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 [TrilinearThreshGridTerm(universe, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], \
self.strength, scaling_factor, self.grid_name, self.max_val, self.Ethresh)]
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']==-2:
from MMTK_trilinear_isqrt_grid import TrilinearISqrtGridTerm
return [TrilinearISqrtGridTerm(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':
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 []
# 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 []
# 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:
# import time
# import os.path
# import MMTK_trilinear_one_fourth_grid
# trilinear_grid_path = MMTK_trilinear_one_fourth_grid.__file__
# print """
# in {0}
# last modified {1}
# """.format(trilinear_grid_path, \
# time.ctime(os.path.getmtime(trilinear_grid_path)))
from MMTK_trilinear_one_fourth_grid import TrilinearOneFourthGridTerm
return [TrilinearOneFourthGridTerm(universe._spec, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], \
scaling_factor.array, \
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:
# print "self.params['name']", self.params['name']
if self.use_C:
# import time
# import os.path
# import MMTK_trilinear_grid
# trilinear_grid_path = MMTK_trilinear_grid.__file__
# print """
# in {0}
# last modified {1}
# """.format(trilinear_grid_path, \
# time.ctime(os.path.getmtime(trilinear_grid_path)))
from MMTK_trilinear_grid import TrilinearGridTerm
return [TrilinearGridTerm(universe._spec, \
self.grid_data['spacing'], self.grid_data['counts'], \
self.grid_data['vals'], self.params['strength'], \
scaling_factor.array, \
self.params['name'])]
else:
# This is for debugging
from MMTK_trilinear_grid_cython import TrilinearGridTerm as TrilinearGridTerm_cython
return [TrilinearGridTerm_cython(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
