amici.petab_import
PEtab Import
Import a model in the petab
(https://github.com/PEtab-dev/PEtab) format
into AMICI.
Functions
|
Check that the model is consistent with the PEtab problem. |
|
Determine, set and return fixed model parameters. |
|
Get observables, sigmas, and noise distributions from PEtab observation table in a format suitable for |
|
Create AMICI model from PEtab problem |
|
Create AMICI model from PEtab problem |
|
Import model from petab problem. |
|
Map from the petab to the amici format of noise distribution identifiers. |
|
Convert PEtab parameter scaling string to AMICI scaling integer |
|
Log some model quantities |
|
Turn a SBML species into parameters and replace species references inside the model instance. |
- amici.petab_import.check_model(amici_model, petab_problem)[source]
Check that the model is consistent with the PEtab problem.
- Return type
- amici.petab_import.get_fixed_parameters(petab_problem, non_estimated_parameters_as_constants=True)[source]
Determine, set and return fixed model parameters.
Non-estimated parameters and parameters specified in the condition table are turned into constants (unless they are overridden). Only global SBML parameters are considered. Local parameters are ignored.
- Parameters
petab_problem (
petab.problem.Problem
) – The PEtab problem instancenon_estimated_parameters_as_constants – Whether parameters marked as non-estimated in PEtab should be considered constant in AMICI. Setting this to
True
will reduce model size and simulation times. If sensitivities with respect to those parameters are required, this should be set toFalse
.
- Return type
- Returns
List of IDs of parameters which are to be considered constant.
- amici.petab_import.get_observation_model(observable_df)[source]
Get observables, sigmas, and noise distributions from PEtab observation table in a format suitable for
amici.sbml_import.SbmlImporter.sbml2amici()
.- Parameters
observable_df (
pandas.core.frame.DataFrame
) – PEtab observables table- Return type
typing.Tuple
[typing.Dict
[str
,typing.Dict
[str
,str
]],typing.Dict
[str
,str
],typing.Dict
[str
,typing.Union
[str
,float
]]]- Returns
Tuple of dicts with observables, noise distributions, and sigmas.
- amici.petab_import.import_model(sbml_model=None, condition_table=None, observable_table=None, measurement_table=None, petab_problem=None, model_name=None, model_output_dir=None, verbose=True, allow_reinit_fixpar_initcond=True, validate=True, non_estimated_parameters_as_constants=True, output_parameter_defaults=None, discard_sbml_annotations=False, **kwargs)
Create AMICI model from PEtab problem
- Parameters
sbml_model (
typing.Union
[str
,pathlib.Path
,libsbml.Model
,None
]) – PEtab SBML model or SBML file name. Deprecated, passpetab_problem
instead.condition_table (
typing.Union
[str
,pathlib.Path
,pandas.core.frame.DataFrame
,None
]) – PEtab condition table. If provided, parameters from there will be turned into AMICI constant parameters (i.e. parameters w.r.t. which no sensitivities will be computed). Deprecated, passpetab_problem
instead.observable_table (
typing.Union
[str
,pathlib.Path
,pandas.core.frame.DataFrame
,None
]) – PEtab observable table. Deprecated, passpetab_problem
instead.measurement_table (
typing.Union
[str
,pathlib.Path
,pandas.core.frame.DataFrame
,None
]) – PEtab measurement table. Deprecated, passpetab_problem
instead.petab_problem (
typing.Optional
[petab.problem.Problem
]) – PEtab problem.model_name (
typing.Optional
[str
]) – Name of the generated model. If model file name was provided, this defaults to the file name without extension, otherwise the SBML model ID will be used.model_output_dir (
typing.Union
[pathlib.Path
,str
,None
]) – Directory to write the model code to. Will be created if doesn’t exist. Defaults to current directory.verbose (
typing.Union
[bool
,int
,None
]) – Print/log extra information.allow_reinit_fixpar_initcond (
bool
) – Seeamici.de_export.ODEExporter
. Must be enabled if initial states are to be reset after preequilibration.validate (
bool
) – Whether to validate the PEtab problemnon_estimated_parameters_as_constants – Whether parameters marked as non-estimated in PEtab should be considered constant in AMICI. Setting this to
True
will reduce model size and simulation times. If sensitivities with respect to those parameters are required, this should be set toFalse
.output_parameter_defaults (
typing.Optional
[typing.Dict
[str
,float
]]) – Optional default parameter values for output parameters introduced in the PEtab observables table, in particular for placeholder parameters. Dictionary mapping parameter IDs to default values.discard_sbml_annotations (
bool
) – Discard information contained in AMICI SBML annotations (debug).kwargs – Additional keyword arguments to be passed to
amici.sbml_import.SbmlImporter.sbml2amici()
.
- Return type
- Returns
The created
amici.sbml_import.SbmlImporter
instance.
- amici.petab_import.import_model_sbml(sbml_model=None, condition_table=None, observable_table=None, measurement_table=None, petab_problem=None, model_name=None, model_output_dir=None, verbose=True, allow_reinit_fixpar_initcond=True, validate=True, non_estimated_parameters_as_constants=True, output_parameter_defaults=None, discard_sbml_annotations=False, **kwargs)[source]
Create AMICI model from PEtab problem
- Parameters
sbml_model (
typing.Union
[str
,pathlib.Path
,libsbml.Model
,None
]) – PEtab SBML model or SBML file name. Deprecated, passpetab_problem
instead.condition_table (
typing.Union
[str
,pathlib.Path
,pandas.core.frame.DataFrame
,None
]) – PEtab condition table. If provided, parameters from there will be turned into AMICI constant parameters (i.e. parameters w.r.t. which no sensitivities will be computed). Deprecated, passpetab_problem
instead.observable_table (
typing.Union
[str
,pathlib.Path
,pandas.core.frame.DataFrame
,None
]) – PEtab observable table. Deprecated, passpetab_problem
instead.measurement_table (
typing.Union
[str
,pathlib.Path
,pandas.core.frame.DataFrame
,None
]) – PEtab measurement table. Deprecated, passpetab_problem
instead.petab_problem (
typing.Optional
[petab.problem.Problem
]) – PEtab problem.model_name (
typing.Optional
[str
]) – Name of the generated model. If model file name was provided, this defaults to the file name without extension, otherwise the SBML model ID will be used.model_output_dir (
typing.Union
[pathlib.Path
,str
,None
]) – Directory to write the model code to. Will be created if doesn’t exist. Defaults to current directory.verbose (
typing.Union
[bool
,int
,None
]) – Print/log extra information.allow_reinit_fixpar_initcond (
bool
) – Seeamici.de_export.ODEExporter
. Must be enabled if initial states are to be reset after preequilibration.validate (
bool
) – Whether to validate the PEtab problemnon_estimated_parameters_as_constants – Whether parameters marked as non-estimated in PEtab should be considered constant in AMICI. Setting this to
True
will reduce model size and simulation times. If sensitivities with respect to those parameters are required, this should be set toFalse
.output_parameter_defaults (
typing.Optional
[typing.Dict
[str
,float
]]) – Optional default parameter values for output parameters introduced in the PEtab observables table, in particular for placeholder parameters. Dictionary mapping parameter IDs to default values.discard_sbml_annotations (
bool
) – Discard information contained in AMICI SBML annotations (debug).kwargs – Additional keyword arguments to be passed to
amici.sbml_import.SbmlImporter.sbml2amici()
.
- Return type
- Returns
The created
amici.sbml_import.SbmlImporter
instance.
- amici.petab_import.import_petab_problem(petab_problem, model_output_dir=None, model_name=None, force_compile=False, non_estimated_parameters_as_constants=True, **kwargs)[source]
Import model from petab problem.
- Parameters
petab_problem (
petab.problem.Problem
) – A petab problem containing all relevant information on the model.model_output_dir (
typing.Union
[pathlib.Path
,str
,None
]) – Directory to write the model code to. Will be created if doesn’t exist. Defaults to current directory.model_name (
typing.Optional
[str
]) – Name of the generated model. If model file name was provided, this defaults to the file name without extension, otherwise the model ID will be used.force_compile (
bool
) – Whether to compile the model even if the target folder is not empty, or the model exists already.non_estimated_parameters_as_constants – Whether parameters marked as non-estimated in PEtab should be considered constant in AMICI. Setting this to
True
will reduce model size and simulation times. If sensitivities with respect to those parameters are required, this should be set toFalse
.kwargs – Additional keyword arguments to be passed to
amici.sbml_import.SbmlImporter.sbml2amici()
.
- Return type
- Returns
The imported model.
- amici.petab_import.petab_noise_distributions_to_amici(observable_df)[source]
Map from the petab to the amici format of noise distribution identifiers.
- Parameters
observable_df (
pandas.core.frame.DataFrame
) – PEtab observable table- Return type
- Returns
Dictionary of observable_id => AMICI noise-distributions
- amici.petab_import.petab_scale_to_amici_scale(scale_str)[source]
Convert PEtab parameter scaling string to AMICI scaling integer
- Return type
- amici.petab_import.species_to_parameters(species_ids, sbml_model)[source]
Turn a SBML species into parameters and replace species references inside the model instance.
- Parameters
species_ids (
typing.List
[str
]) – List of SBML species ID to convert to parameters with the same ID as the replaced species.sbml_model (
libsbml.Model
) – SBML model to modify
- Return type
- Returns
List of IDs of species which have been converted to parameters