def saveNormalizedFiles4AllUsers(listOfUsersNames, sizeSingleUserSpectrum, normalizedSpectrumOfAllUsers, outputPath, matInternalName ='fixed_spectrum_matrix'):
for k in range(1, len(listOfUsersNames) + 1):
indexStart = (k - 1) * sizeSingleUserSpectrum[1]
indexEnd = (k) * sizeSingleUserSpectrum[1]
new_spectrum = normalizedSpectrumOfAllUsers[:, indexStart: indexEnd]
savePath = "".join([outputPath, listOfUsersNames[k - 1]])
saver.saveMatrixAsMat(new_spectrum, savePath, matInternalName)
def cutSpectrumMat(audioRecordingId,
inputSpectrumFolder,
outputSpectrumSlicesFolder,
sizeSquareImages,
additionalText='_spectrum.mat',
structName='new_spectrum'):
spectrumFilePath = "".join(
[inputSpectrumFolder, audioRecordingId, additionalText])
if (os.path.exists(spectrumFilePath)):
spectrum_data = (loader.loadMatFiles(spectrumFilePath))[structName]
sizeSpectrum = np.shape(spectrum_data)
rst = sizeSpectrum[-1] % sizeSquareImages
if (rst != 0):
cols2add = sizeSquareImages - rst
minimum = spectrum_data.min()
spectrum_data = np.hstack([
np.ones([sizeSpectrum[0], cols2add]) * minimum, spectrum_data
])
numSlices = int((np.shape(spectrum_data)[1]) / sizeSquareImages)
print("CREATING SLICES: nImages", numSlices, " for file: ",
audioRecordingId)
for sliceIndex in range(0, numSlices):
indexColumnStart = (sliceIndex) * sizeSquareImages
indexColumnEnd = ((sliceIndex + 1) * sizeSquareImages)
spectrum_data_slice = spectrum_data[
0:sizeSquareImages, indexColumnStart:indexColumnEnd]
imgOutputPath = "".join([
outputSpectrumSlicesFolder, "/", audioRecordingId, "_",
str(sliceIndex), '.mat'
])
saver.saveMatrixAsMat(spectrum_data_slice,
imgOutputPath,
nameMatrixMatlab=structName)
def saveNormalizedFunctionals4allUsers(listOfUsersNames, sizeSingleUserSamples, normalizedFunctionalsOfAllUsers, outputPath, matInternalName ='new_functionals', text2add = ''):
for k in range(1, len(listOfUsersNames) + 1):
indexStart = (k - 1) * sizeSingleUserSamples
indexEnd = (k) * sizeSingleUserSamples
new_functionals = normalizedFunctionalsOfAllUsers[indexStart: indexEnd, :]
if('.arff' in listOfUsersNames[k - 1]):
nameNormalizedFile = listOfUsersNames[k - 1].split('.arff')[0]
else:
nameNormalizedFile = listOfUsersNames[k - 1]
new_functionals = new_functionals.transpose() #guardamos el traspuesta con features como filas para que la creación de imágenes sea más fácil
savePath = "".join([outputPath, nameNormalizedFile,text2add])
saver.saveMatrixAsMat(new_functionals, savePath, matInternalName)
def normPercentileZscorePerUser(input_file_path, typeOfNormalization, newSupValue, newInfValue, outputPath,text2add, percentile):
functionals_data = (loader.loadMatFiles(input_file_path))['fixed_spectrum_matrix']
output_spectrum_name = (input_file_path.split(settings.operative_system)[-1].split('.mat')[0]) + text2add
#functionals_data_modi = np.reshape(functionals_data, [-1])
# plt.figure()
# plt.hist(functionals_data_modi, bins = 200)
#plt.show()
#nameFig = "".join([outputPath, (input_file_path.split('/')[-1].split('.mat')[0]), '_distribution.png'])
#plt.savefig(nameFig)
#zscore
if (typeOfNormalization == 7): # total of the whole audio file
meanValue = functionals_data.mean()
stdValue = functionals_data.std()
else:
print("Error in type of normalization")
return -1
zscore_data = getZscoreData(functionals_data, meanValue, stdValue) #no debería importar
#zscore_data_modi = np.reshape(zscore_data, [-1])
# plt.figure()
# plt.hist(zscore_data_modi, bins = 200)
#plt.show()
#percentile
percentileValue = np.percentile(zscore_data, percentile)
zscore_data[zscore_data < percentileValue] = percentileValue
# zscore_data_modiP = np.reshape(zscore_data, [-1])
# plt.figure()
# plt.hist(zscore_data_modiP, bins=200)
# plt.show()
#normalization
if (typeOfNormalization == 7): # total of the whole audio file
minimumValue = zscore_data.min()
maximumValue = zscore_data.max()
else:
print("Error in type of normalization")
return -1
new_spectrum = getNormalizedData(zscore_data, minimumValue, maximumValue, newSupValue=newSupValue, newInfValue=newInfValue)
# img = Image.fromarray(new_spectrum[:, :].astype('uint8'), 'L')
# imgOutputPath = '/home/cris/PycharmProjects/MasterProject/data/RESULTS/otrasPruebas/zscore.png'
# img.save(imgOutputPath, "PNG")
# plt.figure()
# plt.imshow(new_spectrum)
# plt.show()
#new_spectrum_modi = np.reshape(new_spectrum, [-1])
# plt.figure()
# plt.hist(new_spectrum_modi, bins = 200)
#plt.show()
savePath = "".join([outputPath, output_spectrum_name])
saver.saveMatrixAsMat(new_spectrum, savePath, 'fixed_spectrum_matrix')
return meanValue, stdValue, minimumValue, maximumValue
def normalizationPerUser(input_file_path,typeOfNormalization,newSupValue,newInfValue,outputPath, text2add='_norm0255.mat', name = 'fixed_spectrum_matrix', matInternalName = 'fixed_spectrum_matrix'):
functionals_data = (loader.loadMatFiles(input_file_path))[name]
if (typeOfNormalization == 1): # total of the whole audio file
minimumValue = functionals_data.min()
maximumValue = functionals_data.max()
elif (typeOfNormalization == 2): # min/max per frequency (160, 224 or 672 depending on number of FFT points)
minimumValue = functionals_data.min(1)
maximumValue = functionals_data.max(1)
minimumValue = np.reshape(minimumValue, [-1, 1])
maximumValue = np.reshape(maximumValue, [-1, 1])
else:
print("Error in type of normalization")
return -1
new_spectrum = getNormalizedData(functionals_data,minimumValue,maximumValue,newSupValue,newInfValue)
#save data
output_spectrum_name = (input_file_path.split('/')[-1].split('.mat')[0]) + text2add
savePath = "".join([outputPath, output_spectrum_name])
saver.saveMatrixAsMat(new_spectrum, savePath, matInternalName)
return minimumValue,maximumValue
def normPercentilePerUser(input_file_path,typeOfNormalization,newSupValue,newInfValue,outputPath, text2add='_norm0255.mat', percentile = 25):
functionals_data = (loader.loadMatFiles(input_file_path))['fixed_spectrum_matrix']
minimumValue=[]
maximumValue=[]
percentileValue = np.percentile(functionals_data, percentile)
functionals_data[functionals_data < percentileValue] = percentileValue
if (typeOfNormalization == 5): # total of the whole audio file
minimumValue = functionals_data.min()
maximumValue = functionals_data.max()
else:
print("Error in type of normalization")
return -1
output_spectrum_name = (input_file_path.split('/')[-1].split('.mat')[0]) + text2add
new_spectrum = getNormalizedData(functionals_data, minimumValue, maximumValue, newSupValue, newInfValue)
#img = Image.fromarray(new_spectrum[:, :].astype('uint8'), 'L')
# imgOutputPath = '/home/cris/PycharmProjects/MasterProject/data/RESULTS/otrasPruebas/norm.png'
# img.save(imgOutputPath, "PNG")
savePath = "".join([outputPath, output_spectrum_name])
saver.saveMatrixAsMat(new_spectrum, savePath, 'fixed_spectrum_matrix')
def zscorePerUser(input_file_path,typeOfNormalization,newSupValue,newInfValue,outputPath,zscoreThreshold=None, text2add='_zscore0255.mat', name = 'fixed_spectrum_matrix', newName ='fixed_spectrum_matrix' ):
functionals_data = (loader.loadMatFiles(input_file_path))[name]
meanValue = []
stdValue = []
minimumValue = []
maximumValue = []
if (typeOfNormalization == 3): # total of the whole audio file
meanValue = functionals_data.mean()
stdValue = functionals_data.std()
elif (typeOfNormalization == 4): # min/max per frequency (224 or 672 depending on number of FFT points)
meanValue = functionals_data.mean(1)
stdValue = functionals_data.std(1)
meanValue = np.reshape(meanValue, [-1, 1])
stdValue = np.reshape(stdValue, [-1, 1])
else:
print("Error in type of normalization")
return -1
output_spectrum_name = (input_file_path.split(settings.operative_system)[-1].split('.mat')[0]) + text2add
new_spectrum,minimumValue,maximumValue = getZscoreNormData(functionals_data, meanValue, stdValue, minimumValue, maximumValue, newSupValue, newInfValue, zscoreThreshold)
savePath = "".join([outputPath, output_spectrum_name])
saver.saveMatrixAsMat(new_spectrum, savePath, newName)
return meanValue, stdValue, minimumValue, maximumValue