ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

29392d7c293c0b529284bdff29493ae4994d22ba

206

py

Python

example.py

n0emis/pycodimd

cec7135babe63f0c40fdb9eac7ede50e145cd512

[ "MIT" ]

1

2020-04-20T22:06:49.000Z

example.py

n0emis/pycodimd

cec7135babe63f0c40fdb9eac7ede50e145cd512

[ "MIT" ]

null

example.py

n0emis/pycodimd

cec7135babe63f0c40fdb9eac7ede50e145cd512

[ "MIT" ]

null

from pycodimd import CodiMD cmd = CodiMD('https://md.noemis.me') #cmd.login('[email protected]','CorrectHorseBatteryStaple') cmd.load_cookies() print(cmd.history()[-1]['text']) # Print Name of latest Note

29.428571

61

0.73301

0

113

0.548544

293ac2ae42d575f893f18bae2751d93e4e138ae8

75

py

Python

PP4E-Examples-1.4/Examples/PP4E/System/Environment/echoenv.py

AngelLiang/PP4E

3a7f63b366e1e4700b4d2524884696999a87ba9d

[ "MIT" ]

null

PP4E-Examples-1.4/Examples/PP4E/System/Environment/echoenv.py

AngelLiang/PP4E

3a7f63b366e1e4700b4d2524884696999a87ba9d

[ "MIT" ]

null

PP4E-Examples-1.4/Examples/PP4E/System/Environment/echoenv.py

AngelLiang/PP4E

3a7f63b366e1e4700b4d2524884696999a87ba9d

[ "MIT" ]

null

import os print('echoenv...', end=' ') print('Hello,', os.environ['USER'])

18.75

35

0.613333

0

29

0.386667

293afc12acd3adc92103d2c686f2476332649203

4,137

py

Python

plix/displays.py

freelan-developers/plix

69114b3e522330af802800e09a432c1a84220f88

[ "MIT" ]

1

2017-05-22T11:52:01.000Z

plix/displays.py

freelan-developers/plix

69114b3e522330af802800e09a432c1a84220f88

[ "MIT" ]

4

2015-03-12T16:59:36.000Z

2015-03-12T17:34:15.000Z

plix/displays.py

freelan-developers/plix

69114b3e522330af802800e09a432c1a84220f88

[ "MIT" ]

1

2018-03-04T21:43:33.000Z

""" Display command results. """ from __future__ import unicode_literals from contextlib import contextmanager from argparse import Namespace from io import BytesIO from colorama import AnsiToWin32 from chromalog.stream import stream_has_color_support from chromalog.colorizer import Colorizer from chromalog.mark.helpers.simple import ( warning, important, success, error, ) class BaseDisplay(object): """ Provides general display logic to its subclasses. """ @contextmanager def command(self, index, command): """ Contextmanager that wraps calls to :func:`start_command` and :func:`stop_command`. :param index: The index of the command. :param command: The command that is about to be executed, as an unicode string. """ self.start_command( index=index, command=command, ) result = Namespace(returncode=None) try: yield result finally: self.stop_command( index=index, command=command, returncode=result.returncode, ) class StreamDisplay(BaseDisplay): """ Displays commands output to an output stream. """ def __init__(self, stream, colorizer=Colorizer()): """ Initialize the :class:`StreamDisplay`. :param stream: The stream to be attached too. """ super(StreamDisplay, self).__init__() self.colorizer = colorizer self.output_map = {} if stream_has_color_support(stream): self.stream = AnsiToWin32(stream).stream else: self.stream = stream # Python 3 differentiates binary streams. if hasattr(stream, 'buffer'): self.binary_stream = stream.buffer else: self.binary_stream = stream def format_output(self, message, *args, **kwargs): """ Format some output in regards to the output stream color-capability. :param message: A message. :returns: The formatted message. """ if stream_has_color_support(self.stream): return self.colorizer.colorize_message(message, *args, **kwargs) else: return message.format(*args, **kwargs) def set_context(self, commands): """ Set the context for display. :param commands: The list of commands to be executed. """ self.longest_len = max(map(len, commands)) def start_command(self, index, command): """ Indicate that a command stopped. :param index: The index of the command. :param command: The command that is about to be executed, as an unicode string. """ self.stream.write(self.format_output( "{}) {}", warning(important(index + 1)), command, )) self.stream.flush() self.output_map[index] = BytesIO() def stop_command(self, index, command, returncode): """ Indicate that a command stopped. :param index: The index of the command. :param command: The command that was executed, as an unicode string. :param returncode: The exit status. """ self.stream.write(self.format_output( "{}\t[{}]\n", " " * (self.longest_len - len(command)), success("success") if returncode == 0 else error("failed"), )) if returncode != 0: self.binary_stream.write(self.output_map[index].getvalue()) self.stream.write(self.format_output( "{}) {} {}\n", warning(important(index + 1)), error("Command exited with"), important(error(returncode)), )) del self.output_map[index] def command_output(self, index, data): """ Add some output for a command. :param index: The index of the command. :param data: The output data (as bytes). """ self.output_map[index].write(data)

27.397351

79

0.583273

3,735

0.902828

653

0.157844

673

0.162678

0

1,528

0.36935

293dd5d900ef2c6130d4549dd1b873aa939a8cba

6,167

py

Python

plugins/Autocomplete/plugin.py

mogad0n/Limnoria

f31e5c4b9a77e30918d6b93f69d69f3b8f910e3c

[ "BSD-3-Clause" ]

476

2015-01-04T17:42:59.000Z

2021-08-13T07:40:54.000Z

plugins/Autocomplete/plugin.py

mogad0n/Limnoria

f31e5c4b9a77e30918d6b93f69d69f3b8f910e3c

[ "BSD-3-Clause" ]

491

2015-01-01T04:12:23.000Z

2021-08-12T19:24:47.000Z

plugins/Autocomplete/plugin.py

mogad0n/Limnoria

f31e5c4b9a77e30918d6b93f69d69f3b8f910e3c

[ "BSD-3-Clause" ]

203

2015-01-02T18:29:43.000Z

2021-08-15T12:52:22.000Z

### # Copyright (c) 2020-2021, The Limnoria Contributors # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions, and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions, and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the author of this software nor the name of # contributors to this software may be used to endorse or promote products # derived from this software without specific prior written consent. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. ### from supybot import conf, ircutils, ircmsgs, callbacks from supybot.i18n import PluginInternationalization _ = PluginInternationalization("Autocomplete") REQUEST_TAG = "+draft/autocomplete-request" RESPONSE_TAG = "+draft/autocomplete-response" def _commonPrefix(L): """Takes a list of lists, and returns their longest common prefix.""" assert L if len(L) == 1: return L[0] for n in range(1, max(map(len, L)) + 1): prefix = L[0][:n] for item in L[1:]: if prefix != item[:n]: return prefix[0:-1] assert False def _getAutocompleteResponse(irc, msg, payload): """Returns the value of the +draft/autocomplete-response tag for the given +draft/autocomplete-request payload.""" tokens = callbacks.tokenize( payload, channel=msg.channel, network=irc.network ) normalized_payload = " ".join(tokens) candidate_commands = _getCandidates(irc, normalized_payload) if len(candidate_commands) == 0: # No result return None elif len(candidate_commands) == 1: # One result, return it directly commands = candidate_commands else: # Multiple results, return only the longest common prefix + one word tokenized_candidates = [ callbacks.tokenize(c, channel=msg.channel, network=irc.network) for c in candidate_commands ] common_prefix = _commonPrefix(tokenized_candidates) words_after_prefix = { candidate[len(common_prefix)] for candidate in tokenized_candidates } commands = [ " ".join(common_prefix + [word]) for word in words_after_prefix ] # strip what the user already typed assert all(command.startswith(normalized_payload) for command in commands) normalized_payload_length = len(normalized_payload) response_items = [ command[normalized_payload_length:] for command in commands ] return "\t".join(sorted(response_items)) def _getCandidates(irc, normalized_payload): """Returns a list of commands starting with the normalized_payload.""" candidates = set() for cb in irc.callbacks: cb_commands = cb.listCommands() # copy them with the plugin name (optional when calling a command) # at the beginning plugin_name = cb.canonicalName() cb_commands += [plugin_name + " " + command for command in cb_commands] candidates |= { command for command in cb_commands if command.startswith(normalized_payload) } return candidates class Autocomplete(callbacks.Plugin): """Provides command completion for IRC clients that support it.""" def _enabled(self, irc, msg): return ( conf.supybot.protocols.irc.experimentalExtensions() and self.registryValue("enabled", msg.channel, irc.network) ) def doTagmsg(self, irc, msg): if REQUEST_TAG not in msg.server_tags: return if "msgid" not in msg.server_tags: return if not self._enabled(irc, msg): return msgid = msg.server_tags["msgid"] text = msg.server_tags[REQUEST_TAG] # using callbacks._addressed instead of callbacks.addressed, as # callbacks.addressed would tag the m payload = callbacks._addressed(irc, msg, payload=text) if not payload: # not addressed return # marks used by '_addressed' are usually prefixes (char, string, # nick), but may also be suffixes (with # supybot.reply.whenAddressedBy.nick.atEnd); but there is no way to # have it in the middle of the message AFAIK. assert payload in text if not text.endswith(payload): # If there is a suffix, it means the end of the text is used to # address the bot, so it can't be a method to be completed. return autocomplete_response = _getAutocompleteResponse(irc, msg, payload) if not autocomplete_response: return target = msg.channel or ircutils.nickFromHostmask(msg.prefix) irc.queueMsg( ircmsgs.IrcMsg( server_tags={ "+draft/reply": msgid, RESPONSE_TAG: autocomplete_response, }, command="TAGMSG", args=[target], ) ) Class = Autocomplete # vim:set shiftwidth=4 softtabstop=4 expandtab textwidth=79:

34.071823

79

0.66564

1,852

0.300308

0

2,765

0.448354

293e981880ad85e96c9f610aaeaa19c42550d236

2,237

py

Python

utils/preprocess_twitter.py

arnavk/tumblr-emotions

0ed03201ab833b8b400cb0cff6c5b064fac5edfb

[ "Apache-2.0" ]

null

utils/preprocess_twitter.py

arnavk/tumblr-emotions

0ed03201ab833b8b400cb0cff6c5b064fac5edfb

[ "Apache-2.0" ]

null

utils/preprocess_twitter.py

arnavk/tumblr-emotions

0ed03201ab833b8b400cb0cff6c5b064fac5edfb

[ "Apache-2.0" ]

null

""" preprocess-twitter.py python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)" Script for preprocessing tweets by Romain Paulus with small modifications by Jeffrey Pennington with translation to Python by Motoki Wu Translation of Ruby script to create features for GloVe vectors for Twitter data. http://nlp.stanford.edu/projects/glove/preprocess-twitter.rb """ import sys import regex as re FLAGS = re.MULTILINE | re.DOTALL def hashtag(text): text = text.group() hashtag_body = text[1:] if hashtag_body.isupper(): result = " {} ".format(hashtag_body.lower()) else: result = " ".join(["<hashtag>"] + re.split(r"(?=[A-Z])", hashtag_body, flags=FLAGS)) return result def allcaps(text): text = text.group() return text.lower() + " <allcaps>" def tokenize(text): # Different regex parts for smiley faces eyes = r"[8:=;]" nose = r"['`\-]?" # function so code less repetitive def re_sub(pattern, repl): return re.sub(pattern, repl, text, flags=FLAGS) text = re_sub(r"https?:\/\/\S+\b|www\.(\w+\.)+\S*", "<url>") text = re_sub(r"@\w+", "<user>") text = re_sub(r"{}{}[)dD]+|[)dD]+{}{}".format(eyes, nose, nose, eyes), "<smile>") text = re_sub(r"{}{}p+".format(eyes, nose), "<lolface>") text = re_sub(r"{}{}$+|$+{}{}".format(eyes, nose, nose, eyes), "<sadface>") text = re_sub(r"{}{}[\/|l*]".format(eyes, nose), "<neutralface>") text = re_sub(r"/"," / ") text = re_sub(r"<3","<heart>") text = re_sub(r"[-+]?[.\d]*[\d]+[:,.\d]*", "<number>") text = re_sub(r"#\S+", hashtag) text = re_sub(r"([!?.]){2,}", r"\1 <repeat>") text = re_sub(r"\b(\S*?)(.)\2{2,}\b", r"\1\2 <elong>") ## -- I just don't understand why the Ruby script adds <allcaps> to everything so I limited the selection. # text = re_sub(r"([^a-z0-9()<>'`\-]){2,}", allcaps) text = re_sub(r"([A-Z]){2,}", allcaps) return text.lower() if __name__ == '__main__': _, text = sys.argv if text == "test": text = "I TEST alllll kinds of #hashtags and #HASHTAGS, @mentions and 3000 (http://t.co/dkfjkdf). w/ <3 :) haha!!!!!" tokens = tokenize(text) print(tokens)

34.415385

125

0.591417

0

1,155

0.516316

2940e9042fa0fc027376618fe6d76d1057e9e9bd

37,124

py

Python

pyPLANES/pw/pw_classes.py

matael/pyPLANES

7f591090446303884c9a3d049e42233efae0b7f4

[ "MIT" ]

null

pyPLANES/pw/pw_classes.py

matael/pyPLANES

7f591090446303884c9a3d049e42233efae0b7f4

[ "MIT" ]

null

pyPLANES/pw/pw_classes.py

matael/pyPLANES

7f591090446303884c9a3d049e42233efae0b7f4

[ "MIT" ]

1

2020-12-15T16:24:08.000Z

#! /usr/bin/env python # -*- coding:utf8 -*- # # pw_classes.py # # This file is part of pyplanes, a software distributed under the MIT license. # For any question, please contact one of the authors cited below. # # Copyright (c) 2020 # Olivier Dazel <[email protected]> # Mathieu Gaborit <[email protected]> # Peter Göransson <[email protected]> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # import numpy as np import numpy.linalg as LA import matplotlib.pyplot as plt from mediapack import from_yaml from mediapack import Air, PEM, EqFluidJCA from pyPLANES.utils.io import initialisation_out_files_plain from pyPLANES.core.calculus import PwCalculus from pyPLANES.core.multilayer import MultiLayer from pyPLANES.pw.pw_layers import FluidLayer from pyPLANES.pw.pw_interfaces import FluidFluidInterface, RigidBacking Air = Air() # def initialise_PW_solver(L, b): # nb_PW = 0 # dofs = [] # for _layer in L: # if _layer.medium.MODEL == "fluid": # dofs.append(nb_PW+np.arange(2)) # nb_PW += 2 # elif _layer.medium.MODEL == "pem": # dofs.append(nb_PW+np.arange(6)) # nb_PW += 6 # elif _layer.medium.MODEL == "elastic": # dofs.append(nb_PW+np.arange(4)) # nb_PW += 4 # interface = [] # for i_l, _layer in enumerate(L[:-1]): # interface.append((L[i_l].medium.MODEL, L[i_l+1].medium.MODEL)) # return nb_PW, interface, dofs class PwProblem(PwCalculus, MultiLayer): """ Plane Wave Problem """ def __init__(self, **kwargs): PwCalculus.__init__(self, **kwargs) termination = kwargs.get("termination","rigid") self.method = kwargs.get("termination","global") MultiLayer.__init__(self, **kwargs) self.kx, self.ky, self.k = None, None, None self.shift_plot = kwargs.get("shift_pw", 0.) self.plot = kwargs.get("plot_results", [False]*6) self.result = {} self.outfiles_directory = False if self.method == "global": self.layers.insert(0,FluidLayer(Air,1.e-2)) if self.layers[1].medium.MEDIUM_TYPE == "fluid": self.interfaces.append(FluidFluidInterface(self.layers[0],self.layers[1])) self.nb_PW = 0 for _layer in self.layers: if _layer.medium.MODEL == "fluid": _layer.dofs = self.nb_PW+np.arange(2) self.nb_PW += 2 elif _layer.medium.MODEL == "pem": _layer.dofs = self.nb_PW+np.arange(6) self.nb_PW += 6 elif _layer.medium.MODEL == "elastic": _layer.dofs = self.nb_PW+np.arange(4) self.nb_PW += 4 def update_frequency(self, f): PwCalculus.update_frequency(self, f) MultiLayer.update_frequency(self, f, self.k, self.kx) def create_linear_system(self, f): self.A = np.zeros((self.nb_PW-1, self.nb_PW), dtype=complex) i_eq = 0 # Loop on the interfaces for _int in self.interfaces: if self.method == "global": i_eq = _int.update_M_global(self.A, i_eq) # for i_inter, _inter in enumerate(self.interfaces): # if _inter[0] == "fluid": # if _inter[1] == "fluid": # i_eq = self.interface_fluid_fluid(i_eq, i_inter, Layers, dofs, M) # if _inter[1] == "pem": # i_eq = self.interface_fluid_pem(i_eq, i_inter, Layers, dofs, M) # if _inter[1] == "elastic": # i_eq = self.interface_fluid_elastic(i_eq, i_inter, Layers, dofs, M) # elif _inter[0] == "pem": # if _inter[1] == "fluid": # i_eq = self.interface_pem_fluid(i_eq, i_inter, Layers, dofs, M) # if _inter[1] == "pem": # i_eq = self.interface_pem_pem(i_eq, i_inter, Layers, dofs, M) # if _inter[1] == "elastic": # i_eq = self.interface_pem_elastic(i_eq, i_inter, Layers, dofs, M) # elif _inter[0] == "elastic": # if _inter[1] == "fluid": # i_eq = self.interface_elastic_fluid(i_eq, i_inter, Layers, dofs, M) # if _inter[1] == "pem": # i_eq = self.interface_elastic_pem(i_eq, i_inter, Layers, dofs, M) # if _inter[1] == "elastic": # i_eq = self.interface_elastic_elastic(i_eq, i_inter, Layers, dofs, M) # if self.backing == backing.rigid: # if Layers[-1].medium.MODEL == "fluid": # i_eq = self.interface_fluid_rigid(M, i_eq, Layers[-1], dofs[-1] ) # elif Layers[-1].medium.MODEL == "pem": # i_eq = self.interface_pem_rigid(M, i_eq, Layers[-1], dofs[-1]) # elif Layers[-1].medium.MODEL == "elastic": # i_eq = self.interface_elastic_rigid(M, i_eq, Layers[-1], dofs[-1]) # elif self.backing == "transmission": # i_eq = self.semi_infinite_medium(M, i_eq, Layers[-1], dofs[-1] ) self.F = -self.A[:, 0]*np.exp(1j*self.ky*self.layers[0].d) # - is for transposition, exponential term is for the phase shift self.A = np.delete(self.A, 0, axis=1) # print(self.A) X = LA.solve(self.A, self.F) # print(X) # R_pyPLANES_PW = X[0] # if self.backing == "transmission": # T_pyPLANES_PW = X[-2] # else: # T_pyPLANES_PW = 0. # X = np.delete(X, 0) # del(dofs[0]) # for i, _ld in enumerate(dofs): # dofs[i] -= 2 # if self.plot: # self.plot_sol_PW(X, dofs) # out["R"] = R_pyPLANES_PW # out["T"] = T_pyPLANES_PW # return out # class Solver_PW(PwCalculus): # def __init__(self, **kwargs): # PwCalculus.__init__(self, **kwargs) # ml = kwargs.get("ml") # termination = kwargs.get("termination") # self.layers = [] # for _l in ml: # if _l[0] == "Air": # mat = Air # else: # mat = from_yaml(_l[0]+".yaml") # d = _l[1] # self.layers.append(Layer(mat,d)) # if termination in ["trans", "transmission","Transmission"]: # self.backing = "Transmission" # else: # self.backing = backing.rigid # self.kx, self.ky, self.k = None, None, None # self.shift_plot = kwargs.get("shift_pw", 0.) # self.plot = kwargs.get("plot_results", [False]*6) # self.result = {} # self.outfiles_directory = False # initialisation_out_files_plain(self) # def write_out_files(self, out): # self.out_file.write("{:.12e}\t".format(self.current_frequency)) # abs = 1-np.abs(out["R"])**2 # self.out_file.write("{:.12e}\t".format(abs)) # self.out_file.write("\n") # def interface_fluid_fluid(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = fluid_SV(self.kx, self.k, L[iinter].medium.K) # SV_2, k_y_2 = fluid_SV(self.kx, self.k, L[iinter+1].medium.K) # M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[0, 1] # M[ieq, d[iinter+1][0]] = -SV_2[0, 0] # M[ieq, d[iinter+1][1]] = -SV_2[0, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[1, 1] # M[ieq, d[iinter+1][0]] = -SV_2[1, 0] # M[ieq, d[iinter+1][1]] = -SV_2[1, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness) # ieq += 1 # return ieq # def interface_fluid_rigid(self, M, ieq, L, d): # SV, k_y = fluid_SV(self.kx, self.k, L.medium.K) # M[ieq, d[0]] = SV[0, 0]*np.exp(-1j*k_y*L.thickness) # M[ieq, d[1]] = SV[0, 1] # ieq += 1 # return ieq # def semi_infinite_medium(self, M, ieq, L, d): # M[ieq, d[1]] = 1. # ieq += 1 # return ieq # def interface_pem_pem(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = PEM_SV(L[iinter].medium, self.kx) # SV_2, k_y_2 = PEM_SV(L[iinter+1].medium, self.kx) # for _i in range(6): # M[ieq, d[iinter+0][0]] = SV_1[_i, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[_i, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[_i, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = SV_1[_i, 3] # M[ieq, d[iinter+0][4]] = SV_1[_i, 4] # M[ieq, d[iinter+0][5]] = SV_1[_i, 5] # M[ieq, d[iinter+1][0]] = -SV_2[_i, 0] # M[ieq, d[iinter+1][1]] = -SV_2[_i, 1] # M[ieq, d[iinter+1][2]] = -SV_2[_i, 2] # M[ieq, d[iinter+1][3]] = -SV_2[_i, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = -SV_2[_i, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = -SV_2[_i, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # return ieq # def interface_fluid_pem(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = fluid_SV(self.kx, self.k, L[iinter].medium.K) # SV_2, k_y_2 = PEM_SV(L[iinter+1].medium,self.kx) # # print(k_y_2) # M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[0, 1] # M[ieq, d[iinter+1][0]] = -SV_2[2, 0] # M[ieq, d[iinter+1][1]] = -SV_2[2, 1] # M[ieq, d[iinter+1][2]] = -SV_2[2, 2] # M[ieq, d[iinter+1][3]] = -SV_2[2, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = -SV_2[2, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = -SV_2[2, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[1, 1] # M[ieq, d[iinter+1][0]] = -SV_2[4, 0] # M[ieq, d[iinter+1][1]] = -SV_2[4, 1] # M[ieq, d[iinter+1][2]] = -SV_2[4, 2] # M[ieq, d[iinter+1][3]] = -SV_2[4, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = -SV_2[4, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = -SV_2[4, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+1][0]] = SV_2[0, 0] # M[ieq, d[iinter+1][1]] = SV_2[0, 1] # M[ieq, d[iinter+1][2]] = SV_2[0, 2] # M[ieq, d[iinter+1][3]] = SV_2[0, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = SV_2[0, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = SV_2[0, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+1][0]] = SV_2[3, 0] # M[ieq, d[iinter+1][1]] = SV_2[3, 1] # M[ieq, d[iinter+1][2]] = SV_2[3, 2] # M[ieq, d[iinter+1][3]] = SV_2[3, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = SV_2[3, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = SV_2[3, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # return ieq # def interface_elastic_pem(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = elastic_SV(L[iinter].medium,self.kx, self.omega) # SV_2, k_y_2 = PEM_SV(L[iinter+1].medium,self.kx) # # print(k_y_2) # M[ieq, d[iinter+0][0]] = -SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[0, 2] # M[ieq, d[iinter+0][3]] = -SV_1[0, 3] # M[ieq, d[iinter+1][0]] = SV_2[0, 0] # M[ieq, d[iinter+1][1]] = SV_2[0, 1] # M[ieq, d[iinter+1][2]] = SV_2[0, 2] # M[ieq, d[iinter+1][3]] = SV_2[0, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = SV_2[0, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = SV_2[0, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = -SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[1, 2] # M[ieq, d[iinter+0][3]] = -SV_1[1, 3] # M[ieq, d[iinter+1][0]] = SV_2[1, 0] # M[ieq, d[iinter+1][1]] = SV_2[1, 1] # M[ieq, d[iinter+1][2]] = SV_2[1, 2] # M[ieq, d[iinter+1][3]] = SV_2[1, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = SV_2[1, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = SV_2[1, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = -SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[1, 2] # M[ieq, d[iinter+0][3]] = -SV_1[1, 3] # M[ieq, d[iinter+1][0]] = SV_2[2, 0] # M[ieq, d[iinter+1][1]] = SV_2[2, 1] # M[ieq, d[iinter+1][2]] = SV_2[2, 2] # M[ieq, d[iinter+1][3]] = SV_2[2, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = SV_2[2, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = SV_2[2, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = -SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[2, 2] # M[ieq, d[iinter+0][3]] = -SV_1[2, 3] # M[ieq, d[iinter+1][0]] = (SV_2[3, 0]-SV_2[4, 0]) # M[ieq, d[iinter+1][1]] = (SV_2[3, 1]-SV_2[4, 1]) # M[ieq, d[iinter+1][2]] = (SV_2[3, 2]-SV_2[4, 2]) # M[ieq, d[iinter+1][3]] = (SV_2[3, 3]-SV_2[4, 3])*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = (SV_2[3, 4]-SV_2[4, 4])*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = (SV_2[3, 5]-SV_2[4, 5])*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = -SV_1[3, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[3, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[3, 2] # M[ieq, d[iinter+0][3]] = -SV_1[3, 3] # M[ieq, d[iinter+1][0]] = SV_2[5, 0] # M[ieq, d[iinter+1][1]] = SV_2[5, 1] # M[ieq, d[iinter+1][2]] = SV_2[5, 2] # M[ieq, d[iinter+1][3]] = SV_2[5, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][4]] = SV_2[5, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # M[ieq, d[iinter+1][5]] = SV_2[5, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness) # ieq += 1 # return ieq # def interface_pem_elastic(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = PEM_SV(L[iinter].medium,self.kx) # SV_2, k_y_2 = elastic_SV(L[iinter+1].medium,self.kx, self.omega) # # print(k_y_2) # M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[0, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = SV_1[0, 3] # M[ieq, d[iinter+0][4]] = SV_1[0, 4] # M[ieq, d[iinter+0][5]] = SV_1[0, 5] # M[ieq, d[iinter+1][0]] = -SV_2[0, 0] # M[ieq, d[iinter+1][1]] = -SV_2[0, 1] # M[ieq, d[iinter+1][2]] = -SV_2[0, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = -SV_2[0, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[1, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = SV_1[1, 3] # M[ieq, d[iinter+0][4]] = SV_1[1, 4] # M[ieq, d[iinter+0][5]] = SV_1[1, 5] # M[ieq, d[iinter+1][0]] = -SV_2[1, 0] # M[ieq, d[iinter+1][1]] = -SV_2[1, 1] # M[ieq, d[iinter+1][2]] = -SV_2[1, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = -SV_2[1, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[2, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = SV_1[2, 3] # M[ieq, d[iinter+0][4]] = SV_1[2, 4] # M[ieq, d[iinter+0][5]] = SV_1[2, 5] # M[ieq, d[iinter+1][0]] = -SV_2[1, 0] # M[ieq, d[iinter+1][1]] = -SV_2[1, 1] # M[ieq, d[iinter+1][2]] = -SV_2[1, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = -SV_2[1, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = (SV_1[3, 0]-SV_1[4, 0])*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = (SV_1[3, 1]-SV_1[4, 1])*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = (SV_1[3, 2]-SV_1[4, 2])*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = (SV_1[3, 3]-SV_1[4, 3]) # M[ieq, d[iinter+0][4]] = (SV_1[3, 4]-SV_1[4, 4]) # M[ieq, d[iinter+0][5]] = (SV_1[3, 5]-SV_1[4, 5]) # M[ieq, d[iinter+1][0]] = -SV_2[2, 0] # M[ieq, d[iinter+1][1]] = -SV_2[2, 1] # M[ieq, d[iinter+1][2]] = -SV_2[2, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = -SV_2[2, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = SV_1[5, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[5, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[5, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = SV_1[5, 3] # M[ieq, d[iinter+0][4]] = SV_1[5, 4] # M[ieq, d[iinter+0][5]] = SV_1[5, 5] # M[ieq, d[iinter+1][0]] = -SV_2[3, 0] # M[ieq, d[iinter+1][1]] = -SV_2[3, 1] # M[ieq, d[iinter+1][2]] = -SV_2[3, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = -SV_2[3, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # return ieq # def interface_elastic_elastic(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = elastic_SV(L[iinter].medium,self.kx, self.omega) # SV_2, k_y_2 = elastic_SV(L[iinter+1].medium,self.kx, self.omega) # for _i in range(4): # M[ieq, d[iinter+0][0]] = SV_1[_i, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[_i, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[_i, 2] # M[ieq, d[iinter+0][3]] = SV_1[_i, 3] # M[ieq, d[iinter+1][0]] = -SV_2[_i, 0] # M[ieq, d[iinter+1][1]] = -SV_2[_i, 1] # M[ieq, d[iinter+1][2]] = -SV_2[_i, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = -SV_2[_i, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # return ieq # def interface_fluid_elastic(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = fluid_SV(self.kx, self.k, L[iinter].medium.K) # SV_2, k_y_2 = elastic_SV(L[iinter+1].medium, self.kx, self.omega) # # Continuity of u_y # M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[0, 1] # M[ieq, d[iinter+1][0]] = -SV_2[1, 0] # M[ieq, d[iinter+1][1]] = -SV_2[1, 1] # M[ieq, d[iinter+1][2]] = -SV_2[1, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = -SV_2[1, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # # sigma_yy = -p # M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[1, 1] # M[ieq, d[iinter+1][0]] = SV_2[2, 0] # M[ieq, d[iinter+1][1]] = SV_2[2, 1] # M[ieq, d[iinter+1][2]] = SV_2[2, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = SV_2[2, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # # sigma_xy = 0 # M[ieq, d[iinter+1][0]] = SV_2[0, 0] # M[ieq, d[iinter+1][1]] = SV_2[0, 1] # M[ieq, d[iinter+1][2]] = SV_2[0, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness) # M[ieq, d[iinter+1][3]] = SV_2[0, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness) # ieq += 1 # return ieq # def interface_pem_fluid(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = PEM_SV(L[iinter].medium, self.kx) # SV_2, k_y_2 = fluid_SV(self.kx, self.k, L[iinter+1].medium.K) # # print(k_y_2) # M[ieq, d[iinter+0][0]] = -SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[2, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = -SV_1[2, 3] # M[ieq, d[iinter+0][4]] = -SV_1[2, 4] # M[ieq, d[iinter+0][5]] = -SV_1[2, 5] # M[ieq, d[iinter+1][0]] = SV_2[0, 0] # M[ieq, d[iinter+1][1]] = SV_2[0, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = -SV_1[4, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[4, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[4, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = -SV_1[4, 3] # M[ieq, d[iinter+0][4]] = -SV_1[4, 4] # M[ieq, d[iinter+0][5]] = -SV_1[4, 5] # M[ieq, d[iinter+1][0]] = SV_2[1, 0] # M[ieq, d[iinter+1][1]] = SV_2[1, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness) # ieq += 1 # M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[0, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = SV_1[0, 3] # M[ieq, d[iinter+0][4]] = SV_1[0, 4] # M[ieq, d[iinter+0][5]] = SV_1[0, 5] # ieq += 1 # M[ieq, d[iinter+0][0]] = SV_1[3, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[3, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[3, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness) # M[ieq, d[iinter+0][3]] = SV_1[3, 3] # M[ieq, d[iinter+0][4]] = SV_1[3, 4] # M[ieq, d[iinter+0][5]] = SV_1[3, 5] # ieq += 1 # return ieq # def interface_elastic_fluid(self, ieq, iinter, L, d, M): # SV_1, k_y_1 = elastic_SV(L[iinter].medium, self.kx, self.omega) # SV_2, k_y_2 = fluid_SV(self.kx, self.k, L[iinter+1].medium.K) # # Continuity of u_y # M[ieq, d[iinter+0][0]] = -SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = -SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = -SV_1[1, 2] # M[ieq, d[iinter+0][3]] = -SV_1[1, 3] # M[ieq, d[iinter+1][0]] = SV_2[0, 0] # M[ieq, d[iinter+1][1]] = SV_2[0, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness) # ieq += 1 # # sigma_yy = -p # M[ieq, d[iinter+0][0]] = SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[2, 2] # M[ieq, d[iinter+0][3]] = SV_1[2, 3] # M[ieq, d[iinter+1][0]] = SV_2[1, 0] # M[ieq, d[iinter+1][1]] = SV_2[1, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness) # ieq += 1 # # sigma_xy = 0 # M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness) # M[ieq, d[iinter+0][1]] = SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness) # M[ieq, d[iinter+0][2]] = SV_1[0, 2] # M[ieq, d[iinter+0][3]] = SV_1[0, 3] # ieq += 1 # return ieq # def interface_elastic_rigid(self, M, ieq, L, d): # SV, k_y = elastic_SV(L.medium,self.kx, self.omega) # M[ieq, d[0]] = SV[1, 0]*np.exp(-1j*k_y[0]*L.thickness) # M[ieq, d[1]] = SV[1, 1]*np.exp(-1j*k_y[1]*L.thickness) # M[ieq, d[2]] = SV[1, 2] # M[ieq, d[3]] = SV[1, 3] # ieq += 1 # M[ieq, d[0]] = SV[3, 0]*np.exp(-1j*k_y[0]*L.thickness) # M[ieq, d[1]] = SV[3, 1]*np.exp(-1j*k_y[1]*L.thickness) # M[ieq, d[2]] = SV[3, 2] # M[ieq, d[3]] = SV[3, 3] # ieq += 1 # return ieq # def interface_pem_rigid(self, M, ieq, L, d): # SV, k_y = PEM_SV(L.medium, self.kx) # M[ieq, d[0]] = SV[1, 0]*np.exp(-1j*k_y[0]*L.thickness) # M[ieq, d[1]] = SV[1, 1]*np.exp(-1j*k_y[1]*L.thickness) # M[ieq, d[2]] = SV[1, 2]*np.exp(-1j*k_y[2]*L.thickness) # M[ieq, d[3]] = SV[1, 3] # M[ieq, d[4]] = SV[1, 4] # M[ieq, d[5]] = SV[1, 5] # ieq += 1 # M[ieq, d[0]] = SV[2, 0]*np.exp(-1j*k_y[0]*L.thickness) # M[ieq, d[1]] = SV[2, 1]*np.exp(-1j*k_y[1]*L.thickness) # M[ieq, d[2]] = SV[2, 2]*np.exp(-1j*k_y[2]*L.thickness) # M[ieq, d[3]] = SV[2, 3] # M[ieq, d[4]] = SV[2, 4] # M[ieq, d[5]] = SV[2, 5] # ieq += 1 # M[ieq, d[0]] = SV[5, 0]*np.exp(-1j*k_y[0]*L.thickness) # M[ieq, d[1]] = SV[5, 1]*np.exp(-1j*k_y[1]*L.thickness) # M[ieq, d[2]] = SV[5, 2]*np.exp(-1j*k_y[2]*L.thickness) # M[ieq, d[3]] = SV[5, 3] # M[ieq, d[4]] = SV[5, 4] # M[ieq, d[5]] = SV[5, 5] # ieq += 1 # return ieq # def plot_sol_PW(self, X, dofs): # x_start = self.shift_plot # for _l, _layer in enumerate(self.layers): # x_f = np.linspace(0, _layer.thickness,200) # x_b = x_f-_layer.thickness # if _layer.medium.MODEL == "fluid": # SV, k_y = fluid_SV(self.kx, self.k, _layer.medium.K) # pr = SV[1, 0]*np.exp(-1j*k_y*x_f)*X[dofs[_l][0]] # pr += SV[1, 1]*np.exp( 1j*k_y*x_b)*X[dofs[_l][1]] # ut = SV[0, 0]*np.exp(-1j*k_y*x_f)*X[dofs[_l][0]] # ut += SV[0, 1]*np.exp( 1j*k_y*x_b)*X[dofs[_l][1]] # if self.plot[2]: # plt.figure(2) # plt.plot(x_start+x_f, np.abs(pr), 'r') # plt.plot(x_start+x_f, np.imag(pr), 'm') # plt.title("Pressure") # # plt.figure(5) # # plt.plot(x_start+x_f,np.abs(ut),'b') # # plt.plot(x_start+x_f,np.imag(ut),'k') # if _layer.medium.MODEL == "pem": # SV, k_y = PEM_SV(_layer.medium, self.kx) # ux, uy, pr, ut = 0*1j*x_f, 0*1j*x_f, 0*1j*x_f, 0*1j*x_f # for i_dim in range(3): # ux += SV[1, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # ux += SV[1, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]] # uy += SV[5, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # uy += SV[5, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]] # pr += SV[4, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # pr += SV[4, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]] # ut += SV[2, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # ut += SV[2, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]] # if self.plot[0]: # plt.figure(0) # plt.plot(x_start+x_f, np.abs(uy), 'r') # plt.plot(x_start+x_f, np.imag(uy), 'm') # plt.title("Solid displacement along x") # if self.plot[1]: # plt.figure(1) # plt.plot(x_start+x_f, np.abs(ux), 'r') # plt.plot(x_start+x_f, np.imag(ux), 'm') # plt.title("Solid displacement along y") # if self.plot[2]: # plt.figure(2) # plt.plot(x_start+x_f, np.abs(pr), 'r') # plt.plot(x_start+x_f, np.imag(pr), 'm') # plt.title("Pressure") # if _layer.medium.MODEL == "elastic": # SV, k_y = elastic_SV(_layer.medium, self.kx, self.omega) # ux, uy, pr, sig = 0*1j*x_f, 0*1j*x_f, 0*1j*x_f, 0*1j*x_f # for i_dim in range(2): # ux += SV[1, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # ux += SV[1, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]] # uy += SV[3, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # uy += SV[3, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]] # pr -= SV[2, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # pr -= SV[2, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]] # sig -= SV[0, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]] # sig -= SV[0, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]] # if self.plot[0]: # plt.figure(0) # plt.plot(x_start+x_f, np.abs(uy), 'r') # plt.plot(x_start+x_f, np.imag(uy), 'm') # plt.title("Solid displacement along x") # if self.plot[1]: # plt.figure(1) # plt.plot(x_start+x_f, np.abs(ux), 'r') # plt.plot(x_start+x_f, np.imag(ux), 'm') # plt.title("Solid displacement along y") # # if self.plot[2]: # # plt.figure(2) # # plt.plot(x_start+x_f, np.abs(pr), 'r') # # plt.plot(x_start+x_f, np.imag(pr), 'm') # # plt.title("Sigma_yy") # # if self.plot[2]: # # plt.figure(3) # # plt.plot(x_start+x_f, np.abs(sig), 'r') # # plt.plot(x_start+x_f, np.imag(sig), 'm') # # plt.title("Sigma_xy") # x_start += _layer.thickness # def PEM_SV(mat,ky): # ''' S={0:\hat{\sigma}_{xy}, 1:u_y^s, 2:u_y^t, 3:\hat{\sigma}_{yy}, 4:p, 5:u_x^s}''' # kx_1 = np.sqrt(mat.delta_1**2-ky**2) # kx_2 = np.sqrt(mat.delta_2**2-ky**2) # kx_3 = np.sqrt(mat.delta_3**2-ky**2) # kx = np.array([kx_1, kx_2, kx_3]) # delta = np.array([mat.delta_1, mat.delta_2, mat.delta_3]) # alpha_1 = -1j*mat.A_hat*mat.delta_1**2-1j*2*mat.N*kx[0]**2 # alpha_2 = -1j*mat.A_hat*mat.delta_2**2-1j*2*mat.N*kx[1]**2 # alpha_3 = -2*1j*mat.N*kx[2]*ky # SV = np.zeros((6,6), dtype=complex) # SV[0:6, 0] = np.array([-2*1j*mat.N*kx[0]*ky, kx[0], mat.mu_1*kx[0], alpha_1, 1j*delta[0]**2*mat.K_eq_til*mat.mu_1, ky]) # SV[0:6, 3] = np.array([ 2*1j*mat.N*kx[0]*ky,-kx[0],-mat.mu_1*kx[0], alpha_1, 1j*delta[0]**2*mat.K_eq_til*mat.mu_1, ky]) # SV[0:6, 1] = np.array([-2*1j*mat.N*kx[1]*ky, kx[1], mat.mu_2*kx[1],alpha_2, 1j*delta[1]**2*mat.K_eq_til*mat.mu_2, ky]) # SV[0:6, 4] = np.array([ 2*1j*mat.N*kx[1]*ky,-kx[1],-mat.mu_2*kx[1],alpha_2, 1j*delta[1]**2*mat.K_eq_til*mat.mu_2, ky]) # SV[0:6, 2] = np.array([1j*mat.N*(kx[2]**2-ky**2), ky, mat.mu_3*ky, alpha_3, 0., -kx[2]]) # SV[0:6, 5] = np.array([1j*mat.N*(kx[2]**2-ky**2), ky, mat.mu_3*ky, -alpha_3, 0., kx[2]]) # return SV, kx # def elastic_SV(mat,ky, omega): # ''' S={0:\sigma_{xy}, 1: u_y, 2 \sigma_{yy}, 3 u_x}''' # P_mat = mat.lambda_ + 2.*mat.mu # delta_p = omega*np.sqrt(mat.rho/P_mat) # delta_s = omega*np.sqrt(mat.rho/mat.mu) # kx_p = np.sqrt(delta_p**2-ky**2) # kx_s = np.sqrt(delta_s**2-ky**2) # kx = np.array([kx_p, kx_s]) # alpha_p = -1j*mat.lambda_*delta_p**2 - 2j*mat.mu*kx[0]**2 # alpha_s = 2j*mat.mu*kx[1]*ky # SV = np.zeros((4, 4), dtype=np.complex) # SV[0:4, 0] = np.array([-2.*1j*mat.mu*kx[0]*ky, kx[0], alpha_p, ky]) # SV[0:4, 2] = np.array([ 2.*1j*mat.mu*kx[0]*ky, -kx[0], alpha_p, ky]) # SV[0:4, 1] = np.array([1j*mat.mu*(kx[1]**2-ky**2), ky,-alpha_s, -kx[1]]) # SV[0:4, 3] = np.array([1j*mat.mu*(kx[1]**2-ky**2), ky, alpha_s, kx[1]]) # return SV, kx # def fluid_SV(kx, k, K): # ''' S={0:u_y , 1:p}''' # ky = np.sqrt(k**2-kx**2) # SV = np.zeros((2, 2), dtype=complex) # SV[0, 0:2] = np.array([ky/(1j*K*k**2), -ky/(1j*K*k**2)]) # SV[1, 0:2] = np.array([1, 1]) # return SV, ky # def resolution_PW_imposed_displacement(S, p): # # print("k={}".format(p.k)) # Layers = S.layers.copy() # n, interfaces, dofs = initialise_PW_solver(Layers, S.backing) # M = np.zeros((n, n), dtype=complex) # i_eq = 0 # # Loop on the layers # for i_inter, _inter in enumerate(interfaces): # if _inter[0] == "fluid": # if _inter[1] == "fluid": # i_eq = interface_fluid_fluid(i_eq, i_inter, Layers, dofs, M, p) # if _inter[1] == "pem": # i_eq = interface_fluid_pem(i_eq, i_inter, Layers, dofs, M, p) # elif _inter[0] == "pem": # if _inter[1] == "fluid": # i_eq = interface_pem_fluid(i_eq, i_inter, Layers, dofs, M, p) # if _inter[1] == "pem": # i_eq = interface_pem_pem(i_eq, i_inter, Layers, dofs, M, p) # if S.backing == backing.rigid: # if Layers[-1].medium.MODEL == "fluid": # i_eq = interface_fluid_rigid(M, i_eq, Layers[-1], dofs[-1], p) # elif Layers[-1].medium.MODEL == "pem": # i_eq = interface_pem_rigid(M, i_eq, Layers[-1], dofs[-1], p) # if Layers[0].medium.MODEL == "fluid": # F = np.zeros(n, dtype=complex) # SV, k_y = fluid_SV(p.kx, p.k, Layers[0].medium.K) # M[i_eq, dofs[0][0]] = SV[0, 0] # M[i_eq, dofs[0][1]] = SV[0, 1]*np.exp(-1j*k_y*Layers[0].thickness) # F[i_eq] = 1. # elif Layers[0].medium.MODEL == "pem": # SV, k_y = PEM_SV(Layers[0].medium, p.kx) # M[i_eq, dofs[0][0]] = SV[2, 0] # M[i_eq, dofs[0][1]] = SV[2, 1] # M[i_eq, dofs[0][2]] = SV[2, 2] # M[i_eq, dofs[0][3]] = SV[2, 3]*np.exp(-1j*k_y[0]*Layers[0].thickness) # M[i_eq, dofs[0][4]] = SV[2, 4]*np.exp(-1j*k_y[1]*Layers[0].thickness) # M[i_eq, dofs[0][5]] = SV[2, 5]*np.exp(-1j*k_y[2]*Layers[0].thickness) # F = np.zeros(n, dtype=complex) # F[i_eq] = 1. # i_eq +=1 # M[i_eq, dofs[0][0]] = SV[0, 0] # M[i_eq, dofs[0][1]] = SV[0, 1] # M[i_eq, dofs[0][2]] = SV[0, 2] # M[i_eq, dofs[0][3]] = SV[0, 3]*np.exp(-1j*k_y[0]*Layers[0].thickness) # M[i_eq, dofs[0][4]] = SV[0, 4]*np.exp(-1j*k_y[1]*Layers[0].thickness) # M[i_eq, dofs[0][5]] = SV[0, 5]*np.exp(-1j*k_y[2]*Layers[0].thickness) # i_eq += 1 # M[i_eq, dofs[0][0]] = SV[3, 0] # M[i_eq, dofs[0][1]] = SV[3, 1] # M[i_eq, dofs[0][2]] = SV[3, 2] # M[i_eq, dofs[0][3]] = SV[3, 3]*np.exp(-1j*k_y[0]*Layers[0].thickness) # M[i_eq, dofs[0][4]] = SV[3, 4]*np.exp(-1j*k_y[1]*Layers[0].thickness) # M[i_eq, dofs[0][5]] = SV[3, 5]*np.exp(-1j*k_y[2]*Layers[0].thickness) # X = LA.solve(M, F) # # print("|R pyPLANES_PW| = {}".format(np.abs(X[0]))) # print("R pyPLANES_PW = {}".format(X[0])) # plot_sol_PW(S, X, dofs, p)

48.911726

132

0.502721

4,392

0.118303

0

33,888

0.912808

29419686dd2aebba28a504da3cc741b420dcf049

9,001

py

Python

mmtbx/conformation_dependent_library/mcl.py

pcxod/cctbx_project

d43dfb157cd7432292b30c0329b7491d5a356657

[ "BSD-3-Clause-LBNL" ]

null

mmtbx/conformation_dependent_library/mcl.py

pcxod/cctbx_project

d43dfb157cd7432292b30c0329b7491d5a356657

[ "BSD-3-Clause-LBNL" ]

1

2020-05-26T17:46:17.000Z

2020-05-26T17:55:19.000Z

mmtbx/conformation_dependent_library/mcl.py

pcxod/cctbx_project

d43dfb157cd7432292b30c0329b7491d5a356657

[ "BSD-3-Clause-LBNL" ]

1

2022-02-08T10:11:07.000Z

from __future__ import absolute_import, division, print_function import sys import time from cctbx.array_family import flex from scitbx.math import superpose from mmtbx.conformation_dependent_library import mcl_sf4_coordination from six.moves import range from mmtbx.conformation_dependent_library import metal_coordination_library def get_pdb_hierarchy_from_restraints(code): from mmtbx.monomer_library import server from iotbx import pdb mon_lib_server = server.server() path = mon_lib_server.get_comp_comp_id_direct(code, return_filename=True) cif_obj = server.read_cif(path) ligand_inp=pdb.pdb_input(source_info="Model from %s" % path, lines=flex.split_lines("")) ligand_hierarchy = ligand_inp.construct_hierarchy() model=pdb.hierarchy.model() chain=pdb.hierarchy.chain() chain.id='Z' rg=pdb.hierarchy.residue_group() ag=pdb.hierarchy.atom_group() for block, loops in cif_obj.blocks.items(): if block=='comp_list': continue for loop in loops.iterloops(): for row in loop.iterrows(): if '_chem_comp_atom.comp_id' not in row: break ag.resname = row['_chem_comp_atom.comp_id'] atom = pdb.hierarchy.atom() atom.name = row['_chem_comp_atom.atom_id'] atom.element = '%2s' % row['_chem_comp_atom.type_symbol'] atom.xyz = ( float(row['_chem_comp_atom.x']), float(row['_chem_comp_atom.y']), float(row['_chem_comp_atom.z']), ) ag.append_atom(atom) rg.append_atom_group(ag) chain.append_residue_group(rg) model.append_chain(chain) ligand_hierarchy.append_model(model) ligand_hierarchy.atoms().reset_i_seq() return ligand_hierarchy def update(grm, pdb_hierarchy, link_records=None, log=sys.stdout, verbose=False, ): def _atom_id(a, show_i_seq=False): if show_i_seq: return '%s (%5d)' % (a.id_str(), a.i_seq) else: return '%s' % (a.id_str()) if link_records is None: link_records={} link_records.setdefault('LINK', []) hooks = [ ["Iron sulfur cluster coordination", mcl_sf4_coordination.get_sulfur_iron_cluster_coordination, mcl_sf4_coordination.get_all_proxies, ], ['Zn2+ tetrahedral coordination', metal_coordination_library.get_metal_coordination_proxies, metal_coordination_library.get_proxies, ], ] outl = '' outl_debug = '' for label, get_coordination, get_all_proxies in hooks: rc = get_coordination( pdb_hierarchy=pdb_hierarchy, nonbonded_proxies=grm.pair_proxies( sites_cart=pdb_hierarchy.atoms().extract_xyz()).nonbonded_proxies, verbose=verbose, ) bproxies, aproxies = get_all_proxies(rc) if bproxies is None: continue if len(bproxies): outl += ' %s\n' % label outl += ' %s\n' % label atoms = pdb_hierarchy.atoms() sf4_coordination = {} for bp in bproxies: sf4_ag = atoms[bp.i_seqs[0]].parent() sf4_coordination.setdefault(sf4_ag.id_str(), []) sf4_coordination[sf4_ag.id_str()].append((atoms[bp.i_seqs[0]], atoms[bp.i_seqs[1]])) link = (atoms[bp.i_seqs[0]], atoms[bp.i_seqs[1]], 'x,y,z') if link not in link_records: link_records['LINK'].append(link) for sf4, aas in sorted(sf4_coordination.items()): outl += '%spdb="%s"\n' % (' '*6, sf4) outl_debug += '%spdb="%s"\n' % (' '*6, sf4) for aa in sorted(aas): outl += '%s%s - %s\n' % (' '*8, _atom_id(aa[0]), _atom_id(aa[1])) outl_debug += '%s%s - %s\n' % (' '*8, _atom_id(aa[0], True), _atom_id(aa[1], True)) if bproxies: try: grm.add_new_bond_restraints_in_place( proxies=bproxies, sites_cart=pdb_hierarchy.atoms().extract_xyz(), ) except RuntimeError as e: print('\n\n%s' % outl_debug) raise e # done = [] remove = [] for i, angle in enumerate(aproxies): i_seqs = list(angle.i_seqs) i_seqs.sort() if i_seqs in done: remove.append(i) else: done.append(i_seqs) if remove: remove.reverse() for r in remove: del aproxies[r] # if aproxies: outl += '%s%s' % (' '*6, 'Number of angles added : %d\n' % len(aproxies)) grm.add_angles_in_place(aproxies) if outl: print(' Dynamic metal coordination', file=log) print(outl, file=log) def _extract_sites_cart(ag, element=None): selection = [] for atom in ag.atoms(): if element and atom.element.upper().strip()!=element.upper().strip(): continue selection.append(atom.xyz) return flex.vec3_double(selection) def generate_sites_fixed(pdb_hierarchy, resname, element=None): for ag in pdb_hierarchy.atom_groups(): if ag.resname.strip().upper()==resname.upper(): yield _extract_sites_cart(ag, element), ag def superpose_ideal_residue_coordinates(pdb_hierarchy, resname, superpose_element=None, ): element_lookup = {'SF4' : 'Fe', 'F3S' : 'S', #'F4S' : 'S', # not done yet #'CLF' : 'Fe', # too flexible 'DVT' : 'V', } from mmtbx.monomer_library import pdb_interpretation t0=time.time() rmsd_list = {} if superpose_element is None: superpose_element = element_lookup.get(resname, None) if resname in pdb_interpretation.ideal_ligands: ideal_hierarchy = get_pdb_hierarchy_from_restraints(resname) else: assert 0 sites_moving = _extract_sites_cart(ideal_hierarchy, superpose_element) assert len(sites_moving), 'No atoms %s found' % superpose_element for ideal_ag in ideal_hierarchy.atom_groups(): break for sites_fixed, ag in generate_sites_fixed(pdb_hierarchy, resname, superpose_element, ): assert sites_fixed.size() == sites_moving.size(), '%(resname)s residue is missing atoms' % locals() import random min_rmsd = 1e9 min_sites_cart = None for i in range(100): random.shuffle(sites_moving) lsq_fit = superpose.least_squares_fit( reference_sites = sites_fixed, other_sites = sites_moving) new_atoms = ideal_ag.detached_copy().atoms() sites_new = new_atoms.extract_xyz() sites_new = lsq_fit.r.elems * sites_new + lsq_fit.t.elems rmsd = sites_fixed.rms_difference(lsq_fit.other_sites_best_fit()) if rmsd<min_rmsd: min_rmsd=rmsd min_sites_cart = sites_new rmsd_list[ag.id_str()] = min_rmsd sites_new = min_sites_cart new_atoms.set_xyz(sites_new) for atom1 in ag.atoms(): for atom2 in new_atoms: if atom1.name.strip()==atom2.name.strip(): atom1.xyz=atom2.xyz break else: assert 0, 'not all atoms updated - missing %s' % atom1.quote() outl = '' if rmsd_list: outl = '\n %(resname)s Regularisation' % locals() outl+= '\n residue rmsd' for id_str, rmsd in sorted(rmsd_list.items()): outl += '\n "%s" %0.1f' % (id_str, rmsd) outl += '\n Time to superpose : %0.2fs\n' % (time.time()-t0) return outl def superpose_ideal_ligand_on_poor_ligand(ideal_hierarchy, poor_hierarchy, ): """Function superpose an ideal ligand onto the mangled ligand from a ligand fitting procedure Args: ideal_hierarchy (pdb_hierarchy): Ideal ligand poor_hierarchy (pdb_hierarchy): Poor ligand with correct c.o.m. and same atom names in order. Could become more sophisticated. """ sites_moving = flex.vec3_double() sites_fixed = flex.vec3_double() for atom1, atom2 in zip(ideal_hierarchy.atoms(), poor_hierarchy.atoms()): assert atom1.name==atom2.name, '%s!=%s' % (atom1.quote(),atom2.quote()) sites_moving.append(atom1.xyz) sites_fixed.append(atom2.xyz) lsq_fit = superpose.least_squares_fit( reference_sites = sites_fixed, other_sites = sites_moving) sites_new = ideal_hierarchy.atoms().extract_xyz() sites_new = lsq_fit.r.elems * sites_new + lsq_fit.t.elems # rmsd = sites_fixed.rms_difference(lsq_fit.other_sites_best_fit()) ideal_hierarchy.atoms().set_xyz(sites_new) return ideal_hierarchy if __name__=="__main__": from iotbx import pdb ideal_inp=pdb.pdb_input(sys.argv[1]) ideal_hierarchy = ideal_inp.construct_hierarchy() poor_inp=pdb.pdb_input(sys.argv[2]) poor_hierarchy = poor_inp.construct_hierarchy() ideal_hierarchy = superpose_ideal_ligand_on_poor_ligand(ideal_hierarchy, poor_hierarchy) ideal_hierarchy.write_pdb_file('new.pdb')

37.504167

103

0.63093

0

205

0.022775

0

1,155

0.128319

294225b79ce42a07375fda887c5ff1ca0b02cbd1

15,778

py

Python

tests/test_install.py

dfroger/conda

c0f99ff46b217d081501e66f4dcd7bcdb5d9c6aa

[ "BSD-3-Clause" ]

null

tests/test_install.py

dfroger/conda

c0f99ff46b217d081501e66f4dcd7bcdb5d9c6aa

[ "BSD-3-Clause" ]

null

tests/test_install.py

dfroger/conda

c0f99ff46b217d081501e66f4dcd7bcdb5d9c6aa

[ "BSD-3-Clause" ]

null

from contextlib import contextmanager import random import shutil import stat import tempfile import unittest from os.path import join from conda import install from conda.install import (PaddingError, binary_replace, update_prefix, warn_failed_remove, duplicates_to_remove) from .decorators import skip_if_no_mock from .helpers import mock patch = mock.patch if mock else None def generate_random_path(): return '/some/path/to/file%s' % random.randint(100, 200) class TestBinaryReplace(unittest.TestCase): def test_simple(self): self.assertEqual( binary_replace(b'xxxaaaaaxyz\x00zz', b'aaaaa', b'bbbbb'), b'xxxbbbbbxyz\x00zz') def test_shorter(self): self.assertEqual( binary_replace(b'xxxaaaaaxyz\x00zz', b'aaaaa', b'bbbb'), b'xxxbbbbxyz\x00\x00zz') def test_too_long(self): self.assertRaises(PaddingError, binary_replace, b'xxxaaaaaxyz\x00zz', b'aaaaa', b'bbbbbbbb') def test_no_extra(self): self.assertEqual(binary_replace(b'aaaaa\x00', b'aaaaa', b'bbbbb'), b'bbbbb\x00') def test_two(self): self.assertEqual( binary_replace(b'aaaaa\x001234aaaaacc\x00\x00', b'aaaaa', b'bbbbb'), b'bbbbb\x001234bbbbbcc\x00\x00') def test_spaces(self): self.assertEqual( binary_replace(b' aaaa \x00', b'aaaa', b'bbbb'), b' bbbb \x00') def test_multiple(self): self.assertEqual( binary_replace(b'aaaacaaaa\x00', b'aaaa', b'bbbb'), b'bbbbcbbbb\x00') self.assertEqual( binary_replace(b'aaaacaaaa\x00', b'aaaa', b'bbb'), b'bbbcbbb\x00\x00\x00') self.assertRaises(PaddingError, binary_replace, b'aaaacaaaa\x00', b'aaaa', b'bbbbb') class FileTests(unittest.TestCase): def setUp(self): self.tmpdir = tempfile.mkdtemp() self.tmpfname = join(self.tmpdir, 'testfile') def tearDown(self): shutil.rmtree(self.tmpdir) def test_default_text(self): with open(self.tmpfname, 'w') as fo: fo.write('#!/opt/anaconda1anaconda2anaconda3/bin/python\n' 'echo "Hello"\n') update_prefix(self.tmpfname, '/usr/local') with open(self.tmpfname, 'r') as fi: data = fi.read() self.assertEqual(data, '#!/usr/local/bin/python\n' 'echo "Hello"\n') def test_binary(self): with open(self.tmpfname, 'wb') as fo: fo.write(b'\x7fELF.../some-placeholder/lib/libfoo.so\0') update_prefix(self.tmpfname, '/usr/local', placeholder='/some-placeholder', mode='binary') with open(self.tmpfname, 'rb') as fi: data = fi.read() self.assertEqual( data, b'\x7fELF.../usr/local/lib/libfoo.so\0\0\0\0\0\0\0\0' ) class remove_readonly_TestCase(unittest.TestCase): def test_takes_three_args(self): with self.assertRaises(TypeError): install._remove_readonly() with self.assertRaises(TypeError): install._remove_readonly(True) with self.assertRaises(TypeError): install._remove_readonly(True, True) with self.assertRaises(TypeError): install._remove_readonly(True, True, True, True) @skip_if_no_mock def test_calls_os_chmod(self): some_path = generate_random_path() with patch.object(install.os, 'chmod') as chmod: install._remove_readonly(mock.Mock(), some_path, {}) chmod.assert_called_with(some_path, stat.S_IWRITE) @skip_if_no_mock def test_calls_func(self): some_path = generate_random_path() func = mock.Mock() with patch.object(install.os, 'chmod'): install._remove_readonly(func, some_path, {}) func.assert_called_with(some_path) class rm_rf_file_and_link_TestCase(unittest.TestCase): @contextmanager def generate_mock_islink(self, value): with patch.object(install, 'islink', return_value=value) as islink: yield islink @contextmanager def generate_mock_isdir(self, value): with patch.object(install, 'isdir', return_value=value) as isdir: yield isdir @contextmanager def generate_mock_isfile(self, value): with patch.object(install, 'isfile', return_value=value) as isfile: yield isfile @contextmanager def generate_mock_os_access(self, value): with patch.object(install.os, 'access', return_value=value) as os_access: yield os_access @contextmanager def generate_mock_unlink(self): with patch.object(install.os, 'unlink') as unlink: yield unlink @contextmanager def generate_mock_rmtree(self): with patch.object(install.shutil, 'rmtree') as rmtree: yield rmtree @contextmanager def generate_mock_sleep(self): with patch.object(install.time, 'sleep') as sleep: yield sleep @contextmanager def generate_mock_log(self): with patch.object(install, 'log') as log: yield log @contextmanager def generate_mock_on_win(self, value): original = install.on_win install.on_win = value yield install.on_win = original @contextmanager def generate_mock_check_call(self): with patch.object(install.subprocess, 'check_call') as check_call: yield check_call @contextmanager def generate_mocks(self, islink=True, isfile=True, isdir=True, on_win=False, os_access=True): with self.generate_mock_islink(islink) as mock_islink: with self.generate_mock_isfile(isfile) as mock_isfile: with self.generate_mock_os_access(os_access) as mock_os_access: with self.generate_mock_isdir(isdir) as mock_isdir: with self.generate_mock_unlink() as mock_unlink: with self.generate_mock_rmtree() as mock_rmtree: with self.generate_mock_sleep() as mock_sleep: with self.generate_mock_log() as mock_log: with self.generate_mock_on_win(on_win): with self.generate_mock_check_call() as check_call: yield { 'islink': mock_islink, 'isfile': mock_isfile, 'isdir': mock_isdir, 'os_access': mock_os_access, 'unlink': mock_unlink, 'rmtree': mock_rmtree, 'sleep': mock_sleep, 'log': mock_log, 'check_call': check_call, } def generate_directory_mocks(self, on_win=False): return self.generate_mocks(islink=False, isfile=False, isdir=True, on_win=on_win) def generate_all_false_mocks(self): return self.generate_mocks(False, False, False) @property def generate_random_path(self): return generate_random_path() @skip_if_no_mock def test_calls_islink(self): with self.generate_mocks() as mocks: some_path = self.generate_random_path install.rm_rf(some_path) mocks['islink'].assert_called_with(some_path) @skip_if_no_mock def test_calls_unlink_on_true_islink(self): with self.generate_mocks() as mocks: some_path = self.generate_random_path install.rm_rf(some_path) mocks['unlink'].assert_called_with(some_path) @skip_if_no_mock def test_calls_unlink_on_os_access_false(self): with self.generate_mocks(os_access=False) as mocks: some_path = self.generate_random_path install.rm_rf(some_path) mocks['unlink'].assert_called_with(some_path) @skip_if_no_mock def test_does_not_call_isfile_if_islink_is_true(self): with self.generate_mocks() as mocks: some_path = self.generate_random_path install.rm_rf(some_path) self.assertFalse(mocks['isfile'].called) @skip_if_no_mock def test_calls_isfile_with_path(self): with self.generate_mocks(islink=False, isfile=True) as mocks: some_path = self.generate_random_path install.rm_rf(some_path) mocks['isfile'].assert_called_with(some_path) @skip_if_no_mock def test_calls_unlink_on_false_islink_and_true_isfile(self): with self.generate_mocks(islink=False, isfile=True) as mocks: some_path = self.generate_random_path install.rm_rf(some_path) mocks['unlink'].assert_called_with(some_path) @skip_if_no_mock def test_does_not_call_unlink_on_false_values(self): with self.generate_mocks(islink=False, isfile=False) as mocks: some_path = self.generate_random_path install.rm_rf(some_path) self.assertFalse(mocks['unlink'].called) @skip_if_no_mock def test_does_not_call_shutil_on_false_isdir(self): with self.generate_all_false_mocks() as mocks: some_path = self.generate_random_path install.rm_rf(some_path) self.assertFalse(mocks['rmtree'].called) @skip_if_no_mock def test_calls_rmtree_at_least_once_on_isdir_true(self): with self.generate_directory_mocks() as mocks: some_path = self.generate_random_path install.rm_rf(some_path) mocks['rmtree'].assert_called_with( some_path, onerror=warn_failed_remove, ignore_errors=False) @skip_if_no_mock def test_calls_rmtree_only_once_on_success(self): with self.generate_directory_mocks() as mocks: some_path = self.generate_random_path install.rm_rf(some_path) self.assertEqual(1, mocks['rmtree'].call_count) @skip_if_no_mock def test_raises_final_exception_if_it_cant_remove(self): with self.generate_directory_mocks() as mocks: mocks['rmtree'].side_effect = OSError some_path = self.generate_random_path with self.assertRaises(OSError): install.rm_rf(some_path) @skip_if_no_mock def test_retries_six_times_to_ensure_it_cant_really_remove(self): with self.generate_directory_mocks() as mocks: mocks['rmtree'].side_effect = OSError some_path = self.generate_random_path with self.assertRaises(OSError): install.rm_rf(some_path) self.assertEqual(6, mocks['rmtree'].call_count) @skip_if_no_mock def test_retries_as_many_as_max_retries_plus_one(self): max_retries = random.randint(7, 10) with self.generate_directory_mocks() as mocks: mocks['rmtree'].side_effect = OSError some_path = self.generate_random_path with self.assertRaises(OSError): install.rm_rf(some_path, max_retries=max_retries) self.assertEqual(max_retries + 1, mocks['rmtree'].call_count) @skip_if_no_mock def test_stops_retrying_after_success(self): with self.generate_directory_mocks() as mocks: mocks['rmtree'].side_effect = [OSError, OSError, None] some_path = self.generate_random_path install.rm_rf(some_path) self.assertEqual(3, mocks['rmtree'].call_count) @skip_if_no_mock def test_pauses_for_same_number_of_seconds_as_max_retries(self): with self.generate_directory_mocks() as mocks: mocks['rmtree'].side_effect = OSError max_retries = random.randint(1, 10) with self.assertRaises(OSError): install.rm_rf(self.generate_random_path, max_retries=max_retries) expected = [mock.call(i) for i in range(max_retries)] mocks['sleep'].assert_has_calls(expected) @skip_if_no_mock def test_logs_messages_generated_for_each_retry(self): with self.generate_directory_mocks() as mocks: random_path = self.generate_random_path mocks['rmtree'].side_effect = OSError(random_path) max_retries = random.randint(1, 10) with self.assertRaises(OSError): install.rm_rf(random_path, max_retries=max_retries) log_template = "\n".join([ "Unable to delete %s" % random_path, "%s" % OSError(random_path), "Retrying after %d seconds...", ]) expected_call_list = [mock.call(log_template % i) for i in range(max_retries)] mocks['log'].debug.assert_has_calls(expected_call_list) @skip_if_no_mock def test_tries_extra_kwarg_on_windows(self): with self.generate_directory_mocks(on_win=True) as mocks: random_path = self.generate_random_path mocks['rmtree'].side_effect = [OSError, None] install.rm_rf(random_path) expected_call_list = [ mock.call(random_path, ignore_errors=False, onerror=warn_failed_remove), mock.call(random_path, onerror=install._remove_readonly) ] mocks['rmtree'].assert_has_calls(expected_call_list) self.assertEqual(2, mocks['rmtree'].call_count) class duplicates_to_remove_TestCase(unittest.TestCase): def test_1(self): linked = ['conda-3.18.8-py27_0', 'conda-3.19.0', 'python-2.7.10-2', 'python-2.7.11-0', 'zlib-1.2.8-0'] keep = ['conda-3.19.0', 'python-2.7.11-0'] self.assertEqual(duplicates_to_remove(linked, keep), ['conda-3.18.8-py27_0', 'python-2.7.10-2']) def test_2(self): linked = ['conda-3.19.0', 'python-2.7.10-2', 'python-2.7.11-0', 'zlib-1.2.7-1', 'zlib-1.2.8-0', 'zlib-1.2.8-4'] keep = ['conda-3.19.0', 'python-2.7.11-0'] self.assertEqual(duplicates_to_remove(linked, keep), ['python-2.7.10-2', 'zlib-1.2.7-1', 'zlib-1.2.8-0']) def test_3(self): linked = ['python-2.7.10-2', 'python-2.7.11-0', 'python-3.4.3-1'] keep = ['conda-3.19.0', 'python-2.7.11-0'] self.assertEqual(duplicates_to_remove(linked, keep), ['python-2.7.10-2', 'python-3.4.3-1']) def test_nokeep(self): linked = ['python-2.7.10-2', 'python-2.7.11-0', 'python-3.4.3-1'] self.assertEqual(duplicates_to_remove(linked, []), ['python-2.7.10-2', 'python-2.7.11-0']) def test_misc(self): d1 = 'a-1.3-0' self.assertEqual(duplicates_to_remove([], []), []) self.assertEqual(duplicates_to_remove([], [d1]), []) self.assertEqual(duplicates_to_remove([d1], [d1]), []) self.assertEqual(duplicates_to_remove([d1], []), []) d2 = 'a-1.4-0' li = set([d1, d2]) self.assertEqual(duplicates_to_remove(li, [d2]), [d1]) self.assertEqual(duplicates_to_remove(li, [d1]), [d2]) self.assertEqual(duplicates_to_remove(li, []), [d1]) self.assertEqual(duplicates_to_remove(li, [d1, d2]), []) if __name__ == '__main__': unittest.main()

38.20339

97

0.609266

15,214

0.964254

2,936

0.186082

9,855

0.624604

0

1,693

0.107301

29422d091e83652a21c0e3588c5f7b69d97c82a9

728

py

Python

django_elastic_appsearch/slicer.py

CorrosiveKid/django_elastic_appsearch

85da7642aac566164b8bc06894e97a048fd3116e

[ "MIT" ]

11

2019-08-07T01:31:42.000Z

2021-02-02T08:12:24.000Z

django_elastic_appsearch/slicer.py

CorrosiveKid/django_elastic_appsearch

85da7642aac566164b8bc06894e97a048fd3116e

[ "MIT" ]

148

2019-08-01T04:22:28.000Z

2021-05-10T19:06:31.000Z

django_elastic_appsearch/slicer.py

infoxchange/django_elastic_appsearch

65229586f0392d8d8cb143ab625081c89fa4cb64

[ "MIT" ]

6

2019-08-26T10:00:42.000Z

2021-02-01T03:54:02.000Z

"""A Queryset slicer for Django.""" def slice_queryset(queryset, chunk_size): """Slice a queryset into chunks.""" start_pk = 0 queryset = queryset.order_by('pk') while True: # No entry left if not queryset.filter(pk__gt=start_pk).exists(): break try: # Fetch chunk_size entries if possible end_pk = queryset.filter(pk__gt=start_pk).values_list( 'pk', flat=True)[chunk_size - 1] # Fetch rest entries if less than chunk_size left except IndexError: end_pk = queryset.values_list('pk', flat=True).last() yield queryset.filter(pk__gt=start_pk).filter(pk__lte=end_pk) start_pk = end_pk

28

69

0.60989

0

689

0.946429

0

184

0.252747

29424d0f4478d5925df5fb2792f4b3b4b39494a0

402

py

Python

newsite/news/urls.py

JasperStfun/Django_C

1307f2e9c827f751e8640f50179f1b744c222d63

[ "Unlicense" ]

null

newsite/news/urls.py

JasperStfun/Django_C

1307f2e9c827f751e8640f50179f1b744c222d63

[ "Unlicense" ]

null

newsite/news/urls.py

JasperStfun/Django_C

1307f2e9c827f751e8640f50179f1b744c222d63

[ "Unlicense" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.news_home, name='news_home'), path('create', views.create, name='create'), path('<int:pk>', views.NewsDetailView.as_view(), name='news-detail'), path('<int:pk>/update', views.NewsUpdateView.as_view(), name='news-update'), path('<int:pk>/delete', views.NewsDeleteView.as_view(), name='news-delete'), ]

36.545455

80

0.674129

0

112

0.278607

29428a3c880266295d54c48af9bca30d4cdda98d

412

py

Python

module/phase_one/headers.py

cqr-cryeye-forks/Florid

21ea7abbe5448dca0c485232bdcf870ba2648d68

[ "Apache-2.0" ]

7

2020-03-22T02:44:26.000Z

2022-02-23T01:57:29.000Z

module/phase_one/headers.py

h4zze1/Florid-Scanner

0a8600ce2bdd24f16e45504b00c714ecbb8930af

[ "Apache-2.0" ]

1

2019-02-07T13:41:47.000Z

module/phase_one/headers.py

h4zze1/Florid-Scanner

0a8600ce2bdd24f16e45504b00c714ecbb8930af

[ "Apache-2.0" ]

3

2020-03-22T02:44:27.000Z

2021-08-03T00:52:38.000Z

import requests import lib.common MODULE_NAME = 'headers' def run(): r = requests.get(lib.common.SOURCE_URL) # X-Forwarded-By: if 'X-Powered-By' in r.headers: lib.common.RESULT_ONE_DICT['X-Powered-By'] = r.headers['X-Powered-By'] # Server: if 'Server' in r.headers: lib.common.RESULT_ONE_DICT['Server'] = r.headers['Server'] lib.common.ALIVE_LINE[MODULE_NAME] += 1

20.6

78

0.652913

0

101

0.245146

2942faf9418139b387fac9d36b23ead11b7dcd5e

1,234

py

Python

ekorpkit/io/fetch/edgar/edgar.py

entelecheia/ekorpkit

400cb15005fdbcaa2ab0c311e338799283f28fe0

[ "CC-BY-4.0" ]

4

2022-02-26T10:54:16.000Z

2022-02-26T11:01:56.000Z

ekorpkit/io/fetch/edgar/edgar.py

entelecheia/ekorpkit

400cb15005fdbcaa2ab0c311e338799283f28fe0

[ "CC-BY-4.0" ]

1

2022-03-25T06:37:12.000Z

2022-03-25T06:45:53.000Z

ekorpkit/io/fetch/edgar/edgar.py

entelecheia/ekorpkit

400cb15005fdbcaa2ab0c311e338799283f28fe0

[ "CC-BY-4.0" ]

null

import os import requests from bs4 import BeautifulSoup from ekorpkit import eKonf from ekorpkit.io.download.web import web_download, web_download_unzip class EDGAR: def __init__(self, **args): self.args = eKonf.to_config(args) self.base_url = self.args.base_url self.url = self.args.url self.output_dir = self.args.output_dir os.makedirs(self.output_dir, exist_ok=True) self.force_download = self.args.force_download self.name = self.args.name self.build() def build(self): if self.force_download or not os.listdir(self.output_dir): self.download_edgar() else: print(f"{self.name} is already downloaded") def download_edgar(self): user_agent = "Mozilla/5.0" headers = {"User-Agent": user_agent} page = requests.get(self.url, headers=headers) soup = BeautifulSoup(page.content, "html.parser") filelist = soup.find_all("a", class_="filename") for file in filelist: link = self.base_url + file.get("href") file_path = self.output_dir + "/" + file.get_text().strip() web_download(link, file_path, self.name, self.force_download)

31.641026

73

0.644246

1,078

0.873582

0

96

0.077796

2944646b37b0ab25dfa73f854ed036b7d6e77c63

3,470

py

Python

HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py

michiboo/HARK

de2aab467de19da2ce76de1b58fb420f421bc85b

[ "Apache-2.0" ]

null

HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py

michiboo/HARK

de2aab467de19da2ce76de1b58fb420f421bc85b

[ "Apache-2.0" ]

null

HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py

michiboo/HARK

de2aab467de19da2ce76de1b58fb420f421bc85b

[ "Apache-2.0" ]

null

from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType import numpy as np import unittest class testPerfForesightConsumerType(unittest.TestCase): def setUp(self): self.agent = PerfForesightConsumerType() self.agent_infinite = PerfForesightConsumerType(cycles=0) PF_dictionary = { 'CRRA' : 2.5, 'DiscFac' : 0.96, 'Rfree' : 1.03, 'LivPrb' : [0.98], 'PermGroFac' : [1.01], 'T_cycle' : 1, 'cycles' : 0, 'AgentCount' : 10000 } self.agent_alt = PerfForesightConsumerType( **PF_dictionary) def test_default_solution(self): self.agent.solve() c = self.agent.solution[0].cFunc self.assertEqual(c.x_list[0], -0.9805825242718447) self.assertEqual(c.x_list[1], 0.01941747572815533) self.assertEqual(c.y_list[0], 0) self.assertEqual(c.y_list[1], 0.511321002804608) self.assertEqual(c.decay_extrap, False) def test_another_solution(self): self.agent_alt.DiscFac = 0.90 self.agent_alt.solve() self.assertAlmostEqual( self.agent_alt.solution[0].cFunc(10).tolist(), 3.9750093524820787) def test_checkConditions(self): self.agent_infinite.checkConditions() self.assertTrue(self.agent_infinite.AIC) self.assertTrue(self.agent_infinite.GICPF) self.assertTrue(self.agent_infinite.RIC) self.assertTrue(self.agent_infinite.FHWC) def test_simulation(self): self.agent_infinite.solve() # Create parameter values necessary for simulation SimulationParams = { "AgentCount" : 10000, # Number of agents of this type "T_sim" : 120, # Number of periods to simulate "aNrmInitMean" : -6.0, # Mean of log initial assets "aNrmInitStd" : 1.0, # Standard deviation of log initial assets "pLvlInitMean" : 0.0, # Mean of log initial permanent income "pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income "PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor "T_age" : None, # Age after which simulated agents are automatically killed } self.agent_infinite(**SimulationParams) # This implicitly uses the assignParameters method of AgentType # Create PFexample object self.agent_infinite.track_vars = ['mNrmNow'] self.agent_infinite.initializeSim() self.agent_infinite.simulate() self.assertAlmostEqual( np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40], -23.008063500363942 ) self.assertAlmostEqual( np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100], -27.164608851546927 ) ## Try now with the manipulation at time step 80 self.agent_infinite.initializeSim() self.agent_infinite.simulate(80) self.agent_infinite.aNrmNow += -5. # Adjust all simulated consumers' assets downward by 5 self.agent_infinite.simulate(40) self.assertAlmostEqual( np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40], -23.008063500363942 ) self.assertAlmostEqual( np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100], -29.140261331951606 )

35.408163

111

0.625072

3,354

0.966571

0

738

0.21268

2944814c5ae01dfc5daf1a2ce4f89caabba6e70c

3,893

py

Python

src-gen/openapi_server/models/config.py

etherisc/bima-bolt-api

14201a3055d94ff9c42afbb755109a69e77248f4

[ "Apache-2.0" ]

null

src-gen/openapi_server/models/config.py

etherisc/bima-bolt-api

14201a3055d94ff9c42afbb755109a69e77248f4

[ "Apache-2.0" ]

null

src-gen/openapi_server/models/config.py

etherisc/bima-bolt-api

14201a3055d94ff9c42afbb755109a69e77248f4

[ "Apache-2.0" ]

null

# coding: utf-8 from __future__ import absolute_import from datetime import date, datetime # noqa: F401 from typing import List, Dict # noqa: F401 from openapi_server.models.base_model_ import Model from openapi_server.models.component import Component from openapi_server import util from openapi_server.models.component import Component # noqa: E501 class Config(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). Do not edit the class manually. """ def __init__(self, mongo=None, s3=None, arc2=None, created_at=None): # noqa: E501 """Config - a model defined in OpenAPI :param mongo: The mongo of this Config. # noqa: E501 :type mongo: Component :param s3: The s3 of this Config. # noqa: E501 :type s3: Component :param arc2: The arc2 of this Config. # noqa: E501 :type arc2: Component :param created_at: The created_at of this Config. # noqa: E501 :type created_at: datetime """ self.openapi_types = { 'mongo': Component, 's3': Component, 'arc2': Component, 'created_at': datetime } self.attribute_map = { 'mongo': 'mongo', 's3': 's3', 'arc2': 'arc2', 'created_at': 'created_at' } self._mongo = mongo self._s3 = s3 self._arc2 = arc2 self._created_at = created_at @classmethod def from_dict(cls, dikt) -> 'Config': """Returns the dict as a model :param dikt: A dict. :type: dict :return: The Config of this Config. # noqa: E501 :rtype: Config """ return util.deserialize_model(dikt, cls) @property def mongo(self): """Gets the mongo of this Config. :return: The mongo of this Config. :rtype: Component """ return self._mongo @mongo.setter def mongo(self, mongo): """Sets the mongo of this Config. :param mongo: The mongo of this Config. :type mongo: Component """ if mongo is None: raise ValueError("Invalid value for `mongo`, must not be `None`") # noqa: E501 self._mongo = mongo @property def s3(self): """Gets the s3 of this Config. :return: The s3 of this Config. :rtype: Component """ return self._s3 @s3.setter def s3(self, s3): """Sets the s3 of this Config. :param s3: The s3 of this Config. :type s3: Component """ if s3 is None: raise ValueError("Invalid value for `s3`, must not be `None`") # noqa: E501 self._s3 = s3 @property def arc2(self): """Gets the arc2 of this Config. :return: The arc2 of this Config. :rtype: Component """ return self._arc2 @arc2.setter def arc2(self, arc2): """Sets the arc2 of this Config. :param arc2: The arc2 of this Config. :type arc2: Component """ if arc2 is None: raise ValueError("Invalid value for `arc2`, must not be `None`") # noqa: E501 self._arc2 = arc2 @property def created_at(self): """Gets the created_at of this Config. Creation timestamp, omit this property for post requests # noqa: E501 :return: The created_at of this Config. :rtype: datetime """ return self._created_at @created_at.setter def created_at(self, created_at): """Sets the created_at of this Config. Creation timestamp, omit this property for post requests # noqa: E501 :param created_at: The created_at of this Config. :type created_at: datetime """ self._created_at = created_at

25.444444

96

0.579245

3,532

0.907269

0

2,348

0.603134

0

2,196

0.564089

2944fda074b1c1551c4b520622df91dd49749873

1,003

py

Python

txt_annotation.py

bubbliiiing/classification-keras

b914c5d8526cccbeb3ae8d8f2fea4c8bbabf1d94

[ "MIT" ]

30

2021-01-23T15:51:20.000Z

2022-03-26T13:37:49.000Z

txt_annotation.py

PARILM/classification-keras

91e558b5a128449b81acc4f6983f01e420b2039d

[ "MIT" ]

4

2021-01-22T08:58:57.000Z

2022-03-17T14:21:07.000Z

txt_annotation.py

PARILM/classification-keras

91e558b5a128449b81acc4f6983f01e420b2039d

[ "MIT" ]

10

2021-01-31T01:23:35.000Z

2022-02-17T11:53:05.000Z

import os from os import getcwd #---------------------------------------------# # 训练前一定要注意修改classes # 种类顺序需要和model_data下的txt一样 #---------------------------------------------# classes = ["cat", "dog"] sets = ["train", "test"] wd = getcwd() for se in sets: list_file = open('cls_' + se + '.txt', 'w') datasets_path = "datasets/" + se types_name = os.listdir(datasets_path) for type_name in types_name: if type_name not in classes: continue cls_id = classes.index(type_name) photos_path = os.path.join(datasets_path, type_name) photos_name = os.listdir(photos_path) for photo_name in photos_name: _, postfix = os.path.splitext(photo_name) if postfix not in ['.jpg', '.png', '.jpeg']: continue list_file.write(str(cls_id) + ";" + '%s/%s'%(wd, os.path.join(photos_path, photo_name))) list_file.write('\n') list_file.close()

31.34375

101

0.521436

0

271

0.25933

29451165b051aed5989a0318d992368267c8109d

5,272

py

Python

S4/S4 Library/simulation/relationships/sim_knowledge.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

1

2021-05-20T19:33:37.000Z

S4/S4 Library/simulation/relationships/sim_knowledge.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

S4/S4 Library/simulation/relationships/sim_knowledge.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

from protocolbuffers import SimObjectAttributes_pb2 as protocols from careers.career_unemployment import CareerUnemployment import services import sims4 logger = sims4.log.Logger('Relationship', default_owner='jjacobson') class SimKnowledge: __slots__ = ('_rel_data', '_known_traits', '_knows_career', '_known_stats', '_knows_major') def __init__(self, rel_data): self._rel_data = rel_data self._known_traits = None self._knows_career = False self._known_stats = None self._knows_major = False def add_known_trait(self, trait, notify_client=True): if trait.is_personality_trait: if self._known_traits is None: self._known_traits = set() self._known_traits.add(trait) if notify_client: self._rel_data.relationship.send_relationship_info() else: logger.error("Try to add non personality trait {} to Sim {}'s knowledge about to Sim {}", trait, self._rel_data.sim_id, self._rel_data.target_sim_id) @property def known_traits(self): if self._known_traits is None: return () return self._known_traits @property def knows_career(self): return self._knows_career def add_knows_career(self, notify_client=True): self._knows_career = True if notify_client: self._rel_data.relationship.send_relationship_info() def remove_knows_career(self, notify_client=True): self._knows_career = False if notify_client: self._rel_data.relationship.send_relationship_info() def get_known_careers(self): if self._knows_career: target_sim_info = self._rel_data.find_target_sim_info() if target_sim_info is not None: if target_sim_info.career_tracker.has_career: careers = tuple(career for career in target_sim_info.careers.values() if career.is_visible_career if not career.is_course_slot) if careers: return careers if target_sim_info.career_tracker.retirement is not None: return (target_sim_info.career_tracker.retirement,) else: return (CareerUnemployment(target_sim_info),) return () def get_known_careertrack_ids(self): return (career_track.current_track_tuning.guid64 for career_track in self.get_known_careers()) def add_known_stat(self, stat, notify_client=True): if self._known_stats is None: self._known_stats = set() self._known_stats.add(stat) if notify_client: self._rel_data.relationship.send_relationship_info() def get_known_stats(self): return self._known_stats @property def knows_major(self): return self._knows_major def add_knows_major(self, notify_client=True): self._knows_major = True if notify_client: self._rel_data.relationship.send_relationship_info() def remove_knows_major(self, notify_client=True): self._knows_major = False if notify_client: self._rel_data.relationship.send_relationship_info() def get_known_major(self): if self._knows_major: target_sim_info = self._rel_data.find_target_sim_info() if target_sim_info is not None and target_sim_info.degree_tracker: return target_sim_info.degree_tracker.get_major() def get_known_major_career(self): if self._knows_major: target_sim_info = self._rel_data.find_target_sim_info() if target_sim_info is not None and target_sim_info.career_tracker.has_career: careers = tuple(career for career in target_sim_info.careers.values() if career.is_visible_career if career.is_course_slot) if careers: return careers return () def get_save_data(self): save_data = protocols.SimKnowledge() for trait in self.known_traits: save_data.trait_ids.append(trait.guid64) save_data.knows_career = self._knows_career if self._known_stats is not None: for stat in self._known_stats: save_data.stats.append(stat.guid64) save_data.knows_major = self._knows_major return save_data def load_knowledge(self, save_data): trait_manager = services.get_instance_manager(sims4.resources.Types.TRAIT) stat_manager = services.get_instance_manager(sims4.resources.Types.STATISTIC) for trait_inst_id in save_data.trait_ids: trait = trait_manager.get(trait_inst_id) if trait is not None: if self._known_traits is None: self._known_traits = set() self._known_traits.add(trait) for stat_id in save_data.stats: if self._known_stats is None: self._known_stats = set() stat = stat_manager.get(stat_id) if stat is not None: self._known_stats.add(stat) self._knows_career = save_data.knows_career if hasattr(save_data, 'knows_major'): self._knows_major = save_data.knows_major

39.939394

161

0.660281

5,048

0.957511

0

272

0.051593

0

182

0.034522

29452ec5be15d28b45cb5711c4822ec7f8c5c51e

1,001

py

Python

233_number_of_digt_one.py

gengwg/leetcode

0af5256ec98149ef5863f3bba78ed1e749650f6e

[ "Apache-2.0" ]

2

2018-04-24T19:17:40.000Z

2018-04-24T19:33:52.000Z

233_number_of_digt_one.py

gengwg/leetcode

0af5256ec98149ef5863f3bba78ed1e749650f6e

[ "Apache-2.0" ]

null

233_number_of_digt_one.py

gengwg/leetcode

0af5256ec98149ef5863f3bba78ed1e749650f6e

[ "Apache-2.0" ]

3

2020-06-17T05:48:52.000Z

2021-01-02T06:08:25.000Z

# Given an integer n, count the total number of digit 1 appearing # in all non-negative integers less than or equal to n. # # For example: # Given n = 13, # Return 6, because digit 1 occurred in the following numbers: # 1, 10, 11, 12, 13. # class Solution: def countDigitOne(self, n): """ :type n: int :rtype: int """ # sum all the '1's inside the n numbers count = 0 for i in range(1, n+1): # count including n count += self.numberOfDigitOne(i) return count def numberOfDigitOne(self, n): """ function to count number of digit ones in a number n. mod by 10 to test if 1st digit is 1; then divide by 10 to get next digit; next test if next digit is 1. """ result = 0 while n: if n % 10 == 1: result += 1 n = n / 10 return result if __name__ == "__main__": print Solution().countDigitOne(13)

22.75

65

0.548452

689

0.688312

0

565

0.564436

29458e025d37036dcc3d6da38653c530afc7e75e

13,954

py

Python

Search Algorithms.py

fzehracetin/A-Star-and-Best-First-Search

78be430f0c3523aa78d9822ec8aa19615fd3e500

[ "Apache-2.0" ]

1

2021-02-24T10:13:22.000Z

Search Algorithms.py

fzehracetin/A-Star-and-Best-First-Search

78be430f0c3523aa78d9822ec8aa19615fd3e500

[ "Apache-2.0" ]

null

Search Algorithms.py

fzehracetin/A-Star-and-Best-First-Search

78be430f0c3523aa78d9822ec8aa19615fd3e500

[ "Apache-2.0" ]

null

from PIL import Image from math import sqrt import numpy as np import time import matplotlib.backends.backend_tkagg import matplotlib.pyplot as plt class Point: x: float y: float f: float h: float g: float def __init__(self, x, y, f): self.x = x self.y = y self.f = f self.g = 0 self.h = 0 self.parent = None def equal(self, other): if self.x == other.x and self.y == other.y: return True class Output: result_image: Image total_time: float n_elements: int max_elements: int def __init__(self, result_image, total_time, n_elements, max_elements): self.result_image = result_image self.total_time = total_time self.n_elements = n_elements self.max_elements = max_elements self.name = None def plot_times(self, other1, other2, other3): fig, ax = plt.subplots() ax.bar([self.name, other1.name, other2.name, other3.name], [self.total_time, other1.total_time, other2.total_time, other3.total_time]) fig.suptitle("Toplam Zamanlar") fname = image_name.split('.') plt.savefig(fname[0] + "times.png") plt.show() def plot_n_elements(self, other1, other2, other3): fig, ax = plt.subplots() ax.bar([self.name, other1.name, other2.name, other3.name], [self.n_elements, other1.n_elements, other2.n_elements, other3.n_elements]) fig.suptitle("Stack'ten Çekilen Toplam Eleman Sayısı") fname = image_name.split('.') plt.savefig(fname[0] + "n_elements.png") plt.show() def plot_max_elements(self, other1, other2, other3): fig, ax = plt.subplots() ax.bar([self.name, other1.name, other2.name, other3.name], [self.max_elements, other1.max_elements, other2.max_elements, other3.max_elements]) fig.suptitle("Stack'te Bulunan Maksimum Eleman Sayısı") fname = image_name.split('.') plt.savefig(fname[0] + "max_elements.png") plt.show() def distance(point, x, y): return sqrt((point.x - x)**2 + (point.y - y)**2) def insert_in_heap(heap, top, point): heap.append(point) i = top parent = (i - 1)/2 while i >= 1 and heap[int(i)].f < heap[int(parent)].f: heap[int(i)], heap[int(parent)] = heap[int(parent)], heap[int(i)] # swap i = parent parent = (i - 1) / 2 return def calculate_weight(x, y, liste, top, point, visited, index1, index2): if visited[int(x)][int(y)] == 0: r, g, b = image.getpixel((x, y)) if x == end.x and y == end.y: print("Path found.") if r is 0: r = 1 new_point = Point(x, y, 0) new_point.parent = point new_point.h = distance(end, x, y) * (256 - r) new_point.g = 0 if index1 == 1: # a_star new_point.g = new_point.parent.g + 256 - r new_point.f = new_point.h + new_point.g # bfs'de g = 0 if index2 == 0: # stack liste.append(new_point) else: # heap insert_in_heap(liste, top, new_point) top += 1 visited[int(x)][int(y)] = 1 return top def add_neighbours(point, liste, top, visited, index1, index2): # print(point.x, point.y) if (point.x == width - 1 and point.y == height - 1) or (point.x == 0 and point.y == 0) or \ (point.x == 0 and point.y == height - 1) or (point.x == width - 1 and point.y == 0): # print("first if") if point.x == width - 1 and point.y == height - 1: constx = -1 consty = -1 elif point.x == 0 and point.y == 0: constx = 1 consty = 1 elif point.x == width - 1 and point.y == 0: constx = 1 consty = -1 else: constx = -1 consty = 1 top = calculate_weight(point.x + constx, point.y, liste, top, point, visited, index1, index2) top = calculate_weight(point.x, point.y + consty, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + constx, point.y + consty, liste, top, point, visited, index1, index2) elif point.x == 0 or point.x == width - 1: # print("nd if") top = calculate_weight(point.x, point.y - 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x, point.y + 1, liste, top, point, visited, index1, index2) if point.x == 0: const = 1 else: const = -1 top = calculate_weight(point.x + const, point.y - 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + const, point.y + 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + const, point.y, liste, top, point, visited, index1, index2) elif point.y == 0 or point.y == height - 1: # print("3rd if") top = calculate_weight(point.x - 1, point.y, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + 1, point.y, liste, top, point, visited, index1, index2) if point.y == 0: const = 1 else: const = -1 top = calculate_weight(point.x - 1, point.y + const, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + 1, point.y + const, liste, top, point, visited, index1, index2) top = calculate_weight(point.x, point.y + const, liste, top, point, visited, index1, index2) else: # print("4th if") top = calculate_weight(point.x - 1, point.y, liste, top, point, visited, index1, index2) top = calculate_weight(point.x - 1, point.y - 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x - 1, point.y + 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + 1, point.y - 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + 1, point.y, liste, top, point, visited, index1, index2) top = calculate_weight(point.x + 1, point.y + 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x, point.y + 1, liste, top, point, visited, index1, index2) top = calculate_weight(point.x, point.y - 1, liste, top, point, visited, index1, index2) return top def paint(point): yol = [] while not point.equal(start): yol.append(point) image.putpixel((int(point.x), int(point.y)), (60, 255, 0)) point = point.parent end_time = time.time() # image.show() '''print("--------------YOL------------------") for i in range(len(yol)): print("x: {}, y:{}, distance:{}".format(yol[i].x, yol[i].y, yol[i].f)) print("------------------------------------")''' return image, (end_time - start_time) def bfs_and_a_star_with_stack(index): stack = [] top = 0 found = False point = None stack.append(start) visited = np.zeros((width, height)) visited[int(start.x)][int(start.y)] = 1 j = 0 max_element = 0 while stack and not found: point = stack.pop(top) # print("x: {}, y:{}, f:{}".format(point.x, point.y, point.f)) top -= 1 if point.equal(end): found = True else: top = add_neighbours(point, stack, top, visited, index, 0) stack.sort(key=lambda point: point.f, reverse=True) if len(stack) > max_element: max_element = len(stack) j += 1 if found: result_image, total_time = paint(point) # print("Stackten çekilen eleman sayısı: ", j) # print("Stackteki maksimum eleman sayısı: ", max_element) return result_image, total_time, j, max_element def find_smallest_child(heap, i, top): if 2 * i + 2 < top: # has two child if heap[2*i + 1].f < heap[2*i + 2].f: return 2*i + 1 else: return 2*i + 2 elif 2*i + 1 < top: # has one child return 2*i + 1 else: # has no child return 0 def remove_min(heap, top): if top == 0: return None min_point = heap[0] top -= 1 heap[0] = heap[top] del heap[top] i = 0 index = find_smallest_child(heap, i, top) while index != 0 and heap[i].f > heap[index].f: heap[i], heap[index] = heap[index], heap[i] i = index index = find_smallest_child(heap, i, top) return min_point, top def bfs_and_a_star_with_heap(index): heap = [] found = False yol = [] point = None heap.append(start) visited = np.zeros((width, height)) visited[int(start.x)][int(start.y)] = 1 j = 0 top = 1 max_element = 0 while heap and not found: point, top = remove_min(heap, top) # print("x: {}, y:{}, f:{}".format(point.x, point.y, point.f)) if point.equal(end): found = True else: top = add_neighbours(point, heap, top, visited, index, 1) if len(heap) > max_element: max_element = len(heap) j += 1 if found: result_image, total_time = paint(point) else: return return result_image, total_time, j, max_element if __name__ == "__main__": print("UYARI: Seçilecek görüntü exe dosyası ile aynı klasörde olmalıdır.") image_name = input("Algoritmanın üzerinde çalışacağı görüntünün ismini giriniz (Örnek input: image.png): ") print(image_name) print("-------------------Algoritmalar------------------") print("1- Best First Search with Stack") print("2- Best First Search with Heap") print("3- A* with Stack") print("4- A* with Heap") print("5- Analiz (tüm algoritmaların çalışmalarını ve kıyaslamalarını gör)") alg = input("Algoritmayı ve veri yapısının numarasını seçiniz (Örnek input: 1): ") image = Image.open(image_name) width, height = image.size image = image.convert('RGB') print("Görüntünün genişliği: {}, yüksekliği: {}".format(width, height)) print("NOT: Başlangıç ve bitiş noktasının koordinatları genişlik ve uzunluktan küçük olmalıdır.") sx, sy = input("Başlangıç noktasının x ve y piksel koordinatlarını sırasıyla giriniz (Örnek input: 350 100): ").split() ex, ey = input("Bitiş noktasının x ve y piksel koordinatlarını sırasıyla giriniz (Örnek input: 200 700): ").split() start = Point(int(sx), int(sy), -1) start.parent = -1 end = Point(int(ex), int(ey), -1) start_time = time.time() if int(alg) == 1: result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(0) elif int(alg) == 2: result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(0) elif int(alg) == 3: result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(1) elif int(alg) == 4: result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(1) elif int(alg) == 5: result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(0) output1 = Output(result_image, total_time, n_elements, max_elements) print(n_elements, total_time, max_elements) output1.name = "BFS with Stack" print("1/4") image = Image.open(image_name) width, height = image.size image = image.convert('RGB') start_time = time.time() result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(0) output2 = Output(result_image, total_time, n_elements, max_elements) print(n_elements, total_time, max_elements) output2.name = "BFS with Heap" print("2/4") image = Image.open(image_name) width, height = image.size image = image.convert('RGB') start_time = time.time() result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(1) output3 = Output(result_image, total_time, n_elements, max_elements) output3.name = "A* with Stack" print(n_elements, total_time, max_elements) print("3/4") image = Image.open(image_name) width, height = image.size image = image.convert('RGB') start_time = time.time() result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(1) output4 = Output(result_image, total_time, n_elements, max_elements) output4.name = "A* with Heap" print("4/4") output1.plot_times(output2, output3, output4) output1.plot_max_elements(output2, output3, output4) output1.plot_n_elements(output2, output3, output4) print("Bastırılan görüntüler sırasıyla BFS stack, BFS heap, A* stack ve A* heap şeklindedir.") fname = image_name.split('.') output1.result_image.show() output1.result_image.save(fname[0] + "BFS_stack.png") output2.result_image.show() output2.result_image.save(fname[0] + "BFS_heap.png") output3.result_image.show() output3.result_image.save(fname[0] + "A_star_stack.png") output4.result_image.show() output4.result_image.save(fname[0] + "A_star_heap.png") exit(0) else: print("Algoritma numarası hatalı girildi, tekrar deneyin.") exit(0) print("Stackten çekilen eleman sayısı: ", n_elements) print("Stackteki maksimum eleman sayısı: ", max_elements) print("Toplam süre: ", total_time) result_image.show()

35.68798

124

0.580264

1,985

0.141191

0

2,083

0.148161

2945bb791202db0434b867efcbc0fdb23fb1256d

624

py

Python

time_test.py

Shb742/rnnoise_python

e370e85984d5909111c9e6e7e4a627bf4de76648

[ "BSD-3-Clause" ]

32

2019-05-24T08:51:36.000Z

2022-03-10T06:10:08.000Z

time_test.py

Shb742/rnnoise_python

e370e85984d5909111c9e6e7e4a627bf4de76648

[ "BSD-3-Clause" ]

3

2020-08-06T09:40:51.000Z

2021-04-21T08:50:20.000Z

time_test.py

Shb742/rnnoise_python

e370e85984d5909111c9e6e7e4a627bf4de76648

[ "BSD-3-Clause" ]

5

2019-09-19T05:54:33.000Z

2021-04-21T08:50:29.000Z

#Author Shoaib Omar import time import rnnoise import numpy as np def time_rnnoise(rounds=1000): a = rnnoise.RNNoise() timer = 0.0 st = time.time() for i in range(rounds): inp = np.random.bytes(960) timer = (time.time() - st) print(timer) st = time.time() for i in range(rounds): inp = np.random.bytes(960) va,out = a.process_frame(inp) time_taken_per_frame = ((time.time()-st)-timer) /rounds print("time taken for one frame - " + str(time_taken_per_frame )) print("time in a frame - " +str(480.0/48000.0)) print(str((480.0/48000.0)/time_taken_per_frame )+"X faster than real") a.destroy() time_rnnoise()

28.363636

71

0.692308

0

88

0.141026

29461dc478380b16ce5a78cc8afb8aa1b8e6189a

1,092

py

Python

tests/test_shell.py

jakubtyniecki/pact

c23547a2aed1d612180528e33ec1ce021f9badb6

[ "MIT" ]

2

2017-01-12T10:24:31.000Z

2020-06-11T16:05:05.000Z

tests/test_shell.py

jakubtyniecki/pact

c23547a2aed1d612180528e33ec1ce021f9badb6

[ "MIT" ]

null

tests/test_shell.py

jakubtyniecki/pact

c23547a2aed1d612180528e33ec1ce021f9badb6

[ "MIT" ]

null

""" shell sort tests module """ import unittest import random from sort import shell from tests import helper class ShellSortTests(unittest.TestCase): """ shell sort unit tests class """ max = 100 arr = [] def setUp(self): """ setting up for the test """ self.arr = random.sample(range(self.max), self.max) def test_null_input(self): """ should raise when input array is None """ # arrange inp = None # act with self.assertRaises(TypeError) as ex: shell.sort(inp) # assert self.assertEqual("'NoneType' object is not iterable", str(ex.exception)) def test_empty_input(self): """ should return [] when input array is empty """ # arrange inp = [] # act res = shell.sort(inp) # assert self.assertEqual(len(inp), len(res)) def test_sort_a_given_array(self): """ should sort a given array """ # act res = shell.sort(self.arr[:]) # assert self.assertTrue(helper.is_sorted(res))

22.285714

80

0.574176

977

0.894689

0

317

0.290293

2946888881fb3eee8c4a9270d71f7bab3158abad

666

py

Python

k8s_apps/admin/dump_inventory_file.py

AkadioInc/firefly

d6c48ff9999ffedcaa294fcd956eb97b90408583

[ "BSD-2-Clause" ]

null

k8s_apps/admin/dump_inventory_file.py

AkadioInc/firefly

d6c48ff9999ffedcaa294fcd956eb97b90408583

[ "BSD-2-Clause" ]

null

k8s_apps/admin/dump_inventory_file.py

AkadioInc/firefly

d6c48ff9999ffedcaa294fcd956eb97b90408583

[ "BSD-2-Clause" ]

null

import h5pyd from datetime import datetime import tzlocal BUCKET="firefly-hsds" inventory_domain = "/FIREfly/inventory.h5" def formatTime(timestamp): local_timezone = tzlocal.get_localzone() # get pytz timezone local_time = datetime.fromtimestamp(timestamp, local_timezone) return local_time f = h5pyd.File(inventory_domain, "r", bucket=BUCKET) table = f["inventory"] for row in table: filename = row[0].decode('utf-8') if row[1]: start = formatTime(row[1]) else: start = 0 if row[2]: stop = formatTime(row[2]) else: stop = 0 print(f"{filename}\t{start}\t{stop}") print(f"{table.nrows} rows")

22.965517

66

0.666667

0

128

0.192192

2946dbe0237daa4f111129ff8959628dbb456b22

2,640

py

Python

enaml/qt/qt_timer.py

xtuzy/enaml

a1b5c0df71c665b6ef7f61d21260db92d77d9a46

[ "BSD-3-Clause-Clear" ]

1,080

2015-01-04T14:29:34.000Z

2022-03-29T05:44:51.000Z

enaml/qt/qt_timer.py

xtuzy/enaml

a1b5c0df71c665b6ef7f61d21260db92d77d9a46

[ "BSD-3-Clause-Clear" ]

308

2015-01-05T22:44:13.000Z

2022-03-30T21:19:18.000Z

enaml/qt/qt_timer.py

xtuzy/enaml

a1b5c0df71c665b6ef7f61d21260db92d77d9a46

[ "BSD-3-Clause-Clear" ]

123

2015-01-25T16:33:48.000Z

2022-02-25T19:57:10.000Z

#------------------------------------------------------------------------------ # Copyright (c) 2013-2017, Nucleic Development Team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. #------------------------------------------------------------------------------ from atom.api import Typed from enaml.widgets.timer import ProxyTimer from .QtCore import QTimer from .qt_toolkit_object import QtToolkitObject class QtTimer(QtToolkitObject, ProxyTimer): """ A Qt implementation of an Enaml ProxyTimer. """ #: A reference to the widget created by the proxy. widget = Typed(QTimer) #-------------------------------------------------------------------------- # Initialization #-------------------------------------------------------------------------- def create_widget(self): """ Create the underlying timer object. """ self.widget = QTimer() def init_widget(self): """ Initialize the widget. """ super(QtTimer, self).init_widget() d = self.declaration self.set_interval(d.interval) self.set_single_shot(d.single_shot) self.widget.timeout.connect(self.on_timeout) def destroy(self): """ A reimplemented destructor. This stops the timer before invoking the superclass destructor. """ self.widget.stop() super(QtTimer, self).destroy() #-------------------------------------------------------------------------- # Signal Handlers #-------------------------------------------------------------------------- def on_timeout(self): """ Handle the timeout signal for the timer. """ d = self.declaration if d is not None: d.timeout() #-------------------------------------------------------------------------- # ProxyTimer API #-------------------------------------------------------------------------- def set_interval(self, interval): """ Set the interval on the timer. """ self.widget.setInterval(interval) def set_single_shot(self, single_shot): """ Set the single shot flag on the timer. """ self.widget.setSingleShot(single_shot) def start(self): """ Start or restart the timer. """ self.widget.start() def stop(self): """ Stop the timer. """ self.widget.stop() def is_running(self): """ Get whether or not the timer is running. """ return self.widget.isActive()

27.789474

79

0.46553

2,139

0.810227

0

1,449

0.548864

29487962f697ad1bbd8acf9245d0ea5da17bae4f

12,488

py

Python

mindhome_alpha/erpnext/erpnext_integrations/doctype/mpesa_settings/test_mpesa_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:55:29.000Z

mindhome_alpha/erpnext/erpnext_integrations/doctype/mpesa_settings/test_mpesa_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

null

mindhome_alpha/erpnext/erpnext_integrations/doctype/mpesa_settings/test_mpesa_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:39:01.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2020, Frappe Technologies Pvt. Ltd. and Contributors # See license.txt from __future__ import unicode_literals from json import dumps import frappe import unittest from erpnext.erpnext_integrations.doctype.mpesa_settings.mpesa_settings import process_balance_info, verify_transaction from erpnext.accounts.doctype.pos_invoice.test_pos_invoice import create_pos_invoice class TestMpesaSettings(unittest.TestCase): def tearDown(self): frappe.db.sql('delete from `tabMpesa Settings`') frappe.db.sql('delete from `tabIntegration Request` where integration_request_service = "Mpesa"') def test_creation_of_payment_gateway(self): create_mpesa_settings(payment_gateway_name="_Test") mode_of_payment = frappe.get_doc("Mode of Payment", "Mpesa-_Test") self.assertTrue(frappe.db.exists("Payment Gateway Account", {'payment_gateway': "Mpesa-_Test"})) self.assertTrue(mode_of_payment.name) self.assertEquals(mode_of_payment.type, "Phone") def test_processing_of_account_balance(self): mpesa_doc = create_mpesa_settings(payment_gateway_name="_Account Balance") mpesa_doc.get_account_balance_info() callback_response = get_account_balance_callback_payload() process_balance_info(**callback_response) integration_request = frappe.get_doc("Integration Request", "AG_20200927_00007cdb1f9fb6494315") # test integration request creation and successful update of the status on receiving callback response self.assertTrue(integration_request) self.assertEquals(integration_request.status, "Completed") # test formatting of account balance received as string to json with appropriate currency symbol mpesa_doc.reload() self.assertEquals(mpesa_doc.account_balance, dumps({ "Working Account": { "current_balance": "Sh 481,000.00", "available_balance": "Sh 481,000.00", "reserved_balance": "Sh 0.00", "uncleared_balance": "Sh 0.00" } })) integration_request.delete() def test_processing_of_callback_payload(self): create_mpesa_settings(payment_gateway_name="Payment") mpesa_account = frappe.db.get_value("Payment Gateway Account", {"payment_gateway": 'Mpesa-Payment'}, "payment_account") frappe.db.set_value("Account", mpesa_account, "account_currency", "KES") frappe.db.set_value("Customer", "_Test Customer", "default_currency", "KES") pos_invoice = create_pos_invoice(do_not_submit=1) pos_invoice.append("payments", {'mode_of_payment': 'Mpesa-Payment', 'account': mpesa_account, 'amount': 500}) pos_invoice.contact_mobile = "093456543894" pos_invoice.currency = "KES" pos_invoice.save() pr = pos_invoice.create_payment_request() # test payment request creation self.assertEquals(pr.payment_gateway, "Mpesa-Payment") # submitting payment request creates integration requests with random id integration_req_ids = frappe.get_all("Integration Request", filters={ 'reference_doctype': pr.doctype, 'reference_docname': pr.name, }, pluck="name") callback_response = get_payment_callback_payload(Amount=500, CheckoutRequestID=integration_req_ids[0]) verify_transaction(**callback_response) # test creation of integration request integration_request = frappe.get_doc("Integration Request", integration_req_ids[0]) # test integration request creation and successful update of the status on receiving callback response self.assertTrue(integration_request) self.assertEquals(integration_request.status, "Completed") pos_invoice.reload() integration_request.reload() self.assertEquals(pos_invoice.mpesa_receipt_number, "LGR7OWQX0R") self.assertEquals(integration_request.status, "Completed") frappe.db.set_value("Customer", "_Test Customer", "default_currency", "") integration_request.delete() pr.reload() pr.cancel() pr.delete() pos_invoice.delete() def test_processing_of_multiple_callback_payload(self): create_mpesa_settings(payment_gateway_name="Payment") mpesa_account = frappe.db.get_value("Payment Gateway Account", {"payment_gateway": 'Mpesa-Payment'}, "payment_account") frappe.db.set_value("Account", mpesa_account, "account_currency", "KES") frappe.db.set_value("Mpesa Settings", "Payment", "transaction_limit", "500") frappe.db.set_value("Customer", "_Test Customer", "default_currency", "KES") pos_invoice = create_pos_invoice(do_not_submit=1) pos_invoice.append("payments", {'mode_of_payment': 'Mpesa-Payment', 'account': mpesa_account, 'amount': 1000}) pos_invoice.contact_mobile = "093456543894" pos_invoice.currency = "KES" pos_invoice.save() pr = pos_invoice.create_payment_request() # test payment request creation self.assertEquals(pr.payment_gateway, "Mpesa-Payment") # submitting payment request creates integration requests with random id integration_req_ids = frappe.get_all("Integration Request", filters={ 'reference_doctype': pr.doctype, 'reference_docname': pr.name, }, pluck="name") # create random receipt nos and send it as response to callback handler mpesa_receipt_numbers = [frappe.utils.random_string(5) for d in integration_req_ids] integration_requests = [] for i in range(len(integration_req_ids)): callback_response = get_payment_callback_payload( Amount=500, CheckoutRequestID=integration_req_ids[i], MpesaReceiptNumber=mpesa_receipt_numbers[i] ) # handle response manually verify_transaction(**callback_response) # test completion of integration request integration_request = frappe.get_doc("Integration Request", integration_req_ids[i]) self.assertEquals(integration_request.status, "Completed") integration_requests.append(integration_request) # check receipt number once all the integration requests are completed pos_invoice.reload() self.assertEquals(pos_invoice.mpesa_receipt_number, ', '.join(mpesa_receipt_numbers)) frappe.db.set_value("Customer", "_Test Customer", "default_currency", "") [d.delete() for d in integration_requests] pr.reload() pr.cancel() pr.delete() pos_invoice.delete() def test_processing_of_only_one_succes_callback_payload(self): create_mpesa_settings(payment_gateway_name="Payment") mpesa_account = frappe.db.get_value("Payment Gateway Account", {"payment_gateway": 'Mpesa-Payment'}, "payment_account") frappe.db.set_value("Account", mpesa_account, "account_currency", "KES") frappe.db.set_value("Mpesa Settings", "Payment", "transaction_limit", "500") frappe.db.set_value("Customer", "_Test Customer", "default_currency", "KES") pos_invoice = create_pos_invoice(do_not_submit=1) pos_invoice.append("payments", {'mode_of_payment': 'Mpesa-Payment', 'account': mpesa_account, 'amount': 1000}) pos_invoice.contact_mobile = "093456543894" pos_invoice.currency = "KES" pos_invoice.save() pr = pos_invoice.create_payment_request() # test payment request creation self.assertEquals(pr.payment_gateway, "Mpesa-Payment") # submitting payment request creates integration requests with random id integration_req_ids = frappe.get_all("Integration Request", filters={ 'reference_doctype': pr.doctype, 'reference_docname': pr.name, }, pluck="name") # create random receipt nos and send it as response to callback handler mpesa_receipt_numbers = [frappe.utils.random_string(5) for d in integration_req_ids] callback_response = get_payment_callback_payload( Amount=500, CheckoutRequestID=integration_req_ids[0], MpesaReceiptNumber=mpesa_receipt_numbers[0] ) # handle response manually verify_transaction(**callback_response) # test completion of integration request integration_request = frappe.get_doc("Integration Request", integration_req_ids[0]) self.assertEquals(integration_request.status, "Completed") # now one request is completed # second integration request fails # now retrying payment request should make only one integration request again pr = pos_invoice.create_payment_request() new_integration_req_ids = frappe.get_all("Integration Request", filters={ 'reference_doctype': pr.doctype, 'reference_docname': pr.name, 'name': ['not in', integration_req_ids] }, pluck="name") self.assertEquals(len(new_integration_req_ids), 1) frappe.db.set_value("Customer", "_Test Customer", "default_currency", "") frappe.db.sql("delete from `tabIntegration Request` where integration_request_service = 'Mpesa'") pr.reload() pr.cancel() pr.delete() pos_invoice.delete() def create_mpesa_settings(payment_gateway_name="Express"): if frappe.db.exists("Mpesa Settings", payment_gateway_name): return frappe.get_doc("Mpesa Settings", payment_gateway_name) doc = frappe.get_doc(dict( #nosec doctype="Mpesa Settings", payment_gateway_name=payment_gateway_name, consumer_key="5sMu9LVI1oS3oBGPJfh3JyvLHwZOdTKn", consumer_secret="VI1oS3oBGPJfh3JyvLHw", online_passkey="LVI1oS3oBGPJfh3JyvLHwZOd", till_number="174379" )) doc.insert(ignore_permissions=True) return doc def get_test_account_balance_response(): """Response received after calling the account balance API.""" return { "ResultType":0, "ResultCode":0, "ResultDesc":"The service request has been accepted successfully.", "OriginatorConversationID":"10816-694520-2", "ConversationID":"AG_20200927_00007cdb1f9fb6494315", "TransactionID":"LGR0000000", "ResultParameters":{ "ResultParameter":[ { "Key":"ReceiptNo", "Value":"LGR919G2AV" }, { "Key":"Conversation ID", "Value":"AG_20170727_00004492b1b6d0078fbe" }, { "Key":"FinalisedTime", "Value":20170727101415 }, { "Key":"Amount", "Value":10 }, { "Key":"TransactionStatus", "Value":"Completed" }, { "Key":"ReasonType", "Value":"Salary Payment via API" }, { "Key":"TransactionReason" }, { "Key":"DebitPartyCharges", "Value":"Fee For B2C Payment|KES|33.00" }, { "Key":"DebitAccountType", "Value":"Utility Account" }, { "Key":"InitiatedTime", "Value":20170727101415 }, { "Key":"Originator Conversation ID", "Value":"19455-773836-1" }, { "Key":"CreditPartyName", "Value":"254708374149 - John Doe" }, { "Key":"DebitPartyName", "Value":"600134 - Safaricom157" } ] }, "ReferenceData":{ "ReferenceItem":{ "Key":"Occasion", "Value":"aaaa" } } } def get_payment_request_response_payload(Amount=500): """Response received after successfully calling the stk push process request API.""" CheckoutRequestID = frappe.utils.random_string(10) return { "MerchantRequestID": "8071-27184008-1", "CheckoutRequestID": CheckoutRequestID, "ResultCode": 0, "ResultDesc": "The service request is processed successfully.", "CallbackMetadata": { "Item": [ { "Name": "Amount", "Value": Amount }, { "Name": "MpesaReceiptNumber", "Value": "LGR7OWQX0R" }, { "Name": "TransactionDate", "Value": 20201006113336 }, { "Name": "PhoneNumber", "Value": 254723575670 } ] } } def get_payment_callback_payload(Amount=500, CheckoutRequestID="ws_CO_061020201133231972", MpesaReceiptNumber="LGR7OWQX0R"): """Response received from the server as callback after calling the stkpush process request API.""" return { "Body":{ "stkCallback":{ "MerchantRequestID":"19465-780693-1", "CheckoutRequestID":CheckoutRequestID, "ResultCode":0, "ResultDesc":"The service request is processed successfully.", "CallbackMetadata":{ "Item":[ { "Name":"Amount", "Value":Amount }, { "Name":"MpesaReceiptNumber", "Value":MpesaReceiptNumber }, { "Name":"Balance" }, { "Name":"TransactionDate", "Value":20170727154800 }, { "Name":"PhoneNumber", "Value":254721566839 } ] } } } } def get_account_balance_callback_payload(): """Response received from the server as callback after calling the account balance API.""" return { "Result":{ "ResultType": 0, "ResultCode": 0, "ResultDesc": "The service request is processed successfully.", "OriginatorConversationID": "16470-170099139-1", "ConversationID": "AG_20200927_00007cdb1f9fb6494315", "TransactionID": "OIR0000000", "ResultParameters": { "ResultParameter": [ { "Key": "AccountBalance", "Value": "Working Account|KES|481000.00|481000.00|0.00|0.00" }, { "Key": "BOCompletedTime", "Value": 20200927234123 } ] }, "ReferenceData": { "ReferenceItem": { "Key": "QueueTimeoutURL", "Value": "https://internalsandbox.safaricom.co.ke/mpesa/abresults/v1/submit" } } } }

35.177465

124

0.738629

7,996

0.640295

0

5,594

0.44795

2948b21202accf70d658d0b73f9aafb72b41be55

114

py

Python

b2accessdeprovisioning/configparser.py

EUDAT-B2ACCESS/b2access-deprovisioning-report

2260347a4e1f522386c188c0dfae2e94bc5b2a40

[ "Apache-2.0" ]

null

b2accessdeprovisioning/configparser.py

EUDAT-B2ACCESS/b2access-deprovisioning-report

2260347a4e1f522386c188c0dfae2e94bc5b2a40

[ "Apache-2.0" ]

null

b2accessdeprovisioning/configparser.py

EUDAT-B2ACCESS/b2access-deprovisioning-report

2260347a4e1f522386c188c0dfae2e94bc5b2a40

[ "Apache-2.0" ]

2

2017-10-05T07:26:39.000Z

2017-10-05T07:27:54.000Z

from __future__ import absolute_import import yaml with open("config.yml", "r") as f: config = yaml.load(f)

16.285714

38

0.710526

0

15

0.131579

2948ba6edb0a75f155add6e7fa7726939cd6ba56

3,870

py

Python

res_mods/mods/packages/xvm_main/python/vehinfo_tiers.py

peterbartha/ImmunoMod

cbf8cd49893d7082a347c1f72c0e39480869318a

[ "MIT" ]

null

res_mods/mods/packages/xvm_main/python/vehinfo_tiers.py

peterbartha/ImmunoMod

cbf8cd49893d7082a347c1f72c0e39480869318a

[ "MIT" ]

1

2016-04-03T13:31:39.000Z

2016-04-03T16:48:26.000Z

res_mods/mods/packages/xvm_main/python/vehinfo_tiers.py

peterbartha/ImmunoMod

cbf8cd49893d7082a347c1f72c0e39480869318a

[ "MIT" ]

null

""" XVM (c) www.modxvm.com 2013-2017 """ # PUBLIC def getTiers(level, cls, key): return _getTiers(level, cls, key) # PRIVATE from logger import * from gui.shared.utils.requesters import REQ_CRITERIA from helpers import dependency from skeletons.gui.shared import IItemsCache _special = { # Data from http://forum.worldoftanks.ru/index.php?/topic/41221- # Last update: 23.05.2017 # level 2 'germany:G53_PzI': [ 2, 2 ], 'uk:GB76_Mk_VIC': [ 2, 2 ], 'usa:A19_T2_lt': [ 2, 4 ], 'usa:A93_T7_Combat_Car': [ 2, 2 ], # level 3 'germany:G36_PzII_J': [ 3, 4 ], 'japan:J05_Ke_Ni_B': [ 3, 4 ], 'ussr:R34_BT-SV': [ 3, 4 ], 'ussr:R50_SU76I': [ 3, 4 ], 'ussr:R56_T-127': [ 3, 4 ], 'ussr:R67_M3_LL': [ 3, 4 ], 'ussr:R86_LTP': [ 3, 4 ], # level 4 'france:F14_AMX40': [ 4, 6 ], 'germany:G35_B-1bis_captured': [ 4, 4 ], 'japan:J06_Ke_Ho': [ 4, 6 ], 'uk:GB04_Valentine': [ 4, 6 ], 'uk:GB60_Covenanter': [ 4, 6 ], 'ussr:R12_A-20': [ 4, 6 ], 'ussr:R31_Valentine_LL': [ 4, 4 ], 'ussr:R44_T80': [ 4, 6 ], 'ussr:R68_A-32': [ 4, 5 ], # level 5 'germany:G104_Stug_IV': [ 5, 6 ], 'germany:G32_PzV_PzIV': [ 5, 6 ], 'germany:G32_PzV_PzIV_ausf_Alfa': [ 5, 6 ], 'germany:G70_PzIV_Hydro': [ 5, 6 ], 'uk:GB20_Crusader': [ 5, 7 ], 'uk:GB51_Excelsior': [ 5, 6 ], 'uk:GB68_Matilda_Black_Prince': [ 5, 6 ], 'usa:A21_T14': [ 5, 6 ], 'usa:A44_M4A2E4': [ 5, 6 ], 'ussr:R32_Matilda_II_LL': [ 5, 6 ], 'ussr:R33_Churchill_LL': [ 5, 6 ], 'ussr:R38_KV-220': [ 5, 6 ], 'ussr:R38_KV-220_beta': [ 5, 6 ], 'ussr:R78_SU_85I': [ 5, 6 ], # level 6 'germany:G32_PzV_PzIV_CN': [ 6, 7 ], 'germany:G32_PzV_PzIV_ausf_Alfa_CN': [ 6, 7 ], 'uk:GB63_TOG_II': [ 6, 7 ], # level 7 'germany:G48_E-25': [ 7, 8 ], 'germany:G78_Panther_M10': [ 7, 8 ], 'uk:GB71_AT_15A': [ 7, 8 ], 'usa:A86_T23E3': [ 7, 8 ], 'ussr:R98_T44_85': [ 7, 8 ], 'ussr:R99_T44_122': [ 7, 8 ], # level 8 'china:Ch01_Type59': [ 8, 9 ], 'china:Ch03_WZ-111': [ 8, 9 ], 'china:Ch14_T34_3': [ 8, 9 ], 'china:Ch23_112': [ 8, 9 ], 'france:F65_FCM_50t': [ 8, 9 ], 'germany:G65_JagdTiger_SdKfz_185': [ 8, 9 ], 'usa:A45_M6A2E1': [ 8, 9 ], 'usa:A80_T26_E4_SuperPershing': [ 8, 9 ], 'ussr:R54_KV-5': [ 8, 9 ], 'ussr:R61_Object252': [ 8, 9 ], 'ussr:R61_Object252_BF': [ 8, 9 ], } def _getTiers(level, cls, key): if key in _special: return _special[key] # HT: (=T4 max+1) if level == 4 and cls == 'heavyTank': return (4, 5) # default: (<T3 max+1) & (>=T3 max+2) & (>T9 max=11) return (level, level + 1 if level < 3 else 11 if level > 9 else level + 2) def _test_specials(): for veh_name in _special.keys(): itemsCache = dependency.instance(IItemsCache) if not itemsCache.items.getVehicles(REQ_CRITERIA.VEHICLE.SPECIFIC_BY_NAME(veh_name)): warn('vehinfo_tiers: vehicle %s declared in _special does not exist!' % veh_name)

36.857143

93

0.445478

0

1,424

0.367959

294933d7ee4435c7faf58b9337983fadc1b0d19b

6,099

py

Python

pypy/module/cpyext/test/test_pystrtod.py

m4sterchain/mesapy

ed546d59a21b36feb93e2309d5c6b75aa0ad95c9

[ "Apache-2.0", "OpenSSL" ]

381

2018-08-18T03:37:22.000Z

2022-02-06T23:57:36.000Z

pypy/module/cpyext/test/test_pystrtod.py

m4sterchain/mesapy

ed546d59a21b36feb93e2309d5c6b75aa0ad95c9

[ "Apache-2.0", "OpenSSL" ]

16

2018-09-22T18:12:47.000Z

2022-02-22T20:03:59.000Z

pypy/module/cpyext/test/test_pystrtod.py

m4sterchain/mesapy

ed546d59a21b36feb93e2309d5c6b75aa0ad95c9

[ "Apache-2.0", "OpenSSL" ]

30

2018-08-20T03:16:34.000Z

2022-01-12T17:39:22.000Z

import math from pypy.module.cpyext import pystrtod from pypy.module.cpyext.test.test_api import BaseApiTest, raises_w from rpython.rtyper.lltypesystem import rffi from rpython.rtyper.lltypesystem import lltype from pypy.module.cpyext.pystrtod import PyOS_string_to_double class TestPyOS_string_to_double(BaseApiTest): def test_simple_float(self, space): s = rffi.str2charp('0.4') null = lltype.nullptr(rffi.CCHARPP.TO) r = PyOS_string_to_double(space, s, null, None) assert r == 0.4 rffi.free_charp(s) def test_empty_string(self, space): s = rffi.str2charp('') null = lltype.nullptr(rffi.CCHARPP.TO) with raises_w(space, ValueError): PyOS_string_to_double(space, s, null, None) rffi.free_charp(s) def test_bad_string(self, space): s = rffi.str2charp(' 0.4') null = lltype.nullptr(rffi.CCHARPP.TO) with raises_w(space, ValueError): PyOS_string_to_double(space, s, null, None) rffi.free_charp(s) def test_overflow_pos(self, space): s = rffi.str2charp('1e500') null = lltype.nullptr(rffi.CCHARPP.TO) r = PyOS_string_to_double(space, s, null, None) assert math.isinf(r) assert r > 0 rffi.free_charp(s) def test_overflow_neg(self, space): s = rffi.str2charp('-1e500') null = lltype.nullptr(rffi.CCHARPP.TO) r = PyOS_string_to_double(space, s, null, None) assert math.isinf(r) assert r < 0 rffi.free_charp(s) def test_overflow_exc(self, space): s = rffi.str2charp('1e500') null = lltype.nullptr(rffi.CCHARPP.TO) with raises_w(space, ValueError): PyOS_string_to_double(space, s, null, space.w_ValueError) rffi.free_charp(s) def test_endptr_number(self, space): s = rffi.str2charp('0.4') endp = lltype.malloc(rffi.CCHARPP.TO, 1, flavor='raw') r = PyOS_string_to_double(space, s, endp, None) assert r == 0.4 endp_addr = rffi.cast(rffi.LONG, endp[0]) s_addr = rffi.cast(rffi.LONG, s) assert endp_addr == s_addr + 3 rffi.free_charp(s) lltype.free(endp, flavor='raw') def test_endptr_tail(self, space): s = rffi.str2charp('0.4 foo') endp = lltype.malloc(rffi.CCHARPP.TO, 1, flavor='raw') r = PyOS_string_to_double(space, s, endp, None) assert r == 0.4 endp_addr = rffi.cast(rffi.LONG, endp[0]) s_addr = rffi.cast(rffi.LONG, s) assert endp_addr == s_addr + 3 rffi.free_charp(s) lltype.free(endp, flavor='raw') def test_endptr_no_conversion(self, space): s = rffi.str2charp('foo') endp = lltype.malloc(rffi.CCHARPP.TO, 1, flavor='raw') with raises_w(space, ValueError): PyOS_string_to_double(space, s, endp, None) endp_addr = rffi.cast(rffi.LONG, endp[0]) s_addr = rffi.cast(rffi.LONG, s) assert endp_addr == s_addr rffi.free_charp(s) lltype.free(endp, flavor='raw') class TestPyOS_double_to_string(BaseApiTest): def test_format_code(self, api): ptype = lltype.malloc(rffi.INTP.TO, 1, flavor='raw') r = api.PyOS_double_to_string(150.0, 'e', 1, 0, ptype) assert '1.5e+02' == rffi.charp2str(r) type_value = rffi.cast(lltype.Signed, ptype[0]) assert pystrtod.Py_DTST_FINITE == type_value rffi.free_charp(r) lltype.free(ptype, flavor='raw') def test_precision(self, api): ptype = lltype.malloc(rffi.INTP.TO, 1, flavor='raw') r = api.PyOS_double_to_string(3.14159269397, 'g', 5, 0, ptype) assert '3.1416' == rffi.charp2str(r) type_value = rffi.cast(lltype.Signed, ptype[0]) assert pystrtod.Py_DTST_FINITE == type_value rffi.free_charp(r) lltype.free(ptype, flavor='raw') def test_flags_sign(self, api): ptype = lltype.malloc(rffi.INTP.TO, 1, flavor='raw') r = api.PyOS_double_to_string(-3.14, 'g', 3, 1, ptype) assert '-3.14' == rffi.charp2str(r) type_value = rffi.cast(lltype.Signed, ptype[0]) assert pystrtod.Py_DTST_FINITE == type_value rffi.free_charp(r) lltype.free(ptype, flavor='raw') def test_flags_add_dot_0(self, api): ptype = lltype.malloc(rffi.INTP.TO, 1, flavor='raw') r = api.PyOS_double_to_string(3, 'g', 5, 2, ptype) assert '3.0' == rffi.charp2str(r) type_value = rffi.cast(lltype.Signed, ptype[0]) assert pystrtod.Py_DTST_FINITE == type_value rffi.free_charp(r) lltype.free(ptype, flavor='raw') def test_flags_alt(self, api): ptype = lltype.malloc(rffi.INTP.TO, 1, flavor='raw') r = api.PyOS_double_to_string(314., 'g', 3, 4, ptype) assert '314.' == rffi.charp2str(r) type_value = rffi.cast(lltype.Signed, ptype[0]) assert pystrtod.Py_DTST_FINITE == type_value rffi.free_charp(r) lltype.free(ptype, flavor='raw') def test_ptype_nan(self, api): ptype = lltype.malloc(rffi.INTP.TO, 1, flavor='raw') r = api.PyOS_double_to_string(float('nan'), 'g', 3, 4, ptype) assert 'nan' == rffi.charp2str(r) type_value = rffi.cast(lltype.Signed, ptype[0]) assert pystrtod.Py_DTST_NAN == type_value rffi.free_charp(r) lltype.free(ptype, flavor='raw') def test_ptype_infinity(self, api): ptype = lltype.malloc(rffi.INTP.TO, 1, flavor='raw') r = api.PyOS_double_to_string(1e200 * 1e200, 'g', 0, 0, ptype) assert 'inf' == rffi.charp2str(r) type_value = rffi.cast(lltype.Signed, ptype[0]) assert pystrtod.Py_DTST_INFINITE == type_value rffi.free_charp(r) lltype.free(ptype, flavor='raw') def test_ptype_null(self, api): ptype = lltype.nullptr(rffi.INTP.TO) r = api.PyOS_double_to_string(3.14, 'g', 3, 0, ptype) assert '3.14' == rffi.charp2str(r) assert ptype == lltype.nullptr(rffi.INTP.TO) rffi.free_charp(r)

37.881988

70

0.624529

5,819

0.954091

0

234

0.038367

294a474ec8bf0bc2d0dc645a827ce6425f19ce7f

3,529

py

Python

mathics/core/systemsymbols.py

Mathics3/mathics-core

54dc3c00a42cd893c6430054e125291b6eb55ead

[ "Apache-2.0" ]

90

2021-09-11T14:14:00.000Z

2022-03-29T02:08:29.000Z

mathics/core/systemsymbols.py

Mathics3/mathics-core

54dc3c00a42cd893c6430054e125291b6eb55ead

[ "Apache-2.0" ]

187

2021-09-13T01:00:41.000Z

2022-03-31T11:52:52.000Z

mathics/core/systemsymbols.py

Mathics3/mathics-core

54dc3c00a42cd893c6430054e125291b6eb55ead

[ "Apache-2.0" ]

10

2021-10-05T15:44:26.000Z

2022-03-21T12:34:33.000Z

# -*- coding: utf-8 -*- from mathics.core.symbols import Symbol # Some other common Symbols. This list is sorted in alphabetic order. SymbolAssumptions = Symbol("$Assumptions") SymbolAborted = Symbol("$Aborted") SymbolAll = Symbol("All") SymbolAlternatives = Symbol("Alternatives") SymbolAnd = Symbol("And") SymbolAppend = Symbol("Append") SymbolApply = Symbol("Apply") SymbolAssociation = Symbol("Association") SymbolAutomatic = Symbol("Automatic") SymbolBlank = Symbol("Blank") SymbolBlend = Symbol("Blend") SymbolByteArray = Symbol("ByteArray") SymbolCatalan = Symbol("Catalan") SymbolColorData = Symbol("ColorData") SymbolComplex = Symbol("Complex") SymbolComplexInfinity = Symbol("ComplexInfinity") SymbolCondition = Symbol("Condition") SymbolConditionalExpression = Symbol("ConditionalExpression") Symbol_Context = Symbol("$Context") Symbol_ContextPath = Symbol("$ContextPath") SymbolCos = Symbol("Cos") SymbolD = Symbol("D") SymbolDerivative = Symbol("Derivative") SymbolDirectedInfinity = Symbol("DirectedInfinity") SymbolDispatch = Symbol("Dispatch") SymbolE = Symbol("E") SymbolEdgeForm = Symbol("EdgeForm") SymbolEqual = Symbol("Equal") SymbolExpandAll = Symbol("ExpandAll") SymbolEulerGamma = Symbol("EulerGamma") SymbolFailed = Symbol("$Failed") SymbolFunction = Symbol("Function") SymbolGamma = Symbol("Gamma") SymbolGet = Symbol("Get") SymbolGoldenRatio = Symbol("GoldenRatio") SymbolGraphics = Symbol("Graphics") SymbolGreater = Symbol("Greater") SymbolGreaterEqual = Symbol("GreaterEqual") SymbolGrid = Symbol("Grid") SymbolHoldForm = Symbol("HoldForm") SymbolIndeterminate = Symbol("Indeterminate") SymbolImplies = Symbol("Implies") SymbolInfinity = Symbol("Infinity") SymbolInfix = Symbol("Infix") SymbolInteger = Symbol("Integer") SymbolIntegrate = Symbol("Integrate") SymbolLeft = Symbol("Left") SymbolLess = Symbol("Less") SymbolLessEqual = Symbol("LessEqual") SymbolLog = Symbol("Log") SymbolMachinePrecision = Symbol("MachinePrecision") SymbolMakeBoxes = Symbol("MakeBoxes") SymbolMessageName = Symbol("MessageName") SymbolMinus = Symbol("Minus") SymbolMap = Symbol("Map") SymbolMatrixPower = Symbol("MatrixPower") SymbolMaxPrecision = Symbol("$MaxPrecision") SymbolMemberQ = Symbol("MemberQ") SymbolMinus = Symbol("Minus") SymbolN = Symbol("N") SymbolNeeds = Symbol("Needs") SymbolNIntegrate = Symbol("NIntegrate") SymbolNone = Symbol("None") SymbolNot = Symbol("Not") SymbolNull = Symbol("Null") SymbolNumberQ = Symbol("NumberQ") SymbolNumericQ = Symbol("NumericQ") SymbolOptionValue = Symbol("OptionValue") SymbolOr = Symbol("Or") SymbolOverflow = Symbol("Overflow") SymbolPackages = Symbol("$Packages") SymbolPattern = Symbol("Pattern") SymbolPi = Symbol("Pi") SymbolPiecewise = Symbol("Piecewise") SymbolPoint = Symbol("Point") SymbolPossibleZeroQ = Symbol("PossibleZeroQ") SymbolQuiet = Symbol("Quiet") SymbolRational = Symbol("Rational") SymbolReal = Symbol("Real") SymbolRow = Symbol("Row") SymbolRowBox = Symbol("RowBox") SymbolRGBColor = Symbol("RGBColor") SymbolSuperscriptBox = Symbol("SuperscriptBox") SymbolRule = Symbol("Rule") SymbolRuleDelayed = Symbol("RuleDelayed") SymbolSequence = Symbol("Sequence") SymbolSeries = Symbol("Series") SymbolSeriesData = Symbol("SeriesData") SymbolSet = Symbol("Set") SymbolSimplify = Symbol("Simplify") SymbolSin = Symbol("Sin") SymbolSlot = Symbol("Slot") SymbolStringQ = Symbol("StringQ") SymbolStyle = Symbol("Style") SymbolTable = Symbol("Table") SymbolToString = Symbol("ToString") SymbolUndefined = Symbol("Undefined") SymbolXor = Symbol("Xor")

33.932692

69

0.765373

0

1,007

0.28535

294bff20d8c499704a706ccaf6f51e0e5fd8ce4d

5,821

py

Python

exercises/ali/cartpole-MCTS/cartpole.py

alik604/ra

6058a9adb47db93bb86bcb2c224930c5731d663d

[ "Unlicense" ]

null

exercises/ali/cartpole-MCTS/cartpole.py

alik604/ra

6058a9adb47db93bb86bcb2c224930c5731d663d

[ "Unlicense" ]

5

2021-03-26T01:30:13.000Z

2021-04-22T22:19:03.000Z

exercises/ali/cartpole-MCTS/cartpole.py

alik604/ra

6058a9adb47db93bb86bcb2c224930c5731d663d

[ "Unlicense" ]

1

2021-05-05T00:57:43.000Z

# from https://github.com/kvwoerden/mcts-cartpole # ---------------------------------------------------------------------------- # # Imports # # ---------------------------------------------------------------------------- # import os import time import random import argparse <<<<<<< HEAD ======= from types import SimpleNamespace >>>>>>> MCTS import gym from gym import logger from gym.wrappers.monitoring.video_recorder import VideoRecorder from Simple_mcts import MCTSAgent <<<<<<< HEAD # ---------------------------------------------------------------------------- # # Constants # # ---------------------------------------------------------------------------- # SEED = 28 EPISODES = 1 ENVIRONMENT = 'CartPole-v0' LOGGER_LEVEL = logger.WARN ITERATION_BUDGET = 80 LOOKAHEAD_TARGET = 100 MAX_EPISODE_STEPS = 1500 VIDEO_BASEPATH = '.\\video' # './video' START_CP = 20 ======= from Agent import dqn_agent # ---------------------------------------------------------------------------- # # Constants # # ---------------------------------------------------------------------------- # LOGGER_LEVEL = logger.WARN args = dict() args['env_name'] = 'CartPole-v0' args['episodes'] = 10 args['seed'] = 28 args['iteration_budget'] = 8000 # The number of iterations for each search step. Increasing this should lead to better performance.') args['lookahead_target'] = 10000 # The target number of steps the agent aims to look forward.' args['max_episode_steps'] = 1500 # The maximum number of steps to play. args['video_basepath'] = '.\\video' # './video' args['start_cp'] = 20 # The start value of C_p, the value that the agent changes to try to achieve the lookahead target. Decreasing this makes the search tree deeper, increasing this makes the search tree wider. args = SimpleNamespace(**args) >>>>>>> MCTS # ---------------------------------------------------------------------------- # # Main loop # # ---------------------------------------------------------------------------- # if __name__ == '__main__': <<<<<<< HEAD random.seed(SEED) parser = argparse.ArgumentParser( description='Run a Monte Carlo Tree Search agent on the Cartpole environment', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--env_id', nargs='?', default=ENVIRONMENT, help='The environment to run (only CartPole-v0 is supperted)') parser.add_argument('--episodes', nargs='?', default=EPISODES, type=int, help='The number of episodes to run.') parser.add_argument('--iteration_budget', nargs='?', default=ITERATION_BUDGET, type=int, help='The number of iterations for each search step. Increasing this should lead to better performance.') parser.add_argument('--lookahead_target', nargs='?', default=LOOKAHEAD_TARGET, type=int, help='The target number of steps the agent aims to look forward.') parser.add_argument('--max_episode_steps', nargs='?', default=MAX_EPISODE_STEPS, type=int, help='The maximum number of steps to play.') parser.add_argument('--video_basepath', nargs='?', default=VIDEO_BASEPATH, help='The basepath where the videos will be stored.') parser.add_argument('--start_cp', nargs='?', default=START_CP, type=int, help='The start value of C_p, the value that the agent changes to try to achieve the lookahead target. Decreasing this makes the search tree deeper, increasing this makes the search tree wider.') parser.add_argument('--seed', nargs='?', default=SEED, type=int, help='The random seed.') args = parser.parse_args() logger.set_level(LOGGER_LEVEL) env = gym.make(args.env_id) env.seed(args.seed) agent = MCTSAgent(args.iteration_budget, args.env_id) ======= logger.set_level(LOGGER_LEVEL) random.seed(args.seed) env = gym.make(args.env_name) env.seed(args.seed) Q_net = dqn_agent() agent = MCTSAgent(args.iteration_budget, env, Q_net) >>>>>>> MCTS timestr = time.strftime("%Y%m%d-%H%M%S") reward = 0 done = False for i in range(args.episodes): ob = env.reset() env._max_episode_steps = args.max_episode_steps video_path = os.path.join( args.video_basepath, f"output_{timestr}_{i}.mp4") <<<<<<< HEAD rec = VideoRecorder(env, path=video_path) ======= # rec = VideoRecorder(env, path=video_path) >>>>>>> MCTS try: sum_reward = 0 node = None all_nodes = [] C_p = args.start_cp while True: print("################") env.render() <<<<<<< HEAD rec.capture_frame() ======= # rec.capture_frame() >>>>>>> MCTS action, node, C_p = agent.act(env.state, n_actions=env.action_space.n, node=node, C_p=C_p, lookahead_target=args.lookahead_target) ob, reward, done, _ = env.step(action) print("### observed state: ", ob) sum_reward += reward print("### sum_reward: ", sum_reward) if done: <<<<<<< HEAD rec.close() break except KeyboardInterrupt as e: rec.close() ======= # rec.close() break except KeyboardInterrupt as e: # rec.close() >>>>>>> MCTS env.close() raise e env.close()

39.067114

219

0.519842

0

2,522

0.433259

294c561a401bd6bdb0db578e7797d3a5175a9a58

318

py

Python

wildlifecompliance/components/applications/cron.py

preranaandure/wildlifecompliance

bc19575f7bccf7e19adadbbaf5d3eda1d1aee4b5

[ "Apache-2.0" ]

1

2020-12-07T17:12:40.000Z

wildlifecompliance/components/applications/cron.py

preranaandure/wildlifecompliance

bc19575f7bccf7e19adadbbaf5d3eda1d1aee4b5

[ "Apache-2.0" ]

14

2020-01-08T08:08:26.000Z

2021-03-19T22:59:46.000Z

wildlifecompliance/components/applications/cron.py

preranaandure/wildlifecompliance

bc19575f7bccf7e19adadbbaf5d3eda1d1aee4b5

[ "Apache-2.0" ]

15

2020-01-08T08:02:28.000Z

2021-11-03T06:48:32.000Z

from django_cron import CronJobBase, Schedule class VerifyLicenceSpeciesJob(CronJobBase): """ Verifies LicenceSpecies against TSC server. """ RUN_AT_TIMES = ['00:00'] schedule = Schedule(run_at_times=RUN_AT_TIMES) code = 'applications.verify_licence_species' def do(self): pass

21.2

50

0.704403

269

0.845912

0

103

0.323899

294ddecc4d289926d35a18bd81582fdedcf038ee

2,999

py

Python

optional-plugins/CSVPlugin/CSVContext.py

owlfish/pubtal

fb20a0acf2769b2c06012b65bd462f02da12bd1c

[ "BSD-3-Clause" ]

null

optional-plugins/CSVPlugin/CSVContext.py

owlfish/pubtal

fb20a0acf2769b2c06012b65bd462f02da12bd1c

[ "BSD-3-Clause" ]

null

optional-plugins/CSVPlugin/CSVContext.py

owlfish/pubtal

fb20a0acf2769b2c06012b65bd462f02da12bd1c

[ "BSD-3-Clause" ]

null

import ASV from simpletal import simpleTAL, simpleTALES try: import logging except: import InfoLogging as logging import codecs class ColumnSorter: def __init__ (self, columnList): self.columnList = columnList self.log = logging.getLogger ('ColumnSorter') def setup (self, fieldNames): mapList = [] for columnName, translationMap in self.columnList: try: colNum = fieldNames.index (columnName) mapList.append ((colNum, translationMap)) except ValueError, e: self.log.error ("No such column name as %s" % name) raise e self.mapList = mapList def sort (self, row1, row2): result = 0 for colNum, map in self.mapList: result = self.doSort (row1, row2, colNum, map) if (result != 0): return result return result def doSort (self, row1, row2, colNum, map): if (map is None): col1 = row1[colNum] col2 = row2[colNum] else: try: col1 = map [row1[colNum]] except KeyError, e: self.log.warn ("No key found for key %s - assuming low value" % row1[colNum]) return -1 try: col2 = map [row2[colNum]] except KeyError, e: self.log.warn ("No key found for key %s - assuming low value" % row1[colNum]) return 1 if (col1 < col2): return -1 if (col1 == col2): return 0 if (col1 > col2): return 1 class CsvContextCreator: def __init__ (self, fileName, fileCodec): self.log = logging.getLogger ("CSVTemplate.CsvContextCreator") self.csvData = ASV.ASV() self.csvData.input_from_file(fileName, ASV.CSV(), has_field_names = 1) self.fieldNames = self.csvData.get_field_names() self.conv = fileCodec def getContextMap (self, sorter=None): orderList = [] for row in self.csvData: orderList.append (row) if (sorter is not None): sorter.setup (self.fieldNames) try: orderList.sort (sorter.sort) except Exception, e: self.log.error ("Exception occured executing sorter: " + str (e)) raise e contextList = [] for row in orderList: rowMap = {} colCount = 0 for col in row: if (col != ""): rowMap[self.fieldNames[colCount]] = self.conv(col)[0] colCount += 1 contextList.append (rowMap) return contextList def getRawData (self): return unicode (self.csvData) class CSVTemplateExpander: def __init__ (self, sourceFile, name="csvList"): self.contextFactory = CsvContextCreator (sourceFile) self.name = name self.template=None def expandTemplate (self, templateName, outputName, additionalContext = None, sorter=None): context = simpleTALES.Context() context.addGlobal (self.name, self.contextFactory.getContextMap (sorter)) if (additionalContext is not None): context.addGlobal (additionalContext[0], additionalContext[1]) if (self.template is None): templateFile = open (templateName, 'r') self.template = simpleTAL.compileHTMLTemplate (templateFile) templateFile.close() outputFile = open (outputName, 'w') self.template.expand (context, outputFile) outputFile.close()

26.307018

92

0.686896

2,857

0.952651

0

219

0.073024

294e1d0fe03b7258df243ff2841d037d1b8158e8

2,484

py

Python

wagtail/admin/forms/comments.py

stephiescastle/wagtail

391f46ef91ca4a7bbf339bf9e9a738df3eb8e179

[ "BSD-3-Clause" ]

null

wagtail/admin/forms/comments.py

stephiescastle/wagtail

391f46ef91ca4a7bbf339bf9e9a738df3eb8e179

[ "BSD-3-Clause" ]

null

wagtail/admin/forms/comments.py

stephiescastle/wagtail

391f46ef91ca4a7bbf339bf9e9a738df3eb8e179

[ "BSD-3-Clause" ]

null

from django.forms import BooleanField, ValidationError from django.utils.timezone import now from django.utils.translation import gettext as _ from .models import WagtailAdminModelForm class CommentReplyForm(WagtailAdminModelForm): class Meta: fields = ("text",) def clean(self): cleaned_data = super().clean() user = self.for_user if not self.instance.pk: self.instance.user = user elif self.instance.user != user: # trying to edit someone else's comment reply if any(field for field in self.changed_data): # includes DELETION_FIELD_NAME, as users cannot delete each other's individual comment replies # if deleting a whole thread, this should be done by deleting the parent Comment instead self.add_error( None, ValidationError(_("You cannot edit another user's comment.")) ) return cleaned_data class CommentForm(WagtailAdminModelForm): """ This is designed to be subclassed and have the user overridden to enable user-based validation within the edit handler system """ resolved = BooleanField(required=False) class Meta: formsets = { "replies": { "form": CommentReplyForm, "inherit_kwargs": ["for_user"], } } def clean(self): cleaned_data = super().clean() user = self.for_user if not self.instance.pk: self.instance.user = user elif self.instance.user != user: # trying to edit someone else's comment if any( field for field in self.changed_data if field not in ["resolved", "position"] ): # users can resolve each other's base comments and change their positions within a field self.add_error( None, ValidationError(_("You cannot edit another user's comment.")) ) return cleaned_data def save(self, *args, **kwargs): if self.cleaned_data.get("resolved", False): if not getattr(self.instance, "resolved_at"): self.instance.resolved_at = now() self.instance.resolved_by = self.for_user else: self.instance.resolved_by = None self.instance.resolved_at = None return super().save(*args, **kwargs)

34.5

129

0.594605

2,292

0.922705

0

667

0.268519

294e291b1d27799d1015e0d511b66da83b03b728

1,039

py

Python

run_db_data.py

MahirMahbub/email-client

71ab85f987f783b703b58780444c072bd683927e

[ "MIT" ]

null

run_db_data.py

MahirMahbub/email-client

71ab85f987f783b703b58780444c072bd683927e

[ "MIT" ]

4

2021-08-01T16:29:48.000Z

2021-08-01T16:58:36.000Z

run_db_data.py

MahirMahbub/email-client

71ab85f987f783b703b58780444c072bd683927e

[ "MIT" ]

null

import os from sqlalchemy.orm import Session from db.database import SessionLocal class DbData: def __init__(self): self.root_directory: str = "db_merge_scripts" self.scripts = [ "loader.sql" ] def sync(self, db: Session): for script in self.scripts: try: directory = os.path.join(self.root_directory, script) print(directory) sql = open(directory, "r").read() db.execute(sql) db.commit() print(greed("Data file processed: " + directory)) except Exception as e: print(red("Error to process data file: " + directory)) print(e) def colored(text, r, g, b): return "\033[38;2;{};{};{}m{} \033[38;2;255;255;255m".format(r, g, b, text) def red(text): return colored(text, 255, 0, 0) def greed(text): return colored(text, 0, 255, 0) def add_master_data(): db = SessionLocal() DbData().sync(db) db.close()

23.088889

79

0.549567

648

0.623677

0

132

0.127045

295015d1c787b58239c947be3f7d6287049d840f

6,274

py

Python

scobra/analysis/compare_elements.py

nihalzp/scobra

de1faa73fb4d186d9567bfa8e174b3fd6f1833ef

[ "MIT" ]

7

2016-03-16T09:03:41.000Z

2019-09-20T05:55:02.000Z

scobra/analysis/compare_elements.py

nihalzp/scobra

de1faa73fb4d186d9567bfa8e174b3fd6f1833ef

[ "MIT" ]

11

2019-10-03T15:04:58.000Z

2020-05-11T17:27:10.000Z

scobra/analysis/compare_elements.py

nihalzp/scobra

de1faa73fb4d186d9567bfa8e174b3fd6f1833ef

[ "MIT" ]

6

2016-03-16T09:04:54.000Z

2021-07-24T15:03:41.000Z

def compareMetaboliteDicts(d1, d2): sorted_d1_keys = sorted(d1.keys()) sorted_d2_keys = sorted(d2.keys()) for i in range(len(sorted_d1_keys)): if not compareMetabolites(sorted_d1_keys[i], sorted_d2_keys[i], naive=True): return False elif not d1[sorted_d1_keys[i]] == d2[sorted_d2_keys[i]]: return False else: return True def compareMetabolites(met1, met2, naive=False): if isinstance(met1, set): return compareReactions(list(met1), list(met2), naive) if isinstance(met1, list): if not isinstance(met2, list): return False elif len(met1) != len(met2): return False else: for i in range(len(met1)): if not compareMetabolites(met1[i], met2[i], naive): return False else: return True else: if not True: #can never be entered pass elif not met1._bound == met2._bound: return False elif not met1._constraint_sense == met2._constraint_sense: return False #elif not met1.annotation == met2.annotation: # return False elif not met1.charge == met2.charge: return False elif not met1.compartment == met2.compartment: return False elif not met1.name == met2.name: return False elif not met1.compartment == met2.compartment: return False #elif not met1.notes == met2.notes: # return False elif not naive: if not compareReactions(met1._reaction, met2._reaction, naive=True): return False elif not compareModels(met1._model, met2._model, naive=True): return False else: return True else: return True def compareReactions(r1, r2, naive=False): if isinstance(r1, set): return compareReactions(list(r1), list(r2), naive) if isinstance(r1, list): if not isinstance(r2, list): return False elif len(r1) != len(r2): return False else: for i in range(len(r1)): if not compareReactions(r1[i], r2[i],naive): return False else: return True else: if not True: #can never be entered pass #elif not r1._compartments == r2._compartments: # return False #elif not r1._forward_variable == r2._forward_variable: # return False elif not r1._gene_reaction_rule == r2._gene_reaction_rule: return False elif not r1._id == r2._id: return False elif not r1._lower_bound == r2._lower_bound: return False #elif not r1._model == r2._model: # return False #elif not r1._reverse_variable == r2._reverse_variable: # return False elif not r1._upper_bound == r2._upper_bound: return False #elif not r1.annotation == r2.annotation: # return False elif not r1.name== r2.name: return False #elif not r1.notes == r2.notes: # return False elif not r1.subsystem == r2.subsystem: return False elif not r1.variable_kind == r2.variable_kind: return False elif not naive: if not compareMetaboliteDicts(r1._metabolites, r2._metabolites): return False elif not compareGenes(r1._genes,r2._genes, naive=True): return False else: return True else: return True def compareGenes(g1, g2, naive=False): if isinstance(g1, set): return compareGenes(list(g1), list(g2), naive) if isinstance(g1, list): if not isinstance(g2, list): return False elif len(g1) != len(g2): return False else: for i in range(len(g1)): if not compareGenes(g1[i], g2[i], naive): return False else: return True else: if not True: #can never be entered pass elif not g1._functional == g2._functional: return False elif not g1._id == g2._id: return False #elif not g1._model == g2._model: # return False elif not g1.annotation == g2.annotation: return False elif not g1.name == g2.name: return False #elif not g1.notes == g2.notes: # return False elif not naive: if not compareReactions(g1._reaction,g2._reaction, naive=True): return False else: return True else: return True def compareModels(m1, m2, naive=False): if not True: #can never be entered pass #elif not m1._compartments == m2._compartments: # return False #elif not m1._contexts == m2._contexts: # return False #elif not m1._solver == m2._solver: # return False elif not m1._id == m2._id: return False #elif not m1._trimmed == m2.trimmed: # return False #elif not m1._trimmed_genes == m2._trimmed_genes: # return False #elif not m1._trimmed_reactions == m2._trimmed_reactions: # return False #elif not m1.annotation == m2.annotation: # return False elif not m1.bounds == m2.bounds: return False elif not m1.name == m2.name: return False #elif not m1.notes == m2.notes: # return False #elif not m1.quadratic_component == m2.quadratic_component: # return False elif not naive: if not compareGenes(m1.genes, m2.genes): return False elif not compareMetabolites(m1.metabolites, m2.metabolites): return False elif not compareReactions(m1.reactions,m2.reactions): return False else: return True else: return True

33.021053

84

0.54176

0

1,229

0.195888

29509faf87f0d6a17ff1205ace918609c71b08fe

1,750

py

Python

five/five_copy.py

ngd-b/python-demo

0341c1620bcde1c1d886cb9e75dc6db3722273c8

[ "MIT" ]

1

2019-10-09T13:40:13.000Z

five/five_copy.py

ngd-b/python-demo

0341c1620bcde1c1d886cb9e75dc6db3722273c8

[ "MIT" ]

null

five/five_copy.py

ngd-b/python-demo

0341c1620bcde1c1d886cb9e75dc6db3722273c8

[ "MIT" ]

null

#!/usr/bin/python # -*- coding:utf-8 -*- print("hello world") f = None try: f = open("./hello.txt","r",encoding="utf8") print(f.read(5),end='') print(f.read(5),end='') print(f.read(5)) except IOError as e: print(e) finally: if f: f.close() # with auto call the methods' close with open("./hello.txt","r",encoding="utf8") as f: print(f.read()) # readlines() 按行读取文件 with open("./hello.txt","r",encoding="utf8") as f: for line in f.readlines(): print(line.strip()) # 写入数据 with open("./hello_1.txt","w",encoding="utf8") as f: f.write("北京欢迎你!") with open("./hello.txt","a",encoding="utf8") as f: f.write("祖国 70!") # StringIO / BytesIO from io import StringIO # 创建 str = StringIO('init') # 读取初始化进去的值 while True: s = str.readline() if s == '': break print(s.strip()) # 写入 str.write("你好!") str.write(" 南京") # 获取 print(str.getvalue()) ''' while True: s = str.readline() if s == '': break print(s.strip()) ''' # 写入二进制数据 from io import BytesIO bi = BytesIO() bi.write("你好".encode("utf-8")) print(bi.getvalue()) by = BytesIO(b'\xe4\xbd\xa0\xe5\xa5\xbd') print(by.read()) # 操作系统文件目录 OS import os # 当前环境 nt print(os.name) # python 执行文件 <module 'ntpath' from 'G:\\python-3.7\\lib\\ntpath.py'> print(os.path) # 系统环境配置目录包括系统环境变量、用户变量、 print(os.environ) # 获取当前控制台的管理用户名 'bobol' print(os.getlogin()) # 创建一个文件、目录 os.mkdir("./foo/") # 删除一个目录 os.rmdir("./foo/") ''' os.path 可以处理部分路径问题 ''' # 获取指定路径的绝对路径 'G:\\pythonDemo\\python-demo\\five' print(os.path.abspath("./")) # 返回指定路径是否存在系统文件路径中 False print(os.path.exists("./foo")) # 获取指定路径下文件的大小 4096 print(os.path.getsize("../")) # 返回指定路径是否为绝对路径 False print(os.path.isabs("../"))

19.444444

72

0.6

0

1,155

0.550524

2951af032672242812dfd6a7fa6b331872d27c9b

210

py

Python

prepare_sets.py

mechtal/Vaccination_UK

f3ad91c128b8b7711fc03d12a115cbf3e660318f

[ "MIT" ]

null

prepare_sets.py

mechtal/Vaccination_UK

f3ad91c128b8b7711fc03d12a115cbf3e660318f

[ "MIT" ]

17

2022-03-12T11:19:44.000Z

2022-03-28T08:26:38.000Z

prepare_sets.py

mechtal/Vaccination_UK

f3ad91c128b8b7711fc03d12a115cbf3e660318f

[ "MIT" ]

null

def prepare_sets(dataset, feature_columns, y_column): train_X, val_X, train_y, val_y = train_test_split(dataset[feature_columns], dataset[y_column], random_state=1) return train_X, val_X, train_y, val_y

70

114

0.785714

0

2951e1c21121343a134fe48bfcc73abc7a482cb1

6,355

py

Python

examples/batch_ts_insert.py

bureau14/qdb-api-python

2a010df3252d39bc4d529f545547c5cefb9fe86e

[ "BSD-3-Clause" ]

9

2015-09-02T20:13:13.000Z

2020-07-16T14:17:36.000Z

examples/batch_ts_insert.py

bureau14/qdb-api-python

2a010df3252d39bc4d529f545547c5cefb9fe86e

[ "BSD-3-Clause" ]

5

2018-02-20T10:47:02.000Z

2020-05-20T10:05:49.000Z

examples/batch_ts_insert.py

bureau14/qdb-api-python

2a010df3252d39bc4d529f545547c5cefb9fe86e

[ "BSD-3-Clause" ]

1

2018-04-01T11:12:56.000Z

# Copyright (c) 2009-2020, quasardb SAS. All rights reserved. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of quasardb nor the names of its contributors may # be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY QUASARDB AND CONTRIBUTORS ``AS IS'' AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # from __future__ import print_function from builtins import range as xrange, int import os from socket import gethostname import sys import inspect import traceback import random import time import datetime import locale import numpy as np import quasardb STOCK_COLUMN = "stock_id" OPEN_COLUMN = "open" CLOSE_COLUMN = "close" HIGH_COLUMN = "high" LOW_COLUMN = "low" VOLUME_COLUMN = "volume" def time_execution(str, f, *args): print(" - ", str, end='') start_time = time.time() res = f(*args) end_time = time.time() print(" [duration: {}s]".format(end_time - start_time)) return res def gen_ts_name(): return "test.{}.{}.{}".format(gethostname(), os.getpid(), random.randint(0, 100000)) def create_ts(q, name): ts = q.ts(name) ts.create([quasardb.ColumnInfo(quasardb.ColumnType.Int64, STOCK_COLUMN), quasardb.ColumnInfo(quasardb.ColumnType.Double, OPEN_COLUMN), quasardb.ColumnInfo(quasardb.ColumnType.Double, CLOSE_COLUMN), quasardb.ColumnInfo(quasardb.ColumnType.Double, HIGH_COLUMN), quasardb.ColumnInfo(quasardb.ColumnType.Double, LOW_COLUMN), quasardb.ColumnInfo(quasardb.ColumnType.Int64, VOLUME_COLUMN)]) return ts def create_many_ts(q, names): return [create_ts(q, x) for x in names] def generate_prices(price_count): return np.random.uniform(-100.0, 100.0, price_count) def generate_points(points_count): start_time = np.datetime64('2017-01-01', 'ns') dates = np.array([(start_time + np.timedelta64(i, 'm')) for i in range(points_count)]).astype('datetime64[ns]') stock_ids = np.random.randint(1, 25, size=points_count) prices = np.array([generate_prices(60) for i in range(points_count)]).astype('double') volumes = np.random.randint(0, 10000, points_count) return (dates, stock_ids, prices, volumes) def batch_ts_columns(ts_name, prealloc_size): return (quasardb.BatchColumnInfo(ts_name, STOCK_COLUMN, prealloc_size), quasardb.BatchColumnInfo(ts_name, OPEN_COLUMN, prealloc_size), quasardb.BatchColumnInfo(ts_name, CLOSE_COLUMN, prealloc_size), quasardb.BatchColumnInfo(ts_name, HIGH_COLUMN, prealloc_size), quasardb.BatchColumnInfo(ts_name, LOW_COLUMN, prealloc_size), quasardb.BatchColumnInfo(ts_name, VOLUME_COLUMN, prealloc_size)) def calculate_minute_bar(prices): # Takes all prices for a single minute, and calculate OHLC return (prices[0], prices[-1], np.amax(prices), np.amin(prices)) def bulk_insert(q, ts_names, dates, stock_ids, prices, volumes): # We generate a flattened list of columns for each timeseries; for example, # for 2 columns for 4 timeseries each, we have 8 columns. columns = [column for nested in (batch_ts_columns(ts_name, len(dates)) for ts_name in ts_names) for column in nested] batch_inserter = q.ts_batch(columns) for i in range(len(stock_ids)): # We use the known layout of column (2 for each timeseries, alternating with # STOCK_COLUMN and PRICE_COLUMN) to set the values. for j in range(0, len(ts_names) * 6, 6): (o, c, h, l) = calculate_minute_bar(prices[i]) batch_inserter.start_row(dates[i]) batch_inserter.set_int64(j, stock_ids[i]) # set stock_id batch_inserter.set_double(j + 1, o) # open batch_inserter.set_double(j + 2, c) # close batch_inserter.set_double(j + 3, h) # high batch_inserter.set_double(j + 4, l) # low batch_inserter.set_int64(j + 5, volumes[i]) # low batch_inserter.push() def make_it_so(q, points_count): ts_names = [gen_ts_name(), gen_ts_name()] ts = time_execution("Creating a time series with names {}".format(ts_names), create_many_ts, q, ts_names) (dates, stock_ids, prices, volumes) = time_execution("Generating {:,} points".format(points_count), generate_points, points_count) time_execution("Inserting {:,} points into timeseries with names {}".format(points_count, ts_names), bulk_insert, q, ts_names, dates, stock_ids, prices, volumes) return (ts_names, dates, np.unique(stock_ids)) def main(quasardb_uri, points_count): print("Connecting to: ", quasardb_uri) q = quasardb.Cluster(uri=quasardb_uri) print(" *** Inserting {:,} into {}".format(points_count, quasardb_uri)) make_it_so(q, points_count) if __name__ == "__main__": try: if len(sys.argv) != 3: print("usage: ", sys.argv[0], " quasardb_uri points_count") sys.exit(1) main(sys.argv[1], int(sys.argv[2])) except Exception as ex: # pylint: disable=W0703 print("An error ocurred:", str(ex)) traceback.print_exc()

40.737179

165

0.70181

0

2,270

0.357199

295371af41debc44d7d6cd681954bb737b9ceb2b

2,128

py

Python

tests/backends/test_flashtext_backend.py

openredact/pii-identifier

97eaef56d6de59718501095d631a0fb49700e45a

[ "MIT" ]

14

2020-07-31T18:45:29.000Z

2022-02-21T13:24:00.000Z

tests/backends/test_flashtext_backend.py

openredact/pii-identifier

97eaef56d6de59718501095d631a0fb49700e45a

[ "MIT" ]

7

2020-07-31T06:17:21.000Z

2021-05-23T08:40:24.000Z

tests/backends/test_flashtext_backend.py

openredact/pii-identifier

97eaef56d6de59718501095d631a0fb49700e45a

[ "MIT" ]

1

2020-09-30T01:42:57.000Z

from nerwhal.backends.flashtext_backend import FlashtextBackend from nerwhal.recognizer_bases import FlashtextRecognizer def test_single_recognizer(embed): class TestRecognizer(FlashtextRecognizer): TAG = "XX" SCORE = 1.0 @property def keywords(self): return ["abc", "cde"] backend = FlashtextBackend() backend.register_recognizer(TestRecognizer) text = "Das ist abc und cde." ents = backend.run(text) assert embed(text, ents) == "Das ist XX und XX." assert ents[0].start_char == 8 assert ents[0].end_char == 11 assert ents[0].tag == "XX" assert ents[0].text == "abc" assert ents[0].score == 1.0 assert ents[0].recognizer == "TestRecognizer" def test_multiple_recognizers(embed): class TestRecognizerA(FlashtextRecognizer): TAG = "A" SCORE = 1.0 @property def keywords(self): return ["abc"] class TestRecognizerB(FlashtextRecognizer): TAG = "B" SCORE = 0.5 @property def keywords(self): return ["cde"] backend = FlashtextBackend() backend.register_recognizer(TestRecognizerA) backend.register_recognizer(TestRecognizerB) text = "Das ist abc und cde." ents = backend.run(text) assert embed(text, ents) == "Das ist A und B." assert ents[0].tag == "A" assert ents[0].score == 1.0 assert ents[1].tag == "B" assert ents[1].score == 0.5 def test_overlapping_recognizers(embed): class TestRecognizerA(FlashtextRecognizer): TAG = "A" SCORE = 1.0 @property def keywords(self): return ["abc", "cde"] class TestRecognizerB(FlashtextRecognizer): TAG = "B" SCORE = 0.5 @property def keywords(self): return ["cde", "fgh"] backend = FlashtextBackend() backend.register_recognizer(TestRecognizerA) backend.register_recognizer(TestRecognizerB) text = "Das ist cde." ents = backend.run(text) # Recognizer B overwrites the keyword "cde" assert embed(text, ents) == "Das ist B."

26.6

63

0.62218

796

0.37406

0

341

0.160244

0

239

0.112312

2954339ee63d8f3aeb46e217258769ecc01fa43c

1,444

py

Python

new_rdsmysql.py

AdminTurnedDevOps/AWS_Solutions_Architect_Python

5389f8c9dfbda7b0b49a94a93e9b070420ca9ece

[ "MIT" ]

30

2019-01-13T20:14:07.000Z

2022-02-06T15:08:01.000Z

new_rdsmysql.py

AdminTurnedDevOps/AWS_Solutions_Architect_Python

5389f8c9dfbda7b0b49a94a93e9b070420ca9ece

[ "MIT" ]

1

2019-01-13T23:52:39.000Z

2019-01-14T14:39:45.000Z

new_rdsmysql.py

AdminTurnedDevOps/AWS_Solutions_Architect_Python

5389f8c9dfbda7b0b49a94a93e9b070420ca9ece

[ "MIT" ]

26

2019-01-13T21:32:23.000Z

2022-03-20T05:19:03.000Z

import boto3 import sys import time import logging import getpass def new_rdsmysql(dbname, instanceID, storage, dbInstancetype, dbusername): masterPass = getpass.getpass('DBMasterPassword: ') if len(masterPass) < 10: logging.warning('Password is not at least 10 characters. Please try again') time.sleep(5) exit else: None try: rds_instance = boto3.client('rds') create_instance = rds_instance.create_db_instance( DBName = dbname, DBInstanceIdentifier = instanceID, AllocatedStorage = int(storage), DBInstanceClass = dbInstancetype, Engine = 'mysql', MasterUsername = dbusername, MasterUserPassword = str(masterPass), MultiAZ = True, EngineVersion = '5.7.23', AutoMinorVersionUpgrade = False, LicenseModel = 'general-public-license', PubliclyAccessible = False, Tags = [ { 'Key': 'Name', 'Value' : dbname } ] ) print(create_instance) except Exception as e: logging.warning('An error has occured') print(e) dbname = sys.argv[1] instanceID = sys.argv[2] storage = sys.argv[3] dbInstancetype = sys.argv[4] dbusername = sys.argv[5] new_rdsmysql(dbname, instanceID, storage, dbInstancetype, dbusername)

27.769231

83

0.587258

0

162

0.112188

295527972ae5a65fd8aad67870244e225d07dc77

2,657

py

Python

src/tzscan/tzscan_block_api.py

Twente-Mining/tezos-reward-distributor

8df0745fdb44cbd765084303882545202d2427f3

[ "MIT" ]

null

src/tzscan/tzscan_block_api.py

Twente-Mining/tezos-reward-distributor

8df0745fdb44cbd765084303882545202d2427f3

[ "MIT" ]

null

src/tzscan/tzscan_block_api.py

Twente-Mining/tezos-reward-distributor

8df0745fdb44cbd765084303882545202d2427f3

[ "MIT" ]

null

import random import requests from api.block_api import BlockApi from exception.tzscan import TzScanException from log_config import main_logger logger = main_logger HEAD_API = {'MAINNET': {'HEAD_API_URL': 'https://api%MIRROR%.tzscan.io/v2/head'}, 'ALPHANET': {'HEAD_API_URL': 'http://api.alphanet.tzscan.io/v2/head'}, 'ZERONET': {'HEAD_API_URL': 'http://api.zeronet.tzscan.io/v2/head'} } REVELATION_API = {'MAINNET': {'HEAD_API_URL': 'https://api%MIRROR%.tzscan.io/v1/operations/%PKH%?type=Reveal'}, 'ALPHANET': {'HEAD_API_URL': 'https://api.alphanet.tzscan.io/v1/operations/%PKH%?type=Reveal'}, 'ZERONET': {'HEAD_API_URL': 'https://api.zeronet.tzscan.io/v1/operations/%PKH%?type=Reveal'} } class TzScanBlockApiImpl(BlockApi): def __init__(self, nw): super(TzScanBlockApiImpl, self).__init__(nw) self.head_api = HEAD_API[nw['NAME']] if self.head_api is None: raise Exception("Unknown network {}".format(nw)) self.revelation_api = REVELATION_API[nw['NAME']] def get_current_level(self, verbose=False): uri = self.head_api['HEAD_API_URL'].replace("%MIRROR%", str(self.rand_mirror())) if verbose: logger.debug("Requesting {}".format(uri)) resp = requests.get(uri) if resp.status_code != 200: # This means something went wrong. raise TzScanException('GET {} {}'.format(uri, resp.status_code)) root = resp.json() if verbose: logger.debug("Response from tzscan is: {}".format(root)) current_level = int(root["level"]) return current_level def get_revelation(self, pkh, verbose=False): uri = self.revelation_api['HEAD_API_URL'].replace("%MIRROR%", str(self.rand_mirror())).replace("%PKH%", pkh) if verbose: logger.debug("Requesting {}".format(uri)) resp = requests.get(uri) if resp.status_code != 200: # This means something went wrong. raise TzScanException('GET {} {}'.format(uri, resp.status_code)) root = resp.json() if verbose: logger.debug("Response from tzscan is: {}".format(root)) return len(root) > 0 def rand_mirror(self): mirror = random.randint(1, 6) if mirror == 4: # has problem lately mirror = 3 return mirror def test_get_revelation(): address_api = TzScanBlockApiImpl({"NAME":"ALPHANET"}) address_api.get_revelation("tz3WXYtyDUNL91qfiCJtVUX746QpNv5i5ve5") if __name__ == '__main__': test_get_revelation()

32.402439

116

0.621377

1,657

0.623636

0

802

0.301844

29554b0b9e721e4b0e9ff426e2c29a4e943ecd1c

10,086

py

Python

python/cuxfilter/tests/charts/core/test_core_non_aggregate.py

Anhmike/cuxfilter

a8b25b1c37ac0e5435acb7261f6fcbf677d96bfa

[ "Apache-2.0" ]

201

2018-12-21T18:32:40.000Z

2022-03-22T11:50:29.000Z

python/cuxfilter/tests/charts/core/test_core_non_aggregate.py

Anhmike/cuxfilter

a8b25b1c37ac0e5435acb7261f6fcbf677d96bfa

[ "Apache-2.0" ]

258

2018-12-27T07:37:50.000Z

2022-03-31T20:01:32.000Z

python/cuxfilter/tests/charts/core/test_core_non_aggregate.py

Anhmike/cuxfilter

a8b25b1c37ac0e5435acb7261f6fcbf677d96bfa

[ "Apache-2.0" ]

51

2019-01-10T19:03:09.000Z

2022-03-08T01:37:11.000Z

import pytest import cudf import mock from cuxfilter.charts.core.non_aggregate.core_non_aggregate import ( BaseNonAggregate, ) from cuxfilter.dashboard import DashBoard from cuxfilter import DataFrame from cuxfilter.layouts import chart_view class TestCoreNonAggregateChart: def test_variables(self): bnac = BaseNonAggregate() # BaseChart variables assert bnac.chart_type is None assert bnac.x is None assert bnac.y is None assert bnac.aggregate_fn == "count" assert bnac.color is None assert bnac.height == 0 assert bnac.width == 0 assert bnac.add_interaction is True assert bnac.chart is None assert bnac.source is None assert bnac.source_backup is None assert bnac.data_points == 0 assert bnac._library_specific_params == {} assert bnac.stride is None assert bnac.stride_type == int assert bnac.min_value == 0.0 assert bnac.max_value == 0.0 assert bnac.x_label_map == {} assert bnac.y_label_map == {} assert bnac.title == "" # test chart name setter bnac.x = "x" bnac.y = "y" bnac.chart_type = "test_chart_type" assert bnac.name == "x_y_count_test_chart_type_" # BaseNonAggregateChart variables assert bnac.use_data_tiles is False assert bnac.reset_event is None assert bnac.x_range is None assert bnac.y_range is None assert bnac.aggregate_col is None def test_label_mappers(self): bnac = BaseNonAggregate() library_specific_params = { "x_label_map": {"a": 1, "b": 2}, "y_label_map": {"a": 1, "b": 2}, } bnac.library_specific_params = library_specific_params assert bnac.x_label_map == {"a": 1, "b": 2} assert bnac.y_label_map == {"a": 1, "b": 2} @pytest.mark.parametrize("chart, _chart", [(None, None), (1, 1)]) def test_view(self, chart, _chart): bnac = BaseNonAggregate() bnac.chart = chart bnac.width = 400 bnac.title = "test_title" assert str(bnac.view()) == str( chart_view(_chart, width=bnac.width, title=bnac.title) ) def test_get_selection_geometry_callback(self): bnac = BaseNonAggregate() df = cudf.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]}) dashboard = DashBoard(dataframe=DataFrame.from_dataframe(df)) assert ( bnac.get_selection_geometry_callback(dashboard).__name__ == "selection_callback" ) assert callable(type(bnac.get_selection_geometry_callback(dashboard))) def test_box_selection_callback(self): bnac = BaseNonAggregate() bnac.x = "a" bnac.y = "b" bnac.chart_type = "temp" self.result = None def t_function(data, patch_update=False): self.result = data bnac.reload_chart = t_function df = cudf.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]}) dashboard = DashBoard(dataframe=DataFrame.from_dataframe(df)) dashboard._active_view = bnac class evt: geometry = dict(x0=1, x1=2, y0=3, y1=4, type="rect") t = bnac.get_selection_geometry_callback(dashboard) t(evt) assert self.result.equals(df.query("1<=a<=2 and 3<=b<=4")) def test_lasso_election_callback(self): bnac = BaseNonAggregate() bnac.x = "a" bnac.y = "b" bnac.chart_type = "temp" def t_function(data, patch_update=False): self.result = data bnac.reload_chart = t_function df = cudf.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]}) dashboard = DashBoard(dataframe=DataFrame.from_dataframe(df)) class evt: geometry = dict(x=[1, 1, 2], y=[1, 2, 1], type="poly") final = True t = bnac.get_selection_geometry_callback(dashboard) with mock.patch("cuspatial.point_in_polygon") as pip: pip.return_value = cudf.DataFrame( {"selection": [True, False, True]} ) t(evt) assert pip.called @pytest.mark.parametrize( "data, _data", [ (cudf.DataFrame(), cudf.DataFrame()), ( cudf.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]}), cudf.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]}), ), ], ) def test_calculate_source(self, data, _data): """ Calculate source just calls to the format_source_data function which is implemented by chart types inheriting this class. """ bnac = BaseNonAggregate() self.result = None def t_function(data, patch_update=False): self.result = data bnac.format_source_data = t_function bnac.calculate_source(data) assert self.result.equals(_data) @pytest.mark.parametrize( "x_range, y_range, query, local_dict", [ ( (1, 2), (3, 4), "@x_min<=x<=@x_max and @y_min<=y<=@y_max", {"x_min": 1, "x_max": 2, "y_min": 3, "y_max": 4}, ), ( (0, 2), (3, 5), "@x_min<=x<=@x_max and @y_min<=y<=@y_max", {"x_min": 0, "x_max": 2, "y_min": 3, "y_max": 5}, ), ], ) def test_compute_query_dict(self, x_range, y_range, query, local_dict): bnac = BaseNonAggregate() bnac.chart_type = "test" bnac.x = "x" bnac.y = "y" bnac.x_range = x_range bnac.y_range = y_range df = cudf.DataFrame({"x": [1, 2, 2], "y": [3, 4, 5]}) dashboard = DashBoard(dataframe=DataFrame.from_dataframe(df)) bnac.compute_query_dict( dashboard._query_str_dict, dashboard._query_local_variables_dict ) bnac_key = ( f"{bnac.x}_{bnac.y}" f"{'_' + bnac.aggregate_col if bnac.aggregate_col else ''}" f"_{bnac.aggregate_fn}_{bnac.chart_type}_{bnac.title}" ) assert dashboard._query_str_dict[bnac_key] == query for key in local_dict: assert ( dashboard._query_local_variables_dict[key] == local_dict[key] ) @pytest.mark.parametrize( "add_interaction, reset_event, event_1, event_2", [ (True, None, "selection_callback", None), (True, "test_event", "selection_callback", "reset_callback"), (False, "test_event", None, "reset_callback"), ], ) def test_add_events(self, add_interaction, reset_event, event_1, event_2): bnac = BaseNonAggregate() bnac.add_interaction = add_interaction bnac.reset_event = reset_event df = cudf.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]}) dashboard = DashBoard(dataframe=DataFrame.from_dataframe(df)) self.event_1 = None self.event_2 = None def t_func(fn): self.event_1 = fn.__name__ def t_func1(event, fn): self.event_2 = fn.__name__ bnac.add_selection_geometry_event = t_func bnac.add_event = t_func1 bnac.add_events(dashboard) assert self.event_1 == event_1 assert self.event_2 == event_2 def test_add_reset_event(self): bnac = BaseNonAggregate() bnac.chart_type = "test" bnac.x = "a" bnac.x_range = (0, 2) bnac.y_range = (3, 5) df = cudf.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]}) dashboard = DashBoard(dataframe=DataFrame.from_dataframe(df)) dashboard._active_view = bnac def t_func1(event, fn): fn("event") bnac.add_event = t_func1 bnac.add_reset_event(dashboard) assert bnac.x_range is None assert bnac.y_range is None def test_query_chart_by_range(self): bnac = BaseNonAggregate() bnac.chart_type = "test" bnac.x = "a" bnac_1 = BaseNonAggregate() bnac_1.chart_type = "test" bnac_1.x = "b" query_tuple = (4, 5) df = cudf.DataFrame({"a": [1, 2, 3, 4], "b": [3, 4, 5, 6]}) bnac.source = df self.result = None self.patch_update = None def t_func(data, patch_update): self.result = data self.patch_update = patch_update # creating a dummy reload chart fn as its not implemented in core # non aggregate chart class bnac.reload_chart = t_func bnac.query_chart_by_range( active_chart=bnac_1, query_tuple=query_tuple, datatile=None ) assert self.result.to_string() == " a b\n1 2 4\n2 3 5" assert self.patch_update is False @pytest.mark.parametrize( "new_indices, result", [ ([4, 5], " a b\n1 2 4\n2 3 5"), ([], " a b\n0 1 3\n1 2 4\n2 3 5\n3 4 6"), ([3], " a b\n0 1 3"), ], ) def test_query_chart_by_indices(self, new_indices, result): bnac = BaseNonAggregate() bnac.chart_type = "test" bnac.x = "a" bnac_1 = BaseNonAggregate() bnac_1.chart_type = "test" bnac_1.x = "b" new_indices = new_indices df = cudf.DataFrame({"a": [1, 2, 3, 4], "b": [3, 4, 5, 6]}) bnac.source = df self.result = None self.patch_update = None def t_func(data, patch_update): self.result = data self.patch_update = patch_update # creating a dummy reload chart fn as its not implemented in core # non aggregate chart class bnac.reload_chart = t_func bnac.query_chart_by_indices( active_chart=bnac_1, old_indices=[], new_indices=new_indices, datatile=None, ) assert self.result.to_string() == result assert self.patch_update is False

30.288288

78

0.561967

9,836

0.975213

0

4,746

0.470553

0

1,395

0.138311

29560939d9082f0d01fcc95be50270dfe0f453ac

4,265

py

Python

tunobase/tagging/migrations/0001_initial.py

unomena/tunobase-core

fd24e378c87407131805fa56ade8669fceec8dfa

[ "BSD-3-Clause" ]

null

tunobase/tagging/migrations/0001_initial.py

unomena/tunobase-core

fd24e378c87407131805fa56ade8669fceec8dfa

[ "BSD-3-Clause" ]

null

tunobase/tagging/migrations/0001_initial.py

unomena/tunobase-core

fd24e378c87407131805fa56ade8669fceec8dfa

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'Tag' db.create_table(u'tagging_tag', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('title', self.gf('django.db.models.fields.CharField')(max_length=32, db_index=True)), ('description', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('site', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['sites.Site'], null=True, blank=True)), )) db.send_create_signal(u'tagging', ['Tag']) # Adding unique constraint on 'Tag', fields ['title', 'site'] db.create_unique(u'tagging_tag', ['title', 'site_id']) # Adding model 'ContentObjectTag' db.create_table(u'tagging_contentobjecttag', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('content_type', self.gf('django.db.models.fields.related.ForeignKey')(related_name='content_type_set_for_contentobjecttag', to=orm['contenttypes.ContentType'])), ('object_pk', self.gf('django.db.models.fields.PositiveIntegerField')()), ('site', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['sites.Site'])), ('tag', self.gf('django.db.models.fields.related.ForeignKey')(related_name='content_object_tags', to=orm['tagging.Tag'])), )) db.send_create_signal(u'tagging', ['ContentObjectTag']) def backwards(self, orm): # Removing unique constraint on 'Tag', fields ['title', 'site'] db.delete_unique(u'tagging_tag', ['title', 'site_id']) # Deleting model 'Tag' db.delete_table(u'tagging_tag') # Deleting model 'ContentObjectTag' db.delete_table(u'tagging_contentobjecttag') models = { u'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'sites.site': { 'Meta': {'ordering': "(u'domain',)", 'object_name': 'Site', 'db_table': "u'django_site'"}, 'domain': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, u'tagging.contentobjecttag': { 'Meta': {'object_name': 'ContentObjectTag'}, 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'content_type_set_for_contentobjecttag'", 'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'object_pk': ('django.db.models.fields.PositiveIntegerField', [], {}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['sites.Site']"}), 'tag': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'content_object_tags'", 'to': u"orm['tagging.Tag']"}) }, u'tagging.tag': { 'Meta': {'unique_together': "[('title', 'site')]", 'object_name': 'Tag'}, 'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['sites.Site']", 'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '32', 'db_index': 'True'}) } } complete_apps = ['tagging']

56.118421

182

0.597655

4,098

0.960844

0

2,565

0.601407

2957082f2761f3302a5b658af0d68aab4daff24f

1,004

py

Python

recogym/envs/session.py

philomenec/reco-gym

f8553d197f42ec2f415aefce48525d0e9b10ddaa

[ "Apache-2.0" ]

413

2018-09-18T17:49:44.000Z

2022-03-23T12:25:41.000Z

recogym/envs/session.py

aliang-rec/reco-gym

f8553d197f42ec2f415aefce48525d0e9b10ddaa

[ "Apache-2.0" ]

15

2018-11-08T17:04:21.000Z

2021-11-30T19:20:27.000Z

recogym/envs/session.py

aliang-rec/reco-gym

f8553d197f42ec2f415aefce48525d0e9b10ddaa

[ "Apache-2.0" ]

81

2018-09-22T02:28:55.000Z

2022-03-30T14:03:01.000Z

class Session(list): """Abstract Session class""" def to_strings(self, user_id, session_id): """represent session as list of strings (one per event)""" user_id, session_id = str(user_id), str(session_id) session_type = self.get_type() strings = [] for event, product in self: columns = [user_id, session_type, session_id, event, str(product)] strings.append(','.join(columns)) return strings def get_type(self): raise NotImplemented class OrganicSessions(Session): def __init__(self): super(OrganicSessions, self).__init__() def next(self, context, product): self.append( { 't': context.time(), 'u': context.user(), 'z': 'pageview', 'v': product } ) def get_type(self): return 'organic' def get_views(self): return [p for _, _, e, p in self if e == 'pageview']

27.135135

78

0.548805

1,000

0.996016

0

130

0.129482

29589b62fc1cb372684ea22b3b76f315253769f1

219

py

Python

message_handlers/location_handler.py

pratyushmore/lunch-tag-bot

e0a055a2ee914c3bc5434244b17cfb7e3b1a7a9a

[ "MIT" ]

null

message_handlers/location_handler.py

pratyushmore/lunch-tag-bot

e0a055a2ee914c3bc5434244b17cfb7e3b1a7a9a

[ "MIT" ]

1

2019-10-21T15:15:48.000Z

2019-10-21T19:28:10.000Z

message_handlers/location_handler.py

pratyushmore/lunch-tag-bot

e0a055a2ee914c3bc5434244b17cfb7e3b1a7a9a

[ "MIT" ]

null

def location(messaging_adaptor, user, channel, location): message = "Your location has been set to `{}`. You are ready to be matched for Lunch Tag :)".format(location) messaging_adaptor.send_message(channel, message)

54.75

110

0.767123

0

82

0.374429

2959af169e729db8be7ba1725d5b1686b6c154d4

6,462

py

Python

b.py

lbarchive/b.py

18b533dc40e5fdf7ba62209b51584927c2dd9ba0

[ "MIT" ]

null

b.py

lbarchive/b.py

18b533dc40e5fdf7ba62209b51584927c2dd9ba0

[ "MIT" ]

null

b.py

lbarchive/b.py

18b533dc40e5fdf7ba62209b51584927c2dd9ba0

[ "MIT" ]

null

#!/usr/bin/env python # Copyright (C) 2013-2016 by Yu-Jie Lin # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. """ ============ b.py command ============ Commands ======== ============= ======================= command supported services ============= ======================= ``blogs`` ``b`` ``post`` ``b``, ``wp`` ``generate`` ``base``, ``b``, ``wp`` ``checklink`` ``base``, ``b``, ``wp`` ``search`` ``b`` ============= ======================= Descriptions: ``blogs`` list blogs. This can be used for blog IDs lookup. ``post`` post or update a blog post. ``generate`` generate HTML file at ``<TEMP>/draft.html``, where ``<TEMP>`` is the system's temporary directory. The generation can output a preview html at ``<TEMP>/preview.html`` if there is ``tmpl.html``. It will replace ``%%Title%%`` with post title and ``%%Content%%`` with generated HTML. ``checklink`` check links in generated HTML using lnkckr_. ``search`` search blog .. _lnkckr: https://pypi.python.org/pypi/lnkckr """ from __future__ import print_function import argparse as ap import codecs import imp import logging import os import sys import traceback from bpy.handlers import handlers from bpy.services import find_service, services __program__ = 'b.py' __description__ = 'Post to Blogger or WordPress in markup language seamlessly' __copyright__ = 'Copyright 2013-2016, Yu Jie Lin' __license__ = 'MIT License' __version__ = '0.11.0' __website__ = 'http://bitbucket.org/livibetter/b.py' __author__ = 'Yu-Jie Lin' __author_email__ = '[email protected]' # b.py stuff ############ # filename of local configuration without '.py' suffix. BRC = 'brc' def parse_args(): p = ap.ArgumentParser() p.add_argument('--version', action='version', version='%(prog)s ' + __version__) p.add_argument('-d', '--debug', action='store_true', help='turn on debugging messages') p.add_argument('-s', '--service', default='base', help='what service to use. (Default: %(default)s)') sp = p.add_subparsers(help='commands') pblogs = sp.add_parser('blogs', help='list blogs') pblogs.set_defaults(subparser=pblogs, command='blogs') psearch = sp.add_parser('search', help='search for posts') psearch.add_argument('-b', '--blog', help='Blog ID') psearch.add_argument('q', nargs='+', help='query text') psearch.set_defaults(subparser=psearch, command='search') pgen = sp.add_parser('generate', help='generate html') pgen.add_argument('filename') pgen.set_defaults(subparser=pgen, command='generate') pchk = sp.add_parser('checklink', help='check links in chkerateed html') pchk.add_argument('filename') pchk.set_defaults(subparser=pchk, command='checklink') ppost = sp.add_parser('post', help='post or update a blog post') ppost.add_argument('filename') ppost.set_defaults(subparser=ppost, command='post') args = p.parse_args() return args def load_config(): rc = None try: search_path = [os.getcwd()] _mod_data = imp.find_module(BRC, search_path) print('Loading local configuration...') try: rc = imp.load_module(BRC, *_mod_data) finally: if _mod_data[0]: _mod_data[0].close() except ImportError: pass except Exception: traceback.print_exc() print('Error in %s, aborted.' % _mod_data[1]) sys.exit(1) return rc def main(): args = parse_args() logging.basicConfig( format=( '%(asctime)s ' '%(levelname).4s ' '%(module)5.5s:%(funcName)-10.10s:%(lineno)04d ' '%(message)s' ), datefmt='%H:%M:%S', ) if args.debug: logging.getLogger().setLevel(logging.DEBUG) encoding = sys.stdout.encoding if not encoding.startswith('UTF'): msg = ( 'standard output encoding is %s, ' 'try to set with UTF-8 if there is output issues.' ) logging.warning(msg % encoding) if sys.version_info.major == 2: sys.stdout = codecs.getwriter(encoding)(sys.stdout, 'replace') sys.stderr = codecs.getwriter(encoding)(sys.stderr, 'replace') elif sys.version_info.major == 3: sys.stdout = codecs.getwriter(encoding)(sys.stdout.buffer, 'replace') sys.stderr = codecs.getwriter(encoding)(sys.stderr.buffer, 'replace') rc = load_config() service_options = {'blog': None} if rc: if hasattr(rc, 'handlers'): for name, handler in rc.handlers.items(): if name in handlers: handlers[name].update(handler) else: handlers[name] = handler.copy() if hasattr(rc, 'services'): for name, service in rc.services.items(): if name in services: services[name].update(service) else: services[name] = service.copy() if hasattr(rc, 'service'): args.service = rc.service if hasattr(rc, 'service_options'): service_options.update(rc.service_options) if hasattr(args, 'blog') and args.blog is not None: service_options['blog'] = args.blog filename = args.filename if hasattr(args, 'filename') else None service = find_service(args.service, service_options, filename) if args.command == 'blogs': service.list_blogs() elif args.command == 'search': service.search(' '.join(args.q)) elif args.command == 'generate': service.generate() elif args.command == 'checklink': service.checklink() elif args.command == 'post': service.post() if __name__ == '__main__': main()

29.108108

79

0.660786

0

3,125

0.483596

295a62c87a95c13de9ca2d600326020d699ab2e2

8,729

py

Python

spyder/plugins/outlineexplorer/api.py

suokunlong/spyder

2d5d450fdcef232fb7f38e7fefc27f0e7f704c9a

[ "MIT" ]

1

2018-05-03T02:14:15.000Z

spyder/plugins/outlineexplorer/api.py

jastema/spyder

0ef48ea227c53f57556cd8002087dc404b0108b0

[ "MIT" ]

null

spyder/plugins/outlineexplorer/api.py

jastema/spyder

0ef48ea227c53f57556cd8002087dc404b0108b0

[ "MIT" ]

1

2020-03-05T03:09:11.000Z

# -*- coding: utf-8 -*- # # Copyright © Spyder Project Contributors # Licensed under the terms of the MIT License # (see spyder/__init__.py for details) """Outline explorer API. You need to declare a OutlineExplorerProxy, and a function for handle the edit_goto Signal. class OutlineExplorerProxyCustom(OutlineExplorerProxy): ... def handle_go_to(name, line, text): ... outlineexplorer = OutlineExplorerWidget(None) oe_proxy = OutlineExplorerProxyCustom(name) outlineexplorer.set_current_editor(oe_proxy, update=True, clear=False) outlineexplorer.edit_goto.connect(handle_go_to) """ import re from qtpy.QtCore import Signal, QObject from qtpy.QtGui import QTextBlock from spyder.config.base import _ from spyder.config.base import running_under_pytest def document_cells(block, forward=True): """ Get cells oedata before or after block in the document. Parameters ---------- forward : bool, optional Whether to iterate forward or backward from the current block. """ if not block.isValid(): # Not a valid block return if forward: block = block.next() else: block = block.previous() while block.isValid(): data = block.userData() if (data and data.oedata and data.oedata.def_type == OutlineExplorerData.CELL): yield data.oedata if forward: block = block.next() else: block = block.previous() def is_cell_header(block): """Check if the given block is a cell header.""" if not block.isValid(): return False data = block.userData() return (data and data.oedata and data.oedata.def_type == OutlineExplorerData.CELL) def cell_index(block): """Get the cell index of the given block.""" index = len(list(document_cells(block, forward=False))) if is_cell_header(block): return index + 1 return index def cell_name(block): """ Get the cell name the block is in. If the cell is unnamed, return the cell index instead. """ if is_cell_header(block): header = block.userData().oedata else: try: header = next(document_cells(block, forward=False)) except StopIteration: # This cell has no header, so it is the first cell. return 0 if header.has_name(): return header.def_name else: # No name, return the index return cell_index(block) class OutlineExplorerProxy(QObject): """ Proxy class between editors and OutlineExplorerWidget. """ sig_cursor_position_changed = Signal(int, int) sig_outline_explorer_data_changed = Signal() def __init__(self): super(OutlineExplorerProxy, self).__init__() self.fname = None def is_python(self): """Return whether the editor is a python file or not.""" raise NotImplementedError def get_id(self): """Return an unique id, used for identify objects in a dict""" raise NotImplementedError def give_focus(self): """Give focus to the editor, called when toogling visibility of OutlineExplorerWidget.""" raise NotImplementedError def get_line_count(self): """Return the number of lines of the editor (int).""" raise NotImplementedError def parent(self): """This is used for diferenciate editors in multi-window mode.""" return None def get_cursor_line_number(self): """Return the cursor line number.""" raise NotImplementedError def outlineexplorer_data_list(self): """Returns a list of outline explorer data.""" raise NotImplementedError class OutlineExplorerData(QObject): CLASS, FUNCTION, STATEMENT, COMMENT, CELL = list(range(5)) FUNCTION_TOKEN = 'def' CLASS_TOKEN = 'class' # Emitted if the OutlineExplorerData was changed sig_update = Signal() def __init__(self, block, text=None, fold_level=None, def_type=None, def_name=None, color=None): """ Args: text (str) fold_level (int) def_type (int): [CLASS, FUNCTION, STATEMENT, COMMENT, CELL] def_name (str) color (PyQt.QtGui.QTextCharFormat) """ super(OutlineExplorerData, self).__init__() self.text = text self.fold_level = fold_level self.def_type = def_type self.def_name = def_name self.color = color if running_under_pytest(): # block might be a dummy self.block = block else: # Copy the text block to make sure it is not deleted self.block = QTextBlock(block) def is_not_class_nor_function(self): return self.def_type not in (self.CLASS, self.FUNCTION) def is_class_or_function(self): return self.def_type in (self.CLASS, self.FUNCTION) def is_comment(self): return self.def_type in (self.COMMENT, self.CELL) def get_class_name(self): if self.def_type == self.CLASS: return self.def_name def get_function_name(self): if self.def_type == self.FUNCTION: return self.def_name def get_token(self): if self.def_type == self.FUNCTION: token = self.FUNCTION_TOKEN elif self.def_type == self.CLASS: token = self.CLASS_TOKEN return token @property def def_name(self): """Get the cell name.""" # Non cell don't need unique names. if self.def_type != self.CELL: return self._def_name def get_name(oedata): name = oedata._def_name if not name: name = _('Unnamed Cell') return name self_name = get_name(self) existing_numbers = [] def check_match(oedata): # Look for "string" other_name = get_name(oedata) pattern = '^' + re.escape(self_name) + r'(?:, #(\d+))?$' match = re.match(pattern, other_name) if match: # Check if already has a number number = match.groups()[0] if number: existing_numbers.append(int(number)) return True return False # Count cells N_prev = 0 for oedata in document_cells(self.block, forward=False): if check_match(oedata): N_prev += 1 N_fix_previous = len(existing_numbers) N_next = 0 for oedata in document_cells(self.block, forward=True): if check_match(oedata): N_next += 1 # Get the remaining indexeswe can use free_indexes = [idx for idx in range(N_prev + N_next + 1) if idx + 1 not in existing_numbers] idx = free_indexes[N_prev - N_fix_previous] if N_prev + N_next > 0: return self_name + ', #{}'.format(idx + 1) return self_name @def_name.setter def def_name(self, value): """Set name.""" self._def_name = value def update(self, other): """Try to update to avoid reloading everything.""" if (self.def_type == other.def_type and self.fold_level == other.fold_level): self.text = other.text old_def_name = self._def_name self._def_name = other._def_name self.color = other.color self.sig_update.emit() if self.def_type == self.CELL: if self.cell_level != other.cell_level: return False # Must update all other cells whose name has changed. for oedata in document_cells(self.block, forward=True): if oedata._def_name in [self._def_name, old_def_name]: oedata.sig_update.emit() return True return False def is_valid(self): """Check if the oedata has a valid block attached.""" block = self.block return (block and block.isValid() and block.userData() and hasattr(block.userData(), 'oedata') and block.userData().oedata == self ) def has_name(self): """Check if cell has a name.""" if self._def_name: return True else: return False def get_block_number(self): """Get the block number.""" if not self.is_valid(): # Avoid calling blockNumber if not a valid block return None return self.block.blockNumber()

29.096667

74

0.595601

6,207

0.710997

721

0.082589

1,692

0.193814

0

2,426

0.277892

295bcd4e3374d50cf1562ad240b9c1e9e4ac0fc7

3,132

py

Python

seamless/core/__init__.py

sjdv1982/seamless

1b814341e74a56333c163f10e6f6ceab508b7df9

[ "MIT" ]

15

2017-06-07T12:49:12.000Z

2020-07-25T18:06:04.000Z

seamless/core/__init__.py

sjdv1982/seamless

1b814341e74a56333c163f10e6f6ceab508b7df9

[ "MIT" ]

110

2016-06-21T23:20:44.000Z

2022-02-24T16:15:22.000Z

seamless/core/__init__.py

sjdv1982/seamless

1b814341e74a56333c163f10e6f6ceab508b7df9

[ "MIT" ]

6

2016-06-21T11:19:22.000Z

2019-01-21T13:45:39.000Z

import weakref class IpyString(str): def _repr_pretty_(self, p, cycle): return p.text(str(self)) class SeamlessBase: _destroyed = False _context = None _cached_path = None name = None def _get_macro(self): return self._context()._macro @property def path(self): if self._cached_path is not None: return self._cached_path if self._context is None: return () elif self._context() is None: return ("<None>", self.name) elif self._context().path is None: return ("<None>", self.name) else: return self._context().path + (self.name,) def _validate_path(self, required_path=None): if required_path is None: required_path = self.path else: assert self.path == required_path, (self.path, required_path) return required_path def _set_context(self, context, name): from .context import Context, UnboundContext assert isinstance(context, (Context, UnboundContext)) if self._context is not None and self._context() is context: assert self.name in context._auto context._children.pop(self.name) context._auto.discard(self.name) self.name = name return if isinstance(context, UnboundContext): assert self._context is None else: assert self._context is None or isinstance(self._context(), UnboundContext), self._context ctx = weakref.ref(context) self._context = ctx self.name = name return self def _get_manager(self): assert self._context is not None, self.name #worker/cell must have a context assert self._context() is not None, self.name #worker/cell must have a context return self._context()._get_manager() def _root(self): if self._context is None: return None if self._context() is None: return None return self._context()._root() def _format_path(self): path = self.path if path is None: ret = "<None>" else: path = [str(p) for p in path] ret = "." + ".".join(path) return ret def __str__(self): ret = "Seamless object: " + self._format_path() return ret def __repr__(self): return self.__str__() def _set_macro_object(self, macro_object): self._macro_object = macro_object @property def self(self): return self def destroy(self, **kwargs): self._destroyed = True from .mount import mountmanager from .macro_mode import get_macro_mode, macro_mode_on from . import cell as cell_module from .cell import Cell, cell from . import context as context_module from .context import Context, context from .worker import Worker from .transformer import Transformer, transformer from .structured_cell import StructuredCell, Inchannel, Outchannel from .macro import Macro, macro, path from .reactor import Reactor, reactor from .unilink import unilink

30.705882

102

0.628672

2,638

0.842273

0

445

0.142082

0

113

0.036079

295bf91559d8557674834d5e4100c334bcac0923

11,613

py

Python

oguilem/configuration/config.py

dewberryants/oGUIlem

28271fdc0fb6ffba0037f30f9f9858bec32b0d13

[ "BSD-3-Clause" ]

2

2022-02-23T13:16:47.000Z

2022-03-07T09:47:29.000Z

oguilem/configuration/config.py

dewberryants/oGUIlem

28271fdc0fb6ffba0037f30f9f9858bec32b0d13

[ "BSD-3-Clause" ]

null

oguilem/configuration/config.py

dewberryants/oGUIlem

28271fdc0fb6ffba0037f30f9f9858bec32b0d13

[ "BSD-3-Clause" ]

1

2022-02-23T13:16:49.000Z

import os import re import sys from oguilem.configuration.fitness import OGUILEMFitnessFunctionConfiguration from oguilem.configuration.ga import OGUILEMGlobOptConfig from oguilem.configuration.geometry import OGUILEMGeometryConfig from oguilem.configuration.utils import ConnectedValue, ConfigFileManager from oguilem.resources import options class OGUILEMConfig: def __init__(self): self.ui = OGUILEMUIConfig() self.globopt = OGUILEMGlobOptConfig() self.options = OGUILEMGeneralConfig() self.geometry = OGUILEMGeometryConfig() self.fitness = OGUILEMFitnessFunctionConfiguration() self.file_manager = ConfigFileManager() def save_to_file(self, path: str): content = "###OGOLEM###\n" content += self.globopt.get_finished_config() content += self.geometry.get_finished_config(path) content += self.fitness.get_finished_config() content += self.options.get_finished_config() with open(path, "w") as conf_file: conf_file.write(content) self.file_manager.signal_saved(path) def load_from_file(self, path: str, preset=False): self.options.set_to_default() with open(path, "r") as conf_file: content = conf_file.readlines() # Find geometry block and split off iter_content = iter(content) geo_block = list() backend_defs = list() charge_block = list() spin_block = list() offset = 0 # Separate off blocks for n, line in enumerate(iter_content): # Charge and Spin Blocks if line.strip().startswith("<CHARGES>"): start = n + offset try: charge_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Config ends after <CHARGES> tag!?") while not charge_line.startswith("</CHARGES>"): charge_block.append(charge_line) try: charge_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Dangling <GEOMETRY> tag in configuration!") end = start + len(charge_block) + 2 content = content[:start] + content[end:] offset -= 1 if line.strip().startswith("<SPINS>"): start = n + offset try: spin_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Config ends after <SPINS> tag!?") while not spin_line.startswith("</SPINS>"): spin_block.append(spin_line) try: spin_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Dangling <SPINS> tag in configuration!") end = start + len(spin_block) + 2 content = content[:start] + content[end:] offset -= 1 # Geometry Block if line.strip().startswith("<GEOMETRY>"): start = n + offset try: geo_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Config ends after <GEOMETRY> tag!?") while not geo_line.startswith("</GEOMETRY>"): geo_block.append(geo_line) try: geo_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Dangling <GEOMETRY> tag in configuration!") end = start + len(geo_block) + 2 content = content[:start] + content[end:] offset -= 1 # Any Backend Definitions if line.strip().startswith("<CLUSTERBACKEND>"): back_block = list() start = n + offset try: back_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Config ends after <CLUSTERBACKEND> tag!?") while not back_line.startswith("</CLUSTERBACKEND>"): back_block.append(back_line) try: back_line = next(iter_content).strip() except StopIteration: raise RuntimeError("Dangling <CLUSTERBACKEND> tag in configuration!") end = start + len(back_block) + 2 backend_defs.append(back_block) content = content[:start] + content[end:] offset -= 1 # Parse them self.geometry.parse_from_block(geo_block) self.geometry.parse_charge_block(charge_block) self.geometry.parse_spin_block(spin_block) self.fitness.parse_backend_tags(backend_defs) # Deal with the rest for line in content: if line.strip().startswith("LocOptAlgo="): self.fitness.parse_locopt_algo(line.strip()[11:]) elif line.strip().startswith("GlobOptAlgo="): self.globopt.parse_globopt_string(line.strip()[12:]) else: for key in self.options.values: type = self.options.values[key].type if re.match(key + "=", line.strip()): value, index = parse_value(line.strip()[len(key) + 1:], type) if value is not None: print("Option {:>30} set to: {:>30}".format(key, str(value))) self.options.values[key].set(value, index) else: print("ERROR: Could not set Option %s. Set to default instead!" % key) self.options.values[key].set(self.options.defaults[key]) if not preset: self.file_manager.signal_saved(path) else: self.file_manager.signal_modification() def parse_value(line, type): value = None index = -1 work = line.strip() if type is str: value = work elif type is int: value = int(work) elif type is float: value = float(work) elif type is bool: value = work.lower() == "true" elif type is list: tmp = work.split(";") value = [float(tmp[0]), float(tmp[1]), float(tmp[2])] return value, index class OGUILEMGeneralConfig: def __init__(self): self.defaults = dict() self.values = dict() for key in options: type, default = options[key] if type == "str": self.defaults[key] = default elif type == "int": self.defaults[key] = int(default) elif type == "float": self.defaults[key] = float(default) elif type == "bool": self.defaults[key] = (default.lower() == "true") elif type == "3;float": default = default.strip().split(";") self.defaults[key] = [float(default[0]), float(default[1]), float(default[2])] else: raise IOError("Could not parse xml key %s in general configs!" % key) self.values[key] = ConnectedValue(self.defaults[key]) def set_to_default(self): for key in options: self.values[key].set(self.defaults[key]) def get_finished_config(self) -> str: content = "" for key in self.values: self.values[key].request_update() value = self.values[key].value if value != self.defaults[key]: content += "\n" + key + "=" + str(self.values[key]) return content def find_config_folder(): if sys.platform == 'Windows': path = os.path.join(os.environ['APPDATA'], 'oguilem') else: path = os.path.join(os.environ['HOME'], '.config', 'oguilem') if not os.path.isdir(path): os.mkdir(path) return path class OGUILEMUIConfig: def __init__(self): self.window_size = None self.window_position = None self.java_path = None self.java_vm_variables = None self.ogo_path = None self.ogo_args = None self.environmental_variables = None self.recover_from_file() def get_run_command(self, custom_run_command=""): run_cmd = custom_run_command if custom_run_command else self.ogo_args if not all([self.java_path, self.ogo_path, self.ogo_args]): raise RuntimeError("Cannot run ogolem without knowing java and ogolem paths as well as ogolem arguments!") if self.java_vm_variables: return "%s %s -jar %s %s" % (self.java_path, self.java_vm_variables, self.ogo_path, run_cmd) return "%s -jar %s %s" % (self.java_path, self.ogo_path, run_cmd) def recover_from_file(self): path = os.path.join(find_config_folder(), "oguilem.cfg") try: with open(path, "r") as config: lines = config.readlines() for line in lines: work = line.strip() if work.startswith("WINDOWSIZE"): self.window_size = (int(work.split()[1]), int(work.split()[2])) elif work.startswith("WINDOWPOS"): self.window_position = (int(work.split()[1]), int(work.split()[2])) elif work.startswith("JAVAPATH"): self.java_path = work[8:].strip() elif work.startswith("JAVAVM"): self.java_vm_variables = work[6:].strip() elif work.startswith("OGOPATH"): self.ogo_path = work[7:].strip() elif work.startswith("OGOARGS"): self.ogo_args = work[7:].strip() elif work.startswith("ENV"): self.environmental_variables = work[3:].strip() except ValueError: print("There are format errors in the UI config file in '%s'. Using defaults." % find_config_folder()) except IOError: print("Config file not found. A new one will generate once the program exits.") def save_to_file(self): path = os.path.join(find_config_folder(), "oguilem.cfg") with open(path, "w") as config: if self.window_size: config.write("WINDOWSIZE %d %d\n" % (self.window_size[0], self.window_size[1])) if self.window_position: config.write("WINDOWPOS %d %d\n" % (self.window_position[0], self.window_position[1])) if self.java_path: config.write("JAVAPATH %s\n" % self.java_path) if self.java_vm_variables: config.write("JAVAVM %s\n" % self.java_vm_variables) if self.ogo_path: config.write("OGOPATH %s\n" % self.ogo_path) if self.ogo_args: config.write("OGOARGS %s\n" % self.ogo_args) if self.java_path: config.write("ENV %s\n" % self.environmental_variables)

43.494382

118

0.533023

10,555

0.908895

0

1,341

0.115474

295cb6523225a5b823029ec4f2d16b55a8369739

8,705

py

Python

xpd_workflow/temp_graph.py

CJ-Wright/xpd_workflow

f3fd84831b86b696631759946c3af9b16b45de26

[ "BSD-3-Clause" ]

null

xpd_workflow/temp_graph.py

CJ-Wright/xpd_workflow

f3fd84831b86b696631759946c3af9b16b45de26

[ "BSD-3-Clause" ]

4

2016-08-25T02:59:05.000Z

2016-09-28T22:32:34.000Z

xpd_workflow/temp_graph.py

CJ-Wright/xpd_workflow

f3fd84831b86b696631759946c3af9b16b45de26

[ "BSD-3-Clause" ]

null

from __future__ import (division, print_function) import matplotlib.cm as cmx import matplotlib.colors as colors from matplotlib import gridspec from metadatastore.api import db_connect as mds_db_connect from filestore.api import db_connect as fs_db_connect fs_db_connect( **{'database': 'data-processing-dev', 'host': 'localhost', 'port': 27017}) mds_db_connect( **{'database': 'data-processing-dev', 'host': 'localhost', 'port': 27017}) from databroker import db, get_events from datamuxer import DataMuxer from sidewinder_spec.utils.handlers import * import logging from xpd_workflow.parsers import parse_xrd_standard logger = logging.getLogger(__name__) if __name__ == '__main__': import os import numpy as np import matplotlib.pyplot as plt save = True lam = 1.54059 # Standard reflections for sample components niox_hkl = ['111', '200', '220', '311', '222', '400', '331', '420', '422', '511'] niox_tth = np.asarray( [37.44, 43.47, 63.20, 75.37, 79.87, 95.58, 106.72, 111.84, 129.98, 148.68]) pr3_hkl = ['100', '001', '110', '101', '111', '200', '002', '210', '211', '112', '202'] pr3_tth = np.asarray( [22.96, 24.33, 32.70, 33.70, 41.18, 46.92, 49.86, 52.86, 59.00, 60.91, 70.87] ) pr4_hkl = ['111', '113', '008', '117', '200', '119', '028', '0014', '220', '131', '1115', '0214', '317', '31Na', '2214', '040', '400'] pr4_tth = np.asarray( [23.43, 25.16, 25.86, 32.62, 33.36, 37.67, 42.19, 46.11, 47.44, 53.18, 55.55, 57.72, 59.10, 59.27, 68.25, 68.71, 70.00] ) pr2_tth, pr2int, pr2_hkl = parse_xrd_standard( '/mnt/bulk-data/research_data/Pr2NiO4orthorhombicPDF#97-008-1577.txt') pr2_tth = pr2_tth[pr2int > 5.] prox_hkl = ['111', '200', '220', '311', '222', '400', '331', '420', '422', '511', '440', '531', '600'] prox_tth = np.asarray( [28.25, 32.74, 46.99, 55.71, 58.43, 68.59, 75.73, 78.08, 87.27, 94.12, 105.63, 112.90, 115.42] ) standard_names = [ # 'NiO', 'Pr3Ni2O7', 'Pr2NiO4', # 'Pr4' 'Pr6O11' ] master_hkl = [ # niox_hkl, pr3_hkl, pr2_hkl, # pr4_hkl prox_hkl ] master_tth = [ # niox_tth, pr3_tth, pr2_tth, # pr4_tth prox_tth ] color_map = [ # 'red', 'blue', 'black', 'red' ] line_style = ['--', '-.', ':', ] ns = [1, 2, 3, 4, 5, # 18, 20, 22, 16, 28, 29, 27, 26 ] # ns = [26] ns.sort() # for i in ns: legended_hkl = [] print(i) folder = '/mnt/bulk-data/research_data/USC_beamtime/APS_March_2016/S' + str( i) + '/temp_exp' hdr = db(run_folder=folder)[0] dm = DataMuxer() dm.append_events(get_events(hdr)) df = dm.to_sparse_dataframe() print(df.keys()) binned = dm.bin_on('img', interpolation={'T': 'linear'}) # key_list = [f for f in os.listdir(folder) if # f.endswith('.gr') and not f.startswith('d')] key_list = [f for f in os.listdir(folder) if f.endswith('.chi') and not f.startswith('d') and f.strip( '0.chi') != '' and int( f.lstrip('0').strip('.chi')) % 2 == 1] key_list.sort() key_list = key_list[:-1] # key_list2.sort() idxs = [int(os.path.splitext(f)[0]) for f in key_list] Ts = binned['T'].values[idxs] output = os.path.splitext(key_list[0])[-1][1:] if key_list[0].endswith('.gr'): offset = .1 skr = 0 else: skr = 8 offset = .001 data_list = [(np.loadtxt(os.path.join(folder, f), skiprows=skr )[:, 0], np.loadtxt(os.path.join(folder, f), skiprows=skr )[:, 1]) for f in key_list] ylim_min = None for xmax, length in zip( [len(data_list[0][0]) - 1, len(data_list[0][0]) - 1], ['short', 'full']): fig = plt.figure(figsize=(26, 12)) gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1]) ax1 = plt.subplot(gs[0]) if length == 'short': ax1.set_xlim(1.5, 4.5) ax2 = plt.subplot(gs[1], sharey=ax1) plt.setp(ax2.get_yticklabels(), visible=False) cm = plt.get_cmap('viridis') cNorm = colors.Normalize(vmin=0, vmax=len(key_list)) scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm) for idx in range(len(key_list)): xnm, y = data_list[idx] colorVal = scalarMap.to_rgba(idx) if output == 'chi': x = xnm / 10. ax1.plot(x[:xmax], y[:xmax] + idx * offset, color=colorVal) ax2.plot(Ts[idx], y[-1] + idx * offset, marker='o', color=colorVal) if ylim_min is None or ylim_min > np.min( y[:xmax + idx * offset]): ylim_min = np.min(y[:xmax + idx * offset]) ax2.set_xticklabels([str(f) for f in ax2.get_xticks()], rotation=90) if output == 'gr': bnds = ['O-Pr', 'O-Ni', 'Ni-Ni', 'Pr-Pr', 'Ni-Pr', 'O-Pr', 'O-Ni', 'Ni-Ni-Ni', 'Pr-Ni', 'Pr-Pr', 'Pr-Ni-O', 'Ni-Pr-Ni', 'Pr-Pr', 'Rs:Pr-Pr', 'Rs:Pr_Pr'] bnd_lens = [2.320, 1.955, 3.883, 3.765, 3.186, 2.771, 2.231, 7.767, 4.426, 6.649, 4.989, 5.404, 3.374, 3.910, 8.801] # ax1.grid(True) # ax2.grid(True) for bnd, bnd_len in zip(bnds, bnd_lens): ax1.axvline(bnd_len, color='grey', linestyle='--') ax3 = ax1.twiny() ax3.set_xticks(np.asarray(bnd_lens) / x[xmax]) ax3.set_xticklabels(bnds, rotation=90) else: std_axis = [] for n, hkls, tths, color, ls in zip(standard_names, master_hkl, master_tth, color_map, line_style): std_axis.append(ax1.twiny()) ax3 = std_axis[-1] hkl_q = np.pi * 4 * np.sin(np.deg2rad(tths / 2)) / lam for k, (hkl, q) in enumerate(zip(hkls, hkl_q)): if n not in legended_hkl: ax1.axvline(q, color=color, linestyle=ls, lw=2, label=n ) legended_hkl.append(n) else: ax1.axvline(q, color=color, linestyle=ls, lw=2, ) a = hkl_q > ax1.get_xlim()[0] b = hkl_q < ax1.get_xlim()[1] c = a & b ax3.set_xticks(list((hkl_q[c] - ax1.get_xlim()[0]) / ( ax1.get_xlim()[1] - ax1.get_xlim()[0]) )) ax3.set_xticklabels(hkls, rotation=90, color=color) ax2.set_xlabel('Temperature C') if output == 'gr': fig.suptitle('S{} PDF'.format(i)) ax1.set_xlabel(r"$r (\AA)$") ax1.set_ylabel(r"$G (\AA^{-2})$") elif output == 'chi': fig.suptitle('S{} I(Q)'.format(i)) ax1.set_xlabel(r"$Q (\AA^{-1})$") ax1.set_ylabel(r"$I (Q) $") ax1.set_ylim(ylim_min) ax1.legend() gs.tight_layout(fig, rect=[0, 0, 1, .98], w_pad=1e-6) if save: fig.savefig(os.path.join('/mnt/bulk-data/Dropbox/', 'S{}_{}_output_{}.png'.format( i, length, output))) fig.savefig(os.path.join('/mnt/bulk-data/Dropbox/', 'S{}_{}_output_{}.eps'.format( i, length, output))) else: plt.show()

38.688889

84

0.443538

0

1,290

0.148191

295d0342f32753f768fc55a488c38b501e122b06

12,101

py

Python

winnow/core.py

bgschiller/winnow

0fde7fcc9e2fe3519528feb9115658aa3b3954e5

[ "MIT" ]

3

2017-08-10T16:20:29.000Z

2018-09-19T01:33:13.000Z

winnow/core.py

bgschiller/winnow

0fde7fcc9e2fe3519528feb9115658aa3b3954e5

[ "MIT" ]

null

winnow/core.py

bgschiller/winnow

0fde7fcc9e2fe3519528feb9115658aa3b3954e5

[ "MIT" ]

1

2019-11-29T20:17:23.000Z

from __future__ import unicode_literals import copy import json from six import string_types from . import default_operators from . import sql_prepare from . import values from .error import WinnowError from .templating import SqlFragment from .templating import WinnowSql class Winnow(object): """ Winnow is a SQL query builder specifically designed for powerful filtering on a table. It is designed to be efficient and low-magic. """ # Take care here -- In order to avoid mucking up the parent's copy of this # static value we have to deep copy it to every subclass. _special_cases = {} sql_class = WinnowSql def __init__(self, table, sources): self.table = table self.sources = sources self.sql = self.sql_class() def prepare_query(self, *args, **kwargs): """ Proxy to self.sql """ return self.sql.prepare_query(*args, **kwargs) def resolve(self, filt): """ Given a filter, resolve (expand) all it's clauses. A resolved clause includes information about the value type of the data source, and how to perform queries against that data source. return the modified filter. """ filt['logical_op'] = filt.get('logical_op', '&') if filt['logical_op'] not in '&|': raise WinnowError("Logical op must be one of &, |. Given: {}".format( filt['logical_op'])) for ix in range(len(filt['filter_clauses'])): filt['filter_clauses'][ix] = self.resolve_clause( filt['filter_clauses'][ix]) return filt def validate(self, filt): """ Make sure a filter is valid (resolves properly), but avoid bulking up the json object (probably because it's about to go into the db, or across the network) """ self.resolve(copy.deepcopy(filt)) return filt def resolve_clause(self, filter_clause): """ Given a filter_clause, check that it's valid. Return a dict-style filter_clause with a vivified value field. """ if 'logical_op' in filter_clause: # nested filter return self.resolve(filter_clause) ds, op = self.resolve_components(filter_clause) value = self.vivify(op['value_type'], filter_clause['value']) filter_clause['data_source_resolved'] = ds filter_clause['operator_resolved'] = op filter_clause['value_vivified'] = value filter_clause['summary'] = self.summarize(filter_clause) return filter_clause def summarize(self, filter_clause): ds = filter_clause['data_source_resolved'] op = filter_clause['operator_resolved'] value = filter_clause['value_vivified'] cvt = self.coalesce_value_type(op['value_type']) value_string = value operator_string = op.get('summary_template') or '{{data_source}} {} {{value}}'.format(op['name']) if cvt == 'collection': operator_string, value_string = self.summarize_collection(filter_clause) elif cvt == 'relative_date': value_string = value.replace('_', ' ') elif cvt == 'numeric': value_string = '{:,}'.format(value) return operator_string.format(data_source=ds['display_name'], value=value_string) @classmethod def coalesce_value_type(cls, value_type): for op in cls.operators: if op['value_type'] == value_type: return op.get('coalesced_value_type', value_type) return value_type @classmethod def summarize_collection(cls, filter_clause): value = filter_clause['value'] if isinstance(filter_clause['value'], list) else json.loads(filter_clause['value']) operator_string = '{data_source} any of {value}' if len(value) != 1 else '{data_source} is {value}' if not value: value_string = '(none)' else: value_string = ', '.join(value) return operator_string, value_string @staticmethod def empty_filter(): return dict(logial_op='&', filter_clauses=[]) @classmethod def vivify(cls, value_type, value): """De-stringify <value> into <value_type> Raises WinnowError if <value> is not well formatted for that type.""" cvt = cls.coalesce_value_type(value_type) if cvt == 'string': return values.vivify_string(value) elif cvt == 'collection': return values.vivify_collection(value) elif cvt in ('numeric', 'string_length'): return values.vivify_numeric(value) elif cvt == 'relative_date': return values.vivify_relative_date(value) elif cvt == 'absolute_date': return values.vivify_absolute_date(value) elif cvt in ('bool', 'nullable'): return values.vivify_bool(value) elif cvt == 'single_choice': return values.vivify_single_choice(value) else: raise WinnowError("Unknown value_type, '{}'".format(value_type)) @classmethod def stringify(cls, value_type, value): cvt = cls.coalesce_value_type(value_type) if isinstance(value, string_types): value = cls.vivify(value_type, value) if cvt == 'string': return values.stringify_string(value) elif cvt == 'collection': return values.stringify_collection(value) elif cvt in ('numeric', 'string_length'): return values.stringify_numeric(value) elif cvt == 'relative_date': return values.stringify_relative_date(value) elif cvt == 'absolute_date': return values.stringify_absolute_date(value) elif cvt in ('bool', 'nullable'): return values.stringify_bool(value) elif cvt == 'single_choice': return values.stringify_single_choice(value) raise WinnowError("Unknown value_type, '{}'".format(value_type)) operators = default_operators.OPERATORS def resolve_operator(self, op_name, value_types): '''Given an operator name, return an Op object. Raise an error if the operator is not found''' if not isinstance(op_name, string_types): raise WinnowError("Bad operator type, '{}'. expected string".format(type(op_name))) op_name = op_name.lower() matches = [op for op in self.operators if op['name'].lower() == op_name and op['value_type'] in value_types] if len(matches) == 0: raise WinnowError("Unknown operator '{}'".format(op_name)) return matches.pop() def resolve_source(self, source_name): """ Given a source name, return a resolved data source. Raise an error if the source name is not allowable """ matches = [source for source in self.sources if source['display_name'] == source_name] if len(matches) == 0: raise WinnowError("Unknown data source '{}'".format(source_name)) elif len(matches) > 1: raise WinnowError("Ambiguous data source '{}'".format(source_name)) return matches.pop() def resolve_components(self, clause): source = self.resolve_source(clause['data_source']) operator = self.resolve_operator(clause['operator'], source['value_types']) return source, operator def query(self, filt): return self.prepare_query( "SELECT * FROM {{ table | sqlsafe }} WHERE {{ condition }}", table=self.table, condition=self.where_clauses(filt)) def strip(self, filt): """ Perform the opposite of resolving a filter. """ for k in ('data_source_resolved', 'operator_resolved', 'value_vivified'): filt.pop(k, None) if 'filter_clauses' in filt: filt['filter_clauses'] = [self.strip(f) for f in filt['filter_clauses']] return filt def where_clauses(self, filt): ''' Apply a user filter. Returns a paren-wrapped WHERE clause suitable for using in a SELECT statement on the opportunity table. ''' if not filt['filter_clauses']: return True filt = self.resolve(filt) where_clauses = [] for clause in filt['filter_clauses']: if 'logical_op' in clause: # nested filter where_clauses.append(self.where_clauses(clause)) elif 'data_source_resolved' in clause: where_clauses.append(self._dispatch_clause(clause)) else: # I don't expect to ever get here, because we should hit this # issue when we call `filt = self.resolve(filt)` raise WinnowError("Somehow, this is neither a nested filter, nor a resolved clause") if not where_clauses: return True sep = '\nAND \n ' if filt['logical_op'] == '&' else '\nOR \n ' self.strip(filt) sql_frag = SqlFragment.join(sep, where_clauses) sql_frag.query = '(' + sql_frag.query + ')' return sql_frag def _dispatch_clause(self, clause): """ Evaluates whether a clause is standard, special, or custom and calls the appropriate specialization function. Each specialization returns a paren-wrapped WHERE clause, to be AND'd or OR'd together to produce a final clause.""" for k in ('data_source_resolved', 'operator_resolved', 'value_vivified'): if k not in clause: raise WinnowError('failed to resolve component: {}'.format(k)) op = clause['operator_resolved'] special_handler = self.special_case_handler( source_name=clause['data_source'], value_type=op['value_type']) if special_handler is not None: return special_handler(self, clause) return self._default_clause(clause) def where_clause(self, data_source, operator, value): return sql_prepare.where_clause(data_source['column'], operator, value) def _default_clause(self, clause): """ Given a filter_clause, convert it to a WHERE clause """ ds = clause['data_source_resolved'] op = clause['operator_resolved'] value = clause['value_vivified'] return self.where_clause(ds, op, value) @classmethod def special_case(cls, source_name, *value_types): """ Register a special case handler. A special case handler is a function s: s(Winnow(), clause) -> WHERE clause string """ if cls._special_cases is getattr(super(cls, cls), '_special_cases', None): raise RuntimeError('Please define your own _special_cases dict, so as to avoid modifying your parent. ' 'Note to self: come up with a more durable way to handle this.') # ideas: # proxy the _special_cases as the union of own and parent's version. def decorator(func): """ Register a function in the handler table. """ for value_type in value_types: if (source_name, value_type) in cls._special_cases: raise WinnowError("Conflicting handlers registered for ({},{}): {} and {}".format( value_type, source_name, cls._special_cases[(source_name, value_type)].__name__, func.__name__)) cls._special_cases[(source_name, value_type)] = func return func return decorator def special_case_handler(self, source_name, value_type): """ Check if a given value_type, source_name pair has a special case handler. :return: A function handler for that case accepting the winnow instance and the clause. """ return self._special_cases.get((source_name, value_type))

37.934169

122

0.614081

11,823

0.977027

0

3,841

0.317412

0

4,277

0.353442

295d64816bed48df8774a68b70c332508540215b

12,525

py

Python

ibis/bigquery/client.py

tswast/ibis

2f6d47e4c33cefd7ea1d679bb1d9253c2245993b

[ "Apache-2.0" ]

null

ibis/bigquery/client.py

tswast/ibis

2f6d47e4c33cefd7ea1d679bb1d9253c2245993b

[ "Apache-2.0" ]

null

ibis/bigquery/client.py

tswast/ibis

2f6d47e4c33cefd7ea1d679bb1d9253c2245993b

[ "Apache-2.0" ]

null

import regex as re import time import collections import datetime import six import pandas as pd import google.cloud.bigquery as bq from multipledispatch import Dispatcher import ibis import ibis.common as com import ibis.expr.operations as ops import ibis.expr.types as ir import ibis.expr.schema as sch import ibis.expr.datatypes as dt import ibis.expr.lineage as lin from ibis.compat import parse_version from ibis.client import Database, Query, SQLClient from ibis.bigquery import compiler as comp from google.api.core.exceptions import BadRequest NATIVE_PARTITION_COL = '_PARTITIONTIME' def _ensure_split(table_id, dataset_id): split = table_id.split('.') if len(split) > 1: assert len(split) == 2 if dataset_id: raise ValueError( "Can't pass a fully qualified table name *AND* a dataset_id" ) (dataset_id, table_id) = split return (table_id, dataset_id) _IBIS_TYPE_TO_DTYPE = { 'string': 'STRING', 'int64': 'INT64', 'double': 'FLOAT64', 'boolean': 'BOOL', 'timestamp': 'TIMESTAMP', 'date': 'DATE', } _DTYPE_TO_IBIS_TYPE = { 'INT64': dt.int64, 'FLOAT64': dt.double, 'BOOL': dt.boolean, 'STRING': dt.string, 'DATE': dt.date, # FIXME: enforce no tz info 'DATETIME': dt.timestamp, 'TIME': dt.time, 'TIMESTAMP': dt.timestamp, 'BYTES': dt.binary, } _LEGACY_TO_STANDARD = { 'INTEGER': 'INT64', 'FLOAT': 'FLOAT64', 'BOOLEAN': 'BOOL', } @dt.dtype.register(bq.schema.SchemaField) def bigquery_field_to_ibis_dtype(field): typ = field.field_type if typ == 'RECORD': fields = field.fields assert fields names = [el.name for el in fields] ibis_types = list(map(dt.dtype, fields)) ibis_type = dt.Struct(names, ibis_types) else: ibis_type = _LEGACY_TO_STANDARD.get(typ, typ) ibis_type = _DTYPE_TO_IBIS_TYPE.get(ibis_type, ibis_type) if field.mode == 'REPEATED': ibis_type = dt.Array(ibis_type) return ibis_type @sch.infer.register(bq.table.Table) def bigquery_schema(table): pairs = [(el.name, dt.dtype(el)) for el in table.schema] try: if table.list_partitions(): pairs.append((NATIVE_PARTITION_COL, dt.timestamp)) except BadRequest: pass return sch.schema(pairs) class BigQueryCursor(object): """Cursor to allow the BigQuery client to reuse machinery in ibis/client.py """ def __init__(self, query): self.query = query def fetchall(self): return list(self.query.fetch_data()) @property def columns(self): return [field.name for field in self.query.schema] def __enter__(self): # For compatibility when constructed from Query.execute() return self def __exit__(self, exc_type, exc_value, traceback): pass def _find_scalar_parameter(expr): """:func:`~ibis.expr.lineage.traverse` function to find all :class:`~ibis.expr.types.ScalarParameter` instances and yield the operation and the parent expresssion's resolved name. Parameters ---------- expr : ibis.expr.types.Expr Returns ------- Tuple[bool, object] """ op = expr.op() if isinstance(op, ops.ScalarParameter): result = op, expr.get_name() else: result = None return lin.proceed, result class BigQueryQuery(Query): def __init__(self, client, ddl, query_parameters=None): super(BigQueryQuery, self).__init__(client, ddl) # self.expr comes from the parent class query_parameter_names = dict( lin.traverse(_find_scalar_parameter, self.expr)) self.query_parameters = [ bigquery_param( param.to_expr().name(query_parameter_names[param]), value ) for param, value in (query_parameters or {}).items() ] def _fetch(self, cursor): df = pd.DataFrame(cursor.fetchall(), columns=cursor.columns) return self.schema().apply_to(df) def execute(self): # synchronous by default with self.client._execute( self.compiled_sql, results=True, query_parameters=self.query_parameters ) as cur: result = self._fetch(cur) return self._wrap_result(result) class BigQueryAPIProxy(object): def __init__(self, project_id): self._client = bq.Client(project_id) @property def client(self): return self._client @property def project_id(self): return self.client.project def get_datasets(self): return list(self.client.list_datasets()) def get_dataset(self, dataset_id): return self.client.dataset(dataset_id) def get_table(self, table_id, dataset_id, reload=True): (table_id, dataset_id) = _ensure_split(table_id, dataset_id) table = self.client.dataset(dataset_id).table(table_id) if reload: table.reload() return table def get_schema(self, table_id, dataset_id): return self.get_table(table_id, dataset_id).schema def run_sync_query(self, stmt): query = self.client.run_sync_query(stmt) query.use_legacy_sql = False query.run() # run_sync_query is not really synchronous: there's a timeout while not query.job.done(): query.job.reload() time.sleep(0.1) return query class BigQueryDatabase(Database): pass bigquery_param = Dispatcher('bigquery_param') @bigquery_param.register(ir.StructScalar, collections.OrderedDict) def bq_param_struct(param, value): field_params = [bigquery_param(param[k], v) for k, v in value.items()] return bq.StructQueryParameter(param.get_name(), *field_params) @bigquery_param.register(ir.ArrayValue, list) def bq_param_array(param, value): param_type = param.type() assert isinstance(param_type, dt.Array), str(param_type) try: bigquery_type = _IBIS_TYPE_TO_DTYPE[str(param_type.value_type)] except KeyError: raise com.UnsupportedBackendType(param_type) else: return bq.ArrayQueryParameter(param.get_name(), bigquery_type, value) @bigquery_param.register( ir.TimestampScalar, six.string_types + (datetime.datetime, datetime.date) ) def bq_param_timestamp(param, value): assert isinstance(param.type(), dt.Timestamp) # TODO(phillipc): Not sure if this is the correct way to do this. timestamp_value = pd.Timestamp(value, tz='UTC').to_pydatetime() return bq.ScalarQueryParameter( param.get_name(), 'TIMESTAMP', timestamp_value) @bigquery_param.register(ir.StringScalar, six.string_types) def bq_param_string(param, value): return bq.ScalarQueryParameter(param.get_name(), 'STRING', value) @bigquery_param.register(ir.IntegerScalar, six.integer_types) def bq_param_integer(param, value): return bq.ScalarQueryParameter(param.get_name(), 'INT64', value) @bigquery_param.register(ir.FloatingScalar, float) def bq_param_double(param, value): return bq.ScalarQueryParameter(param.get_name(), 'FLOAT64', value) @bigquery_param.register(ir.BooleanScalar, bool) def bq_param_boolean(param, value): return bq.ScalarQueryParameter(param.get_name(), 'BOOL', value) @bigquery_param.register(ir.DateScalar, six.string_types) def bq_param_date_string(param, value): return bigquery_param(param, pd.Timestamp(value).to_pydatetime().date()) @bigquery_param.register(ir.DateScalar, datetime.datetime) def bq_param_date_datetime(param, value): return bigquery_param(param, value.date()) @bigquery_param.register(ir.DateScalar, datetime.date) def bq_param_date(param, value): return bq.ScalarQueryParameter(param.get_name(), 'DATE', value) class BigQueryClient(SQLClient): sync_query = BigQueryQuery database_class = BigQueryDatabase proxy_class = BigQueryAPIProxy dialect = comp.BigQueryDialect def __init__(self, project_id, dataset_id): self._proxy = type(self).proxy_class(project_id) self._dataset_id = dataset_id @property def project_id(self): return self._proxy.project_id @property def dataset_id(self): return self._dataset_id @property def _table_expr_klass(self): return ir.TableExpr def table(self, *args, **kwargs): t = super(BigQueryClient, self).table(*args, **kwargs) if NATIVE_PARTITION_COL in t.columns: col = ibis.options.bigquery.partition_col assert col not in t return (t .mutate(**{col: t[NATIVE_PARTITION_COL]}) .drop([NATIVE_PARTITION_COL])) return t def _build_ast(self, expr, context): result = comp.build_ast(expr, context) return result def _execute_query(self, dml, async=False): klass = self.async_query if async else self.sync_query inst = klass(self, dml, query_parameters=dml.context.params) df = inst.execute() return df def _fully_qualified_name(self, name, database): dataset_id = database or self.dataset_id return dataset_id + '.' + name def _get_table_schema(self, qualified_name): return self.get_schema(qualified_name) def _execute(self, stmt, results=True, query_parameters=None): # TODO(phillipc): Allow **kwargs in calls to execute query = self._proxy.client.run_sync_query(stmt) query.use_legacy_sql = False query.query_parameters = query_parameters or [] query.run() # run_sync_query is not really synchronous: there's a timeout while not query.job.done(): query.job.reload() time.sleep(0.1) return BigQueryCursor(query) def database(self, name=None): if name is None: name = self.dataset_id return self.database_class(name, self) @property def current_database(self): return self.database(self.dataset_id) def set_database(self, name): self._dataset_id = name def exists_database(self, name): return self._proxy.get_dataset(name).exists() def list_databases(self, like=None): results = [dataset.name for dataset in self._proxy.get_datasets()] if like: results = [ dataset_name for dataset_name in results if re.match(like, dataset_name) ] return results def exists_table(self, name, database=None): (table_id, dataset_id) = _ensure_split(name, database) return self._proxy.get_table(table_id, dataset_id).exists() def list_tables(self, like=None, database=None): dataset = self._proxy.get_dataset(database or self.dataset_id) result = [table.name for table in dataset.list_tables()] if like: result = [ table_name for table_name in result if re.match(like, table_name) ] return result def get_schema(self, name, database=None): (table_id, dataset_id) = _ensure_split(name, database) bq_table = self._proxy.get_table(table_id, dataset_id) return sch.infer(bq_table) @property def version(self): return parse_version(bq.__version__) _DTYPE_TO_IBIS_TYPE = { 'INT64': dt.int64, 'FLOAT64': dt.double, 'BOOL': dt.boolean, 'STRING': dt.string, 'DATE': dt.date, # FIXME: enforce no tz info 'DATETIME': dt.timestamp, 'TIME': dt.time, 'TIMESTAMP': dt.timestamp, 'BYTES': dt.binary, } _LEGACY_TO_STANDARD = { 'INTEGER': 'INT64', 'FLOAT': 'FLOAT64', 'BOOLEAN': 'BOOL', } def _discover_type(field): typ = field.field_type if typ == 'RECORD': fields = field.fields assert fields names = [el.name for el in fields] ibis_types = [_discover_type(el) for el in fields] ibis_type = dt.Struct(names, ibis_types) else: ibis_type = _LEGACY_TO_STANDARD.get(typ, typ) ibis_type = _DTYPE_TO_IBIS_TYPE.get(ibis_type, ibis_type) if field.mode == 'REPEATED': ibis_type = dt.Array(ibis_type) return ibis_type def bigquery_table_to_ibis_schema(table): pairs = [(el.name, _discover_type(el)) for el in table.schema] try: if table.list_partitions(): pairs.append((NATIVE_PARTITION_COL, dt.timestamp)) except BadRequest: pass return ibis.schema(pairs)

28.020134

79

0.660918

6,190

0.494212

0

3,641

0.290699

0

1,319

0.105309

295d6dddae668ee8a211bf176e96dec0fc246700

1,583

py

Python

5 kyu/Family Tree Ancestors.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

6

2020-09-03T09:32:25.000Z

2020-12-07T04:10:01.000Z

5 kyu/Family Tree Ancestors.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

1

2021-12-13T15:30:21.000Z

5 kyu/Family Tree Ancestors.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

null

from math import log, ceil def chart(person): res=helper(tuple(sorted(person.parents(), key=lambda x: x.sex, reverse=True)), 2, [], 16, 16) res.append((person.name, 16)) dict={j:i for i,j in res} dict2=helper2(16) chart=[] for i in range(1, 32): temp=((4-depth(i))*11-1)*" "+("|" if 4-depth(i)!=0 else "") name=dict.get(i, "_______") number=str(dict2[i]).rjust(2, "0") temp+=f"{number} {name}" chart.append(list(temp)) for index,i in enumerate(chart): digits=int("".join(j for j in "".join(i) if j.isdigit())) num=5-ceil(log(digits+1, 2)) if digits==1 or num==0: continue for k in range(1, 2**num): chart[index+(-k if digits%2 else k)][44-num*11-1]="|" chart[16][32]=" " chart[14][32]=" " return "\n".join("".join(i) for i in chart)+"\n" def helper(person, index, arr, row, rate): if person==None or index>31: return rate//=2 for i,j in enumerate(person): if (index+i)<=31: arr.append((j.name, row-rate if j.sex=="M" else row+rate)) helper(tuple(sorted(j.parents(), key=lambda x: x.sex, reverse=True)), (index+i)*2, arr, row-rate if j.sex=="M" else row+rate, rate) return arr def depth(num): total=0 while num%2==0: num//=2 total+=1 return total def helper2(num): start=0 dict={} while num>0: increment=2**(start+1) for i in range(num): dict[2**start+i*increment]=num+i start+=1 num//=2 return dict

32.979167

139

0.53885

0

67

0.042325

295da24723071b30363f5dee9937e755f296d5c6

690

py

Python

tests/make_expected_lookup.py

bfis/coffea

e5e67d410e86faee1172fcc864774d7024d97653

[ "BSD-3-Clause" ]

77

2019-06-09T14:23:33.000Z

2022-03-22T21:34:01.000Z

tests/make_expected_lookup.py

bfis/coffea

e5e67d410e86faee1172fcc864774d7024d97653

[ "BSD-3-Clause" ]

353

2019-06-05T23:54:39.000Z

2022-03-31T21:21:47.000Z

tests/make_expected_lookup.py

bfis/coffea

e5e67d410e86faee1172fcc864774d7024d97653

[ "BSD-3-Clause" ]

71

2019-06-07T02:04:11.000Z

2022-03-05T21:03:45.000Z

import numpy as np import ROOT from dummy_distributions import dummy_pt_eta counts, test_in1, test_in2 = dummy_pt_eta() f = ROOT.TFile.Open("samples/testSF2d.root") sf = f.Get("scalefactors_Tight_Electron") xmin, xmax = sf.GetXaxis().GetXmin(), sf.GetXaxis().GetXmax() ymin, ymax = sf.GetYaxis().GetXmin(), sf.GetYaxis().GetXmax() test_out = np.empty_like(test_in1) for i, (eta, pt) in enumerate(zip(test_in1, test_in2)): if xmax <= eta: eta = xmax - 1.0e-5 elif eta < xmin: eta = xmin if ymax <= pt: pt = ymax - 1.0e-5 elif pt < ymin: pt = ymin ib = sf.FindBin(eta, pt) test_out[i] = sf.GetBinContent(ib) print(repr(test_out))

24.642857

61

0.649275

0

52

0.075362

295e24a9ef2f154bf2eab43ba3f883adfaf8378d

5,755

py

Python

engine/sentiment_analysis.py

zgeorg03/nesase

4dae70994cd0c730a88b4a54e6b8e29868aafb09

[ "BSD-3-Clause" ]

2

2020-12-30T18:03:01.000Z

2021-08-08T21:05:43.000Z

engine/sentiment_analysis.py

zgeorg03/nesase

4dae70994cd0c730a88b4a54e6b8e29868aafb09

[ "BSD-3-Clause" ]

null

engine/sentiment_analysis.py

zgeorg03/nesase

4dae70994cd0c730a88b4a54e6b8e29868aafb09

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Mar 14 17:42:27 2018 @author: zgeorg03 """ import re import json # Used for converting json to dictionary import datetime # Used for date conversions import matplotlib.pyplot as plt import numpy as np from sentiment import Sentiment import json class NewsArticle: def __init__(self,hash,title,author,url,content,date,topics, feed): self.hash = hash self.title = title self.author = author self.url = url self.content = content self.date = datetime.datetime.fromtimestamp(date/1000.0) self.topics = topics self.feed = feed self.sep = re.compile("[.!?]") def __repr__(self): return "hash={},title={},author={},date={},topics={}".format( self.hash, self.title, self.author, self.date, self.topics, self.feed) def __str__(self): return self.__repr__() def produce_title_scores(self, sentiment): lines = self.sep.split(self.title) sentiment.score(lines) neg,neu,pos,com,count = sentiment.get_avg_scores() return (float("{0:.2f}".format(neg*100)), float("{0:.2f}".format(neu*100)) , float("{0:.2f}".format(pos*100)), float("{0:.2f}".format(com*100)),count ) def produce_content_scores(self, sentiment): lines = self.sep.split(self.content) sentiment.score(lines) neg,neu,pos,com,count = sentiment.get_avg_scores() return (float("{0:.2f}".format(neg*100)), float("{0:.2f}".format(neu*100)) , float("{0:.2f}".format(pos*100)), float("{0:.2f}".format(com*100)),count ) class Parser: def __init__(self,file_in,max_articles=None,file_out=None): self.file_name = file_name self.max_articles = max_articles self.articles = [] self.sentiment = Sentiment() self.results = [] self.file_out = file_out def parse(self): count = 0 with open(self.file_name,"r",encoding="UTF-8") as file: for line in file: if line.startswith(','): continue self.articles.append(self.parse_news_article(line)) count += 1 if self.max_articles: if count >= self.max_articles: break def write(self): for i,article in enumerate(self.articles): if i % 100 == 0: print('Finished: {} docs'.format(i)) self.write_article(article) if self.file_out: with open(self.file_out, 'w') as outfile: json.dump(self.results, outfile,sort_keys=True,indent=4) else: print(json.dumps(self.results,sort_keys=True,indent=4)) def write_article(self,article): res = {} res['neg_title'],res['neu_title'],res['pos_title'],res['score_title'], _ = article.produce_title_scores(self.sentiment) res['neg_content'],res['neu_content'],res['pos_content'],res['score_content'], _ = article.produce_content_scores(self.sentiment) res['id'] = article.hash res['title'] = article.title res['date'] = int(article.date.timestamp()) res['content'] = article.content res['topics'] = article.topics res['feed'] = article.feed res['url'] = article.url res['author'] = article.author res['overall_score']= float(res['score_title'])*0.75 + float(res['score_content'])*0.25 overall_score = res['overall_score'] if overall_score <= -50: res['class']= 'Very Negative' res['class_code'] = 4 elif overall_score <= 0: res['class']= 'Negative' res['class_code'] = 3 elif overall_score <= 50: res['class']= 'Positive' res['class_code'] = 2 elif overall_score <= 100: res['class']= 'Very Positive' res['class_code'] = 1 self.results.append(res) def parse_news_article(self, line): data = json.loads(line) hash = data['hash'] title = data['title'] author = data['author'] content = data['content'] date = data['date'] topics = list(set(data['topics'])) feed = data['feed'] url = data['link'] return NewsArticle(hash,title,author,url,content,date,topics,feed) if __name__ == '__main__': file_name = "./log" #max_articles = 1000 p = Parser(file_name,file_out='data-26-04.json') p.parse() p.write() print('Finished') def test(): plt.figure(figsize=(12,9)) plt.title('Articles: {}'.format(max_articles)) plt.plot(x[:,0],'x',label="Negative {0:.2f}".format(np.average(x[:,0]))) plt.plot(x[:,2],'+',label="Positive {0:.2f}".format(np.average(x[:,2]))) plt.plot(x[:,1],'.',label="Neutral {0:.2f}".format(np.average(x[:,1]))) plt.plot(x[:,3],'.',label="Compound {0:.2f}".format(np.average(x[:,3]))) plt.legend() x = [] for i in range(0,max_articles): x.append(articles[i].produce_content_scores(sentiment)) x = np.array(x) print(x[:,0]) plt.figure(figsize=(12,9)) plt.title('Articles: {}'.format(max_articles)) plt.plot(x[:,0],'x',label="Negative {0:.2f}".format(np.average(x[:,0]))) plt.plot(x[:,2],'+',label="Positive {0:.2f}".format(np.average(x[:,2]))) plt.plot(x[:,1],'.',label="Neutral {0:.2f}".format(np.average(x[:,1]))) plt.plot(x[:,3],'.',label="Compound {0:.2f}".format(np.average(x[:,3]))) plt.legend()

31.277174

137

0.559687

4,216

0.73258

0

985

0.171156

295e89c3127cdc64f86ba1f4504dbc0c0e95c2df

1,214

py

Python

ch05/recursion.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

74

2021-05-06T22:03:18.000Z

2022-03-25T04:37:51.000Z

ch05/recursion.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

null

ch05/recursion.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

19

2021-07-16T11:42:00.000Z

2022-03-22T00:25:49.000Z

"""Recursive implementations.""" def find_max(A): """invoke recursive function to find maximum value in A.""" def rmax(lo, hi): """Use recursion to find maximum value in A[lo:hi+1].""" if lo == hi: return A[lo] mid = (lo+hi) // 2 L = rmax(lo, mid) R = rmax(mid+1, hi) return max(L, R) return rmax(0, len(A)-1) def find_max_with_count(A): """Count number of comparisons.""" def frmax(lo, hi): """Use recursion to find maximum value in A[lo:hi+1] incl. count""" if lo == hi: return (0, A[lo]) mid = (lo+hi)//2 ctleft,left = frmax(lo, mid) ctright,right = frmax(mid+1, hi) return (1+ctleft+ctright, max(left, right)) return frmax(0, len(A)-1) def count(A,target): """invoke recursive function to return number of times target appears in A.""" def rcount(lo, hi, target): """Use recursion to find maximum value in A[lo:hi+1].""" if lo == hi: return 1 if A[lo] == target else 0 mid = (lo+hi)//2 left = rcount(lo, mid, target) right = rcount(mid+1, hi, target) return left + right return rcount(0, len(A)-1, target)

26.977778

82

0.555189

0

382

0.314662

295eeef6c40b7545564ffef7ae9d385146c2bde6

3,084

py

Python

setup.py

koonimaru/DeepGMAP

7daac354229fc25fba81649b741921345dc5db05

[ "Apache-2.0" ]

11

2018-06-27T11:45:47.000Z

2021-07-01T15:32:56.000Z

setup.py

koonimaru/DeepGMAP

7daac354229fc25fba81649b741921345dc5db05

[ "Apache-2.0" ]

3

2020-01-28T21:45:15.000Z

2020-04-20T02:40:48.000Z

setup.py

koonimaru/DeepGMAP

7daac354229fc25fba81649b741921345dc5db05

[ "Apache-2.0" ]

1

2018-10-19T19:43:27.000Z

#from distutils.core import setup from setuptools import setup, find_packages from distutils.extension import Extension import re import os import codecs here = os.path.abspath(os.path.dirname(__file__)) def read(*parts): # intentionally *not* adding an encoding option to open, See: # https://github.com/pypa/virtualenv/issues/201#issuecomment-3145690 with codecs.open(os.path.join(here, *parts), 'r') as fp: return fp.read() def find_version(*file_paths): version_file = read(*file_paths) version_match = re.search( r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M, ) if version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") try: from Cython.Distutils import build_ext except ImportError: use_cython = False else: use_cython = True cmdclass = { } ext_modules = [ ] if use_cython: ext_modules += [ Extension("deepgmap.data_preprocessing_tools.seq_to_binary2", [ "deepgmap/data_preprocessing_tools/seq_to_binary2.pyx" ]), #Extension("data_preprocessing_tools.queue", [ "deepgmap/data_preprocessing_tools/queue.pyx" ],libraries=["calg"]), Extension("deepgmap.post_train_tools.cython_util", [ "deepgmap/post_train_tools/cython_util.pyx" ]), ] cmdclass.update({ 'build_ext': build_ext }) else: ext_modules += [ Extension("deepgmap.data_preprocessing_tools.seq_to_binary2", [ "deepgmap/data_preprocessing_tools/seq_to_binary2.c" ]), Extension("deepgmap.post_train_tools.cython_util", [ "deepgmap/post_train_tools/cython_util.c" ]), ] #print(find_version("deepgmap", "__init__.py")) setup( name='DeepGMAP', #version=VERSION, version=find_version("deepgmap", "__init__.py"), description='Learning and predicting gene regulatory sequences in genomes', author='Koh Onimaru', author_email='[email protected]', url='', packages=['deepgmap','deepgmap.train','deepgmap.network_constructors','deepgmap.post_train_tools','deepgmap.data_preprocessing_tools','deepgmap.misc'], #packages=find_packages('deepgmap'), #packages=['deepgmap.'], package_dir={'DeepGMAP':'deepgmap'}, #package_data = { # '': ['enhancer_prediction/*', '*.pyx', '*.pxd', '*.c', '*.h'], #}, scripts=['bin/deepgmap', ], #packages=find_packages(), cmdclass = cmdclass, ext_modules=ext_modules, classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Console', 'Intended Audience :: Developers', 'Programming Language :: Python :: 3.6', 'License :: OSI Approved :: Apache Software License ', 'Operating System :: POSIX :: Linux', 'Topic :: Scientific/Engineering :: Bio-Informatics', 'Topic :: Scientific/Engineering :: Artificial Intelligence', ], install_requires=['tensorflow>=1.15', 'numpy', 'matplotlib', 'sklearn', 'tornado', 'natsort', 'psutil', 'pyBigWig'], long_description=open('README.rst').read(), )

34.651685

155

0.664073

0

1,693

0.548962

295f637700f993cfd8e37b0ff39f106d2c2a6469

1,716

py

Python

{{cookiecutter.project_slug}}/api/__init__.py

Steamboat/cookiecutter-devops

6f07329c9e54b76e671a0308d343d2d9ebff5343

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/api/__init__.py

Steamboat/cookiecutter-devops

6f07329c9e54b76e671a0308d343d2d9ebff5343

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/api/__init__.py

Steamboat/cookiecutter-devops

6f07329c9e54b76e671a0308d343d2d9ebff5343

[ "BSD-3-Clause" ]

null

import logging from flask import Flask from flask_sqlalchemy import SQLAlchemy as _BaseSQLAlchemy from flask_migrate import Migrate from flask_cors import CORS from flask_talisman import Talisman from flask_ipban import IpBan from config import Config, get_logger_handler # database class SQLAlchemy(_BaseSQLAlchemy): def apply_pool_defaults(self, app, options): super(SQLAlchemy, self).apply_pool_defaults(app, options) options["pool_pre_ping"] = True db = SQLAlchemy() migrate = Migrate() cors = CORS() talisman = Talisman() global_config = Config() ip_ban = IpBan(ban_seconds=200, ban_count=global_config.IP_BAN_LIST_COUNT) # logging logger = logging.getLogger('frontend') def create_app(config_class=None): app = Flask(__name__) if config_class is None: config_class = Config() app.config.from_object(config_class) db.init_app(app) migrate.init_app(app, db) # TODO - Refine and update when build pipeline is stable. Get from global_config cors.init_app(app, origins=["http://localhost:5000", "http://localhost:3000", '*']) if app.config["ENV"] in ("staging", "production"): # Secure the application and implement best practice https redirects and a content security policy talisman.init_app(app, content_security_policy=None) # ip_ban.init_app(app) # ip_ban.load_nuisances(global_config.IP_BAN_REGEX_FILE) from api.routes import bp as api_bp app.register_blueprint(api_bp) if not app.debug and not app.testing: app.logger.addHandler(get_logger_handler()) @app.teardown_appcontext def shutdown_session(exception=None): db.session.remove() return app from api import models

32.377358

106

0.740093

189

0.11014

0

94

0.054779

0

375

0.218531

295f7531aae2696a47947cc69a933b6673909fb5

4,937

py

Python

weibospider/pipelines.py

czyczyyzc/WeiboSpider

41b9c97cb01d41cb4a62efdd452451b5ef25bdbc

[ "MIT" ]

2

2021-03-26T03:02:52.000Z

2021-04-01T11:08:46.000Z

weibospider/pipelines.py

czyczyyzc/WeiboSpider

41b9c97cb01d41cb4a62efdd452451b5ef25bdbc

[ "MIT" ]

null

weibospider/pipelines.py

czyczyyzc/WeiboSpider

41b9c97cb01d41cb4a62efdd452451b5ef25bdbc

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import csv import pymongo from pymongo.errors import DuplicateKeyError from settings import MONGO_HOST, MONGO_PORT, SAVE_ROOT class MongoDBPipeline(object): def __init__(self): client = pymongo.MongoClient(MONGO_HOST, MONGO_PORT) db = client['weibo'] self.Users = db["Users"] self.Tweets = db["Tweets"] self.Comments = db["Comments"] self.Relationships = db["Relationships"] self.Reposts = db["Reposts"] def process_item(self, item, spider): if spider.name == 'comment_spider': self.insert_item(self.Comments, item) elif spider.name == 'fan_spider': self.insert_item(self.Relationships, item) elif spider.name == 'follower_spider': self.insert_item(self.Relationships, item) elif spider.name == 'user_spider': self.insert_item(self.Users, item) elif spider.name == 'tweet_spider': self.insert_item(self.Tweets, item) elif spider.name == 'repost_spider': self.insert_item(self.Reposts, item) return item @staticmethod def insert_item(collection, item): try: collection.insert(dict(item)) except DuplicateKeyError: pass class CSVPipeline(object): def __init__(self): if not os.path.exists(SAVE_ROOT): os.makedirs(SAVE_ROOT) users_file = open(os.path.join(SAVE_ROOT, 'users.csv'), 'w', encoding='utf-8-sig', newline='') tweets_file = open(os.path.join(SAVE_ROOT, 'tweets.csv'), 'w', encoding='utf-8-sig', newline='') comments_file = open(os.path.join(SAVE_ROOT, 'comments.csv'), 'w', encoding='utf-8-sig', newline='') relationships_file = open(os.path.join(SAVE_ROOT, 'relationships.csv'), 'w', encoding='utf-8-sig', newline='') reposts_file = open(os.path.join(SAVE_ROOT, 'reposts.csv'), 'w', encoding='utf-8-sig', newline='') self.users_writer = csv.writer(users_file, dialect='excel') self.tweets_writer = csv.writer(tweets_file, dialect='excel') self.comments_writer = csv.writer(comments_file, dialect='excel') self.relationships_writer = csv.writer(relationships_file, dialect='excel') self.reposts_writer = csv.writer(reposts_file, dialect='excel') self.users_head = False self.tweets_head = False self.comments_head = False self.relationships_head = False self.reposts_head = False self.users_ids = [] self.tweets_ids = [] self.comments_ids = [] self.relationships_ids = [] self.reposts_ids = [] def process_item(self, item, spider): item = dict(item) if spider.name == 'comment_spider': if not self.comments_head: self.comments_writer.writerow(list(item.keys())) self.comments_head = True # if item['_id'] not in self.comments_ids: self.comments_writer.writerow(list(item.values())) self.comments_ids.append(item['_id']) elif spider.name == 'fan_spider': if not self.relationships_head: self.relationships_writer.writerow(list(item.keys())) self.relationships_head = True # if item['_id'] not in self.relationships_ids: self.relationships_writer.writerow(list(item.values())) self.relationships_ids.append(item['_id']) elif spider.name == 'follower_spider': if not self.relationships_head: self.relationships_writer.writerow(list(item.keys())) self.relationships_head = True # if item['_id'] not in self.relationships_ids: self.relationships_writer.writerow(list(item.values())) self.relationships_ids.append(item['_id']) elif spider.name == 'user_spider': if not self.users_head: self.users_writer.writerow(list(item.keys())) self.users_head = True # if item['_id'] not in self.users_ids: self.users_writer.writerow(list(item.values())) self.users_ids.append(item['_id']) elif spider.name == 'tweet_spider': if not self.tweets_head: self.tweets_writer.writerow(list(item.keys())) self.tweets_head = True # if item['_id'] not in self.tweets_ids: self.tweets_writer.writerow(list(item.values())) self.tweets_ids.append(item['_id']) elif spider.name == 'repost_spider': if not self.reposts_head: self.reposts_writer.writerow(list(item.keys())) self.reposts_head = True # if item['_id'] not in self.reposts_ids: self.reposts_writer.writerow(list(item.values())) self.reposts_ids.append(item['_id']) return item

40.467213

118

0.611302

4,771

0.966376

0

158

0.032003

0

723

0.146445

295f767e353179afb030c3f6f2c390f8073634e9

6,020

py

Python

tests/test_gc3_config.py

ericmharris/gc3-query

0bf5226130aafbb1974aeb96d93ee1996833e87d

[ "MIT" ]

null

tests/test_gc3_config.py

ericmharris/gc3-query

0bf5226130aafbb1974aeb96d93ee1996833e87d

[ "MIT" ]

null

tests/test_gc3_config.py

ericmharris/gc3-query

0bf5226130aafbb1974aeb96d93ee1996833e87d

[ "MIT" ]

null

from pathlib import Path from requests.auth import _basic_auth_str import pytest from bravado_core.formatter import SwaggerFormat, NO_OP from gc3_query.lib.gc3_config import GC3Config, IDMCredential TEST_BASE_DIR: Path = Path(__file__).parent.joinpath("GC3Config") config_dir = TEST_BASE_DIR.joinpath("config") def test_setup(): assert TEST_BASE_DIR.exists() assert config_dir.exists() def test_init(): gc3_config = GC3Config() assert 'gc30003' in gc3_config['idm']['domains'] assert gc3_config.user.cloud_username == '[email protected]' def test_set_credential(): gc3_config = GC3Config() assert 'gc3test' in gc3_config['idm']['domains'] assert gc3_config.user.cloud_username == '[email protected]' credential = gc3_config.set_credential(idm_domain_name='gc3test', password='Welcome123' ) assert credential assert credential.password == 'Welcome123' assert credential.idm_domain_name == 'gc3test' def test_set_gc3pilot_credential(): gc3_config = GC3Config() assert 'gc3pilot' in gc3_config['idm']['domains'] assert gc3_config.user.cloud_username == '[email protected]' credential = gc3_config.set_credential(idm_domain_name='gc3pilot', password='V@nadium123!' ) assert credential assert credential.password == 'V@nadium123!' assert credential.idm_domain_name == 'gc3pilot' @pytest.fixture() def get_credential_setup() -> IDMCredential: gc3_config = GC3Config() assert 'gc3test' in gc3_config['idm']['domains'] assert gc3_config.user.cloud_username == '[email protected]' credential = gc3_config.set_credential(idm_domain_name='gc3test', password='123Welcome' ) yield (credential) def test_load_atoml_files_individually(get_credential_setup): credential = get_credential_setup gc3_config = GC3Config() assert 'gc3test' in gc3_config['idm']['domains'] assert gc3_config.user.cloud_username == '[email protected]' check_credential = gc3_config.get_credential(idm_domain_name='gc3test') assert check_credential==credential def test_credential_basic_auth(get_credential_setup): credential = get_credential_setup credential_expected_basic_auth =_basic_auth_str('[email protected]', '123Welcome') gc3_config = GC3Config() check_credential = gc3_config.get_credential(idm_domain_name='gc30003') assert gc3_config.user.cloud_username == '[email protected]' assert check_credential.idm_domain_name=='gc30003' assert check_credential.basic_auth_str.startswith('Basic') assert check_credential.basic_auth_str != credential.basic_auth_str def test_get_main_credential(): gc3_config = GC3Config() check_credential = gc3_config.get_credential(idm_domain_name='gc30003') assert gc3_config.user.cloud_username == '[email protected]' assert check_credential.idm_domain_name=='gc30003' # @pytest.fixture() # def get_bravado_config_setup(): # gc3_config = GC3Config() # assert 'iaas_classic' in gc3_config # yield (gc3_config) # # def test_bravado_client_config(get_bravado_config_setup): # gc3_config = get_bravado_config_setup # assert 'iaas_classic' in gc3_config # bravado_client_config = gc3_config.bravado_client_config # assert bravado_client_config # assert 'formats' not in bravado_client_config # assert not 'include_missing_properties' in bravado_client_config # assert 'also_return_response' in bravado_client_config # bravado_client_config_2 = gc3_config.bravado_client_config # assert bravado_client_config==bravado_client_config_2 # assert bravado_client_config is not bravado_client_config_2 # assert isinstance(bravado_client_config, dict) # # def test_bravado_core_config(get_bravado_config_setup): # gc3_config = get_bravado_config_setup # assert 'iaas_classic' in gc3_config # bravado_core_config = gc3_config.bravado_core_config # assert bravado_core_config # assert 'formats' in bravado_core_config # assert 'include_missing_properties' in bravado_core_config # assert not 'also_return_response' in bravado_core_config # bravado_core_config_2 = gc3_config.bravado_core_config # assert bravado_core_config==bravado_core_config_2 # assert bravado_core_config is not bravado_core_config_2 # assert isinstance(bravado_core_config, dict) # assert isinstance(bravado_core_config['formats'], list) # # # # def test_bravado_config(get_bravado_config_setup): # gc3_config = get_bravado_config_setup # assert 'iaas_classic' in gc3_config # bravado_config = gc3_config.bravado_config # assert bravado_config # assert 'formats' in bravado_config # assert 'include_missing_properties' in bravado_config # assert 'also_return_response' in bravado_config # bravado_config_2 = gc3_config.bravado_config # assert bravado_config==bravado_config_2 # assert bravado_config is not bravado_config_2 # assert isinstance(bravado_config, dict) # assert isinstance(bravado_config['formats'], list) # @pytest.fixture() def get_constants_setup(): gc3_config = GC3Config() assert 'iaas_classic' in gc3_config yield (gc3_config) def test_open_api_catalog_dir(get_constants_setup): gc3_config = get_constants_setup open_api_catalog_dir = gc3_config.OPEN_API_CATALOG_DIR assert open_api_catalog_dir # def test_BRAVADO_CONFIG(get_constants_setup): # gc3_config = get_constants_setup # bravado_config = gc3_config.BRAVADO_CONFIG # assert bravado_config # assert 'formats' in bravado_config # assert 'include_missing_properties' in bravado_config # assert 'also_return_response' in bravado_config # assert isinstance(bravado_config, dict) # assert isinstance(bravado_config['formats'], list) # assert bravado_config['formats'] # formats = [f.format for f in bravado_config['formats']] # assert 'json-bool' in formats # assert all([isinstance(i , SwaggerFormat) for i in bravado_config['formats']])

39.605263

96

0.767608

0

431

0.071595

467

0.077575

0

3,259

0.541362

2960cfa3589dae062b2a5ee5a75ad678bb175e9d

2,871

py

Python

lab6/server/datapredict.py

zhiji95/iot

4202f00a79b429d5f5083bca6e914fcff09df294

[ "Apache-2.0" ]

2

2019-09-20T01:38:40.000Z

2020-10-13T21:18:18.000Z

lab6/server/datapredict.py

zw2497/4764

28caec1947c1b1479d2ec9c8ecba8cd599d66d23

[ "Apache-2.0" ]

null

lab6/server/datapredict.py

zw2497/4764

28caec1947c1b1479d2ec9c8ecba8cd599d66d23

[ "Apache-2.0" ]

null

import machine from machine import * import ssd1306 import time import socket import urequests as requests import json word = {'body':8} labels = ['c', 'o', 'l', 'u', 'm', 'b', 'i', 'a','null'] HOST = '18.218.158.249' PORT = 8080 flag = 0 stop = False data = {} xdata = [] ydata = [] n = 0 def dp(d): if (d > 128): return d - 255 return d def do_connect(): import network wlan = network.WLAN(network.STA_IF) wlan.active(True) if not wlan.isconnected(): print('connecting to network...') wlan.connect(b'Columbia University') while not wlan.isconnected(): pass print('network config:', wlan.ifconfig()) do_connect() def http_post(url, d): r = requests.post(url, data=json.dumps(d)) return r.json() def sendData(): global label global xdata global ydata s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((HOST, PORT)) l = { "label": 'a', "n": 0, "number": len(xdata), "content": { "data": { "x": xdata, "y": ydata } } } l = json.dumps(l).encode() s.sendall(l) data = s.recv(1024) data = json.loads(data.decode()) xdata, ydata = [], [] return data def switchAcallback(p): global flag time.sleep(0.1) if p.value() == 1: flag = 1 def switchCcallback(p): global stop if p.value() == 1: stop = True switchA = machine.Pin(0, machine.Pin.IN, machine.Pin.PULL_UP) switchA.irq(trigger=machine.Pin.IRQ_RISING, handler=switchAcallback) switchC = machine.Pin(2, machine.Pin.IN, machine.Pin.PULL_UP) switchC.irq(trigger=machine.Pin.IRQ_RISING, handler=switchCcallback) spi = machine.SPI(1, baudrate=2000000, polarity=1, phase=1) cs = machine.Pin(15, machine.Pin.OUT) cs.value(0) spi.write(b'\x2d') spi.write(b'\x2b') cs.value(1) cs.value(0) spi.write(b'\x31') spi.write(b'\x0f') cs.value(1) i2c = machine.I2C(-1, machine.Pin(5), machine.Pin(4)) oled = ssd1306.SSD1306_I2C(128, 32, i2c) while True: x = 0 y = 0 sendstatus = "null" if (flag): cs.value(0) test1 = spi.read(5, 0xf2) cs.value(1) cs.value(0) test2 = spi.read(5, 0xf3) cs.value(1) cs.value(0) test3 = spi.read(5, 0xf4) cs.value(1) cs.value(0) test4 = spi.read(5, 0xf5) cs.value(1) x = dp(test2[1]) y = dp(test4[1]) xdata.append(x) ydata.append(y) sendstatus = "collect" + str(len(xdata)) + ' '+ ' ' + str(x) + ' ' + str(y) if send: word = sendData() sendstatus = "send success" flag = 0 send = False oled.fill(0) oled.text(labels[word['body']], 0, 0) oled.text(sendstatus, 0,10) oled.show()

20.804348

83

0.554859

0

231

0.08046

2960f549fc004cf3590c25e915c7395ebd3b5e4d

79

py

Python

Geometry/VeryForwardGeometry/python/dd4hep/geometryRPFromDD_2021_cfi.py

PKUfudawei/cmssw

8fbb5ce74398269c8a32956d7c7943766770c093

[ "Apache-2.0" ]

2

2020-10-26T18:40:32.000Z

2021-04-10T16:33:25.000Z

Geometry/VeryForwardGeometry/python/dd4hep/geometryRPFromDD_2021_cfi.py

gartung/cmssw

3072dde3ce94dcd1791d778988198a44cde02162

[ "Apache-2.0" ]

25

2016-06-24T20:55:32.000Z

2022-02-01T19:24:45.000Z

Geometry/VeryForwardGeometry/python/dd4hep/geometryRPFromDD_2021_cfi.py

gartung/cmssw

3072dde3ce94dcd1791d778988198a44cde02162

[ "Apache-2.0" ]

8

2016-03-25T07:17:43.000Z

2021-07-08T17:11:21.000Z

from Geometry.VeryForwardGeometry.dd4hep.v5.geometryRPFromDD_2021_cfi import *

39.5

78

0.886076

0

2962e10ff2cdb13a6dd7a8ef80474fffa61365b3

1,464

py

Python

examples/plots/warmup_schedule.py

shuoyangd/pytorch_warmup

b3557afa6fcfc04e9ddc6ff08a1ae51e8a0ce5df

[ "MIT" ]

170

2019-11-03T06:14:42.000Z

2022-03-18T08:21:44.000Z

examples/plots/warmup_schedule.py

shuoyangd/pytorch_warmup

b3557afa6fcfc04e9ddc6ff08a1ae51e8a0ce5df

[ "MIT" ]

5

2020-05-18T16:53:33.000Z

2021-11-12T13:03:14.000Z

examples/plots/warmup_schedule.py

shuoyangd/pytorch_warmup

b3557afa6fcfc04e9ddc6ff08a1ae51e8a0ce5df

[ "MIT" ]

21

2019-11-06T10:55:21.000Z

2022-02-23T21:38:12.000Z

import argparse import matplotlib.pyplot as plt import torch from pytorch_warmup import * def get_rates(warmup_cls, beta2, max_step): rates = [] p = torch.nn.Parameter(torch.arange(10, dtype=torch.float32)) optimizer = torch.optim.Adam([{'params': p}], lr=1.0, betas=(0.9, beta2)) lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda step: 1.0) warmup_scheduler = warmup_cls(optimizer) for step in range(1, max_step+1): rates.append(optimizer.param_groups[0]['lr']) optimizer.zero_grad() optimizer.step() lr_scheduler.step() warmup_scheduler.dampen() return rates parser = argparse.ArgumentParser(description='Warmup schedule') parser.add_argument('--output', type=str, default='none', choices=['none', 'png', 'pdf'], help='Output file type (default: none)') args = parser.parse_args() beta2 = 0.999 max_step = 3000 plt.plot(range(1, max_step+1), get_rates(RAdamWarmup, beta2, max_step), label='RAdam') plt.plot(range(1, max_step+1), get_rates(UntunedExponentialWarmup, beta2, max_step), label='Untuned Exponential') plt.plot(range(1, max_step+1), get_rates(UntunedLinearWarmup, beta2, max_step), label='Untuned Linear') plt.legend() plt.title('Warmup Schedule') plt.xlabel('Iteration') plt.ylabel(r'Warmup factor $(\omega_t)$') if args.output == 'none': plt.show() else: plt.savefig(f'warmup_schedule.{args.output}')

34.857143

113

0.693306

0

234

0.159836

2965377859485f3e331393d42e82329e9f5b3052

2,107

py

Python

plugins/httpev.py

wohali/gizzy

c9d4ee9cdcf6fdbf260869365b944f29c660e6aa

[ "Apache-2.0" ]

3

2015-09-11T23:34:36.000Z

2018-04-05T21:17:08.000Z

plugins/httpev.py

wohali/gizzy

c9d4ee9cdcf6fdbf260869365b944f29c660e6aa

[ "Apache-2.0" ]

null

plugins/httpev.py

wohali/gizzy

c9d4ee9cdcf6fdbf260869365b944f29c660e6aa

[ "Apache-2.0" ]

null

"""\ This plugin merely enables other plugins to accept data over HTTP. If a plugin defines a module level function named "httpev" it will be invoked for POST requests to the url http://$hostname/event/$pluginname. The function is invoked from the thread in the web.py request context and as such has access to the full web.py API. """ import base64 import json import web web.config.debug = False class Event(object): def POST(self, plugin): self.check_authorized() func = self.find_handler(plugin) try: func() except web.webapi.HTTPError: raise except: log.exception("Plugin '%s' broke handling HTTP event" % plugin) raise web.webapi.internalerror() def check_authorized(self): auth = web.ctx.env.get('HTTP_AUTHORIZATION') if auth is None: raise web.webapi.unauthorized() if not auth.startswith("Basic "): raise web.webapi.unauthorized() try: auth = auth.split(None, 1)[1] raw = base64.decodestring(auth) if tuple(raw.split(":", 1)) == config.httpev["auth"]: return except: raise web.webapi.badrequest("Invalid Authorization header") raise web.webapi.unauthorized() def find_handler(self, name): for p in plugin_manager.plugins: if p.name == name: func = p.data.get("httpev") if callable(func): return func raise web.webapi.notfound() class Server(threading.Thread): def __init__(self): super(Server, self).__init__() self.setDaemon(True) self.urls = ("/event/(.+)", "Event") self.app = web.application(self.urls, {"Event": Event}) self.addr = ('0.0.0.0', config.httpev["port"]) self.srv = web.httpserver.WSGIServer(self.addr, self.app.wsgifunc()) def stop(self): self.srv.stop() def run(self): self.srv.start() def load(): s = Server() s.start() return s def unload(s): s.stop()

26.670886

76

0.591362

1,610

0.76412

0

491

0.233033

29656dc8827f4e4fcb777d91bc04e2895b6de0ad

773

py

Python

ex056.py

danilodelucio/Exercicios_Curso_em_Video

d59e1b4efaf27dd0fc828a608201613c69ac333d

[ "MIT" ]

null

ex056.py

danilodelucio/Exercicios_Curso_em_Video

d59e1b4efaf27dd0fc828a608201613c69ac333d

[ "MIT" ]

null

ex056.py

danilodelucio/Exercicios_Curso_em_Video

d59e1b4efaf27dd0fc828a608201613c69ac333d

[ "MIT" ]

null

somaIdade = 0 maiorIdade = 0 nomeVelho = '' totmulher20 = 0 for p in range(1, 3): print('---- {}ª PESSOA ----'.format(p)) nome = str(input('Nome: ')).strip() idade = int(input('Idade: ')) sexo = str(input('Sexo [M/F]: ')) somaIdade += idade if p == 1 and sexo in 'Mm': maiorIdade = idade nomeVelho = nome if sexo in 'Mm' and idade > maiorIdade: maiorIdade = idade nomeVelho = nome if sexo in 'Ff' and idade < 20: totmulher20 += 1 mediaIdade = int(somaIdade / 4) print('A média de idade do grupo de pessoas é de {} anos.'.format(mediaIdade)) print('O homem mais velho tem {} anos e se chama {}.'.format(maiorIdade, nomeVelho)) print('Ao todo são {} mulher com menos de 20 anos.'.format(totmulher20))

30.92

84

0.606727

0

215

0.276705

2966debf755863b57841211c2eb24e99ff45937a

6,583

py

Python

python/promort.py

simleo/promort_pipeline

03b9d3553a3dade57d0007e230230b02dd70832f

[ "MIT" ]

null

python/promort.py

simleo/promort_pipeline

03b9d3553a3dade57d0007e230230b02dd70832f

[ "MIT" ]

null

python/promort.py

simleo/promort_pipeline

03b9d3553a3dade57d0007e230230b02dd70832f

[ "MIT" ]

3

2020-07-29T15:03:40.000Z

2020-10-06T11:16:04.000Z

"""\ PROMORT example. """ import argparse import random import sys import pyecvl.ecvl as ecvl import pyeddl.eddl as eddl from pyeddl.tensor import Tensor import models def VGG16(in_layer, num_classes): x = in_layer x = eddl.ReLu(eddl.Conv(x, 64, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 64, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 128, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 128, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3])) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 256, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 512, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 512, [3, 3])), [2, 2], [2, 2]) x = eddl.Reshape(x, [-1]) x = eddl.ReLu(eddl.Dense(x, 256)) x = eddl.Softmax(eddl.Dense(x, num_classes)) return x def main(args): num_classes = 2 size = [256, 256] # size of images in_ = eddl.Input([3, size[0], size[1]]) out = models.VGG16_promort(in_, num_classes) net = eddl.Model([in_], [out]) eddl.build( net, eddl.rmsprop(1e-6), #eddl.sgd(0.001, 0.9), ["soft_cross_entropy"], ["categorical_accuracy"], eddl.CS_GPU([1], mem="low_mem") if args.gpu else eddl.CS_CPU() ) eddl.summary(net) eddl.setlogfile(net, "promort_VGG16_classification") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size) #ecvl.AugMirror(.5), #ecvl.AugFlip(.5), #ecvl.AugRotate([-180, 180]), #ecvl.AugAdditivePoissonNoise([0, 10]), #ecvl.AugGammaContrast([0.5, 1.5]), #ecvl.AugGaussianBlur([0, 0.8]), #ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0.5) ]) validation_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) dataset_augs = ecvl.DatasetAugmentations( [training_augs, validation_augs, None] ) print("Reading dataset") #d = ecvl.DLDataset(args.in_ds, args.batch_size) d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs) x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y = Tensor([args.batch_size, len(d.classes_)]) num_samples_train = len(d.GetSplit()) num_batches_train = num_samples_train // args.batch_size d.SetSplit(ecvl.SplitType.validation) num_samples_val = len(d.GetSplit()) num_batches_val = num_samples_val // args.batch_size indices = list(range(args.batch_size)) metric = eddl.getMetric("categorical_accuracy") print("Starting training") ### Main loop across epochs for e in range(args.epochs): print("Epoch {:d}/{:d} - Training".format(e + 1, args.epochs), flush=True) if args.out_dir: current_path = os.path.join(args.out_dir, "Epoch_%d" % e) for c in d.classes_: c_dir = os.path.join(current_path, c) os.makedirs(c_dir, exist_ok=True) d.SetSplit(ecvl.SplitType.training) eddl.reset_loss(net) total_metric = [] s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetAllBatches() ### Looping across batches of training data for b in range(num_batches_train): print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, args.epochs, b + 1, num_batches_train ), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) tx, ty = [x], [y] #print (tx[0].info()) eddl.train_batch(net, tx, ty, indices) #eddl.print_loss(net, b) instances = (b+1) * args.batch_size print ("loss = %.3f, acc = %.3f" % (net.fiterr[0]/instances, net.fiterr[1]/instances)) #print() print("Saving weights") eddl.save(net, "promort_checkpoint_%s.bin" % e, "bin") ### Evaluation on validation set print("Epoch %d/%d - Evaluation" % (e + 1, args.epochs), flush=True) d.SetSplit(ecvl.SplitType.validation) for b in range(num_batches_val): n = 0 print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, args.epochs, b + 1, num_batches_val ), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) eddl.forward(net, [x]) output = eddl.getOutput(out) sum_ = 0.0 for k in range(args.batch_size): result = output.select([str(k)]) target = y.select([str(k)]) ca = metric.value(target, result) total_metric.append(ca) sum_ += ca if args.out_dir: result_a = np.array(result, copy=False) target_a = np.array(target, copy=False) classe = np.argmax(result_a).item() gt_class = np.argmax(target_a).item() single_image = x.select([str(k)]) img_t = ecvl.TensorToView(single_image) img_t.colortype_ = ecvl.ColorType.BGR single_image.mult_(255.) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s_gt_class_%s.png" % (head, gt_class) cur_path = os.path.join( current_path, d.classes_[classe], bname ) ecvl.ImWrite(cur_path, img_t) n += 1 print("categorical_accuracy:", sum_ / args.batch_size) total_avg = sum(total_metric) / len(total_metric) print("Total categorical accuracy:", total_avg) if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("in_ds", metavar="INPUT_DATASET") parser.add_argument("--epochs", type=int, metavar="INT", default=50) parser.add_argument("--batch-size", type=int, metavar="INT", default=32) parser.add_argument("--gpu", action="store_true") parser.add_argument("--out-dir", metavar="DIR", help="if set, save images in this directory") main(parser.parse_args())

37.19209

98

0.556433

0

1,058

0.160717

2967592aac9355f4e077c19d82c1790326f4a71b

343

py

Python

src/view/services_update_page.py

nbilbo/services_manager

74e0471a1101305303a96d39963cc98fc0645a64

[ "MIT" ]

null

src/view/services_update_page.py

nbilbo/services_manager

74e0471a1101305303a96d39963cc98fc0645a64

[ "MIT" ]

null

src/view/services_update_page.py

nbilbo/services_manager

74e0471a1101305303a96d39963cc98fc0645a64

[ "MIT" ]

null

from src.view.services_page import ServicesPage from src.view.services_add_page import ServicesAddPage class ServicesUpdatePage(ServicesAddPage): def __init__(self, parent, *args, **kwargs): super().__init__(parent, *args, **kwargs) self.set_title("Update service") self.set_confirm_button_text("Update")

34.3

54

0.723032

229

0.667638

0

24

0.069971

2967a056b02745df6754455d5a9a7411cbb1bfd2

7,543

py

Python

Lib/site-packages/wagtail/utils/l18n/translation.py

SyahmiAmin/belikilo

0a26dadb514683456ea0dbdcbcfcbf65e09d5dbb

[ "bzip2-1.0.6" ]

null

Lib/site-packages/wagtail/utils/l18n/translation.py

SyahmiAmin/belikilo

0a26dadb514683456ea0dbdcbcfcbf65e09d5dbb

[ "bzip2-1.0.6" ]

null

Lib/site-packages/wagtail/utils/l18n/translation.py

SyahmiAmin/belikilo

0a26dadb514683456ea0dbdcbcfcbf65e09d5dbb

[ "bzip2-1.0.6" ]

null

import os import gettext import bisect from locale import getdefaultlocale from collections.abc import MutableMapping from copy import copy, deepcopy import six class Trans: def __init__(self): self.registry = {} self.current = None self.set(getdefaultlocale()[0]) def __getitem__(self, language): if language: try: return self.registry[language] except KeyError: self.registry[language] = gettext.translation( 'l18n', os.path.join(os.path.dirname(__file__), 'locale'), languages=[language], fallback=True ) return self.registry[language] else: return None def set(self, language): self.current = self[language] def gettext(self, s): try: return self.current.gettext(s) except AttributeError: return s if six.PY2: def ugettext(self, s): try: return self.current.ugettext(s) except AttributeError: return s _trans = Trans() def set_language(language=None): _trans.set(language) if six.PY2: def translate(s, utf8=True, trans=_trans): if trans: if utf8: return trans.ugettext(s) return trans.gettext(s) else: return s else: def translate(s, utf8=True, trans=_trans): if trans: t = trans.gettext(s) if utf8: return t return t.encode() else: return s class L18NLazyObject: def _value(self, utf8=True): raise NotImplementedError def __str__(self): return self._value(utf8=six.PY3) def __bytes__(self): return self._value(utf8=False) def __unicode__(self): return self._value(utf8=True) class L18NLazyString(L18NLazyObject): def __init__(self, s): self._str = s def __copy__(self): return self.__class__(self._str) def __deepcopy__(self, memo): result = self.__copy__() memo[id(self)] = result return result def _value(self, utf8=True): return translate(self._str, utf8) def __repr__(self): return 'L18NLazyString <%s>' % repr(self._str) def __getattr__(self, name): # fallback to call the value's attribute in case it's not found in # L18NLazyString return getattr(self._value(), name) class L18NLazyStringsList(L18NLazyObject): def __init__(self, sep='/', *s): # we assume that the separator and the strings have the same encoding # (text_type) self._sep = sep self._strings = s def __copy__(self): return self.__class__(self._sep, *self._strings) def __deepcopy__(self, memo): result = self.__copy__() memo[id(self)] = result return result def _value(self, utf8=True): sep = self._sep if utf8 and isinstance(sep, six.binary_type): sep = sep.decode(encoding='utf-8') elif not utf8 and isinstance(sep, six.text_type): sep = sep.encode(encoding='utf-8') return sep.join([translate(s, utf8) for s in self._strings]) def __repr__(self): return 'L18NLazyStringsList <%s>' % self._sep.join([ repr(s) for s in self._strings ]) def __getattr__(self, name): # fallback to call the value's attribute in case it's not found in # L18NLazyStringsList return getattr(self._value(), name) class L18NBaseMap(MutableMapping): """ Generic dictionary that returns lazy string or lazy string lists """ def __init__(self, *args, **kwargs): self.store = dict(*args, **kwargs) self.sorted = {} def __copy__(self): result = self.__class__() result.store = self.store result.sorted = self.sorted return result def __deepcopy__(self, memo): result = self.__class__() memo[id(self)] = result result.store = deepcopy(self.store, memo) result.sorted = deepcopy(self.sorted, memo) return result def __getitem__(self, key): raise NotImplementedError def __setitem__(self, key, value): self.store[key] = value for locale, (keys, values) in six.iteritems(self.sorted): tr = translate(value, trans=_trans[locale]) i = bisect.bisect_left(values, tr) keys.insert(i, key) values.insert(i, tr) def __delitem__(self, key): del self.store[key] for keys, values in self.sorted.values(): i = keys.index(key) del keys[i] del values[i] def __iter__(self): loc = _trans.current._info['language'] if _trans.current else None try: return iter(self.sorted[loc][0]) except KeyError: keys = [] values = [] # we can't use iteritems here, as we need to call __getitem__ # via self[key] for key in iter(self.store): value = six.text_type(self[key]) i = bisect.bisect_left(values, value) keys.insert(i, key) values.insert(i, value) self.sorted[loc] = (keys, values) return iter(keys) def __len__(self): return len(self.store) def subset(self, keys): """ Generates a subset of the current map (e.g. to retrieve only tzs in common_timezones from the tz_cities or tz_fullnames maps) """ sub = self.__class__() self_keys = set(self.store.keys()) subset_keys = self_keys.intersection(keys) removed_keys = self_keys.difference(subset_keys) sub.store = {k: self.store[k] for k in subset_keys} for loc, sorted_items in six.iteritems(self.sorted): loc_keys = copy(self.sorted[loc][0]) loc_values = copy(self.sorted[loc][1]) for k in removed_keys: i = loc_keys.index(k) del loc_keys[i] del loc_values[i] sub.sorted[loc] = (loc_keys, loc_values) return sub class L18NMap(L18NBaseMap): def __getitem__(self, key): return L18NLazyString(self.store[key]) class L18NListMap(L18NBaseMap): def __init__(self, sep='/', aux=None, *args, **kwargs): self._sep = sep self._aux = aux super(L18NListMap, self).__init__(*args, **kwargs) def __copy__(self): result = super(L18NListMap, self).__copy__() result._sep = self._sep result._aux = self._aux return result def __deepcopy__(self, memo): result = super(L18NListMap, self).__deepcopy__(memo) result._sep = self._sep result._aux = None if self._aux is None else deepcopy(self._aux, memo) return result def __getitem__(self, key): strs = key.split(self._sep) strs[-1] = key lst = [] for s in strs: try: lst.append(self.store[s]) except KeyError: lst.append(self._aux[s]) return L18NLazyStringsList(self._sep, *lst) def subset(self, keys): sub = super(L18NListMap, self).subset(keys) sub._sep = self._sep sub._aux = deepcopy(self._aux) return sub

27.32971

78

0.571258

6,854

0.908657

0

655

0.086835

2967c010afb3c90f1b88a872839f1b992255abcc

272

py

Python

playground/sockets/server.py

tunki/lang-training

79b9f59a7187053f540f9057c585747762ca8890

[ "MIT" ]

null

playground/sockets/server.py

tunki/lang-training

79b9f59a7187053f540f9057c585747762ca8890

[ "MIT" ]

4

2020-03-10T19:20:21.000Z

2021-06-07T15:39:48.000Z

proglangs-learning/python/example_sockets/server.py

helq/old_code

a432faf1b340cb379190a2f2b11b997b02d1cd8d

[ "CC0-1.0" ]

null

import socket s = socket.socket() s.bind(("localhost", 9999)) s.listen(1) sc, addr = s.accept() while True: recibido = sc.recv(1024) if recibido == "quit": break print "Recibido:", recibido sc.send(recibido) print "adios" sc.close() s.close()

13.6

31

0.617647

0

35

0.128676

29682fb767c90bc573a3f797e4f0ca061a3378d9

743

py

Python

examples/example_contour.py

moghimis/geojsoncontour

23f298cb5c5ae4b7000024423493e109a9cc908d

[ "MIT" ]

63

2016-10-31T06:55:47.000Z

2022-02-04T06:47:32.000Z

examples/example_contour.py

moghimis/geojsoncontour

23f298cb5c5ae4b7000024423493e109a9cc908d

[ "MIT" ]

20

2016-09-26T15:25:53.000Z

2020-11-11T18:26:32.000Z

examples/example_contour.py

moghimis/geojsoncontour

23f298cb5c5ae4b7000024423493e109a9cc908d

[ "MIT" ]

26

2016-06-15T02:39:10.000Z

2022-02-04T06:48:15.000Z

import numpy import matplotlib.pyplot as plt import geojsoncontour # Create lat and lon vectors and grid data grid_size = 1.0 latrange = numpy.arange(-90.0, 90.0, grid_size) lonrange = numpy.arange(-180.0, 180.0, grid_size) X, Y = numpy.meshgrid(lonrange, latrange) Z = numpy.sqrt(X * X + Y * Y) n_contours = 10 levels = numpy.linspace(start=0, stop=100, num=n_contours) # Create a contour plot plot from grid (lat, lon) data figure = plt.figure() ax = figure.add_subplot(111) contour = ax.contour(lonrange, latrange, Z, levels=levels, cmap=plt.cm.jet) # Convert matplotlib contour to geojson geojsoncontour.contour_to_geojson( contour=contour, geojson_filepath='out.geojson', min_angle_deg=10.0, ndigits=3, unit='m' )

26.535714

75

0.729475

0

151

0.20323

296855e3082fc927d6f123b69b223d9a6934f75b

1,861

py

Python

Graphs/ConnectedComponents.py

PK-100/Competitive_Programming

d0863feaaa99462b2999e85dcf115f7a6c08bb8d

[ "MIT" ]

70

2018-06-25T21:20:15.000Z

2022-03-24T03:55:17.000Z

Graphs/ConnectedComponents.py

An3sha/Competitive_Programming

ee7eadf51939a360d0b004d787ebabda583e92f0

[ "MIT" ]

4

2018-09-04T13:12:20.000Z

2021-06-20T08:29:12.000Z

Graphs/ConnectedComponents.py

An3sha/Competitive_Programming

ee7eadf51939a360d0b004d787ebabda583e92f0

[ "MIT" ]

24

2018-12-26T05:15:32.000Z

2022-01-23T23:04:54.000Z

#!/bin/python3 import math import os import random import re import sys # # Complete the 'countGroups' function below. # # The function is expected to return an INTEGER. # The function accepts STRING_ARRAY related as parameter. # class Graph: def __init__(self, V): self.V = V self.adj = [[] for i in range(V)] def addEdge(self, a,b): self.adj[a].append(b) self.adj[b].append(a) def dfs_util(self, temp, node, visited): visited[node] = True temp.append(node) for i in self.adj[node]: if not visited[i]: temp = self.dfs_util(temp, i, visited) return temp def countGroups(self): """ This is the classical concept of connected components in a Graph """ visited = [False] * self.V groups = [] for node in range(self.V): if not visited[node]: temp = [] groups.append(self.dfs_util(temp, node, visited)) return groups def convertMatrixToGraph(mat): """ Accept the input which is an adjacency matrix and return a Graph, which is an adjacency list """ n = len(mat) g = Graph(n) for i in range(n): for j in range(n): if j > i and mat[i][j] == '1': g.addEdge(i,j) return g def countGroups(related): # Write your code here graph = convertMatrixToGraph(related) groups = graph.countGroups() # print(groups) return len(groups) if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') related_count = int(input().strip()) related = [] for _ in range(related_count): related_item = input() related.append(related_item) result = countGroups(related) fptr.write(str(result) + '\n') fptr.close()

22.695122

96

0.576034

804

0.432026

0

432

0.232133

29699db1cd4c1b7712dc1f31ae88eb868493c3ed

1,597

py

Python

recognition/ml_model.py

hurschler/pig-face-recognition

5834f3c89448a645ee0eaf2bbdade064f0c4be93

[ "Apache-2.0" ]

1

2021-11-19T05:33:39.000Z

recognition/ml_model.py

hurschler/pig-face-recognition

5834f3c89448a645ee0eaf2bbdade064f0c4be93

[ "Apache-2.0" ]

null

recognition/ml_model.py

hurschler/pig-face-recognition

5834f3c89448a645ee0eaf2bbdade064f0c4be93

[ "Apache-2.0" ]

1

2022-01-05T12:57:12.000Z

import logging.config import util.logger_init import numpy as np import tensorflow as tf from sklearn.metrics import confusion_matrix from util.tensorboard_util import plot_confusion_matrix, plot_to_image from tensorflow.python.keras.callbacks_v1 import TensorBoard from keras import backend as K class MlModel: def get_model(self): return self.model def summary_print(self): self.model.summary() # Define your scheduling function def scheduler(self, epoch): return 0.001 * 0.95 ** epoch def log_confusion_matrix(self, epoch, logs): # Use the model to predict the values from the test_images. test_pred_raw = self.model.predict(self.ml_data.x_test) test_pred = np.argmax(test_pred_raw, axis=1) # Calculate the confusion matrix using sklearn.metrics cm = confusion_matrix(self.ml_data.y_test, test_pred) figure = plot_confusion_matrix(cm, class_names=self.ml_data.pig_dict.values()) cm_image = plot_to_image(figure) # Log the confusion matrix as an image summary. with self.file_writer_cm.as_default(): tf.summary.image("Confusion Matrix", cm_image, step=epoch) # Define TensorBoard callback child class class LRTensorBoard(TensorBoard): def __init__(self, log_dir, **kwargs): # add other arguments to __init__ if you need super().__init__(log_dir=log_dir, **kwargs) def on_epoch_end(self, epoch, logs=None): logs = logs or {} logs.update({'lr': K.eval(self.model.optimizer.lr)}) super().on_epoch_end(epoch, logs)

31.313725

89

0.708203

1,251

0.783344

0

301

0.188478

296af987b0de54ddfc1944dd10981828dfe9bc78

1,803

py

Python

requests/UpdateSubscriptionRequest.py

divinorum-webb/python-tableau-api

9d3f130d63b15307ad2b23e2273b52790b8d9018

[ "Apache-2.0" ]

1

2019-06-08T22:19:40.000Z

requests/UpdateSubscriptionRequest.py

divinorum-webb/python-tableau-api

9d3f130d63b15307ad2b23e2273b52790b8d9018

[ "Apache-2.0" ]

null

requests/UpdateSubscriptionRequest.py

divinorum-webb/python-tableau-api

9d3f130d63b15307ad2b23e2273b52790b8d9018

[ "Apache-2.0" ]

null

from .BaseRequest import BaseRequest class UpdateSubscriptionRequest(BaseRequest): """ Update subscription request for generating API request URLs to Tableau Server. :param ts_connection: The Tableau Server connection object. :type ts_connection: class :param new_subscription_subject: (Optional) A new subject for the subscription. :type new_subscription_subject: string :param new_schedule_id: (Optional) The ID of a schedule to associate this subscription with. :type new_schedule_id: string """ def __init__(self, ts_connection, new_subscription_subject=None, new_schedule_id=None): super().__init__(ts_connection) self._new_subscription_subject = new_subscription_subject self._new_schedule_id = new_schedule_id @property def base_update_subscription_request(self): if self._new_subscription_subject and self._new_schedule_id: self._request_body.update({ 'subscription': { 'subject': self._new_subscription_subject, 'schedule': {'id': self._new_schedule_id} } }) elif self._new_subscription_subject and not self._new_schedule_id: self._request_body.update({ 'subscription': { 'subject': self._new_subscription_subject } }) else: self._request_body.update({ 'subscription': { 'schedule': {'id': self._new_schedule_id} } }) return self._request_body def get_request(self): return self.base_update_subscription_request

36.795918

108

0.601775

1,763

0.977815

0

819

0.454243

0

597

0.331115

296b3f67c1ceb01054558b270075a2c69d09bb88

2,293

py

Python

doc/examples/cython/cython_main.py

hershg/ray

a1744f67fe954d8408c5b84e28ecccc130157f8e

[ "Apache-2.0" ]

2

2019-06-17T12:38:24.000Z

2020-11-11T07:52:26.000Z

doc/examples/cython/cython_main.py

hershg/ray

a1744f67fe954d8408c5b84e28ecccc130157f8e

[ "Apache-2.0" ]

3

2018-08-15T19:19:25.000Z

2021-06-30T01:54:46.000Z

doc/examples/cython/cython_main.py

hershg/ray

a1744f67fe954d8408c5b84e28ecccc130157f8e

[ "Apache-2.0" ]

2

2017-10-31T23:20:07.000Z

2019-11-13T20:16:03.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import ray import click import inspect import numpy as np import cython_examples as cyth def run_func(func, *args, **kwargs): """Helper function for running examples""" ray.init() func = ray.remote(func) # NOTE: kwargs not allowed for now result = ray.get(func.remote(*args)) # Inspect the stack to get calling example caller = inspect.stack()[1][3] print("%s: %s" % (caller, str(result))) return result @click.group(context_settings={"help_option_names": ["-h", "--help"]}) def cli(): """Working with Cython actors and functions in Ray""" @cli.command() def example1(): """Cython def function""" run_func(cyth.simple_func, 1, 2, 3) @cli.command() def example2(): """Cython def function, recursive""" run_func(cyth.fib, 10) @cli.command() def example3(): """Cython def function, built-in typed parameter""" # NOTE: Cython will attempt to cast argument to correct type # NOTE: Floats will be cast to int, but string, for example will error run_func(cyth.fib_int, 10) @cli.command() def example4(): """Cython cpdef function""" run_func(cyth.fib_cpdef, 10) @cli.command() def example5(): """Cython wrapped cdef function""" # NOTE: cdef functions are not exposed to Python run_func(cyth.fib_cdef, 10) @cli.command() def example6(): """Cython simple class""" ray.init() cls = ray.remote(cyth.simple_class) a1 = cls.remote() a2 = cls.remote() result1 = ray.get(a1.increment.remote()) result2 = ray.get(a2.increment.remote()) print(result1, result2) @cli.command() def example7(): """Cython with function from BrainIAK (masked log)""" run_func(cyth.masked_log, np.array([-1.0, 0.0, 1.0, 2.0])) @cli.command() def example8(): """Cython with blas. NOTE: requires scipy""" # See cython_blas.pyx for argument documentation mat = np.array( [[[2.0, 2.0], [2.0, 2.0]], [[2.0, 2.0], [2.0, 2.0]]], dtype=np.float32) result = np.zeros((2, 2), np.float32, order="C") run_func(cyth.compute_kernel_matrix, "L", "T", 2, 2, 1.0, mat, 0, 2, 1.0, result, 2) if __name__ == "__main__": cli()

20.845455

79

0.64239

0

1,670

0.728304

0

750

0.327082

296c4de61a467a172ab5bf54ab6a35b296d94e43

17,314

py

Python

Simon/dev/main_age_classification.py

uncharted-distil/simon

26e4e54e6de455bde8ee1a24634d060e1ec7babb

[ "MIT" ]

null

Simon/dev/main_age_classification.py

uncharted-distil/simon

26e4e54e6de455bde8ee1a24634d060e1ec7babb

[ "MIT" ]

2

2021-01-27T14:57:26.000Z

2021-10-07T19:34:09.000Z

Simon/dev/main_age_classification.py

uncharted-distil/simon

26e4e54e6de455bde8ee1a24634d060e1ec7babb

[ "MIT" ]

1

2021-03-18T19:13:05.000Z

from DataGenerator import * from Encoder import * import pandas as pd from keras.models import Model from keras.layers import Dense, Activation, Flatten, Input, Dropout, MaxPooling1D, Convolution1D from keras.layers import LSTM, Lambda, merge, Masking from keras.layers import Embedding, TimeDistributed from keras.layers.normalization import BatchNormalization from keras.optimizers import SGD from keras.utils import np_utils import numpy as np import tensorflow as tf import re from keras import backend as K import keras.callbacks import sys import os import time import matplotlib.pyplot as plt import pickle def binarize(x, sz=71): return tf.to_float(tf.one_hot(x, sz, on_value=1, off_value=0, axis=-1)) def custom_multi_label_accuracy(y_true, y_pred): # need some threshold-specific rounding code here, presently only for 0.5 thresh. return K.mean(K.round(np.multiply(y_true,y_pred)),axis=0) def eval_binary_accuracy(y_test, y_pred): correct_indices = y_test==y_pred all_correct_predictions = np.zeros(y_test.shape) all_correct_predictions[correct_indices] = 1 #print("DEBUG::binary accuracy matrix") #print(all_correct_predictions) return np.mean(all_correct_predictions),np.mean(all_correct_predictions, axis=0),all_correct_predictions def eval_confusion(y_test, y_pred): wrong_indices = y_test!=y_pred all_wrong_predictions = np.zeros(y_test.shape) all_wrong_predictions[wrong_indices] = 1 #print("DEBUG::confusion matrix") #print(all_wrong_predictions) return np.mean(all_wrong_predictions),np.mean(all_wrong_predictions, axis=0),all_wrong_predictions def eval_false_positives(y_test, y_pred): false_positive_matrix = np.zeros((y_test.shape[1],y_test.shape[1])) false_positives = np.multiply(y_pred,1-y_test) # print(precision_matrix) for i in np.arange(y_test.shape[0]): for j in np.arange(y_test.shape[1]) : if(false_positives[i,j]==1): #positive label for ith sample and jth predicted category for k in np.arange(y_test.shape[1]): if(y_test[i,k]==1): #positive label for ith sample and kth true category # print("DEBUG::i,j,k") # print("%d,%d,%d"%(i,j,k)) false_positive_matrix[j,k] +=1 # print("DEBUG::precision matrix") # print(precision_matrix) return np.sum(false_positive_matrix),np.sum(false_positive_matrix, axis=0),false_positive_matrix def binarize_outshape(in_shape): return in_shape[0], in_shape[1], 71 def max_1d(x): return K.max(x, axis=1) def striphtml(html): p = re.compile(r'<.*?>') return p.sub('', html) def clean(s): return re.sub(r'[^\x00-\x7f]', r'', s) def setup_test_sets(X, y): ids = np.arange(len(X)) np.random.shuffle(ids) # shuffle X = X[ids] y = y[ids] train_end = int(X.shape[0] * .6) cross_validation_end = int(X.shape[0] * .3 + train_end) test_end = int(X.shape[0] * .1 + cross_validation_end) X_train = X[:train_end] X_cv_test = X[train_end:cross_validation_end] X_test = X[cross_validation_end:test_end] y_train = y[:train_end] y_cv_test = y[train_end:cross_validation_end] y_test = y[cross_validation_end:test_end] data = type('data_type', (object,), {'X_train' : X_train, 'X_cv_test': X_cv_test, 'X_test': X_test, 'y_train': y_train, 'y_cv_test': y_cv_test, 'y_test':y_test}) return data def generate_model(max_len, max_cells, category_count): filter_length = [1, 3, 3] nb_filter = [40, 200, 1000] pool_length = 2 # document input document = Input(shape=(max_cells, max_len), dtype='int64') # sentence input in_sentence = Input(shape=(max_len,), dtype='int64') # char indices to one hot matrix, 1D sequence to 2D embedded = Lambda(binarize, output_shape=binarize_outshape)(in_sentence) # embedded: encodes sentence for i in range(len(nb_filter)): embedded = Convolution1D(nb_filter=nb_filter[i], filter_length=filter_length[i], border_mode='valid', activation='relu', init='glorot_normal', subsample_length=1)(embedded) embedded = Dropout(0.1)(embedded) embedded = MaxPooling1D(pool_length=pool_length)(embedded) forward_sent = LSTM(256, return_sequences=False, dropout_W=0.2, dropout_U=0.2, consume_less='gpu')(embedded) backward_sent = LSTM(256, return_sequences=False, dropout_W=0.2, dropout_U=0.2, consume_less='gpu', go_backwards=True)(embedded) sent_encode = merge([forward_sent, backward_sent], mode='concat', concat_axis=-1) sent_encode = Dropout(0.3)(sent_encode) # sentence encoder encoder = Model(input=in_sentence, output=sent_encode) print(encoder.summary()) encoded = TimeDistributed(encoder)(document) # encoded: sentences to bi-lstm for document encoding forwards = LSTM(128, return_sequences=False, dropout_W=0.2, dropout_U=0.2, consume_less='gpu')(encoded) backwards = LSTM(128, return_sequences=False, dropout_W=0.2, dropout_U=0.2, consume_less='gpu', go_backwards=True)(encoded) merged = merge([forwards, backwards], mode='concat', concat_axis=-1) output = Dropout(0.3)(merged) output = Dense(128, activation='relu')(output) output = Dropout(0.3)(output) output = Dense(category_count, activation='softmax')(output) # output = Activation('softmax')(output) model = Model(input=document, output=output) return model # record history of training class LossHistory(keras.callbacks.Callback): def on_train_begin(self, logs={}): self.losses = [] self.accuracies = [] def on_batch_end(self, batch, logs={}): self.losses.append(logs.get('loss')) self.accuracies.append(logs.get('binary_accuracy')) def plot_loss(history): # summarize history for accuracy plt.subplot('121') plt.plot(history.history['binary_accuracy']) plt.plot(history.history['val_binary_accuracy']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') # summarize history for loss plt.subplot('122') plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() def train_model(batch_size, checkpoint_dir, model, nb_epoch, data): print("starting learning") check_cb = keras.callbacks.ModelCheckpoint(checkpoint_dir + "text-class" + '.{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss', verbose=0, save_best_only=True, mode='min') earlystop_cb = keras.callbacks.EarlyStopping(monitor='val_loss', patience=7, verbose=1, mode='auto') tbCallBack = keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None) loss_history = LossHistory() history = model.fit(data.X_train, data.y_train, validation_data=(data.X_cv_test, data.y_cv_test), batch_size=batch_size, nb_epoch=nb_epoch, shuffle=True, callbacks=[earlystop_cb, check_cb, loss_history, tbCallBack]) print('losses: ') print(history.history['loss']) print('accuracies: ') # print(history.history['acc']) print(history.history['val_binary_accuracy']) plot_loss(history) def evaluate_model(max_cells, model, data, encoder, p_threshold): print("Starting predictions:") start = time.time() scores = model.evaluate(data.X_test, data.y_test, verbose=0) end = time.time() print("Accuracy: %.2f%% \n Time: {0}s \n Time/example : {1}s/ex".format( end - start, (end - start) / data.X_test.shape[0]) % (scores[1] * 100)) # return all predictions above a certain threshold # first, the maximum probability/class probabilities = model.predict(data.X_test, verbose=1) # print("The prediction probabilities are:") # print(probabilities) m = np.amax(probabilities, axis=1) max_index = np.argmax(probabilities, axis=1) # print("Associated fixed category indices:") # print(max_index) with open('Categories.txt','r') as f: Categories = f.read().splitlines() print("Remember that the fixed categories are:") print(Categories) print("Most Likely Predicted Category/Labels are: ") print((np.array(Categories))[max_index]) print("Associated max probabilities/confidences:") print(m) # next, all probabilities above a certain threshold print("DEBUG::y_test:") print(data.y_test) prediction_indices = probabilities > p_threshold y_pred = np.zeros(data.y_test.shape) y_pred[prediction_indices] = 1 print("DEBUG::y_pred:") print(y_pred) print("'Binary' accuracy (true positives + true negatives) is:") print(eval_binary_accuracy(data.y_test,y_pred)) print("'Binary' confusion (false positives + false negatives) is:") print(eval_confusion(data.y_test,y_pred)) print("False positive matrix is:") print(eval_false_positives(data.y_test,y_pred)) def main(checkpoint, data_count, data_cols, should_train, nb_epoch, null_pct, try_reuse_data, batch_size, execution_config): maxlen = 280 max_cells = 1 p_threshold = 0.5 checkpoint_dir = "checkpoints/age_classification/" if not os.path.isdir(checkpoint_dir): os.makedirs(checkpoint_dir) # read test data dataset_name = "training_age" print("Beginning Age Classifier Training...") raw_data = pd.read_csv('data/age_classification/'+dataset_name+'.csv', dtype='str', header=0, skip_blank_lines=True) #print(raw_data) print(raw_data.shape) raw_data_low = raw_data.ix[raw_data['label']=='14_17',0] raw_data_medium = raw_data.ix[raw_data['label']=='18_23',0] raw_data_high = raw_data.ix[raw_data['label']=='24_plus',0] print(raw_data_low.shape) print(raw_data_medium.shape) print(raw_data_high.shape) raw_data = np.asarray(raw_data_low)[np.newaxis] print(raw_data) print(raw_data.shape) header = [['14_17'],]*raw_data_low.shape[0] raw_data = np.column_stack((raw_data,np.asarray(raw_data_medium)[np.newaxis])) header.extend([['18_23'],]*raw_data_medium.shape[0]) raw_data = np.column_stack((raw_data,np.asarray(raw_data_high)[np.newaxis])) header.extend([['24_plus'],]*raw_data_high.shape[0]) print("Final raw_data size is:") print(raw_data.shape) print("Corresponding header length is:") print(len(header)) #print(header) # transpose the data raw_data = np.char.lower(np.transpose(raw_data).astype('U')) # do other processing and encode the data config = {} if not should_train: if execution_config is None: raise TypeError config = load_config(execution_config, checkpoint_dir) encoder = config['encoder'] if checkpoint is None: checkpoint = config['checkpoint'] else: encoder = Encoder() encoder.process(raw_data, max_cells) # encode the data X, y = encoder.encode_data(raw_data, header, maxlen) max_cells = encoder.cur_max_cells data = None if should_train: data = setup_test_sets(X, y) else: data = type('data_type', (object,), {'X_test': X, 'y_test':y}) print('Sample chars in X:{}'.format(X[2, 0:10])) print('y:{}'.format(y[2])) # need to know number of fixed categories to create model category_count = y.shape[1] print('Number of fixed categories is :') print(category_count) model = generate_model(maxlen, max_cells, category_count) load_weights(checkpoint, config, model, checkpoint_dir) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['binary_accuracy']) if(should_train): start = time.time() train_model(batch_size, checkpoint_dir, model, nb_epoch, data) end = time.time() print("Time for training is %f sec"%(end-start)) config = { 'encoder' : encoder, 'checkpoint' : get_best_checkpoint(checkpoint_dir) } save_config(config, checkpoint_dir) #print("DEBUG::The actual headers are:") #print(header) evaluate_model(max_cells, model, data, encoder, p_threshold) # Now, label unlabeled tweets dataset_name = "jordans_collection2" print("Beginning Age Classifier Labeling...") raw_data = pd.read_csv('data/age_classification/'+dataset_name+'.csv', dtype='str', header=0, skip_blank_lines=True,lineterminator='\n') raw_data = raw_data.ix[:,2] raw_data = np.asarray(raw_data)[np.newaxis] print(raw_data.shape) raw_data = np.char.lower(np.transpose(raw_data).astype('U')) X, y = encoder.encode_data(raw_data, [['14_17'],]*raw_data.shape[0], maxlen) data = type('data_type', (object,), {'X_test': X, 'y_test':y}) probabilities = model.predict(data.X_test, verbose=1) print(encoder.reverse_label_encode(probabilities,p_threshold)) def resolve_file_path(filename, dir): if os.path.isfile(str(filename)): return str(filename) elif os.path.isfile(str(dir + str(filename))): return dir + str(filename) def load_weights(checkpoint_name, config, model, checkpoint_dir): if config and not checkpoint_name: checkpoint_name = config['checkpoint'] if checkpoint_name: checkpoint_path = resolve_file_path(checkpoint_name, checkpoint_dir) print("Checkpoint : %s" % str(checkpoint_path)) model.load_weights(checkpoint_path) def save_config(execution_config, checkpoint_dir): filename = "" if not execution_config["checkpoint"] is None: filename = execution_config["checkpoint"].rsplit( ".", 1 )[ 0 ] + ".pkl" else: filename = time.strftime("%Y%m%d-%H%M%S") + ".pkl" with open(checkpoint_dir + filename, 'wb') as output: pickle.dump(execution_config, output, pickle.HIGHEST_PROTOCOL) def load_config(execution_config_path, dir): execution_config_path = resolve_file_path(execution_config_path, dir) return pickle.load( open( execution_config_path, "rb" ) ) def get_best_checkpoint(checkpoint_dir): max_mtime = 0 max_file = '' for dirname,subdirs,files in os.walk(checkpoint_dir): for fname in files: full_path = os.path.join(dirname, fname) mtime = os.stat(full_path).st_mtime if mtime > max_mtime: max_mtime = mtime max_dir = dirname max_file = fname return max_file if __name__ == '__main__': import argparse parser = argparse.ArgumentParser( description='attempts to discern data types looking at columns holistically.') parser.add_argument('--cp', dest='checkpoint', help='checkpoint to load') parser.add_argument('--config', dest='execution_config', help='execution configuration to load. contains max_cells, and encoder config.') parser.add_argument('--train', dest='should_train', action="store_true", default="True", help='run training') parser.add_argument('--no_train', dest='should_train', action="store_false", default="True", help='do not run training') parser.set_defaults(should_train=True) parser.add_argument('--data_count', dest='data_count', action="store", type=int, default=100, help='number of data rows to create') parser.add_argument('--data_cols', dest='data_cols', action="store", type=int, default=10, help='number of data cols to create') parser.add_argument('--nullpct', dest='null_pct', action="store", type=float, default=0, help='percent of Nulls to put in each column') parser.add_argument('--nb_epoch', dest='nb_epoch', action="store", type=int, default=5, help='number of epochs') parser.add_argument('--try_reuse_data', dest='try_reuse_data', action="store_true", default="True", help='loads existing data if the dimensions have been stored') parser.add_argument('--force_new_data', dest='try_reuse_data', action="store_false", default="True", help='forces the creation of new data, even if the dimensions have been stored') parser.add_argument('--batch_size', dest='batch_size', action="store", type=int, default=64, help='batch size for training') args = parser.parse_args() main(args.checkpoint, args.data_count, args.data_cols, args.should_train, args.nb_epoch, args.null_pct, args.try_reuse_data, args.batch_size, args.execution_config)

39.083521

165

0.661315

287

0.016576

0

3,843

0.221959

296d4245a81dda89f65dda7069ff2c63e222474b

287

py

Python

desdeo_tools/solver/__init__.py

phoopies/desdeo-tools

d3cb48c16b35114762386ee8368214b4b432eee0

[ "MIT" ]

1

2022-03-30T17:24:55.000Z

desdeo_tools/solver/__init__.py

phoopies/desdeo-tools

d3cb48c16b35114762386ee8368214b4b432eee0

[ "MIT" ]

2

2022-01-13T04:05:05.000Z

2022-03-12T01:07:03.000Z

desdeo_tools/solver/__init__.py

phoopies/desdeo-tools

d3cb48c16b35114762386ee8368214b4b432eee0

[ "MIT" ]

null

"""This module implements methods for solving scalar valued functions. """ __all__ = ["DiscreteMinimizer", "ScalarMethod", "ScalarMinimizer", "ScalarSolverException"] from desdeo_tools.solver.ScalarSolver import DiscreteMinimizer, ScalarMethod, ScalarMinimizer, ScalarSolverException

35.875

116

0.818815

0

148

0.515679

296eaf3a25df674b47f03f8d1d4dfa8c276636f7

1,661

py

Python

tests/test_root_to_hdf.py

lundeenj/hawc_hal

21b7cfd28490e870d1ded39735827e63684556df

[ "BSD-3-Clause" ]

null

tests/test_root_to_hdf.py

lundeenj/hawc_hal

21b7cfd28490e870d1ded39735827e63684556df

[ "BSD-3-Clause" ]

null

tests/test_root_to_hdf.py

lundeenj/hawc_hal

21b7cfd28490e870d1ded39735827e63684556df

[ "BSD-3-Clause" ]

null

from hawc_hal.maptree.map_tree import map_tree_factory from hawc_hal.response import hawc_response_factory import os from conftest import check_map_trees, check_responses def test_root_to_hdf_response(response): r = hawc_response_factory(response) test_filename = "response.hd5" # Make sure it doesn't exist yet, if it does,remove it if os.path.exists(test_filename): os.remove(test_filename) r.write(test_filename) # Try to open and use it r2 = hawc_response_factory(test_filename) check_responses(r, r2) os.remove(test_filename) def do_one_test_maptree(geminga_roi, geminga_maptree, fullsky=False): # Test both with a defined ROI and full sky (ROI is None) if fullsky: roi_ = None else: roi_ = geminga_roi m = map_tree_factory(geminga_maptree, roi_) test_filename = "maptree.hd5" # Make sure it doesn't exist yet, if it does,remove it if os.path.exists(test_filename): os.remove(test_filename) m.write(test_filename) # Try to open and use it m2 = map_tree_factory(test_filename, roi_) check_map_trees(m, m2) os.remove(test_filename) def test_root_to_hdf_maptree_roi(geminga_roi, geminga_maptree): do_one_test_maptree(geminga_roi, geminga_maptree, fullsky=False) def test_root_to_hdf_maptree_full_sky(geminga_roi, geminga_maptree): do_one_test_maptree(geminga_roi, geminga_maptree, fullsky=True)

24.072464

61

0.642986

0

240

0.144491

296f3e0d144e44aa8925c39df61ed0e143aa64d0

1,577

py

Python

clickhouse_driver/compression/zstd.py

risicle/clickhouse-driver

d36569f52d3e62ad2e275b1d63ad79b75a06402d

[ "MIT" ]

17

2019-12-19T13:37:14.000Z

2022-03-30T15:43:12.000Z

clickhouse_driver/compression/zstd.py

risicle/clickhouse-driver

d36569f52d3e62ad2e275b1d63ad79b75a06402d

[ "MIT" ]

null

clickhouse_driver/compression/zstd.py

risicle/clickhouse-driver

d36569f52d3e62ad2e275b1d63ad79b75a06402d

[ "MIT" ]

4

2019-05-15T14:58:05.000Z

2021-10-14T03:24:12.000Z

from __future__ import absolute_import from io import BytesIO import zstd from .base import BaseCompressor, BaseDecompressor from ..protocol import CompressionMethod, CompressionMethodByte from ..reader import read_binary_uint32 from ..writer import write_binary_uint32, write_binary_uint8 class Compressor(BaseCompressor): method = CompressionMethod.ZSTD method_byte = CompressionMethodByte.ZSTD def get_compressed_data(self, extra_header_size): rv = BytesIO() data = self.get_value() compressed = zstd.compress(data) header_size = extra_header_size + 4 + 4 # sizes write_binary_uint32(header_size + len(compressed), rv) write_binary_uint32(len(data), rv) rv.write(compressed) return rv.getvalue() class Decompressor(BaseDecompressor): method = CompressionMethod.ZSTD method_byte = CompressionMethodByte.ZSTD def get_decompressed_data(self, method_byte, compressed_hash, extra_header_size): size_with_header = read_binary_uint32(self.stream) compressed_size = size_with_header - extra_header_size - 4 compressed = BytesIO(self.stream.read(compressed_size)) block_check = BytesIO() write_binary_uint8(method_byte, block_check) write_binary_uint32(size_with_header, block_check) block_check.write(compressed.getvalue()) self.check_hash(block_check.getvalue(), compressed_hash) compressed = compressed.read(compressed_size - 4) return zstd.decompress(compressed)

30.326923

66

0.723526

1,279

0.811034

0

7

0.004439

2970702f348227149035bca1abec42b22aa901b1

1,019

py

Python

tmdb_client.py

SztMar/movies_catalogue

8140a37e916d9f67314aa679b46134e1794588e6

[ "MIT" ]

null

tmdb_client.py

SztMar/movies_catalogue

8140a37e916d9f67314aa679b46134e1794588e6

[ "MIT" ]

null

tmdb_client.py

SztMar/movies_catalogue

8140a37e916d9f67314aa679b46134e1794588e6

[ "MIT" ]

null

import requests import json import os API_TOKEN = os.environ.get("TMDB_API_TOKEN", "") def call_tmdb_api(_endpoint): endpoint = f"https://api.themoviedb.org/3/{_endpoint}" full_url = f'{endpoint}?api_key={API_TOKEN}' response = requests.get(full_url) response.raise_for_status() return response.json() def get_popular_movies(): return call_tmdb_api(f"movie/popular") def get_movies_list(list_type): return call_tmdb_api(f"movie/{list_type}") def get_poster_url(poster_api_path, size="w324"): base_url = "https://image.tmdb.org/t/p/" return f"{base_url}{size}/{poster_api_path}" def get_single_movie(movie_id): return call_tmdb_api(f"movie/{movie_id}") def get_single_movie_cast(movie_id): return call_tmdb_api(f"movie/{movie_id}/credits") def get_movies(how_many, list_type='popular'): data = get_movies_list(list_type) return data["results"][:how_many] def get_movie_images(movie_id): return call_tmdb_api(f"movie/{movie_id}/images")

26.128205

58

0.723258

0

292

0.286555

2970d09ae49d5ea0ddb5185266b5f2bed4f79bf9

300

py

Python

hanibal/ans_escuela/tipo_colaborador.py

Christian-Castro/castro_odoo8

8247fdb20aa39e043b6fa0c4d0af509462ab3e00

[ "Unlicense" ]

null

hanibal/ans_escuela/tipo_colaborador.py

Christian-Castro/castro_odoo8

8247fdb20aa39e043b6fa0c4d0af509462ab3e00

[ "Unlicense" ]

null

hanibal/ans_escuela/tipo_colaborador.py

Christian-Castro/castro_odoo8

8247fdb20aa39e043b6fa0c4d0af509462ab3e00

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- from openerp.osv import fields, osv from openerp import models, fields, api, _ class Tipo_Colaborador(models.Model): _name = 'tipo.colaborador' _rec_name = 'name' name=fields.Char(string='Nombre') active=fields.Boolean(string='Activo',default=True)

25

55

0.683333

195

0.65

0

63

0.21

2970d64cd60442ad64c58f0629d552521a791dbc

967

py

Python

spyder/utils/tests/test_environ.py

Nicztin/spyder

1c7b2232d2abbf1e101a1e2ab0552b32e8472bd6

[ "MIT" ]

1

2019-09-25T15:08:24.000Z

spyder/utils/tests/test_environ.py

jorgeps86/spyder

1c7b2232d2abbf1e101a1e2ab0552b32e8472bd6

[ "MIT" ]

null

spyder/utils/tests/test_environ.py

jorgeps86/spyder

1c7b2232d2abbf1e101a1e2ab0552b32e8472bd6

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright © Spyder Project Contributors # Licensed under the terms of the MIT License # """ Tests for environ.py """ # Standard library imports import os # Test library imports import pytest # Third party imports from qtpy.QtCore import QTimer # Local imports from spyder.utils.test import close_message_box @pytest.fixture def environ_dialog(qtbot): "Setup the Environment variables Dialog taking into account the os." QTimer.singleShot(1000, lambda: close_message_box(qtbot)) if os.name == 'nt': from spyder.utils.environ import WinUserEnvDialog dialog = WinUserEnvDialog() else: from spyder.utils.environ import EnvDialog dialog = EnvDialog() qtbot.addWidget(dialog) return dialog def test_environ(environ_dialog, qtbot): """Test the environment variables dialog.""" environ_dialog.show() assert environ_dialog if __name__ == "__main__": pytest.main()

20.574468

72

0.713547

0

432

0.446281

0

350

0.36157

2970ef8fae53d89f446cb52a1542393d10955b37

4,680

py

Python

comsole.py

MumuNiMochii/Dumb_Dump

ee431c934a591bc0868792601f5844615f869af1

[ "MIT" ]

1

2020-09-21T01:56:41.000Z

comsole.py

MumuNiMochii/Dumb_Dump

ee431c934a591bc0868792601f5844615f869af1

[ "MIT" ]

null

comsole.py

MumuNiMochii/Dumb_Dump

ee431c934a591bc0868792601f5844615f869af1

[ "MIT" ]

1

2020-10-09T01:09:20.000Z

import math def main(): print(""" \tComsole by MumuNiMochii version beta 1.6.23 \t\"Originally made with C\" \tMAIN MENU \tWhat do you want to execute and evaluate? \t1.) Add two addends \t2.) Subtract a minuend from its subtrahend \t3.) Multiply a multiplicand to its multiplier \t4.) Divide a dividend to its divisor \t5.) Raise to power a base number \t6.) Get the square root of a number \t7.) Compare two numbers \t8.) Compare three numbers \t9.) Auto-summation up to inputted value \t10.) Auto-factorial up to inputted value \t0.) Exit """) opt = int(input("\t\tEnter the number of your choice: ")) if opt == 1: def add(): print("\n\tADD VALUES") x = float(input("\t1.) Enter a first value: ")) y = float(input("\t2.) Enter an second value: ")) print("\t3.) The number " + str(x) + " is added by " + str(y) + ", and is equals to " + str(float(x + y))) add() elif opt == 2: def sub(): print("\n\tSUBTRACT VALUES") x = float(input("\t1.) Enter a first value: ")) y = float(input("\t2.) Enter an second value: ")) print("\t3.) The number " + str(x) + " is subtracted by " + str(y) + ", and is equals to " + str(float(x-y))) sub() elif opt == 3: def mul(): print("\n\tMULTIPLY VALUES") x = float(input("\t1.) Enter a first value: ")) y = float(input("\t2.) Enter an second value: ")) print("\t3.) The number "+str(x)+" is multiplied by "+str(y)+", and is equals to "+str(float(x*y))) mul() elif opt == 4: def div(): print("\n\tDIVIDE VALUES") x = float(input("\t1.) Enter a first value: ")) y = float(input("\t2.) Enter an second value: ")) print("\t3.) The number "+str(x)+" is divided by "+str(y)+", and is equals to "+str(float(x/y))) div() elif opt == 5: def pow(): print("\n\tPOWERED VALUE") x = float(input("\t1.) Enter a base value: ")) y = int(input("\t2.) Enter an exponent value: ")) print("\t3.) The number "+str(x)+" is raised to "+str(y)+", and is equals to "+str(math.pow(x, y))+".") pow() elif opt == 6: def sqrt(): print("\n\tRADICAL VALUE") x = float(input("\t1.) Enter a value: ")) y = math.sqrt(x) print("\t2.) The number is "+str(int(x))+" and its square root is: "+str(y)+".") sqrt() elif opt == 7: def comp2(): print("\n\tCOMPARE TWO VALUES") x = int(input("\t1.) Enter a first value: ")) y = int(input("\t2.) Enter a second value: ")) msg = "\t3.) Your numbers are "+str(x)+", and "+str(y)+", where " if x > y: print(msg + str(x) + " is greater than " + str(y)+".") else: print(msg + str(y) + " is greater than " + str(x)+".") comp2() elif opt == 8: def comp3(): print("\n\tCOMPARE THREE VALUES") x = int(input("\t1.) Enter a first value: ")) y = int(input("\t2.) Enter a second value: ")) z = int(input("\t3.) Enter a third value: ")) msg = "\t4.) Your numbers are "+str(x)+", "+str(y)+", and "+str(z)+", where " if x > y and x > z: print(msg+str(x)+" is greater than the values "+str(y)+" and "+str(z)+".") elif y > x and y > z: print(msg+str(y)+" is greater than the values "+str(x)+" and "+str(z)+".") else: print(msg+str(z)+" is greater than the values "+str(x)+" and "+str(y)+".") comp3() elif opt == 9: def summ(): print("\n\tSUMMATION UP TO INPUT VALUE") x = int(input("\t1.) Count up to inputted number: ")) a = list(range(0, x)) a.append(x) print("\t2.) Summation of numbers: " + str(a)) b = [] b.extend(a) total = 0 for i in b: total += i print("\t3.) Sum: " + str(total)) summ() elif opt == 10: def fact(): print("\n\tFACTORIAL INPUT VALUE") x = int(input("\t1.) Factorial the inputted number: ")) a = list(range(1, x)) a.append(x) print("\t2.) List of factorials: "+str(a)) b = [] b.extend(a) total = 1 for i in b: total *= i print("\t3.) Product: "+str(total)) fact() else: print("Invalid input.") main()

38.04878

121

0.47906

0

2,032

0.434188

2971ec9257acdac3ee8d9c3bae4d48e78eefdf76

3,485

py

Python

test/test_discussions.py

fibasile/ticket-gateway

811a216281a17150adca3edf691f9cf5a1478d2f

[ "MIT" ]

null

test/test_discussions.py

fibasile/ticket-gateway

811a216281a17150adca3edf691f9cf5a1478d2f

[ "MIT" ]

null

test/test_discussions.py

fibasile/ticket-gateway

811a216281a17150adca3edf691f9cf5a1478d2f

[ "MIT" ]

null

import unittest import json from server import server from models.abc import db from repositories import ChannelRepository, GitlabProvider from unittest.mock import MagicMock, Mock # from flask import make_response # from flask.json import jsonify from util import test_client class TestDiscussions(unittest.TestCase): @classmethod def setUpClass(cls): cls.client = test_client(server) cls._getTicket = GitlabProvider.getTicket cls._addTicketDiscussion = GitlabProvider.addTicketDiscussion cls._createTicketDiscussion = GitlabProvider.createTicketDiscussion def setUp(self): db.create_all() ChannelRepository.create( slug='a-channel', title='test channel', path='/dummy/path' ) def tearDown(self): db.session.remove() db.drop_all() cls = TestDiscussions GitlabProvider.getTicket = cls._getTicket GitlabProvider.addTicketDiscussion = cls._addTicketDiscussion GitlabProvider.createTicketDiscussion = cls._createTicketDiscussion def test_get(self): """The GET on `/api/channel/a-channel/tickets/ticket_id/discussions`""" GitlabProvider.getTicket = MagicMock() GitlabProvider.getTicket.return_value = Mock( discussions=Mock(list=Mock(return_value=[ {"id": "3243", "title": "test"} ]))) response = self.client.get( '/api/channel/a-channel/tickets/some_ticket/discussions') self.assertEqual(response.status_code, 200) GitlabProvider.getTicket.assert_called_with( '/dummy/path', 'some_ticket') # GitlabProvider.getMembers.assert_called_with('/dummy/path') response_json = json.loads(response.data.decode('utf-8')) self.assertEqual( response_json, {'data': [{"id": "3243", "title": "test"}]} ) def test_post_new(self): """POST on `/api/channel/a-channel/tickets/ticket_id/discussions`""" """should create a comment in a bew discussion """ GitlabProvider.addTicketDiscussion = MagicMock( name="addTicketDiscussion") GitlabProvider.addTicketDiscussion.return_value = {"status": "success"} response = self.client.post( '/api/channel/a-channel/tickets/some_ticket/discussions', json={ "discussion_id": "3232", "user_id": "3234", "body": "Some comment" }) self.assertEqual(response.status_code, 201) GitlabProvider.addTicketDiscussion.assert_called_with( '/dummy/path', 'some_ticket', '3232', '3234', 'Some comment') def test_post_existing(self): """POST on `/api/channel/a-channel/tickets/ticket_id/discussions`""" """should create a comment in an existing discussion """ GitlabProvider.createTicketDiscussion = MagicMock( name="createTicketDiscussion") GitlabProvider.createTicketDiscussion.return_value = { "status": "success"} response = self.client.post( '/api/channel/a-channel/tickets/some_ticket/discussions', json={ "user_id": "3234", "body": "Some comment" }) self.assertEqual(response.status_code, 201) GitlabProvider.createTicketDiscussion.assert_called_with( '/dummy/path', 'some_ticket', '3234', 'Some comment')

37.880435

79

0.63759

3,204

0.919369

0

274

0.078623

0

998

0.28637

29726a4c7ce2a690926dd277befe7e4b116e0d83

24,943

py

Python

cogs/music.py

ETJeanMachine/Pouty-Bot-Discord

9b005e43885db2e066d525449e05c7eb5d03cd23

[ "MIT" ]

null

cogs/music.py

ETJeanMachine/Pouty-Bot-Discord

9b005e43885db2e066d525449e05c7eb5d03cd23

[ "MIT" ]

null

cogs/music.py

ETJeanMachine/Pouty-Bot-Discord

9b005e43885db2e066d525449e05c7eb5d03cd23

[ "MIT" ]

null

""" This is an example cog that shows how you would make use of Lavalink.py. This example cog requires that you have python 3.6 or higher due to the f-strings. """ import math import re import discord import lavalink from discord.ext import commands from discord.ext import menus from .utils import checks from typing import List import asyncio import logging url_rx = re.compile('https?:\\/\\/(?:www\\.)?.+') # noqa: W605 class LavalinkVoiceClient(discord.VoiceProtocol): def __init__(self, client: discord.Client, channel: discord.abc.Connectable): self.client = client self.channel = channel self.connect_event = asyncio.Event() async def on_voice_server_update(self, data): lavalink_data = { 't': 'VOICE_SERVER_UPDATE', 'd': data } await self.lavalink.voice_update_handler(lavalink_data) async def on_voice_state_update(self, data): lavalink_data = { 't': 'VOICE_STATE_UPDATE', 'd': data } await self.lavalink.voice_update_handler(lavalink_data) async def connect(self, *, timeout: float, reconnect: bool) -> None: await self.channel.guild.change_voice_state(channel=self.channel) try: self.lavalink : lavalink.Client = self.client.lavalink except AttributeError: self.client.lavalink = self.lavalink = lavalink.Client(self.client.user.id) self.client.lavalink.add_node( 'localhost', 2333, 'youshallnotpass', 'us', 'default-node') async def disconnect(self, *, force: bool) -> None: await self.channel.guild.change_voice_state(channel=None) player = self.lavalink.player_manager.get(self.channel.guild.id) if player: player.channel_id = False await player.stop() self.cleanup() class MusicQueue(menus.ListPageSource): def __init__(self, data: List[lavalink.AudioTrack] , ctx: commands.Context, player): self.ctx = ctx self.player = player super().__init__(data, per_page=10) async def format_page(self, menu, entries): offset = menu.current_page * self.per_page embed = discord.Embed(title="Queue", description="\n".join( f'`{i+1}.` [{v.title}]({v.uri}) requested by **{self.ctx.guild.get_member(v.requester).name}**' for i, v in enumerate(entries, start=offset) ) ) status = (f"\N{TWISTED RIGHTWARDS ARROWS} Shuffle: {'enabled' if self.player.shuffle else 'disabled'} | " f"\N{CLOCKWISE RIGHTWARDS AND LEFTWARDS OPEN CIRCLE ARROWS} Repeat: {'enabled' if self.player.repeat else 'disabled'} | " f"\N{SPEAKER} Volume : {self.player.volume}") embed.set_footer(text=status) return embed def can_stop(): def predicate(ctx): if not ctx.guild: raise commands.CheckFailure("Only usable within a server") if not ctx.guild.me.voice: return True my_voice = ctx.guild.me.voice.channel try: if checks.is_owner_or_moderator_check(ctx.message): return True except commands.CheckFailure: pass if ctx.guild.me.voice: if len(my_voice.members) == 2 and ctx.author in my_voice.members: return True if len(my_voice.members) == 1: return True raise commands.CheckFailure( "Can only use this when nobody or " "only one user in voice channel with me" ) return commands.check(predicate) class Music(commands.Cog): def __init__(self, bot): self.bot = bot if not hasattr(self.bot, 'lavalink'): self.bot.lavalink = lavalink.Client(self.bot.user.id) self.bot.lavalink.add_node( 'localhost', 2333, 'youshallnotpass', 'us', 'default-node') self.bot.lavalink.add_event_hook(self.track_hook) self.pages = {} self.skip_votes = {} def current_voice_channel(self, ctx): if ctx.guild and ctx.guild.me.voice: return ctx.guild.me.voice.channel return None def cog_unload(self): self.bot.lavalink._event_hooks.clear() async def cog_before_invoke(self, ctx): guild_check = ctx.guild is not None # This is essentially the same as `@commands.guild_only()` # except it saves us repeating ourselves (and also a few lines). if guild_check: # Ensure that the bot and command author # share a mutual voicechannel. await self.ensure_voice(ctx) return guild_check async def cog_after_invoke(self,ctx): for page in self.pages.get(ctx.message.guild.id, []): await page.show_page(page.current_page) @commands.Cog.listener() async def on_voice_state_update(self, member, before, after): """ deafen yourself when joining a voice channel """ if member.id == member.guild.me.id and after.channel is None: if member.guild.voice_client: await member.guild.voice_client.disconnect(force=True) await self.bot.change_presence(activity=None) if member.id != member.guild.me.id or not after.channel: return my_perms = after.channel.permissions_for(member) if not after.deaf and my_perms.deafen_members: await member.edit(deafen=True) async def track_hook(self, event): if isinstance(event, lavalink.events.QueueEndEvent): guild_id = int(event.player.guild_id) guild : discord.Guild = self.bot.get_guild(guild_id) await guild.voice_client.disconnect(force=True) # Disconnect from the channel -- there's nothing else to play. if isinstance(event, lavalink.events.TrackEndEvent): if self.skip_votes and guild_id in self.skip_votes.keys(): self.skip_votes[guild_id].clear() if isinstance(event, lavalink.events.TrackStartEvent): await self.bot.change_presence( activity=discord.Activity(type=discord.ActivityType.listening, name=event.player.current.title) ) if isinstance(event, lavalink.events.TrackExceptionEvent): channel = event.player.fetch('channel') await channel.send(f"Error while playing Track: " f"**{event.track.title}**:" f"\n`{event.exception}`") @commands.group(aliases=['p'],invoke_without_command=True) async def play(self, ctx, *, query: str): """ Searches and plays a song from a given query. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) query = query.strip('<>') if not url_rx.match(query): query = f'ytsearch:{query}' results = await player.node.get_tracks(query) if not results or not results['tracks']: return await ctx.send('Nothing found!') embed = discord.Embed(color=discord.Color.blurple()) if results['loadType'] == 'PLAYLIST_LOADED': tracks = results['tracks'] for track in tracks: player.add(requester=ctx.author.id, track=track) embed.title = 'Playlist Enqueued!' embed.description = (f'{results["playlistInfo"]["name"]}' f'- {len(tracks)} tracks') else: track = results['tracks'][0] embed.title = 'Track Enqueued' embed.description = (f'[{track["info"]["title"]}]' f'({track["info"]["uri"]})') player.add(requester=ctx.author.id, track=track) await ctx.send(embed=embed) if not player.is_playing: await player.play() @play.command("soundcloud", aliases=['sc']) async def sc_play(self, ctx, *, query: str): """ search and play songs from soundcloud """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) query = query.strip('<>') if not url_rx.match(query): query = f'scsearch:{query}' results = await player.node.get_tracks(query) if not results or not results['tracks']: return await ctx.send('Nothing found!') embed = discord.Embed(color=discord.Color.blurple()) if results['loadType'] == 'PLAYLIST_LOADED': tracks = results['tracks'] for track in tracks: player.add(requester=ctx.author.id, track=track) embed.title = 'Playlist Enqueued!' embed.description = (f'{results["playlistInfo"]["name"]}' f'- {len(tracks)} tracks') else: track = results['tracks'][0] embed.title = 'Track Enqueued' embed.description = (f'[{track["info"]["title"]}]' f'({track["info"]["uri"]})') player.add(requester=ctx.author.id, track=track) await ctx.send(embed=embed) if not player.is_playing: await player.play() @commands.command() async def seek(self, ctx, *, seconds: int): """ Seeks to a given position in a track. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if ctx.author.id != player.current.requester: return await ctx.send("Only requester can seek.") track_time = player.position + (seconds * 1000) await player.seek(track_time) await ctx.send( f'Moved track to **{lavalink.utils.format_time(track_time)}**' ) @commands.command(name="fskip", aliases=['forceskip']) @checks.is_owner_or_moderator() async def force_skip(self, ctx): """ can only be invoked by moderators, immediately skips the current song """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.is_playing: return await ctx.send('Not playing.') await player.skip() if self.skip_votes: self.skip_votes[ctx.guild.id].clear() await ctx.send("⏭ | Skipped by moderator") @commands.command() async def skip(self, ctx): """ if invoked by requester skips the current song otherwise starts a skip vote, use again to remove skip vote """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.is_playing: return await ctx.send('Not playing.') current_voice = self.current_voice_channel(ctx) if (ctx.author.id == player.current.requester or len(current_voice.members) <= 2): await player.skip() if ctx.guild.id in self.skip_votes.keys(): self.skip_votes[ctx.guild.id].clear() await ctx.send('⏭ | Skipped by requester.') else: if ctx.guild.id not in self.skip_votes.keys(): self.skip_votes[ctx.guild.id] = {ctx.author.id} else: if ctx.author.id in self.skip_votes.values(): self.skip_votes[ctx.guild.id].remove(ctx.author.id) else: self.skip_votes[ctx.guild.id].add(ctx.author.id) skip_vote_number = len(self.skip_votes[ctx.guild.id]) number_of_users_in_voice = len(current_voice.members)-1 if skip_vote_number >= number_of_users_in_voice / 2: await player.skip() self.skip_votes[ctx.guild.id].clear() await ctx.send('⏭ | Skip vote passed.') else: votes_needed = \ math.ceil(number_of_users_in_voice/2) - skip_vote_number await ctx.send(f"current skip vote: " f"{votes_needed}" f"more vote(s) needed " f"for skip") @commands.command(aliases=['np', 'n', 'playing']) async def now(self, ctx): """ Shows some stats about the currently playing song. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.current: return await ctx.send('Nothing playing.') position = lavalink.utils.format_time(player.position) requester = ctx.guild.get_member(player.current.requester) if player.current.stream: duration = '🔴 LIVE' else: duration = lavalink.utils.format_time(player.current.duration) song = (f'**[{player.current.title}]({player.current.uri})**\n' f'({position}/{duration}) ' f'requested by ' f'**{requester.display_name if requester else "?"}**') embed = discord.Embed(color=discord.Color.blurple(), title='Now Playing', description=song) status = (f"\N{TWISTED RIGHTWARDS ARROWS} Shuffle: {'enabled' if player.shuffle else 'disabled'} | " f"\N{CLOCKWISE RIGHTWARDS AND LEFTWARDS OPEN CIRCLE ARROWS} Repeat: {'enabled' if player.repeat else 'disabled'} | " f"\N{SPEAKER} Volume : {player.volume}") embed.set_footer(text=status) await ctx.send(embed=embed) @commands.Cog.listener(name="on_reaction_clear") async def remove_page_on_menu_close(self, message, reactions): current_pages = self.pages.get(message.guild.id, None) if not current_pages: return found_page = next(filter(lambda p: p.message == message, current_pages), None) if found_page: self.pages[message.guild.id].remove(found_page) @commands.command(aliases=['q', 'playlist']) async def queue(self, ctx): """ Shows the player's queue. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.queue: return await ctx.send('Nothing queued.') pages= menus.MenuPages(source=MusicQueue(player.queue, ctx, player), clear_reactions_after=True) await pages.start(ctx) if ctx.guild.id in self.pages: self.pages[ctx.guild.id].append(pages) else: self.pages[ctx.guild.id] = [pages] @commands.command(aliases=['resume']) @checks.is_owner_or_moderator() async def pause(self, ctx): """ Pauses/Resumes the current track. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.is_playing: return await ctx.send('Not playing.') if player.paused: await player.set_pause(False) await ctx.send('⏯ | Resumed') else: await player.set_pause(True) await ctx.send('⏯ | Paused') @commands.command(aliases=['vol']) @checks.is_owner_or_moderator() async def volume(self, ctx, volume: int = None): """ Changes the player's volume (0-1000). """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not volume: return await ctx.send(f'🔈 | {player.volume}%') # Lavalink will automatically cap values between, or equal to 0-1000. await player.set_volume(volume) await ctx.send(f'🔈 | Set to {player.volume}%') @commands.command() async def shuffle(self, ctx): """ Shuffles the player's queue. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.is_playing: return await ctx.send('Nothing playing.') player.shuffle = not player.shuffle await ctx.send(f'🔀 | Shuffle ' f'{"enabled" if player.shuffle else "disabled"}') @commands.command(aliases=['loop']) async def repeat(self, ctx): """ Repeats the current song until the command is invoked again or until a new song is queued. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.is_playing: return await ctx.send('Nothing playing.') player.repeat = not player.repeat await ctx.send('🔁 | Repeat ' + ('enabled' if player.repeat else 'disabled')) @commands.command() async def remove(self, ctx, index: int): """ Removes an item from the player's queue with the given index. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) can_remove = False try: can_remove = checks.is_owner_or_moderator_check(ctx.message) except commands.CheckFailure: pass if can_remove or ctx.author.id == player.queue[index-1].requester: if not player.queue: return await ctx.send('Nothing queued.') if index > len(player.queue) or index < 1: return await ctx.send(f'Index has to be **between** 1 and {len(player.queue)}') removed = player.queue.pop(index - 1) # Account for 0-index. await ctx.send(f'Removed **{removed.title}** from the queue.') else: await ctx.send("Only requester and moderators can remove from the list") @commands.group(aliases=["search"], invoke_without_command=True) async def find(self, ctx, *, query): """ Lists the first 10 search results from a given query. also allows you to queue one of the results (use p and the index number) for example p 1 to play the first song in the results. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) original_query = query if not query.startswith('ytsearch:') and not query.startswith('scsearch:'): query = 'ytsearch:' + query results = await player.node.get_tracks(query) if not results or not results['tracks']: return await ctx.send('Nothing found.') tracks = results['tracks'][:10] # First 10 results o = (f"The first 10 results found via query `{original_query}`\n" f"use `queue` or `play` followed by the number of the result to queue that song\n") for index, track in enumerate(tracks, start=1): track_title = track['info']['title'] track_uri = track['info']['uri'] o += f'`{index}.` [{track_title}]({track_uri})\n' embed = discord.Embed(color=discord.Color.blurple(), description=o) await ctx.send(embed=embed) def queue_check(message): if not re.match(r"(q(uery)?|p(lay)?)", message.content): return False try: get_message_numbers = ''.join(c for c in message.content if c.isdigit()) number = int(get_message_numbers) except ValueError: raise commands.CommandError("please choose a number between 1 and 10") return (number >= 1 or number <= 10) and message.channel == ctx.channel and message.author == ctx.author try: msg = await ctx.bot.wait_for("message", check=queue_check, timeout=10.0) except asyncio.TimeoutError: return get_message_numbers = ''.join(c for c in msg.content if c.isdigit()) result_number = int(get_message_numbers) ctx.command = self.play await self.cog_before_invoke(ctx) await ctx.invoke(self.play, query=tracks[result_number-1]['info']['uri']) @find.group(name="scsearch",aliases=["sc", "soundcloud"], invoke_without_command=True) async def find_sc(self, ctx, *, query): """ Lists the first 10 soundcloud search results from a given query. also allows you to queue one of the results (use p and the index number) for example p 1 to play the first song in the results. """ player = self.bot.lavalink.player_manager.get(ctx.guild.id) original_query = query query = 'scsearch:' + query results = await player.node.get_tracks(query) if not results or not results['tracks']: return await ctx.send('Nothing found.') tracks = results['tracks'][:10] # First 10 results o = (f"The first 10 results found via query `{original_query}`\n" f"use `queue` or `play` followed by the number of the result to queue that song\n") for index, track in enumerate(tracks, start=1): track_title = track['info']['title'] track_uri = track['info']['uri'] o += f'`{index}.` [{track_title}]({track_uri})\n' embed = discord.Embed(color=discord.Color.blurple(), description=o) await ctx.send(embed=embed) def queue_check(message): if not re.match(r"(q(uery)?|p(lay)?)", message.content): return False try: get_message_numbers = ''.join(c for c in message.content if c.isdigit()) number = int(get_message_numbers) except ValueError: raise commands.CommandError("please choose a number between 1 and 10") return (number >= 1 or number <= 10) and message.channel == ctx.channel and message.author == ctx.author try: msg = await ctx.bot.wait_for("message", check=queue_check, timeout=10.0) except asyncio.TimeoutError: return get_message_numbers = ''.join(c for c in msg.content if c.isdigit()) result_number = int(get_message_numbers) ctx.command = self.play await self.cog_before_invoke(ctx) await ctx.invoke(self.play, query=tracks[result_number-1]['info']['uri']) @commands.command(aliases=['dc','stop','leave','quit']) @can_stop() async def disconnect(self, ctx: commands.Context): """ Disconnects the player from the voice channel and clears its queue. """ await ctx.voice_client.disconnect(force=True) player = self.bot.lavalink.player_manager.get(ctx.guild.id) if not player.is_connected: return await ctx.send('Not connected.') player.queue.clear() await player.stop() await ctx.send('*⃣ | Disconnected.') async def ensure_voice(self, ctx): """ This check ensures that the bot and command author are in the same voicechannel. """ should_connect = ctx.command.name in ('play', 'junbi_ok','soundcloud') # Add commands that require joining voice to work. if should_connect and self.current_voice_channel(ctx) is None: if self.bot.lavalink.node_manager.available_nodes: voice_client = await ctx.author.voice.channel.connect(cls=LavalinkVoiceClient) player : lavalink.DefaultPlayer = self.bot.lavalink.player_manager.create(ctx.guild.id) player.store("channel", ctx.channel) else: raise commands.CommandError("No audio player nodes available. Please wait a few minutes for a reconnect") elif self.current_voice_channel(ctx) is not None and not self.bot.lavalink.node_manager.available_nodes: await ctx.guild.voice_client.disconnect(force=True) raise commands.CommandError("No audio player nodes available. Please wait a few minutes for a reconnect") if ctx.command.name in ('find', 'scsearch', 'disconnect', 'now', 'queue'): return if not ctx.author.voice or not ctx.author.voice.channel: raise commands.CheckFailure('Join a voicechannel first.') permissions = ctx.author.voice.channel.permissions_for(ctx.me) if not permissions.connect or not permissions.speak: # Check user limit too? raise commands.CheckFailure('I need the `CONNECT` and `SPEAK` permissions.') @commands.command(name="lc") @checks.is_owner() async def lavalink_reconnect(self, ctx): self.bot.lavalink.add_node( 'localhost', 2333, 'youshallnotpass', 'us', 'default-node') async def setup(bot): await bot.add_cog(Music(bot))

41.092257

161

0.583611

23,599

0.945094

0

16,823

0.673728

21,206

0.849259

5,774

0.231237

2973ac2848fca5ea3493059da1e9b46b9746f3f2

17,005

py

Python

utils/data_processing.py

LisaAnne/LocalizingMoments

b6a555c8134581305d0ed4716fbc192860e0b88c

[ "BSD-2-Clause" ]

157

2017-08-04T21:56:46.000Z

2022-03-05T13:49:02.000Z

utils/data_processing.py

Naman-Bhalla/LocalizingMoments

618bebfe6c4b897e94770b8011d34aa45c941e62

[ "BSD-2-Clause" ]

19

2017-09-26T15:27:47.000Z

2022-02-27T23:21:00.000Z

utils/data_processing.py

Naman-Bhalla/LocalizingMoments

618bebfe6c4b897e94770b8011d34aa45c941e62

[ "BSD-2-Clause" ]

48

2017-08-08T11:18:39.000Z

2021-11-15T04:20:33.000Z

import numpy as np import sys import os sys.path.append('utils/') from config import * from utils import * sys.path.append(pycaffe_dir) import time import pdb import random import pickle as pkl import caffe from multiprocessing import Pool from threading import Thread import random import h5py import itertools import math import re glove_dim = 300 glove_path = 'data/glove.6B.%dd.txt' %glove_dim #glove_path = 'data/glove_debug_path.txt' #for debugging if glove_path == 'data/glove_debug_path.txt': print "continue?" pdb.set_trace() possible_segments = [(0,0), (1,1), (2,2), (3,3), (4,4), (5,5)] for i in itertools.combinations(range(6), 2): possible_segments.append(i) length_prep_word = 40 length_prep_character = 250 vocab_file = 'data/vocab_glove_complete.txt' def word_tokenize(s): sent = s.lower() sent = re.sub('[^A-Za-z0-9\s]+',' ', sent) return sent.split() def sentences_to_words(sentences): words = [] for s in sentences: words.extend(word_tokenize(str(s.lower()))) return words class glove_embedding(object): ''' Creates glove embedding object ''' def __init__(self, glove_file=glove_path): glove_txt = open(glove_file).readlines() glove_txt = [g.strip() for g in glove_txt] glove_vector = [g.split(' ') for g in glove_txt] glove_words = [g[0] for g in glove_vector] glove_vecs = [g[1:] for g in glove_vector] glove_array = np.zeros((glove_dim, len(glove_words))) glove_dict = {} for i, w in enumerate(glove_words): glove_dict[w] = i for i, vec in enumerate(glove_vecs): glove_array[:,i] = np.array(vec) self.glove_array = glove_array self.glove_dict = glove_dict self.glove_words = glove_words class zero_language_vector(object): def __init__(self, data): self.dim = glove_dim def get_vector_dim(self): return self.dim def get_vocab_size(self): return 0 def preprocess(self, data): embedding = np.zeros((self.get_vector_dim(),)) for d in data: d['language_input'] = embedding d['gt'] = (d['gt'][0], d['gt'][1]) return data class recurrent_language(object): def get_vocab_size(self): return len(self.vocab_dict.keys()) def preprocess_sentence(self, words): vector_dim = self.get_vector_dim() sentence_mat = np.zeros((len(words), vector_dim)) count_words = 0 for i, w in enumerate(words): try: sentence_mat[count_words,:] = self.vocab_dict[w] count_words += 1 except: if '<unk>' in self.vocab_dict.keys(): sentence_mat[count_words,:] = self.vocab_dict['<unk>'] count_words += 1 else: pass sentence_mat = sentence_mat[:count_words] return sentence_mat def preprocess(self, data): for d in data: words = sentences_to_words([d['description']]) d['language_input'] = self.preprocess(words) return data class recurrent_word(recurrent_language): def __init__(self, data): self.data = data vocab = open(vocab_file).readlines() vocab = [v.strip() for v in vocab] if '<unk>' not in vocab: vocab.append('<unk>') vocab_dict = {} for i, word in enumerate(vocab): vocab_dict[word] = i self.vocab_dict = vocab_dict def get_vector_dim(self): return 1 class recurrent_embedding(recurrent_language): def read_embedding(self): print "Reading glove embedding" embedding = glove_embedding(glove_path) self.embedding = embedding def get_vector_dim(self): return glove_dim def __init__(self, data): self.read_embedding() embedding = self.embedding vector_dim = self.get_vector_dim() self.data = data self.data = data vocab = open(vocab_file).readlines() vocab = [v.strip() for v in vocab] if '<unk>' in vocab: vocab.remove('<unk>') #don't have an <unk> vector. Alternatively, could map to random vector... vocab_dict = {} for i, word in enumerate(vocab): try: vocab_dict[word] = embedding.glove_array[:,embedding.glove_dict[word]] except: print "%s not in glove embedding" %word self.vocab_dict = vocab_dict def preprocess(self, data): vector_dim = self.get_vector_dim() for d in data: d['language_input'] = sentences_to_words([d['description']]) return data def get_vocab_dict(self): return self.vocab_dict #Methods for extracting visual features def feature_process_base(start, end, features): return np.mean(features[start:end+1,:], axis = 0) def feature_process_norm(start, end, features): base_feature = np.mean(features[start:end+1,:], axis = 0) return base_feature/(np.linalg.norm(base_feature) + 0.00001) def feature_process_context(start, end, features): feature_dim = features.shape[1] full_feature = np.zeros((feature_dim*2,)) if np.sum(features[5,:]) > 0: full_feature[:feature_dim] = feature_process_norm(0,6, features) else: full_feature[:feature_dim] = feature_process_norm(0,5, features) full_feature[feature_dim:feature_dim*2] = feature_process_norm(start, end, features) return full_feature feature_process_dict = {'feature_process_base': feature_process_base, 'feature_process_norm': feature_process_norm, 'feature_process_context': feature_process_context, } class extractData(object): """ General class to extract data. """ def increment(self): #uses iteration, batch_size, data_list, and num_data to extract next batch identifiers next_batch = [None]*self.batch_size if self.iteration + self.batch_size >= self.num_data: next_batch[:self.num_data-self.iteration] = self.data_list[self.iteration:] next_batch[self.num_data-self.iteration:] = self.data_list[:self.batch_size -(self.num_data-self.iteration)] random.shuffle(self.data_list) self.iteration = self.num_data - self.iteration else: next_batch = self.data_list[self.iteration:self.iteration+self.batch_size] self.iteration += self.batch_size assert self.iteration > -1 assert len(next_batch) == self.batch_size return next_batch class extractLanguageFeatures(extractData): def __init__(self, dataset, params, result=None): self.data_list = range(len(dataset)) self.num_data = len(self.data_list) self.dataset = dataset self.iteration = 0 self.vocab_dict = params['vocab_dict'] self.batch_size = params['batch_size'] self.num_glove_centroids = self.vocab_dict.values()[0].shape[0] self.T = params['sentence_length'] if isinstance(result, dict): self.result = result self.query_key = params['query_key'] self.cont_key = params['cont_key'] self.top_keys = [self.query_key, self.cont_key] self.top_shapes = [(self.T, self.batch_size, self.num_glove_centroids), (self.T, self.batch_size)] else: print "Will only be able to run in test mode" def get_features(self, query): feature = np.zeros((self.T, self.num_glove_centroids)) cont = np.zeros((self.T,)) len_query = min(len(query), self.T) if len_query < len(query): query = query[:len_query] for count_word, word in enumerate(query): try: feature[-(len_query)+count_word,:] = self.vocab_dict[word] except: feature[-(len_query)+count_word,:] = np.zeros((glove_dim,)) cont[-(len_query-1):] = 1 assert np.sum(feature[:-len_query,:]) == 0 return feature, cont def get_data_test(self, data): query = data['language_input'] return self.get_features(query) def get_data(self, next_batch): data = self.dataset query_mat = np.zeros((self.T, self.batch_size, self.num_glove_centroids)) cont = np.zeros((self.T, self.batch_size)) for i, nb in enumerate(next_batch): query = data[nb]['language_input'] query_mat[:,i,:], cont[:,i] = self.get_features(query) self.result[self.query_key] = query_mat self.result[self.cont_key] = cont class extractVisualFeatures(extractData): def __init__(self, dataset, params, result): self.data_list = range(len(dataset)) self.feature_process_algo = params['feature_process'] self.loc_feature = params['loc_feature'] self.num_data = len(self.data_list) self.dataset = dataset self.iteration = 0 self.loc = params['loc_feature'] loss_type = params['loss_type'] assert loss_type in ['triplet', 'inter', 'intra'] self.inter = False self.intra = False if loss_type in ['triplet', 'inter']: self.inter = True if loss_type in ['triplet', 'intra']: self.intra = True self.batch_size = params['batch_size'] self.num_glove_centroids = params['num_glove_centroids'] features_h5py = h5py.File(params['features']) features = {} for key in features_h5py.keys(): features[key] = np.array(features_h5py[key]) features_h5py.close() self.features = features assert self.feature_process_algo in feature_process_dict.keys() self.feature_process = feature_process_dict[self.feature_process_algo] self.feature_dim = self.feature_process(0,0,self.features[self.dataset[0]['video']]).shape[-1] self.result = result self.feature_key_p = params['feature_key_p'] self.feature_time_stamp_p = params['feature_time_stamp_p'] self.feature_time_stamp_n = params['feature_time_stamp_n'] self.top_keys = [self.feature_key_p, self.feature_time_stamp_p, self.feature_time_stamp_n] self.top_shapes = [(self.batch_size, self.feature_dim), (self.batch_size, 2), (self.batch_size,2)] if self.inter: self.feature_key_inter = 'features_inter' self.top_keys.append(self.feature_key_inter) self.top_shapes.append((self.batch_size, self.feature_dim)) if self.intra: self.feature_key_intra = 'features_intra' self.top_keys.append(self.feature_key_intra) self.top_shapes.append((self.batch_size, self.feature_dim)) self.possible_annotations = possible_segments def get_data_test(self, d): video_feats = self.features[d['video']] features = np.zeros((len(self.possible_annotations), self.feature_dim)) loc_feats = np.zeros((len(self.possible_annotations), 2)) for i, p in enumerate(self.possible_annotations): features[i,:] = self.feature_process(p[0], p[1], video_feats) loc_feats[i,:] = [p[0]/6., p[1]/6.] return features, loc_feats def get_data(self, next_batch): feature_process = self.feature_process data = self.dataset features_p = np.zeros((self.batch_size, self.feature_dim)) if self.inter: features_inter = np.zeros((self.batch_size, self.feature_dim)) if self.intra: features_intra = np.zeros((self.batch_size, self.feature_dim)) features_time_stamp_p = np.zeros((self.batch_size, 2)) features_time_stamp_n = np.zeros((self.batch_size, 2)) for i, nb in enumerate(next_batch): rint = random.randint(0,len(data[nb]['times'])-1) gt_s = data[nb]['times'][rint][0] gt_e = data[nb]['times'][rint][1] possible_n = list(set(self.possible_annotations) - set(((gt_s,gt_e),))) random.shuffle(possible_n) n = possible_n[0] assert n != (gt_s, gt_e) video = data[nb]['video'] feats = self.features[video] if self.inter: other_video = data[nb]['video'] while (other_video == video): other_video_index = int(random.random()*len(data)) other_video = data[other_video_index]['video'] feats_inter = self.features[other_video] features_p[i,:] = feature_process(gt_s, gt_e, feats) if self.intra: features_intra[i,:] = feature_process(n[0], n[1], feats) if self.inter: try: features_inter[i,:] = feature_process(gt_s, gt_e, feats_inter) except: pdb.set_trace() if self.loc: features_time_stamp_p[i,0] = gt_s/6. features_time_stamp_p[i,1] = gt_e/6. features_time_stamp_n[i,0] = n[0]/6. features_time_stamp_n[i,1] = n[1]/6. else: features_time_stamp_p[i,0] = 0 features_time_stamp_p[i,1] = 0 features_time_stamp_n[i,0] = 0 features_time_stamp_n[i,1] = 0 assert not math.isnan(np.mean(self.features[data[nb]['video']][n[0]:n[1]+1,:])) assert not math.isnan(np.mean(self.features[data[nb]['video']][gt_s:gt_e+1,:])) self.result[self.feature_key_p] = features_p self.result[self.feature_time_stamp_p] = features_time_stamp_p self.result[self.feature_time_stamp_n] = features_time_stamp_n if self.inter: self.result[self.feature_key_inter] = features_inter if self.intra: self.result[self.feature_key_intra] = features_intra class batchAdvancer(object): def __init__(self, extractors): self.extractors = extractors self.increment_extractor = extractors[0] def __call__(self): #The batch advancer just calls each extractor next_batch = self.increment_extractor.increment() for e in self.extractors: e.get_data(next_batch) class python_data_layer(caffe.Layer): """ General class to extract data. """ def setup(self, bottom, top): random.seed(10) self.params = eval(self.param_str) params = self.params assert 'top_names' in params.keys() #set up prefetching self.thread_result = {} self.thread = None self.setup_extractors() self.batch_advancer = batchAdvancer(self.data_extractors) shape_dict = {} self.top_names = [] for de in self.data_extractors: for top_name, top_shape in zip(de.top_keys, de.top_shapes): shape_dict[top_name] = top_shape self.top_names.append((params['top_names'].index(top_name), top_name)) self.dispatch_worker() self.top_shapes = [shape_dict[tn[1]] for tn in self.top_names] print 'Outputs:', self.top_names if len(top) != len(self.top_names): raise Exception('Incorrect number of outputs (expected %d, got %d)' % (len(self.top_names), len(top))) self.join_worker() #for top_index, name in enumerate(self.top_names.keys()): top_count = 0 for top_index, name in self.top_names: shape = self.top_shapes[top_count] print 'Top name %s has shape %s.' %(name, shape) top[top_index].reshape(*shape) top_count += 1 def reshape(self, bottom, top): pass def forward(self, bottom, top): if self.thread is not None: self.join_worker() for top_index, name in self.top_names: top[top_index].data[...] = self.thread_result[name] self.dispatch_worker() def dispatch_worker(self): assert self.thread is None self.thread = Thread(target=self.batch_advancer) self.thread.start() def join_worker(self): assert self.thread is not None self.thread.join() self.thread = None def backward(self, top, propoagate_down, bottom): pass feature_process_dict = {'feature_process_base': feature_process_base, 'feature_process_norm': feature_process_norm, 'feature_process_context': feature_process_context, } language_feature_process_dict = {'zero_language': zero_language_vector, 'recurrent_embedding': recurrent_embedding} class dataLayer_ExtractPairedLanguageVision(python_data_layer): def setup_extractors(self): assert 'top_names' in self.params.keys() assert 'descriptions' in self.params.keys() assert 'features' in self.params.keys() if 'batch_size' not in self.params.keys(): self.params['batch_size'] = 120 self.params['query_key'] = 'query' self.params['feature_key_n'] = 'features_n' self.params['feature_key_p'] = 'features_p' self.params['feature_key_t'] = 'features_t' self.params['feature_time_stamp_p'] = 'features_time_stamp_p' self.params['feature_time_stamp_n'] = 'features_time_stamp_n' self.params['cont_key'] = 'cont' language_extractor_fcn = extractLanguageFeatures visual_extractor_fcn = extractVisualFeatures language_process = recurrent_embedding data_orig = read_json(self.params['descriptions']) random.shuffle(data_orig) language_processor = language_process(data_orig) data = language_processor.preprocess(data_orig) self.params['vocab_dict'] = language_processor.vocab_dict num_glove_centroids = language_processor.get_vector_dim() self.params['num_glove_centroids'] = num_glove_centroids visual_feature_extractor = visual_extractor_fcn(data, self.params, self.thread_result) textual_feature_extractor = language_extractor_fcn(data, self.params, self.thread_result) self.data_extractors = [visual_feature_extractor, textual_feature_extractor]

32.267552

114

0.673625

14,574

0.857042

0

1,843

0.10838

297530f3746153e98984e1af10f206c3aa39875c

17,856

py

Python

old_metrics/bleu.py

Danial-Alh/fast-bleu

d8726ae829b2cc275192e70819ee9c60c9d4bed6

[ "MIT" ]

21

2020-08-09T12:06:58.000Z

2022-02-13T19:15:11.000Z

old_metrics/bleu.py

Danial-Alh/FastBLEU

182584a6593aa36d57b15705da6f317bdb06f83e

[ "MIT" ]

4

2020-08-11T22:00:40.000Z

2021-09-01T07:35:52.000Z

old_metrics/bleu.py

Danial-Alh/FastBLEU

182584a6593aa36d57b15705da6f317bdb06f83e

[ "MIT" ]

2

2020-08-12T14:49:18.000Z

2021-06-25T12:51:42.000Z

import math import os from collections import Counter from fractions import Fraction import numpy as np from nltk import ngrams from nltk.translate.bleu_score import SmoothingFunction from .utils import get_ngrams, Threader def corpus_bleu(references, hypothesis, reference_max_counts, ref_lens, weights=(0.25, 0.25, 0.25, 0.25), smoothing_function=None, auto_reweight=False, ): """ Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all the hypotheses and their respective references. Instead of averaging the sentence level BLEU scores (i.e. marco-average precision), the original BLEU metric (Papineni et al. 2002) accounts for the micro-average precision (i.e. summing the numerators and denominators for each hypothesis-reference(s) pairs before the division). >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', ... 'ensures', 'that', 'the', 'military', 'always', ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', ... 'ensures', 'that', 'the', 'military', 'will', 'forever', ... 'heed', 'Party', 'commands'] >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which', ... 'guarantees', 'the', 'military', 'forces', 'always', ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party'] >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', ... 'army', 'always', 'to', 'heed', 'the', 'directions', ... 'of', 'the', 'party'] >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', ... 'interested', 'in', 'world', 'history'] >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', ... 'because', 'he', 'read', 'the', 'book'] >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] >>> hypotheses = [hyp1, hyp2] >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS 0.5920... The example below show that corpus_bleu() is different from averaging sentence_bleu() for hypotheses >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1) >>> score2 = sentence_bleu([ref2a], hyp2) >>> (score1 + score2) / 2 # doctest: +ELLIPSIS 0.6223... :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses :type list_of_references: list(list(list(str))) :param hypotheses: a list of hypothesis sentences :type hypotheses: list(list(str)) :param weights: weights for unigrams, bigrams, trigrams and so on :type weights: list(float) :param smoothing_function: :type smoothing_function: SmoothingFunction :param auto_reweight: Option to re-normalize the weights uniformly. :type auto_reweight: bool :return: The corpus-level BLEU score. :rtype: float """ # Before proceeding to compute BLEU, perform sanity checks. p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches. p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref. hyp_lengths, ref_lengths = 0, 0 # Iterate through each hypothesis and their corresponding references. # For each order of ngram, calculate the numerator and # denominator for the corpus-level modified precision. for i, _ in enumerate(weights, start=1): p_i = modified_precision(reference_max_counts, hypothesis, i) p_numerators[i] += p_i.numerator p_denominators[i] += p_i.denominator # Calculate the hypothesis length and the closest reference length. # Adds them to the corpus-level hypothesis and reference counts. hyp_len = len(hypothesis) hyp_lengths += hyp_len ref_lengths += closest_ref_length(ref_lens, hyp_len) # Calculate corpus-level brevity penalty. bp = brevity_penalty(ref_lengths, hyp_lengths) # Uniformly re-weighting based on maximum hypothesis lengths if largest # order of n-grams < 4 and weights is set at default. if auto_reweight: if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25): weights = (1 / hyp_lengths,) * hyp_lengths # Collects the various precision values for the different ngram orders. p_n = [ Fraction(p_numerators[i], p_denominators[i], _normalize=False) for i, _ in enumerate(weights, start=1) ] # Returns 0 if there's no matching n-grams # We only need to check for p_numerators[1] == 0, since if there's # no unigrams, there won't be any higher order ngrams. if p_numerators[1] == 0: return 0 # If there's no smoothing, set use method0 from SmoothinFunction class. if not smoothing_function: smoothing_function = SmoothingFunction().method0 # Smoothen the modified precision. # Note: smoothing_function() may convert values into floats; # it tries to retain the Fraction object as much as the # smoothing method allows. p_n = smoothing_function( p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths ) s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n)) s = bp * math.exp(math.fsum(s)) return s def modified_precision(reference_max_counts, hypothesis, n): """ Calculate modified ngram precision. The normal precision method may lead to some wrong translations with high-precision, e.g., the translation, in which a word of reference repeats several times, has very high precision. This function only returns the Fraction object that contains the numerator and denominator necessary to calculate the corpus-level precision. To calculate the modified precision for a single pair of hypothesis and references, cast the Fraction object into a float. The famous "the the the ... " example shows that you can get BLEU precision by duplicating high frequency words. >>> reference1 = 'the cat is on the mat'.split() >>> reference2 = 'there is a cat on the mat'.split() >>> hypothesis1 = 'the the the the the the the'.split() >>> references = [reference1, reference2] >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS 0.2857... In the modified n-gram precision, a reference word will be considered exhausted after a matching hypothesis word is identified, e.g. >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', ... 'ensures', 'that', 'the', 'military', 'will', ... 'forever', 'heed', 'Party', 'commands'] >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', ... 'guarantees', 'the', 'military', 'forces', 'always', ... 'being', 'under', 'the', 'command', 'of', 'the', ... 'Party'] >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', ... 'army', 'always', 'to', 'heed', 'the', 'directions', ... 'of', 'the', 'party'] >>> hypothesis = 'of the'.split() >>> references = [reference1, reference2, reference3] >>> float(modified_precision(references, hypothesis, n=1)) 1.0 >>> float(modified_precision(references, hypothesis, n=2)) 1.0 An example of a normal machine translation hypothesis: >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', ... 'ensures', 'that', 'the', 'military', 'always', ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops', ... 'forever', 'hearing', 'the', 'activity', 'guidebook', ... 'that', 'party', 'direct'] >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', ... 'ensures', 'that', 'the', 'military', 'will', ... 'forever', 'heed', 'Party', 'commands'] >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', ... 'guarantees', 'the', 'military', 'forces', 'always', ... 'being', 'under', 'the', 'command', 'of', 'the', ... 'Party'] >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', ... 'army', 'always', 'to', 'heed', 'the', 'directions', ... 'of', 'the', 'party'] >>> references = [reference1, reference2, reference3] >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS 0.9444... >>> float(modified_precision(references, hypothesis2, n=1)) # doctest: +ELLIPSIS 0.5714... >>> float(modified_precision(references, hypothesis1, n=2)) # doctest: +ELLIPSIS 0.5882352941176471 >>> float(modified_precision(references, hypothesis2, n=2)) # doctest: +ELLIPSIS 0.07692... :param references: A list of reference translations. :type references: list(list(str)) :param hypothesis: A hypothesis translation. :type hypothesis: list(str) :param n: The ngram order. :type n: int :return: BLEU's modified precision for the nth order ngram. :rtype: Fraction """ # Extracts all ngrams in hypothesis # Set an empty Counter if hypothesis is empty. counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter() # Extract a union of references' counts. # max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references]) max_counts = reference_max_counts[n - 1] # Assigns the intersection between hypothesis and references' counts. clipped_counts = { ngram: min(count, max_counts.get(ngram, 0)) for ngram, count in counts.items() } numerator = sum(clipped_counts.values()) # Ensures that denominator is minimum 1 to avoid ZeroDivisionError. # Usually this happens when the ngram order is > len(reference). denominator = max(1, sum(counts.values())) return Fraction(numerator, denominator, _normalize=False) def closest_ref_length(ref_lens, hyp_len): """ This function finds the reference that is the closest length to the hypothesis. The closest reference length is referred to as *r* variable from the brevity penalty formula in Papineni et. al. (2002) :param references: A list of reference translations. :type references: list(list(str)) :param hyp_len: The length of the hypothesis. :type hyp_len: int :return: The length of the reference that's closest to the hypothesis. :rtype: int """ closest_ref_len = min( ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len) ) return closest_ref_len def brevity_penalty(closest_ref_len, hyp_len): """ Calculate brevity penalty. As the modified n-gram precision still has the problem from the short length sentence, brevity penalty is used to modify the overall BLEU score according to length. An example from the paper. There are three references with length 12, 15 and 17. And a concise hypothesis of the length 12. The brevity penalty is 1. >>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12 >>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15 >>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17 >>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12 >>> references = [reference1, reference2, reference3] >>> hyp_len = len(hypothesis) >>> closest_ref_len = closest_ref_length(references, hyp_len) >>> brevity_penalty(closest_ref_len, hyp_len) 1.0 In case a hypothesis translation is shorter than the references, penalty is applied. >>> references = [['a'] * 28, ['a'] * 28] >>> hypothesis = ['a'] * 12 >>> hyp_len = len(hypothesis) >>> closest_ref_len = closest_ref_length(references, hyp_len) >>> brevity_penalty(closest_ref_len, hyp_len) 0.2635971381157267 The length of the closest reference is used to compute the penalty. If the length of a hypothesis is 12, and the reference lengths are 13 and 2, the penalty is applied because the hypothesis length (12) is less then the closest reference length (13). >>> references = [['a'] * 13, ['a'] * 2] >>> hypothesis = ['a'] * 12 >>> hyp_len = len(hypothesis) >>> closest_ref_len = closest_ref_length(references, hyp_len) >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS 0.9200... The brevity penalty doesn't depend on reference order. More importantly, when two reference sentences are at the same distance, the shortest reference sentence length is used. >>> references = [['a'] * 13, ['a'] * 11] >>> hypothesis = ['a'] * 12 >>> hyp_len = len(hypothesis) >>> closest_ref_len = closest_ref_length(references, hyp_len) >>> bp1 = brevity_penalty(closest_ref_len, hyp_len) >>> hyp_len = len(hypothesis) >>> closest_ref_len = closest_ref_length(reversed(references), hyp_len) >>> bp2 = brevity_penalty(closest_ref_len, hyp_len) >>> bp1 == bp2 == 1 True A test example from mteval-v13a.pl (starting from the line 705): >>> references = [['a'] * 11, ['a'] * 8] >>> hypothesis = ['a'] * 7 >>> hyp_len = len(hypothesis) >>> closest_ref_len = closest_ref_length(references, hyp_len) >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS 0.8668... >>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7] >>> hypothesis = ['a'] * 7 >>> hyp_len = len(hypothesis) >>> closest_ref_len = closest_ref_length(references, hyp_len) >>> brevity_penalty(closest_ref_len, hyp_len) 1.0 :param hyp_len: The length of the hypothesis for a single sentence OR the sum of all the hypotheses' lengths for a corpus :type hyp_len: int :param closest_ref_len: The length of the closest reference for a single hypothesis OR the sum of all the closest references for every hypotheses. :type closest_ref_len: int :return: BLEU's brevity penalty. :rtype: float """ if hyp_len > closest_ref_len: return 1 # If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0 elif hyp_len == 0: return 0 else: return math.exp(1 - closest_ref_len / hyp_len) class Bleu(): # this class speedup computation when reference is same for multisample # Base on https://www.nltk.org/_modules/nltk/translate/bleu_score.html def __init__(self, references, weights=np.ones(3) / 3., smoothing_function=SmoothingFunction().method1, auto_reweight=False, process_num=None, other_instance=None): self.references = references self.weights = weights self.smoothing_function = smoothing_function self.auto_reweight = auto_reweight self.max_n = len(weights) if process_num is None: self.process_num = os.cpu_count() else: self.process_num = process_num print('bleu{} init!'.format(self.max_n)) if other_instance is None: self.ref_lens = list(len(reference) for reference in references) self.references_ngrams = [get_ngrams(references, n + 1) for n in range(self.max_n)] self.references_counts = [[Counter(l) for l in self.references_ngrams[n]] for n in range(self.max_n)] self.reference_max_counts = [self.get_reference_max_counts(n) for n in range(self.max_n)] else: assert other_instance.max_n >= self.max_n, 'invalid cache!' assert isinstance(other_instance, Bleu), 'invalid cache!' ref_lens, \ references_ngrams, \ references_counts, \ reference_max_counts = other_instance.get_cached_fields() self.ref_lens = ref_lens self.references_ngrams = references_ngrams[:self.max_n] self.references_counts = references_counts[:self.max_n] self.reference_max_counts = reference_max_counts[:self.max_n] def get_cached_fields(self): return self.ref_lens, \ self.references_ngrams, \ self.references_counts, \ self.reference_max_counts def get_score(self, samples, compute_in_parallel=True): print('evaluating bleu {}!'.format(self.max_n)) if compute_in_parallel: return Threader(samples, self.tmp_get_score, self.process_num, show_tqdm=False).run() return [self.tmp_get_score(sample) for sample in samples] def tmp_get_score(self, item): return corpus_bleu(self.references, item, self.reference_max_counts, self.ref_lens, self.weights, self.smoothing_function, self.auto_reweight) def get_reference_max_counts(self, n): print('calculating max counts n = %d!' % ((n + 1),)) ngram_keys = list(set([x for y in self.references_ngrams[n] for x in y])) return dict(zip(ngram_keys, Threader(ngram_keys, self.tmp_get_reference_max_counts, show_tqdm=True).run())) # return dict(zip(ngram_keys, multi_run(ngram_keys, self.tmp_get_reference_max_counts, show_tqdm=True))) def tmp_get_reference_max_counts(self, ngram): counts = [x.get(ngram, 0) for x in self.references_counts[len(ngram) - 1]] return np.max(counts)

44.19802

115

0.620128

3,041

0.170307

0

12,547

0.702677

2975d99cfa9a3c77e63e816ac890c17807b18244

1,880

py

Python

setup.py

gspracklin/bwtools

08159b86738fed978ef442a60032cf8b00b5635a

[ "MIT" ]

4

2021-07-23T02:49:54.000Z

2022-03-22T19:56:13.000Z

setup.py

gspracklin/bwtools

08159b86738fed978ef442a60032cf8b00b5635a

[ "MIT" ]

1

2020-08-24T19:03:46.000Z

setup.py

gspracklin/bwtools

08159b86738fed978ef442a60032cf8b00b5635a

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import io import os import re from setuptools import setup, find_packages # classifiers = """\ # Development Status :: 4 - Beta # Programming Language :: Python # Programming Language :: Python :: 3 # Programming Language :: Python :: 3.4 # Programming Language :: Python :: 3.5 # Programming Language :: Python :: 3.6 # Programming Language :: Python :: 3.7 # Programming Language :: Python :: 3.8 # """ def _read(*parts, **kwargs): filepath = os.path.join(os.path.dirname(__file__), *parts) encoding = kwargs.pop('encoding', 'utf-8') with io.open(filepath, encoding=encoding) as fh: text = fh.read() return text def get_version(): version = re.search( r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', _read('bwtools', '__init__.py'), re.MULTILINE).group(1) return version def get_long_description(): return _read('README.md') def get_requirements(path): content = _read(path) return [ req for req in content.split("\n") if req != '' and not req.startswith('#') ] install_requires = get_requirements('requirements.txt') packages = find_packages() setup( name='bwtools', author='George Spracklin', author_email='@mit.edu', version=get_version(), license='MIT', description='tools for bigwigs', long_description=get_long_description(), long_description_content_type='text/markdown', keywords=['genomics', 'bioinformatics', 'Hi-C', 'analysis', 'cooler'], url='https://github.com/gspracklin/bwtools', zip_safe=False, # classifiers=[s.strip() for s in classifiers.split('\n') if s], packages=packages, install_requires=install_requires, entry_points={ 'console_scripts': [ 'bwtools = bwtools.cli:cli', ] } )

24.736842

74

0.623404

0

788

0.419149

2976116c00e8fbc7eadad3295e61cb99c5280023

3,018

py

Python

hoogberta/encoder.py

KateSatida/HoogBERTa_SuperAI2

e903054bc752a50c391ab610507fdeccc4f5d482

[ "MIT" ]

null

hoogberta/encoder.py

KateSatida/HoogBERTa_SuperAI2

e903054bc752a50c391ab610507fdeccc4f5d482

[ "MIT" ]

null

hoogberta/encoder.py

KateSatida/HoogBERTa_SuperAI2

e903054bc752a50c391ab610507fdeccc4f5d482

[ "MIT" ]

null

from .trainer.models import MultiTaskTagger from .trainer.utils import load_dictionaries,Config from .trainer.tasks.multitask_tagging import MultiTaskTaggingModule from fairseq.data.data_utils import collate_tokens from attacut import tokenize class HoogBERTaEncoder(object): def __init__(self,layer=12,cuda=False,base_path="."): args = Config(base_path=base_path) self.base_path = base_path self.pos_dict, self.ne_dict, self.sent_dict = load_dictionaries(self.base_path) self.model = MultiTaskTagger(args,[len(self.pos_dict), len(self.ne_dict), len(self.sent_dict)]) if cuda == True: self.model = self.model.cuda() def extract_features(self,sentence): all_sent = [] sentences = sentence.split(" ") for sent in sentences: all_sent.append(" ".join(tokenize(sent)).replace("_","[!und:]")) sentence = " _ ".join(all_sent) tokens = self.model.bert.encode(sentence).unsqueeze(0) all_layers = self.model.bert.extract_features(tokens, return_all_hiddens=True) return tokens[0], all_layers[-1][0] def extract_features_batch(self,sentenceL): inputList = [] for sentX in sentenceL: sentences = sentX.split(" ") all_sent = [] for sent in sentences: all_sent.append(" ".join(tokenize(sent)).replace("_","[!und:]")) sentence = " _ ".join(all_sent) inputList.append(sentence) batch = collate_tokens([self.model.bert.encode(sent) for sent in inputList], pad_idx=1) #tokens = self.model.bert.encode(inputList) return self.extract_features_from_tensor(batch) def extract_features_from_tensor(self,batch): all_layers = self.model.bert.extract_features(batch, return_all_hiddens=True) return batch, all_layers[-1] def extract_features2(self,sentence): # all_sent = [] # sentences = sentence.split(" ") # for sent in sentences: # all_sent.append(" ".join(tokenize(sent)).replace("_","[!und:]")) # sentence = " _ ".join(all_sent) tokens = self.model.bert.encode(sentence).unsqueeze(0) all_layers = self.model.bert.extract_features(tokens, return_all_hiddens=True) return tokens[0], all_layers[-1][0] def extract_features_batch2(self,sentenceL): # inputList = [] # for sentX in sentenceL: # sentences = sentX.split(" ") # all_sent = [] # for sent in sentences: # all_sent.append(" ".join(tokenize(sent)).replace("_","[!und:]")) # sentence = " _ ".join(all_sent) # inputList.append(sentence) batch = collate_tokens([self.model.bert.encode(sent) for sent in sentenceL], pad_idx=1) #tokens = self.model.bert.encode(inputList) return self.extract_features_from_tensor(batch)

38.202532

103

0.61829

2,771

0.918158

0

575

0.190524

29765d30f6c468ab494ba5307c14ec5427857135

146

py

Python

computer_equipment_control/model/__init__.py

hugonp/Proyecto-Triples

ff4c77e37d49a1674d17f3137ef06b5c56ab9c29

[ "Apache-2.0" ]

null

computer_equipment_control/model/__init__.py

hugonp/Proyecto-Triples

ff4c77e37d49a1674d17f3137ef06b5c56ab9c29

[ "Apache-2.0" ]

null

computer_equipment_control/model/__init__.py

hugonp/Proyecto-Triples

ff4c77e37d49a1674d17f3137ef06b5c56ab9c29

[ "Apache-2.0" ]

null

from . import usuario from . import equipo_cambio from . import equipo_computo from . import sucursales from . import depto from . import usuario

20.857143

28

0.794521

0

297664fa1ad9becfe5e46a57677c012794d49c7e

697

py

Python

gui/window.py

frlnx/melee

db2670453771c6d3635e97e28bb8667b14643b05

[ "CC0-1.0" ]

null

gui/window.py

frlnx/melee

db2670453771c6d3635e97e28bb8667b14643b05

[ "CC0-1.0" ]

null

gui/window.py

frlnx/melee

db2670453771c6d3635e97e28bb8667b14643b05

[ "CC0-1.0" ]

null

from pyglet.window import Window as PygletWindow from .controllers import ComponentContainerController from .models.container import ComponentContainerModel from .views import OrthoViewport class Window(PygletWindow): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._model = ComponentContainerModel([], 0, self.width, 0, self.height) self._controller = ComponentContainerController(self._model) self.push_handlers(self._controller) self._view = OrthoViewport(self._model) def add_component(self, model): self._model.add_component(model) def on_draw(self): self.clear() self._view.draw()

30.304348

80

0.714491

503

0.721664

0

2979426aee0cb9d3f7a35f048ea9d20f5f7f25ea

1,806

py

Python

conductor_calculator.py

aj83854/project-lightning-rod

77867d6c4ee30650023f3ec2a8318edd92530264

[ "MIT" ]

null

conductor_calculator.py

aj83854/project-lightning-rod

77867d6c4ee30650023f3ec2a8318edd92530264

[ "MIT" ]

null

conductor_calculator.py

aj83854/project-lightning-rod

77867d6c4ee30650023f3ec2a8318edd92530264

[ "MIT" ]

null

from pyconductor import load_test_values, calculate_conductance def conductance_calc(): preloaded_dict = load_test_values() while preloaded_dict: print( "[1] - Show currently available materials in Material Dictionary\n" "[2] - Add a material (will not be saved upon restart)\n" "[3] - Quit\n" "To test the conductive properties of a material, simply type in its name.\n" "Otherwise, type the corresponding number for an option above.\n" ) main_prompt = input(">>> ").lower() if main_prompt == "1": print(f"\nCurrently contains the following materials:\n{preloaded_dict.keys()}\n") elif main_prompt == "2": preloaded_dict.addmat() elif main_prompt == "3": quit() else: try: calculate_conductance(preloaded_dict[main_prompt]) while True: again_prompt = input( "Would you like to try another calculation? [Y]es or [N]o: ").lower() if again_prompt in ("y", "yes"): break elif again_prompt in ("n", "no"): print("\nGoodbye!\n") quit() except KeyError: if main_prompt == "": print("\nNo material specified.\nPlease enter a valid material name " "listed in option [1], or use option [2] to add your own.\n") else: # TODO: add logic handling whether user wants to add missing material print(f"\n{main_prompt} is not a valid material or command!\n") else: pass if __name__ == "__main__": conductance_calc()

42

94

0.530454

0

719

0.398117

297a9dbff855963c67a53faea767533cf768f0af

2,073

py

Python

compiler/python_compiler/engines/py3_8/Variable.py

unknowncoder05/app-architect

083278e1386562797614f320649ca85d1c44e009

[ "MIT" ]

3

2021-08-12T12:59:27.000Z

2021-08-29T15:30:49.000Z

compiler/python_compiler/engines/py3_8/Variable.py

unknowncoder05/app-architect

083278e1386562797614f320649ca85d1c44e009

[ "MIT" ]

null

compiler/python_compiler/engines/py3_8/Variable.py

unknowncoder05/app-architect

083278e1386562797614f320649ca85d1c44e009

[ "MIT" ]

null

from .Fragment import Fragment from utils.flags import * from utils.CustomLogging import CustomLogging #from python_compiler.engines.utils.types import get_python_type_str, ANY DEFAULT_ASSIGN_OPERATOR = "=" ASSIGN_OPERATORS = { "=":"=", "+=":"+=", "-=":"-=", "*=":"*=", "/=":"/=", "//=":"//=", "%=":"%=", "**=":"**=", "&=":"&=", "|=":"|=", "^=":"^=", ">>=":">>=", "<<=":"<<=", } def get_variable_name(fragment) -> str: if not (variable_name := fragment.get(ATTRIBUTE_VARIABLE_NAME)): CustomLogging.critical(f"Fragment type variable '{ATTRIBUTE_VARIABLE_NAME}' attribute does not exist") return variable_name def get_variable_type(fragment) -> str: if not (variable_type := fragment.get(ATTRIBUTE_VARIABLE_TYPE)): variable_type = "" else: variable_type = ":"+variable_type return variable_type def get_variable_assign_operator(fragment) -> str: if not (variable_assign_operator := fragment.get(ATTRIBUTE_VARIABLE_ASSIGN_OPERATOR)): variable_assign_operator = DEFAULT_ASSIGN_OPERATOR return ASSIGN_OPERATORS.get(variable_assign_operator) def get_variable_expression(fragment) -> str: if not (variable_expression := fragment.get(ATTRIBUTE_VARIABLE_EXPRESSION)): CustomLogging.critical(f"Fragment type variable '{ATTRIBUTE_VARIABLE_EXPRESSION}' attribute does not exist") return variable_expression class Variable(Fragment): name:str variable_type:str assign_operator:str expression:str def __init__(self, blueprint, *args, **kwargs) -> None: super().__init__(blueprint, *args, **kwargs) self.name = get_variable_name(blueprint) self.variable_type = get_variable_type(blueprint) self.assign_operator = get_variable_assign_operator(blueprint) self.expression = get_variable_expression(blueprint) def compile(self)->str: fragment_build = "" fragment_build = f"{self.name}{self.variable_type} {self.assign_operator} {self.expression}" return fragment_build

33.983607

116

0.675832

642

0.309696

0

430

0.207429

297b1b7d9f0a6e280fa0145471626d7d01aa1943

266

py

Python

test/win/vs-macros/test_exists.py

chlorm-forks/gyp

a8921fcaab1a18c8cf7e4ab09ceb940e336918ec

[ "BSD-3-Clause" ]

77

2018-07-01T15:55:34.000Z

2022-03-30T09:16:54.000Z

test/win/vs-macros/test_exists.py

chlorm-forks/gyp

a8921fcaab1a18c8cf7e4ab09ceb940e336918ec

[ "BSD-3-Clause" ]

116

2021-05-29T16:32:51.000Z

2021-08-13T16:05:29.000Z

test/win/vs-macros/test_exists.py

chlorm-forks/gyp

a8921fcaab1a18c8cf7e4ab09ceb940e336918ec

[ "BSD-3-Clause" ]

53

2018-04-13T12:06:06.000Z

2022-03-25T13:54:38.000Z

# Copyright (c) 2012 Google Inc. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import os import sys if not os.path.exists(sys.argv[1]): raise Exception() open(sys.argv[2], 'w').close()

24.181818

72

0.718045

0

156

0.586466

297bd9ae840483948b9e83e2e47fb15cc768315a

37,440

py

Python

Lib/site-packages/qwt/scale_draw.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

[ "bzip2-1.0.6", "0BSD" ]

null

Lib/site-packages/qwt/scale_draw.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

[ "bzip2-1.0.6", "0BSD" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

Lib/site-packages/qwt/scale_draw.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

[ "bzip2-1.0.6", "0BSD" ]

null

# -*- coding: utf-8 -*- # # Licensed under the terms of the Qwt License # Copyright (c) 2002 Uwe Rathmann, for the original C++ code # Copyright (c) 2015 Pierre Raybaut, for the Python translation/optimization # (see LICENSE file for more details) """ QwtAbstractScaleDraw -------------------- .. autoclass:: QwtAbstractScaleDraw :members: QwtScaleDraw ------------ .. autoclass:: QwtScaleDraw :members: """ from qwt.scale_div import QwtScaleDiv from qwt.scale_map import QwtScaleMap from qwt.text import QwtText from qwt._math import qwtRadians from qtpy.QtGui import QPalette, QFontMetrics, QTransform from qtpy.QtCore import Qt, qFuzzyCompare, QLocale, QRectF, QPointF, QRect, QPoint from math import ceil import numpy as np class QwtAbstractScaleDraw_PrivateData(object): def __init__(self): self.spacing = 4 self.penWidth = 0 self.minExtent = 0.0 self.components = ( QwtAbstractScaleDraw.Backbone | QwtAbstractScaleDraw.Ticks | QwtAbstractScaleDraw.Labels ) self.tick_length = { QwtScaleDiv.MinorTick: 4.0, QwtScaleDiv.MediumTick: 6.0, QwtScaleDiv.MajorTick: 8.0, } self.tick_lighter_factor = { QwtScaleDiv.MinorTick: 100, QwtScaleDiv.MediumTick: 100, QwtScaleDiv.MajorTick: 100, } self.map = QwtScaleMap() self.scaleDiv = QwtScaleDiv() self.labelCache = {} class QwtAbstractScaleDraw(object): """ A abstract base class for drawing scales `QwtAbstractScaleDraw` can be used to draw linear or logarithmic scales. After a scale division has been specified as a `QwtScaleDiv` object using `setScaleDiv()`, the scale can be drawn with the `draw()` member. Scale components: * `QwtAbstractScaleDraw.Backbone`: Backbone = the line where the ticks are located * `QwtAbstractScaleDraw.Ticks`: Ticks * `QwtAbstractScaleDraw.Labels`: Labels .. py:class:: QwtAbstractScaleDraw() The range of the scale is initialized to [0, 100], The spacing (distance between ticks and labels) is set to 4, the tick lengths are set to 4,6 and 8 pixels """ # enum ScaleComponent Backbone = 0x01 Ticks = 0x02 Labels = 0x04 def __init__(self): self.__data = QwtAbstractScaleDraw_PrivateData() def extent(self, font): """ Calculate the extent The extent is the distance from the baseline to the outermost pixel of the scale draw in opposite to its orientation. It is at least minimumExtent() pixels. :param QFont font: Font used for drawing the tick labels :return: Number of pixels .. seealso:: :py:meth:`setMinimumExtent()`, :py:meth:`minimumExtent()` """ return 0.0 def drawTick(self, painter, value, len_): """ Draw a tick :param QPainter painter: Painter :param float value: Value of the tick :param float len: Length of the tick .. seealso:: :py:meth:`drawBackbone()`, :py:meth:`drawLabel()` """ pass def drawBackbone(self, painter): """ Draws the baseline of the scale :param QPainter painter: Painter .. seealso:: :py:meth:`drawTick()`, :py:meth:`drawLabel()` """ pass def drawLabel(self, painter, value): """ Draws the label for a major scale tick :param QPainter painter: Painter :param float value: Value .. seealso:: :py:meth:`drawTick()`, :py:meth:`drawBackbone()` """ pass def enableComponent(self, component, enable): """ En/Disable a component of the scale :param int component: Scale component :param bool enable: On/Off .. seealso:: :py:meth:`hasComponent()` """ if enable: self.__data.components |= component else: self.__data.components &= ~component def hasComponent(self, component): """ Check if a component is enabled :param int component: Component type :return: True, when component is enabled .. seealso:: :py:meth:`enableComponent()` """ return self.__data.components & component def setScaleDiv(self, scaleDiv): """ Change the scale division :param qwt.scale_div.QwtScaleDiv scaleDiv: New scale division """ self.__data.scaleDiv = scaleDiv self.__data.map.setScaleInterval(scaleDiv.lowerBound(), scaleDiv.upperBound()) self.__data.labelCache.clear() def setTransformation(self, transformation): """ Change the transformation of the scale :param qwt.transform.QwtTransform transformation: New scale transformation """ self.__data.map.setTransformation(transformation) def scaleMap(self): """ :return: Map how to translate between scale and pixel values """ return self.__data.map def scaleDiv(self): """ :return: scale division """ return self.__data.scaleDiv def setPenWidth(self, width): """ Specify the width of the scale pen :param int width: Pen width .. seealso:: :py:meth:`penWidth()` """ if width < 0: width = 0 if width != self.__data.penWidth: self.__data.penWidth = width def penWidth(self): """ :return: Scale pen width .. seealso:: :py:meth:`setPenWidth()` """ return self.__data.penWidth def draw(self, painter, palette): """ Draw the scale :param QPainter painter: The painter :param QPalette palette: Palette, text color is used for the labels, foreground color for ticks and backbone """ painter.save() pen = painter.pen() pen.setWidth(self.__data.penWidth) pen.setCosmetic(False) painter.setPen(pen) if self.hasComponent(QwtAbstractScaleDraw.Labels): painter.save() painter.setPen(palette.color(QPalette.Text)) majorTicks = self.__data.scaleDiv.ticks(QwtScaleDiv.MajorTick) for v in majorTicks: if self.__data.scaleDiv.contains(v): self.drawLabel(painter, v) painter.restore() if self.hasComponent(QwtAbstractScaleDraw.Ticks): painter.save() pen = painter.pen() pen.setCapStyle(Qt.FlatCap) default_color = palette.color(QPalette.WindowText) for tickType in range(QwtScaleDiv.NTickTypes): tickLen = self.__data.tick_length[tickType] if tickLen <= 0.0: continue factor = self.__data.tick_lighter_factor[tickType] pen.setColor(default_color.lighter(factor)) painter.setPen(pen) ticks = self.__data.scaleDiv.ticks(tickType) for v in ticks: if self.__data.scaleDiv.contains(v): self.drawTick(painter, v, tickLen) painter.restore() if self.hasComponent(QwtAbstractScaleDraw.Backbone): painter.save() pen = painter.pen() pen.setColor(palette.color(QPalette.WindowText)) pen.setCapStyle(Qt.FlatCap) painter.setPen(pen) self.drawBackbone(painter) painter.restore() painter.restore() def setSpacing(self, spacing): """ Set the spacing between tick and labels The spacing is the distance between ticks and labels. The default spacing is 4 pixels. :param float spacing: Spacing .. seealso:: :py:meth:`spacing()` """ if spacing < 0: spacing = 0 self.__data.spacing = spacing def spacing(self): """ Get the spacing The spacing is the distance between ticks and labels. The default spacing is 4 pixels. :return: Spacing .. seealso:: :py:meth:`setSpacing()` """ return self.__data.spacing def setMinimumExtent(self, minExtent): """ Set a minimum for the extent The extent is calculated from the components of the scale draw. In situations, where the labels are changing and the layout depends on the extent (f.e scrolling a scale), setting an upper limit as minimum extent will avoid jumps of the layout. :param float minExtent: Minimum extent .. seealso:: :py:meth:`extent()`, :py:meth:`minimumExtent()` """ if minExtent < 0.0: minExtent = 0.0 self.__data.minExtent = minExtent def minimumExtent(self): """ Get the minimum extent :return: Minimum extent .. seealso:: :py:meth:`extent()`, :py:meth:`setMinimumExtent()` """ return self.__data.minExtent def setTickLength(self, tick_type, length): """ Set the length of the ticks :param int tick_type: Tick type :param float length: New length .. warning:: the length is limited to [0..1000] """ if tick_type not in self.__data.tick_length: raise ValueError("Invalid tick type: %r" % tick_type) self.__data.tick_length[tick_type] = min([1000.0, max([0.0, length])]) def tickLength(self, tick_type): """ :param int tick_type: Tick type :return: Length of the ticks .. seealso:: :py:meth:`setTickLength()`, :py:meth:`maxTickLength()` """ if tick_type not in self.__data.tick_length: raise ValueError("Invalid tick type: %r" % tick_type) return self.__data.tick_length[tick_type] def maxTickLength(self): """ :return: Length of the longest tick Useful for layout calculations .. seealso:: :py:meth:`tickLength()`, :py:meth:`setTickLength()` """ return max([0.0] + list(self.__data.tick_length.values())) def setTickLighterFactor(self, tick_type, factor): """ Set the color lighter factor of the ticks :param int tick_type: Tick type :param int factor: New factor """ if tick_type not in self.__data.tick_length: raise ValueError("Invalid tick type: %r" % tick_type) self.__data.tick_lighter_factor[tick_type] = min([0, factor]) def tickLighterFactor(self, tick_type): """ :param int tick_type: Tick type :return: Color lighter factor of the ticks .. seealso:: :py:meth:`setTickLighterFactor()` """ if tick_type not in self.__data.tick_length: raise ValueError("Invalid tick type: %r" % tick_type) return self.__data.tick_lighter_factor[tick_type] def label(self, value): """ Convert a value into its representing label The value is converted to a plain text using `QLocale().toString(value)`. This method is often overloaded by applications to have individual labels. :param float value: Value :return: Label string """ return QLocale().toString(value) def tickLabel(self, font, value): """ Convert a value into its representing label and cache it. The conversion between value and label is called very often in the layout and painting code. Unfortunately the calculation of the label sizes might be slow (really slow for rich text in Qt4), so it's necessary to cache the labels. :param QFont font: Font :param float value: Value :return: Tick label """ lbl = self.__data.labelCache.get(value) if lbl is None: lbl = QwtText(self.label(value)) lbl.setRenderFlags(0) lbl.setLayoutAttribute(QwtText.MinimumLayout) lbl.textSize(font) self.__data.labelCache[value] = lbl return lbl def invalidateCache(self): """ Invalidate the cache used by `tickLabel()` The cache is invalidated, when a new `QwtScaleDiv` is set. If the labels need to be changed. while the same `QwtScaleDiv` is set, `invalidateCache()` needs to be called manually. """ self.__data.labelCache.clear() class QwtScaleDraw_PrivateData(object): def __init__(self): self.len = 0 self.alignment = QwtScaleDraw.BottomScale self.labelAlignment = 0 self.labelRotation = 0.0 self.labelAutoSize = True self.pos = QPointF() class QwtScaleDraw(QwtAbstractScaleDraw): """ A class for drawing scales QwtScaleDraw can be used to draw linear or logarithmic scales. A scale has a position, an alignment and a length, which can be specified . The labels can be rotated and aligned to the ticks using `setLabelRotation()` and `setLabelAlignment()`. After a scale division has been specified as a QwtScaleDiv object using `QwtAbstractScaleDraw.setScaleDiv(scaleDiv)`, the scale can be drawn with the `QwtAbstractScaleDraw.draw()` member. Alignment of the scale draw: * `QwtScaleDraw.BottomScale`: The scale is below * `QwtScaleDraw.TopScale`: The scale is above * `QwtScaleDraw.LeftScale`: The scale is left * `QwtScaleDraw.RightScale`: The scale is right .. py:class:: QwtScaleDraw() The range of the scale is initialized to [0, 100], The position is at (0, 0) with a length of 100. The orientation is `QwtAbstractScaleDraw.Bottom`. """ # enum Alignment BottomScale, TopScale, LeftScale, RightScale = list(range(4)) Flags = ( Qt.AlignHCenter | Qt.AlignBottom, # BottomScale Qt.AlignHCenter | Qt.AlignTop, # TopScale Qt.AlignLeft | Qt.AlignVCenter, # LeftScale Qt.AlignRight | Qt.AlignVCenter, # RightScale ) def __init__(self): QwtAbstractScaleDraw.__init__(self) self.__data = QwtScaleDraw_PrivateData() self.setLength(100) self._max_label_sizes = {} def alignment(self): """ :return: Alignment of the scale .. seealso:: :py:meth:`setAlignment()` """ return self.__data.alignment def setAlignment(self, align): """ Set the alignment of the scale :param int align: Alignment of the scale Alignment of the scale draw: * `QwtScaleDraw.BottomScale`: The scale is below * `QwtScaleDraw.TopScale`: The scale is above * `QwtScaleDraw.LeftScale`: The scale is left * `QwtScaleDraw.RightScale`: The scale is right The default alignment is `QwtScaleDraw.BottomScale` .. seealso:: :py:meth:`alignment()` """ self.__data.alignment = align def orientation(self): """ Return the orientation TopScale, BottomScale are horizontal (`Qt.Horizontal`) scales, LeftScale, RightScale are vertical (`Qt.Vertical`) scales. :return: Orientation of the scale .. seealso:: :py:meth:`alignment()` """ if self.__data.alignment in (self.TopScale, self.BottomScale): return Qt.Horizontal elif self.__data.alignment in (self.LeftScale, self.RightScale): return Qt.Vertical def getBorderDistHint(self, font): """ Determine the minimum border distance This member function returns the minimum space needed to draw the mark labels at the scale's endpoints. :param QFont font: Font :return: tuple `(start, end)` Returned tuple: * start: Start border distance * end: End border distance """ start, end = 0, 1.0 if not self.hasComponent(QwtAbstractScaleDraw.Labels): return start, end ticks = self.scaleDiv().ticks(QwtScaleDiv.MajorTick) if len(ticks) == 0: return start, end minTick = ticks[0] minPos = self.scaleMap().transform(minTick) maxTick = minTick maxPos = minPos for tick in ticks: tickPos = self.scaleMap().transform(tick) if tickPos < minPos: minTick = tick minPos = tickPos if tickPos > self.scaleMap().transform(maxTick): maxTick = tick maxPos = tickPos s = 0.0 e = 0.0 if self.orientation() == Qt.Vertical: s = -self.labelRect(font, minTick).top() s -= abs(minPos - round(self.scaleMap().p2())) e = self.labelRect(font, maxTick).bottom() e -= abs(maxPos - self.scaleMap().p1()) else: s = -self.labelRect(font, minTick).left() s -= abs(minPos - self.scaleMap().p1()) e = self.labelRect(font, maxTick).right() e -= abs(maxPos - self.scaleMap().p2()) return max(ceil(s), 0), max(ceil(e), 0) def minLabelDist(self, font): """ Determine the minimum distance between two labels, that is necessary that the texts don't overlap. :param QFont font: Font :return: The maximum width of a label .. seealso:: :py:meth:`getBorderDistHint()` """ if not self.hasComponent(QwtAbstractScaleDraw.Labels): return 0 ticks = self.scaleDiv().ticks(QwtScaleDiv.MajorTick) if not ticks: return 0 fm = QFontMetrics(font) vertical = self.orientation() == Qt.Vertical bRect1 = QRectF() bRect2 = self.labelRect(font, ticks[0]) if vertical: bRect2.setRect(-bRect2.bottom(), 0.0, bRect2.height(), bRect2.width()) maxDist = 0.0 for tick in ticks: bRect1 = bRect2 bRect2 = self.labelRect(font, tick) if vertical: bRect2.setRect(-bRect2.bottom(), 0.0, bRect2.height(), bRect2.width()) dist = fm.leading() if bRect1.right() > 0: dist += bRect1.right() if bRect2.left() < 0: dist += -bRect2.left() if dist > maxDist: maxDist = dist angle = qwtRadians(self.labelRotation()) if vertical: angle += np.pi / 2 sinA = np.sin(angle) if qFuzzyCompare(sinA + 1.0, 1.0): return np.ceil(maxDist) fmHeight = fm.ascent() - 2 labelDist = fmHeight / np.sin(angle) * np.cos(angle) if labelDist < 0: labelDist = -labelDist if labelDist > maxDist: labelDist = maxDist if labelDist < fmHeight: labelDist = fmHeight return np.ceil(labelDist) def extent(self, font): """ Calculate the width/height that is needed for a vertical/horizontal scale. The extent is calculated from the pen width of the backbone, the major tick length, the spacing and the maximum width/height of the labels. :param QFont font: Font used for painting the labels :return: Extent .. seealso:: :py:meth:`minLength()` """ d = 0.0 if self.hasComponent(QwtAbstractScaleDraw.Labels): if self.orientation() == Qt.Vertical: d = self.maxLabelWidth(font) else: d = self.maxLabelHeight(font) if d > 0: d += self.spacing() if self.hasComponent(QwtAbstractScaleDraw.Ticks): d += self.maxTickLength() if self.hasComponent(QwtAbstractScaleDraw.Backbone): pw = max([1, self.penWidth()]) d += pw return max([d, self.minimumExtent()]) def minLength(self, font): """ Calculate the minimum length that is needed to draw the scale :param QFont font: Font used for painting the labels :return: Minimum length that is needed to draw the scale .. seealso:: :py:meth:`extent()` """ startDist, endDist = self.getBorderDistHint(font) sd = self.scaleDiv() minorCount = len(sd.ticks(QwtScaleDiv.MinorTick)) + len( sd.ticks(QwtScaleDiv.MediumTick) ) majorCount = len(sd.ticks(QwtScaleDiv.MajorTick)) lengthForLabels = 0 if self.hasComponent(QwtAbstractScaleDraw.Labels): lengthForLabels = self.minLabelDist(font) * majorCount lengthForTicks = 0 if self.hasComponent(QwtAbstractScaleDraw.Ticks): pw = max([1, self.penWidth()]) lengthForTicks = np.ceil((majorCount + minorCount) * (pw + 1.0)) return startDist + endDist + max([lengthForLabels, lengthForTicks]) def labelPosition(self, value): """ Find the position, where to paint a label The position has a distance that depends on the length of the ticks in direction of the `alignment()`. :param float value: Value :return: Position, where to paint a label """ tval = self.scaleMap().transform(value) dist = self.spacing() if self.hasComponent(QwtAbstractScaleDraw.Backbone): dist += max([1, self.penWidth()]) if self.hasComponent(QwtAbstractScaleDraw.Ticks): dist += self.tickLength(QwtScaleDiv.MajorTick) px = 0 py = 0 if self.alignment() == self.RightScale: px = self.__data.pos.x() + dist py = tval elif self.alignment() == self.LeftScale: px = self.__data.pos.x() - dist py = tval elif self.alignment() == self.BottomScale: px = tval py = self.__data.pos.y() + dist elif self.alignment() == self.TopScale: px = tval py = self.__data.pos.y() - dist return QPointF(px, py) def drawTick(self, painter, value, len_): """ Draw a tick :param QPainter painter: Painter :param float value: Value of the tick :param float len: Length of the tick .. seealso:: :py:meth:`drawBackbone()`, :py:meth:`drawLabel()` """ if len_ <= 0: return pos = self.__data.pos tval = self.scaleMap().transform(value) pw = self.penWidth() a = 0 if self.alignment() == self.LeftScale: x1 = pos.x() + a x2 = pos.x() + a - pw - len_ painter.drawLine(x1, tval, x2, tval) elif self.alignment() == self.RightScale: x1 = pos.x() x2 = pos.x() + pw + len_ painter.drawLine(x1, tval, x2, tval) elif self.alignment() == self.BottomScale: y1 = pos.y() y2 = pos.y() + pw + len_ painter.drawLine(tval, y1, tval, y2) elif self.alignment() == self.TopScale: y1 = pos.y() + a y2 = pos.y() - pw - len_ + a painter.drawLine(tval, y1, tval, y2) def drawBackbone(self, painter): """ Draws the baseline of the scale :param QPainter painter: Painter .. seealso:: :py:meth:`drawTick()`, :py:meth:`drawLabel()` """ pos = self.__data.pos len_ = self.__data.len off = 0.5 * self.penWidth() if self.alignment() == self.LeftScale: x = pos.x() - off painter.drawLine(x, pos.y(), x, pos.y() + len_) elif self.alignment() == self.RightScale: x = pos.x() + off painter.drawLine(x, pos.y(), x, pos.y() + len_) elif self.alignment() == self.TopScale: y = pos.y() - off painter.drawLine(pos.x(), y, pos.x() + len_, y) elif self.alignment() == self.BottomScale: y = pos.y() + off painter.drawLine(pos.x(), y, pos.x() + len_, y) def move(self, *args): """ Move the position of the scale The meaning of the parameter pos depends on the alignment: * `QwtScaleDraw.LeftScale`: The origin is the topmost point of the backbone. The backbone is a vertical line. Scale marks and labels are drawn at the left of the backbone. * `QwtScaleDraw.RightScale`: The origin is the topmost point of the backbone. The backbone is a vertical line. Scale marks and labels are drawn at the right of the backbone. * `QwtScaleDraw.TopScale`: The origin is the leftmost point of the backbone. The backbone is a horizontal line. Scale marks and labels are drawn above the backbone. * `QwtScaleDraw.BottomScale`: The origin is the leftmost point of the backbone. The backbone is a horizontal line Scale marks and labels are drawn below the backbone. .. py:method:: move(x, y) :noindex: :param float x: X coordinate :param float y: Y coordinate .. py:method:: move(pos) :noindex: :param QPointF pos: position .. seealso:: :py:meth:`pos()`, :py:meth:`setLength()` """ if len(args) == 2: x, y = args self.move(QPointF(x, y)) elif len(args) == 1: (pos,) = args self.__data.pos = pos self.updateMap() else: raise TypeError( "%s().move() takes 1 or 2 argument(s) (%s given)" % (self.__class__.__name__, len(args)) ) def pos(self): """ :return: Origin of the scale .. seealso:: :py:meth:`pos()`, :py:meth:`setLength()` """ return self.__data.pos def setLength(self, length): """ Set the length of the backbone. The length doesn't include the space needed for overlapping labels. :param float length: Length of the backbone .. seealso:: :py:meth:`move()`, :py:meth:`minLabelDist()` """ if length >= 0 and length < 10: length = 10 if length < 0 and length > -10: length = -10 self.__data.len = length self.updateMap() def length(self): """ :return: the length of the backbone .. seealso:: :py:meth:`setLength()`, :py:meth:`pos()` """ return self.__data.len def drawLabel(self, painter, value): """ Draws the label for a major scale tick :param QPainter painter: Painter :param float value: Value .. seealso:: :py:meth:`drawTick()`, :py:meth:`drawBackbone()`, :py:meth:`boundingLabelRect()` """ lbl = self.tickLabel(painter.font(), value) if lbl is None or lbl.isEmpty(): return pos = self.labelPosition(value) labelSize = lbl.textSize(painter.font()) transform = self.labelTransformation(pos, labelSize) painter.save() painter.setWorldTransform(transform, True) lbl.draw(painter, QRect(QPoint(0, 0), labelSize.toSize())) painter.restore() def boundingLabelRect(self, font, value): """ Find the bounding rectangle for the label. The coordinates of the rectangle are absolute (calculated from `pos()`) in direction of the tick. :param QFont font: Font used for painting :param float value: Value :return: Bounding rectangle .. seealso:: :py:meth:`labelRect()` """ lbl = self.tickLabel(font, value) if lbl.isEmpty(): return QRect() pos = self.labelPosition(value) labelSize = lbl.textSize(font) transform = self.labelTransformation(pos, labelSize) return transform.mapRect(QRect(QPoint(0, 0), labelSize.toSize())) def labelTransformation(self, pos, size): """ Calculate the transformation that is needed to paint a label depending on its alignment and rotation. :param QPointF pos: Position where to paint the label :param QSizeF size: Size of the label :return: Transformation matrix .. seealso:: :py:meth:`setLabelAlignment()`, :py:meth:`setLabelRotation()` """ transform = QTransform() transform.translate(pos.x(), pos.y()) transform.rotate(self.labelRotation()) flags = self.labelAlignment() if flags == 0: flags = self.Flags[self.alignment()] if flags & Qt.AlignLeft: x = -size.width() elif flags & Qt.AlignRight: x = 0.0 else: x = -(0.5 * size.width()) if flags & Qt.AlignTop: y = -size.height() elif flags & Qt.AlignBottom: y = 0 else: y = -(0.5 * size.height()) transform.translate(x, y) return transform def labelRect(self, font, value): """ Find the bounding rectangle for the label. The coordinates of the rectangle are relative to spacing + tick length from the backbone in direction of the tick. :param QFont font: Font used for painting :param float value: Value :return: Bounding rectangle that is needed to draw a label """ lbl = self.tickLabel(font, value) if not lbl or lbl.isEmpty(): return QRectF(0.0, 0.0, 0.0, 0.0) pos = self.labelPosition(value) labelSize = lbl.textSize(font) transform = self.labelTransformation(pos, labelSize) br = transform.mapRect(QRectF(QPointF(0, 0), labelSize)) br.translate(-pos.x(), -pos.y()) return br def labelSize(self, font, value): """ Calculate the size that is needed to draw a label :param QFont font: Label font :param float value: Value :return: Size that is needed to draw a label """ return self.labelRect(font, value).size() def setLabelRotation(self, rotation): """ Rotate all labels. When changing the rotation, it might be necessary to adjust the label flags too. Finding a useful combination is often the result of try and error. :param float rotation: Angle in degrees. When changing the label rotation, the label flags often needs to be adjusted too. .. seealso:: :py:meth:`setLabelAlignment()`, :py:meth:`labelRotation()`, :py:meth:`labelAlignment()` """ self.__data.labelRotation = rotation def labelRotation(self): """ :return: the label rotation .. seealso:: :py:meth:`setLabelRotation()`, :py:meth:`labelAlignment()` """ return self.__data.labelRotation def setLabelAlignment(self, alignment): """ Change the label flags Labels are aligned to the point tick length + spacing away from the backbone. The alignment is relative to the orientation of the label text. In case of an flags of 0 the label will be aligned depending on the orientation of the scale: * `QwtScaleDraw.TopScale`: `Qt.AlignHCenter | Qt.AlignTop` * `QwtScaleDraw.BottomScale`: `Qt.AlignHCenter | Qt.AlignBottom` * `QwtScaleDraw.LeftScale`: `Qt.AlignLeft | Qt.AlignVCenter` * `QwtScaleDraw.RightScale`: `Qt.AlignRight | Qt.AlignVCenter` Changing the alignment is often necessary for rotated labels. :param Qt.Alignment alignment Or'd `Qt.AlignmentFlags` .. seealso:: :py:meth:`setLabelRotation()`, :py:meth:`labelRotation()`, :py:meth:`labelAlignment()` .. warning:: The various alignments might be confusing. The alignment of the label is not the alignment of the scale and is not the alignment of the flags (`QwtText.flags()`) returned from `QwtAbstractScaleDraw.label()`. """ self.__data.labelAlignment = alignment def labelAlignment(self): """ :return: the label flags .. seealso:: :py:meth:`setLabelAlignment()`, :py:meth:`labelRotation()` """ return self.__data.labelAlignment def setLabelAutoSize(self, state): """ Set label automatic size option state When drawing text labels, if automatic size mode is enabled (default behavior), the axes are drawn in order to optimize layout space and depends on text label individual sizes. Otherwise, width and height won't change when axis range is changing. This option is not implemented in Qwt C++ library: this may be used either as an optimization (updating plot layout is faster when this option is enabled) or as an appearance preference (with Qwt default behavior, the size of axes may change when zooming and/or panning plot canvas which in some cases may not be desired). :param bool state: On/off .. seealso:: :py:meth:`labelAutoSize()` """ self.__data.labelAutoSize = state def labelAutoSize(self): """ :return: True if automatic size option is enabled for labels .. seealso:: :py:meth:`setLabelAutoSize()` """ return self.__data.labelAutoSize def _get_max_label_size(self, font): key = (font.toString(), self.labelRotation()) size = self._max_label_sizes.get(key) if size is None: size = self.labelSize(font, -999999) # -999999 is the biggest label size.setWidth(np.ceil(size.width())) size.setHeight(np.ceil(size.height())) return self._max_label_sizes.setdefault(key, size) else: return size def maxLabelWidth(self, font): """ :param QFont font: Font :return: the maximum width of a label """ ticks = self.scaleDiv().ticks(QwtScaleDiv.MajorTick) if not ticks: return 0 if self.labelAutoSize(): vmax = sorted( [v for v in ticks if self.scaleDiv().contains(v)], key=lambda obj: len(QLocale().toString(obj)), )[-1] return np.ceil(self.labelSize(font, vmax).width()) ## Original implementation (closer to Qwt's C++ code, but slower): # return np.ceil(max([self.labelSize(font, v).width() # for v in ticks if self.scaleDiv().contains(v)])) else: return self._get_max_label_size(font).width() def maxLabelHeight(self, font): """ :param QFont font: Font :return: the maximum height of a label """ ticks = self.scaleDiv().ticks(QwtScaleDiv.MajorTick) if not ticks: return 0 if self.labelAutoSize(): vmax = sorted( [v for v in ticks if self.scaleDiv().contains(v)], key=lambda obj: len(QLocale().toString(obj)), )[-1] return np.ceil(self.labelSize(font, vmax).height()) ## Original implementation (closer to Qwt's C++ code, but slower): # return np.ceil(max([self.labelSize(font, v).height() # for v in ticks if self.scaleDiv().contains(v)])) else: return self._get_max_label_size(font).height() def updateMap(self): pos = self.__data.pos len_ = self.__data.len sm = self.scaleMap() if self.orientation() == Qt.Vertical: sm.setPaintInterval(pos.y() + len_, pos.y()) else: sm.setPaintInterval(pos.x(), pos.x() + len_)

30.967742

130

0.559161

36,686

0.979861

0

18,416

0.49188

297beccf46f124350d5429aa7d77baa3ba0a7320

3,524

py

Python

fuzzers/LIFCL/090-sysconfig/fuzzer.py

mfkiwl/prjoxide

318331f8b30c2e2a31cc41d51f104b671e180a8a

[ "0BSD" ]

80

2019-12-10T21:06:12.000Z

2021-02-06T09:12:37.000Z

fuzzers/LIFCL/090-sysconfig/fuzzer.py

mfkiwl/prjoxide

318331f8b30c2e2a31cc41d51f104b671e180a8a

[ "0BSD" ]

13

2021-03-18T12:59:25.000Z

2022-03-30T11:35:51.000Z

fuzzers/LIFCL/090-sysconfig/fuzzer.py

mfkiwl/prjoxide

318331f8b30c2e2a31cc41d51f104b671e180a8a

[ "0BSD" ]

4

2020-10-04T22:23:15.000Z

2021-01-29T21:51:25.000Z

from fuzzconfig import FuzzConfig import nonrouting import fuzzloops import re cfgs = [ FuzzConfig(job="SYSCONFIG40", device="LIFCL-40", sv="../shared/empty_40.v", tiles=["CIB_R0C75:EFB_0", "CIB_R0C72:BANKREF0", "CIB_R0C77:EFB_1_OSC", "CIB_R0C79:EFB_2", "CIB_R0C81:I2C_EFB_3", "CIB_R0C85:PMU", "CIB_R0C87:MIB_CNR_32_FAFD", "CIB_R1C87:IREF_P33", "CIB_R2C87:POR"]), FuzzConfig(job="SYSCONFIG17", device="LIFCL-17", sv="../shared/empty_17.v", tiles=["CIB_R1C75:IREF_15K", "CIB_R0C75:PPT_QOUT_15K", "CIB_R0C74:PVTCAL33_15K", "CIB_R0C73:POR_15K", "CIB_R0C72:I2C_15K", "CIB_R0C71:OSC_15K", "CIB_R0C70:PMU_15K", "CIB_R0C66:EFB_15K"]) ] def main(): for cfg in cfgs: cfg.setup() empty = cfg.build_design(cfg.sv, {}) cfg.sv = "../shared/empty_presyn_40.v" cfg.struct_mode = False def get_substs(k, v): return dict(sysconfig="{}={}".format(k, v)) nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.MASTER_SPI_PORT", ["DISABLE", "SERIAL", "DUAL", "QUAD"], lambda x: get_substs("MASTER_SPI_PORT", x), False, assume_zero_base=True, desc="status of master SPI port after configuration") nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.SLAVE_SPI_PORT", ["DISABLE", "SERIAL", "DUAL", "QUAD"], lambda x: get_substs("SLAVE_SPI_PORT", x), False, assume_zero_base=True, desc="status of slave SPI port after configuration") nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.SLAVE_I2C_PORT", ["DISABLE", "ENABLE"], lambda x: get_substs("SLAVE_I2C_PORT", x), False, assume_zero_base=True, desc="status of slave I2C port after configuration") nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.SLAVE_I3C_PORT", ["DISABLE", "ENABLE"], lambda x: get_substs("SLAVE_I3C_PORT", x), False, assume_zero_base=True, desc="status of slave I3C port after configuration") nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.JTAG_PORT", ["DISABLE", "ENABLE"], lambda x: get_substs("JTAG_PORT", x), False, assume_zero_base=True, desc="status of JTAG port after configuration") nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.DONE_PORT", ["DISABLE", "ENABLE"], lambda x: get_substs("DONE_PORT", x), False, assume_zero_base=True, desc="use DONE output after configuration") nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.INITN_PORT", ["DISABLE", "ENABLE"], lambda x: get_substs("INITN_PORT", x), False, assume_zero_base=True, desc="use INITN input after configuration") nonrouting.fuzz_enum_setting(cfg, empty, "SYSCONFIG.PROGRAMN_PORT", ["DISABLE", "ENABLE"], lambda x: get_substs("PROGRAMN_PORT", x), False, assume_zero_base=True, desc="use PROGRAMN input after configuration") if __name__ == "__main__": main()

61.824561

117

0.557605

0

1,280

0.363224

297cba1ed48f76e8524cfa4c59799c094d9928a0

368

py

Python

test.py

jdonovanCS/CS-352-Evolutionary-Computation

dca64068c1824dac8ec3983d7215effa426bc3bc

[ "BSD-3-Clause" ]

null

test.py

jdonovanCS/CS-352-Evolutionary-Computation

dca64068c1824dac8ec3983d7215effa426bc3bc

[ "BSD-3-Clause" ]

null

test.py

jdonovanCS/CS-352-Evolutionary-Computation

dca64068c1824dac8ec3983d7215effa426bc3bc

[ "BSD-3-Clause" ]

null

import argparse import collections import os import random import json from copy import deepcopy import ConfigSpace import numpy as np # from tabular_benchmarks import FCNetProteinStructureBenchmark, FCNetSliceLocalizationBenchmark,\ # FCNetNavalPropulsionBenchmark, FCNetParkinsonsTelemonitoringBenchmark from tabular_benchmarks import NASCifar10A, NASCifar10B

26.285714

98

0.872283

0

173

0.470109

297cf3d7631c7db955fd11e56c685c2b4f84aac0

1,254

py

Python

tests/test_base64_uuid.py

cds-snc/notifier-utils

c3a205ac4381312fe1884a39ffafa7ffb862736f

[ "MIT" ]

3

2020-04-29T17:13:43.000Z

2020-12-04T21:08:33.000Z

tests/test_base64_uuid.py

cds-snc/notifier-utils

c3a205ac4381312fe1884a39ffafa7ffb862736f

[ "MIT" ]

21

2020-04-16T12:29:46.000Z

2022-02-28T17:17:15.000Z

tests/test_base64_uuid.py

cds-snc/notifier-utils

c3a205ac4381312fe1884a39ffafa7ffb862736f

[ "MIT" ]

4

2020-02-21T20:20:00.000Z

2021-02-11T19:00:59.000Z

from uuid import UUID import os import pytest from notifications_utils.base64_uuid import base64_to_uuid, uuid_to_base64, base64_to_bytes, bytes_to_base64 def test_bytes_to_base64_to_bytes(): b = os.urandom(32) b64 = bytes_to_base64(b) assert base64_to_bytes(b64) == b @pytest.mark.parametrize( "url_val", [ "AAAAAAAAAAAAAAAAAAAAAQ", "AAAAAAAAAAAAAAAAAAAAAQ=", # even though this has invalid padding we put extra =s on the end so this is okay "AAAAAAAAAAAAAAAAAAAAAQ==", ], ) def test_base64_converter_to_python(url_val): assert base64_to_uuid(url_val) == UUID(int=1) @pytest.mark.parametrize("python_val", [UUID(int=1), "00000000-0000-0000-0000-000000000001"]) def test_base64_converter_to_url(python_val): assert uuid_to_base64(python_val) == "AAAAAAAAAAAAAAAAAAAAAQ" @pytest.mark.parametrize( "url_val", [ "this_is_valid_base64_but_is_too_long_to_be_a_uuid", "this_one_has_emoji_➕➕➕", ], ) def test_base64_converter_to_python_raises_validation_error(url_val): with pytest.raises(Exception): base64_to_uuid(url_val) def test_base64_converter_to_url_raises_validation_error(): with pytest.raises(Exception): uuid_to_base64(object())

27.26087

117

0.739234

0

837

0.664286

0

329

0.261111

297d3ac09c9100be856d7c8b2496765760397058

3,571

py

Python

homeassistant/components/devolo_home_control/__init__.py

dummys/home-assistant

dd908caebade15adf061fade686355b94ed2f43a

[ "Apache-2.0" ]

11

2018-02-16T15:35:47.000Z

2020-01-14T15:20:00.000Z

homeassistant/components/devolo_home_control/__init__.py

dummys/home-assistant

dd908caebade15adf061fade686355b94ed2f43a

[ "Apache-2.0" ]

64

2020-10-01T06:39:48.000Z

2022-03-31T06:02:17.000Z

homeassistant/components/devolo_home_control/__init__.py

dummys/home-assistant

dd908caebade15adf061fade686355b94ed2f43a

[ "Apache-2.0" ]

6

2018-02-04T03:48:55.000Z

2022-01-24T20:37:04.000Z

"""The devolo_home_control integration.""" from __future__ import annotations import asyncio from functools import partial from types import MappingProxyType from typing import Any from devolo_home_control_api.exceptions.gateway import GatewayOfflineError from devolo_home_control_api.homecontrol import HomeControl from devolo_home_control_api.mydevolo import Mydevolo from homeassistant.components import zeroconf from homeassistant.config_entries import ConfigEntry from homeassistant.const import CONF_PASSWORD, CONF_USERNAME, EVENT_HOMEASSISTANT_STOP from homeassistant.core import Event, HomeAssistant from homeassistant.exceptions import ConfigEntryAuthFailed, ConfigEntryNotReady from .const import ( CONF_MYDEVOLO, DEFAULT_MYDEVOLO, DOMAIN, GATEWAY_SERIAL_PATTERN, PLATFORMS, ) async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: """Set up the devolo account from a config entry.""" hass.data.setdefault(DOMAIN, {}) mydevolo = configure_mydevolo(entry.data) credentials_valid = await hass.async_add_executor_job(mydevolo.credentials_valid) if not credentials_valid: raise ConfigEntryAuthFailed if await hass.async_add_executor_job(mydevolo.maintenance): raise ConfigEntryNotReady gateway_ids = await hass.async_add_executor_job(mydevolo.get_gateway_ids) if entry.unique_id and GATEWAY_SERIAL_PATTERN.match(entry.unique_id): uuid = await hass.async_add_executor_job(mydevolo.uuid) hass.config_entries.async_update_entry(entry, unique_id=uuid) try: zeroconf_instance = await zeroconf.async_get_instance(hass) hass.data[DOMAIN][entry.entry_id] = {"gateways": [], "listener": None} for gateway_id in gateway_ids: hass.data[DOMAIN][entry.entry_id]["gateways"].append( await hass.async_add_executor_job( partial( HomeControl, gateway_id=gateway_id, mydevolo_instance=mydevolo, zeroconf_instance=zeroconf_instance, ) ) ) except GatewayOfflineError as err: raise ConfigEntryNotReady from err hass.config_entries.async_setup_platforms(entry, PLATFORMS) def shutdown(event: Event) -> None: for gateway in hass.data[DOMAIN][entry.entry_id]["gateways"]: gateway.websocket_disconnect( f"websocket disconnect requested by {EVENT_HOMEASSISTANT_STOP}" ) # Listen when EVENT_HOMEASSISTANT_STOP is fired hass.data[DOMAIN][entry.entry_id]["listener"] = hass.bus.async_listen_once( EVENT_HOMEASSISTANT_STOP, shutdown ) return True async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: """Unload a config entry.""" unload = await hass.config_entries.async_unload_platforms(entry, PLATFORMS) await asyncio.gather( *[ hass.async_add_executor_job(gateway.websocket_disconnect) for gateway in hass.data[DOMAIN][entry.entry_id]["gateways"] ] ) hass.data[DOMAIN][entry.entry_id]["listener"]() hass.data[DOMAIN].pop(entry.entry_id) return unload def configure_mydevolo(conf: dict[str, Any] | MappingProxyType[str, Any]) -> Mydevolo: """Configure mydevolo.""" mydevolo = Mydevolo() mydevolo.user = conf[CONF_USERNAME] mydevolo.password = conf[CONF_PASSWORD] mydevolo.url = conf.get(CONF_MYDEVOLO, DEFAULT_MYDEVOLO) return mydevolo

35.009804

86

0.711845

0

2,444

0.684402

327

0.091571

297d9c0aeb9cbc7ee91fb0bd8b0450dda847eca4

1,199

py

Python

trab1/hillClimbing.py

RafaelPedruzzi/IA-2019-2

7d99a8f02ec826403bd48c6eba574d802e558c36

[ "MIT" ]

null

trab1/hillClimbing.py

RafaelPedruzzi/IA-2019-2

7d99a8f02ec826403bd48c6eba574d802e558c36

[ "MIT" ]

null

trab1/hillClimbing.py

RafaelPedruzzi/IA-2019-2

7d99a8f02ec826403bd48c6eba574d802e558c36

[ "MIT" ]

null

## -------------------------------------------------------- ## # Trab 1 IA 2019-2 # # Rafael Belmock Pedruzzi # # hillClimbing.py: implements the hill climbing metaheuristic for the bag problem # # Python version: 3.7.4 ## -------------------------------------------------------- ## import bagProblem as bp from time import time # Returns True and the valid state with the biggest value, or False if no state is valid: def select_Best(si, T, OBJs): sn = -1 # best state position sv = 0 # state value for i in range(len(si)): v = bp.state_Value(si[i], OBJs) # current value if bp.state_Verify(si[i], T, OBJs) and v > sv: sv = v sn = i if sn == -1: return False, [] return True, si[sn] # Hill Climbing: def hill_Climbing(T, OBJs, execTime, *args): sn = [0]*len(OBJs) # initial state c = True # continue flag start = time() while c: if time() - start > execTime: break cs = sn # storing current state c, sn = select_Best(bp.state_Expansion(cs), T, OBJs) return cs # T = 19 # bag size # OBJs = [(1,3), (4,6), (5,7)] # object list (v,t) # print(hill_Climbing(T,OBJs))

28.547619

89

0.533778

0

588

0.490409

2980dcb521571569813c10466d1c0e11144f30c7

151

py

Python

ssb_pseudonymization/__init__.py

statisticsnorway/ssb-pseudonymization-py

cc8e1abd1cf00b403bfaf52fcb335f09ee71b591

[ "MIT" ]

1

2021-02-17T09:51:12.000Z

ssb_pseudonymization/__init__.py

statisticsnorway/ssb-pseudonymization-py

cc8e1abd1cf00b403bfaf52fcb335f09ee71b591

[ "MIT" ]

null

ssb_pseudonymization/__init__.py

statisticsnorway/ssb-pseudonymization-py

cc8e1abd1cf00b403bfaf52fcb335f09ee71b591

[ "MIT" ]

3

2020-08-25T07:12:17.000Z

2021-04-20T12:26:42.000Z

"""ssb-pseudonymization - Data pseudonymization functions used by SSB""" __version__ = '0.0.2' __author__ = 'Statistics Norway (ssb.no)' __all__ = []

25.166667

72

0.721854

0

107

0.708609

2980f0b218fed38559f7aa3fa0718ba902a95fb9

7,657

py

Python

hwilib/devices/keepkey.py

cjackie/HWI

8c1b50aaaac37714b5d61f720b4b06f8aa24c73a

[ "MIT" ]

285

2019-01-31T03:10:19.000Z

2022-03-31T10:38:37.000Z

hwilib/devices/keepkey.py

cjackie/HWI

8c1b50aaaac37714b5d61f720b4b06f8aa24c73a

[ "MIT" ]

426

2019-01-31T10:38:02.000Z

2022-03-28T15:58:13.000Z

hwilib/devices/keepkey.py

cjackie/HWI

8c1b50aaaac37714b5d61f720b4b06f8aa24c73a

[ "MIT" ]

128

2019-01-30T22:32:32.000Z

2022-03-28T19:23:46.000Z

""" Keepkey ******* """ from ..errors import ( DEVICE_NOT_INITIALIZED, DeviceNotReadyError, common_err_msgs, handle_errors, ) from .trezorlib import protobuf as p from .trezorlib.transport import ( hid, udp, webusb, ) from .trezor import TrezorClient, HID_IDS, WEBUSB_IDS from .trezorlib.messages import ( DebugLinkState, Features, HDNodeType, ResetDevice, ) from typing import ( Any, Dict, List, Optional, ) py_enumerate = enumerate # Need to use the enumerate built-in but there's another function already named that KEEPKEY_HID_IDS = {(0x2B24, 0x0001)} KEEPKEY_WEBUSB_IDS = {(0x2B24, 0x0002)} KEEPKEY_SIMULATOR_PATH = '127.0.0.1:11044' HID_IDS.update(KEEPKEY_HID_IDS) WEBUSB_IDS.update(KEEPKEY_WEBUSB_IDS) class KeepkeyFeatures(Features): # type: ignore def __init__( self, *, firmware_variant: Optional[str] = None, firmware_hash: Optional[bytes] = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.firmware_variant = firmware_variant self.firmware_hash = firmware_hash @classmethod def get_fields(cls) -> Dict[int, p.FieldInfo]: return { 1: ('vendor', p.UnicodeType, None), 2: ('major_version', p.UVarintType, None), 3: ('minor_version', p.UVarintType, None), 4: ('patch_version', p.UVarintType, None), 5: ('bootloader_mode', p.BoolType, None), 6: ('device_id', p.UnicodeType, None), 7: ('pin_protection', p.BoolType, None), 8: ('passphrase_protection', p.BoolType, None), 9: ('language', p.UnicodeType, None), 10: ('label', p.UnicodeType, None), 12: ('initialized', p.BoolType, None), 13: ('revision', p.BytesType, None), 14: ('bootloader_hash', p.BytesType, None), 15: ('imported', p.BoolType, None), 16: ('unlocked', p.BoolType, None), 21: ('model', p.UnicodeType, None), 22: ('firmware_variant', p.UnicodeType, None), 23: ('firmware_hash', p.BytesType, None), 24: ('no_backup', p.BoolType, None), 25: ('wipe_code_protection', p.BoolType, None), } class KeepkeyResetDevice(ResetDevice): # type: ignore def __init__( self, *, auto_lock_delay_ms: Optional[int] = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.auto_lock_delay_ms = auto_lock_delay_ms @classmethod def get_fields(cls) -> Dict[int, p.FieldInfo]: return { 1: ('display_random', p.BoolType, None), 2: ('strength', p.UVarintType, 256), # default=256 3: ('passphrase_protection', p.BoolType, None), 4: ('pin_protection', p.BoolType, None), 5: ('language', p.UnicodeType, "en-US"), # default=en-US 6: ('label', p.UnicodeType, None), 7: ('no_backup', p.BoolType, None), 8: ('auto_lock_delay_ms', p.UVarintType, None), 9: ('u2f_counter', p.UVarintType, None), } class KeepkeyDebugLinkState(DebugLinkState): # type: ignore def __init__( self, *, recovery_cipher: Optional[str] = None, recovery_auto_completed_word: Optional[str] = None, firmware_hash: Optional[bytes] = None, storage_hash: Optional[bytes] = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.recovery_cipher = recovery_cipher self.recovery_auto_completed_word = recovery_auto_completed_word self.firmware_hash = firmware_hash self.storage_hash = storage_hash @classmethod def get_fields(cls) -> Dict[int, p.FieldType]: return { 1: ('layout', p.BytesType, None), 2: ('pin', p.UnicodeType, None), 3: ('matrix', p.UnicodeType, None), 4: ('mnemonic_secret', p.BytesType, None), 5: ('node', HDNodeType, None), 6: ('passphrase_protection', p.BoolType, None), 7: ('reset_word', p.UnicodeType, None), 8: ('reset_entropy', p.BytesType, None), 9: ('recovery_fake_word', p.UnicodeType, None), 10: ('recovery_word_pos', p.UVarintType, None), 11: ('recovery_cipher', p.UnicodeType, None), 12: ('recovery_auto_completed_word', p.UnicodeType, None), 13: ('firmware_hash', p.BytesType, None), 14: ('storage_hash', p.BytesType, None), } class KeepkeyClient(TrezorClient): def __init__(self, path: str, password: str = "", expert: bool = False) -> None: """ The `KeepkeyClient` is a `HardwareWalletClient` for interacting with the Keepkey. As Keepkeys are clones of the Trezor 1, please refer to `TrezorClient` for documentation. """ super(KeepkeyClient, self).__init__(path, password, expert, KEEPKEY_HID_IDS, KEEPKEY_WEBUSB_IDS, KEEPKEY_SIMULATOR_PATH) self.type = 'Keepkey' self.client.vendors = ("keepkey.com") self.client.minimum_versions = {"K1-14AM": (0, 0, 0)} self.client.map_type_to_class_override[KeepkeyFeatures.MESSAGE_WIRE_TYPE] = KeepkeyFeatures self.client.map_type_to_class_override[KeepkeyResetDevice.MESSAGE_WIRE_TYPE] = KeepkeyResetDevice if self.simulator: self.client.debug.map_type_to_class_override[KeepkeyDebugLinkState.MESSAGE_WIRE_TYPE] = KeepkeyDebugLinkState def enumerate(password: str = "") -> List[Dict[str, Any]]: results = [] devs = hid.HidTransport.enumerate(usb_ids=KEEPKEY_HID_IDS) devs.extend(webusb.WebUsbTransport.enumerate(usb_ids=KEEPKEY_WEBUSB_IDS)) devs.extend(udp.UdpTransport.enumerate(KEEPKEY_SIMULATOR_PATH)) for dev in devs: d_data: Dict[str, Any] = {} d_data['type'] = 'keepkey' d_data['model'] = 'keepkey' d_data['path'] = dev.get_path() client = None with handle_errors(common_err_msgs["enumerate"], d_data): client = KeepkeyClient(d_data['path'], password) try: client.client.refresh_features() except TypeError: continue if 'keepkey' not in client.client.features.vendor: continue d_data['label'] = client.client.features.label if d_data['path'].startswith('udp:'): d_data['model'] += '_simulator' d_data['needs_pin_sent'] = client.client.features.pin_protection and not client.client.features.unlocked d_data['needs_passphrase_sent'] = client.client.features.passphrase_protection # always need the passphrase sent for Keepkey if it has passphrase protection enabled if d_data['needs_pin_sent']: raise DeviceNotReadyError('Keepkey is locked. Unlock by using \'promptpin\' and then \'sendpin\'.') if d_data['needs_passphrase_sent'] and not password: raise DeviceNotReadyError("Passphrase needs to be specified before the fingerprint information can be retrieved") if client.client.features.initialized: d_data['fingerprint'] = client.get_master_fingerprint().hex() d_data['needs_passphrase_sent'] = False # Passphrase is always needed for the above to have worked, so it's already sent else: d_data['error'] = 'Not initialized' d_data['code'] = DEVICE_NOT_INITIALIZED if client: client.close() results.append(d_data) return results

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0.337861

0

1,613

0.210657

298195d9b4f163d34048ccfa931e4a45670c98bc

15,195

py

Python

sklearn_extra/cluster/_k_medoids.py

chkoar/scikit-learn-extra

9e62f1ac18b40f444009db9f6557196ddc6c5e86

[ "BSD-3-Clause" ]

1

2019-08-27T01:10:48.000Z

sklearn_extra/cluster/_k_medoids.py

KonstantinKlepikov/scikit-learn-extra

12fb111acdfa7aada0ed29f53b9b92c44d8de7c3

[ "BSD-3-Clause" ]

null

sklearn_extra/cluster/_k_medoids.py

KonstantinKlepikov/scikit-learn-extra

12fb111acdfa7aada0ed29f53b9b92c44d8de7c3

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """K-medoids clustering""" # Authors: Timo Erkkilä <[email protected]> # Antti Lehmussola <[email protected]> # Kornel Kiełczewski <[email protected]> # Zane Dufour <[email protected]> # License: BSD 3 clause import warnings import numpy as np from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin from sklearn.metrics.pairwise import ( pairwise_distances, pairwise_distances_argmin, ) from sklearn.utils import check_array, check_random_state from sklearn.utils.extmath import stable_cumsum from sklearn.utils.validation import check_is_fitted from sklearn.exceptions import ConvergenceWarning class KMedoids(BaseEstimator, ClusterMixin, TransformerMixin): """k-medoids clustering. Read more in the :ref:`User Guide <k_medoids>`. Parameters ---------- n_clusters : int, optional, default: 8 The number of clusters to form as well as the number of medoids to generate. metric : string, or callable, optional, default: 'euclidean' What distance metric to use. See :func:metrics.pairwise_distances init : {'random', 'heuristic', 'k-medoids++'}, optional, default: 'heuristic' Specify medoid initialization method. 'random' selects n_clusters elements from the dataset. 'heuristic' picks the n_clusters points with the smallest sum distance to every other point. 'k-medoids++' follows an approach based on k-means++_, and in general, gives initial medoids which are more separated than those generated by the other methods. .. _k-means++: https://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf max_iter : int, optional, default : 300 Specify the maximum number of iterations when fitting. random_state : int, RandomState instance or None, optional Specify random state for the random number generator. Used to initialise medoids when init='random'. Attributes ---------- cluster_centers_ : array, shape = (n_clusters, n_features) or None if metric == 'precomputed' Cluster centers, i.e. medoids (elements from the original dataset) medoid_indices_ : array, shape = (n_clusters,) The indices of the medoid rows in X labels_ : array, shape = (n_samples,) Labels of each point inertia_ : float Sum of distances of samples to their closest cluster center. Examples -------- >>> from sklearn_extra.cluster import KMedoids >>> import numpy as np >>> X = np.asarray([[1, 2], [1, 4], [1, 0], ... [4, 2], [4, 4], [4, 0]]) >>> kmedoids = KMedoids(n_clusters=2, random_state=0).fit(X) >>> kmedoids.labels_ array([0, 0, 0, 1, 1, 1]) >>> kmedoids.predict([[0,0], [4,4]]) array([0, 1]) >>> kmedoids.cluster_centers_ array([[1, 2], [4, 2]]) >>> kmedoids.inertia_ 8.0 See scikit-learn-extra/examples/plot_kmedoids_digits.py for examples of KMedoids with various distance metrics. References ---------- Kaufman, L. and Rousseeuw, P.J., Statistical Data Analysis Based on the L1–Norm and Related Methods, edited by Y. Dodge, North-Holland, 405–416. 1987 See also -------- KMeans The KMeans algorithm minimizes the within-cluster sum-of-squares criterion. It scales well to large number of samples. Notes ----- Since all pairwise distances are calculated and stored in memory for the duration of fit, the space complexity is O(n_samples ** 2). """ def __init__( self, n_clusters=8, metric="euclidean", init="heuristic", max_iter=300, random_state=None, ): self.n_clusters = n_clusters self.metric = metric self.init = init self.max_iter = max_iter self.random_state = random_state def _check_nonnegative_int(self, value, desc): """Validates if value is a valid integer > 0""" if ( value is None or value <= 0 or not isinstance(value, (int, np.integer)) ): raise ValueError( "%s should be a nonnegative integer. " "%s was given" % (desc, value) ) def _check_init_args(self): """Validates the input arguments. """ # Check n_clusters and max_iter self._check_nonnegative_int(self.n_clusters, "n_clusters") self._check_nonnegative_int(self.max_iter, "max_iter") # Check init init_methods = ["random", "heuristic", "k-medoids++"] if self.init not in init_methods: raise ValueError( "init needs to be one of " + "the following: " + "%s" % init_methods ) def fit(self, X, y=None): """Fit K-Medoids to the provided data. Parameters ---------- X : {array-like, sparse matrix}, shape = (n_samples, n_features), \ or (n_samples, n_samples) if metric == 'precomputed' Dataset to cluster. y : Ignored Returns ------- self """ random_state_ = check_random_state(self.random_state) self._check_init_args() X = check_array(X, accept_sparse=["csr", "csc"]) if self.n_clusters > X.shape[0]: raise ValueError( "The number of medoids (%d) must be less " "than the number of samples %d." % (self.n_clusters, X.shape[0]) ) D = pairwise_distances(X, metric=self.metric) medoid_idxs = self._initialize_medoids( D, self.n_clusters, random_state_ ) labels = None # Continue the algorithm as long as # the medoids keep changing and the maximum number # of iterations is not exceeded for self.n_iter_ in range(0, self.max_iter): old_medoid_idxs = np.copy(medoid_idxs) labels = np.argmin(D[medoid_idxs, :], axis=0) # Update medoids with the new cluster indices self._update_medoid_idxs_in_place(D, labels, medoid_idxs) if np.all(old_medoid_idxs == medoid_idxs): break elif self.n_iter_ == self.max_iter - 1: warnings.warn( "Maximum number of iteration reached before " "convergence. Consider increasing max_iter to " "improve the fit.", ConvergenceWarning, ) # Set the resulting instance variables. if self.metric == "precomputed": self.cluster_centers_ = None else: self.cluster_centers_ = X[medoid_idxs] # Expose labels_ which are the assignments of # the training data to clusters self.labels_ = labels self.medoid_indices_ = medoid_idxs self.inertia_ = self._compute_inertia(self.transform(X)) # Return self to enable method chaining return self def _update_medoid_idxs_in_place(self, D, labels, medoid_idxs): """In-place update of the medoid indices""" # Update the medoids for each cluster for k in range(self.n_clusters): # Extract the distance matrix between the data points # inside the cluster k cluster_k_idxs = np.where(labels == k)[0] if len(cluster_k_idxs) == 0: warnings.warn( "Cluster {k} is empty! " "self.labels_[self.medoid_indices_[{k}]] " "may not be labeled with " "its corresponding cluster ({k}).".format(k=k) ) continue in_cluster_distances = D[ cluster_k_idxs, cluster_k_idxs[:, np.newaxis] ] # Calculate all costs from each point to all others in the cluster in_cluster_all_costs = np.sum(in_cluster_distances, axis=1) min_cost_idx = np.argmin(in_cluster_all_costs) min_cost = in_cluster_all_costs[min_cost_idx] curr_cost = in_cluster_all_costs[ np.argmax(cluster_k_idxs == medoid_idxs[k]) ] # Adopt a new medoid if its distance is smaller then the current if min_cost < curr_cost: medoid_idxs[k] = cluster_k_idxs[min_cost_idx] def transform(self, X): """Transforms X to cluster-distance space. Parameters ---------- X : {array-like, sparse matrix}, shape (n_query, n_features), \ or (n_query, n_indexed) if metric == 'precomputed' Data to transform. Returns ------- X_new : {array-like, sparse matrix}, shape=(n_query, n_clusters) X transformed in the new space of distances to cluster centers. """ X = check_array(X, accept_sparse=["csr", "csc"]) if self.metric == "precomputed": check_is_fitted(self, "medoid_indices_") return X[:, self.medoid_indices_] else: check_is_fitted(self, "cluster_centers_") Y = self.cluster_centers_ return pairwise_distances(X, Y=Y, metric=self.metric) def predict(self, X): """Predict the closest cluster for each sample in X. Parameters ---------- X : {array-like, sparse matrix}, shape (n_query, n_features), \ or (n_query, n_indexed) if metric == 'precomputed' New data to predict. Returns ------- labels : array, shape = (n_query,) Index of the cluster each sample belongs to. """ X = check_array(X, accept_sparse=["csr", "csc"]) if self.metric == "precomputed": check_is_fitted(self, "medoid_indices_") return np.argmin(X[:, self.medoid_indices_], axis=1) else: check_is_fitted(self, "cluster_centers_") # Return data points to clusters based on which cluster assignment # yields the smallest distance return pairwise_distances_argmin( X, Y=self.cluster_centers_, metric=self.metric ) def _compute_inertia(self, distances): """Compute inertia of new samples. Inertia is defined as the sum of the sample distances to closest cluster centers. Parameters ---------- distances : {array-like, sparse matrix}, shape=(n_samples, n_clusters) Distances to cluster centers. Returns ------- Sum of sample distances to closest cluster centers. """ # Define inertia as the sum of the sample-distances # to closest cluster centers inertia = np.sum(np.min(distances, axis=1)) return inertia def _initialize_medoids(self, D, n_clusters, random_state_): """Select initial mediods when beginning clustering.""" if self.init == "random": # Random initialization # Pick random k medoids as the initial ones. medoids = random_state_.choice(len(D), n_clusters) elif self.init == "k-medoids++": medoids = self._kpp_init(D, n_clusters, random_state_) elif self.init == "heuristic": # Initialization by heuristic # Pick K first data points that have the smallest sum distance # to every other point. These are the initial medoids. medoids = np.argpartition(np.sum(D, axis=1), n_clusters - 1)[ :n_clusters ] else: raise ValueError( "init value '{init}' not recognized".format(init=self.init) ) return medoids # Copied from sklearn.cluster.k_means_._k_init def _kpp_init(self, D, n_clusters, random_state_, n_local_trials=None): """Init n_clusters seeds with a method similar to k-means++ Parameters ----------- D : array, shape (n_samples, n_samples) The distance matrix we will use to select medoid indices. n_clusters : integer The number of seeds to choose random_state : RandomState The generator used to initialize the centers. n_local_trials : integer, optional The number of seeding trials for each center (except the first), of which the one reducing inertia the most is greedily chosen. Set to None to make the number of trials depend logarithmically on the number of seeds (2+log(k)); this is the default. Notes ----- Selects initial cluster centers for k-medoid clustering in a smart way to speed up convergence. see: Arthur, D. and Vassilvitskii, S. "k-means++: the advantages of careful seeding". ACM-SIAM symposium on Discrete algorithms. 2007 Version ported from http://www.stanford.edu/~darthur/kMeansppTest.zip, which is the implementation used in the aforementioned paper. """ n_samples, _ = D.shape centers = np.empty(n_clusters, dtype=int) # Set the number of local seeding trials if none is given if n_local_trials is None: # This is what Arthur/Vassilvitskii tried, but did not report # specific results for other than mentioning in the conclusion # that it helped. n_local_trials = 2 + int(np.log(n_clusters)) center_id = random_state_.randint(n_samples) centers[0] = center_id # Initialize list of closest distances and calculate current potential closest_dist_sq = D[centers[0], :] ** 2 current_pot = closest_dist_sq.sum() # pick the remaining n_clusters-1 points for cluster_index in range(1, n_clusters): rand_vals = ( random_state_.random_sample(n_local_trials) * current_pot ) candidate_ids = np.searchsorted( stable_cumsum(closest_dist_sq), rand_vals ) # Compute distances to center candidates distance_to_candidates = D[candidate_ids, :] ** 2 # Decide which candidate is the best best_candidate = None best_pot = None best_dist_sq = None for trial in range(n_local_trials): # Compute potential when including center candidate new_dist_sq = np.minimum( closest_dist_sq, distance_to_candidates[trial] ) new_pot = new_dist_sq.sum() # Store result if it is the best local trial so far if (best_candidate is None) or (new_pot < best_pot): best_candidate = candidate_ids[trial] best_pot = new_pot best_dist_sq = new_dist_sq centers[cluster_index] = best_candidate current_pot = best_pot closest_dist_sq = best_dist_sq return centers

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