petab.parameter_mapping¶
Functions related to mapping parameter from model to parameter estimation problem
Functions
Create list of mapping dicts from PEtab-problem to SBML parameters. |
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Create dictionary of parameter value and parameter scale mappings from PEtab-problem to SBML parameters for the given condition. |
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Find all observable parameters and noise parameters that were not mapped and set their mapping to np.nan. |
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Merge preequilibration and simulation parameters and scales for a list of conditions while checking for compatibility. |
Merge preequilibration and simulation parameters and scales for a single condition while checking for compatibility. |
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petab.parameter_mapping.
_apply_condition_parameters
(par_mapping: Dict[str, Union[str, numbers.Number]], scale_mapping: Dict[str, str], condition_id: str, condition_df: pandas.core.frame.DataFrame, sbml_model: libsbml.Model) → None¶ Replace parameter IDs in parameter mapping dictionary by condition table parameter values (in-place).
- Parameters
par_mapping – see get_parameter_mapping_for_condition
condition_id – ID of condition to work on
condition_df – PEtab condition table
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petab.parameter_mapping.
_apply_output_parameter_overrides
(mapping: Dict[str, Union[str, numbers.Number]], cur_measurement_df: pandas.core.frame.DataFrame) → None¶ Apply output parameter overrides to the parameter mapping dict for a given condition as defined in the measurement table (
observableParameter
,noiseParameters
).- Parameters
mapping – parameter mapping dict as obtained from
get_parameter_mapping_for_condition
cur_measurement_df – Subset of the measurement table for the current condition
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petab.parameter_mapping.
_apply_overrides_for_observable
(mapping: Dict[str, Union[str, numbers.Number]], observable_id: str, override_type: str, overrides: List[str]) → None¶ Apply parameter-overrides for observables and noises to mapping matrix.
- Parameters
mapping – mapping dict to which to apply overrides
observable_id – observable ID
override_type – ‘observable’ or ‘noise’
overrides – list of overrides for noise or observable parameters
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petab.parameter_mapping.
_apply_parameter_table
(par_mapping: Dict[str, Union[str, numbers.Number]], scale_mapping: Dict[str, str], parameter_df: Optional[pandas.core.frame.DataFrame] = None, scaled_parameters: bool = False, fill_fixed_parameters: bool = True) → None¶ Replace parameters from parameter table in mapping list for a given condition and set the corresponding scale.
Replace non-estimated parameters by
nominalValues
(un-scaled / lin-scaled), replace estimated parameters by the respective ID.- Parameters
par_mapping – mapping dict obtained from
get_parameter_mapping_for_condition
parameter_df – PEtab parameter table
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petab.parameter_mapping.
_map_condition
(packed_args)¶ Helper function for parallel condition mapping.
For arguments see get_optimization_to_simulation_parameter_mapping
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petab.parameter_mapping.
_map_condition_arg_packer
(simulation_conditions, measurement_df, condition_df, parameter_df, sbml_model, simulation_parameters, warn_unmapped, scaled_parameters, fill_fixed_parameters)¶ Helper function to pack extra arguments for _map_condition
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petab.parameter_mapping.
_output_parameters_to_nan
(mapping: Dict[str, Union[str, numbers.Number]]) → None¶ Set output parameters in mapping dictionary to nan
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petab.parameter_mapping.
_perform_mapping_checks
(measurement_df: pandas.core.frame.DataFrame) → None¶ Check for PEtab features which we can’t account for during parameter mapping.
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petab.parameter_mapping.
get_optimization_to_simulation_parameter_mapping
(condition_df: pandas.core.frame.DataFrame, measurement_df: pandas.core.frame.DataFrame, parameter_df: Optional[pandas.core.frame.DataFrame] = None, observable_df: Optional[pandas.core.frame.DataFrame] = None, sbml_model: Optional[libsbml.Model] = None, simulation_conditions: Optional[pandas.core.frame.DataFrame] = None, warn_unmapped: Optional[bool] = True, scaled_parameters: bool = False, fill_fixed_parameters: bool = True) → List[Tuple[Dict[str, Union[str, numbers.Number]], Dict[str, Union[str, numbers.Number]], Dict[str, str], Dict[str, str]]]¶ Create list of mapping dicts from PEtab-problem to SBML parameters.
Mapping can be performed in parallel. The number of threads is controlled by the environment variable with the name of petab.ENV_NUM_THREADS.
- Parameters
condition_df – The dataframes in the PEtab format.
measurement_df – The dataframes in the PEtab format.
parameter_df – The dataframes in the PEtab format.
observable_df – The dataframes in the PEtab format.
sbml_model – The sbml model with observables and noise specified according to the PEtab format.
simulation_conditions – Table of simulation conditions as created by
petab.get_simulation_conditions
.warn_unmapped – If
True
, log warning regarding unmapped parametersscaled_parameters – Whether parameter values should be scaled.
fill_fixed_parameters – Whether to fill in nominal values for fixed parameters (estimate=0 in parameters table).
- Returns
Parameter value and parameter scale mapping for all conditions.
The length of the returned array is the number of unique combinations of
simulationConditionId``s and ``preequilibrationConditionId``s from the measurement table. Each entry is a tuple of four dicts of length equal to the number of model parameters. The first two dicts map simulation parameter IDs to optimization parameter IDs or values (where values are fixed) for preequilibration and simulation condition, respectively. The last two dicts map simulation parameter IDs to the parameter scale of the respective parameter, again for preequilibration and simulation condition. If no preequilibration condition is defined, the respective dicts will be empty. ``NaN
is used where no mapping exists.
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petab.parameter_mapping.
get_parameter_mapping_for_condition
(condition_id: str, is_preeq: bool, cur_measurement_df: pandas.core.frame.DataFrame, sbml_model: libsbml.Model, condition_df: pandas.core.frame.DataFrame, parameter_df: Optional[pandas.core.frame.DataFrame] = None, simulation_parameters: Optional[Dict[str, str]] = None, warn_unmapped: bool = True, scaled_parameters: bool = False, fill_fixed_parameters: bool = True) → Tuple[Dict[str, Union[str, numbers.Number]], Dict[str, str]]¶ Create dictionary of parameter value and parameter scale mappings from PEtab-problem to SBML parameters for the given condition.
- Parameters
condition_id – Condition ID for which to perform mapping
is_preeq – If
True
, output parameters will not be mappedcur_measurement_df – Measurement sub-table for current condition
condition_df – PEtab condition DataFrame
parameter_df – PEtab parameter DataFrame
sbml_model – The sbml model with observables and noise specified according to the PEtab format used to retrieve simulation parameter IDs.
simulation_parameters – Model simulation parameter IDs mapped to parameter values (output of
petab.sbml.get_model_parameters(.., with_values=True)
). Optional, saves time if precomputed.warn_unmapped – If
True
, log warning regarding unmapped parametersfill_fixed_parameters – Whether to fill in nominal values for fixed parameters (estimate=0 in parameters table).
- Returns
Tuple of two dictionaries. First dictionary mapping model parameter IDs to mapped parameters IDs to be estimated or to filled-in values in case of non-estimated parameters. Second dictionary mapping model parameter IDs to their scale. NaN is used where no mapping exists.
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petab.parameter_mapping.
handle_missing_overrides
(mapping_par_opt_to_par_sim: Dict[str, Union[str, numbers.Number]], warn: bool = True, condition_id: Optional[str] = None) → None¶ Find all observable parameters and noise parameters that were not mapped and set their mapping to np.nan.
Assumes that parameters matching “(noise|observable)Parameter[0-9]+_” were all supposed to be overwritten.
- Parameters
mapping_par_opt_to_par_sim – Output of get_parameter_mapping_for_condition
warn – If True, log warning regarding unmapped parameters
condition_id – Optional condition ID for more informative output
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petab.parameter_mapping.
merge_preeq_and_sim_pars
(parameter_mappings: Iterable[Tuple[Dict[str, Union[str, numbers.Number]], Dict[str, Union[str, numbers.Number]]]], scale_mappings: Iterable[Tuple[Dict[str, str], Dict[str, str]]]) → Tuple[List[Tuple[Dict[str, Union[str, numbers.Number]], Dict[str, Union[str, numbers.Number]]]], List[Tuple[Dict[str, str], Dict[str, str]]]]¶ Merge preequilibration and simulation parameters and scales for a list of conditions while checking for compatibility.
- Parameters
parameter_mappings – As returned by petab.get_optimization_to_simulation_parameter_mapping
scale_mappings – As returned by petab.get_optimization_to_simulation_scale_mapping.
- Returns
The parameter and scale simulation mappings, modified and checked.
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petab.parameter_mapping.
merge_preeq_and_sim_pars_condition
(condition_map_preeq: Dict[str, Union[str, numbers.Number]], condition_map_sim: Dict[str, Union[str, numbers.Number]], condition_scale_map_preeq: Dict[str, str], condition_scale_map_sim: Dict[str, str], condition: Any) → None¶ Merge preequilibration and simulation parameters and scales for a single condition while checking for compatibility.
This function is meant for the case where we cannot have different parameters (and scales) for preequilibration and simulation. Therefore, merge both and ensure matching scales and parameters.
condition_map_sim
andcondition_scale_map_sim
will ne modified in place.- Parameters
condition_map_preeq – Parameter mapping as obtained from get_parameter_mapping_for_condition
condition_map_sim – Parameter mapping as obtained from get_parameter_mapping_for_condition
condition_scale_map_preeq – Parameter scale mapping as obtained from get_get_scale_mapping_for_condition
condition_scale_map_sim – Parameter scale mapping as obtained from get_get_scale_mapping_for_condition
condition – Condition identifier for more informative error messages