numblk+1,1} = Xdiag;\n        Z{numblk+1,1} = Zdiag;\n        parbarrier = parbarrier(idx_keepblk);\n        X = X(idx_keepblk);\n        Z = Z(idx_keepblk);\n    end\nend\n%%******************************************************************\n\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "gdcomp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/gdcomp.m", "size": 4877, "source_encoding": "utf_8", "md5": "edb240000afc00b0bd8d14fdc4ab944c", "text": "%%*********************************************************************\n%% gdcomp: Compute gd = 1/td in Equation (15) of the paper:\n%%\n%% R.M. Freund, F. Ordonez, and K.C. Toh,\n%% Behavioral measures and their correlation with IPM iteration counts\n%% on semi-definite programming problems,\n%% Mathematical Programming, 109 (2007), pp. 445--475.\n%%\n%% [gd,info,yfeas,Zfeas,blk2,At2,C2,b2] = gdcomp(blk,At,C,b,OPTIONS,solveyes);\n%%\n%% yfeas,Zfeas: a dual feasible pair when gd is finite.\n%%              That is, if\n%%              Aty = Atyfun(blk,At,[],[],yfeas);\n%%              Rd = ops(C,'-',ops(Zfeas,'+',Aty));\n%%              then\n%%              ops(Rd,'norm') should be small.\n%%\n%%*********************************************************************\n\nfunction [gd,info,yfeas,Zfeas,blk2,At2,C2,b2] = gdcomp(blk,At,C,b,OPTIONS,solveyes)\n\nif (nargin < 6); solveyes = 1; end\nif (nargin < 5)\n    OPTIONS = sqlparameters;\n    OPTIONS.vers   = 1;\n    OPTIONS.gaptol = 1e-10;\n    OPTIONS.printlevel = 3;\nend\nif isempty(OPTIONS); OPTIONS = sqlparameters; end\nif ~isfield(OPTIONS,'solver'); OPTIONS.solver = 'HSDsqlp'; end\nif ~isfield(OPTIONS,'printlevel'); OPTIONS.printlevel = 3; end\nif ~iscell(C); tmp = C; clear C; C{1} = tmp; end\n%%\n%% convert ublk to lblk\n%%\n% exist_ublk = 0;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'u');\n        % exist_ublk = 1;\n        fprintf('\\n converting ublk into the difference of two non-negative vectors');\n        blk{p,1} = 'l'; blk{p,2} = 2*sum(blk{p,2});\n        At{p} = [At{p}; -At{p}];\n        C{p} = [C{p}; -C{p}];\n    end\nend\n%%\nm = length(b);\nblk2 = blk;\nAt2 = cell(size(blk,1),1);\nC2 = cell(size(blk,1),1);\nEE = cell(size(blk,1),1);\n%%\n%%\n%%\ndd = zeros(1,m);\nalp = 0;\nbeta = 0;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'s')\n        C2{p,1} = sparse(n,n);\n    else\n        C2{p,1} = zeros(n,1);\n    end\n    dd = dd + sqrt(sum(At{p}.*At{p}));\n    beta = beta + norm(C{p},'fro');\n    alp = alp + sqrt(n);\nend\nalp  = 1./max(1,alp);\nbeta = 1./max(1,beta);\ndd   = 1./max(1,dd);\n%%\n%% New multipliers in dual problem:\n%% [v; tt; theta].\n%%\nD = spdiags(dd',0,m,m);\n\nss = 0; cc = 0; aa = zeros(1,m);\n% exist_ublk = 0;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'s')\n        At2{p} = [At{p}*D, svec(pblk,alp*speye(n,n),1), -svec(pblk,beta*C{p},1)];\n        ss = ss + n;\n        cc = cc + trace(C{p});\n        aa = aa + svec(pblk,speye(n),1)'*At{p};\n        EE{p} = speye(n,n);\n    elseif strcmp(pblk{1},'q')\n        eq = zeros(n,1);\n        idx1 = 1+[0,cumsum(pblk{2})];\n        idx1 = idx1(1:length(idx1)-1);\n        eq(idx1) = ones(length(idx1),1);\n        At2{p} = [At{p}*D, 2*sparse(alp*eq), -sparse(beta*C{p})];\n        ss = ss + 2*length(pblk{2});\n        cc = cc + sum(C{p}(idx1));\n        aa = aa + eq'*At{p};\n        EE{p} = eq;\n    elseif strcmp(pblk{1},'l')\n        el = ones(n,1);\n        At2{p} = [At{p}*D, sparse(alp*el), -sparse(beta*C{p})];\n        ss = ss + n;\n        cc = cc + el'*C{p};\n        aa = aa + el'*At{p};\n        EE{p} = el;\n    elseif strcmp(pblk{1},'u')\n        At2{p} = [At{p}*D, sparse(n,1), -sparse(beta*C{p})];\n        % exist_ublk = 1;\n        EE{p} = sparse(n,1);\n    end\nend\naa = aa.*dd;\ncc = cc*beta;\n%%\n%% 4 additional inequality constraints in dual problem.\n%%\nnumblk = size(blk,1);\nblk2{numblk+1,1} = 'l'; blk2{numblk+1,2} = 4;\nC2{numblk+1,1}  = [1; 1; 0; 0];\nAt2{numblk+1,1} = [-aa,         0,   cc;\n    zeros(1,m),   0,   beta;\n    zeros(1,m),  alp, -beta\n    zeros(1,m), -alp,  0];\nAt2{numblk+1} = sparse(At2{numblk+1});\nb2 = [zeros(m,1); alp; 0];\n%%\n%% Solve SDP\n%%\ngd = []; info = []; yfeas = []; Zfeas = [];\nif (solveyes)\n    if strcmp(OPTIONS.solver,'sqlp')\n        [X0,y0,Z0] = infeaspt(blk2,At2,C2,b2,2,100);\n        [obj,X,y,Z,info] = sqlp(blk2,At2,C2,b2,OPTIONS,X0,y0,Z0); %#ok\n    elseif strcmp(OPTIONS.solver,'HSDsqlp');\n        [obj,X,y,Z,info] = HSDsqlp(blk2,At2,C2,b2,OPTIONS); %#ok\n    else\n        [obj,X,y,Z,info] = sdpt3(blk2,At2,C2,b2,OPTIONS); %#ok\n    end\n    tt = alp*abs(y(m+1)); theta = beta*abs(y(m+2));\n    yfeas = D*y(1:m)/theta;\n    Zfeas = ops(ops(Z(1:numblk),'+',EE,tt),'/',theta);\n    %%\n    if (obj(2) > 0) || (abs(obj(2)) < 1e-8)\n        gd = 1/abs(obj(2));\n    elseif (obj(1) > 0)\n        gd = 1/obj(1);\n    else\n        gd = 1/exp(mean(log(abs(obj))));\n    end\n    err = max(info.dimacs([1,3,6]));\n    if (OPTIONS.printlevel)\n        fprintf('\\n ******** gd = %3.2e, err = %3.1e\\n',gd,err);\n        if (err > 1e-6);\n            fprintf('\\n----------------------------------------------------')\n            fprintf('\\n gd problem is not solved to sufficient accuracy');\n            fprintf('\\n----------------------------------------------------\\n')\n        end\n    end\nend\n%%*********************************************************************\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "blkbarrier.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/blkbarrier.m", "size": 1795, "source_encoding": "utf_8", "md5": "77a6eb1f1708aa69217000d27735efa0", "text": "%%********************************************************************\n%% blkbarrier: calculate\n%% [-v(p)*logdet(X{p}),   v(p)*logdet(Z{p}) + n*v(p)*(1-log(v(p)))]\n%% [-v(p)*log(gam(X{p})), v(p)*log(gam(Z{p})) + v(p)]\n%% [-v(p)*log(X{p}),      v(p)*log(Z{p}) + n*v(p)*(1-log(v(p)))]\n%%********************************************************************\n\nfunction objadd = blkbarrier(blk,X,Z,Xchol,Zchol,v)\n\nobjadd = zeros(1,2); tmp = zeros(1,2);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    vp = v{p};\n    idx = find(vp > 0);\n    if ~isempty(idx)\n        vpsub = vp(idx);\n        if size(vpsub,1) < size(vpsub,2); vpsub = vpsub'; end\n        if strcmp(pblk{1},'s')\n            ss = [0, cumsum(pblk{2})];\n            logdetX = 2*log(diag(Xchol{p}));\n            logdetZ = 2*log(diag(Zchol{p}));\n            logdetXsub = zeros(length(idx),1);\n            logdetZsub = zeros(length(idx),1);\n            for k = 1:length(idx)\n                idxtmp = ss(idx(k))+1:ss(idx(k)+1);\n                logdetXsub(k) = sum(logdetX(idxtmp));\n                logdetZsub(k) = sum(logdetZ(idxtmp));\n            end\n            tmp(1) = -sum(vpsub.*logdetXsub);\n            tmp(2) = sum(vpsub.*logdetZsub + (pblk{2}(idx)').*vpsub.*(1-log(vpsub)));\n        elseif strcmp(pblk{1},'q')\n            gamX = sqrt(qops(pblk,X{p},X{p},2));\n            gamZ = sqrt(qops(pblk,Z{p},Z{p},2));\n            tmp(1) = -sum(vpsub.*log(gamX(idx)));\n            tmp(2) = sum(vpsub.*log(gamZ(idx)) + vpsub);\n        elseif strcmp(pblk{1},'l')\n            logX = log(X{p}); logZ = log(Z{p});\n            tmp(1) = -sum(vpsub.*logX(idx));\n            tmp(2) = sum(vpsub.*logZ(idx) + vpsub.*(1-log(vpsub)));\n        end\n        objadd = objadd + tmp;\n    end\nend\n%%********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlpmain.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlpmain.m", "size": 29812, "source_encoding": "utf_8", "md5": "97c549fe923480dc73e59887089d5656", "text": "%%*************************************************************************\n%% sqlp: main solver\n%%\n%%*************************************************************************\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction [obj,X,y,Z,info,runhist] = sqlpmain(blk,At,C,b,par,parbarrier,X0,y0,Z0)\n\nglobal spdensity smallblkdim  printlevel msg\nglobal solve_ok  use_LU  exist_analytic_term numpertdiagschur\nglobal schurfun  schurfun_par\n%%\n% matlabversion = par.matlabversion;\nisoctave = exist( 'OCTAVE_VERSION', 'builtin' );\nif isoctave,\n  w1 = warning('off','Octave:nearly-singular-matrix');\nelse\n  w1 = warning('off','MATLAB:nearlySingularMatrix');\n  w2 = warning('off','MATLAB:singularMatrix');\nend\nvers          = par.vers;\npredcorr      = par.predcorr;\ngam           = par.gam;\nexpon         = par.expon;\ngaptol        = par.gaptol + ( par.gaptol == 0 );\nsteptol       = par.steptol;\nmaxit         = par.maxit;\nprintlevel    = par.printlevel;\nstoplevel     = par.stoplevel;\nscale_data    = par.scale_data;\nspdensity     = par.spdensity;\nrmdepconstr   = par.rmdepconstr;\nsmallblkdim   = par.smallblkdim;\nschurfun      = par.schurfun;\nschurfun_par  = par.schurfun_par;\nublksize      = par.ublksize;\n%%\ntstart = clock;\nX = X0; y = y0; Z = Z0;\nfor p = 1:size(blk,1)\n    if strcmp(blk{p,1},'u'); Z{p} = zeros(blk{p,2},1); end\nend\n%%\n%%-----------------------------------------\n%% convert unrestricted blk to linear blk.\n%%-----------------------------------------\n%%\nconvertlen = 0;\n[blk,At,C,X,Z,u2lblk,ublkidx] = sqlpu2lblk(blk,At,C,X,Z,par,convertlen);\nfor p = 1:size(blk,1)\n    % pblk = blk(p,:);\n    if (u2lblk(p) == 1)\n        n = 2*blk{p,2};\n        blk{p,1} = 'l';  blk{p,2} = n;\n        parbarrier{p} = zeros(1,n);\n        At{p} = [At{p}; -At{p}];\n        % tau = max(1,norm(C{p}));\n        C{p} = [C{p}; -C{p}];\n        msg = 'convert ublk to lblk';\n        if (printlevel); fprintf(' *** %s',msg); end\n        b2 = 1 + abs(b');\n        normCtmp = 1+norm(C{p});\n        normAtmp = 1+sqrt(sum(At{p}.*At{p}));\n        if (n > 1000)\n            const = sqrt(n);\n        else\n            const = n;\n        end\n        if (par.startpoint == 1)\n            X{p} = const* max([1,b2./normAtmp]) *ones(n,1);\n            Z{p} = const* max([1,normAtmp/sqrt(n),normCtmp/sqrt(n)]) *ones(n,1);\n            X{p} = X{p}.*(1+1e-10*randmat(n,1,0,'u'));\n            Z{p} = Z{p}.*(1+1e-10*randmat(n,1,0,'u'));\n        else\n            const = max(abs(X{p})) + 100;\n            X{p} = [X{p}+const; const*ones(n/2,1)];\n            %%old: const = 100; Z{p} = [const*ones(n/2,1); const*ones(n/2,1)];\n            Z{p} = [abs(Z0{p}); abs(Z0{p})] + 1e-4;\n        end\n    end\nend\n%%-----------------------------------------\n%% check whether {A1,...,Am} is\n%% linearly independent.\n%%-----------------------------------------\n%%\nm0 = length(b);\n[At,b,y,indeprows,par.depconstr,feasible,par.AAt] = ...\n    checkdepconstr(blk,At,b,y,rmdepconstr);\nif (~feasible)\n    obj = []; X = cell(size(blk,1),1); y = []; Z = cell(size(blk,1),1);\n    runhist = [];\n    msg = 'SQLP is not feasible';\n    if (printlevel); fprintf('\\n %s \\n',msg); end\n    return;\nend\npar.normAAt = norm(par.AAt,'fro');\n%%\n%%-----------------------------------------\n%% scale SQLP data. Note: must be done only\n%% after checkdepconstr\n%%-----------------------------------------\n%%\nnormA2 = 1+ops(At,'norm');\nnormb2 = 1+norm(b);\nnormC2 = 1+ops(C,'norm');\nnormX0 = 1+ops(X0,'norm');\nnormZ0 = 1+ops(Z0,'norm');\nif (scale_data)\n    [At,C,b,normA,normC,normb,X,y,Z] = scaling(blk,At,C,b,X,y,Z);\nelse\n    normA = 1; normC = 1; normb = 1;\nend\n%%\n%%-----------------------------------------\n%% find the combined list of non-zero\n%% elements of Aj, j = 1:k, for each k.\n%% IMPORTANT NOTE: Ak, C are permuted.\n%%-----------------------------------------\n%%\npar.numcolAt = length(b);\n[At,C,X,Z,par.permA,par.permZ] = sortA(blk,At,C,b,X,Z);\n[par.isspA,par.nzlistA,par.nzlistAsum,par.isspAy,par.nzlistAy] = nzlist(blk,At,par);\n%%\n%%-----------------------------------------\n%% create an artifical non-negative block\n%% for a purely log-barrier problem\n%%-----------------------------------------\n%%\nnumblkold = size(blk,1);\nnn = 0;\nfor p = 1:size(blk,1);\n    pblk = blk(p,:);\n    idx = find(parbarrier{p}==0);\n    if ~isempty(idx);\n        if strcmp(pblk{1},'l')\n            nn = nn + length(idx);\n        elseif strcmp(pblk{1},'q')\n            nn = nn + sum(pblk{2}(idx));\n        elseif strcmp(pblk{1},'s')\n            nn = nn + sum(pblk{2}(idx));\n        end\n    end\nend\nif (nn==0)\n    analytic_prob = 1;\n    numblk = size(blk,1)+1;\n    blk{numblk,1} = 'l'; blk{numblk,2} = 1;\n    At{numblk,1} = sparse(1,length(b));\n    C{numblk,1} = 1;\n    X{numblk,1} = 1e3;\n    Z{numblk,1} = 1e3;\n    parbarrier{numblk,1} = 0;\n    u2lblk(numblk,1) = 0;\n    nn = nn + 1;\nelse\n    analytic_prob = 0;\nend\n%%\nexist_analytic_term = 0;\nfor p = 1:size(blk,1);\n    if any(parbarrier{p} > 0),\n        exist_analytic_term = 1;\n    end\nend\n%%-----------------------------------------\n%% initialization\n%%-----------------------------------------\n%%\nEE = ops(blk,'identity');\nnormE2 = ops(EE,'norm'); Zpertold = 1;\nfor p = 1:size(blk,1)\n    % normCC(p) = 1+ops(C(p),'norm');\n    normEE(p) = 1+ops(EE(p),'norm'); %#ok\nend\n[Xchol,indef(1)] = blkcholfun(blk,X);\n[Zchol,indef(2)] = blkcholfun(blk,Z);\nif any(indef)\n    msg = 'stop: X or Z not positive definite';\n    if (printlevel); fprintf('\\n  %s\\n',msg); end\n    info.termcode = -3;\n    info.msg1 = msg;\n    obj = []; X = cell(size(blk,1),1); y = []; Z = cell(size(blk,1),1);\n    runhist = [];\n    return;\nend\nAX = AXfun(blk,At,par.permA,X);\nrp = b-AX;\nZpATy = ops(Z,'+',Atyfun(blk,At,par.permA,par.isspAy,y));\nZpATynorm = ops(ZpATy,'norm');\nRd = ops(C,'-',ZpATy);\nobjadd0 = 0;\nif (scale_data)\n    for p = 1:size(blk,1)\n        pblk = blk(p,:);\n        objadd0 = objadd0 + sum(parbarrier{p}.*pblk{2})*log(normA{p});\n    end\nend\nobjadd = blkbarrier(blk,X,Z,Xchol,Zchol,parbarrier) + objadd0;\nobj = (normb*normC)*[blktrace(blk,C,X), b'*y] + objadd;\ngap = (normb*normC)*blktrace(blk,X,Z) - diff(objadd);\nrelgap = gap/(1+sum(abs(obj)));\nprim_infeas = norm(rp)/normb2;\ndual_infeas = ops(Rd,'norm')/normC2;\ninfeas = max(prim_infeas,dual_infeas);\nif (scale_data)\n    infeas_org(1) = prim_infeas*normb;\n    infeas_org(2) = dual_infeas*normC;\nelse\n    infeas_org = [0,0];\nend\ntrXZ = blktrace(blk,X,Z,parbarrier);\nif (nn > 0); mu  = trXZ/nn; else mu = gap/ops(X,'getM'); end\nnormX = ops(X,'norm');\n%%\ntermcode = 0; restart = 0;\npstep = 1; dstep = 1; pred_convg_rate = 1; corr_convg_rate = 1;\nbesttol = max( relgap, infeas );\nhomRd = inf; homrp = inf; dy = zeros(length(b),1);\nmsg = []; msg2 = []; msg3 = [];\nrunhist.pobj    = obj(1);\nrunhist.dobj    = obj(2);\nrunhist.gap     = gap;\nrunhist.relgap  = relgap;\nrunhist.pinfeas = prim_infeas;\nrunhist.dinfeas = dual_infeas;\nrunhist.infeas  = infeas;\nrunhist.pstep   = 0;\nrunhist.dstep   = 0;\nrunhist.step    = 0;\nrunhist.normX   = normX;\nrunhist.cputime = etime(clock,tstart);\nttime.preproc   = runhist.cputime;\nttime.pred = 0; ttime.pred_pstep = 0; ttime.pred_dstep = 0;\nttime.corr = 0; ttime.corr_pstep = 0; ttime.corr_dstep = 0;\nttime.pchol = 0; ttime.dchol = 0; ttime.misc = 0;\n%%\n%%-----------------------------------------\n%% display parameters and initial info\n%%-----------------------------------------\n%%\nif (printlevel >= 2)\n    fprintf('\\n********************************************');\n    fprintf('***********************\\n');\n    fprintf('   SDPT3: Infeasible path-following algorithms');\n    fprintf('\\n********************************************');\n    fprintf('***********************\\n');\n    [hh,mm,ss] = mytime(ttime.preproc);\n    if (printlevel>=3)\n        fprintf(' version  predcorr  gam  expon  scale_data\\n');\n        if (vers == 1); fprintf('   HKM '); elseif (vers == 2); fprintf('    NT '); end\n        fprintf('     %1.0f      %4.3f',predcorr,gam);\n        fprintf('   %1.0f        %1.0f    %1.0f\\n',expon,scale_data);\n        fprintf('\\nit pstep dstep pinfeas dinfeas  gap')\n        fprintf('      prim-obj      dual-obj    cputime\\n');\n        fprintf('------------------------------------------------');\n        fprintf('-------------------\\n');\n        fprintf('%2.0f|%4.3f|%4.3f|%2.1e|%2.1e|',0,0,0,prim_infeas,dual_infeas);\n        fprintf('%2.1e|%- 7.6e %- 7.6e| %s:%s:%s|',gap,obj(1),obj(2),hh,mm,ss);\n    end\nend\n%%\n%%---------------------------------------------------------------\n%% start main loop\n%%---------------------------------------------------------------\n%%\nparam.termcode    = termcode;\nparam.iter        = 0;\nparam.obj         = obj;\nparam.relgap      = relgap;\nparam.prim_infeas = prim_infeas;   param.dual_infeas = dual_infeas;\nparam.homRd       = homRd;         param.homrp       = homrp;\nparam.AX          = AX;            param.ZpATynorm   = ZpATynorm;\nparam.normA       = normA;\nparam.normb       = normb;         param.normC       = normC;\nparam.normX0      = normX0;        param.normZ0      = normZ0;\nparam.m0          = m0;            param.indeprows   = indeprows;\nparam.prim_infeas_bad = 0;\nparam.dual_infeas_bad = 0;\nparam.prim_infeas_min = prim_infeas;\nparam.dual_infeas_min = dual_infeas;\nparam.gaptol      = par.gaptol;\nparam.inftol      = par.inftol;\nparam.maxit       = maxit;\nparam.scale_data  = scale_data;\nparam.printlevel  = printlevel;\nparam.ublksize    = ublksize;\nXbest = X; ybest = y; Zbest = Z;\n%%\nfor iter = 1:maxit;\n    tstart  = clock;\n    timeold = tstart;\n    update_iter = 0; breakyes = 0;\n    pred_slow = 0; corr_slow = 0; % step_short = 0;\n    par.parbarrier = parbarrier;\n    par.iter    = iter;\n    par.obj     = obj;\n    par.relgap  = relgap;\n    par.pinfeas = prim_infeas;\n    par.dinfeas = dual_infeas;\n    par.rp      = rp;\n    par.y       = y;\n    par.dy      = dy;\n    par.normX   = normX;\n    par.ZpATynorm = ZpATynorm;\n    %%if (printlevel > 2); fprintf(' %2.1e',par.normX); end\n    if (iter == 1 || restart); Cpert = min(1,normC2/ops(EE,'norm')); end\n    if (runhist.dinfeas(1) > 1e-3) && (~exist_analytic_term) ...\n            && (relgap > 1e-4)\n        if (par.normX > 5e3 && iter < 20)\n            Cpert = Cpert*0.5;\n        elseif (par.normX > 5e2 && iter < 20);\n            Cpert = Cpert*0.3;\n        else\n            Cpert = Cpert*0.1;\n        end\n        Rd = ops(Rd,'+',EE,Cpert);\n        %%if (printlevel > 2); fprintf('|%2.1e',Cpert); end\n    end\n    %%---------------------------------------------------------------\n    %% predictor step.\n    %%---------------------------------------------------------------\n    %%\n    if (predcorr)\n        sigma = 0;\n    else\n        sigma = 1-0.9*min(pstep,dstep);\n        if (iter == 1); sigma = 0.5; end;\n    end\n    sigmu = cell(size(blk,1),1);\n    for p = 1:size(blk,1)\n        sigmu{p} = max(sigma*mu, parbarrier{p}');\n    end\n    invXchol = cell(size(blk,1),1);\n    invZchol = ops(Zchol,'inv');\n    if (vers == 1);\n        [par,dX,dy,dZ,coeff,L,hRd] = ...\n            HKMpred(blk,At,par,rp,Rd,sigmu,X,Z,invZchol);\n    elseif (vers == 2);\n        [par,dX,dy,dZ,coeff,L,hRd] = ...\n            NTpred(blk,At,par,rp,Rd,sigmu,X,Z,Zchol,invZchol);\n    end\n    if (solve_ok <= 0)\n        msg = 'stop: difficulty in computing predictor directions';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        runhist.pinfeas(iter+1) = runhist.pinfeas(iter);\n        runhist.dinfeas(iter+1) = runhist.dinfeas(iter);\n        runhist.relgap(iter+1)  = runhist.relgap(iter);\n        runhist.cputime(iter+1) = etime(clock,tstart);\n        termcode = -4;\n        break; %% do not ues breakyes = 1\n    end\n    timenew = clock;\n    ttime.pred = ttime.pred + etime(timenew,timeold); timeold = timenew;\n    %%\n    %%-----------------------------------------\n    %% step-lengths for predictor step\n    %%-----------------------------------------\n    %%\n    if (gam == 0)\n        gamused = 0.9 + 0.09*min(pstep,dstep);\n    else\n        gamused = gam;\n    end\n    [Xstep,invXchol] = steplength(blk,X,dX,Xchol,invXchol);\n    pstep = min(1,gamused*full(Xstep));\n    timenew = clock;\n    ttime.pred_pstep = ttime.pred_pstep + etime(timenew,timeold); timeold = timenew;\n    Zstep = steplength(blk,Z,dZ,Zchol,invZchol);\n    dstep = min(1,gamused*full(Zstep));\n    trXZnew = trXZ + pstep*blktrace(blk,dX,Z,parbarrier) ...\n        + dstep*blktrace(blk,X,dZ,parbarrier) ...\n        + pstep*dstep*blktrace(blk,dX,dZ,parbarrier);\n    if (nn > 0); mupred  = trXZnew/nn; else mupred = 1e-16; end\n    mupredhist(iter) = mupred; %#ok\n    timenew = clock;\n    ttime.pred_dstep = ttime.pred_dstep + etime(timenew,timeold); timeold = timenew;\n    %%\n    %%-----------------------------------------\n    %%  stopping criteria for predictor step.\n    %%-----------------------------------------\n    %%\n    if (min(pstep,dstep) < steptol) && (stoplevel) && (iter > 10)\n        msg = 'stop: steps in predictor too short';\n        if (printlevel)\n            fprintf('\\n  %s',msg);\n            fprintf(': pstep = %3.2e,  dstep = %3.2e\\n',pstep,dstep);\n        end\n        runhist.cputime(iter+1) = etime(clock,tstart);\n        termcode = -2;\n        breakyes = 1;\n    end\n    if (~predcorr)\n        if (iter >= 2)\n            idx = max(2,iter-2) : iter;\n            pred_slow = all(mupredhist(idx)./mupredhist(idx-1) > 0.4);\n            idx = max(2,iter-5) : iter;\n            pred_convg_rate = mean(mupredhist(idx)./mupredhist(idx-1));\n            pred_slow = pred_slow + (mupred/mu > 5*pred_convg_rate);\n        end\n        if (max(mu,infeas) < 1e-6) && (pred_slow) && (stoplevel)\n            msg = 'stop: lack of progress in predictor';\n            if (printlevel)\n                fprintf('\\n  %s',msg);\n                fprintf(': mupred/mu = %3.2f, pred_convg_rate = %3.2f.',...\n                    mupred/mu,pred_convg_rate);\n            end\n            runhist.cputime(iter+1) = etime(clock,tstart);\n            termcode = -2;\n            breakyes = 1;\n        else\n            update_iter = 1;\n        end\n    end\n    %%---------------------------------------------------------------\n    %% corrector step.\n    %%---------------------------------------------------------------\n    %%\n    if (predcorr) && (~breakyes)\n        step_pred = min(pstep,dstep);\n        if (mu > 1e-6)\n            if (step_pred < 1/sqrt(3));\n                expon_used = 1;\n            else\n                expon_used = max(expon,3*step_pred^2);\n            end\n        else\n            expon_used = max(1,min(expon,3*step_pred^2));\n        end\n        if (nn==0)\n            sigma = 0.2;\n        elseif (mupred < 0)\n            sigma = 0.8;\n        else\n            sigma = min(1, (mupred/mu)^expon_used);\n        end\n        sigmu = cell(size(blk,1),1);\n        for p = 1:size(blk,1)\n            sigmu{p} = max(sigma*mu, parbarrier{p}');\n        end\n        if (vers == 1)\n            [dX,dy,dZ] = HKMcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n                dX,dZ,coeff,L,X,Z);\n        elseif (vers == 2)\n            [dX,dy,dZ] = NTcorr(blk,At,par,rp,Rd,sigmu,hRd,...\n                dX,dZ,coeff,L,X,Z);\n        end\n        if (solve_ok <= 0)\n            msg = 'stop: difficulty in computing corrector directions';\n            if (printlevel); fprintf('\\n  %s',msg); end\n            runhist.pinfeas(iter+1) = runhist.pinfeas(iter);\n            runhist.dinfeas(iter+1) = runhist.dinfeas(iter);\n            runhist.relgap(iter+1)  = runhist.relgap(iter);\n            runhist.cputime(iter+1) = etime(clock,tstart);\n            termcode = -4;\n            break; %% do not ues breakyes = 1\n        end\n        timenew = clock;\n        ttime.corr = ttime.corr + etime(timenew,timeold); timeold = timenew;\n        %%\n        %%-----------------------------------\n        %% step-lengths for corrector step\n        %%-----------------------------------\n        %%\n        if (gam == 0)\n            gamused = 0.9 + 0.09*min(pstep,dstep);\n        else\n            gamused = gam;\n        end\n        Xstep = steplength(blk,X,dX,Xchol,invXchol);\n        pstep = min(1,gamused*full(Xstep));\n        timenew = clock;\n        ttime.corr_pstep = ttime.corr_pstep+etime(timenew,timeold); timeold = timenew;\n        Zstep = steplength(blk,Z,dZ,Zchol,invZchol);\n        dstep = min(1,gamused*full(Zstep));\n        trXZnew = trXZ + pstep*blktrace(blk,dX,Z,parbarrier) ...\n            + dstep*blktrace(blk,X,dZ,parbarrier)...\n            + pstep*dstep*blktrace(blk,dX,dZ,parbarrier);\n        if (nn > 0); mucorr  = trXZnew/nn; else mucorr = 1e-16; end\n        timenew = clock;\n        ttime.corr_dstep = ttime.corr_dstep+etime(timenew,timeold); timeold = timenew;\n        %%\n        %%-----------------------------------------\n        %%  stopping criteria for corrector step\n        %%-----------------------------------------\n        if (iter >= 2)\n            idx = max(2,iter-2) : iter;\n            corr_slow = all(runhist.gap(idx)./runhist.gap(idx-1) > 0.8);\n            idx = max(2,iter-5) : iter;\n            corr_convg_rate = mean(runhist.gap(idx)./runhist.gap(idx-1));\n            corr_slow = corr_slow + (mucorr/mu > max(min(1,5*corr_convg_rate),0.8));\n        end\n        if (max(relgap,infeas) < 1e-6) && (iter > 20) ...\n                && (corr_slow > 1) && (stoplevel)\n            msg = 'stop: lack of progress in corrector';\n            if (printlevel)\n                fprintf('\\n  %s',msg);\n                fprintf(': mucorr/mu = %3.2f, corr_convg_rate = %3.2f',...\n                    mucorr/mu,corr_convg_rate);\n            end\n            runhist.cputime(iter+1) = etime(clock,tstart);\n            termcode = -2;\n            breakyes = 1;\n        else\n            update_iter = 1;\n        end\n    end\n    %%---------------------------------------------------------------\n    %% udpate iterate\n    %%---------------------------------------------------------------\n    indef = [1,1];\n    if (update_iter)\n        for t = 1:5\n            [Xchol,indef(1)] = blkcholfun(blk,ops(X,'+',dX,pstep));\n            timenew = clock;\n            ttime.pchol = ttime.pchol + etime(timenew,timeold); timeold = timenew;\n            if (indef(1)); pstep = 0.8*pstep; else break; end\n        end\n        if (t > 1); pstep = gamused*pstep; end\n        for t = 1:5\n            [Zchol,indef(2)] = blkcholfun(blk,ops(Z,'+',dZ,dstep));\n            timenew = clock;\n            ttime.dchol = ttime.dchol + etime(timenew,timeold); timeold = timenew;\n            if (indef(2)); dstep = 0.8*dstep; else break; end\n        end\n        if (t > 1); dstep = gamused*dstep; end\n        %%-------------------------------------------\n        AXtmp = AX + pstep*AXfun(blk,At,par.permA,dX);\n        prim_infeasnew = norm(b-AXtmp)/normb2;\n        if (relgap < 5*infeas); alpha = 1e2; else alpha = 1e3; end\n        if any(indef)\n            if indef(1); msg = 'stop: X not positive definite'; end\n            if indef(2); msg = 'stop: Z not positive definite'; end\n            if (printlevel); fprintf('\\n  %s',msg); end\n            termcode = -3;\n            breakyes = 1;\n        elseif (prim_infeasnew > max([1e-8,relgap,20*prim_infeas]) && iter > 10) ...\n                || (prim_infeasnew > max([1e-7,1e3*prim_infeas,0.1*relgap]) && relgap < 1e-2) ...\n                || (prim_infeasnew > alpha*max([1e-9,param.prim_infeas_min]) ...\n                && (prim_infeasnew > max([3*prim_infeas,0.1*relgap])) ...\n                && (iter > 25) && (dual_infeas < 1e-6) && (relgap < 0.1)) ...\n                || ((prim_infeasnew > 1e3*prim_infeas && prim_infeasnew > 1e-12) ...\n                && (max(relgap,dual_infeas) < 1e-8))\n            if (stoplevel)\n                msg = 'stop: primal infeas has deteriorated too much';\n                if (printlevel); fprintf('\\n  %s, %2.1e',msg,prim_infeasnew); end\n                termcode = -7;\n                breakyes = 1;\n            end\n        elseif (trXZnew > 1.05*runhist.gap(iter)) && (~exist_analytic_term) ...\n                && ((infeas < 1e-5) && (relgap < 1e-4) && (iter > 20) ...\n                || (max(infeas,relgap) < 1e-7) && (iter > 10))\n            if (stoplevel)\n                msg = 'stop: progress in duality gap has deteriorated';\n                if (printlevel); fprintf('\\n  %s, %2.1e',msg,trXZnew); end\n                termcode = -8;\n                breakyes = 1;\n            end\n        else\n            X = ops(X,'+',dX,pstep);\n            y = y + dstep*dy;\n            Z = ops(Z,'+',dZ,dstep);\n        end\n    end\n    %%---------------------------------------------------------------\n    %% adjust linear blk arising from unrestricted blk\n    %%---------------------------------------------------------------\n    if (~breakyes)\n        for p = 1:size(blk,1)\n            if (u2lblk(p) == 1)\n                len = blk{p,2}/2;\n                xtmp = min(X{p}(1:len),X{p}(len+1:2*len));\n                alpha = 0.8;\n                X{p}(1:len)     = X{p}(1:len) - alpha*xtmp;\n                X{p}(len+1:2*len) = X{p}(len+1:2*len) - alpha*xtmp;\n                if (mu < 1e-4) %% old: (mu < 1e-7)\n                    Z{p} = 0.5*mu./max(1,X{p}); %% good to keep this step\n                else\n                    ztmp = min(1,max(Z{p}(1:len),Z{p}(len+1:2*len)));\n                    if (dual_infeas > 1e-4 && dstep < 0.2)\n                        beta = 0.3;\n                    else\n                        beta = 0.0;\n                    end\n                    %% important to set beta = 0 at later stage.\n                    Z{p}(1:len)     = Z{p}(1:len) + beta*ztmp;\n                    Z{p}(len+1:2*len) = Z{p}(len+1:2*len) + beta*ztmp;\n                end\n            end\n        end\n    end\n    %%--------------------------------------------------\n    %% perturb Z: do this step before checking for break\n    %%--------------------------------------------------\n    if (~breakyes) && (~exist_analytic_term)\n        trXZtmp = blktrace(blk,X,Z);\n        trXE  = blktrace(blk,X,EE);\n        Zpert = max(1e-12,0.2*min(relgap,prim_infeas)).*normC2./normE2;\n        Zpert = min(Zpert,0.1*trXZtmp./trXE);\n        Zpert = min([1,Zpert,1.5*Zpertold]);\n        if (infeas < 0.1)\n            Z = ops(Z,'+',EE,Zpert);\n            [Zchol,indef(2)] = blkcholfun(blk,Z);\n            if any(indef(2))\n                msg = 'stop: Z not positive definite';\n                if (printlevel); fprintf('\\n  %s',msg); end\n                termcode = -3;\n                breakyes = 1;\n            end\n            %%if (printlevel > 2); fprintf(' %2.1e',Zpert); end\n        end\n        Zpertold = Zpert;\n    end\n    %%---------------------------------------------------------------\n    %% compute rp, Rd, infeasibities, etc\n    %%---------------------------------------------------------------\n    %%\n    AX  = AXfun(blk,At,par.permA,X);\n    rp  = b-AX;\n    ZpATy = ops(Z,'+',Atyfun(blk,At,par.permA,par.isspAy,y));\n    ZpATynorm = ops(ZpATy,'norm');\n    Rd  = ops(C,'-',ZpATy);\n    objadd = blkbarrier(blk,X,Z,Xchol,Zchol,parbarrier) + objadd0;\n    obj = (normb*normC)*[blktrace(blk,C,X), b'*y] + objadd;\n    gap = (normb*normC)*blktrace(blk,X,Z) - diff(objadd);\n    relgap = gap/(1+sum(abs(obj)));\n    prim_infeas = norm(rp)/normb2;\n    dual_infeas = ops(Rd,'norm')/normC2;\n    infeas = max(prim_infeas,dual_infeas);\n    if (scale_data)\n        infeas_org(1) = prim_infeas*normb;\n        infeas_org(2) = dual_infeas*normC;\n    end\n    homRd = inf; homrp = inf;\n    if (ops(parbarrier,'norm') == 0)\n        if (obj(2) > 0); homRd = ZpATynorm/(obj(2)); end\n        if (obj(1) < 0); homrp = norm(AX)/(-obj(1))/(normC); end\n    end\n    trXZ = blktrace(blk,X,Z,parbarrier);\n    if (nn > 0); mu = trXZ/nn; else mu = gap/ops(X,'getM'); end\n    normX = ops(X,'norm');\n    %%\n    runhist.pobj(iter+1)  = obj(1);\n    runhist.dobj(iter+1)  = obj(2);\n    runhist.gap(iter+1)   = gap;\n    runhist.relgap(iter+1)  = relgap;\n    runhist.pinfeas(iter+1) = prim_infeas;\n    runhist.dinfeas(iter+1) = dual_infeas;\n    runhist.infeas(iter+1)  = infeas;\n    runhist.pstep(iter+1)   = pstep;\n    runhist.dstep(iter+1)   = dstep;\n    runhist.step(iter+1)    = min(pstep,dstep);\n    runhist.normX(iter+1)   = normX;\n    runhist.cputime(iter+1) = etime(clock,tstart);\n    timenew = clock;\n    ttime.misc = ttime.misc + etime(timenew,timeold); % timeold = timenew;\n    [hh,mm,ss] = mytime(sum(runhist.cputime));\n    if (printlevel>=3)\n        fprintf('\\n%2.0f|%4.3f|%4.3f',iter,pstep,dstep);\n        fprintf('|%2.1e|%2.1e|%2.1e|',prim_infeas,dual_infeas,gap);\n        fprintf('%- 7.6e %- 7.6e| %s:%s:%s|',obj(1),obj(2),hh,mm,ss);\n    end\n    %%--------------------------------------------------\n    %% check convergence\n    %%--------------------------------------------------\n    param.use_LU      = use_LU;\n    param.stoplevel   = stoplevel;\n    param.termcode    = termcode;\n    param.iter        = iter;\n    param.obj         = obj;\n    param.gap         = gap;\n    param.relgap      = relgap;\n    param.prim_infeas = prim_infeas;\n    param.dual_infeas = dual_infeas;\n    param.mu        = mu;\n    param.homRd     = homRd;\n    param.homrp     = homrp;\n    param.AX        = AX;\n    param.ZpATynorm = ZpATynorm;\n    param.normX     = ops(X,'norm');\n    param.normZ     = ops(Z,'norm');\n    param.numpertdiagschur = numpertdiagschur;\n    if (~breakyes)\n        [param,breakyes,restart,msg2] = sqlpcheckconvg(param,runhist);\n    end\n    if (restart)\n        [X,y,Z] = infeaspt(blk,At,C,b,2,1e5);\n        rp  = b-AXfun(blk,At,par.permA,X);\n        ZpATy = ops(Z,'+',Atyfun(blk,At,par.permA,par.isspAy,y));\n        Rd  = ops(C,'-',ZpATy);\n        trXZ = blktrace(blk,X,Z,parbarrier);\n        mu   = trXZ/nn;\n        gap  =  (normb*normC)*blktrace(blk,X,Z) - diff(objadd);\n        prim_infeas = norm(rp)/normb2;\n        dual_infeas = ops(Rd,'norm')/normC2;\n        infeas = max(prim_infeas,dual_infeas);\n        [Xchol,indef(1)] = blkcholfun(blk,X);\n        [Zchol,indef(2)] = blkcholfun(blk,Z); %#ok\n        stoplevel = 3;\n    end\n    %%--------------------------------------------------\n    %% check for break\n    %%--------------------------------------------------    \n    newtol = max(relgap,infeas);\n    update_best(iter+1) = ~( newtol >= besttol ); %#ok\n    if update_best(iter+1),\n        Xbest = X; ybest = y; Zbest = Z;\n        besttol = newtol;\n    end\n    if besttol < 1e-4 && ~any(update_best(max(1,iter-1):iter+1))\n        msg = 'lack of progress in infeas';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -9;\n        breakyes = 1;\n    end\n    if (breakyes); break; end\nend\n%%---------------------------------------------------------------\n%% end of main loop\n%%---------------------------------------------------------------\n%%\nuse_bestiter = 1;\nif (use_bestiter) && (param.termcode <= 0)\n    X = Xbest; y = ybest; Z = Zbest;\n    Xchol = blkcholfun(blk,X);\n    Zchol = blkcholfun(blk,Z);\n    AX = AXfun(blk,At,par.permA,X);\n    rp = b-AX;\n    ZpATy = ops(Z,'+',Atyfun(blk,At,par.permA,par.isspAy,y));\n    Rd = ops(C,'-',ZpATy);\n    objadd = blkbarrier(blk,X,Z,Xchol,Zchol,parbarrier) + objadd0;\n    obj = (normb*normC)*[blktrace(blk,C,X), b'*y] + objadd;\n    gap = (normb*normC)*blktrace(blk,X,Z) - diff(objadd);\n    relgap = gap/(1+sum(abs(obj)));\n    prim_infeas = norm(rp)/normb2;\n    dual_infeas = ops(Rd,'norm')/normC2;\n    infeas = max(prim_infeas,dual_infeas);\n    runhist.pobj(iter+1)  = obj(1);\n    runhist.dobj(iter+1)  = obj(2);\n    runhist.gap(iter+1)   = gap;\n    runhist.relgap(iter+1)  = relgap;\n    runhist.pinfeas(iter+1) = prim_infeas;\n    runhist.dinfeas(iter+1) = dual_infeas;\n    runhist.infeas(iter+1)  = infeas;\nend\n%%---------------------------------------------------------------\n%% unscale and produce infeasibility certificates if appropriate\n%%---------------------------------------------------------------\nif (iter >= 1)\n    [X,y,Z,termcode,resid,reldist,msg3] = ...\n        sqlpmisc(blk,At,C,b,X,y,Z,par.permZ,param);\nend\n%%---------------------------------------------------------------\n%% recover unrestricted blk from linear blk\n%%---------------------------------------------------------------\n%%\nfor p = 1:size(blk,1)\n    if (u2lblk(p) == 1)\n        n = blk{p,2}/2;\n        X{p} = X{p}(1:n)-X{p}(n+1:2*n);\n        Z{p} = Z{p}(1:n);\n    end\nend\nfor p = 1:size(ublkidx,1)\n    if ~isempty(ublkidx{p,2})\n        n0 = ublkidx{p,1}; idxB = setdiff((1:n0)',ublkidx{p,2});\n        tmp = zeros(n0,1); tmp(idxB) = X{p}; X{p} = tmp;\n        tmp = zeros(n0,1); tmp(idxB) = Z{p}; Z{p} = tmp;\n    end\nend\nif (analytic_prob)\n    X = X(1:numblkold); Z = Z(1:numblkold);\nend\n%%---------------------------------------------------------------\n%% print summary\n%%---------------------------------------------------------------\n%%\nmaxC = 1+ops(ops(C,'abs'),'max');\nmaxb = 1+max(abs(b));\nif (scale_data)\n    dimacs = [infeas_org(1)*normb2/maxb; 0; infeas_org(2)*normC2/maxC; 0];\nelse\n    dimacs = [prim_infeas*normb2/maxb; 0; dual_infeas*normC2/maxC; 0];\nend\ndimacs = [dimacs; [-diff(obj); gap]/(1+sum(abs(obj)))];\ninfo.dimacs   = dimacs;\ninfo.termcode = termcode;\ninfo.iter     = iter;\ninfo.obj      = obj;\ninfo.gap      = gap;\ninfo.relgap   = relgap;\ninfo.pinfeas  = prim_infeas;\ninfo.dinfeas  = dual_infeas;\ninfo.cputime  = sum(runhist.cputime);\ninfo.time     = ttime;\ninfo.resid    = resid;\ninfo.reldist  = reldist;\ninfo.normX    = ops(X,'norm');\ninfo.normy    = norm(y);\ninfo.normZ    = ops(Z,'norm');\ninfo.normb    = normb2; info.maxb = maxb;\ninfo.normC    = normC2; info.maxC = maxC;\ninfo.normA    = normA2;\ninfo.msg1     = msg;\ninfo.msg2     = msg2;\ninfo.msg3     = msg3;\nsqlpsummary(info,ttime,infeas_org,printlevel);\nif isoctave,\n  warning(w1.state,w1.identifier);\nelse\n  warning(w2.state,w2.identifier);\n  warning(w1.state,w1.identifier);\nend\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HKMpred.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/HKMpred.m", "size": 2818, "source_encoding": "utf_8", "md5": "da0e8d9efacd9a7cebaae5ea2ad63de9", "text": "%%*******************************************************************\n%% HKMpred: Compute (dX,dy,dZ) for the H..K..M direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [par,dX,dy,dZ,coeff,L,hRd] = ...\n    HKMpred(blk,At,par,rp,Rd,sigmu,X,Z,invZchol)\n\nglobal schurfun schurfun_par\n%%\n%% compute HKM scaling\n%%\nZinv = cell(size(blk,1),1);   dd = cell(size(blk,1),1);\ngamx = cell(size(blk,1),1);   gamz = cell(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    numblk = length(pblk{2});\n    if strcmp(pblk{1},'l')\n        Zinv{p} = 1./Z{p};\n        dd{p} = X{p}./Z{p}; %% do not add perturbation, it badly affects cre-a\n    elseif strcmp(pblk{1},'q')\n        gaptmp  = qops(pblk,X{p},Z{p},1);\n        gamz2   = qops(pblk,Z{p},Z{p},2);\n        gamz{p} = sqrt(gamz2);\n        Zinv{p} = qops(pblk,-1./gamz2,Z{p},4);\n        dd{p}   = qops(pblk,gaptmp./gamz2,ones(n,1),4);\n    elseif strcmp(pblk{1},'s')\n        if (numblk == 1)\n            Zinv{p} = Prod2(pblk,full(invZchol{p}),invZchol{p}',1);\n            %% to fix the anonmaly when Zinv has very small elements\n            if (par.iter==2 || par.iter==3) && ~issparse(Zinv{p});\n                Zinv{p} = Zinv{p} + 1e-16;\n            end\n        else\n            Zinv{p} = Prod2(pblk,invZchol{p},invZchol{p}',1);\n        end\n    end\nend\npar.Zinv = Zinv; par.gamx = gamx; par.gamz = gamz; par.dd = dd;\n%%\n%% compute schur matrix\n%%\nm = length(rp);\nschur = sparse(m,m);\nUU = [];  EE = [];  Afree = [];\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        [schur,UU,EE] = schurmat_lblk(blk,At,par,schur,UU,EE,p,par.dd);\n    elseif strcmp(pblk{1},'q');\n        [schur,UU,EE] = schurmat_qblk(blk,At,par,schur,UU,EE,p,par.dd,par.Zinv,X);\n    elseif strcmp(pblk{1},'s')\n        if isempty(schurfun{p})\n            schur = schurmat_sblk(blk,At,par,schur,p,X,par.Zinv);\n        elseif ischar(schurfun{p})\n            if ~isempty(par.permZ{p})\n                Zpinv = Zinv{p}(par.permZ{p},par.permZ{p});\n                Xp = X{p}(par.permZ{p},par.permZ{p});\n            else\n                Xp = X{p};\n                Zpinv = Zinv{p};\n            end\n            schurtmp = feval(schurfun{p},Xp,Zpinv,schurfun_par(p,:));\n            schur = schur + schurtmp;\n        end\n    elseif strcmp(pblk{1},'u')\n        Afree = [Afree, At{p}']; %#ok\n    end\nend\n%%\n%% compute rhs\n%%\n[rhs,EinvRc,hRd] = HKMrhsfun(blk,At,par,X,Z,rp,Rd,sigmu);\n%%\n%% solve linear system\n%%\n[xx,coeff,L] = linsysolve(par,schur,UU,Afree,EE,rhs);\n%%\n%% compute (dX,dZ)\n%%\n[dX,dy,dZ] = HKMdirfun(blk,At,par,Rd,EinvRc,X,xx,m);\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "read_sdpa.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/read_sdpa.m", "size": 7591, "source_encoding": "utf_8", "md5": "211ea10474df1ffe50a0c5456e1cbe41", "text": "%%*******************************************************************\n%%  Read in a problem in SDPA sparse format.\n%%\n%%  [blk,At,C,b] = read_sdpa(fname)\n%%\n%%  Input: fname = name of the file containing SDP data in\n%%                 SDPA foramt.\n%%  Important: the data is assumed to contain only\n%%             semidefinite and linear blocks.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%******************************************************************\n\nfunction [blk,At,C,b] = read_sdpa(fname)\n\n%%\n%%  Open the file for input\n%%\ncompressed = 0;\nif exist(fname,'file')\n    fid = fopen(fname,'r');\nelseif exist([fname,'.Z'],'file');\n    compressed = 1;\n    unix(['uncompress ',fname,'.Z']);\n    fid = fopen(fname,'r');\nelseif exist([fname,'.gz'],'file');\n    compressed = 2;\n    unix(['gunzip ',fname,'.gz']);\n    fid = fopen(fname,'r');\nelse\n    fprintf('*** Problem not found, please specify the correct path or problem name. \\n');\n    blk = []; At = []; C = []; b = [];\n    return;\nend\n%%\n%%  Clean up special characters and comments from the file\n%%\n[datavec,count] = fscanf(fid,'%c'); %#ok\nlinefeeds = strfind(datavec,char(10));\ncomment_chars = '*\"=';\ncumidx = [];\nfor i=1:length(comment_chars)\n    idx = strfind(datavec,comment_chars(i));\n    cumidx = [cumidx,idx]; %#ok\nend\nfor j=length(cumidx):-1:1\n    if (cumidx(j)==1) || (strcmp(datavec(cumidx(j)-1),char(10)))\n        datavec(cumidx(j):linefeeds(find(cumidx(j)<linefeeds,1,'first')))='';\n    else\n        datavec(cumidx(j):linefeeds(find(cumidx(j)<linefeeds,1,'first'))-1)='';\n    end\nend\nspecial_chars = ',{}()';\ncumidx=[];\nfor i=1:length(special_chars)\n    idx = strfind(datavec,special_chars(i));\n    cumidx = [cumidx,idx]; %#ok\nend\ndatavec(cumidx) = blanks(length(cumidx));\nclear linefeeds;\n%%\n%% Close the file\n%%\nfclose(fid);\nif compressed==1; unix(['compress ',fname]); end;\nif compressed==2; unix(['gzip ',fname]); end;\n%%\n%%  Next, read in basic problem size parameters.\n%%\ndatavec = sscanf(datavec,'%f');\nif size(datavec,1) < size(datavec,2); datavec = datavec'; end;\nm = datavec(1);\nnumblk  = datavec(2);\nblksize = datavec(3:numblk+2);\nif size(blksize,1) > size(blksize,2); blksize = blksize'; end\n%%\n%% Get input  b.\n%%\nidxstrt = 2+numblk;\nb = datavec(idxstrt+1:idxstrt+m);\nidxstrt = idxstrt+m;\nb = -b;\n%%\n%% Construct blk\n%%\ndeblksize = 100;\nspblkidxtmp = find( (blksize>1) & (blksize < deblksize) );\nspblkidxtmp = sort(spblkidxtmp);\ndeblkidx = find( (blksize<=1) | (blksize >= deblksize) );\ndenumblk = length(deblkidx);\nlinblkidx = zeros(1,denumblk);\nfor p = 1:denumblk\n    n = blksize(deblkidx(p));\n    if (n > 1);\n        blk{p,1} = 's'; blk{p,2} = n; %#ok\n        n2 = n*(n+1)/2;\n        At{p,1} = sparse(n2,m); %#ok\n        C{p,1} = sparse(n,n); %#ok\n    else\n        linblkidx(p) = p;\n        blk{p,1} = 'l'; blk{p,2} = abs(n); %#ok\n        At{p,1} = sparse(abs(n),m); %#ok\n        C{p,1} = sparse(abs(n),1); %#ok\n    end\nend\nif ~isempty(spblkidxtmp)\n    maxnumblk = 200;\n    spnumblk = ceil(length(spblkidxtmp)/maxnumblk);\n    for q = 1:spnumblk\n        if (q < spnumblk)\n            spblkidxall{q} = spblkidxtmp([(q-1)*maxnumblk+1: q*maxnumblk]); %#ok\n        else\n            spblkidxall{q} = spblkidxtmp([(q-1)*maxnumblk+1: length(spblkidxtmp)]); %#ok\n        end\n        tmp = blksize(spblkidxall{q});\n        blk{denumblk+q,1} = 's'; %#ok\n        blk{denumblk+q,2} = tmp; %#ok\n        n2 = sum(tmp.*(tmp+1))/2;\n        At{denumblk+q,1} = sparse(n2,m); %#ok\n        C{denumblk+q,1} = sparse(sum(tmp),sum(tmp)); %#ok\n    end\nelse\n    spnumblk = 0;\nend\nlinblkidx(denumblk+1:denumblk+spnumblk) = 0;\n%%\n%% Construct single blocks of A,C\n%%\nlen = length(datavec);\nY = reshape(datavec(idxstrt+1:len),5,(len-idxstrt)/5)';\nclear datavec;\nY = sortrows(Y,[1 2]);\nmatidx = [0; find(diff(Y(:,1)) ~= 0); size(Y,1)];\n%%\nfor k = 1:length(matidx)-1\n    idx = matidx(k)+1 : matidx(k+1);\n    Ytmp  = Y(idx,1:5);\n    matno = Ytmp(1,1);\n    Ytmp2 = Ytmp(:,2);\n    for p = 1:denumblk\n        n  = blksize(deblkidx(p));\n        idx = find(Ytmp2 == deblkidx(p));\n        ii = Ytmp(idx,3); jj = Ytmp(idx,4); vv =Ytmp(idx,5);\n        len = length(idx);\n        if (n > 1)\n            idxtmp = find(ii > jj);\n            if ~isempty(idxtmp);\n                tmp = jj(idxtmp);\n                jj(idxtmp) = ii(idxtmp); ii(idxtmp) = tmp;\n            end\n            tmp = -sparse(ii,jj,vv,n,n);\n            tmp = tmp + triu(tmp,1)';\n        else\n            tmp = -sparse(ii,ones(len,1),vv,abs(n),1);\n        end\n        if (matno == 0)\n            C{p,1} = tmp; %#ok\n        else\n            if (n > 1)\n                At{p,1}(:,matno) = svec(blk(p,:),tmp,1); %#ok\n            else\n                At{p,1}(:,matno) = tmp; %#ok\n            end\n        end\n    end\nend\n%%\n%% Construct big sparse block of A,C\n%%\nif (spnumblk > 0)\n    Y1 = Y(:,1);\n    diffY1 = find(diff([-1; Y1; inf]));\n    for kk = 1:length(diffY1)-1\n        idx = diffY1(kk) : diffY1(kk+1)-1;\n        matno = Y1(diffY1(kk));\n        Ytmp  = Y(idx,1:5);\n        Ytmp2 = Ytmp(:,2);\n        maxYtmp2  = Ytmp2(length(Ytmp2));\n        minYtmp2  = Ytmp2(1);\n        diffYtmp2 = Ytmp2(diff([-1; Ytmp2])~=0);\n        for q = 1:spnumblk\n            spblkidx    = spblkidxall{q};\n            maxspblkidx = spblkidx(length(spblkidx));\n            minspblkidx = spblkidx(1);\n            count = 0;\n            if (minYtmp2 <= maxspblkidx) && (maxYtmp2 >= minspblkidx)\n                tmpblksize = blksize(spblkidx);\n                n = sum(tmpblksize);\n                cumspblksize = [0 cumsum(tmpblksize)];\n                n2 = sum(tmpblksize.*(tmpblksize+1))/2;\n                idx = zeros(n2,1); offset = zeros(n2,1);\n                for t = 1:length(diffYtmp2)\n                    p = find(spblkidx == diffYtmp2(t));\n                    if ~isempty(p)\n                        idxtmp = find(Ytmp2 == spblkidx(p));\n                        len = length(idxtmp);\n                        idx(count+1:count+len) = idxtmp;\n                        offset(count+1:count+len) = cumspblksize(p);\n                        count = count + len;\n                    end\n                end\n                idx = idx(1:count); offset = offset(1:count);\n                ii = Ytmp(idx,3)+offset; jj = Ytmp(idx,4)+offset; vv = Ytmp(idx,5);\n                idxtmp = find(ii > jj);\n                if ~isempty(idxtmp);\n                    tmp = jj(idxtmp);\n                    jj(idxtmp) = ii(idxtmp); ii(idxtmp) = tmp;\n                end\n                idxeq = find(ii==jj);\n                tmp = spconvert([ii jj -vv; jj ii -vv; n n 0]) ...\n                    + spconvert([ii(idxeq) jj(idxeq) vv(idxeq); n n 0]);\n                if (matno == 0)\n                    C{denumblk+q,1} = tmp; %#ok\n                else\n                    At{denumblk+q,1}(:,matno) = svec(blk(denumblk+q,:),tmp,1); %#ok\n                end\n            end\n        end\n    end\nend\n%%\n%% put all linear blocks together as a single linear block\n%%\nidx = find(linblkidx);\nif (length(idx) > 1)\n    sdpidx = find(linblkidx==0);\n    blktmp = 0; Atmp = []; Ctmp = [];\n    for k = 1:length(idx)\n        tmp = linblkidx(idx(k));\n        blktmp = blktmp+blk{tmp,2};\n        Atmp = [Atmp; At{tmp}]; %#ok\n        Ctmp = [Ctmp; C{tmp}]; %#ok\n    end\n    At = At(sdpidx); C = C(sdpidx); blk = blk(sdpidx,:);\n    len = length(sdpidx);\n    blk(2:len+1,:) = blk;\n    blk{1,1} = 'l'; blk{1,2} = blktmp;\n    At(2:len+1,1) = At; C(2:len+1,1) = C;\n    At{1,1} = Atmp; C{1,1} = Ctmp;\nend\n%%******************************************************************\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sortA.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sortA.m", "size": 2639, "source_encoding": "utf_8", "md5": "c998ea7df87432b78f6c001d0bbc5318", "text": "%%*********************************************************************\n%% sortA: sort columns of At{p} in ascending order according to the\n%%        number of nonzero elements.\n%%\n%% [At,C,b,X0,Z0,permA,permZ] = sortA(blk,At,C,b,X0,Z0);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*********************************************************************\n\nfunction [At,C,X0,Z0,permA,permZ] = sortA(blk,At,C,b,X0,Z0)\n\nglobal spdensity smallblkdim\n%%\nif isempty(spdensity); spdensity = 0.4; end\nif isempty(smallblkdim); smallblkdim = 50; end\n%%\nnumblk = size(blk,1);\nm = length(b);\nnnzA = zeros(numblk,m);\npermA = kron(ones(numblk,1),1:m);\npermZ = cell(size(blk,1),1);\n%%\nfor p=1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    % numblk = length(pblk{2});\n    if strcmp(pblk{1},'s') && (max(pblk{2}) > smallblkdim)\n        % n2 = sum(pblk{2}.*pblk{2});\n        n22 = sum(pblk{2}.*(pblk{2}+1))/2;\n        m1 = size(At{p,1},2);\n        if (length(pblk{2}) == 1)\n            tmp = abs(C{p}) + abs(Z0{p});\n            if  (~isempty(At{p,1}))\n                tmp = tmp + smat(blk(p,:),abs(At{p,1})*ones(m1,1),1);\n            end\n            if (nnz(tmp) < spdensity*n22);\n                per = symamd(tmp);\n                invper = zeros(n,1); invper(per) = 1:n;\n                permZ{p} = invper;\n                if (~isempty(At{p,1}))\n                    isspAt = issparse(At{p,1});\n                    for k = 1:m1\n                        Ak = smat(pblk,At{p,1}(:,k),1);\n                        At{p,1}(:,k) = svec(pblk,Ak(per,per),isspAt);\n                    end\n                end\n                C{p}  = C{p}(per,per);\n                Z0{p} = Z0{p}(per,per);\n                X0{p} = X0{p}(per,per);\n            else\n                per = [];\n            end\n            if (length(pblk) > 2) && (~isempty(per))\n                % m2 = length(pblk{3});\n                P = spconvert([(1:n)', per', ones(n,1)]);\n                At{p,2} = P*At{p,2};\n            end\n        end\n        if ~isempty(At{p,1}) && (mexnnz(At{p,1}) < m*n22/2)\n            for k = 1:m1\n                Ak = At{p,1}(:,k);\n                nnzA(p,k) = length(find(abs(Ak) > eps));\n            end\n            [dummy,permAp] = sort(nnzA(p,1:m1)); %#ok\n            At{p,1}  = At{p,1}(:,permAp);\n            permA(p,1:m1) = permAp;\n        end\n    elseif strcmp(pblk{1},'q') || strcmp(pblk{1},'l') || strcmp(pblk{1},'u');\n        if ~issparse(At{p,1});\n            At{p,1} = sparse(At{p,1});\n        end\n    end\nend\n%%*********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "blkeig.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/blkeig.m", "size": 2412, "source_encoding": "utf_8", "md5": "97da97f8165585de45eae52a86e0f127", "text": "%%***************************************************************************\n%% blkeig: compute eigenvalue decomposition of a cell array\n%%         whose contents are square matrices or the diagonal\n%%         of a diagonal matrix.\n%%\n%% [d,V] = blkeig(blk,X);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***************************************************************************\n\nfunction [d,V] = blkeig(blk,X)\n\nspdensity = 0.5;\n\nif ~iscell(X);\n    if strcmp(blk{1},'s');\n        blktmp = blk{2};\n        if (length(blktmp) == 1);\n            if (nargout == 1);\n                d = eig(full(X));\n            elseif (nargout == 2);\n                [V,d] = eig(full(X));\n                d = diag(d);\n            end\n        else\n            if (nargout == 2);\n                V = sparse(length(X),length(X));\n            end\n            d = zeros(sum(blktmp),1);\n            xx = mexsvec(blk,X,0);\n            blktmp2 = blktmp.*(blktmp+1)/2;\n            s2 = [0, cumsum(blktmp2)];\n            blksub{1,1} = 's'; blksub{1,2} = 0;\n            s = [0, cumsum(blktmp)];\n            for i = 1:length(blktmp)\n                pos = s(i)+1 : s(i+1);\n                blksub{2} = blktmp(i);\n                Xsub = mexsmat(blksub,xx(s2(i)+1:s2(i+1)),0);\n                if (nargout == 1);\n                    lam = eig(Xsub);\n                elseif (nargout == 2);\n                    [evec,lam] = eig(Xsub);\n                    lam = diag(lam);\n                    V(pos,pos) = sparse(evec); %#ok\n                end\n                d(pos,1) = lam;\n            end\n        end\n        n2 = sum(blktmp.*blktmp);\n        if (nargout == 2);\n            if (nnz(V) <= spdensity*n2);\n                V = sparse(V);\n            else\n                V = full(V);\n            end\n        end\n    elseif strcmp(blk{1},'l');\n        if (nargout == 2);\n            V = ones(size(X)); d = X;\n        elseif (nargout == 1);\n            d = X;\n        end\n    end\nelse\n    if (nargout == 2);\n        V = cell(size(X));  d = cell(size(X));\n        for p = 1:size(blk,1);\n            [d{p},V{p}] = blkeig(blk(p,:),X{p});\n        end\n    elseif (nargout == 1);\n        d = cell(size(X));\n        for p = 1:size(blk,1);\n            d{p} = blkeig(blk(p,:),X{p});\n        end\n    end\nend\n%%***************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HKMrhsfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/HKMrhsfun.m", "size": 3358, "source_encoding": "utf_8", "md5": "b1e6a8305128af799634b9ef7324cfeb", "text": "%%*******************************************************************\n%% HKMrhsfun: compute the right-hand side vector of the\n%%            Schur complement equation for the  HKM direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [rhs,EinvRc,hRd] = HKMrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ)\n\nm = length(rp);\nif (nargin > 8)\n    corrector = 1;\nelse\n    corrector = 0;\n    hRd = zeros(m,1);\nend\nhEinvRc = zeros(m,1);\nEinvRc = cell(size(blk,1),1);\nrhsfree = [];\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'l')\n        if iscell(sigmu)\n            EinvRc{p} = sigmu{p}./Z{p} -X{p};\n        else\n            EinvRc{p} = sigmu./Z{p} -X{p};\n        end\n        Rq = sparse(n,1);\n        if (corrector) && (norm(par.parbarrier{p})==0)\n            Rq = dX{p}.*dZ{p}./Z{p};\n        else\n            tmp  = par.dd{p}.*Rd{p};\n            tmp2 = mexMatvec(At{p},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = EinvRc{p} - Rq;\n        tmp2 = mexMatvec(At{p,1},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'q')\n        if iscell(sigmu)\n            EinvRc{p} = qops(pblk,sigmu{p},par.Zinv{p},3) -X{p};\n        else\n            EinvRc{p} = sigmu*par.Zinv{p} -X{p};\n        end\n        Rq = sparse(n,1);\n        if (corrector) && (norm(par.parbarrier{p})==0)\n            ff{p} = qops(pblk,1./par.gamz{p},Z{p},3); %#ok\n            hdx = qops(pblk,par.gamz{p},ff{p},5,dX{p});\n            hdz = qops(pblk,par.gamz{p},ff{p},6,dZ{p});\n            hdxdz = Arrow(pblk,hdx,hdz);\n            Rq = qops(pblk,par.gamz{p},ff{p},6,hdxdz);\n        else\n            tmp = par.dd{p}.*Rd{p} ...\n                + qops(pblk,qops(pblk,Rd{p},par.Zinv{p},1),X{p},3) ...\n                + qops(pblk,qops(pblk,Rd{p},X{p},1),par.Zinv{p},3);\n            tmp2 = mexMatvec(At{p,1},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = EinvRc{p} - Rq;\n        tmp2 = mexMatvec(At{p,1},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'s')\n        if iscell(sigmu)\n            %%ss = [0,cumsum(pblk{2})];\n            %%sigmuvec = zeros(n,1);\n            %%for k = 1:length(pblk{2});\n            %%   sigmuvec(ss(k)+1:ss(k+1)) = sigmu{p}(k)*ones(pblk{2}(k),1);\n            %%end\n            sigmuvec = mexexpand(pblk{2},sigmu{p});\n            EinvRc{p} = par.Zinv{p}*spdiags(sigmuvec,0,n,n) -X{p};\n        else\n            EinvRc{p} = sigmu*par.Zinv{p} -X{p};\n        end\n        Rq = sparse(n,n);\n        if (corrector) && (norm(par.parbarrier{p})==0)\n            Rq = Prod3(pblk,dX{p},dZ{p},par.Zinv{p},0);\n            Rq = 0.5*(Rq+Rq');\n        else\n            tmp = Prod3(pblk,X{p},Rd{p},par.Zinv{p},0,par.nzlistAy{p});\n            tmp = 0.5*(tmp+tmp');\n            tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),{tmp});\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = EinvRc{p} - Rq;\n        tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),EinvRc(p));\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'u')\n        rhsfree = [rhsfree; Rd{p}]; %#ok\n    end\nend\n%%\nrhs = rp + hRd - hEinvRc;\nrhs = full([rhs; rhsfree]);\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "linsysolve.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/linsysolve.m", "size": 7741, "source_encoding": "utf_8", "md5": "2091e9d1c0858acfd5c4e07842ed787d", "text": "%%***************************************************************\n%% linsysolve: solve linear system to get dy, and direction\n%%             corresponding to unrestricted variables.\n%%\n%% [xx,coeff,L,resnrm] = linsysolve(schur,UU,Afree,EE,rhs);\n%%\n%% child functions: symqmr.m, mybicgstable.m, linsysolvefun.m\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***************************************************************\n\nfunction [xx,coeff,L,resnrm] = linsysolve(par,schur,UU,Afree,EE,rhs)\n\nglobal solve_ok exist_analytic_term\nglobal nnzmat nnzmatold matfct_options matfct_options_old use_LU\nglobal msg diagR diagRold numpertdiagschur\n\nspdensity  = par.spdensity;\nprintlevel = par.printlevel;\niter       = par.iter;\nif isfield(par,'relgap') && isfield(par,'pinfeas') && isfield(par,'dinfeas')\n    err = max([par.relgap,par.pinfeas,par.dinfeas]);\nelse\n    err = inf;\nend\n%%\nm = length(schur);\nif (iter==1);\n    use_LU = 0;\n    matfct_options_old = ''; %#ok\n    diagR = ones(m,1);\n    numpertdiagschur = 0;\nend\nif isempty(nnzmatold)\n    nnzmatold = 0; %#ok\nend\ndiagRold = diagR;\n%%\n%% schur = schur + rho*diagschur + lam*AAt\n%%\ndiagschur = abs(full(diag(schur)));\nif (par.ublksize)\n    minrho(1) = 1e-15;\nelse\n    minrho(1) = 1e-17;\nend\nminrho(1) = max(minrho(1), 1e-6/3.0^iter); %% old: 1e-6/3.0^iter\nminrho(2) = max(1e-04, 0.7^iter);\nminlam = max(1e-10, 1e-4/2.0^iter);\nrho = min(minrho(1), minrho(2)*(1+norm(rhs))/(1+norm(diagschur.*par.y)));\nlam = min(minlam, 0.1*rho*norm(diagschur)/par.normAAt);\nif (exist_analytic_term); rho = 0; end; %% important\nratio = max(diagR)/min(diagR);\nif (par.depconstr) || (ratio > 1e10) || (iter < 5)\n    %% important: do not perturb beyond certain threshold\n    %% since it will adversely affect prim_infeas of fp43\n    %%\n    pertdiagschur = min(rho*diagschur,1e-4./max(1,abs(par.dy)));\n    mexschurfun(schur,full(pertdiagschur));\n    %%if (printlevel>2); fprintf(' %2.1e',rho); end\nend\nif (par.depconstr) || (par.ZpATynorm > 1e10) || (par.ublksize) || (iter < 10)\n    %% Note: do not add this perturbation even if ratio is large.\n    %% It adversely affects hinf15.\n    %%\n    lam = min(lam,1e-4/max(1,norm(par.AAt*par.dy)));\n    if (exist_analytic_term); lam = 0; end\n    mexschurfun(schur,lam*par.AAt);\n    %%if (printlevel>2); fprintf('*'); end\nend\nif (max(diagschur)/min(diagschur) > 1e14) && (par.blkdim(2) == 0) ...\n        && (iter > 10)\n    tol = 1e-8;\n    idx = find(diagschur < tol); len = length(idx);\n    pertdiagschur = zeros(m,1);\n    if (len > 0 && len < 5) && (norm(rhs(idx)) < tol)\n        pertdiagschur(idx) = 1*ones(length(idx),1);\n        mexschurfun(schur,pertdiagschur);\n        numpertdiagschur = numpertdiagschur + 1;\n        if (printlevel>2); fprintf('#'); end\n    end\nend\n%%\n%% assemble coefficient matrix\n%%\nlen = size(Afree,2);\nif ~isempty(EE)\n    EE(:,[1 2]) = len + EE(:,[1 2]); %% adjust for ublk\nend\nEE = [(1:len)' (1:len)' zeros(len,1); EE];\nif isempty(EE)\n    coeff.mat22 = [];\nelse\n    coeff.mat22 = spconvert(EE);\nend\nif (size(Afree,2) || size(UU,2))\n    coeff.mat12 = [Afree, UU];\nelse\n    coeff.mat12 = [];\nend\ncoeff.mat11 = schur; %% important to use perturbed schur matrix\nncolU = size(coeff.mat12,2);\n%%\n%% pad rhs with zero vector\n%% decide which solution methods to use\n%%\nrhs = [rhs; zeros(m+ncolU-length(rhs),1)];\nif (ncolU > 300); use_LU = 1; end\n%%\n%% Cholesky factorization\n%%\nL = []; resnrm = norm(rhs); xx = inf*ones(m,1);\nif (~use_LU)\n    solve_ok = 1;  solvesys = 1;\n    nnzmat = mexnnz(coeff.mat11);\n    % nnzmatdiff = (nnzmat ~= nnzmatold);\n    if (nnzmat > spdensity*m^2) || (m < 500)\n        matfct_options = 'chol';\n    else\n        matfct_options = 'spchol';\n    end\n    if (printlevel>2); fprintf(' %s ',matfct_options); end\n    L.matdim = length(schur);\n    if strcmp(matfct_options,'chol')\n        if issparse(schur); schur = full(schur); end;\n        if (iter<=5); %%--- to fix strange anonmaly in Matlab\n            mexschurfun(schur,1e-20,2);\n        end\n        L.matfct_options = 'chol';\n        [L.R,indef] = chol(schur);\n        L.perm = 1:m;\n        diagR  = diag(L.R).^2;\n    elseif strcmp(matfct_options,'spchol')\n        if ~issparse(schur); schur = sparse(schur); end;\n        L.matfct_options = 'spchol';\n        [L.R,indef,L.perm] = chol(schur,'vector');\n        L.Rt  = L.R';\n        diagR = full(diag(L.R)).^2;\n    end\n    if (indef)\n        diagR = diagRold;\n        solve_ok = -2; solvesys = 0;\n        msg = 'linsysolve: Schur complement matrix not positive definite';\n        if (printlevel); fprintf('\\n  %s',msg); end\n    end\n    if (solvesys)\n        if (ncolU)\n            tmp = coeff.mat12'*linsysolvefun(L,coeff.mat12)-coeff.mat22;\n            if issparse(tmp); tmp = full(tmp); end\n            tmp = 0.5*(tmp + tmp');\n            [L.Ml,L.Mu,L.Mp] = lu(tmp);\n            tol = 1e-16;\n            condest = max(abs(diag(L.Mu)))/min(abs(diag(L.Mu)));\n            if any(abs(diag(L.Mu)) < tol) || (condest > 1e30);  %%old: 1e18\n                solvesys = 0; %#ok\n                solve_ok = -4; %#ok\n                use_LU = 1;\n                msg = 'SMW too ill-conditioned, switch to LU factor';\n                if (printlevel); fprintf('\\n  %s, %2.1e.',msg,condest); end\n            end\n        end\n        [xx,resnrm,solve_ok] = symqmr(coeff,rhs,L,[],[],printlevel);\n        if (solve_ok <= 0.3) && (printlevel)\n            fprintf('\\n  warning: symqmr failed: %3.1f ',solve_ok);\n        end\n    end\n    if (solve_ok <= 0.3)\n        tol = 1e-10;\n        if (m < 1e4 && strcmp(matfct_options,'chol') && (err > tol)) ...\n                || (m < 2e5 && strcmp(matfct_options,'spchol') && (err > tol))\n            use_LU = 1;\n            if (printlevel); fprintf('\\n  switch to LU factor.'); end\n        end\n    end\nend\n%%\n%% LU factorization\n%%\nif (use_LU)\n    nnzmat = mexnnz(coeff.mat11)+mexnnz(coeff.mat12);\n    % nnzmatdiff = (nnzmat ~= nnzmatold);\n    solve_ok = 1; solvesys = 1; %#ok\n    if ~isempty(coeff.mat22)\n        raugmat = [coeff.mat11, coeff.mat12; coeff.mat12', coeff.mat22];\n    else\n        raugmat = coeff.mat11;\n    end\n    if (nnzmat > spdensity*m^2) || (m+ncolU < 500)\n        matfct_options = 'lu';  %% lu is better than ldl\n    else\n        matfct_options = 'splu'; %% faster than spldl\n    end\n    if (printlevel>2); fprintf(' %s ',matfct_options); end\n    L.matdim = length(raugmat);\n    if strcmp(matfct_options,'lu')\n        if issparse(raugmat); raugmat = full(raugmat); end\n        L.matfct_options = 'lu';\n        [L.L,L.U,L.p] = lu(raugmat,'vector');\n    elseif strcmp(matfct_options,'splu')\n        if ~issparse(raugmat); raugmat = sparse(raugmat); end\n        L.matfct_options = 'splu';\n        [L.L,L.U,L.p,L.q,L.s] = lu(raugmat,'vector');\n        L.s = full(diag(L.s));\n    elseif strcmp(matfct_options,'ldl')\n        if issparse(raugmat); raugmat = full(raugmat); end\n        L.matfct_options = 'ldl';\n        [L.L,L.D,L.p] = ldl(raugmat,'vector');\n        L.D = sparse(L.D);\n    elseif strcmp(matfct_options,'spldl')\n        if ~issparse(raugmat); raugmat = sparse(raugmat); end\n        L.matfct_options = 'spldl';\n        [L.L,L.D,L.p,L.s] = ldl(raugmat,'vector');\n        L.s  = full(diag(L.s));\n        L.Lt = L.L';\n    end\n    if (solvesys)\n        [xx,resnrm,solve_ok] = symqmr(coeff,rhs,L,[],[],printlevel);\n        %%[xx,resnrm,solve_ok] = mybicgstab(coeff,rhs,L,[],[],printlevel);\n        if (solve_ok<=0) && (printlevel)\n            fprintf('\\n  warning: bicgstab fails: %3.1f,',solve_ok);\n        end\n    end\nend\nif (printlevel>2); fprintf('%2.0d ',length(resnrm)-1); end\n%%\nnnzmatold = nnzmat; matfct_options_old = matfct_options;\n%%***************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "schurmat_qblk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/schurmat_qblk.m", "size": 3205, "source_encoding": "utf_8", "md5": "d154ddca57828c83c43efd37fbe64829", "text": "%%*******************************************************************\n%% schurmat_qblk: compute schur matrix corresponding to SOCP blocks.\n%%\n%% HKM direction: output = schur + Ax*Ae' + Ae*Ax' - Ad*Ad'\n%% NT  direction: output = schur + Ae*Ae' - Ad*Ad'\n%%\n%% where schur = A*D*A', and Ad is the modification to ADA'\n%% so that the latter is positive definite.\n%%\n%% [schur,UU,EE] = schurmat_qblk(blk,At,schur,UU,EE,p,dd,ee,xx);\n%%\n%% UU: stores the dense columns of Ax, Ae, Ad, and possibly\n%%     those of A*D^{1/2}. It has the form UU = [Ax Ae Ad].\n%% EE: stores the assocaited (2,2) block matrix when the\n%%     output matrix is expressed as an augmented matrix.\n%%     It has the form EE = [0 -lam 0; -lam 0 0; 0 0 I].\n%%\n%% options = 0, HKM\n%%         = 1, NT\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [schur,UU,EE] = schurmat_qblk(blk,At,par,schur,UU,EE,p,dd,ee,xx)\n\nglobal idxdenAq % nnzschur_qblk\n\nif (nargin == 10); options = 0; else options = 1; end;\niter = par.iter;\n\nif isempty(EE)\n    count = 0;\nelse\n    count = max(max(EE(:,2)),max(EE(:,1)));\nend\npblk = blk(p,:); n = sum(pblk{2}); numblk = length(pblk{2});\n%%\nAe = qprod(pblk,At{p}',ee{p});\nif (options == 0)\n    Ax = qprod(pblk,At{p}',xx{p});\nend\nidxden = checkdense(Ae);\nddsch = dd{p};\nif ~isempty(idxden);\n    spcolidx = setdiff(1:numblk,idxden);\n    s = 1 + [0, cumsum(pblk{2})];\n    idx = s(idxden);\n    tmp = zeros(n,1);\n    tmp(idx) = sqrt(2*abs(ddsch(idx)));\n    Ad = qprod(pblk,At{p}',tmp);\n    ddsch(idx) = abs(ddsch(idx));\n    if (options == 0)\n        len = length(idxden);\n        gamzsub = par.gamz{p}(idxden);\n        lam = gamzsub.*gamzsub;\n        UU = [UU, Ax(:,idxden), Ae(:,idxden)*spdiags(lam,0,len,len), Ad(:,idxden)];\n        tmp = (count+1:count+len)';\n        EE = [EE; [tmp, len+tmp, -lam; len+tmp, tmp, -lam; ...\n            2*len+tmp, 2*len+tmp, ones(len,1)] ];\n        count = count+3*len;\n        Ax = Ax(:,spcolidx); Ae = Ae(:,spcolidx);\n        tmp = Ax*Ae';\n        schur = schur + (tmp + tmp');\n    else\n        len = length(idxden);\n        w2 = par.gamz{p}./par.gamx{p};\n        lam = w2(idxden);\n        UU = [UU, Ae(:,idxden)*spdiags(sqrt(lam),0,len,len), Ad(:,idxden)];\n        tmp = (count+1:count+len)';\n        EE = [EE; [tmp, tmp, -lam; len+tmp, len+tmp, ones(len,1)] ];\n        count = count + 2*len;\n        Ae = Ae(:,spcolidx);\n        schur = schur + Ae*Ae';\n    end\nelse\n    if (options == 0)\n        tmp = Ax*Ae';\n        schur = schur + (tmp+tmp');\n    else\n        tmp = Ae*Ae';\n        schur = schur + tmp;\n    end\nend\nif (iter==1)\n    idxdenAq{p} = checkdense(At{p}');\nend\nif ~isempty(idxdenAq{p});\n    idxden = idxdenAq{p};\n    len = length(idxden);\n    Ad = At{p}(idxden,:)'*spdiags(sqrt(abs(ddsch(idxden))),0,len,len);\n    UU = [UU, Ad];\n    tmp = (count+1:count+len)';\n    EE = [EE; [tmp, tmp, -sign(ddsch(idxden))]];\n    % count = count + len;\n    ddsch(idxden) = zeros(len,1);\nend\nschurtmp = At{p}' *spdiags(ddsch,0,n,n) *At{p};\nschur = schur + schurtmp;\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "ops.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/ops.m", "size": 11548, "source_encoding": "utf_8", "md5": "0931c97ce9eb7f01a28fac581153ca1c", "text": "%%******************************************************************\n%% ops:\n%%\n%%   Z = ops(X,operand,Y,alpha);\n%%\n%%  INPUT:        X = a matrix or a scalar\n%%                    or a CELL ARRAY consisting only of matrices\n%%          operand = sym, transpose, triu, tril,\n%%                    real, imag, sqrt, abs, max, min, nnz,\n%%                    spdiags, ones, zeros, norm, sum, row-norm, blk-norm\n%%                    rank1, rank1inv, inv\n%%                    +,  -, *, .*,  ./, .^\n%%     Y (optional) = a matrix or a scalar\n%%                    or a CELL ARRAY consisting only of matrices\n%% alpha (optional) = a scalar\n%%                    or the variable blk.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%******************************************************************\n\nfunction Z = ops(X,operand,Y,alpha)\n\nspdensity = 0.4;\n\nif (nargin == 2)\n    if strcmp(operand,'sym');\n        if ~iscell(X);\n            [m,n] = size(X);\n            if (m == n);\n                Z = (X+X')/2;\n            elseif (n == 1);\n                Z = X;\n            else\n                error('X must be square matrix or a column vector');\n            end;\n        else\n            Z = cell(size(X));\n            for p = 1:length(X);\n                [m,n] = size(X{p});\n                if (m == n);\n                    Z{p} = (X{p}+X{p}')/2;\n                elseif (n == 1);\n                    Z{p} = X{p};\n                else\n                    error('X{p} must be square matrix or a column vector');\n                end;\n            end;\n        end;\n    elseif strcmp(operand,'sqrt') || strcmp(operand,'abs') || ...\n            strcmp(operand,'real') || strcmp(operand,'imag');\n        if ~iscell(X);\n            eval(['Z = ',operand,'(X);']);\n        else\n            Z = cell(size(X));\n            for p = 1:length(X);\n                eval(['Z{p} = ',operand,'(X{p});']);\n            end;\n        end;\n    elseif strcmp(operand,'max') || strcmp(operand,'min') || ...\n            strcmp(operand,'sum');\n        if ~iscell(X);\n            eval(['Z = ',operand,'(X);']);\n        else\n            Z = [];\n            for p = 1:length(X);\n                eval(['Z = [Z  ',operand,'(X{p})','  ];']);\n            end;\n        end;\n        eval(['Z = ',operand,'(Z);']);\n    elseif strcmp(operand,'transpose') || strcmp(operand,'triu') || ...\n            strcmp(operand,'tril');\n        if ~iscell(X);\n            if (size(X,1) == size(X,2));\n                Z = feval(operand,X);\n            elseif (size(X,2) == 1);\n                Z = X;\n            else\n                error('X must be square matrix or a column vector');\n            end;\n        else\n            Z = cell(size(X));\n            for p = 1:length(X);\n                if (size(X{p},1) == size(X{p},2));\n                    Z{p} = feval(operand,X{p});\n                elseif (size(X{p},2) == 1);\n                    Z{p} = X{p};\n                else\n                    error('X{p} must be square matrix or a column vector');\n                end;\n            end;\n        end;\n    elseif strcmp(operand,'norm');\n        if ~iscell(X);\n            Z = full(sqrt(sum(sum(X.*X))));\n        else\n            Z = 0;\n            for p = 1:length(X); Z = Z + sum(sum(X{p}.*X{p})); end;\n            Z = sqrt(Z);\n        end;\n    elseif strcmp(operand,'blk-norm');\n        if ~iscell(X);\n            Z = full(sqrt(sum(sum(X.*X))));\n        else\n            Z = zeros(length(X),1);\n            for p = 1:length(X); Z(p) = sum(sum(X{p}.*X{p})); end;\n            Z = sqrt(Z);\n        end;\n    elseif strcmp(operand,'inv');\n        if ~iscell(X);\n            [m,n] = size(X); n2 = n*n;\n            if (m==n)\n                Z = inv(X);\n                if (nnz(Z) > spdensity*n2)\n                    Z = full(Z);\n                else\n                    Z = sparse(Z);\n                end\n            elseif (m > 1 && n == 1);\n                Z = 1./X;\n                if (nnz(Z) > spdensity*n)\n                    Z = full(Z);\n                else\n                    Z = sparse(Z);\n                end\n            end\n        else\n            Z = cell(size(X));\n            for p = 1:length(X);\n                [m,n] = size(X{p}); n2 = n*n;\n                if (m==n)\n                    Z{p} = inv(X{p});\n                    if (nnz(Z{p}) > spdensity*n2)\n                        Z{p} = full(Z{p});\n                    else\n                        Z{p} = sparse(Z{p});\n                    end\n                elseif (m > 1 && n == 1);\n                    Z{p} = 1./X{p};\n                    if (nnz(Z{p}) > spdensity*n)\n                        Z{p} = full(Z{p});\n                    else\n                        Z{p} = sparse(Z{p});\n                    end\n                end\n            end\n        end\n    elseif strcmp(operand,'getM');\n        if ~iscell(X);\n            Z = size(X,1);\n        else\n            for p = 1:length(X); Z(p) = size(X{p},1); end; %#ok\n            Z = sum(Z);\n        end;\n    elseif strcmp(operand,'nnz');\n        if ~iscell(X);\n            Z = nnz(X);\n        else\n            for p = 1:length(X);\n                Z(p) = nnz(X{p}); %#ok\n            end;\n            Z = sum(Z);\n        end;\n    elseif strcmp(operand,'ones');\n        if ~iscell(X);\n            Z = ones(size(X));\n        else\n            Z = cell(size(X));\n            for p = 1:length(X);\n                Z{p} = ones(size(X{p}));\n            end\n        end\n    elseif strcmp(operand,'zeros');\n        if ~iscell(X);\n            [m,n] = size(X);\n            Z = sparse(m,n);\n        else\n            Z = cell(size(X));\n            for p = 1:length(X);\n                [m,n] = size(X{p});\n                Z{p} = sparse(m,n);\n            end\n        end\n    elseif strcmp(operand,'identity');\n        blk = X;\n        Z = cell(size(blk,1),1);\n        for p = 1:size(blk,1)\n            pblk = blk(p,:); n = sum(pblk{2});\n            if strcmp(pblk{1},'s')\n                Z{p} = speye(n,n);\n            elseif strcmp(pblk{1},'q')\n                s = 1+[0, cumsum(pblk{2})];\n                len = length(pblk{2});\n                Z{p} = zeros(n,1);\n                Z{p}(s(1:len)) = ones(len,1);\n            elseif strcmp(pblk{1},'l')\n                Z{p} = ones(n,1);\n            elseif strcmp(pblk{1},'u')\n                Z{p} = zeros(n,1);\n            end\n        end\n    elseif strcmp(operand,'row-norm');\n        if ~iscell(X);\n            if (size(X,2) == size(X,1));\n                Z = sqrt(sum((X.*conj(X))'))'; %#ok\n            elseif (size(X,2) == 1);\n                Z = abs(X);\n            end\n        else\n            Z = cell(size(X));\n            for p = 1:length(X);\n                if (size(X{p},2) == size(X{p},1));\n                    Z{p} = sqrt(sum((X{p}.*conj(X{p}))'))'; %#ok\n                elseif (size(X{p},2) == 1);\n                    Z{p} = abs(X{p});\n                end\n            end\n        end\n    end\nend\n%%\nif (nargin == 3)\n    if strcmp(operand,'spdiags');\n        if ~iscell(Y);\n            [m,n] = size(Y);\n            if (m == n);\n                Z = spdiags(X,0,m,n);\n            else\n                Z = X;\n            end\n        else\n            Z = cell(size(Y));\n            for p = 1:length(Y);\n                [m,n] = size(Y{p});\n                if (m == n);\n                    Z{p} = spdiags(X{p},0,m,n);\n                else\n                    Z{p} = X{p};\n                end\n            end\n        end\n    elseif strcmp(operand,'inprod')\n        if ~iscell(X) && ~iscell(Y)\n            Z = (Y'*X)';\n        elseif iscell(X) && iscell(Y)\n            Z = zeros(size(X{1},2),1);\n            for p=1:length(X)\n                Z = Z + (Y{p}'*X{p})';\n            end\n        end\n    elseif strcmp(operand,'+') || strcmp(operand,'-') || ...\n            strcmp(operand,'/') || strcmp(operand,'./') || ...\n            strcmp(operand,'*') || strcmp(operand,'.*') || ...\n            strcmp(operand,'.^');\n        if (~iscell(X) && ~iscell(Y));\n            eval(['Z = X',operand,'Y;']);\n        elseif (iscell(X) && iscell(Y))\n            Z = cell(size(X));\n            for p = 1:length(X);\n                if (size(X{p},2) == 1) && (size(Y{p},2) == 1) && ...\n                        (strcmp(operand,'*') || strcmp(operand,'/'));\n                    eval(['Z{p} = X{p}.',operand,'Y{p};']);\n                else\n                    eval(['Z{p} = X{p} ',operand,'Y{p};']);\n                end\n            end\n        elseif (iscell(X) && ~iscell(Y));\n            if (length(Y) == 1); Y = Y*ones(length(X),1); end\n            Z = cell(size(X));\n            for p = 1:length(X);\n                eval(['Z{p} = X{p}',operand,'Y(p);']);\n            end\n        elseif (~iscell(X) && iscell(Y));\n            Z = cell(size(Y));\n            if (length(X) == 1); X = X*ones(length(Y),1); end\n            for p = 1:length(Y);\n                eval(['Z{p} = X(p)',operand,'Y{p};']);\n            end\n        end\n    else\n        error([operand,' is not available, check input arguments']);\n    end\nend\n%%\nif (nargin == 4)\n    if strcmp(operand,'rank1') || strcmp(operand,'rank1inv');\n        Z = cell(size(alpha,1),1);\n        for p = 1:size(alpha,1);\n            if ~strcmp(alpha{p,1},'diag');\n                blktmp = alpha{p,2};\n                if (length(blktmp) == 1);\n                    if strcmp(operand,'rank1');\n                        Z{p} = (X{p}*Y{p}' + Y{p}*X{p}')/2;\n                    else\n                        Z{p} = 2./(X{p}*Y{p}' + Y{p}*X{p}');\n                    end\n                else\n                    Xp = X{p};\n                    Yp = Y{p};\n                    n = sum(blktmp);\n                    Zp = sparse(n,n);\n                    s = [0 cumsum(blktmp)];\n                    if strcmp(operand,'rank1');\n                        for i = 1:length(blktmp)\n                            pos = s(i)+1 : s(i+1);\n                            x = Xp(pos);\n                            y = Yp(pos);\n                            Zp(pos,pos) = sparse((x*y' + y*x')/2); %#ok\n                        end;\n                        Z{p} = Zp;\n                    else\n                        for i = 1:length(blktmp)\n                            pos = s(i)+1 : s(i+1);\n                            x = Xp(pos);\n                            y = Yp(pos);\n                            Zp(pos,pos) = sparse(2./(x*y' + y*x')); %#ok\n                        end\n                        Z{p} = Zp;\n                    end\n                end\n            elseif strcmp(alpha{p,1},'diag');\n                if strcmp(operand,'rank1');\n                    Z{p} = X{p}.*Y{p};\n                else\n                    Z{p} = 1./(X{p}.*Y{p});\n                end\n            end\n        end\n    elseif strcmp(operand,'+') || strcmp(operand,'-');\n        if ~iscell(X) && ~iscell(Y);\n            eval(['Z = X',operand,'alpha*Y;']);\n        elseif (iscell(X) && iscell(Y));\n            Z = cell(size(X));\n            if (length(alpha) == 1);\n                alpha = alpha*ones(length(X),1);\n            end\n            if strcmp(operand,'+'),\n                for p = 1:length(X)\n                    Z{p} = X{p} + alpha(p) * Y{p};\n                end\n            else\n                for p = 1:length(X);\n                    Z{p} = X{p} - alpha(p) * Y{p};\n                end\n            end\n        else\n            error('X, Y are different objects');\n        end\n    else\n        error([operand,' is not available']);\n    end\nend\n%%============================================================\n\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "schurmat_lblk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/schurmat_lblk.m", "size": 1010, "source_encoding": "utf_8", "md5": "11b66919604e457c631f5ed67225fc45", "text": "%%*******************************************************************\n%% schurmat_lblk: compute A*D*A'\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [schur,UU,EE] = schurmat_lblk(blk,At,par,schur,UU,EE,p,dd)\n\nglobal idxdenAl\n\niter = par.iter;\nn = sum(blk{p,2});\n\nif (iter==1)\n    idxdenAl{p} = checkdense(At{p}');\nend\nddsch = dd{p};\nif ~isempty(idxdenAl{p});\n    idxden = idxdenAl{p};\n    len = length(idxden);\n    Ad = At{p}(idxden,:)' *spdiags(sqrt(ddsch(idxden)),0,len,len);\n    UU = [UU, Ad];\n    if isempty(EE)\n        count = 0;\n    else\n        count = max(max(EE(:,1)),max(EE(:,2)));\n    end\n    tmp = (count + 1: count+len)';\n    EE = [EE; [tmp, tmp, -ones(len,1)] ];\n    ddsch(idxden) = zeros(len,1);\nend\nschurtmp = At{p}' *spdiags(ddsch,0,n,n) *At{p};\nschur = schur + schurtmp;\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlpmisc.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlpmisc.m", "size": 4078, "source_encoding": "utf_8", "md5": "1dc032b6e1fd72eb26f2f3e519a746dd", "text": "%%*****************************************************************************\n%% sqlpmisc:\n%% unscale and produce infeasibility certificates if appropriate\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004.\n%%*****************************************************************************\n\nfunction [X,y,Z,termcode,resid,reldist,msg] = sqlpmisc(blk,At,C,b,X,y,Z,permZ,param)\n\ntermcode    = param.termcode;\niter        = param.iter;\nobj         = param.obj;\nrelgap      = param.relgap;\nprim_infeas = param.prim_infeas;\ndual_infeas = param.dual_infeas;\nhomRd       = param.homRd;\nhomrp       = param.homrp;\nAX          = param.AX;\nZpATynorm   = param.ZpATynorm;\nm0          = param.m0;\nindeprows   = param.indeprows;\nnormX0      = param.normX0;\nnormZ0      = param.normZ0;\ninftol      = param.inftol;\nmaxit       = param.maxit;\nscale_data  = param.scale_data;\nprintlevel  = param.printlevel;\n%%\nresid = []; reldist = []; msg = [];\nif (scale_data)\n    normA = param.normA; normC = param.normC; normb = param.normb;\nelse\n    normA = 1; normC = 1; normb = 1;\nend\nAnorm = ops(At,'norm'); xnorm = ops(X,'norm'); ynorm = norm(y);\ninfeas = max(prim_infeas,dual_infeas);\n%%\nif (iter >= maxit)\n    termcode = -6;\n    msg = 'sqlp stop: maximum number of iterations reached';\n    if (printlevel); fprintf('\\n  %s',msg); end\nend\nif (termcode <= 0)\n    %%\n    %% To detect near-infeasibility when the algorithm provides\n    %% a \"better\" certificate of infeasibility than of optimality.\n    %%\n    err = max(infeas,relgap);\n    iflag = 0;\n    if (obj(2) > 0)\n        if (homRd < 0.1*sqrt(err*inftol))\n            iflag = 1;\n            msg = sprintf('prim_inf,dual_inf,relgap = %3.2e, %3.2e, %3.2e',...\n                prim_infeas,dual_infeas,relgap);\n            if (printlevel); fprintf('\\n  %s',msg); end\n            termcode = 1;\n        end\n    end\n    if (obj(1) < 0)\n        if (homrp < 0.1*sqrt(err*inftol))\n            iflag = 1;\n            msg = sprintf('prim_inf,dual_inf,relgap = %3.2e, %3.2e, %3.2e',...\n                prim_infeas,dual_infeas,relgap);\n            if (printlevel); fprintf('\\n  %s',msg); end\n            termcode = 2;\n        end\n    end\n    if (iflag == 0)\n        if (scale_data == 1)\n            X = ops(ops(X,'./',normA),'*',normb);\n            y = y*normC;\n            Z = ops(ops(Z,'.*',normA),'*',normC);\n        end\n    end\nend\nif (termcode == 1) && (iter > 3)\n    msg = 'sqlp stop: primal problem is suspected of being infeasible';\n    if (printlevel); fprintf('\\n  %s',msg); end\n    if (scale_data == 1)\n        X = ops(X,'./',normA); b = b*normb;\n    end\n    rby = 1/(b'*y); y = rby*y; Z = ops(Z,'*',rby);\n    resid = ZpATynorm * rby;\n    reldist = ZpATynorm/(Anorm*ynorm);\nend\nif (termcode == 2) && (iter > 3)\n    msg = 'sqlp stop: dual problem is suspected of being infeasible';\n    if (printlevel); fprintf('\\n  %s',msg); end\n    if (scale_data == 1)\n        C = ops(C,'.*',normC);\n        Z = ops(Z,'.*',normA);\n    end\n    tCX = blktrace(blk,C,X);\n    X = ops(X,'*',1/(-tCX));\n    resid = norm(AX)/(-tCX);\n    reldist = norm(AX)/(Anorm*xnorm);\nend\nif (termcode == 3)\n    maxblowup = max(ops(X,'norm')/normX0,ops(Z,'norm')/normZ0);\n    msg = sprintf('sqlp stop: primal or dual is diverging, %3.1e',maxblowup);\n    if (printlevel); fprintf('\\n  %s',msg); end\nend\n[X,Z] = unperm(blk,permZ,X,Z);\nif ~isempty(indeprows)\n    ytmp = zeros(m0,1);\n    ytmp(indeprows) = y;\n    y = ytmp;\nend\n%%*****************************************************************************\n%% unperm: undo the permutations applied in validate.\n%%\n%% [X,Z] = unperm(blk,permZ,X,Z);\n%%\n%% undoes the permutation introduced in validate.\n%%*****************************************************************************\n\nfunction [X,Z] = unperm(blk,permZ,X,Z)\n%%\nfor p = 1:size(blk,1)\n    if (strcmp(blk{p,1},'s') && ~isempty(permZ{p}))\n        per = permZ{p};\n        X{p} = X{p}(per,per);\n        Z{p} = Z{p}(per,per);\n    end\nend\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "checkdense.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/checkdense.m", "size": 730, "source_encoding": "utf_8", "md5": "6d44b41643ef16f78b42927e1fc03dd8", "text": "%%********************************************************************\n%% checkdense : identify the dense columns of a matrix\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%********************************************************************\n\nfunction  idxden = checkdense(A)\n\n[m,n] = size(A);\nidxden = [];\nnzratio = 1;\nif (m > 1000); nzratio = 0.20; end;\nif (m > 2000); nzratio = 0.10; end;\nif (m > 5000); nzratio = 0.05; end;\nif (nzratio < 1)\n    nzcolA = sum(spones(A));\n    idxden = find(nzcolA > nzratio*m);\n    if (length(idxden) > max(200,0.1*n))\n        idxden = [];\n    end\nend\n%%********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "NTrhsfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/NTrhsfun.m", "size": 4253, "source_encoding": "utf_8", "md5": "51c74223afc232c456def8488b51aa3f", "text": "%%*******************************************************************\n%% NTrhsfun: compute the right-hand side vector of the\n%%           Schur complement equation for the NT direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [rhs,EinvRc,hRd] = NTrhsfun(blk,At,par,X,Z,rp,Rd,sigmu,hRd,dX,dZ)\n\nspdensity = par.spdensity;\nm = length(rp);\nif (nargin > 8)\n    corrector = 1;\nelse\n    corrector = 0;\n    hRd = zeros(m,1);\nend\nhEinvRc = zeros(m,1);\nEinvRc  = cell(size(blk,1),1);\nrhsfree = [];\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});  numblk = length(pblk{2});\n    if strcmp(pblk{1},'l')\n        if iscell(sigmu)\n            EinvRc{p} = sigmu{p}./Z{p} -X{p};\n        else\n            EinvRc{p} = sigmu./Z{p} -X{p};\n        end\n        Rq = sparse(n,1);\n        if (corrector) && (norm(par.parbarrier{p})==0)\n            Rq = dX{p}.*dZ{p}./Z{p};\n        else\n            tmp  = par.dd{p}.*Rd{p};\n            tmp2 = mexMatvec(At{p},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = EinvRc{p} - Rq;\n        tmp2 = mexMatvec(At{p},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'q')\n        if iscell(sigmu)\n            EinvRc{p} = qops(pblk,-sigmu{p}./(par.gamz{p}.*par.gamz{p}),Z{p},4) -X{p};\n        else\n            EinvRc{p} = qops(pblk,-sigmu./(par.gamz{p}.*par.gamz{p}),Z{p},4) -X{p};\n        end\n        Rq = sparse(n,1);\n        if (corrector) && (norm(par.parbarrier{p})==0)\n            w = sqrt(par.gamz{p}./par.gamx{p});\n            hdx = qops(pblk,w,par.ff{p},5,dX{p});\n            hdz = qops(pblk,w,par.ff{p},6,dZ{p});\n            hdxdz = Arrow(pblk,hdx,hdz);\n            vv = qops(pblk,w,par.ff{p},5,X{p});\n            Vihdxdz = Arrow(pblk,vv,hdxdz,1);\n            Rq = qops(pblk,w,par.ff{p},6,Vihdxdz);\n        else\n            tmp  = par.dd{p}.*Rd{p} + qops(pblk,qops(pblk,Rd{p},par.ee{p},1),par.ee{p},3);\n            tmp2 = mexMatvec(At{p},tmp,1);\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = EinvRc{p} - Rq;\n        tmp2 = mexMatvec(At{p},EinvRc{p},1);\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'s')\n        n2 = pblk{2}.*(pblk{2}+1)/2;\n        if iscell(sigmu)\n            %%ss = [0,cumsum(pblk{2})];\n            %%sigmuvec = zeros(n,1);\n            %%for k = 1:length(pblk{2});\n            %%   sigmuvec(ss(k)+1:ss(k+1)) = sigmu{p}(k)*ones(pblk{2}(k),1);\n            %%end\n            sigmuvec = mexexpand(pblk{2},sigmu{p});\n            tmp = spdiags(sigmuvec./par.sv{p} -par.sv{p},0,n,n);\n        else\n            tmp = spdiags(sigmu./par.sv{p} -par.sv{p},0,n,n);\n        end\n        EinvRc{p} = Prod3(pblk,par.G{p}',tmp,par.G{p},1);\n        Rq = sparse(n,n);\n        if (corrector) && (norm(par.parbarrier{p})==0)\n            hdZ = Prod3(pblk,par.G{p},dZ{p},par.G{p}',1);\n            hdX = spdiags(qops(pblk,par.parbarrier{p}',1./par.sv{p},3)-par.sv{p},0,n,n)-hdZ;\n            tmp = Prod2(pblk,hdX,hdZ,0);\n            tmp = 0.5*(tmp+tmp');\n            if (numblk == 1)\n                d = par.sv{p};\n                e = ones(pblk{2},1);\n                Rq = 2*tmp./(d*e'+e*d');\n                if (nnz(Rq) <= spdensity*n2); Rq = sparse(Rq); end\n            else\n                Rq = sparse(n,n);\n                ss = [0, cumsum(pblk{2})];\n                for i = 1:numblk\n                    pos = ss(i)+1 : ss(i+1);\n                    d = par.sv{p}(pos); e = ones(length(pos),1);\n                    Rq(pos,pos) = 2*tmp(pos,pos)./(d*e' + e*d'); %#ok\n                end\n            end\n            Rq = Prod3(pblk,par.G{p}',Rq,par.G{p},1);\n        else\n            tmp = Prod3(pblk,par.W{p},Rd{p},par.W{p},1,par.nzlistAy{p});\n            tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),{tmp});\n            hRd = hRd + tmp2;\n        end\n        EinvRc{p} = EinvRc{p} - Rq;\n        tmp2 = AXfun(pblk,At(p,:),par.permA(p,:),EinvRc(p));\n        hEinvRc = hEinvRc + tmp2;\n    elseif strcmp(pblk{1},'u')\n        rhsfree = [rhsfree; Rd{p}]; %#ok\n    end\nend\n%%\nrhs = rp + hRd - hEinvRc;\nrhs = full([rhs; rhsfree]);\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "SDPvalBounds.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/SDPvalBounds.m", "size": 1843, "source_encoding": "utf_8", "md5": "fa3af856805cc15685c38e69218a8ffb", "text": "%%*****************************************************************\n%% compute lower and upper bounds for the exact primal\n%%         optimal value.\n%%\n%%  LB <= true optimal dual value = true optimal primal value <= UB.\n%%\n%%*****************************************************************\n\nfunction [LB,UB] = SDPvalBounds(blk,At,C,b,X,y,mu)\n\nif (nargin < 7); mu = 1.1; end\nAty = Atyfun(blk,At,[],[],y);\nZnew = ops(C,'-',Aty);\n%%\neigX = cell(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        eigX{p} = eig(full(X{p}));\n    elseif strcmp(pblk{1},'l')\n        eigX{p} = X{p};\n    end\nend\n%%\n%% compute lower bound\n%%\npert = 0;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        eigtmp = eig(full(Znew{p}));\n        idx  = find(eigtmp < 0);\n        Xbar = mu*max(eigX{p});\n    elseif strcmp(pblk{1},'l')\n        eigtmp = Znew{p};\n        idx  = find(eigtmp < 0);\n        Xbar = mu*max(eigX{p});\n    end\n    numneg = length(idx);\n    if (numneg)\n        %%mineig = min(eigtmp(idx));\n        pert = pert + Xbar*sum(eigtmp(idx));\n        %%fprintf('\\n numneg = %3.0d,  mineigZnew = %- 3.2e',numneg,mineig);\n    end\nend\nLB0 = b'*y;\nLB  = b'*y + pert;\nfprintf('\\n <b,y> = %-10.9e, LB = %-10.9e\\n',LB0,LB);\n%%\n%% compute upper bound\n%%\nXbar = X;\n%% construct Xbar that is positive semidefinite\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    eigXp = eigX{p};\n    if strcmp(pblk{1},'s')\n        Xbar{p} = Xbar{p} - min(eigXp)*speye(n,n);\n    elseif strcmp(pblk{1},'l')\n        Xbar{p} = Xbar{p} - min(eigXp)*ones(n,1);\n    end\nend\nRp  = b-AXfun(blk,At,[],Xbar);\nUB  = blktrace(blk,C,Xbar) + mu*abs(y)'*abs(Rp);\nUB0 = blktrace(blk,C,X);\nfprintf('\\n <C,X> = %-10.9e, UB = %-10.9e\\n',UB0,UB);\n%%*****************************************************************\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "NTscaling.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/NTscaling.m", "size": 1847, "source_encoding": "utf_8", "md5": "82a266e8bf9cf33993f0bff10650c15c", "text": "%%**********************************************************************\n%% NTscaling: Compute NT scaling matrix\n%%\n%% compute SVD of Xchol*Zchol via eigenvalue decompostion of\n%%     Zchol * X * Zchol' = V * diag(sv2) * V'.\n%% compute W satisfying W*Z*W = X.\n%%     W = G'*G,  where G = diag(sqrt(sv)) * (invZchol*V)'\n%%     important to keep W symmertic.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************************\n\nfunction [W,G,sv,gamx,gamz,dd,ee,ff] = ...\n    NTscaling(blk,X,Z,Zchol,invZchol)\n\nnumblk = size(blk,1);\nW = cell(numblk,1); G = cell(numblk,1); sv = cell(numblk,1);\ngamx = cell(numblk,1); gamz = cell(numblk,1);\ndd = cell(numblk,1); ee = cell(numblk,1); ff = cell(numblk,1);\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'l')\n        dd{p} = X{p}./Z{p}; %% do not add perturbation, it badly affects cre-a\n    elseif strcmp(pblk{1},'q');\n        gamx{p} = sqrt(qops(pblk,X{p},X{p},2));\n        gamz{p} = sqrt(qops(pblk,Z{p},Z{p},2));\n        w2 = gamz{p}./gamx{p};  w = sqrt(w2);\n        dd{p} = qops(pblk,1./w2,ones(n,1),4);\n        tt = qops(pblk,1./w,Z{p},3) - qops(pblk,w,X{p},4);\n        gamtt = sqrt(qops(pblk,tt,tt,2));\n        ff{p} = qops(pblk,1./gamtt,tt,3);\n        ee{p} = qops(pblk,sqrt(2)./w,ff{p},4);\n    elseif strcmp(pblk{1},'s')\n        tmp = Prod2(pblk,Zchol{p},X{p},0);\n        tmp = Prod2(pblk,tmp,Zchol{p}',1);\n        [sv2,V] = blkeig(pblk,tmp);\n        sv2 = max(1e-20,sv2);\n        sv{p} = sqrt(sv2);\n        tmp  = Prod2(pblk,invZchol{p},V);\n        G{p} = Prod2(pblk,spdiags(sqrt(sv{p}),0,n,n),tmp');\n        W{p} = Prod2(pblk,G{p}',G{p},1);\n    end\nend\n%%**********************************************************************\n\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "linsysolvefun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/linsysolvefun.m", "size": 1276, "source_encoding": "utf_8", "md5": "7ca4a2896ad33a210a95d53138f0676f", "text": "%%*************************************************************************\n%% linsysolvefun: Solve H*x = b\n%%\n%% x = linsysolvefun(L,b)\n%% where L contains the triangular factors of H.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction x = linsysolvefun(L,b)\n\nx = zeros(size(b));\nfor k=1:size(b,2)\n    if strcmp(L.matfct_options,'chol')\n        x(L.perm,k) = mextriang(L.R, mextriang(L.R,b(L.perm,k),2) ,1);\n        %% x(L.perm,k) = L.R \\ (b(L.perm,k)' / L.R)';\n    elseif strcmp(L.matfct_options,'spchol')\n        x(L.perm,k) = mextriangsp(L.Rt,mextriangsp(L.R,b(L.perm,k),2),1);\n    elseif strcmp(L.matfct_options,'ldl')\n        x(L.p,k) = ((L.D\\ (L.L \\ b(L.p,k)))' / L.L)';\n    elseif strcmp(L.matfct_options,'spldl')\n        btmp = b(:,k).*L.s;\n        xtmp(L.p,1) = L.Lt\\ (L.D\\ (L.L \\ btmp(L.p))); %#ok\n        x(:,k) = xtmp.*L.s;\n    elseif strcmp(L.matfct_options,'lu')\n        x(:,k) = L.U \\ (L.L \\ b(L.p,k));\n    elseif strcmp(L.matfct_options,'splu')\n        btmp = b(:,k)./L.s;\n        x(L.q,k) = L.U \\ (L.L \\ (btmp(L.p)));\n    end\nend\n%%*************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlpdemo.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlpdemo.m", "size": 6011, "source_encoding": "utf_8", "md5": "793deabad8688259781993ce346cea24", "text": "%%*****************************************************************\n%% Examples of SQLP.\n%%\n%% this is an illustration on how to use our SQLP solvers\n%% coded in sqlp.m\n%%\n%% feas = 1 if want feasible initial iterate\n%%      = 0 otherwise\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*****************************************************************\n\nfunction  sqlpdemo\n\nrandn('seed',0); rand('seed',0); %#ok\nfeas = input('using feasible starting point? [yes = 1, no = 0] ');\nif (feas)\n    fprintf('\\n  using feasible starting point\\n\\n');\nelse\n    fprintf('\\n  using infeasible starting point\\n\\n');\nend\npause(1);\n\nntrials = 1;\n% iterm = zeros(2,6); infom = zeros(2,6); timem = zeros(2,6);\n\nsqlparameters;\n\nfor trials = 1:ntrials\n    for eg = 1:6\n        if (eg == 1);\n            disp('******** random sdp **********')\n            n = 10; m = 10;\n            [blk,At,C,b,X0,y0,Z0] = randsdp(n,[],[],m,feas);\n            text = 'random SDP';\n        elseif (eg == 2);\n            disp('******** Norm minimization problem. **********')\n            n = 10; m = 5; B = [];\n            for k = 1:m+1; B{k} = randn(n); end; %#ok\n            [blk,At,C,b,X0,y0,Z0] = norm_min(B,feas);\n            text = 'Norm min. pbm';\n        elseif (eg == 3);\n            disp('******** Max-cut *********');\n            N = 10;\n            B = graph(N);\n            [blk,At,C,b,X0,y0,Z0] = maxcut(B,feas);\n            text = 'Maxcut';\n        elseif (eg == 4);\n            disp('********* ETP ***********')\n            N = 10;\n            B = randn(N); B = B*B';\n            [blk,At,C,b,X0,y0,Z0] = etp(B,feas);\n            text = 'ETP';\n        elseif (eg == 5);\n            disp('**** Lovasz theta function ****')\n            N = 10;\n            B = graph(N);\n            [blk,At,C,b,X0,y0,Z0] = thetaproblem(B,feas);\n            text = 'Lovasz theta fn.';\n        elseif (eg == 6);\n            disp('**** Logarithmic Chebyshev approx. pbm. ****')\n            N = 20; m = 5;\n            B = rand(N,m); f = rand(N,1);\n            [blk,At,C,b,X0,y0,Z0] = logcheby(B,f,feas);\n            text = 'Log. Chebyshev approx. pbm';\n        end;\n        %%\n        % m = length(b);\n        nn = 0;\n        for p = 1:size(blk,1),\n            nn = nn + sum(blk{p,2});\n        end\n        %%\n        Gap = []; Feas = []; legendtext = {};\n        for vers = [1 2];\n            OPTIONS.vers = vers;\n            [obj,X,y,Z,infoall,runhist] = ...\n                sqlp(blk,At,C,b,OPTIONS,X0,y0,Z0); %#ok\n            gaphist = runhist.gap;\n            infeashist = max([runhist.pinfeas; runhist.dinfeas]);\n            Gap(vers,1:length(gaphist)) = gaphist; %#ok\n            Feas(vers,1:length(infeashist)) = infeashist; %#ok\n            if (vers==1); legendtext{end+1} = 'HKM'; %#ok\n            elseif (vers==2); legendtext{end+1} = 'NT'; %#ok\n            end;\n        end;\n        h = plotgap(Gap,Feas);\n        xlabel(text);\n        legend(h(h~=0),legendtext{:});\n        fprintf('\\n**** press enter to continue ****\\n'); pause\n    end\nend\n%%\n%%======================================================================\n%% plotgap: plot the convergence curve of\n%%          duality gap and infeasibility measure.\n%%\n%%         h = plotgap(Gap,Feas);\n%%\n%% Input:  Gap  = each row of Gap corresponds to a convergence curve\n%%                of the duality gap for an SDP.\n%%         Feas = each row of Feas corresponds to a convergence curve\n%%                of the infeasibility measure for an SDP.\n%%\n%% Output: h = figure handle.\n%%\n%% SDPT3: version 3.0\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last modified: 7 Jul 99\n%%********************************************************************\n\nfunction  h = plotgap(Gap,Feas)\n\nclf;\nset(0,'defaultaxesfontsize',12);\nset(0,'defaultlinemarkersize',2);\nset(0,'defaulttextfontsize',12);\n%%\n%% get axis scale for plotting duality gap\n%%\ntmp = [];\nfor k = 1:size(Gap,1);\n    g = Gap(k,:);\n    if ~isempty(g);\n        g = g(g > 5*eps);\n        tmp = [tmp abs(g)]; %#ok\n        iter(k) = length(g); %#ok\n    else\n        iter(k) = 0; %#ok\n    end\nend;\nymax = exp(log(10)*(round(log10(max(tmp)))+0.5));\nymin = exp(log(10)*min(floor(log10(tmp)))-0.5);\nxmax = 5*ceil(max(iter)/5);\n%%\n%% plot duality gap\n%%\ncolor = '-r --b--m-c ';\nif nargin == 2; subplot('position',[0.05 0.3 0.45 0.45]); end;\nfor k = 1:size(Gap,1);\n    g = Gap(k,:);\n    if ~isempty(g);\n        idx = find(g > 5*eps);\n        if ~isempty(idx); g = g(idx); len = length(g);\n            semilogy(len-1,g(len),'.b','markersize',12); hold on;\n            h(k) = semilogy(idx-1,g,color([3*(k-1)+1:3*k]),'linewidth',2); %#ok\n        end;\n    end;\nend;\ntitle('duality gap');  axis('square');\nif nargin == 1;  axis([0 xmax ymin ymax]); end;\nhold off;\n%%\n%% get axis scale for plotting infeasibility\n%%\nif nargin == 2;\n    tmp = [];\n    for k = 1:size(Feas,1);\n        f = Feas(k,:);\n        if ~isempty(f);\n            f = f(f>0);\n            tmp = [tmp abs(f)]; %#ok\n            iter(k) = length(f); %#ok\n        else\n            iter(k) = 0; %#ok\n        end\n    end;\n    fymax = exp(log(10)*(round(log10(max(tmp)))+0.5));\n    fymin = exp(log(10)*(min(floor(log10(tmp)))-0.5));\n    ymax = max(ymax,fymax); ymin = min(ymin,fymin);\n    xmax = 5*ceil(max(iter)/5);\n    axis([0 xmax ymin ymax]);\n    %%\n    %% plot infeasibility\n    %%\n    subplot('position',[0.5 0.3 0.45 0.45]);\n    for k = 1:size(Feas,1);\n        f = Feas(k,:);\n        f(1) = max(f(1),eps);\n        if ~isempty(f);\n            idx = find(f > 1e-20);\n            if ~isempty(idx); f = f(idx); len = length(f);\n                semilogy(len-1,f(len),'.b','markersize',12); hold on;\n                h(k) = semilogy(idx-1,f,color([3*(k-1)+1:3*k]),'linewidth',2); %#ok\n            end;\n        end;\n    end;\n    title('infeasibility measure');\n    axis('square');  axis([0 xmax ymin max(1,ymax)]);\n    hold off;\nend;\n%%====================================================================\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "gpcomp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/gpcomp.m", "size": 5095, "source_encoding": "utf_8", "md5": "7e23e98588f834156a9f13545f77cb3b", "text": "%%*********************************************************************\n%% gpcomp: Compute tp=1/gp in Proposition 2 of the paper:\n%%\n%% R.M. Freund, F. Ordonez, and K.C. Toh,\n%% Behavioral measures and their correlation with IPM iteration counts\n%% on semi-definite programming problems,\n%% Mathematical Programming, 109 (2007), pp. 445--475.\n%%\n%% [gp,info,Xfeas,blk2,At2,C2,b2] = gpcomp(blk,At,C,b,OPTIONS,solveyes);\n%%\n%% Xfeas = a feasible X for the primal problem if gp is finite.\n%%         That is,\n%%         norm(b-AXfun(blk,At,[],Xfeas))\n%%         should be small\n%%*********************************************************************\n\nfunction [gp,info,Xfeas,blk2,At2,C2,b2] = gpcomp(blk,At,C,b,OPTIONS,solveyes)\n\nif (nargin < 6); solveyes = 1; end\nif (nargin < 5)\n    OPTIONS = sqlparameters;\n    OPTIONS.vers   = 1;\n    OPTIONS.gaptol = 1e-10;\n    OPTIONS.printlevel = 3;\nend\nif isempty(OPTIONS); OPTIONS = sqlparameters; end\nif ~isfield(OPTIONS,'solver'); OPTIONS.solver = 'sqlp'; end\nif ~isfield(OPTIONS,'printlevel'); OPTIONS.printlevel = 3; end\nif ~iscell(C); tmp = C; clear C; C{1} = tmp; end\n%%\n%% convert ublk to lblk\n%%\n% exist_ublk = 0;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'u');\n        % exist_ublk = 1;\n        fprintf('\\n converting ublk into the difference of two non-negative vectors');\n        blk{p,1} = 'l'; blk{p,2} = 2*sum(blk{p,2});\n        At{p} = [At{p}; -At{p}];\n        C{p} = [C{p}; -C{p}];\n    end\nend\n%%\nm = length(b);\nblk2 = blk;\nAt2 = At;\nC2 = cell(size(blk,1),1);\nb2 = zeros(m,1);\n%%\n%%\n%%\ndd = ones(1,m);\nee = zeros(1,m); EE = cell(size(blk,1),1);\n% exist_ublk = 0;\nnn = zeros(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'s')\n        ee = ee + svec(pblk,speye(n),1)'*At{p};\n        C2{p,1} = sparse(n,n);\n        EE{p} = speye(n);\n        nn(p) = n;\n    elseif strcmp(pblk{1},'q')\n        eq = zeros(n,1);\n        idx1 = 1+[0,cumsum(pblk{2})];\n        idx1 = idx1(1:length(idx1)-1);\n        eq(idx1) = ones(length(idx1),1);\n        ee = ee + 2*eq'*At{p};\n        C2{p,1} = zeros(n,1);\n        EE{p} = eq;\n        nn(p) = length(pblk{2});\n    elseif strcmp(pblk{1},'l')\n        ee = ee + ones(1,n)*At{p};\n        C2{p,1} = zeros(n,1);\n        EE{p} = ones(n,1);\n        nn(p) = n;\n    elseif strcmp(pblk{1},'u')\n        C2{p,1} = zeros(n,1);\n        % exist_ublk = 1;\n        EE{p} = sparse(n,1);\n        nn(p) = n;\n    end\n    dd = dd + sqrt(sum(At{p}.*At{p}));\nend\ndd = 1./min(1e4,max(1,dd));\nee = ee.*dd;\nb  = b.*dd';\n%%\n%% scale data\n%%\nD = spdiags(dd',0,m,m);\nfor p = 1:size(blk,1)\n    % pblk = blk(p,:);\n    At2{p} = At2{p}*D;\nend\n%%\n%% New variables in primal problem:\n%% [x; tt; theta].\n%%\nnumblk = size(blk,1);\nblk2{numblk+1,1} = 'l'; blk2{numblk+1,2} = 2;\nAt2{numblk+1,1} = [ee; -b'];\nC2{numblk+1,1} = [-1; 0];\n%%\n%% 3 additional inequality constraints in primal problem.\n%%\nss = 0;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    if strcmp(pblk{1},'s')\n        n2 = sum(pblk{2}.*(pblk{2}+1))/2;\n        At2{p} = [At2{p}, svec(pblk,speye(n,n),1), sparse(n2,2)];\n        ss = ss + n;\n    elseif strcmp(pblk{1},'q')\n        eq = zeros(n,1);\n        idx1 = 1+[0,cumsum(pblk{2})];\n        idx1 = idx1(1:length(idx1)-1);\n        eq(idx1) = ones(length(idx1),1);\n        At2{p} = [At2{p}, sparse(eq), sparse(n,2)];\n        ss = ss + 2*length(pblk{2});\n    elseif strcmp(pblk{1},'l')\n        At2{p} = [At2{p}, sparse(ones(n,1)), sparse(n,2)];\n        ss = ss + n;\n    elseif strcmp(pblk{1},'u')\n        At2{p} = [At2{p}, sparse(n,3)];\n    end\nend\nAt2{numblk+1} = sparse([At2{numblk+1}, [ss;0], [0;1], [1;-1]]);\nb2 = [b2; 1; 1; 0];\n%%\n%% Add in the linear block corresponding to the 3 slack variables.\n%%\nblk2{numblk+2,1} = 'l'; blk2{numblk+2,2} = 3;\nAt2{numblk+2,1} = [sparse(3,m), speye(3,3)];\nC2{numblk+2,1}  = zeros(3,1);\n%%\n%% Solve SDP\n%%\ngp = []; info = []; Xfeas = [];\nif (solveyes)\n    if strcmp(OPTIONS.solver,'sqlp')\n        [X0,y0,Z0] = infeaspt(blk2,At2,C2,b2,2,100);\n        [obj,X,y,Z,info] = sqlp(blk2,At2,C2,b2,OPTIONS,X0,y0,Z0); %#ok\n    elseif strcmp(OPTIONS.solver,'HSDsqlp')\n        [obj,X,y,Z,info] = HSDsqlp(blk2,At2,C2,b2,OPTIONS); %#ok\n    else\n        [obj,X,y,Z,info] = sdpt3(blk2,At2,C2,b2,OPTIONS); %#ok\n    end\n    obj = -obj;\n    tt = X{numblk+1}(1); theta = X{numblk+1}(2);\n    Xfeas = ops(ops(X(1:numblk),'+',EE(1:numblk),tt),'/',theta);\n    %%\n    if (obj(1) > 0) || (abs(obj(1)) < 1e-8)\n        gp = 1/abs(obj(1));\n    elseif (obj(2) > 0)\n        gp = 1/obj(2);\n    else\n        gp = 1/exp(mean(log(abs(obj))));\n    end\n    err = max(info.dimacs([1,3,6]));\n    if (OPTIONS.printlevel)\n        fprintf('\\n ******** gp = %3.2e, err = %3.1e\\n',gp,err);\n        if (err > 1e-6);\n            fprintf('\\n----------------------------------------------------')\n            fprintf('\\n gp problem is not solved to sufficient accuracy');\n            fprintf('\\n----------------------------------------------------\\n')\n        end\n    end\nend\n%%*********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlparameters.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlparameters.m", "size": 5137, "source_encoding": "utf_8", "md5": "76bbd09e284250f7770836baabcc95e5", "text": "%%*************************************************************************\n%% parameters.m: set OPTIONS structure to specify default\n%%               parameters for sqlp.m\n%%\n%% OPTIONS.vers        : version of direction to use.\n%%                       1 for HKM direction\n%%                       2 for NT direction\n%%                       0 for the default (which uses the HKM direction if\n%%                       semidefinite blocks exist; and NT direction of SOCP problms)\n%% OPTIONS.gam         : step-length parameter,\n%% OPTIONS.predcorr    : whether to use Mehrotra predictor-corrector.\n%% OPTIONS.expon       : exponent in decrease of centering parameter sigma.\n%% OPTIONS.gaptol      : tolerance for duality gap as a fraction of the\n%%                       value of the objective functions.\n%% OPTIONS.inftol      : tolerance for stopping due to suspicion of\n%%                       infeasibility.\n%% OPTIONS.steptol     : toloerance for stopping due to small steps.\n%% OPTIONS.maxit       : maximum number of iteration allowed\n%% OPTIONS.printlevel  : 3, if want to display result in each iteration,\n%%                       2, if want to display only summary,\n%%                       1, if want to display warning message,\n%%                       0, no display at all.\n%% OPTIONS.stoplevel   : 2, if want to automatically detect termination;\n%%                       1, if want to automatically detect termination, but\n%%                          restart automatically with a new iterate\n%%                          when the algorithm stagnants because of tiny step-lengths.\n%%                       0, if want the algorithm to continue forever except for\n%%                          successful completion, maximum number of iterations, or\n%%                          numerical failures. Note, do not change this field unless\n%%                          you very sure.\n%% OPTIONS.scale_data  : 1, if want to scale the data before solving the problem,\n%%                          else = 0\n%% OPTIONS.rmdepconstr : 1, if want to remove nearly dependent constraints,\n%%                          else = 0.\n%% OPTIONS.smallblkdim : block-size threshold determining what method to compute the\n%%                       schur complement matrix corresponding to semidefintie block.\n%%                       NOTE: this number should be small, say less than 20.\n%% OPTIONS.parbarrier  : parameter values of the log-barrier terms in the SQLP problem.\n%%                       Default = [],  meaning that the parameter values are all 0.\n%% OPTIONS.schurfun    : [], if no user supplied routine to compute the Schur matrix,\n%%                       else, it is a cell array where each cell is either [],\n%%                       or contains a string that is the file name where the Schur matrix\n%%                       of the associated block data is computed.\n%%                       For example, if the SQLP data has the block structure\n%%                       blk{1,1} = '1'; blk{1,2} = 10;\n%%                       blk{2,1} = 's'; blk{2,2} = 50;\n%%                       and\n%%                       OPTIONS.schurfun{1} = [];\n%%                       OPTIONS.schurfun{2} = 'myownschur', where\n%%                             'myownschur' is a function with the calling sequence:\n%%                              function   schur = myownschur(X2,Z2inv,schurfun_par(2,:));\n%%                       This means that for the first block, the Schur\n%%                       matrix is computed by the default method in SDPT3,\n%%                       and for the second block, the user supplies the\n%%                       routine to compute the associated Schur matrix.\n%% OPTIONS.schurfun_par: [], if no user supplied routine to compute the Schur matrix,\n%%                       else, it is a cell array where the p-th row is either [],\n%%                       or is a cell array containing the parameters needed in\n%%                       the user supplied Schur routine OPTIONS.schurfun{p}.\n%%                       For example, for the block structure described\n%%                       above, we may have:\n%%                       OPTIONS.schurfun_par{1} = [];\n%%                       OPTIONS.schurfun_par{2,1} = par1;\n%%                       OPTIONS.schurfun_par{2,2} = par2;\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction OPTIONS = sqlparameters\n\nOPTIONS.vers           = 0;\nOPTIONS.gam            = 0;\nOPTIONS.predcorr       = 1;\nOPTIONS.expon          = 1;\nOPTIONS.gaptol         = 1e-8;\nOPTIONS.inftol         = 1e-8;\nOPTIONS.steptol        = 1e-6;\nOPTIONS.maxit          = 100;\nOPTIONS.printlevel     = 3;\nOPTIONS.stoplevel      = 1; %% do not change this field unless you very sure.\nOPTIONS.scale_data     = 0;\nOPTIONS.spdensity      = 0.4;\nOPTIONS.rmdepconstr    = 0;\nOPTIONS.smallblkdim    = 50;\nOPTIONS.parbarrier     = [];\nOPTIONS.schurfun       = [];\nOPTIONS.schurfun_par   = [];\n%%*************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "convertcmpsdp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/convertcmpsdp.m", "size": 3465, "source_encoding": "utf_8", "md5": "6bd9a62cfb0249da5d75665cfb26063b", "text": "%%*********************************************************\n%% convertcmpsdp: convert SDP with complex data into one\n%%                with real data by converting\n%%\n%%  C - sum_{k=1}^m yk*Ak psd\n%%  to\n%%  [CR,-CI] - sum ykR*[AkR,-AkI] psd\n%%  [CI, CR]           [AkI, AkR]\n%%\n%%  ykI = 0 for k = 1:m\n%%\n%% [bblk,AAt,CC,bb] = convertcmpsdp(blk,A,C,b);\n%%\n%%*********************************************************\n\nfunction [bblk,AAt,CC,bb,iscmp] = convertcmpsdp(blk,A,C,b)\n\nm = length(b);\npp = size(A,1);\nif (pp ~= size(blk,1))\n    error('blk and A not compatible');\nend\nnumblk = size(blk,1);\niscmp = zeros(numblk,m+1);\nfor p = 1:size(blk,1)\n    len = size(A(p),2);\n    for k = 1:len\n        if ~isempty(A{p,k})\n            iscmp(p,k) = 1-isreal(A{p,k});\n        end\n    end\n    iscmp(p,m+1) = 1-isreal(C{p});\nend\niscmp = norm(iscmp,'fro');\n%%\nif (iscmp == 0)\n    %% data is real\n    bblk = blk; AAt = A; CC = C; bb = b;\n    return;\nend\n%%\nbb = real(b);\nbblk = cell(size(blk,1),2);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if (size(pblk{2},1) > size(pblk{2},2))\n        pblk{2} = pblk{2}';\n    end\n    if strcmp(pblk{1},'s')\n        ss = [0,cumsum(pblk{2})];\n        ss2 = [0,cumsum(2*pblk{2})];\n        n = sum(pblk{2});\n        n2 = sum(pblk{2}.*(pblk{2}+1))/2;\n        AR = cell(1,m); Ctmp = sparse(2*n,2*n);\n        if (size(A{p},1)==n2 && size(A{p},2)==m);\n            Atype = 1;\n        elseif (size(A(p),1)==1 && size(A(p),2)==1);\n            Atype = 2;\n        else\n            error('convertcmp: At is not properly coded');\n        end\n        for k = 0:m\n            if (k == 0)\n                Ak = C{p};\n            else\n                if (Atype == 1)\n                    Ak = smat(pblk,A{p}(:,k),1);\n                elseif (Atype == 2)\n                    Ak = A{p,k};\n                end\n            end\n            Atmp = sparse(2*n,2*n);\n            if (length(pblk{2}) == 1)\n                tmp = [real(Ak),-imag(Ak);  imag(Ak), real(Ak)];\n                if (k==0)\n                    Ctmp = tmp;\n                else\n                    Atmp = tmp;\n                end\n            else\n                for j = 1:length(pblk{2})\n                    idx = ss(j)+1: ss(j+1);\n                    Akj = Ak(idx,idx);\n                    tmp = [real(Akj),-imag(Akj);  imag(Akj), real(Akj)];\n                    idx2 = ss2(j)+1: ss2(j+1);\n                    if (k==0)\n                        Ctmp(idx2,idx2) = tmp; %#ok\n                    else\n                        Atmp(idx2,idx2) = tmp; %#ok\n                    end\n                end\n            end\n            if (k==0);\n                CC{p,1} = Ctmp; %#ok\n            else\n                AR{k} = Atmp;\n            end\n        end\n        bblk{p,1} = 's'; bblk{p,2} = 2*pblk{2};\n        AAt(p,1) = svec(bblk(p,:),AR); %#ok\n    elseif strcmp(pblk{1},'q');\n        error('SOCP block with complex data is currently not allowed');\n    elseif strcmp(pblk{1},'l');\n        if isreal(A{p}) && isreal(C{p})\n            bblk(p,:) = blk(p,:);\n            AAt{p,1} = A{p}; CC{p,1} = C{p}; %#ok\n        else\n            error('data for linear block must be real');\n        end\n    elseif strcmp(pblk{1},'u');\n        if isreal(A{p}) && isreal(C{p})\n            bblk(p,:) = blk(p,:);\n            AAt{p,1} = A{p}; CC{p,1} = C{p}; %#ok\n        else\n            error('data for unrestricted block must be real');\n        end\n    end\nend\n%%*********************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlpsummary.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlpsummary.m", "size": 4047, "source_encoding": "utf_8", "md5": "78361a4e3dfeaeb73a7102b08ad30733", "text": "%%*****************************************************************************\n%% sqlpsummary: print summary\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*****************************************************************************\n\nfunction  sqlpsummary(info,ttime,infeas_org,printlevel)\n\niter        = info.iter;\nobj         = info.obj;\ngap         = info.gap;\nrelgap      = info.relgap;\nprim_infeas = info.pinfeas;\ndual_infeas = info.dinfeas;\ntermcode    = info.termcode;\nreldist     = info.reldist;\nresid       = info.resid;\ndimacs      = info.dimacs;\ntotaltime   = info.cputime;\n%%\npreproctime = ttime.preproc; pcholtime = ttime.pchol; dcholtime = ttime.dchol;\npredtime  = ttime.pred; pstep_predtime = ttime.pred_pstep; dstep_predtime = ttime.pred_pstep;\ncorrtime  = ttime.corr; pstep_corrtime = ttime.corr_pstep; dstep_corrtime = ttime.corr_dstep;\nmisctime  = ttime.misc;\n%%\nif (printlevel >= 2)\n    fprintf('\\n------------------------------------------------');\n    fprintf('-------------------\\n');\n    fprintf(' number of iterations   = %2.0f\\n',iter);\nend\nif (termcode <= 0)\n    if (printlevel >=2)\n        fprintf(' primal objective value = %- 9.8e\\n',obj(1));\n        fprintf(' dual   objective value = %- 9.8e\\n',obj(2));\n        fprintf(' gap := trace(XZ)       = %3.2e\\n',gap);\n        fprintf(' relative gap           = %3.2e\\n',relgap);\n        fprintf(' actual relative gap    = %3.2e\\n',-diff(obj)/(1+sum(abs(obj))));\n        if ~isempty(infeas_org)\n            fprintf(' rel. primal infeas (scaled problem)   = %3.2e\\n',prim_infeas);\n            fprintf(' rel. dual     \"        \"       \"      = %3.2e\\n',dual_infeas);\n            fprintf(' rel. primal infeas (unscaled problem) = %3.2e\\n',infeas_org(1));\n            fprintf(' rel. dual     \"        \"       \"      = %3.2e\\n',infeas_org(2));\n        else\n            fprintf(' rel. primal infeas     = %3.2e\\n',prim_infeas);\n            fprintf(' rel. dual   infeas     = %3.2e\\n',dual_infeas);\n        end\n        fprintf(' norm(X), norm(y), norm(Z) = %3.1e, %3.1e, %3.1e\\n',...\n            info.normX,info.normy,info.normZ);\n        fprintf(' norm(A), norm(b), norm(C) = %3.1e, %3.1e, %3.1e\\n',...\n            info.normA,info.normb,info.normC);\n    end\nelseif (termcode == 1)\n    if (printlevel >=2)\n        fprintf(' residual of primal infeasibility      \\n')\n        fprintf(' certificate (y,Z)      = %3.2e\\n',resid);\n        fprintf(' reldist to infeas.    <= %3.2e\\n',reldist);\n    end\nelseif (termcode == 2)\n    if (printlevel >=2)\n        fprintf(' residual of dual infeasibility        \\n')\n        fprintf(' certificate X          = %3.2e\\n',resid);\n        fprintf(' reldist to infeas.    <= %3.2e\\n',reldist);\n    end\nend\nif (printlevel >=2)\n    fprintf(' Total CPU time (secs)  = %3.2f  \\n',totaltime);\n    fprintf(' CPU time per iteration = %3.2f  \\n',totaltime/iter);\n    fprintf(' termination code       = %2.0f\\n',termcode);\n    fprintf(' DIMACS: %.1e  %.1e  %.1e  %.1e  %.1e  %.1e\\n',dimacs);\n    fprintf('------------------------------------------------');\n    fprintf('-------------------\\n');\n    if (printlevel > 3)\n        fprintf(' Percentage of CPU time spent in various parts \\n');\n        fprintf('------------------------------------------------');\n        fprintf('-------------------\\n');\n        fprintf(' preproc Xchol Zchol pred pred_steplen  corr corr_steplen  misc\\n')\n        tt = [preproctime, pcholtime, dcholtime, predtime, pstep_predtime, dstep_predtime];\n        tt = [tt, corrtime, pstep_corrtime, dstep_corrtime, misctime];\n        tt = tt/sum(tt)*100;\n        fprintf('   %3.1f   %3.1f   %3.1f   %3.1f  %3.1f  %3.1f ',...\n            tt(1),tt(2),tt(3),tt(4),tt(5),tt(6));\n        fprintf('    %3.1f  %3.1f  %3.1f     %3.1f\\n',tt(7),tt(8),tt(9),tt(10));\n        fprintf('------------------------------------------------');\n        fprintf('-------------------\\n');\n    end\nend\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "checkdepconstr.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/checkdepconstr.m", "size": 6926, "source_encoding": "utf_8", "md5": "186362786fef9d7d3328ab9e5a9e117c", "text": "%%*****************************************************************************\n%% checkdepconst: compute AAt to determine if the\n%%             constraint matrices Ak are linearly independent.\n%%\n%% [At,b,y,idxB,neardepconstr,feasible,AAt] = checkdepconstr(blk,At,b,y,rmdepconstr);\n%%\n%% rmdepconstr = 1, if want to remove dependent columns in At\n%%             = 0, otherwise.\n%%\n%% idxB = indices of linearly independent columns of original At.\n%% neardepconstr = 1 if there is nearly dependent columns in At\n%%            = 0, otherwise.\n%% Note: the definition of \"nearly dependent\" is dependent on the\n%%       threshold used to determine the small diagonal elements in\n%%       the LDLt factorization of A*At.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*****************************************************************************\n\nfunction  [At,b,y,idxB,neardepconstr,feasible,AAt] = ...\n    checkdepconstr(blk,At,b,y,rmdepconstr)\n\nglobal existlowrank printlevel\nglobal nnzmatold\n%%\n%% compute AAt\n%%\nm = length(b);\nAAt = sparse(m,m);  numdencol = 0; UU = [];\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s');\n        m1 = size(At{p,1},2); m2 = m - m1;\n        if (m2 > 0)\n            if (m2 > 0.3*m); AAt = full(AAt); end\n            dd = At{p,3};\n            lenn = size(dd,1);\n            idxD = [0; find(diff(dd(:,1))); lenn];\n            ss = [0, cumsum(pblk{3})];\n            if (existlowrank)\n                AAt(1:m1,1:m1) = AAt(1:m1,1:m1) + At{p,1}'*At{p,1}; %#ok\n                for k = 1:m2\n                    idx = ss(k)+1 : ss(k+1);\n                    idx2 = idxD(k)+1: idxD(k+1);\n                    ii = dd(idx2,2)-ss(k); %% undo cumulative indexing\n                    jj = dd(idx2,3)-ss(k);\n                    len = pblk{3}(k);\n                    Dk = spconvert([ii,jj,dd(idx2,4); len,len,0]);\n                    tmp = svec(pblk,At{p,2}(:,idx)*Dk*At{p,2}(:,idx)');\n                    tmp2 = AAt(1:m1,m1+k) + (tmp'*At{p,1})';\n                    AAt(1:m1,m1+k) = tmp2; %#ok\n                    AAt(m1+k,1:m1) = tmp2'; %#ok\n                end\n            end\n            DD = spconvert([dd(:,2:4); sum(pblk{3}),sum(pblk{3}),0]);\n            VtVD = (At{p,2}'*At{p,2})*DD;\n            VtVD2 = VtVD'.*VtVD;\n            for k = 1:m2\n                idx0 = ss(k)+1 : ss(k+1);\n                %%tmp = VtVD(idx0,:)' .* VtVD(:,idx0);\n                tmp = VtVD2(:,idx0);\n                tmp = tmp*ones(length(idx0),1);\n                tmp3 = AAt(m1+1:m1+m2,m1+k) + mexqops(pblk{3},tmp,ones(length(tmp),1),1);\n                AAt(m1+1:m1+m2,m1+k) = tmp3; %#ok\n            end\n        else\n            AAt = AAt + abs(At{p,1})'*abs(At{p,1});\n        end\n    else\n        decolidx = checkdense(At{p,1}');\n        if ~isempty(decolidx);\n            n2 = size(At{p,1},1);\n            dd = ones(n2,1);\n            len= length(decolidx);\n            dd(decolidx) = zeros(len,1);\n            AAt = AAt + (abs(At{p,1})' *spdiags(dd,0,n2,n2)) *abs(At{p,1});\n            tmp = At{p,1}(decolidx,:)';\n            UU = [UU, tmp]; %#ok\n            numdencol = numdencol + len;\n        else\n            AAt = AAt + abs(At{p,1})'*abs(At{p,1});\n        end\n    end\nend\nif (numdencol > 0) && (printlevel)\n    fprintf('\\n number of dense column in A = %d',numdencol);\nend\nnumdencol = size(UU,2);\n%%\n%%\n%%\nfeasible = 1; neardepconstr = 0;\nif ~issparse(AAt); AAt = sparse(AAt); end\nnnzmatold = mexnnz(AAt);\nrho = 1e-15;\ndiagAAt = diag(AAt);\nmexschurfun(AAt,rho*max(diagAAt,1));\n[L.R,indef,L.perm] = chol(AAt,'vector');\nL.d = full(diag(L.R)).^2;\nif (indef)\n    msg = 'AAt is not pos. def.';\n    idxB = 1:m;\n    neardepconstr = 1;\n    if (printlevel); fprintf('\\n checkdepconstr: %s',msg); end\n    return;\nend\n%%\n%% find independent rows of A\n%%\ndd = zeros(m,1);\nidxB = (1:m)';\ndd(L.perm) = abs(L.d);\nidxN = find(dd < 1e-13*mean(L.d));\nddB = dd(setdiff(1:m,idxN));\nddN = dd(idxN);\nif ~isempty(ddN) && ~isempty(ddB) && (min(ddB)/max(ddN) < 10)\n    %% no clear separation of elements in dd\n    %% do not label constraints as dependent\n    idxN = [];\nend\nif ~isempty(idxN)\n    neardepconstr = 1;\n    if (printlevel)\n        fprintf('\\n number of nearly dependent constraints = %1.0d',length(idxN));\n    end\n    if (numdencol==0)\n        if (rmdepconstr)\n            idxB = setdiff((1:m)',idxN);\n            if (printlevel)\n                fprintf('\\n checkdepconstr: removing dependent constraints...');\n            end\n            [W,resnorm] = findcoeffsub(blk,At,idxB,idxN);\n            tol = 1e-8;\n            if (resnorm > sqrt(tol))\n                idxB = (1:m)';\n                neardepconstr = 0;\n                if (printlevel)\n                    fprintf('\\n checkdepconstr: basis rows cannot be reliably identified,');\n                    fprintf(' abort removing nearly dependent constraints');\n                end\n                return;\n            end\n            tmp = W'*b(idxB) - b(idxN);\n            nnorm = norm(tmp)/max(1,norm(b));\n            if (nnorm > tol)\n                feasible = 0;\n                if (printlevel)\n                    fprintf('\\n checkdepconstr: inconsistent constraints exist,');\n                    fprintf(' problem is infeasible.');\n                end\n            else\n                feasible = 1;\n                for p = 1:size(blk,1)\n                    At{p,1} = At{p,1}(:,idxB);\n                end\n                b = b(idxB);\n                y = y(idxB);\n                AAt = AAt(idxB,idxB);\n            end\n        else\n            if (printlevel)\n                fprintf('\\n To remove these constraints,');\n                fprintf(' re-run sqlp.m with OPTIONS.rmdepconstr = 1.');\n            end\n        end\n    else\n        if (printlevel)\n            fprintf('\\n warning: the sparse part of AAt may be nearly singular.');\n        end\n    end\nend\n%%*****************************************************************************\n%% findcoeffsub:\n%%\n%% [W,resnorm] = findcoeffsub(blk,At,idXB,idXN);\n%%\n%% idXB = indices of independent columns of At.\n%% idxN = indices of   dependent columns of At.\n%%\n%% AB = At(:,idxB); AN = At(:,idxN) = AB*W\n%%\n%% SDPT3: version 3.0\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last modified: 2 Feb 01\n%%*****************************************************************************\n\nfunction [W,resnorm] = findcoeffsub(blk,At,idxB,idxN)\n\nAB = []; AN = [];\nfor p = 1:size(blk,1)\n    AB = [AB; At{p,1}(:,idxB)]; %#ok\n    AN = [AN; At{p,1}(:,idxN)]; %#ok\nend\nn = size(AB,2);\n%%\n%%-----------------------------------------\n%% find W so that AN = AB*W\n%%-----------------------------------------\n%%\n[L,U,P,Q] = lu(sparse(AB));\nrhs  = P*AN;\nLhat = L(1:n,:);\nW = Q*( U \\ (Lhat \\ rhs(1:n,:)));\nresnorm = norm(AN-AB*W,'fro')/max(1,norm(AN,'fro'));\n%%*****************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "convertRcone.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/convertRcone.m", "size": 948, "source_encoding": "utf_8", "md5": "afa3b210699dbf796a257cb53e92ef0d", "text": "%%***************************************************************\n%% convertRcone: convert rotated cone to socp cone\n%%\n%% [blk,At,C,b,T] = convertRcone(blk,At,C,b);\n%%\n%%***************************************************************\n\nfunction [blk,At,C,b,T] = convertRcone(blk,At,C,b)\n\nT = cell(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'r')\n        if (min(pblk{2}) < 3)\n            error('rotated cones must be at least 3-dimensional');\n        end\n        n = sum(pblk{2}); len = length(pblk{2});\n        ss = [0,cumsum(pblk{2})];\n        idx = 1+ss(1:len)';\n        ir2 = 1/sqrt(2)*ones(len,1);\n        dd = [idx,idx,ir2-1; idx,idx+1,ir2;\n            idx+1,idx,ir2; idx+1,idx+1,-ir2-1];\n        T{p} = speye(n,n) + spconvert([dd; n,n,0]);\n        blk{p,1} = 'q';\n        At{p,1} = T{p}*At{p,1};\n        C{p,1} = T{p}*C{p,1};\n    end\nend\n%%***************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "qops.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/qops.m", "size": 1421, "source_encoding": "utf_8", "md5": "bddd4e93643ef0eb5b6868ef41d2bec7", "text": "%%********************************************************\n%% qops:  Fu = qops(pblk,w,f,options,u);\n%%\n%% options = 1, Fu(i) = <wi,fi>\n%%         = 2, Fu(i) = 2*wi(1)*fi(1)-<wi,fi>\n%%         = 3, Fui   = w(i)*fi\n%%         = 4, Fui   = w(i)*fi, Fui(1) = -Fui(1).\n%% options = 5, Fu = w [ f'*u ; ub + fb*alp ], where\n%%              alp = (f'*u + u0)/(1+f0);\n%% options = 6, compute Finv*u.\n%%\n%% Note F = w [f0  fb'; fb  I+ fb*fb'/(1+f0) ], where\n%%      f0*f0 - fb*fb' = 1.\n%%      Finv = (1/w) [f0  -fb'; -fb  I+ fb*fb'/(1+f0) ].\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%********************************************************\n\nfunction  Fu = qops(pblk,w,f,options,u)\n\nif (options >= 1 && options <= 4)\n    Fu = mexqops(pblk{2},w,f,options);\nelseif (options == 5)\n    s = 1 + [0 cumsum(pblk{2})];\n    idx1 = s(1:length(pblk{2}));\n    inprod = mexqops(pblk{2},f,u,1);\n    tmp = (u(idx1)+inprod)./(1+f(idx1));\n    Fu  = u + mexqops(pblk{2},tmp,f,3);\n    Fu(idx1) = inprod;\n    Fu = mexqops(pblk{2},w,Fu,3);\nelseif (options == 6)\n    s = 1 + [0 cumsum(pblk{2})];\n    idx1 = s(1:length(pblk{2}));\n    gamprod = mexqops(pblk{2},f,u,2);\n    tmp = (u(idx1)+gamprod)./(1+f(idx1));\n    Fu  = u - mexqops(pblk{2},tmp,f,3);\n    Fu(idx1) = gamprod;\n    Fu = mexqops(pblk{2},1./w,Fu,3);\nend\n%%********************************************************\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "HKMdirfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/HKMdirfun.m", "size": 1703, "source_encoding": "utf_8", "md5": "2e90b8de8daed6f5a9a6894232dbff6a", "text": "%%*******************************************************************\n%% HKMdirfun: compute (dX,dZ), given dy, for the HKM direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [dX,dy,dZ] = HKMdirfun(blk,At,par,Rd,EinvRc,X,xx,m)\n\nglobal solve_ok\n\ndX = cell(size(blk,1),1); dZ = cell(size(blk,1),1); dy = [];\nif (any(isnan(xx)) || any(isinf(xx)))\n    solve_ok = 0;\n    fprintf('\\n  linsysolve: solution contains NaN or inf');\n    return;\nend\n%%\ndy = xx(1:m);\ncount = m;\n%%\nfor p=1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        %%dZ{p} = Rd{p} - At{p}*dy;\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy));\n        dX{p} = EinvRc{p} - par.dd{p}.*dZ{p};\n    elseif strcmp(pblk{1},'q')\n        %%dZ{p} = Rd{p} - At{p}*dy;\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy));\n        tmp = par.dd{p}.*dZ{p} ...\n            + qops(pblk,qops(pblk,dZ{p},par.Zinv{p},1),X{p},3) ...\n            + qops(pblk,qops(pblk,dZ{p},X{p},1),par.Zinv{p},3);\n        dX{p} = EinvRc{p} - tmp;\n    elseif strcmp(pblk{1},'s')\n        %%dZ{p} = Rd{p} -smat(pblk,At{p}*dy(par.permA(p,:)),par.isspAy(p));\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),par.permA(p,:),par.isspAy(p),dy));\n        tmp = Prod3(pblk,X{p},dZ{p},par.Zinv{p},0);\n        tmp = 0.5*(tmp+tmp');\n        dX{p} = EinvRc{p}-tmp;\n    elseif strcmp(pblk{1},'u');\n        n = sum(pblk{2});\n        dZ{p} = zeros(n,1);\n        dX{p} = xx(count+1:count+n);\n        count = count + n;\n    end\nend\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "symqmr.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/symqmr.m", "size": 3957, "source_encoding": "utf_8", "md5": "bf28cb72305cb0378a7b572d42f39ff6", "text": "%%*************************************************************************\n%% symqmr: symmetric QMR with left (symmetric) preconditioner.\n%%         The preconditioner used is based on the analytical\n%%         expression of inv(A).\n%%\n%% [x,resnrm,solve_ok] = symqmr(A,b,L,tol,maxit)\n%%\n%% child function: linsysolvefun.m\n%%\n%% A = [mat11 mat12; mat12' mat22].\n%% b = rhs vector.\n%% if matfct_options = 'chol' or 'spchol'\n%%    L = Cholesky factorization of (1,1) block.\n%%    M = Cholesky factorization of\n%%        Schur complement of A ( = mat12'*inv(mat11)*mat12-mat22).\n%% else\n%%    L = triangular factors of A.\n%%    M = not relevant.\n%% end\n%% resnrm = norm of qmr-generated residual vector b-Ax.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*************************************************************************\n\nfunction  [xx,resnrm,solve_ok] = symqmr(A,b,L,tol,maxit,printlevel)\n\nN = length(b);\nif (nargin < 6); printlevel = 1; end\nif (nargin < 5) || isempty(maxit); maxit = max(30,length(A.mat22)); end;\nif (nargin < 4) || isempty(tol); tol = 1e-10; end;\ntolb = min(1e-4,tol*norm(b));\n\nsolve_ok = 1;\nx = zeros(N,1);\nif (norm(x))\n    if isstruct(A); Aq = matvec(A,x); else Aq=A*x; end;\n    r = b-Aq;\nelse\n    r = b;\nend\nerr = norm(r); resnrm(1) = err; minres = err; xx = x;\nif (err < 1e-3*tolb); return; end\n\nq = precond(A,L,r);\ntau_old   = norm(q);\nrho_old   = r'*q;\ntheta_old = 0;\nd = zeros(N,1);\nres = r; Ad = zeros(N,1);\n%%\n%% main loop\n%%\ntiny = 1e-30;\nfor iter = 1:maxit\n    \n    if isstruct(A); Aq = matvec(A,q); else Aq=A*q; end;\n    sigma = q'*Aq;\n    if (abs(sigma) < tiny)\n        solve_ok = 2;\n        if (printlevel); fprintf('*'); end;\n        break;\n    else\n        alpha = rho_old/sigma;\n        r = r - alpha*Aq;\n    end\n    u = precond(A,L,r);\n    \n    theta = norm(u)/tau_old; c = 1/sqrt(1+theta^2);\n    tau = tau_old*theta*c;\n    gam = (c^2*theta_old^2); eta = (c^2*alpha);\n    d = gam*d + eta*q;\n    x = x + d;\n    %%\n    Ad = gam*Ad + eta*Aq;\n    res = res - Ad;\n    err = norm(res); resnrm(iter+1) = err; %#ok\n    if (err < minres); xx = x; minres = err; end\n    if (err < tolb); break; end\n    if (iter > 10)\n        if (err > 0.98*mean(resnrm(iter-10:iter)))\n            solve_ok = 0.5; break;\n        end\n    end\n    %%\n    if (abs(rho_old) < tiny)\n        solve_ok = 2;\n        if (printlevel); fprintf('*'); end;\n        break;\n    else\n        rho  = r'*u;\n        beta = rho/rho_old;\n        q = u + beta*q;\n    end\n    rho_old = rho;\n    tau_old = tau;\n    theta_old = theta;\nend\nif (iter == maxit); solve_ok = 0.3; end;\n%%\n%%*************************************************************************\n%% precond:\n%%*************************************************************************\n\nfunction Mx = precond(A,L,x)\n\nm = L.matdim; m2 = length(x)-m;\nMx = zeros(length(x),1);\n\nfor iter = 1:1\n    if norm(Mx); r = full(x - matvec(A,Mx)); else r = full(x); end\n    r1 = r(1:m);\n    if (m2 > 0)\n        r2 = r(m+1:m+m2);\n        w = linsysolvefun(L,r1);\n        z = mexMatvec(A.mat12,w,1) - r2;\n        z = L.Mu \\ (L.Ml \\ (L.Mp*z));\n        r1 = r1 - mexMatvec(A.mat12,z);\n    end\n    d = linsysolvefun(L,r1);\n    if (m2 > 0)\n        d = [d; z]; %#ok\n    end\n    Mx = Mx + d;\nend\n%%*************************************************************************\n%% matvec: matrix-vector multiply.\n%% matrix = [A.mat11, A.mat12; A.mat12', A.mat22]\n%%*************************************************************************\n\nfunction Ax = matvec(A,x)\n\nm = length(A.mat11); m2 = length(x)-m;\n\nif issparse(x); x = full(x); end\nif (m2 > 0)\n    x1 = x(1:m);\nelse\n    x1 = x;\nend\nAx = mexMatvec(A.mat11,x1);\nif (m2 > 0)\n    x2 = x(m+1:m+m2);\n    Ax = Ax + mexMatvec(A.mat12,x2);\n    Ax2 = mexMatvec(A.mat12,x1,1) + mexMatvec(A.mat22,x2);\n    Ax = [full(Ax); full(Ax2)];\nend\n%%*************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "validate_startpoint.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/validate_startpoint.m", "size": 3067, "source_encoding": "utf_8", "md5": "572146bf8639c3d5066b6a790bca3ba8", "text": "%%***********************************************************************\n%% validate_startpoint: validate_startpoint starting point X0,y0,Z0\n%%\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%***********************************************************************\n\nfunction [X0,Z0] = validate_startpoint(blk,X0,Z0,spdensity,iscmp)\n\nif (nargin < 5); iscmp = 0; end\nif (nargin < 4); spdensity = 0.4; end\n%%\nif ~iscell(X0) || ~iscell(Z0);\n    error('validate_startpoint: X0, Z0 must be cell arrays');\nend\nif (min(size(X0))~=1 || min(size(Z0))~=1);\n    error('validate_startpoint: cell array X, Z can only have 1 column or row');\nend\nif (size(X0,2) > size(X0,1)); X0 = X0'; end;\nif (size(Z0,2) > size(Z0,1)); Z0 = Z0'; end;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    n = sum(pblk{2});\n    n2 = sum(pblk{2}.*pblk{2});\n    numblk = length(pblk{2});\n    if strcmp(pblk{1},'s');\n        if (iscmp)\n            X0{p} = [real(X0{p}),-imag(X0{p}); imag(X0{p}), real(X0{p})];\n            Z0{p} = [real(Z0{p}),-imag(Z0{p}); imag(Z0{p}), real(Z0{p})];\n        end\n        if ~all(size(X0{p}) == n) || ~all(size(Z0{p}) == n);\n            error('validate_startpoint: blk and X0,Z0 are not compatible');\n        end\n        if (norm([X0{p}-X0{p}' Z0{p}-Z0{p}'],inf) > 2e-13);\n            error('validate_startpoint: X0,Z0 not symmetric');\n        end\n        if (nnz(X0{p}) < spdensity*n2) || (numblk > 1) ;\n            if ~issparse(X0{p}); X0{p} = sparse(X0{p}); end;\n        else\n            if issparse(X0{p}); X0{p} = full(X0{p}); end;\n        end\n        if (nnz(Z0{p}) < spdensity*n2) || (numblk > 1);\n            if ~issparse(Z0{p}); Z0{p} = sparse(Z0{p}); end;\n        else\n            if issparse(Z0{p}); Z0{p} = full(Z0{p}); end;\n        end\n    elseif strcmp(pblk{1},'q') || strcmp(pblk{1},'l') || strcmp(pblk{1},'u');\n        if ~all([size(X0{p},2) size(Z0{p},2)]==1);\n            error(['validate_startpoint: ',num2str(p),...\n                '-th block of X0,Z0 must be column vectors']);\n        end\n        if ~all([size(X0{p},1) size(Z0{p},1)]==n);\n            error('validate_startpoint: blk, and X0,Z0, are not compatible');\n        end\n        if (nnz(X0{p}) < spdensity*n);\n            if ~issparse(X0{p}); X0{p} = sparse(X0{p}); end;\n        else\n            if issparse(X0{p}); X0{p} = full(X0{p}); end;\n        end\n        if (nnz(Z0{p}) < spdensity*n);\n            if ~issparse(Z0{p}); Z0{p} = sparse(Z0{p}); end;\n        else\n            if issparse(Z0{p}); Z0{p} = full(Z0{p}); end;\n        end\n        if strcmp(pblk{1},'l') && (any(X0{p} < 1e-12) || any(Z0{p} < 1e-12))\n            error('X0 or Z0 is not in nonnegative cone');\n        end\n        if strcmp(pblk{1},'q');\n            s = 1+[0, cumsum(pblk{2})]; len = length(pblk{2});\n            if any(X0{p}(s(1:len)) < 1e-12) || any(Z0{p}(s(1:len)) < 1e-12)\n                error('X0 or Z0 is not in socp cone');\n            end\n        end\n    end\nend\n%%***********************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "Atyfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/Atyfun.m", "size": 1471, "source_encoding": "utf_8", "md5": "5af173811d5528ab49ca2a48bd4748ce", "text": "%%*********************************************************\n%% Atyfun: compute sum_{k=1}^m yk*Ak.\n%%\n%%  Q = Atyfun(blk,At,permA,isspAy,y);\n%%\n%% Note: permA and isspAy may be set to [].\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************\n\nfunction Q = Atyfun(blk,At,permA,isspAy,y)\n\nif isempty(permA); ismtpermA = 1; else ismtpermA = 0; end\n\nQ = cell(size(blk,1),1);\nif isempty(isspAy); isspAy = ones(size(blk,1),1); end\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        n = sum(pblk{2});\n        m1 = size(At{p,1},2);\n        if (~isempty(At{p,1}))\n            if (ismtpermA)\n                tmp = At{p,1}*y(1:m1);\n            else\n                tmp = At{p,1}*y(permA(p,1:m1),1);\n            end\n            Q{p} = smat(pblk,tmp,isspAy(p));\n        else\n            Q{p} = sparse(n,n);\n        end\n        if (length(pblk) > 2) %% for low rank constraints\n            len = sum(pblk{3});\n            m2 = length(pblk{3});\n            y2 = y(m1+1:m1+m2);\n            dd = At{p,3};\n            idxD = [0; find(diff(dd(:,1))); size(dd,1)];\n            yy2 = mexexpand(diff(idxD),y2);\n            DD = spconvert([dd(:,2:3),dd(:,4).*yy2; len,len,0]);\n            Q{p} = Q{p} + At{p,2}*DD*At{p,2}';\n        end\n    else\n        Q{p} = At{p,1}*y;\n    end\nend\n%%*********************************************************\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "blkcholfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/blkcholfun.m", "size": 1247, "source_encoding": "utf_8", "md5": "6d78842d748fad818d79a003e1238916", "text": "%%******************************************************************\n%% blkcholfun: compute Cholesky factorization of X.\n%%\n%%  [Xchol,indef] = blkcholfun(blk,X,permX);\n%%\n%%  X = Xchol'*Xchol;\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%******************************************************************\n\nfunction [Xchol,indef] = blkcholfun(blk,X,permX)\n\nif (nargin == 2); permX = []; end;\n\nif ~iscell(X);\n    indef = 0;\n    if strcmp(blk{1},'s');\n        if isempty(permX) || ~issparse(X);\n            [Xchol,indef] = chol(X);\n        else\n            [tmp,indef] = chol(X(permX,permX));\n            Xchol(:,permX) = tmp;\n        end\n    elseif strcmp(blk{1},'q')\n        gamx = qops(blk,X,X,2);\n        if any(gamx <= 0)\n            indef = 1;\n        end\n        Xchol = [];\n    elseif strcmp(blk{1},'l');\n        if any(X <= 0)\n            indef = 1;\n        end\n        Xchol = [];\n    elseif strcmp(blk{1},'u')\n        Xchol = [];\n    end\nelse\n    Xchol = cell(size(X));\n    for p = 1:size(blk,1)\n        [Xchol{p},indef(p)] = blkcholfun(blk(p,:),X{p}); %#ok\n    end\n    indef = max(indef);\nend\n%%=================================================================\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "smat.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/smat.m", "size": 880, "source_encoding": "utf_8", "md5": "184f4081013cebeae6150a22b224ebf9", "text": "%%*********************************************************\n%% smat: compute the matrix smat(x).\n%%\n%%   M = smat(blk,x,isspM);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************\n\nfunction M = smat(blk,xvec,isspM)\n\nif (nargin < 3); isspM = zeros(size(blk,1),1); end\n%%\nif ~iscell(xvec)\n    if strcmp(blk{1},'s')\n        M = mexsmat(blk,xvec,isspM);\n    else\n        M = xvec;\n    end\nelse\n    M = cell(size(blk,1),1);\n    if (length(isspM)==1)\n        isspM = isspM*ones(size(blk,1),1);\n    end\n    for p=1:size(blk,1)\n        pblk = blk(p,:);\n        if strcmp(pblk{1},'s');\n            M{p} = mexsmat(pblk,xvec{p},isspM(p));\n        else\n            M{p} = xvec{p};\n        end\n    end\nend\n%%*********************************************************\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sqlpcheckconvg.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/sqlpcheckconvg.m", "size": 8298, "source_encoding": "utf_8", "md5": "6acb41aefb05d5a53dbd501dabab2b33", "text": "%%*****************************************************************************\n%% sqlpcheckconvg: check convergence.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*****************************************************************************\n\nfunction [param,breakyes,restart,msg] = sqlpcheckconvg(param,runhist)\n\ntermcode    = param.termcode;\niter        = param.iter;\nobj         = param.obj;\nrelgap      = param.relgap;\ngap         = param.gap;\nprim_infeas = param.prim_infeas;\ndual_infeas = param.dual_infeas;\nmu          = param.mu;\n% homrp       = param.homrp;\n% homRd       = param.homRd;\nprim_infeas_bad = param.prim_infeas_bad;\ndual_infeas_bad = param.dual_infeas_bad;\nif (iter > 15)\n    prim_infeas_min = min(param.prim_infeas_min, max(prim_infeas,1e-10));\n    dual_infeas_min = min(param.dual_infeas_min, max(dual_infeas,1e-10));\nelse\n    prim_infeas_min = inf;\n    dual_infeas_min = inf;\nend\nprintlevel  = param.printlevel;\nstoplevel   = param.stoplevel;\nublksize    = param.ublksize;\nuse_LU      = param.use_LU;\nnumpertdiagschur = param.numpertdiagschur;\ninfeas      = max(prim_infeas,dual_infeas);\nrestart     = 0;\nbreakyes    = 0;\nmsg         = [];\n%%\nif (param.normX > 1e15*param.normX0 || param.normZ > 1e15*param.normZ0)\n    termcode = 3;\n    breakyes = 1;\nend\nerr = max(infeas,relgap);\nidx = max(2,iter-9): iter+1;\npratio = (1-runhist.pinfeas(idx)./runhist.pinfeas(idx-1))./runhist.pstep(idx);\ndratio = (1-runhist.dinfeas(idx)./runhist.dinfeas(idx-1))./runhist.dstep(idx);\nif (param.homRd < 0.1*sqrt(err*max(param.inftol,1e-13))) ...\n        && (iter > 30 || termcode==3) && (mean(abs(dratio-1)) > 0.5)\n    termcode = 1;\n    breakyes = 1;\nend\nif (param.homrp < 0.1*sqrt(err*max(param.inftol,1e-13))) ...\n        && (iter > 30 || termcode==3) && (mean(abs(pratio-1)) > 0.5)\n    termcode = 2;\n    breakyes = 1;\nend\nif (stoplevel) && (iter > 2) && (~breakyes)\n    prim_infeas_bad = ...\n        + (prim_infeas > max(1e-10,1e2*prim_infeas_min) && (prim_infeas_min < 1e-2)) ...\n        + (prim_infeas > prod(1.5-runhist.step(iter+1:iter-1))*runhist.pinfeas(iter-2));\n    dual_infeas_bad = ...\n        + (dual_infeas > max(1e-8,1e3*dual_infeas_min) && (dual_infeas_min < 1e-2));\n    if (mu < 1e-8) || (use_LU)\n        idx = max(1,iter-1): iter;\n    elseif (mu < 1e-4);\n        idx = max(1,iter-2): iter;\n    else\n        idx = max(1,iter-3): iter;\n    end\n    gap_progress_bad = (infeas < 1e-4) && (relgap < 5e-3) ...\n        && (gap > 0.9*exp(mean(log(runhist.gap(idx)))));\n    gap_progress_bad2 = (infeas < 1e-4) && (relgap < 1) ...\n        && (gap > 0.95*exp(mean(log(runhist.gap(idx)))));\n    gap_ratio = runhist.gap(idx+1)./runhist.gap(idx);\n    idxtmp = max(1,iter-4): iter;\n    gap_ratio_tmp = runhist.gap(idxtmp+1)./runhist.gap(idxtmp);\n    gap_slowrate = min(0.8,max(0.6,2*mean(gap_ratio_tmp)));\n    idx2 = max(1,iter-10): iter;\n    gap_ratio2 = runhist.gap(idx2+1)./runhist.gap(idx2);\n    gap_slow  = all(gap_ratio > gap_slowrate);\n    gap_slow2 = all(gap_ratio2 > gap_slowrate);\n    if (iter > 20) && (infeas < 1e-4 || prim_infeas_bad) ...\n            && (max(infeas,relgap) < 1) ...\n            && ~(min(runhist.step(idx)) > 0.2 && ublksize)\n        if (gap_slow && prim_infeas_bad && (relgap < 1e-3)) ...\n                || (gap_slow2 && prim_infeas_bad && ublksize && (runhist.step(iter+1) > 0.2))\n            msg = 'stop: progress is too slow';\n            if (printlevel); fprintf('\\n  %s',msg); end\n            termcode = -5;\n            breakyes = 1;\n        elseif (max(infeas,relgap) < 1e-2) && (prim_infeas_bad)\n            if (relgap < max(0.2*prim_infeas,1e-2*dual_infeas))\n                msg = 'stop: relative gap < infeasibility';\n                if (printlevel); fprintf('\\n  %s',msg); end\n                termcode = -1;\n                breakyes = 1;\n            end\n        end\n    end\n    if (iter > 20) && (gap_progress_bad) ...\n            && (prim_infeas_bad || any(runhist.step(idx) > 0.5))\n        msg = 'stop: progress is bad';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -5;\n        breakyes = 1;\n    end\n    if (iter > 20) && (gap_progress_bad2) ...\n            && (numpertdiagschur > 10);\n        msg = 'stop: progress is bad';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -5;\n        breakyes = 1;\n    end\n    if (iter > 30) && (prim_infeas_bad) && (gap_slow) && (relgap < 1e-3) ...\n            && (dual_infeas < 1e-5) && ~(min(runhist.step(idx)) > 0.2 && ublksize)\n        msg = 'stop: progress is bad*';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -5;\n        breakyes = 1;\n    end\n    if (iter > 30) && (dual_infeas_bad) && (relgap < 1e-3) ...\n            && (dual_infeas < 1e-5) ...\n            && ~(min(runhist.step(idx)) > 0.2 && ublksize) %#ok\n        msg = 'stop: dual infeas has deteriorated too much';\n        if (printlevel); fprintf('\\n  %s',msg); end\n        termcode = -5;\n        breakyes = 1;\n    end\n    if (iter > 50) && (prim_infeas/runhist.pinfeas(1) < 1e-6) ...\n            && (dual_infeas/runhist.dinfeas(1) > 1e-3) ...\n            && (runhist.step(iter+1) > 0.2) && (relgap > 1e-3)\n        msg = 'stop: lack of progress in dual infeas';\n        if (printlevel); fprintf('\\n  %s, homrp=%2.1e',msg,param.homrp); end\n        termcode = -5;\n        breakyes = 1;\n    end\n    if (iter > 50) && (dual_infeas/runhist.dinfeas(1) < 1e-6) ...\n            && (prim_infeas/runhist.pinfeas(1) > 1e-3) ...\n            && (runhist.step(iter+1) > 0.2) && (relgap > 1e-3)\n        msg = 'stop: lack of progress in primal infeas';\n        if (printlevel); fprintf('\\n  %s, homRd=%2.1e',msg,param.homRd); end\n        termcode = -5;\n        breakyes = 1;\n    end\n    if (min(runhist.infeas) < 1e-4 || (prim_infeas_bad && iter > 10)) ...\n            && (max(runhist.infeas) > 1e-5) || (iter > 20)\n        relgap2 = abs(diff(obj))/(1+sum(abs(obj)));\n        if (relgap2 < 1e-3);\n            step_short = all(runhist.step(iter:iter+1) < 0.05) ;\n        elseif (relgap2 < 1) || (use_LU)\n            idx = max(1,iter-3): iter+1;\n            step_short = all(runhist.step(idx) < 0.03);\n        else\n            step_short = 0;\n        end\n        if (step_short) && (relgap2 < 1e-2)\n            msg = 'stop: steps too short consecutively';\n            if (printlevel); fprintf('\\n  %s',msg); end\n            termcode = -5;\n            breakyes = 1;\n        end\n    end\n    if (iter > 3 && iter < 20) && (infeas > 1) ...\n            && (min(param.homrp,param.homRd) > min(1e-8,param.inftol)) ...\n            && (max(runhist.step(max(1,iter-3):iter+1)) < 1e-3)\n        if (stoplevel == 2)\n            msg = 'stop: steps too short consecutively*';\n            if (printlevel)\n                fprintf('\\n *** Too many tiny steps, advisable to restart');\n                fprintf(' with the following iterate.')\n                fprintf('\\n *** Suggestion: [X0,y0,Z0] = infeaspt(blk,At,C,b,2,1e5);');\n                fprintf('\\n  %s',msg);\n            end\n            termcode = -5;\n            breakyes = 1;\n        elseif (stoplevel == 3)\n            msg = 'stop: steps too short consecutively*';\n            if (printlevel)\n                fprintf('\\n *** Too many tiny steps even')\n                fprintf(' after restarting');\n                fprintf('\\n  %s',msg);\n            end\n            termcode = -5;\n            breakyes = 1;\n        else\n            if (printlevel)\n                fprintf('\\n *** Too many tiny steps:')\n                fprintf('  restarting with the following iterate.')\n                fprintf('\\n *** [X,y,Z] = infeaspt(blk,At,C,b,2,1e5);');\n            end\n            prim_infeas_min = 1e20;\n            prim_infeas_bad = 0;\n            restart = 1;\n        end\n    end\nend\nif (max(relgap,infeas) < param.gaptol)\n    msg = sprintf('stop: max(relative gap, infeasibilities) < %3.2e',param.gaptol);\n    if (printlevel); fprintf('\\n  %s',msg); end\n    termcode = 0;\n    breakyes = 1;\nend\n%%\nparam.prim_infeas_bad = prim_infeas_bad;\nparam.prim_infeas_min = prim_infeas_min;\nparam.dual_infeas_bad = dual_infeas_bad;\nparam.dual_infeas_min = dual_infeas_min;\nparam.termcode = termcode;\n%%**************************************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "SDPT3soln_SEDUMIsoln.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/SDPT3soln_SEDUMIsoln.m", "size": 2743, "source_encoding": "utf_8", "md5": "1f7236a38116d782e7084afc40e90a10", "text": "%%**********************************************************\n%% SDPT3soln_SEDUMIsoln: convert SQLP solution in SDPT3 format to\n%%                       SeDuMi format\n%%\n%% [xx,yy,zz] = SDPT3soln_SEDUMIsoln(blk,X,y,Z,perm);\n%%\n%% usage: load SEDUMI_data_file (containing say, A,b,c,K)\n%%        [blk,At,C,b,perm] = read_sedumi(A,b,c,K);\n%%        [obj,X,y,Z]  = sdpt3(blk,At,C,b);\n%%        [xx,yy,zz]   = SDPT3soln_SEDUMIsoln(blk,X,y,Z,perm);\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%**********************************************************\n\nfunction [xx,yy,zz] = SDPT3soln_SEDUMIsoln(blk,X,y,Z,perm)\n\nyy = y;\nxx = []; zz = [];\n%%\n%% extract unrestricted blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'u')\n        xx  = [xx; X{p,1}]; %#ok\n        zz  = [zz; Z{p,1}]; %#ok\n    end\nend\n%%\n%% extract linear blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        xx  = [xx; X{p,1}]; %#ok\n        zz  = [zz; Z{p,1}]; %#ok\n    end\nend\n%%\n%% extract second order cone blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'q')\n        xx  = [xx; X{p,1}]; %#ok\n        zz  = [zz; Z{p,1}]; %#ok\n    end\nend\n%%\n%% extract rotated cone blk\n%%\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'r')\n        xx  = [xx; X{p,1}]; %#ok\n        zz  = [zz; Z{p,1}]; %#ok\n    end\nend\n%%\n%% extract semidefinite cone blk\n%%\nper = [];\nlen = 0;\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        sblk(p) = length(pblk{2}); %#ok\n        per = [per, perm{p}];  %#ok\n        len = len + sum(pblk{2}.*pblk{2});\n    end\nend\nsblk = sum(sblk);\ncnt = 1;\nXsblk = cell(sblk,1); Zsblk = cell(sblk,1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        ss = [0,cumsum(pblk{2})];\n        numblk = length(pblk{2});\n        Xp = X{p,1};\n        Zp = Z{p,1};\n        for tt = 1:numblk\n            if (numblk > 1)\n                idx = ss(tt)+1: ss(tt+1);\n                Xsblk{cnt} = full(Xp(idx,idx));\n                Zsblk{cnt} = full(Zp(idx,idx));\n            else\n                Xsblk{cnt} = Xp;\n                Zsblk{cnt} = Zp;\n            end\n            cnt = cnt + 1;\n        end\n    end\nend\nif ~isempty(per)\n    Xsblk(per) = Xsblk; Zsblk(per) = Zsblk;\n    xtmp = zeros(len,1); ztmp = zeros(len,1);\n    cnt = 0;\n    for p = 1:sblk\n        if strcmp(pblk{1},'s')\n            idx = 1:length(Xsblk{p})^2;\n            xtmp(cnt+idx) = Xsblk{p}(:);\n            ztmp(cnt+idx) = Zsblk{p}(:);\n            cnt = cnt + length(idx);\n        end\n    end\n    xx = [xx; xtmp];\n    zz = [zz; ztmp];\nend\n%%**********************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "detect_ublk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/detect_ublk.m", "size": 2815, "source_encoding": "utf_8", "md5": "819f087d73a97e1717b952ca2f3a407d", "text": "%%*******************************************************************\n%% detect_ublk: search for implied free variables in linear\n%%              block.\n%% [blk2,At2,C2,ublkinfo] = detect_ublk(blk,At,C);\n%%\n%% i1,i2: indices corresponding to splitting of unrestricted varaibles\n%% i3   : remaining indices in the linear block\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [blk2,At2,C2,ublkinfo,parbarrier2,X2,Z2] = ...\n    detect_ublk(blk,At,C,parbarrier,X,Z,printlevel)\n\nif (nargin < 7); printlevel = 1; end\n\nblk2 = blk; At2 = At; C2 = C;\nif (nargin >= 6)\n    parbarrier2 = parbarrier;\n    X2 = X; Z2 = Z;\nelse\n    X2 = []; Z2 = [];\nend\nnumblk = size(blk,1);\nublkinfo = cell(size(blk,1),3);\ntol = 1e-14;\n%%\nnumblknew = numblk;\n%%\nfor p = 1:numblk\n    pblk = blk(p,:);\n    m = size(At{p},2);\n    if strcmp(pblk{1},'l')\n        r = randmat(1,m,0,'n');\n        % stime = cputime;\n        Ap = At{p}'; Cp = C{p};\n        ApTr = (r*Ap)';\n        [sApTr,perm] = sort(abs(ApTr));\n        idx0 = find(abs(diff(sApTr)) < tol);\n        if ~isempty(idx0)\n            n = pblk{2};\n            i1 = perm(idx0); i2 = perm(idx0+1);\n            Api1 = Ap(:,i1);\n            Api2 = Ap(:,i2);\n            Cpi1 = Cp(i1)';\n            Cpi2 = Cp(i2)';\n            idxzr = abs(Cpi1+Cpi2) < tol & sum(abs(Api1+Api2),1) < tol;\n            if any(idxzr)\n                i1 = i1(idxzr');\n                i2 = i2(idxzr');\n                blk2{p,1} = 'u';\n                blk2{p,2} = length(i1);\n                At2{p} = Ap(:,i1)';\n                C2{p}  = Cp(i1);\n                if (printlevel)\n                    fprintf('\\n %1.0d linear variables from unrestricted variable.\\n',...\n                        2*length(i1));\n                end\n                if (nargin >= 6)\n                    parbarrier2{p} = parbarrier{p}(i1);\n                    X2{p} = X{p}(i1)-X{p}(i2);\n                    Z2{p} = zeros(length(i1),1);\n                end\n                i3 = setdiff(1:n,union(i1,i2));\n                if ~isempty(i3)\n                    numblknew = numblknew + 1;\n                    blk2{numblknew,1} = 'l';\n                    blk2{numblknew,2} = length(i3);\n                    At2{numblknew,1}  = Ap(:,i3)';\n                    C2{numblknew,1}   = Cp(i3);\n                    if (nargin >= 6)\n                        parbarrier2{numblknew,1} = parbarrier{p}(i3);\n                        X2{numblknew,1} = X{p}(i3); Z2{numblknew,1} = Z{p}(i3);\n                    end\n                end\n                ublkinfo{p,1} = i1; ublkinfo{p,2} = i2; ublkinfo{p,3} = i3;\n            end\n        end\n    end\nend\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "combine_blk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/combine_blk.m", "size": 2358, "source_encoding": "utf_8", "md5": "205279faec8f7b11c04bc75707a6eb77", "text": "%%*******************************************************************\n%% combine_blk: combine small SDP blocks together,\n%%              combine all SOCP blocks together, etc\n%%\n%% [blk2,At2,C2,blkinfo] = combine_blk(blk,At,C);\n%%\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [blk2,At2,C2,blkinfo] = combine_blk(blk,At,C)\n\nblkinfo = zeros(size(blk,1),1);\nfor p = 1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'s')\n        if (sum(pblk{2}) < 100)\n            blkinfo(p) = 1;\n        end\n    elseif strcmp(pblk{1},'q')\n        blkinfo(p) = 2;\n    elseif strcmp(pblk{1},'r')\n        blkinfo(p) = 3;\n    elseif strcmp(pblk{1},'l')\n        blkinfo(p) = 4;\n    elseif strcmp(pblk{1},'u')\n        blkinfo(p) = 5;\n    end\nend\nnumblk0 = length(find(blkinfo == 0));\nnumblk = numblk0 + length(union(blkinfo(blkinfo > 0),[]));\nblk2 = cell(numblk,2); At2 = cell(numblk,1); C2 = cell(numblk,1);\ncnt = 0;\nidx = find(blkinfo==0); %% larger SDP blocks\nif ~isempty(idx)\n    len = length(idx);\n    blk2(1:len,:) = blk(idx,:);\n    At2(1:len) = At(idx); C2(1:len) = C(idx);\n    cnt = len;\nend\nidx = find(blkinfo==1); %% smaller SDP blocks\nCtmp = []; idxstart = 0;\nif ~isempty(idx)\n    cnt = cnt + 1;\n    blk2{cnt,1} = 's'; blk2{cnt,2} = [];\n    len = length(idx);\n    for k = 1:len\n        blk2{cnt,2} = [blk2{cnt,2}, blk{idx(k),2}];\n        At2{cnt} = [At2{cnt}; At{idx(k)}];\n        [ii,jj,vv] = find(C{idx(k)});\n        Ctmp = [Ctmp; [idxstart+ii,idxstart+jj,vv]]; %#ok\n        idxstart = idxstart + sum(blk{idx(k),2});\n    end\nend\nn = sum(blk2{cnt,2});\nC2{cnt} = spconvert([Ctmp; n,n,0]);\n%%\nfor L = 2:5\n    idx = find(blkinfo==L);\n    if ~isempty(idx)\n        cnt = cnt + 1;\n        if (L==2)\n            blk2{cnt,1} = 'q';\n        elseif (L==3)\n            blk2{cnt,1} = 'r';\n        elseif (L==4)\n            blk2{cnt,1} = 'l';\n        elseif (L==5)\n            blk2{cnt,1} = 'u';\n        end\n        blk2{cnt,2} = [];\n        len = length(idx);\n        for k = 1:len\n            blk2{cnt,2} = [blk2{cnt,2}, blk{idx(k),2}];\n            At2{cnt} = [At2{cnt}; At{idx(k)}];\n            C2{cnt} = [C2{cnt}; C{idx(k)}];\n        end\n    end\nend\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "Prod2.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/Prod2.m", "size": 1882, "source_encoding": "utf_8", "md5": "b291dcf07608872ed75bd82e48ff0b54", "text": "%%*******************************************************************\n%% Prod2: compute the block diagonal matrix A*B\n%%\n%%    C = Prod2(blk,A,B,options);\n%%\n%% INPUT:  blk = a cell array describing the block structure of A and B\n%%         A,B = square matrices or column vectors.\n%%\n%%   options = 0  if no special structure\n%%             1  if C is symmetric\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction  C = Prod2(blk,A,B,options)\n\nglobal spdensity\n\nif (nargin == 3); options = 0; end;\niscellA = iscell(A); iscellB = iscell(B);\n%%\nif (~iscellA && ~iscellB)\n    if (size(blk,1) > 1);\n        error('Prod2: blk and A,B are not compatible');\n    end;\n    if strcmp(blk{1},'s')\n        numblk = length(blk{2});\n        isspA = issparse(A); isspB = issparse(B);\n        if (numblk > 1)\n            if ~isspA; A=sparse(A); isspA=1; end\n            if ~isspB; B=sparse(B); isspB=1; end\n        end\n        %%use_matlab = (options==0 && ~isspA && ~isspB) || (isspA && isspB);\n        use_matlab = (~isspA && ~isspB) || (isspA && isspB);\n        if (use_matlab)\n            C = A*B;\n            if (options==1); C = 0.5*(C+C'); end;\n        else\n            C = mexProd2(blk,A,B,options);\n        end\n        checksparse = (numblk==1) && (isspA || isspB);\n        if (checksparse)\n            n2 = sum(blk{2}.*blk{2});\n            if (mexnnz(C) <= spdensity*n2);\n                if ~issparse(C); C = sparse(C); end;\n            else\n                if issparse(C); C = full(C); end;\n            end\n        end\n    elseif (strcmp(blk{1},'q') || strcmp(blk{1},'l') || strcmp(blk{1},'u'))\n        C = A.*B;\n    end\nelse\n    error('Prod2: A,B must be matrices');\nend\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "NTdirfun.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sdpt3/Solver/NTdirfun.m", "size": 1614, "source_encoding": "utf_8", "md5": "a236e4d45e59db8824375b14ed6090a1", "text": "%%*******************************************************************\n%% NTdirfun: compute (dX,dZ), given dy, for the NT direction.\n%%\n%% SDPT3: version 3.1\n%% Copyright (c) 1997 by\n%% K.C. Toh, M.J. Todd, R.H. Tutuncu\n%% Last Modified: 16 Sep 2004\n%%*******************************************************************\n\nfunction [dX,dy,dZ] = NTdirfun(blk,At,par,Rd,EinvRc,xx,m)\n\nglobal solve_ok\n\ndX = cell(size(blk,1),1); dZ = cell(size(blk,1),1); dy = [];\nif (any(isnan(xx)) || any(isinf(xx)))\n    solve_ok = 0;\n    fprintf('\\n  linsysolve: solution contains NaN or inf.');\n    return;\nend\n%%\ndy = xx(1:m);\ncount = m;\n%%\nfor p=1:size(blk,1)\n    pblk = blk(p,:);\n    if strcmp(pblk{1},'l')\n        %%dZ{p} = Rd{p} - At{p}*dy;\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy));\n        tmp   = par.dd{p}.*dZ{p};\n        dX{p} = EinvRc{p} - tmp;\n    elseif strcmp(pblk{1},'q')\n        %%dZ{p} = Rd{p} - At{p}*dy;\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),[],[],dy));\n        tmp = par.dd{p}.*dZ{p} + qops(pblk,qops(pblk,dZ{p},par.ee{p},1),par.ee{p},3);\n        dX{p} = EinvRc{p} - tmp;\n    elseif strcmp(pblk{1},'s')\n        %%dZ{p} = Rd{p} - smat(pblk,At{p}*dy(par.permA(p,:)),par.isspAy(p));\n        dZ(p) = ops(Rd(p),'-',Atyfun(pblk,At(p,:),par.permA(p,:),par.isspAy(p),dy));\n        tmp   = Prod3(pblk,par.W{p},dZ{p},par.W{p},1);\n        dX{p} = EinvRc{p}-tmp;\n    elseif strcmp(pblk{1},'u');\n        n = sum(pblk{2});\n        dZ{p} = zeros(n,1);\n        dX{p} = xx(count+1:count+n);\n        count = count + n;\n    end\nend\n%%*******************************************************************\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "make.m", "ext": ".m", "path": "Programs-master/matlab/cvx/examples/make.m", "size": 23079, "source_encoding": "utf_8", "md5": "8b04233b872cb2ab50851644ae0e486a", "text": "function make( varargin )\n\n%\n% Determine the base path\n%\n\nodir = pwd;\nbase = mfilename('fullpath');\nbase = fileparts( base );\n\n%\n% Check the force and runonly flags\n%\n\nargs = varargin;\nis_octave = exist( 'OCTAVE_VERSION', 'builtin' );\nif is_octave,\n    force = true;\n    runonly = true;\n    indexonly = false;\n    page_output_immediately(true);\nelse\n    temp = strcmp( args, '-force' );\n    force = any( temp );\n    if force, args(temp) = []; end\n    temp = strcmp( args, '-runonly' );\n    runonly = any( temp );\n    if runonly, args(temp) = []; end\n    temp = strcmp( args, '-indexonly' );\n    indexonly = any( temp );\n    if indexonly, args(temp) = []; end\n    if ~runonly,\n      close all;\n      fclose all;\n    end\nend\nif isempty( args ), \n    args = { base }; \nend\n\n%\n% Process the arguments\n%\n\nfor k = 1 : length( args ),\n    file = args{k};\n    if any( file == '*' ),\n        files = dir( file );\n        files = { files.name };\n    else\n        files = { file };\n    end\n    for j = 1 : length( files );\n\n        %\n        % Check the validity of the file or directory\n        %\n\n        file = files{j};\n        switch exist( file, 'file' ),\n            case 0,\n                error( 'Cannot find file or directory: %s', file );\n            case 2,\n                file = which( file );\n                if isempty( file ), \n                    file = files{j};\n                    if file(1) ~= filesep,\n                        file = [ base, filesep, file ];\n                    end\n                end\n                [ mpath, file, ext ] = fileparts( file );\n                file = [ file, ext ];\n                if ~strcmp( ext, '.m' ),\n                    error( 'Must be an m-file: %s' );\n                elseif strcmp( file, 'Contents.m' ) && length( files ) > 1,\n                    continue;\n                elseif strcmp( file, 'make.m' ) && strcmp( mpath, base ),\n                    continue;\n                end\n            case 7,\n                cd( file );\n                mpath = pwd;\n                cd( odir );\n                file = '';\n            otherwise,\n                error( 'Invalid file: %s', file );\n        end\n        if length( mpath ) < length( base ) || strncmpi( mpath, base, length( base ) ) == 0,\n            error( 'Not a valid a subdirectory of cvx/examples/: %s', mpath );\n        end\n\n        %\n        % Process the file or directory\n        %\n\n        if ~runonly && isempty( file ) && strcmp( mpath, base ),\n            [ fidr, message ] = fopen( 'index.html', 'r' );\n            if fidr < 0,\n                error( 'Cannot open index.html\\n   %s', message );\n            end\n            [ fidw, message ] = fopen( 'index.html.new', 'w+' );\n            if fidw < 0,\n                error( 'Cannot open index.html.new\\n   %s', message );\n            end\n            while ~feof( fidr ),\n                temp = fgetl( fidr );\n                fprintf( fidw, '%s\\n', temp );\n                if strcmp(temp,'<ul class=\"mktree\" id=\"tree1\">'),\n                    while ~feof( fidr ),\n                        temp = fgetl( fidr );\n                        if strcmp(temp,'</ul>'), break; end\n                    end\n                    break;\n                end\n            end\n        else\n            fidw = -1;\n        end\n        if isempty( file ),\n            generate_directory( mpath, '', force, runonly, indexonly, fidw, base, 0, is_octave );\n        else\n            cd( mpath );\n            generate_file( file, '', force, runonly, indexonly, is_octave );\n        end\n        cd( odir );\n        if fidw >= 0,\n            fprintf( fidw, '</ul>\\n' );\n            while ~feof( fidr ),\n                fprintf( fidw, '%s\\n', fgetl( fidr ) );\n            end\n            fclose( fidr );\n            fclose( fidw );\n            cd( mpath )\n            compare_and_replace( '', 'index.html' );\n        end\n    end\nend\n\nfunction [ title, files ] = generate_directory( mpath, prefix, force, runonly, indexonly, fidc, base, depth, is_octave )\n\nfprintf( 1, '%sDirectory: %s\\n', prefix, mpath );\nprefix = [ prefix, '   ' ];\ncd( mpath );\nmpath = pwd;\n\n%\n% Open Contents.m file and retrieve title and comments\n%\n\ntitle = '';\nif ~runonly,\n    comments = {};\n    fcomments = {};\n    [ fidr, message ] = fopen( 'Contents.m', 'r' );\n    if fidr >= 0,\n        temp = fgetl( fidr );\n        if length( temp ) > 2 && temp( 1 ) == '%' && temp( 2 ) == ' ' && temp( 3 ) ~= ' ',\n            title = temp( min( find( temp ~= '%' & temp ~= ' ' ) ) : end );\n            while ~feof( fidr ),\n                temp = fgetl( fidr );\n                if isempty(temp) || temp( 1 ) ~= '%' || ~any( temp ~= '%' & temp ~= ' ' ), break; end\n                temp = temp( min( find( temp ~= '%' & temp ~= ' ' ) ) : end );\n                if strcmp(title(end-2:end),'...'),\n                    title = [ title(1:end-3), temp ];\n                else\n                    if ~isempty(fcomments) && strcmp( fcomments{end}(end-2:end),'...' ),\n                        fcomments{end} = [ fcomments{end}(1:end-3), temp ];\n                    else\n                        fcomments{end+1} = temp;\n                    end\n                    comments{end+1} = temp;\n                end\n            end\n        end\n        fclose( fidr );\n    elseif ~isempty( dir( 'Contents.m' ) ),\n        error( 'Cannot open Contents.m for reading\\n   %s', message );\n    end\nend\nif isempty(title),\n    title = '(no title)';\nend\n\n%\n% Read the entries, and process the scripts and functions\n%\n\ndd = dir;\nmlen = 0;\nfiles = struct( 'name', {}, 'title', {}, 'type', {} );\nfor k = 1 : length( dd ),\n    name = dd(k).name;\n    if dd(k).isdir,\n        if name(1) == '.' || strcmp( name, 'eqs' ) || strcmp( name, 'html' ), continue; end\n        name(end+1) = '/';\n        files( end + 1 ) = struct( 'name', name, 'title', '', 'type', 'dir' );\n    elseif length( name ) > 2,\n        ndx = max(find(name=='.'));\n        if isempty( ndx ), continue; end\n        switch name(ndx+1:end),\n            case 'm',\n                if strcmp( name, 'Contents.m' ) || strcmp( name, 'make.m' ) || name(end-2) == '_', continue; end\n                [ temp, isfunc ] = generate_file( name, prefix, force, runonly, indexonly, is_octave );\n                if isfunc, type = 'func'; else type = 'script'; end\n                files( end + 1 ) = struct( 'name', name, 'title', temp, 'type', type );\n            case 'tex',\n                temp = generate_doc( name, prefix, force );\n                files( end + 1 ) = struct( 'name', name, 'title', temp, 'type', 'tex' );\n            case { 'pdf', 'ps' },\n                if any( strcmp( { dd.name }, [name(1:ndx+1),'tex'] ) ), continue; end\n                files( end + 1 ) = struct( 'name', name, 'title', '', 'type', 'doc' );\n            case { 'dat', 'mat', 'txt' },\n                if strcmp( name, 'index.dat' ), continue; end\n                files( end + 1 ) = struct( 'name', name, 'title', '', 'type', 'dat' );\n            otherwise,\n                continue;\n        end\n    end\n    mlen = max( mlen, length(name) );\nend\n\n%\n% Sort the files\n%\n\nif ~isempty( files ),\n    [ fnames, ndxs ] = sort( { files.title } );\n    files = files(ndxs);\n    ftypes = { files.type };\n    tdir  = strcmp( ftypes, 'dir' );\n    tfun  = strcmp( ftypes, 'func' );\n    tdoc  = strcmp( ftypes, 'doc' ) | strcmp( ftypes, 'tex' );\n    tdat  = strcmp( ftypes, 'dat' );\n    tscr  = ~( tdir | tfun | tdoc | tdat );\n    t1    = strncmp( fnames, 'Exercise', 8 ) & tscr;\n    t2    = strncmp( fnames, 'Example',  7 ) & tscr;\n    t3    = strncmp( fnames, 'Section',  7 ) & tscr;\n    t4    = strncmp( fnames, 'Figure',   6 ) & tscr;\n    t5    = ~( t1 | t2 | t3 | t4 ) & tscr;\n    tdir  = find(tdir(:));\n    tscr  = [ find(t3(:)); find(t4(:)); find(t2(:)); find(t5(:)); find(t1(:)); ];\n    tfun  = find(tfun(:));\n    tdoc  = find(tdoc(:));\n    tdat  = find(tdat(:));\n    files = files( [ tdoc ; tdir ; tscr ; tfun ; tdat ] );\n    tdoc  = [ 1, length(tdoc) ];\n    tdir  = tdoc(end) + [ 1, length(tdir) ];\n    tscr  = tdir(end) + [ 1, length(tscr) ];\n    tfun  = tscr(end) + [ 1, length(tfun) ];\n    tdat  = tfun(end) + [ 1, length(tdat) ];\nend\n\n%\n% Fill out the index.jemdoc file\n%\n\nif fidc >= 0,\n    \n    dots = sprintf('\\t');\n    dots = dots(ones(1,depth+1));\n    dpath = mpath( length(base) + 2 : end );\n    dpath(dpath=='\\') = '/';\n    if ~isempty(dpath), dpath(end+1) = '/'; end\n    \n    % Directory title---skip for the top level\n    \n    if depth,\n        title = regexprep(title,'</?b>','');\n        title = regexprep(title,' target=\"_blank\"','');\n        title2 = regexprep(title,'<a ([^>]*>)','</b><a target=\"_blank\" $1<b>');\n        title2 = regexprep(title2,'</a>','</b></a><b>');\n        title2 = regexprep(['<b>',title2,'</b>'],'<b></b>','');\n        fprintf( fidc, '%s<li>%s<ul>\\n', dots(1:end-1), title2 );\n    end\n    \n    if tdoc(2) >= tdoc(1) || ~isempty( fcomments ),\n        for k = tdoc(1) : tdoc(2),\n            name = files( k ).name;\n            if strcmp( files(k).type, 'tex' ),\n                name = [ name(1:end-4), 'pdf' ];\n            end\n            temp = files( k ).title;\n            if isempty( temp ),\n                fprintf( fidc, '%s<li>Reference: <a href=\"%s%s\" target=\"_blank\">%s</a></li>\\n', dots, dpath, name, name );\n            else\n                fprintf( fidc, '%s<li>Reference: <a href=\"%s%s\" target=\"_blank\">%s (%s)</a></li>\\n', dots, dpath, name, temp, name );\n            end\n        end\n        for k = 1 : length(fcomments),\n            fprintf( fidc, '%s<li>Reference: %s</li>\\n', dots, regexprep(fcomments{k},'<a href=','<a target=\"_blank\" href='));\n        end\n    end\n\n    for k = tdir(1) : tdir(2),\n        files(k).title = generate_directory( files(k).name(1:end-1), prefix, force, runonly, indexonly, fidc, base, depth+1, is_octave );\n        cd(mpath);\n    end\n    \n    if tscr(2) >= tscr(1),\n        if ~depth,\n            fprintf( fidc, '%s<li><b>Miscellaneous examples</b>\\n', dots );\n            dots(end+1) = dots(end);\n            fprintf( fidc, '%s<ul>\\n', dots );\n        end\n        for k = tscr(1) : tscr(2),\n            name = files( k ).name;\n            temp = files( k ).title;\n            if isempty( temp ),\n                fprintf( fidc, '%s<li><a href=\"%s%s\">%s</a></li>\\n', dots, dpath, name, name );\n            else\n                fprintf( fidc, '%s<li><a href=\"%shtml/%shtml\">%s</a> (<a href=\"%s%s\">%s</a>)</li>\\n', dots, dpath, name(1:end-1), temp, dpath, name, name );\n            end\n        end\n        if ~depth,\n            fprintf( fidc, '%s</ul>\\n', dots );\n            dots(end) = [];\n            fprintf( fidc, '%s</li>\\n', dots );\n        end\n    end\n\n    if tfun(2) >= tfun(1),\n        pref = 'Utility: ';\n        for k = tfun(1) : tfun(2),\n            name = files( k ).name;\n            temp = files( k ).title;\n            if isempty( temp ),\n                fprintf( fidc, '%s<li>Utility: <a href=\"%s%s\">%s</a></li>\\n', dots, dpath, name, name );\n            else\n                fprintf( fidc, '%s<li>Utility: <a href=\"%shtml/%shtml\">%s</a> (<a href=\"%s%s\">%s</a>)</li>\\n', dots, dpath, name(1:end-1), temp, dpath, name, name );\n            end\n        end\n    end\n    \n    if tdat(2) >= tdat(1),\n        pref = '- Data: ';\n        for k = tdat(1) : tdat(2),\n            name = files( k ).name;\n            temp = files( k ).title;\n            if isempty( temp ),\n                fprintf( fidc, '%s<li>Data: <a href=\"%s%s\">%s</a></li>\\n', dots, dpath, name, name );\n            else\n                fprintf( fidc, '%s<li>Data: <a href=\"%s%s\">%s (%s)</a></li>\\n', dots, dpath, name, temp, name );\n            end\n        end\n    end\n    \n    if depth, \n        fprintf( fidc, '%s</ul></li>\\n', dots(1:end-1) );\n    end\n        \nelseif any( tdir ),\n    \n    for k = 1 : length( files ),\n        if strcmp( files(k).type, 'dir' ),\n            files(k).title = generate_directory( files(k).name(1:end-1), prefix, force, runonly, indexonly, fidc, base, depth+1, is_octave );\n            cd(mpath);\n        end\n    end\n\nend\n        \n\n%\n% Create Contents.m.new\n%\n\nif ~runonly,\n    [ fidw, message ] = fopen( 'Contents.m.new', 'w+' );\n    if fidw < 0,\n        if fidr >= 0, fclose( fidr ); end\n        error( 'Cannot open Contents.m.new\\n   %s', message );\n    elseif ~isempty( title ),\n        fprintf( fidw, '%% %s\\n', title );\n        for k = 1 : length( comments ),\n            fprintf( fidw, '%% %s\\n', comments{k} );\n        end\n        fprintf( fidw, '%%\\n' );\n    end\n    for k = 1 : length( files ),\n        tfile = files(k);\n        tfile.name(end+1:mlen) = ' ';\n        if isempty( tfile.title ),\n            fprintf( fidw, '%%  %s - (no title)\\n', tfile.name );\n        else\n            fprintf( fidw, '%%  %s - %s\\n', tfile.name, tfile.title );\n        end\n    end\n    fprintf( fidw, 'help Contents\\n' );\n    fclose( fidw );\nelse\n    fidw = -1;\nend\n\n%\n% Compare Contents.m and Contents.m.new and update if necessary\n%\n\ncd( mpath )\nif fidw >= 0,\n    compare_and_replace( prefix, 'Contents.m' );\nend\n\nfunction [ title, isfunc ] = generate_file( name, prefix, force, runonly, indexonly, is_octave )\n\nif length( name ) < 2 || ~strcmp( name(end-1:end), '.m' ),\n    error( 'Not an m-file.' );\nelseif strcmp( name, 'Contents.m' ),\n    error( 'To generate the Contents.m file, you must run this function on the entire directory.' );\nelse\n    fprintf( 1, '%s%s: ', prefix, name );\nend\n\ndd = dir( name );\nndate = date_convert( dd.date );\n[ fidr, message ] = fopen( name, 'r' );\nif fidr < 0,\n    error( 'Cannot open the source file\\n   %s', message );\nend\ntitle = '';\nisfunc = false;\nlasttitle = false;\nfounddata = false;\nprefixes = {};\nwhile ~feof( fidr ) && ( ~founddata || isempty( title ) || lasttitle ),\n    temp1 = fgetl( fidr );\n    if isempty( temp1 ),\n        if lasttitle, continue; end\n    else\n        temp2 = find( temp1 ~= ' ' );\n        if isempty( temp2 ),\n            if lasttitle, continue; end\n        elseif temp1(temp2(1)) == '%',\n            temp2 = temp1(temp2(1):temp2(end));\n            temp3 = find( temp2 ~= '%' );\n            if isempty( temp3 ),\n                if lasttitle, continue; end\n            else\n                temp3 = temp2( temp3(1) : end );\n                temp4 = find( temp3 ~= ' ' );\n                if isempty( temp4 ),\n                    if lasttitle, continue; end\n                elseif isempty( title ),\n                    title = temp3(temp4(1):temp4(end));\n                    lasttitle = true;\n                    continue;\n                else\n                    lasttitle = false;\n                end\n            end\n        else\n            lasttitle = false;\n            founddata = true;\n            temp2 = temp1(temp2(1):temp2(end));\n            if strncmp( temp2, 'function', 8 ) && ( length( temp2 ) == 8 || ~isvarname( temp2( 1 : 9 ) ) ),\n                isfunc = true;\n            end\n        end\n    end\n    prefixes{end+1} = temp1;\nend\nif runonly,\n    fclose( fidr );\n    if isfunc, return; end\nend\nhfile = [ name(1:end-1), 'html' ];\nodir = pwd;\nhdir = 'html';\nhdate = 0;\nif exist( hdir, 'dir' ),\n    cd( hdir );\n    df = dir( hfile );\n    if length( df ) == 1,\n        hdate = date_convert( df.date );\n    end\n    cd( odir );\nend\nif indexonly,\n    fprintf( 1, 'done.\\n' );\nelseif force || hdate <= ndate,\n    if runonly,\n        fprintf( 1, 'running %s ...', name );\n    elseif hdate == 0,\n        fprintf( 1, 'creating %s ...', hfile );\n    else\n        fprintf( 1, 'updating %s ...', hfile );\n    end\n    name = name(1:end-2);\n    if ~runonly,\n        [ fidw, message ] = fopen( [ name, '_.m' ], 'w+' );\n        if fidw < 0,\n            error( 'Cannot open the temporary file\\n   %s', message );\n        end\n        if isempty( title ),\n            fprintf( fidw, '%%%% %s\\n\\n', name );\n        else\n            fprintf( fidw, '%%%% %s\\n\\n', title );\n        end\n        fprintf( fidw, '%s\\n', prefixes{:} );\n        fwrite( fidw, fread( fidr, Inf, 'uint8' ), 'uint8' );\n        fclose( fidw );\n        fclose( fidr );\n    end\n    evalin( 'base', 'clear' );\n    cvx_clear;\n    cvx_quiet( false );\n    cvx_precision default;\n    success = true;\n    try\n        out___ = [];\n        if is_octave,\n            run_clean_octave( name );\n        elseif runonly,\n            out___ = run_clean( name );\n            fprintf( 1, ' done.\\n' );\n        else\n            opts.format = 'html';\n            opts.useNewFigure = false;\n            opts.createThumbnail =  false;\n            opts.evalCode = ~isfunc;\n            opts.showCode = true;\n            opts.catchError = false;\n            publish( [ name, '_' ], opts );\n            prefixes = { '<style', '<!--', '<p class=\"footer\"', '<meta name=', '<link rel=' };\n            suffixes = { '</style>', '-->', '</p>', '>', '>' };\n            suffix = '';\n            f_in = fopen( [ 'html', filesep, name, '_.html' ], 'r' );\n            data = fread( f_in, Inf, 'uint8=>char' )';\n            fclose( f_in );\n            backpath = '';\n            for k = 1 : 10,\n                if exist( [ backpath, filesep, 'examples.css' ], 'file' ), break; end\n                backpath = [ '..', filesep, backpath ];\n            end\n            backpath = [ '..', filesep, backpath ];\n            canon = [regexprep(pwd,'.*/cvx/examples','http://cvxr.com/cvx/examples'),'/html/',hfile];\n            data = regexprep( data, '<!--.*?-->|<link rel=.*?>|<style.*?</style>|<meta name=.*?>|<p class=\"footer\".*?</p>', '' );\n            data = regexprep( data, '</head>', sprintf( '\\n<link rel=\"canonical\" href=\"%s\"/>\\n<link rel=\"stylesheet\" href=\"%sexamples.css\" type=\"text/css\"/>\\n</head>', canon, backpath ) );\n            data = regexprep( data, '<div class=\"content\"><h1>(.*?)</h1>','<div id=\"header\">\\n<h1>$1</h1>\\n<!--control--></div><div id=\"content\">' );\n            data = regexprep( data, '<pre class=\"codeinput\">\\n?', '\\n<a id=\"source\"></a><pre class=\"codeinput\">\\n' );\n            if ~isempty( regexp( data, '<pre class=\"codeoutput\">' ) ),\n                control_o = '<a href=\"#output\">Text output</a>\\n';\n                data = regexprep( data, '<pre class=\"codeoutput\">\\n?', '\\n<a id=\"output\"></a><pre class=\"codeoutput\">\\n' );\n            else\n                control_o = 'Text output\\n';\n            end\n            if ~isempty( regexp( data, '</pre>\\s*<img', 'once' ) ),\n                control_p = '<a href=\"#plots\">Plots</a>\\n';\n                data = regexprep( data, '</pre>\\s*<img', '</pre>\\n<a id=\"plots\"></a><div id=\"plotoutput\">\\n<img' );\n                data = regexprep( data, '</div>\\s*</body>', '</div></div></body>' );\n            else\n                control_p = 'Plots\\n';\n            end\n            control = sprintf( 'Jump to:&nbsp;&nbsp;&nbsp;&nbsp;\\n<a href=\"#source\">Source code</a>&nbsp;&nbsp;&nbsp;&nbsp;\\n%s&nbsp;&nbsp;&nbsp;&nbsp;\\n%s&nbsp;&nbsp;&nbsp;&nbsp;<a href=\"%sindex.html\">Library index</a>', control_o, control_p, backpath );\n            data = regexprep( data, '<!--control-->', control );\n            data = regexprep( data, '<html>', '<html>\\n' );\n            data = regexprep( data, '(<div|<pre|</div>|<body>|</body>|</html>)', '\\n$1' );\n            data = regexprep( data, '^\\s*<!DOCTYPE.*?>','<!DOCTYPE HTML>' );\n            data = regexprep( data, '<meta http-equiv.*?>', '<meta charset=\"UTF-8\">' );\n            data = regexprep( data, '\\s*((v|h)space=\\S*)', '' );\n            data = regexprep( data, '\\s*(<meta|<title)','\\n$1' );\n            data = regexprep( data, '/>', '>' );\n            f_out = fopen( [ 'html', filesep, name, '.html' ], 'w' );\n            fwrite( f_out, data );\n            fclose( f_out );\n            delete( [ 'html', filesep, name, '_.html' ] );\n            fprintf( 1, ' done.\\n' );\n        end\n    catch\n        err = lasterror;\n        fprintf( 1, ' aborted.\\n' );\n        cd( odir );\n        fprintf( 1, '===> ERROR: %s\\n', err.message );\n        success = false;\n    end\n    if runonly,\n        disp( out___ );\n    else\n        delete( [ name, '_.m' ] );\n    end\n    cd( odir );\n    if ~success && ~runonly && exist( hdir, 'dir' ),\n        cd( hdir );\n        df = dir( hfile );\n        if length( df ) == 1,\n            delete( hfile );\n        end\n        cd( odir );\n    end\n    close all\nelse\n    fprintf( 1, 'up to date.\\n' );\nend\n\nfunction title = generate_doc( name, prefix, force )\n\nif length( name ) < 5 || ~strcmp( name(end-3:end), '.tex' ),\n    error( 'Not an valid TeX file.' );\nelse\n    fprintf( 1, '%s%s: ', prefix, name );\nend\n\ndd = dir( name );\nndate = date_convert( dd.date );\n[ fidr, message ] = fopen( name, 'r' );\nif fidr < 0,\n    error( 'Cannot open the source file\\n   %s', message );\nend\ntitle = '';\nwhile ~feof( fidr ),\n    temp = strtrim( fgetl( fidr ) );\n    kndx = strfind( temp, '\\title{' );\n    if isempty( kndx ), continue; end\n    knd2 = strfind( temp(kndx(1):end), '}' );\n    if isempty( knd2 ), continue; end\n    title = strtrim(temp(kndx(1)+7:kndx(1)+kndx(2)-2));\n    break;\nend\npdffile = [ name(1:end-3), 'pdf' ];\nhdate = 0;\ndf = dir( pdffile );\nif length( df ) == 1,\n    hdate = date_convert( df.date );\nend\nif force || hdate < ndate,\n    if hdate == 0,\n        fprintf( 1, 'creating %s:', hfile );\n    else\n        fprintf( 1, 'updating %s:', hfile );\n    end\n    name2 = name(1:end-4);\n    eval( sprintf( '!latex %s', name2 ) );\n    eval( sprintf( '!latex %s', name2 ) );\n    eval( sprintf( '!bibtex %s', name2 ) );\n    eval( sprintf( '!latex %s', name2 ) );\n    eval( sprintf( '!latex %s', name2 ) );\n    eval( sprintf( '!latex %s', name2 ) );\n    eval( sprintf( '!dvips %s', name2 ) );\n    eval( sprintf( '!ps2pdf %s.ps', name2 ) );\nend\n\nfunction dnum = date_convert( dstr )\npersistent mstrs\nif isempty( mstrs ),\n    mstrs = { 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' };\nend\n% DD-MMM-YY HH:MM:SS\nS = sscanf( dstr, '%d-%3s-%d %d:%d:%d' );\nS = [ S(5), find(strcmp(char(S(2:4)'),mstrs)), S(1), S(6), S(7), S(8) ];\ndnum = S(6) + 100 * ( S(5) + 100 * ( S(4) + 100 * ( S(3) + 100 * ( S(2) + 100 * S(1) ) ) ) );\n\nfunction compare_and_replace( prefix, oldname )\n\nnames = { oldname, [ oldname, '.new' ] };\nfprintf( 1, '%s%s ... ', prefix, oldname );\nfids = [];\nc = {};\nfor k = 1 : 2,\n    [ fids(k), message ] = fopen( names{k}, 'r' );\n    if fids(k) < 0 && ~isempty( dir( names{k} ) ),\n        error( 'Cannot open file %s for reading:\\n   %s', names{k}, message );\n    end\n    c{k} = fread( fids(k), Inf, 'uint8' );\n    fclose( fids(k) );\nend\nif isempty( c{2} ),\n    if fids(k) >= 0,\n        fprintf( 1, ' removed.\\n' );\n        delete( oldname );\n    end\n    delete( names{2} );\nelseif length( c{1} ) ~= length( c{2} ) || any( c{1} ~= c{2} ),\n    [ success, message ] = movefile( names{2}, names{1}, 'f' );\n    if ~success,\n        error( 'Cannot move %s into place\\n   %s', names{2}, message );\n        delete( names{2} )\n    end\n    if ~isempty( c{1} ),\n        fprintf( 1, ' updated.\\n' );\n    else\n        fprintf( 1, ' created.\\n' );\n    end\nelse\n    delete( names{2} )\n    fprintf( 1, ' up to date.\\n' );\nend\n\nfunction run_clean_octave( name )\nfeval( name );\n\nfunction out___ = run_clean( name )\nout___ = evalc( name );\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cantilever_beam_plot.m", "ext": ".m", "path": "Programs-master/matlab/cvx/examples/cvxbook/Ch04_cvx_opt_probs/cantilever_beam_plot.m", "size": 1050, "source_encoding": "utf_8", "md5": "e8c8c9e1b601e4102f96e0436649d132", "text": "% Plots a cantilever beam as a 3D figure.\n% This is a helper function for the optimal cantilever beam example.\n%\n% Inputs:\n%    values: an array of heights and widths of each segment\n%            [h1 h2 ... hN w1 w2 ... wN]\n%\n% Almir Mutapcic 01/25/06\n\nfunction cantilever_beam_plot(values)\n\nN = length(values)/2;\nfor k = 0:N-1\n  [X Y Z] = data_rect3(values(2*N-k),values(N-k),k);\n  plot3(X,Y,Z); hold on;\nend\nhold off;\n\nxlabel('width')\nylabel('height')\nzlabel('length')\nreturn;\n\n%****************************************************************\nfunction [X, Y, Z] = data_rect3(w,h,d)\n%****************************************************************\n% back face\nX = [-w/2 w/2 w/2 -w/2 -w/2];\nY = [-h/2 -h/2 h/2 h/2 -h/2];\nZ = [d d d d d];\n% side face\nX = [X -w/2 -w/2 -w/2 -w/2 -w/2];\nY = [Y -h/2 -h/2 h/2 h/2 -h/2];\nZ = [Z d d+1 d+1 d d];\n% front face\nX = [X -w/2 w/2 w/2 -w/2 -w/2];\nY = [Y -h/2 -h/2 h/2 h/2 -h/2];\nZ = [Z d+1 d+1 d+1 d+1 d+1];\n% back side face\nX = [X w/2 w/2 w/2 w/2 w/2];\nY = [Y -h/2 h/2 h/2 -h/2 -h/2];\nZ = [Z d+1 d+1 d d d+1];\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "simple_step.m", "ext": ".m", "path": "Programs-master/matlab/cvx/examples/circuit_design/simple_step.m", "size": 235, "source_encoding": "utf_8", "md5": "b8043326fe5966f9432b69b584891e0f", "text": "% Computes the step response of a linear system\n\nfunction X = simple_step(A,B,DT,N)\nn  = size(A,1);\nAd = expm( full( A * DT ) );\nBd = ( Ad - eye(n) ) * B;\nBd = A \\ Bd;\nX  = zeros(n,N);\nfor k = 2 : N,\n    X(:,k) = Ad*X(:,k-1)+Bd;\nend\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "spectral_fact.m", "ext": ".m", "path": "Programs-master/matlab/cvx/examples/filter_design/spectral_fact.m", "size": 1292, "source_encoding": "utf_8", "md5": "014eebfa2dfbbd038c1383ff2ef97b0e", "text": "% Spectral factorization using Kolmogorov 1939 approach.\n% (code follows pp. 232-233, Signal Analysis, by A. Papoulis)\n%\n% Computes the minimum-phase impulse response which satisfies\n% given auto-correlation.\n%\n% Input:\n%   r: top-half of the auto-correlation coefficients\n%      starts from 0th element to end of the auto-corelation\n%      should be passed in as a column vector\n% Output\n%   h: impulse response that gives the desired auto-correlation\n\nfunction h = spectral_fact(r)\n\n% length of the impulse response sequence\nn = length(r);\n\n% over-sampling factor\nmult_factor = 100;        % should have mult_factor*(n) >> n \nm = mult_factor*n;\n\n% computation method:\n% H(exp(jTw)) = alpha(w) + j*phi(w)\n% where alpha(w) = 1/2*ln(R(w)) and phi(w) = Hilbert_trans(alpha(w))\n\n% compute 1/2*ln(R(w))\nw = 2*pi*[0:m-1]/m;\nR = [ ones(m,1) 2*cos(kron(w',[1:n-1])) ]*r;\nalpha = 1/2*log(R);\n\n% find the Hilbert transform \nalphatmp = fft(alpha);\nalphatmp(floor(m/2)+1:m) = -alphatmp(floor(m/2)+1:m);\nalphatmp(1) = 0;\nalphatmp(floor(m/2)+1) = 0;\nphi = real(ifft(j*alphatmp));\n\n% now retrieve the original sampling \nindex  = find(rem([0:m-1],mult_factor)==0);\nalpha1 = alpha(index);\nphi1   = phi(index);\n\n% compute the impulse response (inverse Fourier transform)\nh = real(ifft(exp(alpha1+j*phi1),n));\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "polar_plot_ant.m", "ext": ".m", "path": "Programs-master/matlab/cvx/examples/antenna_array_design/polar_plot_ant.m", "size": 1149, "source_encoding": "utf_8", "md5": "34a08a3bc75c474d61e01ea58b16e54e", "text": "% Plot a polar plot of an antenna array sensitivity\n% with lines denoting the target direction and beamwidth.\n% This is a helper function used in the broadband antenna examples.\n%\n% Inputs:\n%    X:      an array of abs(y(theta)) where y is the antenna array pattern\n%    theta0: target direction\n%    bw:     total beamwidth\n%    label:  a string displayed as the plot legend\n%\n% Original code by Lieven Vandenberghe\n% Updated for CVX by Almir Mutapcic 02/17/06\n\nfunction polar_plot_ant(X,theta0,bw,label)\n\n% polar plot\nnumpoints = length(X);\nthetas2 = linspace(1,360,numpoints)';\n\nplot(X.*cos(pi*thetas2/180), X.*sin(pi*thetas2/180), '-');\nplot(X.*cos(pi*thetas2/180), X.*sin(pi*thetas2/180), '-');\nhold on;\naxis('equal');\n\nplot(cos(pi*[thetas2;1]/180), sin(pi*[thetas2;1]/180), '--');\ntext(1.1,0,'1');\n\nplot([0 cos(pi*theta0/180)], [0 sin(pi*theta0/180)], '--');\nsl1 = find(thetas2-theta0 > bw/2);\nsl2 = find(thetas2-theta0 < -bw/2);\nGsl = max(max(X(sl1)), max(X(sl2)));\nplot(Gsl*cos(pi*thetas2(sl1)/180), Gsl*sin(pi*thetas2(sl1)/180), '--');\nplot(Gsl*cos(pi*thetas2(sl2)/180), Gsl*sin(pi*thetas2(sl2)/180), '--');\n\ntext(-1,1.1,label);\n\naxis off;\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "spectral_fact.m", "ext": ".m", "path": "Programs-master/matlab/cvx/examples/antenna_array_design/spectral_fact.m", "size": 1385, "source_encoding": "utf_8", "md5": "570e7ae2165d19abd477494c52e609f8", "text": "% Spectral factorization using Kolmogorov 1939 approach\n% (code follows pp. 232-233, Signal Analysis, by A. Papoulis)\n%\n% Computes the minimum-phase impulse response which satisfies\n% given auto-correlation.\n%\n% Input:\n%   r: top-half of the auto-correlation coefficients\n%      starts from 0th element to end of the auto-corelation\n%      should be passed in as a column vector\n% Output\n%   h: impulse response that gives the desired auto-correlation\n\nfunction h = spectral_fact(r)\n\n% length of the impulse response sequence\nnr = length(r);\nn  = (nr+1)/2;\n\n% over-sampling factor\nmult_factor = 30;        % should have mult_factor*(n) >> n\nm = mult_factor*n;\n\n% computation method:\n% H(exp(jTw)) = alpha(w) + j*phi(w)\n% where alpha(w) = 1/2*ln(R(w)) and phi(w) = Hilbert_trans(alpha(w))\n\n% compute 1/2*ln(R(w))\nw = 2*pi*[0:m-1]/m;\nR = exp( -j*kron(w',[-(n-1):n-1]) )*r;\nR = abs(real(R)); % remove numerical noise from the imaginary part\nfigure; plot(20*log10(R));\nalpha = 1/2*log(R);\n\n% find the Hilbert transform\nalphatmp = fft(alpha);\nalphatmp(floor(m/2)+1:m) = -alphatmp(floor(m/2)+1:m);\nalphatmp(1) = 0;\nalphatmp(floor(m/2)+1) = 0;\nphi = real(ifft(j*alphatmp));\n\n% now retrieve the original sampling\nindex  = find(rem([0:m-1],mult_factor)==0);\nalpha1 = alpha(index);\nphi1   = phi(index);\n\n% compute the impulse response (inverse Fourier transform)\nh = ifft(exp(alpha1+j*phi1),n);\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "plotgraph.m", "ext": ".m", "path": "Programs-master/matlab/cvx/examples/graph_laplacian/plotgraph.m", "size": 3172, "source_encoding": "utf_8", "md5": "a46b1d761798c492e96a5b9504aea9aa", "text": "function plotgraph(A,xy,weights)\n% Plots a graph with each edge width proportional to its weight.\n%\n% Edges with positive weights are drawn in blue; negative weights in red.\n%\n% Input parameters:\n% A       --- incidence matrix of the graph (size is n x m)\n%             (n is the number of nodes and m is the number of edges)\n% xy      --- horizontal and vertical positions of the nodes (n x 2 matrix)\n% weights --- m vector giving edge weights\n%\n% Original by Lin Xiao\n% Modified by Almir Mutapcic\n\n% graph size\n[n,m]= size(A);\n\n% set the graph scale and normalize the coordinates to lay in [-1,1] square\nR = max(max(abs(xy))); % maximum abs value of the xy coordinates\nx = xy(:,1)/R; y = xy(:,2)/R;\n\n% normalize weight vector to range between +1 and -1\nweights = weights/max(abs(weights));\n\n% internal parameters (tune these parameters to make the plot look pretty)\n% (note that the graph coordinates and the weights have been rescaled\n% to a common unity scale)\n%rNode = 0.005;     % radius of the node circles\nrNode = 0;          % set the node radius to zero if you do not want the nodes\nwNode = 2;          % line width of the node circles\nPWColor = [0 0 1];  % color of the edges with positive weights\nNWColor = [1 0 0];  % color of the edges with negative weights\nWmin = 0.0001;      % minimum weight value for which we draw an edge\nmax_width = 0.05;   % drawn width of edge with maximum absolute weight\n\n% first draw the edges with patch widths proportional to the weights\nfor i=1:m\n  if ( abs(weights(i)) > Wmin )\n    Isrc = find( sign(weights(i))*A(:,i)>0 );\n    Idst = find( sign(weights(i))*A(:,i)<0 );\n  else\n    Isrc = find( A(:,i)>0 );\n    Idst = find( A(:,i)<0 );\n  end\n\n  % obtain edge patch coordinates\n  xdelta = x(Idst) - x(Isrc); ydelta = y(Idst) - y(Isrc);\n  RotAgl = atan2( ydelta, xdelta );\n  xstart = x(Isrc) + rNode*cos(RotAgl); ystart = y(Isrc) + rNode*sin(RotAgl);\n  xend   = x(Idst) - rNode*cos(RotAgl); yend   = y(Idst) - rNode*sin(RotAgl);\n  L = sqrt( xdelta^2 + ydelta^2 ) - 2*rNode;\n\n  if ( weights(i) > Wmin )\n    W = abs(weights(i))*max_width;\n    drawedge(xstart, ystart, RotAgl, L, W, PWColor);\n    hold on;\n  elseif ( weights(i) < -Wmin )\n    W = abs(weights(i))*max_width;\n    drawedge(xstart, ystart, RotAgl, L, W, NWColor);\n    hold on;\n  else\n    plot([xstart xend],[ystart yend],'k:','LineWidth',2.5);\n  end\nend\n\n% the circle to draw around each node\nangle = linspace(0,2*pi,100);\nxbd = rNode*cos(angle);\nybd = rNode*sin(angle);\n\n% draw the nodes\nfor i=1:n\n  plot( x(i)+xbd, y(i)+ybd, 'k', 'LineWidth', wNode );\nend;\naxis equal;\nset(gca,'Visible','off');\nhold off;\n\n%********************************************************************\n% helper function to draw edges in the graph \n%********************************************************************\nfunction drawedge( x0, y0, RotAngle, L, W, color )\nxp = [     0   L      L    L   L     L     0      0  ];\nyp = [-0.5*W -0.5*W -0.5*W 0 0.5*W 0.5*W 0.5*W -0.5*W];\nRotMat = [cos(RotAngle) -sin(RotAngle); sin(RotAngle) cos(RotAngle)];\n\nDrawCoordinates = RotMat*[ xp; yp ];\nxd = x0 + DrawCoordinates(1,:);\nyd = y0 + DrawCoordinates(2,:);\n\n% draw the edge\npatch( xd, yd, color );\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "disp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/lib/@cvxprob/disp.m", "size": 5405, "source_encoding": "utf_8", "md5": "c8f4506efcff564b81f1f3cc1dbb8a9c", "text": "function disp( prob, prefix )\n\nif nargin < 2, prefix = ''; end\n\nglobal cvx___\np = cvx___.problems( prob.index_ );\n\nif isempty( p.variables ),\n    nvars = 0;\nelse\n    nvars = length( fieldnames( p.variables ) );\nend\nif isempty( p.duals ),\n    nduls = 0;\nelse\n    nduls = length( fieldnames( p.duals ) );\nend\nneqns = ( length( cvx___.equalities ) - p.n_equality ) + ...\n        ( length( cvx___.linforms )   - p.n_linform  ) + ...\n        ( length( cvx___.uniforms )   - p.n_uniform  );\nnineqs = nnz( cvx___.needslack( p.n_equality + 1 : end ) ) + ...\n         nnz( cvx_vexity( cvx___.linrepls( p.n_linform + 1 : end ) ) ) + ...\n         nnz( cvx_vexity( cvx___.unirepls( p.n_uniform + 1 : end ) ) );\nneqns = neqns - nineqs;     \nif isempty( p.name ) || strcmp( p.name, 'cvx_' ),\n    nm = '';\nelse\n    nm = [ p.name, ': ' ];\nend\n\nrsv = cvx___.reserved;\nnt  = length( rsv );\nfv = length( p.t_variable );\nqv = fv + 1 : nt;\ntt = p.t_variable;\nni = nnz( tt ) - 1;\nndup = sum( rsv ) - nnz( rsv );\nneqns = neqns + ndup;\nnv = nt - fv + ni + ndup;\ntt( qv ) = true;\ngfound = nnz( cvx___.logarithm( tt ) );\ncfound = false;\nfor k = 1 : length( cvx___.cones ),\n    if any( any( tt( cvx___.cones( k ).indices ) ) ),\n        cfound = true;\n        break;\n    end\nend\n\nif all( [ numel( p.objective ), nv, nvars, nduls, neqns, nineqs, cfound, gfound ] == 0 ),\n    disp( [ prefix, nm, 'cvx problem object' ] );\nelse\n    if ( p.gp ),\n        ptype =' geometric ';\n    elseif ( p.sdp ),\n        ptype = ' semidefinite ';\n    else\n        ptype = ' ';\n    end\n    if isempty( p.objective ),\n        tp = 'feasibility';\n    else\n        switch p.direction,\n            case 'minimize',  tp = 'minimization';\n            case 'epigraph',  tp = 'epigraph minimization';\n            case 'hypograph', tp = 'hypograph maximization';\n            case 'maximize',  tp = 'maximization';\n        end\n        if numel( p.objective ) > 1,\n            sz = sprintf( '%dx', size( p.objective ) );\n            tp = [ sz(1:end-1), '-objective ', tp ];\n        end\n    end\n    disp( [ prefix, nm, 'cvx', ptype, tp, ' problem' ] );\n    if nvars > 0,\n        disp( [ prefix, 'variables: ' ] );\n        [ vnam, vsiz ] = dispvar( p.variables, '' );\n        vnam = strvcat( vnam ); %#ok\n        vsiz = strvcat( vsiz ); %#ok\n        for k = 1 : size( vnam ),\n            disp( [ prefix, '   ', vnam( k, : ), '  ', vsiz( k, : ) ] );\n        end\n    end\n    if nduls > 0,\n        disp( [ prefix, 'dual variables: ' ] );\n        [ vnam, vsiz ] = dispvar( p.duals, '' );\n        vnam = strvcat( vnam ); %#ok\n        vsiz = strvcat( vsiz ); %#ok\n        for k = 1 : size( vnam ),\n            disp( [ prefix, '   ', vnam( k, : ), '  ', vsiz( k, : ) ] );\n        end\n    end\n    if neqns > 0 || nineqs > 0,\n        disp( [ prefix, 'linear constraints:' ] );\n        if neqns > 0,\n            if neqns > 1, plural = 'ies'; else plural = 'y'; end\n            fprintf( 1, '%s   %d equalit%s\\n', prefix, neqns, plural );\n        end\n        if nineqs > 0,\n            if nineqs > 1, plural = 'ies'; else plural = 'y'; end\n            fprintf( 1, '%s   %d inequalit%s\\n', prefix, nineqs, plural );\n        end\n    end\n    if cfound || gfound,\n        disp( [ prefix, 'nonlinearities:' ] );\n        if gfound > 0,\n            if gfound > 1, plural = 's'; else plural = ''; end\n            fprintf( 1, '%s   %d exponential pair%s\\n', prefix, gfound, plural );\n        end\n        if cfound,\n            for k = 1 : length( cvx___.cones ),\n                ndxs = cvx___.cones( k ).indices;\n                ndxs = ndxs( :, any( reshape( tt( ndxs ), size( ndxs ) ), 1 ) );\n                if ~isempty( ndxs ),\n                    if isequal( cvx___.cones( k ).type, 'nonnegative' ),\n                        ncones = 1;\n                        csize = numel(  ndxs  );\n                    else\n                        [ csize, ncones ] = size( ndxs );\n                    end\n                    if ncones == 1, plural = ''; else plural = 's'; end\n                    fprintf( 1, '%s   %d order-%d %s cone%s\\n', prefix, ncones, csize, cvx___.cones( k ).type, plural );\n                end\n            end\n        end\n    end\nend\n\nfunction [ names, sizes ] = dispvar( v, name )\n\nswitch class( v ),\n    case 'struct',\n        fn = fieldnames( v );\n        if ~isempty( name ), name( end + 1 ) = '.'; end\n        names = {}; sizes = {};\n        for k = 1 : length( fn ),\n            [ name2, size2 ] = dispvar( subsref(v,struct('type','.','subs',fn{k})), [ name, fn{k} ] );\n            names( end + 1 : end + length( name2 ) ) = name2;\n            sizes( end + 1 : end + length( size2 ) ) = size2;\n            if k == 1 && ~isempty( name ), name( 1 : end - 1 ) = ' '; end\n        end\n    case 'cell',\n        names = {}; sizes = {};\n        for k = 1 : length( v ),\n            [ name2, size2 ] = dispvar( v{k}, sprintf( '%s{%d}', name, k ) );\n            names( end + 1 : end + length( name2 ) ) = name2;\n            sizes( end + 1 : end + length( size2 ) ) = size2;\n            if k == 1, name( 1 : end ) = ' '; end\n        end\n    case 'double',\n        names = { name };\n        sizes = { '(constant)' };\n    otherwise,\n        names = { name };\n        sizes = { [ '(', type( v, true ), ')' ] };\nend\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "apply.m", "ext": ".m", "path": "Programs-master/matlab/cvx/lib/@cvxtuple/apply.m", "size": 505, "source_encoding": "utf_8", "md5": "f59f25021974462a358e5189ea5415e8", "text": "function y = apply( func, x )\ny = do_apply( func, x.value_ );\n\nfunction y = do_apply( func, x )\nswitch class( x ),\n    case 'struct',\n        y = cell2struct( do_apply( func, struct2cell( x ) ), fieldnames( x ), 1 );\n    case 'cell',\n        y = cellfun( func, x, 'UniformOutput', false );\n    otherwise,\n        y = feval( func, x );\nend\n\n% Copyright 2005-2016 CVX Research, Inc. \n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cvx_setdual.m", "ext": ".m", "path": "Programs-master/matlab/cvx/lib/@cvxtuple/cvx_setdual.m", "size": 954, "source_encoding": "utf_8", "md5": "b6deb370985cc299023b091bbba5dfd6", "text": "function x = setdual( x, y )\nx.dual_ = y;\nx.value_ = do_setdual( x.value_, y );\n\nfunction x = do_setdual( x, y )\nswitch class( x ),\n    case 'struct',\n        nx = numel( x );\n        if nx > 1,\n            error( 'Dual variables may not be attached to struct arrays.' );\n        end\n        f = fieldnames(x);\n        y(end+1).type = '{}';\n        for k = 1 : length(f),\n            y(end).subs = {1,k};\n            x.(f{k}) = do_setdual( x.(f{k}), y );\n        end\n    case 'cell',\n        y(end+1).type = '{}';\n        y(end+1).subs = cell(1,ndims(x));\n        for k = 1 : numel(nx),\n            [ y(end).subs{:} ] = { 1, k };\n            x{k} = do_setdual( x{k}, y );\n        end\n    case 'cvx',\n        x = setdual( x, y );\n    case 'double',\n        x = setdual( cvx( x ), y );\nend\n\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "testall.m", "ext": ".m", "path": "Programs-master/matlab/cvx/lib/@cvxtuple/testall.m", "size": 452, "source_encoding": "utf_8", "md5": "be9d0553e0e560f2c73fdb02a37b08b6", "text": "function y = testall( func, x )\ny = do_test( func, x.value_ );\n\nfunction y = do_test( func, x )\nswitch class( x ),\n    case 'struct',\n        y = do_test( func, struct2cell( x ) );\n    case 'cell',\n        y = all( cellfun( func, x ) );\n    otherwise,\n        y = feval( func, x );\nend\n\n% Copyright 2005-2016 CVX Research, Inc. \n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "disp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/lib/@cvxtuple/disp.m", "size": 1366, "source_encoding": "utf_8", "md5": "ed9c53cbe23c1f5ab4440b5d9a10cbfd", "text": "function disp( x, prefix )\nif nargin < 2,\n    prefix = '';\nend\ndisp( [ prefix, 'cvx tuple object: ' ] );\nprefix = [ prefix, '   ' ];\ndo_disp( x.value_, {}, prefix, prefix, '' );\nif ~isempty( x.dual_ ),\n    dn = cvx_subs2str( x.dual_ );\n    disp( [ prefix, 'dual variable: ', dn(2:end) ] );\nend\n\nfunction do_disp( x, f, fprefix, prefix, suffix )\nswitch class( x ),\n    case 'struct',\n        do_disp( struct2cell(x), fieldnames(x), fprefix, prefix, suffix );\n    case 'cell',\n        fprefix = [ fprefix, '{ ' ];\n        prefix  = [ prefix, '  ' ];\n        nsuffix = '';\n        kend = numel( x );\n        for k = 1 : kend,\n            if k == kend, nsuffix = [ ' }', suffix ]; end\n            if ~isempty( f ),\n                fprefix = sprintf( '%s%s: ', fprefix, f{k} );\n            end\n            do_disp( x{k}, {}, fprefix, prefix, nsuffix );\n            fprefix = prefix;\n        end\n    case 'cvx',\n        dual = cvx_getdual( x );\n        if ~isempty( dual ),\n            suffix = sprintf( ' (dual: %s)%s', dual, suffix );\n        end\n        disp( [ fprefix, cvx_class( x, true, true ), ' ', type( x ), suffix ] );\n    case 'double',\n        fprintf( 1, '%s%g%s\\n', fprefix, x, suffix );\nend\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sparsify.m", "ext": ".m", "path": "Programs-master/matlab/cvx/lib/@cvx/sparsify.m", "size": 4013, "source_encoding": "utf_8", "md5": "2eda31274fafa84387d3aad5be748107", "text": "function x = sparsify( x, mode )\n\nglobal cvx___\nnarginchk(2,2);\npersistent remap\n\n%\n% Check mode argument\n%\n\nif ~ischar( mode ) || size( mode, 1 ) ~= 1,\n    error( 'Second arugment must be a string.' );\nend\nisobj = strcmp( mode, 'objective' );\n\npr = cvx___.problems( end );\ntouch( pr.self, x );\nbz = x.basis_ ~= 0;\nbs = sum( bz, 1 );\nbc = bz( 1, : );\ntt = bs > bc + 1;\nat = any( tt );\nif at,\n    \n    %\n    % Replace posynomials with log-convex monomials --- that is, unless\n    % we are taking the exponential of one. (Not that I know why we \n    % would do that!) In that case, we should just use a standard\n    % linear replacement and leave it at that.\n    %\n    \n    if ~isequal( mode, 'exponential' ),\n        if isempty( remap ),\n            remap = cvx_remap( 'posynomial' );\n        end\n        t2 = remap( cvx_classify( x ) );\n        if any( t2 ),\n            if all( t2 ),\n                x = exp( log( x ) );\n            else\n                x = cvx_subsasgn( x, t2, exp( log( cvx_subsref( x, t2 ) ) ) );\n            end\n            bc( t2 ) = 0;\n            tt = tt & ~t2;\n            at = any( tt );\n        end\n    end\n    \n    %\n    % Replace other multivariable forms with single-variable forms\n    %\n    \n    if at,\n        abc = any( bc( :, tt ) );\n        if abc,\n            xc = cvx_constant( x );\n            x = x - xc;\n        end\n        forms = cvx___.linforms;\n        repls = cvx___.linrepls;\n        [ x, forms, repls ] = replcols( x, tt, 'full', forms, repls, isobj );\n        cvx___.linforms = forms;\n        cvx___.linrepls = repls;\n        if abc,\n            x = x + xc;\n        end\n    end\n    \nend\n\n%\n% Arguments: no constraints on coefficients or constant values\n% Objectives:     all replaced with coefficient > 0, constant terms preserved\n% Exponentiation: coefficient == 1, constant terms perserved\n% Logarithm:      coefficient >  0, constant terms eliminated\n%\n\nswitch mode,\n    case { 'argument', 'constraint' },\n        tt = false;\n    case 'objective',\n        tt = ~tt | sum( x.basis_, 1 ) < x.basis_( 1, : );\n        usexc = true;\n    case 'logarithm',\n        tt = sum( x.basis_, 1 ) < x.basis_( 1, : );\n        usexc = false;\n    case 'exponential';\n        tt = sum( x.basis_, 1 ) ~= x.basis_( 1, : ) + 1;\n        usexc = true;\n    otherwise,\n        error( [ 'Invalid normalization mode: ', mode ] );\nend\nif any( tt ),\n    if usexc,\n        abc = any( bc );\n        if abc,\n            xc = cvx_constant( x );\n            x = x - xc;\n        end\n    else\n        abc = false;\n    end\n    forms = cvx___.uniforms;\n    repls = cvx___.unirepls;\n    [ x, forms, repls ] = replcols( x, tt, 'none', forms, repls, isobj );\n    cvx___.uniforms = forms;\n    cvx___.unirepls = repls;\n    if abc,\n        x = x + xc;\n    end\nend\n\nfunction [ x, forms, repls ] = replcols( x, tt, mode, forms, repls, isobj )\n\n%\n% Sift through the forms, removing duplicates\n%\n\nglobal cvx___\nbN = vec( cvx_subsref( x, tt ) );\nnO = length( forms );\nnN = length( bN );\nif nO ~= 0,\n    bN = [ forms ; bN ];\nend\n[ bNR, bN ] = bcompress( bN, mode, nO );\nbNR = bNR( :, nO + 1 : end );\nnB = length( bN ) - nO;\n\n%\n% Create the replacement variables\n%\n\nif nB ~= 0,\n    forms   = bN;\n    bN      = cvx_subsref( bN, nO + 1 : nO + nB, 1 );\n    newrepl = newvar( cvx___.problems( end ).self, '', nB );\n    [ ndxs, temp ] = find( newrepl.basis_ ); %#ok\n    repls = [ repls ; newrepl ];\n    bV = cvx_vexity( bN );\n    cvx___.vexity( ndxs ) = bV;\n    cvx___.readonly( ndxs ) = vec( cvx_readlevel( bN ) );\n    if ~isobj,\n        ss = bV == 0;\n        if any( ss ),\n            temp = cvx_basis( bN );\n            temp = any( temp( :, ss ), 2 );\n            temp( ndxs( ss ) ) = true;\n            cvx___.canslack( temp ) = false;\n        end\n    end\nend\n\n%\n% Re-expand the structure\n%\n\nx = cvx_subsasgn( x, tt, buncompress( bNR, repls, nN ) );\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "prelp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sedumi/conversion/prelp.m", "size": 3887, "source_encoding": "utf_8", "md5": "4d1e23d094e3f1b35ec666df11a34003", "text": "% PRELP  Loads and preprocesses LP from an MPS file.\n%\n% > [A,b,c,lenx,lbounds] = PRELP('problemname')\n%    The above command results in an LP in standard form,\n%    - Instead of specifying the problemname, you can also use PRELP([]), to\n%    get the problem from the file /tmp/default.mat.\n%    - Also, you may type PRELP without any input arguments, and get prompted\n%    for a name.\n%\n% MINIMIZE  c'*x SUCH THAT  A*x = b AND  x>= 0.\n%\n%   So, you can solve it with SeDuMi:\n% > [x,y,info] = SEDUMI(A,b,c);\n%\n% After solving, post-process it with\n% > [x,objp] = POSTPROCESS(x(1:lenx),lbounds).\n%\n% REMARK  x(lenx+1:length(x)) will contain upper-bound slacks.\n%\n% IMPORTANT works only if LIPSOL is installed on your system.\n%\n% See also sedumi, getproblem, postprocess (LIPSOL), frompack, lipsol.\n\nfunction [A,b,c,lenx,lbounds,times] = prelp(pname)\n%\n% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko\n% Copyright (C) 2005 McMaster University, Hamilton, CANADA  (since 1.1)\n%\n% Copyright (C) 2001 Jos F. Sturm (up to 1.05R5)\n%   Dept. Econometrics & O.R., Tilburg University, the Netherlands.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% Affiliation SeDuMi 1.03 and 1.04Beta (2000):\n%   Dept. Quantitative Economics, Maastricht University, the Netherlands.\n%\n% Affiliations up to SeDuMi 1.02 (AUG1998):\n%   CRL, McMaster University, Canada.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% This program is free software; you can redistribute it and/or modify\n% it under the terms of the GNU General Public License as published by\n% the Free Software Foundation; either version 2 of the License, or\n% (at your option) any later version.\n%\n% This program is distributed in the hope that it will be useful,\n% but WITHOUT ANY WARRANTY; without even the implied warranty of\n% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n% GNU General Public License for more details.\n%\n% You should have received a copy of the GNU General Public License\n% along with this program; if not, write to the Free Software\n% Foundation, Inc.,  51 Franklin Street, Fifth Floor, Boston, MA\n% 02110-1301, USA%\n\n\nglobal OUTFID\nglobal Ubounds_exist\nif ~exist('loadata','file') || ~exist('preprocess','file')\nif ~exist('loadata','file') | ~exist('preprocess','file')\n    error('To use PRELP, you need to have LIPSOL installed.')\nend\n\n%--------------------------------------------------\n% LOAD LP PROBLEM INTO MEMORY\n%--------------------------------------------------\nif (nargin == 0)\n    pname = input('Enter problem name: ','s');\nend\n[A,b,c,lbounds,ubounds,BIG] = loadata(pname);\nt0 = cputime;\n[A,b,c,lbounds,ubounds,BIG,NAME] = loadata(pname);\ntimes(1) = cputime - t0;\n\n%--------------------------------------------------\n% PREPROCESS LP PROBLEM\n% NB: Y.Zhang's preprocess returns lbounds for post-\n% processing; the pre-processed problem has x>=0.\n%--------------------------------------------------\nt0 = cputime;\n[A,b,c,lbounds,ubounds,FEASIBLE] = ...\n    preprocess(A,b,c,lbounds,ubounds,BIG);\nif ~FEASIBLE\n    fprintf('\\n');\n    if isempty(OUTFID)\n        return;\n    end;\n    msginf =  'Infeasibility detected in preprocessing';\n    fprintf(OUTFID, [pname '  0   ' msginf '\\n']);\n    return;\nend;\n%[A,b,c,ubounds] = scaling(A,b,c,ubounds);\n%--------------------------------------------------\n% INSERT UBOUND- CONSTRAINTS IN THE A-MATRIX\n%--------------------------------------------------\nb = full(b); c = full(c);\n[m,lenx] = size(A);\nif Ubounds_exist\n    nub = nnz(ubounds);\n    A= [ A sparse(m,nub); sparse(1:nub,find(ubounds),1,nub,lenx) speye(nub) ];\n    b = [b; nonzeros(ubounds)];\n    c = [c; zeros(nub,1)];\nelse\n    ubounds = [];\nend\n%--------------------------------------------------\n% LOCATE DENSE COLUMNS\n%--------------------------------------------------\n%checkdense(A);\ntimes(2) = cputime - t0;\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "feasreal.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sedumi/conversion/feasreal.m", "size": 4144, "source_encoding": "utf_8", "md5": "454dcb6c42c0642ed5c5a524e84bec58", "text": "% FEASREAL Generates a random sparse optimization problem with\n%   linear, quadratic and semi-definite constraints. Output\n%   can be used by SEDUMI. All data will be real-valued.\n%\n%   The following two lines are typical:\n% > [AT,B,C,K] = FEASREAL;\n% > [X,Y,INFO] = SEDUMI(AT,B,C,K);\n%\n%   An extended version is:\n% > [AT,B,C,K] = FEASREAL(m,lpN,lorL,rconeL,sdpL,denfac)\n%\n% SEE ALSO sedumi AND feascpx.\n\nfunction [At,b,c,K] = feasreal(m,nLP,qL,rL,nL,denfac)\n\n%\n% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko\n% Copyright (C) 2005 McMaster University, Hamilton, CANADA  (since 1.1)\n%\n% Copyright (C) 2001 Jos F. Sturm (up to 1.05R5)\n%   Dept. Econometrics & O.R., Tilburg University, the Netherlands.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% Affiliation SeDuMi 1.03 and 1.04Beta (2000):\n%   Dept. Quantitative Economics, Maastricht University, the Netherlands.\n%\n% Affiliations up to SeDuMi 1.02 (AUG1998):\n%   CRL, McMaster University, Canada.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% This program is free software; you can redistribute it and/or modify\n% it under the terms of the GNU General Public License as published by\n% the Free Software Foundation; either version 2 of the License, or\n% (at your option) any later version.\n%\n% This program is distributed in the hope that it will be useful,\n% but WITHOUT ANY WARRANTY; without even the implied warranty of\n% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n% GNU General Public License for more details.\n%\n% You should have received a copy of the GNU General Public License\n% along with this program; if not, write to the Free Software\n% Foundation, Inc.,  51 Franklin Street, Fifth Floor, Boston, MA\n% 02110-1301, USA%\n\nif nargin < 6\n    denfac = 0.1;\n    if nargin < 5\n        nL = 1+ round(7*rand(1,5));\n        fprintf('Choosing random SDP-product cone K.s.\\n')\n        if nargin < 4\n            rL = 3 + round(4*rand(1,5));\n            fprintf('Choosing random LORENTZ R-product cone K.r.\\n')\n            if nargin < 3\n                qL = 3 + round(4*rand(1,5));\n                fprintf('Choosing random LORENTZ-product cone K.q.\\n')\n                if(nargin < 2)\n                    nLP = 10;\n                    fprintf('Choosing default K.l=%3.0f.\\n',nLP)\n                    if nargin < 1\n                        m=10;\n                        fprintf('Choosing default m=%3.0f.\\n',m)\n                    end\n                end\n            end\n        end\n    end\nend\nif isempty(nLP)\n    nLP = 0;\nend\nnblk = length(nL) + length(rL) + length(qL) + nLP;\ndenfac = min(1, max(denfac,2/nblk));\nfprintf('Choosing block density denfac=%6.4f per row\\n',denfac)\nApattern=sparse(m,nblk);\nfor j=1:m\n    pati = sprandn(1,nblk,denfac);\n    Apattern(j,:) = (pati~= 0);\nend\nsumnLsqr = sum(nL.^2);\nx = zeros(nLP+sum(qL)+sum(rL)+sumnLsqr,1);\nx(1:nLP) = rand(nLP,1);\nAt = sparse(length(x),m);\nAt(1:nLP,:) = sprandn(Apattern(:,1:nLP)');\nfirstk = nLP+1;\n%[nA, mA] = size(At);\n% LORENTZ:\nfor k=1:length(qL)\n    nk = qL(k);  lastk = firstk + nk - 1;\n    x(firstk) = rand;\n    for j=1:m\n        if Apattern(j,nLP+k)==1\n            At(firstk:lastk,j) = sprand(nk,1,1/sqrt(nk));\n        end\n    end\n    firstk = lastk + 1;\nend\n% RCONE (Rotated Lorentz):\nfor k=1:length(rL)\n    nk = rL(k);  lastk = firstk + nk - 1;\n    x(firstk) = rand; x(firstk+1) = rand;\n    for j=1:m\n        if Apattern(j,nLP+length(qL)+k)==1\n            At(firstk:lastk,j) = sprand(nk,1,1/sqrt(nk));\n        end\n    end\n    firstk = lastk + 1;\nend\n% SDP:\nfor k=1:length(nL)\n    nk = nL(k);  lastk = firstk + nk*nk - 1;\n    Xk = diag(rand(nk,1));            % diagonal, to keep sparsity structure\n    x(firstk:lastk) = Xk;             %although symmetric, store nk^2 elts.\n    for j=1:m\n        if Apattern(j,nLP+length(qL)+length(rL)+k)==1\n            Aik = sprandsym(nk,1/nk);       %on average 1 nonzero per row\n            At(firstk:lastk,j) = vec( (Aik+Aik') );\n        end\n    end\n    firstk = lastk + 1;\nend\nb = full(At'*x);\ny = rand(m,1)-0.5;\nc = sparse(At*y)+x;\nK.l = nLP; K.q = qL; K.r = rL; K.s = nL;\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "sdpa2vec.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sedumi/conversion/sdpa2vec.m", "size": 2352, "source_encoding": "utf_8", "md5": "f055b4df357f6cf3b426ce27869bc738", "text": "% x = sdpavec(E,K)\n% Takes an SDPA type sparse data description E, i.e.\n%   E(1,:) = block, E(2,:) = row, E(3,:) = column, E(4,:) = entry,\n% and transforms it into a \"long\" vector, with vectorized matrices for\n% each block stacked under each other. The size of each matrix block\n% is given in the field K.s.\n% **********  INTERNAL FUNCTION OF SEDUMI **********\n\nfunction x = sdpa2vec(E,K,invperm)\n\n%\n% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko\n% Copyright (C) 2005 McMaster University, Hamilton, CANADA  (since 1.1)\n%\n% Copyright (C) 2001 Jos F. Sturm (up to 1.05R5)\n%   Dept. Econometrics & O.R., Tilburg University, the Netherlands.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% Affiliation SeDuMi 1.03 and 1.04Beta (2000):\n%   Dept. Quantitative Economics, Maastricht University, the Netherlands.\n%\n% Affiliations up to SeDuMi 1.02 (AUG1998):\n%   CRL, McMaster University, Canada.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% This program is free software; you can redistribute it and/or modify\n% it under the terms of the GNU General Public License as published by\n% the Free Software Foundation; either version 2 of the License, or\n% (at your option) any later version.\n%\n% This program is distributed in the hope that it will be useful,\n% but WITHOUT ANY WARRANTY; without even the implied warranty of\n% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n% GNU General Public License for more details.\n%\n% You should have received a copy of the GNU General Public License\n% along with this program; if not, write to the Free Software\n% Foundation, Inc.,  51 Franklin Street, Fifth Floor, Boston, MA\n% 02110-1301, USA %\n\n\n\n% ------------------------------------------------------------\n% Split E into blocks E[1], E[2], ..., E[length(K.s)]\n% ------------------------------------------------------------\n[xir,xjc] = sdpasplit(E(1,:),length(K.s));\nx = sparse([],[],[],0,0,2*size(E,2));\nfor knz = 1:length(xir)\n    permk = xir(knz);\n    k = invperm(permk);\n    if k > K.l\n        k = k - K.l;                       % matrix\n        nk = K.s(k);\n        Ek = E(2:4,xjc(knz):xjc(knz+1)-1);\n        Xk = sparse(Ek(1,:),Ek(2,:),Ek(3,:),nk,nk);\n        Xk = Xk + triu(Xk,1)';\n        x=[x;Xk(:)];\n    else\n        x=[x;E(4,xjc(knz))];\n    end\nend\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "blk2vec.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sedumi/conversion/blk2vec.m", "size": 1653, "source_encoding": "utf_8", "md5": "0d48b96f66e746fce480e7a3e9c271a5", "text": "% x = blk2vec(X,nL)\n%\n% Converts a block diagonal matrix into a vector.\n%\n% **********  INTERNAL FUNCTION OF FROMPACK  **********\n\nfunction x = blk2vec(X,nL)\n\n%\n% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko\n% Copyright (C) 2005 McMaster University, Hamilton, CANADA  (since 1.1)\n%\n% Copyright (C) 2001 Jos F. Sturm (up to 1.05R5)\n%   Dept. Econometrics & O.R., Tilburg University, the Netherlands.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% Affiliation SeDuMi 1.03 and 1.04Beta (2000):\n%   Dept. Quantitative Economics, Maastricht University, the Netherlands.\n%\n% Affiliations up to SeDuMi 1.02 (AUG1998):\n%   CRL, McMaster University, Canada.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% This program is free software; you can redistribute it and/or modify\n% it under the terms of the GNU General Public License as published by\n% the Free Software Foundation; either version 2 of the License, or\n% (at your option) any later version.\n%\n% This program is distributed in the hope that it will be useful,\n% but WITHOUT ANY WARRANTY; without even the implied warranty of\n% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n% GNU General Public License for more details.\n%\n% You should have received a copy of the GNU General Public License\n% along with this program; if not, write to the Free Software\n% Foundation, Inc.,  51 Franklin Street, Fifth Floor, Boston, MA\n% 02110-1301, USA %\n\nnblk = length(nL);\nsumnk = 0;\nx = [];\nfor k = 1:nblk\n    nk = nL(k);\n    x = [x; vec(X(sumnk+1:sumnk + nk,sumnk+1:sumnk + nk))] ;\n    sumnk = sumnk + nk;\nend\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "writesdp.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sedumi/conversion/writesdp.m", "size": 4708, "source_encoding": "utf_8", "md5": "31f196bca4c11b9610c98de8e4d7b107", "text": "%  This function takes a problem in SeDuMi MATLAB format and writes it out \n%  in SDPpack format.  \n%\n%  Usage:\n%\n%  writesdp(fname,A,b,c,K)\n%\n%      fname           Name of SDPpack file, in quotes\n%      A,b,c,K         Problem in SeDuMi form\n%\n%  Notes:\n%\n%     Problems with complex data are not allowed.    \n%\n%     Rotated cone constraints are not supported.  \n%\n%     Nonsymmetric A.s and C.s matrices are symmetrized with A=(A+A')/2\n%     a warning is given when this happens.\n%\n%     Floating point numbers are written out with 18 decimal digits for\n%     accuracy.\n%\n%  Please contact the author (Brian Borchers, borchers@nmt.edu) with any\n%  questions or bug reports.\n% \nfunction writesdp(fname,A,b,c,K)\n\n%From: \tBrian Borchers[SMTP:borchers@nmt.edu]\n%Sent: \tWednesday, December 08, 1999 12:33 PM\n%To: \tJ.Sturm@KE.UNIMAAS.NL\n%\n%Here's a MATLAB routine that will take a problem in SeDuMi's MATLAB format\n%and write it out in SDPpack format.\n\n%\n%  First, check for complex numbers in A, b, or c.\n%\nif (isreal(A) ~= 1),\n  disp('A is not real!');\n  return;\nend;\nif (isreal(b) ~= 1),\n  disp('b is not real!');\n  return;\nend;\nif (isreal(c) ~= 1),\n  disp('c is not real!');\n  return;\nend;\n%\n%  Check for any rotated cone constraints.\n%\nif (isfield(K,'r') && (~isempty(K.r)) && (K.r ~= 0)),\n  disp('rotated cone constraints are not yet supported.');\n  return;\nend; \n%\n%  Get the size data.\n%\nif (isfield(K,'l')),\n  nlin=K.l;\n  sizelin=nlin;\n  if (isempty(sizelin)),\n    sizelin=0;\n    nlin=0;\n  end;\nelse\n  nlin=0;\n  sizelin=0;\nend;\n\nif (isfield(K,'s')),\n  nsdpblocks=length(K.s);\n  %sizesdp=sum((K.s).^2);\n  %if (isempty(sizesdp)),\n    %sizesdp=0;\n    %nsdpblocks=0;\n  %end;\nelse\n  %sizesdp=0;\n  nsdpblocks=0;\nend;\n\nif (isfield(K,'q')),\n  nqblocks=length(K.q);\n  sizeq=sum(K.q);\n  if (isempty(sizeq)),\n    sizeq=0;\n    nqblocks=0;\n  end;\nelse\n  nqblocks=0;\n  sizeq=0;\nend;\n\nm=length(b);\n\n%\n%  Open up the file for writing.\n%\nfid=fopen(fname,'w');\n%\n%  Print out m, the number of constraints.\n%\nfprintf(fid,'%d \\n',m);\n%\n%  Next, b, with one entry per line.\n%\nfprintf(fid,'%.18e\\n',full(b));\n%\n%  Next, the semidefinite part.\n%\nif (nsdpblocks == 0),\n  fprintf(fid,'0\\n');\nelse\n%\n%  Print out the number of semidefinite blocks.\n%\n  fprintf(fid,'%d\\n',nsdpblocks);\n%\n%  For each block, print out its size.\n%\n  fprintf(fid,'%d\\n',full(K.s));\n%\n%  Next, the cost matrix C.s.\n%\n%\n%  First, calculate where in c things start.\n%\n  base=sizelin+sizeq+1;\n%\n%  Next, work through the blocks.\n%\n  for i=1:nsdpblocks,\n    fprintf(fid,'1\\n');\n    work=c(base:base+K.s(i)^2-1);\n    if nnz(work ~= work'),\n    if (work ~= work'),\n      disp('Non symmetric C.s matrix!');\n      work=(work+work')/2;\n    end;\n    work=triu(work);\n    [II,JJ,V]=find(work);\n    cnt=length(II);\n    fprintf(fid,'%d\\n',cnt);\n    if (cnt ~= 0),\n      fprintf(fid,'%d\\n%d\\n%.18e\\n',[II JJ V]');\n    end;\n%\n%  Next, update to the next base.\n%\n    base=base+K.s(i)^2;\n  end;\n%\n%  Now, loop through the constraints, one at a time.\n%\n  for cn=1:m,\n%\n%  Print out the SDP part of constraint cn.\n%\n    base=sizelin+sizeq+1;\n    for i=1:nsdpblocks,\n      fprintf(fid,'1\\n');\n      if nnz(work ~= work'),\n      work=reshape(work,K.s(i),K.s(i));\n      if (work ~= work'),\n        disp('Non symmetric A.s matrix!');\n        work=(work+work')/2;\n      end;\n      work=triu(work);\n\n      [II,JJ,V]=find(work);\n      cnt=length(II);\n      fprintf(fid,'%d\\n',cnt);\n      if (cnt ~= 0),\n        fprintf(fid,'%d\\n%d\\n%.18e\\n',[II JJ V]');\n      end;\n%\n%  Next, update to the next base.\n%\n      base=base+K.s(i)^2;\n    end;\n%\n% Done with constraint cn\n%\n  end;\n%\n% Done with SDP part.\n%\nend;\n%\n% Next, handle the Quadratic part.\n%\n%\n% Describe the Q blocks.\n%\nif (nqblocks == 0),\n  fprintf(fid,'0\\n');\nelse\n  fprintf(fid,'%d\\n',nqblocks);\n  fprintf(fid,'%d\\n',full(K.q));\n%\n%  Find C.q.\n%\n  base=sizelin+1;\n  cq=c(base:base+sizeq-1);\n%\n% Print out the C.q coefficients.\n%\n  fprintf(fid,'%.18e\\n',full(cq));\n%\n%  Next, the constraint matrix A.q. \n%\n  Aq=A(:,base:base+sizeq-1);\n%\n%  Print out the count of nonzeros.\n%\n  [II,JJ,V]=find(Aq);\n  cnt=length(II);\n  fprintf(fid,'1\\n');\n  fprintf(fid,'%d\\n',cnt);\n  if (cnt ~= 0),\n    fprintf(fid,'%d\\n%d\\n%.18e\\n',[II JJ V]');\n  end;\n%\n% End of handling quadratic part.\n%\nend;\n%\n%\n% Finally, handle the linear part.\n%\nif (nlin == 0),\n  fprintf(fid,'0\\n');\nelse\n%\n% Print out the number of linear variables.\n%\n  fprintf(fid,'%d\\n',nlin);\n%\n% Print out C.l\n%\n  fprintf(fid,'%.18e\\n',full(c(1:nlin)));\n%\n%  Print out the A matrix.\n%\n  Al=A(:,1:nlin);\n  [II,JJ,V]=find(Al);\n  cnt=length(II);\n  fprintf(fid,'1\\n');\n  fprintf(fid,'%d\\n',cnt);\n  if (cnt ~= 0),\n    fprintf(fid,'%d\\n%d\\n%.18e\\n',[II JJ V]');\n  end;\nend;\n\n\n\n\n\n\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "frompack.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sedumi/conversion/frompack.m", "size": 2509, "source_encoding": "utf_8", "md5": "a4730dcb4ec069944953dce753da973d", "text": "% FROMPACK Converts a cone problem in SDPPACK format to SEDUMI format.\n%\n% [At,c] = frompack(A,b,C,blk)  Given a problem (A,b,C,blk) in the\n%   SDPPACK-0.9-beta format, this produces At and c for use with\n%   SeDuMi. This lets you execute\n%\n% [x,y,info] = SEDUMI(At,b,c,blk);\n%\n% IMPORTANT: this function assumes that the SDPPACK function `smat'\n%    exists in your search path.\n%\n% SEE ALSO sedumi.\n\nfunction [At,c] = frompack(A,b,C,blk)\n%\n% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko\n% Copyright (C) 2005 McMaster University, Hamilton, CANADA  (since 1.1)\n%\n% Copyright (C) 2001 Jos F. Sturm (up to 1.05R5)\n%   Dept. Econometrics & O.R., Tilburg University, the Netherlands.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% Affiliation SeDuMi 1.03 and 1.04Beta (2000):\n%   Dept. Quantitative Economics, Maastricht University, the Netherlands.\n%\n% Affiliations up to SeDuMi 1.02 (AUG1998):\n%   CRL, McMaster University, Canada.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% This program is free software; you can redistribute it and/or modify\n% it under the terms of the GNU General Public License as published by\n% the Free Software Foundation; either version 2 of the License, or\n% (at your option) any later version.\n%\n% This program is distributed in the hope that it will be useful,\n% but WITHOUT ANY WARRANTY; without even the implied warranty of\n% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n% GNU General Public License for more details.\n%\n% You should have received a copy of the GNU General Public License\n% along with this program; if not, write to the Free Software\n% Foundation, Inc.,  51 Franklin Street, Fifth Floor, Boston, MA\n% 02110-1301, USA%\n\nm = length(b);\n% ----------------------------------------\n% In SDPPACK, 0s are sometimes used  as emptyset and vice versa.\n% ----------------------------------------\nif blk.l == 0\n    A.l = []; C.l = [];\nend\nif isempty(blk.q)\n    A.q = []; C.q = [];\nend\nif isempty(blk.s)\n    A.s = []; C.s = [];\nend\n% ----------------------------------------\n% SDP:\n% ----------------------------------------\nAsdp = [];\nif sum(blk.s) == 0\n    csdp = [];\nelse\n    for i=1:m\n        Asdp = [Asdp blk2vec( smat(sparse(A.s(i,:)),blk.s), blk.s ) ];\n    end\n    csdp = blk2vec(C.s,blk.s);\nend\n% ----------------------------------------\n% Assemble LP, LORENTZ and SDP.\n%----------------------------------------\nAt = [A.l'; A.q'; Asdp];\nc = [C.l; C.q; csdp];\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "feascpx.m", "ext": ".m", "path": "Programs-master/matlab/cvx/sedumi/conversion/feascpx.m", "size": 4385, "source_encoding": "utf_8", "md5": "c48c6cf336cdb94ad12b04e1efd2417b", "text": "% FEASCPX Generates a random sparse optimization problem with\n%   linear, quadratic and semi-definite constraints. Output\n%   can be used by SEDUMI. Includes complex-valued data.\n%\n%   The following two lines are typical:\n% > [AT,B,C,K] = FEASCPX;\n% > [X,Y,INFO] = SEDUMI(AT,B,C,K);\n%\n%   An extended version is:\n% > [AT,B,C,K] = FEASCPX(m,lpN,lorL,rconeL,sdpL,denfac)\n%\n% SEE ALSO sedumi AND feasreal.\n\nfunction [At,b,c,K] = feascpx(m,nLP,qL,rL,nL,denfac)\n\n%\n% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko\n% Copyright (C) 2005 McMaster University, Hamilton, CANADA  (since 1.1)\n%\n% Copyright (C) 2001 Jos F. Sturm (up to 1.05R5)\n%   Dept. Econometrics & O.R., Tilburg University, the Netherlands.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% Affiliation SeDuMi 1.03 and 1.04Beta (2000):\n%   Dept. Quantitative Economics, Maastricht University, the Netherlands.\n%\n% Affiliations up to SeDuMi 1.02 (AUG1998):\n%   CRL, McMaster University, Canada.\n%   Supported by the Netherlands Organization for Scientific Research (NWO).\n%\n% This program is free software; you can redistribute it and/or modify\n% it under the terms of the GNU General Public License as published by\n% the Free Software Foundation; either version 2 of the License, or\n% (at your option) any later version.\n%\n% This program is distributed in the hope that it will be useful,\n% but WITHOUT ANY WARRANTY; without even the implied warranty of\n% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n% GNU General Public License for more details.\n%\n% You should have received a copy of the GNU General Public License\n% along with this program; if not, write to the Free Software\n% Foundation, Inc.,  51 Franklin Street, Fifth Floor, Boston, MA\n% 02110-1301, USA%\n\nif nargin < 6\n    denfac = 0.1;\n    if nargin < 5\n        nL = 1+ round(7*rand(1,5));\n        fprintf('Choosing random SDP-product cone K.s.\\n')\n        if nargin < 4\n            rL = 3 + round(4*rand(1,5));\n            fprintf('Choosing random LORENTZ R-product cone K.r.\\n')\n            if nargin < 3\n                qL = 3 + round(4*rand(1,5));\n                fprintf('Choosing random LORENTZ-product cone K.q.\\n')\n                if(nargin < 2)\n                    nLP = 10;\n                    fprintf('Choosing default K.l=%3.0f.\\n',nLP)\n                    if nargin < 1\n                        m=10;\n                        fprintf('Choosing default m=%3.0f.\\n',m)\n                    end\n                end\n            end\n        end\n    end\nend\nif isempty(nLP)\n    nLP = 0;\nend\nnblk = length(nL) + length(rL) + length(qL) + nLP;\ndenfac = min(1, max(denfac,2/nblk));\nfprintf('Choosing block density denfac=%6.4f per row\\n',denfac)\nApattern=sparse(m,nblk);\nfor j=1:m\n    pati = sprandn(1,nblk,denfac);\n    Apattern(j,:) = (pati~= 0);\nend\nsumnLsqr = sum(nL.^2);\nx = zeros(nLP+sum(qL)+sum(rL)+sumnLsqr,1);\nx(1:nLP) = rand(nLP,1);\nAt = sparse(length(x),m);\nAt(1:nLP,:) = sprandn(Apattern(:,1:nLP)');\nfirstk = nLP+1;\n%[nA, mA] = size(At);\n% LORENTZ:\nfor k=1:length(qL)\n    nk = qL(k);  lastk = firstk + nk - 1;\n    x(firstk) = rand;\n    for j=1:m\n        if Apattern(j,nLP+k)==1\n            At(firstk:lastk,j) = sprand(nk,1,1/sqrt(nk)) + sqrt(-1) * ...\n                sparse([0;sprand(nk-1,1,1/sqrt(nk))]);\n        end\n    end\n    firstk = lastk + 1;\nend\n% RCONE (Rotated Lorentz):\nfor k=1:length(rL)\n    nk = rL(k);  lastk = firstk + nk - 1;\n    x(firstk) = rand; x(firstk+1) = rand;\n    for j=1:m\n        if Apattern(j,nLP+length(qL)+k)==1\n            At(firstk:lastk,j) = sprand(nk,1,1/sqrt(nk)) + sqrt(-1) * ...\n                sparse([0;0;sprand(nk-2,1,1/sqrt(nk-1))]);\n        end\n    end\n    firstk = lastk + 1;\nend\n% SDP:\nfor k=1:length(nL)\n    nk = nL(k);  lastk = firstk + nk*nk - 1;\n    Xk = diag(rand(nk,1));            % diagonal, to keep sparsity structure\n    x(firstk:lastk) = Xk;             %although symmetric, store nk^2 elts.\n    for j=1:m\n        if Apattern(j,nLP+length(qL)+length(rL)+k)==1\n            rAik = sprandsym(nk,1/nk);       %on average 1 nonzero per row\n            iAik = sprand(nk,nk,1/nk);       %on average 1 nonzero per row\n            At(firstk:lastk,j) = vec(rAik + sqrt(-1)*(iAik-iAik'));\n        end\n    end\n    firstk = lastk + 1;\nend\nb = real(full(At'*x));\ny = rand(m,1)-0.5;\nc = sparse(At*y)+x;\nK.l = nLP; K.q = qL; K.r = rL; K.s = nL;\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cvx_glpk.m", "ext": ".m", "path": "Programs-master/matlab/cvx/shims/cvx_glpk.m", "size": 4344, "source_encoding": "utf_8", "md5": "fc695a24b6757c72ebcc0c9422e98dca", "text": "function shim = cvx_glpk( shim )\n\n% CVX_SOLVER_SHIM\tGLPK interface for CVX.\n%   This procedure returns a 'shim': a structure containing the necessary\n%   information CVX needs to use this solver in its modeling framework.\n\nif ~isempty( shim.solve ),\n    return\nend\nif isempty( shim.name ),\n    fname = 'glpk.m';\n    ps = pathsep;\n    shim.name = 'GLPK';\n    shim.dualize = true;\n    flen = length(fname);\n    fpaths = which( fname, '-all' );\n    if ~iscell(fpaths),\n      fpaths = { fpaths };\n    end\n    old_dir = pwd;\n    oshim = shim;\n    shim = [];\n    for k = 1 : length(fpaths),\n        fpath = fpaths{k};\n        if ~exist( fpath, 'file' ) || any( strcmp( fpath, fpaths(1:k-1) ) ),\n            continue\n        end\n        new_dir = fpath(1:end-flen-1);\n        cd( new_dir );\n        tshim = oshim;\n        tshim.fullpath = fpath;\n        tshim.version = 'unknown';\n        tshim.location = new_dir;\n        if isempty( tshim.error ),\n            tshim.check = @check;\n            tshim.solve = @solve;\n            tshim.eargs = {};\n            if k ~= 1,\n                tshim.path = [ new_dir, ps ];\n            end\n        end\n        shim = [ shim, tshim ]; %#ok\n    end\n    cd( old_dir );\n    if isempty( shim ),\n        shim = oshim;\n        shim.error = 'Could not find a GLPK installation.';\n    end\nelse\n    shim.check = @check;\n    shim.solve = @solve;\nend\n    \nfunction found_bad = check( nonls ) %#ok\nfound_bad = false;\n\nfunction [ x, status, tol, iters, y, z ] = solve( At, b, c, nonls, quiet, prec, settings )\n\nn  = length( c );\nm  = length( b );\nlb = -Inf(n,1);\nub = +Inf(n,1);\nvtype = 'C';\nvtype = vtype(ones(n,1));\nctype = 'S';\nctype = ctype(ones(m,1));\nrr = zeros(0,1);\ncc = rr; \nvv = rr;\nzinv = rr;\nis_ip = false;\nfor k = 1 : length( nonls ),\n    temp = nonls( k ).indices;\n    nn = size( temp, 1 );\n    nv = size( temp, 2 );\n    tt = nonls( k ).type;\n    if strncmp( tt, 'i_', 2 ),\n      is_ip = true;\n      vartype(temp) = 'I';\n      if strcmp(tt,'i_binary'),\n        lb(temp) = 0;\n        ub(temp) = 1;\n      end\n    elseif nn == 1 || isequal( tt, 'nonnegative' ),\n        lb(temp) = 0;\n    elseif isequal( tt, 'lorentz' ),\n        if nn == 2,\n            rr2  = [ temp ; temp ];\n            cc2  = reshape( floor( 1 : 0.5 : 2 * nv + 0.5 ), 4, nv );\n            vv2  = [1;1;-1;1]; vv = vv(:,ones(1,nv));\n            rr   = [ rr ; rr(:) ];\n            cc   = [ cc ; cc(:) ];\n            vv   = [ vv ; vv(:) ];\n            zinv = [ zinv ; temp(:) ];\n        else\n            error('GLPK does not support nonlinear constraints.' );\n        end\n    else\n      error('GLPK does not support nonlinear constraints.' );\n    end\nend\nif ~isempty(rr),\n  znorm = [1:n]';\n  znorm(zinv) = [];\n  rr = [ rr ; znorm ];\n  cc = [ cc ; znorm ];\n  vv = [ vv ; ones(size(znorm)) ];\n  reord = sparse( rr, cc, vv, n, n );\n  At = reord' * At;\n  c  = reord' * c;\nend\nif quiet,\n  param.msglev = 0;\nelse\n  param.msglev = 2;\nend\nparam.scale = 128;\nparam.tolbnd = prec(1);\nparam.toldj = prec(1);\nparam.tolobj = prec(1);\n[ xx, fmin, errnum, extra ] = cvx_run_solver( @glpk, c, At', b, lb, ub, ctype, vtype, 1, param, 'xx', 'fmin', 'errnum', 'extra', settings, 9 );\ntol   = [];\niters = [];\nx = full( xx );\ny = full( extra.lambda );\nz = full( extra.redcosts );\nif ~isempty( rr ),\n  x = reord * x;\n  z = reord * z;\n  z(zinv) = z(zinv) * 0.5;\nend\nstatus = 'Failed';\nswitch errnum,\ncase 0,\n  switch extra.status,\n  case 2,\n    if is_ip,\n      status = 'Suboptimal';\n    elseif errnumn == 0,\n      status = 'Solved';\n    else\n      status = 'Inaccurate/Solved';\n    end  \n  case {3,4},\n    status = 'Infeasible';\n  case 5,\n    status = 'Solved';\n  case 6,\n    status = 'Unbounded';\n  end\ncase 10,\n  status = 'Infeasible';\ncase 11,\n  status = 'Unbounded';\ncase {5,17,15,19}\n  status = 'Failed';\ncase {6,7,8,9,13,14},\n  switch extra.status,\n  case {2,5}\n    if is_ip,\n      status = 'Suboptimal';\n    else\n      status = 'Inaccurate/Solved';\n    end  \n  case { 3,4 },\n    status = 'Inaccurate/Infeasible';\n  case 6,\n    status = 'Inaccurate/Unbounded';\n  end\nend\nif strcmp(status,'Failed'),\n  tol = Inf;\nelseif strncmp(status,'Inaccurate/',11),\n  tol = prec(3);\nelse\n  tol = prec(2);\nend\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cvx_sedumi.m", "ext": ".m", "path": "Programs-master/matlab/cvx/shims/cvx_sedumi.m", "size": 10740, "source_encoding": "utf_8", "md5": "42324556d877c6a43224d410f1a12290", "text": "function shim = cvx_sedumi( shim )\n\n% CVX_SOLVER_SHIM\tSeDuMi interface for CVX.\n%   This procedure returns a 'shim': a structure containing the necessary\n%   information CVX needs to use this solver in its modeling framework.\n\nglobal cvx___\nif ~isempty( shim.solve ),\n    return\nend\nif isempty( shim.name ),\n    fname = 'sedumi.m';\n    fs = cvx___.fs;\n    ps = cvx___.ps;\n    int_path = [ cvx___.where, fs ];\n    int_plen = length( int_path );\n    shim.name = 'SeDuMi';\n    shim.dualize = true;\n    flen = length(fname);\n    fpaths = { [ int_path, 'sedumi', fs, fname ] };\n    fpaths = [ fpaths ; which( fname, '-all' ) ];\n    old_dir = pwd;\n    oshim = shim;\n    shim = [];\n    for k = 1 : length(fpaths),\n        fpath = fpaths{k};\n        if ~exist( fpath, 'file' ) || any( strcmp( fpath, fpaths(1:k-1) ) ),\n            continue\n        end\n        new_dir = fpath(1:end-flen-1);\n        cd( new_dir );\n        tshim = oshim;\n        tshim.fullpath = fpath;\n        tshim.version = 'unknown';\n        is_internal = strncmp( new_dir, int_path, int_plen );\n        if is_internal,\n            tshim.location = [ '{cvx}', new_dir(int_plen:end) ];\n        else\n            tshim.location = new_dir;\n        end\n        try\n            fid = fopen(fname);\n            otp = fread(fid,Inf,'uint8=>char')';\n            fclose(fid);\n        catch errmsg\n            tshim.error = sprintf( 'Unexpected error:\\n%s\\n', errmsg.message );\n        end\n        if isempty( tshim.error ),\n            otp = regexp( otp, 'SeDuMi \\d\\S+', 'match' );\n            if ~isempty(otp), tshim.version = otp{end}(8:end); end\n            vnum = str2double( tshim.version );\n            tshim.check = @check;\n            tshim.solve = @solve;\n            tshim.eargs = { vnum >= 1.3 && vnum < 1.32 };\n            if k ~= 2,\n                tshim.path = [ new_dir, ps ];\n                if ~isempty(cvx___.msub) && exist([new_dir,fs,cvx___.msub],'dir'),\n                   tshim.path = [ new_dir, fs, cvx___.msub, ps, tshim.path ];\n                end\n            end\n        end\n        shim = [ shim, tshim ]; %#ok\n    end\n    cd( old_dir );\n    if isempty( shim ),\n        shim = oshim;\n        shim.error = 'Could not find a SeDuMi installation.';\n    end\nelse\n    shim.check = @check;\n    shim.solve = @solve;\n    vnum = str2double( shim.version );\n    shim.eargs = { vnum >= 1.3 && vnum < 1.32 };\nend\n    \nfunction found_bad = check( nonls ) %#ok\nfound_bad = false;\n\nfunction [ x, status, tol, iters, y, z ] = solve( At, b, c, nonls, quiet, prec, settings, nocplx )\n\nn = length( c );\nm = length( b );\nK = struct( 'f', 0, 'l', 0, 'q', [], 'r', [], 's', [], 'scomplex', [], 'ycomplex', [] );\nreord = struct( 'n', 0, 'r', [], 'c', [], 'v', [] );\nreord = struct( 'f', reord, 'l', reord, 'a', reord, 'q', reord, 'r', reord, 's', reord, 'h', reord );\nreord.f.n = n;\nzinv = [];\nfor k = 1 : length( nonls ),\n    temp = nonls( k ).indices;\n    nn = size( temp, 1 );\n    nv = size( temp, 2 );\n    nnv = nn * nv;\n    tt = nonls( k ).type;\n    reord.f.n = reord.f.n - nnv;\n    if strncmp( tt, 'i_', 2 ),\n        error( 'SeDuMi does not support integer variables.' );\n    elseif nn == 1 || isequal( tt, 'nonnegative' ),\n        reord.l.r = [ reord.l.r ; temp(:) ];\n        reord.l.c = [ reord.l.c ; reord.l.n + ( 1 : nnv )' ];\n        reord.l.v = [ reord.l.v ; ones( nnv, 1 ) ];\n        reord.l.n = reord.l.n + nnv;\n    elseif isequal( tt, 'lorentz' ),\n        if nn == 2,\n            rr = [ temp ; temp ];\n            cc = reshape( floor( 1 : 0.5 : 2 * nv + 0.5 ), 4, nv );\n            vv = [1;1;-1;1]; vv = vv(:,ones(1,nv));\n            reord.a.r = [ reord.a.r ; rr(:) ];\n            reord.a.c = [ reord.a.c ; cc(:) + reord.a.n ];\n            reord.a.v = [ reord.a.v ; vv(:) ];\n            reord.a.n = reord.a.n + nnv;\n            zinv = [ zinv ; temp(:) ]; %#ok\n        else\n            temp = temp( [ end, 1 : end - 1 ], : );\n            reord.q.r = [ reord.q.r ; temp(:) ];\n            reord.q.c = [ reord.q.c ; reord.q.n + ( 1 : nnv )' ];\n            reord.q.v = [ reord.q.v ; ones(nnv,1) ];\n            reord.q.n = reord.q.n + nnv;\n            K.q = [ K.q, nn * ones( 1, nv ) ];\n        end\n    elseif isequal( tt, 'semidefinite' ),\n        if nn == 3,\n            temp = temp( [1,3,2], : );\n            tempv = [sqrt(2);sqrt(2);1] * ones(1,nv);\n            reord.r.r = [ reord.r.r ; temp(:) ];\n            reord.r.c = [ reord.r.c ; reord.r.n + ( 1 : nnv )' ];\n            reord.r.v = [ reord.r.v ; tempv(:) ];\n            reord.r.n = reord.r.n + nnv;\n            K.r = [ K.r, 3 * ones( 1, nv ) ];\n            temp = temp(1:2,:);\n            zinv = [ zinv ; temp(:) ]; %#ok\n        else\n            nn = 0.5 * ( sqrt( 8 * nn + 1 ) - 1 );\n            str = cvx_create_structure( [ nn, nn, nv ], 'symmetric' );\n            K.s = [ K.s, nn * ones( 1, nv ) ];\n            [ cc, rr, vv ] = find( cvx_invert_structure( str, 'compact' ) );\n            rr = temp( rr );\n            reord.s.r = [ reord.s.r; rr( : ) ];\n            reord.s.c = [ reord.s.c; cc( : ) + reord.s.n ];\n            reord.s.v = [ reord.s.v; vv( : ) ];\n            reord.s.n = reord.s.n + nn * nn * nv;\n            reord.s.z = reord.s.v;\n        end\n    elseif isequal( tt, 'hermitian-semidefinite' ),\n        if nn == 4,\n            temp = temp( [1,4,2,3], : );\n            tempv = [sqrt(2);sqrt(2);1;1] * ones(1,nv);\n            reord.r.r = [ reord.r.r ; temp(:) ];\n            reord.r.c = [ reord.r.c ; reord.r.n + ( 1 : nnv )' ];\n            reord.r.v = [ reord.r.v ; tempv(:) ];\n            reord.r.n = reord.r.n + nnv;\n            reord.r.z = reord.r.v;\n            K.r = [ K.r, 4 * ones( 1, nv ) ];\n            temp = temp(1:2,:);\n            zinv = [ zinv ; temp(:) ]; %#ok\n        elseif nocplx,\n            % SeDuMi's complex SDP support was broken with the 1.3 update. So\n            % we must use the following complex-to-real SDP conversion to work\n            % around it, at a modest cost of problem size.\n            %   X >= 0 <==> exists [ Y1, Y2^T ; Y2, Y3 ] >= 0 s.t.\n            %               Y1 + Y3 == real(X), Y2 - Y2^T == imag(X)\n            nsq = nn; nn = sqrt( nn );\n            str = cvx_create_structure( [ nn, nn, nv ], 'hermitian' );\n            [ cc, rr, vv ] = find( cvx_invert_structure( str, 'compact' ) );\n            cc = cc - 1;\n            mm = floor( cc / nsq );\n            cc = cc - mm * nsq;\n            jj = floor( cc / nn );\n            ii = cc - jj * nn + 1;\n            jj = jj + 1;\n            mm = mm + 1;\n            vr = real( vv );\n            vi = imag( vv );\n            ii = [ ii + nn * ~vr ; ii + nn * ~vi ];\n            jj = [ jj ; jj + nn ]; %#ok\n            vv = sqrt( 0.5 ) * [ vr + vi ; vr - vi ];\n            rr = [ rr ; rr ]; %#ok\n            mm = [ mm ; mm ]; %#ok\n            [ jj, ii ] = deal( min( ii, jj ), max( ii, jj ) );\n            cc = ii + ( jj - 1 ) * ( 2 * nn ) + ( mm - 1 ) * ( 4 * nsq );\n            K.s = [ K.s, 2 * nn * ones( 1, nv ) ];\n            rr = temp( rr );\n            reord.s.r = [ reord.s.r; rr( : ) ];\n            reord.s.c = [ reord.s.c; cc( : ) + reord.s.n ];\n            reord.s.v = [ reord.s.v; vv( : ) ];\n            reord.s.n = reord.s.n + 4 * nsq * nv;\n            reord.s.z = reord.s.v;\n        else\n            % SeDuMi's complex SDP support was restored in v1.33.\n            K.scomplex = [ K.scomplex, length( K.s ) + ( 1 : nv ) ];\n            nn = sqrt( nn );\n            str = cvx_create_structure( [ nn, nn, nv ], 'hermitian' );\n            K.s = [ K.s, nn * ones( 1, nv ) ];\n            stri = cvx_invert_structure( str, 'compact' )';\n            [ rr, cc, vv ] = find( stri );\n            rr = temp( rr );\n            reord.s.r = [ reord.s.r; rr( : ) ];\n            reord.s.c = [ reord.s.c; cc( : ) + reord.s.n ];\n            reord.s.v = [ reord.s.v; vv( : ) ];\n            reord.s.n = reord.s.n + size( stri, 2 );\n            reord.s.z = reord.s.v;\n        end\n    else\n        error( 'Unsupported nonlinearity: %s', tt );\n    end\nend\nif reord.f.n > 0,\n    reord.f.r = ( 1 : n )';\n    reord.f.r( [ reord.l.r ; reord.a.r ; reord.q.r ; reord.r.r ; reord.s.r ] ) = [];\n    reord.f.c = ( 1 : reord.f.n )';\n    reord.f.v = ones(reord.f.n,1);\nend\nn_d = max( m - n - reord.f.n + 1, isempty( At ) );\nif n_d,\n    reord.l.n = reord.l.n + n_d;\nend\nK.f = reord.f.n;\nK.l = reord.l.n + reord.a.n;\nn_out = reord.f.n;\nreord.l.c = reord.l.c + n_out; n_out = n_out + reord.l.n;\nreord.a.c = reord.a.c + n_out; n_out = n_out + reord.a.n;\nreord.q.c = reord.q.c + n_out; n_out = n_out + reord.q.n;\nreord.r.c = reord.r.c + n_out; n_out = n_out + reord.r.n;\nreord.s.c = reord.s.c + n_out; n_out = n_out + reord.s.n;\nreord = sparse( ...\n    [ reord.f.r ; reord.l.r ; reord.a.r ; reord.q.r ; reord.r.r ; reord.s.r ], ...\n    [ reord.f.c ; reord.l.c ; reord.a.c ; reord.q.c ; reord.r.c ; reord.s.c ], ...\n    [ reord.f.v ; reord.l.v ; reord.a.v ; reord.q.v ; reord.r.v ; reord.s.v ], ...\n    n, n_out );\n\nAt = reord' * At;\nc  = reord' * c;\npars.free = K.f > 1 && nnz( K.q );\npars.eps     = prec(1);\npars.bigeps  = prec(3);\nif quiet,\n    pars.fid = 0;\nend\nadd_row = isempty( At );\nif add_row,\n    K.f = K.f + 1;\n    At = sparse( 1, 1, 1, n_out + 1, 1 );\n    b = 1;\n    c = [ 0 ; c ];\nend\n[ xx, yy, info ] = cvx_run_solver( @sedumi, At, b, c, K, pars, 'xx', 'yy', 'info', settings, 5 );\nif add_row,\n    xx = xx(2:end);\n    yy = zeros(0,1);\n    At = zeros(n_out,0);\n    % b  = zeros(0,1);\n    c  = c(2:end);\nend\nif ~isfield( info, 'r0' ) && info.pinf,\n    info.r0 = 0;\n    info.iter = 0;\n    info.numerr = 0;\nend\ntol = info.r0;\niters = info.iter;\nxx = full( xx );\nyy = full( yy );\nstatus = '';\nif info.pinf ~= 0,\n    status = 'Infeasible';\n    x = NaN * ones( n, 1 );\n    y = yy;\n    z = - real( reord * ( At * yy ) );\n    if add_row, y = zeros( 0, 1 ); end\nelseif info.dinf ~= 0\n    status = 'Unbounded';\n    y = NaN * ones( m, 1 );\n    z = NaN * ones( n, 1 );\n    x = real( reord * xx );\nelse\n    x = real( reord * xx );\n    y = yy;\n    z = real( reord * ( c - At * yy ) );\n    if add_row, y = zeros( 0, 1 ); end\nend\nif ~isempty(zinv),\n    z(zinv) = z(zinv) * 0.5;\nend\nif info.numerr == 2,\n    status = 'Failed';\n    if any( K.q == 2 ),\n        warning( 'CVX:SeDuMi', cvx_error_format( 'This solver failure may possibly be due to a known bug in the SeDuMi solver. Try switching to SDPT3 by inserting \"cvx_solver sdpt3\" into your model.', ...\n            [66,75], false, '' ) );\n    end\nelse\n    if isempty( status ),\n        status = 'Solved';\n    end\n    if info.numerr == 1 && info.r0 > prec(2),\n        status = [ 'Inaccurate/', status ];\n    end\nend\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "xiaoxiaojiangshang/Programs-master", "name": "cvx_sdpt3.m", "ext": ".m", "path": "Programs-master/matlab/cvx/shims/cvx_sdpt3.m", "size": 12657, "source_encoding": "utf_8", "md5": "b42871a1a82cd274121bc52919caa778", "text": "function shim = cvx_sdpt3( shim )\n\n% CVX_SOLVER_SHIM\tSDPT3 interface for CVX.\n%   This procedure returns a 'shim': a structure containing the necessary\n%   information CVX needs to use this solver in its modeling framework.\n\nglobal cvx___\nif ~isempty( shim.solve ),\n    return\nend\nif isempty( shim.name ),\n    fname = 'sdpt3.m';\n    ps = cvx___.ps;\n    fs = cvx___.fs;\n    int_path = [ cvx___.where, fs ];\n    int_plen = length( int_path );\n    shim.name = 'SDPT3';\n    shim.dualize = true;\n    flen = length(fname);\n    fpaths = { [ int_path, 'sdpt3', fs, fname ] };\n    fpaths = [ fpaths ; which( fname, '-all' ) ];\n    old_dir = pwd;\n    oshim = shim;\n    shim = [];\n    for k = 1 : length(fpaths),\n        fpath = fpaths{k};\n        if ~exist( fpath, 'file' ) || any( strcmp( fpath, fpaths(1:k-1) ) ),\n            continue\n        end\n        new_dir = fpath(1:end-flen-1);\n        cd( new_dir );\n        tshim = oshim;\n        tshim.fullpath = fpath;\n        tshim.version = 'unknown';\n        is_internal = strncmp( new_dir, int_path, int_plen );\n        if is_internal,\n            tshim.location = [ '{cvx}', new_dir(int_plen:end) ];\n        else\n            tshim.location = new_dir;\n        end\n        try\n            fid = fopen(fname);\n            otp = fread(fid,Inf,'uint8=>char')';\n            fclose(fid);\n        catch errmsg\n            tshim.error = sprintf( 'Unexpected error:\\n%s\\n', errmsg.message );\n        end\n        if isempty( tshim.error ),\n            otp = regexp( otp, 'SDPT3: version \\d+\\.\\d+', 'match' );\n            if ~isempty(otp), tshim.version = otp{1}(16:end); end\n            if k ~= 2,\n                tpath = { new_dir, [ new_dir, fs, 'Solver' ], [ new_dir, fs, 'HSDSolver' ], [ new_dir, fs, 'Solver', fs, 'Mexfun' ] };\n                if ~isempty(cvx___.msub) && exist( [ tpath{end}, fs, cvx___.msub ], 'dir' ),\n                    tpath{end} = [ tpath{end}, fs, cvx___.msub ];\n                end\n                tpath = sprintf( [ '%s', ps ], tpath{:} ) ;\n                tshim.path = tpath;\n            end\n            tshim.check = @check;\n            tshim.solve = @solve;\n            tshim.eargs = {};\n        end\n        shim = [ shim, tshim ]; %#ok\n    end\n    cd( old_dir );\n    if isempty( shim ),\n        shim = oshim;\n        shim.error = 'Could not find a SDPT3 installation.';\n    end\nelse\n    shim.check = @check;\n    shim.solve = @solve;\n    shim.eargs = {};\nend\n\nfunction found_bad = check( nonls ) %#ok\nfound_bad = false;\n\nfunction [ x, status, tol, iters, y, z ] = solve( At, b, c, nonls, quiet, prec, settings )\n[n,m] = size(At);\n\n% SDPT3 cannot handle empty equality constraints or square systems. So\n% add an extra row or column, as needed, to rectify this problem.\n\nmzero = m == 0 | isempty( nonls ) | m == n;\nif mzero,    \n    n = n + 1;\n    c = [ c ; 0 ];\n    if m == 0,\n        azero = true;\n        At = sparse( n, 1, 1 );\n        b  = 1;\n        m  = 1;\n    elseif m + 1 < n,\n        azero = true;\n        At(end+1,end+1) = 1;\n        b(end+1) = 1;\n        m = m + 1;\n    else\n        At(end+1,end) = 0;\n        azero = false;\n    end\n    nonls(end+1).type = 'nonnegative';\n    nonls(end).indices = n;\nelse\n    azero = false;\nend\n\ntypes = { nonls.type };\nindices = { nonls.indices };\nfound = false(1,length(types));\nused = false(1,n);\n\nblk  = cell( 0, 2 );\nAvec = cell( 0, 1 );\nCvec = cell( 0, 1 );\nxvec = cell( 0, 1 );\ntvec = cell( 0, 1 );\nif nargout > 3,\n    need_z = true;\n    zvec = cell( 0, 1 );\nelse\n    need_z = false;\nend\n\n%\n% Reject integer variables\n%\n\nif any( strncmp( types, 'i_', 2 ) ),\n    error( 'SDTP3 does not support integer variables.' );\nend\n\n%\n% Nonnegative variables preserved as-is\n%\n\ntt = find( strcmp( types, 'nonnegative' ) );\nif ~isempty( tt ),\n    for k = tt,\n        ti = indices{k};\n        indices{k} = reshape(ti,1,numel(ti));\n    end\n    ti = cat( 2, indices{tt} );\n    ni = length(ti);\n    blk {end+1,1} = 'l';\n    blk {end,  2} = ni;\n    Avec{end+1,1} = At(ti,:);\n    Cvec{end+1,1} = c(ti);\n    tvec{end+1,1} = ti;\n    xvec{end+1,1} = 1;\n    if need_z,\n        zvec{end+1,1} = 1;\n    end\n    found(tt) = true;\n    used(ti) = true;\nend\n\n%\n% SDTP3 places the epigraph variable of an SOC { (x,y) | ||x||<=y }\n% at the beginning of the vector; CVX places it at the end. So we must \n% reverse our ordering here.\n%\n\ntt = find( strcmp( types, 'lorentz' ) );\nif ~isempty( tt ),\n    blk{end+1,1} = 'q';\n    blk{end,2}   = zeros(1,0);\n    for k = tt,\n        ti = indices{k};\n        ti = ti([end,1:end-1],:);\n        blk{end,2} = [ blk{end,2}, size(ti,1) * ones(1,size(ti,2)) ];\n        indices{k} = reshape(ti,1,numel(ti));\n    end\n    ti = cat( 2, indices{tt} );\n    Avec{end+1,1} = At(ti,:);\n    Cvec{end+1,1} = c(ti);\n    tvec{end+1,1} = ti;\n    xvec{end+1,1} = 1;\n    if need_z,\n        zvec{end+1,1} = 1;\n    end\n    found(tt) = true;\n    used(ti) = true;\nend\n\ntt = find( strcmp( types, 'semidefinite' ) | strcmp( types, 'hermitian-semidefinite' ) );\nif ~isempty( tt ),\n    blk{end+1,1}  = 's';\n    blk{end,2}    = {};\n    Avec{end+1,1} = {};\n    Cvec{end+1,1} = {};\n    tvec{end+1,1} = {};\n    xvec{end+1,1} = {};\n    if need_z,\n        zvec{end+1,1} = {};\n    end\n    nnn = 0;\n    for k = tt,\n        ti = indices{k};\n        [nt,nv] = size(ti);\n        if types{k}(1) == 's',\n            nn = 0.5*(sqrt(8*nt+1)-1);\n        else\n            nn = 2*sqrt(nt);\n        end\n        nnn = nnn + nn*nv;\n    end\n    for k = tt,\n        ti = indices{k};\n        [nt,nv] = size(ti);\n        if types{k}(1) == 's',\n            nn = 0.5 * ( sqrt(8*nt+1) - 1 );\n            nt2 = nt;\n            n2 = nn;\n        else\n            nn = sqrt(nt);\n            n2 = 2 * nn;\n            nt2 = 0.5 * n2 * ( n2 + 1 );\n        end\n        blk{end,2}{end+1} = n2 * ones(1,nv);\n        tvec{end}{end+1} = ti(:);\n        \n        if types{k}(1) == 's',\n            \n            %\n            % CVX stores symmetric matrix variables in packed lower triangle\n            % form; and str_1 is the adjoint of the mapping from packed form to\n            % unpacked form. That is, if x is an n(n+1)/2 by 1 vector, then\n            % str_1' * x is the n x n symmetric matrix. To preserve inner\n            % products we need the inverse operator as well:\n            % <a,x> = <a,str_2'*str_1'*x> = <str_2*a,str_1'*x>\n            %\n\n            str_1 = cvx_create_structure( [nn,nn], 'symmetric' );\n            \n        else\n            \n            %\n            % SDPT3 does not do complex SDP natively. To convert to real SDPs\n            % there are two approaches;\n            %   X >= 0 <==> [ real(X), -imag(X) ; imag(X), real(X) ] >= 0\n            %   X >= 0 <==> exists [ Y1, Y2^T ; Y2, Y3 ] >= 0 s.t.\n            %                        Y1 + Y3 == real(X), Y2 - Y2^T == imag(X)\n            % For primal standard form, this second form is the best choice,\n            % and what we end up using here.\n            %\n        \n            str_1 = cvx_create_structure( [nn,nn], 'hermitian' );\n            [rr,cr,vr] = find(real(str_1));\n            cr = cr + floor((cr-1)/nn)*nn;\n            [ri,ci,vi] = find(imag(str_1));\n            ci = ci + floor((ci-1)/nn)*nn;\n            str_1 = sparse( [rr;ri;ri;rr], [cr;ci+nn;ci+n2*nn;cr+nn*(n2+1)], [vr;vi;-vi;vr], nt, n2^2 );\n            \n        end\n        str_2 = cvx_invert_structure( str_1 );\n        \n        %\n        % SDPT3, on the other hand, stores symmetric matrix variables in\n        % packed upper triangle format, with off-diagonal elements scaled \n        % by sqrt(2) to preserve inner products. The operator is unitary:\n        % <str_2*a,str_1'*x> = <str_3*str_2*a,str_3*str_1'*x>\n        %\n        \n        str_3 = sqrt(0.5) * ones(nt2,1); str_3(cumsum(1:n2)) = 1;\n        str_3 = spdiags( str_3, 0, nt2, nt2 ) * cvx_s_symmetric_ut( n2, n2, true );\n        Avec{end}{end+1} = reshape( ( str_3 * str_2 ) * reshape( At(ti,:), nt, nv * m ), nt2 * nv, m );\n        \n        %\n        % SDPT3 expects C to be in the form of a symmetric matrix, so we\n        % only need to do half the work.\n        % <c,x> == <str_2*c,str_1'*x>\n        %\n        \n        cc = reshape( str_2 * reshape( c(ti,:), nt, nv ), n2, n2 * nv );\n        if nv > 1,\n            % For multiple matrices, SDPT3 expects the blocks to be stacked\n            % along the diagonal, but our calculation above stacks them into\n            % a single row. A simple row offset fixes this.\n            [ rr, cc, vv ] = find( cc );\n            cc = sparse( rr + floor((cc-1)/n2)*n2, cc, vv, n2 * nv, n2 * nv );\n        end\n        Cvec{end}{end+1} = cc;\n        \n        %\n        % SDPT3 presents X in symmetric form. We must extract the lower\n        % triangle for CVX. No scaling is needed. For multiple blocks we\n        % must add row offsets to handle the block diagonal form.\n        %\n        \n        [ rr, cc, vv ] = find( str_2' );\n        cc = cc + floor((cc-1)/n2)*(nnn-n2);\n        if nv > 1,\n            ov = ones(1,nv); sv = 0:nv-1;\n            or = ones(length(rr),1);\n            rr = rr(:,ov) + or*(sv*nt);\n            cc = cc(:,ov) + or*(sv*(n2*(nnn+1)));\n            vv = vv(:,ov);\n        end\n        xvec{end}{end+1} = sparse( cc, rr, vv, n2*nv*(nnn+1), nt*nv );\n        if need_z,\n            [ rr, cc, vv ] = find( str_1 );\n            cc = cc + floor((cc-1)/n2)*(nnn-n2);\n            if nv > 1,\n                ov = ones(1,nv); sv = 0:nv-1;\n                or = ones(length(rr),1);\n                rr = rr(:,ov) + or*(sv*nt);\n                cc = cc(:,ov) + or*(sv*(n2*(nnn+1)));\n                vv = vv(:,ov);\n            end\n            zvec{end}{end+1} = sparse( cc, rr, vv, n2*nv*(nnn+1), nt*nv );\n        end\n        used(ti) = true;\n    end\n    blk{end,2} = horzcat( blk{end,2}{:} );\n    Avec{end}  = vertcat( Avec{end}{:} );\n    Cvec{end}  = cvx_blkdiag( Cvec{end}{:} );\n    tvec{end}  = vertcat( tvec{end}{:} );\n    xvec{end}  = cvx_blkdiag( xvec{end}{:} );\n    xvec{end}(nnn*nnn+1:end,:) = [];\n    if need_z,\n        zvec{end} = cvx_blkdiag( zvec{end}{:} );\n        zvec{end}(nnn*nnn+1:end,:) = [];\n    end\n    found(tt)  = true;\nend\n\nti = find(~found);\nif ~isempty(ti),\n    types = unique( types(ti) );\n    types = sprintf( ' %s', types{:} );\n    error( 'One or more unsupported nonlinearities detected: %s', types );\nend\n\nti = find(~used);\nif ~isempty(ti),\n    ni = numel(ti);\n    ti = reshape( ti, 1, ni );\n    blk {end+1,1} = 'u';\n    blk {end,  2} = ni;\n    Avec{end+1,1} = At(ti,:);\n    Cvec{end+1,1} = c(ti);\n    tvec{end+1,1} = ti;\n    xvec{end+1,1} = 1;\n    if need_z,\n        zvec{end+1,1} = 1;\n    end\n    % found(tt) = true;\n    % used(ti) = true;\nend\n\n%\n% Call SDPT3\n%\n\nb = full(b);\nOPTIONS = sqlparameters;\nOPTIONS.gaptol = prec(1);\nOPTIONS.printlevel = 3 * ~quiet;\nwarn_save = warning;\n[ obj, xx, y, zz, info ] = cvx_run_solver( @sqlp, blk, Avec, Cvec, b, OPTIONS, 'obj', 'x', 'y', 'z', 'info', settings, 5 ); %#ok\nwarning( warn_save );\ntol = max( [ info.relgap, info.pinfeas, info.dinfeas ] );\nif ~quiet, disp(' '); end\n\n%\n% Interpret status codes\n%\n\nx_good = true;\ny_good = true;\nswitch info.termcode,\n    case 0, \n        status = 'Solved';\n        tol = min( prec(2), tol );\n    case 1,\n        status = 'Infeasible';\n        x_good = false;\n        tol = min( info.dinfeas, prec(2) );\n    case 2,\n        status = 'Unbounded';\n        y_good = false;\n        tol = min( info.pinfeas, prec(2) );\n    otherwise,\n        if isnan(tol),\n            tol = Inf;\n            status = 'Failed';\n        elseif tol > prec(3),\n            status = 'Failed';\n        elseif tol <= prec(2),\n            status = 'Solved';\n            info.termcode = 0;\n        else\n            status = 'Inaccurate/Solved';\n            info.termcode = 0;\n        end\nend\n\n% Primal point\n\nif x_good,\n    x = zeros(1,n);\n    for k = 1 : length(xx),\n        x(1,tvec{k}) = x(1,tvec{k}) + xx{k}(:)' * xvec{k};\n    end\nend\nif x_good && all(isfinite(x)),\n    x = full( x )';\nelse\n    x = NaN * ones(n,1);\nend\nif mzero,\n    x(end) = [];\nend\n\n% Lagrange multipliers\n\nif y_good,\n    y = full(y);\nend\nif ~y_good || ~all(isfinite(y)),\n    y = NaN * ones(m,1);\nend\nif azero,\n    y(end,:) = [];\nend\n\n% Iteration count\n\niters = info.iter;\n\n% Dual cone\n\nif need_z,\n    if y_good,\n        z = zeros(1,n);\n        for k = 1 : length(zz),\n            z(1,tvec{k}) = z(1,tvec{k}) + zz{k}(:)' * zvec{k};\n        end\n    end\n    if y_good && all(isfinite(z)),\n        z = full( z )';\n    else\n        z = NaN * ones(n,1);\n    end\n    if mzero,\n        z(end) = [];\n    end\nend\n\n% Copyright 2005-2016 CVX Research, Inc.\n% See the file LICENSE.txt for full copyright information.\n% The command 'cvx_where' will show where this file is located.\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "apply_mask.m", "ext": ".m", "path": "selectiveConvFeature-master/utils/apply_mask.m", "size": 926, "source_encoding": "utf_8", "md5": "d86c8eba84407eed59d1110068a3caf0", "text": "function [ masked_fea ] = apply_mask( filename, maskmethod )\n% apply_mask\n% + Load the feature map file.\n% + Create the mask then apply\n% + Return a set of local features\n\nl = load(filename);\nk = size(l.fea, 3);\n\nswitch maskmethod\n    case 'max'\n        mask = create_max_mask( l.fea );\n    case {'sum50', 'sum'}\n        mask = create_sum_mask( l.fea );\n    case 'none'\n        mask = true(size(l.fea, 1), size(l.fea, 2));\nend\n\nmask = repmat(mask, [1, 1, k]);\nmasked_fea =  l.fea(mask);\nmasked_fea = reshape(masked_fea, [length(masked_fea)/k, k])';\nend\n\nfunction [ mask ] = create_max_mask( fea )\n% Return a binary mask\n\nmask = false(size(fea, 1), size(fea, 2));\nfor j=1:size(fea, 3)\n    [v1, p1] = max(fea(:, :, j));\n    [~, p2] = max(v1);\n    mask(p1(p2), p2) = 1;\nend\nmask = mask(:);\nend\n\n\nfunction [ mask ] = create_sum_mask( fea )\n% Return a binary mask\nmask = sum(fea, 3);\nmask = (mask(:) >= prctile(mask(:), 50));\nend\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "vecpostproc.m", "ext": ".m", "path": "selectiveConvFeature-master/utils/vecpostproc.m", "size": 354, "source_encoding": "utf_8", "md5": "d516f36867714475f5635721288ec358", "text": "function x = vecpostproc(x, a) \n\tif ~exist('a'), a = 1; end\n\tx = replacenan (yael_vecs_normalize (powerlaw (x, a)));\n\n% replace all nan values in a matrix (with zero)\nfunction y = replacenan (x, v)\n\nif ~exist ('v')\n  v = 0;\nend\n\ny = x;\ny(isnan(x)) = v;\t\n\n% apply powerlaw\nfunction x = powerlaw (x, a)\n\nif a == 1, return; end\nx = sign (x) .* abs(x)  .^ a;"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "sinkhornm.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/democratic/sinkhornm.m", "size": 746, "source_encoding": "utf_8", "md5": "00b5563e8a441037d97fe9e0a1f3b82c", "text": "% This is a modified version of the SINKHORN algorithm (see sinhorn.m)\n% \n% It converts a square positive matrix to a matrix that sums to a constant C\n% (rows and columns sum to one) based on the Knight variant.\n% \n% Usage: [kn, d1, nbiter] = sinkhornm(kn, reg, nbiter)\n%\n% The algorithm steps after nbiter iterations\n\nfunction [kn, lambda, nbiter] = sinkhornm(kn, reg, nbiter, verbose)\n\nif ~exist ('reg', 'var'),    reg = 0.5; end\nif ~exist ('nbiter', 'var'), nbiter = 10; end\nif ~exist ('verbose', 'var'), verbose = false; end\n\nn = size (kn, 1);\nlambda = ones (1, size(kn, 1), 'single');\n\nfor k=1:nbiter\n    delta = 1./(sum(kn).^reg);\n    kn = bsxfun (@times, kn, delta);\n    kn = bsxfun (@times, kn, delta');\n    lambda = delta .* lambda;\nend\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "qdemocratic.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/democratic/qdemocratic.m", "size": 1100, "source_encoding": "utf_8", "md5": "69cffbcf97d19ad0fa67caf9f827c86d", "text": "% Compute the weights to tend towards a democratic kernel\n% Usage: alpha = qdemocratic (x, method, tau)\n%  method   'sum'\n%           'sinkhorn'    regular Sinkhorn\n%           'zca'         ZCA\n%\n%  tau       sparsificatino of the Gram matrix\n%\n%  param1, param2, ...    Method-dependent\n%\nfunction [y, alpha] = qdemocratic (x, method, param1, param2, param3)\n\n% d = size (x, 1);\n% n = size (x, 2);\n% \n% alpha = [];\n% \n% if n == 0\n%   alpha = [];\n%   y = zeros (d, 1);\n%   return;\n% end\n\n%--------------------------------------\n% if strcmp (method, 'sum')\n%   alpha = ones (1, n);\n%   y = sum (x, 2);\n%   return;\n% end\n\n\n% if strcmp (method, 'sinkhorn')\n  % Gram matrix\n  G = x' * x ;\n\n  % Filter out kernel elements below 0\n  G = (G + abs(G)) / 2; % Faster than finding negative values and putting 0\n\n%   if exist ('param1')  reg = param1;\n%   else                 reg = 0.5;  end\n  reg = 0.5;\n\n%   if exist ('param2')  nbiter = param2;\n%   else                 nbiter = 10;  end\n  nbiter = 10;\n  [~, alpha, ~] = sinkhornm(G, reg, nbiter);\n\n  y = sum(bsxfun(@times, x, alpha), 2);\n%   return\n% end\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "compute_map.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/evaluation/compute_map.m", "size": 1846, "source_encoding": "utf_8", "md5": "97ec2a86542fd2a35fd509732ae5f396", "text": "% This function computes the mAP for a given set of returned results.\n%\n% Usage: map = compute_map (ranks, gnd);\n%\n% Notes:\n% 1) ranks starts from 1, size(ranks) = db_size X #queries\n% 2) The junk results (e.g., the query itself) should be declared in the gnd stuct array\nfunction [map, aps] = compute_map (ranks, gnd, verbose)\n\nif nargin < 3\n  verbose = false;\nend\n\nmap = 0;\nnq = numel (gnd);   % number of queries\naps = zeros (nq, 1);\n\nfor i = 1:nq\n  qgnd = gnd(i).ok; \n  if isfield (gnd(i), 'junk')\n    qgndj = gnd(i).junk; \n  else \n    qgndj = []; \n  end\n  \n\t% positions of positive and junk images\n  [~, pos] = intersect (ranks (:,i), qgnd);\n  [~, junk] = intersect (ranks (:,i), qgndj);\n\n\tpos = sort(pos);\n\tjunk = sort(junk);\n\n\tk = 0;  \n\tij = 1;\n\n\tif length (junk)\n\t\t% decrease positions of positives based on the number of junk images appearing before them\n\t\tip = 1;\n\t\twhile ip <= numel (pos)\n\n\t\t\twhile ( ij <= length (junk) & pos (ip) > junk (ij) )\n\t\t\t\tk = k + 1;\n\t\t\t\tij = ij + 1;\n\t\t\tend\n\n\t\t\tpos (ip) = pos (ip) - k;\n\t\t\tip = ip + 1;\n\t\tend\n\tend\n\n  ap = score_ap_from_ranks1 (pos, length (qgnd));\n\t\n  if verbose\n    fprintf ('query no %d -> gnd = ', i);\n    fprintf ('%d ', qgnd);\n    fprintf ('\\n              tp ranks = ');\n    fprintf ('%d ', pos);\n    fprintf (' -> ap=%.3f\\n', ap);\n  end\n  map = map + ap;\n\taps (i) = ap;\n\nend\nmap = map / nq;\n\nend\n\n\n% This function computes the AP for a query\nfunction ap = score_ap_from_ranks1 (ranks, nres)\n\n% number of images ranked by the system\nnimgranks = length (ranks);  \nranks = ranks - 1;\t\n  \n% accumulate trapezoids in PR-plot\nap = 0;\n\nrecall_step = 1 / nres;\n\nfor j = 1:nimgranks\n  rank = ranks(j);\n  \n  if rank == 0\n    precision_0 = 1.0;\n  else\n    precision_0 = (j - 1) / rank;\n  end\n  \n  precision_1 = j / (rank + 1);\n  ap = ap + (precision_0 + precision_1) * recall_step / 2;\nend\n\nend\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "projection_learn_batch.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/faemb/projection_learn_batch.m", "size": 1767, "source_encoding": "utf_8", "md5": "38ada347cd49eae4254d173697dea39a", "text": "%% Function for learning projection params used for whitening stage\n\nfunction [Xmean, eigvec, eigval] = projection_learn_batch (X, B, gama_all_final, k, hes)\n\nif (~exist('hes', 'var'))\n    hes = 1;\nend\n\nm = size (X, 2) ;    % number of input vectors\nn = size(X, 1)   ;   % dimesion of input vectors\nD = n * (n+1) * k / 2 ;          % input vector dimensionality\ndout = D; % output dimensionality\n\nslicesize = 10^4; % we can adjust this param correspond to the capability of memory's size for faster computing\n% e.g. RAM = 64 GB --> slicesize = 10^5, etc.\n\nXsum = zeros (D, 1);\n% fprintf(2,'Computing Xmean...\\n\\n');\n\nfor i=1:slicesize:m\n    endi = min(i+slicesize-1, m);\n%     fprintf ('\\r%d-%d/%d', i, endi, m);\n    \n    %tmp = embeding( X(:,i:endi), B, gama_all_final(:,i:endi), hes );\n    tmp = fa_embedding (single(X(:,i:endi)), single(B), single(gama_all_final(:,i:endi)));\n    \n    Xsum = Xsum + sum (tmp, 2);\nend\nXmean = Xsum / m;\n% fprintf(2,'\\nFinish computing Xmean!\\n\\n');\n\n% Compute whitening parameters\ncovD = zeros(D);\n\n% fprintf(2,'\\nComputing covariance matrix...\\n\\n');\n\nfor i=1:slicesize:m\n\n  endi = min(i+slicesize-1, m);\n%   fprintf ('\\r%d-%d/%d', i, endi, m);\n\n%   [ PHIX ] = embeding( X(:,i:endi), B, gama_all_final(:,i:endi), hes );\n  [ PHIX ] = fa_embedding( single(X(:,i:endi)), single(B), single(gama_all_final(:,i:endi) ));\n  \n  PHIX = bsxfun(@minus, PHIX, Xmean);\n\n  covD = covD + PHIX * PHIX';\n\nend\ncovD = covD/(m-1);\n% fprintf(2,'\\nFinish computing covariance matrix!\\n\\n');\n\n[eigvec, eigval] = eig (covD); %output by increasing orders of eigenvalue\neigvec = eigvec (:, end:-1:end-dout+1); %sort to decreasing orders\neigval = diag (eigval);\neigval = eigval (end:-1:end-dout+1);\n\n\n% fprintf('\\nLearning projection matrix finished!\\n\\n');\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "bvecs_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/bvecs_read.m", "size": 1520, "source_encoding": "utf_8", "md5": "977df50a2a45c709849888179f2f1e39", "text": "% Read a set of vectors stored in the bvec format (int + n * float)\n% The function returns a set of output uint8 vector (one vector per column)\n%\n% Syntax: \n%   v = bvecs_read (filename)        -> read all vectors\n%   v = bvecs_read (filename, n)      -> read n vectors \n%   v = bvecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\nfunction v = bvecs_read (filename, bounds)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\n\nvecsizeof = 1 * 4 + d;\n\n% Get the number of vectrors\nfseek (fid, 0, 1);\na = 1;\nbmax = ftell (fid) / vecsizeof;\nb = bmax;\n\nif nargin >= 2\n  if length (bounds) == 1\n    b = bounds;\n   \n  elseif length (bounds) == 2\n    a = bounds(1);\n    b = bounds(2);    \n  end\nend\n\nassert (a >= 1);\nif b > bmax\n  b = bmax;\nend\n\nif b == 0 | b < a\n  v = [];\n  fclose (fid);\n  return;\nend\n\n% compute the number of vectors that are really read and go in starting positions\nn = b - a + 1;\nfseek (fid, (a - 1) * vecsizeof, -1);\n\n% read n vectors\nv = fread (fid, (d + 4) * n, 'uint8=>uint8');\nv = reshape (v, d + 4, n);\n\n\n% Check if the first column (dimension of the vectors) is correct\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\nassert (sum (v (2, 2:end) == v(2, 1)) == n - 1);\nassert (sum (v (3, 2:end) == v(3, 1)) == n - 1);\nassert (sum (v (4, 2:end) == v(4, 1)) == n - 1);\nv = v (5:end, :);\n  \nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "bvecs_size.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/bvecs_size.m", "size": 494, "source_encoding": "utf_8", "md5": "f740fcd630ac853920781a4a1a4f742c", "text": "% Return the number of vectors contained in a bvecs files and their dimension\n%\n% Syntax:   [n,d] = bvecs_size (filename) \nfunction [n, d] = bvecs_size (filename)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\n\n% Read the number of vectors\nfseek (fid, 0, 1);\nn = ftell (fid) / (1 * 4 + d);\nfseek (fid, 0, -1);\n\nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "fvecs_write.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/fvecs_write.m", "size": 635, "source_encoding": "utf_8", "md5": "69a30552f6ea46eddf3605642048b76b", "text": "% This function reads a vector of float vectors\n% \n% Usage: fvecs_write (filename, v)\n%   where v is a set of vector (stored columnwise)\nfunction fvecs_write (filename, v)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'wb');\nd = size (v, 1);\nn = size (v, 2);\n\nfor i = 1:n\n  \n  % first write the vector size\n  count = fwrite (fid, d, 'int');\n\n  if count ~= 1 \n    error ('Unable to write vector dimension: count !=1 \\n');\n  end\n\n  % write the vector components\n  count = fwrite (fid, v(:, i), 'float');\n\n  if count ~= d \n    error ('Unable to write vector elements: count !=1 \\n');\n  end\nend\n\nfclose (fid);"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_pca.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_pca.m", "size": 1665, "source_encoding": "utf_8", "md5": "aa7a3cf0b3e26bc890a66a46fd9d94f1", "text": "% PCA with automatic selection of the method: covariance or gram matrix\n% Usage: [X, eigvec, eigval, Xm] = pca (X, dout, center, verbose)\n%   X       input vector set (1 vector per column)\n%   dout    number of principal components to be computed\n%   center  need to center data?\n% \n% Note: the eigenvalues are given in decreasing order of magnitude\n%\n% Author: Herve Jegou, 2011. \n% Last revision: 08/10/2013\nfunction [X, eigvec, eigval, Xm] = yael_pca (X, dout, center, verbose)\n\nif nargin < 3,         center = true; end\nif ~exist ('verbose'), verbose = false; end\n\nX = double (X);\nd = size (X, 1);\nn = size (X, 2);\n\nif nargin < 2\n  dout = d;\nend\n\nif center\n  Xm = mean (X, 2);\n  X = bsxfun (@minus, X, Xm);\nelse\n  Xm = zeros (d, 1);\nend\n\n\nopts.issym = true;\nopts.isreal = true;\nopts.tol = eps;\nopts.disp = 0;\n\n% PCA with covariance matrix\nif n > d \n  if verbose, fprintf ('PCA with covariance matrix: %d -> %d\\n', d, dout); end\n  Xcov = X * X';\n  Xcov = (Xcov + Xcov') / (2 * n);\n  \n  if dout < d\n    [eigvec, eigval] = eigs (Xcov, dout, 'LM', opts);\n  else\n    [eigvec, eigval] = eig (Xcov);\n  end\nelse\n  % PCA with gram matrix\n  if verbose, fprintf ('PCA with gram matrix: %d -> %d\\n', d, dout); end\n  Xgram = X' * X;\n  Xgram = (Xgram + Xgram') / 2;\n  if dout < d\n    [eigvec, eigval] = eigs (Xgram, dout, 'LM', opts);\n  else\n    [eigvec, eigval] = eig (Xgram);\n  end\n  eigvec = single (X * eigvec);\n  eigvec = yael_vecs_normalize (eigvec);\nend\n           \n\nX = eigvec' * X;\nX = single (X);\neigval = diag(eigval);\n\n% We prefer a consistent order\n[~, eigord] = sort (eigval, 'descend');\neigval = eigval (eigord);\neigvec = eigvec (:, eigord);\nX = X(eigord, :);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "bvecs_write.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/bvecs_write.m", "size": 561, "source_encoding": "utf_8", "md5": "6cec679cf64f52656b11ffacfefce339", "text": "% This function reads a vector from a file in the libit format\n\nfunction bvecs_write (filename, v)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'wb');\nd = size (v, 1);\nn = size (v, 2);\n\nfor i = 1:n\n  \n  % first write the vector size\n  count = fwrite (fid, d, 'int');\n\n  if count ~= 1 \n    error ('Unable to write vector dimension: count !=1 \\n');\n  end\n\n  % write the vector components\n  count = fwrite (fid, v(:,i), 'uint8');\n\n  if count ~= d \n    error ('Unable to write vector elements: count !=1 \\n');\n  end\nend\n\nfclose (fid);"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_hamming.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_hamming.m", "size": 1225, "source_encoding": "utf_8", "md5": "b630ed71a4baee7ecbfa561b2d12cee8", "text": "% Compute the hamming distances between binary vectors in compact format\r\n%\r\n% Usage:  D = yael_hamming(X, Y);\r\n% Input:  X and Y are set of bit vectors, 1 vector per column\r\n% Output: Set of Hamming distances, in uint16 format\r\n\r\nfunction D = yael_hamming(X, Y);\r\n\r\n% Tabulate Hamming distance.\r\ntabhamdis = uint16([...\r\n    0 1 1 2 1 2 2 3 1 2 2 3 2 3 3 4 1 2 2 3 2 3 ...\r\n    3 4 2 3 3 4 3 4 4 5 1 2 2 3 2 3 3 4 2 3 3 4 ...\r\n    3 4 4 5 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 1 2 ...\r\n    2 3 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 ...\r\n    3 4 4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 ...\r\n    5 6 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 1 2 2 3 ...\r\n    2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 3 4 ...\r\n    4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 ...\r\n    3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 2 3 3 4 3 4 ...\r\n    4 5 3 4 4 5 4 5 5 6 3 4 4 5 4 5 5 6 4 5 5 6 ...\r\n    5 6 6 7 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 4 5 ...\r\n    5 6 5 6 6 7 5 6 6 7 6 7 7 8]);\r\n\r\nnX = size(X, 2);\r\ndd = size(X, 1);  % True dimensionality is dd*8\r\nnY = size(Y, 2);\r\nassert (size(Y, 1) == dd);\r\n\r\nD = zeros(nX, nY, 'uint16');\r\n\r\nfor i = 1:nX\r\na = repmat(X(:,i), 1, nY);\r\nb = uint16 (bitxor (a, Y));\r\nc = tabhamdis (b + 1);\r\n  D (i, :) = sum (c);\r\nend\r\n          \r\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_vecs_normalize.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_vecs_normalize.m", "size": 797, "source_encoding": "utf_8", "md5": "4c8a85a6b713f2aa9654ea5e8312d7af", "text": "% This function normalize a set of vectors\n% Parameters:\n%   v     the set of vectors to be normalized (column stored)\n%   nr    the norm for which the normalization is performed (Default: Euclidean)\n%   rval  replace value in case the vector is 0-norm\n%\n% Output:\n%   vout  the normalized vector\n%   vnr   the norms of the input vectors\n%\n% Remark: the function return Nan for vectors of null norm\nfunction [vout, vnr] = yael_vecs_normalize (v, nr, rval)\n\n% if nargin < 2, nr = 2; end\nnr = 2;\n\n% norm of each column\nvnr = (sum (v.^nr)) .^ (1 / nr);\n\n% sparse multiplication to apply the norm\nvout = bsxfun (@times, v, 1 ./ vnr);\n\n% if exist('rval')\n%   [~, ko] = find (isnan (vout));\n%   ko = unique (ko);\n% %   if (~isempty(ko))\n% %       disp('hello');\n% %   end\n%   vout (:, ko) = rval;\n% end\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_kmin.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_kmin.m", "size": 1029, "source_encoding": "utf_8", "md5": "9c0a18bd3826f63bcfcce260fdfe4ae3", "text": "% This function returns the k smallest values of a vector \n%\n% Usage: [val, idx] = yael_kmin (v,k)\n%\n% Parameters:\n%   v     the vector to be partially ranked. If v is a matrix, the function\n%         returns the k largest values of each column (like min function)\n%   k     the number of neighbors to be returned. Must be smaller than vector length\n%\n% Output:\n%   val   a k-dimensional vector containing the (ordered) set of smallest values\n%         In case v was a matrix, val is a k*n matrix with one column per vector\n%   idx   the indexes, in the original vector, where the smallest values have been found\n%         This output parameter is not mandatory\n%\n% Remarks: if k=1, this function is equivalent to searching the min. \n%          if k is equal to the vector length, it is equivalent to the sort function\nfunction [val, idx] = yael_kmin (v, k)\n\nfprintf ('# Warning: This is NOT the fast implementation. \\n#You should use the Mex version instead\\n');\n\n[val, idx] = sort (v);\n\nval = val (1:k, :);\nidx = idx (1:k, :);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_L2sqr.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_L2sqr.m", "size": 860, "source_encoding": "utf_8", "md5": "81282dfbf310860445f74620ccb58948", "text": "% Compute all the distances between two sets of vectors\n%\n% Usage: [dis] = dis_L2sqr(q, v)\n%\n% Parameters:\n%   q, v        sets of vectors (1 vector per column)\n%\n% Returned values\n%   dis         the corresponding *square* distances\n%               vectors of q corresponds to row, and columns for v\nfunction dis = dis_L2sqr (q, v)\n\n\n% vector dimension and number of vectors in the dataset\nn = size (v, 2);\nd = size (v, 1);\nk = n;\n\n% number of query vectors\nnq = size (q, 2);\n\n% Compute the square norm of the dataset vectors\nv_nr = sum (v .* v);\n\n% first compute the square norm the queries of the slice\nd_nr = sum (q .* q)';\n\n% the most efficient way I found to compute distances in matlab\ndis = repmat (v_nr, nq, 1) + repmat (d_nr, 1, n) - 2 * q' * v;\ndis = repmat (v_nr, nq, 1) + repmat (d_nr, 1, n) - 2 * q' * v;\n\nneg = find (dis < 0) ;\n\ndis(neg) = 0;\n\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "fvec_write.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/fvec_write.m", "size": 401, "source_encoding": "utf_8", "md5": "0eb88a1df37dd296f56f52331ed8e54d", "text": "% This function reads a vector from a file in the libit format\n\nfunction fvec_write (fid, v)\n\n% first read the vector size\ncount = fwrite (fid, length(v), 'int');\n\nif (count ~= 1) \n  error ('Unable to write vector dimension: count !=1 \\n');\nend\n\n% write the vector components\ncount = fwrite (fid, v, 'float');\n\nif (count ~= length (v)) \n  error ('Unable to write vector elements: count !=1 \\n');\nend\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "ivecs_write.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/ivecs_write.m", "size": 561, "source_encoding": "utf_8", "md5": "b6fbc8815b3445b63ff6e0650507879d", "text": "% This function writes a vector from a file in the ivecs format\n\nfunction ivecs_write (filename, v)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'wb');\nd = size (v, 1);\nn = size (v, 2);\n\nfor i = 1:n\n  \n  % first write the vector size\n  count = fwrite (fid, d, 'int');\n\n  if count ~= 1 \n    error ('Unable to write vector dimension: count !=1 \\n');\n  end\n\n  % write the vector components\n  count = fwrite (fid, v(:,i), 'int');\n\n  if count ~= d \n    error ('Unable to write vector elements: count !=1 \\n');\n  end\nend\n\nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "fvecs_size.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/fvecs_size.m", "size": 498, "source_encoding": "utf_8", "md5": "6d37c7e1e4d00bd9fa8dc774eaf3ca1c", "text": "% Return the number of vectors contained in a fvecs files and their dimension\n%\n% Syntax:   [n,d] = fvecs_size (filename) \nfunction [n, d] = fvecs_size (filename)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\n\n% Read the number of vectors\nfseek (fid, 0, 1);\nn = ftell (fid) / (1 * 4 + d * 4);\nfseek (fid, 0, -1);\n\nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "uint8tobit.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/uint8tobit.m", "size": 326, "source_encoding": "utf_8", "md5": "1074f77e291a69382ecc5b7d60c2c334", "text": "% This function translates a uint8 vector into a binary vector\n% Usage: b = uint8tobit (v)\n% The vectors are column-stored\nfunction b = uint8tobit (v)\n\nn = size (v, 2);\ndbytes = size (v, 1);\nd = dbytes * 8;\n\nb = zeros(d, n, 'uint8');\n\nfor i = 1:n\n  for j = 1:dbytes\n  b((j-1)*8+1:j*8 ,i) = bitget (v(j, i), 1:8);  \n  end\nend\n "}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_kmax.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_kmax.m", "size": 1038, "source_encoding": "utf_8", "md5": "fc1109c025b4398d6ac82e9a403db3cf", "text": "% This function returns the k largest values of a vector \n% \n% Usage: [val, idx] = yael_kmax (v,k)\n%\n% Parameters:\n%   v     the vector to be partially ranked. If v is a matrix, the function\n%         returns the k largest values of each column (like max function)\n%   k     the number of neighbors to be returned. Must be smaller than vector length\n%\n% Output:\n%   val   a k-dimensional vector containing the (ordered) set of largest values\n%         In case v was a matrix, val is a k*n matrix with one column per vector\n%   idx   the indexes, in the original vector, where the largest values have been found\n%         This output parameter is not mandatory\n%\n% Remarks: if k=1, this function is equivalent to searching the min. \n%          if k is equal to the vector length, it is equivalent to the sort function\nfunction [val, idx] = yael_kmax (v, k)\n\nfprintf ('# Warning: This is NOT the fast implementation. \\n#You should use the Mex version instead\\n');\n\n[val, idx] = sort (v, 'descend');\n\nval = val (1:k, :);\nidx = idx (1:k, :);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "siftgeo_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/siftgeo_read.m", "size": 994, "source_encoding": "utf_8", "md5": "65eb5ad63f3acbafad26f4c850e05a31", "text": "% This function reads a siftgeo binary file\n%\n% Usage: [v, meta] = siftgeo_read (filename)\n%   filename    the input filename\n%\n% Returned values\n%   v           the sift descriptors (1 descriptor per line)\n%   meta        meta data for each descriptor, i.e., per line:\n%               x, y, scale, angle, mi11, mi12, mi21, mi22, cornerness\nfunction [v, meta] = siftgeo_read (filename, dv, maxn)\n\nif nargin < 2, dv = 128; end\n\nif nargin < 3, maxn = 10^9+1; end\n\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'r');\n\nif fid==-1\n  error('could not open %s',filename)\nend\n\nfseek (fid, 0, 1);\nn = ftell (fid) / (9 * 4 + 1 * 4 + dv);\nfseek (fid, 0, -1);\n\n% first read the meta information associated with the descriptor\nmeta = zeros (9, n,'single');\nv = zeros (dv, n, 'single');\nd = 0;\n\n% read the elements\nfor i = 1:n\n  meta(:,i) = fread (fid, 9, 'float');\n  d = fread (fid, 1, 'int');\n  assert (d == dv);\n  v(:,i) = fread (fid, d, 'uint8=>single');\nend\n\nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "fvec_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/fvec_read.m", "size": 213, "source_encoding": "utf_8", "md5": "363d7d860bcd190414ecad7c6a887f8d", "text": "% This function reads a vector from a file in the libit format\n\nfunction [v,d] = fvec_read (fid)\n\n% first read the vector size\nd = fread (fid, 1, 'int');\n\n% read the elements\nv = fread (fid, d, 'float=>single');\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "gmm_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/gmm_read.m", "size": 637, "source_encoding": "utf_8", "md5": "4a76011f253e15048c463832a1fdf432", "text": "% This function reads the parameters of a gmm file\n% \n% Usage: [w, mu, sigma] = gmm_read (filename)\nfunction [w, mu, sigma] = gmm_read (filename)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n\n% first read the vector size and the number of centroids\nd = fread (fid, 1, 'int');\nk = fread (fid, 1, 'int');\n\n% read the elements\nw = fread (fid, k, 'float=>single');\nmu = fread (fid, d*k, 'float=>single');\nsigma = fread (fid, d*k, 'float=>single');\n\nmu = reshape (mu, d, k);\nsigma = reshape (sigma, d, k);\n\nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_nn.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_nn.m", "size": 1908, "source_encoding": "utf_8", "md5": "adc08b31a08f01d9b0735a94cc01fca9", "text": "% Return the k nearest neighbors of a set of query vectors\n%\n% Usage: [ids,dis] = nn(v, q, k, distype)\n%   v                the dataset to be searched (one vector per column)\n%   q                the set of queries (one query per column)\n%   k  (default:1)   the number of nearest neigbors we want\n%   distype          distance type: 1=L1, \n%                                   2=L2         -> Warning: return the square L2 distance\n%                                   3=chi-square -> Warning: return the square Chi-square\n%                                   4=signed chis-square\n%                                   16=cosine    -> Warning: return the *smallest* cosine \n%                                                   Use -query to obtain the largest\n%                    available in Mex-version only\n%\n% Returned values\n%   idx         the vector index of the nearest neighbors\n%   dis         the corresponding *square* distances\n%\n% Both v and q contains vectors stored in columns, so transpose them if needed\nfunction [idx, dis] = yael_nn (X, Q, k, distype)\n\n\nif ~exist('k'), k = 1; end\nif ~exist('distype'), distype = 2; end \nassert (size (X, 1) == size (Q, 1));\n\nswitch distype\n\ncase {2,'L2'}\n  % Compute half square norm\n  X_nr = sum (X.^2) / 2;\n  Q_nr = sum (Q.^2) / 2;\n\n  sim = bsxfun (@plus, Q_nr', bsxfun (@minus, X_nr, Q'*X));\n%  sim = bsxfun (@minus, X_nr, Q'*X)\n%  sim = bsxfun (@plus, Q_nr', sim);\n\n  if k == 1\n    [dis, idx] = min (sim, [], 2);\n  else  \n    [dis, idx] = sort (sim, 2);\n    dis = dis (:, 1:k);\n    idx = idx (:, 1:k);\n  end\n  \n  dis = dis' * 2;\n  idx = idx';\n\ncase {16,'COS'}\n  sim = Q' * X;\n                \n  if k == 1\n    [dis, idx] = min (sim, [], 2);\n    dis = dis';\n    idx = idx';\n  else  \n    [dis, idx] = sort (sim, 2);\n    dis = dis (:, 1:k)';\n    idx = idx (:, 1:k)';\n  end\n                 \notherwise\n   error ('Unknown distance type');\nend\n\n                \n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "ivecs_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/ivecs_read.m", "size": 1446, "source_encoding": "utf_8", "md5": "bccc2d5437bc4c9f3372086f6f6b6d6d", "text": "% Read a set of vectors stored in the ivec format (int + n * int)\n% The function returns a set of output vector (one vector per column)\n%\n% Syntax: \n%   v = ivecs_read (filename)     -> read all vectors\n%   v = ivecs_read (filename, n)  -> read n vectors \n%   v = ivecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\nfunction v = ivecs_read (filename, bounds)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\nvecsizeof = 1 * 4 + d * 4;\n\n% Get the number of vectrors\nfseek (fid, 0, 1);\na = 1;\nbmax = ftell (fid);\nif bmax == 0\n  v = [];\n  return;\nend\nbmax = bmax / vecsizeof;\nif bmax == 0\n  v = [];\n  return;\nend\nb = bmax;\n\nif nargin >= 2\n  if length (bounds) == 1\n    b = bounds;\n   \n  elseif length (bounds) == 2\n    a = bounds(1);\n    b = bounds(2);    \n  end\nend\n\nassert (a >= 1);\nif b > bmax\n  b = bmax;\nend\n\nif b == 0 | b < a\n  v = [];\n  fclose (fid);\n  return;\nend\n\n% compute the number of vectors that are really read and go in starting positions\nn = b - a + 1;\nfseek (fid, (a - 1) * vecsizeof, -1);\n\n% read n vectors\nv = fread (fid, (d + 1) * n, 'int=>int32');\nv = reshape (v, d + 1, n);\n\n% Check if the first column (dimension of the vectors) is correct\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\nv = v (2:end, :);\n  \nfclose (fid);\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "ivecs_size.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/ivecs_size.m", "size": 496, "source_encoding": "utf_8", "md5": "10a6970b9239b5eee1b6831b3665e93a", "text": "% Return the number of vectors contained in a ivecs file and their dimension\n%\n% Syntax:  [n,d] = ivecs_size (filename) \nfunction [n, d] = ivecs_size (filename)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\n\n% Read the number of vectors\nfseek (fid, 0, 1);\nn = ftell (fid) / (1 * 4 + d * 4);\nfseek (fid, 0, -1);\n\nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "ivec_write.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/ivec_write.m", "size": 409, "source_encoding": "utf_8", "md5": "cc96e4c56543e2c74419151a3823e9d4", "text": "% This function writes a vector from a file in the libit format\n\nfunction [v,d] = ivec_write (fid, v)\n\n\n  \n% first write the vector size\ncount = fwrite (fid, length(v), 'int');\n\nif count ~= 1 \n  error ('Unable to write vector dimension: count !=1 \\n');\nend\n\n% write the vector components\ncount = fwrite (fid, v, 'int');\n\nif count ~= length (v) \n  error ('Unable to write vector elements: count !=1 \\n');\nend\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_ivf_he.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_ivf_he.m", "size": 8187, "source_encoding": "utf_8", "md5": "a3d9ade30faad13177030bc79a4fdaed", "text": "% Construct Hamming Embedding structure from a learning set\n% WARNING: this is a very slow implementation of HE, for exposition purpose only\n% \n% Usage: ivf = ivfhe_new (k, nbits, v, quantizer, C, idx)\n%        ivf = vifhe_new (k, nbits, v, C)\n% where\n%   k          number of centroids (=visual words) for the inverted file\n%   nbits      number of bits for the signature\n%   v          the set of vectors using for learning the structure\n%   C          use a pre-learned set of centroids instead of learning it with v\n%   quantizer  is a user-defined function for quantization \n%              If defined, C is its first parameter (of any desired type)\n%\t\tidx \t\t\t precomputed quantization cells for learning vectors v\n%\n% This software is governed by the CeCILL license under French law and\n% abiding by the rules of distribution of free software. \n% See http://www.cecill.info/licences.en.html\n%\n% This package was written by Herve Jegou\n% Copyright (C) INRIA 2011-2013\n% Last change: May 2013. \n\nfunction ivfhe = yael_ivf_he (k, nbits, v, quantizer, quantizer_params, idx)\n\n% If a filename is provided as sole argument, then load from disk\nif nargin == 1\n   ivfhe = ivfhe_load (k); \n   return;\nend\n\nivfhe.verbose = false;\nn = size (v, 2);     % number of vectors in the training set\nd = size (v, 1);     % vector dimension\nniter = 50;\n\nivfhe.k = k;\nivfhe.d = size (v, 1);\nivfhe.nbits = nbits;\nivfhe.elemsize = nbits / 8;\nassert (mod(nbits, 8) == 0);\n\n%--- first draw a random rotation matrix ---\n[ivfhe.Q, ~] = qr (randn(d));\nivfhe.Q = ivfhe.Q (1:ivfhe.nbits, :);\n\n%--- first learn the coarse quantizer and a random rotation matrix ---\nif ~exist('quantizer')\n  ivfhe.quantizer = @yael_nn;   % NN rule by default\nelse\n  ivfhe.quantizer = quantizer;\nend\n\nif ~exist('quantizer_params')\n  ivfhe.quantizer_params = yael_kmeans (v, k, 'niter', niter, 'verbose', 0);\nelse\n  ivfhe.quantizer_params = quantizer_params; \nend\n\n%--- compute the median values ---\n\n% Assign and re-organize the vector per cell using the given quantization method\nif ~exist('idx')\n\t[idx, ~] = ivfhe.quantizer (ivfhe.quantizer_params, v);\nend\n\n% Learn the median values\nivfhe.medians = ivfhe_learn_medians (ivfhe, v, idx);\n\n% Construct a matrix to help the binarization\nm = 2.^(0:7);\nM = m;\nfor i = 2:ivfhe.elemsize\n  M = blkdiag (M, m);\nend\nivfhe.bin2compactbin = M;\n\nyael_ivf ('free');\nyael_ivf ('new', ivfhe.k, ivfhe.elemsize);\n\n\n% The methods associated with the ivfhe structure\nivfhe.binsign = @ivfhe_binsign;\nivfhe.add = @ivfhe_add;\nivfhe.query = @ivfhe_query;\nivfhe.queryw = @ivfhe_queryw;\nivfhe.getids = @ivfhe_getids;\nivfhe.save = @ivfhe_save;\nivfhe.load = @ivfhe_load;\nivfhe.imbfactor = @ivfhe_imbfactor;\nivfhe.findbs = @ivfhe_findbs;\n\n%------------------------------------------------------------\nfunction medians = ivfhe_learn_medians (ivfhe, v, idx)\n\nn = size (v, 2);     % number of vectors in the training set\nd = size (v, 1);     % vector dimension\nassert (ivfhe.d == d);\n\n\n%--- compute the median values ---\n\n% Assign and re-organize the vector per cell\n[~, ids] = sort (idx);\nv = v (:, ids);\nh = hist (double(idx), 1:ivfhe.k);\n\n% check if there is at least one value per cell\nnovalcell = find (h == 0);\nlnovalcell = length (novalcell);\nif lnovalcell > 0\n  fprintf ('# Error: found %d cell with no vector to learn the median values\\n', lnovalcell);\nend\n\ncumh = [0 cumsum(h)];\n\n% Fixed random projection\nv = ivfhe.Q * v;\n\n% By default, if no descriptor in the cell, use as median the projected centroid itself\nfor i = 1:ivfhe.k\n  hstart = cumh (i) + 1;\n  hend = cumh (i+1);\n  \n  % no descriptor in cell\n  if hstart > hend,  continue; end\n  \n  vsub = v (:, hstart:hend);\n  medians (:, i) = median (vsub, 2);\nend\n\n\n%------------------------------------------------------------\n% ivfhe_binsign: Compute the binary signature associated with a set of vectors\n% Usage: bs = ivfhe_binsign (v);\n% Usage: bs = ivfhe_binsign (v, idx, w);\n%\n% Parameters:\n%  ivfhe   The inverted file parameter structure\n%  v       The input vector \n%  idx     (optional, computed if not given) the set of quantized indexes associated with\n%  w       (optional, default=1) multiple assignment\nfunction bs = ivfhe_binsign (ivfhe, v, idx, w)\n\n% w not used at this stage\nassert (nargin < 4);\n\n% find the indexes for the coarse quantizer\nif nargin < 3\n  [idx,~] = ivfhe.quantizer (ivfhe.quantizer_params, v);\nend\n\n% project the vectors using the rotation matrix and compute the binary signature\nbs = (ivfhe.medians(:, idx) < (ivfhe.Q * v));\n\n% Convert this binary matrix into a compact format\nbs = uint8 (ivfhe.bin2compactbin*bs);\n\n\n%------------------------------------------------------------\n% ivfhe_add: Add some vector to the index\n%   ivfhe \n%   ids     vectors identifiers\n%   idx     quantization cell\n%   codes:  binary code (int uint8 format)\nfunction [vidx, codes] = ivfhe_add (ivfhe, ids, v, vidx, codes)\n\nif ~exist ('vidx')\n  tic\n  [vidx, dis] = ivfhe.quantizer (ivfhe.quantizer_params, v);\n  if ivfhe.verbose, fprintf ('* Quantization done in %.3f seconds\\n', toc); end\nend\n\nif ~exist ('codes')\n  tic\n  codes = ivfhe.binsign (ivfhe, v, vidx);\n  if ivfhe.verbose, fprintf ('* Binary signatures computed in %.3f seconds\\n', toc); end\nend\n\nyael_ivf ('add', int32(ids), int32(vidx), codes);\n\n\n%------------------------------------------------------------\n% ivfhe_query: Make some queries\n%   ids: vectors identifiers\n%   idx: quantization cell\n%   codes:  binary code (int uint8 format)\nfunction matches = ivfhe_query (ivfhe, ids, v, ht, vidx, codes)\n\nif ~exist ('vidx')\n  [vidx,dis] = ivfhe.quantizer (ivfhe.quantizer_params, v);\nend\n\nif ~exist ('codes')\n  codes = ivfhe.binsign (ivfhe, v, vidx);\nend\n\nmatches = yael_ivf ('queryhe', int32(ids), int32(vidx), codes, ht);\n\n\n%------------------------------------------------------------\n% ivfhe_query_w: alternative implementation to weights the score\n%   ids: vectors identifiers\n%   idx: quantization cell\n%   codes:  binary code (int uint8 format)\nfunction [mids, msc] = ivfhe_queryw (ivfhe, ids, v, ht, vidx, codes)\n\nif ~exist ('vidx')\n  [vidx,dis] = ivfhe.quantizer (ivfhe.quantizer_params, v);\nend\n\nif ~exist ('codes')\n  codes = ivfhe.binsign (ivfhe, v, vidx);\nend\n\nif isfield (ivfhe, 'scoremap') \n  if isfield (ivfhe, 'listw')\n    [mids, msc] = yael_ivf ('queryhew', int32(ids), int32(vidx), codes, ht, ivfhe.scoremap, ivfhe.listw);\n  else\n    [mids, msc] = yael_ivf ('queryhew', int32(ids), int32(vidx), codes, ht, ivfhe.scoremap);\n  end\nelse \n  [mids, msc] = yael_ivf ('queryhew', int32(ids), int32(vidx), codes, ht);\nend\n\n%------------------------------------------------------------\n% ivfhe_getids: Get ids associated with a quantization cell\n%   idx: quantization cell\nfunction ids = ivfhe_getids (idx)\n\nids = yael_ivf ('getids', int32(idx));\n\n\n%------------------------------------------------------------\n% ivfhe_imbfactor: Compute the imbalance factor\nfunction imf = ivfhe_imbfactor ()\n\nimf = yael_ivf ('imbfactor');\n\n\n%------------------------------------------------------------\n% ivfhe_findbs: Get binary signatures associated with a quantization cell\n% and particular ids\nfunction bs = ivfhe_findbs (keys, ids)\n\nbs = yael_ivf ('findbs', keys, int32(ids));\n\n\n%------------------------------------------------------------\nfunction ivfhe = ivfhe_load (fivf_name)\n\n% Parameter file\nivfhe = load ([fivf_name '.mat'], '-mat', 'd', 'k', ...\n              'nbits', 'Q', 'quantizer_params', 'medians', 'bin2compactbin');\nyael_ivf ('free');\nyael_ivf ('load', fivf_name);\n\n% The methods associated with the ivfhe structure\nivfhe.quantizer = @yael_nn;\n\nivfhe.binsign = @ivfhe_binsign;\nivfhe.add = @ivfhe_add;\nivfhe.query = @ivfhe_query;\nivfhe.queryw = @ivfhe_queryw;\nivfhe.getids = @ivfhe_getids;\nivfhe.save = @ivfhe_save;\nivfhe.load = @ivfhe_load;\nivfhe.imbfactor = @ivfhe_imbfactor;\nivfhe.findbs = @ivfhe_findbs;\n\n\n%------------------------------------------------------------\nfunction ivfhe_save (ivfhe, fivf_name, quantizer)\n\n% Parameter file (The matlab part)\nsave ([fivf_name '.mat'], '-mat', '-struct', 'ivfhe', 'd', 'k', ...\n      'nbits', 'Q', 'quantizer_params', 'medians', 'bin2compactbin');\n\n% Content of the inverted file\nyael_ivf ('save', fivf_name);\n\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "load_ext.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/load_ext.m", "size": 2715, "source_encoding": "utf_8", "md5": "2cec807c8443df302331fc4cd049076f", "text": "% Generic way to load files depending on the type (determined by extension)\n%\nfunction [X,Y] = load_ext (filename, nrows, bounds, verbose)\n\n% Retrieve the extension of the file\next = regexp (filename, '\\.(\\w)*$');\nif length(ext) == 0\n  error ('The filename should have an extension');\nend\next = filename (ext:end);\n\nnmin = 1;\nnmax = inf;\n\n% Default\nY = [];\nif ~exist('verbose'), verbose = false; end;\nif ~exist('bounds'),\n  nmin = 1;\n  nmax = inf;\nelseif size (bounds, 2) == 2\n  nmin = bounds(1);\n  nmax = bounds(2) - bounds(1) + 1;\nelseif size (bounds, 2) == 1\n  nmin = 1;\n  nmax = bounds;\nend\n\n\nswitch ext\n case '.siftgeo'\n  if ~exist('nrows'), nrows = 128; end;\n\n  if verbose, fprintf ('< load siftgeo file %s\\n', filename); end\n  if nmax == Inf,   [X, Y] = siftgeo_read (filename, nrows);\n  elseif nmin == 1, [X, Y] = siftgeo_read (filename, nrows, nmax);\n  else error ('### This call of siftgeo_read is not implemented\\');\n  end\n\n case '.fvecs'\n%  assert (nargout == 1);\n  if verbose, fprintf ('< load fvecs file %s\\n', filename); end\n  if nmax == Inf,   X = fvecs_read (filename);\n  elseif nmin == 1, X = fvecs_read (filename, nmax);\n  else              X = fvecs_read (filename, bounds);\n  end\n\n case '.ivecs'\n%  assert (nargout == 1);\n  if verbose, fprintf ('< load ivecs file %s\\n', filename); end\n  if nmax == Inf,      X = ivecs_read (filename);\n  elseif nmin == 1,    X = ivecs_read (filename, nmax);\n  else                 X = ivecs_read (filename, bounds);\n  end\n\n case {'.uint8','.uchar'}\n  assert (nargout == 1);\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('< load uint8 file %s\\n', filename); end\n\n  fid = fopen (filename, 'rb');\n  fseek (fid, (nmin-1) * nrows, 'bof');\n  X = fread (fid, [nrows,nmax], 'uint8=>uint8');\n  fclose (fid);\n\n  case {'.int','.int32'}\n  assert (nargout == 1);\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('< load int file %s\\n', filename); end\n\n  fid = fopen (filename, 'rb');\n  fseek (fid, 4 * (nmin-1) * nrows, 'bof');\n  X = fread (fid, [nrows,nmax], 'int32=>int32');\n  fclose (fid);\n\n  case {'.uint','.uint32'}\n  assert (nargout == 1);\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('< load int file %s\\n', filename); end\n\n  fid = fopen (filename, 'rb');\n  fseek (fid, 4 * (nmin-1) * nrows, 'bof');\n  X = fread (fid, [nrows,nmax], 'uint32=>uint32');\n  fclose (fid);\n\n case {'.float','.f32','.float32','.single'}\n  assert (nargout == 1);\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('< load raw float32 file %s\\n', filename); end\n\n  fid = fopen (filename, 'rb');\n  fseek (fid, 4 * (nmin-1) * nrows, 'bof');\n  X = fread (fid, [nrows,nmax], 'real*4=>single');\n  fclose (fid);\n\n otherwise\n  error ('Unknown extension: %s', ext);\n\nend\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "ivec_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/ivec_read.m", "size": 203, "source_encoding": "utf_8", "md5": "68cb23cdec0879a0f09f0773c5ff6c95", "text": "% This function reads a vector from a file in the libit format\n\nfunction [v,d] = ivec_read (fid)\n\n% first read the vector size\nd = fread (fid, 1, 'int');\n\n% read the elements\nv = fread (fid, d, 'int');\n\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "save_ext.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/save_ext.m", "size": 1473, "source_encoding": "utf_8", "md5": "225eee847e71d3efc0dc3137d172cfd5", "text": "% Generic way to save files depending on the type (determined by extension)\nfunction save_ext (filename, X, verbose)\n\n% Retrieve the extension of the file\next = regexp (filename, '\\.(\\w)*$');\nif length(ext) == 0\n  error ('The filename should have an extension');\nend\next = filename (ext:end);\n\n% Default\nY = [];\nif nargin < 3, verbose = true; end;\n\nswitch ext\n \n case '.fvecs'\n  if verbose, fprintf ('> write fvecs file %s\\n', filename); end\n  fvecs_write (filename, X);\n  \n case '.ivecs'\n  if verbose, fprintf ('> write ivecs file %s\\n', filename); end\n  ivecs_write (filename, X);\n  \n case {'.uint8','.uchar'}\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('> write uint8 file %s\\n', filename); end\n\n  fid = fopen (filename, 'wb');\n  fwrite (fid, X, 'uint8');\n  fclose (fid);\n  \n case {'.int','.int32'}\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('> write int file %s\\n', filename); end\n\n  fid = fopen (filename, 'wb');\n  fwrite (fid, X, 'int32');\n  fclose (fid);\n\n case {'.uint','.uint32'}\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('> write int file %s\\n', filename); end\n\n  fid = fopen (filename, 'wb');\n  fwrite (fid, X, 'uint32');\n  fclose (fid);\n  \n case {'.float','.f32','.float32','.single'}\n  if nargin < 2, nrows = 1; end\n  if verbose, fprintf ('> write raw float32 file %s\\n', filename); end\n  \n  fid = fopen (filename, 'wb');\n  fwrite (fid, X, 'single');\n  fclose (fid);\n\n otherwise\n  error ('Unknown extension: %s', ext);\n\nend\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_fvecs_normalize.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_fvecs_normalize.m", "size": 628, "source_encoding": "utf_8", "md5": "1687902f2d255497b8c79ecbdb31c6b4", "text": "% This function normalize a set of vectors \n% Parameters:\n%   v     the set of vectors to be normalized (column stored)\n%   nr    the norm for which the normalization is performed (Default: Euclidean)\n%\n% Output:\n%   vout  the normalized vector\n%   vnr   the norms of the input vectors\n%\n% Remark: the function return Nan for vectors of null norm\nfunction [vout, vnr] = yael_fvecs_normalize (v, nr)\n\nfprintf ('# Warning: Obsolete. Use yael_vecs_normalize instead\\n');\n\nif nargin < 2, nr = 2; end\n\n% norm of each column\nvnr = (sum (v.^nr)) .^ (-1 / nr);\n\n% sparse multiplication to apply the norm\nvout = bsxfun (@times, v, vnr);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "yael_cross_distances.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/yael_cross_distances.m", "size": 825, "source_encoding": "utf_8", "md5": "af5bf753183dea59cc7b4a0e4b367958", "text": "% Compute all the distances between two sets of vectors\n%\n% Usage: [dis] = dis_cross_distances(q, v, distype, nt)\n%\n% Parameters:\n%   q, v        sets of vectors (1 vector per column)\n%   distype          distance type: 1=L1, \n%                                   2=L2         -> Warning: return the square L2 distance\n%                                   3=chi-square -> Warning: return the square Chi-square\n%                                   4=signed chi-square\n%                                   16=cosine\n%   nt          number of threads (not used for L2 distance)\n%\n% Returned values\n%   dis         the corresponding distances\n%               vectors of q corresponds to row, and columns for v\nfunction dis = yael_cross_distances (q, v, distype, nt)\n\nerror ('This function is available only if compiled (Mex-file)');\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "fvecs_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/fvecs_read.m", "size": 1457, "source_encoding": "utf_8", "md5": "0318bd7f465153c725687e87ddd7c3ca", "text": "% Read a set of vectors stored in the fvec format (int + n * float)\n% The function returns a set of output vector (one vector per column)\n%\n% Syntax: \n%   v = fvecs_read (filename)     -> read all vectors\n%   v = fvecs_read (filename, n)  -> read n vectors \n%   v = fvecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\nfunction v = fvecs_read (filename, bounds)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\nvecsizeof = 1 * 4 + d * 4;\n\n% Get the number of vectrors\nfseek (fid, 0, 1);\na = 1;\nbmax = ftell (fid);\nif bmax == 0\n  v = [];\n  return;\nend\nbmax = floor(bmax / vecsizeof);\nif bmax == 0\n  v = [];\n  return;\nend\nb = bmax;\n\nif nargin >= 2\n  if length (bounds) == 1\n    b = bounds;\n   \n  elseif length (bounds) == 2\n    a = bounds(1);\n    b = bounds(2);    \n  end\nend\n\nassert (a >= 1);\nif b > bmax\n  b = bmax;\nend\n\nif b == 0 | b < a\n  v = [];\n  fclose (fid);\n  return;\nend\n\n% compute the number of vectors that are really read and go in starting positions\nn = b - a + 1;\nfseek (fid, (a - 1) * vecsizeof, -1);\n\n% read n vectors\nv = fread (fid, (d + 1) * n, 'float=>single');\nv = reshape (v, d + 1, n);\n\n% Check if the first column (dimension of the vectors) is correct\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\nv = v (2:end, :);\n  \nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "b2fvecs_read.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/yael/b2fvecs_read.m", "size": 1609, "source_encoding": "utf_8", "md5": "58b9445ea5835d74f5186f5fddef4b21", "text": "% Read a set of vectors stored in the bvec format (int + n * float)\n% The function returns a set of output floating point vector (one vector per column)\n%\n% Syntax: \n%   v = b2fvecs_read (filename)        -> read all vectors\n%   v = b2fvecs_read (filename, n)      -> read n vectors \n%   v = b2fvecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\nfunction v = b2fvecs_read (filename, bounds)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n  error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\n\nvecsizeof = 1 * 4 + d;\n\n% Get the number of vectrors\nfseek (fid, 0, 1);\na = 1;\nbmax = ftell (fid) / vecsizeof;\nb = bmax;\n\nif nargin >= 2\n  if length (bounds) == 1\n    b = bounds;\n   \n  elseif length (bounds) == 2\n    a = bounds(1);\n    b = bounds(2);    \n  end\nend\n\nassert (a >= 1);\nif b > bmax\n  b = bmax;\nend\n\nif b == 0 | b < a\n  v = [];\n  fclose (fid);\n  return;\nend\n\n% compute the number of vectors that are really read and go in starting positions\nn = b - a + 1;\na\n(a-1)*vecsizeof\n\nfseek (fid, (a - 1) * vecsizeof, -1);\n\nfprintf ('b2fvecs_read -> pos=%d\\n', ftell (fid));\n\n% read n vectors\nv = fread (fid, (d + 4) * n, 'uint8=>single');\nv = reshape (v, d + 4, n);\n\n\n% Check if the first column (dimension of the vectors) is correct\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\nassert (sum (v (2, 2:end) == v(2, 1)) == n - 1);\nassert (sum (v (3, 2:end) == v(3, 1)) == n - 1);\nassert (sum (v (4, 2:end) == v(4, 1)) == n - 1);\nv = v (5:end, :);\n  \nfclose (fid);\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "triemb_learn.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/triemb/triemb_learn.m", "size": 1390, "source_encoding": "utf_8", "md5": "fc74130ad165ccb5eeca3bf1abaa78b1", "text": "% Learn the embedding parameters for triangulation embedding\n%\n% Usage:  [Xmean, eigvec, eigval] = triemb_learn (vtrain, C, dout)\n%   vtrain    vector set for learning\n%   C         centroids\n%   dout      request output dimensionality\nfunction [Xmean, eigvec, eigval] = triemb_learn (vtrain, C)\n\nnlearn = size (vtrain, 2);     % number of input vectors\nk = size (C, 2);               % number of support centroids\nd = size (vtrain, 1);          % input vector dimensionality\nD = k * d;                     % output dimensionality\ndout = D;\n\nslicesize = 10000;\nnslices = nlearn / slicesize;\nXmean = zeros (D, 1, 'single');\n\n% Compute mean embedded vector\nXsum = zeros (D, 1);\nfor i=1:slicesize:nlearn\n  endi = min(i+slicesize-1, nlearn);\n  X = triemb_res (vtrain (:,i:endi), C, Xmean);\n  Xsum = Xsum + sum (X, 2);\nend\nXmean = Xsum / nlearn;\n\n% Compute whitening parameters\ncovD = zeros(d * k);\nfor i=1:slicesize:nlearn\n  endi = min(i+slicesize-1, nlearn);\n  X = triemb_res (vtrain (:,i:endi), C, Xmean);\n  covD = covD + X * X';\nend\nfprintf ('\\n');\n\n% Eigen-decomposition\nif 3 * dout < D\n  eigopts.issym = true;\n  eigopts.isreal = true;\n  eigopts.tol = eps;\n  eigopts.disp = 0;\n\n  [eigvec, eigval] = eigs (double(covD), dout, 'LM', eigopts);\nelse\n  [eigvec, eigval] = eig (covD);\n  eigvec = eigvec (:, end:-1:end-dout+1);\n  eigval = diag (eigval);\n  eigval = eigval (end:-1:end-dout+1);\nend\n"}
+{"plateform": "github", "repo_name": "hnanhtuan/selectiveConvFeature-master", "name": "triemb_res.m", "ext": ".m", "path": "selectiveConvFeature-master/tools/triemb/triemb_res.m", "size": 413, "source_encoding": "utf_8", "md5": "febf3e798c5d4b3f7cd8d098445e011b", "text": "% Usage: Y = triemb_res (X, C, Xm)\n% \n% Perform the triangulation embedding \n%    X   input vectors\n%    C   centroids\n%    Xm  mean to be removed \nfunction Y = triemb_res (X, C,  Xm)\n\nn = size(X, 2);\nd = size(X, 1);\nkc = size(C, 2);\nD = d * kc;\n\nY = bsxfun (@minus, repmat(X, kc, 1), C(:));\n\nfor j = 1:kc\n  idxj = 1+(j-1)*d : j*d;\n  Y(idxj, :) = yael_vecs_normalize (Y(idxj,:));\nend\n\nY = bsxfun (@minus, Y, Xm);\n"}
+{"plateform": "github", "repo_name": "Dicksonlab/MateBook-master", "name": "xlswriteonly.m", "ext": ".m", "path": "MateBook-master/scripts/moonwalk_20130106/xlswriteonly.m", "size": 11637, "source_encoding": "utf_8", "md5": "24492874563c3aba69017ed30e90f2f6", "text": "function [success,message]=xlswriteonly(xlshandle,data,sheet,range)\n% XLSWRITE Stores numeric array or cell array in Excel workbook.\n%   [SUCCESS,MESSAGE]=XLSWRITE(FILE,ARRAY,SHEET,RANGE) writes ARRAY to the Excel\n%   workbook, FILE, into the area, RANGE in the worksheet specified in SHEET.\n%   FILE and ARRAY must be specified. If either FILE or ARRAY is empty, an\n%   error is thrown and XLSWRITE terminates. The first worksheet of the\n%   workbook is the default. If SHEET does not exist, a new sheet is added at\n%   the end of the worksheet collection. If SHEET is an index larger than the\n%   number of worksheets, new sheets are appended until the number of worksheets\n%   in the workbook equals SHEET. The size defined  by the  RANGE should fit the\n%   size of ARRAY or contain only the first cell, e.g. 'A2'. If RANGE is larger\n%   than the size of ARRAY, Excel will fill the remainder of the region with\n%   #N/A. If RANGE is smaller than the size of ARRAY, only the sub-array that\n%   fits into RANGE will be written to FILE. The success of the operation is\n%   returned in SUCCESS and any accompanying message, in MESSAGE. On error,\n%   MESSAGE shall be a struct, containing the error message and message ID.\n%   See NOTE 1.\n%\n%   To specify SHEET or RANGE, but not both, you can call XLSWRITE with\n%   just three inputs. If the third input is a string that includes a colon \n%   character (e.g., 'D2:H4'), it specifies RANGE. If it is not (e.g., \n%   'SALES'), it specifies the worksheet to write to. See the next two \n%   syntaxes below.\n%\n%   [SUCCESS,MESSAGE]=XLSWRITE(FILE,ARRAY,SHEET) writes ARRAY to the Excel\n%   workbook, FILE, starting at cell A1 and using SHEET as described above.\n%\n%   [SUCCESS,MESSAGE]=XLSWRITE(FILE,ARRAY,RANGE) writes ARRAY to the Excel\n%   workbook, FILE, in the first worksheet and using RANGE as described above.\n%\n%   [SUCCESS,MESSAGE]=XLSWRITE(FILE,ARRAY) writes ARRAY to the Excel\n%   workbook, FILE, starting at cell A1 of the first worksheet. The return\n%   values are as for the above example.\n%\n%   XLSWRITE ARRAY FILE, is the command line version of the above example.\n%\n%   INPUT PARAMETERS:\n%       file:   string defining the workbook file to write to.\n%               Default directory is pwd; default extension 'xls'.\n%       array:  m x n numeric array or cell array.\n%       sheet:  string defining worksheet name;\n%               double, defining worksheet index.\n%       range:  string defining data region in worksheet, using the Excel\n%               'A1' notation.\n%\n%   RETURN PARAMETERS:\n%       SUCCESS: logical scalar.\n%       MESSAGE: struct containing message field and message_id field.\n%\n%   EXAMPLES:\n%\n%   SUCCESS = XLSWRITE('c:\\matlab\\work\\myworkbook.xls',A,'A2:C4') will write A to\n%   the workbook file, myworkbook.xls, and attempt to fit the elements of A into\n%   the rectangular worksheet region, A2:C4. On success, SUCCESS will contain true,\n%   while on failure, SUCCESS will contain false.\n%\n%   NOTE 1: The above functionality depends upon Excel as a COM server. In\n%   absence of Excel, ARRAY shall be written as a text file in CSV format. In\n%   this mode, the SHEET and RANGE arguments shall be ignored.\n%\n% See also XLSREAD, CSVWRITE.\n%\n\n%   Copyright 1984-2008 The MathWorks, Inc.\n%   $Revision: 1.1.6.17.4.1 $  $Date: 2010/06/24 19:34:38 $\n%==============================================================================\n% Set default values.\nSheet1 = 1;\n\nif nargin < 3\n    sheet = Sheet1;\n    range = '';\nelseif nargin < 4\n    range = '';\nend\n\nif nargout > 0\n    success = true;\n    message = struct('message',{''},'identifier',{''});\nend\n\n% Handle input.\ntry\n    % Check for empty input data\n    if isempty(data)\n        error('MATLAB:xlswrite:EmptyInput','Input array is empty.');\n    end\n\n    % Check for N-D array input data\n    if ndims(data)>2\n        error('MATLAB:xlswrite:InputDimension',...\n            'Dimension of input array cannot be higher than two.');\n    end\n\n    % Check class of input data\n    if ~(iscell(data) || isnumeric(data) || ischar(data)) && ~islogical(data)\n        error('MATLAB:xlswrite:InputClass',...\n            'Input data must be a numeric, cell, or logical array.');\n    end\n\n\n    % convert input to cell array of data.\n     if iscell(data)\n        A=data;\n     else\n         A=num2cell(data);\n     end\n\n    if nargin > 2\n        % Verify class of sheet parameter.\n        if ~(ischar(sheet) || (isnumeric(sheet) && sheet > 0))\n            error('MATLAB:xlswrite:InputClass',...\n                'Sheet argument must be a string or a whole number greater than 0.');\n        end\n        if isempty(sheet)\n            sheet = Sheet1;\n        end\n        % parse REGION into sheet and range.\n        % Parse sheet and range strings.\n        if ischar(sheet) && ~isempty(strfind(sheet,':'))\n            range = sheet; % only range was specified.\n            sheet = Sheet1;% Use default sheet.\n        elseif ~ischar(range)\n            error('MATLAB:xlswrite:InputClass',...\n                'Range argument must be a string in Excel A1 notation.');\n        end\n    end\n\ncatch exception\n    if ~isempty(nargchk(2,4,nargin))\n        error('MATLAB:xlswrite:InputArguments',nargchk(2,4,nargin));\n    else\n        success = false;\n        message = exceptionHandler(nargout, exception);\n    end\n    return;\nend\n%------------------------------------------------------------------------------\n    Excel = xlshandle.Excel;\n%------------------------------------------------------------------------------\ntry\n    % Construct range string\n    if isempty(strfind(range,':'))\n        % Range was partly specified or not at all. Calculate range.\n        [m,n] = size(A);\n        range = calcrange(range,m,n);\n    end\ncatch exception\n    success = false;\n    message = exceptionHandler(nargout, exception);\n    return;\nend\n\n%------------------------------------------------------------------------------\n            try % select region.\n                % Activate indicated worksheet.\n                message = activate_sheet(Excel,sheet);\n\n                % Select range in worksheet.\n                Select(Range(Excel,sprintf('%s',range)));\n\n            catch exceptionInner % Throw data range error.\n                throw(MException('MATLAB:xlswrite:SelectDataRange', sprintf('Excel returned: %s.', exceptionInner.message))); \n            end\n\n            % Export data to selected region.\n            set(Excel.selection,'Value',A);\nend\n%--------------------------------------------------------------------------\nfunction message = activate_sheet(Excel,Sheet)\n% Activate specified worksheet in workbook.\n\n% Initialise worksheet object\nWorkSheets = Excel.sheets;\nmessage = struct('message',{''},'identifier',{''});\n\n% Get name of specified worksheet from workbook\ntry\n    TargetSheet = get(WorkSheets,'item',Sheet);\ncatch exception  %#ok<NASGU>\n    % Worksheet does not exist. Add worksheet.\n    TargetSheet = addsheet(WorkSheets,Sheet);\n    warning('MATLAB:xlswrite:AddSheet','Added specified worksheet.');\n    if nargout > 0\n        [message.message,message.identifier] = lastwarn;\n    end\nend\n\n% activate worksheet\nActivate(TargetSheet);\nend\n%------------------------------------------------------------------------------\nfunction newsheet = addsheet(WorkSheets,Sheet)\n% Add new worksheet, Sheet into worsheet collection, WorkSheets.\n\nif isnumeric(Sheet)\n    % iteratively add worksheet by index until number of sheets == Sheet.\n    while WorkSheets.Count < Sheet\n        % find last sheet in worksheet collection\n        lastsheet = WorkSheets.Item(WorkSheets.Count);\n        newsheet = WorkSheets.Add([],lastsheet);\n    end\nelse\n    % add worksheet by name.\n    % find last sheet in worksheet collection\n    lastsheet = WorkSheets.Item(WorkSheets.Count);\n    newsheet = WorkSheets.Add([],lastsheet);\nend\n% If Sheet is a string, rename new sheet to this string.\nif ischar(Sheet)\n    set(newsheet,'Name',Sheet);\nend\nend\n%------------------------------------------------------------------------------\nfunction range = calcrange(range,m,n)\n% Calculate full target range, in Excel A1 notation, to include array of size\n% m x n\n\nrange = upper(range);\ncols = isletter(range);\nrows = ~cols;\n% Construct first row.\nif ~any(rows)\n    firstrow = 1; % Default row.\nelse\n    firstrow = str2double(range(rows)); % from range input.\nend\n% Construct first column.\nif ~any(cols)\n    firstcol = 'A'; % Default column.\nelse\n    firstcol = range(cols); % from range input.\nend\ntry\n    lastrow = num2str(firstrow+m-1);   % Construct last row as a string.\n    firstrow = num2str(firstrow);      % Convert first row to string image.\n    lastcol = dec2base27(base27dec(firstcol)+n-1); % Construct last column.\n\n    range = [firstcol firstrow ':' lastcol lastrow]; % Final range string.\ncatch exception \n    error('MATLAB:xlswrite:CalculateRange', 'Invalid data range: %s.', range);\nend\nend\n%----------------------------------------------------------------------\nfunction string = index_to_string(index, first_in_range, digits)\n\nletters = 'A':'Z';\nworking_index = index - first_in_range;\noutputs = cell(1,digits);\n[outputs{1:digits}] = ind2sub(repmat(26,1,digits), working_index);\nstring = fliplr(letters([outputs{:}]));\nend\n%----------------------------------------------------------------------\nfunction [digits first_in_range] = calculate_range(num_to_convert)\n\ndigits = 1;\nfirst_in_range = 0;\ncurrent_sum = 26;\nwhile num_to_convert > current_sum\n    digits = digits + 1;\n    first_in_range = current_sum;\n    current_sum = first_in_range + 26.^digits;\nend\nend\n%------------------------------------------------------------------------------\nfunction s = dec2base27(d)\n\n%   DEC2BASE27(D) returns the representation of D as a string in base 27,\n%   expressed as 'A'..'Z', 'AA','AB'...'AZ', and so on. Note, there is no zero\n%   digit, so strictly we have hybrid base26, base27 number system.  D must be a\n%   negative integer bigger than 0 and smaller than 2^52.\n%\n%   Examples\n%       dec2base(1) returns 'A'\n%       dec2base(26) returns 'Z'\n%       dec2base(27) returns 'AA'\n%-----------------------------------------------------------------------------\n\nd = d(:);\nif d ~= floor(d) || any(d(:) < 0) || any(d(:) > 1/eps)\n    error('MATLAB:xlswrite:Dec2BaseInput',...\n        'D must be an integer, 0 <= D <= 2^52.');\nend\n[num_digits begin] = calculate_range(d);\ns = index_to_string(d, begin, num_digits);\nend\n%------------------------------------------------------------------------------\nfunction d = base27dec(s)\n%   BASE27DEC(S) returns the decimal of string S which represents a number in\n%   base 27, expressed as 'A'..'Z', 'AA','AB'...'AZ', and so on. Note, there is\n%   no zero so strictly we have hybrid base26, base27 number system.\n%\n%   Examples\n%       base27dec('A') returns 1\n%       base27dec('Z') returns 26\n%       base27dec('IV') returns 256\n%-----------------------------------------------------------------------------\n\nif length(s) == 1\n   d = s(1) -'A' + 1;\nelse\n    cumulative = 0;\n    for i = 1:numel(s)-1\n        cumulative = cumulative + 26.^i;\n    end\n    indexes_fliped = 1 + s - 'A';\n    indexes = fliplr(indexes_fliped);\n    indexes_in_cells = mat2cell(indexes, 1, ones(1,numel(indexes)));\n    d = cumulative + sub2ind(repmat(26, 1,numel(s)), indexes_in_cells{:});\nend\nend\n%-------------------------------------------------------------------------------\n\nfunction messageStruct = exceptionHandler(nArgs, exception)\n    if nArgs == 0\n        throwAsCaller(exception);  \t   \n    else\n        messageStruct.message = exception.message;       \n        messageStruct.identifier = exception.identifier;\n    end\nend\n"}
+{"plateform": "github", "repo_name": "Dicksonlab/MateBook-master", "name": "xlsopen.m", "ext": ".m", "path": "MateBook-master/scripts/moonwalk_20130106/xlsopen.m", "size": 4622, "source_encoding": "utf_8", "md5": "1537b40528e736ab3fb72dd4f606b876", "text": "function xlshandle=xlsopen(file)\n% XLSOPEN Opens an Excel workbook or creates it if it doesn't exist.\n%==============================================================================\ntry\n    % handle requested Excel workbook filename.\n    if ~isempty(file)\n        if ~ischar(file)\n            error('MATLAB:xlswrite:InputClass','Filename must be a string.');\n        end\n        % check for wildcards in filename\n        if any(findstr('*', file))\n            error('MATLAB:xlswrite:FileName', 'Filename must not contain *.');\n        end\n        [Directory,file,ext]=fileparts(file);\n        if isempty(ext) % add default Excel extension;\n            ext = '.xls';\n        end\n        file = abspath(fullfile(Directory,[file ext]));\n        [a1 a2] = fileattrib(file);\n        if a1 && ~(a2.UserWrite == 1)\n            error('MATLAB:xlswrite:FileReadOnly', 'File cannot be read-only.');\n        end\n\telse\n        error('MATLAB:xlswrite:EmptyFileName','Filename is empty.');\n    end\ncatch exception\n    if ~isempty(nargchk(2,4,nargin))\n        error('MATLAB:xlswrite:InputArguments',nargchk(2,4,nargin));\n    else\n        success = false;\n        message = exceptionHandler(nargout, exception);\n    end\n    return;\nend\n%------------------------------------------------------------------------------\n% Attempt to start Excel as ActiveX server.\ntry\n\txlshandle.Excel = actxserver('Excel.Application');\ncatch exception\n    error('MATLAB:xlswrite:NoCOMServer', 'Could not start Excel server for export.');\nend\n%------------------------------------------------------------------------------\ntry\n    bCreated = ~exist(file,'file');\n    ExecuteWrite;\ncatch exception\n    if (bCreated && exist(file, 'file') == 2)\n        delete(file);\n    end\n    success = false;\n    message = exceptionHandler(nargout, exception);\nend        \n    function ExecuteWrite\n            if bCreated\n                % Create new workbook.  \n                %This is in place because in the presence of a Google Desktop\n                %Search installation, calling Add, and then SaveAs after adding data,\n                %to create a new Excel file, will leave an Excel process hanging.  \n                %This workaround prevents it from happening, by creating a blank file,\n                %and saving it.  It can then be opened with Open.\n                ExcelWorkbook = xlshandle.Excel.workbooks.Add;\n                switch ext\n                    case '.xls' %xlExcel8 or xlWorkbookNormal\n                       xlFormat = -4143;\n                    case '.xlsb' %xlExcel12\n                       xlFormat = 50;\n                    case '.xlsx' %xlOpenXMLWorkbook\n                       xlFormat = 51;\n                    case '.xlsm' %xlOpenXMLWorkbookMacroEnabled \n                       xlFormat = 52;\n                    otherwise\n                       xlFormat = -4143;\n                end\n                ExcelWorkbook.SaveAs(file, xlFormat);\n                ExcelWorkbook.Close(false);\n            end\n\n            %Open file\n            xlshandle.ExcelWorkbook = xlshandle.Excel.workbooks.Open(file);\n            if xlshandle.ExcelWorkbook.ReadOnly ~= 0\n                %This means the file is probably open in another process.\n                error('MATLAB:xlswrite:LockedFile', 'The file %s is not writable.  It may be locked by another process.', file);\n            end\n    end\nend\n%------------------------------------------------------------------------------\nfunction [absolutepath]=abspath(partialpath)\n\n% parse partial path into path parts\n[pathname filename ext] = fileparts(partialpath);\n% no path qualification is present in partial path; assume parent is pwd, except\n% when path string starts with '~' or is identical to '~'.\nif isempty(pathname) && isempty(strmatch('~',partialpath))\n    Directory = pwd;\nelseif isempty(regexp(partialpath,'(.:|\\\\\\\\)','once')) && ...\n        isempty(strmatch('/',partialpath)) && ...\n        isempty(strmatch('~',partialpath));\n    % path did not start with any of drive name, UNC path or '~'.\n    Directory = [pwd,filesep,pathname];\nelse\n    % path content present in partial path; assume relative to current directory,\n    % or absolute.\n    Directory = pathname;\nend\n\n% construct absulute filename\nabsolutepath = fullfile(Directory,[filename,ext]);\nend\n%-------------------------------------------------------------------------------\nfunction messageStruct = exceptionHandler(nArgs, exception)\n    if nArgs == 0\n        throwAsCaller(exception);  \t   \n    else\n        messageStruct.message = exception.message;       \n        messageStruct.identifier = exception.identifier;\n    end\nend\n\n"}
+{"plateform": "github", "repo_name": "Dicksonlab/MateBook-master", "name": "getExcelCol.m", "ext": ".m", "path": "MateBook-master/scripts/moonwalk_20130106/getExcelCol.m", "size": 1517, "source_encoding": "utf_8", "md5": "70cced69f70e99027562811ed20aa567", "text": "function s = getExcelCol(d)\r\n% taken from xlswrite.m:\r\n%   DEC2BASE27(D) returns the representation of D as a string in base 27,\r\n%   expressed as 'A'..'Z', 'AA','AB'...'AZ', and so on. Note, there is no zero\r\n%   digit, so strictly we have hybrid base26, base27 number system.  D must be a\r\n%   negative integer bigger than 0 and smaller than 2^52.\r\n%\r\n%   Examples\r\n%       dec2base(1) returns 'A'\r\n%       dec2base(26) returns 'Z'\r\n%       dec2base(27) returns 'AA'\r\n%-----------------------------------------------------------------------------\r\nfunction string = index_to_string(index, first_in_range, digits)\r\n\r\nletters = 'A':'Z';\r\nworking_index = index - first_in_range;\r\noutputs = cell(1,digits);\r\n[outputs{1:digits}] = ind2sub(repmat(26,1,digits), working_index);\r\nstring = fliplr(letters([outputs{:}]));\r\nend\r\n%----------------------------------------------------------------------\r\nfunction [digits first_in_range] = calculate_range(num_to_convert)\r\n\r\ndigits = 1;\r\nfirst_in_range = 0;\r\ncurrent_sum = 26;\r\nwhile num_to_convert > current_sum\r\n    digits = digits + 1;\r\n    first_in_range = current_sum;\r\n    current_sum = first_in_range + 26.^digits;\r\nend\r\nend\r\n%------------------------------------------------------------------------------\r\n\r\n\r\nd = d(:);\r\nif d ~= floor(d) || any(d(:) < 0) || any(d(:) > 1/eps)\r\n    error('MATLAB:xlswrite:Dec2BaseInput',...\r\n        'D must be an integer, 0 <= D <= 2^52.');\r\nend\r\n[num_digits begin] = calculate_range(d);\r\ns = index_to_string(d, begin, num_digits);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "xalinchi/Deep-Learning-WiFi-CSI-master", "name": "get_scaled_csi.m", "ext": ".m", "path": "Deep-Learning-WiFi-CSI-master/matlab code/data segementation/get_scaled_csi.m", "size": 1868, "source_encoding": "utf_8", "md5": "21129db6cce7a77c81c687e679b1f361", "text": "%GET_SCALED_CSI Converts a CSI struct to a channel matrix H.\n%\n% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>\n%\nfunction ret = get_scaled_csi(csi_st)\n    % Pull out CSI\n    csi = csi_st.csi;\n\n    % Calculate the scale factor between normalized CSI and RSSI (mW)\n    csi_sq = csi .* conj(csi);\n    csi_pwr = sum(csi_sq(:));\n    rssi_pwr = dbinv(get_total_rss(csi_st));\n    %   Scale CSI -> Signal power : rssi_pwr / (mean of csi_pwr)\n    scale = rssi_pwr / (csi_pwr / 30);\n\n    % Thermal noise might be undefined if the trace was\n    % captured in monitor mode.\n    % ... If so, set it to -92\n    if (csi_st.noise == -127)\n        noise_db = -92;\n    else\n        noise_db = csi_st.noise;\n    end\n    %thermal_noise_pwr = dbinv(noise_db);\n    thermal_noise_pwr=0;\n    \n    % Quantization error: the coefficients in the matrices are\n    % 8-bit signed numbers, max 127/-128 to min 0/1. Given that Intel\n    % only uses a 6-bit ADC, I expect every entry to be off by about\n    % +/- 1 (total across real & complex parts) per entry.\n    %\n    % The total power is then 1^2 = 1 per entry, and there are\n    % Nrx*Ntx entries per carrier. We only want one carrier's worth of\n    % error, since we only computed one carrier's worth of signal above.\n    quant_error_pwr = scale * (csi_st.Nrx * csi_st.Ntx);\n\n    % Total noise and error power\n    total_noise_pwr = thermal_noise_pwr + quant_error_pwr;\n\n    % Ret now has units of sqrt(SNR) just like H in textbooks\n    ret = csi * sqrt(scale / total_noise_pwr);\n    if csi_st.Ntx == 2\n        ret = ret * sqrt(2);\n    elseif csi_st.Ntx == 3\n        % Note: this should be sqrt(3)~ 4.77 dB. But, 4.5 dB is how\n        % Intel (and some other chip makers) approximate a factor of 3\n        %\n        % You may need to change this if your card does the right thing.\n        ret = ret * sqrt(dbinv(4.5));\n    end\nend\n"}
+{"plateform": "github", "repo_name": "xalinchi/Deep-Learning-WiFi-CSI-master", "name": "get_total_rss.m", "ext": ".m", "path": "Deep-Learning-WiFi-CSI-master/matlab code/data segementation/get_total_rss.m", "size": 587, "source_encoding": "utf_8", "md5": "9c5a5f37a01f792ebe614efd783dd22a", "text": "%GET_TOTAL_RSS Calculates the Received Signal Strength (RSS) in dBm from\n% a CSI struct.\n%\n% (c) 2011 Daniel Halperin <dhalperi@cs.washington.edu>\n%\nfunction ret = get_total_rss(csi_st)\n   nargoutchk(1,1,nargin);\n\n    % Careful here: rssis could be zero\n    rssi_mag = 0;\n    if csi_st.rssi_a ~= 0\n        rssi_mag = rssi_mag + dbinv(csi_st.rssi_a);\n    end\n    if csi_st.rssi_b ~= 0\n        rssi_mag = rssi_mag + dbinv(csi_st.rssi_b);\n    end\n    if csi_st.rssi_c ~= 0\n        rssi_mag = rssi_mag + dbinv(csi_st.rssi_c);\n    end\n    \n    ret = db(rssi_mag, 'pow') - 44 - csi_st.agc;\nend"}
+{"plateform": "github", "repo_name": "xalinchi/Deep-Learning-WiFi-CSI-master", "name": "read_bf_file.m", "ext": ".m", "path": "Deep-Learning-WiFi-CSI-master/matlab code/data segementation/read_bf_file.m", "size": 2573, "source_encoding": "utf_8", "md5": "d25e3fc0ffb85dc441ad47a28aac2965", "text": "%READ_BF_FILE Reads in a file of beamforming feedback logs.\n%   This version uses the *C* version of read_bfee, compiled with\n%   MATLAB's MEX utility.\n%\n% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>\n%\nfunction ret = read_bf_file(filename)\n%% Input check\nnargoutchk(1,1,nargin);\n\n%% Open file\nf = fopen(filename, 'rb');\nif (f < 0)\n    error('Couldn''t open file %s', filename);\n    return;\nend\n\nstatus = fseek(f, 0, 'eof');\nif status ~= 0\n    [msg, errno] = ferror(f);\n    error('Error %d seeking: %s', errno, msg);\n    fclose(f);\n    return;\nend\nlen = ftell(f);\n\nstatus = fseek(f, 0, 'bof');\nif status ~= 0\n    [msg, errno] = ferror(f);\n    error('Error %d seeking: %s', errno, msg);\n    fclose(f);\n    return;\nend\n\n%% Initialize variables\nret = cell(ceil(len/95),1);     % Holds the return values - 1x1 CSI is 95 bytes big, so this should be upper bound\ncur = 0;                        % Current offset into file\ncount = 0;                      % Number of records output\nbroken_perm = 0;                % Flag marking whether we've encountered a broken CSI yet\ntriangle = [1 3 6];             % What perm should sum to for 1,2,3 antennas\n\n%% Process all entries in file\n% Need 3 bytes -- 2 byte size field and 1 byte code\nwhile cur < (len - 3)\n    % Read size and code\n    field_len = fread(f, 1, 'uint16', 0, 'ieee-be');\n    code = fread(f,1);\n    cur = cur+3;\n    \n    % If unhandled code, skip (seek over) the record and continue\n    if (code == 187) % get beamforming or phy data\n        bytes = fread(f, field_len-1, 'uint8=>uint8');\n        cur = cur + field_len - 1;\n        if (length(bytes) ~= field_len-1)\n            fclose(f);\n            return;\n        end\n    else % skip all other info\n        fseek(f, field_len - 1, 'cof');\n        cur = cur + field_len - 1;\n        continue;\n    end\n    \n    if (code == 187) %hex2dec('bb')) Beamforming matrix -- output a record\n        count = count + 1;\n        ret{count} = read_bfee(bytes);\n        \n        perm = ret{count}.perm;\n        Nrx = ret{count}.Nrx;\n        if Nrx == 1 % No permuting needed for only 1 antenna\n            continue;\n        end\n        if sum(perm) ~= triangle(Nrx) % matrix does not contain default values\n            if broken_perm == 0\n                broken_perm = 1;\n                fprintf('WARN ONCE: Found CSI (%s) with Nrx=%d and invalid perm=[%s]\\n', filename, Nrx, int2str(perm));\n            end\n        else\n            ret{count}.csi(:,perm(1:Nrx),:) = ret{count}.csi(:,1:Nrx,:);\n        end\n    end\nend\nret = ret(1:count);\n\n%% Close file\nfclose(f);\nend\n"}
+{"plateform": "github", "repo_name": "xalinchi/Deep-Learning-WiFi-CSI-master", "name": "dbinv.m", "ext": ".m", "path": "Deep-Learning-WiFi-CSI-master/matlab code/data segementation/dbinv.m", "size": 145, "source_encoding": "utf_8", "md5": "f3e0b99630ef3ad7fcc8ca3ac3daade3", "text": "%DBINV Convert from decibels.\n%\n% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>\n%\nfunction ret = dbinv(x)\n    ret = 10.^(x/10);\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex2/machine-learning-ex2/ex2/submit.m", "size": 1605, "source_encoding": "utf_8", "md5": "9b63d386e9bd7bcca66b1a3d2fa37579", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'logistic-regression';\n  conf.itemName = 'Logistic Regression';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'sigmoid.m' }, ...\n      'Sigmoid Function', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'costFunction.m' }, ...\n      'Logistic Regression Cost', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'costFunction.m' }, ...\n      'Logistic Regression Gradient', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'predict.m' }, ...\n      'Predict', ...\n    }, ...\n    { ...\n      '5', ...\n      { 'costFunctionReg.m' }, ...\n      'Regularized Logistic Regression Cost', ...\n    }, ...\n    { ...\n      '6', ...\n      { 'costFunctionReg.m' }, ...\n      'Regularized Logistic Regression Gradient', ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId, auxstring)\n  % Random Test Cases\n  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];\n  y = sin(X(:,1) + X(:,2)) > 0;\n  if partId == '1'\n    out = sprintf('%0.5f ', sigmoid(X));\n  elseif partId == '2'\n    out = sprintf('%0.5f ', costFunction([0.25 0.5 -0.5]', X, y));\n  elseif partId == '3'\n    [cost, grad] = costFunction([0.25 0.5 -0.5]', X, y);\n    out = sprintf('%0.5f ', grad);\n  elseif partId == '4'\n    out = sprintf('%0.5f ', predict([0.25 0.5 -0.5]', X));\n  elseif partId == '5'\n    out = sprintf('%0.5f ', costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1));\n  elseif partId == '6'\n    [cost, grad] = costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1);\n    out = sprintf('%0.5f ', grad);\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex2/machine-learning-ex2/ex2/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex2/machine-learning-ex2/ex2/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex2/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex2/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex2/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex4/ex4/submit.m", "size": 1635, "source_encoding": "utf_8", "md5": "ae9c236c78f9b5b09db8fbc2052990fc", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'neural-network-learning';\n  conf.itemName = 'Neural Networks Learning';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'nnCostFunction.m' }, ...\n      'Feedforward and Cost Function', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'nnCostFunction.m' }, ...\n      'Regularized Cost Function', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'sigmoidGradient.m' }, ...\n      'Sigmoid Gradient', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'nnCostFunction.m' }, ...\n      'Neural Network Gradient (Backpropagation)', ...\n    }, ...\n    { ...\n      '5', ...\n      { 'nnCostFunction.m' }, ...\n      'Regularized Gradient', ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId, auxstring)\n  % Random Test Cases\n  X = reshape(3 * sin(1:1:30), 3, 10);\n  Xm = reshape(sin(1:32), 16, 2) / 5;\n  ym = 1 + mod(1:16,4)';\n  t1 = sin(reshape(1:2:24, 4, 3));\n  t2 = cos(reshape(1:2:40, 4, 5));\n  t  = [t1(:) ; t2(:)];\n  if partId == '1'\n    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);\n    out = sprintf('%0.5f ', J);\n  elseif partId == '2'\n    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);\n    out = sprintf('%0.5f ', J);\n  elseif partId == '3'\n    out = sprintf('%0.5f ', sigmoidGradient(X));\n  elseif partId == '4'\n    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);\n    out = sprintf('%0.5f ', J);\n    out = [out sprintf('%0.5f ', grad)];\n  elseif partId == '5'\n    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);\n    out = sprintf('%0.5f ', J);\n    out = [out sprintf('%0.5f ', grad)];\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex4/ex4/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex4/ex4/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/submit.m", "size": 1318, "source_encoding": "utf_8", "md5": "bfa0b4ffb8a7854d8e84276e91818107", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'support-vector-machines';\n  conf.itemName = 'Support Vector Machines';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'gaussianKernel.m' }, ...\n      'Gaussian Kernel', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'dataset3Params.m' }, ...\n      'Parameters (C, sigma) for Dataset 3', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'processEmail.m' }, ...\n      'Email Preprocessing', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'emailFeatures.m' }, ...\n      'Email Feature Extraction', ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId, auxstring)\n  % Random Test Cases\n  x1 = sin(1:10)';\n  x2 = cos(1:10)';\n  ec = 'the quick brown fox jumped over the lazy dog';\n  wi = 1 + abs(round(x1 * 1863));\n  wi = [wi ; wi];\n  if partId == '1'\n    sim = gaussianKernel(x1, x2, 2);\n    out = sprintf('%0.5f ', sim);\n  elseif partId == '2'\n    load('ex6data3.mat');\n    [C, sigma] = dataset3Params(X, y, Xval, yval);\n    out = sprintf('%0.5f ', C);\n    out = [out sprintf('%0.5f ', sigma)];\n  elseif partId == '3'\n    word_indices = processEmail(ec);\n    out = sprintf('%d ', word_indices);\n  elseif partId == '4'\n    x = emailFeatures(wi);\n    out = sprintf('%d ', x);\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "porterStemmer.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/porterStemmer.m", "size": 9902, "source_encoding": "utf_8", "md5": "7ed5acd925808fde342fc72bd62ebc4d", "text": "function stem = porterStemmer(inString)\n% Applies the Porter Stemming algorithm as presented in the following\n% paper:\n% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,\n%   no. 3, pp 130-137\n\n% Original code modeled after the C version provided at:\n% http://www.tartarus.org/~martin/PorterStemmer/c.txt\n\n% The main part of the stemming algorithm starts here. b is an array of\n% characters, holding the word to be stemmed. The letters are in b[k0],\n% b[k0+1] ending at b[k]. In fact k0 = 1 in this demo program (since\n% matlab begins indexing by 1 instead of 0). k is readjusted downwards as\n% the stemming progresses. Zero termination is not in fact used in the\n% algorithm.\n\n% To call this function, use the string to be stemmed as the input\n% argument.  This function returns the stemmed word as a string.\n\n% Lower-case string\ninString = lower(inString);\n\nglobal j;\nb = inString;\nk = length(b);\nk0 = 1;\nj = k;\n\n\n\n% With this if statement, strings of length 1 or 2 don't go through the\n% stemming process. Remove this conditional to match the published\n% algorithm.\nstem = b;\nif k > 2\n    % Output displays per step are commented out.\n    %disp(sprintf('Word to stem: %s', b));\n    x = step1ab(b, k, k0);\n    %disp(sprintf('Steps 1A and B yield: %s', x{1}));\n    x = step1c(x{1}, x{2}, k0);\n    %disp(sprintf('Step 1C yields: %s', x{1}));\n    x = step2(x{1}, x{2}, k0);\n    %disp(sprintf('Step 2 yields: %s', x{1}));\n    x = step3(x{1}, x{2}, k0);\n    %disp(sprintf('Step 3 yields: %s', x{1}));\n    x = step4(x{1}, x{2}, k0);\n    %disp(sprintf('Step 4 yields: %s', x{1}));\n    x = step5(x{1}, x{2}, k0);\n    %disp(sprintf('Step 5 yields: %s', x{1}));\n    stem = x{1};\nend\n\n% cons(j) is TRUE <=> b[j] is a consonant.\nfunction c = cons(i, b, k0)\nc = true;\nswitch(b(i))\n    case {'a', 'e', 'i', 'o', 'u'}\n        c = false;\n    case 'y'\n        if i == k0\n            c = true;\n        else\n            c = ~cons(i - 1, b, k0);\n        end\nend\n\n% mseq() measures the number of consonant sequences between k0 and j.  If\n% c is a consonant sequence and v a vowel sequence, and <..> indicates\n% arbitrary presence,\n\n%      <c><v>       gives 0\n%      <c>vc<v>     gives 1\n%      <c>vcvc<v>   gives 2\n%      <c>vcvcvc<v> gives 3\n%      ....\nfunction n = measure(b, k0)\nglobal j;\nn = 0;\ni = k0;\nwhile true\n    if i > j\n        return\n    end\n    if ~cons(i, b, k0)\n        break;\n    end\n    i = i + 1;\nend\ni = i + 1;\nwhile true\n    while true\n        if i > j\n            return\n        end\n        if cons(i, b, k0)\n            break;\n        end\n        i = i + 1;\n    end\n    i = i + 1;\n    n = n + 1;\n    while true\n        if i > j\n            return\n        end\n        if ~cons(i, b, k0)\n            break;\n        end\n        i = i + 1;\n    end\n    i = i + 1;\nend\n\n\n% vowelinstem() is TRUE <=> k0,...j contains a vowel\nfunction vis = vowelinstem(b, k0)\nglobal j;\nfor i = k0:j,\n    if ~cons(i, b, k0)\n        vis = true;\n        return\n    end\nend\nvis = false;\n\n%doublec(i) is TRUE <=> i,(i-1) contain a double consonant.\nfunction dc = doublec(i, b, k0)\nif i < k0+1\n    dc = false;\n    return\nend\nif b(i) ~= b(i-1)\n    dc = false;\n    return\nend\ndc = cons(i, b, k0);\n\n\n% cvc(j) is TRUE <=> j-2,j-1,j has the form consonant - vowel - consonant\n% and also if the second c is not w,x or y. this is used when trying to\n% restore an e at the end of a short word. e.g.\n%\n%      cav(e), lov(e), hop(e), crim(e), but\n%      snow, box, tray.\n\nfunction c1 = cvc(i, b, k0)\nif ((i < (k0+2)) || ~cons(i, b, k0) || cons(i-1, b, k0) || ~cons(i-2, b, k0))\n    c1 = false;\nelse\n    if (b(i) == 'w' || b(i) == 'x' || b(i) == 'y')\n        c1 = false;\n        return\n    end\n    c1 = true;\nend\n\n% ends(s) is TRUE <=> k0,...k ends with the string s.\nfunction s = ends(str, b, k)\nglobal j;\nif (str(length(str)) ~= b(k))\n    s = false;\n    return\nend % tiny speed-up\nif (length(str) > k)\n    s = false;\n    return\nend\nif strcmp(b(k-length(str)+1:k), str)\n    s = true;\n    j = k - length(str);\n    return\nelse\n    s = false;\nend\n\n% setto(s) sets (j+1),...k to the characters in the string s, readjusting\n% k accordingly.\n\nfunction so = setto(s, b, k)\nglobal j;\nfor i = j+1:(j+length(s))\n    b(i) = s(i-j);\nend\nif k > j+length(s)\n    b((j+length(s)+1):k) = '';\nend\nk = length(b);\nso = {b, k};\n\n% rs(s) is used further down.\n% [Note: possible null/value for r if rs is called]\nfunction r = rs(str, b, k, k0)\nr = {b, k};\nif measure(b, k0) > 0\n    r = setto(str, b, k);\nend\n\n% step1ab() gets rid of plurals and -ed or -ing. e.g.\n\n%       caresses  ->  caress\n%       ponies    ->  poni\n%       ties      ->  ti\n%       caress    ->  caress\n%       cats      ->  cat\n\n%       feed      ->  feed\n%       agreed    ->  agree\n%       disabled  ->  disable\n\n%       matting   ->  mat\n%       mating    ->  mate\n%       meeting   ->  meet\n%       milling   ->  mill\n%       messing   ->  mess\n\n%       meetings  ->  meet\n\nfunction s1ab = step1ab(b, k, k0)\nglobal j;\nif b(k) == 's'\n    if ends('sses', b, k)\n        k = k-2;\n    elseif ends('ies', b, k)\n        retVal = setto('i', b, k);\n        b = retVal{1};\n        k = retVal{2};\n    elseif (b(k-1) ~= 's')\n        k = k-1;\n    end\nend\nif ends('eed', b, k)\n    if measure(b, k0) > 0;\n        k = k-1;\n    end\nelseif (ends('ed', b, k) || ends('ing', b, k)) && vowelinstem(b, k0)\n    k = j;\n    retVal = {b, k};\n    if ends('at', b, k)\n        retVal = setto('ate', b(k0:k), k);\n    elseif ends('bl', b, k)\n        retVal = setto('ble', b(k0:k), k);\n    elseif ends('iz', b, k)\n        retVal = setto('ize', b(k0:k), k);\n    elseif doublec(k, b, k0)\n        retVal = {b, k-1};\n        if b(retVal{2}) == 'l' || b(retVal{2}) == 's' || ...\n                b(retVal{2}) == 'z'\n            retVal = {retVal{1}, retVal{2}+1};\n        end\n    elseif measure(b, k0) == 1 && cvc(k, b, k0)\n        retVal = setto('e', b(k0:k), k);\n    end\n    k = retVal{2};\n    b = retVal{1}(k0:k);\nend\nj = k;\ns1ab = {b(k0:k), k};\n\n%  step1c() turns terminal y to i when there is another vowel in the stem.\nfunction s1c = step1c(b, k, k0)\nglobal j;\nif ends('y', b, k) && vowelinstem(b, k0)\n    b(k) = 'i';\nend\nj = k;\ns1c = {b, k};\n\n% step2() maps double suffices to single ones. so -ization ( = -ize plus\n% -ation) maps to -ize etc. note that the string before the suffix must give\n% m() > 0.\nfunction s2 = step2(b, k, k0)\nglobal j;\ns2 = {b, k};\nswitch b(k-1)\n    case {'a'}\n        if ends('ational', b, k) s2 = rs('ate', b, k, k0);\n        elseif ends('tional', b, k) s2 = rs('tion', b, k, k0); end;\n    case {'c'}\n        if ends('enci', b, k) s2 = rs('ence', b, k, k0);\n        elseif ends('anci', b, k) s2 = rs('ance', b, k, k0); end;\n    case {'e'}\n        if ends('izer', b, k) s2 = rs('ize', b, k, k0); end;\n    case {'l'}\n        if ends('bli', b, k) s2 = rs('ble', b, k, k0);\n        elseif ends('alli', b, k) s2 = rs('al', b, k, k0);\n        elseif ends('entli', b, k) s2 = rs('ent', b, k, k0);\n        elseif ends('eli', b, k) s2 = rs('e', b, k, k0);\n        elseif ends('ousli', b, k) s2 = rs('ous', b, k, k0); end;\n    case {'o'}\n        if ends('ization', b, k) s2 = rs('ize', b, k, k0);\n        elseif ends('ation', b, k) s2 = rs('ate', b, k, k0);\n        elseif ends('ator', b, k) s2 = rs('ate', b, k, k0); end;\n    case {'s'}\n        if ends('alism', b, k) s2 = rs('al', b, k, k0);\n        elseif ends('iveness', b, k) s2 = rs('ive', b, k, k0);\n        elseif ends('fulness', b, k) s2 = rs('ful', b, k, k0);\n        elseif ends('ousness', b, k) s2 = rs('ous', b, k, k0); end;\n    case {'t'}\n        if ends('aliti', b, k) s2 = rs('al', b, k, k0);\n        elseif ends('iviti', b, k) s2 = rs('ive', b, k, k0);\n        elseif ends('biliti', b, k) s2 = rs('ble', b, k, k0); end;\n    case {'g'}\n        if ends('logi', b, k) s2 = rs('log', b, k, k0); end;\nend\nj = s2{2};\n\n% step3() deals with -ic-, -full, -ness etc. similar strategy to step2.\nfunction s3 = step3(b, k, k0)\nglobal j;\ns3 = {b, k};\nswitch b(k)\n    case {'e'}\n        if ends('icate', b, k) s3 = rs('ic', b, k, k0);\n        elseif ends('ative', b, k) s3 = rs('', b, k, k0);\n        elseif ends('alize', b, k) s3 = rs('al', b, k, k0); end;\n    case {'i'}\n        if ends('iciti', b, k) s3 = rs('ic', b, k, k0); end;\n    case {'l'}\n        if ends('ical', b, k) s3 = rs('ic', b, k, k0);\n        elseif ends('ful', b, k) s3 = rs('', b, k, k0); end;\n    case {'s'}\n        if ends('ness', b, k) s3 = rs('', b, k, k0); end;\nend\nj = s3{2};\n\n% step4() takes off -ant, -ence etc., in context <c>vcvc<v>.\nfunction s4 = step4(b, k, k0)\nglobal j;\nswitch b(k-1)\n    case {'a'}\n        if ends('al', b, k) end;\n    case {'c'}\n        if ends('ance', b, k)\n        elseif ends('ence', b, k) end;\n    case {'e'}\n        if ends('er', b, k) end;\n    case {'i'}\n        if ends('ic', b, k) end;\n    case {'l'}\n        if ends('able', b, k)\n        elseif ends('ible', b, k) end;\n    case {'n'}\n        if ends('ant', b, k)\n        elseif ends('ement', b, k)\n        elseif ends('ment', b, k)\n        elseif ends('ent', b, k) end;\n    case {'o'}\n        if ends('ion', b, k)\n            if j == 0\n            elseif ~(strcmp(b(j),'s') || strcmp(b(j),'t'))\n                j = k;\n            end\n        elseif ends('ou', b, k) end;\n    case {'s'}\n        if ends('ism', b, k) end;\n    case {'t'}\n        if ends('ate', b, k)\n        elseif ends('iti', b, k) end;\n    case {'u'}\n        if ends('ous', b, k) end;\n    case {'v'}\n        if ends('ive', b, k) end;\n    case {'z'}\n        if ends('ize', b, k) end;\nend\nif measure(b, k0) > 1\n    s4 = {b(k0:j), j};\nelse\n    s4 = {b(k0:k), k};\nend\n\n% step5() removes a final -e if m() > 1, and changes -ll to -l if m() > 1.\nfunction s5 = step5(b, k, k0)\nglobal j;\nj = k;\nif b(k) == 'e'\n    a = measure(b, k0);\n    if (a > 1) || ((a == 1) && ~cvc(k-1, b, k0))\n        k = k-1;\n    end\nend\nif (b(k) == 'l') && doublec(k, b, k0) && (measure(b, k0) > 1)\n    k = k-1;\nend\ns5 = {b(k0:k), k};\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex7/machine-learning-ex7/ex7/submit.m", "size": 1438, "source_encoding": "utf_8", "md5": "665ea5906aad3ccfd94e33a40c58e2ce", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'k-means-clustering-and-pca';\n  conf.itemName = 'K-Means Clustering and PCA';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'findClosestCentroids.m' }, ...\n      'Find Closest Centroids (k-Means)', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'computeCentroids.m' }, ...\n      'Compute Centroid Means (k-Means)', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'pca.m' }, ...\n      'PCA', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'projectData.m' }, ...\n      'Project Data (PCA)', ...\n    }, ...\n    { ...\n      '5', ...\n      { 'recoverData.m' }, ...\n      'Recover Data (PCA)', ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId, auxstring)\n  % Random Test Cases\n  X = reshape(sin(1:165), 15, 11);\n  Z = reshape(cos(1:121), 11, 11);\n  C = Z(1:5, :);\n  idx = (1 + mod(1:15, 3))';\n  if partId == '1'\n    idx = findClosestCentroids(X, C);\n    out = sprintf('%0.5f ', idx(:));\n  elseif partId == '2'\n    centroids = computeCentroids(X, idx, 3);\n    out = sprintf('%0.5f ', centroids(:));\n  elseif partId == '3'\n    [U, S] = pca(X);\n    out = sprintf('%0.5f ', abs([U(:); S(:)]));\n  elseif partId == '4'\n    X_proj = projectData(X, Z, 5);\n    out = sprintf('%0.5f ', X_proj(:));\n  elseif partId == '5'\n    X_rec = recoverData(X(:,1:5), Z, 5);\n    out = sprintf('%0.5f ', X_rec(:));\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex7/machine-learning-ex7/ex7/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex7/machine-learning-ex7/ex7/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex7/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex7/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex7/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex5/ex5/submit.m", "size": 1765, "source_encoding": "utf_8", "md5": "b1804fe5854d9744dca981d250eda251", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';\n  conf.itemName = 'Regularized Linear Regression and Bias/Variance';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'linearRegCostFunction.m' }, ...\n      'Regularized Linear Regression Cost Function', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'linearRegCostFunction.m' }, ...\n      'Regularized Linear Regression Gradient', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'learningCurve.m' }, ...\n      'Learning Curve', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'polyFeatures.m' }, ...\n      'Polynomial Feature Mapping', ...\n    }, ...\n    { ...\n      '5', ...\n      { 'validationCurve.m' }, ...\n      'Validation Curve', ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId, auxstring)\n  % Random Test Cases\n  X = [ones(10,1) sin(1:1.5:15)' cos(1:1.5:15)'];\n  y = sin(1:3:30)';\n  Xval = [ones(10,1) sin(0:1.5:14)' cos(0:1.5:14)'];\n  yval = sin(1:10)';\n  if partId == '1'\n    [J] = linearRegCostFunction(X, y, [0.1 0.2 0.3]', 0.5);\n    out = sprintf('%0.5f ', J);\n  elseif partId == '2'\n    [J, grad] = linearRegCostFunction(X, y, [0.1 0.2 0.3]', 0.5);\n    out = sprintf('%0.5f ', grad);\n  elseif partId == '3'\n    [error_train, error_val] = ...\n        learningCurve(X, y, Xval, yval, 1);\n    out = sprintf('%0.5f ', [error_train(:); error_val(:)]);\n  elseif partId == '4'\n    [X_poly] = polyFeatures(X(2,:)', 8);\n    out = sprintf('%0.5f ', X_poly);\n  elseif partId == '5'\n    [lambda_vec, error_train, error_val] = ...\n        validationCurve(X, y, Xval, yval);\n    out = sprintf('%0.5f ', ...\n        [lambda_vec(:); error_train(:); error_val(:)]);\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex5/ex5/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex5/ex5/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex3/machine-learning-ex3/ex3/submit.m", "size": 1567, "source_encoding": "utf_8", "md5": "1dba733a05282b2db9f2284548483b81", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'multi-class-classification-and-neural-networks';\n  conf.itemName = 'Multi-class Classification and Neural Networks';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'lrCostFunction.m' }, ...\n      'Regularized Logistic Regression', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'oneVsAll.m' }, ...\n      'One-vs-All Classifier Training', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'predictOneVsAll.m' }, ...\n      'One-vs-All Classifier Prediction', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'predict.m' }, ...\n      'Neural Network Prediction Function' ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId, auxdata)\n  % Random Test Cases\n  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];\n  y = sin(X(:,1) + X(:,2)) > 0;\n  Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...\n          1 1 ;  1 2 ;  2 1 ; 2 2 ; ...\n         -1 1 ;  -1 2 ;  -2 1 ; -2 2 ; ...\n          1 -1 ; 1 -2 ;  -2 -1 ; -2 -2 ];\n  ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]';\n  t1 = sin(reshape(1:2:24, 4, 3));\n  t2 = cos(reshape(1:2:40, 4, 5));\n\n  if partId == '1'\n    [J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);\n    out = sprintf('%0.5f ', J);\n    out = [out sprintf('%0.5f ', grad)];\n  elseif partId == '2'\n    out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));\n  elseif partId == '3'\n    out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));\n  elseif partId == '4'\n    out = sprintf('%0.5f ', predict(t1, t2, Xm));\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex3/machine-learning-ex3/ex3/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex3/machine-learning-ex3/ex3/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex3/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex3/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex3/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/submit.m", "size": 2135, "source_encoding": "utf_8", "md5": "eebb8c0a1db5a4df20b4c858603efad6", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';\n  conf.itemName = 'Anomaly Detection and Recommender Systems';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'estimateGaussian.m' }, ...\n      'Estimate Gaussian Parameters', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'selectThreshold.m' }, ...\n      'Select Threshold', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'cofiCostFunc.m' }, ...\n      'Collaborative Filtering Cost', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'cofiCostFunc.m' }, ...\n      'Collaborative Filtering Gradient', ...\n    }, ...\n    { ...\n      '5', ...\n      { 'cofiCostFunc.m' }, ...\n      'Regularized Cost', ...\n    }, ...\n    { ...\n      '6', ...\n      { 'cofiCostFunc.m' }, ...\n      'Regularized Gradient', ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId, auxstring)\n  % Random Test Cases\n  n_u = 3; n_m = 4; n = 5;\n  X = reshape(sin(1:n_m*n), n_m, n);\n  Theta = reshape(cos(1:n_u*n), n_u, n);\n  Y = reshape(sin(1:2:2*n_m*n_u), n_m, n_u);\n  R = Y > 0.5;\n  pval = [abs(Y(:)) ; 0.001; 1];\n  Y = (Y .* double(R));  % set 'Y' values to 0 for movies not reviewed\n  yval = [R(:) ; 1; 0];\n  params = [X(:); Theta(:)];\n  if partId == '1'\n    [mu sigma2] = estimateGaussian(X);\n    out = sprintf('%0.5f ', [mu(:); sigma2(:)]);\n  elseif partId == '2'\n    [bestEpsilon bestF1] = selectThreshold(yval, pval);\n    out = sprintf('%0.5f ', [bestEpsilon(:); bestF1(:)]);\n  elseif partId == '3'\n    [J] = cofiCostFunc(params, Y, R, n_u, n_m, ...\n                       n, 0);\n    out = sprintf('%0.5f ', J(:));\n  elseif partId == '4'\n    [J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ...\n                             n, 0);\n    out = sprintf('%0.5f ', grad(:));\n  elseif partId == '5'\n    [J] = cofiCostFunc(params, Y, R, n_u, n_m, ...\n                       n, 1.5);\n    out = sprintf('%0.5f ', J(:));\n  elseif partId == '6'\n    [J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ...\n                             n, 1.5);\n    out = sprintf('%0.5f ', grad(:));\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submit.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex1/machine-learning-ex1/ex1/submit.m", "size": 1876, "source_encoding": "utf_8", "md5": "8d1c467b830a89c187c05b121cb8fbfd", "text": "function submit()\n  addpath('./lib');\n\n  conf.assignmentSlug = 'linear-regression';\n  conf.itemName = 'Linear Regression with Multiple Variables';\n  conf.partArrays = { ...\n    { ...\n      '1', ...\n      { 'warmUpExercise.m' }, ...\n      'Warm-up Exercise', ...\n    }, ...\n    { ...\n      '2', ...\n      { 'computeCost.m' }, ...\n      'Computing Cost (for One Variable)', ...\n    }, ...\n    { ...\n      '3', ...\n      { 'gradientDescent.m' }, ...\n      'Gradient Descent (for One Variable)', ...\n    }, ...\n    { ...\n      '4', ...\n      { 'featureNormalize.m' }, ...\n      'Feature Normalization', ...\n    }, ...\n    { ...\n      '5', ...\n      { 'computeCostMulti.m' }, ...\n      'Computing Cost (for Multiple Variables)', ...\n    }, ...\n    { ...\n      '6', ...\n      { 'gradientDescentMulti.m' }, ...\n      'Gradient Descent (for Multiple Variables)', ...\n    }, ...\n    { ...\n      '7', ...\n      { 'normalEqn.m' }, ...\n      'Normal Equations', ...\n    }, ...\n  };\n  conf.output = @output;\n\n  submitWithConfiguration(conf);\nend\n\nfunction out = output(partId)\n  % Random Test Cases\n  X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))'];\n  Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2));\n  X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25];\n  Y2 = Y1.^0.5 + Y1;\n  if partId == '1'\n    out = sprintf('%0.5f ', warmUpExercise());\n  elseif partId == '2'\n    out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]'));\n  elseif partId == '3'\n    out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10));\n  elseif partId == '4'\n    out = sprintf('%0.5f ', featureNormalize(X2(:,2:4)));\n  elseif partId == '5'\n    out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]'));\n  elseif partId == '6'\n    out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10));\n  elseif partId == '7'\n    out = sprintf('%0.5f ', normalEqn(X2, Y2));\n  end \nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "submitWithConfiguration.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex1/machine-learning-ex1/ex1/lib/submitWithConfiguration.m", "size": 5562, "source_encoding": "utf_8", "md5": "4ac719ea6570ac228ea6c7a9c919e3f5", "text": "function submitWithConfiguration(conf)\n  addpath('./lib/jsonlab');\n\n  parts = parts(conf);\n\n  fprintf('== Submitting solutions | %s...\\n', conf.itemName);\n\n  tokenFile = 'token.mat';\n  if exist(tokenFile, 'file')\n    load(tokenFile);\n    [email token] = promptToken(email, token, tokenFile);\n  else\n    [email token] = promptToken('', '', tokenFile);\n  end\n\n  if isempty(token)\n    fprintf('!! Submission Cancelled\\n');\n    return\n  end\n\n  try\n    response = submitParts(conf, email, token, parts);\n  catch\n    e = lasterror();\n    fprintf('\\n!! Submission failed: %s\\n', e.message);\n    fprintf('\\n\\nFunction: %s\\nFileName: %s\\nLineNumber: %d\\n', ...\n      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);\n    fprintf('\\nPlease correct your code and resubmit.\\n');\n    return\n  end\n\n  if isfield(response, 'errorMessage')\n    fprintf('!! Submission failed: %s\\n', response.errorMessage);\n  elseif isfield(response, 'errorCode')\n    fprintf('!! Submission failed: %s\\n', response.message);\n  else\n    showFeedback(parts, response);\n    save(tokenFile, 'email', 'token');\n  end\nend\n\nfunction [email token] = promptToken(email, existingToken, tokenFile)\n  if (~isempty(email) && ~isempty(existingToken))\n    prompt = sprintf( ...\n      'Use token from last successful submission (%s)? (Y/n): ', ...\n      email);\n    reenter = input(prompt, 's');\n\n    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')\n      token = existingToken;\n      return;\n    else\n      delete(tokenFile);\n    end\n  end\n  email = input('Login (email address): ', 's');\n  token = input('Token: ', 's');\nend\n\nfunction isValid = isValidPartOptionIndex(partOptions, i)\n  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));\nend\n\nfunction response = submitParts(conf, email, token, parts)\n  body = makePostBody(conf, email, token, parts);\n  submissionUrl = submissionUrl();\n\n  responseBody = getResponse(submissionUrl, body);\n  jsonResponse = validateResponse(responseBody);\n  response = loadjson(jsonResponse);\nend\n\nfunction body = makePostBody(conf, email, token, parts)\n  bodyStruct.assignmentSlug = conf.assignmentSlug;\n  bodyStruct.submitterEmail = email;\n  bodyStruct.secret = token;\n  bodyStruct.parts = makePartsStruct(conf, parts);\n\n  opt.Compact = 1;\n  body = savejson('', bodyStruct, opt);\nend\n\nfunction partsStruct = makePartsStruct(conf, parts)\n  for part = parts\n    partId = part{:}.id;\n    fieldName = makeValidFieldName(partId);\n    outputStruct.output = conf.output(partId);\n    partsStruct.(fieldName) = outputStruct;\n  end\nend\n\nfunction [parts] = parts(conf)\n  parts = {};\n  for partArray = conf.partArrays\n    part.id = partArray{:}{1};\n    part.sourceFiles = partArray{:}{2};\n    part.name = partArray{:}{3};\n    parts{end + 1} = part;\n  end\nend\n\nfunction showFeedback(parts, response)\n  fprintf('== \\n');\n  fprintf('== %43s | %9s | %-s\\n', 'Part Name', 'Score', 'Feedback');\n  fprintf('== %43s | %9s | %-s\\n', '---------', '-----', '--------');\n  for part = parts\n    score = '';\n    partFeedback = '';\n    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));\n    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));\n    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);\n    fprintf('== %43s | %9s | %-s\\n', part{:}.name, score, partFeedback);\n  end\n  evaluation = response.evaluation;\n  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);\n  fprintf('==                                   --------------------------------\\n');\n  fprintf('== %43s | %9s | %-s\\n', '', totalScore, '');\n  fprintf('== \\n');\nend\n\n% use urlread or curl to send submit results to the grader and get a response\nfunction response = getResponse(url, body)\n% try using urlread() and a secure connection\n  params = {'jsonBody', body};\n  [response, success] = urlread(url, 'post', params);\n\n  if (success == 0)\n    % urlread didn't work, try curl & the peer certificate patch\n    if ispc\n      % testing note: use 'jsonBody =' for a test case\n      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);\n    else\n      % it's linux/OS X, so use the other form\n      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);\n    end\n    % get the response body for the peer certificate patch method\n    [code, response] = system(json_command);\n    % test the success code\n    if (code ~= 0)\n      fprintf('[error] submission with curl() was not successful\\n');\n    end\n  end\nend\n\n% validate the grader's response\nfunction response = validateResponse(resp)\n  % test if the response is json or an HTML page\n  isJson = length(resp) > 0 && resp(1) == '{';\n  isHtml = findstr(lower(resp), '<html');\n\n  if (isJson)\n    response = resp;\n  elseif (isHtml)\n    % the response is html, so it's probably an error message\n    printHTMLContents(resp);\n    error('Grader response is an HTML message');\n  else\n    error('Grader sent no response');\n  end\nend\n\n% parse a HTML response and print it's contents\nfunction printHTMLContents(response)\n  strippedResponse = regexprep(response, '<[^>]+>', ' ');\n  strippedResponse = regexprep(strippedResponse, '[\\t ]+', ' ');\n  fprintf(strippedResponse);\nend\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Service configuration\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction submissionUrl = submissionUrl()\n  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "savejson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex1/machine-learning-ex1/ex1/lib/jsonlab/savejson.m", "size": 17462, "source_encoding": "utf_8", "md5": "861b534fc35ffe982b53ca3ca83143bf", "text": "function json=savejson(rootname,obj,varargin)\n%\n% json=savejson(rootname,obj,filename)\n%    or\n% json=savejson(rootname,obj,opt)\n% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript\n% Object Notation) string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09\n%\n% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array).\n%      filename: a string for the file name to save the output JSON data.\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element\n%                         of a 1D/2D array;\n%        opt.ArrayIndent [1|0]: if 1, output explicit data array with\n%                         precedent indentation; if 0, no indentation\n%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.Inf ['\"$1_Inf_\"'|string]: a customized regular expression pattern\n%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'\n%                         and $1 represents the sign. For those who want to use\n%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'\n%        opt.NaN ['\"_NaN_\"'|string]: a customized regular expression pattern\n%                         to represent NaN\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSONP='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.\n%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a string in the JSON format (see http://json.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      savejson('jmesh',jsonmesh)\n%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\\t%.5g')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\n\nwhitespaces=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nif(jsonopt('Compact',0,opt)==1)\n    whitespaces=struct('tab','','newline','','sep',',');\nend\nif(~isfield(opt,'whitespaces_'))\n    opt.whitespaces_=whitespaces;\nend\n\nnl=whitespaces.newline;\n\njson=obj2json(rootname,obj,rootlevel,opt);\nif(rootisarray)\n    json=sprintf('%s%s',json,nl);\nelse\n    json=sprintf('{%s%s%s}\\n',nl,json,nl);\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=sprintf('%s(%s);%s',jsonp,json,nl);\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    if(jsonopt('SaveBinary',0,opt)==1)\n\t    fid = fopen(opt.FileName, 'wb');\n\t    fwrite(fid,json);\n    else\n\t    fid = fopen(opt.FileName, 'wt');\n\t    fwrite(fid,json,'char');\n    end\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2json(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2json(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2json(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2json(name,item,level,varargin{:});\nelse\n    txt=mat2json(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2json(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=jsonopt('whitespaces_',struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n')),varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nif(len>1)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": [%s',padding0, checkname(name,varargin{:}),nl); name=''; \n    else\n        txt=sprintf('%s[%s',padding0,nl); \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=sprintf('%s\"%s\": []',padding0, checkname(name,varargin{:})); name=''; \n    else\n        txt=sprintf('%s[]',padding0); \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n    for i=1:dim(1)\n       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));\n       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    end\n    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2json(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding0=repmat(ws.tab,1,level);\npadding2=repmat(ws.tab,1,level+1);\npadding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));\nnl=ws.newline;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding0,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding0,nl); end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end\n  for i=1:dim(1)\n    names = fieldnames(item(i,j));\n    if(~isempty(name) && len==1)\n        txt=sprintf('%s%s\"%s\": {%s',txt,padding1, checkname(name,varargin{:}),nl); \n    else\n        txt=sprintf('%s%s{%s',txt,padding1,nl); \n    end\n    if(~isempty(names))\n      for e=1:length(names)\n\t    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));\n        if(e<length(names)) txt=sprintf('%s%s',txt,','); end\n        txt=sprintf('%s%s',txt,nl);\n      end\n    end\n    txt=sprintf('%s%s}',txt,padding1);\n    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\n  end\n  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end\n  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end\nend\nif(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2json(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(~isempty(name)) \n    if(len>1) txt=sprintf('%s\"%s\": [%s',padding1,checkname(name,varargin{:}),nl); end\nelse\n    if(len>1) txt=sprintf('%s[%s',padding1,nl); end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    if(isoct)\n        val=regexprep(item(e,:),'\\\\','\\\\');\n        val=regexprep(val,'\"','\\\"');\n        val=regexprep(val,'^\"','\\\"');\n    else\n        val=regexprep(item(e,:),'\\\\','\\\\\\\\');\n        val=regexprep(val,'\"','\\\\\"');\n        val=regexprep(val,'^\"','\\\\\"');\n    end\n    val=escapejsonstring(val);\n    if(len==1)\n        obj=['\"' checkname(name,varargin{:}) '\": ' '\"',val,'\"'];\n\tif(isempty(name)) obj=['\"',val,'\"']; end\n        txt=sprintf('%s%s%s%s',txt,padding1,obj);\n    else\n        txt=sprintf('%s%s%s%s',txt,padding0,['\"',val,'\"']);\n    end\n    if(e==len) sep=''; end\n    txt=sprintf('%s%s',txt,sep);\nend\nif(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2json(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\npadding1=repmat(ws.tab,1,level);\npadding0=repmat(ws.tab,1,level+1);\nnl=ws.newline;\nsep=ws.sep;\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))\n    if(isempty(name))\n    \ttxt=sprintf('%s{%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    else\n    \ttxt=sprintf('%s\"%s\": {%s%s\"_ArrayType_\": \"%s\",%s%s\"_ArraySize_\": %s,%s',...\n              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\\s+',','),nl);\n    end\nelse\n    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)\n        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\\[',''),']','');\n    else\n        numtxt=matdata2json(item,level+1,varargin{:});\n    end\n    if(isempty(name))\n    \ttxt=sprintf('%s%s',padding1,numtxt);\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n           \ttxt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        else\n    \t    txt=sprintf('%s\"%s\": %s',padding1,checkname(name,varargin{:}),numtxt);\n        end\n    end\n    return;\nend\ndataformat='%s%s%s%s%s';\n\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n    end\n    txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsSparse_\": ','1', sep);\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([iy(:),data'],level+2,varargin{:}), nl);\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,data],level+2,varargin{:}), nl);\n    else\n        % General case, store row and column indices.\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n           matdata2json([ix,iy,data],level+2,varargin{:}), nl);\n    end\nelse\n    if(isreal(item))\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json(item(:)',level+2,varargin{:}), nl);\n    else\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayIsComplex_\": ','1', sep);\n        txt=sprintf(dataformat,txt,padding0,'\"_ArrayData_\": ',...\n            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);\n    end\nend\ntxt=sprintf('%s%s%s',txt,padding1,'}');\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2json(mat,level,varargin)\n\nws=struct('tab',sprintf('\\t'),'newline',sprintf('\\n'),'sep',sprintf(',\\n'));\nws=jsonopt('whitespaces_',ws,varargin{:});\ntab=ws.tab;\nnl=ws.newline;\n\nif(size(mat,1)==1)\n    pre='';\n    post='';\n    level=level-1;\nelse\n    pre=sprintf('[%s',nl);\n    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));\nend\n\nif(isempty(mat))\n    txt='null';\n    return;\nend\nfloatformat=jsonopt('FloatFormat','%.10g',varargin{:});\n%if(numel(mat)>1)\n    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];\n%else\n%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\\n')]];\n%end\n\nif(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)\n    formatstr=[repmat(tab,1,level) formatstr];\nend\n\ntxt=sprintf(formatstr,mat');\ntxt(end-length(nl):end)=[];\nif(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)\n   txt=regexprep(txt,'1','true');\n   txt=regexprep(txt,'0','false');\nend\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],\\n['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\ntxt=[pre txt post];\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n\n%%-------------------------------------------------------------------------\nfunction newstr=escapejsonstring(str)\nnewstr=str;\nisoct=exist('OCTAVE_VERSION','builtin');\nif(isoct)\n   vv=sscanf(OCTAVE_VERSION,'%f');\n   if(vv(1)>=3.8) isoct=0; end\nend\nif(isoct)\n  escapechars={'\\a','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},escapechars{i});\n  end\nelse\n  escapechars={'\\a','\\b','\\f','\\n','\\r','\\t','\\v'};\n  for i=1:length(escapechars);\n    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\\\','\\\\\\\\'));\n  end\nend\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex1/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m", "size": 18732, "source_encoding": "ibm852", "md5": "ab98cf173af2d50bbe8da4d6db252a20", "text": "function data = loadjson(fname,varargin)\n%\n% data=loadjson(fname,opt)\n%    or\n% data=loadjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2011/09/09, including previous works from \n%\n%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713\n%            created on 2009/11/02\n%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393\n%            created on  2009/03/22\n%         Joel Feenstra:\n%         http://www.mathworks.com/matlabcentral/fileexchange/20565\n%            created on 2008/07/03\n%\n% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a JSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a\n%                         speed-optimized array parser when loading an \n%                         array object. The fast array parser may \n%                         collapse block arrays into a single large\n%                         array similar to rules defined in cell2mat; 0 to \n%                         use a legacy parser; if set to a larger-than-1\n%                         value, this option will specify the minimum\n%                         dimension to enable the fast array parser. For\n%                         example, if the input is a 3D array, setting\n%                         FastArrayParser to 1 will return a 3D array;\n%                         setting to 2 will return a cell array of 2D\n%                         arrays; setting to 3 will return to a 2D cell\n%                         array of 1D vectors; setting to 4 will return a\n%                         3D cell array.\n%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      dat=loadjson('{\"obj\":{\"string\":\"value\",\"array\":[1,2,3]}}')\n%      dat=loadjson(['examples' filesep 'example1.json'])\n%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\n\nif(jsonopt('ShowProgress',0,opt)==1)\n    opt.progressbar_=waitbar(0,'loading ...');\nend\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\nif(isfield(opt,'progressbar_'))\n    close(opt.progressbar_);\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=data(j).x0x5F_ArraySize_;\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    if next_char ~= '}'\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            parse_char(':');\n            val = parse_value(varargin{:});\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}'\n                break;\n            end\n            parse_char(',');\n        end\n    end\n    parse_char('}');\n\n%%-------------------------------------------------------------------------\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim2=[];\n    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});\n    pbar=jsonopt('progressbar_',-1,varargin{:});\n\n    if next_char ~= ']'\n\tif(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))\n            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);\n            arraystr=['[' inStr(pos:endpos)];\n            arraystr=regexprep(arraystr,'\"_NaN_\"','NaN');\n            arraystr=regexprep(arraystr,'\"([-+]*)_Inf_\"','$1Inf');\n            arraystr(arraystr==sprintf('\\n'))=[];\n            arraystr(arraystr==sprintf('\\r'))=[];\n            %arraystr=regexprep(arraystr,'\\s*,',','); % this is slow,sometimes needed\n            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D\n        \tastr=inStr((e1l+1):(e1r-1));\n        \tastr=regexprep(astr,'\"_NaN_\"','NaN');\n        \tastr=regexprep(astr,'\"([-+]*)_Inf_\"','$1Inf');\n        \tastr(astr==sprintf('\\n'))=[];\n        \tastr(astr==sprintf('\\r'))=[];\n        \tastr(astr==' ')='';\n        \tif(isempty(find(astr=='[', 1))) % array is 2D\n                    dim2=length(sscanf(astr,'%f,',[1 inf]));\n        \tend\n            else % array is 1D\n        \tastr=arraystr(2:end-1);\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);\n        \tif(nextidx>=length(astr)-1)\n                    object=obj;\n                    pos=endpos;\n                    parse_char(']');\n                    return;\n        \tend\n            end\n            if(~isempty(dim2))\n        \tastr=arraystr;\n        \tastr(astr=='[')='';\n        \tastr(astr==']')='';\n        \tastr(astr==' ')='';\n        \t[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);\n        \tif(nextidx>=length(astr)-1)\n                    object=reshape(obj,dim2,numel(obj)/dim2)';\n                    pos=endpos;\n                    parse_char(']');\n                    if(pbar>0)\n                        waitbar(pos/length(inStr),pbar,'loading ...');\n                    end\n                    return;\n        \tend\n            end\n            arraystr=regexprep(arraystr,'\\]\\s*,','];');\n\telse\n            arraystr='[';\n\tend\n        try\n           if(isoct && regexp(arraystr,'\"','once'))\n                error('Octave eval can produce empty cells for JSON-like input');\n           end\n           object=eval(arraystr);\n           pos=endpos;\n        catch\n         while 1\n            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);\n            val = parse_value(newopt);\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            parse_char(',');\n         end\n        end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    parse_char(']');\n    \n    if(pbar>0)\n        waitbar(pos/length(inStr),pbar,'loading ...');\n    end\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr len  esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    if inStr(pos) ~= '\"'\n        error_pos('String starting with \" expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    str = '';\n    while pos <= len\n        while index_esc <= len_esc && esc(index_esc) < pos\n            index_esc = index_esc + 1;\n        end\n        if index_esc > len_esc\n            str = [str inStr(pos:len)];\n            pos = len + 1;\n            break;\n        else\n            str = [str inStr(pos:esc(index_esc)-1)];\n            pos = esc(index_esc);\n        end\n        nstr = length(str); switch inStr(pos)\n            case '\"'\n                pos = pos + 1;\n                if(~isempty(str))\n                    if(strcmp(str,'_Inf_'))\n                        str=Inf;\n                    elseif(strcmp(str,'-_Inf_'))\n                        str=-Inf;\n                    elseif(strcmp(str,'_NaN_'))\n                        str=NaN;\n                    end\n                end\n                return;\n            case '\\'\n                if pos+1 > len\n                    error_pos('End of file reached right after escape character');\n                end\n                pos = pos + 1;\n                switch inStr(pos)\n                    case {'\"' '\\' '/'}\n                        str(nstr+1) = inStr(pos);\n                        pos = pos + 1;\n                    case {'b' 'f' 'n' 'r' 't'}\n                        str(nstr+1) = sprintf(['\\' inStr(pos)]);\n                        pos = pos + 1;\n                    case 'u'\n                        if pos+4 > len\n                            error_pos('End of file reached in escaped unicode character');\n                        end\n                        str(nstr+(1:6)) = inStr(pos-1:pos+4);\n                        pos = pos + 5;\n                end\n            otherwise % should never happen\n                str(nstr+1) = inStr(pos), keyboard\n                pos = pos + 1;\n        end\n    end\n    error_pos('End of file while expecting end of inStr');\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct\n    currstr=inStr(pos:end);\n    numstr=0;\n    if(isoct~=0)\n        numstr=regexp(currstr,'^\\s*-?(?:0|[1-9]\\d*)(?:\\.\\d+)?(?:[eE][+\\-]?\\d+)?','end');\n        [num, one] = sscanf(currstr, '%f', 1);\n        delta=numstr+1;\n    else\n        [num, one, err, delta] = sscanf(currstr, '%f', 1);\n        if ~isempty(err)\n            error_pos('Error reading number at position %d');\n        end\n    end\n    pos = pos + delta-1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n    \n    pbar=jsonopt('progressbar_',-1,varargin{:});\n    if(pbar>0)\n        waitbar(pos/len,pbar,'loading ...');\n    end\n    \n    switch(inStr(pos))\n        case '\"'\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'-','0','1','2','3','4','5','6','7','8','9'}\n            val = parse_number(varargin{:});\n            return;\n        case 't'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')\n                val = true;\n                pos = pos + 4;\n                return;\n            end\n        case 'f'\n            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')\n                val = false;\n                pos = pos + 5;\n                return;\n            end\n        case 'n'\n            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')\n                val = [];\n                pos = pos + 4;\n                return;\n            end\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "loadubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex1/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m", "size": 15574, "source_encoding": "utf_8", "md5": "5974e78e71b81b1e0f76123784b951a4", "text": "function data = loadubjson(fname,varargin)\n%\n% data=loadubjson(fname,opt)\n%    or\n% data=loadubjson(fname,'param1',value1,'param2',value2,...)\n%\n% parse a JSON (JavaScript Object Notation) file or string\n%\n% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/01\n%\n% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      fname: input file name, if fname contains \"{}\" or \"[]\", fname\n%             will be interpreted as a UBJSON string\n%      opt: a struct to store parsing options, opt can be replaced by \n%           a list of ('param',value) pairs - the param string is equivallent\n%           to a field in opt. opt can have the following \n%           fields (first in [.|.] is the default)\n%\n%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat\n%                         for each element of the JSON data, and group \n%                         arrays based on the cell2mat rules.\n%           opt.IntEndian [B|L]: specify the endianness of the integer fields\n%                         in the UBJSON input data. B - Big-Endian format for \n%                         integers (as required in the UBJSON specification); \n%                         L - input integer fields are in Little-Endian order.\n%\n% output:\n%      dat: a cell array, where {...} blocks are converted into cell arrays,\n%           and [...] are converted to arrays\n%\n% examples:\n%      obj=struct('string','value','array',[1 2 3]);\n%      ubjdata=saveubjson('obj',obj);\n%      dat=loadubjson(ubjdata)\n%      dat=loadubjson(['examples' filesep 'example1.ubj'])\n%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details \n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nglobal pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian\n\nif(regexp(fname,'[\\{\\}\\]\\[]','once'))\n   string=fname;\nelseif(exist(fname,'file'))\n   fid = fopen(fname,'rb');\n   string = fread(fid,inf,'uint8=>char')';\n   fclose(fid);\nelse\n   error('input file does not exist');\nend\n\npos = 1; len = length(string); inStr = string;\nisoct=exist('OCTAVE_VERSION','builtin');\narraytoken=find(inStr=='[' | inStr==']' | inStr=='\"');\njstr=regexprep(inStr,'\\\\\\\\','  ');\nescquote=regexp(jstr,'\\\\\"');\narraytoken=sort([arraytoken escquote]);\n\n% String delimiters and escape chars identified to improve speed:\nesc = find(inStr=='\"' | inStr=='\\' ); % comparable to: regexp(inStr, '[\"\\\\]');\nindex_esc = 1; len_esc = length(esc);\n\nopt=varargin2struct(varargin{:});\nfileendian=upper(jsonopt('IntEndian','B',opt));\n[os,maxelem,systemendian]=computer;\n\njsoncount=1;\nwhile pos <= len\n    switch(next_char)\n        case '{'\n            data{jsoncount} = parse_object(opt);\n        case '['\n            data{jsoncount} = parse_array(opt);\n        otherwise\n            error_pos('Outer level structure must be an object or an array');\n    end\n    jsoncount=jsoncount+1;\nend % while\n\njsoncount=length(data);\nif(jsoncount==1 && iscell(data))\n    data=data{1};\nend\n\nif(~isempty(data))\n      if(isstruct(data)) % data can be a struct array\n          data=jstruct2array(data);\n      elseif(iscell(data))\n          data=jcell2array(data);\n      end\nend\n\n\n%%\nfunction newdata=parse_collection(id,data,obj)\n\nif(jsoncount>0 && exist('data','var')) \n    if(~iscell(data))\n       newdata=cell(1);\n       newdata{1}=data;\n       data=newdata;\n    end\nend\n\n%%\nfunction newdata=jcell2array(data)\nlen=length(data);\nnewdata=data;\nfor i=1:len\n      if(isstruct(data{i}))\n          newdata{i}=jstruct2array(data{i});\n      elseif(iscell(data{i}))\n          newdata{i}=jcell2array(data{i});\n      end\nend\n\n%%-------------------------------------------------------------------------\nfunction newdata=jstruct2array(data)\nfn=fieldnames(data);\nnewdata=data;\nlen=length(data);\nfor i=1:length(fn) % depth-first\n    for j=1:len\n        if(isstruct(getfield(data(j),fn{i})))\n            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));\n        end\n    end\nend\nif(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))\n  newdata=cell(len,1);\n  for j=1:len\n    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);\n    iscpx=0;\n    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))\n        if(data(j).x0x5F_ArrayIsComplex_)\n           iscpx=1;\n        end\n    end\n    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))\n        if(data(j).x0x5F_ArrayIsSparse_)\n            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n                dim=double(data(j).x0x5F_ArraySize_);\n                if(iscpx && size(ndata,2)==4-any(dim==1))\n                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));\n                end\n                if isempty(ndata)\n                    % All-zeros sparse\n                    ndata=sparse(dim(1),prod(dim(2:end)));\n                elseif dim(1)==1\n                    % Sparse row vector\n                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));\n                elseif dim(2)==1\n                    % Sparse column vector\n                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));\n                else\n                    % Generic sparse array.\n                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));\n                end\n            else\n                if(iscpx && size(ndata,2)==4)\n                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));\n                end\n                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));\n            end\n        end\n    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))\n        if(iscpx && size(ndata,2)==2)\n             ndata=complex(ndata(:,1),ndata(:,2));\n        end\n        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);\n    end\n    newdata{j}=ndata;\n  end\n  if(len==1)\n      newdata=newdata{1};\n  end\nend\n\n%%-------------------------------------------------------------------------\nfunction object = parse_object(varargin)\n    parse_char('{');\n    object = [];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1); % TODO\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        count=double(parse_number());\n    end\n    if next_char ~= '}'\n        num=0;\n        while 1\n            str = parseStr(varargin{:});\n            if isempty(str)\n                error_pos('Name of value at position %d cannot be empty');\n            end\n            %parse_char(':');\n            val = parse_value(varargin{:});\n            num=num+1;\n            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );\n            if next_char == '}' || (count>=0 && num>=count)\n                break;\n            end\n            %parse_char(',');\n        end\n    end\n    if(count==-1)\n        parse_char('}');\n    end\n\n%%-------------------------------------------------------------------------\nfunction [cid,len]=elem_info(type)\nid=strfind('iUIlLdD',type);\ndataclass={'int8','uint8','int16','int32','int64','single','double'};\nbytelen=[1,1,2,4,8,4,8];\nif(id>0)\n    cid=dataclass{id};\n    len=bytelen(id);\nelse\n    error_pos('unsupported type at position %d');\nend\n%%-------------------------------------------------------------------------\n\n\nfunction [data adv]=parse_block(type,count,varargin)\nglobal pos inStr isoct fileendian systemendian\n[cid,len]=elem_info(type);\ndatastr=inStr(pos:pos+len*count-1);\nif(isoct)\n    newdata=int8(datastr);\nelse\n    newdata=uint8(datastr);\nend\nid=strfind('iUIlLdD',type);\nif(id<=5 && fileendian~=systemendian)\n    newdata=swapbytes(typecast(newdata,cid));\nend\ndata=typecast(newdata,cid);\nadv=double(len*count);\n\n%%-------------------------------------------------------------------------\n\n\nfunction object = parse_array(varargin) % JSON array is written in row-major order\nglobal pos inStr isoct\n    parse_char('[');\n    object = cell(0, 1);\n    dim=[];\n    type='';\n    count=-1;\n    if(next_char == '$')\n        type=inStr(pos+1);\n        pos=pos+2;\n    end\n    if(next_char == '#')\n        pos=pos+1;\n        if(next_char=='[')\n            dim=parse_array(varargin{:});\n            count=prod(double(dim));\n        else\n            count=double(parse_number());\n        end\n    end\n    if(~isempty(type))\n        if(count>=0)\n            [object adv]=parse_block(type,count,varargin{:});\n            if(~isempty(dim))\n                object=reshape(object,dim);\n            end\n            pos=pos+adv;\n            return;\n        else\n            endpos=matching_bracket(inStr,pos);\n            [cid,len]=elem_info(type);\n            count=(endpos-pos)/len;\n            [object adv]=parse_block(type,count,varargin{:});\n            pos=pos+adv;\n            parse_char(']');\n            return;\n        end\n    end\n    if next_char ~= ']'\n         while 1\n            val = parse_value(varargin{:});\n            object{end+1} = val;\n            if next_char == ']'\n                break;\n            end\n            %parse_char(',');\n         end\n    end\n    if(jsonopt('SimplifyCell',0,varargin{:})==1)\n      try\n        oldobj=object;\n        object=cell2mat(object')';\n        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)\n            object=oldobj;\n        elseif(size(object,1)>1 && ndims(object)==2)\n            object=object';\n        end\n      catch\n      end\n    end\n    if(count==-1)\n        parse_char(']');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction parse_char(c)\n    global pos inStr len\n    skip_whitespace;\n    if pos > len || inStr(pos) ~= c\n        error_pos(sprintf('Expected %c at position %%d', c));\n    else\n        pos = pos + 1;\n        skip_whitespace;\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction c = next_char\n    global pos inStr len\n    skip_whitespace;\n    if pos > len\n        c = [];\n    else\n        c = inStr(pos);\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction skip_whitespace\n    global pos inStr len\n    while pos <= len && isspace(inStr(pos))\n        pos = pos + 1;\n    end\n\n%%-------------------------------------------------------------------------\nfunction str = parseStr(varargin)\n    global pos inStr esc index_esc len_esc\n % len, ns = length(inStr), keyboard\n    type=inStr(pos);\n    if type ~= 'S' && type ~= 'C' && type ~= 'H'\n        error_pos('String starting with S expected at position %d');\n    else\n        pos = pos + 1;\n    end\n    if(type == 'C')\n        str=inStr(pos);\n        pos=pos+1;\n        return;\n    end\n    bytelen=double(parse_number());\n    if(length(inStr)>=pos+bytelen-1)\n        str=inStr(pos:pos+bytelen-1);\n        pos=pos+bytelen;\n    else\n        error_pos('End of file while expecting end of inStr');\n    end\n\n%%-------------------------------------------------------------------------\n\nfunction num = parse_number(varargin)\n    global pos inStr len isoct fileendian systemendian\n    id=strfind('iUIlLdD',inStr(pos));\n    if(isempty(id))\n        error_pos('expecting a number at position %d');\n    end\n    type={'int8','uint8','int16','int32','int64','single','double'};\n    bytelen=[1,1,2,4,8,4,8];\n    datastr=inStr(pos+1:pos+bytelen(id));\n    if(isoct)\n        newdata=int8(datastr);\n    else\n        newdata=uint8(datastr);\n    end\n    if(id<=5 && fileendian~=systemendian)\n        newdata=swapbytes(typecast(newdata,type{id}));\n    end\n    num=typecast(newdata,type{id});\n    pos = pos + bytelen(id)+1;\n\n%%-------------------------------------------------------------------------\n\nfunction val = parse_value(varargin)\n    global pos inStr len\n    true = 1; false = 0;\n\n    switch(inStr(pos))\n        case {'S','C','H'}\n            val = parseStr(varargin{:});\n            return;\n        case '['\n            val = parse_array(varargin{:});\n            return;\n        case '{'\n            val = parse_object(varargin{:});\n            if isstruct(val)\n                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))\n                    val=jstruct2array(val);\n                end\n            elseif isempty(val)\n                val = struct;\n            end\n            return;\n        case {'i','U','I','l','L','d','D'}\n            val = parse_number(varargin{:});\n            return;\n        case 'T'\n            val = true;\n            pos = pos + 1;\n            return;\n        case 'F'\n            val = false;\n            pos = pos + 1;\n            return;\n        case {'Z','N'}\n            val = [];\n            pos = pos + 1;\n            return;\n    end\n    error_pos('Value expected at position %d');\n%%-------------------------------------------------------------------------\n\nfunction error_pos(msg)\n    global pos inStr len\n    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);\n    if poShow(3) == poShow(2)\n        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after\n    end\n    msg = [sprintf(msg, pos) ': ' ...\n    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];\n    error( ['JSONparser:invalidFormat: ' msg] );\n\n%%-------------------------------------------------------------------------\n\nfunction str = valid_field(str)\nglobal isoct\n% From MATLAB doc: field names must begin with a letter, which may be\n% followed by any combination of letters, digits, and underscores.\n% Invalid characters will be converted to underscores, and the prefix\n% \"x0x[Hex code]_\" will be added if the first character is not a letter.\n    pos=regexp(str,'^[^A-Za-z]','once');\n    if(~isempty(pos))\n        if(~isoct)\n            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');\n        else\n            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));\n        end\n    end\n    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end\n    if(~isoct)\n        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');\n    else\n        pos=regexp(str,'[^0-9A-Za-z_]');\n        if(isempty(pos)) return; end\n        str0=str;\n        pos0=[0 pos(:)' length(str)];\n        str='';\n        for i=1:length(pos)\n            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];\n        end\n        if(pos(end)~=length(str))\n            str=[str str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\n    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';\n\n%%-------------------------------------------------------------------------\nfunction endpos = matching_quote(str,pos)\nlen=length(str);\nwhile(pos<len)\n    if(str(pos)=='\"')\n        if(~(pos>1 && str(pos-1)=='\\'))\n            endpos=pos;\n            return;\n        end        \n    end\n    pos=pos+1;\nend\nerror('unmatched quotation mark');\n%%-------------------------------------------------------------------------\nfunction [endpos e1l e1r maxlevel] = matching_bracket(str,pos)\nglobal arraytoken\nlevel=1;\nmaxlevel=level;\nendpos=0;\nbpos=arraytoken(arraytoken>=pos);\ntokens=str(bpos);\nlen=length(tokens);\npos=1;\ne1l=[];\ne1r=[];\nwhile(pos<=len)\n    c=tokens(pos);\n    if(c==']')\n        level=level-1;\n        if(isempty(e1r)) e1r=bpos(pos); end\n        if(level==0)\n            endpos=bpos(pos);\n            return\n        end\n    end\n    if(c=='[')\n        if(isempty(e1l)) e1l=bpos(pos); end\n        level=level+1;\n        maxlevel=max(maxlevel,level);\n    end\n    if(c=='\"')\n        pos=matching_quote(tokens,pos+1);\n    end\n    pos=pos+1;\nend\nif(endpos==0) \n    error('unmatched \"]\"');\nend\n\n"}
+{"plateform": "github", "repo_name": "wgshun/AndrewNG-Machinelearning-master", "name": "saveubjson.m", "ext": ".m", "path": "AndrewNG-Machinelearning-master/homework/machine-learning-ex1/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m", "size": 16123, "source_encoding": "utf_8", "md5": "61d4f51010aedbf97753396f5d2d9ec0", "text": "function json=saveubjson(rootname,obj,varargin)\n%\n% json=saveubjson(rootname,obj,filename)\n%    or\n% json=saveubjson(rootname,obj,opt)\n% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)\n%\n% convert a MATLAB object (cell, struct or array) into a Universal \n% Binary JSON (UBJSON) binary string\n%\n% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)\n% created on 2013/08/17\n%\n% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $\n%\n% input:\n%      rootname: the name of the root-object, when set to '', the root name\n%        is ignored, however, when opt.ForceRootName is set to 1 (see below),\n%        the MATLAB variable name will be used as the root name.\n%      obj: a MATLAB object (array, cell, cell array, struct, struct array)\n%      filename: a string for the file name to save the output UBJSON data\n%      opt: a struct for additional options, ignore to use default values.\n%        opt can have the following fields (first in [.|.] is the default)\n%\n%        opt.FileName [''|string]: a file name to save the output JSON data\n%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D\n%                         array in JSON array format; if sets to 1, an\n%                         array will be shown as a struct with fields\n%                         \"_ArrayType_\", \"_ArraySize_\" and \"_ArrayData_\"; for\n%                         sparse arrays, the non-zero elements will be\n%                         saved to _ArrayData_ field in triplet-format i.e.\n%                         (ix,iy,val) and \"_ArrayIsSparse_\" will be added\n%                         with a value of 1; for a complex array, the \n%                         _ArrayData_ array will include two columns \n%                         (4 for sparse) to record the real and imaginary \n%                         parts, and also \"_ArrayIsComplex_\":1 is added. \n%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem\n%                         will use true/false rather than 1/0.\n%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single\n%                         numerical element will be shown without a square\n%                         bracket, unless it is the root object; if 0, square\n%                         brackets are forced for any numerical arrays.\n%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson\n%                         will use the name of the passed obj variable as the \n%                         root object name; if obj is an expression and \n%                         does not have a name, 'root' will be used; if this \n%                         is set to 0 and rootname is empty, the root level \n%                         will be merged down to the lower level.\n%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),\n%                         for example, if opt.JSON='foo', the JSON data is\n%                         wrapped inside a function call as 'foo(...);'\n%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson \n%                         back to the string form\n%\n%        opt can be replaced by a list of ('param',value) pairs. The param \n%        string is equivallent to a field in opt and is case sensitive.\n% output:\n%      json: a binary string in the UBJSON format (see http://ubjson.org)\n%\n% examples:\n%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... \n%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...\n%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...\n%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...\n%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...\n%               'SpecialData',[nan, inf, -inf]);\n%      saveubjson('jsonmesh',jsonmesh)\n%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')\n%\n% license:\n%     BSD, see LICENSE_BSD.txt files for details\n%\n% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)\n%\n\nif(nargin==1)\n   varname=inputname(1);\n   obj=rootname;\n   if(isempty(varname)) \n      varname='root';\n   end\n   rootname=varname;\nelse\n   varname=inputname(2);\nend\nif(length(varargin)==1 && ischar(varargin{1}))\n   opt=struct('FileName',varargin{1});\nelse\n   opt=varargin2struct(varargin{:});\nend\nopt.IsOctave=exist('OCTAVE_VERSION','builtin');\nrootisarray=0;\nrootlevel=1;\nforceroot=jsonopt('ForceRootName',0,opt);\nif((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)\n    rootisarray=1;\n    rootlevel=0;\nelse\n    if(isempty(rootname))\n        rootname=varname;\n    end\nend\nif((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)\n    rootname='root';\nend\njson=obj2ubjson(rootname,obj,rootlevel,opt);\nif(~rootisarray)\n    json=['{' json '}'];\nend\n\njsonp=jsonopt('JSONP','',opt);\nif(~isempty(jsonp))\n    json=[jsonp '(' json ')'];\nend\n\n% save to a file if FileName is set, suggested by Patrick Rapin\nif(~isempty(jsonopt('FileName','',opt)))\n    fid = fopen(opt.FileName, 'wb');\n    fwrite(fid,json);\n    fclose(fid);\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=obj2ubjson(name,item,level,varargin)\n\nif(iscell(item))\n    txt=cell2ubjson(name,item,level,varargin{:});\nelseif(isstruct(item))\n    txt=struct2ubjson(name,item,level,varargin{:});\nelseif(ischar(item))\n    txt=str2ubjson(name,item,level,varargin{:});\nelse\n    txt=mat2ubjson(name,item,level,varargin{:});\nend\n\n%%-------------------------------------------------------------------------\nfunction txt=cell2ubjson(name,item,level,varargin)\ntxt='';\nif(~iscell(item))\n        error('input is not a cell');\nend\n\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item); % let's handle 1D cell first\nif(len>1) \n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) '[']; name=''; \n    else\n        txt='['; \n    end\nelseif(len==0)\n    if(~isempty(name))\n        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; \n    else\n        txt='Z'; \n    end\nend\nfor j=1:dim(2)\n    if(dim(1)>1) txt=[txt '[']; end\n    for i=1:dim(1)\n       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];\n    end\n    if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=struct2ubjson(name,item,level,varargin)\ntxt='';\nif(~isstruct(item))\n\terror('input is not a struct');\nend\ndim=size(item);\nif(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now\n    item=reshape(item,dim(1),numel(item)/dim(1));\n    dim=size(item);\nend\nlen=numel(item);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nfor j=1:dim(2)\n  if(dim(1)>1) txt=[txt '[']; end\n  for i=1:dim(1)\n     names = fieldnames(item(i,j));\n     if(~isempty(name) && len==1)\n        txt=[txt S_(checkname(name,varargin{:})) '{']; \n     else\n        txt=[txt '{']; \n     end\n     if(~isempty(names))\n       for e=1:length(names)\n\t     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...\n             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];\n       end\n     end\n     txt=[txt '}'];\n  end\n  if(dim(1)>1) txt=[txt ']']; end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=str2ubjson(name,item,level,varargin)\ntxt='';\nif(~ischar(item))\n        error('input is not a string');\nend\nitem=reshape(item, max(size(item),[1 0]));\nlen=size(item,1);\n\nif(~isempty(name)) \n    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end\nelse\n    if(len>1) txt='['; end\nend\nisoct=jsonopt('IsOctave',0,varargin{:});\nfor e=1:len\n    val=item(e,:);\n    if(len==1)\n        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];\n\tif(isempty(name)) obj=['',S_(val),'']; end\n        txt=[txt,'',obj];\n    else\n        txt=[txt,'',['',S_(val),'']];\n    end\nend\nif(len>1) txt=[txt ']']; end\n\n%%-------------------------------------------------------------------------\nfunction txt=mat2ubjson(name,item,level,varargin)\nif(~isnumeric(item) && ~islogical(item))\n        error('input is not an array');\nend\n\nif(length(size(item))>2 || issparse(item) || ~isreal(item) || ...\n   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))\n      cid=I_(uint32(max(size(item))));\n      if(isempty(name))\n    \ttxt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];\n      else\n          if(isempty(item))\n              txt=[S_(checkname(name,varargin{:})),'Z'];\n              return;\n          else\n    \t      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];\n          end\n      end\nelse\n    if(isempty(name))\n    \ttxt=matdata2ubjson(item,level+1,varargin{:});\n    else\n        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)\n            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\\[',''),']','');\n           \ttxt=[S_(checkname(name,varargin{:})) numtxt];\n        else\n    \t    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];\n        end\n    end\n    return;\nend\nif(issparse(item))\n    [ix,iy]=find(item);\n    data=full(item(find(item)));\n    if(~isreal(item))\n       data=[real(data(:)),imag(data(:))];\n       if(size(item,1)==1)\n           % Kludge to have data's 'transposedness' match item's.\n           % (Necessary for complex row vector handling below.)\n           data=data';\n       end\n       txt=[txt,S_('_ArrayIsComplex_'),'T'];\n    end\n    txt=[txt,S_('_ArrayIsSparse_'),'T'];\n    if(size(item,1)==1)\n        % Row vector, store only column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([iy(:),data'],level+2,varargin{:})];\n    elseif(size(item,2)==1)\n        % Column vector, store only row indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,data],level+2,varargin{:})];\n    else\n        % General case, store row and column indices.\n        txt=[txt,S_('_ArrayData_'),...\n           matdata2ubjson([ix,iy,data],level+2,varargin{:})];\n    end\nelse\n    if(isreal(item))\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson(item(:)',level+2,varargin{:})];\n    else\n        txt=[txt,S_('_ArrayIsComplex_'),'T'];\n        txt=[txt,S_('_ArrayData_'),...\n            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];\n    end\nend\ntxt=[txt,'}'];\n\n%%-------------------------------------------------------------------------\nfunction txt=matdata2ubjson(mat,level,varargin)\nif(isempty(mat))\n    txt='Z';\n    return;\nend\nif(size(mat,1)==1)\n    level=level-1;\nend\ntype='';\nhasnegtive=(mat<0);\nif(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))\n    if(isempty(hasnegtive))\n       if(max(mat(:))<=2^8)\n           type='U';\n       end\n    end\n    if(isempty(type))\n        % todo - need to consider negative ones separately\n        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);\n        if(isempty(find(id)))\n            error('high-precision data is not yet supported');\n        end\n        key='iIlL';\n\ttype=key(find(id));\n    end\n    txt=[I_a(mat(:),type,size(mat))];\nelseif(islogical(mat))\n    logicalval='FT';\n    if(numel(mat)==1)\n        txt=logicalval(mat+1);\n    else\n        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];\n    end\nelse\n    if(numel(mat)==1)\n        txt=['[' D_(mat) ']'];\n    else\n        txt=D_a(mat(:),'D',size(mat));\n    end\nend\n\n%txt=regexprep(mat2str(mat),'\\s+',',');\n%txt=regexprep(txt,';',sprintf('],['));\n% if(nargin>=2 && size(mat,1)>1)\n%     txt=regexprep(txt,'\\[',[repmat(sprintf('\\t'),1,level) '[']);\n% end\nif(any(isinf(mat(:))))\n    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','\"$1_Inf_\"',varargin{:}));\nend\nif(any(isnan(mat(:))))\n    txt=regexprep(txt,'NaN',jsonopt('NaN','\"_NaN_\"',varargin{:}));\nend\n\n%%-------------------------------------------------------------------------\nfunction newname=checkname(name,varargin)\nisunpack=jsonopt('UnpackHex',1,varargin{:});\nnewname=name;\nif(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))\n    return\nend\nif(isunpack)\n    isoct=jsonopt('IsOctave',0,varargin{:});\n    if(~isoct)\n        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');\n    else\n        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');\n        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');\n        if(isempty(pos)) return; end\n        str0=name;\n        pos0=[0 pend(:)' length(name)];\n        newname='';\n        for i=1:length(pos)\n            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];\n        end\n        if(pos(end)~=length(name))\n            newname=[newname str0(pos0(end-1)+1:pos0(end))];\n        end\n    end\nend\n%%-------------------------------------------------------------------------\nfunction val=S_(str)\nif(length(str)==1)\n  val=['C' str];\nelse\n  val=['S' I_(int32(length(str))) str];\nend\n%%-------------------------------------------------------------------------\nfunction val=I_(num)\nif(~isinteger(num))\n    error('input is not an integer');\nend\nif(num>=0 && num<255)\n   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];\n   return;\nend\nkey='iIlL';\ncid={'int8','int16','int32','int64'};\nfor i=1:4\n  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))\n    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];\n    return;\n  end\nend\nerror('unsupported integer');\n\n%%-------------------------------------------------------------------------\nfunction val=D_(num)\nif(~isfloat(num))\n    error('input is not a float');\nend\n\nif(isa(num,'single'))\n  val=['d' data2byte(num,'uint8')];\nelse\n  val=['D' data2byte(num,'uint8')];\nend\n%%-------------------------------------------------------------------------\nfunction data=I_a(num,type,dim,format)\nid=find(ismember('iUIlL',type));\n\nif(id==0)\n  error('unsupported integer array');\nend\n\n% based on UBJSON specs, all integer types are stored in big endian format\n\nif(id==1)\n  data=data2byte(swapbytes(int8(num)),'uint8');\n  blen=1;\nelseif(id==2)\n  data=data2byte(swapbytes(uint8(num)),'uint8');\n  blen=1;\nelseif(id==3)\n  data=data2byte(swapbytes(int16(num)),'uint8');\n  blen=2;\nelseif(id==4)\n  data=data2byte(swapbytes(int32(num)),'uint8');\n  blen=4;\nelseif(id==5)\n  data=data2byte(swapbytes(int64(num)),'uint8');\n  blen=8;\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];\n  end\n  data=['[' data(:)'];\nelse\n  data=reshape(data,blen,numel(data)/blen);\n  data(2:blen+1,:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction data=D_a(num,type,dim,format)\nid=find(ismember('dD',type));\n\nif(id==0)\n  error('unsupported float array');\nend\n\nif(id==1)\n  data=data2byte(single(num),'uint8');\nelseif(id==2)\n  data=data2byte(double(num),'uint8');\nend\n\nif(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))\n  format='opt';\nend\nif((nargin<4 || strcmp(format,'opt')) && numel(num)>1)\n  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))\n      cid=I_(uint32(max(dim)));\n      data=['$' type '#' I_a(dim,cid(1)) data(:)'];\n  else\n      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];\n  end\n  data=['[' data];\nelse\n  data=reshape(data,(id*4),length(data)/(id*4));\n  data(2:(id*4+1),:)=data;\n  data(1,:)=type;\n  data=data(:)';\n  data=['[' data(:)' ']'];\nend\n%%-------------------------------------------------------------------------\nfunction bytes=data2byte(varargin)\nbytes=typecast(varargin{:});\nbytes=bytes(:)';\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "buildWpyr.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildWpyr.m", "size": 2705, "source_encoding": "utf_8", "md5": "1c4ff4ecab086742bb93ea70f1e9e015", "text": "% [PYR, INDICES] = buildWpyr(IM, HEIGHT, FILT, EDGES)\r\n%\r\n% Construct a separable orthonormal QMF/wavelet pyramid on matrix (or vector) IM.\r\n%\r\n% HEIGHT (optional) specifies the number of pyramid levels to build. Default\r\n% is maxPyrHt(IM,FILT).  You can also specify 'auto' to use this value.\r\n%\r\n% FILT (optional) can be a string naming a standard filter (see\r\n% namedFilter), or a vector which will be used for (separable)\r\n% convolution.  Filter can be of even or odd length, but should be symmetric. \r\n% Default = 'qmf9'.  EDGES specifies edge-handling, and\r\n% defaults to 'reflect1' (see corrDn).\r\n%\r\n% PYR is a vector containing the N pyramid subbands, ordered from fine\r\n% to coarse.  INDICES is an Nx2 matrix containing the sizes of\r\n% each subband.  This is compatible with the MatLab Wavelet toolbox.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction [pyr,pind] = buildWpyr(im, ht, filt, edges)\r\n\r\nif (nargin < 1)\r\n  error('First argument (IM) is required');\r\nend\r\n\r\n%------------------------------------------------------------\r\n%% OPTIONAL ARGS:\r\n\r\nif (exist('filt') ~= 1)\r\n  filt = 'qmf9';\r\nend\r\n\r\nif (exist('edges') ~= 1)\r\n  edges= 'reflect1';\r\nend\r\n\r\nif isstr(filt)\r\n  filt = namedFilter(filt);\r\nend\r\n\r\nif ( (size(filt,1) > 1) & (size(filt,2) > 1) )\r\n  error('FILT should be a 1D filter (i.e., a vector)');\r\nelse\r\n  filt = filt(:);\r\nend\r\n\r\nhfilt = modulateFlip(filt);\r\n\r\n% Stagger sampling if filter is odd-length:\r\nif (mod(size(filt,1),2) == 0)\r\n  stag = 2;\r\nelse\r\n  stag = 1;\r\nend\r\n\r\nim_sz = size(im);\r\n\r\nmax_ht = maxPyrHt(im_sz, size(filt,1));\r\nif ( (exist('ht') ~= 1) | (ht == 'auto') )\r\n  ht = max_ht;\r\nelse\r\n  if (ht > max_ht)\r\n    error(sprintf('Cannot build pyramid higher than %d levels.',max_ht));\r\n  end\r\nend\r\n\r\nif (ht <= 0)\r\n\r\n  pyr = im(:);\r\n  pind = im_sz;\r\n\r\nelse\r\n\r\n  if (im_sz(2) == 1)\r\n    lolo = corrDn(im, filt, edges, [2 1], [stag 1]);\r\n    hihi = corrDn(im, hfilt, edges, [2 1], [2 1]);\r\n  elseif (im_sz(1) == 1)\r\n    lolo = corrDn(im, filt', edges, [1 2], [1 stag]);\r\n    hihi = corrDn(im, hfilt', edges, [1 2], [1 2]);\r\n  else\r\n    lo = corrDn(im, filt, edges, [2 1], [stag 1]);\r\n    hi = corrDn(im, hfilt, edges, [2 1], [2 1]);\r\n    lolo = corrDn(lo, filt', edges, [1 2], [1 stag]);\r\n    lohi = corrDn(hi, filt', edges, [1 2], [1 stag]); % horizontal\r\n    hilo = corrDn(lo, hfilt', edges, [1 2], [1 2]); % vertical\r\n    hihi = corrDn(hi, hfilt', edges, [1 2], [1 2]); % diagonal\r\n  end\r\n\r\n  [npyr,nind] = buildWpyr(lolo, ht-1, filt, edges);\r\n\r\n  if ((im_sz(1) == 1) | (im_sz(2) == 1))\r\n    pyr = [hihi(:); npyr];\r\n    pind = [size(hihi); nind];\r\n  else\r\n    pyr = [lohi(:); hilo(:); hihi(:); npyr];\r\n    pind = [size(lohi); size(hilo); size(hihi); nind];\r\n  end\r\n\r\nend\r\n  \r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "pyrBand.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pyrBand.m", "size": 406, "source_encoding": "utf_8", "md5": "0cad3c43324c84276383d06f6f6a5b60", "text": "% RES = pyrBand(PYR, INDICES, BAND_NUM)\r\n%\r\n% Access a subband from a pyramid (gaussian, laplacian, QMF/wavelet, \r\n% or steerable).  Subbands are numbered consecutively, from finest\r\n% (highest spatial frequency) to coarsest (lowest spatial frequency).\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction res =  pyrBand(pyr, pind, band)\r\n\r\nres = reshape( pyr(pyrBandIndices(pind,band)), pind(band,1), pind(band,2) );\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "buildFullSFpyr2.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildFullSFpyr2.m", "size": 3031, "source_encoding": "utf_8", "md5": "4a1191e10f08e0d219040bdc7864a575", "text": "% [PYR, INDICES, STEERMTX, HARMONICS] = buildFullSFpyr2(IM, HEIGHT, ORDER, TWIDTH)\n%\n% Construct a steerable pyramid on matrix IM, in the Fourier domain.\n% Unlike the standard transform, subdivides the highpass band into\n% orientations.\n\nfunction [pyr,pind,steermtx,harmonics] = buildFullSFpyr2(im, ht, order, twidth)\n\n%-----------------------------------------------------------------\n%% DEFAULTS:\n\nmax_ht = floor(log2(min(size(im)))+1);\n\nif (exist('ht') ~= 1)\n  ht = max_ht;\nelse\n  if (ht > max_ht)\n    error(sprintf('Cannot build pyramid higher than %d levels.',max_ht));\n  end\nend\n\nif (exist('order') ~= 1)\n  order = 3;\nelseif ((order > 15)  | (order < 0))\n  fprintf(1,'Warning: ORDER must be an integer in the range [0,15]. Truncating.\\n');\n  order = min(max(order,0),15);\nelse\n  order = round(order);\nend\nnbands = order+1;\n\nif (exist('twidth') ~= 1)\n  twidth = 1;\nelseif (twidth <= 0)\n  fprintf(1,'Warning: TWIDTH must be positive.  Setting to 1.\\n');\n  twidth = 1;\nend\n\n%-----------------------------------------------------------------\n%% Steering stuff:\n\nif (mod((nbands),2) == 0)\n  harmonics = [0:(nbands/2)-1]'*2 + 1;\nelse\n  harmonics = [0:(nbands-1)/2]'*2;\nend\n\nsteermtx = steer2HarmMtx(harmonics, pi*[0:nbands-1]/nbands, 'even');\n\n%-----------------------------------------------------------------\n\ndims = size(im);\nctr = ceil((dims+0.5)/2);\n\n[xramp,yramp] = meshgrid( ([1:dims(2)]-ctr(2))./(dims(2)/2), ...\n    ([1:dims(1)]-ctr(1))./(dims(1)/2) );\nangle = atan2(yramp,xramp);\nlog_rad = sqrt(xramp.^2 + yramp.^2);\nlog_rad(ctr(1),ctr(2)) =  log_rad(ctr(1),ctr(2)-1);\nlog_rad  = log2(log_rad);\n\n%% Radial transition function (a raised cosine in log-frequency):\n[Xrcos,Yrcos] = rcosFn(twidth,(-twidth/2),[0 1]);\nYrcos = sqrt(Yrcos);\n\nYIrcos = sqrt(1.0 - Yrcos.^2);\nlo0mask = pointOp(log_rad, YIrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\nimdft = fftshift(fft2(im));\nlo0dft =  imdft .* lo0mask;\n\n[pyr,pind] = buildSFpyrLevs(lo0dft, log_rad, Xrcos, Yrcos, angle, ht, nbands);\n\n%% Split the highpass band into orientations\n\nhi0mask = pointOp(log_rad, Yrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\n\nlutsize = 1024;\nXcosn = pi*[-(2*lutsize+1):(lutsize+1)]/lutsize;  % [-2*pi:pi]\norder = nbands-1;\nconst = (2^(2*order))*(factorial(order)^2)/(nbands*factorial(2*order));\nYcosn = sqrt(const) * (cos(Xcosn)).^order;\n\nbands = zeros(prod(size(imdft)), nbands);\nbind = zeros(nbands,2);\n\nfor b = 1:nbands\n    anglemask = pointOp(angle, Ycosn, Xcosn(1)+pi*(b-1)/nbands, Xcosn(2)-Xcosn(1));\n    Mask = ((-sqrt(-1))^(nbands-1))*anglemask.*hi0mask;\n    % make real the contents in the HF cross (to avoid information loss in these freqs.)\n    % It distributes evenly these contents among the nbands orientations\n    Mask(1,:) = ones(1,size(im,2))/sqrt(nbands);\n    Mask(2:size(im,1),1) = ones(size(im,1)-1,1)/sqrt(nbands);\n\n    banddft =  imdft .* Mask;\n    band = real(ifft2(fftshift(banddft)));\n    bands(:,b) = real(band(:));\n    bind(b,:)  = size(band);\nend\n\npyr = [bands(:); pyr];\npind = [bind; pind];\n\npind = [ [0 0]; pind];  %% Dummy highpass\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "var2.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/var2.m", "size": 393, "source_encoding": "utf_8", "md5": "46f01727b1895ebb42fea033f942c562", "text": "% V = VAR2(MTX,MEAN)\r\n%\r\n% Sample variance of a matrix.\r\n%  Passing MEAN (optional) makes the calculation faster.\r\n\r\nfunction res = var2(mtx, mn)\r\n\r\nif (exist('mn') ~= 1)\r\n  mn =  mean2(mtx);\r\nend\r\n\r\nif (isreal(mtx))\r\n  res = sum(sum(abs(mtx-mn).^2)) / (prod(size(mtx)) - 1);\r\nelse\r\n  res = sum(sum(real(mtx-mn).^2)) + i*sum(sum(imag(mtx-mn).^2));\r\n  res = res  / (prod(size(mtx)) - 1);\r\nend\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "reconSFpyrLevs.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconSFpyrLevs.m", "size": 2013, "source_encoding": "utf_8", "md5": "bff5aabf51101f5a06bf4a3a59d0de00", "text": "% RESDFT = reconSFpyrLevs(PYR,INDICES,LOGRAD,XRCOS,YRCOS,ANGLE,NBANDS,LEVS,BANDS)\r\n%\r\n% Recursive function for reconstructing levels of a steerable pyramid\r\n% representation.  This is called by reconSFpyr, and is not usually\r\n% called directly.\r\n\r\n% Eero Simoncelli, 5/97.\r\n\r\nfunction resdft = reconSFpyrLevs(pyr,pind,log_rad,Xrcos,Yrcos,angle,nbands,levs,bands);\r\n\r\nlo_ind = nbands+1;\r\ndims = pind(1,:);\r\nctr = ceil((dims+0.5)/2);\r\n\r\nlog_rad = log_rad + 1;\r\n\r\nif any(levs > 1)\r\n\r\n  lodims = ceil((dims-0.5)/2);\r\n  loctr = ceil((lodims+0.5)/2);\r\n  lostart = ctr-loctr+1;\r\n  loend = lostart+lodims-1;\r\n  nlog_rad = log_rad(lostart(1):loend(1),lostart(2):loend(2));\r\n  nangle = angle(lostart(1):loend(1),lostart(2):loend(2));\r\n\r\n  if  (size(pind,1) > lo_ind)\r\n    nresdft = reconSFpyrLevs( pyr(1+sum(prod(pind(1:lo_ind-1,:)')):size(pyr,1)),...\r\n\tpind(lo_ind:size(pind,1),:), ...\r\n\tnlog_rad, Xrcos, Yrcos, nangle, nbands,levs-1, bands);\r\n  else\r\n    nresdft = fftshift(fft2(pyrBand(pyr,pind,lo_ind)));\r\n  end\r\n\r\n  YIrcos = sqrt(abs(1.0 - Yrcos.^2));\r\n  lomask = pointOp(nlog_rad, YIrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\r\n\r\n  resdft = zeros(dims);\r\n  resdft(lostart(1):loend(1),lostart(2):loend(2)) = nresdft .* lomask;\r\n\r\nelse\r\n\r\n  resdft = zeros(dims);\r\n\r\nend\r\n\r\n\t\r\nif any(levs == 1)\r\n\r\n  lutsize = 1024;\r\n  Xcosn = pi*[-(2*lutsize+1):(lutsize+1)]/lutsize;  % [-2*pi:pi]\r\n  order = nbands-1;\r\n  %% divide by sqrt(sum_(n=0)^(N-1)  cos(pi*n/N)^(2(N-1)) )\r\n  const = (2^(2*order))*(factorial(order)^2)/(nbands*factorial(2*order));\r\n  Ycosn = sqrt(const) * (cos(Xcosn)).^order;\r\n  himask = pointOp(log_rad, Yrcos, Xrcos(1), Xrcos(2)-Xrcos(1),0);\r\n\r\n  ind = 1;\r\n  for b = 1:nbands\r\n    if any(bands == b)\r\n      anglemask = pointOp(angle,Ycosn,Xcosn(1)+pi*(b-1)/nbands,Xcosn(2)-Xcosn(1));\r\n      band = reshape(pyr(ind:ind+prod(dims)-1), dims(1), dims(2));\r\n      banddft = fftshift(fft2(band));\r\n      resdft = resdft + (i)^(nbands-1) * banddft.*anglemask.*himask;\r\n    end\r\n    ind = ind + prod(dims);\r\n  end\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "rcosFn.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/rcosFn.m", "size": 1167, "source_encoding": "utf_8", "md5": "e668c06ae18191dea3d63f2c3035cdcb", "text": "% [X, Y] = rcosFn(WIDTH, POSITION, VALUES)\r\n%\r\n% Return a lookup table (suitable for use by INTERP1) \r\n% containing a \"raised cosine\" soft threshold function:\r\n% \r\n%    Y =  VALUES(1) + (VALUES(2)-VALUES(1)) *\r\n%              cos^2( PI/2 * (X - POSITION + WIDTH)/WIDTH )\r\n%\r\n% WIDTH is the width of the region over which the transition occurs\r\n% (default = 1). POSITION is the location of the center of the\r\n% threshold (default = 0).  VALUES (default = [0,1]) specifies the\r\n% values to the left and right of the transition.\r\n\r\n% Eero Simoncelli, 7/96.\r\n\r\nfunction [X, Y] = rcosFn(width,position,values)\r\n\r\n%------------------------------------------------------------\r\n% OPTIONAL ARGS:\r\n\r\nif (exist('width') ~= 1)\r\n  width = 1;\r\nend\r\n\r\nif (exist('position') ~= 1)\r\n  position = 0;\r\nend\r\n\r\nif (exist('values') ~= 1)\r\n  values = [0,1];\r\nend\r\n\r\n%------------------------------------------------------------\r\n\r\nsz = 256;  %% arbitrary!\r\n\r\nX    = pi * [-sz-1:1] / (2*sz);\r\n\r\nY = values(1) + (values(2)-values(1)) * cos(X).^2;\r\n\r\n%    Make sure end values are repeated, for extrapolation...\r\nY(1) = Y(2);\r\nY(sz+3) = Y(sz+2);\r\n\r\nX = position + (2*width/pi) * (X + pi/4);\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "vector.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/vector.m", "size": 240, "source_encoding": "utf_8", "md5": "99db83250fc29065ecdb3bff900669d3", "text": "% [VEC] = vector(MTX)\r\n% \r\n% Pack elements of MTX into a column vector.  Same as VEC = MTX(:)\r\n% Previously named \"vectorize\" (changed to avoid overlap with Matlab's\r\n% \"vectorize\" function).\r\n\r\nfunction vec = vector(mtx)\r\n\r\nvec = mtx(:);\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "showIm.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/showIm.m", "size": 6332, "source_encoding": "utf_8", "md5": "fc722445cecdb4685413ce683c5c414f", "text": "% RANGE = showIm (MATRIX, RANGE, ZOOM, LABEL, NSHADES )\r\n% \r\n% Display a MatLab MATRIX as a grayscale image in the current figure,\r\n% inside the current axes.  If MATRIX is complex, the real and imaginary \r\n% parts are shown side-by-side, with the same grayscale mapping.\r\n% \r\n% If MATRIX is a string, it should be the name of a variable bound to a \r\n% MATRIX in the base (global) environment.  This matrix is displayed as an \r\n% image, with the title set to the string.\r\n% \r\n% RANGE (optional) is a 2-vector specifying the values that map to\r\n% black and white, respectively.  Passing a value of 'auto' (default)\r\n% sets RANGE=[min,max] (as in MatLab's imagesc).  'auto2' sets\r\n% RANGE=[mean-2*stdev, mean+2*stdev].  'auto3' sets\r\n% RANGE=[p1-(p2-p1)/8, p2+(p2-p1)/8], where p1 is the 10th percentile\r\n% value of the sorted MATRIX samples, and p2 is the 90th percentile\r\n% value.\r\n% \r\n% ZOOM specifies the number of matrix samples per screen pixel.  It\r\n% will be rounded to an integer, or 1 divided by an integer.  A value\r\n% of 'same' or 'auto' (default) causes the zoom value to be chosen\r\n% automatically to fit the image into the current axes.  A value of\r\n% 'full' fills the axis region (leaving no room for labels).  See\r\n% pixelAxes.m.\r\n% \r\n% If LABEL (optional, default = 1, unless zoom='full') is non-zero, the range \r\n% of values that are mapped into the gray colormap and the dimensions \r\n% (size) of the matrix and zoom factor are printed below the image.  If label \r\n% is a string, it is used as a title.\r\n% \r\n% NSHADES (optional) specifies the number of gray shades, and defaults\r\n% to the size of the current colormap.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\n%%TODO: should use \"newplot\"\r\n\r\nfunction range = showIm( im, range, zoom, label, nshades );\r\n\r\n%------------------------------------------------------------\r\n%% OPTIONAL ARGS:\r\n\r\nif (nargin < 1)\r\n  error('Requires at least one input argument.'); \r\nend\r\n\r\nMLv = version;\r\n\r\nif isstr(im)\r\n  if (strcmp(MLv(1),'4'))\r\n    error('Cannot pass string arg for MATRIX in MatLab version 4.x');\r\n  end\r\n  label = im;\r\n  im = evalin('base',im);\r\nend\r\n\r\nif (exist('range') ~= 1)\r\n  range = 'auto1';\r\nend\r\n\r\nif (exist('nshades') ~= 1)\r\n  nshades = size(colormap,1);\r\nend\r\nnshades = max( nshades, 2 );\r\n\r\nif (exist('zoom') ~= 1)\r\n  zoom = 'auto';\r\nend\r\n\r\nif (exist('label') ~= 1)\r\n  if strcmp(zoom,'full')\r\n    label = 0;\t\t\t\t% no labeling\r\n  else\t\t\t\t\t\r\n    label = 1;\t\t\t\t% just print grayrange & dims\r\n  end\r\nend\r\n\r\n%------------------------------------------------------------\r\n\r\n%% Automatic range calculation: \r\nif (strcmp(range,'auto1') | strcmp(range,'auto'))\r\n  if isreal(im)\r\n    [mn,mx] = range2(im);\r\n  else\r\n    [mn1,mx1] = range2(real(im));\r\n    [mn2,mx2] =  range2(imag(im));\r\n    mn = min(mn1,mn2);\r\n    mx = max(mx1,mx2);\r\n  end\r\n  if any(size(im)==1)\r\n    pad = (mx-mn)/12;\t\t\t% MAGIC NUMBER: graph padding\r\n    range = [mn-pad, mx+pad];\r\n  else\r\n    range = [mn,mx];\r\n  end\r\n\r\nelseif strcmp(range,'auto2')\r\n  if isreal(im)\r\n    stdev = sqrt(var2(im));\r\n    av = mean2(im);\r\n  else\r\n    stdev = sqrt((var2(real(im)) + var2(imag(im)))/2);\r\n    av = (mean2(real(im)) + mean2(imag(im)))/2;\r\n  end\r\n  range = [av-2*stdev,av+2*stdev]; \t% MAGIC NUMBER: 2 stdevs\r\n\r\nelseif strcmp(range, 'auto3')\r\n  percentile = 0.1;\t\t\t% MAGIC NUMBER: 0<p<0.5\r\n  [N,X] = histo(im);\r\n  binsz = X(2)-X(1);\r\n  N = N+1e-10;  % Ensure cumsum will be monotonic for call to interp1\r\n  cumN = [0, cumsum(N)]/sum(N);\r\n  cumX = [X(1)-binsz, X] + (binsz/2);\r\n  ctrRange = interp1(cumN,cumX, [percentile, 1-percentile]);\r\n  range = mean(ctrRange) + (ctrRange-mean(ctrRange))/(1-2*percentile);\r\n\r\nelseif isstr(range)\r\n  error(sprintf('Bad RANGE argument: %s',range))\r\n\r\nend\r\n\r\nif ((range(2) - range(1)) <= eps)\r\n  range(1) = range(1) - 0.5;\r\n  range(2) = range(2) + 0.5;\r\nend\r\n\r\n\r\nif isreal(im)\r\n  factor=1;\r\nelse\r\n  factor = 1+sqrt(-1);\r\nend\r\n\r\nxlbl_offset = 0; % default value\r\n\r\nif (~any(size(im)==1))\r\n  %% MatLab's \"image\" rounds when mapping to the colormap, so we compute\r\n  %%      (im-r1)*(nshades-1)/(r2-r1) + 1.5 \r\n  mult = ((nshades-1) / (range(2)-range(1)));\r\n  d_im = (mult * im) + factor*(1.5 - range(1)*mult);\r\nend\r\n\r\nif isreal(im)\r\n  if (any(size(im)==1))\r\n    hh = plot( im);\r\n    axis([1, prod(size(im)), range]);\r\n  else\r\n    hh = image( d_im );\r\n    axis('off');\r\n    zoom = pixelAxes(size(d_im),zoom);\r\n  end\r\nelse\r\n  if (any(size(im)==1))\r\n    subplot(2,1,1);\r\n    hh = plot(real(im));\r\n    axis([1, prod(size(im)), range]);\r\n    subplot(2,1,2);\r\n    hh = plot(imag(im));\r\n    axis([1, prod(size(im)), range]);\r\n  else\r\n    subplot(1,2,1);\r\n    hh = image(real(d_im));\r\n    axis('off'); zoom = pixelAxes(size(d_im),zoom);\r\n    ax = gca; orig_units = get(ax,'Units');\r\n    set(ax,'Units','points');\r\n    pos1 = get(ax,'Position');\r\n    set(ax,'Units',orig_units);\r\n    subplot(1,2,2);\r\n    hh = image(imag(d_im));\r\n    axis('off'); zoom = pixelAxes(size(d_im),zoom);\r\n    ax = gca; orig_units = get(ax,'Units');\r\n    set(ax,'Units','points');\r\n    pos2 = get(ax,'Position');\r\n    set(ax,'Units',orig_units);\r\n    xlbl_offset = (pos1(1)-pos2(1))/2;\r\n  end\r\nend  \r\n\r\nif ~any(size(im)==1)\r\n  colormap(gray(nshades));\r\nend\r\n\r\nif ((label ~= 0))\r\n  if isstr(label)\r\n    title(label);\r\n    h = get(gca,'Title');\r\n    orig_units = get(h,'Units');\r\n    set(h,'Units','points');\r\n    pos = get(h,'Position');\r\n    pos(1:2) = pos(1:2) + [xlbl_offset, -3]; % MAGIC NUMBER: y pixel offset\r\n    set(h,'Position',pos);\r\n    set(h,'Units',orig_units);\r\n  end\r\n\r\n  if (~any(size(im)==1))\r\n    if (zoom > 1)\r\n      zformat = sprintf('* %d',round(zoom));\r\n    else\r\n      zformat = sprintf('/ %d',round(1/zoom));\r\n    end\r\n    if isreal(im) \r\n      format=[' Range: [%.3g, %.3g] \\n Dims: [%d, %d] ', zformat];\r\n        else\r\n      format=['Range: [%.3g, %.3g]  ----  Dims: [%d, %d]', zformat];\r\n    end\r\n    xlabel(sprintf(format, range(1), range(2), size(im,1), size(im,2)));\r\n    h = get(gca,'Xlabel');\r\n    set(h,'FontSize', 9); \t\t% MAGIC NUMBER: font size!!!\r\n\r\n    orig_units = get(h,'Units');\r\n    set(h,'Units','points');  \r\n    pos = get(h,'Position');\r\n    pos(1:2) = pos(1:2) + [xlbl_offset, 10]; % MAGIC NUMBER: y offset in points\r\n    set(h,'Position',pos);\r\n    set(h,'Units',orig_units);\r\n\r\n    set(h,'Visible','on');\t\t% axis('image') turned the  xlabel  off...\r\n  end\r\nend\r\n\r\nreturn;\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "upConv.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/upConv.m", "size": 2750, "source_encoding": "utf_8", "md5": "c396c00dd8d50ee2a930427fead849be", "text": "% RES = upConv(IM, FILT, EDGES, STEP, START, STOP, RES)\r\n%\r\n% Upsample matrix IM, followed by convolution with matrix FILT.  These\r\n% arguments should be 1D or 2D matrices, and IM must be larger (in\r\n% both dimensions) than FILT.  The origin of filt\r\n% is assumed to be floor(size(filt)/2)+1.\r\n%\r\n% EDGES is a string determining boundary handling:\r\n%    'circular' - Circular convolution\r\n%    'reflect1' - Reflect about the edge pixels\r\n%    'reflect2' - Reflect, doubling the edge pixels\r\n%    'repeat'   - Repeat the edge pixels\r\n%    'zero'     - Assume values of zero outside image boundary\r\n%    'extend'   - Reflect and invert\r\n%    'dont-compute' - Zero output when filter overhangs OUTPUT boundaries\r\n%\r\n% Upsampling factors are determined by STEP (optional, default=[1 1]),\r\n% a 2-vector [y,x].\r\n% \r\n% The window over which the convolution occurs is specfied by START \r\n% (optional, default=[1,1], and STOP (optional, default = \r\n% step .* (size(IM) + floor((start-1)./step))).\r\n%\r\n% RES is an optional result matrix.  The convolution result will be \r\n% destructively added into this matrix.  If this argument is passed, the \r\n% result matrix will not be returned. DO NOT USE THIS ARGUMENT IF \r\n% YOU DO NOT UNDERSTAND WHAT THIS MEANS!!\r\n% \r\n% NOTE: this operation corresponds to multiplication of a signal\r\n% vector by a matrix whose columns contain copies of the time-reversed\r\n% (or space-reversed) FILT shifted by multiples of STEP.  See corrDn.m\r\n% for the operation corresponding to the transpose of this matrix.\r\n\r\n% Eero Simoncelli, 6/96.  revised 2/97.\r\n\r\nfunction result = upConv(im,filt,edges,step,start,stop,res)\r\n\r\n%% THIS CODE IS NOT ACTUALLY USED! (MEX FILE IS CALLED INSTEAD)\r\n\r\nfprintf(1,'Warning: You should compile the MEX code for \"upConv\", found in the MEX subdirectory.  It is much faster.\\n');\r\n\r\n%------------------------------------------------------------\r\n%% OPTIONAL ARGS:\r\n\r\nif (exist('edges') == 1) \r\n  if (strcmp(edges,'reflect1') ~= 1)\r\n    warning('Using REFLECT1 edge-handling (use MEX code for other options).');\r\n  end\r\nend\r\n\r\nif (exist('step') ~= 1)\r\n  step = [1,1];\r\nend\t\r\n\r\nif (exist('start') ~= 1)\r\n  start = [1,1];\r\nend\t\r\n\r\n% A multiple of step\r\nif (exist('stop') ~= 1)\r\n  stop = step .* (floor((start-ones(size(start)))./step)+size(im))\r\nend\t\r\n\r\nif ( ceil((stop(1)+1-start(1)) / step(1)) ~= size(im,1) )\r\n  error('Bad Y result dimension');\r\nend\r\nif ( ceil((stop(2)+1-start(2)) / step(2)) ~= size(im,2) )\r\n  error('Bad X result dimension');\r\nend\r\n\r\nif (exist('res') ~= 1)\r\n  res = zeros(stop-start+1);\r\nend\t\r\n\r\n%------------------------------------------------------------\r\n\r\ntmp = zeros(size(res));\r\ntmp(start(1):step(1):stop(1),start(2):step(2):stop(2)) = im;\r\n\r\nresult = rconv2(tmp,filt) + res;\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "range2.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/range2.m", "size": 477, "source_encoding": "utf_8", "md5": "4b8ed3c6da04efbf2862f0b36b91d524", "text": "% [MIN, MAX] = range2(MTX)\r\n%\r\n% Compute minimum and maximum values of MTX, returning them as a 2-vector.\r\n\r\n% Eero Simoncelli, 3/97.\r\n\r\nfunction [mn, mx] = range2(mtx)\r\n\r\n%% NOTE: THIS CODE IS NOT ACTUALLY USED! (MEX FILE IS CALLED INSTEAD)\r\n\r\nfprintf(1,'WARNING: You should compile the MEX code for \"range2\", found in the MEX subdirectory.  It is MUCH faster.\\n');\r\n\r\nif (~isreal(mtx))\r\n  error('MTX must be real-valued');  \r\nend\r\n\r\nmn = min(min(mtx));\r\nmx = max(max(mtx));\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "reconFullSFpyr2.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconFullSFpyr2.m", "size": 3257, "source_encoding": "utf_8", "md5": "447f5204894b159fbb4ea4de2dccf877", "text": "% RES = reconFullSFpyr2(PYR, INDICES, LEVS, BANDS, TWIDTH)\n%\n% Reconstruct image from its steerable pyramid representation, in the Fourier\n% domain, as created by buildSFpyr.\n% Unlike the standard transform, subdivides the highpass band into\n% orientations.\n\n\nfunction res = reconFullSFpyr2(pyr, pind, levs, bands, twidth)\n\n%%------------------------------------------------------------\n%% DEFAULTS:\n\nif (exist('levs') ~= 1)\n  levs = 'all';\nend\n\nif (exist('bands') ~= 1)\n  bands = 'all';\nend\n\nif (exist('twidth') ~= 1)\n  twidth = 1;\nelseif (twidth <= 0)\n  fprintf(1,'Warning: TWIDTH must be positive.  Setting to 1.\\n');\n  twidth = 1;\nend\n\n%%------------------------------------------------------------\n\nnbands = spyrNumBands(pind)/2;\n\nmaxLev =  2+spyrHt(pind(nbands+1:size(pind,1),:));\nif strcmp(levs,'all')\n  levs = [0:maxLev]';\nelse\n  if (any(levs > maxLev) | any(levs < 0))\n    error(sprintf('Level numbers must be in the range [0, %d].', maxLev));\n  end\n  levs = levs(:);\nend\n\nif strcmp(bands,'all')\n  bands = [1:nbands]';\nelse\n  if (any(bands < 1) | any(bands > nbands))\n    error(sprintf('Band numbers must be in the range [1,3].', nbands));\n  end\n  bands = bands(:);\nend\n\n%----------------------------------------------------------------------\n\ndims = pind(2,:);\nctr = ceil((dims+0.5)/2);\n\n[xramp,yramp] = meshgrid( ([1:dims(2)]-ctr(2))./(dims(2)/2), ...\n    ([1:dims(1)]-ctr(1))./(dims(1)/2) );\nangle = atan2(yramp,xramp);\nlog_rad = sqrt(xramp.^2 + yramp.^2);\nlog_rad(ctr(1),ctr(2)) =  log_rad(ctr(1),ctr(2)-1);\nlog_rad  = log2(log_rad);\n\n%% Radial transition function (a raised cosine in log-frequency):\n[Xrcos,Yrcos] = rcosFn(twidth,(-twidth/2),[0 1]);\nYrcos = sqrt(Yrcos);\nYIrcos = sqrt(1.0 - Yrcos.^2);\n\nif (size(pind,1) == 2)\n  if (any(levs==1))\n    resdft = fftshift(fft2(pyrBand(pyr,pind,2)));\n  else\n    resdft = zeros(pind(2,:));\n  end\nelse\n  resdft = reconSFpyrLevs(pyr(1+sum(prod(pind(1:nbands+1,:)')):size(pyr,1)), ...\n      pind(nbands+2:size(pind,1),:), ...\n      log_rad, Xrcos, Yrcos, angle, nbands, levs, bands);\nend\n \nlo0mask = pointOp(log_rad, YIrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\nresdft = resdft .* lo0mask;\n\n%% Oriented highpass bands:\nif any(levs == 0)\n  lutsize = 1024;\n  Xcosn = pi*[-(2*lutsize+1):(lutsize+1)]/lutsize;  % [-2*pi:pi]\n  order = nbands-1;\n  %% divide by sqrt(sum_(n=0)^(N-1)  cos(pi*n/N)^(2(N-1)) )\n  const = (2^(2*order))*(factorial(order)^2)/(nbands*factorial(2*order));\n  Ycosn = sqrt(const) * (cos(Xcosn)).^order;\n\n  hi0mask = pointOp(log_rad, Yrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\n\n  ind = 1;\n  for b = 1:nbands\n    if any(bands == b)\n      anglemask = pointOp(angle,Ycosn,Xcosn(1)+pi*(b-1)/nbands,Xcosn(2)-Xcosn(1));\n      band = reshape(pyr(ind:ind+prod(dims)-1), dims(1), dims(2));\n      banddft = fftshift(fft2(band));\n      % make real the contents in the HF cross (to avoid information loss in these freqs.)\n      % It distributes evenly these contents among the nbands orientations\n      Mask = (sqrt(-1))^(nbands-1) * anglemask.*hi0mask;\n      Mask(1,:) = ones(1,size(Mask,2))/sqrt(nbands);\n      Mask(2:size(Mask,1),1) = ones(size(Mask,1)-1,1)/sqrt(nbands);\n      \n      resdft = resdft + banddft.*Mask;\n    end\n    ind = ind + prod(dims);\n  end\nend\n\nres = real(ifft2(ifftshift(resdft)));\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "steer2HarmMtx.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/steer2HarmMtx.m", "size": 1874, "source_encoding": "utf_8", "md5": "8efc390a04b19bdec63526c7bbd1407e", "text": "% MTX = steer2HarmMtx(HARMONICS, ANGLES, REL_PHASES)\r\n%\r\n% Compute a steering matrix (maps a directional basis set onto the\r\n% angular Fourier harmonics).  HARMONICS is a vector specifying the\r\n% angular harmonics contained in the steerable basis/filters.  ANGLES \r\n% (optional) is a vector specifying the angular position of each filter.  \r\n% REL_PHASES (optional, default = 'even') specifies whether the harmonics \r\n% are cosine or sine phase aligned about those positions.\r\n% The result matrix is suitable for passing to the function STEER.\r\n\r\n% Eero Simoncelli, 7/96.\r\n\r\nfunction mtx = steer2HarmMtx(harmonics, angles, evenorodd)\r\n\r\n%%=================================================================\r\n%%% Optional Parameters:\r\n\r\nif (exist('evenorodd') ~= 1)\r\n  evenorodd = 'even';\r\nend\r\n\r\n% Make HARMONICS a row vector\r\nharmonics = harmonics(:)';\r\n\r\nnumh = 2*size(harmonics,2) - any(harmonics == 0);\r\n\r\nif (exist('angles') ~= 1)\r\n  angles = pi * [0:numh-1]'/numh;\r\nelse\r\n  angles = angles(:);\r\nend\r\n\r\n%%=================================================================\r\n\r\nif isstr(evenorodd)\r\n  if strcmp(evenorodd,'even')\r\n    evenorodd = 0;\r\n  elseif strcmp(evenorodd,'odd')\r\n    evenorodd = 1;\r\n  else\r\n    error('EVEN_OR_ODD should be the string  EVEN or ODD');\r\n  end\r\nend\r\n\r\n%% Compute inverse matrix, which maps Fourier components onto \r\n%% steerable basis.\r\nimtx = zeros(size(angles,1),numh);\r\ncol = 1;\r\nfor h=harmonics\r\n  args = h*angles;\r\n  if (h == 0)\r\n    imtx(:,col) = ones(size(angles));\r\n    col = col+1;\r\n  elseif evenorodd\r\n    imtx(:,col) = sin(args);\r\n    imtx(:,col+1) = -cos(args);\r\n    col = col+2;\r\n  else\r\n    imtx(:,col) = cos(args);\r\n    imtx(:,col+1) = sin(args);\r\n    col = col+2;\r\n  end\r\nend\r\n  \r\nr = rank(imtx);\r\nif (( r ~= numh ) & ( r ~= size(angles,1) ))\r\n  fprintf(2,'WARNING: matrix is not full rank');\r\nend  \r\n\r\nmtx = pinv(imtx);\r\n\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "subMtx.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/subMtx.m", "size": 441, "source_encoding": "utf_8", "md5": "18fe3fa6f65d3fbc8cd38682559c3619", "text": "% MTX = subMtx(VEC, DIMENSIONS, START_INDEX)\r\n%\r\n% Reshape a portion of VEC starting from START_INDEX (optional,\r\n% default=1) to the given dimensions.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction mtx = subMtx(vec, sz, offset)\r\n\r\nif (exist('offset') ~= 1)\r\n   offset = 1;\r\nend\r\n\r\nvec = vec(:);\r\nsz = sz(:);\r\n\r\nif (size(sz,1) ~= 2)\r\n  error('DIMENSIONS must be a 2-vector.');\r\nend\r\n\r\nmtx = reshape( vec(offset:offset+prod(sz)-1), sz(1), sz(2) );\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "spyrHt.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/spyrHt.m", "size": 321, "source_encoding": "utf_8", "md5": "acae1ee5a657f2e4d75b2e60e954a515", "text": "% [HEIGHT] = spyrHt(INDICES)\r\n%\r\n% Compute height of steerable pyramid with given index matrix.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction [ht] =  spyrHt(pind)\r\n\r\nnbands = spyrNumBands(pind);\r\n\r\n% Don't count lowpass, or highpass residual bands\r\nif (size(pind,1) > 2)\r\n  ht = (size(pind,1)-2)/nbands;\r\nelse\r\n  ht = 0;\r\nend\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "spyrBand.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/spyrBand.m", "size": 853, "source_encoding": "utf_8", "md5": "24b93860a4de0346982a44edcb390ab5", "text": "% [LEV,IND] = spyrBand(PYR,INDICES,LEVEL,BAND)\r\n%\r\n% Access a band from a steerable pyramid.\r\n% \r\n%   LEVEL indicates the scale (finest = 1, coarsest = spyrHt(INDICES)).\r\n% \r\n%   BAND (optional, default=1) indicates which subband \r\n%     (1 = vertical, rest proceeding anti-clockwise).\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction res =  spyrBand(pyr,pind,level,band)\r\n\r\nif (exist('level') ~= 1)\r\n  level = 1;\r\nend\r\n\r\nif (exist('band') ~= 1)\r\n  band = 1;\r\nend\r\n\r\nnbands = spyrNumBands(pind);\r\nif ((band > nbands) | (band < 1))\r\n  error(sprintf('Bad band number (%d) should be in range [1,%d].', band, nbands));\r\nend\t\r\n\r\nmaxLev = spyrHt(pind);\r\nif ((level > maxLev) | (level < 1))\r\n  error(sprintf('Bad level number (%d), should be in range [1,%d].', level, maxLev));\r\nend\r\n\r\nfirstband = 1 + band + nbands*(level-1);\r\nres  = pyrBand(pyr, pind, firstband);\r\n\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "wpyrBand.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/wpyrBand.m", "size": 912, "source_encoding": "utf_8", "md5": "ec3f9e1a26cc9775110888b67417876a", "text": "% RES = wpyrBand(PYR, INDICES, LEVEL, BAND)\r\n%\r\n% Access a subband from a separable QMF/wavelet pyramid.  \r\n% \r\n% LEVEL (optional, default=1) indicates the scale (finest = 1,\r\n% coarsest = wpyrHt(INDICES)).  \r\n% \r\n% BAND (optional, default=1) indicates which subband (1=horizontal,\r\n% 2=vertical, 3=diagonal).\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction im =  wpyrBand(pyr,pind,level,band)\r\n\r\nif (exist('level') ~= 1)\r\n  level = 1;\r\nend\r\n\r\nif (exist('band') ~= 1)\r\n  band = 1;\r\nend\r\n\r\nif ((pind(1,1) == 1) | (pind(1,2) ==1))\r\n  nbands = 1;\r\nelse\r\n  nbands = 3;\r\nend\r\n\t\t\r\nif ((band > nbands) | (band < 1))\r\n  error(sprintf('Bad band number (%d) should be in range [1,%d].', band, nbands));\r\nend\r\n\t\r\nmaxLev = wpyrHt(pind);\r\nif ((level > maxLev) | (level < 1))\r\n  error(sprintf('Bad level number (%d), should be in range [1,%d].', level, maxLev));\r\nend\r\n\r\nband = band + nbands*(level-1);\r\nim = pyrBand(pyr,pind,band);\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "innerProd.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/innerProd.m", "size": 415, "source_encoding": "utf_8", "md5": "e759ef690a2e4eadf5f81e0b8282888f", "text": "% RES = innerProd(MTX)\r\n%\r\n% Compute (MTX' * MTX) efficiently (i.e., without copying the matrix)\r\n\r\nfunction res = innerProd(mtx)\r\n\r\n%% NOTE: THIS CODE SHOULD NOT BE USED! (MEX FILE IS CALLED INSTEAD)\r\n\r\nfprintf(1,'WARNING: You should compile the MEX version of \"innerProd.c\",\\n         found in the MEX subdirectory of matlabPyrTools, and put it in your matlab path.  It is MUCH faster.\\n');\r\n\r\nres = mtx' * mtx;\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "reconSFpyr.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconSFpyr.m", "size": 3141, "source_encoding": "utf_8", "md5": "bb26483e3afdf1b4b46da4b35efaec7b", "text": "% RES = reconSFpyr(PYR, INDICES, LEVS, BANDS, TWIDTH)\r\n%\r\n% Reconstruct image from its steerable pyramid representation, in the Fourier\r\n% domain, as created by buildSFpyr.\r\n%\r\n% PYR is a vector containing the N pyramid subbands, ordered from fine\r\n% to coarse.  INDICES is an Nx2 matrix containing the sizes of\r\n% each subband.  This is compatible with the MatLab Wavelet toolbox.\r\n%\r\n% LEVS (optional) should be a list of levels to include, or the string\r\n% 'all' (default).  0 corresonds to the residual highpass subband.  \r\n% 1 corresponds to the finest oriented scale.  The lowpass band\r\n% corresponds to number spyrHt(INDICES)+1.\r\n%\r\n% BANDS (optional) should be a list of bands to include, or the string\r\n% 'all' (default).  1 = vertical, rest proceeding anti-clockwise.\r\n%\r\n% TWIDTH is the width of the transition region of the radial lowpass\r\n% function, in octaves (default = 1, which gives a raised cosine for\r\n% the bandpass filters).\r\n\r\n% Eero Simoncelli, 5/97.\r\n\r\nfunction res = reconSFpyr(pyr, pind, levs, bands, twidth)\r\n\r\n%%------------------------------------------------------------\r\n%% DEFAULTS:\r\n\r\nif (exist('levs') ~= 1)\r\n  levs = 'all';\r\nend\r\n\r\nif (exist('bands') ~= 1)\r\n  bands = 'all';\r\nend\r\n\r\nif (exist('twidth') ~= 1)\r\n  twidth = 1;\r\nelseif (twidth <= 0)\r\n  fprintf(1,'Warning: TWIDTH must be positive.  Setting to 1.\\n');\r\n  twidth = 1;\r\nend\r\n\r\n%%------------------------------------------------------------\r\n\r\nnbands = spyrNumBands(pind);\r\n\r\nmaxLev =  1+spyrHt(pind);\r\nif strcmp(levs,'all')\r\n  levs = [0:maxLev]';\r\nelse\r\n  if (any(levs > maxLev) | any(levs < 0))\r\n    error(sprintf('Level numbers must be in the range [0, %d].', maxLev));\r\n  end\r\n  levs = levs(:);\r\nend\r\n\r\nif strcmp(bands,'all')\r\n  bands = [1:nbands]';\r\nelse\r\n  if (any(bands < 1) | any(bands > nbands))\r\n    error(sprintf('Band numbers must be in the range [1,3].', nbands));\r\n  end\r\n  bands = bands(:);\r\nend\r\n\r\n%----------------------------------------------------------------------\r\n\r\ndims = pind(1,:);\r\nctr = ceil((dims+0.5)/2);\r\n\r\n[xramp,yramp] = meshgrid( ([1:dims(2)]-ctr(2))./(dims(2)/2), ...\r\n    ([1:dims(1)]-ctr(1))./(dims(1)/2) );\r\nangle = atan2(yramp,xramp);\r\nlog_rad = sqrt(xramp.^2 + yramp.^2);\r\nlog_rad(ctr(1),ctr(2)) =  log_rad(ctr(1),ctr(2)-1);\r\nlog_rad  = log2(log_rad);\r\n\r\n%% Radial transition function (a raised cosine in log-frequency):\r\n[Xrcos,Yrcos] = rcosFn(twidth,(-twidth/2),[0 1]);\r\nYrcos = sqrt(Yrcos);\r\nYIrcos = sqrt(abs(1.0 - Yrcos.^2));\r\n\r\nif (size(pind,1) == 2)\r\n  if (any(levs==1))\r\n    resdft = fftshift(fft2(pyrBand(pyr,pind,2)));\r\n  else\r\n    resdft = zeros(pind(2,:));\r\n  end\r\nelse\r\n  resdft = reconSFpyrLevs(pyr(1+prod(pind(1,:)):size(pyr,1)), ...\r\n      pind(2:size(pind,1),:), ...\r\n      log_rad, Xrcos, Yrcos, angle, nbands, levs, bands);\r\nend\r\n\r\nlo0mask = pointOp(log_rad, YIrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\r\nresdft = resdft .* lo0mask;\r\n\r\n%% residual highpass subband\r\nif any(levs == 0)\r\n  hi0mask = pointOp(log_rad, Yrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\r\n  hidft = fftshift(fft2(subMtx(pyr, pind(1,:))));\r\n  resdft = resdft + hidft .* hi0mask;\r\nend\r\n \r\nres = real(ifft2(ifftshift(resdft)));\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "corrDn.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/corrDn.m", "size": 2195, "source_encoding": "utf_8", "md5": "61533e2b3b4b039c523120d2ec1363aa", "text": "% RES = corrDn(IM, FILT, EDGES, STEP, START, STOP)\r\n%\r\n% Compute correlation of matrices IM with FILT, followed by\r\n% downsampling.  These arguments should be 1D or 2D matrices, and IM\r\n% must be larger (in both dimensions) than FILT.  The origin of filt\r\n% is assumed to be floor(size(filt)/2)+1.\r\n% \r\n% EDGES is a string determining boundary handling:\r\n%    'circular' - Circular convolution\r\n%    'reflect1' - Reflect about the edge pixels\r\n%    'reflect2' - Reflect, doubling the edge pixels\r\n%    'repeat'   - Repeat the edge pixels\r\n%    'zero'     - Assume values of zero outside image boundary\r\n%    'extend'   - Reflect and invert\r\n%    'dont-compute' - Zero output when filter overhangs input boundaries\r\n%\r\n% Downsampling factors are determined by STEP (optional, default=[1 1]), \r\n% which should be a 2-vector [y,x].\r\n% \r\n% The window over which the convolution occurs is specfied by START \r\n% (optional, default=[1,1], and STOP (optional, default=size(IM)).\r\n% \r\n% NOTE: this operation corresponds to multiplication of a signal\r\n% vector by a matrix whose rows contain copies of the FILT shifted by\r\n% multiples of STEP.  See upConv.m for the operation corresponding to\r\n% the transpose of this matrix.\r\n\r\n% Eero Simoncelli, 6/96, revised 2/97.\r\n\r\nfunction res = corrDn(im, filt, edges, step, start, stop)\r\n\r\n%% NOTE: THIS CODE IS NOT ACTUALLY USED! (MEX FILE IS CALLED INSTEAD)\r\n\r\nfprintf(1,'Warning: You should compile the MEX code for \"corrDn\", found in the MEX subdirectory.  It is MUCH faster.\\n');\r\n\r\n%------------------------------------------------------------\r\n%% OPTIONAL ARGS:\r\n\r\nif (exist('edges') == 1) \r\n  if (strcmp(edges,'reflect1') ~= 1)\r\n    warning('Using REFLECT1 edge-handling (use MEX code for other options).');\r\n  end\r\nend\r\n\r\nif (exist('step') ~= 1)\r\n\tstep = [1,1];\r\nend\t\r\n\r\nif (exist('start') ~= 1)\r\n\tstart = [1,1];\r\nend\t\r\n\r\nif (exist('stop') ~= 1)\r\n\tstop = size(im);\r\nend\t\r\n\r\n%------------------------------------------------------------\r\n\r\n% Reverse order of taps in filt, to do correlation instead of convolution\r\nfilt = filt(size(filt,1):-1:1,size(filt,2):-1:1);\r\n\r\ntmp = rconv2(im,filt);\r\nres = tmp(start(1):step(1):stop(1),start(2):step(2):stop(2));\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "maxPyrHt.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/maxPyrHt.m", "size": 628, "source_encoding": "utf_8", "md5": "00ee02d58475fcdf592ef59a15fa0af3", "text": "% HEIGHT = maxPyrHt(IMSIZE, FILTSIZE)\r\n%\r\n% Compute maximum pyramid height for given image and filter sizes.\r\n% Specifically: the number of corrDn operations that can be sequentially\r\n% performed when subsampling by a factor of 2.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction height = maxPyrHt(imsz, filtsz)\r\n\r\nimsz = imsz(:);\r\nfiltsz = filtsz(:);\r\n\r\nif any(imsz == 1) % 1D image\r\n  imsz = prod(imsz);\r\n  filtsz = prod(filtsz);\r\nelseif any(filtsz == 1)              % 2D image, 1D filter\r\n  filtsz = [filtsz(1); filtsz(1)];\r\nend\r\n\r\nif any(imsz < filtsz)\r\n  height = 0;\r\nelse\r\n  height = 1 + maxPyrHt( floor(imsz/2), filtsz ); \r\nend\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "buildSFpyrLevs.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildSFpyrLevs.m", "size": 1887, "source_encoding": "utf_8", "md5": "e58fd43a3b9e8101ef5ada17c5116eed", "text": "% [PYR, INDICES] = buildSFpyrLevs(LODFT, LOGRAD, XRCOS, YRCOS, ANGLE, HEIGHT, NBANDS)\r\n%\r\n% Recursive function for constructing levels of a steerable pyramid.  This\r\n% is called by buildSFpyr, and is not usually called directly.\r\n\r\n% Eero Simoncelli, 5/97.\r\n\r\nfunction [pyr,pind] = buildSFpyrLevs(lodft,log_rad,Xrcos,Yrcos,angle,ht,nbands);\r\n\r\nif (ht <= 0)\r\n\r\n  lo0 = ifft2(ifftshift(lodft));\r\n  pyr = real(lo0(:));\r\n  pind = size(lo0);\r\n\r\nelse\r\n\r\n  bands = zeros(prod(size(lodft)), nbands);\r\n  bind = zeros(nbands,2);\r\n\r\n  log_rad = log_rad + 1;\r\n\r\n  lutsize = 1024;\r\n  Xcosn = pi*[-(2*lutsize+1):(lutsize+1)]/lutsize;  % [-2*pi:pi]\r\n  order = nbands-1;\r\n  %% divide by sqrt(sum_(n=0)^(N-1)  cos(pi*n/N)^(2(N-1)) )\r\n  %% Thanks to Patrick Teo for writing this out :)\r\n  const = (2^(2*order))*(factorial(order)^2)/(nbands*factorial(2*order));\r\n  Ycosn = sqrt(const) * (cos(Xcosn)).^order;\r\n  himask = pointOp(log_rad, Yrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\r\n\r\n  for b = 1:nbands\r\n    anglemask = pointOp(angle, Ycosn, Xcosn(1)+pi*(b-1)/nbands, Xcosn(2)-Xcosn(1));\r\n    banddft = ((-i)^(nbands-1)) .* lodft .* anglemask .* himask;\r\n    band = ifft2(ifftshift(banddft));\r\n\r\n    bands(:,b) = real(band(:));\r\n    bind(b,:)  = size(band);\r\n  end\r\n\r\n  dims = size(lodft);\r\n  ctr = ceil((dims+0.5)/2);\r\n  lodims = ceil((dims-0.5)/2);\r\n  loctr = ceil((lodims+0.5)/2);\r\n  lostart = ctr-loctr+1;\r\n  loend = lostart+lodims-1;\r\n\r\n  log_rad = log_rad(lostart(1):loend(1),lostart(2):loend(2));\r\n  angle = angle(lostart(1):loend(1),lostart(2):loend(2));\r\n  lodft = lodft(lostart(1):loend(1),lostart(2):loend(2));\r\n  YIrcos = abs(sqrt(1.0 - Yrcos.^2));\r\n  lomask = pointOp(log_rad, YIrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\r\n\r\n  lodft = lomask .* lodft;\r\n\r\n  [npyr,nind] = buildSFpyrLevs(lodft, log_rad, Xrcos, Yrcos, angle, ht-1, nbands);\r\n\r\n  pyr = [bands(:); npyr];\r\n  pind = [bind; nind];\r\n\r\nend\r\n\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "pixelAxes.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pixelAxes.m", "size": 2053, "source_encoding": "utf_8", "md5": "177c4d9d58d2280676a45dd83ef3e50a", "text": "% [ZOOM] = pixelAxes(DIMS, ZOOM)\r\n%\r\n% Set the axes of the current plot to cover a multiple of DIMS pixels,\r\n% thereby eliminating screen aliasing artifacts when displaying an\r\n% image of size DIMS.  \r\n% \r\n% ZOOM (optional, default='same') expresses the desired number of\r\n% samples displayed per screen pixel.  It should be a scalar, which\r\n% will be rounded to the nearest integer, or 1 over an integer.  It\r\n% may also be the string 'same' or 'auto', in which case the value is chosen so\r\n% as to produce an image closest in size to the currently displayed\r\n% image.  It may also be the string 'full', in which case the image is\r\n% made as large as possible while still fitting in the window.\r\n\r\n% Eero Simoncelli, 2/97.\r\n\r\nfunction [zoom] = pixelAxes(dims, zoom)\r\n\r\n%------------------------------------------------------------\r\n%% OPTIONAL ARGS:\r\n\r\nif (exist('zoom') ~= 1)\r\n  zoom = 'same';\r\nend\r\n\r\n%% Reverse  dimension order, since Figure Positions reported as (x,y).\r\ndims = dims(2:-1:1);\r\n\r\n%% Use MatLab's axis function to force square pixels, etc:\r\naxis('image');\r\nax = gca;\r\n\r\noldunits = get(ax,'Units');\r\n\r\nif strcmp(zoom,'full');\r\n  set(ax,'Units','normalized');\r\n  set(ax,'Position',[0 0 1 1]);\r\n  zoom = 'same';\r\nend\r\n\r\nset(ax,'Units','pixels');\r\npos = get(ax,'Position');\r\nctr = pos(1:2)+pos(3:4)/2;\r\n\r\nif (strcmp(zoom,'same') | strcmp(zoom,'auto'))\r\n  %% HACK: enlarge slightly so that floor doesn't round down \r\n  zoom = min( pos(3:4) ./ (dims - 1) );\r\nelseif isstr(zoom)\r\n  error(sprintf('Bad ZOOM argument: %s',zoom));\r\nend\r\n\r\n%% Force zoom value to be an integer, or inverse integer.\r\nif (zoom < 0.75)\r\n  zoom = 1/ceil(1/zoom);\r\n  %% Round upward, subtracting 0.5 to avoid floating point errors.\r\n  newsz = ceil(zoom*(dims-0.5));\r\nelse\r\n  zoom = floor(zoom + 0.001);\t\t% Avoid floating pt errors\r\n  if (zoom < 1.5)\t\t\t% zoom=1\r\n    zoom = 1;\r\n    newsz = dims + 0.5;\r\n  else\r\n    newsz = zoom*(dims-1) + mod(zoom,2);\r\n  end\r\nend \r\n\r\nset(ax,'Position', [floor(ctr-newsz/2)+0.5, newsz] )\r\n\r\n% Restore units\r\nset(ax,'Units',oldunits);\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "pyrBandIndices.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pyrBandIndices.m", "size": 613, "source_encoding": "utf_8", "md5": "a5387a946b44f72051b9a72faae6130c", "text": "% RES = pyrBandIndices(INDICES, BAND_NUM)\r\n%\r\n% Return indices for accessing a subband from a pyramid \r\n% (gaussian, laplacian, QMF/wavelet, steerable).\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction indices =  pyrBandIndices(pind,band)\r\n\r\nif ((band > size(pind,1)) | (band < 1))\r\n  error(sprintf('BAND_NUM must be between 1 and number of pyramid bands (%d).', ...\r\n      size(pind,1)));\r\nend\r\n\r\nif (size(pind,2) ~= 2)\r\n  error('INDICES must be an Nx2 matrix indicating the size of the pyramid subbands');\r\nend\r\n\r\nind = 1;\r\nfor l=1:band-1\r\n  ind = ind + prod(pind(l,:));\r\nend\r\n\r\nindices = ind:ind+prod(pind(band,:))-1;\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "pointOp.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pointOp.m", "size": 1159, "source_encoding": "utf_8", "md5": "040e1c3bc4afc4f9cfab4aa964e16082", "text": "% RES = pointOp(IM, LUT, ORIGIN, INCREMENT, WARNINGS)\r\n%\r\n% Apply a point operation, specified by lookup table LUT, to image IM.\r\n% LUT must be a row or column vector, and is assumed to contain\r\n% (equi-spaced) samples of the function.  ORIGIN specifies the\r\n% abscissa associated with the first sample, and INCREMENT specifies the\r\n% spacing between samples.  Between-sample values are estimated via\r\n% linear interpolation.  If WARNINGS is non-zero, the function prints\r\n% a warning whenever the lookup table is extrapolated.\r\n%\r\n% This function is much faster than MatLab's interp1, and allows\r\n% extrapolation beyond the lookup table domain.  The drawbacks are\r\n% that the lookup table must be equi-spaced, and the interpolation is\r\n% linear.\r\n\r\n% Eero Simoncelli, 8/96.\r\n\r\nfunction res = pointOp(im, lut, origin, increment, warnings)\r\n\r\n%% NOTE: THIS CODE IS NOT ACTUALLY USED! (MEX FILE IS CALLED INSTEAD)\r\n\r\nfprintf(1,'WARNING: You should compile the MEX code for \"pointOp\", found in the MEX subdirectory.  It is MUCH faster.\\n');\r\n\r\nX = origin + increment*[0:size(lut(:),1)-1];\r\nY = lut(:);\r\n\r\nres = reshape(interp1(X, Y, im(:), 'linear'),size(im));\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "reconWpyr.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconWpyr.m", "size": 4140, "source_encoding": "utf_8", "md5": "de1ba8ead0f28186c92b026ec2d0631a", "text": "% RES = reconWpyr(PYR, INDICES, FILT, EDGES, LEVS, BANDS)\r\n%\r\n% Reconstruct image from its separable orthonormal QMF/wavelet pyramid\r\n% representation, as created by buildWpyr.\r\n%\r\n% PYR is a vector containing the N pyramid subbands, ordered from fine\r\n% to coarse.  INDICES is an Nx2 matrix containing the sizes of\r\n% each subband.  This is compatible with the MatLab Wavelet toolbox.\r\n%\r\n% FILT (optional) can be a string naming a standard filter (see\r\n% namedFilter), or a vector which will be used for (separable)\r\n% convolution.  Default = 'qmf9'.  EDGES specifies edge-handling,\r\n% and defaults to 'reflect1' (see corrDn).\r\n%\r\n% LEVS (optional) should be a vector of levels to include, or the string\r\n% 'all' (default).  1 corresponds to the finest scale.  The lowpass band\r\n% corresponds to wpyrHt(INDICES)+1.\r\n%\r\n% BANDS (optional) should be a vector of bands to include, or the string\r\n% 'all' (default).   1=horizontal, 2=vertical, 3=diagonal.  This is only used\r\n% for pyramids of 2D images.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction res = reconWpyr(pyr, ind, filt, edges, levs, bands)\r\n\r\nif (nargin < 2)\r\n  error('First two arguments (PYR INDICES) are required');\r\nend\r\n\r\n%%------------------------------------------------------------\r\n%% OPTIONAL ARGS:\r\n\r\nif (exist('filt') ~= 1)\r\n  filt = 'qmf9';\r\nend\r\n\r\nif (exist('edges') ~= 1)\r\n  edges= 'reflect1';\r\nend\r\n\r\nif (exist('levs') ~= 1)\r\n  levs = 'all';\r\nend\r\n\r\nif (exist('bands') ~= 1)\r\n  bands = 'all';\r\nend\r\n\r\n%%------------------------------------------------------------\r\n\r\nmaxLev = 1+wpyrHt(ind);\r\nif strcmp(levs,'all')\r\n  levs = [1:maxLev]';\r\nelse\r\n  if (any(levs > maxLev))\r\n    error(sprintf('Level numbers must be in the range [1, %d].', maxLev));\r\n  end\r\n  levs = levs(:);\r\nend\r\n\r\nif strcmp(bands,'all')\r\n  bands = [1:3]';\r\nelse\r\n  if (any(bands < 1) | any(bands > 3))\r\n    error('Band numbers must be in the range [1,3].');\r\n  end\r\n  bands = bands(:);\r\nend\r\n\r\nif isstr(filt)\r\n  filt = namedFilter(filt);\r\nend\r\n\r\nfilt = filt(:);\r\nhfilt = modulateFlip(filt);\r\n\r\n%% For odd-length filters, stagger the sampling lattices:\r\nif (mod(size(filt,1),2) == 0)\r\n\tstag = 2;\r\nelse\r\n\tstag = 1;\r\nend\r\n\r\n%% Compute size of result image: assumes critical sampling (boundaries correct)\r\nres_sz = ind(1,:);\r\nif (res_sz(1) == 1)\r\n  loind = 2;\r\n  res_sz(2) = sum(ind(:,2));\r\nelseif (res_sz(2) == 1)\t\r\n  loind = 2;\r\n  res_sz(1) = sum(ind(:,1));\r\nelse\r\n  loind = 4;\r\n  res_sz = ind(1,:) + ind(2,:);  %%horizontal + vertical bands.\r\n  hres_sz = [ind(1,1), res_sz(2)];\r\n  lres_sz = [ind(2,1), res_sz(2)];\r\nend\r\n\t\r\n\r\n%% First, recursively collapse coarser scales:\r\nif any(levs > 1)  \r\n\r\n  if (size(ind,1) > loind)\r\n    nres = reconWpyr( pyr(1+sum(prod(ind(1:loind-1,:)')):size(pyr,1)), ...\r\n\tind(loind:size(ind,1),:), filt, edges, levs-1, bands);\r\n  else\r\n    nres = pyrBand(pyr, ind, loind); \t% lowpass subband\r\n  end\r\n\r\n  if (res_sz(1) == 1)\r\n    res = upConv(nres, filt', edges, [1 2], [1 stag], res_sz);\r\n  elseif (res_sz(2) == 1)\r\n    res = upConv(nres, filt, edges, [2 1], [stag 1], res_sz);\r\n  else\r\n    ires = upConv(nres, filt', edges, [1 2], [1 stag], lres_sz); \r\n    res = upConv(ires, filt, edges, [2 1], [stag 1], res_sz);\r\n  end\r\n  \r\nelse\r\n\r\n  res = zeros(res_sz);\r\n\r\nend\r\n\r\n\t\r\n%% Add  in reconstructed bands from this level:\r\nif any(levs == 1)\r\n  if (res_sz(1) == 1)\r\n    upConv(pyrBand(pyr,ind,1), hfilt', edges, [1 2], [1 2], res_sz, res);\r\n  elseif (res_sz(2) == 1)\r\n    upConv(pyrBand(pyr,ind,1), hfilt, edges, [2 1], [2 1], res_sz, res);\r\n  else\r\n    if any(bands == 1) % horizontal\r\n      ires = upConv(pyrBand(pyr,ind,1),filt',edges,[1 2],[1 stag],hres_sz);\r\n      upConv(ires,hfilt,edges,[2 1],[2 1],res_sz,res);  %destructively modify res\r\n    end\r\n    if any(bands == 2) % vertical\r\n      ires = upConv(pyrBand(pyr,ind,2),hfilt',edges,[1 2],[1 2],lres_sz);\r\n      upConv(ires,filt,edges,[2 1],[stag 1],res_sz,res);  %destructively modify res\r\n    end\r\n    if any(bands == 3) % diagonal\r\n      ires =  upConv(pyrBand(pyr,ind,3),hfilt',edges,[1 2],[1 2],hres_sz);\r\n      upConv(ires,hfilt,edges,[2 1],[2 1],res_sz,res);  %destructively modify res\r\n    end\r\n  end\r\nend\r\n  \r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "shift.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/shift.m", "size": 453, "source_encoding": "utf_8", "md5": "3e12f9ab9679c3cc88b56885c167121a", "text": "% [RES] = shift(MTX, OFFSET)\r\n% \r\n% Circular shift 2D matrix samples by OFFSET (a [Y,X] 2-vector),\r\n% such that  RES(POS) = MTX(POS-OFFSET).\r\n\r\nfunction res = shift(mtx, offset)\r\n\r\ndims = size(mtx);\r\n\r\noffset = mod(-offset,dims);\r\n\r\nres = [ mtx(offset(1)+1:dims(1), offset(2)+1:dims(2)),  ...\r\n          mtx(offset(1)+1:dims(1), 1:offset(2));        ...\r\n        mtx(1:offset(1), offset(2)+1:dims(2)),          ...\r\n\t  mtx(1:offset(1), 1:offset(2)) ];\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "namedFilter.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/namedFilter.m", "size": 3278, "source_encoding": "utf_8", "md5": "837312a94d58a44dd9503ab3736cffe7", "text": "% KERNEL = NAMED_FILTER(NAME)\r\n%\r\n% Some standard 1D filter kernels.  These are scaled such that\r\n% their L2-norm is 1.0.\r\n%\r\n%  binomN             - binomial coefficient filter of order N-1\r\n%  haar:              - Haar wavelet.\r\n%  qmf8, qmf12, qmf16 - Symmetric Quadrature Mirror Filters [Johnston80]\r\n%  daub2,daub3,daub4  - Daubechies wavelet [Daubechies88].\r\n%  qmf5, qmf9, qmf13: - Symmetric Quadrature Mirror Filters [Simoncelli88,Simoncelli90]\r\n%\r\n%  See bottom of file for full citations.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction [kernel] = named_filter(name)\r\n\r\nif strcmp(name(1:min(5,size(name,2))), 'binom')\r\n  kernel = sqrt(2) * binomialFilter(str2num(name(6:size(name,2))));\r\nelseif strcmp(name,'qmf5')\r\n  kernel = [-0.076103 0.3535534 0.8593118 0.3535534 -0.076103]';\r\nelseif strcmp(name,'qmf9')\r\n  kernel = [0.02807382 -0.060944743 -0.073386624 0.41472545 0.7973934 ...\r\n      0.41472545 -0.073386624 -0.060944743 0.02807382]';\r\nelseif strcmp(name,'qmf13')\r\n  kernel = [-0.014556438 0.021651438 0.039045125 -0.09800052 ...\r\n\t  -0.057827797 0.42995453 0.7737113 0.42995453 -0.057827797 ...\r\n\t  -0.09800052 0.039045125 0.021651438 -0.014556438]';\r\nelseif strcmp(name,'qmf8')\r\n  kernel = sqrt(2) * [0.00938715 -0.07065183 0.06942827 0.4899808 ...\r\n    0.4899808 0.06942827 -0.07065183 0.00938715 ]';\r\nelseif strcmp(name,'qmf12')\r\n  kernel = sqrt(2) * [-0.003809699 0.01885659 -0.002710326 -0.08469594 ...\r\n\t0.08846992 0.4843894 0.4843894 0.08846992 -0.08469594 -0.002710326 ...\r\n\t0.01885659 -0.003809699 ]';\r\nelseif strcmp(name,'qmf16')\r\n  kernel = sqrt(2) * [0.001050167 -0.005054526 -0.002589756 0.0276414 -0.009666376 ...\r\n\t-0.09039223 0.09779817 0.4810284 0.4810284 0.09779817 -0.09039223 -0.009666376 ...\r\n\t0.0276414 -0.002589756 -0.005054526 0.001050167 ]';\r\nelseif strcmp(name,'haar')\r\n  kernel = [1 1]' / sqrt(2);\r\nelseif strcmp(name,'daub2')\r\n  kernel = [0.482962913145 0.836516303738 0.224143868042 -0.129409522551]';\r\nelseif strcmp(name,'daub3')\r\n  kernel = [0.332670552950 0.806891509311 0.459877502118 -0.135011020010 ...\r\n\t-0.085441273882  0.035226291882]';\r\nelseif strcmp(name,'daub4')\r\n  kernel = [0.230377813309 0.714846570553 0.630880767930 -0.027983769417 ...\r\n\t-0.187034811719 0.030841381836 0.032883011667 -0.010597401785]';\r\nelseif strcmp(name,'gauss5')  % for backward-compatibility\r\n  kernel = sqrt(2) * [0.0625 0.25 0.375 0.25 0.0625]';\r\nelseif strcmp(name,'gauss3')  % for backward-compatibility\r\n  kernel = sqrt(2) * [0.25 0.5 0.25]';\r\nelse\r\n  error(sprintf('Bad filter name: %s\\n',name));\r\nend\r\n  \r\n% [Johnston80] - J D Johnston, \"A filter family designed for use in quadrature \r\n%    mirror filter banks\", Proc. ICASSP, pp 291-294, 1980.\r\n%\r\n% [Daubechies88] - I Daubechies, \"Orthonormal bases of compactly supported wavelets\",\r\n%    Commun. Pure Appl. Math, vol. 42, pp 909-996, 1988.\r\n%\r\n% [Simoncelli88] - E P Simoncelli,  \"Orthogonal sub-band image transforms\",\r\n%     PhD Thesis, MIT Dept. of Elec. Eng. and Comp. Sci. May 1988.\r\n%     Also available as: MIT Media Laboratory Vision and Modeling Technical \r\n%     Report #100.\r\n%\r\n% [Simoncelli90] -  E P Simoncelli and E H Adelson, \"Subband image coding\",\r\n%    Subband Transforms, chapter 4, ed. John W Woods, Kluwer Academic \r\n%    Publishers,  Norwell, MA, 1990, pp 143--192.\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "wpyrHt.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/wpyrHt.m", "size": 285, "source_encoding": "utf_8", "md5": "5b256ddf8ffadaa7888328a31159035e", "text": "% [HEIGHT] = wpyrHt(INDICES)\r\n%\r\n% Compute height of separable QMF/wavelet pyramid with given index matrix.\r\n\r\n% Eero Simoncelli, 6/96.\r\n\r\nfunction [ht] =  wpyrHt(pind)\r\n\r\nif ((pind(1,1) == 1) | (pind(1,2) ==1))\r\n\tnbands = 1;\r\nelse\r\n\tnbands = 3;\r\nend\r\n\r\nht = (size(pind,1)-1)/nbands;\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "buildSFpyr.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildSFpyr.m", "size": 3362, "source_encoding": "utf_8", "md5": "c23de2136a85b1bc58eff471a98d44aa", "text": "% [PYR, INDICES, STEERMTX, HARMONICS] = buildSFpyr(IM, HEIGHT, ORDER, TWIDTH)\r\n%\r\n% Construct a steerable pyramid on matrix IM, in the Fourier domain.\r\n% This is similar to buildSpyr, except that:\r\n%\r\n%    + Reconstruction is exact (within floating point errors)\r\n%    + It can produce any number of orientation bands.\r\n%    - Typically slower, especially for non-power-of-two sizes.\r\n%    - Boundary-handling is circular.\r\n%\r\n% HEIGHT (optional) specifies the number of pyramid levels to build. Default\r\n% is maxPyrHt(size(IM),size(FILT));\r\n%\r\n% The squared radial functions tile the Fourier plane, with a raised-cosine\r\n% falloff.  Angular functions are cos(theta-k\\pi/(K+1))^K, where K is\r\n% the ORDER (one less than the number of orientation bands, default= 3).\r\n%\r\n% TWIDTH is the width of the transition region of the radial lowpass\r\n% function, in octaves (default = 1, which gives a raised cosine for\r\n% the bandpass filters).\r\n%\r\n% PYR is a vector containing the N pyramid subbands, ordered from fine\r\n% to coarse.  INDICES is an Nx2 matrix containing the sizes of\r\n% each subband.  This is compatible with the MatLab Wavelet toolbox.\r\n% See the function STEER for a description of STEERMTX and HARMONICS.\r\n\r\n% Eero Simoncelli, 5/97.\r\n% See http://www.cis.upenn.edu/~eero/steerpyr.html for more\r\n% information about the Steerable Pyramid image decomposition.\r\n\r\nfunction [pyr,pind,steermtx,harmonics] = buildSFpyr(im, ht, order, twidth)\r\n\r\n%-----------------------------------------------------------------\r\n%% DEFAULTS:\r\n\r\nmax_ht = floor(log2(min(size(im)))+2);\r\n\r\nif (exist('ht') ~= 1)\r\n  ht = max_ht;\r\nelse\r\n  if (ht > max_ht)\r\n    error(sprintf('Cannot build pyramid higher than %d levels.',max_ht));\r\n  end\r\nend\r\n\r\nif (exist('order') ~= 1)\r\n  order = 3;\r\nelseif ((order > 15)  | (order < 0))\r\n  fprintf(1,'Warning: ORDER must be an integer in the range [0,15]. Truncating.\\n');\r\n  order = min(max(order,0),15);\r\nelse\r\n  order = round(order);\r\nend\r\nnbands = order+1;\r\n\r\nif (exist('twidth') ~= 1)\r\n  twidth = 1;\r\nelseif (twidth <= 0)\r\n  fprintf(1,'Warning: TWIDTH must be positive.  Setting to 1.\\n');\r\n  twidth = 1;\r\nend\r\n\r\n%-----------------------------------------------------------------\r\n%% Steering stuff:\r\n\r\nif (mod((nbands),2) == 0)\r\n  harmonics = [0:(nbands/2)-1]'*2 + 1;\r\nelse\r\n  harmonics = [0:(nbands-1)/2]'*2;\r\nend\r\n\r\nsteermtx = steer2HarmMtx(harmonics, pi*[0:nbands-1]/nbands, 'even');\r\n\r\n%-----------------------------------------------------------------\r\n\r\ndims = size(im);\r\nctr = ceil((dims+0.5)/2);\r\n\r\n[xramp,yramp] = meshgrid( ([1:dims(2)]-ctr(2))./(dims(2)/2), ...\r\n    ([1:dims(1)]-ctr(1))./(dims(1)/2) );\r\nangle = atan2(yramp,xramp);\r\nlog_rad = sqrt(xramp.^2 + yramp.^2);\r\nlog_rad(ctr(1),ctr(2)) =  log_rad(ctr(1),ctr(2)-1);\r\nlog_rad  = log2(log_rad);\r\n\r\n%% Radial transition function (a raised cosine in log-frequency):\r\n[Xrcos,Yrcos] = rcosFn(twidth,(-twidth/2),[0 1]);\r\nYrcos = sqrt(Yrcos);\r\n\r\nYIrcos = sqrt(1.0 - Yrcos.^2);\r\nlo0mask = pointOp(log_rad, YIrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\r\nimdft = fftshift(fft2(im));\r\nlo0dft =  imdft .* lo0mask;\r\n\r\n[pyr,pind] = buildSFpyrLevs(lo0dft, log_rad, Xrcos, Yrcos, angle, ht, nbands);\r\n\r\nhi0mask = pointOp(log_rad, Yrcos, Xrcos(1), Xrcos(2)-Xrcos(1), 0);\r\nhi0dft =  imdft .* hi0mask;\r\nhi0 = ifft2(ifftshift(hi0dft));\r\n\r\npyr = [real(hi0(:)) ; pyr];\r\npind = [size(hi0); pind];\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "modulateFlip.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/modulateFlip.m", "size": 480, "source_encoding": "utf_8", "md5": "9f2392c42cf1ad73accf1b25c3327ac7", "text": "% [HFILT] = modulateFlipShift(LFILT)\r\n%\r\n% QMF/Wavelet highpass filter construction: modulate by (-1)^n,\r\n% reverse order (and shift by one, which is handled by the convolution\r\n% routines).  This is an extension of the original definition of QMF's\r\n% (e.g., see Simoncelli90).\r\n\r\n% Eero Simoncelli, 7/96.\r\n\r\nfunction [hfilt] = modulateFlipShift(lfilt)\r\n\r\nlfilt = lfilt(:);\r\n\r\nsz = size(lfilt,1);\r\nsz2 = ceil(sz/2);\r\n\r\nind = [sz:-1:1]';\r\n\r\nhfilt = lfilt(ind) .* (-1).^(ind-sz2);\r\n"}
+{"plateform": "github", "repo_name": "phcerdan/BLS-GSM_Denoising_Portilla-master", "name": "spyrNumBands.m", "ext": ".m", "path": "BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/spyrNumBands.m", "size": 500, "source_encoding": "utf_8", "md5": "2173f8841142e73d6f87cb5242a2f9a7", "text": "% [NBANDS] = spyrNumBands(INDICES)\r\n%\r\n% Compute number of orientation bands in a steerable pyramid with\r\n% given index matrix.  If the pyramid contains only the highpass and\r\n% lowpass bands (i.e., zero levels), returns 0.\r\n\r\n% Eero Simoncelli, 2/97.\r\n\r\nfunction [nbands] =  spyrNumBands(pind)\r\n\r\nif (size(pind,1) == 2)\r\n  nbands  = 0;\r\nelse\r\n  % Count number of orientation bands:\r\n  b = 3;\r\n  while ((b <= size(pind,1)) & all( pind(b,:) == pind(2,:)) )\r\n    b = b+1;\r\n  end\r\n  nbands = b-2;\r\nend\r\n"}