Source code for pysb.simulator.bng

from pysb.simulator.base import Simulator, SimulationResult, SimulatorException
from pysb.bng import BngFileInterface, load_equations, generate_hybrid_model
import numpy as np
import logging
from pysb.logging import EXTENDED_DEBUG
from pysb.core import as_complex_pattern, Parameter, \
import collections
from import Iterable
import os

[docs]class BngSimulator(Simulator): """ Simulate a model using BioNetGen """ _supports = { 'multi_initials': True, 'multi_param_values': True } _SIMULATOR_TYPES = ['ssa', 'nf', 'pla', 'ode'] def __init__(self, model, tspan=None, initials=None, param_values=None, cleanup=True, verbose=False): super(BngSimulator, self).__init__(model, tspan=tspan, initials=initials, param_values=param_values, verbose=verbose) self.cleanup = cleanup self._outdir = None
[docs] 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 =, 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, 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 = [ for g in self._model.parameters] n_param_sets = self.initials_length total_sims = n_runs * n_param_sets'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(params_names)): 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)
[docs]class PopulationMap(object): """ Population map for BioNetGen hybrid particle/population simulation For use with the :class:`BngSimulator`. References ---------- Hogg et al. 2014: BioNetGen HPP documentation: """ def __init__(self, complex_pattern, lumping_rate, counter_species=None): try: self.complex_pattern = as_complex_pattern(complex_pattern) except InvalidComplexPatternException: raise ValueError('complex_pattern must be a ComplexPattern') if not isinstance(lumping_rate, Parameter): raise ValueError('lumping_rate must be a %s' % Parameter.__class__) self.lumping_rate = lumping_rate if counter_species is None: self.counter_species = None else: self.counter_species = str(counter_species) def __repr__(self): return 'PopulationMap({}, {}, {})'.format( self.complex_pattern, self.lumping_rate, 'None' if self.counter_species is None else '{}'.format( self.counter_species) )