# silence, = plt.plot(ecogSilence[expSentence].ix[:,0].reset_index(drop = True)) # plt.legend([speaking, silence],["Speaking neural signal", "Silence neural signal"]) # plt.savefig("path/to/saving/place") #============================================================================== # Data Scaling(new data; Parallelized) #============================================================================== # Scaling ecogNewScaled = DataManipulation.ScalingBySilenceNew(ecogNew, ecogSilence, rawIntervals) # UserDefinedTest.ScaledDataTestNew(ecogNewScaled) #for i in range(70): # plt.plot(ecogNewScaled[expSentence].ix[:,i].reset_index(drop = True)) # phone labeling ecogNewScaledLabled = DataManipulation.PhoneLabelingNew( ecogNewScaled, rawIntervals) # Output DataManipulation.ExportScalingDataNew(ecogNewScaledLabled) #============================================================================== # Data Loading(pandas trunk reading & HDF5) #============================================================================== sentenceTrain = load.loadOrderedSentence() # sentence ecogTrain = load.loadEcog() # ecog data(neural signal) fileName = 'AlignedTime.txt' rawIntervals = load.loadPhone(fileName) # raw split point data #============================================================================== # Data Scaling(Parallelized)