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Added ability to provide an axis to the plot_xs routines, and added a type for slowing-down power so we can plot moderating ratios. Why not right?

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This commit is contained in:
Adam Nelson 2016-11-11 14:50:24 -05:00
parent 4fe275b381
commit 240028d13d
4 changed files with 76 additions and 23 deletions
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21
openmc/element.py
View file

@ -261,7 +261,7 @@ class Element(object):
return isotopes
def plot_xs(self, types, divisor_types=None, temperature=294.,
def plot_xs(self, types, divisor_types=None, temperature=294., axis=None,
Erange=(1.E-5, 20.E6), sab_name=None, cross_sections=None,
enrichment=None, **kwargs):
"""Creates a figure of continuous-energy microscopic cross sections
@ -280,6 +280,9 @@ class Element(object):
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.
axis : matplotlib.axes, optional
A previously generated axis to use for plotting. If not specified,
a new axis and figure will be generated.
Erange : tuple of floats
Energy range (in eV) to plot the cross section within
sab_name : str, optional
@ -297,7 +300,10 @@ class Element(object):
Returns
-------
fig : matplotlib.figure.Figure
Matplotlib Figure of the generated macroscopic cross section
If axis is None, then a Matplotlib Figure of the generated
macroscopic cross section will be returned. Otherwise, a value of
None will be returned as the figure and axes have already been
generated.
"""
@ -327,8 +333,12 @@ class Element(object):
data = data_new
# Generate the plot
fig = plt.figure(**kwargs)
ax = fig.add_subplot(111)
if axis is None:
fig = plt.figure(**kwargs)
ax = fig.add_subplot(111)
else:
fig = None
ax = axis
for i in range(len(data)):
# Set to loglog or semilogx depending on if we are plotting a data
# type which we expect to vary linearly
@ -337,11 +347,12 @@ class Element(object):
else:
plot_func = ax.loglog
if np.sum(data[i, :]) > 0.:
print('max = {:.2E}'.format(np.max(data[i, :])))
plot_func(E, data[i, :], label=types[i])
ax.set_xlabel('Energy [eV]')
if divisor_types:
ax.set_ylabel('Elemental Cross Section')
ax.set_ylabel('Elemental Data')
else:
ax.set_ylabel('Elemental Cross Section [b]')
ax.legend(loc='best')

26
openmc/material.py
View file

@ -666,8 +666,7 @@ class Material(object):
nuc_densities = []
nuc_density_types = []
for nuclide in nuclides.items():
nuc, nuc_data = nuclide
nuc, nuc_density, nuc_density_type = nuc_data
nuc, nuc_density, nuc_density_type = nuclide[1]
nucs.append(nuc)
nuc_densities.append(nuc_density)
nuc_density_types.append(nuc_density_type)
@ -713,7 +712,8 @@ class Material(object):
return nuclides
def plot_xs(self, types, divisor_types=None, temperature=294.,
Erange=(1.E-5, 20.E6), cross_sections=None, **kwargs):
axis=None, Erange=(1.E-5, 20.E6), cross_sections=None,
**kwargs):
"""Creates a figure of continuous-energy macroscopic cross sections
for this material
@ -730,6 +730,9 @@ class Material(object):
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.
axis : matplotlib.axes, optional
A previously generated axis to use for plotting. If not specified,
a new axis and figure will be generated.
Erange : tuple of floats, optional
Energy range (in eV) to plot the cross section within
cross_sections : str, optional
@ -740,8 +743,11 @@ class Material(object):
Returns
-------
fig : matplotlib.figure.Figure
Matplotlib Figure of the generated macroscopic cross section
fig : matplotlib.figure.Figure or None
If axis is None, then a Matplotlib Figure of the generated
macroscopic cross section will be returned. Otherwise, a value of
None will be returned as the figure and axes have already been
generated.
"""
@ -770,8 +776,12 @@ class Material(object):
data = data_new
# Generate the plot
fig = plt.figure(**kwargs)
ax = fig.add_subplot(111)
if axis is None:
fig = plt.figure(**kwargs)
ax = fig.add_subplot(111)
else:
fig = None
ax = axis
for i in range(len(data)):
# Set to loglog or semilogx depending on if we are plotting a data
# type which we expect to vary linearly
@ -784,7 +794,7 @@ class Material(object):
ax.set_xlabel('Energy [eV]')
if divisor_types:
ax.set_ylabel('Macroscopic Cross Section')
ax.set_ylabel('Macroscopic Data')
else:
ax.set_ylabel('Macroscopic Cross Section [1/cm]')
ax.legend(loc='best')

27
openmc/nuclide.py
View file

@ -97,7 +97,7 @@ class Nuclide(object):
self._scattering = scattering
def plot_xs(self, types, divisor_types=None, temperature=294.,
def plot_xs(self, types, divisor_types=None, temperature=294., axis=None,
Erange=(1.E-5, 20.E6), sab_name=None, cross_sections=None,
**kwargs):
"""Creates a figure of continuous-energy cross sections for this
@ -116,6 +116,9 @@ class Nuclide(object):
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.
axis : matplotlib.axes, optional
A previously generated axis to use for plotting. If not specified,
a new axis and figure will be generated.
Erange : tuple of floats
Energy range (in eV) to plot the cross section within
sab_name : str, optional
@ -129,7 +132,10 @@ class Nuclide(object):
Returns
-------
fig : matplotlib.figure.Figure
Matplotlib Figure of the generated macroscopic cross section
If axis is None, then a Matplotlib Figure of the generated
macroscopic cross section will be returned. Otherwise, a value of
None will be returned as the figure and axes have already been
generated.
"""
@ -158,8 +164,12 @@ class Nuclide(object):
data = data_new
# Generate the plot
fig = plt.figure(**kwargs)
ax = fig.add_subplot(111)
if axis is None:
fig = plt.figure(**kwargs)
ax = fig.add_subplot(111)
else:
fig = None
ax = axis
for i in range(len(data)):
to_plot = data[i](E)
# Set to loglog or semilogx depending on if we are plotting a data
@ -173,7 +183,7 @@ class Nuclide(object):
ax.set_xlabel('Energy [eV]')
if divisor_types:
ax.set_ylabel('Microscopic Cross Section')
ax.set_ylabel('Microscopic Data')
else:
ax.set_ylabel('Microscopic Cross Section [b]')
ax.legend(loc='best')
@ -357,8 +367,13 @@ class Nuclide(object):
funcs.append(nuc[mt].xs[nucT])
else:
funcs.append(nuc[mt].xs[nucT])
elif mt == 0:
elif mt == UNITY_MT:
funcs.append(lambda x: 1.)
elif mt == XI_MT:
awr = nuc.atomic_weight_ratio
alpha = ((awr - 1.) / (awr + 1.))**2
xi = 1. + alpha * np.log(alpha) / (1. - alpha)
funcs.append(lambda x: xi)
else:
funcs.append(lambda x: 0.)
xs.append(openmc.data.Combination(funcs, op))

25
openmc/plot_data.py
View file

@ -2,7 +2,12 @@ import numpy as np
# Supported keywords for material xs plotting
PLOT_TYPES = ['total', 'scatter', 'elastic', 'inelastic', 'fission',
'absorption', 'capture', 'nu-fission', 'nu-scatter', 'unity']
'absorption', 'capture', 'nu-fission', 'nu-scatter', 'unity',
'slowing-down power']
# Special MT values
UNITY_MT = -1
XI_MT = -2
# MTs to combine to generate associated plot_types
PLOT_TYPES_MT = {'total': (2, 3,),
@ -31,7 +36,15 @@ PLOT_TYPES_MT = {'total': (2, 3,),
172, 173, 174, 175, 176, 177, 178, 179, 180,
181, 183, 184, 190, 194, 196, 198, 199, 200,
875, 891),
'unity': (0,)}
'unity': (UNITY_MT,),
'slowing-down power': (2, 4, 11, 16, 17, 22, 23, 24, 25, 28,
29, 30, 32, 33, 34, 35, 36, 37, 41, 42,
44, 45, 152, 153, 154, 156, 157, 158,
159, 160, 161, 162, 163, 164, 165, 166,
167, 168, 169, 170, 171, 172, 173, 174,
175, 176, 177, 178, 179, 180, 181, 183,
184, 190, 194, 196, 198, 199, 200, 875,
891, XI_MT)}
# Operations to use when combining MTs the first np.add is used in reference
# to zero
PLOT_TYPES_OP = {'total': (np.add,),
@ -41,7 +54,9 @@ PLOT_TYPES_OP = {'total': (np.add,),
'fission': (), 'absorption': (),
'capture': (), 'nu-fission': (),
'nu-scatter': (np.add,) * (len(PLOT_TYPES_MT['nu-scatter']) - 1),
'unity': ()}
'unity': (),
'slowing-down power':
(np.add,) * (len(PLOT_TYPES_MT['slowing-down power']) - 2) + (np.multiply,)}
# Whether or not to multiply the reaction by the yield as well
PLOT_TYPES_YIELD = {'total': (False, False),
@ -51,7 +66,9 @@ PLOT_TYPES_YIELD = {'total': (False, False),
'fission': (False,), 'absorption': (False,),
'capture': (False,), 'nu-fission': (True,),
'nu-scatter': (True,) * len(PLOT_TYPES_MT['nu-scatter']),
'unity': (False,)}
'unity': (False,),
'slowing-down power':
(True,) * len(PLOT_TYPES_MT['slowing-down power'])}
# Types of plots to plot linearly in y
PLOT_TYPES_LINEAR = ['nu-fission / fission', 'nu-scatter / scatter']