def trialMakePolyList(v): # [i think this is experimental code by Huaicai, never called, perhaps never tested. -- bruce 051117]
pMat = []
for i in range(size(v)):
pMat += [polyMat[i] * v[i]]
segs = []
for corner, edges, planes in polyTab:
pts = size(planes)
for i in range(pts):
segs += [pMat[corner], pMat[planes[i]]]
segs += [pMat[planes[i]], pMat[planes[(i+1)%pts]]]
return segs
def trialMakePolyList(
v
): # [i think this is experimental code by Huaicai, never called, perhaps never tested. -- bruce 051117]
pMat = []
for i in range(size(v)):
pMat += [polyMat[i] * v[i]]
segs = []
for corner, edges, planes in polyTab:
pts = size(planes)
for i in range(pts):
segs += [pMat[corner], pMat[planes[i]]]
segs += [pMat[planes[i]], pMat[planes[(i + 1) % pts]]]
return segs
def povStrVec(va): # review: refile in povheader or so? [bruce 071215 comment]
# used in other modules too
rstr = '<'
for ii in range(size(va)):
rstr += str(va[ii]) + ', '
return rstr
def povStrVec(va): # review: refile in povheader or so? [bruce 071215 comment]
# used in other modules too
rstr = '<'
for ii in range(size(va)):
rstr += str(va[ii]) + ', '
return rstr
def __alignMatrixDimension(self, cm, thisSeq, castSeq, axis=0):
"""
Correct one dimension of contactMatrix by inserting and deleting
columns, so that it can be later compared to contact matrices based
on slightly different sequences.
@param cm: contact matrix, 2D matrix of residue contacts
recceptor x ligand sequence
@type cm: array
@param thisSeq: AA sequence of this dimension of the contactMatrix
@type thisSeq: string
@param castSeq: AA sequence of this dimension in the other contact
@type castSeq: string
@param axis: which dimension to adapt (0=receptor, 1=ligand)
@type axis: 1|0
@return: contact matrix with residue contacts compatible to refSeq.
@rtype: 2D array
"""
# compare the two sequences
seqdiff = SequenceMatcher(None, thisSeq, castSeq)
seqDiff = seqdiff.get_opcodes()
## print seqDiff
# decide which dimension to work on
if not axis:
cm = N.transpose( cm )
seqCount = 0 # keep track of sequence length changes
i=0
for list in seqDiff:
# remove the column corresponding to the deletion in the
# docked sequence
if str( seqDiff[i][0] ) == 'delete':
# separate matrix into before and after deletion
matrixSeg1 = cm[ :, : seqDiff[i][1] + seqCount ]
matrixSeg2 = cm[ :, seqDiff[i][2] + seqCount : ]
# concatenate part
cm = N.concatenate( ( matrixSeg1, matrixSeg2 ), 1)
seqCount = seqCount + seqDiff[i][1] - seqDiff[i][2]
# inserts zeros in the column where there is a insertion in the
# docked sequence
if str( seqDiff[i][0] ) == 'insert':
# create a matrix to be inserted
insertZeros= seqDiff[i][4] - seqDiff[i][3]
insertColumns = N.array( [ [0] * insertZeros ] * N.size(cm,0) )
# separate matrix into before and after insertion
matrixSeg1 = cm[ :, : seqDiff[i][1] + seqCount ]
matrixSeg2 = cm[ :, seqDiff[i][2] + seqCount : ]
# concatenate parts with the zero matrix
cm = N.concatenate( (matrixSeg1,insertColumns,matrixSeg2), 1)
seqCount = seqCount + seqDiff[i][4] - seqDiff[i][3]
i=i+1
if not axis:
return N.transpose( cm )
return cm
def calc_sse_dist_matrix(self, ptnode_list):
"""
Build the matrix of SSE distance, i.e. min dist between residues
in the SSEs.
NOTE: the self-distance (i.e. matrix elements [i,i]) are set to
infinity rather than 0, so we can efficiently use argmin
in get_sse_min_distance() to find SSE (not same one) with min
distance - we are wanting to find minimum distances
in ptgraph2, not maximum distances.
Parameters:
ptnode_list - iterable over PTNode objects represneting SSEs
Return value:
None. (sets data members)
Uses data members (WRITE):
sse_dist_matrix - square symmetric
Numeric.array matrix of dimensions
len(ptnode_list) x len(ptnode_list) where each
elementis distance between the two SSEs
represented by the ptnodes,
as defined by calc_sse_dist() (min residue distance)
sse_index_map - dict of { ptnode : array_index } mapping a PTNode
object to index in sse_dist_matrix
reverse_sse_index_map - list of PTNode objects mapping
index back to PTNode
(i.e. reverse of index_map)
sse_residue_map - dict of {(ptnode1, ptnode2) : (residue1, residue2)}
which for every pair of sses gives the residue
in each which are closest (used in the distance
matrix). Note both (ptnode1,ptnode2) and
(ptnode2,ptnode1) are stored, with residues
swapped appropriately.
"""
self.sse_dist_matrix = Numeric.zeros(
(len(ptnode_list), len(ptnode_list)), Numeric.Float)
# set the self-distances to infinity (see comments above and in
# get_sse_min_distance()
# TODO: maybe if we used NaN instead of inf, this would allow
# both min/max and argmin/argmax rather than just min/argmin
# (as we actualy use) to be useful. I tried it with Python 2.5.1
# on Linux and it worked (ie NaN is neither max nor min) but
# not really sure how reliable that behaviour is... so sticking
# with inf for now since we only need min/argmin anyway.
for i in range(0, Numeric.size(self.sse_dist_matrix, 0)):
self.sse_dist_matrix[i, i] = float("inf")
self.reverse_sse_index_map = len(ptnode_list) * [-1] #will in 0..len-1
index_maplist = list(enumerate(ptnode_list))
for i in range(len(index_maplist)):
row, sse_one = index_maplist[i]
self.sse_index_map[sse_one] = row
self.reverse_sse_index_map[row] = sse_one
for j in range(i + 1, len(index_maplist)):
col, sse_two = index_maplist[j]
(dist, res_one, res_two) = self.calc_sse_dist(sse_one, sse_two)
self.sse_dist_matrix[row, col] = dist
self.sse_dist_matrix[col, row] = dist
self.sse_residue_map[sse_one, sse_two] = (res_one, res_two)
self.sse_residue_map[sse_two, sse_one] = (res_two, res_one)
def calc_sse_dist_matrix(self, ptnode_list):
"""
Build the matrix of SSE distance, i.e. min dist between residues
in the SSEs.
NOTE: the self-distance (i.e. matrix elements [i,i]) are set to
infinity rather than 0, so we can efficiently use argmin
in get_sse_min_distance() to find SSE (not same one) with min
distance - we are wanting to find minimum distances
in ptgraph2, not maximum distances.
Parameters:
ptnode_list - iterable over PTNode objects represneting SSEs
Return value:
None. (sets data members)
Uses data members (WRITE):
sse_dist_matrix - square symmetric
Numeric.array matrix of dimensions
len(ptnode_list) x len(ptnode_list) where each
elementis distance between the two SSEs
represented by the ptnodes,
as defined by calc_sse_dist() (min residue distance)
sse_index_map - dict of { ptnode : array_index } mapping a PTNode
object to index in sse_dist_matrix
reverse_sse_index_map - list of PTNode objects mapping
index back to PTNode
(i.e. reverse of index_map)
sse_residue_map - dict of {(ptnode1, ptnode2) : (residue1, residue2)}
which for every pair of sses gives the residue
in each which are closest (used in the distance
matrix). Note both (ptnode1,ptnode2) and
(ptnode2,ptnode1) are stored, with residues
swapped appropriately.
"""
self.sse_dist_matrix =Numeric.zeros((len(ptnode_list),len(ptnode_list)),
Numeric.Float)
# set the self-distances to infinity (see comments above and in
# get_sse_min_distance()
# TODO: maybe if we used NaN instead of inf, this would allow
# both min/max and argmin/argmax rather than just min/argmin
# (as we actualy use) to be useful. I tried it with Python 2.5.1
# on Linux and it worked (ie NaN is neither max nor min) but
# not really sure how reliable that behaviour is... so sticking
# with inf for now since we only need min/argmin anyway.
for i in range(0, Numeric.size(self.sse_dist_matrix,0)):
self.sse_dist_matrix[i,i] = float("inf")
self.reverse_sse_index_map = len(ptnode_list) * [ -1 ] #will in 0..len-1
index_maplist = list(enumerate(ptnode_list))
for i in range(len(index_maplist)):
row, sse_one = index_maplist[i]
self.sse_index_map[sse_one] = row
self.reverse_sse_index_map[row] = sse_one
for j in range(i+1, len(index_maplist)):
col, sse_two = index_maplist[j]
(dist, res_one, res_two) = self.calc_sse_dist(sse_one, sse_two)
self.sse_dist_matrix[row, col] = dist
self.sse_dist_matrix[col, row] = dist
self.sse_residue_map[sse_one, sse_two] = (res_one, res_two)
self.sse_residue_map[sse_two, sse_one] = (res_two, res_one)
