def __unmaskedMatrix(self, contacts, rec_mask, lig_mask):
"""
Map contacts between selected rec and lig atoms back to all atoms
matrix.
@param contacts: contact matrix, array sum_rec_mask x sum_lig_mask
@type contacts: array
@param rec_mask: atom mask
@type rec_mask: [1|0]
@param lig_mask: atom mask
@type lig_mask: [1|0]
@return: atom contact matrix, array N_atoms_rec x N_atoms_lig
@rtype: array
"""
l_rec = len(self.rec_model)
l_lig = len(self.lig_model)
## map contacts back to all atoms matrix
r = N0.zeros(l_rec * l_lig)
rMask = N0.ravel(N0.outerproduct(rec_mask, lig_mask))
## (Optimization: nonzero is time consuming step)
N0.put(r, N0.nonzero(rMask), N0.ravel(contacts))
return N0.resize(r, (l_rec, l_lig))
def arrayEqual(a, b):
"""
Compare 2 arrays or lists of numbers for equality.
:param a: first array (multi-dimensional is supported)
:type a: array / list
:param b: second array (multi-dimensional is supported)
:type b: array / list
:return: 1 if array/list a equals array/list b
:rtype: 1|0
"""
if a is None or b is None:
return a is b
if len(a) != len(b):
return 0
if type(a) is list: a = N0.array(a)
if type(b) is list: b = N0.array(b)
a = N0.ravel(a)
b = N0.ravel(b)
return N0.sum(a == b) == len(a)
def arrayEqual( a, b ):
"""
Compare 2 arrays or lists of numbers for equality.
:param a: first array (multi-dimensional is supported)
:type a: array / list
:param b: second array (multi-dimensional is supported)
:type b: array / list
:return: 1 if array/list a equals array/list b
:rtype: 1|0
"""
if a is None or b is None:
return a is b
if len(a) != len(b):
return 0
if type(a) is list: a = N0.array( a )
if type(b) is list: b = N0.array( b )
a = N0.ravel( a )
b = N0.ravel( b )
return N0.sum( a==b ) == len(a)
def __unmaskedMatrix( self, contacts, rec_mask, lig_mask ):
"""
Map contacts between selected rec and lig atoms back to all atoms
matrix.
@param contacts: contact matrix, array sum_rec_mask x sum_lig_mask
@type contacts: array
@param rec_mask: atom mask
@type rec_mask: [1|0]
@param lig_mask: atom mask
@type lig_mask: [1|0]
@return: atom contact matrix, array N_atoms_rec x N_atoms_lig
@rtype: array
"""
l_rec = len( self.rec_model )
l_lig = len( self.lig_model )
## map contacts back to all atoms matrix
r = N0.zeros( l_rec * l_lig )
rMask = N0.ravel( N0.outerproduct( rec_mask, lig_mask ) )
## (Optimization: nonzero is time consuming step)
N0.put( r, N0.nonzero( rMask ), N0.ravel( contacts ) )
return N0.resize( r, (l_rec, l_lig))
def avgRmsd( self, cluster, aMask=None, threshold=0. ):
"""
Claculate the average pairwise rmsd (in Angstrom) for members
of a cluter.
@param cluster: cluster number
@type cluster: int
@param aMask: atom mask applied before calculation
@type aMask: [1|0]
@param threshold: float 0-1, minimal cluster membership,
see L{memberTraj()}
@type threshold: float
@return: average rmsd and the standard deviation
@rtype: float, float
"""
try:
rms = self.memberTraj(cluster,threshold).pairwiseRmsd( aMask )
rms = aboveDiagonal( rms )
except:
rms = []
if len(N0.ravel(rms)) == 1:
## was: return N0.average(rms)[0], 0.0
return N0.average(rms), 0.0
if len(N0.ravel(rms)) == 0:
return 0.0, 0.0
return N0.average( rms ), SD( rms )
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 takeMembers( self, mIndices ):
"""
Take all frames belonging to the members in mIndices::
takeMembers( mIndices ) -> EnsembleTraj with frames of given members
:param mIndices: list of member indices
:type mIndices: [int] OR array('i')
:return: EnsembleTraj with specified members
:rtype: EnsembleTraj
@todo: return self.__class__ instead of EnsembleTraj
"""
try:
## assumes that each member traj has same number of frames
fi = N0.array( [ self.memberIndices( i ) for i in mIndices ] )
fi = N0.ravel( N0.transpose( fi ) )
n_members = len( mIndices )
## has wrong n_members and member order
t = self.takeFrames( fi )
result = EnsembleTraj( n_members=n_members )
result.__dict__.update( t.__dict__ )
result.n_members = n_members
result.resetFrameNames()
return result
except TypeError:
raise EnsembleTrajError('takeMembers TypeError '+\
str(mIndices)+\
"\nlenFrames: %i; n_members: %i" %(len(self), self.n_members))
def takeMembers( self, mIndices ):
"""
Take all frames belonging to the members in mIndices::
takeMembers( mIndices ) -> EnsembleTraj with frames of given members
:param mIndices: list of member indices
:type mIndices: [int] OR array('i')
:return: EnsembleTraj with specified members
:rtype: EnsembleTraj
@todo: return self.__class__ instead of EnsembleTraj
"""
try:
## assumes that each member traj has same number of frames
fi = N0.array( [ self.memberIndices( i ) for i in mIndices ] )
fi = N0.ravel( N0.transpose( fi ) )
n_members = len( mIndices )
## has wrong n_members and member order
t = self.takeFrames( fi )
result = EnsembleTraj( n_members=n_members )
result.__dict__.update( t.__dict__ )
result.n_members = n_members
result.resetFrameNames()
return result
except TypeError:
raise EnsembleTrajError('takeMembers TypeError '+\
str(mIndices)+\
"\nlenFrames: %i; n_members: %i" %(len(self), self.n_members))
def test_ComplexTraj(self):
"""Dock.ComplexTraj test"""
import biskit.tools as T
## there is no complex trajectory in the test folder so will have
## to create a fake trajectory with a complex
f = [T.testRoot() + '/com/1BGS.pdb'] * 5
t = Trajectory(f, verbose=self.local)
t = ComplexTraj(t, recChains=[0])
#if self.local:
#print 'plotting contact density...'
#t.plotContactDensity( step=2 )
## create a fake second chain in the ligand
for i in range(1093 + 98, 1968):
t.ref.atoms['chain_id'][i] = 'B'
t.ref.chainIndex(force=1, cache=1)
t.cl = [1, 2]
r = N0.concatenate(
(list(range(1093,
1191)), list(range(0, 1093)), list(range(1191, 1968))))
tt = t.takeAtoms(r)
contactMat = tt.atomContacts(1)
if self.local:
print('Receptor chains: %s Ligand chains: %s' % (t.cr, t.cl))
self.assertEqual(N0.sum(N0.ravel(contactMat)), 308)
def test_rmsFit(self):
"""rmsFit test"""
from . import tools as T
self.traj = T.load(T.testRoot('lig_pcr_00/traj.dat'))
rt, rmsdLst = match(self.traj.ref.xyz, self.traj[-1].xyz)
if self.local:
print('RMSD: %.2f' % rmsdLst[0][1])
# return rotation matrix
r = abs(N0.sum(N0.ravel(rt[0])))
e = abs(N0.sum(N0.ravel(self.EXPECT)))
self.assertAlmostEqual(r, e, 6)
def test_rmsFit( self ):
"""rmsFit test"""
from . import tools as T
self.traj = T.load( T.testRoot('lig_pcr_00/traj.dat') )
rt, rmsdLst = match( self.traj.ref.xyz, self.traj[-1].xyz)
if self.local:
print('RMSD: %.2f' % rmsdLst[0][1])
# return rotation matrix
r = abs( N0.sum( N0.ravel( rt[0] )))
e = abs( N0.sum( N0.ravel( self.EXPECT )))
self.assertAlmostEqual(r, e, 6)
def test_ComplexTraj(self):
"""Dock.ComplexTraj test"""
import biskit.tools as T
## there is no complex trajectory in the test folder so will have
## to create a fake trajectory with a complex
f = [ T.testRoot()+ '/com/1BGS.pdb' ] * 5
t = Trajectory( f, verbose=self.local )
t = ComplexTraj( t, recChains=[0] )
#if self.local:
#print 'plotting contact density...'
#t.plotContactDensity( step=2 )
## create a fake second chain in the ligand
for i in range( 1093+98, 1968 ):
t.ref.atoms['chain_id'][i] = 'B'
t.ref.chainIndex( force=1, cache=1 )
t.cl = [1,2]
r = N0.concatenate((list(range(1093,1191)), list(range(0,1093)), list(range(1191,1968))))
tt = t.takeAtoms( r )
contactMat = tt.atomContacts( 1 )
if self.local:
print('Receptor chains: %s Ligand chains: %s'%(t.cr, t.cl))
self.assertEqual( N0.sum(N0.ravel(contactMat)), 308 )
def plotContactDensity(self, step=1, cutoff=4.5):
"""
Example. plot histogramm of contact density. Somehing wrong??
@raise ComplexTrajError: if gnuplot program is not installed
"""
if not gnuplot.installed:
raise ComplexTrajError('gnuplot program is not installed')
r = self.averageContacts(step, cutoff)
r = N0.ravel(r)
r = N0.compress(r, r)
gnuplot.plot(hist.density(r, 10))
def plotContactDensity( self, step=1, cutoff=4.5 ):
"""
Example. plot histogramm of contact density. Somehing wrong??
@raise ComplexTrajError: if gnuplot program is not installed
"""
if not gnuplot.installed:
raise ComplexTrajError('gnuplot program is not installed')
r = self.averageContacts( step, cutoff )
r = N0.ravel( r )
r = N0.compress( r, r )
gnuplot.plot( hist.density( r, 10 ) )
def __init__(self, models, name=None, profileName='relAS', verbose=1):
"""
@param models: List of models display a Ramachandran plot for
@type models: [ PDBModel ] OR PDBModel
@param name: model name, will show up in plot
@type name: str
@param profileName: name of profile to use for coloring
(default: 'relAS')
@type profileName: str
@param verbose: verbosity level (default: 1)
@type verbose: 1|0
"""
if not biggles:
raise ImportError('biggles module could not be imported.')
if type(models) != type([]):
models = [models]
self.psi = []
self.phi = []
self.gly = []
self.pro = []
self.prof = []
self.profileName = profileName
self.name = name
self.verbose = verbose
# calculate angles, profiles ...
self.calc(models)
self.prof = N0.ravel(self.prof)
self.gly = N0.ravel(self.gly)
self.pro = N0.ravel(self.pro)
def __init__( self, models, name=None, profileName='relAS', verbose=1 ):
"""
@param models: List of models display a Ramachandran plot for
@type models: [ PDBModel ] OR PDBModel
@param name: model name, will show up in plot
@type name: str
@param profileName: name of profile to use for coloring
(default: 'relAS')
@type profileName: str
@param verbose: verbosity level (default: 1)
@type verbose: 1|0
"""
if not biggles:
raise ImportError('biggles module could not be imported.')
if type(models) != type([]):
models = [ models ]
self.psi = []
self.phi = []
self.gly = []
self.pro = []
self.prof=[]
self.profileName = profileName
self.name=name
self.verbose = verbose
# calculate angles, profiles ...
self.calc( models )
self.prof = N0.ravel(self.prof)
self.gly = N0.ravel(self.gly)
self.pro = N0.ravel(self.pro)
def getFirstCrdLine(self):
"""
Return the first line of Amber crd.
:return: first line of Amber crd formatted coordinate block
:rtype: str
"""
if not self.xyz:
self.getXyz()
result = ""
for x in N0.ravel(self.xyz)[:10]:
result += "%8.3f" % x
return result + "\n"
def getFirstCrdLine( self ):
"""
Return the first line of Amber crd.
:return: first line of Amber crd formatted coordinate block
:rtype: str
"""
if not self.xyz:
self.getXyz()
result = ""
for x in N0.ravel( self.xyz )[:10]:
result += "%8.3f" % x
return result + "\n"
def writeCrd( self, fcrd, append=1, lastAtom=None ):
"""
Write/Append Amber-formatted block of coordinates to a file.
If a file handle is given, the file will not be closed.
:param fcrd: file to write to
:type fcrd: str or file object
:param append: append to existing file (default: 1)
:type append: 0|1
:param lastAtom: skip all atoms beyond this one (default: None)
:type lastAtom: int
"""
if not self.xyz:
self.getXyz()
if type( fcrd ) == file:
## take file handle
f = fcrd
else:
## create new file handle
mode = 'w'
if append:
mode = 'a'
f = open( T.absfile( fcrd ), mode )
newf = (mode=='w' or not os.path.exists( T.absfile(fcrd) ))
if newf:
f.write("\n")
i = 0
for x in N0.ravel( self.xyz ):
i = i + 1
f.write( "%8.3f" % x )
if (i % 10) == 0:
f.write("\n")
if lastAtom and i / 3.0 == lastAtom:
break
if ((i % 10) != 0):
f.write("\n")
if type( fcrd ) != file:
## don't close file that was already given
f.close()
def writeCrd(self, fcrd, append=1, lastAtom=None):
"""
Write/Append Amber-formatted block of coordinates to a file.
If a file handle is given, the file will not be closed.
:param fcrd: file to write to
:type fcrd: str or file object
:param append: append to existing file (default: 1)
:type append: 0|1
:param lastAtom: skip all atoms beyond this one (default: None)
:type lastAtom: int
"""
if not self.xyz:
self.getXyz()
if type(fcrd) == file:
## take file handle
f = fcrd
else:
## create new file handle
mode = 'w'
if append:
mode = 'a'
f = open(T.absfile(fcrd), mode)
newf = (mode == 'w' or not os.path.exists(T.absfile(fcrd)))
if newf:
f.write("\n")
i = 0
for x in N0.ravel(self.xyz):
i = i + 1
f.write("%8.3f" % x)
if (i % 10) == 0:
f.write("\n")
if lastAtom and i / 3.0 == lastAtom:
break
if ((i % 10) != 0):
f.write("\n")
if type(fcrd) != file:
## don't close file that was already given
f.close()
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 packBinaryMatrix(cm):
"""
Compress sparse array of 0 and ones to list of one-positions
(space saving function, upack with :class:`unpackBinaryMatrix`).
:param cm: X by Y array of int
:type cm: 2D array
:return: {'shape':(X,Y), 'nonzero':[int] }
:rtype: dict
"""
if cm is None or type(cm) == dict:
return cm
result = {}
result['shape'] = N0.shape(cm)
result['nonzero'] = N0.nonzero(N0.ravel(cm))
result['nonzero'] = result['nonzero'].tolist()
return result
def packBinaryMatrix( cm ):
"""
Compress sparse array of 0 and ones to list of one-positions
(space saving function, upack with :class:`unpackBinaryMatrix`).
:param cm: X by Y array of int
:type cm: 2D array
:return: {'shape':(X,Y), 'nonzero':[int] }
:rtype: dict
"""
if cm is None or type( cm ) == dict:
return cm
result = {}
result['shape'] = N0.shape( cm )
result['nonzero'] = N0.nonzero( N0.ravel( cm ) )
result['nonzero'] = result['nonzero'].tolist()
return result
def matrixToList(cm):
"""
Convert matrix into standard python list remembering the dimensions.
Unpack with :class:`listToMatrix`.
Note: Not used right now.
:param cm: array of int
:type cm: 2D array
:return: {'shape':(int,..), 'lst':[..] }
:rtype: dict
"""
if cm is None or type(cm) == dict:
return cm
result = {}
result['shape'] = N0.shape(cm)
result['lst'] = N0.ravel(cm).tolist()
return result
def matrixToList( cm ):
"""
Convert matrix into standard python list remembering the dimensions.
Unpack with :class:`listToMatrix`.
Note: Not used right now.
:param cm: array of int
:type cm: 2D array
:return: {'shape':(int,..), 'lst':[..] }
:rtype: dict
"""
if cm is None or type( cm ) == dict:
return cm
result = {}
result['shape'] = N0.shape( cm )
result['lst'] = N0.ravel( cm ).tolist()
return result
def slim(self):
"""
Remove coordinates and atoms of ligand and receptor from memory,
if they can be restored from file, compress contact matrix.
@note: CALLED BEFORE PICKLING
"""
self.lig_transformed = None
self.pw_dist = None
## self.ligandMatrix = self.ligandMatrix.tolist()
if 'matrix' in self.info:
del self.info['matrix']
## compress contact matrix array
if self.contacts is not None and \
len(N0.shape( self.contacts['result'] ) )==2:
m = self.contacts['result']
self.contacts['shape'] = N0.shape( m )
self.contacts['result'] = N0.nonzero( N0.ravel( m ) ).astype(N0.Int32)
def slim(self):
"""
Remove coordinates and atoms of ligand and receptor from memory,
if they can be restored from file, compress contact matrix.
@note: CALLED BEFORE PICKLING
"""
self.lig_transformed = None
self.pw_dist = None
## self.ligandMatrix = self.ligandMatrix.tolist()
if 'matrix' in self.info:
del self.info['matrix']
## compress contact matrix array
if self.contacts is not None and \
len(N0.shape( self.contacts['result'] ) )==2:
m = self.contacts['result']
self.contacts['shape'] = N0.shape(m)
self.contacts['result'] = N0.nonzero(N0.ravel(m)).astype(N0.Int32)
def writeCrd( self, fname, frames=None ):
"""
Write frames to Amber crd file (w/o box info).
:param fname: output file name
:type fname: str
:param frames: frame indices (default: all)
:type frames: [int]
"""
if frames is None:
frames = list(range( self.lenFrames()))
template = " %7.3f" * 10 + '\n'
## open new file
out = open( T.absfile(fname), 'wt')
__write = out.write ## cache function address for speed
__write('\n')
n_lines = None
for fi in frames:
f = N0.ravel( self.frames[ fi ] )
if n_lines is None:
n_lines = n_lines or len( f ) // 10
overhang= ( 0 != len( f ) % 10 )
i_lines = range( n_lines )
for i in i_lines:
__write( template % tuple( f[10*i:10*i+10] ) )
if overhang:
for x in f[i*10+10:]:
__write(" %7.3f" % x )
__write('\n')
out.close()
def thin( self, step=1 ):
"""
Keep only each step'th frame from trajectory with 10 ensemble members.
:param step: 1..keep all frames, 2..skip first and every second, ..
(default: 1)
:type step: int
:return: reduced EnsembleTraj
:rtype: EnsembleTraj
"""
T.ensure( step, int, forbidden=[0] )
## 10 x lenFrames/10, frame indices of each member
mI = [ self.memberIndices( i ) for i in range(self.n_members) ]
mI = N0.array( mI )
mI = N0.take( mI, range( -1, N0.shape( mI )[1], step )[1:], 1 )
mI = N0.transpose( mI )
return self.takeFrames( N0.ravel( mI ))
def thin( self, step=1 ):
"""
Keep only each step'th frame from trajectory with 10 ensemble members.
:param step: 1..keep all frames, 2..skip first and every second, ..
(default: 1)
:type step: int
:return: reduced EnsembleTraj
:rtype: EnsembleTraj
"""
T.ensure( step, int, forbidden=[0] )
## 10 x lenFrames/10, frame indices of each member
mI = [ self.memberIndices( i ) for i in range(self.n_members) ]
mI = N0.array( mI )
mI = N0.take( mI, range( -1, N0.shape( mI )[1], step )[1:], 1 )
mI = N0.transpose( mI )
return self.takeFrames( N0.ravel( mI ))
def conservationScore( self, cons_type='cons_ent', ranNr=150,
log=StdLog(), verbose=1 ):
"""
Score of conserved residue pairs in the interaction surface.
Optionally, normalized by radom surface contacts.
@param cons_type: precalculated conservation profile name,
see L{Biskit.PDBDope}.
@type cons_type: str
@param ranNr: number of random matricies to use (default: 150)
@type ranNr: int
@param log: log file [STDOUT]
@type log: Biskit.LogFile
@param verbose: give progress report [1]
@type verbose: bool | int
@return: conservation score
@rtype: float
"""
try:
recCons = self.rec().profile( cons_type, updateMissing=1 )
except:
if verbose:
log.add('\n'+'*'*30+'\nNO HHM PROFILE FOR RECEPTOR\n'+\
'*'*30+'\n')
recCons = N0.ones( self.rec().lenResidues() )
try:
ligCons = self.lig().profile( cons_type, updateMissing=1 )
except:
if verbose:
log.add(\
'\n'+'*'*30+'\nNO HHM PROFILE FOR LIGAND\n'+'*'*30+'\n')
ligCons = N0.ones( self.lig().lenResidues() )
if self.rec().profile( 'surfMask' ):
recSurf = self.rec().profile( 'surfMask' )
else:
d = PDBDope(self.rec())
d.addSurfaceMask()
if self.lig().profile( 'surfMask' ):
ligSurf = self.lig().profile( 'surfMask' )
else:
d = PDBDope(self.lig())
d.addSurfaceMask()
surfMask = N0.ravel(N0.outerproduct( recSurf, ligSurf ))
missing = N0.outerproduct( N0.equal( recCons, 0), N0.equal(ligCons,0))
cont = self.resContacts() * N0.logical_not(missing)
consMat = N0.outerproduct( recCons, ligCons )
score = cont* consMat
# get a random score
if ranNr != 0:
if self.verbose:
self.log.write('.')
ranMat = mathUtils.random2DArray( cont, ranNr, mask=surfMask )
random_score = N0.sum(N0.sum( ranMat * consMat ))/( ranNr*1.0 )
return N0.sum(N0.sum(score))/random_score
else:
return N0.sum(N0.sum(score))/ N0.sum(N0.sum(cont))
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
def conservationScore(self,
cons_type='cons_ent',
ranNr=150,
log=StdLog(),
verbose=1):
"""
Score of conserved residue pairs in the interaction surface.
Optionally, normalized by radom surface contacts.
@param cons_type: precalculated conservation profile name,
see L{Biskit.PDBDope}.
@type cons_type: str
@param ranNr: number of random matricies to use (default: 150)
@type ranNr: int
@param log: log file [STDOUT]
@type log: Biskit.LogFile
@param verbose: give progress report [1]
@type verbose: bool | int
@return: conservation score
@rtype: float
"""
try:
recCons = self.rec().profile(cons_type, updateMissing=1)
except:
if verbose:
log.add('\n'+'*'*30+'\nNO HHM PROFILE FOR RECEPTOR\n'+\
'*'*30+'\n')
recCons = N0.ones(self.rec().lenResidues())
try:
ligCons = self.lig().profile(cons_type, updateMissing=1)
except:
if verbose:
log.add(\
'\n'+'*'*30+'\nNO HHM PROFILE FOR LIGAND\n'+'*'*30+'\n')
ligCons = N0.ones(self.lig().lenResidues())
if self.rec().profile('surfMask'):
recSurf = self.rec().profile('surfMask')
else:
d = PDBDope(self.rec())
d.addSurfaceMask()
if self.lig().profile('surfMask'):
ligSurf = self.lig().profile('surfMask')
else:
d = PDBDope(self.lig())
d.addSurfaceMask()
surfMask = N0.ravel(N0.outerproduct(recSurf, ligSurf))
missing = N0.outerproduct(N0.equal(recCons, 0), N0.equal(ligCons, 0))
cont = self.resContacts() * N0.logical_not(missing)
consMat = N0.outerproduct(recCons, ligCons)
score = cont * consMat
# get a random score
if ranNr != 0:
if self.verbose:
self.log.write('.')
ranMat = mathUtils.random2DArray(cont, ranNr, mask=surfMask)
random_score = N0.sum(N0.sum(ranMat * consMat)) / (ranNr * 1.0)
return N0.sum(N0.sum(score)) / random_score
else:
return N0.sum(N0.sum(score)) / N0.sum(N0.sum(cont))
