def randomMask( nOnes, length ):
"""
Create random array of given lenght and number of ones.
@param nOnes: number of ones
@type nOnes: int
@param length: lenght of array
@type length: int
@return: array with ones and zeros
@rtype: array( 1|0 )
"""
r = N.zeros( length )
pos = []
## add random ones
for i in range( nOnes ):
pos += [ int( random.random() * length ) ]
N.put( r, pos, 1 )
## if two ones ended up on the same position
while nOnes != N.sum(r):
pos = int( random.random() * length )
N.put( r, pos, 1 )
return r
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 != 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 != None and \
len( N.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 = N.zeros( lenRec * lenLig )
N.put( m, self.contacts['result'], 1 )
self.contacts['result'] = N.reshape( m, (lenRec, lenLig) )
return self.contacts
def mergeProfiles(self, p0, p1, maxOverlap=3):
"""
Merge profile p0 with profile p1, as long as they overlap in
at most maxOverlap positions
@param p0: profile
@type p0: [float]
@param p1: profile
@type p1: [float]
@param maxOverlap: maximal allowed overlap between profiles
@type maxOverlap: int
@return: array
@rtype:
"""
p0 = self.__list2array(p0)
p1 = self.__list2array(p1)
overlap = N.greater(N.greater(p0, 0) + N.greater(p1, 0), 1)
if N.sum(overlap) <= maxOverlap:
## one of the two profiles will in most cases not belong to these
## positions. We can't decide which one is wrong, let's eliminate
## both values. Alternatively we could keep one, or the average, ..
N.put(p1, N.nonzero(overlap), 0)
N.put(p0, N.nonzero(overlap), 0)
p0 = p0 + p1
return p0
def shuffledLists( self, n, lst, mask=None ):
"""
shuffle order of a list n times, leaving masked(0) elements untouched
@param n: number of times to shuffle the list
@type n: int
@param lst: list to shuffle
@type lst: [any]
@param mask: mask to be applied to lst
@type mask: [1|0]
@return: list of shuffeled lists
@rtype: [[any]]
"""
if not mask:
mask = N.ones( len(lst) )
if type( lst ) == list:
lst = N.array( lst )
pos = N.nonzero( mask )
rand_pos = N.array( [ self.__shuffleList( pos ) for i in range(n) ] )
result = []
for p in rand_pos:
r = copy.copy( lst )
N.put( r, p, N.take( lst, pos ) )
result += [r]
return result
def mergeProfiles( self, p0, p1, maxOverlap=3 ):
"""
Merge profile p0 with profile p1, as long as they overlap in
at most maxOverlap positions
@param p0: profile
@type p0: [float]
@param p1: profile
@type p1: [float]
@param maxOverlap: maximal allowed overlap between profiles
@type maxOverlap: int
@return: array
@rtype:
"""
p0 = self.__list2array( p0 )
p1 = self.__list2array( p1 )
overlap = N.greater( N.greater(p0,0) + N.greater(p1,0), 1 )
if N.sum( overlap ) <= maxOverlap:
## one of the two profiles will in most cases not belong to these
## positions. We can't decide which one is wrong, let's eliminate
## both values. Alternatively we could keep one, or the average, ..
N.put( p1, N.nonzero( overlap ), 0 )
N.put( p0, N.nonzero( overlap ), 0 )
p0 = p0 + p1
return p0
def shuffledLists(self, n, lst, mask=None):
"""
shuffle order of a list n times, leaving masked(0) elements untouched
@param n: number of times to shuffle the list
@type n: int
@param lst: list to shuffle
@type lst: [any]
@param mask: mask to be applied to lst
@type mask: [1|0]
@return: list of shuffeled lists
@rtype: [[any]]
"""
if not mask:
mask = N.ones(len(lst))
if type(lst) == list:
lst = N.array(lst)
pos = N.nonzero(mask)
rand_pos = N.array([self.__shuffleList(pos) for i in range(n)])
result = []
for p in rand_pos:
r = copy.copy(lst)
N.put(r, p, N.take(lst, pos))
result += [r]
return result
def castHmmDic( self, hmmDic, repete, hmmGap, key ):
"""
Blow up hmmDic to the number of repetes of the profile used.
Correct scores for possible deletions in the search sequence.
@param hmmDic: dictionary from L{getHmmProfile}
@type hmmDic: dict
@param repete: repete information from L{align}
@type repete: int
@param hmmGap: information about gaps from L{align}
@type hmmGap: [int]
@param key: name of scoring method to adjust for gaps and repetes
@type key: str
@return: dictionary with information about the profile
@rtype: dict
"""
s = hmmDic[key]
for i in range( repete ):
mask = N.ones( len(s) )
N.put( mask, hmmGap[i], 0 )
if i == 0:
score = N.compress( mask, s, 0 )
if i > 0:
score = N.concatenate( ( N.compress( mask, s, 0 ), score ) )
hmmDic[key] = score
return hmmDic
def randomMask(nOnes, length):
"""
Create random array of given lenght and number of ones.
@param nOnes: number of ones
@type nOnes: int
@param length: lenght of array
@type length: int
@return: array with ones and zeros
@rtype: array( 1|0 )
"""
r = N.zeros(length)
pos = []
## add random ones
for i in range(nOnes):
pos += [int(random.random() * length)]
N.put(r, pos, 1)
## if two ones ended up on the same position
while nOnes != N.sum(r):
pos = int(random.random() * length)
N.put(r, pos, 1)
return r
def blockFit(self, ref=None, mask=None):
"""
RMSD-fit the average of each member trajectory (i.e. the trajectory
en block) onto the overall average (default) or a given structure.
@param ref: reference structure (default: average structure)
@type ref: PDBModel
@param mask: atoms to consider (default: None, all heavy)
@type mask: [1|0] OR None
"""
ref = ref or self.avgModel()
for m in range(self.n_members):
indices = self.memberIndices(m)
## get a copy of this member's Trajectory
traj = self.takeFrames(indices)
m_avg = traj.avgModel()
r, t = m_avg.transformation(ref, mask)
traj.transform(r, t)
## replace original frames of this member
N.put(self.frames, indices, traj.frames)
def blockFit( self, ref=None, mask=None ):
"""
RMSD-fit the average of each member trajectory (i.e. the trajectory
en block) onto the overall average (default) or a given structure.
@param ref: reference structure (default: average structure)
@type ref: PDBModel
@param mask: atoms to consider (default: None, all heavy)
@type mask: [1|0] OR None
"""
ref = ref or self.avgModel()
for m in range( self.n_members ):
indices = self.memberIndices( m )
## get a copy of this member's Trajectory
traj = self.takeFrames( indices )
m_avg = traj.avgModel()
r, t = m_avg.transformation( ref, mask )
traj.transform( r, t )
## replace original frames of this member
N.put( self.frames, indices, traj.frames )
def unpackBinaryMatrix(pcm, raveled=0):
"""
Uncompress array of 0 and 1 that was compressed with L{packBinaryMatrix}.
@param pcm: {'shape':(X,Y,..), 'nonzero':[int]}
@type pcm: dict
@param raveled: return raveled (default: 0)
@type raveled: 1|0
@return: N.array(X by Y by ..) of int
@rtype: 2D array
"""
if type(pcm) != dict:
return pcm
s = pcm['shape']
m = N.zeros(N.cumproduct(s)[-1], N.Int)
pass ## m.savespace( 1 )
N.put(m, pcm['nonzero'], 1)
if raveled:
return m
return N.reshape(m, s)
def castHmmDic(self, hmmDic, repete, hmmGap, key):
"""
Blow up hmmDic to the number of repetes of the profile used.
Correct scores for possible deletions in the search sequence.
@param hmmDic: dictionary from L{getHmmProfile}
@type hmmDic: dict
@param repete: repete information from L{align}
@type repete: int
@param hmmGap: information about gaps from L{align}
@type hmmGap: [int]
@param key: name of scoring method to adjust for gaps and repetes
@type key: str
@return: dictionary with information about the profile
@rtype: dict
"""
s = hmmDic[key]
for i in range(repete):
mask = N.ones(len(s))
N.put(mask, hmmGap[i], 0)
if i == 0:
score = N.compress(mask, s, 0)
if i > 0:
score = N.concatenate((N.compress(mask, s, 0), score))
hmmDic[key] = score
return hmmDic
def unpackBinaryMatrix( pcm, raveled=0 ):
"""
Uncompress array of 0 and 1 that was compressed with L{packBinaryMatrix}.
@param pcm: {'shape':(X,Y,..), 'nonzero':[int]}
@type pcm: dict
@param raveled: return raveled (default: 0)
@type raveled: 1|0
@return: N.array(X by Y by ..) of int
@rtype: 2D array
"""
if type( pcm ) != dict:
return pcm
s = pcm['shape']
m = N.zeros( N.cumproduct( s )[-1], N.Int)
pass ## m.savespace( 1 )
N.put( m, pcm['nonzero'], 1 )
if raveled:
return m
return N.reshape( m, s )
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 = N.zeros( l_rec * l_lig )
rMask = N.ravel( N.outerproduct( rec_mask, lig_mask ) )
## (Optimization: nonzero is time consuming step)
N.put( r, N.nonzero( rMask ), N.ravel( contacts ) )
return N.resize( r, (l_rec, l_lig))
def indices2condition(indices, length):
"""Input: indices=[0,3]; output: condition=[1,0,0,1]
"""
indices = Numeric.asarray(indices)
assert len(indices.shape) == 1
assert length >= indices.shape[0]
c = Numeric.zeros((length,), Numeric.Int)
Numeric.put(c, indices, 1)
return c
def rankData(n, inverse=False):
"""Returns ranks of 1D Numeric array in range 1...shape[0].
"""
n = Numeric.asarray(n)
assert Numeric.rank(n) == 1
r = Numeric.zeros(n.shape[0], Numeric.Float)
Numeric.put(r, Numeric.argsort(n), Numeric.arange(n.shape[0]))
if inverse:
return -1*r+n.shape[0]
else:
return r+1
def patchAround(self, center, nAtoms):
"""
patchAround( float_center, int_nAtoms ) -> mask for self.model
Create single patch of nAtoms atoms that are closest to center.
"""
dist = self.__distances(center)
order = N.argsort(dist)
r = N.zeros(len(self.model), 'i')
N.put(r, order[:nAtoms], 1)
return self.centerPatch(r)
def __inverseIndices(self, model, i_atoms):
"""
@param model: model
@type model: PDBMode
@param i_atoms: atom index
@type i_atoms: [int]
@return: remaining atom indices of m that are NOT in i_atoms
@rtype: [int]
"""
mask = N.zeros(len(model), N.Int)
N.put(mask, i_atoms, 1)
return N.nonzero(N.logical_not(mask))
def __inverseIndices( self, model, i_atoms ):
"""
@param model: model
@type model: PDBMode
@param i_atoms: atom index
@type i_atoms: [int]
@return: remaining atom indices of m that are NOT in i_atoms
@rtype: [int]
"""
mask = N.zeros( len( model ),N.Int )
N.put( mask, i_atoms, 1 )
return N.nonzero( N.logical_not( mask ) )
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 = N.shape( matrix )
## get array from matrix that is to be randomized
if mask is not None:
if len(mask) == len( N.ravel(matrix) ):
array = N.compress( mask, N.ravel(matrix) )
if len(mask) != len( N.ravel(matrix) ):
raise MathUtilError(
'MatUtils.random2DArray - mask of incorrect length' +
'\tMatrix length: %i Mask length: %i'\
%(len( N.ravel(matrix) ), len(mask)))
if not mask:
array = N.ravel(matrix)
## number of ones and length of array
nOnes = int( N.sum( array ) )
lenArray = len( array )
ranArray = N.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 = N.zeros(a*b)
N.put( r, N.nonzero(mask), ranArray)
return N.reshape( r, (a,b) )
if not mask:
return N.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 = N.shape(matrix)
## get array from matrix that is to be randomized
if mask is not None:
if len(mask) == len(N.ravel(matrix)):
array = N.compress(mask, N.ravel(matrix))
if len(mask) != len(N.ravel(matrix)):
raise MathUtilError(
'MatUtils.random2DArray - mask of incorrect length' +
'\tMatrix length: %i Mask length: %i'\
%(len( N.ravel(matrix) ), len(mask)))
if not mask:
array = N.ravel(matrix)
## number of ones and length of array
nOnes = int(N.sum(array))
lenArray = len(array)
ranArray = N.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 = N.zeros(a * b)
N.put(r, N.nonzero(mask), ranArray)
return N.reshape(r, (a, b))
if not mask:
return N.reshape(ranArray, (a, b))
def centerPatch(self, patch_mask):
"""
patch_mask - [ 1|0 ], mask of non-centered patch
-> [ 1|0 ], mask of patch around geometric center of first patch
"""
c = self.model.center(patch_mask)
dist = self.__distances(c)
n_atoms = len(N.nonzero(patch_mask))
i_dist = N.argsort(dist)[:n_atoms]
result = N.zeros(len(patch_mask))
N.put(result, i_dist, 1)
return result
def toarray( self ):
"""
Reconstruct dense array::
L.toarray() -> Numeric.array, normal dense array
@return: normal dense array
@rtype: array
"""
if self.default() is 0:
a = N.zeros( ( self.shape ), self.typecode() )
else:
a = N.ones( (self.shape ), self.typecode() ) * self.default()
N.put( a, self.nondefault(), self.nondefault_values() )
return a
def toarray(self):
"""
Reconstruct dense array::
L.toarray() -> Numeric.array, normal dense array
@return: normal dense array
@rtype: array
"""
if self.default() is 0:
a = N.zeros((self.shape), self.typecode())
else:
a = N.ones((self.shape), self.typecode()) * self.default()
N.put(a, self.nondefault(), self.nondefault_values())
return a
def draw(self):
# XXX This method is not speed-optimized. I just wrote it to
# get the job done. (Nonetheless, it seems faster than the C
# version commented out above.)
p = self.parameters # shorthand
now_sec = VisionEgg.time_func()
if self.start_times_sec is not None:
# compute extinct dots and generate new positions
replace_indices = Numeric.nonzero( Numeric.greater( now_sec - self.start_times_sec, p.dot_lifespan_sec) )
Numeric.put( self.start_times_sec, replace_indices, now_sec )
new_centers = np.random.standard_normal((3,len(replace_indices)))
for i in range(3):
Numeric.put( self.centers[i,:], replace_indices, new_centers[i,:] )
else:
# initialize dot extinction values to random (uniform) distribution
self.start_times_sec = RandomArray.uniform( now_sec - p.dot_lifespan_sec, now_sec,
(self.constant_parameters.num_dots,))
time_delta_sec = now_sec - self.last_time_sec
self.last_time_sec = now_sec # reset for next loop
self.centers = self.centers + np.array(p.signal_vec)[:,np.newaxis]*time_delta_sec
xyz = self.centers*p.start_position_variance + np.array(p.start_position_mean)[:,np.newaxis]
xs = xyz[0,:]
ys = xyz[1,:]
zs = xyz[2,:]
if p.on:
gl.glEnable( gl.GL_POINT_SMOOTH )
# allow max_alpha value to control blending
gl.glEnable( gl.GL_BLEND )
gl.glBlendFunc( gl.GL_SRC_ALPHA, gl.GL_ONE_MINUS_SRC_ALPHA )
gl.glPointSize(p.dot_size)
# Clear the modeview matrix
gl.glMatrixMode(gl.GL_MODELVIEW)
gl.glPushMatrix()
gl.glDisable(gl.GL_TEXTURE_2D)
draw_dots(xs,ys,zs,self.colors)
gl.glDisable( gl.GL_POINT_SMOOTH ) # turn off
gl.glPopMatrix()
def random_contacts( self, contMat, n, maskRec=None, maskLig=None ):
"""
Create randomized surface contact matrix with same number of
contacts and same shape as given contact matrix.
@param contMat: template contact matrix
@type contMat: matrix
@param n: number of matrices to generate
@type n: int
@param maskRec: surface masks (or something similar)
@type maskRec: [1|0]
@param maskLig: surface masks (or something similar)
@type maskLig: [1|0]
@return: list of [n] random contact matricies
@rtype: [matrix]
"""
a,b = N.shape( contMat )
nContacts = N.sum( N.sum( contMat ))
if not maskLig:
r_size, l_size = N.shape( contMat )
maskLig = N.ones( l_size )
maskRec = N.ones( r_size )
c_mask = N.ravel( N.outerproduct( maskRec, maskLig ) )
c_pos = N.nonzero( c_mask )
# get array with surface positions from complex
cont = N.take( N.ravel(contMat), c_pos )
length = len( cont )
result = []
for i in range( n ):
# create random array
ranCont = mathUtils.randomMask( nContacts,length )
# blow up to size of original matrix
r = N.zeros(a*b)
N.put( r, c_pos, ranCont)
result += [ N.reshape( r, (a,b) ) ]
return result
def random_contacts(self, contMat, n, maskRec=None, maskLig=None):
"""
Create randomized surface contact matrix with same number of
contacts and same shape as given contact matrix.
@param contMat: template contact matrix
@type contMat: matrix
@param n: number of matrices to generate
@type n: int
@param maskRec: surface masks (or something similar)
@type maskRec: [1|0]
@param maskLig: surface masks (or something similar)
@type maskLig: [1|0]
@return: list of [n] random contact matricies
@rtype: [matrix]
"""
a, b = N.shape(contMat)
nContacts = N.sum(N.sum(contMat))
if not maskLig:
r_size, l_size = N.shape(contMat)
maskLig = N.ones(l_size)
maskRec = N.ones(r_size)
c_mask = N.ravel(N.outerproduct(maskRec, maskLig))
c_pos = N.nonzero(c_mask)
# get array with surface positions from complex
cont = N.take(N.ravel(contMat), c_pos)
length = len(cont)
result = []
for i in range(n):
# create random array
ranCont = mathUtils.randomMask(nContacts, length)
# blow up to size of original matrix
r = N.zeros(a * b)
N.put(r, c_pos, ranCont)
result += [N.reshape(r, (a, b))]
return result
def memberMask( self, member ):
"""
Get mask for all frames belonging to a given ensemble member.
@param member: member index starting with 0
@type member: int
@return: member mask, N.array( N_frames x 1) of 1||0
@rtype: [1|0]
"""
result = N.zeros( self.lenFrames() )
if isinstance( member , types.IntType):
N.put( result, self.memberIndices( member ), 1 )
if type( member ) == types.ListType:
for m in member:
N.put( result, self.memberIndices( m ), 1 )
return result
def memberMask(self, member):
"""
Get mask for all frames belonging to a given ensemble member.
@param member: member index starting with 0
@type member: int
@return: member mask, N.array( N_frames x 1) of 1||0
@rtype: [1|0]
"""
result = N.zeros(self.lenFrames())
if isinstance(member, types.IntType):
N.put(result, self.memberIndices(member), 1)
if type(member) == types.ListType:
for m in member:
N.put(result, self.memberIndices(m), 1)
return result
def compareSequences( seqAA_1, seqAA_2 ):
"""
"""
seqAA_1 = list( seqAA_1 )
seqAA_2 = list( seqAA_2 )
seqNr_1 = range( len( seqAA_1 ) )
seqNr_2 = range( len( seqAA_2 ) )
# get mask
mask_1 = N.zeros( len( seqNr_1 ) )
mask_2 = N.zeros( len( seqNr_2 ) )
# compare sequences
seqDiff = getOpCodes( seqAA_1, seqAA_2)
# get delete lists
del_1, del_2 = getSkipLists( seqDiff )
del_1 = [ expandRepeats( seqAA_1, *pos ) for pos in del_1 ]
del_2 = [ expandRepeats( seqAA_2, *pos ) for pos in del_2 ]
mask1 = del2mask( seqAA_1, *del_1 )
mask2 = del2mask( seqAA_2, *del_2 )
seqAA_1 = N.compress( mask1, seqAA_1 ).tolist()
seqNr_1 = N.compress( mask1, seqNr_1 ).tolist()
seqAA_2 = N.compress( mask2, seqAA_2 ).tolist()
seqNr_2 = N.compress( mask2, seqNr_2 ).tolist()
# get equal parts
seqDiff = getOpCodes( seqAA_1, seqAA_2 )
equal_1, equal_2 = getEqualLists( seqDiff )
seqAA_1, seqNr_1 = getEqual( seqAA_1, seqNr_1, equal_1)
seqAA_2, seqNr_2 = getEqual( seqAA_2, seqNr_2, equal_2 )
N.put( mask_1, seqNr_1 , 1 )
N.put( mask_2, seqNr_2 , 1 )
return mask_1, mask_2
def compareSequences(seqAA_1, seqAA_2):
"""
"""
seqAA_1 = list(seqAA_1)
seqAA_2 = list(seqAA_2)
seqNr_1 = range(len(seqAA_1))
seqNr_2 = range(len(seqAA_2))
# get mask
mask_1 = N.zeros(len(seqNr_1))
mask_2 = N.zeros(len(seqNr_2))
# compare sequences
seqDiff = getOpCodes(seqAA_1, seqAA_2)
# get delete lists
del_1, del_2 = getSkipLists(seqDiff)
del_1 = [expandRepeats(seqAA_1, *pos) for pos in del_1]
del_2 = [expandRepeats(seqAA_2, *pos) for pos in del_2]
mask1 = del2mask(seqAA_1, *del_1)
mask2 = del2mask(seqAA_2, *del_2)
seqAA_1 = N.compress(mask1, seqAA_1).tolist()
seqNr_1 = N.compress(mask1, seqNr_1).tolist()
seqAA_2 = N.compress(mask2, seqAA_2).tolist()
seqNr_2 = N.compress(mask2, seqNr_2).tolist()
# get equal parts
seqDiff = getOpCodes(seqAA_1, seqAA_2)
equal_1, equal_2 = getEqualLists(seqDiff)
seqAA_1, seqNr_1 = getEqual(seqAA_1, seqNr_1, equal_1)
seqAA_2, seqNr_2 = getEqual(seqAA_2, seqNr_2, equal_2)
N.put(mask_1, seqNr_1, 1)
N.put(mask_2, seqNr_2, 1)
return mask_1, mask_2
def memberMap(self, traj):
"""
Tell which traj frame belongs to which member trajectory.
@param traj: Trajectory
@type traj: Trajectory
@return: member index of each frame OR None if traj is
not a EnsembleTraj
@rtype: [ int ] OR None
"""
if not isinstance( traj, EnsembleTraj ):
return None
r = N.zeros( len(traj), N.Int )
for i in range( traj.n_members ):
mi = traj.memberIndices( i )
N.put( r, mi, i )
return r.tolist()
def go(self, model_list = None, reference = None):
"""
Run benchmarking.
@param model_list: list of models
(default: None S{->} outFolder/L{F_PDBModels})
@type model_list: ModelList
@param reference: reference model
(default: None S{->} outFolder/L{F_INPUT_REFERENCE})
@type reference: PDBModel
"""
model_list = model_list or self.outFolder + self.F_PDBModels
reference = reference or self.outFolder + self.F_INPUT_REFERENCE
pdb_list = T.load('%s'%model_list)
reference = PDBModel(reference)
# check with python 2.4
iref, imodel = reference.compareAtoms(pdb_list[0])
mask_casting = N.zeros(len(pdb_list[0]))
N.put(mask_casting, imodel, 1)
reference = reference.take(iref)
#reference_mask_CA = reference_rmsd.maskCA()
atom_mask = N.zeros(len(pdb_list[0]))
N.put(atom_mask,imodel,1)
rmask = pdb_list[0].profile2mask("n_templates", 1,1000)
amask = pdb_list[0].res2atomMask(rmask)
mask_final_ref = N.compress(mask_casting, amask)
mask_final = mask_casting * amask
reference = reference.compress(mask_final_ref)
for i in range(len(pdb_list)):
#self.cad(reference, pdb_list[i])
pdb_list[i], pdb_wo_if = self.output_fittedStructures(\
pdb_list[i], reference, i, mask_final)
fitted_model_if = pdb_list[i].compress(mask_final)
fitted_model_wo_if = pdb_wo_if.compress(mask_final)
coord1 = reference.getXyz()
coord2 = fitted_model_if.getXyz()
aprofile = self.rmsd_res(coord1,coord2)
self.calc_rmsd(fitted_model_if, fitted_model_wo_if,
reference, pdb_list[i])
pdb_list[i].atoms.set('rmsd2ref_if', aprofile,
mask=mask_final, default = -1,
comment="rmsd to known reference structure")
self.output_rmsd_aa(pdb_list)
self.output_rmsd_ca(pdb_list)
self.output_rmsd_res(pdb_list)
self.write_PDBModels(pdb_list)
def go(self, model_list=None, reference=None):
"""
Run benchmarking.
@param model_list: list of models
(default: None S{->} outFolder/L{F_PDBModels})
@type model_list: ModelList
@param reference: reference model
(default: None S{->} outFolder/L{F_INPUT_REFERENCE})
@type reference: PDBModel
"""
model_list = model_list or self.outFolder + self.F_PDBModels
reference = reference or self.outFolder + self.F_INPUT_REFERENCE
pdb_list = T.load('%s' % model_list)
reference = PDBModel(reference)
# check with python 2.4
iref, imodel = reference.compareAtoms(pdb_list[0])
mask_casting = N.zeros(len(pdb_list[0]))
N.put(mask_casting, imodel, 1)
reference = reference.take(iref)
#reference_mask_CA = reference_rmsd.maskCA()
atom_mask = N.zeros(len(pdb_list[0]))
N.put(atom_mask, imodel, 1)
rmask = pdb_list[0].profile2mask("n_templates", 1, 1000)
amask = pdb_list[0].res2atomMask(rmask)
mask_final_ref = N.compress(mask_casting, amask)
mask_final = mask_casting * amask
reference = reference.compress(mask_final_ref)
for i in range(len(pdb_list)):
#self.cad(reference, pdb_list[i])
pdb_list[i], pdb_wo_if = self.output_fittedStructures(\
pdb_list[i], reference, i, mask_final)
fitted_model_if = pdb_list[i].compress(mask_final)
fitted_model_wo_if = pdb_wo_if.compress(mask_final)
coord1 = reference.getXyz()
coord2 = fitted_model_if.getXyz()
aprofile = self.rmsd_res(coord1, coord2)
self.calc_rmsd(fitted_model_if, fitted_model_wo_if, reference,
pdb_list[i])
pdb_list[i].atoms.set('rmsd2ref_if',
aprofile,
mask=mask_final,
default=-1,
comment="rmsd to known reference structure")
self.output_rmsd_aa(pdb_list)
self.output_rmsd_ca(pdb_list)
self.output_rmsd_res(pdb_list)
self.write_PDBModels(pdb_list)
def draw(self):
# XXX This method is not speed-optimized. I just wrote it to
# get the job done. (Nonetheless, it seems faster than the C
# version commented out above.)
p = self.parameters # shorthand
if p.center is not None:
if not hasattr(VisionEgg.config, "_GAVE_CENTER_DEPRECATION"):
logger = logging.getLogger('VisionEgg.Dots')
logger.warning("Specifying DotArea2D by deprecated "
"'center' parameter deprecated. Use "
"'position' parameter instead. (Allows "
"use of 'anchor' parameter to set to "
"other values.)")
VisionEgg.config._GAVE_CENTER_DEPRECATION = 1
p.anchor = 'center'
p.position = p.center[0], p.center[
1] # copy values (don't copy ref to tuple)
if p.on:
# calculate center
center = VisionEgg._get_center(p.position, p.anchor, p.size)
if p.anti_aliasing:
if len(p.color) == 4 and not self._gave_alpha_warning:
if p.color[3] != 1.0:
logger = logging.getLogger('VisionEgg.Dots')
logger.warning("The parameter anti_aliasing is "
"set to true in the DotArea2D "
"stimulus class, but the color "
"parameter specifies an alpha "
"value other than 1.0. To "
"acheive the best anti-aliasing, "
"ensure that the alpha value for "
"the color parameter is 1.0.")
self._gave_alpha_warning = 1
gl.glEnable(gl.GL_POINT_SMOOTH)
# allow max_alpha value to control blending
gl.glEnable(gl.GL_BLEND)
gl.glBlendFunc(gl.GL_SRC_ALPHA, gl.GL_ONE_MINUS_SRC_ALPHA)
else:
gl.glDisable(gl.GL_BLEND)
now_sec = VisionEgg.time_func()
if self.start_times_sec is not None:
# compute extinct dots and generate new positions
replace_indices = Numeric.nonzero(
Numeric.greater(now_sec - self.start_times_sec,
p.dot_lifespan_sec))
Numeric.put(self.start_times_sec, replace_indices, now_sec)
new_x_positions = RandomArray.uniform(0.0, 1.0,
(len(replace_indices), ))
Numeric.put(self.x_positions, replace_indices, new_x_positions)
new_y_positions = RandomArray.uniform(0.0, 1.0,
(len(replace_indices), ))
Numeric.put(self.y_positions, replace_indices, new_y_positions)
new_random_directions_radians = RandomArray.uniform(
0.0, 2 * math.pi, (len(replace_indices), ))
Numeric.put(self.random_directions_radians, replace_indices,
new_random_directions_radians)
else:
# initialize dot extinction values to random (uniform) distribution
self.start_times_sec = RandomArray.uniform(
now_sec - p.dot_lifespan_sec, now_sec,
(self.constant_parameters.num_dots, ))
signal_num_dots = int(
round(p.signal_fraction * self.constant_parameters.num_dots))
time_delta_sec = now_sec - self.last_time_sec
self.last_time_sec = now_sec # reset for next loop
x_increment_normalized = math.cos(
p.signal_direction_deg / 180.0 * math.pi
) * p.velocity_pixels_per_sec / p.size[0] * time_delta_sec
y_increment_normalized = -math.sin(
p.signal_direction_deg / 180.0 * math.pi
) * p.velocity_pixels_per_sec / p.size[1] * time_delta_sec
self.x_positions[:signal_num_dots] += x_increment_normalized
self.y_positions[:signal_num_dots] += y_increment_normalized
num_random_dots = self.constant_parameters.num_dots - signal_num_dots
random_x_increment_normalized = Numeric.cos(
self.random_directions_radians[signal_num_dots:]
) * p.velocity_pixels_per_sec / p.size[0] * time_delta_sec
random_y_increment_normalized = -Numeric.sin(
self.random_directions_radians[signal_num_dots:]
) * p.velocity_pixels_per_sec / p.size[1] * time_delta_sec
self.x_positions[signal_num_dots:] += random_x_increment_normalized
self.y_positions[signal_num_dots:] += random_y_increment_normalized
self.x_positions = Numeric.fmod(self.x_positions, 1.0) # wrap
self.y_positions = Numeric.fmod(self.y_positions, 1.0)
self.x_positions = Numeric.fmod(self.x_positions + 1,
1.0) # wrap again for values < 1
self.y_positions = Numeric.fmod(self.y_positions + 1, 1.0)
xs = (self.x_positions - 0.5) * p.size[0] + center[0]
ys = (self.y_positions - 0.5) * p.size[1] + center[1]
if len(p.color) == 3:
gl.glColor3f(*p.color)
elif len(p.color) == 4:
gl.glColor4f(*p.color)
gl.glPointSize(p.dot_size)
# Clear the modeview matrix
gl.glMatrixMode(gl.GL_MODELVIEW)
gl.glPushMatrix()
gl.glDisable(gl.GL_TEXTURE_2D)
if p.depth is None:
depth = 0.0
else:
gl.glEnable(gl.GL_DEPTH_TEST)
depth = p.depth
zs = (depth, ) * len(xs) # make N tuple with repeat value of depth
draw_dots(xs, ys, zs)
if p.anti_aliasing:
gl.glDisable(gl.GL_POINT_SMOOTH) # turn off
gl.glPopMatrix()
def draw(self):
# XXX This method is not speed-optimized. I just wrote it to
# get the job done. (Nonetheless, it seems faster than the C
# version commented out above.)
p = self.parameters # shorthand
if p.center is not None:
if not hasattr(VisionEgg.config,"_GAVE_CENTER_DEPRECATION"):
logger = logging.getLogger('VisionEgg.Dots')
logger.warning("Specifying DotArea2D by deprecated "
"'center' parameter deprecated. Use "
"'position' parameter instead. (Allows "
"use of 'anchor' parameter to set to "
"other values.)")
VisionEgg.config._GAVE_CENTER_DEPRECATION = 1
p.anchor = 'center'
p.position = p.center[0], p.center[1] # copy values (don't copy ref to tuple)
if p.on:
# calculate center
center = VisionEgg._get_center(p.position,p.anchor,p.size)
if p.anti_aliasing:
if len(p.color) == 4 and not self._gave_alpha_warning:
if p.color[3] != 1.0:
logger = logging.getLogger('VisionEgg.Dots')
logger.warning("The parameter anti_aliasing is "
"set to true in the DotArea2D "
"stimulus class, but the color "
"parameter specifies an alpha "
"value other than 1.0. To "
"acheive the best anti-aliasing, "
"ensure that the alpha value for "
"the color parameter is 1.0.")
self._gave_alpha_warning = 1
gl.glEnable( gl.GL_POINT_SMOOTH )
# allow max_alpha value to control blending
gl.glEnable( gl.GL_BLEND )
gl.glBlendFunc( gl.GL_SRC_ALPHA, gl.GL_ONE_MINUS_SRC_ALPHA )
else:
gl.glDisable( gl.GL_BLEND )
now_sec = VisionEgg.time_func()
if self.start_times_sec is not None:
# compute extinct dots and generate new positions
replace_indices = Numeric.nonzero( Numeric.greater( now_sec - self.start_times_sec, p.dot_lifespan_sec) )
Numeric.put( self.start_times_sec, replace_indices, now_sec )
new_x_positions = RandomArray.uniform(0.0,1.0,
(len(replace_indices),))
Numeric.put( self.x_positions, replace_indices, new_x_positions )
new_y_positions = RandomArray.uniform(0.0,1.0,
(len(replace_indices),))
Numeric.put( self.y_positions, replace_indices, new_y_positions )
new_random_directions_radians = RandomArray.uniform(0.0,2*math.pi,
(len(replace_indices),))
Numeric.put( self.random_directions_radians, replace_indices, new_random_directions_radians )
else:
# initialize dot extinction values to random (uniform) distribution
self.start_times_sec = RandomArray.uniform( now_sec - p.dot_lifespan_sec, now_sec,
(self.constant_parameters.num_dots,))
signal_num_dots = int(round(p.signal_fraction * self.constant_parameters.num_dots))
time_delta_sec = now_sec - self.last_time_sec
self.last_time_sec = now_sec # reset for next loop
x_increment_normalized = math.cos(p.signal_direction_deg/180.0*math.pi) * p.velocity_pixels_per_sec / p.size[0] * time_delta_sec
y_increment_normalized = -math.sin(p.signal_direction_deg/180.0*math.pi) * p.velocity_pixels_per_sec / p.size[1] * time_delta_sec
self.x_positions[:signal_num_dots] += x_increment_normalized
self.y_positions[:signal_num_dots] += y_increment_normalized
num_random_dots = self.constant_parameters.num_dots - signal_num_dots
random_x_increment_normalized = Numeric.cos(self.random_directions_radians[signal_num_dots:]) * p.velocity_pixels_per_sec / p.size[0] * time_delta_sec
random_y_increment_normalized = -Numeric.sin(self.random_directions_radians[signal_num_dots:]) * p.velocity_pixels_per_sec / p.size[1] * time_delta_sec
self.x_positions[signal_num_dots:] += random_x_increment_normalized
self.y_positions[signal_num_dots:] += random_y_increment_normalized
self.x_positions = Numeric.fmod( self.x_positions, 1.0 ) # wrap
self.y_positions = Numeric.fmod( self.y_positions, 1.0 )
self.x_positions = Numeric.fmod( self.x_positions+1, 1.0 ) # wrap again for values < 1
self.y_positions = Numeric.fmod( self.y_positions+1, 1.0 )
xs = (self.x_positions - 0.5) * p.size[0] + center[0]
ys = (self.y_positions - 0.5) * p.size[1] + center[1]
if len(p.color)==3:
gl.glColor3f(*p.color)
elif len(p.color)==4:
gl.glColor4f(*p.color)
gl.glPointSize(p.dot_size)
# Clear the modeview matrix
gl.glMatrixMode(gl.GL_MODELVIEW)
gl.glPushMatrix()
gl.glDisable(gl.GL_TEXTURE_2D)
if p.depth is None:
depth = 0.0
else:
gl.glEnable(gl.GL_DEPTH_TEST)
depth = p.depth
zs = (depth,)*len(xs) # make N tuple with repeat value of depth
draw_dots(xs,ys,zs)
if p.anti_aliasing:
gl.glDisable( gl.GL_POINT_SMOOTH ) # turn off
gl.glPopMatrix()