gillespy2.core package

Submodules

gillespy2.core.assignmentrule module

class gillespy2.core.assignmentrule.AssignmentRule(variable=None, formula=None, name=None)

Bases: SortableObject, Jsonify

An AssignmentRule is used to express equations that set the values of variables. This would correspond to a function in the form of x = f(V)

Parameters:

• name (str) – Name of the Rule

• variable (str) – Target Species/Parameter to be modified by rule

• formula (str) – String representation of formula to be evaluated

sanitized_formula(species_mappings, parameter_mappings)

Sanitize the assignment rule formula.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized formula.

Return type:

str

gillespy2.core.cleanup module

gillespy2.core.cleanup.cleanup_tempfiles()

Cleanup all tempfiles in gillespy2 build.

gillespy2.core.events module

class gillespy2.core.events.Event(name='', delay=None, assignments=[], priority='0', trigger=None, use_values_from_trigger_time=False)

Bases: Jsonify

An Event can be given as an assignment_expression (function) or directly as a value (scalar). If given an assignment_expression, it should be understood as evaluable in the namespace of a parent Model.

Parameters:

• name (str) – The name by which this Event is called or referenced in reactions.

• assignments (str) – List of EventAssignments to be executed at trigger or delay

• trigger (EventTrigger) – contains math expression which can be evaluated to a boolean result. Upon the transition from ‘False’ to ‘True’, event assignments may be executed immediately, or after a designated delay.

• delay (str) – contains math expression evaluable within model namespace. This expression designates a delay between the trigger of an event and the execution of its assignments.

• priority (str) – Contains math expression evaluable within model namespace.

add_assignment(assignment)

Adds an EventAssignment or a list of EventAssignment.

Parameters:

assignment (EventAssignment or list[EventAssignment]) – The event or list of events to be added to this event.

class gillespy2.core.events.EventAssignment(name=None, variable=None, expression=None)

Bases: Jsonify

An EventAssignment describes a change to be performed to the current model simulation. This is assignment can either be fired at the time its associated trigger changes from false to true, or after a specified delay, depending on how the Event to which it is assigned is configured.

Parameters:

• variable (gillespy2.Species, gillespy2.Parameter) – Target model component to be modified by the EventAssignment expression. Valid target variables include gillespy2 Species, Parameters, and Compartments.

• expression (str) – String to be evaluated when the event is fired. This expression must be evaluable within the model namespace, and the results of it’s evaluation will be assigned to the EventAssignment variable.

class gillespy2.core.events.EventTrigger(expression=None, initial_value=False, persistent=False)

Bases: Jsonify

Trigger detects changes in model/environment conditions in order to fire an event. A Trigger contains an expression, a mathematical function which can be evaluated to a boolean value within a model’s namespace. Upon transitioning from ‘false’ to ‘true’, this trigger will cause the immediate execution of an event’s list of assignments if no delay is present, otherwise, the delay evaluation will be initialized.

Parameters:

• expression (str) – String for a function calculating EventTrigger values. Should be evaluable in namespace of Model.

• value (bool) – Value of EventTrigger at simulation start, with time t=0

• persistent (bool) – Determines if trigger condition is persistent or not

sanitized_expression(species_mappings, parameter_mappings)

Sanitize the event trigger expression.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized expression.

Return type:

str

gillespy2.core.functiondefinition module

class gillespy2.core.functiondefinition.FunctionDefinition(name='', function=None, args=[])

Bases: SortableObject, Jsonify

Object representation defining an evaluable function to be used during simulation of a GillesPy2 model

Parameters:

• name (str) – Name of the function to be made and called

• function (str) – Defined function body of operation to be performed.

• variables (list[str]) – String names of Variables to be used as arguments to function.

get_arg_string() → str

Convert function’s argument list into a comma-separated formatted string.

Returns:

Argument list as a comma-separated formatted string.

sanitized_function(species_mappings, parameter_mappings)

Sanitize the function definition function.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized function.

Return type:

str

gillespy2.core.gillespyError module

exception gillespy2.core.gillespyError.AssignmentRuleError

Bases: ModelError

exception gillespy2.core.gillespyError.BuildError

Bases: SolverError

exception gillespy2.core.gillespyError.DirectoryError

Bases: SolverError

exception gillespy2.core.gillespyError.EventError

Bases: ModelError

exception gillespy2.core.gillespyError.ExecutionError

Bases: SolverError

exception gillespy2.core.gillespyError.FunctionDefinitionError

Bases: ModelError

exception gillespy2.core.gillespyError.InvalidModelError

Bases: SimulationError

exception gillespy2.core.gillespyError.InvalidStochMLError

Bases: SimulationError

exception gillespy2.core.gillespyError.ModelError

Bases: Exception

exception gillespy2.core.gillespyError.ParameterError

Bases: ModelError

exception gillespy2.core.gillespyError.RateRuleError

Bases: ModelError

exception gillespy2.core.gillespyError.ReactionError

Bases: ModelError

exception gillespy2.core.gillespyError.ResultsError

Bases: Exception

exception gillespy2.core.gillespyError.SBMLError

Bases: Exception

exception gillespy2.core.gillespyError.SimulationError

Bases: Exception

exception gillespy2.core.gillespyError.SimulationTimeoutError

Bases: SimulationError

exception gillespy2.core.gillespyError.SolverError

Bases: Exception

exception gillespy2.core.gillespyError.SpeciesError

Bases: ModelError

exception gillespy2.core.gillespyError.StochMLImportError

Bases: SimulationError

exception gillespy2.core.gillespyError.TimespanError

Bases: ModelError

exception gillespy2.core.gillespyError.ValidationError

Bases: ResultsError

gillespy2.core.gillespySolver module

class gillespy2.core.gillespySolver.GillesPySolver

Bases: object

Abstract class for a solver.

get_solver_settings()

Abstract function for getting a solvers settings.

classmethod get_supported_features() → Set[Type]

Get the set of supported features.

classmethod get_supported_integrator_options() → Set[str]

Get the supported integrator options

name = 'GillesPySolver'

run(model, t=20, number_of_trajectories=1, increment=0.05, seed=None, debug=False, profile=False, **kwargs)

Call out and run the solver. Collect the results.

Parameters:

• model (gillespy.Model) – The model on which the solver will operate.

• t (int) – The end time of the solver

• number_of_trajectories (int) – The number of times to sample the chemical master equation. Each trajectory will be returned at the end of the simulation.

• increment (float) – The time step of the solution

• seed (int) – The random seed for the simulation. Defaults to None.

• debug (bool) – Set to True to provide additional debug information about the simulation.

Returns:

Simulation trajectories.

classmethod validate_integrator_options(options: dict[str, float]) → dict[str, float]

Validate the integrator options.

Parameters:

options (dict) – Integrator options to validate.

Returns:

Dict of supported integrator options.

Return type:

dict

classmethod validate_model(sol_model, model)

Validate that the solvers model is the same as the model passed to solver.run.

Parameters:

• sol_model (gillespy2.Model) – Solver model object.

• model (gillespy2.Model) – Model object passed to solver.run.

Raises:

SimulationError – If the models are not equal.

classmethod validate_sbml_features(model)

Validate the models SBML features.

Parameters:

model (gillespy2.Model) – The model object to be checked.

Raises:

ModelError – If the model contains unsupported SBML features.

validate_tspan(increment, t)

Validate the models time span and set it if not provided.

Parameters:

• increment (int) – The current value of increment.

• t (int) – The end time of the simulation.

Raises:

SimulationError – if timespan and increment are both set by the user or neither are set by the user.

gillespy2.core.jsonify module

class gillespy2.core.jsonify.ComplexJsonCoder(translation_table=None, encode_private=True, **kwargs)

Bases: JSONEncoder

This class delegates the encoding and decoding of objects to one or more implementees.

Parameters:

• translation_table (TranslationTable) – A TranslationTable instance that will be used to translate objects.

• encode_private (bool) – If set to True then all private and public variables will be converted to JSON. If False, only public.

decode(json_dict: dict)

Decode the JSON dictionary into a valid Python object.

Parameters:

json_dict (dict) – The JSON dictionary to decode.

Returns:

The decoded form of the JSON dictionary.

default(o: object)

This function is called when json.dumps() fires. default() is a bad name for the function, but anything else makes JSONEncoder freak out.

Parameters:

o – The object that is currently being encoded into JSON.

class gillespy2.core.jsonify.Int64Coder

Bases: Jsonify

This JSON coder enables support for the numpy.int64 type.

static from_json(obj)

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

static to_dict(obj)

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

class gillespy2.core.jsonify.Jsonify

Bases: object

Interface to allow for instances of arbitrary types to be encoded into json strings and decoded into new objects.

classmethod from_dict(src_dict: dict) → object

Convert some dict into a new instance of a python type. This function will return a __new__ instance of the type.

Parameters:

src_dict (dict) – The dictionary to apply onto the new instance.

Returns:

A new object with its backing __dict__ set to a copy of src_dict.

classmethod from_json(json_str: str) → object

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

get_json_hash(ignore_whitespace=True, hash_private_vals=False) → str

Get the hash of the json representation of self.

Parameters:

• ignore_whitespace (bool) – If set to True all whitespace will be stripped from the JSON prior to being hashed.

• hash_private_vals (bool) – If set to True all private and non-private variables will be included in the hash.

Returns:

An MD5 hash of the object’s sorted JSON representation.

get_translation_table() → TranslationTable

Make and/or return the translation table.

Returns:

A TranslationTable instance.

make_translation_table() → TranslationTable

Make a translation table that describes key:value pairs to convert user-define data into generic equivalents.

Returns:

A newly generated TranslationTable instance.

public_vars() → dict

Gets a dictionary of public vars that exist on self. Keys starting with ‘_’ are ignored.

Returns:

A dictionary containing all public (‘_’-prefixed) variables on the object.

to_anon()

Converts self into an anonymous instance of self.

to_dict() → dict

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

to_json(encode_private=True) → str

Convert self into a json string.

Parameters:

encode_private (bool) – If True all private (prefixed of ‘_’) will be encoded. None if False.

Returns:

The JSON representation of self.

to_named()

Converts self into a named instance of self.

class gillespy2.core.jsonify.NdArrayCoder

Bases: Jsonify

This JSON coder enables support for the numpy.ndarray type.

static from_json(obj)

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

static to_dict(obj)

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

class gillespy2.core.jsonify.SetCoder

Bases: Jsonify

This JSON coder enables support for the set type.

static from_json(obj)

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

static to_dict(obj)

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

class gillespy2.core.jsonify.TranslationTable(to_anon: dict[str, str])

Bases: Jsonify

This class contains functions to enable arbitrary object trees to be “translated” to anonymous and named objects. This behavior is defined by a map of ‘named’ and ‘anon’ key values.

Parameters:

to_anon (dict[str, str]) – A mapping of ‘named’ to ‘anonymous’ strings to be used when converting user-defined names to anon.

obj_to_anon(obj: object)

Recursively anonymise all named properties on the object.

Parameters:

obj (object) – The object to anonymize.

Returns:

An anonymized instance of self.

obj_to_named(obj)

Recursively identify all anonymous properties on the object.

Parameters:

obj (object) – The object that will be converted to named.

Returns:

A named instance of self.

recursive_translate(obj: object, translation_table: dict[str, str])

Recursively search through the object tree searching for property value matches in the translation table. If a match is found, substitute.

Parameters:

• obj (object) – The object that will be translated.

• translation_table (TranslationTable) – The mapping to translate by.

class gillespy2.core.jsonify.TypeCoder

Bases: Jsonify

This JSON coder enables support for the ‘type’ type.

static from_json(obj)

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

static to_dict(obj)

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

gillespy2.core.liveGraphing module

class gillespy2.core.liveGraphing.CRepeatTimer(interval, function, args=None, kwargs=None)

Bases: Timer

Threading timer which repeatedly calls the given function instead of simply ending.

Used for C solver live graphing support

pause = False

run()

Method representing the thread’s activity.

You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.

class gillespy2.core.liveGraphing.LiveDisplayer(model=None, timeline=None, number_of_trajectories=1, live_output_options={}, resume=False)

Bases: object

holds information required for displaying information when live_output = True

display(curr_state, curr_time, trajectory_base, finished=False)

Display the output for the live grapher.

Parameters:

• curr_state (list) – Current state of the simulation. Should be a list of len 1 to get reference.

• curr_time (list) – Current time of the simulation. Should be a list of len 1 to get reference.

• trajectory_base (list) – Current results of the simulation.

• finished (bool) – Indicates whether or not the simulation has finished.

increment_trajectory(trajectory_num)

Increment the trejectory counter.

print_text_header(file_obj)

Print the header for text display type.

Parameters:

file_obj (file object) – File object to write text output.

trajectory_header()

Create the trajectory header for the output.

class gillespy2.core.liveGraphing.RepeatTimer(interval, function, args=None, kwargs=None)

Bases: Timer

Threading timer which repeatedly calls the given function instead of simply ending

pause = False

run()

Method representing the thread’s activity.

You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.

gillespy2.core.liveGraphing.display_types()

Get the list of supported display types.

Returns:

Supported display types.

Return type:

list

gillespy2.core.liveGraphing.valid_graph_params(live_output_options)

Validated the live output options.

Parameters:

live_output_options (dict) – Options to be validated.

Raises:

SimulationError – If the display type is invalid.

gillespy2.core.model module

class gillespy2.core.model.Model(name='', population=True, volume=1.0, tspan=None, annotation='model')

Bases: SortableObject, Jsonify

Representation of a well mixed biochemical model. Contains reactions, parameters, species.

Parameters:

• name (str) – The name of the model, or an annotation describing it.

• population (bool) – The type of model being described. A discrete stochastic model is a population model (True), a deterministic model is a concentration model (False). Automatic conversion from population to concentration models may be used, by setting the volume parameter.

• volume (float) – The volume of the system matters when converting to from population to concentration form. This will also set a parameter “vol” for use in custom (i.e. non-mass-action) propensity functions.

• tspan (numpy ndarray) – The timepoints at which the model should be simulated. If None, a default timespan is added. May be set later, see Model.timespan

• annotation (str) – Option further description of model

add(components)

Adds a component, or list of components to the model. If a list is provided, Species and Parameters are added before other components. Lists may contain any combination of accepted types other than lists and do not need to be in any particular order.

Parameters:

components (Species, Parameters, Reactions, Events, Rate Rules, Assignment Rules, FunctionDefinitions, TimeSpan, or list) – The component or list of components to be added the the model.

Returns:

The components that were added to the model.

Return type:

Species, Parameters, Reactions, Events, Rate Rules, Assignment Rules, FunctionDefinitions, TimeSpan, or list

Raises:

ModelError – Component is invalid.

add_assignment_rule(assignment_rule)

Add an assignment rule, or list of assignment rules to the model.

Parameters:

assignment_rules (gillespy2.AssignmentRule or list of gillespy2.AssignmentRules) – The assignment rule or list of assignment rules to be added to the model object.

Returns:

The assignment rule or list of assignment rules that were added to the model.

Return type:

gillespy2.AssignmentRule | list of gillespy2.AssignmentRule

Raises:

ModelError – If an invalid assignment rule is provided or if assignment rule validation fails.

add_event(event)

Adds an event, or list of events to the model.

Parameters:

event (gillespy2.Event | list of gillespy2.Events) – The event or list of event to be added to the model object.

Returns:

The event or list of events that were added to the model.

Return type:

gillespy2.Event | list of gillespy2.Event

Raises:

ModelError – If an invalid event is provided or if event validation fails.

add_function_definition(function_definition)

Add function definition, or list of function definitions to the model

Parameters:

function_definition (gillespy2.FunctionDefinition | list of gillespy2.FunctionDefinitions.) – The function definition, or list of function definitions to be added to the model object.

Returns:

The function defintion or list of function definitions that were added to the model.

Return type:

gillespy2.FunctionDefinitions | list of gillespy2.FunctionDefinitions

Raises:

ModelError – If an invalid function definition is provided.

add_parameter(parameters)

Adds a parameter, or list of parameters to the model.

Parameters:

parameters (gillespy2.Parameter | list of gillespy2.Parameter) – The parameter or list of parameters to be added to the model object.

Returns:

A parameter or list of Parameters that were added to the model.

Return type:

gillespy2.Parameter | list of gillespy2.Parameter

Raises:

ModelError – If an invalid parameter is provided or if Parameter.validate fails.

add_rate_rule(rate_rule)

Adds a rate rule, or list of rate rules to the model.

Parameters:

rate_rule (gillespy2.RateRule | list of gillespy2.RateRules) – The rate rule or list of rate rules to be added to the model object.

Returns:

The rate rule or list of rate rules that were added to the model.

Return type:

gillespy2.RateRule | list of gillespy2.RateRule

Raises:

ModelError – If an invalid rate rule is provided or if rate rule validation fails.

add_reaction(reactions)

Adds a reaction, or list of reactions to the model.

Parameters:

reactions (gillespy2.Reaction | list of gillespy2.Reaction) – The reaction or list of reactions to be added to the model object

Returns:

The reaction or list of reactions that were added to the model.

Return type:

gillespy2.Reaction | list of gillespy2.Reaction

Raises:

ModelError – If an invalid reaction is provided or if Reaction.validate fails.

add_species(species)

Adds a species, or list of species to the model.

Parameters:

species (gillespy2.Species | list of gillespy2.Species) – The species or list of species to be added to the model object

Returns:

The species or list of species that were added to the model.

Return type:

gillespy2.Species | list of gillespy2.Species

Raises:

ModelError – If an invalid species is provided or if Species.validate fails.

compile_prep()

Prepare the model for export or simulation.

delete_all_assignment_rules()

Removes all assignment rules from the model object.

delete_all_events()

Removes all events from the model object.

delete_all_function_definitions()

Removes all function definitions from the model object.

delete_all_parameters()

Removes all parameters from model object.

delete_all_rate_rules()

Removes all rate rules from the model object.

delete_all_reactions()

Removes all reactions from the model object.

delete_all_species()

Removes all species from the model object.

delete_assignment_rule(name)

Removes an assignment rule object by model.

Parameters:

name (str) – Name of the assignment rule object to be removed.

Raises:

ModelError – If the assignment rule is not part of the model.

delete_event(name)

Removes an event object by name.

Parameters:

name (str) – Name of the event object to be removed.

delete_function_definition(name)

Removes a function definition object by name.

Parameters:

name (str) – Name of the function definition object to be removed.

delete_parameter(name)

Removes a parameter object by name.

Parameters:

name (str) – Name of the parameter object to be removed.

Raises:

ModelError – If the parameter is not part of the model.

delete_rate_rule(name)

Removes rate rule object by name.

Parameters:

name (str) – Name of the rate rule to be removed.

Raises:

ModelError – If the rate rule is not part of the model.

delete_reaction(name)

Removes a reaction object by name.

Parameters:

name (str) – Name of the reaction object to be removed.

Raises:

ModelError – If the reaction is not part of the model.

delete_species(name)

Removes a species object by name.

Parameters:

name (str) – Name of the species object to be removed.

Raises:

ModelError – If the species is not part of the model.

get_all_assignment_rules()

Get all of the assignment rules in the model object.

Returns:

A dict of all assignemt rules in the model, in the form: {name: reaction object}.

get_all_events()

Get all of the events in the model object.

Returns:

A dict of all evetns in the model, in the form: {name : event object}

get_all_function_definitions()

Get all of the function definitions in the model object.

Returns:

A dict of all function definitions in the model, in the form {name : function definition object}.

Return type:

OrderedDict

get_all_parameters()

Get all of the parameters in the model object.

Returns:

A dict of all parameters in the model, in the form: {name : parameter object}

Return type:

OrderedDict

get_all_rate_rules()

Get all of the rate rules in the model object.

Returns:

A dict of all rate rules in the model, in the form: {name : rate rule object}.

Return type:

OrderedDict

get_all_reactions()

Get all of the reactions in the model object.

Returns:

A dict of all reactions in the model, in the form: {name : reaction object}.

Return type:

OrderedDict

get_all_species()

Get all of the species in the model object.

Returns:

A dict of all species in the model, in the form: {name : species object}.

Return type:

OrderedDict

get_assignment_rule(name)

Returns an assignment rule object by name.

Parameters:

name (str) – Name of the assignment rule object to be returned.

Returns:

The specified assignment rule object.

Return type:

gillespy2.AssignmentRule

Raises:

ModelError – If the assignment rule is not part of the model.

get_best_solver()

Finds best solver for the users simulation. Currently, AssignmentRules, RateRules, FunctionDefinitions, Events, and Species with a dynamic, or continuous population must use the TauHybridSolver.

Parameters:

precompile (bool) – If True, and the model contains no AssignmentRules, RateRules, FunctionDefinitions, Events, or Species with a dynamic or continuous population, it will choose SSACSolver

Returns:

gillespy2.gillespySolver

get_best_solver_algo(algorithm)

If user has specified a particular algorithm, we return either the Python or C++ version of that algorithm

get_element(name)

Get a model element specified by name.

Parameters:

name (str) – Name of the element to be returned.

Returns:

The specified gillespy2.Model element.

Return type:

Species, Parameters, Reactions, Events, RateRules, AssignmentRules, FunctionDefinitions, or TimeSpan

Raises:

ModelError – If the element is not part of the model.

get_event(name)

Returns an event object by name.

Parameters:

name (str) – Name of the event object to be returned.

Returns:

The specified event object.

Return type:

gillespy2.Event

Raises:

ModelError – If the event is not part of the model.

get_function_definition(name)

Returns a function definition object by name.

Parameters:

name (str) – Name of the function definition object to be returned.

Returns:

The specified function definition object.

Return type:

gillespy2.FunctionDefinition

get_model_features() → Set[Type]

Determine what solver-specific model features are present on the model. Used to validate that the model is compatible with the given solver.

Returns:

Set containing the classes of every solver-specific feature present on the model.

get_parameter(name)

Returns a parameter object by name.

Parameters:

name (str) – Name of the parameter object to be returned

Returns:

The specified parameter object.

Return type:

gillespy2.Parameter

Raises:

ModelError – If the parameter is not part of the model.

get_rate_rule(name)

Returns a rate rule object by name.

Parameters:

name (str) – Name of the rate rule object to be returned.

Returns:

The specified rate rule object.

Return type:

gillespy2.RateRule

Raises:

ModelError – If the rate rule is not part of the model.

get_reaction(name)

Returns a reaction object by name.

Parameters:

name (str) – Name of the reaction object to be returned

Returns:

The specified reaction object.

Return type:

gillespy2.Reaction

Raises:

ModelError – If the reaction is not part of the model.

get_species(name)

Returns a species object by name.

Parameters:

name (str) – Name of the species object to be returned.

Returns:

The specified species object.

Return type:

gillespy2.Species

Raises:

ModelError – If the species is not part of the model.

make_translation_table()

Make a translation table that describes key:value pairs to convert user-define data into generic equivalents.

Returns:

A newly generated TranslationTable instance.

problem_with_name(*args)

(deprecated)

reserved_names = ['vol']

run(solver=None, timeout=0, t=None, increment=None, algorithm=None, **solver_args)

Function calling simulation of the model. There are a number of parameters to be set here.

Parameters:

• solver (gillespy.GillesPySolver) – The solver by which to simulate the model. This solver object may be initialized separately to specify an algorithm. Optional, defaults to ssa solver.

• timeout (int) – Allows a time_out value in seconds to be sent to a signal handler, restricting simulation run-time

• t (int) – End time of simulation

• solver_args – Solver-specific arguments to be passed to solver.run()

• algorithm (str) – Specify algorithm (‘ODE’, ‘Tau-Leaping’, or ‘SSA’) for GillesPy2 to automatically pick best solver using that algorithm.

Returns:

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.**

sanitized_parameter_names()

Generate a dictionary mapping user chosen parameter names to simplified formats which will be used later on by GillesPySolvers evaluating reaction propensity functions.

Returns:

the dictionary mapping user parameter names to their internal GillesPy notation.

sanitized_species_names()

Generate a dictionary mapping user chosen species names to simplified formats which will be used later on by GillesPySolvers evaluating reaction propensity functions.

Returns:

the dictionary mapping user species names to their internal GillesPy notation.

serialize()

Serializes the Model object to valid StochML.

set_parameter(p_name, expression)

Set the value of an existing parameter “pname” to “expression” (deprecated).

Parameters:

• p_name (str) – Name of the parameter whose value will be set.

• expression (str) – String that may be executed in C, describing the value of the parameter. May reference other parameters by name. (e.g. “k1*4”)

set_units(units)

Sets the units of the model to either “population” or “concentration”

Parameters:

units (str) – Either “population” or “concentration”

special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']

timespan(time_span)

Set the time span of simulation. GillesPy2 does not support non-uniform timespans.

Parameters:

time_span (gillespy2.TimeSpan | iterator) – Evenly-spaced list of times at which to sample the species populations during the simulation. Best to use the form gillespy2.TimeSpan(np.linspace( <start time>, <end time>, <number of time-points, inclusive>))

update_namespace()

Create a dict with flattened parameter and species objects.

validate_reactants_and_products(reactions)

Internal function (deprecated): Ensure that the rate and all reactants and products are present in the model for the given reaction. This methods must be called before exporting the model.

Parameters:

reaction (gillespy2.Reaction) – The target reaction to resolve.

Raises:

ModelError – If the reaction can’t be resolved.

class gillespy2.core.model.StochMLDocument

Bases: object

Serializiation and deserialization of a Model to/from the native XML format.

classmethod from_file(filepath)

Intializes the document from an exisiting native XML file read from disk.

classmethod from_model(model)

Creates an XML document from an exisiting Model object. This method assumes that all the parameters in the model are already resolved to scalar floats (see Model.resolveParamters).

Note, this method is intended to be used internally by the models ‘serialization’ function, which performs additional operations and tests on the model prior to writing out the XML file.

You should NOT do:

document = StochMLDocument.fromModel(model)
print document.toString()

You SHOULD do:

print model.serialize()

classmethod from_string(string)

Intializes the document from an exisiting native XML file read from disk.

to_model(name)

Instantiates a Model object from a StochMLDocument.

to_string()

Returns the document as a string.

gillespy2.core.model.export_SBML(gillespy_model, filename=None)

GillesPy model to SBML converter

Parameters:

• gillespy_model (gillespy.Model) – GillesPy model to be converted to SBML

• filename (str) – Path to the SBML file for conversion

gillespy2.core.model.export_StochSS(gillespy_model, filename=None, return_stochss_model=False)

GillesPy model to StochSS converter

Parameters:

• gillespy_model (gillespy.Model) – GillesPy model to be converted to StochSS

• filename (str) – Path to the StochSS file for conversion

gillespy2.core.model.import_SBML(filename, name=None, gillespy_model=None, report_silently_with_sbml_error=False)

SBML to GillesPy model converter. NOTE: non-mass-action rates in terms of concentrations may not be converted for population simulation. Use caution when importing SBML.

Parameters:

• filename (str) – Path to the SBML file for conversion.

• name (str) – Name of the resulting model

• gillespy_model (gillespy.Model) – If desired, the SBML model may be added to an existing GillesPy model

• report_silently_with_sbml_error (bool) – SBML import will fail silently and SBML errors will not output to the user.

gillespy2.core.parameter module

class gillespy2.core.parameter.Parameter(name=None, expression=None)

Bases: SortableObject, Jsonify

A parameter can be given as an expression (function) or directly as a value (scalar). If given an expression, it should be understood as evaluable in the namespace of a parent Model.

Parameters:

• name (str) – The name by which this parameter is called or referenced in reactions, rate rules, assignment rules, and events.

• expression (str) – String for a function calculating parameter values. Should be evaluable in namespace of Model.

Raises:

ParameterError – Arg is of invalid type. Required arg set to None. Arg value is outside of accepted bounds.

sanitized_expression(species_mappings, parameter_mappings)

validate(expression=None, coverage='all')

Validate the parameter.

Parameters:

• expression (str) – String for a function calculating parameter values. Should be evaluable in namespace of Model.

• coverage (str) – The scope of attributes to validate. Set to an attribute name to restrict validation to a specific attribute.

Raises:

ParameterError – Attribute is of invalid type. Required attribute set to None. Attribute value is outside of accepted bounds.

gillespy2.core.raterule module

class gillespy2.core.raterule.RateRule(variable=None, formula='', name=None)

Bases: SortableObject, Jsonify

A RateRule is used to express equations that determine the rates of change of variables. This would correspond to a function in the form of dx/dt=f(W)

Parameters:

• name (str) – Name of Rule

• variable (str) – Target Species/Parameter to be modified by rule

• formula (str) – String representation of formula to be evaluated

sanitized_formula(species_mappings, parameter_mappings)

gillespy2.core.reaction module

class gillespy2.core.reaction.Reaction(name=None, reactants=None, products=None, propensity_function=None, ode_propensity_function=None, rate=None, annotation=None, massaction=None)

Bases: SortableObject, Jsonify

Models a single reaction. A reaction has its own dicts of species (reactants and products) and parameters. The reaction’s propensity function needs to be evaluable (and result in a non-negative scalar value) in the namespace defined by the union of those dicts.

Parameters:

• name (str) – The name by which the reaction is called (optional).

• reactants (dict) – The reactants that are consumed in the reaction, with stoichiometry. An example would be {R1 : 1, R2 : 2} if the reaction consumes two of R1 and one of R2, where R1 and R2 are Species objects.

• products (dict) – The species that are created by the reaction event, with stoichiometry. Same format as reactants.

• propensity_function (str) – The custom propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

• ode_propensity_function (str) – The custom ode propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

• massaction (bool) – (deprecated) The switch to use a mass-action reaction. If set to True, a rate value is required.

• rate (float) – The rate of the mass-action reaction, take care to note the units.

• annotation (str) – An optional note about the reaction.

Notes

For a species that is NOT consumed in the reaction but is part of a mass action reaction, add it as both a reactant and a product.

Mass-action reactions must also have a rate term added. Note that the input rate represents the mass-action constant rate independent of volume.

if a name is not provided for reactions, the name will be populated by the model based on the order it was added. This could impact seeded simulation results if the order of addition is not preserved.

Annotate(*args, **kwargs)

Add an annotation to this reaction (deprecated).

Parameters:

annotation (str) – Annotation note to be added to reaction

addProduct(*args, **kwargs)

Add a product to this reaction (deprecated)

Parameters:

• species (spatialpy.core.species.Species) – Species object to be produced by the reaction

• stoichiometry (int) – Stoichiometry of this product.

addReactant(*args, **kwargs)

Add a reactant to this reaction (deprecated)

Parameters:

• species (spatialpy.core.species.Species) – reactant Species object

• stoichiometry (int) – Stoichiometry of this participant reactant

add_product(species, stoichiometry)

Add a product to this reaction

Parameters:

• species (spatialpy.core.species.Species | str) – Species object to be produced by the reaction

• stoichiometry (int) – Stoichiometry of this product.

add_reactant(species, stoichiometry)

Add a reactant to this reaction

Parameters:

• species (spatialpy.core.species.Species) – reactant Species object

• stoichiometry (int) – Stoichiometry of this participant reactant

create_mass_action(*args, **kwargs)

Initializes the mass action propensity function given self.reactants and a single parameter value.

classmethod from_json(json_object)

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

sanitized_propensity_function(species_mappings, parameter_mappings, ode=False)

Sanitize the reaction propensity.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized propensity.

Return type:

str

setType(rxntype)

Sets reaction type to either “mass-action” or “customized” (deprecated)

Parameters:

rxntype (str) – Either “mass-action” or “customized”

set_annotation(annotation)

Add an annotation to this reaction.

Parameters:

annotation (str) – Annotation note to be added to reaction

set_propensities(propensity_function=None, ode_propensity_function=None)

Change the reaction to a customized reaction and set the propensities.

Parameters:

• propensity_function (str) – The custom propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

• ode_propensity_function (str) – The custom ode propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

set_rate(rate)

Change the reaction to a mass-action reaction and set the rate.

Parameters:

rate (int | float | str | Parameter) – The rate of the mass-action reaction, take care to note the units.

to_dict()

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

validate(reactants=None, products=None, propensity_function=None, ode_propensity_function=None, marate=None, annotation=None, coverage='build')

Check if the reaction is properly formatted. Does nothing on sucesss, raises and error on failure.

verify(*args, **kwargs)

Check if the reaction is properly formatted. Does nothing on sucesss, raises and error on failure.

gillespy2.core.results module

class gillespy2.core.results.Results(data)

Bases: UserList, Jsonify

List of Trajectory objects created by a gillespy2 solver, extends the UserList object.

Parameters:

data (UserList) – A list of trajectory objects

average_ensemble()

Generate a single Results object with a Trajectory that is made of the means of all trajectories’ outputs

Returns:

The Results object

classmethod build_from_solver_results(solver, live_output_options)

Build a gillespy2.Results object using the provided solver results.

Parameters:

• solver (gillespy2.GillesPySolver) – The solver used to run the simulation.

• live_output_options (dict) – 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

plot(index=None, xaxis_label='Time', xscale='linear', yscale='linear', yaxis_label='Value', style='default', title=None, show_title=False, show_legend=True, multiple_graphs=False, included_species_list=[], save_png=False, figsize=(18, 10))

Plots the Results using matplotlib.

Parameters:

• index (int) – If not none, the index of the Trajectory to be plotted.

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• multiple_graphs (bool) – IF each trajectory should have its own graph or if they should overlap.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• save_png (bool or str) – Should the graph be saved as a png file. If True, File name is title of graph. If a string is given, file is named after that string.

• figsize (tuple of ints (x,y)) – The size of the graph. A tuple of the form (width,height). Is (18,10) by default.

plot_mean_stdev(xscale='linear', yscale='linear', xaxis_label='Time', yaxis_label='Value', title=None, show_title=False, style='default', show_legend=True, included_species_list=[], ddof=0, save_png=False, figsize=(18, 10))

Plot a matplotlib graph depicting mean and standard deviation of a results object.

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – Default False, if True, title will be displayed on the graph.

• style (str) – Matplotlib style to be displayed on graph.

• show_legend (bool) – Default to True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• figsize (tuple of ints (x,y)) – The size of the graph. A tuple of the form (width,height). Is (18,10) by default.

plot_std_dev_range(**kwargs)

Plot a matplotlib graph depicting mean and standard deviation of a results object.

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – Default False, if True, title will be displayed on the graph.

• style (str) – Matplotlib style to be displayed on graph.

• show_legend (bool) – Default to True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• figsize (tuple of ints (x,y)) – The size of the graph. A tuple of the form (width,height). Is (18,10) by default.

plotplotly(index=None, xaxis_label='Time', yaxis_label='Value', title=None, show_title=False, show_legend=True, multiple_graphs=False, included_species_list=[], return_plotly_figure=False, **layout_args)

Plots the Results using plotly. Can only be viewed in a Jupyter Notebook.

Parameters:

• index (int) – If not none, the index of the Trajectory to be plotted.

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – If True, title will be shown on graph.

• show_legend (bool) – Default True, if False, legend will not be shown on graph.

• multiple_graphs (bool) – IF each trajectory should have its own graph or if they should overlap.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• return_plotly_figure (bool) – Whether or not to return a figure dictionary of data(graph object traces) and layout which may be edited by the user

• **layout_args –

  Optional additional arguments to be passed to plotlys layout constructor.

plotplotly_mean_stdev(xaxis_label='Time', yaxis_label='Value', title=None, show_title=False, show_legend=True, included_species_list=[], return_plotly_figure=False, ddof=0, **layout_args)

Plot a plotly graph depicting the mean and standard deviation of a results object

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – If True, title will be shown on graph.

• show_legend (bool) – Default True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• return_plotly_figure (bool) – Whether or not to return a figure dicctionary of data(graph object traces) and layout which may be edited by the user

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• **layout_args –

  Optional additional arguments to be passed to plotlys layout constructor.

plotplotly_std_dev_range(**kwargs)

Plot a plotly graph depicting the mean and standard deviation of a results object

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – If True, title will be shown on graph.

• show_legend (bool) – Default True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• return_plotly_figure (bool) – Whether or not to return a figure dicctionary of data(graph object traces) and layout which may be edited by the user

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• **layout_args –

  Optional additional arguments to be passed to plotlys layout constructor.

stddev_ensemble(ddof=0)

Generate a single Results object with a Trajectory that is made of the sample standard deviations of all trajectories’ outputs.

Parameters:

ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

Returns:

the Results object

to_array()

Convert the results object into a numpy array.

Returns:

Array containing the result of the simulation.

Return type:

numpy.ndarray

to_csv(path='.', nametag=None, stamp=None, postfix='.odf', verbose=False)

Outputs the Results to one or more .csv files in a new directory.

Parameters:

• nametag (str) – allows the user to optionally “tag” the directory and included files. Defaults to the model name.

• path (str) – the location for the new directory and included files. Defaults to model location.

• stamp (str) – Allows the user to optionally “tag” the directory (not included files). Default is timestamp.

• verbose (str) – Print useful informataion.

Returns:

Path to the observed data files.

Return type:

str

class gillespy2.core.results.Trajectory(data, model=None, solver_name='Undefined solver name', rc=0)

Bases: UserDict, Jsonify

Trajectory Dict created by a gillespy2 solver containing single trajectory, extends the UserDict object.

Parameters:

• data (UserDict) – A dictionary of trajectory values created by a solver

• model (str) – The name of the model used to create the trajectory

• solver_name (str) – The name of the solver used to create the trajectory

• rc (int) – The solvers status return code.

• status – The solver status (‘Success’,’Timed out’)

gillespy2.core.results.common_rgb_values()

List of 50 hex color values used for plotting graphs

gillespy2.core.sortableobject module

class gillespy2.core.sortableobject.SortableObject

Bases: object

Base class for GillesPy2 objects that are sortable.

gillespy2.core.species module

class gillespy2.core.species.Species(name=None, initial_value=0, constant=False, boundary_condition=False, mode=None, allow_negative_populations=False, switch_min=0, switch_tol=0.03)

Bases: SortableObject, Jsonify

Chemical species. Can be added to Model object to interact with other species or time.

Parameters:

• name (str) – The name by which this species will be called in reactions and within the model.

• initial_value (int | float) – Initial population (discrete) or concentration (continuous) of this species.

• constant (bool) – If true, the value of the species cannot be changed (currently TauHybridSolver only)

• boundary_condition (bool) – If true, species can be changed by events and rate rules, but not by reactions. (TauHybridSolver only)

• mode (str) – *FOR USE WITH TauHybridSolver ONLY* Sets the mode of representation of this species for the TauHybridSolver, can be discrete, continuous, or dynamic. mode=’dynamic’ - Allows a species to be represented as either discrete or continuous mode=’continuous’ - Species will only be represented as continuous mode=’discrete’ - Species will only be represented as discrete

• allow_negative_populations (bool) – If true, population can be reduced below 0.

• switch_tol (float) – *FOR USE WITH TauHybridSolver ONLY* Tolerance level for considering a dynamic species deterministically, value is compared to an estimated sd/mean population of a species after a given time step. This value will be used if a switch_min is not provided. The default value is 0.03

• switch_min (float) – *FOR USE WITH TauHybridSolver ONLY* Minimum population value at which species will be represented as continuous. If a value is given, switch_min will be used instead of switch_tol

Raises:

SpeciesError – Arg is of invalid type. Required arg set to None. Arg value is outside of accepted bounds.

set_initial_value(initial_value)

Setter method for initial_value of a population

Parameters:

initial_value (int | float) – Initial population (discrete) or concentration (continuous) of this species.

Raises:

SpeciesError – initial_value is of invalid type. initial_value set to None. initial_value is a float when mode != ‘continuous’. initial_value is negative when allow_negative_populations=False.

validate(initial_value=None, coverage='all')

Validate the species.

Parameters:

• initial_value (int | float) – Initial population (discrete) or concentration (continuous) of this species.

• coverage (str) – The scope of attributes to validate. Set to an attribute name to restrict validation to a specific attribute.

Raises:

SpeciesError – Attribute is of invalid type. Required attribute set to None. Attribute is value outside of accepted bounds.

gillespy2.core.timespan module

class gillespy2.core.timespan.TimeSpan(items)

Bases: Iterator, Jsonify

Model timespan that describes the duration to run the simulation and at which timepoint to sample the species populations during the simulation.

Parameters:

items (list, tuple, range, or numpy.ndarray) – Evenly-spaced list of times at which to sample the species populations during the simulation. Best to use the form np.linspace(<start time>, <end time>, <number of time-points, inclusive>)

Raises:

TimespanError – items is an invalid type.

classmethod arange(increment, t=20)

Creates a timespan using the form np.arange(0, <t, inclusive>, <increment>).

Parameters:

• increment (float | int) – Distance between sample points for the species populations during the simulation.

• t (float | int) – End time for the simulation.

Returns:

Timespan for the model.

Return type:

gillespy2.TimeSpan

Raises:

TimespanError – t or increment are None, <= 0, or invalid type.

classmethod linspace(t=20, num_points=None)

Creates a timespan using the form np.linspace(0, <t>, <num_points, inclusive>).

Parameters:

• t (float | int) – End time for the simulation.

• num_points (int) – Number of sample points for the species populations during the simulation.

Returns:

Timespan for the model.

Return type:

gillespy2.TimeSpan

Raises:

TimespanError – t or num_points are None, <= 0, or invalid type.

validate()

Validate the models time span

Raises:

TimespanError – Timespan is an invalid type, empty, not uniform, contains a single repeated value, or contains a negative initial time.

Module contents

class gillespy2.core.AssignmentRule(variable=None, formula=None, name=None)

Bases: SortableObject, Jsonify

An AssignmentRule is used to express equations that set the values of variables. This would correspond to a function in the form of x = f(V)

Parameters:

• name (str) – Name of the Rule

• variable (str) – Target Species/Parameter to be modified by rule

• formula (str) – String representation of formula to be evaluated

sanitized_formula(species_mappings, parameter_mappings)

Sanitize the assignment rule formula.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized formula.

Return type:

str

exception gillespy2.core.AssignmentRuleError

Bases: ModelError

exception gillespy2.core.BuildError

Bases: SolverError

class gillespy2.core.ChainMap(*maps)

Bases: MutableMapping

A ChainMap groups multiple dicts (or other mappings) together to create a single, updateable view.

The underlying mappings are stored in a list. That list is public and can be accessed or updated using the maps attribute. There is no other state.

Lookups search the underlying mappings successively until a key is found. In contrast, writes, updates, and deletions only operate on the first mapping.

clear()

Clear maps[0], leaving maps[1:] intact.

copy()

New ChainMap or subclass with a new copy of maps[0] and refs to maps[1:]

classmethod fromkeys(iterable, *args)

Create a ChainMap with a single dict created from the iterable.

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.

new_child(m=None)

New ChainMap with a new map followed by all previous maps. If no map is provided, an empty dict is used.

property parents

].

Type:

New ChainMap from maps[1

pop(key, *args)

Remove key from maps[0] and return its value. Raise KeyError if key not in maps[0].

popitem()

Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.

exception gillespy2.core.DirectoryError

Bases: SolverError

class gillespy2.core.Event(name='', delay=None, assignments=[], priority='0', trigger=None, use_values_from_trigger_time=False)

Bases: Jsonify

An Event can be given as an assignment_expression (function) or directly as a value (scalar). If given an assignment_expression, it should be understood as evaluable in the namespace of a parent Model.

Parameters:

• name (str) – The name by which this Event is called or referenced in reactions.

• assignments (str) – List of EventAssignments to be executed at trigger or delay

• trigger (EventTrigger) – contains math expression which can be evaluated to a boolean result. Upon the transition from ‘False’ to ‘True’, event assignments may be executed immediately, or after a designated delay.

• delay (str) – contains math expression evaluable within model namespace. This expression designates a delay between the trigger of an event and the execution of its assignments.

• priority (str) – Contains math expression evaluable within model namespace.

add_assignment(assignment)

Adds an EventAssignment or a list of EventAssignment.

Parameters:

assignment (EventAssignment or list[EventAssignment]) – The event or list of events to be added to this event.

class gillespy2.core.EventAssignment(name=None, variable=None, expression=None)

Bases: Jsonify

An EventAssignment describes a change to be performed to the current model simulation. This is assignment can either be fired at the time its associated trigger changes from false to true, or after a specified delay, depending on how the Event to which it is assigned is configured.

Parameters:

• variable (gillespy2.Species, gillespy2.Parameter) – Target model component to be modified by the EventAssignment expression. Valid target variables include gillespy2 Species, Parameters, and Compartments.

• expression (str) – String to be evaluated when the event is fired. This expression must be evaluable within the model namespace, and the results of it’s evaluation will be assigned to the EventAssignment variable.

exception gillespy2.core.EventError

Bases: ModelError

class gillespy2.core.EventTrigger(expression=None, initial_value=False, persistent=False)

Bases: Jsonify

Trigger detects changes in model/environment conditions in order to fire an event. A Trigger contains an expression, a mathematical function which can be evaluated to a boolean value within a model’s namespace. Upon transitioning from ‘false’ to ‘true’, this trigger will cause the immediate execution of an event’s list of assignments if no delay is present, otherwise, the delay evaluation will be initialized.

Parameters:

• expression (str) – String for a function calculating EventTrigger values. Should be evaluable in namespace of Model.

• value (bool) – Value of EventTrigger at simulation start, with time t=0

• persistent (bool) – Determines if trigger condition is persistent or not

sanitized_expression(species_mappings, parameter_mappings)

Sanitize the event trigger expression.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized expression.

Return type:

str

exception gillespy2.core.ExecutionError

Bases: SolverError

class gillespy2.core.FunctionDefinition(name='', function=None, args=[])

Bases: SortableObject, Jsonify

Object representation defining an evaluable function to be used during simulation of a GillesPy2 model

Parameters:

• name (str) – Name of the function to be made and called

• function (str) – Defined function body of operation to be performed.

• variables (list[str]) – String names of Variables to be used as arguments to function.

get_arg_string() → str

Convert function’s argument list into a comma-separated formatted string.

Returns:

Argument list as a comma-separated formatted string.

sanitized_function(species_mappings, parameter_mappings)

Sanitize the function definition function.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized function.

Return type:

str

exception gillespy2.core.FunctionDefinitionError

Bases: ModelError

class gillespy2.core.GillesPySolver

Bases: object

Abstract class for a solver.

get_solver_settings()

Abstract function for getting a solvers settings.

classmethod get_supported_features() → Set[Type]

Get the set of supported features.

classmethod get_supported_integrator_options() → Set[str]

Get the supported integrator options

name = 'GillesPySolver'

run(model, t=20, number_of_trajectories=1, increment=0.05, seed=None, debug=False, profile=False, **kwargs)

Call out and run the solver. Collect the results.

Parameters:

• model (gillespy.Model) – The model on which the solver will operate.

• t (int) – The end time of the solver

• number_of_trajectories (int) – The number of times to sample the chemical master equation. Each trajectory will be returned at the end of the simulation.

• increment (float) – The time step of the solution

• seed (int) – The random seed for the simulation. Defaults to None.

• debug (bool) – Set to True to provide additional debug information about the simulation.

Returns:

Simulation trajectories.

classmethod validate_integrator_options(options: dict[str, float]) → dict[str, float]

Validate the integrator options.

Parameters:

options (dict) – Integrator options to validate.

Returns:

Dict of supported integrator options.

Return type:

dict

classmethod validate_model(sol_model, model)

Validate that the solvers model is the same as the model passed to solver.run.

Parameters:

• sol_model (gillespy2.Model) – Solver model object.

• model (gillespy2.Model) – Model object passed to solver.run.

Raises:

SimulationError – If the models are not equal.

classmethod validate_sbml_features(model)

Validate the models SBML features.

Parameters:

model (gillespy2.Model) – The model object to be checked.

Raises:

ModelError – If the model contains unsupported SBML features.

validate_tspan(increment, t)

Validate the models time span and set it if not provided.

Parameters:

• increment (int) – The current value of increment.

• t (int) – The end time of the simulation.

Raises:

SimulationError – if timespan and increment are both set by the user or neither are set by the user.

exception gillespy2.core.InvalidModelError

Bases: SimulationError

exception gillespy2.core.InvalidStochMLError

Bases: SimulationError

class gillespy2.core.Jsonify

Bases: object

Interface to allow for instances of arbitrary types to be encoded into json strings and decoded into new objects.

classmethod from_dict(src_dict: dict) → object

Convert some dict into a new instance of a python type. This function will return a __new__ instance of the type.

Parameters:

src_dict (dict) – The dictionary to apply onto the new instance.

Returns:

A new object with its backing __dict__ set to a copy of src_dict.

classmethod from_json(json_str: str) → object

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

get_json_hash(ignore_whitespace=True, hash_private_vals=False) → str

Get the hash of the json representation of self.

Parameters:

• ignore_whitespace (bool) – If set to True all whitespace will be stripped from the JSON prior to being hashed.

• hash_private_vals (bool) – If set to True all private and non-private variables will be included in the hash.

Returns:

An MD5 hash of the object’s sorted JSON representation.

get_translation_table() → TranslationTable

Make and/or return the translation table.

Returns:

A TranslationTable instance.

make_translation_table() → TranslationTable

Make a translation table that describes key:value pairs to convert user-define data into generic equivalents.

Returns:

A newly generated TranslationTable instance.

public_vars() → dict

Gets a dictionary of public vars that exist on self. Keys starting with ‘_’ are ignored.

Returns:

A dictionary containing all public (‘_’-prefixed) variables on the object.

to_anon()

Converts self into an anonymous instance of self.

to_dict() → dict

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

to_json(encode_private=True) → str

Convert self into a json string.

Parameters:

encode_private (bool) – If True all private (prefixed of ‘_’) will be encoded. None if False.

Returns:

The JSON representation of self.

to_named()

Converts self into a named instance of self.

class gillespy2.core.Model(name='', population=True, volume=1.0, tspan=None, annotation='model')

Bases: SortableObject, Jsonify

Representation of a well mixed biochemical model. Contains reactions, parameters, species.

Parameters:

• name (str) – The name of the model, or an annotation describing it.

• population (bool) – The type of model being described. A discrete stochastic model is a population model (True), a deterministic model is a concentration model (False). Automatic conversion from population to concentration models may be used, by setting the volume parameter.

• volume (float) – The volume of the system matters when converting to from population to concentration form. This will also set a parameter “vol” for use in custom (i.e. non-mass-action) propensity functions.

• tspan (numpy ndarray) – The timepoints at which the model should be simulated. If None, a default timespan is added. May be set later, see Model.timespan

• annotation (str) – Option further description of model

add(components)

Adds a component, or list of components to the model. If a list is provided, Species and Parameters are added before other components. Lists may contain any combination of accepted types other than lists and do not need to be in any particular order.

Parameters:

components (Species, Parameters, Reactions, Events, Rate Rules, Assignment Rules, FunctionDefinitions, TimeSpan, or list) – The component or list of components to be added the the model.

Returns:

The components that were added to the model.

Return type:

Species, Parameters, Reactions, Events, Rate Rules, Assignment Rules, FunctionDefinitions, TimeSpan, or list

Raises:

ModelError – Component is invalid.

add_assignment_rule(assignment_rule)

Add an assignment rule, or list of assignment rules to the model.

Parameters:

assignment_rules (gillespy2.AssignmentRule or list of gillespy2.AssignmentRules) – The assignment rule or list of assignment rules to be added to the model object.

Returns:

The assignment rule or list of assignment rules that were added to the model.

Return type:

gillespy2.AssignmentRule | list of gillespy2.AssignmentRule

Raises:

ModelError – If an invalid assignment rule is provided or if assignment rule validation fails.

add_event(event)

Adds an event, or list of events to the model.

Parameters:

event (gillespy2.Event | list of gillespy2.Events) – The event or list of event to be added to the model object.

Returns:

The event or list of events that were added to the model.

Return type:

gillespy2.Event | list of gillespy2.Event

Raises:

ModelError – If an invalid event is provided or if event validation fails.

add_function_definition(function_definition)

Add function definition, or list of function definitions to the model

Parameters:

function_definition (gillespy2.FunctionDefinition | list of gillespy2.FunctionDefinitions.) – The function definition, or list of function definitions to be added to the model object.

Returns:

The function defintion or list of function definitions that were added to the model.

Return type:

gillespy2.FunctionDefinitions | list of gillespy2.FunctionDefinitions

Raises:

ModelError – If an invalid function definition is provided.

add_parameter(parameters)

Adds a parameter, or list of parameters to the model.

Parameters:

parameters (gillespy2.Parameter | list of gillespy2.Parameter) – The parameter or list of parameters to be added to the model object.

Returns:

A parameter or list of Parameters that were added to the model.

Return type:

gillespy2.Parameter | list of gillespy2.Parameter

Raises:

ModelError – If an invalid parameter is provided or if Parameter.validate fails.

add_rate_rule(rate_rule)

Adds a rate rule, or list of rate rules to the model.

Parameters:

rate_rule (gillespy2.RateRule | list of gillespy2.RateRules) – The rate rule or list of rate rules to be added to the model object.

Returns:

The rate rule or list of rate rules that were added to the model.

Return type:

gillespy2.RateRule | list of gillespy2.RateRule

Raises:

ModelError – If an invalid rate rule is provided or if rate rule validation fails.

add_reaction(reactions)

Adds a reaction, or list of reactions to the model.

Parameters:

reactions (gillespy2.Reaction | list of gillespy2.Reaction) – The reaction or list of reactions to be added to the model object

Returns:

The reaction or list of reactions that were added to the model.

Return type:

gillespy2.Reaction | list of gillespy2.Reaction

Raises:

ModelError – If an invalid reaction is provided or if Reaction.validate fails.

add_species(species)

Adds a species, or list of species to the model.

Parameters:

species (gillespy2.Species | list of gillespy2.Species) – The species or list of species to be added to the model object

Returns:

The species or list of species that were added to the model.

Return type:

gillespy2.Species | list of gillespy2.Species

Raises:

ModelError – If an invalid species is provided or if Species.validate fails.

compile_prep()

Prepare the model for export or simulation.

delete_all_assignment_rules()

Removes all assignment rules from the model object.

delete_all_events()

Removes all events from the model object.

delete_all_function_definitions()

Removes all function definitions from the model object.

delete_all_parameters()

Removes all parameters from model object.

delete_all_rate_rules()

Removes all rate rules from the model object.

delete_all_reactions()

Removes all reactions from the model object.

delete_all_species()

Removes all species from the model object.

delete_assignment_rule(name)

Removes an assignment rule object by model.

Parameters:

name (str) – Name of the assignment rule object to be removed.

Raises:

ModelError – If the assignment rule is not part of the model.

delete_event(name)

Removes an event object by name.

Parameters:

name (str) – Name of the event object to be removed.

delete_function_definition(name)

Removes a function definition object by name.

Parameters:

name (str) – Name of the function definition object to be removed.

delete_parameter(name)

Removes a parameter object by name.

Parameters:

name (str) – Name of the parameter object to be removed.

Raises:

ModelError – If the parameter is not part of the model.

delete_rate_rule(name)

Removes rate rule object by name.

Parameters:

name (str) – Name of the rate rule to be removed.

Raises:

ModelError – If the rate rule is not part of the model.

delete_reaction(name)

Removes a reaction object by name.

Parameters:

name (str) – Name of the reaction object to be removed.

Raises:

ModelError – If the reaction is not part of the model.

delete_species(name)

Removes a species object by name.

Parameters:

name (str) – Name of the species object to be removed.

Raises:

ModelError – If the species is not part of the model.

get_all_assignment_rules()

Get all of the assignment rules in the model object.

Returns:

A dict of all assignemt rules in the model, in the form: {name: reaction object}.

get_all_events()

Get all of the events in the model object.

Returns:

A dict of all evetns in the model, in the form: {name : event object}

get_all_function_definitions()

Get all of the function definitions in the model object.

Returns:

A dict of all function definitions in the model, in the form {name : function definition object}.

Return type:

OrderedDict

get_all_parameters()

Get all of the parameters in the model object.

Returns:

A dict of all parameters in the model, in the form: {name : parameter object}

Return type:

OrderedDict

get_all_rate_rules()

Get all of the rate rules in the model object.

Returns:

A dict of all rate rules in the model, in the form: {name : rate rule object}.

Return type:

OrderedDict

get_all_reactions()

Get all of the reactions in the model object.

Returns:

A dict of all reactions in the model, in the form: {name : reaction object}.

Return type:

OrderedDict

get_all_species()

Get all of the species in the model object.

Returns:

A dict of all species in the model, in the form: {name : species object}.

Return type:

OrderedDict

get_assignment_rule(name)

Returns an assignment rule object by name.

Parameters:

name (str) – Name of the assignment rule object to be returned.

Returns:

The specified assignment rule object.

Return type:

gillespy2.AssignmentRule

Raises:

ModelError – If the assignment rule is not part of the model.

get_best_solver()

Finds best solver for the users simulation. Currently, AssignmentRules, RateRules, FunctionDefinitions, Events, and Species with a dynamic, or continuous population must use the TauHybridSolver.

Parameters:

precompile (bool) – If True, and the model contains no AssignmentRules, RateRules, FunctionDefinitions, Events, or Species with a dynamic or continuous population, it will choose SSACSolver

Returns:

gillespy2.gillespySolver

get_best_solver_algo(algorithm)

If user has specified a particular algorithm, we return either the Python or C++ version of that algorithm

get_element(name)

Get a model element specified by name.

Parameters:

name (str) – Name of the element to be returned.

Returns:

The specified gillespy2.Model element.

Return type:

Species, Parameters, Reactions, Events, RateRules, AssignmentRules, FunctionDefinitions, or TimeSpan

Raises:

ModelError – If the element is not part of the model.

get_event(name)

Returns an event object by name.

Parameters:

name (str) – Name of the event object to be returned.

Returns:

The specified event object.

Return type:

gillespy2.Event

Raises:

ModelError – If the event is not part of the model.

get_function_definition(name)

Returns a function definition object by name.

Parameters:

name (str) – Name of the function definition object to be returned.

Returns:

The specified function definition object.

Return type:

gillespy2.FunctionDefinition

get_model_features() → Set[Type]

Determine what solver-specific model features are present on the model. Used to validate that the model is compatible with the given solver.

Returns:

Set containing the classes of every solver-specific feature present on the model.

get_parameter(name)

Returns a parameter object by name.

Parameters:

name (str) – Name of the parameter object to be returned

Returns:

The specified parameter object.

Return type:

gillespy2.Parameter

Raises:

ModelError – If the parameter is not part of the model.

get_rate_rule(name)

Returns a rate rule object by name.

Parameters:

name (str) – Name of the rate rule object to be returned.

Returns:

The specified rate rule object.

Return type:

gillespy2.RateRule

Raises:

ModelError – If the rate rule is not part of the model.

get_reaction(name)

Returns a reaction object by name.

Parameters:

name (str) – Name of the reaction object to be returned

Returns:

The specified reaction object.

Return type:

gillespy2.Reaction

Raises:

ModelError – If the reaction is not part of the model.

get_species(name)

Returns a species object by name.

Parameters:

name (str) – Name of the species object to be returned.

Returns:

The specified species object.

Return type:

gillespy2.Species

Raises:

ModelError – If the species is not part of the model.

make_translation_table()

Make a translation table that describes key:value pairs to convert user-define data into generic equivalents.

Returns:

A newly generated TranslationTable instance.

problem_with_name(*args)

(deprecated)

reserved_names = ['vol']

run(solver=None, timeout=0, t=None, increment=None, algorithm=None, **solver_args)

Function calling simulation of the model. There are a number of parameters to be set here.

Parameters:

• solver (gillespy.GillesPySolver) – The solver by which to simulate the model. This solver object may be initialized separately to specify an algorithm. Optional, defaults to ssa solver.

• timeout (int) – Allows a time_out value in seconds to be sent to a signal handler, restricting simulation run-time

• t (int) – End time of simulation

• solver_args – Solver-specific arguments to be passed to solver.run()

• algorithm (str) – Specify algorithm (‘ODE’, ‘Tau-Leaping’, or ‘SSA’) for GillesPy2 to automatically pick best solver using that algorithm.

Returns:

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.**

sanitized_parameter_names()

Generate a dictionary mapping user chosen parameter names to simplified formats which will be used later on by GillesPySolvers evaluating reaction propensity functions.

Returns:

the dictionary mapping user parameter names to their internal GillesPy notation.

sanitized_species_names()

Generate a dictionary mapping user chosen species names to simplified formats which will be used later on by GillesPySolvers evaluating reaction propensity functions.

Returns:

the dictionary mapping user species names to their internal GillesPy notation.

serialize()

Serializes the Model object to valid StochML.

set_parameter(p_name, expression)

Set the value of an existing parameter “pname” to “expression” (deprecated).

Parameters:

• p_name (str) – Name of the parameter whose value will be set.

• expression (str) – String that may be executed in C, describing the value of the parameter. May reference other parameters by name. (e.g. “k1*4”)

set_units(units)

Sets the units of the model to either “population” or “concentration”

Parameters:

units (str) – Either “population” or “concentration”

special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']

timespan(time_span)

Set the time span of simulation. GillesPy2 does not support non-uniform timespans.

Parameters:

time_span (gillespy2.TimeSpan | iterator) – Evenly-spaced list of times at which to sample the species populations during the simulation. Best to use the form gillespy2.TimeSpan(np.linspace( <start time>, <end time>, <number of time-points, inclusive>))

update_namespace()

Create a dict with flattened parameter and species objects.

validate_reactants_and_products(reactions)

Internal function (deprecated): Ensure that the rate and all reactants and products are present in the model for the given reaction. This methods must be called before exporting the model.

Parameters:

reaction (gillespy2.Reaction) – The target reaction to resolve.

Raises:

ModelError – If the reaction can’t be resolved.

exception gillespy2.core.ModelError

Bases: Exception

class gillespy2.core.OrderedDict

Bases: dict

Dictionary that remembers insertion order

clear() → None.  Remove all items from od.

copy() → a shallow copy of od

fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values

class gillespy2.core.Parameter(name=None, expression=None)

Bases: SortableObject, Jsonify

A parameter can be given as an expression (function) or directly as a value (scalar). If given an expression, it should be understood as evaluable in the namespace of a parent Model.

Parameters:

• name (str) – The name by which this parameter is called or referenced in reactions, rate rules, assignment rules, and events.

• expression (str) – String for a function calculating parameter values. Should be evaluable in namespace of Model.

Raises:

ParameterError – Arg is of invalid type. Required arg set to None. Arg value is outside of accepted bounds.

sanitized_expression(species_mappings, parameter_mappings)

validate(expression=None, coverage='all')

Validate the parameter.

Parameters:

• expression (str) – String for a function calculating parameter values. Should be evaluable in namespace of Model.

• coverage (str) – The scope of attributes to validate. Set to an attribute name to restrict validation to a specific attribute.

Raises:

ParameterError – Attribute is of invalid type. Required attribute set to None. Attribute value is outside of accepted bounds.

exception gillespy2.core.ParameterError

Bases: ModelError

class gillespy2.core.RateRule(variable=None, formula='', name=None)

Bases: SortableObject, Jsonify

A RateRule is used to express equations that determine the rates of change of variables. This would correspond to a function in the form of dx/dt=f(W)

Parameters:

• name (str) – Name of Rule

• variable (str) – Target Species/Parameter to be modified by rule

• formula (str) – String representation of formula to be evaluated

sanitized_formula(species_mappings, parameter_mappings)

exception gillespy2.core.RateRuleError

Bases: ModelError

class gillespy2.core.Reaction(name=None, reactants=None, products=None, propensity_function=None, ode_propensity_function=None, rate=None, annotation=None, massaction=None)

Bases: SortableObject, Jsonify

Models a single reaction. A reaction has its own dicts of species (reactants and products) and parameters. The reaction’s propensity function needs to be evaluable (and result in a non-negative scalar value) in the namespace defined by the union of those dicts.

Parameters:

• name (str) – The name by which the reaction is called (optional).

• reactants (dict) – The reactants that are consumed in the reaction, with stoichiometry. An example would be {R1 : 1, R2 : 2} if the reaction consumes two of R1 and one of R2, where R1 and R2 are Species objects.

• products (dict) – The species that are created by the reaction event, with stoichiometry. Same format as reactants.

• propensity_function (str) – The custom propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

• ode_propensity_function (str) – The custom ode propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

• massaction (bool) – (deprecated) The switch to use a mass-action reaction. If set to True, a rate value is required.

• rate (float) – The rate of the mass-action reaction, take care to note the units.

• annotation (str) – An optional note about the reaction.

Notes

For a species that is NOT consumed in the reaction but is part of a mass action reaction, add it as both a reactant and a product.

Mass-action reactions must also have a rate term added. Note that the input rate represents the mass-action constant rate independent of volume.

if a name is not provided for reactions, the name will be populated by the model based on the order it was added. This could impact seeded simulation results if the order of addition is not preserved.

Annotate(*args, **kwargs)

Add an annotation to this reaction (deprecated).

Parameters:

annotation (str) – Annotation note to be added to reaction

addProduct(*args, **kwargs)

Add a product to this reaction (deprecated)

Parameters:

• species (spatialpy.core.species.Species) – Species object to be produced by the reaction

• stoichiometry (int) – Stoichiometry of this product.

addReactant(*args, **kwargs)

Add a reactant to this reaction (deprecated)

Parameters:

• species (spatialpy.core.species.Species) – reactant Species object

• stoichiometry (int) – Stoichiometry of this participant reactant

add_product(species, stoichiometry)

Add a product to this reaction

Parameters:

• species (spatialpy.core.species.Species | str) – Species object to be produced by the reaction

• stoichiometry (int) – Stoichiometry of this product.

add_reactant(species, stoichiometry)

Add a reactant to this reaction

Parameters:

• species (spatialpy.core.species.Species) – reactant Species object

• stoichiometry (int) – Stoichiometry of this participant reactant

create_mass_action(*args, **kwargs)

Initializes the mass action propensity function given self.reactants and a single parameter value.

classmethod from_json(json_object)

Convert some json_str into a decoded Python type. This function should return a new instance of the type.

Parameters:

json_str (str) – A json str to be converted into a new type instance.

Returns:

A decoded object.

sanitized_propensity_function(species_mappings, parameter_mappings, ode=False)

Sanitize the reaction propensity.

Parameters:

• species_mappings (dict) – Mapping of species names to sanitized species names.

• parameter_mappings (dict) – Mapping of parameter names to sanitized parameter names.

Returns:

The sanitized propensity.

Return type:

str

setType(rxntype)

Sets reaction type to either “mass-action” or “customized” (deprecated)

Parameters:

rxntype (str) – Either “mass-action” or “customized”

set_annotation(annotation)

Add an annotation to this reaction.

Parameters:

annotation (str) – Annotation note to be added to reaction

set_propensities(propensity_function=None, ode_propensity_function=None)

Change the reaction to a customized reaction and set the propensities.

Parameters:

• propensity_function (str) – The custom propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

• ode_propensity_function (str) – The custom ode propensity function for the reaction. Must be evaluable in the namespace of the reaction using C operations.

set_rate(rate)

Change the reaction to a mass-action reaction and set the rate.

Parameters:

rate (int | float | str | Parameter) – The rate of the mass-action reaction, take care to note the units.

to_dict()

Convert the object into a dictionary ready for json encoding. Note: Complex types that inherit from Jsonify do not need to be manually encoded.

By default, this function will return a dictionary of the object’s public types.

Returns:

The backing var dictionary of the object.

validate(reactants=None, products=None, propensity_function=None, ode_propensity_function=None, marate=None, annotation=None, coverage='build')

Check if the reaction is properly formatted. Does nothing on sucesss, raises and error on failure.

verify(*args, **kwargs)

Check if the reaction is properly formatted. Does nothing on sucesss, raises and error on failure.

exception gillespy2.core.ReactionError

Bases: ModelError

class gillespy2.core.Results(data)

Bases: UserList, Jsonify

List of Trajectory objects created by a gillespy2 solver, extends the UserList object.

Parameters:

data (UserList) – A list of trajectory objects

average_ensemble()

Generate a single Results object with a Trajectory that is made of the means of all trajectories’ outputs

Returns:

The Results object

classmethod build_from_solver_results(solver, live_output_options)

Build a gillespy2.Results object using the provided solver results.

Parameters:

• solver (gillespy2.GillesPySolver) – The solver used to run the simulation.

• live_output_options (dict) – 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

plot(index=None, xaxis_label='Time', xscale='linear', yscale='linear', yaxis_label='Value', style='default', title=None, show_title=False, show_legend=True, multiple_graphs=False, included_species_list=[], save_png=False, figsize=(18, 10))

Plots the Results using matplotlib.

Parameters:

• index (int) – If not none, the index of the Trajectory to be plotted.

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• multiple_graphs (bool) – IF each trajectory should have its own graph or if they should overlap.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• save_png (bool or str) – Should the graph be saved as a png file. If True, File name is title of graph. If a string is given, file is named after that string.

• figsize (tuple of ints (x,y)) – The size of the graph. A tuple of the form (width,height). Is (18,10) by default.

plot_mean_stdev(xscale='linear', yscale='linear', xaxis_label='Time', yaxis_label='Value', title=None, show_title=False, style='default', show_legend=True, included_species_list=[], ddof=0, save_png=False, figsize=(18, 10))

Plot a matplotlib graph depicting mean and standard deviation of a results object.

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – Default False, if True, title will be displayed on the graph.

• style (str) – Matplotlib style to be displayed on graph.

• show_legend (bool) – Default to True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• figsize (tuple of ints (x,y)) – The size of the graph. A tuple of the form (width,height). Is (18,10) by default.

plot_std_dev_range(**kwargs)

Plot a matplotlib graph depicting mean and standard deviation of a results object.

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – Default False, if True, title will be displayed on the graph.

• style (str) – Matplotlib style to be displayed on graph.

• show_legend (bool) – Default to True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• figsize (tuple of ints (x,y)) – The size of the graph. A tuple of the form (width,height). Is (18,10) by default.

plotplotly(index=None, xaxis_label='Time', yaxis_label='Value', title=None, show_title=False, show_legend=True, multiple_graphs=False, included_species_list=[], return_plotly_figure=False, **layout_args)

Plots the Results using plotly. Can only be viewed in a Jupyter Notebook.

Parameters:

• index (int) – If not none, the index of the Trajectory to be plotted.

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – If True, title will be shown on graph.

• show_legend (bool) – Default True, if False, legend will not be shown on graph.

• multiple_graphs (bool) – IF each trajectory should have its own graph or if they should overlap.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• return_plotly_figure (bool) – Whether or not to return a figure dictionary of data(graph object traces) and layout which may be edited by the user

• **layout_args –

  Optional additional arguments to be passed to plotlys layout constructor.

plotplotly_mean_stdev(xaxis_label='Time', yaxis_label='Value', title=None, show_title=False, show_legend=True, included_species_list=[], return_plotly_figure=False, ddof=0, **layout_args)

Plot a plotly graph depicting the mean and standard deviation of a results object

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – If True, title will be shown on graph.

• show_legend (bool) – Default True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• return_plotly_figure (bool) – Whether or not to return a figure dicctionary of data(graph object traces) and layout which may be edited by the user

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• **layout_args –

  Optional additional arguments to be passed to plotlys layout constructor.

plotplotly_std_dev_range(**kwargs)

Plot a plotly graph depicting the mean and standard deviation of a results object

Parameters:

• xaxis_label (str) – The label for the x-axis

• yaxis_label (str) – The label for the y-axis

• title (str) – The title of the graph

• show_title (bool) – If True, title will be shown on graph.

• show_legend (bool) – Default True, if False, legend will not be shown on graph.

• included_species_list (list) – A list of strings describing which species to include. By default displays all species.

• return_plotly_figure (bool) – Whether or not to return a figure dicctionary of data(graph object traces) and layout which may be edited by the user

• ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

• **layout_args –

  Optional additional arguments to be passed to plotlys layout constructor.

stddev_ensemble(ddof=0)

Generate a single Results object with a Trajectory that is made of the sample standard deviations of all trajectories’ outputs.

Parameters:

ddof (int) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of trajectories. Sample standard deviation uses ddof of 1. Defaults to population standard deviation where ddof is 0.

Returns:

the Results object

to_array()

Convert the results object into a numpy array.

Returns:

Array containing the result of the simulation.

Return type:

numpy.ndarray

to_csv(path='.', nametag=None, stamp=None, postfix='.odf', verbose=False)

Outputs the Results to one or more .csv files in a new directory.

Parameters:

• nametag (str) – allows the user to optionally “tag” the directory and included files. Defaults to the model name.

• path (str) – the location for the new directory and included files. Defaults to model location.

• stamp (str) – Allows the user to optionally “tag” the directory (not included files). Default is timestamp.

• verbose (str) – Print useful informataion.

Returns:

Path to the observed data files.

Return type:

str

exception gillespy2.core.ResultsError

Bases: Exception

exception gillespy2.core.SBMLError

Bases: Exception

exception gillespy2.core.SimulationError

Bases: Exception

exception gillespy2.core.SimulationTimeoutError

Bases: SimulationError

exception gillespy2.core.SolverError

Bases: Exception

class gillespy2.core.SortableObject

Bases: object

Base class for GillesPy2 objects that are sortable.

class gillespy2.core.Species(name=None, initial_value=0, constant=False, boundary_condition=False, mode=None, allow_negative_populations=False, switch_min=0, switch_tol=0.03)

Bases: SortableObject, Jsonify

Chemical species. Can be added to Model object to interact with other species or time.

Parameters:

• name (str) – The name by which this species will be called in reactions and within the model.

• initial_value (int | float) – Initial population (discrete) or concentration (continuous) of this species.

• constant (bool) – If true, the value of the species cannot be changed (currently TauHybridSolver only)

• boundary_condition (bool) – If true, species can be changed by events and rate rules, but not by reactions. (TauHybridSolver only)

• mode (str) – *FOR USE WITH TauHybridSolver ONLY* Sets the mode of representation of this species for the TauHybridSolver, can be discrete, continuous, or dynamic. mode=’dynamic’ - Allows a species to be represented as either discrete or continuous mode=’continuous’ - Species will only be represented as continuous mode=’discrete’ - Species will only be represented as discrete

• allow_negative_populations (bool) – If true, population can be reduced below 0.

• switch_tol (float) – *FOR USE WITH TauHybridSolver ONLY* Tolerance level for considering a dynamic species deterministically, value is compared to an estimated sd/mean population of a species after a given time step. This value will be used if a switch_min is not provided. The default value is 0.03

• switch_min (float) – *FOR USE WITH TauHybridSolver ONLY* Minimum population value at which species will be represented as continuous. If a value is given, switch_min will be used instead of switch_tol

Raises:

SpeciesError – Arg is of invalid type. Required arg set to None. Arg value is outside of accepted bounds.

set_initial_value(initial_value)

Setter method for initial_value of a population

Parameters:

initial_value (int | float) – Initial population (discrete) or concentration (continuous) of this species.

Raises:

SpeciesError – initial_value is of invalid type. initial_value set to None. initial_value is a float when mode != ‘continuous’. initial_value is negative when allow_negative_populations=False.

validate(initial_value=None, coverage='all')

Validate the species.

Parameters:

• initial_value (int | float) – Initial population (discrete) or concentration (continuous) of this species.

• coverage (str) – The scope of attributes to validate. Set to an attribute name to restrict validation to a specific attribute.

Raises:

SpeciesError – Attribute is of invalid type. Required attribute set to None. Attribute is value outside of accepted bounds.

exception gillespy2.core.SpeciesError

Bases: ModelError

class gillespy2.core.StochMLDocument

Bases: object

Serializiation and deserialization of a Model to/from the native XML format.

classmethod from_file(filepath)

Intializes the document from an exisiting native XML file read from disk.

classmethod from_model(model)

Creates an XML document from an exisiting Model object. This method assumes that all the parameters in the model are already resolved to scalar floats (see Model.resolveParamters).

Note, this method is intended to be used internally by the models ‘serialization’ function, which performs additional operations and tests on the model prior to writing out the XML file.

You should NOT do:

document = StochMLDocument.fromModel(model)
print document.toString()

You SHOULD do:

print model.serialize()

classmethod from_string(string)

Intializes the document from an exisiting native XML file read from disk.

to_model(name)

Instantiates a Model object from a StochMLDocument.

to_string()

Returns the document as a string.

exception gillespy2.core.StochMLImportError

Bases: SimulationError

class gillespy2.core.TimeSpan(items)

Bases: Iterator, Jsonify

Model timespan that describes the duration to run the simulation and at which timepoint to sample the species populations during the simulation.

Parameters:

items (list, tuple, range, or numpy.ndarray) – Evenly-spaced list of times at which to sample the species populations during the simulation. Best to use the form np.linspace(<start time>, <end time>, <number of time-points, inclusive>)

Raises:

TimespanError – items is an invalid type.

classmethod arange(increment, t=20)

Creates a timespan using the form np.arange(0, <t, inclusive>, <increment>).

Parameters:

• increment (float | int) – Distance between sample points for the species populations during the simulation.

• t (float | int) – End time for the simulation.

Returns:

Timespan for the model.

Return type:

gillespy2.TimeSpan

Raises:

TimespanError – t or increment are None, <= 0, or invalid type.

classmethod linspace(t=20, num_points=None)

Creates a timespan using the form np.linspace(0, <t>, <num_points, inclusive>).

Parameters:

• t (float | int) – End time for the simulation.

• num_points (int) – Number of sample points for the species populations during the simulation.

Returns:

Timespan for the model.

Return type:

gillespy2.TimeSpan

Raises:

TimespanError – t or num_points are None, <= 0, or invalid type.

validate()

Validate the models time span

Raises:

TimespanError – Timespan is an invalid type, empty, not uniform, contains a single repeated value, or contains a negative initial time.

exception gillespy2.core.TimespanError

Bases: ModelError

class gillespy2.core.Trajectory(data, model=None, solver_name='Undefined solver name', rc=0)

Bases: UserDict, Jsonify

Trajectory Dict created by a gillespy2 solver containing single trajectory, extends the UserDict object.

Parameters:

• data (UserDict) – A dictionary of trajectory values created by a solver

• model (str) – The name of the model used to create the trajectory

• solver_name (str) – The name of the solver used to create the trajectory

• rc (int) – The solvers status return code.

• status – The solver status (‘Success’,’Timed out’)

class gillespy2.core.TranslationTable(to_anon: dict[str, str])

Bases: Jsonify

This class contains functions to enable arbitrary object trees to be “translated” to anonymous and named objects. This behavior is defined by a map of ‘named’ and ‘anon’ key values.

Parameters:

to_anon (dict[str, str]) – A mapping of ‘named’ to ‘anonymous’ strings to be used when converting user-defined names to anon.

obj_to_anon(obj: object)

Recursively anonymise all named properties on the object.

Parameters:

obj (object) – The object to anonymize.

Returns:

An anonymized instance of self.

obj_to_named(obj)

Recursively identify all anonymous properties on the object.

Parameters:

obj (object) – The object that will be converted to named.

Returns:

A named instance of self.

recursive_translate(obj: object, translation_table: dict[str, str])

Recursively search through the object tree searching for property value matches in the translation table. If a match is found, substitute.

Parameters:

• obj (object) – The object that will be translated.

• translation_table (TranslationTable) – The mapping to translate by.

class gillespy2.core.UserDict(**kwargs)

Bases: MutableMapping

copy()

classmethod fromkeys(iterable, value=None)

class gillespy2.core.UserList(initlist=None)

Bases: MutableSequence

A more or less complete user-defined wrapper around list objects.

append(item)

S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S

copy()

count(value) → integer -- return number of occurrences of value

extend(other)

S.extend(iterable) – extend sequence by appending elements from the iterable

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(i, item)

S.insert(index, value) – insert value before index

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

remove(item)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse()

S.reverse() – reverse IN PLACE

sort(*args, **kwds)

exception gillespy2.core.ValidationError

Bases: ResultsError

gillespy2.core.cleanup_tempfiles()

Cleanup all tempfiles in gillespy2 build.

gillespy2.core.common_rgb_values()

List of 50 hex color values used for plotting graphs

class gillespy2.core.datetime(year, month, day[, hour[, minute[, second[, microsecond[, tzinfo]]]]])

Bases: date

The year, month and day arguments are required. tzinfo may be None, or an instance of a tzinfo subclass. The remaining arguments may be ints.

astimezone()

tz -> convert to local time in new timezone tz

combine()

date, time -> datetime with same date and time fields

ctime()

Return ctime() style string.

date()

Return date object with same year, month and day.

dst()

Return self.tzinfo.dst(self).

fold

fromisoformat()

string -> datetime from datetime.isoformat() output

fromtimestamp()

timestamp[, tz] -> tz’s local time from POSIX timestamp.

hour

isoformat()

[sep] -> string in ISO 8601 format, YYYY-MM-DDT[HH[:MM[:SS[.mmm[uuu]]]]][+HH:MM]. sep is used to separate the year from the time, and defaults to ‘T’. timespec specifies what components of the time to include (allowed values are ‘auto’, ‘hours’, ‘minutes’, ‘seconds’, ‘milliseconds’, and ‘microseconds’).

max = datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)

microsecond

min = datetime.datetime(1, 1, 1, 0, 0)

minute

now()

Returns new datetime object representing current time local to tz.

tz

Timezone object.

If no tz is specified, uses local timezone.

replace()

Return datetime with new specified fields.

resolution = datetime.timedelta(microseconds=1)

second

strptime()

string, format -> new datetime parsed from a string (like time.strptime()).

time()

Return time object with same time but with tzinfo=None.

timestamp()

Return POSIX timestamp as float.

timetuple()

Return time tuple, compatible with time.localtime().

timetz()

Return time object with same time and tzinfo.

tzinfo

tzname()

Return self.tzinfo.tzname(self).

utcfromtimestamp()

Construct a naive UTC datetime from a POSIX timestamp.

utcnow()

Return a new datetime representing UTC day and time.

utcoffset()

Return self.tzinfo.utcoffset(self).

utctimetuple()

Return UTC time tuple, compatible with time.localtime().

gillespy2.core.export_SBML(gillespy_model, filename=None)

GillesPy model to SBML converter

Parameters:

• gillespy_model (gillespy.Model) – GillesPy model to be converted to SBML

• filename (str) – Path to the SBML file for conversion

gillespy2.core.export_StochSS(gillespy_model, filename=None, return_stochss_model=False)

GillesPy model to StochSS converter

Parameters:

• gillespy_model (gillespy.Model) – GillesPy model to be converted to StochSS

• filename (str) – Path to the StochSS file for conversion

gillespy2.core.import_SBML(filename, name=None, gillespy_model=None, report_silently_with_sbml_error=False)

SBML to GillesPy model converter. NOTE: non-mass-action rates in terms of concentrations may not be converted for population simulation. Use caution when importing SBML.

Parameters:

• filename (str) – Path to the SBML file for conversion.

• name (str) – Name of the resulting model

• gillespy_model (gillespy.Model) – If desired, the SBML model may be added to an existing GillesPy model

• report_silently_with_sbml_error (bool) – SBML import will fail silently and SBML errors will not output to the user.