petab.core
PEtab core functions (or functions that don’t fit anywhere else)
Functions
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Concatenate DataFrames provided as DataFrames or filenames, and a parser |
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Create COMBINE archive (https://co.mbine.org/documents/archive) based on PEtab YAML file. |
Flatten timepoint-specific output parameter overrides. |
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Return list of |
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Read PEtab simulation table |
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Read PEtab visualization table |
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Check if the value val, e.g. |
Return input as float if possible, otherwise return as is |
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Return a list of unique elements in Sequence, keeping only the first occurrence of each element |
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Write PEtab simulation table |
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Write PEtab visualization table |
- petab.core.concat_tables(tables: Union[str, Path, DataFrame, Iterable[Union[DataFrame, str, Path]]], file_parser: Optional[Callable] = None) 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, Path], filename: Union[str, 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: DataFrame, candidates: Iterable)[source]
Return list of
df
-columns incandidates
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_simulation_df(simulation_file: str) 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, Path]) 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, otherwisex
- 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