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54 lines
2 KiB
ReStructuredText
.. _pythonapi:
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==========
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Python API
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==========
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OpenMC includes a rich Python API that enables programmatic pre- and
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post-processing. The easiest way to begin using the API is to take a look at the
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examples_. This assumes that you are already familiar with Python and common
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third-party packages such as `NumPy <https://numpy.org/>`_. If you have never
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used Python before, the prospect of learning a new code *and* a programming
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language might sound daunting. However, you should keep in mind that there are
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many substantial benefits to using the Python API, including:
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- The ability to define dimensions using variables.
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- Availability of standard-library modules for working with files.
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- An entire ecosystem of third-party packages for scientific computing.
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- Automated multi-group cross section generation (:mod:`openmc.mgxs`)
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- A fully-featured nuclear data interface (:mod:`openmc.data`)
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- Depletion capability (:mod:`openmc.deplete`)
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- Convenience functions (e.g., a function returning a hexagonal region)
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- Ability to plot individual universes as geometry is being created
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- A :math:`k_\text{eff}` search function (:func:`openmc.search_for_keff`)
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- Random sphere packing for generating TRISO particle locations
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(:func:`openmc.model.pack_spheres`)
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- Ability to create materials based on natural elements or uranium enrichment
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For those new to Python, there are many good tutorials available online. We
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recommend going through the modules from `Codecademy
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<https://www.codecademy.com/learn/learn-python-3>`_ and/or the `Scipy lectures
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<https://scipy-lectures.org/>`_.
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The full API documentation serves to provide more information on a given module
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or class.
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.. tip:: Users are strongly encouraged to use the Python API to generate input
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files and analyze results.
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.. rubric:: Modules
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.. toctree::
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:maxdepth: 1
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base
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model
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examples
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deplete
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mgxs
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stats
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data
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capi
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openmoc
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.. _examples: https://github.com/openmc-dev/openmc/wiki/Example-Jupyter-Notebooks
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