amici.petab_import¶
PEtab Import¶
Import a model in the petab
(https://github.com/PEtab-dev/PEtab) format
into AMICI.
Functions Summary
|
Convert constant species in the SBML model to constant parameters. |
|
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. |
|
Command line interface to import a model in the PEtab (https://github.com/PEtab-dev/PEtab/) format into AMICI. |
Parse command line arguments |
|
|
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. |
Functions
-
amici.petab_import.
constant_species_to_parameters
(sbml_model)[source]¶ Convert constant species in the SBML model to constant parameters.
This can be used e.g. for setting up models with condition-specific constant species for PEtab, since there it is not possible to specify constant species in the condition table.
- Parameters
sbml_model (
libsbml.Model
) – SBML Model- Return type
- Returns
List of IDs of SBML species that have been turned into constants
-
amici.petab_import.
get_fixed_parameters
(sbml_model, condition_df=None, const_species_to_parameters=False)[source]¶ Determine, set and return fixed model parameters.
Parameters specified in condition_df are turned into constants. Only global SBML parameters are considered. Local parameters are ignored.
- Parameters
condition_df (
typing.Optional
[pandas.core.frame.DataFrame
]) – PEtab condition table. If provided, the respective parameters will be turned into AMICI constant parameters.sbml_model (
libsbml.Model
) – libsbml.Model instanceconst_species_to_parameters (
bool
) – If True, species which are marked constant within the SBML model will be turned into constant parameters within the given sbml_model.
- 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, condition_table=None, observable_table=None, measurement_table=None, model_name=None, model_output_dir=None, verbose=True, allow_reinit_fixpar_initcond=True, **kwargs)¶ Create AMICI model from PEtab problem
- Parameters
sbml_model (
typing.Union
[str
,libsbml.Model
]) – PEtab SBML model or SBML file name.condition_table (
typing.Union
[str
,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).observable_table (
typing.Union
[str
,pandas.core.frame.DataFrame
,None
]) – PEtab observable table.measurement_table (
typing.Union
[str
,pandas.core.frame.DataFrame
,None
]) – PEtab measurement table.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.Optional
[str
]) – 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.ode_export.ODEExporter
. Must be enabled if initial states are to be reset after preequilibration.kwargs – Additional keyword arguments to be passed to
amici.sbml_import.SbmlImporter.sbml2amici()
.
- Return type
-
amici.petab_import.
import_model_sbml
(sbml_model, condition_table=None, observable_table=None, measurement_table=None, model_name=None, model_output_dir=None, verbose=True, allow_reinit_fixpar_initcond=True, **kwargs)[source]¶ Create AMICI model from PEtab problem
- Parameters
sbml_model (
typing.Union
[str
,libsbml.Model
]) – PEtab SBML model or SBML file name.condition_table (
typing.Union
[str
,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).observable_table (
typing.Union
[str
,pandas.core.frame.DataFrame
,None
]) – PEtab observable table.measurement_table (
typing.Union
[str
,pandas.core.frame.DataFrame
,None
]) – PEtab measurement table.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.Optional
[str
]) – 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.ode_export.ODEExporter
. Must be enabled if initial states are to be reset after preequilibration.kwargs – Additional keyword arguments to be passed to
amici.sbml_import.SbmlImporter.sbml2amici()
.
- Return type
-
amici.petab_import.
import_petab_problem
(petab_problem, model_output_dir=None, model_name=None, force_compile=False, **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.Optional
[str
]) – 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 SBML 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.kwargs – Additional keyword arguments to be passed to
amici.sbml_import.SbmlImporter.sbml2amici()
.
- Return type
- Returns
The imported model.
-
amici.petab_import.
main
()[source]¶ Command line interface to import a model in the PEtab (https://github.com/PEtab-dev/PEtab/) format into AMICI.
-
amici.petab_import.
parse_cli_args
()[source]¶ Parse command line arguments
- Returns
Parsed CLI arguments from
argparse
.
-
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