def PerformInverseWaveletTransform(data_vector, C, M):
# print C
Result = FWT.getSignal(data_vector, C, len(data_vector), len(C), M)
# print Result1
# print Result2
Signal = np.asarray(Result)
#n=len(data_vector)/2
#FinalMatrix1.resize(M,n)
return Signal
def PerformInverseWaveletTransform(data_vector, ndatavector, C, nC, M):
"""
This functions performs the inverse wavelength transform of a data vector
"""
Result = FWT.getSignal(data_vector, C, ndatavector, nC, M)
Signal = np.asarray(Result)
return Signal
def PerformInverseWaveletTransform(data_vector,C,M): # print C Result=FWT.getSignal(data_vector,C,len(data_vector),len(C),M) # print Result1 # print Result2 Signal=np.asarray(Result) #n=len(data_vector)/2 #FinalMatrix1.resize(M,n) return Signal
def PerformWaveletTransform(data_vector, C, M):
# print C
Result1, Result2 = FWT.getWC(data_vector, C, len(data_vector), len(C), M)
# print Result1
# print Result2
FinalMatrix1 = np.asarray(Result1)
FinalMatrix2 = np.asarray(Result2)
#n=len(data_vector)/2
#FinalMatrix1.resize(M,n)
return FinalMatrix1, FinalMatrix2
def PerformWaveletTransform(data_vector,C,M): # print C Result1,Result2=FWT.getWC(data_vector,C,len(data_vector),len(C),M) # print Result1 # print Result2 FinalMatrix1=np.asarray(Result1) FinalMatrix2=np.asarray(Result2) #n=len(data_vector)/2 #FinalMatrix1.resize(M,n) return FinalMatrix1,FinalMatrix2
def PerformWaveletTransform(data_vector, ndatavector, C, nC, M):
"""
This function performs the wavelet transform of a vector of length 2**i, where i is any integer.
If your dataset does not have this length, pad zeroes at the end with the zero_pad function.
"""
Result1, Result2 = FWT.getWC(data_vector, C, ndatavector, nC, M)
FinalMatrix1 = np.asarray(Result1)
FinalMatrix2 = np.asarray(Result2)
return FinalMatrix1, FinalMatrix2
