# 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)
