petab.visualize.helper_functions¶

This file should contain the functions, which PEtab internally needs for plotting, but which are not meant to be used by non-developers and should hence not be directly visible/usable when using import petab.visualize.

Functions

`check_ex_exp_columns`(exp_data, …)	Check the columns in measurement file, if non-mandotory columns does not exist, create default columns
`check_ex_visu_columns`(vis_spec, exp_data, …)	Check the columns in Visu_Spec file, if non-mandotory columns does not exist, create default columns
`check_vis_spec_consistency`(exp_data, …)	Helper function for plotting data and simulations, which checks the visualization setting, if no visualization specification file is provided.
`create_dataset_id_list`(simcond_id_list, …)	Create dataset id list and corresponding plot legends.
`create_figure`(uni_plot_ids, plots_to_file)	Helper function for plotting data and simulations, open figure and axes
`create_or_update_vis_spec`(exp_data, …)	Helper function for plotting data and simulations, which updates vis_spec file if necessary or creates a default visualization table and updates/creates DATASET_ID column of exp_data.
`expand_vis_spec_settings`(vis_spec, columns_dict)	only makes sense if DATASET_ID is not in vis_spec.columns?
`get_data_to_plot`(plot_spec, m_data, …)	Group the data, which should be plotted and return it as dataframe.
`get_default_vis_specs`(exp_data, …)	Helper function for plotting data and simulations, which creates a default visualization table and updates/creates DATASET_ID column of exp_data
`get_vis_spec_dependent_columns_dict`(…)	Helper function for creating values for columns PLOT_ID, DATASET_ID, LEGEND_ENTRY, Y_VALUES for visualization specification file.
`handle_dataset_plot`(plot_spec, ax, exp_data, …)	Handle dataset plot
`import_from_files`(data_file_path, …)	Helper function for plotting data and simulations, which imports data from PEtab files.
`matches_plot_spec`(df, col_id, x_value, str], …)	constructs an index for subsetting of the dataframe according to what is specified in plot_spec.

petab.visualize.helper_functions.check_ex_exp_columns(exp_data: pandas.core.frame.DataFrame, dataset_id_list: List[List[str]], sim_cond_id_list: List[List[str]], sim_cond_num_list: List[List[int]], observable_id_list: List[List[str]], observable_num_list: List[List[int]], exp_conditions: pandas.core.frame.DataFrame, sim: Optional[bool] = False) → Tuple[pandas.core.frame.DataFrame, List[List[str]], Dict[KT, VT]]¶

Check the columns in measurement file, if non-mandotory columns does not exist, create default columns

Returns:	A tuple of experimental DataFrame, list of datasetIds and dictionary of plot legends, corresponding to the datasetIds

petab.visualize.helper_functions.check_ex_visu_columns(vis_spec: pandas.core.frame.DataFrame, exp_data: pandas.core.frame.DataFrame, exp_conditions: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame¶

Check the columns in Visu_Spec file, if non-mandotory columns does not exist, create default columns

Returns:	Updated visualization specification DataFrame

petab.visualize.helper_functions.check_vis_spec_consistency(exp_data: pandas.core.frame.DataFrame, dataset_id_list: Optional[List[List[str]]] = None, sim_cond_id_list: Optional[List[List[str]]] = None, sim_cond_num_list: Optional[List[List[int]]] = None, observable_id_list: Optional[List[List[str]]] = None, observable_num_list: Optional[List[List[int]]] = None) → str¶

Helper function for plotting data and simulations, which checks the visualization setting, if no visualization specification file is provided.

For documentation, see main function plot_data_and_simulation()

Returns:	Specifies the grouping of data to plot.
Return type:	group_by

petab.visualize.helper_functions.create_dataset_id_list(simcond_id_list: List[List[str]], simcond_num_list: List[List[int]], observable_id_list: List[List[str]], observable_num_list: List[List[int]], exp_data: pandas.core.frame.DataFrame, exp_conditions: pandas.core.frame.DataFrame, group_by: str) → Tuple[pandas.core.frame.DataFrame, List[List[str]], Dict[KT, VT], Dict[KT, VT]]¶

Create dataset id list and corresponding plot legends. Additionally, update/create DATASET_ID column of exp_data

Parameters:	group_by – defines grouping of data to plot
Returns:	A tuple of experimental DataFrame, list of datasetIds and dictionary of plot legends, corresponding to the datasetIds

For additional documentation, see main function plot_data_and_simulation()

petab.visualize.helper_functions.create_figure(uni_plot_ids: numpy.ndarray, plots_to_file: bool) → Tuple[matplotlib.figure.Figure, Union[Dict[str, matplotlib.axes._subplots.AxesSubplot], np.ndarray[plt.Subplot]]]¶

Helper function for plotting data and simulations, open figure and axes

Parameters:

uni_plot_ids – Array with unique plot indices
plots_to_file – Indicator if plots are saved to file

Returns:

fig (Figure object of the created plot.)
ax (Axis object of the created plot.)

petab.visualize.helper_functions.create_or_update_vis_spec(exp_data: pandas.core.frame.DataFrame, exp_conditions: pandas.core.frame.DataFrame, vis_spec: Optional[pandas.core.frame.DataFrame] = None, dataset_id_list: Optional[List[List[str]]] = None, sim_cond_id_list: Optional[List[List[str]]] = None, sim_cond_num_list: Optional[List[List[int]]] = None, observable_id_list: Optional[List[List[str]]] = None, observable_num_list: Optional[List[List[int]]] = None, plotted_noise: Optional[str] = 'MeanAndSD')¶

Helper function for plotting data and simulations, which updates vis_spec file if necessary or creates a default visualization table and updates/creates DATASET_ID column of exp_data. As a result, a visualization specification file exists with columns PLOT_ID, DATASET_ID, Y_VALUES and LEGEND_ENTRY

Returns:	A tuple of visualization specification DataFrame and experimental DataFrame.

petab.visualize.helper_functions.expand_vis_spec_settings(vis_spec, columns_dict)¶

only makes sense if DATASET_ID is not in vis_spec.columns?

Returns:	A visualization specification DataFrame

petab.visualize.helper_functions.get_data_to_plot(plot_spec: pandas.core.series.Series, m_data: pandas.core.frame.DataFrame, simulation_data: pandas.core.frame.DataFrame, condition_ids: numpy.ndarray, col_id: str, simulation_field: str = 'simulation') → pandas.core.frame.DataFrame¶

Group the data, which should be plotted and return it as dataframe.

Parameters:	plot_spec – information about contains defined data format (visualization file) m_data – contains defined data format (measurement file) simulation_data – contains defined data format (simulation file) condition_ids – contains all unique condition IDs which should be plotted in one figure (can be found in measurementData file, column simulationConditionId) col_id – the name of the column in visualization file, whose entries should be unique (depends on condition in column xValues) simulation_field – Column name in `simulation_data` that contains the actual simulation result.
Returns:	Contains the data which should be plotted (Mean and Std)
Return type:	data_to_plot

petab.visualize.helper_functions.get_default_vis_specs(exp_data: pandas.core.frame.DataFrame, exp_conditions: pandas.core.frame.DataFrame, dataset_id_list: Optional[List[List[str]]] = None, sim_cond_id_list: Optional[List[List[str]]] = None, sim_cond_num_list: Optional[List[List[int]]] = None, observable_id_list: Optional[List[List[str]]] = None, observable_num_list: Optional[List[List[int]]] = None, plotted_noise: Optional[str] = 'MeanAndSD') → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]¶

Helper function for plotting data and simulations, which creates a default visualization table and updates/creates DATASET_ID column of exp_data

Returns:	A tuple of visualization specification DataFrame and experimental DataFrame.

For documentation, see main function plot_data_and_simulation()

petab.visualize.helper_functions.get_vis_spec_dependent_columns_dict(exp_data: pandas.core.frame.DataFrame, exp_conditions: pandas.core.frame.DataFrame, dataset_id_list: Optional[List[List[str]]] = None, sim_cond_id_list: Optional[List[List[str]]] = None, sim_cond_num_list: Optional[List[List[int]]] = None, observable_id_list: Optional[List[List[str]]] = None, observable_num_list: Optional[List[List[int]]] = None) → Tuple[pandas.core.frame.DataFrame, Dict[KT, VT]]¶

Helper function for creating values for columns PLOT_ID, DATASET_ID, LEGEND_ENTRY, Y_VALUES for visualization specification file. DATASET_ID column of exp_data is updated accordingly.

Returns:	A tuple of experimental DataFrame and a dictionary with values for columns PLOT_ID, DATASET_ID, LEGEND_ENTRY, Y_VALUES for visualization specification file.

petab.visualize.helper_functions.handle_dataset_plot(plot_spec: pandas.core.series.Series, ax: matplotlib.axes._axes.Axes, exp_data: pandas.core.frame.DataFrame, exp_conditions: pandas.core.frame.DataFrame, sim_data: pandas.core.frame.DataFrame)¶: Handle dataset plot

petab.visualize.helper_functions.import_from_files(data_file_path: str, condition_file_path: str, simulation_file_path: str, dataset_id_list: List[List[str]], sim_cond_id_list: List[List[str]], sim_cond_num_list: List[List[int]], observable_id_list: List[List[str]], observable_num_list: List[List[int]], plotted_noise: str, visualization_file_path: str = None) → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]¶

Helper function for plotting data and simulations, which imports data from PEtab files. If visualization_file_path is not provided, the visualisation specification DataFrame will be generated automatically.

For documentation, see main function plot_data_and_simulation()

Returns:	A tuple of experimental data, experimental conditions, visualization specification and simulation data DataFrames.

petab.visualize.helper_functions.matches_plot_spec(df: pandas.core.frame.DataFrame, col_id: str, x_value: Union[float, str], plot_spec: pandas.core.series.Series) → pandas.core.series.Series¶

constructs an index for subsetting of the dataframe according to what is specified in plot_spec.

Parameters:	df – pandas data frame to subset, can be from measurement file or simulation file col_id – name of the column that will be used for indexing in x variable x_value – subsetted x value plot_spec – visualization spec from the visualization file
Returns:	Boolean series that can be used for subsetting of the passed dataframe
Return type:	index