sbmlsim.simulation.sensitivity
¶
Helpers for calculating model sensitivities and uncertainties.
Allows to get sets of changes from given model instance.
Module Contents¶
Classes¶
Type of sensitivity. |
|
Type of supported distributions. |
|
Helpers for calculating model sensitivity. |
Attributes¶
- class sbmlsim.simulation.sensitivity.DistributionType[source]¶
Bases:
enum.Enum
Type of supported distributions.
# FIXME: support lognormal
- 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
- 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
model (sbmlsim.model.RoadrunnerSBMLModel) –
simulation (sbmlsim.simulation.TimecourseSim) –
cv (float) –
size (int) –
distribution (DistributionType) –
stype (SensitivityType) –
exclude_zero (bool) –
zero_eps (float) –
- Return type
- 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
model (sbmlsim.model.RoadrunnerSBMLModel) –
changes (Dict) –
cv (float) –
size (int) –
distribution (DistributionType) –
stype (SensitivityType) –
exclude_zero (bool) –
zero_eps (float) –
- Return type
- 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
model (sbmlsim.model.RoadrunnerSBMLModel) –
changes (Dict) –
difference (float) –
stype (SensitivityType) –
exclude_zero (bool) –
zero_eps (float) –
- Return type
- 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