def run(self,
model,
t=20,
number_of_trajectories=1,
increment=0.05,
seed=None,
debug=False,
show_labels=True,
live_output=None,
live_output_options={},
timeout=None,
resume=None,
**kwargs):
"""
Run the SSA algorithm using a NumPy for storing the data in arrays and generating the timeline.
:param model: The model on which the solver will operate.
:param t: The end time of the solver.
:param number_of_trajectories: The number of times to sample the chemical master equation. Each
trajectory will be returned at the end of the simulation.
:param increment: The time step of the solution.
:param seed: The random seed for the simulation. Defaults to None.
:param debug: Set to True to provide additional debug information about the
simulation.
:param resume: Result of a previously run simulation, to be resumed
:param live_output : str The type of output to be displayed by solver. Can be "progress", "text", or "graph".
: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
:return: a list of each trajectory simulated.
"""
if isinstance(self, type):
self = NumPySSASolver()
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 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(model._listOfSpecies.keys())
trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(
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:
total_time = [resume['time'][-1]]
else:
total_time = [0]
curr_state = [None]
live_grapher = [None]
sim_thread = Thread(target=self.___run,
args=(
model,
curr_state,
total_time,
timeline,
trajectory_base,
live_grapher,
),
kwargs={
't': t,
'number_of_trajectories':
number_of_trajectories,
'increment': increment,
'seed': seed,
'debug': debug,
'show_labels': show_labels,
'timeout': timeout,
'resume': resume,
})
try:
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(
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,
))
display_timer.start()
sim_thread.join(timeout=timeout)
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.cancel()
self.pause_event.set()
while self.result is None:
pass
if hasattr(self, 'has_raised_exception'):
raise self.has_raised_exception
return self.result, self.rc
def run(self,
model,
t=20,
number_of_trajectories=1,
increment=0.05,
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: GillesPy2 model object to simulate
:type model: gillespy2.Model
:param t: Simulation run time
:type t: int
:param number_of_trajectories: Number of trajectories to simulate
: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:
:param resume:
:param tau_tol:
:param kwargs:
:return:
"""
if isinstance(self, type):
self = TauLeapingSolver(debug=debug, profile=profile)
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(model._listOfSpecies.keys())
trajectory_base, tempSpecies = nputils.numpy_trajectory_base_initialization(
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:
total_time = [resume['time'][-1]]
else:
total_time = [0]
curr_state = [None]
live_grapher = [None]
sim_thread = Thread(target=self.___run,
args=(
model,
curr_state,
total_time,
timeline,
trajectory_base,
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:
sim_thread.start()
if resume is not None:
resumeTest = True # If resuming, relay this information to live_grapher
else:
resumeTest = False
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)
live_grapher[0] = liveGraphing.LiveDisplayer(
model,
timeline,
number_of_trajectories,
live_output_options,
resume=resumeTest)
display_timer = liveGraphing.RepeatTimer(
live_output_options['interval'],
live_grapher[0].display,
args=(
curr_state,
total_time,
trajectory_base,
))
display_timer.start()
sim_thread.join(timeout=timeout)
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.cancel()
self.pause_event.set()
while self.result is None:
pass
if hasattr(self, 'has_raised_exception'):
raise self.has_raised_exception
return self.result, self.rc
def __run(self,
model,
curr_state,
total_time,
timeline,
trajectory_base,
live_grapher,
t=20,
number_of_trajectories=1,
increment=0.05,
seed=None,
debug=False,
profile=False,
timeout=None,
resume=None,
tau_tol=0.03,
**kwargs):
# for use with resume, determines how much excess data to cut off due to
# how species and time are initialized to 0
timeStopped = 0
if resume is not None:
if resume[0].model != model:
raise ModelError(
'When resuming, one must not alter the model being resumed.'
)
if t < resume['time'][-1]:
raise ExecutionError(
"'t' must be greater than previous simulations end time, or set in the run() method as the "
"simulations next end time")
if debug:
print("t = ", t)
print("increment = ", increment)
species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(
model)
# create numpy matrix to mark all state data of time and species
trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(
model, number_of_trajectories, timeline, species, resume=resume)
if seed is not None:
if not isinstance(seed, int):
seed = int(seed)
if seed > 0:
random.seed(seed)
np.random.seed(seed)
else:
raise ModelError('seed must be a positive integer')
simulation_data = []
for trajectory_num in range(number_of_trajectories):
if self.stop_event.is_set():
self.rc = 33
break
elif self.pause_event.is_set():
timeStopped = timeline[entry_count]
# For multi trajectories, live_grapher needs to be informed of trajectory increment
if live_grapher[0] is not None:
live_grapher[0].increment_trajectory(trajectory_num)
start_state = [0] * (len(model.listOfReactions) +
len(model.listOfRateRules))
propensities = {}
curr_state[0] = {}
if resume is not None:
save_time = total_time[0]
curr_time = [save_time]
else:
save_time = 0
curr_time = [0]
curr_state[0]['vol'] = model.volume
data = {'time': timeline}
steps_taken = []
steps_rejected = 0
entry_count = 0
trajectory = trajectory_base[trajectory_num]
HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, critical_threshold = Tau.initialize(
model, tau_tol)
# initialize populations
if not (resume is None):
for spec in model.listOfSpecies:
curr_state[0][spec] = tmpSpecies[spec]
else:
for spec in model.listOfSpecies:
curr_state[0][spec] = model.listOfSpecies[
spec].initial_value
for param in model.listOfParameters:
curr_state[0][param] = model.listOfParameters[param].value
for i, rxn in enumerate(model.listOfReactions):
# set reactions to uniform random number and add to start_state
start_state[i] = (math.log(random.uniform(0, 1)))
if debug:
print("Setting Random number ", start_state[i], " for ",
model.listOfReactions[rxn].name)
compiled_propensities = {}
for i, r in enumerate(model.listOfReactions):
compiled_propensities[r] = compile(
model.listOfReactions[r].propensity_function, '<string>',
'eval')
timestep = 0
# Each save step
while entry_count < timeline.size:
if self.stop_event.is_set():
self.rc = 33
break
elif self.pause_event.is_set():
timeStopped = timeline[entry_count]
break
# Until save step reached
while curr_time[0] < save_time:
if self.stop_event.is_set():
self.rc = 33
break
elif self.pause_event.is_set():
timeStopped = timeline[entry_count]
break
propensity_sum = 0
for i, r in enumerate(model.listOfReactions):
propensities[r] = eval(compiled_propensities[r],
curr_state[0])
propensity_sum += propensities[r]
tau_args = [
HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, tau_tol,
critical_threshold, model, propensities, curr_state[0],
curr_time[0], save_time
]
tau_step = Tau.select(*tau_args)
prev_start_state = start_state.copy()
prev_curr_state = curr_state[0].copy()
prev_curr_time = curr_time[0]
loop_cnt = 0
while True:
loop_cnt += 1
if loop_cnt > 100:
raise Exception(
"Loop over __get_reactions() exceeded loop count"
)
reactions, curr_state[0], curr_time[
0] = self.__get_reactions(tau_step, curr_state[0],
curr_time[0], save_time,
propensities,
model.listOfReactions)
# Update curr_state with the result of the SSA reaction that fired
species_modified = {}
for i, rxn in enumerate(model.listOfReactions):
if reactions[rxn] > 0:
for reactant in model.listOfReactions[
rxn].reactants:
species_modified[reactant.name] = True
curr_state[0][
reactant.
name] -= model.listOfReactions[
rxn].reactants[
reactant] * reactions[rxn]
for product in model.listOfReactions[
rxn].products:
species_modified[product.name] = True
curr_state[0][
product.name] += model.listOfReactions[
rxn].products[product] * reactions[
rxn]
neg_state = False
for spec in species_modified:
if curr_state[0][spec] < 0:
neg_state = True
if debug:
print(
"Negative state detected: curr_state[{0}]= {1}"
.format(spec, curr_state[0][spec]))
if neg_state:
if debug:
print("\trxn={0}".format(reactions))
start_state = prev_start_state.copy()
curr_state[0] = prev_curr_state.copy()
curr_time[0] = prev_curr_time
total_time[0] = prev_curr_time
tau_step = tau_step / 2
steps_rejected += 1
if debug:
print("Resetting curr_state[{0}]= {1}".format(
spec, curr_state[0][spec]))
if debug:
print(
"\tRejecting step, taking step of half size, tau_step={0}"
.format(tau_step))
else:
break # breakout of the while True
# save step reached
for i in range(number_species):
trajectory[entry_count][i + 1] = curr_state[0][species[i]]
save_time += increment
timestep += 1
entry_count += 1
# end of trajectory
for i in range(number_species):
data[species[i]] = trajectory[:, i + 1]
simulation_data.append(data)
if profile:
print(steps_taken)
print("Total Steps Taken: ", len(steps_taken))
print("Total Steps Rejected: ", steps_rejected)
# If simulation has been paused, or tstopped !=0
if timeStopped != 0 or resume is not None:
simulation_data = nputils.numpy_resume(timeStopped,
simulation_data,
resume=resume)
self.result = simulation_data
return self.result, self.rc
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)
def run(self,
model,
t=20,
number_of_trajectories=1,
increment=0.05,
show_labels=True,
integrator='lsoda',
integrator_options={},
live_output=None,
live_output_options={},
timeout=None,
resume=None,
**kwargs):
"""
:param model: gillespy2.model class object
:param t: end time of simulation
:param number_of_trajectories: Should be 1.
This is deterministic and will always have same results
:param increment: time step increment for plotting
:param integrator: integrator to be used form scipy.integrate.ode. Options include 'vode', 'zvode', 'lsoda',
'dopri5', and 'dop835'. For more details,
see https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html
: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}
: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
:param live_output : str The type of output to be displayed by solver. Can be "progress", "text", or "graph".
: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 displa
"""
if isinstance(self, type):
self = ODESolver()
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(model._listOfSpecies.keys())
trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(
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=(
model,
curr_state,
curr_time,
timeline,
trajectory_base,
tmpSpecies,
live_grapher,
),
kwargs={
't': t,
'number_of_trajectories':
number_of_trajectories,
'increment': increment,
'timeout': timeout,
'resume': resume,
'integrator': integrator,
'integrator_options': integrator_options,
})
try:
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(
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,
))
display_timer.start()
sim_thread.join(timeout=timeout)
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.cancel()
self.pause_event.set()
while self.result is None:
pass
if hasattr(self, 'has_raised_exception'):
raise self.has_raised_exception
return self.result, self.rc
