def make_plotplotly(results_json: str, *args, **kwargs) -> str:
import plotly.io as plotlyio
results = Results.from_json(results_json)
plot = Results.plotplotly(results, return_plotly_figure=True, **kwargs)
plot_json = plotlyio.to_json(plot)
return plot_json
def test_to_csv_single_result_no_stamp(self):
test_data = {'time': [0]}
result = Results(data=test_data)
test_nametag = "test_nametag"
with tempfile.TemporaryDirectory() as tempdir:
result.to_csv(nametag=test_nametag, path=tempdir)
assert len(os.listdir(tempdir)) is not 0
def test_to_csv_single_result_no_data(self):
result = Results(data=None)
test_nametag = "test_nametag"
test_stamp = "test_stamp"
with tempfile.TemporaryDirectory() as tempdir:
result.to_csv(stamp=test_stamp, nametag=test_nametag, path=tempdir)
test_path = tempdir + "/" + test_nametag + test_stamp
assert not os.path.isdir(test_path)
def test_to_csv_single_result_no_path(self):
test_data = {'time': [0]}
result = Results(data=test_data)
test_nametag = "test_nametag"
test_stamp = "test_stamp"
with tempfile.TemporaryDirectory() as tempdir:
os.chdir(tempdir)
result.to_csv(stamp=test_stamp, nametag=test_nametag)
assert len(os.listdir(tempdir)) is not 0
def test_to_csv_single_result_no_nametag(self):
test_data = {'time': [0]}
test_model = Model('test_model')
result = Results(data=test_data, model=test_model)
result.solver_name = 'test_solver'
test_stamp = "test_stamp"
with tempfile.TemporaryDirectory() as tempdir:
result.to_csv(stamp=test_stamp, path=tempdir)
assert len(os.listdir(tempdir)) is not 0
def test_to_csv_single_result_file_exists(self):
test_data = {'time': [0]}
result = Results(data=test_data)
test_nametag = "test_nametag"
test_stamp = "test_stamp"
with tempfile.TemporaryDirectory() as tempdir:
result.to_csv(stamp=test_stamp, nametag=test_nametag, path=tempdir)
test_path = tempdir + "/" + test_nametag + test_stamp + "/" + test_nametag + ".csv"
assert os.path.isfile(test_path)
def resolve(self) -> Results:
"""
Finish the remote job and return the results.
This function will block until the remote job is complete.
:returns: Results
"""
# Poll the job status until it finishes.
while not self.__poll_job_status():
time.sleep(5)
# Request the results of the finished job.
results_response = self.server.get(Endpoint.RESULT,
f"/{self.result_id}/get")
if not results_response.ok:
raise Exception(ErrorResponse.parse_raw(results_response.text).msg)
print(f"Results size: {sys.getsizeof(results_response.content)}")
results_json = bz2.decompress(results_response.content).decode()
print(f"Expanded to: {sys.getsizeof(results_json)}")
# Parse and return the response body into a valid Results object.
results = Results.from_json(results_json)
return results
def make_plot(memory_id: str):
# Make sure we can grab a memory with this ID.
if not delegate.job_complete(memory_id):
status = delegate.job_status(memory_id)
return status.status_text, 404
# Grab the memory.
memory: Results = Results.from_json(delegate.job_results(memory_id))
# Swap the pyplot backend so the Results#plot call wont try to write to a GUI.
pyplot.switch_backend("template")
# Write the plot as a .png to the tempfile.
_, temp = tempfile.mkstemp(suffix=".png")
memory.plot(save_png=temp)
# Read the contents of the temp file and cleanup.
with open(temp, "rb") as infile:
plot_bytes = infile.read()
os.remove(temp)
compressed_plot = bz2.compress(plot_bytes)
response = make_response(compressed_plot)
response.headers["Content-Encoding"] = "bzip2"
return response, 200
def test_job(*args, **kwargs):
data = []
for result in args:
data = data + result.data
from gillespy2.core import Results
return Results(data)
def test_equality_of_named_results(self):
"""
Test that Model simulation results can be converted to JSON and back.
"""
for model in self.runnable_models:
target = model()
results = target.run()
results_from = Results.from_json(results.to_json())
self.assertEquals(results.to_json(), results_from.to_json())
def test_equality_of_named_results(self):
"""
Test that anonymous Model simulation results can be converted to JSON and back.
"""
for model in self.runnable_models:
target = model()
results = target.run()
results._translation_table = target.get_translation_table()
results = results.to_anon().to_json()
self.assertEqual(results, Results.from_json(results).to_json())
def join_results(results):
# Save the dependent trajectories in memory.
from distributed import get_client
client = get_client()
client.publish_dataset(dependencies,
name=f"{model_id}-trajectories",
override=True)
data = []
for i, result in enumerate(results):
with open(os.path.join(cache_dir, keys[i]), "w+") as outfile:
outfile.write(result.to_json())
data = data + result.data
from gillespy2.core import Results
result_json = Results(data).to_json()
with open(os.path.join(cache_dir, job_id), "w+") as outfile:
outfile.write(result_json)
return result_json
def __init__(self, result_id: str, server: ComputeServer):
Results.__new__(Results)
self.result_id = result_id
self.server = server
self.local_results = None
def run(self=None,
model: Model = None,
t: int = None,
number_of_trajectories: int = 1,
timeout: int = 0,
increment: int = None,
seed: int = None,
debug: bool = False,
profile: bool = False,
variables={},
resume=None,
live_output: str = None,
live_output_options: dict = {},
**kwargs):
from gillespy2 import log
"""
:param model: The model on which the solver will operate. (Deprecated)
:type model: gillespy2.Model
:param t: End time of simulation.
:type t: int
: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. By default number_of_trajectories = 1.
:type number_of_trajectories: int
:param timeout: If set, if simulation takes longer than timeout, will exit.
:type timeout: int
:param increment: Time step increment for plotting.
:type increment: float
:param seed: The random seed for the simulation. Optional, defaults to None.
:type seed: int
:param variables: Dictionary of species and their data that will override existing species data.
:type variables: dict
:param resume: Result of a previously run simulation, to be resumed.
:type resume: gillespy2.Results
: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
:returns: A result object containing the results of the simulation.
:rtype: gillespy2.Results
"""
if self is None:
# Post deprecation block
# raise SimulationError("SSACSolver must be instantiated to run the simulation")
# Pre deprecation block
log.warning("""
`gillespy2.Model.run(solver=SSACSolver)` is deprecated.
You should use `gillespy2.Model.run(solver=SSACSolver(model=gillespy2.Model))
Future releases of GillesPy2 may not support this feature.
""")
self = SSACSolver(model, resume=resume)
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._set_model(model=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]
# Validate parameters prior to running the model.
self._validate_type(variables, dict,
"'variables' argument must be a dictionary.")
self._validate_resume(t, resume)
self._validate_kwargs(**kwargs)
if resume is not None:
t = abs(t - int(resume["time"][-1]))
number_timesteps = int(round(t / increment + 1))
args = {
"trajectories": number_of_trajectories,
"timesteps": number_timesteps,
"end": t,
"interval": str(number_timesteps),
}
if self.variable:
populations = cutils.update_species_init_values(
self.model.listOfSpecies, self.species, variables, resume)
parameter_values = cutils.change_param_values(
self.model.listOfParameters, self.parameters,
self.model.volume, variables)
args.update({
"init_pop": populations,
"parameters": parameter_values
})
seed = self._validate_seed(seed)
if seed is not None:
args.update({"seed": seed})
if live_output is not None:
live_output_options['type'] = live_output
display_args = {
"model": self.model,
"number_of_trajectories": number_of_trajectories,
"timeline": np.linspace(0, t, number_timesteps),
"live_output_options": live_output_options,
"resume": bool(resume)
}
else:
display_args = None
args = self._make_args(args)
decoder = IterativeSimDecoder.create_default(
number_of_trajectories, number_timesteps,
len(self.model.listOfSpecies))
sim_exec = self._build(self.model, self.target, self.variable, False)
sim_status = self._run(sim_exec, args, decoder, timeout, display_args)
if sim_status == SimulationReturnCode.FAILED:
raise ExecutionError(
"Error encountered while running simulation C++ file:\n"
f"Return code: {int(sim_status)}.\n")
trajectories, time_stopped = decoder.get_output()
simulation_data = self._format_output(trajectories)
if sim_status == SimulationReturnCode.PAUSED:
simulation_data = self._make_resume_data(time_stopped,
simulation_data, t)
if resume is not None:
simulation_data = self._update_resume_data(resume, simulation_data,
time_stopped)
self.result = simulation_data
self.rc = int(sim_status)
return Results.build_from_solver_results(self, live_output_options)
def results_to_json(results_json):
results = Results.from_json(results_json)
return results.average_ensemble().to_json()
