sbmlsim.experiment.experiment

SimulationExperiments and helpers.

Module Contents

Classes

SimulationExperiment	Generic simulation experiment.
ExperimentResult	Result of a simulation experiment.

Attributes

logger

sbmlsim.experiment.experiment.logger

class sbmlsim.experiment.experiment.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.experiment.experiment.ExperimentResult

Result of a simulation experiment.

experiment: SimulationExperiment

output_path: pathlib.Path

to_dict()

Conversion to dictionary.

Used in serialization and required for reports.

Return type:

Dict