def evaluatorTerms(self, universe, subset1, subset2, global_data):
# The energy for subsets is defined as consisting only
# of interactions within that subset, so the contribution
# of an external field is zero. Therefore we just return
# an empty list of energy terms.
if subset1 is not None or subset2 is not None:
return []
# Collect the charges into an array
charges = ParticleScalar(universe)
for o in universe:
for a in o.atomList():
charges[a] = o.getAtomProperty(a, self.charge_property)
# Here we pass all the parameters to
# the energy term code that handles energy calculations.
return [ElectricFieldTerm(universe, self.strength, charges)]
def evaluatorTerms(self, universe, subset1, subset2, global_data):
# The energy for subsets is defined as consisting only
# of interactions within that subset, so the contribution
# of an external field is zero. Therefore we just return
# an empty list of energy terms.
if subset1 is not None or subset2 is not None:
return []
# Collect the scaling_factor into an array
scaling_factor = ParticleScalar(universe)
for o in universe:
for a in o.atomList():
scaling_factor[a] = o.getAtomProperty(a, self.scaling_property)
scaling_factor.scaleBy(self.scaling_prefactor)
# Here we pass all the parameters to
# the energy term code that handles energy calculations.
return [TrilinearISqrtGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], \
self.strength, scaling_factor, self.grid_name)]
def evaluatorTerms(self, universe, subset1, subset2, global_data):
# The energy for subsets is defined as consisting only
# of interactions within that subset, so the contribution
# of an external field is zero. Therefore we just return
# an empty list of energy terms.
if subset1 is not None or subset2 is not None:
return []
# Collect the scaling_factor into an array
scaling_factor = ParticleScalar(universe)
for o in universe:
for a in o.atomList():
scaling_factor[a] = o.getAtomProperty(
a, self.params['scaling_property'])
scaling_factor.scaleBy(self.params['scaling_prefactor'])
# Here we pass all the parameters to
# the energy term code that handles energy calculations.
if self.params['interpolation_type'] == 'Trilinear':
if self.params['energy_thresh'] > 0:
if self.params['inv_power'] is not None:
raise NotImplementedError
else:
from MMTK_trilinear_thresh_grid import TrilinearThreshGridTerm
return [TrilinearThreshGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'], self.params['energy_thresh'])]
elif self.params['inv_power'] is not None:
if self.params['inv_power'] == 4:
from MMTK_trilinear_one_fourth_grid import TrilinearOneFourthGridTerm
return [TrilinearOneFourthGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'])]
else:
from MMTK_trilinear_transform_grid import TrilinearTransformGridTerm
return [TrilinearTransformGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor,
self.params['name'], self.params['inv_power'])]
else:
from MMTK_trilinear_grid import TrilinearGridTerm
return [TrilinearGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'])]
elif self.params['interpolation_type'] == 'BSpline':
if self.params['inv_power'] is not None:
from MMTK_BSpline_transform_grid import BSplineTransformGridTerm
return [BSplineTransformGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'], self.params['inv_power'])]
else:
from MMTK_BSpline_grid import BSplineGridTerm
return [BSplineGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'])]
elif self.params['interpolation_type'] == 'CatmullRom':
if self.params['inv_power'] is not None:
from MMTK_CatmullRom_transform_grid import CatmullRomTransformGridTerm
return [CatmullRomTransformGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'], self.params['inv_power'])]
else:
from MMTK_CatmullRom_grid import CatmullRomGridTerm
return [CatmullRomGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'])]
elif self.params['interpolation_type'] == 'Tricubic':
if self.params['inv_power'] is not None:
from MMTK_Tricubic_transform_grid import TricubicTransformGridTerm
return [TricubicTransformGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'], self.params['inv_power'])]
else:
from MMTK_Tricubic_grid import TricubicGridTerm
return [TricubicGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], scaling_factor, \
self.params['name'])]
print self.params['interpolation_type'] + ' interpolation is unknown'
raise NotImplementedError
def evaluatorTerms(self, universe, subset1, subset2, global_data):
# The energy for subsets is defined as consisting only
# of interactions within that subset, so the contribution
# of an external field is zero. Therefore we just return
# an empty list of energy terms.
if subset1 is not None or subset2 is not None:
return []
import numpy as np
if (self.prmtopFN is not None) and \
(self.inv_prmtop_atom_order is not None):
# Get charges, radii, and scale factors from OpenMM
import simtk.openmm
import simtk.openmm.app as OpenMM_app
prmtop = OpenMM_app.AmberPrmtopFile(self.prmtopFN)
OMM_system = prmtop.createSystem(\
nonbondedMethod=OpenMM_app.CutoffNonPeriodic, \
nonbondedCutoff=1.5, \
constraints=None, \
implicitSolvent=OpenMM_app.OBC2)
f = OMM_system.getForces()[-2]
numParticles = f.getNumParticles()
charges = np.zeros(numParticles)
atomicRadii = np.zeros(numParticles)
scaleFactors = np.zeros(numParticles)
for n in range(numParticles):
(charge, radius, scaleFactor) = f.getParticleParameters(n)
charges[n] = charge / simtk.unit.elementary_charge
atomicRadii[n] = radius / simtk.unit.nanometer
scaleFactors[n] = scaleFactor
charges = charges[self.inv_prmtop_atom_order]
atomicRadii = atomicRadii[self.inv_prmtop_atom_order]
scaleFactors = scaleFactors[self.inv_prmtop_atom_order]
else:
# Get charges, radii, and scale factors from the ligand database (preferred)
charges_ps = ParticleScalar(universe)
atomicRadii_ps = ParticleScalar(universe)
scaleFactors_ps = ParticleScalar(universe)
for o in universe:
for a in o.atomList():
charges_ps[a] = o.getAtomProperty(a, 'amber_charge')
atomicRadii_ps[a] = o.getAtomProperty(
a, 'scaling_factor_BornRadii')
scaleFactors_ps[a] = o.getAtomProperty(
a, 'scaling_factor_BornScreening')
charges = charges_ps.array
atomicRadii = atomicRadii_ps.array
scaleFactors = scaleFactors_ps.array
numParticles = charges.shape[0]
# import time
# import os.path
# import MMTK_OBC
# OBCpath = MMTK_OBC.__file__
# print """
# in {0}
# last modified {1}
# """.format(OBCpath, time.ctime(os.path.getmtime(OBCpath)))
# Here we pass all the parameters as "simple" data types to
# the C code that handles energy calculations.
if self.useDesolvationGrid:
# With desolvation grid
from MMTK_OBC_desolv import OBCDesolvTerm
return [OBCDesolvTerm(universe._spec, numParticles, self.strength, \
charges, atomicRadii, scaleFactors, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.r_min, self.r_max)]
else:
# No desolvation grid
from MMTK_OBC import OBCTerm
return [OBCTerm(universe._spec, numParticles, self.strength, \
charges, atomicRadii, scaleFactors)]
