def __init__(self, dcdfile, pdbfile, selection=None, verbose=False):
self.file = dcdfile
self.verbose = verbose
self.selectDict = {
None: None,
"all": None,
"bb": ['N', 'CA', 'C', 'O'],
"backbone": ['N', 'CA', 'C', 'O'],
"ca": ['CA'],
"trace": ['CA']
}
self.sel = selection if hasattr(selection,
"pop") else self.selectDict[selection]
if hasattr(pdbfile, "pdbfile"):
self.pdbfile = pdbfile
else:
self.pdbfile = pdbReader.PdbReader(
pdbfile if hasattr(pdbfile, "read") else open(pdbfile, 'r'))
self.array = None
self.mean = None
self.covariance = None
self.correlation = None
self.indices = self.pdbfile.getIndices(self.sel)
self.natom = len(self.indices)
self.nframe = 0
self.header = {}
self.load()
def move(self, eigenvalue, eigenvector, pdbFileName, clusterVarComparison=None, outPdbFile='move.pdb', append=False, modelNumber=0, continuousScale=False, ca = True, bb = False):
pdbFile = open(pdbFileName)
pdbR = pdbReader.PdbReader(pdbFile)
if clusterVarComparison is None:
p = abs(eigenvalue*eigenvector)
else:
cl1,cl2,var=clusterVarComparison
# print var.shape,eigenvector.shape
c = (1.*(eigenvector == cl1)+1.*(eigenvector == cl2))
p = var* (numpy.array([c,c,c]).transpose())
pdbFile = open(pdbFileName)
pdbW = pdbWriter.PdbWriter(pdbFile)
pdbW.alterCoordinates(p, outPdbFile, append=append, modelNumber=modelNumber, ca = ca, bb = bb)
def projection(self,
eigenvalue,
eigenvector,
pdbFileName,
clusterVarComparison=None,
outPdbFile='projection.pdb',
append=False,
modelNumber=0,
continuousScale=False,
ca=True,
bb=False):
pdbFile = open(pdbFileName)
pdbR = pdbReader.PdbReader(pdbFile)
if clusterVarComparison is None:
p = abs(eigenvalue * eigenvector)
else:
cl1, cl2, var = clusterVarComparison
# print var.shape,eigenvector.shape
p = var * ((1. * (eigenvector == cl1) - 1. * (eigenvector == cl2)))
pdbFile = open(pdbFileName)
pdbW = pdbWriter.PdbWriter(pdbFile)
pdbW.addBFactor(p,
outPdbFile,
append=append,
modelNumber=modelNumber,
ca=ca,
bb=bb)
pymolScript = open(outPdbFile.replace('.pdb', '.pml'), 'w')
max = numpy.max(eigenvector)
if not continuousScale:
pymolScript.write("""
load %s
hide everything
show cartoon
spectrum b, minimum=0, maximum=%s
""" % (outPdbFile, max))
else:
pymolScript.write("""
load %s
hide everything
show cartoon
spectrum b, blue_white_red, minimum=-1, maximum=1
alter all, b=str(abs(b))
cartoon putty
unset cartoon_smooth_loops
unset cartoon_flat_sheets
""" % (outPdbFile))
pymolScript.close()
def write3Dcorr(self,
matrix,
pdbFileName,
outFileName='corrCoords.txt',
threshold=0.0,
anticorr=True):
mm = matriceManipulation.matrix(matrix)
matrix = mm.symmetryZeros()
def sorter(matrix, anticorr):
l = numpy.ravel(matrix)
sl = numpy.sort(l)
lsl = list(sl)
if not anticorr:
lsl.reverse()
return lsl
def replace(pn, matrix):
"""
pn = +1|-1
"""
for i in range(numpy.shape(matrix)[0]):
for j in range(numpy.shape(matrix)[1]):
e = matrix[i, j]
if pn * e > 0:
matrix[i, j] = 0
return matrix
pdbFile = open(pdbFileName)
pdbR = pdbReader.PdbReader(pdbFile)
caCoords = pdbR.getCAcoord()
resSeqs = pdbR.getResSeqs()
if anticorr == True:
matrix = replace(1, matrix)
else:
matrix = replace(-1, matrix)
lCorrSorted = sorter(matrix, anticorr)
outFile = open(outFileName, 'w')
outFile.write('Sorted Ca coordinates from the most correlated\n')
outFile.write('resSeq1'.rjust(10) + 'resSeq2'.rjust(10) +
'Pearson'.rjust(10) + 'x1'.rjust(10) + 'y1'.rjust(10) +
'z1'.rjust(10) + 'x2'.rjust(10) + 'y2'.rjust(10) +
'z2'.rjust(10) + '\n')
for e in lCorrSorted:
if abs(e) > abs(threshold):
ij = numpy.where(matrix == e)
jValues = itertools.chain(ij[1])
for i in ij[0]:
j = jValues.next()
iCoord = caCoords[i]
jCoord = caCoords[j]
outFile.write(('%s' % resSeqs[i]).rjust(10) +
('%s' % resSeqs[j]).rjust(10) +
('%.3f' % e).rjust(10) +
('%.3f' % iCoord[0]).rjust(10) +
('%.3f' % iCoord[1]).rjust(10) +
('%.3f' % iCoord[2]).rjust(10) +
('%.3f' % jCoord[0]).rjust(10) +
('%.3f' % jCoord[1]).rjust(10) +
('%.3f' % jCoord[2]).rjust(10) + '\n')
else:
break