def _load_trajectories(self, directory):
"""Read simulation results from output files.
Returns `tout` and `trajectories` arrays.
"""
files = [
filename for filename in os.listdir(directory)
if re.match(self._prefix, filename)
]
if len(files) == 0:
raise SimulatorException(
"Cannot find any output files to load data from.")
if len(files) != len(self.param_values):
raise SimulatorException(
"Number of input files (%d) does not match number "
"of requested simulations (%d)." %
(len(files), len(self.param_values)))
n_sims = len(files)
trajectories = [None] * n_sims
tout = [None] * n_sims
traj_n = np.ones((len(self.tspan), self._len_species)) * float('nan')
tout_n = np.ones(len(self.tspan)) * float('nan')
# load the data
indir_prefix = os.path.join(directory, self._prefix)
for n in range(n_sims):
trajectories[n] = traj_n.copy()
tout[n] = tout_n.copy()
filename = indir_prefix + "_" + str(n)
if not os.path.isfile(filename):
raise Exception("Cannot find input file " + filename)
# determine optimal loading method
if n == 0:
(data, use_pandas) = self._test_pandas(filename)
# load data
else:
if use_pandas:
data = self._load_with_pandas(filename)
else:
data = self._load_with_openfile(filename)
# store data
tout[n] = data[:, 0]
trajectories[n][:, self._out_species] = data[:, 1:]
# volume correction
if self.vol:
trajectories[n][:, self._out_species] *= (N_A * self.vol)
return np.array(tout), np.array(trajectories)
def __init__(self,
model,
tspan=None,
initials=None,
param_values=None,
verbose=False,
**kwargs):
super(CupSodaSimulator, self).__init__(model,
tspan=tspan,
initials=initials,
param_values=param_values,
verbose=verbose,
**kwargs)
self.gpu = kwargs.get('gpu', 0)
self._obs_species_only = kwargs.get('obs_species_only', True)
self._cleanup = kwargs.get('cleanup', True)
self._prefix = kwargs.get('prefix', self._model.name.replace('.', '_'))
self._base_dir = kwargs.get('base_dir', None)
self.integrator = kwargs.get('integrator', 'cupsoda')
# generate the equations for the model
pysb.bng.generate_equations(self._model, self._cleanup, self.verbose)
# build integrator options list from our defaults and any kwargs
# passed to this function
options = {}
if self.default_integrator_options.get(self.integrator):
options.update(self.default_integrator_options[
self.integrator]) # default options
else:
raise SimulatorException("Integrator type '" + self.integrator +
"' not recognized.")
options.update(kwargs.get('integrator_options', {})) # overwrite
# defaults
self.opts = options
self._out_species = None
# private variables (to reduce the number of function calls)
self._len_rxns = len(self._model.reactions)
self._len_species = len(self._model.species)
self._len_params = len(self._model.parameters)
self._model_parameters_rules = self._model.parameters_rules()
# Set cupsoda verbosity level
logger_level = self._logger.logger.getEffectiveLevel()
if logger_level <= EXTENDED_DEBUG:
self._cupsoda_verbose = 2
elif logger_level <= logging.DEBUG:
self._cupsoda_verbose = 1
else:
self._cupsoda_verbose = 0
# regex for extracting cupSODA reported running time
self._running_time_regex = re.compile(r'Running time:\s+(\d+\.\d+)')
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_chunk(self, gpus, outdir, chunk_idx, cmtx, sims, trajectories,
tout):
_indirs = {}
_outdirs = {}
p = {}
# Path to cupSODA executable
bin_path = get_path('cupsoda')
# Start simulations
for gpu in gpus:
_indirs[gpu] = os.path.join(
outdir, "INPUT_GPU{}_{}".format(gpu, chunk_idx))
os.mkdir(_indirs[gpu])
_outdirs[gpu] = os.path.join(
outdir, "OUTPUT_GPU{}_{}".format(gpu, chunk_idx))
# Create cupSODA input files
self._create_input_files(_indirs[gpu], sims[gpu], cmtx)
# Build command
# ./cupSODA input_model_folder blocks output_folder simulation_
# file_prefix gpu_number fitness_calculation memory_use dump
command = [
bin_path, _indirs[gpu],
str(self.n_blocks), _outdirs[gpu], self._prefix,
str(gpu), '0', self._memory_usage,
str(self._cupsoda_verbose)
]
self._logger.info("Running cupSODA: " + ' '.join(command))
# Run simulation and return trajectories
p[gpu] = subprocess.Popen(command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# Read results
for gpu in gpus:
(p_out, p_err) = p[gpu].communicate()
p_out = p_out.decode('utf-8')
p_err = p_err.decode('utf-8')
logger_level = self._logger.logger.getEffectiveLevel()
if logger_level <= logging.INFO:
run_time_match = self._running_time_regex.search(p_out)
if run_time_match:
self._logger.info('cupSODA GPU {} chunk {} reported '
'time: {} seconds'.format(
gpu, chunk_idx,
run_time_match.group(1)))
self._logger.debug('cupSODA GPU {} chunk {} stdout:\n{}'.format(
gpu, chunk_idx, p_out))
if p_err:
self._logger.error('cupSODA GPU {} chunk {} '
'stderr:\n{}'.format(gpu, chunk_idx, p_err))
if p[gpu].returncode:
raise SimulatorException(
"cupSODA GPU {} chunk {} exception:\n{}\n{}".format(
gpu, chunk_idx, p_out.rstip("at line"),
p_err.rstrip()))
tout_run, trajectories_run = self._load_trajectories(
_outdirs[gpu], sims[gpu])
if trajectories is None:
tout = tout_run
trajectories = trajectories_run
else:
tout = np.concatenate((tout, tout_run))
trajectories = np.concatenate((trajectories, trajectories_run))
return tout, trajectories
def __init__(self,
model,
tspan=None,
initials=None,
param_values=None,
verbose=False,
**kwargs):
super(CupSodaSimulator, self).__init__(model,
tspan=tspan,
initials=initials,
param_values=param_values,
verbose=verbose,
**kwargs)
self.gpu = kwargs.pop('gpu', (0, ))
if not isinstance(self.gpu, collections.Iterable):
self.gpu = [self.gpu]
self._obs_species_only = kwargs.pop('obs_species_only', True)
self._cleanup = kwargs.pop('cleanup', True)
self._prefix = kwargs.pop('prefix', self._model.name)
# Sanitize the directory - cupsoda doesn't handle spaces etc. well
self._prefix = re.sub('[^0-9a-zA-Z]', '_', self._prefix)
self._base_dir = kwargs.pop('base_dir', None)
self.integrator = kwargs.pop('integrator', 'cupsoda')
integrator_options = kwargs.pop('integrator_options', {})
if kwargs:
raise ValueError('Unknown keyword argument(s): {}'.format(
', '.join(kwargs.keys())))
unknown_integrator_options = set(integrator_options.keys()).difference(
self._integrator_options_allowed)
if unknown_integrator_options:
raise ValueError(
'Unknown integrator_options: {}. Allowed options: {}'.format(
', '.join(unknown_integrator_options),
', '.join(self._integrator_options_allowed)))
# generate the equations for the model
pysb.bng.generate_equations(self._model, self._cleanup, self.verbose)
# build integrator options list from our defaults and any kwargs
# passed to this function
options = {}
if self.default_integrator_options.get(self.integrator):
options.update(self.default_integrator_options[
self.integrator]) # default options
else:
raise SimulatorException("Integrator type '" + self.integrator +
"' not recognized.")
options.update(integrator_options) # overwrite
# defaults
self.opts = options
self._out_species = None
# private variables (to reduce the number of function calls)
self._len_rxns = len(self._model.reactions)
self._len_species = len(self._model.species)
self._len_params = len(self._model.parameters)
self._model_parameters_rules = self._model.parameters_rules()
# Set cupsoda verbosity level
logger_level = self._logger.logger.getEffectiveLevel()
if logger_level <= EXTENDED_DEBUG:
self._cupsoda_verbose = 2
elif logger_level <= logging.DEBUG:
self._cupsoda_verbose = 1
else:
self._cupsoda_verbose = 0
# regex for extracting cupSODA reported running time
self._running_time_regex = re.compile(r'Running time:\s+(\d+\.\d+)')
def run(self,
tspan=None,
initials=None,
param_values=None,
n_runs=1,
algorithm='ssa',
output_dir=None,
num_processors=1,
seed=None,
method=None,
stats=False,
epsilon=None,
threshold=None):
"""
Run a simulation and returns the result (trajectories)
.. note::
``tspan``, ``initials`` and ``param_values`` values supplied to
this method will persist to future :func:`run` calls.
Parameters
----------
tspan
initials
param_values
See parameter definitions in :class:`StochKitSimulator`.
n_runs : int
The number of simulation runs per parameter set. The total
number of simulations is therefore n_runs * max(len(initials),
len(param_values))
algorithm : str
Choice of 'ssa' (Gillespie's stochastic simulation algorithm) or
'tau_leaping' (Tau leaping algorithm)
output_dir : str or None
Directory for StochKit output, or None for a system-specific
temporary directory
num_processors : int
Number of CPU cores for StochKit to use (default: 1)
seed : int or None
A random number seed for StochKit. Set to any integer value for
deterministic behavior.
method : str or None
StochKit "method" argument, default None. Only used by StochKit
2.1 (not yet released at time of writing).
stats : bool
Ask StochKit to generate simulation summary statistics if True
epsilon : float or None
Tolerance parameter for tau-leaping algorithm
threshold : int or None
Threshold parameter for tau-leaping algorithm
Returns
-------
A :class:`SimulationResult` object
"""
super(StochKitSimulator, self).run(tspan=tspan,
initials=initials,
param_values=param_values)
self._logger.info('Running StochKit with {:d} parameter sets, '
'{:d} repeats ({:d} simulations total)'.format(
len(self.initials), n_runs,
len(self.initials) * n_runs))
if output_dir is None:
self._outdir = tempfile.mkdtemp()
else:
self._outdir = output_dir
# Calculate time intervals and validate
t_range = self.tspan[-1] - self.tspan[0]
t_length = len(self.tspan)
if not np.allclose(self.tspan, np.linspace(0, self.tspan[-1],
t_length)):
raise SimulatorException('StochKit requires tspan to be linearly '
'spaced starting at t=0')
try:
trajectories = self._run_stochkit(t=t_range,
number_of_trajectories=n_runs,
t_length=t_length,
seed=seed,
algorithm=algorithm,
method=method,
num_processors=num_processors,
stats=stats,
epsilon=epsilon,
threshold=threshold)
finally:
if self.cleanup:
try:
shutil.rmtree(self._outdir)
except OSError:
pass
# set output time points
trajectories_array = np.array(trajectories)
self.tout = trajectories_array[:, :, 0] + self.tspan[0]
# species
species = trajectories_array[:, :, 1:]
return SimulationResult(self,
self.tout,
species,
simulations_per_param_set=n_runs)
def _run_stochkit(self,
t=20,
t_length=100,
number_of_trajectories=1,
seed=None,
algorithm='ssa',
method=None,
num_processors=1,
stats=False,
epsilon=None,
threshold=None):
extra_args = '-p {:d}'.format(num_processors)
# Random seed for stochastic simulation
if seed is not None:
extra_args += ' --seed {:d}'.format(seed)
# Keep all the trajectories by default
extra_args += ' --keep-trajectories'
# Number of trajectories
extra_args += ' --realizations {:d}'.format(number_of_trajectories)
# We generally don't need the extra stats
if not stats:
extra_args += ' --no-stats'
if method is not None: # This only works for StochKit 2.1
extra_args += ' --method {}'.format(method)
if epsilon is not None:
extra_args += ' --epsilon {:f}'.format(epsilon)
if threshold is not None:
extra_args += ' --threshold {:d}'.format(threshold)
# Find binary for selected algorithm (SSA, Tau-leaping, ...)
if algorithm not in ['ssa', 'tau_leaping']:
raise SimulatorException(
"algorithm must be 'ssa' or 'tau_leaping'")
executable = get_path('stochkit_{}'.format(algorithm))
# Output model file to directory
fname = os.path.join(self._outdir, 'pysb.xml')
trajectories = []
for i in range(len(self.initials)):
# We write all StochKit output files to a temporary folder
prefix_outdir = os.path.join(self._outdir, 'output_{}'.format(i))
# Export model file
stoch_xml = StochKitExporter(self._model).export(
self.initials[i], self.param_values[i])
self._logger.log(EXTENDED_DEBUG, 'StochKit XML:\n' + stoch_xml)
with open(fname, 'w') as f:
f.write(stoch_xml)
# Assemble the argument list
args = '--model {} --out-dir {} -t {:f} -i {:d}'.format(
fname, prefix_outdir, t, t_length - 1)
# If we are using local mode, shell out and run StochKit
# (SSA or Tau-leaping or ODE)
cmd = '{} {} {}'.format(executable, args, extra_args)
self._logger.debug("StochKit run {} of {} (cmd: {})".format(
(i + 1), len(self.initials), cmd))
# Execute
try:
handle = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
return_code = handle.wait()
except OSError as e:
raise SimulatorException("StochKit execution failed: \
{0}\n{1}".format(cmd, e))
try:
stderr = handle.stderr.read()
except Exception as e:
stderr = 'Error reading stderr: {0}'.format(e)
try:
stdout = handle.stdout.read()
except Exception as e:
stdout = 'Error reading stdout: {0}'.format(e)
if return_code != 0:
raise SimulatorException("Solver execution failed: \
'{0}' output:\nSTDOUT:\n{1}\nSTDERR:\n{2}".format(
cmd, stdout, stderr))
traj_dir = os.path.join(prefix_outdir, 'trajectories')
try:
trajectories.extend([
np.loadtxt(os.path.join(traj_dir, f))
for f in sorted(os.listdir(traj_dir))
])
except Exception as e:
raise SimulatorException(
"Error reading StochKit trajectories: {0}"
"\nSTDOUT:{1}\nSTDERR:{2}".format(e, stdout, stderr))
if len(trajectories) == 0 or len(stderr) != 0:
raise SimulatorException("Solver execution failed: \
'{0}' output:\nSTDOUT:\n{1}\nSTDERR:\n{2}".format(
cmd, stdout, stderr))
self._logger.debug("StochKit STDOUT:\n{0}".format(stdout))
# Return data
return trajectories
def run(self, tspan=None, initials=None, param_values=None):
"""Perform a set of integrations.
Returns a :class:`.SimulationResult` object.
Parameters
----------
tspan : list-like, optional
Time values at which the integrations are sampled. The first and
last values define the time range.
initials : list-like, optional
Initial species concentrations for all simulations. Dimensions are
number of simulation x number of species.
param_values : list-like, optional
Parameters for all simulations. Dimensions are number of
simulations x number of parameters.
Returns
-------
A :class:`SimulationResult` object
Notes
-----
1. An exception is thrown if `tspan` is not defined in either
`__init__`or `run`.
2. If neither `initials` nor `param_values` are defined in either
`__init__` or `run` a single simulation is run with the initial
concentrations and parameter values defined in the model.
"""
super(CupSodaSimulator, self).run(tspan=tspan,
initials=initials,
param_values=param_values,
_run_kwargs=[])
# Create directories for cupSODA input and output files
self.outdir = tempfile.mkdtemp(prefix=self._prefix + '_',
dir=self._base_dir)
self._logger.debug("Output directory is %s" % self.outdir)
_cupsoda_infiles_dir = os.path.join(self.outdir, "INPUT")
os.mkdir(_cupsoda_infiles_dir)
self._cupsoda_outfiles_dir = os.path.join(self.outdir, "OUTPUT")
# Path to cupSODA executable
bin_path = get_path('cupsoda')
# Create cupSODA input files
self._create_input_files(_cupsoda_infiles_dir)
# Build command
# ./cupSODA input_model_folder blocks output_folder simulation_
# file_prefix gpu_number fitness_calculation memory_use dump
command = [
bin_path, _cupsoda_infiles_dir,
str(self.n_blocks), self._cupsoda_outfiles_dir, self._prefix,
str(self.gpu), '0', self._memory_usage,
str(self._cupsoda_verbose)
]
self._logger.info("Running cupSODA: " + ' '.join(command))
start_time = time.time()
# Run simulation and return trajectories
p = subprocess.Popen(command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# for line in iter(p.stdout.readline, b''):
# if 'Running time' in line:
# self._logger.info(line[:-1])
(p_out, p_err) = p.communicate()
p_out = p_out.decode('utf-8')
p_err = p_err.decode('utf-8')
logger_level = self._logger.logger.getEffectiveLevel()
if logger_level <= logging.INFO:
run_time_match = self._running_time_regex.search(p_out)
if run_time_match:
self._logger.info('cupSODA reported time: {} '
'seconds'.format(run_time_match.group(1)))
self._logger.debug('cupSODA stdout:\n' + p_out)
if p_err:
self._logger.error('cupsoda strerr:\n' + p_err)
if p.returncode:
raise SimulatorException(
p_out.rstrip("at line") + "\n" + p_err.rstrip())
tout, trajectories = self._load_trajectories(
self._cupsoda_outfiles_dir)
if self._cleanup:
shutil.rmtree(self.outdir)
end_time = time.time()
self._logger.info("cupSODA + I/O time: {} seconds".format(end_time -
start_time))
return SimulationResult(self, tout, trajectories)
def run(self,
tspan=None,
initials=None,
param_values=None,
n_runs=1,
output_dir=None,
output_file_basename=None,
cleanup=None,
**additional_args):
"""
Simulate a model using Kappa
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
output_dir : string, optional
Location for temporary files generated by Kappa. 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 Kappa
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__`.
additional_args: kwargs, optional
Additional arguments to pass to Kappa
* seed : int, optional
Random number seed for Kappa simulation
* perturbation : string, optional
Optional perturbation language syntax to be appended to the
Kappa file. See KaSim manual for more details.
Examples
--------
>>> import numpy as np
>>> from pysb.examples import michment
>>> from pysb.simulator import KappaSimulator
>>> sim = KappaSimulator(michment.model, tspan=np.linspace(0, 1))
>>> x = sim.run(n_runs=1)
"""
super(KappaSimulator, self).run(tspan=tspan,
initials=initials,
param_values=param_values,
_run_kwargs=locals())
if cleanup is None:
cleanup = self.cleanup
tspan_lin_spaced = np.allclose(
self.tspan,
np.linspace(self.tspan[0], self.tspan[-1], len(self.tspan)))
if not tspan_lin_spaced or self.tspan[0] != 0.0:
raise SimulatorException('Kappa requires tspan to be linearly '
'spaced starting at t=0')
points = len(self.tspan)
time = self.tspan[-1]
plot_period = time / (len(self.tspan) - 1)
if output_file_basename is None:
output_file_basename = 'tmpKappa_%s_' % self.model.name
base_directory = tempfile.mkdtemp(prefix=output_file_basename,
dir=output_dir)
base_filename = os.path.join(base_directory, self.model.name)
kappa_filename_pattern = base_filename + '_{}.ka'
out_filename_pattern = base_filename + '_{}_run{}.out'
base_args = ['-u', 'time', '-l', str(time), '-p', '%.5f' % plot_period]
if 'seed' in additional_args:
seed = additional_args.pop('seed')
base_args.extend(['-seed', str(seed)])
kasim_path = pf.get_path('kasim')
n_param_sets = self.initials_length
gen = KappaGenerator(self.model, _exclude_ic_param=True)
file_data_base = gen.get_content()
# Check if a perturbation has been set
try:
perturbation = additional_args.pop('perturbation')
except KeyError:
perturbation = None
# Check no unknown arguments have been set
if additional_args:
raise ValueError('Unknown argument(s): {}'.format(', '.join(
additional_args.keys())))
# Kappa column names, for sanity check
kappa_col_names = tuple(['time'] +
[o.name for o in self.model.observables])
tout = []
observable_traj = []
try:
for pset_idx in range(n_param_sets):
file_data = file_data_base + ''
for param, param_value in zip(self.model.parameters,
self.param_values[pset_idx]):
file_data += "%var: '{}' {:e}\n".format(
param.name, param_value)
file_data += '\n'
for cp, values in self.initials_dict.items():
file_data += "%init: {} {}\n".format(
values[pset_idx], gen.format_complexpattern(cp))
# If any perturbation language code has been passed in, add it
# to the Kappa file:
if perturbation:
file_data += '%s\n' % perturbation
# Generate the Kappa model code from the PySB model and write
# it to the Kappa file:
kappa_filename = kappa_filename_pattern.format(pset_idx)
with open(kappa_filename, 'w') as kappa_file:
self._logger.debug('Kappa file contents:\n\n' + file_data)
kappa_file.write(file_data)
for sim_rpt in range(n_runs):
# Run Kappa
out_filename = out_filename_pattern.format(
pset_idx, sim_rpt)
args = [kasim_path] + base_args + [
'-i', kappa_filename, '-o', out_filename
]
# Run KaSim
self._logger.debug('Running: {}'.format(' '.join(args)))
p = subprocess.Popen(args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=base_directory)
for line in p.stdout:
self._logger.debug('@@' + line.decode('utf8')[:-1])
(p_out, p_err) = p.communicate()
if p.returncode:
raise KasimInterfaceError(
p_out.decode('utf8') + '\n' + p_err.decode('utf8'))
# The simulation data, as a numpy array
data = _parse_kasim_outfile(out_filename)
# Sanity check that observables are in correct order
assert data.dtype.names == kappa_col_names
data = data.view('<f8')
# Handle case with single row output
if data.ndim == 1:
data.shape = (1, data.shape[0])
# Parse into format
tout.append(data[:, 0])
observable_traj.append(data[:, 1:])
finally:
if cleanup:
shutil.rmtree(base_directory)
return SimulationResult(self,
tout=tout,
observables_and_expressions=observable_traj,
simulations_per_param_set=n_runs)
