def test_windowingSignal(self):
sig, force, fs, firings = getSignal(1, 30, "../signals/")
preparedFirings = prepareFiringSignal(firings[0], len(sig[0, 0][0]))
sdSig = calculateSD(sig)
signal, label = windowingSig(sdSig, preparedFirings, windowSize=15)
TestCase.assertTrue(self, len(signal) > 0)
TestCase.assertTrue(self, len(label) > 0)
def test_featureSelection(self):
sig, force, fs, firings = getSignal(1, 30, "../signals/")
sdSig = calculateSD(sig)
whitenSdSig = calculateWhiten(sdSig)
preparedFirings = prepareFiringSignal(firings[0], len(sig[0, 0][0]))
signalWindow, labelWindow = windowingSig(whitenSdSig, preparedFirings, windowSize=124)
signalWindow, labelWindow = calculateNormalizedOnCorr(signalWindow, labelWindow)
featureExtracted=extractFeatures(signalWindow)
TestCase.assertTrue(self, len(featureExtracted) > 0)
def test_train_test_split(self):
sig, force, fs, firings = getSignal(1, 30, "../signals/")
preparedFirings = prepareFiringSignal(firings[0], len(sig[0, 0][0]))
sdSig = calculateSD(sig)
signalWindow, labelWindow = windowingSig(sdSig, preparedFirings, windowSize=15)
X_train, X_test, y_train, y_test = trainTestSplit(signalWindow, labelWindow, 0.7)
TestCase.assertTrue(self, len(X_train) > 0)
TestCase.assertTrue(self, len(X_test) > 0)
TestCase.assertTrue(self, len(y_train, ) > 0)
TestCase.assertTrue(self, len(y_test) > 0)
def test_CKC(self):
sig, force, fs, firings = getSignal(1, 30, "../signals/")
sdSig = calculateSD(sig)
whitenSdSig = calculateWhiten(sdSig)
preparedFirings = prepareFiringSignal(firings[0], len(sig[0, 0][0]))
signalWindow, labelWindow = windowingSig(whitenSdSig, preparedFirings, windowSize=124)
index = 50
Rx = autocorr(signalWindow[index])
t = pinv(Rx) @ signalWindow[index]
plt.plot(t[1, :])
plt.show()
TestCase.assertTrue(self, len(t) > 0)
def prepareDataWithForce(sig, force):
sdSig = calculateSD(sig)
sdSig = butter_bandpass_filter(sdSig, 20, 450, fs2)
# sdSig = calculateICA(sdSig, 64)
signalWindow, labelWindow = windowingSig(
sdSig, force, windowSize=maxFeatures) # should be divideable to 4
# signalWindow = calculateFFTOnWindows(signalWindow)
labelWindow = calculateForceAverage(labelWindow)
# signalWindow, labelWindow = calculateNormalizedOnCorr(signalWindow, labelWindow)
# signalWindow, labelWindow = calculateICA(signalWindow, labelWindow, 4)
# signalWindow = extractPhaseSpace(signalWindow)
# signalWindow = extractSoundFeatures(signalWindow)
return signalWindow, labelWindow
def prepareData(sig, firings):
sdSig = calculateSD(sig)
# sdSig = calculateICA(sdSig, 64)
sdSig = butter_bandpass_filter(sdSig, 20, 450, 2048)
sizeInputSignal = sdSig.shape[1]
preparedFirings = prepareFiringSignal(firings[0], sizeInputSignal)
signalWindow, labelWindow = windowingSig(
sdSig, preparedFirings,
windowSize=maxFeatures) # should be divideable to 4
# signalWindow = calculateFFTOnWindows(signalWindow)
labelWindow = calculateCdr(labelWindow)
# signalWindow, labelWindow = calculateNormalizedOnCorr(signalWindow, labelWindow)
# signalWindow, labelWindow = calculateICA(signalWindow, labelWindow, 4)
signalWindow = extractPhaseSpace(signalWindow)
# signalWindow = extractSoundFeatures(signalWindow)
return signalWindow, labelWindow
def prepareData(self, sig, firings):
sdSig = calculateSD(sig)
sdSig = scale(sdSig, axis=0)
# sdSig = calculateICA(sdSig, 64)
sdSig = butter_bandpass_filter(sdSig, 20, 450, 2048)
sizeInputSignal = sdSig.shape[0]
preparedFirings = prepareFiringSignal(firings[0],
sizeInputSignal,
numSignals=12)
signalWindow, labelWindow = windowingSig(
sdSig.transpose(), preparedFirings,
windowSize=256) # should be divideable to 4
# signalWindow = calculateFFTOnWindows(signalWindow)
# labelWindow = convertLabel2OneDimentional(labelWindow)
# signalWindow, labelWindow = calculateNormalizedOnCorr(signalWindow, labelWindow)
# signalWindow, labelWindow = calculateICA(signalWindow, labelWindow, 18)
# signalWindow = extractPhaseSpace(signalWindow)
# signalWindow = extractSoundFeatures(signalWindow)
return signalWindow, np.asarray(labelWindow)
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv1D, MaxPooling1D
from keras.optimizers import SGD, RMSprop
from keras.layers.advanced_activations import LeakyReLU
import numpy as np
from Utils.SignalUtils import getSignal, calculateSD, prepareFiringSignal, windowingSig, trainTestSplit, \
calculateCdr, calculateNormalizedOnCorr
sig, force, fs, firings = getSignal(1, 30, "../signals/")
sdSig = calculateSD(sig)
# sdSig = calculateICA(sdSig, 64)
sizeInputSignal = sdSig.shape[1]
preparedFirings = prepareFiringSignal(firings[0], sizeInputSignal)
signalWindow, labelWindow = windowingSig(sdSig, preparedFirings, windowSize=8) # should be divideable to 4
labelWindow = calculateCdr(labelWindow)
signalWindow = calculateNormalizedOnCorr(signalWindow)
x_train, x_test, y_train, y_test = trainTestSplit(signalWindow, labelWindow, 0.7)
# convert to one-hot vector
# y_train = to_categorical(y_train)
# y_test = to_categorical(y_test)
signal_size_row = x_train[0].shape[0]
signal_size_col = x_train[0].shape[1]
x_train = np.reshape(x_train, [-1, signal_size_row, signal_size_col])
x_test = np.reshape(x_test, [-1, signal_size_row, signal_size_col])
input_shape = (signal_size_row, signal_size_col)
model = Sequential()
model.add(Conv1D(16, 5, padding='same', input_shape=input_shape))
def test_save_data_as_images(self):
sig, force, fs, firings = getSignal(1, 30, "../signals/")
preparedFirings = prepareFiringSignal(firings[0], len(sig[0, 0][0]))
sdSig = calculateSD(sig)
signalWindow, labelWindow = windowingSig(sdSig, preparedFirings, windowSize=15)
saveDataAsImage(signalWindow, labelWindow)