def testFilterWavelet(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)
N = 10
Xw2, inds = MetabolomicsUtils.filterWavelet(Xw, N)
tol = 10**-6
self.assertEquals(inds.shape[0], N)
self.assertTrue(numpy.linalg.norm( Xw[:, inds] - Xw2[:, inds] ) < tol)
zeroInds = numpy.setdiff1d(numpy.arange(Xw.shape[1]), inds)
self.assertTrue(numpy.linalg.norm(Xw2[:, zeroInds]) < tol)
dataDir = PathDefaults.getDataDir() + "metabolomic/"
X, X2, Xs, XOpls, YList, ages, df = MetabolomicsUtils.loadData()
mode = "cpd"
level = 10
XwDb4 = MetabolomicsUtils.getWaveletFeatures(X, 'db4', level, mode)
XwDb8 = MetabolomicsUtils.getWaveletFeatures(X, 'db8', level, mode)
XwHaar = MetabolomicsUtils.getWaveletFeatures(X, 'haar', level, mode)
#Filter the wavelets
Ns = [10, 25, 50, 75, 100]
dataList = []
for i in range(len(Ns)):
N = Ns[i]
XwDb4F, inds = MetabolomicsUtils.filterWavelet(XwDb4, N)
dataList.append((XwDb4F[:, inds], "Db4-" + str(N)))
XwDb8F, inds = MetabolomicsUtils.filterWavelet(XwDb8, N)
dataList.append((XwDb8F[:, inds], "Db8-" + str(N)))
XwHaarF, inds = MetabolomicsUtils.filterWavelet(XwHaar, N)
dataList.append((XwHaarF[:, inds], "Haar-" + str(N)))
dataList.extend([(Xs, "raw_std"), (XwDb4, "Db4"), (XwDb8, "Db8"), (XwHaar, "Haar"), (X2, "log"), (XOpls, "opls")])
#Data for functional TreeRank
dataListF = [(XwDb4, "Db4"), (XwDb8, "Db8"), (XwHaar, "Haar")]
dataListPCA = ([(Xs, "raw_std"), (XwDb4, "Db4"), (XwDb8, "Db8"), (XwHaar, "Haar"), (X2, "log"), (XOpls, "opls")])
lock = multiprocessing.Lock()
Ns = range(0, 700, 50)
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, :])