def testStudyVariance(self):
alnFileName = "../../alignmentFiles/mmult.list-FINAL.aln"
workingDirectory = "../../alignmentFiles/"
resultsFileName2 = "../../alignmentFiles/results2.results"
generateMultipleAlignment.getResidues(0.8, resultsFileName2, alnFileName, workingDirectory, False, True, False)
AtomicresultsFileName = "../../alignmentFiles/results2.Atomic"
generateMultipleAlignment.composeAtomicMatrix(resultsFileName2, AtomicresultsFileName)
filteredAtomicMatrixFileName = "../../alignmentFiles/filteredResults2.Atomic"
generateMultipleAlignment.filterAtomicMatrix(AtomicresultsFileName, filteredAtomicMatrixFileName)
caMatrixFileName = "../../alignmentFiles/filteredResults2.Atomic"
dimensions = generateMultipleAlignment.getDimensions(caMatrixFileName)
(mask, matrix) = svdAnalysis.loadCAmatrix(caMatrixFileName, dimensions)
(caMatrix, caMask) = svdAnalysis.getCovarianceMatrix(mask, matrix)
(atoms_variance, atoms_variance_mask) = svdAnalysis.getAtomsVariance(caMatrix, caMask)
(max, min, mean, normalizedAtoms) = svdAnalysis.atoms_variance_statistics(atoms_variance, atoms_variance_mask)
print ("max %f \n" % (max,))
print ("min %f \n" % (min,))
print ("mean %f \n" % (mean,))
print normalizedAtoms
print "\n\n"
workingDirectory = "../../alignmentFiles/"
varianceFileName = workingDirectory + "variance.atomic"
maskFileName = workingDirectory + "variance_mask.atomic"
svdAnalysis.serializeAtomsVariance(atoms_variance, atoms_variance_mask, varianceFileName, maskFileName)
(atoms_variance, atoms_variance_mask) = svdAnalysis.encarnateAtomsVariance(varianceFileName, maskFileName)
assert 30 == len(atoms_variance[0])
for variance in atoms_variance[0]:
print ("%3.f \n" % (variance,))
def test5getCovarianceMatrix(self):
mask = numpy.array([[False, False, False, True, True, True], [True, True, True, True, True, True]])
# mask = numpy.array([[True, True, True, True, True, True],[True, True, True, True, True, True]])
matrix = numpy.array([[0, 1, 2, 3, 4, 5], [10, 11, 12, 13, 14, 15]], dtype=float)
(caMatrix, caMask) = svdAnalysis.getCovarianceMatrix(mask, matrix, True)
print ("\n caMatrix\n")
print caMatrix
print ("\ncaMask\n")
print caMask
(ca_atoms, ca_mask) = svdAnalysis.getAtomsVariance(caMatrix, caMask)
def testGlobal(self):
caMatrixFileName = "../../alignmentFiles/filteredResults2.Atomic"
dimensions = generateMultipleAlignment.getDimensions(caMatrixFileName)
print ("%d %d\n" % (dimensions[0], dimensions[1]))
(mask, matrix) = svdAnalysis.loadCAmatrix(caMatrixFileName, dimensions)
(caMatrix, caMask) = svdAnalysis.getCovarianceMatrix(mask, matrix)
print ("%d %d\n" % (caMatrix.shape[0], caMatrix.shape[1]))
matrixFileName = "../../alignmentFiles/matrix.float"
maskFileName = "../../alignmentFiles/mask.float"
# print("\n caMatrix\n")
# print caMatrix
# print("\ncaMask\n")
# print caMask
svdAnalysis.serialize_matrix(caMatrix, matrixFileName)
diagonalFileName = "../../alignmentFiles/diagonal.float"
(ca_atoms, ca_mask) = svdAnalysis.getAtomsVariance(caMatrix, caMask)
svdAnalysis.serialize_matrix(ca_atoms, diagonalFileName)
svdAnalysis.serialize_matrix(ca_mask, maskFileName)
def __load_variability_measures(self):
(self.__mask, self.__matrix) = svdAnalysis.loadCAmatrix(self.__filteredAtomicMatrixFileName, self.dimensions)
(self.__covMatrix, self.__covMask) = svdAnalysis.getCovarianceMatrix(self.__mask, self.__matrix)
(self.atoms_variance, self.atoms_variance_mask) = svdAnalysis.getAtomsVariance(self.__covMatrix, self.__covMask)
