def createDictionary(self):
fileName = QtGui.QFileDialog.getSaveFileName(self , 'Save dictionary to a file' , expanduser('~')+'/dictionary' , 'Matlab file (*.mat);;Python pickle (*.p);;Comma sep. values (*.csv);;Excel file (*.xlsx)')
if len(fileName) == 0:
return
self.displayInformation('Saving dictionary...' , flag='new')
fileName = str(fileName)
self.setDictionaryConfig()
self.setAlgorithmConfig()
config = generateFinalConfig(self.dictionaryConfig , self.dataMatrixes[self.filePath][1] , self.dataMatrixes[self.filePath][2])
time = np.arange(0,self.dataMatrixes[self.filePath][1]['numberOfSamples'])
dictionary = generateDictionary(time , config)
if fileName[-4:] == '.mat':
dic = {col_name : dictionary[col_name].values for col_name in dictionary.columns.values}
savemat(fileName , dic)
elif fileName[-2:] == '.p':
dictionary.to_pickle(fileName)
elif fileName[-4:] == '.csv':
dictionary.to_csv(fileName)
elif fileName[-5:] == '.xlsx':
dictionary.to_excel(fileName, sheet_name='dictionary')
self.displayInformation('Dictionary saved.' , flag='new')
def saveConfig(self):
fileName = QtGui.QFileDialog.getSaveFileName(self , 'Save configuration file' , expanduser('~')+'/config' , 'Python pickle (*.p);;Text file (*.txt);;')
if len(fileName) == 0:
return
self.displayInformation('Saving configuration...' , flag='new')
fileName = str(fileName)
self.setDictionaryConfig()
self.setAlgorithmConfig()
config = generateFinalConfig(self.dictionaryConfig , self.dataMatrixes[self.filePath][1] , self.dataMatrixes[self.filePath][2])
if fileName[-4:] == '.txt':
with open(fileName , 'w') as f:
for key in config.keys():
if isinstance(config[key] , unicode):
stringToWrite = key + ' ' + config[key] + '\n'
else:
stringToWrite = key + ' ' + str(config[key]) + '\n'
f.write(stringToWrite.encode('UTF8'))
elif fileName[-2:] == '.p':
with open(fileName , 'wb') as f:
dump(config , f)
self.displayInformation('Configuration saved.' , flag='new')
def run(self):
self.sig_prb_file_setMax.emit(len(self.files2run))
ind_file = 0
self.sig_prb_file_update.emit(ind_file)
self.sig_lbl_file_update.emit('Files - (' + str(ind_file) + ' / ' +
str(len(self.files2run)) + ')')
for file2run in self.files2run:
dataMatrix = self.input[file2run][0]
dataInfo = self.input[file2run][1]
algorithmConfig = self.input[file2run][2]
config = generateFinalConfig(self.dictionaryConfig, dataInfo,
algorithmConfig)
book = np.empty(shape=(len(config['trials2calc']),
len(config['channels2calc'])),
dtype=pd.core.frame.DataFrame)
self.sig_prb_channel_setMax.emit(len(config['channels2calc']))
self.sig_prb_trial_setMax.emit(len(config['trials2calc']))
ind_channel = 0
self.sig_prb_channel_update.emit(ind_channel)
self.sig_lbl_channel_update.emit(
'Channels - (' + str(ind_channel) + ' / ' +
str(len(config['channels2calc'])) + ')')
for channel in config['channels2calc']:
ind_trial = 0
self.sig_prb_trial_update.emit(ind_trial)
self.sig_lbl_trial_update.emit(
'Trials - (' + str(ind_trial) + ' / ' +
str(len(config['trials2calc'])) + ')')
for trial in config['trials2calc']:
book[ind_trial, ind_channel] = calculateMP(
self.dictionary,
np.squeeze(dataMatrix[trial - 1, channel - 1, :]),
config)
ind_trial += 1
self.sig_prb_trial_update.emit(ind_trial)
self.sig_lbl_trial_update.emit(
'Trials - (' + str(ind_trial) + ' / ' +
str(len(config['trials2calc'])) + ')')
if self.cancelClicked == 1:
return
ind_channel += 1
self.sig_prb_channel_update.emit(ind_channel)
self.sig_lbl_channel_update.emit(
'Channels - (' + str(ind_channel) + ' / ' +
str(len(config['channels2calc'])) + ')')
ind_file += 1
self.sig_prb_file_update.emit(ind_file)
self.sig_lbl_file_update.emit('Files - (' + str(ind_file) + ' / ' +
str(len(self.files2run)) + ')')
self.sig_singleBookDone.emit(book, config, file2run)
def run(self):
self.sig_prb_file_setMax.emit(len(self.files2run))
ind_file = 0
self.sig_prb_file_update.emit(ind_file)
self.sig_lbl_file_update.emit('Files - (' + str(ind_file) + ' / ' + str(len(self.files2run)) + ')')
for file2run in self.files2run:
dataMatrix = self.input[file2run][0]
dataInfo = self.input[file2run][1]
algorithmConfig = self.input[file2run][2]
config = generateFinalConfig(self.dictionaryConfig , dataInfo , algorithmConfig)
book = np.empty(shape=(len(config['trials2calc']),len(config['channels2calc'])) , dtype = pd.core.frame.DataFrame)
self.sig_prb_channel_setMax.emit(len(config['channels2calc']))
self.sig_prb_trial_setMax.emit(len(config['trials2calc']))
ind_channel = 0
self.sig_prb_channel_update.emit(ind_channel)
self.sig_lbl_channel_update.emit('Channels - (' + str(ind_channel) + ' / ' + str(len(config['channels2calc'])) + ')')
for channel in config['channels2calc']:
ind_trial = 0
self.sig_prb_trial_update.emit(ind_trial)
self.sig_lbl_trial_update.emit('Trials - (' + str(ind_trial) + ' / ' + str(len(config['trials2calc'])) + ')')
for trial in config['trials2calc']:
book[ind_trial , ind_channel] = calculateMP(self.dictionary , np.squeeze(dataMatrix[trial-1,channel-1,:]) , config)
ind_trial += 1
self.sig_prb_trial_update.emit(ind_trial)
self.sig_lbl_trial_update.emit('Trials - (' + str(ind_trial) + ' / ' + str(len(config['trials2calc'])) + ')')
if self.cancelClicked == 1:
return
ind_channel += 1
self.sig_prb_channel_update.emit(ind_channel)
self.sig_lbl_channel_update.emit('Channels - (' + str(ind_channel) + ' / ' + str(len(config['channels2calc'])) + ')')
ind_file += 1
self.sig_prb_file_update.emit(ind_file)
self.sig_lbl_file_update.emit('Files - (' + str(ind_file) + ' / ' + str(len(self.files2run)) + ')')
self.sig_singleBookDone.emit(book , config , file2run)
