def test_to_csv_single_result_no_path(self):
test_data = Trajectory({'time': [0]},
model=Model('test_model'),
solver_name='test_solver_name')
result = Results(data=[test_data])
test_nametag = "test_nametag"
test_stamp = "test_stamp"
def test_to_csv_single_result_no_nametag(self):
test_model = Model('test_model')
test_data = Trajectory(data={'time': [0]}, model=test_model)
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_nametag(self):
test_model = Model('test_model')
test_data = Trajectory(data={'time': [0]}, model=test_model)
result = Results(data=[test_data])
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_xscale_plot(self):
from unittest import mock
trajectory = Trajectory(data={
'time': [0.],
'foo': [1.]
},
model=Model('test_model'))
results = Results(data=[trajectory])
with mock.patch('matplotlib.pyplot.xscale') as mock_xscale:
results.plot(xscale='log')
mock_xscale.assert_called_with('log')
def test_pickle_stable_plot_iterate(self):
from unittest import mock
from gillespy2.core.results import _plot_iterate
trajectory = Trajectory(data={
'time': [0.],
'foo': [1.]
},
model=Model('test_model'))
import pickle
trajectory_unpickled = pickle.loads(pickle.dumps(trajectory))
import matplotlib
with mock.patch('matplotlib.pyplot.plot') as mock_method_before_pickle:
_plot_iterate(trajectory)
with mock.patch('matplotlib.pyplot.plot') as mock_method_after_pickle:
_plot_iterate(trajectory_unpickled)
assert mock_method_before_pickle.call_args_list == mock_method_after_pickle.call_args_list
def test_plotly_layout_args(self):
from unittest import mock
trajectory = Trajectory(data={
'time': [0.],
'foo': [1.]
},
model=Model('test_model'))
results = Results(data=[trajectory])
with mock.patch('plotly.offline.init_notebook_mode') as mock_notebook:
with mock.patch('plotly.graph_objs.Scatter') as mock_scatter:
with mock.patch('plotly.graph_objs.Layout') as mock_layout:
results.plotplotly(return_plotly_figure=True, xscale='log')
mock_layout.assert_called_with(showlegend=True,
title='',
xaxis_title='Time ',
xscale='log',
yaxis_title='Species Population')
def test_plotly_std_dev_range_layout_args(self):
from unittest import mock
from unittest.mock import MagicMock
trajectory = Trajectory(data={
'time': [0.],
'foo': [1.]
},
model=Model('test_model'))
results = Results(data=[trajectory])
_plotly_iterate = MagicMock()
with mock.patch('plotly.offline.init_notebook_mode') as mock_notebook:
with mock.patch('plotly.graph_objs.Scatter') as mock_scatter:
with mock.patch('plotly.graph_objs.Layout') as mock_layout:
results.plotplotly_std_dev_range(return_plotly_figure=True,
xscale='log')
mock_layout.assert_called_with(legend={'traceorder': 'normal'},
showlegend=True,
title='Standard Deviation Range',
xaxis_title='Time ',
xscale='log',
yaxis_title='Species Population')
def run(self,
solver=None,
timeout=0,
t=None,
show_labels=True,
cpp_support=False,
**solver_args):
"""
Function calling simulation of the model. There are a number of
parameters to be set here.
:param solver: The solver by which to simulate the model. This solver object may
be initialized separately to specify an algorithm. Optional,
defaults to ssa solver.
:type solver: gillespy.GillesPySolver
:param timeout: Allows a time_out value in seconds to be sent to a signal handler, restricting simulation run-time
:type timeout: int
:param t: End time of simulation
:type t: int
:param solver_args: Solver-specific arguments to be passed to solver.run()
:return If show_labels is False, returns a numpy array of arrays of species population data. If show_labels is
True,returns a Results object that inherits UserList and contains one or more Trajectory objects that
inherit UserDict. Results object supports graphing and csv export.
To pause a simulation and retrieve data before the simulation, keyboard interrupt the simulation by pressing
control+c or pressing stop on a jupyter notebook. To resume a simulation, pass your previously ran results
into the run method, and set t = to the time you wish the resuming simulation to end (run(resume=results, t=x)).
Pause/Resume is only supported for SINGLE TRAJECTORY simulations. T MUST BE SET OR UNEXPECTED BEHAVIOR MAY OCCUR.
"""
if not show_labels:
from gillespy2.core import log
log.warning('show_labels = False is deprecated. Future releases '
'of GillesPy2 may not support this feature.')
if t is None:
t = self.tspan[-1]
if solver is None:
solver = self.get_best_solver()
try:
solver_results, rc = solver.run(model=self,
t=t,
increment=self.tspan[-1] -
self.tspan[-2],
timeout=timeout,
**solver_args)
except Exception as e:
if cpp_support is False:
if not isinstance(solver, str):
if solver.name == 'SSACSolver' or solver.name == 'VariableSSACSolver':
from gillespy2.core import log
log.warning(
"Please install/configure 'g++' and 'make' on your"
" system, to ensure that GillesPy2 C solvers will"
" run properly.")
raise SimulationError(
"argument 'solver={}' to run() failed. Reason Given: {}".
format(solver, e))
if rc == 33:
from gillespy2.core import log
log.warning('GillesPy2 simulation exceeded timeout.')
if hasattr(solver_results[0], 'shape'):
return solver_results
if len(solver_results) > 0:
results_list = []
for i in range(0, len(solver_results)):
temp = Trajectory(data=solver_results[i],
model=self,
solver_name=solver.name,
rc=rc)
results_list.append(temp)
results = Results(results_list)
if show_labels == False:
results = results.to_array()
return results
if len(solver_results) > 0:
results_list = []
for i in range(0, len(solver_results)):
temp = Trajectory(data=solver_results[i],
model=self,
solver_name=solver.name,
rc=rc)
results_list.append(temp)
results = Results(results_list)
if show_labels == False:
results = results.to_array()
return results
else:
raise ValueError(
"number_of_trajectories must be non-negative and non-zero")
