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Merge pull request #765 from nelsonag/mgxs_plot
Extend the data plotter capability to support MGXS Libraries
This commit is contained in:
Paul Romano
GitHub
commit
171fbeda87
3 changed files with 1250 additions and 591 deletions
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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@ -7,11 +7,22 @@ import numpy as np
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import openmc.checkvalue as cv
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import openmc.data
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# Supported keywords for xs plotting
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# Supported keywords for continuous-energy cross section plotting
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PLOT_TYPES = ['total', 'scatter', 'elastic', 'inelastic', 'fission',
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'absorption', 'capture', 'nu-fission', 'nu-scatter', 'unity',
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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', 'scatter', 'fission',
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'kappa-fission', 'nu-fission', 'prompt-nu-fission',
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'deleyed-nu-fission', 'chi', 'chi-prompt', 'chi-delayed',
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'inverse-velocity', 'beta', 'decay rate', 'unity']
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# Create a dictionary which can be used to convert PLOT_TYPES_MGXS to the
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# openmc.XSdata attribute name needed to access the data
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_PLOT_MGXS_ATTR = {line: line.replace(' ', '_').replace('-', '_')
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for line in PLOT_TYPES_MGXS}
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_PLOT_MGXS_ATTR['scatter'] = 'scatter_matrix'
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# Special MT values
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UNITY_MT = -1
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XI_MT = -2
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@ -47,10 +58,16 @@ PLOT_TYPES_OP = {'total': (np.add,),
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PLOT_TYPES_LINEAR = {'nu-fission / fission', 'nu-scatter / scatter',
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'nu-fission / absorption', 'fission / absorption'}
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# Minimum and maximum energies for plotting (units of eV)
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_MIN_E = 1.e-5
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_MAX_E = 20.e6
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def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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sab_name=None, cross_sections=None, enrichment=None, **kwargs):
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"""Creates a figure of continuous-energy cross sections for this item
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sab_name=None, ce_cross_sections=None, mg_cross_sections=None,
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enrichment=None, plot_CE=True, orders=None, divisor_orders=None,
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**kwargs):
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"""Creates a figure of continuous-energy cross sections for this item.
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Parameters
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----------
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@ -73,12 +90,24 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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sab_name : str, optional
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Name of S(a,b) library to apply to MT=2 data when applicable; only used
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for items which are instances of openmc.Element or openmc.Nuclide
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cross_sections : str, optional
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ce_cross_sections : str, optional
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Location of cross_sections.xml file. Default is None.
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mg_cross_sections : str, optional
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Location of MGXS HDF5 Library file. Default is None.
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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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plot_CE : bool, optional
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Denotes whether or not continuous-energy will be plotted. Defaults to
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plotting the continuous-energy data.
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orders : Iterable of Integral, optional
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The scattering order or delayed group index to use for the
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corresponding entry in types. Defaults to the 0th order for scattering
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and the total delayed neutron data. This only applies to plots of
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multi-group data.
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divisor_orders : Iterable of Integral, optional
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Same as orders, but for divisor_types
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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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@ -95,37 +124,60 @@ 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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cv.check_type("plot_CE", plot_CE, bool)
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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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elif isinstance(this, openmc.Macroscopic):
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data_type = 'macroscopic'
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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 plot_CE:
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# Calculate for the CE cross sections
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E, data = calculate_cexs(this, types, temperature, sab_name,
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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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Ediv, data_div = calculate_cexs(this, divisor_types, temperature,
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sab_name, ce_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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data_new = np.zeros((len(types), len(E)))
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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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data_new = np.zeros((len(types), len(E)))
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for line in range(len(types)):
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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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else:
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# Calculate for MG cross sections
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E, data = calculate_mgxs(this, types, orders, temperature,
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mg_cross_sections, ce_cross_sections,
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enrichment)
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for line in range(len(types)):
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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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if divisor_types:
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cv.check_length('divisor types', divisor_types, len(types))
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Ediv, data_div = calculate_mgxs(this, divisor_types,
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divisor_orders, temperature,
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mg_cross_sections,
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ce_cross_sections, enrichment)
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# Perform the division
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for line in range(len(types)):
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data[line, :] /= data_div[line, :]
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if divisor_types[line] != 'unity':
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types[line] += ' / ' + divisor_types[line]
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# Generate the plot
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if axis is None:
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@ -140,6 +192,7 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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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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data[i, :] = np.nan_to_num(data[i, :])
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@ -147,33 +200,38 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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plot_func(E, data[i, :], label=types[i])
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ax.set_xlabel('Energy [eV]')
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if plot_CE:
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ax.set_xlim(_MIN_E, _MAX_E)
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else:
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ax.set_xlim(E[-1], E[0])
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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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elif data_type == 'material' or data_type == 'macroscopic':
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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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elif data_type == 'material' or data_type == 'macroscopic':
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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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# Set to the most likely expected range
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ax.set_xlim((1.E-5, 20.E6))
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if this.name is not None:
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ax.set_title('Cross Section for ' + this.name)
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if len(types) > 1:
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ax.set_title('Cross Sections for ' + this.name)
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else:
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ax.set_title('Cross Section for ' + this.name)
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return fig
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def calculate_xs(this, types, temperature=294., sab_name=None,
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cross_sections=None, enrichment=None):
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"""Calculates continuous-energy cross sections of a requested type
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def calculate_cexs(this, types, temperature=294., sab_name=None,
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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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@ -212,8 +270,8 @@ def calculate_xs(this, types, temperature=294., sab_name=None,
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cv.check_type('enrichment', enrichment, Real)
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if isinstance(this, openmc.Nuclide):
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energy_grid, xs = _calculate_xs_nuclide(this, types, temperature,
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sab_name, cross_sections)
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energy_grid, xs = _calculate_cexs_nuclide(this, types, temperature,
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sab_name, cross_sections)
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# Convert xs (Iterable of Callable) to a grid of cross section values
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# calculated on @ the points in energy_grid for consistency with the
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# element and material functions.
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@ -221,21 +279,21 @@ def calculate_xs(this, types, temperature=294., sab_name=None,
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for line in range(len(types)):
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data[line, :] = xs[line](energy_grid)
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elif isinstance(this, openmc.Element):
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energy_grid, data = _calculate_xs_elem_mat(this, types, temperature,
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cross_sections, sab_name,
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enrichment)
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energy_grid, data = _calculate_cexs_elem_mat(this, types, temperature,
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cross_sections, sab_name,
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enrichment)
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elif isinstance(this, openmc.Material):
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energy_grid, data = _calculate_xs_elem_mat(this, types, temperature,
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cross_sections)
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energy_grid, data = _calculate_cexs_elem_mat(this, types, temperature,
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cross_sections)
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else:
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raise TypeError("Invalid type")
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return energy_grid, data
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def _calculate_xs_nuclide(this, types, temperature=294., sab_name=None,
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cross_sections=None):
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"""Calculates continuous-energy cross sections of a requested type
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def _calculate_cexs_nuclide(this, types, temperature=294., sab_name=None,
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cross_sections=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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@ -426,9 +484,10 @@ def _calculate_xs_nuclide(this, types, temperature=294., sab_name=None,
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return energy_grid, xs
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def _calculate_xs_elem_mat(this, types, temperature=294., cross_sections=None,
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sab_name=None, enrichment=None):
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"""Calculates continuous-energy cross sections of a requested type
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def _calculate_cexs_elem_mat(this, types, temperature=294.,
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cross_sections=None, sab_name=None,
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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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@ -515,7 +574,8 @@ def _calculate_xs_elem_mat(this, types, temperature=294., cross_sections=None,
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name = nuclide[0]
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nuc = nuclide[1]
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sab_tab = sabs[name]
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temp_E, temp_xs = calculate_xs(nuc, types, T, sab_tab, cross_sections)
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temp_E, temp_xs = calculate_cexs(nuc, types, T, sab_tab,
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cross_sections)
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E.append(temp_E)
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# Since the energy grids are different, store the cross sections as
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# a tabulated function so they can be calculated on any grid needed.
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@ -540,3 +600,305 @@ def _calculate_xs_elem_mat(this, types, temperature=294., cross_sections=None,
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xs[name][line](energy_grid))
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return energy_grid, data
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def calculate_mgxs(this, types, orders=None, temperature=294.,
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cross_sections=None, ce_cross_sections=None,
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enrichment=None):
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"""Calculates continuous-energy cross sections of a requested type.
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If the data for the nuclide or macroscopic object in the library is
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represented as angle-dependent data then this method will return the
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geometric average cross section over all angles.
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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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Object to source data from
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types : Iterable of values of PLOT_TYPES_MGXS
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The type of cross sections to calculate
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orders : Iterable of Integral, optional
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The scattering order or delayed group index to use for the
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corresponding entry in types. Defaults to the 0th order for scattering
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and the total delayed neutron data.
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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
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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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energy_grid : numpy.ndarray
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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 energy_grid
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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_iterable_type('types', types, string_types)
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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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mgxs = _calculate_mgxs_nuc_macro(this, types, library, orders,
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temperature)
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elif isinstance(this, (openmc.Element, openmc.Material)):
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mgxs = _calculate_mgxs_elem_mat(this, types, library, orders,
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temperature, ce_cross_sections,
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enrichment)
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else:
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raise TypeError("Invalid type")
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# Convert the data to the format needed
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data = np.zeros((len(types), 2 * library.energy_groups.num_groups))
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energy_grid = np.zeros(2 * library.energy_groups.num_groups)
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for g in range(library.energy_groups.num_groups):
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energy_grid[g * 2: g * 2 + 2] = \
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library.energy_groups.group_edges[g: g + 2]
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# Ensure the energy will show on a log-axis by replacing 0s with a
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# sufficiently small number
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energy_grid[0] = max(energy_grid[0], _MIN_E)
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for line in range(len(types)):
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for g in range(library.energy_groups.num_groups):
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data[g * 2: g * 2 + 2] = mgxs[line, g]
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return energy_grid[::-1], data
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def _calculate_mgxs_nuc_macro(this, types, library, orders=None,
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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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If the data for the nuclide or macroscopic object in the library is
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represented as angle-dependent data then this method will return the
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geometric average cross section over all angles.
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Parameters
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----------
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this : openmc.Nuclide or 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
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library : openmc.MGXSLibrary
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MGXS Library containing the data of interest
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orders : Iterable of Integral, optional
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The scattering order or delayed group index to use for the
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corresponding entry in types. Defaults to the 0th order for scattering
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and the total delayed neutron data.
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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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Returns
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-------
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data : numpy.ndarray
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Cross sections calculated at the energy grid described by energy_grid
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"""
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# Check the parameters and grab order/delayed groups
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if orders:
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cv.check_iterable_type('orders', orders, Integral,
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min_depth=len(types), max_depth=len(types))
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else:
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orders = [None] * len(types)
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for i, line in enumerate(types):
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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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if orders[i]:
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cv.check_greater_than("order value", orders[i], 0, equality=True)
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xsdata = library.get_by_name(this.name)
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if xsdata is not None:
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# Obtain the nearest temperature
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t = np.abs(xsdata.temperatures - temperature).argmin()
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# Get the data
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data = np.zeros((len(types), library.energy_groups.num_groups))
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for i, line in enumerate(types):
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if 'fission' in line and not xsdata.fissionable:
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continue
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elif line == 'unity':
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data[i, :] = 1.
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else:
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# Now we have to get the cross section data and properly
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# treat it depending on the requested type.
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# First get the data in a generic fashion
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temp_data = getattr(xsdata, _PLOT_MGXS_ATTR[line])[t]
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shape = temp_data.shape[:]
|
||||
# If we have angular data, then want the geometric
|
||||
# average over all provided angles. Since the angles are
|
||||
# equi-distant, un-weighted averaging will suffice
|
||||
if xsdata.representation == 'angle':
|
||||
temp_data = np.mean(temp_data, axis=(0, 1))
|
||||
|
||||
# Now we can look at the shape of the data to identify how
|
||||
# it should be modified to produce an array of values
|
||||
# with groups.
|
||||
if shape in (xsdata.xs_shapes["[G']"],
|
||||
xsdata.xs_shapes["[G]"]):
|
||||
# Then the data is already an array vs groups so copy
|
||||
# and move along
|
||||
data[i, :] = temp_data
|
||||
elif shape == xsdata.xs_shapes["[G][G']"]:
|
||||
# Sum the data over outgoing groups to create our array vs
|
||||
# groups
|
||||
data[i, :] = np.sum(temp_data, axis=1)
|
||||
elif shape == xsdata.xs_shapes["[DG]"]:
|
||||
# Then we have a constant vs groups with a value for each
|
||||
# delayed group. The user-provided value of orders tells us
|
||||
# which delayed group we want. If none are provided, then
|
||||
# we sum all the delayed groups together.
|
||||
if orders[i]:
|
||||
if orders[i] < len(shape[0]):
|
||||
data[i, :] = temp_data[orders[i]]
|
||||
else:
|
||||
data[i, :] = np.sum(temp_data[:])
|
||||
elif shape in (xsdata.xs_shapes["[G'][DG]"],
|
||||
xsdata.xs_shapes["[G][DG]"]):
|
||||
# Then we have an array vs groups with values for each
|
||||
# delayed group. The user-provided value of orders tells us
|
||||
# which delayed group we want. If none are provided, then
|
||||
# we sum all the delayed groups together.
|
||||
if orders[i]:
|
||||
if orders[i] < len(shape[1]):
|
||||
data[i, :] = temp_data[:, orders[i]]
|
||||
else:
|
||||
data[i, :] = np.sum(temp_data[:, :], axis=1)
|
||||
elif shape == xsdata.xs_shapes["[G][G'][DG]"]:
|
||||
# Then we have a delayed group matrix. We will first
|
||||
# remove the outgoing group dependency
|
||||
temp_data = np.sum(temp_data, axis=1)
|
||||
# And then proceed in exactly the same manner as the
|
||||
# "[G'][DG]" of "[G][DG]" shapes in the previous block.
|
||||
if orders[i]:
|
||||
if orders[i] < len(shape[1]):
|
||||
data[i, :] = temp_data[:, orders[i]]
|
||||
else:
|
||||
data[i, :] = np.sum(temp_data[:, :], axis=1)
|
||||
elif shape == xsdata.xs_shapes["[G][G'][Order]"]:
|
||||
# This is a scattering matrix with angular data
|
||||
# First remove the outgoing group dependence
|
||||
temp_data = np.sum(temp_data, axis=1)
|
||||
# The user either provided a specific order or we resort
|
||||
# to the default 0th order
|
||||
if orders[i]:
|
||||
order = orders[i]
|
||||
else:
|
||||
order = 0
|
||||
# If the order is available, store the data for that order
|
||||
# if it is not available, then the expansion coefficient
|
||||
# is zero and thus we already have the correct value.
|
||||
if order < shape[1]:
|
||||
data[i, :] = temp_data[:, order]
|
||||
else:
|
||||
raise ValueError("{} not present in provided MGXS "
|
||||
"library".format(this.name))
|
||||
|
||||
return data
|
||||
|
||||
|
||||
def _calculate_mgxs_elem_mat(this, types, library, orders=None,
|
||||
temperature=294., ce_cross_sections=None,
|
||||
enrichment=None):
|
||||
"""Determines the multi-group cross sections of an element or material
|
||||
object.
|
||||
|
||||
If the data for the nuclide or macroscopic object in the library is
|
||||
represented as angle-dependent data then this method will return the
|
||||
geometric average cross section over all angles.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
this : openmc.Element or 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
|
||||
orders : Iterable of Integral, optional
|
||||
The scattering order or delayed group index to use for the
|
||||
corresponding entry in types. Defaults to the 0th order for scattering
|
||||
and the total delayed neutron data.
|
||||
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 calculated at the energy grid described by energy_grid
|
||||
|
||||
"""
|
||||
|
||||
if isinstance(this, openmc.Material):
|
||||
if this.temperature is not None:
|
||||
T = this.temperature
|
||||
else:
|
||||
T = temperature
|
||||
|
||||
# Check to see if we have nuclides/elements or a macrocopic object
|
||||
if this._macroscopic is not None:
|
||||
# We have macroscopics
|
||||
nuclides = {this._macroscopic: (this._macroscopic, this.density)}
|
||||
else:
|
||||
# Expand elements in to nuclides with atomic densities
|
||||
nuclides = this.get_nuclide_atom_densities()
|
||||
|
||||
# For ease of processing split out nuc and nuc_density
|
||||
nuc_fraction = [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_fraction = [nuclide[1] for nuclide in nuclides]
|
||||
|
||||
nuc_data = []
|
||||
for nuclide in nuclides.items():
|
||||
nuc_data.append(_calculate_mgxs_nuc_macro(nuclide[0], types, library,
|
||||
orders, 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_fraction[n] * nuc_data[n][line, :]
|
||||
|
||||
return data
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue