petab.v1.parameters
Functions operating on the PEtab parameter table
Functions
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Create a new PEtab parameter table |
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Get Dictionary with optimization parameter IDs mapped to parameter scaling strings. |
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Get list of optimization parameter IDs from parameter table. |
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Read the provided parameter file into a |
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Create list with information about the parameter priors |
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Get set of parameters which need to go into the parameter table |
Get set of parameters which may be present inside the parameter table |
|
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Scale the parameters, i.e. as |
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Unscale the parameters, i.e. as |
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Add missing columns and fill in default values. |
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Scale parameter according to |
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Unscale parameter according to |
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Write PEtab parameter table |
- petab.v1.parameters.create_parameter_df(sbml_model: Model | None = None, condition_df: DataFrame | None = None, observable_df: DataFrame | None = None, measurement_df: DataFrame | None = None, model: Model | None = None, include_optional: bool = False, parameter_scale: str = 'log10', lower_bound: Iterable | None = None, upper_bound: Iterable | None = None, mapping_df: DataFrame | None = None) DataFrame [source]
Create a new PEtab parameter table
All table entries can be provided as string or list-like with length matching the number of parameters
- Parameters:
sbml_model – SBML Model (deprecated, mutually exclusive with
model
)model – PEtab model (mutually exclusive with
sbml_model
)condition_df – PEtab condition DataFrame
observable_df – PEtab observable DataFrame
measurement_df – PEtab measurement DataFrame
include_optional – By default this only returns parameters that are required to be present in the parameter table. If set to
True
, this returns all parameters that are allowed to be present in the parameter table (i.e. also including parameters specified in the model).parameter_scale – parameter scaling
lower_bound – lower bound for parameter value
upper_bound – upper bound for parameter value
mapping_df – PEtab mapping DataFrame
- Returns:
The created parameter DataFrame
- petab.v1.parameters.get_optimization_parameter_scaling(parameter_df: DataFrame) dict[str, str] [source]
Get Dictionary with optimization parameter IDs mapped to parameter scaling strings.
- Parameters:
parameter_df – PEtab parameter DataFrame
- Returns:
Dictionary with optimization parameter IDs mapped to parameter scaling strings.
- petab.v1.parameters.get_optimization_parameters(parameter_df: DataFrame) list[str] [source]
Get list of optimization parameter IDs from parameter table.
- Parameters:
parameter_df – PEtab parameter DataFrame
- Returns:
List of IDs of parameters selected for optimization.
- petab.v1.parameters.get_parameter_df(parameter_file: str | Path | DataFrame | Iterable[str | Path | DataFrame] | None) DataFrame | None [source]
Read the provided parameter file into a
pandas.Dataframe
.- Parameters:
parameter_file – Name of the file to read from or pandas.Dataframe,
Iterable. (or an)
- Returns:
Parameter
DataFrame
, orNone
ifNone
was passed.
- petab.v1.parameters.get_priors_from_df(parameter_df: DataFrame, mode: Literal['initialization', 'objective'], parameter_ids: Sequence[str] | None = None) list[tuple] [source]
Create list with information about the parameter priors
- Parameters:
parameter_df – PEtab parameter table
mode –
'initialization'
or'objective'
parameter_ids – A sequence of parameter IDs for which to sample starting points. For subsetting or reordering the parameters. Defaults to all estimated parameters.
- Returns:
List with prior information.
- petab.v1.parameters.get_valid_parameters_for_parameter_table(model: Model, condition_df: DataFrame, observable_df: DataFrame, measurement_df: DataFrame, mapping_df: DataFrame | None = None) set[str] [source]
Get set of parameters which may be present inside the parameter table
- Parameters:
model – PEtab model
condition_df – PEtab condition table
observable_df – PEtab observable table
measurement_df – PEtab measurement table
mapping_df – PEtab mapping table for additional checks
- Returns:
Set of parameter IDs which PEtab allows to be present in the parameter table.
- petab.v1.parameters.map_scale(parameters: Sequence[Number], scale_strs: Iterable[Literal['', 'lin', 'log', 'log10']] | Literal['', 'lin', 'log', 'log10']) Iterable[Number] [source]
Scale the parameters, i.e. as
scale()
, but for Sequences.- Parameters:
parameters – Parameters to be scaled.
scale_strs – Scales to apply. Broadcast if a single string.
- Returns:
The scaled parameters.
- petab.v1.parameters.map_unscale(parameters: Sequence[Number], scale_strs: Iterable[Literal['', 'lin', 'log', 'log10']] | Literal['', 'lin', 'log', 'log10']) Iterable[Number] [source]
Unscale the parameters, i.e. as
unscale()
, but for Sequences.- Parameters:
parameters – Parameters to be unscaled.
scale_strs – Scales that the parameters are currently on. Broadcast if a single string.
- Returns:
The unscaled parameters.
- petab.v1.parameters.normalize_parameter_df(parameter_df: DataFrame) DataFrame [source]
Add missing columns and fill in default values.
- petab.v1.parameters.scale(parameter: Number, scale_str: Literal['', 'lin', 'log', 'log10']) Number [source]
Scale parameter according to
scale_str
.- Parameters:
parameter – Parameter to be scaled.
scale_str – One of
'lin'
(synonymous with''
),'log'
,'log10'
.
- Returns:
The scaled parameter.