mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-27 05:35:49 -04:00
Enabled plotting of MGXS data
This commit is contained in:
Adam Nelson
commit
e358afd00b
2 changed files with 367 additions and 114 deletions
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@ -1,4 +1,5 @@
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from numbers import Real, Integral
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import os
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from six import string_types
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import numpy as np
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@ -2125,13 +2126,15 @@ class MGXSLibrary(object):
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file.close()
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@classmethod
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def from_hdf5(cls, filename):
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def from_hdf5(cls, filename=None):
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"""Generate an MGXS Library from an HDF5 group or file
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Parameters
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----------
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filename : str
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Name of HDF5 file containing MGXS data.
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filename : str, optional
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Name of HDF5 file containing MGXS data. Default is None.
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If not provided, the value of the OPENMC_MG_CROSS_SECTIONS
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environmental variable will be used
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Returns
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-------
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@ -2140,6 +2143,16 @@ class MGXSLibrary(object):
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"""
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# If filename is None, get the cross sections from the
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# OPENMC_CROSS_SECTIONS environment variable
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if filename is None:
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filename = os.environ.get('OPENMC_MG_CROSS_SECTIONS')
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# Check to make sure there was an environmental variable.
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if filename is None:
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raise ValueError("Either path or OPENMC_MG_CROSS_SECTIONS "
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"environmental variable must be set")
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check_type('filename', filename, str)
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file = h5py.File(filename, 'r')
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@ -11,9 +11,8 @@ PLOT_TYPES = ['total', 'scatter', 'elastic', 'inelastic', 'fission',
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'slowing-down power', 'damage']
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# Supported keywoards for multi-group cross section plotting
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PLOT_TYPES_MGXS = ['total', 'absorption', 'fission', 'kappa-fission',
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'chi', 'chi-prompt', 'chi-delayed', 'nu-fission',
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'prompt-nu-fission', 'delayed-nu-fission', 'beta',
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'decay rate', 'inverse-velocity']
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'chi', 'chi-prompt', 'nu-fission', 'prompt-nu-fission',
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'inverse-velocity', 'unity']
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# Special MT values
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UNITY_MT = -1
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@ -95,7 +94,7 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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Parameters
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----------
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this : openmc.Element, openmc.Nuclide, openmc.Material, or openmc.Macroscopic
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this : {openmc.Element, openmc.Nuclide, openmc.Material}
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Object to source data from
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types : Iterable of values of PLOT_TYPES
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The type of cross sections to include in the plot.
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@ -138,6 +137,143 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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from matplotlib import pyplot as plt
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if isinstance(this, openmc.Nuclide):
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data_type = 'nuclide'
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elif isinstance(this, openmc.Element):
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data_type = 'element'
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elif isinstance(this, openmc.Material):
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data_type = 'material'
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else:
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raise TypeError("Invalid type for plotting")
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E, data = calculate_xs(this, types, temperature, sab_name, cross_sections,
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enrichment)
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if divisor_types:
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cv.check_length('divisor types', divisor_types, len(types),
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len(types))
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Ediv, data_div = calculate_xs(this, divisor_types, temperature,
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sab_name, cross_sections, enrichment)
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# Create a new union grid, interpolate data and data_div on to that
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# grid, and then do the actual division
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Enum = E[:]
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E = np.union1d(Enum, Ediv)
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if data_type == 'nuclide':
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data_new = []
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else:
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data_new = np.zeros((len(types), len(E)))
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for line in range(len(types)):
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if data_type == 'nuclide':
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data_new.append(openmc.data.Combination([data[line],
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data_div[line]],
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[np.divide]))
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else:
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data_new[line, :] = \
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np.divide(np.interp(E, Enum, data[line, :]),
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np.interp(E, Ediv, data_div[line, :]))
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if divisor_types[line] != 'unity':
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types[line] = types[line] + ' / ' + divisor_types[line]
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data = data_new
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# Generate the plot
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if axis is None:
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fig = plt.figure(**kwargs)
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ax = fig.add_subplot(111)
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else:
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fig = None
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ax = axis
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# Set to loglog or semilogx depending on if we are plotting a data
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# type which we expect to vary linearly
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if set(types).issubset(PLOT_TYPES_LINEAR):
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plot_func = ax.semilogx
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else:
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plot_func = ax.loglog
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# Plot the data
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for i in range(len(data)):
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if data_type == 'nuclide':
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to_plot = data[i](E)
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else:
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to_plot = data[i, :]
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if np.sum(to_plot) > 0.:
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plot_func(E, to_plot, label=types[i])
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ax.set_xlabel('Energy [eV]')
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if divisor_types:
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if data_type == 'nuclide':
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ylabel = 'Nuclidic Microscopic Data'
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elif data_type == 'element':
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ylabel = 'Elemental Microscopic Data'
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elif data_type == 'material':
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ylabel = 'Macroscopic Data'
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else:
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if data_type == 'nuclide':
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ylabel = 'Microscopic Cross Section [b]'
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elif data_type == 'element':
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ylabel = 'Elemental Cross Section [b]'
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elif data_type == 'material':
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ylabel = 'Macroscopic Cross Section [1/cm]'
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ax.set_ylabel(ylabel)
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ax.legend(loc='best')
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ax.set_xlim(energy_range)
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if this.name is not None:
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title = 'Cross Section for ' + this.name
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ax.set_title(title)
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return fig
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def plot_mgxs(this, types, divisor_types=None, temperature=294., axis=None,
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energy_range=(1.E-5, 20.E6), cross_sections=None,
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ce_cross_sections=None, enrichment=None, **kwargs):
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"""Creates a figure of multi-group cross sections for this item
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Parameters
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----------
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this : {openmc.Element, openmc.Nuclide, openmc.Material, openmc.Macroscopic}
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Object to source data from
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types : Iterable of values of PLOT_TYPES
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The type of cross sections to include in the plot.
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divisor_types : Iterable of values of PLOT_TYPES, optional
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Cross section types which will divide those produced by types
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before plotting. A type of 'unity' can be used to effectively not
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divide some types.
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temperature : float, optional
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Temperature in Kelvin to plot. If not specified, a default
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temperature of 294K will be plotted. Note that the nearest
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temperature in the library for each nuclide will be used as opposed
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to using any interpolation.
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axis : matplotlib.axes, optional
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A previously generated axis to use for plotting. If not specified,
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a new axis and figure will be generated.
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energy_range : tuple of floats
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Energy range (in eV) to plot the cross section within
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cross_sections : str, optional
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Location of MGXS HDF5 Library file. Default is None.
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cross_sections : str, optional
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Location of continuous-energy cross_sections.xml file. Default is None.
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This is used only for expanding an openmc.Element object passed as this
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enrichment : float, optional
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Enrichment for U235 in weight percent. For example, input 4.95 for
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4.95 weight percent enriched U. Default is None. This is only used for
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items which are instances of openmc.Element
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**kwargs
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All keyword arguments are passed to
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:func:`matplotlib.pyplot.figure`.
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Returns
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-------
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fig : matplotlib.figure.Figure
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If axis is None, then a Matplotlib Figure of the generated
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cross section will be returned. Otherwise, a value of
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None will be returned as the figure and axes have already been
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generated.
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"""
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from matplotlib import pyplot as plt
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if isinstance(this, openmc.Nuclide):
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data_type = 'nuclide'
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elif isinstance(this, openmc.Element):
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@ -149,82 +285,74 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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else:
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raise TypeError("Invalid type for plotting")
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# Check the type (CE or MG data) and validity of cross_sections
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cv.check_type("cross_sections", cross_sections, str)
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if cross_sections.endswith('.xml'):
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mgxs = False
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elif cross_sections.endswith('.h5'):
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mgxs = True
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E, data = calculate_mgxs(this, types, temperature, cross_sections,
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ce_cross_sections, enrichment)
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if divisor_types:
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cv.check_length('divisor types', divisor_types, len(types),
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len(types))
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Ediv, data_div = calculate_mgxs(this, divisor_types, temperature,
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cross_sections, ce_cross_sections,
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enrichment)
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# Perform the division
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for line in range(len(types)):
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data[line, :] = np.divide(data[line, :], data_div[line, :])
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if divisor_types[line] != 'unity':
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types[line] = types[line] + ' / ' + divisor_types[line]
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# Modify the data such that the it plots the group values as distinct
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# horizontal lines in an efficient manner
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E_plot = []
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data_plot = []
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for line in range(len(types)):
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for g in range(len(E) - 1):
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if line == 0:
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E_plot.append(E[g])
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E_plot.append(E[g + 1])
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E_plot.append(None)
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data_plot.append(data[line, g])
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data_plot.append(data[line, g])
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data_plot.append(None)
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# Generate the plot
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if axis is None:
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fig = plt.figure(**kwargs)
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ax = fig.add_subplot(111)
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else:
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raise ValueError("cross_sections is neither an XML or HDF5 file")
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if not mgxs:
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E, data = calculate_xs(this, types, temperature, sab_name,
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cross_sections, enrichment)
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if divisor_types:
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cv.check_length('divisor types', divisor_types, len(types),
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len(types))
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Ediv, data_div = calculate_xs(this, divisor_types, temperature,
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sab_name, cross_sections, enrichment)
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# Create a new union grid, interpolate data and data_div on to that
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# grid, and then do the actual division
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Enum = E[:]
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E = np.union1d(Enum, Ediv)
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if data_type == 'nuclide':
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data_new = []
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else:
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data_new = np.zeros((len(types), len(E)))
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for line in range(len(types)):
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if data_type == 'nuclide':
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data_new.append(openmc.data.Combination([data[line],
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data_div[line]],
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[np.divide]))
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else:
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data_new[line, :] = \
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np.divide(np.interp(E, Enum, data[line, :]),
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np.interp(E, Ediv, data_div[line, :]))
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if divisor_types[line] != 'unity':
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types[line] = types[line] + ' / ' + divisor_types[line]
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data = data_new
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# Generate the plot
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if axis is None:
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fig = plt.figure(**kwargs)
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ax = fig.add_subplot(111)
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else:
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fig = None
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ax = axis
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# Set to loglog or semilogx depending on if we are plotting a data
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# type which we expect to vary linearly
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if set(types).issubset(PLOT_TYPES_LINEAR):
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plot_func = ax.semilogx
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else:
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plot_func = ax.loglog
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# Plot the data
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for i in range(len(data)):
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if data_type == 'nuclide':
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to_plot = data[i](E)
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else:
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to_plot = data[i, :]
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if np.sum(to_plot) > 0.:
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plot_func(E, to_plot, label=types[i])
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ax.set_xlabel('Energy [eV]')
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if divisor_types:
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ax.set_ylabel('Data')
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else:
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ax.set_ylabel('Cross Section [b]')
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ax.legend(loc='best')
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ax.set_xlim(energy_range)
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if this.name is not None:
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title = 'Cross Section for ' + this.name
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ax.set_title(title)
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fig = None
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ax = axis
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# Set to loglog or semilogx depending on if we are plotting a data
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# type which we expect to vary linearly
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if set(types).issubset(PLOT_TYPES_LINEAR):
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plot_func = ax.semilogx
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else:
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pass
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plot_func = ax.loglog
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# Plot the data
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for i in range(len(data_plot)):
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plot_func(E_plot, data_plot[i, :], label=types[i])
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ax.set_xlabel('Energy [eV]')
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if divisor_types:
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if data_type == 'nuclide':
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ylabel = 'Nuclidic Microscopic Data'
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elif data_type == 'element':
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ylabel = 'Elemental Microscopic Data'
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elif data_type == 'material':
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ylabel = 'Macroscopic Data'
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else:
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if data_type == 'nuclide':
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ylabel = 'Microscopic Cross Section [b]'
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elif data_type == 'element':
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ylabel = 'Elemental Cross Section [b]'
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elif data_type == 'material':
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ylabel = 'Macroscopic Cross Section [1/cm]'
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ax.set_ylabel(ylabel)
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ax.legend(loc='best')
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ax.set_xlim(energy_range)
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if this.name is not None:
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title = 'Cross Section for ' + this.name
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ax.set_title(title)
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return fig
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@ -626,18 +754,71 @@ def _calculate_xs_material(this, types, temperature=294., cross_sections=None):
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return energy_grid, data
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def _calculate_mgxs_nuclide(this, types, cross_sections, temperature=294.):
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"""Determines the multi-group cross sections of a requested type
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def calculate_mgxs(this, types, temperature=294., cross_sections=None,
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ce_cross_sections=None, enrichment=None):
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"""Calculates continuous-energy cross sections of a requested type
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Parameters
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----------
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this : openmc.Nuclide
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Nuclide object to source data from
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this : openmc.Element, openmc.Nuclide, or openmc.Material
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Object to source data from
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types : Iterable of values of PLOT_TYPES
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The type of cross sections to calculate
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temperature : float, optional
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Temperature in Kelvin to plot. If not specified, a default
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temperature of 294K will be plotted. Note that the nearest
|
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temperature in the library for each nuclide will be used as opposed
|
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to using any interpolation.
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cross_sections : str, optional
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Location of MGXS HDF5 Library file. Default is None.
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ce_cross_sections : str, optional
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Location of continuous-energy cross_sections.xml file. Default is None.
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This is used only for expanding an openmc.Element object passed as this
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enrichment : float, optional
|
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Enrichment for U235 in weight percent. For example, input 4.95 for
|
||||
4.95 weight percent enriched U. Default is None
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(natural composition).
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Returns
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-------
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numpy.array
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Energy group structure in units of eV
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data : numpy.ndarray
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Multi-Group Cross sections
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"""
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# Check types
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cv.check_type('temperature', temperature, Real)
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if enrichment:
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cv.check_type('enrichment', enrichment, Real)
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cv.check_type("cross_sections", cross_sections, str)
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library = openmc.MGXSLibrary.from_hdf5(cross_sections)
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if isinstance(this, (openmc.Nuclide, openmc.Macroscopic)):
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data = _calculate_mgxs_nuc_macro(this, types, library, temperature)
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elif isinstance(this, (openmc.Element, openmc.Material)):
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data = _calculate_mgxs_elem_mat(this, types, library, temperature,
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ce_cross_sections, enrichment)
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else:
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raise TypeError("Invalid type")
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return library.energy_groups.group_structure, data
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def _calculate_mgxs_nuc_macro(this, types, library, temperature=294.):
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"""Determines the multi-group cross sections of a nuclide or macroscopic
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object
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Parameters
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----------
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this : {openmc.Nuclide, openmc.Macroscopic}
|
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Object to source data from
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types : Iterable of str
|
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The type of cross sections to calculate; values can either be those
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in openmc.PLOT_TYPES_MGXS or integers which correspond to reaction
|
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channel (MT) numbers.
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cross_sections : openmc.MGXSLibrary
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in openmc.PLOT_TYPES_MGXS
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library : openmc.MGXSLibrary
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MGXS Library containing the data of interest
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temperature : float, optional
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Temperature in Kelvin to plot. If not specified, a default
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|
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@ -647,11 +828,8 @@ def _calculate_mgxs_nuclide(this, types, cross_sections, temperature=294.):
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Returns
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-------
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energy_grid : numpy.array
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Energies at which cross sections are calculated, in units of eV
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data : numpy.ndarray
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Cross sections calculated at the energy grid described by
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energy_grid
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Cross sections values of the requested types
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"""
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@ -660,17 +838,11 @@ def _calculate_mgxs_nuclide(this, types, cross_sections, temperature=294.):
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cv.check_type("line", line, str)
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cv.check_value("line", line, PLOT_TYPES_MGXS)
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cv.check_type("cross_sections", cross_sections, openmc.MGXSLibrary)
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xsdata = cross_sections[this.name]
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xsdata = library[this.name]
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|
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if xsdata is not None:
|
||||
# Get group structure
|
||||
group_structure = cross_sections.energy_groups.group_structure
|
||||
G = cross_sections.energy_groups.num_groups
|
||||
|
||||
# Obtain the nearest temperature
|
||||
data_Ts = cross_sections.temperatures
|
||||
data_Ts = library.temperatures
|
||||
for t in range(len(data_Ts)):
|
||||
# Take off the "K" and convert to a float
|
||||
data_Ts[t] = float(data_Ts[t][:-1])
|
||||
|
|
@ -682,22 +854,90 @@ def _calculate_mgxs_nuclide(this, types, cross_sections, temperature=294.):
|
|||
t = closest_t
|
||||
|
||||
# Get the data
|
||||
energy_grid = []
|
||||
xs = []
|
||||
data = np.zeros((len(types), library.energy_groups.num_groups))
|
||||
for line in types:
|
||||
energy_grid.append([])
|
||||
xs.append([])
|
||||
attr = line.replace(' ', '_').replace('-', '_')
|
||||
values = getattr(xsdata, attr)[t]
|
||||
for g_in in range(G):
|
||||
energy_grid[-1].append(group_structure[g_in])
|
||||
energy_grid[-1].append(group_structure[g_in + 1])
|
||||
energy_grid[-1].append(None)
|
||||
xs[-1].append(values[g_in])
|
||||
xs[-1].append(values[g_in + 1])
|
||||
xs[-1].append(values[None])
|
||||
if line == 'unity':
|
||||
data[line, :] = 1.
|
||||
else:
|
||||
attr = line.replace(' ', '_').replace('-', '_')
|
||||
data[line, :] = getattr(xsdata, attr)[t]
|
||||
else:
|
||||
raise ValueError("{} not present in provided MGXS "
|
||||
"library".format(this.name))
|
||||
|
||||
return energy_grid, xs
|
||||
return data
|
||||
|
||||
|
||||
def _calculate_mgxs_elem_mat(this, types, library, temperature=294.,
|
||||
ce_cross_sections=None, enrichment=None):
|
||||
"""Determines the multi-group cross sections of an element or material
|
||||
object
|
||||
|
||||
Parameters
|
||||
----------
|
||||
this : {openmc.Element, openmc.Material}
|
||||
Object to source data from
|
||||
types : Iterable of str
|
||||
The type of cross sections to calculate; values can either be those
|
||||
in openmc.PLOT_TYPES_MGXS
|
||||
library : openmc.MGXSLibrary
|
||||
MGXS Library containing the data of interest
|
||||
temperature : float, optional
|
||||
Temperature in Kelvin to plot. If not specified, a default
|
||||
temperature of 294K will be plotted. Note that the nearest
|
||||
temperature in the library for each nuclide will be used as opposed
|
||||
to using any interpolation.
|
||||
ce_cross_sections : str, optional
|
||||
Location of continuous-energy cross_sections.xml file. Default is None.
|
||||
This is used only for expanding the elements
|
||||
enrichment : float, optional
|
||||
Enrichment for U235 in weight percent. For example, input 4.95 for
|
||||
4.95 weight percent enriched U. Default is None
|
||||
(natural composition).
|
||||
|
||||
Returns
|
||||
-------
|
||||
data : numpy.ndarray
|
||||
Cross sections values of the requested types
|
||||
|
||||
"""
|
||||
|
||||
# Check the parameters
|
||||
for line in types:
|
||||
cv.check_type("line", line, str)
|
||||
cv.check_value("line", line, PLOT_TYPES_MGXS)
|
||||
|
||||
if isinstance(this, openmc.Material):
|
||||
if this.temperature is not None:
|
||||
T = this.temperature
|
||||
else:
|
||||
T = temperature
|
||||
# Expand elements in to nuclides with atomic densities
|
||||
nuclides = this.get_nuclide_atom_densities(ce_cross_sections)
|
||||
|
||||
# For ease of processing split out nuc and nuc_density
|
||||
nuc_multiplier = [nuclide[1][1] for nuclide in nuclides.items()]
|
||||
else:
|
||||
T = temperature
|
||||
# Expand elements in to nuclides with atomic densities
|
||||
nuclides = this.expand(100., 'ao', enrichment=enrichment,
|
||||
cross_sections=ce_cross_sections)
|
||||
|
||||
# For ease of processing split out nuc and nuc_fractions
|
||||
nuc_multiplier = [nuclide[1] for nuclide in nuclides]
|
||||
|
||||
nuc_data = []
|
||||
for nuclide in nuclides:
|
||||
nuc_data.append(_calculate_mgxs_nuc_macro(nuclide[0], types, library,
|
||||
T))
|
||||
|
||||
# Combine across the nuclides
|
||||
data = np.zeros((len(types), library.energy_groups.num_groups))
|
||||
for line in range(len(types)):
|
||||
if types[line] == 'unity':
|
||||
data[line, :] = 1.
|
||||
else:
|
||||
for n in range(len(nuclides)):
|
||||
data[line, :] += nuc_multiplier[n] * nuc_data[n][line, :]
|
||||
|
||||
return data
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue