sbmlsim.simulation.sensitivity

Helpers for calculating model sensitivities and uncertainties.

Allows to get sets of changes from given model instance.

Module Contents

Classes

SensitivityType

Type of sensitivity.

DistributionType

Type of supported distributions.

ModelSensitivity

Helpers for calculating model sensitivity.

Attributes

logger

model

sbmlsim.simulation.sensitivity.logger[source]
class sbmlsim.simulation.sensitivity.SensitivityType[source]

Bases: enum.Enum

Type of sensitivity.

PARAMETER_SENSITIVITY = 1[source]
SPECIES_SENSITIVITY = 2[source]
All_SENSITIVITY = 3[source]
class sbmlsim.simulation.sensitivity.DistributionType[source]

Bases: enum.Enum

Type of supported distributions.

# FIXME: support lognormal

NORMAL_DISTRIBUTION = 1[source]
class sbmlsim.simulation.sensitivity.ModelSensitivity[source]

Bases: object

Helpers for calculating model sensitivity.

static difference_sensitivity_scan(model, simulation, difference=0.1, stype=SensitivityType.PARAMETER_SENSITIVITY, exclude_filter=None, exclude_zero=True, zero_eps=1e-08)[source]

Create a parameter sensitivity scan for given TimecourseSimulation.

Parameters
  • model (sbmlsim.model.RoadrunnerSBMLModel) – model for execution (needed to select parameters)

  • simulation (sbmlsim.simulation.TimecourseSim) – timecourse simulation to scan

  • difference (float) – change in parameters, i.e. every parameter (which is not excluded) is changed to ‘(1.0 - difference) * value’ and ‘(1.0 + difference) * value’

  • stype (SensitivityType) – which sensitivity (parameters or species)

  • exclude_filter – filter function which defines which parameters should be excluded from scan

  • exclude_zero (bool) – parameters with a value of abs(value)<zero_eps are excluded from scan

  • zero_eps (float) – epsilon for zero values

Returns

Return type

sbmlsim.simulation.ScanSim

static distribution_sensitivity_scan(model, simulation, cv=0.1, size=10, distribution=DistributionType.NORMAL_DISTRIBUTION, stype=SensitivityType.PARAMETER_SENSITIVITY, exclude_filter=None, exclude_zero=True, zero_eps=1e-08)[source]

Get sensitivity scan based on distributions for values.

Parameters
Return type

sbmlsim.simulation.ScanSim

static create_sampling_dimension(model, changes=None, cv=0.1, size=10, distribution=DistributionType.NORMAL_DISTRIBUTION, stype=SensitivityType.PARAMETER_SENSITIVITY, exclude_filter=None, exclude_zero=True, zero_eps=1e-08)[source]

Create list of dimensions for sampling parameter values.

Only parameters relevant for “GU_”, “LI_” and “KI_” models are sampled.

cv: coeffient of variation (sigma/mean) -> sigma = cv*mean

Parameters
Return type

sbmlsim.simulation.Dimension

static create_difference_dimension(model, changes=None, difference=0.1, stype=SensitivityType.PARAMETER_SENSITIVITY, exclude_filter=None, exclude_zero=True, zero_eps=1e-08)[source]

Create list of dimensions for sampling parameter values.

Only parameters relevant for “GU_”, “LI_” and “KI_” models are sampled.

cv: coeffient of variation (sigma/mean) -> sigma = cv*mean

Parameters
Return type

sbmlsim.simulation.Dimension

static reference_dict(model, changes=None, stype=SensitivityType.PARAMETER_SENSITIVITY, exclude_filter=None, exclude_zero=True, zero_eps=1e-08)[source]

Get key:value dict for sensitivity analysis.

Values are based on the reference state of the model with the applied changes. Values in current model state are used.

Parameters
  • model (sbmlsim.model.RoadrunnerSBMLModel) –

  • exclude_filter – filter function to exclude parameters, excludes parameter id if the filter function is True

  • exclude_zero (bool) – exclude parameters which are zero

  • changes (Dict) –

  • stype (SensitivityType) –

  • zero_eps (float) –

Returns

Return type

Dict

static apply_change_to_dict(ref_dict, change=0.1)[source]

Apply relative change to reference dictionary.

Parameters
  • ref_dict – {key: value} dictionary to change

  • change (float) – relative change to apply.

Returns

sbmlsim.simulation.sensitivity.model[source]