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)
config['minS'] = 32 config['maxS'] = numberOfSamples config['density'] = 0.01 config['maxNumberOfIterations'] = 4 config['minEnergyExplained'] = 0.99 config['samplingFrequency'] = samplingFrequency config['minNFFT'] = 256 # 2*samplingFrequency # optional config for t-f map drawing # config['mapFreqRange'] = [0.0 , samplingFrequency/2] # config['mapStructFreqs'] = [0.0 , samplingFrequency/2] # config['mapStructSigmas'] = [0.0 , 4.0] dictionary = generateDictionary(time , config) book = calculateMP(dictionary , signal , config) # print book # plot resulting functions plt.figure() plt.subplot(4,1,1) plt.plot(time,signal,'k') plt.plot(time,sum(book['reconstruction']).real , 'r') plt.subplot(4,1,2) plt.plot(time,book['reconstruction'][0].real , 'r') plt.subplot(4,1,3) plt.plot(time,book['reconstruction'][1].real , 'r') plt.subplot(4,1,4)
flags['useGradientOptimization'] = 1
flags['displayInfo'] = 0
config = {}
config['flags'] = flags
config['algorithm'] = 'smp'
config['minS'] = 32
config['maxS'] = numberOfSamples
config['density'] = 0.01
config['maxNumberOfIterations'] = 4
config['minEnergyExplained'] = 0.99
config['samplingFrequency'] = samplingFrequency
config['minNFFT'] = 2 * samplingFrequency
dictionary = generateDictionary(time, config)
book = calculateMP(dictionary, signal, config)
flags = {}
flags['useAsymA'] = 1
flags['useRectA'] = 0
flags['useGradientOptimization'] = 1
flags['displayInfo'] = 0
config['flags'] = flags
advancedDictionary = generateDictionary(time, config)
advancedBook = calculateMP(advancedDictionary, advancedSignal, config)
flags = {}
flags['useAsymA'] = 1
flags['useRectA'] = 1
flags['useGradientOptimization'] = 1
config['minS'] = 32
config['maxS'] = numberOfSamples
config['density'] = 0.01
config['maxNumberOfIterations'] = 4
config['minEnergyExplained'] = 0.99
config['samplingFrequency'] = samplingFrequency
config['minNFFT'] = 256 # 2*samplingFrequency
# optional config for t-f map drawing
# config['mapFreqRange'] = [0.0 , samplingFrequency/2]
# config['mapStructFreqs'] = [0.0 , samplingFrequency/2]
# config['mapStructSigmas'] = [0.0 , 4.0]
dictionary = generateDictionary(time, config)
book = calculateMP(dictionary, signal, config)
# print book
# plot resulting functions
plt.figure()
plt.subplot(4, 1, 1)
plt.plot(time, signal, 'k')
plt.plot(time, sum(book['reconstruction']).real, 'r')
plt.subplot(4, 1, 2)
plt.plot(time, book['reconstruction'][0].real, 'r')
plt.subplot(4, 1, 3)
plt.plot(time, book['reconstruction'][1].real, 'r')
plt.subplot(4, 1, 4)
config['flags']['useGradientOptimization'] = 1
# config for display
# config['flags']['drawMeanMap'] = 0
# config['flags']['saveMeanMap'] = 0
# config['flags']['drawSingleMaps'] = 0
# config['flags']['saveSingleMaps'] = 0
config['mapFreqRange'] = [0.0, 64.0]
config['mapStructFreqs'] = [0.0, 64.0]
config['mapStructSigmas'] = [0.0, 20.0]
dictionary = generateDictionary(time, config)
for ind1 in np.arange(data.shape[0]):
book = calculateMP(dictionary, data[ind1, :], config)
# print book
break
(T, F, TFmap) = calculateTFMap(book, time, config['samplingFrequency'], 0,
config['mapStructFreqs'],
config['mapStructSigmas'])
# results = {}
# results['mapM'] = TFmap
# results['mapT'] = time
# results['mapF'] = F
fig = plt.figure()
plt.subplot(3, 1, 1)
m = plt.imshow(np.abs(TFmap),
(data, time, info) = loadSyntheticSigmalFromEEGLABFile(nameOfFile)
# config for a dictionary and MP
flags = {}
flags['useAsymA'] = 0
flags['useRectA'] = 0
flags['useGradientOptimization'] = 1
config = {}
config['flags'] = flags
config['algorithm'] = 'mmp'
config['trials2calculate'] = range(info['numberOfTrials'])
# config['channels2calculate'] = range(info['numberOfChannels'])
config['channels2calculate'] = range(5)
config['minS'] = 100
config['maxS'] = info['numberOfSamples']
config['density'] = 0.01
config['maxNumberOfIterations'] = 1
config['minEnergyExplained'] = 0.99
config['samplingFrequency'] = info['samplingFreq']
config['minNFFT'] = 1024
# optional config for t-f map drawing
# config['mapFreqRange'] = [0.0 , samplingFrequency/2]
# config['mapStructFreqs'] = [0.0 , samplingFrequency/2]
# config['mapStructSigmas'] = [0.0 , 4.0]
dictionary = generateDictionary(time, config)
book = calculateMP(dictionary, data, config)
flags["useGradientOptimization"] = 1
flags["displayInfo"] = 0
config = {}
config["flags"] = flags
config["algorithm"] = "smp"
config["minS"] = 32
config["maxS"] = numberOfSamples
config["density"] = 0.01
config["maxNumberOfIterations"] = 4
config["minEnergyExplained"] = 0.99
config["samplingFrequency"] = samplingFrequency
config["minNFFT"] = 2 * samplingFrequency
dictionary = generateDictionary(time, config)
book = calculateMP(dictionary, signal, config)
flags = {}
flags["useAsymA"] = 1
flags["useRectA"] = 0
flags["useGradientOptimization"] = 1
flags["displayInfo"] = 0
config["flags"] = flags
advancedDictionary = generateDictionary(time, config)
advancedBook = calculateMP(advancedDictionary, advancedSignal, config)
flags = {}
flags["useAsymA"] = 1
flags["useRectA"] = 1
flags["useGradientOptimization"] = 1
# config for a dictionary and MP
flags = {}
flags['useAsymA'] = 0
flags['useRectA'] = 0
flags['useGradientOptimization'] = 1
config = {}
config['flags'] = flags
config['algorithm'] = 'mmp'
config['trials2calculate'] = range(info['numberOfTrials'])
# config['channels2calculate'] = range(info['numberOfChannels'])
config['channels2calculate'] = range(5)
config['minS'] = 100
config['maxS'] = info['numberOfSamples']
config['density'] = 0.01
config['maxNumberOfIterations'] = 1
config['minEnergyExplained'] = 0.99
config['samplingFrequency'] = info['samplingFreq']
config['minNFFT'] = 1024
# optional config for t-f map drawing
# config['mapFreqRange'] = [0.0 , samplingFrequency/2]
# config['mapStructFreqs'] = [0.0 , samplingFrequency/2]
# config['mapStructSigmas'] = [0.0 , 4.0]
dictionary = generateDictionary(time , config)
book = calculateMP(dictionary , data , config)
config['flags']['useGradientOptimization'] = 1
# config for display
# config['flags']['drawMeanMap'] = 0
# config['flags']['saveMeanMap'] = 0
# config['flags']['drawSingleMaps'] = 0
# config['flags']['saveSingleMaps'] = 0
config['mapFreqRange'] = [0.0 , 64.0]
config['mapStructFreqs'] = [0.0 , 64.0]
config['mapStructSigmas'] = [0.0 , 20.0]
dictionary = generateDictionary(time , config)
for ind1 in np.arange(data.shape[0]):
book = calculateMP(dictionary , data[ind1,:] , config)
# print book
break
(T,F,TFmap) = calculateTFMap(book,time,config['samplingFrequency'],0,config['mapStructFreqs'],config['mapStructSigmas'])
# results = {}
# results['mapM'] = TFmap
# results['mapT'] = time
# results['mapF'] = F
fig = plt.figure()
plt.subplot(3,1,1)
m = plt.imshow(np.abs(TFmap) , aspect='auto' , origin='lower' , extent=[0.0,numberOfSamples/samplingFrequency , 0.0,samplingFrequency/2])
plt.ylim(config['mapFreqRange'])