def setUp(self) -> None:
# Disable the gillespy2 cache.
gillespy2.cache_enabled = False
self.build_engine = BuildEngine()
self.tmp_dir = self.build_engine.prepare(self.test_model,
variable=False)
def _build(self, model: "Union[Model, SanitizedModel]", simulation_name: str, variable: bool, debug: bool = False) -> str:
"""
Generate and build the simulation from the specified Model and solver_name into the output_dir.
:param model: The Model to simulate.
:type model: gillespy2.Model
:param simulation_name: The name of the simulation to execute.
:type simulation_name: str
:param variable: If True the simulation will be variable, False if not.
:type variable: bool
:param debug: Enables or disables debug behavior.
:type debug: bool
"""
# Prepare the build workspace.
if self.build_engine is None or self.build_engine.get_executable_path() is None:
self.build_engine = BuildEngine(debug=debug, output_dir=self.output_directory)
self.build_engine.prepare(model, variable)
# Compile the simulation, returning the path of the executable.
return self.build_engine.build_simulation(simulation_name)
# Assume that the simulation has already been built.
return self.build_engine.get_executable_path()
def build(root: "str", model: Model, solver: "str") -> "CProfiler":
build = BuildEngine(output_dir=root)
definitions = {
"CXXFLAGS": "-pg -std=c++14 -O0",
"CFLAGS": "-pg -O0",
}
# Prepare the simulation executable.
build.prepare(model)
exe = build.build_simulation(simulation_name=solver,
definitions=definitions)
return CProfiler(root_dir=pathlib.Path(root), exe_path=exe)
def __init__(self, model: Model = None, output_directory: str = None, delete_directory: bool = True, resume=None, variable: bool = False):
if model is None:
raise gillespyError.SimulationError("A model is required to run the simulation.")
if len(BuildEngine.get_missing_dependencies()) > 0:
raise gillespyError.SimulationError(
"Please install/configure 'g++' and 'make' on your system, to ensure that GillesPy2 C solvers will run properly."
)
if platform.system() == "Windows" and " " in gillespy2.__file__:
raise gillespy2Error.SimulationError("GillesPy2 does not support spaces in its path on Windows systems.")
self.delete_directory = False
self.model = copy.deepcopy(model)
self.resume = resume
self.variable = variable
self.build_engine: BuildEngine = None
# Validate output_directory, ensure that it doesn't already exist
if isinstance(output_directory, str):
output_directory = os.path.abspath(output_directory)
if os.path.exists(output_directory):
raise gillespyError.DirectoryError(
f"Could not write to specified output directory: {output_directory}"
" (already exists)"
)
self.output_directory = output_directory
self.delete_directory = delete_directory
if self.model is not None:
self._set_model()
self.is_instantiated = True
class TestBuildEngine(unittest.TestCase):
test_model = create_dimerization()
# List of solver names.
# Each entry represents the Makefile target for a C++ solver.
solver_names = [
"ssa",
"ode",
"tau_leap",
]
def setUp(self) -> None:
# Disable the gillespy2 cache.
gillespy2.cache_enabled = False
self.build_engine = BuildEngine()
self.tmp_dir = self.build_engine.prepare(self.test_model,
variable=False)
def tearDown(self) -> None:
self.build_engine.clean()
if self.tmp_dir is not None and os.path.exists(self.tmp_dir):
shutil.rmtree(self.tmp_dir, ignore_errors=True)
def test_default_layout(self):
"""
Ensure that with the default, cache-less output, the template and obj files
are subdirectories of the temp directory.
This is to ensure that they get cleaned up when the temp directory is cleaned up.
"""
# This test should be attempted with each solver target.
for solver_name in self.solver_names:
with self.subTest(solver=solver_name):
self.build_engine.build_simulation(solver_name)
template_dir = self.build_engine.template_dir
obj_dir = self.build_engine.obj_dir
self.assertTrue(
str(template_dir.resolve()).startswith(
str(Path(self.tmp_dir).resolve())),
"Template directory was not placed in temp directory when cache is disabled"
)
self.assertTrue(
str(obj_dir.resolve()).startswith(str(
Path(self.tmp_dir).resolve())),
"Dependency obj directory was not placed in temp directory when cache is disabled"
)
# Ensure that the dependencies are cleaned up when .clean() is called.
self.build_engine.clean()
self.assertFalse(
template_dir.exists(),
"Template directory was not cleaned up when cache is disabled")
self.assertFalse(
obj_dir.exists(),
"Dependency obj directory was not cleaned up when cache is disabled"
)
def test_clean(self):
"""
Ensure that with the default, cache-less output, the output files are cleaned up.
"""
# Test clean-up with manual call to .clean()
self.build_engine.clean()
self.assertFalse(os.path.exists(self.tmp_dir),
"Build engine output directory not cleaned up.")
def test_template_file(self):
"""
Ensure that the resulting template file exists and was prepared correctly.
"""
template_file = self.build_engine.template_dir.joinpath(
self.build_engine.template_definitions_name)
self.assertTrue(template_file.exists(),
"Simulation template header file could not be found")
# Test to ensure the contents of the output template_file contains ONLY macro definitions,
# nothing else.
with open(template_file) as template:
for line in template.readlines():
self.assertTrue(
line.startswith("#"),
"Output template file contains an invalid definition.")
def test_build_output(self):
"""
Ensure that the default, cache-less output has expected behavior.
Should result in an expected definitions file and an executable simulation.
"""
simulation_file = self.build_engine.output_dir.joinpath(
"GillesPy2_Simulation.exe" if os.name ==
"nt" else "GillesPy2_Simulation.out")
for solver_name in self.solver_names:
with self.subTest(solver=solver_name):
self.build_engine.build_simulation(solver_name)
self.assertTrue(simulation_file.exists(),
"Simulation executable could not be found")
self.assertTrue(os.access(str(simulation_file), os.X_OK),
"Simulation output is not executable")
class CSolver:
"""
This class implements base behavior that will be needed for C++ solver implementees.
:param model: The Model to simulate.
:type model: Model
:param output_directory: The working output directory.
:type output_directory: str
:param delete_directory: If True then the output_directory will be deleted upon completetion.
:type delete_directory: bool
:param resume: Resume data from a previous simulation run.
:param variable: Indicates whether the simulation should be variable.
:type variable: bool
"""
rc = 0
def __init__(self, model: Model = None, output_directory: str = None, delete_directory: bool = True, resume=None, variable: bool = False):
if model is None:
raise gillespyError.SimulationError("A model is required to run the simulation.")
if len(BuildEngine.get_missing_dependencies()) > 0:
raise gillespyError.SimulationError(
"Please install/configure 'g++' and 'make' on your system, to ensure that GillesPy2 C solvers will run properly."
)
if platform.system() == "Windows" and " " in gillespy2.__file__:
raise gillespy2Error.SimulationError("GillesPy2 does not support spaces in its path on Windows systems.")
self.delete_directory = False
self.model = copy.deepcopy(model)
self.resume = resume
self.variable = variable
self.build_engine: BuildEngine = None
# Validate output_directory, ensure that it doesn't already exist
if isinstance(output_directory, str):
output_directory = os.path.abspath(output_directory)
if os.path.exists(output_directory):
raise gillespyError.DirectoryError(
f"Could not write to specified output directory: {output_directory}"
" (already exists)"
)
self.output_directory = output_directory
self.delete_directory = delete_directory
if self.model is not None:
self._set_model()
self.is_instantiated = True
def __del__(self):
if self.build_engine is None:
return
if not self.delete_directory:
return
self.build_engine.clean()
def _build(self, model: "Union[Model, SanitizedModel]", simulation_name: str, variable: bool, debug: bool = False) -> str:
"""
Generate and build the simulation from the specified Model and solver_name into the output_dir.
:param model: The Model to simulate.
:type model: gillespy2.Model
:param simulation_name: The name of the simulation to execute.
:type simulation_name: str
:param variable: If True the simulation will be variable, False if not.
:type variable: bool
:param debug: Enables or disables debug behavior.
:type debug: bool
"""
# Prepare the build workspace.
if self.build_engine is None or self.build_engine.get_executable_path() is None:
self.build_engine = BuildEngine(debug=debug, output_dir=self.output_directory)
self.build_engine.prepare(model, variable)
# Compile the simulation, returning the path of the executable.
return self.build_engine.build_simulation(simulation_name)
# Assume that the simulation has already been built.
return self.build_engine.get_executable_path()
def _run_async(self, sim_exec: str, sim_args: "list[str]", decoder: SimDecoder, timeout: int = 0):
"""
Run the target executable simulation as async.
:param sim_exec: The executable simulation to run.
:type sim_exec: str
:param sim_args: The arguments to pass on simulation run.
:type sim_args: list[str]
:param decoder: The SimDecoder instance that will handle simulation output.
:type decoder: SimDecoder
:returns: A future which represents the currently executing run_simulation job.
"""
executor = ThreadPoolExecutor()
return executor.submit(self._run, sim_exec, sim_args, decoder, timeout)
def _run(self, sim_exec: str, sim_args: "list[str]", decoder: SimDecoder, timeout: int = 0, display_args: dict = None) -> int:
"""
Run the target executable simulation.
:param sim_exec: The executable simulation to run.
:type sim_exec: str
:param sim_args: The arguments to pass on simulation run.
:type sim_args: list[str]
:param decoder: The SimDecoder instance that will handle simulation output.
:type decoder: SimDecoder
:param display_args: The kwargs need to setup the live graphing
:type display_args: dict
:returns: The return_code of the simulation.
"""
# Prefix the executable to the sim arguments.
sim_args = [sim_exec] + sim_args
# nt and *nix require different methods to force shutdown a running process.
if os.name == "nt":
proc_kill = lambda sim: sim.send_signal(signal.CTRL_BREAK_EVENT)
platform_args = {
"creationflags": subprocess.CREATE_NEW_PROCESS_GROUP,
"start_new_session": True
}
else:
proc_kill = lambda sim: os.killpg(sim.pid, signal.SIGINT)
platform_args = {
"start_new_session": True
}
live_grapher = [None]
if display_args is not None:
live_queue = queue.Queue(maxsize=1)
def decoder_cb(curr_time, curr_state, trajectory_base=decoder.trajectories):
try:
old_entry = live_queue.get_nowait()
except queue.Empty as err:
pass
curr_state = {self.species[i]: curr_state[i] for i in range(len(curr_state))}
entry = ([curr_state], [curr_time], trajectory_base)
live_queue.put(entry)
decoder.with_callback(decoder_cb)
from gillespy2.core.liveGraphing import (
LiveDisplayer, CRepeatTimer, valid_graph_params
)
valid_graph_params(display_args['live_output_options'])
live_grapher[0] = LiveDisplayer(**display_args)
display_timer = CRepeatTimer(
display_args['live_output_options']['interval'], live_grapher[0].display,
args=(live_queue, display_args['live_output_options']['type'])
)
timeout_event = [False]
with subprocess.Popen(sim_args, stdout=subprocess.PIPE, **platform_args) as simulation:
def timeout_kill():
timeout_event[0] = True
proc_kill(simulation)
timeout_thread = threading.Timer(timeout, timeout_kill)
reader_thread = threading.Thread(target=decoder.read,
args=(simulation.stdout,))
if timeout > 0:
timeout_thread.start()
try:
reader_thread.start()
if display_args is not None:
display_timer.start()
reader_thread.join()
except KeyboardInterrupt:
if live_grapher[0] is not None:
display_timer.pause = True
proc_kill(simulation)
finally:
if live_grapher[0] is not None:
display_timer.cancel()
timeout_thread.cancel()
return_code = simulation.wait()
reader_thread.join()
if timeout_event[0]:
return SimulationReturnCode.PAUSED
return self._handle_return_code(return_code)
def _make_args(self, args_dict: "dict[str, str]") -> "list[str]":
"""
Convert a dictionary of key, value pairs into a valid Popen argument list.
Note: Do not prefix a key with `-` as this will be handled automatically.
:param args_dict: A dictionary of named arguments.
:type args_dict: dict[str, str]
:returns: A formatted list of arguments.
"""
args_list = []
for key, value in args_dict.items():
args_list.extend([f"--{key}", str(value)])
return args_list
def _format_output(self, trajectories: numpy.ndarray):
# Check the dimensionality of the input trajectory.
if not len(trajectories.shape) == 3:
raise gillespyError.ValidationError("Could not format trajectories, input numpy.ndarray is not 3-dimensional.")
# The trajectory count is the first dimention of the input ndarray.
trajectory_count = trajectories.shape[0]
self.result = []
# Begin iterating through the trajectories, copying each dimension into simulation_data.
for trajectory in range(trajectory_count):
# Copy the first index of the third-dimension into the simulation_data dictionary.
data = {
"time": trajectories[trajectory, :, 0]
}
for i in range(len(self.species)):
data[self.species[i]] = trajectories[trajectory, :, i + 1]
self.result.append(data)
return self.result
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 _make_resume_data(self, time_stopped: int, simulation_data: numpy.ndarray, t: int):
"""
If the simulation was paused then the output data needs to be trimmed to allow for resume.
In the event the simulation was not paused, no data is changed.
"""
# No need to create resume data if the simulation completed without interruption.
# Note that, currently, some C++ solvers write 0 out as the "time stopped" by default.
# This is likely to change in the future.
if not time_stopped < t or time_stopped == 0:
return simulation_data
# Find the index of the time step value which is closest to the time stopped.
cutoff = numpy.searchsorted(simulation_data[-1]["time"], float(time_stopped))
if cutoff < 2:
log.warning('You have paused the simulation too early, and no points have been calculated past'
' initial values. A graphic display will not produce expected results.')
# Break off any extraneous data which goes past the cutoff time.
# Any data in this case is assumed to be untrusted.
for entry_name, entry_data in simulation_data[-1].items():
simulation_data[-1][entry_name] = entry_data[:cutoff]
return simulation_data
def _set_model(self, model=None):
if model is not None:
self.model = copy.deepcopy(model)
self._build(self.model, self.target, self.variable, False)
self.species_mappings = self.model.sanitized_species_names()
self.species = list(self.species_mappings.keys())
self.parameter_mappings = self.model.sanitized_parameter_names()
self.parameters = list(self.parameter_mappings.keys())
self.reactions = list(self.model.listOfReactions.keys())
self.result = []
self.rc = 0
def _update_resume_data(self, resume: Results, simulation_data: "list[dict[str, numpy.ndarray]]", time_stopped: int):
"""
Modify the simulation output to continue from a previous Results object.
Does not handle the case where the simulation was interrupted again.
"""
# No need to update the resume data if there is no previous data, or not enough.
if resume is None or len(resume["time"]) < 2:
return simulation_data
resume_time = float(resume["time"][-1])
increment = resume_time - float(resume["time"][-2])
# Replace the simulation's timespan to continue where the Results object left off.
simulation_data[-1]["time"] = numpy.arange(start=(resume_time),
stop=(resume_time + time_stopped + increment),
step=increment)
for entry_name, entry_data in simulation_data[-1].items():
# The results of the current simulation is treated as an "extension" of the resume data.
# As such, the new simulation output is formed by joining the two end to end.
new_data = numpy.concatenate((resume[entry_name], entry_data[1:]), axis=None)
simulation_data[-1][entry_name] = new_data
return simulation_data
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 _validate_kwargs(self, **kwargs):
"""
Validate any additional kwargs passed to the model. If any exist, warn the user.
"""
if len(kwargs) == 0:
return
for key, val in kwargs.items():
log.warning(f"Unsupported keyword argument for solver {self.name}: {key}")
def _validate_seed(self, seed: int):
if seed is None:
return None
if not isinstance(seed, int):
seed = int(seed)
if seed <= 0:
raise gillespyError.ModelError("`seed` must be a postive integer.")
return seed
def _validate_variables_in_set(self, variables, values):
for var in variables.keys():
if var not in values:
raise gillespyError.SimulationError(f"Argument to variable '{var}' is not a valid variable. Variables must be model species or parameters.")
def _validate_type(self, value, typeof: type, message: str):
if not type(value) == typeof:
raise gillespyError.SimulationError(message)
def check_cpp_support():
return not len(BuildEngine.get_missing_dependencies()) > 0
def test_check_cpp_support(self):
self.assertEqual(not len(BuildEngine.get_missing_dependencies()),
self.old_check_cpp_support())