diff --git a/docs/source/devguide/workflow.rst b/docs/source/devguide/workflow.rst index 3bad04caec..6b5ec63cb5 100644 --- a/docs/source/devguide/workflow.rst +++ b/docs/source/devguide/workflow.rst @@ -119,8 +119,8 @@ pip_. From the root directory of the OpenMC repository, run: pip install -e .[test] This installs the OpenMC Python package in `"editable" mode -`_ so -that 1) it can be imported from a Python interpreter and 2) any changes made are +`_ so that 1) +it can be imported from a Python interpreter and 2) any changes made are immediately reflected in the installed version (that is, you don't need to keep reinstalling it). While the same effect can be achieved using the :envvar:`PYTHONPATH` environment variable, this is generally discouraged as it diff --git a/docs/source/methods/cross_sections.rst b/docs/source/methods/cross_sections.rst index 2210dfa455..3396d7f251 100644 --- a/docs/source/methods/cross_sections.rst +++ b/docs/source/methods/cross_sections.rst @@ -187,9 +187,10 @@ are represented using a multi-group library format specific to the OpenMC code. The format is described in the :ref:`mgxs_lib_spec`. The data itself can be prepared via traditional paths or directly from a continuous-energy OpenMC calculation by use of the Python API as is shown in an `example notebook -<../examples/mg-mode-part-i.ipynb>`_. This multi-group library consists of -meta-data (such as the energy group structure) and multiple `xsdata` objects -which contains the required microscopic or macroscopic multi-group data. +`_. +This multi-group library consists of meta-data (such as the energy group +structure) and multiple `xsdata` objects which contains the required microscopic +or macroscopic multi-group data. At a minimum, the library must contain the absorption cross section (:math:`\sigma_{a,g}`) and a scattering matrix. If the problem is an eigenvalue @@ -275,6 +276,6 @@ or even isotropic scattering. .. _MCNP: https://mcnp.lanl.gov .. _Serpent: http://montecarlo.vtt.fi .. _NJOY: https://www.njoy21.io/NJOY21/ -.. _ENDF/B data: https://www.nndc.bnl.gov/endf/b8.0/ +.. _ENDF/B data: https://www.nndc.bnl.gov/endf-b8.0/ .. _Leppanen: https://doi.org/10.1016/j.anucene.2009.03.019 .. _algorithms: http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package diff --git a/docs/source/methods/neutron_physics.rst b/docs/source/methods/neutron_physics.rst index e9061eea1b..2e9f313d73 100644 --- a/docs/source/methods/neutron_physics.rst +++ b/docs/source/methods/neutron_physics.rst @@ -359,7 +359,7 @@ secondary energy and angle sampling. For a reaction with secondary products, it is necessary to determine the outgoing angle and energy of the products. For any reaction other than elastic and level inelastic scattering, the outgoing energy must be determined based on -tabulated or parameterized data. The `ENDF-6 Format `_ specifies a +tabulated or parameterized data. The `ENDF-6 Format` specifies a variety of ways that the secondary energy distribution can be represented. ENDF File 5 contains uncorrelated energy distribution whereas ENDF File 6 contains correlated energy-angle distributions. The ACE format specifies its own @@ -1416,8 +1416,7 @@ For incoherent elastic scattering, OpenMC has two methods for calculating the cosine of the angle of scattering. The first method uses the Debye-Waller integral, :math:`W'`, and the characteristic bound cross section as given directly in an ENDF-6 formatted file. In this case, the cosine of the angle of -scattering can be sampled by inverting equation 7.4 from the `ENDF-6 Format -Manual `_: +scattering can be sampled by inverting equation 7.4 from the `ENDF-6 Format`_: .. math:: :label: incoherent-elastic-mu-exact @@ -1752,7 +1751,7 @@ types. .. _PREPRO: https://www-nds.iaea.org/ndspub/endf/prepro/ -.. _endf102: https://www.oecd-nea.org/dbdata/data/manual-endf/endf102.pdf +.. _ENDF-6 Format: https://www.oecd-nea.org/dbdata/data/manual-endf/endf102.pdf .. _Monte Carlo Sampler: https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-09721-MS diff --git a/docs/source/methods/tallies.rst b/docs/source/methods/tallies.rst index dc3a63decf..1aae47d6ea 100644 --- a/docs/source/methods/tallies.rst +++ b/docs/source/methods/tallies.rst @@ -510,6 +510,6 @@ improve the estimate of the percentile. .. _Cauchy distribution: https://en.wikipedia.org/wiki/Cauchy_distribution -.. _unpublished rational approximation: https://web.archive.org/web/20150926021742/http://home.online.no/~pjacklam/notes/invnorm/ +.. _unpublished rational approximation: https://stackedboxes.org/2017/05/01/acklams-normal-quantile-function/ .. _MC21: http://www.osti.gov/bridge/servlets/purl/903083-HT5p1o/903083.pdf diff --git a/docs/source/usersguide/cross_sections.rst b/docs/source/usersguide/cross_sections.rst index 58bf1a2933..0cbb581bd4 100644 --- a/docs/source/usersguide/cross_sections.rst +++ b/docs/source/usersguide/cross_sections.rst @@ -124,7 +124,7 @@ OpenMC. .. hint:: The :class:`IncidentNeutron` class allows you to view/modify cross sections, secondary angle/energy distributions, probability tables, etc. For a more thorough overview of the capabilities of this class, - see the `example notebook <../examples/nuclear-data.ipynb>`__. + see the `example notebook `_. Manually Creating a Library from ENDF files ------------------------------------------- @@ -259,7 +259,7 @@ For an example of how to create a multi-group library, see the `example notebook .. _NNDC: https://www.nndc.bnl.gov/endf .. _MCNP: https://mcnp.lanl.gov .. _Serpent: http://montecarlo.vtt.fi -.. _ENDF/B: https://www.nndc.bnl.gov/endf/b7.1/acefiles.html +.. _ENDF/B: https://www.nndc.bnl.gov/endf-b7.1/acefiles.html .. _JEFF: https://www.oecd-nea.org/dbdata/jeff/jeff33/ .. _TENDL: https://tendl.web.psi.ch/tendl_2017/tendl2017.html .. _Seltzer and Berger: https://doi.org/10.1016/0092-640X(86)90014-8 diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index 68ecfce95c..2382609a48 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -478,10 +478,10 @@ OpenMC to verify that the model matches one's expectations. **Note:** DAGMC geometries used in OpenMC are currently required to be clean, meaning that all surfaces have been `imprinted and merged -`_ -successfully and that the model is `watertight -`_. Future -implementations of DAGMC geometry will support small volume overlaps and +`_ successfully +and that the model is `watertight +`_. +Future implementations of DAGMC geometry will support small volume overlaps and un-merged surfaces. Cell, Surface, and Material IDs diff --git a/docs/source/usersguide/install.rst b/docs/source/usersguide/install.rst index 7961b3e38b..fb721c7ce9 100644 --- a/docs/source/usersguide/install.rst +++ b/docs/source/usersguide/install.rst @@ -512,8 +512,9 @@ distributions. are used for several optional features in the API. `pandas `_ - Pandas is used to generate tally DataFrames as demonstrated in - an `example notebook <../examples/pandas-dataframes.ipynb>`_. + Pandas is used to generate tally DataFrames as demonstrated in an `example + notebook + `_. `h5py `_ h5py provides Python bindings to the HDF5 library. Since OpenMC outputs diff --git a/docs/source/usersguide/processing.rst b/docs/source/usersguide/processing.rst index e61ba33bf1..3103b7b7cf 100644 --- a/docs/source/usersguide/processing.rst +++ b/docs/source/usersguide/processing.rst @@ -34,30 +34,20 @@ as requested; it is used in many of the provided plotting utilities, OpenMC's regression test suite, and can be used in user-created scripts to carry out manipulations of the data. -An `example notebook <../examples/post-processing.ipynb>`_ demonstrates how to -extract data from a statepoint using the Python API. +An `example notebook`_ demonstrates how to extract data from a statepoint using +the Python API. Plotting in 2D -------------- -The `notebook example <../examples/post-processing.ipynb>`_ also demonstrates -how to plot a structured mesh tally in two dimensions using the Python API. One -can also use the :ref:`scripts_plot` script which provides an interactive GUI to -explore and plot structured mesh tallies for any scores and filter bins. +The `example notebook`_ also demonstrates how to plot a structured mesh tally in +two dimensions using the Python API. One can also use the :ref:`scripts_plot` +script which provides an interactive GUI to explore and plot structured mesh +tallies for any scores and filter bins. .. image:: ../_images/plotmeshtally.png :width: 400px -Getting Data into MATLAB ------------------------- - -There is currently no front-end utility to dump tally data to MATLAB files, but -the process is straightforward. First extract the data using the Python API via -``openmc.statepoint`` and then use the `Scipy MATLAB IO routines -`_ to save to a MAT -file. Note that all arrays that are accessible in a statepoint are already in -NumPy arrays that can be reshaped and dumped to MATLAB in one step. - .. _usersguide_track: ---------------------------- @@ -101,5 +91,6 @@ For eigenvalue problems, OpenMC will store information on the fission source sites in the statepoint file by default. For each source site, the weight, position, sampled direction, and sampled energy are stored. To extract this data from a statepoint file, the ``openmc.statepoint`` module can be used. An -`example notebook <../examples/post-processing.ipynb>`_ demontrates how to -analyze and plot source information. +`example notebook`_ demontrates how to analyze and plot source information. + +.. _example notebook: https://nbviewer.jupyter.org/github/openmc-dev/openmc-notebooks/blob/main/post-processing.ipynb