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{ "source": "jernejvivod/skrelief", "score": 3 }
#### File: skrelief/skrelief/turf.py ```python import numpy as np from scipy.stats import rankdata from sklearn.base import BaseEstimator, TransformerMixin from julia import Julia Julia(compiled_modules=False) from julia import Relief as Relief_jl from skrelief.relieff import Relieff class TuRF(BaseEstimator, TransformerMixin): """sklearn compatible implementation of the TuRF algorithm. Reference: <NAME> and <NAME>. Tuning ReliefF for genome-wide genetic analysis. In <NAME>, <NAME>, and <NAME>. Rajapakse, editors, Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics, pages 166–175. Springer, 2007. Args: n_features_to_select (int): number of features to select from dataset. num_it (int): number of iterations. rba (object): feature weighting algorithm wrapped by the VLSRelief algorithm. If equal to None, the default ReliefF RBA implemented in Julia is used. Attributes: n_features_to_select (int): number of features to select from dataset. num_it (int): number of iterations. _rba (object): feature weighting algorithm wrapped by the TuRF algorithm. """ def __init__(self, n_features_to_select=10, num_it=10, rba=None): self.n_features_to_select = n_features_to_select self.num_it = num_it self._rba = rba def fit(self, data, target): """ Rank features using TuRF feature selection algorithm Args: data (numpy.ndarray): matrix of data samples target (numpy.ndarray): vector of target values of samples Returns: (object): reference to self """ # Compute feature weights and rank. if self._rba is not None: self.weights = Relief_jl.turf(data, target, self.num_it, self.rba_wrap) else: self.weights = Relief_jl.turf(data, target, self.num_it) self.rank = rankdata(-self.weights, method='ordinal') # Return reference to self. return self def transform(self, data): """ Perform feature selection using computed feature ranks. Args: data (numpy.ndarray): matrix of data samples on which to perform feature selection. Returns: (numpy.ndarray): result of performing feature selection. """ # select n_features_to_select best features and return selected features. msk = self.rank <= self.n_features_to_select # Compute mask. return data[:, msk] # Perform feature selection. ```
{ "source": "jernej-vrscaj/Hello_IoT", "score": 3 }
#### File: Hello_IoT/script/Hello_IoT_CGI_notify.py ```python print('Content-Type: text/html \n') # Import modules # from bluepy import btle import cgi import time import struct import sys import os # Brief: Callback class for notification events # Input args: # valHandle: Tuple of characteristic handle values # # fahr: FORMs fahrenheit value # # v_list: List of characteristic values [temperature, humidity, pressure] # Return val: # None # class MyDelegate(btle.DefaultDelegate): def __init__(self, valHandle, fahr, v_list): btle.DefaultDelegate.__init__(self) self.__envHandle = valHandle self.__fahr = fahr self.__vList = v_list def handleNotification(self, cHandle, data): if cHandle == self.__envHandle[0]: cnv_data = struct.unpack('h', data) if self.__fahr == 'Fahrenheit': self.__vList[0] = 'T = {0:.2f} °F'.format((cnv_data[0]*0.01)*1.8 + 32.0) else: self.__vList[0] = 'T = {0:.2f} °C'.format(cnv_data[0]*0.01) elif cHandle == self.__envHandle[1]: cnv_data = struct.unpack('H', data) self.__vList[1] = 'H = {0:.2f} %'.format(cnv_data[0]*0.01) elif cHandle == self.__envHandle[2]: cnv_data = struct.unpack('I', data) self.__vList[2] = 'P = {0:.2f} hPa'.format(cnv_data[0]*0.001) #### # Brief: Save environmental values to .txt file # Input args: # v_list: List of characteristic values [temperature, humidity, pressure] # Return val: # None # def save_to_textf(v_list): # Append on open # try: fp = open('env_val.txt', 'a', encoding='utf8') except IOError as io_exp: display_cgi_page_err(io_exp) sys.exit() f_data = v_list[0] + ',' + v_list[1] + ',' + v_list[2] + '\n' fp.write(f_data) if not fp.closed: fp.close() #### # Brief: Error HTML page # Input args: # exp: Raised exception # Return val: # None # def display_cgi_page_err(exp): print(''' <!DOCTYPE html> <html> <head> <title>Error</title> <script> </script> <style> html { background: url(/Nature___Sundown_Golden_sunset_above_the_clouds_042961_23.jpg) no-repeat center fixed; background-size: cover; background-color: rgba(128, 128, 128, 0.4); /* Used if the image is unavailable */ height: 100%; width: 100%; } .myfont { color: white; text-shadow: 1px 1px rgba(0, 0, 0, 1); font-family: Trebuchet MS, Helvetica, sans-serif; } </style> </head> <body> ''') print('<p class="myfont" style="font-size: 200%;">Error:',exp,'</p>') print(''' </body> </html> ''') #### # Brief: Read characteristic values # Input args: # sensor: BLE peripheral object # # fahr: FORMs fahrenheit value # # v_list: List of characteristic values [temperature, humidity, pressure] # Return val: # None # def read_ch_values(sensor, fahr, v_list): # Environmental service # uuid_svc_env = btle.UUID('0000181a-0000-1000-8000-00805f9b34fb') svc_env = sensor.getServiceByUUID(uuid_svc_env) # Before reading data, wait some time for sensors to do their readings # and update characteristic values # time.sleep(0.2) # Read data from Temperature characteristic # uuid_ch_temp = btle.UUID('00002a6e-0000-1000-8000-00805f9b34fb') ch_temp = svc_env.getCharacteristics(uuid_ch_temp)[0] val_temp = ch_temp.read() temp_tuple = struct.unpack('h', val_temp) if fahr == 'Fahrenheit': v_list[0] = 'T = {0:.2f} °F'.format((temp_tuple[0]*0.01)*1.8 + 32.0) else: v_list[0] = 'T = {0:.2f} °C'.format(temp_tuple[0]*0.01) # Read data from Humidity characteristic # uuid_ch_humd = btle.UUID('00002a6f-0000-1000-8000-00805f9b34fb') ch_humd = svc_env.getCharacteristics(uuid_ch_humd)[0] val_humd = ch_humd.read() humd_tuple = struct.unpack('H', val_humd) v_list[1] = 'H = {0:.2f} %'.format(humd_tuple[0]*0.01) # Read data from Pressure characteristic # uuid_ch_press = btle.UUID('00002a6d-0000-1000-8000-00805f9b34fb') ch_press = svc_env.getCharacteristics(uuid_ch_press)[0] val_press = ch_press.read() press_tuple = struct.unpack('I', val_press) v_list[2] = 'P = {0:.2f} hPa'.format(press_tuple[0]*0.001) #### # Brief: Read characteristic notification values # Input args: # sensor: BLE peripheral object # # fahr: FORMs fahrenheit value # # UPDT_INT: Sensor values update interval in connection mode, in seconds # # v_list: List of characteristic values [temperature, humidity, pressure] # Return val: # None # def read_ntfcn_values(sensor, fahr, UPDT_INT, v_list): read_ch_values(sensor, fahr, v_list) save_to_textf(v_list) # Environmental service # uuid_svc_env = btle.UUID('0000181a-0000-1000-8000-00805f9b34fb') svc_env = sensor.getServiceByUUID(uuid_svc_env) # Setup to turn notifications ON # # Temperature char # uuid_ch_temp = btle.UUID('00002a6e-0000-1000-8000-00805f9b34fb') ch_temp = svc_env.getCharacteristics(uuid_ch_temp)[0] # Humidity char # uuid_ch_humd = btle.UUID('00002a6f-0000-1000-8000-00805f9b34fb') ch_humd = svc_env.getCharacteristics(uuid_ch_humd)[0] # Pressure char # uuid_ch_press = btle.UUID('00002a6d-0000-1000-8000-00805f9b34fb') ch_press = svc_env.getCharacteristics(uuid_ch_press)[0] # Tuple of characteristic handle values # ch_env_hnd = ch_temp.valHandle, ch_humd.valHandle, ch_press.valHandle # Set callback object for notification events # sensor.setDelegate(MyDelegate(ch_env_hnd, fahr, v_list)) # Temperature notification ON # sensor.writeCharacteristic(ch_temp.valHandle+1, b'\x01\x00') # Humidity notification ON # sensor.writeCharacteristic(ch_humd.valHandle+1, b'\x01\x00') # Pressure notification ON # sensor.writeCharacteristic(ch_press.valHandle+1, b'\x01\x00') while True: try: if sensor.waitForNotifications(UPDT_INT): save_to_textf(v_list) except KeyboardInterrupt: sys.exit() except btle.BTLEDisconnectError as btle_exp: display_cgi_page_err(btle_exp) sys.exit() # Other # except Exception as exp: display_cgi_page_err(exp) sys.exit() #### # Brief: Main # Input args: # None # Return val: # None # def main(): # Read values from the FORM # formdata = cgi.FieldStorage() fahrenheit = formdata.getvalue('fahrenheit') # Sensor values update interval in connection mode, in seconds # UPDT_INT = 1.0 # Environmental values # temperature = '' humidity = '' pressure = '' val_list = [temperature, humidity, pressure] # Directory for the .txt file # os.chdir('/var/www/html/') # Clear content on open # try: fp = open('env_val.txt', 'w', encoding='utf8') except IOError as io_exp: display_cgi_page_err(io_exp) sys.exit() if not fp.closed: fp.close() # Connect to peripheral # try: periph = btle.Peripheral('D0:65:F1:9B:08:4B', btle.ADDR_TYPE_RANDOM) except btle.BTLEDisconnectError as btle_exp: display_cgi_page_err(btle_exp) sys.exit() finally: read_ntfcn_values(periph, fahrenheit, UPDT_INT, val_list) #### if __name__ == "__main__": main() ```
{ "source": "jernelv/SpecAnalysis", "score": 3 }
#### File: modules/libs/PLSRlib.py ```python import numpy as np import scipy def Der(x,y): """Function for finding first derivative of spectral data. Uses finite differences.""" n=len(x) x2=np.zeros(n-1) y2=np.zeros(n-1) for i in range(n-1): x2[i]=0.5*(x[i]+x[i+1]) y2[i]=(y[i+1]-y[i])/(x[i+1]-x[i]) return(x2,y2) def Der2(x,y): """Function for finding second derivative of spectral data. Uses finite differences.""" n=len(x) x2=np.zeros(n-2) y2=np.zeros(n-2) dx2=(x[1]-x[0])**2 # assumed constant for i in range(n-2): x2[i]=x[i+1] y2[i]=(y[i]-2*y[i+1]+y[i+2])/dx2 return(x2,y2) def mlr(x,y,order): """Multiple linear regression fit of the columns of matrix x (dependent variables) to constituent vector y (independent variables) order - order of a smoothing polynomial, which can be included in the set of independent variables. If order is not specified, no background will be included. b - fit coeffs f - fit result (m x 1 column vector) r - residual (m x 1 column vector) """ if order > 0: s=scipy.ones((len(y),1)) for j in range(order): s=scipy.concatenate((s,(scipy.arange(0,1+(1.0/(len(y)-1))-0.5/(len(y)-1),1.0/(len(y)-1))**j)[:,nA]),1) X=scipy.concatenate((x, s),1) else: X = x b = scipy.dot(scipy.dot(scipy.linalg.pinv(scipy.dot(scipy.transpose(X),X)),scipy.transpose(X)),y) f = scipy.dot(X,b) r = y - f return b,f,r def emsc(case, order, fit=None): """Extended multiplicative scatter correction case - spectral data for background correction order - order of polynomial fit - if None then use average spectrum, otherwise provide a spectrum as a column vector to which all others fitted corr - EMSC corrected data mx - fitting spectrum """ if not type(fit)==type(None): mx = fit else: mx = scipy.mean(case,axis=0)[:,nA] corr = scipy.zeros(case.shape) for i in range(len(case)): b,f,r = mlr(mx, case[i,:][:,nA], order) corr[i,:] = scipy.reshape((r/b[0,0]) + mx, (corr.shape[1],)) corr=np.nan_to_num(corr) return corr def baseline_corr(case): """Baseline correction that sets the first independent variable of each spectrum to zero.""" size = case.shape subtract = scipy.transpose(scipy.resize(scipy.transpose(case[:,0]),(size[1],size[0]))) return (case-subtract) def baseline_avg(case): """Baseline correction that subtracts an average of the first and last independent variable from each variable.""" size = case.shape subtract = scipy.transpose(scipy.resize(scipy.transpose((case[:,0]+case[:size[1]-1])/2),(size[1],size[0]))) return (case-subtract) def baseline_linear(case): """Baseline correction that subtracts a linearly increasing baseline between the first and last independent variable.""" size, t = case.shape, 0 subtract = scipy.zeros((size[0],size[1]), 'd') while t < size[0]: a = case[t,0] b = case[t,size[1]-1] div = (b-a)/size[1] if div == 0: div = 1 arr = scipy.arrange(a,b,div,'d') subtract[t,:] = scipy.resize(arr,(size[1],)) t = t+1 return case-subtract ``` #### File: modules/libs/PLSRwavelengthSelection.py ```python import numpy as np import fns from . import PLSRregressionMethods from . import PLSRsave import tkinter import copy import sklearn.model_selection import types from . import PLSRclassifiers def get_buttons(): buttons=[ {'key': 'RNNtab2name', 'type': 'tabname', 'text': 'Wavelength Selection', 'tab': 2} , {'key': 'RegressionL3', 'type': 'label', 'text': 'Type of wavelength selection:', 'tab': 2, 'row': 2} , {'key': 'regression_wavelength_selection', 'type': 'radio:vertical:text', 'texts': ['No wavelength selection', 'Moving Window', 'Genetic Algorithm','Sequential Feature Selector'], 'tab': 2, 'row': 3} , {'key': 'moving_window_min', 'type': 'txt:float', 'text': 'Min window', 'default': '30', 'width': 4, 'tab': 2, 'row': 4} , {'key': 'moving_window_max', 'type': 'txt:float', 'text': 'Max window', 'default': '100', 'width': 4, 'tab': 2, 'row': 4} , {'key': 'RegressionL4', 'type': 'label', 'text': 'GA options ', 'tab': 2, 'row': 5} , {'key': 'GA_number_of_individuals', 'type': 'txt:int', 'text': 'GA num. Individuals', 'default': '100', 'width': 4, 'tab': 2, 'row': 5} , {'key': 'GA_crossover_rate', 'type': 'txt:float', 'text': 'GA crossover rate', 'default': '0.8', 'width': 4, 'tab': 2, 'row': 5} , {'key': 'GA_mutation_rate', 'type': 'txt:float', 'text': 'GA mutation rate', 'default': '0.001', 'width': 6, 'tab': 2, 'row': 5} , {'key': 'GA_max_number_of_generations', 'type': 'txt:int', 'text': 'GA generations', 'default': '20', 'width': 3, 'tab': 2, 'row': 5} , {'key': 'SFS type', 'type': 'radio:text', 'texts': ['Forward', 'Backward'], 'tab': 2, 'row': 6} , {'key': 'SFS_floating', 'type': 'check', 'text': 'Floating', 'tab': 2, 'row': 6} , {'key': 'SFS_num_after_min', 'type': 'txt:int', 'text': 'Iterations after min', 'default': '30', 'width': 4, 'tab': 2, 'row': 6 }, {'key': 'SFS_target', 'type': 'txt:int', 'text': 'Target number', 'default': '20', 'width': 4, 'tab': 2, 'row': 6 }, {'key': 'SFS_max_iterations', 'type': 'txt:int', 'text': 'Max iterations', 'default': '300', 'width': 4, 'tab': 2, 'row': 6 }, {'key': 'WS_loss_type', 'type': 'radio:text', 'texts': ['X-validation on training', 'RMSEC on training', 'RMSEP on validation'], 'tab': 2, 'row': 8} , {'key': 'WS_cross_val_N', 'type': 'txt:int', 'text': 'WS cross val fold', 'default': '1', 'width': 4, 'tab': 2, 'row': 9} , {'key': 'WS_cross_val_max_cases', 'type': 'txt:int', 'text': 'WS cross val num cases', 'default': '-1', 'width': 4, 'tab': 2, 'row': 9} , ] return buttons def MW(case,ui,common_variables,keywords={}): T=case.T V=case.V wavenumbers=case.wavenumbers folder=case.folder try: keywords=case.keywords except: keywords={} WS_getCrossvalSplits([0,1],T,V,ui,use_stored=False) # get regression module reg_module=PLSRregressionMethods.getRegModule(ui['reg_type'],keywords) # Set what datapoints to include, the parameter 'wavenum' is in units cm^-1 if ui['save_check_var']: common_variables.tempax.fig=common_variables.tempfig #len_wavenumbers=len(wavenumbers) dw=wavenumbers[0]-wavenumbers[1] # Windowsize is input in cm^-1, transform to indexes MWmax=int(round(ui['moving_window_max']/abs(dw),0)) MWmin=int(round(ui['moving_window_min']/abs(dw),0)) Wresults=np.zeros((len(wavenumbers),MWmax+1-MWmin)) Wsizes=np.arange(MWmin,MWmax+1) # do moving window for i,Wsize in enumerate(Wsizes): trail_active_wavenumbers=[] for j, Wcenter in enumerate(wavenumbers): Wstart=j-Wsize//2 Wend=Wstart+Wsize #if Wsize < MWmax+1 and i < len(wavenumbers)+1: if Wstart<0: k=j continue elif Wend>len(wavenumbers): l=j break else: trail_active_wavenumbers.append(np.arange(Wstart,Wend)) #Wresults[j,i]=WS_getRMSEP(reg_module,trail_active_wavenumbers[-1],T,V,use_stored=False) print('moving window row '+str(i)+' of '+str(len(Wsizes))) Wresults[k+1:l,i], _ = WS_evaluate_chromosomes(reg_module, T, V, trail_active_wavenumbers, use_stored=True) # done moving window Wresults=Wresults+(Wresults==0)*np.max(Wresults) # set empty datapoints to max value j,i=np.unravel_index(Wresults.argmin(), Wresults.shape) bestVal=Wresults[j,i] bestSize=Wsizes[i] bestStart=j-bestSize//2 # plot MWresults Wresults=np.array(Wresults) # make plot Wwindowsize,Wwavenumbers = np.meshgrid(Wsizes*abs(dw), wavenumbers) unique_keywords=PLSRsave.get_unique_keywords_formatted(common_variables.keyword_lists,keywords) PLSRsave.PcolorMW(Wwavenumbers,Wwindowsize,Wresults,fns.add_axis(common_variables.fig,ui['fig_per_row'],ui['max_plots']),unique_keywords[1:],ui) if ui['save_check_var']: tempCbar=PLSRsave.PcolorMW(Wwavenumbers,Wwindowsize,Wresults,common_variables.tempax,unique_keywords[1:],ui) common_variables.tempfig.subplots_adjust(bottom=0.13,left=0.15, right=0.97, top=0.9) plotFileName=folder+ui['reg_type']+unique_keywords.replace('.','p')+'_moving_window' common_variables.tempfig.savefig(plotFileName+ui['file_extension']) tempCbar.remove() # set result as keywords, so that they are saved bestEnd=bestStart+bestSize Wwidth=wavenumbers[bestStart]-wavenumbers[bestEnd-1] #cm-1 Wcenter=0.5*(wavenumbers[bestStart]+wavenumbers[bestEnd-1]) #cm-1 keywords['MW width']=str(round(Wwidth,1))+r' cm$^{-1}$' keywords['MW center']=str(round(Wcenter,1))+r' cm$^{-1}$' # prepare return vector active_wavenumers=np.zeros(len(wavenumbers), dtype=bool) active_wavenumers[bestStart:bestEnd]=True return active_wavenumers def WS_evaluate_chromosomes(reg_module,T,V,trail_active_wavenumbers,ui=None,use_stored=False,backup_reg_module=None): used_mlr=False losses=np.zeros(len(trail_active_wavenumbers)) for i,active_wavenumers in enumerate(trail_active_wavenumbers): #print(,i,' of ',len(active_wavenumers)) #i+=1 try: losses[i]=WS_getRMSEP(reg_module,active_wavenumers,T,V,ui=ui,use_stored=use_stored) except: used_mlr=True losses[i]=WS_getRMSEP(backup_reg_module,active_wavenumers,T,V,ui=ui,use_stored=use_stored) return losses, used_mlr def WS_getRMSEP(reg_module,chromosome,T,V,ui=None,use_stored=False): # ui is optional only if use_stored=True Ts,Vs=WS_getCrossvalSplits(chromosome,T,V,ui=None,use_stored=use_stored) RMSEP=[] percent_cor_classified_list=[] for curT,curV in zip(Ts,Vs): reg_module.fit(curT.X, curT.Y) curV.pred = reg_module.predict(curV.X)[:,0] if reg_module.type=='regression': RMSEP.append(np.sqrt((np.sum((curV.pred-curV.Y)**2)))/len(curV.Y)) else: #reg_module.type=='classifier' percent_cor_classified_list.append(PLSRclassifiers.get_correct_categorized(curV.pred,curV.Y)) if reg_module.type=='regression': return np.sqrt(np.sum(np.array(RMSEP)**2)/len(RMSEP)) else: return 1-np.average(percent_cor_classified_list) def WS_getCrossvalSplits(chromosome,T,V,ui=None,use_stored=False): global stored_XvalTs global stored_XvalVs if use_stored==True: XvalTs = copy.deepcopy(stored_XvalTs) XvalVs = copy.deepcopy(stored_XvalVs) else: XvalTs=[] XvalVs=[] if ui['WS_loss_type']=='X-validation on training': if ui['WS_cross_val_N']==1 and ui['WS_cross_val_max_cases']==-1: splitmodule=sklearn.model_selection.LeaveOneOut() else: splitmodule=sklearn.model_selection.ShuffleSplit(n_splits=ui['WS_cross_val_max_cases'], test_size=ui['WS_cross_val_N']) for train,val in splitmodule.split(T.X): XvalTs.append(types.SimpleNamespace()) XvalTs[-1].X=np.array(T.X[train]) XvalTs[-1].Y=np.array(T.Y[train]) XvalVs.append(types.SimpleNamespace()) XvalVs[-1].X=np.array(T.X[val]) XvalVs[-1].Y=np.array(T.Y[val]) elif ui['WS_loss_type']=='RMSEC on training': XvalTs.append(copy.deepcopy(T)) XvalVs=XvalTs # pointer to object, no need to copy it else:# ui['WS_loss_type']=='RMSEP on validation': XvalTs.append(copy.deepcopy(T)) XvalVs.append(copy.deepcopy(V)) stored_XvalTs = copy.deepcopy(XvalTs) stored_XvalVs = copy.deepcopy(XvalVs) for T in XvalTs: T.X=T.X[:,chromosome] if len(XvalVs[0].X[0])>len(XvalTs[0].X[0]): # this is just a check to see if T==V, in that case we should not act on for V in XvalVs: V.X=V.X[:,chromosome] return XvalTs,XvalVs ``` #### File: modules/libs/signal_alignment.py ```python import numpy as np from scipy.optimize import minimize from scipy.interpolate import interp1d from scipy.ndimage.interpolation import shift from statsmodels.tsa.stattools import ccovf def chisqr_align(reference, target, roi, order=1, init=0.1, bound=1): ''' Align a target signal to a reference signal within a region of interest (ROI) by minimizing the chi-squared between the two signals. Depending on the shape of your signals providing a highly constrained prior is necessary when using a gradient based optimization technique in order to avoid local solutions. Args: reference (1d array/list): signal that won't be shifted target (1d array/list): signal to be shifted to reference roi (tuple): region of interest to compute chi-squared order (int): order of spline interpolation for shifting target signal init (int): initial guess to offset between the two signals bound (int): symmetric bounds for constraining the shift search around initial guess Returns: shift (float): offset between target and reference signal Todo: * include uncertainties on spectra * update chi-squared metric for uncertainties * include loss function on chi-sqr ''' # convert to int to avoid indexing issues ROI = slice(int(roi[0]), int(roi[1]), 1) # normalize ref within ROI reference = reference/np.mean(reference[ROI]) # define objective function: returns the array to be minimized def fcn2min(x): shifted = shift(target,x,order=order) shifted = shifted/np.mean(shifted[ROI]) return np.sum( ((reference - shifted)**2 )[ROI] ) # set up bounds for pos/neg shifts minb = min( [(init-bound),(init+bound)] ) maxb = max( [(init-bound),(init+bound)] ) # minimize chi-squared between the two signals result = minimize(fcn2min,init,method='L-BFGS-B',bounds=[ (minb,maxb) ]) return result.x[0] def phase_align(reference, target, roi, res=100): ''' Cross-correlate data within region of interest at a precision of 1./res if data is cross-correlated at native resolution (i.e. res=1) this function can only achieve integer precision Args: reference (1d array/list): signal that won't be shifted target (1d array/list): signal to be shifted to reference roi (tuple): region of interest to compute chi-squared res (int): factor to increase resolution of data via linear interpolation Returns: shift (float): offset between target and reference signal ''' # convert to int to avoid indexing issues ROI = slice(int(roi[0]), int(roi[1]), 1) # interpolate data onto a higher resolution grid x,r1 = highres(reference[ROI],kind='linear',res=res) x,r2 = highres(target[ROI],kind='linear',res=res) # subtract mean r1 -= r1.mean() r2 -= r2.mean() # compute cross covariance cc = ccovf(r1,r2,demean=False,unbiased=False) # determine if shift if positive/negative if np.argmax(cc) == 0: cc = ccovf(r2,r1,demean=False,unbiased=False) mod = -1 else: mod = 1 # often found this method to be more accurate then the way below return np.argmax(cc)*mod*(1./res) # interpolate data onto a higher resolution grid x,r1 = highres(reference[ROI],kind='linear',res=res) x,r2 = highres(target[ROI],kind='linear',res=res) # subtract off mean r1 -= r1.mean() r1 -= r2.mean() # compute the phase-only correlation function product = np.fft.fft(r1) * np.fft.fft(r2).conj() cc = np.fft.fftshift(np.fft.ifft(product)) # manipulate the output from np.fft l = reference[ROI].shape[0] shifts = np.linspace(-0.5*l,0.5*l,l*res) # plt.plot(shifts,cc,'k-'); plt.show() return shifts[np.argmax(cc.real)] def highres(y,kind='cubic',res=100): ''' Interpolate data onto a higher resolution grid by a factor of *res* Args: y (1d array/list): signal to be interpolated kind (str): order of interpolation (see docs for scipy.interpolate.interp1d) res (int): factor to increase resolution of data via linear interpolation Returns: shift (float): offset between target and reference signal ''' y = np.array(y) x = np.arange(0, y.shape[0]) f = interp1d(x, y,kind='cubic') xnew = np.linspace(0, x.shape[0]-1, x.shape[0]*res) ynew = f(xnew) return xnew,ynew if __name__ == "__main__": from scipy import signal import matplotlib.pyplot as plt NPTS = 100 SHIFTVAL = 4 NOISE = 1e-2 # can perturb offset retrieval from true print('true signal offset:',SHIFTVAL) # generate some noisy data and simulate a shift y = signal.gaussian(NPTS, std=4) + np.random.normal(1,NOISE,NPTS) shifted = shift( signal.gaussian(NPTS, std=4) ,SHIFTVAL) + np.random.normal(1,NOISE,NPTS) # align the shifted spectrum back to the real s = phase_align(y, shifted, [10,90]) print('phase shift value to align is',s) # chi squared alignment at native resolution s = chisqr_align(y, shifted, [10,90], init=-3.5,bound=2) print('chi square alignment',s) # make some diagnostic plots plt.plot(y,label='original data') plt.plot(shifted,label='shifted data') plt.plot(shift(shifted,s,mode='nearest'),ls='--',label='aligned data') plt.legend(loc='best') plt.show() ``` #### File: SpecAnalysis/modules/PLSR.py ```python from __future__ import print_function import fns import numpy as np import os import matplotlib.pyplot as plt import matplotlib import scipy.signal from scipy import signal #from sklearn.model_selection import LeavePOut #from sklearn.model_selection import KFold from sklearn.model_selection import ShuffleSplit from sklearn.model_selection import LeaveOneOut from sklearn.linear_model import ElasticNet import sklearn.metrics import types from math import sqrt import copy import sys import importlib from .libs import PLSRsave from .libs import PLSRGeneticAlgorithm from .libs import PLSRNN from .libs import PLSRRNN from .libs import PLSRCNN from .libs import PLSR_file_import from .libs import PLSRregressionMethods from .libs import PLSRregressionVisualization from .libs import PLSRpreprocessing from .libs import PLSRwavelengthSelection from .libs import PLSRsequential_feature_selectors from .libs import PLSRclassifiers def eprint(*args, **kwargs): print(*args, file=sys.stderr, **kwargs) #### this '''functions_to_wrap = [[matplotlib.axes.Axes,'pcolormesh'], [matplotlib.figure.Figure,'colorbar'], [matplotlib.figure.Figure,'clf'], [matplotlib.figure.Figure,'set_size_inches'], [matplotlib.figure.Figure,'add_subplot'], [matplotlib.figure.Figure,'subplots'], [matplotlib.figure.Figure,'subplots_adjust'], [matplotlib.axes.Axes,'invert_yaxis'], [matplotlib.axes.Axes,'invert_xaxis'], [matplotlib.axes.Axes,'set_title'], [matplotlib.axes.Axes,'axis'], [matplotlib.axes.Axes,'cla'], [matplotlib.axes.Axes,'plot'], [matplotlib.figure.Figure,'savefig'], [matplotlib.axes.Axes,'set_xlim'], [matplotlib.axes.Axes,'set_position'], [matplotlib.axes.Axes,'bar'], [matplotlib.figure.Figure,'add_axes'], [plt,'figure'], ] for function in functions_to_wrap: if not 'function rimt.<locals>.rimt_this' in str(getattr(function[0], function[1])): setattr(function[0], function[1], fns.rimt(getattr(function[0], function[1])))''' #from multiprocessing import Pool #import datetime #matplotlib.rc('text', usetex=True) #matplotlib.rc('text.latex', preamble=r'\usepackage{upgreek}') def crossval(T,V,ui,case): if not ui['is_validation']=='X-val on training': case.supressplot=0 return [case] else: case.Xval_cases=[] #XvalTs=[] #XvalVs=[] #supressplots=[] if ui['cross_val_N']==1 and ui['cross_val_max_cases']==-1: #ui['cross_val_max_cases']=len(T.Y) splitodule=LeaveOneOut() print('Using sklearn.LeaveOneOut on '+str(len(T.Y))+' measurements. Maxcases set to '+str(len(T.Y))) else: if ui['cross_val_max_cases']==-1: print('cross_val_max_cases set to -1, cross_val_N not set to 1. Setting cross_val_max_cases to default (20)' ) ui['cross_val_max_cases']=20 splitodule=ShuffleSplit(n_splits=ui['cross_val_max_cases'], test_size=ui['cross_val_N']) for train,val in splitodule.split(T.X): case.Xval_cases.append(types.SimpleNamespace()) case.Xval_cases[-1].train=train case.Xval_cases[-1].val=val case.Xval_cases[-1].T=types.SimpleNamespace() case.Xval_cases[-1].T.X=np.array(T.X[train]) case.Xval_cases[-1].T.Y=np.array(T.Y[train]) case.Xval_cases[-1].V=types.SimpleNamespace() case.Xval_cases[-1].V.X=np.array(T.X[val]) case.Xval_cases[-1].V.Y=np.array(T.Y[val]) case.Xval_cases[-1].supressplot=1 case.Xval_cases[-1].supressplot=0 return case.Xval_cases def run_reg_module(Xval_case,case,ui,common_variables,active_wavenumers,logfile,keywords={}): T=Xval_case.T V=Xval_case.V supressplot=Xval_case.supressplot wavenumbers=case.wavenumbers folder=case.folder try: keywords=case.keywords except: keywords={} print('let the developers know if you see this error') # Set what datapoints to include, the parameter 'wavenum' is in units cm^-1 #datapointlists=ui.datapointlists # common_variables.tempax and common_variables.tempfig are for the figure that is saved, common_variables.ax and common_variables.fig are for the figure that is displayed # need to have this for the colorbar if ui['save_check_var']: common_variables.tempax.fig=common_variables.tempfig #plot best result # or only result if not MW reg_module=PLSRregressionMethods.getRegModule(ui['reg_type'],keywords) #reg_module.active_wavenumers=active_wavenumers # get RMSe for E in [T,V]: if len(E.Y)>0: E.Xsmol=E.X[:,active_wavenumers] reg_module.fit(T.Xsmol, T.Y) for E in [T,V]: if len(E.Y)>0: E.pred = reg_module.predict(E.Xsmol)[:,0] else: E.pred = [] Xval_case.RMSECP=np.sqrt((np.sum((T.pred-T.Y)**2)+np.sum((V.pred-V.Y)**2))/(len(T.Y)+len(V.Y))) Xval_case.RMSEC=np.sqrt((np.sum((T.pred-T.Y)**2))/(len(T.Y))) if len(V.Y)>0: Xval_case.RMSEP=np.sqrt((np.sum((V.pred-V.Y)**2))/(len(V.Y))) '''if ui['RMS_type']=='Combined RMSEP+RMSEC' and len(V.Y)>0: RMSe=Xval_case.RMSECP Y_for_r2=np.concatenate((T.Y,V.Y)) pred_for_r2=np.concatenate((T.pred,V.pred)) el''' if ui['RMS_type']=='RMSEP': RMSe=Xval_case.RMSEP Y_for_r2=V.Y pred_for_r2=V.pred else: RMSe=Xval_case.RMSEC Y_for_r2=T.Y pred_for_r2=T.pred case.XvalRMSEs.append(RMSe) #calculating coefficient of determination if not hasattr(case,'X_val_pred'): case.X_val_pred=[pred_for_r2] case.X_val_Y=[Y_for_r2] else: case.X_val_pred.append(pred_for_r2) case.X_val_Y.append(Y_for_r2) if not supressplot: # if plotting this, calculate R^2 for all xval cases X_pred=np.array(case.X_val_pred).reshape(-1) X_Y=np.array(case.X_val_Y).reshape(-1) y_mean = np.sum(X_Y)*(1/len(X_Y)) Xval_case.R_squared = 1 - ((np.sum((X_Y - X_pred)**2))/(np.sum((X_Y - y_mean)**2))) avg=np.average(X_pred-X_Y) n=len(X_pred) Xval_case.SEP=np.sqrt(np.sum( ( X_pred-X_Y-avg )**2 )/(n-1)) Xval_case.mean_absolute_error=sklearn.metrics.mean_absolute_error(X_Y,X_pred) Xval_case.mean_absolute_error_percent=100/len(X_Y) * np.sum(np.abs(X_Y-X_pred)/X_Y) else: Xval_case.R_squared=0 Xval_case.SEP=0 try: Xval_case.R_not_squared=sqrt(Xval_case.R_squared) except: Xval_case.R_not_squared=0 if ui['coeff_det_type']=='R^2': coeff_det = Xval_case.R_squared elif ui['coeff_det_type']=='R': coeff_det = Xval_case.R_not_squared if reg_module.type=='classifier':#'classifier_type' in keywords: frac_cor_lab=PLSRclassifiers.get_correct_categorized(case.X_val_Y[-1],case.X_val_pred[-1]) case.XvalCorrClass.append(frac_cor_lab) else: frac_cor_lab=-1 #plot if not supressplot: if not ui['do_not_save_plots']: PLSRsave.plot_regression(Xval_case,case,ui,fns.add_axis(common_variables.fig,ui['fig_per_row'],ui['max_plots']),keywords,RMSe, coeff_det,frac_cor_lab=frac_cor_lab) if ui['save_check_var']: if not ui['do_not_save_plots']: PLSRsave.plot_regression(Xval_case,case,ui,common_variables.tempax,keywords,RMSe, coeff_det,frac_cor_lab=frac_cor_lab) common_variables.tempfig.subplots_adjust(bottom=0.13,left=0.15, right=0.97, top=0.95) #common_variables.tempfig.savefig(folder+'Best'+'Comp'+str(components)+'Width'+str(round(Wwidth,1))+'Center'+str(round(Wcenter,1))+'.pdf') #common_variables.tempfig.savefig(folder+'Best'+'Comp'+str(components)+'Width'+str(round(Wwidth,1))+'Center'+str(round(Wcenter,1))+'.svg') plotFileName=case.folder+ui['reg_type']+PLSRsave.get_unique_keywords_formatted(common_variables.keyword_lists,case.keywords).replace('.','p') common_variables.tempfig.savefig(plotFileName+ui['file_extension']) PLSRsave.add_line_to_logfile(logfile,Xval_case,case,ui,keywords,RMSe,coeff_det,frac_cor_lab=frac_cor_lab) #draw(common_variables) return reg_module, RMSe class moduleClass(): filetypes=['DPT','dpt','list','txt','laser'] def __init__(self, fig, locations, frame, ui): #reload modules if frame.module_reload_var.get(): if 'modules.libs.PLSRsave' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRsave']) if 'modules.libs.PLSRGeneticAlgorithm' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRGeneticAlgorithm']) if 'modules.libs.PLSRsequential_feature_selectors' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRsequential_feature_selectors']) if 'modules.libs.PLSRNN' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRNN']) if 'modules.libs.PLSRRNN' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRRNN']) if 'modules.libs.PLSRCNN' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRCNN']) if 'modules.libs.PLSR_file_import' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSR_file_import']) if 'modules.libs.PLSRregressionMethods' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRregressionMethods']) if 'modules.libs.PLSRclassifiers' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRclassifiers']) if 'modules.libs.PLSRregressionVisualization' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRregressionVisualization']) if 'modules.libs.PLSRpreprocessing' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRpreprocessing']) if 'modules.libs.PLSRwavelengthSelection' in sys.modules: #reload each time it is run importlib.reload(sys.modules['modules.libs.PLSRwavelengthSelection']) #code for checking for memory leaks global run #global keyword used to connect button clicks to class object run=self self.fig=fig self.locations=locations self.frame=frame self.ui=ui def clear_memory(self): safe_keys=['fig','locations','frame','ui','wrapper_i','wrapper_max'] keys=[] for key in self.__dict__: keys.append(key) for key in keys: if not key in safe_keys: delattr(self,key) def run(self): if not self.ui['use_wrapper']: self.run_wrapper_case() else: import gc gc.collect() #collect garbage to free memory from last run self.wrapper_i=1 self.wrapper_max=len(self.ui['binning']) if self.ui['filter']=='Try all': self.wrapper_max*=6 if self.ui['try_all_scatter_correction']: self.wrapper_max*=4 if self.ui['try_all_normalize']: self.wrapper_max*=4 if self.ui['scaling']=='Try all': self.wrapper_max*=2 if self.ui['mean_centering']=='Try all': self.wrapper_max*=2 bins=self.ui['binning'] for bin in bins: self.ui['binning']=[bin] self.scatter_cor_wrapper() self.ui['binning']=bins def scatter_cor_wrapper(self): #{'key': 'filter', 'type': 'radio:text', 'texts': ['No filter', 'MA', 'Butterworth', 'Hamming','Fourier','Try all'], 'tab': 0, 'row': 7} , if self.ui['filter']=='Try all': self.ui['use_SG']='No SG' for f in ['No filter', 'MA', 'Butterworth', 'Hamming','Fourier','SG']: #print(self.__dict__) self.ui['filter']=f if self.ui['filter']=='SG': self.ui['filter']='No filter' self.ui['use_SG']='use SG' if self.ui['try_all_scatter_correction']: self.ui['try_all_scatter_correction']=0 self.ui['normalize']=0 self.ui['SNV_key']=0 self.ui['MSC_key']=0 self.normalize_wrapper() self.ui['normalize']=1 self.normalize_wrapper() self.ui['normalize']=0 self.ui['SNV_key']=1 self.normalize_wrapper() self.ui['SNV_key']=0 self.ui['MSC_key']=1 self.normalize_wrapper() self.ui['MSC_key']=0 self.ui['try_all_scatter_correction']=1 else: self.normalize_wrapper() self.ui['use_SG']='No SG' self.ui['filter']='Try all' else: if self.ui['try_all_scatter_correction']: self.ui['try_all_scatter_correction']=0 self.ui['normalize']=0 self.ui['SNV_key']=0 self.ui['MSC_key']=0 self.normalize_wrapper() self.ui['normalize']=1 self.normalize_wrapper() self.ui['normalize']=0 self.ui['SNV_key']=1 self.normalize_wrapper() self.ui['SNV_key']=0 self.ui['MSC_key']=1 self.normalize_wrapper() self.ui['MSC_key']=0 self.ui['try_all_scatter_correction']=1 else: self.normalize_wrapper() def normalize_wrapper(self): ui=self.ui if not ui['try_all_normalize']: self.scaling_wrapper() else: ui['try_all_normalize']=0 #ui['normalize']=0 ui['baseline_value']=0 ui['baseline_linear']=0 ui['baseline_background']=0 ui['derivative']='Not der' # self.scaling_wrapper() # #ui['normalize']=1 #self.scaling_wrapper() #ui['normalize']=0 # ui['baseline_value']=1 self.scaling_wrapper() ui['baseline_value']=0 # ui['baseline_linear']=1 self.scaling_wrapper() ui['baseline_linear']=0 # ui['baseline_background']=1 self.scaling_wrapper() ui['baseline_background']=0 # ui['derivative']='1st der' self.scaling_wrapper() ui['derivative']='2nd der' self.scaling_wrapper() ui['derivative']='Not der' ui['try_all_normalize']=1 return #{'key': 'scaling', 'type': 'radio:text', 'texts': ['No scaling', 'Scaling','Try all'], 'tab': 0, 'row': 2} def scaling_wrapper(self): if not self.ui['scaling']=='Try all': self.mean_centering_wrapper() else: self.ui['scaling']='No scaling' self.mean_centering_wrapper() self.ui['scaling']='Scaling' self.mean_centering_wrapper() self.ui['scaling']='Try all' #{'key': 'mean_centering', 'type': 'radio:text', 'texts': ['No mean centering', 'Mean centering','Try all'], 'tab': 0, 'row': 2} , def mean_centering_wrapper(self): if not self.ui['mean_centering']=='Try all': self.clear_memory() print('wrapper i = ',self.wrapper_i, ' of ', self.wrapper_max) self.wrapper_i+=1 self.run_wrapper_case() else: self.ui['mean_centering']='No mean centering' self.clear_memory() print('wrapper i = ',self.wrapper_i, ' of ', self.wrapper_max) self.wrapper_i+=1 self.run_wrapper_case() self.ui['mean_centering']='Mean centering' self.clear_memory() print('wrapper i = ',self.wrapper_i, ' of ', self.wrapper_max) self.wrapper_i+=1 self.run_wrapper_case() self.ui['mean_centering']='Try all' def run_wrapper_case(self): fig=self.fig locations=self.locations frame=self.frame ui=self.ui eprint('running') self.fig=fig fig.clf() self.frame=frame # get variables from buttons common_variables=types.SimpleNamespace() common_variables.draw=self.draw self.common_variables=common_variables common_variables.keyword_lists={} PLSRregressionMethods.get_relevant_keywords(common_variables,ui) ui['multiprocessing']=1-(ui['no_multiprocessing']) save_check_var=frame.save_check_var.get() ui['save_check_var']=save_check_var filename=frame.name_field_string.get() self.filename=filename #prepare figures for display (set correct number of axes, each pointing to the next axis) ######################### if crossval and moving window -> stop ########### if ui['is_validation']=='X-val on training' and ui['regression_wavelength_selection']=='Moving window': print("Use of x-validation with moving window is not supported") return ######################### if RMSEP and no validation -> stop ############## if ui['is_validation']=='Training' and ui['RMS_type']=='RMSEP': print("Unable to calculate RMSEP with only training set") return #################### if RMSEP and RMSEC and no validation -> only RMSEP ### if ui['is_validation']=='Training': ui['RMS_type']='RMSEC' if ui['RMS_type']=='Default': ui['RMS_type']='RMSEC' else: if ui['RMS_type']=='Default': ui['RMS_type']='RMSEP' common_variables.frame=frame common_variables.fig=fig ################################################################################################ ######################### Load data as training or validation ################################## ################################################################################################ T=types.SimpleNamespace() V=types.SimpleNamespace() if len(frame.training_files)==0: print('training set required') return #load training set T.X, T.Y, common_variables.trainingfiles, self.wavenumbers, self.regressionCurControlTypes=PLSR_file_import.get_files(frame.training_files,ui['max_range']) self.original_wavenumbers=self.wavenumbers for i, contrltytpe in enumerate(self.regressionCurControlTypes): frame.button_handles['cur_col'][i]["text"]=contrltytpe if ui['is_validation']=='Training' or ui['is_validation']=='X-val on training':# if training or crossval -> deselect validation frame.nav.deselect() #frame.nav.clear_color('color3') #frame.validation_files=frame.nav.get_paths_of_selected_items() V.X=np.array([]) # set empty validation set V.Y=np.array([]) elif ui['is_validation']=='Training and Validation': if len(frame.validation_files)==0: print('training and validation set, but no validation set in in put') return #load validation set V.X, V.Y, common_variables.validationfiles, _, _2=PLSR_file_import.get_files(frame.validation_files,ui['max_range']) common_variables.original_T=copy.deepcopy(T) common_variables.original_V=copy.deepcopy(V) ################################################################################################ ################################## load reference spectra ####################################### ################################################################################################ if ui['reference_spectra']=='': self.reference_spectra=None else: try: temp, _1, _2, _3, _4=PLSR_file_import.get_files([ui['reference_spectra']],np.inf) if len(temp)>0: print('first reference spectra in list selected for reference spectra selected as reference spectra') self.reference_spectra=np.array(temp[0]) except Exception as e: self.reference_spectra=None print(e) print('error importing referece spectra -> ignoring') if ui['background_spectra']=='': self.background_spectra=None else: try: temp, _1, _2, _3, _4=PLSR_file_import.get_files([ui['background_spectra']],np.inf) if len(temp)>0: print('first background spectra in list selected for reference spectra selected as reference spectra') self.background_spectra=np.array(temp[0]) except Exception as e: self.background_spectra=None print(e) print('error importing referece spectra -> ignoring') ################################################################################################ ################# set up folder, save log and temporary figure for saving ###################### ################################################################################################ if save_check_var: if not os.path.exists(filename): os.makedirs(filename) PLSRsave.SaveLogFile(filename,ui,common_variables) common_variables.tempfig,common_variables.tempax=PLSRsave.make_tempfig(ui,frame) ################################################################################################ ############################## calculate window ranges ######################################### ################################################################################################ common_variables.datapoints=np.arange(len(self.wavenumbers)) #common_variables.datapointlists=[common_variables.datapoints]# declare this for get_or_make_absorbance_ax #common_variables.datapoints, common_variables.datapointlists=PLSRpreprocessing.GetDatapoints(self.wavenumbers, ui) ################################################################################################ ################################### save unprocessed spectra ################################### ################################################################################################ if ui['plot_spectra_before_preprocessing']: eprint('plot abs') if ui['save_check_var']: PLSRsave.PlotAbsorbance(common_variables.tempax,common_variables.tempfig,common_variables.datapoints,ui,self.wavenumbers,T.X,V.X) plotFileName=filename+'/SpectraPrePreprocessing' common_variables.tempfig.savefig(plotFileName.replace('.','p')+ui['file_extension']) common_variables.tempax.cla() ax=PLSRsave.get_or_make_absorbance_ax(self) self.draw() ################################################################################################ ################################### make pychem input file ##################################### ################################################################################################ if int(ui['make_pyChem_input_file']): if ui['is_validation']=='Training and Validation': PLSRsave.writePyChemFile(T.X,T.Y,validation,validationtruevalues) else: PLSRsave.writePyChemFile(T.X,T.Y,[],[]) ################################################################################################ ################## set current control and remove data higher than maxrange #################### ################################################################################################ datasets=[T] if ui['is_validation']=='Training and Validation': datasets.append(V) for E in datasets: keepsamples=[] for i,_ in enumerate(E.Y): if not E.Y[i,ui['cur_col']] > ui['max_range']: keepsamples.append(i) E.X=E.X[keepsamples,:] E.Y=E.Y[keepsamples,ui['cur_col']] ui['cur_control_string']=self.regressionCurControlTypes[ui['cur_col']] PLSRpreprocessing.do_preprocessing(self,T,V) if ui['plot_fourier']: if hasattr(T,'X_fft'): ax=fns.add_axis(fig,ui['fig_per_row'],ui['max_plots']) PLSRsave.plot_fourier(ax,fig,T,V,ui) self.complete_cases=[] for _ in [1]: # is a loop so that you can use 'break' for i,dercase in enumerate(self.preprocessed_cases): #need to set data range in case of derrivative, rerunn in all cases anyways datapoints=PLSRpreprocessing.GetDatapoints(dercase.wavenumbers, ui) #common_variables.datapoints=datapoints #common_variables.datapointlists=datapointlists if ui['plot_spectra_after_preprocessing']: ax=fns.add_axis(fig,ui['fig_per_row'],ui['max_plots']) PLSRsave.PlotAbsorbance(ax,fig,datapoints,ui,dercase.wavenumbers,dercase.T.X,dercase.V.X,dercase=dercase) self.draw() if ui['save_check_var']: PLSRsave.PlotAbsorbance(common_variables.tempax,common_variables.tempfig,datapoints,ui,dercase.wavenumbers,dercase.T.X,dercase.V.X,dercase=dercase) plotFileName=dercase.folder+'/SpectraPostPreprocessing' common_variables.tempfig.savefig(plotFileName.replace('.','p')+ui['file_extension']) common_variables.tempax.cla() for E in [dercase.T,dercase.V]: if len(E.Y)>0: E.X=E.X[:,datapoints] dercase.wavenumbers=dercase.wavenumbers[datapoints] #create complete cases for all pemutations of keyword values in keyword_lists for keyword_case in PLSRregressionMethods.generate_keyword_cases(common_variables.keyword_lists): self.complete_cases.append(types.SimpleNamespace()) self.complete_cases[-1].wavenumbers=dercase.wavenumbers self.complete_cases[-1].folder=dercase.folder self.complete_cases[-1].sg_config=dercase.sg_config self.complete_cases[-1].derrivative=dercase.derrivative self.complete_cases[-1].T=dercase.T self.complete_cases[-1].V=dercase.V self.complete_cases[-1].preprocessing_done=dercase.preprocessing_done self.complete_cases[-1].keywords=keyword_case if ui['reg_type']=='None': break for case in self.complete_cases: case.XvalRMSEs=[] case.XvalCorrClass=[] common_variables.keywords=case.keywords #GeneticAlgorithm(ui,T,V,datapoints,components) if ui['regression_wavelength_selection']=='No wavelength selection': active_wavenumers = np.ones(len(case.wavenumbers), dtype=bool) else: # report to user regarding split module if self.ui['WS_loss_type']=='X-validation on training': if self.ui['WS_cross_val_N']==1 and self.ui['WS_cross_val_max_cases']==-1: print('Using sklearn.LeaveOneOut on '+str(len(case.T.Y))+' measurements. Maxcases set to '+str(len(case.T.Y))) else: if self.ui['WS_cross_val_max_cases']==-1: print('WS_cross_val_max_cases set to -1, GA_cross_val_N not set to 1. Setting GAcross_val_max_cases to default (20)' ) self.ui['WS_cross_val_max_cases']=20 if ui['regression_wavelength_selection']=='Genetic Algorithm': GAobject = PLSRGeneticAlgorithm.GeneticAlgorithm(common_variables,ui,case) active_wavenumers = GAobject.run(fns.add_axis(common_variables.fig,ui['fig_per_row'],ui['max_plots']),case.wavenumbers,case.folder,self.draw) elif ui['regression_wavelength_selection']=='Moving Window': active_wavenumers = PLSRwavelengthSelection.MW(case,ui,common_variables) elif ui['regression_wavelength_selection']=='Sequential Feature Selector': FSobject = PLSRsequential_feature_selectors.sequentialFeatureSelector(common_variables,ui,case,self.draw) active_wavenumers = FSobject.run() Xval_cases=crossval(case.T,case.V,ui,case) # returns [T],[V] if not crossva, otherwise makes cases from validation dataset for Xval_case in Xval_cases: # ui.datapoints=runGeneticAlgorithm(dercase[0],dercase[1],dercase[2],dercase[3],dercase[4],dercase[5],dercase[6],dercase[7]) #def MW(T,V,wavenumbers, folder,ui,sg_config,curDerivative,supressplot): if ui['save_check_var'] and not ui['do_not_save_plots']: active_wavenumbers_file=case.folder+ui['reg_type']+PLSRsave.get_unique_keywords_formatted(common_variables.keyword_lists,case.keywords).replace('.','p')+'active_wavenumers.dpb' PLSRsave.save_active_wavenumbers(active_wavenumbers_file,case.wavenumbers,active_wavenumers) case.active_wavenumers=active_wavenumers self.draw() self.last_reg_module, RMSe = run_reg_module(Xval_case,case,ui,common_variables,active_wavenumers,self.filename+'/results_table',keywords={}) self.draw() self.last_complete_case = case self.last_Xval_case = Xval_case if Xval_case.supressplot==0: if ui['is_validation']=='X-val on training': #if ui['RMS_type']=='Combined RMSEP+RMSEC': # print('RMSEC+RMSEP = '+PLSRsave.custom_round(case.xvalRMSE,3)+' '+ui['unit']) if not 'classifier_type' in case.keywords: case.xvalRMSE=np.sqrt(np.sum(np.array(case.XvalRMSEs)**2)/len(case.XvalRMSEs)) if ui['RMS_type']=='RMSEC': print('RMSEC = '+PLSRsave.custom_round(case.xvalRMSE,3)+' '+ui['unit']) elif ui['RMS_type']=='RMSEP': print('RMSEP = '+PLSRsave.custom_round(case.xvalRMSE,3)+' '+ui['unit']) else: print(case.XvalCorrClass) case.xvalCorrClas=np.average(case.XvalCorrClass) print(case.xvalCorrClas) if ui['RMS_type']=='RMSEC': print('x-val corr classifed training = '+str(round(case.xvalCorrClas*100,3))+' %') elif ui['RMS_type']=='RMSEP': print('x-val corr classifed prediction = '+str(round(case.xvalCorrClas*100,3))+' %') case.XvalRMSEs=[] eprint('done') #plt.close(common_variables.tempfig) #del common_variables.tempfig if save_check_var: # save plot in window fig.savefig(filename+'/'+'_'.join(filename.split('/')[1:])+ui['file_extension']) print('Done') return def callbackClick(self,frame,event): ax=event.inaxes if hasattr(ax,'plot_type'): if ax.plot_type=='NN node map': PLSRregressionVisualization.plot_node_activation_vector(event) return else: print("clicked at", event.xdata, event.ydata) def reorder_plots(self,event): ui=self.ui ui['fig_per_row']=int(self.frame.buttons['fig_per_row'].get()) ui['max_plots']=int(self.frame.buttons['max_plots'].get()) fns.move_all_plots(self.fig,ui['fig_per_row'],ui['max_plots']) self.draw() @fns.rimt def draw(self): self.fig.canvas.draw() self.frame.update() def addButtons(): buttons=[ {'key': 'RNNtab3name', 'type': 'tabname', 'text': 'Import Options', 'tab': 3} , # dataset configuration {'key': 'RegressionL0', 'type': 'label', 'text': 'Data import options: ', 'tab': 3, 'row': 0} , {'key': 'is_validation', 'type': 'radio:text', 'texts': ['Training', 'Training and Validation', 'X-val on training'], 'tab': 3, 'row': 0} , {'key': 'cross_val_N', 'type': 'txt:int', 'text': 'Number of validation samples for cross validation', 'default': '10', 'width': 4, 'tab': 3, 'row': 1} , {'key': 'cross_val_max_cases', 'type': 'txt:int', 'text': 'Iterations', 'default': '-1', 'width': 4, 'tab': 3, 'row': 1} , {'key': 'RegressionL0a', 'type': 'label', 'text': 'Column of data to use: ', 'tab': 3, 'row': 2} , {'key': 'cur_col', 'type': 'radio', 'texts': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'], 'tab': 3, 'row': 2} , {'key': 'max_range', 'type': 'txt:float', 'text': 'Maximum concentration for training set', 'default': '10000', 'width': 6, 'tab': 3, 'row': 3} , {'key': 'unit', 'type': 'txt', 'text': 'Concentration unit', 'default': 'mg/dl', 'width': 6, 'tab': 3, 'row': 4} , # config for creating figure and saving {'key': 'file_extension', 'type': 'radio:text', 'texts': [ '.svg', '.png', '.pdf'], 'tab': 4, 'row': 1} , {'key': 'reorder_plots', 'type': 'click', 'text': 'Reorder plots', 'bind': reorder_plots, 'tab': 4, 'row': 1} , {'key': 'fig_per_row', 'type': 'txt:int', 'text': 'Figures per row', 'default': '2', 'width': 4, 'tab': 4, 'row': 1} , {'key': 'DPI', 'type': 'txt:int', 'text': 'dpi', 'default': '80', 'width': 4, 'tab': 4, 'row': 1} , # graphical user interface options {'key': 'max_plots', 'type': 'txt:int', 'text': 'Max number of plots', 'default': '-1', 'width': 3, 'tab': 4, 'row': 2} , # save options {'key': 'make_pyChem_input_file', 'type': 'check', 'text': 'Make pyChem file', 'tab': 4, 'row': 9} , {'key': 'do_not_save_plots', 'type': 'check', 'text': 'do not save plots', 'tab': 4, 'row': 8} , {'key': 'use_wrapper', 'type': 'check', 'text': 'use wrapper', 'tab': 4, 'row': 8} , # debugging options {'key': 'RNNtab5name', 'type': 'tabname', 'text': 'Other', 'tab': 5} , {'key': 'no_multiprocessing', 'type': 'radio', 'texts': ['use multiprocessing', 'do not use multiprocessing'], 'tab': 5, 'row': 0}, # result {'key': 'RMS_type', 'type': 'radio:text', 'texts': ['Default', 'RMSEC', 'RMSEP'], 'tab': 3, 'row': 6} , {'key': 'coeff_det_type', 'type': 'radio:text', 'texts': ['R^2', 'R'], 'tab': 3, 'row': 7} , {'key': 'SEP_MAE_or_%MAE', 'type': 'radio:text', 'texts': ['SEP', 'MAE','%MAE'], 'tab': 3, 'row': 8} , # declare input {'key': 'set_training', 'type': 'click', 'text': 'Set Training', 'bind': set_training,'color':'color1', 'tab': 10, 'row': 0} , {'key': 'set_validation', 'type': 'click', 'text': 'Set Validation', 'bind': set_validation,'color':'color3', 'tab': 10, 'row': 0} , ] buttons+=PLSRregressionMethods.get_buttons() buttons+=PLSRclassifiers.get_buttons() buttons+=PLSRsave.get_buttons() buttons+=PLSRwavelengthSelection.get_buttons() buttons+=PLSRpreprocessing.get_buttons() return buttons def set_training(event): """Sets the training data set(s) in the GUI.""" frame=event.widget.master.master.master frame.nav.clear_color('color1') frame.nav.color_selected('color1') frame.training_files=frame.nav.get_paths_of_selected_items() frame.nav.deselect() return def set_validation(event): """Sets the validation data set(s) in the GUI.""" frame=event.widget.master.master.master frame.nav.clear_color('color3') frame.nav.color_selected('color3') frame.validation_files=frame.nav.get_paths_of_selected_items() frame.nav.deselect() return def reorder_plots(event): global run run.reorder_plots(run,event) return ``` #### File: SpecAnalysis/modules/scansAveraging_absorbance.py ```python import numpy as np import fns import copy from .libs import signal_alignment from .libs import signal_alignment #function for averaging over n pulses def averageN(y,n=5): nh=int(n/2) rest=n-2*nh y2=np.array(y.copy()) l=len(y) y2[int(nh):l-1-nh]=0.0 for i in range(-nh,nh+rest,1): #print(int(nh)+i,l-1-nh+i,l) y2[int(nh):l-1-nh]+=y[int(nh)+i:l-1-nh+i]/float(n) return y2 def averageM(y,m=5): for j in range(y.shape[1]): y2=np.concatenate([copy.copy(y[:,j]),copy.copy(y[:,j])]) for i in range(y.shape[0]): y[i,j]=np.average(y2[i:i+m]) return y class moduleClass: filetypes = ['bin'] def __init__(self, fig, locations, frame, ui): self.ui=ui self.fig=fig self.scans=[] self.frame=frame for fname in locations: data=np.fromfile(fname,dtype=np.int16) rate=80000000 #only valid for 100kHz repetition rate ####################### get periodicity in data with FFT fftfreq=np.fft.fftfreq(24000)[1:] fft=np.fft.fft(data[0:24000])[1:]/fftfreq # divide by fftfreq period=int(1/fftfreq[np.argmax(abs(fft))]) period=800 #print(period) ####################### Get first pulse somezeroes = np.zeros(100, dtype=np.int16) data = np.concatenate((somezeroes, data)) ##adding some zeroes to the beginning of data #bin1 = np.concatenate((somezeroes, bin1)) ##adding some zeroes to the beginning of data #bin2 = np.concatenate((somezeroes, bin2)) ##adding some zeroes to the beginning of data maxstart=max(data[0:2000]) nextpulse=np.argmax(data[0:2000]-np.arange(0,maxstart,maxstart/(2000.0-0.5))) pulsewindow=50 correctPlace=data[nextpulse-pulsewindow:nextpulse+pulsewindow] #nextpulse+=np.argmax(correctPlace)-40 corvector=np.arange(-pulsewindow,pulsewindow,1) nextpulse+=int(np.sum(correctPlace*corvector)/np.sum(correctPlace)) ####################### get max value of each pulse numpulses=int(len(data)/period)+100 #make array extra long, make sure it is long enough pulseIntensity=np.zeros(numpulses) #np.zeros(len(data)/period) dichal1=np.zeros(numpulses) #np.zeros(len(data)/period) i=0 while nextpulse+20 < len(data) and nextpulse < 80000000: #print(nextpulse,i) if i%500==0: # every 500 pulses: refine pulse position #plt.plot(data[nextpulse-20:nextpulse+20]) correctPlace=data[nextpulse-pulsewindow:nextpulse+pulsewindow] #nextpulse+=np.argmax(correctPlace)-80 #print(np.sum(correctPlace)) if np.sum(correctPlace) >1000: nextpulse+=int(np.sum(correctPlace*corvector)/np.sum(correctPlace)) #pulseIntensity[i]=np.max(data[nextpulse-40:nextpulse+40]) pulseIntensity[i]=np.sum(data[nextpulse-pulsewindow:nextpulse+pulsewindow]) # integrate the pulse #dichal1[i]=bin1[nextpulse] nextpulse+=period i+=1 ####################### cut off excess length of pulseIntensity i=-1 while pulseIntensity[i]==0: i-=1 numpulses=numpulses+i pulseIntensity=pulseIntensity[0:i+1] self.scans.append(pulseIntensity) #self.scans=np.array(self.scans) return def run(self): self.fig.clf() ax=fns.add_axis(self.fig,1) StartWL=1200 EndWL=925 minscanlength=np.inf for scan in self.scans: if len(scan)<minscanlength: minscanlength=len(scan) for i,scan in enumerate(self.scans): self.scans[i]=scan[0:minscanlength] n=255 self.scans[i]=averageN(self.scans[i],n) self.scans=np.array(self.scans) self.averagescans=np.average(self.scans,axis=0) for i,scan in enumerate(self.scans): self.scans[i]=np.log10(scan/self.averagescans) for i, scan in enumerate(self.scans): if i > 0: s = signal_alignment.chisqr_align(self.scans[0], scan, [0,20000], init=0, bound=50) print(s) self.scans[i] = signal_alignment.shift(scan, s, mode='nearest') #StartWL=1200 #EndWL=925 #self.wavenumbers=StartWL+(EndWL-StartWL)*np.arange(minscanlength)/minscanlength StartWL=1200 EndWL=925 self.wavenumbers=StartWL+(EndWL-StartWL)*np.arange(minscanlength)/minscanlength numPulses=1000 step=100 self.ms=[10] self.averaged_scans=[] for i,m in enumerate(self.ms): self.averaged_scans.append(copy.deepcopy(self.scans)) self.averaged_scans[-1]=averageM(self.averaged_scans[-1],m) self.plot_me(ax,step,numPulses,EndWL,StartWL) if self.frame.save_check_var.get(): tempfig = self.frame.hidden_figure tempfig.set_size_inches(4*1.2, 3*1.2) tempfig.set_dpi(300) tempfig.clf() tempax = tempfig.add_subplot(1, 1, 1) tempfig.subplots_adjust(bottom=0.17,left=0.16, right=0.97, top=0.97) self.plot_me(tempax,step,numPulses,EndWL,StartWL) filename=self.frame.name_field_string.get() tempfig.savefig(filename+'.png') tempfig.savefig(filename+'.svg') return def plot_me(self,ax,step,numPulses,EndWL,StartWL): figure=ax.figure #[x,y,width,height] pos = [0.3, 0.25, 0.3, 0.2] newax = figure.add_axes(pos) for i,m in enumerate(self.ms): dat=self.averaged_scans[i][:,step//2:step*numPulses+step//2:step].swapaxes(0,1) dat=np.std(dat,axis=1) #ax.semilogy(self.wavenumbers[step//2:step*numPulses+step//2:step], # dat*100) xax=self.wavenumbers[step//2:step*numPulses+step//2:step] if m==1: label='1 scan' else: label=str(m)+' scans' '''ax.fill_between(xax[1:-1], -dat[1:-1]*100, dat[1:-1]*100, label=label)''' ax.plot(xax[1:-1], dat[1:-1], label=label) newax.loglog([m],[np.average(dat[1:-27000//100])],'x') #1200-1000 ax.legend(loc=2) #ax.text(i+0.5,1.075,str(m)) #ax.set_xticks(np.arange(len(self.ms))+0.5) #ax.set_xticklabels(self.ms) ax.invert_xaxis() #ax.set_xlabel(r'Wavenumbers [cm$^-1$]') ax.set_ylabel(r'Deviation from mean intensity [%]') ax.set_xlabel(r'Wavenumber [cm-1]') #ax.set_ylim([-1,1]) return def addButtons(): buttons = [ #{'key': 'joinPNG_orientation', 'type': 'radio:text', 'texts': ['horizontal', 'vertical'], 'tab': 0, 'row': 0} , ] return buttons ```
{ "source": "Jernic-Technologies/Soka", "score": 3 }
#### File: Jernic-Technologies/Soka/Soka-ES.py ```python import os import tkinter as tk from tkinter import filedialog from setuptools import Command # Codigo Base root = tk.Tk() apps = [] # Icono root.iconbitmap("favicon.ico") root.title('Soka') # Comandos def add_app(): for widget in frame.winfo_children(): widget.destroy() filename = filedialog.askopenfilename(initialdir="/", title="Seleccionar Archivo", filetypes=(("Ejecutable", "*.exe"), ("all files", "*.*"))) apps.append(filename) print(filename) for app in apps: label = tk.Label(frame, text=app, bg="gray") label.pack() def run_apps(): for app in apps: os.startfile(app) def quit(): root.quit() # Menu Bar menu_bar = tk.Menu(root) file_bar = tk.Menu(menu_bar, tearoff=0) file_bar.add_command(label='Add', command=add_app) file_bar.add_command(label='Close Soka', command=root.quit) menu_bar.add_cascade(label='File', menu=file_bar) root.config(menu=menu_bar) run_bar = tk.Menu(menu_bar, tearoff=0) run_bar.add_command(label='Run', command=run_apps) menu_bar.add_cascade(label='Run', menu=run_bar) root.config(menu=menu_bar) # Canvas y Frame canvas = tk.Canvas(root, height=500, width=500, bg="#263D42") canvas.pack() frame = tk.Frame(root, bg="white") frame.place(relwidth=0.8, relheight=0.8, relx=0.1, rely=0.1) # Botones openFile = tk.Button(root, text="Abrir archivo", padx=10, pady=5, fg="white", bg="#263D42", command=add_app) openFile.pack() runApps = tk.Button(root, text="Ejecutar Aplicaciones", padx=10, pady=5, fg="white", bg="#263D42", command=run_apps) runApps.pack() quit_soka= tk.Button(root, text="Cerrar Soka", padx=10,pady=5, fg="white", bg="#263D42", command=quit) quit_soka.pack() for app in apps: label = tk.Label(frame, text=app) label.pack() # Running root.mainloop() ```
{ "source": "jernkuan/thingsboard-python-rest-client", "score": 2 }
#### File: api/api_ce/dashboard_controller_api.py ```python from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from tb_rest_client.api_client import ApiClient class DashboardControllerApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def add_dashboard_customers_using_post(self, body, dashboard_id, **kwargs): # noqa: E501 """addDashboardCustomers # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.add_dashboard_customers_using_post(body, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] body: strCustomerIds (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.add_dashboard_customers_using_post_with_http_info(body, dashboard_id, **kwargs) # noqa: E501 else: (data) = self.add_dashboard_customers_using_post_with_http_info(body, dashboard_id, **kwargs) # noqa: E501 return data def add_dashboard_customers_using_post_with_http_info(self, body, dashboard_id, **kwargs): # noqa: E501 """addDashboardCustomers # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.add_dashboard_customers_using_post_with_http_info(body, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] body: strCustomerIds (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method add_dashboard_customers_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `add_dashboard_customers_using_post`") # noqa: E501 # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `add_dashboard_customers_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/{dashboardId}/customers/add', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def assign_dashboard_to_customer_using_post(self, customer_id, dashboard_id, **kwargs): # noqa: E501 """assignDashboardToCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_dashboard_to_customer_using_post(customer_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.assign_dashboard_to_customer_using_post_with_http_info(customer_id, dashboard_id, **kwargs) # noqa: E501 else: (data) = self.assign_dashboard_to_customer_using_post_with_http_info(customer_id, dashboard_id, **kwargs) # noqa: E501 return data def assign_dashboard_to_customer_using_post_with_http_info(self, customer_id, dashboard_id, **kwargs): # noqa: E501 """assignDashboardToCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_dashboard_to_customer_using_post_with_http_info(customer_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['customer_id', 'dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method assign_dashboard_to_customer_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'customer_id' is set if ('customer_id' not in params or params['customer_id'] is None): raise ValueError("Missing the required parameter `customer_id` when calling `assign_dashboard_to_customer_using_post`") # noqa: E501 # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `assign_dashboard_to_customer_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'customer_id' in params: path_params['customerId'] = params['customer_id'] # noqa: E501 if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/customer/{customerId}/dashboard/{dashboardId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def assign_dashboard_to_edge_using_post(self, edge_id, dashboard_id, **kwargs): # noqa: E501 """assignDashboardToEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_dashboard_to_edge_using_post(edge_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.assign_dashboard_to_edge_using_post_with_http_info(edge_id, dashboard_id, **kwargs) # noqa: E501 else: (data) = self.assign_dashboard_to_edge_using_post_with_http_info(edge_id, dashboard_id, **kwargs) # noqa: E501 return data def assign_dashboard_to_edge_using_post_with_http_info(self, edge_id, dashboard_id, **kwargs): # noqa: E501 """assignDashboardToEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_dashboard_to_edge_using_post_with_http_info(edge_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method assign_dashboard_to_edge_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `assign_dashboard_to_edge_using_post`") # noqa: E501 # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `assign_dashboard_to_edge_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/dashboard/{dashboardId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def assign_dashboard_to_public_customer_using_post(self, dashboard_id, **kwargs): # noqa: E501 """assignDashboardToPublicCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_dashboard_to_public_customer_using_post(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.assign_dashboard_to_public_customer_using_post_with_http_info(dashboard_id, **kwargs) # noqa: E501 else: (data) = self.assign_dashboard_to_public_customer_using_post_with_http_info(dashboard_id, **kwargs) # noqa: E501 return data def assign_dashboard_to_public_customer_using_post_with_http_info(self, dashboard_id, **kwargs): # noqa: E501 """assignDashboardToPublicCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_dashboard_to_public_customer_using_post_with_http_info(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method assign_dashboard_to_public_customer_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `assign_dashboard_to_public_customer_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/customer/public/dashboard/{dashboardId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_dashboard_using_delete(self, dashboard_id, **kwargs): # noqa: E501 """deleteDashboard # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_dashboard_using_delete(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_dashboard_using_delete_with_http_info(dashboard_id, **kwargs) # noqa: E501 else: (data) = self.delete_dashboard_using_delete_with_http_info(dashboard_id, **kwargs) # noqa: E501 return data def delete_dashboard_using_delete_with_http_info(self, dashboard_id, **kwargs): # noqa: E501 """deleteDashboard # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_dashboard_using_delete_with_http_info(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_dashboard_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `delete_dashboard_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/{dashboardId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_customer_dashboards_using_get(self, customer_id, page_size, page, **kwargs): # noqa: E501 """getCustomerDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_customer_dashboards_using_get(customer_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str page_size: pageSize (required) :param str page: page (required) :param bool mobile: mobile :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_customer_dashboards_using_get_with_http_info(customer_id, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_customer_dashboards_using_get_with_http_info(customer_id, page_size, page, **kwargs) # noqa: E501 return data def get_customer_dashboards_using_get_with_http_info(self, customer_id, page_size, page, **kwargs): # noqa: E501 """getCustomerDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_customer_dashboards_using_get_with_http_info(customer_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str page_size: pageSize (required) :param str page: page (required) :param bool mobile: mobile :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ all_params = ['customer_id', 'page_size', 'page', 'mobile', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_customer_dashboards_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'customer_id' is set if ('customer_id' not in params or params['customer_id'] is None): raise ValueError("Missing the required parameter `customer_id` when calling `get_customer_dashboards_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_customer_dashboards_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_customer_dashboards_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'customer_id' in params: path_params['customerId'] = params['customer_id'] # noqa: E501 query_params = [] if 'mobile' in params: query_params.append(('mobile', params['mobile'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/customer/{customerId}/dashboards{?mobile,textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataDashboardInfo', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_dashboard_by_id_using_get(self, dashboard_id, **kwargs): # noqa: E501 """getDashboardById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_dashboard_by_id_using_get(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_dashboard_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501 else: (data) = self.get_dashboard_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501 return data def get_dashboard_by_id_using_get_with_http_info(self, dashboard_id, **kwargs): # noqa: E501 """getDashboardById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_dashboard_by_id_using_get_with_http_info(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_dashboard_by_id_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `get_dashboard_by_id_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/{dashboardId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_dashboard_info_by_id_using_get(self, dashboard_id, **kwargs): # noqa: E501 """getDashboardInfoById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_dashboard_info_by_id_using_get(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: DashboardInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_dashboard_info_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501 else: (data) = self.get_dashboard_info_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501 return data def get_dashboard_info_by_id_using_get_with_http_info(self, dashboard_id, **kwargs): # noqa: E501 """getDashboardInfoById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_dashboard_info_by_id_using_get_with_http_info(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: DashboardInfo If the method is called asynchronously, returns the request thread. """ all_params = ['dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_dashboard_info_by_id_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `get_dashboard_info_by_id_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/info/{dashboardId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DashboardInfo', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_edge_dashboards_using_get(self, edge_id, page_size, page, **kwargs): # noqa: E501 """getEdgeDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_edge_dashboards_using_get(edge_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :param int start_time: startTime :param int end_time: endTime :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_edge_dashboards_using_get_with_http_info(edge_id, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_edge_dashboards_using_get_with_http_info(edge_id, page_size, page, **kwargs) # noqa: E501 return data def get_edge_dashboards_using_get_with_http_info(self, edge_id, page_size, page, **kwargs): # noqa: E501 """getEdgeDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_edge_dashboards_using_get_with_http_info(edge_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :param int start_time: startTime :param int end_time: endTime :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order', 'start_time', 'end_time'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_edge_dashboards_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `get_edge_dashboards_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_edge_dashboards_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_edge_dashboards_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 query_params = [] if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'start_time' in params: query_params.append(('startTime', params['start_time'])) # noqa: E501 if 'end_time' in params: query_params.append(('endTime', params['end_time'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/dashboards{?textSearch,sortProperty,sortOrder,startTime,endTime,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataDashboardInfo', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_home_dashboard_info_using_get(self, **kwargs): # noqa: E501 """getHomeDashboardInfo # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_home_dashboard_info_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: HomeDashboardInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_home_dashboard_info_using_get_with_http_info(self, **kwargs): # noqa: E501 """getHomeDashboardInfo # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_home_dashboard_info_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HomeDashboardInfo If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_home_dashboard_info_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/home/info', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HomeDashboardInfo', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_home_dashboard_using_get(self, **kwargs): # noqa: E501 """getHomeDashboard # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_home_dashboard_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: HomeDashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_home_dashboard_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_home_dashboard_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_home_dashboard_using_get_with_http_info(self, **kwargs): # noqa: E501 """getHomeDashboard # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_home_dashboard_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HomeDashboard If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_home_dashboard_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/home', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HomeDashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_max_datapoints_limit_using_get(self, **kwargs): # noqa: E501 """getMaxDatapointsLimit # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_max_datapoints_limit_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: int If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_max_datapoints_limit_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_max_datapoints_limit_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_max_datapoints_limit_using_get_with_http_info(self, **kwargs): # noqa: E501 """getMaxDatapointsLimit # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_max_datapoints_limit_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: int If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_max_datapoints_limit_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/maxDatapointsLimit', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='int', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_server_time_using_get(self, **kwargs): # noqa: E501 """getServerTime # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_server_time_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: int If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_server_time_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_server_time_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_server_time_using_get_with_http_info(self, **kwargs): # noqa: E501 """getServerTime # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_server_time_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: int If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_server_time_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/serverTime', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='int', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tenant_dashboards_using_get(self, page_size, page, **kwargs): # noqa: E501 """getTenantDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_dashboards_using_get(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param bool mobile: mobile :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_tenant_dashboards_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_tenant_dashboards_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 return data def get_tenant_dashboards_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501 """getTenantDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_dashboards_using_get_with_http_info(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param bool mobile: mobile :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ all_params = ['page_size', 'page', 'mobile', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_tenant_dashboards_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_tenant_dashboards_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_tenant_dashboards_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'mobile' in params: query_params.append(('mobile', params['mobile'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/tenant/dashboards{?mobile,textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataDashboardInfo', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tenant_dashboards_using_get1(self, tenant_id, page_size, page, **kwargs): # noqa: E501 """getTenantDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_dashboards_using_get1(tenant_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str tenant_id: tenantId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_tenant_dashboards_using_get1_with_http_info(tenant_id, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_tenant_dashboards_using_get1_with_http_info(tenant_id, page_size, page, **kwargs) # noqa: E501 return data def get_tenant_dashboards_using_get1_with_http_info(self, tenant_id, page_size, page, **kwargs): # noqa: E501 """getTenantDashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_dashboards_using_get1_with_http_info(tenant_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str tenant_id: tenantId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataDashboardInfo If the method is called asynchronously, returns the request thread. """ all_params = ['tenant_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_tenant_dashboards_using_get1" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'tenant_id' is set if ('tenant_id' not in params or params['tenant_id'] is None): raise ValueError("Missing the required parameter `tenant_id` when calling `get_tenant_dashboards_using_get1`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_tenant_dashboards_using_get1`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_tenant_dashboards_using_get1`") # noqa: E501 collection_formats = {} path_params = {} if 'tenant_id' in params: path_params['tenantId'] = params['tenant_id'] # noqa: E501 query_params = [] if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/tenant/{tenantId}/dashboards{?textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataDashboardInfo', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tenant_home_dashboard_info_using_get(self, **kwargs): # noqa: E501 """getTenantHomeDashboardInfo # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_home_dashboard_info_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: HomeDashboardInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_tenant_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_tenant_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_tenant_home_dashboard_info_using_get_with_http_info(self, **kwargs): # noqa: E501 """getTenantHomeDashboardInfo # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_home_dashboard_info_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HomeDashboardInfo If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_tenant_home_dashboard_info_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/tenant/dashboard/home/info', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HomeDashboardInfo', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def remove_dashboard_customers_using_post(self, body, dashboard_id, **kwargs): # noqa: E501 """removeDashboardCustomers # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.remove_dashboard_customers_using_post(body, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] body: strCustomerIds (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.remove_dashboard_customers_using_post_with_http_info(body, dashboard_id, **kwargs) # noqa: E501 else: (data) = self.remove_dashboard_customers_using_post_with_http_info(body, dashboard_id, **kwargs) # noqa: E501 return data def remove_dashboard_customers_using_post_with_http_info(self, body, dashboard_id, **kwargs): # noqa: E501 """removeDashboardCustomers # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.remove_dashboard_customers_using_post_with_http_info(body, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] body: strCustomerIds (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method remove_dashboard_customers_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `remove_dashboard_customers_using_post`") # noqa: E501 # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `remove_dashboard_customers_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/{dashboardId}/customers/remove', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def save_dashboard_using_post(self, body, **kwargs): # noqa: E501 """saveDashboard # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_dashboard_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param Dashboard body: dashboard (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_dashboard_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.save_dashboard_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def save_dashboard_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """saveDashboard # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_dashboard_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param Dashboard body: dashboard (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method save_dashboard_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `save_dashboard_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def set_tenant_home_dashboard_info_using_post(self, body, **kwargs): # noqa: E501 """setTenantHomeDashboardInfo # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_tenant_home_dashboard_info_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param HomeDashboardInfo body: homeDashboardInfo (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.set_tenant_home_dashboard_info_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.set_tenant_home_dashboard_info_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def set_tenant_home_dashboard_info_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """setTenantHomeDashboardInfo # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_tenant_home_dashboard_info_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param HomeDashboardInfo body: homeDashboardInfo (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method set_tenant_home_dashboard_info_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `set_tenant_home_dashboard_info_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/tenant/dashboard/home/info', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def unassign_dashboard_from_customer_using_delete(self, customer_id, dashboard_id, **kwargs): # noqa: E501 """unassignDashboardFromCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_dashboard_from_customer_using_delete(customer_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.unassign_dashboard_from_customer_using_delete_with_http_info(customer_id, dashboard_id, **kwargs) # noqa: E501 else: (data) = self.unassign_dashboard_from_customer_using_delete_with_http_info(customer_id, dashboard_id, **kwargs) # noqa: E501 return data def unassign_dashboard_from_customer_using_delete_with_http_info(self, customer_id, dashboard_id, **kwargs): # noqa: E501 """unassignDashboardFromCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_dashboard_from_customer_using_delete_with_http_info(customer_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['customer_id', 'dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method unassign_dashboard_from_customer_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'customer_id' is set if ('customer_id' not in params or params['customer_id'] is None): raise ValueError("Missing the required parameter `customer_id` when calling `unassign_dashboard_from_customer_using_delete`") # noqa: E501 # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `unassign_dashboard_from_customer_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'customer_id' in params: path_params['customerId'] = params['customer_id'] # noqa: E501 if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/customer/{customerId}/dashboard/{dashboardId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def unassign_dashboard_from_edge_using_delete(self, edge_id, dashboard_id, **kwargs): # noqa: E501 """unassignDashboardFromEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_dashboard_from_edge_using_delete(edge_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.unassign_dashboard_from_edge_using_delete_with_http_info(edge_id, dashboard_id, **kwargs) # noqa: E501 else: (data) = self.unassign_dashboard_from_edge_using_delete_with_http_info(edge_id, dashboard_id, **kwargs) # noqa: E501 return data def unassign_dashboard_from_edge_using_delete_with_http_info(self, edge_id, dashboard_id, **kwargs): # noqa: E501 """unassignDashboardFromEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_dashboard_from_edge_using_delete_with_http_info(edge_id, dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method unassign_dashboard_from_edge_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `unassign_dashboard_from_edge_using_delete`") # noqa: E501 # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `unassign_dashboard_from_edge_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/dashboard/{dashboardId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def unassign_dashboard_from_public_customer_using_delete(self, dashboard_id, **kwargs): # noqa: E501 """unassignDashboardFromPublicCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_dashboard_from_public_customer_using_delete(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.unassign_dashboard_from_public_customer_using_delete_with_http_info(dashboard_id, **kwargs) # noqa: E501 else: (data) = self.unassign_dashboard_from_public_customer_using_delete_with_http_info(dashboard_id, **kwargs) # noqa: E501 return data def unassign_dashboard_from_public_customer_using_delete_with_http_info(self, dashboard_id, **kwargs): # noqa: E501 """unassignDashboardFromPublicCustomer # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_dashboard_from_public_customer_using_delete_with_http_info(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['dashboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method unassign_dashboard_from_public_customer_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `unassign_dashboard_from_public_customer_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/customer/public/dashboard/{dashboardId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_dashboard_customers_using_post(self, dashboard_id, **kwargs): # noqa: E501 """updateDashboardCustomers # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_dashboard_customers_using_post(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :param list[str] body: strCustomerIds :return: Dashboard If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_dashboard_customers_using_post_with_http_info(dashboard_id, **kwargs) # noqa: E501 else: (data) = self.update_dashboard_customers_using_post_with_http_info(dashboard_id, **kwargs) # noqa: E501 return data def update_dashboard_customers_using_post_with_http_info(self, dashboard_id, **kwargs): # noqa: E501 """updateDashboardCustomers # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_dashboard_customers_using_post_with_http_info(dashboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str dashboard_id: dashboardId (required) :param list[str] body: strCustomerIds :return: Dashboard If the method is called asynchronously, returns the request thread. """ all_params = ['dashboard_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_dashboard_customers_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dashboard_id' is set if ('dashboard_id' not in params or params['dashboard_id'] is None): raise ValueError("Missing the required parameter `dashboard_id` when calling `update_dashboard_customers_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'dashboard_id' in params: path_params['dashboardId'] = params['dashboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/dashboard/{dashboardId}/customers', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Dashboard', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) ``` #### File: api/api_ce/o_auth_2_config_template_controller_api.py ```python from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from tb_rest_client.api_client import ApiClient class OAuth2ConfigTemplateControllerApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def delete_client_registration_template_using_delete(self, client_registration_template_id, **kwargs): # noqa: E501 """deleteClientRegistrationTemplate # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_client_registration_template_using_delete(client_registration_template_id, async_req=True) >>> result = thread.get() :param async_req bool :param str client_registration_template_id: clientRegistrationTemplateId (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_client_registration_template_using_delete_with_http_info(client_registration_template_id, **kwargs) # noqa: E501 else: (data) = self.delete_client_registration_template_using_delete_with_http_info(client_registration_template_id, **kwargs) # noqa: E501 return data def delete_client_registration_template_using_delete_with_http_info(self, client_registration_template_id, **kwargs): # noqa: E501 """deleteClientRegistrationTemplate # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_client_registration_template_using_delete_with_http_info(client_registration_template_id, async_req=True) >>> result = thread.get() :param async_req bool :param str client_registration_template_id: clientRegistrationTemplateId (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['client_registration_template_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_client_registration_template_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'client_registration_template_id' is set if ('client_registration_template_id' not in params or params['client_registration_template_id'] is None): raise ValueError("Missing the required parameter `client_registration_template_id` when calling `delete_client_registration_template_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'client_registration_template_id' in params: path_params['clientRegistrationTemplateId'] = params['client_registration_template_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/oauth2/config/template/{clientRegistrationTemplateId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_client_registration_templates_using_get(self, **kwargs): # noqa: E501 """getClientRegistrationTemplates # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_client_registration_templates_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: list[OAuth2ClientRegistrationTemplate] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_client_registration_templates_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_client_registration_templates_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_client_registration_templates_using_get_with_http_info(self, **kwargs): # noqa: E501 """getClientRegistrationTemplates # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_client_registration_templates_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[OAuth2ClientRegistrationTemplate] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_client_registration_templates_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/oauth2/config/template', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[OAuth2ClientRegistrationTemplate]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def save_client_registration_template_using_post(self, body, **kwargs): # noqa: E501 """saveClientRegistrationTemplate # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_client_registration_template_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param OAuth2ClientRegistrationTemplate body: clientRegistrationTemplate (required) :return: OAuth2ClientRegistrationTemplate If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_client_registration_template_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.save_client_registration_template_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def save_client_registration_template_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """saveClientRegistrationTemplate # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_client_registration_template_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param OAuth2ClientRegistrationTemplate body: clientRegistrationTemplate (required) :return: OAuth2ClientRegistrationTemplate If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method save_client_registration_template_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `save_client_registration_template_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/oauth2/config/template', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='OAuth2ClientRegistrationTemplate', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) ``` #### File: api/api_ce/rpc_v_2_controller_api.py ```python from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from tb_rest_client.api_client import ApiClient class RpcV2ControllerApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def delete_resource_using_delete(self, rpc_id, **kwargs): # noqa: E501 """deleteResource # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_resource_using_delete(rpc_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rpc_id: rpcId (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_resource_using_delete_with_http_info(rpc_id, **kwargs) # noqa: E501 else: (data) = self.delete_resource_using_delete_with_http_info(rpc_id, **kwargs) # noqa: E501 return data def delete_resource_using_delete_with_http_info(self, rpc_id, **kwargs): # noqa: E501 """deleteResource # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_resource_using_delete_with_http_info(rpc_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rpc_id: rpcId (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['rpc_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_resource_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rpc_id' is set if ('rpc_id' not in params or params['rpc_id'] is None): raise ValueError("Missing the required parameter `rpc_id` when calling `delete_resource_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'rpc_id' in params: path_params['rpcId'] = params['rpc_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/rpc/persistent/{rpcId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_persisted_rpc_by_device_using_get(self, device_id, page_size, page, rpc_status, **kwargs): # noqa: E501 """getPersistedRpcByDevice # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_persisted_rpc_by_device_using_get(device_id, page_size, page, rpc_status, async_req=True) >>> result = thread.get() :param async_req bool :param str device_id: deviceId (required) :param int page_size: pageSize (required) :param int page: page (required) :param str rpc_status: rpcStatus (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataRpc If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_persisted_rpc_by_device_using_get_with_http_info(device_id, page_size, page, rpc_status, **kwargs) # noqa: E501 else: (data) = self.get_persisted_rpc_by_device_using_get_with_http_info(device_id, page_size, page, rpc_status, **kwargs) # noqa: E501 return data def get_persisted_rpc_by_device_using_get_with_http_info(self, device_id, page_size, page, rpc_status, **kwargs): # noqa: E501 """getPersistedRpcByDevice # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_persisted_rpc_by_device_using_get_with_http_info(device_id, page_size, page, rpc_status, async_req=True) >>> result = thread.get() :param async_req bool :param str device_id: deviceId (required) :param int page_size: pageSize (required) :param int page: page (required) :param str rpc_status: rpcStatus (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataRpc If the method is called asynchronously, returns the request thread. """ all_params = ['device_id', 'page_size', 'page', 'rpc_status', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_persisted_rpc_by_device_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `get_persisted_rpc_by_device_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_persisted_rpc_by_device_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_persisted_rpc_by_device_using_get`") # noqa: E501 # verify the required parameter 'rpc_status' is set if ('rpc_status' not in params or params['rpc_status'] is None): raise ValueError("Missing the required parameter `rpc_status` when calling `get_persisted_rpc_by_device_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 if 'rpc_status' in params: query_params.append(('rpcStatus', params['rpc_status'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/rpc/persistent/device/{deviceId}{?pageSize,page,rpcStatus,textSearch,sortProperty,sortOrder}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataRpc', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_persisted_rpc_using_get(self, rpc_id, **kwargs): # noqa: E501 """getPersistedRpc # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_persisted_rpc_using_get(rpc_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rpc_id: rpcId (required) :return: Rpc If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_persisted_rpc_using_get_with_http_info(rpc_id, **kwargs) # noqa: E501 else: (data) = self.get_persisted_rpc_using_get_with_http_info(rpc_id, **kwargs) # noqa: E501 return data def get_persisted_rpc_using_get_with_http_info(self, rpc_id, **kwargs): # noqa: E501 """getPersistedRpc # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_persisted_rpc_using_get_with_http_info(rpc_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rpc_id: rpcId (required) :return: Rpc If the method is called asynchronously, returns the request thread. """ all_params = ['rpc_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_persisted_rpc_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rpc_id' is set if ('rpc_id' not in params or params['rpc_id'] is None): raise ValueError("Missing the required parameter `rpc_id` when calling `get_persisted_rpc_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'rpc_id' in params: path_params['rpcId'] = params['rpc_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/rpc/persistent/{rpcId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Rpc', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def handle_one_way_device_rpc_request_using_post1(self, body, device_id, **kwargs): # noqa: E501 """handleOneWayDeviceRPCRequest # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_one_way_device_rpc_request_using_post1(body, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str body: requestBody (required) :param str device_id: deviceId (required) :return: DeferredResultResponseEntity If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.handle_one_way_device_rpc_request_using_post1_with_http_info(body, device_id, **kwargs) # noqa: E501 else: (data) = self.handle_one_way_device_rpc_request_using_post1_with_http_info(body, device_id, **kwargs) # noqa: E501 return data def handle_one_way_device_rpc_request_using_post1_with_http_info(self, body, device_id, **kwargs): # noqa: E501 """handleOneWayDeviceRPCRequest # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_one_way_device_rpc_request_using_post1_with_http_info(body, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str body: requestBody (required) :param str device_id: deviceId (required) :return: DeferredResultResponseEntity If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'device_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method handle_one_way_device_rpc_request_using_post1" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `handle_one_way_device_rpc_request_using_post1`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `handle_one_way_device_rpc_request_using_post1`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/rpc/oneway/{deviceId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeferredResultResponseEntity', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def handle_two_way_device_rpc_request_using_post1(self, body, device_id, **kwargs): # noqa: E501 """handleTwoWayDeviceRPCRequest # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_two_way_device_rpc_request_using_post1(body, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str body: requestBody (required) :param str device_id: deviceId (required) :return: DeferredResultResponseEntity If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.handle_two_way_device_rpc_request_using_post1_with_http_info(body, device_id, **kwargs) # noqa: E501 else: (data) = self.handle_two_way_device_rpc_request_using_post1_with_http_info(body, device_id, **kwargs) # noqa: E501 return data def handle_two_way_device_rpc_request_using_post1_with_http_info(self, body, device_id, **kwargs): # noqa: E501 """handleTwoWayDeviceRPCRequest # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_two_way_device_rpc_request_using_post1_with_http_info(body, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str body: requestBody (required) :param str device_id: deviceId (required) :return: DeferredResultResponseEntity If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'device_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method handle_two_way_device_rpc_request_using_post1" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `handle_two_way_device_rpc_request_using_post1`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `handle_two_way_device_rpc_request_using_post1`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/rpc/twoway/{deviceId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeferredResultResponseEntity', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) ``` #### File: api/api_ce/rule_chain_controller_api.py ```python from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from tb_rest_client.api_client import ApiClient class RuleChainControllerApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def assign_rule_chain_to_edge_using_post(self, edge_id, rule_chain_id, **kwargs): # noqa: E501 """assignRuleChainToEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_rule_chain_to_edge_using_post(edge_id, rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.assign_rule_chain_to_edge_using_post_with_http_info(edge_id, rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.assign_rule_chain_to_edge_using_post_with_http_info(edge_id, rule_chain_id, **kwargs) # noqa: E501 return data def assign_rule_chain_to_edge_using_post_with_http_info(self, edge_id, rule_chain_id, **kwargs): # noqa: E501 """assignRuleChainToEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_rule_chain_to_edge_using_post_with_http_info(edge_id, rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method assign_rule_chain_to_edge_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `assign_rule_chain_to_edge_using_post`") # noqa: E501 # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `assign_rule_chain_to_edge_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/ruleChain/{ruleChainId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_rule_chain_using_delete(self, rule_chain_id, **kwargs): # noqa: E501 """deleteRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_rule_chain_using_delete(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_rule_chain_using_delete_with_http_info(rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.delete_rule_chain_using_delete_with_http_info(rule_chain_id, **kwargs) # noqa: E501 return data def delete_rule_chain_using_delete_with_http_info(self, rule_chain_id, **kwargs): # noqa: E501 """deleteRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_rule_chain_using_delete_with_http_info(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_rule_chain_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `delete_rule_chain_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/{ruleChainId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def export_rule_chains_using_get(self, limit, **kwargs): # noqa: E501 """exportRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.export_rule_chains_using_get(limit, async_req=True) >>> result = thread.get() :param async_req bool :param str limit: limit (required) :return: RuleChainData If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.export_rule_chains_using_get_with_http_info(limit, **kwargs) # noqa: E501 else: (data) = self.export_rule_chains_using_get_with_http_info(limit, **kwargs) # noqa: E501 return data def export_rule_chains_using_get_with_http_info(self, limit, **kwargs): # noqa: E501 """exportRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.export_rule_chains_using_get_with_http_info(limit, async_req=True) >>> result = thread.get() :param async_req bool :param str limit: limit (required) :return: RuleChainData If the method is called asynchronously, returns the request thread. """ all_params = ['limit'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method export_rule_chains_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'limit' is set if ('limit' not in params or params['limit'] is None): raise ValueError("Missing the required parameter `limit` when calling `export_rule_chains_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChains/export{?limit}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChainData', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_auto_assign_to_edge_rule_chains_using_get(self, **kwargs): # noqa: E501 """getAutoAssignToEdgeRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_auto_assign_to_edge_rule_chains_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: list[RuleChain] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_auto_assign_to_edge_rule_chains_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_auto_assign_to_edge_rule_chains_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_auto_assign_to_edge_rule_chains_using_get_with_http_info(self, **kwargs): # noqa: E501 """getAutoAssignToEdgeRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_auto_assign_to_edge_rule_chains_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[RuleChain] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_auto_assign_to_edge_rule_chains_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/autoAssignToEdgeRuleChains', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[RuleChain]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_edge_rule_chains_using_get(self, edge_id, page_size, page, **kwargs): # noqa: E501 """getEdgeRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_edge_rule_chains_using_get(edge_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataRuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_edge_rule_chains_using_get_with_http_info(edge_id, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_edge_rule_chains_using_get_with_http_info(edge_id, page_size, page, **kwargs) # noqa: E501 return data def get_edge_rule_chains_using_get_with_http_info(self, edge_id, page_size, page, **kwargs): # noqa: E501 """getEdgeRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_edge_rule_chains_using_get_with_http_info(edge_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataRuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_edge_rule_chains_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `get_edge_rule_chains_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_edge_rule_chains_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_edge_rule_chains_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 query_params = [] if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/ruleChains{?textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataRuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_latest_rule_node_debug_input_using_get(self, rule_node_id, **kwargs): # noqa: E501 """getLatestRuleNodeDebugInput # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_latest_rule_node_debug_input_using_get(rule_node_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_node_id: ruleNodeId (required) :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_latest_rule_node_debug_input_using_get_with_http_info(rule_node_id, **kwargs) # noqa: E501 else: (data) = self.get_latest_rule_node_debug_input_using_get_with_http_info(rule_node_id, **kwargs) # noqa: E501 return data def get_latest_rule_node_debug_input_using_get_with_http_info(self, rule_node_id, **kwargs): # noqa: E501 """getLatestRuleNodeDebugInput # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_latest_rule_node_debug_input_using_get_with_http_info(rule_node_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_node_id: ruleNodeId (required) :return: str If the method is called asynchronously, returns the request thread. """ all_params = ['rule_node_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_latest_rule_node_debug_input_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_node_id' is set if ('rule_node_id' not in params or params['rule_node_id'] is None): raise ValueError("Missing the required parameter `rule_node_id` when calling `get_latest_rule_node_debug_input_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_node_id' in params: path_params['ruleNodeId'] = params['rule_node_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleNode/{ruleNodeId}/debugIn', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_rule_chain_by_id_using_get(self, rule_chain_id, **kwargs): # noqa: E501 """getRuleChainById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_rule_chain_by_id_using_get(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_rule_chain_by_id_using_get_with_http_info(rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.get_rule_chain_by_id_using_get_with_http_info(rule_chain_id, **kwargs) # noqa: E501 return data def get_rule_chain_by_id_using_get_with_http_info(self, rule_chain_id, **kwargs): # noqa: E501 """getRuleChainById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_rule_chain_by_id_using_get_with_http_info(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_rule_chain_by_id_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `get_rule_chain_by_id_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/{ruleChainId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_rule_chain_meta_data_using_get(self, rule_chain_id, **kwargs): # noqa: E501 """getRuleChainMetaData # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_rule_chain_meta_data_using_get(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChainMetaData If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_rule_chain_meta_data_using_get_with_http_info(rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.get_rule_chain_meta_data_using_get_with_http_info(rule_chain_id, **kwargs) # noqa: E501 return data def get_rule_chain_meta_data_using_get_with_http_info(self, rule_chain_id, **kwargs): # noqa: E501 """getRuleChainMetaData # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_rule_chain_meta_data_using_get_with_http_info(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChainMetaData If the method is called asynchronously, returns the request thread. """ all_params = ['rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_rule_chain_meta_data_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `get_rule_chain_meta_data_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/{ruleChainId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChainMetaData', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_rule_chains_using_get(self, page_size, page, **kwargs): # noqa: E501 """getRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_rule_chains_using_get(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataRuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_rule_chains_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_rule_chains_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 return data def get_rule_chains_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501 """getRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_rule_chains_using_get_with_http_info(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataRuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['page_size', 'page', 'type', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_rule_chains_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_rule_chains_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_rule_chains_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'type' in params: query_params.append(('type', params['type'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChains{?type,textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataRuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def import_rule_chains_using_post(self, body, **kwargs): # noqa: E501 """importRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.import_rule_chains_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param RuleChainData body: ruleChainData (required) :param bool overwrite: overwrite :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.import_rule_chains_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.import_rule_chains_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def import_rule_chains_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """importRuleChains # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.import_rule_chains_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param RuleChainData body: ruleChainData (required) :param bool overwrite: overwrite :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'overwrite'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method import_rule_chains_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `import_rule_chains_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'overwrite' in params: query_params.append(('overwrite', params['overwrite'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChains/import{?overwrite}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def save_rule_chain_meta_data_using_post(self, body, **kwargs): # noqa: E501 """saveRuleChainMetaData # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_rule_chain_meta_data_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param RuleChainMetaData body: ruleChainMetaData (required) :return: RuleChainMetaData If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_rule_chain_meta_data_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.save_rule_chain_meta_data_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def save_rule_chain_meta_data_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """saveRuleChainMetaData # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_rule_chain_meta_data_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param RuleChainMetaData body: ruleChainMetaData (required) :return: RuleChainMetaData If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method save_rule_chain_meta_data_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `save_rule_chain_meta_data_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/metadata', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChainMetaData', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def save_rule_chain_using_post(self, body, **kwargs): # noqa: E501 """saveRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_rule_chain_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param DefaultRuleChainCreateRequest body: request (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_rule_chain_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.save_rule_chain_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def save_rule_chain_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """saveRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_rule_chain_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param DefaultRuleChainCreateRequest body: request (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method save_rule_chain_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `save_rule_chain_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/device/default', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def save_rule_chain_using_post1(self, body, **kwargs): # noqa: E501 """saveRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_rule_chain_using_post1(body, async_req=True) >>> result = thread.get() :param async_req bool :param RuleChain body: ruleChain (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_rule_chain_using_post1_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.save_rule_chain_using_post1_with_http_info(body, **kwargs) # noqa: E501 return data def save_rule_chain_using_post1_with_http_info(self, body, **kwargs): # noqa: E501 """saveRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_rule_chain_using_post1_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param RuleChain body: ruleChain (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method save_rule_chain_using_post1" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `save_rule_chain_using_post1`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def set_auto_assign_to_edge_rule_chain_using_post(self, rule_chain_id, **kwargs): # noqa: E501 """setAutoAssignToEdgeRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_auto_assign_to_edge_rule_chain_using_post(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.set_auto_assign_to_edge_rule_chain_using_post_with_http_info(rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.set_auto_assign_to_edge_rule_chain_using_post_with_http_info(rule_chain_id, **kwargs) # noqa: E501 return data def set_auto_assign_to_edge_rule_chain_using_post_with_http_info(self, rule_chain_id, **kwargs): # noqa: E501 """setAutoAssignToEdgeRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_auto_assign_to_edge_rule_chain_using_post_with_http_info(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method set_auto_assign_to_edge_rule_chain_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `set_auto_assign_to_edge_rule_chain_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/{ruleChainId}/autoAssignToEdge', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def set_edge_template_root_rule_chain_using_post(self, rule_chain_id, **kwargs): # noqa: E501 """setEdgeTemplateRootRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_edge_template_root_rule_chain_using_post(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.set_edge_template_root_rule_chain_using_post_with_http_info(rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.set_edge_template_root_rule_chain_using_post_with_http_info(rule_chain_id, **kwargs) # noqa: E501 return data def set_edge_template_root_rule_chain_using_post_with_http_info(self, rule_chain_id, **kwargs): # noqa: E501 """setEdgeTemplateRootRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_edge_template_root_rule_chain_using_post_with_http_info(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method set_edge_template_root_rule_chain_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `set_edge_template_root_rule_chain_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/{ruleChainId}/edgeTemplateRoot', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def set_root_rule_chain_using_post1(self, rule_chain_id, **kwargs): # noqa: E501 """setRootRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_root_rule_chain_using_post1(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.set_root_rule_chain_using_post1_with_http_info(rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.set_root_rule_chain_using_post1_with_http_info(rule_chain_id, **kwargs) # noqa: E501 return data def set_root_rule_chain_using_post1_with_http_info(self, rule_chain_id, **kwargs): # noqa: E501 """setRootRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_root_rule_chain_using_post1_with_http_info(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method set_root_rule_chain_using_post1" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `set_root_rule_chain_using_post1`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/{ruleChainId}/root', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def test_script_using_post(self, body, **kwargs): # noqa: E501 """testScript # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.test_script_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param str body: inputParams (required) :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.test_script_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.test_script_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def test_script_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """testScript # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.test_script_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param str body: inputParams (required) :return: str If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method test_script_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `test_script_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/testScript', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def unassign_rule_chain_from_edge_using_delete(self, edge_id, rule_chain_id, **kwargs): # noqa: E501 """unassignRuleChainFromEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_rule_chain_from_edge_using_delete(edge_id, rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.unassign_rule_chain_from_edge_using_delete_with_http_info(edge_id, rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.unassign_rule_chain_from_edge_using_delete_with_http_info(edge_id, rule_chain_id, **kwargs) # noqa: E501 return data def unassign_rule_chain_from_edge_using_delete_with_http_info(self, edge_id, rule_chain_id, **kwargs): # noqa: E501 """unassignRuleChainFromEdge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_rule_chain_from_edge_using_delete_with_http_info(edge_id, rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: edgeId (required) :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method unassign_rule_chain_from_edge_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `unassign_rule_chain_from_edge_using_delete`") # noqa: E501 # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `unassign_rule_chain_from_edge_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/ruleChain/{ruleChainId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def unset_auto_assign_to_edge_rule_chain_using_delete(self, rule_chain_id, **kwargs): # noqa: E501 """unsetAutoAssignToEdgeRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unset_auto_assign_to_edge_rule_chain_using_delete(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.unset_auto_assign_to_edge_rule_chain_using_delete_with_http_info(rule_chain_id, **kwargs) # noqa: E501 else: (data) = self.unset_auto_assign_to_edge_rule_chain_using_delete_with_http_info(rule_chain_id, **kwargs) # noqa: E501 return data def unset_auto_assign_to_edge_rule_chain_using_delete_with_http_info(self, rule_chain_id, **kwargs): # noqa: E501 """unsetAutoAssignToEdgeRuleChain # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unset_auto_assign_to_edge_rule_chain_using_delete_with_http_info(rule_chain_id, async_req=True) >>> result = thread.get() :param async_req bool :param str rule_chain_id: ruleChainId (required) :return: RuleChain If the method is called asynchronously, returns the request thread. """ all_params = ['rule_chain_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method unset_auto_assign_to_edge_rule_chain_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'rule_chain_id' is set if ('rule_chain_id' not in params or params['rule_chain_id'] is None): raise ValueError("Missing the required parameter `rule_chain_id` when calling `unset_auto_assign_to_edge_rule_chain_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'rule_chain_id' in params: path_params['ruleChainId'] = params['rule_chain_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/ruleChain/{ruleChainId}/autoAssignToEdge', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RuleChain', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) ``` #### File: api/api_pe/entity_view_controller_api.py ```python from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from tb_rest_client.api_client import ApiClient class EntityViewControllerApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def delete_entity_view_using_delete(self, entity_view_id, **kwargs): # noqa: E501 """deleteEntityView # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_entity_view_using_delete(entity_view_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_id: entityViewId (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_entity_view_using_delete_with_http_info(entity_view_id, **kwargs) # noqa: E501 else: (data) = self.delete_entity_view_using_delete_with_http_info(entity_view_id, **kwargs) # noqa: E501 return data def delete_entity_view_using_delete_with_http_info(self, entity_view_id, **kwargs): # noqa: E501 """deleteEntityView # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_entity_view_using_delete_with_http_info(entity_view_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_id: entityViewId (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_view_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_entity_view_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_view_id' is set if ('entity_view_id' not in params or params['entity_view_id'] is None): raise ValueError("Missing the required parameter `entity_view_id` when calling `delete_entity_view_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_view_id' in params: path_params['entityViewId'] = params['entity_view_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityView/{entityViewId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def find_by_query_using_post4(self, body, **kwargs): # noqa: E501 """findByQuery # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.find_by_query_using_post4(body, async_req=True) >>> result = thread.get() :param async_req bool :param EntityViewSearchQuery body: query (required) :return: list[EntityView] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.find_by_query_using_post4_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.find_by_query_using_post4_with_http_info(body, **kwargs) # noqa: E501 return data def find_by_query_using_post4_with_http_info(self, body, **kwargs): # noqa: E501 """findByQuery # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.find_by_query_using_post4_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param EntityViewSearchQuery body: query (required) :return: list[EntityView] If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method find_by_query_using_post4" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `find_by_query_using_post4`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityViews', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityView]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_customer_entity_views_using_get(self, customer_id, page_size, page, **kwargs): # noqa: E501 """getCustomerEntityViews # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_customer_entity_views_using_get(customer_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_customer_entity_views_using_get_with_http_info(customer_id, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_customer_entity_views_using_get_with_http_info(customer_id, page_size, page, **kwargs) # noqa: E501 return data def get_customer_entity_views_using_get_with_http_info(self, customer_id, page_size, page, **kwargs): # noqa: E501 """getCustomerEntityViews # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_customer_entity_views_using_get_with_http_info(customer_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str customer_id: customerId (required) :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ all_params = ['customer_id', 'page_size', 'page', 'type', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_customer_entity_views_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'customer_id' is set if ('customer_id' not in params or params['customer_id'] is None): raise ValueError("Missing the required parameter `customer_id` when calling `get_customer_entity_views_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_customer_entity_views_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_customer_entity_views_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'customer_id' in params: path_params['customerId'] = params['customer_id'] # noqa: E501 query_params = [] if 'type' in params: query_params.append(('type', params['type'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/customer/{customerId}/entityViews{?type,textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataEntityView', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_entity_view_by_id_using_get(self, entity_view_id, **kwargs): # noqa: E501 """getEntityViewById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_view_by_id_using_get(entity_view_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_id: entityViewId (required) :return: EntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_view_by_id_using_get_with_http_info(entity_view_id, **kwargs) # noqa: E501 else: (data) = self.get_entity_view_by_id_using_get_with_http_info(entity_view_id, **kwargs) # noqa: E501 return data def get_entity_view_by_id_using_get_with_http_info(self, entity_view_id, **kwargs): # noqa: E501 """getEntityViewById # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_view_by_id_using_get_with_http_info(entity_view_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_id: entityViewId (required) :return: EntityView If the method is called asynchronously, returns the request thread. """ all_params = ['entity_view_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_entity_view_by_id_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_view_id' is set if ('entity_view_id' not in params or params['entity_view_id'] is None): raise ValueError("Missing the required parameter `entity_view_id` when calling `get_entity_view_by_id_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_view_id' in params: path_params['entityViewId'] = params['entity_view_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityView/{entityViewId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityView', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_entity_view_types_using_get(self, **kwargs): # noqa: E501 """getEntityViewTypes # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_view_types_using_get(async_req=True) >>> result = thread.get() :param async_req bool :return: list[EntitySubtype] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_view_types_using_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_entity_view_types_using_get_with_http_info(**kwargs) # noqa: E501 return data def get_entity_view_types_using_get_with_http_info(self, **kwargs): # noqa: E501 """getEntityViewTypes # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_view_types_using_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[EntitySubtype] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_entity_view_types_using_get" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityView/types', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntitySubtype]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_entity_views_by_entity_group_id_using_get(self, entity_group_id, page_size, page, **kwargs): # noqa: E501 """getEntityViewsByEntityGroupId # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_views_by_entity_group_id_using_get(entity_group_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: entityGroupId (required) :param str page_size: Page size (required) :param str page: Page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_views_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_entity_views_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501 return data def get_entity_views_by_entity_group_id_using_get_with_http_info(self, entity_group_id, page_size, page, **kwargs): # noqa: E501 """getEntityViewsByEntityGroupId # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_views_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: entityGroupId (required) :param str page_size: Page size (required) :param str page: Page (required) :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_entity_views_by_entity_group_id_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `get_entity_views_by_entity_group_id_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_entity_views_by_entity_group_id_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_entity_views_by_entity_group_id_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/entityViews{?textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataEntityView', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_entity_views_by_ids_using_get(self, entity_view_ids, **kwargs): # noqa: E501 """getEntityViewsByIds # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_views_by_ids_using_get(entity_view_ids, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_ids: entityViewIds (required) :return: list[EntityView] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_views_by_ids_using_get_with_http_info(entity_view_ids, **kwargs) # noqa: E501 else: (data) = self.get_entity_views_by_ids_using_get_with_http_info(entity_view_ids, **kwargs) # noqa: E501 return data def get_entity_views_by_ids_using_get_with_http_info(self, entity_view_ids, **kwargs): # noqa: E501 """getEntityViewsByIds # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_entity_views_by_ids_using_get_with_http_info(entity_view_ids, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_ids: entityViewIds (required) :return: list[EntityView] If the method is called asynchronously, returns the request thread. """ all_params = ['entity_view_ids'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_entity_views_by_ids_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_view_ids' is set if ('entity_view_ids' not in params or params['entity_view_ids'] is None): raise ValueError("Missing the required parameter `entity_view_ids` when calling `get_entity_views_by_ids_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'entity_view_ids' in params: query_params.append(('entityViewIds', params['entity_view_ids'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityViews{?entityViewIds}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityView]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tenant_entity_view_using_get(self, entity_view_name, **kwargs): # noqa: E501 """getTenantEntityView # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_entity_view_using_get(entity_view_name, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_name: entityViewName (required) :return: EntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_tenant_entity_view_using_get_with_http_info(entity_view_name, **kwargs) # noqa: E501 else: (data) = self.get_tenant_entity_view_using_get_with_http_info(entity_view_name, **kwargs) # noqa: E501 return data def get_tenant_entity_view_using_get_with_http_info(self, entity_view_name, **kwargs): # noqa: E501 """getTenantEntityView # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_entity_view_using_get_with_http_info(entity_view_name, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_view_name: entityViewName (required) :return: EntityView If the method is called asynchronously, returns the request thread. """ all_params = ['entity_view_name'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_tenant_entity_view_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_view_name' is set if ('entity_view_name' not in params or params['entity_view_name'] is None): raise ValueError("Missing the required parameter `entity_view_name` when calling `get_tenant_entity_view_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'entity_view_name' in params: query_params.append(('entityViewName', params['entity_view_name'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/tenant/entityViews{?entityViewName}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityView', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tenant_entity_views_using_get(self, page_size, page, **kwargs): # noqa: E501 """getTenantEntityViews # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_entity_views_using_get(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_tenant_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_tenant_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 return data def get_tenant_entity_views_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501 """getTenantEntityViews # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_entity_views_using_get_with_http_info(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ all_params = ['page_size', 'page', 'type', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_tenant_entity_views_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_tenant_entity_views_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_tenant_entity_views_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'type' in params: query_params.append(('type', params['type'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/tenant/entityViews{?type,textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataEntityView', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_user_entity_views_using_get(self, page_size, page, **kwargs): # noqa: E501 """getUserEntityViews # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_user_entity_views_using_get(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_user_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_user_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 return data def get_user_entity_views_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501 """getUserEntityViews # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_user_entity_views_using_get_with_http_info(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str page_size: pageSize (required) :param str page: page (required) :param str type: type :param str text_search: textSearch :param str sort_property: sortProperty :param str sort_order: sortOrder :return: PageDataEntityView If the method is called asynchronously, returns the request thread. """ all_params = ['page_size', 'page', 'type', 'text_search', 'sort_property', 'sort_order'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_user_entity_views_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_user_entity_views_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_user_entity_views_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'type' in params: query_params.append(('type', params['type'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/user/entityViews{?type,textSearch,sortProperty,sortOrder,pageSize,page}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataEntityView', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def save_entity_view_using_post(self, body, **kwargs): # noqa: E501 """saveEntityView # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_entity_view_using_post(body, async_req=True) >>> result = thread.get() :param async_req bool :param EntityView body: entityView (required) :param str entity_group_id: entityGroupId :return: EntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_entity_view_using_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.save_entity_view_using_post_with_http_info(body, **kwargs) # noqa: E501 return data def save_entity_view_using_post_with_http_info(self, body, **kwargs): # noqa: E501 """saveEntityView # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_entity_view_using_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param EntityView body: entityView (required) :param str entity_group_id: entityGroupId :return: EntityView If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'entity_group_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method save_entity_view_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `save_entity_view_using_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'entity_group_id' in params: query_params.append(('entityGroupId', params['entity_group_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityView{?entityGroupId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityView', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) ``` #### File: models/models_ce/entity_data_page_link.py ```python import pprint import re # noqa: F401 import six class EntityDataPageLink(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'page_size': 'int', 'page': 'int', 'text_search': 'str', 'sort_order': 'EntityDataSortOrder', 'dynamic': 'bool' } attribute_map = { 'page_size': 'pageSize', 'page': 'page', 'text_search': 'textSearch', 'sort_order': 'sortOrder', 'dynamic': 'dynamic' } def __init__(self, page_size=None, page=None, text_search=None, sort_order=None, dynamic=None): # noqa: E501 """EntityDataPageLink - a model defined in Swagger""" # noqa: E501 self._page_size = None self._page = None self._text_search = None self._sort_order = None self._dynamic = None self.discriminator = None self.page_size = page_size self.page = page self.text_search = text_search self.sort_order = sort_order self.dynamic = dynamic @property def page_size(self): """Gets the page_size of this EntityDataPageLink. # noqa: E501 :return: The page_size of this EntityDataPageLink. # noqa: E501 :rtype: int """ return self._page_size @page_size.setter def page_size(self, page_size): """Sets the page_size of this EntityDataPageLink. :param page_size: The page_size of this EntityDataPageLink. # noqa: E501 :type: int """ if page_size is None: raise ValueError("Invalid value for `page_size`, must not be `None`") # noqa: E501 self._page_size = page_size @property def page(self): """Gets the page of this EntityDataPageLink. # noqa: E501 :return: The page of this EntityDataPageLink. # noqa: E501 :rtype: int """ return self._page @page.setter def page(self, page): """Sets the page of this EntityDataPageLink. :param page: The page of this EntityDataPageLink. # noqa: E501 :type: int """ if page is None: raise ValueError("Invalid value for `page`, must not be `None`") # noqa: E501 self._page = page @property def text_search(self): """Gets the text_search of this EntityDataPageLink. # noqa: E501 :return: The text_search of this EntityDataPageLink. # noqa: E501 :rtype: str """ return self._text_search @text_search.setter def text_search(self, text_search): """Sets the text_search of this EntityDataPageLink. :param text_search: The text_search of this EntityDataPageLink. # noqa: E501 :type: str """ if text_search is None: raise ValueError("Invalid value for `text_search`, must not be `None`") # noqa: E501 self._text_search = text_search @property def sort_order(self): """Gets the sort_order of this EntityDataPageLink. # noqa: E501 :return: The sort_order of this EntityDataPageLink. # noqa: E501 :rtype: EntityDataSortOrder """ return self._sort_order @sort_order.setter def sort_order(self, sort_order): """Sets the sort_order of this EntityDataPageLink. :param sort_order: The sort_order of this EntityDataPageLink. # noqa: E501 :type: EntityDataSortOrder """ if sort_order is None: raise ValueError("Invalid value for `sort_order`, must not be `None`") # noqa: E501 self._sort_order = sort_order @property def dynamic(self): """Gets the dynamic of this EntityDataPageLink. # noqa: E501 :return: The dynamic of this EntityDataPageLink. # noqa: E501 :rtype: bool """ return self._dynamic @dynamic.setter def dynamic(self, dynamic): """Sets the dynamic of this EntityDataPageLink. :param dynamic: The dynamic of this EntityDataPageLink. # noqa: E501 :type: bool """ if dynamic is None: raise ValueError("Invalid value for `dynamic`, must not be `None`") # noqa: E501 self._dynamic = dynamic def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(EntityDataPageLink, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, EntityDataPageLink): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other ``` #### File: models/models_pe/allowed_permissions_info.py ```python import pprint import re # noqa: F401 import six class AllowedPermissionsInfo(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'allowed_for_group_owner_only_group_operations': 'list[str]', 'allowed_for_group_owner_only_operations': 'list[str]', 'allowed_for_group_role_operations': 'list[str]', 'allowed_resources': 'list[str]', 'operations_by_resource': 'dict(str, list[str])', 'user_owner_id': 'EntityId', 'user_permissions': 'MergedUserPermissions' } attribute_map = { 'allowed_for_group_owner_only_group_operations': 'allowedForGroupOwnerOnlyGroupOperations', 'allowed_for_group_owner_only_operations': 'allowedForGroupOwnerOnlyOperations', 'allowed_for_group_role_operations': 'allowedForGroupRoleOperations', 'allowed_resources': 'allowedResources', 'operations_by_resource': 'operationsByResource', 'user_owner_id': 'userOwnerId', 'user_permissions': 'userPermissions' } def __init__(self, allowed_for_group_owner_only_group_operations=None, allowed_for_group_owner_only_operations=None, allowed_for_group_role_operations=None, allowed_resources=None, operations_by_resource=None, user_owner_id=None, user_permissions=None): # noqa: E501 """AllowedPermissionsInfo - a model defined in Swagger""" # noqa: E501 self._allowed_for_group_owner_only_group_operations = None self._allowed_for_group_owner_only_operations = None self._allowed_for_group_role_operations = None self._allowed_resources = None self._operations_by_resource = None self._user_owner_id = None self._user_permissions = None self.discriminator = None if allowed_for_group_owner_only_group_operations is not None: self.allowed_for_group_owner_only_group_operations = allowed_for_group_owner_only_group_operations if allowed_for_group_owner_only_operations is not None: self.allowed_for_group_owner_only_operations = allowed_for_group_owner_only_operations if allowed_for_group_role_operations is not None: self.allowed_for_group_role_operations = allowed_for_group_role_operations if allowed_resources is not None: self.allowed_resources = allowed_resources if operations_by_resource is not None: self.operations_by_resource = operations_by_resource if user_owner_id is not None: self.user_owner_id = user_owner_id if user_permissions is not None: self.user_permissions = user_permissions @property def allowed_for_group_owner_only_group_operations(self): """Gets the allowed_for_group_owner_only_group_operations of this AllowedPermissionsInfo. # noqa: E501 :return: The allowed_for_group_owner_only_group_operations of this AllowedPermissionsInfo. # noqa: E501 :rtype: list[str] """ return self._allowed_for_group_owner_only_group_operations @allowed_for_group_owner_only_group_operations.setter def allowed_for_group_owner_only_group_operations(self, allowed_for_group_owner_only_group_operations): """Sets the allowed_for_group_owner_only_group_operations of this AllowedPermissionsInfo. :param allowed_for_group_owner_only_group_operations: The allowed_for_group_owner_only_group_operations of this AllowedPermissionsInfo. # noqa: E501 :type: list[str] """ allowed_values = ["ALL", "CREATE", "READ", "WRITE", "DELETE", "RPC_CALL", "READ_CREDENTIALS", "WRITE_CREDENTIALS", "READ_ATTRIBUTES", "WRITE_ATTRIBUTES", "READ_TELEMETRY", "WRITE_TELEMETRY", "ADD_TO_GROUP", "REMOVE_FROM_GROUP", "CHANGE_OWNER", "IMPERSONATE", "CLAIM_DEVICES", "SHARE_GROUP", "ASSIGN_TO_TENANT"] # noqa: E501 if not set(allowed_for_group_owner_only_group_operations).issubset(set(allowed_values)): raise ValueError( "Invalid values for `allowed_for_group_owner_only_group_operations` [{0}], must be a subset of [{1}]" # noqa: E501 .format(", ".join(map(str, set(allowed_for_group_owner_only_group_operations) - set(allowed_values))), # noqa: E501 ", ".join(map(str, allowed_values))) ) self._allowed_for_group_owner_only_group_operations = allowed_for_group_owner_only_group_operations @property def allowed_for_group_owner_only_operations(self): """Gets the allowed_for_group_owner_only_operations of this AllowedPermissionsInfo. # noqa: E501 :return: The allowed_for_group_owner_only_operations of this AllowedPermissionsInfo. # noqa: E501 :rtype: list[str] """ return self._allowed_for_group_owner_only_operations @allowed_for_group_owner_only_operations.setter def allowed_for_group_owner_only_operations(self, allowed_for_group_owner_only_operations): """Sets the allowed_for_group_owner_only_operations of this AllowedPermissionsInfo. :param allowed_for_group_owner_only_operations: The allowed_for_group_owner_only_operations of this AllowedPermissionsInfo. # noqa: E501 :type: list[str] """ allowed_values = ["ALL", "CREATE", "READ", "WRITE", "DELETE", "RPC_CALL", "READ_CREDENTIALS", "WRITE_CREDENTIALS", "READ_ATTRIBUTES", "WRITE_ATTRIBUTES", "READ_TELEMETRY", "WRITE_TELEMETRY", "ADD_TO_GROUP", "REMOVE_FROM_GROUP", "CHANGE_OWNER", "IMPERSONATE", "CLAIM_DEVICES", "SHARE_GROUP", "ASSIGN_TO_TENANT"] # noqa: E501 if not set(allowed_for_group_owner_only_operations).issubset(set(allowed_values)): raise ValueError( "Invalid values for `allowed_for_group_owner_only_operations` [{0}], must be a subset of [{1}]" # noqa: E501 .format(", ".join(map(str, set(allowed_for_group_owner_only_operations) - set(allowed_values))), # noqa: E501 ", ".join(map(str, allowed_values))) ) self._allowed_for_group_owner_only_operations = allowed_for_group_owner_only_operations @property def allowed_for_group_role_operations(self): """Gets the allowed_for_group_role_operations of this AllowedPermissionsInfo. # noqa: E501 :return: The allowed_for_group_role_operations of this AllowedPermissionsInfo. # noqa: E501 :rtype: list[str] """ return self._allowed_for_group_role_operations @allowed_for_group_role_operations.setter def allowed_for_group_role_operations(self, allowed_for_group_role_operations): """Sets the allowed_for_group_role_operations of this AllowedPermissionsInfo. :param allowed_for_group_role_operations: The allowed_for_group_role_operations of this AllowedPermissionsInfo. # noqa: E501 :type: list[str] """ allowed_values = ["ALL", "CREATE", "READ", "WRITE", "DELETE", "RPC_CALL", "READ_CREDENTIALS", "WRITE_CREDENTIALS", "READ_ATTRIBUTES", "WRITE_ATTRIBUTES", "READ_TELEMETRY", "WRITE_TELEMETRY", "ADD_TO_GROUP", "REMOVE_FROM_GROUP", "CHANGE_OWNER", "IMPERSONATE", "CLAIM_DEVICES", "SHARE_GROUP", "ASSIGN_TO_TENANT"] # noqa: E501 if not set(allowed_for_group_role_operations).issubset(set(allowed_values)): raise ValueError( "Invalid values for `allowed_for_group_role_operations` [{0}], must be a subset of [{1}]" # noqa: E501 .format(", ".join(map(str, set(allowed_for_group_role_operations) - set(allowed_values))), # noqa: E501 ", ".join(map(str, allowed_values))) ) self._allowed_for_group_role_operations = allowed_for_group_role_operations @property def allowed_resources(self): """Gets the allowed_resources of this AllowedPermissionsInfo. # noqa: E501 :return: The allowed_resources of this AllowedPermissionsInfo. # noqa: E501 :rtype: list[str] """ return self._allowed_resources @allowed_resources.setter def allowed_resources(self, allowed_resources): """Sets the allowed_resources of this AllowedPermissionsInfo. :param allowed_resources: The allowed_resources of this AllowedPermissionsInfo. # noqa: E501 :type: list[str] """ allowed_values = ["ALL", "PROFILE", "ADMIN_SETTINGS", "ALARM", "DEVICE", "ASSET", "CUSTOMER", "DASHBOARD", "ENTITY_VIEW", "EDGE", "TENANT", "RULE_CHAIN", "USER", "WIDGETS_BUNDLE", "WIDGET_TYPE", "OAUTH2_CONFIGURATION_INFO", "OAUTH2_CONFIGURATION_TEMPLATE", "TENANT_PROFILE", "DEVICE_PROFILE", "CONVERTER", "INTEGRATION", "SCHEDULER_EVENT", "BLOB_ENTITY", "CUSTOMER_GROUP", "DEVICE_GROUP", "ASSET_GROUP", "USER_GROUP", "ENTITY_VIEW_GROUP", "EDGE_GROUP", "DASHBOARD_GROUP", "ROLE", "GROUP_PERMISSION", "WHITE_LABELING", "AUDIT_LOG", "API_USAGE_STATE", "TB_RESOURCE", "OTA_PACKAGE"] # noqa: E501 if not set(allowed_resources).issubset(set(allowed_values)): raise ValueError( "Invalid values for `allowed_resources` [{0}], must be a subset of [{1}]" # noqa: E501 .format(", ".join(map(str, set(allowed_resources) - set(allowed_values))), # noqa: E501 ", ".join(map(str, allowed_values))) ) self._allowed_resources = allowed_resources @property def operations_by_resource(self): """Gets the operations_by_resource of this AllowedPermissionsInfo. # noqa: E501 :return: The operations_by_resource of this AllowedPermissionsInfo. # noqa: E501 :rtype: dict(str, list[str]) """ return self._operations_by_resource @operations_by_resource.setter def operations_by_resource(self, operations_by_resource): """Sets the operations_by_resource of this AllowedPermissionsInfo. :param operations_by_resource: The operations_by_resource of this AllowedPermissionsInfo. # noqa: E501 :type: dict(str, list[str]) """ allowed_values = [ALL, CREATE, READ, WRITE, DELETE, RPC_CALL, READ_CREDENTIALS, WRITE_CREDENTIALS, READ_ATTRIBUTES, WRITE_ATTRIBUTES, READ_TELEMETRY, WRITE_TELEMETRY, ADD_TO_GROUP, REMOVE_FROM_GROUP, CHANGE_OWNER, IMPERSONATE, CLAIM_DEVICES, SHARE_GROUP, ASSIGN_TO_TENANT] # noqa: E501 if not set(operations_by_resource.keys()).issubset(set(allowed_values)): raise ValueError( "Invalid keys in `operations_by_resource` [{0}], must be a subset of [{1}]" # noqa: E501 .format(", ".join(map(str, set(operations_by_resource.keys()) - set(allowed_values))), # noqa: E501 ", ".join(map(str, allowed_values))) ) self._operations_by_resource = operations_by_resource @property def user_owner_id(self): """Gets the user_owner_id of this AllowedPermissionsInfo. # noqa: E501 :return: The user_owner_id of this AllowedPermissionsInfo. # noqa: E501 :rtype: EntityId """ return self._user_owner_id @user_owner_id.setter def user_owner_id(self, user_owner_id): """Sets the user_owner_id of this AllowedPermissionsInfo. :param user_owner_id: The user_owner_id of this AllowedPermissionsInfo. # noqa: E501 :type: EntityId """ self._user_owner_id = user_owner_id @property def user_permissions(self): """Gets the user_permissions of this AllowedPermissionsInfo. # noqa: E501 :return: The user_permissions of this AllowedPermissionsInfo. # noqa: E501 :rtype: MergedUserPermissions """ return self._user_permissions @user_permissions.setter def user_permissions(self, user_permissions): """Sets the user_permissions of this AllowedPermissionsInfo. :param user_permissions: The user_permissions of this AllowedPermissionsInfo. # noqa: E501 :type: MergedUserPermissions """ self._user_permissions = user_permissions def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(AllowedPermissionsInfo, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, AllowedPermissionsInfo): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other ``` #### File: models/models_pe/o_auth2_basic_mapper_config.py ```python import pprint import re # noqa: F401 import six class OAuth2BasicMapperConfig(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'always_full_screen': 'bool', 'customer_name_pattern': 'str', 'default_dashboard_name': 'str', 'email_attribute_key': 'str', 'first_name_attribute_key': 'str', 'last_name_attribute_key': 'str', 'parent_customer_name_pattern': 'str', 'tenant_name_pattern': 'str', 'tenant_name_strategy': 'str', 'user_groups_name_pattern': 'list[str]' } attribute_map = { 'always_full_screen': 'alwaysFullScreen', 'customer_name_pattern': 'customerNamePattern', 'default_dashboard_name': 'defaultDashboardName', 'email_attribute_key': 'emailAttributeKey', 'first_name_attribute_key': 'firstNameAttributeKey', 'last_name_attribute_key': 'lastNameAttributeKey', 'parent_customer_name_pattern': 'parentCustomerNamePattern', 'tenant_name_pattern': 'tenantNamePattern', 'tenant_name_strategy': 'tenantNameStrategy', 'user_groups_name_pattern': 'userGroupsNamePattern' } def __init__(self, always_full_screen=None, customer_name_pattern=None, default_dashboard_name=None, email_attribute_key=None, first_name_attribute_key=None, last_name_attribute_key=None, parent_customer_name_pattern=None, tenant_name_pattern=None, tenant_name_strategy=None, user_groups_name_pattern=None): # noqa: E501 """OAuth2BasicMapperConfig - a model defined in Swagger""" # noqa: E501 self._always_full_screen = None self._customer_name_pattern = None self._default_dashboard_name = None self._email_attribute_key = None self._first_name_attribute_key = None self._last_name_attribute_key = None self._parent_customer_name_pattern = None self._tenant_name_pattern = None self._tenant_name_strategy = None self._user_groups_name_pattern = None self.discriminator = None if always_full_screen is not None: self.always_full_screen = always_full_screen if customer_name_pattern is not None: self.customer_name_pattern = customer_name_pattern if default_dashboard_name is not None: self.default_dashboard_name = default_dashboard_name if email_attribute_key is not None: self.email_attribute_key = email_attribute_key if first_name_attribute_key is not None: self.first_name_attribute_key = first_name_attribute_key if last_name_attribute_key is not None: self.last_name_attribute_key = last_name_attribute_key if parent_customer_name_pattern is not None: self.parent_customer_name_pattern = parent_customer_name_pattern if tenant_name_pattern is not None: self.tenant_name_pattern = tenant_name_pattern if tenant_name_strategy is not None: self.tenant_name_strategy = tenant_name_strategy if user_groups_name_pattern is not None: self.user_groups_name_pattern = user_groups_name_pattern @property def always_full_screen(self): """Gets the always_full_screen of this OAuth2BasicMapperConfig. # noqa: E501 :return: The always_full_screen of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: bool """ return self._always_full_screen @always_full_screen.setter def always_full_screen(self, always_full_screen): """Sets the always_full_screen of this OAuth2BasicMapperConfig. :param always_full_screen: The always_full_screen of this OAuth2BasicMapperConfig. # noqa: E501 :type: bool """ self._always_full_screen = always_full_screen @property def customer_name_pattern(self): """Gets the customer_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :return: The customer_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._customer_name_pattern @customer_name_pattern.setter def customer_name_pattern(self, customer_name_pattern): """Sets the customer_name_pattern of this OAuth2BasicMapperConfig. :param customer_name_pattern: The customer_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ self._customer_name_pattern = customer_name_pattern @property def default_dashboard_name(self): """Gets the default_dashboard_name of this OAuth2BasicMapperConfig. # noqa: E501 :return: The default_dashboard_name of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._default_dashboard_name @default_dashboard_name.setter def default_dashboard_name(self, default_dashboard_name): """Sets the default_dashboard_name of this OAuth2BasicMapperConfig. :param default_dashboard_name: The default_dashboard_name of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ self._default_dashboard_name = default_dashboard_name @property def email_attribute_key(self): """Gets the email_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :return: The email_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._email_attribute_key @email_attribute_key.setter def email_attribute_key(self, email_attribute_key): """Sets the email_attribute_key of this OAuth2BasicMapperConfig. :param email_attribute_key: The email_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ self._email_attribute_key = email_attribute_key @property def first_name_attribute_key(self): """Gets the first_name_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :return: The first_name_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._first_name_attribute_key @first_name_attribute_key.setter def first_name_attribute_key(self, first_name_attribute_key): """Sets the first_name_attribute_key of this OAuth2BasicMapperConfig. :param first_name_attribute_key: The first_name_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ self._first_name_attribute_key = first_name_attribute_key @property def last_name_attribute_key(self): """Gets the last_name_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :return: The last_name_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._last_name_attribute_key @last_name_attribute_key.setter def last_name_attribute_key(self, last_name_attribute_key): """Sets the last_name_attribute_key of this OAuth2BasicMapperConfig. :param last_name_attribute_key: The last_name_attribute_key of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ self._last_name_attribute_key = last_name_attribute_key @property def parent_customer_name_pattern(self): """Gets the parent_customer_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :return: The parent_customer_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._parent_customer_name_pattern @parent_customer_name_pattern.setter def parent_customer_name_pattern(self, parent_customer_name_pattern): """Sets the parent_customer_name_pattern of this OAuth2BasicMapperConfig. :param parent_customer_name_pattern: The parent_customer_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ self._parent_customer_name_pattern = parent_customer_name_pattern @property def tenant_name_pattern(self): """Gets the tenant_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :return: The tenant_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._tenant_name_pattern @tenant_name_pattern.setter def tenant_name_pattern(self, tenant_name_pattern): """Sets the tenant_name_pattern of this OAuth2BasicMapperConfig. :param tenant_name_pattern: The tenant_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ self._tenant_name_pattern = tenant_name_pattern @property def tenant_name_strategy(self): """Gets the tenant_name_strategy of this OAuth2BasicMapperConfig. # noqa: E501 :return: The tenant_name_strategy of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: str """ return self._tenant_name_strategy @tenant_name_strategy.setter def tenant_name_strategy(self, tenant_name_strategy): """Sets the tenant_name_strategy of this OAuth2BasicMapperConfig. :param tenant_name_strategy: The tenant_name_strategy of this OAuth2BasicMapperConfig. # noqa: E501 :type: str """ allowed_values = ["DOMAIN", "EMAIL", "CUSTOM"] # noqa: E501 if tenant_name_strategy not in allowed_values: raise ValueError( "Invalid value for `tenant_name_strategy` ({0}), must be one of {1}" # noqa: E501 .format(tenant_name_strategy, allowed_values) ) self._tenant_name_strategy = tenant_name_strategy @property def user_groups_name_pattern(self): """Gets the user_groups_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :return: The user_groups_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :rtype: list[str] """ return self._user_groups_name_pattern @user_groups_name_pattern.setter def user_groups_name_pattern(self, user_groups_name_pattern): """Sets the user_groups_name_pattern of this OAuth2BasicMapperConfig. :param user_groups_name_pattern: The user_groups_name_pattern of this OAuth2BasicMapperConfig. # noqa: E501 :type: list[str] """ self._user_groups_name_pattern = user_groups_name_pattern def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(OAuth2BasicMapperConfig, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, OAuth2BasicMapperConfig): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other ```
{ "source": "jernsthausen/datesplitter", "score": 3 }
#### File: site-packages/parserator/data_prep_utils.py ```python from lxml import etree import os import csv # appends a labeled list to an existing xml file # calls: appendListToXML, stripFormatting def appendListToXMLfile(labeled_list, module, filepath): # format for labeled_list: [ [ (token, label), (token, label), ...], # [ (token, label), (token, label), ...], # [ (token, label), (token, label), ...], # ... ] if os.path.isfile(filepath): with open( filepath, 'r+' ) as f: tree = etree.parse(filepath) collection_XML = tree.getroot() collection_XML = stripFormatting(collection_XML) else: collection_tag = module.GROUP_LABEL collection_XML = etree.Element(collection_tag) parent_tag = module.PARENT_LABEL collection_XML = appendListToXML(labeled_list, collection_XML, parent_tag) with open(filepath, 'w') as f : f.write(etree.tostring(collection_XML, pretty_print = True)) # given a list of labeled sequences to an xml list, # appends corresponding xml to existing xml # calls: sequence2XML # called by: appendListToXMLfile def appendListToXML(list_to_append, collection_XML, parent_tag) : # format for list_to_append: [ [ (token, label), (token, label), ...], # [ (token, label), (token, label), ...], # [ (token, label), (token, label), ...], # ... ] for labeled_sequence in list_to_append: sequence_xml = sequence2XML(labeled_sequence, parent_tag) collection_XML.append(sequence_xml) return collection_XML # given a labeled sequence, generates xml for that sequence # called by: appendListToXML def sequence2XML(labeled_sequence, parent_tag) : # format for labeled_sequence: [(token, label), (token, label), ...] sequence_xml = etree.Element(parent_tag) for token, label in labeled_sequence: component_xml = etree.Element(label) component_xml.text = token component_xml.tail = ' ' sequence_xml.append(component_xml) sequence_xml[-1].tail = '' return sequence_xml # clears formatting for an xml collection def stripFormatting(collection) : collection.text = None for element in collection : element.text = None element.tail = None return collection # writes a list of strings to a file def list2file(string_list, filepath): with open(filepath, 'wb') as csvfile: writer = csv.writer(csvfile, doublequote=True, quoting=csv.QUOTE_MINIMAL) for string in string_list: writer.writerow([string.encode('utf-8')]) ``` #### File: site-packages/parserator/main.py ```python from __future__ import print_function from __future__ import absolute_import from argparse import ArgumentParser from . import manual_labeling from . import training import os import shutil import fileinput from .parser_template import init_template, setup_template, test_tokenize_template def dispatch(): parser = ArgumentParser(description="") parser_subparsers = parser.add_subparsers() sub_label = parser_subparsers.add_parser('label') sub_train = parser_subparsers.add_parser('train') sub_init = parser_subparsers.add_parser('init') sub_label.add_argument(dest='infile', help='input csv filepath for the label task') sub_label.add_argument(dest='outfile', help='output xml filepath for the label task') sub_label.add_argument(dest='modulename', help='parser module name') sub_label.set_defaults(func=label) sub_train.add_argument(dest='traindata', help='comma separated xml filepaths, or "path/to/traindata/*.xml"') sub_train.add_argument(dest='modulename', help='parser module name') sub_train.set_defaults(func=train) sub_init.add_argument(dest='modulename', help='module name for a new parser') sub_init.set_defaults(func=init) args = parser.parse_args() args.func(args) def label(args) : if args.infile and args.outfile: module = __import__(args.modulename) infile_path = args.infile outfile_path = args.outfile manual_labeling.label(module, infile_path, outfile_path) else: print('Please specify an input csv file [--infile FILE] and an output xml file [--outfile FILE]') def train(args) : if args.traindata: if args.traindata.endswith('*.xml'): train_data_dir = args.traindata[:-5] if not train_data_dir: train_data_dir = '.' train_file_list = [] for filename in os.listdir(train_data_dir): if filename.endswith('.xml'): train_file_list.append(args.traindata[:-5]+filename) else: train_file_list = args.traindata.split(',') module = __import__(args.modulename) training.train(module, train_file_list) else: print('Please specify one or more xml training files (comma separated) [--trainfile FILE]') def init(args) : name = args.modulename data = "raw" training = "training" tests = 'tests' dirs_to_mk = [name, data, training, tests] print('\nInitializing directories for %s' %name) for directory in dirs_to_mk: if not os.path.exists(directory): os.mkdir(directory) print('* %s' %directory) print('\nGenerating __init__.py') init_path = name + '/__init__.py' if os.path.exists(init_path): print(' warning: %s already exists' %init_path) else: with open(init_path, "w") as f: f.write(init_template()) print('* %s' %init_path) print('\nGenerating setup.py') if os.path.exists('setup.py'): print(' warning: setup.py already exists') else: with open('setup.py', 'w') as f: f.write(setup_template(name)) print('* setup.py') print('\nGenerating test file') token_test_path = tests+'/test_tokenizing.py' if os.path.exists(token_test_path): print(' warning: %s already exists' %token_test_path) else: with open(token_test_path, 'w') as f: f.write(test_tokenize_template(name)) print('* %s' %token_test_path) ``` #### File: site-packages/parserator/training.py ```python from __future__ import print_function from __future__ import absolute_import from builtins import zip import pycrfsuite import random import os from lxml import etree from imp import reload from . import data_prep_utils import re import time def trainModel(training_data, module, params_to_set={'c1':0.1, 'c2':0.01, 'feature.minfreq':0}): X = [] Y = [] for raw_string, components in training_data: tokens, labels = list(zip(*components)) X.append(module.tokens2features(tokens)) Y.append(labels) # train model trainer = pycrfsuite.Trainer(verbose=False, params=params_to_set) for xseq, yseq in zip(X, Y): trainer.append(xseq, yseq) trainer.train(module.__name__+'/'+module.MODEL_FILE) # given a list of xml training filepaths & a parser module, # reads the xml & returns training data (for trainModel) def readTrainingData( xml_infile_list, collection_tag ): full_xml = etree.Element(collection_tag) component_string_list = [] # loop through xml training files for xml_infile in xml_infile_list: train_data_filepath = xml_infile if os.path.isfile(train_data_filepath): with open( train_data_filepath, 'r+' ) as f: tree = etree.parse(f) file_xml = tree.getroot() file_xml = data_prep_utils.stripFormatting(file_xml) for component_etree in file_xml: # etree components to string representations component_string_list.append(etree.tostring(component_etree).decode('utf-8')) else: print('WARNING: %s does not exist' % xml_infile) # get rid of duplicates in string representations component_string_list = list(set(component_string_list)) # loop through unique string representations for component_string in component_string_list: # convert string representation back to xml sequence_xml = etree.fromstring(component_string) raw_text = etree.tostring(sequence_xml, method='text', encoding='utf-8') sequence_components = [] for component in list(sequence_xml): sequence_components.append([component.text, component.tag]) yield raw_text, sequence_components def renameModelFile(old_model): if os.path.exists(old_model): t = time.gmtime(os.path.getctime(old_model)) time_str = '_'+str(t.tm_year)+'_'+str(t.tm_mon)+'_'+str(t.tm_mday)+'_'+str(t.tm_hour)+str(t.tm_min)+str(t.tm_sec) renamed = re.sub('.crfsuite', time_str+'.crfsuite', old_model) print("\nrenaming old model: %s -> %s" %(old_model, renamed)) os.rename(old_model, renamed) def train(module, train_file_list) : training_data = list(readTrainingData(train_file_list, module.GROUP_LABEL)) if not training_data: print('ERROR: No training data found. Perhaps double check your training data filepaths?') return model_path = module.__name__+'/'+module.MODEL_FILE renameModelFile(model_path) print('\ntraining model on {num} training examples from {file_list}'.format(num=len(training_data), file_list=train_file_list)) trainModel(training_data, module) print('\ndone training! model file created: {path}'.format(path=model_path)) ```
{ "source": "jernsting/nxt_gem", "score": 3 }
#### File: gem/embedding/node2vec.py ```python import os import numpy as np from subprocess import call from gem.embedding.static_graph_embedding import StaticGraphEmbedding from gem.utils import graph_util class node2vec(StaticGraphEmbedding): hyper_params = { 'method_name': 'node2vec_rw' } def __init__(self, *args, **kwargs): """ Initialize the node2vec class Args: d: dimension of the embedding max_iter: max iterations walk_len: length of random walk num_walks: number of random walks con_size: context size ret_p: return weight inout_p: inout weight """ super(node2vec, self).__init__(*args, **kwargs) def learn_embedding(self, graph=None, is_weighted=False, no_python=False): current_dir = os.path.dirname(os.path.abspath(__file__)) executable = os.path.abspath(os.path.join(current_dir, '../c_exe/node2vec')) args = [executable] if not graph: raise ValueError('graph needed') graph_util.saveGraphToEdgeListTxtn2v(graph, 'tempGraph.graph') args.append("-i:tempGraph.graph") args.append("-o:tempGraph.emb") args.append("-d:%d" % self._d) args.append("-l:%d" % self._walk_len) args.append("-r:%d" % self._num_walks) args.append("-k:%d" % self._con_size) args.append("-e:%d" % self._max_iter) args.append("-p:%f" % self._ret_p) args.append("-q:%f" % self._inout_p) args.append("-v") args.append("-dr") args.append("-w") try: call(args) except Exception as e: # pragma: no cover print(str(e)) raise FileNotFoundError('./node2vec not found. Please compile snap, place node2vec in the system path ' 'and grant executable permission') self._X = graph_util.loadEmbedding('tempGraph.emb') return self._X def get_edge_weight(self, i, j): return np.dot(self._X[i, :], self._X[j, :]) ``` #### File: gem/evaluation/evaluate_graph_reconstruction.py ```python import pickle from gem.evaluation import metrics from gem.utils import evaluation_util, graph_util import networkx as nx import numpy as np def evaluateStaticGraphReconstruction(digraph, graph_embedding, X_stat, node_l=None, file_suffix=None, sample_ratio_e=None, is_undirected=True, is_weighted=False): node_num = len(digraph.nodes) # evaluation if sample_ratio_e: eval_edge_pairs = evaluation_util.get_random_edge_pairs( node_num, sample_ratio_e, is_undirected ) else: eval_edge_pairs = None if file_suffix is None: estimated_adj = graph_embedding.get_reconstructed_adj(X_stat, node_l) else: estimated_adj = graph_embedding.get_reconstructed_adj( X_stat, file_suffix, node_l ) predicted_edge_list = evaluation_util.get_edge_list_from_adj_mtrx( estimated_adj, is_undirected=is_undirected, edge_pairs=eval_edge_pairs ) MAP = metrics.computeMAP(predicted_edge_list, digraph, is_undirected=is_undirected) prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list, digraph) # If weighted, compute the error in reconstructed weights of observed edges if is_weighted: digraph_adj = nx.to_numpy_matrix(digraph) estimated_adj[digraph_adj == 0] = 0 err = np.linalg.norm(digraph_adj - estimated_adj) err_baseline = np.linalg.norm(digraph_adj) else: err = None err_baseline = None return MAP, prec_curv, err, err_baseline ```
{ "source": "jernsting/useful_layers", "score": 3 }
#### File: useful_layers/layers/channel_attention.py ```python import torch from torch.nn import functional as F from useful_layers.utils import reduction_network from useful_layers.layers.ABCLayer import Layer __all__ = ['ChannelAttention2D', 'ChannelAttention3D'] class _ChannelAttention(Layer): def __init__(self): super(_ChannelAttention, self).__init__() def forward(self, x: torch.Tensor) -> torch.Tensor: size = x.size() if isinstance(self, ChannelAttention2D): view = (size[0], size[1], 1, 1) elif isinstance(self, ChannelAttention3D): view = (size[0], size[1], 1, 1, 1) else: raise NotImplementedError(f'Expected to be ChannelAttention2D or -3D, got {self}') avg_comp = torch.mean(x.view(size[0], size[1], -1), dim=-1).view(*view) max_comp = torch.max(x.view(size[0], size[1], -1), dim=-1).values.view(*view) avg_comp = self.conv2(F.relu(self.conv1(avg_comp))) max_comp = self.conv2(F.relu(self.conv1(max_comp))) return F.sigmoid(avg_comp + max_comp) class ChannelAttention2D(_ChannelAttention): """ChannelAttention2D Channel attention layer as presented in https://arxiv.org/pdf/1807.06521v2.pdf. """ def __init__(self, in_channels: int, reduction: int = 2): """Create ChannelAttention2D Layer Args: in_channels (int): Number of input channels reduction (int, optional): Degree of reduction. Defaults to 2. """ super(ChannelAttention2D, self).__init__() self.conv1, self.conv2 = reduction_network(in_channels, reduction, "2d") class ChannelAttention3D(_ChannelAttention): """ChannelAttention3D Channel attention layer as presented in https://arxiv.org/pdf/1807.06521v2.pdf. """ def __init__(self, in_channels: int, reduction: int = 2): """Create ChannelAttention3D Layer Args: in_channels (int): Number of input channels reduction (int, optional): Degree of reduction. Defaults to 2. """ super(ChannelAttention3D, self).__init__() self.conv1, self.conv2 = reduction_network(in_channels, reduction, "3d") ``` #### File: useful_layers/layers/spatial_attention.py ```python from abc import ABC import torch import torch.nn as nn import torch.nn.functional as F from useful_layers.layers.ABCLayer import Layer __all__ = ['SpatialAttention2D', 'SpatialAttention3D'] class _SpatialAttention(Layer, ABC): def __init__(self): super(_SpatialAttention, self).__init__() self.spacial_conv = self.conv(in_channels=2, kernel_size=self.kernel_size, out_channels=1, stride=1, dilation=1, groups=1, bias=False, padding=(self.kernel_size - 1) // 2) if self.batch_norm: self.batch_norm = self.batch_norm(1) def forward(self, x: torch.Tensor) -> torch.Tensor: avg_comp = torch.max(x, 1).values.unsqueeze(1) max_comp = torch.mean(x, 1).unsqueeze(1) conv_input = torch.cat((avg_comp, max_comp), dim=1) attention_map = self.spacial_conv(conv_input) if self.batch_norm: attention_map = self.batch_norm(attention_map) attention_map = F.sigmoid(attention_map) return attention_map class SpatialAttention2D(_SpatialAttention): """Simple spatial attention layer Implementation based on: https://arxiv.org/abs/1807.06521v2 """ def __init__(self, in_channels: int, kernel_size: int = 7, batch_norm: bool = True): """Create new SpatialAttention Layer Args: in_channels: Number of input channels kernel_size: Kernel size to use (integer or tuple of int) batch_norm: If true batch normalization is applied. Defaults to True. """ self.in_channels = in_channels self.kernel_size = kernel_size self.batch_norm = None if batch_norm: self.batch_norm = nn.BatchNorm2d self.conv = nn.Conv2d super(SpatialAttention2D, self).__init__() class SpatialAttention3D(_SpatialAttention): """Simple spatial attention layer Implementation based on: https://arxiv.org/abs/1807.06521v2 """ def __init__(self, in_channels: int, kernel_size: int = 7, batch_norm: bool = True): """Create a SpatialAttention3D layer Args: in_channels: Number of input channels kernel_size: Kernel size to use (integer or tuple of int) batch_norm: If true batch normalization is applied. Defaults to True. """ self.in_channels = in_channels self.kernel_size = kernel_size self.batch_norm = None if batch_norm: self.batch_norm = nn.BatchNorm3d self.conv = nn.Conv3d super(SpatialAttention3D, self).__init__() ```
{ "source": "jernst/mf2py", "score": 3 }
#### File: mf2py/mf2py/http_server.py ```python from __future__ import print_function import os from mf2py import Parser from flask import Flask, Response, request app = Flask(__name__) @app.route("/", methods=["GET"]) def index(): resp = """<!DOCTYPE HTML> <html><head><title>mf2py</title></head> <body><form action="/parse" method="get"> <h1>mf2py test</h1> URL: <input type="text" name="url" /> <input type="submit" /></body></html> """ return Response(resp, status=200, mimetype="text/html") @app.route("/parse", methods=["GET", "POST"]) def parse(): if request.method == 'GET': u = request.args['url'] else: u = request.form['url'] print(u) p = Parser(url=unicode(u)) return Response(p.to_json(pretty_print=True), status=200, mimetype='application/json') if __name__ == "__main__": app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 33507))) ``` #### File: mf2py/test/test_parser.py ```python from __future__ import unicode_literals, print_function import os.path import re import sys import mock from nose.tools import assert_equal, assert_true, assert_false from mf2py import Parser from unittest import TestCase TestCase.maxDiff = None if sys.version < '3': text_type = unicode binary_type = str else: text_type = str binary_type = bytes def parse_fixture(path, url=None): with open(os.path.join("test/examples/", path)) as f: p = Parser(doc=f, url=url, html_parser='html5lib') return p.to_dict() def test_empty(): p = Parser() assert_true(type(p) is not None) assert_true(type(p.to_dict()) is dict) def test_open_file(): p = Parser(doc=open("test/examples/empty.html")) assert_true(p.__doc__ is not None) assert_true(type(p) is not None) assert_true(type(p.to_dict()) is dict) @mock.patch('requests.get') def test_user_agent(getter): ua_expect = 'mf2py - microformats2 parser for python' assert_true(Parser.useragent.startswith(ua_expect)) resp = mock.MagicMock() resp.content = b'' resp.text = '' resp.headers = {} getter.return_value = resp Parser(url='http://example.com') getter.assert_called_with('http://example.com', headers={ 'User-Agent': Parser.useragent }) Parser.useragent = 'something else' assert_equal(Parser.useragent, 'something else') # set back to default. damn stateful classes Parser.useragent = 'mf2py - microformats2 parser for python' def test_base(): p = Parser(doc=open("test/examples/base.html")) assert_equal(p.__url__, "http://tantek.com/") def test_simple_parse(): result = parse_fixture("simple_person_reference.html") assert_equal(result["items"][0]["properties"], {'name': ['<NAME>']}) def test_simple_person_reference_implied(): p = Parser(doc=open("test/examples/simple_person_reference_implied.html")) result = p.to_dict() assert_equal(result["items"][0]["properties"], {'name': ['<NAME>']}) def test_simple_person_reference_same_element(): result = parse_fixture("simple_person_reference_same_element.html") assert_equal(result["items"][0]["properties"], {'name': ['<NAME>']}) def test_person_with_url(): p = Parser(doc=open("test/examples/person_with_url.html")) result = p.to_dict() assert_equal(result["items"][0]["properties"]["name"], ['<NAME>']) assert_equal(result["items"][0]["properties"]["url"], ['http://tommorris.org/']) def test_vcp(): result = parse_fixture("value_class_person.html") assert_equal(result["items"][0]["properties"]["tel"], ['+44 1234 567890']) def test_multiple_root_classnames(): result = parse_fixture("nested_multiple_classnames.html") # order does not matter assert_equal(len(result["items"]), 1) assert_equal(set(result["items"][0]["type"]), set(["h-entry", "h-as-note"])) def test_property_nested_microformat(): result = parse_fixture("nested_multiple_classnames.html") assert_equal(len(result["items"]), 1) assert "author" in result["items"][0]["properties"] assert_equal( result["items"][0]["properties"]["author"][0]["properties"]["name"][0], "<NAME>") assert_equal( result["items"][0]["properties"]["reviewer"][0] ["properties"]["name"][0], "<NAME>") assert_equal( result["items"][0]["properties"]["author"][0] ["properties"]["adr"][0]["properties"]["city"][0], "London") def test_plain_child_microformat(): result = parse_fixture("nested_multiple_classnames.html") assert_equal(len(result["items"]), 1) assert_true("children" in result["items"][0]) assert_equal(len(result["items"][0]["children"]), 1) assert_equal( result["items"][0]["children"][0]["properties"]["name"][0], "Some Citation") def test_implied_name(): result = parse_fixture("implied_properties.html") for i in range(6): assert_equal(result["items"][i]["properties"]["name"][0], "<NAME>") def test_implied_url(): result = parse_fixture("implied_properties.html", url="http://foo.com/") assert_equal( result["items"][1]["properties"]["url"][0], "http://tommorris.org/") # img should not have a "url" property assert_true("url" not in result["items"][4]["properties"]) # href="" is relative to the base url assert_equal(result["items"][5]["properties"]["url"][0], "http://foo.com/") def test_implied_nested_photo(): result = parse_fixture("implied_properties.html", url="http://bar.org") assert_equal(result["items"][2]["properties"]["photo"][0], "http://tommorris.org/photo.png") # src="" is relative to the base url assert_equal(result["items"][5]["properties"]["photo"][0], "http://bar.org") def test_implied_nested_photo_alt_name(): result = parse_fixture("implied_properties.html") assert_equal(result["items"][3]["properties"]["name"][0], "<NAME>") def test_implied_image(): result = parse_fixture("implied_properties.html") assert_equal(result["items"][4]["properties"]["photo"][0], "http://tommorris.org/photo.png") assert_equal(result["items"][4]["properties"]["name"][0], "<NAME>") def test_datetime_parsing(): result = parse_fixture("datetimes.html") assert_equal(result["items"][0]["properties"]["start"][0], "2014-01-01T12:00:00+00:00") assert_equal(result["items"][0]["properties"]["end"][0], "3014-01-01T18:00:00+00:00") assert_equal(result["items"][0]["properties"]["duration"][0], "P1000Y") assert_equal(result["items"][0]["properties"]["updated"][0], "2011-08-26T00:01:21+00:00") assert_equal(result["items"][0]["properties"]["updated"][1], "2011-08-26T00:01:21+00:00") def test_datetime_vcp_parsing(): result = parse_fixture("datetimes.html") assert_equal(result["items"][1]["properties"]["published"][0], "3014-01-01T01:21:00Z") assert_equal(result["items"][2]["properties"]["updated"][0], "2014-03-11 09:55:00") assert_equal(result["items"][3]["properties"]["published"][0], "2014-01-30T15:28:00") assert_equal(result["items"][4]["properties"]["published"][0], "9999-01-14T11:52:00+08:00") assert_equal(result["items"][5]["properties"]["published"][0], "2014-06-01T12:30:00-06:00") def test_dt_end_implied_date(): """Test that events with dt-start and dt-end use the implied date rule http://microformats.org/wiki/value-class-pattern#microformats2_parsers for times without dates""" result = parse_fixture("datetimes.html") event_wo_tz = result["items"][6] assert_equal(event_wo_tz["properties"]["start"][0], "2014-05-21T18:30:00") assert_equal(event_wo_tz["properties"]["end"][0], "2014-05-21T19:30:00") event_w_tz = result["items"][7] assert_equal(event_w_tz["properties"]["start"][0], "2014-06-01T12:30:00-06:00") assert_equal(event_w_tz["properties"]["end"][0], "2014-06-01T19:30:00-06:00") def test_embedded_parsing(): result = parse_fixture("embedded.html") assert_equal( result["items"][0]["properties"]["content"][0]["html"], '\n <p>Blah blah blah blah blah.</p>\n' + ' <p>Blah.</p>\n <p>Blah blah blah.</p>\n ') assert_equal( result["items"][0]["properties"]["content"][0]["value"], '\n Blah blah blah blah blah.\n Blah.\n Blah blah blah.\n ') def test_backcompat(): result = parse_fixture("backcompat.html") assert_true('h-entry' in result['items'][0]['type']) assert_equal('<NAME>', result['items'][0]['properties'] ['author'][0]['properties']['name'][0]) assert_equal('A Title', result['items'][0]['properties']['name'][0]) assert_equal('Some Content', result['items'][0]['properties']['content'][0]['value']) def test_hoisting_nested_hcard(): result = parse_fixture("nested_hcards.html") expected = { 'items': [ { 'properties': { 'author': [ { 'properties': {'name': ['KP1']}, 'type': ['h-card'], 'value': 'KP1' } ], 'in-reply-to': [ { 'properties': {'name': ['KP']}, 'type': ['h-cite'], 'value': 'KP' } ], 'name': ['KP\n KP1'] }, 'type': ['h-entry'] } ], 'rels': {}, 'rel-urls': {} } assert_equal(['KP\n KP1'], result['items'][0]['properties']['name']) assert_equal(expected, result) def test_html_tag_class(): result = parse_fixture("hfeed_on_html_tag.html") assert_equal(['h-feed'], result['items'][0]['type']) assert_equal(['entry1'], result['items'][0]['children'][0] ['properties']['name']) assert_equal(['entry2'], result['items'][0]['children'][1] ['properties']['name']) def test_string_strip(): result = parse_fixture("string_stripping.html") assert result["items"][0]["properties"]["name"][0] == "<NAME>" def test_template_parse(): result = parse_fixture("template_tag.html") assert len(result["items"]) == 0 def test_backcompat_hproduct(): result = parse_fixture("backcompat_hproduct.html") assert len(result["items"]) == 1 assert result["items"][0]["type"] == ["h-product"] assert result["items"][0]["properties"]["category"] == ['bullshit'] expect1 = ['Quacktastic Products'] assert result["items"][0]["properties"]["brand"] == expect1 assert result["items"][0]["properties"]["identifier"] == ['#BULLSHIT-001'] expect2 = "Magical tasty sugar pills that don't do anything." assert result["items"][0]["properties"]['description'][0] == expect2 expect3 = ["Tom's Magical Quack Tincture"] assert result["items"][0]["properties"]["name"] == expect3 def test_backcompat_hproduct_nested_hreview(): result = parse_fixture("backcompat_hproduct_hreview_nested.html") assert result["items"][0]["children"][0]['type'] == ['h-review'] assert type(result["items"][0]["children"][0] ['properties']['name'][0]) == text_type def test_backcompat_rel_bookmark(): """Confirm that rel=bookmark inside of an h-entry is converted to u-url. """ result = parse_fixture('backcompat_feed_with_rel_bookmark.html') for ii, url in enumerate(( '/2014/11/24/jump-rope', '/2014/11/23/graffiti', '/2014/11/21/earth', '/2014/11/19/labor', )): assert result['items'][ii]['type'] == ['h-entry'] assert result['items'][ii]['properties']['url'] == [url] def test_backcompat_rel_tag(): """Confirm that rel=tag inside of an h-entry is converted to a p-category and the last path segment of the href is used. """ result = parse_fixture('backcompat_hentry_with_rel_tag.html') assert result['items'][0]['properties']['category'] == ['cat', 'dog', 'mountain lion'] def test_area_uparsing(): result = parse_fixture("area.html") assert result["items"][0]["properties"] == { 'url': ['http://suda.co.uk'], 'name': ['<NAME>']} assert 'shape' in result["items"][0].keys() assert 'coords' in result["items"][0].keys() def test_src_equiv(): result = parse_fixture("test_src_equiv.html") for item in result["items"]: assert 'x-example' in item['properties'].keys() assert 'http://example.org/' == item['properties']['x-example'][0] def test_rels(): result = parse_fixture("rel.html") assert result['rels'] == { u'in-reply-to': [u'http://example.com/1', u'http://example.com/2'], u'author': [u'http://example.com/a', u'http://example.com/b'], u'alternate': [u'http://example.com/fr'], u'home': [u'http://example.com/fr'], } assert result['rel-urls'] == { u'http://example.com/1': {'text': u"post 1", "rels": [u'in-reply-to']}, u'http://example.com/2': {'text': u"post 2", "rels": [u'in-reply-to']}, u'http://example.com/a': {'text': u"author a", "rels": [u'author']}, u'http://example.com/b': {'text': u"author b", "rels": [u'author']}, u'http://example.com/fr': {'text': u'French mobile homepage', u'media': u'handheld', u'rels': [u'alternate', u'home'], u'hreflang': u'fr'} } def test_alternates(): result = parse_fixture("rel.html") assert result['alternates'] == [{ 'url': 'http://example.com/fr', 'media': 'handheld', 'text': 'French mobile homepage', 'rel': 'home', 'hreflang': 'fr' }] def test_enclosures(): result = parse_fixture("rel_enclosure.html") assert result['rels'] == {'enclosure': ['http://example.com/movie.mp4']} assert result['rel-urls'] == {'http://example.com/movie.mp4': { 'rels': ['enclosure'], 'text': 'my movie', 'type': 'video/mpeg'} } def test_empty_href(): result = parse_fixture("hcard_with_empty_url.html", "http://foo.com") for hcard in result['items']: assert hcard['properties'].get('url') == ['http://foo.com'] def test_link_with_u_url(): result = parse_fixture("link_with_u-url.html", "http://foo.com") assert_equal({ "type": ["h-card"], "properties": { "name": [""], "url": ["http://foo.com/"], }, }, result["items"][0]) def test_complex_e_content(): """When parsing h-* e-* properties, we should fold {"value":..., "html":...} into the parsed microformat object, instead of nesting it under an unnecessary second layer of "value": """ result = Parser(doc="""<!DOCTYPE html><div class="h-entry"> <div class="h-card e-content"><p>Hello</p></div></div>""").to_dict() assert_equal({ "type": ["h-entry"], "properties": { "content": [{ "type": [ "h-card" ], "properties": { "name": ["Hello"] }, "html": "<p>Hello</p>", "value": "Hello" }], "name": ["Hello"] } }, result["items"][0]) def test_nested_values(): """When parsing nested microformats, check that value is the value of the simple property element""" result = parse_fixture("nested_values.html") entry = result["items"][0] assert_equal({ 'properties': { 'name': ['Kyle'], 'url': ['http://about.me/kyle'], }, 'value': 'Kyle', 'type': ['h-card'], }, entry["properties"]["author"][0]) assert_equal({ 'properties': { 'name': ['foobar'], 'url': ['http://example.com/foobar'], }, 'value': 'http://example.com/foobar', 'type': ['h-cite'], }, entry["properties"]["like-of"][0]) assert_equal({ 'properties': { 'name': ['George'], 'url': ['http://people.com/george'], }, 'type': ['h-card'], }, entry["children"][0]) def test_implied_name_empty_alt(): """An empty alt text should not prevent us from including other children in the implied name. """ p = Parser(doc=""" <a class="h-card" href="https://twitter.com/kylewmahan"> <img src="https://example.org/test.jpg" alt=""> @kylewmahan </a>""").to_dict() hcard = p['items'][0] assert_equal({ 'type': ['h-card'], 'properties': { 'name': ['@kylewmahan'], 'url': ['https://twitter.com/kylewmahan'], 'photo': ['https://example.org/test.jpg'], }, }, hcard) def test_implied_properties_silo_pub(): result = parse_fixture('silopub.html') item = result['items'][0] implied_name = item['properties']['name'][0] implied_name = re.sub('\s+', ' ', implied_name).strip() assert_equal('@kylewmahan on Twitter', implied_name) def test_relative_datetime(): result = parse_fixture("implied_relative_datetimes.html") assert_equal('2015-01-02T05:06:00', result[u'items'][0][u'properties'][u'updated'][0]) def assert_unicode_everywhere(obj): if isinstance(obj, dict): for k, v in obj.items(): assert_false(isinstance(k, binary_type), 'key=%r; type=%r' % (k, type(k))) assert_unicode_everywhere(v) elif isinstance(obj, list): for v in obj: assert_unicode_everywhere(v) assert_false(isinstance(obj, binary_type), 'value=%r; type=%r' % (obj, type(obj))) def check_unicode(filename, jsonblob): assert_unicode_everywhere(jsonblob) def test_unicode_everywhere(): for h in os.listdir("test/examples"): result = parse_fixture(h) yield check_unicode, h, result ```
{ "source": "jeroanan/Gyroscope", "score": 2 }
#### File: Gyroscope/Config/Settings.py ```python def __get_setting(site_config, app_config, key, default_value): return site_config.get(key, app_config.get(key, default_value)) def get_logfile_location(app_config): return app_config.get("logfile_location", "") def get_log_level(app_config): app_config.get("log_level", 20) def should_get_pages(site_config, app_config): return __get_setting(site_config, app_config, "get_pages", True) def should_log_too_big(site_config, app_config): return __get_setting(site_config, app_config, "log_too_big", True) def should_log_too_slow(site_config, app_config): return __get_setting(site_config, app_config, "log_too_slow", True) ``` #### File: jeroanan/Gyroscope/GetSite.py ```python def get_site(site, config): import Request import GetAssets import GetPage from Config import Settings from Uri import UriBuilder from functools import partial http_request = Request.get_request(site["uri"], site, config, "index") GetAssets.get_assets(http_request.data, site, UriBuilder.join_uri(site["uri"], ""), config) if Settings.should_get_pages(site, config): list(map(partial(GetPage.request_page, site=site, config=config), site.get("pages", []))) ``` #### File: jeroanan/Gyroscope/gyroscope.py ```python from collections import ChainMap import logging import time import sys import GetArgs import GetSite from Config import Defaults from Config import LoadSites from Config import LoadConfig from Config import Settings def work(): def init_config(): args = GetArgs.get_args() return ChainMap(args, LoadConfig.load_config(args.get("config"), args.get("no_config")), Defaults.get_defaults()) def init_logging(): logfile_location = Settings.get_logfile_location(config) if logfile_location == "" or config.get("no_logfile", False): logging.basicConfig(level=config["log_level"], format="%(asctime)s %(message)s") else: logging.basicConfig(filename=logfile_location, level=config["log_level"], filemode=config["logfile_mode"], format="%(asctime)s %(message)s") def get_site(site): def site_disabled(): return site.get("disabled", False) if not site_disabled(): GetSite.get_site(site, config) config = init_config() init_logging() logging.info("Start") start_time = time.time() list(map(get_site, LoadSites.load_sites(config["sites_file"]))) logging.info("End (total time: %d seconds)" % (time.time() - start_time)) try: work() except KeyboardInterrupt: logging.shutdown() sys.exit(0) ``` #### File: Gyroscope/HttpStatuses/LogStatus.py ```python import logging from HttpStatuses import Status200 def log_status(http_request, uri, page_description, time_elapsed, site, config): def log_400(): logging.error("Bad request: %s" % uri) def log_403(): logging.error("Access denied: %s" % uri) def log_404(): logging.error("Missing page: %s" % uri) def log_200(): Status200.log_ok_status(uri, page_description, http_request.tell() / 1024, time_elapsed, site, config) def log_500(): logging.critical("Error: %s" % uri) def log_default(): logging.warning("%s (%s): %s" % (uri, page_description, http_request.status)) status_methods = { 400: log_400, 403: log_403, 404: log_404, 200: log_200, 500: log_500 } status_methods.get(http_request.status, log_default)() ```
{ "source": "jerocobo/LegalStream", "score": 4 }
#### File: jerocobo/LegalStream/epg.py ```python import datetime import string import math def ToDay(): global year year = datetime.datetime.now().year global month month = '%02d' % datetime.datetime.now().month global day day = '%02d' % datetime.datetime.today().day global hour hour = '%02d' % datetime.datetime.now().hour global minute minute = '%02d' % datetime.datetime.now().minute global second second = '%02d' % datetime.datetime.now().second global numbers numbers = str(year) + str(month) + str(day) + str(day) + str(hour) + str(second) + "00" ToDay() StartYear = int(year) StartMonth = int(month) StartDay = int(day) StartHour = int(hour) StartMinute = int(minute) StartSecond = int(second) ToDay() EndYear = int(year) EndMonth = int(month) EndDay = int(day) EndHour = int(hour) EndMinute = int(minute) EndSecond = int(second) MinuteLength = EndMinute - StartMinute SecondLength = EndSecond - StartSecond def DoubleDigit(Integer): return "%02d"%Integer def PlusOneDay(): global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) global minute minute = int(minute) global second second = int(second) global numbers if day is 30: day = DoubleDigit(0) else: day = DoubleDigit(day + 1) month = DoubleDigit(month) numbers = str(year) + str(month) + str(day) + str(day) + str(hour) + str(second) + "00" def RetPlusOneDay(): global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) global minute minute = int(minute) global second second = int(second) global numbers if day is 30: day = DoubleDigit(0) else: day = DoubleDigit(day + 1) month = DoubleDigit(month) return str(year) + str(month) + str(day) + str(day) + str(hour) + str(second) + "00" def RetPlusOneHour(): global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) if hour is 23: hour = DoubleDigit(0) else: hour = hour + 1 hour = DoubleDigit(hour) global minute minute = int(minute) global second second = int(second) global numbers if day is 30: day = DoubleDigit(0) else: day = DoubleDigit(day + 1) if month is 11: month = DoubleDigit(0) year = year + 1 else: month = DoubleDigit(month + 1) return str(year) + str(month) + str(day) + str(day) + str(hour) + str(second) + "00" print RetPlusOneHour() DoubleDigit(8) prompt = raw_input("Are you sure you want to run this program? Avg. run time: 1m 25s.") if "yes" in prompt: pass elif "Yes" in prompt: pass elif "y" in prompt: pass elif "Y" in prompt: pass elif "yeah" in prompt: pass elif "Yeah" in prompt: pass elif "ok" in prompt: pass elif "OK" in prompt: pass elif "okay" in prompt: pass elif "Okay" in prompt: pass else: exit() def ABC1(): ToDay() global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) global minute minute = int(minute) global second second = int(second) i = 0 Program = [] for i in range(0, 365): Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneHour() + '00 -0400" channel="ABCN1"><title lang="en">Now on ABC News</title><category lang="en">News</category></programme>') i = i + 1 print str(round(float(i)/365*100, 1)) + "% of ABC News Digital 1 Schedule Complete." return Program ABC1 = ABC1() def ABC2(): ToDay() global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) global minute minute = int(minute) global second second = int(second) i = 0 Program = [] for i in range(0, 365): Program.append('<programme start="' + str(year) + str(month) + str(day) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneDay() + '00 -0400" channel="ABCN2"><title lang="en">Regularly Scheduled Programming</title><category lang="en">News</category></programme>') i = i + 1 print str(round(float(i)/365*100, 1)) + "% of ABC News Digital 2 Schedule Complete." return Program ABC2 = ABC2() def ABC3(): ToDay() global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) global minute minute = int(minute) global second second = int(second) i = 0 Program = [] for i in range(0, 365): Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneDay() + '00 -0400" channel="ABCN3"><title lang="en">Regularly Scheduled Programming</title><category lang="en">News</category></programme>') i = i + 1 print str(round(float(i)/365*100, 1)) + "% of ABC News Digital 3 Schedule Complete." return Program ABC3 = ABC3() def ABC4(): ToDay() global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) global minute minute = int(minute) global second second = int(second) i = 0 Program = [] for i in range(0, 365): Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneDay() + '00 -0400" channel="ABCN4"><title lang="en">Regularly Scheduled Programming</title><category lang="en">News</category></programme>') i = i + 1 print str(round(float(i)/365*100, 1)) + "% of ABC News Digital 4 Schedule Complete." return Program ABC4 = ABC4() def ABC5(): ToDay() global year year = int(year) global month month = int(month) global day day = int(day) global hour hour = int(hour) global minute minute = int(minute) global second second = int(second) i = 0 Program = [] for i in range(0, 365): Program.append('<programme start="' + str(year) + str(month) + str(day) + str(hour) + str(second) + '00 -0400" stop="' + RetPlusOneDay() + '00 -0400" channel="ABCN5"><title lang="en">Regularly Scheduled Programming</title><category lang="en">News</category></programme>') i = i + 1 print str(round(float(i)/365*100, 1)) + "% of ABC News Digital 5 Schedule Complete." return Program #File = open('workfile', 'w') Filee = '<?xml version="1.0" encoding="utf-8" ?><!DOCTYPE tv SYSTEM "http://www.teleguide.info/download/xmltv.dtd"><tv generator-info-name="LegalStream Python EPG Generator" generator-info-url=https://github.com/notanewbie/LegalStream/blob/master/epg.py"><channel id="300093"><display-name lang="en">France 24</display-name></channel><channel id="ABCN1"><display-name lang="en">ABC News Digital 1</display-name></channel><channel id="ABCN2"><display-name lang="en">ABC News Digital 2</display-name></channel><channel id="ABCN3"><display-name lang="en">ABC News Digital 3</display-name></channel><channel id="ABCN4"><display-name lang="en">ABC News Digital 4</display-name></channel><channel id="ABCN5"><display-name lang="en">ABC News Digital 5</display-name></channel>' i = 0 for object in ABC1: Filee = Filee + ABC1[i] i = i + 1 i = 0 for object in ABC2: Filee = Filee + ABC2[i] i = i + 1 i = 0 for object in ABC3: Filee = Filee + ABC3[i] i = i + 1 i = 0 for object in ABC4: Filee = Filee + ABC4[i] i = i + 1 file_ = open('output.xml', 'w') file_.write(Filee + "</tv>") file_.close() ToDay() EndYear = int(year) EndMonth = int(month) EndDay = int(day) EndHour = int(hour) EndMinute = int(minute) EndSecond = int(second) MinuteLength = EndMinute - StartMinute SecondLength = EndSecond - StartSecond print "Generating EPG data took " + str(MinuteLength) + "m and " + str(SecondLength) + "s." ```
{ "source": "jerod2000/pytest", "score": 3 }
#### File: jerod2000/pytest/test.py ```python import os import shutil #搜索apk文件并复制到目标目录 def checkApkFile (srcPath,destPath): files = os.listdir(srcPath) for file in files: if file.lower().endswith(".apk"): oldFile=srcPath+"\\"+file newFile=destPath+"\\"+file if os.path.exists(newFile): os.remove(newFile) print("copy " + file,end=" ") shutil.copyfile(oldFile,newFile) print("success") #搜索release目录并在其目录下检测apk文件 def checkReleaseDir (srcPath,destPath): files = os.listdir(srcPath) isTragetDir=False for file in files: if "release" == file.lower(): releaseDir=srcPath+"\\"+file checkApkFile(releaseDir,destPath) isTragetDir=True if isTragetDir:#删除目录 shutil.rmtree(srcPath,True) #返回当前工作目录 curPath=os.getcwd() #打印当前工作目录 print(curPath) #创建目录 releasePath=curPath+"\\Release" if not os.path.exists(releasePath): os.mkdir(releasePath) #收集当前目录所有的文件及目录 files=os.listdir(curPath) #遍历 for f1 in files: filePath = curPath + "\\" + f1 if os.path.isdir(filePath):#如果是目录,则进一步检测目录下是否有Release目录 checkReleaseDir(filePath,releasePath) print("end") ```
{ "source": "jerodray/Checkers", "score": 3 }
#### File: jerodray/Checkers/SingleMove.py ```python import model import view class SingleMove: def __init__(self, friendly, friendly_color, enemy, enemy_color): self.board = model.board self.checkers = model.checkers self.buildGame(friendly, friendly_color, enemy, enemy_color) self.view(view.win1) view.runAI(True) # print("(click once on the left checkerboard to close the application)") # click1 = view.win1.getMouse() input("Press Enter to close application...") def buildGame(self, friendly, friendly_color, enemy, enemy_color): # Init Board ("Pieces" = Squares on the board) for x in range(0, 8): for y in range(0, 8): self.board[x, y] = model.Piece(x * 62.5, y * 62.5) # Set checker color correctly view.color_ai = friendly_color view.color_opponent = enemy_color # Place Checkers ("Checkers" = Circular Plastic Guys) for f in friendly: self.addChecker(f[0], f[1], f[2], True) for e in enemy: self.addChecker(e[0], e[1], e[2], False) def view(self, window): view.drawBoard(window) view.drawCheckers(window) def addChecker(self, x, y, is_king, is_friendly): checker = model.Checker() checker.id = (x, y) checker.index = x * 8 + (y + 1) checker.black = is_friendly checker.x = x checker.y = y checker.king = is_king self.board[x, y].checker = checker self.checkers.append(checker) ```
{ "source": "Jeroen0494/docker-suricata", "score": 2 }
#### File: Jeroen0494/docker-suricata/wrapper.py ```python import sys import os import argparse DEFAULT_IMAGE = "jasonish/suricata" DEFAULT_TAG = "latest" def main(): parser = argparse.ArgumentParser() parser.add_argument( "--image", default=DEFAULT_IMAGE, help="Docker image (default: {})".format(DEFAULT_IMAGE)) parser.add_argument( "--tag", default=DEFAULT_TAG, help="Docker image tag (default: {})".format(DEFAULT_TAG)) parser.add_argument( "--net", default="host", help="Docker networking type (default: host)") parser.add_argument( "remainder", nargs=argparse.REMAINDER, help=argparse.SUPPRESS) parser.add_argument( "--podman", action="store_true", default=False, help="Use podman") parser.add_argument( "-e", "--env", action="append", default=[], help="Set environment variable") args = parser.parse_args() runner = "docker" if args.podman: runner = "podman" volumes = [] user_mode = False log_dir = None suricata_args = [] while args.remainder: arg = args.remainder.pop(0) if arg == "--": continue elif arg == "-S": v = args.remainder.pop(0) volumes += [ "-v", "{}:{}".format(v, v) ] suricata_args += [arg, v] elif arg == "-r": v = args.remainder.pop(0) volumes += [ "-v", "{}:{}".format(v, v) ] suricata_args += [arg, v] user_mode = True elif arg == "-l": v = args.remainder.pop(0) if not v.startswith("/"): v = "{}/{}".format(os.getcwd(), v) volumes += [ "-v", "{}:/var/log/suricata".format(v) ] suricata_args += ["-l", "/var/log/suricata"] log_dir = v else: suricata_args += [arg] docker_args = [ runner, "run", "--net", args.net, "--rm", "-it", "--cap-add", "sys_nice", "--cap-add", "net_admin", "-e", "PUID={}".format(getuid()), "-e", "PGID={}".format(getgid()), ] for e in args.env: docker_args += ["-e", e] if user_mode and log_dir is None: volumes += ["-v", "{}:/work".format(os.getcwd())] docker_args += ["-w", "/work"] docker_args += volumes docker_args += [ "{}:{}".format(DEFAULT_IMAGE, args.tag) ] docker_args += suricata_args print(" ".join(docker_args)) os.execvp(docker_args[0], docker_args) def getuid(): if os.getenv("SUDO_UID") != None: return os.getenv("SUDO_UID") return os.getuid() def getgid(): if os.getenv("SUDO_GID") != None: return os.getenv("SUDO_GID") return os.getgid() if __name__ == "__main__": sys.exit(main()) ```
{ "source": "jeroen11dijk/Choreography", "score": 3 }
#### File: choreography/choreos/test_choreo.py ```python import cmath import math from dataclasses import dataclass from typing import List import numpy from rlbot.utils.structures.game_data_struct import GameTickPacket from rlbot.utils.structures.game_interface import GameInterface from rlutilities.linear_algebra import vec3, rotation, dot, vec2, look_at, mat3, norm, normalize, \ xy, axis_to_rotation, euler_to_rotation from rlutilities.simulation import Input from choreography.choreography_main import Choreography from choreography.drone import Drone from choreography.group_step import BlindBehaviorStep, DroneListStep, PerDroneStep, \ StateSettingStep, TwoTickStateSetStep from choreography.utils.img_to_shape import convert_img_to_shape from .examples import YeetTheBallOutOfTheUniverse, FormACircle, Wait, FlyUp # HEX FLIP COOL CLIP class HexDoubleFlip(Choreography): @staticmethod def get_num_bots(): return 6 def __init__(self, game_interface: GameInterface): super().__init__(game_interface) def generate_sequence(self): self.sequence = [ YeetTheBallOutOfTheUniverse(), HexSetup(), BoostUntilFast(), BackflipBoostyThing() ] class HexSetup(StateSettingStep): radius = 300 center = vec3(-2000, 0, 100) def set_drone_states(self, drones: List[Drone]): for i, drone in enumerate(drones): angle = i * math.pi * 2 / len(drones) rot = rotation(angle) v = vec3(dot(rot, vec2(1, 0))) drone.position = v * self.radius + self.center drone.orientation = look_at(vec3(2, 0, 3), vec3(1, 0, 0)) drone.velocity = vec3(0, 0, 500) drone.angular_velocity = vec3(0, 0, 0) class BoostUntilFast(DroneListStep): def step(self, packet: GameTickPacket, drones: List[Drone]): self.finished = norm(drones[0].velocity) > 1000 for drone in drones: drone.controls.pitch = 0 drone.controls.boost = True class BackflipBoostyThing(BlindBehaviorStep): duration = 6.0 def set_controls(self, controls: Input): controls.pitch = 0.5 controls.boost = True # AUTOMATIC STATE SETTING INTO DRAWING class Dickbutt(Choreography): def __init__(self, game_interface: GameInterface): super().__init__(game_interface) def generate_sequence(self): self.sequence = [ YeetTheBallOutOfTheUniverse(), Drawing('ChoreographyHive/assets/dickbutt.png', origin=vec3(-1000, 1500, 18)), Wait(1.0) ] class Drawing(TwoTickStateSetStep): def __init__(self, image, origin=vec3(0, 0, 18)): super().__init__() self.origin = origin self.shape = convert_img_to_shape(image) def set_drone_states(self, drones: List[Drone]): for i, drone in enumerate(drones): if i < len(self.shape): drone.position = self.origin + self.shape[i] drone.orientation = mat3(1, 0, 0, 0, 1, 0, 0, 0, 1) drone.velocity = vec3(0, 0, 0) else: drone.position = vec3(0, 0, 3000) # CIRCLES AND SPHERE FORMATION TESTS class CirclesAndSpheres(Choreography): @staticmethod def get_num_bots(): return 45 def __init__(self, game_interface: GameInterface): super().__init__(game_interface) def generate_sequence(self): self.sequence = [ YeetTheBallOutOfTheUniverse(), FormACircle(), Wait(1.0), FlyUp(), HoverSpinUp(), HoverSpinDown(), SphereFormation(), HoverOrbit() ] class HoverSpinUp(PerDroneStep): duration = 6.0 def step(self, packet: GameTickPacket, drone: Drone, index: int): drone.hover.up = normalize(drone.position) clockwise_rotation = axis_to_rotation(vec3(0, 0, self.time_since_start / 4)) position_on_circle = normalize(xy(drone.position)) * (2000 - self.time_since_start * 200) drone.hover.target = dot(clockwise_rotation, position_on_circle) drone.hover.target[2] = 1000 drone.hover.step(self.dt) drone.controls = drone.hover.controls class HoverSpinDown(PerDroneStep): duration = 6.0 def step(self, packet: GameTickPacket, drone: Drone, index: int): drone.hover.up = normalize(drone.position) clockwise_rotation = axis_to_rotation(vec3(0, 0, 1.5 - self.time_since_start / 4)) position_on_circle = normalize(xy(drone.position)) * (800 + self.time_since_start * 200) drone.hover.target = dot(clockwise_rotation, position_on_circle) drone.hover.target[2] = 1000 drone.hover.step(self.dt) drone.controls = drone.hover.controls class SphereFormation(DroneListStep): duration = 12.0 separation_duration = 3.0 recirculation_start = 6.5 radius_shrink_start = 3.0 radius_shrink_duration = 6.0 layers = [ [0, 16], [1, 2, 17, 18, 32, 33], [3, 4, 5, 19, 20, 21, 34, 35, 36], [6, 7, 8, 9, 22, 23, 24, 25, 37, 38, 39, 40], [10, 11, 12, 26, 27, 28, 41, 42, 43], [13, 14, 29, 30, 44, 45], [15, 31] ] heights = [ 1500, 1400, 1250, 1000, 750, 600, 500, ] radii = [ 200, 450, 600, 650, 600, 450, 200, ] def step(self, packet: GameTickPacket, drones: List[Drone]): for drone in drones: drone.hover.up = normalize(drone.position) for i, layer in enumerate(self.layers): if drone.id in layer: # Calculate radius if self.time_since_start < self.radius_shrink_start: radius = 2000 elif self.time_since_start < self.radius_shrink_start + self.radius_shrink_duration: diff = 2000 - self.radii[i] radius = 2000 - diff * ( (self.time_since_start - self.radius_shrink_start) / self.radius_shrink_duration) else: radius = self.radii[i] # Calculate xy position if self.time_since_start > self.recirculation_start: a = layer.index(drone.id) angle = a * math.pi * 2 / len(layer) rot = rotation(angle) pos_xy = vec3(dot(rot, vec2(1, 0))) else: pos_xy = xy(drone.position) # Combine xy and radius drone.hover.target = normalize(pos_xy) * radius # Get height if self.time_since_start < self.separation_duration: diff = 1000 - self.heights[i] height = 1000 - diff * (self.time_since_start / self.separation_duration) else: height = self.heights[i] drone.hover.target[2] = height break drone.hover.step(self.dt) drone.controls = drone.hover.controls class HoverOrbit(PerDroneStep): duration = 8.0 layers = [ [0, 16], [1, 2, 17, 18, 32, 33], [3, 4, 5, 19, 20, 21, 34, 35, 36], [6, 7, 8, 9, 22, 23, 24, 25, 37, 38, 39, 40], [10, 11, 12, 26, 27, 28, 41, 42, 43], [13, 14, 29, 30, 44, 45], [15, 31] ] heights = [ 1500, 1400, 1250, 1000, 750, 600, 500, ] radii = [ 200, 450, 600, 650, 600, 450, 200, ] def step(self, packet: GameTickPacket, drone: Drone, index: int): for i, layer in enumerate(self.layers): if index in layer: drone.hover.up = normalize(drone.position) clockwise_rotation = axis_to_rotation(vec3(0, 0, 0.3)) position_on_circle = normalize(xy(drone.position)) * self.radii[i] drone.hover.target = dot(clockwise_rotation, position_on_circle) drone.hover.target[2] = self.heights[i] break drone.hover.step(self.dt) drone.controls = drone.hover.controls # DOUBLE HELIX class DoubleHelix(Choreography): @staticmethod def get_appearances(num_bots: int) -> List[str]: appearances = ['WillRedBlue.cfg'] * num_bots # appearances[0::4] = ['WillYellowGreen.cfg'] * round(num_bots / 4) # appearances[1::4] = ['WillYellowGreen.cfg'] * round(num_bots / 4) return appearances @staticmethod def get_teams(num_bots: int) -> List[int]: # Every other bot is on the orange team. teams = [0] * num_bots teams[1::2] = [1] * round(num_bots / 2) return teams @staticmethod def get_num_bots(): return 32 def __init__(self, game_interface: GameInterface): super().__init__(game_interface) def generate_sequence(self): self.sequence = [ YeetTheBallOutOfTheUniverse(), TwoLineSetup(), Wait(1.0), ForwardThenHelix() ] class TwoLineSetup(StateSettingStep): y_distance = 500 x_distance = 300 gap_offset = 300 def set_drone_states(self, drones: List[Drone]): for i, drone in enumerate(drones): angle = (-1) ** i * -math.pi / 2 x = -self.x_distance * (-1) ** i y = (self.y_distance + self.gap_offset * (i // 2)) * (-1) ** i drone.position = vec3(x, y, 20) drone.orientation = euler_to_rotation(vec3(0, angle, 0)) drone.velocity = vec3(0, 0, 0) drone.angular_velocity = vec3(0, 0, 0) class ForwardThenHelix(PerDroneStep): duration = 13.0 radius = 500 def step(self, packet: GameTickPacket, drone: Drone, index: int): if drone.position[2] < 25: drone.since_jumped = 0.0 # Go forward drone.controls.throttle = 1.0 if abs(drone.velocity[1]) < 500 else 0.01 # If near half-line if abs(drone.position[1]) < 200: drone.controls.jump = True else: drone.since_jumped += self.dt height = 50 + drone.since_jumped * 150 angle = 1.0 + drone.since_jumped * 1.2 if index % 2 == 0: angle += math.pi rot = rotation(angle) v = vec3(dot(rot, vec2(1, 0))) drone.hover.target = v * self.radius drone.hover.target[2] = height drone.hover.up = normalize(drone.position) drone.hover.step(self.dt) drone.controls = drone.hover.controls # F(X,Y) GRAPHER class GraphTest(Choreography): @staticmethod def get_num_bots(): return 64 @staticmethod def get_appearances(num_bots: int) -> List[str]: return 64 * ['graph.cfg'] # @staticmethod # def get_teams(num_bots: int) -> List[int]: # teams = [0] * num_bots # return teams def __init__(self, game_interface: GameInterface): super().__init__(game_interface) def generate_sequence(self): self.sequence = [ YeetTheBallOutOfTheUniverse(), Grid(), BaseGraph(), Wave(), Water(), BaseGraph(), Saddle(), BaseGraph(), Pants(), # Parabola(), # CosSin(), # WindMill(), # YeetEquation(), # Limit(), # Jochem(), # LogarithmReal(), ] class Grid(TwoTickStateSetStep): spacing = 200 def set_drone_states(self, drones: List[Drone]): s = int(math.sqrt(len(drones))) # Side length for i, drone in enumerate(drones): # Get grid pos. x = (i // s) - (s - 1) / 2 y = (i % s) - (s - 1) / 2 drone.position = vec3(x * self.spacing, y * self.spacing, 800) # 800 is base height drone.orientation = euler_to_rotation(vec3(math.pi / 2, 0, 0)) drone.velocity = vec3(0, 0, 100) drone.angular_velocity = vec3(0, 0, 0) class BaseGraph(DroneListStep): duration = 2 rotation_speed = 0 spacing = 200 def func(self, x, y): return 0 def step(self, packet: GameTickPacket, drones: List[Drone]): s = int(math.sqrt(len(drones))) # Side length for i, drone in enumerate(drones): # Get grid pos. x = (i // s) - (s - 1) / 2 y = (i % s) - (s - 1) / 2 # Get height from func. z = 800 + self.func(x, y) # 800 is base height drone.hover.target = vec3(x * self.spacing, y * self.spacing, z) rot = rotation(self.rotation_speed * self.time_since_start * 2) drone.hover.up = vec3(dot(rot, vec2(1, 0))) drone.hover.step(self.dt) drone.controls = drone.hover.controls class Parabola(BaseGraph): def func(self, x, y): return 40 * (x ** 2 + y ** 2) - 200 class CosSin(BaseGraph): def func(self, x, y): return 250 * (math.cos(x) + math.sin(y)) class WindMill(BaseGraph): duration = 4 * math.pi def func(self, x, y): t = self.time_since_start return 1000 * (numpy.sign(x * y) * numpy.sign(1 - (x * 9) ** 2 + (y * 9) ** 2) / 9) class Wave(BaseGraph): duration = 2 * math.pi def func(self, x, y): t = self.time_since_start return 150 * (math.sin(x / 2 + 2 * t)) class YeetEquation(BaseGraph): duration = 5 def func(self, x, y): t = self.time_since_start t_0 = 2 c = 0.5 a = 1 / (4 * math.pi * c * (t + t_0)) b = -(x ** 2 + y ** 2) / (4 * c * (t + t_0)) return 20000 * a * math.exp(b) class Water(BaseGraph): duration = 2 * math.pi def func(self, x, y): t = self.time_since_start return 250 * (math.sin(x / 2 + t)) * (math.cos(y / 2 + t)) class Saddle(BaseGraph): duration = 4 * math.pi def func(self, x, y): t = self.time_since_start return 4 * x * y * t * math.cos(t) class Jochem(BaseGraph): duration = 4 def func(self, x, y): t = self.time_since_start return 300 * t * x / (x ** 2 + y ** 2 + 0.3) class Limit(BaseGraph): duration = 4 * math.pi def func(self, x, y): t = self.time_since_start return 10 * t * math.cos(t) * x / (y + 0.001) class Will(BaseGraph): duration = 5 def func(self, x, y): t = self.time_since_start return 10 * (math.sin(1.5 * t) - 0.5) * (x ** 2 + y ** 2) class LogarithmReal(BaseGraph): duration = 4 * math.pi def func(self, x, y): t = self.time_since_start return 200 * math.cos(t) * (cmath.sqrt(x + y * 1j)).real class Pants(BaseGraph): duration = 4 * math.pi def func(self, x, y): t = self.time_since_start return 275 * math.sin(t) * (cmath.sqrt(x + y * 1j)).imag # HARDCODED CLONES class Clones(Choreography): @staticmethod def get_num_bots(): return 10 def __init__(self, game_interface: GameInterface): super().__init__(game_interface) def generate_sequence(self): self.sequence = [ YeetTheBallOutOfTheUniverse(), StackThemUp(), GoForwardAndThenDoAJumpOrSomething() ] class StackThemUp(StateSettingStep): pos = vec3(0, -2000, 20) height = 50 def set_drone_states(self, drones: List[Drone]): for i, drone in enumerate(drones): drone.position = self.pos drone.position[2] += i * self.height drone.orientation = euler_to_rotation(vec3(0, math.pi / 2, 0)) drone.velocity = vec3(0, 0, 0) drone.angular_velocity = vec3(0, 0, 0) @dataclass class MovementInInterval: start: float end: float controls: Input # Pass in a list of MovementInIntervals and it automatically completes the moves with each drone. # If you have the temptation to use clone_delay = 0, use BlindBehaviourStep instead. class HardcodedMovement(PerDroneStep): def __init__(self, movements: List[MovementInInterval], clone_delay: float = 1.0): self.movements = movements self.clone_delay = clone_delay super().__init__() def step(self, packet: GameTickPacket, drone: Drone, index: int): delay = index * self.clone_delay for movement in self.movements: if movement.start + delay < self.time_since_start < movement.end + delay: drone.controls = movement.controls if index == packet.num_cars - 1: self.finished = self.time_since_start > delay + self.movements[-1].end class GoForwardAndThenDoAJumpOrSomething(HardcodedMovement): def __init__(self): a = Input() a.throttle = True b = Input() b.jump = True b.pitch = 1.0 movements = [ MovementInInterval(0.0, 3.0, a), MovementInInterval(3.0, 4.2, b) ] super().__init__(movements, clone_delay=0.8) # Unused cool sphere class CoolSphere(PerDroneStep): duration = 30.0 height = 1100 radius = 850 unwind_start_time = 10.0 max_frequency = 30.0 def step(self, packet: GameTickPacket, drone: Drone, index: int): if self.time_since_start > self.unwind_start_time: f = self.max_frequency - (self.time_since_start - self.unwind_start_time) else: f = self.max_frequency z = (index - 31.5) / 32 # For 64 bots :^) x = math.sqrt(1 - z ** 2) * math.cos(z * f) y = math.sqrt(1 - z ** 2) * math.sin(z * f) target = vec3(x, y, z) * self.radius target[2] += self.height drone.hover.up = normalize(drone.position) drone.hover.target = target drone.hover.step(self.dt) drone.controls = drone.hover.controls ``` #### File: choreography/utils/vector_math.py ```python from rlutilities.linear_algebra import vec3, norm, normalize def distance(position: vec3, target: vec3) -> float: return norm(position - target) def direction(source: vec3, target: vec3) -> vec3: return normalize(target - source) ```
{ "source": "jeroenbbb/openpilot", "score": 2 }
#### File: selfdrive/can/libdbc_py.py ```python import os import subprocess from selfdrive.swaglog import cloudlog from cffi import FFI can_dir = os.path.dirname(os.path.abspath(__file__)) libdbc_fn = os.path.join(can_dir, "libdbc.so") try: subprocess.check_call(["make"], cwd=can_dir) except subprocess.CalledProcessError: cloudlog.warning("building in can/libdbc_py failed") ffi = FFI() ffi.cdef(""" typedef struct { const char* name; double value; } SignalPackValue; typedef struct { uint32_t address; const char* name; double default_value; } SignalParseOptions; typedef struct { uint32_t address; int check_frequency; } MessageParseOptions; typedef struct { uint32_t address; uint16_t ts; const char* name; double value; } SignalValue; typedef enum { DEFAULT, HONDA_CHECKSUM, HONDA_COUNTER, TOYOTA_CHECKSUM, } SignalType; typedef struct { const char* name; int b1, b2, bo; bool is_signed; double factor, offset; SignalType type; } Signal; typedef struct { const char* name; uint32_t address; unsigned int size; size_t num_sigs; const Signal *sigs; } Msg; typedef struct { const char* name; uint32_t address; const char* def_val; const Signal *sigs; } Val; typedef struct { const char* name; size_t num_msgs; const Msg *msgs; const Val *vals; size_t num_vals; } DBC; void* can_init(int bus, const char* dbc_name, size_t num_message_options, const MessageParseOptions* message_options, size_t num_signal_options, const SignalParseOptions* signal_options, bool sendcan, const char* tcp_addr); void can_update(void* can, uint64_t sec, bool wait); size_t can_query(void* can, uint64_t sec, bool *out_can_valid, size_t out_values_size, SignalValue* out_values); const DBC* dbc_lookup(const char* dbc_name); void* canpack_init(const char* dbc_name); uint64_t canpack_pack(void* inst, uint32_t address, size_t num_vals, const SignalPackValue *vals, int counter); """) try: libdbc = ffi.dlopen(libdbc_fn) except OSError: cloudlog.warning("load library in can/libdbc_py failed") ``` #### File: selfdrive/loggerd/publish_log.py ```python import time import zmq import sys import requests import threading if __name__ == "__main__": sys.path.append("/home/pi/openpilot") import selfdrive.messaging as messaging import selfdrive.loggerd.telegram as telegram from selfdrive.services import service_list from cereal import log # display all services for service in service_list: print (service) print (service_list[service].port) # set upload time interval for every message # name, number of seconds between 2 uploads upload_interval = { "gpsLocationExternal": 5, "navUpdate": 30, "logMessage": 120, "health": 300, "thermal": 30, "liveMapData": 30 } # define list for all last uploads last_upload = {} # define list to remeber last message so it can be communicated to Telegram last_message = {} def upload(msgtype, data): url = "https://esfahaniran.com/openpilot/index.php" post_fields = {'type': msgtype, 'data': data} header = {"Content-type": "application/x-www-form-urlencoded", "Accept": "text/plain"} try: # = requests.post(url, data=post_fields, headers=header,timeout=5) r = requests.post(url, data=post_fields, timeout=5) # json = urlopen(request).read().decode() # r = requests.post(url, data={'data': data, 'type': msgtype}) print(r.status_code, r.reason) print(r.text) except: print ("Timeout, no upload") # check priority, not every message has to be uploaded every time # and some special fields can be extracted from the message # see cereal/log.capnp - struct Event for all possible messages def define_upload_required(evnt): field1 = "" field2 = "" upload_required = False type = evnt.which() if type == 'gpsLocationExternal': # get gps locations field1 = evnt.gpsLocationExternal.latitude field2 = evnt.gpsLocationExternal.longitude # check time in sec since last upload if type in last_upload: time_since_last_upload = (evnt.logMonoTime - last_upload[type]) / 1000000000 else: time_since_last_upload = 1000 # print (time_since_last_upload) if type in upload_interval: if upload_interval[type] < time_since_last_upload: # priority of message type is higher than the last upload # so a next upload is required print ("Upload required") upload_required = True last_upload[type] = evnt.logMonoTime return upload_required, field1, field2 def convert_message(evt): # convert all messages into readible output which = evt.which() output = str(evt) if which == "navUpdate": hlp = evt.navUpdate.segments output = str(hlp[0].instruction) + ", distance=" output = output + str(hlp[0].distance) + " meters" if which == "logMessage": output = evt.logMessage if which == "gpsLocationExternal": lat = str(round(evt.gpsLocationExternal.latitude,5)) lon = str(round(evt.gpsLocationExternal.longitude,5)) speed = str(round(evt.gpsLocationExternal.speed,3)) bearing = str(round(evt.gpsLocationExternal.bearing,0)) accuracy= str(round(evt.gpsLocationExternal.accuracy,0)) time_stamp = time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(evt.gpsLocationExternal.timestamp)) output = lat + " " + lon + " " + speed + " m/s " + bearing + " degrees, accuracy in meters=" + accuracy + " " + time_stamp output = output + " (https://maps.google.com/?q=" + lat output = output + "," + lon + ")" return output def main(gctx=None): context = zmq.Context() poller = zmq.Poller() service_sock = [] count = 0 # start telegram stuff count = 0 last_update_id = None print (telegram.get_me()) # loop through all services to define socks for service in service_list: print (service) print (service_list[service].port) port = service_list[service].port # service_sock.append(messaging.sub_sock(context, service_list[service].port)) sock = messaging.sub_sock(context, port, poller) # count = count + 1 # define poller to listen to all sockets # for i in range(0,count): # poller.register( service_sock[i], zmq.POLLIN ) # poll all incoming messages priority = 1 while True: sock_found = False polld = poller.poll(timeout=100) for sock, mode in polld: sock_found = True #print (str(sock)) #print (mode) msg = sock.recv() # msg = sock.recv_multipart() # print (str(msg)) # print (msg.decode("ascii")) evt = log.Event.from_bytes(msg) print(evt.which()) # remember last message for every message type last_message[evt.which()] = convert_message(evt) # check if the message has to be uploaded or not upload_required, field1, field2 = define_upload_required(evt) if evt.which() == 'liveMapData': print(evt) if priority == 10: upload(evt.which(), evt) priority = 0 print (sock_found) # check if Telegram is asking something # but only when no messages are waiting if sock_found == False: updates = telegram.get_updates(last_update_id) print (updates) if len(updates["result"]) > 0: last_update_id = telegram.get_last_update_id(updates) + 1 telegram.handle_answer(updates, last_message) time.sleep(2) print ("Sleep" + str(count)) count = count + 1 # loop through all services to listen to the socks #while True: # count = 0 # for service in service_list: # # read all messages form this socket # msg = messaging.recv_sock(service_sock[count], wait=False) # while msg is not None: # if isinstance(msg, str): # print (service + "=" + msg) # else: # print ("message received from " + service + " " + str(msg)) # #type(msg) # msg = messaging.recv_sock(service_sock[count], wait=False) # count = count + 1 # time.sleep(5) if __name__ == "__main__": main() ```
{ "source": "JeroenBongers96/suii_3d_vision_ros", "score": 2 }
#### File: suii_3d_vision_ros/yolo/yolo_server.py ```python import cv2 import time import numpy #from processing import PostProcessing from suii_3d_vision_ros.srv import GetRoi, GetRoiResponse from std_msgs.msg import String from sensor_msgs.msg import Image from network import NetworkClient #from cv_bridge import CvBridge, CvBridgeError from cv_bridge import CvBridge, CvBridgeError import rospy import roslib #from yolo import Yolo import struct import socket import base64 import json class Yolo_Wrapper(object): def __init__(self): rospy.init_node('get_roi_server') s = rospy.Service('get_roi', GetRoi, self.handle_get_roi) print("Server is ready") self.client = NetworkClient("localhost", 9999) #data = numpy.load('/home/jeroen/catkin_ws/src/suii_3d_vision_ros/yolo/config/mtx.npz') #self.mtx = data['mtx'] #data = numpy.load('/home/jeroen/catkin_ws/src/suii_3d_vision_ros/yolo/config/dist.npz') #self.dist = data['dist'] rospy.spin() def handle_get_roi(self, req): cvb_de = CvBridge() #cv_image = bridge.imgmsg_to_cv2(req, desired_encoding="bgr8") newimg = cvb_de.imgmsg_to_cv2(req.input, "bgr8") #image = newimg #h, w = image.shape[:2] #newcameramtx, roi=cv2.getOptimalNewCameraMatrix(self.mtx,self.dist,(w,h),1,(w,h)) #dst = cv2.undistort(image, self.mtx, self.dist, None, newcameramtx) #x,y,w,h = roi #image = dst[y:y+h, x:x+w] retval, buff = cv2.imencode('.jpg', newimg) jpg_enc = base64.b64encode(buff) # Do no touch, client encoded request resp = self.client.networkCall(0x00, {"img": jpg_enc}) #End client encoded request list_of_name = resp['names'] arr_list = [] for x in list_of_name: #add start int for y in x: #add data arr_list.append(y) # convert to np array return GetRoiResponse(arr_list) if __name__ == "__main__": wrapper = Yolo_Wrapper() ```
{ "source": "JeroenBos/jeroenbos.partest", "score": 2 }
#### File: partest/fixtures/test_fixtures.py ```python import os import shutil import tempfile from pytest import fixture from jeroenbos.partest.utils import append_to_file # all these fixtures are made available to scope tests/** by the import statement in tests/conftest.py @fixture def temp_test_file(): path = tempfile.mktemp("_test.py") append_to_file( path, """# flake8: noqa # type: ignore import os os.environ["METATESTING"] = "true" import pytest import unittest from unittest import TestCase """, ) yield path os.remove(path) @fixture def successful_test_file(temp_test_file: str) -> str: return append_successful_test_file(temp_test_file) def append_successful_test_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ def test_that_succeeds(): pass """, ) return temp_test_file @fixture def failing_test_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ def test_that_fails(): raise ValueError("Intended to fail") """, ) return temp_test_file @fixture def skipped_test_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ @pytest.mark.skip("Intended to be skipped") def test_that_is_skipped(): raise ValueError("Intended to be skipped") """, ) return temp_test_file @fixture def skipped_test_with_failing_teardown_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestSkippedTestWithFailingTearDown(TestCase): @pytest.mark.skip("Intended to be skipped") def test_that_is_skipped(): raise ValueError("Intended to be skipped") def tearDown(self): raise ValueError("Teardown intended to fail") """, ) return temp_test_file @fixture def test_with_failing_setup_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestWithFailingTearDown(TestCase): def test_after_failed_setup(self): raise ValueError("After setup intended to fail") def setUp(self): raise ValueError("Setup intended to fail") """, ) return temp_test_file @fixture def test_with_failing_teardown_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestWithFailingTearDown(TestCase): def test_before_failing_teardown(self): pass def tearDown(self): raise ValueError("Teardown intended to fail") """, ) return temp_test_file @fixture def test_with_failing_setup_and_teardown_file(test_with_failing_setup_file: str) -> str: append_to_file( test_with_failing_setup_file, """ def tearDown(self): raise ValueError("Teardown intended to fail") """, ) return test_with_failing_setup_file @fixture def test_that_fails_and_teardown_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestThatFailsWithFailingTearDown(TestCase): def test_before_failing_teardown(self): raise ValueError("Test intended to fail") def tearDown(self): raise ValueError("Teardown intended to fail") """, ) return temp_test_file @fixture def skipped_test_with_failing_class_teardown_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestSkippedTestWithFailingTearDown(TestCase): @pytest.mark.skip("Intended to be skipped") def test_that_is_skipped(): raise ValueError("Intended to be skipped") @classmethod def tearDownClass(cls): raise ValueError("Class teardown intended to fail") """, ) return temp_test_file @fixture def test_with_failing_class_setup_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestWithFailingTearDown(TestCase): def test_after_failed_setup(self): raise ValueError("After setup intended to fail") @classmethod def setUpClass(cls): raise ValueError("setUpClass intended to fail") """, ) return temp_test_file @fixture def test_with_failing_class_teardown_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestWithFailingTearDown(TestCase): def test_before_failing_teardown(self): pass @classmethod def tearDownClass(cls): raise ValueError("tearDownClass intended to fail") """, ) return temp_test_file @fixture def test_with_failing_class_setup_and_class_teardown_file(test_with_failing_class_setup_file: str) -> str: append_to_file( test_with_failing_class_setup_file, """ @classmethod def tearDownClass(cls): raise ValueError("Class teardown intended to fail") """, ) return test_with_failing_class_setup_file @fixture def test_that_fails_and_class_teardown_file(temp_test_file: str) -> str: append_to_file( temp_test_file, """ class TestThatFailsWithFailingTearDown(TestCase): def test_before_failing_teardown(self): raise ValueError("Test intended to fail") @classmethod def tearDownClass(cls): raise ValueError("Class teardown intended to fail") """, ) return temp_test_file @fixture def temp_test_directory(): dir = tempfile.mkdtemp() append_successful_test_file(os.path.join(dir, "test_file1.py")) append_successful_test_file(os.path.join(dir, "nested", "test_file2.py")) yield dir try: shutil.rmtree(dir) except PermissionError: pass ```
{ "source": "jeroenbrons/kubepi-amd64", "score": 2 }
#### File: kubepi-amd64/commands/cmd_platform.py ```python from kubepi.cli import pass_environment, logger from kubepi.helpers.git import get_submodules, get_repo import click import click_spinner import git import os import subprocess as s # App group of commands @click.group('platform', short_help='Platform commands') @click.pass_context @pass_environment def cli(ctx, kube_context): """Platform commands to help with handling the codebase and repo""" pass @cli.command('init', short_help='Initialize platform components') @click.argument('submodules', required=True, default='all') @click.argument('repopath', required=True, type=click.Path(exists=True), default=os.getcwd()) @click.pass_context @pass_environment def init(ctx, kube_context, submodules, repopath): """Init the platform by doing submodule init and checkout all submodules on master""" # Get the repo from arguments defaults to cwd repo = get_repo(repopath) submodules = get_submodules(repo, submodules) with click_spinner.spinner(): repo.submodule_update() logger.info('Platform initialized.') @cli.command('info', short_help='Get info on platform') @click.pass_context @pass_environment def info(ctx, kube_context): """Get info on accessing the platform""" kube_context = ctx.kube_context try: k1s_host = s.run(['kubectl', '--context', 'k3d-' + kube_context, '-n', 'k1s', 'get', 'ingressroute', 'ui', '-o', 'jsonpath={.spec.routes[0].match}'], capture_output=True, check=True) k1s_host = k1s_host.stdout.decode('utf-8') k1s_host = k1s_host.split('`') k1s_url = k1s_host[1] logger.info('K1S can be accessed through the URL:') logger.info('https://' + k1s_url + '/') except s.CalledProcessError as error: logger.debug(error.stderr.decode('utf-8')) raise click.Abort() @cli.command('token', short_help='Get the platform token') @click.pass_context @pass_environment def token(ctx, kube_context): """Get the platform token required by Kubernetes Dashboard""" kube_context = ctx.kube_context try: proc1 = s.Popen(['kubectl', '--context', 'k3d-' + kube_context, '-n', 'monitoring', 'describe', 'secret', 'k1s-admin'], stdout=s.PIPE) proc2 = s.Popen(['grep', 'token:'], stdin=proc1.stdout, stdout=s.PIPE, universal_newlines=True) proc1.stdout.close() out = proc2.communicate()[0] logger.info('The platform token is:\n') logger.info(out) except s.CalledProcessError as error: logger.debug(error.stderr.decode('utf-8')) raise click.Abort() @cli.command('version', short_help='Get all versions of components') @click.argument('submodules', required=True, default='all') @click.argument('repopath', required=True, type=click.Path(exists=True), default=os.getcwd()) @click.pass_context @pass_environment def version(ctx, kube_context, submodules, repopath): """Check versions of services in git submodules You can provide a comma separated list of submodules or you can use 'all' for all submodules""" # Get the repo from arguments defaults to cwd repo = get_repo(repopath) submodules = get_submodules(repo, submodules) # Do something with the submodules all_sm_details = [] with click_spinner.spinner(): for submodule in submodules: logger.debug('Switched to submodule: ' + submodule) sm_details = {} sm_details['repo'] = submodule # Are we on an active branch? on a tag? if not then get sha? try: smrepo = git.Repo(submodule) sm_details['present'] = True except git.InvalidGitRepositoryError as error: logger.warning(submodule + ': not present') sm_details['present'] = False all_sm_details.append(sm_details) continue # Get branch try: branch = smrepo.active_branch.name sm_details['branch'] = branch # Check if remotes are ahead or behind origin = smrepo.remotes.origin origin.fetch() commits_behind = smrepo.iter_commits(branch + '..origin/' + branch) commits_ahead = smrepo.iter_commits('origin/' + branch + '..' + branch) sm_details['commits_ahead'] = sum(1 for c in commits_ahead) sm_details['commits_behind'] = sum(1 for c in commits_behind) except TypeError as error: sm_details['branch'] = '' logger.debug(error) # Check if we point to any tags points_at_tag = smrepo.git.tag('--points-at', 'HEAD') sm_details['tag'] = points_at_tag # Get sha of HEAD sha = smrepo.head.commit.hexsha sm_details['sha'] = sha # Add submodule details to the list all_sm_details.append(sm_details) logger.debug('Received following details about the platform submodules:') logger.debug(all_sm_details) for sm_details in all_sm_details: logger.info(sm_details['repo'] + ':') logger.info('Branch: ' + sm_details['branch']) logger.info('SHA: ' + sm_details['sha']) if sm_details['tag']: logger.info('Tag: ' + sm_details['tag']) if sm_details['commits_ahead'] > 0: logger.info('Ahead by: ' + str(sm_details['commits_ahead']) + ' commits') if sm_details['commits_behind'] > 0: logger.info('Behind by: ' + str(sm_details['commits_behind']) + ' commits') ``` #### File: kubepi-amd64/helpers/git.py ```python from kubepi.cli import logger import click import git def get_repo(repopath): try: return git.Repo(repopath, odbt=git.GitDB) except git.InvalidGitRepositoryError: logger.critical('The repo path ' + repopath + ' is not a git repo') raise click.Abort() def get_submodules(repo, submodules): # Based on provided submodules through arguments set the repo objects # that we want to work with if submodules == 'all': submodules = repo.submodules submodule_list = [] for submodule in submodules: submodule_list.append(submodule.name) submodules = submodule_list else: submodules = submodules.split(',') submodule_list = [] for submodule in submodules: submodule_list.append('platform/' + submodule) submodules = submodule_list logger.debug('The provided submodules are:') logger.debug(submodules) return(submodules) ```
{ "source": "jeroenbrouwer/django-tenant-schemas", "score": 3 }
#### File: django-tenant-schemas/tenant_schemas/rename.py ```python from django.core.exceptions import ValidationError from django.db import connection from tenant_schemas.postgresql_backend.base import _is_valid_schema_name from tenant_schemas.utils import schema_exists def rename_schema(*, schema_name, new_schema_name): """ This renames a schema to a new name. It checks to see if it exists first """ cursor = connection.cursor() if schema_exists(new_schema_name): raise ValidationError("New schema name already exists") if not _is_valid_schema_name(new_schema_name): raise ValidationError("Invalid string used for the schema name.") sql = 'ALTER SCHEMA {0} RENAME TO {1}'.format(schema_name, new_schema_name) cursor.execute(sql) cursor.close() ```
{ "source": "JeroenDeDauw/WikidataIntegrator", "score": 2 }
#### File: wikidataintegrator/ref_handlers/strict_overwrite.py ```python from datetime import datetime import copy #### # Example custom ref handler # Always replaces all old refs with new refs #### def strict_overwrite(olditem, newitem): # modifies olditem in place!!! olditem.references = newitem.references ```
{ "source": "jeroen-dhollander/python-paginator", "score": 3 }
#### File: python-paginator/examples/more_with_rainbow_page_plugin.py ```python from more_or_less import MorePlugin, Page, PageOfHeight, RepeatableMixin import more_or_less import random import sys # See https://en.wikipedia.org/wiki/ANSI_escape_code#Colors # http://www.isthe.com/chongo/tech/comp/ansi_escapes.html # Make text bold, faint, normal, underlined, blinking, crossed-out, ... _MODIFIERS = list(range(0, 10)) _FOREGROUND_COLORS = list(range(30, 38)) + list(range(90, 98)) _BACKGROUND_COLORS = list(range(40, 48)) + list(range(100, 108)) def main(): more_or_less.add_plugin(RainbowPlugin) more_or_less.paginate(input=sys.stdin) class RainbowPlugin(MorePlugin): def get_keys(self): # We trigger our plugin on 'r' and 'R' return ['r', 'R'] def build_page(self, page_builder, key_pressed, arguments): # Return our output page. # For the page height, we either use the value provided on the command line # (if the user typed 10r), # or we default to the screen height height = arguments.get('count', page_builder.get_page_height()) return RainbowPage(output=page_builder.get_output(), height=height) def get_help(self): # Help is returned as an iterator over ('key', 'help') tupples yield ('r or R', 'Rainbowify the next k lines of text [current screen height]') class RainbowPage(Page, RepeatableMixin): ''' Rainbowifies every line. By inheriting from 'RepeatableMixin' we support repeating the command by pressing '.' ''' def __init__(self, height, output): self._page = PageOfHeight(height, output=output) def is_full(self): return self._page.is_full() def add_line(self, line): return self._page.add_line(rainbowify(line)) def flush(self): return self._page.flush() def repeat(self): return RainbowPage(self._page.height, self._page.output) def rainbowify(line): modifier = random.choice(_MODIFIERS) foreground = random.choice(_FOREGROUND_COLORS) background = random.choice(_BACKGROUND_COLORS) return f'\x1b[{modifier};{foreground};{background}m{line}\x1b[0m' if __name__ == "__main__": main() ``` #### File: python-paginator/more_or_less/count_plugin.py ```python from .more_plugin import MorePlugin class CountPlugin(MorePlugin): ''' Invoked when the user types any number. Adds a 'count' argument to the next action. ''' def __init__(self): self._digits = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] def get_keys(self): return self._digits def build_page(self, page_builder, key_pressed, arguments): arguments['count'] = self._get_count(page_builder, key_pressed) return page_builder.build_next_page(arguments=arguments) def get_help(self): return [] def _get_count(self, page_builder, first_key): def iter_digits(): # Read characters as long as the user enters digits key_pressed = first_key while key_pressed in self._digits: yield key_pressed key_pressed = input.get_character(prompt_message) input.put_back(key_pressed) input = page_builder.get_input() prompt_message = page_builder.get_prompt_message() return int(''.join(iter_digits())) ``` #### File: python-paginator/more_or_less/fixed_size_screen.py ```python import sys from more_or_less.screen_dimensions import ScreenDimensions _HUGE = sys.maxsize class FixedSizeScreen(ScreenDimensions): def __init__(self, height=_HUGE, width=_HUGE): self._height = height self._width = width def get_height(self): return self._height def get_width(self): return self._width ``` #### File: python-paginator/more_or_less/more_plugin.py ```python from abc import ABC, abstractmethod class MorePlugin(ABC): ''' A plugin that represents an extra action the user can take on the 'more' prompt. ''' @abstractmethod def get_keys(self): ''' Returns a list of the keys the user has to enter to trigger this action. ''' pass @abstractmethod def build_page(self, page_builder, key_pressed, arguments): ''' Called when the user pressed one of the keys to trigger this action. Arguments: ---------- page_builder: The MorePageBuilder instance. key_pressed: The key the user pressed to trigger this action. arguments: A dictionary of arguments the user entered on this line before triggering this action. By default, the only value that can be in there is 'count', which will be set if the user entered a number before your action. For example, if the user entered '10 ' then the '<space>' action is triggered with argument {'count': 10}. ''' pass def wrap_page(self, page): ''' Called when a new page is created. Gives the plugin to return a wrapper page that can be used to modify/register _every_ line, including the ones that are suppressed by other plugins. Example usage is counting all the outputted lines. Must return a 'Page'. Implementing this method is optional. ''' return page @abstractmethod def get_help(self): ''' Returns an iterator over 'command', 'help-text' tuples that describe how to use this plugin. Example: yield (' ', 'Display next line of text') ''' pass ``` #### File: python-paginator/more_or_less/one_line_plugin.py ```python from .more_plugin import MorePlugin from .page_of_height import PageOfHeight class OneLinePlugin(MorePlugin): ''' Displays one more output line. Invoked when the user types '<enter>'. ''' def __init__(self): self._page_height = 1 def get_keys(self): return ['\r', '\n'] def build_page(self, page_builder, key_pressed, arguments): self._update_page_height(arguments) return PageOfHeight(height=self._page_height, output=page_builder.get_output()) def get_help(self): yield ('<return>', 'Display next k lines of text [{}]*'.format(self._page_height)) def _update_page_height(self, arguments): self._page_height = arguments.get('count', self._page_height) ``` #### File: python-paginator/more_or_less/output.py ```python from abc import ABC, abstractmethod class Output(ABC): ''' Example API of what is expected from the 'output' object. This does not mean it must inherit from this. Note that any 'file' object matches this API, so files can natively be used as output. ''' @abstractmethod def write(self, text): pass @abstractmethod def flush(self): pass ``` #### File: python-paginator/more_or_less/paginator.py ```python from .more_page_builder import MorePageBuilder from .page_builder import StopOutput import queue import threading # Signal to send to the input queue when there is no more input END_OF_INPUT = None # Return code if output was interrupted by the user (e.g. the user pressed ctrl+c) OUTPUT_STOPPED = 'OUTPUT_STOPPED' def paginate( input, output=None, prompt=None, screen_dimensions=None, plugins=None, page_builder=None, asynchronous=False): ''' Paginates the input, similar to how 'more' works in bash. Reads from input until the output window is full. Then prompts the user for an action before reading more input. Pseudo-logic: ------------- page = page_builder.build_first_page() for line in <input-lines>: if page.is_full(): page.flush() page = page_builder.build_next_page() page.add_line(line) Arguments: ---------- input: [type iterable or Queue] The input text that should be paginated. This must either be an iterable over text (e.g. a list or a file), or an instance of queue.Queue. It is not required that each returned string is a complete line. The paginator will combine incomplete lines until a '\n' is encountered. If it is a queue.Queue, you must pass 'END_OF_INPUT' into the queue when no more input is expected. This will flush the final incomplete line (if any) to the output. Note that you can NOT use queue.join() to detect all input has been processed (as that just raises issues if the user decides to abort the output halfway through). Instead, if you use 'asynchronous=True' you can join the returned context. output: [type Output] If not specified we print output to stdout prompt: [type Input] Used when prompting the user for actions. Defaults to reading from stdin. screen_dimensions: [type ScreenDimensions] Used to know the height of the output window (which is used to decide how many lines to print before we consider a page 'full'). Defaults to using the dimensions of the terminal window. plugins: [type list of MorePlugin] The plugins to load. These plugins decide what actions are available on the 'more' prompt. If not specified will fetch all plugins from more_plugins.py asynchronous: [type bool] If true the 'paginate' call will return instantly and run asynchronously. In this case a context is returned on which you can call 'context.join([timeout])' to block until all lines are sent to the output. page_builder: [type PageBuilder] The object that will create the output pages whenever a page is full. Must be an instance of 'PageBuilder'. If specified we ignore the values of output, prompt, screen_dimensions and plugins. Returns: -------- A joinable 'context' if asynchronous is True OUTPUT_STOPPED if the user stopped the output (for example using ctrl+c) ''' page_builder = page_builder or MorePageBuilder( input=prompt, output=output, screen_dimensions=screen_dimensions, plugins=plugins) if asynchronous: thread = threading.Thread( target=paginate, kwargs={ 'input': input, 'page_builder': page_builder, 'asynchronous': False, }, ) thread.start() return thread paginator = Paginator(page_builder) if isinstance(input, queue.Queue): return paginator.paginate_from_queue(input) else: return paginator.paginate(input) class Paginator(object): ''' Paginates given input text, similar to how 'more' works in bash. See help of 'paginate' for a more detailed description of the behavior. There are 3 ways to send input text: - pass an iterable to self.paginate. - pass a queue to self.paginate_from_queue. - call 'add_text' repeatedly until all text has been sent in, then call 'flush_incomplete_line'. Each of these methods returns 'OUTPUT_STOPPED' if the user stopped the output (for example using ctrl+c) ''' def __init__(self, page_builder): self._page_builder = page_builder self._lines = _LineCollector() self._page = self._page_builder.build_first_page() def paginate(self, iterable): ''' Iterates over the iterable, and paginates all the text it returns ''' try: for text in iterable: self._try_to_add_text(text) self.flush_incomplete_line() except StopOutput: return OUTPUT_STOPPED def paginate_from_queue(self, input_queue): ''' Iterates over the queue, and paginates all the text it returns. Stops paginating when END_OF_INPUT is encountered on the queue. ''' return self.paginate(QueueIterator(input_queue)) def add_text(self, input_text): ''' Splits the input_text into lines, and paginates them. Can be called multiple times. When you're done you must call 'flush_incomplete_line' to ensure the last incomplete input line is sent to the output. ''' try: self._try_to_add_text(input_text) except StopOutput: return OUTPUT_STOPPED def _try_to_add_text(self, input_text): self._lines.add(input_text) for line in self._lines.pop_complete_lines(): self._paginate_and_print_text(line) def flush_incomplete_line(self): try: self._try_to_flush_incomplete_line() except StopOutput: return OUTPUT_STOPPED def _try_to_flush_incomplete_line(self): if len(self._lines.incomplete_line): self._paginate_and_print_text(self._lines.pop_incomplete_line()) self._page.flush() def _paginate_and_print_text(self, text): if self._page.is_full(): self._start_new_page() self._output_text(text) def _start_new_page(self): self._page.flush() self._page = self._page_builder.build_next_page() def _output_text(self, text): self._page.add_line(text) class _LineCollector(object): ''' Collects the input text and allows us to walk over the complete lines only. example: self.add('first ') self.add('line \nsecond line\n') self.add('incomplete final line') self.pop_complete_lines() <-- returns ['first line', 'second line'] self.pop_incomplete_line() <-- returns 'incomplete final line' ''' def __init__(self): self._complete_lines = [] self.incomplete_line = '' def add(self, text): assert isinstance(text, str), 'expected str got {}'.format(text.__class__) unprocessed_text = self.incomplete_line + text complete_lines, incomplete_line = self._split_lines(unprocessed_text) self._complete_lines += complete_lines self.incomplete_line = incomplete_line def pop_complete_lines(self): try: return self._complete_lines finally: self._complete_lines = [] def pop_incomplete_line(self): try: return self.incomplete_line finally: self.incomplete_line = '' def _split_lines(self, text): lines = text.splitlines(True) if self._has_incomplete_line(lines): complete_lines = lines[:-1] incomplete_line = lines[-1] else: complete_lines = lines incomplete_line = '' return (complete_lines, incomplete_line) def _has_incomplete_line(self, lines): return len(lines) and not lines[-1].endswith('\n') def _make_callable(value): if not callable(value): return lambda: value else: return value class QueueIterator(object): ''' Iterates over a queue, until END_OF_INPUT is encountered ''' def __init__(self, queue): self._queue = queue def __iter__(self): return self def __next__(self): text = self._queue.get() if text is END_OF_INPUT: raise StopIteration return text ``` #### File: python-paginator/more_or_less/search_plugin.py ```python from .more_plugin import MorePlugin from .page import Page from .page_of_height import PageOfHeight from .repeatable_mixin import RepeatableMixin import re _NO_PREVIOUS_REGULAR_EXPRESSION = '--No previous regular expression--' _SKIPPING_MESSAGE = '...skipping\n' class SearchPlugin(MorePlugin): ''' Skips all output until a certain search pattern is found. Invoked when the user types '/'. The search can be repeated by pressing 'n' ''' def __init__(self): self._pattern = None self._match_count = None def get_keys(self): return ['/', 'n'] def build_page(self, page_builder, key_pressed, arguments): self._match_count = arguments.get('count', 1) if key_pressed == '/': return self._do_new_search(page_builder) elif key_pressed == 'n': return self._repeat_last_search(page_builder) else: assert False, 'Unexpected input key' def get_help(self): yield ('/<regular expression>', 'Search for kth occurrence of the regular expression [1]') yield ('n', 'Search for kth occurrence of the last regular expression [1]') def _do_new_search(self, page_builder): self._update_pattern(page_builder.get_input()) return self._create_search_page(page_builder) def _repeat_last_search(self, page_builder): if self._pattern is None: return page_builder.build_next_page(message=_NO_PREVIOUS_REGULAR_EXPRESSION) else: return self._create_search_page(page_builder) def _create_search_page(self, page_builder): page_builder.get_output().write(_SKIPPING_MESSAGE) return SearchPage( pattern=self._pattern, next_page=self._create_full_page(page_builder), match_count=self._match_count, ) def _create_full_page(self, page_builder): return PageOfHeight( height=page_builder.get_page_height(), output=page_builder.get_output()) def _update_pattern(self, input): self._pattern = input.prompt('/') class SearchPage(Page, RepeatableMixin): ''' A page that suppresses all output until a given search pattern is found. After that it displays the passed in page ''' def __init__(self, pattern, next_page, match_count): self.pattern = pattern self.next_page = next_page self._matcher = re.compile(pattern) self._actual_match_count = 0 self.required_match_count = match_count def is_full(self): if self.has_match: return self.next_page.is_full() return False def add_line(self, line): self._match(line) if self.has_match: self.next_page.add_line(line) def _match(self, line): if self._matcher.search(line): self._actual_match_count = self._actual_match_count + 1 def flush(self): if self.has_match: self.next_page.flush() def repeat(self): return SearchPage(self.pattern, self.next_page.repeat(), self.required_match_count) @property def has_match(self): return self._actual_match_count >= self.required_match_count ``` #### File: python-paginator/more_or_less/wrapped_page.py ```python from .page import Page from abc import ABC, abstractmethod class WrappedPage(Page, ABC): ''' Basic class that can be derived from if you need to create a Page that - records all printed lines - can change the printed lines before forwarding. ''' def __init__(self, wrapped_page): self.wrapped_page = wrapped_page def is_full(self): return self.wrapped_page.is_full() def add_line(self, line): new_line = self.on_add_line(line) return self.wrapped_page.add_line(new_line) def flush(self): return self.wrapped_page.flush() @abstractmethod def on_add_line(self, line): ''' Called with every line. Returns the modified version of the line ''' pass def __getattr__(self, name): return getattr(self.wrapped_page, name) ``` #### File: python-paginator/tests/test_line_count_plugin.py ```python from more_or_less.input import Input from more_or_less.output import Output from tests.test_more_page_builder import TestUtil from unittest.mock import Mock, call class TestLineCountPlugin(TestUtil): def setUp(self): self.input = Mock(Input) self.output = Mock(Output) self.builder = self.get_more_page_builder(input=self.input, output=self.output) def print_n_lines(self, n): page = self.builder.build_first_page() for i in range(0, n): page.add_line(f'line {i}\n') def test_prints_line_number_when_user_types_equal(self): self.print_n_lines(10) self.input.get_character.side_effect = ['=', ' '] self.builder.build_next_page() self.input.get_character.assert_has_calls([ call('--More--'), call('--10--'), ]) def test_returns_next_page_after_printing_line_number(self): self.print_n_lines(10) self.input.get_character.side_effect = ['=', ' '] page = self.builder.build_next_page() self.assertIsFullscreenPage(page) def test_prints_line_numbers_after_pressing_l(self): first_page = self.builder.build_first_page() first_page.add_line('before enabling line-numbers\n') self.input.get_character.side_effect = ['l', ' '] page = self.builder.build_next_page() page.add_line('after enabling line-numbers\n') self.output.assert_has_calls([ call.write('before enabling line-numbers\n'), call.write('2: after enabling line-numbers\n'), ]) def test_stops_printing_line_numbers_after_pressing_l_again(self): first_page = self.builder.build_first_page() first_page.add_line('before enabling line-numbers\n') self.input.get_character.side_effect = ['l', ' '] page = self.builder.build_next_page() page.add_line('after enabling line-numbers\n') self.input.get_character.side_effect = ['l', ' '] page = self.builder.build_next_page() page.add_line('after disabling line-numbers\n') self.output.assert_has_calls([ call.write('before enabling line-numbers\n'), call.write('2: after enabling line-numbers\n'), call.write('after disabling line-numbers\n'), ]) def test_prints_status_in_prompt_when_enabling_or_disabling_line_numbers(self): self.input.get_character.side_effect = ['l', 'l', ' '] self.builder.build_next_page() self.input.assert_has_calls([ call.get_character('--Line numbers are now enabled--'), call.get_character('--Line numbers are now disabled--'), ]) ``` #### File: python-paginator/tests/test_more_page_builder.py ```python from more_or_less import PageOfHeight from more_or_less.fixed_size_screen import FixedSizeScreen from more_or_less.input import Input from more_or_less.more_page_builder import MorePageBuilder from more_or_less.output import Output from more_or_less.page_builder import StopOutput from more_or_less.wrapped_page import WrappedPage from unittest.mock import Mock import unittest class TestUtil(unittest.TestCase): def assertIsPageOfType(self, page, page_type): ''' assertIsInstance, but will first strip page-wrappers ''' page = _skip_page_wrappers(page) self.assertIsInstance(page, page_type) def assertIsPageOfHeight(self, page, height): self.assertIsPageOfType(page, PageOfHeight) self.assertEqual(height, page.height) def assertIsFullscreenPage(self, page, screen_height=1000): self.assertIsPageOfHeight(page, _page_height_for_screen(screen_height)) def get_more_page_builder(self, output=None, input=None, plugins=None, screen_height=1000): return MorePageBuilder( input=input or Mock(Input), output=output or Mock(Output), screen_dimensions=FixedSizeScreen(height=screen_height), plugins=plugins, ) class TestMorePageBuilder(TestUtil): def test_build_first_page_returns_page_of_screen_height_minus_one(self): screen_height = 10 builder = self.get_more_page_builder(screen_height=screen_height) page = builder.build_first_page() self.assertIsPageOfHeight(page, screen_height - 1) def test_build_next_page_prompts_user_for_action(self): input = Mock(Input) input.get_character.return_value = ' ' builder = self.get_more_page_builder(input=input) builder.build_next_page() input.get_character.assert_called_once_with('--More--') def test_returns_full_screen_page_if_user_presses_space(self): screen_height = 10 input = Mock(Input) builder = self.get_more_page_builder(input=input, screen_height=10) input.get_character.return_value = ' ' page = builder.build_next_page() self.assertIsFullscreenPage(page, screen_height) def test_returns_one_line_page_if_user_presses_enter(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.return_value = '\r' page = builder.build_next_page() self.assertIsPageOfHeight(page, 1) def test_enter_works_both_on_newline_and_carriage_return(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.return_value = '\n' page = builder.build_next_page() self.assertIsPageOfHeight(page, 1) def test_stops_output_if_user_presses_q(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.return_value = 'q' with self.assertRaises(StopOutput): builder.build_next_page() def test_stops_output_if_user_presses_Q(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.return_value = 'Q' with self.assertRaises(StopOutput): builder.build_next_page() def test_stops_output_on_ctrl_c(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.side_effect = KeyboardInterrupt with self.assertRaises(StopOutput): builder.build_next_page() def test_ignores_unexpected_user_input(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.side_effect = ['a', 'b', 'c', '\r'] builder.build_next_page() self.assertEqual(4, input.get_character.call_count) def test_user_can_enter_count_before_enter(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.side_effect = ['5', '\n'] page = builder.build_next_page() self.assertIsPageOfHeight(page, 5) def test_count_becomes_the_new_default_for_enter(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.side_effect = ['5', '\n'] builder.build_next_page() input.get_character.side_effect = ['\n'] second_page = builder.build_next_page() self.assertIsPageOfHeight(second_page, 5) def test_can_specify_count_bigger_than_10(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.side_effect = ['5', '0', '0', '\n'] page = builder.build_next_page() self.assertIsPageOfHeight(page, 500) def test_user_can_enter_count_before_space(self): input = Mock(Input) builder = self.get_more_page_builder(input=input) input.get_character.side_effect = ['5', ' '] page = builder.build_next_page() self.assertIsPageOfHeight(page, 5) def test_count_does_not_become_the_new_default_for_space(self): input = Mock(Input) screen_height = 666 builder = self.get_more_page_builder(input=input, screen_height=screen_height) input.get_character.side_effect = ['5', ' '] builder.build_next_page() input.get_character.side_effect = [' '] second_page = builder.build_next_page() self.assertIsFullscreenPage(second_page, screen_height) def _page_height_for_screen(screen_height): height_reserved_for_more_prompt = 1 return screen_height - height_reserved_for_more_prompt def _skip_page_wrappers(page): while isinstance(page, WrappedPage): page = page.wrapped_page return page ``` #### File: python-paginator/tests/test_repeat_plugin.py ```python from more_or_less import more_plugins from more_or_less.input import Input from more_or_less.more_plugin import MorePlugin from more_or_less.output import Output from more_or_less.page import Page from more_or_less.search_plugin import SearchPage from tests.test_more_page_builder import TestUtil from unittest.mock import Mock, call _UNREPEATABLE_PAGE_KEY = 'U' class TestRepeatPlugin(TestUtil): def setUp(self): self.input = Mock(Input) self.output = Mock(Output) plugins = more_plugins.get() + [UnrepeatablePagePlugin()] self.builder = self.get_more_page_builder( input=self.input, output=self.output, plugins=plugins) def fill_page(self, page): while not page.is_full(): page.add_line('line \n') def test_can_repeat_enter(self): self.input.get_character.side_effect = ['5', '\n'] page = self.builder.build_next_page() self.fill_page(page) self.input.get_character.side_effect = ['.'] repeated_page = self.builder.build_next_page() self.assertIsPageOfHeight(repeated_page, 5) self.assertFalse(repeated_page.is_full()) def test_can_repeat_space(self): self.input.get_character.side_effect = [' '] page = self.builder.build_next_page() self.fill_page(page) self.input.get_character.side_effect = ['.'] repeated_page = self.builder.build_next_page() self.assertIsPageOfHeight(repeated_page, page.height) def test_can_repeat_search(self): self.input.get_character.side_effect = ['5', '/'] self.input.prompt.return_value = 'the pattern' self.builder.build_next_page() self.input.get_character.side_effect = ['.'] repeated_page = self.builder.build_next_page() self.assertIsPageOfType(repeated_page, SearchPage) self.assertEqual('the pattern', repeated_page.pattern) self.assertEqual(5, repeated_page.required_match_count) def test_prints_warning_on_unrepeatable_command(self): self.input.get_character.side_effect = [_UNREPEATABLE_PAGE_KEY] self.builder.build_next_page() self.input.get_character.side_effect = ['.', ' ', ' '] self.builder.build_next_page() self.input.assert_has_calls([ call.get_character('--More--'), call.get_character('--Previous command can not be repeated--'), ]) class UnrepeatablePage(Page): def is_full(self): return False def add_line(self, line): pass class UnrepeatablePagePlugin(MorePlugin): ''' Plugin that returns a page of type 'DefaultPage' ''' def get_keys(self): return [_UNREPEATABLE_PAGE_KEY] def build_page(self, page_builder, key_pressed, arguments): return UnrepeatablePage() def get_help(self): pass ``` #### File: python-paginator/tests/test_search_plugin.py ```python from more_or_less.input import Input from more_or_less.output import Output from more_or_less.page import Page from more_or_less.search_plugin import SearchPage from tests.test_more_page_builder import TestUtil from unittest.mock import Mock, call import unittest class TestSearchPlugin(TestUtil): def assertIsSearchPageWithPattern(self, page, pattern): self.assertIsPageOfType(page, SearchPage) self.assertEqual(pattern, page.pattern) def assertIsSearchPageWithMatchCount(self, page, match_count): self.assertIsPageOfType(page, SearchPage) self.assertEqual(match_count, page.required_match_count) def test_creates_search_page_when_pressing_slash(self): input = Mock(Input) input.get_character.return_value = '/' input.prompt.return_value = '' builder = self.get_more_page_builder(input=input) page = builder.build_next_page() self.assertIsSearchPageWithPattern(page, pattern='') def test_passes_search_pattern_to_search_page(self): input = Mock(Input) input.get_character.return_value = '/' input.prompt.return_value = 'the-pattern' builder = self.get_more_page_builder(input=input) page = builder.build_next_page() self.assertIsSearchPageWithPattern(page, pattern='the-pattern') def test_n_repeats_previous_search(self): input = Mock(Input) input.get_character.return_value = '/' input.prompt.side_effect = ['the-pattern'] builder = self.get_more_page_builder(input=input) builder.build_next_page() input.get_character.return_value = 'n' second_page = builder.build_next_page() self.assertIsSearchPageWithPattern(second_page, pattern='the-pattern') def test_n_without_previous_search_prints_error_in_prompt(self): input = Mock(Input) input.get_character.side_effect = ['n', ' '] builder = self.get_more_page_builder(input=input) builder.build_next_page() input.get_character.assert_has_calls([ call('--More--'), call('--No previous regular expression--'), ]) def test_prints_skipping_text_to_output(self): input = Mock(Input) input.get_character.return_value = '/' input.prompt.side_effect = ['the-pattern'] output = Mock(Output) builder = self.get_more_page_builder(input=input, output=output) builder.build_next_page() output.write.assert_called_once_with('...skipping\n') def test_passes_full_page_to_search_page(self): screen_height = 100 input = Mock(Input) input.get_character.return_value = '/' input.prompt.side_effect = ['the-pattern'] builder = self.get_more_page_builder(input=input, screen_height=screen_height) page = builder.build_next_page() self.assertIsFullscreenPage(page.next_page, screen_height=screen_height) def test_passes_count_1_to_search_page_by_default(self): input = Mock(Input) input.get_character.return_value = '/' input.prompt.side_effect = ['the-pattern'] builder = self.get_more_page_builder(input=input) page = builder.build_next_page() self.assertIsSearchPageWithMatchCount(page, match_count=1) def test_passes_count_to_search_page(self): input = Mock(Input) input.get_character.side_effect = ['5', '/'] input.prompt.side_effect = ['the-pattern'] builder = self.get_more_page_builder(input=input) page = builder.build_next_page() self.assertIsSearchPageWithMatchCount(page, match_count=5) def test_n_defaults_to_match_count_1(self): input = Mock(Input) input.get_character.side_effect = ['5', '/'] input.prompt.side_effect = ['the-pattern'] builder = self.get_more_page_builder(input=input) builder.build_next_page() input.get_character.side_effect = ['n'] second_page = builder.build_next_page() self.assertIsSearchPageWithMatchCount(second_page, match_count=1) def test_n_accepts_a_count(self): input = Mock(Input) input.get_character.side_effect = ['/'] input.prompt.side_effect = ['the-pattern'] builder = self.get_more_page_builder(input=input) builder.build_next_page() input.get_character.side_effect = ['7', 'n'] second_page = builder.build_next_page() self.assertIsSearchPageWithMatchCount(second_page, match_count=7) class TestSearchPage(unittest.TestCase): def setUp(self): self.next_page = Mock(Page) def create_search_page(self, pattern='', match_count=1): return SearchPage(pattern=pattern, next_page=self.next_page, match_count=match_count) def test_add_line_blackholed_if_it_doesnt_match(self): page = self.create_search_page('the-pattern') page.add_line('this does not match the pattern') self.next_page.add_line.assert_not_called() def test_add_line_forwarded_if_it_matches_the_pattern(self): page = self.create_search_page('the.*pattern') page.add_line('this matches the regex pattern') self.next_page.add_line.assert_called_once_with('this matches the regex pattern') def test_add_line_forwarded_if_previous_line_matched_the_pattern(self): page = self.create_search_page('the.*pattern') page.add_line('this matches the regex pattern') page.add_line('next line') self.next_page.add_line.assert_has_calls([ call('this matches the regex pattern'), call('next line'), ]) def test_must_match_the_provided_number_of_times(self): match_count = 5 page = self.create_search_page('the.*pattern', match_count=match_count - 1) page.add_line('this matches the regex pattern the 1th time') page.add_line('this matches the regex pattern the 2nd time') page.add_line('this matches the regex pattern the 3rd time') page.add_line('this matches the regex pattern the 4th time') page.add_line('this matches the regex pattern the 5th time') self.next_page.add_line.assert_has_calls([ call('this matches the regex pattern the 5th time'), ]) def test_is_full_false_initially(self): page = self.create_search_page() self.assertFalse(page.is_full()) def test_is_full_does_not_contact_next_page_if_pattern_is_not_matched(self): page = self.create_search_page() page.is_full() self.next_page.is_full.assert_not_called() def test_is_full_forwarded_to_next_page_after_pattern_has_been_matched(self): page = self.create_search_page('the.*pattern') page.add_line('this matches the regex pattern') page.is_full() self.next_page.is_full.assert_called_once() def test_flush_not_forwarded_if_pattern_is_not_matched(self): page = self.create_search_page() page.flush() self.next_page.flush.assert_not_called() def test_flush_forwarded_to_next_page_after_pattern_has_been_matched(self): page = self.create_search_page('the.*pattern') page.add_line('this matches the regex pattern') page.flush() self.next_page.flush.assert_called_once() ```
{ "source": "JeroenDM/acrobotics", "score": 2 }
#### File: acrobotics/inverse_kinematics/spherical_wrist.py ```python import numpy as np from .ik_result import IKResult def ik(T, tf_base) -> IKResult: """ TODO add base frame correction """ Rbase = tf_base[:3, :3] Ree = T[:3, :3] Ree_rel = np.dot(Rbase.transpose(), Ree) # ignore position # n s a according to convention Siciliano n = Ree_rel[:3, 0] s = Ree_rel[:3, 1] a = Ree_rel[:3, 2] A = np.sqrt(a[0] ** 2 + a[1] ** 2) # solution with theta2 in (0, pi) t1_1 = np.arctan2(a[1], a[0]) t2_1 = np.arctan2(A, a[2]) t3_1 = np.arctan2(s[2], -n[2]) # solution with theta2 in (-pi, 0) t1_2 = np.arctan2(-a[1], -a[0]) t2_2 = np.arctan2(-A, a[2]) t3_2 = np.arctan2(-s[2], n[2]) q_sol = np.zeros((2, 3)) q_sol[0, 0], q_sol[0, 1], q_sol[0, 2] = t1_1, t2_1, t3_1 q_sol[1, 0], q_sol[1, 1], q_sol[1, 2] = t1_2, t2_2, t3_2 return IKResult(True, q_sol) ``` #### File: src/acrobotics/link.py ```python import numpy as np import casadi as ca from collections import namedtuple from enum import Enum DHLink = namedtuple("DHLink", ["a", "alpha", "d", "theta"]) class JointType(Enum): revolute = "r" prismatic = "p" class LinkKinematics: """ Robot link according to the Denavit-Hartenberg convention. """ def __init__(self, dh_parameters: DHLink, joint_type: JointType): """ Creates a linkf from Denavit-Hartenberg parameters, a joint type ('r' for revolute, 'p' for prismatic) and a Scene of Shapes representing the geometry. """ self.dh = dh_parameters if joint_type in JointType: self.joint_type = joint_type else: raise ValueError(f"Unkown JointType: {joint_type}.") # chache a transform because creating it is slow # but just fillin in an existing one is ok self._T = np.eye(4) def get_link_relative_transform(self, qi): """ transformation matrix from link i relative to i-1 Links and joints are numbered from 1 to ndof, but python indexing of these links goes from 0 to ndof-1! """ if self.joint_type == JointType.revolute: a, alpha, d, theta = self.dh.a, self.dh.alpha, self.dh.d, qi elif self.joint_type == JointType.prismatic: a, alpha, d, theta = self.dh.a, self.dh.alpha, qi, self.dh.theta c_theta = np.cos(theta) s_theta = np.sin(theta) c_alpha = np.cos(alpha) s_alpha = np.sin(alpha) T = self._T T[0, 0], T[0, 1] = c_theta, -s_theta * c_alpha T[0, 2], T[0, 3] = s_theta * s_alpha, a * c_theta T[1, 0], T[1, 1] = s_theta, c_theta * c_alpha T[1, 2], T[1, 3] = -c_theta * s_alpha, a * s_theta T[2, 1], T[2, 2], T[2, 3] = s_alpha, c_alpha, d return T def get_link_relative_transform_casadi(self, qi): """ Link transform according to the Denavit-Hartenberg convention. Casadi compatible function. """ if self.joint_type == JointType.revolute: a, alpha, d, theta = self.dh.a, self.dh.alpha, self.dh.d, qi elif self.joint_type == JointType.prismatic: a, alpha, d, theta = self.dh.a, self.dh.alpha, qi, self.dh.theta c_t, s_t = ca.cos(theta), ca.sin(theta) c_a, s_a = ca.cos(alpha), ca.sin(alpha) row1 = ca.hcat([c_t, -s_t * c_a, s_t * s_a, a * c_t]) row2 = ca.hcat([s_t, c_t * c_a, -c_t * s_a, a * s_t]) row3 = ca.hcat([0, s_a, c_a, d]) row4 = ca.hcat([0, 0, 0, 1]) return ca.vcat([row1, row2, row3, row4]) class Link(LinkKinematics): def __init__(self, dh_parameters, joint_type, geometry): super().__init__(dh_parameters, joint_type) self.geometry = geometry def plot(self, ax, tf, *arg, **kwarg): self.geometry.plot(ax, tf=tf, *arg, **kwarg) ``` #### File: acrobotics/path/factory.py ```python import numpy as np from copy import deepcopy from typing import List from numpy.linalg import norm from acrolib.quaternion import Quaternion from .path_pt import TolPositionPt, PathPt def check_num_points(num_points: int): if num_points < 2: raise Exception(f"Value of num_points must be 2 or more, not {num_points}.") def create_line(start_pt: PathPt, end_pos: np.ndarray, num_points: int) -> List[PathPt]: """ Copy a given toleranced PathPt along a straight line.""" check_num_points(num_points) trans_vec = (end_pos - start_pt.pos) / (num_points - 1) path = [start_pt] for _ in range(num_points - 1): new_pt = deepcopy(path[-1]) new_pt.translate(trans_vec) path.append(new_pt) return path def create_circle( start_pt: PathPt, mid_point: np.ndarray, rotation_axis: np.ndarray, num_points: int ): """Copy a given toleranced PathPt along a circle with a given mid point and rotation axis.""" check_num_points(num_points) return create_arc(start_pt, mid_point, rotation_axis, 2 * np.pi, num_points) def create_arc( start_pt: PathPt, mid_point: np.ndarray, rotation_axis, angle, num_points ): """Copy a given toleranced PathPt along an arc with a given mid point and rotation axis.""" check_num_points(num_points) rotation_axis = rotation_axis / norm(rotation_axis) rotating_vector = start_pt.pos - mid_point a = np.linspace(angle / (num_points - 1), angle, num_points - 1) path = [deepcopy(start_pt)] for ai in a: rot_mat = Quaternion(angle=ai, axis=rotation_axis).rotation_matrix offset = (rot_mat @ rotating_vector) - rotating_vector new_pt = deepcopy(start_pt) new_pt.translate(offset) new_pt.rotate(rot_mat) path.append(new_pt) return path ``` #### File: acrobotics/planning/settings.py ```python from numpy import ndarray from typing import List from ..path.sampling import SamplingSetting from .types import SolveMethod, CostFuntionType class OptSettings: """ Settings for the numerical optimization based planners. """ def __init__( self, q_init: ndarray = None, max_iters: int = None, weights: List[float] = None, con_objective_weight=0.0, ): # q init is handled when whe know the path length and the ndof of the robot self.q_init = q_init self.weights = weights self.con_objective_weight = con_objective_weight if max_iters is None: self.max_iters = 100 else: self.max_iters = max_iters class SolverSettings: def __init__( self, solve_method: SolveMethod, cost_function_type: CostFuntionType, sampling_settings: SamplingSetting = None, opt_settings: OptSettings = None, ): self.solve_method = solve_method self.cost_function_type = cost_function_type if solve_method == SolveMethod.sampling_based: assert sampling_settings is not None self.sampling_settings = sampling_settings # fill in the correct cost function based on the type elif solve_method == SolveMethod.optimization_based: assert opt_settings is not None self.opt_settings = opt_settings ``` #### File: src/acrobotics/robot.py ```python import numpy as np import casadi as ca from abc import ABC from collections import namedtuple from matplotlib import animation from typing import List, Callable from acrobotics.geometry import Scene from acrobotics.link import Link from acrolib.plotting import plot_reference_frame JointLimit = namedtuple("JointLimit", ["lower", "upper"]) class IKResult: def __init__(self, success: bool, solutions: List[np.ndarray] = None): self.success = success if self.success: assert solutions is not None self.solutions = solutions class Tool(Scene): """ Geometric shapes with added atribute tool tip transform tf_tt relative to the last link. """ def __init__(self, shapes, tf_shapes, tf_tool_tip): """ tf_tool_tip relative to last link robot. """ super().__init__(shapes, tf_shapes) self.tf_tool_tip = tf_tool_tip class RobotKinematics: """ Robot kinematics and shape (inital joint values not implemented) """ def __init__(self, links: List[Link], joint_limits: List[JointLimit] = None): self.links = links self.ndof = len(links) # set default joint limits if joint_limits is None: self.joint_limits = [JointLimit(-np.pi, np.pi)] * self.ndof else: self.joint_limits = joint_limits # pose of base with respect to the global reference frame # this is independent from the geometry of the base, # for the whole robot self.tf_base = np.eye(4) # pose of the tool tip relative to last link robot. self.tf_tool_tip = None def fk(self, q) -> np.ndarray: """ Return end effector frame, either last link, or tool frame if tool available """ T = self.tf_base for i in range(0, self.ndof): Ti = self.links[i].get_link_relative_transform(q[i]) T = T @ Ti if self.tf_tool_tip is not None: T = np.dot(T, self.tf_tool_tip) return T def fk_rpy(self, q) -> np.ndarray: T = self.fk(q) s = np.sqrt(T[0, 0] * T[0, 0] + T[0, 1] * T[0, 1]) r_x = np.arctan2(-T[1, 2], T[2, 2]) r_y = np.arctan2(T[0, 2], s) r_z = np.arctan2(-T[0, 1], T[2, 2]) out = np.zeros(6) out[:3] = T[:3, 3] out[3:] = [r_x, r_y, r_z] return out def fk_all_links(self, q) -> List[np.ndarray]: """ Return link frames (not base or tool) """ tf_links = [] T = self.tf_base for i in range(0, self.ndof): Ti = self.links[i].get_link_relative_transform(q[i]) T = T @ Ti tf_links.append(T) return tf_links def ik(self, transformation_matrix) -> IKResult: raise NotImplementedError def estimate_max_extension(self): max_ext = 0 for link in self.links: max_ext += abs(link.dh.a) + abs(link.dh.d) return max_ext def plot_kinematics(self, ax, q, *arg, **kwarg): # base frame (0) plot_reference_frame(ax, self.tf_base) # link frames (1-ndof) tf_links = self.fk_all_links(q) points = [tf[0:3, 3] for tf in tf_links] points = np.array(points) points = np.vstack((self.tf_base[0:3, 3], points)) ax.plot(points[:, 0], points[:, 1], points[:, 2], "o-") for tfi in tf_links: plot_reference_frame(ax, tfi) # tool tip frame if self.tf_tool_tip is not None: tf_tt = np.dot(tf_links[-1], self.tf_tool_tip) plot_reference_frame(ax, tf_tt) class RobotCasadiKinematics(ABC): ndof: int links: List[Link] tf_base: np.ndarray tf_tool_tip: np.ndarray jacobian_position_fun: Callable jacobian_rpy_fun: Callable def fk_casadi(self, q): T = self.tf_base for i in range(0, self.ndof): Ti = self.links[i].get_link_relative_transform_casadi(q[i]) T = T @ Ti if self.tf_tool_tip is not None: T = T @ self.tf_tool_tip return T def fk_rpy_casadi(self, q): T = self.fk_casadi(q) s = ca.sqrt(T[0, 0] * T[0, 0] + T[0, 1] * T[0, 1]) r_x = ca.arctan2(-T[1, 2], T[2, 2]) r_y = ca.arctan2(T[0, 2], s) r_z = ca.arctan2(-T[0, 1], T[2, 2]) return ca.vcat([T[:3, 3], r_x, r_y, r_z]) def fk_all_links_casadi(self, q): """ Return link frames (not base or tool) """ tf_links = [] T = self.tf_base for i in range(0, self.ndof): Ti = self.links[i].get_link_relative_transform_casadi(q[i]) T = T @ Ti tf_links.append(T) return tf_links def jacobian_position(self, q): return self.jacobian_position_fun(q) def jacobian_rpy(self, q): return self.jacobian_rpy_fun(q) def _create_jacobian_position(self): q = ca.MX.sym("q", self.ndof) jac = ca.jacobian(self.fk_casadi(q)[:3, 3], q) return ca.Function("jac_fun", [q], [jac]) def _create_jacobian_rpy(self): q = ca.MX.sym("q", self.ndof) jac = ca.jacobian(self.fk_rpy_casadi(q), q) return ca.Function("jac_fun", [q], [jac]) class Robot(RobotKinematics, RobotCasadiKinematics): def __init__(self, links, joint_limits=None): super().__init__(links, joint_limits) # defaul has no fixed base geometry, no tool and self.geometry_base = None self.geometry_tool = None self.do_check_self_collision = True # self collision matrix # default: do not check link neighbours, create band structure matrix temp = np.ones((self.ndof, self.ndof), dtype="bool") self.collision_matrix = np.tril(temp, k=-3) + np.triu(temp, k=3) # keep track of most likly links to be in collision self.collision_priority = list(range(self.ndof)) # loggers to get performance criteria self.cc_checks = 0 self.jacobian_position_fun = self._create_jacobian_position() self.jacobian_rpy_fun = self._create_jacobian_rpy() @property def tool(self): return self.geometry_tool @tool.setter def tool(self, new_tool: Tool): self.tf_tool_tip = new_tool.tf_tool_tip self.geometry_tool = new_tool def _check_self_collision(self, tf_links, geom_links): for i, ti, gi in zip(range(self.ndof), tf_links, geom_links): for j, tj, gj in zip(range(self.ndof), tf_links, geom_links): if self.collision_matrix[i, j]: if gi.is_in_collision(gj, tf_self=ti, tf_other=tj): return True # do not check tool against the last link where it is mounted if self.geometry_tool is not None: tf_tool = tf_links[-1] for tf_link, geom_link in zip(tf_links[:-1], geom_links[:-1]): if geom_link.is_in_collision( self.geometry_tool, tf_self=tf_link, tf_other=tf_tool ): return True return False def _is_in_limits(self, q): for qi, limit in zip(q, self.joint_limits): if qi > limit.upper or qi < limit.lower: return False return True @staticmethod def _linear_interpolation_path(q_start, q_goal, max_q_step): q_start, q_goal = np.array(q_start), np.array(q_goal) q_diff = np.linalg.norm(q_goal - q_start) num_steps = int(np.ceil(q_diff / max_q_step)) S = np.linspace(0, 1, num_steps) return [(1 - s) * q_start + s * q_goal for s in S] def is_in_self_collision(self, q): geom_links = [l.geometry for l in self.links] tf_links = self.fk_all_links(q) return self._check_self_collision(tf_links, geom_links) def is_in_collision(self, q, collection): self.cc_checks += 1 if collection is not None: geom_links = [l.geometry for l in self.links] tf_links = self.fk_all_links(q) # check collision with tool first if self.geometry_tool is not None: tf_tool = tf_links[-1] if self.geometry_tool.is_in_collision(collection, tf_self=tf_tool): return True # check collision of fixed base geometry base = self.geometry_base if base is not None: if base.is_in_collision(collection, tf_self=self.tf_base): return True # check collision for all links for i in self.collision_priority: if geom_links[i].is_in_collision(collection, tf_self=tf_links[i]): # move current index to front of priority list self.collision_priority.remove(i) self.collision_priority.insert(0, i) return True if self.do_check_self_collision: if self._check_self_collision(tf_links, geom_links): return True return False def is_path_in_collision_discrete( self, q_start, q_goal, collection, max_q_step=0.1 ): """ Check for collision with linear interpolation between start and goal. """ for q in self._linear_interpolation_path(q_start, q_goal, max_q_step): if self.is_in_collision(q, collection): return True return False def is_path_in_collision(self, q_start, q_goal, collection: Scene): """ Check for collision using the continuous collision checking stuff from fcl. - We do not check for self collision on a path. - Base is assumed not to move. """ geom_links = [l.geometry for l in self.links] tf_links = self.fk_all_links(q_start) tf_links_target = self.fk_all_links(q_goal) # check collision with tool first if self.geometry_tool is not None: if self.geometry_tool.is_path_in_collision( tf_links[-1], tf_links_target[-1], collection ): return True # Base is assumed to be always fixed base = self.geometry_base if base is not None: if base.is_in_collision(collection, tf_self=self.tf_base): return True # check collision for all links for i in self.collision_priority: if geom_links[i].is_path_in_collision( tf_links[i], tf_links_target[i], collection ): # move current index to front of priority list self.collision_priority.remove(i) self.collision_priority.insert(0, i) return True return False def plot(self, ax, q, *arg, **kwarg): if self.geometry_base is not None: self.geometry_base.plot(ax, self.tf_base, *arg, **kwarg) tf_links = self.fk_all_links(q) for i, link in enumerate(self.links): link.plot(ax, tf_links[i], *arg, **kwarg) if self.geometry_tool is not None: self.geometry_tool.plot(ax, tf=tf_links[-1], *arg, **kwarg) def plot_path(self, ax, joint_space_path): alpha = np.linspace(1, 0.2, len(joint_space_path)) for i, qi in enumerate(joint_space_path): self.plot(ax, qi, c=(0.1, 0.2, 0.5, alpha[i])) def animate_path(self, fig, ax, joint_space_path): def get_emtpy_lines(ax): lines = [] for l in self.links: for s in l.geometry.shapes: lines.append(s.get_empty_plot_lines(ax, c=(0.1, 0.2, 0.5))) if self.geometry_tool is not None: for s in self.geometry_tool.shapes: lines.append(s.get_empty_plot_lines(ax, c=(0.1, 0.2, 0.5))) return lines def update_lines(frame, q_path, lines): tfs = self.fk_all_links(q_path[frame]) cnt = 0 for tf_l, l in zip(tfs, self.links): for tf_s, s in zip(l.geometry.tf_s, l.geometry.s): Ti = np.dot(tf_l, tf_s) lines[cnt] = s.update_plot_lines(lines[cnt], Ti) cnt = cnt + 1 if self.geometry_tool is not None: for tf_s, s in zip(self.geometry_tool.tf_s, self.geometry_tool.s): tf_j = np.dot(tfs[-1], tf_s) lines[cnt] = s.update_plot_lines(lines[cnt], tf_j) cnt = cnt + 1 ls = get_emtpy_lines(ax) N = len(joint_space_path) self.animation = animation.FuncAnimation( fig, update_lines, N, fargs=(joint_space_path, ls), interval=200, blit=False ) ``` #### File: src/acrobotics/workspace_envelope.py ```python import numpy as np from .robot import Robot from .inverse_kinematics.ik_result import IKResult from tqdm import tqdm from pyquaternion import Quaternion class EnvelopeSettings: def __init__( self, sample_distance: float, num_orientation_samples: int, max_ik_solutions: int, ): self.sample_distance = sample_distance self.num_orientation_samples = num_orientation_samples self.max_ik_solutions = max_ik_solutions def sample_position(position: np.ndarray, n: int): """ Return n random transforms at the given position. """ tf_samples = [Quaternion.random().transformation_matrix for _ in range(n)] for tfi in tf_samples: tfi[:3, 3] = position return tf_samples def process_ik_solution(robot: Robot, ik_solution: IKResult): """ Return the number of collision free ik_solutions. """ if ik_solution.success: q_collision_free = [] for qi in ik_solution.solutions: if not robot.is_in_self_collision(qi): q_collision_free.append(qi) return len(q_collision_free) else: return 0 def calculate_reachability( robot: Robot, position: np.ndarray, num_samples: int = 100, max_ik_solutions: int = 8, ): """ Return the fraction of reachable poses at a given position by solving the inverse kinematics for uniform random orientation samples. """ sampled_transforms = sample_position(position, n=num_samples) reachable_cnt = 0 for transfom in sampled_transforms: ik_sol = robot.ik(transfom) reachable_cnt += process_ik_solution(robot, ik_sol) return reachable_cnt / (max_ik_solutions * num_samples) def scale(X, from_range, to_range): Y = (X - from_range[0]) / (from_range[1] - from_range[0]) Y = Y * (to_range[1] - to_range[0]) + to_range[0] return Y def generate_positions(max_extension, num_points: int): steps = np.linspace(-max_extension, max_extension, num_points) x, y, z = np.meshgrid(steps, steps, steps) return np.vstack((x.flatten(), y.flatten(), z.flatten())).T # return np.stack((x, y, z)) def generate_robot_envelope(robot: Robot, settings: EnvelopeSettings): max_extension = robot.estimate_max_extension() num_points = int(2 * max_extension / settings.sample_distance) points = generate_positions(max_extension, num_points) result = np.zeros((len(points), 4)) for i, point in enumerate(tqdm(points)): reachability = calculate_reachability( robot, point, settings.num_orientation_samples, settings.max_ik_solutions ) # TODO: other params result[i, 0] = reachability result[i, 1:] = point return result ``` #### File: acrobotics/tests/test_continuous_cc.py ```python import time import fcl import numpy as np import matplotlib.pyplot as plt from acrolib.plotting import get_default_axes3d, plot_reference_frame from acrolib.geometry import translation from acrobotics.robot_examples import Kuka from acrobotics.tool_examples import torch2 from acrobotics.geometry import Scene from acrobotics.shapes import Box robot = Kuka() robot.tool = torch2 DEBUG = False def show_animation(robot, scene, qa, qb): q_path = np.linspace(qa, qb, 10) fig, ax = get_default_axes3d([-0.8, 0.8], [0, 1.6], [-0.2, 1.4]) ax.set_axis_off() ax.view_init(elev=31, azim=-15) scene.plot(ax, c="green") robot.animate_path(fig, ax, q_path) plt.show() def test_ccd_1(): table = Box(2, 2, 0.1) T_table = translation(0, 0, -0.2) obstacle = Box(0.01, 0.01, 1.5) T_obs = translation(0, 0.5, 0.55) scene = Scene([table, obstacle], [T_table, T_obs]) q_start = np.array([1.0, 1.5, -0.3, 0, 0, 0]) q_goal = np.array([2.0, 1.5, 0.3, 0, 0, 0]) res = robot.is_path_in_collision(q_start, q_goal, scene) assert res if DEBUG: print("resut test 1: ", res) show_animation(robot, scene, q_start, q_goal) def test_ccd_2(): table = Box(2, 2, 0.1) T_table = translation(0, 0, -0.2) obstacle = Box(0.2, 0.1, 0.01) T_obs = translation(0, 0.9, 0.55) scene = Scene([table, obstacle], [T_table, T_obs]) q_start = np.array([1.5, 1.5, -0.3, 0, 0.3, 0]) q_goal = np.array([1.5, 1.5, 0.3, 0, -0.3, 0]) res = robot.is_path_in_collision(q_start, q_goal, scene) assert res if DEBUG: print("resut test 2: ", res) show_animation(robot, scene, q_start, q_goal) def test_ccd_3(): table = Box(2, 2, 0.1) T_table = translation(0, 0, -0.2) obstacle = Box(0.01, 0.2, 0.2) T_obs = translation(0, 1.2, 0) scene = Scene([table, obstacle], [T_table, T_obs]) q_start = np.array([1.0, 1.2, -0.5, 0, 0, 0]) q_goal = np.array([2.0, 1.2, -0.5, 0, 0, 0]) res = robot.is_path_in_collision(q_start, q_goal, scene) assert res if DEBUG: print("resut test 3: ", res) show_animation(robot, scene, q_start, q_goal) if __name__ == "__main__": test_ccd_1() test_ccd_2() test_ccd_3() ``` #### File: acrobotics/tests/test_planning_sampling_based.py ```python import pytest import numpy as np import acrobotics as ab # import matplotlib.pyplot as plt from numpy.testing import assert_almost_equal from acrolib.quaternion import Quaternion from acrolib.sampling import SampleMethod from acrolib.plotting import get_default_axes3d, plot_reference_frame from acrobotics.robot import Robot class DummyRobot(Robot): def __init__(self, is_colliding=False): self.is_colliding = is_colliding self.ndof = 6 def is_in_collision(self, joint_position, scene=None): return self.is_colliding def fk(self, q): tf = np.eye(4) tf[:3, 3] = np.array([1, -2.09, 3]) return tf def test_tol_quat_pt_with_weights(): path_ori_free = [] for s in np.linspace(0, 1, 3): xi = 0.8 yi = s * 0.2 + (1 - s) * (-0.2) zi = 0.2 path_ori_free.append( ab.TolQuatPt( [xi, yi, zi], Quaternion(axis=[1, 0, 0], angle=np.pi), [ab.NoTolerance(), ab.NoTolerance(), ab.NoTolerance()], ab.QuaternionTolerance(2.0), ) ) table = ab.Box(0.5, 0.5, 0.1) table_tf = np.array( [[1, 0, 0, 0.80], [0, 1, 0, 0.00], [0, 0, 1, 0.12], [0, 0, 0, 1]] ) scene1 = ab.Scene([table], [table_tf]) robot = ab.Kuka() # robot.tool = torch setup = ab.PlanningSetup(robot, path_ori_free, scene1) # weights to express the importance of the joints in the cost function joint_weights = [10.0, 5.0, 1.0, 1.0, 1.0, 1.0] settings = ab.SamplingSetting( ab.SearchStrategy.INCREMENTAL, sample_method=SampleMethod.random_uniform, num_samples=500, iterations=2, tolerance_reduction_factor=2, weights=joint_weights, ) solve_set = ab.SolverSettings( ab.SolveMethod.sampling_based, ab.CostFuntionType.weighted_sum_squared, sampling_settings=settings, ) sol = ab.solve(setup, solve_set) assert sol.success for qi, s in zip(sol.joint_positions, np.linspace(0, 1, 3)): xi = 0.8 yi = s * 0.2 + (1 - s) * (-0.2) zi = 0.2 fk = robot.fk(qi) pos_fk = fk[:3, 3] assert_almost_equal(pos_fk, np.array([xi, yi, zi])) # fig, ax = get_default_axes3d() # scene1.plot(ax, c="g") # robot.animate_path(fig, ax, sol.joint_positions) # plt.show(block=True) def test_tol_position_pt_planning_problem(): robot = ab.Kuka() table = ab.Box(0.5, 0.5, 0.1) table_tf = np.array( [[1, 0, 0, 0.80], [0, 1, 0, 0.00], [0, 0, 1, 0.12], [0, 0, 0, 1]] ) scene1 = ab.Scene([table], [table_tf]) # create path quat = Quaternion(axis=np.array([1, 0, 0]), angle=np.pi) tolerance = [ab.NoTolerance(), ab.SymmetricTolerance(0.05, 10), ab.NoTolerance()] first_point = ab.TolPositionPt(np.array([0.9, -0.2, 0.2]), quat, tolerance) # end_position = np.array([0.9, 0.2, 0.2]) # path = create_line(first_point, end_position, 5) path = ab.create_arc( first_point, np.array([0.9, 0.0, 0.2]), np.array([0, 0, 1]), 2 * np.pi, 5 ) planner_settings = ab.SamplingSetting(ab.SearchStrategy.GRID, iterations=1) solver_settings = ab.SolverSettings( ab.SolveMethod.sampling_based, ab.CostFuntionType.sum_squared, sampling_settings=planner_settings, ) setup = ab.PlanningSetup(robot, path, scene1) sol = ab.solve(setup, solver_settings) assert sol.success for qi, pt in zip(sol.joint_positions, path): fk = robot.fk(qi) pos_fk = fk[:3, 3] pos_pt = pt.pos R_pt = pt.rotation_matrix pos_error = R_pt.T @ (pos_fk - pos_pt) assert_almost_equal(pos_error[0], 0) assert_almost_equal(pos_error[2], 0) assert pos_error[1] <= (0.05 + 1e-6) assert pos_error[1] >= -(0.05 + 1e-6) # TODO fix this test def test_euler_pt_planning_problem(): robot = ab.Kuka() table = ab.Box(0.5, 0.5, 0.1) table_tf = np.array( [[1, 0, 0, 0.80], [0, 1, 0, 0.00], [0, 0, 1, 0.00], [0, 0, 0, 1]] ) scene1 = ab.Scene([table], [table_tf]) # create path quat = Quaternion(axis=np.array([1, 0, 0]), angle=-3 * np.pi / 4) pos_tol = 3 * [ab.NoTolerance()] # rot_tol = 3 * [NoTolerance()] rot_tol = [ ab.NoTolerance(), ab.SymmetricTolerance(np.pi / 4, 20), ab.SymmetricTolerance(np.pi, 20), ] first_point = ab.TolEulerPt(np.array([0.9, -0.1, 0.2]), quat, pos_tol, rot_tol) # end_position = np.array([0.9, 0.1, 0.2]) # path = create_line(first_point, end_position, 5) path = ab.create_arc( first_point, np.array([0.9, 0.0, 0.2]), np.array([0, 0, 1]), 2 * np.pi, 5 ) planner_settings = ab.SamplingSetting( ab.SearchStrategy.GRID, iterations=1, tolerance_reduction_factor=2 ) solver_settings = ab.SolverSettings( ab.SolveMethod.sampling_based, ab.CostFuntionType.sum_squared, sampling_settings=planner_settings, ) setup = ab.PlanningSetup(robot, path, scene1) sol = ab.solve(setup, solver_settings) assert sol.success # fig, ax = get_default_axes3d() # scene1.plot(ax, c="g") # path_tf = [pt.transformation_matrix for pt in path] # for tf in path_tf: # plot_reference_frame(ax, tf, 0.1) # # for tf in path[0].sample_grid(): # # plot_reference_frame(ax, tf, 0.1) # fk_tfs = [robot.fk(qi) for qi in sol.joint_positions] # for tf in fk_tfs: # plot_reference_frame(ax, tf, 0.1) # ax.set_axis_off() # robot.animate_path(fig, ax, sol.joint_positions) # plt.show(block=True) def test_state_cost(): robot = ab.Kuka() table = ab.Box(0.5, 0.5, 0.1) table_tf = np.array( [[1, 0, 0, 0.80], [0, 1, 0, 0.00], [0, 0, 1, 0.00], [0, 0, 0, 1]] ) scene1 = ab.Scene([table], [table_tf]) # create path quat = Quaternion(axis=np.array([1, 0, 0]), angle=-3 * np.pi / 4) pos_tol = 3 * [ab.NoTolerance()] # rot_tol = 3 * [NoTolerance()] rot_tol = [ ab.NoTolerance(), ab.SymmetricTolerance(np.pi / 4, 20), ab.SymmetricTolerance(np.pi, 20), ] first_point = ab.TolEulerPt(np.array([0.9, -0.1, 0.2]), quat, pos_tol, rot_tol) # end_position = np.array([0.9, 0.1, 0.2]) # path = create_line(first_point, end_position, 5) path = ab.create_arc( first_point, np.array([0.9, 0.0, 0.2]), np.array([0, 0, 1]), 2 * np.pi, 5 ) planner_settings = ab.SamplingSetting( ab.SearchStrategy.GRID, iterations=1, tolerance_reduction_factor=2, use_state_cost=True, state_cost_weight=10.0, ) solver_settings = ab.SolverSettings( ab.SolveMethod.sampling_based, ab.CostFuntionType.sum_squared, sampling_settings=planner_settings, ) setup = ab.PlanningSetup(robot, path, scene1) sol = ab.solve(setup, solver_settings) assert sol.success def test_exceptions(): settings = ab.SamplingSetting( ab.SearchStrategy.INCREMENTAL, sample_method=SampleMethod.random_uniform, num_samples=500, iterations=2, tolerance_reduction_factor=2, ) solve_set = ab.SolverSettings( ab.SolveMethod.sampling_based, ab.CostFuntionType.weighted_sum_squared, sampling_settings=settings, ) setup = ab.PlanningSetup(None, None, None) with pytest.raises(Exception) as e: ab.solve(setup, solve_set) assert ( str(e.value) == "No weights specified in SamplingSettings for the weighted cost function." ) robot = ab.Kuka() scene = ab.Scene([], []) pos = np.array([1000, 0, 0]) quat = Quaternion(axis=np.array([1, 0, 0]), angle=-3 * np.pi / 4) path = [ab.TolPositionPt(pos, quat, 3 * [ab.NoTolerance()])] solve_set2 = ab.SolverSettings( ab.SolveMethod.sampling_based, ab.CostFuntionType.sum_squared, sampling_settings=settings, ) setup2 = ab.PlanningSetup(robot, path, scene) with pytest.raises(Exception) as e: ab.solve(setup2, solve_set2) assert str(e.value) == f"No valid joint solutions for path point {0}." ``` #### File: acrobotics/tests/test_shapes.py ```python import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import from numpy.testing import assert_almost_equal from acrolib.geometry import rot_z, rot_y from acrobotics.shapes import Box, Cylinder tf_identity = np.eye(4) def pose_z(alfa, x, y, z): """ Homogenous transform with rotation around z-axis and translation. """ return np.array( [ [np.cos(alfa), -np.sin(alfa), 0, x], [np.sin(alfa), np.cos(alfa), 0, y], [0, 0, 1, z], [0, 0, 0, 1], ] ) class TestShape: def test_init(self): Box(1, 2, 3) def test_get_vertices(self): b = Box(1, 2, 3) v = b.get_vertices(tf_identity) desired = np.array( [ [-0.5, 1, 1.5], [-0.5, 1, -1.5], [-0.5, -1, 1.5], [-0.5, -1, -1.5], [0.5, 1, 1.5], [0.5, 1, -1.5], [0.5, -1, 1.5], [0.5, -1, -1.5], ] ) assert_almost_equal(v, desired) def test_get_normals(self): b = Box(1, 2, 3) n = b.get_normals(tf_identity) desired = np.array( [[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]] ) assert_almost_equal(n, desired) def test_set_transform(self): b = Box(1, 2, 3) tf = np.eye(4) tf[0, 3] = 10.5 v = b.get_vertices(tf) desired = np.array( [ [10, 1, 1.5], [10, 1, -1.5], [10, -1, 1.5], [10, -1, -1.5], [11, 1, 1.5], [11, 1, -1.5], [11, -1, 1.5], [11, -1, -1.5], ] ) assert_almost_equal(v, desired) def test_set_transform2(self): b = Box(1, 2, 3) tf = np.eye(4) # rotate pi / 2 around x-axis tf[1:3, 1:3] = np.array([[0, -1], [1, 0]]) v = b.get_vertices(tf) desired = np.array( [ [-0.5, -1.5, 1], [-0.5, 1.5, 1], [-0.5, -1.5, -1], [-0.5, 1.5, -1], [0.5, -1.5, 1], [0.5, 1.5, 1], [0.5, -1.5, -1], [0.5, 1.5, -1], ] ) assert_almost_equal(v, desired) def test_get_edges(self): b = Box(1, 2, 3) e = b.get_edges(tf_identity) row, col = e.shape assert row == 12 assert col == 6 v = b.get_vertices(tf_identity) # check only one edge v0 = np.hstack((v[0], v[1])) assert_almost_equal(v0, e[0]) def test_polyhedron(self): b = Box(1, 2, 3) A, b = b.get_polyhedron(np.eye(4)) Aa = np.array( [[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]] ) ba = np.array([0.5, 0.5, 1, 1, 1.5, 1.5]) assert_almost_equal(A, Aa) assert_almost_equal(b, ba) def test_polyhedron_transformed(self): b = Box(1, 2, 3) tf = pose_z(0.3, 0.1, 0.2, -0.3) A, b = b.get_polyhedron(tf) Aa = np.array( [[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]] ) ba = np.array([0.5, 0.5, 1, 1, 1.5, 1.5]) Aa = np.dot(Aa, tf[:3, :3].T) ba = ba + np.dot(Aa, tf[:3, 3]) assert_almost_equal(A, Aa) assert_almost_equal(b, ba) def test_is_in_collision(self): b1 = Box(1, 1, 1) b2 = Box(1, 1, 2) actual = b1.is_in_collision(tf_identity, b2, tf_identity) assert actual == True b3 = Box(1, 2, 1) T3 = pose_z(np.pi / 4, 0.7, 0.7, 0) assert b1.is_in_collision(tf_identity, b3, T3) == True b4 = Box(1, 1, 1) b5 = Box(1, 1, 2) T4 = pose_z(0, -1, -1, 0) T5 = pose_z(np.pi / 4, -2, -2, 0) assert b4.is_in_collision(T4, b5, T5) == False def test_plot(self): b1 = Box(1, 2, 3) fig = plt.figure() ax = fig.gca(projection="3d") b1.plot(ax, tf_identity) assert True class TestCylinder: def test_4_faces(self): cyl = Cylinder(1, 2, approx_faces=4) n = cyl.get_normals(np.eye(4)) n_desired = np.array( [[1, 0, 0], [0, 1, 0], [-1, 0, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]] ) assert_almost_equal(n, n_desired) v = cyl.get_vertices(np.eye(4)) v_desired = np.array( [ [1, 0, 1], [0, 1, 1], [-1, 0, 1], [0, -1, 1], [1, 0, -1], [0, 1, -1], [-1, 0, -1], [0, -1, -1], ] ) v_desired = (rot_z(np.pi / 4) @ v_desired.T).T assert_almost_equal(v, v_desired) e = cyl.get_edges(np.eye(4)) e_desired = np.zeros((12, 6)) vd = v_desired e_desired[0] = np.hstack((vd[3], vd[0])) e_desired[1] = np.hstack((vd[0], vd[1])) e_desired[2] = np.hstack((vd[1], vd[2])) e_desired[3] = np.hstack((vd[2], vd[3])) e_desired[4] = np.hstack((vd[7], vd[4])) e_desired[5] = np.hstack((vd[4], vd[5])) e_desired[6] = np.hstack((vd[5], vd[6])) e_desired[7] = np.hstack((vd[6], vd[7])) e_desired[8] = np.hstack((vd[0], vd[4])) e_desired[9] = np.hstack((vd[1], vd[5])) e_desired[10] = np.hstack((vd[2], vd[6])) e_desired[11] = np.hstack((vd[3], vd[7])) assert e.shape == e_desired.shape assert_almost_equal(e[0:4], e_desired[0:4]) assert_almost_equal(e[4:8], e_desired[4:8]) assert_almost_equal(e[8:12], e_desired[8:12]) def test_4_faces_transformed(self): tf = np.eye(4) tf[:3, 3] = np.array([5, -3, 7]) tf[:3, :3] = rot_y(0.5) @ rot_z(-0.3) cyl = Cylinder(1, 2, approx_faces=4) n = cyl.get_normals(tf) n_desired = np.array( [[1, 0, 0], [0, 1, 0], [-1, 0, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]] ) n_desired = (tf[:3, :3] @ n_desired.T).T assert_almost_equal(n, n_desired) v = cyl.get_vertices(tf) v_desired = np.array( [ [1, 0, 1], [0, 1, 1], [-1, 0, 1], [0, -1, 1], [1, 0, -1], [0, 1, -1], [-1, 0, -1], [0, -1, -1], ] ) v_desired = (rot_z(np.pi / 4) @ v_desired.T).T v_desired = (tf[:3, :3] @ v_desired.T).T + tf[:3, 3] assert_almost_equal(v, v_desired) def test_plot_cylinder(self): cyl = Cylinder(1, 2) fig = plt.figure() ax = fig.gca(projection="3d") tf = np.eye(4) tf[:3, 3] = np.array([0, 5, -3]) tf[:3, :3] = rot_y(np.pi / 4) cyl.plot(ax, tf, c="k") # plt.show(block=True) assert True ``` #### File: acrobotics/tests/test_workspace_envelope.py ```python import numpy as np from numpy.testing import assert_almost_equal from acrobotics.workspace_envelope import ( sample_position, process_ik_solution, calculate_reachability, generate_positions, generate_robot_envelope, EnvelopeSettings, ) from acrobotics.inverse_kinematics.ik_result import IKResult from acrobotics.robot import Robot from acrobotics.robot_examples import Kuka def test_sample_position(): pos = np.array([0.1, 0.2, 0.3]) samples = sample_position(pos, 5) for tf in samples: assert_almost_equal(tf[:3, 3], pos) for i in range(1, len(samples)): assert np.any(np.not_equal(samples[i], samples[i - 1])) class DummyRobot(Robot): def __init__(self): pass def is_in_self_collision(self, q): if q[0] < 0.5: return False else: return True def test_process_ik_result(): ik_result = IKResult(True, [[0, 0], [0, 0]]) robot = DummyRobot() res = process_ik_solution(robot, ik_result) assert res == 2 ik_result = IKResult(True, [[0, 0], [1, 1]]) robot = DummyRobot() res = process_ik_solution(robot, ik_result) assert res == 1 ik_result = IKResult(True, [[1, 1], [1, 1]]) robot = DummyRobot() res = process_ik_solution(robot, ik_result) assert res == 0 def test_generate_envelop(): robot = Kuka() settings = EnvelopeSettings(1.0, 10, 8) we = generate_robot_envelope(robot, settings) max_extension = robot.estimate_max_extension() num_points = int(2 * max_extension / settings.sample_distance) assert we.shape == (num_points ** 3, 4) ```
{ "source": "JeroenDM/acrolib", "score": 3 }
#### File: src/acrolib/plotting.py ```python import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import def get_default_axes3d(xlim=[-1, 1], ylim=[-1, 1], zlim=[-1, 1]): """ Create a default `mpl_toolkits.mplot3d.Axes3D` object with default axis limits on all axis from -1 to 1, and labels on the axes. """ fig = plt.figure() ax = fig.gca(projection="3d") ax.set_xlim3d(xlim) ax.set_ylim3d(ylim) ax.set_zlim3d(zlim) ax.set_xlabel("X") ax.set_ylabel("Y") ax.set_zlabel("Z") return fig, ax def plot_reference_frame(ax, tf=None, arrow_length=0.2): """ Plot xyz-axes on axes3d object Parameters ---------- ax : mpl_toolkits.mplot3d.Axes3D Axes object for 3D plotting. tf : np.array of float Transform to specify location of axes. Plots in origin if None. l : float The length of the axes plotted. """ l = arrow_length x_axis = np.array([[0, l], [0, 0], [0, 0]]) y_axis = np.array([[0, 0], [0, l], [0, 0]]) z_axis = np.array([[0, 0], [0, 0], [0, l]]) if tf is not None: # rotation x_axis = np.dot(tf[:3, :3], x_axis) y_axis = np.dot(tf[:3, :3], y_axis) z_axis = np.dot(tf[:3, :3], z_axis) # translation [:, None] numpian way to change shape (add axis) x_axis = x_axis + tf[:3, 3][:, None] y_axis = y_axis + tf[:3, 3][:, None] z_axis = z_axis + tf[:3, 3][:, None] ax.plot(x_axis[0], x_axis[1], x_axis[2], "-", c="r") ax.plot(y_axis[0], y_axis[1], y_axis[2], "-", c="g") ax.plot(z_axis[0], z_axis[1], z_axis[2], "-", c="b") ``` #### File: acrolib/tests/test_plotting.py ```python import numpy as np import matplotlib import mpl_toolkits from acrolib.plotting import get_default_axes3d, plot_reference_frame def test_create_axes_3d(): fig, ax = get_default_axes3d() assert isinstance(fig, matplotlib.pyplot.Figure) assert isinstance(ax, mpl_toolkits.mplot3d.Axes3D) def test_plot_reference_frame(): _, ax = get_default_axes3d() plot_reference_frame(ax) plot_reference_frame(ax, tf=np.eye(4)) plot_reference_frame(ax, tf=np.eye(4), arrow_length=0.3) ```
{ "source": "JeroenDM/moveit_constrained_planning_examples", "score": 3 }
#### File: moveit_constrained_planning_examples/scripts/cart_planning_server.py ```python from __future__ import print_function import sys import time import rospy import rospkg import moveit_commander import geometry_msgs.msg import moveit_msgs.msg import moveit_msgs.srv GROUP_NAME = "panda_arm" # GROUP_NAME = "manipulator" class CartesianPlanningServer: def __init__(self): self.mc = moveit_commander.RobotCommander() self.mg = moveit_commander.MoveGroupCommander(GROUP_NAME) self.server = rospy.Service( "cartesian_planning_server", moveit_msgs.srv.GetMotionPlan, lambda x: self.handle_request(x) ) # default settings for Cartesian planning self.eef_step = 0.01 self.jump_threshold = 0.0 def handle_request(self, request): """ Extract Cartesian waypoints from a MotionPlanRequest and use computeCartesianPath to find a solution. """ rospy.loginfo("Received cartesian motion planning request.") req = request.motion_plan_request print(req) resp = moveit_msgs.msg.MotionPlanResponse() resp.group_name = req.group_name assert(len(req.reference_trajectories) == 1) assert(len(req.reference_trajectories[0].cartesian_trajectory) == 1) cartesian_trajectory = req.reference_trajectories[0].cartesian_trajectory[0] waypoints = [] for ctp in cartesian_trajectory.points: waypoints.append(ctp.point.pose) start_time = time.time() (plan, fraction) = self.mg.compute_cartesian_path( waypoints, self.eef_step, self.jump_threshold) resp.planning_time = time.time() - start_time if fraction == 1.0: resp.error_code.val = moveit_msgs.msg.MoveItErrorCodes.SUCCESS resp.trajectory = plan else: resp.error_code.val = moveit_msgs.msg.MoveItErrorCodes.GOAL_IN_COLLISION return resp if __name__ == '__main__': rospy.init_node('cart_planning_server', anonymous=True) server = CartesianPlanningServer() print("Ready receive planning requests.") rospy.spin() ``` #### File: moveit_constrained_planning_examples/scripts/load_work_object_case_3.py ```python import sys import copy import rospy import rospkg import rosparam import moveit_commander import urdfpy from geometry_msgs.msg import Vector3, Quaternion, Pose, PoseStamped # The location of the urdf file inside the setup 1 support package REL_WORK_PATH = "/urdf/work/" # I moved the task a bit along the x-axis # but this code was not flexible enough to change the position # of the work object, so I hardcoded the x offset for now X_OFFSET = 0.3 def numpy_to_pose(arr): """ Numpy 4x4 array to geometry_msg.Pose Code from: https://github.com/eric-wieser/ros_numpy TODO move this to some utility module if I have one. """ from tf import transformations assert arr.shape == (4, 4) trans = transformations.translation_from_matrix(arr) quat = transformations.quaternion_from_matrix(arr) return Pose(position=Vector3(*trans), orientation=Quaternion(*quat)) def remove_all_objects(scene): """ Given a planning scene, remove all known objects. """ for name in scene.get_known_object_names(): scene.remove_world_object(name) def parse_urdf_file(package_name, work_name): """ Convert urdf file (xml) to python dict. Using the urdfpy package for now. Using the xml package from the standard library could be easier to understand. We can change this in the future if it becomes a mess. """ rospack = rospkg.RosPack() filepath = rospack.get_path(package_name) filepath += REL_WORK_PATH urdf = urdfpy.URDF.load(filepath + work_name + ".urdf") d = {"links": {}, "joints": {}} for link in urdf.links: if link.name == "world" or link.name == "work": continue else: d["links"][link.name] = parse_link(link, filepath) for joint in urdf.joints: p = PoseStamped() p.header.frame_id = joint.parent p.pose = numpy_to_pose(joint.origin) d["joints"][joint.name] = { "pose": p, "parent": joint.parent, "child": joint.child } return d def parse_link(link, mesh_path): """ Assume a link has only a single collision object. Assume this collision object is a box. Assume the link named "world" has no collision objects. link: a urdfpy.urdf.Link object mesh_path: absolute path of the folder where we have to fine the stl files """ assert len(link.collisions) == 1 assert link.name != "world" assert link.name != "work" collision = link.collisions[0] if collision.geometry.box is not None: data = {"type": "box", "size": link.collisions[0].geometry.box.size} elif collision.geometry.mesh is not None: data = { "type": "mesh", "filename": mesh_path + collision.geometry.mesh.filename, "scale": collision.geometry.mesh.scale } else: raise Exception("No mesh of box collision geometry found.") return data def publish_parsed_urdf(parsed_urdf, scene): """ Publish link geometry for every joint's child. TODO: there is an ugly hardcoded x offset for now. """ for name, joint in parsed_urdf["joints"].items(): # get the child link data link = parsed_urdf["links"][joint["child"]] pose_stamped = copy.deepcopy(joint["pose"]) pose_stamped.pose.position.x += X_OFFSET # publish the child links collision geometry if link["type"] == "box": scene.add_box( joint["child"], pose_stamped, link["size"] ) else: scene.add_mesh( joint["child"], pose_stamped, link["filename"], link["scale"] ) if __name__ == "__main__": rospy.init_node("publish_work") scene = moveit_commander.PlanningSceneInterface() rospy.sleep(1.0) # wait for the above things to setup remove_all_objects(scene) work = parse_urdf_file("setup_1_support", "kingpin") publish_parsed_urdf(work, scene) print("Done!") ```
{ "source": "JeroenDM/sampling_based_tube_following_2", "score": 3 }
#### File: sampling_based_tube_following_2/case_2/create_data_figure_9.py ```python import time import numpy as np from acrobotics.path.sampling import SamplingSetting, SampleMethod, SearchStrategy from acrobotics.planning.types import CostFuntionType, SolveMethod, PlanningSetup from acrobotics.planning.settings import SolveMethod, OptSettings, SolverSettings from acrobotics.planning.solver import solve from definition import create_robot, create_path, create_scene # ============================================================================= # Define some utilities to run the experiment and process the results. # ============================================================================== def create_settings_grid(iters, use_constraints_cost, constraints_cost_weight=1.0): s = SamplingSetting( search_strategy=SearchStrategy.GRID, iterations=iters, tolerance_reduction_factor=2.0, use_state_cost=use_constraints_cost, state_cost_weight=constraints_cost_weight, ) s2 = SolverSettings(SolveMethod.sampling_based, CostFuntionType.sum_squared, s) return s2 def create_opt_settins(q_init, cow): s2 = SolverSettings( SolveMethod.optimization_based, CostFuntionType.sum_squared, opt_settings=OptSettings( q_init=q_init, max_iters=500, con_objective_weight=cow ), ) return s2 def calc_mean_deviation(rxyz): """Calculate the mean deviation on the x and y rotation compared to the ideal value, wich is zero since tolerance is expressed in the local path frame.""" rx = rxyz[:, 0] ry = rxyz[:, 1] return np.sum(np.abs(rx) + np.abs(ry)) def JVM(sol): """ Calculate Joint Velocity Measure for a solution path. """ qp = np.array(sol.joint_positions) return np.sum(np.diff(qp, axis=0) ** 2) def Jcon(rxyz): """ Cacluate the value of the objective related to the path constraints. """ return np.sum(rxyz[:, :2] ** 2) def calc_tol_dev(robot, path, sol): """ Calculate deviation of the welding angles from the nominal path point pose. """ rxyz = np.zeros((N_PATH, 3)) for i, qi, pt in zip(range(N_PATH), sol.joint_positions, path): tol_dev = pt.transform_to_rel_tolerance_deviation(robot.fk(qi)) rxyz[i] = tol_dev[3:] return rxyz # ============================================================================= # Run the simulations for the two planners, and different lambda values # Warning: running the simulation can take a couple of minutes. # ============================================================================== N_PATH = 20 robot = create_robot() scene, start, stop = create_scene(np.array([0.85, 0, 0])) path = create_path(start, stop, N_PATH, 5, 5, 30) setup = PlanningSetup(robot, path, scene) lambda_values = [0.0, 1.0, 3.0, 10.0, 30.0, 100.0] with open("case_2_sampling_based.csv", "a") as file: file.write("lambda,mean_dev\n") for w in lambda_values: s = create_settings_grid(1, True, w) sol = solve(setup, s) rxyz = calc_tol_dev(robot, path, sol) mean_dev = calc_mean_deviation(rxyz) file.write(f"{w},{mean_dev}\n") # Use the home position as an initial guess for the next algorithm # in general it is not always trivial to find a good intial guess q_home = np.array([0, 1.5, 0, 0, 0, 0]) q_init = np.ones((N_PATH, 6)) * q_home # I used a slightly different position for the planning scene by accident # This does not influence the results much as we focus on the influence of # lambda here. scene, start, stop = create_scene(np.array([0.8, 0, 0])) path = create_path(start, stop, N_PATH, 5, 5, 30) setup = PlanningSetup(robot, path, scene) with open("case_2_optimization_based.csv", "a") as file: file.write("cost,time,mean_dev,lambda,success\n") for w in lambda_values: start = time.time() try: sol2 = solve(setup, create_opt_settins(q_init, w)) stop = time.time() rxyz = calc_tol_dev(robot, path, sol2) mean_dev = calc_mean_deviation(rxyz) file.write(f"{sol2.path_cost},{stop - start},{mean_dev},{w},1\n") except: stop = time.time() file.write(f"{np.nan},{stop - start},{np.nan},{w},0\n") ```
{ "source": "JeroenDM/urdf_to_opw_kinematics", "score": 3 }
#### File: src/urdf_to_opw_kinematics/main.py ```python import numpy as np from numpy.linalg import norm from urdf_to_opw_kinematics.util import angle, Axis, distance, rot_y DEBUG = True def check_compatibility(robot): """ TODO add compatibility tests now I just check if there are 6 revolute joints """ axes = get_joint_axes_from_urdf(robot) num_joints = len(axes) if num_joints != 6: print(robot.name + " has " + str(num_joints) + " joints, not 6.") return False return True def convert(robot): axes = get_joint_axes_from_urdf(robot) tool0_position = get_tool0_position(robot, axes) jo = get_joint_offsets(axes) sc = get_sign_corrections(axes) params = get_dimensions(axes, tool0_position, jo) params['joint_offsets'] = jo params['sign_corrections'] = sc return params def get_joint_axes_from_urdf(robot): """ Extract joint origin and axis direction from urdf Save absolute position in base_link, and relative position with respect to the previous link. Parameters ---------- robot object from the urdf_parser_py library Returns ------- list of Axis objects """ joints = robot.joints axes = [] for i in range(len(joints)): if joints[i].type == "revolute": if i > 0: p_relative = np.array(joints[i].origin.xyz) p_previous = axes[-1].position axes.append(Axis(p_previous + p_relative, p_relative, np.array(joints[i].axis))) else: axes.append(Axis(np.array(joints[i].origin.xyz), np.array( joints[i].origin.xyz), np.array(joints[i].axis))) return axes def get_tool0_position(robot, axes): """ Search for the tool0 link and get absolut position origin Returns ------- absolut position as a numpy array of length 3 """ for joint in robot.joints: if joint.child == "tool0": return axes[-1].position + np.array(joint.origin.xyz) raise ValueError("Failed to find a joint with child link 'tool0'.") def get_joint_offsets(axes): """ Calculate joint angle difference between reference pose of opw_kinematics and the zero pose of the current robot """ G1 = axes[0] G2 = axes[1] G3 = axes[2] G4 = axes[3] G5 = axes[4] G6 = axes[5] unit_x = np.array([1.0, 0, 0]) unit_y = np.array([0, 1.0, 0]) unit_z = np.array([0, 0, 1.0]) jo1 = angle(unit_y, G2.direction) v23 = distance(G2, G3, return_vector=True) jo2 = angle(unit_z, v23) #g4_positive = np.array([abs(e) for e in axes[3].direction]) jo3 = angle(unit_z, G4.direction) - jo2 jo4 = angle(unit_y, G5.direction) - jo1 jo5 = angle(G4.direction, G6.direction) # TODO get ee_y as input and correct for jo1 and j04 ee_y_direction = unit_y jo6 = angle(ee_y_direction, unit_y) return [-jo1, -jo2, -jo3, -jo4, -jo5, -jo6] def get_sign_corrections(axes): """ Does the axis rotate according to the right hand rule? Assume all z-axis pointed up and axis along one of the main axes """ sc = map(np.sum, [a.direction for a in axes]) return [int(val) for val in sc] def get_dimensions(axes, tool0_position, jo): """ Calculate distance parameters c1, c2, c3, c4 and signed distances a1, a2, b Note ---- The sign of b is not yet implemented and defaults as positive """ params = {} G1 = axes[0] G2 = axes[1] G3 = axes[2] G4 = axes[3] G5 = axes[4] G6 = axes[5] p_ee = tool0_position unit_x = np.array([1.0, 0, 0]) # TODO use joint offset on first joint to make this more general # check if a1 is along positive x and g2 is above x-y plane # this mean that the position of g2 position should be (a1, 0, c1) with a1 > 0 P2 = G2.position if (P2[0] >= 0 and P2[1] == 0 and P2[2] >= 0): params['a1'] = P2[0] params['c1'] = P2[2] else: raise ValueError("Wrong orientations of g2.") # ci's are always positive params['c2'] = distance(G2, G3) params['c3'] = distance(G3, G5, along=G4) # distance between g5 and tool0 along g6 params['c4'] = np.abs(np.dot(G6.direction, p_ee - G5.position)) # calculate sign a2 v34 = distance(G3, G4, return_vector=True) v34 = np.dot(rot_y(jo[1] + jo[2]), v34) a2_sign = np.sign(np.dot(unit_x, v34)) params['a2'] = a2_sign * distance(G3, G4) # TODO sign calculation # but b is zero in most robots params['b'] = distance(G3, G4, along=G3) return params def get_dimensions_new(axes): """ (DOES NOT WORK) Alternative method that could work if we make a lot more assumtions about the given urdf model. """ params = {} P_0_1 = axes[0].p_rel P_1_2 = axes[1].p_rel P_2_3 = axes[2].p_rel P_3_4 = axes[3].p_rel P_4_5 = axes[4].p_rel P_5_6 = axes[5].p_rel params['c1'] = P_0_1[2] + P_1_2[2] params['c2'] = norm(P_2_3) params['c3'] = norm(P_4_5) + P_3_4[0] params['c4'] = norm(P_5_6) params['a1'] = np.sqrt(P_1_2[0]**2 + P_1_2[1]**2) params['a2'] = -np.sqrt(P_3_4[0]**2 + P_3_4[2]**2) # or -P_3_4[2] params['b'] = P_3_4[1] return params ```
{ "source": "jeroendoggen/blackboard-analysis-tools", "score": 3 }
#### File: blackboard-analysis-tools/blackboard_analysis_tools/logger.py ```python """ Blackboard Analysis Tools Copyright 2013, <NAME>, <EMAIL> """ from __future__ import print_function, division # We require Python 2.6+ import logging import sys class Logger(): """ Logging class """ logger = 0 def __init__(self, logfile): self.set_logfile(logfile) self.info("Starting 'analysis tool': ") def set_logfile(self, logfile): """Set the logfile: for error & info messages""" try: self.logfile = logfile logging.basicConfig(filename=self.logfile, level=logging.DEBUG, format="%(asctime)s %(name)s %(levelname)s %(message)s") self.logger = logging.getLogger(__name__) except IOError: self.exit_program("opening the logfile (do you have write permission?)") def exit_program(self, message): """ Exit the program with a message TODO: this should move somewhere else (needed in multiple places) """ print("Error while " + message) print("Closing application") sys.exit() def info(self, message): self.logger.info(message) ```
{ "source": "jeroenh/coldsweat", "score": 2 }
#### File: coldsweat/coldsweat/config.py ```python import os from ConfigParser import SafeConfigParser from utilities import Struct __all__ = [ 'load_config', ] DEFAULTS = { 'min_interval' : '900', 'max_errors' : '50', 'max_history' : '7', 'timeout' : '10', 'processes' : '4', 'level' : 'INFO', 'filename' : '', # Don't log 'static_url' : '', 'load' : '' } def load_config(config_path): ''' Load up configuration settings ''' parser = SafeConfigParser(DEFAULTS) converters = { 'min_interval' : parser.getint, 'max_errors' : parser.getint, 'max_history' : parser.getint, 'timeout' : parser.getint, 'processes' : parser.getint, } if os.path.exists(config_path): parser.read(config_path) else: raise RuntimeError('Could not find configuration file %s' % config_path) config = Struct() for section in parser.sections(): d = { k : converters[k](section, k) if k in converters else v for k, v in parser.items(section) } config[section] = Struct(d) return config ``` #### File: coldsweat/coldsweat/controllers.py ```python import sys, os, re, time, urlparse from datetime import datetime from xml.etree import ElementTree from peewee import JOIN_LEFT_OUTER, fn, IntegrityError import feedparser import requests from requests.exceptions import * from webob.exc import * from models import * from utilities import * from plugins import trigger_event, load_plugins from filters import escape_html, status_title from coldsweat import * from fetcher import * class UserController(object): ''' Base user controller class. Derived classes may need to override the user property ''' @property def user(self): return self._current_user @user.setter def user(self, user): self._current_user = user def add_subscription(self, feed, group): ''' Associate a feed/group pair to current user ''' try: subscription = Subscription.create(user=self.user, feed=feed, group=group) except IntegrityError: logger.debug(u'user %s already has feed %s in her subscriptions' % (self.user.username, feed.self_link)) return None logger.debug(u'subscribed user %s to feed %s' % (self.user.username, feed.self_link)) return subscription def remove_subscription(self, feed): ''' Remove a feed subscription for current user ''' Subscription.delete().where((Subscription.user == self.user) & (Subscription.feed == feed)).execute() # ------------------------------------------------------ # Queries # ------------------------------------------------------ # Entries def mark_entry(self, entry, status): ''' Mark an entry as read|unread|saved|unsaved for current user ''' if status == 'read': try: Read.create(user=self.user, entry=entry) except IntegrityError: logger.debug(u'entry %s already marked as read, ignored' % entry.id) return elif status == 'unread': count = Read.delete().where((Read.user==self.user) & (Read.entry==entry)).execute() if not count: logger.debug(u'entry %s never marked as read, ignored' % entry.id) return elif status == 'saved': try: Saved.create(user=self.user, entry=entry) except IntegrityError: logger.debug(u'entry %s already saved, ignored' % entry.id) return elif status == 'unsaved': count = Saved.delete().where((Saved.user==self.user) & (Saved.entry==entry)).execute() if not count: logger.debug(u'entry %s never saved, ignored' % entry.id) return logger.debug(u'entry %s %s' % (entry.id, status)) def get_unread_entries(self, *select): #@@TODO: include saved information too q = _q(*select).where((Subscription.user == self.user) & ~(Entry.id << Read.select(Read.entry).where(Read.user == self.user))).distinct() return q def get_saved_entries(self, *select): #@@TODO: include read information too q = _q(*select).where((Subscription.user == self.user) & (Entry.id << Saved.select(Saved.entry).where(Saved.user == self.user))).distinct() return q def get_all_entries(self, *select): #@@TODO: include read and saved information too q = _q(*select).where(Subscription.user == self.user).distinct() return q def get_group_entries(self, group, *select): #@@TODO: include read and saved information too q = _q(*select).where((Subscription.user == self.user) & (Subscription.group == group)) return q def get_feed_entries(self, feed, *select): #@@TODO: include read and saved information too q = _q(*select).where((Subscription.user == self.user) & (Subscription.feed == feed)).distinct() return q # Feeds def get_feeds(self, *select): select = select or [Feed, fn.Count(Entry.id).alias('entry_count')] q = Feed.select(*select).join(Entry, JOIN_LEFT_OUTER).switch(Feed).join(Subscription).where(Subscription.user == self.user).group_by(Feed) return q def get_group_feeds(self, group): q = Feed.select().join(Subscription).where((Subscription.user == self.user) & (Subscription.group == group)) return q # Groups def get_groups(self): q = Group.select().join(Subscription).where(Subscription.user == self.user).distinct().order_by(Group.title) return q # Shortcut def _q(*select): select = select or (Entry, Feed) q = Entry.select(*select).join(Feed).join(Subscription) return q class FeedController(object): ''' Feed controller class ''' def add_feed_from_url(self, self_link, fetch_data=False): ''' Save a new feed object to database via its URL ''' feed = Feed(self_link=self_link) return self.add_feed(feed, fetch_data) def add_feed(self, feed, fetch_data=False): ''' Save a new feed object to database ''' feed.self_link = scrub_url(feed.self_link) try: previous_feed = Feed.get(self_link_hash=make_sha1_hash(feed.self_link)) logger.debug(u'feed %s has been already added to database, skipped' % feed.self_link) return previous_feed except Feed.DoesNotExist: pass feed.save() if fetch_data: self.fetch_feeds([feed]) return feed # #@@TODO: delete feed if there are no subscribers # def remove_feed(self, feed): # pass def add_feeds_from_file(self, filename, fetch_data=False): """ Add feeds to database reading from a file containing OPML data. """ # Map OPML attr keys to Feed model feed_allowed_attribs = { 'xmlUrl': 'self_link', 'htmlUrl': 'alternate_link', 'title': 'title', 'text': 'title', # Alias for title } # Map OPML attr keys to Group model group_allowed_attribs = { 'title': 'title', 'text': 'title', # Alias for title } default_group = Group.get(Group.title == Group.DEFAULT_GROUP) feeds = [] groups = [default_group] for event, element in ElementTree.iterparse(filename, events=('start','end')): if event == 'start': if (element.tag == 'outline') and ('xmlUrl' not in element.attrib): # Entering a group group = Group() for k, v in element.attrib.items(): if k in group_allowed_attribs: setattr(group, group_allowed_attribs[k], v) try: group = Group.get(Group.title==group.title) except Group.DoesNotExist: group.save() logger.debug(u'added group %s to database' % group.title) groups.append(group) elif event == 'end': if (element.tag == 'outline') and ('xmlUrl' in element.attrib): # Leaving a feed feed = Feed() for k, v in element.attrib.items(): if k in feed_allowed_attribs: setattr(feed, feed_allowed_attribs[k], v) feed = self.add_feed(feed, fetch_data) feeds.append((feed, groups[-1])) elif element.tag == 'outline': # Leaving a group groups.pop() return feeds # ------------------------------------------------------ # Fetching # ------------------------------------------------------ def fetch_feeds(self, feeds): """ Fetch given feeds, possibly parallelizing requests """ start = time.time() load_plugins() logger.debug(u"starting fetcher") trigger_event('fetch_started') if config.fetcher.processes: from multiprocessing import Pool # Each worker has its own connection p = Pool(config.fetcher.processes, initializer=connect) p.map(feed_worker, feeds) # Exit the worker processes so their connections do not leak p.close() else: # Just sequence requests in this process for feed in feeds: feed_worker(feed) trigger_event('fetch_done', feeds) logger.info(u"%d feeds checked in %.2fs" % (len(feeds), time.time() - start)) def fetch_all_feeds(self): """ Fetch all enabled feeds, possibly parallelizing requests """ q = Feed.select().where(Feed.is_enabled==True) feeds = list(q) if not feeds: logger.debug(u"no feeds found to fetch, halted") return self.fetch_feeds(feeds) def feed_worker(feed): fetcher = Fetcher(feed) fetcher.update_feed() ``` #### File: coldsweat/coldsweat/fetcher.py ```python import sys, os, re, time, urlparse from datetime import datetime from peewee import IntegrityError import feedparser import requests from requests.exceptions import * from webob.exc import * from coldsweat import * from plugins import trigger_event from models import * from utilities import * from translators import * import markup import filters __all__ = [ 'Fetcher', 'fetch_url' ] FETCH_ICONS_DELTA = 30 # Days class Fetcher(object): ''' Fetch a single given feed ''' def __init__(self, feed): # Save timestamp for current fetch operation self.instant = datetime.utcnow() # Extract netloc _, self.netloc, _, _, _ = urlparse.urlsplit(feed.self_link) self.feed = feed def handle_500(self, response): ''' Internal server error ''' self.feed.error_count += 1 self.feed.last_status = response.status_code logger.warn(u"%s has caused an error on server, skipped" % self.netloc) raise HTTPInternalServerError def handle_403(self, response): ''' Forbidden ''' self.feed.error_count += 1 self.feed.last_status = response.status_code logger.warn(u"%s access was denied, skipped" % self.netloc) raise HTTPForbidden def handle_404(self, response): ''' Not Found ''' self.feed.error_count += 1 self.feed.last_status = response.status_code logger.warn(u"%s has been not found, skipped" % self.netloc) raise HTTPNotFound def handle_410(self, response): ''' Gone ''' self.feed.is_enabled = False self.feed.error_count += 1 self.feed.last_status = response.status_code logger.warn(u"%s is gone, disabled" % self.netloc) self._synthesize_entry('Feed has been removed from the origin server.') raise HTTPGone def handle_304(self, response): ''' Not modified ''' logger.debug(u"%s hasn't been modified, skipped" % self.netloc) self.feed.last_status = response.status_code raise HTTPNotModified def handle_301(self, response): ''' Moved permanently ''' self_link = response.url try: Feed.get(self_link=self_link) except Feed.DoesNotExist: self.feed.self_link = self_link self.feed.last_status = response.status_code logger.info(u"%s has changed its location, updated to %s" % (self.netloc, self_link)) else: self.feed.is_enabled = False self.feed.last_status = DuplicatedFeedError.code self.feed.error_count += 1 self._synthesize_entry('Feed has a duplicated web address.') logger.warn(u"new %s location %s is duplicated, disabled" % (self.netloc, self_link)) raise DuplicatedFeedError def handle_200(self, response): ''' OK plus redirects ''' self.feed.etag = response.headers.get('ETag', None) # Save final status code discarding redirects self.feed.last_status = response.status_code handle_307 = handle_200 # Alias handle_302 = handle_200 # Alias def update_feed(self): logger.debug(u"updating %s" % self.netloc) # Check freshness for value in [self.feed.last_checked_on, self.feed.last_updated_on]: if not value: continue # No datetime.timedelta since we need to # deal with large seconds values delta = datetime_as_epoch(self.instant) - datetime_as_epoch(value) if delta < config.fetcher.min_interval: logger.debug(u"%s is below minimun fetch interval, skipped" % self.netloc) return try: response = fetch_url(self.feed.self_link, timeout=config.fetcher.timeout, etag=self.feed.etag, modified_since=self.feed.last_updated_on) except RequestException: # Record any network error as 'Service Unavailable' self.feed.last_status = HTTPServiceUnavailable.code self.feed.error_count += 1 logger.warn(u"a network error occured while fetching %s, skipped" % self.netloc) self.check_feed_health() self.feed.save() return self.feed.last_checked_on = self.instant # Check if we got a redirect first if response.history: status = response.history[0].status_code else: status = response.status_code try: handler = getattr(self, 'handle_%d' % status, None) if handler: logger.debug(u"got status %s from server" % status) handler(response) else: self.feed.last_status = status logger.warn(u"%s replied with unhandled status %d, aborted" % (self.netloc, status)) return self._parse_feed(response.text) self._fetch_icon() except HTTPNotModified: pass # Nothing to do except (HTTPError, DuplicatedFeedError): self.check_feed_health() finally: self.feed.save() def check_feed_health(self): if config.fetcher.max_errors and self.feed.error_count > config.fetcher.max_errors: self._synthesize_entry('Feed has accumulated too many errors (last was %s).' % filters.status_title(self.feed.last_status)) logger.warn(u"%s has accomulated too many errors, disabled" % self.netloc) self.feed.is_enabled = False return def update_feed_with_data(self, data): self._parse_feed(data) self.feed.save() def _parse_feed(self, data): soup = feedparser.parse(data) # Got parsing error? if hasattr(soup, 'bozo') and soup.bozo: logger.debug(u"%s caused a parser error (%s), tried to parse it anyway" % (self.netloc, soup.bozo_exception)) ft = FeedTranslator(soup.feed) self.feed.last_updated_on = ft.get_timestamp(self.instant) self.feed.alternate_link = ft.get_alternate_link() self.feed.title = self.feed.title or ft.get_title() # Do not set again if already set #entries = [] feed_author = ft.get_author() for entry_dict in soup.entries: t = EntryTranslator(entry_dict) link = t.get_link() guid = t.get_guid(default=link) if not guid: logger.warn(u'could not find GUID for entry from %s, skipped' % self.netloc) continue timestamp = t.get_timestamp(self.instant) content_type, content = t.get_content(('text/plain', '')) # Skip ancient entries if config.fetcher.max_history and (self.instant - timestamp).days > config.fetcher.max_history: logger.debug(u"entry %s from %s is over maximum history, skipped" % (guid, self.netloc)) continue try: # If entry is already in database with same hashed GUID, skip it Entry.get(guid_hash=make_sha1_hash(guid)) logger.debug(u"duplicated entry %s, skipped" % guid) continue except Entry.DoesNotExist: pass entry = Entry( feed = self.feed, guid = guid, link = link, title = t.get_title(default='Untitled'), author = t.get_author() or feed_author, content = content, content_type = content_type, last_updated_on = timestamp ) # At this point we are pretty sure we doesn't have the entry # already in the database so alert plugins and save data trigger_event('entry_parsed', entry, entry_dict) entry.save() #@@TODO: entries.append(entry) logger.debug(u"parsed entry %s from %s" % (guid, self.netloc)) #return entries def _fetch_icon(self): if not self.feed.icon or not self.feed.icon_last_updated_on or (self.instant - self.feed.icon_last_updated_on).days > FETCH_ICONS_DELTA: # Prefer alternate_link if available since self_link could # point to Feed Burner or similar services self.feed.icon = self._google_favicon_fetcher(self.feed.alternate_link or self.feed.self_link) self.feed.icon_last_updated_on = self.instant logger.debug(u"fetched favicon %s..." % (self.feed.icon[:70])) def _google_favicon_fetcher(self, url): ''' Fetch a site favicon via Google service ''' endpoint = "http://www.google.com/s2/favicons?domain=%s" % urlparse.urlsplit(url).hostname try: response = fetch_url(endpoint) except RequestException, exc: logger.warn(u"could not fetch favicon for %s (%s)" % (url, exc)) return Feed.DEFAULT_ICON return make_data_uri(response.headers['Content-Type'], response.content) def add_synthesized_entry(self, title, content_type, content): ''' Create an HTML entry for this feed ''' # Since we don't know the mechanism the feed used to build a GUID for its entries # synthesize an tag URI from the link and a random string. This makes # entries internally generated by Coldsweat reasonably globally unique guid = ENTRY_TAG_URI % make_sha1_hash(self.feed.self_link + make_nonce()) entry = Entry( feed = self.feed, guid = guid, title = title, author = 'Coldsweat', content = content, content_type = content_type, last_updated_on = self.instant ) entry.save() logger.debug(u"synthesized entry %s" % guid) return entry def _synthesize_entry(self, reason): title = u'This feed has been disabled' content = render_template(os.path.join(template_dir, '_entry_feed_disabled.html'), {'reason': reason}) return self.add_synthesized_entry(title, 'text/html', content) def fetch_url(url, timeout=10, etag=None, modified_since=None): ''' Fecth a given URL optionally issuing a 'Conditional GET' request ''' request_headers = { 'User-Agent': USER_AGENT } # Conditional GET headers if etag and modified_since: logger.debug(u"fetching %s with a conditional GET (%s %s)" % (url, etag, format_http_datetime(modified_since))) request_headers['If-None-Match'] = etag request_headers['If-Modified-Since'] = format_http_datetime(modified_since) try: response = requests.get(url, timeout=timeout, headers=request_headers) except RequestException, exc: logger.debug(u"tried to fetch %s but got %s" % (url, exc.__class__.__name__)) raise exc return response # ------------------------------------------------------ # Custom error codes 9xx & exceptions # ------------------------------------------------------ class DuplicatedFeedError(Exception): code = 900 title = 'Duplicated feed' explanation = 'Feed address matches another already present in the database.' # Update WebOb status codes map for klass in (DuplicatedFeedError,): status_map[klass.code] = klass ``` #### File: coldsweat/tests/strip.py ```python from ..markup import strip_html def run_tests(): tests = [ ('a', 'a'), # Identity ('a <p class="c"><span>b</span></p> a', 'a b a'), (u'à <p class="c"><span>b</span></p> à', u'à b à'), # Unicode ('a&amp;a&lt;a&gt;', 'a&a<a>'), # Test unescape of entity and char reference too ('<span>a</span>', 'a'), ('<span>a', 'a'), # Unclosed elements ('<p><span>a</p>', 'a'), ('<foo attr=1><bar />a</foo>', 'a'), # Non HTML tags ] for value, wanted in tests: assert strip_html(value) == wanted if __name__ == "__main__": run_tests() ```
{ "source": "jeroenj/youtube-dl", "score": 2 }
#### File: youtube-dl/test/test_youtube_signature.py ```python from __future__ import unicode_literals # Allow direct execution import os import sys import unittest sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import io import re import string from test.helper import FakeYDL from youtube_dl.extractor import YoutubeIE from youtube_dl.jsinterp import JSInterpreter from youtube_dl.compat import compat_str, compat_urlretrieve _SIG_TESTS = [ ( 'https://s.ytimg.com/yts/jsbin/html5player-vflHOr_nV.js', 86, '>=<;:/.-[+*)(\'&%$#"!ZYX0VUTSRQPONMLKJIHGFEDCBA\\yxwvutsrqponmlkjihgfedcba987654321', ), ( 'https://s.ytimg.com/yts/jsbin/html5player-vfldJ8xgI.js', 85, '3456789a0cdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRS[UVWXYZ!"#$%&\'()*+,-./:;<=>?@', ), ( 'https://s.ytimg.com/yts/jsbin/html5player-vfle-mVwz.js', 90, ']\\[@?>=<;:/.-,+*)(\'&%$#"hZYXWVUTSRQPONMLKJIHGFEDCBAzyxwvutsrqponmlkjiagfedcb39876', ), ( 'https://s.ytimg.com/yts/jsbin/html5player-en_US-vfl0Cbn9e.js', 84, 'O1I3456789abcde0ghijklmnopqrstuvwxyzABCDEFGHfJKLMN2PQRSTUVW@YZ!"#$%&\'()*+,-./:;<=', ), ( 'https://s.ytimg.com/yts/jsbin/html5player-en_US-vflXGBaUN.js', '2ACFC7A61CA478CD21425E5A57EBD73DDC78E22A.2094302436B2D377D14A3BBA23022D023B8BC25AA', 'A52CB8B320D22032ABB3A41D773D2B6342034902.A22E87CDD37DBE75A5E52412DC874AC16A7CFCA2', ), ( 'https://s.ytimg.com/yts/jsbin/html5player-en_US-vflBb0OQx.js', 84, '123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQ0STUVWXYZ!"#$%&\'()*+,@./:;<=>' ), ( 'https://s.ytimg.com/yts/jsbin/html5player-en_US-vfl9FYC6l.js', 83, '123456789abcdefghijklmnopqr0tuvwxyzABCDETGHIJKLMNOPQRS>UVWXYZ!"#$%&\'()*+,-./:;<=F' ), ( 'https://s.ytimg.com/yts/jsbin/html5player-en_US-vflCGk6yw/html5player.js', '4646B5181C6C3020DF1D9C7FCFEA.AD80ABF70C39BD369CCCAE780AFBB98FA6B6CB42766249D9488C288', '82C8849D94266724DC6B6AF89BBFA087EACCD963.B93C07FBA084ACAEFCF7C9D1FD0203C6C1815B6B' ), ( 'https://s.ytimg.com/yts/jsbin/html5player-en_US-vflKjOTVq/html5player.js', '312AA52209E3623129A412D56A40F11CB0AF14AE.3EE09501CB14E3BCDC3B2AE808BF3F1D14E7FBF12', '112AA5220913623229A412D56A40F11CB0AF14AE.3EE0950FCB14EEBCDC3B2AE808BF331D14E7FBF3', ) ] _NSIG_TESTS = [ ( 'https://www.youtube.com/s/player/9216d1f7/player_ias.vflset/en_US/base.js', 'SLp9F5bwjAdhE9F-', 'gWnb9IK2DJ8Q1w', ), ( 'https://www.youtube.com/s/player/f8cb7a3b/player_ias.vflset/en_US/base.js', 'oBo2h5euWy6osrUt', 'ivXHpm7qJjJN', ), ( 'https://www.youtube.com/s/player/2dfe380c/player_ias.vflset/en_US/base.js', 'oBo2h5euWy6osrUt', '3DIBbn3qdQ', ), ( 'https://www.youtube.com/s/player/f1ca6900/player_ias.vflset/en_US/base.js', 'cu3wyu6LQn2hse', 'jvxetvmlI9AN9Q', ), ( 'https://www.youtube.com/s/player/8040e515/player_ias.vflset/en_US/base.js', 'wvOFaY-yjgDuIEg5', 'HkfBFDHmgw4rsw', ), ( 'https://www.youtube.com/s/player/e06dea74/player_ias.vflset/en_US/base.js', 'AiuodmaDDYw8d3y4bf', 'ankd8eza2T6Qmw', ), ] class TestPlayerInfo(unittest.TestCase): def test_youtube_extract_player_info(self): PLAYER_URLS = ( ('https://www.youtube.com/s/player/64dddad9/player_ias.vflset/en_US/base.js', '64dddad9'), ('https://www.youtube.com/s/player/64dddad9/player_ias.vflset/fr_FR/base.js', '64dddad9'), ('https://www.youtube.com/s/player/64dddad9/player-plasma-ias-phone-en_US.vflset/base.js', '64dddad9'), ('https://www.youtube.com/s/player/64dddad9/player-plasma-ias-phone-de_DE.vflset/base.js', '64dddad9'), ('https://www.youtube.com/s/player/64dddad9/player-plasma-ias-tablet-en_US.vflset/base.js', '64dddad9'), # obsolete ('https://www.youtube.com/yts/jsbin/player_ias-vfle4-e03/en_US/base.js', 'vfle4-e03'), ('https://www.youtube.com/yts/jsbin/player_ias-vfl49f_g4/en_US/base.js', 'vfl49f_g4'), ('https://www.youtube.com/yts/jsbin/player_ias-vflCPQUIL/en_US/base.js', 'vflCPQUIL'), ('https://www.youtube.com/yts/jsbin/player-vflzQZbt7/en_US/base.js', 'vflzQZbt7'), ('https://www.youtube.com/yts/jsbin/player-en_US-vflaxXRn1/base.js', 'vflaxXRn1'), ('https://s.ytimg.com/yts/jsbin/html5player-en_US-vflXGBaUN.js', 'vflXGBaUN'), ('https://s.ytimg.com/yts/jsbin/html5player-en_US-vflKjOTVq/html5player.js', 'vflKjOTVq'), ) for player_url, expected_player_id in PLAYER_URLS: player_id = YoutubeIE._extract_player_info(player_url) self.assertEqual(player_id, expected_player_id) class TestSignature(unittest.TestCase): def setUp(self): TEST_DIR = os.path.dirname(os.path.abspath(__file__)) self.TESTDATA_DIR = os.path.join(TEST_DIR, 'testdata/sigs') if not os.path.exists(self.TESTDATA_DIR): os.mkdir(self.TESTDATA_DIR) def tearDown(self): try: for f in os.listdir(self.TESTDATA_DIR): os.remove(f) except OSError: pass def t_factory(name, sig_func, url_pattern): def make_tfunc(url, sig_input, expected_sig): m = url_pattern.match(url) assert m, '%r should follow URL format' % url test_id = m.group('id') def test_func(self): basename = 'player-{0}-{1}.js'.format(name, test_id) fn = os.path.join(self.TESTDATA_DIR, basename) if not os.path.exists(fn): compat_urlretrieve(url, fn) with io.open(fn, encoding='utf-8') as testf: jscode = testf.read() self.assertEqual(sig_func(jscode, sig_input), expected_sig) test_func.__name__ = str('test_{0}_js_{1}'.format(name, test_id)) setattr(TestSignature, test_func.__name__, test_func) return make_tfunc def signature(jscode, sig_input): func = YoutubeIE(FakeYDL())._parse_sig_js(jscode) src_sig = ( compat_str(string.printable[:sig_input]) if isinstance(sig_input, int) else sig_input) return func(src_sig) def n_sig(jscode, sig_input): funcname = YoutubeIE(FakeYDL())._extract_n_function_name(jscode) return JSInterpreter(jscode).call_function(funcname, sig_input) make_sig_test = t_factory( 'signature', signature, re.compile(r'.*-(?P<id>[a-zA-Z0-9_-]+)(?:/watch_as3|/html5player)?\.[a-z]+$')) for test_spec in _SIG_TESTS: make_sig_test(*test_spec) make_nsig_test = t_factory( 'nsig', n_sig, re.compile(r'.+/player/(?P<id>[a-zA-Z0-9_-]+)/.+.js$')) for test_spec in _NSIG_TESTS: make_nsig_test(*test_spec) if __name__ == '__main__': unittest.main() ```
{ "source": "jeroenj/yt-dlp", "score": 2 }
#### File: yt-dlp/yt_dlp/compat.py ```python import asyncio import base64 import collections import ctypes import getpass import html import html.parser import http import http.client import http.cookiejar import http.cookies import http.server import itertools import optparse import os import re import shlex import shutil import socket import struct import subprocess import sys import tokenize import urllib import xml.etree.ElementTree as etree from subprocess import DEVNULL # HTMLParseError has been deprecated in Python 3.3 and removed in # Python 3.5. Introducing dummy exception for Python >3.5 for compatible # and uniform cross-version exception handling class compat_HTMLParseError(Exception): pass # compat_ctypes_WINFUNCTYPE = ctypes.WINFUNCTYPE # will not work since ctypes.WINFUNCTYPE does not exist in UNIX machines def compat_ctypes_WINFUNCTYPE(*args, **kwargs): return ctypes.WINFUNCTYPE(*args, **kwargs) class _TreeBuilder(etree.TreeBuilder): def doctype(self, name, pubid, system): pass def compat_etree_fromstring(text): return etree.XML(text, parser=etree.XMLParser(target=_TreeBuilder())) compat_os_name = os._name if os.name == 'java' else os.name if compat_os_name == 'nt': def compat_shlex_quote(s): return s if re.match(r'^[-_\w./]+$', s) else '"%s"' % s.replace('"', '\\"') else: from shlex import quote as compat_shlex_quote def compat_ord(c): if type(c) is int: return c else: return ord(c) def compat_setenv(key, value, env=os.environ): env[key] = value if compat_os_name == 'nt' and sys.version_info < (3, 8): # os.path.realpath on Windows does not follow symbolic links # prior to Python 3.8 (see https://bugs.python.org/issue9949) def compat_realpath(path): while os.path.islink(path): path = os.path.abspath(os.readlink(path)) return path else: compat_realpath = os.path.realpath def compat_print(s): assert isinstance(s, compat_str) print(s) # Fix https://github.com/ytdl-org/youtube-dl/issues/4223 # See http://bugs.python.org/issue9161 for what is broken def workaround_optparse_bug9161(): op = optparse.OptionParser() og = optparse.OptionGroup(op, 'foo') try: og.add_option('-t') except TypeError: real_add_option = optparse.OptionGroup.add_option def _compat_add_option(self, *args, **kwargs): enc = lambda v: ( v.encode('ascii', 'replace') if isinstance(v, compat_str) else v) bargs = [enc(a) for a in args] bkwargs = dict( (k, enc(v)) for k, v in kwargs.items()) return real_add_option(self, *bargs, **bkwargs) optparse.OptionGroup.add_option = _compat_add_option try: compat_Pattern = re.Pattern except AttributeError: compat_Pattern = type(re.compile('')) try: compat_Match = re.Match except AttributeError: compat_Match = type(re.compile('').match('')) try: compat_asyncio_run = asyncio.run # >= 3.7 except AttributeError: def compat_asyncio_run(coro): try: loop = asyncio.get_event_loop() except RuntimeError: loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(coro) asyncio.run = compat_asyncio_run try: # >= 3.7 asyncio.tasks.all_tasks except AttributeError: asyncio.tasks.all_tasks = asyncio.tasks.Task.all_tasks try: import websockets as compat_websockets except ImportError: compat_websockets = None # Python 3.8+ does not honor %HOME% on windows, but this breaks compatibility with youtube-dl # See https://github.com/yt-dlp/yt-dlp/issues/792 # https://docs.python.org/3/library/os.path.html#os.path.expanduser if compat_os_name in ('nt', 'ce') and 'HOME' in os.environ: _userhome = os.environ['HOME'] def compat_expanduser(path): if not path.startswith('~'): return path i = path.replace('\\', '/', 1).find('/') # ~user if i < 0: i = len(path) userhome = os.path.join(os.path.dirname(_userhome), path[1:i]) if i > 1 else _userhome return userhome + path[i:] else: compat_expanduser = os.path.expanduser try: from Cryptodome.Cipher import AES as compat_pycrypto_AES except ImportError: try: from Crypto.Cipher import AES as compat_pycrypto_AES except ImportError: compat_pycrypto_AES = None try: import brotlicffi as compat_brotli except ImportError: try: import brotli as compat_brotli except ImportError: compat_brotli = None WINDOWS_VT_MODE = False if compat_os_name == 'nt' else None def windows_enable_vt_mode(): # TODO: Do this the proper way https://bugs.python.org/issue30075 if compat_os_name != 'nt': return global WINDOWS_VT_MODE startupinfo = subprocess.STARTUPINFO() startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW try: subprocess.Popen('', shell=True, startupinfo=startupinfo) WINDOWS_VT_MODE = True except Exception: pass # Deprecated compat_basestring = str compat_chr = chr compat_filter = filter compat_input = input compat_integer_types = (int, ) compat_kwargs = lambda kwargs: kwargs compat_map = map compat_numeric_types = (int, float, complex) compat_str = str compat_xpath = lambda xpath: xpath compat_zip = zip compat_collections_abc = collections.abc compat_HTMLParser = html.parser.HTMLParser compat_HTTPError = urllib.error.HTTPError compat_Struct = struct.Struct compat_b64decode = base64.b64decode compat_cookiejar = http.cookiejar compat_cookiejar_Cookie = compat_cookiejar.Cookie compat_cookies = http.cookies compat_cookies_SimpleCookie = compat_cookies.SimpleCookie compat_etree_Element = etree.Element compat_etree_register_namespace = etree.register_namespace compat_get_terminal_size = shutil.get_terminal_size compat_getenv = os.getenv compat_getpass = getpass.getpass compat_html_entities = html.entities compat_html_entities_html5 = compat_html_entities.html5 compat_http_client = http.client compat_http_server = http.server compat_itertools_count = itertools.count compat_parse_qs = urllib.parse.parse_qs compat_shlex_split = shlex.split compat_socket_create_connection = socket.create_connection compat_struct_pack = struct.pack compat_struct_unpack = struct.unpack compat_subprocess_get_DEVNULL = lambda: DEVNULL compat_tokenize_tokenize = tokenize.tokenize compat_urllib_error = urllib.error compat_urllib_parse = urllib.parse compat_urllib_parse_quote = urllib.parse.quote compat_urllib_parse_quote_plus = urllib.parse.quote_plus compat_urllib_parse_unquote = urllib.parse.unquote compat_urllib_parse_unquote_plus = urllib.parse.unquote_plus compat_urllib_parse_unquote_to_bytes = urllib.parse.unquote_to_bytes compat_urllib_parse_urlencode = urllib.parse.urlencode compat_urllib_parse_urlparse = urllib.parse.urlparse compat_urllib_parse_urlunparse = urllib.parse.urlunparse compat_urllib_request = urllib.request compat_urllib_request_DataHandler = urllib.request.DataHandler compat_urllib_response = urllib.response compat_urlparse = urllib.parse compat_urlretrieve = urllib.request.urlretrieve compat_xml_parse_error = etree.ParseError # Set public objects __all__ = [ 'WINDOWS_VT_MODE', 'compat_HTMLParseError', 'compat_HTMLParser', 'compat_HTTPError', 'compat_Match', 'compat_Pattern', 'compat_Struct', 'compat_asyncio_run', 'compat_b64decode', 'compat_basestring', 'compat_brotli', 'compat_chr', 'compat_collections_abc', 'compat_cookiejar', 'compat_cookiejar_Cookie', 'compat_cookies', 'compat_cookies_SimpleCookie', 'compat_ctypes_WINFUNCTYPE', 'compat_etree_Element', 'compat_etree_fromstring', 'compat_etree_register_namespace', 'compat_expanduser', 'compat_filter', 'compat_get_terminal_size', 'compat_getenv', 'compat_getpass', 'compat_html_entities', 'compat_html_entities_html5', 'compat_http_client', 'compat_http_server', 'compat_input', 'compat_integer_types', 'compat_itertools_count', 'compat_kwargs', 'compat_map', 'compat_numeric_types', 'compat_ord', 'compat_os_name', 'compat_parse_qs', 'compat_print', 'compat_pycrypto_AES', 'compat_realpath', 'compat_setenv', 'compat_shlex_quote', 'compat_shlex_split', 'compat_socket_create_connection', 'compat_str', 'compat_struct_pack', 'compat_struct_unpack', 'compat_subprocess_get_DEVNULL', 'compat_tokenize_tokenize', 'compat_urllib_error', 'compat_urllib_parse', 'compat_urllib_parse_quote', 'compat_urllib_parse_quote_plus', 'compat_urllib_parse_unquote', 'compat_urllib_parse_unquote_plus', 'compat_urllib_parse_unquote_to_bytes', 'compat_urllib_parse_urlencode', 'compat_urllib_parse_urlparse', 'compat_urllib_parse_urlunparse', 'compat_urllib_request', 'compat_urllib_request_DataHandler', 'compat_urllib_response', 'compat_urlparse', 'compat_urlretrieve', 'compat_websockets', 'compat_xml_parse_error', 'compat_xpath', 'compat_zip', 'windows_enable_vt_mode', 'workaround_optparse_bug9161', ] ``` #### File: yt_dlp/extractor/adn.py ```python from __future__ import unicode_literals import base64 import binascii import json import os import random from .common import InfoExtractor from ..aes import aes_cbc_decrypt_bytes, unpad_pkcs7 from ..compat import ( compat_HTTPError, compat_b64decode, ) from ..utils import ( ass_subtitles_timecode, bytes_to_intlist, bytes_to_long, ExtractorError, float_or_none, int_or_none, intlist_to_bytes, long_to_bytes, pkcs1pad, strip_or_none, try_get, unified_strdate, urlencode_postdata, ) class ADNIE(InfoExtractor): IE_DESC = 'Anime Digital Network' _VALID_URL = r'https?://(?:www\.)?animedigitalnetwork\.fr/video/[^/]+/(?P<id>\d+)' _TEST = { 'url': 'http://animedigitalnetwork.fr/video/blue-exorcist-kyoto-saga/7778-episode-1-debut-des-hostilites', 'md5': '0319c99885ff5547565cacb4f3f9348d', 'info_dict': { 'id': '7778', 'ext': 'mp4', 'title': 'Blue Exorcist - Kyôto Saga - Episode 1', 'description': 'md5:2f7b5aa76edbc1a7a92cedcda8a528d5', 'series': 'Blue Exorcist - Kyôto Saga', 'duration': 1467, 'release_date': '20170106', 'comment_count': int, 'average_rating': float, 'season_number': 2, 'episode': 'Début des hostilités', 'episode_number': 1, } } _NETRC_MACHINE = 'animedigitalnetwork' _BASE_URL = 'http://animedigitalnetwork.fr' _API_BASE_URL = 'https://gw.api.animedigitalnetwork.fr/' _PLAYER_BASE_URL = _API_BASE_URL + 'player/' _HEADERS = {} _LOGIN_ERR_MESSAGE = 'Unable to log in' _RSA_KEY = (0x9B42B08905199A5CCE2026274399CA560ECB209EE9878A708B1C0812E1BB8CB5D1FB7441861147C1A1F2F3A0476DD63A9CAC20D3E983613346850AA6CB38F16DC7D720FD7D86FC6E5B3D5BBC72E14CD0BF9E869F2CEA2CCAD648F1DCE38F1FF916CEFB2D339B64AA0264372344BC775E265E8A852F88144AB0BD9AA06C1A4ABB, 65537) _POS_ALIGN_MAP = { 'start': 1, 'end': 3, } _LINE_ALIGN_MAP = { 'middle': 8, 'end': 4, } def _get_subtitles(self, sub_url, video_id): if not sub_url: return None enc_subtitles = self._download_webpage( sub_url, video_id, 'Downloading subtitles location', fatal=False) or '{}' subtitle_location = (self._parse_json(enc_subtitles, video_id, fatal=False) or {}).get('location') if subtitle_location: enc_subtitles = self._download_webpage( subtitle_location, video_id, 'Downloading subtitles data', fatal=False, headers={'Origin': 'https://animedigitalnetwork.fr'}) if not enc_subtitles: return None # http://animedigitalnetwork.fr/components/com_vodvideo/videojs/adn-vjs.min.js dec_subtitles = unpad_pkcs7(aes_cbc_decrypt_bytes( compat_b64decode(enc_subtitles[24:]), binascii.unhexlify(self._K + 'ab9f52f5baae7c72'), compat_b64decode(enc_subtitles[:24]))) subtitles_json = self._parse_json(dec_subtitles.decode(), None, fatal=False) if not subtitles_json: return None subtitles = {} for sub_lang, sub in subtitles_json.items(): ssa = '''[Script Info] ScriptType:V4.00 [V4 Styles] Format: Name,Fontname,Fontsize,PrimaryColour,SecondaryColour,TertiaryColour,BackColour,Bold,Italic,BorderStyle,Outline,Shadow,Alignment,MarginL,MarginR,MarginV,AlphaLevel,Encoding Style: Default,Arial,18,16777215,16777215,16777215,0,-1,0,1,1,0,2,20,20,20,0,0 [Events] Format: Marked,Start,End,Style,Name,MarginL,MarginR,MarginV,Effect,Text''' for current in sub: start, end, text, line_align, position_align = ( float_or_none(current.get('startTime')), float_or_none(current.get('endTime')), current.get('text'), current.get('lineAlign'), current.get('positionAlign')) if start is None or end is None or text is None: continue alignment = self._POS_ALIGN_MAP.get(position_align, 2) + self._LINE_ALIGN_MAP.get(line_align, 0) ssa += os.linesep + 'Dialogue: Marked=0,%s,%s,Default,,0,0,0,,%s%s' % ( ass_subtitles_timecode(start), ass_subtitles_timecode(end), '{\\a%d}' % alignment if alignment != 2 else '', text.replace('\n', '\\N').replace('<i>', '{\\i1}').replace('</i>', '{\\i0}')) if sub_lang == 'vostf': sub_lang = 'fr' subtitles.setdefault(sub_lang, []).extend([{ 'ext': 'json', 'data': json.dumps(sub), }, { 'ext': 'ssa', 'data': ssa, }]) return subtitles def _perform_login(self, username, password): try: access_token = (self._download_json( self._API_BASE_URL + 'authentication/login', None, 'Logging in', self._LOGIN_ERR_MESSAGE, fatal=False, data=urlencode_postdata({ 'password': password, 'rememberMe': False, 'source': 'Web', 'username': username, })) or {}).get('accessToken') if access_token: self._HEADERS = {'authorization': 'Bearer ' + access_token} except ExtractorError as e: message = None if isinstance(e.cause, compat_HTTPError) and e.cause.code == 401: resp = self._parse_json( e.cause.read().decode(), None, fatal=False) or {} message = resp.get('message') or resp.get('code') self.report_warning(message or self._LOGIN_ERR_MESSAGE) def _real_extract(self, url): video_id = self._match_id(url) video_base_url = self._PLAYER_BASE_URL + 'video/%s/' % video_id player = self._download_json( video_base_url + 'configuration', video_id, 'Downloading player config JSON metadata', headers=self._HEADERS)['player'] options = player['options'] user = options['user'] if not user.get('hasAccess'): self.raise_login_required() token = self._download_json( user.get('refreshTokenUrl') or (self._PLAYER_BASE_URL + 'refresh/token'), video_id, 'Downloading access token', headers={ 'x-player-refresh-token': user['refreshToken'] }, data=b'')['token'] links_url = try_get(options, lambda x: x['video']['url']) or (video_base_url + 'link') self._K = ''.join([random.choice('0123456789abcdef') for _ in range(16)]) message = bytes_to_intlist(json.dumps({ 'k': self._K, 't': token, })) # Sometimes authentication fails for no good reason, retry with # a different random padding links_data = None for _ in range(3): padded_message = intlist_to_bytes(pkcs1pad(message, 128)) n, e = self._RSA_KEY encrypted_message = long_to_bytes(pow(bytes_to_long(padded_message), e, n)) authorization = base64.b64encode(encrypted_message).decode() try: links_data = self._download_json( links_url, video_id, 'Downloading links JSON metadata', headers={ 'X-Player-Token': authorization }, query={ 'freeWithAds': 'true', 'adaptive': 'false', 'withMetadata': 'true', 'source': 'Web' }) break except ExtractorError as e: if not isinstance(e.cause, compat_HTTPError): raise e if e.cause.code == 401: # This usually goes away with a different random pkcs1pad, so retry continue error = self._parse_json(e.cause.read(), video_id) message = error.get('message') if e.cause.code == 403 and error.get('code') == 'player-bad-geolocation-country': self.raise_geo_restricted(msg=message) raise ExtractorError(message) else: raise ExtractorError('Giving up retrying') links = links_data.get('links') or {} metas = links_data.get('metadata') or {} sub_url = (links.get('subtitles') or {}).get('all') video_info = links_data.get('video') or {} title = metas['title'] formats = [] for format_id, qualities in (links.get('streaming') or {}).items(): if not isinstance(qualities, dict): continue for quality, load_balancer_url in qualities.items(): load_balancer_data = self._download_json( load_balancer_url, video_id, 'Downloading %s %s JSON metadata' % (format_id, quality), fatal=False) or {} m3u8_url = load_balancer_data.get('location') if not m3u8_url: continue m3u8_formats = self._extract_m3u8_formats( m3u8_url, video_id, 'mp4', 'm3u8_native', m3u8_id=format_id, fatal=False) if format_id == 'vf': for f in m3u8_formats: f['language'] = 'fr' formats.extend(m3u8_formats) self._sort_formats(formats) video = (self._download_json( self._API_BASE_URL + 'video/%s' % video_id, video_id, 'Downloading additional video metadata', fatal=False) or {}).get('video') or {} show = video.get('show') or {} return { 'id': video_id, 'title': title, 'description': strip_or_none(metas.get('summary') or video.get('summary')), 'thumbnail': video_info.get('image') or player.get('image'), 'formats': formats, 'subtitles': self.extract_subtitles(sub_url, video_id), 'episode': metas.get('subtitle') or video.get('name'), 'episode_number': int_or_none(video.get('shortNumber')), 'series': show.get('title'), 'season_number': int_or_none(video.get('season')), 'duration': int_or_none(video_info.get('duration') or video.get('duration')), 'release_date': unified_strdate(video.get('releaseDate')), 'average_rating': float_or_none(video.get('rating') or metas.get('rating')), 'comment_count': int_or_none(video.get('commentsCount')), } ``` #### File: yt_dlp/extractor/cctv.py ```python from __future__ import unicode_literals import re from .common import InfoExtractor from ..compat import compat_str from ..utils import ( float_or_none, try_get, unified_timestamp, ) class CCTVIE(InfoExtractor): IE_DESC = '央视网' _VALID_URL = r'https?://(?:(?:[^/]+)\.(?:cntv|cctv)\.(?:com|cn)|(?:www\.)?ncpa-classic\.com)/(?:[^/]+/)*?(?P<id>[^/?#&]+?)(?:/index)?(?:\.s?html|[?#&]|$)' _TESTS = [{ # fo.addVariable("videoCenterId","id") 'url': 'http://sports.cntv.cn/2016/02/12/ARTIaBRxv4rTT1yWf1frW2wi160212.shtml', 'md5': 'd61ec00a493e09da810bf406a078f691', 'info_dict': { 'id': '5ecdbeab623f4973b40ff25f18b174e8', 'ext': 'mp4', 'title': '[NBA]二少联手砍下46分 雷霆主场击败鹈鹕(快讯)', 'description': 'md5:7e14a5328dc5eb3d1cd6afbbe0574e95', 'duration': 98, 'uploader': 'songjunjie', 'timestamp': 1455279956, 'upload_date': '20160212', }, }, { # var guid = "id" 'url': 'http://tv.cctv.com/2016/02/05/VIDEUS7apq3lKrHG9Dncm03B160205.shtml', 'info_dict': { 'id': 'efc5d49e5b3b4ab2b34f3a502b73d3ae', 'ext': 'mp4', 'title': '[赛车]“车王”舒马赫恢复情况成谜(快讯)', 'description': '2月4日,蒙特泽莫罗透露了关于“车王”舒马赫恢复情况,但情况是否属实遭到了质疑。', 'duration': 37, 'uploader': 'shujun', 'timestamp': 1454677291, 'upload_date': '20160205', }, 'params': { 'skip_download': True, }, }, { # changePlayer('id') 'url': 'http://english.cntv.cn/special/four_comprehensives/index.shtml', 'info_dict': { 'id': '4bb9bb4db7a6471ba85fdeda5af0381e', 'ext': 'mp4', 'title': 'NHnews008 ANNUAL POLITICAL SEASON', 'description': 'Four Comprehensives', 'duration': 60, 'uploader': 'zhangyunlei', 'timestamp': 1425385521, 'upload_date': '20150303', }, 'params': { 'skip_download': True, }, }, { # loadvideo('id') 'url': 'http://cctv.cntv.cn/lm/tvseries_russian/yilugesanghua/index.shtml', 'info_dict': { 'id': 'b15f009ff45c43968b9af583fc2e04b2', 'ext': 'mp4', 'title': 'Путь,усыпанный космеями Серия 1', 'description': 'Путь, усыпанный космеями', 'duration': 2645, 'uploader': 'renxue', 'timestamp': 1477479241, 'upload_date': '20161026', }, 'params': { 'skip_download': True, }, }, { # var initMyAray = 'id' 'url': 'http://www.ncpa-classic.com/2013/05/22/VIDE1369219508996867.shtml', 'info_dict': { 'id': 'a194cfa7f18c426b823d876668325946', 'ext': 'mp4', 'title': '小泽征尔音乐塾 音乐梦想无国界', 'duration': 2173, 'timestamp': 1369248264, 'upload_date': '20130522', }, 'params': { 'skip_download': True, }, }, { # var ids = ["id"] 'url': 'http://www.ncpa-classic.com/clt/more/416/index.shtml', 'info_dict': { 'id': 'a8606119a4884588a79d81c02abecc16', 'ext': 'mp3', 'title': '来自维也纳的新年贺礼', 'description': 'md5:f13764ae8dd484e84dd4b39d5bcba2a7', 'duration': 1578, 'uploader': 'djy', 'timestamp': 1482942419, 'upload_date': '20161228', }, 'params': { 'skip_download': True, }, 'expected_warnings': ['Failed to download m3u8 information'], }, { 'url': 'http://ent.cntv.cn/2016/01/18/ARTIjprSSJH8DryTVr5Bx8Wb160118.shtml', 'only_matching': True, }, { 'url': 'http://tv.cntv.cn/video/C39296/e0210d949f113ddfb38d31f00a4e5c44', 'only_matching': True, }, { 'url': 'http://english.cntv.cn/2016/09/03/VIDEhnkB5y9AgHyIEVphCEz1160903.shtml', 'only_matching': True, }, { 'url': 'http://tv.cctv.com/2016/09/07/VIDE5C1FnlX5bUywlrjhxXOV160907.shtml', 'only_matching': True, }, { 'url': 'http://tv.cntv.cn/video/C39296/95cfac44cabd3ddc4a9438780a4e5c44', 'only_matching': True, }] def _real_extract(self, url): video_id = self._match_id(url) webpage = self._download_webpage(url, video_id) video_id = self._search_regex( [r'var\s+guid\s*=\s*["\']([\da-fA-F]+)', r'videoCenterId["\']\s*,\s*["\']([\da-fA-F]+)', r'changePlayer\s*\(\s*["\']([\da-fA-F]+)', r'load[Vv]ideo\s*\(\s*["\']([\da-fA-F]+)', r'var\s+initMyAray\s*=\s*["\']([\da-fA-F]+)', r'var\s+ids\s*=\s*\[["\']([\da-fA-F]+)'], webpage, 'video id') data = self._download_json( 'http://vdn.apps.cntv.cn/api/getHttpVideoInfo.do', video_id, query={ 'pid': video_id, 'url': url, 'idl': 32, 'idlr': 32, 'modifyed': 'false', }) title = data['title'] formats = [] video = data.get('video') if isinstance(video, dict): for quality, chapters_key in enumerate(('lowChapters', 'chapters')): video_url = try_get( video, lambda x: x[chapters_key][0]['url'], compat_str) if video_url: formats.append({ 'url': video_url, 'format_id': 'http', 'quality': quality, # Sample clip 'preference': -10 }) hls_url = try_get(data, lambda x: x['hls_url'], compat_str) if hls_url: hls_url = re.sub(r'maxbr=\d+&?', '', hls_url) formats.extend(self._extract_m3u8_formats( hls_url, video_id, 'mp4', entry_protocol='m3u8_native', m3u8_id='hls', fatal=False)) self._sort_formats(formats) uploader = data.get('editer_name') description = self._html_search_meta( 'description', webpage, default=None) timestamp = unified_timestamp(data.get('f_pgmtime')) duration = float_or_none(try_get(video, lambda x: x['totalLength'])) return { 'id': video_id, 'title': title, 'description': description, 'uploader': uploader, 'timestamp': timestamp, 'duration': duration, 'formats': formats, } ``` #### File: yt_dlp/extractor/digitalconcerthall.py ```python from __future__ import unicode_literals from .common import InfoExtractor from ..utils import ( ExtractorError, parse_resolution, traverse_obj, try_get, urlencode_postdata, ) class DigitalConcertHallIE(InfoExtractor): IE_DESC = 'DigitalConcertHall extractor' _VALID_URL = r'https?://(?:www\.)?digitalconcerthall\.com/(?P<language>[a-z]+)/concert/(?P<id>[0-9]+)' _OAUTH_URL = 'https://api.digitalconcerthall.com/v2/oauth2/token' _ACCESS_TOKEN = None _NETRC_MACHINE = 'digitalconcerthall' _TESTS = [{ 'note': 'Playlist with only one video', 'url': 'https://www.digitalconcerthall.com/en/concert/53201', 'info_dict': { 'id': '53201-1', 'ext': 'mp4', 'composer': '<NAME>', 'title': '[Magic Night]', 'thumbnail': r're:^https?://images.digitalconcerthall.com/cms/thumbnails.*\.jpg$', 'upload_date': '20210624', 'timestamp': 1624548600, 'duration': 2798, 'album_artist': 'Members of the Berliner Philharmoniker / <NAME>', }, 'params': {'skip_download': 'm3u8'}, }, { 'note': 'Concert with several works and an interview', 'url': 'https://www.digitalconcerthall.com/en/concert/53785', 'info_dict': { 'id': '53785', 'album_artist': 'Berliner Philharmoniker / <NAME>', 'title': 'Kir<NAME>renko conducts Mendelssohn and Shostakovich', }, 'params': {'skip_download': 'm3u8'}, 'playlist_count': 3, }] def _perform_login(self, username, password): token_response = self._download_json( self._OAUTH_URL, None, 'Obtaining token', errnote='Unable to obtain token', data=urlencode_postdata({ 'affiliate': 'none', 'grant_type': 'device', 'device_vendor': 'unknown', 'app_id': 'dch.webapp', 'app_version': '1.0.0', 'client_secret': '<KEY>', }), headers={ 'Content-Type': 'application/x-www-form-urlencoded', }) self._ACCESS_TOKEN = token_response['access_token'] try: self._download_json( self._OAUTH_URL, None, note='Logging in', errnote='Unable to login', data=urlencode_postdata({ 'grant_type': 'password', 'username': username, 'password': password, }), headers={ 'Content-Type': 'application/x-www-form-urlencoded', 'Referer': 'https://www.digitalconcerthall.com', 'Authorization': f'Bearer {self._ACCESS_TOKEN}' }) except ExtractorError: self.raise_login_required(msg='Login info incorrect') def _real_initialize(self): if not self._ACCESS_TOKEN: self.raise_login_required(method='password') def _entries(self, items, language, **kwargs): for item in items: video_id = item['id'] stream_info = self._download_json( self._proto_relative_url(item['_links']['streams']['href']), video_id, headers={ 'Accept': 'application/json', 'Authorization': f'Bearer {self._ACCESS_TOKEN}', 'Accept-Language': language }) m3u8_url = traverse_obj( stream_info, ('channel', lambda x: x.startswith('vod_mixed'), 'stream', 0, 'url'), get_all=False) formats = self._extract_m3u8_formats(m3u8_url, video_id, 'mp4', 'm3u8_native', fatal=False) self._sort_formats(formats) yield { 'id': video_id, 'title': item.get('title'), 'composer': item.get('name_composer'), 'url': m3u8_url, 'formats': formats, 'duration': item.get('duration_total'), 'timestamp': traverse_obj(item, ('date', 'published')), 'description': item.get('short_description') or stream_info.get('short_description'), **kwargs, 'chapters': [{ 'start_time': chapter.get('time'), 'end_time': try_get(chapter, lambda x: x['time'] + x['duration']), 'title': chapter.get('text'), } for chapter in item['cuepoints']] if item.get('cuepoints') else None, } def _real_extract(self, url): language, video_id = self._match_valid_url(url).group('language', 'id') if not language: language = 'en' thumbnail_url = self._html_search_regex( r'(https?://images\.digitalconcerthall\.com/cms/thumbnails/.*\.jpg)', self._download_webpage(url, video_id), 'thumbnail') thumbnails = [{ 'url': thumbnail_url, **parse_resolution(thumbnail_url) }] vid_info = self._download_json( f'https://api.digitalconcerthall.com/v2/concert/{video_id}', video_id, headers={ 'Accept': 'application/json', 'Accept-Language': language }) album_artist = ' / '.join(traverse_obj(vid_info, ('_links', 'artist', ..., 'name')) or '') return { '_type': 'playlist', 'id': video_id, 'title': vid_info.get('title'), 'entries': self._entries(traverse_obj(vid_info, ('_embedded', ..., ...)), language, thumbnails=thumbnails, album_artist=album_artist), 'thumbnails': thumbnails, 'album_artist': album_artist, } ``` #### File: yt_dlp/extractor/doodstream.py ```python from __future__ import unicode_literals import string import random import time from .common import InfoExtractor class DoodStreamIE(InfoExtractor): _VALID_URL = r'https?://(?:www\.)?dood\.(?:to|watch)/[ed]/(?P<id>[a-z0-9]+)' _TESTS = [{ 'url': 'http://dood.to/e/5s1wmbdacezb', 'md5': '4568b83b31e13242b3f1ff96c55f0595', 'info_dict': { 'id': '5s1wmbdacezb', 'ext': 'mp4', 'title': 'Kat Wonders - Monthly May 2020', 'description': 'Kat Wonders - Monthly May 2020 | DoodStream.com', 'thumbnail': 'https://img.doodcdn.com/snaps/flyus84qgl2fsk4g.jpg', } }, { 'url': 'http://dood.watch/d/5s1wmbdacezb', 'md5': '4568b83b31e13242b3f1ff96c55f0595', 'info_dict': { 'id': '5s1wmbdacezb', 'ext': 'mp4', 'title': 'Kat Wonders - Monthly May 2020', 'description': 'Kat Wonders - Monthly May 2020 | DoodStream.com', 'thumbnail': 'https://img.doodcdn.com/snaps/flyus84qgl2fsk4g.jpg', } }, { 'url': 'https://dood.to/d/jzrxn12t2s7n', 'md5': '3207e199426eca7c2aa23c2872e6728a', 'info_dict': { 'id': 'jzrxn12t2s7n', 'ext': 'mp4', 'title': '<NAME>ute ALLWAYSWELL', 'description': '<NAME> ALLWAYSWELL | DoodStream.com', 'thumbnail': 'https://img.doodcdn.com/snaps/8edqd5nppkac3x8u.jpg', } }] def _real_extract(self, url): video_id = self._match_id(url) url = f'https://dood.to/e/{video_id}' webpage = self._download_webpage(url, video_id) title = self._html_search_meta(['og:title', 'twitter:title'], webpage, default=None) thumb = self._html_search_meta(['og:image', 'twitter:image'], webpage, default=None) token = self._html_search_regex(r'[?&]token=([a-z0-9]+)[&\']', webpage, 'token') description = self._html_search_meta( ['og:description', 'description', 'twitter:description'], webpage, default=None) headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:53.0) Gecko/20100101 Firefox/66.0', 'referer': url } pass_md5 = self._html_search_regex(r'(/pass_md5.*?)\'', webpage, 'pass_md5') final_url = ''.join(( self._download_webpage(f'https://dood.to{pass_md5}', video_id, headers=headers), *(random.choice(string.ascii_letters + string.digits) for _ in range(10)), f'?token={token}&expiry={int(time.time() * 1000)}', )) return { 'id': video_id, 'title': title, 'url': final_url, 'http_headers': headers, 'ext': 'mp4', 'description': description, 'thumbnail': thumb, } ``` #### File: yt_dlp/extractor/eroprofile.py ```python from __future__ import unicode_literals import re from .common import InfoExtractor from ..compat import compat_urllib_parse_urlencode from ..utils import ( ExtractorError, merge_dicts, ) class EroProfileIE(InfoExtractor): _VALID_URL = r'https?://(?:www\.)?eroprofile\.com/m/videos/view/(?P<id>[^/]+)' _LOGIN_URL = 'http://www.eroprofile.com/auth/auth.php?' _NETRC_MACHINE = 'eroprofile' _TESTS = [{ 'url': 'http://www.eroprofile.com/m/videos/view/sexy-babe-softcore', 'md5': 'c26f351332edf23e1ea28ce9ec9de32f', 'info_dict': { 'id': '3733775', 'display_id': 'sexy-babe-softcore', 'ext': 'm4v', 'title': 'sexy babe softcore', 'thumbnail': r're:https?://.*\.jpg', 'age_limit': 18, }, 'skip': 'Video not found', }, { 'url': 'http://www.eroprofile.com/m/videos/view/Try-It-On-Pee_cut_2-wmv-4shared-com-file-sharing-download-movie-file', 'md5': '1baa9602ede46ce904c431f5418d8916', 'info_dict': { 'id': '1133519', 'ext': 'm4v', 'title': 'Try It On Pee_cut_2.wmv - 4shared.com - file sharing - download movie file', 'thumbnail': r're:https?://.*\.jpg', 'age_limit': 18, }, 'skip': 'Requires login', }] def _perform_login(self, username, password): query = compat_urllib_parse_urlencode({ 'username': username, 'password': password, 'url': 'http://www.eroprofile.com/', }) login_url = self._LOGIN_URL + query login_page = self._download_webpage(login_url, None, False) m = re.search(r'Your username or password was incorrect\.', login_page) if m: raise ExtractorError( 'Wrong username and/or password.', expected=True) self.report_login() redirect_url = self._search_regex( r'<script[^>]+?src="([^"]+)"', login_page, 'login redirect url') self._download_webpage(redirect_url, None, False) def _real_extract(self, url): display_id = self._match_id(url) webpage = self._download_webpage(url, display_id) m = re.search(r'You must be logged in to view this video\.', webpage) if m: self.raise_login_required('This video requires login') video_id = self._search_regex( [r"glbUpdViews\s*\('\d*','(\d+)'", r'p/report/video/(\d+)'], webpage, 'video id', default=None) title = self._html_search_regex( (r'Title:</th><td>([^<]+)</td>', r'<h1[^>]*>(.+?)</h1>'), webpage, 'title') info = self._parse_html5_media_entries(url, webpage, video_id)[0] return merge_dicts(info, { 'id': video_id, 'display_id': display_id, 'title': title, 'age_limit': 18, }) class EroProfileAlbumIE(InfoExtractor): _VALID_URL = r'https?://(?:www\.)?eroprofile\.com/m/videos/album/(?P<id>[^/]+)' IE_NAME = 'EroProfile:album' _TESTS = [{ 'url': 'https://www.eroprofile.com/m/videos/album/BBW-2-893', 'info_dict': { 'id': 'BBW-2-893', 'title': 'BBW 2' }, 'playlist_mincount': 486, }, ] def _extract_from_page(self, page): for url in re.findall(r'href=".*?(/m/videos/view/[^"]+)"', page): yield self.url_result(f'https://www.eroprofile.com{url}', EroProfileIE.ie_key()) def _entries(self, playlist_id, first_page): yield from self._extract_from_page(first_page) page_urls = re.findall(rf'href=".*?(/m/videos/album/{playlist_id}\?pnum=(\d+))"', first_page) max_page = max(int(n) for _, n in page_urls) for n in range(2, max_page + 1): url = f'https://www.eroprofile.com/m/videos/album/{playlist_id}?pnum={n}' yield from self._extract_from_page( self._download_webpage(url, playlist_id, note=f'Downloading playlist page {int(n) - 1}')) def _real_extract(self, url): playlist_id = self._match_id(url) first_page = self._download_webpage(url, playlist_id, note='Downloading playlist') playlist_title = self._search_regex( r'<title>Album: (.*) - EroProfile</title>', first_page, 'playlist_title') return self.playlist_result(self._entries(playlist_id, first_page), playlist_id, playlist_title) ``` #### File: yt_dlp/extractor/iprima.py ```python from __future__ import unicode_literals import re import time from .common import InfoExtractor from ..utils import ( determine_ext, js_to_json, urlencode_postdata, ExtractorError, parse_qs ) class IPrimaIE(InfoExtractor): _VALID_URL = r'https?://(?!cnn)(?:[^/]+)\.iprima\.cz/(?:[^/]+/)*(?P<id>[^/?#&]+)' _GEO_BYPASS = False _NETRC_MACHINE = 'iprima' _LOGIN_URL = 'https://auth.iprima.cz/oauth2/login' _TOKEN_URL = 'https://auth.iprima.cz/oauth2/token' access_token = None _TESTS = [{ 'url': 'https://prima.iprima.cz/particka/92-epizoda', 'info_dict': { 'id': 'p51388', 'ext': 'mp4', 'title': 'Partička (92)', 'description': 'md5:859d53beae4609e6dd7796413f1b6cac', 'upload_date': '20201103', 'timestamp': 1604437480, }, 'params': { 'skip_download': True, # m3u8 download }, }, { 'url': 'http://play.iprima.cz/particka/particka-92', 'only_matching': True, }, { # geo restricted 'url': 'http://play.iprima.cz/closer-nove-pripady/closer-nove-pripady-iv-1', 'only_matching': True, }, { 'url': 'https://prima.iprima.cz/my-little-pony/mapa-znameni-2-2', 'only_matching': True, }, { 'url': 'https://prima.iprima.cz/porady/jak-se-stavi-sen/rodina-rathousova-praha', 'only_matching': True, }, { 'url': 'http://www.iprima.cz/filmy/desne-rande', 'only_matching': True, }, { 'url': 'https://zoom.iprima.cz/10-nejvetsich-tajemstvi-zahad/posvatna-mista-a-stavby', 'only_matching': True, }, { 'url': 'https://krimi.iprima.cz/mraz-0/sebevrazdy', 'only_matching': True, }, { 'url': 'https://cool.iprima.cz/derava-silnice-nevadi', 'only_matching': True, }, { 'url': 'https://love.iprima.cz/laska-az-za-hrob/slib-dany-bratrovi', 'only_matching': True, }] def _perform_login(self, username, password): if self.access_token: return login_page = self._download_webpage( self._LOGIN_URL, None, note='Downloading login page', errnote='Downloading login page failed') login_form = self._hidden_inputs(login_page) login_form.update({ '_email': username, '_password': password}) _, login_handle = self._download_webpage_handle( self._LOGIN_URL, None, data=urlencode_postdata(login_form), note='Logging in') code = parse_qs(login_handle.geturl()).get('code')[0] if not code: raise ExtractorError('Login failed', expected=True) token_request_data = { 'scope': 'openid+email+profile+phone+address+offline_access', 'client_id': 'prima_sso', 'grant_type': 'authorization_code', 'code': code, 'redirect_uri': 'https://auth.iprima.cz/sso/auth-check'} token_data = self._download_json( self._TOKEN_URL, None, note='Downloading token', errnote='Downloading token failed', data=urlencode_postdata(token_request_data)) self.access_token = token_data.get('access_token') if self.access_token is None: raise ExtractorError('Getting token failed', expected=True) def _real_initialize(self): if not self.access_token: self.raise_login_required('Login is required to access any iPrima content', method='password') def _raise_access_error(self, error_code): if error_code == 'PLAY_GEOIP_DENIED': self.raise_geo_restricted(countries=['CZ'], metadata_available=True) elif error_code is not None: self.raise_no_formats('Access to stream infos forbidden', expected=True) def _real_extract(self, url): video_id = self._match_id(url) webpage = self._download_webpage(url, video_id) title = self._html_search_meta( ['og:title', 'twitter:title'], webpage, 'title', default=None) video_id = self._search_regex(( r'productId\s*=\s*([\'"])(?P<id>p\d+)\1', r'pproduct_id\s*=\s*([\'"])(?P<id>p\d+)\1'), webpage, 'real id', group='id') metadata = self._download_json( f'https://api.play-backend.iprima.cz/api/v1//products/id-{video_id}/play', video_id, note='Getting manifest URLs', errnote='Failed to get manifest URLs', headers={'X-OTT-Access-Token': self.access_token}, expected_status=403) self._raise_access_error(metadata.get('errorCode')) stream_infos = metadata.get('streamInfos') formats = [] if stream_infos is None: self.raise_no_formats('Reading stream infos failed', expected=True) else: for manifest in stream_infos: manifest_type = manifest.get('type') manifest_url = manifest.get('url') ext = determine_ext(manifest_url) if manifest_type == 'HLS' or ext == 'm3u8': formats += self._extract_m3u8_formats( manifest_url, video_id, 'mp4', entry_protocol='m3u8_native', m3u8_id='hls', fatal=False) elif manifest_type == 'DASH' or ext == 'mpd': formats += self._extract_mpd_formats( manifest_url, video_id, mpd_id='dash', fatal=False) self._sort_formats(formats) final_result = self._search_json_ld(webpage, video_id) or {} final_result.update({ 'id': video_id, 'title': title, 'thumbnail': self._html_search_meta( ['thumbnail', 'og:image', 'twitter:image'], webpage, 'thumbnail', default=None), 'formats': formats, 'description': self._html_search_meta( ['description', 'og:description', 'twitter:description'], webpage, 'description', default=None)}) return final_result class IPrimaCNNIE(InfoExtractor): _VALID_URL = r'https?://cnn\.iprima\.cz/(?:[^/]+/)*(?P<id>[^/?#&]+)' _GEO_BYPASS = False _TESTS = [{ 'url': 'https://cnn.iprima.cz/porady/strunc/24072020-koronaviru-mam-plne-zuby-strasit-druhou-vlnou-je-absurdni-rika-senatorka-dernerova', 'info_dict': { 'id': 'p716177', 'ext': 'mp4', 'title': 'md5:277c6b1ed0577e51b40ddd35602ff43e', }, 'params': { 'skip_download': 'm3u8' } }] def _real_extract(self, url): video_id = self._match_id(url) self._set_cookie('play.iprima.cz', 'ott_adult_confirmed', '1') webpage = self._download_webpage(url, video_id) title = self._og_search_title( webpage, default=None) or self._search_regex( r'<h1>([^<]+)', webpage, 'title') video_id = self._search_regex( (r'<iframe[^>]+\bsrc=["\'](?:https?:)?//(?:api\.play-backend\.iprima\.cz/prehravac/embedded|prima\.iprima\.cz/[^/]+/[^/]+)\?.*?\bid=(p\d+)', r'data-product="([^"]+)">', r'id=["\']player-(p\d+)"', r'playerId\s*:\s*["\']player-(p\d+)', r'\bvideos\s*=\s*["\'](p\d+)'), webpage, 'real id') playerpage = self._download_webpage( 'http://play.iprima.cz/prehravac/init', video_id, note='Downloading player', query={ '_infuse': 1, '_ts': round(time.time()), 'productId': video_id, }, headers={'Referer': url}) formats = [] def extract_formats(format_url, format_key=None, lang=None): ext = determine_ext(format_url) new_formats = [] if format_key == 'hls' or ext == 'm3u8': new_formats = self._extract_m3u8_formats( format_url, video_id, 'mp4', entry_protocol='m3u8_native', m3u8_id='hls', fatal=False) elif format_key == 'dash' or ext == 'mpd': return new_formats = self._extract_mpd_formats( format_url, video_id, mpd_id='dash', fatal=False) if lang: for f in new_formats: if not f.get('language'): f['language'] = lang formats.extend(new_formats) options = self._parse_json( self._search_regex( r'(?s)(?:TDIPlayerOptions|playerOptions)\s*=\s*({.+?});\s*\]\]', playerpage, 'player options', default='{}'), video_id, transform_source=js_to_json, fatal=False) if options: for key, tracks in options.get('tracks', {}).items(): if not isinstance(tracks, list): continue for track in tracks: src = track.get('src') if src: extract_formats(src, key.lower(), track.get('lang')) if not formats: for _, src in re.findall(r'src["\']\s*:\s*(["\'])(.+?)\1', playerpage): extract_formats(src) if not formats and '>GEO_IP_NOT_ALLOWED<' in playerpage: self.raise_geo_restricted(countries=['CZ'], metadata_available=True) self._sort_formats(formats) return { 'id': video_id, 'title': title, 'thumbnail': self._og_search_thumbnail(webpage, default=None), 'formats': formats, 'description': self._og_search_description(webpage, default=None), } ``` #### File: yt_dlp/extractor/lecturio.py ```python from __future__ import unicode_literals import re from .common import InfoExtractor from ..utils import ( clean_html, determine_ext, ExtractorError, float_or_none, int_or_none, str_or_none, url_or_none, urlencode_postdata, urljoin, ) class LecturioBaseIE(InfoExtractor): _API_BASE_URL = 'https://app.lecturio.com/api/en/latest/html5/' _LOGIN_URL = 'https://app.lecturio.com/en/login' _NETRC_MACHINE = 'lecturio' def _perform_login(self, username, password): # Sets some cookies _, urlh = self._download_webpage_handle( self._LOGIN_URL, None, 'Downloading login popup') def is_logged(url_handle): return self._LOGIN_URL not in url_handle.geturl() # Already logged in if is_logged(urlh): return login_form = { 'signin[email]': username, 'signin[password]': password, 'signin[remember]': 'on', } response, urlh = self._download_webpage_handle( self._LOGIN_URL, None, 'Logging in', data=urlencode_postdata(login_form)) # Logged in successfully if is_logged(urlh): return errors = self._html_search_regex( r'(?s)<ul[^>]+class=["\']error_list[^>]+>(.+?)</ul>', response, 'errors', default=None) if errors: raise ExtractorError('Unable to login: %s' % errors, expected=True) raise ExtractorError('Unable to log in') class LecturioIE(LecturioBaseIE): _VALID_URL = r'''(?x) https:// (?: app\.lecturio\.com/([^/]+/(?P<nt>[^/?#&]+)\.lecture|(?:\#/)?lecture/c/\d+/(?P<id>\d+))| (?:www\.)?lecturio\.de/[^/]+/(?P<nt_de>[^/?#&]+)\.vortrag ) ''' _TESTS = [{ 'url': 'https://app.lecturio.com/medical-courses/important-concepts-and-terms-introduction-to-microbiology.lecture#tab/videos', 'md5': '9a42cf1d8282a6311bf7211bbde26fde', 'info_dict': { 'id': '39634', 'ext': 'mp4', 'title': 'Important Concepts and Terms — Introduction to Microbiology', }, 'skip': 'Requires lecturio account credentials', }, { 'url': 'https://www.lecturio.de/jura/oeffentliches-recht-staatsexamen.vortrag', 'only_matching': True, }, { 'url': 'https://app.lecturio.com/#/lecture/c/6434/39634', 'only_matching': True, }] _CC_LANGS = { 'Arabic': 'ar', 'Bulgarian': 'bg', 'German': 'de', 'English': 'en', 'Spanish': 'es', 'Persian': 'fa', 'French': 'fr', 'Japanese': 'ja', 'Polish': 'pl', 'Pashto': 'ps', 'Russian': 'ru', } def _real_extract(self, url): mobj = self._match_valid_url(url) nt = mobj.group('nt') or mobj.group('nt_de') lecture_id = mobj.group('id') display_id = nt or lecture_id api_path = 'lectures/' + lecture_id if lecture_id else 'lecture/' + nt + '.json' video = self._download_json( self._API_BASE_URL + api_path, display_id) title = video['title'].strip() if not lecture_id: pid = video.get('productId') or video.get('uid') if pid: spid = pid.split('_') if spid and len(spid) == 2: lecture_id = spid[1] formats = [] for format_ in video['content']['media']: if not isinstance(format_, dict): continue file_ = format_.get('file') if not file_: continue ext = determine_ext(file_) if ext == 'smil': # smil contains only broken RTMP formats anyway continue file_url = url_or_none(file_) if not file_url: continue label = str_or_none(format_.get('label')) filesize = int_or_none(format_.get('fileSize')) f = { 'url': file_url, 'format_id': label, 'filesize': float_or_none(filesize, invscale=1000) } if label: mobj = re.match(r'(\d+)p\s*\(([^)]+)\)', label) if mobj: f.update({ 'format_id': mobj.group(2), 'height': int(mobj.group(1)), }) formats.append(f) self._sort_formats(formats) subtitles = {} automatic_captions = {} captions = video.get('captions') or [] for cc in captions: cc_url = cc.get('url') if not cc_url: continue cc_label = cc.get('translatedCode') lang = cc.get('languageCode') or self._search_regex( r'/([a-z]{2})_', cc_url, 'lang', default=cc_label.split()[0] if cc_label else 'en') original_lang = self._search_regex( r'/[a-z]{2}_([a-z]{2})_', cc_url, 'original lang', default=None) sub_dict = (automatic_captions if 'auto-translated' in cc_label or original_lang else subtitles) sub_dict.setdefault(self._CC_LANGS.get(lang, lang), []).append({ 'url': cc_url, }) return { 'id': lecture_id or nt, 'title': title, 'formats': formats, 'subtitles': subtitles, 'automatic_captions': automatic_captions, } class LecturioCourseIE(LecturioBaseIE): _VALID_URL = r'https://app\.lecturio\.com/(?:[^/]+/(?P<nt>[^/?#&]+)\.course|(?:#/)?course/c/(?P<id>\d+))' _TESTS = [{ 'url': 'https://app.lecturio.com/medical-courses/microbiology-introduction.course#/', 'info_dict': { 'id': 'microbiology-introduction', 'title': 'Microbiology: Introduction', 'description': 'md5:13da8500c25880c6016ae1e6d78c386a', }, 'playlist_count': 45, 'skip': 'Requires lecturio account credentials', }, { 'url': 'https://app.lecturio.com/#/course/c/6434', 'only_matching': True, }] def _real_extract(self, url): nt, course_id = self._match_valid_url(url).groups() display_id = nt or course_id api_path = 'courses/' + course_id if course_id else 'course/content/' + nt + '.json' course = self._download_json( self._API_BASE_URL + api_path, display_id) entries = [] for lecture in course.get('lectures', []): lecture_id = str_or_none(lecture.get('id')) lecture_url = lecture.get('url') if lecture_url: lecture_url = urljoin(url, lecture_url) else: lecture_url = 'https://app.lecturio.com/#/lecture/c/%s/%s' % (course_id, lecture_id) entries.append(self.url_result( lecture_url, ie=LecturioIE.ie_key(), video_id=lecture_id)) return self.playlist_result( entries, display_id, course.get('title'), clean_html(course.get('description'))) class LecturioDeCourseIE(LecturioBaseIE): _VALID_URL = r'https://(?:www\.)?lecturio\.de/[^/]+/(?P<id>[^/?#&]+)\.kurs' _TEST = { 'url': 'https://www.lecturio.de/jura/grundrechte.kurs', 'only_matching': True, } def _real_extract(self, url): display_id = self._match_id(url) webpage = self._download_webpage(url, display_id) entries = [] for mobj in re.finditer( r'(?s)<td[^>]+\bdata-lecture-id=["\'](?P<id>\d+).+?\bhref=(["\'])(?P<url>(?:(?!\2).)+\.vortrag)\b[^>]+>', webpage): lecture_url = urljoin(url, mobj.group('url')) lecture_id = mobj.group('id') entries.append(self.url_result( lecture_url, ie=LecturioIE.ie_key(), video_id=lecture_id)) title = self._search_regex( r'<h1[^>]*>([^<]+)', webpage, 'title', default=None) return self.playlist_result(entries, display_id, title) ``` #### File: yt_dlp/extractor/myspass.py ```python from __future__ import unicode_literals from .common import InfoExtractor from ..compat import compat_str from ..utils import ( int_or_none, parse_duration, xpath_text, ) class MySpassIE(InfoExtractor): _VALID_URL = r'https?://(?:www\.)?myspass\.de/(?:[^/]+/)*(?P<id>\d+)/?[^/]*$' _TESTS = [{ 'url': 'http://www.myspass.de/myspass/shows/tvshows/absolute-mehrheit/Absolute-Mehrheit-vom-17022013-Die-Highlights-Teil-2--/11741/', 'md5': '0b49f4844a068f8b33f4b7c88405862b', 'info_dict': { 'id': '11741', 'ext': 'mp4', 'description': 'md5:9f0db5044c8fe73f528a390498f7ce9b', 'title': '17.02.2013 - Die Highlights, Teil 2', 'thumbnail': r're:.*\.jpg', 'duration': 323.0, 'episode': '17.02.2013 - Die Highlights, Teil 2', 'season_id': '544', 'episode_number': 1, 'series': 'Absolute Mehrheit', 'season_number': 2, 'season': 'Season 2', }, }, { 'url': 'https://www.myspass.de/shows/tvshows/tv-total/Novak-Puffovic-bei-bester-Laune--/44996/', 'md5': 'eb28b7c5e254192046e86ebaf7deac8f', 'info_dict': { 'id': '44996', 'ext': 'mp4', 'description': 'md5:74c7f886e00834417f1e427ab0da6121', 'title': 'Novak Puffovic bei bester Laune', 'thumbnail': r're:.*\.jpg', 'episode_number': 8, 'episode': 'Novak Puffovic bei bester Laune', 'series': 'TV total', 'season': 'Season 19', 'season_id': '987', 'duration': 2941.0, 'season_number': 19, }, }, { 'url': 'https://www.myspass.de/channels/tv-total-raabigramm/17033/20831/', 'md5': '7b293a6b9f3a7acdd29304c8d0dbb7cc', 'info_dict': { 'id': '20831', 'ext': 'mp4', 'description': 'Gefühle pur: Schaut euch die ungeschnittene Version von <NAME> an die Moderationsgrazie von Welt, Verona Feldbusch, an.', 'title': 'Raabigramm Verona Feldbusch', 'thumbnail': r're:.*\.jpg', 'episode_number': 6, 'episode': 'Raabigramm Verona Feldbusch', 'series': 'TV total', 'season': 'Season 1', 'season_id': '34', 'duration': 105.0, 'season_number': 1, }, }] def _real_extract(self, url): video_id = self._match_id(url) metadata = self._download_xml('http://www.myspass.de/myspass/includes/apps/video/getvideometadataxml.php?id=' + video_id, video_id) title = xpath_text(metadata, 'title', fatal=True) video_url = xpath_text(metadata, 'url_flv', 'download url', True) video_id_int = int(video_id) for group in self._search_regex(r'/myspass2009/\d+/(\d+)/(\d+)/(\d+)/', video_url, 'myspass', group=(1, 2, 3), default=[]): group_int = int(group) if group_int > video_id_int: video_url = video_url.replace(group, compat_str(group_int // video_id_int)) return { 'id': video_id, 'url': video_url, 'title': title, 'thumbnail': xpath_text(metadata, 'imagePreview'), 'description': xpath_text(metadata, 'description'), 'duration': parse_duration(xpath_text(metadata, 'duration')), 'series': xpath_text(metadata, 'format'), 'season_number': int_or_none(xpath_text(metadata, 'season')), 'season_id': xpath_text(metadata, 'season_id'), 'episode': title, 'episode_number': int_or_none(xpath_text(metadata, 'episode')), } ``` #### File: yt_dlp/extractor/nfb.py ```python from __future__ import unicode_literals from .common import InfoExtractor from ..utils import int_or_none class NFBIE(InfoExtractor): _VALID_URL = r'https?://(?:www\.)?nfb\.ca/film/(?P<id>[^/?#&]+)' _TESTS = [{ 'url': 'https://www.nfb.ca/film/trafficopter/', 'info_dict': { 'id': 'trafficopter', 'ext': 'mp4', 'title': 'Trafficopter', 'description': 'md5:060228455eb85cf88785c41656776bc0', 'thumbnail': r're:^https?://.*\.jpg$', 'uploader': '<NAME>', 'release_year': 1972, }, }] def _real_extract(self, url): video_id = self._match_id(url) webpage = self._download_webpage('https://www.nfb.ca/film/%s/' % video_id, video_id) iframe = self._html_search_regex( r'<[^>]+\bid=["\']player-iframe["\'][^>]*src=["\']([^"\']+)', webpage, 'iframe', default=None, fatal=True) if iframe.startswith('/'): iframe = f'https://www.nfb.ca{iframe}' player = self._download_webpage(iframe, video_id) source = self._html_search_regex( r'source:\s*\'([^\']+)', player, 'source', default=None, fatal=True) formats, subtitles = self._extract_m3u8_formats_and_subtitles(source, video_id, ext='mp4') self._sort_formats(formats) return { 'id': video_id, 'title': self._html_search_regex( r'<[^>]+\bid=["\']titleHeader["\'][^>]*>\s*<h1[^>]*>\s*([^<]+?)\s*</h1>', webpage, 'title', default=None), 'description': self._html_search_regex( r'<[^>]+\bid=["\']tabSynopsis["\'][^>]*>\s*<p[^>]*>\s*([^<]+)', webpage, 'description', default=None), 'thumbnail': self._html_search_regex( r'poster:\s*\'([^\']+)', player, 'thumbnail', default=None), 'uploader': self._html_search_regex( r'<[^>]+\bitemprop=["\']name["\'][^>]*>([^<]+)', webpage, 'uploader', default=None), 'release_year': int_or_none(self._html_search_regex( r'<[^>]+\bitemprop=["\']datePublished["\'][^>]*>([^<]+)', webpage, 'release_year', default=None)), 'formats': formats, 'subtitles': subtitles, } ``` #### File: yt_dlp/extractor/openload.py ```python from __future__ import unicode_literals import json import os import subprocess import tempfile from ..compat import ( compat_urlparse, compat_kwargs, ) from ..utils import ( check_executable, encodeArgument, ExtractorError, get_exe_version, is_outdated_version, Popen, ) def cookie_to_dict(cookie): cookie_dict = { 'name': cookie.name, 'value': cookie.value, } if cookie.port_specified: cookie_dict['port'] = cookie.port if cookie.domain_specified: cookie_dict['domain'] = cookie.domain if cookie.path_specified: cookie_dict['path'] = cookie.path if cookie.expires is not None: cookie_dict['expires'] = cookie.expires if cookie.secure is not None: cookie_dict['secure'] = cookie.secure if cookie.discard is not None: cookie_dict['discard'] = cookie.discard try: if (cookie.has_nonstandard_attr('httpOnly') or cookie.has_nonstandard_attr('httponly') or cookie.has_nonstandard_attr('HttpOnly')): cookie_dict['httponly'] = True except TypeError: pass return cookie_dict def cookie_jar_to_list(cookie_jar): return [cookie_to_dict(cookie) for cookie in cookie_jar] class PhantomJSwrapper(object): """PhantomJS wrapper class This class is experimental. """ _TEMPLATE = r''' phantom.onError = function(msg, trace) {{ var msgStack = ['PHANTOM ERROR: ' + msg]; if(trace && trace.length) {{ msgStack.push('TRACE:'); trace.forEach(function(t) {{ msgStack.push(' -> ' + (t.file || t.sourceURL) + ': ' + t.line + (t.function ? ' (in function ' + t.function +')' : '')); }}); }} console.error(msgStack.join('\n')); phantom.exit(1); }}; var page = require('webpage').create(); var fs = require('fs'); var read = {{ mode: 'r', charset: 'utf-8' }}; var write = {{ mode: 'w', charset: 'utf-8' }}; JSON.parse(fs.read("{cookies}", read)).forEach(function(x) {{ phantom.addCookie(x); }}); page.settings.resourceTimeout = {timeout}; page.settings.userAgent = "{ua}"; page.onLoadStarted = function() {{ page.evaluate(function() {{ delete window._phantom; delete window.callPhantom; }}); }}; var saveAndExit = function() {{ fs.write("{html}", page.content, write); fs.write("{cookies}", JSON.stringify(phantom.cookies), write); phantom.exit(); }}; page.onLoadFinished = function(status) {{ if(page.url === "") {{ page.setContent(fs.read("{html}", read), "{url}"); }} else {{ {jscode} }} }}; page.open(""); ''' _TMP_FILE_NAMES = ['script', 'html', 'cookies'] @staticmethod def _version(): return get_exe_version('phantomjs', version_re=r'([0-9.]+)') def __init__(self, extractor, required_version=None, timeout=10000): self._TMP_FILES = {} self.exe = check_executable('phantomjs', ['-v']) if not self.exe: raise ExtractorError('PhantomJS executable not found in PATH, ' 'download it from http://phantomjs.org', expected=True) self.extractor = extractor if required_version: version = self._version() if is_outdated_version(version, required_version): self.extractor._downloader.report_warning( 'Your copy of PhantomJS is outdated, update it to version ' '%s or newer if you encounter any errors.' % required_version) self.options = { 'timeout': timeout, } for name in self._TMP_FILE_NAMES: tmp = tempfile.NamedTemporaryFile(delete=False) tmp.close() self._TMP_FILES[name] = tmp def __del__(self): for name in self._TMP_FILE_NAMES: try: os.remove(self._TMP_FILES[name].name) except (IOError, OSError, KeyError): pass def _save_cookies(self, url): cookies = cookie_jar_to_list(self.extractor._downloader.cookiejar) for cookie in cookies: if 'path' not in cookie: cookie['path'] = '/' if 'domain' not in cookie: cookie['domain'] = compat_urlparse.urlparse(url).netloc with open(self._TMP_FILES['cookies'].name, 'wb') as f: f.write(json.dumps(cookies).encode('utf-8')) def _load_cookies(self): with open(self._TMP_FILES['cookies'].name, 'rb') as f: cookies = json.loads(f.read().decode('utf-8')) for cookie in cookies: if cookie['httponly'] is True: cookie['rest'] = {'httpOnly': None} if 'expiry' in cookie: cookie['expire_time'] = cookie['expiry'] self.extractor._set_cookie(**compat_kwargs(cookie)) def get(self, url, html=None, video_id=None, note=None, note2='Executing JS on webpage', headers={}, jscode='saveAndExit();'): """ Downloads webpage (if needed) and executes JS Params: url: website url html: optional, html code of website video_id: video id note: optional, displayed when downloading webpage note2: optional, displayed when executing JS headers: custom http headers jscode: code to be executed when page is loaded Returns tuple with: * downloaded website (after JS execution) * anything you print with `console.log` (but not inside `page.execute`!) In most cases you don't need to add any `jscode`. It is executed in `page.onLoadFinished`. `saveAndExit();` is mandatory, use it instead of `phantom.exit()` It is possible to wait for some element on the webpage, for example: var check = function() { var elementFound = page.evaluate(function() { return document.querySelector('#b.done') !== null; }); if(elementFound) saveAndExit(); else window.setTimeout(check, 500); } page.evaluate(function(){ document.querySelector('#a').click(); }); check(); """ if 'saveAndExit();' not in jscode: raise ExtractorError('`saveAndExit();` not found in `jscode`') if not html: html = self.extractor._download_webpage(url, video_id, note=note, headers=headers) with open(self._TMP_FILES['html'].name, 'wb') as f: f.write(html.encode('utf-8')) self._save_cookies(url) replaces = self.options replaces['url'] = url user_agent = headers.get('User-Agent') or self.extractor.get_param('http_headers')['User-Agent'] replaces['ua'] = user_agent.replace('"', '\\"') replaces['jscode'] = jscode for x in self._TMP_FILE_NAMES: replaces[x] = self._TMP_FILES[x].name.replace('\\', '\\\\').replace('"', '\\"') with open(self._TMP_FILES['script'].name, 'wb') as f: f.write(self._TEMPLATE.format(**replaces).encode('utf-8')) if video_id is None: self.extractor.to_screen('%s' % (note2,)) else: self.extractor.to_screen('%s: %s' % (video_id, note2)) p = Popen( [self.exe, '--ssl-protocol=any', self._TMP_FILES['script'].name], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = p.communicate_or_kill() if p.returncode != 0: raise ExtractorError( 'Executing JS failed\n:' + encodeArgument(err)) with open(self._TMP_FILES['html'].name, 'rb') as f: html = f.read().decode('utf-8') self._load_cookies() return (html, encodeArgument(out)) ```
{ "source": "JeroenKnoops/functiondefextractor", "score": 2 }
#### File: JeroenKnoops/functiondefextractor/setup.py ```python import setuptools def get_license(): """ replace the license content while creating the package""" with open("LICENSE.md", "r", encoding="utf8") as fh: license_description = fh.read() return license_description def get_install(): """ replace the install content while creating the package""" with open("INSTALL.md", "r", encoding="utf8") as fh: install_description = fh.read() return install_description def get_maintainers(): """ replace the maintainers content while creating the package""" with open("MAINTAINERS.md", "r", encoding="utf8") as fh: maintainers_description = fh.read() return maintainers_description with open("README.md", "r", encoding="utf8") as fh: long_description = fh.read() if "[INSTALL.md](INSTALL.md)" in long_description: long_description = long_description.replace("[INSTALL.md](INSTALL.md)", str(get_install())) if "[MAINTAINERS.md](MAINTAINERS.md)" in long_description: long_description = long_description.replace("[MAINTAINERS.md](MAINTAINERS.md)", str(get_maintainers())) if "[License.md](License.md)" in long_description: long_description = long_description.replace("[License.md](License.md)", str(get_license())) with open('requirements.txt') as f: required = f.read().splitlines() setuptools.setup( name="functiondefextractor", version="0.0.2", author="Brijesh", author_email="<EMAIL>", description="Function Definition Extractor", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/bkk003/FunctionDefExtractor", packages=setuptools.find_packages(include=['functiondefextractor'], exclude=['test', '*.test', '*.test.*']), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], install_requires=required, python_requires='>=3.6', ) ```
{ "source": "JeroenKnoops/TextSimilarityProcessor", "score": 4 }
#### File: TextSimilarityProcessor/test/test_unit.py ```python import unittest from collections import Counter import similarity_processor.similarity_core as cc class MyUnitTestCase(unittest.TestCase): """This class verifies the individual functionality of the units: get_cosine() text_to_vector() check_ tolerance() methods with valid and invalid inputs, which verifies the function behaviour""" def test_get_positive_cosine(self): """This method checks the value returned by the cosine_core.get_cosine() for vec1, vec2: Input vector from the texts to be compared - positive cosine """ positive_cosine = cc.get_cosine(Counter({"hello": 16, "Language": 30, "python": 66}), Counter({"Mac": 9, "MANGO": 27, "python": 88, "hello": 5})) self.assertEqual(0.8562387195638202, positive_cosine, "Value should not be equal to 0") def test_get_negative_cosine(self): """This method checks the value returned by the cosine_core.get_cosine() for vec1, vec2: Input vector from the texts to be compared - negative cosine value""" negative_cosine = cc.get_cosine(Counter({"hello_World": 99}), Counter({"TEST": 888})) self.assertEqual(0.0, negative_cosine, "Value should be 0.0") def test_get_cosine_same(self): """This method checks the value returned by the cosine_core.get_cosine() for vec1, vec2: Input vector from the texts to be compared""" positive_cosine = cc.get_cosine(Counter({"hello": 16, "Language": 30, "python": 66}), Counter({"hello": 16, "Language": 30, "python": 66})) self.assertEqual(1.0, positive_cosine, "Value should not be equal to 0") def test_get_cosine_none(self): """This method checks the value returned by the cosine_core.get_cosine() for vec1, vec2: Input vector from the texts to be compared""" _cosine = cc.get_cosine(Counter({"": 0}), Counter({"": 0})) self.assertEqual(0.0, _cosine, "Value should be equal to 0") def test_text_to_invalid_vector(self): """This method checks the value returned by the cosine_core.text_to_vector() for converting text to vector for invalid input """ negative_text = None try: negative_text = cc.text_to_vector(1234567.988766) except TypeError as err: print('Error: ', str(err)) self.assertIsNone(negative_text, "Vector should not be generated") def test_text_to_valid_vector(self): """This method checks the value returned by the cosine_core.text_to_vector() for converting text to vector for valid input """ text = "What is generator in Python with example Python generators \ are a simple, A Counter is a container that" positive_text = cc.text_to_vector(text) self.assertEqual(Counter, type(positive_text), "Counter vector should be generated from text") if __name__ == '__main__': unittest.main() ```
{ "source": "JeroenKools/covid19", "score": 3 }
#### File: JeroenKools/covid19/interactive_plot.py ```python from covid19_processing import * import ipywidgets as widgets def run(): data = Covid19Processing(False) data.process(rows=0, debug=False) data.set_default_countries([ "World", "California, US", "Mongolia", "United States", "India", "Netherlands"]) widgets.interact(data.plot_interactive, x_metric=["calendar_date", "day_number"], y_metric=["confirmed", "deaths", "active", "new confirmed", "new deaths", "confirmed/population", "active/population", "deaths/population", "new confirmed/population", "new deaths/population", "recent confirmed", "recent deaths", "recent confirmed/population", "recent deaths/population", "deaths/confirmed", "recent deaths/recent confirmed"], smoothing_days=widgets.IntSlider(min=0, max=31, step=1, value=7), use_log_scale=True) ```
{ "source": "jeroenmanders/aws-python", "score": 2 }
#### File: aws/route53/client.py ```python import boto3 class Client(object): _instance = None @staticmethod def get_instance(): if Client._instance == None: Client._instance = Client() return Client._instance def __init__(self): self._client = boto3.client('route53', region_name="us-east-1") def get_client(self): return self._client ``` #### File: aws/ses/ses_mailer.py ```python import boto3 from botocore.exceptions import ClientError class SesMailer(object): def __init__(self): pass def mail(self, sender, recipient, subject, body_text, body_html): CONFIGURATION_SET = "ConfigSet" AWS_REGION = "us-east-1" CHARSET = "UTF-8" client = boto3.client('ses', region_name=AWS_REGION) try: response = client.send_email( Destination={ 'ToAddresses': [ recipient, ], }, Message={ 'Body': { 'Html': { 'Charset': CHARSET, 'Data': body_html, }, 'Text': { 'Charset': CHARSET, 'Data': body_text, }, }, 'Subject': { 'Charset': CHARSET, 'Data': subject, }, }, Source=sender, #ConfigurationSetName=CONFIGURATION_SET, ) # Display an error if something goes wrong. except ClientError as e: print(e.response['Error']['Message']) else: print("Email sent! Message ID:"), print(response['MessageId']) ```
{ "source": "Jeroen-Matthijssens/jsx-lexer", "score": 3 }
#### File: jsx-lexer/tests/test_lexer.py ```python import os from unittest import TestCase from pygments import lexers from jsx import lexer as lexer_mod from jsx.lexer import JsxLexer from .tokens import TOKENS as expected_tokens CURRENT_DIR = os.path.abspath(os.path.dirname(__file__)) lexer = lexers.load_lexer_from_file(lexer_mod.__file__, "JsxLexer") with open(os.path.join(CURRENT_DIR, 'example.jsx'), 'r') as fh: text = fh.read() class JsxLexerTestCase(TestCase): def test_guess_lexer_for_filename(self): guessed_lexer = lexers.guess_lexer_for_filename('test.jsx', text) self.assertEqual(guessed_lexer.name, JsxLexer.name) def test_get_lexer_by_name(self): lexer = lexers.get_lexer_by_name('jsx') self.assertEqual(lexer.name, JsxLexer.name) def test_get_tokens(self): lexer = lexers.get_lexer_by_name('jsx') tokens = lexer.get_tokens(text) self.assertEqual([i for i in tokens], expected_tokens) ```
{ "source": "jeroen-meijer/lightly", "score": 4 }
#### File: local_server/led_controller/led_controller.py ```python from typing import Protocol, Callable class LedController(Protocol): """ A protocol for controlling the LEDs. Fields: - pin: int, the GPIO pin the LEDs are connected to. - count: int, the number of LEDs. - order: str, the order of the LEDs. Methods: - setPixels(pixels: list[tuple[int, int, int]]) -> None, sets the LEDs to the given colors. """ # Define the three fields above pin: int count: int order: str def setPixels(self, pixels: dict[int, tuple[int, int, int]]): """Sets the LEDs at the given indices to the given colors.""" print("Unimplemented method setPixels() called with", pixels) def buildPixels( self, buildColor: Callable[[int], tuple[int, int, int]] ) -> dict[int, tuple[int, int, int]]: """Runs the given pixel builder callback for each pixel in the chain and returns the resulting dictionary of pixels. The callback should take a single argument, which is the current pixel index. """ pixels: dict[int, tuple[int, int, int]] = {} for i in range(self.count): pixel = buildColor(i) if pixel is not None: pixels[i] = pixel return pixels # Neopixel # pixels = neopixel.NeoPixel( # PIXEL_PIN, # NUM_PIXELS, # pixel_order=ORDER, # auto_write=False, # brightness=0.2, # ) # def wheel(pos): # # Input a value 0 to 255 to get a color value. # # The colours are a transition r - g - b - back to r. # if pos < 0 or pos > 255: # r = g = b = 0 # elif pos < 85: # r = int(pos * 3) # g = int(255 - pos * 3) # b = 0 # elif pos < 170: # pos -= 85 # r = int(255 - pos * 3) # g = 0 # b = int(pos * 3) # else: # pos -= 170 # r = 0 # g = int(pos * 3) # b = int(255 - pos * 3) # return (r, g, b) if ORDER in (neopixel.RGB, neopixel.GRB) else (r, g, b, 0) # def rainbow_cycle(wait): # for j in range(255): # for i in range(NUM_PIXELS): # pixel_index = (i * 256 // NUM_PIXELS) + j # pixels[i] = wheel(pixel_index & 255) # pixels.show() # time.sleep(wait) # while True: # # Comment this line out if you have RGBW/GRBW NeoPixels # pixels.fill((255, 0, 0)) # # Uncomment this line if you have RGBW/GRBW NeoPixels # # pixels.fill((255, 0, 0, 0)) # pixels.show() # time.sleep(1) # # Comment this line out if you have RGBW/GRBW NeoPixels # pixels.fill((0, 255, 0)) # # Uncomment this line if you have RGBW/GRBW NeoPixels # # pixels.fill((0, 255, 0, 0)) # pixels.show() # time.sleep(1) # # Comment this line out if you have RGBW/GRBW NeoPixels # pixels.fill((0, 0, 255)) # # Uncomment this line if you have RGBW/GRBW NeoPixels # # pixels.fill((0, 0, 255, 0)) # pixels.show() # time.sleep(1) # rainbow_cycle(0.001) # rainbow cycle with 1ms delay per step # firstArg = sys.argv[1] # print("Arg: ", firstArg) # jsonPayload = json.loads(firstArg) # print("Json payload: ", jsonPayload) # rgb = jsonPayload # pixels = neopixel.NeoPixel(board.D18, 200, brightness=1) # for _i in range(5): # print("%s Blackout" % datetime.now()) # pixels.fill((0, 0, 0)) # time.sleep(0.5) # print("%s Show LED" % datetime.now()) # # G, R, B # pixels.fill((rgb[1], rgb[0], rgb[2])) # pixels.show() # time.sleep(0.5) # print("--------------") ```
{ "source": "jeroenmeulendijks/gdax-tradebot", "score": 4 }
#### File: gdax-tradebot/model/Indicators.py ```python import pandas as pd import matplotlib.pyplot as plt from config import * from stockstats import StockDataFrame from abc import ABC, abstractmethod class Signal(object): BUY = "BUY" SELL = "SELL" value = None class Indicator(ABC): def __init__(self): super().__init__() def plotWithPrice(self): # We assume that an indicator will not be plotted together with the prices # when you want to plot the indicator with the prices override this method # and return True return False @abstractmethod def plot(self, subplot, stock): pass def signal(self, dataframes): return Signal() @classmethod def isEnabled(cls): return (cls.__str__(cls) in INDICATORS) @abstractmethod def __str__(self): pass class EMA(Indicator): def __init__(self): super().__init__() def plotWithPrice(self): return True def plot(self, subplot, stock): plt.plot(stock['datetime'], stock['close_5_ema']) plt.plot(stock['datetime'], stock['close_20_ema']) def signal(self, stock): s = Signal() if stock.shape[0] > 5: EMA5 = stock['close_5_ema'].tail(2).reset_index(drop=True) EMA20 = stock['close_20_ema'].tail(2).reset_index(drop=True) if (EMA5[1] <= EMA20[1]) & (EMA5[0] >= EMA20[0]): s.value = Signal.SELL elif (EMA5[1] >= EMA20[1]) & (EMA5[0] <= EMA20[0]): s.value = Signal.BUY return s def __str__(self): return "EMA" class RSI(Indicator): def __init__(self): super().__init__() def plot(self, subplot, stock): subplot.cla() plt.plot(stock['datetime'], stock['rsi_14']) def __str__(self): return "RSI" def calculateRSI(self, dataframe, period): # Calculate RSI and add to dataframe length = dataframe.shape[0] if (length >= period): delta = dataframe['close'].dropna().apply(float).diff() dUp, dDown = delta.copy(), delta.copy() dUp[dUp < 0] = 0 dDown[dDown > 0] = 0 RollUp = dUp.rolling(window=period).mean() RollDown = dDown.rolling(window=period).mean().abs() RS = RollUp / RollDown RSI = 100.0 - (100.0 / (1.0 + RS)) dataframe['RSI'] = RSI class MACD(Indicator): def __init__(self): super().__init__() def plot(self, subplot, stock): subplot.cla() plt.plot(stock['datetime'], stock['macd']) plt.plot(stock['datetime'], stock['macds']) def signal(self, stock): s = Signal() if stock.shape[0] > 2: signal = stock['macds'].tail(2).reset_index(drop=True) macd = stock['macd'].tail(2).reset_index(drop=True) # If the MACD crosses the signal line upward BUY! if macd[1] > signal[1] and macd[0] <= signal[0]: s.value = Signal.BUY # The other way around. SELL elif macd[1] < signal[1] and macd[0] >= signal[0]: s.value = Signal.SELL # Do nothing if not crossed else: pass return s def __str__(self): return "MACD" class DMI(Indicator): def __init__(self): super().__init__() def plot(self, subplot, stock): subplot.cla() # +DI, default to 14 days plt.plot(stock['datetime'], stock['pdi']) # -DI, default to 14 days plt.plot(stock['datetime'], stock['mdi']) plt.plot(stock['datetime'], stock['adx']) plt.plot(stock['datetime'], stock['adxr']) def __str__(self): return "DMI" ```
{ "source": "JeroenMols/AdventOfCode2021", "score": 3 }
#### File: AdventOfCode2021/day_10/day_10.py ```python def load_input(file_name): a_file = open(file_name, "r") input = [] for line in a_file: input.append(line.strip()) return input def problem_a(): # lines = load_input("day_10_sample.txt") lines = load_input("day_10.txt") char_to_points = {')': 3, ']': 57, '}': 1197, '>': 25137} score = 0 for line in lines: replaced = replace_valid_chucks(line) invalid_chars = get_invalid_chars(replaced) if len(invalid_chars) != 0: score += char_to_points.get(invalid_chars[0]) print('Result: ', score) def problem_b(): # lines = load_input("day_10_sample.txt") lines = load_input("day_10.txt") char_to_points = {'(': 1, '[': 2, '{': 3, '<': 4} scores = [] for line in lines: replaced = replace_valid_chucks(line) if len(get_invalid_chars(replaced)) == 0: score = 0 for char in replaced[::-1]: score = score * 5 + char_to_points[char] scores.append(score) scores.sort() print('Result: ', scores[int(len(scores) / 2)]) def replace_valid_chucks(line): before = line while True: after = before.replace('()', '').replace('[]', '').replace('{}', '').replace('<>', '') if len(after) == len(before): break else: before = after return after def get_invalid_chars(replaced): return replaced.replace('(', '').replace('[', '').replace('{', '').replace('<', '') if __name__ == '__main__': problem_b() ``` #### File: AdventOfCode2021/day_11/day_11.py ```python def load_input(file_name): a_file = open(file_name, "r") input = [] for line in a_file: input.append([Octopus(element, True) for element in line.strip()]) return input class Octopus: value: int flashed: bool processed: bool = False def __init__(self, value, flashed): self.value = int(value) self.flashed = flashed def reset(self): self.flashed = False self.processed = False def step(self): if not self.flashed: self.value += 1 if self.value > 9: self.flashed = True self.value = 0 def __str__(self): return str(self.value) def problem_a(): # octopi = load_input("day_11_sample.txt") octopi = load_input("day_11.txt") flashes = 0 for step in range(0, 100): perform_step(octopi) # Count flashes flashes_in_step = count_flashes(octopi) flashes += flashes_in_step print(step + 1, " flashes: ", flashes_in_step) print_matrix(octopi) print('Result: ', flashes) def problem_b(): # octopi = load_input("day_11_sample.txt") octopi = load_input("day_11.txt") step = 0 while True: step += 1 perform_step(octopi) if count_flashes(octopi) == 100: break print('Result: ', step) def perform_step(octopi): # Reset octopi for x in range(0, len(octopi[0])): for y in range(0, len(octopi)): octopi[y][x].reset() # First add for x in range(0, len(octopi[0])): for y in range(0, len(octopi)): octopi[y][x].step() # Snowball has_more_flashes = True while has_more_flashes: has_more_flashes = False # Process all flashed octopi that haven't been processed yet for x in range(0, len(octopi[0])): for y in range(0, len(octopi)): if octopi[y][x].flashed and not octopi[y][x].processed: # add to neighbours has_more_flashes = True octopi[y][x].processed = True process_neighbours(octopi, x, y) def count_flashes(octopi): flashes_in_step = 0 for x in range(0, len(octopi[0])): for y in range(0, len(octopi)): if octopi[y][x].flashed: flashes_in_step += 1 return flashes_in_step def get_value_or_default(matrix, x, y): if y < 0 or y >= len(matrix) or x < 0 or x >= len(matrix[0]): return Octopus(0, False) else: return matrix[y][x] def process_neighbours(matrix, x, y): get_value_or_default(matrix, x - 1, y - 1).step() get_value_or_default(matrix, x - 1, y).step() get_value_or_default(matrix, x - 1, y + 1).step() get_value_or_default(matrix, x, y - 1).step() get_value_or_default(matrix, x, y + 1).step() get_value_or_default(matrix, x + 1, y - 1).step() get_value_or_default(matrix, x + 1, y).step() get_value_or_default(matrix, x + 1, y + 1).step() def print_matrix(matrix): for y in range(0, len(matrix)): line = '' for x in range(0, len(matrix[0])): line += str(matrix[y][x]) print(line) if __name__ == '__main__': problem_a() ``` #### File: AdventOfCode2021/day_12/day_12.py ```python def load_input(file_name): a_file = open(file_name, "r") input = [] for line in a_file: route = line.strip().split('-') input.append(Path(route[0], route[1])) return input class Path: start: str end: str def __init__(self, start, end): self.start = start self.end = end class Route: nodes: [] allow_one_double_visit: bool def __init__(self, nodes, allow_one_double_visit=False): self.nodes = nodes self.allow_one_double_visit = allow_one_double_visit def end(self): return self.nodes[-1] def can_pass_by(self, node): if node == 'start' and 'start' in self.nodes: return False if node.islower() and node in self.nodes: if not self.allow_one_double_visit: return False elif not self.has_double(): return True else: return False else: return True def has_double(self): lower_nodes = [] for node in self.nodes: if not node.islower(): continue elif node in lower_nodes: return True else: lower_nodes.append(node) return False def finished(self): if self.nodes[-1] == 'end': return True else: return False def __str__(self): to_string = '' for node in self.nodes: to_string += node + ',' return to_string[:-1] def problem_a(): # paths = load_input("day_12_sample.txt") paths = load_input("day_12.txt") new_routes = [Route(['start'])] completed_routes = [] while len(new_routes) > 0: new_routes, completed = routes_step(new_routes, paths, False) completed_routes += completed print_routes(completed_routes) print("Result: ", len(completed_routes)) def problem_b(): # paths = load_input("day_12_sample.txt") paths = load_input("day_12.txt") new_routes = [Route(['start'])] completed_routes = [] while len(new_routes) > 0: new_routes, completed = routes_step(new_routes, paths, True) completed_routes += completed print_routes(completed_routes) print("Result: ", len(completed_routes)) def routes_step(routes, paths, allow_double): new_routes = [] completed_routes = [] for route in routes: if route.finished(): completed_routes.append(route) continue for path in paths: if route.end() == path.start: if route.can_pass_by(path.end): new_nodes = route.nodes.copy() + [path.end] new_routes.append(Route(new_nodes, allow_double)) elif route.end() == path.end: if route.can_pass_by(path.start): new_nodes = route.nodes.copy() + [path.start] new_routes.append(Route(new_nodes, allow_double)) return new_routes, completed_routes def print_routes(routes): for route in routes: print(route) if __name__ == '__main__': problem_b() ``` #### File: AdventOfCode2021/day_14/day_14.py ```python def load_input(file_name): a_file = open(file_name, "r") template = '' insertions = {} for line in a_file: if '->' in line: raw_insertions = line.strip().split(' -> ') insertions[raw_insertions[0]] = raw_insertions[1] elif line.strip() == '': continue else: template = line.strip() return template, insertions def problem_a(): template, insertions = load_input("day_14_sample.txt") # template, insertions = load_input("day_14.txt") processed = perform_insertions(template, insertions, 10) occurrences = get_occurrences(processed) print (occurrences) print("Result: ", max(occurrences.values()) - min(occurrences.values())) def problem_b(): # template, insertions = load_input("day_14_sample.txt") template, insertions = load_input("day_14.txt") times = 40 initial = 20 first_pass = perform_insertions(template, insertions, initial) # Split in smaller shards shards = [] for index in range(0, len(first_pass) - 1): shards.append(first_pass[index] + first_pass[index + 1]) shard_occurrences = process_shards(initial, insertions, shards, times) occurrences = merge_shard_occurrences(shard_occurrences) print("Result: ", max(occurrences.values()) - min(occurrences.values())) def process_shards(initial, insertions, shards, times): cache = {} shard_occurrences = [] for index, shard in enumerate(shards): if index == len(shards) - 1: processed = perform_insertions(shard, insertions, times - initial) shard_occurrences.append(get_occurrences(processed)) else: if shard not in cache.keys(): # Exclude last element as that's duplicate in the next shard. processed = perform_insertions(shard, insertions, times - initial) cache[shard] = get_occurrences(processed[0:len(processed) - 1]) shard_occurrences.append(cache[shard]) print(index,'/', len(shards)) return shard_occurrences def perform_insertions(shard, insertions, times): for step in range(0, times): new_shard = [shard[0]] for index in range(1, len(shard)): new_shard.append(insertions[(shard[index - 1] + shard[index])]) new_shard.append(shard[index]) shard = new_shard.copy() return shard def get_occurrences(template): occurrences = {} for char in template: if char in occurrences: occurrences[char] = occurrences[char] + 1 else: occurrences[char] = 1 return occurrences def merge_shard_occurrences(shard_occurrences): occurrences = {} for shard_occurrences in shard_occurrences: for char in shard_occurrences: if char in occurrences: occurrences[char] = occurrences[char] + shard_occurrences[char] else: occurrences[char] = shard_occurrences[char] return occurrences if __name__ == '__main__': problem_b() ``` #### File: AdventOfCode2021/day_15/day_15.py ```python from dataclasses import dataclass def load_input(file_name): a_file = open(file_name, "r") cavern = [] for line in a_file: cavern.append([int(element) for element in list(line.strip())]) return cavern @dataclass(eq=True, frozen=True) class Path: points: [] cost: int @dataclass(eq=True, frozen=True) class Point: x: int y: int def problem_a(): # cavern = load_input("day_15_sample.txt") cavern = load_input("day_15.txt") min_cost = brute_force_all_paths(cavern) print("Result: ", min_cost) # Does't work, need to use an algorithm like Dykstra or A+ # After 2hours of running, made it to step 571 def problem_b(): # cavern = load_input("day_15_sample.txt") cavern = load_input("day_15.txt") data = repeat_input(cavern, 5) min_cost = brute_force_all_paths(data) print('Result:', min_cost) def repeat_input(cavern, times): data = [] for y in range(0, len(cavern) * times): data.append([]) for x in range(0, len(cavern[0]) * times): value = cavern[(y % len(cavern))][(x % len(cavern[0]))] value = (value + int(y / len(cavern)) + int(x / len(cavern[0]))) if value > 9: value -= 9 data[y].append(value) return data # Very fast approach, but only considers moving down + right def down_right_minimum(cavern): min_cost = [] for y in range(0, len(cavern)): min_cost.append([0] * len(cavern[0])) for y in range(0, len(cavern)): cost_down = get(min_cost, 0, y - 1) + get(cavern, 0, y) min_cost[y][0] = cost_down for x in range(0, len(cavern[0])): cost_right = get(min_cost, x - 1, 0) + get(cavern, x, 0) min_cost[0][x] = cost_right for y in range(1, len(cavern)): for x in range(1, len(cavern[0])): cost_down = get(min_cost, x, y - 1) + get(cavern, x, y) cost_right = get(min_cost, x - 1, y) + get(cavern, x, y) min_cost[y][x] = min(cost_down, cost_right) return min_cost[-1][-1] - cavern[0][0] def get(data, x, y, default=0): if x < 0 or y < 0: return default else: return data[y][x] def print_data(data): for y in range(0, len(data)): line = '' for x in range(0, len(data[0])): line += str(data[y][x]) # if data[y][x] < 10: # line += '0' + str(data[y][x]) + '|' # else: # line += str(data[y][x]) + '|' print(line) # Steps in all directions, tries to smartly discard paths but is still very slow def brute_force_all_paths(cavern): next_paths = [Path([Point(0, 0)], cavern[0][0])] # take path straight down and to right as first guess of minimum min_cost = down_right_minimum(cavern) + cavern[0][0] print(min_cost) end_point = Point(len(cavern[0]) - 1, len(cavern) - 1) for step in range(0, len(cavern) * len(cavern[0])): print(step, '/', len(cavern) * len(cavern[0])) paths = next_paths next_paths_dict = {} for path in paths: next_points = [] if path.points[-1].x > 0: left = Point(path.points[-1].x - 1, path.points[-1].y) next_points.append(left) if path.points[-1].x < len(cavern[0]) - 1: right = Point(path.points[-1].x + 1, path.points[-1].y) next_points.append(right) if path.points[-1].y > 0: up = Point(path.points[-1].x, path.points[-1].y - 1) next_points.append(up) if path.points[-1].y < len(cavern) - 1: down = Point(path.points[-1].x, path.points[-1].y + 1) next_points.append(down) for point in next_points: # Reached end point if point == end_point: new_points = path.points.copy() new_points.append(point) min_cost = min(path.cost + cavern[point.y][point.x], min_cost) continue # Don't allow loops if point in path.points: continue new_points = path.points.copy() new_points.append(point) new_cost = path.cost + cavern[point.y][point.x] # Stop processing path when cost higher than current min if new_cost > min_cost: continue # Only consider path if cheaper than other path to same point elif point in next_paths_dict: cost = next_paths_dict[point].cost if new_cost < cost: next_paths_dict[point] = Path(new_points, new_cost) else: next_paths_dict[point] = Path(new_points, new_cost) next_paths = next_paths_dict.values() if len(next_paths) == 0: print("done") break return min_cost - cavern[0][0] def calculate_min_cost(cavern, paths_to_end): costs = [] for path in paths_to_end: cost = 0 for point in path.points: cost += cavern[point.y][point.x] costs.append(cost) return min(costs) def calculate_cost(cavern, points): cost = 0 for point in points: cost += cavern[point.y][point.x] return cost if __name__ == '__main__': problem_b() ```
{ "source": "jeroenmoons/drl_banana", "score": 3 }
#### File: drl_banana/agent/dqn.py ```python import numpy as np import torch import torch.nn.functional as F import torch.optim as optim from agent.base import UnityAgent from agent.estimate.neural import FullyConnectedNetwork from agent.memory.buffer import ReplayBuffer class DqnAgent(UnityAgent): """Chooses epsilon-greedy actions using a NN to estimate action values.""" # Default params DEVICE_DEFAULT = 'cpu' # pytorch device HIDDEN_LAYER_SIZES_DEFAULT = (50, 50) # default q network hidden layer sizes REPLAY_BUFFER_SIZE_DEFAULT = 100000 # max nr of experiences in memory ALPHA_DEFAULT = .1 # default learning rate GAMMA_DEFAULT = .9 # default reward discount factor EPSILON_DEFAULT = 1. # starting value for epsilon EPSILON_DECAY_DEFAULT = .9999 # used to decay epsilon over time EPSILON_MIN_DEFAULT = .005 # minimum value for decayed epsilon LEARN_BATCH_SIZE_DEFAULT = 50 # batch size to use when learning from memory def __init__(self, brain_name, state_size, action_size, params): super().__init__(brain_name, state_size, action_size, params) # pytorch device self.device = params.get('device', self.DEVICE_DEFAULT) # learning parameters self.alpha = params.get('alpha', self.ALPHA_DEFAULT) self.gamma = params.get('gamma', self.GAMMA_DEFAULT) self.epsilon = params.get('epsilon', self.EPSILON_DEFAULT) self.epsilon_decay = params.get('epsilon_decay', self.EPSILON_DECAY_DEFAULT) self.epsilon_min = params.get('epsilon_min', self.EPSILON_MIN_DEFAULT) self.learn_batch_size = params.get('learn_batch_size', self.LEARN_BATCH_SIZE_DEFAULT) # memory self.memory_size = params.get('memory_size', self.REPLAY_BUFFER_SIZE_DEFAULT) self.memory = ReplayBuffer(action_size, self.memory_size) # online and target Q-network models self.hidden_layer_sizes = params.get('hidden_layer_sizes', self.HIDDEN_LAYER_SIZES_DEFAULT) self.online_network = FullyConnectedNetwork(self.state_size, self.hidden_layer_sizes, self.action_size) self.target_network = FullyConnectedNetwork(self.state_size, self.hidden_layer_sizes, self.action_size) self.optimizer = optim.Adam(self.online_network.parameters(), lr=self.alpha) def select_action(self, state): """ Selects an epsilon-greedy action from the action space, using the online_network and target_network to estimate action values. """ # with probability epsilon, explore by choosing a random action if self.training and np.random.rand() < self.epsilon: return np.random.choice(self.action_size) # else, use the online network to choose the action it currently estimates to be the best one state_tensor = torch.from_numpy(state).float() state_tensor = state_tensor.unsqueeze(0) # wrap state in extra array so vector becomes a (single state) batch state_tensor = state_tensor.to(self.device) # move the tensor to the configured device (cpu or cuda/gpu) self.online_network.eval() # switch to evaluation mode for more efficient evaluation of the state tensor with torch.no_grad(): action_values = self.online_network(state_tensor) self.online_network.train() # and back to training mode best_action = torch.argmax(action_values.squeeze()).numpy().item(0) # pick action with highest Q value return best_action def step(self, state, action, result): next_state = result.vector_observations[0] reward = result.rewards[0] done = result.local_done[0] self.memory.add(state, action, reward, next_state, done) experiences = self.memory.sample(self.learn_batch_size) self.learn(experiences, self.gamma) # learn every step self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay) # decay epsilon up to minimum return reward, done def learn(self, experiences, gamma): """Performs gradient descent of the local network on the batch of experiences.""" # create pytorch tensors from the sampled experiences states, actions, rewards, next_states, dones = self.tensorize_experiences(experiences) # get estimated q values for next states from target_network next_state_values = self.target_network(next_states) # get q for each action in next_states next_targets = next_state_values.detach().max(1)[0].unsqueeze(1) # select maximum action value for each one # calculate q values for current states: reward + (gamma * target value of next_state) # multiplication by (1 - dones) sets next_state value to 0 if state was end of episode. targets = rewards + (gamma * next_targets * (1 - dones)) # calculate expected q values for current state by evaluation through online_network expecteds = self.online_network(states).gather(1, actions) # calculate error between expected and target (= loss) loss = F.mse_loss(expecteds, targets) # minimize that loss to make the network perform better next time self.optimizer.zero_grad() loss.backward() self.optimizer.step() # update the target network's weights only slightly to stabilize training # (instead of copying all weights every X episodes) self.soft_update_target_network(self.online_network, self.target_network, 1e-3) def tensorize_experiences(self, experiences): """Turns a set of experiences into separate tensors.""" states = torch.tensor(np.array([e.state for e in experiences])).float().to(self.device) actions = torch.tensor(np.array([[e.action] for e in experiences])).long().to(self.device) rewards = torch.tensor(np.array([[e.reward] for e in experiences])).float().to(self.device) next_states = torch.tensor(np.array([e.next_state for e in experiences])).float().to(self.device) dones = torch.tensor(np.array([[int(e.done)] for e in experiences]).astype(np.uint8)).float().to(self.device) return states, actions, rewards, next_states, dones def soft_update_target_network(self, source, target, tau): """Soft update target network weights from source.""" for target_w, source_w in zip(target.parameters(), source.parameters()): # move target weights slightly closer to source weights. target_w.data.copy_(tau * source_w.data + (1.0 - tau) * target_w.data) def save_checkpoint(self, name='checkpoint'): torch.save(self.online_network.state_dict(), 'saved_models/dqn_agent_{}.pth'.format(name)) def load_checkpoint(self, checkpoint): weights = torch.load(checkpoint) self.online_network.load_state_dict(weights) self.target_network.load_state_dict(weights) def get_params(self): return { 'device': self.device, 'memory_size': self.memory_size, 'learn_batch_size': self.learn_batch_size, 'alpha': self.alpha, 'gamma': self.gamma, 'epsilon': self.epsilon, 'epsilon_decay': self.epsilon_decay, 'epsilon_min': self.epsilon_min, 'online_network': self.online_network, 'target_network': self.target_network } ``` #### File: jeroenmoons/drl_banana/train.py ```python import config import sys import numpy as np import matplotlib.pyplot as plt from unityagents import UnityEnvironment from agent.factory import AgentFactory def train(env, agent): """ Performs the main training loop. """ max_score = 0 scores = [] scores_avg = [] iterations = 0 solved = False print('Training agent.') while iterations < config.MAX_ITERATIONS and not solved: # show a progress indication print('\repisode {}, max score so far is {}'.format(iterations, max_score), end='') sys.stdout.flush() iterations += 1 done = False score = 0 env_info = env.reset(train_mode=True)[agent.brain_name] # reset the environment episode_steps = 0 while not done and episode_steps < config.MAX_EPISODE_STEPS: episode_steps += 1 state = env_info.vector_observations[0] action = agent.select_action(state) # choose an action env_info = env.step(action)[agent.brain_name] # execute that action reward, done = agent.step(state, action, env_info) # give the agent the chance to learn from the results score += reward # update score with the reward scores.append(score) # keep track of the episode score avg_score = np.mean(scores[-100:]) # calculate average score over the last 100 episodes scores_avg.append(avg_score) # keep track of the average score max_score = score if max_score < score else max_score # keep track of max score so far # print periodic progress report if iterations % 100 == 0: print('\rIteration {} - avg score of {} over last 100 episodes'.format(iterations, avg_score)) agent.save_checkpoint(name='checkpoint') # if the environment is solved, stop training if not solved and avg_score > config.SOLVED_SCORE: print('\rEnvironment solved in {} iterations with a score of {}'.format(iterations, avg_score)) solved = True agent.save_checkpoint(name='solved') print('Training ended with an avg score of {} over last 100 episodes'.format(scores_avg[-1])) plot_scores(scores, scores_avg) return scores def plot_scores(scores, scores_avg): """Creates plots of score track record.""" # plot all scores plt.plot(np.arange(len(scores)), scores) plt.ylabel('Score') plt.xlabel('Episode #') plt.show() # plot average scores plt.plot(np.arange(len(scores_avg)), scores_avg) plt.ylabel('Avg Score over last 100 eps') plt.xlabel('Episode #') plt.show() if __name__ == '__main__': """ This shows an agent performing a single episode. """ print('training a new agent to master {}'.format(config.ENV_APP)) # Create the Unity environment banana_env = UnityEnvironment(file_name=config.ENV_APP) # Select the brain (= Unity ML agent) to work with and examine action space brain_name = banana_env.brain_names[0] brain = banana_env.brains[brain_name] action_size = brain.vector_action_space_size # Examine state space initial_env_info = banana_env.reset(train_mode=True)[brain_name] state_size = len(initial_env_info.vector_observations[0]) # Create a new DQN agent agent_factory = AgentFactory() an_agent = agent_factory.create_agent('dqn_new', brain_name, state_size, action_size) an_agent.training = True # True is default, but just in case print('Agent params: {}'.format(an_agent.get_params())) # Train the agent result = train(banana_env, an_agent) # Close the environment, no longer needed banana_env.close() print("Max score: {}".format(np.array(result).max())) ```
{ "source": "jeroennijhuis/Energy", "score": 3 }
#### File: Raspberry Pi/SmartMeter/EnergyData.py ```python from EnergyTariff import EnergyTariff class EnergyData: ConsumedRate1 = -1 ConsumedRate2 = -1 ReturnedRate1 = -1 ReturnedRate2 = -1 Tariff = EnergyTariff.Unknown Consumed = -1 Returned = -1 Gas = -1 def IsValid(self): return self.ConsumedRate1 >= 0 \ and self.ConsumedRate2 >= 0 \ and self.ReturnedRate1 >= 0 \ and self.ReturnedRate2 >= 0 \ and self.Tariff != EnergyTariff.Unknown \ and self.Consumed >= 0 \ and self.Returned >= 0 \ and self.Gas >= 0 ```
{ "source": "jeroenpeters1986/clockify-api-aclient", "score": 3 }
#### File: clockify_api_client/models/project.py ```python import logging from urllib.parse import urlencode from clockify_api_client.abstract_clockify import AbstractClockify class Project(AbstractClockify): def __init__(self, api_key, api_url): super(Project, self).__init__(api_key=api_key, api_url=api_url) def get_projects(self, workspace_id, params=None): """Returns projects from given workspace with applied params if provided. :param workspace_id Id of workspace. :param params Dictionary with request parameters. :return List of projects. """ try: if params: url_params = urlencode(params, doseq=True) url = self.base_url + '/workspaces/' + workspace_id + '/projects?' + url_params else: url = self.base_url + '/workspaces/' + workspace_id + '/projects/' return self.get(url) except Exception as e: logging.error("API error: {0}".format(e)) raise e def add_project(self, workspace_id, project_name, client_id, billable=False, public=False, color="#16407B"): """Add new project into workspace. :param workspace_id Id of workspace. :param project_name Name of new project. :param client_id Id of client. :param billable Bool flag. :return Dictionary representation of new project. """ try: url = self.base_url + '/workspaces/' + workspace_id + '/projects/' data = { 'name': project_name, "clientId": client_id, "isPublic": "true" if public else "false", "billable": billable } return self.post(url, data) except Exception as e: logging.error("API error: {0}".format(e)) raise e ```
{ "source": "JeroenProoth/BDO-MARKET-API", "score": 3 }
#### File: BDO-MARKET-API/market_api/client.py ```python import requests class Client(): base_url = 'https://marketweb-eu.blackdesertonline.com/' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36', } cookies = { 'ASP.NET_SessionId': None, '__RequestVerificationToken': None, } data = { '__RequestVerificationToken': None, 'keyType' : '0', 'subKey' : '0', #Grade of the item. 0 is +0, 1 is +1 (or Pri for accessories) 'isUp' : 'true', 'mainKey' : None, } def __init__(self, session_id, cookie_token, form_token): self.http = requests.session() self.cookies['ASP.NET_SessionId'] = session_id self.cookies['__RequestVerificationToken'] = cookie_token self.data['__RequestVerificationToken'] = form_token def set_item(self, item_id): self.data['mainKey'] = item_id def set_main_category(self, category_id): self.data['mainCategory'] = category_id def set_sub_category(self, category_id): self.data['subCategory'] = category_id def connect(self, method = None): try: request = self.http.post( self.base_url + str(method), cookies = self.cookies, headers = self.headers, data = self.data ) except requests.exceptions.ConnectionError: print('Connection Refused') return if request.status_code == 200: if request.text: data = request.json() error = data.get('error') if not error: return data else: print('Bad Request for url: {}.'.format(request.url)) ``` #### File: JeroenProoth/BDO-MARKET-API/market_scrubber.py ```python import time import pandas as pd from market_api.methods import Methods from masteries.cooking import CookingMastery from webscraper import WebScraper class MarketScrubber(): def __init__(self, session_id, cookie_token, form_token, item_dataframe, material_groups): self.methods = Methods(session_id, cookie_token, form_token) self.item_dataframe = item_dataframe self.material_groups = material_groups def get_item_id(self, item_name): """Returns item_id given an item_id.""" return self.item_dataframe.loc[(self.item_dataframe.name).apply(lambda x : x.casefold()) == str(item_name).casefold()].mainKey.values[0] def get_item_name(self, item_id): """Returns item_name given an item_id.""" return self.item_dataframe.loc[self.item_dataframe.mainKey == int(item_id)].name.values[0] def get_market_depth(self, item): """Returns a pandas DataFrame sorted from low -> high. Market depth is given by the buy- and sellCounts. returns a DataFrame """ if not item.isdigit(): item = self.get_item_id(item) data = self.methods.get_item_sell_buy_info(item)['marketConditionList'] market_conditions = pd.DataFrame.from_dict(data)[['pricePerOne', 'buyCount', 'sellCount']] return market_conditions def get_items_sold(self, item, time_format = 'unix'): """Returns the total amount of items sold for a given item. returns a tuple with (time_data (unix or local), total items sold) time_format: 'unix' (default) or 'local'. """ if not item.isdigit(): item = self.get_item_id(item) time_data = time.time() if time_format == 'local': time_data = time.strftime("%d:%m:%y %H:%M:%S", time.localtime(time_data)) data = self.methods.get_world_market_sub_list(item)['detailList'] items_sold = data[0]['totalTradeCount'] return (time_data, items_sold) def get_item_price(self, item): """Returns the price of an item. """ if not item.isdigit(): item = self.get_item_id(item) data = self.methods.get_world_market_sub_list(item)['detailList'] item_value = data[0]['pricePerOne'] return item_value def calculate_recipe_profitability(self, recipe, mastery): """Returns the input and output value of a recipe based on mastery level. Used formulas provided by https://docs.google.com/spreadsheets/d/1D7mFcXYFm4BUS_MKxTvgBY2lXkGtwWqn2AW91ntUvzE/edit#gid=1519713712 returns a tuple (input value, output value) """ cooking_master = CookingMastery(mastery) max_proc_chance = cooking_master.regular_rare_max_chance() rare_proc_chance = cooking_master.rare_proc_chance() outputs = recipe['output'] inputs = recipe['input'] input_value = 0 output_value = 0 for item, amount in inputs.items(): input_value += float(amount) * self.get_item_price(item) for item, rarity in outputs.items(): if rarity == 'normal': items_created = 2.5 + 1.5 * max_proc_chance output_value += items_created * self.get_item_price(item) else: items_created = (1.5 + 0.5 * max_proc_chance) * (0.2 + rare_proc_chance) output_value += items_created * self.get_item_price(item) return (input_value, output_value) ``` #### File: JeroenProoth/BDO-MARKET-API/webscraper.py ```python import requests import time from bs4 import BeautifulSoup from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC class WebScraper(): def __init__(self, item_dataframe): self.item_dataframe = item_dataframe def get_item_id(self, item_name): """Returns item_id given an item_id.""" return self.item_dataframe.loc[(self.item_dataframe.name).apply(lambda x : x.casefold()) == str(item_name).casefold()].mainKey.values[0] def get_item_name(self, item_id): """Returns item_name given an item_id.""" return self.item_dataframe.loc[self.item_dataframe.mainKey == int(item_id)].name.values[0] def get_recipe(self, item): if not item.isdigit(): item = self.get_item_id(item) URL = 'https://bdocodex.com/us/item/{}/'.format(item) page = requests.get(URL) driver = webdriver.Chrome() driver.get(URL) # WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.CLASS_NAME, "dt-reward"))) materials = [] total_time = 0 recipe = {self.get_item_name(item) : {'output' : {}, 'input' : {}}} """ Sometimes it doesn't work, so I did this. This work quite consistently within 2 seconds, so don't change it. I tried all kinds of shit with WebDriverWait, I couldn't make it work. """ while not materials: page = BeautifulSoup(driver.page_source, 'lxml') product = page.find(id='tabs-productofrecipe') materials = product.find_all("td", {"class": "dt-reward"}, limit = 2) time.sleep(1) total_time +=1 if total_time > 60: break inputs = materials[0] outputs = materials[1] lines = inputs.find_all("a", class_ = 'qtooltip') for line in lines: amount = line.find("div", class_='quantity_small nowrap') for attr in str(line).split(" "): if 'data-id' in attr: item_id = attr.replace('data-id=', '').replace('item--', '').replace('"', '').replace('--', ' ') if amount != None: amount = int(amount.text) else: amount = 1 recipe[self.get_item_name(item)]['input'][item_id] = amount lines = outputs.find_all("a", class_ = 'qtooltip') for line in lines: amount = line.find("div", class_='quantity_small nowrap') for attr in str(line).split(" "): if 'data-id' in attr: item_id = attr.replace('data-id=', '').replace('item--', '').replace('"', '').replace('--', ' ') if amount != None: amount = int(amount.text) else: amount = 1 if len(recipe[self.get_item_name(item)]['output']) < 1: recipe[self.get_item_name(item)]['output'][item_id] = 'normal' else: recipe[self.get_item_name(item)]['output'][item_id] = 'rare' return recipe def get_material_group(self, material_group): """There are a total of 56 material groups""" try: group = material_group.split(" ")[1] group = {material_group : {} } page = requests.get('https://bdocodex.com/us/materialgroup/{}/'.format(group)) soup = BeautifulSoup(page.content, 'html.parser') insider = soup.find("div", class_='insider') for line in insider: for attr in str(line).split(" "): if 'data-id' in attr: item_id = attr.replace('data-id=', '').replace('item--', '').replace('"', '').replace('--', ' ') group[material_group][self.get_item_name(item_id)] = item_id return group except (IndexError, TypeError): return None ```
{ "source": "jeroenschoonderbeek/Clappform-Python-Connector", "score": 3 }
#### File: Clappform-Python-Connector/Clappform/collection.py ```python from .settings import settings from .dataFrame import _DataFrame from .item import _Item from .auth import Auth import requests class _Collection: app_id = None id = None def __init__(self, app, collection = None): self.app_id = app self.id = collection def DataFrame(self): return _DataFrame(self.app_id, self.id) def Item(self, item = None): return _Item(self.app_id, self.id, item) def ReadOne(self, extended = False): if not Auth.tokenValid(): Auth.refreshToken() extended = str(extended).lower() response = requests.get(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.id + '?extended=' + extended, headers={'Authorization': 'Bearer ' + settings.token}) if response.json()["code"] is 200: return response.json()["data"] else: raise Exception(response.json()["message"]) def Create(self, slug, name, encryption): if not Auth.tokenValid(): Auth.refreshToken() response = requests.post(settings.baseURL + 'api/metric/' + self.app_id, json={ 'slug': slug, 'name': name, 'encryption': encryption }, headers={ 'Authorization': 'Bearer ' + settings.token }) if response.json()["code"] is 200: return _Collection(self.app_id, id) else: raise Exception(response.json()["message"]) def Update(self, slug = None, name = None, encryption = None): if not Auth.tokenValid(): Auth.refreshToken() properties = {} if name is not None: properties["name"] = name if slug is not None: properties["slug"] = slug if encryption is not None: properties["encryption"] = encryption response = requests.post(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.id, json=properties, headers={ 'Authorization': 'Bearer ' + settings.token }) if response.json()["code"] is 200: return _Collection(self.app_id, id) else: raise Exception(response.json()["message"]) def Delete(self): if not Auth.tokenValid(): Auth.refreshToken() response = requests.delete(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.id, headers={'Authorization': 'Bearer ' + settings.token}) if response.json()["code"] is 200: return True else: raise Exception(response.json()["message"]) def Empty(self): if not Auth.tokenValid(): Auth.refreshToken() response = requests.delete(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.id + '/dataframe', headers={'Authorization': 'Bearer ' + settings.token}) if response.json()["code"] is 200: return True else: raise Exception(response.json()["message"]) ``` #### File: Clappform-Python-Connector/Clappform/dataFrame.py ```python from .settings import settings import pandas as pd import json import requests import math from .auth import Auth class _DataFrame: app_id = None collection_id = None def __init__(self, app, collection): self.app_id = app self.collection_id = collection def Read(self): if not Auth.tokenValid(): Auth.refreshToken() response = requests.get(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.collection_id, headers={ 'Authorization': 'Bearer ' + settings.token }) data = [] loopCount = math.ceil(response.json()["data"]["items"] / 500) for x in range(0, loopCount): response = requests.get(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.collection_id + '?extended=true&offset=' + str(x * 500), headers={ 'Authorization': 'Bearer ' + settings.token }) for item in response.json()["data"]["items"]: data.append(item["data"]) return pd.DataFrame(data) def Synchronize(self, dataframe): if not Auth.tokenValid(): Auth.refreshToken() response = requests.put(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.collection_id + '/dataframe', json=json.loads(dataframe.to_json(orient='index')), headers={ 'Authorization': 'Bearer ' + settings.token }) if response.json()["code"] is 200: return True else: raise Exception(response.json()["message"]) def Append(self, dataframe): if not Auth.tokenValid(): Auth.refreshToken() dataframe.reset_index(inplace=True, drop=True) response = requests.get(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.collection_id, headers={ 'Authorization': 'Bearer ' + settings.token }) if 'index' in dataframe: dataframe = dataframe.drop(columns=["index"]) offset = response.json()["data"]["items"] count = 0 for x in range(0 + offset, len(dataframe.index) + offset): if (count + 1) % 100 is 0: portion = dataframe.iloc[x - 99 - offset:x + 1 - offset] portion.reset_index(inplace=True, drop=True) if 'index' in portion: portion = portion.drop(columns=["index"]) portion.index += offset + count - 99 items = json.loads(portion.to_json(orient='index')) response = requests.post(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.collection_id + '/dataframe', json=items, headers={ 'Authorization': 'Bearer ' + settings.token }) elif len(dataframe.index) + offset == x + 1: portion = dataframe.tail(len(dataframe.index) - int(math.floor(len(dataframe.index) / 100.0)) * 100) portion.reset_index(inplace=True, drop=True) if 'index' in portion: portion = portion.drop(columns=["index"]) portion.index += offset + count - 99 items = json.loads(portion.to_json(orient='index')) response = requests.post(settings.baseURL + 'api/metric/' + self.app_id + '/' + self.collection_id + '/dataframe', json=items, headers={ 'Authorization': 'Bearer ' + settings.token }) count += 1 return True ```
{ "source": "JeroenSlobbe/Scripts", "score": 4 }
#### File: Scripts/Crypto CTF challenges/XOR challenge.py ```python import os def encrypt(key: bytes, data: bytes) -> bytes: cipherText = b'' for i in range(len(data)): cipherText += bytes([data[i] ^ key[i % len(key)]]) return cipherText; def decrypt(key: bytes, data: bytes) -> bytes: plaintext = b'' for i in range(len(data)): plaintext += bytes([data[i] ^ key[i % len(key)]]) return plaintext def generateKey(length) -> bytes: returnKey = b'' returnKey = os.urandom(length); return returnKey; def testWalk(): print("\nGenerating random key with keylength 4:") key = generateKey(4) print(key) print("\nEncrypting message: 'testwalk':") input = b'testwalk' cipher = encrypt(key, input) print(cipher) plaintext = decrypt(key, cipher) print("\nDecrypting cipher:") print(plaintext) def main(): # Because the key is re-used time after time, and we know the first 4 bytes due to default flag format, we can recover the key and decrypt the whole message # Lesson learned, in python3, starting with b'' defines something as a bytestring. Using bytes.fromhex creates a bytestring based on something that once was a normal string key = b'HTB{' input = bytes.fromhex("134af6e1297bc4a96f6a87fe046684e8047084ee046d84c5282dd7ef292dc9") # Using the gibberish would also work, but from hex looks much cleaner # Input = b'\x13J\xf6\xe1){\xc4\xa9oj\x87\xfe\x04f\x84\xe8\x04p\x84\xee\x04m\x84\xc5(-\xd7\xef)-\xc9' realKey = decrypt(key,input)[0:4] print(decrypt(realKey,input)) main() ``` #### File: Scripts/CSV crawlers and parsers/ICS-CERT2CSV.py ```python import re from urllib.request import urlopen,Request def getExploitability(input,link): result = "unkown" # List that indicates unknown (not to be coded as this is default # Public exploits may exist that could target this vulnerability. # List of explicit mentioning of no exploit code ne1 = "No known public exploits specifically target this vulnerability" ne2 = "No known public exploits specifically target these vulnerabilities" ne4 = "No known exploits specifically target this vulnerability." ne5 = "No known exploits specifically target these vulnerable components." ne7 = "No known exploits specifically target these vulnerabilities." ne8 = "No known public exploits specifically target the other" ne11 = "No known public exploits have targeted this vulnerability." ne12 = "No known exploits are specifically targeting this vulnerability." ne16 = "No known public exploits exist that target these vulnerabilities." ne20 = "No known publicly available exploit exists" ne23 = "No known public exploits specifically target this vulnerability" ne25 = "No publicly available exploits are known to specifically target this vulnerability." ne26 = "No known public exploit specifically targets this vulnerability." ne27 = "No known public exploits specifically target these products" ne28 = "No known public exploits specifically target this vulnerability " ne31 = "No known public exploits exist that target this vulnerability." ne10 = "No publicly available exploit is known to exist." ne3 = "No publicly available exploits are known to exist for this vulnerability." ne13 = "No publicly known exploits specifically target these vulnerabilities" ne18 = "No publicly available exploit code is known to exist that specifically targets this vulnerability." ne24 = "No publicly known exploits specifically target this vulnerability." ne32 = "No publicly available exploits specifically targeting these vulnerabilities are known to exist." ne15 = "No exploits are known specifically to target this vulnerability." ne30 = "No exploits are known that target this vulnerability." ne9 = "No exploits are known that specifically target this vulnerability" ne17 = "Exploits that target these vulnerabilities are not publicly available." ne19 = "Exploits that target this vulnerability are not known to be publicly available." ne6 = "There are currently no publicly known exploits specifically targeting this vulnerability." ne29 = "There are currently no known exploits specifically targeting these vulnerabilities." ne21 = "There are currently no known exploits specifically targeting this vulnerability." ne22 = "ICS-CERT is unaware of any exploits that target this vulnerability." ne14 = "Currently, no known exploits are specifically targeting this vulnerability." # List of explicit mentioning of exploit code e1 = "Public exploits are known to target this vulnerability." e11 = "Public exploits are known to target these vulnerabilities." e6 = "public exploits are available" e12 = "Public exploits are available." e8 = "publicly available exploit code is known to exist that targets these vulnerabilities." e17 = "Public exploits are known to exist that target these vulnerabilities." e27 = "Public exploits are known that target these vulnerabilities." e29 = "Public exploits are known that specifically target this vulnerability." e7 = "Exploits that target this vulnerability are known to be publicly available." e2 = "Exploits that target these vulnerabilities are publicly available." e10 = "Exploits that target this vulnerability are publicly available." e4 = "Exploit code is publicly available for each of the vulnerabilities" e5 = "Exploit code is publicly available for these vulnerabilities." e15 = "Exploits that target these vulnerabilities exist and are publicly available." e16 = "Exploits that target some of these vulnerabilities are known to be publicly available." e18 = "Exploit code specifically targeting this vulnerability has been released" e22 = "Exploits that target some of these vulnerabilities are publicly availabl" e24 = "Exploits that target some vulnerabilities are publicly available." e28 = "Exploit code for this vulnerability is publicly available" e30 = "Exploit code for this vulnerability has been recently published." e3 = "An exploit that targets one of these vulnerabilities is publicly available" e9 = "An exploit of this vulnerability has been posted publicly." e13 = "Publicly released PoC code exists for these vulnerabilities." e14 = "Public exploit(s) are known to target these vulnerabilities." e19 = "An exploit targeting this vulnerability is publicly available." e20 = "This exploit is publicly known and available." e21 = "An exploit for this vulnerability is publicly available." e23 = "Publicly available exploits are known to specifically target vulnerabilities" e25 = "Known exploits are now targeting this vulnerability." e26 = "The researcher has publicly released exploits that specifically target these vulnerabilities." # List of exploit demonstrated but not disclosed nde1 = "Exploitation of vulnerabilities has been publicly demonstrated; however, exploit code is not publicly available." nde2 = "General exploits are publicly available that utilize this attack vector" nde3 = "No known public exploits specifically target this vulnerability, but information regarding this vulnerability has been publicly disclosed." nde4 = "No exploit code is known to exist beyond the test code developed by the researcher" nde5 = "Detailed vulnerability information is publicly available that could be used to develop an exploit that targets these vulnerabilities." nde6 = "Detailed vulnerability information is publicly available that could be used to develop an exploit that targets this vulnerability." nde7 = "Proof-of-concept code is expected to be made public by the researcher." nde8 = "No known public exploits specifically target this vulnerability; however, common techniques may be used to exploit." nde9 = "Exploits that target these vulnerabilities are potentially available" nde10 = "Public exploits that target these vulnerabilities may exist." # Metasploit / easy tool available ae1 = "A Metasploit module is publicly available." ae2 = "Tools are publicly available that aid in exploiting this cross-site scripting vulnerability." ae3 = "Malware and public exploits are known to target this vulnerability." ae4 = "Tools are publicly available that could aid in exploiting this" if input.count(ne32) + input.count(ne31) + input.count(ne30) + input.count(ne29) + input.count(ne28) + input.count(ne27) + input.count(ne26) + input.count(ne25) + input.count(ne24) + input.count(ne23) + input.count(ne22) + input.count(ne21) + input.count(ne20) + input.count(ne19) + input.count(ne18) + input.count(ne17) + input.count(ne16) + input.count(ne15) + input.count(ne14) + input.count(ne13) + input.count(ne12) + input.count(ne11) + input.count(ne10) + input.count(ne9) + input.count(ne8) + input.count(ne7) + input.count(ne6) + input.count(ne1) + input.count(ne2) + input.count(ne3) + input.count(ne4) + input.count(ne5) > 0: result = "no" elif input.count(ae4) + input.count(ae3) + input.count(ae2) + input.count(ae1): result="yes, autoexploit" elif input.count(e30) + input.count(e29) + input.count(e28) + input.count(e27) + input.count(e26) + input.count(e25) + input.count(e24) + input.count(e23) + input.count(e22) + input.count(e21) + input.count(e20) + input.count(e19) + input.count(e18) + input.count(e17) + input.count(e16) + input.count(e15) + input.count(e14) + input.count(e13) + input.count(e12) + input.count(e1) + input.count(e2) + input.count(e3) + input.count(e4) + input.count(e5) + input.count(e6) + input.count(e7) + input.count(e8) + input.count(e9) + input.count(e10) + input.count(e11) > 0: result = "yes" elif input.count(nde10) + input.count(nde9) + input.count(nde8) + input.count(nde7) + input.count(nde6) + input.count(nde5) + input.count(nde4) + input.count(nde3) + input.count(nde2) + input.count(nde1) > 0: result = "Partialy" else: # print(link) #DEBUG result = "unkown" return result def getCVSS(input,link): result = "" if(len(re.findall("CVSS",input, re.IGNORECASE)) > 0): CVSSOption1 = "CVSS V2 base score of ([0-9]*\.?[0-9])" CVSSOption2 = "CVSS v3 ([0-9]*\.?[0-9])" CVSSOption3 = "CVSS v3 base score of ([0-9]*\.?[0-9])" CVSSregex = CVSSOption1 + "|" + CVSSOption2 + "|" + CVSSOption3 CVSS = re.findall(CVSSregex,input, re.IGNORECASE) for item in CVSS: result = max(item) else: result = "unspecified" #print("No CVSS specified for: " + link) #DEBUG return result def getAffectedVersions(input,link): result = "" affectedVersions = "" # Versions: Based on website structure, products are in list items after header2. First and last contain generic stuff (so lets remove that). Afterthat clean up the data. rav1 = "<h2>Affected Products</h2>(.*?)<h2" rav2 = "AFFECTED PRODUCTS</h3>(.*?)<h3" rav3 = "<h2>AFFECTED PRODUCTS</h2>(.*?)<h2" rav = rav1 + "|" + rav2 + "|" + rav3 affectedVersionsList = re.findall(rav, input) for item in affectedVersionsList: if(len(re.findall("<li>",max(item), re.IGNORECASE)) > 0): affectedVersions = max(item).split("<li>")[1:] for itemAV in affectedVersions: result = result + itemAV.replace('</li>','').replace('</ul>','') + ";" elif(len(re.findall("<li class=\"BodyTextBlack",max(item), re.IGNORECASE)) > 0): affectedVersions = max(item).split("<li")[1:] for itemAV in affectedVersions: result = result + itemAV.replace('</li>','').replace('</ul>','') + ";" elif(len(re.findall("<li class=\"MsoListBullet",max(item), re.IGNORECASE)) > 0): affectedVersions = max(item).split("<li")[1:] for itemAV in affectedVersions: result = result + itemAV.replace('</li>','').replace('</ul>','').replace(' class="MsoListBullet">','') + ";" elif(len(re.findall("<li class=\"margin-left: 40px;",max(item), re.IGNORECASE)) > 0): affectedVersions = max(item).split("<li")[1:] for itemAV in affectedVersions: result = result + itemAV.replace('</li>','').replace('</ul>','').replace(' style="margin-left: 40px;">','') + ";" elif(len(re.findall("<p>(.*?)</p>",max(item), re.IGNORECASE)) > 0): result = re.findall("<p>(.*?)</p>",max(item), re.IGNORECASE)[0] # Improve data quality g1 = 'class="BodyTextBlack" style="margin: 6pt 0in;">' g2 = '<ul style="margin-left: 40px;">' g3 = "</p>" g4 = "<p>" g5 = "<ul>" g6 = '<font face="Times New Roman"><font face="Times New Roman">' g7 = "</font></font>;" g8 = '<p class="red_title">>' g9 = '<h4>' g10 = "</h4>" g11 = '<p class="red_title">><strong>3</strong><strong> --------</strong>From CIMPLICITY 6.1 forward, users have been advised that S90 drivers were no longer supported and an alternate tool was provided. CIMPLICITY 9.5 removed the drivers from the product.<p class="red_title"><strong>--------- End Update A Part 1 of 3 ----------</strong>' g12 = '<ul style="list-style-type:circle">' g17 = '<ul style="list-style-type:circle;">' g13 = '<h3 style="color:red;">></h3>' g14 = '<div class="red_title"><strong>--------- End Update C Part 1 of 2 ---------</strong></div>' g15 = '<h3 class="red_title">></h3>' g16 = '<div class="red_title"><strong>--------- End Update A Part 2 of 4 ---------</strong></div>' g19 = '<div class="red_title"><strong>--------- End Update A Part 3 of 5 --------</strong></div>' g18 = '<h3 style="color: red;">></h3>' g20 = '<h3 style="color:red;">></h3>' try: start = 0 stop = 0 if(len(re.findall("<strong>",max(item),re.IGNORECASE)) > 0): start = result.find('<strong>') stop = result.find('</strong>') + 8 result = result[:start] + result[stop:] if(len(re.findall("<em>",max(item),re.IGNORECASE)) > 0): start = result.find('<em>') stop = result.find('</em>') + 5 result = result[:start] + result[stop:] if(len(re.findall('<div class="red_title">',max(item),re.IGNORECASE)) > 0): start = result.find('<div class="red_title">') stop = result.find('</div>') + 6 result = result[:start] + result[stop:] result = result.replace(g20,'').replace(g19,'').replace(g18,'').replace(g17,'').replace(g16,'').replace(g15,'').replace(g14,'').replace(g1,'').replace(g2,'').replace(g3,'').replace("<br />",';').replace(g4,'').replace(g5,'').replace(g6,'').replace(g7,'').replace(g8,'').replace(g9,'').replace(g10,'').replace(g11,'').replace(g12,'').replace(g13,'') except: print(link) pass return(result) def getVulType(input,link): result = "" # Vuln types are based on CVE types: https://cwe.mitre.org/data/definitions/1008.html mitrefile = open("mitredefinitions.source",'r') rv = "" for line in mitrefile: rv = rv + re.escape(line.split(' - (')[0]) + "|" vulnTypes = re.findall(rv, input, re.IGNORECASE) tmpArray = [] for v in vulnTypes: if (v.strip() and (v.lower() not in tmpArray)): tmpArray.append(v.lower()) result = result + v + ";" return(result) def mainProgram(): textfile = open("data.txt", 'r') crawler = {'User-Agent': "ICS-Info-Crawler"} #fullList = urlopen(Request(url="https://ics-cert.us-cert.gov/advisories-by-vendor", headers=crawler)).read().decode('ISO-8859-1') #vendorChunk = str(((fullList.split('<div class="view-content">')[1]).split('</div></section>')[0]).encode("utf-8","ignore")) staticURL = "https://www.us-cert.gov/ics/advisories-by-vendor?page=" currentVendor = "" advisories = "" link = "" product = "" CVSS = "" exploitability = "" versions = "" vultypes = "" result = "" for x in range(0, 12): tmpUrl = staticURL + str(x) tmpList = urlopen(Request(url=tmpUrl, headers=crawler)).read().decode('ISO-8859-1') tmpVendors = tmpList.split('<div class="view-content">')[1].split('<nav class="pager-nav text-center"')[0].replace('<div class="item-list">','').split('<h3>') for tmpVendor in tmpVendors: currentVendor = tmpVendor.split("</h3>")[0] advisories = re.findall("<a href=\"(.*)</a>", tmpVendor) for advisory in advisories: tmpLinkProduct = "https://www.us-cert.gov/" + advisory.replace('" hreflang="en','') link = tmpLinkProduct.split(">")[0].replace('"','') product = tmpLinkProduct.split(">")[1] vulnerabilityDetails = urlopen(Request(url=link, headers=crawler)).read().decode('ISO-8859-1') CVSS = getCVSS(vulnerabilityDetails, link) exploitability = getExploitability(vulnerabilityDetails, link) versions = getAffectedVersions(vulnerabilityDetails, link) vultypes = getVulType(vulnerabilityDetails,link) result = str(currentVendor).replace(',','') + "," + str(link).replace(',','') + "," + str(CVSS).replace(',','') + "," + str(exploitability).replace(',','') + "," + str(versions).replace(',','') + "," + str(vultypes).replace(',','') print(result.encode("utf-8","ignore")) # Main program print("Vendor, product, advisoryLink,CVSS, public exploit, affected versions, vulnerability type") mainProgram() ```
{ "source": "jeroenstalenburg/judgment_aggregation", "score": 3 }
#### File: data/constraints/CNF.py ```python from .Constraint import Constraint # import sympy as sp import pycosat as ps import shlex class CNF(Constraint): def reset(self): self.lines = 0 self.clauses_loaded = 0 self.var_amount = 0 self.clause_amount = 0 self.clauses = [] # self.boolean_expression = sp.true self.boolean_vars = [] self.p_initialised = False def __str__(self): return ("JA CNF constraint object {\n Clauses: %s\n}" % self.clauses) def initialise_data(self, var_amount, clause_amount): """Initialise the data for the class for future use""" self.clauses_loaded = 0 self.var_amount = var_amount self.clause_amount = clause_amount self.clauses = [[]] * clause_amount # self.boolean_expression = sp.true # self.boolean_vars = [var(str(i + 1)) for i in range(var_amount)] self.p_initialised = True def load_lines(self, iterable): """Load the contents of a iterable of strings, such as a file or list of strings. The strings need to be in the valid format args: iterable: the iterable object with the correctly formatted lines path: is used to resolve the location of files which may be needed by the scenario.""" self.lines = 0 for line in iterable: self.lines += 1 contents = shlex.split(line.replace('\n', '')) if contents == []: continue if contents[0] == 'p': self.load_p_line(*contents[1:]) else: self.load_clause_line(*contents) self.lines = 0 self.finalise() def load_p_line(self, *args): if self.p_initialised: self.throw_error("May not redefine amount of issues and " "judges in the middle of a cnf file") if args[0] != "cnf": self.throw_error("The given file is not a scenario file") if len(args) != 3: self.throw_error("Expected two arguments after 'p cnf'") try: var_amount = int(args[1]) clause_amount = int(args[2]) except ValueError: self.throw_error("'%s' and/or '%s' not a number" % (args[1], args[2])) self.initialise_data(var_amount, clause_amount) def load_clause_line(self, *args): try: clause = list(map(int, args)) except ValueError: self.throw_error("clause line should consist of only integers") if clause[-1] != 0: self.throw_error("clause line should end with a 0") self.add_clause(clause[:-1]) def add_clause(self, clause): """Add a clause to the current CNF constraint""" self.check_initialised() if (not all([type(c) == int for c in clause])): self.throw_error("Clause must be consist of only integers") if (self.clauses_loaded >= self.clause_amount): self.throw_error("May not add more clauses than specified while " "initialising the object") if (not all([c != 0 and abs(c) <= self.var_amount for c in clause])): self.throw_error("Variables referenced should be between 1 and " "the amount given during initialisation (%s)" % self.var_amount) self.clauses[self.clauses_loaded] = clause # sp_clause = sp.false # for c in clause: # atom = self.boolean_vars[abs(c) - 1] # if c < 0: # atom = ~atom # sp_clause = sp_clause | atom # self.boolean_expression = self.boolean_expression & sp_clause # self.boolean_lambda = None self.clauses_loaded += 1 def check_clause(self, clause, judgment): """Return True if any of the elements of the clause are True.""" for index in clause: if (index < 0) ^ judgment[abs(index) - 1]: return True return False def check_judgment(self, judgment): """Return True if the judgment satisfies the current costraint""" for clause in self.clauses: if not self.check_clause(clause, judgment): return False return True def generate_all_valid_judgments(self): """Generate all valid judgments according to the current constraint""" self.check_initialised() for judgment in ps.itersolve(self.clauses, vars=self.var_amount): yield list(map(lambda x: int(x > 0), judgment)) def get_var_amount(self): """Get the var amount of the current constraint""" return self.var_amount ``` #### File: judgment_aggregation/test/test_scenario.py ```python from unittest import TestCase from ..data.Scenario import Scenario class TestScenario(TestCase): def test_creating_scenario(self): s = Scenario() s.load_file("kemenyslaterdiff.ja") s.solve('ASP') self.assertEqual(s.collective_judgments, [[0, 1, 1, 0, 0, 0, 1, 1]]) ```
{ "source": "JeroenSwart/warmstart", "score": 2 }
#### File: src/utils/thesis_utils.py ```python import pandas as pd from src.pipeline_optimization.bayesian_hopt import Config from plotly.subplots import make_subplots import plotly.graph_objects as go def thesis_lookup_objective(name): def objective(configs): # import lookup table lookup_table = pd.read_csv( "../../data/metadata/raw/" + name + ".csv", index_col=0, header=[0, 1] ) lookup_table.loc[:, ("hyperparameters", "learning_rate")] = lookup_table[ "hyperparameters" ]["learning_rate"].round(13) idx = lookup_table.index[ (lookup_table["hyperparameters"]["max_depth"] == configs["max_depth"]) & ( lookup_table["hyperparameters"]["learning_rate"] == configs["learning_rate"] ) & ( lookup_table["hyperparameters"]["min_child_weight"] == configs["min_child_weight"] ) & (lookup_table["hyperparameters"]["subsample"] == configs["subsample"]) & (lookup_table["hyperparameters"]["num_trees"] == configs["num_trees"]) ] result = lookup_table.iloc[idx]["diagnostics"]["mae"].squeeze() walltime = lookup_table.iloc[idx]["diagnostics"]["walltime"].squeeze() crossval = lookup_table.iloc[idx]["crossval_diag"]["mae"].squeeze() return result, walltime, crossval return objective def thesis_search_space(): search_space = { "num_trees": Config(100, 800, granularity=6, rounding=1), "learning_rate": Config(-2.5, -0.5, granularity=10, scale="log", rounding=13), "max_depth": Config(5, 20, granularity=8, rounding=0), "min_child_weight": Config(5, 40, granularity=3, rounding=1), "subsample": Config(0.5, 1.0, granularity=3, rounding=2), } return search_space def get_standard_dataset(dataset_name): # load data df = pd.read_csv("../../data/timeseries/raw/final_data.csv", index_col=0) # select the dataset split_name = dataset_name.split("_") end_name = split_name[0] + "_target_" + split_name[1] ex_name = split_name[0] + "_temp_" + split_name[1] time_based_features = ["Hour of Day", "Day of Week", "Day of Year", "Holiday"] data = df[[end_name, ex_name] + time_based_features].rename( columns={end_name: "endogenous", ex_name: "exogenous"} ) dataset = data.dropna(subset=["endogenous"]) # divide in training and test data start = dataset.index.get_loc("2012-01-01 00:00:00+00:00") train_nr = int(split_name[2]) dataset = dataset[start : start + train_nr] dataset.index = pd.DatetimeIndex(dataset.index) test_data = data.dropna(subset=["endogenous"])[ start + train_nr : start + train_nr + 365 * 24 ] test_data.index = pd.DatetimeIndex(test_data.index) return dataset, test_data def visualize_avg_performance_single_datasets(hopt_exp, sample_ids): fig = make_subplots(rows=1, cols=len(sample_ids)) for i, sample_id in enumerate(sample_ids): # transform to best so far dataframe data = hopt_exp.best_so_far[sample_id].mean(level="iterations") for identifier in [hopt.identifier for hopt in hopt_exp._hopts]: fig.add_trace( go.Scatter(y=data[identifier], name=identifier), row=1, col=i + 1 ) fig.update_layout( title=sample_id, xaxis=go.layout.XAxis(title="Iterations"), yaxis=go.layout.YAxis(title="MAE"), ) fig.update_layout(height=600, width=1500, title_text="Subplots") fig.show() ```
{ "source": "jeroenterheerdt/nexia", "score": 3 }
#### File: nexia/tests/test_home.py ```python import json import os from os.path import dirname import unittest import pytest from nexia.home import NexiaHome def load_fixture(filename): """Load a fixture.""" test_dir = dirname(__file__) path = os.path.join(test_dir, "fixtures", filename) with open(path) as fptr: return fptr.read() class TestNexiaThermostat(unittest.TestCase): """Tests for nexia thermostat.""" def test_update(self): nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(2059661) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [83261002, 83261005, 83261008, 83261011]) nexia.update_from_json(devices_json) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [83261002, 83261005, 83261008, 83261011]) nexia.update_from_json(devices_json) def test_idle_thermo(self): """Get methods for an idle thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(2059661) self.assertEqual(thermostat.get_model(), "XL1050") self.assertEqual(thermostat.get_firmware(), "5.9.1") self.assertEqual(thermostat.get_dev_build_number(), "1581321824") self.assertEqual(thermostat.get_device_id(), "000000") self.assertEqual(thermostat.get_type(), "XL1050") self.assertEqual(thermostat.get_name(), "Downstairs East Wing") self.assertEqual(thermostat.get_deadband(), 3) self.assertEqual(thermostat.get_setpoint_limits(), (55, 99)) self.assertEqual(thermostat.get_variable_fan_speed_limits(), (0.35, 1.0)) self.assertEqual(thermostat.get_unit(), "F") self.assertEqual(thermostat.get_humidity_setpoint_limits(), (0.35, 0.65)) self.assertEqual(thermostat.get_fan_mode(), "Auto") self.assertEqual(thermostat.get_fan_modes(), ["Auto", "On", "Circulate"]) self.assertEqual(thermostat.get_outdoor_temperature(), 88.0) self.assertEqual(thermostat.get_relative_humidity(), 0.36) self.assertEqual(thermostat.get_current_compressor_speed(), 0.0) self.assertEqual(thermostat.get_requested_compressor_speed(), 0.0) self.assertEqual(thermostat.get_fan_speed_setpoint(), 0.35) self.assertEqual(thermostat.get_dehumidify_setpoint(), 0.50) self.assertEqual(thermostat.has_dehumidify_support(), True) self.assertEqual(thermostat.has_dehumidify_support(), True) self.assertEqual(thermostat.has_emergency_heat(), False) self.assertEqual(thermostat.get_system_status(), "System Idle") self.assertEqual(thermostat.has_air_cleaner(), True) self.assertEqual(thermostat.get_air_cleaner_mode(), "auto") self.assertEqual(thermostat.is_blower_active(), False) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [83261002, 83261005, 83261008, 83261011]) def test_idle_thermo_issue_33758(self): """Get methods for an idle thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_issue_33758.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(12345678) self.assertEqual(thermostat.get_model(), "XL1050") self.assertEqual(thermostat.get_firmware(), "5.9.1") self.assertEqual(thermostat.get_dev_build_number(), "1581321824") self.assertEqual(thermostat.get_device_id(), "xxxxxx") self.assertEqual(thermostat.get_type(), "XL1050") self.assertEqual(thermostat.get_name(), "Thermostat") self.assertEqual(thermostat.get_deadband(), 3) self.assertEqual(thermostat.get_setpoint_limits(), (55, 99)) self.assertEqual(thermostat.get_variable_fan_speed_limits(), (0.35, 1.0)) self.assertEqual(thermostat.get_unit(), "F") self.assertEqual(thermostat.get_humidity_setpoint_limits(), (0.35, 0.65)) self.assertEqual(thermostat.get_fan_mode(), "Auto") self.assertEqual(thermostat.get_fan_modes(), ["Auto", "On", "Circulate"]) self.assertEqual(thermostat.get_outdoor_temperature(), 55.0) self.assertEqual(thermostat.get_relative_humidity(), 0.43) self.assertEqual(thermostat.get_current_compressor_speed(), 0.0) self.assertEqual(thermostat.get_requested_compressor_speed(), 0.0) self.assertEqual(thermostat.get_fan_speed_setpoint(), 1) self.assertEqual(thermostat.get_dehumidify_setpoint(), 0.55) self.assertEqual(thermostat.has_dehumidify_support(), True) self.assertEqual(thermostat.has_humidify_support(), True) self.assertEqual(thermostat.has_emergency_heat(), True) self.assertEqual(thermostat.is_emergency_heat_active(), False) self.assertEqual(thermostat.get_system_status(), "System Idle") self.assertEqual(thermostat.has_air_cleaner(), True) self.assertEqual(thermostat.get_air_cleaner_mode(), "auto") self.assertEqual(thermostat.is_blower_active(), False) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [12345678]) def test_idle_thermo_issue_33968_thermostat_1690380(self): """Get methods for an cooling thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_issue_33968.json")) nexia.update_from_json(devices_json) thermostat_ids = nexia.get_thermostat_ids() self.assertEqual(thermostat_ids, [1690380]) thermostat = nexia.get_thermostat_by_id(1690380) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [83037337, 83037340, 83037343]) self.assertEqual(thermostat.get_model(), "XL1050") self.assertEqual(thermostat.get_firmware(), "5.9.1") self.assertEqual(thermostat.get_dev_build_number(), "1581321824") self.assertEqual(thermostat.get_device_id(), "removed") self.assertEqual(thermostat.get_type(), "XL1050") self.assertEqual(thermostat.get_name(), "Thermostat") self.assertEqual(thermostat.get_deadband(), 3) self.assertEqual(thermostat.get_setpoint_limits(), (55, 99)) self.assertEqual(thermostat.get_variable_fan_speed_limits(), (0.35, 1.0)) self.assertEqual(thermostat.get_unit(), "F") self.assertEqual(thermostat.get_humidity_setpoint_limits(), (0.35, 0.65)) self.assertEqual(thermostat.get_fan_mode(), "Auto") self.assertEqual(thermostat.get_fan_modes(), ["Auto", "On", "Circulate"]) self.assertEqual(thermostat.get_outdoor_temperature(), 80.0) self.assertEqual(thermostat.get_relative_humidity(), 0.55) self.assertEqual(thermostat.get_current_compressor_speed(), 0.41) self.assertEqual(thermostat.get_requested_compressor_speed(), 0.41) self.assertEqual(thermostat.get_fan_speed_setpoint(), 0.5) self.assertEqual(thermostat.get_dehumidify_setpoint(), 0.55) self.assertEqual(thermostat.has_dehumidify_support(), True) self.assertEqual(thermostat.has_humidify_support(), False) self.assertEqual(thermostat.has_emergency_heat(), True) self.assertEqual(thermostat.is_emergency_heat_active(), False) self.assertEqual(thermostat.get_system_status(), "Cooling") self.assertEqual(thermostat.has_air_cleaner(), True) self.assertEqual(thermostat.get_air_cleaner_mode(), "auto") self.assertEqual(thermostat.is_blower_active(), True) def test_active_thermo(self): """Get methods for an active thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(2293892) self.assertEqual(thermostat.get_model(), "XL1050") self.assertEqual(thermostat.get_firmware(), "5.9.1") self.assertEqual(thermostat.get_dev_build_number(), "1581321824") self.assertEqual(thermostat.get_device_id(), "0281B02C") self.assertEqual(thermostat.get_type(), "XL1050") self.assertEqual(thermostat.get_name(), "Master Suite") self.assertEqual(thermostat.get_deadband(), 3) self.assertEqual(thermostat.get_setpoint_limits(), (55, 99)) self.assertEqual(thermostat.get_variable_fan_speed_limits(), (0.35, 1.0)) self.assertEqual(thermostat.get_unit(), "F") self.assertEqual(thermostat.get_humidity_setpoint_limits(), (0.35, 0.65)) self.assertEqual(thermostat.get_fan_mode(), "Auto") self.assertEqual(thermostat.get_fan_modes(), ["Auto", "On", "Circulate"]) self.assertEqual(thermostat.get_outdoor_temperature(), 87.0) self.assertEqual(thermostat.get_relative_humidity(), 0.52) self.assertEqual(thermostat.get_current_compressor_speed(), 0.69) self.assertEqual(thermostat.get_requested_compressor_speed(), 0.69) self.assertEqual(thermostat.get_fan_speed_setpoint(), 0.35) self.assertEqual(thermostat.get_dehumidify_setpoint(), 0.45) self.assertEqual(thermostat.has_dehumidify_support(), True) self.assertEqual(thermostat.has_humidify_support(), False) self.assertEqual(thermostat.has_emergency_heat(), False) self.assertEqual(thermostat.get_system_status(), "Cooling") self.assertEqual(thermostat.has_air_cleaner(), True) self.assertEqual(thermostat.get_air_cleaner_mode(), "auto") self.assertEqual(thermostat.is_blower_active(), True) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [83394133, 83394130, 83394136, 83394127, 83394139]) @pytest.mark.skip(reason="not yet supported") def test_xl624(self): """Get methods for an xl624 thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_xl624.json")) nexia.update_from_json(devices_json) thermostat_ids = nexia.get_thermostat_ids() self.assertEqual(thermostat_ids, [2222222, 3333333]) thermostat = nexia.get_thermostat_by_id(1111111) self.assertEqual(thermostat.get_model(), None) self.assertEqual(thermostat.get_firmware(), "2.8") self.assertEqual(thermostat.get_dev_build_number(), "0603340208") self.assertEqual(thermostat.get_device_id(), None) self.assertEqual(thermostat.get_type(), None) self.assertEqual(thermostat.get_name(), "Downstairs Hall") self.assertEqual(thermostat.get_deadband(), 3) self.assertEqual(thermostat.get_setpoint_limits(), (55, 99)) self.assertEqual(thermostat.has_variable_fan_speed(), False) self.assertEqual(thermostat.get_unit(), "F") self.assertEqual(thermostat.get_humidity_setpoint_limits(), (0.35, 0.65)) self.assertEqual(thermostat.get_fan_mode(), "Auto") self.assertEqual(thermostat.get_fan_modes(), ["Auto", "On", "Cycler"]) self.assertEqual(thermostat.get_current_compressor_speed(), 0.0) self.assertEqual(thermostat.get_requested_compressor_speed(), 0.0) self.assertEqual(thermostat.has_dehumidify_support(), False) self.assertEqual(thermostat.has_humidify_support(), False) self.assertEqual(thermostat.has_emergency_heat(), False) self.assertEqual(thermostat.get_system_status(), "System Idle") self.assertEqual(thermostat.has_air_cleaner(), False) self.assertEqual(thermostat.is_blower_active(), False) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [12345678]) def test_xl824_1(self): """Get methods for an xl824 thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_xl624.json")) nexia.update_from_json(devices_json) thermostat_ids = nexia.get_thermostat_ids() self.assertEqual(thermostat_ids, [2222222, 3333333]) thermostat = nexia.get_thermostat_by_id(2222222) self.assertEqual(thermostat.get_model(), "XL824") self.assertEqual(thermostat.get_firmware(), "5.9.1") self.assertEqual(thermostat.get_dev_build_number(), "1581314625") self.assertEqual(thermostat.get_device_id(), "0167CA48") self.assertEqual(thermostat.get_type(), "XL824") self.assertEqual(thermostat.get_name(), "Family Room") self.assertEqual(thermostat.get_deadband(), 3) self.assertEqual(thermostat.get_setpoint_limits(), (55, 99)) self.assertEqual(thermostat.has_variable_fan_speed(), True) self.assertEqual(thermostat.get_unit(), "F") self.assertEqual(thermostat.get_humidity_setpoint_limits(), (0.35, 0.65)) self.assertEqual(thermostat.get_fan_mode(), "Circulate") self.assertEqual(thermostat.get_fan_modes(), ["Auto", "On", "Circulate"]) self.assertEqual(thermostat.get_current_compressor_speed(), 0.0) self.assertEqual(thermostat.get_requested_compressor_speed(), 0.0) self.assertEqual(thermostat.has_dehumidify_support(), True) self.assertEqual(thermostat.has_humidify_support(), False) self.assertEqual(thermostat.has_emergency_heat(), False) self.assertEqual(thermostat.get_system_status(), "System Idle") self.assertEqual(thermostat.has_air_cleaner(), True) self.assertEqual(thermostat.is_blower_active(), False) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [88888888]) def test_xl824_2(self): """Get methods for an xl824 thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_xl624.json")) nexia.update_from_json(devices_json) thermostat_ids = nexia.get_thermostat_ids() self.assertEqual(thermostat_ids, [2222222, 3333333]) thermostat = nexia.get_thermostat_by_id(3333333) self.assertEqual(thermostat.get_model(), "XL824") self.assertEqual(thermostat.get_firmware(), "5.9.1") self.assertEqual(thermostat.get_dev_build_number(), "1581314625") self.assertEqual(thermostat.get_device_id(), "01573380") self.assertEqual(thermostat.get_type(), "XL824") self.assertEqual(thermostat.get_name(), "Upstairs") self.assertEqual(thermostat.get_deadband(), 3) self.assertEqual(thermostat.get_setpoint_limits(), (55, 99)) self.assertEqual(thermostat.has_variable_fan_speed(), True) self.assertEqual(thermostat.get_unit(), "F") self.assertEqual(thermostat.get_humidity_setpoint_limits(), (0.35, 0.65)) self.assertEqual(thermostat.get_fan_mode(), "Circulate") self.assertEqual(thermostat.get_fan_modes(), ["Auto", "On", "Circulate"]) self.assertEqual(thermostat.get_current_compressor_speed(), 0.0) self.assertEqual(thermostat.get_requested_compressor_speed(), 0.0) self.assertEqual(thermostat.has_dehumidify_support(), True) self.assertEqual(thermostat.has_humidify_support(), False) self.assertEqual(thermostat.has_emergency_heat(), False) self.assertEqual(thermostat.get_system_status(), "System Idle") self.assertEqual(thermostat.has_air_cleaner(), True) self.assertEqual(thermostat.is_blower_active(), False) zone_ids = thermostat.get_zone_ids() self.assertEqual(zone_ids, [99999999]) class TestNexiaHome(unittest.TestCase): """Tests for nexia home.""" def test_basic(self): """Basic tests for NexiaHome.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) self.assertEqual(nexia.get_name(), "Hidden") thermostat_ids = nexia.get_thermostat_ids() self.assertEqual(thermostat_ids, [2059661, 2059676, 2293892, 2059652]) def test_basic_issue_33758(self): """Basic tests for NexiaHome.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_issue_33758.json")) nexia.update_from_json(devices_json) self.assertEqual(nexia.get_name(), "Hidden") thermostat_ids = nexia.get_thermostat_ids() self.assertEqual(thermostat_ids, [12345678]) class TestNexiaThermostatZone(unittest.TestCase): """Tests for nexia thermostat zone.""" def test_zone_issue_33968_zone_83037337(self): """Tests for nexia thermostat zone that is cooling.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_issue_33968.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(1690380) zone = thermostat.get_zone_by_id(83037337) self.assertEqual(zone.thermostat, thermostat) self.assertEqual(zone.get_name(), "Family Room") self.assertEqual(zone.get_cooling_setpoint(), 77) self.assertEqual(zone.get_heating_setpoint(), 74) self.assertEqual(zone.get_current_mode(), "COOL") self.assertEqual( zone.get_requested_mode(), "COOL", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Damper Closed", ) self.assertEqual( zone.get_setpoint_status(), "Permanent Hold", ) self.assertEqual(zone.is_calling(), False) self.assertEqual(zone.is_in_permanent_hold(), True) def test_zone_issue_33968_zone_83037340(self): """Tests for nexia thermostat zone that is cooling.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_issue_33968.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(1690380) zone = thermostat.get_zone_by_id(83037340) self.assertEqual(zone.thermostat, thermostat) self.assertEqual(zone.get_name(), "Office") self.assertEqual(zone.get_cooling_setpoint(), 77) self.assertEqual(zone.get_heating_setpoint(), 74) self.assertEqual(zone.get_current_mode(), "COOL") self.assertEqual( zone.get_requested_mode(), "COOL", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Damper Open", ) self.assertEqual( zone.get_setpoint_status(), "Permanent Hold", ) self.assertEqual(zone.is_calling(), True) self.assertEqual(zone.is_in_permanent_hold(), True) def test_zone_issue_33968_zone_83037343(self): """Tests for nexia thermostat zone that is cooling.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_issue_33968.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(1690380) zone = thermostat.get_zone_by_id(83037343) self.assertEqual(zone.thermostat, thermostat) self.assertEqual(zone.get_name(), "Master") self.assertEqual(zone.get_cooling_setpoint(), 77) self.assertEqual(zone.get_heating_setpoint(), 68) self.assertEqual(zone.get_current_mode(), "COOL") self.assertEqual( zone.get_requested_mode(), "COOL", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Damper Open", ) self.assertEqual( zone.get_setpoint_status(), "Permanent Hold", ) self.assertEqual(zone.is_calling(), True) self.assertEqual(zone.is_in_permanent_hold(), True) def test_zone_issue_33758(self): """Tests for nexia thermostat zone relieving air.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_issue_33758.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(12345678) zone = thermostat.get_zone_by_id(12345678) self.assertEqual(zone.thermostat, thermostat) self.assertEqual(zone.get_name(), "Thermostat NativeZone") self.assertEqual(zone.get_cooling_setpoint(), 73) self.assertEqual(zone.get_heating_setpoint(), 68) self.assertEqual(zone.get_current_mode(), "AUTO") self.assertEqual( zone.get_requested_mode(), "AUTO", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Idle", ) self.assertEqual( zone.get_setpoint_status(), "Run Schedule - None", ) self.assertEqual(zone.is_calling(), False) self.assertEqual(zone.is_in_permanent_hold(), False) def test_zone_relieving_air(self): """Tests for nexia thermostat zone relieving air.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(2293892) zone = thermostat.get_zone_by_id(83394133) self.assertEqual(zone.thermostat, thermostat) self.assertEqual(zone.get_name(), "Bath Closet") self.assertEqual(zone.get_cooling_setpoint(), 79) self.assertEqual(zone.get_heating_setpoint(), 63) self.assertEqual(zone.get_current_mode(), "AUTO") self.assertEqual( zone.get_requested_mode(), "AUTO", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Relieving Air", ) self.assertEqual( zone.get_setpoint_status(), "Permanent Hold", ) self.assertEqual(zone.is_calling(), True) self.assertEqual(zone.is_in_permanent_hold(), True) def test_zone_cooling_air(self): """Tests for nexia thermostat zone cooling.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(2293892) zone = thermostat.get_zone_by_id(83394130) self.assertEqual(zone.get_name(), "Master") self.assertEqual(zone.get_cooling_setpoint(), 71) self.assertEqual(zone.get_heating_setpoint(), 63) self.assertEqual(zone.get_current_mode(), "AUTO") self.assertEqual( zone.get_requested_mode(), "AUTO", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Damper Open", ) self.assertEqual( zone.get_setpoint_status(), "Permanent Hold", ) self.assertEqual(zone.is_calling(), True) self.assertEqual(zone.is_in_permanent_hold(), True) def test_zone_idle(self): """Tests for nexia thermostat zone idle.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) thermostat = nexia.get_thermostat_by_id(2059661) zone = thermostat.get_zone_by_id(83261002) self.assertEqual(zone.get_name(), "Living East") self.assertEqual(zone.get_cooling_setpoint(), 79) self.assertEqual(zone.get_heating_setpoint(), 63) self.assertEqual(zone.get_current_mode(), "AUTO") self.assertEqual( zone.get_requested_mode(), "AUTO", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Idle", ) self.assertEqual( zone.get_setpoint_status(), "Permanent Hold", ) self.assertEqual(zone.is_calling(), False) self.assertEqual(zone.is_in_permanent_hold(), True) def test_xl824_idle(self): """Tests for nexia xl824 zone idle.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_house_xl624.json")) nexia.update_from_json(devices_json) thermostat_ids = nexia.get_thermostat_ids() self.assertEqual(thermostat_ids, [2222222, 3333333]) thermostat = nexia.get_thermostat_by_id(3333333) zone = thermostat.get_zone_by_id(99999999) self.assertEqual(zone.get_name(), "Upstairs NativeZone") self.assertEqual(zone.get_cooling_setpoint(), 74) self.assertEqual(zone.get_heating_setpoint(), 62) self.assertEqual(zone.get_current_mode(), "COOL") self.assertEqual( zone.get_requested_mode(), "COOL", ) self.assertEqual( zone.get_presets(), ["None", "Home", "Away", "Sleep"], ) self.assertEqual( zone.get_preset(), "None", ) self.assertEqual( zone.get_status(), "Idle", ) self.assertEqual( zone.get_setpoint_status(), "Permanent Hold", ) self.assertEqual(zone.is_calling(), False) self.assertEqual(zone.is_in_permanent_hold(), True) class TestNexiaAutomation(unittest.TestCase): def test_automations(self): """Get methods for an active thermostat.""" nexia = NexiaHome(auto_login=False) devices_json = json.loads(load_fixture("mobile_houses_123456.json")) nexia.update_from_json(devices_json) automation_ids = nexia.get_automation_ids() self.assertEqual( automation_ids, [3467876, 3467870, 3452469, 3452472, 3454776, 3454774, 3486078, 3486091], ) automation_one = nexia.get_automation_by_id(3467876) self.assertEqual(automation_one.name, "Away for 12 Hours") self.assertEqual( automation_one.description, "When IFTTT activates the automation Upstairs West Wing will " "permanently hold the heat to 62.0 and cool to 83.0 AND " "Downstairs East Wing will permanently hold the heat to 62.0 " "and cool to 83.0 AND Downstairs West Wing will permanently " "hold the heat to 62.0 and cool to 83.0 AND Activate the mode " "named 'Away 12' AND Master Suite will permanently hold the " "heat to 62.0 and cool to 83.0", ) self.assertEqual(automation_one.enabled, True) self.assertEqual(automation_one.automation_id, 3467876) ```
{ "source": "jeroenterheerdt/TeespringStorefrontParser", "score": 3 }
#### File: jeroenterheerdt/TeespringStorefrontParser/parser.py ```python from pyquery import PyQuery import requests import json InputFile = "input/store.html" def getProductImageUrl(product_card_pq): product_image_str = str(product_card_pq(".product_card__image")) a = product_image_str.find('https://') b = product_image_str.find(');" data-reactid') product_image_url = product_image_str[a:b] return product_image_url def getProductCardTitle(product_card_pq): return product_card_pq(".product_card__title").text() def getProductCardProductName(product_card_pq): return product_card_pq(".product_card__product_name").text() def getProductCardPrice(product_card_pq): return product_card_pq(".product_card__meta").text() def getProductCardProductPageUrl(product_card_pq): s = str(product_card_pq(".product_card").html()) a = s.find('https://') b = s.find('" data-reactid') return s[a:b] def getProductSizes(product_page_url): sizes = {} response = requests.get(product_page_url) product_page = PyQuery(response.content) product_size_drop_down = product_page("#select-size-campaign-page") for o in product_size_drop_down.items('option'): option_text= o.val('value').text() if not option_text == '--': o_str = str(o.val('value')) if not "disabled" in o_str: a_find_str = 'data-usd-price-with-tax="' a = o_str.find(a_find_str) b = o_str.find('" data-gbp-price="') price = o_str[a+len(a_find_str):b] sizes.update({option_text:price}) return sizes def getProductColors(product_page_url): colors = [] response = requests.get(product_page_url) product_page = PyQuery(response.content) color_list = product_page(".product__color_list") for c in color_list.items('li'): col = str(c.html()) col_find_str = "background-color:" a = col.find(col_find_str) b = col.find('"/>') color_hex = col[a+len(col_find_str):b] colors.append(color_hex.upper()) #deduplicate return list(set(colors)) class Product: def __init__(self, title, name, price = None, image_url = None, page_url = None, sizes = None, colors = None): self.title = title self.name = name self.price = price self.image_url = image_url self.page_url = page_url self.sizes = sizes self.colors = colors with open(InputFile,encoding="utf8") as f: html = f.read() pq = PyQuery(html) product_cards = pq(".product_card") print("Found "+str(len(product_cards))+" products!") products = [] for product_card in product_cards: product_card_pq = PyQuery(product_card) #product_image_url = getProductImageUrl(product_card_pq) product_title = getProductCardTitle(product_card_pq) product_name = getProductCardProductName(product_card_pq) #product_price = getProductCardPrice(product_card_pq) product_page_url = getProductCardProductPageUrl(product_card_pq) #sizes = getProductSizes(product_page_url) #colors = getProductColors(product_page_url) #p = Product(product_title, product_name, product_price, product_image_url, product_page_url, sizes, colors) p = Product(product_title, product_name, None, None, product_page_url) products.append(p) print("Parsed "+str(len(products))) ```
{ "source": "Jeroentetje3/PyBitmessage", "score": 3 }
#### File: src/tests/test_addresses.py ```python import unittest from binascii import unhexlify from pybitmessage import addresses from .samples import sample_address, sample_ripe class TestAddresses(unittest.TestCase): """Test addresses manipulations""" def test_decode(self): """Decode some well known addresses and check the result""" self.assertEqual( addresses.decodeAddress(sample_address), ('success', 2, 1, unhexlify(sample_ripe))) status, version, stream, ripe1 = addresses.decodeAddress( '2cWzSnwjJ7yRP3nLEWUV5LisTZyREWSzUK') self.assertEqual(status, 'success') self.assertEqual(stream, 1) self.assertEqual(version, 4) status, version, stream, ripe2 = addresses.decodeAddress( '2DBPTgeSawWYZceFD69AbDT5q4iUWtj1ZN') self.assertEqual(status, 'success') self.assertEqual(stream, 1) self.assertEqual(version, 3) self.assertEqual(ripe1, ripe2) def test_encode(self): """Encode sample ripe and compare the result to sample address""" self.assertEqual( sample_address, addresses.encodeAddress(2, 1, unhexlify(sample_ripe))) ```
{ "source": "jeroenubbink/commercetools-python-sdk", "score": 2 }
#### File: commercetools-python-sdk/codegen/raml_types.py ```python from typing import Dict, List, Optional import attr from codegen.utils import snakeit @attr.s(auto_attribs=True) class DataType: name: str type: Optional[str] properties: List["Property"] = attr.Factory(lambda: []) base: "DataType" = attr.ib(repr=False, default=None) children: List["DataType"] = attr.ib(repr=False, default=attr.Factory(lambda: [])) discriminator_value: Optional[str] = None discriminator: Optional[str] = None enum: List[str] = attr.Factory(lambda: []) package_name: Optional[str] = None #: Annotations are additional free fields, not specified in the #: raml specs annotations: Dict[str, object] = attr.Factory(lambda: {}) @property def is_scalar_type(self): return self.type in [ "string", "number", "float", "integer", "boolean", "date", "file", "any", ] def get_bases(self): bases = [] cur = self while cur: bases.append(cur) cur = cur.base return bases def get_all_properties(self) -> List["Property"]: """Return all the properties for this datatype including parent types. Note that we need to remove duplicate properties in case a sub resoruce overrides a property of the parent. """ properties = {} # assume ordered dict bases = reversed(self.get_bases()) # bottom to top for base in bases: for prop in base.properties: properties[prop.name] = prop return list(properties.values()) def get_discriminator_field(self): field = None bases = self.get_bases() for base in bases: if base.discriminator: field = base.discriminator break if field: all_properties = self.get_all_properties() all_properties = {prop.name: prop for prop in all_properties} return all_properties[field] def get_all_children(self): children = list(self.children) for child in self.children: children.extend(child.get_all_children()) return children @attr.s(auto_attribs=True, slots=True) class Property: name: str types: List[DataType] optional: bool = False many: bool = False items: List[str] = attr.Factory(lambda: []) items_types: Optional[List["DataType"]] = None @property def type(self): if self.types: return self.types[0] @type.setter def type(self, value): if self.types: self.types[0] = value else: self.types = [value] @property def attribute_name(self) -> Optional[str]: name = snakeit(self.name) if not name or not name.isidentifier(): return None return name @attr.s(auto_attribs=True) class UnresolvedType: name: str ``` #### File: commercetools-python-sdk/codegen/service_processor.py ```python import typing import attr from codegen.utils import ( class_name, create_codename, create_method_name, extract_name, snakeit, ) @attr.s(auto_attribs=True) class ServiceParameter: name: str type: str required: bool multiple: bool = False extra_data: dict = None @property def pytype(self): if self.type == "string": return str return self.type @attr.s(auto_attribs=True) class ServiceMethod: name: str path: str method: str type: str context_name: str = None description: str = None path_params: typing.List[ServiceParameter] = attr.Factory(list) query_params: typing.List[ServiceParameter] = attr.Factory(list) extra_params: typing.List[ServiceParameter] = attr.Factory(list) input_type: str = None returns: str = None is_fileupload: bool = False traits: typing.List["TraitInfo"] = attr.Factory(list) @attr.s(auto_attribs=True) class ServiceDomain: path: str parent: "ServiceDomain" = None context_name: str = None description: str = None path_parameters: typing.List[ServiceParameter] = None methods: typing.List[ServiceMethod] = None resource_draft: str = None resource_type: str = None resource_querytype: str = None def add_method(self, *methods): for method in methods: method.context_name = self.context_name self.methods.append(method) @attr.s(auto_attribs=True) class TraitInfo: name: str class_name: str params: typing.List[ServiceParameter] = attr.Factory(list) class ServiceProcessor: def __init__(self): pass def load(self, source): self._source = source["/{projectKey}"] self.traits = self._parse_traits(source["traits"]) self._resource_types = source["resourceTypes"] def __iter__(self): for path, data in self._source.items(): if path.startswith("/"): yield self._parse_service(path, data) def _parse_traits(self, source) -> typing.Dict[str, TraitInfo]: result = {} for name, data in source.items(): param_data = data.get("queryParameters", {}) params = _parse_query_parameters(param_data) for param in params: param.multiple = True result[name] = TraitInfo( name=name, class_name=class_name(name), params=params ) return result def _parse_service(self, path, source, parent=None): domain = ServiceDomain( path=path if not parent else parent.path + path, parent=parent, methods=[], description=source.get("description"), path_parameters=self._get_parameters(source), resource_draft=extract_name( _get_value(source, "type", "baseDomain", "resourceDraft") ), resource_type=extract_name( _get_value(source, "type", "baseDomain", "resourceType") ), resource_querytype=extract_name( _get_value(source, "type", "baseDomain", "resourceQueryType") ), ) if parent and parent.path_parameters: domain.path_parameters = parent.path_parameters + domain.path_parameters domain.context_name = domain.resource_type or create_codename(path).title() for endpoint, endpoint_data in source.items(): endpoint_type = _get_item_type(endpoint_data) if endpoint_type == "baseDomain": child = self._parse_service(endpoint, endpoint_data, domain) domain.add_method(*child.methods) del child elif endpoint in ["post", "get", "delete"]: method = self._get_domain_methods( domain, endpoint, endpoint_data, source ) if method: domain.add_method(method) elif endpoint.startswith("/"): subparams = self._get_parameters(endpoint_data) if not subparams: method = self._get_action_method( domain, endpoint, endpoint_data, source ) if method: domain.add_method(method) else: for method in self._get_resource_methods( domain, endpoint, endpoint_data ): domain.add_method(method) return domain def _get_domain_methods(self, service_domain, method, method_data, parent_data): method_name = "" if service_domain.parent: method_name = snakeit(service_domain.context_name) + "_" if method == "get": method = ServiceMethod( name=method_name + "query", path=service_domain.path, path_params=list(service_domain.path_parameters), query_params=[], type="query", method=method, returns=_get_return_type( method_data, service_domain.resource_querytype ), ) return self._add_metadata(method, method_data, parent_data) elif method == "post": method = ServiceMethod( name=method_name + "create", path=service_domain.path, path_params=list(service_domain.path_parameters), query_params=[], type="create", method=method, input_type=service_domain.resource_draft, returns=_get_return_type(method_data, service_domain.resource_type), ) return self._add_metadata(method, method_data, parent_data) def _get_action_method(self, service_domain, path, data, parent_data): if "get" in data and "post" in data: if data["post"].get("responses"): endpoint_data = data["post"] method = "post" else: endpoint_data = data["get"] method = "get" elif "post" in data: endpoint_data = data["post"] method = "post" elif "get" in data: endpoint_data = data["get"] method = "get" else: return None method_name = create_method_name(path) if service_domain.parent: method_name = snakeit(service_domain.context_name) + "_" + method_name method = ServiceMethod( name=method_name, path=service_domain.path + path, path_params=[], query_params=[], type="action", method=method, input_type=_get_input_type(endpoint_data), returns=_get_return_type(endpoint_data, service_domain.resource_type), ) return self._add_metadata(method, endpoint_data, data) def _get_resource_methods(self, service_domain, path, data): params = list(service_domain.path_parameters) params.extend(self._get_parameters(data)) method_name = "_%s" % snakeit(data["(methodName)"]) method_name = method_name.replace("with", "by") type_name = _get_item_type(data) name_prefix = "" if service_domain.parent: name_prefix = snakeit(service_domain.context_name) + "_" for endpoint_path, endpoint_data in data.items(): if endpoint_path == "post": input_type = data["type"][type_name]["resourceUpdateType"] method = ServiceMethod( name=name_prefix + "update" + method_name, path=service_domain.path + path, path_params=list(params), type="update", method="post", input_type=service_domain.resource_type + input_type, returns=_get_return_type( endpoint_data, service_domain.resource_type ), ) yield self._add_metadata(method, endpoint_data, data) elif endpoint_path == "get": method = ServiceMethod( name=name_prefix + "get" + method_name, path=service_domain.path + path, path_params=list(params), type="get", method="get", returns=_get_return_type( endpoint_data, service_domain.resource_type ), ) yield self._add_metadata(method, endpoint_data, data) elif endpoint_path == "delete": method = ServiceMethod( name=name_prefix + "delete" + method_name, path=service_domain.path + path, path_params=list(params), type="delete", method="delete", returns=_get_return_type( endpoint_data, service_domain.resource_type ), ) yield self._add_metadata(method, endpoint_data, data) elif endpoint_path.startswith("/"): yield self._get_action_method( service_domain, endpoint_path, endpoint_data, data ) def _get_parameters(self, data) -> typing.List[ServiceParameter]: if isinstance(data, str): return [] if "uriParameters" in data: params = [] for key, val in data["uriParameters"].items(): param = ServiceParameter(name=key, type=val, required=True) params.append(param) return params type_name = _get_item_type(data) if not isinstance(data.get("type"), dict): return [] value = data["type"][type_name].get("uriParameterName") if value: param = ServiceParameter(name=value, type="string", required=True) return [param] return [] def _add_metadata(self, method: ServiceMethod, data, parent_data): if isinstance(data, str): return method method.description = data.get("description", "") if parent_data.get("description"): method.description += "\n\n" + parent_data["description"].strip() type_name = _get_item_type(parent_data) traits = [] # Get traits from base resource type_data = self._resource_types[type_name] type_data = type_data.get(method.method) or type_data.get(method.method + "?") traits.extend(type_data.get("is", [])) # Missing in raml specs? if method.type == "update": traits.append("versioned") # Get params from traits traits.extend(data.get("is", [])) for trait in traits: if isinstance(trait, str): trait_info = self.traits[trait] else: trait_key = list(trait.keys())[0] trait_info = self.traits[trait_key] method.query_params.extend(trait_info.params) method.traits.append(trait_info) # Get params specified on method params = data.get("queryParameters", {}) if params: method.extra_params = _parse_query_parameters(params) method.query_params.extend(method.extra_params) # Check if this is a file upload (?) if data.get("body", {}).get("type") == "file": method.is_fileupload = True method.query_params.append( ServiceParameter(name="fh", type="file", required=True, extra_data=None) ) # De-duplicate param names deduplicated = {} for param in method.query_params: deduplicated[param.name] = param method.query_params = list(deduplicated.values()) return method def _parse_query_parameters(params): result = [] for name, value in params.items(): required = value.get("required", True) if name.endswith("?"): name = name[:-1] required = False param = ServiceParameter( name=name, type=value.get("type", "string"), required=required, extra_data=value, ) result.append(param) return result def _get_value(data, *keys): val = data for key in keys: try: val = val[key] except (KeyError, TypeError): return None return val def _get_input_type(data, default=None): for code in [200, 201]: try: return data["body"]["application/json"]["type"] except (KeyError, TypeError): continue return default def _get_return_type(data, default=None): for code in [200, 201]: try: return data["responses"][code]["body"]["application/json"]["type"] except (KeyError, TypeError): continue return default def _get_item_type(data): if isinstance(data, str): return "base" try: typeval = data.get("type") if not typeval: return "base" if isinstance(typeval, str): return typeval return list(typeval.keys())[0] except (TypeError, KeyError): pass return "base" ``` #### File: codegen/tests/test_utils.py ```python import pytest import utils @pytest.mark.parametrize( "test_input,test_output", [("fooBar", "foo_bar"), ("externalOAuth", "external_oauth")], ) def test_snakit(test_input, test_output): assert utils.snakeit(test_input) == test_output def test_format_docstring(): value = "Returns a customer by its ID from a specific Store. The {storeKey} path parameter maps to a Store's key.\nIt also considers customers that do not have the stores field.\nIf the customer exists in the commercetools project but the stores field references different stores,\nthis method returns a ResourceNotFound error.\n" newvalue = utils.format_docstring(value) assert ( newvalue == """Returns a customer by its ID from a specific Store. The {storeKey} path parameter maps to a Store's key. It also considers customers that do not have the stores field. If the customer exists in the commercetools project but the stores field references different stores, this method returns a ResourceNotFound error. """ ) ``` #### File: src/commercetools/client.py ```python import os import typing import urllib.parse import requests from marshmallow.base import SchemaABC from oauthlib.oauth2 import BackendApplicationClient from requests.adapters import HTTPAdapter from requests_oauthlib import OAuth2Session from urllib3.util.retry import Retry from commercetools._schemas._error import ErrorResponseSchema from commercetools.constants import HEADER_CORRELATION_ID from commercetools.exceptions import CommercetoolsError from commercetools.helpers import _concurrent_retry from commercetools.services import ServicesMixin from commercetools.utils import BaseTokenSaver, DefaultTokenSaver, fix_token_url class RefreshingOAuth2Session(OAuth2Session): def refresh_token(self, token_url, **kwargs): kwargs.update(self.auto_refresh_kwargs) kwargs["scope"] = self.scope return self.fetch_token(token_url, **kwargs) class Client(ServicesMixin): """The Commercetools Client, used to interact with the Commercetools API. :param project_key: the key for the project with which you want to interact :param client_id: the oauth2 client id :param client_secret: the oauth2 client secret :param scope: the oauth2 scope. If None then 'manage_project:{project_key}' :param url: the api endpoint :param token_url: the oauth2 token url endpoint. This should be the full path to the token url. :param token_saver: optional custom token saver to store and retrieve the oauth2 tokens. """ def __init__( self, project_key: str = None, client_id: str = None, client_secret: str = None, scope: typing.List[str] = None, url: str = None, token_url: str = None, token_saver: BaseTokenSaver = None, ) -> None: # Use environment variables as fallback config = { "project_key": project_key, "client_id": client_id, "client_secret": client_secret, "url": url, "token_url": token_url, "scope": scope, } # Make sure we use the config vars del project_key, client_id, client_secret, url, token_url, scope self._config = self._read_env_vars(config) self._config["token_url"] = fix_token_url(self._config["token_url"]) self._token_saver = token_saver or DefaultTokenSaver() self._url = self._config["url"] self._base_url = f"{self._config['url']}/{self._config['project_key']}/" # Fetch token from the token saver token = self._token_saver.get_token( self._config["client_id"], self._config["scope"] ) client = BackendApplicationClient( client_id=self._config["client_id"], scope=self._config["scope"] ) self._http_client = RefreshingOAuth2Session( client=client, scope=self._config["scope"], auto_refresh_url=self._config["token_url"], auto_refresh_kwargs={ "client_id": self._config["client_id"], "client_secret": self._config["client_secret"], }, token_updater=self._save_token, ) # Register retry handling for Connection errors and 502, 503, 504. retry = Retry(status=3, connect=3, status_forcelist=[502, 503, 504]) adapter = HTTPAdapter(max_retries=retry) self._http_client.mount("http://", adapter) self._http_client.mount("https://", adapter) if token: self._http_client.token = token else: token = self._http_client.fetch_token( token_url=self._config["token_url"], scope=self._config["scope"], client_id=self._config["client_id"], client_secret=self._config["client_secret"], ) self._save_token(token) def _save_token(self, token): self._token_saver.add_token( self._config["client_id"], self._config["scope"], token ) def _get( self, endpoint: str, params: typing.Dict[str, typing.Any], schema_cls: SchemaABC ) -> typing.Any: """Retrieve a single object from the commercetools platform""" response = self._http_client.get(self._base_url + endpoint, params=params) if response.status_code == 200: return schema_cls().load(response.json()) return self._process_error(response) def _post( self, endpoint: str, params: typing.Dict[str, str], data_object: typing.Any, request_schema_cls: SchemaABC, response_schema_cls: SchemaABC, form_encoded: bool = False, force_update: bool = False, ) -> typing.Any: """Retrieve a single object from the commercetools platform""" @_concurrent_retry(3 if force_update else 0) def remote_http_call(data): if form_encoded: kwargs = {"data": data} else: kwargs = {"json": data} if params: kwargs["params"] = params response = self._http_client.post(self._base_url + endpoint, **kwargs) if response.status_code in (200, 201): return response_schema_cls().load(response.json()) return self._process_error(response) data = request_schema_cls().dump(data_object) return remote_http_call(data) def _upload( self, endpoint: str, params: typing.Dict[str, str], file: typing.IO, response_schema_cls: SchemaABC, ) -> typing.Any: """Retrieve a single object from the commercetools platform""" response = self._http_client.post( self._base_url + endpoint, data=file.read(), params=params ) if response.status_code in (200, 201): return response_schema_cls().load(response.json()) return self._process_error(response) def _delete( self, endpoint: str, params: typing.Dict[str, str], response_schema_cls: SchemaABC, force_delete: bool = False, ) -> typing.Any: """Delete an object from the commercetools platform""" @_concurrent_retry(3 if force_delete else 0) def remote_http_call(data): response = self._http_client.delete( self._base_url + endpoint, params=params ) if response.status_code == 200: return response_schema_cls().load(response.json()) return self._process_error(response) return remote_http_call(params) def _process_error(self, response: requests.Response) -> None: correlation_id = response.headers.get(HEADER_CORRELATION_ID) if not response.content: response.raise_for_status() obj = ErrorResponseSchema().loads(response.content) # We'll fetch the 'raw' errors from the response because some of the # attributes are not included in the schemas. # With the raw errors in the CommercetoolsError object we can use that # information later to render more detailed error messages errors_raw = [] try: response_json = response.json() except ValueError: pass else: errors_raw = response_json.get("errors", []) raise CommercetoolsError(obj.message, errors_raw, obj, correlation_id) def _read_env_vars(self, config: dict) -> dict: if not config.get("project_key"): config["project_key"] = os.environ.get("CTP_PROJECT_KEY") if not config.get("client_id"): config["client_id"] = os.environ.get("CTP_CLIENT_ID") if not config.get("client_secret"): config["client_secret"] = os.environ.get("CTP_CLIENT_SECRET") if not config.get("url"): config["url"] = os.environ.get("CTP_API_URL") if not config.get("token_url"): config["token_url"] = os.environ.get("CTP_AUTH_URL") if not config["scope"]: config["scope"] = os.environ.get("CTP_SCOPES") if config["scope"]: config["scope"] = config["scope"].split(",") else: config["scope"] = ["manage_project:%s" % config["project_key"]] for key, value in config.items(): if value is None: raise ValueError(f"No value set for {key}") return config ``` #### File: src/commercetools/exceptions.py ```python import typing from commercetools.types import ErrorResponse class CommercetoolsError(Exception): response: ErrorResponse correlation_id: typing.Optional[str] def __init__( self, message: typing.Any, errors: typing.List[dict], response: ErrorResponse, correlation_id: str = None, ) -> None: super().__init__(message) self.response = response self.errors = errors self.correlation_id = correlation_id def __str__(self): result = super().__str__() if self.details: return f"{result} ({', '.join(self.details)})" return result @property def details(self) -> typing.List[str]: return [ e["detailedErrorMessage"] for e in self.errors if "detailedErrorMessage" in e ] @property def codes(self) -> typing.List[str]: try: return [e.code for e in self.response.errors] except AttributeError: return [] @property def code(self) -> str: """Convenience property to easily get the error code. Returns the code of the first error, just as 'message' is always the message of the first error. """ try: return self.codes[0] except KeyError: return "" ``` #### File: commercetools/services/messages.py ```python import typing from commercetools._schemas._message import ( MessagePagedQueryResponseSchema, MessageSchema, ) from commercetools.helpers import RemoveEmptyValuesMixin from commercetools.types._message import Message, MessagePagedQueryResponse from commercetools.typing import OptionalListStr from . import abstract, traits class _MessageQuerySchema( traits.ExpandableSchema, traits.SortableSchema, traits.PagingSchema, traits.QuerySchema, ): pass class MessageService(abstract.AbstractService): """A message represents a change or an action performed on a resource (like an Order or a Product).""" def get_by_id(self, id: str, *, expand: OptionalListStr = None) -> Message: params = self._serialize_params({"expand": expand}, traits.ExpandableSchema) return self._client._get( endpoint=f"messages/{id}", params=params, schema_cls=MessageSchema ) def query( self, *, expand: OptionalListStr = None, sort: OptionalListStr = None, limit: int = None, offset: int = None, with_total: bool = None, where: OptionalListStr = None, predicate_var: typing.Dict[str, str] = None, ) -> MessagePagedQueryResponse: """A message represents a change or an action performed on a resource (like an Order or a Product). """ params = self._serialize_params( { "expand": expand, "sort": sort, "limit": limit, "offset": offset, "withTotal": with_total, "where": where, "predicate_var": predicate_var, }, _MessageQuerySchema, ) return self._client._get( endpoint="messages", params=params, schema_cls=MessagePagedQueryResponseSchema, ) ``` #### File: commercetools/services/shopping_lists.py ```python import typing from commercetools._schemas._shopping_list import ( ShoppingListDraftSchema, ShoppingListPagedQueryResponseSchema, ShoppingListSchema, ShoppingListUpdateSchema, ) from commercetools.helpers import RemoveEmptyValuesMixin from commercetools.types._shopping_list import ( ShoppingList, ShoppingListDraft, ShoppingListPagedQueryResponse, ShoppingListUpdate, ShoppingListUpdateAction, ) from commercetools.typing import OptionalListStr from . import abstract, traits class _ShoppingListQuerySchema( traits.ExpandableSchema, traits.SortableSchema, traits.PagingSchema, traits.QuerySchema, ): pass class _ShoppingListUpdateSchema(traits.ExpandableSchema, traits.VersionedSchema): pass class _ShoppingListDeleteSchema( traits.VersionedSchema, traits.ExpandableSchema, traits.DataErasureSchema ): pass class ShoppingListService(abstract.AbstractService): """shopping-lists e. g. for wishlist support """ def get_by_id(self, id: str, *, expand: OptionalListStr = None) -> ShoppingList: """Gets a shopping list by ID.""" params = self._serialize_params({"expand": expand}, traits.ExpandableSchema) return self._client._get( endpoint=f"shopping-lists/{id}", params=params, schema_cls=ShoppingListSchema, ) def get_by_key(self, key: str, *, expand: OptionalListStr = None) -> ShoppingList: """Gets a shopping list by Key.""" params = self._serialize_params({"expand": expand}, traits.ExpandableSchema) return self._client._get( endpoint=f"shopping-lists/key={key}", params=params, schema_cls=ShoppingListSchema, ) def query( self, *, expand: OptionalListStr = None, sort: OptionalListStr = None, limit: int = None, offset: int = None, with_total: bool = None, where: OptionalListStr = None, predicate_var: typing.Dict[str, str] = None, ) -> ShoppingListPagedQueryResponse: """shopping-lists e.g. for wishlist support """ params = self._serialize_params( { "expand": expand, "sort": sort, "limit": limit, "offset": offset, "withTotal": with_total, "where": where, "predicate_var": predicate_var, }, _ShoppingListQuerySchema, ) return self._client._get( endpoint="shopping-lists", params=params, schema_cls=ShoppingListPagedQueryResponseSchema, ) def create( self, draft: ShoppingListDraft, *, expand: OptionalListStr = None ) -> ShoppingList: """shopping-lists e.g. for wishlist support """ params = self._serialize_params({"expand": expand}, traits.ExpandableSchema) return self._client._post( endpoint="shopping-lists", params=params, data_object=draft, request_schema_cls=ShoppingListDraftSchema, response_schema_cls=ShoppingListSchema, ) def update_by_id( self, id: str, version: int, actions: typing.List[ShoppingListUpdateAction], *, expand: OptionalListStr = None, force_update: bool = False, ) -> ShoppingList: params = self._serialize_params({"expand": expand}, _ShoppingListUpdateSchema) update_action = ShoppingListUpdate(version=version, actions=actions) return self._client._post( endpoint=f"shopping-lists/{id}", params=params, data_object=update_action, request_schema_cls=ShoppingListUpdateSchema, response_schema_cls=ShoppingListSchema, force_update=force_update, ) def update_by_key( self, key: str, version: int, actions: typing.List[ShoppingListUpdateAction], *, expand: OptionalListStr = None, force_update: bool = False, ) -> ShoppingList: """Update a shopping list found by its Key.""" params = self._serialize_params({"expand": expand}, _ShoppingListUpdateSchema) update_action = ShoppingListUpdate(version=version, actions=actions) return self._client._post( endpoint=f"shopping-lists/key={key}", params=params, data_object=update_action, request_schema_cls=ShoppingListUpdateSchema, response_schema_cls=ShoppingListSchema, force_update=force_update, ) def delete_by_id( self, id: str, version: int, *, expand: OptionalListStr = None, data_erasure: bool = None, force_delete: bool = False, ) -> ShoppingList: params = self._serialize_params( {"version": version, "expand": expand, "dataErasure": data_erasure}, _ShoppingListDeleteSchema, ) return self._client._delete( endpoint=f"shopping-lists/{id}", params=params, response_schema_cls=ShoppingListSchema, force_delete=force_delete, ) def delete_by_key( self, key: str, version: int, *, expand: OptionalListStr = None, data_erasure: bool = None, force_delete: bool = False, ) -> ShoppingList: params = self._serialize_params( {"version": version, "expand": expand, "dataErasure": data_erasure}, _ShoppingListDeleteSchema, ) return self._client._delete( endpoint=f"shopping-lists/key={key}", params=params, response_schema_cls=ShoppingListSchema, force_delete=force_delete, ) ``` #### File: commercetools/types/_tax_category.py ```python import datetime import typing from commercetools.types._abstract import _BaseType from commercetools.types._common import ( BaseResource, Reference, ReferenceTypeId, ResourceIdentifier, ) if typing.TYPE_CHECKING: from ._common import CreatedBy, LastModifiedBy __all__ = [ "SubRate", "TaxCategory", "TaxCategoryAddTaxRateAction", "TaxCategoryChangeNameAction", "TaxCategoryDraft", "TaxCategoryPagedQueryResponse", "TaxCategoryReference", "TaxCategoryRemoveTaxRateAction", "TaxCategoryReplaceTaxRateAction", "TaxCategoryResourceIdentifier", "TaxCategorySetDescriptionAction", "TaxCategorySetKeyAction", "TaxCategoryUpdate", "TaxCategoryUpdateAction", "TaxRate", "TaxRateDraft", ] class SubRate(_BaseType): #: :class:`str` name: str #: :class:`float` amount: float def __init__(self, *, name: str = None, amount: float = None) -> None: self.name = name self.amount = amount super().__init__() def __repr__(self) -> str: return "SubRate(name=%r, amount=%r)" % (self.name, self.amount) class TaxCategory(BaseResource): #: :class:`str` id: str #: :class:`int` version: int #: :class:`datetime.datetime` `(Named` ``createdAt`` `in Commercetools)` created_at: datetime.datetime #: :class:`datetime.datetime` `(Named` ``lastModifiedAt`` `in Commercetools)` last_modified_at: datetime.datetime #: Optional :class:`commercetools.types.LastModifiedBy` `(Named` ``lastModifiedBy`` `in Commercetools)` last_modified_by: typing.Optional["LastModifiedBy"] #: Optional :class:`commercetools.types.CreatedBy` `(Named` ``createdBy`` `in Commercetools)` created_by: typing.Optional["CreatedBy"] #: :class:`str` name: str #: Optional :class:`str` description: typing.Optional[str] #: List of :class:`commercetools.types.TaxRate` rates: typing.List["TaxRate"] #: Optional :class:`str` key: typing.Optional[str] def __init__( self, *, id: str = None, version: int = None, created_at: datetime.datetime = None, last_modified_at: datetime.datetime = None, last_modified_by: typing.Optional["LastModifiedBy"] = None, created_by: typing.Optional["CreatedBy"] = None, name: str = None, description: typing.Optional[str] = None, rates: typing.List["TaxRate"] = None, key: typing.Optional[str] = None ) -> None: self.id = id self.version = version self.created_at = created_at self.last_modified_at = last_modified_at self.last_modified_by = last_modified_by self.created_by = created_by self.name = name self.description = description self.rates = rates self.key = key super().__init__( id=id, version=version, created_at=created_at, last_modified_at=last_modified_at, ) def __repr__(self) -> str: return ( "TaxCategory(id=%r, version=%r, created_at=%r, last_modified_at=%r, last_modified_by=%r, created_by=%r, name=%r, description=%r, rates=%r, key=%r)" % ( self.id, self.version, self.created_at, self.last_modified_at, self.last_modified_by, self.created_by, self.name, self.description, self.rates, self.key, ) ) class TaxCategoryDraft(_BaseType): #: :class:`str` name: str #: Optional :class:`str` description: typing.Optional[str] #: List of :class:`commercetools.types.TaxRateDraft` rates: typing.List["TaxRateDraft"] #: Optional :class:`str` key: typing.Optional[str] def __init__( self, *, name: str = None, description: typing.Optional[str] = None, rates: typing.List["TaxRateDraft"] = None, key: typing.Optional[str] = None ) -> None: self.name = name self.description = description self.rates = rates self.key = key super().__init__() def __repr__(self) -> str: return "TaxCategoryDraft(name=%r, description=%r, rates=%r, key=%r)" % ( self.name, self.description, self.rates, self.key, ) class TaxCategoryPagedQueryResponse(_BaseType): #: :class:`int` limit: int #: :class:`int` count: int #: Optional :class:`int` total: typing.Optional[int] #: :class:`int` offset: int #: List of :class:`commercetools.types.TaxCategory` results: typing.Sequence["TaxCategory"] def __init__( self, *, limit: int = None, count: int = None, total: typing.Optional[int] = None, offset: int = None, results: typing.Sequence["TaxCategory"] = None ) -> None: self.limit = limit self.count = count self.total = total self.offset = offset self.results = results super().__init__() def __repr__(self) -> str: return ( "TaxCategoryPagedQueryResponse(limit=%r, count=%r, total=%r, offset=%r, results=%r)" % (self.limit, self.count, self.total, self.offset, self.results) ) class TaxCategoryReference(Reference): #: Optional :class:`commercetools.types.TaxCategory` obj: typing.Optional["TaxCategory"] def __init__( self, *, type_id: "ReferenceTypeId" = None, id: str = None, obj: typing.Optional["TaxCategory"] = None ) -> None: self.obj = obj super().__init__(type_id=ReferenceTypeId.TAX_CATEGORY, id=id) def __repr__(self) -> str: return "TaxCategoryReference(type_id=%r, id=%r, obj=%r)" % ( self.type_id, self.id, self.obj, ) class TaxCategoryResourceIdentifier(ResourceIdentifier): def __init__( self, *, type_id: typing.Optional["ReferenceTypeId"] = None, id: typing.Optional[str] = None, key: typing.Optional[str] = None ) -> None: super().__init__(type_id=ReferenceTypeId.TAX_CATEGORY, id=id, key=key) def __repr__(self) -> str: return "TaxCategoryResourceIdentifier(type_id=%r, id=%r, key=%r)" % ( self.type_id, self.id, self.key, ) class TaxCategoryUpdate(_BaseType): #: :class:`int` version: int #: :class:`list` actions: list def __init__(self, *, version: int = None, actions: list = None) -> None: self.version = version self.actions = actions super().__init__() def __repr__(self) -> str: return "TaxCategoryUpdate(version=%r, actions=%r)" % ( self.version, self.actions, ) class TaxCategoryUpdateAction(_BaseType): #: :class:`str` action: str def __init__(self, *, action: str = None) -> None: self.action = action super().__init__() def __repr__(self) -> str: return "TaxCategoryUpdateAction(action=%r)" % (self.action,) class TaxRate(_BaseType): #: Optional :class:`str` id: typing.Optional[str] #: :class:`str` name: str #: :class:`float` amount: float #: :class:`bool` `(Named` ``includedInPrice`` `in Commercetools)` included_in_price: bool #: :class:`str` country: "str" #: Optional :class:`str` state: typing.Optional[str] #: Optional list of :class:`commercetools.types.SubRate` `(Named` ``subRates`` `in Commercetools)` sub_rates: typing.Optional[typing.List["SubRate"]] def __init__( self, *, id: typing.Optional[str] = None, name: str = None, amount: float = None, included_in_price: bool = None, country: "str" = None, state: typing.Optional[str] = None, sub_rates: typing.Optional[typing.List["SubRate"]] = None ) -> None: self.id = id self.name = name self.amount = amount self.included_in_price = included_in_price self.country = country self.state = state self.sub_rates = sub_rates super().__init__() def __repr__(self) -> str: return ( "TaxRate(id=%r, name=%r, amount=%r, included_in_price=%r, country=%r, state=%r, sub_rates=%r)" % ( self.id, self.name, self.amount, self.included_in_price, self.country, self.state, self.sub_rates, ) ) class TaxRateDraft(_BaseType): #: :class:`str` name: str #: Optional :class:`float` amount: typing.Optional[float] #: :class:`bool` `(Named` ``includedInPrice`` `in Commercetools)` included_in_price: bool #: :class:`str` country: "str" #: Optional :class:`str` state: typing.Optional[str] #: Optional list of :class:`commercetools.types.SubRate` `(Named` ``subRates`` `in Commercetools)` sub_rates: typing.Optional[typing.List["SubRate"]] def __init__( self, *, name: str = None, amount: typing.Optional[float] = None, included_in_price: bool = None, country: "str" = None, state: typing.Optional[str] = None, sub_rates: typing.Optional[typing.List["SubRate"]] = None ) -> None: self.name = name self.amount = amount self.included_in_price = included_in_price self.country = country self.state = state self.sub_rates = sub_rates super().__init__() def __repr__(self) -> str: return ( "TaxRateDraft(name=%r, amount=%r, included_in_price=%r, country=%r, state=%r, sub_rates=%r)" % ( self.name, self.amount, self.included_in_price, self.country, self.state, self.sub_rates, ) ) class TaxCategoryAddTaxRateAction(TaxCategoryUpdateAction): #: :class:`commercetools.types.TaxRateDraft` `(Named` ``taxRate`` `in Commercetools)` tax_rate: "TaxRateDraft" def __init__(self, *, action: str = None, tax_rate: "TaxRateDraft" = None) -> None: self.tax_rate = tax_rate super().__init__(action="addTaxRate") def __repr__(self) -> str: return "TaxCategoryAddTaxRateAction(action=%r, tax_rate=%r)" % ( self.action, self.tax_rate, ) class TaxCategoryChangeNameAction(TaxCategoryUpdateAction): #: :class:`str` name: str def __init__(self, *, action: str = None, name: str = None) -> None: self.name = name super().__init__(action="changeName") def __repr__(self) -> str: return "TaxCategoryChangeNameAction(action=%r, name=%r)" % ( self.action, self.name, ) class TaxCategoryRemoveTaxRateAction(TaxCategoryUpdateAction): #: :class:`str` `(Named` ``taxRateId`` `in Commercetools)` tax_rate_id: str def __init__(self, *, action: str = None, tax_rate_id: str = None) -> None: self.tax_rate_id = tax_rate_id super().__init__(action="removeTaxRate") def __repr__(self) -> str: return "TaxCategoryRemoveTaxRateAction(action=%r, tax_rate_id=%r)" % ( self.action, self.tax_rate_id, ) class TaxCategoryReplaceTaxRateAction(TaxCategoryUpdateAction): #: :class:`str` `(Named` ``taxRateId`` `in Commercetools)` tax_rate_id: str #: :class:`commercetools.types.TaxRateDraft` `(Named` ``taxRate`` `in Commercetools)` tax_rate: "TaxRateDraft" def __init__( self, *, action: str = None, tax_rate_id: str = None, tax_rate: "TaxRateDraft" = None ) -> None: self.tax_rate_id = tax_rate_id self.tax_rate = tax_rate super().__init__(action="replaceTaxRate") def __repr__(self) -> str: return ( "TaxCategoryReplaceTaxRateAction(action=%r, tax_rate_id=%r, tax_rate=%r)" % (self.action, self.tax_rate_id, self.tax_rate) ) class TaxCategorySetDescriptionAction(TaxCategoryUpdateAction): #: Optional :class:`str` description: typing.Optional[str] def __init__( self, *, action: str = None, description: typing.Optional[str] = None ) -> None: self.description = description super().__init__(action="setDescription") def __repr__(self) -> str: return "TaxCategorySetDescriptionAction(action=%r, description=%r)" % ( self.action, self.description, ) class TaxCategorySetKeyAction(TaxCategoryUpdateAction): #: Optional :class:`str` key: typing.Optional[str] def __init__(self, *, action: str = None, key: typing.Optional[str] = None) -> None: self.key = key super().__init__(action="setKey") def __repr__(self) -> str: return "TaxCategorySetKeyAction(action=%r, key=%r)" % (self.action, self.key) ``` #### File: src/commercetools/utils.py ```python import threading import urllib.parse tls = threading.local() class BaseTokenSaver: def get_token(self, client_id, scopes): raise NotImplementedError() def add_token(self, client_id, scopes, token): raise NotImplementedError() def _create_token_hash(self, client_id, scopes): assert scopes is not None return "%s:%s" % (client_id, ";".join(scopes)) class DefaultTokenSaver(BaseTokenSaver): @property def storage(self): items = getattr(tls, "tokens", None) if items is None: items = {} setattr(tls, "tokens", items) return items def add_token(self, client_id, scopes, token): name = self._create_token_hash(client_id, scopes) self.storage[name] = token def get_token(self, client_id, scopes): name = self._create_token_hash(client_id, scopes) return self.storage.get(name) @classmethod def clear_cache(cls): items = getattr(tls, "tokens", {}) items.clear() def fix_token_url(token_url: str) -> str: """ Ensure the token url has the right format. Often clients only pass the base url instead of the complete token url, which gets confusing for users. """ parts = urllib.parse.urlparse(token_url) if parts.path == "": token_url = urllib.parse.urlunparse((*parts[:2], "/oauth/token", *parts[3:])) return token_url ``` #### File: commercetools-python-sdk/tests/test_mock_server.py ```python import os import requests from commercetools import Client from commercetools.types import ( ChannelDraft, ChannelResourceIdentifier, ChannelRoleEnum, LocalizedString, ProductDraft, StoreDraft, ) def test_http_server(commercetools_client, commercetools_http_server): os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1" client = Client( project_key="unittest", client_id="client-id", client_secret="client-secret", scope=[], url=commercetools_http_server.api_url, token_url=f"{commercetools_http_server.api_url}/oauth/token", ) query_result = client.products.query() assert query_result.count == 0 product = client.products.create(ProductDraft(name=LocalizedString(nl="Testje"))) client.products.get_by_id(product.id) url = commercetools_http_server.api_url + f"/unittest/products/{product.id}" response = requests.get(url, headers={"Authorization": "Bearer token"}) assert response.status_code == 200, response.text data = response.json() assert data["masterData"]["staged"]["name"]["nl"] == "Testje" def test_http_server_expanding(commercetools_client, commercetools_http_server): os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1" client = Client( project_key="unittest", client_id="client-id", client_secret="client-secret", scope=[], url=commercetools_http_server.api_url, token_url=f"{commercetools_http_server.api_url}/oauth/token", ) client.channels.create( ChannelDraft(key="FOO", roles=[ChannelRoleEnum.PRODUCT_DISTRIBUTION]) ) store = client.stores.create( StoreDraft( key="FOO", distribution_channels=[ChannelResourceIdentifier(key="FOO")] ) ) url = commercetools_http_server.api_url + f"/unittest/stores/{store.id}" response = requests.get( url, params={"expand": "distributionChannels[*]"}, headers={"Authorization": "Bearer token"}, ) assert response.status_code == 200, response.text data = response.json() assert data["distributionChannels"][0]["obj"]["key"] == "FOO" ``` #### File: commercetools-python-sdk/tests/test_service_custom_objects.py ```python import pytest from requests.exceptions import HTTPError from commercetools import types def test_custom_object_get_by_id(client): custom_object = client.custom_objects.create_or_update( types.CustomObjectDraft(container="unittest", key="test-object", value=1234) ) assert custom_object.id assert custom_object.container == "unittest" assert custom_object.key == "test-object" assert custom_object.value == 1234 custom_object = client.custom_objects.get_by_id(custom_object.id) assert custom_object.container == "unittest" assert custom_object.key == "test-object" assert custom_object.value == 1234 with pytest.raises(HTTPError): client.custom_objects.get_by_id("invalid") def test_custom_object_query(client): client.custom_objects.create_or_update( types.CustomObjectDraft(container="unittest", key="test-object-1", value=1234) ) client.custom_objects.create_or_update( types.CustomObjectDraft(container="unittest", key="test-object-2", value=1234) ) # single sort query result = client.custom_objects.query(sort="id asc") assert len(result.results) == 2 assert result.total == 2 # multiple sort queries result = client.custom_objects.query(sort=["id asc", "name asc"]) assert len(result.results) == 2 assert result.total == 2 def test_custom_object_update(client): """Test the return value of the update methods. It doesn't test the actual update itself. TODO: See if this is worth testing since we're using a mocking backend """ custom_object = client.custom_objects.create_or_update( types.CustomObjectDraft(container="unittest", key="test-object-1", value=1234) ) assert custom_object.key == "test-object-1" custom_object = client.custom_objects.create_or_update( types.CustomObjectDraft(container="unittest", key="test-object-1", value=2345) ) assert custom_object.key == "test-object-1" ``` #### File: commercetools-python-sdk/tests/test_service_extensions.py ```python from commercetools import types def test_extension_create(client): extension = client.extensions.create(types.ExtensionDraft()) assert extension.id def test_extension_get_by_id(client): extension = client.extensions.create(types.ExtensionDraft()) assert extension.id extension = client.extensions.get_by_id(extension.id) assert extension.id ``` #### File: commercetools-python-sdk/tests/test_service_payment.py ```python from commercetools import types def test_payments_get_by_id(client): payment = client.payments.create( types.PaymentDraft( key="test-payment", amount_planned=types.Money(cent_amount=2000, currency_code="GBP"), payment_method_info=types.PaymentMethodInfo( payment_interface="ADYEN", method="mc" ), transactions=[ types.TransactionDraft( type=types.TransactionType.CHARGE, amount=types.Money(cent_amount=2000, currency_code="GBP"), interaction_id="8525483242578266", state=types.TransactionState.SUCCESS, ) ], interface_interactions=[ types.CustomFieldsDraft( fields=types.FieldContainer( { "operations": "CANCEL,CAPTURE,REFUND", "success": True, "psp_reference": "8525483242578266", "merchant_reference": "some reference", "reason": "82132:0005:10/2020", "amount": 2000, "payment_method": "mc", "event_date": "2019-01-24T11:04:17.000000Z", "currency_code": "GBP", "event_code": "AUTHORISATION", "merchant_account_code": "TestMerchant", } ) ) ], ) ) assert payment.id assert payment.key == "test-payment" def test_update_actions(client): payment = client.payments.create( types.PaymentDraft( key="test-payment", amount_planned=types.Money(cent_amount=2000, currency_code="GBP"), payment_method_info=types.PaymentMethodInfo( payment_interface="ADYEN", method="mc" ), transactions=[ types.TransactionDraft( type=types.TransactionType.CHARGE, amount=types.Money(cent_amount=2000, currency_code="GBP"), state=types.TransactionState.PENDING, ) ], ) ) existing_transaction = payment.transactions[0] payment = client.payments.update_by_id( payment.id, payment.version, actions=[ types.PaymentAddInterfaceInteractionAction( fields=types.FieldContainer({"pspRef": "1337"}) ), types.PaymentChangeTransactionInteractionIdAction( transaction_id=existing_transaction.id, interaction_id="1337" ), types.PaymentAddTransactionAction( transaction=types.TransactionDraft( type=types.TransactionType.CHARGE, amount=types.Money(currency_code="GBP", cent_amount=1000), interaction_id="123", state=types.TransactionState.INITIAL, ) ), types.PaymentChangeTransactionStateAction( transaction_id=existing_transaction.id, state=types.TransactionState.SUCCESS, ), ], ) assert payment.interface_interactions[0].fields == {"pspRef": "1337"} assert payment.transactions[0].interaction_id == "1337" assert len(payment.transactions) == 2 assert payment.transactions[0].state == types.TransactionState.SUCCESS ``` #### File: commercetools-python-sdk/tests/test_utils.py ```python import pytest from commercetools.utils import fix_token_url @pytest.mark.parametrize( "token_url,expected_url", [ ("https://auth.sphere.io", "https://auth.sphere.io/oauth/token"), ("https://auth.sphere.io/oauth/token", "https://auth.sphere.io/oauth/token"), ("https://auth.commercetools.co", "https://auth.commercetools.co/oauth/token"), ( "https://auth.sphere.io?test=123", "https://auth.sphere.io/oauth/token?test=123", ), ], ) def test_fix_token_url(token_url, expected_url): assert fix_token_url(token_url) == expected_url ```
{ "source": "jeroenubbink/mach-composer", "score": 2 }
#### File: src/mach/git.py ```python import os import re import subprocess from dataclasses import dataclass from typing import List, Optional import click from mach import exceptions PRETTY_FMT = { "commit": "%H", "author": "%aN <%aE>", "date": "%ad", "message": "%s", } PRETTY_FMT_STR = "format:" + "|".join([fmt for fmt in PRETTY_FMT.values()]) class GitError(exceptions.MachError): pass @dataclass class Commit: id: str msg: str def commit(message: str): result = _run(["git", "status", "--short"]) if not result: click.echo("No changes detected, won't commit anything") return _run(["git", "commit", "-m", message]) def add(file: str): _run(["git", "add", file]) def ensure_local(repo: str, dest: str): """Ensure the repository is present on the given dest.""" reference = "" reference_match = re.match(r"(.*)(?:(?:@)(\w+))$", repo) if reference_match: repo, reference = reference_match.groups() if os.path.exists(dest): _run(["git", "pull"], cwd=dest) else: clone(repo, dest) if reference: try: _run(["git", "reset", "--hard", reference], cwd=dest) except GitError as e: raise GitError(f"Unable to swtich to reference {reference}: {e}") def clone(repo: str, dest: str): _run(["git", "clone", repo, dest]) def history(dir: str, from_ref: str, *, branch: Optional[str] = "") -> List[Commit]: if branch: _run(["git", "checkout", branch], cwd=dir) cmd = ["git", "log", f"--pretty={PRETTY_FMT_STR}"] if from_ref: cmd.append(f"{from_ref}..{branch or ''}") lines = _run(cmd, cwd=dir).decode("utf-8").splitlines() commits = [] for line in lines: commit_id, author, date, message = line.split("|", 3) commits.append( Commit(id=_clean_commit_id(commit_id), msg=_clean_commit_msg(message)) ) return commits def _clean_commit_msg(msg: str) -> str: return msg def _clean_commit_id(commit_id: str) -> str: """Get the correct commit ID for this commit. It will trim the short_id since mach and the components are using a different commit id format (7 chars long). """ return commit_id[:7] def _run(cmd: List, *args, **kwargs) -> bytes: kwargs["stderr"] = subprocess.STDOUT try: return subprocess.check_output(cmd, *args, **kwargs) except subprocess.CalledProcessError as e: raise GitError(e.output.decode() if e.output else str(e)) from e ``` #### File: mach/types/base.py ```python from dataclasses import dataclass, field from pathlib import Path from typing import Any, Dict, List, Optional from dataclasses_json import config, dataclass_json from dataclasses_jsonschema import JsonSchemaMixin from . import fields from .components import ComponentConfig from .general_config import GlobalConfig from .mach import MachComposerConfig from .sites import Site __all__ = ["MachConfig"] @dataclass_json @dataclass class MachConfig(JsonSchemaMixin): """Main MACH configuration object.""" mach_composer: MachComposerConfig general_config: GlobalConfig = field(metadata=config(field_name="global")) sites: List[Site] components: List[ComponentConfig] = fields.list_() # Items that are not used in the configuration itself by set by the parser output_path: str = "deployments" file: Optional[str] = fields.none() # Indicates that the config file is SOPS encrypted file_encrypted: bool = False variables: Dict[str, Any] = fields.dict_() variables_path: str = fields.none() variables_encrypted: bool = False @property def deployment_path(self) -> Path: return Path(self.output_path) def deployment_path_for(self, site: Site): return self.deployment_path / Path(site.identifier) def get_component(self, name: str) -> Optional[ComponentConfig]: for comp in self.components: if comp.name == name: return comp return None ``` #### File: tests/unittests/test_variables.py ```python import pytest from mach import variables def test_resolve_variables(): vars = { "my-value": "foo", "secrets": {"site1": {"my-value": "bar"}}, "list": ["one", "two", {"nested-key": "three"}], } variables.resolve_variable("my-value", vars) == "foo" with pytest.raises(variables.VariableNotFound): variables.resolve_variable("my-other-value", vars) variables.resolve_variable("secrets.site1.my-value", vars) == "bar" with pytest.raises(variables.VariableNotFound): variables.resolve_variable("secrets.site2.my-value", vars) variables.resolve_variable("list.0", vars) == "one" variables.resolve_variable("list.1", vars) == "two" variables.resolve_variable("list.2", vars) == {"nested-key": "three"} variables.resolve_variable("list.2.nested-key", vars) == "three" with pytest.raises(variables.VariableNotFound): variables.resolve_variable("my-value.string-attribute", vars) ```
{ "source": "jeroenubbink/syncthing2piwigo", "score": 2 }
#### File: syncthing2piwigo/syncthing/event.py ```python from typing import cast from marshmallow import Schema, fields, EXCLUDE class EventBaseSchema(Schema): ... # FIXME: unsure how this works, should probably do something with timezone # accept we have a dict for the nested schema right now. class EventDateTimeField(fields.DateTime): def _to_iso_format(self, syncthing_event_time: str) -> str: # look at the crap we have to deal with... # "2014-07-13T21:22:03.414609034+02:00" # first transform nano_seconds into micro_seconds _nano_seconds = syncthing_event_time.split(".")[1][:-6] _micro_seconds = _nano_seconds[:6] # correct tz _syncthing_tz = syncthing_event_time[-6:] _iso_format_tz = _syncthing_tz.replace(":", "") _fixed_nano_seconds = syncthing_event_time.replace(_nano_seconds, _micro_seconds) iso_format_string = _fixed_nano_seconds.replace(_syncthing_tz, _iso_format_tz) return iso_format_string def _to_syncthing_format(self, iso_format_time: str) -> str: # let's add the crap and forget about the lost nano seconds _micro_seconds = iso_format_time.split(".")[1][:-5] _nano_seconds = f"{_micro_seconds}000" _iso_format_tz = iso_format_time[-5:] _syncthing_tz = f"{_iso_format_tz[:-2]}:{_iso_format_tz[-2:]}" _fixed_micro_seconds = iso_format_time.replace(_micro_seconds, _nano_seconds) syncthing_format_string = _fixed_micro_seconds.replace(_iso_format_tz, _syncthing_tz) return syncthing_format_string def _deserialize(self, value, attr, data, **kwargs): print(f"deserialize value: {value}") event_time: str = cast(str, value) value_iso_format = self._to_iso_format(syncthing_event_time=event_time) super()._deserialize(value_iso_format, attr, data, **kwargs) def _serialize(self, value, attr, obj, **kwargs): print(f"serialize value: {value}") iso_time: str = super()._serialize(value, attr, obj, **kwargs) value_syncthing_format = self._to_syncthing_format(iso_format_time=iso_time) return value_syncthing_format class EventDataSchema(EventBaseSchema): item = fields.Str() folder = fields.Str() error = fields.Str(required=False, allow_none=True) type = fields.Str() action = fields.Str() class Meta(EventBaseSchema.Meta): unknown = EXCLUDE class EventSchema(EventBaseSchema): id = fields.Int() globalID = fields.Int() type = fields.Str() time = EventDateTimeField() data = fields.Nested(EventDataSchema(partial=True)) ```
{ "source": "jeroenvanbaar/ReciprocityMotives", "score": 2 }
#### File: Code/Functions/costFunctions.py ```python import numpy as np import pandas as pd import choiceModels def MP_costfun(param,subDat,printStep=False,printPredictions=False,resid_share=False): theta = param[0] phi = param[1] for trial in range(subDat.shape[0]): subDat.loc[trial,'prediction'] = choiceModels.MP_model( subDat.loc[trial,'inv'], subDat.loc[trial,'mult'], subDat.loc[trial,'baseMult'], subDat.loc[trial,'exp'], theta, phi) if resid_share == True: residuals = (subDat.loc[:,'ret'] - subDat.loc[:,'prediction'])/(subDat.loc[:,'inv'] * subDat.loc[:,'mult']) else: residuals = subDat.loc[:,'ret'] - subDat.loc[:,'prediction'] residuals = residuals.astype('float') SSE = np.sum(np.square(residuals)) if printStep==True: print('theta = %.2f, phi = %.2f, SSE = %.2f'%(theta,phi,SSE)) if printPredictions == True: print(subDat) return residuals def MP_costfun_ppSOE(param,subDat,printStep=False,printPredictions=False,resid_share=False): theta = param[0] phi = param[1] for trial in range(subDat.shape[0]): subDat.loc[trial,'prediction'] = choiceModels.MP_model_ppSOE( subDat.loc[trial,'inv'], subDat.loc[trial,'mult'], subDat.loc[trial,'baseMult'], subDat.loc[trial,'exp'], theta, phi) if resid_share == True: residuals = (subDat.loc[:,'ret'] - subDat.loc[:,'prediction'])/(subDat.loc[:,'inv'] * subDat.loc[:,'mult']) else: residuals = subDat.loc[:,'ret'] - subDat.loc[:,'prediction'] residuals = residuals.astype('float') SSE = np.sum(np.square(residuals)) if printStep==True: print('theta = %.2f, phi = %.2f, SSE = %.2f'%(theta,phi,SSE)) if printPredictions == True: print(subDat) return residuals def IA_costfun(theta,subDat,printStep=False,printPredictions=False,resid_share=False): for trial in range(subDat.shape[0]): subDat.loc[trial,'prediction'] = choiceModels.IA_model( subDat.loc[trial,'inv'], subDat.loc[trial,'mult'], theta) if resid_share == True: residuals = (subDat.loc[:,'ret'] - subDat.loc[:,'prediction'])/(subDat.loc[:,'inv'] * subDat.loc[:,'mult']) else: residuals = subDat.loc[:,'ret'] - subDat.loc[:,'prediction'] residuals = residuals.astype('float') SSE = np.sum(np.square(residuals)) if printStep==True: print('theta = %.2f, SSE = %.2f'%(theta,SSE)) if printPredictions == True: print(subDat) return residuals def GA_costfun(theta,subDat,printStep=False,printPredictions=False,resid_share=False): for trial in range(subDat.shape[0]): subDat.loc[trial,'prediction'] = choiceModels.GA_model( subDat.loc[trial,'inv'], subDat.loc[trial,'mult'], subDat.loc[trial,'baseMult'], subDat.loc[trial,'exp'], theta) if resid_share == True: residuals = (subDat.loc[:,'ret'] - subDat.loc[:,'prediction'])/(subDat.loc[:,'inv'] * subDat.loc[:,'mult']) else: residuals = subDat.loc[:,'ret'] - subDat.loc[:,'prediction'] residuals = residuals.astype('float') SSE = np.sum(np.square(residuals)) if printStep==True: print('theta = %.2f, SSE = %.2f'%(theta,SSE)) if printPredictions == True: print(subDat) return residuals def GR_costfun(subDat,printPredictions=False,resid_share=False): for trial in range(subDat.shape[0]): subDat.loc[trial,'prediction'] = choiceModels.GR_model() if resid_share == True: residuals = (subDat.loc[:,'ret'] - subDat.loc[:,'prediction'])/(subDat.loc[:,'inv'] * subDat.loc[:,'mult']) else: residuals = subDat.loc[:,'ret'] - subDat.loc[:,'prediction'] residuals = residuals.astype('float') SSE = np.sum(np.square(residuals)) if printPredictions == True: print(subDat) return residuals ```
{ "source": "JeroenvdSande/dash-sample-apps", "score": 3 }
#### File: dash-3d-image-partitioning/plotly-common/app_utils.py ```python from os import environ def get_env(name, default=None, conv=None, check_if_none=False): try: ret = environ[name] except KeyError: ret = default if check_if_none and ret is None: raise Exception("Specify " + name + ".") return ret if conv is not None: return conv(ret) return ret ``` #### File: apps/dash-airfoil-design/app.py ```python import plotly.express as px import plotly.graph_objects as go import dash import dash_core_components as dcc import dash_html_components as html import dash_bootstrap_components as dbc from dash.dependencies import Input, Output, State import aerosandbox as asb import aerosandbox.numpy as np import copy import plotly.figure_factory as ff import pandas as pd from app_components import * ### Build the app app = dash.Dash( __name__, external_stylesheets=[dbc.themes.MINTY], title="Airfoil Analysis" ) server = app.server app.layout = dbc.Container( [ dbc.Row( [ dbc.Col( [ dcc.Markdown( """ # Airfoil Analysis with [AeroSandbox](https://github.com/peterdsharpe/AeroSandbox) and [Dash](https://plotly.com/dash/) By [<NAME>](https://peterdsharpe.github.io/). Uses potential flow theory (viscous effects neglected, for now). [Source code here](https://github.com/peterdsharpe/Automotive-Airfoil-Design). """ ) ], width=True, ), dbc.Col( [ html.Img( src="assets/MIT-logo-red-gray-72x38.svg", alt="MIT Logo", height="30px", ), ], width=1, ), ], align="end", ), html.Hr(), dbc.Row( [ dbc.Col( [ dbc.Button( "Modify Operating Conditions", id="operating_button" ), dbc.Collapse( dbc.Card(dbc.CardBody(operating_slider_components,)), id="operating_collapse", is_open=False, ), html.Hr(), dbc.Button( "Modify Shape Parameters (Kulfan)", id="shape_button" ), dbc.Collapse( dbc.Card(dbc.CardBody(kulfan_slider_components,)), id="shape_collapse", is_open=False, ), html.Hr(), dbc.Button( "Show Raw Coordinates (*.dat format)", id="coordinates_button", ), dbc.Collapse( dbc.Card( dbc.CardBody(dcc.Markdown(id="coordinates_output")) ), id="coordinates_collapse", is_open=False, ), html.Hr(), dcc.Markdown("##### Commands"), dbc.Button( "Analyze", id="analyze", color="primary", style={"margin": "5px"}, ), html.Hr(), dcc.Markdown("##### Aerodynamic Performance"), dbc.Spinner(html.P(id="text_output"), color="primary",), ], width=3, ), dbc.Col( [dcc.Graph(id="display", style={"height": "90vh"}),], width=9, align="start", ), ] ), html.Hr(), dcc.Markdown( """ Aircraft design tools powered by [AeroSandbox](https://github.com/peterdsharpe/AeroSandbox). Build beautiful UIs for your scientific computing apps with [Plot.ly](https://plotly.com/) and [Dash](https://plotly.com/dash/)! """ ), ], fluid=True, ) ### Callback to make shape parameters menu expand @app.callback( Output("shape_collapse", "is_open"), [Input("shape_button", "n_clicks")], [State("shape_collapse", "is_open")], ) def toggle_shape_collapse(n_clicks, is_open): if n_clicks: return not is_open return is_open ### Callback to make operating parameters menu expand @app.callback( Output("operating_collapse", "is_open"), [Input("operating_button", "n_clicks")], [State("operating_collapse", "is_open")], ) def toggle_shape_collapse(n_clicks, is_open): if n_clicks: return not is_open return is_open ### Callback to make coordinates menu expand @app.callback( Output("coordinates_collapse", "is_open"), [Input("coordinates_button", "n_clicks")], [State("coordinates_collapse", "is_open")], ) def toggle_shape_collapse(n_clicks, is_open): if n_clicks: return not is_open return is_open ### Callback to make operating sliders display proper values @app.callback( Output("alpha_slider_output", "children"), [Input("alpha_slider_input", "drag_value")], ) def display_alpha_slider(drag_value): return f"Angle of Attack: {drag_value}" @app.callback( Output("height_slider_output", "children"), [Input("height_slider_input", "drag_value")], ) def display_alpha_slider(drag_value): return f"Height: {drag_value}" @app.callback( Output("streamline_density_slider_output", "children"), [Input("streamline_density_slider_input", "drag_value")], ) def display_streamline_density_slider(drag_value): return f"Streamline Density: {drag_value}" ### The callback to make the kulfan sliders display proper values for side in sides: for i in range(n_kulfan_inputs_per_side): @app.callback( Output(f"kulfan_{side.lower()}_{i}_output", "children"), [Input(f"kulfan_{side.lower()}_{i}_input", "drag_value")], ) def display_slider_value(drag_value): return f"Parameter: {drag_value}" def make_table(dataframe): return dbc.Table.from_dataframe( dataframe, bordered=True, hover=True, responsive=True, striped=True, style={} ) last_analyze_timestamp = None n_clicks_last = 0 ### The callback to draw the airfoil on the graph @app.callback( Output("display", "figure"), Output("text_output", "children"), Output("coordinates_output", "children"), [ Input("analyze", "n_clicks"), Input("alpha_slider_input", "value"), Input("height_slider_input", "value"), Input("streamline_density_slider_input", "value"), Input("operating_checklist", "value"), ] + [ Input(f"kulfan_{side.lower()}_{i}_input", "value") for side in sides for i in range(n_kulfan_inputs_per_side) ], ) def display_graph( n_clicks, alpha, height, streamline_density, operating_checklist, *kulfan_inputs ): ### Figure out if a button was pressed global n_clicks_last if n_clicks is None: n_clicks = 0 analyze_button_pressed = n_clicks > n_clicks_last n_clicks_last = n_clicks ### Parse the checklist ground_effect = "ground_effect" in operating_checklist ### Start constructing the figure airfoil = asb.Airfoil( coordinates=asb.get_kulfan_coordinates( lower_weights=np.array(kulfan_inputs[n_kulfan_inputs_per_side:]), upper_weights=np.array(kulfan_inputs[:n_kulfan_inputs_per_side]), TE_thickness=0, enforce_continuous_LE_radius=False, n_points_per_side=200, ) ) ### Do coordinates output coordinates_output = "\n".join( ["```"] + ["AeroSandbox Airfoil"] + ["\t%f\t%f" % tuple(coordinate) for coordinate in airfoil.coordinates] + ["```"] ) ### Continue doing the airfoil things airfoil = airfoil.rotate(angle=-np.radians(alpha)) airfoil = airfoil.translate(0, height + 0.5 * np.sind(alpha)) fig = go.Figure() fig.add_trace( go.Scatter( x=airfoil.x(), y=airfoil.y(), mode="lines", name="Airfoil", fill="toself", line=dict(color="blue"), ) ) ### Default text output text_output = 'Click "Analyze" to compute aerodynamics!' xrng = (-0.5, 1.5) yrng = (-0.6, 0.6) if not ground_effect else (0, 1.2) if analyze_button_pressed: analysis = asb.AirfoilInviscid( airfoil=airfoil.repanel(50), op_point=asb.OperatingPoint(velocity=1, alpha=0,), ground_effect=ground_effect, ) x = np.linspace(*xrng, 100) y = np.linspace(*yrng, 100) X, Y = np.meshgrid(x, y) u, v = analysis.calculate_velocity(x_field=X.flatten(), y_field=Y.flatten()) U = u.reshape(X.shape) V = v.reshape(Y.shape) streamline_fig = ff.create_streamline( x, y, U, V, arrow_scale=1e-16, density=streamline_density, line=dict(color="#ff82a3"), name="Streamlines", ) fig = go.Figure(data=streamline_fig.data + fig.data) text_output = make_table( pd.DataFrame( {"Engineering Quantity": ["C_L"], "Value": [f"{analysis.Cl:.3f}"]} ) ) fig.update_layout( xaxis_title="x/c", yaxis_title="y/c", showlegend=False, yaxis=dict(scaleanchor="x", scaleratio=1), margin={"t": 0}, title=None, ) fig.update_xaxes(range=xrng) fig.update_yaxes(range=yrng) return fig, text_output, [coordinates_output] if __name__ == "__main__": app.run_server(debug=False) ``` #### File: apps/dash-baseball-statistics/index.py ```python import os import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output # Dash Bootstrap components import dash_bootstrap_components as dbc # Navbar, layouts, custom callbacks from navbar import Navbar from layouts import ( appMenu, menuSlider, playerMenu, teamLayout, battingLayout, fieldingLayout, ) import callbacks # Import app from app import app # Import server for deployment from app import srv as server app_name = os.getenv("DASH_APP_PATH", "/dash-baseball-statistics") # Layout variables, navbar, header, content, and container nav = Navbar() header = dbc.Row( dbc.Col( html.Div( [ html.H2(children="Major League Baseball History"), html.H3(children="A Visualization of Historical Data"), ] ) ), className="banner", ) content = html.Div([dcc.Location(id="url"), html.Div(id="page-content")]) container = dbc.Container([header, content]) # Menu callback, set and return # Declair function that connects other pages with content to container @app.callback(Output("page-content", "children"), [Input("url", "pathname")]) def display_page(pathname): if pathname in [app_name, app_name + "/"]: return html.Div( [ dcc.Markdown( """ ### The Applicaiton This application is a portfolio project built by [<NAME>](https://devparra.github.io/) using Plotly's Dash, faculty.ai's Dash Bootstrap Components, and Pandas. Using historical MLB (Major League Baseball) data, this application provides visualizations for team and player statistics dating from 1903 to 2020. Selecting from a dropdown menu, the era will update the list of available teams and players in the range set on the years slider. The slider allows the user to adjust the range of years with which the data is presented. ### The Analysis The applicaiton breaks down each baseballs teams win/loss performance within a range of the teams history. Additionally, the application will break down the batting performance with the team batting average, BABIP, and strikeout rate. The application also brakes down the piching perfomance using the teams ERA and strikeout to walk ratio. Finally the feilding performance of each team is illustrated with total errors and double plays. The applicaiton will also breakdown each of teams players statistics within the given era. ### The Data The data used in this application was retrieved from [Seanlahman.com](http://www.seanlahman.com/baseball-archive/statistics/). Provided by [Chadwick Baseball Bureau's GitHub](https://github.com/chadwickbureau/baseballdatabank/) . This database is copyright 1996-2021 by <NAME>. This data is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. For details see: [CreativeCommons](http://creativecommons.org/licenses/by-sa/3.0/) """ ) ], className="home", ) elif pathname.endswith("/team"): return appMenu, menuSlider, teamLayout elif pathname.endswith("/player"): return appMenu, menuSlider, playerMenu, battingLayout elif pathname.endswith("/field"): return appMenu, menuSlider, playerMenu, fieldingLayout else: return "ERROR 404: Page not found!" # Main index function that will call and return all layout variables def index(): layout = html.Div([nav, container]) return layout # Set layout to index function app.layout = index() # Call app server if __name__ == "__main__": # set debug to false when deploying app app.run_server(debug=True) ``` #### File: apps/dash-brain-viewer/app.py ```python import os import json import dash import dash_core_components as dcc import dash_html_components as html import dash_colorscales as dcs from dash.dependencies import Input, Output, State from dash.exceptions import PreventUpdate from mni import create_mesh_data, default_colorscale app = dash.Dash( __name__, meta_tags=[{"name": "viewport", "content": "width=device-width, initial-scale=1"}], ) app.title = "Brain Surface Viewer" server = app.server GITHUB_LINK = os.environ.get( "GITHUB_LINK", "https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-brain-viewer", ) default_colorscale_index = [ea[1] for ea in default_colorscale] axis_template = { "showbackground": True, "backgroundcolor": "#141414", "gridcolor": "rgb(255, 255, 255)", "zerolinecolor": "rgb(255, 255, 255)", } plot_layout = { "title": "", "margin": {"t": 0, "b": 0, "l": 0, "r": 0}, "font": {"size": 12, "color": "white"}, "showlegend": False, "plot_bgcolor": "#141414", "paper_bgcolor": "#141414", "scene": { "xaxis": axis_template, "yaxis": axis_template, "zaxis": axis_template, "aspectratio": {"x": 1, "y": 1.2, "z": 1}, "camera": {"eye": {"x": 1.25, "y": 1.25, "z": 1.25}}, "annotations": [], }, } app.layout = html.Div( [ html.Div( [ html.Div( [ html.Div( [ html.Div( [ html.Img( src=app.get_asset_url("dash-logo.png") ), html.H4("MRI Reconstruction"), ], className="header__title", ), html.Div( [ html.P( "Click on the brain to add an annotation. Drag the black corners of the graph to rotate." ) ], className="header__info pb-20", ), html.Div( [ html.A( "View on GitHub", href=GITHUB_LINK, target="_blank", ) ], className="header__button", ), ], className="header pb-20", ), html.Div( [ dcc.Graph( id="brain-graph", figure={ "data": create_mesh_data("human_atlas"), "layout": plot_layout, }, config={"editable": True, "scrollZoom": False}, ) ], className="graph__container", ), ], className="container", ) ], className="two-thirds column app__left__section", ), html.Div( [ html.Div( [ html.Div( [ html.P( "Click colorscale to change", className="subheader" ), dcs.DashColorscales( id="colorscale-picker", colorscale=default_colorscale_index, ), ] ) ], className="colorscale pb-20", ), html.Div( [ html.P("Select option", className="subheader"), dcc.RadioItems( options=[ {"label": "Brain Atlas", "value": "human_atlas"}, {"label": "Cortical Thickness", "value": "human"}, {"label": "Mouse Brain", "value": "mouse"}, ], value="human_atlas", id="radio-options", labelClassName="label__option", inputClassName="input__option", ), ], className="pb-20", ), html.Div( [ html.Span("Click data", className="subheader"), html.Span(" | "), html.Span( "Click on points in the graph.", className="small-text" ), dcc.Loading( html.Pre(id="click-data", className="info__container"), type="dot", ), ], className="pb-20", ), html.Div( [ html.Span("Relayout data", className="subheader"), html.Span(" | "), html.Span( "Drag the graph corners to rotate it.", className="small-text", ), dcc.Loading( html.Pre(id="relayout-data", className="info__container"), type="dot", ), ], className="pb-20", ), html.Div( [ html.P( [ "Dash/Python code on ", html.A( children="GitHub.", target="_blank", href=GITHUB_LINK, className="red-ish", ), ] ), html.P( [ "Brain data from Mcgill's ACE Lab ", html.A( children="Surface Viewer.", target="_blank", href="https://brainbrowser.cbrain.mcgill.ca/surface-viewer#ct", className="red-ish", ), ] ), ] ), ], className="one-third column app__right__section", ), dcc.Store(id="annotation_storage"), ] ) def add_marker(x, y, z): """ Create a plotly marker dict. """ return { "x": [x], "y": [y], "z": [z], "mode": "markers", "marker": {"size": 25, "line": {"width": 3}}, "name": "Marker", "type": "scatter3d", "text": ["Click point to remove annotation"], } def add_annotation(x, y, z): """ Create plotly annotation dict. """ return { "x": x, "y": y, "z": z, "font": {"color": "black"}, "bgcolor": "white", "borderpad": 5, "bordercolor": "black", "borderwidth": 1, "captureevents": True, "ay": -100, "arrowcolor": "white", "arrowwidth": 2, "arrowhead": 0, "text": "Click here to annotate<br>(Click point to remove)", } def marker_in_points(points, marker): """ Checks if the marker is in the list of points. :params points: a list of dict that contains x, y, z :params marker: a dict that contains x, y, z :returns: index of the matching marker in list """ for index, point in enumerate(points): if ( point["x"] == marker["x"] and point["y"] == marker["y"] and point["z"] == marker["z"] ): return index return None @app.callback( Output("brain-graph", "figure"), [ Input("brain-graph", "clickData"), Input("radio-options", "value"), Input("colorscale-picker", "colorscale"), ], [State("brain-graph", "figure"), State("annotation_storage", "data")], ) def brain_graph_handler(click_data, val, colorscale, figure, current_anno): """ Listener on colorscale, option picker, and graph on click to update the graph. """ # new option select if figure["data"][0]["name"] != val: figure["data"] = create_mesh_data(val) figure["layout"] = plot_layout cs = [[i / (len(colorscale) - 1), rgb] for i, rgb in enumerate(colorscale)] figure["data"][0]["colorscale"] = cs return figure # modify graph markers if click_data is not None and "points" in click_data: y_value = click_data["points"][0]["y"] x_value = click_data["points"][0]["x"] z_value = click_data["points"][0]["z"] marker = add_marker(x_value, y_value, z_value) point_index = marker_in_points(figure["data"], marker) # delete graph markers if len(figure["data"]) > 1 and point_index is not None: figure["data"].pop(point_index) anno_index_offset = 2 if val == "mouse" else 1 try: figure["layout"]["scene"]["annotations"].pop( point_index - anno_index_offset ) except Exception as error: print(error) pass # append graph markers else: # iterate through the store annotations and save it into figure data if current_anno is not None: for index, annotations in enumerate( figure["layout"]["scene"]["annotations"] ): for key in current_anno.keys(): if str(index) in key: figure["layout"]["scene"]["annotations"][index][ "text" ] = current_anno[key] figure["data"].append(marker) figure["layout"]["scene"]["annotations"].append( add_annotation(x_value, y_value, z_value) ) cs = [[i / (len(colorscale) - 1), rgb] for i, rgb in enumerate(colorscale)] figure["data"][0]["colorscale"] = cs return figure @app.callback(Output("click-data", "children"), [Input("brain-graph", "clickData")]) def display_click_data(click_data): return json.dumps(click_data, indent=4) @app.callback( Output("relayout-data", "children"), [Input("brain-graph", "relayoutData")] ) def display_relayout_data(relayout_data): return json.dumps(relayout_data, indent=4) @app.callback( Output("annotation_storage", "data"), [Input("brain-graph", "relayoutData")], [State("annotation_storage", "data")], ) def save_annotations(relayout_data, current_data): """ Update the annotations in the dcc store. """ if relayout_data is None: raise PreventUpdate if current_data is None: return {} for key in relayout_data.keys(): # to determine if the relayout has to do with annotations if "scene.annotations" in key: current_data[key] = relayout_data[key] return current_data if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-canvas-ocr/app.py ```python from PIL import Image import base64 from io import BytesIO import dash import numpy as np import dash_html_components as html import dash_core_components as dcc from dash_canvas import DashCanvas from dash_canvas.utils import array_to_data_url, parse_jsonstring from dash.dependencies import Input, Output, State from dash.exceptions import PreventUpdate import pytesseract app = dash.Dash(__name__) server = app.server canvas_width = 800 canvas_height = 200 app.layout = html.Div( [ # Banner html.Div( [ html.Img(src=app.get_asset_url("ocr-logo.png"), className="app__logo"), html.H4("Dash OCR", className="header__text"), ], className="app__header", ), # Canvas html.Div( [ html.Div( [ html.P( "Write inside the canvas with your stylus and press Sign", className="section_title", ), html.Div( DashCanvas( id="canvas", lineWidth=8, width=canvas_width, height=canvas_height, hide_buttons=[ "zoom", "pan", "line", "pencil", "rectangle", "select", ], add_only=False, lineColor="black", goButtonTitle="Sign", ), className="canvas-outer", style={"margin-top": "1em"}, ), ], className="v-card-content", ), html.Div( html.Button(id="clear", children="clear"), className="v-card-content-markdown-outer", ), html.Div( [ html.B("Text Recognition Output", className="section_title"), dcc.Loading(dcc.Markdown(id="text-output", children="")), ], className="v-card-content", style={"margin-top": "1em"}, ), ], className="app__content", ), ] ) @app.callback(Output("canvas", "json_objects"), [Input("clear", "n_clicks")]) def clear_canvas(n): if n is None: return dash.no_update strings = ['{"objects":[ ]}', '{"objects":[]}'] return strings[n % 2] @app.callback( Output("text-output", "children"), [Input("canvas", "json_data")], ) def update_data(string): if string: try: mask = parse_jsonstring(string, shape=(canvas_height, canvas_width)) except: return "Out of Bounding Box, click clear button and try again" # np.savetxt('data.csv', mask) use this to save the canvas annotations as a numpy array # Invert True and False mask = (~mask.astype(bool)).astype(int) image_string = array_to_data_url((255 * mask).astype(np.uint8)) # this is from canvas.utils.image_string_to_PILImage(image_string) img = Image.open(BytesIO(base64.b64decode(image_string[22:]))) text = "{}".format( pytesseract.image_to_string(img, lang="eng", config="--psm 6") ) return text else: raise PreventUpdate if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-chess-analytics/app.py ```python import dash import ast import dash_core_components as dcc import dash_html_components as html import dash_bootstrap_components as dbc from dash.dependencies import Input, Output import pandas as pd import numpy as np import plotly.express as px import plotly.graph_objs as go from whitenoise import WhiteNoise from chessboard import getChessboard, getHeatmap, getStackedBar, getBoard from styles import * # Read the .csv file with the preprocessed data. url = "https://raw.githubusercontent.com/Exileus/DataVis2021_proj2/main/chess_app.csv" df_original = pd.read_csv( url, sep=",", dtype={"pawns": int, "knights": int, "bishops": int, "rooks": int, "queens": int}, converters={ "wKing_sqr": ast.literal_eval, "bKing_sqr": ast.literal_eval, "wQueen_sqr": ast.literal_eval, "bQueen_sqr": ast.literal_eval, "wRook_sqr": ast.literal_eval, "bRook_sqr": ast.literal_eval, "wRook2_sqr": ast.literal_eval, "bRook2_sqr": ast.literal_eval, "wBishop_sqr": ast.literal_eval, "bBishop_sqr": ast.literal_eval, "wBishop2_sqr": ast.literal_eval, "bBishop2_sqr": ast.literal_eval, "wKnight_sqr": ast.literal_eval, "bKnight_sqr": ast.literal_eval, "wKnight2_sqr": ast.literal_eval, "bKnight2_sqr": ast.literal_eval, }, ) max_moves = df_original["moves"].max() min_elo, max_elo = df_original["avg_Elo"].min(), df_original["avg_Elo"].max() # Define function to output an 8*8 dataframe based on a df and a list of column names to parse. def board_output(df, col_list): brd = np.zeros((8, 8)) for col_name in col_list: for tup in df[col_name]: if tup == (None, None): pass else: brd[tup] += 1 return pd.DataFrame(brd) # Define global variables for later. g_color = "white_color" g_piece = "King" g_status, g_winner, g_time_control, g_game_type = ".*", ".*", ".*", ".*" pieces_list = ["King", "Queen", "Rook", "Bishop", "Knight"] # Define a dictionary to be used to update the board with the correct columns. color_piece_dict = cp_dict = { ("white_color", "King"): ["wKing_sqr"], ("black_color", "King"): ["bKing_sqr"], ("white_color", "Queen"): ["wQueen_sqr"], ("black_color", "Queen"): ["bQueen_sqr"], ("white_color", "Rook"): ["wRook_sqr", "wRook2_sqr"], ("black_color", "Rook"): ["bRook_sqr", "bRook2_sqr"], ("white_color", "Bishop"): ["wBishop_sqr", "wBishop2_sqr"], ("black_color", "Bishop"): ["bBishop_sqr", "bBishop2_sqr"], ("white_color", "Knight"): ["wKnight_sqr", "wKnight2_sqr"], ("black_color", "Knight"): ["bKnight_sqr", "bKnight2_sqr"], } # Define an additional dict for dropdown status to use for callbacks. dropdown_status_dict = st_dict = { "st_all": ".*", "st_draw": "draw", "st_mate": "mate", "st_resign": "resign", "st_outoftime": "outoftime", } dropdown_winner_dict = wn_dict = { "wn_all": ".*", "wn_white": "white", "wn_black": "black", } dropdown_time_control_dict = tc_dict = { "tc_all": ".*", "tc_bullet": "Bullet", "tc_blitz": "Blitz", "tc_classic": "Classical", "tc_none": "Correspondence", } dropdown_game_type_dict = gt_dict = { "gt_all": ".*", "gt_std": "game", "gt_tourney": "tournament", } # Set stylesheets and app. # ["https://codepen.io/chriddyp/pen/bWLwgP.css"] FA = "https://use.fontawesome.com/releases/v5.12.1/css/all.css" external_stylesheets = [dbc.themes.LUX, FA] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) app.title = "CHESS KINGDOM" server = app.server server.wsgi_app = WhiteNoise(server.wsgi_app, root="static/") # Defining app layout margin_bottom = "30px" # Banner banner = dbc.Row( children=[ dbc.Col( html.Img( src=app.get_asset_url("apple-touch-icon.png"), id="logo", style={"border-radius": "50%"}, ), width=2, align="left", ), dbc.Col( html.H1("A Visualization of Endgame Chess Pieces"), align="center", width=10, ), ], style={"margin-bottom": "50px", "margin-top": "-30px"}, align="center", ) # Graph graph = dbc.Row( style={"margin-bottom": "30px", "margin-left": "auto", "margin-right": "auto"}, children=[ dcc.Graph( id="chessboard", animate=True, style={ "margin-left": "auto", "margin-right": "auto", "background-color": "lightgray", }, config={ "displayModeBar": False, "scrollZoom": False, "showAxisDragHandles": False, }, ) ], ) # Stacked Bar stacked_graph = dbc.Row( style={"margin-bottom": "30px"}, justify="center", children=[ dbc.Col( width=10, children=[ dcc.Graph( id="stackedbar", animate=True, config={ "displayModeBar": False, "scrollZoom": False, "showAxisDragHandles": False, }, ) ], ) ], ) text_margin = "6px" c_total_games = dbc.Row( style={"margin-bottom": "20px"}, justify="center", children=[ dbc.Col( children=[ html.Div(id="game_count", style={"text-align": "center"}), html.Div( "TOTAL GAMES", style={"margin-left": text_margin, "text-align": "center"}, ), ], ), dbc.Col( children=[ html.Div(id="white_wins", style={"text-align": "center"}), html.Div( "WINS BY WHITE", style={"margin-left": text_margin, "text-align": "center"}, ), ], ), dbc.Col( children=[ html.Div(id="black_wins", style={"text-align": "center"}), html.Div( "WINS BY BLACK", style={"margin-left": text_margin, "text-align": "center"}, ), ], ), dbc.Col( children=[ html.Div(id="draw", style={"text-align": "center"}), html.Div( "DRAWS", style={"margin-left": text_margin, "text-align": "center"} ), ], ), ], ) # BLACK / WHITE c_choose_side = dbc.Col( style={"margin-bottom": margin_bottom}, children=[ html.Div( str("Choose side").upper(), style={"text-align": "center", "margin-bottom": text_margin}, ), dbc.Row( justify="center", children=[ dbc.ButtonGroup( style={"text-align": "center"}, children=[ dbc.Button( "White", color="secondary", n_clicks=0, id="white_color", outline=True, active=True, ), dbc.Button( "Black", color="dark", n_clicks=0, id="black_color", outline=True, active=False, ), ], ), ], ), ], ) c_select_piece = dbc.Col( style={"margin-bottom": margin_bottom}, width=9, children=[ html.Div( str("Select Piece").upper(), style={"text-align": "center", "margin-bottom": text_margin}, ), dbc.Row( justify="center", children=[ dbc.ButtonGroup( children=[ dbc.Button( [ html.I(className=f"fas fa-chess-{name.lower()} mr-2"), name, ], color="primary", n_clicks=0, outline=True, id=name, active=False, ) for name in pieces_list ], ) ], ), ], ) c_elo_slider = dbc.Col( style={ "margin-bottom": margin_bottom, "margin-left": "auto", "margin-right": "auto", }, width=12, children=[ html.Div( str("Elo range").upper(), style={"text-align": "center", "margin-bottom": text_margin}, ), dcc.RangeSlider( id="elo_slider", min=min_elo, max=max_elo, value=[min_elo, max_elo], step=10, pushable=1, allowCross=False, marks={ i: str(i) for i in range( int(min_elo) - 1, int(max_elo) + 1, int((max_elo - min_elo + 2) // 10), ) }, ), ], ) c_moves_slider = dbc.Col( style={ "margin-bottom": margin_bottom, "margin-left": "auto", "margin-right": "auto", }, width=12, children=[ html.Div( str("Number of Moves").upper(), style={"text-align": "center", "margin-bottom": text_margin}, ), dcc.RangeSlider( id="moves_slider", min=1, max=max_moves, value=[0, max_moves], step=1, pushable=1, allowCross=False, marks={i: str(i) for i in range(0, max_moves, 5)}, ), ], ) dropdown_status = dbc.DropdownMenu( [ dbc.DropdownMenuItem(str("Status",).upper(), header=True,), dbc.DropdownMenuItem(str("All").upper(), id="st_all", n_clicks=0), dbc.DropdownMenuItem(str("Draws").upper(), id="st_draw", n_clicks=0), dbc.DropdownMenuItem(str("Checkmate").upper(), id="st_mate", n_clicks=0), dbc.DropdownMenuItem(str("Resignation").upper(), id="st_resign", n_clicks=0), dbc.DropdownMenuItem( str("Time Forfeit").upper(), id="st_outoftime", n_clicks=0 ), ], label="Status", id="dropdown_status", ) dropdown_winner = dbc.Collapse( dbc.DropdownMenu( [ dbc.DropdownMenuItem(str("Winning Side").upper(), header=True), dbc.DropdownMenuItem(str("All").upper(), id="wn_all", n_clicks=0), dbc.DropdownMenuItem(str("White").upper(), id="wn_white", n_clicks=0), dbc.DropdownMenuItem(str("Black").upper(), id="wn_black", n_clicks=0), ], label="Winning Side", id="dropdown_winner", ), id="wn_menu", ) dropdown_time_control = dbc.DropdownMenu( [ dbc.DropdownMenuItem(str("Time Control").upper(), header=True), dbc.DropdownMenuItem(str("All").upper(), id="tc_all", n_clicks=0), dbc.DropdownMenuItem(str("Bullet").upper(), id="tc_bullet", n_clicks=0), dbc.DropdownMenuItem(str("Blitz").upper(), id="tc_blitz", n_clicks=0), # dbc.DropdownMenuItem("Rapid",id="tc_rpd",n_clicks=0), if this shows up later then include it. dbc.DropdownMenuItem(str("Classical").upper(), id="tc_classic", n_clicks=0), dbc.DropdownMenuItem(str("No Time Control").upper(), id="tc_none", n_clicks=0), ], label="Time Control", id="dropdown_time_control", ) dropdown_game_type = dbc.DropdownMenu( [ dbc.DropdownMenuItem(str("Game Type").upper(), header=True), dbc.DropdownMenuItem(str("All").upper(), id="gt_all", n_clicks=0), dbc.DropdownMenuItem(str("Standard").upper(), id="gt_std", n_clicks=0), dbc.DropdownMenuItem(str("Tournament").upper(), id="gt_tourney", n_clicks=0), ], label="Game Type", id="dropdown_game_type", ) dropdown_states = dbc.Row( justify="center", children=[ html.Tbody("xsxsxs", id="g_status", style={"margin": "10px"}), html.Tbody("xsxsxs", id="g_winner", style={"margin": "10px"}), html.Tbody("xsxsxs", id="g_time_control", style={"margin": "10px"}), html.Tbody(children="111", id="g_game_type", style={"margin": "10px"}), ], ) popover_status = dbc.Popover( [ dbc.PopoverHeader("Status of the Game"), dbc.PopoverBody( "Games can be over in a myriad of ways, either by checkmate, draw, player resignation, or when a player runs out of time. Filter the games by these conditions here." ), ], trigger="hover", target="dropdown_status", placement="left", ) popover_time_control = dbc.Popover( [ dbc.PopoverHeader("Time Control Filter"), dbc.PopoverBody( "Players have a specific time to make their moves. The games in the dataset follow this convention: Bullet Games (0-3 minutes), Blitz(3-10 minutes), Classical(10 minutes+). Note: Lichess uses a slight different system today." ), ], trigger="hover", target="dropdown_time_control", placement="left", ) popover_game_type = dbc.Popover( [ dbc.PopoverHeader("Type of Competitive Setting"), dbc.PopoverBody( "This dataset contains games played in specific tournaments, hosted by Lichess." ), ], trigger="hover", target="dropdown_game_type", placement="left", ) about_this = dbc.Row( justify="end", children=[ dbc.Button(str("About this Visualization").upper(), id="abt_us"), dbc.Popover( [ dbc.PopoverHeader("Powered by Lichess"), dbc.PopoverBody( """This visualization is powered by a dataset of games played in April, 2017, sourced from the publicly available lichess database.\n Authors: <NAME> 20200604, <NAME> 20200994, <NAME> 20200613.\nNova IMS, Data Visualization Course, 2021.""" ), ], trigger="click", target="abt_us", ), ], ) dropdown_menus = dbc.Row( style={"margin-bottom": margin_bottom}, justify="center", children=[ dropdown_status, popover_status, dropdown_winner, dropdown_time_control, popover_time_control, dropdown_game_type, popover_game_type, ], ) app.layout = dbc.Jumbotron( style={"background-color": "#ebebeb"}, # ADD SETTINGS HERE children=[ # Banner # Main Layout dbc.Row( # ADD SETTINGS HERE children=[ # PARAMETER SETTINGS COLUMN dbc.Col( children=[ banner, c_total_games, stacked_graph, dbc.Row( style={"margin-bottom": margin_bottom}, children=[c_choose_side, c_select_piece], ), c_elo_slider, c_moves_slider, dropdown_menus, dropdown_states, ] ), # CHESS BOARD COLUMN dbc.Col(width={"size": 6}, children=[graph, about_this]), ], ), ], ) @app.callback( Output("chessboard", "figure"), Output("stackedbar", "figure"), Output("game_count", "children"), Output("white_wins", "children"), Output("black_wins", "children"), Output("draw", "children"), Output("wn_menu", "is_open"), Output("white_color", "active"), Output("black_color", "active"), Output("King", "active"), Output("Queen", "active"), Output("Rook", "active"), Output("Bishop", "active"), Output("Knight", "active"), Output("moves_slider", "value"), Output("g_status", "children"), Output("g_winner", "children"), Output("g_time_control", "children"), Output("g_game_type", "children"), [ Input("white_color", "n_clicks"), Input("black_color", "n_clicks"), Input("King", "n_clicks"), Input("Queen", "n_clicks"), Input("Rook", "n_clicks"), Input("Bishop", "n_clicks"), Input("Knight", "n_clicks"), Input("elo_slider", "value"), Input("st_all", "n_clicks"), Input("st_draw", "n_clicks"), Input("st_mate", "n_clicks"), Input("st_resign", "n_clicks"), Input("st_outoftime", "n_clicks"), Input("wn_all", "n_clicks"), Input("wn_white", "n_clicks"), Input("wn_black", "n_clicks"), Input("tc_all", "n_clicks"), Input("tc_blitz", "n_clicks"), Input("tc_bullet", "n_clicks"), Input("tc_classic", "n_clicks"), Input("tc_none", "n_clicks"), Input("gt_all", "n_clicks"), Input("gt_std", "n_clicks"), Input("gt_tourney", "n_clicks"), Input("moves_slider", "value"), ], ) def update_chessboard( white_color, black_color, King, Queen, Rook, Bishop, Knight, elo_range, st_all, st_draw, st_mate, st_resign, st_outoftime, wn_all, wn_white, wn_black, tc_all, tc_blitz, tc_bullet, tc_classic, tc_none, gt_all, gt_std, gt_tourney, move_range, ): # Trigger button here, for when a button is pressed. trigger_button = dash.callback_context.triggered[0]["prop_id"].split(".")[0] global g_status global g_winner global g_time_control global g_game_type if trigger_button in st_dict.keys(): g_status = st_dict[trigger_button] elif trigger_button in wn_dict.keys(): g_winner = wn_dict[trigger_button] elif trigger_button in tc_dict.keys(): g_time_control = tc_dict[trigger_button] elif trigger_button in gt_dict.keys(): g_game_type = gt_dict[trigger_button] # Filters go here. dff = df_original[ (df_original["avg_Elo"] >= int(elo_range[0])) & (df_original["avg_Elo"] <= int(elo_range[1])) & (df_original["moves"] >= int(move_range[0])) & (df_original["moves"] <= int(move_range[-1])) & (df_original["victory_status"].str.contains(g_status)) & (df_original["Winner"].str.contains(g_winner)) & (df_original["Event"].str.contains(g_time_control)) & (df_original["Event"].str.contains(g_game_type)) ] if dff.shape[0] == 0: return dash.no_update min_moves_, max_moves_ = dff["moves"].min(), dff["moves"].max() # print(f"{min_moves_ = }, {max_moves_ = }") value_ = [min_moves_, max_moves_] # Before further manipulation, get the number of games from the filtered dataframe. game_count = dff.shape[0] game_results = dff.Winner.value_counts().to_dict() game_results_norm = np.round( dff.Winner.str.upper().value_counts(normalize=True), 4 ).to_dict() if "white" in game_results.keys(): white_wins = game_results["white"] else: white_wins = 0 if "black" in game_results.keys(): black_wins = game_results["black"] else: black_wins = 0 if "draw" in game_results.keys(): draw = game_results["draw"] else: draw = 0 print(game_results_norm) stackedbar = getStackedBar(game_results_norm) # Then retrieve the column of interest. global g_color global g_piece if trigger_button in ["white_color", "black_color"]: g_color = trigger_button if trigger_button in pieces_list: g_piece = trigger_button df = board_output(dff, cp_dict[g_color, g_piece]) # Additionally: if g_status == "draw": is_open = False else: is_open = True # Additionaly pt.2: if g_color == "white_color": wc_act, bc_act = True, False else: wc_act, bc_act = False, True # Additionaly pt3: k_act, q_act, r_act, b_act, n_act = [x == g_piece for x in pieces_list] # Transform it for the heatmap. df = ( df.stack() .reset_index() .rename(columns={"level_0": "rows", "level_1": "cols", 0: "freq"}) ) df["rows"] = df["rows"].replace({i: list(range(8))[::-1][i] for i in range(8)}) chessboard = getChessboard(800) getBoard(chessboard) chessboard.add_trace(getHeatmap(dataframe=df)) # print( # f"{g_color = }, {g_game_type = }, {g_piece = }, {g_status = }, {g_time_control = }, {g_winner = }" # ) g_status_ = { ".*": "Status: all", "draw": "Status: draw", "mate": "Status: checkmate", "resign": "Status: resignation", "outoftime": "Status: time forfeit", }[g_status] g_winner_ = { ".*": "winner: All", "white": "winner: white", "black": "winner: black", }[g_winner] g_time_control_ = { ".*": "time control: all", "Bullet": "time control: Bullet", "Blitz": "time control: Blitz", "Classical": "time control: Classical", "Correspondence": "time control: No Time Control", }[g_time_control] g_game_type_ = { ".*": "game type: all", "game": "game type: standard", "tournament": "game type: tournament", }[g_game_type] return ( chessboard, stackedbar, game_count, white_wins, black_wins, draw, is_open, wc_act, bc_act, k_act, q_act, r_act, b_act, n_act, value_, g_status_.upper(), g_winner_.upper(), g_time_control_.upper(), g_game_type_.upper(), ) # Statring the dash app if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-daq-satellite-dashboard/app.py ```python import time import pathlib import os import pandas as pd import dash import dash_core_components as dcc import dash_html_components as html from dash.dependencies import State, Input, Output import dash_daq as daq app = dash.Dash( __name__, meta_tags=[ {"name": "viewport", "content": "width=device-width, initial-scale=1.0"} ], ) # This is for gunicorn server = app.server # Mapbox MAPBOX_ACCESS_TOKEN = "<KEY>" MAPBOX_STYLE = "mapbox://styles/plotlymapbox/cjyivwt3i014a1dpejm5r7dwr" # Dash_DAQ elements utc = html.Div( id="control-panel-utc", children=[ daq.LEDDisplay( id="control-panel-utc-component", value="16:23", label="Time", size=40, color="#fec036", backgroundColor="#2b2b2b", ) ], n_clicks=0, ) speed = html.Div( id="control-panel-speed", children=[ daq.Gauge( id="control-panel-speed-component", label="Speed", min=0, max=40, showCurrentValue=True, value=27.859, size=175, units="1000km/h", color="#fec036", ) ], n_clicks=0, ) elevation = html.Div( id="control-panel-elevation", children=[ daq.Tank( id="control-panel-elevation-component", label="Elevation", min=0, max=1000, value=650, units="kilometers", showCurrentValue=True, color="#303030", ) ], n_clicks=0, ) temperature = html.Div( id="control-panel-temperature", children=[ daq.Tank( id="control-panel-temperature-component", label="Temperature", min=0, max=500, value=290, units="Kelvin", showCurrentValue=True, color="#303030", ) ], n_clicks=0, ) fuel_indicator = html.Div( id="control-panel-fuel", children=[ daq.GraduatedBar( id="control-panel-fuel-component", label="Fuel Level", min=0, max=100, value=76, step=1, showCurrentValue=True, color="#fec036", ) ], n_clicks=0, ) battery_indicator = html.Div( id="control-panel-battery", children=[ daq.GraduatedBar( id="control-panel-battery-component", label="Battery-Level", min=0, max=100, value=85, step=1, showCurrentValue=True, color="#fec036", ) ], n_clicks=0, ) longitude = html.Div( id="control-panel-longitude", children=[ daq.LEDDisplay( id="control-panel-longitude-component", value="0000.0000", label="Longitude", size=24, color="#fec036", style={"color": "#black"}, backgroundColor="#2b2b2b", ) ], n_clicks=0, ) latitude = html.Div( id="control-panel-latitude", children=[ daq.LEDDisplay( id="control-panel-latitude-component", value="0050.9789", label="Latitude", size=24, color="#fec036", style={"color": "#black"}, backgroundColor="#2b2b2b", ) ], n_clicks=0, ) solar_panel_0 = daq.Indicator( className="panel-lower-indicator", id="control-panel-solar-panel-0", label="Solar-Panel-0", labelPosition="bottom", value=True, color="#fec036", style={"color": "#black"}, ) solar_panel_1 = daq.Indicator( className="panel-lower-indicator", id="control-panel-solar-panel-1", label="Solar-Panel-1", labelPosition="bottom", value=True, color="#fec036", style={"color": "#black"}, ) camera = daq.Indicator( className="panel-lower-indicator", id="control-panel-camera", label="Camera", labelPosition="bottom", value=True, color="#fec036", style={"color": "#black"}, ) thrusters = daq.Indicator( className="panel-lower-indicator", id="control-panel-thrusters", label="Thrusters", labelPosition="bottom", value=True, color="#fec036", style={"color": "#black"}, ) motor = daq.Indicator( className="panel-lower-indicator", id="control-panel-motor", label="Motor", labelPosition="bottom", value=True, color="#fec036", style={"color": "#black"}, ) communication_signal = daq.Indicator( className="panel-lower-indicator", id="control-panel-communication-signal", label="Signal", labelPosition="bottom", value=True, color="#fec036", style={"color": "#black"}, ) map_toggle = daq.ToggleSwitch( id="control-panel-toggle-map", value=True, label=["Hide path", "Show path"], color="#ffe102", style={"color": "#black"}, ) minute_toggle = daq.ToggleSwitch( id="control-panel-toggle-minute", value=True, label=["Past Hour", "Past Minute"], color="#ffe102", style={"color": "#black"}, ) # Side panel satellite_dropdown = dcc.Dropdown( id="satellite-dropdown-component", options=[ {"label": "H45-K1", "value": "h45-k1"}, {"label": "L12-5", "value": "l12-5"}, ], clearable=False, value="h45-k1", ) satellite_dropdown_text = html.P( id="satellite-dropdown-text", children=["Satellite", html.Br(), " Dashboard"] ) satellite_title = html.H1(id="satellite-name", children="") satellite_body = html.P( className="satellite-description", id="satellite-description", children=[""] ) side_panel_layout = html.Div( id="panel-side", children=[ satellite_dropdown_text, html.Div(id="satellite-dropdown", children=satellite_dropdown), html.Div(id="panel-side-text", children=[satellite_title, satellite_body]), ], ) # Satellite location tracker # Helper to straighten lines on the map def flatten_path(xy1, xy2): diff_rate = (xy2 - xy1) / 100 res_list = [] for i in range(100): res_list.append(xy1 + i * diff_rate) return res_list map_data = [ { "type": "scattermapbox", "lat": [0], "lon": [0], "hoverinfo": "text+lon+lat", "text": "Satellite Path", "mode": "lines", "line": {"width": 2, "color": "#707070"}, }, { "type": "scattermapbox", "lat": [0], "lon": [0], "hoverinfo": "text+lon+lat", "text": "Current Position", "mode": "markers", "marker": {"size": 10, "color": "#fec036"}, }, ] map_layout = { "mapbox": { "accesstoken": MAPBOX_ACCESS_TOKEN, "style": MAPBOX_STYLE, "center": {"lat": 45}, }, "showlegend": False, "autosize": True, "paper_bgcolor": "#1e1e1e", "plot_bgcolor": "#1e1e1e", "margin": {"t": 0, "r": 0, "b": 0, "l": 0}, } map_graph = html.Div( id="world-map-wrapper", children=[ map_toggle, dcc.Graph( id="world-map", figure={"data": map_data, "layout": map_layout}, config={"displayModeBar": False, "scrollZoom": False}, ), ], ) # Histogram histogram = html.Div( id="histogram-container", children=[ html.Div( id="histogram-header", children=[ html.H1( id="histogram-title", children=["Select A Property To Display"] ), minute_toggle, ], ), dcc.Graph( id="histogram-graph", figure={ "data": [ { "x": [i for i in range(60)], "y": [i for i in range(60)], "type": "scatter", "marker": {"color": "#fec036"}, } ], "layout": { "margin": {"t": 30, "r": 35, "b": 40, "l": 50}, "xaxis": {"dtick": 5, "gridcolor": "#636363", "showline": False}, "yaxis": {"showgrid": False}, "plot_bgcolor": "#2b2b2b", "paper_bgcolor": "#2b2b2b", "font": {"color": "gray"}, }, }, config={"displayModeBar": False}, ), ], ) # Control panel + map main_panel_layout = html.Div( id="panel-upper-lower", children=[ dcc.Interval(id="interval", interval=1 * 2000, n_intervals=0), map_graph, html.Div( id="panel", children=[ histogram, html.Div( id="panel-lower", children=[ html.Div( id="panel-lower-0", children=[elevation, temperature, speed, utc], ), html.Div( id="panel-lower-1", children=[ html.Div( id="panel-lower-led-displays", children=[latitude, longitude], ), html.Div( id="panel-lower-indicators", children=[ html.Div( id="panel-lower-indicators-0", children=[solar_panel_0, thrusters], ), html.Div( id="panel-lower-indicators-1", children=[solar_panel_1, motor], ), html.Div( id="panel-lower-indicators-2", children=[camera, communication_signal], ), ], ), html.Div( id="panel-lower-graduated-bars", children=[fuel_indicator, battery_indicator], ), ], ), ], ), ], ), ], ) # Data generation # Pandas APP_PATH = str(pathlib.Path(__file__).parent.resolve()) # Satellite H45-K1 data df_non_gps_h_0 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "non_gps_data_h_0.csv")) ) df_non_gps_m_0 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "non_gps_data_m_0.csv")) ) df_gps_m_0 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "gps_data_m_0.csv")) ) df_gps_h_0 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "gps_data_h_0.csv")) ) # Satellite L12-5 data df_non_gps_h_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "non_gps_data_h_1.csv")) ) df_non_gps_m_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "non_gps_data_m_1.csv")) ) df_gps_m_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "gps_data_m_1.csv")) ) df_gps_h_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "gps_data_h_1.csv")) ) # Root root_layout = html.Div( id="root", children=[ dcc.Store(id="store-placeholder"), dcc.Store( id="store-data", data={ "hour_data_0": { "elevation": [df_non_gps_h_0["elevation"][i] for i in range(60)], "temperature": [ df_non_gps_h_0["temperature"][i] for i in range(60) ], "speed": [df_non_gps_h_0["speed"][i] for i in range(60)], "latitude": [ "{0:09.4f}".format(df_gps_h_0["lat"][i]) for i in range(60) ], "longitude": [ "{0:09.4f}".format(df_gps_h_0["lon"][i]) for i in range(60) ], "fuel": [df_non_gps_h_0["fuel"][i] for i in range(60)], "battery": [df_non_gps_h_0["battery"][i] for i in range(60)], }, "minute_data_0": { "elevation": [df_non_gps_m_0["elevation"][i] for i in range(60)], "temperature": [ df_non_gps_m_0["temperature"][i] for i in range(60) ], "speed": [df_non_gps_m_0["speed"][i] for i in range(60)], "latitude": [ "{0:09.4f}".format(df_gps_m_0["lat"][i]) for i in range(60) ], "longitude": [ "{0:09.4f}".format(df_gps_m_0["lon"][i]) for i in range(60) ], "fuel": [df_non_gps_m_0["fuel"][i] for i in range(60)], "battery": [df_non_gps_m_0["battery"][i] for i in range(60)], }, "hour_data_1": { "elevation": [df_non_gps_h_1["elevation"][i] for i in range(60)], "temperature": [ df_non_gps_h_1["temperature"][i] for i in range(60) ], "speed": [df_non_gps_h_1["speed"][i] for i in range(60)], "latitude": [ "{0:09.4f}".format(df_gps_h_1["lat"][i]) for i in range(60) ], "longitude": [ "{0:09.4f}".format(df_gps_h_1["lon"][i]) for i in range(60) ], "fuel": [df_non_gps_h_1["fuel"][i] for i in range(60)], "battery": [df_non_gps_h_1["battery"][i] for i in range(60)], }, "minute_data_1": { "elevation": [df_non_gps_m_1["elevation"][i] for i in range(60)], "temperature": [ df_non_gps_m_1["temperature"][i] for i in range(60) ], "speed": [df_non_gps_m_1["speed"][i] for i in range(60)], "latitude": [ "{0:09.4f}".format(df_gps_m_1["lat"][i]) for i in range(60) ], "longitude": [ "{0:09.4f}".format(df_gps_m_1["lon"][i]) for i in range(60) ], "fuel": [df_non_gps_m_1["fuel"][i] for i in range(60)], "battery": [df_non_gps_m_1["battery"][i] for i in range(60)], }, }, ), # For the case no components were clicked, we need to know what type of graph to preserve dcc.Store(id="store-data-config", data={"info_type": "", "satellite_type": 0}), side_panel_layout, main_panel_layout, ], ) app.layout = root_layout # Callbacks Data # Add new data every second/minute @app.callback( Output("store-data", "data"), [Input("interval", "n_intervals")], [State("store-data", "data")], ) def update_data(interval, data): new_data = data # Update H45-K1 data when sat==0, update L12-5 data when sat==1 for sat in range(2): if sat == 0: gps_minute_file = df_gps_m_0 gps_hour_file = df_gps_h_0 else: gps_minute_file = df_gps_m_1 gps_hour_file = df_gps_h_1 m_data_key = "minute_data_" + str(sat) h_data_key = "hour_data_" + str(sat) new_data[m_data_key]["elevation"].append(data[m_data_key]["elevation"][0]) new_data[m_data_key]["elevation"] = new_data[m_data_key]["elevation"][1:61] new_data[m_data_key]["temperature"].append(data[m_data_key]["temperature"][0]) new_data[m_data_key]["temperature"] = new_data[m_data_key]["temperature"][1:61] new_data[m_data_key]["speed"].append(data[m_data_key]["speed"][0]) new_data[m_data_key]["speed"] = new_data[m_data_key]["speed"][1:61] new_data[m_data_key]["latitude"].append( "{0:09.4f}".format(gps_minute_file["lat"][(60 + interval) % 3600]) ) new_data[m_data_key]["latitude"] = new_data[m_data_key]["latitude"][1:61] new_data[m_data_key]["longitude"].append( "{0:09.4f}".format(gps_minute_file["lon"][(60 + interval) % 3600]) ) new_data[m_data_key]["longitude"] = new_data[m_data_key]["longitude"][1:61] new_data[m_data_key]["fuel"].append(data[m_data_key]["fuel"][0]) new_data[m_data_key]["fuel"] = new_data[m_data_key]["fuel"][1:61] new_data[m_data_key]["battery"].append(data[m_data_key]["battery"][0]) new_data[m_data_key]["battery"] = new_data["minute_data_0"]["battery"][1:61] if interval % 60000 == 0: new_data[h_data_key]["elevation"].append(data[h_data_key]["elevation"][0]) new_data[h_data_key]["elevation"] = new_data[h_data_key]["elevation"][1:61] new_data[h_data_key]["temperature"].append( data[h_data_key]["temperature"][0] ) new_data[h_data_key]["temperature"] = new_data[h_data_key]["temperature"][ 1:61 ] new_data[h_data_key]["speed"].append(data[h_data_key]["speed"][0]) new_data[h_data_key]["speed"] = new_data[h_data_key]["speed"][1:61] new_data[h_data_key]["latitude"].append( "{0:09.4f}".format(gps_hour_file["lat"][interval % 60]) ) new_data[h_data_key]["latitude"] = new_data[h_data_key]["latitude"][1:61] new_data[h_data_key]["longitude"].append( "{0:09.4f}".format(gps_hour_file["lon"][interval % 60]) ) new_data[h_data_key]["longitude"] = new_data[h_data_key]["longitude"][1:61] new_data[h_data_key]["fuel"].append(data[h_data_key]["fuel"][0]) new_data[h_data_key]["fuel"] = new_data[h_data_key]["fuel"][1:61] new_data[h_data_key]["battery"].append(data[h_data_key]["battery"][0]) new_data[h_data_key]["battery"] = new_data[h_data_key]["battery"] return new_data # Callbacks Histogram # Update the graph @app.callback( [ Output("histogram-graph", "figure"), Output("store-data-config", "data"), Output("histogram-title", "children"), ], [ Input("interval", "n_intervals"), Input("satellite-dropdown-component", "value"), Input("control-panel-toggle-minute", "value"), Input("control-panel-elevation", "n_clicks"), Input("control-panel-temperature", "n_clicks"), Input("control-panel-speed", "n_clicks"), Input("control-panel-latitude", "n_clicks"), Input("control-panel-longitude", "n_clicks"), Input("control-panel-fuel", "n_clicks"), Input("control-panel-battery", "n_clicks"), ], [ State("store-data", "data"), State("store-data-config", "data"), State("histogram-graph", "figure"), State("store-data-config", "data"), State("histogram-title", "children"), ], ) def update_graph( interval, satellite_type, minute_mode, elevation_n_clicks, temperature_n_clicks, speed_n_clicks, latitude_n_clicks, longitude_n_clicks, fuel_n_clicks, battery_n_clicks, data, data_config, old_figure, old_data, old_title, ): new_data_config = data_config info_type = data_config["info_type"] ctx = dash.callback_context # Check which input fired off the component if not ctx.triggered: trigger_input = "" else: trigger_input = ctx.triggered[0]["prop_id"].split(".")[0] # Update store-data-config['satellite_type'] if satellite_type == "h45-k1": new_data_config["satellite_type"] = 0 elif satellite_type == "l12-5": new_data_config["satellite_type"] = 1 else: new_data_config["satellite_type"] = None # Decide the range of Y given if minute_mode is on def set_y_range(data_key): if data_key == "elevation": if minute_mode: figure["layout"]["yaxis"] = {"rangemode": "normal", "autorange": True} else: figure["layout"]["yaxis"] = { "rangemode": "normal", "range": [0, 1000], "autorange": False, } elif data_key == "temperature": if minute_mode: figure["layout"]["yaxis"] = {"rangemode": "normal", "autorange": True} else: figure["layout"]["yaxis"] = { "rangemode": "normal", "range": [0, 500], "autorange": False, } elif data_key == "speed": if minute_mode: figure["layout"]["yaxis"] = {"rangemode": "normal", "autorange": True} else: figure["layout"]["yaxis"] = { "rangemode": "normal", "range": [0, 40], "autorange": False, } elif data_key == "latitude": if minute_mode: figure["layout"]["yaxis"] = {"rangemode": "normal", "autorange": True} else: figure["layout"]["yaxis"] = { "rangemode": "normal", "range": [-90, 90], "autorange": False, "dtick": 30, } elif data_key == "longitude": if minute_mode: figure["layout"]["yaxis"] = {"rangemode": "normal", "autorange": True} else: figure["layout"]["yaxis"] = { "rangemode": "normal", "range": [0, 360], "autorange": False, } elif data_key == "fuel" or data_key == "battery": if minute_mode: figure["layout"]["yaxis"] = {"rangemode": "normal", "autorange": True} else: figure["layout"]["yaxis"] = { "rangemode": "normal", "range": [0, 100], "autorange": False, } # Function to update values def update_graph_data(data_key): string_buffer = "" if data_config["satellite_type"] == 0: string_buffer = "_0" elif data_config["satellite_type"] == 1: string_buffer = "_1" if minute_mode: figure["data"][0]["y"] = list( reversed(data["minute_data" + string_buffer][data_key]) ) else: figure["data"][0]["y"] = list( reversed(data["hour_data" + string_buffer][data_key]) ) # Graph title changes depending on graphed data new_title = data_key.capitalize() + " Histogram" return [data_key, new_title] # A default figure option to base off everything else from figure = old_figure # First pass checks if a component has been selected if trigger_input == "control-panel-elevation": set_y_range("elevation") info_type, new_title = update_graph_data("elevation") elif trigger_input == "control-panel-temperature": set_y_range("temperature") info_type, new_title = update_graph_data("temperature") elif trigger_input == "control-panel-speed": set_y_range("speed") info_type, new_title = update_graph_data("speed") elif trigger_input == "control-panel-latitude": set_y_range("latitude") info_type, new_title = update_graph_data("latitude") elif trigger_input == "control-panel-longitude": set_y_range("longitude") info_type, new_title = update_graph_data("longitude") elif trigger_input == "control-panel-fuel": set_y_range("fuel") info_type, new_title = update_graph_data("fuel") elif trigger_input == "control-panel-battery": set_y_range("battery") info_type, new_title = update_graph_data("battery") # If no component has been selected, check for most recent info_type, to prevent graph from always resetting else: if info_type in [ "elevation", "temperature", "speed", "latitude", "longitude", "fuel", "battery", ]: set_y_range(info_type) nil, new_title = update_graph_data(info_type) return [figure, new_data_config, new_title] else: return [old_figure, old_data, old_title] new_data_config["info_type"] = info_type return [figure, new_data_config, new_title] # Callbacks Dropdown @app.callback( Output("satellite-name", "children"), [Input("satellite-dropdown-component", "value")], ) def update_satellite_name(val): if val == "h45-k1": return "Satellite\nH45-K1" elif val == "l12-5": return "Satellite\nL12-5" else: return "" @app.callback( Output("satellite-description", "children"), [Input("satellite-dropdown-component", "value")], ) def update_satellite_description(val): text = "Select a satellite to view using the dropdown above." if val == "h45-k1": text = ( "H45-K1, also known as GPS IIR-9 and GPS SVN-45, is an American navigation satellite which forms part " "of the Global Positioning System. It was the ninth Block IIR GPS satellite to be launched, out of " "thirteen in the original configuration, and twenty one overall. It was built by <NAME>, using " "the AS-4000 satellite bus. -168 was launched at 22:09:01 UTC on 31 March 2003, atop a Delta II carrier " "rocket, flight number D297, flying in the 7925-9.5 configuration. The launch took place from Space " "Launch Complex 17A at the Cape Canaveral Air Force Station, and placed H45-K1 into a transfer orbit. " "The satellite raised itself into medium Earth orbit using a Star-37FM apogee motor." ) elif val == "l12-5": text = ( "L12-5, also known as NRO Launch 22 or NROL-22, is an American signals intelligence satellite, " "operated by the National Reconnaissance Office. Launched in 2006, it has been identified as the first " "in a new series of satellites which are replacing the earlier Trumpet spacecraft. L12-5 was launched " "by Boeing, using a Delta IV carrier rocket flying in the Medium+(4,2) configuration. The rocket was the " "first Delta IV to launch from Vandenberg Air Force Base, flying from Space Launch Complex 6, a launch " "pad originally constructed as part of abandoned plans for manned launches from Vandenberg, originally " "using Titan rockets, and later Space Shuttles. The launch also marked the first launch of an Evolved " "Expendable Launch Vehicle from Vandenberg, and the first launch of an NRO payload on an EELV." ) return text # Callbacks Map @app.callback( Output("world-map", "figure"), [ Input("interval", "n_intervals"), Input("control-panel-toggle-map", "value"), Input("satellite-dropdown-component", "value"), ], [ State("world-map", "figure"), State("store-data", "data"), State("store-data-config", "data"), ], ) def update_word_map(clicks, toggle, satellite_type, old_figure, data, data_config): figure = old_figure string_buffer = "" # Set string buffer as well as drawing the satellite path if data_config["satellite_type"] == 0: string_buffer = "_0" figure["data"][0]["lat"] = [df_gps_m_0["lat"][i] for i in range(3600)] figure["data"][0]["lon"] = [df_gps_m_0["lon"][i] for i in range(3600)] elif data_config["satellite_type"] == 1: string_buffer = "_1" figure["data"][0]["lat"] = [df_gps_m_1["lat"][i] for i in range(3600)] figure["data"][0]["lon"] = [df_gps_m_1["lon"][i] for i in range(3600)] else: figure["data"][0]["lat"] = [df_gps_m["lat"][i] for i in range(3600)] figure["data"][0]["lon"] = [df_gps_m["lon"][i] for i in range(3600)] if not string_buffer: figure["data"][1]["lat"] = [1.0] figure["data"][1]["lon"] = [1.0] elif clicks % 2 == 0: figure["data"][1]["lat"] = [ float(data["minute_data" + string_buffer]["latitude"][-1]) ] figure["data"][1]["lon"] = [ float(data["minute_data" + string_buffer]["longitude"][-1]) ] # If toggle is off, hide path if not toggle: figure["data"][0]["lat"] = [] figure["data"][0]["lon"] = [] return figure # Callbacks Components @app.callback( Output("control-panel-utc-component", "value"), [Input("interval", "n_intervals")] ) def update_time(interval): hour = time.localtime(time.time())[3] hour = str(hour).zfill(2) minute = time.localtime(time.time())[4] minute = str(minute).zfill(2) return hour + ":" + minute @app.callback( [ Output("control-panel-elevation-component", "value"), Output("control-panel-temperature-component", "value"), Output("control-panel-speed-component", "value"), Output("control-panel-fuel-component", "value"), Output("control-panel-battery-component", "value"), ], [Input("interval", "n_intervals"), Input("satellite-dropdown-component", "value")], [State("store-data-config", "data"), State("store-data", "data")], ) def update_non_gps_component(clicks, satellite_type, data_config, data): string_buffer = "" if data_config["satellite_type"] == 0: string_buffer = "_0" if data_config["satellite_type"] == 1: string_buffer = "_1" new_data = [] components_list = ["elevation", "temperature", "speed", "fuel", "battery"] # Update each graph value for component in components_list: new_data.append(data["minute_data" + string_buffer][component][-1]) return new_data @app.callback( [ Output("control-panel-latitude-component", "value"), Output("control-panel-longitude-component", "value"), ], [Input("interval", "n_intervals"), Input("satellite-dropdown-component", "value")], [State("store-data-config", "data"), State("store-data", "data")], ) def update_gps_component(clicks, satellite_type, data_config, data): string_buffer = "" if data_config["satellite_type"] == 0: string_buffer = "_0" if data_config["satellite_type"] == 1: string_buffer = "_1" new_data = [] for component in ["latitude", "longitude"]: val = list(data["minute_data" + string_buffer][component][-1]) if val[0] == "-": new_data.append("0" + "".join(val[1::])) else: new_data.append("".join(val)) return new_data @app.callback( [ Output("control-panel-latitude-component", "color"), Output("control-panel-longitude-component", "color"), ], [Input("interval", "n_intervals"), Input("satellite-dropdown-component", "value")], [State("store-data-config", "data"), State("store-data", "data")], ) def update_gps_color(clicks, satellite_type, data_config, data): string_buffer = "" if data_config["satellite_type"] == 0: string_buffer = "_0" if data_config["satellite_type"] == 1: string_buffer = "_1" new_data = [] for component in ["latitude", "longitude"]: value = float(data["minute_data" + string_buffer][component][-1]) if value < 0: new_data.append("#ff8e77") else: new_data.append("#fec036") return new_data @app.callback( Output("control-panel-communication-signal", "value"), [Input("interval", "n_intervals"), Input("satellite-dropdown-component", "value")], ) def update_communication_component(clicks, satellite_type): if clicks % 2 == 0: return False else: return True if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-daq-tektronix350/osc_tds350.py ```python import visa import numpy as np oscilloscope = None # Adapted from code seen here: # https://github.com/baroobob/TektronixTDS2024B/blob/master/TektronixTDS2024B.py def get_data(): global oscilloscope rm = visa.ResourceManager() oscilloscope = rm.open_resource("GPIB0::1::INSTR") write("DATA:SOURCE CH1") write("DATA:WIDTH 2") write("DATa:ENCdg SRIbinary") ymult = float(query("WFMPRE:CH1:YMULT?")) yzero = float(query("WFMPRE:CH1:YZERO?")) yoff = float(query("WFMPRE:CH1:YOFF?")) xincr = float(query("WFMPRE:CH1:XINCR?")) write("AUTOSET EXECUTE") write("CURVE?") data = oscilloscope.read_raw() headerlen = 2 + int(data[1]) header = data[:headerlen] ADC_wave = data[headerlen:-1] ADC_wave = np.fromstring(ADC_wave, dtype=np.int16) y = (ADC_wave - yoff) * ymult + yzero x = np.arange(0, xincr * len(y), xincr) oscilloscope.close() return [ { "x": x, "y": y, "type": "line", "showscale": False, "colorscale": [[0, "rgba(255, 255, 255,0)"], [1, "rgba(0,0,255,1)"]], } ] def get_data_tuple(): global oscilloscope rm = visa.ResourceManager() oscilloscope = rm.open_resource("GPIB0::1::INSTR") write("DATA:SOURCE CH1") write("DATA:WIDTH 2") write("DATa:ENCdg SRIbinary") ymult = float(query("WFMPRE:CH1:YMULT?")) yzero = float(query("WFMPRE:CH1:YZERO?")) yoff = float(query("WFMPRE:CH1:YOFF?")) xincr = float(query("WFMPRE:CH1:XINCR?")) write("AUTOSET EXECUTE") write("CURVE?") data = oscilloscope.read_raw() headerlen = 2 + int(data[1]) header = data[:headerlen] ADC_wave = data[headerlen:-1] ADC_wave = np.fromstring(ADC_wave, dtype=np.int16) y = (ADC_wave - yoff) * ymult + yzero x = np.arange(0, xincr * len(y), xincr) return (x, y) def query(command): return oscilloscope.query(command) def write(command): oscilloscope.write(command) # page 204 ``` #### File: apps/dash-deck-explorer/app.py ```python from importlib import import_module import inspect from textwrap import dedent import os import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output, State from tqdm import tqdm def Header(name, app): title = html.H1(name, style={"margin-top": 5}) logo = html.Img( src=app.get_asset_url("dash-logo.png"), style={"float": "right", "height": 60} ) link = html.A(logo, href="https://plotly.com/dash/") return dbc.Row([dbc.Col(title, md=8), dbc.Col(link, md=4)]) def format_demo_name(demo): return demo.replace("usage-", "").replace("-", " ").title() ignored_demos = ["usage-events.py", "usage-style-prop.py"] deck_demos = [ n.replace(".py", "").replace("usage-", "") for n in sorted(os.listdir("./demos")) if ".py" in n and n not in ignored_demos ] deck_modules = {demo: import_module(f"demos.usage-{demo}") for demo in tqdm(deck_demos)} print("Demos Loaded:", deck_demos) app = dash.Dash(__name__, external_stylesheets=[dbc.themes.DARKLY]) server = app.server app_selection = dbc.FormGroup( [ dbc.Label("Select Demo", width=3), dbc.Col( dbc.Select( id="demo-selection", options=[ {"label": demo.replace("-", " ").title(), "value": demo} for demo in deck_demos ], className="form-control-plaintext", ), width=9, ), ], row=True, ) tab_style = {"height": "calc(100vh - 230px)", "padding": "15px"} # tab_style = {'max-height': 'calc(100vh - 210px)'} tabs = dbc.Tabs( [ dbc.Tab(dcc.Markdown(id="description", style=tab_style), label="Description"), dbc.Tab(dcc.Markdown(id="source-code", style=tab_style), label="Source Code"), ] ) layout = [ Header("Dash Deck Explorer", app), html.Br(), dcc.Location(id="url", refresh=False), dbc.Row( [ dbc.Col( dbc.Card( id="deck-card", style={"height": "calc(100vh - 110px)"}, body=True ), md=6, ), dbc.Col([app_selection, tabs], md=6), ] ), ] app.layout = dbc.Container(layout, fluid=True) @app.callback( Output("url", "pathname"), Input("demo-selection", "value"), State("url", "pathname"), ) def update_url(name, pathname): if name is None: if pathname in ["/dash-deck-explorer/", None, "/dash-deck-explorer"]: name = deck_demos[0] else: return dash.no_update return "/dash-deck-explorer/" + name @app.callback( [ Output("deck-card", "children"), Output("description", "children"), Output("source-code", "children"), ], Input("url", "pathname"), ) def update_demo(pathname): if pathname in ["/dash-deck-explorer/", None, "/"]: return dash.no_update name = pathname.split("/")[-1] module = deck_modules[name] deck_component = module.app.layout desc = module.__doc__ code = f"```\n{inspect.getsource(module)}\n```" end = dedent( f""" ----- * Source Code on GitHub: [Link to demo](https://github.com/plotly/dash-deck/blob/master/demos/usage-{name}.py) * Dash Deck for enterprises: [Contact us](https://plotly.com/contact-us) * Download it now: [PyPi](https://pypi.org/project/dash-deck) * About Dash Deck: [Readme](https://github.com/plotly/dash-deck/blob/master/README.md) | [Announcement](https://community.plotly.com/) """ ) return deck_component, desc + end, code if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-drug-discovery/app.py ```python import dash import pandas as pd import pathlib import dash_html_components as html import dash_core_components as dcc from dash.dependencies import Input, Output from dash.exceptions import PreventUpdate from helpers import make_dash_table, create_plot app = dash.Dash( __name__, meta_tags=[{"name": "viewport", "content": "width=device-width, initial-scale=1"}], ) server = app.server DATA_PATH = pathlib.Path(__file__).parent.joinpath("data").resolve() # read from datasheet df = pd.read_csv(DATA_PATH.joinpath("small_molecule_drugbank.csv")).drop( ["Unnamed: 0"], axis=1 ) STARTING_DRUG = "Levobupivacaine" DRUG_DESCRIPTION = df.loc[df["NAME"] == STARTING_DRUG]["DESC"].iloc[0] DRUG_IMG = df.loc[df["NAME"] == STARTING_DRUG]["IMG_URL"].iloc[0] FIGURE = create_plot( x=df["PKA"], y=df["LOGP"], z=df["SOL"], size=df["MW"], color=df["MW"], name=df["NAME"], ) app.layout = html.Div( [ html.Div( [html.Img(src=app.get_asset_url("dash-logo.png"))], className="app__banner" ), html.Div( [ html.Div( [ html.Div( [ html.H3( "dash for drug discovery", className="uppercase title", ), html.Span("Hover ", className="uppercase bold"), html.Span( "over a drug in the graph to see its structure." ), html.Br(), html.Span("Select ", className="uppercase bold"), html.Span( "a drug in the dropdown to add it to the drug candidates at the bottom." ), ] ) ], className="app__header", ), html.Div( [ dcc.Dropdown( id="chem_dropdown", multi=True, value=[STARTING_DRUG], options=[{"label": i, "value": i} for i in df["NAME"]], ) ], className="app__dropdown", ), html.Div( [ html.Div( [ dcc.RadioItems( id="charts_radio", options=[ {"label": "3D Scatter", "value": "scatter3d"}, {"label": "2D Scatter", "value": "scatter"}, { "label": "2D Histogram", "value": "histogram2d", }, ], labelClassName="radio__labels", inputClassName="radio__input", value="scatter3d", className="radio__group", ), dcc.Graph( id="clickable-graph", hoverData={"points": [{"pointNumber": 0}]}, figure=FIGURE, ), ], className="two-thirds column", ), html.Div( [ html.Div( [ html.Img( id="chem_img", src=DRUG_IMG, className="chem__img", ) ], className="chem__img__container", ), html.Div( [ html.A( STARTING_DRUG, id="chem_name", href="https://www.drugbank.ca/drugs/DB01002", target="_blank", ), html.P(DRUG_DESCRIPTION, id="chem_desc"), ], className="chem__desc__container", ), ], className="one-third column", ), ], className="container card app__content bg-white", ), html.Div( [ html.Table( make_dash_table([STARTING_DRUG], df), id="table-element", className="table__container", ) ], className="container bg-white p-0", ), ], className="app__container", ), ] ) def df_row_from_hover(hoverData): """ Returns row for hover point as a Pandas Series. """ try: point_number = hoverData["points"][0]["pointNumber"] molecule_name = str(FIGURE["data"][0]["text"][point_number]).strip() return df.loc[df["NAME"] == molecule_name] except KeyError as error: print(error) return pd.Series() @app.callback( Output("clickable-graph", "figure"), [Input("chem_dropdown", "value"), Input("charts_radio", "value")], ) def highlight_molecule(chem_dropdown_values, plot_type): """ Selected chemical dropdown values handler. :params chem_dropdown_values: selected dropdown values :params plot_type: selected plot graph """ return create_plot( x=df["PKA"], y=df["LOGP"], z=df["SOL"], size=df["MW"], color=df["MW"], name=df["NAME"], markers=chem_dropdown_values, plot_type=plot_type, ) @app.callback(Output("table-element", "children"), [Input("chem_dropdown", "value")]) def update_table(chem_dropdown_value): """ Update the table rows. :params chem_dropdown_values: selected dropdown values """ return make_dash_table(chem_dropdown_value, df) @app.callback( [ Output("chem_name", "children"), Output("chem_name", "href"), Output("chem_img", "src"), Output("chem_desc", "children"), ], [Input("clickable-graph", "hoverData")], ) def chem_info_on_hover(hoverData): """ Display chemical information on graph hover. Update the image, link, description. :params hoverData: data on graph hover """ if hoverData is None: raise PreventUpdate try: row = df_row_from_hover(hoverData) if row.empty: raise Exception return ( row["NAME"].iloc[0], row["PAGE"].iloc[0], row["IMG_URL"].iloc[0], row["DESC"].iloc[0], ) except Exception as error: print(error) raise PreventUpdate if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-fashion-mnist-explorer/app.py ```python import base64 from io import BytesIO import numpy as np from keras.models import load_model from PIL import Image import dash import dash_html_components as html import dash_core_components as dcc from dash.dependencies import Input, Output, State import pickle import plotly.express as px from helpers import load_mnist, parse_image, numpy_to_b64, create_img, label_mapping train_images, train_labels = load_mnist("./fashion", subset="train") test_images, test_labels = load_mnist("./fashion", subset="test") all_labels = np.concatenate((train_labels, test_labels)) X_train = train_images.reshape(60000, 28, 28, 1) X_test = test_images.reshape(10000, 28, 28, 1) all_images = np.concatenate((X_train, X_test), axis=0) train_X_hat = np.load("trained_data/train_tsne.npy") test_X_hat = np.load("trained_data/test_tsne.npy") all_X_hat = np.load("trained_data/all_images_tsne.npy") model = load_model("trained_data/fashion_mnist_cnn.h5") intro_text = """ This app applies T-SNE on the images from the [fashion mnist dataset](https://github.com/zalandoresearch/fashion-mnist), which reduces each image to a two dimensional embedding to visualize the similarity between images. Clusters represent similar images, and the greater the distance between two points, the less similar the images are. The app also allows to predict the class of each image using a simple convolutional neural network (CNN). The T-SNE embeddings were generated using [RAPIDS cuML](https://github.com/rapidsai/cuml), and the CNN was trained using Keras. Hover over each point in the tsne graph to see the image it represents. You can click an individual point to see the CNN's prediction for that point, as well as the ground-truth label. You can also upload your own image to see how the CNN would classify it, as well as to display the images' approximate location in the T-SNE space (new embeddings are approximated using a linear model fit on the mnist images, using the original mnist T-SNE embeddings as the dependent variable). """ def create_tsne_graph(data, uploaded_point=None): colors = px.colors.qualitative.Pastel traces = [] for i, key in enumerate(label_mapping.keys()): # Training data idx = np.where(train_labels == key) x = all_X_hat[idx, 0].flatten() y = all_X_hat[idx, 1].flatten() if data in ["Train", "All"]: opacity = 0.9 hoverinfo = "all" showlegend = True visible = True else: opacity = 0.5 hoverinfo = "none" showlegend = False visible = "legendonly" trace = { "x": x, "y": y, "mode": "markers", "type": "scattergl", "marker": {"color": colors[i], "size": 3}, "name": label_mapping[key], "text": label_mapping[key], "customdata": idx[0], "opacity": opacity, "hoverinfo": hoverinfo, "visible": visible, "showlegend": showlegend, "selected": {"marker": {"size": 10, "color": "black"}}, } traces.append(trace) for i, key in enumerate(label_mapping.keys()): # Test data idx = np.where(test_labels == key) x = all_X_hat[(idx[0] + len(train_labels)), 0].flatten() y = all_X_hat[(idx[0] + len(train_labels)), 1].flatten() if data in ["Test", "All"]: opacity = 0.9 hoverinfo = "all" showlegend = True if data == "Test" else False visible = True else: opacity = 0.5 hoverinfo = "none" showlegend = False visible = "legendonly" trace = { "x": x, "y": y, "mode": "markers", "type": "scattergl", "marker": {"color": colors[i], "size": 3}, "name": label_mapping[key], "text": label_mapping[key], "customdata": idx[0] + len(train_labels), "opacity": opacity, "hoverinfo": hoverinfo, "visible": visible, "showlegend": showlegend, "selected": {"marker": {"size": 10, "color": "black"}}, } traces.append(trace) annotation = [] if uploaded_point: annotation.append( { "x": uploaded_point[0][0], "y": uploaded_point[0][1], "xref": "x", "yref": "y", "text": "Predicted Embedding for Uploaded Image", "showarrow": True, "arrowhead": 1, "ax": 10, "ay": -40, "font": {"size": 20}, } ) layout = { "xaxis": {"visible": False}, "yaxis": {"visible": False}, "clickmode": "event+select", "annotations": annotation, } figure = {"data": traces, "layout": layout} return figure app = dash.Dash(name=__name__) server = app.server app.css.config.serve_locally = False app.config.suppress_callback_exceptions = True header = html.Div( id="app-header", children=[ html.Img(src=app.get_asset_url("dash-logo.png"), className="logo"), "Fashion MNIST Explorer: T-SNE and CNN", ], ) app.layout = html.Div( children=[ header, html.Br(), html.Details( id="intro-text", children=[html.Summary(html.B("About This App")), dcc.Markdown(intro_text)], ), # html.Div(html.Div(id="intro-text", children=dcc.Markdown(intro_text),),), html.Div( id="app-body", children=[ html.Div( id="control-card", children=[ html.Span( className="control-label", children="Display Train or Test Data", ), dcc.Dropdown( id="train-test-dropdown", className="control-dropdown", options=[ {"label": i, "value": i} for i in ["Train", "Test", "All"] ], value="Train", ), html.Span( className="control-label", children="Upload an Image" ), dcc.Upload( id="img-upload", className="upload-component", children=html.Div( ["Drag and Drop or ", html.A("Select Files")] ), ), html.Div(id="output-img-upload"), ], ), html.Div( style={"width": "75vw"}, children=[ html.Div( id="tsne-graph-div", children=[ html.Div( id="tsne-graph-outer", children=[ # html.Div( # id="intro-text", # children=dcc.Markdown(intro_text), # ), html.H3( className="graph-title", children="Fashion MNIST Images Reduced to 2 Dimensions with T-SNE", ), dcc.Graph( id="tsne-graph", figure=create_tsne_graph("Test"), ), ], ) ], ), html.Div( id="image-card-div", children=[ html.Div( id="hover-point-outer", className="img-card", children=[ html.Div( "Hover Point:", style={"height": "20%"} ), html.Br(), html.Br(), html.Br(), html.Img( id="hover-point-graph", className="image" ), ], ), html.Div( id="prediction-div", className="img-card", children=[ html.Div( id="selected-data-graph-outer", children=[ html.Div( children=[ html.Div("Selected Point:"), html.Div( id="prediction", children=[ "Click on a point to display the Network's prediction", html.Br(), html.Br(), ], ), ], style={"height": "20%"}, ), html.Br(), html.Img( id="selected-data-graph", className="image", src=create_img(np.zeros((28, 28))), ), ], ) ], ), ], ), ], ), ], ), ] ) @app.callback( Output("output-img-upload", "children"), [Input("img-upload", "contents")], [State("img-upload", "filename"), State("img-upload", "last_modified")], ) def display_uploaded_img(contents, fname, date): if contents is not None: original_img, resized_img = parse_image(contents, fname, date) img = np.expand_dims(resized_img, axis=0) prediction_array = model.predict(img) prediction = np.argmax(prediction_array) children = [ "Your uploaded image: ", html.Img(className="image", src=original_img), "Image fed the model: ", html.Img(className="image", src=create_img(resized_img)), f"The model thinks this is a {label_mapping[prediction]}", html.Button( id="clear-button", children="Remove Uploaded Image", n_clicks=0 ), ] return children @app.callback(Output("img-upload", "contents"), [Input("clear-button", "n_clicks")]) def clear_upload(n_clicks): if n_clicks >= 1: return None raise dash.exceptions.PreventUpdate @app.callback( Output("tsne-graph", "figure"), [Input("train-test-dropdown", "value"), Input("img-upload", "contents")], [State("img-upload", "filename"), State("img-upload", "last_modified")], ) def display_train_test(value, contents, fname, date): embedding_prediction = None if contents is not None: original_img, resized_img = parse_image(contents, fname, date) linear_model = pickle.load( open("trained_data/linear_model_embeddings.sav", "rb") ) embedding_prediction = linear_model.predict(resized_img.reshape(1, -1)).tolist() return create_tsne_graph(value, embedding_prediction) @app.callback(Output("hover-point-graph", "src"), [Input("tsne-graph", "hoverData")]) def display_selected_point(hoverData): if not hoverData: return create_img(train_images[0]) idx = hoverData["points"][0]["customdata"] return create_img(all_images[idx]) @app.callback( [Output("selected-data-graph", "src"), Output("prediction", "children")], [Input("tsne-graph", "clickData")], ) def display_selected_point(clickData): if not clickData: raise dash.exceptions.PreventUpdate idx = clickData["points"][0]["customdata"] img = np.expand_dims(all_images[idx], axis=0) prediction_array = model.predict(img) prediction = np.argmax(prediction_array) probability = np.round(prediction_array[0, prediction] * 100, 2) ground_truth = all_labels[idx] correct = prediction == ground_truth if correct: color = "green" else: color = "red" return [ create_img(all_images[idx]), [ f"prediction: {label_mapping[prediction]} ({probability}% certainty)", html.Br(), f"actual: {label_mapping[ground_truth]}", ], ] if __name__ == "__main__": app.run_server(debug=False) ``` #### File: apps/dash-floris-gch/app.py ```python import base64 from io import BytesIO import dash import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output, State import floris.tools as wfct import matplotlib.pyplot as plt import reusable_components as rc # see reusable_components.py # ############ Create helper functions ############ def mpl_to_b64(fig, format="png", dpi=300, **kwargs): b_io = BytesIO() fig.savefig(b_io, format=format, bbox_inches="tight", dpi=dpi, **kwargs) b64_enc = base64.b64encode(b_io.getvalue()).decode("utf-8") return f"data:image/{format};base64," + b64_enc def build_visualizations(x_loc, y_loc, yaw_1, wd, gch, minSpeed=4, maxSpeed=8.0): fi = wfct.floris_interface.FlorisInterface("./data/example_input.json") fi.set_gch(gch) fi.reinitialize_flow_field( wind_direction=wd, layout_array=((0, 126 * 7, 126 * 14), (0, 0, 0)) ) fi.calculate_wake(yaw_angles=[yaw_1, 0, 0]) # Horizontal plane fig, ax = plt.subplots() wfct.visualization.visualize_cut_plane( fi.get_hor_plane(), ax=ax, minSpeed=minSpeed, maxSpeed=maxSpeed ) ax.axhline(y_loc, color="w", ls="--", lw=1) ax.axvline(x_loc, color="w", ls="--", lw=1) horiz_b64 = mpl_to_b64(fig) plt.close(fig) # Cross (x-normal) plane fig, ax = plt.subplots() wfct.visualization.visualize_cut_plane( fi.get_cross_plane(x_loc=x_loc), ax=ax, minSpeed=minSpeed, maxSpeed=maxSpeed ) wfct.visualization.reverse_cut_plane_x_axis_in_plot(ax) x_plane_b64 = mpl_to_b64(fig) plt.close(fig) # Cross (y-normal) plane fig, ax = plt.subplots() wfct.visualization.visualize_cut_plane( fi.get_y_plane(y_loc=y_loc), ax=ax, minSpeed=minSpeed, maxSpeed=maxSpeed ) wfct.visualization.reverse_cut_plane_x_axis_in_plot(ax) y_plane_b64 = mpl_to_b64(fig) plt.close(fig) return horiz_b64, x_plane_b64, y_plane_b64 # ############ Initialize app ############ app = dash.Dash(__name__, external_stylesheets=[rc.MATERALIZE_CSS]) server = app.server # ############ Build components and layouts ############ navbar = html.Nav( html.Div( className="nav-wrapper teal", children=[ html.Img( src=app.get_asset_url("dash-logo.png"), style={"float": "right", "height": "100%", "padding-right": "15px"}, ), html.A( "GCH and Cut Plane Visualization in FLORIS", className="brand-logo", href="https://plotly.com/dash/", style={"padding-left": "15px"}, ), ], ) ) controls = [ rc.CustomSlider(id="wind-direction", min=250, max=290, label="Wind Direction"), rc.CustomSlider(id="yaw-angle", min=-30, max=30, label="Yaw angle T1"), rc.CustomSlider( id="x-loc", min=0, max=3000, value=500, label="X Normal Plane Intercept" ), rc.CustomSlider(id="y-loc", min=-100, max=100, label="Y Normal Plane Intercept"), ] left_section = rc.Card( rc.CardContent( [ rc.CardTitle("Horizontal Cut Plane"), html.Img(id="gch-horizontal", style={"width": "100%"}), rc.CardTitle("Cross (X-Normal) Cut Plane"), html.Img(id="gch-x-normal", style={"width": "100%"}), rc.CardTitle("Cross (Y-Normal) Cut Plane"), html.Img(id="gch-y-normal", style={"width": "100%"}), ] ) ) right_section = rc.Card( rc.CardContent( [ rc.CardTitle("Horizontal Cut Plane"), html.Img(id="no-gch-horizontal", style={"width": "100%"}), rc.CardTitle("Cross (X-Normal) Cut Plane"), html.Img(id="no-gch-x-normal", style={"width": "100%"}), rc.CardTitle("Cross (Y-Normal) Cut Plane"), html.Img(id="no-gch-y-normal", style={"width": "100%"}), ] ) ) app.layout = html.Div( style={"--slider_active": "teal"}, # className="container", children=[ navbar, html.Br(), rc.Row( rc.Col( rc.Card(rc.CardContent(rc.Row([rc.Col(c, width=3) for c in controls]))), width=12, ) ), rc.Row( [ rc.Col([html.H4("Results with GCH"), left_section], width=6), rc.Col([html.H4("Results without GCH"), right_section], width=6), ] ), ], ) @app.callback( Output("gch-horizontal", "src"), Output("gch-x-normal", "src"), Output("gch-y-normal", "src"), Input("x-loc", "value"), Input("y-loc", "value"), Input("yaw-angle", "value"), Input("wind-direction", "value"), ) def gch_update(x_loc, y_loc, yaw_1, wd): return build_visualizations(x_loc, y_loc, yaw_1, wd, gch=True) @app.callback( Output("no-gch-horizontal", "src"), Output("no-gch-x-normal", "src"), Output("no-gch-y-normal", "src"), Input("x-loc", "value"), Input("y-loc", "value"), Input("yaw-angle", "value"), Input("wind-direction", "value"), ) def no_gch_update(x_loc, y_loc, yaw_1, wd): return build_visualizations(x_loc, y_loc, yaw_1, wd, gch=False) if __name__ == "__main__": app.run_server(debug=True, threaded=False, processes=2) ``` #### File: apps/dash-interest-rate/utils.py ```python import dash import dash_html_components as html import dash_core_components as dcc import dash_bootstrap_components as dbc def Header(name, app): title = html.H2(name, style={"margin-top": 7}) logo = html.Img( src=app.get_asset_url("dash-logo.png"), style={"float": "right", "height": 60} ) link = html.A(logo, href="https://plotly.com/dash/") btn_style = {"margin-top": "13px", "float": "right", "margin-right": "10px"} demo_btn = html.A( dbc.Button("Enterprise Demo", style=btn_style, color="primary"), href="https://plotly.com/get-demo/", ) code_btn = html.A( dbc.Button("Source Code", style=btn_style, color="secondary"), href="https://github.com/plotly/dash-sample-apps/tree/main/apps/dash-interest-rate", ) return dbc.Row([dbc.Col(title, md=7), dbc.Col([link, demo_btn, code_btn], md=5)]) def OptionMenu(values, label, **kwargs): options = [{"label": s.replace("_", " ").capitalize(), "value": s} for s in values] kwargs["value"] = kwargs.get("value", values[0]) if len(options) <= 4: component = dbc.RadioItems kwargs["inline"] = True else: component = dbc.Select return dbc.FormGroup([dbc.Label(label), component(options=options, **kwargs)]) def CustomRangeSlider(values, label, **kwargs): values = sorted(values) marks = {i: f"{i//1000}k" for i in values} return dbc.FormGroup( [ dbc.Label(label), dcc.RangeSlider( min=values[0], max=values[-1], step=1000, value=[values[1], values[-2]], marks=marks, **kwargs, ), ] ) def get_unique(connection, db, col): query = f""" SELECT DISTINCT {col} FROM {db}.PUBLIC.LOAN_CLEAN; """ return [x[0] for x in connection.execute(query).fetchall()] def get_range(connection, db, col): query = f""" SELECT MIN({col}), MAX({col}) FROM {db}.PUBLIC.LOAN_CLEAN; """ return connection.execute(query).fetchall()[0] ``` #### File: apps/dash-live-model-training/demo_utils.py ```python import dash_core_components as dcc import dash_html_components as html import pandas as pd from dash.dependencies import Input, Output, State import pathlib # get relative data folder PATH = pathlib.Path(__file__).parent DATA_PATH = PATH.joinpath("data").resolve() def demo_explanation(demo_mode): if demo_mode: # Markdown files with open(PATH.joinpath("demo.md"), "r") as file: demo_md = file.read() return html.Div( html.Div([dcc.Markdown(demo_md, className="markdown")]), style={"margin": "10px"}, ) def demo_callbacks(app, demo_mode): if demo_mode: @app.server.before_first_request def load_demo_run_logs(): global data_dict, demo_md names = [ "step", "train accuracy", "val accuracy", "train cross entropy", "val cross entropy", ] data_dict = { "softmax": { "cifar": pd.read_csv( DATA_PATH.joinpath("cifar_softmax_run_log.csv"), names=names ), "mnist": pd.read_csv( DATA_PATH.joinpath("mnist_softmax_run_log.csv"), names=names ), "fashion": pd.read_csv( DATA_PATH.joinpath("fashion_softmax_run_log.csv"), names=names ), }, "cnn": { "cifar": pd.read_csv( DATA_PATH.joinpath("cifar_cnn_run_log.csv"), names=names ), "mnist": pd.read_csv( DATA_PATH.joinpath("mnist_cnn_run_log.csv"), names=names ), "fashion": pd.read_csv( DATA_PATH.joinpath("fashion_cnn_run_log.csv"), names=names ), }, } @app.callback( Output("storage-simulated-run", "data"), [Input("interval-simulated-step", "n_intervals")], [ State("dropdown-demo-dataset", "value"), State("dropdown-simulation-model", "value"), ], ) def simulate_run(n_intervals, demo_dataset, simulation_model): if simulation_model and demo_dataset and n_intervals > 0: step = n_intervals * 5 run_logs = data_dict[simulation_model][demo_dataset] run_below_steps = run_logs[run_logs["step"] <= step] json = run_below_steps.to_json(orient="split") return json @app.callback( Output("interval-simulated-step", "n_intervals"), [ Input("dropdown-demo-dataset", "value"), Input("dropdown-simulation-model", "value"), ], ) def reset_interval_simulated_step(*_): return 0 @app.callback( Output("run-log-storage", "data"), [Input("interval-log-update", "n_intervals")], [State("storage-simulated-run", "data")], ) def get_run_log(_, simulated_run): if simulate_run: return simulated_run @app.callback( Output("div-total-step-count", "children"), [Input("dropdown-demo-dataset", "value")], ) def total_step_count(dataset_name): if dataset_name is not None: dataset = data_dict["softmax"][dataset_name] return html.H6( f"Total Steps: {dataset['step'].iloc[-1]}", style={"margin-top": "3px", "float": "right"}, ) ``` #### File: dash-live-model-training/examples/cifar_deep_modified.py ```python from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # Modified Import import numpy as np from sklearn.model_selection import train_test_split from skimage.transform import rescale from skimage import color from tfutils import write_data from sklearn.preprocessing import OneHotEncoder FLAGS = None def deepnn(x): """deepnn builds the graph for a deep net for classifying digits. Args: x: an input tensor with the dimensions (N_examples, 784), where 784 is the number of pixels in a standard MNIST image. Returns: A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values equal to the logits of classifying the digit into one of 10 classes (the digits 0-9). keep_prob is a scalar placeholder for the probability of dropout. """ # Reshape to use within a convolutional neural net. # Last dimension is for "features" - there is three here, since images are # rgb -- it would be 1 for a grayscale image, 4 for RGBA, etc. x_image = tf.reshape(x, [-1, 32, 32, 3]) # Convolutional layers 1 and 2 - maps 3-color image to 32 feature maps. W_conv1 = weight_variable([3, 3, 3, 32]) # 3x3 filters b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) W_conv2 = weight_variable([3, 3, 32, 32]) b_conv2 = bias_variable([32]) h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2) # Pooling layer - downsamples by 2X. h_pool2 = max_pool_2x2(h_conv2) # Dropout h_pool2_drop = tf.nn.dropout(h_pool2, 0.75) # Convolutional layers 3 and 4 - maps 32 feature maps to 64. W_conv3 = weight_variable([3, 3, 32, 64]) # 3x3 filters b_conv3 = bias_variable([64]) h_conv3 = tf.nn.relu(conv2d(h_pool2_drop, W_conv3) + b_conv3) W_conv4 = weight_variable([3, 3, 64, 64]) # 3x3 filters b_conv4 = bias_variable([64]) h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4) # Second pooling layer. h_pool4 = max_pool_2x2(h_conv4) # Dropout h_pool4_drop = tf.nn.dropout(h_pool4, 0.75) # Fully connected layer 1 -- after 2 round of downsampling, our 32x32 image # is down to 8x8x64 feature maps -- maps this to 512 features. W_fc1 = weight_variable([8 * 8 * 64, 512]) b_fc1 = bias_variable([512]) h_pool4_flat = tf.reshape(h_pool4_drop, [-1, 8 * 8 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat, W_fc1) + b_fc1) # Dropout - controls the complexity of the model, prevents co-adaptation of # features. keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # Map the 512 features to 10 classes, one for each digit W_fc2 = weight_variable([512, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 return y_conv, keep_prob def conv2d(x, W): """conv2d returns a 2d convolution layer with full stride.""" return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME") def max_pool_2x2(x): """max_pool_2x2 downsamples a feature map by 2X.""" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") def weight_variable(shape): """weight_variable generates a weight variable of a given shape.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): """bias_variable generates a bias variable of a given shape.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def main(_): # Import data print("Starting to generate CIFAR10 images.") (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() x_train = np.moveaxis(x_train, 1, 3) / 255.0 # Normalize values x_train_vec = x_train.reshape(50000, -1) x_test = np.moveaxis(x_test, 1, 3) / 255.0 # Normalize values x_test_vec = x_test.reshape(10000, -1) X_train, X_val, y_train, y_val = train_test_split( x_train_vec, y_train, test_size=0.1, random_state=42 ) print("Finished generating CIFAR10 images.") # Create the model x = tf.placeholder(tf.float32, [None, 32 * 32 * 3]) # Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) # Build the graph for the deep net y_conv, keep_prob = deepnn(x) cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y_conv) ) train_step = tf.train.AdamOptimizer(1e-4).minimize( cross_entropy ) # RMS is used in keras example, Adam is better correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.Session() as sess: y_train = OneHotEncoder(sparse=False).fit_transform(y_train) y_val = OneHotEncoder(sparse=False).fit_transform(y_val) sess.run(tf.global_variables_initializer()) for i in range(20001): start_train = i * 50 % y_train.shape[0] end_train = start_train + 50 start_val = i * 50 % y_val.shape[0] end_val = start_val + 50 batch = (X_train[start_train:end_train], y_train[start_train:end_train]) batch_val = (X_val[start_val:end_val], y_val[start_val:end_val]) feed_dict_train = {x: batch[0], y_: batch[1], keep_prob: 1.0} feed_dict_val = {x: batch_val[0], y_: batch_val[1], keep_prob: 1.0} # Writes data into run log csv file write_data( accuracy=accuracy, cross_entropy=cross_entropy, feed_dict_train=feed_dict_train, feed_dict_val=feed_dict_val, step=i, ) if i % 100 == 0: train_accuracy = accuracy.eval( feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0} ) print("step %d, training accuracy %g" % (i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) print( "test accuracy %g" % accuracy.eval(feed_dict={x: x_test_vec, y_: y_test, keep_prob: 1.0}) ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--data_dir", type=str, default="/tmp/tensorflow/mnist/input_data", help="Directory for storing input data", ) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) ``` #### File: dash-peaky-finders/peaky_finders/predictor.py ```python from typing import List import datetime as dt from datetime import timedelta import requests import os import pickle from typing import Dict, Tuple import geopandas as gpd import pandas as pd import pytz, datetime from shapely import wkt from timezonefinderL import TimezoneFinder from peaky_finders.data_acquisition.train_model import ( LoadCollector, GEO_COORDS, CATEGORICAL_FEATURES, MONTH_TO_SEASON, ) from peaky_finders.training_pipeline import MODEL_OUTPUT_DIR, MODEL_INPUT_DIR from peaky_finders.data_acquisition.train_model import GEO_COORDS ISO_MAP_IDS = { 56669: "MISO", 14725: "PJM", 2775: "CAISO", 13434: "ISONE", 13501: "NYISO", } ISO_LIST = ["NYISO", "ISONE", "PJM", "MISO", "CAISO"] PEAK_DATA_PATH = os.path.join(os.path.dirname(__file__), "historical_peaks") tz_finder = TimezoneFinder() def get_iso_map(): iso_df = pd.read_csv("iso_map_final.csv") iso_df["geometry"] = iso_df["geometry"].apply(wkt.loads) iso_gdf = gpd.GeoDataFrame(iso_df, crs="EPSG:4326", geometry="geometry") return iso_gdf class Predictor: def __init__(self, iso_name: str, start: str, end: str) -> None: self.start = start self.end = end self.iso_name = iso_name self.load_collector: LoadCollector = None def get_load(self, start: str, end: str): self.load_collector = LoadCollector(self.iso_name, start, end) def featurize(self): self.load_collector.engineer_features() def add_future(self, load: pd.Series) -> pd.Series: future = pd.date_range( start=load.index[-1], end=(load.index[-1] + timedelta(days=1)), freq="H" ).to_frame(name="load_MW") tz_finder = TimezoneFinder() lon = float(GEO_COORDS[self.iso_name]["lon"]) lat = float(GEO_COORDS[self.iso_name]["lat"]) tz_name = tz_finder.timezone_at(lng=lon, lat=lat) future["load_MW"] = None future.index = future.index.tz_convert(tz_name) return future def prepare_predictions(self): self.get_load(self.start, self.end) load = self.load_collector.load self.load_collector.engineer_features() model_input = self.load_collector.load.copy() for feature in CATEGORICAL_FEATURES: dummies = pd.get_dummies( model_input[feature], prefix=feature, drop_first=True ) model_input = model_input.drop(feature, axis=1) model_input = pd.concat([model_input, dummies], axis=1) return model_input, load def predict_load(self, model_input: pd.DataFrame) -> Tuple[pd.Series, pd.Series]: model_path = os.path.join( MODEL_OUTPUT_DIR, (f"xg_boost_{self.iso_name}_load_model.pkl") ) xgb = pickle.load(open(model_path, "rb")) if "holiday_True" not in model_input.columns: model_input["holiday_True"] = 0 X = model_input.drop("load_MW", axis=1).astype(float).dropna() weekday_cols = [f"weekday_{i + 1}" for i in range(0, 6)] if len(set(weekday_cols) - set(X.columns)) > 0: for col in list(set(weekday_cols) - set(X.columns)): X[col] = 0 predictions = xgb.predict(X[xgb.get_booster().feature_names]) X["predicted_load"] = predictions return X["predicted_load"] def predict_load(self,): for iso in ISO_LIST: model_input_path = os.path.join(MODEL_INPUT_DIR, MODEL_INPUT_DATA[iso]) model_path = os.path.join( MODEL_OUTPUT_DIR, (f"xg_boost_{self.iso_name}_load_model.pkl") ) def predict_all(iso_list: list, start: str, end: str) -> Tuple[Dict[str, pd.DataFrame]]: historical_vs_predicted = {} for iso in iso_list: predictor = Predictor(iso, start, end) model_input, historical_load = predictor.prepare_predictions() predictions = predictor.predict_load(model_input) comparison_df = pd.concat([model_input, predictions], axis=1)[ ["load_MW", "predicted_load"] ] historical_vs_predicted[iso] = comparison_df return historical_vs_predicted def get_peak_data(iso_list: list) -> Tuple[Dict[str, pd.DataFrame]]: peak_data = {} for iso in iso_list: path = "https://raw.githubusercontent.com/kbaranko/peaky-finders/master/peaky_finders/historical_peaks" iso_data = pd.read_csv( f"{path}/{iso}_historical_peaks.csv", parse_dates=["timestamp"] ) iso_data["timestamp"] = iso_data["timestamp"].apply( lambda x: x.astimezone(pytz.utc) ) tz_name = tz_finder.timezone_at( lng=float(GEO_COORDS[iso]["lon"]), lat=float(GEO_COORDS[iso]["lat"]) ) iso_data.index = pd.DatetimeIndex(iso_data["timestamp"]) iso_data.index = iso_data.index.tz_convert(tz_name) peak_data[iso] = iso_data return peak_data def get_temperature_forecast(iso: str) -> dict: lon = GEO_COORDS[iso]["lon"] lat = GEO_COORDS[iso]["lat"] API_KEY = os.environ["DARKSKY_KEY"] url = f"https://api.darksky.net/forecast/{API_KEY}/{lat},{lon}" response = requests.get(url) if response.status_code == 200: print(response.status_code) else: raise ValueError( f"Error getting data from DarkSky API: " f"Response Code {response.status_code}" ) info = response.json() hourly_data = info["hourly"]["data"] hourly_temp = {} for info in hourly_data: timestamp = datetime.datetime.fromtimestamp(info["time"]) tz = tz_finder.timezone_at(lng=float(lon), lat=float(lat)) timestamp = timestamp.astimezone(pytz.timezone(tz)) hourly_temp[timestamp] = info["temperature"] return hourly_temp def create_load_duration(peak_data: Dict[str, pd.DataFrame]) -> Dict[str, pd.Series]: load_duration_curves = {} for iso in ISO_LIST: load_duration_curves[iso] = pd.Series( peak_data[iso]["load_MW"].values ).sort_values(ascending=False) return load_duration_curves def get_forecasts(iso_list: List[str]): predictions = {} historical_load = {} temperature = {} for iso in iso_list: path = f"https://raw.githubusercontent.com/kbaranko/peaky-finders/master/peaky_finders/forecasts/{iso}_forecasts.csv" iso_data = pd.read_csv(path, parse_dates=["timestamp"]) iso_data["timestamp"] = iso_data["timestamp"].apply( lambda x: x.astimezone(pytz.utc) ) tz_name = tz_finder.timezone_at( lng=float(GEO_COORDS[iso]["lon"]), lat=float(GEO_COORDS[iso]["lat"]) ) iso_data.index = pd.DatetimeIndex(iso_data["timestamp"]) iso_data.index = iso_data.index.tz_convert(tz_name) historical_load[iso] = iso_data["load_MW"] predictions[iso] = iso_data["predicted_load"] temperature[iso] = iso_data["temperature"] return predictions, historical_load, temperature ``` #### File: apps/dash-pileup-demo/app.py ```python import os import dash_html_components as html import dash_core_components as dcc from dash.dependencies import Input, Output import dash_bio import pandas as pd import numpy as np import math import plotly.graph_objects as go from layout_helper import run_standalone_app text_style = {"color": "#506784", "font-family": "Open Sans"} _COMPONENT_ID = "pileup-browser" def description(): return "An interactive in-browser track viewer." def azure_url(file): return os.path.join( "https://sampleappsdata.blob.core.windows.net/dash-pileup-demo/rna/", file ) def header_colors(): return { "bg_color": "#0F5BA7", "font_color": "white", } def rna_differential(app): basal_lactate = { "url": azure_url("SRR1552454.fastq.gz.sampled.bam"), "indexUrl": azure_url("SRR1552454.fastq.gz.sampled.bam.bai"), } luminal_lactate = { "url": azure_url("SRR1552448.fastq.gz.sampled.bam"), "indexUrl": azure_url("SRR1552448.fastq.gz.sampled.bam.bai"), } HOSTED_TRACKS = { "range": {"contig": "chr1", "start": 54986297, "stop": 54991347}, "celltype": [ {"viz": "scale", "label": "Scale"}, {"viz": "location", "label": "Location"}, { "viz": "genes", "label": "genes", "source": "bigBed", "sourceOptions": {"url": azure_url("mm10.ncbiRefSeq.sorted.bb")}, }, { "viz": "coverage", "label": "Basal", "source": "bam", "sourceOptions": basal_lactate, }, { "viz": "pileup", "vizOptions": {"viewAsPairs": True}, "label": "Basal", "source": "bam", "sourceOptions": basal_lactate, }, { "viz": "coverage", "label": "Luminal", "source": "bam", "sourceOptions": luminal_lactate, }, { "viz": "pileup", "label": "Luminal", "source": "bam", "sourceOptions": luminal_lactate, }, ], } return HOSTED_TRACKS REFERENCE = { "label": "mm10", "url": "https://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/mm10.2bit", } DATAPATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets/data") # Differentially expressed genes (identified in R, see assets/data/rna/README.md) DE_dataframe = pd.read_csv(azure_url("DE_genes.csv")) # filter for the cell type condition DE_dataframe = DE_dataframe[ DE_dataframe["Comparison"] == "luminal__v__basal" ].reset_index() # add SNP column DE_dataframe["SNP"] = "NA" # get min and max effect sizes df_min = math.floor(min(DE_dataframe["log2FoldChange"])) df_max = math.ceil(max(DE_dataframe["log2FoldChange"])) def layout(app): HOSTED_CASE_DICT = rna_differential(app) return html.Div( id="pileup-body", className="app-body", children=[ html.Div( id="pileup-control-tabs", className="control-tabs", children=[ dcc.Tabs( id="pileup-tabs", value="data", children=[ dcc.Tab( label="Volcano plot", value="data", children=html.Div( className="control-tab", children=[ "Effect Size", dcc.RangeSlider( id="pileup-volcanoplot-input", min=df_min, max=df_max, step=None, marks={ i: {"label": str(i)} for i in range(df_min, df_max + 1, 2) }, value=[-1, 1], ), html.Br(), dcc.Graph( id="pileup-dashbio-volcanoplot", figure=dash_bio.VolcanoPlot( dataframe=DE_dataframe, margin=go.layout.Margin(l=0, r=0, b=0), legend={ "orientation": "h", "yanchor": "bottom", "y": 1.02, "bgcolor": "#f2f5fa", }, effect_size="log2FoldChange", effect_size_line=[-1, 1], title="Differentially Expressed Genes", genomewideline_value=-np.log10(0.05), p="padj", snp="SNP", gene="Gene", ), ), ], ), ), dcc.Tab( label="About this tutorial", value="description", children=html.Div( className="control-tab", children=[ html.H4( className="description", children="""Visualizing RNA-seq data with pileup.js and volcano plots""", ), dcc.Markdown( """ In this example, we use the pileup.js and volcano plot components from dash-bio to visualize two RNA-sequencing (RNA-seq) samples from two conditions. RNA-seq allows us to learn how the expression of genes changes between different samples of interest. Here, we are looking at RNA-seq from two samples that are taken from two different mouse cell types. We refer to these different cell types as basal and luminal cell types. On the right, we use pileup.js to visualize aligned reads from RNA-seq samples. On the left, we have a volcano plot, that visualizes the magnitude of change in gene expression between the two samples. On the x-axis, the `Effect Size` indicates the log2 fold change in expression between the two conditions. On the y-axis, `-log10(p)` indicates the -log10(p-value) for each gene. This p-value, along with the effect size, can help determine whether each gene is significantly differentially expressed between the conditions of interest. To explore a gene, you can click on a gene in the volcano plot. After clicking on a gene, the genomic region overlapping that gene will show up in the pileup.js browser on the right. Now, you can investigate RNA-seq alignments at each gene of interest. You may notice that genes with a negative effect size in the volcano plot have more RNA-seq reads in the top sample (the basal cell type), while genes with a positive effect size have more reads in the bottom sample (the luminal cell type). """ ), ], ), ), dcc.Tab( label="About pileup.js", value="what-is", children=html.Div( className="control-tab", children=[ html.H4( className="what-is", children="What is pileup.js?", ), dcc.Markdown( """ The Dash pileup.js component is a high-performance genomics data visualization component developed originally by the Hammer Lab (https://github.com/hammerlab/pileup.js). pileup.js supports visualization of genomic file formats, such as vcf, bam, and bigbed files. pileup.js additionally allows flexible interaction with non-standard data formats. Users can visualize GA4GH JSON formatted alignments, features and variants. Users can also connect with and visualize data stored in GA4GH formatted data stores. """ ), ], ), ), ], ) ], ), dcc.Loading( parent_className="dashbio-loading", id="pileup-output", children=html.Div( [ dash_bio.Pileup( id=_COMPONENT_ID, range=HOSTED_CASE_DICT["range"], reference=REFERENCE, tracks=HOSTED_CASE_DICT["celltype"], ) ] ), ), ], ) def callbacks(_app): HOSTED_CASE_DICT = rna_differential(_app) @_app.callback( Output("pileup-dashbio-volcanoplot", "figure"), [Input("pileup-volcanoplot-input", "value")], ) def update_volcano(effects): return dash_bio.VolcanoPlot( dataframe=DE_dataframe, margin=go.layout.Margin(l=0, r=0, b=0), legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "x": 0.0,}, effect_size="log2FoldChange", effect_size_line=effects, title="Differentially Expressed Genes", genomewideline_value=-np.log10(0.05), p="padj", snp="SNP", gene="Gene", ) @_app.callback( Output(_COMPONENT_ID, "range"), Input("pileup-dashbio-volcanoplot", "clickData") ) def update_range(point): if point is None: range = HOSTED_CASE_DICT["range"] else: # get genomic location of selected genes and goto pointText = point["points"][0]["text"] gene = pointText.split("GENE: ")[-1] row = DE_dataframe[DE_dataframe["Gene"] == gene].iloc[0] range = {"contig": row["chr"], "start": row["start"], "stop": row["end"]} return range app = run_standalone_app(layout, callbacks, header_colors, __file__) server = app.server if __name__ == "__main__": app.run_server(debug=True, port=8050) ``` #### File: apps/dash-pivottable/app.py ```python import dash from dash.dependencies import Input, Output import dash_html_components as html import dash_pivottable from data import data def Header(name, app): img_style = {"float": "right", "height": 40, "margin-right": 10} dash_logo = html.Img(src=app.get_asset_url("dash.png"), style=img_style) ghub_logo = html.Img(src=app.get_asset_url("github.png"), style=img_style) return html.Div( [ html.H1(name, style={"margin": 10, "display": "inline"}), html.A(dash_logo, href="https://plotly.com/dash/"), html.A(ghub_logo, href="https://github.com/plotly/dash-pivottable"), html.A( html.Button( "Enterprise Demo", style={ "float": "right", "margin-right": "10px", "margin-top": "5px", "padding": "5px 10px", "font-size": "15px", }, ), href="https://plotly.com/get-demo/", ), html.Hr(), ] ) app = dash.Dash(__name__) app.title = "Dash Pivottable" server = app.server app.layout = html.Div( [ Header("Dash Pivottable", app), dash_pivottable.PivotTable( id="table", data=data, cols=["Day of Week"], colOrder="key_a_to_z", rows=["Party Size"], rowOrder="key_a_to_z", rendererName="Grouped Column Chart", aggregatorName="Average", vals=["Total Bill"], valueFilter={"Day of Week": {"Thursday": False}}, ), html.Div(id="output"), ] ) @app.callback( Output("output", "children"), [ Input("table", "cols"), Input("table", "rows"), Input("table", "rowOrder"), Input("table", "colOrder"), Input("table", "aggregatorName"), Input("table", "rendererName"), ], ) def display_props(cols, rows, row_order, col_order, aggregator, renderer): return [ html.P(str(cols), id="columns"), html.P(str(rows), id="rows"), html.P(str(row_order), id="row_order"), html.P(str(col_order), id="col_order"), html.P(str(aggregator), id="aggregator"), html.P(str(renderer), id="renderer"), ] if __name__ == "__main__": app.run_server(debug=True) ``` #### File: app/ui/tab_comparison_controls.py ```python import dash_core_components as dcc import dash_html_components as html from config import strings def make_port_comparison_controls( port_1_arr: list, port_1_val: str, port_2_arr: list, port_2_val: str, vessel_types_arr: list, vessel_type_val: str, ) -> html.Div: """ Returns dropdown controls options for the Compare tab. :param port_1_arr: list, possible values for the first port :param port_1_val: str, current value for the first port :param port_2_arr: list, possible values for the second port :param port_2_val: str, current value for the second port :param vessel_types_arr: list, possible values for the vessel types :param vessel_type_val: str, current value for the vessel type :return: HTML div """ return html.Div( className="tab-port-map-controls", children=[ html.Div( className="tab-port-map-single-control-container area-a", children=[ html.Label( className="control-label", children=[strings.LABEL_PORT] ), dcc.Dropdown( id="port-compare-port-1-dpd", clearable=False, options=[{"label": port, "value": port} for port in port_1_arr], value=port_1_val, ), ], ), html.Div(className="tab-port-map-single-control-separator area-b"), html.Div( className="tab-port-map-single-control-container area-c", children=[ html.Label( className="control-label", children=[strings.LABEL_VESSEL] ), dcc.Dropdown( id="port-compare-vessel-type-dpd", clearable=False, options=[ {"label": vessel, "value": vessel} for vessel in vessel_types_arr ], value=vessel_type_val, ), ], ), html.Div(className="tab-port-map-single-control-separator area-d"), html.Div( className="tab-port-map-single-control-container area-e", children=[ html.Label( className="control-label", children=[strings.LABEL_PORT_COMPARE] ), dcc.Dropdown( id="port-compare-port-2-dpd", clearable=False, options=[{"label": port, "value": port} for port in port_2_arr], value=port_2_val, ), ], ), ], ) ``` #### File: app/ui/tab_map_controls.py ```python import dash_core_components as dcc import dash_html_components as html from config import strings def make_tab_port_map_controls( port_arr: list, port_val: str, vessel_types_arr: list, vessel_type_val: str, year_arr: list, year_val: int, month_arr: list, month_val: int, ) -> html.Div: """ Returns a HTML div of user controls found on top of the map tab. :param port_arr: list, all possible ports :param port_val: str, current port value :param vessel_types_arr: list, all possible vessel types :param vessel_type_val: str, current vessel type value :param year_arr: list, all possible years :param year_val: str, current year value :param month_arr: list, all possible months :param month_val: str, current month value :return: HTML div """ return html.Div( className="tab-port-map-controls", children=[ html.Div( className="tab-port-map-single-control-container area-a", children=[ html.Label( className="control-label", children=[strings.LABEL_PORT] ), dcc.Dropdown( id="port-map-dropdown-port", clearable=False, options=[{"label": port, "value": port} for port in port_arr], value=port_val, ), ], ), html.Div(className="tab-port-map-single-control-separator area-b"), html.Div( className="tab-port-map-single-control-container area-c", children=[ html.Label( className="control-label", children=[strings.LABEL_VESSEL] ), dcc.Dropdown( id="port-map-dropdown-vessel-type", clearable=False, options=[ {"label": vessel_type, "value": vessel_type} for vessel_type in vessel_types_arr ], value=vessel_type_val, ), ], ), html.Div(className="tab-port-map-single-control-separator area-d"), html.Div( className="tab-port-map-single-control-container date-grid area-e", children=[ html.Div( className="tab-port-map-single-control-container-date", children=[ html.Label( className="control-label", children=[strings.LABEL_YEAR] ), dcc.Dropdown( id="port-map-dropdown-year", clearable=False, options=[ {"label": year, "value": year} for year in year_arr ], value=year_val, ), ], ), html.Div( className="tab-port-map-single-control-separator smaller-line" ), html.Div( className="tab-port-map-single-control-container-date", children=[ html.Label( className="control-label", children=[strings.LABEL_MONTH], ), dcc.Dropdown( id="port-map-dropdown-month", clearable=False, options=[ {"label": month, "value": month} for month in month_arr ], value=month_val, ), ], ), ], ), ], ) ``` #### File: apps/dash-salesforce-crm/index.py ```python import dash import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output, State from app import sf_manager, app from panels import opportunities, cases, leads server = app.server app.layout = html.Div( [ html.Div( className="row header", children=[ html.Button(id="menu", children=dcc.Markdown("&#8801")), html.Span( className="app-title", children=[ dcc.Markdown("**CRM App**"), html.Span( id="subtitle", children=dcc.Markdown("&nbsp using Salesforce API"), style={"font-size": "1.8rem", "margin-top": "15px"}, ), ], ), html.Img(src=app.get_asset_url("logo.png")), html.A( id="learn_more", children=html.Button("Learn More"), href="https://plot.ly/dash/", ), ], ), html.Div( id="tabs", className="row tabs", children=[ dcc.Link("Opportunities", href="/"), dcc.Link("Leads", href="/"), dcc.Link("Cases", href="/"), ], ), html.Div( id="mobile_tabs", className="row tabs", style={"display": "none"}, children=[ dcc.Link("Opportunities", href="/"), dcc.Link("Leads", href="/"), dcc.Link("Cases", href="/"), ], ), dcc.Store( # opportunities df id="opportunities_df", data=sf_manager.get_opportunities().to_json(orient="split"), ), dcc.Store( # leads df id="leads_df", data=sf_manager.get_leads().to_json(orient="split") ), dcc.Store( id="cases_df", data=sf_manager.get_cases().to_json(orient="split") ), # cases df dcc.Location(id="url", refresh=False), html.Div(id="tab_content"), html.Link( href="https://use.fontawesome.com/releases/v5.2.0/css/all.css", rel="stylesheet", ), html.Link( href="https://fonts.googleapis.com/css?family=Dosis", rel="stylesheet" ), html.Link( href="https://fonts.googleapis.com/css?family=Open+Sans", rel="stylesheet" ), html.Link( href="https://fonts.googleapis.com/css?family=Ubuntu", rel="stylesheet" ), ], className="row", style={"margin": "0%"}, ) # Update the index @app.callback( [ Output("tab_content", "children"), Output("tabs", "children"), Output("mobile_tabs", "children"), ], [Input("url", "pathname")], ) def display_page(pathname): tabs = [ dcc.Link("Opportunities", href="/dash-salesforce-crm/opportunities"), dcc.Link("Leads", href="/dash-salesforce-crm/leads"), dcc.Link("Cases", href="/dash-salesforce-crm/cases"), ] if pathname == "/dash-salesforce-crm/opportunities": tabs[0] = dcc.Link( dcc.Markdown("**&#9632 Opportunities**"), href="/dash-salesforce-crm/opportunities", ) return opportunities.layout, tabs, tabs elif pathname == "/dash-salesforce-crm/cases": tabs[2] = dcc.Link( dcc.Markdown("**&#9632 Cases**"), href="/dash-salesforce-crm/cases" ) return cases.layout, tabs, tabs tabs[1] = dcc.Link( dcc.Markdown("**&#9632 Leads**"), href="/dash-salesforce-crm/leads" ) return leads.layout, tabs, tabs @app.callback( Output("mobile_tabs", "style"), [Input("menu", "n_clicks")], [State("mobile_tabs", "style")], ) def show_menu(n_clicks, tabs_style): if n_clicks: if tabs_style["display"] == "none": tabs_style["display"] = "flex" else: tabs_style["display"] = "none" return tabs_style if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-soccer-analytics/fig_generator.py ```python import plotly.io as pio # This script takes the motion_graph output json file and converts it back into a figure which can be # displayed as an animated graph using the main app.py Dash app def fig_from_json(filename): with open(filename, "r") as f: fig = pio.from_json(f.read()) return fig ``` #### File: apps/dash-spatial-clustering/helpers.py ```python import json import os import shutil import time import pandas as pd import numpy as np import pathlib import geopandas as gpd import pysal as ps from sklearn import cluster from sklearn.preprocessing import scale # Data reading & Processing app_path = pathlib.Path(__file__).parent.resolve() data_path = pathlib.Path(__file__).parent.joinpath("data") geo_json_path = data_path.joinpath("Zipcodes.geojson") austin_listings = pd.read_csv( "https://raw.githubusercontent.com/plotly/datasets/master/dash-sample-apps/dash-spatial-clustering/data/listings.csv", low_memory=False, ) # Refractor zipcode outlier, modify in place zip_outlier = austin_listings[austin_listings["zipcode"] == "TX 78702"].index austin_listings.loc[zip_outlier, "zipcode"] = "78702" austin_listings = austin_listings.dropna(axis=0, subset=["zipcode"]) review_columns = [c for c in austin_listings.columns if "review_" in c] # Geojson loading with open(geo_json_path) as response: zc_link = json.load(response) # Add id for choropleth layer for feature in zc_link["features"]: feature["id"] = feature["properties"]["zipcode"] listing_zipcode = austin_listings["zipcode"].unique() def apply_clustering(): """ # Apply KMeans clustering to group zipcodes into categories based on type of houses listed(i.e. property type) :return: Dataframe. db: scaled proportions of house types by zipcode, use for plotting Choropleth map layer. barh_df : scaled proportion of house type grouped by cluster, use for prop type chart and review chart. """ variables = ["bedrooms", "bathrooms", "beds"] aves = austin_listings.groupby("zipcode")[variables].mean() review_aves = austin_listings.groupby("zipcode")[review_columns].mean() types = pd.get_dummies(austin_listings["property_type"]) prop_types = types.join(austin_listings["zipcode"]).groupby("zipcode").sum() prop_types_pct = (prop_types * 100.0).div(prop_types.sum(axis=1), axis=0) aves_props = aves.join(prop_types_pct) # Standardize a dataset along any axis, Center to the mean and component wise scale to unit variance. db = pd.DataFrame( scale(aves_props), index=aves_props.index, columns=aves_props.columns ).rename(lambda x: str(x)) # Apply clustering on scaled df km5 = cluster.KMeans(n_clusters=5) km5cls = km5.fit(db.reset_index().values) # print(len(km5cls.labels_)) db["cl"] = km5cls.labels_ # sort by labels since every time cluster is running, label 0-4 is randomly assigned db["count"] = db.groupby("cl")["cl"].transform("count") db.sort_values("count", inplace=True, ascending=True) barh_df = prop_types_pct.assign(cl=km5cls.labels_).groupby("cl").mean() # Join avg review columns for updating review plot db = db.join(review_aves) grouped = db.groupby("cl")[review_columns].mean() barh_df = barh_df.join(grouped) return db.reset_index(), barh_df def rating_clustering(threshold): start = time.time() # Explore boundaries/ areas where customers are have similar ratings. Different from # predefined number of output regions, it takes target variable(num of reviews, and # apply a minimum threshold (5% per region) on it. # Bring review columns at zipcode level rt_av = austin_listings.groupby("zipcode")[review_columns].mean().dropna() # Regionalization requires building of spatial weights zc = gpd.read_file(geo_json_path) zrt = zc[["geometry", "zipcode"]].join(rt_av, on="zipcode").dropna() zrt.to_file("tmp") w = ps.queen_from_shapefile("tmp/tmp.shp", idVariable="zipcode") # Remove temp tmp/* we created for spatial weights if os.path.isdir(os.path.join(app_path, "tmp")): print("removing tmp folder") shutil.rmtree(os.path.join(app_path, "tmp")) # Impose that every resulting region has at least 5% of the total number of reviews n_review = ( austin_listings.groupby("zipcode") .sum()["number_of_reviews"] .rename(lambda x: str(int(x))) .reindex(zrt["zipcode"]) ) thr = np.round(int(threshold) / 100 * n_review.sum()) # Set the seed for reproducibility np.random.seed(1234) z = zrt.drop(["geometry", "zipcode"], axis=1).values # Create max-p algorithm, note that this API is upgraded in pysal>1.11.1 maxp = ps.region.Maxp(w, z, thr, n_review.values[:, None], initial=100) maxp.cinference(nperm=99) # p value compared with randomly assigned region p_value = maxp.cpvalue print("p_value:", p_value) lbls = pd.Series(maxp.area2region).reindex(zrt["zipcode"]) regionalization_df = ( pd.DataFrame(lbls).reset_index().rename(columns={"zipcode": "zipcode", 0: "cl"}) ) end = time.time() # The larger threshold, the longer time it takes for computing print( "Computing threshold {}%".format(threshold), "time cost for clustering: ", end - start, ) types = pd.get_dummies(austin_listings["property_type"]) prop_types = types.join(austin_listings["zipcode"]).groupby("zipcode").sum() merged = pd.merge( prop_types.reset_index(), regionalization_df, on="zipcode", how="inner" ) d_merged = merged.drop(["zipcode", "cl"], axis=1) prop_types_pct = (d_merged * 100.0).div(d_merged.sum(axis=1), axis=0) pct_d = ( prop_types_pct.assign(cl=merged["cl"], zipcode=merged["zipcode"]) .groupby("cl") .mean() ) zrt = zrt[review_columns].groupby(lbls.values).mean() joined_prop = pct_d.join(zrt) return regionalization_df, p_value, joined_prop # # # rating = rating_clustering(5) ``` #### File: apps/dash-stitching/registration.py ```python import numpy as np from skimage import io, measure, feature from scipy import ndimage def autocrop(img): """ Remove zero-valued rectangles at the border of the image. Parameters ---------- img: ndarray Image to be cropped """ slices = ndimage.find_objects(img > 0)[0] return img[slices] def _blending_mask(shape): mask = np.zeros(shape, dtype=np.int) mask[1:-1, 1:-1] = 1 return ndimage.distance_transform_cdt(mask) + 1 def register_tiles( imgs, n_rows, n_cols, overlap_global=None, overlap_local=None, pad=None, blending=True, ): """ Stitch together overlapping tiles of a mosaic, using Fourier-based registration to estimate the shifts between neighboring tiles. Parameters ---------- imgs: array of tiles, of shape (n_rows, n_cols, l_r, l_r) with (l_c, l_r) the shape of individual tiles. n_rows: int number of rows of the mosaic. n_cols : int number of columns of the mosaic. overlap_global : float Fraction of overlap between tiles. overlap_local : dictionary Local overlaps between pairs of tiles. overlap_local[(i, j)] is a pair of (x, y) shifts giving the 2D shift vector between tiles i and j. Indices (i, j) are the raveled indices of the tile numbers. pad : int Value of the padding used at the border of the stitched image. An autocrop is performed at the end to remove the unnecessary padding. Notes ----- Fourier-based registration is used in this function (skimage.feature.register_translation). """ if pad is None: pad = 200 l_r, l_c = imgs.shape[2:4] if overlap_global is None: overlap_global = 0.15 overlap_value = int(float(overlap_global) * l_r) imgs = imgs.astype(np.float) if blending: blending_mask = _blending_mask((l_r, l_c)) else: blending_mask = np.ones((l_r, l_c)) if imgs.ndim == 4: canvas = np.zeros( (2 * pad + n_rows * l_r, 2 * pad + n_cols * l_c), dtype=imgs.dtype ) else: canvas = np.zeros( (2 * pad + n_rows * l_r, 2 * pad + n_cols * l_c, 3), dtype=imgs.dtype ) blending_mask = np.dstack((blending_mask,) * 3) weights = np.zeros_like(canvas) init_r, init_c = pad, pad weighted_img = imgs[0, 0] * blending_mask canvas[init_r : init_r + l_r, init_c : init_c + l_c] = weighted_img weights[init_r : init_r + l_r, init_c : init_c + l_c] = blending_mask shifts = np.empty((n_rows, n_cols, 2), dtype=np.int) shifts[0, 0] = init_r, init_c for i_rows in range(n_rows): # Shifts between rows if i_rows >= 1: index_target = np.ravel_multi_index((i_rows, 0), (n_rows, n_cols)) index_orig = index_target - n_cols try: overlap = overlap_local[(index_orig, index_target)] except (KeyError, TypeError): overlap = np.array([overlap_value, 0]) init_r, init_c = shifts[i_rows - 1, 0] init_r += l_r shift_vert = feature.register_translation( imgs[i_rows - 1, 0, -overlap[0] :, : (l_c - overlap[1])], imgs[i_rows, 0, : overlap[0], -(l_c - overlap[1]) :], )[0] init_r += int(shift_vert[0]) - overlap[0] init_c += int(shift_vert[1]) - overlap[1] shifts[i_rows, 0] = init_r, init_c # Fill canvas and weights weighted_img = imgs[i_rows, 0] * blending_mask canvas[init_r : init_r + l_r, init_c : init_c + l_c] += weighted_img weights[init_r : init_r + l_r, init_c : init_c + l_c] += blending_mask # Shifts between columns for j_cols in range(n_cols - 1): index_orig = np.ravel_multi_index((i_rows, j_cols), (n_rows, n_cols)) index_target = index_orig + 1 try: overlap = overlap_local[(index_orig, index_target)] except (KeyError, TypeError): overlap = np.array([0, overlap_value]) init_c += l_c if overlap[0] < 0: print("up") row_start_1 = -(l_r + overlap[0]) row_end_1 = None row_start_2 = None row_end_2 = l_r + overlap[0] else: print("down") row_start_1 = None row_end_1 = l_r - overlap[0] row_start_2 = -(l_r - overlap[0]) row_end_2 = None shift_horiz = feature.register_translation( imgs[i_rows, j_cols, row_start_1:row_end_1, -overlap[1] :], imgs[i_rows, j_cols + 1, row_start_2:row_end_2, : overlap[1]], )[0] init_r += int(shift_horiz[0]) - (overlap[0]) init_c += int(shift_horiz[1]) - overlap[1] shifts[i_rows, j_cols + 1] = init_r, init_c # Fill canvas and weights weighted_img = imgs[i_rows, j_cols + 1] * blending_mask canvas[init_r : init_r + l_r, init_c : init_c + l_c] += weighted_img weights[init_r : init_r + l_r, init_c : init_c + l_c] += blending_mask canvas /= weights + 1.0e-5 return autocrop(np.rint(canvas).astype(np.uint8)) ``` #### File: apps/dash-svm/app.py ```python import time import importlib import dash import dash_core_components as dcc import dash_html_components as html import numpy as np from dash.dependencies import Input, Output, State from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn import datasets from sklearn.svm import SVC import utils.dash_reusable_components as drc import utils.figures as figs app = dash.Dash( __name__, meta_tags=[ {"name": "viewport", "content": "width=device-width, initial-scale=1.0"} ], ) app.title = "Support Vector Machine" server = app.server def generate_data(n_samples, dataset, noise): if dataset == "moons": return datasets.make_moons(n_samples=n_samples, noise=noise, random_state=0) elif dataset == "circles": return datasets.make_circles( n_samples=n_samples, noise=noise, factor=0.5, random_state=1 ) elif dataset == "linear": X, y = datasets.make_classification( n_samples=n_samples, n_features=2, n_redundant=0, n_informative=2, random_state=2, n_clusters_per_class=1, ) rng = np.random.RandomState(2) X += noise * rng.uniform(size=X.shape) linearly_separable = (X, y) return linearly_separable else: raise ValueError( "Data type incorrectly specified. Please choose an existing dataset." ) app.layout = html.Div( children=[ # .container class is fixed, .container.scalable is scalable html.Div( className="banner", children=[ # Change App Name here html.Div( className="container scalable", children=[ # Change App Name here html.H2( id="banner-title", children=[ html.A( "Support Vector Machine (SVM) Explorer", href="https://github.com/plotly/dash-svm", style={ "text-decoration": "none", "color": "inherit", }, ) ], ), html.A( id="banner-logo", children=[ html.Img(src=app.get_asset_url("dash-logo-new.png")) ], href="https://plot.ly/products/dash/", ), ], ) ], ), html.Div( id="body", className="container scalable", children=[ html.Div( id="app-container", # className="row", children=[ html.Div( # className="three columns", id="left-column", children=[ drc.Card( id="first-card", children=[ drc.NamedDropdown( name="Select Dataset", id="dropdown-select-dataset", options=[ {"label": "Moons", "value": "moons"}, { "label": "Linearly Separable", "value": "linear", }, { "label": "Circles", "value": "circles", }, ], clearable=False, searchable=False, value="moons", ), drc.NamedSlider( name="Sample Size", id="slider-dataset-sample-size", min=100, max=500, step=100, marks={ str(i): str(i) for i in [100, 200, 300, 400, 500] }, value=300, ), drc.NamedSlider( name="Noise Level", id="slider-dataset-noise-level", min=0, max=1, marks={ i / 10: str(i / 10) for i in range(0, 11, 2) }, step=0.1, value=0.2, ), ], ), drc.Card( id="button-card", children=[ drc.NamedSlider( name="Threshold", id="slider-threshold", min=0, max=1, value=0.5, step=0.01, ), html.Button( "Reset Threshold", id="button-zero-threshold", ), ], ), drc.Card( id="last-card", children=[ drc.NamedDropdown( name="Kernel", id="dropdown-svm-parameter-kernel", options=[ { "label": "Radial basis function (RBF)", "value": "rbf", }, {"label": "Linear", "value": "linear"}, { "label": "Polynomial", "value": "poly", }, { "label": "Sigmoid", "value": "sigmoid", }, ], value="rbf", clearable=False, searchable=False, ), drc.NamedSlider( name="Cost (C)", id="slider-svm-parameter-C-power", min=-2, max=4, value=0, marks={ i: "{}".format(10 ** i) for i in range(-2, 5) }, ), drc.FormattedSlider( id="slider-svm-parameter-C-coef", min=1, max=9, value=1, ), drc.NamedSlider( name="Degree", id="slider-svm-parameter-degree", min=2, max=10, value=3, step=1, marks={ str(i): str(i) for i in range(2, 11, 2) }, ), drc.NamedSlider( name="Gamma", id="slider-svm-parameter-gamma-power", min=-5, max=0, value=-1, marks={ i: "{}".format(10 ** i) for i in range(-5, 1) }, ), drc.FormattedSlider( id="slider-svm-parameter-gamma-coef", min=1, max=9, value=5, ), html.Div( id="shrinking-container", children=[ html.P(children="Shrinking"), dcc.RadioItems( id="radio-svm-parameter-shrinking", labelStyle={ "margin-right": "7px", "display": "inline-block", }, options=[ { "label": " Enabled", "value": "True", }, { "label": " Disabled", "value": "False", }, ], value="True", ), ], ), ], ), ], ), html.Div( id="div-graphs", children=dcc.Graph( id="graph-sklearn-svm", figure=dict( layout=dict( plot_bgcolor="#282b38", paper_bgcolor="#282b38" ) ), ), ), ], ) ], ), ] ) @app.callback( Output("slider-svm-parameter-gamma-coef", "marks"), [Input("slider-svm-parameter-gamma-power", "value")], ) def update_slider_svm_parameter_gamma_coef(power): scale = 10 ** power return {i: str(round(i * scale, 8)) for i in range(1, 10, 2)} @app.callback( Output("slider-svm-parameter-C-coef", "marks"), [Input("slider-svm-parameter-C-power", "value")], ) def update_slider_svm_parameter_C_coef(power): scale = 10 ** power return {i: str(round(i * scale, 8)) for i in range(1, 10, 2)} @app.callback( Output("slider-threshold", "value"), [Input("button-zero-threshold", "n_clicks")], [State("graph-sklearn-svm", "figure")], ) def reset_threshold_center(n_clicks, figure): if n_clicks: Z = np.array(figure["data"][0]["z"]) value = -Z.min() / (Z.max() - Z.min()) else: value = 0.4959986285375595 return value # Disable Sliders if kernel not in the given list @app.callback( Output("slider-svm-parameter-degree", "disabled"), [Input("dropdown-svm-parameter-kernel", "value")], ) def disable_slider_param_degree(kernel): return kernel != "poly" @app.callback( Output("slider-svm-parameter-gamma-coef", "disabled"), [Input("dropdown-svm-parameter-kernel", "value")], ) def disable_slider_param_gamma_coef(kernel): return kernel not in ["rbf", "poly", "sigmoid"] @app.callback( Output("slider-svm-parameter-gamma-power", "disabled"), [Input("dropdown-svm-parameter-kernel", "value")], ) def disable_slider_param_gamma_power(kernel): return kernel not in ["rbf", "poly", "sigmoid"] @app.callback( Output("div-graphs", "children"), [ Input("dropdown-svm-parameter-kernel", "value"), Input("slider-svm-parameter-degree", "value"), Input("slider-svm-parameter-C-coef", "value"), Input("slider-svm-parameter-C-power", "value"), Input("slider-svm-parameter-gamma-coef", "value"), Input("slider-svm-parameter-gamma-power", "value"), Input("dropdown-select-dataset", "value"), Input("slider-dataset-noise-level", "value"), Input("radio-svm-parameter-shrinking", "value"), Input("slider-threshold", "value"), Input("slider-dataset-sample-size", "value"), ], ) def update_svm_graph( kernel, degree, C_coef, C_power, gamma_coef, gamma_power, dataset, noise, shrinking, threshold, sample_size, ): t_start = time.time() h = 0.3 # step size in the mesh # Data Pre-processing X, y = generate_data(n_samples=sample_size, dataset=dataset, noise=noise) X = StandardScaler().fit_transform(X) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=42 ) x_min = X[:, 0].min() - 0.5 x_max = X[:, 0].max() + 0.5 y_min = X[:, 1].min() - 0.5 y_max = X[:, 1].max() + 0.5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) C = C_coef * 10 ** C_power gamma = gamma_coef * 10 ** gamma_power if shrinking == "True": flag = True else: flag = False # Train SVM clf = SVC(C=C, kernel=kernel, degree=degree, gamma=gamma, shrinking=flag) clf.fit(X_train, y_train) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. if hasattr(clf, "decision_function"): Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) else: Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] prediction_figure = figs.serve_prediction_plot( model=clf, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test, Z=Z, xx=xx, yy=yy, mesh_step=h, threshold=threshold, ) roc_figure = figs.serve_roc_curve(model=clf, X_test=X_test, y_test=y_test) confusion_figure = figs.serve_pie_confusion_matrix( model=clf, X_test=X_test, y_test=y_test, Z=Z, threshold=threshold ) return [ html.Div( id="svm-graph-container", children=dcc.Loading( className="graph-wrapper", children=dcc.Graph(id="graph-sklearn-svm", figure=prediction_figure), style={"display": "none"}, ), ), html.Div( id="graphs-container", children=[ dcc.Loading( className="graph-wrapper", children=dcc.Graph(id="graph-line-roc-curve", figure=roc_figure), ), dcc.Loading( className="graph-wrapper", children=dcc.Graph( id="graph-pie-confusion-matrix", figure=confusion_figure ), ), ], ), ] # Running the server if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-synapse-demo/utils.py ```python import dash import dash_html_components as html import dash_core_components as dcc import dash_bootstrap_components as dbc def OptionMenu(values, label, **kwargs): options = [{"label": s.replace("_", " ").capitalize(), "value": s} for s in values] kwargs["value"] = kwargs.get("value", values[0]) if len(options) <= 4: component = dbc.RadioItems kwargs["inline"] = True else: component = dbc.Select return dbc.FormGroup([dbc.Label(label), component(options=options, **kwargs)]) def CustomRangeSlider(values, label, **kwargs): values = sorted(values) marks = {i: f"{i//1000}k" for i in values} return dbc.FormGroup( [ dbc.Label(label), dcc.RangeSlider( min=values[0], max=values[-1], step=1000, value=[values[1], values[-2]], marks=marks, **kwargs, ), ] ) def get_unique(connection, db, table, col): query = f""" SELECT DISTINCT {col} FROM {db}.dbo.{table}; """ return [x[0] for x in connection.execute(query).fetchall()] def get_range(connection, db, table, col): query = f""" SELECT MIN({col}), MAX({col}) FROM {db}.dbo.{table}; """ return connection.execute(query).fetchall()[0] def get_column_strings(df): # Load the actual csv file # Create SQL columns based on the columns of that dataframe types = ( df.dtypes.copy() .replace("float64", "FLOAT") .replace("int64", "INT") .replace("object", "VARCHAR(100) COLLATE Latin1_General_BIN2") ) ls = [f"{ix.lower()} {t}" for ix, t in zip(types.index, types.values)] return ",\n".join(ls) ``` #### File: apps/dash-video-detection/app.py ```python import time from base64 import b64encode from pprint import pprint import cv2 import dash import dash_player import dash_table import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output, State import pandas as pd from PIL import ImageColor, Image import plotly.express as px import tensorflow as tf import tensorflow_hub as hub def Header(name, app): title = html.H2(name, style={"margin-top": 5}) logo = html.Img( src=app.get_asset_url("dash-logo.png"), style={"float": "right", "height": 50} ) link = html.A(logo, href="https://plotly.com/dash/") return dbc.Row([dbc.Col(title, md=8), dbc.Col(link, md=4)]) def add_editable_box( fig, x0, y0, x1, y1, name=None, color=None, opacity=1, group=None, text=None ): fig.add_shape( editable=True, x0=x0, y0=y0, x1=x1, y1=y1, line_color=color, opacity=opacity, line_width=3, name=name, ) # Load colors and detector colors = list(ImageColor.colormap.values()) module_handle = "https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1" detector = hub.load(module_handle).signatures["default"] # Start app app = dash.Dash(__name__, external_stylesheets=[dbc.themes.UNITED]) server = app.server controls = [ dbc.Select( id="scene", options=[{"label": f"Scene #{i}", "value": i} for i in range(1, 4)], value=1, ), dbc.Button( "Detect Frame", id="detect-frame", n_clicks=0, color="primary", block=True ), html.A( dbc.Button("Download", n_clicks=0, color="info", outline=True, block=True), download="annotations.csv", id="download", ), ] video = dbc.Card( [ dbc.CardBody( dash_player.DashPlayer( id="video", width="100%", height="auto", controls=True ) ) ] ) graph_detection = dbc.Card( [ dbc.CardBody( dcc.Graph( id="graph-detection", config={"modeBarButtonsToAdd": ["drawrect"]}, style={"height": "calc(50vh - 100px)"}, ) ) ] ) record_table = dbc.Card( dash_table.DataTable( id="record-table", editable=True, columns=[ {"name": i, "id": i} for i in [ "scene", "time", "order", "object", "xmin", "xmax", "ymin", "ymax", ] ], data=[], page_size=10, ), body=True, ) app.layout = dbc.Container( [ Header("Dash AV Video Detection", app), html.Hr(), dbc.Row( [ dbc.Col( [ video, html.Br(), dbc.Card(dbc.Row([dbc.Col(c) for c in controls]), body=True), ], md=7, ), dbc.Col([graph_detection, html.Br(), record_table], md=5), ] ), dcc.Store(id="store-figure"), # dcc.Location(id='url'), ], fluid=True, ) @app.callback(Output("video", "url"), [Input("scene", "value")]) def update_scene(i): return app.get_asset_url(f"scene_{i}.mov") @app.callback(Output("download", "href"), [Input("record-table", "data")]) def update_download_href(data): df = pd.DataFrame.from_records(data) df_b64 = b64encode(df.to_csv(index=False).encode()) return "data:text/csv;base64," + df_b64.decode() @app.callback( Output("record-table", "data"), [Input("graph-detection", "relayoutData")], [ State("graph-detection", "figure"), State("record-table", "data"), State("video", "currentTime"), State("scene", "value"), ], ) def update_table(relayout_data, figure, table_data, curr_time, scene): if relayout_data is None or figure is None: return dash.no_update keys = list(relayout_data.keys()) shapes = figure["layout"]["shapes"] if len(keys) == 0: return dash.no_update elif "shapes" in keys: shapes = relayout_data["shapes"] i = len(shapes) - 1 elif "shapes[" in keys[0]: i = int(keys[0].replace("shapes[", "").split("].")[0]) else: return dash.no_update if i >= len(shapes): return dash.no_update filtered_table_data = [ row for row in table_data if not ( row["order"] == i and row["time"] == round(curr_time, 6) and row["scene"] == scene ) ] new_shape = shapes[i] new = { "time": round(curr_time, 6), "scene": scene, "object": new_shape.get("name", "New"), "order": i, "xmin": round(new_shape["x0"], 1), "xmax": round(new_shape["x1"], 1), "ymin": round(new_shape["y0"], 1), "ymax": round(new_shape["y1"], 1), } filtered_table_data.append(new) return filtered_table_data @app.callback( Output("graph-detection", "figure"), [Input("store-figure", "data"), Input("graph-detection", "relayoutData")], ) def store_to_graph(data, relayout_data): ctx = dash.callback_context if not ctx.triggered: return dash.no_update prop_id = ctx.triggered[0]["prop_id"] if prop_id == "store-figure.data": return data if "shapes" in relayout_data: data["layout"]["shapes"] = relayout_data.get("shapes") return data else: return dash.no_update @app.callback( Output("store-figure", "data"), [Input("detect-frame", "n_clicks")], [State("scene", "value"), State("video", "currentTime")], ) def show_time(n_clicks, scene, ms): if ms is None or scene is None: return dash.no_update t0 = time.time() cap = cv2.VideoCapture(f"./data/scene-{scene}.mov") cap.read() cap.set(cv2.CAP_PROP_POS_MSEC, 1000 * ms) ret, frame = cap.read() img = cv2.rotate(frame, cv2.ROTATE_90_COUNTERCLOCKWISE) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) resized = cv2.resize(img, (512, 512)) image_tensor = tf.image.convert_image_dtype(resized, tf.float32)[tf.newaxis, ...] result = detector(image_tensor) boxes = result["detection_boxes"].numpy() scores = result["detection_scores"].numpy() labels = result["detection_class_entities"].numpy() class_ids = result["detection_class_labels"].numpy() # Start build figure im = Image.fromarray(img) fig = px.imshow(im, binary_format="jpg") fig.update_xaxes(visible=False) fig.update_yaxes(visible=False) fig.update_layout( showlegend=False, margin=dict(l=0, r=0, t=0, b=0), uirevision=n_clicks ) for i in range(min(10, boxes.shape[0])): class_id = scores[i].argmax() label = labels[i].decode("ascii") confidence = scores[i].max() # ymin, xmin, ymax, xmax y0, x0, y1, x1 = boxes[i] x0 *= im.size[0] x1 *= im.size[0] y0 *= im.size[1] y1 *= im.size[1] color = colors[class_ids[i] % len(colors)] text = f"{label}: {int(confidence*100)}%" if confidence > 0.1: add_editable_box( fig, x0, y0, x1, y1, group=label, name=label, color=color, text=text ) print(f"Detected in {time.time() - t0:.2f}s.") return fig if __name__ == "__main__": app.run_server(debug=True) ``` #### File: apps/dash-video-detection/generate_video.py ```python import cv2 import plotly.express as px import os from tqdm import tqdm import tensorflow_hub as hub import tensorflow as tf from PIL import Image # For drawing onto the image. import numpy as np from PIL import Image from PIL import ImageColor from PIL import ImageDraw from PIL import ImageFont from PIL import ImageOps def draw_bounding_box_on_image( image, ymin, xmin, ymax, xmax, color, font, thickness=4, display_str_list=() ): """Adds a bounding box to an image.""" draw = ImageDraw.Draw(image) im_width, im_height = image.size (left, right, top, bottom) = ( xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height, ) draw.line( [(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=thickness, fill=color, ) # If the total height of the display strings added to the top of the bounding # box exceeds the top of the image, stack the strings below the bounding box # instead of above. display_str_heights = [font.getsize(ds)[1] for ds in display_str_list] # Each display_str has a top and bottom margin of 0.05x. total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights) if top > total_display_str_height: text_bottom = top else: text_bottom = top + total_display_str_height # Reverse list and print from bottom to top. for display_str in display_str_list[::-1]: text_width, text_height = font.getsize(display_str) margin = np.ceil(0.05 * text_height) draw.rectangle( [ (left, text_bottom - text_height - 2 * margin), (left + text_width, text_bottom), ], fill=color, ) draw.text( (left + margin, text_bottom - text_height - margin), display_str, fill="black", font=font, ) text_bottom -= text_height - 2 * margin def draw_boxes( image, boxes, class_ids, class_names, scores, font, max_boxes=10, min_score=0.1 ): """Overlay labeled boxes on an image with formatted scores and label names.""" colors = list(ImageColor.colormap.values()) for i in range(min(boxes.shape[0], max_boxes)): if scores[i] >= min_score: ymin, xmin, ymax, xmax = tuple(boxes[i]) display_str = "{}: {}%".format( class_names[i].decode("ascii"), int(100 * scores[i]) ) color = colors[class_ids[i] % len(colors)] image_pil = Image.fromarray(np.uint8(image)).convert("RGB") draw_bounding_box_on_image( image_pil, ymin, xmin, ymax, xmax, color, font, display_str_list=[display_str], ) np.copyto(image, np.array(image_pil)) return np.array(image_pil) def fast_draw_boxes( image, boxes, class_ids, class_names, scores, font, max_boxes=10, min_score=0.1 ): """Overlay labeled boxes on an image with formatted scores and label names.""" colors = list(ImageColor.colormap.values()) image_pil = Image.fromarray(np.uint8(image)).convert("RGB") for i in range(min(boxes.shape[0], max_boxes)): if scores[i] >= min_score: ymin, xmin, ymax, xmax = tuple(boxes[i]) display_str = "{}: {}%".format( class_names[i].decode("ascii"), int(100 * scores[i]) ) color = colors[class_ids[i] % len(colors)] draw_bounding_box_on_image( image_pil, ymin, xmin, ymax, xmax, color, font, display_str_list=[display_str], ) # np.copyto(image, np.array(image_pil)) return np.array(image_pil) module_handle = "https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1" detector = hub.load(module_handle).signatures["default"] codec = "XVID" try: font = ImageFont.truetype( "/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf", 25 ) except IOError: print("Font not found, using default font.") font = ImageFont.load_default() i = 2 # Define the codec and create VideoWriter object VIDEO_PATH = f"./data/scene-{i}.mov" VIDEO_OUT = f"./data/processed/scene_{i}.mov" frames = [] cap = cv2.VideoCapture(VIDEO_PATH) ret = True while ret: ret, frame = cap.read() if ret: frame = cv2.rotate(frame, cv2.ROTATE_90_COUNTERCLOCKWISE) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(frame) processed_frames = [] sample_rate = 3 for i, img in enumerate(tqdm(frames)): if i % sample_rate == 0: resized = cv2.resize(img, (512, 512)) image_tensor = tf.image.convert_image_dtype(resized, tf.float32)[ tf.newaxis, ... ] result = detector(image_tensor) image_with_boxes = fast_draw_boxes( img.copy(), result["detection_boxes"].numpy(), result["detection_class_labels"].numpy(), result["detection_class_entities"].numpy(), result["detection_scores"].numpy(), font=font, ) processed_frames.append(image_with_boxes) fourcc = cv2.VideoWriter_fourcc(*codec) out = cv2.VideoWriter(VIDEO_OUT, fourcc, 30, (1280, 720)) for frame in tqdm(processed_frames): frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) out.write(frame) out.release() ```
{ "source": "jeroenvermeulen/netbox-dns", "score": 2 }
#### File: netbox-dns/netbox_dns/filters.py ```python import django_filters from django.db.models import Q from extras.filters import TagFilter from netbox.filtersets import PrimaryModelFilterSet from .models import NameServer, Record, Zone class ZoneFilter(PrimaryModelFilterSet): """Filter capabilities for Zone instances.""" q = django_filters.CharFilter( method="search", label="Search", ) name = django_filters.CharFilter( lookup_expr="icontains", ) status = django_filters.ChoiceFilter( choices=Zone.CHOICES, ) tag = TagFilter() class Meta: model = Zone fields = ("name", "status", "nameservers", "tag") def search(self, queryset, name, value): """Perform the filtered search.""" if not value.strip(): return queryset qs_filter = Q(name__icontains=value) | Q(status__icontains=value) return queryset.filter(qs_filter) class NameServerFilter(PrimaryModelFilterSet): """Filter capabilities for NameServer instances.""" name = django_filters.CharFilter( lookup_expr="icontains", ) tag = TagFilter() class Meta: model = NameServer fields = ("name", "tag") class RecordFilter(PrimaryModelFilterSet): """Filter capabilities for Record instances.""" q = django_filters.CharFilter( method="search", label="Search", ) type = django_filters.MultipleChoiceFilter( choices=Record.CHOICES, null_value=None, ) name = django_filters.CharFilter( lookup_expr="icontains", ) value = django_filters.CharFilter( lookup_expr="icontains", ) zone_id = django_filters.ModelMultipleChoiceFilter( queryset=Zone.objects.all(), label="Parent Zone ID", ) zone = django_filters.ModelMultipleChoiceFilter( field_name="zone__name", to_field_name="name", queryset=Zone.objects.all(), label="Parent Zone", ) tag = TagFilter() managed = django_filters.BooleanFilter() class Meta: model = Record fields = ("type", "name", "value", "tag", "managed") def search(self, queryset, name, value): """Perform the filtered search.""" if not value.strip(): return queryset qs_filter = ( Q(name__icontains=value) | Q(value__icontains=value) | Q(zone__name__icontains=value) ) return queryset.filter(qs_filter) ``` #### File: netbox-dns/netbox_dns/forms.py ```python from django import forms from django.conf import settings from django.core.exceptions import ValidationError from django.core.validators import ( MinValueValidator, MaxValueValidator, validate_ipv6_address, validate_ipv4_address, ) from django.forms import ( CharField, IntegerField, BooleanField, NullBooleanField, ) from django.urls import reverse_lazy from extras.forms import AddRemoveTagsForm from extras.models.tags import Tag from utilities.forms import ( CSVModelForm, BootstrapMixin, BulkEditNullBooleanSelect, DynamicModelMultipleChoiceField, TagFilterField, StaticSelect, CSVChoiceField, CSVModelChoiceField, DynamicModelChoiceField, APISelect, StaticSelectMultiple, add_blank_choice, ) from .fields import CustomDynamicModelMultipleChoiceField from .models import NameServer, Record, Zone class BulkEditForm(forms.Form): """Base form for editing multiple objects in bulk.""" def __init__(self, model, *args, **kwargs): super().__init__(*args, **kwargs) self.model = model self.nullable_fields = [] if hasattr(self.Meta, "nullable_fields"): self.nullable_fields = self.Meta.nullable_fields class ZoneForm(BootstrapMixin, forms.ModelForm): """Form for creating a new Zone object.""" def __init__(self, *args, **kwargs): """Override the __init__ method in order to provide the initial value for the default fields""" super().__init__(*args, **kwargs) defaults = settings.PLUGINS_CONFIG.get("netbox_dns") def _initialize(initial, setting): if initial.get(setting, None) in (None, ""): initial[setting] = defaults.get(f"zone_{setting}", None) for setting in ( "default_ttl", "soa_ttl", "soa_rname", "soa_serial_auto", "soa_refresh", "soa_retry", "soa_expire", "soa_minimum", ): _initialize(self.initial, setting) if self.initial.get("soa_ttl", None) is None: self.initial["soa_ttl"] = self.initial.get("default_ttl", None) if self.initial.get("soa_serial_auto"): self.initial["soa_serial"] = None if self.initial.get("soa_mname", None) in (None, ""): default_soa_mname = defaults.get("zone_soa_mname", None) if default_soa_mname is not None: try: self.initial["soa_mname"] = NameServer.objects.get( name=default_soa_mname ) except NameServer.DoesNotExist: pass if not self.initial.get("nameservers", []): default_nameservers = defaults.get("zone_nameservers", []) if default_nameservers: self.initial["nameservers"] = NameServer.objects.filter( name__in=default_nameservers ) def clean_default_ttl(self): return ( self.cleaned_data["default_ttl"] if self.cleaned_data["default_ttl"] else self.initial["default_ttl"] ) nameservers = CustomDynamicModelMultipleChoiceField( queryset=NameServer.objects.all(), required=False, ) tags = DynamicModelMultipleChoiceField( queryset=Tag.objects.all(), required=False, ) default_ttl = IntegerField( required=False, label="Default TTL", help_text="Default TTL for new records in this zone", validators=[MinValueValidator(1)], ) soa_ttl = IntegerField( required=True, label="SOA TTL", help_text="TTL for the SOA record of the zone", validators=[MinValueValidator(1)], ) soa_rname = CharField( required=True, label="SOA Responsible", help_text="Mailbox of the zone's administrator", ) soa_serial_auto = BooleanField( required=False, label="Generate SOA Serial", help_text="Automatically generate the SOA Serial", ) soa_serial = IntegerField( required=False, label="SOA Serial", help_text="Serial number of the current zone data version", validators=[MinValueValidator(1)], ) soa_refresh = IntegerField( required=True, label="SOA Refresh", help_text="Refresh interval for secondary name servers", validators=[MinValueValidator(1)], ) soa_retry = IntegerField( required=True, label="SOA Retry", help_text="Retry interval for secondary name servers", validators=[MinValueValidator(1)], ) soa_expire = IntegerField( required=True, label="SOA Expire", help_text="Expire time after which the zone is considered unavailable", validators=[MinValueValidator(1)], ) soa_minimum = IntegerField( required=True, label="SOA Minimum TTL", help_text="Minimum TTL for negative results, e.g. NXRRSET", validators=[MinValueValidator(1)], ) class Meta: model = Zone fields = ( "name", "status", "nameservers", "default_ttl", "tags", "soa_ttl", "soa_mname", "soa_rname", "soa_serial_auto", "soa_serial", "soa_refresh", "soa_retry", "soa_expire", "soa_minimum", ) widgets = { "status": StaticSelect(), "soa_mname": StaticSelect(), } help_texts = { "soa_mname": "Primary name server for the zone", } class ZoneFilterForm(BootstrapMixin, forms.Form): """Form for filtering Zone instances.""" model = Zone q = CharField( required=False, widget=forms.TextInput(attrs={"placeholder": "Name or Status"}), label="Search", ) status = forms.ChoiceField( choices=add_blank_choice(Zone.CHOICES), required=False, widget=StaticSelect(), ) name = CharField( required=False, label="Name", ) nameservers = CustomDynamicModelMultipleChoiceField( queryset=NameServer.objects.all(), required=False, ) tag = TagFilterField(Zone) class ZoneCSVForm(CSVModelForm, BootstrapMixin, forms.ModelForm): status = CSVChoiceField( choices=Zone.CHOICES, help_text="Zone status", ) default_ttl = IntegerField( required=False, help_text="Default TTL", ) soa_ttl = IntegerField( required=False, help_text="TTL for the SOA record of the zone", ) soa_mname = CSVModelChoiceField( queryset=NameServer.objects.all(), required=False, to_field_name="name", help_text="Primary name server for the zone", error_messages={ "invalid_choice": "Nameserver not found.", }, ) soa_rname = CharField( required=False, help_text="Mailbox of the zone's administrator", ) soa_serial_auto = BooleanField( required=False, help_text="Generate the SOA serial", ) soa_serial = IntegerField( required=False, help_text="Serial number of the current zone data version", ) soa_refresh = IntegerField( required=False, help_text="Refresh interval for secondary name servers", ) soa_retry = IntegerField( required=False, help_text="Retry interval for secondary name servers", ) soa_expire = IntegerField( required=False, help_text="Expire time after which the zone is considered unavailable", ) soa_minimum = IntegerField( required=False, help_text="Minimum TTL for negative results, e.g. NXRRSET", ) def _get_default_value(self, field): _default_values = settings.PLUGINS_CONFIG.get("netbox_dns", dict()) if _default_values.get("zone_soa_ttl", None) is None: _default_values["zone_soa_ttl"] = _default_values.get( "zone_default_ttl", None ) return _default_values.get(f"zone_{field}", None) def _clean_field_with_defaults(self, field): if self.cleaned_data[field]: value = self.cleaned_data[field] else: value = self._get_default_value(field) if value is None: raise ValidationError(f"{field} not set and no default value available") return value def clean_default_ttl(self): return self._clean_field_with_defaults("default_ttl") def clean_soa_ttl(self): return self._clean_field_with_defaults("soa_ttl") def clean_soa_mname(self): return self._clean_field_with_defaults("soa_mname") def clean_soa_rname(self): return self._clean_field_with_defaults("soa_rname") def clean_soa_serial_auto(self): try: return self._clean_field_with_defaults("soa_serial_auto") except ValidationError: if self.cleaned_data["soa_serial"] or self._get_default_value("soa_serial"): return None else: raise def clean_soa_serial(self): try: return self._clean_field_with_defaults("soa_serial") except ValidationError: if self.cleaned_data["soa_serial_auto"] or self._get_default_value( "soa_serial_auto" ): return None else: raise def clean_soa_refresh(self): return self._clean_field_with_defaults("soa_refresh") def clean_soa_retry(self): return self._clean_field_with_defaults("soa_retry") def clean_soa_expire(self): return self._clean_field_with_defaults("soa_expire") def clean_soa_minimum(self): return self._clean_field_with_defaults("soa_minimum") class Meta: model = Zone fields = ( "name", "status", "default_ttl", "soa_ttl", "soa_mname", "soa_rname", "soa_serial_auto", "soa_serial", "soa_refresh", "soa_retry", "soa_expire", "soa_minimum", ) class ZoneBulkEditForm(BootstrapMixin, AddRemoveTagsForm, BulkEditForm): pk = forms.ModelMultipleChoiceField( queryset=Zone.objects.all(), widget=forms.MultipleHiddenInput(), ) status = forms.ChoiceField( choices=add_blank_choice(Zone.CHOICES), required=False, widget=StaticSelect(), ) nameservers = CustomDynamicModelMultipleChoiceField( queryset=NameServer.objects.all(), required=False, ) default_ttl = IntegerField( required=False, label="Default TTL", validators=[MinValueValidator(1)], ) soa_ttl = IntegerField( required=False, label="SOA TTL", validators=[MinValueValidator(1)], ) soa_mname = DynamicModelChoiceField( queryset=NameServer.objects.all(), required=False, label="SOA Primary Nameserver", widget=APISelect( attrs={ "data-url": reverse_lazy("plugins-api:netbox_dns-api:nameserver-list") } ), ) soa_rname = CharField( required=False, label="SOA Responsible", ) soa_serial_auto = NullBooleanField( required=False, widget=BulkEditNullBooleanSelect(), label="Generate SOA Serial", ) soa_serial = IntegerField( required=False, label="SOA Serial", validators=[MinValueValidator(1), MaxValueValidator(4294967295)], ) soa_refresh = IntegerField( required=False, label="SOA Refresh", validators=[MinValueValidator(1)], ) soa_retry = IntegerField( required=False, label="SOA Retry", validators=[MinValueValidator(1)], ) soa_expire = IntegerField( required=False, label="SOA Expire", validators=[MinValueValidator(1)], ) soa_minimum = IntegerField( required=False, label="SOA Minimum TTL", validators=[MinValueValidator(1)], ) def clean(self): """ If soa_serial_auto is True, set soa_serial to None. """ cleaned_data = super().clean() if cleaned_data.get("soa_serial_auto"): cleaned_data["soa_serial"] = None class Meta: nullable_fields = [] model = Zone fields = ( "name", "status", "nameservers", "default_ttl", "tags", "soa_ttl", "soa_rname", "soa_serial_auto", "soa_serial", "soa_refresh", "soa_retry", "soa_expire", "soa_minimum", ) widgets = { "status": StaticSelect(), } class NameServerForm(BootstrapMixin, forms.ModelForm): """Form for creating a new NameServer object.""" tags = DynamicModelMultipleChoiceField( queryset=Tag.objects.all(), required=False, ) class Meta: model = NameServer fields = ("name", "tags") class NameServerFilterForm(BootstrapMixin, forms.Form): """Form for filtering NameServer instances.""" model = NameServer name = CharField( required=False, label="Name", ) tag = TagFilterField(NameServer) class NameServerCSVForm(CSVModelForm, BootstrapMixin, forms.ModelForm): class Meta: model = NameServer fields = ("name",) class NameServerBulkEditForm(BootstrapMixin, AddRemoveTagsForm, BulkEditForm): pk = forms.ModelMultipleChoiceField( queryset=NameServer.objects.all(), widget=forms.MultipleHiddenInput(), ) class Meta: nullable_fields = [] model = NameServer fields = ("name", "tags") class RecordForm(BootstrapMixin, forms.ModelForm): """Form for creating a new Record object.""" def clean(self): """ For A and AAA records, verify that a valid IPv4 or IPv6 was passed as value and raise a ValidationError exception otherwise. """ cleaned_data = super().clean() type = cleaned_data.get("type") if type not in (Record.A, Record.AAAA): return value = cleaned_data.get("value") try: ip_version = "4" if type == Record.A else "6" if type == Record.A: validate_ipv4_address(value) else: validate_ipv6_address(value) except ValidationError: raise forms.ValidationError( { "value": f"A valid IPv{ip_version} address is required for record type {type}." } ) if cleaned_data.get("disable_ptr"): return pk = cleaned_data.get("pk") conflicts = Record.objects.filter(value=value, type=type, disable_ptr=False) if self.instance.pk: conflicts = conflicts.exclude(pk=self.instance.pk) if len(conflicts): raise forms.ValidationError( { "value": f"There is already an {type} record with value {value} and PTR enabled." } ) def clean_ttl(self): ttl = self.cleaned_data["ttl"] if ttl is not None: if ttl <= 0: raise ValidationError("TTL must be greater than zero") return ttl else: return self.cleaned_data["zone"].default_ttl disable_ptr = BooleanField( label="Disable PTR", required=False, ) tags = DynamicModelMultipleChoiceField( queryset=Tag.objects.all(), required=False, ) ttl = IntegerField( required=False, label="TTL", ) class Meta: model = Record fields = ("zone", "type", "disable_ptr", "name", "value", "ttl", "tags") widgets = { "zone": StaticSelect(), "type": StaticSelect(), } class RecordFilterForm(BootstrapMixin, forms.Form): """Form for filtering Record instances.""" model = Record q = CharField( required=False, widget=forms.TextInput(attrs={"placeholder": "Name, Zone or Value"}), label="Search", ) type = forms.MultipleChoiceField( choices=add_blank_choice(Record.CHOICES), required=False, widget=StaticSelectMultiple(), ) name = CharField( required=False, label="Name", ) value = CharField( required=False, label="Value", ) zone_id = CustomDynamicModelMultipleChoiceField( queryset=Zone.objects.all(), required=False, label="Zone", ) tag = TagFilterField(Record) class RecordCSVForm(CSVModelForm, BootstrapMixin, forms.ModelForm): zone = CSVModelChoiceField( queryset=Zone.objects.all(), to_field_name="name", required=True, help_text="Assigned zone", ) type = CSVChoiceField( choices=Record.CHOICES, required=True, help_text="Record Type", ) ttl = IntegerField( required=False, help_text="TTL", ) disable_ptr = forms.BooleanField( required=False, label="Disable PTR", help_text="Disable generation of a PTR record", ) def clean(self): """ For A and AAA records, verify that a valid IPv4 or IPv6 was passed as value and raise a ValidationError exception otherwise. """ cleaned_data = super().clean() type = cleaned_data.get("type") if type not in (Record.A, Record.AAAA): return value = cleaned_data.get("value") try: ip_version = "4" if type == Record.A else "6" if type == Record.A: validate_ipv4_address(value) else: validate_ipv6_address(value) except ValidationError: raise forms.ValidationError( { "value": f"A valid IPv{ip_version} address is required for record type {type}." } ) if cleaned_data.get("disable_ptr"): return conflicts = Record.objects.filter(value=value, type=type, disable_ptr=False) if len(conflicts): raise forms.ValidationError( { "value": f"There is already an {type} record with value {value} and PTR enabled." } ) def clean_ttl(self): ttl = self.cleaned_data["ttl"] if ttl is not None: if ttl <= 0: raise ValidationError("TTL must be greater than zero") return ttl elif "zone" in self.cleaned_data: return self.cleaned_data["zone"].default_ttl class Meta: model = Record fields = ("zone", "type", "name", "value", "ttl", "disable_ptr") class RecordBulkEditForm(BootstrapMixin, AddRemoveTagsForm, BulkEditForm): pk = forms.ModelMultipleChoiceField( queryset=Record.objects.all(), widget=forms.MultipleHiddenInput() ) zone = DynamicModelChoiceField( queryset=Zone.objects.all(), required=False, widget=APISelect( attrs={"data-url": reverse_lazy("plugins-api:netbox_dns-api:zone-list")} ), ) disable_ptr = NullBooleanField( required=False, widget=BulkEditNullBooleanSelect(), label="Disable PTR" ) ttl = IntegerField( required=False, label="TTL", ) def clean(self): """ For A and AAA records, verify that a valid IPv4 or IPv6 was passed as value and raise a ValidationError exception otherwise. """ cleaned_data = super().clean() disable_ptr = cleaned_data.get("disable_ptr") if disable_ptr is None or disable_ptr: return for record in cleaned_data.get("pk"): conflicts = ( Record.objects.filter(Record.unique_ptr_qs) .filter(value=record.value) .exclude(pk=record.pk) ) if len(conflicts): raise forms.ValidationError( { "disable_ptr": f"Multiple {record.type} records with value {record.value} and PTR enabled." } ) def clean_ttl(self): ttl = self.cleaned_data["ttl"] if ttl is not None: if ttl <= 0: raise ValidationError("TTL must be greater than zero") return ttl class Meta: model = Record fields = ("zone", "ttl", "disable_ptr", "tags") nullable_fields = [] widgets = { "zone": StaticSelect(), } ``` #### File: netbox_dns/migrations/0008_zone_status_names.py ```python from django.db import migrations from netbox_dns.models import Zone, Record def rename_passive_status_to_parked(apps, schema_editor): Zone = apps.get_model("netbox_dns", "Zone") for zone in Zone.objects.filter(status="passive"): zone.update(status=Zone.STATUS_PARKED) class Migration(migrations.Migration): dependencies = [ ("netbox_dns", "0005_update_ns_records"), ] operations = [ migrations.RunPython(rename_passive_status_to_parked), ] ``` #### File: netbox-dns/netbox_dns/models.py ```python import ipaddress from math import ceil from datetime import datetime from django.core.validators import MinValueValidator, MaxValueValidator from django.core.exceptions import ObjectDoesNotExist from django.db import models, transaction from django.db.models import Q, Max, ExpressionWrapper, BooleanField from django.db.models.functions import Length from django.urls import reverse from django.db.models.signals import m2m_changed from django.dispatch import receiver from extras.utils import extras_features from netbox.models import PrimaryModel, TaggableManager from utilities.querysets import RestrictedQuerySet @extras_features("custom_links", "export_templates", "webhooks") class NameServer(PrimaryModel): name = models.CharField( unique=True, max_length=255, ) tags = TaggableManager( through="extras.TaggedItem", blank=True, ) objects = RestrictedQuerySet.as_manager() clone_fields = ["name"] class Meta: ordering = ("name",) def __str__(self): return self.name def get_absolute_url(self): return reverse("plugins:netbox_dns:nameserver", kwargs={"pk": self.pk}) class ZoneManager(models.Manager.from_queryset(RestrictedQuerySet)): """Special Manager for zones providing the activity status annotation""" def get_queryset(self): return ( super(ZoneManager, self) .get_queryset() .annotate( active=ExpressionWrapper( Q(status__in=Zone.ACTIVE_STATUS_LIST), output_field=BooleanField() ) ) ) @extras_features("custom_links", "export_templates", "webhooks") class Zone(PrimaryModel): STATUS_ACTIVE = "active" STATUS_RESERVED = "reserved" STATUS_DEPRECATED = "deprecated" STATUS_PARKED = "parked" CHOICES = ( (STATUS_ACTIVE, "Active"), (STATUS_RESERVED, "Reserved"), (STATUS_DEPRECATED, "Deprecated"), (STATUS_PARKED, "Parked"), ) CSS_CLASSES = { STATUS_ACTIVE: "primary", STATUS_RESERVED: "info", STATUS_DEPRECATED: "danger", STATUS_PARKED: "warning", } ACTIVE_STATUS_LIST = (STATUS_ACTIVE,) name = models.CharField( unique=True, max_length=255, ) status = models.CharField( max_length=50, choices=CHOICES, default=STATUS_ACTIVE, blank=True, ) nameservers = models.ManyToManyField( NameServer, related_name="zones", blank=True, ) tags = TaggableManager( through="extras.TaggedItem", blank=True, ) default_ttl = models.PositiveIntegerField( blank=True, verbose_name="Default TTL", validators=[MinValueValidator(1)], ) soa_ttl = models.PositiveIntegerField( blank=False, null=False, verbose_name="SOA TTL", validators=[MinValueValidator(1)], ) soa_mname = models.ForeignKey( NameServer, related_name="zones_soa", verbose_name="SOA MName", on_delete=models.PROTECT, blank=False, null=False, ) soa_rname = models.CharField( max_length=255, blank=False, null=False, verbose_name="SOA RName", ) soa_serial = models.BigIntegerField( blank=True, null=True, verbose_name="SOA Serial", validators=[MinValueValidator(1), MaxValueValidator(4294967295)], ) soa_refresh = models.PositiveIntegerField( blank=False, null=False, verbose_name="SOA Refresh", validators=[MinValueValidator(1)], ) soa_retry = models.PositiveIntegerField( blank=False, null=False, verbose_name="SOA Retry", validators=[MinValueValidator(1)], ) soa_expire = models.PositiveIntegerField( blank=False, null=False, verbose_name="SOA Expire", validators=[MinValueValidator(1)], ) soa_minimum = models.PositiveIntegerField( blank=False, null=False, verbose_name="SOA Minimum TTL", validators=[MinValueValidator(1)], ) soa_serial_auto = models.BooleanField( verbose_name="Generate SOA Serial", help_text="Automatically generate the SOA Serial field", default=True, ) objects = ZoneManager() clone_fields = [ "name", "status", "nameservers", "default_ttl", "soa_ttl", "soa_mname", "soa_rname", "soa_refresh", "soa_retry", "soa_expire", "soa_minimum", ] class Meta: ordering = ("name",) def __str__(self): return self.name def get_absolute_url(self): return reverse("plugins:netbox_dns:zone", kwargs={"pk": self.pk}) def get_status_class(self): return self.CSS_CLASSES.get(self.status) def update_soa_record(self): soa_name = "@" soa_ttl = self.soa_ttl soa_value = ( f"({self.soa_mname} {self.soa_rname} {self.soa_serial}" f" {self.soa_refresh} {self.soa_retry} {self.soa_expire}" f" {self.soa_minimum})" ) old_soa_records = self.record_set.filter(type=Record.SOA, name=soa_name) if len(old_soa_records): for index, record in enumerate(old_soa_records): if index > 0: record.delete() continue if record.ttl != soa_ttl or record.value != soa_value: record.ttl = soa_ttl record.value = soa_value record.managed = True record.save() else: Record.objects.create( zone_id=self.pk, type=Record.SOA, name=soa_name, ttl=soa_ttl, value=soa_value, managed=True, ) def update_ns_records(self, nameservers): ns_name = "@" ns_ttl = self.default_ttl delete_ns = self.record_set.filter(type=Record.NS, managed=True).exclude( value__in=nameservers ) for record in delete_ns: record.delete() for ns in nameservers: Record.raw_objects.update_or_create( zone_id=self.pk, type=Record.NS, name=ns_name, ttl=ns_ttl, value=ns, managed=True, ) def check_nameservers(self): nameservers = self.nameservers.all() ns_warnings = [] ns_errors = [] if not nameservers: ns_errors.append(f"No nameservers are configured for zone {self.name}") for nameserver in nameservers: ns_domain = ".".join(nameserver.name.split(".")[1:]) if not ns_domain: continue try: ns_zone = Zone.objects.get(name=ns_domain) except ObjectDoesNotExist: continue ns_name = nameserver.name.split(".")[0] address_records = Record.objects.filter( Q(zone=ns_zone), Q(Q(name=f"{nameserver.name}.") | Q(name=ns_name)), Q(Q(type=Record.A) | Q(type=Record.AAAA)), ) if not address_records: ns_warnings.append( f"Nameserver {nameserver.name} does not have an address record in zone {ns_zone.name}" ) return ns_warnings, ns_errors def get_auto_serial(self): records = Record.objects.filter(zone=self).exclude(type=Record.SOA) if records: soa_serial = ( records.aggregate(Max("last_updated")) .get("last_updated__max") .timestamp() ) else: soa_serial = ceil(datetime.now().timestamp()) if self.last_updated: soa_serial = ceil(max(soa_serial, self.last_updated.timestamp())) return soa_serial def update_serial(self): self.last_updated = datetime.now() self.save() def parent_zones(self): zone_fields = self.name.split(".") return [ f'{".".join(zone_fields[length:])}' for length in range(1, len(zone_fields)) ] def save(self, *args, **kwargs): new_zone = self.pk is None if not new_zone: renamed_zone = Zone.objects.get(pk=self.pk).name != self.name else: renamed_zone = False if self.soa_serial_auto: self.soa_serial = self.get_auto_serial() super().save(*args, **kwargs) if (new_zone or renamed_zone) and self.name.endswith(".arpa"): address_records = Record.objects.filter( Q(ptr_record__isnull=True) | Q(ptr_record__zone__name__in=self.parent_zones()), type__in=(Record.A, Record.AAAA), disable_ptr=False, ) for record in address_records: record.update_ptr_record() elif renamed_zone: for record in self.record_set.filter(ptr_record__isnull=False): record.update_ptr_record() self.update_soa_record() def delete(self, *args, **kwargs): with transaction.atomic(): address_records = list(self.record_set.filter(ptr_record__isnull=False)) for record in address_records: record.ptr_record.delete() ptr_records = self.record_set.filter(address_record__isnull=False) update_records = [ record.pk for record in Record.objects.filter(ptr_record__in=ptr_records) ] super().delete(*args, **kwargs) for record in Record.objects.filter(pk__in=update_records): record.update_ptr_record() @receiver(m2m_changed, sender=Zone.nameservers.through) def update_ns_records(**kwargs): if kwargs.get("action") not in ["post_add", "post_remove"]: return zone = kwargs.get("instance") nameservers = zone.nameservers.all() new_nameservers = [f'{ns.name.rstrip(".")}.' for ns in nameservers] zone.update_ns_records(new_nameservers) class RecordManager(models.Manager.from_queryset(RestrictedQuerySet)): """Special Manager for records providing the activity status annotation""" def get_queryset(self): return ( super(RecordManager, self) .get_queryset() .annotate( active=ExpressionWrapper( Q( Q(zone__status__in=Zone.ACTIVE_STATUS_LIST) & Q( Q(address_record__isnull=True) | Q( address_record__zone__status__in=Zone.ACTIVE_STATUS_LIST ) ) ), output_field=BooleanField(), ) ) ) return queryset @extras_features("custom_links", "export_templates", "webhooks") class Record(PrimaryModel): A = "A" AAAA = "AAAA" CNAME = "CNAME" MX = "MX" TXT = "TXT" NS = "NS" SOA = "SOA" SRV = "SRV" PTR = "PTR" SPF = "SPF" CAA = "CAA" DS = "DS" SSHFP = "SSHFP" TLSA = "TLSA" AFSDB = "AFSDB" APL = "APL" DNSKEY = "DNSKEY" CDNSKEY = "CDNSKEY" CERT = "CERT" DCHID = "DCHID" DNAME = "DNAME" HIP = "HIP" IPSECKEY = "IPSECKEY" LOC = "LOC" NAPTR = "NAPTR" NSEC = "NSEC" RRSIG = "RRSIG" RP = "RP" CHOICES = ( (A, A), (AAAA, AAAA), (CNAME, CNAME), (MX, MX), (TXT, TXT), (SOA, SOA), (NS, NS), (SRV, SRV), (PTR, PTR), (SPF, SPF), (CAA, CAA), (DS, DS), (SSHFP, SSHFP), (TLSA, TLSA), (AFSDB, AFSDB), (APL, APL), (DNSKEY, DNSKEY), (CDNSKEY, CDNSKEY), (CERT, CERT), (DCHID, DCHID), (DNAME, DNAME), (HIP, HIP), (IPSECKEY, IPSECKEY), (LOC, LOC), (NAPTR, NAPTR), (NSEC, NSEC), (RRSIG, RRSIG), (RP, RP), ) unique_ptr_qs = Q(Q(disable_ptr=False), Q(Q(type="A") | Q(type="AAAA"))) zone = models.ForeignKey( Zone, on_delete=models.CASCADE, ) type = models.CharField( choices=CHOICES, max_length=10, ) name = models.CharField( max_length=255, ) value = models.CharField( max_length=1000, ) ttl = models.PositiveIntegerField( verbose_name="TTL", ) tags = TaggableManager( through="extras.TaggedItem", blank=True, ) managed = models.BooleanField( null=False, default=False, ) ptr_record = models.OneToOneField( "self", on_delete=models.SET_NULL, related_name="address_record", verbose_name="PTR record", null=True, blank=True, ) disable_ptr = models.BooleanField( verbose_name="Disable PTR", help_text="Disable PTR record creation", default=False, ) objects = RecordManager() raw_objects = RestrictedQuerySet.as_manager() clone_fields = ["zone", "type", "name", "value", "ttl", "disable_ptr"] class Meta: ordering = ("zone", "name", "type", "value") constraints = ( models.UniqueConstraint( name="unique_pointer_for_address", fields=["type", "value"], condition=( models.Q( models.Q(disable_ptr=False), models.Q(type="A") | models.Q(type="AAAA"), ) ), ), ) def __str__(self): if self.name.endswith("."): return f"{self.name} [{self.type}]" else: return f"{self.name}.{self.zone.name} [{self.type}]" def get_absolute_url(self): return reverse("plugins:netbox_dns:record", kwargs={"pk": self.id}) def fqdn(self): return f"{self.name}.{self.zone.name}." def ptr_zone(self): address = ipaddress.ip_address(self.value) if address.version == 4: lengths = range(1, 4) else: lengths = range(16, 32) zone_names = [ ".".join(address.reverse_pointer.split(".")[length:]) for length in lengths ] ptr_zones = Zone.objects.filter(Q(name__in=zone_names)).order_by( Length("name").desc() ) if len(ptr_zones): return ptr_zones[0] def update_ptr_record(self): ptr_zone = self.ptr_zone() if ptr_zone is None or self.disable_ptr: if self.ptr_record is not None: with transaction.atomic(): self.ptr_record.delete() self.ptr_record = None return ptr_name = ipaddress.ip_address(self.value).reverse_pointer.replace( f".{ptr_zone.name}", "" ) ptr_value = self.fqdn() ptr_record = self.ptr_record with transaction.atomic(): if ptr_record is not None: if ptr_record.zone.pk != ptr_zone.pk: ptr_record.delete() ptr_record = None else: if ( ptr_record.name != ptr_name or ptr_record.value != ptr_value or ptr_record.ttl != self.ttl ): ptr_record.name = ptr_name ptr_record.value = ptr_value ptr_record.ttl = self.ttl ptr_record.save() if ptr_record is None: ptr_record = Record.objects.create( zone_id=ptr_zone.pk, type=Record.PTR, name=ptr_name, ttl=self.ttl, value=ptr_value, managed=True, ) self.ptr_record = ptr_record super().save() def save(self, *args, **kwargs): if self.type in (self.A, self.AAAA): self.update_ptr_record() super().save(*args, **kwargs) zone = self.zone if self.type != self.SOA and zone.soa_serial_auto: zone.update_serial() def delete(self, *args, **kwargs): if self.ptr_record: self.ptr_record.delete() super().delete(*args, **kwargs) zone = self.zone if zone.soa_serial_auto: zone.update_serial() ``` #### File: netbox_dns/tests/test_auto_soa.py ```python import re from django.test import TestCase from netbox_dns.models import NameServer, Zone, Record def parse_soa_value(soa): soa_match = re.match( r"^\((\S+)\s+(\S+)\s+(\S+)\s+(\S+)\s+(\S+)\s+(\S+)\s+(\S+)\)", soa ) return { "soa_mname": soa_match.group(1), "soa_rname": soa_match.group(2), "soa_serial": int(soa_match.group(3)), "soa_refresh": int(soa_match.group(4)), "soa_retry": int(soa_match.group(5)), "soa_expire": int(soa_match.group(6)), "soa_minimum": int(soa_match.group(7)), } class AutoSOATest(TestCase): zone_data = { "default_ttl": 86400, "soa_rname": "hostmaster.example.com", "soa_refresh": 172800, "soa_retry": 7200, "soa_expire": 2592000, "soa_ttl": 86400, "soa_minimum": 3600, "soa_serial": 1, "soa_serial_auto": False, } @classmethod def setUpTestData(cls): cls.nameservers = [ NameServer(name="ns1.example.com"), NameServer(name="ns2.example.com"), ] NameServer.objects.bulk_create(cls.nameservers) cls.zone = Zone.objects.create( name="zone1.example.com", **cls.zone_data, soa_mname=cls.nameservers[0] ) def test_zone_soa(self): zone = self.zone nameserver = self.nameservers[0] soa_records = Record.objects.filter(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_records[0].value) self.assertTrue( all( ( zone.soa_mname.name == soa.get("soa_mname"), zone.soa_rname == soa.get("soa_rname"), zone.soa_serial == soa.get("soa_serial"), zone.soa_refresh == soa.get("soa_refresh"), zone.soa_retry == soa.get("soa_retry"), zone.soa_expire == soa.get("soa_expire"), zone.soa_minimum == soa.get("soa_minimum"), zone.soa_ttl == soa_records[0].ttl, len(soa_records) == 1, ) ) ) def test_zone_soa_change_mname(self): zone = self.zone nameserver2 = self.nameservers[1] zone.soa_mname = nameserver2 zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_record.value) self.assertEqual(nameserver2.name, soa.get("soa_mname")) def test_zone_soa_change_rname(self): zone = self.zone rname = "new-hostmaster.example.com" zone.soa_rname = rname zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_record.value) self.assertEqual(rname, soa.get("soa_rname")) def test_zone_soa_change_serial(self): zone = self.zone serial = 42 zone.soa_serial = serial zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_record.value) self.assertEqual(serial, soa.get("soa_serial")) def test_zone_soa_change_refresh(self): zone = self.zone refresh = 23 zone.soa_refresh = refresh zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_record.value) self.assertEqual(refresh, soa.get("soa_refresh")) def test_zone_soa_change_retry(self): zone = self.zone retry = 2342 zone.soa_retry = retry zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_record.value) self.assertEqual(retry, soa.get("soa_retry")) def test_zone_soa_change_expire(self): zone = self.zone expire = 4223 zone.soa_expire = expire zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_record.value) self.assertEqual(expire, soa.get("soa_expire")) def test_zone_soa_change_minimum(self): zone = self.zone minimum = 4223 zone.soa_minimum = minimum zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) soa = parse_soa_value(soa_record.value) self.assertEqual(minimum, soa.get("soa_minimum")) def test_zone_soa_change_ttl(self): zone = self.zone ttl = 422342 zone.soa_ttl = ttl zone.save() soa_record = Record.objects.get(type=Record.SOA, zone=zone) self.assertEqual(ttl, soa_record.ttl) ```
{ "source": "JeroenvO/CZ-Stats", "score": 3 }
#### File: JeroenvO/CZ-Stats/__init__.py ```python import gc from time import sleep from math import ceil import buttons import defines import rgb import system import uinterface import urequests import wifi # globals stat = 0 old_stat = 0 l = None # colors colors = [ # cmy [(0, 255, 255), (255, 0, 255), (255, 255, 0)], [(255, 255, 0), (0, 255, 255), (255, 0, 255)], [(255, 0, 255), (255, 255, 0), (0, 255, 255)], # rgb [(255, 0, 0), (0, 255, 0), (0, 0, 255)], [(0, 0, 255), (255, 0, 0), (0, 255, 0)], [(0, 255, 0), (0, 0, 255), (255, 0, 0)], # white [(255, 255, 255), (255, 255, 255), (255, 255, 255)], ] color = 0 # buttons UP, DOWN, LEFT, RIGHT = defines.BTN_UP, defines.BTN_DOWN, defines.BTN_LEFT, defines.BTN_RIGHT A, B = defines.BTN_A, defines.BTN_B def input_up(pressed): if pressed: global color color = (color + 1) % (len(colors)) draw_text() def input_down(pressed): if pressed: global color color = (color - 1) % (len(colors)) draw_text() def input_left(pressed): if pressed: global stat stat = 1 - stat def input_right(pressed): if pressed: global stat stat = 1 - stat def input_B(pressed): if pressed: rgb.clear() rgb.text("Bye!") sleep(0.5) system.reboot() def input_A(pressed): if pressed: rgb.background((255, 100, 100)) else: rgb.background((0, 0, 0)) def draw_error(e): rgb.clear() rgb.pixel((255, 0, 0), (REFRESH_RATE, 7)) # red for error rgb.text('E {}'.format(e)) def draw_text(): global l, color rgb.clear() if l: rgb.pixel((0, 150, 0), (REFRESH_RATE, 7)) # green for new data for i, d in enumerate(l): rgb.text(d, colors[color][i], (ceil(31/len(l))*i, 0)) else: rgb.text('E Data') # init buttons.register(UP, input_up) buttons.register(DOWN, input_down) buttons.register(LEFT, input_left) buttons.register(RIGHT, input_right) buttons.register(B, input_B) buttons.register(A, input_A) rgb.setfont(rgb.FONT_6x3) rgb.framerate(10) # second updates REFRESH_RATE = 31 # times framerate updates. # wifi connect if not wifi.status(): if not uinterface.connect_wifi(): system.reboot() rgb.text('Hi!') # main loop count = REFRESH_RATE - 1 # start fast while True: if not wifi.status(): if not uinterface.connect_wifi(): system.reboot() if count < REFRESH_RATE and stat == old_stat: gc.collect() sleep(0.1) rgb.pixel((150, 150, 0), (count, 7)) # refresh counter count += 1 continue else: count = 0 old_stat = stat if stat == 0: # generator try: r = urequests.post("https://dashboard.eventinfra.org/api/datasources/proxy/1/render", data='target=infra.ACT_PWR_1_generator_tot_kva&target=infra.ACT_PWR_2_generator_tot_kva&target=infra.ACT_PWR_3_generator_tot_kva&from=-3min&until=now&format=json&maxDataPoints=768') except: draw_error('req') continue if r.status_code == 200: # rgb.clear() try: l = [str(int(i['datapoints'][-1][0])) for i in r.json()] except: draw_error('json') continue draw_text() else: draw_error(r.status_code) elif stat == 1: # up/down link try: r = urequests.post("https://dashboard.eventinfra.org/api/datasources/proxy/1/render", data='target=scale(scaleToSeconds(nonNegativeDerivative(net.kvm2.snmp.if_octets-eth3_300.tx),1),8)&target=scale(scaleToSeconds(nonNegativeDerivative(net.kvm2.snmp.if_octets-eth3_300.rx),1),8)&from=-5min&until=now&format=json&maxDataPoints=768') except: rgb.text("E req") continue if r.status_code == 200: try: l = [str(int(i['datapoints'][-1][0] / 1e6)) for i in r.json()] except: draw_error('json') continue draw_text() else: # non 200 status code draw_error(str(r.status_code)) ```
{ "source": "JeroenvO/pulsedpowerplasmaplots", "score": 3 }
#### File: analyze/scope_parse/c_get_lines.py ```python import os from analyze.scope_parse.a_easyscope_parser import parse_file from analyze.scope_parse.b_correct_lines import correct_lines from analyze.defines import * def get_vol_cur_single(filename, current_scaling = 0.5, delay=0, voltage_offset=None, current_offset=None, splitted_pulse=False): """ Parse voltage and current from waveforms. :param filename: filepath without extension :return: [time, v, i] of waveform """ line_objs = parse_file(filename) # file to parse offsets = [ {'v_shift': delay, # -16 works fine for exact match of waveforms with different cable length. Otherwise 0 'div_zero': voltage_offset}, # if voltage has another div_zero than current {'val_div_correct': current_scaling, # -100 for Pearson 0.1v/a inverted. 'div_zero': current_offset} # {}, # {} ] time_axis, y_axes = correct_lines(line_objs, offsets=offsets) v = y_axes[0] i = y_axes[1] # for splitted pulse, disable these assertions. assert MAX_VOLTAGE_MIN < max(v) < MAX_VOLTAGE_MAX, "Voltage max (%r) scaling incorrect!" % max(v) assert MIN_VOLTAGE_MIN < min(v) < MIN_VOLTAGE_MAX, "Voltage min (%r) scaling incorrect!" % min(v) if not MAX_CURRENT_MIN < max(i) < MAX_CURRENT_MAX: # max current between 2A and 30A if max(i) < 0.03: print("Warning!, scope current corrected for mV to V!") i *= 1000 elif max(i) > 1000: # minimum 1A max print("Warning!, scope current corrected for V to mV!") i /= 1000 else: raise Exception("Current scaling is incorrect! Max: "+str(max(i))+', Min: '+str(min(i))) assert MAX_CURRENT_MIN < max(i) < MAX_CURRENT_MAX, "Current max (%r) scaling incorrect!" % max(i) assert MIN_CURRENT_MIN < min(i) < MIN_CURRENT_MAX, "Current min (%r) scaling incorrect!" % min(i) # assert i.argmax() < i.argmin(), 'Current valley before peak, signal is inverted!' return [time_axis, v, i] def get_vol_cur_dir(path): """ Get list of [[time, vol, cur], .. ] for each file in 'path' :param path: search path :return: list of lists with time, vol and cur. """ dir = os.listdir(path) lines = [] for file in dir: lines.append(get_vol_cur_single(path+'/'+file) + [file]) return lines def get_vol_cur_multiple(base_filename, **kwargs): """ Used if multiple scope waveforms are captured per measurement. These waveforms are all appended to the data in calc_run.py It will be used in e_average.py to calculate average powers for pulses. :param base_filename: base filename/path without extension or _. :return: list of lists with [time, v, i] waveforms. One for each obtained waveform """ i = 0 lines = [] while True: try: # print(base_filename, i) lines.append(get_vol_cur_single(base_filename+'_'+str(i), **kwargs)) i += 1 except IOError: break except Exception as e: raise Exception(e) return lines ``` #### File: visualize/final_v1/plot_pe.py ```python import matplotlib.pyplot as plt from visualize.helpers.plot import save_file, set_plot from visualize.helpers.data import filter_data def plot_pe(data, reactor): """ Plot power and energy waveform, in two subplots :param data: :param reactor: :return: """ data = filter_data(data, input_v_output=15e3, input_f=10, input_l=1)[0] fig, ax = plt.subplots(2, 1, sharex=True) x_axis = data['output_t'][0]*1e6 p_axis = data['output_p'][0]/1e3 e_axis = data['output_e'][0]*1e3 ax[0].plot(x_axis, p_axis, color='black') ax[1].plot(x_axis, e_axis, color='black') ax[0].set_ylabel('P [kW]') ax[1].set_ylabel('E [mJ]') set_plot(fig, 2, pulse=True) save_file(fig, name='pe-'+reactor, path='G:/Prive/MIJN-Documenten/TU/62-Stage/05_python/plots_final_v1') if __name__ == '__main__': from visualize.helpers.data import load_pickle data = load_pickle('20180115/run1') plot_pe(data, 'short-glass') ``` #### File: final_v2/burst/plot_edens_yield_paper.py ```python import matplotlib.pyplot as plt import numpy as np from visualize.helpers.colors import color_plasma from visualize.helpers.data import filter_data, reactor_inducance_index from visualize.helpers.plot import save_file, set_plot, set_unique_legend from visualize.helpers.burst import calc_burst def plot_edens_yield(datas): """ Make various plots to energy density :param datas: :return: """ fig, ax = plt.subplots(2, 1, sharex=True) m = 'o' ui = np.array([200, 150, 100, 75, 50]) offset = 1 # skip bright yellow color colors = color_plasma(len(ui)+offset) # sort data, to keep the legend in the right order. datas = sorted(datas, key=lambda x:x[0]['burst_inner_f']) for i, data in enumerate(datas): l = str(data[0]['burst_inner_f']) + ' kHz' data = filter_data(data, input_v_output=15e3, output_yield_gkwh__gt=25) c = colors[np.where(data[0]['burst_inner_f'] == ui)[0][0]+offset] burstdata = calc_burst(data) line = data[0] # because all data is the same in one burst run edens = burstdata['output_energy_dens'] ax[1].scatter(edens, burstdata['output_yield_gkwh'], label=l, c=c, marker=m) ax[0].scatter(edens, line['o3_ppm'], label=l, c=c, marker=m) ax[1].set_ylabel('Yield [g/kWh]') # ax[1].set_ylabel('Production [g/h]') # ax_dens[1].set_ylim([0, 7e-5]) # ax_dens[2].set_ylim([0, 2e3]) # ax[0].set_ylim([0, 120]) ax[0].set_ylabel('Ozone [ppm]') ax[0].text(20, 220, '50 Hz') ax[0].text(45, 250, '100 Hz') ax[0].text(85, 550, '200 Hz') ax[1].set_xlabel('Energy density [J/l]') set_unique_legend(ax[0]) set_plot(fig, plot_height=2, from_zero=False) save_file(fig, name='edens-all-burst-paper', path='plots_final_v2/burst') if __name__ == '__main__': pass ``` #### File: final_v2/burst/plot_f_epulse.py ```python import matplotlib.lines as mlines import matplotlib.pyplot as plt import numpy as np from visualize.helpers.colors import color2 from visualize.helpers.data import filter_data, get_values from visualize.helpers.plot import markers from visualize.helpers.plot import save_file, set_plot, interpolate_plot def plot_f_epulse(datas): """ Plots energy per pulse for various frequencies 5 pulses, 100hz :param data: :param reactor: :return: """ fig, ax = plt.subplots() marker_legends = [] for i, data in enumerate(datas): data = filter_data(data, input_v_output=15e3, input_l=1) # l = str(data[0]['burst_inner_f']) + ' kHz, ' + str(data[0]['burst_pulses']) c = color2[i] center = get_values(data, 'output_e_plasma')*1000 all = [np.array(list)*1000 for list in get_values(data, 'output_e_plasma_single')] x = range(1,1+len(data)) interpolate_plot(ax, x, center) m = markers[i] for j, line in enumerate(data): epuls = line['output_e_plasma']*1000 # array of values, to mJ. plt.scatter(j+1, epuls, c=c, marker=m) marker_legends.append( mlines.Line2D([], [], marker=m, label=str(len(data))+' pulses', color='grey', markerfacecolor=c, markeredgewidth=0)) # mi = [y2a - min(z2a) for z2a, y2a in zip(all, center)] # list of minima of y # ma = [max(z2a) - y2a for z2a, y2a in zip(all, center)] # list of maxima of y std = [np.std(z2a) for z2a in all] ax.errorbar(x, center, yerr=std, xerr=None, ecolor=c, fmt='none', capsize=3) # add x labels ax.set_xlabel('Pulse number') ax.set_ylabel('Pulse plasma energy [mJ]') ax.legend(handles=marker_legends) ax.set_xlim(left=0) set_plot(fig) save_file(fig, name='epulse-burst', path='plots_final_v2/burst') ``` #### File: final_v2/normal/plot_f_epulse.py ```python import matplotlib.lines as mlines import matplotlib.pyplot as plt import numpy as np from visualize.helpers.data import load_pickle, filter_data, get_values from visualize.helpers.plot import save_file, set_plot def plot_f_epulse(data, reactor): """ Plots energy per pulse for various frequencies as boxplot :param data: :param reactor: :return: """ data = filter_data(data, input_v_output=15e3, input_l=1) fig, ax = plt.subplots() uf = np.unique(get_values(data, 'input_f')) plotdata = [] for f in uf: d = filter_data(data, input_f=f) l = get_values(d, key='output_e_plasma_single') # returns list of arrays with values. v = np.concatenate(l) epuls = np.array(v)*1000 # array of values plotdata.append(epuls) plt.boxplot(plotdata) # add x labels num_boxes = len(uf) plt.xticks(range(num_boxes+1), ['']+list(uf), rotation=45) # add top x-labels with number of sample points upperLabels = [''] + [str(len(s)) for s in plotdata] ax2 = ax.twiny() ax2.set_xlim(ax.get_xlim()) ax2.set_xticks(ax.get_xticks()) ax2.set_xticklabels(upperLabels) ax2.set_xlabel("Number of samples") ax.set_xlabel('Frequency [Hz]') ax.set_ylabel('Pulse plasma energy [mJ]') set_plot(fig, plot_height=1.4) save_file(fig, name='epulse-'+reactor, path='plots_final_v2/normal') if __name__ == '__main__': reactors = ['long-glass-46uH', 'long-glass-26uH', 'short-glass-nocoil', 'short-glass-26uH', 'short-glass-8uH'] reactor = reactors[3] print(reactor) if reactor == 'long-glass-46uH': data = load_pickle("20180115-def1/run6") elif reactor == 'long-glass-26uH': data = load_pickle("20180115-def1/run5") elif reactor == 'short-glass-26uH': data = load_pickle("20180115-def1/run2") elif reactor == 'short-glass-8uH': data = load_pickle("20180115-def1/run3") elif reactor == 'short-glass-nocoil': data = load_pickle("20180115-def1/run1") else: raise Exception("No input!") plot_f_epulse(data, reactor) ``` #### File: visualize/helpers/burst.py ```python import numpy as np from visualize.helpers.data import get_values def calc_burst(data): """ Recalculate values from d_calc for a run with bursts where each measurement is one pulse form the burst :param data: :return: """ pulse_energy = [] burst_energy = 0 burst = len(data) freq = data[0]['burst_f'] print("Burst with "+str(burst) + ' pulses') pulse_energy = np.average(get_values(data, key='output_e_plasma')) # average energy in each burst pulse burst_energy = sum(get_values(data, key='output_e_plasma')) # sum energy in one burst of n pulses output_p_plasma = freq * burst_energy lss = np.average(get_values(data, 'airflow_ls')) o3f = np.average(get_values(data, 'o3_gramsec')) ppm = np.average(get_values(data, 'o3_ppm')) input_p = np.average(get_values(data, 'input_p')) dic ={ 'e_plasma_burst': burst_energy, 'e_plasma_avg': pulse_energy, 'p_plasma': output_p_plasma, 'output_energy_dens': output_p_plasma / lss, 'output_yield_gj': o3f / output_p_plasma if output_p_plasma else 0, 'output_yield_gkwh': o3f / (output_p_plasma / 3.6e6) if output_p_plasma else 0, 'e_eff': output_p_plasma / input_p if output_p_plasma else 0, 'ppm': ppm, } return dic ``` #### File: visualize/helpers/data.py ```python import operator import os import pickle import numpy as np from analyze.defines import * def load_pickle(path): if path[-4:] != '.pkl': if path[-5] == '.' or path[-4] == '.': # random file with extension return None elif path[-8:] != 'data.pkl': path = path + '/data.pkl' else: path = path + '.pkl' if not os.path.exists(path): # path = 'G:/Prive/MIJN-Documenten/TU/62-Stage/' + path # try full path. path = 'D:/ownCloud/DATA/MIJN-Documenten/TU/6-Stage/' + path assert os.path.exists(path) with open(path, 'rb') as f: d = pickle.load(f) assert any(d) return d def get_values(dicts, key): """ Get all values from a list of dicts with a given key stop if list is empty or zero. Takes one 'column' of the data, as analogy to the generated excel file. :param dicts: the list of dicts to search :param key: the key to search each dict for :return: list of values """ assert any(dicts) assert key in dicts[0] a = np.array([d[key] if key in d else 0 for d in dicts]) # assert any(a) return a def load_pickles(dir, filename='data.pkl'): """ Load pickles from all directories in a path. :param dir: dir with subdirs which have data.pkl :return: list of dicts with processed measure data """ data = [] if not os.path.exists(dir): dir = 'D:/ownCloud/DATA/MIJN-Documenten/TU/6-Stage/' + dir # try full path. dirs = os.listdir(dir, ) for tdir in dirs: if os.path.isdir(dir+'/'+tdir): try: data += load_pickle(dir + '/' + tdir + '/' + filename) except: pass # invalid dir assert any(data) return data def filter_data(data, **kwargs): """ Filter a list of dicts for given key=value in the dict append '__<operator>' at key to choose custom operator from operator module. :param data: data to filter, array of dicts from pickle file :param kwargs: key=value, where key is key of dict and value is value to filter. :return: filtered data """ assert any(data) for key, value in kwargs.items(): key = key.split('__') op = key[1] if len(key) == 2 else 'eq' f = getattr(operator, op) # only check data[0], assume all dicts have the same keys assert key[0] in data[0], '%r is not found in dictionary!' % key[0] if op in ['contains']: # reverse order of arguments for these ops. data = [d for d in data if f(value, d[key[0]])] else: data = [d for d in data if f(d[key[0]], value)] assert any(data), "Filter on key %r returned no data!" % str(key) return data def sort_data(data, key): """ Sort a list of dicts by a given key :param data: input list of dicts :param key: key to sort :return: sorted list of dicts """ assert any(data) return sorted(data, key=lambda k: k[key]) def reactor_inducance_index(reactor, inductance): """ Return an index based on combination of reactor and inductance :param reactor: :param inductance: :return: """ if reactor == REACTOR_GLASS_SHORT_QUAD: assert inductance in INDUCTANCE_SHORT_REACTOR return INDUCTANCE_SHORT_REACTOR.index(inductance) elif reactor == REACTOR_GLASS_LONG: assert inductance in INDUCTANCE_LONG_REACTOR return INDUCTANCE_LONG_REACTOR.index(inductance) + len(INDUCTANCE_SHORT_REACTOR) else: raise Exception("Invalid reactor!") def annotate_data(data, **kwargs): """ Annotate each item in data with a key=value from kwargs :param data: input list of dicts :param kwargs: key=val to annotate :return: """ for key, val in kwargs.items(): for i, line in enumerate(data): data[i][key] = val return data ``` #### File: poster/normal/plot_edens_yield.py ```python import matplotlib.pyplot as plt from visualize.helpers.colors import color_plasma_3 from visualize.helpers.data import filter_data, reactor_inducance_index from visualize.helpers.plot import save_file, set_plot, set_unique_legend, markers def plot_edens_yield(data): """ Make various plots to energy density as scatterplot :param data: :param reactor: :return: """ data = filter_data(data, input_v_output=15e3, input_l=1, output_yield_gkwh__gt=25) fig, ax = plt.subplots(4, 1, sharex=True) colors = color_plasma_3 # m = 'o' # interpolate_plot(ax[0], x, get_values(data, 'output_yield_gkwh')) # interpolate_plot(ax[1], x, get_values(data, 'o3_gramsec')*3600) # interpolate_plot(ax[2], x, get_values(data, 'o3_ppm')) # interpolate_plot(ax[3], x, get_values(data, 'input_p')) # interpolate_plot(ax[3], x, get_values(data, 'output_p_avg')) # interpolate_plot(ax[4], x, get_values(data, 'input_f')) for line in data: reactor = line['reactor'] inductance = line['inductance'] i = reactor_inducance_index(reactor, inductance) l = reactor + ' ' + (str(inductance)+'$\,\mu H$' if inductance else 'no coil') c = colors[i] m = markers[i] edens = line['output_energy_dens'] ax[0].scatter(edens, line['output_yield_gkwh'], label=l, c=c, marker=m) # ax_freq[0].scatter(freq, line['input_yield_gkwh']) ax[1].scatter(edens, line['o3_ppm'], label=l, c=c, marker=m) ax[2].scatter(edens, line['e_eff']*100, label=l, c=c, marker=m) ax[3].scatter(edens, line['input_f'], label=l, c=c, marker=m) ax[0].set_ylabel('Yield [g/kWh]') # ax[1].set_ylabel('Production [g/h]') # ax_dens[1].set_ylim([0, 7e-5]) # ax_dens[2].set_ylim([0, 2e3]) # ax[0].set_ylim([0, 120]) ax[1].set_ylabel('Ozone [ppm]') ax[2].set_ylabel('Energy efficiency [%]') ax[3].set_ylabel('Frequency [Hz]') ax[3].set_xlabel('Energy density [J/l]') set_unique_legend(ax[1]) set_plot(fig, plot_height=3) save_file(fig, name='edens-all', path='plots_poster/normal') if __name__ == '__main__': pass ```
{ "source": "jeroenvuurens/pipetorch", "score": 2 }
#### File: pipetorch/data/imagecollection.py ```python import pandas as pd import numpy as np import torch import math import torch.nn.functional as F from torch.utils.data import DataLoader, Dataset, random_split, Subset import matplotlib.pyplot as plt from torchvision.datasets import MNIST, ImageFolder, CIFAR10 from torchvision.transforms import transforms import os import matplotlib import matplotlib.patheffects as PathEffects from IPython.core import pylabtools from pathlib import Path import sys from IPython import get_ipython from tqdm.notebook import tqdm import ipywidgets as widgets import io from PIL import Image, ImageStat from getpass import getuser from ..evaluate.evaluate import Evaluator ipython = get_ipython() back2gui = { b:g for g, b in pylabtools.backends.items() } class plt_gui: def __init__(self, gui): self.gui = gui def __enter__(self): backend = matplotlib.get_backend() self.old_gui = back2gui[backend] ipython.magic('matplotlib ' + self.gui) def __exit__(self, *args): ipython.magic('matplotlib ' + self.old_gui) class plt_inline(plt_gui): def __init__(self): super().__init__('inline') class plt_notebook(plt_gui): def __init__(self): super().__init__('notebook') def subplots(rows, cols, imgsize=4, figsize=None, title=None, **kwargs): "Like `plt.subplots` but with consistent axs shape, `kwargs` passed to `fig.suptitle` with `title`" if figsize is None: figsize = (imgsize*cols, imgsize*rows) fig, axs = plt.subplots(rows,cols,figsize=figsize) if rows==cols==1: axs = [[axs]] elif (rows==1 and cols!=1) or (cols==1 and rows!=1): axs = [axs] if title is not None: fig.suptitle(title, **kwargs) return np.array(axs) def sample(self, device=None): X, y = self.one_batch() if device is not None: return X.to(device), y.to(device) return X, y class ImageDataset(Dataset): """Image dataset.""" def __init__(self, *args, transform=None, **kwargs): """ Args: transform (callable, optional): Optional transform to be applied on a sample. """ super.__init__(*args, **kwargs) self.transform = transform def __getitem__(self, idx): item = super.__getitem__(idx) if self.transform: item = self.transform(item) return item class image_databunch: def __init__(self, train_ds, valid_ds, batch_size=32, valid_batch_size=None, shuffle=True, num_workers=0, pin_memory=False, valid_pin_memory=None, normalized_mean=None, normalized_std=None, classes=None, class_to_idx=None): self.train_ds = train_ds self.valid_ds = valid_ds self.batch_size = batch_size self.valid_batch_size = batch_size if valid_batch_size is None else valid_batch_size self.valid_pin_memory = pin_memory if valid_pin_memory is None else valid_pin_memory self.num_workers = num_workers self.shuffle = shuffle self.pin_memory = pin_memory self.normalized_mean = normalized_mean self.normalized_std = normalized_std self.classes = classes self.class_to_idx = class_to_idx @staticmethod def balance(X, y): indices = [np.where(y==l)[0] for l in np.unique(y)] classlengths = [len(i) for i in indices] n = max(classlengths) mask = np.hstack([np.random.choice(i, n-l, replace=True) for l,i in zip(classlengths, indices)]) indices = np.hstack([mask, range(len(y))]) return X[indices], y[indices] def to(self, device): try: self.train_ds.data.to(device) except: pass try: self.train_ds.targets.to(device) except: pass try: self.valid_ds.data.to(device) except: pass try: self.valid_ds.targets.to(device) except: pass self.device=device return self def cpu(self): return self.to(torch.device('cpu')) def gpu(self): return self.to(torch.device('cuda:0')) @property def batch_size(self): return self._batch_size @batch_size.setter def batch_size(self, value): self._batch_size = min(value, len(self.train_ds)) self.reset() @property def num_workers(self): return self._num_workers @num_workers.setter def num_workers(self, value): self._num_workers = value self.reset() def evaluate(self, *metrics): #assert len(metrics) > 0, 'You need to provide at least one metric for the evaluation' return Evaluator(self, *metrics) @property def labels(self): return self._labels @property def train_dl(self): try: return self._train_dl except: self._train_dl = DataLoader(self.train_ds, num_workers=self.num_workers, shuffle=self.shuffle, batch_size=self.batch_size, pin_memory=self.pin_memory) return self._train_dl @train_dl.setter def train_dl(self, dl): self._train_dl = dl @property def valid_dl(self): try: return self._valid_dl except: self._valid_dl = DataLoader(self.valid_ds, shuffle=False, num_workers=self.num_workers, batch_size=self.valid_batch_size, pin_memory=self.valid_pin_memory) return self._valid_dl @valid_dl.setter def valid_dl(self, dl): self._valid_dl = dl @property def train_X(self): return self.train_ds.data @property def train_y(self): return self.train_ds.targets @property def valid_X(self): return self.valid_ds.data @property def valid_y(self): return self.valid_ds.targets @property def train_numpy(self): return to_numpy(self.train_X), to_numpy(self.train_y) @property def valid_numpy(self): return to_numpy(self.valid_X), to_numpy(self.valid_y) def sample(self, device=None): X, y = next(iter(self.train_dl)) if device is not None: return X.to(device), y.to(device) return X, y def reset(self): try: del self.valid_dl except: pass try: del self._train_dl except: pass def show_batch(self, rows=3, imgsize=(20,20), figsize=(10,10)): with plt_inline(): old_backend = matplotlib.get_backend() Xs, ys = next(iter(self.train_dl)) Xs = Xs[:rows*rows] ys = ys[:rows*rows] axs = subplots(rows, rows, imgsize=imgsize, figsize=figsize) invnormalize = self.inv_normalize() for x,y,ax in zip(Xs, ys, axs.flatten()): x = x.cpu() x = invnormalize(x) im = transforms.ToPILImage()(x).convert("RGB") im = transforms.Resize([100,100])(im) ax.imshow(im) try: y = self.classes[y] except: pass ax.set_title(f'y={y}') for ax in axs.flatten()[len(Xs):]: ax.axis('off') plt.tight_layout() plt.show() @classmethod def get_transformations_train(cls, size=224, crop_size=None, crop_padding=None, color_jitter=None, rotate=None, do_flip=True, normalize_mean=None, normalize_std=None): return cls.get_transformations(size=size, crop_size=crop_size, crop_padding=crop_padding, color_jitter=color_jitter, rotate=rotate, do_flip=do_flip, normalize_mean=normalize_mean, normalize_std=normalize_std) @classmethod def get_transformations(cls, size=224, crop_size=None, crop_padding=None, color_jitter=None, rotate=None, do_flip=None, normalize_mean=None, normalize_std=None): t = [] if rotate is not None: t.append(transforms.RandomRotation(rotate)) if color_jitter is not None: t.append(transforms.ColorJitter(*color_jitter)) if crop_size is not None or crop_padding is not None: if crop_size is None: crop_size = size if crop_padding is None: crop_padding = 0 t.append(transforms.RandomCrop(crop_size, padding=crop_padding, pad_if_needed=True)) if size is not None: t.append(transforms.Resize([size,size])) if do_flip: t.append(transforms.RandomHorizontalFlip()) t.append(transforms.ToTensor()) if normalize_mean is not None and normalize_std is not None: t.append(transforms.Normalize(mean=normalize_mean, std=normalize_std)) return transforms.Compose( t ) def inv_normalize(self): if self.normalized_std is not None and self.normalized_mean is not None: return transforms.Normalize(mean=tuple(-m/s for m, s in zip(self.normalized_mean, self.normalized_std)), std=tuple(1/s for s in self.normalized_std)) try: for l in self.train_ds.transform.transforms: if type(l) == transforms.Normalize: return transforms.Normalize(mean=tuple(-m/s for m, s in zip(l.mean, l.std)), std=tuple(1/s for s in l.std)) except:pass try: for l in self.train_ds.dataset.transform.transforms: if type(l) == transforms.Normalize: return transforms.Normalize(mean=tuple(-m/s for m, s in zip(l.mean, l.std)), std=tuple(1/s for s in l.std)) except:pass return lambda x:x @staticmethod def tensor_ds(ds): try: ds1 = TransformableDataset(ds, transforms.ToTensor()) ds1[0][0].shape[0] return ds1 except: return ds @staticmethod def channels(ds): return image_databunch.tensor_ds(ds)[0][0].shape[0] @classmethod def train_normalize(cls, ds): ds = image_databunch.tensor_ds(ds) channels = image_databunch.channels(ds) total_mean = [] total_std = [] for c in range(channels): s = torch.cat([X[c].view(-1) for X, y in ds]) total_mean.append(s.mean()) total_std.append(s.std()) return torch.tensor(total_mean), torch.tensor(total_std) @classmethod def from_image_folder(cls, path, valid_size=0.2, target_transform=None, size=224, crop_size=None, crop_padding=None, color_jitter=None, rotate=None, do_flip=None, normalize_mean=None, normalize_std=None, normalize=False, **kwargs): ds = ImageFolder(root=path, target_transform=target_transform) split = int((1-valid_size) * len(ds)) indices = list(range(len(ds))) np.random.shuffle(indices) train_idx, valid_idx = indices[:split], indices[split:] if normalize: assert normalize_mean is None and normalize_std is None, 'You cannot set normalize=True and give the mean or std' normalize_mean, normalize_std = cls.train_normalize(Subset(ds, train_idx)) train_transforms = cls.get_transformations_train(size=size, crop_size=crop_size, crop_padding=crop_padding, color_jitter=color_jitter, rotate=rotate, do_flip=do_flip, normalize_mean=normalize_mean, normalize_std=normalize_std) valid_transforms = cls.get_transformations(size=size, normalize_mean=normalize_mean, normalize_std=normalize_std) train_ds = TransformableDataset(Subset(ds, train_idx), train_transforms) valid_ds = TransformableDataset(Subset(ds, valid_idx), valid_transforms) return cls(train_ds, valid_ds, classes=ds.classes, class_to_idx=ds.class_to_idx, normalized_mean=normalize_mean, normalized_std=normalize_std, **kwargs) @classmethod def from_image_folders(cls, trainpath, validpath, size=None, transform=None, target_transform=None, **kwargs): if type(transform) is int: train_transforms = cls.get_transformations_train(size=transform) valid_transforms = cls.get_transformations(size=transform) elif type(transform) is dict: train_transforms = cls.get_transformations_train(**transform) valid_transforms = cls.get_transformations(**transform) elif type(transform) is tuple: train_transforms, valid_transforms = transform elif transform is None: train_transforms = transforms.Compose( [transforms.ToTensor()] ) valid_transforms = train_transforms else: train_transforms = transform valid_transforms = transform train_ds = ImageFolder(root=trainpath, transform=train_transforms, target_transform=target_transform) valid_ds = ImageFolder(root=validpath, transform=valid_transforms, target_transform=target_transform) return cls(train_ds, valid_ds, classes=train_ds.classes, class_to_idx=train_ds.class_to_idx, **kwargs) class TransformableDataset(Dataset): def __init__(self, dataset, transform=None): self.dataset = dataset self.transform = transform def __getitem__(self, index): x, y = self.dataset[index] if self.transform: x = self.transform(x) return x, y def __len__(self): return len(self.dataset) class Resize(object): def __init__(self, size, interpolation=Image.BILINEAR): self.size = size self.interpolation = interpolation def __call__(self, img): old_size = img.size # old_size[0] is in (width, height) format ratio = float(self.size)/min(old_size) new_size = tuple([int(x * ratio) for x in old_size]) return img.resize(new_size, resample=self.interpolation) class FastCIFAR(CIFAR10): def __init__(self, root='/data/datasets/cifarnew/', train=True, transform=None, device=None, size=None, **kwargs): super().__init__(root=root, train=train, **kwargs) self.transform=transform # Scale data to [0,1] self.data = torch.tensor(self.data).float().div(255) self.data = self.data.permute(0, 3, 1, 2) if size is not None: self.data = F.interpolate(self.data, (3, size, size)) # Normalize it with the usual MNIST mean and std self.data[:,0] = self.data[:,0].sub_(0.4057).div_(0.2039) self.data[:,1] = self.data[:,1].sub_(0.5112).div_(0.2372) self.data[:,2] = self.data[:,2].sub_(0.5245).div_(0.3238) self.targets = torch.tensor(self.targets) # Put both data and targets on GPU in advance if device is not None: self.data, self.targets = self.data.to(device), self.targets.to(device) def __getitem__(self, index): img, target = self.data[index], self.targets[index] if self.transform: img = self.transform(img) return img, target class FastMNIST(MNIST): def __init__(self, *args, transform=None, device=torch.device('cuda:0'), size=None, **kwargs): super().__init__(*args, **kwargs) self.transform=transform # Scale data to [0,1] self.data = self.data.unsqueeze(1).float().div(255) if size is not None: self.data = F.interpolate(self.data, (size, size)) # Normalize it with the usual MNIST mean and std self.data = self.data.sub_(0.1307).div_(0.3081) # Put both data and targets on GPU in advance if device is not None: self.data, self.targets = self.data.to(device), self.targets.to(device) def __getitem__(self, index): img, target = self.data[index], self.targets[index] if self.transform: img = self.transform(img) return img, target class FastMNIST3(FastMNIST): def __init__(self, *args, transform=None, device=torch.device('cuda:0'), size=None, **kwargs): super().__init__(*args, transform=None, device=torch.device('cuda:0'), **kwargs) self.size = size def __getitem__(self, index): img, target = self.data[index], self.targets[index] if self.size is not None: img = F.interpolate(img.unsqueeze(0), (self.size, self.size)).squeeze(0) if self.transform: img = self.transform(img) img = torch.cat([img, img, img], axis=0) return img, target def mnist(path='/data/datasets/mnist2', batch_size=64, transform=None, size=None, **kwargs): ''' returns an image_databunch of the mnist dataset in greyscale (shape is (1,28,28). path: folder where the mnist dataset is, or will be downloaded to if it does not exist batch_size (64): batch_size used for segmenting for training. transform (None): pipeline of transformations that are applied to the images size (None): resizes the images to (size, size). ''' train_ds = FastMNIST(path, transform=transform, train=True, size=size, **kwargs) valid_ds = FastMNIST(path, transform=transform, train=False, size=size, **kwargs) db = image_databunch(train_ds, valid_ds, batch_size=batch_size, normalized_mean=(0.1307,), normalized_std=(0.3081,)) return db def mnist3(path='/data/datasets/mnist2', batch_size=64, size=None, transform=None, **kwargs): ''' returns an image_databunch of the mnist dataset in rgb (shape is (3,28,28). path: folder where the mnist dataset is, or will be downloaded to if it does not exist batch_size (64): batch_size used for segmenting for training. transform (None): pipeline of transformations that are applied to the images size (None): resizes the images to (size, size). ''' train_ds = FastMNIST3(path, transform=transform, train=True, size=size, **kwargs) valid_ds = FastMNIST3(path, transform=transform, train=False, size=size, **kwargs) db = image_databunch(train_ds, valid_ds, batch_size=batch_size, normalized_mean=(0.1307, 0.1307, 0.1307), normalized_std=(0.3081, 0.3081, 0.3081)) return db def cifar(path='/data/datasets/cifarnew/', batch_size=64, size=None, transform=None, **kwargs): train_ds = FastCIFAR(root=path, transform=transform, train=True, size=size, **kwargs) valid_ds = FastCIFAR(root=path, transform=transform, train=False, size=size, **kwargs) db = image_databunch(train_ds, valid_ds, batch_size=batch_size, normalized_mean=(0.4057, 0.5112, 0.5245), normalized_std=(0.2039, 0.2372, 0.3238)) return db def create_path(p, mode=0o777): path = Path(p) os.makedirs(path, mode, exist_ok=True) return path def image_folder(): return f'/tmp/{getuser()}/images' def _gis_args(keywords, output_directory=None, image_directory=None, limit=200, format='jpg', color_type='full-color', size='medium', type='photo', delay=0, **kwargs): if output_directory is None: output_directory = str(create_path(image_folder())) if image_directory is None: image_directory = '_'.join(keywords.split()) arguments = {"keywords":keywords, "limit":limit, "format":format, "color_type":color_type, "size":size, "type":type, "delay":delay, "image_directory":image_directory, "output_directory":output_directory, "chromedriver":"/usr/bin/chromedriver" } arguments.update(kwargs) return arguments def crawl_images(keywords, output_directory=None, image_directory=None, limit=200, format='jpg', color_type='full-color', size='medium', type='photo', delay=0, **kwargs): """ Downloads images through Google Image Search, see https://google-images-download.readthedocs.io/en/latest/arguments.html for info on the arguments. keywords: the keywords passed to google image search to retrieve images limit: maximum number of images to retrieve (default=200). You will actually receive less iamges because many links will not work output_directory: base folder for the downloads (default: /tmp/username/images/) image_directory: subpath to store the images for this query (by default uses the query name) format: compression type of photos that are downloaded (default='jpg') color-type: default='full-color', see https://google-images-download.readthedocs.io/en/latest/arguments.html size: default='medium', see https://google-images-download.readthedocs.io/en/latest/arguments.html type: default='photo', see https://google-images-download.readthedocs.io/en/latest/arguments.html delay: default=0, to pause between downloads, see https://google-images-download.readthedocs.io/en/latest/arguments.html kwargs: any additional arguments that google-images-download accepts. """ try: from .google_images_download import googleimagesdownload except: raise NotImplemented('Need google images download for this') kwargs = _gis_args(keywords, output_directory=output_directory, image_directory=image_directory, limit=limit, format=format, color_type=color_type, size=size, type=type, delay=delay, **kwargs) response = googleimagesdownload() #class instantiation paths = response.download(kwargs) #passing the arguments to the function def filter_images(keywords, folder=None, columns=4, height=200, width=200): """ Removes duplicate images and shows the remaining images so that the user can manually select images to remove from the folder by pressing the DELETE button below. keywords: subfolder of 'folder' in which the images are stored folder: folder/output_directory where the crawled images are stored (e.g. /tmp/username/images) columns (4): number of images displayed per row height (200): height of the images in pixels width (200): width of the images in pixels """ def on_click(button): for r in rows: if type(r) is widgets.HBox: for c in r.children: checkbox = c.children[1] if checkbox.value: print(checkbox.description_tooltip) os.remove(checkbox.description_tooltip) if folder is None: folder = Path(image_folder()) keywords = '_'.join(keywords.split()) imagefiles = [f for f in folder.glob(keywords + '/*')] rows = [] cols = [] bymean = {} for i, imgfile in enumerate(tqdm(imagefiles)): row = i // columns col = i % columns img = Image.open(imgfile) m = hash(tuple(ImageStat.Stat(img).mean)) buff = io.BytesIO() img.save(buff, format='JPEG') if m in bymean: os.remove(imgfile) else: bymean[m] = imgfile image = widgets.Image( value=buff.getvalue(), width=width, height=height ) button = widgets.Checkbox( description='Delete', description_tooltip = str(imgfile) ) box = widgets.VBox([image, button]) cols.append(box) if len(cols) == columns: rows.append(widgets.HBox(cols)) cols = [] if len(cols) > 0: rows.append(widgets.HBox(cols)) button = widgets.Button( description='Delete' ) button.on_click(on_click) rows.append(button) return widgets.VBox(rows) ``` #### File: pipetorch/data/ptdataset.py ```python import numpy as np import pandas as pd from pandas.core.groupby.generic import DataFrameGroupBy, SeriesGroupBy from sklearn.preprocessing import StandardScaler, PolynomialFeatures import matplotlib.pyplot as plt from sklearn.utils import resample import copy import os def to_numpy(arr): try: return arr.data.cpu().numpy() except: pass try: return arr.to_numpy() except: pass return arr class PTDS: _metadata = ['_df', '_dfindices', '_pt_categoryx', '_pt_categoryy', '_pt_dummiesx', '_pt_dummiesy', '_pt_columny', '_pt_columnx', '_pt_transposey', '_pt_bias', '_pt_polynomials', '_pt_dtype', '_pt_sequence_window', '_pt_sequence_shift_y', '_pt_is_test'] _internal_names = pd.DataFrame._internal_names + ["_pt__indices", "_pt__x_sequence"] _internal_names_set = set(_internal_names) def to_ptdataframe(self): cls = self._df.__class__ r = cls(self) r._pt_columnx = self._pt_columnx r._pt_columny = self._pt_columny r._pt_transposey = self._pt_transposey r._pt_bias = self._pt_bias r._pt_polynomials = self._pt_polynomials r._pt_sequence_window = self._pt_sequence_window r._pt_sequence_shift_y = self._pt_sequence_shift_y r._pt__train = self r._pt__full = self r._pt__valid = None r._pt__test = None r._pt_indices = list(range(len(self))) r._pt__train_indices = r._pt_indices r._pt__valid_indices = [] r._pt__test_indices = [] r._pt_split = None r._pt_random_state = None r._pt_balance = None r._pt_shuffle = False return r def _copy_meta(self, r): r._df = self._df r._dfindices = self._dfindices r._pt_categoryx = self._pt_categoryx r._pt_categoryy = self._pt_categoryy r._pt_dummiesx = self._pt_dummiesx r._pt_dummiesy = self._pt_dummiesy r._pt_columny = self._pt_columny r._pt_columnx = self._pt_columnx r._pt_is_test = self._pt_is_test r._pt_transposey = self._pt_transposey r._pt_polynomials = self._pt_polynomials r._pt_bias = self._pt_bias r._pt_dtype = self._pt_dtype r._pt_sequence_window = self._pt_sequence_window r._pt_sequence_shift_y = self._pt_sequence_shift_y return r def _ptdataset(self, data): return self._copy_meta( PTDataSet(data) ) def _not_nan(self, a): a = np.isnan(a) while len(a.shape) > 1: a = np.any(a, -1) return np.where(~a)[0] @property def _dtype(self): return self._pt_dtype @property def indices(self): try: return self._pt__indices except: if self._pt_is_test: self._pt__indices = self._not_nan(self._x_sequence) else: s = set(self._not_nan(self._y_transposed)) self._pt__indices = [ i for i in self._not_nan(self._x_sequence) if i in s] return self._pt__indices @property def _scalerx(self): return self._df._scalerx @property def _scalery(self): return self._df._scalery @property def _categoryx(self): return self._pt_categoryx() @property def _categoryy(self): return self._pt_categoryy() @property def _dummiesx(self): return self._pt_dummiesx() @property def _dummiesy(self): return self._pt_dummiesy() @property def _shift_y(self): if self._pt_sequence_shift_y is not None: return self._pt_sequence_shift_y else: return 0 @property def _sequence_window(self): try: if self._is_sequence: return self._pt_sequence_window except:pass return 1 @property def _sequence_index_y(self): return self._pt_sequence_window+self._shift_y-1 @property def _columny(self): return [ self.columns[-1] ] if self._pt_columny is None else self._pt_columny @property def _transposey(self): return True if self._pt_transposey is None else self._pt_transposey @property def _columnx(self): if self._pt_columnx is None: return [ c for c in self.columns if c not in self._columny ] return self._pt_columnx @property def _polynomials(self): return self._pt_polynomials @property def _bias(self): return self._pt_bias def _transform(self, scalers, array): out = [] for i, scaler in enumerate(scalers): if scaler is not None: out.append(scaler.transform(array[:, i:i+1])) else: out.append(array[:, i:i+1]) return np.concatenate(out, axis=1) def resample_rows(self, n=True): r = self._ptdataset(self) if n == True: n = len(r) if n < 1: n = n * len(r) return r.iloc[resample(list(range(len(r))), n_samples = int(n))] def interpolate_factor(self, factor=2, sortcolumn=None): if not sortcolumn: sortcolumn = self.columns[0] df = self.sort_values(by=sortcolumn) for i in range(factor): i = df.rolling(2).sum()[1:] / 2.0 df = pd.concat([df, i], axis=0) df = df.sort_values(by=sortcolumn) return self._df._ptdataset(df).reset_index(drop=True) @property def _x_category(self): if self._is_sequence: self = self.iloc[:-self._shift_y] if self._categoryx is None: return self[self._columnx] r = copy.copy(self[self._columnx]) for c, cat in zip(r._columnx, r._categoryx): if cat is not None: r[c] = cat.transform(r[c]) return r @property def _x_dummies(self): if self._dummiesx is None: return self._x_category r = copy.copy(self._x_category) r1 = [] for d, onehot in zip(r._columnx, r._dummiesx): if onehot is not None: a = onehot.transform(r[[d]]) r1.append( pd.DataFrame(a.toarray(), columns=onehot.get_feature_names_out([d])) ) r = r.drop(columns = d) r1.insert(0, r.reset_index(drop=True)) r = pd.concat(r1, axis=1) return r @property def _x_numpy(self): return self._x_dummies.to_numpy() @property def _x_polynomials(self): try: return self._polynomials.fit_transform(self._x_numpy) except: return self._x_numpy @property def _x_scaled(self): if len(self) > 0: return self._transform(self._scalerx, self._x_polynomials) return self._x_polynomials @property def _x_biased(self): a = self._x_scaled if self._bias: return np.concatenate([np.ones((len(a),1)), a], axis=1) return a @property def _x_sequence(self): try: return self._pt__x_sequence except: if not self._is_sequence: self._pt__x_sequence = self._x_biased else: X = self._x_biased window = self._sequence_window len_seq_mode = max(0, len(X) - window + 1) self._pt__x_sequence = np.concatenate([np.expand_dims(X[ii:ii+window], axis=0) for ii in range(len_seq_mode)], axis=0) return self._pt__x_sequence @property def X(self): return self._x_sequence[self.indices] @property def X_tensor(self): import torch if self._dtype is None: return torch.tensor(self.X).type(torch.FloatTensor) else: return torch.tensor(self.X) @property def y_tensor(self): import torch if self._dtype is None: return torch.tensor(self.y).type(torch.FloatTensor) else: return torch.tensor(self.y) @property def _is_sequence(self): return self._pt_sequence_window is not None @property def tensors(self): return self.X_tensor, self.y_tensor @property def _range_y(self): stop = len(self) if self._shift_y >= 0 else len(self) + self._shift_y start = min(stop, self._sequence_window + self._shift_y - 1) return slice(start, stop) @property def _y_category(self): if self._is_sequence: self = self.iloc[self._range_y] if self._categoryy is None: return self[self._columny] r = copy.copy(self[self._columny]) for d, onehot in zip(r._columny, r._dummiesy): if onehot is not None: r[c] = cat.transform(r[c]) return r @property def _y_dummies(self): if self._dummiesy is None: return self._y_category r = copy.copy(self._y_category) r1 = [] for d, onehot in zip(r._columny, r._dummiesy): if onehot is not None: a = onehot.transform(r[[d]]) r1.append( pd.DataFrame(a.toarray(), columns=onehot.get_feature_names_out([d])) ) r = r.drop(columns = d) r1.insert(0, r.reset_index(drop=True)) r = pd.concat(r1, axis=1) return r @property def _y_numpy(self): return self._y_dummies.to_numpy() @property def _y_scaled(self): if len(self) > 0: return self._transform(self._scalery, self._y_numpy) return self._y_numpy @property def _y_transposed(self): return self._y_scaled.squeeze() if self._transposey else self._y_scaled @property def y(self): return self._y_transposed[self.indices] def replace_y(self, new_y): y_pred = self._predict(new_y) offset = self._range_y.start indices = [ i + offset for i in self.indices ] assert len(y_pred) == len(indices), f'The number of predictions ({len(y_pred)}) does not match the number of samples ({len(indices)})' r = copy.deepcopy(self) r[self._columny] = np.NaN columns = [r.columns.get_loc(c) for c in self._columny] r.iloc[indices, columns] = y_pred.values return r def to_dataset(self): """ returns: a list with a train, valid and test DataSet. Every DataSet contains an X and y, where the input data matrix X contains all columns but the last, and the target y contains the last column columns: list of columns to convert, the last column is always the target. default=None means all columns. """ from torch.utils.data import TensorDataset return TensorDataset(*self.tensors) def _predict_y(self, predict): if not callable(predict): return predict try: from torch import nn import torch with torch.set_grad_enabled(False): return to_numpy(predict(self.X_tensor)).reshape(len(self)) except: raise try: return predict(self.X).reshape(len(self)) except: raise raise ValueError('predict mus be a function that works on Numpy arrays or PyTorch tensors') def _predict(self, predict): return self.inverse_transform_y(self._predict_y(predict)) def predict(self, predict, drop=True): y_pred = self._predict_y(predict) if drop: return self._df.inverse_transform(self.X, y_pred) return self._df.inverse_transform(self.X, self.y, y_pred) def add_column(self, y_pred, *columns): y_pred = to_numpy(y_pred) offset = self._range_y.start indices = [ i + offset for i in self.indices ] assert len(y_pred) == len(indices), f'The number of predictions ({len(y_pred)}) does not match the number of samples ({len(indices)})' r = copy.deepcopy(self) y_pred = self.inverse_transform_y(y_pred) if len(columns) == 0: columns = [ c + '_pred' for c in self._columny ] for c in columns: r[c] = np.NaN columns = [r.columns.get_loc(c) for c in columns] r.iloc[indices, columns] = y_pred.values return r def inverse_transform_y(self, y_pred): return self._df.inverse_transform_y(y_pred) def line(self, x=None, y=None, xlabel = None, ylabel = None, title = None, **kwargs ): self._df.evaluate().line(x=x, y=y, xlabel=xlabel, ylabel=ylabel, title=title, df=self, **kwargs) def scatter(self, x=None, y=None, xlabel = None, ylabel = None, title = None, **kwargs ): self._df.evaluate().scatter(x=x, y=y, xlabel=xlabel, ylabel=ylabel, title=title, df=self, **kwargs) def scatter2d_class(self, x1=None, x2=None, y=None, xlabel=None, ylabel=None, title=None, loc='upper right', noise=0, **kwargs): self._df.evaluate().scatter2d_class(x1=x1, x2=x2, y=y, xlabel=xlabel, ylabel=ylabel, title=title, loc=loc, noise=noise, df=self, **kwargs) def scatter2d_color(self, x1=None, x2=None, c=None, xlabel=None, ylabel=None, title=None, noise=0, **kwargs): self._df.evaluate().scatter2d_color(x1=x1, x2=x2, c=c, xlabel=xlabel, ylabel=ylabel, title=title, noise=noise, df=self, **kwargs) def scatter2d_size(self, x1=None, x2=None, s=None, xlabel=None, ylabel=None, title=None, noise=0, **kwargs): self._df.evaluate().scatter2d_size(x1=x1, x2=x2, s=s, xlabel=xlabel, ylabel=ylabel, title=title, noise=noise, df=self, **kwargs) def plot_boundary(self, predict): self._df.evaluate().plot_boundary(predict) def plot_contour(self, predict): self._df.evaluate().plot_contour(predict) class PTDataSet(pd.DataFrame, PTDS): _metadata = PTDS._metadata _internal_names = PTDS._internal_names _internal_names_set = PTDS._internal_names_set @property def _constructor(self): return PTDataSet @classmethod def from_ptdataframe(cls, data, df, dfindices): r = cls(data) r._df = df r._dfindices = dfindices r._pt_categoryx = df._categoryx r._pt_categoryy = df._categoryy r._pt_dummiesx = df._dummiesx r._pt_dummiesy = df._dummiesy r._pt_columny = df._columny r._pt_columnx = df._columnx r._pt_transposey = df._transposey r._pt_polynomials = df._pt_polynomials r._pt_bias = df._pt_bias r._pt_dtype = df._pt_dtype r._pt_is_test = False r._pt_sequence_window = df._pt_sequence_window r._pt_sequence_shift_y = df._pt_sequence_shift_y return r @classmethod def df_to_testset(cls, data, df, dfindices): r = cls.from_ptdataframe(data, df, dfindices) r._pt_is_test = True return r def groupby(self, by, axis=0, level=None, as_index=True, sort=True, group_keys=True, observed=False, dropna=True): r = super().groupby(by, axis=axis, level=level, as_index=as_index, sort=sort, group_keys=group_keys, observed=observed, dropna=dropna) return self._copy_meta( PTGroupedDataSet(r) ) class PTGroupedDataSetSeries(SeriesGroupBy, PTDS): _metadata = PTDS._metadata #_internal_names = PTDS._internal_names #_internal_names_set = PTDS._internal_names_set @property def _constructor(self): return PTGroupedDataSetSeries @property def _constructor_expanddim(self): return PTGroupedDataFrame class PTGroupedDataSet(DataFrameGroupBy, PTDS): _metadata = PTDS._metadata #_internal_names = PTDS._internal_names #_internal_names_set = PTDS._internal_names_set def __init__(self, data=None): super().__init__(obj=data.obj, keys=data.keys, axis=data.axis, level=data.level, grouper=data.grouper, exclusions=data.exclusions, selection=data._selection, as_index=data.as_index, sort=data.sort, group_keys=data.group_keys, observed=data.observed, mutated=data.mutated, dropna=data.dropna) @property def _constructor(self): return PTGroupedDataSet @property def _constructor_sliced(self): return PTGroupedDataSetSeries def __iter__(self): for group, subset in super().__iter__(): yield group, self._copy_meta(subset) def astype(self, dtype, copy=True, errors='raise'): PTDataSet.astype(self, dtype, copy=copy, errors=errors) def get_group(self, name, obj=None): return self._ptdataset( super().get_group(name, obj=obj) ) def to_dataset(self): from torch.utils.data import ConcatDataset dss = [] for key, group in self: dss.append( group.to_dataset()) return ConcatDataset(dss) ``` #### File: pipetorch/model/perceptron.py ```python from torchvision.models import * import torch import torch.nn as nn identity=lambda x:x class SingleLayerPerceptron(nn.Module): def __init__(self, input, output, last_activation=identity): super().__init__() self.w1 = nn.Linear(input, output) self.a1 = last_activation def forward(self, x): return self.a1(self.w1(x)) #return pred_y.view(-1) class SingleLayerPerceptron_BinaryClass(SingleLayerPerceptron): def __init__(self, input, output): super().__init__(input, output, nn.Sigmoid()) def post_forward(self, y): return (y > 0.5).float() class SingleLayerPerceptron_MultiClass(SingleLayerPerceptron): def __init__(self, input, output): super().__init__(input, output, nn.LogSoftmax(dim=1)) def flatten_r_image(x): return x[:,0,:,:].view(x.shape[0], -1) class MultiLayerPerceptron(nn.Module): def __init__(self, *width, preprocess=identity, inner_activation=nn.ReLU(), drop_prob=None, last_activation=None): super().__init__() self.actions = [preprocess] for n, (i, o) in enumerate(zip(width[:-1], width[1:])): l = nn.Linear(i, o) self.actions.append(l) self.__setattr__(f'w{n+1}', l) if n < len(width) - 2: if drop_prob is not None: self.actions.append(nn.Dropout(p=drop_prob)) self.__setattr__(f'drop{n+1}', self.actions[-1]) self.actions.append(inner_activation) self.__setattr__(f'activation{n+1}', self.actions[-1]) elif last_activation is not None: self.actions.append(last_activation) self.__setattr__(f'activation{n+1}', self.actions[-1]) #if width[-1] == 1: # self.reshape = (-1) #else: # self.reshape = (-1, width[-1]) def forward(self, x): for a in self.actions: x = a(x) return x #.view(self.reshape) class MultiLayerPerceptron_BinaryClass(MultiLayerPerceptron): def __init__(self, *width, preprocess=identity, inner_activation=nn.ReLU(), drop_prob=None): super().__init__(*width, preprocess=preprocess, inner_activation=inner_activation, drop_prob=drop_prob, last_activation=nn.nn.Sigmoid()) def post_forward(self, y): return (y > 0.5).float() class MultiLayerPerceptron_MultiClass(MultiLayerPerceptron): def __init__(self, *width, preprocess=identity, inner_activation=nn.ReLU(), drop_prob=None): super().__init__(*width, preprocess=preprocess, inner_activation=inner_activation, drop_prob=drop_prob) def post_forward(self, y): return torch.argmax(y, axis=1) class TwoLayerPerceptron(nn.Module): def __init__(self, input, hidden, output, last_activation=None): super().__init__() self.w1 = nn.Linear(input, hidden) self.a1 = nn.ReLU() self.w2 = nn.Linear(hidden, output) if last_activation: self.a2 = last_activation def forward(self, x): x = self.a1(self.w1(x)) pred_y = self.a2(self.w2(x)) return pred_y #.view(-1) def post_forward(self, y): return y class TwoLayerPerceptron_BinaryClass(TwoLayerPerceptron): def __init__(self, input, hidden, output): super().__init__(input, hidden, output, last_activation=nn.Sigmoid()) def post_forward(self, y): return (y > 0.5).float() class TwoLayerPerceptron_MultiClass(TwoLayerPerceptron): def __init__(self, input, hidden, output): super().__init__(input, hidden, output, last_activation=nn.LogSoftmax(dim=1)) def zero_embedding(rows, columns): e = nn.Embedding(rows, columns) e.weight.data.zero_() return e class factorization(nn.Module): def __init__(self, n_users, n_items, n_factors=20): super().__init__() self.user_factors = nn.Embedding( n_users,n_factors) self.item_factors = nn.Embedding( n_items,n_factors) self.user_bias = zero_embedding( n_users, 1) self.item_bias = zero_embedding( n_items, 1) self.fc = nn.Linear(n_factors, 4) def forward(self, X): user = X[:,0] - 1 item = X[:,1] - 1 return (self.user_factors(user) * self.item_factors(item)).sum(1) + self.user_bias(user).squeeze() + self.item_bias(item).squeeze() ``` #### File: pipetorch/train/helper.py ```python import torch import os import matplotlib from matplotlib import pyplot as plt import matplotlib.patheffects as PathEffects from IPython.core import pylabtools as pt from pathlib2 import Path from sklearn.manifold import TSNE import seaborn as sns import numpy as np import sys from IPython import get_ipython ipython = get_ipython() back2gui = { b:g for g, b in pt.backends.items() } class plt_gui: def __init__(self, gui): self.gui = gui def __enter__(self): backend = matplotlib.get_backend() self.old_gui = back2gui[backend] ipython.magic('matplotlib ' + self.gui) def __exit__(self, *args): ipython.magic('matplotlib ' + self.old_gui) class plt_inline(plt_gui): def __init__(self): super().__init__('inline') class plt_notebook(plt_gui): def __init__(self): super().__init__('notebook') def getsizeof(o, ids=set()): d = deep_getsizeof if id(o) in ids: return 0 r = sys.getsizeof(o) ids.add(id(o)) if isinstance(o, str) or isinstance(0, unicode): return r if isinstance(o, Mapping): return r + sum(d(k, ids) + d(v, ids) for k, v in o.iteritems()) if isinstance(o, Container): return r + sum(d(x, ids) for x in o) return r class Plot: def __init__(self, xlabel=None, ylabel='Loss', xscale=None, yscale='log', **kwargs): self.figure = plt.figure() self.ax = self.figure.add_subplot(111) self.figure.show() self.xlabel = xlabel self.ylabel = ylabel self.xscale = xscale self.yscale = yscale def __enter__(self): plt.ion() return self def __exit__(self, *args): plt.ioff() def set_ylim(self, y): y = np.array(y) while True: mean_y = np.mean(y) sd_y = np.std(y) keep = (y >= mean_y - 4 * sd_y) & (y <= mean_y + 4 * sd_y) if sum(keep) == len(y): break y = y[keep] if min(y) < max(y): self.ax.set_ylim(max(y) - (max(y) - min(y)) * 1.1, min(y) + (max(y) - min(y))) def set_ylim_multi(self, yy): min_y = None max_y = None for y in yy.values(): y = np.array(y) while True: mean_y = np.mean(y) sd_y = np.std(y) keep = (y >= mean_y - 3 * sd_y) & (y <= mean_y + 3 * sd_y) if sum(keep) == len(y): break y = y[keep] if min_y is not None: min_y = min(min_y, min(y)) max_y = max(max_y, max(y)) else: min_y = min(y) max_y = max(y) if min_y < max_y: self.ax.set_ylim(max_y - (max_y - min_y) * 1.05, min_y + (max_y - min_y)*1.05) def replot(self, x, y): self.ax.clear() if self.xlabel: self.ax.set_xlabel(self.xlabel) if self.ylabel: self.ax.set_ylabel(self.ylabel) if self.xscale: self.ax.set_xscale(self.xscale) if self.yscale: self.ax.set_yscale(self.yscale) self.set_ylim(y) self.ax.plot( x, y) plt.show() self.figure.canvas.draw() def multiplot(self, x, yy): self.ax.clear() if self.xlabel: self.ax.set_xlabel(self.xlabel) if self.ylabel: self.ax.set_ylabel(self.ylabel) if self.xscale: self.ax.set_xscale(self.xscale) if self.yscale: self.ax.set_yscale(self.yscale) self.set_ylim_multi(yy) for name, y in yy.items(): self.ax.plot( x, y, label=str(name)) self.ax.legend() self.figure.canvas.draw() def to_numpy(arr): if type(arr) is torch.Tensor: if arr.device.type == 'cuda': return arr.data.cpu().numpy() else: return arr.data.numpy() return arr def plot_histories(metric, history, train=True, valid=True, **kwargs): plt.figure(**kwargs) for label, t in history.items(): h = t.history x = [ epoch['epoch'] for epoch in h.epochs['train'] ] if train: plt.plot(x, h.train(metric), label=f'train_{label}') if valid: plt.plot(x, h.valid(metric), label=f'valid_{label}') plt.ylabel(metric.__name__) plt.xlabel("epochs") plt.legend() plt.show() def create_path(p, mode=0o777): path = Path(p) os.makedirs(path, mode, exist_ok=True) return path def scatter(x, colors): num_classes = len(np.unique(colors)) palette = np.array(sns.color_palette("hls", num_classes)) f = plt.figure(figsize=(8, 8)) ax = plt.subplot(aspect='equal') sc = ax.scatter(x[:,0], x[:,1], lw=0, s=40, c=palette[colors.astype(np.int)]) plt.xlim(-25, 25) plt.ylim(-25, 25) ax.axis('off') ax.axis('tight') txts = [] for i in range(num_classes): xtext, ytext = np.median(x[colors == i, :], axis=0) txt = ax.text(xtext, ytext, str(i), fontsize=24) txt.set_path_effects([ PathEffects.Stroke(linewidth=5, foreground="w"), PathEffects.Normal()]) txts.append(txt) #return f, ax, sc, txts def to_numpy1(a): try: a = a.detach() except: pass try: a = a.numpy() except: pass return a def draw_regression(x, y_true, y_pred): f = plt.figure(figsize=(8, 8)) x, y_true, y_pred = [to_numpy(a) for a in (x, y_true, y_pred)] plt.scatter(x, y_true) indices = np.argsort(x) plt.plot(x[indices], y_pred[indices]) def line_predict(x, y_true, y_pred): draw_regression(x, y_true, y_pred) def scatter(x, y): f = plt.figure(figsize=(8, 8)) x, y = [to_numpy(a) for a in (x, y)] plt.scatter(x, y) def range3(start, end): while start < end: yield start yield start * 3 start *= 10 def plot_tsne(X, y, random_state=0): t = TSNE(random_state=random_state).fit_transform(X) scatter(t, y) def trace_warnings(): import traceback import warnings import sys def warn_with_traceback(message, category, filename, lineno, file=None, line=None): log = file if hasattr(file,'write') else sys.stderr traceback.print_stack(file=log) log.write(warnings.formatwarning(message, category, filename, lineno, line)) warnings.showwarning = warn_with_traceback def expand_features(df, target, *features): if len(features) == 0: return [c for c in df.columns if c != target] else: return [c for c in features if c != target] def read_csv(filename, nrows=100, drop=None, columns=None, dtype=dict(), intcols=[], **kwargs): df = pd.read_csv(filename, nrows=nrows, engine='python', **kwargs) if drop: df = df.drop(columns=drop) elif columns: df = df[columns] float_cols = [c for c in df if df[c].dtype.kind == "f" or df[c].dtype.kind == "i"] float32_cols = {c:np.float32 for c in float_cols} float32_cols.update({ c:np.int64 for c in intcols }) float32_cols.update(dtype) df = pd.read_csv(filename, dtype=float32_cols, engine='python', low_memory=False, **kwargs) if drop: df = df.drop(columns=drop) elif columns: df = df[columns] return df class nonondict(dict): """ A dict that does not store None values, which is used to keep a dict of parameters for function calls, in which setting to None does not override the default setting. """ def __init__(self, *args, **kwargs): super().__init__() self.update(*args, **kwargs) def __setitem__(self, key, value): if value is None: try: del self[key] except: pass else: super().__setitem__(key, value) def setifnone(self, key, value): """ Set a key to a value, only if that key does not yet exists. Since None values are not added, this also applies to keys that are previously set to None. Arguments: key: str value: any """ if key not in self: self[key] = value def update(self, *args, **kwargs): for k, v in dict(*args, **kwargs).items(): self[k] = v ``` #### File: pipetorch/train/trainer.py ```python import torch from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F from torch.optim import AdamW import timeit import sys import copy import inspect import numpy as np import math from tqdm.notebook import tqdm from ..evaluate.evaluate import Evaluator from torch.optim.lr_scheduler import OneCycleLR, ConstantLR from .tuner import * from .helper import nonondict from functools import partial import os try: GPU = int(os.environ['GPU']) GPU = 0 except: GPU = -1 # def last_container(last): # try: # l = last_container(last.children()) # if l is not None: # return l # except: pass # try: # if len(last._modules) > 0 and next(reversed(last._modules.values())).out_features > 0: # return last # except: pass def to_numpy(arr): try: return arr.data.cpu().numpy() except: pass try: return arr.to_numpy() except: pass return arr # class DLModel(nn.Module): # def __init__(self): # super().__init__() # def set_last_linear(self, out_features): # container = self.last_container() # name, last = container._modules.popitem() # container.add_module(name, nn.Linear(last.in_features, out_features)) # def last_container(self): # return last_container(self) def UniformLR(*args, **kwargs): class Uniform_Scheduler: def step(self): pass return Uniform_Scheduler() def onecycle(optimizer, lr, steps): return OneCycleLR(optimizer, lr[1], total_steps=steps) class ordered_dl: def __init__(self, dl): self.dl = dl def __enter__(self): self.oldsampler = self.dl.batch_sampler.sampler self.newsampler = torch.utils.data.sampler.SequentialSampler(self.oldsampler.data_source) self.dl.batch_sampler.sampler = self.newsampler return self.dl def __exit__(self, exc_type, exc_value, tb): self.dl.batch_sampler.sampler = self.oldsampler if exc_type is not None: return False class trainer: """ A general purpose trainer for PyTorch. Arguments: model: nn.Module a PyTorch Module that will be trained loss: callable a PyTorch or custom loss function data: databunch or a list of iterables (DataLoaders) a databunch is an object that has a train_dl, valid_dl, and optionally test_dl property. otherwise, a list of iterables can also be given. Most often, these iterables are PyTorch DataLoaders that are used to iterate over the respective datasets for training and validation. metrics: callable or list of callable One or more functions that can be called with (y, y_pred) to compute an evaluation metric. This will automatically be done during training, for both the train and valid sets. Typically, the callable is a function from SKLearn.metrics like mean_squared_error or recall_score. optimizer: PyTorch Optimizer (AdamW) The PyTorch or custom optimizer class that is used during training optimizerparams: dict (None) the parameters that are passed (along with the model parameters) to initialize an optimizer. A 'nonondict' is used, meaning that when a None value is set, the key is removed, so that the default value is used instead. random_state: int used to set a random state for reproducible results scheduler: None, OneCycleLR, ConstantLR used to adapt the learning rate: - None will use a constant learning rate - OneCycleLR will will use a cyclic annealing learning rate between an upper and lower bound. - ConstantLR will use a linear decaying learning rate between an upper bound and lower bound. You can optionally use 'cycle' when calling 'train' to restart ConstantLR every 'cycle' epochs. weight_decay: float Apply weight_decay regularization with the AdamW optimizer momentum: float Apply momentum with the AdamW optimizer gpu: bool, int or torch.device The device to train on: False or -1: cpu True: cuda:0, this is probably what you want to train on gpu int: cuda:gpu Setting the device will automatically move the model and data to the given device. Note that the model is not automatically transfered back to cpu afterwards. evaluator: PipeTorch evaluator An evaluator that was created by a different trainer or DataFrame, to combine the results of different training sessions. """ def __init__(self, model, loss, *data, metrics = [], optimizer=AdamW, optimizerparams=None, random_state=None, scheduler=None, weight_decay=None, momentum=None, gpu=False, evaluator=None, **kwargs): self.report_frequency = 1 self.loss = loss self.random_state = random_state self.gpu(gpu) self.set_data(*data) self._model = model try: self.post_forward = model.post_forward except: pass self.optimizer = optimizer self.optimizer_params = optimizerparams self.scheduler = scheduler if self.random_state is not None: torch.backends.cudnn.deterministic=True torch.manual_seed(self.random_state) self._commit = {} self.epochid = 0 self.weight_decay = weight_decay self.momentum = momentum self.lowest_score=None self.highest_score=None if evaluator is not None: assert len(metrics) == 0, 'When you assign an evaluator, you cannot assign different metrics to a trainer' self._evaluator = evaluator self.metrics = evaluator.metrics else: self.metrics = metrics def set_data(self, *data): """ Changes the dataset that is used by the trainer Arguments: data: databunch or a list of iterables (DataLoaders) a databunch is an object that has a train_dl, valid_dl, and optionally test_dl property. otherwise, a list of iterables can also be given. Most often, these iterables are PyTorch DataLoaders that are used to iterate over the respective datasets for training and validation. """ assert len(data) > 0, 'You have to specify a data source. Either a databunch or a set of dataloaders' if len(data) == 1: db = data[0] self.databunch = db elif len(data) < 4: try: _ = iter(data[0]) self.train_dl = data[0] except TypeError: raise TypeError('The first data source must be iterable, preferably a DataLoader that provide an X and y') try: _ = iter(data[1]) self.valid_dl = data[1] except TypeError: raise TypeError('The second data source must be iterable, preferably a DataLoader that provide an X and y') if len(data) > 2: try: _ = iter(data[2]) self.test_dl = data[2] except TypeError: raise TypeError('The third data source must be iterable, preferably a DataLoader that provide an X and y') @property def evaluator(self): """ The (PipeTorch) evaluator that is used to log training progress """ try: return self._evaluator except: try: self._evaluator = self.db.to_evaluator( *self.metrics ) except: self._evaluator = Evaluator(self, *self.metrics) return self._evaluator def __repr__(self): return 'Trainer( ' + self.model + ')' def to(self, device): """ Configures the device to train on Arguments: device: bool, int or torch.device The device to train on: False or -1: cpu True: cuda:0, this is probably what you want to train on gpu int: cuda:gpu Setting the device will automatically move the model and data to the given device. Note that the model is not automatically transfered back to cpu afterwards. """ if device is True or (type(device) == int and device == 0): device = torch.device('cuda:0') elif device is False or (type(device) == int and device == -1): device = torch.device('cpu') elif type(device) == int: assert device < torch.cuda.device_count(), 'Cannot use gpu {device}, note that if a gpu has already been selected it is always renumbered to 0' device = torch.device(f'cuda:{device}') try: if device != self.device: self.device = device try: del self._optimizer except: pass except: self.device = device def cpu(self): """ Configure the trainer to train on cpu """ self.to(False) def gpu(self, gpu=True): """ Configure the trainer to train on gpu, see to(device) """ self.to(gpu) @property def metrics(self): """ Returns: list of metrics that is collected while training """ return self._metrics @metrics.setter def metrics(self, value): """ Sets the metric(s) that are collected while training """ try: iter(value) self._metrics = value except: self._metrics = [] if value is None else [value] @property def databunch(self): """ Returns: the databunch that is used thows an exception if no databunch has been configured """ return self._databunch @databunch.setter def databunch(self, db): """ Setter to use a databunch. The databunch object must have at least a train_dl and a valid_dl property, and optional a test_dl. These are often PyTorch DataLoaders, but can be any iterable over a DataSet. """ assert hasattr(db, 'train_dl'), 'A single data source must be an object with a train_dl property (like a databunch)' assert hasattr(db, 'valid_dl'), 'A single data source must be an object with a valid_dl property (like a databunch)' self._databunch = db self.train_dl = self.databunch.train_dl self.valid_dl = self.databunch.valid_dl try: self.test_dl = self.databunch.test_dl except: pass @property def lr(self): """ return: the learning rate that was set, could be an interval """ return self._lr @lr.setter def lr(self, lr): """ Sets the learning rate that is used for training. You can either use a single value for a fixed lr, a tuple with an interval of two values for a linear decaying scheduler, or a tuple with an interval of two values for a OneCyleLR scheduler. The allocation of a scheduler can be overruled by setting a scheduler manually. If the lr did not change, nothing happens, otherwise a new optimizer is created when needed. """ if type(lr) is tuple: lr = tuple(sorted(lr)) elif type(lr) is list: lr = sorted(lr) try: if self.lr == lr: return except: pass try: del self._optimizer except: pass self._lr = lr def set_lr(self, lr): """ sets the learning rate without changing the learning rate settings the scheduler or optimizer. is used by tuners like find_lr. """ for param_group in self.optimizer.param_groups: param_group['lr'] = lr @property def min_lr(self): """ the learning rate or lowest of an interval of learning rates """ try: return self.lr[0] except: try: return self.lr except: return 1e-2 @property def max_lr(self): """ the learning rate or highest of an interval of learning rates """ try: return self.lr[1] except: pass try: return self.lr[0] except: pass return self.lr def set_optimizer_param(self, key, value): """ Set a parameter for the optimizer. A 'nonondict' is used, meaning that setting a value to None will cause the default to be used. Argument: key: str the key to use value: any the value to use. When set to None, the key is removed. """ self.optimizer_params[key] = value try: del self._optimizer del self._scheduler except: pass @property def weight_decay(self): """ Returns: the current value for the weight decay regularization only works when using an Adam(W) optimizer """ return self.optimizer.param_groups[0]['weight_decay'] @weight_decay.setter def weight_decay(self, value): """ Sets the weight decay regularization on the Adam(W) optimizer """ self.set_optimizer_param('weight_decay', value) @property def momentum(self): """ Returns the momentum value on the Adam(W) optimizer """ return self.optimizer.param_groups[0]['betas'] @momentum.setter def momentum(self, value): """ Sets the momentum value on the Adam(W) optimizer """ self.set_optimizer_param('betas', value) @property def optimizer(self): """ Returns: an optimizer for training the model, using the applied configuration (e.g. weight_decay, momentum, learning_rate). If no optimizer exists, a new one is created using the configured optimizerclass (default: AdamW) and settings. """ try: return self._optimizer except: self.set_optimizer_param('lr', self.min_lr) self._optimizer = self._optimizer_class(self.model.parameters(), **self.optimizer_params) return self._optimizer @optimizer.setter def optimizer(self, value): """ Sets the optimizer class to use. """ self._optimizer_class = value try: del self._optimizer del self._scheduler except: pass @property def optimizer_params(self): try: return self._optimizer_params except: self._optimizer_params = nonondict() return self._optimizer_params @optimizer_params.setter def optimizer_params(self, value): """ Setter for the optimizer parameters used, only applies them if the value is set other than None. If you want to remove all params, set them to an empty dict. Arguments: value: dict conform the optimizer class that is used """ if value is not None: assert instanceof(value) == dict, 'you have set optimizer_params to a dict' self._optimizer_params = nonondict(value) @property def scheduler_params(self): try: return self._scheduler_params except: self._scheduler_params = nonondict() return self._scheduler_params @scheduler_params.setter def scheduler_params(self, value): """ Setter for the scheduler parameters used, only applies them if the value is set other than None. If you want to remove all params, set them to an empty dict. Arguments: value: dict conform the scheduler class/initializer that is used """ if value is not None: assert instanceof(value) == dict, 'you have set scheduler_params to a dict' self._optimizer_params = nonondict(value) def del_optimizer(self): try: del self._optimizer except: pass self.del_scheduler() def del_scheduler(self): try: del self._scheduler except: pass @property def scheduler(self): """ Returns: scheduler that is used to adapt the learning rate When you have set a (partial) function to initialze a scheduler, it should accepts (optimizer, lr, scheduler_params) as its parameters. Otherwise, one of three standard schedulers is used based on the value of the learning rate. If the learning rate is - float: no scheduler is used - [max, min]: a linear decaying scheduler is used. - (max, min): a OneCyleLR scheduler is used. """ try: return self._scheduler except: try: #steps = int(round((len(self.train_dl) * self.cycle_epochs))) if self._scheduler_class is None: try: self.lr[1] if type(self.lr) == tuple: schedulerclass = OneCycleLR elif type(self.lr) == list: schedulerclass = ConstantLR else: raise NotImplementedError(f'Provide either an single value learning rate for a Uniform scheduler, list [low, high] for a Linear Decay, or tuple (low, high) for a OneCycleLR scheduler') except: schedulerclass = UniformLR else: schedulerclass = self._scheduler_class if schedulerclass == ConstantLR: factor = (self.min_lr / self.max_lr) ** (1 / self._scheduler_epochs) self._scheduler = ConstantLR(self.optimizer, factor, self._scheduler_epochs, **self.scheduler_params) elif schedulerclass == OneCycleLR: scheduler_params = self.scheduler_params scheduler_params['epochs'] = self._scheduler_epochs scheduler_params['steps_per_epoch'] = len(self.train_dl) self._scheduler = OneCycleLR(self.optimizer, self.min_lr, **scheduler_params) else: self._scheduler = schedulerclass(self.optimizer, self.lr, **self.scheduler_params) except: raise NotImplementedError(f'The provided function does not work with (optim, {self.lr}, {self._scheduler_epochs}, {len(self.train_dl)}) to instantiate a scheduler') return self._scheduler @scheduler.setter def scheduler(self, value): """ Sets the schedulerclass (or function to initialize a scheduler) to use. At this moment, there is no uniform way to initialize all PyTorch schedulers. PipeTorch provides easy support for using a scheduler through the learning rate: - float: no scheduler is used - [max, min]: a linear annealing scheduler is used. - (max, min): a OneCyleLR scheduler is used. To use another scheduler, set this to a function that accepts the following parameters: (optimizer instance, learning rate, **scheduler_params) The scheduler_params can be supplied when calling train. """ try: del self._scheduler except: pass self._scheduler_class = value # @property # def out_features(self): # try: # return self._out_features # except: pass # try: # self._out_features = last_container(self.model).out_features # return self._out_features # except: # print('cannot infer out_features from the model, please specify it in the constructor of the trainer') # raise # @property # def in_features(self): # first = next(iter(self._model.modules())) # while type(first) is nn.Sequential: # first = next(iter(first.modules())) # return first.in_features @property def valid_ds(self): return self.valid_dl.dataset @property def train_ds(self): return self.train_dl.dataset @property def test_ds(self): return self.test_dl.dataset @property def train_Xy(self): for batch in self.train_dl: yield [ t.to(self.model.device) for t in batch ] @property def valid_Xy(self): for batch in self.valid_dl: yield [ t.to(self.model.device) for t in batch ] @property def test_Xy(self): for batch in self.test_dl: yield [ t.to(self.model.device) for t in batch ] @property def valid_tensors(self): return self.valid_dl.dataset.tensors @property def train_tensors(self): return self.train_dl.dataset.tensors @property def test_tensors(self): return self.test_dl.dataset.tensors @property def train_X(self): return self.train_tensors[0] @property def train_y(self): return self.train_tensors[-1] @property def valid_X(self): return self.valid_tensors[0] @property def valid_y(self): return self.valid_tensors[-1] @property def test_X(self): return self.test_tensors[0] @property def test_y(self): return self.test_tensors[-1] @property def model(self): """ When a device is configured to train the model on, the model is automatically transferred to the device. A device property is set on the model to transfer the data to the same device as the model before using. Returns: the model """ try: if self.device is not self._model.device: self._model.device = self.device self._model.to(self.device) try: del self._optimizer except: pass except: try: self._model.device = self.device self._model.to(self.device) #print('change device') try: del self._optimizer except: pass except: pass return self._model def parameters(self): """ Prints the (trainable) model parameters """ for name, param in self.model.named_parameters(): if param.requires_grad: print(name, param.data) def forward(self, *X): """ Returns the results of the model's forward on the given input X. Arguments: *X: tensor or collection of tensors the tensor of collection of tensors that is passed to the forward of the model. The inputs are automatically transfered to the same device as the model is on. Returns: tensor outputs that are returned by first the forward pass on the model. """ X = [ x.to(self.model.device) for x in X ] return self.model(*X) def predict(self, *X): """ Returns model predictions for the given input. The difference with forward is that the outputs of the model are optionally processed by a post_forward (for classification). Arguments: *X: tensor or collection of tensors the tensor of collection of tensors that is passed to the forward of the model. The inputs are automatically transfered to the same device as the model is on. Returns: tensor Predictions that are returned by first the forward pass on the model and optionally a post_forward for classification tasks """ return self.post_forward(self.forward(*X)) def post_forward(self, y): """ For classification tasks, training may require a different pred_y than the evaluation metrics do. Typically, the predictions are logits or an estimated likelihood (e.g. 0.2), while the evaluation function need a class label (e.g. 0 or 1). Using PipeTorch, you need to add a post_forward(y) method to your model, that will be called on the predictions before they are passed to the evaluation functions. Returns: tensor If the model has a post_forward to convert pred_y to predictions, this returns the the results calling post_forward, otherise, it will just return pred_y """ post_forward = getattr(self.model, "post_forward", None) if callable(post_forward): return self.model.post_forward(y) return y def list_commits(self): """ Returns: a list of the keys of committed (saved) models, during or after training. """ return self._commit.keys() def commit(self, label): """ Save the model and optimizer state, allowing to revert to a previous state/version of the model. Arguments: label: str The key to save the model under """ model_state = copy.deepcopy(self.model.state_dict()) optimizer_state = copy.deepcopy(self.optimizer.state_dict()) self._commit[label] = (model_state, optimizer_state) def _model_filename(self, folder=None, filename=None, extension=None): if folder is None: folder = '.' if filename is not None: path = f'{folder}/{filename}' else: path = f'{folder}/{self.model.__class__.__name__}' if '.pyt' not in path: if extension is None: return f'{path}.pyt{torch.__version__}' else: return f'{path}.{extension}' return path def save(self, folder=None, filename=None, extension=None): """ Saves a (trained) model to file. This will only save the model parameters. To load the model, you will first have to initialize a model with the same configuration, and then use trainer.load(path) to load the model from file. Aruments: folder: str (None) folder to save the model, default is the current folder filename: str (None) the basename of the saved file, default is the classname extension: str (None) the extension of the saved file, default is pyt with the pytorch version name """ path = self._model_filename(folder, filename, extension) torch.save(self.model.state_dict(), path) print(f'Saved the model as {path}') def load(self, folder=None, filename=None, extension=None): """ Load a saved (trained) model from file. For this to work, the model for this trainer has to be configured in the exact same way as the model that was saved. This will only load the model parameters. Aruments: folder: str (None) folder to save the model, default is the current folder filename: str (None) the basename of the saved file, default is the classname extension: str (None) the extension of the saved file, default is pyt with the pytorch version name """ self.model.load_state_dict(torch.load(self._model_filename(folder, filename, extension))) def to_trt(self): """ Converts the (trained) model into a TRT model that can be used on a Jetson Returns: TRTModule The converted model """ from torch2trt import torch2trt x = next(iter(self.train_Xy))[0] print(x.shape) return torch2trt(self.model, [x]) def save_trt(self, folder=None, filename=None, extension='trt'): """ Converts the (trained) model to TRT and saves it. Aruments: folder: str (None) folder to save the model, default is the current folder filename: str (None) the basename of the saved file, default is the classname extension: str ('trt') the extension of the saved file """ path = self._model_filename(folder, filename, extension) torch.save(self.to_trt().state_dict(), path) print(f'Saved the TRT model as {path}') def save_onnx(self, folder=None, filename=None, extension='onnx'): """ Converts the (trained) model to ONNX and saves it. Aruments: folder: str (None) folder to save the model, default is the current folder filename: str (None) the basename of the saved file, default is the classname extension: str ('onnx') the extension of the saved file """ path = self._model_filename(folder, filename, extension) x = next(iter(self.train_Xy))[0][:1] torch.onnx.export(self.model, x, path, verbose=True) print(f'Saved the ONNX model as {path}') def revert(self, label): """ Revert the model and optimizer to a previously commited state, and deletes the commit point to free memory. Prints a warning when the label was not found. Arguments: label: str The key under which the model was commited """ if label in self._commit: model_state, optimizer_state = self._commit.pop(label) self.model.load_state_dict(model_state) self.del_optimizer() self.optimizer.load_state_dict(optimizer_state) else: print('commit point {label} not found') def checkout(self, label): """ Loads a previously commited state of the model and optimizer but keeps the commit point. Prints a warning when the label was not found. Arguments: label: str The key under which the model was commited """ if label in self._commit: model_state, optimizer_state = self._commit[label] self.model.load_state_dict(model_state) self.del_optimizer() self.optimizer.load_state_dict(optimizer_state) else: print('commit point {label} not found') def remove_checkpoint(self, label): """ Removes a previously committed state of the model. Arguments: label: str The key under which the model was commited """ self._commit.pop(label) def purge(self, label): """ Switches the model and optimizer to a previously commited state, and keeps only that commit point and removes all other versions. Arguments: label: str The key under which the model was commited """ if label in self._commit: self.checkout(label) self._commit = { l:s for l, s in self._commit.items() if l == label } else: print(f'commit point {label} not found') def _loss_xy(self, *X, y=None): """ Computes predictions for the given X. Arguments: *X: tensor inputs that are used by the forward of the model y: tensor ground truth labels, the predictions are compared against Returns: (float, tensor) a tuple with the loss for the predictions on X, and a tensor with the predicted values """ assert y is not None, 'Call _loss_xy with y=None' y_pred = self.forward(*X) return self.loss(y_pred, y), self.post_forward(y_pred) def loss_dl(self, dl): """ Iterates over the given dataloader, the loss is computed in evaluation mode and accumulated over the dataset. Arguments: dl: DataLoader the dataloader that is used to iterate over. Returns: float weighted average loss over the given dataloader/set. """ if not dl: dl = self.valid_Xy losses = [] leny = 0 for *X, y in dl: y_pred = self.forward(*X) l = self.loss(y_pred, y) losses.append(l.item() * len(y)) leny += len(y) return sum(losses) / leny def validate_loss(self): """ Returns: weighted average loss over the validation set, or the data that is provided. """ return self.loss_dl(self.valid_Xy) @property def eval_mode(self): """ A ContextManager to put the model in evaluation mode """ class CM(object): def __init__(self, trainer): self.trainer = trainer def __enter__(self): self.trainer.model.eval() self.prev = torch.is_grad_enabled() torch.set_grad_enabled(False) return self.trainer.model def __exit__(self, type, value, traceback): torch.set_grad_enabled(self.prev) self.trainer.model.train() return CM(self) @property def train_mode(self): """ A ContextManager to put the model in training mode """ class CM(object): def __init__(self, trainer): self.trainer = trainer def __enter__(self): self.trainer.model.train() self.prev = torch.is_grad_enabled() torch.set_grad_enabled(True) return self.trainer.model def __exit__(self, type, value, traceback): torch.set_grad_enabled(self.prev) self.trainer.model.eval() return CM(self) def validate(self, pbar=None, log={}): """ Run the validation set (in evaluation mode) and store the loss and metrics into the evaluator. Arguments: pbar: tqdm progress bar (None) if not None, progress is reported on the progress bar log: dict additional labels to log when storing the results in the evaluator. Returns: float weighted average loss over the validation set """ epochloss = 0 n = 0 epoch_y_pred = [] epoch_y = [] with self.eval_mode: for *X, y in self.valid_Xy: loss, y_pred = self._loss_xy(*X, y=y) epochloss += loss.item() * len(y_pred) n += len(y_pred) epoch_y_pred.append(to_numpy(y_pred)) epoch_y.append(to_numpy(y)) if pbar is not None: pbar.update(self.valid_dl.batch_size) epochloss /= n epoch_y = np.concatenate(epoch_y, axis=0) epoch_y_pred = np.concatenate(epoch_y_pred, axis=0) self.evaluator._store(epoch_y, epoch_y_pred, loss=epochloss, phase='valid', epoch=self.epochid, **log) return epochloss def train_batch(self, *X, y=None): """ Train the model on a single batch X, y. The model should already be in training mode. Arguments: *X: tensor inputs that are used by the forward of the model y: tensor ground truth labels, the predictions are compared against Returns: (float, tensor) a tuple with the loss for the predictions on X, and a tensor with the predicted values """ self.optimizer.zero_grad() loss, y_pred = self._loss_xy(*X, y=y) loss.backward() self.optimizer.step() return loss, y_pred def _time(self): try: t = self._start_time except: t = timeit.default_timer() self._start_time = timeit.default_timer() return timeit.default_timer() - t def train(self, epochs, lr=None, report_frequency=None, save=None, optimizer=None, optimizer_params=None, scheduler=False, scheduler_params=None, weight_decay=None, momentum=None, save_lowest=None, save_highest=None, log={}): """ Train the model for the given number of epochs. Loss and metrics are logged during training in an evaluator. If a model was already (partially) trained, training will continue where it was left off. Arguments: epochs: int the number of epochs to train the model lr: float, tuple of floats, or list of floats float: set the learning (upper, lower): switch the scheduler to OneCycleLR and use a cyclic annealing learning rate between an upper and lower bound. [upper, lower]: switch the scheduler to Linear Decay and use a linearly decaying learning rate between an upper and lower bound. report_frequency: int configures after how many epochs the loss and metrics are logged and reported during training. This is remembered for consecutive calls to train. save: str (None) If not None, saves (commits) the model after each reported epoch, under the name 'save'-epochnr optimizer: PyTorch Optimizer (None) If not None, changes the optimizer class to use. optimizer_params: dict (None) If not None, the parameters to configure the optimizer. scheduler: None, custom scheduler class used to adapt the learning rate. Set OneCycleLR or Linear Decay through the learning rate. Otherwise, provide a custom class/function to initialize a scheduler by accepting (optimizer, learning_rate, scheduler_cycle) scheduler_params: dict (None) additional parameters that are passed when initializing the scheduler weight_decay: float Apply weight_decay regularization with the AdamW optimizer momentum: float Apply momentum with the AdamW optimizer save_lowest: bool (False) Automatically commit/save the model when reporting an epoch and the validation loss is lowest than seen before. The model is saved as 'lowest' and can be checked out by calling lowest() on the trainer. """ self._scheduler_start = self.epochid # used by OneCycleScheduler self._scheduler_epochs = epochs self.scheduler_params = scheduler_params self.del_optimizer() self.lr = lr or self.lr if weight_decay is not None and self.weight_decay != weight_decay: self.weight_decay = weight_decay if momentum is not None and self.momentum != momentum: self.momentum = momentum if optimizer and self._optimizerclass != optimizer: self.optimizer = optimizer if scheduler is not False: self.scheduler = scheduler self.report_frequency = report_frequency or self.report_frequency model = self.model torch.set_grad_enabled(False) reports = math.ceil(epochs / self.report_frequency) maxepoch = self.epochid + epochs epochspaces = int(math.log(maxepoch)/math.log(10)) + 1 batches = len(self.train_dl) * self.train_dl.batch_size * epochs + len(self.valid_dl) * self.valid_dl.batch_size * reports pbar = tqdm(range(batches), desc='Total', leave=False) self._time() for i in range(epochs): self.epochid += 1 epochloss = 0 n = 0 epoch_y_pred = [] epoch_y = [] self.scheduler report = (((i + 1) % self.report_frequency) == 0 or i == epochs - 1) with self.train_mode: for *X, y in self.train_Xy: loss, y_pred = self.train_batch(*X, y=y) self.scheduler.step() try: # TODO naam aanpassen y_pred = model.post_forward(y_pred) except: pass if report: epochloss += loss.item() * len(y_pred) n += len(y_pred) epoch_y_pred.append(to_numpy(y_pred)) epoch_y.append(to_numpy(y)) pbar.update(self.train_dl.batch_size) if report: epochloss /= n epoch_y = np.concatenate(epoch_y, axis=0) epoch_y_pred = np.concatenate(epoch_y_pred, axis=0) self.evaluator._store(epoch_y, epoch_y_pred, loss=epochloss, phase='train', epoch=self.epochid, **log) validloss = self.validate(pbar = pbar, log=log) metric = '' v = self.evaluator.valid.iloc[-1] for m in self.metrics: m = m.__name__ value = v[m] try: metric += f'{m}={value:.5f} ' except: pass print(f'{self.epochid:>{epochspaces}} {self._time():.2f}s trainloss={epochloss:.5f} validloss={validloss:.5f} {metric}') if save is not None: self.commit(f'{save}-{self.epochid}') if save_lowest is not None: if self.lowest_score is None or validloss < self.lowest_score: self.lowest_score = validloss self.commit('lowest') def lowest(self): """ Checkout the model with the lowest validation loss, that was committed when training with save_lowest=True """ self.checkout('lowest') def learning_curve(self, y='loss', series='phase', select=None, xlabel = None, ylabel = None, title=None, label_prefix='', **kwargs): """ Plot a learning curve with the train and valid loss on the y-axis over the epoch on the x-axis. The plot is generated by the evaluator that logged training progress. By default the evaluator logs: - epoch: the epoch number - phase: 'train' or 'valid' - loss: the weighted average loss under the name of each metric function, the resulting value when called with (y, y_pred) and the additional values that are passed to train() through the log parameter. Arguments: y: str or function the metric that is used for the y-axis. It has to be a metric that was collected during training. if a function is passed, the name of the function is used. series: str ('phase') the label to use as a series. By default, 'phase' is used to plot both the train and valid results. select: see evaluator.select using the values 'train' and 'valid' you can select to plot only the train or valid sets. xlabel: str the label used on the x-axis ylabel: str the label used on the y-axis title: str the title of the plot label_prefix: str prefixes the label, so that you can combine a plot with results from different metrics or models **kwargs: dict forwarded to matplotlib's plot or scatter function """ return self.evaluator.line_metric(x='epoch', series=series, select=select, y=y, xlabel = xlabel, ylabel = ylabel, title=title, label_prefix=label_prefix, **kwargs) def validation_curve(self, y=None, x='epoch', series='phase', select=None, xlabel = None, ylabel = None, title=None, label_prefix='', **kwargs): """ Plot a metric for the train and valid set, over epoch on the x-axis. The plot is generated by the evaluator that logged training progress. By default the evaluator logs: - epoch: the epoch number - phase: 'train' or 'valid' - loss: the weighted average loss under the name of each metric function, the resulting value when called with (y, y_pred) and the additional values that are passed to train() through the log parameter. Arguments: y: str or function the metric that is used for the y-axis. It has to be a metric that was collected during training. if a function is passed, the name of the function is used. x: str ('epoch') the label used for the x-axis. series: str ('phase') the label to use as a series. By default, 'phase' is used to plot both the train and valid results. select: see evaluator.select using the values 'train' and 'valid' you can select to plot only the train or valid sets. xlabel: str the label used on the x-axis ylabel: str the label used on the y-axis title: str the title of the plot label_prefix: str prefixes the label, so that you can combine a plot with results from different metrics or models **kwargs: dict forwarded to matplotlib's plot or scatter function """ if y is not None and type(y) != str: y = y.__name__ return self.evaluator.line_metric(x=x, series=series, select=select, y=y, xlabel = xlabel, ylabel = ylabel, title=title, label_prefix=label_prefix, **kwargs) def freeze(self, last=-1): """ Mostly used for transfer learning, to freeze all parameters of a model, until the given layer (exclusive). Arguments: last: int (-1) Freeze all layers up to this layer number. -1 is the last layer. """ for c in list(self.model.children())[:last]: for p in c.parameters(): p.requires_grad=False def unfreeze(self): """ Mostly used for transfer learning, to unfreeze all parameters of a model. """ for c in list(self.model.children()): for p in c.parameters(): p.requires_grad=True def tune(self, params,setter, lr=[1e-6, 1e-2], steps=40, smooth=0.05, label=None, **kwargs): lr_values = exprange(*lr, steps) if label is None: label = str(setter) if len(params) == 2: params = range3(*params) with tuner(self, lr_values, self.set_lr, smooth=0.05, label=label) as t: t.run_multi(params, setter) def tune_weight_decay(self, lr=[1e-6,1e-4], params=[1e-6, 1], steps=40, smooth=0.05, yscale='log', **kwargs): self.tune( params, partial(self.set_optimizer_param, 'weight_decay'), lr=lr, steps=steps, smooth=smooth, label='weight decay', yscale=yscale, **kwargs) def lr_find(self, lr=[1e-6, 10], steps=40, smooth=0.05, cache_valid=True, **kwargs): """ Run a learning rate finder on the dataset (as propesed by <NAME> and implemented in FastAI). This saves the model, then starting with a very low learning rate iteratively trains the model on a single mini-batch and logs the loss on the validation set. Gradually, the learning rate is raised. The idea is that the graph contains information on a stable setting of the learning rate. This does not always work, and often after some training, if learning is not stable, the learning rate still needs to be adjusted. The result is a plot of the validation loss over the change in learning rate. Arguments: lr: [small float, big float] ([1e-6, 10]) Interval of learning rates to inspect steps: int (40) number of (exponential) steps to divide the learning rate interval in smooth: float (0.05) smoothing parameter, to generate a more readable graph cache_valid: bool (True) whether to keep the validation set if possible in memory. Switch of if there is insufficient memory """ with tuner(self, exprange(lr[0], lr[1], steps), self.set_lr, label='lr', yscale='log', smooth=smooth, cache_valid=cache_valid, **kwargs) as t: t.run() ```
{ "source": "jeroenwinkelhorst/ToHyDAMOgml", "score": 3 }
#### File: ToHyDAMOgml/tohydamogml/read_filegdb.py ```python import fiona import geopandas as gpd import pandas as pd from tohydamogml.config import COLNAME_OID def read_filegdb(filegdb, layer): """Read filegdb with fiona to get original objectid. Return geopandas dataframe or pandas dataframe""" if layer in fiona.listlayers(filegdb): features = _yield_features(filegdb, layer) if next(features)["geometry"] is not None: gdf = gpd.GeoDataFrame.from_features(features, crs=get_crs(filegdb, layer)) gdf[COLNAME_OID] = gdf[COLNAME_OID].astype(int) return gdf else: df = pd.DataFrame.from_records(_yield_table(filegdb, layer)) df[COLNAME_OID] = df[COLNAME_OID].astype(int) return df else: raise ValueError(f"layer '{layer}' not in layer list: {fiona.listlayers(filegdb)}") def _yield_features(path, layer, colname_oid=COLNAME_OID): """Read filegdb with fiona to get original objectid""" with fiona.open(path, 'r', layer=layer) as f: for feature in f: feature['properties'][colname_oid] = feature['id'] yield feature def _yield_table(path, layer, colname_oid=COLNAME_OID): """Read filegdb table with fiona to get original objectid""" with fiona.open(path, 'r', layer=layer) as f: for feature in f: feature['properties'][colname_oid] = feature['id'] yield feature['properties'] def get_crs(path, layer): with fiona.open(path, 'r', layer=layer) as f: if type(f.crs) == dict: if 'init' in f.crs.keys(): return f.crs['init'] return None ```
{ "source": "jeroenzeegers/panopuppet", "score": 2 }
#### File: pano/views/analytics.py ```python import pytz from django.contrib.auth.decorators import login_required from django.shortcuts import redirect, render from django.views.decorators.cache import cache_page from pano.puppetdb.pdbutils import run_puppetdb_jobs, json_to_datetime from pano.puppetdb.puppetdb import set_server, get_server from pano.settings import AVAILABLE_SOURCES, CACHE_TIME __author__ = 'etaklar' @login_required @cache_page(CACHE_TIME) def analytics(request): context = {'timezones': pytz.common_timezones, 'SOURCES': AVAILABLE_SOURCES} if request.method == 'GET': if 'source' in request.GET: source = request.GET.get('source') set_server(request, source) if request.method == 'POST': request.session['django_timezone'] = request.POST['timezone'] return redirect(request.POST['return_url']) source_url, source_certs, source_verify = get_server(request) events_class_params = { 'query': { 1: '["and",["=","latest_report?",true],["in","certname",["extract","certname",["select_nodes",["null?","deactivated",true]]]]]' }, 'summarize_by': 'containing_class', } events_resource_params = { 'query': { 1: '["and",["=","latest_report?",true],["in","certname",["extract","certname",["select_nodes",["null?","deactivated",true]]]]]' }, 'summarize_by': 'resource', } events_status_params = { 'query': { 1: '["and",["=","latest_report?",true],["in","certname",["extract","certname",["select_nodes",["null?","deactivated",true]]]]]' }, 'summarize_by': 'resource', } reports_runavg_params = { 'limit': 100, 'order_by': { 'order_field': { 'field': 'receive_time', 'order': 'desc', }, 'query_field': {'field': 'certname'}, }, } jobs = { 'events_class_list': { 'url': source_url, 'certs': source_certs, 'verify': source_verify, 'id': 'events_class_list', 'path': '/event-counts', 'api_version': 'v4', 'params': events_class_params, 'request': request }, 'events_resource_list': { 'url': source_url, 'certs': source_certs, 'verify': source_verify, 'id': 'events_resource_list', 'path': '/event-counts', 'api_version': 'v4', 'params': events_resource_params, 'request': request }, 'events_status_list': { 'url': source_url, 'certs': source_certs, 'verify': source_verify, 'id': 'events_status_list', 'path': '/aggregate-event-counts', 'api_version': 'v4', 'params': events_status_params, 'request': request }, 'reports_run_avg': { 'url': source_url, 'certs': source_certs, 'verify': source_verify, 'id': 'reports_run_avg', 'path': '/reports', 'api_version': 'v4', 'params': reports_runavg_params, 'request': request }, } job_results = run_puppetdb_jobs(jobs, 4) reports_run_avg = job_results['reports_run_avg'] events_class_list = job_results['events_class_list'] events_resource_list = job_results['events_resource_list'] events_status_list = job_results['events_status_list'] num_runs_avg = len(reports_run_avg) run_avg_times = [] avg_run_time = 0 for report in reports_run_avg: run_time = "{0:.0f}".format( (json_to_datetime(report['end_time']) - json_to_datetime(report['start_time'])).total_seconds()) avg_run_time += int(run_time) run_avg_times.append(run_time) if num_runs_avg != 0: avg_run_time = "{0:.0f}".format(avg_run_time / num_runs_avg) else: avg_run_time = 0 class_event_results = [] class_resource_results = [] class_status_results = [] for item in events_class_list: class_name = item['subject']['title'] class_total = item['skips'] + item['failures'] + item['noops'] + item['successes'] class_event_results.append((class_name, class_total)) for item in events_resource_list: class_name = item['subject']['type'] class_total = item['skips'] + item['failures'] + item['noops'] + item['successes'] class_resource_results.append((class_name, class_total)) print(events_status_list) if events_status_list: for status, value in events_status_list[0].items(): print(status, value) if value is 0 or status == 'total' or status == 'summarize_by': continue class_status_results.append((status, value)) context['class_events'] = class_event_results context['class_status'] = class_status_results context['resource_events'] = class_resource_results context['run_times'] = run_avg_times context['run_num'] = num_runs_avg context['run_avg'] = avg_run_time return render(request, 'pano/analytics/analytics.html', context) ``` #### File: views/api/report_agent_log.py ```python import arrow import json from django.contrib.auth.decorators import login_required from django.shortcuts import HttpResponse from django.template import defaultfilters as filters from django.utils.timezone import localtime from django.views.decorators.cache import cache_page from pano.puppetdb import puppetdb from pano.puppetdb.puppetdb import get_server from pano.settings import CACHE_TIME __author__ = 'etaklar' @login_required @cache_page(CACHE_TIME) def report_log_json(request, report_hash=None): source_url, source_certs, source_verify = get_server(request) # Redirects to the events page if GET param latest is true.. context = {} if report_hash is None: context['error'] = 'Report Hash not provided.' return HttpResponse(json.dumps(context), content_type="application/json") report_logs = puppetdb.api_get( api_url=source_url, cert=source_certs, verify=source_verify, path='/reports/' + report_hash + '/logs', api_version='v4', ) if 'error' in report_logs: context = report_logs return HttpResponse(json.dumps(context), content_type="application/json") # Remove the dict from the list... for log in report_logs: # Parse... 2015-09-18T18:02:04.753163330+02:00 # Puppetlabs... has a super long millisecond counter (9 digits!!!) # We need to trim those down... time = log['time'][0:26] + log['time'][-6:-3] + log['time'][-2:] time = arrow.get(time).to('UTC').datetime log['time'] = filters.date(localtime(time), 'Y-m-d H:i:s') context['agent_log'] = report_logs context['report_hash'] = report_hash return HttpResponse(json.dumps(context), content_type="application/json") ``` #### File: views/api/report_data.py ```python import json from django.contrib.auth.decorators import login_required from django.shortcuts import HttpResponse from django.template import defaultfilters as filters from django.utils.timezone import localtime from django.views.decorators.cache import cache_page from pano.puppetdb import puppetdb from pano.puppetdb.pdbutils import json_to_datetime from pano.puppetdb.puppetdb import get_server from pano.settings import CACHE_TIME __author__ = 'etaklar' @login_required @cache_page(CACHE_TIME) def reports_json(request, certname=None): source_url, source_certs, source_verify = get_server(request) # Redirects to the events page if GET param latest is true.. context = {} # Cur Page Number if request.GET.get('page', False): if request.session['report_page'] != int(request.GET.get('page', 1)): request.session['report_page'] = int(request.GET.get('page', 1)) if request.session['report_page'] <= 0: request.session['report_page'] = 1 else: if 'report_page' not in request.session: request.session['report_page'] = 1 if request.session['report_page'] <= 0: offset = 0 else: offset = (25 * request.session['report_page']) - 25 reports_params = { 'query': { 1: '["=","certname","' + certname + '"]' }, 'order_by': { 'order_field': { 'field': 'start_time', 'order': 'desc', }, }, 'limit': 25, 'include_total': 'true', 'offset': offset, } reports_list, headers = puppetdb.api_get( api_url=source_url, cert=source_certs, verify=source_verify, path='/reports', api_version='v4', params=puppetdb.mk_puppetdb_query( reports_params, request), ) # Work out the number of pages from the xrecords response xrecords = headers['X-Records'] num_pages_wdec = float(xrecords) / 25 num_pages_wodec = float("{:.0f}".format(num_pages_wdec)) if num_pages_wdec > num_pages_wodec: num_pages = num_pages_wodec + 1 else: num_pages = num_pages_wodec report_status = [] for report in reports_list: found_report = False events_params = { 'query': { 1: '["=","report","' + report['hash'] + '"]' }, 'summarize_by': 'certname', } eventcount_list = puppetdb.api_get( path='event-counts', api_url=source_url, api_version='v4', params=puppetdb.mk_puppetdb_query(events_params, request), ) # Make list of the results for event in eventcount_list: if event['subject']['title'] == report['certname']: found_report = True report_status.append({ 'hash': report['hash'], 'certname': report['certname'], 'environment': report['environment'], 'start_time': filters.date(localtime(json_to_datetime(report['start_time'])), 'Y-m-d H:i:s'), 'end_time': filters.date(localtime(json_to_datetime(report['end_time'])), 'Y-m-d H:i:s'), 'events_successes': event['successes'], 'events_noops': event['noops'], 'events_failures': event['failures'], 'events_skipped': event['skips'], 'report_status': report['status'], 'config_version': report['configuration_version'], 'run_duration': "{0:.0f}".format( (json_to_datetime(report['end_time']) - json_to_datetime(report['start_time'])).total_seconds()) }) break if found_report is False: report_status.append({ 'hash': report['hash'], 'certname': report['certname'], 'environment': report['environment'], 'start_time': filters.date(localtime(json_to_datetime(report['start_time'])), 'Y-m-d H:i:s'), 'end_time': filters.date(localtime(json_to_datetime(report['end_time'])), 'Y-m-d H:i:s'), 'events_successes': 0, 'events_noops': 0, 'events_failures': 0, 'events_skipped': 0, 'report_status': report['status'], 'config_version': report['configuration_version'], 'run_duration': "{0:.0f}".format( (json_to_datetime(report['end_time']) - json_to_datetime(report['start_time'])).total_seconds()) }) context['certname'] = certname context['reports_list'] = report_status context['curr_page'] = request.session['report_page'] context['tot_pages'] = "{:.0f}".format(num_pages) return HttpResponse(json.dumps(context), content_type="application/json") def reports_search_json(request): context = dict() if request.method == 'GET': if 'search' in request.GET: search = request.GET.get('search') if 'certname' in request.GET: certname = request.GET.get('certname') if not certname or not search: context['error'] = 'Must specify both certname and search query.' return HttpResponse(json.dumps(context), content_type="application/json") source_url, source_certs, source_verify = get_server(request) # Redirects to the events page if GET param latest is true.. reports_params = { 'query': { 'operator': 'and', 1: '["=","certname","' + certname + '"]', 2: '["~","hash","^' + search + '"]' }, 'order_by': { 'order_field': { 'field': 'start_time', 'order': 'desc', }, } } reports_list = puppetdb.api_get( path='/reports', api_url=source_url, api_version='v4', params=puppetdb.mk_puppetdb_query(reports_params, request), ) return HttpResponse(json.dumps(reports_list), content_type="application/json") ``` #### File: pano/views/event_analytics.py ```python import pytz from django.contrib.auth.decorators import login_required from django.shortcuts import redirect, render from django.views.decorators.cache import cache_page from pano.methods import events from pano.puppetdb.puppetdb import set_server from pano.settings import AVAILABLE_SOURCES, CACHE_TIME __author__ = 'etaklar' @login_required @cache_page(CACHE_TIME) def event_analytics(request, view='summary'): context = {'timezones': pytz.common_timezones, 'SOURCES': AVAILABLE_SOURCES} if request.method == 'GET': if 'source' in request.GET: source = request.GET.get('source') set_server(request, source) if request.method == 'POST': request.session['django_timezone'] = request.POST['timezone'] return redirect(request.POST['return_url']) summary = events.get_events_summary(timespan='latest', request=request) context['summary'] = summary # Show Classes if request.GET.get('value', False): if view == 'classes': class_name = request.GET.get('value') title = "Class: %s" % class_name class_events = events.get_report(key='containing_class', value=class_name, request=request) context['events'] = class_events # Show Nodes elif view == 'nodes': node_name = request.GET.get('value') title = "Node: %s" % node_name node_events = events.get_report(key='certname', value=node_name, request=request) context['events'] = node_events # Show Resources elif view == 'resources': resource_name = request.GET.get('value') title = "Resource: %s" % resource_name resource_events = events.get_report(key='resource_title', value=resource_name, request=request) context['events'] = resource_events # Show Types elif view == 'types': type_name = request.GET.get('value') title = "Type: %s" % type_name type_events = events.get_report(key='resource_type', value=type_name, request=request) context['events'] = type_events # Show summary if none of the above matched else: sum_avail = ['classes', 'nodes', 'resources', 'types'] stat_avail = ['failed', 'noop', 'success', 'skipped' ''] show_summary = request.GET.get('show_summary', 'classes') show_status = request.GET.get('show_status', 'failed') if show_summary in sum_avail and show_status in stat_avail: title = "%s with status %s" % (show_summary.capitalize(), show_status.capitalize()) context['show_title'] = title else: title = 'Failed Classes' context['show_title'] = title return render(request, 'pano/analytics/events_details.html', context) # Add title to context context['show_title'] = title # if the above went well and did not reach the else clause we can also return the awesome. return render(request, 'pano/analytics/events_inspect.html', context) ``` #### File: pano/views/radiator.py ```python import pytz from django.contrib.auth.decorators import login_required from django.shortcuts import redirect, render from django.views.decorators.cache import cache_page from pano.puppetdb import puppetdb from pano.puppetdb.puppetdb import set_server, get_server from pano.settings import AVAILABLE_SOURCES, CACHE_TIME, NODES_DEFAULT_FACTS __author__ = 'etaklar' @login_required @cache_page(CACHE_TIME) def radiator(request, certname=None): context = {'timezones': pytz.common_timezones, 'SOURCES': AVAILABLE_SOURCES} if request.method == 'GET': if 'source' in request.GET: source = request.GET.get('source') set_server(request, source) if request.method == 'POST': request.session['django_timezone'] = request.POST['timezone'] return redirect(request.POST['return_url']) context['certname'] = certname context['node_facts'] = ','.join(NODES_DEFAULT_FACTS) return render(request, 'pano/radiator.html', context) ``` #### File: pano/views/report_agent_logs.py ```python import pytz from django.contrib.auth.decorators import login_required from django.shortcuts import redirect, render from django.views.decorators.cache import cache_page from pano.puppetdb.puppetdb import set_server from pano.settings import AVAILABLE_SOURCES, CACHE_TIME __author__ = 'etaklar' @login_required @cache_page(CACHE_TIME * 60) # Cache for cache_time multiplied 60 because the report will never change... def agent_logs(request, certname=None, report_hash=None): context = {'timezones': pytz.common_timezones, 'SOURCES': AVAILABLE_SOURCES} if request.method == 'GET': if 'source' in request.GET: source = request.GET.get('source') set_server(request, source) if request.method == 'POST': request.session['django_timezone'] = request.POST['timezone'] return redirect(request.POST['return_url']) context['certname'] = certname context['report_hash'] = report_hash return render(request, 'pano/report_agent_logs.html', context) ``` #### File: panopuppet/tests/test_puppetdb_functions.py ```python from django.test import TestCase from pano.puppetdb.puppetdb import mk_puppetdb_query __author__ = 'etaklar' class CreatePuppetdbQueries(TestCase): def test_single_search_query(self): content = { 'query': { 1: '["=","certname","hostname.example.com"]' }, } expected_results = { 'query': '["and",["=","certname","hostname.example.com"]]' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_double_search_query_with_operator(self): content = { 'query': { 'operator': 'and', 1: '["=","hash","e4fug294hf3293hf9348g3804hg3084h"]', 2: '["=","latest_report?",true]' }, } expected_results = { 'query': '["and",["=","hash","e4fug294hf3293hf9348g3804hg3084h"],["=","latest_report?",true]]' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_single_search_query_with_operator(self): content = { 'query': { 'operator': 'and', 1: '["=","hash","e4fug294hf3293hf9348g3804hg3084h"]', }, } expected_results = { 'query': '["and",["=","hash","e4fug294hf3293hf9348g3804hg3084h"]]' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_summarize_by_query(self): content = { 'summarize_by': 'containing_class', } expected_results = { 'summarize_by': 'containing_class' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_summarize_by_query_with_single_search_query(self): content = { 'query': { 1: '["=","certname","hostname.example.com"]' }, 'summarize_by': 'containing_class', } expected_results = { 'query': '["and",["=","certname","hostname.example.com"]]', 'summarize_by': 'containing_class' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_order_by_query(self): content = { 'order_by': { 'order_field': { 'field': 'report_timestamp', 'order': 'desc', }, } } expected_results = { 'order_by': '[{"field":"report_timestamp","order":"desc"}]' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_order_by_query_with_single_search_query(self): content = { 'query': { 1: '["=","certname","hostname.example.com"]' }, 'order_by': { 'order_field': { 'field': 'report_timestamp', 'order': 'desc', }, } } expected_results = { 'order_by': '[{"field":"report_timestamp","order":"desc"}]', 'query': '["and",["=","certname","hostname.example.com"]]' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_order_by_query_with_double_search_query_with_operator(self): content = { 'query': { 'operator': 'or', 1: '["=","certname","hostname1.example.com"]', 2: '["=","certname","hostname2.example.com"]' }, 'order_by': { 'order_field': { 'field': 'report_timestamp', 'order': 'desc', }, } } expected_results = { 'query': '["and",["=","certname","hostname1.example.com"],["=","certname","hostname2.example.com"]]', 'order_by': '[{"field":"report_timestamp","order":"desc"}]' } results = mk_puppetdb_query(content) self.assertEqual(expected_results, results) def test_query_with_string(self): content = "string value" self.assertRaises(TypeError, mk_puppetdb_query, params=content) def test_query_with_list(self): content = ['test1', 'test2'] self.assertRaises(TypeError, mk_puppetdb_query, params=content) def test_query_with_integer(self): content = 1 self.assertRaises(TypeError, mk_puppetdb_query, params=content) def test_query_with_empty_dict(self): content = {} expected_results = {} self.assertEquals(content, expected_results) ``` #### File: panopuppet/tests/test_puppetdb_utils.py ```python from datetime import datetime, timedelta from django.test import TestCase from pano.puppetdb.pdbutils import is_unreported __author__ = 'etaklar' class CheckIfUnreported(TestCase): def test_none_date(self): """ Should fail because if there is no report timestamp the node has not managed to complete a puppet run. """ """ :return: """ date = None results = is_unreported(date) self.assertEquals(results, True) def test_date_reported_within_two_hours(self): """ Should return False since the node has reported within the default value of 2 hours. """ date = (datetime.utcnow() - timedelta(hours=1)).strftime('%Y-%m-%dT%H:%M:%S.%fZ') results = is_unreported(date) self.assertEquals(results, False) def test_date_unreported_within_two_hours(self): """ Should return True since the node has not reported within the default value of 2 hours. """ date = (datetime.utcnow() - timedelta(hours=3)).strftime('%Y-%m-%dT%H:%M:%S.%fZ') results = is_unreported(date) self.assertEquals(results, True) def test_invalid_formatted_date(self): """ Since a date in the incorrect format can not be read datetime should raise an error because it does not match the format %Y-%m-%dT%H:%M:%S.%fZ """ date = 'not_a_real_date' self.assertRaises(ValueError, is_unreported, node_report_timestamp=date) def test_unreported_date_with_hours_set_to_24_hours(self): """ Test timestamp set to 25 hours ago, it should count as a unreported timestamp since the unreported time is set to 24 hours. """ date = (datetime.utcnow() - timedelta(hours=25)).strftime('%Y-%m-%dT%H:%M:%S.%fZ') results = is_unreported(date, unreported=24) self.assertEquals(results, True) def test_reported_date_with_hours_set_to_30_minutes_using_float_value(self): """ Test unreported parameter to is_unreported accepts float value. It is set to .5 hours which is effectively 30 minutes. With a time set to 15 minutes ago it should return that the node is not unreported. """ date = (datetime.utcnow() - timedelta(minutes=15)).strftime('%Y-%m-%dT%H:%M:%S.%fZ') results = is_unreported(date, unreported=.5) self.assertEquals(results, False) ```
{ "source": "jeroFlo/robotsVision_openCV", "score": 3 }
#### File: samples/python/practice_2p1.py ```python from __future__ import print_function import numpy as np import cv2 as cv import math bins = np.arange(256).reshape(256,1) def hist_curve(im): h = np.zeros((300,256,3)) if len(im.shape) == 2: color = [(255,255,255)] elif im.shape[2] == 3: color = [ (255,0,0),(0,255,0),(0,0,255) ] for ch, col in enumerate(color): hist_item = cv.calcHist([np.uint8(im)],[ch],None,[256],[0,256]) cv.normalize(hist_item,hist_item,0,255,cv.NORM_MINMAX) hist=np.int32(np.around(hist_item)) pts = np.int32(np.column_stack((bins,hist))) cv.polylines(h,[pts],False,col) y=np.flipud(h) return y def binary(img, threshold = 127): im = img im = cv.cvtColor(im,cv.COLOR_BGR2GRAY) #blur = cv.GaussianBlur(im,(3,3),0) #ret3,th3 = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU) ret1,th3 = cv.threshold(im,threshold,255,cv.THRESH_BINARY) return th3 def contrast(im, alpha): img = np.where(im*alpha < im, 255, alpha*im) return img def brightness(im, beta): if beta > 0: img = np.where(im+beta < im, 255, im+beta) elif beta < 0: img = np.where(im+beta < 0, 0, im+beta) return np.uint8(img) else: return im return img def main(): import sys if len(sys.argv)>1: fname = sys.argv[1] #../../../practice_1_2_fft/resources/gato_2.jpg else : fname = 'lena.jpg' print("usage : python hist.py <image_file>") im = cv.imread(cv.samples.findFile(fname)) if im is None: print('Failed to load image file:', fname) sys.exit(1) gray = cv.cvtColor(im,cv.COLOR_BGR2GRAY) print(''' Histogram plotting \n show histogram for color image in curve mode \n show binarization from the input image \n Esc - exit \n ''') cv.imshow('image',im) curve = hist_curve(im) #obtener el histograma cv.imshow('histogram original image',curve)#mostrar el histograma img = contrast(im, 2) cv.imshow('contrast', img) curve = hist_curve(img) #obtener el histograma cv.imshow('histogram contrast',curve) img = brightness(im, -100) cv.imshow('brightness mine', img) curve = hist_curve(img) #obtener el histograma cv.imshow('histogram brightness mine',curve) #for contrast and brightness given function img = cv.convertScaleAbs(im, alpha=1, beta=100) cv.imshow('brightness', img) curve = hist_curve(img) #obtener el histograma cv.imshow('histogram brightness',curve) #Edge detection #gaussiana = cv2.GaussianBlur(gris, (n,n), 0) img_gauss = cv.GaussianBlur(gray, (3,3), 0) # 3x3 kernel img = binary(im, 150) cv.imshow('image',img) # Canny #canny = cv2.Canny(imagen, umbral_minimo histeresis, umbral_maximo) img_canny = cv.Canny(img, 100, 200) cv.imshow("Canny", img_canny) # Sobel img_sobelx = cv.Sobel(img_gauss, cv.CV_8U, 1, 0, ksize=3) img_sobely = cv.Sobel(img_gauss, cv.CV_8U, 0, 1, ksize=3) img_sobel = img_sobelx + img_sobely cv.imshow("Sobel X", img_sobelx) cv.imshow("Sobel Y", img_sobely) cv.imshow("Sobel", img_sobel) # Prewitt kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]]) kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]]) img_prewittx = cv.filter2D(img_gauss, -1, kernelx) img_prewitty = cv.filter2D(img_gauss, -1, kernely) cv.imshow("Prewitt X", img_prewittx) cv.imshow("Prewitt Y", img_prewitty) cv.imshow("Prewitt", img_prewittx + img_prewitty) cv.waitKey(0) print('Done') if __name__ == '__main__': print(__doc__) main() cv.destroyAllWindows() ``` #### File: samples/python/practice_3p1.py ```python from __future__ import print_function from matplotlib.colors import hsv_to_rgb from skimage.transform import resize from skimage.feature import hog from skimage import exposure import numpy as np import cv2 as cv import math import matplotlib.pyplot as plt import imutils import time import re from numpy import savetxt def scale(img, scale_percent = 50):# percent of original size width = int(img.shape[1] * scale_percent / 100) height = int(img.shape[0] * scale_percent / 100) dim = (width, height) return cv.resize(img, dim, interpolation= cv.INTER_AREA) def pyramid(image, scale=1.5, minSize=(30,30)): yield image while True: w = int(image.shape[1]/scale) image = imutils.resize(image, width=w) if image.shape[0] < minSize[1] or image.shape[1] < minSize[0]: break yield image def sliding_window(image, stepSize, windowSize): # slide a window across the image for y in range(0, image.shape[0], stepSize): for x in range(0, image.shape[1], stepSize): # yield the current window yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]]) def HOG(crop_img, orient=9, pixels_per_cell=(8,8), cells_per_block=(2,2)): return hog(crop_img, orientations=orient, pixels_per_cell=pixels_per_cell, cells_per_block=cells_per_block, visualize=True, multichannel=True) def main(): image = cv.imread("../data/puzzle_part.jpg") image = scale(image, 20) print(image.shape) ''' for (i, resized) in enumerate(pyramid(image)): # show the resized image cv.imshow("Layer {}".format(i + 1), resized) cv.waitKey(0) ''' f = open("../data/features_hog.csv", "w") (winW, winH) = (64,128) # loop over the image pyramid for resized in pyramid(image, scale=2, minSize=(64,128)): # loop over the sliding window for each layer of the pyramid for (x, y, window) in sliding_window(resized, stepSize=32, windowSize=(winW, winH)): # if the window does not meet our desired window size, ignore it if window.shape[0] != winH or window.shape[1] != winW: continue #print("resized size {}".format(resized.shape)) #print("window size {}".format(window.shape)) fd, hog_image = HOG(window) #print(window) f.write(','.join(map(str,fd))) f.write('\n') #cv.imshow('window', window) #cv.imshow('hog', hog_image) #cv.waitKey(0) # we'll just draw the window #clone = resized.copy() #cv.rectangle(clone, (x, y), (x + winW, y + winH), (0, 255, 0), 2) #cv.imshow("Window", clone) #cv.waitKey(1) #time.sleep(0.025) f.close() print('Done') if __name__ == '__main__': print(__doc__) main() cv.destroyAllWindows() ``` #### File: samples/python/segmentation.py ```python from __future__ import print_function import numpy as np import cv2 as cv from matplotlib import pyplot as plt import random as rng rng.seed(12345) def scale(img, scale_percent = 50):# percent of original size width = int(img.shape[1] * scale_percent / 100) height = int(img.shape[0] * scale_percent / 100) dim = (width, height) return cv.resize(img, dim, interpolation= cv.INTER_AREA) def binarization(img, thres = 127, otsu=False, inv=True): gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY) #cv.imshow('gray', gray) if not otsu: if inv: ret1,thresh = cv.threshold(gray,thres,255,cv.THRESH_BINARY_INV) else: ret1,thresh = cv.threshold(gray,thres,255,cv.THRESH_BINARY) return thresh if inv: ret, thresh = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV+cv.THRESH_OTSU) else: ret, thresh = cv.threshold(gray,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU) return thresh def erosion(img, size = 3, iter=1): kernel = np.ones((size,size), np.uint8) # The first parameter is the original image, # kernel is the matrix with which image is # convolved and third parameter is the number # of iterations, which will determine how much # you want to erode/dilate a given image. img_erosion = cv.erode(img, kernel, iterations=iter) return img_erosion def dilation(img, size = 3, iter=1): kernel = np.ones((size,size), np.uint8) img_dilation = cv.dilate(img, kernel, iterations=iter) return img_dilation def closing(img, size =3 ,iter = 1): for i_iter in range(iter): img = erosion(dilation(img, size), size) return img def opening(img, size =3 ,iter = 1): for i_iter in range(iter): img = dilation(erosion(img, size), size) return img def main(): #image_path = '../data/puzzle_part_edit.jpg' image_path = '../data/one_piece_puzzle.jpeg' img = cv.imread(image_path) print(img.shape) img = scale(img, 20) print(img.shape) #cv.imshow('Scaled', img) binary = binarization(img, 110) #binary_n = binarization(img, 105, inv=False) #cv.imshow('binary not inverted', binary_n) binary = dilation(binary, size=6, iter=2) #cv.imshow('binary', binary) sure_bg = closing(binary, 4, iter=2) #binary = dilation(binary, size=4) cv.imshow('closing', binary) dist = cv.distanceTransform(sure_bg,distanceType=cv.DIST_L2,maskSize=3) cv.normalize(dist, dist, 0, 1.0, cv.NORM_MINMAX) cv.imshow('dist', dist) _, binary = cv.threshold(dist, 0.29, 1.0, cv.THRESH_BINARY) cv.imshow('binary normalize', binary) #binary = opening(binary) sure_fg = erosion(binary, 4) cv.imshow('opening', sure_fg) # Finding unknown region sure_fg = np.uint8(sure_fg) unknown = cv.subtract(sure_bg,sure_fg) # Marker labelling ret, markers = cv.connectedComponents(sure_fg) # Add one to all labels so that sure background is not 0, but 1 markers = markers+1 # Now, mark the region of unknown with zero markers[unknown==255] = 0 #markers = np.uint8(markers) #cv.imshow('markers', markers) markers = cv.watershed(img,markers) img[markers == -1] = [0,255,0] cv.imshow('result',img) ''' # Marker labelling binary = np.uint8(binary) contours, _= cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) #print(binary) #ret, markers = cv.connectedComponents(binary) # Add one to all labels so that sure background is not 0, but 1 #print(markers[0][20]) #markers = markers+1 # Now, mark the region of unknown with zero #markerrs[unknown==255] = 0 markers = cv.watershed(img,markers) print(markers) img[markers == -1] = [255,0,0] cv.imshow('x', img) ''' # Create the CV_8U version of the distance image # It is needed for findContours() #dist_8u = binary.astype('uint8') # Find total markers #contours, _= cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) #print(contours) # Create the marker image for the watershed algorithm #markers = np.zeros(binary.shape, dtype=np.int32) # Draw the foreground markers #for i in range(len(contours)): # print(i) # cv.drawContours(markers, contours, i, (i+1), -1) # Draw the background marker #cv.circle(markers, (5,5), 3, (255,0,0), -1) #markers = cv.watershed(img, markers) #img[markers == -1] = [0,255,0] #print(markers) #cv.imshow('x', img) ''' #mark = np.zeros(markers.shape, dtype=np.uint8) mark = markers.astype('uint8') mark = cv.bitwise_not(mark) # uncomment this if you want to see how the mark # image looks like at that point cv.imshow('Markers_v2', mark) # Generate random colors colors = [] for contour in contours: colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))) # Create the result image dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8) # Fill labeled objects with random colors for i in range(markers.shape[0]): for j in range(markers.shape[1]): index = markers[i,j] if index > 0 and index <= len(contours): dst[i,j,:] = colors[index-1] # Visualize the final image cv.imshow('Final Result', dst) #img[markers==-1] = [255,0,0] #cv.imshow('Markers', markers) ''' cv.waitKey() if __name__ == '__main__': print(__doc__) main() cv.destroyAllWindows() ```
{ "source": "jerogee/mdl-ling-chunks", "score": 2 }
#### File: mdl-ling-chunks/src/est.py ```python import os import sys import gzip import logging import tempfile import psutil from estimators import lzss def mem_usage_percent(): process = psutil.Process(os.getpid()) return process.memory_percent() def get_tmpfilename(): try: return tempfile.mktemp(prefix=tempfile.template) except TypeError: return tempfile.mktemp() def file_append(f, s): with open(f, 'a') as fh: fh.write(s) def lzss_calculate_compression_ratio(f): # Temporary filename for compressed version f_c = get_tmpfilename() # Get file handles of input and output files fh_i = open(f, 'rb') fh_o = open(f_c, 'wb') # Compress, get # bytes, clean up [bytes_i, bytes_o] = lzss.encode(fh_i, fh_o) os.remove(f_c) return bytes_i/bytes_o def run_lzss_file(fn_i, fn_o): # Create temporary file name tfn = get_tmpfilename() # Process corpus line by line and produce output cnt_sen = 0 cnt_crs = 0 logging.info('loading [%s]', fn_i) logging.info('writing [%s]', fn_o) with open(fn_i, 'r') as fhi, open(fn_o, 'w') as fho: # Write output file header fho.write('nr\tlength\tratio\n') # Iterate over input sentences for sentence in fhi: cnt_sen += 1 if cnt_sen % 50 == 0: logging.info('... %d lines', cnt_sen) # Strip POS tags tokens = [word.split('|')[0] for word in sentence.split()] # Append sentence w/o spaces tokenstring = ''.join(tokens) cnt_crs += len(tokenstring) file_append(tfn, tokenstring) # Get compression ratio and write output cr = lzss_calculate_compression_ratio(tfn) fho.write('%d\t%d\t%.8f\n' % (cnt_sen, cnt_crs, cr)) # Cleanup os.remove(tfn) # Some reporting logging.info('%d lines processed', cnt_sen) def run_lz77_mem(fn_i, fn_o): # Process corpus line by line and produce output cnt_sen = 0 txt_plain = '' logging.info('loading [%s]', fn_i) logging.info('writing [%s]', fn_o) with open(fn_i, 'r') as fhi, open(fn_o, 'w') as fho: # Write output file header fho.write('nr\tlength\tratio\n') # Iterate over input sentences for sentence in fhi: cnt_sen += 1 if cnt_sen % 50 == 0: mem_usage = mem_usage_percent() logging.info('... %d lines\tusing %.2f%% RAM', cnt_sen, mem_usage) if mem_usage > 90: logging.error('Insufficient RAM. QUITTING!') exit(1) # Strip POS tags tokens = [word.split('|')[0] for word in sentence.split()] # Append sentence w/o spaces, then add sentence with space sep tokenstring = ''.join(tokens) txt_plain += tokenstring + ' ' txt_compr = gzip.compress(str.encode(txt_plain)) # Get compression ratio and write output cr = len(txt_plain) / len(txt_compr) fho.write('%d\t%d\t%.8f\n' % (cnt_sen, len(txt_plain), cr)) # Some reporting logging.info('%d lines processed', cnt_sen) def main(argv): if len(argv) < 2: logging.error('Insufficient arguments') exit(1) # Run lz77 - in memory run_lz77_mem(sys.argv[1],sys.argv[2]) if __name__ == '__main__': logging.basicConfig( stream=sys.stderr, level=logging.INFO, format='%(asctime)s - %(message)s', datefmt='%H:%M:%S') main(sys.argv[1:]) ```
{ "source": "jerojero/Requiem-BOT", "score": 3 }
#### File: jerojero/Requiem-BOT/main.py ```python import sys import socket import string import topics import random import time import threading SERVER = "irc.rizon.net" CHANNEL = "#" + sys.argv[1] NICK = sys.argv[2] OWNER = "dude1" OWNERVHOST = "singing.a.sad.song" # Your vhost is unique so this way only you (identified) may be able to part and join channels. INTRO = "Every hour it gives you a literary topic to think about your meaningless life." channelsJoined = [CHANNEL, ] def join_channel(channel): ircsock.send(bytes("JOIN "+ channel +"\n", "UTF-8")) ircsock.send(bytes("PRIVMSG "+ channel +" :"+ INTRO +"\n", "UTF-8")) ircsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) ircsock.connect((SERVER, 6667)) ircsock.send(bytes("USER "+ NICK +" "+ NICK +" "+ NICK +" :connected\n", "UTF-8")) ircsock.send(bytes("NICK "+ NICK +"\n", "UTF-8")) # ircsock.send(bytes("NICKSERV :IDENTIFY %s\r\n" % password, "UTF-8")) time.sleep(2) join_channel(CHANNEL) # Bot functions def send_to_channel_normal(message, channel): ircsock.send(bytes("PRIVMSG "+ channel +" :"+ message +"\n", "UTF-8")) def send_to_channel_bold(message, channel): ircsock.send(bytes("PRIVMSG "+ channel +" :""\u0002"+message +"\n", "UTF-8")) def send_to_channel_cursive(message, channel): ircsock.send(bytes("PRIVMSG "+ channel +" :""\u001D"+message +"\n", "UTF-8")) def part(channel): ircsock.send(bytes("PART "+ channel +"\n", "UTF-8")) def get_time(delay): timeNow = [time.strftime("%H"), time.strftime("%M"), time.strftime("%S")] time.sleep(delay) return(timeNow) def timer(): while True: currentTime = get_time(30) if(currentTime[1] == "00"): topicName = random.choice(list(topics.topics.keys())) topicTran = topics.topics[topicName][0] topicDesc = topics.topics[topicName][1] for channel in channelsJoined: send_to_channel_bold(topicName, channel) send_to_channel_cursive(topicTran, channel) send_to_channel_normal(topicDesc, channel) time.sleep(60) def ircBuffer(): readbuffer = "" while True: readbuffer = readbuffer+ircsock.recv(1024).decode("UTF-8") temp = str.split(readbuffer, "\n") readbuffer=temp.pop( ) try: for line in temp: line = str.rstrip(line) line = str.split(line) print(line) channel = "" try: for char in line[2]: if(char != ":"): channel += char except: pass if(line[0] == "PING"): ircsock.send(bytes("PONG %s\r\n" % line[1], "UTF-8")) if(line[3] == ":!!topic"): topicName = random.choice(list(topics.topics.keys())) topicTran = topics.topics[topicName][0] topicDesc = topics.topics[topicName][1] send_to_channel_bold(topicName, channel) send_to_channel_cursive(topicTran, channel) send_to_channel_normal(topicDesc, channel) if(line[3] in (":!!" + word for word in topics.first_word)): # checks if the message starts with !! if it does checks if the next words is in a list of first words, is a valid phrase in topics size = len(line) # some topics are longer so this makes sure you get all the words index = 3 # starts at the fourth element, first element (0) is the username, second (1) is PRIVMSG, third (2) is the channel and fourth is the first word (:!!firstword) phrase = "" while (index < size): phrase += line[index] + " " index += 1 phrase = phrase.replace(":!!","") phrase = phrase.capitalize() phrase = phrase.rstrip() sender = "" for char in line[0]: if(char == "!"): break if(char != ":"): sender += char if(sender != NICK): topicName = phrase topicTran = topics.topics[topicName][0] topicDesc = topics.topics[topicName][1] send_to_channel_bold(topicName, channel) send_to_channel_cursive(topicTran, channel) send_to_channel_normal(topicDesc, channel) if(line[3] == ":!!help"): message = "Every hour it gives a random phrase so you think about your meaningless life, do !!topic to get one right this moment. Also you can do !!name of topic to get an explanation on that one (ej: !!carpe diem)." send_to_channel_normal(message, channel) if(line[3] == ":!!quit") and (line[4] == NICK): sender = line[0].split("@")[1] if(sender == OWNERVHOST): part(channel) if(line[3] == ":!!join") and (line[4] == NICK): sender = line[0].split("@")[1] if(sender == OWNERVHOST): channelToJoin = "" for char in line[4]: channelToJoin += char join_channel(channelToJoin) channelsJoined.append(channelToJoin) except: continue t1 = threading.Thread(target = timer) t2 = threading.Thread(target = ircBuffer) t1.start() t2.start() ```
{ "source": "Jerold25/DarkWeb-Crawling-Indexing", "score": 3 }
#### File: Code/crawler/crawl_bot.py ```python from get_domains import * from file_manage import * from link_finder import link_crawler from urllib.request import urlopen import tldextract #Importing Stem libraries from stem import Signal from stem.control import Controller import socks, socket #Initiating Connection with Controller.from_port(port=9051) as controller: controller.authenticate("insert-your-key") controller.signal(Signal.NEWNYM) # TOR SETUP GLOBAL Vars SOCKS_PORT = 9050 # TOR proxy port that is default from torrc, change to whatever torrc is configured to socks.setdefaultproxy(socks.PROXY_TYPE_SOCKS5, "127.0.0.1", SOCKS_PORT) socket.socket = socks.socksocket # Perform DNS resolution through the socket def getaddrinfo(*args): return [(socket.AF_INET, socket.SOCK_STREAM, 6, '', (args[0], args[1]))] socket.getaddrinfo = getaddrinfo class Crawl_bot: folder_name, start_link, domain_name, queued_data, crawled_data = '', '', '', '', '' queue = set() data_crawled = set() def __init__(self, folder_name, start_link, domain_name): Crawl_bot.folder_name = folder_name Crawl_bot.start_link = start_link Crawl_bot.domain_name = domain_name Crawl_bot.queued_data = Crawl_bot.folder_name + '/queue.txt' Crawl_bot.crawled_data = Crawl_bot.folder_name + '/crawled.txt' self.initiate_directory() self.crawl_page('Spider starts here', Crawl_bot.start_link) @staticmethod def initiate_directory(): # Define and create new directory on the first run create_project_folder(Crawl_bot.folder_name) create_data_files(Crawl_bot.folder_name, Crawl_bot.start_link) Crawl_bot.queue = convert_to_set(Crawl_bot.queued_data) Crawl_bot.data_crawled = convert_to_set(Crawl_bot.crawled_data) @staticmethod def crawl_page(thread_name, web_url): # Fill queue and then update files, also updating user display print(web_url) if web_url not in Crawl_bot.data_crawled: print(thread_name + ' now crawl starts ' + web_url) print('Queue_url ' + str(len(Crawl_bot.queue)) + ' | Crawled_url ' + str(len(Crawl_bot.data_crawled))) Crawl_bot.add_url_to_queue(Crawl_bot.collect_url(web_url)) Crawl_bot.queue.remove(web_url) Crawl_bot.data_crawled.add(web_url) Crawl_bot.update_folder() # Converts raw response data into readable information and checks for proper html formatting @staticmethod def collect_url(web_url): html_data_string = '' try: received_response = urlopen(web_url) if 'text/html' in received_response.getheader('Content-Type'): data_bytes = received_response.read() html_data_string = data_bytes.decode("latin-1") link_finder = link_crawler(Crawl_bot.start_link, web_url) link_finder.feed(html_data_string) ############################################################################################################################################################################################## #######################################FOR SCRAPPING PURPOSES################################################################################################################################# f = open(Crawl_bot.folder_name + '/' + ((tldextract.extract(web_url)).domain), 'a') f.write(html_data_string + "\n\n\n" + '#####EOF#####' + "\n\n\n") f.close() ############################################################################################################################################################################################### ############################################################################################################################################################################################### except Exception as e: print(str(e)) return set() return link_finder.page_urls() @staticmethod def add_url_to_queue(links): # Queue data saves to project files for url in links: if (url in Crawl_bot.queue) or (url in Crawl_bot.data_crawled): continue Crawl_bot.queue.add(url) @staticmethod def update_folder(): # Update the project directory set_to_file(Crawl_bot.queue, Crawl_bot.queued_data) set_to_file(Crawl_bot.data_crawled, Crawl_bot.crawled_data) ```
{ "source": "Jerold25/textanalysis_yelp", "score": 2 }
#### File: textanalysis_yelp/backend/app.py ```python import string import time import bson from collections import OrderedDict import datetime from bson.json_util import dumps, loads from flask import Flask, redirect, url_for from flask import jsonify from flask import request, make_response from flask_pymongo import PyMongo from flask_cors import CORS from werkzeug.contrib.cache import SimpleCache app = Flask(__name__) cors = CORS(app) cache = SimpleCache() app.config['MONGO_DBNAME'] = 'yelp-db' app.config['MONGO_URI'] = 'mongodb://localhost:27017/yelp-db' mongo = PyMongo(app) from escapejson import escapejson @app.route('/get-data', methods=['GET']) def get_resturant_info(): res = [] try: mile = 3963 # Retrieve 1000 businesses from the center of Tempe -> nearest to the farthest x = mongo.db.business.find({"location": {"$nearSphere": [-111.9400, 33.4255], "$minDistance": 0 / mile}}).limit( 1000) for i in x: temp = {'type': "Feature"} props = {'city': i['city'], 'review_count': i['review_count'], 'name': i['name'], 'business_id': i['business_id'], 'hours': i['hours'], 'state': i['state'], 'postal_code': i['postal_code'], 'stars': i['stars'], 'address': i['address'], 'is_open': i['is_open'], 'attributes': i['attributes'], 'categories': i['categories']} temp['properties'] = props geo = {'type': 'Point', 'coordinates': i['location']} temp['geometry'] = geo res.append(temp) except Exception as ex: print(ex) return make_response(dumps(res)) @app.route('/', methods=['GET']) def start(): return redirect(url_for('get_all_details')) def get_liveliness(business_id): obj = mongo.db.checkin.find_one({"business_id": business_id}) date_str = obj['date'] dates = date_str.split(', ') data = [] heat_map = [[0 for i in range(24)] for j in range(7)] date_map = {0: set(), 1: set(), 2: set(), 3: set(), 4: set(), 5: set(), 6: set()} for i in dates: date, time = i.split(' ') day = int(datetime.datetime.strptime(date, '%Y-%m-%d').strftime('%w')) date_map[day].add(date) hour = int(time.split(':')[0]) heat_map[day][hour] += 1 for i in range(7): for j in range(24): x = len(date_map[i]) if x>1: heat_map[i][j] /= x data.append(OrderedDict([('day', i), ('hour', j), ('value', heat_map[i][j])])) return data @app.route('/all-details', methods=['GET']) def get_all_details(): # args = request.args # business_id = args["business-id"] business_id = "mKf7pIkOYpEhJTqjw4_Fxg" response = cache.get(business_id) if not response: word_cloud_data = get_word_cloud_trend(business_id) rating_trend_data = get_rating_trend(business_id) sentiment_trend_data = get_sentiment_trend(business_id) liveliness = get_liveliness(business_id) combined_trend = get_combined_trend(business_id) response = {"ratingTrend": rating_trend_data, "sentimentTrend": sentiment_trend_data, "liveliness": liveliness, "wordCloudData": word_cloud_data, "combinedTrends": combined_trend} cache.set(business_id, response, timeout=15 * 60) return make_response(escapejson(dumps(response))) # @app.route('/rating_trend', methods=['GET']) def get_rating_trend(business_id): cursor = mongo.db.review.aggregate( [ {"$match": {"business_id": business_id}}, {'$project': {'part_date': {'$substr': ['$date', 0, 7]}, 'stars': '$stars', 'business_id': '$business_id'}}, {"$group": {"_id": '$part_date', "avgRating": {"$avg": '$stars'}}}, {"$sort": {"_id": 1}} ] ) data = [] c = 0 cum = 0 for i in cursor: val = (cum * c) + i['avgRating'] c = c + 1 cum = val / c data.append({'date': i['_id'], 'count': cum}) return data def get_sentiment_trend(business_id): cursor = mongo.db.sentiment_info.aggregate( [ {"$match": {"business_id": business_id}}, {'$project': {'part_date': {'$substr': ['$date', 0, 7]}, 'pos_sentiment': {'$arrayElemAt': ["$sentiment", 1]}, 'business_id': 1}}, {"$group": {"_id": '$part_date', "avgSenti": {"$avg": '$pos_sentiment'}}}, {"$sort": {"_id": 1}} ] ) data = [] c = 0 cum = 0 for i in cursor: val = (cum * c) + i['avgSenti'] c = c + 1 cum = val / c data.append({'date': i['_id'], 'count': cum}) return data def get_combined_trend(business_id): #SENTIMENTS cursor = mongo.db.sentiment_info.aggregate( [ {"$match": {"business_id": business_id}}, {'$project': {'part_date': {'$substr': ['$date', 0, 10]}, 'pos_sentiment': {'$arrayElemAt': ["$sentiment", 1]}, 'business_id': 1}}, {"$group": {"_id": '$part_date', "avgSenti": {"$avg": '$pos_sentiment'}}}, {"$sort": {"_id": 1}} ] ) sentiments = {} c = 0 cum = 0 for i in cursor: val = (cum * c) + i['avgSenti'] c = c + 1 cum = val / c dat = i['_id'] date_time_obj = time.strptime(dat, "%Y-%m-%d") epoch = int(time.mktime(date_time_obj)) sentiments[epoch] = cum #RATINGS cursor = mongo.db.review.aggregate( [ {"$match": {"business_id": business_id}}, {'$project': {'part_date': {'$substr': ['$date', 0, 10]}, 'stars': '$stars', 'business_id': '$business_id'}}, {"$group": {"_id": '$part_date', "avgRating": {"$avg": '$stars'}}}, {"$sort": {"_id": 1}} ] ) rating = {} c = 0 cum = 0 for i in cursor: val = (cum * c) + i['avgRating'] c = c + 1 cum = val / c dat = i['_id'] date_time_obj = time.strptime(dat, "%Y-%m-%d") epoch = int(time.mktime(date_time_obj)) rating[epoch] = cum trend = [] for key, val in rating.items(): senti = 0 if key in sentiments: senti = sentiments[key] * 5 temp = {'date': key, 'ratings': val, 'sentiment': senti} trend.append(temp) return trend # @app.route('/word-cloud-trend', methods=['GET']) def get_word_cloud_trend(business_id): # args = request.args # restuarant_id = args["resturant-id"] word_value_dict = dict() word_reviews_dict = dict() for review in mongo.db.food_items_info.find({"business_id": business_id}): for word in review["food_items"]: word = escape_keys(word) if word not in word_value_dict: word_value_dict[word] = 0 if word not in word_reviews_dict: word_reviews_dict[word] = [] word_value_dict[word] = word_value_dict[word] + review["stars"] if len(word_reviews_dict[word]) < 10: word_reviews_dict[word].append({"text": escape_quotes(review["text"]), "stars": review["stars"], "date": review["date"]}) # words = [] # for word, value in word_value_dict.items(): # words.append({"text": word, "weight": value}) sample_title = {} for word, count in word_value_dict.items(): sample_title[word] = "Got {} stars!".format(count) response = {"count": word_value_dict, "sample_title": sample_title, "word_reviews":word_reviews_dict } return response def remove_punctuations(text): return str(text).translate(str.maketrans('', '', string.punctuation)) def escape_keys(text): text = text.lower() return remove_punctuations(text) def escape_quotes(text): text = text.replace('"', '\"') return text if __name__ == '__main__': app.run(host='0.0.0.0', debug=True, port=5001) ```
{ "source": "jeroldalbertson-wf/rick_roller", "score": 3 }
#### File: rick_roller/src/models.py ```python import json from google.appengine.ext import ndb class RickRoll(ndb.Model): ip = ndb.StringProperty() def get_rick_rolls_list(): rick_rolls = RickRoll.query().fetch() if not rick_rolls: rick_rolls = [] return rick_rolls ```
{ "source": "JeroldLeo/ursina", "score": 4 }
#### File: ursina/samples/clicker_game.py ```python from ursina import * app = Ursina() window.color = color._20 gold = 0 counter = Text(text='0', y=.25, z=-1, scale=2, origin=(0, 0), background=True) button = Button(text='+', color=color.azure, scale=.125) def button_click(): global gold gold += 1 counter.text = str(gold) button.on_click = button_click button_2 = Button(cost=10, x=.2, scale=.125, color=color.dark_gray, disabled=True) button_2.tooltip = Tooltip(f'<gold>Gold Generator\n<default>Earn 1 gold every second.\nCosts {button_2.cost} gold.') def buy_auto_gold(): global gold if gold >= button_2.cost: gold -= button_2.cost counter.text = str(gold) invoke(auto_generate_gold, 1, 1) button_2.on_click = buy_auto_gold def auto_generate_gold(value=1, interval=1): global gold gold += 1 counter.text = str(gold) button_2.animate_scale(.125 * 1.1, duration=.1) button_2.animate_scale(.125, duration=.1, delay=.1) invoke(auto_generate_gold, value, delay=interval) def update(): global gold for b in (button_2,): if gold >= b.cost: b.disabled = False b.color = color.green else: b.disabled = True b.color = color.gray app.run() ``` #### File: ursina/samples/minecraft_clone.py ```python from ursina import * from ursina.prefabs.first_person_controller import FirstPersonController app = Ursina() # Define a Voxel class. # By setting the parent to scene and the model to 'cube' it becomes a 3d button. class Voxel(Button): def __init__(self, position=(0, 0, 0)): super().__init__( parent=scene, position=position, model='cube', origin_y=.5, texture='white_cube', color=color.color(0, 0, random.uniform(.9, 1.0)), highlight_color=color.lime, ) def input(self, key): if self.hovered: if key == 'left mouse down': voxel = Voxel(position=self.position + mouse.normal) if key == 'right mouse down': destroy(self) for z in range(8): for x in range(8): voxel = Voxel(position=(x, 0, z)) player = FirstPersonController() app.run() ```
{ "source": "jeromaerts/ESMValTool", "score": 2 }
#### File: diag_scripts/emergent_constraints/cox18nature.py ```python import logging import os import iris import matplotlib.lines as mlines import matplotlib.pyplot as plt import numpy as np import esmvaltool.diag_scripts.emergent_constraints as ec import esmvaltool.diag_scripts.shared.iris_helpers as ih from esmvaltool.diag_scripts.shared import ( ProvenanceLogger, get_diagnostic_filename, get_plot_filename, group_metadata, io, plot, run_diagnostic, select_metadata) logger = logging.getLogger(os.path.basename(__file__)) plt.style.use(plot.get_path_to_mpl_style()) COLOR_SMALL_LAMBDA = '#800060' COLOR_LARGE_LAMBDA = '#009900' (FIG, AXES) = plt.subplots() ECS_ATTRS = { 'short_name': 'ecs', 'long_name': 'Effective Climate Sensitivity (ECS)', 'units': 'K', } TASA_ATTRS = { 'short_name': 'tasa', 'long_name': 'Near-Surface Air Temperature Anomaly', 'units': 'K', } PSI_ATTRS = { 'short_name': 'psi', 'long_name': 'Temperature variability metric', 'units': 'K', } def _get_ancestor_files(cfg, obs_name, projects=None): """Get ancestor files for provenance.""" if projects is None: projects = _get_project(cfg) if isinstance(projects, str): projects = [projects] datasets = [] for project in projects: datasets.extend( select_metadata(cfg['input_data'].values(), project=project)) datasets.extend( select_metadata(cfg['input_data'].values(), dataset=obs_name)) return [d['filename'] for d in datasets] def _get_model_color(model, lambda_cube): """Get color of model dependent on climate feedback parameter.""" clim_sens = lambda_cube.extract(iris.Constraint(dataset=model)).data if clim_sens < 1.0: col = COLOR_SMALL_LAMBDA else: col = COLOR_LARGE_LAMBDA return col def _plot_model_point(model, psi_cube, ecs_cube, lambda_cube): """Plot a single model point for emergent relationship.""" col = _get_model_color(model, lambda_cube) style = plot.get_dataset_style(model, 'cox18nature') AXES.plot( psi_cube.extract(iris.Constraint(dataset=model)).data, ecs_cube.extract(iris.Constraint(dataset=model)).data, linestyle='none', marker=style['mark'], markeredgecolor=col, markerfacecolor=col, markersize=style['size']) def _get_line_plot_legend(): """Add legend for line plots.""" color_obs = plot.get_dataset_style('OBS', 'cox18nature')['color'] handles = [ mlines.Line2D([], [], color=COLOR_SMALL_LAMBDA, label=r'$\lambda < 1.0$ Wm$^{-2}$K$^{-1}$'), mlines.Line2D([], [], color=COLOR_LARGE_LAMBDA, label=r'$\lambda > 1.0$ Wm$^{-2}$K$^{-1}$'), mlines.Line2D([], [], linestyle='none', marker='o', markeredgecolor=color_obs, markerfacecolor=color_obs, label='Observations'), ] return AXES.legend(handles=handles, loc='upper left') def _get_project(cfg): """Extract project from cfg.""" input_data = cfg['input_data'].values() projects = list(group_metadata(input_data, 'project').keys()) projects = [p for p in projects if 'obs' not in p.lower()] if len(projects) == 1: return projects[0] return projects def _save_fig(cfg, basename, legend=None): """Save matplotlib figure.""" path = get_plot_filename(basename, cfg) if legend is None: legend = [] else: legend = [legend] FIG.savefig( path, additional_artists=legend, bbox_inches='tight', orientation='landscape') logger.info("Wrote %s", path) AXES.cla() return path def get_external_cubes(cfg): """Get external cubes for psi, ECS and lambda.""" cubes = iris.cube.CubeList() for filename in ('psi.nc', 'ecs.nc', 'lambda.nc'): filepath = io.get_ancestor_file(cfg, filename) cube = iris.load_cube(filepath) cube = cube.extract( ih.iris_project_constraint(['OBS'], cfg, negate=True)) cubes.append(cube) cubes = ih.intersect_dataset_coordinates(cubes) return (cubes[0], cubes[1], cubes[2]) def get_provenance_record(caption, statistics, plot_type, ancestor_files): """Create a provenance record describing the diagnostic data and plot.""" record = { 'ancestors': ancestor_files, 'authors': ['schlund_manuel'], 'caption': caption, 'domains': ['global'], 'plot_type': plot_type, 'realms': ['atmos'], 'references': ['cox18nature'], 'statistics': statistics, 'themes': ['EC'], } return record def plot_temperature_anomaly(cfg, tas_cubes, lambda_cube, obs_name): """Plot temperature anomaly versus time.""" for cube in tas_cubes.values(): cube.data -= np.mean( cube.extract( iris.Constraint(year=lambda cell: 1961 <= cell <= 1990)).data) # Save netcdf file and provencance filename = 'temperature_anomaly_{}'.format(obs_name) netcdf_path = get_diagnostic_filename(filename, cfg) io.save_1d_data(tas_cubes, netcdf_path, 'year', TASA_ATTRS) project = _get_project(cfg) provenance_record = get_provenance_record( "Simulated change in global temperature from {} models (coloured " "lines), compared to the global temperature anomaly from the {} " "dataset (black dots). The anomalies are relative to a baseline " "period of 1961-1990.".format(project, obs_name), ['anomaly'], ['times'], _get_ancestor_files(cfg, obs_name)) # Plot if cfg['write_plots']: models = lambda_cube.coord('dataset').points # Plot lines for model in models: cube = tas_cubes[model] AXES.plot( cube.coord('year').points, cube.data, color=_get_model_color(model, lambda_cube)) obs_style = plot.get_dataset_style('OBS', 'cox18nature') obs_cube = tas_cubes[obs_name] AXES.plot( obs_cube.coord('year').points, obs_cube.data, linestyle='none', marker='o', markeredgecolor=obs_style['color'], markerfacecolor=obs_style['color']) # Plot appearance AXES.set_title('Simulation of global warming record') AXES.set_xlabel('Year') AXES.set_ylabel('Temperature anomaly / K') legend = _get_line_plot_legend() # Save plot provenance_record['plot_file'] = _save_fig(cfg, filename, legend) # Write provenance with ProvenanceLogger(cfg) as provenance_logger: provenance_logger.log(netcdf_path, provenance_record) def plot_psi(cfg, psi_cubes, lambda_cube, obs_name): """Plot temperature variability metric psi versus time.""" filename = 'temperature_variability_metric_{}'.format(obs_name) netcdf_path = get_diagnostic_filename(filename, cfg) io.save_1d_data(psi_cubes, netcdf_path, 'year', PSI_ATTRS) project = _get_project(cfg) provenance_record = get_provenance_record( "Psi metric of variability versus time, from the {0} models " "(coloured lines), and the {1} observational data (black circles). " "The psi values are calculated for windows of width {2} yr, after " "linear de-trending in each window. These {2}-yr windows are shown " "for different end times.".format(project, obs_name, cfg.get('window_length', 55)), ['corr', 'var'], ['times'], _get_ancestor_files(cfg, obs_name)) # Plot if cfg['write_plots']: models = lambda_cube.coord('dataset').points # Plot lines for model in models: cube = psi_cubes[model] AXES.plot( cube.coord('year').points, cube.data, color=_get_model_color(model, lambda_cube)) obs_style = plot.get_dataset_style('OBS', 'cox18nature') obs_cube = psi_cubes[obs_name] AXES.plot( obs_cube.coord('year').points, obs_cube.data, linestyle='none', marker='o', markeredgecolor=obs_style['color'], markerfacecolor=obs_style['color']) # Plot appearance AXES.set_title('Metric of variability versus time') AXES.set_xlabel('Year') AXES.set_ylabel(r'$\Psi$ / K') legend = _get_line_plot_legend() # Save plot provenance_record['plot_file'] = _save_fig(cfg, filename, legend) # Write provenance with ProvenanceLogger(cfg) as provenance_logger: provenance_logger.log(netcdf_path, provenance_record) def plot_emergent_relationship(cfg, psi_cube, ecs_cube, lambda_cube, obs_cube): """Plot emergent relationship.""" filename = 'emergent_relationship_{}'.format( obs_cube.attributes['dataset']) cube = ecs_cube.copy() cube.add_aux_coord( iris.coords.AuxCoord(psi_cube.data, **ih.convert_to_iris(PSI_ATTRS)), 0) netcdf_path = get_diagnostic_filename(filename, cfg) io.iris_save(cube, netcdf_path) provenance_record = get_provenance_record( "Emergent relationship between ECS and the psi metric. The black dot-" "dashed line shows the best-fit linear regression across the model " "ensemble, with the prediction error for the fit given by the black " "dashed lines. The vertical blue lines show the observational " "constraint from the {} observations: the mean (dot-dashed line) and " "the mean plus and minus one standard deviation (dashed lines).". format(obs_cube.attributes['dataset']), ['mean', 'corr', 'var'], ['scatter'], _get_ancestor_files(cfg, obs_cube.attributes['dataset'])) # Plot if cfg['write_plots']: obs_mean = np.mean(obs_cube.data) obs_std = np.std(obs_cube.data) # Calculate regression line lines = ec.regression_line(psi_cube.data, ecs_cube.data) logger.info("Found emergent relationship with slope %.2f (r = %.2f)", lines['slope'], lines['rvalue']) # Plot points for model in psi_cube.coord('dataset').points: _plot_model_point(model, psi_cube, ecs_cube, lambda_cube) # Plot lines AXES.set_xlim(auto=False) AXES.set_ylim(auto=False) AXES.plot( lines['x'], lines['y_best_estim'], color='black', linestyle='dashdot', label='Linear regression') AXES.plot( lines['x'], lines['y_minus_err'], color='black', linestyle='dashed') AXES.plot( lines['x'], lines['y_plus_err'], color='black', linestyle='dashed') AXES.axvline( obs_mean, color='blue', linestyle='dashdot', label='Observational constraint') AXES.axvline(obs_mean - obs_std, color='blue', linestyle='dashed') AXES.axvline(obs_mean + obs_std, color='blue', linestyle='dashed') # Plot appearance AXES.set_title('Emergent relationship fit') AXES.set_xlabel(r'$\Psi$ / K') AXES.set_ylabel('ECS / K') legend = AXES.legend(loc='upper left') # Save plot provenance_record['plot_file'] = _save_fig(cfg, filename, legend) # Write provenance with ProvenanceLogger(cfg) as provenance_logger: provenance_logger.log(netcdf_path, provenance_record) def plot_pdf(cfg, psi_cube, ecs_cube, obs_cube): """Plot probability density function of ECS.""" obs_mean = np.mean(obs_cube.data) obs_std = np.std(obs_cube.data) (ecs_lin, ecs_pdf) = ec.gaussian_pdf(psi_cube.data, ecs_cube.data, obs_mean, obs_std) # Provenance filename = 'pdf_{}'.format(obs_cube.attributes['dataset']) netcdf_path = get_diagnostic_filename(filename, cfg) cube = iris.cube.Cube( ecs_pdf, var_name='pdf', long_name='Probability density function', units='K-1') cube.add_aux_coord( iris.coords.AuxCoord(ecs_lin, **ih.convert_to_iris(ECS_ATTRS)), 0) io.iris_save(cube, netcdf_path) project = _get_project(cfg) provenance_record = get_provenance_record( "The PDF for ECS. The orange histograms show the prior distributions " "that arise from equal weighting of the {} models in 0.5 K bins.". format(project), ['mean'], ['other'], _get_ancestor_files(cfg, obs_cube.attributes['dataset'])) # Plot if cfg['write_plots']: AXES.plot( ecs_lin, ecs_pdf, color='black', linewidth=2.0, label='Emergent constraint') AXES.hist( ecs_cube.data, bins=6, range=(2.0, 5.0), density=True, color='orange', label='{} models'.format(project)) # Plot appearance AXES.set_title('PDF of emergent constraint') AXES.set_xlabel('ECS / K') AXES.set_ylabel('Probability density') legend = AXES.legend(loc='upper left') # Save plot provenance_record['plot_file'] = _save_fig(cfg, filename, legend) # Write provenance with ProvenanceLogger(cfg) as provenance_logger: provenance_logger.log(netcdf_path, provenance_record) def plot_cdf(cfg, psi_cube, ecs_cube, obs_cube): """Plot cumulative distribution function of ECS.""" confidence_level = cfg.get('confidence_level', 0.66) (ecs_lin, ecs_pdf) = ec.gaussian_pdf(psi_cube.data, ecs_cube.data, np.mean(obs_cube.data), np.std(obs_cube.data)) ecs_cdf = ec.cdf(ecs_lin, ecs_pdf) # Provenance filename = 'cdf_{}'.format(obs_cube.attributes['dataset']) netcdf_path = get_diagnostic_filename(filename, cfg) cube = iris.cube.Cube( ecs_cdf, var_name='cdf', long_name='Cumulative distribution function', units='1') cube.add_aux_coord( iris.coords.AuxCoord(ecs_lin, **ih.convert_to_iris(ECS_ATTRS)), 0) io.iris_save(cube, netcdf_path) project = _get_project(cfg) provenance_record = get_provenance_record( "The CDF for ECS. The horizontal dot-dashed lines show the {}% " "confidence limits. The orange histograms show the prior " "distributions that arise from equal weighting of the {} models in " "0.5 K bins.".format(int(confidence_level * 100), project), ['mean'], ['other'], _get_ancestor_files(cfg, obs_cube.attributes['dataset'])) # Plot if cfg['write_plots']: AXES.plot( ecs_lin, ecs_cdf, color='black', linewidth=2.0, label='Emergent constraint') AXES.hist( ecs_cube.data, bins=6, range=(2.0, 5.0), cumulative=True, density=True, color='orange', label='{} models'.format(project)) AXES.axhline( (1.0 - confidence_level) / 2.0, color='black', linestyle='dashdot') AXES.axhline( (1.0 + confidence_level) / 2.0, color='black', linestyle='dashdot') # Plot appearance AXES.set_title('CDF of emergent constraint') AXES.set_xlabel('ECS / K') AXES.set_ylabel('CDF') legend = AXES.legend(loc='upper left') # Save plot provenance_record['plot_file'] = _save_fig(cfg, filename, legend) # Write provenance with ProvenanceLogger(cfg) as provenance_logger: provenance_logger.log(netcdf_path, provenance_record) def get_ecs_range(cfg, psi_cube, ecs_cube, obs_cube): """Get constrained ecs range.""" confidence_level = cfg.get('confidence_level', 0.66) conf_low = (1.0 - confidence_level) / 2.0 conf_high = (1.0 + confidence_level) / 2.0 # Calculate PDF and CDF (ecs_lin, ecs_pdf) = ec.gaussian_pdf(psi_cube.data, ecs_cube.data, np.mean(obs_cube.data), np.std(obs_cube.data)) ecs_cdf = ec.cdf(ecs_lin, ecs_pdf) # Calculate constrained ECS range ecs_mean = ecs_lin[np.argmax(ecs_pdf)] ecs_index_range = np.where((ecs_cdf >= conf_low) & (ecs_cdf <= conf_high))[0] ecs_range = ecs_lin[ecs_index_range] ecs_low = min(ecs_range) ecs_high = max(ecs_range) return (ecs_mean, ecs_low, ecs_high) def main(cfg): """Run the diagnostic.""" input_data = ( select_metadata(cfg['input_data'].values(), short_name='tas') + select_metadata(cfg['input_data'].values(), short_name='tasa')) if not input_data: raise ValueError("This diagnostics needs 'tas' or 'tasa' variable") # Get tas data tas_cubes = {} tas_obs = [] for (dataset, [data]) in group_metadata(input_data, 'dataset').items(): cube = iris.load_cube(data['filename']) cube = cube.aggregated_by('year', iris.analysis.MEAN) tas_cubes[dataset] = cube if data['project'] == 'OBS': tas_obs.append(dataset) # Get time-dependent psi data psi_cubes = {} psi_obs = [] for (dataset, [data]) in group_metadata( io.netcdf_to_metadata(cfg, pattern='psi_*.nc'), 'dataset').items(): cube = iris.load_cube(data['filename']) cube = cube.aggregated_by('year', iris.analysis.MEAN) psi_cubes[dataset] = cube if data['project'] == 'OBS': psi_obs.append(dataset) # Get psi, ECS and psi for models (psi_cube, ecs_cube, lambda_cube) = get_external_cubes(cfg) # Plots for obs_name in tas_obs: logger.info("Observation for tas: %s", obs_name) plot_temperature_anomaly(cfg, tas_cubes, lambda_cube, obs_name) for obs_name in psi_obs: logger.info("Observation for psi: %s", obs_name) plot_psi(cfg, psi_cubes, lambda_cube, obs_name) obs_cube = psi_cubes[obs_name] plot_emergent_relationship(cfg, psi_cube, ecs_cube, lambda_cube, obs_cube) plot_pdf(cfg, psi_cube, ecs_cube, obs_cube) plot_cdf(cfg, psi_cube, ecs_cube, obs_cube) # Print ECS range ecs_range = get_ecs_range(cfg, psi_cube, ecs_cube, obs_cube) logger.info("Observational constraint: Ψ = (%.2f ± %.2f) K", np.mean(obs_cube.data), np.std(obs_cube.data)) logger.info( "Constrained ECS range: (%.2f - %.2f) K with best " "estimate %.2f K", ecs_range[1], ecs_range[2], ecs_range[0]) if __name__ == '__main__': with run_diagnostic() as config: main(config) plt.close() ``` #### File: diag_scripts/shared/io.py ```python import fnmatch import logging import os import iris import numpy as np from .iris_helpers import unify_1d_cubes logger = logging.getLogger(__name__) VAR_KEYS = [ 'long_name', 'units', ] NECESSARY_KEYS = VAR_KEYS + [ 'dataset', 'filename', 'project', 'short_name', ] def _has_necessary_attributes(metadata, only_var_attrs=False, log_level='debug'): """Check if dataset metadata has necessary attributes.""" keys_to_check = (VAR_KEYS + ['short_name'] if only_var_attrs else NECESSARY_KEYS) for dataset in metadata: for key in keys_to_check: if key not in dataset: getattr(logger, log_level)("Dataset '%s' does not have " "necessary attribute '%s'", dataset, key) return False return True def get_all_ancestor_files(cfg, pattern=None): """Return a list of all files in the ancestor directories. Parameters ---------- cfg : dict Diagnostic script configuration. pattern : str, optional Only return files which match a certain pattern. Returns ------- list of str Full paths to the ancestor files. """ ancestor_files = [] input_dirs = [ d for d in cfg['input_files'] if not d.endswith('metadata.yml') ] for input_dir in input_dirs: for (root, _, files) in os.walk(input_dir): if pattern is not None: files = fnmatch.filter(files, pattern) files = [os.path.join(root, f) for f in files] ancestor_files.extend(files) return ancestor_files def get_ancestor_file(cfg, pattern): """Return a desired file in the ancestor directories. Parameters ---------- cfg : dict Diagnostic script configuration. pattern : str Pattern which specifies the name of the file. Returns ------- str or None Full path to the file or `None` if file not found. """ files = get_all_ancestor_files(cfg, pattern=pattern) if not files: logger.warning( "No file with requested name %s found in ancestor " "directories", pattern) return None if len(files) != 1: logger.warning( "Multiple files with requested pattern %s found (%s), returning " "first appearance", pattern, files) return files[0] def netcdf_to_metadata(cfg, pattern=None, root=None): """Convert attributes of netcdf files to list of metadata. Parameters ---------- cfg : dict Diagnostic script configuration. pattern : str, optional Only consider files which match a certain pattern. root : str, optional (default: ancestor directories) Root directory for the search. Returns ------- list of dict List of dataset metadata. """ if root is None: all_files = get_all_ancestor_files(cfg, pattern) else: all_files = [] for (base, _, files) in os.walk(root): if pattern is not None: files = fnmatch.filter(files, pattern) files = [os.path.join(base, f) for f in files] all_files.extend(files) all_files = fnmatch.filter(all_files, '*.nc') # Iterate over netcdf files metadata = [] for path in all_files: cube = iris.load_cube(path) dataset_info = dict(cube.attributes) for var_key in VAR_KEYS: dataset_info[var_key] = getattr(cube, var_key) dataset_info['short_name'] = cube.var_name dataset_info['standard_name'] = cube.standard_name dataset_info['filename'] = path # Check if necessary keys are available if _has_necessary_attributes([dataset_info], log_level='warning'): metadata.append(dataset_info) else: logger.warning("Skipping '%s'", path) return metadata def metadata_to_netcdf(cube, metadata): """Convert single metadata dictionary to netcdf file. Parameters ---------- cube : iris.cube.Cube Cube to be written. metadata : dict Metadata for the cube. """ metadata = dict(metadata) if not _has_necessary_attributes([metadata], log_level='warning'): logger.warning("Cannot save cube\n%s", cube) return for var_key in VAR_KEYS: setattr(cube, var_key, metadata.pop(var_key)) cube.var_name = metadata.pop('short_name') cube.standard_name = None if 'standard_name' in metadata: standard_name = metadata.pop('standard_name') try: cube.standard_name = standard_name except ValueError: logger.debug("Got invalid standard_name '%s'", standard_name) for (attr, val) in metadata.items(): if isinstance(val, bool): metadata[attr] = str(val) cube.attributes.update(metadata) iris_save(cube, metadata['filename']) def save_1d_data(cubes, path, coord_name, var_attrs, attributes=None): """Save 1D data for multiple datasets. Create 2D cube with the dimensionsal coordinate `coord_name` and the auxiliary coordinate `dataset` and save 1D data for every dataset given. The cube is filled with missing values where no data exists for a dataset at a certain point. Note ---- Does not check metadata of the `cubes`, i.e. different names or units will be ignored. Parameters ---------- cubes : dict of iris.cube.Cube 1D `iris.cube.Cube`s (values) and corresponding datasets (keys). path : str Path to the new file. coord_name : str Name of the coordinate. var_attrs : dict Attributes for the variable (`short_name`, `long_name`, or `units`). attributes : dict, optional Additional attributes for the cube. """ var_attrs = dict(var_attrs) if not cubes: logger.warning("Cannot save 1D data, no cubes given") return if not _has_necessary_attributes( [var_attrs], only_var_attrs=True, log_level='warning'): logger.warning("Cannot write file '%s'", path) return datasets = list(cubes.keys()) cube_list = iris.cube.CubeList(list(cubes.values())) cube_list = unify_1d_cubes(cube_list, coord_name) data = [c.data for c in cube_list] dataset_coord = iris.coords.AuxCoord(datasets, long_name='dataset') coord = cube_list[0].coord(coord_name) if attributes is None: attributes = {} var_attrs['var_name'] = var_attrs.pop('short_name') # Create new cube cube = iris.cube.Cube(np.ma.array(data), aux_coords_and_dims=[(dataset_coord, 0), (coord, 1)], attributes=attributes, **var_attrs) iris_save(cube, path) def iris_save(source, path): """Save :mod:`iris` objects with correct attributes. Parameters ---------- source : iris.cube.Cube or iterable of iris.cube.Cube Cube(s) to be saved. path : str Path to the new file. """ if isinstance(source, iris.cube.Cube): source.attributes['filename'] = path else: for cube in source: cube.attributes['filename'] = path iris.save(source, path) logger.info("Wrote %s", path) def save_scalar_data(data, path, var_attrs, aux_coord=None, attributes=None): """Save scalar data for multiple datasets. Create 1D cube with the auxiliary dimension `dataset` and save scalar data for every dataset given. Note ---- Missing values can be added by `np.nan`. Parameters ---------- data : dict Scalar data (values) and corresponding datasets (keys). path : str Path to the new file. var_attrs : dict Attributes for the variable (`short_name`, `long_name` and `units`). aux_coord : iris.coords.AuxCoord, optional Optional auxiliary coordinate. attributes : dict, optional Additional attributes for the cube. """ var_attrs = dict(var_attrs) if not data: logger.warning("Cannot save scalar data, no data given") return if not _has_necessary_attributes( [var_attrs], only_var_attrs=True, log_level='warning'): logger.warning("Cannot write file '%s'", path) return dataset_coord = iris.coords.AuxCoord(list(data), long_name='dataset') if attributes is None: attributes = {} var_attrs['var_name'] = var_attrs.pop('short_name') coords = [(dataset_coord, 0)] if aux_coord is not None: coords.append((aux_coord, 0)) cube = iris.cube.Cube(np.ma.masked_invalid(list(data.values())), aux_coords_and_dims=coords, attributes=attributes, **var_attrs) iris_save(cube, path) ``` #### File: diag_scripts/thermodyn_diagtool/fourier_coefficients.py ```python import numpy as np from netCDF4 import Dataset GP_RES = np.array([16, 32, 48, 64, 96, 128, 256, 384, 512, 1024, 2048, 4096]) FC_RES = np.array([5, 10, 15, 21, 31, 43, 85, 127, 171, 341, 683, 1365]) G_0 = 9.81 # Gravity acceleration GAM = 0.0065 # Standard atmosphere lapse rate GAS_CON = 287.0 # Gas constant P_0 = 10000 # Reference tropospheric pressure def fourier_coeff(tadiagfile, outfile, ta_input, tas_input): """Compute Fourier coefficients in lon direction. Receive as input: - tadiagfile: the name of a file to store modified t fields; - outfile: the name of a file to store the Fourier coefficients; - ta_input: the name of a file containing t,u,v,w fields; - tas_input: the name of a file containing t2m field. """ with Dataset(ta_input) as dataset: lon = dataset.variables['lon'][:] lat = dataset.variables['lat'][:] lev = dataset.variables['plev'][:] time = dataset.variables['time'][:] t_a = dataset.variables['ta'][:, :, :, :] u_a = dataset.variables['ua'][:, :, :, :] v_a = dataset.variables['va'][:, :, :, :] wap = dataset.variables['wap'][:, :, :, :] nlon = len(lon) nlat = len(lat) nlev = len(lev) ntime = len(time) i = np.min(np.where(2 * nlat <= GP_RES)) trunc = FC_RES[i] + 1 wave2 = np.linspace(0, trunc - 1, trunc) with Dataset(tas_input) as dataset: tas = dataset.variables['tas'][:, :, :] tas = tas[:, ::-1, :] ta1_fx = np.array(t_a) deltat = np.zeros([ntime, nlev, nlat, nlon]) p_s = np.full([ntime, nlat, nlon], P_0) for i in np.arange(nlev - 1, 0, -1): h_1 = np.ma.masked_where(ta1_fx[:, i, :, :] != 0, ta1_fx[:, i, :, :]) if np.any(h_1.mask > 0): deltat[:, i - 1, :, :] = np.where(ta1_fx[:, i - 1, :, :] != 0, deltat[:, i - 1, :, :], (ta1_fx[:, i, :, :] - tas)) deltat[:, i - 1, :, :] = ( (1 * np.array(h_1.mask)) * np.array(deltat[:, i - 1, :, :])) d_p = -( (P_0 * G_0 / (GAM * GAS_CON)) * deltat[:, i - 1, :, :] / tas) p_s = np.where(ta1_fx[:, i - 1, :, :] != 0, p_s, lev[i - 1] + d_p) for k in np.arange(0, nlev - i - 1, 1): h_3 = np.ma.masked_where(ta1_fx[:, i + k, :, :] != 0, ta1_fx[:, i + k, :, :]) if np.any(h_3.mask > 0): deltat[:, i - 1, :, :] = np.where( ta1_fx[:, i + k, :, :] != 0, deltat[:, i - 1, :, :], (ta1_fx[:, i + k + 1, :, :] - tas)) d_p = -((P_0 * G_0 / (GAM * GAS_CON)) * deltat[:, i - 1, :, :] / tas) p_s = np.where(ta1_fx[:, i + k, :, :] != 0, p_s, lev[i + k] + d_p) ta2_fx = np.array(t_a) mask = np.zeros([nlev, ntime, nlat, nlon]) dat = np.zeros([nlev, ntime, nlat, nlon]) tafr_bar = np.zeros([nlev, ntime, nlat, nlon]) deltap = np.zeros([ntime, nlev, nlat, nlon]) for i in np.arange(nlev): deltap[:, i, :, :] = p_s - lev[i] h_2 = np.ma.masked_where(ta2_fx[:, i, :, :] == 0, ta2_fx[:, i, :, :]) mask[i, :, :, :] = np.array(h_2.mask) tafr_bar[i, :, :, :] = (1 * np.array(mask[i, :, :, :]) * ( tas - GAM * GAS_CON / (G_0 * p_s) * deltap[:, i, :, :] * tas)) dat[i, :, :, :] = ( ta2_fx[:, i, :, :] * (1 - 1 * np.array(mask[i, :, :, :]))) t_a[:, i, :, :] = dat[i, :, :, :] + tafr_bar[i, :, :, :] pr_output_diag(t_a, ta_input, tadiagfile, 'ta') tafft_p = np.fft.fft(t_a, axis=3)[:, :, :, :int(trunc / 2)] / (nlon) uafft_p = np.fft.fft(u_a, axis=3)[:, :, :, :int(trunc / 2)] / (nlon) vafft_p = np.fft.fft(v_a, axis=3)[:, :, :, :int(trunc / 2)] / (nlon) wapfft_p = np.fft.fft(wap, axis=3)[:, :, :, :int(trunc / 2)] / (nlon) tafft = np.zeros([ntime, nlev, nlat, trunc]) uafft = np.zeros([ntime, nlev, nlat, trunc]) vafft = np.zeros([ntime, nlev, nlat, trunc]) wapfft = np.zeros([ntime, nlev, nlat, trunc]) tafft[:, :, :, 0::2] = np.real(tafft_p) tafft[:, :, :, 1::2] = np.imag(tafft_p) uafft[:, :, :, 0::2] = np.real(uafft_p) uafft[:, :, :, 1::2] = np.imag(uafft_p) vafft[:, :, :, 0::2] = np.real(vafft_p) vafft[:, :, :, 1::2] = np.imag(vafft_p) wapfft[:, :, :, 0::2] = np.real(wapfft_p) wapfft[:, :, :, 1::2] = np.imag(wapfft_p) dict_v = {'ta': tafft, 'ua': uafft, 'va': vafft, 'wap': wapfft} file_desc = 'Fourier coefficients' pr_output(dict_v, ta_input, outfile, file_desc, wave2) def pr_output(dict_v, nc_f, fileo, file_desc, wave2): """Print outputs to NetCDF. Save fields to NetCDF, retrieving information from an existing NetCDF file. Metadata are transferred from the existing file to the new one. Arguments: - var1, var2, var3, var4: the fields to be stored, with shape (time,level,wave,lon); - nc_f: the existing dataset, from where the metadata are retrieved. Coordinates time,level and lon have to be the same dimension as the fields to be saved to the new files; - fileo: the name of the output file; - wave2: an array containing the zonal wavenumbers; - name1, name2, name3, name4: the name of the variables to be saved; PROGRAMMER(S) <NAME> (2014), modified by <NAME> (2018). """ # Writing NetCDF files with Dataset(fileo, 'w', format='NETCDF4') as var_nc_fid: var_nc_fid.description = file_desc with Dataset(nc_f, 'r') as nc_fid: extr_time(nc_fid, var_nc_fid) extr_lat(nc_fid, var_nc_fid, 'lat') extr_plev(nc_fid, var_nc_fid) # Write the wave dimension var_nc_fid.createDimension('wave', len(wave2)) var_nc_fid.createVariable('wave', nc_fid.variables['plev'].dtype, ('wave', )) var_nc_fid.variables['wave'][:] = wave2 for key in dict_v: value = dict_v[key] var1_nc_var = var_nc_fid.createVariable( key, 'f8', ('time', 'plev', 'lat', 'wave')) varatts(var1_nc_var, key) var_nc_fid.variables[key][:, :, :, :] = value def pr_output_diag(var1, nc_f, fileo, name1): """Print processed ta field to NetCDF file. Save fields to NetCDF, retrieving information from an existing NetCDF file. Metadata are transferred from the existing file to the new one. Arguments: - var1: the field to be stored, with shape (time,level,lat,lon); - nc_f: the existing dataset, from where the metadata are retrieved. Coordinates time,level, lat and lon have to be the same dimension as the fields to be saved to the new files; - fileo: the name of the output file; - name1: the name of the variable to be saved; PROGRAMMER(S) <NAME> (2014), modified by <NAME> (2018). """ with Dataset(fileo, 'w', format='NETCDF4') as var_nc_fid: var_nc_fid.description = "Fourier coefficients" with Dataset(nc_f, 'r') as nc_fid: # Extract data from NetCDF file nad write them to the new file extr_time(nc_fid, var_nc_fid) extr_lat(nc_fid, var_nc_fid, 'lat') extr_lon(nc_fid, var_nc_fid) extr_plev(nc_fid, var_nc_fid) var1_nc_var = var_nc_fid.createVariable(name1, 'f8', ('time', 'plev', 'lat', 'lon')) varatts(var1_nc_var, name1) var_nc_fid.variables[name1][:, :, :, :] = var1 var_nc_fid.close() # close the new file def extr_lat(nc_fid, var_nc_fid, latn): """Extract lat coord. from NC files and save them to a new NC file. Arguments: - nc_f: the existing dataset, from where the metadata are retrieved. Time,level and lon dimensions are retrieved; - var_nc_fid: the id of the new NC dataset previously created; - latn: the name of the latitude dimension; """ # Extract coordinates from NetCDF file lats = nc_fid.variables['lat'][:] var_nc_fid.createDimension(latn, len(lats)) var_nc_dim = var_nc_fid.createVariable(latn, nc_fid.variables['lat'].dtype, (latn, )) for ncattr in nc_fid.variables['lat'].ncattrs(): var_nc_dim.setncattr(ncattr, nc_fid.variables['lat'].getncattr(ncattr)) var_nc_fid.variables[latn][:] = lats def extr_lon(nc_fid, var_nc_fid): """Extract lat coord. from NC files and save them to a new NC file. Arguments: - nc_f: the existing dataset, from where the metadata are retrieved. Time,level and lon dimensions are retrieved; - var_nc_fid: the id of the new NC dataset previously created; """ # Extract coordinates from NetCDF file lons = nc_fid.variables['lon'][:] var_nc_fid.createDimension('lon', len(lons)) var_nc_dim = var_nc_fid.createVariable( 'lon', nc_fid.variables['lon'].dtype, ('lon', )) for ncattr in nc_fid.variables['lon'].ncattrs(): var_nc_dim.setncattr(ncattr, nc_fid.variables['lon'].getncattr(ncattr)) var_nc_fid.variables['lon'][:] = lons def extr_plev(nc_fid, var_nc_fid): """Extract plev coord. from NC files and save them to a new NC file. Arguments: - nc_f: the existing dataset, from where the metadata are retrieved. Time,level and lon dimensions are retrieved; - var_nc_fid: the id of the new NC dataset previously created; """ plev = nc_fid.variables['plev'][:] var_nc_fid.createDimension('plev', len(plev)) var_nc_dim = var_nc_fid.createVariable( 'plev', nc_fid.variables['plev'].dtype, ('plev', )) for ncattr in nc_fid.variables['plev'].ncattrs(): var_nc_dim.setncattr(ncattr, nc_fid.variables['plev'].getncattr(ncattr)) var_nc_fid.variables['plev'][:] = plev def extr_time(nc_fid, var_nc_fid): """Extract time coord. from NC files and save them to a new NC file. Arguments: - nc_f: the existing dataset, from where the metadata are retrieved. Time,level and lon dimensions are retrieved; - var_nc_fid: the id of the new NC dataset previously created; """ # Extract coordinates from NetCDF file time = nc_fid.variables['time'][:] # Using our previous dimension info, we can create the new dimensions. var_nc_fid.createDimension('time', len(time)) var_nc_dim = var_nc_fid.createVariable( 'time', nc_fid.variables['time'].dtype, ('time', )) for ncattr in nc_fid.variables['time'].ncattrs(): var_nc_dim.setncattr(ncattr, nc_fid.variables['time'].getncattr(ncattr)) var_nc_fid.variables['time'][:] = time def varatts(w_nc_var, varname): """Add attibutes to the variables, depending on their name. Arguments: - w_nc_var: a variable object; - varname: the name of the variable, among ta, ua, va and wap. """ if varname == 'ta': w_nc_var.setncatts({ 'long_name': "Air temperature", 'units': "K", 'level_desc': 'pressure levels' }) elif varname == 'ua': w_nc_var.setncatts({ 'long_name': "Eastward wind", 'units': "m s-1", 'level_desc': 'pressure levels' }) elif varname == 'va': w_nc_var.setncatts({ 'long_name': "Northward wind", 'units': "m s-1", 'level_desc': 'pressure levels' }) elif varname == 'wap': w_nc_var.setncatts({ 'long_name': 'Lagrangian tendency of ' 'air pressure', 'units': "Pa s-1", 'level_desc': 'pressure levels' }) ```
{ "source": "jeromaerts/eWaterCycle_example_notebooks", "score": 3 }
#### File: eWaterCycle_example_notebooks/utils/usgs_streamflow_download.py ```python import os import sys import urllib.request as urllib2 import numpy as np import pandas as pd def download_usgs_data( usgs_info_file, outputfolder, output_format, startDT, endDT, parameterCd, basin_start, basin_end, convert_unit_timestep=True ): # More information: https://waterservices.usgs.gov/rest/IV-Test-Tool.html # output_format e.g. ('json', 'rdb') # startDT e.g. ('1980-01-01') # endDT e.g.('2018-12-31') # parameterCd e.g. ('00060') discharge, cubic feet per second # 30208 m3/s # excel_format : [USGS_ID, lat, lon]! # Load USGS gauge ids stations = pd.read_table(usgs_info_file, delimiter=";") # Drop n number of rows, Remove after testing! stations = stations.iloc[basin_start:basin_end] # Create output folder if not os.path.exists(outputfolder): os.makedirs(outputfolder) for index, station in stations.iterrows(): # Fix import error -> adds 0 value leading gauge id usgsid = str(np.array(station["gauge_id"], dtype=np.int)) if len(usgsid) == 7: usgsid = "0" + usgsid # Create download link url = ( "https://waterservices.usgs.gov/nwis/iv/?format=" + output_format + ",1.0&sites=" + usgsid + "&startDT=" + startDT + "&endDT=" + endDT + "&parameterCd=" + parameterCd + "&siteStatus=all" ) out = outputfolder + "/" + usgsid + "." + output_format urllib2.urlretrieve(url, out) if convert_unit_timestep is True: # Changes format table to [datetime, discharge] df = pd.read_table( out, skiprows=55, usecols=[2, 4], header=None, names=["datetime", "discharge"], ) # Read rdb table and set column headers # Set Ice and Dis values to 0 df["discharge"] = df["discharge"].replace("Ice", 0) df["discharge"] = df["discharge"].replace("Dis", 0) # Get equipment malfunction values indexvals = df.index[df["discharge"] == "Eqp"].tolist() for i in indexvals: # Overwrite with previous measurement df["discharge"].loc[i] = df["discharge"].loc[i - 1] # Convert to datetime and set index df.index = pd.to_datetime( df["datetime"], infer_datetime_format=True ) # Drop obsolete column df = df.drop(columns="datetime") # Resample to hourly values df = df.resample("H").mean() # Print USGS ID for log print(" USGSID") print(usgsid) # Set table id and coordinates df["USGS_ID"] = usgsid df["lat"] = station["gauge_lat"] df["lon"] = station["gauge_lon"] # Convert series to float df["discharge"] = df["discharge"].apply(lambda x: float(x)) # Convert to cubic meters per second df["discharge"] = df["discharge"].apply(lambda x: x / 35.315) # Write daily UTC files df.index = df.index.tz_localize("UTC") df = df.resample("D").mean() df["USGS_ID"] = usgsid df.to_csv(outputfolder + "/" + usgsid + "_UTC_daily.csv") log = print(usgsid + " Downloaded") else: exit() return log # Set system variables from bash usgs_info_file = sys.argv[1] outputfolder = sys.argv[2] start_date = sys.argv[3] end_date = sys.argv[4] basin_start = int(sys.argv[5]) basin_end = int(sys.argv[6]) download_usgs_data( usgs_info_file, outputfolder, "rdb", start_date, end_date, "00060", basin_start, basin_end ) ```
{ "source": "jeromaerts/flood_hazard_map_comparison_2019", "score": 3 }
#### File: jeromaerts/flood_hazard_map_comparison_2019/MAI_calculation_example.py ```python import os import numpy as np import time import rasterio # Set rastersum and haversine filenames raster = "rastersum_RP100.tif" haversine = "haversine_grid.tif" def inundated_area(aggr_cat): # Function calculates the inundated area using a haversine grid for each aggregate category src_A = rasterio.open(raster) profile = src_A.meta.copy() src_B = rasterio.open(haversine) inundated_area = np.zeros([1,73800]) i = 0 for ji, window in src_A.block_windows(1): i += 1 affine = rasterio.windows.transform(window, src_A.transform) height, width = rasterio.windows.shape(window) bbox = rasterio.windows.bounds(window, src_A.transform) profile.update({ 'height': height, 'width': width, 'affine': affine}) array_A = src_A.read(window=window) array_B = src_B.read(window=window) if array_A.shape[0] == 1: #Reshape 3 dimensional array to 2 dimensional array array_A = array_A.reshape(array_A.shape[1:]) #Reshape 3 dimensional array to 2 dimensional array if array_B.shape[0] == 1: #Reshape 3 dimensional array to 2 dimensional array array_B = array_B.reshape(array_B.shape[1:]) #Reshape 3 dimensional array to 2 dimensional array array_A = np.where(array_A == aggr_cat,1,0) #extract binary values # Calculate inundated area in km2 for each aggregate_category array_combined = array_A * array_B array_combined = np.sum(array_combined) inundated_area = np.append(inundated_area, array_combined) inundated_area = np.sum(inundated_area,0) return inundated_area # Calculate inundated area for each aggregate category inun_1 = inundated_area(1) inun_2 = inundated_area(2) inun_3 = inundated_area(3) inun_4 = inundated_area(4) inun_5 = inundated_area(5) inun_6 = inundated_area(6) # Calculate total inundated area area_total = inun_1+inun_2+inun_3+inun_4+inun_5+inun_6 a2 = (2/6)*inun_2 a3 = (3/6)*inun_3 a4 = (4/6)*inun_4 a5 = (5/6)*inun_5 a6 = (6/6)*inun_6 # Calculate MAI MAI = (a2+a3+a4+a5+a6)/area_total ```
{ "source": "jeromba6/transip_api_v6", "score": 3 }
#### File: transip_api_v6/transipApiV6/Generic.py ```python from OpenSSL import crypto import base64 import requests import random import string import json def randomDigits(self, stringLength=10): """Generate a random string of letters and digits """ return ''.join(random.choice(string.digits) for i in range(stringLength)) class Generic: base_url='https://api.transip.nl/v6/auth' def __init__(self, login, key, demo = False): self.login = login self.key = key self.demo = demo def get_jwt(self): if self.demo: return '<KEY>' pkey = crypto.load_privatekey(crypto.FILETYPE_PEM, self.key) data = '{ "login": "' + self.login + '", "nonce": ' + randomDigits(10) + ' }' signature = base64.b64encode(crypto.sign(pkey, data.encode(), "sha512")).decode() headers = {'Signature': signature, 'Accept': 'application/json'} res = requests.post(self.base_url, headers=headers, data=data.encode()) if res.status_code != 201: print('Could not create a JWT. Status_code was: ' + str(res.status_code)) print(res.text) exit(1) return json.loads(res.text)['token'] def get_headers(self): return {'Authorization': 'Bearer ' + Generic.get_jwt(self), 'Accept': 'application/json'} ```
{ "source": "Jerome1434/ampscan", "score": 3 }
#### File: ampscan/ampscan/core.py ```python import numpy as np import os import struct from ampscan.trim import trimMixin from ampscan.smooth import smoothMixin from ampscan.vis import visMixin # The file path used in doc examples filename = os.path.join(os.getcwd(), "tests", "stl_file.stl") class AmpObject(trimMixin, smoothMixin, visMixin): r""" Base class for the ampscan project. Stores mesh data and extra information Inherits methods via mixins Flexible class able to deal with surface data using 3 or 4 node faces and visualise nodal data such as FEA outputs or shape deviations Parameters ---------- data : str or dict Data input as either a string to import from an external file or a dictionary to pull values directly stype : str, optional descriptor of the type of data the AmpObject is representing, e.g 'limb' or 'socket'. Default is 'limb' Returns ------- AmpObject Initiation of the object Examples ------- >>> amp = AmpObject(filename) """ def __init__(self, data=None, stype='limb', unify=True, struc=True): self.stype = stype self.createCMap() if isinstance(data, str): self.read_stl(data, unify, struc) elif isinstance(data, dict): for k, v in data.items(): setattr(self, k, v) self.calcStruct() elif isinstance(data, bytes): self.read_bytes(data, unify, struc) def read_stl(self, filename, unify=True, struc=True): """ Function to read .stl file from filename and import data into the AmpObj Parameters ----------- filename: str file path of the .stl file to read unify: boolean, default True unify the coincident vertices of each face struc: boolean, default True Calculate the underlying structure of the mesh, such as edges """ with open(filename, 'rb') as fh: # Defined no of bytes for header and no of faces HEADER_SIZE = 80 COUNT_SIZE = 4 # State the data type and length in bytes of the normals and vertices data_type = np.dtype([('normals', np.float32, (3, )), ('vertices', np.float32, (9, )), ('atttr', '<i2', (1, ))]) # Read the header of the STL head = fh.read(HEADER_SIZE).lower() # Read the number of faces NFaces, = struct.unpack('@i', fh.read(COUNT_SIZE)) # Read the remaining data and save as void, then close file data = np.fromfile(fh, data_type) # Test if the file is ascii if str(head[:5], 'utf-8') == 'solid': raise ValueError("ASCII files not supported") # Write the data to a numpy arrays in AmpObj tfcond = NFaces==data['vertices'].shape[0] #assigns true or false to tfcond if not tfcond: #if tfcond is false, raise error raise ValueError("File is corrupt") #if true, move on vert = np.resize(np.array(data['vertices']), (NFaces*3, 3)) norm = np.array(data['normals']) faces = np.reshape(range(NFaces*3), [NFaces,3]) self.faces = faces self.vert = vert self.norm = norm # Call function to unify vertices of the array if unify is True: self.unifyVert() # Call function to calculate the edges array # self.fixNorm() if struc is True: self.calcStruct() self.values = np.zeros([len(self.vert)]) def read_bytes(self, data, unify=True, struc=True): """ Function to read .stl file from filename and import data into the AmpObj Parameters ----------- filename: str file path of the .stl file to read unify: boolean, default True unify the coincident vertices of each face struc: boolean, default True Calculate the underlying structure of the mesh, such as edges """ # Defined no of bytes for header and no of faces HEADER_SIZE = 80 COUNT_SIZE = 4 # State the data type and length in bytes of the normals and vertices data_type = np.dtype([('normals', np.float32, (3, )), ('vertices', np.float32, (9, )), ('atttr', '<i2', (1, ))]) # Read the header of the STL head = data[:HEADER_SIZE].lower() # Read the number of faces NFaces, = struct.unpack('@i', data[HEADER_SIZE:HEADER_SIZE+COUNT_SIZE]) # Read the remaining data and save as void, then close file data = np.frombuffer(data[COUNT_SIZE+HEADER_SIZE:], data_type) # Test if the file is ascii if str(head[:5], 'utf-8') == 'solid': raise ValueError("ASCII files not supported") # Write the data to a numpy arrays in AmpObj tfcond = NFaces==data['vertices'].shape[0] #assigns true or false to tfcond if not tfcond: #if tfcond is false, raise error raise ValueError("File is corrupt") #if true, move on vert = np.resize(np.array(data['vertices']), (NFaces*3, 3)) norm = np.array(data['normals']) faces = np.reshape(range(NFaces*3), [NFaces,3]) self.faces = faces self.vert = vert self.norm = norm # Call function to unify vertices of the array if unify is True: self.unifyVert() # Call function to calculate the edges array # self.fixNorm() if struc is True: self.calcStruct() self.values = np.zeros([len(self.vert)]) def calcStruct(self, norm=True, edges=True, edgeFaces=True, faceEdges=True, vNorm=False): r""" Top level function to calculate the underlying structure of the AmpObject Parameters ---------- norm: boolean, default True If true, the normals of each face in the mesh will be calculated edges: boolean, default True If true, the edges of the mesh will be calculated, the refers to the vertex index that make up any edge edgeFaces: boolean, default True If true, the edgeFaces array of the mesh will be calculated, this refers to the index of the three edges that make up each face faceEdges: boolean, default True If true, the faceEdges array will be calculated, this refers to index of the faces that are coincident to each edge. Normally, there are two faces per edge, if there is only one, then -99999 will be used to indicate this vNorm: boolean, default False If true, the normals of each vertex in the mesh will be calculated """ if norm is True: self.calcNorm() if edges is True: self.calcEdges() if edgeFaces is True: self.calcEdgeFaces() if faceEdges is True: self.calcFaceEdges() if vNorm is True: self.calcVNorm() def unifyVert(self): r""" Function to unify coincident vertices of the mesh to reduce size of the vertices array enabling speed increases when performing calculations using the vertex array Examples -------- >>> amp = AmpObject(filename, unify=False) >>> amp.vert.shape (44832, 3) >>> amp.unifyVert() >>> amp.vert.shape (7530, 3) """ # Requires numpy 1.13 self.vert, indC = np.unique(self.vert, return_inverse=True, axis=0) # Maps the new vertices index to the face array self.faces = np.resize(indC[self.faces], (len(self.norm), 3)).astype(np.int32) def calcEdges(self): """ Function to compute the edges array ie the index of the two vertices that make up each edge Returns ------- edges: ndarray Denoting the indicies of two vertices on each edge """ # Get edges array self.edges = np.reshape(self.faces[:, [0, 1, 0, 2, 1, 2]], [-1, 2]) self.edges = np.sort(self.edges, 1) # Unify the edges self.edges, indC = np.unique(self.edges, return_inverse=True, axis=0) def calcEdgeFaces(self): r""" Function that calculates the indicies of the three edges that make up each face Returns ------- edgesFace: ndarray Denoting the indicies of the three edges on each face """ edges = np.reshape(self.faces[:, [0, 1, 0, 2, 1, 2]], [-1, 2]) edges = np.sort(edges, 1) # Unify the edges edges, indC = np.unique(edges, return_inverse=True, axis=0) # Get edges on each face self.edgesFace = np.reshape(range(len(self.faces)*3), [-1,3]) #Remap the edgesFace array self.edgesFace = indC[self.edgesFace].astype(np.int32) def calcFaceEdges(self): r""" Function that calculates the indicies of the faces on each edge Returns ------- faceEdges: ndarray The indicies of the faces in each edge, edges may have either 1 or 2 faces, if 1 then the second index will be NaN """ #Initiate the faceEdges array self.faceEdges = np.empty([len(self.edges), 2], dtype=np.int32) self.faceEdges.fill(-99999) # Denote the face index for flattened edge array fInd = np.repeat(np.array(range(len(self.faces))), 3) # Flatten edge array eF = np.reshape(self.edgesFace, [-1]) eFInd = np.unique(eF, return_index=True)[1] logic = np.zeros([len(eF)], dtype=bool) logic[eFInd] = True self.faceEdges[eF[logic], 0] = fInd[logic] self.faceEdges[eF[~logic], 1] = fInd[~logic] def calcNorm(self): r""" Calculate the normal of each face of the AmpObj Returns ------- norm: ndarray normal of each face """ norms = np.cross(self.vert[self.faces[:,1]] - self.vert[self.faces[:,0]], self.vert[self.faces[:,2]] - self.vert[self.faces[:,0]]) mag = np.linalg.norm(norms, axis=1) self.norm = np.divide(norms, mag[:,None]) def fixNorm(self): r""" Fix normals of faces so they all face outwards """ fC = self.vert[self.faces].mean(axis=1) cent = self.vert.mean(axis=0) # polarity = np.sum(self.norm * (fC-cent), axis=1) < 0 # if polarity.mean() > 0.5: # self.faces[:, [1,2]] = self.faces[:, [2,1]] # self.calcNorm() # if hasattr(self, 'vNorm'): self.calcVNorm() polarity = np.einsum('ij, ij->i', fC - cent, self.norm) < 0 # self.faces[polarity, [1,2]] = self.faces[polarity, [2,1]] for i, f in enumerate(self.faces): if polarity[i] == True: self.faces[i, :] = [f[0], f[2], f[1]] self.calcNorm() if hasattr(self, 'vNorm'): self.calcVNorm() def calcVNorm(self): """ Function to compute the vertex normals based upon the mean of the connected face normals Returns ------- vNorm: ndarray normal of each vertex """ f = self.faces.flatten() o_idx = f.argsort() row, col = np.unravel_index(o_idx, self.faces.shape) ndx = np.searchsorted(f[o_idx], range(self.vert.shape[0]), side='right') ndx = np.r_[0, ndx] norms = self.norm[row, :] self.vNorm = np.zeros(self.vert.shape) for i in range(self.vert.shape[0]): self.vNorm[i, :] = np.nanmean(norms[ndx[i]:ndx[i+1], :], axis=0) def save(self, filename): r""" Function to save the AmpObj as a binary .stl file Parameters ----------- filename: str file path of the .stl file to save to """ self.calcNorm() fv = self.vert[np.reshape(self.faces, len(self.faces)*3)] with open(filename, 'wb') as fh: header = '%s' % (filename) header = header.split('/')[-1].encode('utf-8') header = header[:80].ljust(80, b' ') packed = struct.pack('@i', len(self.faces)) fh.write(header) fh.write(packed) data_type = np.dtype([('normals', np.float32, (3, )), ('vertices', np.float32, (9, )), ('atttr', '<i2', (1, ))]) data_write = np.zeros(len(self.faces), dtype=data_type) data_write['normals'] = self.norm data_write['vertices'] = np.reshape(fv, (len(self.faces), 9)) data_write.tofile(fh) def translate(self, trans): r""" Translate the AmpObj in 3D space Parameters ----------- trans: array_like Translation in [x, y, z] """ # Check that trans is array like if isinstance(trans, (list, np.ndarray, tuple)): # Check that trans has exactly 3 dimensions if len(trans) == 3: self.vert[:] += trans else: raise ValueError("Translation has incorrect dimensions. Expected 3 but found: " + str(len(trans))) else: raise TypeError("Translation is not array_like: " + trans) def centre(self): r""" Centre the AmpObject based upon the mean of all the vertices """ self.translate(-self.vert.mean(axis=0)) def centreStatic(self, static): r""" Centre this AmpObject on the static AmpObject's centroid based upon the mean of all the vertices Parameters ---------- static : AmpObject The static shape to center this object onto """ if isinstance(static, AmpObject): self.translate(-self.vert.mean(axis=0)+static.vert.mean(axis=0)) else: raise TypeError("centre_static method expects AmpObject, found: {}".format(type(static))) def rotateAng(self, rot, ang='rad', norms=True): r""" Rotate the AmpObj in 3D space according to three angles Parameters ----------- rot: array_like Rotation around [x, y, z] ang: str, default 'rad' Specify if the euler angles are in degrees or radians. Default is radians Examples -------- >>> amp = AmpObject(filename) >>> ang = [np.pi/2, -np.pi/4, np.pi/3] >>> amp.rotateAng(ang, ang='rad') """ # Check that ang is valid if ang not in ('rad', 'deg'): raise ValueError("Ang expected 'rad' or 'deg' but {} was found".format(ang)) if isinstance(rot, (tuple, list, np.ndarray)): R = self.rotMatrix(rot, ang) self.rotate(R, norms) else: raise TypeError("rotateAng requires a list") def rotate(self, R, norms=True): r""" Rotate the AmpObject using a rotation matrix Parameters ---------- R: array_like A 3x3 array specifying the rotation matrix norms: boolean, default True """ if isinstance(R, (list, tuple)): # Make R a np array if its a list or tuple R = np.array(R, np.float) elif not isinstance(R, np.ndarray): # If raise TypeError("Expected R to be array-like but found: " + str(type(R))) if len(R) != 3 or len(R[0]) != 3: # Incorrect dimensions if isinstance(R, np.ndarray): raise ValueError("Expected 3x3 array, but found: {}".format(R.shape)) else: raise ValueError("Expected 3x3 array, but found: 3x"+str(len(R))) self.vert[:, :] = np.dot(self.vert, R.T) if norms is True: self.norm[:, :] = np.dot(self.norm, R.T) if hasattr(self, 'vNorm'): self.vNorm[:, :] = np.dot(self.vNorm, R.T) def rigidTransform(self, R=None, T=None): r""" Perform a rigid transformation on the AmpObject, first the rotation, then the translation Parameters ---------- R: array_like, default None A 3x3 array specifying the rotation matrix T: array_like, defauly None An array of the form [x, y, z] which specifies the translation """ if R is not None: if isinstance(R, (tuple, list, np.ndarray)): self.rotate(R, True) else: raise TypeError("Expecting array-like rotation, but found: "+type(R)) if T is not None: if isinstance(T, (tuple, list, np.ndarray)): self.translate(T) else: raise TypeError("Expecting array-like translation, but found: "+type(T)) @staticmethod def rotMatrix(rot, ang='rad'): r""" Calculate the rotation matrix from three angles, the order is assumed as around the x, then y, then z axis Parameters ---------- rot: array_like Rotation around [x, y, z] ang: str, default 'rad' Specify if the Euler angles are in degrees or radians Returns ------- R: array_like The calculated 3x3 rotation matrix """ # Check that rot is valid if not isinstance(rot, (tuple, list, np.ndarray)): raise TypeError("Expecting array-like rotation, but found: "+type(rot)) elif len(rot) != 3: raise ValueError("Expecting 3 arguments but found: {}".format(len(rot))) # Check that ang is valid if ang not in ('rad', 'deg'): raise ValueError("Ang expected 'rad' or 'deg' but {} was found".format(ang)) if ang == 'deg': rot = np.deg2rad(rot) [angx, angy, angz] = rot Rx = np.array([[1, 0, 0], [0, np.cos(angx), -np.sin(angx)], [0, np.sin(angx), np.cos(angx)]]) Ry = np.array([[np.cos(angy), 0, np.sin(angy)], [0, 1, 0], [-np.sin(angy), 0, np.cos(angy)]]) Rz = np.array([[np.cos(angz), -np.sin(angz), 0], [np.sin(angz), np.cos(angz), 0], [0, 0, 1]]) R = np.dot(np.dot(Rz, Ry), Rx) return R def flip(self, axis=1): r""" Flip the mesh in a plane Parameters ---------- axis: int, default 1 The axis in which to flip the mesh """ if isinstance(axis, int): if 0 <= axis < 3: # Check axis is between 0-2 self.vert[:, axis] *= -1.0 # Switch face order to normals face same direction self.faces[:, [1, 2]] = self.faces[:, [2, 1]] self.calcNorm() self.calcVNorm() else: raise ValueError("Expected axis to be within range 0-2 but found: {}".format(axis)) else: raise TypeError("Expected axis to be int, but found: {}".format(type(axis))) ``` #### File: ampscan/ampscan/smooth.py ```python import numpy as np import copy class smoothMixin(object): def lp_smooth(self, n=1, brim = True): r""" Function to apply a Laplacian smooth to the mesh. This method replaces each vertex with the mean of its connected neighbours Parameters ---------- n: int, default 1 number of iterations of smoothing """ if brim is True: eidx = (self.faceEdges == -99999).sum(axis=1).astype(bool) vBrim = np.unique(self.edges[eidx, :]) else: vBrim = [] # Flatten the edges array to 1D e = self.edges.flatten() # Get the indicies to sort edges o_idx = e.argsort() # Get indicies of sorted array where last of each vertex index # occurs ndx = np.searchsorted(e[o_idx], np.arange(len(self.vert)), side='right') ndx = np.r_[0, ndx] # Map indicies between flatted edges array and standard row, col = np.unravel_index(o_idx, self.edges.shape) for i in np.arange(n): # List all vertices vert = copy.deepcopy(self.vert) neighVerts = vert[self.edges[row, 1-col], :] vRange = np.arange(self.vert.shape[0]) log = np.isin(vRange, vBrim) vRange = vRange[~log] for j in vRange: # Calculate the mean of the vertex set self.vert[j, :] = neighVerts[ndx[j]:ndx[j+1]].mean(axis=0) self.calcNorm() self.calcVNorm() def hc_smooth(self, n=1 ,beta=0.6, brim=True): r""" Function to apply a Humphrey’s Classes smooth to the mesh. Note, this assumes that alpha=0 (ie the original point through the iteration has no effect). If beta=1, then this effectively acts as the Laplacian smooth Parameters ---------- n: int, default 1 number of iterations of smoothing beta: float, default 0.6 scalar between [0, 1] which dictates influence of distance from adjacent to original point. If beta=1, then this effectively acts as the Laplacian smooth brim: bool, default True If true, then this will not smooth the vertices on the brim """ if brim is True: eidx = (self.faceEdges == -99999).sum(axis=1).astype(bool) vBrim = np.unique(self.edges[eidx, :]) else: vBrim = [] # Flatten the edges array to 1D e = self.edges.flatten() # Get the indicies to sort edges o_idx = e.argsort() # Get indicies of sorted array where last of each vertex index # occurs ndx = np.searchsorted(e[o_idx], np.arange(len(self.vert)), side='right') ndx = np.r_[0, ndx] # Map indicies between flatted edges array and standard row, col = np.unravel_index(o_idx, self.edges.shape) for i in np.arange(n): # List all vertices vert = copy.deepcopy(self.vert) neighVerts = vert[self.edges[row, 1-col], :] vRange = np.arange(self.vert.shape[0]) log = np.isin(vRange, vBrim) vRange = vRange[~log] for j in vRange: # Get the adjacent vertices adj = neighVerts[ndx[j]:ndx[j+1]] # Get the original vertex q = self.vert[j, :] # calculate new Laplacian location p = adj.mean(axis=0) # Distance between Laplacian and original b = p - q # Mean distance adjacent between original d = (adj - q).mean(axis=0) # Based upon beta, get the updated location self.vert[j, :] = q + beta*b - (1-beta)*d self.calcNorm() self.calcVNorm() def smoothValues(self, n=1): """ Function to apply a simple Laplacian smooth to the values array. Identical to the vertex smoothing except it applies the smoothing to the values Parameters ---------- n: int, default 1 number of iterations of smoothing """ # Flatten the edges array to 1D e = self.edges.flatten() # Get the indicies to sort edges o_idx = e.argsort() # Get indicies of sorted array where last of each vertex index # occurs ndx = np.searchsorted(e[o_idx], np.arange(len(self.values)), side='right') ndx = np.r_[0, ndx] # Map indicies between flatted edges array and standard row, col = np.unravel_index(o_idx, self.edges.shape) for i in np.arange(n): neighValues = self.values[self.edges[row, 1-col]] for j in np.arange(self.values.shape[0]): # Calculate mean of values set self.values[j] = neighValues[ndx[j]:ndx[j+1]].mean() ``` #### File: ampscan/ampscan/trim.py ```python import numpy as np from numbers import Number import os from scipy import spatial import copy # Used by doc tests filename = os.path.join(os.getcwd(), "tests", "stl_file.stl") class trimMixin(object): r""" Methods for trimming the AmpObject mesh """ def planarTrim(self, height, plane = 2, above = True): r""" Trim the vertices using a flat plane, all vertices above plane will be trimmed Parameters ----------- height: float Trim height, values above this will be deleted plane: int, default 2 plane for slicing Examples -------- >>> from ampscan import AmpObject >>> amp = AmpObject(filename) >>> amp.planarTrim(100, 2) """ if isinstance(height, Number) and isinstance(plane, int): # planar values for each vert on face fv = self.vert[self.faces, plane] # Number points on each face are above cut plane fvlogic = (fv > height).sum(axis=1) # Faces with points both above and below cut plane adjf = self.faces[np.logical_or(fvlogic == 2, fvlogic == 1)] # Get adjacent vertices adjv = np.unique(adjf) # Get vert above height and set to height abvInd = adjv[self.vert[adjv, plane] > height] self.vert[abvInd, plane] = height # Find all verts above plane delv = self.vert[:, plane] > height # Reorder verts to account for deleted one vInd = np.cumsum(~delv) - 1 self.faces = self.faces[fvlogic != 3, :] self.faces = vInd[self.faces] self.vert = self.vert[~delv, :] self.values = self.values[~delv] self.calcStruct() else: raise TypeError("height arg must be a float") def threePointTrim(self, p0, p1, p2, above = True): r""" Trim the vertices using a plane defined by three points. By default, all points above the plane are deleted. Parameters ----------- p0: array_like The co-ordinates of the first point to define the plane p1: array_like The co-ordinates of the second point to define the plane p2: array_like The co-ordinates of the third point to define the plane Examples -------- >>> from ampscan import AmpObject >>> amp = AmpObject(filename) >>> p0 = [50, 50, 0] >>> p1 = [50, -50, -40] >>> p2 = [-50, 50, 10] >>> amp.threePointTrim(p0, p1, p2) """ # Ensure asarrays p0 = np.asarray(p0) p1 = np.asarray(p1) p2 = np.asarray(p2) # Calculate plane v0 = p1 - p0 v1 = p2 - p0 c = np.cross(v0, v1) c = c/np.linalg.norm(c) k = -np.multiply(c, p0).sum() # planar values for each vert on face height = -(self.vert[:, 0]*c[0] + self.vert[:, 1]*c[1] + k)/c[2] # Number points on each face are above cut plane fv = self.vert[self.faces, 2] fvHeight = height[self.faces] fvlogic = (fv > fvHeight).sum(axis=1) # Faces with points both above and below cut plane adjf = self.faces[np.logical_or(fvlogic == 2, fvlogic == 1)] # Get adjacent vertices adjv = np.unique(adjf) # Get vert above height and set to height abvInd = adjv[self.vert[adjv, 2] > height[adjv]] self.vert[abvInd, 2] = height[abvInd] # Find all verts above plane delv = self.vert[:, 2] > height # Reorder verts to account for deleted one vInd = np.cumsum(~delv) - 1 self.faces = self.faces[fvlogic != 3, :] self.faces = vInd[self.faces] self.vert = self.vert[~delv, :] self.values = self.values[~delv] self.calcStruct() def dynamicTrim(self, s, maxdist = 20): """ This function trims vertices and faces from the AmpObject. It calculates the distance between the AmpObject mesh centroids and their nearest neighbour on the s mesh. If this distance is more than maxdist, the face is removed, and subsequently the vertices no longer connected to a face. Parameters ---------- s : AmpObject The target object maxdist : float The threshold distance. Faces on the m mesh that have a higher distance with their nearest neighbour on the s mesh than maxdist will be removed, as will the vertices no longer connected to a face afterwards. """ kdTree = spatial.cKDTree(s.vert) fC = self.vert[self.faces].mean(axis=1) [dist, idx] = kdTree.query(fC,1) # faceid = np.arange(len(dist))[dist < maxdist] # Find the faces with a centroid outside maxdist self.faces = self.faces[dist <= maxdist, :] # Index any vertices to keep keepV = np.zeros([self.vert.shape[0]], dtype = bool) keepV[np.unique(self.faces)] = 1 vInd = np.cumsum(keepV) - 1 # Set the vertices and faces self.faces = vInd[self.faces] self.vert = self.vert[keepV, :] self.calcStruct() ``` #### File: ampscan/tests/test_trim.py ```python import unittest from util import get_path import numpy as np class TestTrim(unittest.TestCase): def setUp(self): """Runs before each unit test Sets up the AmpObject object using "stl_file.stl" """ from ampscan.core import AmpObject stl_path = get_path("stl_file.stl") self.amp = AmpObject(stl_path) stl_path = get_path("stl_file_4.stl") # R=1.2 self.amp2 = AmpObject(stl_path) def test_trim(self): """Tests the trim method of AmpObject for TypeErrors""" # Testing that the method runs self.amp.planarTrim(0.6, plane=2) # Testing invalid data types raise TypeErrors with self.assertRaises(TypeError): self.amp.planarTrim(0.6, plane=[]) with self.assertRaises(TypeError): self.amp.planarTrim(0.6, plane=0.9) with self.assertRaises(TypeError): self.amp.planarTrim([], plane=[]) def test_trim_2(self): """Tests the trim method of AmpObject by checking no vertices are above trim line""" # Test no points are above 10 h = 10 self.amp.planarTrim(h, plane=2) self.assertLessEqual(self.amp.vert[:, 2].max(), h) # Test no points are above 0 h = 0 self.amp.planarTrim(h, plane=2) self.assertLessEqual(self.amp.vert[:, 2].max(), h) def test_trim_3(self): """Tests the trim method of AmpObject by checking no vertices are above trim line""" # Test no points are above 10 p0 = np.array([50, 50, 0]) p1 = np.array([50, -50, -40]) p2 = np.array([-50, 50, 10]) v0 = p1 - p0 v1 = p2 - p0 c = np.cross(v0, v1) c = c/np.linalg.norm(c) k = -np.multiply(c, p0).sum() # planar values for each vert on face self.amp.threePointTrim(p0, p1, p2) height = -(self.amp.vert[:, 0]*c[0] + self.amp.vert[:, 1]*c[0] + k)/c[2] self.assertLessEqual(self.amp.vert[:, 2].max(), height.max()) def test_trim_3(self): """Tests the trim method of AmpObject by checking no vertices are above trim line""" # Test no points are above 10 v = self.amp.vert.shape[0] self.amp.dynamicTrim(self.amp2, 100) self.assertLess(self.amp.vert.shape[0], v) ```
{ "source": "jerome89/graphene", "score": 3 }
#### File: v1.9.1/es-index-migration/es-index-migration.py ```python import sys from getpass import getpass try: from elasticsearch import Elasticsearch, helpers except ImportError: raise ImportError("Please install python elasticsearch dependency.") def migrate(es, tag_index, fetch_size, bulk_size): print("Migrate documents in index: " + tag_index) resp = es.search( index=tag_index, body={}, size=fetch_size, scroll='60s', request_timeout=30 ) if resp['hits']['total'] == 0: print("SKIP! No documents to migrate.") return print("Migration will be done on total " + str(resp['hits']['total']) + " document(s) ...") print("Fetching all documents from index: " + tag_index) scroll_id = resp['_scroll_id'] docs_to_migrate = [] docs_to_migrate.extend(resp['hits']['hits']) while len(resp['hits']['hits']): resp = es.scroll( scroll_id=scroll_id, scroll='60s', request_timeout=30 ) print("Fetched total " + str(len(docs_to_migrate)) + " document(s).") docs_to_migrate.extend(resp['hits']['hits']) migrate_docs(es, docs_to_migrate, bulk_size) print("Finished migration in index: " + tag_index) def migrate_docs(es, docs, bulk_size): if len(docs) == 0: return bulk = [] total = len(docs) count = 0 for doc in docs: if count > 0 and count % bulk_size == 0: print("Bulk updating ... (" + str(count) + "/" + str(total) + ")") helpers.bulk(es, bulk) bulk = [] if '_id' not in doc.keys() or '_type' not in doc.keys() or '_index' not in doc.keys(): print("Wrong doc!") continue doc_id = doc['_id'] doc_type = doc['_type'] doc_index = doc['_index'] tag_list = [] for tag in doc['_source'].keys(): if tag not in tag_list and not tag.startswith('@'): tag_list.append(tag) update_doc = {'_op_type': 'update', 'doc': {}, '_index': doc_index, '_id': doc_id, '_type': doc_type} update_doc['doc']['@tags'] = tag_list bulk.append(update_doc) count = count + 1 if len(bulk) > 0: print("Bulk updating remaining " + str(len(bulk)) + " document(s) ...") helpers.bulk(es, bulk) print("Finished migration for " + str(count) + " among total " + str(total) + " document(s)!") if len(sys.argv) < 3: raise Exception("Please provide elasticsearch host and port.") es_host = str(sys.argv[1]) es_port = int(sys.argv[2]) es_username = str(input("Please enter the Elasticsearch username(leave empty for no authentication): ")) if len(es_username.strip()) != 0: es_password = str(getpass("Please enter the Elasticsearch password: ")) if len(es_username.strip()) > 0: es = Elasticsearch( [es_host], port=es_port, http_auth=(es_username, es_password) ) else: es = Elasticsearch( [es_host], port=es_port ) bulk_size = 10000 fetch_size = 10000 tag_indices = list(es.indices.get_alias("tag*").keys()) for tag_index in tag_indices: migrate(es, tag_index, fetch_size, bulk_size) print("Migration Complete!") ```
{ "source": "jerome-auguste/Movie-Selector", "score": 3 }
#### File: jerome-auguste/Movie-Selector/sparql_queries.py ```python from SPARQLWrapper import JSON from utils import get_sparql, get_prefix, search, resp_format # , pprint def get_movie(title: str = None, director: str = None, actor: str = None, genre: str = None, score: int = 0) -> list: """Result of queries movies matching some research criteria (film title, director name, actor name, genre and/or minimum score) Args: title (str, optional): Searched title. Defaults to None. director (str, optional): Searched director. Defaults to None. actor (str, optional): Searched actor. Defaults to None. genre (str, optional): Searched genre. Defaults to None. score (int, optional): Minimum score. Defaults to 0. Returns: list: result of the queries (list of movies with id, title, director's name, score, poster if exists, actors list and genres list) """ query = f""" {get_prefix()} SELECT ?film ?filmLabel ?directorLabel ?score (SAMPLE(?poster) as ?poster) (GROUP_CONCAT(DISTINCT ?actorLabel; separator=";") as ?actorsList) (GROUP_CONCAT(DISTINCT ?genreLabel; separator=";") as ?genresList) WHERE {{ ?film wdt:P57 ?director; wdt:P161 ?actor; wdt:P136 ?genre; wdt:P444 ?brutScore. OPTIONAL {{?film wdt:P3383 ?poster }} OPTIONAL {{?film wdt:P18 ?poster }} OPTIONAL {{?film wdt:P154 ?poster }} SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". ?film rdfs:label ?filmLabel. ?director rdfs:label ?directorLabel. ?actor rdfs:label ?actorLabel. ?genre rdfs:label ?genreLabel. }} {search('?film', title)} {search('?director', director)} {search('?actor', actor)} {search('?genre', genre)} FILTER regex(?brutScore, "^[0-9]+%$") BIND(xsd:integer(REPLACE(?brutScore, "%$", "")) AS ?score) FILTER (?score >= {score}) }} GROUP BY ?film ?filmLabel ?directorLabel ?score ORDER BY DESC(?score) LIMIT 100 """ # print(query) sp_wrapper = get_sparql() sp_wrapper.setQuery(query) sp_wrapper.setReturnFormat(JSON) return resp_format(sp_wrapper.query().convert()['results']['bindings']) def recommendation_topic(film: str, limit: int=20) -> list: """Movie recommandations based on common main subjects with selected movie Args: film (str): URI of the selected movie limit (int, optional): Maximum number of results to return. Defaults to 20. Returns: list: matching moveis with URI, title, number of awards recieved, score on Rotten Tomato and a "relevance score" """ # In the query, we assume that movies have a score < 100 # (removes noise - movies with few reviews) query = f""" {get_prefix()} SELECT ?film ?filmLabel ?topicLabel (COUNT(DISTINCT ?award) AS ?numAwards) ?score ((?score + ?numAwards)*100/138 AS ?totalScore) WHERE {{ {{ SELECT ?topic WHERE {{ wd:{film} wdt:P921 ?topic. }} }} ?film wdt:P31 wd:Q11424; wdt:P921 ?topic; wdt:P444 ?brutScore. OPTIONAL {{?film wdt:P166 ?award.}} SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". ?film rdfs:label ?filmLabel. ?topic rdfs:label ?topicLabel. }} FILTER regex(?brutScore, "^[0-9]+%$") BIND(xsd:integer(REPLACE(?brutScore, "%$", "")) AS ?score) FILTER (?score != 100) FILTER (?film != wd:{film}) }} GROUP BY ?film ?filmLabel ?topicLabel ?score ORDER BY DESC(?totalScore) LIMIT {limit} """ # print(query) sp_wrapper = get_sparql() sp_wrapper.setQuery(query) sp_wrapper.setReturnFormat(JSON) return resp_format(sp_wrapper.query().convert()['results']['bindings']) def recommendation_based_on(film: str, limit: int=20) -> list: """Movie recommandations based on same story with selected movie Args: film (str): URI of the selected movie limit (int, optional): Maximum number of results to return. Defaults to 20. Returns: list: matching moveis with URI, title, story on which the movie is based on, number of awards recieved, score on Rotten Tomato and a "relevance score" """ # In the query, we assume that movies have a score < 100 # (removes noise - movies with few reviews) query = f""" {get_prefix()} SELECT ?film ?filmLabel ?basedOnLabel (COUNT(DISTINCT ?award) AS ?numAwards) ?score ((?score + ?numAwards)*100/138 AS ?totalScore) WHERE {{ {{ SELECT ?originBasedOn WHERE {{ OPTIONAL{{ wd:{film} wdt:P144 ?originBasedOn }} }} }} ?film wdt:P31 wd:Q11424; wdt:P136 ?genre; wdt:P444 ?brutScore. OPTIONAL{{?film wdt:P144 ?basedOn}} OPTIONAL {{?film wdt:P166 ?award.}} SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". ?film rdfs:label ?filmLabel. ?basedOn rdfs:label ?basedOnLabel. }} FILTER (?basedOn IN (?originBasedOn)) FILTER regex(?brutScore, "^[0-9]+%$") BIND(xsd:integer(REPLACE(?brutScore, "%$", "")) AS ?score) FILTER (?score != 100) FILTER(?film != wd:{film}) }} GROUP BY ?film ?filmLabel ?score ?basedOnLabel ORDER BY DESC(?totalScore) LIMIT {limit} """ # print(query) sp_wrapper = get_sparql() sp_wrapper.setQuery(query) sp_wrapper.setReturnFormat(JSON) return resp_format(sp_wrapper.query().convert()['results']['bindings']) def recommendation_part_of_series(film: str, limit: int=20) -> list: """Movie recommandations from the same series with selected movie Args: film (str): URI of the selected movie limit (int, optional): Maximum number of results to return. Defaults to 20. Returns: list: matching moveis with URI, title, series title, number of awards recieved, score on Rotten Tomato and a "relevance score" """ # In the query, we assume that movies have a score < 100 # (removes noise - movies with few reviews) query = f""" {get_prefix()} SELECT ?film ?filmLabel ?seriesLabel (COUNT(DISTINCT ?award) AS ?numAwards) ?score ((?score + ?numAwards)*100/138 AS ?totalScore) WHERE {{ {{ SELECT ?series WHERE {{ wd:{film} wdt:P179 ?series. }} }} ?film wdt:P31 wd:Q11424; wdt:P179 ?series; wdt:P444 ?brutScore. OPTIONAL {{?film wdt:P166 ?award.}} SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". ?film rdfs:label ?filmLabel. ?basedOn rdfs:label ?basedOnLabel. }} FILTER regex(?brutScore, "^[0-9]+%$") BIND(xsd:integer(REPLACE(?brutScore, "%$", "")) AS ?score) FILTER (?score != 100) FILTER(?film != wd:{film}) }} GROUP BY ?film ?filmLabel ?seriesLabel ?score ORDER BY DESC(?totalScore) LIMIT {limit} """ print(query) sp_wrapper = get_sparql() sp_wrapper.setQuery(query) sp_wrapper.setReturnFormat(JSON) return resp_format(sp_wrapper.query().convert()['results']['bindings']) def recommendation_genre(film: str, limit: int=20) -> list: """Movie recommandations based on common genres with selected movie Args: film (str): URI of the selected movie limit (int, optional): Maximum number of results to return. Defaults to 20. Returns: list: matching moveis with URI, title, number of awards recieved, score on Rotten Tomato and a "relevance score" (genre list could not be displayed because of a timeout issue with wikidata) """ # In the query, we assume that movies have a score < 100 # (removes noise - movies with few reviews) query = f""" {get_prefix()} SELECT ?film ?filmLabel (COUNT(DISTINCT ?award) AS ?numAwards) ?score ((?score + ?numAwards)*100/138 AS ?totalScore) WHERE {{ {{ SELECT ?originGenre WHERE {{ wd:{film} wdt:P136 ?originGenre . }} }} ?film wdt:P31 wd:Q11424; wdt:P136 ?genre; wdt:P444 ?brutScore. OPTIONAL {{?film wdt:P166 ?award.}} SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". ?film rdfs:label ?filmLabel. ?genre rdfs:label ?genreLabel. }} FILTER (?genre IN (?originGenre)) FILTER regex(?brutScore, "^[0-9]+%$") BIND(xsd:integer(REPLACE(?brutScore, "%$", "")) AS ?score) FILTER (?score != 100) FILTER(?film != wd:{film}) }} GROUP BY ?film ?filmLabel ?score ORDER BY DESC(?totalScore) LIMIT {limit} """ print(query) sp_wrapper = get_sparql() sp_wrapper.setQuery(query) sp_wrapper.setReturnFormat(JSON) return resp_format(sp_wrapper.query().convert()['results']['bindings']) def recommendation_performer(film: str, limit: int=20) -> list: """Movie recommandations having the same original soundtrack artist with selected movie Args: film (str): URI of the selected movie limit (int, optional): Maximum number of results to return. Defaults to 20. Returns: list: matching moveis with URI, title, list of performers (artists), number of awards recieved, score on Rotten Tomato and a "relevance score" """ # In the query, we assume that movies have a score < 100 # (removes noise - movies with few reviews) query = f""" {get_prefix()} SELECT ?film ?filmLabel (GROUP_CONCAT(DISTINCT ?performerLabel; separator="; ") AS ?performersList) (COUNT(DISTINCT ?award) AS ?numAwards) ?score ((?score + ?numAwards)*100/138 AS ?totalScore) WHERE {{ {{ SELECT ?originPerformer WHERE {{ wd:{film} wdt:P175 ?originPerformer. }} }} ?film wdt:P31 wd:Q11424; wdt:P175 ?performer; wdt:P444 ?brutScore. OPTIONAL {{?film wdt:P166 ?award.}} SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". ?film rdfs:label ?filmLabel. ?performer rdfs:label ?performerLabel. }} FILTER (?performer IN (?originPerformer)) FILTER regex(?brutScore, "^[0-9]+%$") BIND(xsd:integer(REPLACE(?brutScore, "%$", "")) AS ?score) FILTER (?score != 100) FILTER(?film != wd:{film}) }} GROUP BY ?film ?filmLabel ?score ORDER BY DESC(?totalScore) LIMIT {limit} """ print(query) sp_wrapper = get_sparql() sp_wrapper.setQuery(query) sp_wrapper.setReturnFormat(JSON) return resp_format(sp_wrapper.query().convert()['results']['bindings']) def recommendation_inspiredby(film: str, limit: int=20) -> list: """Movie recommandations from the same inspiration with selected movie Args: film (str): URI of the selected movie limit (int, optional): Maximum number of results to return. Defaults to 20. Returns: list: matching moveis with URI, title, inspiration list, number of awards recieved, score on Rotten Tomato and a "relevance score" """ # In the query, we assume that movies have a score < 100 # (removes noise - movies with few reviews) query = f""" {get_prefix()} SELECT ?film ?filmLabel (GROUP_CONCAT(DISTINCT ?inspiredbyLabel; separator="; ") AS ?inspiredbyList) (COUNT(DISTINCT ?award) AS ?numAwards) ?score ((?score + ?numAwards)*100/138 AS ?totalScore) WHERE {{ {{ SELECT ?originInspiredby WHERE {{ wd:{film} wdt:P941 ?originInspiredby . }} }} ?film wdt:P31 wd:Q11424; wdt:P941 ?inspiredby; wdt:P444 ?brutScore. OPTIONAL {{?film wdt:P166 ?award.}} SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". ?film rdfs:label ?filmLabel. ?inspiredby rdfs:label ?inspiredbyLabel. }} FILTER (?inspiredby IN (?originInspiredby)) FILTER regex(?brutScore, "^[0-9]+%$") BIND(xsd:integer(REPLACE(?brutScore, "%$", "")) AS ?score) FILTER (?score != 100) FILTER(?film != wd:{film}) }} GROUP BY ?film ?filmLabel ?score ORDER BY DESC(?totalScore) LIMIT {limit} """ print(query) sp_wrapper = get_sparql() sp_wrapper.setQuery(query) sp_wrapper.setReturnFormat(JSON) return resp_format(sp_wrapper.query().convert()['results']['bindings']) # res = get_film(director="<NAME>") # pprint(res) # res = recommendation_genre('Q44578', 10) # pprint(res) ```
{ "source": "jeromebchouinard/jbnetwork", "score": 3 }
#### File: jeromebchouinard/jbnetwork/jbnetworkfactory.py ```python import jbnetwork as jbn def build_star_network(size): """Build a star network. Returns Network object.""" network = jbn.Network() for i in range(1, size): network.add_link(0, i) return network def build_chain_network(size): """Build a chain network. Returns Network object.""" network = jbn.Network() for i in range(size-1): network.add_link(i, i+1) return network def build_ring_network(size): """Build a ring network. Returns Network object.""" network = jbn.Network() for i in range(size-1): network.add_link(i, i+1) network.add_link(0, size-1) return network def build_random_network(size, prob): """Build a random (Erdos-Renyi) network. Returns Network object.""" network = jbn.Network() for i in range(size): network.add_node(i) for i in range(size-1): for j in range(i+1, size): if random.random() < prob: network.add_link(i, j) return network def build_clique_network(size): """Build a clique network. Returns Network object.""" network = jbn.Network() for i in range(size-1): for j in range(i+1, size): network.add_link(i, j) return network def build_hypercube_network(size): """Build a hypercube network. Returns Network object.""" # pylint: disable=missing-docstring def _rec_build_hc_net(size): if size == 1: return {0:{}} network = {} network1 = _rec_build_hc_net(size/2) for node1 in network1: network[node1] = network1[node1] network[node1 + size/2] = {} for node2 in network1[node1]: network[node1 + size/2][node2 + size/2] = 1 network[node1][node1 + size/2] = 1 network[node1 + size/2][node1] = 1 return network # Find largest power of 2 <= size pow2size = 2**int(math.log(size, 2)) network = _rec_build_hc_net(pow2size) return Network(from_dict=network) def build_grid_network(dim): """Build a grid network. Returns Network object. arguments dim -- (x, y) tuple of dimensions """ network = jbn.Network() for node in range(size[0] * size[1]): if (node+1) % size[0] != 0: network.add_link(node, node+1) if node < (size[1] - 1)*size[0]: network.add_link(node, node+size[0]) return network ``` #### File: jeromebchouinard/jbnetwork/jbomap.py ```python from jbheap import KeyValueHeap class OrderedMap(object): """A mutable ordered sequence with optional labels. Labels can be any hashable value and must be unique. Note that if integers are used as labels, accessing by label will not work, since omap[i] where i is an int accesses by (positional) index. Methods append insert remove remove_by_label index index_by_label Supported operators + (concatenates) Indexing with [] Slice indexing with [:] Usage omap = OMap({(0, 'foo'):'bar', (1, 'baz'):'banana'}) omap[0] > 'bar' omap['foo'] > 'bar' omap[1] = 'apple' omap['baz'] > 'apple' """ def __init__(self, map=None): self.omap = [] self.lmap = KeyValueHeap() if map is not None: for key in map: try: self.insert(key[0], key[1], map[key]) except IndexError: self.insert(key[0], None, map[key]) def __add__(self, operand2): pass def __len__(self): pass def __iter__(self): pass def append(self, val, label=None): pass def insert(self, ii, val, label=None): if not hash(label) in self.lmap.keys(): self.omap.insert(ii, (val, label)) if label is not None: self.lmap.insert((hash(label), val)) else: raise AttributeError('Label already exists.') def remove(self, ii): pass def remove_by_label(self, label): pass def index(self, ii): pass def index_by_label(self, label): pass ``` #### File: jeromebchouinard/jbnetwork/jbutils.py ```python import time from pprint import pprint import math def partition(L, v): """ Partition list L at value V. """ left = [] right = [] for i in range(len(L)): if L[i] < v: left.append(L[i]) elif L[i] > v: right.append(L[i]) return (left, v, right) def top_k(L, k): """ Find the top k elements in list L. """ i = int(random.random() * len(L)) (left, v, right) = partition(L, L[i]) if len(left) == k: return left if len(left) == k-1: return left + [v] if len(left) < k: return left + [v] + top_k(right, k - (len(left) + 1)) else: return top_k(left, k) def timeit(f): """ Modify a function to print cpu and wall-clock elapsed time when called. Can be used as a decorator: @timeit def func(x): ... """ def g(*args, **kwargs): start_etime = time.perf_counter() start_cputime = time.process_time() rvalue = f(*args, **kwargs) end_etime = time.perf_counter() end_cputime = time.process_time() print('elapsed time (s): ', end_etime - start_etime) print('cpu time (s)', end_cputime - start_cputime) return rvalue return g def profile(func, input_gen, max_time=5, max_n=2**20, start_n=1, keep_returns=False): """ Time a function for different input sizes and check if O(n^2), O(n), or O(log(n)) """ runtimes = [] returns = [] if keep_returns else None last_runtime = 0 n = start_n max_time_ms = max_time * 1000 input_sizes = [] while last_runtime <= max_time_ms and n <= max_n: inp = input_gen(n) start_time = time.process_time() rvalue = func(inp) end_time = time.process_time() last_runtime = end_time - start_time runtimes.append(last_runtime) input_sizes.append(n) if keep_returns: returns.append(rvalue) n *= 2 lognfactors = [] nfactors = [] n2factors = [] for i in range(1,len(runtimes)): lognfactors.append(runtimes[i]-runtimes[i-1]) nfactors.append(runtimes[i]/runtimes[i-1]) n2factors.append(math.sqrt(runtimes[i])/math.sqrt(runtimes[i-1])) print('If func is O(log(n)) these numbers should be the same:') pprint(lognfactors) print('\n') print('If func is O(n) these numbers should be the same:') pprint(nfactors) print('\n') print('If func is O(n^2) these numbers should be the same: ') pprint(n2factors) print('\n') return (input_sizes, runtimes, returns) ```
{ "source": "JeromeBlanchet/Neuraxle", "score": 3 }
#### File: neuraxle/hyperparams/distributions.py ```python import copy import math import random import sys from abc import abstractmethod, ABCMeta from typing import List import numpy as np class HyperparameterDistribution(metaclass=ABCMeta): """Base class for other hyperparameter distributions.""" def __init__(self): """ Create a HyperparameterDistribution. This method should still be called with super if it gets overriden. """ self.first_id = id(self) @abstractmethod def rvs(self): """ Sample the random variable. :return: The randomly sampled value. """ pass def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.0) -> 'HyperparameterDistribution': """ Takes a value that is estimated to be the best one of the space, and restrict the space near that value. By default, this function will completely replace the returned value by the new guess if not overriden. :param best_guess: the value towards which we want to narrow down the space. :param kept_space_ratio: what proportion of the space is kept. Should be between 0.0 and 1.0. Default is to keep only the best_guess (0.0). :return: a new HyperparameterDistribution object that has been narrowed down. """ return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) def was_narrowed_from( self, kept_space_ratio: float, original_hp: 'HyperparameterDistribution' ) -> 'HyperparameterDistribution': """ Keep track of the original distribution to restore it. :param kept_space_ratio: the ratio which made the current object narrower than the ``original_hp``. :param original_hp: The original HyperparameterDistribution, which will be kept in a private variable for an eventual restore. :return: self. """ self.kept_space_ratio_trace = ( self.get_current_narrowing_value() * kept_space_ratio * original_hp.get_current_narrowing_value() ) self.original_hp: HyperparameterDistribution = original_hp.unnarrow() return self def get_current_narrowing_value(self): if not hasattr(self, 'kept_space_ratio_trace'): self.kept_space_ratio_trace: float = 1.0 return self.kept_space_ratio_trace def unnarrow(self) -> 'HyperparameterDistribution': """ Return the original distribution before narrowing of the distribution. If the distribution was never narrowed, will return a copy of self. :return: the original HyperparameterDistribution before narrowing, or else self if the distribution is virgin. """ if not hasattr(self, 'original_hp'): return copy.deepcopy(self) return copy.deepcopy(self.original_hp.unnarrow()) def __eq__(self, other): return self.first_id == other.first_id class FixedHyperparameter(HyperparameterDistribution): """This is an hyperparameter that won't change again, but that is still expressed as a distribution.""" def __init__(self, value): """ Create a still hyperparameter :param value: what will be returned by calling ``.rvs()``. """ self.value = value super(FixedHyperparameter, self).__init__() def rvs(self): """ Sample the non-random anymore value. :return: the value given at creation. """ return self.value # TODO: Mixin this or something: # class DelayedAdditionOf(MalleableDistribution): # """A HyperparameterDistribution (MalleableDistribution mixin) that """ # # def __init__(self, *dists): # self.dists = dists # # def rvs(self): # rvss = [d.rvs if hasattr(d, 'rvs') else d for d in self.dists] # return sum(rvss) # # # class MalleableDistribution(metaclass=ABCMeta): # """An hyperparameter distribution to which it's possible to do additional math using defaut python operators.""" # # def __add__(self, other): # return DelayedAdditionOf(self, other) # # max min + - / * % ** // == != < > <= >= # class Boolean(HyperparameterDistribution): """Get a random boolean hyperparameter.""" def rvs(self): """ Get a random True or False. :return: True or False (random). """ return random.choice([True, False]) class Choice(HyperparameterDistribution): """Get a random value from a choice list of possible value for this hyperparameter. When narrowed, the choice will only collapse to a single element when narrowed enough. For example, if there are 4 items in the list, only at a narrowing value of 0.25 that the first item will be kept alone. """ def __init__(self, choice_list: List): """ Create a random choice hyperparameter from the given list. :param choice_list: a list of values to sample from. """ self.choice_list = choice_list super(Choice, self).__init__() def rvs(self): """ Get one of the items randomly. :return: one of the items of the list. """ return random.choice(self.choice_list) def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.0) -> HyperparameterDistribution: """ Will narrow the space. If the cumulative kept_space_ratio gets to be under or equal to 1/len(choice_list), then the list is crunched to a single item as a FixedHyperparameter to reflect this narrowing. So once a small enough kept_space_ratio is reached, the list becomes a fixed unique item from the best guess. Otherwise, a deepcopy of self is returned. :param best_guess: the best item of the list to keep if truly narrowing. :param kept_space_ratio: the ratio of the space to keep. :return: a deepcopy of self, or else a FixedHyperparameter of the best_guess. """ new_narrowing = self.get_current_narrowing_value() * kept_space_ratio if len(self.choice_list) == 0 or len(self.choice_list) == 1 or new_narrowing <= 1.0 / len(self.choice_list): return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) return copy.deepcopy(self).was_narrowed_from(kept_space_ratio, self) def __len__(self): """ Return the number of choices. :return: the number of choices. """ return len(self.choice_list) class PriorityChoice(HyperparameterDistribution): """Get a random value from a choice list of possible value for this hyperparameter. The first parameters are kept until the end when the list is narrowed (it is narrowed progressively), unless there is a best guess that surpasses some of the top choices. """ def __init__(self, choice_list: List): """ Create a random choice hyperparameter from the given list (choice_list). The first parameters in the choice_list will be kept longer when narrowing the space. :param choice_list: a list of values to sample from. First placed, first kept when space is narrowed. """ self.choice_list = choice_list super(PriorityChoice, self).__init__() def rvs(self): """ Get one of the items randomly. :return: one of the items of the list. """ return random.choice(self.choice_list) def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.0) -> HyperparameterDistribution: """ Will narrow the space. If the cumulative kept_space_ratio gets to be under or equal to 1-1/len(choice_list), then the list is crunched to discard the last items to reflect this narrowing. After a few narrowing (or a big one), the list may become a FixedHyperparameter. Otherwise if the list is unchanged, a deepcopy of self is returned. :param best_guess: the best item of the list, which will be brought back as the first item. :param kept_space_ratio: the ratio of the space to keep. :return: a deepcopy of self, or a subchoice of self, or else a FixedHyperparameter of the best_guess. """ new_size = int(len(self) * kept_space_ratio + sys.float_info.epsilon) if ( len(self.choice_list) == 0 or len(self.choice_list) == 1 or new_size <= 1 or kept_space_ratio <= 1.0 / len(self.choice_list) ): return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) # Bring best_guess to front idx = self.choice_list.index(best_guess) del self.choice_list[idx] self.choice_list = [best_guess] + self.choice_list # Narrowing of the list. maybe_reduced_list = self.choice_list[:new_size] return PriorityChoice(maybe_reduced_list).was_narrowed_from(kept_space_ratio, self) def __len__(self): """ Return the number of choices. :return: the number of choices. """ return len(self.choice_list) class WrappedHyperparameterDistributions(HyperparameterDistribution): def __init__(self, hd: HyperparameterDistribution = None, hds: List[HyperparameterDistribution] = None): """ Create a wrapper that will surround another HyperparameterDistribution. The wrapper might use one (hd) and/or many (hds) HyperparameterDistribution depending on the argument(s) used. :param hd: the other HyperparameterDistribution to wrap. :param hds: the others HyperparameterDistribution to wrap. """ self.hd: HyperparameterDistribution = hd self.hds: List[HyperparameterDistribution] = hds super(WrappedHyperparameterDistributions, self).__init__() def __repr__(self): return self.__class__.__name__ + "(" + repr(self.hd) + ", hds=" + repr(self.hds) + ")" def __str__(self): return self.__class__.__name__ + "(" + str(self.hd) + ", hds=" + str(self.hds) + ")" class Quantized(WrappedHyperparameterDistributions): """A quantized wrapper for another distribution: will round() the rvs number.""" def rvs(self) -> int: """ Will return an integer, rounded from the output of the previous distribution. :return: an integer. """ return round(self.hd.rvs()) def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> 'Quantized': """ Will narrow the underlying distribution and re-wrap it under a Quantized. :param best_guess: the value towards which we want to narrow down the space. :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5). :return: """ return Quantized( self.hd.narrow_space_from_best_guess(best_guess, kept_space_ratio) ).was_narrowed_from(kept_space_ratio, self) class RandInt(HyperparameterDistribution): """Get a random integer within a range""" def __init__(self, min_included: int, max_included: int): """ Create a quantized random uniform distribution. A random integer between the two values inclusively will be returned. :param min_included: minimum integer, included. :param max_included: maximum integer, included. """ self.min_included = min_included self.max_included = max_included super(RandInt, self).__init__() def rvs(self) -> int: """ Will return an integer in the specified range as specified at creation. :return: an integer. """ return random.randint(self.min_included, self.max_included) def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution: """ Will narrow the underlying distribution towards the best guess. :param best_guess: the value towards which we want to narrow down the space. Should be between 0.0 and 1.0. :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5). :return: a new HyperparameterDistribution that has been narrowed down. """ lost_space_ratio = 1.0 - kept_space_ratio new_min_included = round(self.min_included * kept_space_ratio + best_guess * lost_space_ratio) new_max_included = round(self.max_included * kept_space_ratio + best_guess * lost_space_ratio) if new_max_included <= new_min_included or kept_space_ratio == 0.0: return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) return RandInt(new_min_included, new_max_included).was_narrowed_from(kept_space_ratio, self) class Uniform(HyperparameterDistribution): """Get a uniform distribution.""" def __init__(self, min_included: int, max_included: int): """ Create a random uniform distribution. A random float between the two values somehow inclusively will be returned. :param min_included: minimum integer, included. :param max_included: maximum integer, might be included - for more info, see https://docs.python.org/2/library/random.html#random.uniform """ self.min_included = min_included self.max_included = max_included super(Uniform, self).__init__() def rvs(self) -> float: """ Will return a float value in the specified range as specified at creation. :return: a float. """ return random.random() * (self.max_included - self.min_included) + self.min_included def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution: """ Will narrow the underlying distribution towards the best guess. :param best_guess: the value towards which we want to narrow down the space. Should be between 0.0 and 1.0. :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5). :return: a new HyperparameterDistribution that has been narrowed down. """ lost_space_ratio = 1.0 - kept_space_ratio new_min_included = self.min_included * kept_space_ratio + best_guess * lost_space_ratio new_max_included = self.max_included * kept_space_ratio + best_guess * lost_space_ratio if new_max_included <= new_min_included or kept_space_ratio == 0.0: return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) return Uniform(new_min_included, new_max_included).was_narrowed_from(kept_space_ratio, self) class LogUniform(HyperparameterDistribution): """Get a LogUniform distribution. For example, this is good for neural networks' learning rates: that vary exponentially.""" def __init__(self, min_included: float, max_included: float): """ Create a quantized random log uniform distribution. A random float between the two values inclusively will be returned. :param min_included: minimum integer, should be somehow included. :param max_included: maximum integer, should be somehow included. """ self.log2_min_included = math.log2(min_included) self.log2_max_included = math.log2(max_included) super(LogUniform, self).__init__() def rvs(self) -> float: """ Will return a float value in the specified range as specified at creation. :return: a float. """ return 2 ** random.uniform(self.log2_min_included, self.log2_max_included) def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution: """ Will narrow, in log space, the distribution towards the new best_guess. :param best_guess: the value towards which we want to narrow down the space. Should be between 0.0 and 1.0. :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5). :return: a new HyperparameterDistribution that has been narrowed down. """ log2_best_guess = math.log2(best_guess) lost_space_ratio = 1.0 - kept_space_ratio new_min_included = self.log2_min_included * kept_space_ratio + log2_best_guess * lost_space_ratio new_max_included = self.log2_max_included * kept_space_ratio + log2_best_guess * lost_space_ratio if new_max_included <= new_min_included or kept_space_ratio == 0.0: return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) return LogUniform(2 ** new_min_included, 2 ** new_max_included).was_narrowed_from(kept_space_ratio, self) class Normal(HyperparameterDistribution): """Get a normal distribution.""" def __init__(self, mean: float, std: float, hard_clip_min: float = None, hard_clip_max: float = None): """ Create a normal distribution from mean and standard deviation. :param mean: the most common value to pop :param std: the standard deviation (that is, the sqrt of the variance). :param hard_clip_min: if not none, rvs will return max(result, hard_clip_min). :param hard_clip_max: if not none, rvs will return min(result, hard_clip_min). """ self.mean = mean, self.std = std self.hard_clip_min = hard_clip_min self.hard_clip_max = hard_clip_max super(Normal, self).__init__() def rvs(self) -> float: """ Will return a float value in the specified range as specified at creation. :return: a float. """ result = float(np.random.normal(self.mean, self.std)) if not math.isfinite(result): return self.rvs() # TODO: replace hard_clip with malleable max and min? also remove in doc if so (search for "hard clip"). if self.hard_clip_max is not None: result = min(result, self.hard_clip_max) if self.hard_clip_min is not None: result = max(result, self.hard_clip_min) return float(result) def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution: """ Will narrow the distribution towards the new best_guess. The mean will move towards the new best guess, and the standard deviation will be multiplied by the kept_space_ratio. The hard clip limit is unchanged. :param best_guess: the value towards which we want to narrow down the space's mean. Should be between 0.0 and 1.0. :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5). :return: a new HyperparameterDistribution that has been narrowed down. """ lost_space_ratio = 1.0 - kept_space_ratio if isinstance(self.mean, tuple): self.mean = self.mean[0] new_mean = self.mean * kept_space_ratio + best_guess * lost_space_ratio new_std = self.std * kept_space_ratio if new_std <= 0.0: return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) return Normal( new_mean, new_std, self.hard_clip_min, self.hard_clip_max ).was_narrowed_from(kept_space_ratio, self) class LogNormal(HyperparameterDistribution): """Get a LogNormal distribution.""" def __init__(self, log2_space_mean: float, log2_space_std: float, hard_clip_min: float = None, hard_clip_max: float = None): """ Create a LogNormal distribution. :param log2_space_mean: the most common value to pop, but before taking 2**value. :param log2_space_std: the standard deviation of the most common value to pop, but before taking 2**value. :param hard_clip_min: if not none, rvs will return max(result, hard_clip_min). This value is not checked in logspace (so it is checked after the exp). :param hard_clip_max: if not none, rvs will return min(result, hard_clip_min). This value is not checked in logspace (so it is checked after the exp). """ self.log2_space_mean = log2_space_mean self.log2_space_std = log2_space_std self.hard_clip_min = hard_clip_min self.hard_clip_max = hard_clip_max super(LogNormal, self).__init__() def rvs(self) -> float: """ Will return a float value in the specified range as specified at creation. Note: the range at creation was in log space. The return value is after taking an exponent. :return: a float. """ result = 2 ** float(np.random.normal(self.log2_space_mean, self.log2_space_std)) if not math.isfinite(result): return self.rvs() if self.hard_clip_max is not None: result = min(result, self.hard_clip_max) if self.hard_clip_min is not None: result = max(result, self.hard_clip_min) return float(result) def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution: """ Will narrow the distribution towards the new best_guess. The log2_space_mean (log space mean) will move, in log space, towards the new best guess, and the log2_space_std (log space standard deviation) will be multiplied by the kept_space_ratio. :param best_guess: the value towards which we want to narrow down the space's mean. Should be between 0.0 and 1.0. :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5). :return: a new HyperparameterDistribution that has been narrowed down. """ log2_best_guess = math.log2(best_guess) lost_space_ratio = 1.0 - kept_space_ratio new_mean = self.log2_space_mean * kept_space_ratio + log2_best_guess * lost_space_ratio new_std = self.log2_space_std * kept_space_ratio if new_std <= 0.0: return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self) return Normal( new_mean, new_std, self.hard_clip_min, self.hard_clip_max ).was_narrowed_from(kept_space_ratio, self) ``` #### File: neuraxle/hyperparams/space.py ```python from collections import OrderedDict from neuraxle.hyperparams.distributions import HyperparameterDistribution PARAMS_SPLIT_SEQ = "__" def nested_dict_to_flat(nested_hyperparams, dict_ctor=OrderedDict): """ Convert a nested hyperparameter dictionary to a flat one. :param nested_hyperparams: a nested hyperparameter dictionary. :param dict_ctor: ``OrderedDict`` by default. Will use this as a class to create the new returned dict. :return: a flat hyperparameter dictionary. """ ret = dict_ctor() for k, v in nested_hyperparams.items(): if isinstance(v, dict) or isinstance(v, OrderedDict) or isinstance(v, dict_ctor): _ret = nested_dict_to_flat(v) for key, val in _ret.items(): ret[k + PARAMS_SPLIT_SEQ + key] = val else: ret[k] = v return ret def flat_to_nested_dict(flat_hyperparams, dict_ctor=OrderedDict): """ Convert a flat hyperparameter dictionary to a nested one. :param flat_hyperparams: a flat hyperparameter dictionary. :param dict_ctor: ``OrderedDict`` by default. Will use this as a class to create the new returned dict. :return: a nested hyperparameter dictionary. """ pre_ret = dict_ctor() ret = dict_ctor() for k, v in flat_hyperparams.items(): k, _, key = k.partition(PARAMS_SPLIT_SEQ) if len(key) > 0: if k not in pre_ret.keys(): pre_ret[k] = dict_ctor() pre_ret[k][key] = v else: ret[k] = v for k, v in pre_ret.items(): ret[k] = flat_to_nested_dict(v) return ret class HyperparameterSamples(OrderedDict): """Wraps an hyperparameter nested dict or flat dict, and offer a few more functions. This can be set on a Pipeline with the method ``set_hyperparams``. HyperparameterSamples are often the result of calling ``.rvs()`` on an HyperparameterSpace.""" def to_flat(self) -> 'HyperparameterSamples': """ Will create an equivalent flat HyperparameterSamples. :return: an HyperparameterSamples like self, flattened. """ return nested_dict_to_flat(self, dict_ctor=HyperparameterSamples) def to_nested_dict(self) -> 'HyperparameterSamples': """ Will create an equivalent nested dict HyperparameterSamples. :return: an HyperparameterSamples like self, as a nested dict. """ return flat_to_nested_dict(self, dict_ctor=HyperparameterSamples) def to_flat_as_dict_primitive(self) -> dict: """ Will create an equivalent flat HyperparameterSpace, as a dict. :return: an HyperparameterSpace like self, flattened. """ return nested_dict_to_flat(self, dict_ctor=dict) def to_nested_dict_as_dict_primitive(self) -> dict: """ Will create an equivalent nested dict HyperparameterSpace, as a dict. :return: a nested primitive dict type of self. """ return flat_to_nested_dict(self, dict_ctor=dict) def to_flat_as_ordered_dict_primitive(self) -> OrderedDict: """ Will create an equivalent flat HyperparameterSpace, as a dict. :return: an HyperparameterSpace like self, flattened. """ return nested_dict_to_flat(self, dict_ctor=OrderedDict) def to_nested_dict_as_ordered_dict_primitive(self) -> OrderedDict: """ Will create an equivalent nested dict HyperparameterSpace, as a dict. :return: a nested primitive dict type of self. """ return flat_to_nested_dict(self, dict_ctor=OrderedDict) class HyperparameterSpace(HyperparameterSamples): """Wraps an hyperparameter nested dict or flat dict, and offer a few more functions to process all contained HyperparameterDistribution. This can be set on a Pipeline with the method ``set_hyperparams_space``. Calling ``.rvs()`` on an ``HyperparameterSpace`` results in ``HyperparameterSamples``.""" def rvs(self) -> 'HyperparameterSpace': """ Sample the space of random variables. :return: a random HyperparameterSamples, sampled from a point of the present HyperparameterSpace. """ new_items = [] for k, v in self.items(): if isinstance(v, HyperparameterDistribution) or isinstance(v, HyperparameterSpace): v = v.rvs() new_items.append((k, v)) return HyperparameterSpace(new_items) def narrow_space_from_best_guess( self, best_guesses: 'HyperparameterSpace', kept_space_ratio: float = 0.5 ) -> 'HyperparameterSpace': """ Takes samples estimated to be the best ones of the space as of yet, and restrict the whole space towards that. :param best_guess: sampled HyperparameterSpace (the result of rvs on each parameter, but still stored as a HyperparameterSpace). :param kept_space_ratio: what proportion of the space is kept. Should be between 0.0 and 1.0. Default is 0.5. :return: a new HyperparameterSpace containing the narrowed HyperparameterDistribution objects. """ new_items = [] for k, v in self.items(): if isinstance(v, HyperparameterDistribution) or isinstance(v, HyperparameterSpace): best_guess_v = best_guesses[k] v = v.narrow_space_from_best_guess(best_guess_v, kept_space_ratio) new_items.append((k, v)) return HyperparameterSpace(new_items) def unnarrow(self) -> 'HyperparameterSpace': """ Return the original space before narrowing of the distribution. If the distribution was never narrowed, the values in the dict will be copies. :return: the original HyperparameterSpace before narrowing. """ new_items = [] for k, v in self.items(): if isinstance(v, HyperparameterDistribution) or isinstance(v, HyperparameterSpace): v = v.unnarrow() new_items.append((k, v)) return HyperparameterSpace(new_items) def to_flat(self) -> 'HyperparameterSpace': """ Will create an equivalent flat HyperparameterSpace. :return: an HyperparameterSpace like self, flattened. """ return nested_dict_to_flat(self, dict_ctor=HyperparameterSpace) def to_nested_dict(self) -> 'HyperparameterSpace': """ Will create an equivalent nested dict HyperparameterSpace. :return: an HyperparameterSpace like self, as a nested dict. """ return flat_to_nested_dict(self, dict_ctor=HyperparameterSpace) ``` #### File: testing/steps/test_utils.py ```python import copy import numpy as np from neuraxle.pipeline import Pipeline from neuraxle.steps.util import TapeCallbackFunction, TransformCallbackStep, StepClonerForEachDataInput from neuraxle.union import Identity, AddFeatures def test_tape_callback(): expected_tape = ["1", "2", "3", "a", "b", "4"] tape = TapeCallbackFunction() p = Pipeline([ Identity(), TransformCallbackStep(tape.callback, ["1"]), TransformCallbackStep(tape.callback, ["2"]), TransformCallbackStep(tape.callback, ["3"]), AddFeatures([ TransformCallbackStep(tape.callback, ["a"]), TransformCallbackStep(tape.callback, ["b"]), ]), TransformCallbackStep(tape.callback, ["4"]), Identity() ]) p.fit_transform(np.ones((1, 1))) assert tape.get_name_tape() == expected_tape def test_step_cloner(): tape = TapeCallbackFunction() data = [[1], [2], [3]] sc = StepClonerForEachDataInput(TransformCallbackStep(tape, ["-"]), copy_op=copy.copy) sc.fit_transform(data) print(tape) print(tape.get_name_tape()) print(tape.get_data()) assert tape.get_data() == data assert tape.get_name_tape() == ["-"] * 3 ```
{ "source": "JeromeBriot/fusion360-open-folders", "score": 2 }
#### File: JeromeBriot/fusion360-open-folders/OpenFolders.py ```python import adsk.core, adsk.fusion, traceback # pylint: disable=import-error import platform import os import plistlib import subprocess import json import re thisAddinName = 'OpenFolders' thisAddinTitle = 'Open Folders' thisAddinVersion = '0.4.0' thisAddinAuthor = '<NAME>' thisAddinContact = '<EMAIL>' thisFilePath = os.path.join(os.path.dirname(os.path.realpath(__file__))) app = adsk.core.Application.get() ui = app.userInterface # https://forums.autodesk.com/t5/fusion-360-api-and-scripts/api-bug-cannot-click-menu-items-in-nested-dropdown/m-p/9669144#M10876 nestedMenuBugFixed = False showUndocumentedFolders = True controls = { 'titles': [], 'ids': [], 'parentsIds': [], 'types': [], 'paths': [], 'separators': [], 'icons': [] } undocumentedControls = { 'titles': [], 'ids': [], 'parentsIds': [], 'types': [], 'paths': [], 'separators': [], 'icons': [] } handlers = [] def getDefaultControls(): global controls if platform.system() == 'Windows': desktopPath = os.path.join(os.getenv('USERPROFILE'), 'Desktop') # https://stackoverflow.com/questions/2014554/find-the-newest-folder-in-a-directory-in-python directory = os.path.join(os.getenv('LOCALAPPDATA'), 'Autodesk', 'webdeploy', 'production') fusion360Install = max([os.path.join(directory,d) for d in os.listdir(directory)], key=os.path.getctime) fusion360ApiCpp = os.path.join(fusion360Install, 'CPP') fusion360ApiPython = os.path.join(fusion360Install, 'Api', 'Python') fusion360Python = os.path.join(fusion360Install, 'Python') autodeskLocal = os.path.join(os.getenv('LOCALAPPDATA'), 'Autodesk') autodeskRoaming = os.path.join(os.getenv('APPDATA'), 'Autodesk') controls = { 'titles': [ 'Install', 'API', 'C++', 'Python', 'Python', 'Autodesk (Roaming)', 'Autodesk (Local)', 'Desktop', 'Appdata (Roaming)', 'Appdata (Local)', 'Temp', 'Preferences' ], 'ids': [ 'Fusion360Install', 'Fusion360Api', 'Fusion360ApiCpp', 'Fusion360ApiPython', 'Fusion360Python', 'AutodeskRoaming', 'AutodeskLocal', 'WindowsDesktop', 'WindowsAppdataRoaming', 'WindowsAppdataLocal', 'WindowsTemp', 'Preferences' ], 'parentsIds': [ 'root', 'root', 'Fusion360Api', 'Fusion360Api', 'root', 'root', 'root', 'root', 'root', 'root', 'root', 'root' ], 'types': [ 'command', 'dropdown', 'command', 'command', 'command', 'command', 'command', 'command', 'command', 'command', 'command', 'command' ], 'paths': [ fusion360Install, None, fusion360ApiCpp, fusion360ApiPython, fusion360Python, autodeskRoaming, autodeskLocal, os.path.join(os.getenv('USERPROFILE'), 'Desktop'), os.path.join(os.getenv('APPDATA')), os.path.join(os.getenv('LOCALAPPDATA')), os.path.join(os.getenv('TMP')), getUserDataPath() ], 'separators': [False, False, False, False, True, False, True, False, False, False, True, True], 'icons': [ 'fusion360', 'fusion360', 'fusion360', 'fusion360', 'fusion360', 'autodesk', 'autodesk', 'windows', 'windows', 'windows', 'windows', '' ]} # if not nestedMenuBugFixed: # controls['separators'][1] = True else: userPath = os.path.expanduser('~') desktopPath = os.path.join(userPath, 'Desktop') autodeskPath = os.path.join(userPath, 'Library', 'Application Support', 'Autodesk') fusionAppPath = os.path.realpath(os.path.join(autodeskPath, 'webdeploy', 'production', 'Autodesk Fusion 360.app')) fusion360Install = os.path.join(fusionAppPath, 'Contents') fusion360ApiCpp = os.path.join(fusion360Install, 'Libraries', 'Neutron', 'CPP') fusion360ApiPython = os.path.join(fusion360Install, 'Api', 'Python') fusion360Python = os.path.join(fusion360Install, 'Frameworks', 'Python.framework', 'Versions') controls = { 'titles': [ 'Install', 'API', 'C++', 'Python', 'Python', 'Autodesk', 'Desktop', 'Preferences' ], 'ids': [ 'Fusion360Install', 'Fusion360Api', 'Fusion360ApiCpp', 'Fusion360ApiPython', 'Fusion360Python', 'Autodesk', 'Desktop', 'Preferences' ], 'parentsIds': [ 'root', 'root', 'Fusion360Api', 'Fusion360Api', 'root', 'root', 'root', 'root' ], 'types': [ 'command', 'dropdown', 'command', 'command', 'command', 'command', 'command', 'command' ], 'paths': [ fusion360Install, None, fusion360ApiCpp, fusion360ApiPython, fusion360Python, autodeskPath, desktopPath, getUserDataPath() ], 'separators': [False, False, False, False, True, True, True, True], 'icons': [ 'fusion360', 'fusion360', 'fusion360', 'fusion360', 'fusion360', 'autodesk', 'macos', '' ] } # if not nestedMenuBugFixed: # controls['separators'][1] = True def getUndocumentedControls(): global undocumentedControls if not nestedMenuBugFixed: undocumentedControls = { 'titles': [], 'ids' : [], 'parentsIds': [], 'types': [], 'paths': [], 'separators': [], 'icons': [] } else: idx = 4 pathsDict = json.loads(app.executeTextCommand('Paths.Get')) if nestedMenuBugFixed: controls['titles'].insert(idx, 'Undocumented') controls['ids'].insert(idx, 'Undocumented') controls['parentsIds'].insert(idx, 'root') controls['types'].insert(idx, 'dropdown') controls['paths'].insert(idx, None) controls['separators'].insert(idx, True) controls['icons'].insert(idx, 'fusion360') for key in pathsDict.keys(): if key != 'isInstalledBuild': pn = ' '.join(re.findall(r'[A-Z](?:[a-z]+|[A-Z]*(?=[A-Z]|$))', key[0].upper() + key[1:])) if pathsDict[key].startswith('Auto-save location is '): pp = pathsDict[key].replace('Auto-save location is ', '') else: pp = pathsDict[key] if key == 'AppLogFilePath': pp = os.path.dirname(pp) if not pp.endswith('/'): pp += '/' if nestedMenuBugFixed: idx += 1 controls['titles'].insert(idx, pn) controls['ids'].insert(idx, pn.replace(' ', '')) controls['parentsIds'].insert(idx, 'Undocumented') controls['types'].insert(idx, 'command') controls['paths'].insert(idx, pp) controls['separators'].insert(idx, False) controls['icons'].insert(idx, 'fusion360') else: undocumentedControls['titles'].append(pn) undocumentedControls['ids'].append(pn.replace(' ', '')) undocumentedControls['parentsIds'].append('root') undocumentedControls['types'].append('command') undocumentedControls['paths'].append(pp) undocumentedControls['separators'].append(False) undocumentedControls['icons'].append('fusion360') def getCustomControls(): userDataPath = getUserDataPath() customPathFile = os.path.join(userDataPath, 'customPaths.json') if not os.path.exists(customPathFile): createJsonFiles(customPathFile) else: with open(customPathFile, 'r') as file: customControls = json.load(file) controls['titles'] = controls['titles'][0:-1] + customControls['titles'] + [controls['titles'][-1]] controls['ids'] = controls['ids'][0:-1] + customControls['ids'] + [controls['ids'][-1]] controls['parentsIds'] = controls['parentsIds'][0:-1] + customControls['parentsIds'] + [controls['parentsIds'][-1]] controls['types'] = controls['types'][0:-1] + customControls['types'] + [controls['types'][-1]] controls['paths'] = controls['paths'][0:-1] + customControls['paths'] + [controls['paths'][-1]] controls['separators'] = controls['separators'][0:-1] + customControls['separators'] + [controls['separators'][-1]] controls['icons'] = controls['icons'][0:-1] + customControls['icons'] + [controls['icons'][-1]] def checkResources(): global controls for i in range(0, len(controls['icons'])): if controls['icons'][i] != '': resourcePath = os.path.join(thisFilePath, 'resources', controls['icons'][i]) if os.path.exists(resourcePath): controls['icons'][i] = 'resources/' + controls['icons'][i] else: controls['icons'][i] = '' class commandCreatedEventHandler(adsk.core.CommandCreatedEventHandler): def __init__(self): super().__init__() def notify(self, args): try: senderId = args.firingEvent.sender.id[len(thisAddinName):] if senderId in controls['ids']: idx = controls['ids'].index(senderId) if controls['paths'][idx]: path = os.path.realpath(controls['paths'][idx]) elif senderId in undocumentedControls['ids']: idx = undocumentedControls['ids'].index(senderId) if undocumentedControls['paths'][idx]: path = os.path.realpath(undocumentedControls['paths'][idx]) else: ui.messageBox('Control not in list', '{} v{}'.format(thisAddinTitle, thisAddinVersion), adsk.core.MessageBoxButtonTypes.OKButtonType, adsk.core.MessageBoxIconTypes.CriticalIconType) path = None if path: if os.path.exists(path): if platform.system() == 'Windows': os.startfile(path) else: subprocess.check_call(["open", "--", path]) else: ui.messageBox('Path not found: ' + path, '{} v{}'.format(thisAddinTitle, thisAddinVersion), adsk.core.MessageBoxButtonTypes.OKButtonType, adsk.core.MessageBoxIconTypes.CriticalIconType) except: if ui: ui.messageBox('Failed:\n{}'.format(traceback.format_exc()), '{} v{}'.format(thisAddinTitle, thisAddinVersion), adsk.core.MessageBoxButtonTypes.OKButtonType, adsk.core.MessageBoxIconTypes.CriticalIconType) def getUserDataPath(): if platform.system() == 'Windows': dataPath = os.path.join(os.getenv('APPDATA'), thisAddinName + 'ForFusion360') else: dataPath = os.path.join(os.path.expanduser('~'), 'Library', 'Application Support', thisAddinName + 'ForFusion360') if not os.path.exists(dataPath): os.mkdir(dataPath) userDataPath = os.path.join(dataPath, app.userId) if not os.path.exists(userDataPath): os.mkdir(userDataPath) return userDataPath def createJsonFiles(customPathFile): emptyControls = { 'titles': [], 'ids': [], 'parentsIds': [], 'types': [], 'paths': [], 'separators': [], 'icons': [] } with open(customPathFile, 'w') as f: json.dump(emptyControls, f, indent=2) def cleanUI(): solidScripts = ui.allToolbarPanels.itemById('SolidScriptsAddinsPanel') cntrls = solidScripts.controls separator = cntrls.itemById(thisAddinName + 'separator') if separator: separator.deleteMe() cmdDefs = ui.commandDefinitions for i in range(0, len(controls['titles'])): cmdDef = cmdDefs.itemById(thisAddinName + controls['ids'][i]) if cmdDef: cmdDef.deleteMe() if not nestedMenuBugFixed: for i in range(0, len(undocumentedControls['titles'])): cmdDef = cmdDefs.itemById(thisAddinName + undocumentedControls['ids'][i]) if cmdDef: cmdDef.deleteMe() dropdownCntr = cntrls.itemById(thisAddinName + 'root' + 'Dropdown') if dropdownCntr: for i in range(0, len(controls['titles'])): cntrl = dropdownCntr.controls.itemById(thisAddinName + controls['ids'][i]) if cntrl: cntrl.isPromoted = False cntrl.deleteMe() if controls['separators'][i]: cntrl = dropdownCntr.controls.itemById(thisAddinName + controls['ids'][i] + 'separator') if cntrl: cntrl.isPromoted = False cntrl.deleteMe() dropdownCntr.deleteMe() if not nestedMenuBugFixed: dropdownCntr = cntrls.itemById(thisAddinName + 'root' + 'Dropdown' + 'Undoc') if dropdownCntr: for i in range(0, len(undocumentedControls['titles'])): cntrl = dropdownCntr.controls.itemById(thisAddinName + undocumentedControls['ids'][i]) if cntrl: cntrl.isPromoted = False cntrl.deleteMe() if undocumentedControls['separators'][i]: cntrl = dropdownCntr.controls.itemById(thisAddinName + undocumentedControls['ids'][i] + 'separator') if cntrl: cntrl.isPromoted = False cntrl.deleteMe() dropdownCntr.deleteMe() def run(context): try: getDefaultControls() if nestedMenuBugFixed and showUndocumentedFolders: getUndocumentedControls() getCustomControls() cmdDefs = ui.commandDefinitions commandCreated = commandCreatedEventHandler() solidScripts = ui.allToolbarPanels.itemById('SolidScriptsAddinsPanel') solidScripts.controls.addSeparator(thisAddinName + 'separator', '') solidScripts.controls.addDropDown(thisAddinTitle, '', thisAddinName + 'root' + 'Dropdown', '', False) for i in range(0, len(controls['icons'])): if controls['icons'][i] != '': resourcePath = os.path.join(thisFilePath, 'resources', controls['icons'][i]) if os.path.exists(resourcePath): controls['icons'][i] = 'resources/' + controls['icons'][i] else: controls['icons'][i] = '' for i in range(0, len(controls['titles'])): if controls['types'][i] == 'command': button = cmdDefs.addButtonDefinition(thisAddinName + controls['ids'][i], controls['titles'][i], controls['paths'][i], controls['icons'][i]) button.commandCreated.add(commandCreated) handlers.append(commandCreated) if controls['parentsIds'][i] == 'root': dropdown = solidScripts.controls.itemById(thisAddinName + 'root' + 'Dropdown') else: rootDropdown = solidScripts.controls.itemById(thisAddinName + 'root' + 'Dropdown') dropdown = rootDropdown.controls.itemById(thisAddinName + controls['parentsIds'][i]) dropdown.controls.addCommand(button) else: dropdown = solidScripts.controls.itemById(thisAddinName + 'root' + 'Dropdown') dropdown.controls.addDropDown(controls['titles'][i], controls['icons'][i], thisAddinName + controls['ids'][i], '', False) if controls['separators'][i]: dropdown.controls.addSeparator(thisAddinName + controls['ids'][i] + 'separator', '') if not nestedMenuBugFixed and showUndocumentedFolders: getUndocumentedControls() for i in range(0, len(undocumentedControls['icons'])): if undocumentedControls['icons'][i] != '': resourcePath = os.path.join(thisFilePath, 'resources', undocumentedControls['icons'][i]) if os.path.exists(resourcePath): undocumentedControls['icons'][i] = 'resources/' + undocumentedControls['icons'][i] else: undocumentedControls['icons'][i] = '' solidScripts.controls.addDropDown(thisAddinTitle + ' (undocumented)', '', thisAddinName + 'root' + 'Dropdown' + 'Undoc', '', False) for i in range(0, len(undocumentedControls['titles'])): if undocumentedControls['types'][i] == 'command': button = cmdDefs.addButtonDefinition(thisAddinName + undocumentedControls['ids'][i], undocumentedControls['titles'][i], undocumentedControls['paths'][i], undocumentedControls['icons'][i]) button.commandCreated.add(commandCreated) handlers.append(commandCreated) if undocumentedControls['parentsIds'][i] == 'root': dropdown = solidScripts.controls.itemById(thisAddinName + 'root' + 'Dropdown' + 'Undoc') else: rootDropdown = solidScripts.controls.itemById(thisAddinName + 'root' + 'Dropdown' + 'Undoc') dropdown = rootDropdown.controls.itemById(thisAddinName + undocumentedControls['parentsIds'][i]) dropdown.controls.addCommand(button) else: dropdown = solidScripts.controls.itemById(thisAddinName + 'root' + 'Dropdown' + 'Undoc') dropdown.controls.addDropDown(undocumentedControls['titles'][i], undocumentedControls['icons'][i], thisAddinName + undocumentedControls['ids'][i], '', False) if undocumentedControls['separators'][i]: dropdown.controls.addSeparator(thisAddinName + undocumentedControls['ids'][i] + 'separator', '') if context['IsApplicationStartup'] is False: ui.messageBox("The '{}' command has been added\nto the ADD-INS panel of the DESIGN workspace.".format(thisAddinTitle), '{} v{}'.format(thisAddinTitle, thisAddinVersion)) except: if ui: cleanUI() ui.messageBox('Failed:\n{}'.format(traceback.format_exc()), '{} v{}'.format(thisAddinTitle, thisAddinVersion)) def stop(context): try: cleanUI() except: if ui: ui.messageBox('Failed:\n{}'.format(traceback.format_exc()), '{} v{}'.format(thisAddinTitle, thisAddinVersion)) ```
{ "source": "Jerome-Celle/Blitz-API", "score": 3 }
#### File: blitz_api/tests/tests_model_Domain.py ```python from django.db import IntegrityError, transaction from rest_framework.test import APITestCase from ..models import Domain, Organization class DomainTests(APITestCase): @classmethod def setUpClass(cls): super(DomainTests, cls).setUpClass() cls.org = Organization.objects.create(name="random_university") def test_create(self): """ Ensure that we can create a domain with a valid organization. """ domain = Domain.objects.create( name="random_domain", organization_id=self.org.id ) self.assertEqual(domain.__str__(), "random_domain") ``` #### File: blitz_api/tests/tests_view_UsersExport.py ```python import json import re from rest_framework import status from rest_framework.response import Response from rest_framework.test import APIClient, APITestCase from django.contrib.auth import get_user_model from django.urls import reverse from django.conf import settings from xlrd import open_workbook from xlrd.sheet import Sheet from ..factories import UserFactory, AdminFactory User = get_user_model() class UsersTests(APITestCase): @classmethod def setUpClass(cls): super(UsersTests, cls).setUpClass() cls.client = APIClient() cls.client_authenticate = APIClient() cls.export_url = reverse('user-export') cls.regex_file_name = f'({settings.MEDIA_ROOT}.*\\.xls)' def setUp(self): self.user = UserFactory() self.user.set_password('<PASSWORD>!') self.user.save() self.admin = AdminFactory() self.admin.set_password('<PASSWORD>!') self.admin.save() self.client_authenticate.force_authenticate(user=self.admin) self.nb_setup_user = 2 def test_export_content(self): response: Response = self.client_authenticate.get( self.export_url ) self.assertEqual( response.status_code, status.HTTP_200_OK, response.content ) export_response = json.loads(response.content) self.assertEqual( export_response['count'], self.nb_setup_user, "Count value of export is different than expected" ) self.assertEqual( export_response['limit'], 1000, ) self.assertIn(settings.MEDIA_ROOT, export_response['file_url'], export_response['file_url']) file_path = re.findall(self.regex_file_name, export_response['file_url'])[0] wb = open_workbook(file_path) first_sheet: Sheet = wb.sheets()[0] col_infos = [] for col_number in range(first_sheet.ncols): try: col_infos.append({ 'col_number': col_number, 'col_name': first_sheet.cell(0, col_number).value }) except Exception: pass users = [] for row in range(1, first_sheet.nrows): user_data = dict() try: for col_info in col_infos: user_info = first_sheet.cell( row, col_info['col_number']).value user_data[col_info['col_name']] = user_info except Exception: pass users.append(user_data) user_0_id = users[0]['id'] user_0 = User.objects.get(id=user_0_id) self.assertEqual( users[0]['first_name'], user_0.first_name, users[0] ) ``` #### File: retirement/tests/tests_model_Reservation.py ```python from datetime import datetime, timedelta import pytz from django.conf import settings from django.contrib.contenttypes.models import ContentType from django.utils import timezone from rest_framework.test import APITestCase from blitz_api.factories import UserFactory from store.models import Order, OrderLine from ..models import Reservation, Retirement LOCAL_TIMEZONE = pytz.timezone(settings.TIME_ZONE) class ReservationTests(APITestCase): @classmethod def setUpClass(cls): super(ReservationTests, cls).setUpClass() cls.user = UserFactory() cls.retirement_type = ContentType.objects.get_for_model(Retirement) cls.retirement = Retirement.objects.create( name="random_retirement", details="This is a description of the retirement.", seats=40, address_line1="123 random street", postal_code="123 456", state_province="Random state", country="Random country", price=3, start_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 15, 8)), end_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 17, 12)), min_day_refund=7, min_day_exchange=7, refund_rate=100, is_active=True, accessibility=True, form_url="example.com", carpool_url='example2.com', review_url='example3.com', has_shared_rooms=True ) cls.order = Order.objects.create( user=cls.user, transaction_date=timezone.now(), authorization_id=1, settlement_id=1, ) cls.order_line = OrderLine.objects.create( order=cls.order, quantity=999, content_type=cls.retirement_type, object_id=1, ) def test_create(self): """ Ensure that we can create a time_slot. """ reservation = Reservation.objects.create( user=self.user, retirement=self.retirement, order_line=self.order_line, is_active=True, ) self.assertEqual(str(reservation), str(self.user)) ``` #### File: retirement/tests/tests_viewset_WaitQueue.py ```python import json from datetime import datetime, timedelta import pytz from django.conf import settings from django.contrib.auth import get_user_model from django.urls import reverse from django.utils import timezone from rest_framework import status from rest_framework.test import APIClient, APITestCase from blitz_api.factories import AdminFactory, UserFactory from ..models import Retirement, WaitQueue User = get_user_model() LOCAL_TIMEZONE = pytz.timezone(settings.TIME_ZONE) class WaitQueueTests(APITestCase): @classmethod def setUpClass(cls): super(WaitQueueTests, cls).setUpClass() cls.client = APIClient() cls.user = UserFactory() cls.user2 = UserFactory() cls.admin = AdminFactory() def setUp(self): self.retirement = Retirement.objects.create( name="mega_retirement", details="This is a description of the mega retirement.", seats=400, address_line1="123 random street", postal_code="123 456", state_province="Random state", country="Random country", price=199, start_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 15, 8)), end_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 17, 12)), min_day_refund=7, min_day_exchange=7, refund_rate=50, is_active=True, activity_language='FR', next_user_notified=3, accessibility=True, form_url="example.com", carpool_url='example2.com', review_url='example3.com', has_shared_rooms=True, ) self.wait_queue_subscription = WaitQueue.objects.create( user=self.user2, retirement=self.retirement, ) def test_create(self): """ Ensure we can subscribe a user to a retirement wait_queue. """ self.client.force_authenticate(user=self.user) data = { 'retirement': reverse( 'retirement:retirement-detail', args=[self.retirement.id] ), # The 'user' field is ignored when the calling user is not admin. # The field is REQUIRED nonetheless. 'user': reverse('user-detail', args=[self.admin.id]), } response = self.client.post( reverse('retirement:waitqueue-list'), data, format='json', ) self.assertEqual( response.status_code, status.HTTP_201_CREATED, response.content, ) content = { 'list_size': 2, 'retirement': 'http://testserver/retirement/retirements/' + str(self.retirement.id), 'user': ''.join(['http://testserver/users/', str(self.user.id)]), 'created_at': json.loads(response.content)['created_at'], } response_data = json.loads(response.content) del response_data['id'] del response_data['url'] self.assertEqual( response_data, content ) def test_create_as_admin_for_user(self): """ Ensure we can subscribe another user to a retirement wait_queue as an admin user. """ self.client.force_authenticate(user=self.admin) data = { 'retirement': reverse( 'retirement:retirement-detail', args=[self.retirement.id] ), 'user': reverse('user-detail', args=[self.user.id]), } response = self.client.post( reverse('retirement:waitqueue-list'), data, format='json', ) self.assertEqual( response.status_code, status.HTTP_201_CREATED, response.content, ) content = { 'list_size': 2, 'retirement': 'http://testserver/retirement/retirements/' + str(self.retirement.id), 'user': ''.join(['http://testserver/users/', str(self.user.id)]), } response_data = json.loads(response.content) del response_data['id'] del response_data['url'] del response_data['created_at'] self.assertEqual( response_data, content ) def test_create_not_authenticated(self): """ Ensure we can't subscribe to a retirement waitqueue if user has no permission. """ data = { 'retirement': reverse( 'retirement:retirement-detail', args=[self.retirement.id] ), 'user': reverse('user-detail', args=[self.user.id]), } response = self.client.post( reverse('retirement:waitqueue-list'), data, format='json', ) content = { 'detail': 'Authentication credentials were not provided.' } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) def test_create_duplicate(self): """ Ensure we can't subscribe to a retirement waitqueue twice. """ self.client.force_authenticate(user=self.admin) data = { 'retirement': reverse( 'retirement:retirement-detail', args=[self.retirement.id] ), 'user': reverse('user-detail', args=[self.user2.id]), } response = self.client.post( reverse('retirement:waitqueue-list'), data, format='json', ) content = { "non_field_errors": [ "The fields user, retirement must make a unique set." ] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_missing_field(self): """ Ensure we can't subscribe to a retirement waitqueue when required field are missing. """ self.client.force_authenticate(user=self.admin) data = {} response = self.client.post( reverse('retirement:waitqueue-list'), data, format='json', ) content = { "retirement": ["This field is required."], "user": ["This field is required."] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_invalid_field(self): """ Ensure we can't subscribe to a retirement waitqueue with invalid fields. """ self.client.force_authenticate(user=self.admin) data = { 'retirement': (1,), 'user': "http://testserver/invalid/999" } response = self.client.post( reverse('retirement:waitqueue-list'), data, format='json', ) content = { 'retirement': [ 'Incorrect type. Expected URL string, received list.' ], 'user': ['Invalid hyperlink - No URL match.'] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_update(self): """ Ensure we can't update a subscription to a retirement waitqueue. """ self.client.force_authenticate(user=self.admin) data = { 'retirement': reverse( 'retirement:retirement-detail', args=[self.retirement.id] ), 'user': reverse('user-detail', args=[self.user2.id]), } response = self.client.put( reverse( 'retirement:waitqueue-detail', kwargs={'pk': 1}, ), data, format='json', ) self.assertEqual( response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED ) def test_partial_update(self): """ Ensure we can't partially a subscription to a retirement waitqueue. """ self.client.force_authenticate(user=self.admin) data = { 'retirement': reverse( 'retirement:retirement-detail', args=[self.retirement.id] ), 'user': reverse('user-detail', args=[self.user2.id]), } response = self.client.put( reverse( 'retirement:waitqueue-detail', kwargs={'pk': 1}, ), data, format='json', ) self.assertEqual( response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED ) def test_delete(self): """ Ensure we can delete a subscription to a retirement waitqueue. The index determining the next user to be notified should be corrected. """ self.client.force_authenticate(user=self.admin) response = self.client.delete( reverse( 'retirement:waitqueue-detail', kwargs={'pk': self.wait_queue_subscription.id}, ), ) self.assertEqual( response.status_code, status.HTTP_204_NO_CONTENT, response.content ) self.retirement.refresh_from_db() self.assertEqual(self.retirement.next_user_notified, 2) def test_list(self): """ Ensure we can list subscriptions to retirement waitqueues as an authenticated user. """ self.client.force_authenticate(user=self.user2) response = self.client.get( reverse('retirement:waitqueue-list'), format='json', ) response_data = json.loads(response.content) content = { 'count': 1, 'next': None, 'previous': None, 'results': [{ 'created_at': response_data['results'][0]['created_at'], 'id': self.wait_queue_subscription.id, 'list_size': 1, 'retirement': 'http://testserver/retirement/retirements/' + str(self.retirement.id), 'url': 'http://testserver/retirement/wait_queues/' + str(self.wait_queue_subscription.id), 'user': 'http://testserver/users/' + str(self.user2.id) }] } self.assertEqual(response_data, content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_list_not_authenticated(self): """ Ensure we can't list subscriptions to retirement waitqueues as an unauthenticated user. """ response = self.client.get( reverse('retirement:waitqueue-list'), format='json', ) content = {'detail': 'Authentication credentials were not provided.'} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) def test_read(self): """ Ensure we can read read a subscription to a retirement as an authenticated user. """ self.client.force_authenticate(user=self.user2) response = self.client.get( reverse( 'retirement:waitqueue-detail', kwargs={'pk': self.wait_queue_subscription.id}, ), ) content = { 'id': self.wait_queue_subscription.id, 'list_size': 1, 'retirement': 'http://testserver/retirement/retirements/' + str(self.retirement.id), 'url': 'http://testserver/retirement/wait_queues/' + str(self.wait_queue_subscription.id), 'user': ''.join(['http://testserver/users/', str(self.user2.id)]), 'created_at': json.loads(response.content)['created_at'], } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_read_not_authenticated(self): """ Ensure we can't read a subscription to a retirement waitqueues as an unauthenticated user. """ response = self.client.get( reverse( 'retirement:waitqueue-detail', kwargs={'pk': 1}, ), format='json', ) content = {'detail': 'Authentication credentials were not provided.'} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) def test_read_as_admin(self): """ Ensure we can read read a subscription to a retirement as an admin user. """ self.client.force_authenticate(user=self.admin) response = self.client.get( reverse( 'retirement:waitqueue-detail', kwargs={'pk': self.wait_queue_subscription.id}, ), ) response_data = json.loads(response.content) content = { 'id': self.wait_queue_subscription.id, 'list_size': 1, 'retirement': 'http://testserver/retirement/retirements/' + str(self.retirement.id), 'url': 'http://testserver/retirement/wait_queues/' + str(self.wait_queue_subscription.id), 'user': ''.join(['http://testserver/users/', str(self.user2.id)]), 'created_at': json.loads(response.content)['created_at'], } self.assertEqual(response_data, content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_read_non_existent(self): """ Ensure we get not found when asking for a subscription to a retirement that doesn't exist. """ self.client.force_authenticate(user=self.admin) response = self.client.get( reverse( 'retirement:waitqueue-detail', kwargs={'pk': 999}, ), ) content = {'detail': 'Not found.'} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) ``` #### File: Blitz-API/store/permissions.py ```python from rest_framework import permissions class IsAdminOrReadOnly(permissions.BasePermission): """ Custom permission to only allow admins to modify objects. """ def has_permission(self, request, view): # Read permissions are allowed to any request, # so we'll always allow GET, HEAD or OPTIONS requests. if request.method in permissions.SAFE_METHODS: return True return request.user.is_staff class IsAdminOrCreateReadOnly(permissions.BasePermission): """ Custom permission to only allow admins to update/delete objects. """ def has_permission(self, request, view): # Read permissions are allowed to any request, # so we'll always allow GET, HEAD or OPTIONS requests. if request.method in permissions.SAFE_METHODS: return True # Always allow object creation if request.method == 'POST': return True return request.user.is_staff class IsOwner(permissions.BasePermission): """ Custom permission to only allow admins or owners of an object to view/edit. """ def has_object_permission(self, request, view, obj): # Test if the object is the user himself otherwise verifies # if a owner field exists and equals the user. return (request.user.is_staff or obj == request.user or (hasattr(obj, 'owner') and obj.owner == request.user), (hasattr(obj, 'user') and obj.user == request.user)) ``` #### File: Blitz-API/store/resources.py ```python from django.apps import apps from django.contrib.auth import get_user_model from import_export import fields, resources from import_export.widgets import (ForeignKeyWidget, ManyToManyWidget, DateTimeWidget) from blitz_api.models import AcademicLevel from blitz_api.services import get_model_from_name from .models import (Membership, Order, OrderLine, Package, CustomPayment, Coupon, CouponUser, Refund, ) User = get_user_model() # django-import-export models declaration # These represent the models data that will be importd/exported class MembershipResource(resources.ModelResource): academic_levels = fields.Field( column_name='academic_levels', attribute='academic_levels', widget=ManyToManyWidget(AcademicLevel, ',', 'name'), ) class Meta: model = Membership fields = ( 'id', 'name', 'details', 'price', 'duration', 'academic_levels', 'available', ) export_order = ( 'id', 'name', 'details', 'price', 'duration', 'academic_levels', 'available', ) class OrderResource(resources.ModelResource): user = fields.Field( column_name='user', attribute='user', widget=ForeignKeyWidget(User, 'email'), ) coupon = fields.Field( column_name='coupon', attribute='coupon', widget=ForeignKeyWidget(Coupon, 'code'), ) class Meta: model = Order fields = ( 'id', 'user', 'transaction_date', 'authorization_id', 'settlement_id', 'coupon', ) export_order = ( 'id', 'user', 'transaction_date', 'authorization_id', 'settlement_id', 'coupon', ) class OrderLineResource(resources.ModelResource): user = fields.Field( column_name='user', attribute='order__user', widget=ForeignKeyWidget(User, 'email'), ) item_type = fields.Field( column_name='item_type', attribute='content_type__model', ) item_name = fields.Field() item_id = fields.Field() def dehydrate_item_name(self, orderline): model = get_model_from_name(orderline.content_type.model) return model.objects.get(id=orderline.object_id).name def dehydrate_item_id(self, orderline): model = get_model_from_name(orderline.content_type.model) return model.objects.get(id=orderline.object_id).id class Meta: model = OrderLine fields = ( 'id', 'user', 'item_type', 'item_name', 'item_id', 'quantity', 'order', ) export_order = ( 'id', 'user', 'item_type', 'item_name', 'item_id', 'quantity', 'order', ) class PackageResource(resources.ModelResource): memberships = fields.Field( column_name='memberships', attribute='exclusive_memberships', widget=ManyToManyWidget(Membership, ',', 'name'), ) class Meta: model = Package fields = ( 'id', 'name', 'details', 'price', 'reservations', 'memberships', 'available', ) export_order = ( 'id', 'name', 'details', 'price', 'reservations', 'memberships', 'available', ) class CustomPaymentResource(resources.ModelResource): user = fields.Field( column_name='user', attribute='user', widget=ForeignKeyWidget(User, 'email'), ) class Meta: model = CustomPayment fields = ( 'id', 'name', 'details', 'price', 'user', 'transaction_date', 'authorization_id', 'settlement_id', ) export_order = ( 'id', 'name', 'details', 'price', 'user', 'transaction_date', 'authorization_id', 'settlement_id', ) class CouponResource(resources.ModelResource): owner = fields.Field( column_name='owner', attribute='owner', widget=ForeignKeyWidget(User, 'email'), ) total_use = fields.Field() def dehydrate_total_use(self, coupon): uses = CouponUser.objects.filter(coupon=coupon) return sum(uses.values_list('uses', flat=True)) class Meta: model = Coupon fields = ( 'id', 'details', 'value', 'percent_off', 'code', 'owner', 'start_time', 'end_time', 'total_use', ) export_order = ( 'id', 'details', 'value', 'percent_off', 'code', 'owner', 'start_time', 'end_time', 'total_use', ) class CouponUserResource(resources.ModelResource): user_email = fields.Field( column_name='user_email', attribute='user', widget=ForeignKeyWidget(User, 'email'), ) user_firstname = fields.Field( column_name='user_firstname', attribute='user', widget=ForeignKeyWidget(User, 'first_name'), ) user_lastname = fields.Field( column_name='user_lastname', attribute='user', widget=ForeignKeyWidget(User, 'last_name'), ) student_number = fields.Field( column_name='student_number', attribute='user', widget=ForeignKeyWidget(User, 'student_number'), ) academic_program_code = fields.Field( column_name='academic_program_code', attribute='user', widget=ForeignKeyWidget(User, 'academic_program_code'), ) university = fields.Field( column_name='university', attribute='user', widget=ForeignKeyWidget(User, 'university__name'), ) class Meta: model = CouponUser fields = ( 'user_email', 'university', 'user_firstname', 'user_lastname', 'student_number', 'academic_program_code', 'uses', ) export_order = ( 'user_email', 'university', 'user_firstname', 'user_lastname', 'student_number', 'academic_program_code', 'uses', ) class RefundResource(resources.ModelResource): orderline = fields.Field( column_name='orderline', attribute='orderline', widget=ForeignKeyWidget(OrderLine, 'content_type__model'), ) product_name = fields.Field( column_name='product_name', attribute='orderline', widget=ForeignKeyWidget(OrderLine, 'content_object__name'), ) class Meta: model = Refund fields = ( 'id', 'orderline', 'product_name', 'amount', 'details', 'refund_date', ) export_order = ( 'id', 'orderline', 'product_name', 'amount', 'details', 'refund_date', ) ``` #### File: store/tests/tests_model_Refund.py ```python from datetime import timedelta from django.utils import timezone from django.contrib.contenttypes.models import ContentType from rest_framework.test import APITestCase from blitz_api.factories import UserFactory from ..models import Membership, Order, OrderLine, Refund class RefundTests(APITestCase): @classmethod def setUpClass(cls): super(RefundTests, cls).setUpClass() cls.membership_type = ContentType.objects.get_for_model(Membership) cls.membership = Membership.objects.create( name="basic_membership", details="1-Year student membership", available=True, price=50, duration=timedelta(days=365), ) cls.user = UserFactory() cls.order = Order.objects.create( user=cls.user, transaction_date=timezone.now(), authorization_id=1, settlement_id=1, ) cls.orderline = OrderLine.objects.create( order=cls.order, quantity=999, content_type=cls.membership_type, object_id=cls.membership.id, ) def test_create(self): """ Ensure that we can create a membership. """ refund = Refund.objects.create( orderline=self.orderline, refund_date=timezone.now(), amount=10.00, details="Refund details", ) self.assertEqual(str(refund), 'basic_membership, qt:999, 10.0$') ``` #### File: Blitz-API/workplace/fields.py ```python import pytz from rest_framework import serializers from django.utils.translation import ugettext_lazy as _ class TimezoneField(serializers.CharField): def to_internal_value(self, value): tz = super().to_representation(value) try: return str(pytz.timezone(tz)) except pytz.exceptions.UnknownTimeZoneError: raise serializers.ValidationError(_("Unknown timezone")) ``` #### File: workplace/tests/tests_viewset_Period.py ```python import json import pytz from datetime import datetime, timedelta from rest_framework import status from rest_framework.test import APIClient, APITestCase from django.urls import reverse from django.utils import timezone from django.conf import settings from django.core import mail from django.contrib.auth import get_user_model from django.test.utils import override_settings from blitz_api.factories import UserFactory, AdminFactory from blitz_api.services import remove_translation_fields from ..models import Workplace, Period, TimeSlot, Reservation User = get_user_model() LOCAL_TIMEZONE = pytz.timezone(settings.TIME_ZONE) class PeriodTests(APITestCase): @classmethod def setUpClass(cls): super(PeriodTests, cls).setUpClass() cls.client = APIClient() cls.user = UserFactory() cls.admin = AdminFactory() cls.workplace = Workplace.objects.create( name="Blitz", seats=40, details="short_description", address_line1="123 random street", postal_code="123 456", state_province="Random state", country="Random country", ) cls.period = Period.objects.create( name="random_period", workplace=cls.workplace, start_date=timezone.now(), end_date=timezone.now() + timedelta(weeks=4), price=3, is_active=False, ) cls.period_active = Period.objects.create( name="random_period_active", workplace=cls.workplace, start_date=timezone.now(), end_date=timezone.now() + timedelta(weeks=4), price=3, is_active=True, ) cls.time_slot_active = TimeSlot.objects.create( name="evening_time_slot_active", period=cls.period_active, price=3, start_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 15, 18)), end_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 15, 22)), ) cls.reservation = Reservation.objects.create( user=cls.user, timeslot=cls.time_slot_active, is_active=True, ) def test_create(self): """ Ensure we can create a period if user has permission. """ self.client.force_authenticate(user=self.admin) data = { 'name': "random_period", 'workplace': reverse('workplace-detail', args=[self.workplace.id]), 'start_date': LOCAL_TIMEZONE.localize( datetime.now() + timedelta(weeks=5)), 'end_date': LOCAL_TIMEZONE.localize( datetime.now() + timedelta(weeks=10)), 'price': '3.00', 'is_active': True, } response = self.client.post( reverse('period-list'), data, format='json', ) content = { 'end_date': data['end_date'].isoformat(), 'is_active': True, 'name': 'random_period', 'price': '3.00', 'total_reservations': 0, 'start_date': data['start_date'].isoformat(), 'workplace': f'http://testserver/workplaces/{self.workplace.id}' } response_content = json.loads(response.content) del response_content['id'] del response_content['url'] self.assertEqual( remove_translation_fields(response_content), content ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) def test_create_without_permission(self): """ Ensure we can't create a period if user has no permission. """ self.client.force_authenticate(user=self.user) data = { 'name': "random_period", 'workplace': reverse('workplace-detail', args=[self.workplace.id]), 'start_date': timezone.now(), 'end_date': timezone.now() + timedelta(weeks=4), 'price': '3.00', 'is_active': True, } response = self.client.post( reverse('period-list'), data, format='json', ) content = { 'detail': 'You do not have permission to perform this action.' } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_create_overlapping(self): """ Ensure we can't create overlapping period in the same workplace. """ self.client.force_authenticate(user=self.admin) data = { 'name': "random_period", 'workplace': reverse('workplace-detail', args=[self.workplace.id]), 'start_date': timezone.now(), 'end_date': timezone.now() + timedelta(weeks=4), 'price': '3.00', 'is_active': True, } response = self.client.post( reverse('period-list'), data, format='json', ) content = { 'non_field_errors': [ 'An active period associated to the same workplace overlaps ' 'with the provided start_date and end_date.' ] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_invalid_start_end(self): """ Ensure we can't create periods with start_date greater than end_date. """ self.client.force_authenticate(user=self.admin) data = { 'name': "random_period", 'workplace': reverse('workplace-detail', args=[self.workplace.id]), 'start_date': timezone.now(), 'end_date': timezone.now() - timedelta(weeks=4), 'price': '3.00', 'is_active': True, } response = self.client.post( reverse('period-list'), data, format='json', ) content = { 'end_date': ['End date must be later than start_date.'], 'start_date': ['Start date must be earlier than end_date.'] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_non_existent_workplace(self): """ Ensure we can't create a period with a non-existent workplace. """ self.client.force_authenticate(user=self.admin) data = { 'name': "random_period", 'workplace': reverse('workplace-detail', args=[999]), 'start_date': timezone.now(), 'end_date': timezone.now() + timedelta(weeks=4), 'price': '3.00', 'is_active': True, } response = self.client.post( reverse('period-list'), data, format='json', ) content = {'workplace': ['Invalid hyperlink - Object does not exist.']} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_missing_field(self): """ Ensure we can't create a period when required field are missing. """ self.client.force_authenticate(user=self.admin) data = {} response = self.client.post( reverse('period-list'), data, format='json', ) content = { 'end_date': ['This field is required.'], 'is_active': ['This field is required.'], 'price': ['This field is required.'], 'start_date': ['This field is required.'], 'workplace': ['This field is required.'] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_blank_field(self): """ Ensure we can't create a period when required field are blank. """ self.client.force_authenticate(user=self.admin) data = { 'name': None, 'workplace': None, 'start_date': None, 'end_date': None, 'price': None, 'is_active': None, } response = self.client.post( reverse('period-list'), data, format='json', ) content = { 'name': ['This field may not be null.'], 'start_date': ['This field may not be null.'], 'end_date': ['This field may not be null.'], 'price': ['This field may not be null.'], 'is_active': ['This field may not be null.'] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_invalid_field(self): """ Ensure we can't create a timeslot when required field are invalid. """ self.client.force_authenticate(user=self.admin) data = { 'name': "", 'workplace': "invalid", 'start_date': "", 'end_date': "", 'price': "", 'is_active': "", } response = self.client.post( reverse('period-list'), data, format='json', ) content = { 'end_date': [ 'Datetime has wrong format. Use one of these formats instead: ' 'YYYY-MM-DDThh:mm[:ss[.uuuuuu]][+HH:MM|-HH:MM|Z].' ], 'is_active': ['Must be a valid boolean.'], 'name': ['This field may not be blank.'], 'price': ['A valid number is required.'], 'start_date': [ 'Datetime has wrong format. Use one of these formats instead: ' 'YYYY-MM-DDThh:mm[:ss[.uuuuuu]][+HH:MM|-HH:MM|Z].' ], 'workplace': ['Invalid hyperlink - No URL match.'] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) """ Full updates and partial updates are limited. If reservations exist, these actions are forbidden. In a future iteration, we could allow updates with the exception of: - Postpone start_date - Bring forward end_date - Set is_active to False """ def test_update(self): """ Ensure we can update a period without reservations. """ self.client.force_authenticate(user=self.admin) data = { 'name': "new_period", 'workplace': reverse('workplace-detail', args=[self.workplace.id]), 'start_date': LOCAL_TIMEZONE.localize( datetime.now() + timedelta(weeks=5)), 'end_date': LOCAL_TIMEZONE.localize( datetime.now() + timedelta(weeks=10)), 'price': '3.00', 'is_active': True, } response = self.client.put( reverse( 'period-detail', args=[self.period.id] ), data, format='json', ) content = { 'id': self.period.id, 'end_date': data['end_date'].isoformat(), 'is_active': True, 'name': 'new_period', 'price': '3.00', 'total_reservations': 0, 'start_date': data['start_date'].isoformat(), 'url': f'http://testserver/periods/{self.period.id}', 'workplace': f'http://testserver/workplaces/{self.workplace.id}' } self.assertEqual( remove_translation_fields(json.loads(response.content)), content ) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_update_with_reservations(self): """ Ensure we can't update a period that contains time slots with reservations. """ self.client.force_authenticate(user=self.admin) data = { 'name': "new_period", 'workplace': reverse('workplace-detail', args=[self.workplace.id]), 'start_date': timezone.now() + timedelta(weeks=5), 'end_date': timezone.now() + timedelta(weeks=10), 'price': '3.00', 'is_active': True, } response = self.client.put( reverse( 'period-detail', args=[self.period_active.id] ), data, format='json', ) content = { 'non_field_errors': [ "The period contains timeslots with user reservations." ] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_update_partial(self): """ Ensure we can partially update a period. """ self.client.force_authenticate(user=self.admin) data = { 'name': "updated_period", 'start_date': LOCAL_TIMEZONE.localize( datetime.now() + timedelta(weeks=1)), 'price': '2000.00', } response = self.client.patch( reverse( 'period-detail', args=[self.period.id] ), data, format='json', ) response_data = json.loads(response.content) content = { 'id': self.period.id, 'is_active': False, 'name': 'updated_period', 'price': '2000.00', 'total_reservations': 0, 'end_date': response_data['end_date'], 'start_date': data['start_date'].isoformat(), 'url': f'http://testserver/periods/{self.period.id}', 'workplace': f'http://testserver/workplaces/{self.workplace.id}' } self.assertEqual( remove_translation_fields(json.loads(response.content)), content ) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_update_partial_with_reservations(self): """ Ensure we can't partially update a period that contains time slots with reservations. The next step is to allow only these actions: - The start_date can be set to an earlier date. - The end_date can be set to a later date. - The is_active field can be set to True. - The name can change. """ self.client.force_authenticate(user=self.admin) data = { 'name': "updated_period", 'start_date': timezone.now() + timedelta(weeks=1), 'price': '2000.00', } response = self.client.patch( reverse( 'period-detail', args=[self.period_active.id] ), data, format='json', ) content = { 'non_field_errors': [ "The period contains timeslots with user reservations." ] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_update_partial_overlapping(self): """ Ensure we can't partially update an active period if it overlaps with another active period. """ self.client.force_authenticate(user=self.admin) data = { 'name': "updated_period", 'start_date': timezone.now() + timedelta(weeks=1), 'price': '2000.00', 'is_active': True, } response = self.client.patch( reverse( 'period-detail', args=[self.period.id] ), data, format='json', ) content = { 'non_field_errors': [ 'An active period associated to the same workplace overlaps ' 'with the provided start_date and end_date.' ] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_delete(self): """ Ensure we can delete a period that has no reservations. """ self.client.force_authenticate(user=self.admin) response = self.client.delete( reverse( 'period-detail', args=[self.period.id] ), ) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) def test_delete_with_reservations(self): """ Ensure we can delete a period that has reservations. """ self.client.force_authenticate(user=self.admin) reservation_2 = Reservation.objects.create( user=self.user, timeslot=self.time_slot_active, is_active=True, ) data = { 'force_delete': True, } response = self.client.delete( reverse( 'period-detail', args=[self.period_active.id] ), data, format='json', ) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.reservation.refresh_from_db() self.user.refresh_from_db() self.admin.refresh_from_db() # Make sure the timeslot was deleted (cascade) self.assertFalse( TimeSlot.objects.filter( name="evening_time_slot_active" ).exists() ) self.assertFalse(self.reservation.is_active) self.assertEqual(self.reservation.cancelation_reason, 'TD') self.assertTrue(self.reservation.cancelation_date) self.assertEqual(len(mail.outbox), 2) self.assertEqual(self.user.tickets, 3) self.assertEqual(self.admin.tickets, 1) self.reservation.is_active = True self.reservation.cancelation_date = None self.reservation.cancelation_reason = None self.reservation.save() self.reservation.refresh_from_db() reservation_2.delete() self.user.tickets = 0 self.user.save() self.admin.tickets = 0 self.admin.save() def test_delete_with_reservations_no_force(self): """ Ensure we can't delete a period that has reservations if the force_delete field is not provided and set to True. """ self.client.force_authenticate(user=self.admin) data = { # 'force_delete': True, } response = self.client.delete( reverse( 'period-detail', args=[self.period_active.id] ), data, format='json', ) content = { "non_field_errors": [ "Trying to do a Period deletion that affects " "users without providing `force_delete` field set to True." ] } self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(json.loads(response.content), content) def test_delete_with_reservations_invalid_force_delete(self): """ Ensure we can't delete a timeslot that has reservations if the force_delete field is not provided and set to True. """ self.client.force_authenticate(user=self.admin) data = { 'force_delete': "invalid", } response = self.client.delete( reverse( 'period-detail', args=[self.period.id] ), data, format='json', ) content = { 'force_delete': [ 'Must be a valid boolean.' ] } self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(json.loads(response.content), content) def test_list(self): """ Ensure we can list active periods as an unauthenticated user if is active. """ response = self.client.get( reverse('period-list'), format='json', ) data = json.loads(response.content) data['results'] = [ remove_translation_fields(m) for m in data['results'] ] content = { 'count': 1, 'next': None, 'previous': None, 'results': [{ 'id': self.period_active.id, 'end_date': data['results'][0]['end_date'], 'is_active': True, 'name': 'random_period_active', 'price': '3.00', 'total_reservations': 1, 'start_date': data['results'][0]['start_date'], 'url': f'http://testserver/periods/{self.period_active.id}', 'workplace': f'http://testserver/workplaces/' f'{self.workplace.id}' }] } self.assertEqual(data, content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_list_inactive(self): """ Ensure we can list all periods as an admin user. """ self.client.force_authenticate(user=self.admin) response = self.client.get( reverse('period-list'), format='json', ) data = json.loads(response.content) data['results'] = [ remove_translation_fields(m) for m in data['results'] ] content = { 'count': 2, 'next': None, 'previous': None, 'results': [{ 'id': self.period.id, 'end_date': data['results'][0]['end_date'], 'is_active': False, 'name': 'random_period', 'price': '3.00', 'total_reservations': 0, 'start_date': data['results'][0]['start_date'], 'url': f'http://testserver/periods/{self.period.id}', 'workplace': f'http://testserver/workplaces/{self.workplace.id}' }, { 'id': self.period_active.id, 'end_date': data['results'][1]['end_date'], 'is_active': True, 'name': 'random_period_active', 'price': '3.00', 'total_reservations': 1, 'start_date': data['results'][1]['start_date'], 'url': f'http://testserver/periods/{self.period_active.id}', 'workplace': f'http://testserver/workplaces/{self.workplace.id}' }] } self.assertEqual(data, content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_read(self): """ Ensure we can read a period as an unauthenticated user if it is active. """ response = self.client.get( reverse( 'period-detail', args=[self.period_active.id] ), ) data = json.loads(response.content) content = { 'id': self.period_active.id, 'end_date': data['end_date'], 'is_active': True, 'name': 'random_period_active', 'price': '3.00', 'total_reservations': 1, 'start_date': data['start_date'], 'url': f'http://testserver/periods/{self.period_active.id}', 'workplace': f'http://testserver/workplaces/{self.workplace.id}' } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_read_inactive(self): """ Ensure we can read a period as admin if it is inactive. """ self.client.force_authenticate(user=self.admin) response = self.client.get( reverse( 'period-detail', args=[self.period.id] ), ) data = json.loads(response.content) content = { 'id': self.period.id, 'end_date': data['end_date'], 'is_active': False, 'name': 'random_period', 'price': '3.00', 'total_reservations': 0, 'start_date': data['start_date'], 'url': f'http://testserver/periods/{self.period.id}', 'workplace': f'http://testserver/workplaces/{self.workplace.id}' } self.assertEqual( remove_translation_fields(json.loads(response.content)), content ) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_read_inactive_non_admin(self): """ Ensure we can't read a period as non_admin if it is inactive. """ response = self.client.get( reverse( 'period-detail', args=[self.period.id] ), ) content = {'detail': 'Not found.'} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_read_non_existent(self): """ Ensure we get not found when asking for a period that doesn't exist. """ response = self.client.get( reverse( 'period-detail', kwargs={'pk': 999}, ), ) content = {'detail': 'Not found.'} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) ``` #### File: workplace/tests/tests_viewset_Workplace.py ```python import json from rest_framework import status from rest_framework.test import APIClient, APITestCase from django.urls import reverse from django.contrib.auth import get_user_model from blitz_api.factories import UserFactory, AdminFactory from blitz_api.services import remove_translation_fields from ..models import Workplace User = get_user_model() class WorkplaceTests(APITestCase): @classmethod def setUpClass(cls): super(WorkplaceTests, cls).setUpClass() cls.client = APIClient() cls.user = UserFactory() cls.admin = AdminFactory() def setUp(self): self.workplace = Workplace.objects.create( name="Blitz", seats=40, details="short_description", address_line1="random_address_1", postal_code="RAN_DOM", city='random_city', state_province="Random_State", country="Random_Country", timezone="America/Montreal", ) def test_create(self): """ Ensure we can create a workplace if user has permission. """ self.client.force_authenticate(user=self.admin) data = { 'name': "random_workplace", 'seats': 40, 'details': "short_description", 'address_line1': 'random_address_1', 'city': 'random_city', 'country': 'Random_Country', 'postal_code': 'RAN_DOM', 'state_province': 'Random_State', 'timezone': "America/Montreal", 'volunteers': [f"http://testserver/users/{self.user.id}"], } response = self.client.post( reverse('workplace-list'), data, format='json', ) response_content = json.loads(response.content) self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content) content = { 'details': 'short_description', 'address_line1': 'random_address_1', 'address_line2': None, 'city': 'random_city', 'country': 'Random_Country', 'postal_code': 'RAN_DOM', 'state_province': 'Random_State', 'latitude': None, 'longitude': None, 'name': 'random_workplace', 'pictures': [], 'seats': 40, 'timezone': "America/Montreal", 'place_name': '', 'volunteers': [ f'http://testserver/users/{self.user.id}' ], } del response_content['id'] del response_content['url'] self.assertEqual( remove_translation_fields(response_content), content ) def test_create_without_permission(self): """ Ensure we can't create a workplace if user has no permission. """ self.client.force_authenticate(user=self.user) data = { 'name': "random_workplace", 'seats': 40, 'details': "short_description", 'address_line1': 'random_address_1', 'city': 'random_city', 'country': 'Random_Country', 'postal_code': 'RAN_DOM', 'state_province': 'Random_State', 'timezone': "America/Montreal" } response = self.client.post( reverse('workplace-list'), data, format='json', ) content = { 'detail': 'You do not have permission to perform this action.' } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_create_duplicate_name(self): """ Ensure we can't create a workplace with same name. """ self.client.force_authenticate(user=self.admin) data = { 'name': "Blitz", 'seats': 40, 'details': "short_description", 'address_line1': 'random_address_1', 'city': 'random_city', 'country': 'Random_Country', 'postal_code': 'RAN_DOM', 'state_province': 'Random_State', 'timezone': "America/Montreal" } response = self.client.post( reverse('workplace-list'), data, format='json', ) content = {'name': ['This field must be unique.']} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_missing_field(self): """ Ensure we can't create a workplace when required field are missing. """ self.client.force_authenticate(user=self.admin) data = {} response = self.client.post( reverse('workplace-list'), data, format='json', ) content = { 'details': ['This field is required.'], 'address_line1': ['This field is required.'], 'city': ['This field is required.'], 'country': ['This field is required.'], 'name': ['This field is required.'], 'postal_code': ['This field is required.'], 'seats': ['This field is required.'], 'state_province': ['This field is required.'], 'timezone': ['This field is required.'] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_invalid_field(self): """ Ensure we can't create a workplace with invalid fields. """ self.client.force_authenticate(user=self.admin) data = { 'name': ("invalid",), 'seats': "invalid", 'details': ("invalid",), 'postal_code': (1,), 'city': (1,), 'address_line1': (1,), 'country': (1,), 'state_province': (1,), 'timezone': ("invalid",), 'place_name': (1,), 'volunteers': (1,), } response = self.client.post( reverse('workplace-list'), data, format='json', ) content = { 'details': ['Not a valid string.'], 'name': ['Not a valid string.'], 'city': ['Not a valid string.'], 'address_line1': ['Not a valid string.'], 'postal_code': ['Not a valid string.'], 'state_province': ['Not a valid string.'], 'country': ['Not a valid string.'], 'seats': ['A valid integer is required.'], 'timezone': ['Unknown timezone'], 'place_name': ['Not a valid string.'], 'volunteers': [ 'Incorrect type. Expected URL string, received int.' ], } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_update(self): """ Ensure we can update a workplace. """ self.client.force_authenticate(user=self.admin) data = { 'name': "new_workplace", 'seats': 200, 'details': "new_short_description", 'address_line1': 'new_address', 'city': 'new_city', 'country': 'Random_Country', 'postal_code': 'NEW_CIT', 'state_province': 'Random_State', 'timezone': "America/Montreal", } response = self.client.put( reverse( 'workplace-detail', kwargs={'pk': self.workplace.id}, ), data, format='json', ) content = { 'details': 'new_short_description', 'id': self.workplace.id, 'longitude': None, 'latitude': None, 'address_line1': 'new_address', 'address_line2': None, 'city': 'new_city', 'country': 'Random_Country', 'postal_code': 'NEW_CIT', 'state_province': 'Random_State', 'name': 'new_workplace', 'pictures': [], 'seats': 200, 'timezone': 'America/Montreal', 'place_name': '', 'volunteers': [], 'url': f'http://testserver/workplaces/{self.workplace.id}' } self.assertEqual( remove_translation_fields(json.loads(response.content)), content ) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_delete(self): """ Ensure we can delete a workplace. """ self.client.force_authenticate(user=self.admin) response = self.client.delete( reverse( 'workplace-detail', kwargs={'pk': self.workplace.id}, ), ) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) def test_list(self): """ Ensure we can list workplaces as an unauthenticated user. """ response = self.client.get( reverse('workplace-list'), format='json', ) content = { 'count': 1, 'next': None, 'previous': None, 'results': [{ 'details': 'short_description', 'id': self.workplace.id, 'latitude': None, 'longitude': None, 'address_line1': 'random_address_1', 'address_line2': None, 'city': 'random_city', 'country': 'Random_Country', 'postal_code': 'RAN_DOM', 'state_province': 'Random_State', 'name': 'Blitz', 'pictures': [], 'seats': 40, 'timezone': 'America/Montreal', 'place_name': '', 'volunteers': [], 'url': f'http://testserver/workplaces/{self.workplace.id}' }] } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_read(self): """ Ensure we can read a workplace as an unauthenticated user. """ response = self.client.get( reverse( 'workplace-detail', kwargs={'pk': self.workplace.id}, ), ) content = { 'details': 'short_description', 'id': self.workplace.id, 'address_line1': 'random_address_1', 'address_line2': None, 'city': 'random_city', 'country': 'Random_Country', 'longitude': None, 'latitude': None, 'postal_code': 'RAN_DOM', 'state_province': 'Random_State', 'name': 'Blitz', 'pictures': [], 'seats': 40, 'place_name': '', 'timezone': 'America/Montreal', 'volunteers': [], 'url': f'http://testserver/workplaces/{self.workplace.id}' } self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_read_non_existent_workplace(self): """ Ensure we get not found when asking for a workplace that doesn't exist. """ response = self.client.get( reverse( 'workplace-detail', kwargs={'pk': 999}, ), ) content = {'detail': 'Not found.'} self.assertEqual(json.loads(response.content), content) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) ```
{ "source": "jeromechiu/Travel_Salesman_Proble", "score": 3 }
#### File: jeromechiu/Travel_Salesman_Proble/address_to_wgs84.py ```python import googlemaps import pandas as pd from key import gmap_key gmaps = googlemaps.Client(key=gmap_key) """ Sample of address destinations = [[0, '新北市中和區中正路209號'], [1, '新北市中和區建一路92號'], [3, '新北市中和區景平路634-2號B1'], [2, '新北市中和區連城路258號18樓'] ] Sample of WGS84 dest_coord = [[0, (24.993484, 121.497134)], [1, (25.0007671, 121.4879088)], [3, (24.9986295, 121.5007544)], [2, (24.99663, 121.4869139)]] """ def to_coord(address): return gmaps.geocode(address) def transfer_address_geocord(destinations): dest_coord = list() for i, addr in destinations: data = to_coord(addr) lat, long = data[0]['geometry']['location']['lat'], data[0]['geometry']['location']['lng'] dest_coord.append([i, (lat, long)]) dest_coord = pd.DataFrame(dest_coord, columns=['id', 'coord']) dest_coord.set_index('id', inplace=True) dest_coord.sort_index(inplace=True) return dest_coord ``` #### File: jeromechiu/Travel_Salesman_Proble/main.py ```python import travel_point_grouping # import routing from address_to_wgs84 import transfer_address_geocord from routing import calculate_tsp from travel_point_grouping import wgs84_to_cartesian, grouping, wgs84_to_cartesian destinations = [[0, '新北市中和區中正路209號'], [1, '新北市中和區建一路92號'], [3, '新北市中和區景平路634-2號B1'], [2, '新北市中和區連城路258號18樓'] ] dest_coord = [[0, (24.993484, 121.497134)], [1, (25.0007671, 121.4879088)], [3, (24.9986295, 121.5007544)], [2, (24.99663, 121.4869139)], [4, (24.99675656624081, 121.50636226818159)], [5, (25.002060969852035, 121.51072200728377)], [6, (24.99648971473095, 121.50066515392008)], [7, (24.99725077478079, 121.50031353934627)], [8, (24.99674027185629, 121.49756159310002)], [9, (24.996839941641497, 121.49789418699332)], [10, (24.997515749272218, 121.49955447425104)], [11, (24.995498039188387, 121.50097604502005)], [12, (24.99587241199133, 121.50172974569763)], [13, (24.99577760340233, 121.4988369834885)], [14, (24.99635496227342, 121.50022636767291)], [15, (24.996587107412616, 121.50234704426815)] ] def main(): # dest_coord = transfer_address_geocord(destinations) groupped = grouping(wgs84_to_cartesian(dest_coord), dest_coord) print(groupped) delivery_plan = calculate_tsp(groupped) print(delivery_plan) if __name__ == '__main__': main() ```
{ "source": "jeromecn/caravel_viz_full", "score": 2 }
#### File: caravel_viz_full/caravel/models.py ```python from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from collections import OrderedDict import functools import json import logging import pickle import re import textwrap from collections import namedtuple from copy import deepcopy, copy from datetime import timedelta, datetime, date import humanize import pandas as pd import requests import sqlalchemy as sqla from sqlalchemy.engine.url import make_url from sqlalchemy.orm import subqueryload import sqlparse from dateutil.parser import parse from flask import escape, g, Markup, request from flask_appbuilder import Model from flask_appbuilder.models.mixins import AuditMixin from flask_appbuilder.models.decorators import renders from flask_babel import lazy_gettext as _ from pydruid.client import PyDruid from pydruid.utils.filters import Dimension, Filter from pydruid.utils.postaggregator import Postaggregator from pydruid.utils.having import Aggregation from six import string_types from sqlalchemy import ( Column, Integer, String, ForeignKey, Text, Boolean, DateTime, Date, Table, Numeric, create_engine, MetaData, desc, asc, select, and_, func ) from sqlalchemy.ext.compiler import compiles from sqlalchemy.ext.declarative import declared_attr from sqlalchemy.orm import backref, relationship from sqlalchemy.orm.session import make_transient from sqlalchemy.sql import table, literal_column, text, column from sqlalchemy.sql.expression import ColumnClause, TextAsFrom from sqlalchemy_utils import EncryptedType from werkzeug.datastructures import ImmutableMultiDict import caravel from caravel import app, db, db_engine_specs, get_session, utils, sm from caravel.source_registry import SourceRegistry from caravel.viz import viz_types from caravel.jinja_context import get_template_processor from caravel.utils import ( flasher, MetricPermException, DimSelector, wrap_clause_in_parens ) config = app.config QueryResult = namedtuple('namedtuple', ['df', 'query', 'duration']) FillterPattern = re.compile(r'''((?:[^,"']|"[^"]*"|'[^']*')+)''') class JavascriptPostAggregator(Postaggregator): def __init__(self, name, field_names, function): self.post_aggregator = { 'type': 'javascript', 'fieldNames': field_names, 'name': name, 'function': function, } self.name = name class ImportMixin(object): def override(self, obj): """Overrides the plain fields of the dashboard.""" for field in obj.__class__.export_fields: setattr(self, field, getattr(obj, field)) def copy(self): """Creates a copy of the dashboard without relationships.""" new_obj = self.__class__() new_obj.override(self) return new_obj def alter_params(self, **kwargs): d = self.params_dict d.update(kwargs) self.params = json.dumps(d) @property def params_dict(self): if self.params: return json.loads(self.params) else: return {} class AuditMixinNullable(AuditMixin): """Altering the AuditMixin to use nullable fields Allows creating objects programmatically outside of CRUD """ created_on = Column(DateTime, default=datetime.now, nullable=True) changed_on = Column( DateTime, default=datetime.now, onupdate=datetime.now, nullable=True) @declared_attr def created_by_fk(cls): # noqa return Column(Integer, ForeignKey('ab_user.id'), default=cls.get_user_id, nullable=True) @declared_attr def changed_by_fk(cls): # noqa return Column( Integer, ForeignKey('ab_user.id'), default=cls.get_user_id, onupdate=cls.get_user_id, nullable=True) @renders('created_on') def creator(self): # noqa return '{}'.format(self.created_by or '') @property def changed_by_(self): return '{}'.format(self.changed_by or '') @renders('changed_on') def changed_on_(self): return Markup( '<span class="no-wrap">{}</span>'.format(self.changed_on)) @renders('changed_on') def modified(self): s = humanize.naturaltime(datetime.now() - self.changed_on) return Markup('<span class="no-wrap">{}</span>'.format(s)) @property def icons(self): return """ <a href="{self.datasource_edit_url}" data-toggle="tooltip" title="{self.datasource}"> <i class="fa fa-database"></i> </a> """.format(**locals()) class Url(Model, AuditMixinNullable): """Used for the short url feature""" __tablename__ = 'url' id = Column(Integer, primary_key=True) url = Column(Text) class CssTemplate(Model, AuditMixinNullable): """CSS templates for dashboards""" __tablename__ = 'css_templates' id = Column(Integer, primary_key=True) template_name = Column(String(250)) css = Column(Text, default='') slice_user = Table('slice_user', Model.metadata, Column('id', Integer, primary_key=True), Column('user_id', Integer, ForeignKey('ab_user.id')), Column('slice_id', Integer, ForeignKey('slices.id')) ) class Slice(Model, AuditMixinNullable, ImportMixin): """A slice is essentially a report or a view on data""" __tablename__ = 'slices' id = Column(Integer, primary_key=True) slice_name = Column(String(250)) datasource_id = Column(Integer) datasource_type = Column(String(200)) datasource_name = Column(String(2000)) viz_type = Column(String(250)) params = Column(Text) description = Column(Text) cache_timeout = Column(Integer) perm = Column(String(2000)) owners = relationship("User", secondary=slice_user) export_fields = ('slice_name', 'datasource_type', 'datasource_name', 'viz_type', 'params', 'cache_timeout') def __repr__(self): return self.slice_name @property def cls_model(self): return SourceRegistry.sources[self.datasource_type] @property def datasource(self): return self.get_datasource @datasource.getter @utils.memoized def get_datasource(self): ds = db.session.query( self.cls_model).filter_by( id=self.datasource_id).first() return ds @renders('datasource_name') def datasource_link(self): datasource = self.datasource if datasource: return self.datasource.link @property def datasource_edit_url(self): self.datasource.url @property @utils.memoized def viz(self): d = json.loads(self.params) viz_class = viz_types[self.viz_type] return viz_class(self.datasource, form_data=d) @property def description_markeddown(self): return utils.markdown(self.description) @property def data(self): """Data used to render slice in templates""" d = {} self.token = '' try: d = self.viz.data self.token = d.get('token') except Exception as e: d['error'] = str(e) d['slice_id'] = self.id d['slice_name'] = self.slice_name d['description'] = self.description d['slice_url'] = self.slice_url d['edit_url'] = self.edit_url d['description_markeddown'] = self.description_markeddown return d @property def json_data(self): return json.dumps(self.data) @property def slice_url(self): """Defines the url to access the slice""" try: slice_params = json.loads(self.params) except Exception as e: logging.exception(e) slice_params = {} slice_params['slice_id'] = self.id slice_params['json'] = "false" slice_params['slice_name'] = self.slice_name from werkzeug.urls import Href href = Href( "/caravel/explore/{obj.datasource_type}/" "{obj.datasource_id}/".format(obj=self)) return href(slice_params) @property def slice_id_url(self): return ( "/caravel/{slc.datasource_type}/{slc.datasource_id}/{slc.id}/" ).format(slc=self) @property def edit_url(self): return "/slicemodelview/edit/{}".format(self.id) @property def slice_link(self): url = self.slice_url name = escape(self.slice_name) return Markup('<a href="{url}">{name}</a>'.format(**locals())) def get_viz(self, url_params_multidict=None): """Creates :py:class:viz.BaseViz object from the url_params_multidict. :param werkzeug.datastructures.MultiDict url_params_multidict: Contains the visualization params, they override the self.params stored in the database :return: object of the 'viz_type' type that is taken from the url_params_multidict or self.params. :rtype: :py:class:viz.BaseViz """ slice_params = json.loads(self.params) # {} slice_params['slice_id'] = self.id slice_params['json'] = "false" slice_params['slice_name'] = self.slice_name slice_params['viz_type'] = self.viz_type if self.viz_type else "table" if url_params_multidict: slice_params.update(url_params_multidict) to_del = [k for k in slice_params if k not in url_params_multidict] for k in to_del: del slice_params[k] immutable_slice_params = ImmutableMultiDict(slice_params) return viz_types[immutable_slice_params.get('viz_type')]( self.datasource, form_data=immutable_slice_params, slice_=self ) @classmethod def import_obj(cls, slc_to_import, import_time=None): """Inserts or overrides slc in the database. remote_id and import_time fields in params_dict are set to track the slice origin and ensure correct overrides for multiple imports. Slice.perm is used to find the datasources and connect them. """ session = db.session make_transient(slc_to_import) slc_to_import.dashboards = [] slc_to_import.alter_params( remote_id=slc_to_import.id, import_time=import_time) # find if the slice was already imported slc_to_override = None for slc in session.query(Slice).all(): if ('remote_id' in slc.params_dict and slc.params_dict['remote_id'] == slc_to_import.id): slc_to_override = slc slc_to_import = slc_to_import.copy() params = slc_to_import.params_dict slc_to_import.datasource_id = SourceRegistry.get_datasource_by_name( session, slc_to_import.datasource_type, params['datasource_name'], params['schema'], params['database_name']).id if slc_to_override: slc_to_override.override(slc_to_import) session.flush() return slc_to_override.id else: session.add(slc_to_import) logging.info('Final slice: {}'.format(slc_to_import.to_json())) session.flush() return slc_to_import.id def set_perm(mapper, connection, target): # noqa src_class = target.cls_model id_ = target.datasource_id ds = db.session.query(src_class).filter_by(id=int(id_)).first() target.perm = ds.perm sqla.event.listen(Slice, 'before_insert', set_perm) sqla.event.listen(Slice, 'before_update', set_perm) dashboard_slices = Table( 'dashboard_slices', Model.metadata, Column('id', Integer, primary_key=True), Column('dashboard_id', Integer, ForeignKey('dashboards.id')), Column('slice_id', Integer, ForeignKey('slices.id')), ) dashboard_user = Table( 'dashboard_user', Model.metadata, Column('id', Integer, primary_key=True), Column('user_id', Integer, ForeignKey('ab_user.id')), Column('dashboard_id', Integer, ForeignKey('dashboards.id')) ) class Dashboard(Model, AuditMixinNullable, ImportMixin): """The dashboard object!""" __tablename__ = 'dashboards' id = Column(Integer, primary_key=True) dashboard_title = Column(String(500)) position_json = Column(Text) description = Column(Text) css = Column(Text) json_metadata = Column(Text) slug = Column(String(255), unique=True) slices = relationship( 'Slice', secondary=dashboard_slices, backref='dashboards') owners = relationship("User", secondary=dashboard_user) export_fields = ('dashboard_title', 'position_json', 'json_metadata', 'description', 'css', 'slug') def __repr__(self): return self.dashboard_title @property def table_names(self): return ", ".join({"{}".format(s.datasource) for s in self.slices}) @property def url(self): return "/caravel/dashboard/{}/".format(self.slug or self.id) @property def datasources(self): return {slc.datasource for slc in self.slices} @property def sqla_metadata(self): metadata = MetaData(bind=self.get_sqla_engine()) return metadata.reflect() def dashboard_link(self): title = escape(self.dashboard_title) return Markup( '<a href="{self.url}">{title}</a>'.format(**locals())) @property def json_data(self): d = { 'id': self.id, 'metadata': self.params_dict, 'dashboard_title': self.dashboard_title, 'slug': self.slug, 'slices': [slc.data for slc in self.slices], 'position_json': json.loads(self.position_json) if self.position_json else [], } return json.dumps(d) @property def params(self): return self.json_metadata @params.setter def params(self, value): self.json_metadata = value @property def position_array(self): if self.position_json: return json.loads(self.position_json) return [] @classmethod def import_obj(cls, dashboard_to_import, import_time=None): """Imports the dashboard from the object to the database. Once dashboard is imported, json_metadata field is extended and stores remote_id and import_time. It helps to decide if the dashboard has to be overridden or just copies over. Slices that belong to this dashboard will be wired to existing tables. This function can be used to import/export dashboards between multiple caravel instances. Audit metadata isn't copies over. """ def alter_positions(dashboard, old_to_new_slc_id_dict): """ Updates slice_ids in the position json. Sample position json: [{ "col": 5, "row": 10, "size_x": 4, "size_y": 2, "slice_id": "3610" }] """ position_array = dashboard.position_array for position in position_array: if 'slice_id' not in position: continue old_slice_id = int(position['slice_id']) if old_slice_id in old_to_new_slc_id_dict: position['slice_id'] = '{}'.format( old_to_new_slc_id_dict[old_slice_id]) dashboard.position_json = json.dumps(position_array) logging.info('Started import of the dashboard: {}' .format(dashboard_to_import.to_json())) session = db.session logging.info('Dashboard has {} slices' .format(len(dashboard_to_import.slices))) # copy slices object as Slice.import_slice will mutate the slice # and will remove the existing dashboard - slice association slices = copy(dashboard_to_import.slices) old_to_new_slc_id_dict = {} new_filter_immune_slices = [] new_expanded_slices = {} i_params_dict = dashboard_to_import.params_dict for slc in slices: logging.info('Importing slice {} from the dashboard: {}'.format( slc.to_json(), dashboard_to_import.dashboard_title)) new_slc_id = Slice.import_obj(slc, import_time=import_time) old_to_new_slc_id_dict[slc.id] = new_slc_id # update json metadata that deals with slice ids new_slc_id_str = '{}'.format(new_slc_id) old_slc_id_str = '{}'.format(slc.id) if ('filter_immune_slices' in i_params_dict and old_slc_id_str in i_params_dict['filter_immune_slices']): new_filter_immune_slices.append(new_slc_id_str) if ('expanded_slices' in i_params_dict and old_slc_id_str in i_params_dict['expanded_slices']): new_expanded_slices[new_slc_id_str] = ( i_params_dict['expanded_slices'][old_slc_id_str]) # override the dashboard existing_dashboard = None for dash in session.query(Dashboard).all(): if ('remote_id' in dash.params_dict and dash.params_dict['remote_id'] == dashboard_to_import.id): existing_dashboard = dash dashboard_to_import.id = None alter_positions(dashboard_to_import, old_to_new_slc_id_dict) dashboard_to_import.alter_params(import_time=import_time) if new_expanded_slices: dashboard_to_import.alter_params( expanded_slices=new_expanded_slices) if new_filter_immune_slices: dashboard_to_import.alter_params( filter_immune_slices=new_filter_immune_slices) new_slices = session.query(Slice).filter( Slice.id.in_(old_to_new_slc_id_dict.values())).all() if existing_dashboard: existing_dashboard.override(dashboard_to_import) existing_dashboard.slices = new_slices session.flush() return existing_dashboard.id else: # session.add(dashboard_to_import) causes sqlachemy failures # related to the attached users / slices. Creating new object # allows to avoid conflicts in the sql alchemy state. copied_dash = dashboard_to_import.copy() copied_dash.slices = new_slices session.add(copied_dash) session.flush() return copied_dash.id @classmethod def export_dashboards(cls, dashboard_ids): copied_dashboards = [] datasource_ids = set() for dashboard_id in dashboard_ids: # make sure that dashboard_id is an integer dashboard_id = int(dashboard_id) copied_dashboard = ( db.session.query(Dashboard) .options(subqueryload(Dashboard.slices)) .filter_by(id=dashboard_id).first() ) make_transient(copied_dashboard) for slc in copied_dashboard.slices: datasource_ids.add((slc.datasource_id, slc.datasource_type)) # add extra params for the import slc.alter_params( remote_id=slc.id, datasource_name=slc.datasource.name, schema=slc.datasource.name, database_name=slc.datasource.database.database_name, ) copied_dashboard.alter_params(remote_id=dashboard_id) copied_dashboards.append(copied_dashboard) eager_datasources = [] for dashboard_id, dashboard_type in datasource_ids: eager_datasource = SourceRegistry.get_eager_datasource( db.session, dashboard_type, dashboard_id) eager_datasource.alter_params( remote_id=eager_datasource.id, database_name=eager_datasource.database.database_name, ) make_transient(eager_datasource) eager_datasources.append(eager_datasource) return pickle.dumps({ 'dashboards': copied_dashboards, 'datasources': eager_datasources, }) class Queryable(object): """A common interface to objects that are queryable (tables and datasources)""" @property def column_names(self): return sorted([c.column_name for c in self.columns]) @property def main_dttm_col(self): return "timestamp" @property def groupby_column_names(self): return sorted([c.column_name for c in self.columns if c.groupby]) @property def filterable_column_names(self): return sorted([c.column_name for c in self.columns if c.filterable]) @property def dttm_cols(self): return [] @property def url(self): return '/{}/edit/{}'.format(self.baselink, self.id) @property def explore_url(self): if self.default_endpoint: return self.default_endpoint else: return "/caravel/explore/{obj.type}/{obj.id}/".format(obj=self) class Database(Model, AuditMixinNullable): """An ORM object that stores Database related information""" __tablename__ = 'dbs' id = Column(Integer, primary_key=True) database_name = Column(String(250), unique=True) sqlalchemy_uri = Column(String(1024)) password = Column(EncryptedType(String(1024), config.get('SECRET_KEY'))) cache_timeout = Column(Integer) select_as_create_table_as = Column(Boolean, default=False) expose_in_sqllab = Column(Boolean, default=False) allow_run_sync = Column(Boolean, default=True) allow_run_async = Column(Boolean, default=False) allow_ctas = Column(Boolean, default=False) allow_dml = Column(Boolean, default=False) force_ctas_schema = Column(String(250)) extra = Column(Text, default=textwrap.dedent("""\ { "metadata_params": {}, "engine_params": {} } """)) def __repr__(self): return self.database_name @property def name(self): return self.database_name @property def backend(self): url = make_url(self.sqlalchemy_uri_decrypted) return url.get_backend_name() def set_sqlalchemy_uri(self, uri): password_mask = "X" * 10 conn = sqla.engine.url.make_url(uri) if conn.password != password_mask: # do not over-write the password with the password mask self.password = <PASSWORD> conn.password = password_mask if conn.password else None self.sqlalchemy_uri = str(conn) # hides the password def get_sqla_engine(self, schema=None): extra = self.get_extra() url = make_url(self.sqlalchemy_uri_decrypted) params = extra.get('engine_params', {}) if self.backend == 'presto' and schema: if '/' in url.database: url.database = url.database.split('/')[0] + '/' + schema else: url.database += '/' + schema elif schema: url.database = schema return create_engine(url, **params) def get_reserved_words(self): return self.get_sqla_engine().dialect.preparer.reserved_words def get_quoter(self): return self.get_sqla_engine().dialect.identifier_preparer.quote def get_df(self, sql, schema): sql = sql.strip().strip(';') eng = self.get_sqla_engine(schema=schema) cur = eng.execute(sql, schema=schema) cols = [col[0] for col in cur.cursor.description] df = pd.DataFrame(cur.fetchall(), columns=cols) return df def compile_sqla_query(self, qry, schema=None): eng = self.get_sqla_engine(schema=schema) compiled = qry.compile(eng, compile_kwargs={"literal_binds": True}) return '{}'.format(compiled) def select_star( self, table_name, schema=None, limit=100, show_cols=False, indent=True): """Generates a ``select *`` statement in the proper dialect""" for i in range(10): print(schema) quote = self.get_quoter() fields = '*' table = self.get_table(table_name, schema=schema) if show_cols: fields = [quote(c.name) for c in table.columns] if schema: table_name = schema + '.' + table_name qry = select(fields).select_from(text(table_name)) if limit: qry = qry.limit(limit) sql = self.compile_sqla_query(qry) if indent: sql = sqlparse.format(sql, reindent=True) return sql def wrap_sql_limit(self, sql, limit=1000): qry = ( select('*') .select_from(TextAsFrom(text(sql), ['*']) .alias('inner_qry')).limit(limit) ) return self.compile_sqla_query(qry) def safe_sqlalchemy_uri(self): return self.sqlalchemy_uri @property def inspector(self): engine = self.get_sqla_engine() return sqla.inspect(engine) def all_table_names(self, schema=None): return sorted(self.inspector.get_table_names(schema)) def all_view_names(self, schema=None): views = [] try: views = self.inspector.get_view_names(schema) except Exception as e: pass return views def all_schema_names(self): return sorted(self.inspector.get_schema_names()) @property def db_engine_spec(self): engine_name = self.get_sqla_engine().name or 'base' return db_engine_specs.engines.get( engine_name, db_engine_specs.BaseEngineSpec) def grains(self): """Defines time granularity database-specific expressions. The idea here is to make it easy for users to change the time grain form a datetime (maybe the source grain is arbitrary timestamps, daily or 5 minutes increments) to another, "truncated" datetime. Since each database has slightly different but similar datetime functions, this allows a mapping between database engines and actual functions. """ return self.db_engine_spec.time_grains def grains_dict(self): return {grain.name: grain for grain in self.grains()} def get_extra(self): extra = {} if self.extra: try: extra = json.loads(self.extra) except Exception as e: logging.error(e) return extra def get_table(self, table_name, schema=None): extra = self.get_extra() meta = MetaData(**extra.get('metadata_params', {})) return Table( table_name, meta, schema=schema or None, autoload=True, autoload_with=self.get_sqla_engine()) def get_columns(self, table_name, schema=None): return self.inspector.get_columns(table_name, schema) def get_indexes(self, table_name, schema=None): return self.inspector.get_indexes(table_name, schema) def get_pk_constraint(self, table_name, schema=None): return self.inspector.get_pk_constraint(table_name, schema) def get_foreign_keys(self, table_name, schema=None): return self.inspector.get_foreign_keys(table_name, schema) @property def sqlalchemy_uri_decrypted(self): conn = sqla.engine.url.make_url(self.sqlalchemy_uri) conn.password = self.password return str(conn) @property def sql_url(self): return '/caravel/sql/{}/'.format(self.id) @property def perm(self): return ( "[{obj.database_name}].(id:{obj.id})").format(obj=self) class SqlaTable(Model, Queryable, AuditMixinNullable, ImportMixin): """An ORM object for SqlAlchemy table references""" type = "table" __tablename__ = 'tables' id = Column(Integer, primary_key=True) table_name = Column(String(250)) main_dttm_col = Column(String(250)) description = Column(Text) default_endpoint = Column(Text) database_id = Column(Integer, ForeignKey('dbs.id'), nullable=False) is_featured = Column(Boolean, default=False) user_id = Column(Integer, ForeignKey('ab_user.id')) owner = relationship('User', backref='tables', foreign_keys=[user_id]) database = relationship( 'Database', backref=backref('tables', cascade='all, delete-orphan'), foreign_keys=[database_id]) offset = Column(Integer, default=0) cache_timeout = Column(Integer) schema = Column(String(255)) sql = Column(Text) params = Column(Text) baselink = "tablemodelview" export_fields = ( 'table_name', 'main_dttm_col', 'description', 'default_endpoint', 'database_id', 'is_featured', 'offset', 'cache_timeout', 'schema', 'sql', 'params') __table_args__ = ( sqla.UniqueConstraint( 'database_id', 'schema', 'table_name', name='_customer_location_uc'),) def __repr__(self): return self.table_name @property def description_markeddown(self): return utils.markdown(self.description) @property def link(self): table_name = escape(self.table_name) return Markup( '<a href="{self.explore_url}">{table_name}</a>'.format(**locals())) @property def perm(self): return ( "[{obj.database}].[{obj.table_name}]" "(id:{obj.id})").format(obj=self) @property def name(self): return self.table_name @property def full_name(self): return utils.get_datasource_full_name( self.database, self.table_name, schema=self.schema) @property def dttm_cols(self): l = [c.column_name for c in self.columns if c.is_dttm] if self.main_dttm_col not in l: l.append(self.main_dttm_col) return l @property def num_cols(self): return [c.column_name for c in self.columns if c.isnum] @property def any_dttm_col(self): cols = self.dttm_cols if cols: return cols[0] @property def html(self): t = ((c.column_name, c.type) for c in self.columns) df = pd.DataFrame(t) df.columns = ['field', 'type'] return df.to_html( index=False, classes=( "dataframe table table-striped table-bordered " "table-condensed")) @property def metrics_combo(self): return sorted( [ (m.metric_name, m.verbose_name or m.metric_name) for m in self.metrics], key=lambda x: x[1]) @property def sql_url(self): return self.database.sql_url + "?table_name=" + str(self.table_name) @property def time_column_grains(self): return { "time_columns": self.dttm_cols, "time_grains": [grain.name for grain in self.database.grains()] } def get_col(self, col_name): columns = self.columns for col in columns: if col_name == col.column_name: return col def query( # sqla self, groupby, metrics, granularity, from_dttm, to_dttm, filter=None, # noqa is_timeseries=True, timeseries_limit=15, timeseries_limit_metric=None, row_limit=None, inner_from_dttm=None, inner_to_dttm=None, orderby=None, extras=None, columns=None): """Querying any sqla table from this common interface""" template_processor = get_template_processor( table=self, database=self.database) # For backward compatibility if granularity not in self.dttm_cols: granularity = self.main_dttm_col cols = {col.column_name: col for col in self.columns} metrics_dict = {m.metric_name: m for m in self.metrics} qry_start_dttm = datetime.now() if not granularity and is_timeseries: raise Exception(_( "Datetime column not provided as part table configuration " "and is required by this type of chart")) metrics_exprs = [metrics_dict.get(m).sqla_col for m in metrics] timeseries_limit_metric = metrics_dict.get(timeseries_limit_metric) timeseries_limit_metric_expr = None if timeseries_limit_metric: timeseries_limit_metric_expr = \ timeseries_limit_metric.sqla_col if metrics: main_metric_expr = metrics_exprs[0] else: main_metric_expr = literal_column("COUNT(*)").label("ccount") select_exprs = [] groupby_exprs = [] if groupby: select_exprs = [] inner_select_exprs = [] inner_groupby_exprs = [] for s in groupby: col = cols[s] outer = col.sqla_col inner = col.sqla_col.label(col.column_name + '__') groupby_exprs.append(outer) select_exprs.append(outer) inner_groupby_exprs.append(inner) inner_select_exprs.append(inner) elif columns: for s in columns: select_exprs.append(cols[s].sqla_col) metrics_exprs = [] if granularity: # TODO: sqlalchemy 1.2 release should be doing this on its own. # Patch only if the column clause is specific for DateTime set and # granularity is selected. @compiles(ColumnClause) def visit_column(element, compiler, **kw): text = compiler.visit_column(element, **kw) try: if element.is_literal and hasattr(element.type, 'python_type') and \ type(element.type) is DateTime: text = text.replace('%%', '%') except NotImplementedError: pass # Some elements raise NotImplementedError for python_type return text dttm_col = cols[granularity] dttm_expr = dttm_col.sqla_col.label('timestamp') timestamp = dttm_expr # Transforming time grain into an expression based on configuration time_grain_sqla = extras.get('time_grain_sqla') if time_grain_sqla: db_engine_spec = self.database.db_engine_spec if dttm_col.python_date_format == 'epoch_s': dttm_expr = \ db_engine_spec.epoch_to_dttm().format(col=dttm_expr) elif dttm_col.python_date_format == 'epoch_ms': dttm_expr = \ db_engine_spec.epoch_ms_to_dttm().format(col=dttm_expr) udf = self.database.grains_dict().get(time_grain_sqla, '{col}') timestamp_grain = literal_column( udf.function.format(col=dttm_expr), type_=DateTime).label('timestamp') else: timestamp_grain = timestamp if is_timeseries: select_exprs += [timestamp_grain] groupby_exprs += [timestamp_grain] outer_from = text(dttm_col.dttm_sql_literal(from_dttm)) outer_to = text(dttm_col.dttm_sql_literal(to_dttm)) time_filter = [ timestamp >= outer_from, timestamp <= outer_to, ] inner_time_filter = copy(time_filter) if inner_from_dttm: inner_time_filter[0] = timestamp >= text( dttm_col.dttm_sql_literal(inner_from_dttm)) if inner_to_dttm: inner_time_filter[1] = timestamp <= text( dttm_col.dttm_sql_literal(inner_to_dttm)) else: inner_time_filter = [] select_exprs += metrics_exprs qry = select(select_exprs) tbl = table(self.table_name) if self.schema: tbl.schema = self.schema # Supporting arbitrary SQL statements in place of tables if self.sql: tbl = TextAsFrom(sqla.text(self.sql), []).alias('expr_qry') if not columns: qry = qry.group_by(*groupby_exprs) where_clause_and = [] having_clause_and = [] for col, op, eq in filter: col_obj = cols[col] if op in ('in', 'not in'): splitted = FillterPattern.split(eq)[1::2] values = [types.replace("'", '').strip() for types in splitted] cond = col_obj.sqla_col.in_(values) if op == 'not in': cond = ~cond where_clause_and.append(cond) if extras: where = extras.get('where') if where: where_clause_and += [wrap_clause_in_parens( template_processor.process_template(where))] having = extras.get('having') if having: having_clause_and += [wrap_clause_in_parens( template_processor.process_template(having))] if granularity: qry = qry.where(and_(*(time_filter + where_clause_and))) else: qry = qry.where(and_(*where_clause_and)) qry = qry.having(and_(*having_clause_and)) if groupby: qry = qry.order_by(desc(main_metric_expr)) elif orderby: for col, ascending in orderby: direction = asc if ascending else desc qry = qry.order_by(direction(col)) qry = qry.limit(row_limit) if timeseries_limit and groupby: # some sql dialects require for order by expressions # to also be in the select clause inner_select_exprs += [main_metric_expr] subq = select(inner_select_exprs) subq = subq.select_from(tbl) subq = subq.where(and_(*(where_clause_and + inner_time_filter))) subq = subq.group_by(*inner_groupby_exprs) ob = main_metric_expr if timeseries_limit_metric_expr is not None: ob = timeseries_limit_metric_expr subq = subq.order_by(desc(ob)) subq = subq.limit(timeseries_limit) on_clause = [] for i, gb in enumerate(groupby): on_clause.append( groupby_exprs[i] == column(gb + '__')) tbl = tbl.join(subq.alias(), and_(*on_clause)) qry = qry.select_from(tbl) engine = self.database.get_sqla_engine() sql = "{}".format( qry.compile( engine, compile_kwargs={"literal_binds": True},), ) df = pd.read_sql_query( sql=sql, con=engine ) sql = sqlparse.format(sql, reindent=True) return QueryResult( df=df, duration=datetime.now() - qry_start_dttm, query=sql) def get_sqla_table_object(self): return self.database.get_table(self.table_name, schema=self.schema) def fetch_metadata(self): """Fetches the metadata for the table and merges it in""" try: table = self.get_sqla_table_object() except Exception: raise Exception( "Table doesn't seem to exist in the specified database, " "couldn't fetch column information") TC = TableColumn # noqa shortcut to class M = SqlMetric # noqa metrics = [] any_date_col = None for col in table.columns: try: datatype = "{}".format(col.type).upper() except Exception as e: datatype = "UNKNOWN" logging.error( "Unrecognized data type in {}.{}".format(table, col.name)) logging.exception(e) dbcol = ( db.session .query(TC) .filter(TC.table == self) .filter(TC.column_name == col.name) .first() ) db.session.flush() if not dbcol: dbcol = TableColumn(column_name=col.name, type=datatype) dbcol.groupby = dbcol.is_string dbcol.filterable = dbcol.is_string dbcol.sum = dbcol.isnum dbcol.avg = dbcol.isnum dbcol.is_dttm = dbcol.is_time db.session.merge(self) self.columns.append(dbcol) if not any_date_col and dbcol.is_time: any_date_col = col.name quoted = "{}".format( column(dbcol.column_name).compile(dialect=db.engine.dialect)) if dbcol.sum: metrics.append(M( metric_name='sum__' + dbcol.column_name, verbose_name='sum__' + dbcol.column_name, metric_type='sum', expression="SUM({})".format(quoted) )) if dbcol.avg: metrics.append(M( metric_name='avg__' + dbcol.column_name, verbose_name='avg__' + dbcol.column_name, metric_type='avg', expression="AVG({})".format(quoted) )) if dbcol.max: metrics.append(M( metric_name='max__' + dbcol.column_name, verbose_name='max__' + dbcol.column_name, metric_type='max', expression="MAX({})".format(quoted) )) if dbcol.min: metrics.append(M( metric_name='min__' + dbcol.column_name, verbose_name='min__' + dbcol.column_name, metric_type='min', expression="MIN({})".format(quoted) )) if dbcol.count_distinct: metrics.append(M( metric_name='count_distinct__' + dbcol.column_name, verbose_name='count_distinct__' + dbcol.column_name, metric_type='count_distinct', expression="COUNT(DISTINCT {})".format(quoted) )) dbcol.type = datatype db.session.merge(self) db.session.commit() metrics.append(M( metric_name='count', verbose_name='COUNT(*)', metric_type='count', expression="COUNT(*)" )) for metric in metrics: m = ( db.session.query(M) .filter(M.metric_name == metric.metric_name) .filter(M.table_id == self.id) .first() ) metric.table_id = self.id if not m: db.session.add(metric) db.session.commit() if not self.main_dttm_col: self.main_dttm_col = any_date_col @classmethod def import_obj(cls, datasource_to_import, import_time=None): """Imports the datasource from the object to the database. Metrics and columns and datasource will be overrided if exists. This function can be used to import/export dashboards between multiple caravel instances. Audit metadata isn't copies over. """ session = db.session make_transient(datasource_to_import) logging.info('Started import of the datasource: {}' .format(datasource_to_import.to_json())) datasource_to_import.id = None database_name = datasource_to_import.params_dict['database_name'] datasource_to_import.database_id = session.query(Database).filter_by( database_name=database_name).one().id datasource_to_import.alter_params(import_time=import_time) # override the datasource datasource = ( session.query(SqlaTable).join(Database) .filter( SqlaTable.table_name == datasource_to_import.table_name, SqlaTable.schema == datasource_to_import.schema, Database.id == datasource_to_import.database_id, ) .first() ) if datasource: datasource.override(datasource_to_import) session.flush() else: datasource = datasource_to_import.copy() session.add(datasource) session.flush() for m in datasource_to_import.metrics: new_m = m.copy() new_m.table_id = datasource.id logging.info('Importing metric {} from the datasource: {}'.format( new_m.to_json(), datasource_to_import.full_name)) imported_m = SqlMetric.import_obj(new_m) if imported_m not in datasource.metrics: datasource.metrics.append(imported_m) for c in datasource_to_import.columns: new_c = c.copy() new_c.table_id = datasource.id logging.info('Importing column {} from the datasource: {}'.format( new_c.to_json(), datasource_to_import.full_name)) imported_c = TableColumn.import_obj(new_c) if imported_c not in datasource.columns: datasource.columns.append(imported_c) db.session.flush() return datasource.id class SqlMetric(Model, AuditMixinNullable, ImportMixin): """ORM object for metrics, each table can have multiple metrics""" __tablename__ = 'sql_metrics' id = Column(Integer, primary_key=True) metric_name = Column(String(512)) verbose_name = Column(String(1024)) metric_type = Column(String(32)) table_id = Column(Integer, ForeignKey('tables.id')) table = relationship( 'SqlaTable', backref=backref('metrics', cascade='all, delete-orphan'), foreign_keys=[table_id]) expression = Column(Text) description = Column(Text) is_restricted = Column(Boolean, default=False, nullable=True) d3format = Column(String(128)) export_fields = ( 'metric_name', 'verbose_name', 'metric_type', 'table_id', 'expression', 'description', 'is_restricted', 'd3format') @property def sqla_col(self): name = self.metric_name return literal_column(self.expression).label(name) @property def perm(self): return ( "{parent_name}.[{obj.metric_name}](id:{obj.id})" ).format(obj=self, parent_name=self.table.full_name) if self.table else None @classmethod def import_obj(cls, metric_to_import): session = db.session make_transient(metric_to_import) metric_to_import.id = None # find if the column was already imported existing_metric = session.query(SqlMetric).filter( SqlMetric.table_id == metric_to_import.table_id, SqlMetric.metric_name == metric_to_import.metric_name).first() metric_to_import.table = None if existing_metric: existing_metric.override(metric_to_import) session.flush() return existing_metric session.add(metric_to_import) session.flush() return metric_to_import class TableColumn(Model, AuditMixinNullable, ImportMixin): """ORM object for table columns, each table can have multiple columns""" __tablename__ = 'table_columns' id = Column(Integer, primary_key=True) table_id = Column(Integer, ForeignKey('tables.id')) table = relationship( 'SqlaTable', backref=backref('columns', cascade='all, delete-orphan'), foreign_keys=[table_id]) column_name = Column(String(255)) verbose_name = Column(String(1024)) is_dttm = Column(Boolean, default=False) is_active = Column(Boolean, default=True) type = Column(String(32), default='') groupby = Column(Boolean, default=False) count_distinct = Column(Boolean, default=False) sum = Column(Boolean, default=False) avg = Column(Boolean, default=False) max = Column(Boolean, default=False) min = Column(Boolean, default=False) filterable = Column(Boolean, default=False) expression = Column(Text, default='') description = Column(Text, default='') python_date_format = Column(String(255)) database_expression = Column(String(255)) num_types = ('DOUBLE', 'FLOAT', 'INT', 'BIGINT', 'LONG') date_types = ('DATE', 'TIME') str_types = ('VARCHAR', 'STRING', 'CHAR') export_fields = ( 'table_id', 'column_name', 'verbose_name', 'is_dttm', 'is_active', 'type', 'groupby', 'count_distinct', 'sum', 'avg', 'max', 'min', 'filterable', 'expression', 'description', 'python_date_format', 'database_expression' ) def __repr__(self): return self.column_name @property def isnum(self): return any([t in self.type.upper() for t in self.num_types]) @property def is_time(self): return any([t in self.type.upper() for t in self.date_types]) @property def is_string(self): return any([t in self.type.upper() for t in self.str_types]) @property def sqla_col(self): name = self.column_name if not self.expression: col = column(self.column_name).label(name) else: col = literal_column(self.expression).label(name) return col @classmethod def import_obj(cls, column_to_import): session = db.session make_transient(column_to_import) column_to_import.id = None column_to_import.table = None # find if the column was already imported existing_column = session.query(TableColumn).filter( TableColumn.table_id == column_to_import.table_id, TableColumn.column_name == column_to_import.column_name).first() column_to_import.table = None if existing_column: existing_column.override(column_to_import) session.flush() return existing_column session.add(column_to_import) session.flush() return column_to_import def dttm_sql_literal(self, dttm): """Convert datetime object to a SQL expression string If database_expression is empty, the internal dttm will be parsed as the string with the pattern that the user inputted (python_date_format) If database_expression is not empty, the internal dttm will be parsed as the sql sentence for the database to convert """ tf = self.python_date_format or '%Y-%m-%d %H:%M:%S.%f' if self.database_expression: return self.database_expression.format(dttm.strftime('%Y-%m-%d %H:%M:%S')) elif tf == 'epoch_s': return str((dttm - datetime(1970, 1, 1)).total_seconds()) elif tf == 'epoch_ms': return str((dttm - datetime(1970, 1, 1)).total_seconds() * 1000.0) else: s = self.table.database.db_engine_spec.convert_dttm( self.type, dttm) return s or "'{}'".format(dttm.strftime(tf)) class DruidCluster(Model, AuditMixinNullable): """ORM object referencing the Druid clusters""" __tablename__ = 'clusters' id = Column(Integer, primary_key=True) cluster_name = Column(String(250), unique=True) coordinator_host = Column(String(255)) coordinator_port = Column(Integer) coordinator_endpoint = Column( String(255), default='druid/coordinator/v1/metadata') broker_host = Column(String(255)) broker_port = Column(Integer) broker_endpoint = Column(String(255), default='druid/v2') metadata_last_refreshed = Column(DateTime) cache_timeout = Column(Integer) def __repr__(self): return self.cluster_name def get_pydruid_client(self): cli = PyDruid( "http://{0}:{1}/".format(self.broker_host, self.broker_port), self.broker_endpoint) return cli def get_datasources(self): endpoint = ( "http://{obj.coordinator_host}:{obj.coordinator_port}/" "{obj.coordinator_endpoint}/datasources" ).format(obj=self) return json.loads(requests.get(endpoint).text) def get_druid_version(self): endpoint = ( "http://{obj.coordinator_host}:{obj.coordinator_port}/status" ).format(obj=self) return json.loads(requests.get(endpoint).text)['version'] def refresh_datasources(self): self.druid_version = self.get_druid_version() for datasource in self.get_datasources(): if datasource not in config.get('DRUID_DATA_SOURCE_BLACKLIST'): DruidDatasource.sync_to_db(datasource, self) @property def perm(self): return "[{obj.cluster_name}].(id:{obj.id})".format(obj=self) @property def name(self): return self.cluster_name class DruidDatasource(Model, AuditMixinNullable, Queryable): """ORM object referencing Druid datasources (tables)""" type = "druid" baselink = "druiddatasourcemodelview" __tablename__ = 'datasources' id = Column(Integer, primary_key=True) datasource_name = Column(String(255), unique=True) is_featured = Column(Boolean, default=False) is_hidden = Column(Boolean, default=False) description = Column(Text) default_endpoint = Column(Text) user_id = Column(Integer, ForeignKey('ab_user.id')) owner = relationship( 'User', backref=backref('datasources', cascade='all, delete-orphan'), foreign_keys=[user_id]) cluster_name = Column( String(250), ForeignKey('clusters.cluster_name')) cluster = relationship( 'DruidCluster', backref='datasources', foreign_keys=[cluster_name]) offset = Column(Integer, default=0) cache_timeout = Column(Integer) @property def database(self): return self.cluster @property def metrics_combo(self): return sorted( [(m.metric_name, m.verbose_name) for m in self.metrics], key=lambda x: x[1]) @property def database(self): return self.cluster @property def num_cols(self): return [c.column_name for c in self.columns if c.isnum] @property def name(self): return self.datasource_name @property def perm(self): return ( "[{obj.cluster_name}].[{obj.datasource_name}]" "(id:{obj.id})").format(obj=self) @property def link(self): name = escape(self.datasource_name) return Markup('<a href="{self.url}">{name}</a>').format(**locals()) @property def full_name(self): return utils.get_datasource_full_name( self.cluster_name, self.datasource_name) @property def time_column_grains(self): return { "time_columns": [ 'all', '5 seconds', '30 seconds', '1 minute', '5 minutes', '1 hour', '6 hour', '1 day', '7 days', 'week', 'week_starting_sunday', 'week_ending_saturday', 'month', ], "time_grains": ['now'] } def __repr__(self): return self.datasource_name @renders('datasource_name') def datasource_link(self): url = "/caravel/explore/{obj.type}/{obj.id}/".format(obj=self) name = escape(self.datasource_name) return Markup('<a href="{url}">{name}</a>'.format(**locals())) def get_metric_obj(self, metric_name): return [ m.json_obj for m in self.metrics if m.metric_name == metric_name ][0] @staticmethod def version_higher(v1, v2): """is v1 higher than v2 >>> DruidDatasource.version_higher('0.8.2', '0.9.1') False >>> DruidDatasource.version_higher('0.8.2', '0.6.1') True >>> DruidDatasource.version_higher('0.8.2', '0.8.2') False >>> DruidDatasource.version_higher('0.8.2', '0.9.BETA') False >>> DruidDatasource.version_higher('0.8.2', '0.9') False """ def int_or_0(v): try: v = int(v) except (TypeError, ValueError): v = 0 return v v1nums = [int_or_0(n) for n in v1.split('.')] v2nums = [int_or_0(n) for n in v2.split('.')] v1nums = (v1nums + [0, 0, 0])[:3] v2nums = (v2nums + [0, 0, 0])[:3] return v1nums[0] > v2nums[0] or \ (v1nums[0] == v2nums[0] and v1nums[1] > v2nums[1]) or \ (v1nums[0] == v2nums[0] and v1nums[1] == v2nums[1] and v1nums[2] > v2nums[2]) def latest_metadata(self): """Returns segment metadata from the latest segment""" client = self.cluster.get_pydruid_client() results = client.time_boundary(datasource=self.datasource_name) if not results: return max_time = results[0]['result']['maxTime'] max_time = parse(max_time) # Query segmentMetadata for 7 days back. However, due to a bug, # we need to set this interval to more than 1 day ago to exclude # realtime segments, which trigged a bug (fixed in druid 0.8.2). # https://groups.google.com/forum/#!topic/druid-user/gVCqqspHqOQ start = (0 if self.version_higher(self.cluster.druid_version, '0.8.2') else 1) intervals = (max_time - timedelta(days=7)).isoformat() + '/' intervals += (max_time - timedelta(days=start)).isoformat() segment_metadata = client.segment_metadata( datasource=self.datasource_name, intervals=intervals) if segment_metadata: return segment_metadata[-1]['columns'] def generate_metrics(self): for col in self.columns: col.generate_metrics() @classmethod def sync_to_db_from_config(cls, druid_config, user, cluster): """Merges the ds config from druid_config into one stored in the db.""" session = db.session() datasource = ( session.query(DruidDatasource) .filter_by( datasource_name=druid_config['name']) ).first() # Create a new datasource. if not datasource: datasource = DruidDatasource( datasource_name=druid_config['name'], cluster=cluster, owner=user, changed_by_fk=user.id, created_by_fk=user.id, ) session.add(datasource) dimensions = druid_config['dimensions'] for dim in dimensions: col_obj = ( session.query(DruidColumn) .filter_by( datasource_name=druid_config['name'], column_name=dim) ).first() if not col_obj: col_obj = DruidColumn( datasource_name=druid_config['name'], column_name=dim, groupby=True, filterable=True, # TODO: fetch type from Hive. type="STRING", datasource=datasource ) session.add(col_obj) # Import Druid metrics for metric_spec in druid_config["metrics_spec"]: metric_name = metric_spec["name"] metric_type = metric_spec["type"] metric_json = json.dumps(metric_spec) if metric_type == "count": metric_type = "longSum" metric_json = json.dumps({ "type": "longSum", "name": metric_name, "fieldName": metric_name, }) metric_obj = ( session.query(DruidMetric) .filter_by( datasource_name=druid_config['name'], metric_name=metric_name) ).first() if not metric_obj: metric_obj = DruidMetric( metric_name=metric_name, metric_type=metric_type, verbose_name="%s(%s)" % (metric_type, metric_name), datasource=datasource, json=metric_json, description=( "Imported from the airolap config dir for %s" % druid_config['name']), ) session.add(metric_obj) session.commit() @classmethod def sync_to_db(cls, name, cluster): """Fetches metadata for that datasource and merges the Caravel db""" logging.info("Syncing Druid datasource [{}]".format(name)) session = get_session() datasource = session.query(cls).filter_by(datasource_name=name).first() if not datasource: datasource = cls(datasource_name=name) session.add(datasource) flasher("Adding new datasource [{}]".format(name), "success") else: flasher("Refreshing datasource [{}]".format(name), "info") session.flush() datasource.cluster = cluster session.flush() cols = datasource.latest_metadata() if not cols: return for col in cols: col_obj = ( session .query(DruidColumn) .filter_by(datasource_name=name, column_name=col) .first() ) datatype = cols[col]['type'] if not col_obj: col_obj = DruidColumn(datasource_name=name, column_name=col) session.add(col_obj) if datatype == "STRING": col_obj.groupby = True col_obj.filterable = True if datatype == "hyperUnique" or datatype == "thetaSketch": col_obj.count_distinct = True if col_obj: col_obj.type = cols[col]['type'] session.flush() col_obj.datasource = datasource col_obj.generate_metrics() session.flush() @staticmethod def time_offset(granularity): if granularity == 'week_ending_saturday': return 6 * 24 * 3600 * 1000 # 6 days return 0 # uses https://en.wikipedia.org/wiki/ISO_8601 # http://druid.io/docs/0.8.0/querying/granularities.html # TODO: pass origin from the UI @staticmethod def granularity(period_name, timezone=None): if not period_name or period_name == 'all': return 'all' iso_8601_dict = { '5 seconds': 'PT5S', '30 seconds': 'PT30S', '1 minute': 'PT1M', '5 minutes': 'PT5M', '1 hour': 'PT1H', '6 hour': 'PT6H', 'one day': 'P1D', '1 day': 'P1D', '7 days': 'P7D', 'week': 'P1W', 'week_starting_sunday': 'P1W', 'week_ending_saturday': 'P1W', 'month': 'P1M', } granularity = {'type': 'period'} if timezone: granularity['timezone'] = timezone if period_name in iso_8601_dict: granularity['period'] = iso_8601_dict[period_name] if period_name in ('week_ending_saturday', 'week_starting_sunday'): # use Sunday as start of the week granularity['origin'] = '2016-01-03T00:00:00' elif not isinstance(period_name, string_types): granularity['type'] = 'duration' granularity['duration'] = period_name elif period_name.startswith('P'): # identify if the string is the iso_8601 period granularity['period'] = period_name else: granularity['type'] = 'duration' granularity['duration'] = utils.parse_human_timedelta( period_name).total_seconds() * 1000 return granularity def query( # druid self, groupby, metrics, granularity, from_dttm, to_dttm, filter=None, # noqa is_timeseries=True, timeseries_limit=None, timeseries_limit_metric=None, row_limit=None, inner_from_dttm=None, inner_to_dttm=None, orderby=None, extras=None, # noqa select=None, # noqa columns=None, ): """Runs a query against Druid and returns a dataframe. This query interface is common to SqlAlchemy and Druid """ # TODO refactor into using a TBD Query object qry_start_dttm = datetime.now() inner_from_dttm = inner_from_dttm or from_dttm inner_to_dttm = inner_to_dttm or to_dttm # add tzinfo to native datetime with config from_dttm = from_dttm.replace(tzinfo=config.get("DRUID_TZ")) to_dttm = to_dttm.replace(tzinfo=config.get("DRUID_TZ")) timezone = from_dttm.tzname() query_str = "" metrics_dict = {m.metric_name: m for m in self.metrics} all_metrics = [] post_aggs = {} def recursive_get_fields(_conf): _fields = _conf.get('fields', []) field_names = [] for _f in _fields: _type = _f.get('type') if _type in ['fieldAccess', 'hyperUniqueCardinality']: field_names.append(_f.get('fieldName')) elif _type == 'arithmetic': field_names += recursive_get_fields(_f) return list(set(field_names)) for metric_name in metrics: metric = metrics_dict[metric_name] if metric.metric_type != 'postagg': all_metrics.append(metric_name) else: conf = metric.json_obj all_metrics += recursive_get_fields(conf) all_metrics += conf.get('fieldNames', []) if conf.get('type') == 'javascript': post_aggs[metric_name] = JavascriptPostAggregator( name=conf.get('name'), field_names=conf.get('fieldNames'), function=conf.get('function')) else: post_aggs[metric_name] = Postaggregator( conf.get('fn', "/"), conf.get('fields', []), conf.get('name', '')) aggregations = OrderedDict() for m in self.metrics: if m.metric_name in all_metrics: aggregations[m.metric_name] = m.json_obj rejected_metrics = [ m.metric_name for m in self.metrics if m.is_restricted and m.metric_name in aggregations.keys() and not sm.has_access('metric_access', m.perm) ] if rejected_metrics: raise MetricPermException( "Access to the metrics denied: " + ', '.join(rejected_metrics) ) qry = dict( datasource=self.datasource_name, dimensions=groupby, aggregations=aggregations, granularity=DruidDatasource.granularity( granularity, timezone=timezone), post_aggregations=post_aggs, intervals=from_dttm.isoformat() + '/' + to_dttm.isoformat(), ) filters = self.get_filters(filter) if filters: qry['filter'] = filters having_filters = self.get_having_filters(extras.get('having_druid')) if having_filters: qry['having'] = having_filters client = self.cluster.get_pydruid_client() orig_filters = filters if len(groupby) == 0: del qry['dimensions'] client.timeseries(**qry) if len(groupby) == 1: if not timeseries_limit: timeseries_limit = 10000 qry['threshold'] = timeseries_limit qry['dimension'] = qry.get('dimensions')[0] del qry['dimensions'] qry['metric'] = list(qry['aggregations'].keys())[0] client.topn(**qry) elif len(groupby) > 1: if timeseries_limit and is_timeseries: order_by = metrics[0] if metrics else self.metrics[0] if timeseries_limit_metric: order_by = timeseries_limit_metric # Limit on the number of timeseries, doing a two-phases query pre_qry = deepcopy(qry) pre_qry['granularity'] = "all" pre_qry['limit_spec'] = { "type": "default", "limit": timeseries_limit, 'intervals': ( inner_from_dttm.isoformat() + '/' + inner_to_dttm.isoformat()), "columns": [{ "dimension": order_by, "direction": "descending", }], } client.groupby(**pre_qry) query_str += "// Two phase query\n// Phase 1\n" query_str += json.dumps( client.query_builder.last_query.query_dict, indent=2) query_str += "\n" query_str += ( "//\nPhase 2 (built based on phase one's results)\n") df = client.export_pandas() if df is not None and not df.empty: dims = qry['dimensions'] filters = [] for unused, row in df.iterrows(): fields = [] for dim in dims: f = Dimension(dim) == row[dim] fields.append(f) if len(fields) > 1: filt = Filter(type="and", fields=fields) filters.append(filt) elif fields: filters.append(fields[0]) if filters: ff = Filter(type="or", fields=filters) if not orig_filters: qry['filter'] = ff else: qry['filter'] = Filter(type="and", fields=[ ff, orig_filters]) qry['limit_spec'] = None if row_limit: qry['limit_spec'] = { "type": "default", "limit": row_limit, "columns": [{ "dimension": ( metrics[0] if metrics else self.metrics[0]), "direction": "descending", }], } client.groupby(**qry) query_str += json.dumps( client.query_builder.last_query.query_dict, indent=2) df = client.export_pandas() if df is None or df.size == 0: raise Exception(_("No data was returned.")) if ( not is_timeseries and granularity == "all" and 'timestamp' in df.columns): del df['timestamp'] # Reordering columns cols = [] if 'timestamp' in df.columns: cols += ['timestamp'] cols += [col for col in groupby if col in df.columns] cols += [col for col in metrics if col in df.columns] df = df[cols] time_offset = DruidDatasource.time_offset(granularity) def increment_timestamp(ts): dt = utils.parse_human_datetime(ts).replace( tzinfo=config.get("DRUID_TZ")) return dt + timedelta(milliseconds=time_offset) if 'timestamp' in df.columns and time_offset: df.timestamp = df.timestamp.apply(increment_timestamp) return QueryResult( df=df, query=query_str, duration=datetime.now() - qry_start_dttm) @staticmethod def get_filters(raw_filters): filters = None for col, op, eq in raw_filters: cond = None if op == '==': cond = Dimension(col) == eq elif op == '!=': cond = ~(Dimension(col) == eq) elif op in ('in', 'not in'): fields = [] # Distinguish quoted values with regular value types splitted = FillterPattern.split(eq)[1::2] values = [types.replace("'", '') for types in splitted] if len(values) > 1: for s in values: s = s.strip() fields.append(Dimension(col) == s) cond = Filter(type="or", fields=fields) else: cond = Dimension(col) == eq if op == 'not in': cond = ~cond elif op == 'regex': cond = Filter(type="regex", pattern=eq, dimension=col) if filters: filters = Filter(type="and", fields=[ cond, filters ]) else: filters = cond return filters def _get_having_obj(self, col, op, eq): cond = None if op == '==': if col in self.column_names: cond = DimSelector(dimension=col, value=eq) else: cond = Aggregation(col) == eq elif op == '>': cond = Aggregation(col) > eq elif op == '<': cond = Aggregation(col) < eq return cond def get_having_filters(self, raw_filters): filters = None reversed_op_map = { '!=': '==', '>=': '<', '<=': '>' } for col, op, eq in raw_filters: cond = None if op in ['==', '>', '<']: cond = self._get_having_obj(col, op, eq) elif op in reversed_op_map: cond = ~self._get_having_obj(col, reversed_op_map[op], eq) if filters: filters = filters & cond else: filters = cond return filters class Log(Model): """ORM object used to log Caravel actions to the database""" __tablename__ = 'logs' id = Column(Integer, primary_key=True) action = Column(String(512)) user_id = Column(Integer, ForeignKey('ab_user.id')) dashboard_id = Column(Integer) slice_id = Column(Integer) json = Column(Text) user = relationship('User', backref='logs', foreign_keys=[user_id]) dttm = Column(DateTime, default=func.now()) dt = Column(Date, default=date.today()) @classmethod def log_this(cls, f): """Decorator to log user actions""" @functools.wraps(f) def wrapper(*args, **kwargs): user_id = None if g.user: user_id = g.user.get_id() d = request.args.to_dict() d.update(kwargs) slice_id = d.get('slice_id', 0) try: slice_id = int(slice_id) if slice_id else 0 except ValueError: slice_id = 0 params = "" try: params = json.dumps(d) except: pass log = cls( action=f.__name__, json=params, dashboard_id=d.get('dashboard_id') or None, slice_id=slice_id, user_id=user_id) db.session.add(log) db.session.commit() return f(*args, **kwargs) return wrapper class DruidMetric(Model, AuditMixinNullable): """ORM object referencing Druid metrics for a datasource""" __tablename__ = 'metrics' id = Column(Integer, primary_key=True) metric_name = Column(String(512)) verbose_name = Column(String(1024)) metric_type = Column(String(32)) datasource_name = Column( String(255), ForeignKey('datasources.datasource_name')) # Setting enable_typechecks=False disables polymorphic inheritance. datasource = relationship( 'DruidDatasource', backref=backref('metrics', cascade='all, delete-orphan'), enable_typechecks=False) json = Column(Text) description = Column(Text) is_restricted = Column(Boolean, default=False, nullable=True) d3format = Column(String(128)) @property def json_obj(self): try: obj = json.loads(self.json) except Exception: obj = {} return obj @property def perm(self): return ( "{parent_name}.[{obj.metric_name}](id:{obj.id})" ).format(obj=self, parent_name=self.datasource.full_name ) if self.datasource else None class DruidColumn(Model, AuditMixinNullable): """ORM model for storing Druid datasource column metadata""" __tablename__ = 'columns' id = Column(Integer, primary_key=True) datasource_name = Column( String(255), ForeignKey('datasources.datasource_name')) # Setting enable_typechecks=False disables polymorphic inheritance. datasource = relationship( 'DruidDatasource', backref=backref('columns', cascade='all, delete-orphan'), enable_typechecks=False) column_name = Column(String(255)) is_active = Column(Boolean, default=True) type = Column(String(32)) groupby = Column(Boolean, default=False) count_distinct = Column(Boolean, default=False) sum = Column(Boolean, default=False) avg = Column(Boolean, default=False) max = Column(Boolean, default=False) min = Column(Boolean, default=False) filterable = Column(Boolean, default=False) description = Column(Text) def __repr__(self): return self.column_name @property def isnum(self): return self.type in ('LONG', 'DOUBLE', 'FLOAT', 'INT') def generate_metrics(self): """Generate metrics based on the column metadata""" M = DruidMetric # noqa metrics = [] metrics.append(DruidMetric( metric_name='count', verbose_name='COUNT(*)', metric_type='count', json=json.dumps({'type': 'count', 'name': 'count'}) )) # Somehow we need to reassign this for UDAFs if self.type in ('DOUBLE', 'FLOAT'): corrected_type = 'DOUBLE' else: corrected_type = self.type if self.sum and self.isnum: mt = corrected_type.lower() + 'Sum' name = 'sum__' + self.column_name metrics.append(DruidMetric( metric_name=name, metric_type='sum', verbose_name='SUM({})'.format(self.column_name), json=json.dumps({ 'type': mt, 'name': name, 'fieldName': self.column_name}) )) if self.avg and self.isnum: mt = corrected_type.lower() + 'Avg' name = 'avg__' + self.column_name metrics.append(DruidMetric( metric_name=name, metric_type='avg', verbose_name='AVG({})'.format(self.column_name), json=json.dumps({ 'type': mt, 'name': name, 'fieldName': self.column_name}) )) if self.min and self.isnum: mt = corrected_type.lower() + 'Min' name = 'min__' + self.column_name metrics.append(DruidMetric( metric_name=name, metric_type='min', verbose_name='MIN({})'.format(self.column_name), json=json.dumps({ 'type': mt, 'name': name, 'fieldName': self.column_name}) )) if self.max and self.isnum: mt = corrected_type.lower() + 'Max' name = 'max__' + self.column_name metrics.append(DruidMetric( metric_name=name, metric_type='max', verbose_name='MAX({})'.format(self.column_name), json=json.dumps({ 'type': mt, 'name': name, 'fieldName': self.column_name}) )) if self.count_distinct: name = 'count_distinct__' + self.column_name if self.type == 'hyperUnique' or self.type == 'thetaSketch': metrics.append(DruidMetric( metric_name=name, verbose_name='COUNT(DISTINCT {})'.format(self.column_name), metric_type=self.type, json=json.dumps({ 'type': self.type, 'name': name, 'fieldName': self.column_name }) )) else: mt = 'count_distinct' metrics.append(DruidMetric( metric_name=name, verbose_name='COUNT(DISTINCT {})'.format(self.column_name), metric_type='count_distinct', json=json.dumps({ 'type': 'cardinality', 'name': name, 'fieldNames': [self.column_name]}) )) session = get_session() new_metrics = [] for metric in metrics: m = ( session.query(M) .filter(M.metric_name == metric.metric_name) .filter(M.datasource_name == self.datasource_name) .filter(DruidCluster.cluster_name == self.datasource.cluster_name) .first() ) metric.datasource_name = self.datasource_name if not m: new_metrics.append(metric) session.add(metric) session.flush() utils.init_metrics_perm(caravel, new_metrics) class FavStar(Model): __tablename__ = 'favstar' id = Column(Integer, primary_key=True) user_id = Column(Integer, ForeignKey('ab_user.id')) class_name = Column(String(50)) obj_id = Column(Integer) dttm = Column(DateTime, default=func.now()) class QueryStatus: CANCELLED = 'cancelled' FAILED = 'failed' PENDING = 'pending' RUNNING = 'running' SCHEDULED = 'scheduled' SUCCESS = 'success' TIMED_OUT = 'timed_out' class Query(Model): """ORM model for SQL query""" __tablename__ = 'query' id = Column(Integer, primary_key=True) client_id = Column(String(11), unique=True, nullable=False) database_id = Column(Integer, ForeignKey('dbs.id'), nullable=False) # Store the tmp table into the DB only if the user asks for it. tmp_table_name = Column(String(256)) user_id = Column( Integer, ForeignKey('ab_user.id'), nullable=True) status = Column(String(16), default=QueryStatus.PENDING) tab_name = Column(String(256)) sql_editor_id = Column(String(256)) schema = Column(String(256)) sql = Column(Text) # Query to retrieve the results, # used only in case of select_as_cta_used is true. select_sql = Column(Text) executed_sql = Column(Text) # Could be configured in the caravel config. limit = Column(Integer) limit_used = Column(Boolean, default=False) limit_reached = Column(Boolean, default=False) select_as_cta = Column(Boolean) select_as_cta_used = Column(Boolean, default=False) progress = Column(Integer, default=0) # 1..100 # # of rows in the result set or rows modified. rows = Column(Integer) error_message = Column(Text) # key used to store the results in the results backend results_key = Column(String(64)) # Using Numeric in place of DateTime for sub-second precision # stored as seconds since epoch, allowing for milliseconds start_time = Column(Numeric(precision=3)) end_time = Column(Numeric(precision=3)) changed_on = Column( DateTime, default=datetime.utcnow, onupdate=datetime.utcnow, nullable=True) database = relationship( 'Database', foreign_keys=[database_id], backref='queries') user = relationship( 'User', backref=backref('queries', cascade='all, delete-orphan'), foreign_keys=[user_id]) __table_args__ = ( sqla.Index('ti_user_id_changed_on', user_id, changed_on), ) @property def limit_reached(self): return self.rows == self.limit if self.limit_used else False def to_dict(self): return { 'changedOn': self.changed_on, 'changed_on': self.changed_on.isoformat(), 'dbId': self.database_id, 'db': self.database.database_name, 'endDttm': self.end_time, 'errorMessage': self.error_message, 'executedSql': self.executed_sql, 'id': self.client_id, 'limit': self.limit, 'progress': self.progress, 'rows': self.rows, 'schema': self.schema, 'ctas': self.select_as_cta, 'serverId': self.id, 'sql': self.sql, 'sqlEditorId': self.sql_editor_id, 'startDttm': self.start_time, 'state': self.status.lower(), 'tab': self.tab_name, 'tempTable': self.tmp_table_name, 'userId': self.user_id, 'user': self.user.username, 'limit_reached': self.limit_reached, 'resultsKey': self.results_key, } @property def name(self): ts = datetime.now().isoformat() ts = ts.replace('-', '').replace(':', '').split('.')[0] tab = self.tab_name.replace(' ', '_').lower() if self.tab_name else 'notab' tab = re.sub(r'\W+', '', tab) return "sqllab_{tab}_{ts}".format(**locals()) class DatasourceAccessRequest(Model, AuditMixinNullable): """ORM model for the access requests for datasources and dbs.""" __tablename__ = 'access_request' id = Column(Integer, primary_key=True) datasource_id = Column(Integer) datasource_type = Column(String(200)) ROLES_BLACKLIST = set(['Admin', 'Alpha', 'Gamma', 'Public']) @property def cls_model(self): return SourceRegistry.sources[self.datasource_type] @property def username(self): return self.creator() @property def datasource(self): return self.get_datasource @datasource.getter @utils.memoized def get_datasource(self): ds = db.session.query(self.cls_model).filter_by( id=self.datasource_id).first() return ds @property def datasource_link(self): return self.datasource.link @property def roles_with_datasource(self): action_list = '' pv = sm.find_permission_view_menu( 'datasource_access', self.datasource.perm) for r in pv.role: if r.name in self.ROLES_BLACKLIST: continue url = ( '/caravel/approve?datasource_type={self.datasource_type}&' 'datasource_id={self.datasource_id}&' 'created_by={self.created_by.username}&role_to_grant={r.name}' .format(**locals()) ) href = '<a href="{}">Grant {} Role</a>'.format(url, r.name) action_list = action_list + '<li>' + href + '</li>' return '<ul>' + action_list + '</ul>' @property def user_roles(self): action_list = '' for r in self.created_by.roles: url = ( '/caravel/approve?datasource_type={self.datasource_type}&' 'datasource_id={self.datasource_id}&' 'created_by={self.created_by.username}&role_to_extend={r.name}' .format(**locals()) ) href = '<a href="{}">Extend {} Role</a>'.format(url, r.name) if r.name in self.ROLES_BLACKLIST: href = "{} Role".format(r.name) action_list = action_list + '<li>' + href + '</li>' return '<ul>' + action_list + '</ul>' ```
{ "source": "jerome-colin/mtools", "score": 2 }
#### File: mtools/common/Roi.py ```python __author__ = "jerome.colin'at'ces<EMAIL>" __license__ = "MIT" __version__ = "1.0.3" import numpy as np from scipy import stats import sys class Roi_collection: """ A collection of ROIs defined according to the coordinate file given to roistats """ def __init__(self, fname, extent, logger, delimiter=','): """ :param fname: the coordinate file :param extent: in mters :param logger: :param delimiter: """ self.fname = fname self.extent = extent self.logger = logger self.delimiter = delimiter self.logger.info("Checking coordinates consistency...") try: self.coord_arr = np.loadtxt(self.fname, delimiter=self.delimiter) self.logger.info("Found %i coordinates pairs" % (len(self.coord_arr))) if len(self.coord_arr) == 0: self.logger.error("Coordinates file empty ?") sys.exit(2) for c in range(len(self.coord_arr)): self.logger.debug(self.coord_arr[c]) except ValueError as err: self.logger.error(err) self.logger.error("Wrong value in coordinates file (or un-managed header line)") sys.exit(2) except FileNotFoundError as err: self.logger.error(err) sys.exit(1) # Extent value consistency check self.logger.info("Checking extent value consistency...") if self.extent <= 0: self.logger.error("Wrong extent given : %i" % self.extent) sys.exit(2) def compute_stats_all_bands(self, product, logger, stdout=False, withAOT=False, withVAP=False): """ Print statistiques for all bands of a product for all ROIs in collectoin :param product: a Product instance :param logger: :param quicklook: not yet implemented :return: """ # Get the list of bands to compute stats for bands = product.band_names if withAOT: bands.append("AOT.") if withVAP: bands.append("VAP.") list_stats = [] # For each Roi in Roi_collection: for i in range(len(self.coord_arr)): # Get an ROI object roi_n = Roi(self.coord_arr[i], self.extent, logger) # Get the corresponding mask clm = product.get_band_subset(product.find_band(product.clm_name), roi=roi_n) edg = product.get_band_subset(product.find_band(product.edg_name), roi=roi_n) mask = product.get_mask(clm, edg) # For each SRE band in product, extract a subset according to ROI and return stats for band in bands: # samples, minmax, avg, variance, skewness, kurtosis band_size = mask.size stats = self.compute_stats_oneband(roi_n, product, band, mask=mask) list_stats.append(stats) if stdout: if stats is not None: print("%s, %s, %s, %i, %i, %6.1f%%, %10.8f, %10.8f, %10.8f, %10.8f" % (product.name, roi_n.id, band[:-1], band_size, stats[0], stats[0]/band_size*100, stats[1][0], stats[1][1], stats[2], stats[3])) else: print("%s, %s, %s, no valid pixel in ROI (fully cloudy or out of edge)" % (product.name, roi_n.id, band[:-1])) return list_stats def compute_stats_oneband(self, roi, product, band, mask=None): """ :param roi: Roi object :param product: Product object :param band: a string that helps identify a file :return: """ if band == "AOT.": subset = product.get_band_subset(product.find_band(product.aot_name), roi=roi, scalef=product.aot_scalef, layer=product.aot_layer) elif band == "VAP.": subset = product.get_band_subset(product.find_band(product.vap_name), roi=roi, scalef=product.vap_scalef, layer=product.vap_layer) else: subset = product.get_band_subset(product.find_band(band), roi=roi, scalef=product.sre_scalef) if mask is not None: search = np.where(mask == 1) valid_pixels = subset[search] else: valid_pixels = subset try: return stats.describe(valid_pixels, axis=None) except ValueError: return None class Roi: def __init__(self, id_utmx_utmy, extent, logger): """ Returns an ROI instance :param id_utmx_utmy: a vector containing an id(int), utmx(float) and utmy(float). :param extent: in meters :param logger: """ self.id = str(int(id_utmx_utmy[0])) self.utmx = id_utmx_utmy[1] self.utmy = id_utmx_utmy[2] self.extent = extent # Compute ulx, uly, lrx, lry assuming UTM coordinates self.ulx = self.utmx - self.extent / 2 self.uly = self.utmy + self.extent / 2 self.lrx = self.utmx + self.extent / 2 self.lry = self.utmy - self.extent / 2 logger.info('ROI id %s: ulx=%i, uly=%i, lrx=%i, lry=%i' % (self.id, self.ulx, self.uly, self.lrx, self.lry)) ``` #### File: mtools/common/test_Product.py ```python __author__ = "jerome.colin'at'<EMAIL>" __license__ = "MIT" __version__ = "1.0.3" import os import pytest import Product import Roi import utilities import numpy import osgeo.gdal from matplotlib import pylab as pl TEST_DATA_PATH = os.environ['TEST_DATA_PATH'] logger = utilities.get_logger('test_Product', verbose=True) # TEST REFACTORED PRODUCT FROM HERE ## TESTING PRODUCT_DIR (DEFAULT) def test_product_dir(): logger.info("TESTING PRODUCT_DIR (DEFAULT)") p_dir = Product.Product(TEST_DATA_PATH + "acix_carpentras/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0", logger) assert type(p_dir.content_list) is list p_dir_filename = p_dir.find_band("SRE_B4.") assert p_dir_filename == TEST_DATA_PATH + "acix_carpentras/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0_SRE_B4.tif" p_dir_band = p_dir.get_band(p_dir.find_band("SRE_B4.")) assert type(p_dir_band) is numpy.ndarray assert p_dir_band[0,0] == 850 assert p_dir_band[6, 2] == 1249 b4_subset = p_dir.get_band_subset(p_dir.find_band("SRE_B4."), ulx=649455, uly=4238445, lrx=649465, lry=4238435) assert type(b4_subset) is numpy.ndarray def test_product_mask_use_nan(): logger.info("TESTING PRODUCT GET_MASK WITH NAN") p_dir = Product.Product(TEST_DATA_PATH + "acix_carpentras/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0", logger) clm = numpy.zeros((3,3)) + 33 edg = numpy.zeros_like(clm) + 1 clm[1,:] = numpy.nan clm[2, :] = numpy.nan assert utilities.count_nan(clm) == 6 edg[:,1] = numpy.nan assert utilities.count_nan(edg) == 3 mask, ratio = p_dir.get_mask(clm,edg, stats=True, use_nodata=True) assert numpy.sum(mask) == 2 assert ratio == pytest.approx(2/9*100) assert mask[1,1] == 1 assert mask[2,1] == 1 logger.debug("test_product_mask ratio=%6.4f" % ratio) def test_product_mask_use_zeros(): logger.info("TESTING PRODUCT GET_MASK WITH ZEROS") p_dir = Product.Product(TEST_DATA_PATH + "acix_carpentras/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0", logger) clm = numpy.zeros((3,3)) + 33 edg = numpy.zeros_like(clm) + 1 clm[1,:] = 0 clm[2, :] = 0 assert numpy.count_nonzero(clm) == 3 edg[:,1] = 0 assert numpy.count_nonzero(edg) == 6 mask, ratio = p_dir.get_mask(clm,edg, stats=True, use_nodata=False) assert numpy.sum(mask) == 2 assert ratio == pytest.approx(2/9*100) assert mask[1,1] == 1 assert mask[2,1] == 1 logger.debug("test_product_mask ratio=%6.4f" % ratio) ## TEST PRODUCT_ZIP_VENUS def test_product_zip_venus(): logger.info("TEST PRODUCT_ZIP_VENUS") p_zip_venus = Product.Product_zip_venus(TEST_DATA_PATH + "VENUS-XS_20200402-191352-000_L2A_GALLOP30_D.zip", logger) assert p_zip_venus.name == "VENUS-XS_20200402-191352-000_L2A_GALLOP30_D.zip" assert p_zip_venus.band_names[0] == "SRE_B1." assert p_zip_venus.clm_name == "CLM_XS" assert p_zip_venus.edg_name == "EDG_XS" logger.info("test_product_zip_venus_get_content_list") b4_filename = p_zip_venus.find_band("SRE_B4.") assert b4_filename == "VENUS-XS_20200402-191352-000_L2A_GALLOP30_C_V2-2/VENUS-XS_20200402-191352-000_L2A_GALLOP30_C_V2-2_SRE_B4.tif" logger.info("test_product_zip_venus_get_band") b4 = p_zip_venus.get_band(b4_filename) assert type(b4) is numpy.ndarray logger.debug("b4 is of size %i, with shape %s" % (numpy.size(b4), str(numpy.shape(b4)))) assert numpy.shape(b4)[0] == 11686 assert numpy.shape(b4)[1] == 11711 b4 = None logger.info("test_product_zip_venus_get_band_aot") atb_filename = p_zip_venus.find_band("ATB_XS") assert atb_filename == "VENUS-XS_20200402-191352-000_L2A_GALLOP30_C_V2-2/VENUS-XS_20200402-191352-000_L2A_GALLOP30_C_V2-2_ATB_XS.tif" atb_bands = p_zip_venus.get_band(atb_filename) logger.debug("atb_bands is of type %s" % str(type(atb_bands))) logger.debug("atb_bands is of size %i, with shape %s" % (numpy.size(atb_bands), str(numpy.shape(atb_bands)))) assert type(atb_bands) is numpy.ndarray assert numpy.shape(atb_bands)[0] == 2 atb_bands = None aot = p_zip_venus.get_band(atb_filename, layer=1) logger.debug("aot is of size %i, with shape %s" % (numpy.size(aot), str(numpy.shape(aot)))) assert numpy.shape(aot)[0] == 11686 assert numpy.shape(aot)[1] == 11711 aot = None aot = p_zip_venus.get_band(p_zip_venus.find_band(p_zip_venus.aot_name), scalef=p_zip_venus.aot_scalef, layer=p_zip_venus.aot_layer) logger.debug("aot is of size %i, with shape %s" % (numpy.size(aot), str(numpy.shape(aot)))) assert numpy.shape(aot)[0] == 11686 assert numpy.shape(aot)[1] == 11711 aot = None vap = p_zip_venus.get_band(p_zip_venus.find_band(p_zip_venus.vap_name), scalef=p_zip_venus.vap_scalef, layer=p_zip_venus.vap_layer) logger.debug("vap is of size %i, with shape %s" % (numpy.size(vap), str(numpy.shape(vap)))) assert numpy.shape(vap)[0] == 11686 assert numpy.shape(vap)[1] == 11711 vap = None logger.info("test_product_zip_venus_get_band_subset") b4_subset = p_zip_venus.get_band_subset(b4_filename, ulx=649455, uly=4238445, lrx=649465, lry=4238435) assert type(b4_subset) is numpy.ndarray assert b4_subset[0, 0] == 93 assert b4_subset[1, 0] == 86 assert b4_subset[0, 1] == 113 assert b4_subset[1, 1] == 94 assert p_zip_venus.sre_scalef == 1000 b4_subset_SRE = p_zip_venus.get_band_subset(b4_filename, ulx=649455, uly=4238445, lrx=649465, lry=4238435, scalef=p_zip_venus.sre_scalef) assert b4_subset_SRE[0, 0] == 0.093 assert b4_subset_SRE[1, 0] == 0.086 assert b4_subset_SRE[0, 1] == 0.113 assert b4_subset_SRE[1, 1] == 0.094 logger.info("test_product_zip_venus_get_band_subset_withROI") roi = Roi.Roi([99, 649460, 4238440], 10, logger) b4_subset_SRE = p_zip_venus.get_band_subset(b4_filename, roi=roi, scalef=p_zip_venus.sre_scalef) assert b4_subset_SRE[0, 0] == 0.093 assert b4_subset_SRE[1, 0] == 0.086 assert b4_subset_SRE[0, 1] == 0.113 assert b4_subset_SRE[1, 1] == 0.094 aot = p_zip_venus.get_band_subset(p_zip_venus.find_band(p_zip_venus.aot_name), ulx=649455, uly=4238445, lrx=649465, lry=4238435, scalef=p_zip_venus.aot_scalef, layer=p_zip_venus.aot_layer) logger.debug("aot is of size %i, with shape %s" % (numpy.size(aot), str(numpy.shape(aot)))) assert aot[0, 0] == 0.195 assert aot[1, 0] == 0.195 assert aot[0, 1] == 0.195 assert aot[1, 1] == 0.195 vap = p_zip_venus.get_band_subset(p_zip_venus.find_band(p_zip_venus.vap_name), ulx=649455, uly=4238445, lrx=649465, lry=4238435, scalef=p_zip_venus.vap_scalef, layer=p_zip_venus.vap_layer) logger.debug("vap is of size %i, with shape %s" % (numpy.size(vap), str(numpy.shape(vap)))) assert vap[0, 0] == 0.55 assert vap[1, 0] == 0.55 assert vap[0, 1] == 0.55 assert vap[1, 1] == 0.55 ## TEST PRODUCT_HDF def test_product_hdf(): logger.info("TEST PRODUCT_HDF") p_hdf_vermote = Product.Product_hdf(TEST_DATA_PATH + "vermote_carpentras/refsrs2-L1C_T31TFJ_A003037_20171005T104550-Carpentras.hdf", logger) assert p_hdf_vermote.ptype == "HDF" logger.info("test_product_hdf_content_list") logger.debug(type(p_hdf_vermote.content_list)) logger.debug(p_hdf_vermote.content_list) assert type(p_hdf_vermote.content_list) is list logger.info("test_product_hdf_find_band") subdsid = p_hdf_vermote.find_band("band04-red") assert type(subdsid) is int assert subdsid == 3 logger.info("test_product_hdf_get_band") assert type(p_hdf_vermote.get_band(p_hdf_vermote.find_band("band04-red"))) is numpy.ndarray assert p_hdf_vermote.get_band(p_hdf_vermote.find_band("band04-red"))[0][0] == 596 assert p_hdf_vermote.get_band(p_hdf_vermote.find_band("band04-red"))[6][2] == 1096 def test_product_hdf_acix(): p_hdf_acix = Product.Product_hdf_acix(TEST_DATA_PATH + "vermote_carpentras/refsrs2-L1C_T31TFJ_A012260_20171027T103128-Carpentras.hdf", logger) assert p_hdf_acix.sre_scalef == 10000 b7 = p_hdf_acix.get_band(p_hdf_acix.find_band("band07"), scalef=p_hdf_acix.sre_scalef) assert type(b7) is numpy.ndarray assert b7[12,5] == 0.1829 assert b7[899,899] == 0.2021 ## TESTING PRODUCT_DIR_MAJA def test_product_dir_maja(): logger.info("TESTING PRODUCT_DIR_MAJA") p_dir_maja = Product.Product_dir_maja(TEST_DATA_PATH + "acix_carpentras/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0", logger) assert type(p_dir_maja.content_list) is list p_dir_b4_filename = p_dir_maja.find_band("SRE_B4.") assert p_dir_b4_filename == TEST_DATA_PATH + "acix_carpentras/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0/SENTINEL2A_20171007-103241-161_L2A_T31TFJ_C_V1-0_SRE_B4.tif" p_dir_band = p_dir_maja.get_band(p_dir_b4_filename, scalef=p_dir_maja.sre_scalef) assert type(p_dir_band) is numpy.ndarray assert p_dir_band[0,0] == 0.0850 assert p_dir_band[6, 2] == 0.1249 roi = Roi.Roi([88, 660260, 4887620], 20, logger) b4_subset = p_dir_maja.get_band_subset(p_dir_maja.find_band("SRE_B4."), roi=roi, scalef=p_dir_maja.sre_scalef) assert type(b4_subset) is numpy.ndarray assert b4_subset[0, 0] == 0.1362 assert b4_subset[1, 0] == 0.1451 assert b4_subset[0, 1] == 0.1173 assert b4_subset[1, 1] == 0.1306 ```
{ "source": "jeromecoutant/pyOCD", "score": 2 }
#### File: pyocd/probe/common.py ```python import logging LOG = logging.getLogger(__name__) ## Whether the warning about no libusb was printed already. # # Used to prevent warning spewage if repeatedly scanning for probes, such as when ConnectHelper # is used in blocking mode and no probes are connected. did_show_no_libusb_warning = False def show_no_libusb_warning(): global did_show_no_libusb_warning if not did_show_no_libusb_warning: LOG.warning("STLink and CMSIS-DAPv2 probes are not supported because no libusb library was found.") did_show_no_libusb_warning = True ``` #### File: target/builtin/target_MAX32630.py ```python from ...flash.flash import Flash from ...core.coresight_target import CoreSightTarget from ...core.memory_map import (FlashRegion, RamRegion, MemoryMap) from ...debug.svd.loader import SVDFile import logging FLASH_ALGO = { 'load_address' : 0x20000000, 'instructions' : [ 0xE00ABE00, 0x062D780D, 0x24084068, 0xD3000040, 0x1E644058, 0x1C49D1FA, 0x2A001E52, 0x4770D1F2, 0x4603B510, 0x4893460C, 0x68414448, 0xF0006888, 0xB1087080, 0xBD102001, 0x4448488E, 0x60486880, 0xE7F82000, 0x488B4602, 0x68414448, 0xF0206888, 0x60884070, 0x47702000, 0x44484886, 0x68886841, 0x7080F000, 0x2001B108, 0x6A484770, 0x2000B148, 0x6A486248, 0x2002B128, 0x6A486248, 0x2001B108, 0x6888E7F2, 0x4070F020, 0x5000F040, 0x20006088, 0xB510E7EA, 0x44484877, 0xF7FF6844, 0xB108FFDD, 0xBD102001, 0xF42068A0, 0xF440407F, 0x60A0402A, 0xF04068A0, 0x60A00002, 0x68A0BF00, 0x7080F000, 0xD1FA2800, 0xF02068A0, 0x60A04070, 0xF0006A60, 0xB1080002, 0xE7E42001, 0xE7E22000, 0x4605B570, 0x44484864, 0xF7FF6844, 0xB108FFB7, 0xBD702001, 0xF42068A0, 0xF440407F, 0x60A040AA, 0x68A06025, 0x0004F040, 0xBF0060A0, 0xF00068A0, 0x28007080, 0x68A0D1FA, 0x4070F020, 0x6A6060A0, 0x0002F000, 0x2001B108, 0x2000E7E3, 0xE92DE7E1, 0x460747F0, 0x4690468A, 0x4448484F, 0x46566844, 0xF0084645, 0xB1100003, 0xE8BD2001, 0x464587F0, 0xFF84F7FF, 0x2001B108, 0x68A0E7F7, 0x6000F020, 0x68A060A0, 0x0010F040, 0xE00E60A0, 0xCD016027, 0x68A06320, 0x0001F040, 0xBF0060A0, 0xF00068A0, 0x28007080, 0x1D3FD1FA, 0x2E041F36, 0xF007D303, 0x2800001F, 0x4838D1EA, 0x68C04448, 0xD1212880, 0xD31F2E10, 0xF02068A0, 0x60A00010, 0xF04068A0, 0x60A06000, 0x6027E014, 0x6320CD01, 0x6360CD01, 0x63A0CD01, 0x63E0CD01, 0xF04068A0, 0x60A00001, 0x68A0BF00, 0x7080F000, 0xD1FA2800, 0x3E103710, 0xD2E82E10, 0xD3192E04, 0xF02068A0, 0x60A06000, 0xF04068A0, 0x60A00010, 0x6027E00E, 0x6320CD01, 0xF04068A0, 0x60A00001, 0x68A0BF00, 0x7080F000, 0xD1FA2800, 0x1F361D3F, 0xD2EE2E04, 0x68A2B306, 0x6200F022, 0x68A260A2, 0x0210F042, 0xF04F60A2, 0x21FF30FF, 0x682AE005, 0x0201EA62, 0x02094010, 0x2E001E76, 0x6027D1F7, 0x68A26320, 0x0201F042, 0xBF0060A2, 0xF00268A2, 0x2A007280, 0xBF00D1FA, 0xF02068A0, 0x60A04070, 0xF0006A60, 0xB1080002, 0xE76A2001, 0xE7682000, 0x00000004, 0x00000000, 0x00000000, # FLC_BASE, CLK_DIV, BRST_SIZE, FLASH_BASE, FLASH_SIZE, FLASH_SECTOR 0x40002000, 0x00000060, 0x00000020, 0x00000000, 0x00200000, 0x00002000 ], 'pc_init' : 0x20000021, 'pc_eraseAll' : 0x20000093, 'pc_erase_sector' : 0x200000DD, 'pc_program_page' : 0x2000012B, 'begin_data' : 0x20004000, # Analyzer uses a max of 128B data (32 pages * 4 bytes / page) 'page_buffers' : [0x20006000, 0x20008000], # Enable double buffering 'begin_stack' : 0x20002000, 'static_base' : 0x20000278, 'min_program_length' : 4, 'analyzer_supported' : True, 'analyzer_address' : 0x2000A000 # Analyzer 0x2000A000..0x2000A600 } class MAX32630(CoreSightTarget): VENDOR = "Maxim" memoryMap = MemoryMap( FlashRegion( start=0, length=0x200000, blocksize=0x2000, is_boot_memory=True, algo=FLASH_ALGO), RamRegion( start=0x20000000, length=0x40000), ) def __init__(self, link): super(MAX32630, self).__init__(link, self.memoryMap) self._svd_location = SVDFile.from_builtin("max32630.svd") ``` #### File: target/builtin/target_MK64FN1M0xxx12.py ```python from ..family.target_kinetis import Kinetis from ..family.flash_kinetis import Flash_Kinetis from ...core.memory_map import (FlashRegion, RamRegion, MemoryMap) from ...debug.svd.loader import SVDFile import logging FLASH_ALGO = { 'load_address' : 0x20000000, 'instructions' : [ 0xE00ABE00, 0x062D780D, 0x24084068, 0xD3000040, 0x1E644058, 0x1C49D1FA, 0x2A001E52, 0x4770D1F2, 0x4604b570, 0x4616460d, 0x5020f24c, 0x81c84932, 0x1028f64d, 0x460881c8, 0xf0208800, 0x80080001, 0x4448482e, 0xf8dcf000, 0x2001b108, 0x2000bd70, 0x4601e7fc, 0x47702000, 0x4929b510, 0x44484827, 0xf8b8f000, 0xb92c4604, 0x48242100, 0xf0004448, 0x4604f9a9, 0xf837f000, 0xbd104620, 0x4604b570, 0x4448481e, 0x46214b1e, 0xf00068c2, 0x4605f85d, 0x481ab93d, 0x23004448, 0x68c24621, 0xf946f000, 0xf0004605, 0x4628f820, 0xb5febd70, 0x460c4605, 0x46234616, 0x46294632, 0x44484810, 0xf8f8f000, 0xb9674607, 0x22012000, 0x2000e9cd, 0x46224633, 0x90024629, 0x44484809, 0xf984f000, 0xf0004607, 0x4638f802, 0x4807bdfe, 0xf4206840, 0xf5000070, 0x49040070, 0x47706048, 0x40052000, 0x00000004, 0x6b65666b, 0x4001f000, 0x4a0e2070, 0x20807010, 0xbf007010, 0x7800480b, 0x280009c0, 0x4809d0fa, 0xf0017801, 0xb1080020, 0x47702067, 0x0010f001, 0x2068b108, 0xf001e7f9, 0xb1080001, 0xe7f42069, 0xe7f22000, 0x40020000, 0x4df0e92d, 0x460d4604, 0x469a4690, 0xf0004650, 0x4606f891, 0x4630b116, 0x8df0e8bd, 0x46422310, 0x46204629, 0xf86cf000, 0xb10e4606, 0xe7f34630, 0x0008eb05, 0x68e01e47, 0xf1f0fbb7, 0x7011fb00, 0x68e0b140, 0xf0f0fbb7, 0x0b01f100, 0xfb0068e0, 0x1e47f00b, 0x480be011, 0x68004478, 0x20096005, 0x71c84909, 0xffacf7ff, 0x69a04606, 0x69a0b108, 0xb1064780, 0x68e0e003, 0x42bd4405, 0xbf00d9eb, 0xe7c94630, 0x000002ec, 0x40020000, 0x4604b570, 0x4628460d, 0xf84ef000, 0xb10e4606, 0xbd704630, 0x2004b90c, 0x2044e7fb, 0x71c84902, 0xff88f7ff, 0x0000e7f5, 0x40020000, 0xb9094601, 0x47702004, 0x6cc04826, 0x6003f3c0, 0x447b4b25, 0x0010f833, 0xb90a0302, 0xe7f22064, 0x60082000, 0x2002604a, 0x02c06088, 0x200060c8, 0x61486108, 0xbf006188, 0x4602e7e5, 0x2004b90a, 0x61914770, 0xe7fb2000, 0x4604b530, 0x2004b90c, 0x1e58bd30, 0xb9104008, 0x40101e58, 0x2065b108, 0x6820e7f6, 0xd8054288, 0x0500e9d4, 0x188d4428, 0xd20142a8, 0xe7eb2066, 0xe7e92000, 0x480b4601, 0xd0014281, 0x4770206b, 0xe7fc2000, 0xb90b4603, 0x47702004, 0xd801290f, 0xd0012a04, 0xe7f82004, 0xe7f62000, 0x40048000, 0x0000025a, 0x6b65666b, 0x41f0e92d, 0x46884607, 0x461d4614, 0x2004b914, 0x81f0e8bd, 0x462a2308, 0x46384641, 0xffbcf7ff, 0xb10e4606, 0xe7f34630, 0x4812e01f, 0x68004478, 0x8000f8c0, 0x490fcc01, 0x390c4479, 0x60486809, 0x490ccc01, 0x39184479, 0x60886809, 0x490a2007, 0xf7ff71c8, 0x4606ff01, 0xb10869b8, 0x478069b8, 0xe004b106, 0x0808f108, 0x2d003d08, 0xbf00d1dd, 0xe7cd4630, 0x000001b0, 0x40020000, 0x4dffe92d, 0x4682b082, 0x2310460c, 0x46504621, 0xf7ff9a04, 0x4683ff83, 0x0f00f1bb, 0x4658d003, 0xe8bdb006, 0xe9da8df0, 0xfbb00101, 0x4260f7f1, 0x40084279, 0x42a54245, 0x443dd100, 0xe0229e04, 0x0804eba5, 0xd90045b0, 0xea4f46b0, 0x90011018, 0x4478480f, 0x60046800, 0x490e2001, 0x980171c8, 0x72c80a00, 0x72889801, 0x72489805, 0xfeb6f7ff, 0xf1bb4683, 0xd0010f00, 0xe7d14658, 0x0608eba6, 0x443d4444, 0x2e00bf00, 0x2000d1da, 0x0000e7c8, 0x0000010e, 0x40020000, 0x4604b570, 0xb90c460d, 0xbd702004, 0x49032040, 0x460871c8, 0xf7ff7185, 0xe7f6fe95, 0x40020000, 0x4dffe92d, 0x4617460c, 0xe9dd461d, 0xf8ddb80c, 0xb91da038, 0xb0042004, 0x8df0e8bd, 0x463a2304, 0x98004621, 0xff1ef7ff, 0xb10e4606, 0xe7f24630, 0x4814e022, 0x68004478, 0x20026004, 0x71c84912, 0xf8804608, 0x490fb00b, 0x39144479, 0x68096828, 0xf7ff6088, 0x4606fe67, 0xf1b8b15e, 0xd0010f00, 0x4000f8c8, 0x0f00f1ba, 0x2000d002, 0x0000f8ca, 0x1f3fe004, 0x1d241d2d, 0xd1da2f00, 0x4630bf00, 0x0000e7c9, 0x00000074, 0x40020000, 0x00000000, 0x00080000, 0x00100000, 0x00200000, 0x00400000, 0x00800000, 0x01000000, 0x01000000, 0x40020004, 0x00000000, ], 'pc_init' : 0x20000021, 'pc_eraseAll' : 0x20000059, 'pc_erase_sector' : 0x2000007D, 'pc_program_page' : 0x200000AB, 'begin_stack' : 0x20001000, 'begin_data' : 0x20003000, # Analyzer uses a max of 1024 B data (256 pages * 4 bytes / page) 'page_buffers' : [0x20003000, 0x20004000], # Enable double buffering 'static_base' : 0x20000000 + 0x20 + 0x474, 'min_program_length' : 8, 'analyzer_supported' : True, 'analyzer_address' : 0x1ffff000 # Analyzer 0x1ffff000..0x1ffff600 } class K64F(Kinetis): memoryMap = MemoryMap( FlashRegion( start=0, length=0x100000, blocksize=0x1000, is_boot_memory=True, algo=FLASH_ALGO, flash_class=Flash_Kinetis), RamRegion( start=0x1fff0000, length=0x40000) ) def __init__(self, link): super(K64F, self).__init__(link, self.memoryMap) self._svd_location = SVDFile.from_builtin("MK64F12.svd") ```
{ "source": "JeromeCui/annoy", "score": 3 }
#### File: annoy/test/hamming_index_test.py ```python import numpy import random from common import TestCase from annoy import AnnoyIndex class HammingIndexTest(TestCase): def test_basic_conversion(self): f = 100 i = AnnoyIndex(f, 'hamming') u = numpy.random.binomial(1, 0.5, f) v = numpy.random.binomial(1, 0.5, f) i.add_item(0, u) i.add_item(1, v) u2 = i.get_item_vector(0) v2 = i.get_item_vector(1) self.assertAlmostEqual(numpy.dot(u - u2, u - u2), 0.0) self.assertAlmostEqual(numpy.dot(v - v2, v - v2), 0.0) self.assertAlmostEqual(i.get_distance(0, 0), 0.0) self.assertAlmostEqual(i.get_distance(1, 1), 0.0) self.assertAlmostEqual(i.get_distance(0, 1), numpy.dot(u - v, u - v)) self.assertAlmostEqual(i.get_distance(1, 0), numpy.dot(u - v, u - v)) def test_basic_nns(self): f = 100 i = AnnoyIndex(f, 'hamming') u = numpy.random.binomial(1, 0.5, f) v = numpy.random.binomial(1, 0.5, f) i.add_item(0, u) i.add_item(1, v) i.build(10) self.assertEquals(i.get_nns_by_item(0, 99), [0, 1]) self.assertEquals(i.get_nns_by_item(1, 99), [1, 0]) rs, ds = i.get_nns_by_item(0, 99, include_distances=True) self.assertEquals(rs, [0, 1]) self.assertAlmostEqual(ds[0], 0) self.assertAlmostEqual(ds[1], numpy.dot(u-v, u-v)) def test_save_load(self): f = 100 i = AnnoyIndex(f, 'hamming') u = numpy.random.binomial(1, 0.5, f) v = numpy.random.binomial(1, 0.5, f) i.add_item(0, u) i.add_item(1, v) i.build(10) i.save('blah.ann') j = AnnoyIndex(f, 'hamming') j.load('blah.ann') rs, ds = j.get_nns_by_item(0, 99, include_distances=True) self.assertEquals(rs, [0, 1]) self.assertAlmostEqual(ds[0], 0) self.assertAlmostEqual(ds[1], numpy.dot(u-v, u-v)) def test_many_vectors(self): f = 10 i = AnnoyIndex(f, 'hamming') for x in range(100000): i.add_item(x, numpy.random.binomial(1, 0.5, f)) i.build(10) rs, ds = i.get_nns_by_vector([0]*f, 10000, include_distances=True) self.assertGreaterEqual(min(ds), 0) self.assertLessEqual(max(ds), f) dists = [] for x in range(1000): rs, ds = i.get_nns_by_vector(numpy.random.binomial(1, 0.5, f), 1, search_k=1000, include_distances=True) dists.append(ds[0]) avg_dist = 1.0 * sum(dists) / len(dists) self.assertLessEqual(avg_dist, 0.42) ```
{ "source": "jeromecyang/ltsoj", "score": 2 }
#### File: ltsoj/_utils/lib.py ```python import sys, re, os, yaml from collections import OrderedDict path = os.path.dirname(os.path.realpath(__file__)) + '/../_episodes/' def get_all_episodes(): return sorted(os.listdir(path)) def add_to_data(raw_content, data_to_add): data_list = raw_content.split('---') data_list[1] = data_list[1] + data_to_add return "---".join(data_list) def read_data(episode): f = open(path + episode) content = f.read() data_list = content.split('---') return yaml.load(data_list[1], Loader=yaml.SafeLoader) def write_data(episode, data): f = open(path + episode) content = f.read() data_list = content.split('---') str_data = '\n' + yaml.dump(data, sort_keys=False, allow_unicode=True, width=1000) new_content = "---".join([data_list[0], str_data, *data_list[2:]]) g = open(path + episode, 'w') g.write(new_content) g.close() def split_and_get_nth(text, pattern, index): parts = text.split(pattern) if (len(parts) > index): return parts[index] return '' def get_section(content, index): sections = re.split(r'[^#]##\s.*?\n', split_and_get_nth(content, '---\n', 2), flags=re.S) return sections[index] def read_content(episode): f = open(path + episode) content = f.read() return content def write_content(episode, content): g = open(path + episode, 'w') g.write(content) g.close() ```
{ "source": "jeromedockes/cogspaces", "score": 3 }
#### File: cogspaces/modules/factored.py ```python import math import numpy as np import torch from torch import nn from torch.nn import functional as F from cogspaces.datasets import fetch_atlas_modl from cogspaces.modules.linear import DropoutLinear class LatentClassifier(nn.Module): def __init__(self, latent_size, target_size, var_penalty, dropout=0., adaptive=False, batch_norm=True): """ One third-layer classification head. Simply combines batch-norm -> DropoutLinear -> softmax. Parameters ---------- latent_size : int Size of the latent space. target_size : int Number of targets for the classifier. var_penalty : float Penalty to apply for variational latent_dropout dropout : float, [0, 1] Dropout rate to apply at the input adaptive : bool Use adaptive latent_dropout rate batch_norm : bool Use batch normalization at the input """ super().__init__() if batch_norm: self.batch_norm = nn.BatchNorm1d(latent_size, affine=False, ) self.linear = DropoutLinear(latent_size, target_size, bias=True, p=dropout, var_penalty=var_penalty, adaptive=adaptive, level='layer') def forward(self, input, logits=False): if hasattr(self, 'batch_norm'): if not self.training or len(input) > 1: input = self.batch_norm(input) logits_ = self.linear(input) if logits: return logits_ else: return F.log_softmax(logits_, dim=1) def reset_parameters(self): self.linear.reset_parameters() self.batch_norm.reset_parameters() def penalty(self): return self.linear.penalty() def get_dropout(self): return self.linear.get_dropout() class VarMultiStudyModule(nn.Module): def __init__(self, in_features, latent_size, target_sizes, lengths, input_dropout=0., regularization=1., latent_dropout=0., init='orthogonal', batch_norm=True, adaptive=False, ): """ Second and third-layer of the models. Parameters ---------- in_features : int Size of the input before the second layer (number of resting-state loadings). latent_size : int Size of the latent dimension, in between the second and third layer. target_sizes: Dict[str, int] For each study, number of contrasts to predict lengths: Dict[str, int] Length of each study (for variational regularization) input_dropout: float, [0, 1] regularization: float, default=1 Regularization to apply for variational latent_dropout latent_dropout: float, default=1 Dropout rate to apply in between the second and third layer. init: str, {'normal', 'orthogonal', 'resting-state'} How to initialize the second layer. If 'resting-state', then it must be in_features = 453 and latent_size = 128 batch_norm: bool, Batch norm between the second and third layer adaptive: bool, Dropout rate should be adaptive """ super().__init__() total_length = sum(list(lengths.values())) self.embedder = DropoutLinear( in_features, latent_size, adaptive=adaptive, var_penalty=regularization / total_length, level='layer', p=input_dropout, bias=True) self.classifiers = {study: LatentClassifier( latent_size, target_size, dropout=latent_dropout, var_penalty=regularization / lengths[study], batch_norm=batch_norm, adaptive=adaptive, ) for study, target_size in target_sizes.items()} for study, classifier in self.classifiers.items(): self.add_module('classifier_%s' % study, classifier) self.init = init def reset_parameters(self): self.embedder.weight.data = self.get_embedder_init() nn.init.zeros_(self.embedder.bias.data) self.embedder.reset_dropout() for classifier in self.classifiers.values(): classifier.reset_parameters() def get_embedder_init(self): weight = torch.empty_like(self.embedder.weight.data) gain = 1. / math.sqrt(weight.shape[1]) if self.init == 'normal': self.weight.data.uniform_(-gain, gain) elif self.init == 'orthogonal': nn.init.orthogonal_(weight, gain=gain) elif self.init == 'resting-state': dataset = fetch_atlas_modl() weight = np.load(dataset['loadings_128_gm']) weight = torch.from_numpy(np.array(weight)) return weight def forward(self, inputs, logits=False): preds = {} for study, input in inputs.items(): preds[study] = self.classifiers[study](self.embedder(input), logits=logits) return preds def penalty(self, studies): """ Return the variational penalty of the model. Parameters ---------- studies: Iterable[str], Studies to consider when computing the penalty Returns ------- penalty: torch.tensor, Scalar penalty """ return (self.embedder.penalty() + sum(self.classifiers[study].penalty() for study in studies)) def get_dropout(self): return (self.embedder.get_dropout(), {study: classifier.get_dropout() for study, classifier in self.classifiers.items()}) ``` #### File: cogspaces/modules/linear.py ```python import math import torch from torch import nn from torch.nn import Parameter, functional as F k1 = 0.63576 k2 = 1.87320 k3 = 1.48695 class DropoutLinear(nn.Linear): def __init__(self, in_features, out_features, bias=True, p=1e-8, level='layer', var_penalty=0., adaptive=False, sparsify=False): super().__init__(in_features, out_features, bias) self.p = p self.var_penalty = var_penalty if level == 'layer': self.log_alpha = Parameter(torch.Tensor(1, 1), requires_grad=adaptive) elif level == 'atom': self.log_alpha = Parameter(torch.Tensor(1, in_features), requires_grad=adaptive) elif level == 'coef': self.log_alpha = Parameter(torch.Tensor(out_features, in_features), requires_grad=adaptive) elif level == 'additive': assert adaptive self.log_sigma2 = Parameter( torch.Tensor(out_features, in_features), requires_grad=True) else: raise ValueError() self.sparsify = sparsify self.adaptive = adaptive self.level = level self.reset_dropout() def reset_parameters(self): super().reset_parameters() if hasattr(self, 'level'): self.reset_dropout() def reset_dropout(self): if self.p > 0: log_alpha = math.log(self.p) - math.log(1 - self.p) if self.level != 'additive': self.log_alpha.data.fill_(log_alpha) else: self.log_sigma2.data = log_alpha + torch.log( self.weight.data ** 2 + 1e-8) def make_additive(self): assert self.level != 'additive' self.log_alpha.requires_grad = False self.level = 'additive' self.adaptive = True out_features, in_features = self.weight.shape self.log_sigma2 = Parameter(torch.Tensor(out_features, in_features), requires_grad=True) self.log_sigma2.data = (self.log_alpha.expand(*self.weight.shape) + torch.log(self.weight ** 2 + 1e-8) ).detach() self.log_alpha.requires_grad = False def make_non_adaptive(self): assert self.level != 'additive' self.adaptive = False self.log_alpha.requires_grad = False def make_adaptive(self): assert self.level != 'additive' self.adaptive = True self.log_alpha.requires_grad = True def get_var_weight(self): if self.level == 'additive': return torch.exp(self.log_sigma2) # return self.sigma ** 2 else: return torch.exp(self.log_alpha) * self.weight ** 2 def get_log_alpha(self): if self.level == 'additive': return torch.clamp( self.log_sigma2 - torch.log(self.weight ** 2 + 1e-8), -8, 8) else: return torch.clamp(self.log_alpha, -8, 8) def get_dropout(self): return 1 / (1 + torch.exp(-self.get_log_alpha())).squeeze().detach() def forward(self, input): if self.training: if self.p == 0: return F.linear(input, self.weight, self.bias) if self.adaptive: output = F.linear(input, self.weight, self.bias) # Local reparemtrization trick: gaussian latent_dropout noise on input # <-> gaussian noise on output std = torch.sqrt( F.linear(input ** 2, self.get_var_weight(), None) + 1e-8) eps = torch.randn_like(output, requires_grad=False) return output + std * eps else: eps = torch.randn_like(input, requires_grad=False) input = input * ( 1 + torch.exp(.5 * self.get_log_alpha()) * eps) return F.linear(input, self.weight, self.bias) else: if self.sparsify: weight = self.sparse_weight else: weight = self.weight return F.linear(input, weight, self.bias) def penalty(self): if not self.adaptive or self.var_penalty == 0: return torch.tensor(0., device=self.weight.device, dtype=torch.float) else: log_alpha = self.get_log_alpha() var_penalty = - k1 * (torch.sigmoid(k2 + k3 * log_alpha) - .5 * F.softplus(-log_alpha) - 1).expand(*self.weight.shape).sum() return var_penalty * self.var_penalty @property def density(self): return (self.sparse_weight != 0).float().mean().item() @property def sparse_weight(self): mask = self.get_log_alpha() > 3 return self.weight.masked_fill(mask, 0) ``` #### File: cogspaces/modules/loss.py ```python from typing import Dict, Tuple import torch from torch import nn from torch.nn import functional as F class MultiStudyLoss(nn.Module): def __init__(self, study_weights: Dict[str, float], ) -> None: super().__init__() self.study_weights = study_weights def forward(self, preds: Dict[str, torch.FloatTensor], targets: Dict[str, torch.LongTensor]) \ -> Tuple[torch.FloatTensor, torch.FloatTensor]: loss = 0 for study in preds: pred = preds[study] target = targets[study] this_loss = F.nll_loss(pred, target, reduction='elementwise_mean') loss += this_loss * self.study_weights[study] return loss ``` #### File: cogspaces/plotting/volume.py ```python import os from itertools import repeat from os.path import join import numpy as np from cogspaces.datasets import fetch_mask from joblib import Parallel, delayed from matplotlib.colors import LinearSegmentedColormap, rgb_to_hsv, hsv_to_rgb from nilearn._utils import check_niimg from nilearn.datasets import fetch_surf_fsaverage5 from nilearn.image import iter_img from nilearn.input_data import NiftiMasker def make_cmap(color, rotation=.5, white=False, transparent_zero=False): h, s, v = rgb_to_hsv(color) h = h + rotation if h > 1: h -= 1 r, g, b = color ri, gi, bi = hsv_to_rgb((h, s, v)) colors = {'direct': (ri, gi, bi), 'inverted': (r, g, b)} cdict = {} for direction, (r, g, b) in colors.items(): if white: cdict[direction] = {color: [(0.0, 0.0416, 0.0416), (0.18, c, c), (0.5, 1, 1), (0.62, 0.0, 0.0), (1.0, 0.0416, 0.0416)] for color, c in [('blue', b), ('red', r), ('green', g)]} else: cdict[direction] = {color: [(0.0, 1, 1), (0.32, c, c), (0.5, 0.0416, 0.0416), (0.5, 0.0, 0.0), (0.87, 0.0, 0.0), (1.0, 1, 1)] for color, c in [('blue', b), ('red', r), ('green', g)]} if transparent_zero: cdict[direction]['alpha']: [(0, 1, 1), (0.5, 0, 0), (1, 1, 1)] cmap = LinearSegmentedColormap('cmap', cdict['direct']) cmapi = LinearSegmentedColormap('cmap', cdict['inverted']) cmap._init() cmapi._init() cmap._lut = np.maximum(cmap._lut, cmapi._lut[::-1]) # Big hack from nilearn (WTF !?) cmap._lut[-1, -1] = 0 return cmap def plot_single(img, name, output_dir, view_types=['stat_map'], color=None, threshold=0): from nilearn.plotting import plot_stat_map, find_xyz_cut_coords, plot_glass_brain if color is not None: cmap = make_cmap(color, rotation=.5) cmap_white = make_cmap(color, rotation=.5, white=True) else: cmap = 'cold_hot' cmap_white = 'cold_white_hot' srcs = [] vmax = np.abs(img.get_data()).max() threshold = vmax / 8 for view_type in view_types: src = join(output_dir, '%s_%s.png' % (name, view_type)) cut_coords = find_xyz_cut_coords(img, activation_threshold=vmax / 3) if view_type == 'stat_map': plot_stat_map(img, threshold=threshold, cut_coords=cut_coords, vmax=vmax, colorbar=True, output_file=src, # cmap=cmap ) elif view_type == 'glass_brain': plot_glass_brain(img, threshold=threshold, vmax=vmax, plot_abs=False, output_file=src, colorbar=True, # cmap=cmap_white ) else: raise ValueError('Wrong view type in `view_types`: got %s' % view_type) srcs.append(src) return srcs, name def numbered_names(name): i = 0 while True: yield '%s_%i' % (name, i) i += 1 def plot_4d_image(img, names=None, output_dir=None, colors=None, view_types=['stat_map'], threshold=True, n_jobs=1, verbose=10): if not os.path.exists(output_dir): os.makedirs(output_dir) if colors is None: colors = repeat(None) if 'surf_stat_map_right' in view_types or 'surf_stat_map_left' in view_types: fetch_surf_fsaverage5() filename = img img = check_niimg(img, ensure_ndim=4) img.get_data() if names is None or isinstance(names, str): if names is None: dirname, filename = os.path.split(filename) names = filename.replace('.nii.gz', '') names = numbered_names(names) else: assert len(names) == img.get_shape()[3] mask = fetch_mask() masker = NiftiMasker(mask_img=mask).fit() components = masker.transform(img) n_components = len(components) threshold = np.percentile(np.abs(components), 100. * (1 - 1. / n_components)) if threshold else 0 imgs = Parallel(n_jobs=n_jobs, verbose=verbose)( delayed(plot_single)(img, name, output_dir, view_types, color, threshold=threshold) for name, img, color in zip(names, iter_img(img), colors)) return imgs ```
{ "source": "jeromedockes/dataset_shift_biomarkers", "score": 3 }
#### File: simulations/datashift/datasets.py ```python import numpy as np from scipy.stats import multivariate_normal, norm import pandas as pd from sklearn.utils import check_random_state def quadratic_additive_noise( x_mean=0, x_std=1.0, n_points=100, noise_level=1.0, random_state=0 ): """y = x**2 + e""" rng = check_random_state(random_state) x = rng.normal(x_mean, x_std, size=n_points) noise = rng.normal(size=n_points) * noise_level y = x ** 2 + noise return x, y def blobs(mean=[0], cov=[1.0], size=100, random_state=0): X = np.empty((len(mean) * size, len(mean[0])), dtype=float) y = np.empty(len(mean) * size) for i, (m, c) in enumerate(zip(mean, cov)): ii = i * size distrib = multivariate_normal(mean=m, cov=c) X[ii : ii + size] = distrib.rvs(size=size, random_state=random_state) y[ii : ii + size] = i return X, y def parabola( rot=0, offset=0, c=0.2, noise=1.0, size=200, x=None, mode="laplace", loc=0 ): rng = np.random.RandomState(0) if x is None: if mode == "uniform": xmin, xmax = -5, 5 x = rng.uniform(xmin, xmax, size) elif mode == "laplace": x = rng.laplace(loc=loc, size=size) else: raise ValueError("mode not understood: {}".format(mode)) y = c * x ** 2 + offset + noise * rng.normal(size=size) A = np.asarray([[np.cos(rot), np.sin(rot)], [-np.sin(rot), np.cos(rot)]]) return A.dot([x, y]).T def parabola_multiclass(size=100, **kwargs): Xt = parabola(np.pi / 4, size=size, **kwargs) yt = np.zeros(len(Xt), dtype=int) Xf = parabola(np.pi / 4, 3.0, size=size, **kwargs) yf = np.ones(len(Xf), dtype=int) X = np.concatenate((Xt, Xf), axis=0) y = np.concatenate((yt, yf), axis=0) return X, y class BlobsGenerator: def __init__( self, random_state=0, class_1_mean=(7., 0.), z_direction=(1, 1), z_noise=.3 ): self.rng = check_random_state(random_state) self.class_0_mean = (0, 0) self.class_1_mean = class_1_mean self.z_direction = np.asarray( (np.cos(z_direction), np.sin(z_direction))) self.z_noise = z_noise def sample(self, size=200): y = self.rng.binomial(1, 0.5, size=size) n0 = (y == 0).sum() n1 = (y == 1).sum() X = np.empty((size, 2)) X[y == 0] = self.rng.multivariate_normal( self.class_0_mean, np.eye(2), size=n0) X[y == 1] = self.rng.multivariate_normal( self.class_1_mean, np.eye(2), size=n1) z = self.z_direction.dot(X.T) + self.rng.normal(0., self.z_noise, size) return pd.DataFrame({"x1": X[:, 0], "x2": X[:, 1], "z": z, "y": y}) class ParabolasGenerator: def __init__( self, z_loc=0, z_scale=1.0, c=0.3, rot=np.pi / 4, class_offset=3.0, x1_noise=0.7, x2_noise=1.0, y_noise=0.2, random_state=0, ): self.rng = check_random_state(random_state) self.z_loc = z_loc self.z_scale = z_scale self.c = c self.rot = rot self.class_offset = class_offset self.x1_noise = x1_noise self.x2_noise = x2_noise self.y_noise = y_noise self.A = np.asarray( [ [np.cos(self.rot), np.sin(self.rot)], [-np.sin(self.rot), np.cos(self.rot)], ] ) def sample(self, size=200): z = self.rng.normal(self.z_loc, self.z_scale, size=size) y = (z > 0).astype(int) y_noise = self.rng.binomial(1, self.y_noise, size=size) y = y * (1 - y_noise) + (1 - y) * y_noise x1 = z + self.rng.normal(0.0, self.x1_noise, size=size) offset = self.class_offset * y x2 = ( self.c * x1 ** 2 + offset + self.x2_noise * self.rng.normal(size=size) ) x1, x2 = self.A.dot([x1, x2]) return pd.DataFrame({"x1": x1, "x2": x2, "z": z, "y": y}) def score(self, z, normalize=True): distrib = norm(self.z_loc, self.z_scale) scores = distrib.pdf(z) if normalize: scores /= scores.sum() return scores ```
{ "source": "jeromedockes/neuroquery_image_search", "score": 2 }
#### File: neuroquery_image_search/tests/conftest.py ```python from pathlib import Path import tempfile from unittest.mock import MagicMock import pytest import numpy as np import pandas as pd from scipy import sparse import nibabel import nilearn from nilearn.datasets import _testing from nilearn.datasets._testing import request_mocker # noqa: F401 def make_fake_img(): rng = np.random.default_rng(0) img = rng.random(size=(4, 3, 5)) return nibabel.Nifti1Image(img, np.eye(4)) @pytest.fixture() def fake_img(): return make_fake_img() def make_fake_data(): n_voxels, n_components, n_studies, n_terms = 23, 8, 12, 9 rng = np.random.default_rng(0) difumo_maps = rng.random((n_components, n_voxels)) difumo_maps[rng.binomial(1, 0.3, size=difumo_maps.shape).astype(int)] = 0 difumo_inverse_covariance = np.linalg.pinv(difumo_maps.dot(difumo_maps.T)) difumo_maps = sparse.csr_matrix(difumo_maps) projections = rng.random((n_studies, n_components)) term_projections = rng.random((n_terms, n_components)) articles_info = pd.DataFrame({"pmid": np.arange(n_studies) + 100}) articles_info["title"] = [ f"title {pmid}" for pmid in articles_info["pmid"] ] articles_info["pubmed_url"] = [ f"url {pmid}" for pmid in articles_info["pmid"] ] mask = np.zeros(4 * 3 * 5, dtype=int) mask[:n_voxels] = 1 mask = mask.reshape((4, 3, 5)) mask_img = nibabel.Nifti1Image(mask, np.eye(4)) doc_freq = pd.DataFrame( { "term": ["term_{i}" for i in range(n_terms)], "document_frequency": np.arange(n_terms), } ) with tempfile.TemporaryDirectory() as temp_dir: temp_dir = Path(temp_dir) sparse.save_npz(temp_dir / "difumo_maps.npz", difumo_maps) np.save( temp_dir / "difumo_inverse_covariance.npy", difumo_inverse_covariance, ) np.save(temp_dir / "projections.npy", projections) np.save(temp_dir / "term_projections.npy", term_projections) articles_info.to_csv(temp_dir / "articles-info.csv", index=False) mask_img.to_filename(str(temp_dir / "mask.nii.gz")) doc_freq.to_csv( str(temp_dir / "document_frequencies.csv"), index=False ) archive = _testing.dict_to_archive( {"neuroquery_image_search_data": temp_dir} ) return archive @pytest.fixture(autouse=True) def temp_data_dir(tmp_path_factory, monkeypatch): home_dir = tmp_path_factory.mktemp("temp_home") monkeypatch.setenv("HOME", str(home_dir)) monkeypatch.setenv("USERPROFILE", str(home_dir)) data_dir = home_dir / "neuroquery_data" data_dir.mkdir() monkeypatch.setenv("NEUROQUERY_DATA", str(data_dir)) @pytest.fixture(autouse=True, scope="function") def map_mock_requests(request_mocker): request_mocker.url_mapping[ "https://osf.io/mx3t4/download" ] = make_fake_data() return request_mocker @pytest.fixture(autouse=True) def patch_nilearn(monkeypatch): def fake_motor_task(*args, **kwargs): return {"images": [make_fake_img()]} monkeypatch.setattr( nilearn.datasets, "fetch_neurovault_motor_task", fake_motor_task ) monkeypatch.setattr("webbrowser.open", MagicMock()) ``` #### File: neuroquery_image_search/tests/test_searching.py ```python import numpy as np import pandas as pd from nilearn import image import json import pytest from neuroquery_image_search import _searching, _datasets def test_image_search(tmp_path, fake_img): img_path = str(tmp_path / "img.nii.gz") fake_img.to_filename(img_path) results_path = tmp_path / "results.json" _searching.image_search( f"{img_path} -o {results_path} --n_studies 7 --n_terms 3".split() ) results = json.loads(results_path.read_text()) study_results = pd.DataFrame(results["studies"]) assert study_results.shape == (7, 4) assert np.allclose(study_results.reset_index().at[0, "similarity"], 1.0) results_path = tmp_path / "results.html" _searching.image_search( [img_path, "-o", str(results_path), "--n_studies", "1"] ) results = results_path.read_text() assert results.strip().startswith("<!DOCTYPE html>") _searching.image_search(["-o", str(results_path), "--n_studies", "7"]) results = results_path.read_text() assert "Image" in results _searching.image_search([]) def test_json_encoder(): df = pd.DataFrame({"A": [2, 3]}, index=list("ab")) data = {"a": {"B": 3.3}, "b": df} as_json = json.dumps(data, cls=_searching._JSONEncoder) loaded = json.loads(as_json) loaded_df = pd.DataFrame(loaded["b"]) assert (df == loaded_df).all().all() with pytest.raises(TypeError): json.dumps({"a": json}, cls=_searching._JSONEncoder) def test_neuroquery_image_search(fake_img): search = _searching.NeuroQueryImageSearch() results = search(fake_img, 20, transform="identity", rescale_similarities=False) assert results["studies"]["similarity"].max() != pytest.approx(1.) results = search(fake_img, 20, transform="identity") assert results["terms"]["similarity"].min() == pytest.approx(0.) assert results["studies"]["similarity"].max() == pytest.approx(1.) results = results["studies"] neg_img = image.new_img_like(fake_img, image.get_data(fake_img) * -1.0) neg_results = search(neg_img, 20, transform="identity")["studies"] assert (neg_results["pmid"].values == results["pmid"].values[::-1]).all() results = search(fake_img, 20, transform="absolute_value")["studies"] neg_results = search(neg_img, 20, transform="absolute_value")["studies"] assert (neg_results["pmid"].values == results["pmid"]).all() pos_img = image.new_img_like( fake_img, np.maximum(0, image.get_data(fake_img)) ) results = search(fake_img, 20, transform="positive_part")["studies"] pos_results = search(pos_img, 20, transform="identity")["studies"] assert (pos_results["pmid"].values == results["pmid"]).all() data = _datasets.fetch_data() assert (search.data["studies_info"] == data["studies_info"]).all().all() assert ( (search.data["document_frequencies"] == data["document_frequencies"]) .all() .all() ) ```
{ "source": "jeromedockes/pylabelbuddy", "score": 2 }
#### File: pylabelbuddy/tests/test_annotations_manager.py ```python from pylabelbuddy import _annotations_manager, _database def fill_db(data_dir): _database.add_docs_from_file(data_dir / "docs_1.csv") _database.add_labels_from_json(data_dir / "labels_1.json") def test_annotations_manager(root, data_dir, prepare_db): manager = _annotations_manager.AnnotationsManager(root) assert manager.n_docs() == 30 new_db = data_dir / "new_db.sqlite" _database.set_db_path(new_db) con = _database.get_connection() manager.change_database() assert manager.n_docs() == 0 manager.visit_document(1) assert manager.current_doc_id is None for direction in ["prev", "next"]: for kind in ["", "_labelled", "_unlabelled"]: getattr(manager, f"visit_{direction}{kind}")() assert manager.current_doc_id is None fill_db(data_dir) labels = manager.labels_info assert len(labels) == 8 assert labels["something"] == {"id": 1, "color": "#ff00ff"} manager.visit_document() assert manager.current_doc_id == 1 manager.visit_document(2) assert manager.current_doc_id == 2 assert ( manager.content == con.execute("select content from document where id = 2").fetchone()[ "content" ] ) manager.visit_document(2000) assert manager.current_doc_id == 2 with con: con.execute("update app_state set last_visited_doc = null") manager.visit_document(2000) assert manager.current_doc_id == 1 assert ( con.execute("select last_visited_doc from app_state").fetchone()[0] == 1 ) with con: con.executemany( "insert into annotation (doc_id, label_id, start_char, end_char)" " values (?, ?, ?, ?)", [(7, 1, 2, 5), (10, 3, 5, 9)], ) manager.visit_next() assert manager.current_doc_id == 2 manager.visit_next_labelled() assert manager.current_doc_id == 7 manager.visit_next_labelled() assert manager.current_doc_id == 10 manager.visit_prev_unlabelled() assert manager.current_doc_id == 9 manager.visit_prev_labelled() assert manager.current_doc_id == 7 manager.visit_prev_labelled() assert manager.current_doc_id == 7 manager.visit_prev_unlabelled() assert manager.current_doc_id == 6 manager.visit_prev() assert manager.current_doc_id == 5 manager.visit_document(6) manager.visit_next_unlabelled() assert manager.current_doc_id == 8 with con: con.execute("delete from document where id = 8") manager.refresh() assert manager.current_doc_id == 1 manager.visit_document(1) manager.visit_prev() assert manager.current_doc_id == 1 manager.visit_prev_labelled() assert manager.current_doc_id == 1 manager.visit_prev_unlabelled() assert manager.current_doc_id == 1 manager.visit_document(30) manager.visit_next() assert manager.current_doc_id == 30 manager.visit_next_labelled() assert manager.current_doc_id == 30 manager.visit_next_unlabelled() assert manager.current_doc_id == 30 assert len(list(manager.existing_regions())) == 0 manager.visit_document(7) assert len(list(manager.existing_regions())) == 1 assert manager.add_annotation("something-else", 0, 1) == 3 assert manager.add_annotation("unknown label", 0, 1) is None assert len(list(manager.existing_regions())) == 2 manager.delete_annotation("1") assert len(list(manager.existing_regions())) == 1 manager.delete_annotation("3") assert len(list(manager.existing_regions())) == 0 manager.visit_prev() manager.visit_next_labelled() assert manager.current_doc_id == 10 manager.update_annotation_label("2", "unknown label") manager.update_annotation_label("2", "something-else") assert ( con.execute( "select label_id from annotation where rowid = 2" ).fetchone()[0] == 2 ) assert manager.last_doc() == 30 assert manager.first_doc() == 1 assert manager.first_unlabelled() == 1 assert manager.last_unlabelled() == 30 assert manager.first_labelled() == 10 assert manager.last_labelled() == 10 ``` #### File: pylabelbuddy/tests/test_annotations_navigator.py ```python from unittest.mock import MagicMock from pylabelbuddy import _annotations_navigator def test_annotations_navigator(root): annotations_manager = MagicMock() annotations_manager.first_doc.return_value = 2 annotations_manager.last_doc.return_value = 123 annotations_manager.first_labelled.return_value = 7 annotations_manager.last_labelled.return_value = 123 annotations_manager.first_unlabelled.return_value = 2 annotations_manager.last_unlabelled.return_value = 32 annotations_manager.current_doc_id = 9 navigator = _annotations_navigator.AnnotationsNavigator( root, annotations_manager ) for button_name in ["", "labelled_", "unlabelled_"]: for direction in ["prev", "next"]: assert ( getattr(navigator.nav_bar, f"{direction}_{button_name}button")[ "state" ] == "normal" ) annotations_manager.current_doc_id = 5 navigator.refresh() assert navigator.nav_bar.prev_labelled_button["state"] == "disabled" ``` #### File: pylabelbuddy/tests/test_annotations_notebook.py ```python from pylabelbuddy import _annotations_notebook def test_annotations_notebook(root, annotations_mock, dataset_mock): nb = _annotations_notebook.AnnotationsNotebook( root, annotations_mock, dataset_mock ) nb.change_database() assert nb.notebook.index(nb.notebook.select()) == 2 nb.go_to_annotations() assert nb.notebook.index(nb.notebook.select()) == 0 ``` #### File: pylabelbuddy/tests/test_dataset_menu.py ```python from unittest.mock import MagicMock from pylabelbuddy import _dataset_menu class _DB: def __init__(self): self.lab_ids = list(range(7, 40, 4)) self.unlab_ids = list(range(9, 59, 4)) self.all_ids = sorted(self.lab_ids + self.unlab_ids) self.doc_ids = { "all docs": self.all_ids, "unlabelled docs": self.unlab_ids, "labelled docs": self.lab_ids, } def get_docs(self, offset, page_size, doc_filter): docs_ids = self.doc_ids[doc_filter][offset : offset + page_size] return [ {"id": doc_id, "trunc_content": f"doc {doc_id} {doc_filter}"} for doc_id in docs_ids ] def n_docs(self, doc_filter): return len(self.get_docs(0, 1000, doc_filter)) def test_documents_list(root, example_labels): db = _DB() assert db.n_docs("all docs") == 22 assert db.n_docs("labelled docs") == 9 assert db.n_docs("unlabelled docs") == 13 manager = MagicMock() manager.get_labels.return_value = [ {"id": i + 1, "string_form": la["text"], "color": "#aabbcc"} for i, la in enumerate(example_labels) ] manager.total_n_docs.side_effect = db.n_docs manager.get_docs.side_effect = db.get_docs menu = _dataset_menu.DatasetMenu(root, manager) doc_list = menu.documents_list doc_list.page_size = 4 doc_list.fill() assert doc_list.docs_info == db.get_docs(0, 4, "all docs") doc_list.next_page() assert doc_list.docs_info == db.get_docs(4, 4, "all docs") doc_list.next_page() assert doc_list.offset == 8 doc_list.doc_filter.set("labelled docs") doc_list._filter_change() assert doc_list.offset == 0 doc_list.last_page() assert doc_list.offset == 8 doc_list.next_page() doc_list.next_page() assert doc_list.offset == 8 assert len(doc_list.docs_info) == 1 assert doc_list.docs_info == db.get_docs(8, 4, "labelled docs") doc_list.docs_list.listbox.selection_set(0) doc_list.go_to_annotations() assert doc_list.requested_doc_id == 39 doc_list.delete_selection() assert manager.delete_docs.call_args[0] == ([39],) doc_list.prev_page() assert doc_list.offset == 4 doc_list.first_page() assert doc_list.offset == 0 manager.total_n_docs.side_effect = lambda *args: 0 doc_list.fill() assert hasattr(doc_list, "empty_banner") def test_labels_list(root, example_labels, monkeypatch): color_chooser = MagicMock() color_chooser.return_value = ((1, 2, 3), "#010203") monkeypatch.setattr("tkinter.colorchooser.askcolor", color_chooser) db = _DB() manager = MagicMock() labels = [ {"id": i + 1, "string_form": la["text"], "color": "#aabbcc"} for i, la in enumerate(example_labels) ] manager.get_labels.return_value = labels manager.total_n_docs.side_effect = db.n_docs manager.get_docs.side_effect = db.get_docs menu = _dataset_menu.DatasetMenu(root, manager) lab_list = menu.labels_list assert lab_list.labels_info == labels lab_list.labels_list.listbox.selection_set([1]) lab_list.labels_list.listbox.selection_set([3]) lab_list._update_button_states() assert lab_list.delete_button["state"] == "normal" lab_list._set_color_for_selection() assert manager.set_label_color.call_args[0] == ( labels[1]["id"], "#010203", ) lab_list.delete_selection() assert manager.delete_labels.call_args[0] == ( [labels[1]["id"], labels[3]["id"]], ) manager.get_labels.side_effect = lambda *args: [] lab_list.fill() assert hasattr(lab_list, "empty_banner") ``` #### File: pylabelbuddy/tests/test_searchable_text.py ```python from pylabelbuddy import _searchable_text def test_searchable_text(example_text): root = None text = example_text searchable_text = _searchable_text.SearchableText(root, None) searchable_text._fill(text) assert searchable_text.text["state"] == "disabled" searchable_text.search_box.search_string.set("maçã") selected = searchable_text.text.tag_ranges("sel") start = searchable_text.text.count("1.0", selected[0])[0] end = searchable_text.text.count("1.0", selected[1])[0] assert text[start : end + 1] == "maçã1" searchable_text._search_next() selected = searchable_text.text.tag_ranges("sel") start = searchable_text.text.count("1.0", selected[0])[0] end = searchable_text.text.count("1.0", selected[1])[0] assert text[start : end + 1] == "maçã2" searchable_text._search_prev() selected = searchable_text.text.tag_ranges("sel") start = searchable_text.text.count("1.0", selected[0])[0] end = searchable_text.text.count("1.0", selected[1])[0] assert text[start : end + 1] == "maçã1" searchable_text._search_prev() selected = searchable_text.text.tag_ranges("sel") start = searchable_text.text.count("1.0", selected[0])[0] end = searchable_text.text.count("1.0", selected[1])[0] assert text[start : end + 1] == "maçã3" ```
{ "source": "jeromedontdev/tinkup", "score": 2 }
#### File: jeromedontdev/tinkup/tinkup.py ```python from cgitb import text import queue from random import seed import serial import serial.tools.list_ports from signal import signal, SIGINT import sys import threading import time import tkinter from tkinter import END, W, PhotoImage, filedialog as fd, scrolledtext as sd global fw_filename fw_filename = "" COM_OVERRIDE=None VERSION='1.0' DEBUG=False running = True class PrintLogger(): def __init__(self, textbox): self.textbox = textbox def write(self, text): self.textbox.insert(tkinter.END, text) self.textbox.see(END) def flush(self): pass def on_closing(): global running running = False def sig_handler(signal_received, frame): on_closing() class Tink: cmd = { 'CmdGetVer': b'\x01', 'CmdErase': b'\x02', 'CmdWrite': b'\x03', 'JumpApp': b'\x05', } ctrl = { 'SOH': b'\x01', 'EOT': b'\x04', 'DLE': b'\x10', } rxfsm = { 'RxIdle': 0, 'RxBuffer': 1, 'RxEscape': 2, } blfsm = { 'BlIdle': 0, 'BlVersion': 1, 'BlErase': 2, 'BlWrite': 3, 'BlJump': 4, } serial = None rx_state = rxfsm['RxIdle'] def timer(self, timestamp): # 100ms interval timer if running: timestamp += 0.1 self.timer_thread = threading.Timer(timestamp - time.time(), self.timer, args=(timestamp,)).start() def calc_crc(self, b): # NOTE: This is the CRC lookup table for polynomial 0x1021 lut = [ 0, 4129, 8258, 12387,\ 16516, 20645, 24774, 28903,\ 33032, 37161, 41290, 45419,\ 49548, 53677, 57806, 61935] num1 = 0 for num2 in b: num3 = (num1 >> 12) ^ (num2 >> 4) num4 = (lut[num3 & 0x0F] ^ (num1 << 4)) & 0xFFFF num5 = (num4 >> 12) ^ num2 num1 = (lut[num5 & 0x0F] ^ (num4 << 4)) & 0xFFFF return num1 def rx_process(self, packet, debug=DEBUG): if debug: print('Processing packet: %s' % packet.hex()) crc_rx = (packet[-1] << 8) | packet[-2] if self.calc_crc(packet[0:-2]) != crc_rx: print('Bad CRC received, resetting state') self.bl_state = self.blfsm['BlIdle'] else: cmd = bytes([packet[0]]) payload = packet[1:-2] if self.bl_state == self.blfsm['BlVersion']: if cmd == self.cmd['CmdGetVer']: print('Found device ID: %s' % payload.decode().split('\x00')[0]) print('Erasing device... ', end='') self.tx_packet(self.cmd['CmdErase']) self.bl_state = self.blfsm['BlErase'] else: print('ERROR: Expected response code CmdGetVer, got %s' % packet[0]) elif self.bl_state == self.blfsm['BlErase']: if cmd == self.cmd['CmdErase']: print('OKAY') self.hex_line = 1 self.fw_file = open(self.fw_name, 'r') tx = bytearray(self.cmd['CmdWrite']) hex_line = bytes.fromhex(self.fw_file.readline().rstrip()[1:]) tx += hex_line print('Writing firmware %d/%d... ' % (self.hex_line, self.hex_nline), end='') self.tx_packet(tx) self.bl_state = self.blfsm['BlWrite'] else: print('ERROR: Expected response code CmdErase, got %s' % packet[0]) elif self.bl_state == self.blfsm['BlWrite']: if cmd == self.cmd['CmdWrite']: print('OKAY') self.hex_line = self.hex_line + 1 # hex_line starts at 1, so we need to send up to and # including hex_nline if self.hex_line > self.hex_nline: print('Update complete, booting firmware') self.bl_state = self.blfsm['BlJump'] self.tx_packet(self.cmd['JumpApp']) button_state() return # There doesnt seem to be a response to the JumpApp # command, so at this point we're done. self.running = False else: tx = bytearray(self.cmd['CmdWrite']) hex_line = bytes.fromhex(self.fw_file.readline().rstrip()[1:]) tx += hex_line print('Writing firmware %d/%d... ' % (self.hex_line, self.hex_nline), end='') self.tx_packet(tx) else: print('ERROR: Expected response code CmdWrite, got %s' % packet[0]) def rx_buffer(self, b, debug=DEBUG): state_begin = self.rx_state if self.rx_state == self.rxfsm['RxIdle']: # Ignore bytes until we see SOH if b == self.ctrl['SOH']: self.rxbuf = bytearray() self.rx_state = self.rxfsm['RxBuffer'] elif self.rx_state == self.rxfsm['RxBuffer']: if b == self.ctrl['DLE']: # Escape the next control sequence self.rx_state = self.rxfsm['RxEscape'] elif b == self.ctrl['EOT']: # End of transmission self.rx_state = self.rxfsm['RxIdle'] self.rx_process(self.rxbuf) else: # Buffer the byte self.rxbuf += b elif self.rx_state == self.rxfsm['RxEscape']: # Unconditionally buffer any byte following the escape sequence self.rxbuf += b self.rx_state = self.rxfsm['RxBuffer'] else: # Shouldn't get here print('Unknown state') self.rx_state = self.rxfsm['RxIdle'] if debug: keys = list(self.rxfsm.keys()) vals = list(self.rxfsm.values()) s0 = vals.index(state_begin) s1 = vals.index(self.rx_state) print('RX: %s, RX FSM state: %s -> %s' % (b.hex(), keys[s0], keys[s1])) def rx(self): while running: if self.serial: b = self.serial.read(1) if b: self.rx_buffer(b) else: print('RX timeout?') else: print('Lost serial port') time.sleep(1) def tx(self, b, debug=DEBUG): if debug: print('TX: %s' % b.hex()) if self.serial and self.serial.is_open: try: self.serial.write(b) self.serial.flush() except: print('TX failure') button_state() return else: print('TX failure, serial port not writeable') button_state() return def tx_packet(self, b): # b should be a bytearray crc = self.calc_crc(b) b += bytes([crc & 0xFF]) b += bytes([(crc >> 8) & 0xFF]) b_tx = bytearray(self.ctrl['SOH']) for bb in b: bb = bytes([bb]) # Escape any control characters that appear in the TX buffer if bb == self.ctrl['SOH'] or bb == self.ctrl['EOT'] or bb == self.ctrl['DLE']: b_tx += self.ctrl['DLE'] b_tx += bb b_tx += self.ctrl['EOT'] self.tx(b_tx) def __init__(self, fw_name=None, port=None): self.rx_state = self.rxfsm['RxIdle'] self.bl_state = self.blfsm['BlIdle'] self.fw_name = fw_name self.hex_nline = 0 self.hex_line = 0 # Ensure the file exists, has valid Intel Hex checksums, and count lines try: with open(self.fw_name) as fw_file: for line in fw_file: self.hex_nline = self.hex_nline + 1 line = line.rstrip()[1:] try: checksum = bytes.fromhex(line[-2:]) except: print('%s is not a valid hex file' % fw_name) button_state() return # It seems to just load hex if it's blank data = bytes.fromhex(line[:-2]) s = bytes([((~(sum(data) & 0xFF) & 0xFF) + 1) & 0xFF]) if checksum != s: print('%s is not a valid hex file' % fw_name) button_state() return except: print('No file selected') button_state() return comports = [] try: if port == None: comports_all = [comport for comport in serial.tools.list_ports.comports()] for com in comports_all: if com.manufacturer == 'FTDI': comports.append(com.device) else: comports.append(port) if comports: if len(comports) > 1: print('Several FTDI devices detected - not sure which to target. Aborting.') # TODO: Add interactive device selector? button_state() return for com in comports: try: self.serial = serial.Serial(com, baudrate=115200, timeout=None, rtscts=True) print('Opened device at %s' % com) except Exception as ex: print('Could not open device at %s' % com) print('Exception: %s' % ex) button_state() return else: print('No RetroTINK devices found') button_state() return except: print('No communication with device') button_state() return if self.serial: self.rx_process_thread = threading.Thread(target=self.rx, args=()) self.rx_process_thread.daemon = True self.rx_process_thread.start() self.timer_thread = threading.Thread(target=self.timer, args=(time.time() + 0.1,)) self.timer_thread.daemon = True self.timer_thread.start() else: button_state() return self.running = True retries = 1 self.bl_state = self.blfsm['BlVersion'] while retries and running: retries = retries - 1 print('Probing device... ', end='') self.tx_packet(self.cmd['CmdGetVer']) time.sleep(1) # Need to add a timeout def file_select(): filetypes = ( ('hex files', '*.hex'), ('All files', '*.*') ) fw_filename = fd.askopenfilename( title='Select hex', initialdir='/', filetypes=filetypes) browse_box.configure(state="normal") browse_box.delete(0, END) browse_box.insert(0,fw_filename) browse_box.configure(state="readonly") def tink_flash(): fw_filename = browse_box.get() try: button_state() tink = Tink(fw_name=fw_filename, port=COM_OVERRIDE) except: print('Could not execute flash') button_state() return def button_state(): if browse_button['state'] == "normal": browse_button.configure(state="disabled") flash_button.configure(state="disabled") else: browse_button.configure(state="normal") flash_button.configure(state="normal") if __name__ == '__main__': signal(SIGINT, sig_handler) window = tkinter.Tk() window.geometry('680x380') window.iconbitmap(default='./assets/icon.ico') window.title('tinkup-gui') window.resizable(False,False) window.eval('tk::PlaceWindow . center') tink_logo = PhotoImage(file='./assets/RetroTINK-logo.png') tink_logo = tink_logo.subsample(4,4) tink_label = tkinter.Label(window,image=tink_logo) tink_label.place(x=285, y=10) fw_label = tkinter.Label(window,text="Hex File:") fw_label.place(x=325, y=90) browse_box = tkinter.Entry(window,textvariable=fw_filename) browse_box.configure(state="readonly") browse_box.place(x=10, y=120, width=582) browse_button = tkinter.Button(window,text='Load HEX',command=file_select) browse_button.place(x=610, y=115) flash_button = tkinter.Button(window, text="Flash", command=tink_flash) flash_button.place(x=330, y=145) print_text = sd.ScrolledText(window, undo=True) print_text.place(x=10, y=180, height=180) logger = PrintLogger(print_text) sys.stdout = logger try: from ctypes import windll windll.shcore.SetProcessDpiAwareness(1) finally: window.mainloop() on_closing() ```
{ "source": "JeromeEippers/pyside_maya_class", "score": 4 }
#### File: JeromeEippers/pyside_maya_class/ControlFlow.py ```python x = 2 print(x == 2) print(x == 3) print(x < 3) print(x != 3) print(x <> 3 ) print('John' in ["John", "Rick"]) print('Jeff' in ["John", "Rick"]) print('Jeff' not in ["John", "Rick"]) #--------------------- IF --------------------------- #if else x = 0 if x < 0: print 'negative' else: print 'positive' #if elif else x = 0 if x < 0: print 'negative' elif x == 0: print 'zero' elif x == 1: print 'one' else: print 'big number' #and or name = "John" age = 23 if name == "John" and age == 23: print("Your name is John, and you are also 23 years old.") if name == "John" or name == "Rick": print("Your name is either John or Rick.") #empty list is tested as False myList = [] if myList: print('there is some values') else: print('there is no value') myList = [1] if myList: print('there is some values') else: print('there is no value') #EXERCICE 1 #Change the values of the 4 variables of this exercice so all tests return true number = 10 second_number = 2 first_array = [] second_array = [1,2,3] if number > 15: print("1") if first_array: print("2") if len(second_array) == 2: print("3") if len(first_array) + len(second_array) == 5: print("4") if first_array and first_array[0] == 1: print("5") if not second_number: print("6") #------ #--------------------- LOOPS --------------------------- # Measure some strings: words = ['cat', 'window', 'dog'] for w in words: print w, len(w) #range function print( range(5) ) print( range(3,9) ) print( range(3,9,2) ) for x in range(5): print x #mix range and len function to loop over indexes of list a = ['Mary', 'had', 'a', 'little', 'lamb'] for i in range(len(a)): print i, a[i] #break myList = list() for x in range(10): myList.append( x ) if x == 5: break print myList #continue myList = list() for x in range(10): myList.append( x ) if x == 5: continue print myList #EXERCICE 2 #loop on this list and print all the even numbers but stop at the number 237 numbers = [ 951, 402, 984, 651, 360, 69, 408, 319, 601, 485, 980, 507, 725, 547, 544, 615, 83, 165, 141, 501, 263, 617, 865, 575, 219, 390, 984, 592, 236, 105, 942, 941, 386, 462, 47, 418, 907, 344, 236, 375, 823, 566, 597, 978, 328, 615, 953, 345, 399, 162, 758, 219, 918, 237, 412, 566, 826, 248, 866, 950, 626, 949, 687, 217, 815, 67, 104, 58, 512, 24, 892, 894, 767, 553, 81, 379, 843, 831, 445, 742, 717, 958, 609, 842, 451, 688, 753, 854, 685, 93, 857, 440, 380, 126, 721, 328, 753, 470, 743, 527 ] #>>> [402, 984, 360, 408, 980, 544, 390, 984, 592, 236, 942, 386, 462, 418, 344, 236, 566, 978, 328, 162, 758, 918] #loop and dictionnaries myDict = {1:"Aa" , 2:"Bb", 3:"Cc"} for key in myDict.keys(): print key myDict = {1:"Aa" , 2:"Bb", 3:"Cc"} for value in myDict.values(): print value myDict = {'first': 100, 'second': 'YES', 5: 100, 6: 'NO'} for key, value in myDict.iteritems(): print key, value #--------------------- FUNCTIONS --------------------------- def my_function(): print("Hello From My Function!") def my_function_with_args(username, greeting): print("Hello, %s , From My Function!, I wish you %s"%(username, greeting)) def sum_two_numbers(a, b): return a + b # print(a simple greeting) my_function() #prints - "Hello, <NAME>, From My Function!, I wish you a great year!" my_function_with_args("<NAME>", "a great year!") # after this line x will hold the value 3! x = sum_two_numbers(1,2) print x #default argument ------ def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'): print "-- This parrot wouldn't", action, print "if you put", voltage, "volts through it." print "-- Lovely plumage, the", type print "-- It's", state, "!" parrot(1000) # 1 positional argument parrot(voltage=1000) # 1 keyword argument parrot(voltage=1000000, action='VOOOOOM') # 2 keyword arguments parrot(action='VOOOOOM', voltage=1000000) # 2 keyword arguments parrot('a million', 'bereft of life', 'jump') # 3 positional arguments parrot('a thousand', state='pushing up the daisies') # 1 positional, 1 keyword #multiple return and unpack def myFunction(): return 10 , 20 x, y = myFunction() print x print y #EXERCICE 3 #Modify this funciton to return a list of names def get_name_list(): pass #Modify this function to return the name concatenated with the sentence 'was here' def get_sentence( name ): pass def test_exercice(): for name in get_name_list(): print( get_sentence( name ) ) test_exercice() ``` #### File: pyside_maya_class/fbx_exporter/exporter.py ```python from PySide2 import QtWidgets, QtGui import maya.cmds as cmds import maya.mel as mel import os from maya.app.general.mayaMixin import MayaQWidgetDockableMixin class GroupText(QtWidgets.QWidget): """Group text create a line edit inside a groupbox""" def __init__(self, label, text="", placeHolderText="", parent=None): super(GroupText, self).__init__(parent) layV = QtWidgets.QVBoxLayout(self) self.setLayout(layV) layV.setContentsMargins(0,0,0,0) grp = QtWidgets.QGroupBox(label, self) layV.addWidget(grp) layH = QtWidgets.QHBoxLayout(self) layH.setContentsMargins(2,0,2,0) grp.setLayout(layH) self._line = QtWidgets.QLineEdit(text, self) self._line.setPlaceholderText(placeHolderText) layH.addWidget(self._line,100) layV.addStretch() def line(self): return self._line class AbstractPickerWidget(QtWidgets.QWidget): """Base class for a picker You have to implement the onClicked method""" def __init__(self, label, text="", placeHolderText="", btnText="", iconBtn="", parent=None): super(AbstractPickerWidget, self).__init__(parent) layV = QtWidgets.QVBoxLayout(self) self.setLayout(layV) layV.setContentsMargins(0,0,0,0) grp = QtWidgets.QGroupBox(label, self) layV.addWidget(grp) layH = QtWidgets.QHBoxLayout(self) layH.setContentsMargins(2,0,2,0) grp.setLayout(layH) self._line = QtWidgets.QLineEdit(text, self) self._line.setPlaceholderText(placeHolderText) layH.addWidget(self._line,100) if iconBtn: icon = QtGui.QIcon(iconBtn) btn = QtWidgets.QPushButton(icon, btnText) else: btn = QtWidgets.QPushButton(btnText) btn.clicked.connect( self.onClicked ) layH.addWidget(btn,1) layV.addStretch() def line(self): return self._line def onClicked(self): pass class MayaPickerWidget( AbstractPickerWidget ): """Maya picker widget This allows to quicly pick an object in Maya""" def __init__(self, label, text="", placeHolderText="", btnText="", iconBtn="", parent=None): placeHolderText= placeHolderText or "Pick Maya Object" btnText = btnText or "<" super(MayaPickerWidget, self).__init__(label, text, placeHolderText, btnText, iconBtn, parent) def onClicked(self): selection = cmds.ls(sl=True) if selection: self._line.setText(selection[0]) class FolderPickerWidget( AbstractPickerWidget ): """Folder picker widget pick a folder on the disk""" def __init__(self, label, text="", placeHolderText="", btnText="", iconBtn="", parent=None): iconBtn = iconBtn or ":/folder-closed.png" placeHolderText= placeHolderText or "Select Folder" super(FolderPickerWidget, self).__init__(label, text, placeHolderText, btnText, iconBtn, parent) def onClicked(self): dir = QtWidgets.QFileDialog.getExistingDirectory( self, "Select Folder" ) self._line.setText(dir) class MyWindow(MayaQWidgetDockableMixin, QtWidgets.QWidget): """Main exporter widget""" SAVE_OBJ = "exporter_options" def __init__(self, parent=None): """Set the layout""" super(MyWindow, self).__init__(parent) self.setWindowTitle("Exporter") lay = QtWidgets.QVBoxLayout(self) self.rootFolder = FolderPickerWidget("Export Folder") lay.addWidget(self.rootFolder) self.rigName = GroupText("Rig Name") self.rigName.line().textChanged.connect(self._canExportValidator) lay.addWidget(self.rigName) self.exportRig = QtWidgets.QGroupBox("Export Rig") self.exportRig.setCheckable(True) self.exportRig.setChecked(False) self.exportRig.toggled.connect(self._canExportValidator) groupLayout = QVBoxLayout(self) self.exportRig.setLayout(groupLayout) lay.addWidget(self.exportRig) self.rigRoot = MayaPickerWidget("Rig Root Object") self.rigRoot.line().textChanged.connect(self._canExportValidator) groupLayout.addWidget(self.rigRoot) self.exportAnim = QtWidgets.QGroupBox("Export Animation") self.exportAnim.setCheckable(True) self.exportAnim.toggled.connect(self._canExportValidator) groupLayout = QVBoxLayout(self) self.exportAnim.setLayout(groupLayout) lay.addWidget(self.exportAnim) self.animName = GroupText("Animation Name") self.animName.line().textChanged.connect(self._canExportValidator) groupLayout.addWidget(self.animName) self.animRoot = MayaPickerWidget("Anim Root Object") self.animRoot.line().textChanged.connect(self._canExportValidator) groupLayout.addWidget(self.animRoot) self.startFrame = GroupText("start frame", text="0") self.startFrameValidator = QtGui.QIntValidator() self.startFrame.line().setValidator(self.startFrameValidator) self.startFrame.line().textChanged.connect(self._canExportValidator) groupLayout.addWidget(self.startFrame) self.endFrame = GroupText("end frame", text="30") self.endFrameValidator = QtGui.QIntValidator() self.endFrame.line().setValidator(self.endFrameValidator) self.endFrame.line().textChanged.connect(self._canExportValidator) groupLayout.addWidget(self.endFrame) lay.addStretch(2000) buttonsLayout = QtWidgets.QHBoxLayout(self) lay.addLayout(buttonsLayout) self.saveBtn = QtWidgets.QPushButton("Save") self.saveBtn.clicked.connect(self.onSave) buttonsLayout.addWidget(self.saveBtn) self.exportBtn = QtWidgets.QPushButton("EXPORT") self.exportBtn.clicked.connect(self.onExport) buttonsLayout.addWidget(self.exportBtn,100) self._canExportValidator() def _canExportValidator(self, event=None): """call back on all modification, so we can enable the export button""" self.exportBtn.setEnabled(self.canExport()) def options(self): """return the options of the tool as a dictionary""" return { 'export_folder' : ( str( self.rootFolder.line().text() )).replace('\\', '/'), 'rig_name' : str( self.rigName.line().text() ), 'export_rig' : self.exportRig.isChecked(), 'rig_root' : str( self.rigRoot.line().text() ), 'export_animation' : self.exportAnim.isChecked(), 'animation_name' : str( self.animName.line().text() ), 'animation_root' : str( self.animRoot.line().text() ), 'startFrame' : int( self.startFrame.line().text() ), 'endFrame' : int( self.endFrame.line().text() ) } def canExport(self, options=None): """check if we can export something using the options from the tool, or from a specific set of options""" options = options or self.options() if options['export_folder'] == '' or options['rig_name'] == '': return False if options['export_rig'] == False and options['export_animation'] == False: return False if options['export_rig'] == True: if options['rig_root'] == '': return False if options['export_animation'] == True: if options['animation_name'] == '' or options['animation_root'] == '': return False return True def export_fbx(self, path, root, startFrame=0, endFrame=0): """Export an FBX file""" cmds.select( root ) mel.eval('FBXExportAnimationOnly -v false') mel.eval('FBXExportBakeComplexAnimation -v true') mel.eval('FBXExportBakeComplexStart -v {0}'.format(startFrame)) mel.eval('FBXExportBakeComplexEnd -v {0}'.format(endFrame)) mel.eval('FBXExportBakeResampleAnimation -v true') mel.eval('FBXExportConstraints -v false') mel.eval('FBXExportInputConnections -v false') mel.eval('FBXExportSkeletonDefinitions -v true') mel.eval('FBXExportSkins -v true') mel.eval('FBXExport -f "{0}" -s'.format( path.replace('\\', '/') ) ) def onExport(self, options=None): """export using the options from the tool, or from a specific set of options""" options = options or self.options() if self.canExport( options ): folder = os.path.normpath ( options['export_folder'] ) if options['export_rig']: path = os.path.join(folder, options['rig_name'] + '.fbx') self.export_fbx(path, options['rig_root']) if options['export_animation']: path = os.path.join(folder, options['rig_name'] + '@' + options['animation_name'] + '.fbx') self.export_fbx(path, options['animation_root'], options['startFrame'], options['endFrame']) def onSave(self): """saving the options in the file""" if cmds.objExists( self.SAVE_OBJ ): cmds.delete(self.SAVE_OBJ) node = cmds.createNode('transform', name=self.SAVE_OBJ) cmds.addAttr( node, longName='options', dt='string' ) cmds.setAttr( node + '.options', str(self.options()), type='string' ) win = MyWindow() win.show(dockable=True) ``` #### File: pyside_maya_class/maya_devkit_scripts/cacheFileConverter.py ```python import os import os.path import getopt import sys import xml.dom.minidom import string import re import array """ This example shows how to convert float channels found in cache files in Maya 8.5 and later to double channels, so that the cache file would then be compatible with the geometry cache in Maya 8.0. It parses the XML file in addition to the cache data files and handles caches that are one file per frame as well as one file. To use: python cacheFileConverter.py -f mayaCacheFile.xml -o outputFileName Overview of Maya Caches: ======================== Conceptually, a Maya cache consists of 1 or more channels of data. Each channel has a number of properties, such as: - start/end time - data type of the channel (eg. "DoubleVectorArray" to represents a point array) - interpretation (eg. "positions" the vector array represents position data, as opposed to per vertex normals, for example) - sampling type (eg. "regular" or "irregular") - sampling rate (meaningful only if sampling type is "regular") Each channel has a number of data points in time, not necessarily regularly spaced, and not necessarily co-incident in time with data in other channels. At the highest level, a Maya cache is simply made up of channels and their data in time. On disk, the Maya cache is made up of a XML description file, and 1 or more data files. The description file provides a high level overview of what the cache contains, such as the cache type (one file, or one file per frame), channel names, interpretation, etc. The data files contain the actual data for the channels. In the case of one file per frame, a naming convention is used so the cache can check its available data at runtime. Here is a visualization of the data format of the OneFile case: // |---CACH (Group) // Header // | |---VRSN // Version Number (char*) // | |---STIM // Start Time of the Cache File (int) // | |---ETIM // End Time of the Cache File (int) // | // |---MYCH (Group) // 1st Time // | |---TIME // Time (int) // | |---CHNM // 1st Channel Name (char*) // | |---SIZE // 1st Channel Size // | |---DVCA // 1st Channel Data (Double Vector Array) // | |---CHNM // n-th Channel Name // | |---SIZE // n-th Channel Size // | |---DVCA // n-th Channel Data (Double Vector Array) // | |.. // | // |---MYCH (Group) // 2nd Time // | |---TIME // Time // | |---CHNM // 1st Channel Name // | |---SIZE // 1st Channel Size // | |---DVCA // 1st Channel Data (Double Vector Array) // | |---CHNM // n-th Channel Name // | |---SIZE // n-th Channel Size // | |---DVCA // n-th Channel Data (Double Vector Array) // | |.. // | // |---.. // | // In a multiple file caches, the only difference is that after the header "CACH" group, there is only one MYCH group and there is no TIME chunk. In the case of one file per frame, the time is part of the file name - allowing Maya to scan at run time to see what data is actually available, and it allows users to move data in time by manipulating the file name. !Note that it's not necessary to have data for every channel at every time. """ class CacheChannel: m_channelName = "" m_channelType = "" m_channelInterp = "" m_sampleType = "" m_sampleRate = 0 m_startTime = 0 m_endTime = 0 def __init__(self,channelName,channelType,interpretation,samplingType,samplingRate,startTime,endTime): self.m_channelName = channelName self.m_channelType = channelType self.m_channelInterp = interpretation self.m_sampleType = samplingType self.m_sampleRate = samplingRate self.m_startTime = startTime self.m_endTime = endTime class CacheFile: m_baseFileName = "" m_directory = "" m_fullPath = "" m_cacheType = "" m_cacheStartTime = 0 m_cacheEndTime = 0 m_timePerFrame = 0 m_version = 0.0 m_channels = [] ######################################################################## # Description: # Class constructor - tries to figure out full path to cache # xml description file before calling parseDescriptionFile() # def __init__(self,fileName): # fileName can be the full path to the .xml description file, # or just the filename of the .xml file, with or without extension # if it is in the current directory dir = os.path.dirname(fileName) fullPath = "" if dir == "": currDir = os.getcwd() fullPath = os.path.join(currDir,fileName) if not os.path.exists(fullPath): fileName = fileName + '.xml'; fullPath = os.path.join(currDir,fileName) if not os.path.exists(fullPath): print "Sorry, can't find the file %s to be opened\n" % fullPath sys.exit(2) else: fullPath = fileName self.m_baseFileName = os.path.basename(fileName).split('.')[0] self.m_directory = os.path.dirname(fullPath) self.m_fullPath = fullPath self.parseDescriptionFile(fullPath) ######################################################################## # Description: # Writes a converted description file, where all instances of "FloatVectorArray" # are replaced with "DoubleVectorArray" # def writeConvertedDescriptionFile(self,outputFileName): newXmlFileName = outputFileName + ".xml" newXmlFullPath = os.path.join(self.m_directory,newXmlFileName) fd = open(self.m_fullPath,"r") fdOut = open(newXmlFullPath,"w") lines = fd.readlines() for line in lines: if line.find("FloatVectorArray") >= 0: line = line.replace("FloatVectorArray","DoubleVectorArray") fdOut.write(line) ######################################################################## # Description: # Given the full path to the xml cache description file, this # method parses its contents and sets the relevant member variables # def parseDescriptionFile(self,fullPath): dom = xml.dom.minidom.parse(fullPath) root = dom.getElementsByTagName("Autodesk_Cache_File") allNodes = root[0].childNodes for node in allNodes: if node.nodeName == "cacheType": self.m_cacheType = node.attributes.item(0).nodeValue if node.nodeName == "time": timeRange = node.attributes.item(0).nodeValue.split('-') self.m_cacheStartTime = int(timeRange[0]) self.m_cacheEndTime = int(timeRange[1]) if node.nodeName == "cacheTimePerFrame": self.m_timePerFrame = int(node.attributes.item(0).nodeValue) if node.nodeName == "cacheVersion": self.m_version = float(node.attributes.item(0).nodeValue) if node.nodeName == "Channels": self.parseChannels(node.childNodes) ######################################################################## # Description: # helper method to extract channel information # def parseChannels(self,channels): for channel in channels: if re.compile("channel").match(channel.nodeName) != None : channelName = "" channelType = "" channelInterp = "" sampleType = "" sampleRate = 0 startTime = 0 endTime = 0 for index in range(0,channel.attributes.length): attrName = channel.attributes.item(index).nodeName if attrName == "ChannelName": channelName = channel.attributes.item(index).nodeValue if attrName == "ChannelInterpretation": channelInterp = channel.attributes.item(index).nodeValue if attrName == "EndTime": endTime = int(channel.attributes.item(index).nodeValue) if attrName == "StartTime": startTime = int(channel.attributes.item(index).nodeValue) if attrName == "SamplingRate": sampleRate = int(channel.attributes.item(index).nodeValue) if attrName == "SamplingType": sampleType = channel.attributes.item(index).nodeValue if attrName == "ChannelType": channelType = channel.attributes.item(index).nodeValue channelObj = CacheChannel(channelName,channelType,channelInterp,sampleType,sampleRate,startTime,endTime) self.m_channels.append(channelObj) def fileFormatError(): print "Error: unable to read cache format\n"; sys.exit(2) def readInt(fd,needSwap): intArray = array.array('l') intArray.fromfile(fd,1) if needSwap: intArray.byteswap() return intArray[0] def writeInt(fd,outInt,needSwap): intArray = array.array('l') intArray.insert(0,outInt) if needSwap: intArray.byteswap() intArray.tofile(fd) ######################################################################## # Description: # method to parse and convert the contents of the data file, for the # One large file case ("OneFile") def parseDataOneFile(cacheFile,outFileName): dataFilePath = os.path.join(cacheFile.m_directory,cacheFile.m_baseFileName) dataFileNameOut = outFileName + ".mc" dataFilePathOut = os.path.join(cacheFile.m_directory,dataFileNameOut) dataFilePath = dataFilePath + ".mc" if not os.path.exists(dataFilePath): print "Error: unable to open cache data file at %s\n" % dataFilePath sys.exit(2) fd = open(dataFilePath,"rb") fdOut = open(dataFilePathOut,"wb") blockTag = fd.read(4) fdOut.write(blockTag) #blockTag must be FOR4 if blockTag != "FOR4": fileFormatError() platform = sys.platform needSwap = False if re.compile("win").match(platform) != None : needSwap = True if re.compile("linux").match(platform) != None : needSwap = True offset = readInt(fd,needSwap) writeInt(fdOut,offset,needSwap) #The 1st block is the header, not used. #just write out as is header = fd.read(offset) fdOut.write(header) while True: #From now on the file is organized in blocks of time #Each block holds the data for all the channels at that #time blockTag = fd.read(4) fdOut.write(blockTag) if blockTag == "": #EOF condition...we are done return if blockTag != "FOR4": fileFormatError() blockSize = readInt(fd,needSwap) #We cannot just write out the old block size, since we are potentially converting #Float channels to doubles, the block size may increase. newBlockSize = 0 bytesRead = 0 #Since we don't know the size of the block yet, we will cache everything in a dictionary, #and write everything out in the end. blockContents = {} mychTag = fd.read(4) if mychTag != "MYCH": fileFormatError() bytesRead += 4 blockContents['mychTag'] = mychTag timeTag = fd.read(4) if timeTag != "TIME": fileFormatError() bytesRead += 4 blockContents['timeTag']= timeTag #Next 32 bit int is the size of the time variable, #this is always 4 timeVarSize = readInt(fd,needSwap) bytesRead += 4 blockContents['timeVarSize']= timeVarSize #Next 32 bit int is the time itself, in ticks #1 tick = 1/6000 of a second time = readInt(fd,needSwap) bytesRead += 4 blockContents['time']= time newBlockSize = bytesRead channels = [] blockContents['channels'] = channels print "Converting Data found at time %f seconds...\n"%(time/6000.0) while bytesRead < blockSize: channelContents = {} #channel name is next. #the tag for this must be CHNM chnmTag = fd.read(4) if chnmTag != "CHNM": fileFormatError() bytesRead += 4 newBlockSize += 4 channelContents['chnmTag'] = chnmTag #Next comes a 32 bit int that tells us how long the #channel name is chnmSize = readInt(fd,needSwap) bytesRead += 4 newBlockSize += 4 channelContents['chnmSize'] = chnmSize #The string is padded out to 32 bit boundaries, #so we may need to read more than chnmSize mask = 3 chnmSizeToRead = (chnmSize + mask) & (~mask) channelName = fd.read(chnmSize) paddingSize = chnmSizeToRead-chnmSize channelContents['channelName'] = channelName channelContents['paddingSize'] = paddingSize if paddingSize > 0: padding = fd.read(paddingSize) channelContents['padding'] = padding bytesRead += chnmSizeToRead newBlockSize += chnmSizeToRead #Next is the SIZE field, which tells us the length #of the data array sizeTag = fd.read(4) channelContents['sizeTag'] = sizeTag if sizeTag != "SIZE": fileFormatError() bytesRead += 4 newBlockSize += 4 #Next 32 bit int is the size of the array size variable, #this is always 4, so we'll ignore it for now #though we could use it as a sanity check. arrayVarSize = readInt(fd,needSwap) bytesRead += 4 newBlockSize += 4 channelContents['arrayVarSize'] = arrayVarSize #finally the actual size of the array: arrayLength = readInt(fd,needSwap) bytesRead += 4 newBlockSize += 4 channelContents['arrayLength'] = arrayLength #data format tag: dataFormatTag = fd.read(4) #buffer length - how many bytes is the actual data bufferLength = readInt(fd,needSwap) bytesRead += 8 newBlockSize += 8 numPointsToPrint = 5 if dataFormatTag == "FVCA": #FVCA == Float Vector Array outDataTag = "DVCA" channelContents['dataFormatTag'] = outDataTag if bufferLength != arrayLength*3*4: fileFormatError() outBufLength = bufferLength*2 channelContents['bufferLength'] = outBufLength floatArray = array.array('f') floatArray.fromfile(fd,arrayLength*3) doubleArray = array.array('d') bytesRead += arrayLength*3*4 newBlockSize += arrayLength*3*8 if needSwap: floatArray.byteswap() for index in range(0,arrayLength*3): doubleArray.append(floatArray[index]) if needSwap: doubleArray.byteswap() channelContents['doubleArray'] = doubleArray channels.append(channelContents) elif dataFormatTag == "DVCA": #DVCA == Double Vector Array channelContents['dataFormatTag'] = dataFormatTag if bufferLength != arrayLength*3*8: fileFormatError() channelContents['bufferLength'] = bufferLength doubleArray = array.array('d') doubleArray.fromfile(fd,arrayLength*3) channelContents['doubleArray'] = doubleArray channels.append(channelContents) bytesRead += arrayLength*3*8 newBlockSize += arrayLength*3*8 else: fileFormatError() #Now that we have completely parsed this block, we are ready to output it writeInt(fdOut,newBlockSize,needSwap) fdOut.write(blockContents['mychTag']) fdOut.write(blockContents['timeTag']) writeInt(fdOut,blockContents['timeVarSize'],needSwap) writeInt(fdOut,blockContents['time'],needSwap) for channelContents in channels: fdOut.write(channelContents['chnmTag']) writeInt(fdOut,channelContents['chnmSize'],needSwap) fdOut.write(channelContents['channelName']) if channelContents['paddingSize'] > 0: fdOut.write(channelContents['padding']) fdOut.write(channelContents['sizeTag']) writeInt(fdOut,channelContents['arrayVarSize'],needSwap) writeInt(fdOut,channelContents['arrayLength'],needSwap) fdOut.write(channelContents['dataFormatTag']) writeInt(fdOut,channelContents['bufferLength'],needSwap) channelContents['doubleArray'].tofile(fdOut) ######################################################################## # Description: # method to parse and convert the contents of the data file, for the # file per frame case ("OneFilePerFrame") def parseDataFilePerFrame(cacheFile,outFileName): allFilesInDir = os.listdir(cacheFile.m_directory) matcher = re.compile(cacheFile.m_baseFileName) dataFiles = [] for afile in allFilesInDir: if os.path.splitext(afile)[1] == ".mc" and matcher.match(afile) != None: dataFiles.append(afile) for dataFile in dataFiles: fileName = os.path.split(dataFile)[1] baseName = os.path.splitext(fileName)[0] frameAndTickNumberStr = baseName.split("Frame")[1] frameAndTickNumber = frameAndTickNumberStr.split("Tick") frameNumber = int(frameAndTickNumber[0]) tickNumber = 0 if len(frameAndTickNumber) > 1: tickNumber = int(frameAndTickNumber[1]) timeInTicks = frameNumber*cacheFile.m_timePerFrame + tickNumber print "--------------------------------------------------------------\n" print "Converting data at time %f seconds:\n"%(timeInTicks/6000.0) fd = open(dataFile,"rb") dataFileOut = outFileName + "Frame" + frameAndTickNumberStr + ".mc" dataFileOutPath = os.path.join(cacheFile.m_directory,dataFileOut) fdOut = open(dataFileOutPath,"wb") blockTag = fd.read(4) #blockTag must be FOR4 if blockTag != "FOR4": fileFormatError() fdOut.write(blockTag) platform = sys.platform needSwap = False if re.compile("win").match(platform) != None : needSwap = True if re.compile("linux").match(platform) != None : needSwap = True offset = readInt(fd,needSwap) writeInt(fdOut,offset,needSwap) #The 1st block is the header, not used. #write out as is. header = fd.read(offset) fdOut.write(header) blockTag = fd.read(4) if blockTag != "FOR4": fileFormatError() fdOut.write(blockTag) blockSize = readInt(fd,needSwap) #We cannot just write out the old block size, since we are potentially converting #Float channels to doubles, the block size may increase. newBlockSize = 0 bytesRead = 0 #Since we don't know the size of the block yet, we will cache everything in a dictionary, #and write everything out in the end. blockContents = {} mychTag = fd.read(4) blockContents['mychTag'] = mychTag if mychTag != "MYCH": fileFormatError() bytesRead += 4 #Note that unlike the oneFile case, for file per frame there is no #TIME tag at this point. The time of the data is embedded in the #file name itself. newBlockSize = bytesRead channels = [] blockContents['channels'] = channels while bytesRead < blockSize: channelContents = {} #channel name is next. #the tag for this must be CHNM chnmTag = fd.read(4) if chnmTag != "CHNM": fileFormatError() bytesRead += 4 newBlockSize += 4 channelContents['chnmTag'] = chnmTag #Next comes a 32 bit int that tells us how long the #channel name is chnmSize = readInt(fd,needSwap) bytesRead += 4 newBlockSize += 4 channelContents['chnmSize'] = chnmSize #The string is padded out to 32 bit boundaries, #so we may need to read more than chnmSize mask = 3 chnmSizeToRead = (chnmSize + mask) & (~mask) channelName = fd.read(chnmSize) paddingSize = chnmSizeToRead-chnmSize channelContents['channelName'] = channelName channelContents['paddingSize'] = paddingSize if paddingSize > 0: padding = fd.read(paddingSize) channelContents['padding'] = padding bytesRead += chnmSizeToRead newBlockSize += chnmSizeToRead #Next is the SIZE field, which tells us the length #of the data array sizeTag = fd.read(4) channelContents['sizeTag'] = sizeTag if sizeTag != "SIZE": fileFormatError() bytesRead += 4 newBlockSize += 4 #Next 32 bit int is the size of the array size variable, #this is always 4, so we'll ignore it for now #though we could use it as a sanity check. arrayVarSize = readInt(fd,needSwap) bytesRead += 4 newBlockSize += 4 channelContents['arrayVarSize'] = arrayVarSize #finally the actual size of the array: arrayLength = readInt(fd,needSwap) bytesRead += 4 newBlockSize += 4 channelContents['arrayLength'] = arrayLength #data format tag: dataFormatTag = fd.read(4) #buffer length - how many bytes is the actual data bufferLength = readInt(fd,needSwap) bytesRead += 8 newBlockSize += 8 numPointsToPrint = 5 if dataFormatTag == "FVCA": #FVCA == Float Vector Array outDataTag = "DVCA" channelContents['dataFormatTag'] = outDataTag if bufferLength != arrayLength*3*4: fileFormatError() outBufLength = bufferLength*2 channelContents['bufferLength'] = outBufLength floatArray = array.array('f') floatArray.fromfile(fd,arrayLength*3) bytesRead += arrayLength*3*4 newBlockSize += arrayLength*3*8 doubleArray = array.array('d') if needSwap: floatArray.byteswap() for index in range(0,arrayLength*3): doubleArray.append(floatArray[index]) if needSwap: doubleArray.byteswap() channelContents['doubleArray'] = doubleArray channels.append(channelContents) elif dataFormatTag == "DVCA": #DVCA == Double Vector Array channelContents['dataFormatTag'] = dataFormatTag if bufferLength != arrayLength*3*8: fileFormatError() channelContents['bufferLength'] = bufferLength doubleArray = array.array('d') doubleArray.fromfile(fd,arrayLength*3) channelContents['doubleArray'] = doubleArray channels.append(channelContents) bytesRead += arrayLength*3*8 newBlockSize += arrayLength*3*8 else: fileFormatError() #Now that we have completely parsed this block, we are ready to output it writeInt(fdOut,newBlockSize,needSwap) fdOut.write(blockContents['mychTag']) for channelContents in channels: fdOut.write(channelContents['chnmTag']) writeInt(fdOut,channelContents['chnmSize'],needSwap) fdOut.write(channelContents['channelName']) if channelContents['paddingSize'] > 0: fdOut.write(channelContents['padding']) fdOut.write(channelContents['sizeTag']) writeInt(fdOut,channelContents['arrayVarSize'],needSwap) writeInt(fdOut,channelContents['arrayLength'],needSwap) fdOut.write(channelContents['dataFormatTag']) writeInt(fdOut,channelContents['bufferLength'],needSwap) channelContents['doubleArray'].tofile(fdOut) def usage(): print "Use -f to indicate the cache description file (.xml) you wish to convert\nUse -o to indicate the output filename" try: (opts, args) = getopt.getopt(sys.argv[1:], "f:o:") except getopt.error: # print help information and exit: usage() sys.exit(2) if len(opts) != 2: usage() sys.exit(2) fileName = "" outFileName = "" for o,a in opts: if o == "-f": fileName = a if o == "-o": outFileName = a cacheFile = CacheFile(fileName) if cacheFile.m_version > 2.0: print "Error: this script can only parse cache files of version 2 or lower\n" sys.exit(2) print "Outputing new description file...\n" cacheFile.writeConvertedDescriptionFile(outFileName) print "Beginning Conversion of data files...\n" if cacheFile.m_cacheType == "OneFilePerFrame": parseDataFilePerFrame(cacheFile,outFileName) elif cacheFile.m_cacheType == "OneFile": parseDataOneFile(cacheFile,outFileName) else: print "unknown cache type!\n" ``` #### File: pyside_maya_class/maya_pyside/003_button.py ```python from PySide2 import QtWidgets import maya.cmds as cmds class MyWindow(QtWidgets.QDialog): def __init__(self, parent=None): super(MyWindow, self).__init__(parent) layV = QtWidgets.QVBoxLayout(self) label = QtWidgets.QLabel('This is rad') layV.addWidget(label) btn = QtWidgets.QPushButton('PRESS ME') btn.clicked.connect( self.onClicked ) layV.addWidget(btn) def onClicked(self): cmds.polyCube() win = MyWindow() win.show() ``` #### File: pyside_maya_class/maya_pyside/005_main_window_dialog.py ```python from PySide2 import QtWidgets import shiboken2 import maya.OpenMayaUI as apiUI def getMayaWindow(): """ Get the main Maya window as a QtGui.QMainWindow instance @return: QtGui.QMainWindow instance of the top level Maya windows """ ptr = apiUI.MQtUtil.mainWindow() if ptr is not None: return shiboken2.wrapInstance(long(ptr), QtWidgets.QWidget) class MyWindow(QtWidgets.QDialog): def __init__(self, parent=None): super(MyWindow, self).__init__(parent) lay = QtWidgets.QVBoxLayout(self) label = QtWidgets.QLabel('label1') lay.addWidget(label) label = QtWidgets.QLabel('label2') lay.addWidget(label) win = MyWindow( getMayaWindow() ) win.show() ``` #### File: maya_pyside/exercice/001_layout.py ```python from PySide2 import QtWidgets class MyWindow (QtWidgets.QDialog): def __init__(self, parent=None): super(MyWindow, self).__init__(parent) lay = QtWidgets.QVBoxLayout(self) layH = QtWidgets.QHBoxLayout(self) lay.addLayout(layH) label = QtWidgets.QLabel('hello', self) layH.addWidget(label) label = QtWidgets.QLabel('world', self) layH.addWidget(label) label = QtWidgets.QLabel('this is rad', self) lay.addWidget(label) win = MyWindow() win.show() ``` #### File: unityTool/dataType/Prefab.py ```python from Component import Component class Prefab(Component): def __init__(self, fileId, data, file): super(Prefab, self).__init__(fileId, data, file, 'Prefab') def parentPrefabGuid(self): return self._data['m_ParentPrefab']['guid'] def isPrefabParent(self): return self._data['m_IsPrefabParent'] ```
{ "source": "JeromeEippers/python_rnd_collection", "score": 2 }
#### File: npk/animation_framework/fbxreader.py ```python import sys import numpy as np from .skeleton import Skeleton, Bone from . import posquat as pq fbxsdkpath = r'D:\Software\fbx_python37_x64' if fbxsdkpath not in sys.path: sys.path.append(fbxsdkpath) import FbxCommon as fb import fbx def find_mesh_node(pScene): def _get_mesh(pNode): if isinstance(pNode.GetNodeAttribute(), fbx.FbxMesh): return pNode for i in range(pNode.GetChildCount()): ret = _get_mesh(pNode.GetChild(i)) if ret: return ret node = _get_mesh(pScene.GetRootNode()) if node : return node return None def read_vertices_buffer(lMeshNode): lMesh = lMeshNode.GetNodeAttribute() lControlPointsCount = lMesh.GetControlPointsCount() lControlPoints = lMesh.GetControlPoints() m = lMeshNode.EvaluateGlobalTransform() # 3pos, 3normal vertexstride = 6 vertices = np.zeros((lControlPointsCount, vertexstride), dtype=np.float32) for i in range(lControlPointsCount): # get positions vertices[i, :3] = list(m.MultT(lControlPoints[i]))[:3] # get normals for j in range(lMesh.GetLayerCount()): leNormals = lMesh.GetLayer(j).GetNormals() if leNormals: if leNormals.GetMappingMode() == fbx.FbxLayerElement.eByControlPoint: if leNormals.GetReferenceMode() == fbx.FbxLayerElement.eDirect: vertices[i, 3:6] = list(m.MultT(leNormals.GetDirectArray().GetAt(i)))[:3] return vertices def read_index_buffer(lMeshNode): lMesh = lMeshNode.GetNodeAttribute() lPolygonCount = lMesh.GetPolygonCount() faces = np.zeros(lPolygonCount * 10, dtype=np.int) arrayid = 0 for i in range(lPolygonCount): lPolygonSize = lMesh.GetPolygonSize(i) # retriangulate for j in range(2, lPolygonSize): faces[arrayid] = lMesh.GetPolygonVertex(i, j - 2) arrayid += 1 faces[arrayid] = lMesh.GetPolygonVertex(i, j - 1) arrayid += 1 faces[arrayid] = lMesh.GetPolygonVertex(i, j) arrayid += 1 return faces[:arrayid] def read_skeleton(pScene): skeleton = Skeleton() def _skel(pNode, pParent): bone = Bone(pNode.GetName(), pParent) if pParent > -1: skeleton.bones[pParent].children.append(bone) skeleton.bones.append(bone) boneid = len(skeleton.bones) - 1 m = pNode.EvaluateGlobalTransform() for i in range(4): for j in range(4): skeleton.bindpose[boneid, i, j] = m.Get(i, j) skeleton.initialpose[boneid, i, j] = m.Get(i, j) for i in range(pNode.GetChildCount()): childnode = pNode.GetChild(i) if isinstance(childnode.GetNodeAttribute(), fbx.FbxMesh) == False: _skel(childnode, boneid) lRootNode = pScene.GetRootNode() _skel(lRootNode.GetChild(0), -1) #add cop bone cop = Bone('COP', 0) skeleton.bones[0].children.append(cop) skeleton.bones.append(cop) skeleton.bindpose = skeleton.bindpose[:len(skeleton.bones), :, :] skeleton.initialpose = skeleton.initialpose[:len(skeleton.bones), :, :] skeleton.parentlist = [bone.parent for bone in skeleton.bones] skeleton.upleglength = np.linalg.norm(skeleton.initialpose[skeleton.boneid('Model:LeftUpLeg'), 3, :3] - skeleton.initialpose[skeleton.boneid('Model:LeftLeg'), 3, :3]) skeleton.leglength = np.linalg.norm(skeleton.initialpose[skeleton.boneid('Model:LeftLeg'), 3, :3] - skeleton.initialpose[skeleton.boneid('Model:LeftFoot'), 3, :3]) skeleton.hipsid = skeleton.boneid('Model:Hips') skeleton.leftlegids = [skeleton.boneid('Model:LeftUpLeg'), skeleton.boneid('Model:LeftLeg'), skeleton.boneid('Model:LeftFoot')] skeleton.rightlegids = [skeleton.boneid('Model:RightUpLeg'), skeleton.boneid('Model:RightLeg'), skeleton.boneid('Model:RightFoot')] skeleton.leftfootid = skeleton.leftlegids[-1] skeleton.rightfootid = skeleton.rightlegids[-1] skeleton.copid = skeleton.boneid('COP') skeleton.bindpose[skeleton.copid, ...] = np.eye(4) skeleton.initialpose[skeleton.copid, ...] = np.eye(4) skeleton.localinitialpq = skeleton.global_to_local(pq.pose_to_pq(skeleton.initialpose)) return skeleton def read_bindpose(lMeshNode, skeleton): lMesh = lMeshNode.GetNodeAttribute() skin = lMesh.GetDeformer(0,fbx.FbxDeformer.eSkin) clustercount = skin.GetClusterCount() for clusterid in range(clustercount): cluster = skin.GetCluster(clusterid) linkedNode = cluster.GetLink() boneid = skeleton.boneid(linkedNode.GetName()) if boneid < 0: raise Exception('bone {} not found in skeleton'.format(linkedNode.GetName())) m = fbx.FbxAMatrix() m = cluster.GetTransformLinkMatrix(m) m = m.Inverse() for i in range(4): for j in range(4): skeleton.bindpose[boneid,i,j] = m.Get(i,j) def read_skinning(lMeshNode, skeleton): lMesh = lMeshNode.GetNodeAttribute() lControlPointsCount = lMesh.GetControlPointsCount() weights = np.zeros([lControlPointsCount, 8]) indices = np.zeros([lControlPointsCount, 8], dtype=np.int32) counts = np.zeros([lControlPointsCount], dtype=np.int32) skin = lMesh.GetDeformer(0, fbx.FbxDeformer.eSkin) clustercount = skin.GetClusterCount() for clusterid in range(clustercount): cluster = skin.GetCluster(clusterid) linkedNode = cluster.GetLink() boneid = skeleton.boneid(linkedNode.GetName()) if boneid < 0: raise Exception('bone {} not found in skeleton'.format(linkedNode.GetName())) vertcount = cluster.GetControlPointIndicesCount() for k in range(vertcount): vertindex = cluster.GetControlPointIndices()[k] index = counts[vertindex] indices[vertindex, index] = boneid weights[vertindex, index] = cluster.GetControlPointWeights()[k] counts[vertindex] += 1 ind = np.argsort(weights)[:,-4:] normalizeweights = np.zeros([lControlPointsCount, 4]) normalizeindices = np.zeros([lControlPointsCount, 4], dtype=np.int32) for i in range(lControlPointsCount): normalizeweights[i,:] = weights[i,ind[i]] normalizeweights[i, :] /= np.sum(normalizeweights[i, :]) normalizeindices[i, :] = indices[i, ind[i]] return normalizeindices, normalizeweights def read_animations(pScene, skeleton): animations = {} time = fbx.FbxTime() lRootNode = pScene.GetRootNode() mapping = {bone.name:lRootNode.FindChild(bone.name,True,True) for bone in skeleton.bones } for i in range(pScene.GetSrcObjectCount(fbx.FbxCriteria.ObjectType(fbx.FbxAnimStack.ClassId))): lAnimStack = pScene.GetSrcObject(fbx.FbxCriteria.ObjectType(fbx.FbxAnimStack.ClassId), i) pScene.SetCurrentAnimationStack(lAnimStack) start = lAnimStack.LocalStart.Get() stop = lAnimStack.LocalStop.Get() name = lAnimStack.GetName() animlen = stop.GetFrameCount() - start.GetFrameCount() + 1 bonelen = len(skeleton.bones) animation = np.repeat(skeleton.initialpose[np.newaxis,...], animlen, axis=0) for frame in range(start.GetFrameCount(), stop.GetFrameCount() + 1): animframe = frame - start.GetFrameCount() time.SetFrame(frame) for boneid in range(bonelen): bone = skeleton.bones[boneid] if bone.name in mapping and mapping[bone.name] is not None: localMatrix = mapping[bone.name].EvaluateGlobalTransform(time) for i in range(4): for j in range(4): animation[animframe, boneid, i, j] = localMatrix.Get(i, j) animations[name] = animation return animations class FbxReader(object): def __init__(self, path): lSdkManager, lScene = fb.InitializeSdkObjects() status = fb.LoadScene(lSdkManager, lScene, path) if not status: raise Exception('error in fbx file') self._scene = lScene self._mesh = find_mesh_node(self._scene) self._vertices = None self._indices = None self._skinning = None self._skeleton = None self._animations = None def vertices_and_indices(self): if self._mesh: if self._vertices is None: self._vertices = read_vertices_buffer(self._mesh) if self._indices is None: self._indices = read_index_buffer(self._mesh) return self._vertices, self._indices raise Exception('no mesh') def skeleton(self): if self._skeleton is None: self._skeleton = read_skeleton(self._scene) if self._mesh: read_bindpose(self._mesh, self._skeleton) return self._skeleton def skinning_indices_weights(self): if self._mesh: if self._skinning is None: self._skinning = read_skinning(self._mesh, self.skeleton()) return self._skinning raise Exception('no mesh') def animation_dictionary(self, skeleton=None): if self._animations is None: if skeleton is None: skeleton = self.skeleton() self._animations = read_animations(self._scene, skeleton) return self._animations ``` #### File: npk/animation_framework/__init__.py ```python from pathlib import Path import pickle from .viewer import viewer from . import inertialize from . import fbxreader from . import modifier from . import modifier_displacement from . import posquat from . import skeleton from . import utilities resource_dir = Path(__file__).parent.resolve() / 'resources' # global so we don't reload the character all the time g_vertices, g_indices, g_skinningindices, g_skinningweights, g_skeleton = None, None, None, None, None def get_character_constructor_parameters(): global g_vertices, g_indices, g_skinningindices, g_skinningweights, g_skeleton # LOAD CHARACTER if g_skeleton != None: return g_vertices, g_indices, g_skinningindices, g_skinningweights, g_skeleton try: g_vertices, g_indices, g_skinningindices, g_skinningweights, g_skeleton = pickle.load( open(str(resource_dir / 'simplified_man_average.dump'), 'rb')) except Exception: reader = fbxreader.FbxReader(str(resource_dir / 'simplified_man_average.fbx')) x = pickle.dumps( (*reader.vertices_and_indices(), *reader.skinning_indices_weights(), reader.skeleton()) ) with open(str(resource_dir / 'simplified_man_average.dump'), 'wb') as f: f.write(x) return g_vertices, g_indices, g_skinningindices, g_skinningweights, g_skeleton def get_skeleton() -> skeleton.Skeleton: return get_character_constructor_parameters()[4] def run_main_window(widgets=None, widgets_addon=None): if widgets is None: widgets = [viewer.CharacterWidget(True, *get_character_constructor_parameters())] #create widget viewer.ViewerWindow.widgets.extend(widgets) if widgets_addon is not None: viewer.ViewerWindow.widgets.extend(widgets_addon) #run viewer.mglw.run_window_config(viewer.ViewerWindow) ``` #### File: npk/animation_framework/posquat.py ```python import numpy as np def vec_cross3(a, b): """Compute a cross product for a list of vectors""" return np.concatenate([ a[..., 1:2] * b[..., 2:3] - a[..., 2:3] * b[..., 1:2], a[..., 2:3] * b[..., 0:1] - a[..., 0:1] * b[..., 2:3], a[..., 0:1] * b[..., 1:2] - a[..., 1:2] * b[..., 0:1], ], axis=-1) def quat_mul(x, y, check_flip=False): if check_flip: y = np.where((np.sum(x * y, axis=-1) < 0)[..., np.newaxis].repeat(4, axis=-1), quat_flip(y), y) x0, x1, x2, x3 = x[..., 0:1], x[..., 1:2], x[..., 2:3], x[..., 3:4] y0, y1, y2, y3 = y[..., 0:1], y[..., 1:2], y[..., 2:3], y[..., 3:4] return np.concatenate([ y0 * x0 - y1 * x1 - y2 * x2 - y3 * x3, y0 * x1 + y1 * x0 - y2 * x3 + y3 * x2, y0 * x2 + y1 * x3 + y2 * x0 - y3 * x1, y0 * x3 - y1 * x2 + y2 * x1 + y3 * x0], axis=-1) def quat_mul_vec(quaternions, vectors): q2 = np.zeros_like(quaternions) q2[..., 1:] = vectors return ( (quat_mul(quat_conj(quaternions), quat_mul(q2, quaternions)))[..., 1:] ) def quat_conj(x): return np.array([1, -1, -1, -1], dtype=np.float32) * x def quat_flip(x): return np.array([-1, -1, -1, -1], dtype=np.float32) * x def quat_slerp(x, y, a, eps=1e-10): # WARNING CANNOT SLERP IDENTITY WITH IDENTITY y = np.where((np.sum(x * y, axis=-1) < 0)[..., np.newaxis].repeat(4, axis=-1), quat_flip(y), y) l = np.sum(x * y, axis=-1) o = np.arccos(np.clip(l, -1.0, 1.0)) a0 = np.sin((1.0 - a) * o) / (np.sin(o) + eps) a1 = np.sin((a) * o) / (np.sin(o) + eps) return a0[..., np.newaxis] * x + a1[..., np.newaxis] * y def quat_from_angle_axis(angle, axis): x, y, z = axis[..., 0:1], axis[..., 1:2], axis[..., 2:3] theta = angle/2 sintheta = np.ones_like(x[..., 0])[...,np.newaxis] * np.sin(theta) return np.concatenate([ np.ones_like(x[..., 0])[...,np.newaxis] * np.cos(theta), x * sintheta, y * sintheta, z * sintheta], axis=-1) def quat_to_angle_axis(quats): w, v = quats[..., 0:1], quats[..., 1:] theta = np.arccos(w) * 2.0 return theta, vec_normalize(v, 1E-6) def quat_average(Q, weights=None): ''' Averaging Quaternions. Arguments: Q(ndarray): an Mx4 ndarray of quaternions. weights(list): an M elements list, a weight for each quaternion. ''' # TODO does not support multidimension at this point # Form the symmetric accumulator matrix A = np.zeros((4, 4)) M = Q.shape[0] wSum = 0 if weights is None: weights = np.ones(M) for i in range(M): q = Q[i, :] w_i = weights[i] A += w_i * (np.outer(q, q)) # rank 1 update wSum += w_i # scale A /= wSum # Get the eigenvector corresponding to largest eigen value return np.linalg.eigh(A)[1][:, -1] def vec_normalize(x, eps=1e-08): return x / (np.sqrt(np.sum(x * x, axis=-1, keepdims=True)) + eps) def quat_lerp(a, b, t): b = np.where((np.sum(b * a, axis=-1) < 0)[..., np.newaxis].repeat(4, axis=-1), quat_flip(b), b) return vec_normalize(a * (1.0 - t) + b * t) def quat_to_m33(x): qw, qx, qy, qz = x[..., 0:1], x[..., 1:2], x[..., 2:3], x[..., 3:4] x2, y2, z2 = qx + qx, qy + qy, qz + qz xx, yy, wx = qx * x2, qy * y2, qw * x2 xy, yz, wy = qx * y2, qy * z2, qw * y2 xz, zz, wz = qx * z2, qz * z2, qw * z2 return np.concatenate([ np.concatenate([1.0 - (yy + zz), xy - wz, xz + wy], axis=-1)[..., np.newaxis, :], np.concatenate([xy + wz, 1.0 - (xx + zz), yz - wx], axis=-1)[..., np.newaxis, :], np.concatenate([xz - wy, yz + wx, 1.0 - (xx + yy)], axis=-1)[..., np.newaxis, :], ], axis=-2) def m33_to_quat(ts, eps=1e-10): qs = np.empty_like(ts[..., :1, 0].repeat(4, axis=-1)) t = ts[..., 0, 0] + ts[..., 1, 1] + ts[..., 2, 2] s = 0.5 / np.sqrt(np.maximum(t + 1, eps)) qs = np.where((t > 0)[..., np.newaxis].repeat(4, axis=-1), np.concatenate([ (0.25 / s)[..., np.newaxis], (s * (ts[..., 2, 1] - ts[..., 1, 2]))[..., np.newaxis], (s * (ts[..., 0, 2] - ts[..., 2, 0]))[..., np.newaxis], (s * (ts[..., 1, 0] - ts[..., 0, 1]))[..., np.newaxis] ], axis=-1), qs) c0 = (ts[..., 0, 0] > ts[..., 1, 1]) & (ts[..., 0, 0] > ts[..., 2, 2]) s0 = 2.0 * np.sqrt(np.maximum(1.0 + ts[..., 0, 0] - ts[..., 1, 1] - ts[..., 2, 2], eps)) qs = np.where(((t <= 0) & c0)[..., np.newaxis].repeat(4, axis=-1), np.concatenate([ ((ts[..., 2, 1] - ts[..., 1, 2]) / s0)[..., np.newaxis], (s0 * 0.25)[..., np.newaxis], ((ts[..., 0, 1] + ts[..., 1, 0]) / s0)[..., np.newaxis], ((ts[..., 0, 2] + ts[..., 2, 0]) / s0)[..., np.newaxis] ], axis=-1), qs) c1 = (~c0) & (ts[..., 1, 1] > ts[..., 2, 2]) s1 = 2.0 * np.sqrt(np.maximum(1.0 + ts[..., 1, 1] - ts[..., 0, 0] - ts[..., 2, 2], eps)) qs = np.where(((t <= 0) & c1)[..., np.newaxis].repeat(4, axis=-1), np.concatenate([ ((ts[..., 0, 2] - ts[..., 2, 0]) / s1)[..., np.newaxis], ((ts[..., 0, 1] + ts[..., 1, 0]) / s1)[..., np.newaxis], (s1 * 0.25)[..., np.newaxis], ((ts[..., 1, 2] + ts[..., 2, 1]) / s1)[..., np.newaxis] ], axis=-1), qs) c2 = (~c0) & (~c1) s2 = 2.0 * np.sqrt(np.maximum(1.0 + ts[..., 2, 2] - ts[..., 0, 0] - ts[..., 1, 1], eps)) qs = np.where(((t <= 0) & c2)[..., np.newaxis].repeat(4, axis=-1), np.concatenate([ ((ts[..., 1, 0] - ts[..., 0, 1]) / s2)[..., np.newaxis], ((ts[..., 0, 2] + ts[..., 2, 0]) / s2)[..., np.newaxis], ((ts[..., 1, 2] + ts[..., 2, 1]) / s2)[..., np.newaxis], (s2 * 0.25)[..., np.newaxis] ], axis=-1), qs) return qs def quat_from_lookat(aim, up): matrices = np.zeros([3, 3]) * np.ones_like(aim[..., :1, np.newaxis].repeat(3, axis=-1)) x = vec_normalize(aim) z = vec_normalize(vec_cross3(x, up)) y = vec_normalize(vec_cross3(z, x)) matrices[..., 0, :] = x matrices[..., 1, :] = y matrices[..., 2, :] = z return m33_to_quat(matrices) def vec3_flip(x): return np.array([-1, -1, -1], dtype=np.float32) * x def pose_to_pq(pose): quaternions = m33_to_quat(pose[..., :3, :3]) positions = pose[..., 3, :3] return positions, quaternions def pq_to_pose(pqs=None, positions=None, quaternions=None): if pqs is not None: positions, quaternions = pqs matrices = np.eye(4, dtype=np.float32) * np.ones_like(positions[..., :1, np.newaxis].repeat(4, axis=-1)) matrices[..., :3, :3] = quat_to_m33(quaternions) matrices[..., 3, :3] = positions return matrices def inv(pqs=None, positions=None, quaternions=None): if pqs is not None: positions, quaternions = pqs qs = quat_conj(quaternions) ps = vec3_flip(positions) return quat_mul_vec(qs, ps), qs def mult(a, b): positions = quat_mul_vec(b[1], a[0]) positions += b[0] quaternions = quat_mul(a[1], b[1]) return positions, quaternions def sub(a, b): positions = a[0] - b[0] quaternions = quat_mul(quat_conj(b[1]), a[1]) return positions, quaternions def add(a, b): positions = a[0] + b[0] quaternions = quat_mul(a[1], b[1]) return positions, quaternions def lerp(a, b, t): positions = a[0] * (1.0 - t) + b[0] * t quaternions = quat_lerp(a[1], b[1], t) return positions, quaternions def transform_point(pqs, positions): pos = quat_mul_vec(pqs[1], positions) return pos + pqs[0] def transform_vector(pqs, vector): return quat_mul_vec(pqs[1], vector) def __take_one_pq(pqs, index, as_array=True): positions, quaternions = pqs if as_array: return positions[index][np.newaxis, ...], quaternions[index][np.newaxis, ...] return positions[index], quaternions[index] def _tests_(): a = np.array([ [5.05513623e-04, 9.98390697e-01, 5.67055689e-02, 0.00000000e+00], [4.59858199e-02, -5.66687993e-02, 9.97333361e-01, 0.00000000e+00], [9.98941905e-01, 2.10348673e-03, -4.59404671e-02, 0.00000000e+00], [2.34800407e+01, 1.03402939e+02, -2.17692762e+01, 1.00000000e+00]]) b = np.array([ [9.71705084e-01, -2.32879069e-01, -3.94458240e-02, 0.00000000e+00], [-2.06373974e-02, 8.26563821e-02, -9.96364162e-01, 0.00000000e+00], [2.35292892e-01, 9.68986527e-01, 7.55116358e-02, 0.00000000e+00], [2.81058541e+01, 1.51051439e+02, -2.08025977e+01, 1.00000000e+00]]) a_b = np.concatenate([a[np.newaxis, ...], b[np.newaxis, ...]], axis=0) # test conversion back and forth pq = pose_to_pq(a_b) na_b = pq_to_pose(pq) assert (np.allclose(a_b, na_b, rtol=1e-04, atol=1e-06)) # test inverse inv_a = np.linalg.inv(a) inv_pq = inv(pq) inv_a_b = pq_to_pose(inv_pq) assert (np.allclose(inv_a, inv_a_b[0, ...], rtol=1e-04, atol=1e-06)) # dot product a_dot_b = np.dot(a, b) p_a_dot_b = mult(__take_one_pq(pq, 0), __take_one_pq(pq, 1)) n_a_dot_b = pq_to_pose(p_a_dot_b)[0, ...] assert (np.allclose(a_dot_b, n_a_dot_b, rtol=1e-04, atol=1e-06)) # more dimensions pq = pose_to_pq(a_b[np.newaxis, ...]) identities = pq_to_pose(mult(pq, inv(pq))) assert (np.allclose(identities, np.eye(4, dtype=np.float32) * np.ones([1, 2, 1, 1]))) ``` #### File: animation_framework/viewer/axisrender.py ```python import moderngl import numpy as np class AxisRender(object): def __init__(self, ctx, scale=10): self.ctx = ctx self.program = self.ctx.program( vertex_shader=''' #version 430 uniform mat4 Mvp; in vec3 in_vert; in vec3 in_color; out vec3 v_color; void main() { gl_Position = Mvp * vec4(in_vert, 1.0); v_color = in_color; } ''', fragment_shader=''' #version 430 in vec3 v_color; out vec4 f_color; void main() { f_color = vec4(v_color, 1.0); } ''', ) vertices = np.array([ # x, y ,z red, green, blue 0, 0, 0, 1, 0, 0, scale, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, scale, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, scale, 0, 0, 1, ], dtype='f4') vbo = self.ctx.buffer(vertices) self.vao = self.ctx.vertex_array( self.program, [ (vbo, '3f4 3f4', 'in_vert', 'in_color') ], ) def render(self, mvp, globalBoneMatrices=None): if globalBoneMatrices is None: self.program['Mvp'].write(mvp) self.vao.render(moderngl.LINES) else: for b in globalBoneMatrices: self.program['Mvp'].write((mvp * b).astype('f4')) self.vao.render(moderngl.LINES) ``` #### File: animation/npk/animations.py ```python from pathlib import Path import pickle import numpy as np from animation_framework import modifier from animation_framework import posquat as pq from animation_framework import animation as AN import animation_framework as fw from animation_framework.utilities import compute_bone_speed, is_foot_static from animation_framework import skeleton as sk from animation_framework import modifier_displacement as disp from animation_framework import fbxreader import footphase_extraction as FPE resource_dir = Path(__file__).parent.resolve() / 'resources' def convert_fbx_animation(name, need_rotation=False): skel = fw.get_skeleton() reader = fbxreader.FbxReader(str(resource_dir / '{}.fbx'.format(name))) animation = reader.animation_dictionary(skel)['Take 001'] if need_rotation: animation = np.dot(animation, np.array([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])) disp.update_matrix_anim_projecting_disp_on_ground(animation) x = pickle.dumps(animation) with open(str(resource_dir / '{}.dump'.format(name)), 'wb') as f: f.write(x) def get_raw_animation(name, with_foot_phase=False): anm = pq.pose_to_pq(pickle.load(open(str(resource_dir / '{}.dump'.format(name)), 'rb'))) animation = AN.Animation(anm, name=name) if with_foot_phase: raise NotImplementedError() # use the new attribute method to add this info return animation def get_raw_db_animations(with_foot_phase=False): skel = fw.get_skeleton() animations = [] animation = get_raw_animation('on_spot', with_foot_phase=with_foot_phase) animation.pq = modifier.lock_feet(skel, animation.pq, 5, 10) ranges = [[33, 130], [465, 528], [558, 647], [790, 857], [892, 961], [1120, 1190], [1465, 1528]] animations += [animation[r[0]: r[1]] for r in ranges] animation = get_raw_animation('side_steps', with_foot_phase=with_foot_phase) animation.pq = modifier.lock_feet(skel, animation.pq, 5, 10) ranges = [[185,256], [256,374], [374,463], [463,550], [550,636], [636,735], [735,816], [816,900], [900,990], [990,1080], [1080,1165], [1165,1260]] animations += [animation[r[0]-185: r[1]-185] for r in ranges] animation = get_raw_animation('turn_steps', with_foot_phase=with_foot_phase) animation.pq = modifier.lock_feet(skel, animation.pq, 10, 5) ranges = [[184, 280], [280, 378], [375, 498], [490, 576], [576, 704], [704, 811], [811, 924], [920, 1026]] animations += [animation[r[0]-184: r[1]-184] for r in ranges] for anim in animations: anim.pq = disp.reset_displacement_origin(skel, anim.pq) return animations def generate_augmentation(skel:sk.Skeleton, animations): # 7 first are on spot # 12 next are side steps # make small steps ''' animcount = 12 for i in range(7, 7+animcount): print('generate pass {} / {}'.format(i, animcount)) animations += [displacement.scale_displacement(skel, copy.deepcopy(animations[i]), 0.6, 0.6)] animations = [anim for anim in animations if is_animation_valid(skel, anim)] ''' animcount = len(animations) for i in range(animcount): print('generate mirrors {} / {}'.format(i, animcount)) animations += [modifier.mirror_animation(animations[i])] ''' animcount = len(animations) rots = [pq.quat_from_angle_axis(np.array([0 * 3.1415 / 180]), np.array([[0, 0, 1]]))] rots += [pq.quat_from_angle_axis(np.array([25 * 3.1415 / 180]), np.array([[0, 0, 1]]))] rots += [pq.quat_from_angle_axis(np.array([-25 * 3.1415 / 180]), np.array([[0, 0, 1]]))] movs = [np.array([15, 0, 0])] movs += [np.array([0, 0, 15])] movs += [np.array([0, 0, -15])] for i in range(animcount): print('generate pass {} / {}'.format(i, animcount)) for rot in rots: for mov in movs: animations += [displacement.offset_displacement_at_end(skel, copy.deepcopy(animations[i]), mov, rot)] ''' animcount = len(animations) print('generate blends {}'.format(animcount * animcount)) for i in range(animcount): for j in range(animcount): if i != j: try: a = modifier.blend_anim_foot_phase(skel, animations[i], animations[j], 0.5) animations.append(a) except Exception: pass ''' animations = [anim for anim in animations if is_animation_valid(skel, anim)] animcount = len(animations) for i in range(animcount): print('generate pass {} / {}'.format(i, animcount)) animations += [displacement.scale_displacement(skel, copy.deepcopy(animations[i]), 0.6, 0.8)] animations = [anim for anim in animations if is_animation_valid(skel, anim)] ''' print('generate {} animations'.format(len(animations))) return animations def save_animation_database(animations): x = pickle.dumps(animations) with open(str(resource_dir / 'animation_database.dump'), 'wb') as f: f.write(x) def load_animation_database(): return pickle.load(open(str(resource_dir / 'animation_database.dump'), 'rb')) ``` #### File: animation/npk/footphase_extraction.py ```python import numpy as np import sinusoidal_fit def get_foot_phase_sinusoidal(phase, window_size=20, normalized=False): anim_len = len(phase) window_size = min(window_size, int(anim_len / 4)) phase_normal = phase if normalized: phase_normal = np.zeros(anim_len) for t in range(anim_len): s = max(0, t - window_size) e = min(anim_len, t + window_size) mean = np.mean(phase[s:e]) std = np.std(phase[s:e]) phase_normal[t] = (phase[t] - mean) / (std + 1e-8) fitted = np.zeros((anim_len, 5), dtype=np.float32) fitted[:, 0] = phase_normal last_solution = None for t in range(0, anim_len): s = max(0, t - window_size) e = min(anim_len, t + window_size) lnspace = np.array(range(s, e), dtype=np.float32) / 30.0 data = phase_normal[s:e] solution, fitness = sinusoidal_fit.fast_fit(lnspace, data, original_fit=last_solution) #solution, fitness = sinusoidal_fit.fit(lnspace, data) last_solution = solution print(fitness, float(t)/anim_len) #a, f, s, b = solution fitted[t, 1:] = solution #fitted[t] = a * np.sin(f * t - s) + b #fitted[t] = (f * t - s) % (np.pi*2) return fitted ``` #### File: animation/npk/motionmatching.py ```python import numpy as np import animation_framework as fw from animation_framework import modifier from animation_framework import posquat as pq from animation_framework import animation as AN from animation_framework import utilities as tr from animation_framework import modifier_displacement as disp from animation_framework import skeleton as skl INITIAL_SCORE = 1 FEATURE_SCORE = 1 TIMING_SCORE = 20 class motionDB(object): def __init__(self, stride): self.stride = stride self.clips = [] def build_motion_db(animations, skeleton: skl.Skeleton, stride=10): db = motionDB(stride) def _build_motion(animation): animation_len = len(animation) last_clip = None for r in range(0, animation_len-stride-2, stride): anim = animation[r:] anim.pq = disp.reset_displacement_origin(skeleton, anim.pq) anim_len = len(anim) features_keys = list(range(0, anim_len-stride, stride)) hipspos = anim.pq[0][:, skeleton.hipsid, :] lfpos = anim.pq[0][:, skeleton.leftfootid, :] rfpos = anim.pq[0][:, skeleton.rightfootid, :] hipsvec = tr.compute_vector(hipspos) lfvec = tr.compute_vector(lfpos) rfvec = tr.compute_vector(rfpos) frames = anim.pq[0][:stride+1], anim.pq[1][:stride+1] features = np.zeros((len(features_keys), 6, 3)) features[:, 0, :] = hipspos[features_keys, :] features[:, 1, :] = lfpos[features_keys, :] features[:, 2, :] = rfpos[features_keys, :] features[:, 3, :] = hipsvec[features_keys, :] features[:, 4, :] = lfvec[features_keys, :] features[:, 5, :] = rfvec[features_keys, :] #create the animation of this clip clip = AN.Animation(frames, anim.name) clip.attributes.append(AN.Attribute(features, 'mm_features', False)) if last_clip is not None: last_clip.attributes.append(AN.Attribute([clip], 'mm_next', False)) last_clip = clip db.clips.append(clip) for animation in animations: _build_motion(animation) return db def compute_score(feature, query): vec = feature - query dist = np.sum(vec * vec, axis=1) * np.array([2, 1, 1, 4, 6, 6]) return np.sum(dist) def find_segment(a, b, t, db, skeleton, debug_dict=None): # create a temp animation with a end b pos, quat = np.zeros((4, len(skeleton.bindpose), 3)), np.zeros((4, len(skeleton.bindpose), 4)) pos[:2, :, :], quat[:2, :, :] = a[0][-2:, :, :], a[1][-2:, :, :] pos[2:, :, :], quat[2:, :, :] = b[0][:2, :, :], b[1][:2, :, :] # set in local space pos, quat = disp.reset_displacement_origin(skeleton, (pos, quat)) hipspos = pos[:, skeleton.hipsid, :] lfpos = pos[:, skeleton.leftfootid, :] rfpos = pos[:, skeleton.rightfootid, :] hipsvec = tr.compute_vector(hipspos) lfvec = tr.compute_vector(lfpos) rfvec = tr.compute_vector(rfpos) if debug_dict != None: debug_dict['segment_a_{}'.format(t)] = a debug_dict['segment_b_{}'.format(t)] = b debug_dict['local_{}'.format(t)] = (pos, quat) a_query = np.zeros((6, 3)) a_query[0, :] = hipspos[1, :] a_query[1, :] = lfpos[1, :] a_query[2, :] = rfpos[1, :] a_query[3, :] = hipsvec[0, :] a_query[4, :] = lfvec[0, :] a_query[5, :] = rfvec[0, :] b_query = np.zeros((6, 3)) b_query[0, :] = hipspos[-2, :] b_query[1, :] = lfpos[-2, :] b_query[2, :] = rfpos[-2, :] b_query[3, :] = hipsvec[-1, :] b_query[4, :] = lfvec[-1, :] b_query[5, :] = rfvec[-1, :] # check all the clips for matching features features_index = int(t / db.stride) best_score = 1e8 best_clip = None best_feature = -1 for clip in db.clips: features = clip.attribute('mm_features').data features_count = len(features) # initial state score init_score = compute_score(features[0], a_query) # features scores for f in range(1, features_count): feature_distance_score = np.abs(f - features_index) feature_score = compute_score(features[f], b_query) score = init_score * INITIAL_SCORE + \ feature_distance_score * TIMING_SCORE + \ feature_score * FEATURE_SCORE if best_score > score: best_score = score best_clip = clip best_feature = f if debug_dict != None: debug_dict['frames_{}'.format(t)] = best_clip return best_clip, best_score def create_motion_transition(db, skeleton, anim_a, anim_b, transition_time, debug_dict=None): ''' Create a transition using a pure motionmatching implementation that goes from stride to stride :param db: :param skeleton: :param anim_a: :param anim_b: :param transition_time: :param debug_dict: :return: ''' # combine transition p, q = np.zeros((transition_time*2, len(skeleton.bones), 3)), np.zeros((transition_time*2, len(skeleton.bones), 4)) transition_len = 0 lastp, lastq = anim_a.pq[0][-2:, :, :], anim_a.pq[1][-2:, :, :] endp, endq = anim_b.pq[0][:2, :, :], anim_b.pq[1][:2, :, :] t = transition_time last_segment = None best_score = 1e8 while t >= db.stride: segment, score = find_segment((lastp, lastq), (endp, endq), t, db, skeleton, debug_dict) if last_segment is None or score < best_score: segment.pq = disp.set_displacement_origin(skeleton, segment.pq, (lastp[-1, 0, :], lastq[-1, 0, :])) last_segment = segment best_score = score p[transition_len: transition_len+db.stride, :, :] = last_segment.pq[0][:db.stride] q[transition_len: transition_len + db.stride, :, :] = last_segment.pq[1][:db.stride] transition_len += db.stride t -= db.stride lastp, lastq = last_segment.pq[0][-2:, :, :], last_segment.pq[1][-2:, :, :] next_clip = last_segment.attribute('mm_next') if next_clip is None: last_segment = None best_score = 1e8 else: last_segment = next_clip.data[0] return AN.Animation((p[:transition_len:, :, :], q[:transition_len:, :, :]), name='transition') def create_segment_transition(db, skeleton, anim_a, anim_b, transition_time, debug_dict=None): ''' pick the best segment in the motion database and stick to it for the entire animation length :param db: :param skeleton: :param anim_a: :param anim_b: :param transition_time: :param debug_dict: :return: ''' # combine transition p, q = np.zeros((transition_time * 2, len(skeleton.bones), 3)), np.zeros( (transition_time * 2, len(skeleton.bones), 4)) transition_len = 0 lastp, lastq = anim_a.pq[0][-2:, :, :], anim_a.pq[1][-2:, :, :] endp, endq = anim_b.pq[0][:2, :, :], anim_b.pq[1][:2, :, :] segment, score = find_segment((lastp, lastq), (endp, endq), transition_time, db, skeleton, debug_dict) while True: segment.pq = disp.set_displacement_origin(skeleton, segment.pq, (lastp[-1, 0, :], lastq[-1, 0, :])) p[transition_len: transition_len + db.stride, :, :] = segment.pq[0][:db.stride] q[transition_len: transition_len + db.stride, :, :] = segment.pq[1][:db.stride] lastp, lastq = segment.pq[0][-2:, :, :], segment.pq[1][-2:, :, :] next_clip = segment.attribute('mm_next') if next_clip is None: break segment = next_clip.data[0] transition_len += db.stride return AN.Animation((p[:transition_len:, :, :], q[:transition_len:, :, :]), name='transition') ``` #### File: animation/npk/sinusoidal_fit.py ```python import numpy as np from scipy import optimize def fit(X, y, population_count=100, elite_count=2, velocity_rate=0.001, epoch_count=25): params_count = 4 lower_limits = np.array([0, 0, -np.pi, -1]) upper_limits = np.array([1, np.pi * 2, np.pi, 1]) bounds = np.array([(l, u) for l, u in zip(lower_limits, upper_limits)]) def function(afsb, t): return afsb[..., 0:1] * np.sin(afsb[..., 1:2] * t - afsb[..., 2:3]) + afsb[..., 3:4] def error(params, X, y): y_ = function(params, X) return np.sqrt(np.sum((y - y_) ** 2, axis=-1) / X.shape[-1]) def extinctions(fitness): return (swarm_fitness + np.min(swarm_fitness) * ( ((params_count - 1.0) / (population_count - 1.0)) - 1.0)) / np.max( swarm_fitness) # initial population swarm_positions = np.random.uniform(lower_limits, upper_limits, (population_count, params_count)) swarm_velocities = np.random.uniform(-0.1, 0.1, population_count * params_count).reshape( (population_count, params_count)) swarm_fitness = error(swarm_positions, X[np.newaxis, :], y) swarm_extinction = extinctions(swarm_fitness) swarm_sorted_args = np.argsort(swarm_fitness, axis=0) # global best solution = swarm_positions[swarm_sorted_args[0], ...] best_fitness = swarm_fitness[swarm_sorted_args[0]] # iterate for epoch in range(epoch_count): # early exit if close enough if best_fitness < 1e-6: break # pick elites and do a gradient descent using l-bfgs-b algorithm for e in range(elite_count): x, _, _ = optimize.fmin_l_bfgs_b( func=error, x0=swarm_positions[swarm_sorted_args[e], ...], args=(X[np.newaxis, :], y), approx_grad=True, bounds=bounds, maxiter=100) swarm_velocities[swarm_sorted_args[e], ...] = np.random.uniform() * \ swarm_velocities[swarm_sorted_args[e], ...] + x - \ swarm_positions[swarm_sorted_args[e], ...] swarm_positions[swarm_sorted_args[e], ...] = x # create the offsprings offspring_positions = np.zeros((population_count, params_count), dtype=np.float32) offspring_velocities = np.zeros((population_count, params_count), dtype=np.float32) offspring_fitness = np.zeros(population_count, dtype=np.float32) # populate offsprings for off in range(population_count): parents_count = len(swarm_sorted_args) # rank based selection probabilities = np.array([parents_count - i for i in range(parents_count)], dtype=np.float32) probabilities /= np.sum(probabilities) a, b, prot = np.random.choice(swarm_sorted_args, 3, p=probabilities, replace=False) # combine parents mix_values = np.random.uniform(size=params_count) offspring_positions[off, :] = swarm_positions[a, :] * mix_values + \ swarm_positions[b, :] * (1.0 - mix_values) # add a bit of the velocity from the parents offspring_positions[off, :] += velocity_rate * (swarm_velocities[a, :] + swarm_velocities[b, :]) # use the velocities from the parents offspring_velocities[off, :] = np.random.uniform(size=params_count) * swarm_velocities[a, :] + \ np.random.uniform(size=params_count) * swarm_velocities[b, :] # mutate p = (np.mean(swarm_extinction[[a, b]]) * (params_count - 1.0) + 1.0) / params_count if p < np.random.uniform(): swarm_min = np.min(swarm_positions, axis=0) swarm_max = np.max(swarm_positions, axis=0) x = np.random.uniform(-1, 1, size=params_count) * np.mean(swarm_extinction[[a, b]]) * ( swarm_max - swarm_min) offspring_velocities[off, :] += x offspring_positions[off, :] += x # adoption mix_values = np.random.uniform(size=params_count) average_parents = np.mean(swarm_positions[[a, b], :], axis=0) x = mix_values * (average_parents - offspring_positions[off, :]) mix_values = np.random.uniform(size=params_count) x += mix_values * (offspring_positions[prot, :] - offspring_positions[off, :]) offspring_velocities[off, :] += x offspring_positions[off, :] += x # clip offspring_positions[off, :] = np.clip(offspring_positions[off, :], a_min=lower_limits, a_max=upper_limits) # compute fitness of this offspring offspring_fitness[off] = error(offspring_positions[off, :], X, y) # assign offsprings to population swarm_positions = offspring_positions swarm_velocities = offspring_velocities swarm_fitness = offspring_fitness # sort everyone swarm_sorted_args = np.argsort(swarm_fitness, axis=0) swarm_extinction = extinctions(swarm_fitness) # try update solution if swarm_fitness[swarm_sorted_args[0]] < best_fitness: best_fitness = swarm_fitness[swarm_sorted_args[0]] solution = swarm_positions[swarm_sorted_args[0], ...] return solution, best_fitness def fast_fit(X, y, population_count=200, epoch_count=400, original_fit=None): weights = np.ones_like(X) #weights[:len(X)-2] = np.linspace(0.2, 1.0, len(X) - 2) #weights[len(X) - 2:] = np.linspace(1.0, 0.2, len(X) - 2) def function(afsb, t): return afsb[..., 0:1] * np.sin(afsb[..., 1:2] * t - afsb[..., 2:3]) + afsb[..., 3:4] def error(params, X, y): y_ = function(params, X) return np.sqrt(np.sum(((y - y_) ** 2) * weights, axis=-1) / X.shape[-1]) params_count = 4 lower_limits = np.array([0, 0, -np.pi, -.5]) upper_limits = np.array([1, np.pi * 2, np.pi, .5]) bounds = np.array([(l, u) for l, u in zip(lower_limits, upper_limits)]) lower_limits = lower_limits[np.newaxis, :] * np.ones((population_count, 1)) upper_limits = upper_limits[np.newaxis, :] * np.ones((population_count, 1)) steps_size = (upper_limits - lower_limits) * 0.1 population = np.random.uniform(lower_limits, upper_limits, (population_count, params_count)) if original_fit is not None: population = original_fit[np.newaxis, :] * np.ones((population_count, 1)) population = np.random.normal(population, steps_size) fitness = error(population, X[np.newaxis, :], y) for epoch in range(epoch_count): new_population = np.random.normal(population, steps_size) new_population = np.clip(new_population, a_min=lower_limits, a_max=upper_limits) new_fitness = error(new_population, X[np.newaxis, :], y) is_better = new_fitness < fitness population[is_better] = new_population[is_better] fitness[is_better] = new_fitness[is_better] steps_size *= 0.999 sorted_args = np.argsort(fitness, axis=0) x, f, _ = optimize.fmin_l_bfgs_b( func=error, x0=population[sorted_args[0], :], args=(X[np.newaxis, :], y), approx_grad=True, bounds=bounds) return x, f ``` #### File: animation/npk/test.py ```python import pickle import numpy as np import animations as IN from animation_framework import modifier_displacement as disp from animation_framework import posquat as pq from animation_framework import skeleton as sk import transition_type_b as trn def create_transition_animation(resource_dir, skel:sk.Skeleton): anim_db = IN.load_animation_database() mapping_db = pickle.load(open(str(resource_dir / 'mapping.dump'), 'rb')) idle = pq.pose_to_pq(pickle.load(open(str(resource_dir / 'idle.dump'), 'rb'))) idle = disp.reset_displacement_origin(skel, idle) relax = pq.pose_to_pq(pickle.load(open(str(resource_dir / 'idle_relax.dump'), 'rb'))) relax = disp.reset_displacement_origin(skel, relax) alert = pq.pose_to_pq(pickle.load(open(str(resource_dir / 'idle_alerted.dump'), 'rb'))) alert = disp.reset_displacement_origin(skel, alert) rootup = pq.quat_from_angle_axis(np.array([90 * 3.1415 / 180]), np.array([[1, 0, 0]])) is_transition = np.ones(6000) a = trn.create_transition( skel, anim_db, mapping_db, (idle[0][260:300, ...], idle[1][260:300, ...]), (idle[0][1440:1500, ...], idle[1][1440:1500, ...]) ) is_transition[:40] = 0 is_transition[len(a[0]) - 60:len(a[0])] = 0 a = trn.create_transition( skel, anim_db, mapping_db, a, (alert[0][0:40, ...], alert[1][0:40, ...]) ) is_transition[len(a[0]) - 40:len(a[0])] = 0 a = trn.create_transition( skel, anim_db, mapping_db, a, disp.set_displacement_origin( skel, (idle[0][500:550, ...], idle[1][500:550, ...]), ( np.array([0, 0, 0]), pq.quat_mul(rootup, pq.quat_from_angle_axis(np.array([-120 * 3.1415 / 180]), np.array([[0, 1, 0]]))) ) ) ) is_transition[len(a[0]) - 50:len(a[0])] = 0 a = trn.create_transition( skel, anim_db, mapping_db, a, disp.set_displacement_origin( skel, (relax[0][00:50, ...], relax[1][00:50, ...]), ( np.array([-5, 0, 0]), pq.quat_mul(rootup, pq.quat_from_angle_axis(np.array([-130 * 3.1415 / 180]), np.array([[0, 1, 0]]))) ) ) ) is_transition[len(a[0]) - 50:len(a[0])] = 0 a = trn.create_transition( skel, anim_db, mapping_db, a, (idle[0][250:260, ...], idle[1][250:260, ...]) ) is_transition[len(a[0]) - 10:len(a[0])] = 0 return a, is_transition[:len(a[0])] ``` #### File: python_rnd_collection/deep_learning/deep_q_learning_atari.py ```python import numpy as np import gym import collections import cv2 import os import torch as T import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import matplotlib.pyplot as plt class RepeatSkipScaleStack(gym.Wrapper): def __init__(self, env, repeat=4, output_shape=(4, 84, 84)): super(RepeatSkipScaleStack, self).__init__(env) self.repeat = repeat self.frame_buffer = np.zeros((2, *env.observation_space.low.shape), dtype=np.uint8) self.stack = collections.deque(maxlen=output_shape[0]) self.observation_space = gym.spaces.Box(low=0.0, high=1.0, shape=output_shape, dtype=np.float32) def _resize_observation(self, observation): new_frame = cv2.cvtColor(observation, cv2.COLOR_BGR2GRAY) resize_screen = cv2.resize(new_frame, (84, 84), interpolation=cv2.INTER_AREA) new_obs = np.array(resize_screen, dtype=np.float32).reshape(self.observation_space.low.shape[1:]) new_obs /= 255.0 return new_obs def reset(self, **kwargs): self.stack.clear() observation = self.env.reset() observation = self._resize_observation(observation) for _ in range(self.stack.maxlen): self.stack.append(observation) return np.array(self.stack).reshape(self.observation_space.low.shape) def step(self, action): t_reward = 0.0 done = False for i in range(self.repeat): obs, reward, done, info = self.env.step(action) t_reward += reward idx = i % 2 self.frame_buffer[idx] = obs if done: break max_frame = np.maximum(self.frame_buffer[0], self.frame_buffer[1]) observation = self._resize_observation(max_frame) self.stack.append(observation) frames = np.array(self.stack).reshape(self.observation_space.low.shape) return frames, t_reward, done, info def make_env(env_name, shape=(4,84,84), repeat=4): env = gym.make(env_name) env = RepeatSkipScaleStack(env, repeat=repeat, output_shape=shape) return env class ReplayBuffer(object): def __init__(self, max_size, input_shapes): self.mem_size = max_size self.mem_ctr = 0 self.state = np.zeros((self.mem_size, *input_shapes), dtype=np.float32) self.new_state = np.zeros((self.mem_size, *input_shapes), dtype=np.float32) self.action = np.zeros(self.mem_size, dtype=np.int64) self.reward = np.zeros(self.mem_size, dtype=np.float32) self.terminal = np.zeros(self.mem_size, dtype=np.bool) def store_transition(self, state, action, reward, state_, done): idx = self.mem_ctr % self.mem_size self.state[idx] = state self.new_state[idx] = state_ self.action[idx] = action self.reward[idx] = reward self.terminal[idx] = done self.mem_ctr += 1 def sample_buffer(self, batch_size): max_mem = min(self.mem_size, self.mem_ctr) batch = np.random.choice(max_mem, batch_size, replace=False) return ( self.state[batch], self.action[batch], self.reward[batch], self.new_state[batch], self.terminal[batch] ) class DeepQNetwork(nn.Module): def __init__(self, lr, n_actions, name, input_dims, chkpt_dir): super(DeepQNetwork, self).__init__() self.checkpoint_dir = chkpt_dir self.checkpoint_file = os.path.join(self.checkpoint_dir, name+'.ckp') self.conv1 = nn.Conv2d(input_dims[0], 32, 8, stride=4) self.conv2 = nn.Conv2d(32, 64, 4, stride=2) self.conv3 = nn.Conv2d(64, 64, 3, stride=1) fc_input_dims = self.calculate_conv_output_dims(input_dims) self.fc1 = nn.Linear(fc_input_dims, 512) self.fc2 = nn.Linear(512, n_actions) self.optimizer = optim.RMSprop(self.parameters(), lr=lr) self.loss = nn.MSELoss() self.device = T.device('cuda:0') self.to(self.device) def calculate_conv_output_dims(self, input_dims): state = T.zeros(1, *input_dims) dims = self.conv1(state) dims = self.conv2(dims) dims = self.conv3(dims) return int(np.prod(dims.size())) def forward(self, state): conv1 = F.relu(self.conv1(state)) conv2 = F.relu(self.conv2(conv1)) conv3 = F.relu(self.conv3(conv2)) # conv3 shape is BS * n_filters * H * W conv_state = conv3.view(conv3.size()[0], -1) flat1 = F.relu(self.fc1(conv_state)) actions = self.fc2(flat1) return actions def save_checkpoint(self): print('... saving checkpoint ...') T.save(self.state_dict(), self.checkpoint_file) def load_checkpoint(self): print('... load checkpoint ...') self.load_state_dict(T.load(self.checkpoint_file)) class DQNAgent(object): def __init__(self, gamma, epsilon, lr, n_actions, input_dims, mem_size, batch_size, eps_min=0.01, eps_dec=5e-7, replace=1000, algo=None, env_name=None, chkpt_dir='tmp/dqn'): self.gamma = gamma self.epsilon = epsilon self.lr = lr self.n_actions = n_actions self.input_dims = input_dims self.mem_size = mem_size self.batch_size = batch_size self.eps_min = eps_min self.eps_dec = eps_dec self.replace_target = replace self.algo = algo self.env_name = env_name self.chkpt_dir = chkpt_dir self.action_space = [i for i in range(self.n_actions)] self.learn_step_counter = 0 self.memory = ReplayBuffer(mem_size, input_dims) self.q_eval = DeepQNetwork(self.lr, self.n_actions, input_dims=self.input_dims, name=self.env_name+'_'+self.algo+'_q_eval', chkpt_dir=self.chkpt_dir) self.q_next = DeepQNetwork(self.lr, self.n_actions, input_dims=self.input_dims, name=self.env_name + '_' + self.algo + '_q_next', chkpt_dir=self.chkpt_dir) def choose_action(self, observation): if np.random.random() > self.epsilon: state = T.tensor([observation], dtype=T.float).to(self.q_eval.device) actions = self.q_eval.forward(state) action = T.argmax(actions).item() else: action = np.random.choice(self.action_space) return action def store_transition(self, state, action, reward, state_, done): self.memory.store_transition(state, action, reward, state_, done) def sample_memory(self): state, action, reward, new_state, done = self.memory.sample_buffer(self.batch_size) state = T.tensor(state).to(self.q_eval.device) action = T.tensor(action).to(self.q_eval.device) reward = T.tensor(reward).to(self.q_eval.device) new_state = T.tensor(new_state).to(self.q_eval.device) done = T.tensor(done).to(self.q_eval.device) return state, action, reward, new_state, done def replace_target_network(self): if self.learn_step_counter % self.replace_target == 0: self.q_next.load_state_dict(self.q_eval.state_dict()) def decrement_epsilon(self): self.epsilon = self.epsilon - self.eps_dec self.epsilon = max(self.epsilon, self.eps_min) def save_models(self): self.q_eval.save_checkpoint() self.q_next.save_checkpoint() def load_models(self): self.q_eval.load_checkpoint() self.q_next.load_checkpoint() def learn(self): if self.memory.mem_ctr > self.batch_size: self.q_eval.optimizer.zero_grad() self.replace_target_network() states, actions, rewards, states_, dones = self.sample_memory() indices = np.arange(self.batch_size) q_pred = self.q_eval.forward(states)[indices, actions] q_next = self.q_next.forward(states_).max(dim=1)[0] q_next[dones] = 0.0 #if we are done we dont have any reward q_target = rewards + self.gamma*q_next loss = self.q_eval.loss(q_target, q_pred).to(self.q_eval.device) loss.backward() self.q_eval.optimizer.step() self.learn_step_counter += 1 self.decrement_epsilon() def plot_learning_curve(x, scores, epsilons, filename, lines=None): fig=plt.figure() ax=fig.add_subplot(111, label="1") ax2=fig.add_subplot(111, label="2", frame_on=False) ax.plot(x, epsilons, color="C0") ax.set_xlabel("Training Steps", color="C0") ax.set_ylabel("Epsilon", color="C0") ax.tick_params(axis='x', colors="C0") ax.tick_params(axis='y', colors="C0") N = len(scores) running_avg = np.empty(N) for t in range(N): running_avg[t] = np.mean(scores[max(0, t-20):(t+1)]) ax2.scatter(x, running_avg, color="C1") ax2.axes.get_xaxis().set_visible(False) ax2.yaxis.tick_right() ax2.set_ylabel('Score', color="C1") ax2.yaxis.set_label_position('right') ax2.tick_params(axis='y', colors="C1") if lines is not None: for line in lines: plt.axvline(x=line) plt.savefig(filename) if __name__ == '__main__': np.seterr(all='raise') env = make_env('PongNoFrameskip-v4') best_score = -np.inf learning_mode = True load_checkpoint = False n_games = 1000 agent = DQNAgent(gamma=0.99, epsilon=1.0, lr=0.0001, input_dims=env.observation_space.shape, n_actions=env.action_space.n, mem_size=50000, eps_min=0.1, batch_size=32, replace=1000, eps_dec=1e-5, chkpt_dir='models/', algo='DQNAgent', env_name='PongNoFrameskip-v4') if load_checkpoint: agent.load_models() fname = 'plots/{}_{}_{}_lr_{}_games.png'.format(agent.algo, agent.env_name, str(agent.lr), str(n_games)) n_steps = 0 scores, eps_history, steps_array = [], [], [] for i in range(n_games): done = False score = 0 observation = env.reset() while not done: action = agent.choose_action(observation) observation_, reward, done, info = env.step(action) score += reward if learning_mode: agent.store_transition(observation, action, reward, observation_, int(done)) agent.learn() observation = observation_ n_steps += 1 scores.append(score) steps_array.append(n_steps) avg_score = np.mean(scores[-100:]) print('episode', i, 'score', score, 'average score {} best score {} epsilon {}'.format(avg_score, best_score, agent.epsilon), 'steps', n_steps) if avg_score > best_score: if learning_mode: agent.save_models() best_score = avg_score eps_history.append(agent.epsilon) plot_learning_curve(steps_array, scores, eps_history, fname) ``` #### File: python_rnd_collection/deep_learning/Reinforce.py ```python import numpy as np import torch as T import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import gym import matplotlib.pyplot as plt class PolicyNetwork(nn.Module): def __init__(self, lr, input_dims, n_actions): super(PolicyNetwork, self).__init__() if T.cuda.is_available() == False: raise Exception('no cuda') self.fc1 = nn.Linear(*input_dims, 128) self.fc2 = nn.Linear(128, 128) self.fc3 = nn.Linear(128, n_actions) self.optimizer = optim.Adam(self.parameters(), lr=lr) self.device = T.device('cuda:0') self.to(self.device) def forward(self, state): x = F.relu(self.fc1(state)) x = F.relu(self.fc2(x)) actions = self.fc3(x) return actions class PolicyGradientAgent(object): def __init__(self, lr, input_dims, gamma=0.99, n_actions=4): self.gamma = gamma self.lr = lr self.reward_memory = [] self.action_memory = [] self.policy = PolicyNetwork(self.lr, input_dims, n_actions) def choose_action(self, observation): state = T.Tensor([observation]).to(self.policy.device) probabilities = F.softmax(self.policy.forward(state), dim=1) action_probs = T.distributions.Categorical(probabilities) action = action_probs.sample() log_probs = action_probs.log_prob(action) self.action_memory.append(log_probs) return action.item() def store_rewards(self, reward): self.reward_memory.append(reward) def learn(self): self.policy.optimizer.zero_grad() G = np.zeros_like(self.reward_memory) for t in range(len(self.reward_memory)): G_sum = 0 discount = 1 for k in range(t, len(self.reward_memory)): G_sum += self.reward_memory[k] * discount discount *= self.gamma G[t] = G_sum G = T.tensor(G, dtype=T.float).to(self.policy.device) loss = 0 for g, logprob in zip(G, self.action_memory): loss += -g * logprob loss.backward() self.policy.optimizer.step() self.action_memory = [] self.reward_memory = [] def plot_learnin_curve(scores, x, filename): running_avg = np.zeros(len(scores)) for i in range(len(running_avg)): running_avg[i] = np.mean(scores[max(0, i-100):(i+1)]) plt.plot(x, running_avg) plt.title('running average of previous 100 scores') plt.savefig(filename) if __name__ == '__main__': env = gym.make('LunarLander-v2') n_games = 3000 agent = PolicyGradientAgent(gamma=0.99, lr=0.0005, input_dims=[8], n_actions=4) fname = 'plots/{}_{}_{}_lr_{}_games.png'.format('REINFORCE', 'LunarLander', str(agent.lr), str(n_games)) scores = [] for i in range(n_games): done = False observation = env.reset() score = 0 while not done: action = agent.choose_action(observation) observation_, reward, done, info = env.step(action) score += reward agent.store_rewards(reward) observation = observation_ agent.learn() scores.append(score) avg_score = np.mean(scores[-100:]) print('episode', i, 'score %.2f' % score, 'average score %.2f' % avg_score) x = [i+1 for i in range(len(scores))] plot_learnin_curve(scores, x, fname) ``` #### File: custom_nodes_python/rbfSolver/rbfSolver.py ```python import math, sys import maya.OpenMaya as OpenMaya import maya.OpenMayaMPx as OpenMayaMPx kPluginNodeTypeName = "RBFSolver" rbfNodeId = OpenMaya.MTypeId(0x8800) """MATRIX MATH""" def zeros (rows, cols): return [[0]*cols for i in range(rows)]; def identity (rows): M = zeros(rows, rows) for r in range(rows): M[r][r] = 1.0 return M def copyMatrix (M): return [[v for v in col] for col in M] def transpose(A): rowsA = len(A) colsA = len(A[0]) return [[A[i][j] for i in range(rowsA)] for j in range(colsA)] def scale(A, scale): return [[v*scale for v in col] for col in A] def dot(A, B): rowsA = len(A) colsA = len(A[0]) rowsB = len(B) colsB = len(B[0]) if colsA != rowsB: raise Exception('Number of A columns must equal number of B rows.') C = zeros(rowsA, colsB) for i in range(rowsA): for j in range(colsB): C[i][j] = sum([A[i][k] * B[k][j] for k in range(colsA)]) return C def inverse(A): rowsA = len(A) colsA = len(A[0]) if rowsA != colsA: raise Exception('Matrix must be square') AM = copyMatrix(A) IM = identity(rowsA) for fd in range(rowsA): fdScaler = 1.0 / AM[fd][fd] for j in range(rowsA): AM[fd][j] *= fdScaler IM[fd][j] *= fdScaler for i in list(range(rowsA))[0:fd] + list(range(rowsA))[fd+1:]: crScaler = AM[i][fd] for j in range(rowsA): AM[i][j] = AM[i][j] - crScaler * AM[fd][j] IM[i][j] = IM[i][j] - crScaler * IM[fd][j] return IM def solve(A, b): Inv = inverse(A) return dot(Inv, b) """VECTOR MATH""" import math def vAdd(a,b): return [ia + ib for ia,ib in zip(a,b)] def vSub(a,b): return [ia + ib for ia,ib in zip(a,b)] def vScale(a, scale): return [ia * scale for ia in a] def vDot(a,b): return sum([ia * ib for ia,ib in zip(a,b)]) def vLength(a): return math.sqrt(vDot(a,a)) def vNormalize(a): return vScale(a, 1.0/vLength(a)) """RBF""" def gaussian(x, sigma=1): return math.exp(-x * x / math.pow(sigma, 2.0)) def multiQuadratic(x, sigma=1): return math.sqrt(1+ (x*sigma)**2) def inverseQuadratic(x, sigma=1): return 1.0 / (1.0 + (x*sigma)**2) def inverseMultiQuadratic(x, sigma=1): return 1.0 / math.sqrt(1.0 + (x*sigma)**2) def thinPlate(r): return r * r * math.log(max(1e-8, r)) def normDist(va, vb): return vLength(vSub(va,vb)) def dotDist(va, vb): d = 1.0 - vDot(vNormalize(va), vNormalize(vb)) return d class RBF (object): def __init__(self, centers, values, distfunc, kernelfunc ): """ centers {matrix} : the list of coordinates values {matrix} : the list of values for each center distfunc {function} : the method used to compute the distance between centers kernelfunc {function} : the method used to compute the kernel value """ count = len(centers) Phi = zeros(count, count) for i in range(count): for j in range(i, count): dist = distfunc(centers[i], centers[j]) Phi[i][j] = Phi[j][i] = kernelfunc(dist) A = Phi b = values self.centers = centers self.values = values self.dist = distfunc self.kernel = kernelfunc self.coeffs = solve(A, b) def evaluate(self, centers): """ centers {matrix} : the list of coordinates we want to evaluate """ Phi = [ [self.kernel(self.dist(value, center)) for center in self.centers] for value in centers ] return dot(Phi, self.coeffs) # Node definition class RbfSolver(OpenMayaMPx.MPxNode): # class variables inputCenters = OpenMaya.MObject() inputValues = OpenMaya.MObject() inputEval = OpenMaya.MObject() inputSigma = OpenMaya.MObject() inputKernel = OpenMaya.MObject() inputDistance = OpenMaya.MObject() inputForceUpdate = OpenMaya.MObject() output = OpenMaya.MObject() def __init__(self): OpenMayaMPx.MPxNode.__init__(self) self._rbf = None def shouldSave(self): return True def compute(self, plug, dataBlock): if ( plug == RbfSolver.output ): recompute = dataBlock.inputValue( RbfSolver.inputForceUpdate ).asBool() if self._rbf == None or recompute: self._rbf = None centers = dataBlock.inputArrayValue( RbfSolver.inputCenters ) values = dataBlock.inputArrayValue( RbfSolver.inputValues ) if centers.elementCount() == values.elementCount(): centersmatrix = [] valuesmatrix = [] kernel = dataBlock.inputValue( RbfSolver.inputKernel ).asInt() dist = dataBlock.inputValue( RbfSolver.inputDistance ).asInt() sigma = dataBlock.inputValue( RbfSolver.inputSigma ).asDouble() gaussParam = max(0.1, sigma) for ci in range(centers.elementCount()): centers.jumpToElement(ci) values.jumpToElement(ci) centersmatrix.append ( list(centers.inputValue().asDouble3()) ) valuesmatrix.append ( list(values.inputValue().asDouble3()) ) if centersmatrix: kernelFunction = lambda x:gaussian(x, sigma) if kernel == 1: kernelFunction = lambda x:multiQuadratic(x, sigma) if kernel == 2: kernelFunction = lambda x:inverseQuadratic(x, sigma) if kernel == 3: kernelFunction = lambda x:inverseMultiQuadratic(x, sigma) if kernel == 4: kernelFunction = lambda x:thinPlate(x) distfunction = normDist if dist == 1: distfunction = dotDist self._rbf = RBF(centersmatrix, valuesmatrix, distfunction, kernelFunction ) if self._rbf : center = [list(dataBlock.inputValue( RbfSolver.inputEval ).asDouble3())] result = self._rbf.evaluate(center) outputHandle = dataBlock.outputValue( RbfSolver.output ) outputHandle.set3Double( *result[0] ) dataBlock.setClean( plug ) # creator def nodeCreator(): return OpenMayaMPx.asMPxPtr( RbfSolver() ) # initializer def nodeInitializer(): # input nAttr = OpenMaya.MFnNumericAttribute() RbfSolver.inputCenters = nAttr.create( "centers", "cin", OpenMaya.MFnNumericData.k3Double, 0.0 ) nAttr.setArray(1) nAttr.setStorable(1) nAttr = OpenMaya.MFnNumericAttribute() RbfSolver.inputValues = nAttr.create( "values", "vin", OpenMaya.MFnNumericData.k3Double, 0.0 ) nAttr.setArray(1) nAttr.setStorable(1) nAttr = OpenMaya.MFnNumericAttribute() RbfSolver.inputEval = nAttr.create( "input", "in", OpenMaya.MFnNumericData.k3Double, 0.0 ) nAttr.setStorable(1) nAttr = OpenMaya.MFnNumericAttribute() RbfSolver.inputForceUpdate = nAttr.create( "update", "update", OpenMaya.MFnNumericData.kBoolean, 0.0 ) nAttr.setStorable(1) nAttr = OpenMaya.MFnNumericAttribute() RbfSolver.inputSigma = nAttr.create( "sigma", "sig", OpenMaya.MFnNumericData.kDouble, 1.0 ) nAttr.setStorable(1) enAttr = OpenMaya.MFnEnumAttribute() RbfSolver.inputKernel = enAttr.create( "kernel", "k", 0 ) enAttr.addField('Gaussian', 0) enAttr.addField('Multi Quadratic', 1) enAttr.addField('Inverse Quadratic', 2) enAttr.addField('Inverse Multi Quadratic', 3) enAttr.addField('Thin Plate', 4) enAttr.setStorable(1) enAttr = OpenMaya.MFnEnumAttribute() RbfSolver.inputDistance = enAttr.create( "distance", "dis", 0 ) enAttr.addField('euclidian distance', 0) enAttr.addField('angular distance', 1) enAttr.setStorable(1) # output nAttr = OpenMaya.MFnNumericAttribute() RbfSolver.output = nAttr.create( "output", "out", OpenMaya.MFnNumericData.k3Double, 0.0 ) nAttr.setStorable(1) nAttr.setWritable(1) # add attributes RbfSolver.addAttribute( RbfSolver.inputCenters ) RbfSolver.addAttribute( RbfSolver.inputValues ) RbfSolver.addAttribute( RbfSolver.inputEval ) RbfSolver.addAttribute( RbfSolver.inputForceUpdate ) RbfSolver.addAttribute( RbfSolver.inputSigma ) RbfSolver.addAttribute( RbfSolver.inputKernel ) RbfSolver.addAttribute( RbfSolver.inputDistance ) RbfSolver.addAttribute( RbfSolver.output ) RbfSolver.attributeAffects( RbfSolver.inputCenters, RbfSolver.output ) RbfSolver.attributeAffects( RbfSolver.inputValues, RbfSolver.output ) RbfSolver.attributeAffects( RbfSolver.inputEval, RbfSolver.output ) RbfSolver.attributeAffects( RbfSolver.inputSigma, RbfSolver.output ) RbfSolver.attributeAffects( RbfSolver.inputKernel, RbfSolver.output ) RbfSolver.attributeAffects( RbfSolver.inputDistance, RbfSolver.output ) RbfSolver.attributeAffects( RbfSolver.inputForceUpdate, RbfSolver.output ) # initialize the script plug-in def initializePlugin(mobject): mplugin = OpenMayaMPx.MFnPlugin(mobject) try: mplugin.registerNode( kPluginNodeTypeName, rbfNodeId, nodeCreator, nodeInitializer ) except: sys.stderr.write( "Failed to register node: %s" % kPluginNodeTypeName ) raise # uninitialize the script plug-in def uninitializePlugin(mobject): mplugin = OpenMayaMPx.MFnPlugin(mobject) try: mplugin.deregisterNode( rbfNodeId ) except: sys.stderr.write( "Failed to register node: %s" % kPluginNodeTypeName ) raise ``` #### File: custom_nodes_python/retargetSolver/CapsuleLink.py ```python import mtypes as t class CapsuleLink(object): def __init__(self, distance, ratioA, ratioB, normalA, normalB, relativePq): self.distance = distance self.ratioA = ratioA self.ratioB = ratioB self.normalA = normalA self.normalB = normalB self.relativePq = relativePq def __repr__(self): return """CapsuleLink( distance={}, ratioA={}, ratioB={}, normalA={}, normalB={}, relativePq={} )""".format(self.distance, self.ratioA, self.ratioB, self.normalA, self.normalB, self.relativePq) @classmethod def gather(cls, capsuleA, capsuleB): return cls(*(capsuleA.distance(capsuleB))) def solve(self, capsuleA, capsuleB, weight=1.0, ABRatio=0.0, AOrientationRatio=1.0): A = t.PosQuat(capsuleA.globalSurfacePosition(self.ratioA, self.normalA), capsuleA.pq.q) B = t.PosQuat(capsuleB.globalSurfacePosition(self.ratioB, self.normalB), capsuleB.pq.q) resultA = capsuleA.pq.copy() resultB = capsuleB.pq.copy() #compute the target pq target = A * self.relativePq localATarget = capsuleA.pq.inverse() * target localB = capsuleB.pq.inverse() * B if ABRatio < 0.999 : #compute how we will move capsuleB so B matches the target resultB = target * (B.inverse() * capsuleB.pq) #if the ratio is not 1.0 we will move B a little then compute the motion we have to do on A to reach also the target if ABRatio > 0.001 : resultB = t.PosQuat.lerp( capsuleB.pq, resultB, 1.0-ABRatio ) if ABRatio > 0.001 : #compute how we will move primA so that target matches bPQ goalB = (resultB * localB) #check if we want to move only in translation or not #in that case we change the goalB (to reach) to have the same orientation as what the target is already, so no rotation will happen if AOrientationRatio < 0.999: goalB = (resultB * localB) goalB.q = t.Quaternion.lerp(target.q, goalB.q, AOrientationRatio) resultA = goalB * ( target.inverse() * capsuleA.pq ) #check that primA has been moved completly and not only on translation #otherwise we move back the primB to make sure we are solving the constraint if AOrientationRatio < 0.999: resultB = (resultA * localATarget) * ( B.inverse() * capsuleB.pq ) #solve weights resultA = t.PosQuat.lerp( capsuleA.pq, resultA, weight ) resultB = t.PosQuat.lerp( capsuleB.pq, resultB, weight ) return resultA, resultB ``` #### File: custom_nodes_python/retargetSolver/PrimitiveLink.py ```python import maya.api.OpenMaya as OpenMaya import mtypes as t import CapsuleLink as CL class PrimitiveLink(object): def __init__(self, primitiveIdA, capsuleIdA, primitiveIdB, capsuleIdB, weight, ABRatio, AOrientationRatio): self.primitiveIdA = primitiveIdA self.capsuleIdA = capsuleIdA self.primitiveIdB = primitiveIdB self.capsuleIdB = capsuleIdB self.weight = weight self.ABRatio = ABRatio self.AOrientationRatio = AOrientationRatio self.link = None def __repr__(self): return """PrimitiveLink( primitiveIdA={}, capsuleIdA={}, primitiveIdB={}, capsuleIdB={}, weight={}, ABRatio={}, AOrientationRatio={}, link={} )""".format(self.primitiveIdA, self.capsuleIdA, self.primitiveIdB, self.capsuleIdB, self.weight, self.ABRatio, self.AOrientationRatio, self.link) def gather(self, primitives, skeleton): primitiveA = primitives[self.primitiveIdA] primitiveB = primitives[self.primitiveIdB] capsuleA = primitiveA.capsules[self.capsuleIdA].copy() capsuleB = primitiveB.capsules[self.capsuleIdB].copy() capsuleA.pq = skeleton.globalPq(primitiveA.boneParent) * capsuleA.pq capsuleB.pq = skeleton.globalPq(primitiveB.boneParent) * capsuleB.pq self.link = CL.CapsuleLink.gather(capsuleA, capsuleB) def solve(self, primitives, skeleton): primitiveA = primitives[self.primitiveIdA] primitiveB = primitives[self.primitiveIdB] capsuleA = primitiveA.capsules[self.capsuleIdA].copy() capsuleB = primitiveB.capsules[self.capsuleIdB].copy() capsuleA.pq = skeleton.globalPq(primitiveA.boneParent) * capsuleA.pq capsuleB.pq = skeleton.globalPq(primitiveB.boneParent) * capsuleB.pq resultA, resultB = self.link.solve(capsuleA, capsuleB, self.weight, self.ABRatio, self.AOrientationRatio) return ( resultA * primitiveA.capsules[self.capsuleIdA].pq.inverse(), resultB * primitiveB.capsules[self.capsuleIdB].pq.inverse() ) def create_primitive_links_compound(classtype, name): mComp = OpenMaya.MFnCompoundAttribute() compound = mComp.create(name, name) setattr(classtype, name, compound) mComp.array = True mComp.storable = True mComp.writable = True nAttr = OpenMaya.MFnNumericAttribute() primitiveA = nAttr.create( name + "PrimitiveA", name + "PrimitiveA", OpenMaya.MFnNumericData.kInt, 0 ) setattr(classtype, name + "PrimitiveA", primitiveA) nAttr.array = False nAttr.storable = True nAttr.writable = True nAttr = OpenMaya.MFnNumericAttribute() capsuleA = nAttr.create( name + "CapsuleA", name + "CapsuleA", OpenMaya.MFnNumericData.kInt, 0 ) setattr(classtype, name + "CapsuleA", capsuleA) nAttr.array = False nAttr.storable = True nAttr.writable = True nAttr = OpenMaya.MFnNumericAttribute() primitiveB = nAttr.create( name + "PrimitiveB", name + "PrimitiveB", OpenMaya.MFnNumericData.kInt, 0 ) setattr(classtype, name + "PrimitiveB", primitiveB) nAttr.array = False nAttr.storable = True nAttr.writable = True nAttr = OpenMaya.MFnNumericAttribute() capsuleB = nAttr.create( name + "CapsuleB", name + "CapsuleB", OpenMaya.MFnNumericData.kInt, 0 ) setattr(classtype, name + "CapsuleB", capsuleB) nAttr.array = False nAttr.storable = True nAttr.writable = True nAttr = OpenMaya.MFnNumericAttribute() weight = nAttr.create( name +"Weight",name + "Weight", OpenMaya.MFnNumericData.kDouble, 1.0 ) setattr(classtype, name + "Weight", weight) nAttr.array = False nAttr.storable = True nAttr.writable = True nAttr = OpenMaya.MFnNumericAttribute() abRatio = nAttr.create( name +"ABRatio",name + "ABRatio", OpenMaya.MFnNumericData.kDouble, 0.0 ) setattr(classtype, name + "ABRatio", abRatio) nAttr.array = False nAttr.storable = True nAttr.writable = True nAttr = OpenMaya.MFnNumericAttribute() aOrientationRatio = nAttr.create( name +"AOrientationRatio",name + "AOrientationRatio", OpenMaya.MFnNumericData.kDouble, 1.0 ) setattr(classtype, name + "AOrientationRatio", aOrientationRatio) nAttr.array = False nAttr.storable = True nAttr.writable = True classtype.addAttribute(primitiveA) classtype.addAttribute(capsuleA) classtype.addAttribute(primitiveB) classtype.addAttribute(capsuleB) classtype.addAttribute(weight) classtype.addAttribute(abRatio) classtype.addAttribute(aOrientationRatio) mComp.addChild(primitiveA) mComp.addChild(capsuleA) mComp.addChild(primitiveB) mComp.addChild(capsuleB) mComp.addChild(weight) mComp.addChild(abRatio) mComp.addChild(aOrientationRatio) classtype.addAttribute(compound) return compound def create_primitives_links_from_input(classtype, name, dataBlock): links = [] linksHandle = dataBlock.inputArrayValue( getattr(classtype, name)) while linksHandle.isDone() == False: linkHandle = linksHandle.inputValue() primitiveA = linkHandle.child(getattr(classtype, name + "PrimitiveA")).asInt() capsuleA = linkHandle.child(getattr(classtype, name + "CapsuleA")).asInt() primitiveB = linkHandle.child(getattr(classtype, name + "PrimitiveB")).asInt() capsuleB = linkHandle.child(getattr(classtype, name + "CapsuleB")).asInt() weight = linkHandle.child(getattr(classtype, name + "Weight")).asDouble() abRatio = linkHandle.child(getattr(classtype, name + "ABRatio")).asDouble() aOrientationRatio = linkHandle.child(getattr(classtype, name + "AOrientationRatio")).asDouble() links.append(PrimitiveLink( primitiveA, capsuleA, primitiveB, capsuleB, weight, abRatio, aOrientationRatio )) linksHandle.next() return links ```
{ "source": "JeromeErasmus/browserstack_automate", "score": 2 }
#### File: automate/server/__init__.py ```python import os from flask import Flask, render_template #from flask_login import LoginManager #from flask_bcrypt import Bcrypt from flask_debugtoolbar import DebugToolbarExtension #from flask_bootstrap import Bootstrap from flask_sqlalchemy import SQLAlchemy ################ #### config #### ################ app = Flask( __name__, template_folder='../client/templates', static_folder='../client/static' ) app_settings = 'automate.server.config.DevelopmentConfig'#os.getenv('AUTOMATE_APP_SETTINGS', 'automate.server.config.DevelopmentConfig') app.config.from_object(app_settings) #################### #### extensions #### #################### toolbar = DebugToolbarExtension(app) #bootstrap = Bootstrap(app) db = SQLAlchemy(app) ################### ### blueprints #### ################### #from automate.server.user.views import user_blueprint from automate.server.main.views import main_blueprint #app.register_blueprint(user_blueprint) app.register_blueprint(main_blueprint) ######################### ##### error handlers #### ######################### @app.errorhandler(404) def page_not_found(error): return render_template("errors/404.html"), 404 @app.errorhandler(500) def server_error_page(error): return render_template("errors/500.html"), 500 ``` #### File: JeromeErasmus/browserstack_automate/manage.py ```python from subprocess import Popen, PIPE, CalledProcessError import platform import os import unittest from flask_script import Manager from flask_migrate import Migrate, MigrateCommand from automate.server import app, db from automate.server.models import Project, Config migrate = Migrate(app, db) manager = Manager(app) # migrations manager.add_command('db', MigrateCommand) @manager.command def test(): """Runs the unit tests without test coverage.""" tests = unittest.TestLoader().discover('automate/tests', pattern='test*.py') result = unittest.TextTestRunner(verbosity=2).run(tests) if result.wasSuccessful(): return 0 return 1 @manager.command def create_db(): """Creates the db tables.""" db.create_all() @manager.command def drop_db(): """Drops the db tables.""" db.drop_all() @manager.command def create_data(): """Creates sample data.""" pass @manager.command def install(): """ Install the app """ create_db() create_data() if __name__ == '__main__': manager.run() ```
{ "source": "JeromeErasmus/raycast-aws", "score": 2 }
#### File: raycast-aws/src/rds_describe_db_clusters.py ```python import sys import botocore import botostubs import boto3 from core.config import AWSConfig from core.requests import AWSRequests from core.functions import Functions config = AWSConfig() client = config.session.client('rds') # type: botostubs.RDS table_headers = { 'DBClusterIdentifier': 'DBClusterIdentifier', 'Status': 'Status', 'BackupRetentionPeriod': 'Retention', 'Capacity': 'Capacity', 'EngineMode': 'EngineMode'} table_columns = {'DBClusterIdentifier', 'Status', 'BackupRetentionPeriod', 'EngineMode', 'Capacity'} def describe_db_clusters(*args): response = AWSRequests.send_request( client.describe_db_clusters ) items = Functions.search_list(args[0], 'DBClusterIdentifier', response['DBClusters']) Functions.display(items, table_headers, table_columns) if len(sys.argv) > 1: describe_db_clusters(sys.argv[1]) else: describe_db_clusters(None) exit(0) ``` #### File: raycast-aws/src/ssm_get_parameter.py ```python import sys import botocore import botostubs import boto3 from collections import OrderedDict from core.config import AWSConfig from core.requests import AWSRequests from core.functions import Functions, Fontcol config = AWSConfig() client = config.session.client('ssm') # type: botostubs.SSM table_headers = {'Name': 'Name', 'Value': 'Value'} table_columns = {'Name', 'Value'} def get_parameter(*args): response = AWSRequests.send_request( client.get_parameter, Name=args[0] ) Functions.display([response['Parameter']], table_headers, table_columns) Functions.copyClipboard(response['Parameter']['Value']) if len(sys.argv) > 1: get_parameter(sys.argv[1]) else: get_parameter(None) exit(0) ```
{ "source": "JeromeErasmus/raycast-commands", "score": 3 }
#### File: src/core/config.py ```python from dotenv import dotenv_values from github import Github from jira import JIRA __all__ = ['CommandsConfig', 'get_github_client', 'get_jira_client'] class CommandsConfig: config = None github_client = None jira_client = None github_repo = None github_branch = None def __init__(self, **kwargs): self.config = dotenv_values() self.github_client = Github(self.config['GITHUB_TOKEN'], per_page=30) self.jira_client = JIRA(server=self.config['JIRA_SERVER'], basic_auth=(self.config['JIRA_USER_EMAIL'], self.config['JIRA_TOKEN'])) if kwargs['repository']: self.github_repo = kwargs['repository'] else: self.github_repo = self.config['GITHUB_DEFAULT_REPOSITORY'] if kwargs['branch']: self.github_branch= kwargs['branch'] else: self.github_branch = self.config['GITHUB_DEFAULT_BRANCH'] def get_github_client(self, **kwargs): """Gets a Github Client configuration """ return self.github_client def get_jira_client(self, **kwargs): """Gets a Jira Client configuration """ return self.jira_client ``` #### File: raycast-commands/src/git_create_release.py ```python import sys import re import json from core.config import CommandsConfig from datetime import datetime from itertools import groupby from jira import JIRA, JIRAError from github import GithubException from core.functions import Functions, Fontcol config = None github_client = None jira_client = None repo = None def main(*args): global config, github_client, jira_client, repo config = CommandsConfig(repository=args[0], branch=args[1]) github_client = config.get_github_client() jira_client = config.get_jira_client() repo = get_repository() last_release = get_last_release() if not last_release: print('Error. Previous release not found') return False if last_release.prerelease: print('Previous release is a PreRelease. Publish the previous release') return False issues = create_issues_list(last_release) grouped_issues = group_issues_list(issues) notes = format_notes(grouped_issues) create_release(last_release, notes) def create_issues_list(release): issues = [] for issue in search_issues(release): ticket_key = extract_ticket(issue.title) lable_name = extract_lable(issue.title) issues.append(dict( number=issue.number, title=issue.title, ticket_key=ticket_key, lable_name=lable_name, )) return issues def group_issues_list(issues): sorted_issues = sorted(issues, key=sort_key_func) grouped = dict() for key, value in groupby(sorted_issues, sort_key_func): grouped[key] = dict(children=list(value), ticket_key=key) for key in grouped: issue = grouped[key] jira_issue = get_jira_issue(issue['ticket_key']) if not jira_issue: issue['valid_issue'] = False else: issue['valid_issue'] = True issue['issue_summary'] = jira_issue.fields.summary return grouped def format_notes(issues): notes = [] for key in issues: issue = issues[key] summary = [] numbers = [] for child in issue['children']: numbers.append(str(child['number'])) if not issue['valid_issue']: for child in issue['children']: summary.append(child['lable_name']) else: summary = [issue['issue_summary']] notes.append("#{0} [{1}] {2} \n\r".format( ' #'.join(numbers), issue['ticket_key'], ', '.join(summary), ) ) return ''.join(notes) def get_jira_issue(ticket_key): try: issue = jira_client.issue(ticket_key) if issue: return issue except JIRAError as error: pass return None def extract_ticket(string): ticket_label = re.search(r"(?<=\[)(.*?)(?=\])", string) if ticket_label: return ticket_label.group(0).lstrip().rstrip() else: return '' def extract_lable(string): index = string.rfind(']') if index != -1: return string[index+1:].lstrip().capitalize() else: return '' def search_issues(release): date = release.published_at.strftime('%Y-%m-%dT%H:%M:%S') query = 'repo:{0} type:pr merged:>{1}'.format(config.github_repo, date) try: result = github_client.search_issues(query=query) return result except GithubException as error: print(error) return False def sort_key_func(k): return k['ticket_key'] def get_repository(): try: repo = github_client.get_repo(config.github_repo) if repo: return repo except GithubException as error: print(error) return False def get_last_release(): try: releases = repo.get_releases() if releases and releases[0]: return releases[0] except GithubException as error: print(error) return False def get_branch_head(): try: branch = repo.get_branch(config.github_branch) return branch except GithubException as error: print(error) return False def create_release(last_release, notes): if not last_release: print('Error. Last release not found') return False last_tag = last_release.tag_name m = int(last_tag[last_tag.rfind('.')+1:]) + 1 tag = last_tag[:last_tag.rfind('.')+1] + str(m) name = "{0}-{1}".format(tag, datetime.today().strftime('%Y-%m-%d')) try: release = repo.create_git_release( tag=tag, name=name, prerelease=True, message=notes, target_commitish=config.github_branch ) if release: print(Fontcol.YELLOW, 'Version: {0}'.format(name)) print('Url: {0}'.format(release.html_url)) print(Fontcol.WHITE, '\n{0}\n{1}'.format('-'*10, notes)) except GithubException as error: print(error) return False if len(sys.argv) > 1: main(sys.argv[1], sys.argv[2]) else: print('Error. Invalid argumnet count') exit(0) ```
{ "source": "jeromefiot/FEED2TW", "score": 3 }
#### File: FEED2TW/app/classes_feed.py ```python import feedparser import tweepy import time from datetime import datetime import random from threading import Timer from flask import current_app, flash from flask.ext.login import current_user from . import db from . import infos_tweet from .models import Feed, Article # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- # THREADING TEST # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- class RepeatedTimer(object): """ Run function (arg or not) every interval seconds http://stackoverflow.com/questions/3393612/ run-certain-code-every-n-seconds """ def __init__(self, interval, function, *args, **kwargs): self._timer = None self.interval = interval self.function = function self.args = args self.kwargs = kwargs self.is_running = False self.start() def _run(self): self.is_running = False self.start() self.function(*self.args, **self.kwargs) def start(self): if not self.is_running: self._timer = Timer(self.interval, self._run) self._timer.start() self.is_running = True def stop(self): self._timer.cancel() self.is_running = False class RssFlux(): """ Activate get_articles (and func to deactivate : desactivate_collect). Activate tweet_articles (and func to deactivate : desactivate_tweet). functions : > refresh (default rate = 1800 sec.) > activate() / deactivate() > get_articles > Tweet articles from (self) Feed """ def __init__(self, idflux): """Connection init.""" self.app = current_app._get_current_object() self.idflux = idflux flux_info = Feed.query.filter_by(id=self.idflux).first() self.name = flux_info.name self.url = flux_info.url self.collect_actif = flux_info.collect_actif self.Tweet_actif = flux_info.Tweet_actif # resfresh rate for geting articles (28800.0 = 8h) self.refresh = 610.0 # every 10mn # self.frequency = (24/flux_info.frequency) * 3600 self.frequency = 600.0 # every 10mn if flux_info.hashtag: self.hashtag = flux_info.hashtag else: self.hashtag = '' self.rt = None self.rt2 = None # thread name # self.name_Thread = '{0} {1}'.format('thread', idflux) # print self.name_Thread def get_articles(self): """Get every self.refresh all new artle of feed and insert bdd.""" # repeat in a thread every self.refresh the get_articles function # self.name_Thread = threading.Timer(self.refresh, self.get_articles).start() # Timer(self.refresh, self.get_articles).start() rss = self.url feeds = feedparser.parse(rss) with self.app.app_context(): db.session.expunge_all() # titles list of all articles in bdd title_articles = [element.title for element in Article.query.filter(Article.feed_id == self.idflux)] # list title/link from last 10 items of Rss feed not in bdd feedss = [(feeds.entries[i]['title'], feeds.entries[i]['link']) for i in range(1, 10) if feeds.entries[i]['title'] not in title_articles] # Add new items from list feedss to bdd for elem in feedss: article = Article(title=elem[0], url=elem[1], feed_id=self.idflux) db.session.add(article) db.session.commit() print "SCRAPP ARTICLE EFFECTUE" def tweet_articles(self): """Format and tweet articles from bdd for self.flux.""" with self.app.app_context(): articles_to_tweet = Article.query.\ filter(Article.feed_id == self.idflux).\ filter(Article.tweeted == 0).all() # checkingarticles to tweet if articles_to_tweet: auth = tweepy.OAuthHandler(infos_tweet.Key_consumer, infos_tweet.Consumer_secret) auth.set_access_token(infos_tweet.Access_token, infos_tweet.Access_token_secret) api = tweepy.API(auth) try: for tweets in articles_to_tweet: # TITLE // LINK -> tweet_content title = tweets.title[:100] link_article = tweets.url # FEED name for VIA -> tweet_content name_feed = Feed.query.\ filter(Feed.id == Article.feed_id).first() via_article = name_feed.name.split()[0] tweet_content = "%s // %s - via %s" %\ (title, link_article, via_article) # update twitted tweets.tweeted = 1 tweets.date_tweeted = datetime.utcnow() db.session.commit() # send it api.update_status(tweet_content) # wait randomly time.sleep(600 + random.randint(30, 60)) print "Tweet ID : "+str(tweets.id)+" : ENVOYE" # check rate limit except tweepy.RateLimitError: print "Rate limite reach...sarace" time.sleep(16 * 60) else: # no tweet to send message = flash('No tweets to send') print message def activate_get(self): """Activate Flux to get Articles.""" print self.collect_actif if not self.collect_actif: print "enter activate_get" self.rt2 = RepeatedTimer(self.refresh, self.get_articles) # update Feed flux_info = Feed.query.filter_by(id=self.idflux).first() flux_info.collect_actif = True db.session.commit() print self.rt2 else: print 'Collect already enable' def desactivate_get(self): """Desactivate Flux to get Articles.""" if self.rt2: self.rt2.stop() # update Feed flux_info = Feed.query.filter_by(id=self.idflux).first() flux_info.collect_actif = False db.session.commit() else: print 'Collect already disable' def activate_tweet(self): """Activate Flux to get Articles.""" print "State TWEET (Tweet_actif) : " print self.Tweet_actif if not self.Tweet_actif: print "enter activate_tweet" self.rt = RepeatedTimer(self.frequency, self.tweet_articles) # update Feed flux_info = Feed.query.filter_by(id=self.idflux).first() flux_info.Tweet_actif = True db.session.commit() print self.rt else: print 'Tweet already enable' def desactivate_tweet(self): """Desactivate Flux to get Articles.""" if self.rt: self.rt.stop() # update Feed flux_info = Feed.query.filter_by(id=self.idflux).first() flux_info.Tweet_actif = False db.session.commit() else: print 'Tweet already disable' def state(self): """Print effective actions (tweet_articles / get_articles).""" if self.rt.is_running is True: if self.rt2.is_running is True: return self.name+" : Collecting and Tweeting actif." return self.name+" : Tweeting is actif." elif self.rt2.is_running is True: return self.name+" : Collecting is actif." else: print 'No actions' def print_info(self): self.attrs = vars(self) print ', '.join("%s: %s" % item for item in self.attrs.items()) if __name__ == '__main__': pass ```
{ "source": "jeromefiot/FLASK_base", "score": 2 }
#### File: static/img/views.py ```python from flask import render_template, redirect, url_for, abort, flash, request, current_app from flask.ext.login import login_required, current_user from flask.ext.admin import Admin, BaseView, expose, AdminIndexView from flask.ext.admin.contrib.sqla import ModelView from flask.ext.admin.contrib.fileadmin import FileAdmin from . import main from ..email import send_email from .forms import EditProfileForm, EditProfileAdminForm, AddClientForm, ContactForm from .. import db, admin from ..models import Role, User, Client, Document from ..decorators import admin_required # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- # PAGES PUBLIQUES # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- @main.route('/') def index(): return render_template('index.html') @main.route('/contact', methods=['GET', 'POST']) def contact(): form = ContactForm() ################################################### # prevoir 2 mails : un pour admin et un pour envoyeur ################################################### if form.validate_on_submit(): app = current_app._get_current_object() # envoi a l'admin send_email(app.config['FLASKY_ADMIN'], form.titre.data, '/mail/contact', envoyeur=form.nom.data, mail=form.mail.data, message=form.message.data, depuis=app.config['FLASKY_MAIL_SUBJECT_PREFIX']) # envoi merci a l'envoyeur send_email(form.mail.data, 'Confirmation message', '/mail/merci_contact') flash('Message bien envoye !') return redirect(url_for('main.index')) return render_template('contact.html', form=form) @main.route('/credits') def credits(): return render_template('credits.html') # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- # PAGES INSCRITS # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- @main.route('/user/<username>') @login_required ################################################### # prevoir un login restricted a cet utilisateur only ################################################### def user(username): user = User.query.filter_by(username=username).first_or_404() clients = Client.query.filter_by(user_id=user.id).all() return render_template('user.html', user=user, clients=clients) @main.route('/user/clients/<username>') @login_required ################################################### # prevoir un login restricted a cet utilisateur only ################################################### def clients_user(username): user = User.query.filter_by(username=username).first_or_404() clients = Client.query.filter_by(user_id=user.id).all() return render_template('clients_user.html', user=user, clients=clients) @main.route('/user/ajout_client/<username>', methods=['GET', 'POST']) @login_required ################################################### # prevoir un login restricted a cet utilisateur only ################################################### def add_user(username): form = AddClientForm() user = User.query.filter_by(username=username).first_or_404() if form.validate_on_submit(): client = Client(nom=form.nom.data, entreprise=form.entreprise.data, telephone = form.telephone.data, mail = form.mail.data, adresse = form.adresse.data, codepostal = form.codepostal.data, user_id=current_user.id) db.session.add(client) flash('Client ajoute.') return redirect(url_for('.clients_user', username=current_user.username)) return render_template('add_user.html', user=user, form=form) @main.route('/edit-profile', methods=['GET', 'POST']) @login_required def edit_profile(): form = EditProfileForm() if form.validate_on_submit(): current_user.name = form.name.data current_user.telephone = form.telephone.data current_user.location = form.location.data current_user.codepostal = form.codepostal.data current_user.entreprise = form.entreprise.data db.session.add(current_user) flash('Your profile has been updated.') return redirect(url_for('.user', username=current_user.username)) form.name.data = current_user.name form.telephone.data = current_user.telephone form.location.data = current_user.location form.codepostal.data = current_user.codepostal form.entreprise.data = current_user.entreprise return render_template('edit_profile.html', form=form) # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- # PAGES @ADMIN_REQUIRED # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- @main.route('/edit-profile/<int:id>', methods=['GET', 'POST']) @login_required @admin_required def edit_profile_admin(id): user = User.query.get_or_404(id) form = EditProfileAdminForm(user=user) if form.validate_on_submit(): user.email = form.email.data user.username = form.username.data user.confirmed = form.confirmed.data user.role = Role.query.get(form.role.data) user.name = form.name.data user.telephone = form.telephone.data user.location = form.location.data user.codepostal = form.codepostal.data user.entreprise = form.entreprise.data db.session.add(user) flash('The profile has been updated.') return redirect(url_for('.user', username=user.username)) form.email.data = user.email form.username.data = user.username form.confirmed.data = user.confirmed form.role.data = user.role_id form.name.data = user.name form.telephone.data = user.telephone form.location.data = user.location form.codepostal.data = user.codepostal form.entreprise.data = user.entreprise return render_template('edit_profile.html', form=form, user=user) @main.route('/list_users/', methods=['GET', 'POST']) @login_required @admin_required def list_users(): user = User.query.all() if request.method == 'POST': flash("Suppression de id") return render_template('list_users.html', user=user) # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- # PAGES ADMIN # --------------------------------------------------------------------------------- # --------------------------------------------------------------------------------- class UserModelView(ModelView): # remove "password_hash" # http://flask-admin.readthedocs.org/en/latest/api/mod_model/#flask.ext.admin.model.BaseModelView) column_exclude_list = ('password_hash') # Accessible only by admin @admin_required def is_accessible(self): return current_user.is_administrator() class ClientModelView(ModelView): # Accessible only by admin @admin_required def is_accessible(self): return current_user.is_administrator() class DocumentModelView(ModelView): # Accessible only by admin @admin_required def is_accessible(self): return current_user.is_administrator() admin.add_view(UserModelView(User, db.session)) admin.add_view(ClientModelView(Client, db.session)) admin.add_view(DocumentModelView(Document, db.session)) # pour la vue avec tous les arg, sinon Myview3 #admin.add_view(ModelView(User, db.session)) ```
{ "source": "jeromefischer/fountain", "score": 3 }
#### File: jeromefischer/fountain/Valve.py ```python import RPi.GPIO as GPIO from Logger import logger class Valve: def __init__(self, pin, name='Valve'): self.pin = pin self.name = name self.init_gpio() def init_gpio(self): logger.info('initialize: {} pin: {}'.format(self.name, self.pin)) GPIO.setmode(GPIO.BCM) GPIO.setup(self.pin, GPIO.OUT) def get_valve_status(self): pass def set_valve_on(self): GPIO.output(self.pin, GPIO.HIGH) # Turn valve on logger.info('valve switched on') pass def set_valve_off(self): GPIO.output(self.pin, GPIO.LOW) # Turn valve off logger.info('valve switched off') pass ```
{ "source": "jerome-f/polyfun", "score": 3 }
#### File: polyfun/ldsc_polyfun/irwls.py ```python import numpy as np from . import jackknife as jk import logging class IRWLS(object): ''' Iteratively re-weighted least squares (FLWS). Parameters ---------- x : np.matrix with shape (n, p) Independent variable. y : np.matrix with shape (n, 1) Dependent variable. update_func : function Transforms output of np.linalg.lstsq to new weights. n_blocks : int Number of jackknife blocks (for estimating SE via block jackknife). w : np.matrix with shape (n, 1) Initial regression weights (default is the identity matrix). These should be on the inverse CVF scale. slow : bool Use slow block jackknife? (Mostly for testing) Attributes ---------- est : np.matrix with shape (1, p) IRWLS estimate. jknife_est : np.matrix with shape (1, p) Jackknifed estimate. jknife_var : np.matrix with shape (1, p) Variance of jackknifed estimate. jknife_se : np.matrix with shape (1, p) Standard error of jackknifed estimate, equal to sqrt(jknife_var). jknife_cov : np.matrix with shape (p, p) Covariance matrix of jackknifed estimate. delete_values : np.matrix with shape (n_blocks, p) Jackknife delete values. Methods ------- wls(x, y, w) : Weighted Least Squares. _weight(x, w) : Weight x by w. ''' def __init__(self, x, y, update_func, n_blocks, w=None, slow=False, separators=None): n, p = jk._check_shape(x, y) if w is None: w = np.ones_like(y) if w.shape != (n, 1): raise ValueError( 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) jknife = self.irwls( x, y, update_func, n_blocks, w, slow=slow, separators=separators) self.est = jknife.est self.jknife_se = jknife.jknife_se self.jknife_est = jknife.jknife_est self.jknife_var = jknife.jknife_var self.jknife_cov = jknife.jknife_cov self.delete_values = jknife.delete_values self.separators = jknife.separators @classmethod def irwls(cls, x, y, update_func, n_blocks, w, slow=False, separators=None): ''' Iteratively re-weighted least squares (IRWLS). Parameters ---------- x : np.matrix with shape (n, p) Independent variable. y : np.matrix with shape (n, 1) Dependent variable. update_func: function Transforms output of np.linalg.lstsq to new weights. n_blocks : int Number of jackknife blocks (for estimating SE via block jackknife). w : np.matrix with shape (n, 1) Initial regression weights. slow : bool Use slow block jackknife? (Mostly for testing) separators : list or None Block jackknife block boundaries (optional). Returns ------- jknife : jk.LstsqJackknifeFast Block jackknife regression with the final IRWLS weights. ''' (n, p) = x.shape if y.shape != (n, 1): raise ValueError( 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) if w.shape != (n, 1): raise ValueError( 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) w = np.sqrt(w) for i in range(2): # update this later new_w = np.sqrt(update_func(cls.wls(x, y, w))) if new_w.shape != w.shape: logging.info('IRWLS update:', new_w.shape, w.shape) raise ValueError('New weights must have same shape.') else: w = new_w x = cls._weight(x, w) y = cls._weight(y, w) if slow: jknife = jk.LstsqJackknifeSlow( x, y, n_blocks, separators=separators) else: jknife = jk.LstsqJackknifeFast( x, y, n_blocks, separators=separators) return jknife @classmethod def wls(cls, x, y, w): ''' Weighted least squares. Parameters ---------- x : np.matrix with shape (n, p) Independent variable. y : np.matrix with shape (n, 1) Dependent variable. w : np.matrix with shape (n, 1) Regression weights (1/CVF scale). Returns ------- coef : list with four elements (coefficients, residuals, rank, singular values) Output of np.linalg.lstsq ''' (n, p) = x.shape if y.shape != (n, 1): raise ValueError( 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) if w.shape != (n, 1): raise ValueError( 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) x = cls._weight(x, w) y = cls._weight(y, w) coef = np.linalg.lstsq(x, y) return coef @classmethod def _weight(cls, x, w): ''' Weight x by w. Parameters ---------- x : np.matrix with shape (n, p) Rows are observations. w : np.matrix with shape (n, 1) Regression weights (1 / sqrt(CVF) scale). Returns ------- x_new : np.matrix with shape (n, p) x_new[i,j] = x[i,j] * w'[i], where w' is w normalized to have sum 1. Raises ------ ValueError : If any element of w is <= 0 (negative weights are not meaningful in WLS). ''' if np.any(w <= 0): raise ValueError('Weights must be > 0') (n, p) = x.shape if w.shape != (n, 1): raise ValueError( 'w has shape {S}. w must have shape (n, 1).'.format(S=w.shape)) w = w / float(np.sum(w)) x *= w return x ``` #### File: polyfun/ldsc_polyfun/jackknife.py ```python import numpy as np from scipy.optimize import nnls np.seterr(divide='raise', invalid='raise') from tqdm import tqdm from sklearn.linear_model import Lasso import logging import warnings warnings.filterwarnings('ignore', message='Coordinate descent with alpha=0 may lead to unexpected results and is discouraged.') warnings.filterwarnings('ignore', message='Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.') from sklearn.metrics import r2_score def _check_shape(x, y): '''Check that x and y have the correct shapes (for regression jackknives).''' if len(x.shape) != 2 or len(y.shape) != 2: raise ValueError('x and y must be 2D arrays.') if x.shape[0] != y.shape[0]: raise ValueError( 'Number of datapoints in x != number of datapoints in y.') if y.shape[1] != 1: raise ValueError('y must have shape (n_snp, 1)') n, p = x.shape if p > n: raise ValueError('More dimensions than datapoints.') return (n, p) def _check_shape_block(xty_block_values, xtx_block_values): '''Check that xty_block_values and xtx_block_values have correct shapes.''' if xtx_block_values.shape[0:2] != xty_block_values.shape: raise ValueError( 'Shape of xty_block_values must equal shape of first two dimensions of xty_block_values.') if len(xtx_block_values.shape) < 3: raise ValueError('xtx_block_values must be a 3D array.') if xtx_block_values.shape[1] != xtx_block_values.shape[2]: raise ValueError( 'Last two axes of xtx_block_values must have same dimension.') return xtx_block_values.shape[0:2] class Jackknife(object): ''' Base class for jackknife objects. Input involves x,y, so this base class is tailored for statistics computed from independent and dependent variables (e.g., regressions). The __delete_vals_to_pseudovalues__ and __jknife__ methods will still be useful for other sorts of statistics, but the __init__ method will need to be overriden. Parameters ---------- x : np.matrix with shape (n, p) Independent variable. y : np.matrix with shape (n, 1) Dependent variable. n_blocks : int Number of jackknife blocks *args, **kwargs : Arguments for inheriting jackknives. Attributes ---------- n_blocks : int Number of jackknife blocks p : int Dimensionality of the independent varianble N : int Number of datapoints (equal to x.shape[0]) Methods ------- jknife(pseudovalues): Computes jackknife estimate and variance from the jackknife pseudovalues. delete_vals_to_pseudovalues(delete_vals, est): Converts delete values and the whole-data estimate to pseudovalues. get_separators(): Returns (approximately) evenly-spaced jackknife block boundaries. ''' def __init__(self, x, y, n_blocks=None, separators=None): self.N, self.p = _check_shape(x, y) if separators is not None: if max(separators) != self.N: raise ValueError( 'Max(separators) must be equal to number of data points.') if min(separators) != 0: raise ValueError('Max(separators) must be equal to 0.') self.separators = sorted(separators) self.n_blocks = len(separators) - 1 elif n_blocks is not None: self.n_blocks = n_blocks self.separators = self.get_separators(self.N, self.n_blocks) else: raise ValueError('Must specify either n_blocks are separators.') if self.n_blocks > self.N: raise ValueError('More blocks than data points.') @classmethod def jknife(cls, pseudovalues): ''' Converts pseudovalues to jackknife estimate and variance. Parameters ---------- pseudovalues : np.matrix pf floats with shape (n_blocks, p) Returns ------- jknife_est : np.matrix with shape (1, p) Jackknifed estimate. jknife_var : np.matrix with shape (1, p) Variance of jackknifed estimate. jknife_se : np.matrix with shape (1, p) Standard error of jackknifed estimate, equal to sqrt(jknife_var). jknife_cov : np.matrix with shape (p, p) Covariance matrix of jackknifed estimate. ''' n_blocks = pseudovalues.shape[0] jknife_cov = np.atleast_2d(np.cov(pseudovalues.T, ddof=1) / n_blocks) jknife_var = np.atleast_2d(np.diag(jknife_cov)) jknife_se = np.atleast_2d(np.sqrt(jknife_var)) jknife_est = np.atleast_2d(np.mean(pseudovalues, axis=0)) return (jknife_est, jknife_var, jknife_se, jknife_cov) @classmethod def delete_values_to_pseudovalues(cls, delete_values, est): ''' Converts whole-data estimate and delete values to pseudovalues. Parameters ---------- delete_values : np.matrix with shape (n_blocks, p) Delete values. est : np.matrix with shape (1, p): Whole-data estimate. Returns ------- pseudovalues : np.matrix with shape (n_blocks, p) Psuedovalues. Raises ------ ValueError : If est.shape != (1, delete_values.shape[1]) ''' n_blocks, p = delete_values.shape if est.shape != (1, p): raise ValueError( 'Different number of parameters in delete_values than in est.') return n_blocks * est - (n_blocks - 1) * delete_values @classmethod def get_separators(cls, N, n_blocks): '''Define evenly-spaced block boundaries.''' return np.floor(np.linspace(0, N, n_blocks + 1)).astype(int) class LstsqJackknifeSlow(Jackknife): ''' Slow linear-regression block jackknife. This class computes delete values directly, rather than forming delete values from block values. Useful for testing and for non-negative least squares (which as far as I am aware does not admit a fast block jackknife algorithm). Inherits from Jackknife class. Parameters ---------- x : np.matrix with shape (n, p) Independent variable. y : np.matrix with shape (n, 1) Dependent variable. n_blocks : int Number of jackknife blocks nn: bool Non-negative least-squares? Attributes ---------- est : np.matrix with shape (1, p) FWLS estimate. jknife_est : np.matrix with shape (1, p) Jackknifed estimate. jknife_var : np.matrix with shape (1, p) Variance of jackknifed estimate. jknife_se : np.matrix with shape (1, p) Standard error of jackknifed estimate, equal to sqrt(jknife_var). jknife_cov : np.matrix with shape (p, p) Covariance matrix of jackknifed estimate. delete_vals : np.matrix with shape (n_blocks, p) Jackknife delete values. ''' @classmethod def delete_values(cls, x, y, func, s): ''' Compute delete values by deleting one block at a time. Parameters ---------- x : np.matrix with shape (n, p) Independent variable. y : np.matrix with shape (n, 1) Dependent variable. func : function (n, p) , (n, 1) --> (1, p) Function of x and y to be jackknived. s : list of ints Block separators. Returns ------- delete_values : np.matrix with shape (n_blocks, p) Delete block values (with n_blocks blocks defined by parameter s). Raises ------ ValueError : If x.shape[0] does not equal y.shape[0] or x and y are not 2D. ''' _check_shape(x, y) d = [] logging.info('Starting non-negative jackknife...') for i in tqdm(range(len(s) - 1)): jk_est = func(np.vstack([x[0:s[i], ...], x[s[i + 1]:, ...]]), np.vstack([y[0:s[i], ...], y[s[i + 1]:, ...]])) d.append(jk_est) return np.concatenate(d, axis=0) def __init__(self, x, y, is_large_chi2, n_blocks=None, nn=False, separators=None, chr_num=None, evenodd_split=False, nnls_exact=False): Jackknife.__init__(self, x, y, n_blocks, separators) #estimate taus if nn: # non-negative least squares if nnls_exact: self.est = np.atleast_2d(nnls(x, np.array(y).T[0])[0]) else: xtx = x.T.dot(x) lasso = Lasso(alpha=1e-100, fit_intercept=False, normalize=False, precompute=xtx, positive=True, max_iter=10000, random_state=0) self.est = lasso.fit(x,y[:,0]).coef_.reshape((1, x.shape[1])) else: self.est = np.atleast_2d(np.linalg.lstsq(x, np.array(y).T[0])[0]) #move large_chi2 SNPs to the end of x and y (don't include them in the separator definition, so that they'll never get removed during jackknife) if np.any(is_large_chi2): x_large = x[is_large_chi2] y_large = y[is_large_chi2] x = x[~is_large_chi2] y = y[~is_large_chi2] Jackknife.__init__(self, x, y, n_blocks, separators) x = np.concatenate((x,x_large), axis=0) y = np.concatenate((y,y_large), axis=0) #jackknife if nn: d = [] s = self.separators for i in tqdm(range(len(s) - 1), disable=False): x_noblock = np.delete(x, slice(s[i], s[i+1]), axis=0) y_noblock = np.delete(y, slice(s[i], s[i+1]), axis=0) if nnls_exact: jk_est = np.atleast_2d(nnls(x_noblock, y_noblock[:,0])[0]) else: x_block = x[s[i] : s[i+1]] xtx_noblock = xtx - x_block.T.dot(x_block) lasso_noblock = Lasso(alpha=1e-100, fit_intercept=False, normalize=False, precompute=xtx_noblock, positive=True, max_iter=10000, random_state=0) jk_est = lasso_noblock.fit(x_noblock, y_noblock[:,0]).coef_.reshape((1, x.shape[1])) ###z = nnls(x_noblock, y_noblock[:,0])[0] ###assert np.allclose(z, jk_est[0]) d.append(jk_est) self.delete_values = np.concatenate(d, axis=0) else: self.delete_values = self.delete_values(x, y, func, self.separators) self.pseudovalues = self.delete_values_to_pseudovalues( self.delete_values, self.est) (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ self.jknife(self.pseudovalues) if evenodd_split: assert y.shape[1]==1 assert chr_num is not None assert len(np.unique(chr_num)) > 1 self.chr_list = np.sort(np.unique(chr_num)) self.est_loco = np.empty((len(self.chr_list), x.shape[1]), dtype=np.float32) for chr_i, left_out_chr in enumerate(tqdm(self.chr_list)): is_loco = ((chr_num%2)==(left_out_chr%2)) & (chr_num != left_out_chr) x_loco = x[is_loco] y_loco = y[is_loco] self.est_loco[chr_i, :] = nnls(x_loco, y_loco[:,0])[0] class LstsqJackknifeFast(Jackknife): def __init__(self, x, y, is_large_chi2, n_blocks=None, separators=None, chr_num=None, evenodd_split=False): #compute jackknife estimates using all SNPs Jackknife.__init__(self, x, y, n_blocks, separators) xty, xtx = self.block_values(x, y, self.separators) self.est = self.block_values_to_est(xty, xtx) #compute xtx_tot and xty_tot xty_tot = np.sum(xty, axis=0) xtx_tot = np.sum(xtx, axis=0) #exclude large-chi2 SNPs from xtx and xty for the jackknife if np.any(is_large_chi2): x = x[~is_large_chi2] y = y[~is_large_chi2] Jackknife.__init__(self, x, y, n_blocks, separators) xty, xtx = self.block_values(x, y, self.separators) self.delete_values = self.block_values_to_delete_values(xty, xtx, xtx_tot=xtx_tot, xty_tot=xty_tot) self.pseudovalues = self.delete_values_to_pseudovalues( self.delete_values, self.est) (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ self.jknife(self.pseudovalues) if evenodd_split: assert y.shape[1]==1 assert chr_num is not None assert len(np.unique(chr_num)) > 1 x_even = x[chr_num %2 == 0] y_even = y[chr_num %2 == 0] XTX_even = x_even.T.dot(x_even) XTy_even = y_even[:,0].dot(x_even) del x_even, y_even x_odd = x[chr_num %2 != 0] y_odd = y[chr_num %2 != 0] XTX_odd = x_odd.T.dot(x_odd) XTy_odd = y_odd[:,0].dot(x_odd) del x_odd, y_odd assert np.allclose(XTy_even + XTy_odd, y[:,0].dot(x)) assert np.allclose(XTX_even + XTX_odd, x.T.dot(x)) self.chr_list = np.sort(np.unique(chr_num)) #self.est_chr = np.empty((len(self.chr_list), x.shape[1]), dtype=np.float32) self.est_loco = np.empty((len(self.chr_list), x.shape[1]), dtype=np.float32) for chr_i, left_out_chr in enumerate(self.chr_list): x_chr = x[chr_num == left_out_chr] y_chr = y[chr_num == left_out_chr, 0] XTX_chr = x_chr.T.dot(x_chr) XTy_chr = y_chr.dot(x_chr) if left_out_chr %2 == 0: XTX_loco = XTX_even - XTX_chr XTy_loco = XTy_even - XTy_chr else: XTX_loco = XTX_odd - XTX_chr XTy_loco = XTy_odd - XTy_chr self.est_loco[chr_i, :] = np.linalg.solve(XTX_loco, XTy_loco) #self.est_chr[chr_i, :] = np.linalg.solve(XTX_chr, XTy_chr) @classmethod def block_values_to_delete_values(cls, xty_block_values, xtx_block_values, xtx_tot, xty_tot): n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) delete_values = np.zeros((n_blocks, p)) for j in range(n_blocks): delete_xty = xty_tot - xty_block_values[j] delete_xtx = xtx_tot - xtx_block_values[j] delete_values[j, ...] = np.linalg.solve( delete_xtx, delete_xty).reshape((1, p)) return delete_values @classmethod def block_values(cls, x, y, s): ''' Compute block values. Parameters ---------- x : np.matrix with shape (n, p) Independent variable. y : np.matrix with shape (n, 1) Dependent variable. n_blocks : int Number of jackknife blocks s : list of ints Block separators. Returns ------- xty_block_values : np.matrix with shape (n_blocks, p) Block values of X^T Y. xtx_block_values : 3d np array with shape (n_blocks, p, p) Block values of X^T X. Raises ------ ValueError : If x.shape[0] does not equal y.shape[0] or x and y are not 2D. ''' n, p = _check_shape(x, y) n_blocks = len(s) - 1 xtx_block_values = np.zeros((n_blocks, p, p)) xty_block_values = np.zeros((n_blocks, p)) for i in range(n_blocks): xty_block_values[i, ...] = np.dot( x[s[i]:s[i + 1], ...].T, y[s[i]:s[i + 1], ...]).reshape((1, p)) xtx_block_values[i, ...] = np.dot( x[s[i]:s[i + 1], ...].T, x[s[i]:s[i + 1], ...]) return (xty_block_values, xtx_block_values) @classmethod def block_values_to_est(cls, xty_block_values, xtx_block_values): ''' Converts block values to the whole-data linear regression estimate. Parameters ---------- xty_block_values : np.matrix with shape (n_blocks, p) Block values of X^T Y. xtx_block_values : 3D np.array with shape (n_blocks, p, p) Block values of X^T X Returns ------- est : np.matrix with shape (1, p) Whole data estimate. Raises ------ LinAlgError : If design matrix is singular. ValueError : If the last two dimensions of xtx_block_values are not equal or if the first two dimensions of xtx_block_values do not equal the shape of xty_block_values. ''' n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) xty = np.sum(xty_block_values, axis=0) xtx = np.sum(xtx_block_values, axis=0) return np.linalg.solve(xtx, xty).reshape((1, p)) class RatioJackknife(Jackknife): ''' Block jackknife ratio estimate. Jackknife. Parameters ---------- est : float or np.array with shape (1, p) Whole data ratio estimate numer_delete_values : np.matrix with shape (n_blocks, p) Delete values for the numerator. denom_delete_values: np.matrix with shape (n_blocks, p) Delete values for the denominator. Methods ------- delete_vals_to_pseudovalues(est, denom, num): Converts denominator/ numerator delete values and the whole-data estimate to pseudovalues. Raises ------ FloatingPointError : If any entry of denom_delete_values is zero. Note that it is possible for the denominator to cross zero (i.e., be both positive and negative) and still have a finite ratio estimate and SE, for example if the numerator is fixed to 0 and the denominator is either -1 or 1. If the denominator is noisily close to zero, then it is unlikely that the denominator will yield zero exactly (and therefore yield an inf or nan), but delete values will be of the form (numerator / close to zero) and -(numerator / close to zero), i.e., (big) and -(big), and so the jackknife will (correctly) yield huge SE. ''' def __init__(self, est, numer_delete_values, denom_delete_values): if numer_delete_values.shape != denom_delete_values.shape: raise ValueError( 'numer_delete_values.shape != denom_delete_values.shape.') if len(numer_delete_values.shape) != 2: raise ValueError('Delete values must be matrices.') if len(est.shape) != 2 or est.shape[0] != 1 or est.shape[1] != numer_delete_values.shape[1]: raise ValueError( 'Shape of est does not match shape of delete values.') self.n_blocks = numer_delete_values.shape[0] self.est = est self.pseudovalues = self.delete_values_to_pseudovalues(self.est, denom_delete_values, numer_delete_values) (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ self.jknife(self.pseudovalues) @classmethod def delete_values_to_pseudovalues(cls, est, denom, numer): ''' Converts delete values to pseudovalues. Parameters ---------- est : np.matrix with shape (1, p) Whole-data ratio estimate. denom : np.matrix with shape (n_blocks, p) Denominator delete values. numer : np.matrix with shape (n_blocks, p) Numerator delete values. Returns ------- pseudovalues : Ratio Jackknife Pseudovalues. Raises ------ ValueError : If numer.shape != denom.shape. ''' n_blocks, p = denom.shape pseudovalues = np.zeros((n_blocks, p)) for j in range(0, n_blocks): pseudovalues[j, ...] = n_blocks * est - \ (n_blocks - 1) * numer[j, ...] / denom[j, ...] return pseudovalues class Jackknife_Ridge(Jackknife): def __init__(self, x, y, n_blocks=None, separators=None, chr_num=None, verbose=True, num_lambdas=100, approx_ridge=False, ridge_lambda=None, use_1se=False, has_intercept=False, standardize=True, skip_ridge_jackknife=True, num_chr_sets=2): #sanity checks assert chr_num is not None # # # chr_num[:100000]=1 # # # chr_num[100000:]=2 assert len(np.unique(chr_num)) > 1 #init stuff Jackknife.__init__(self, x, y, n_blocks=n_blocks, separators=separators) self.use_1se = use_1se self.verbose=verbose self.has_intercept = has_intercept ###define chromosome sets assert num_chr_sets>1 if num_chr_sets == 2: #Use the good old fashioned odd/even chromosome split chromosomes = np.sort(np.unique(chr_num)) self.chromosome_sets = [] self.chromosome_sets.append(chromosomes[chromosomes%2==0]) self.chromosome_sets.append(chromosomes[chromosomes%2!=0]) elif num_chr_sets == 22: self.chromosome_sets = [np.array([c]) for c in range(1,23)] else: chr_sizes = np.bincount(chr_num)[1:] assert num_chr_sets<=len(chr_sizes) chr_assignments = self._divide_chromosomes_to_sets(chr_sizes, num_chr_sets) self.chromosome_sets = [] for set_i in range(num_chr_sets): self.chromosome_sets.append(np.where(chr_assignments==set_i)[0]+1) #make sure we work with numpy arrays, not dataframes try: x=x.values except: pass try: y=y.values except: pass try: constraints=constraints.values except: pass try: chr_num=chr_num.values except: pass #make y look like a vector assert y.shape[1]==1 y = y[:,0] #standardize x if standardize: x_l2 = np.sqrt(np.einsum('ij,ij->j', x, x)) x /= x_l2 else: x_l2 = None #Create a set of ridge lambdas to evaluate XTX_all = x.T.dot(x) XTy_all = y.dot(x) mean_diag = np.mean(np.diag(XTX_all)) self.ridge_lambdas = np.logspace(np.log10(mean_diag*1e-8), np.log10(mean_diag*1e2), num=num_lambdas) #find best lambda (using off-chromosome estimation) and estimate taus if ridge_lambda is not None: assert self.approx_ridge best_lambda = ridge_lambda else: best_lambda, r2_best_lambda = self._find_best_lambda(x, y, XTX_all, XTy_all, chr_num) self.est = np.atleast_2d(self._est_ridge(XTX_all, XTy_all, best_lambda)) self.r2_best_lambda = r2_best_lambda if standardize: self.est /= x_l2 #LOCO (leave one chromosome out) computations self.est_chr_lstsq, self.est_chr_ridge, self.est_loco_lstsq, self.est_loco_ridge = \ self._est_taus_loco(x, y, XTX_all, XTy_all, chr_num, best_lambda, standardize, x_l2) #run jackknife if not skip_ridge_jackknife: self.delete_values = np.empty((len(self.separators)-1, self.est.shape[1]), dtype=np.float32) self.est_chr_lstsq_jk_list = [] self.est_chr_ridge_jk_list = [] self.est_loco_lstsq_jk_list = [] self.est_loco_ridge_jk_list = [] logging.info('Running ridge jackknife...') self.best_r2_jk_noblock = np.zeros(len(self.separators) - 1) for block_i in tqdm(range(len(self.separators) - 1)): #prepare data structures x_block = x[self.separators[block_i]:self.separators[block_i+1], ...] y_block = y[self.separators[block_i]:self.separators[block_i+1], ...] XTX_noblock = XTX_all - x_block.T.dot(x_block) XTy_noblock = XTy_all - y_block.dot(x_block) slice_block = slice(self.separators[block_i], self.separators[block_i+1]) x_noblock = np.delete(x, slice_block, axis=0) y_noblock = np.delete(y, slice_block, axis=0) chr_noblock = np.delete(chr_num, slice_block, axis=0) #find best lambda for this jackknife block if approx_ridge: best_lambda_noblock = best_lambda else: best_lambda_noblock, r2_noblock = self._find_best_lambda(x_noblock, y_noblock, XTX_noblock, XTy_noblock, chr_noblock) self.best_r2_jk_noblock[block_i] = r2_noblock #main jackknife estimation est_block = self._est_ridge(XTX_noblock, XTy_noblock, best_lambda_noblock) self.delete_values[block_i, ...] = est_block #jackknife LOCO computation est_chr_lstsq, est_chr_ridge, est_loco_lstsq, est_loco_ridge = \ self._est_taus_loco(x_noblock, y_noblock, XTX_noblock, XTy_noblock, chr_noblock, best_lambda_noblock, standardize, x_l2) self.est_chr_lstsq_jk_list.append(est_chr_lstsq) self.est_chr_ridge_jk_list.append(est_chr_ridge) self.est_loco_lstsq_jk_list.append(est_loco_lstsq) self.est_loco_ridge_jk_list.append(est_loco_ridge) if standardize: self.delete_values /= x_l2 #compute jackknife pseudo-values self.pseudovalues = self.delete_values_to_pseudovalues(self.delete_values, self.est) (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) = self.jknife(self.pseudovalues) #restore original x if standardize: x *= x_l2 def _divide_chromosomes_to_sets(self, chr_sizes, num_sets): chr_order = np.argsort(chr_sizes)[::-1] #np.arange(len(chr_sizes)) chr_assignments = np.zeros(22, dtype=np.int) - 1 chr_assignments[chr_order[:num_sets]] = np.arange(num_sets) set_sizes = chr_sizes[chr_order[:num_sets]].copy() for c_i in chr_order[num_sets : len(chr_sizes)]: smallest_set = np.argmin(set_sizes) chr_assignments[c_i] = smallest_set set_sizes[smallest_set] += chr_sizes[c_i] assert set_sizes.sum() == chr_sizes.sum() return chr_assignments def _est_taus_loco(self, x, y, XTX, XTy, chr_num, ridge_lambda, standardize, x_l2=None, reestimate_lambda=False): chromosomes = np.sort(np.unique(chr_num)) est_set_lstsq = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) est_noset_lstsq = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) est_set_ridge = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) est_noset_ridge = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) tqdm_chr_sets = tqdm(self.chromosome_sets) logging.info('Estimating annotation coefficients for each chromosomes set') for set_i, chromosome_set in enumerate(tqdm_chr_sets): is_in_set = np.isin(chr_num, chromosome_set) if not np.any(is_in_set): continue x_set = x[is_in_set] y_set = y[is_in_set] XTX_set = x_set.T.dot(x_set) XTy_set = y_set.dot(x_set) XTX_noset = XTX - XTX_set XTy_noset = XTy - XTy_set if (not reestimate_lambda) or (len(chromosomes) <= 2): best_lambda_noset = ridge_lambda best_lambda_set = ridge_lambda else: x_loco = x[~is_in_set] y_loco = y[~is_in_set] chr_loco = chr_num[~is_in_set] best_lambda_noset, r2_noset = self._find_best_lambda(x_loco, y_loco, XTX_noset, XTy_noset, chr_loco) if len(chromosome_set) == 1: best_lambda_set = ridge_lambda else: best_lambda_set, r2_set = self._find_best_lambda(x_set, y_set, XTX_set, XTy_set, chr_num[is_in_set]) est_set_lstsq[set_i, :] = self._est_ridge(XTX_set, XTy_set, ridge_lambda=0) est_set_ridge[set_i, :] = self._est_ridge(XTX_set, XTy_set, best_lambda_set) est_noset_lstsq[set_i, :] = self._est_ridge(XTX_noset, XTy_noset, ridge_lambda=0) est_noset_ridge[set_i, :] = self._est_ridge(XTX_noset, XTy_noset, best_lambda_noset) ###import ipdb; ipdb.set_trace() if standardize: est_set_lstsq /= x_l2 est_set_ridge /= x_l2 est_noset_lstsq /= x_l2 est_noset_ridge /= x_l2 return est_set_lstsq, est_set_ridge, est_noset_lstsq, est_noset_ridge def _find_best_lambda(self, x, y, XTX, XTy, chr_num): chromosomes = np.sort(np.unique(chr_num)) assert len(chromosomes) > 1 num_lambdas = len(self.ridge_lambdas) y_pred_lambdas = np.empty((chr_num.shape[0], num_lambdas), dtype=np.float32) if self.verbose: y_pred_lambdas_lstsq = np.empty(chr_num.shape[0], dtype=np.float32) logging.info('iterating over chromosomes to compute XTX, XTy...') for chr_i, left_out_chr in enumerate(tqdm(chromosomes)): is_chr = (chr_num == left_out_chr) chr_inds = np.where(is_chr)[0] assert np.all(chr_inds == np.arange(chr_inds[0], chr_inds[-1]+1)) chr_slice = slice(chr_inds[0], chr_inds[-1]+1) x_chr = x[chr_slice] y_chr = y[chr_slice] XTX_loco = XTX - x_chr.T.dot(x_chr) XTy_loco = XTy - y_chr.dot(x_chr) y_pred_lambdas[chr_slice, :] = self._predict_lambdas(XTX_loco, XTy_loco, x_chr) if self.verbose: tau_lstsq_loco = self._est_ridge(XTX_loco, XTy_loco, 0) y_pred_lambdas_lstsq[chr_slice] = x_chr.dot(tau_lstsq_loco) #Assign an r2 score to each lambda score_lambdas = np.empty(num_lambdas, dtype=np.float32) logging.info('Evaluating Ridge lambdas...') for r_i in tqdm(range(num_lambdas)): score_lambdas[r_i] = r2_score(y, y_pred_lambdas[:,r_i]) #choose lambda based on the 1SE rule? if self.use_1se: score_folds = np.empty(len(chromosomes), dtype=np.float32) for chr_i, left_out_chr in enumerate(chromosomes): is_chr = (chr_num == left_out_chr) score_folds[chr_i] = r2_score(y[is_chr], y_pred_lambdas[is_chr, best_lambda_index]) scores_std = np.std(score_folds) best_score = score_lambdas[best_lambda_index] assert np.isclose(best_score, np.max(score_lambdas)) best_lambda_index = np.where(score_lambdas > best_score - scores_std)[0][-1] else: best_lambda_index = np.argmax(score_lambdas) best_lambda = self.ridge_lambdas[best_lambda_index] if self.verbose: score_lstsq = r2_score(y, y_pred_lambdas_lstsq) logging.info('Selected ridge lambda: %0.4e (%d/%d) score: %0.4e score lstsq: %0.4e'%(best_lambda, best_lambda_index+1, num_lambdas, score_lambdas[best_lambda_index], score_lstsq)) return best_lambda, score_lambdas[best_lambda_index] def _predict_lambdas(self, XTX_train, XTy_train, X_validation): tau_est_ridge = np.empty((XTX_train.shape[0], len(self.ridge_lambdas)), dtype=np.float32) for r_i, r in enumerate(self.ridge_lambdas): tau_est_ridge[:, r_i] = self._est_ridge(XTX_train, XTy_train, r) y_pred = X_validation.dot(tau_est_ridge) return y_pred def _est_ridge(self, XTX, XTy, ridge_lambda): I = np.eye(XTX.shape[0]) * ridge_lambda if self.has_intercept: I[-1,-1]=0 return np.linalg.solve(XTX+I, XTy) ``` #### File: polyfun/ldsc_polyfun/sumstats.py ```python import numpy as np import pandas as pd from scipy import stats import itertools as it from . import parse as ps from . import regressions as reg import sys import traceback import copy import os _N_CHR = 22 # complementary bases COMPLEMENT = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'} # bases BASES = list(COMPLEMENT.keys()) # true iff strand ambiguous STRAND_AMBIGUOUS = {''.join(x): x[0] == COMPLEMENT[x[1]] for x in it.product(BASES, BASES) if x[0] != x[1]} # SNPS we want to keep (pairs of alleles) VALID_SNPS = {x for x in [''.join(y) for y in it.product(BASES, BASES)] if x[0] != x[1] and not STRAND_AMBIGUOUS[x]} # T iff SNP 1 has the same alleles as SNP 2 (allowing for strand or ref allele flip). MATCH_ALLELES = {x for x in [''.join(y) for y in it.product(VALID_SNPS, VALID_SNPS)] # strand and ref match if ((x[0] == x[2]) and (x[1] == x[3])) or # ref match, strand flip ((x[0] == COMPLEMENT[x[2]]) and (x[1] == COMPLEMENT[x[3]])) or # ref flip, strand match ((x[0] == x[3]) and (x[1] == x[2])) or ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]]))} # strand and ref flip # T iff SNP 1 has the same alleles as SNP 2 w/ ref allele flip. FLIP_ALLELES = {''.join(x): ((x[0] == x[3]) and (x[1] == x[2])) or # strand match # strand flip ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]])) for x in MATCH_ALLELES} def _splitp(fstr): flist = fstr.split(',') flist = [os.path.expanduser(os.path.expandvars(x)) for x in flist] return flist def _select_and_log(x, ii, log, msg): '''Fiter down to rows that are True in ii. Log # of SNPs removed.''' new_len = ii.sum() if new_len == 0: raise ValueError(msg.format(N=0)) else: x = x[ii] log.log(msg.format(N=new_len)) return x def smart_merge(x, y): '''Check if SNP columns are equal. If so, save time by using concat instead of merge.''' assert len(set(x.columns).intersection(set(y.drop(columns=['SNP']).columns))) == 0 if len(x) == len(y) and (x.index == y.index).all() and (x.SNP == y.SNP).all(): #x = x.reset_index(drop=True) #y = y.reset_index(drop=True).drop(columns=['SNP']) #out = pd.concat([x, y], axis=1) out = pd.concat([x, y.drop(columns=['SNP'])], axis=1) else: if x.index.name == 'snpid' and y.index.name == 'snpid': out = pd.merge(x, y.drop(columns=['SNP']), how='inner', left_index=True, right_index=True) else: out = pd.merge(x, y, how='inner', on='SNP') return out def _read_ref_ld(args, log): '''Read reference LD Scores.''' ref_ld = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, 'reference panel LD Score', ps.ldscore_fromlist) log.log( 'Read reference panel LD Scores for {N} SNPs.'.format(N=len(ref_ld))) return ref_ld def _read_annot(args, log): '''Read annot matrix.''' if (args.anno is not None): annotations = args.anno.split(',') else: annotations = None try: if args.ref_ld is not None: overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, 'annot matrix', ps.annot, frqfile=args.frqfile, anno=annotations) elif args.ref_ld_chr is not None: overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, 'annot matrix', ps.annot, frqfile=args.frqfile_chr, anno=annotations) except Exception: log.log('Error parsing .annot file.') raise return overlap_matrix, M_tot def _read_M(args, log, n_annot): '''Read M (--M, --M-file, etc).''' if args.M: try: M_annot = [float(x) for x in _splitp(args.M)] except ValueError as e: raise ValueError('Could not cast --M to float: ' + str(e.args)) else: if args.ref_ld: M_annot = ps.M_fromlist( _splitp(args.ref_ld), common=(not args.not_M_5_50)) elif args.ref_ld_chr: M_annot = ps.M_fromlist( _splitp(args.ref_ld_chr), _N_CHR, common=(not args.not_M_5_50)) try: M_annot = np.array(M_annot).reshape((1, n_annot)) except ValueError as e: raise ValueError( '# terms in --M must match # of LD Scores in --ref-ld.\n' + str(e.args)) return M_annot def _read_w_ld(args, log): '''Read regression SNP LD.''' if (args.w_ld and ',' in args.w_ld) or (args.w_ld_chr and ',' in args.w_ld_chr): raise ValueError( '--w-ld must point to a single fileset (no commas allowed).') w_ld = _read_chr_split_files(args.w_ld_chr, args.w_ld, log, 'regression weight LD Score', ps.ldscore_fromlist) w_ld.drop(['CHR'], axis=1, inplace=True) if len(w_ld.columns) != 2: raise ValueError('--w-ld may only have one LD Score column.') w_ld.columns = ['SNP', 'LD_weights'] # prevent colname conflicts w/ ref ld log.log( 'Read regression weight LD Scores for {N} SNPs.'.format(N=len(w_ld))) return w_ld def _read_chr_split_files(chr_arg, not_chr_arg, log, noun, parsefunc, **kwargs): '''Read files split across 22 chromosomes (annot, ref_ld, w_ld).''' try: if not_chr_arg: log.log('Reading {N} from {F} ...'.format(F=not_chr_arg, N=noun)) out = parsefunc(_splitp(not_chr_arg), **kwargs) elif chr_arg: f = ps.sub_chr(chr_arg, '[1-22]') log.log('Reading {N} from {F} ...'.format(F=f, N=noun)) out = parsefunc(_splitp(chr_arg), _N_CHR, **kwargs) except ValueError as e: log.log('Error parsing {N}.'.format(N=noun)) raise e return out def _read_sumstats(args, log, fh, alleles=True, dropna=False): '''Parse summary statistics.''' log.log('Reading summary statistics from {S} ...'.format(S=fh)) sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna) log_msg = 'Read summary statistics for {N} SNPs.' log.log(log_msg.format(N=len(sumstats))) if np.any(sumstats.index.duplicated()): m = len(sumstats) sumstats = sumstats.loc[~sumstats.index.duplicated()] log.log('Dropped {M} duplicated SNPs.'.format(M=m - len(sumstats))) return sumstats def _check_ld_condnum(args, log, ref_ld): '''Check condition number of LD Score matrix.''' if len(ref_ld.shape) >= 2: cond_num = int(np.linalg.cond(ref_ld)) if cond_num > 100000: if args.invert_anyway: warn = "WARNING: LD Score matrix condition number is {C}. " warn += "Inverting anyway because the --invert-anyway flag is set." log.log(warn.format(C=cond_num)) else: warn = "WARNING: LD Score matrix condition number is {C}. " warn += "Remove collinear LD Scores. " raise ValueError(warn.format(C=cond_num)) def _check_variance(log, M_annot, ref_ld): '''Remove zero-variance LD Scores.''' ###ii = ref_ld.iloc[:, 2:].var(axis=0) == 0 # NB there is a SNP and CHR column here ii = np.array([(ref_ld[c].var() == 0) for c in ref_ld.columns[2:]]) #This command uses way way less memory if ii.all(): raise ValueError('All LD Scores have zero variance.') elif ii.any(): log.log('Removing partitioned LD Scores with zero variance: %s'%(','.join(ref_ld.columns[2:][ii]))) ii_snp = np.array([True, True] + list(~ii)) ii_m = np.array(~ii) ref_ld = ref_ld.loc[:, ii_snp] M_annot = M_annot[:, ii_m] return M_annot, ref_ld, ii def _warn_length(log, sumstats): if len(sumstats) < 200000: log.log( 'WARNING: number of SNPs less than 200k; this is almost always bad.') def _print_cov(ldscore_reg, ofh, log): '''Prints covariance matrix of slopes.''' log.log( 'Printing covariance matrix of the estimates to {F}.'.format(F=ofh)) np.savetxt(ofh, ldscore_reg.coef_cov) def _print_delete_values(ldscore_reg, ofh, log): '''Prints block jackknife delete-k values''' log.log('Printing block jackknife delete values to {F}.'.format(F=ofh)) np.savetxt(ofh, ldscore_reg.tot_delete_values) def _print_part_delete_values(ldscore_reg, ofh, log): '''Prints partitioned block jackknife delete-k values''' log.log('Printing partitioned block jackknife delete values to {F}.'.format(F=ofh)) np.savetxt(ofh, ldscore_reg.part_delete_values) def _merge_and_log(ld, sumstats, noun, log): '''Wrap smart merge with log messages about # of SNPs.''' sumstats = smart_merge(ld, sumstats) msg = 'After merging with {F}, {N} SNPs remain.' if len(sumstats) == 0: msg += ' Please make sure that your annotation files include the SNPs in your sumstats files (please see the PolyFun wiki for details on downloading functional annotations)' raise ValueError(msg.format(N=len(sumstats), F=noun)) else: log.log(msg.format(N=len(sumstats), F=noun)) return sumstats def _read_ld_sumstats(args, log, fh, alleles=True, dropna=True): sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna) ref_ld = _read_ref_ld(args, log) n_annot = len(ref_ld.columns) - 2 #Changed to -2 because we also have chromosome column now M_annot = _read_M(args, log, n_annot) #keep only requested annotations if --anno was specified if args.anno is not None: cols_to_keep = np.zeros(len(ref_ld.columns), dtype=np.bool) annotations = args.anno.split(',') is_found1 = np.isin(annotations, ref_ld.columns.str[:-2]) is_found2 = np.isin(annotations, ref_ld.columns.str[:-4]) is_found = is_found1 | is_found2 if np.any(~is_found): raise ValueError('Not all annotations specified with --anno are found in the LD scores file') cols_to_keep = (ref_ld.columns.str[:-2].isin(annotations)) | (ref_ld.columns.str[:-4].isin(annotations)) | (ref_ld.columns.isin(['CHR', 'SNP'])) assert np.sum(cols_to_keep) == len(annotations)+2 cols_nochrsnp = ref_ld.drop(columns=['CHR', 'SNP']).columns M_cols_to_keep = (cols_nochrsnp.str[:-2].isin(annotations)) | (cols_nochrsnp.str[:-4].isin(annotations)) assert np.sum(M_cols_to_keep) == len(annotations) ref_ld = ref_ld.loc[:, cols_to_keep] M_annot = M_annot[:, M_cols_to_keep] log.log('Keeping only annotations specified with --anno') M_annot, ref_ld, novar_cols = _check_variance(log, M_annot, ref_ld) w_ld = _read_w_ld(args, log) sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log) sumstats = _merge_and_log(sumstats, w_ld, 'regression SNP LD', log) w_ld_cname = sumstats.columns[-1] ref_ld_cnames = ref_ld.drop(columns=['CHR', 'SNP']).columns return M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols def estimate_h2(args, log): '''Estimate h2 and partitioned h2.''' args = copy.deepcopy(args) if args.samp_prev is not None and args.pop_prev is not None: args.samp_prev, args.pop_prev = list(map( float, [args.samp_prev, args.pop_prev])) if args.intercept_h2 is not None: args.intercept_h2 = float(args.intercept_h2) if args.no_intercept: args.intercept_h2 = 1 M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols = _read_ld_sumstats( args, log, args.h2) ref_ld = np.array(sumstats[ref_ld_cnames], dtype=np.float32) if not args.skip_cond_check: _check_ld_condnum(args, log, ref_ld_cnames) _warn_length(log, sumstats) n_snp = len(sumstats) n_blocks = min(n_snp, args.n_blocks) n_annot = len(ref_ld_cnames) chisq_max = args.chisq_max old_weights = False if n_annot == 1: if args.two_step is None and args.intercept_h2 is None: args.two_step = 30 else: old_weights = True if args.chisq_max is None: chisq_max = max(0.001*sumstats.N.max(), args.max_chi2) s = lambda x: np.array(x).reshape((n_snp, 1)) chisq = s(sumstats.Z**2).astype(np.float32) if chisq_max is not None and not args.keep_large: ii = np.ravel(chisq < chisq_max) sumstats = sumstats.loc[ii, :] log.log('Removed {M} SNPs with chi^2 > {C} ({N} SNPs remain)'.format( C=chisq_max, N=np.sum(ii), M=n_snp-np.sum(ii))) n_snp = np.sum(ii) # lambdas are late-binding, so this works ref_ld = np.array(sumstats[ref_ld_cnames], dtype=np.float32) chisq = chisq[ii].reshape((n_snp, 1)) if args.two_step is not None: log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) hsqhat = reg.Hsq(chisq, ref_ld, s(sumstats[w_ld_cname]), s(sumstats.N), M_annot, n_blocks=n_blocks, intercept=args.intercept_h2, twostep=args.two_step, old_weights=old_weights, chr_num=sumstats['CHR'], loco=args.loco, ridge_lambda=args.ridge_lambda, standardize_ridge=not args.no_standardize_ridge, approx_ridge=not args.reestimate_lambdas, skip_ridge_jackknife=not args.ridge_jackknife, num_chr_sets = args.num_chr_sets, evenodd_split=args.evenodd_split, nn=args.nn, keep_large=args.keep_large, nnls_exact=args.nnls_exact ) if args.print_cov: _print_cov(hsqhat, args.out + '.cov', log) if args.print_delete_vals: _print_delete_values(hsqhat, args.out + '.delete', log) _print_part_delete_values(hsqhat, args.out + '.part_delete', log) #save ridge-regression lambdas if possible if args.loco and args.ridge_jackknife and args.reestimate_lambdas: np.savetxt(args.out+'.out_of_chrom_r2.txt', [hsqhat.jknife_ridge.r2_best_lambda]) df = pd.Series(hsqhat.jknife_ridge.best_r2_jk_noblock) df.to_csv(args.out+'.out_of_chrom_r2_jk.txt', index=False, header=False) log.log(hsqhat.summary(ref_ld_cnames, P=args.samp_prev, K=args.pop_prev, overlap = args.overlap_annot)) if args.overlap_annot: overlap_matrix, M_tot = _read_annot(args, log) # overlap_matrix = overlap_matrix[np.array(~novar_cols), np.array(~novar_cols)]#np.logical_not df_results = hsqhat._overlap_output(ref_ld_cnames, overlap_matrix, M_annot, M_tot, args.print_coefficients) df_results.to_csv(args.out+'.results', sep="\t", index=False, na_rep='NA', float_format='%0.4e') log.log('Results printed to '+args.out+'.results') return hsqhat def estimate_rg(args, log): '''Estimate rg between trait 1 and a list of other traits.''' args = copy.deepcopy(args) rg_paths, rg_files = _parse_rg(args.rg) n_pheno = len(rg_paths) f = lambda x: _split_or_none(x, n_pheno) args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev = list(map(f, (args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev))) list(map(lambda x: _check_arg_len(x, n_pheno), ((args.intercept_h2, '--intercept-h2'), (args.intercept_gencov, '--intercept-gencov'), (args.samp_prev, '--samp-prev'), (args.pop_prev, '--pop-prev')))) if args.no_intercept: args.intercept_h2 = [1 for _ in range(n_pheno)] args.intercept_gencov = [0 for _ in range(n_pheno)] p1 = rg_paths[0] out_prefix = args.out + rg_files[0] M_annot, w_ld_cname, ref_ld_cnames, sumstats, _ = _read_ld_sumstats(args, log, p1, alleles=True, dropna=True) RG = [] n_annot = M_annot.shape[1] if n_annot == 1 and args.two_step is None and args.intercept_h2 is None: args.two_step = 30 if args.two_step is not None: log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) for i, p2 in enumerate(rg_paths[1:n_pheno]): log.log( 'Computing rg for phenotype {I}/{N}'.format(I=i + 2, N=len(rg_paths))) try: loop = _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames) rghat = _rg(loop, args, log, M_annot, ref_ld_cnames, w_ld_cname, i) RG.append(rghat) _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, i == 0) out_prefix_loop = out_prefix + '_' + rg_files[i + 1] if args.print_cov: _print_rg_cov(rghat, out_prefix_loop, log) if args.print_delete_vals: _print_rg_delete_values(rghat, out_prefix_loop, log) except Exception: # keep going if phenotype 50/100 causes an error msg = 'ERROR computing rg for phenotype {I}/{N}, from file {F}.' log.log(msg.format(I=i + 2, N=len(rg_paths), F=rg_paths[i + 1])) ex_type, ex, tb = sys.exc_info() log.log(traceback.format_exc(ex) + '\n') if len(RG) <= i: # if exception raised before appending to RG RG.append(None) log.log('\nSummary of Genetic Correlation Results\n' + _get_rg_table(rg_paths, RG, args)) return RG def _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames): loop = _read_sumstats(args, log, p2, alleles=True, dropna=False) loop = _merge_sumstats_sumstats(args, sumstats, loop, log) loop = loop.dropna(how='any') alleles = loop.A1 + loop.A2 + loop.A1x + loop.A2x if not args.no_check_alleles: loop = _select_and_log(loop, _filter_alleles(alleles), log, '{N} SNPs with valid alleles.') loop['Z2'] = _align_alleles(loop.Z2, alleles) loop = loop.drop(['A1', 'A1x', 'A2', 'A2x'], axis=1) _check_ld_condnum(args, log, loop[ref_ld_cnames]) _warn_length(log, loop) return loop def _get_rg_table(rg_paths, RG, args): '''Print a table of genetic correlations.''' t = lambda attr: lambda obj: getattr(obj, attr, 'NA') x = pd.DataFrame() x['p1'] = [rg_paths[0] for i in range(1, len(rg_paths))] x['p2'] = rg_paths[1:len(rg_paths)] x['rg'] = list(map(t('rg_ratio'), RG)) x['se'] = list(map(t('rg_se'), RG)) x['z'] = list(map(t('z'), RG)) x['p'] = list(map(t('p'), RG)) if args.samp_prev is not None and args.pop_prev is not None and\ all((i is not None for i in args.samp_prev)) and all((i is not None for it in args.pop_prev)): c = reg.h2_obs_to_liab(1, args.samp_prev[1], args.pop_prev[1]) x['h2_liab'] = [c * x for x in list(map(t('tot'), list(map(t('hsq2'), RG))))] x['h2_liab_se'] = [c * x for x in list(map(t('tot_se'), list(map(t('hsq2'), RG))))] else: x['h2_obs'] = list(map(t('tot'), list(map(t('hsq2'), RG)))) x['h2_obs_se'] = list(map(t('tot_se'), list(map(t('hsq2'), RG)))) x['h2_int'] = list(map(t('intercept'), list(map(t('hsq2'), RG)))) x['h2_int_se'] = list(map(t('intercept_se'), list(map(t('hsq2'), RG)))) x['gcov_int'] = list(map(t('intercept'), list(map(t('gencov'), RG)))) x['gcov_int_se'] = list(map(t('intercept_se'), list(map(t('gencov'), RG)))) return x.to_string(header=True, index=False) + '\n' def _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, print_hsq1): l = lambda x: x + ''.join(['-' for i in range(len(x.replace('\n', '')))]) P = [args.samp_prev[0], args.samp_prev[i + 1]] K = [args.pop_prev[0], args.pop_prev[i + 1]] if args.samp_prev is None and args.pop_prev is None: args.samp_prev = [None, None] args.pop_prev = [None, None] if print_hsq1: log.log(l('\nHeritability of phenotype 1\n')) log.log(rghat.hsq1.summary(ref_ld_cnames, P=P[0], K=K[0])) log.log( l('\nHeritability of phenotype {I}/{N}\n'.format(I=i + 2, N=len(rg_paths)))) log.log(rghat.hsq2.summary(ref_ld_cnames, P=P[1], K=K[1])) log.log(l('\nGenetic Covariance\n')) log.log(rghat.gencov.summary(ref_ld_cnames, P=P, K=K)) log.log(l('\nGenetic Correlation\n')) log.log(rghat.summary() + '\n') def _merge_sumstats_sumstats(args, sumstats1, sumstats2, log): '''Merge two sets of summary statistics.''' sumstats1.rename(columns={'N': 'N1', 'Z': 'Z1'}, inplace=True) sumstats2.rename( columns={'A1': 'A1x', 'A2': 'A2x', 'N': 'N2', 'Z': 'Z2'}, inplace=True) x = _merge_and_log(sumstats1, sumstats2, 'summary statistics', log) return x def _filter_alleles(alleles): '''Remove bad variants (mismatched alleles, non-SNPs, strand ambiguous).''' ii = alleles.apply(lambda y: y in MATCH_ALLELES) return ii def _align_alleles(z, alleles): '''Align Z1 and Z2 to same choice of ref allele (allowing for strand flip).''' try: z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y]) except KeyError as e: msg = 'Incompatible alleles in .sumstats files: %s. ' % e.args msg += 'Did you forget to use --merge-alleles with munge_sumstats.py?' raise KeyError(msg) return z def _rg(sumstats, args, log, M_annot, ref_ld_cnames, w_ld_cname, i): '''Run the regressions.''' n_snp = len(sumstats) s = lambda x: np.array(x).reshape((n_snp, 1)) if args.chisq_max is not None: ii = sumstats.Z1**2*sumstats.Z2**2 < args.chisq_max**2 n_snp = np.sum(ii) # lambdas are late binding, so this works sumstats = sumstats[ii] n_blocks = min(args.n_blocks, n_snp) ref_ld = sumstats.as_matrix(columns=ref_ld_cnames) intercepts = [args.intercept_h2[0], args.intercept_h2[ i + 1], args.intercept_gencov[i + 1]] rghat = reg.RG(s(sumstats.Z1), s(sumstats.Z2), ref_ld, s(sumstats[w_ld_cname]), s( sumstats.N1), s(sumstats.N2), M_annot, intercept_hsq1=intercepts[0], intercept_hsq2=intercepts[1], intercept_gencov=intercepts[2], n_blocks=n_blocks, twostep=args.two_step) return rghat def _parse_rg(rg): '''Parse args.rg.''' rg_paths = _splitp(rg) rg_files = [x.split('/')[-1] for x in rg_paths] if len(rg_paths) < 2: raise ValueError( 'Must specify at least two phenotypes for rg estimation.') return rg_paths, rg_files def _print_rg_delete_values(rg, fh, log): '''Print block jackknife delete values.''' _print_delete_values(rg.hsq1, fh + '.hsq1.delete', log) _print_delete_values(rg.hsq2, fh + '.hsq2.delete', log) _print_delete_values(rg.gencov, fh + '.gencov.delete', log) def _print_rg_cov(rghat, fh, log): '''Print covariance matrix of estimates.''' _print_cov(rghat.hsq1, fh + '.hsq1.cov', log) _print_cov(rghat.hsq2, fh + '.hsq2.cov', log) _print_cov(rghat.gencov, fh + '.gencov.cov', log) def _split_or_none(x, n): if x is not None: y = list(map(float, x.replace('N', '-').split(','))) else: y = [None for _ in range(n)] return y def _check_arg_len(x, n): x, m = x if len(x) != n: raise ValueError( '{M} must have the same number of arguments as --rg/--h2.'.format(M=m)) ``` #### File: polyfun/ldstore/parse.py ```python import numpy as np def areSNPsIncluded( snps, n_snps, fname ): for snp in snps: if snp >= n_snps: print('Cannot read dosages for ' + str( snp ) + 'th SNP. File "' + fname + '" contains only ' + str( n_snps ) + ' SNPs!') return def convertIntToFloat( x, n_bytes ): if type( x ) is np.ndarray: return convertIntToFloat_array( x, n_bytes ) else: return convertIntToFloat_scalar( x, n_bytes ) def convertIntToFloat_array( x, n_bytes ): int_na = getIntNA( n_bytes ) y = np.zeros( len( x ) ) y[ x == int_na ] = np.nan y[ x != int_na ] = np.ldexp( x[ x != int_na ], -1 * ( 8 * n_bytes - 2 ) ) return y def convertIntToFloat_scalar( x, n_bytes ): if x == getIntNA( n_bytes ): return np.nan else: return np.ldexp( x, -1 * ( 8 * n_bytes - 2 ) ) def getIntNA( n_bytes ): if n_bytes == 2: return 53248 elif n_bytes == 4: return 3489660928 elif n_bytes == 8: return 14987979559889010688 elif n_bytes == 1: return 208 else: print('Only 1, 2, 4 and 8 bytes are supported!') return ```
{ "source": "jerome-f/susieR", "score": 2 }
#### File: inst/code/monitor_memory.py ```python import time import psutil import subprocess class ProcessTimer: def __init__(self, command, interval = 1): self.command = command self.execution_state = False self.interval = interval def execute(self): self.max_vms_memory = 0 self.max_rss_memory = 0 self.t0 = time.time() self.t1 = None self.max_t = [self.t0] try: self.p = subprocess.Popen(self.command, shell=False) except FileNotFoundError: self.p = None sys.exit("Invalid command `{}`".format(sys.argv[1])) self.execution_state = True def poll(self): if not self.check_execution_state(): return False self.t1 = time.time() try: pp = psutil.Process(self.p.pid) # Obtain a list of the subprocess and all its descendants. descendants = list(pp.children(recursive=True)) descendants = descendants + [pp] rss_memory = 0 vms_memory = 0 # Calculate and sum up the memory of the subprocess and all its # descendants. for descendant in descendants: try: mem_info = descendant.memory_info() rss_memory += mem_info[0] vms_memory += mem_info[1] except (psutil.NoSuchProcess, psutil.ZombieProcess, psutil.AccessDenied): # Sometimes a subprocess descendant will have terminated # between the time we obtain a list of descendants, and the # time we actually poll this descendant's memory usage. pass if int(self.max_vms_memory * 1E-8) < int(vms_memory * 1E-8): # Peak memory updated, at ~100-MB resolution. self.max_t = [self.t1] if int(self.max_vms_memory * 1E-8) == int(vms_memory * 1E-8): # Peak memory maintained. self.max_t = [self.max_t[0], self.t1] self.max_vms_memory = max(self.max_vms_memory,vms_memory) self.max_rss_memory = max(self.max_rss_memory,rss_memory) except (psutil.NoSuchProcess, psutil.ZombieProcess, psutil.AccessDenied): return self.check_execution_state() return self.check_execution_state() def is_running(self): return psutil.pid_exists(self.p.pid) and self.p.poll() == None def check_execution_state(self): if not self.execution_state: return False if self.is_running(): return True self.executation_state = False self.t1 = time.time() return False def close(self,kill=False): if self.p is not None: try: pp = psutil.Process(self.p.pid) if kill: pp.kill() else: pp.terminate() except (psutil.NoSuchProcess, psutil.ZombieProcess, psutil.AccessDenied): pass def takewhile_excluding(iterable, value = ['|', '<', '>']): for it in iterable: if it in value: return yield it if __name__ == '__main__': import sys, os if len(sys.argv) <= 1: sys.exit() interval = float(os.environ['MEM_CHECK_INTERVAL']) if 'MEM_CHECK_INTERVAL' in os.environ else 1 ptimer = ProcessTimer(takewhile_excluding(sys.argv[1:]), interval) try: ptimer.execute() # Poll as often as possible; otherwise the subprocess might # "sneak" in some extra memory usage while you aren't looking. while ptimer.poll(): time.sleep(ptimer.interval) finally: # Make sure that we don't leave the process dangling. ptimer.close() sys.stderr.write('\ntime elapsed: {:.2f}s\n'.format(max(0, ptimer.t1 - ptimer.t0 - ptimer.interval * 0.5))) sys.stderr.write('peak first occurred: {:.2f}s\n'.format(min(ptimer.max_t) - ptimer.t0)) sys.stderr.write('peak last occurred: {:.2f}s\n'.format(max(ptimer.max_t) - ptimer.t0)) sys.stderr.write('max vms_memory: {:.2f}GB\n'.format(ptimer.max_vms_memory * 1.07E-9)) sys.stderr.write('max rss_memory: {:.2f}GB\n'.format(ptimer.max_rss_memory * 1.07E-9)) sys.stderr.write('memory check interval: %ss\n' % ptimer.interval) sys.stderr.write('return code: %s\n' % ptimer.p.returncode) ```