def run(self=None, model=None, t=None, number_of_trajectories=1, increment=None, seed=None,
debug=False, profile=False, live_output=None, live_output_options={},
timeout=None, resume=None, tau_tol=0.03, **kwargs):
"""
Function calling simulation of the model.
This is typically called by the run function in GillesPy2 model objects
and will inherit those parameters which are passed with the model
as the arguments this run function.
:param model: The model on which the solver will operate. (Deprecated)
:type model: gillespy2.Model
:param t: Simulation run time.
:type t: int or float
:param number_of_trajectories: Number of trajectories to simulate. By default number_of_trajectories = 1.
:type number_of_trajectories: int
:param increment: Save point increment for recording data.
:type increment: float
:param seed: The random seed for the simulation. Optional, defaults to None.
:type seed: int
:param debug: Set to True to provide additional debug information about the simulation.
:type debug: bool
:param profile: Set to True to provide information about step size (tau) taken at each step.
:type profile: bool
:param live_output: The type of output to be displayed by solver. Can be "progress", "text", or "graph".
:type live_output: str
:param live_output_options: Contains options for live_output. By default {"interval":1}. "interval"
specifies seconds between displaying. "clear_output" specifies if display should be refreshed with each
display.
:type live_output_options: dict
:param timeout: If set, if simulation takes longer than timeout, will exit.
:type timeout: int
:param resume: Result of a previously run simulation, to be resumed.
:type resume: gillespy2.Results
:param tau_tol: Tolerance level for Tau leaping algorithm. Larger tolerance values will
result in larger tau steps. Default value is 0.03.
:type tau_tol: float
:returns: A result object containing the results of the simulation.
:rtype: gillespy2.Results
"""
from gillespy2 import log
if self is None:
# Post deprecation block
# raise SimulationError("TauLeapingSolver must be instantiated to run the simulation")
# Pre deprecation block
log.warning(
"""
`gillespy2.Model.run(solver=TauLeapingSolver)` is deprecated.
You should use `gillespy2.Model.run(solver=TauLeapingSolver(model=gillespy2.Model))
Future releases of GillesPy2 may not support this feature.
"""
)
self = TauLeapingSolver(model=model, debug=debug, profile=profile)
if model is not None:
log.warning('model = gillespy2.model is deprecated. Future releases '
'of GillesPy2 may not support this feature.')
if self.model is None:
if model is None:
raise SimulationError("A model is required to run the simulation.")
self.model = copy.deepcopy(model)
self.model.compile_prep()
self.validate_model(self.model, model)
self.validate_sbml_features(model=self.model)
self.validate_tspan(increment=increment, t=t)
if increment is None:
increment = self.model.tspan[-1] - self.model.tspan[-2]
if t is None:
t = self.model.tspan[-1]
self.stop_event = Event()
self.pause_event = Event()
if timeout is not None and timeout <= 0:
timeout = None
if len(kwargs) > 0:
for key in kwargs:
log.warning('Unsupported keyword argument to {0} solver: {1}'.format(self.name, key))
# create numpy array for timeline
if resume is not None:
# start where we last left off if resuming a simulatio
lastT = resume['time'][-1]
step = lastT - resume['time'][-2]
timeline = np.arange(lastT, t+step, step)
else:
timeline = np.linspace(0, t, int(round(t / increment + 1)))
species = list(self.model._listOfSpecies.keys())
trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(self.model, number_of_trajectories,
timeline, species, resume=resume)
# total_time and curr_state are list of len 1 so that __run receives reference
if resume is not None:
total_time = [resume['time'][-1]]
else:
total_time = [0]
curr_state = [None]
live_grapher = [None]
sim_thread = Thread(target=self.___run, args=(curr_state, total_time, timeline, trajectory_base, tmpSpecies,
live_grapher,), kwargs={'t': t,
'number_of_trajectories':
number_of_trajectories,
'increment': increment, 'seed': seed,
'debug': debug, 'resume': resume,
'timeout': timeout, 'tau_tol': tau_tol
})
try:
time = 0
sim_thread.start()
if live_output is not None:
import gillespy2.core.liveGraphing
live_output_options['type'] = live_output
gillespy2.core.liveGraphing.valid_graph_params(
live_output_options)
if resume is not None:
resumeTest = True # If resuming, relay this information to live_grapher
else:
resumeTest = False
live_grapher[
0] = gillespy2.core.liveGraphing.LiveDisplayer(self.model,
timeline,
number_of_trajectories,
live_output_options,
resume=resumeTest)
display_timer = gillespy2.core.liveGraphing.RepeatTimer(
live_output_options['interval'],
live_grapher[0].display, args=(curr_state,
total_time,
trajectory_base,
live_output
)
)
display_timer.start()
if timeout is not None:
while sim_thread.is_alive():
sim_thread.join(.1)
time += .1
if time >= timeout:
break
else:
while sim_thread.is_alive():
sim_thread.join(.1)
if live_grapher[0] is not None:
display_timer.cancel()
self.stop_event.set()
while self.result is None:
pass
except KeyboardInterrupt:
if live_output:
display_timer.pause = True
display_timer.cancel()
self.pause_event.set()
while self.result is None:
pass
if hasattr(self, 'has_raised_exception'):
raise SimulationError(
f"Error encountered while running simulation:\nReturn code: {int(self.rc)}.\n"
) from self.has_raised_exception
return Results.build_from_solver_results(self, live_output_options)
def run(self=None, model=None, t=None, number_of_trajectories=1, increment=None, integrator='lsoda',
integrator_options={}, live_output=None, live_output_options={}, timeout=None, resume=None, **kwargs):
"""
:param model: The model on which the solver will operate. (Deprecated)
:type model: gillespy2.Model
:param t: End time of simulation.
:type t: int or float
:param number_of_trajectories: Number of trajectories to simulate. By default number_of_trajectories = 1.
This is deterministic and will always have same results.
:type number_of_trajectories: int
:param increment: Time step increment for plotting.
:type increment: float
:param integrator: integrator to be used from scipy.integrate.ode. Options include 'vode', 'zvode', 'lsoda',
'dopri5', and 'dop853'. For more details,
see https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html
:type integrator: str
:param integrator_options: a dictionary containing options to the scipy integrator. for a list of options,
see https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html.
Example use: {max_step : 0, rtol : .01}
:type integrator_options: dict
:param live_output: The type of output to be displayed by solver. Can be "progress", "text", or "graph".
:type live_output: str
:param live_output_options: dictionary contains options for live_output. By default {"interval":1}.
"interval" specifies seconds between displaying.
"clear_output" specifies if display should be refreshed with each display.
:type live_output_options: dict
:param timeout: If set, if simulation takes longer than timeout, will exit.
:type timeout: int
:param resume: Result of a previously run simulation, to be resumed.
:type resume: gillespy2.Results
:returns: A result object containing the results of the simulation.
:rtype: gillespy2.Results
"""
from gillespy2 import log
if self is None:
# Post deprecation block
# raise SimulationError("ODESolver must be instantiated to run the simulation")
# Pre deprecation block
log.warning(
"""
`gillespy2.Model.run(solver=ODESolver)` is deprecated.
You should use `gillespy2.Model.run(solver=ODESolver(model=gillespy2.Model))
Future releases of GillesPy2 may not support this feature.
"""
)
self = ODESolver(model=model)
if model is not None:
log.warning('model = gillespy2.model is deprecated. Future releases '
'of GillesPy2 may not support this feature.')
if self.model is None:
if model is None:
raise SimulationError("A model is required to run the simulation.")
self.model = copy.deepcopy(model)
self.model.compile_prep()
self.validate_model(self.model, model)
self.validate_sbml_features(model=self.model)
self.validate_tspan(increment=increment, t=t)
if increment is None:
increment = self.model.tspan[-1] - self.model.tspan[-2]
if t is None:
t = self.model.tspan[-1]
self.stop_event = Event()
self.pause_event = Event()
if timeout is not None and timeout <= 0:
timeout = None
if len(kwargs) > 0:
for key in kwargs:
log.warning('Unsupported keyword argument to {0} solver: {1}'.format(self.name, key))
if number_of_trajectories > 1:
log.warning("Generating duplicate trajectories for model with ODE Solver. "
"Consider running with only 1 trajectory.")
if resume is not None:
# start where we last left off if resuming a simulation
lastT = resume['time'][-1]
step = lastT - resume['time'][-2]
timeline = np.arange(lastT, t+step, step)
else:
timeline = np.linspace(0, t, int(round(t / increment + 1)))
species = list(self.model._listOfSpecies.keys())
trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(self.model, number_of_trajectories,
timeline, species, resume=resume)
# curr_time and curr_state are list of len 1 so that __run receives reference
if resume is not None:
curr_time = [resume['time'][-1]]
else:
curr_time = [0] # Current Simulation Time
curr_state = [None]
live_grapher = [None]
sim_thread = Thread(target=self.___run, args=(curr_state, curr_time, timeline, trajectory_base,
tmpSpecies, live_grapher,), kwargs={'t': t,
'number_of_trajectories':
number_of_trajectories,
'increment': increment,
'resume': resume,
'integrator': integrator,
'integrator_options':
integrator_options, })
try:
time = 0
sim_thread.start()
if live_output is not None:
import gillespy2.core.liveGraphing
live_output_options['type'] = live_output
gillespy2.core.liveGraphing.valid_graph_params(live_output_options)
if resume is not None:
resumeTest = True # If resuming, relay this information to live_grapher
else:
resumeTest = False
live_grapher[0] = gillespy2.core.liveGraphing.LiveDisplayer(self.model, timeline, number_of_trajectories,
live_output_options, resume=resumeTest)
display_timer = gillespy2.core.liveGraphing.RepeatTimer(live_output_options['interval'],
live_grapher[0].display,
args=(curr_state, curr_time, trajectory_base,live_output))
display_timer.start()
if timeout is not None:
while sim_thread.is_alive():
sim_thread.join(.1)
time += .1
if time >= timeout:
break
else:
while sim_thread.is_alive():
sim_thread.join(.1)
if live_grapher[0] is not None:
display_timer.cancel()
self.stop_event.set()
while self.result is None:
pass
except KeyboardInterrupt:
if live_output:
display_timer.pause = True
display_timer.cancel()
self.pause_event.set()
while self.result is None:
pass
if hasattr(self, 'has_raised_exception'):
raise SimulationError(
f"Error encountered while running simulation:\nReturn code: {int(self.rc)}.\n"
) from self.has_raised_exception
return Results.build_from_solver_results(self, live_output_options)
