petab.core

PEtab core functions (or functions that don’t fit anywhere else)

Functions

`concat_tables`(tables[, file_parser])	Concatenate DataFrames provided as DataFrames or filenames, and a parser
`create_combine_archive`(yaml_file, filename)	Create COMBINE archive (https://co.mbine.org/documents/archive) based on PEtab YAML file.
`flatten_timepoint_specific_output_overrides`(...)	Flatten timepoint-specific output parameter overrides.
`get_notnull_columns`(df, candidates)	Return list of `df`-columns in `candidates` which are not all null/nan.
`get_observable_id`(parameter_id)	Get PEtab observable ID from PEtab-style sigma or observable AssignmentRule-target `parameter_id`.
`get_simulation_df`(simulation_file)	Read PEtab simulation table
`get_visualization_df`(visualization_file)	Read PEtab visualization table
`is_empty`(val)	Check if the value val, e.g.
`to_float_if_float`(x)	Return input as float if possible, otherwise return as is
`unique_preserve_order`(seq)	Return a list of unique elements in Sequence, keeping only the first occurrence of each element
`write_simulation_df`(df, filename)	Write PEtab simulation table
`write_visualization_df`(df, filename)	Write PEtab visualization table

petab.core.concat_tables(tables: Union[str, pathlib.Path, pandas.core.frame.DataFrame, Iterable[Union[pandas.core.frame.DataFrame, str, pathlib.Path]]], file_parser: Optional[Callable] = None) → pandas.core.frame.DataFrame[source]

Concatenate DataFrames provided as DataFrames or filenames, and a parser

Parameters

tables – Iterable of tables to join, as DataFrame or filename.
file_parser – Function used to read the table in case filenames are provided, accepting a filename as only argument.

Returns

The concatenated DataFrames

petab.core.create_combine_archive(yaml_file: Union[str, pathlib.Path], filename: Union[str, pathlib.Path], family_name: Optional[str] = None, given_name: Optional[str] = None, email: Optional[str] = None, organization: Optional[str] = None) → None[source]

Create COMBINE archive (https://co.mbine.org/documents/archive) based on PEtab YAML file.

Parameters

yaml_file – Path to PEtab YAML file
filename – Destination file name
family_name – Family name of archive creator
given_name – Given name of archive creator
email – E-mail address of archive creator
organization – Organization of archive creator

petab.core.flatten_timepoint_specific_output_overrides(petab_problem: petab.problem.Problem) → None[source]

Flatten timepoint-specific output parameter overrides.

If the PEtab problem definition has timepoint-specific observableParameters or noiseParameters for the same observable, replace those by replicating the respective observable.

This is a helper function for some tools which may not support such timepoint-specific mappings. The observable table and measurement table are modified in place.

Parameters: petab_problem – PEtab problem to work on

petab.core.get_notnull_columns(df: pandas.core.frame.DataFrame, candidates: Iterable)[source]

Return list of df-columns in candidates which are not all null/nan.

The output can e.g. be used as input for pandas.DataFrame.groupby.

Parameters

df – Dataframe
candidates – Columns of df to consider

petab.core.get_observable_id(parameter_id: str) → str[source]

Get PEtab observable ID from PEtab-style sigma or observable AssignmentRule-target parameter_id.

e.g. for ‘observable_obs1’ -> ‘obs1’, for ‘sigma_obs1’ -> ‘obs1’

Parameters: parameter_id – Some parameter ID
Returns: Observable ID

petab.core.get_simulation_df(simulation_file: str) → pandas.core.frame.DataFrame[source]

Read PEtab simulation table

Parameters: simulation_file – URL or filename of PEtab simulation table
Returns: Simulation DataFrame

petab.core.get_visualization_df(visualization_file: Union[str, pathlib.Path]) → pandas.core.frame.DataFrame[source]

Read PEtab visualization table

Parameters: visualization_file – URL or filename of PEtab visualization table
Returns: Visualization DataFrame

petab.core.is_empty(val) → bool[source]

Check if the value val, e.g. a table entry, is empty.

Parameters: val – The value to check.
Returns: Whether the field is to be considered empty.

petab.core.to_float_if_float(x: Any) → Any[source]

Return input as float if possible, otherwise return as is

Parameters: x – Anything
Returns: x as float if possible, otherwise x

petab.core.unique_preserve_order(seq: Sequence) → List[source]

Return a list of unique elements in Sequence, keeping only the first occurrence of each element

Parameters: seq – Sequence to prune
Returns: List of unique elements in seq

petab.core.write_simulation_df(df: pandas.core.frame.DataFrame, filename: str) → None[source]

Write PEtab simulation table

Parameters

df – PEtab simulation table
filename – Destination file name

petab.core.write_visualization_df(df: pandas.core.frame.DataFrame, filename: Union[str, pathlib.Path]) → None[source]

Write PEtab visualization table

Parameters

df – PEtab visualization table
filename – Destination file name