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84 lines
1.6 KiB
ReStructuredText
84 lines
1.6 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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example Jupyter_ notebooks provided. However, this assumes that you are already
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familiar with Python and common third-party packages such as NumPy_. If you have
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never programmed in Python before, there are many good tutorials available
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online. We recommend going through the modules from Codecademy_ and/or the
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`Scipy lectures`_. The full API documentation serves to provide more information
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on a given module or class.
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**Handling nuclear data:**
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.. toctree::
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:maxdepth: 1
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ace
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**Creating input files:**
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.. toctree::
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:maxdepth: 1
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cmfd
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element
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filter
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geometry
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material
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mesh
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nuclide
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opencg_compatible
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plots
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settings
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surface
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tallies
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trigger
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universe
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**Running OpenMC:**
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.. toctree::
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:maxdepth: 1
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executor
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**Post-processing:**
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.. toctree::
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:maxdepth: 1
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particle_restart
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statepoint
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summary
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tallies
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**Multi-Group Cross Section Generation**
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.. toctree::
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:maxdepth: 1
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mgxs
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energy_groups
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mgxs_library
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**Example Jupyter Notebooks:**
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.. toctree::
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:maxdepth: 1
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examples/post-processing
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examples/pandas-dataframes
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examples/tally-arithmetic
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examples/mgxs-part-i
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examples/mgxs-part-ii
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examples/mgxs-part-iii
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.. _Jupyter: https://jupyter.org/
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.. _NumPy: http://www.numpy.org/
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.. _Codecademy: https://www.codecademy.com/tracks/python
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.. _Scipy lectures: https://scipy-lectures.github.io/
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