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simulation.sensitivity

Helpers for calculating model sensitivities and uncertainties.

Allows to get sets of changes from given model instance.

SensitivityType

Bases: Enum

Type of sensitivity.

DistributionType

Bases: Enum

Type of supported distributions.

FIXME: support lognormal

ModelSensitivity

Helpers for calculating model sensitivity.

difference_sensitivity_scan staticmethod 

difference_sensitivity_scan(
    model,
    simulation,
    difference=0.1,
    stype=PARAMETER_SENSITIVITY,
    exclude_filter=None,
    exclude_zero=True,
    zero_eps=1e-08,
)

Create a parameter sensitivity scan for given TimecourseSimulation.

:param model: model for execution (needed to select parameters) :param simulation: timecourse simulation to scan :param difference: change in parameters, i.e. every parameter (which is not excluded) is changed to '(1.0 - difference) * value' and '(1.0 + difference) * value' :param stype: which sensitivity (parameters or species) :param exclude_filter: filter function which defines which parameters should be excluded from scan :param exclude_zero: parameters with a value of abs(value)<zero_eps are excluded from scan :param zero_eps: epsilon for zero values :return:

distribution_sensitivity_scan staticmethod 

distribution_sensitivity_scan(
    model,
    simulation,
    cv=0.1,
    size=10,
    distribution=NORMAL_DISTRIBUTION,
    stype=PARAMETER_SENSITIVITY,
    exclude_filter=None,
    exclude_zero=True,
    zero_eps=1e-08,
)

Get sensitivity scan based on distributions for values.

create_sampling_dimension staticmethod 

create_sampling_dimension(
    model,
    changes=None,
    cv=0.1,
    size=10,
    distribution=NORMAL_DISTRIBUTION,
    stype=PARAMETER_SENSITIVITY,
    exclude_filter=None,
    exclude_zero=True,
    zero_eps=1e-08,
)

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

create_difference_dimension staticmethod 

create_difference_dimension(
    model,
    changes=None,
    difference=0.1,
    stype=PARAMETER_SENSITIVITY,
    exclude_filter=None,
    exclude_zero=True,
    zero_eps=1e-08,
)

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

reference_dict staticmethod 

reference_dict(
    model,
    changes=None,
    stype=PARAMETER_SENSITIVITY,
    exclude_filter=None,
    exclude_zero=True,
    zero_eps=1e-08,
)

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.

:param model: :param exclude_filter: filter function to exclude parameters, excludes parameter id if the filter function is True :param exclude_zero: exclude parameters which are zero :return:

apply_change_to_dict staticmethod 

apply_change_to_dict(ref_dict, change=0.1)

Apply relative change to reference dictionary.

:param ref_dict: {key: value} dictionary to change :param change: relative change to apply. :return: