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 __load_atoms_variance(self): (self.atoms_variance, self.atoms_variance_mask) = svdAnalysis.encarnateAtomsVariance(self.__varianceFileName, self.__maskFileName)