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https://github.com/openmc-dev/openmc.git
synced 2026-07-26 13:15:39 -04:00
Some major simplifications: combined plot_xs and plot_mgxs so now the same interface works for both, and added support in a very generic way for all the different types of data one would expect to plot for MGXS plots (i.e., all the types in the library itself including choices of certain orders/delayed groups)
This commit is contained in:
Adam Nelson
parent
39dcd86e54
commit
f76ee0867c
1 changed files with 161 additions and 197 deletions
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@ -13,11 +13,14 @@ PLOT_TYPES = ['total', 'scatter', 'elastic', 'inelastic', 'fission',
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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', 'chi', 'chi-prompt', 'nu-fission',
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'prompt-nu-fission', 'inverse-velocity', 'unity']
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# Add on values for scattering moments
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PLOT_TYPES_MGXS += ['scatter-' + str(i)
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for i in range(0, openmc.mgxs.MAX_LEGENDRE + 1)]
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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_ATTR = {line: line.replace(' ', '_').replace('-', '_')
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for line in PLOT_TYPES_MGXS}
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_PLOT_ATTR['scatter'] = 'scatter_matrix'
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# Special MT values
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UNITY_MT = -1
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@ -56,7 +59,9 @@ PLOT_TYPES_LINEAR = {'nu-fission / fission', 'nu-scatter / scatter',
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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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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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@ -80,138 +85,24 @@ 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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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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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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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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# 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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data[i, :] = np.nan_to_num(data[i, :])
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if np.sum(data[i, :]) > 0.:
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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 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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# 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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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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mg_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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plot_CE : bool
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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
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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
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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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@ -228,6 +119,8 @@ def plot_mgxs(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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@ -239,21 +132,49 @@ def plot_mgxs(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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E, data = calculate_mgxs(this, types, temperature, cross_sections,
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ce_cross_sections, 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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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_mgxs(this, divisor_types, temperature,
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cross_sections, ce_cross_sections,
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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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# 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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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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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,
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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, :] = 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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# Generate the plot
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if axis is None:
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@ -268,9 +189,12 @@ def plot_mgxs(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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plot_func(E, data[i], label=types[i])
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data[i, :] = np.nan_to_num(data[i, :])
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if np.sum(data[i, :]) > 0.:
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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 divisor_types:
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@ -289,16 +213,14 @@ def plot_mgxs(this, types, divisor_types=None, temperature=294., axis=None,
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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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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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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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@ -338,8 +260,8 @@ 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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@ -347,20 +269,20 @@ 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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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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@ -548,8 +470,9 @@ 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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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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@ -637,7 +560,8 @@ 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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@ -664,8 +588,9 @@ def _calculate_xs_elem_mat(this, types, temperature=294., cross_sections=None,
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return energy_grid, data
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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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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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Parameters
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@ -674,6 +599,10 @@ def calculate_mgxs(this, types, temperature=294., cross_sections=None,
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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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orders : Iterable of Integral
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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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@ -702,15 +631,18 @@ def calculate_mgxs(this, types, temperature=294., cross_sections=None,
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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, temperature)
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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, temperature,
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ce_cross_sections, enrichment)
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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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@ -727,7 +659,8 @@ def calculate_mgxs(this, types, temperature=294., cross_sections=None,
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return np.flipud(energy_grid), data
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def _calculate_mgxs_nuc_macro(this, types, library, temperature=294.):
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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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@ -740,6 +673,10 @@ def _calculate_mgxs_nuc_macro(this, types, library, temperature=294.):
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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
|
||||
orders : Iterable of Integral
|
||||
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
|
||||
|
|
@ -753,10 +690,17 @@ def _calculate_mgxs_nuc_macro(this, types, library, temperature=294.):
|
|||
|
||||
"""
|
||||
|
||||
# Check the parameters
|
||||
for line in types:
|
||||
# Check the parameters and grab order/delayed groups
|
||||
if orders:
|
||||
cv.check_iterable_type('orders', orders, Integral,
|
||||
min_depth=len(types), max_depth=len(types))
|
||||
else:
|
||||
orders = [None] * len(types)
|
||||
for i, line in enumerate(types):
|
||||
cv.check_type("line", line, str)
|
||||
cv.check_value("line", line, PLOT_TYPES_MGXS)
|
||||
if orders[i]:
|
||||
cv.check_greater_than("order value", orders[i], 0, equality=True)
|
||||
|
||||
xsdata = library[this.name]
|
||||
|
||||
|
|
@ -767,26 +711,46 @@ def _calculate_mgxs_nuc_macro(this, types, library, temperature=294.):
|
|||
# Get the data
|
||||
data = np.zeros((len(types), library.energy_groups.num_groups))
|
||||
for i, line in enumerate(types):
|
||||
if line == 'unity':
|
||||
if 'fission' in line and not xsdata.fissionable:
|
||||
continue
|
||||
elif line == 'unity':
|
||||
data[i, :] = 1.
|
||||
elif line.startswith('scatter'):
|
||||
# We have to remove the outgoing dependence
|
||||
attr = line.replace(' ', '_').replace('-', '_')
|
||||
matrix = xsdata.scatter_matrix[t]
|
||||
# Sum over outgoing groups
|
||||
vector = np.sum(matrix, axis=1)
|
||||
# Now get the actual order of interest
|
||||
if line == 'scatter':
|
||||
order = 0
|
||||
else:
|
||||
order = int(line.split('-')[1])
|
||||
if order < xsdata.xs_shapes["[G][G'][Order]"][-1]:
|
||||
data[i, :] = vector[:, order]
|
||||
else:
|
||||
data[i, :] = 0.
|
||||
else:
|
||||
attr = line.replace(' ', '_').replace('-', '_')
|
||||
data[i, :] = getattr(xsdata, attr)[t]
|
||||
attr = _PLOT_ATTR[line]
|
||||
temp_data = getattr(xsdata, attr)[t]
|
||||
if temp_data.shape in (xsdata.xs_shapes["[G']"],
|
||||
xsdata.xs_shapes["[G]"]):
|
||||
data[i, :] = temp_data
|
||||
elif temp_data.shape == xsdata.xs_shapes["[G][G']"]:
|
||||
data[i, :] = np.sum(temp_data, axis=1)
|
||||
elif temp_data.shape == xsdata.xs_shapes["[DG]"]:
|
||||
if orders[i]:
|
||||
if orders[i] < len(temp_data.shape[0]):
|
||||
data[i, :] = temp_data[orders[i]]
|
||||
else:
|
||||
data[i, :] = np.sum(temp_data[:])
|
||||
elif temp_data.shape in (xsdata.xs_shapes["[G'][DG]"],
|
||||
xsdata.xs_shapes["[G][DG]"]):
|
||||
if orders[i]:
|
||||
if orders[i] < len(temp_data.shape[1]):
|
||||
data[i, :] = temp_data[:, orders[i]]
|
||||
else:
|
||||
data[i, :] = np.sum(temp_data[:, :], axis=1)
|
||||
elif temp_data.shape == xsdata.xs_shapes["[G][G'][DG]"]:
|
||||
temp_data = np.sum(temp_data, axis=1)
|
||||
if orders[i]:
|
||||
if orders[i] < len(temp_data.shape[1]):
|
||||
data[i, :] = temp_data[:, orders[i]]
|
||||
else:
|
||||
data[i, :] = np.sum(temp_data[:, :], axis=1)
|
||||
elif temp_data.shape == xsdata.xs_shapes["[G][G'][Order]"]:
|
||||
temp_data = np.sum(temp_data, axis=1)
|
||||
if orders[i]:
|
||||
order = orders[i]
|
||||
else:
|
||||
order = 0
|
||||
if order < temp_data.shape[1]:
|
||||
data[i, :] = temp_data[:, order]
|
||||
else:
|
||||
raise ValueError("{} not present in provided MGXS "
|
||||
"library".format(this.name))
|
||||
|
|
@ -794,8 +758,9 @@ def _calculate_mgxs_nuc_macro(this, types, library, temperature=294.):
|
|||
return data
|
||||
|
||||
|
||||
def _calculate_mgxs_elem_mat(this, types, library, temperature=294.,
|
||||
ce_cross_sections=None, enrichment=None):
|
||||
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
|
||||
|
||||
|
|
@ -808,6 +773,10 @@ def _calculate_mgxs_elem_mat(this, types, library, temperature=294.,
|
|||
in openmc.PLOT_TYPES_MGXS
|
||||
library : openmc.MGXSLibrary
|
||||
MGXS Library containing the data of interest
|
||||
orders : Iterable of Integral
|
||||
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
|
||||
|
|
@ -828,11 +797,6 @@ def _calculate_mgxs_elem_mat(this, types, library, temperature=294.,
|
|||
|
||||
"""
|
||||
|
||||
# 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
|
||||
|
|
@ -861,7 +825,7 @@ def _calculate_mgxs_elem_mat(this, types, library, temperature=294.,
|
|||
nuc_data = []
|
||||
for nuclide in nuclides.items():
|
||||
nuc_data.append(_calculate_mgxs_nuc_macro(nuclide[0], types, library,
|
||||
T))
|
||||
orders, T))
|
||||
|
||||
# Combine across the nuclides
|
||||
data = np.zeros((len(types), library.energy_groups.num_groups))
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue