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# Trajectory generators in Cython
#
# Written by Konrad Hinsen
#
import_MMTK_universe()
import_MMTK_trajectory()
import_MMTK_forcefield()
from MMTK import Features
import numpy as N
cimport numpy as N
import cython
cdef extern from "stdlib.h":
ctypedef long size_t
cdef void *malloc(size_t size)
cdef void free(void *ptr)
#
# Base class for trajectory generators
#
cdef class TrajectoryGenerator(object):
"""
Trajectory generator base class
This base class implements the common aspects of everything that
generates trajectories: integrators, minimizers, etc.
"""
def __init__(self, universe, options, name):
self.universe = universe
self.options = options
self.name = name
self.call_options = {}
self.features = []
self.tvars = NULL
self.tspec = NULL
def setCallOptions(self, options):
self.call_options = options
def getActions(self):
try:
steps = self.getOption('steps')
except ValueError:
steps = None
return [a.getSpecificationList(self, steps) for a in self.actions]
def cleanupActions(self):
for a in self.actions:
a.cleanup()
def getOption(self, option):
try:
value = self.call_options[option]
except KeyError:
try:
value = self.options[option]
except KeyError:
try:
value = self.default_options[option]
except KeyError:
raise ValueError('undefined option: ' + option)
return value
def optionString(self, options):
return sum((o + '=' + `self.getOption(o)` + ', ' for o in options),
'')[:-2]
def __call__(self, **options):
self.setCallOptions(options)
try:
self.actions = self.getOption('actions')
except ValueError:
self.actions = []
try:
if self.getOption('background'):
import MMTK_state_accessor
self.state_accessor = MMTK_state_accessor.StateAccessor()
self.actions.append(self.state_accessor)
except ValueError:
pass
Features.checkFeatures(self, self.universe)
if self.tvars != NULL:
free(self.tvars)
self.tvars = NULL
configuration = self.universe.configuration()
self.conf_array = configuration.array
self.declareTrajectoryVariable_array(self.conf_array,
"configuration",
"Configuration:\n",
length_unit_name,
PyTrajectory_Configuration)
self.universe_spec = <PyUniverseSpecObject *>self.universe._spec
if self.universe_spec.geometry_data_length > 0:
self.declareTrajectoryVariable_box(
self.universe_spec.geometry_data,
self.universe_spec.geometry_data_length)
masses = self.universe.masses()
self.declareTrajectoryVariable_array(masses.array,
"masses",
"Masses:\n",
mass_unit_name,
PyTrajectory_Internal)
self.natoms = self.universe.numberOfAtoms()
self.df = self.universe.degreesOfFreedom()
self.declareTrajectoryVariable_int(&self.df,
"degrees_of_freedom",
"Degrees of freedom: %d\n",
"", PyTrajectory_Internal)
if self.getOption('background'):
from MMTK import ThreadManager
return ThreadManager.TrajectoryGeneratorThread(
self.universe, self.start_py, (), self.state_accessor)
else:
self.start()
return None
def start_py(self):
self.start()
cdef start(self):
pass
cdef void _addTrajectoryVariable(self, PyTrajectoryVariable v) except *:
cdef PyTrajectoryVariable *tv
cdef int i, n
if self.tvars == NULL:
self.tvars = <PyTrajectoryVariable *> \
malloc(2*sizeof(PyTrajectoryVariable))
if self.tvars == NULL:
raise MemoryError
self.tvars[0] = v
self.tvars[1].name = NULL
else:
n = 0
tv = self.tvars
while tv.name != NULL:
n += 1
tv += 1
tv = <PyTrajectoryVariable *> \
malloc((n+2)*sizeof(PyTrajectoryVariable))
if tv == NULL:
raise MemoryError
for i in range(n):
tv[i] = self.tvars[i]
tv[n] = v
tv[n+1].name = NULL
free(self.tvars)
self.tvars = tv
cdef void declareTrajectoryVariable_double(self, double * var, char *name,
char *text, char*unit,
int data_class) except *:
cdef PyTrajectoryVariable v
v.name = name
v.text = text
v.unit = unit
v.type = PyTrajectory_Scalar
v.data_class = data_class
v.value.dp = var
self._addTrajectoryVariable(v)
cdef void declareTrajectoryVariable_int(self, int * var, char *name,
char *text, char*unit,
int data_class) except *:
cdef PyTrajectoryVariable v
v.name = name
v.text = text
v.unit = unit
v.type = PyTrajectory_IntScalar
v.data_class = data_class
v.value.ip = var
self._addTrajectoryVariable(v)
cdef void declareTrajectoryVariable_array(self, N.ndarray array, char*name,
char *text, char*unit,
int data_class) except *:
cdef PyTrajectoryVariable v
v.name = name
v.text = text
v.unit = unit
v.data_class = data_class
v.value.array = <PyArrayObject *>array
if array.ndim == 1:
v.type = PyTrajectory_ParticleScalar
elif array.ndim == 2:
v.type = PyTrajectory_ParticleVector
else:
v.type = 0 # suppress warning from gcc
assert ValueError("array must be 1D or 2D")
self._addTrajectoryVariable(v)
cdef void declareTrajectoryVariable_box(self, double * var, int l) except *:
cdef PyTrajectoryVariable v
v.name = "box_size"
v.text = "Box size:"
v.unit = length_unit_name
v.type = PyTrajectory_BoxSize
v.data_class = PyTrajectory_Configuration
v.value.dp = var
v.length = l
self._addTrajectoryVariable(v)
cdef void initializeTrajectoryActions(self) except *:
actions = self.getActions()
self.tspec = PyTrajectory_OutputSpecification(self.universe,
<PyListObject *>actions,
self.name, self.tvars);
if self.tspec == NULL:
raise MemoryError
cdef void finalizeTrajectoryActions(self, int last_step,
int error=False) except *:
if error:
PyTrajectory_OutputFinish(self.tspec, last_step, 1, 1, self.tvars)
else:
PyTrajectory_OutputFinish(self.tspec, last_step, 0, 1, self.tvars)
cdef int trajectoryActions(self, int step) except -1:
return PyTrajectory_Output(self.tspec, step, self.tvars, NULL)
cdef void foldCoordinatesIntoBox(self) nogil:
self.universe_spec.correction_function(<vector3 *>self.conf_array.data,
self.natoms,
self.universe_spec.geometry_data)
cdef void acquireReadLock(self) nogil:
PyUniverseSpec_StateLock(self.universe_spec, 1)
self.lock_state = 1
cdef void releaseReadLock(self) nogil:
PyUniverseSpec_StateLock(self.universe_spec, 2)
self.lock_state = 0
cdef void acquireWriteLock(self) nogil:
PyUniverseSpec_StateLock(self.universe_spec, -1)
self.lock_state = -1
cdef void releaseWriteLock(self) nogil:
PyUniverseSpec_StateLock(self.universe_spec, -2)
self.lock_state = 0
#
# Base class for trajectory generators that call the C-level
# energy evaluators. It implements a mechanism that makes such
# generators thread-safe.
#
cdef class EnergyBasedTrajectoryGenerator(TrajectoryGenerator):
def __init__(self, universe, options, name):
TrajectoryGenerator.__init__(self, universe, options, name)
self.evaluator_object = None
self.c_evaluator_object = None
self.evaluator = NULL
cdef void initializeTrajectoryActions(self) except *:
TrajectoryGenerator.initializeTrajectoryActions(self)
# Construct a C evaluator object for the force field, using
# the specified number of threads or the default value
nt = self.getOption('threads')
self.evaluator_object = self.universe.energyEvaluator(threads=nt)
self.c_evaluator_object = self.evaluator_object.CEvaluator()
self.evaluator = <PyFFEvaluatorObject*>self.c_evaluator_object
cdef void finalizeTrajectoryActions(self, int last_step,
int error=False) except *:
TrajectoryGenerator.finalizeTrajectoryActions(self, last_step, error)
self.evaluator = NULL
self.c_evaluator_object = None
self.evaluator_object = None
cdef int trajectoryActions(self, int step) except -1:
cdef int ret_code
self.evaluator.tstate_save = PyEval_SaveThread()
if self.lock_state != 0:
PyUniverseSpec_StateLock(self.universe_spec, 2*self.lock_state)
ret_code = PyTrajectory_Output(self.tspec, step, self.tvars,
&self.evaluator.tstate_save)
if self.lock_state != 0:
PyUniverseSpec_StateLock(self.universe_spec, self.lock_state)
PyEval_RestoreThread(self.evaluator.tstate_save)
return ret_code
cdef void calculateEnergies(self, N.ndarray[double, ndim=2] conf_array,
energy_data *energy, int small_change=0) \
except *:
self.evaluator.tstate_save = PyEval_SaveThread()
if self.lock_state == -1:
PyUniverseSpec_StateLock(self.universe_spec, -2)
if self.lock_state != 1:
PyUniverseSpec_StateLock(self.universe_spec, 1)
self.evaluator.eval_func(self.evaluator, energy, <PyArrayObject *>conf_array,
small_change)
if self.lock_state != 1:
PyUniverseSpec_StateLock(self.universe_spec, 2)
if self.lock_state == -1:
PyUniverseSpec_StateLock(self.universe_spec, -1)
PyEval_RestoreThread(self.evaluator.tstate_save)
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