def bngl_import_compare_nfsim(bng_file):
m = model_from_bngl(bng_file)
BNG_SEED = 123
# Simulate using the BNGL file directly
with BngFileInterface(model=None) as bng:
bng.action('readFile', file=bng_file, skip_actions=1)
bng.action('simulate',
method='nf',
n_steps=10,
t_end=100,
seed=BNG_SEED)
bng.execute()
yfull1 = bng.read_simulation_results()
# Convert to a PySB model, then simulate using BNG
with BngFileInterface(model=m) as bng:
bng.action('simulate',
method='nf',
n_steps=10,
t_end=100,
seed=BNG_SEED)
bng.execute()
yfull2 = bng.read_simulation_results()
# Check all species trajectories are equal (within numerical tolerance)
for i in range(len(m.observables)):
print(i)
print(yfull1[i])
print(yfull2[i])
print(yfull1[i] == yfull2[i])
assert yfull1[i] == yfull2[i]
def bngl_import_compare_simulations(bng_file,
force=False,
precision=1e-12,
sim_times=range(0, 100, 10)):
"""
Test BNGL file import by running an ODE simulation on the imported model
and on the BNGL file directly to compare trajectories.
"""
m = model_from_bngl(bng_file, force=force)
if sim_times is None:
# Skip simulation check
return
# Simulate using the BNGL file directly
with BngFileInterface(model=None) as bng:
bng.action('readFile', file=bng_file, skip_actions=1)
bng.action('generate_network')
bng.action('simulate', method='ode', sample_times=sim_times)
bng.execute()
yfull1 = bng.read_simulation_results()
# Convert to a PySB model, then simulate using BNG
with BngFileInterface(model=m) as bng:
bng.action('generate_network')
bng.action('simulate', method='ode', sample_times=sim_times)
bng.execute()
yfull2 = bng.read_simulation_results()
# Don't check trajectories on forced examples
if force:
return
# Check all species trajectories are equal (within numerical tolerance)
assert len(yfull1.dtype.names) == len(yfull2.dtype.names)
for species in yfull1.dtype.names:
logger.debug(species)
logger.debug(yfull1[species])
if species in yfull2.dtype.names:
renamed_species = species
else:
renamed_species = 'Obs_{}'.format(species)
logger.debug(yfull2[renamed_species])
assert numpy.allclose(yfull1[species],
yfull2[renamed_species],
atol=precision,
rtol=precision)
def __init__(self, filename, force=False, cleanup=True):
super(BnglBuilder, self).__init__()
with BngFileInterface(model=None, cleanup=cleanup) as con:
con.action('readFile', file=filename, skip_actions=1)
con.action('writeXML', evaluate_expressions=0)
con.execute()
self._force = force
self._x = xml.etree.ElementTree.parse('%s.xml' %
con.base_filename)\
.getroot().find(_ns('{0}model'))
self._model_env = {}
self._parse_bng_xml()
def bngl_import_compare_simulations(bng_file,
force=False,
sim_times=range(0, 100, 10)):
"""
Test BNGL file import by running an ODE simulation on the imported model
and on the BNGL file directly to compare trajectories.
"""
m = model_from_bngl(bng_file, force=force)
# Simulate using the BNGL file directly
with BngFileInterface(model=None) as bng:
bng.action('readFile', file=bng_file, skip_actions=1)
bng.action('generate_network')
bng.action('simulate', method='ode', sample_times=sim_times)
bng.execute()
yfull1 = bng.read_simulation_results()
# Convert to a PySB model, then simulate using BNG
with BngFileInterface(model=m) as bng:
bng.action('generate_network')
bng.action('simulate', method='ode', sample_times=sim_times)
bng.execute()
yfull2 = bng.read_simulation_results()
# Check all species trajectories are equal (within numerical tolerance)
for species in m.species:
print(species)
print(yfull1[species])
print(yfull2[species])
print(
numpy.allclose(yfull1[species],
yfull2[species],
atol=1e-8,
rtol=1e-8))
assert numpy.allclose(yfull1[species],
yfull2[species],
atol=1e-8,
rtol=1e-8)
def __init__(self, filename, force=False, cleanup=True):
super(BnglBuilder, self).__init__()
filename = os.path.abspath(filename)
with BngFileInterface(model=None, cleanup=cleanup) as con:
con.action('readFile', file=filename, skip_actions=1)
con.action('writeXML', evaluate_expressions=0)
con.execute()
self._x = xml.etree.ElementTree.parse('%s.xml' %
con.base_filename)\
.getroot().find(_ns('{0}model'))
self.model.name = os.path.splitext(os.path.basename(filename))[0]
self._force = force
self._model_env = {}
self._renamed_states = collections.defaultdict(dict)
self._log = pysb.logging.get_logger(__name__)
self._parse_bng_xml()
def run(self,
tspan=None,
initials=None,
param_values=None,
n_runs=1,
method='ssa',
output_dir=None,
output_file_basename=None,
cleanup=None,
population_maps=None,
**additional_args):
"""
Simulate a model using BioNetGen
Parameters
----------
tspan: vector-like
time span of simulation
initials: vector-like, optional
initial conditions of model
param_values : vector-like or dictionary, optional
Values to use for every parameter in the model. Ordering is
determined by the order of model.parameters.
If not specified, parameter values will be taken directly from
model.parameters.
n_runs: int
number of simulations to run
method : str
Type of simulation to run. Must be one of:
* 'ssa' - Stochastic Simulation Algorithm (direct method with
propensity sorting)
* 'nf' - Stochastic network free simulation with NFsim.
Performs Hybrid Particle/Population simulation if
population_maps argument is supplied
* 'pla' - Partioned-leaping algorithm (variant of tau-leaping
algorithm)
* 'ode' - ODE simulation (Sundials CVODE algorithm)
output_dir : string, optional
Location for temporary files generated by BNG. If None (the
default), uses a temporary directory provided by the system. A
temporary directory with a random name is created within the
supplied location.
output_file_basename : string, optional
This argument is used as a prefix for the temporary BNG
output directory, rather than the individual files.
cleanup : bool, optional
If True (default), delete the temporary files after the
simulation is finished. If False, leave them in place (Useful for
debugging). The default value, None, means to use the value
specified in :py:func:`__init__`.
population_maps: list of PopulationMap
List of :py:class:`PopulationMap` objects for hybrid
particle/population modeling. Only used when method='nf'.
additional_args: kwargs, optional
Additional arguments to pass to BioNetGen
Examples
--------
Simulate a model using network free simulation (NFsim):
>>> from pysb.examples import robertson
>>> from pysb.simulator.bng import BngSimulator
>>> model = robertson.model
>>> sim = BngSimulator(model, tspan=np.linspace(0, 1))
>>> x = sim.run(n_runs=1, method='nf')
"""
super(BngSimulator, self).run(tspan=tspan,
initials=initials,
param_values=param_values,
_run_kwargs=locals())
if cleanup is None:
cleanup = self.cleanup
if method not in self._SIMULATOR_TYPES:
raise ValueError("Method must be one of " +
str(self._SIMULATOR_TYPES))
if method != 'nf' and population_maps:
raise ValueError('population_maps argument is only used when '
'method is "nf"')
if method == 'nf':
if population_maps is not None and (
not isinstance(population_maps, collections.Iterable)
or any(not isinstance(pm, PopulationMap)
for pm in population_maps)):
raise ValueError('population_maps should be a list of '
'PopulationMap objects')
model_additional_species = self.initials_dict.keys()
else:
model_additional_species = None
tspan_lin_spaced = np.allclose(
self.tspan,
np.linspace(self.tspan[0], self.tspan[-1], len(self.tspan)))
if method == 'nf' and (not tspan_lin_spaced or self.tspan[0] != 0.0):
raise SimulatorException('NFsim requires tspan to be linearly '
'spaced starting at t=0')
# BNG requires t_start even when supplying sample_times
additional_args['t_start'] = self.tspan[0]
if tspan_lin_spaced:
# Just supply t_end and n_steps
additional_args['n_steps'] = len(self.tspan) - 1
additional_args['t_end'] = self.tspan[-1]
else:
additional_args['sample_times'] = self.tspan
additional_args['method'] = method
additional_args['print_functions'] = True
verbose_bool = self._logger.logger.getEffectiveLevel() <= logging.DEBUG
extended_debug = self._logger.logger.getEffectiveLevel() <= \
EXTENDED_DEBUG
additional_args['verbose'] = extended_debug
params_names = [g.name for g in self._model.parameters]
n_param_sets = self.initials_length
total_sims = n_runs * n_param_sets
self._logger.info('Running %d BNG %s simulations' %
(total_sims, method))
model_to_load = None
hpp_bngl = None
if population_maps:
self._logger.debug('Generating hybrid particle-population model')
hpp_bngl = generate_hybrid_model(self._model,
population_maps,
model_additional_species,
verbose=extended_debug)
else:
model_to_load = self._model
with BngFileInterface(
model_to_load,
verbose=verbose_bool,
output_dir=output_dir,
output_prefix=output_file_basename,
cleanup=cleanup,
model_additional_species=model_additional_species) as bngfile:
if hpp_bngl:
hpp_bngl_filename = os.path.join(bngfile.base_directory,
'hpp_model.bngl')
self._logger.debug('HPP BNGL:\n\n' + hpp_bngl)
with open(hpp_bngl_filename, 'w') as f:
f.write(hpp_bngl)
if method != 'nf':
# TODO: Write existing netfile if already generated
bngfile.action('generate_network',
overwrite=True,
verbose=extended_debug)
if output_file_basename is None:
prefix = 'pysb'
else:
prefix = output_file_basename
sim_prefix = 0
for pset_idx in range(n_param_sets):
for n in range(len(self.param_values[pset_idx])):
bngfile.set_parameter(params_names[n],
self.param_values[pset_idx][n])
for cp, values in self.initials_dict.items():
if population_maps:
for pm in population_maps:
if pm.complex_pattern.is_equivalent_to(cp):
cp = pm.counter_species
break
bngfile.set_concentration(cp, values[pset_idx])
for sim_rpt in range(n_runs):
tmp = additional_args.copy()
tmp['prefix'] = '{}{}'.format(prefix, sim_prefix)
bngfile.action('simulate', **tmp)
bngfile.action('resetConcentrations')
sim_prefix += 1
if hpp_bngl:
bngfile.execute(reload_netfile=hpp_bngl_filename,
skip_file_actions=True)
else:
bngfile.execute()
if method != 'nf':
load_equations(self.model, bngfile.net_filename)
list_of_yfull = \
BngFileInterface.read_simulation_results_multi(
[bngfile.base_filename + str(n) for n in range(total_sims)])
tout = []
species_out = []
obs_exp_out = []
for i in range(total_sims):
yfull = list_of_yfull[i]
yfull_view = yfull.view(float).reshape(len(yfull), -1)
tout.append(yfull_view[:, 0])
if method == 'nf':
obs_exp_out.append(yfull_view[:, 1:])
else:
species_out.append(yfull_view[:,
1:(len(self.model.species) + 1)])
if len(self.model.observables) or len(self.model.expressions):
obs_exp_out.append(
yfull_view[:, (len(self.model.species) +
1):(len(self.model.species) + 1) +
len(self.model.observables) +
len(self.model.expressions_dynamic())])
return SimulationResult(self,
tout=tout,
trajectories=species_out,
observables_and_expressions=obs_exp_out,
simulations_per_param_set=n_runs)
def run(self, tspan=None, initials=None, param_values=None, n_runs=1,
method='ssa', output_dir=None, output_file_basename=None,
cleanup=None, population_maps=None, **additional_args):
"""
Simulate a model using BioNetGen
Parameters
----------
tspan: vector-like
time span of simulation
initials: vector-like, optional
initial conditions of model
param_values : vector-like or dictionary, optional
Values to use for every parameter in the model. Ordering is
determined by the order of model.parameters.
If not specified, parameter values will be taken directly from
model.parameters.
n_runs: int
number of simulations to run
method : str
Type of simulation to run. Must be one of:
* 'ssa' - Stochastic Simulation Algorithm (direct method with
propensity sorting)
* 'nf' - Stochastic network free simulation with NFsim.
Performs Hybrid Particle/Population simulation if
population_maps argument is supplied
* 'pla' - Partioned-leaping algorithm (variant of tau-leaping
algorithm)
* 'ode' - ODE simulation (Sundials CVODE algorithm)
output_dir : string, optional
Location for temporary files generated by BNG. If None (the
default), uses a temporary directory provided by the system. A
temporary directory with a random name is created within the
supplied location.
output_file_basename : string, optional
This argument is used as a prefix for the temporary BNG
output directory, rather than the individual files.
cleanup : bool, optional
If True (default), delete the temporary files after the
simulation is finished. If False, leave them in place (Useful for
debugging). The default value, None, means to use the value
specified in :py:func:`__init__`.
population_maps: list of PopulationMap
List of :py:class:`PopulationMap` objects for hybrid
particle/population modeling. Only used when method='nf'.
additional_args: kwargs, optional
Additional arguments to pass to BioNetGen
Examples
--------
Simulate a model using network free simulation (NFsim):
>>> from pysb.examples import robertson
>>> from pysb.simulator.bng import BngSimulator
>>> model = robertson.model
>>> sim = BngSimulator(model, tspan=np.linspace(0, 1))
>>> x = sim.run(n_runs=1, method='nf')
"""
super(BngSimulator, self).run(tspan=tspan,
initials=initials,
param_values=param_values,
_run_kwargs=locals()
)
if cleanup is None:
cleanup = self.cleanup
if method not in self._SIMULATOR_TYPES:
raise ValueError("Method must be one of " +
str(self._SIMULATOR_TYPES))
if method != 'nf' and population_maps:
raise ValueError('population_maps argument is only used when '
'method is "nf"')
if method == 'nf':
if population_maps is not None and (not isinstance(
population_maps, collections.Iterable) or
any(not isinstance(pm, PopulationMap) for pm in
population_maps)):
raise ValueError('population_maps should be a list of '
'PopulationMap objects')
model_additional_species = self.initials_dict.keys()
else:
model_additional_species = None
tspan_lin_spaced = np.allclose(
self.tspan,
np.linspace(self.tspan[0], self.tspan[-1], len(self.tspan))
)
if method == 'nf' and (not tspan_lin_spaced or self.tspan[0] != 0.0):
raise SimulatorException('NFsim requires tspan to be linearly '
'spaced starting at t=0')
# BNG requires t_start even when supplying sample_times
additional_args['t_start'] = self.tspan[0]
if tspan_lin_spaced:
# Just supply t_end and n_steps
additional_args['n_steps'] = len(self.tspan) - 1
additional_args['t_end'] = self.tspan[-1]
else:
additional_args['sample_times'] = self.tspan
additional_args['method'] = method
additional_args['print_functions'] = True
verbose_bool = self._logger.logger.getEffectiveLevel() <= logging.DEBUG
extended_debug = self._logger.logger.getEffectiveLevel() <= \
EXTENDED_DEBUG
additional_args['verbose'] = extended_debug
params_names = [g.name for g in self._model.parameters]
n_param_sets = self.initials_length
total_sims = n_runs * n_param_sets
self._logger.info('Running %d BNG %s simulations' % (total_sims,
method))
model_to_load = None
hpp_bngl = None
if population_maps:
self._logger.debug('Generating hybrid particle-population model')
hpp_bngl = generate_hybrid_model(
self._model,
population_maps,
model_additional_species,
verbose=extended_debug)
else:
model_to_load = self._model
with BngFileInterface(model_to_load,
verbose=verbose_bool,
output_dir=output_dir,
output_prefix=output_file_basename,
cleanup=cleanup,
model_additional_species=model_additional_species
) as bngfile:
if hpp_bngl:
hpp_bngl_filename = os.path.join(bngfile.base_directory,
'hpp_model.bngl')
self._logger.debug('HPP BNGL:\n\n' + hpp_bngl)
with open(hpp_bngl_filename, 'w') as f:
f.write(hpp_bngl)
if method != 'nf':
# TODO: Write existing netfile if already generated
bngfile.action('generate_network', overwrite=True,
verbose=extended_debug)
if output_file_basename is None:
prefix = 'pysb'
else:
prefix = output_file_basename
sim_prefix = 0
for pset_idx in range(n_param_sets):
for n in range(len(self.param_values[pset_idx])):
bngfile.set_parameter(params_names[n],
self.param_values[pset_idx][n])
for cp, values in self.initials_dict.items():
if population_maps:
for pm in population_maps:
if pm.complex_pattern.is_equivalent_to(cp):
cp = pm.counter_species
break
bngfile.set_concentration(cp, values[pset_idx])
for sim_rpt in range(n_runs):
tmp = additional_args.copy()
tmp['prefix'] = '{}{}'.format(prefix, sim_prefix)
bngfile.action('simulate', **tmp)
bngfile.action('resetConcentrations')
sim_prefix += 1
if hpp_bngl:
bngfile.execute(reload_netfile=hpp_bngl_filename,
skip_file_actions=True)
else:
bngfile.execute()
if method != 'nf':
load_equations(self.model, bngfile.net_filename)
list_of_yfull = \
BngFileInterface.read_simulation_results_multi(
[bngfile.base_filename + str(n) for n in range(total_sims)])
tout = []
species_out = []
obs_exp_out = []
for i in range(total_sims):
yfull = list_of_yfull[i]
yfull_view = yfull.view(float).reshape(len(yfull), -1)
tout.append(yfull_view[:, 0])
if method == 'nf':
obs_exp_out.append(yfull_view[:, 1:])
else:
species_out.append(yfull_view[:,
1:(len(self.model.species) + 1)])
if len(self.model.observables) or len(self.model.expressions):
obs_exp_out.append(yfull_view[:,
(len(self.model.species) + 1):
(len(self.model.species) + 1) +
len(self.model.observables) +
len(self.model.expressions_dynamic())])
return SimulationResult(self, tout=tout, trajectories=species_out,
observables_and_expressions=obs_exp_out,
simulations_per_param_set=n_runs)