#/*########################################################################## # # The PyMca X-Ray Fluorescence Toolkit # # Copyright (c) 2004-2019 European Synchrotron Radiation Facility # # This file is part of the PyMca X-ray Fluorescence Toolkit developed at # the ESRF by the Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "V. Armando Sole - ESRF Data Analysis" __contact__ = "sole@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import unittest import numpy import numpy.linalg try: import mdp MDP = True except: # MDP can give very weird errors MDP = False class testPCATools(unittest.TestCase): def testPCAToolsImport(self): from PyMca5.PyMcaMath.mva import PCATools def testPCAToolsCovariance(self): from PyMca5.PyMcaMath.mva.PCATools import getCovarianceMatrix x = numpy.array([[0.0, 2.0, 3.0], [3.0, 0.0, -1.0], [4.0, -4.0, 4.0], [4.0, 4.0, 4.0]]) nSpectra = x.shape[0] # test just multiplication tmpArray = numpy.dot(x.T, x) for force in [True, False]: pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x, force=force, center=False) self.assertTrue(numpy.allclose(tmpArray, pymcaCov * (nData - 1))) # calculate covariance using numpy numpyCov = numpy.cov(x.T) numpyAvg = x.sum(axis=0).reshape(-1, 1) / nSpectra tmpArray = x.T - numpyAvg numpyCov2 = numpy.dot(tmpArray, tmpArray.T) / nSpectra numpyAvg = numpyAvg.reshape(1, -1) # calculate covariance using PCATools and 2D stack # directly and dynamically loading data for force in [False, True]: pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x, force=force, center=True) self.assertTrue(numpy.allclose(numpyCov, pymcaCov)) self.assertTrue(numpy.allclose(numpyAvg, pymcaAvg)) self.assertTrue(nData == nSpectra) # calculate covariance using PCATools and 3D stack # directly and dynamically loading data x.shape = 2, 2, -1 for force in [False, True]: pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x, force=force, center=True) self.assertTrue(numpy.allclose(numpyCov, pymcaCov)) self.assertTrue(numpy.allclose(numpyAvg, pymcaAvg)) self.assertTrue(nData == nSpectra) def testPCAToolsPCA(self): from PyMca5.PyMcaMath.mva.PCATools import numpyPCA x = numpy.array([[0.0, 2.0, 3.0], [3.0, 0.0, -1.0], [4.0, -4.0, 4.0], [4.0, 4.0, 4.0]]) # that corresponds to 4 spectra of 3 channels nSpectra = x.shape[0] # calculate eigenvalues and eigenvectors with numpy tmpArray = numpy.dot(x.T, x)/(nSpectra - 1) numpyEigenvalues, numpyEigenvectors = numpy.linalg.eigh(tmpArray) # sort from higher to lower idx = list(range(numpyEigenvalues.shape[0]-1, -1 , -1)) numpyEigenvalues = numpy.take(numpyEigenvalues, idx) numpyEigenvectors = numpyEigenvectors[:, ::-1].T # now use PyMca # centering has to be false to obtain the same results ncomp = x.shape[1] for force in [True, False]: images, eigenvalues, eigenvectors = numpyPCA(x, ncomponents=ncomp, force=force, center=False, scale=False) self.assertTrue(numpy.allclose(eigenvalues, numpyEigenvalues)) for i in range(ncomp): if (eigenvectors[i,0] >= 0 and numpyEigenvectors[i,0] >=0) or\ (eigenvectors[i,0] <= 0 and numpyEigenvectors[i,0] <=0): # both same sign self.assertTrue(numpy.allclose(eigenvectors[i], numpyEigenvectors[i])) else: self.assertTrue(numpy.allclose(-eigenvectors[i], numpyEigenvectors[i])) # test with a different shape x.shape = 2, 2, -1 ncomp = 3 for force in [True, False]: images, eigenvalues, eigenvectors = numpyPCA(x, ncomponents=ncomp, force=force, center=False, scale=False) self.assertTrue(numpy.allclose(eigenvalues, numpyEigenvalues)) for i in range(ncomp): if (eigenvectors[i,0] >= 0 and numpyEigenvectors[i,0] >=0) or\ (eigenvectors[i,0] <= 0 and numpyEigenvectors[i,0] <=0): # both same sign self.assertTrue(numpy.allclose(eigenvectors[i], numpyEigenvectors[i])) else: self.assertTrue(numpy.allclose(-eigenvectors[i], numpyEigenvectors[i])) if MDP: def testPCAToolsMDP(self): from PyMca5.PyMcaMath.mva.PCATools import getCovarianceMatrix, numpyPCA x = numpy.array([[0.0, 2.0, 3.0], [3.0, 0.0, -1.0], [4.0, -4.0, 4.0], [4.0, 4.0, 4.0]]) # use mdp pcaNode = mdp.nodes.PCANode() pcaNode.train(x) pcaNode.stop_training() pcaEigenvectors = pcaNode.v.T # and compare with PyMca ncomp = x.shape[1] for force in [True, False]: images, eigenvalues, eigenvectors = numpyPCA(x, ncomponents=ncomp, force=force, center=True, scale=False) # the eigenvalues must be the same self.assertTrue(numpy.allclose(eigenvalues, pcaNode.d)) # the eigenvectors can be multiplied by -1 for i in range(ncomp): if (eigenvectors[i,0] >= 0 and pcaEigenvectors[i,0] >=0) or\ (eigenvectors[i,0] <= 0 and pcaEigenvectors[i,0] <=0): # both same sign self.assertTrue(numpy.allclose(eigenvectors[i], pcaEigenvectors[i])) else: self.assertTrue(numpy.allclose(-eigenvectors[i], pcaEigenvectors[i])) def getSuite(auto=True): testSuite = unittest.TestSuite() if auto: testSuite.addTest(\ unittest.TestLoader().loadTestsFromTestCase(testPCATools)) else: # use a predefined order testSuite.addTest(testPCATools("testPCAToolsImport")) testSuite.addTest(testPCATools("testPCAToolsCovariance")) testSuite.addTest(testPCATools("testPCAToolsPCA")) if MDP: testSuite.addTest(testPCATools("testPCAToolsMDP")) return testSuite def test(auto=False): unittest.TextTestRunner(verbosity=2).run(getSuite(auto=auto)) if __name__ == '__main__': test()