def _validate_resume(self, t: int, resume):
"""
Validate `resume`. An exception will be thrown if resume['time'][-1] is > t.
"""
if resume is None:
return
if t < resume["time"][-1]:
raise gillespyError.ExecutionError(
"'t' must be greater than previous simulations end time, or set in the run() method as the "
"simulations next end time"
)
def run(self=None,
model=None,
t=20,
number_of_trajectories=1,
increment=0.05,
seed=None,
debug=False,
profile=False,
show_labels=True,
**kwargs):
if self is None:
self = SSACSolver(model)
if self.compiled:
self.simulation_data = None
number_timesteps = int(t // increment + 1)
# Execute simulation.
args = [
os.path.join(self.output_directory, 'UserSimulation'),
'-trajectories',
str(number_of_trajectories), '-timesteps',
str(number_timesteps), '-end',
str(t)
]
if isinstance(seed, int):
args.append('-seed')
args.append(str(seed))
simulation = subprocess.run(args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# Parse/return results.
if simulation.returncode == 0:
trajectory_base = parse_binary_output(simulation.stdout,
number_of_trajectories,
number_timesteps,
len(self.species))
# Format results
if show_labels:
self.simulation_data = []
for trajectory in range(number_of_trajectories):
data = {'time': trajectory_base[trajectory, :, 0]}
for i in range(len(self.species)):
data[self.species[i]] = trajectory_base[
trajectory, :, i + 1]
self.simulation_data.append(data)
else:
self.simulation_data = trajectory_base
else:
raise gillespyError.ExecutionError(
"Error encountered while running simulation C++ file:\nReturn code: {0}.\nError:\n{1}\n"
.format(simulation.returncode, simulation.stderr))
return self.simulation_data
def _handle_return_code(self, return_code: "int") -> "SimulationReturnCode":
"""
Default return code handler; determines whether the simulation succeeded or failed.
Intended to be overridden by solver subclasses, which handles solver-specific return codes.
Does nothing if the return code checks out, otherwise raises an error.
:param return_code: Return code returned by a simulation.
:type return_code: int
"""
if return_code == 33:
return SimulationReturnCode.PAUSED
if return_code == 0:
return SimulationReturnCode.DONE
raise gillespyError.ExecutionError("Error encountered while running simulation C++ file "
f"(return code: {int(return_code)})")
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,
show_labels=True,
resume=None,
timeout=None):
# 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 gillespyError.ModelError(
'When resuming, one must not alter the model being resumed.'
)
if t < resume['time'][-1]:
raise gillespyError.ExecutionError(
"'t' must be greater than previous simulations end time, or set in the run() method as the "
"simulations next end time")
random.seed(seed)
species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(
model)
# create dictionary of all constant parameters for propensity evaluation
parameters = {'V': model.volume}
for paramName, param in model.listOfParameters.items():
parameters[parameter_mappings[paramName]] = param.value
# create mapping of reaction dictionary to array indices
reactions = list(model.listOfReactions.keys())
# create mapping of reactions, and which reactions depend on their reactants/products
dependent_rxns = nputils.dependency_grapher(model, reactions)
number_reactions = len(reactions)
propensity_functions = {}
# create an array mapping reactions to species modified
species_changes = np.zeros((number_reactions, number_species))
# pre-evaluate propensity equations from strings:
for i, reaction in enumerate(reactions):
# replace all references to species with array indices
for j, spec in enumerate(species):
species_changes[i][j] = model.listOfReactions[reaction].products.get(model.listOfSpecies[spec], 0) \
- model.listOfReactions[reaction].reactants.get(model.listOfSpecies[spec], 0)
if debug:
print('species_changes: {0},i={1}, j={2}... {3}'.format(
species, i, j, species_changes[i][j]))
propensity_functions[reaction] = [
eval(
'lambda S:' + model.
listOfReactions[reaction].sanitized_propensity_function(
species_mappings, parameter_mappings), parameters), i
]
if debug:
print('propensity_functions', propensity_functions)
# begin simulating each trajectory
simulation_data = []
for trajectory_num in range(number_of_trajectories):
total_time[0] = 0
if self.stop_event.is_set():
self.rc = 33
break
elif self.pause_event.is_set():
timeStopped = timeline[entry_count]
break
# 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)
# copy initial state data
trajectory = trajectory_base[trajectory_num]
entry_count = 1
curr_state[0] = {}
# 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]
for spec in model.listOfSpecies:
if resume is not None:
curr_state[0][spec] = resume[spec][-1]
else:
curr_state[0][spec] = model.listOfSpecies[
spec].initial_value
propensity_sums = np.zeros(number_reactions)
# calculate initial propensity sums
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
# determine next reaction
species_states = list(curr_state[0].values())
for i in range(number_reactions):
propensity_sums[i] = propensity_functions[reactions[i]][0](
species_states)
if debug:
print('propensity: ', propensity_sums[i])
propensity_sum = np.sum(propensity_sums)
if debug:
print('propensity_sum: ', propensity_sum)
# if no more reactions, quit
if propensity_sum <= 0:
trajectory[entry_count:, 1:] = list(species_states)
break
cumulative_sum = random.uniform(0, propensity_sum)
curr_time[0] += -math.log(random.random()) / propensity_sum
total_time[0] += -math.log(random.random()) / propensity_sum
if debug:
print('cumulative sum: ', cumulative_sum)
print('entry count: ', entry_count)
print('timeline.size: ', timeline.size)
print('curr_time: ', curr_time[0])
# determine time passed in this reaction
while entry_count < timeline.size and timeline[
entry_count] <= curr_time[0]:
if self.stop_event.is_set():
self.rc = 33
break
elif self.pause_event.is_set():
timeStopped = timeline[entry_count]
break
trajectory[entry_count, 1:] = species_states
entry_count += 1
for potential_reaction in range(number_reactions):
cumulative_sum -= propensity_sums[potential_reaction]
if debug:
print('if <=0, fire: ', cumulative_sum)
if cumulative_sum <= 0:
for i, spec in enumerate(model.listOfSpecies):
curr_state[0][spec] += species_changes[
potential_reaction][i]
reacName = reactions[potential_reaction]
if debug:
print('current state: ', curr_state[0])
print('species_changes: ', species_changes)
print('updating: ', potential_reaction)
species_states = list(curr_state[0].values())
for i in dependent_rxns[reacName]['dependencies']:
propensity_sums[propensity_functions[i]
[1]] = propensity_functions[i][0](
species_states)
if debug:
print('new propensity sum: ',
propensity_sums[i])
break
data = {'time': timeline}
for i in range(number_species):
data[species[i]] = trajectory[:, i + 1]
simulation_data.append(data)
# 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=20,
number_of_trajectories=1,
timeout=0,
increment=0.05,
seed=None,
debug=False,
profile=False,
resume=None,
**kwargs):
pause = False
if resume is not None:
if t < resume['time'][-1]:
raise gillespyError.ExecutionError(
"'t' must be greater than previous simulations end time, or set in the run() method as the "
"simulations next end time")
if resume is not None:
self = SSACSolver(model, resume=resume)
else:
if self is None or self.model is None:
self = SSACSolver(model)
if len(kwargs) > 0:
for key in kwargs:
log.warning(
'Unsupported keyword argument to {0} solver: {1}'.format(
self.name, key))
unsupported_sbml_features = {
'Rate Rules': len(model.listOfRateRules),
'Assignment Rules': len(model.listOfAssignmentRules),
'Events': len(model.listOfEvents),
'Function Definitions': len(model.listOfFunctionDefinitions)
}
detected_features = []
for feature, count in unsupported_sbml_features.items():
if count:
detected_features.append(feature)
if len(detected_features):
raise gillespyError.ModelError(
'Could not run Model. SBML Feature: {} not supported by SSACSolver.'
.format(detected_features))
if self.__compiled:
self.simulation_data = None
if resume is not None:
t = abs(t - resume['time'][-1])
number_timesteps = int(round(t / increment + 1))
# Execute simulation.
args = [
os.path.join(self.output_directory, 'UserSimulation'),
'-trajectories',
str(number_of_trajectories), '-timesteps',
str(number_timesteps), '-end',
str(t)
]
if seed is not None:
if isinstance(seed, int):
args.append('-seed')
args.append(str(seed))
else:
seed_int = int(seed)
if seed_int > 0:
args.append('-seed')
args.append(str(seed_int))
else:
raise gillespyError.ModelError(
"seed must be a positive integer")
# begin subprocess c simulation with timeout (default timeout=0 will not timeout)
with subprocess.Popen(args,
stdout=subprocess.PIPE,
start_new_session=True) as simulation:
return_code = 0
try:
if timeout > 0:
stdout, stderr = simulation.communicate(
timeout=timeout)
else:
stdout, stderr = simulation.communicate()
return_code = simulation.wait()
except KeyboardInterrupt:
os.killpg(
simulation.pid,
signal.SIGINT) # send signal to the process group
stdout, stderr = simulation.communicate()
pause = True
return_code = 33
except subprocess.TimeoutExpired:
os.killpg(
simulation.pid,
signal.SIGINT) # send signal to the process group
stdout, stderr = simulation.communicate()
pause = True
return_code = 33
# Parse/return results.
if return_code in [0, 33]:
trajectory_base, timeStopped = cutils._parse_binary_output(
stdout,
number_of_trajectories,
number_timesteps,
len(model.listOfSpecies),
pause=pause)
if model.tspan[2] - model.tspan[1] == 1:
timeStopped = int(timeStopped)
# Format results
self.simulation_data = []
for trajectory in range(number_of_trajectories):
data = {'time': trajectory_base[trajectory, :, 0]}
for i in range(len(self.species)):
data[self.species[i]] = trajectory_base[trajectory, :,
i + 1]
self.simulation_data.append(data)
else:
raise gillespyError.ExecutionError(
"Error encountered while running simulation C++ file:"
"\nReturn code: {0}.\nError:\n{1}\n".format(
simulation.returncode, simulation.stderr))
if resume is not None or timeStopped != 0:
self.simulation_data = cutils.c_solver_resume(
timeStopped, self.simulation_data, t, resume=resume)
return self.simulation_data, return_code
def __run(self, curr_state, curr_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
number_of_trajectories=1, increment=0.05, integrator='lsoda',
integrator_options={}, resume=None, **kwargs):
timeStopped = 0
if resume is not None:
if resume[0].model != self.model:
raise gillespyError.ModelError('When resuming, one must not alter the model being resumed.')
if t < resume['time'][-1]:
raise gillespyError.ExecutionError(
"'t' must be greater than previous simulations end time, or set in the run() method as the "
"simulations next end time")
# compile reaction propensity functions for eval
c_prop = OrderedDict()
for r_name, reaction in self.model.listOfReactions.items():
c_prop[r_name] = compile(reaction.ode_propensity_function, '<string>', 'eval')
result = trajectory_base[0]
entry_count = 0
y0 = [0] * len(self.model.listOfSpecies)
curr_state[0] = OrderedDict()
if resume is not None:
for i, s in enumerate(tmpSpecies):
curr_state[0][s] = tmpSpecies[s]
y0[i] = tmpSpecies[s]
else:
for i, s in enumerate(self.model.listOfSpecies.values()):
curr_state[0][s.name] = s.initial_value
y0[i] = s.initial_value
for p_name, param in self.model.listOfParameters.items():
curr_state[0][p_name] = param.value
if 'vol' not in curr_state[0]:
curr_state[0]['vol'] = 1.0
rhs = ode(ODESolver.__f).set_integrator(integrator, **integrator_options)
rhs.set_initial_value(y0, curr_time[0]).set_f_params(curr_state, self.model, c_prop)
while entry_count < timeline.size - 1:
if self.stop_event.is_set():
self.rc = 33
break
if self.pause_event.is_set():
timeStopped = timeline[entry_count]
break
int_time = curr_time[0] + increment
entry_count += 1
y0 = rhs.integrate(int_time)
curr_time[0] += increment
for i, spec in enumerate(self.model.listOfSpecies):
curr_state[0][spec] = y0[i]
result[entry_count][i+1] = curr_state[0][spec]
results_as_dict = {
'time': timeline
}
for i, species in enumerate(self.model.listOfSpecies):
results_as_dict[species] = result[:, i+1]
results = [results_as_dict] * number_of_trajectories
if timeStopped != 0 or resume is not None:
results = nputils.numpy_resume(timeStopped, results, resume=resume)
self.result = results
return results, self.rc
