def testReconstructSignal(self):
numExamples = 100
numFeatures = 16
X = numpy.random.rand(numExamples, numFeatures)
level = 10
mode = "cpd"
waveletStr = "db4"
C = pywt.wavedec(X[0, :], waveletStr, mode, level=10)
Xw = MetabolomicsUtils.getWaveletFeatures(X, waveletStr, level, mode)
X2 = MetabolomicsUtils.reconstructSignal(X, Xw, waveletStr, mode, C)
tol = 10**-6
self.assertTrue(numpy.linalg.norm(X - X2) < tol)
waveletStrs = ['haar', 'db4', 'db8']
errors = numpy.zeros((len(waveletStrs), len(Ns)))
mode = "cpd"
standardiser = Standardiser()
#X = standardiser.centreArray(X)
for i in range(len(waveletStrs)):
waveletStr = waveletStrs[i]
Xw = MetabolomicsUtils.getWaveletFeatures(X, waveletStr, level, mode)
C = pywt.wavedec(X[0, :], waveletStr, level=level, mode=mode)
for j in range(len(Ns)):
N = Ns[j]
Xw2, inds = MetabolomicsUtils.filterWavelet(Xw, N)
X2 = MetabolomicsUtils.reconstructSignal(X, Xw2, waveletStr, mode, C)
errors[i, j] = numpy.linalg.norm(X - X2)
#Plot example wavelet after filtering
waveletStr = "haar"
N = 100
Xw = MetabolomicsUtils.getWaveletFeatures(X, waveletStr, level, mode)
C = pywt.wavedec(X[0, :], waveletStr, level=level, mode=mode)
Xw2, inds = MetabolomicsUtils.filterWavelet(Xw, N)
X2 = MetabolomicsUtils.reconstructSignal(X, Xw2, waveletStr, mode, C)
plt.figure(3)
plt.plot(range(X.shape[1]), X[0, :])
plt.plot(range(X.shape[1]), X2[0, :])
