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#/*##########################################################################
#
# 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()
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