def centerAndMomentOfInertia(self, conf = None):
"""
:param conf: a configuration object, or None for the
current configuration
:type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
:returns: the center of mass and the moment of inertia tensor
in the given configuration
"""
from Scientific.Geometry import delta
offset = None
universe = self.universe()
if universe is not None:
offset = universe.contiguousObjectOffset([self], conf)
m = 0.
mr = Vector(0.,0.,0.)
t = Tensor(3*[3*[0.]])
for a in self.atomIterator():
ma = a._mass
if offset is None:
r = a.position(conf)
else:
r = a.position(conf)+offset[a]
m += ma
mr += ma*r
t += ma*r.dyadicProduct(r)
cm = mr/m
t -= m*cm.dyadicProduct(cm)
t = t.trace()*delta - t
return cm, t
def centerAndMomentOfInertia(self, conf=None):
"""
:param conf: a configuration object, or None for the
current configuration
:type conf: :class:~MMTK.ParticleProperties.Configuration or NoneType
:returns: the center of mass and the moment of inertia tensor
in the given configuration
"""
from Scientific.Geometry import delta
offset = None
universe = self.universe()
if universe is not None:
offset = universe.contiguousObjectOffset([self], conf)
m = 0.
mr = Vector(0., 0., 0.)
t = Tensor(3 * [3 * [0.]])
for a in self.atomIterator():
ma = a._mass
if offset is None:
r = a.position(conf)
else:
r = a.position(conf) + offset[a]
m += ma
mr += ma * r
t += ma * r.dyadicProduct(r)
cm = mr / m
t -= m * cm.dyadicProduct(cm)
t = t.trace() * delta - t
return cm, t
def changeBasis(pt, op, ip, jp, kp):
"""Returns the coordinates of a point after a change of basis.
@param pt: the coordinates of the point in the old basis.
@type pt: Scientific Vector
@param op: the coordinates of the new origin in the old basis.
@type op: Scientific Vector
@param ip: the coordinates of the new x axis in the old basis.
@type ip: Scientific Vector
@param jp: the coordinates of the new y axis in the old basis.
@type jp: Scientific Vector
@param kp: the coordinates of the new z axis in the old basis.
@type kp: Scientific Vector
@return: the coordinates of the point in the new basis.
@rtype: Scientific Vector
"""
mat = Tensor(N.array([ip, jp, kp], typecode=N.Float).transpose())
matinv = mat.inverse()
# This is the formula of the change of basis.
mprim = matinv * (pt - op)
return mprim
def __getitem__(self, item):
i1, i2 = item
if not isinstance(i1, int):
i1 = i1.index
if not isinstance(i2, int):
i2 = i2.index
if i1 > i2:
i1, i2 = i2, i1
return Tensor(N.transpose(self.array[i1, :, i2, :]))
else:
return Tensor(self.array[i1, :, i2, :])
def __init__(self, universe, lower, upper, number, initial=None):
basis = universe.reciprocalBasisVectors()
if basis is None:
self.basis = None
if initial is None:
self._makeRandomKList(lower, upper, number)
else:
self.klist = [initial]
else:
self.basis = map(lambda v: 2. * N.pi * v, basis)
inverse = N.array(map(list, universe.basisVectors()))
self.inverse = Tensor(inverse) / (2. * N.pi)
cell_volume = self.basis[0] * (self.basis[1].cross(self.basis[2]))
if initial is None:
center = 0.5 * (lower + upper)
shell_volume = 4. * N.pi * center**2 * (upper - lower)
ncells = shell_volume / cell_volume
if number > 1.5 * ncells:
number = int(1.5 * ncells)
if ncells > 500:
self._makeRandomKList(lower, upper, number)
else:
self._makeExplicitKList(lower, upper, number)
else:
ki = map(round, self.inverse * initial)
k = ki[0]*self.basis[0] + ki[1]*self.basis[1] + \
ki[2]*self.basis[2]
self.klist = [k]
def zero(self):
"""
@returns: a tensor of the same rank as the field values
with all elements equal to zero
@rtype: L{Scientifc.Geometry.Tensor}
"""
if self.rank == 0:
return 0.
else:
return Tensor(Numeric.zeros(self.rank*(3,), Numeric.Float))
def __call__(self, *points):
if len(points) == 1:
points = tuple(points[0])
value = apply(Interpolation.InterpolatingFunction.__call__,
(self, ) + points)
if self.rank == 0:
return value
elif self.rank == 1:
return Vector(value)
else:
return Tensor(value)
def centerAndMomentOfInertia(self, conf = None):
"Returns the center of mass and the moment of inertia tensor."
from Scientific.Geometry.TensorModule import delta
offset = None
universe = self.universe()
if universe is not None:
offset = universe.contiguousObjectOffset([self], conf)
m = 0.
mr = Vector(0.,0.,0.)
t = Tensor(3*[3*[0.]])
for a in self.atomList():
ma = a._mass
if offset is None:
r = a.position(conf)
else:
r = a.position(conf)+offset[a]
m = m + ma
mr = mr + ma*r
t = t + ma*r.dyadicProduct(r)
cm = mr/m
t = t - m*cm.dyadicProduct(cm)
t = t.trace()*delta - t
return cm, t
def centerAndMomentOfInertia(self, conf=None):
"Returns the center of mass and the moment of inertia tensor."
from Scientific.Geometry import delta
offset = None
universe = self.universe()
if universe is not None:
offset = universe.contiguousObjectOffset([self], conf)
m = 0.
mr = Vector(0., 0., 0.)
t = Tensor(3 * [3 * [0.]])
for a in self.atomList():
ma = a._mass
if offset is None:
r = a.position(conf)
else:
r = a.position(conf) + offset[a]
m = m + ma
mr = mr + ma * r
t = t + ma * r.dyadicProduct(r)
cm = mr / m
t = t - m * cm.dyadicProduct(cm)
t = t.trace() * delta - t
return cm, t
def __init__(self, universe, qVectorsDict):
if not isinstance(qVectorsDict, dict):
raise Error("%s: not a valid python dictionnary." % qVectorsDict)
self.qVectorsDict = copy.copy(qVectorsDict)
# A copy of the input universe.
self.universe = universe
if self.universe.reciprocalBasisVectors() is None:
# The direct basis.
self.dirBasis = None
# The reciprocal basis.
self.recBasis = None
else:
# The direct basis.
self.dirBasis = Tensor(
N.array([N.array(v) for v in self.universe.basisVectors()],
typecode=N.Float)) / (2.0 * N.pi)
# The reciprocal basis.
self.recBasis = (2.0 * N.pi) * Tensor(
N.array([
N.array(v) for v in self.universe.reciprocalBasisVectors()
],
typecode=N.Float))
self.transRecBasis = self.recBasis.transpose()
# These tree qttributes will the "output" for the class.
self.qRadii = []
self.qVectors = []
self.hkls = []
self.qGeometry = self.qVectorsDict.get("qgeometry", None)
if self.qGeometry in ["spatial", "planar", "axial"]:
qShells = self.qVectorsDict.get("qshellvalues", None)
self.qShells = Generate_QShells(qShells)
try:
self.qShellWidth = float(self.qVectorsDict.get("qshellwidth"))
except:
raise Error("Invalid value for qshellwidth parameter.")
try:
self.qVectorsPerShell = int(
self.qVectorsDict.get("qvectorspershell"))
except:
raise Error("Invalid value for qvectorspershell parameter.")
self.qGeometry = self.qVectorsDict.get("qgeometry", None)
if self.qGeometry == "spatial":
self._spatial_qvectors()
elif self.qGeometry == "planar":
self._planar_qvectors()
elif self.qGeometry == "axial":
self._axial_qvectors()
else:
raise Error("%s is not a valid Qvector geometry." %
self.qGeometry)
elif self.qGeometry == "userdefined":
self._userdefined_qvectors()
else:
raise Error("%s is not a valid Qvector geometry." % self.qGeometry)
self.qRadii = N.array(self.qRadii, typecode=N.Float)
self.qvectors_statistics()
self._display_qvectors()
def NeRFinte2cart(self, dihe): # Needs debug
"""
Reconstructs atomlist positions
2005 Parsons et al - NeRF algorithm
:param universe: MMTK universe
:type universe: MMTK universe
:param dihe: list of dihedrals
:type dihe: [[[],[],aval] , [[],[],aval] , dval] where [] is a bond
"""
known_atoms = []
unknown_atoms = copy.deepcopy(universe.atomList())
C = Vector(0, 0, 0)
D = Vector(0, 0, 0)
D2 = Vector(0, 0, 0)
l = len(dihe)
for di in range(l): # Create the START stack
dihe.append(self.swap_dihe(dihe[di]))
#for i in dihe:
# print_zmat_dihe(i)
# Assign initial values
i = dihe[0]
a1 = i[0][0][0]
a2 = i[0][0][1]
a3 = i[1][1][0]
a4 = i[1][1][1]
R1 = i[0][0][2]
R2 = i[1][1][2]
theta1 = i[0][2]
phi = i[2]
universe.atomList()[a1.index].setPosition(Vector(0, 0, 0))
universe.atomList()[a2.index].setPosition(Vector(R1, 0, 0))
universe.atomList()[a3.index].setPosition(
Vector(R1 - R2 * cos(theta1), R2 * sin(theta1), 0))
known_atoms.append(a1)
known_atoms.append(a2)
known_atoms.append(a3)
#print 'initialize at: ', a1, a2, a3
#bPDBprint_atom('HETATM', a1.name, 10*a1.position())
#bPDBprint_atom('HETATM', a2.name, 10*a2.position())
#bPDBprint_atom('HETATM', a3.name, 10*a3.position())
# Main loop
cnt = 0
while len(known_atoms) < len(universe.atomList()):
cnt += 1
if cnt > 9999:
print 'NeRFinte2cart() cnt EXCEEDED: 9999'
break
ai = -1
for a in unknown_atoms:
ai += 1
for i in dihe: # Search if atom a is a4 in any dihe
a1 = i[0][0][0]
a2 = i[0][0][1]
a3 = i[1][0][1]
a4 = i[1][1][1]
a1known = 0
a2known = 0
a3known = 0
a4known = 0
if a4.index == a.index:
for k in known_atoms: # Check if atom is in KNOWN stack
if a4.index == k.index:
a4known = 1
break
if a1.index == k.index:
a1known = 1
if a2.index == k.index:
a2known = 1
if a3.index == k.index:
a3known = 1
if ((a4known == 0) and \
(a1known == 1) and (a2known == 1) and (a3known == 1)):
# Move everything so that a3 is in the center
M = Translation(-1 * a3.position())
universe.applyTransformation(M)
for k in known_atoms:
M(k.position())
# Solve a4
#print 'SOLVE: ', a4
print_zmat_dihe(i)
A = a1.position()
B = a2.position()
C = a3.position()
#print 'A = ', A, '(', a1.name , ')'
#print 'B = ', B, '(', a2.name , ')'
#print 'C = ', C, '(', a3.name , ')'
R = i[1][1][2]
#print 'R = ', R
theta = i[1][2]
if theta > pi / 2:
theta = pi - theta
#print 'theta = ', theta, numpy.rad2deg(theta)
phi = i[2]
if phi < 0:
phi = pi - phi
#print 'phi = ', phi, numpy.rad2deg(phi)
AB = B - A # B - A
#print 'AB = ', AB
BC = C - B # C - B
#print 'BC = ', BC
bc = BC.normal()
#print 'bc = ', bc
n = AB.cross(bc)
n = n.normal()
#print 'n = ', n
Mx = bc
#print 'Mx = ', Mx
My = n.cross(bc)
My = My.normal()
#print 'My = ', My
Mz = n
#print 'Mz = ', Mz
M = Tensor([ [Mx.x(), Mx.y(), Mx.z()],\
[My.x(), My.y(), My.z()],\
[Mz.x(), Mz.y(), Mz.z()] ])
#print 'M: '
#print M
D2 = Vector(R * cos(theta),
R * cos(phi) * sin(theta),
R * sin(phi) * sin(theta))
#print 'D2: '
#print D2
D = M * D2
D = D + C
#print 'D: '
#print D
a.setPosition(D)
universe.atomList()[a4.index].setPosition(D)
# Add a4 to KNOWN
known_atoms.append(a4)
unknown_atoms.pop(ai)
#print '========================'
break
for k in known_atoms:
bPDBprint_atom('HETATM', k.name, 10 * k.position())
def readLine(self):
"""Returns the contents of the next non-blank line (= record).
The return value is a tuple whose first element (a string)
contains the record type. For supported record types (HEADER,
ATOM, HETATM, ANISOU, TERM, MODEL, CONECT), the items from the
remaining fields are put into a dictionary which is returned
as the second tuple element. Most dictionary elements are
strings or numbers; atom positions are returned as a vector,
and anisotropic temperature factors are returned as a rank-2
tensor, already multiplied by 1.e-4. White space is stripped
from all strings except for atom names, whose correct
interpretation can depend on an initial space. For unsupported
record types, the second tuple element is a string containing
the remaining part of the record.
"""
while 1:
line = self.file.readline()
if not line: return ('END', '')
if line[-1] == '\n': line = line[:-1]
line = string.strip(line)
if line: break
line = string.ljust(line, 80)
type = string.strip(line[:6])
if type == 'ATOM' or type == 'HETATM':
line = FortranLine(line, atom_format)
data = {
'serial_number': line[1],
'name': line[2],
'alternate': string.strip(line[3]),
'residue_name': string.strip(line[4]),
'chain_id': string.strip(line[5]),
'residue_number': line[6],
'insertion_code': string.strip(line[7]),
'position': Vector(line[8:11]),
'occupancy': line[11],
'temperature_factor': line[12],
'segment_id': string.strip(line[13]),
'element': string.strip(line[14]),
'charge': string.strip(line[15])
}
return type, data
elif type == 'ANISOU':
line = FortranLine(line, anisou_format)
data = {
'serial_number':
line[1],
'name':
line[2],
'alternate':
string.strip(line[3]),
'residue_name':
string.strip(line[4]),
'chain_id':
string.strip(line[5]),
'residue_number':
line[6],
'insertion_code':
string.strip(line[7]),
'u':
1.e-4 * Tensor([[line[8], line[11], line[12]],
[line[11], line[9], line[13]],
[line[12], line[13], line[10]]]),
'segment_id':
string.strip(line[14]),
'element':
string.strip(line[15]),
'charge':
string.strip(line[16])
}
return type, data
elif type == 'TER':
line = FortranLine(line, ter_format)
data = {
'serial_number': line[1],
'residue_name': string.strip(line[2]),
'chain_id': string.strip(line[3]),
'residue_number': line[4],
'insertion_code': string.strip(line[5])
}
return type, data
elif type == 'CONECT':
line = FortranLine(line, conect_format)
data = {
'serial_number': line[1],
'bonded': filter(lambda i: i > 0, line[2:6]),
'hydrogen_bonded': filter(lambda i: i > 0, line[6:10]),
'salt_bridged': filter(lambda i: i > 0, line[10:12])
}
return type, data
elif type == 'MODEL':
line = FortranLine(line, model_format)
data = {'serial_number': line[1]}
return type, data
elif type == 'HEADER':
line = FortranLine(line, header_format)
data = {'compound': line[1], 'date': line[2], 'pdb_code': line[3]}
return type, data
else:
return type, line[6:]
def zero(self):
return Tensor(3 * [[0., 0., 0.]])
def sumOverParticles(self):
return Tensor(N.add.reduce(self.array, 0))
def zero(self):
return Tensor([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]])
def __getitem__(self, item):
if not isinstance(item, int):
item = item.index
return Tensor(self.array[item])