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'])