pyRBM.Simulation package

Submodules

pyRBM.Simulation.Compartment module

class Compartment(index, name: str, comp_type, label_mapping, initial_class_values, compartment_constants)

Bases: object

reset() → None

updateCompartmentValues(new_values) → None

pyRBM.Simulation.EventQueue module

pyRBM.Simulation.Rule module

class Rule(propensity: list[str], stoichiometry: list[numpy.ndarray], rule_name: str, num_builtin_classes: int, compartments: list[pyRBM.Simulation.Compartment.Compartment], rule_index_sets: list[list[int]], random_source, event_time_distrib_and_args: str | None = None)

Bases: object

partial_evaluation(compartments)

returnEventRate(compartments, builtin_classes, index_set_i, is_laplace: bool)

returnPropensity(compartments, builtin_classes, index_set_i)

triggerAttemptedRuleChange(compartments, times_triggered: int = 1, allow_negative: bool = True) → bool

pyRBM.Simulation.RuleChain module

returnOneStepRuleUpdates(rules, compartments, matched_indices, base_classes)

pyRBM.Simulation.Solvers module

pyRBM.Simulation.State module

class ModelState(model_classes: Iterable[str], start_datetime: datetime)

Bases: object

changeDate(new_date: datetime) → None

changeModelClassValue(class_name: str, new_value: Any)

changeMonth(old_month, new_month, new_month_index)

processUpdate(new_time) → None

reset() → None

resetClassVars()

returnChangedVars() → list[str]

returnModelClasses()

returnModelClassesValues()

pyRBM.Simulation.StaticSeries module

pyRBM.Simulation.Trajectory module

class Trajectory(compartments: list[pyRBM.Simulation.Compartment.Compartment])

Bases: object

addEntry(time, compartment_values, compartment_index: int) → None

plotAllClassesOverTime(compartment_index: int, figure_position: str = 'center left') → None

pyRBM.Simulation.WaitTimeDistributions module

class Distribution(params: dict[str, float], random_source=None)

Bases: object

checkParams()

checkParamsLaplace()

isLaplaceGillespieCompatible()

isNMGACompatible()

returnLaplaceRate(propensity=0)

returnRandomLaplaceFunc()

returnRandomNMGAFunc()

returnRate(propensity=0)

returnStringRepr()

class DistributionFactory

Bases: object

Used to create distributions from their string representations

createDistribution(distrib_name: str, distribution_args: dict[str, float], random_source=None)

class DoublePowerLawDistribution(params, random_source=None)

Bases: Distribution

checkParamsLaplace()

returnRandomLaplaceFunc()

class ExponentialDistribution(params, random_source=None)

Bases: Distribution

checkParamsLaplace()

returnRandomLaplaceFunc()

class GammaDistribution(params, random_source=None)

Bases: Distribution

checkParamsLaplace()

returnRandomLaplaceFunc()

class GammaPowerLawDistribution(params, random_source=None)

Bases: Distribution

checkParamsLaplace()

returnRandomLaplaceFunc()

class MittagLefflerDistribution(params: dict[str, float], random_source=None)

Bases: Distribution

tba()

class ShiftedGammaPowerLawDistribution(params, random_source=None)

Bases: Distribution

checkParamsLaplace()

returnRandomLaplaceFunc()

class UniformPowerLawDistribution(params, random_source=None)

Bases: Distribution

checkParamsLaplace()

returnRandomLaplaceFunc()

processDistributionString(distribution_string: str)

Module contents