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Added IPython notebook with post-processing examples.
Since the notebook shows an example of how to histogram relative errors from a statepoint, we don't really need the openmc-statepoint-histogram utility, so it's been deleted. The user's guide section on post processing has been updated as well.
This commit is contained in:
Paul Romano
parent
c57b2c22d9
commit
42032210fb
9 changed files with 1207 additions and 318 deletions
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@ -16,5 +16,4 @@ as debugging.
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styleguide
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workflow
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xml-parsing
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voxel
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docbuild
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@ -1,52 +0,0 @@
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.. _devguide_voxel:
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=====================================
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Voxel Plot Binary File Specifications
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=====================================
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The current revision of the voxel plot binary file is 1.
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**integer(4) n_voxels_x**
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Number of voxels in the x direction
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**integer(4) n_voxels_y**
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Number of voxels in the y direction
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**integer(4) n_voxels_z**
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Number of voxels in the z direction
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**real(8) width_voxel_x**
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Width of voxels in the x direction
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**real(8) width_voxel_y**
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Width of voxels in the y direction
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**real(8) width_voxel_z**
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Width of voxels in the z direction
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**real(8) lower_left_x**
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Lower left x point of the voxel grid
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**real(8) lower_left_y**
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Lower left y point of the voxel grid
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**real(8) lower_left_z**
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Lower left z point of the voxel grid
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*do x = 1, n_voxels_x*
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*do y = 1, n_voxels_y*
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*do z = 1, n_voxels_z*
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**integer(4) id**
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Cell or material id number at this voxel center. Set to -1 when
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cell not_found.
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1121
docs/source/pythonapi/examples/post-processing.ipynb
Normal file
1121
docs/source/pythonapi/examples/post-processing.ipynb
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File diff suppressed because one or more lines are too long
13
docs/source/pythonapi/examples/post-processing.rst
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docs/source/pythonapi/examples/post-processing.rst
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@ -0,0 +1,13 @@
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.. _notebook_post_processing:
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===============
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Post Processing
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===============
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.. only:: html
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.. notebook:: post-processing.ipynb
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.. only:: latex
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IPython notebooks must be viewed in the online HTML documentation.
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@ -62,6 +62,7 @@ on a given module or class.
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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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@ -1,8 +1,8 @@
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.. _usersguide_voxel:
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=================
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Voxel File Format
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=================
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======================
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Voxel Plot File Format
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======================
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**/filetype** (*char[]*)
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@ -6,31 +6,33 @@ Data Processing and Visualization
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This section is intended to explain in detail the recommended procedures for
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carrying out common post-processing tasks with OpenMC. While several utilities
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of varying complexity are provided to help automate the process, in many cases
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it will be extremely beneficial to do some coding in Python to quickly obtain
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results. In these cases, and for many of the provided utilities, it is necessary
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for your Python installation to contain:
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of varying complexity are provided to help automate the process, the most
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powerful capabilities for post-processing derive from use of the :ref:`Python
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API <pythonapi>`. Both the provided scripts and the Python API rely on a number
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third-party Python packages, including:
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* [1]_ `Numpy <http://www.numpy.org/>`_
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* [1]_ `Scipy <http://www.scipy.org/>`_
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* [2]_ `h5py <http://code.google.com/p/h5py/>`_
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* [3]_ `Matplotlib <http://matplotlib.org/>`_
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* [1]_ `NumPy <http://www.numpy.org/>`_
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* [2]_ `h5py <http://www.h5py.org>`_
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* [3]_ `pandas <http://pandas.pydata.org>`_
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* [3]_ `matplotlib <http://matplotlib.org/>`_
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* [3]_ `Silomesh <https://github.com/nhorelik/silomesh>`_
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* [3]_ `VTK <http://www.vtk.org/>`_
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* [3]_ `lxml <http://lxml.de>`_
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Most of these are easily obtainable in Ubuntu through the package manager, or
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are easily installed with distutils.
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Most of these are can easily be installed with `pip <https://pip.pypa.io>`_
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or alternatively obtaining through a package manager.
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.. [1] Required for tally data extraction from statepoints with statepoint.py
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.. [2] Required only if reading HDF5 statepoint files.
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.. [3] Optional for plotting utilities
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.. [1] Required for most post-processing tasks
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.. [2] Required for reading HDF5 output files
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.. [3] Not used directly by the Python API, but are optional dependencies for a
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number of scripts.
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----------------------
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Geometry Visualization
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----------------------
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Geometry plotting is carried out by creating a plots.xml, specifying plots, and
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running OpenMC with the -plot or -p command-line option (See
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running OpenMC with the --plot or -p command-line option (See
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:ref:`usersguide_plotting`).
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Plotting in 2D
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@ -128,27 +130,26 @@ capabilities of 3D voxel plots.
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Voxel plots are built the same way 2D slice plots are, by determining the cell
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or material id of a particle at the center of each voxel. In this example, the
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space covered is the cube between the points (-5,-5,-5) and (5,5,5), with voxel
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centers 10/500 = 0.02 cm apart. The binary VOXEL files that are produced do not
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centers 10/500 = 0.02 cm apart. The HDF5 voxel files that are produced do not
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specify any color - instead containing only material or cell ids (material id
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in this example) - and thus the ``background``, ``col_spec``, and ``mask``
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elements are not used. If no cell is found at a voxel center, an id of -1 is
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stored.
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The binary VOXEL files output by OpenMC can not be viewed directly by any
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existing viewers. In order to view them, they must be converted into a standard
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mesh format that can be viewed in ParaView, Visit, etc. This typically will
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compress the size of the file significantly. The provided utility voxel.py
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accomplishes this for SILO:
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The voxel plot data is written to an HDF5 file. The voxel file can subsequently
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be converted into a standard mesh format that can be viewed in ParaView, Visit,
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etc. This typically will compress the size of the file significantly. The
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provided utility openmc-voxel-to-silovtk accomplishes this for SILO:
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.. code-block:: sh
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<openmc_root>/src/utils/voxel.py myplot.voxel -o output.silo
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openmc-voxel-to-silovtk myplot.voxel -o output.silo
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and VTK file formats:
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.. code-block:: sh
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<openmc_root>/src/utils/voxel.py myplot.voxel --vtk -o output.vti
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openmc-voxel-to-silovtk myplot.voxel --vtk -o output.vti
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To use this utility you need either
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@ -156,11 +157,10 @@ To use this utility you need either
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or
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* `VTK <http://www.vtk.org/>`_ with python bindings - On Ubuntu, these are
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easily obtained with ``sudo apt-get install python-vtk``
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* `VTK <http://www.vtk.org/>`_ with python bindings. On debian derivatives,
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these are easily obtained with ``sudo apt-get install python-vtk``
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Users can process the binary into any other format if desired by following the
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example of voxel.py. For the binary file structure, see :ref:`devguide_voxel`.
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For the HDF5 file structure, see :ref:`usersguide_voxel`.
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Once processed into a standard 3D file format, colors and masks can be defined
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using the stored id numbers to better explore the geometry. The process for
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@ -183,150 +183,38 @@ doing this will depend on the 3D viewer, but should be straightforward.
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Tally Visualization
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-------------------
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Tally results are saved in both a text file (tallies.out) as well as a binary
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Tally results are saved in both a text file (tallies.out) as well as an HDF5
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statepoint file. While the tallies.out file may be fine for simple tallies, in
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many cases the user requires more information about the tally or the run, or
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has to deal with a large number of result values (e.g. for mesh tallies). In
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these cases, extracting data from the statepoint file via Python scripting is
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the preferred method of data analysis and visualization.
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many cases the user requires more information about the tally or the run, or has
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to deal with a large number of result values (e.g. for mesh tallies). In these
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cases, extracting data from the statepoint file via the Python API is the
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preferred method of data analysis and visualization.
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Data Extraction
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---------------
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A great deal of information is available in statepoint files (See
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:ref:`usersguide_statepoint`), most of which is easily extracted by the provided
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utility statepoint.py. This utility provides a Python class to load statepoints
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and extract data - it is used in many of the provided plotting utilities, and
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can be used in user-created scripts to carry out manipulations of the data. To
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read tallies using this utility, make sure statepoint.py is in your PYTHONPATH,
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and then import the class, instantiate it, and call read_results:
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:ref:`usersguide_statepoint`), all of which is accessible through the Python
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API. The ``openmc.statepoint`` module (see :ref:`pythonapi_statepoint`) provides
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a class to load statepoints and access data as requested; it is used in many of
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the provided plotting utilities, OpenMC's regression test suite, and can be used
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in user-created scripts to carry out manipulations of the data.
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.. code-block:: python
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from statepoint import StatePoint
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sp = StatePoint('statepoint.100.binary')
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sp.read_results()
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At this point the user can extract entire scores from tallies into a data
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dictionary containing numpy arrays:
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.. code-block:: python
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tallyid = 1
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score = 'flux'
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data = sp.extract_results(tallyid, score)
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means = data['means']
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print data.keys()
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The results from this function contain all filter bins (all mesh points, all
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energy groups, etc.), which can be reshaped with the bin ordering also contained
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in the output dictionary. This is the best choice of output for easily
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integrating ranges of data.
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Alternatively the user can extract specific values for a single score/filter
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combination:
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.. code-block:: python
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tallyid = 1
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score = 'flux'
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filters = [('mesh', (1, 1, 5)), ('energyin', 0)]
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value, error = sp.get_value(tallyid, filters, score)
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In the future more documentation may become available here for statepoint.py and
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the data extraction functions of StatePoint objects. However, for now it is up
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to the user to explore the classes in statepoint.py to discover what data is
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available in StatePoint objects (we highly recommend interactively exploring
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with `IPython <http://ipython.org/>`_). Many examples can be found by looking
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through the other utilities that use statepoint.py, and a few common
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visualization tasks will be described here in the following sections.
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An :ref:`example IPython notebook <notebook_post_processing>` demonstrates how
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to extract data from a statepoint using the Python API.
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Plotting in 2D
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--------------
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The :ref:`IPython notebook example <notebook_post_processing>` also demonstrates
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how to plot a mesh tally in two dimensions using the Python API. Note, however,
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that there is also a script distributed with OpenMC, ``openmc-plot-mesh-tally``,
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that interactive GUI to explore and plot mesh tallies for any scores and filter
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bins.
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.. image:: ../_images/plotmeshtally.png
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:height: 200px
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For simple viewing of 2D slices of a mesh plot, the utility plot_mesh_tally.py
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is provided. This utility provides an interactive GUI to explore and plot
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mesh tallies for any scores and filter bins. It requires statepoint.py.
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.. image:: ../_images/fluxplot.png
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:height: 200px
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Alternatively, the user can write their own Python script to manipulate the data
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appropriately. Consider a run where the first tally contains a 105x105x20 mesh
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over a small core, with a flux score and two energyin filter bins. To explicitly
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extract the data and create a plot with gnuplot, the following script can be
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used. The script operates in several steps for clarity, and is not necessarily
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the most efficient way to extract data from large mesh tallies. This creates the
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two heatmaps in the previous figure.
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.. code-block:: python
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#!/usr/bin/env python
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import os
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import statepoint
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# load and parse the statepoint file
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sp = statepoint.StatePoint('statepoint.300.binary')
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sp.read_results()
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tallyid = 0 # This is tally 1
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score = 0 # This corresponds to flux (see tally.scores)
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# get mesh dimensions
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meshid = sp.tallies[tallyid].filters['mesh'].bins[0]
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for i,m in enumerate(sp.meshes):
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if m.id == meshid:
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mesh = m
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break
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nx,ny,nz = mesh.dimension
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# loop through mesh and extract values to python dictionaries
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thermal = {}
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fast = {}
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for x in range(1,nx+1):
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for y in range(1,ny+1):
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for z in range(1,nz+1):
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val,err = sp.get_value(tallyid,
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[('mesh',(x,y,z)),('energyin',0)],
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score)
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thermal[(x,y,z)] = val
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val,err = sp.get_value(tallyid,
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[('mesh',(x,y,z)),('energyin',1)],
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score)
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fast[(x,y,z)] = val
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# sum up the axial values and write datafile for gnuplot
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with open('meshdata.dat','w') as fh:
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for x in range(1,nx+1):
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for y in range(1,ny+1):
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thermalval = 0.
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fastval = 0.
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for z in range(1,nz+1):
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thermalval += thermal[(x,y,z)]
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fastval += fast[(x,y,z)]
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fh.write("{} {} {} {}\n".format(x,y,thermalval,fastval))
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# write gnuplot file
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with open('tmp.gnuplot','w') as fh:
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fh.write(r"""set terminal png size 1000 400
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set output 'fluxplot.png'
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set nokey
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set autoscale fix
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set multiplot layout 1,2 title "Pin Mesh Flux Tally"
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set title "Thermal"
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plot 'meshdata.dat' using 1:2:3 with image
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set title "Fast"
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plot 'meshdata.dat' using 1:2:4 with image
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""")
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# make plot
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os.system("gnuplot < tmp.gnuplot")
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Plotting in 3D
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--------------
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@ -334,22 +222,23 @@ Plotting in 3D
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:height: 200px
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As with 3D plots of the geometry, meshtally data needs to be put into a standard
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format for viewing. The utility statepoint_3d.py is provided to accomplish this
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for both VTK and SILO. By default statepoint_3d.py processes a statepoint into a
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3D file with all mesh tallies and filter/score combinations,
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format for viewing. The utility ``openmc-statepoint-3d`` is provided to
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accomplish this for both VTK and SILO. By default ``openmc-statepoint-3d``
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processes a statepoint into a 3D file with all mesh tallies and filter/score
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combinations,
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.. code-block:: sh
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<openmc_root>/src/utils/statepoint_3d.py <statepoint_file> -o output.silo
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<openmc_root>/src/utils/statepoint_3d.py <statepoint_file> --vtk -o output.vtm
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openmc-statepoint-3d <statepoint_file> -o output.silo
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openmc-statepoint-3d <statepoint_file> --vtk -o output.vtm
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but it also provides several command-line options to selectively process only
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certain data arrays in order to keep file sizes down.
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.. code-block:: sh
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statepoint_3d.py <statepoint_file> --tallies 2,4 --scores 4.1,4.3 -o output.silo
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statepoint_3d.py <statepoint_file> --filters 2.energyin.1 --vtk -o output.vtm
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openmc-statepoint-3d <statepoint_file> --tallies 2,4 --scores 4.1,4.3 -o output.silo
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openmc-statepoint-3d <statepoint_file> --filters 2.energyin.1 --vtk -o output.vtm
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All available options for specifying a subset of tallies, scores, and filters
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can be listed with the ``--list`` or ``-l`` command line options.
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@ -426,13 +315,11 @@ Getting Data into MATLAB
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------------------------
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There is currently no front-end utility to dump tally data to MATLAB files, but
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the process is straightforward. First extract the data using a custom Python
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script with statepoint.py, put the data into appropriately-shaped numpy arrays,
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and then use the `Scipy MATLAB IO routines
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the process is straightforward. First extract the data using the Python API via
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``openmc.statepoint`` and then use the `Scipy MATLAB IO routines
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<http://docs.scipy.org/doc/scipy/reference/tutorial/io.html>`_ to save to a MAT
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file. Note that the data contained in the output from
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``StatePoint.extract_result`` is already in a Numpy array that can be reshaped
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and dumped to MATLAB in one step.
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file. Note that all arrays that are accessible in a statepoint are already in
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NumPy arrays that can be reshaped and dumped to MATLAB in one step.
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----------------------------
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Particle Track Visualization
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@ -463,15 +350,15 @@ particle numbers, respectively. For example, to output the tracks for particles
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</track>
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After running OpenMC, the directory should contain a file of the form
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"track_(batch #)_(generation #)_(particle #).(binary or h5)" for each particle
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tracked. These track files can be converted into VTK poly data files with the
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"track.py" utility. The usage of track.py is of the form "track.py [-o OUT] IN"
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where OUT is the optional output filename and IN is one or more filenames
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describing track files. The default output name is "track.pvtp". A common
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usage of track.py is "track.py track*.binary" which will use the data from all
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binary track files in the directory to write a "track.pvtp" VTK output file.
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The .pvtp file can then be read and plotted by 3d visualization programs such as
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ParaView.
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"track_(batch #)_(generation #)_(particle #).h5" for each particle tracked.
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||||
These track files can be converted into VTK poly data files with the
|
||||
``openmc-track-to-vtk`` utility. The usage of ``openmc-track-to-vtk`` is of the
|
||||
form "openmc-track-to-vtk [-o OUT] IN" where OUT is the optional output filename
|
||||
and IN is one or more filenames describing track files. The default output name
|
||||
is "track.pvtp". A common usage of track.py is "openmc-track-to-vtk track*.h5"
|
||||
which will use the data from all binary track files in the directory to write a
|
||||
"track.pvtp" VTK output file. The .pvtp file can then be read and plotted by 3d
|
||||
visualization programs such as ParaView.
|
||||
|
||||
----------------------
|
||||
Source Site Processing
|
||||
|
|
@ -480,43 +367,6 @@ Source Site Processing
|
|||
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 statepoint.py Python module can be used. Below is an
|
||||
example of an interactive ipython session using the statepoint.py Python module:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
In [1]: import statepoint
|
||||
|
||||
In [2]: sp = statepoint.StatePoint('statepoint.100.h5')
|
||||
|
||||
In [3]: sp.read_source()
|
||||
|
||||
In [4]: len(sp.source)
|
||||
Out[4]: 1000
|
||||
|
||||
In [5]: sp.source[0:10]
|
||||
Out[5]:
|
||||
[<SourceSite: xyz=[ 2.21980946 -8.92686048 87.93720485] at E=0.932923263566>,
|
||||
<SourceSite: xyz=[ 2.21980946 -8.92686048 87.93720485] at E=0.349240220512>,
|
||||
<SourceSite: xyz=[-31.21542213 -30.26762771 72.10845757] at E=3.75843584486>,
|
||||
<SourceSite: xyz=[-31.21542213 -30.26762771 72.10845757] at E=0.80550137267>,
|
||||
<SourceSite: xyz=[ 0.18805099 -69.13376508 103.67726838] at E=1.67922461097>,
|
||||
<SourceSite: xyz=[ 0.18805099 -69.13376508 103.67726838] at E=1.16304110199>,
|
||||
<SourceSite: xyz=[ -50.42189115 -9.96571672 123.34077905] at E=0.710937974074>,
|
||||
<SourceSite: xyz=[ -32.80427668 -15.49316628 125.26301151] at E=1.61907104162>,
|
||||
<SourceSite: xyz=[ 53.20376026 -15.38643708 120.58071044] at E=3.33962024907>,
|
||||
<SourceSite: xyz=[ 53.20376026 -15.38643708 120.58071044] at E=1.90185680329>]
|
||||
|
||||
In [6]: site = sp.source[0]
|
||||
|
||||
In [7]: site.weight
|
||||
Out[7]: 1.0
|
||||
|
||||
In [8]: site.xyz
|
||||
Out[8]: array([ 2.21980946, -8.92686048, 87.93720485])
|
||||
|
||||
In [9]: site.uvw
|
||||
Out[9]: array([ 0.06740523, 0.50612814, 0.85982024])
|
||||
|
||||
In [10]: site.E
|
||||
Out[10]: 0.93292326356564159
|
||||
from a statepoint file, the ``openmc.statepoint`` module can be used. An
|
||||
:ref:`example IPython notebook <notebook_post_processing>` demontrates how to
|
||||
analyze and plot source information.
|
||||
|
|
|
|||
|
|
@ -1,43 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
from __future__ import print_function
|
||||
from sys import argv
|
||||
from math import sqrt
|
||||
|
||||
import numpy as np
|
||||
import scipy.stats
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from openmc.statepoint import StatePoint
|
||||
|
||||
# Get filename
|
||||
filename = argv[1]
|
||||
|
||||
# Create StatePoint object
|
||||
sp = StatePoint(filename)
|
||||
sp.read_results()
|
||||
sp.compute_ci()
|
||||
|
||||
# Check if tallies are present
|
||||
if not sp.tallies_present:
|
||||
raise Exception("No tally data in state point!")
|
||||
|
||||
# Loop over all tallies
|
||||
for i, t in sp.tallies.items():
|
||||
# Determine relative error and fraction of bins with less than 1% half-width
|
||||
# of CI
|
||||
n_bins = t.mean.size
|
||||
relative_error = t.std_dev[t.mean > 0.] / t.mean[t.mean > 0.]
|
||||
fraction = float(sum(relative_error < 0.01))/n_bins
|
||||
|
||||
# Display results
|
||||
print("Tally " + str(i))
|
||||
print(" Fraction under 1% = {0}".format(fraction))
|
||||
print(" Min relative error = {0}".format(min(relative_error)))
|
||||
print(" Max relative error = {0}".format(max(relative_error)))
|
||||
print(" Non-scoring bins = {0}".format(
|
||||
1.0 - float(relative_error.size)/n_bins))
|
||||
|
||||
# Plot histogram
|
||||
plt.hist(relative_error, 100)
|
||||
plt.show()
|
||||
2
setup.py
2
setup.py
|
|
@ -32,7 +32,7 @@ kwargs = {'name': 'openmc',
|
|||
if have_setuptools:
|
||||
kwargs.update({
|
||||
# Required dependencies
|
||||
'install_requires': ['numpy', 'scipy', 'h5py', 'matplotlib'],
|
||||
'install_requires': ['numpy', 'h5py', 'matplotlib'],
|
||||
|
||||
# Optional dependencies
|
||||
'extras_require': {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue