def plot_single_mass_fraction_vs_property(file, prop, species,
**keyword_parameters):
root = etree.parse(file).getroot()
props = wn_xml.get_properties_in_zones(root, [prop])
m = wn_xml.get_mass_fractions_in_zones(root, species)
x = np.array(map(float, props[prop]))
if ('xfactor' in keyword_parameters):
x /= float(keyword_parameters['xfactor'])
plp.set_plot_params(plt, keyword_parameters)
if ('use_latex_names' in keyword_parameters):
if (keyword_parameters['use_latex_names'] == 'yes'):
plt.ylabel('X(' +
wn_utilities.get_latex_names([species])[species] + ')')
for my_species in species:
y = np.array(map(float, m[my_species]))
plt.plot(x, y)
plt.show()
def plot_group_mass_fractions_vs_property(file, group, prop, species,
**keyword_parameters):
plp.set_plot_params(mpl, keyword_parameters)
fig = plt.figure()
x = []
y = []
l = []
latex_names = []
props = wn_h5.get_group_properties_in_zones(file, group, [prop])
m = wn_h5.get_group_mass_fractions(file, group)
nuclide_data = wn_h5.get_nuclide_data_hash(file)
if ('xfactor' in keyword_parameters):
xfactor = keyword_parameters['xfactor']
else:
xfactor = 1
x = np.array(map(float, props[prop])) / xfactor
if ('use_latex_names' in keyword_parameters):
if (keyword_parameters['use_latex_names'] == 'yes'):
laxtex_names = wn_utilities.get_latex_names(species)
iy = 0
for sp in species:
y = np.array(map(float, m[:, nuclide_data[sp]['index']]))
if (len(latex_names) != 0):
lab = latex_names[sp]
else:
lab = sp
l.append(plt.plot(x, y, label=lab))
if (len(species) != 1):
plt.legend(loc='upper right', prop={'size': 14})
if ('ylabel' not in keyword_parameters):
if (len(species) != 1):
plt.ylabel('Mass Fraction')
else:
if (len(latex_names) == 0):
plt.ylabel('X(' + species[0] + ')')
else:
plt.ylabel('X(' + latex_names[species[0]] + ')')
if ('xlabel' not in keyword_parameters):
plt.xlabel(prop)
plp.apply_class_methods(plt, keyword_parameters)
plt.show()
def plot_zone_mass_fractions_vs_property(file, zone, prop, species,
**keyword_parameters):
plp.set_plot_params(mpl, keyword_parameters)
fig = plt.figure()
x = []
y = []
l = []
latex_names = []
props = wn_h5.get_zone_properties_in_groups(file, zone, [prop])
m = wn_h5.get_zone_nuclide_mass_fractions_in_groups(file, zone, species)
if ('xfactor' in keyword_parameters):
xfactor = keyword_parameters['xfactor']
else:
xfactor = 1
x = np.array(map(float, props[prop])) / xfactor
if ('use_latex_names' in keyword_parameters):
if (keyword_parameters['use_latex_names'] == 'yes'):
latex_names = wn_utilities.get_latex_names(species)
for i in range(len(species)):
y = np.array(list(map(float, m[species[i]])))
if (len(latex_names) != 0):
lab = latex_names[species[i]]
else:
lab = species[i]
l.append(plt.plot(x, y, label=lab))
if (len(species) != 1):
plt.legend(loc='upper left', prop={'size': 14})
if ('ylabel' not in keyword_parameters):
if (len(species) != 1):
plt.ylabel('Mass Fraction')
else:
if (len(latex_names) == 0):
plt.ylabel('X(' + species[0] + ')')
else:
plt.ylabel('X(' + latex_names[species[0]] + ')')
if ('xlabel' not in keyword_parameters):
plt.xlabel(prop)
plp.apply_class_methods(plt, keyword_parameters)
plt.show()
def plot_mass_fractions_vs_property(file, prop, species, **keyword_parameters):
plp.set_plot_params(mpl, keyword_parameters)
fig = plt.figure()
x = []
y = []
l = []
latex_names = []
root = etree.parse(file).getroot()
props = wn_xml.get_properties_in_zones(root, [prop])
x = np.array(list(map(float, props[prop])))
if ('xfactor' in keyword_parameters):
x /= float(keyword_parameters['xfactor'])
m = wn_xml.get_mass_fractions_in_zones(root, species)
if ('use_latex_names' in keyword_parameters):
if (keyword_parameters['use_latex_names'] == 'yes'):
latex_names = wn_utilities.get_latex_names(species)
for i in range(len(species)):
y = np.array(list(map(float, m[species[i]])))
if (len(latex_names) != 0):
lab = latex_names[species[i]]
else:
lab = species[i]
l.append(plt.plot(x, y, label=lab))
if ('ylabel' not in keyword_parameters):
if (len(species) != 1):
plt.ylabel('Mass Fraction')
else:
if (len(latex_names) == 0):
plt.ylabel('X(' + species[0] + ')')
else:
plt.ylabel('X(' + latex_names[species[0]] + ')')
if len(species) != 1:
plt.legend()
if ('xlabel' not in keyword_parameters):
plt.xlabel(prop)
plp.apply_class_methods(plt, keyword_parameters)
plt.show()
def plot_mass_fractions(file, species, **keyword_parameters):
plp.set_plot_params(mpl, keyword_parameters)
fig = plt.figure()
y = []
l = []
latex_names = []
root = etree.parse(file).getroot()
m = wn_xml.get_mass_fractions_in_zones(root, species)
if ('use_latex_names' in keyword_parameters):
if (keyword_parameters['use_latex_names'] == 'yes'):
latex_names = wn_utilities.get_latex_names(species)
for i in range(len(species)):
y = np.array(list(map(float, m[species[i]])))
if (len(latex_names) != 0):
lab = latex_names[species[i]]
else:
lab = species[i]
l.append(plt.plot(y, label=lab))
# if( len( species ) != 1 ):
# plt.legend(loc='upper right', prop={'size':14})
if ('ylabel' not in keyword_parameters):
if (len(species) != 1):
plt.ylabel('Mass Fraction')
else:
if (len(latex_names) == 0):
plt.ylabel('X(' + species[0] + ')')
else:
plt.ylabel('X(' + latex_names[species[0]] + ')')
if ('xlabel' not in keyword_parameters):
plt.xlabel('step')
plp.apply_class_methods(plt, keyword_parameters)
plt.show()
def plot_single_mass_fraction_vs_property_in_files(files, prop, species,
**keyword_parameters):
plp.set_plot_params(plt, keyword_parameters)
if ('legend_labels' in keyword_parameters):
if (len(keyword_parameters['legend_labels']) != len(files)):
print("Invalid legend labels for input files.")
exit(1)
fig = plt.figure()
roots = []
x = []
y = []
l = []
for file in files:
roots.append(etree.parse(file).getroot())
for i in range(len(roots)):
props = wn_xml.get_properties_in_zones(roots[i], [prop])
x = np.array(list(map(float, props[prop])))
if ('xfactor' in keyword_parameters):
x /= float(keyword_parameters['xfactor'])
m = wn_xml.get_mass_fractions_in_zones(roots[i], [species])
y = np.array(list(map(float, m[species])))
if ('legend_labels' in keyword_parameters):
ll, = plt.plot(x, y, label=keyword_parameters['legend_labels'][i])
else:
ll, = plt.plot(x, y)
l.append(ll)
plp.apply_class_methods(plt, keyword_parameters)
if ('use_latex_names' in keyword_parameters):
if (keyword_parameters['use_latex_names'] == 'yes'):
plt.ylabel('X(' +
wn_utilities.get_latex_names([species])[species] + ')')
plt.show()
def plot_single_mass_fraction_vs_property(file, zone, prop, species,
**keyword_parameters):
props = wn_h5.get_zone_properties_in_groups(file, zone, [prop])
m = wn_h5.get_zone_nuclide_mass_fractions_in_groups(file, zone, [species])
if ('xfactor' in keyword_parameters):
xfactor = keyword_parameters['xfactor']
else:
xfactor = 1
x = np.array(map(float, props[prop])) / xfactor
y = np.array(map(float, m[species]))
plp.set_plot_params(plt, keyword_parameters)
if ('use_latex_names' in keyword_parameters):
if (keyword_parameters['use_latex_names'] == 'yes'):
plt.ylabel('X(' +
wn_utilities.get_latex_names([species])[species] + ')')
plt.plot(x, y)
plt.show()
def movie_mass_fractions_vs_property(file, movie_name, prop, species,
**keyword_parameters):
import matplotlib.animation as manimation
FFMpegWriter = manimation.writers['ffmpeg']
metadata = dict(title='Movie Test',
artist='Matplotlib',
comment='Movie support!')
if ('fps' in keyword_parameters):
fps = keyword_parameters['fps']
else:
fps = 15
writer = FFMpegWriter(fps=fps, metadata=metadata)
ts_factor = {'yr': 3.15e7, 'kyr': 3.15e10, 'Myr': 3.15e13}
fig = plt.figure()
groups = wn_h5.get_iterable_groups(file)
nuclide_data = wn_h5.get_nuclide_data_hash(file)
plp.set_plot_params(plt, keyword_parameters)
names = wu.get_latex_names(species)
l = {}
for name in names:
l[name], = plt.plot([], [], label=name)
legend = plt.legend(loc='upper center', shadow=True)
with writer.saving(fig, movie_name, 200):
for group in groups:
m = wn_h5.get_group_mass_fractions(file, group)
t = wn_h5.get_group_properties_in_zones(file, group, ['time'])
tt = float(t['time'][0])
title_str = "time (s) = %7.2e" % tt
if ('time_scale' in keyword_parameters):
title_str = \
"time (" + keyword_parameters['time_scale'] + ") = %7.2e" \
% (tt / ts_factor[keyword_parameters['time_scale']] )
plt.title(title_str)
if prop == '':
x = np.linspace(0, len(m[:, 0]) - 1, len(m[:, 0]))
else:
props = wn_h5.get_group_properties_in_zones(
file, group, [prop])
if ('xfactor' in keyword_parameters):
xfactor = keyword_parameters['xfactor']
else:
xfactor = 1
x = np.array(map(float, props[prop])) / xfactor
for sp in species:
y = np.array(map(float, m[:, nuclide_data[sp]['index']]))
l[sp].set_data(x, y)
writer.grab_frame()