def random2DArray( matrix, ranNr=1, mask=None):
"""
Create randomized 2D array containing ones and zeros.
:param matrix: matrix to randomize
:type matrix: 2D array
:param mask: mask OR None (default: None)
:type mask: list(1|0)
:param ranNr: number of matricies to add up (default: 1)
:type ranNr: integer
:return: 2D array or |ranNr| added contact matricies
:rtype:2D array
:raise MathUtilError: if mask does not fit matrix
"""
## get shape of matrix
a,b = N0.shape( matrix )
## get array from matrix that is to be randomized
if mask is not None:
if len(mask) == len( N0.ravel(matrix) ):
array = N0.compress( mask, N0.ravel(matrix) )
if len(mask) != len( N0.ravel(matrix) ):
raise MathUtilError(
'MatUtils.random2DArray - mask of incorrect length' +
'\tMatrix length: %i Mask length: %i'\
%(len( N0.ravel(matrix) ), len(mask)))
if not mask:
array = N0.ravel(matrix)
## number of ones and length of array
nOnes = int( N0.sum( array ) )
lenArray = len( array )
ranArray = N0.zeros( lenArray )
## create random array
for n in range(ranNr):
ranArray += randomMask( nOnes, lenArray )
## blow up to size of original matix
if mask is not None:
r = N0.zeros(a*b)
N0.put( r, N0.nonzero(mask), ranArray)
return N0.reshape( r, (a,b) )
if not mask:
return N0.reshape( ranArray, (a,b) )
def random2DArray(matrix, ranNr=1, mask=None):
"""
Create randomized 2D array containing ones and zeros.
:param matrix: matrix to randomize
:type matrix: 2D array
:param mask: mask OR None (default: None)
:type mask: list(1|0)
:param ranNr: number of matricies to add up (default: 1)
:type ranNr: integer
:return: 2D array or |ranNr| added contact matricies
:rtype:2D array
:raise MathUtilError: if mask does not fit matrix
"""
## get shape of matrix
a, b = N0.shape(matrix)
## get array from matrix that is to be randomized
if mask is not None:
if len(mask) == len(N0.ravel(matrix)):
array = N0.compress(mask, N0.ravel(matrix))
if len(mask) != len(N0.ravel(matrix)):
raise MathUtilError(
'MatUtils.random2DArray - mask of incorrect length' +
'\tMatrix length: %i Mask length: %i'\
%(len( N0.ravel(matrix) ), len(mask)))
if not mask:
array = N0.ravel(matrix)
## number of ones and length of array
nOnes = int(N0.sum(array))
lenArray = len(array)
ranArray = N0.zeros(lenArray)
## create random array
for n in range(ranNr):
ranArray += randomMask(nOnes, lenArray)
## blow up to size of original matix
if mask is not None:
r = N0.zeros(a * b)
N0.put(r, N0.nonzero(mask), ranArray)
return N0.reshape(r, (a, b))
if not mask:
return N0.reshape(ranArray, (a, b))
def unpackBinaryMatrix( pcm, raveled=0 ):
"""
Uncompress array of 0 and 1 that was compressed with :class:`packBinaryMatrix`.
:param pcm: {'shape':(X,Y,..), 'nonzero':[int]}
:type pcm: dict
:param raveled: return raveled (default: 0)
:type raveled: 1|0
:return: N0.array(X by Y by ..) of int
:rtype: 2D array
"""
if type( pcm ) != dict:
return pcm
s = pcm['shape']
m = N0.zeros( N0.cumproduct( s )[-1], N0.Int)
pass ## m.savespace( 1 )
N0.put( m, pcm['nonzero'], 1 )
if raveled:
return m
return N0.reshape( m, s )
def loadResContacts(self):
"""
Uncompress residue contact matrix if necessary.
@return: dict with contact matrix and parameters OR None
@rtype: dict OR None
"""
## Backwards compatibility
if self.contacts is not None and type(self.contacts) == str:
self.contacts = t.load(self.contacts)
EHandler.warning("loading old-style pickled contacts.")
return self.contacts
## New, uncompression from list of indices into raveled array
if self.contacts is not None and \
len( N0.shape( self.contacts['result'])) == 1:
try:
lenRec, lenLig = self.contacts['shape']
except:
EHandler.warning("uncompressing contacts without shape")
lenRec = self.rec().lenResidues()
lenLig = self.lig().lenResidues()
m = N0.zeros(lenRec * lenLig)
N0.put(m, self.contacts['result'], 1)
self.contacts['result'] = N0.reshape(m, (lenRec, lenLig))
return self.contacts
def unpackBinaryMatrix(pcm, raveled=0):
"""
Uncompress array of 0 and 1 that was compressed with :class:`packBinaryMatrix`.
:param pcm: {'shape':(X,Y,..), 'nonzero':[int]}
:type pcm: dict
:param raveled: return raveled (default: 0)
:type raveled: 1|0
:return: N0.array(X by Y by ..) of int
:rtype: 2D array
"""
if type(pcm) != dict:
return pcm
s = pcm['shape']
m = N0.zeros(N0.cumproduct(s)[-1], N0.Int)
pass ## m.savespace( 1 )
N0.put(m, pcm['nonzero'], 1)
if raveled:
return m
return N0.reshape(m, s)
def loadResContacts( self ):
"""
Uncompress residue contact matrix if necessary.
@return: dict with contact matrix and parameters OR None
@rtype: dict OR None
"""
## Backwards compatibility
if self.contacts is not None and type( self.contacts ) == str:
self.contacts = t.load( self.contacts )
EHandler.warning("loading old-style pickled contacts.")
return self.contacts
## New, uncompression from list of indices into raveled array
if self.contacts is not None and \
len( N0.shape( self.contacts['result'])) == 1:
try:
lenRec, lenLig = self.contacts['shape']
except:
EHandler.warning("uncompressing contacts without shape")
lenRec = self.rec().lenResidues()
lenLig = self.lig().lenResidues()
m = N0.zeros( lenRec * lenLig )
N0.put( m, self.contacts['result'], 1 )
self.contacts['result'] = N0.reshape( m, (lenRec, lenLig) )
return self.contacts
def centers( self ):
"""
Get 'center structure' for each cluster.
@return: N0.array( n_clusters x n_atoms_masked x 3 )
@rtype: array
"""
lenAtoms = N0.shape( self.fcCenters )[1] / 3
return N0.reshape( self.fcCenters, ( self.n_clusters, lenAtoms, 3))
def __frame(self):
"""Collect next complete coordinate frame
:return: coordinate frame
:rtype: array
"""
self.xyz = [self.__nextLine() for i in range(self.lines_per_frame)]
return N0.reshape(self.xyz, (self.n, 3)).astype(N0.Float32)
def __frame( self ):
"""Collect next complete coordinate frame
:return: coordinate frame
:rtype: array
"""
self.xyz = [ self.__nextLine() for i in range(self.lines_per_frame) ]
return N0.reshape(self.xyz, ( self.n, 3 ) ).astype(N0.Float32)
def listToMatrix( lcm ):
"""
Convert result of :class:`matrixToList` back into Numeric array
Note: Not used right now.
:param lcm: {'shape':(int,..), 'lst':[..] }
:type lcm: dict
:return: array
:rtype:
"""
if type( lcm ) != dict:
return lcm
s = lcm['shape']
return N0.reshape( lcm['lst'], s )
def listToMatrix(lcm):
"""
Convert result of :class:`matrixToList` back into Numeric array
Note: Not used right now.
:param lcm: {'shape':(int,..), 'lst':[..] }
:type lcm: dict
:return: array
:rtype:
"""
if type(lcm) != dict:
return lcm
s = lcm['shape']
return N0.reshape(lcm['lst'], s)
def color_array( self, a, resetLimits=1 ):
"""
:param a: array of float
:type a: array of float
:param resetLimits: re-define color range on max and min of values
(default: 1)
:type resetLimits: 1|0
:return: matrix of color codes with same dimensions as a
:rtype: array of float
"""
s = N0.shape( a )
v = N0.ravel( a )
r = self.colors( v, resetLimits=resetLimits )
r = N0.reshape( r, s )
return r
def pcMovie( self, ev, steps, factor=1., ref=0, morph=1 ):
"""
Morph between the two extreme values of a single principal
component.
:param ev: EigenVector to visualize
:type ev: int
:param steps: number of intermediate frames
:type steps: int
:param factor: exageration factor (default: 1 = No exageration)
:type factor: float
:param ref: take other eigenvecors from this frame (default: 1)
:type ref: int
:param morph: morph between min and max (1) or take real values (0)
(default: 1)
:type morph: 1|0
:return: Trajectory with frames visualizing the morphing.
:rtype: Trajectory
"""
fit = 1
if self.pc is not None:
fit = self.pc['fit']
pc = self.getPca( fit=fit )
## eigenvectors (rows)
U = pc['u']
## raveled and centered frames
x_avg = N0.average(self.frames, 0)
X = N0.array( [N0.ravel(x) for x in self.frames - x_avg] )
## ev'th eigenvector of reference frame
alpha_0 = N0.dot( X[ref], U[ev] )
## list of deviations of ev'th eigenvector of each frame from ref
alpha_range = N0.dot(X, U[ev]) - alpha_0
## get some representative alphas...
if morph:
a_min = factor * min(alpha_range)
a_max = factor * max(alpha_range)
delta = (a_max - a_min) / steps
alpha_range = [ a_min + i*(delta) for i in range(0, steps) ]
else:
alpha_range = N0.sort( alpha_range )
delta = len(alpha_range) / (steps * 1.0)
alpha_range = [ alpha_range[ int(round( i*delta )) ]
for i in range(0,steps) ]
## scale ev'th eigenvector of ref with different alphas
Y = N0.array( [ X[ref] + alpha * U[ev] for alpha in alpha_range] )
## back convert to N x 3 coordinates
Y = N0.reshape(Y, (Y.shape[0], -1, 3))
Y = x_avg + Y
result = self.__class__()
result.ref = self.ref
result.frames = Y
return result