sbmlsim.examples.experiments.midazolam.experiments

Submodules

• sbmlsim.examples.experiments.midazolam.experiments.kupferschmidt1995
• sbmlsim.examples.experiments.midazolam.experiments.mandema1992

Package Contents

Classes

SimulationExperiment	Generic simulation experiment.
AbstractModel	Abstract base class to store a model in sbmlsim.
TimecourseSim	Timecourse simulation consisting of multiple concatenated timecourses.
ModelSensitivity	Helpers for calculating model sensitivity.
Task	Tasks combine models with simulations.
MidazolamSimulationExperiment	Base class for all GlucoseSimulationExperiments.

Functions

exclude_parameters_midazolam(pid)

Filter for excluding parameter ids in sensitivity.

Attributes

MODEL_PATH
MolecularWeights

class sbmlsim.examples.experiments.midazolam.experiments.SimulationExperiment(sid=None, base_path=None, data_path=None, ureg=None, **kwargs)

Generic simulation experiment.

Consists of models, datasets, simulations, tasks, results, processing, figures

Parameters:

• sid (str) –

• base_path (pathlib.Path) –

• data_path (pathlib.Path) –

• ureg (sbmlsim.units.UnitRegistry) –

property results: Dict[str, sbmlsim.xresult.XResult]

Access simulation results.

Results are mapped on tasks based on the task_ids. E.g. to get the results for the task with id ‘task_glciv’ use ```

simexp.results[“task_glciv”] self.results[“task_glciv”]

```

Return type:

Dict[str, sbmlsim.xresult.XResult]

initialize()

Initialize SimulationExperiment.

Initialization must be separated from object construction due to the parallel execution of the problem later on. Certain objects cannot be serialized and must be initialized. :return:

Return type:

None

__str__()

Get string representation.

Return type:

str

models()

Define model definitions.

The child classes fill out the information.

Return type:

Dict[str, Union[sbmlsim.model.AbstractModel, pathlib.Path]]

datasets()

Define dataset definitions (experimental data).

The child classes fill out the information.

Return type:

Dict[str, sbmlsim.data.DataSet]

simulations()

Define simulation definitions.

The child classes fill out the information.

Return type:

Dict[str, sbmlsim.simulation.AbstractSim]

tasks()

Define task definitions.

The child classes fill out the information.

Return type:

Dict[str, sbmlsim.task.Task]

data()

Define DataGenerators including functions.

This determines the selection in the model.

All data which is accessed in a simulation result must be defined in a data generator. The data generators are important for defining the selections of a simulation experiment.

Return type:

Dict[str, sbmlsim.data.Data]

figures()

Figure definition.

Selections accessed in figures and analyses must be registered beforehand via datagenerators.

Most figures do not require access to concrete data, but only abstract data concepts.

Return type:

Dict[str, sbmlsim.plot.Figure]

figures_mpl()

Matplotlib figure definition.

Selections accessed in figures and analyses must be registered beforehand via datagenerators.

Most figures do not require access to concrete data, but only abstract data concepts.

Return type:

Dict[str, sbmlsim.plot.serialization_matplotlib.FigureMPL]

fit_mappings()

Define fit mappings.

Mapping reference data on observables. Used for the optimization of parameters. The child classes fill out the information.

Return type:

Dict[str, sbmlsim.fit.FitMapping]

reports()

Define reports.

Reports are defined by a hashmap label:Data. Reports can be serialized in multiple manners.

Return type:

Dict[str, Dict[str, str]]

add_data(d)

Add data to the tracked data.

Parameters:

d (sbmlsim.data.Data) –

Return type:

None

add_selections_data(selections, task_ids=None)

Add selections to given tasks.

The data for the selections will be part of the results.

Selections are necessary to access data from simulations. Here these selections are added to the tasks. If no tasks are given, the selections are added to all tasks.

Parameters:

• reset – drop and reset all selections.

• selections (Iterable[str]) –

• task_ids (Iterable[str]) –

Return type:

None

_check_keys()

Check keys in information dictionaries.

_check_types()

Check for correctness of types.

run(simulator, output_path=None, show_figures=True, save_results=False, figure_formats=None, reduced_selections=True)

Execute given experiment and store results.

Parameters:

• output_path (pathlib.Path) –

• show_figures (bool) –

• save_results (bool) –

• figure_formats (List[str]) –

• reduced_selections (bool) –

Return type:

ExperimentResult

_run_tasks(simulator, reduced_selections=True)

Run simulations and scans.

This should not be called directly, but the results of the simulations should be requested by the results property. This allows to hash executed simulations.

Parameters:

reduced_selections (bool) –

evaluate_fit_mappings()

Evaluate fit mappings.

to_json(path=None, indent=2)

Convert experiment to JSON for exchange.

Parameters:

• path (pathlib.Path) – path for file, if None JSON str is returned

• indent (int) –

Returns:

to_dict()

Convert to dictionary.

This is the basis for the JSON serialization.

classmethod from_json(json_info)

Load experiment from json path or str.

Parameters:

json_info (Union[pathlib.Path, str]) –

Return type:

SimulationExperiment

save_datasets(results_path)

Save datasets.

Parameters:

results_path (pathlib.Path) –

Return type:

None

save_results(results_path)

Save results (mean timecourse).

Parameters:

results_path (pathlib.Path) –

Returns:

Return type:

None

create_mpl_figures()

Create matplotlib figures.

Return type:

Dict[str, Union[sbmlsim.plot.serialization_matplotlib.FigureMPL, sbmlsim.plot.Figure]]

show_mpl_figures(mpl_figures)

Show matplotlib figures.

Parameters:

mpl_figures (Dict[str, sbmlsim.plot.serialization_matplotlib.FigureMPL]) –

Return type:

None

save_mpl_figures(results_path, mpl_figures, figure_formats=None)

Save matplotlib figures.

Parameters:

• results_path (pathlib.Path) –

• mpl_figures (Dict[str, sbmlsim.plot.serialization_matplotlib.FigureMPL]) –

• figure_formats (List[str]) –

Return type:

Dict[str, List[pathlib.Path]]

classmethod close_mpl_figures(mpl_figures)

Close matplotlib figures.

Parameters:

mpl_figures (Dict[str, sbmlsim.plot.serialization_matplotlib.FigureMPL]) –

class sbmlsim.examples.experiments.midazolam.experiments.AbstractModel(source, sid=None, name=None, language=None, language_type=LanguageType.SBML, base_path=None, changes=None, selections=None)

Bases: object

Abstract base class to store a model in sbmlsim.

Depending on the model language different subclasses are implemented.

Parameters:

• source (Union[str, pathlib.Path]) –

• sid (Optional[str]) –

• name (Optional[str]) –

• language (Optional[str]) –

• language_type (LanguageType) –

• base_path (Optional[pathlib.Path]) –

• changes (Dict) –

• selections (List[str]) –

class LanguageType

Bases: enum.Enum

Language types.

SBML = 1

CELLML = 2

class SourceType

Bases: enum.Enum

Source types.

PATH = 1

URN = 2

URL = 3

__repr__()

Get string representation.

Return type:

str

normalize(uinfo)

Normalize values to model units for all changes.

Parameters:

uinfo (sbmlsim.units.UnitsInformation) –

to_dict()

Convert to dictionary.

class sbmlsim.examples.experiments.midazolam.experiments.TimecourseSim(timecourses, selections=None, reset=True, time_offset=0.0)

Bases: sbmlsim.simulation.AbstractSim

Timecourse simulation consisting of multiple concatenated timecourses.

In case of a single timecourse, only the single timecourse is executed.

Parameters:

• timecourses (Union[List[Timecourse], Timecourse]) –

• selections (Optional[List[str]]) –

• reset (bool) –

• time_offset (float) –

__repr__()

Get representation.

Return type:

str

_time()

Calculate the time vector complete simulation.

Return type:

numpy.ndarray

dimensions()

Get dimensions.

Return type:

List[sbmlsim.simulation.Dimension]

add_model_changes(model_changes)

Add model changes to given simulation.

Parameters:

model_changes (Dict) –

Return type:

None

normalize(uinfo)

Normalize timecourse simulation.

Parameters:

uinfo (sbmlsim.units.UnitsInformation) –

Return type:

None

strip_units()

Strip units from simulation.

Return type:

None

to_dict()

Convert to dictionary.

Return type:

Dict[str, Any]

to_json(path=None)

Convert definition to JSON.

Parameters:

path (pathlib.Path) –

Return type:

str

static from_json(json_info)

Load from JSON.

Parameters:

json_info (Union[str, pathlib.Path]) –

Return type:

TimecourseSim

__str__()

Get string representation.

Return type:

str

class sbmlsim.examples.experiments.midazolam.experiments.ModelSensitivity

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)

Create a parameter sensitivity scan for given TimecourseSimulation.

Parameters:

• model (sbmlsim.model.rr_model.roadrunner.RoadRunner) – 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)

Get sensitivity scan based on distributions for values.

Parameters:

• model (sbmlsim.model.rr_model.roadrunner.RoadRunner) –

• simulation (sbmlsim.simulation.TimecourseSim) –

• cv (float) –

• size (int) –

• distribution (DistributionType) –

• stype (SensitivityType) –

• exclude_zero (bool) –

• zero_eps (float) –

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)

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.rr_model.roadrunner.RoadRunner) –

• changes (Dict) –

• cv (float) –

• size (int) –

• distribution (DistributionType) –

• stype (SensitivityType) –

• exclude_zero (bool) –

• zero_eps (float) –

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)

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.rr_model.roadrunner.RoadRunner) –

• changes (Dict) –

• difference (float) –

• stype (SensitivityType) –

• exclude_zero (bool) –

• zero_eps (float) –

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)

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.rr_model.roadrunner.RoadRunner) –

• 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)

Apply relative change to reference dictionary.

Parameters:

• ref_dict – {key: value} dictionary to change

• change (float) – relative change to apply.

Returns:

class sbmlsim.examples.experiments.midazolam.experiments.Task(model, simulation, sid=None, name=None)

Tasks combine models with simulations.

This allows to execute the same simulation with different model variants.

Parameters:

• model (str) –

• simulation (str) –

• sid (str) –

• name (str) –

__repr__()

Get representation.

Return type:

str

to_dict()

Convert to dictionary.

Return type:

Dict[str, str]

sbmlsim.examples.experiments.midazolam.experiments.MODEL_PATH

sbmlsim.examples.experiments.midazolam.experiments.MolecularWeights

class sbmlsim.examples.experiments.midazolam.experiments.MidazolamSimulationExperiment(sid=None, base_path=None, data_path=None, ureg=None, **kwargs)

Bases: sbmlsim.experiment.SimulationExperiment

Base class for all GlucoseSimulationExperiments.

Parameters:

• sid (str) –

• base_path (pathlib.Path) –

• data_path (pathlib.Path) –

• ureg (sbmlsim.units.UnitRegistry) –

property Mr

models()

Define model definitions.

The child classes fill out the information.

Return type:

Dict[str, sbmlsim.model.AbstractModel]

tasks()

Define task definitions.

The child classes fill out the information.

Return type:

Dict[str, sbmlsim.task.Task]

simulations(simulations=None)

Define simulation definitions.

The child classes fill out the information.

Return type:

Dict[str, sbmlsim.simulation.TimecourseSim]

sbmlsim.examples.experiments.midazolam.experiments.exclude_parameters_midazolam(pid)

Filter for excluding parameter ids in sensitivity.

Parameters:

pid (str) –

Return type:

bool