AMICI Python API
Modules
AMICI |
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Core C++ bindings This module encompasses the complete public C++ API of AMICI, which was exposed via swig. All functions listed here are directly accessible in the main amici package, i.e., amici.amici.ExpData is available as amici.ExpData. Usage of functions and classes from the base amici package is generally recommended as they often include convenience wrappers that avoid common pitfalls when accessing C++ types from python and implement some nonstandard type conversions. |
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SBML Import This module provides all necessary functionality to import a model specified in the Systems Biology Markup Language (SBML). |
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PySB Import This module provides all necessary functionality to import a model specified in the pysb.core.Model format. |
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PEtab Import Import a model in the petab (https://github.com/PEtab-dev/PEtab) format into AMICI. |
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PySB-PEtab Import Import a model in the PySB-adapted petab (https://github.com/PEtab-dev/PEtab) format into AMICI. |
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PEtab Objective Functionality related to running simulations or evaluating the objective function as defined by a PEtab problem |
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PEtab Simulate Functionality related to the use of AMICI for simulation with PEtab's Simulator class. |
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Miscellaneous functions related to model import, independent of any specific model format |
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C++ Export This module provides all necessary functionality specify an ODE model and generate executable C++ simulation code. The user generally won't have to directly call any function from this module as this will be done by amici.pysb_import.pysb2amici(), amici.sbml_import.SbmlImporter.sbml2amici() and amici.petab_import.import_model(). |
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Plotting Plotting related functions |
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Pandas Wrappers This module contains convenience wrappers that allow for easy interconversion between C++ objects from amici.amici and pandas DataFrames |
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Logging This module provides custom logging functionality for other amici modules |
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Finite Difference Check This module provides functions to automatically check correctness of amici computed sensitivities using finite difference approximations |
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Parameter mapping |