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Used more pythonic commands in plotter module and commented the block which obtains MGXS data for plotting
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Adam Nelson
parent
346deb258f
commit
a45652d785
3 changed files with 105 additions and 78 deletions
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@ -1975,7 +1975,7 @@
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"\n",
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"One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the `openmc.plotter` module to plot continuous-energy cross sections from the openly available cross section library distributed by NNDC.\n",
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"\n",
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"There is a simpler way to plot the MGXS data (using the same interface as is used for the continuous-energy data), however this example series has not yet introduced the pre-requisite information and so we will do this manually here."
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"The MGXS data can also be plotted using the openmc.plot_xs command, however we will do this manually here to show how the openmc.Mgxs.get_xs method can be used to obtain data."
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]
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},
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{
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File diff suppressed because one or more lines are too long
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@ -67,7 +67,7 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
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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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"""Creates a figure of continuous-energy cross sections for this item.
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Parameters
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----------
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@ -233,7 +233,7 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=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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"""Calculates continuous-energy cross sections of a requested type.
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Parameters
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----------
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@ -295,7 +295,7 @@ def calculate_cexs(this, types, temperature=294., sab_name=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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"""Calculates continuous-energy cross sections of a requested type.
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Parameters
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----------
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@ -489,7 +489,7 @@ def _calculate_cexs_nuclide(this, types, temperature=294., sab_name=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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"""Calculates continuous-energy cross sections of a requested type.
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Parameters
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----------
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@ -607,11 +607,11 @@ def _calculate_cexs_elem_mat(this, types, temperature=294.,
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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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"""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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average cross section over all angles.
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geometric average cross section over all angles.
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Parameters
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----------
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@ -669,20 +669,16 @@ def calculate_mgxs(this, types, orders=None, temperature=294.,
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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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i = 0
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for g in range(library.energy_groups.num_groups):
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energy_grid[i: i + 2] = library.energy_groups.group_edges[g: g + 2]
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i += 2
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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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if energy_grid[0] <= 0.:
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energy_grid[0] = _MIN_E
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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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i = 0
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for g in range(library.energy_groups.num_groups):
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data[line, i: i + 2] = mgxs[line, g]
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i += 2
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data[g * 2: g * 2 + 2] = mgxs[line, g]
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return np.flipud(energy_grid), data
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@ -690,11 +686,11 @@ def calculate_mgxs(this, types, orders=None, 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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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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average cross section over all angles.
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geometric average cross section over all angles.
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Parameters
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----------
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@ -748,19 +744,34 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None,
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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[:]
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# If we have angular data, then we will plot the average
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# over all provided angles. Since the angles are equi-distant,
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# un-weighted averaging will suffice
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# If we have angular data, then want the geometric
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# average over all provided angles. Since the angles are
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# equi-distant, un-weighted averaging will suffice
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if xsdata.representation == 'angle':
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temp_data = np.mean(temp_data, axis=(0, 1))
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# Now we can look at the shape of the data to identify how
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# it should be modified to produce an array of values
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# with groups.
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if shape in (xsdata.xs_shapes["[G']"],
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xsdata.xs_shapes["[G]"]):
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# Then the data is already an array vs groups so copy
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# and move along
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data[i, :] = temp_data
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elif shape == xsdata.xs_shapes["[G][G']"]:
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# Sum the data over outgoing groups to create our array vs
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# groups
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data[i, :] = np.sum(temp_data, axis=1)
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elif shape == xsdata.xs_shapes["[DG]"]:
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# Then we have a constant vs groups with a value for each
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# delayed group. The user-provided value of orders tells us
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# which delayed group we want. If none are provided, then
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# we sum all the delayed groups together.
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if orders[i]:
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if orders[i] < len(shape[0]):
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data[i, :] = temp_data[orders[i]]
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@ -768,24 +779,39 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None,
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data[i, :] = np.sum(temp_data[:])
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elif shape in (xsdata.xs_shapes["[G'][DG]"],
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xsdata.xs_shapes["[G][DG]"]):
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# Then we have an array vs groups with values for each
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# delayed group. The user-provided value of orders tells us
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# which delayed group we want. If none are provided, then
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# we sum all the delayed groups together.
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if orders[i]:
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if orders[i] < len(shape[1]):
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data[i, :] = temp_data[:, orders[i]]
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else:
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data[i, :] = np.sum(temp_data[:, :], axis=1)
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elif shape == xsdata.xs_shapes["[G][G'][DG]"]:
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# Then we have a delayed group matrix. We will first
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# remove the outgoing group dependency
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temp_data = np.sum(temp_data, axis=1)
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# And then proceed in exactly the same manner as the
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# "[G'][DG]" of "[G][DG]" shapes in the previous block.
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if orders[i]:
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if orders[i] < len(shape[1]):
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data[i, :] = temp_data[:, orders[i]]
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else:
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data[i, :] = np.sum(temp_data[:, :], axis=1)
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elif shape == xsdata.xs_shapes["[G][G'][Order]"]:
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# This is a scattering matrix with angular data
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# First remove the outgoing group dependence
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temp_data = np.sum(temp_data, axis=1)
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# The user either provided a specific order or we resort
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# to the default 0th order
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if orders[i]:
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order = orders[i]
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else:
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order = 0
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# If the order is available, store the data for that order
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# if it is not available, then the expansion coefficient
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# is zero and thus we already have the correct value.
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if order < shape[1]:
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data[i, :] = temp_data[:, order]
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else:
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@ -799,11 +825,11 @@ def _calculate_mgxs_elem_mat(this, types, library, orders=None,
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temperature=294., ce_cross_sections=None,
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enrichment=None):
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"""Determines the multi-group cross sections of an element or material
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object
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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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average cross section over all angles.
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geometric average cross section over all angles.
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Parameters
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----------
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