def get_e_release(results_dir):
"""Return a list of energy generation from all completed sub-directories in results_dir."""
import wdmerger_grid_diag_analysis as grid_diag
import numpy as np
dir_list = wdmerger.get_parameter_list(results_dir)
if (dir_list == []):
return
diag_filename_list = [
results_dir + '/' + directory + '/output/grid_diag.out'
for directory in dir_list
]
e_arr = [
np.amax(wdmerger.get_column('TOTAL ENERGY', diag_filename)) -
np.amin(wdmerger.get_column('TOTAL ENERGY', diag_filename))
for diag_filename in diag_filename_list
]
# Divide by 10**51
e_arr = [e / 1.0e51 for e in e_arr]
return e_arr
def get_ni56(results_dir, prefix=''):
"""Return a list of maximum 56Ni production from all completed sub-directories in results_dir."""
import numpy as np
ni56_arr = []
dir_list = wdmerger.get_parameter_list(results_dir)
# Strip out those directories that don't match the prefix.
if (prefix != ''):
dir_list = [dir for dir in dir_list if dir[0:len(prefix)] == prefix]
if (dir_list == []):
return ni56_arr
diag_filename_list = [
results_dir + '/' + directory + '/output/species_diag.out'
for directory in dir_list
]
# Ensure all of the output files are there.
for diag_file in diag_filename_list:
if not os.path.isfile(diag_file):
return ni56_arr
ni56_arr = [
np.amax(wdmerger.get_column('Mass ni56', diag_filename)) -
np.amin(wdmerger.get_column('Mass ni56', diag_filename))
for diag_filename in diag_filename_list
]
return ni56_arr
def stellar_masses(eps_filename, results_base):
"""Plot the WD masses as a function of time."""
if not is_root():
return
import os
if not os.path.exists(results_base):
return
import matplotlib.pyplot as plt
import numpy as np
res_list = wdmerger.get_parameter_list(results_base)
directory_list = [results_base + '/' + res for res in res_list]
for i, directory in enumerate(directory_list):
eps_filename_curr = eps_filename.replace('.eps', '_n_' + res_list[i][1:] + '.eps')
if os.path.isfile(eps_filename_curr):
continue
p_diag = directory + '/output/primary_diag.out'
s_diag = directory + '/output/secondary_diag.out'
p_time = wdmerger.get_column('TIME', p_diag)
p_mass = wdmerger.get_column('PRIMARY MASS', p_diag)
s_time = wdmerger.get_column('TIME', s_diag)
s_mass = wdmerger.get_column('SECONDARY MASS', s_diag)
p_mask = np.where(p_time <= 100.0)
s_mask = np.where(s_time <= 100.0)
plt.plot(np.array(p_time)[p_mask], np.array(p_mass)[p_mask] / np.array(p_mass)[0], lw = 4.0, linestyle = linestyles[0], markersize = 12.0, markevery = 2000, label = 'Primary')
plt.plot(np.array(s_time)[s_mask], np.array(s_mass)[s_mask] / np.array(s_mass)[0], lw = 4.0, linestyle = linestyles[1], markersize = 12.0, markevery = 2000, label = 'Secondary')
plt.tick_params(labelsize = 20)
plt.xlabel('Time (s)', fontsize = 24)
plt.ylabel('WD Mass / Initial Mass', fontsize = 24)
plt.legend(loc = 'best', prop = {'size':20}, fancybox = True, framealpha = 0.5)
plt.tight_layout()
plt.savefig(eps_filename_curr)
plt.close()
def stellar_distance(eps_filename, results_base):
"""Plot the WD distance as a function of time."""
if not is_root():
return
import os
if os.path.isfile(eps_filename):
return
if not os.path.exists(results_base):
return
import matplotlib.pyplot as plt
import numpy as np
res_list = wdmerger.get_parameter_list(results_base)
res_list = sorted([int(res[1:]) for res in res_list])
diag_list = [results_base + '/n' + str(res) + '/output/star_diag.out' for res in res_list]
for i, diag in enumerate(diag_list):
eps_filename_curr = eps_filename.replace('.eps', '_n_' + str(res_list[i]) + '.eps')
if os.path.isfile(eps_filename_curr):
return
time = np.array(wdmerger.get_column('TIME', diag))
dist = np.array(wdmerger.get_column('WD DISTANCE', diag))
mask = np.where(dist > 0.0)
plt.plot(time[mask], dist[mask] / dist[0], lw = 4.0, linestyle = linestyles[0])
plt.tick_params(labelsize = 20)
plt.xlabel('Time (s)', fontsize = 24)
plt.ylabel('WD Distance / Initial Distance', fontsize = 24)
plt.tight_layout()
plt.savefig(eps_filename_curr)
plt.close()
def stellar_distance_convergence(eps_filename, results_base):
"""Plot the WD distance as a function of resolution."""
if not is_root():
return
import os
if os.path.isfile(eps_filename):
return
if not os.path.exists(results_base):
return
import matplotlib.pyplot as plt
import numpy as np
res_list = wdmerger.get_parameter_list(results_base)
res_list = sorted([int(res[1:]) for res in res_list])
diag_list = [results_base + '/n' + str(res) + '/output/star_diag.out' for res in res_list]
for i, diag in enumerate(diag_list):
time = wdmerger.get_column('TIME', diag)
dist = wdmerger.get_column('WD DISTANCE', diag)
mask = np.where(time <= 50.0)
plt.plot(np.array(time)[mask], np.array(dist)[mask] / np.array(dist)[0], lw = 4.0, linestyle = linestyles[i],
marker = markers[i], markersize = 12.0, markevery = 2000, label = 'n = ' + str(res_list[i]))
plt.tick_params(labelsize = 20)
plt.xlabel('Time (s)', fontsize = 24)
plt.ylabel('WD Distance / Initial Distance', fontsize = 24)
plt.legend(loc = 'best', prop = {'size':20}, fancybox = True, framealpha = 0.5)
plt.tight_layout()
plt.savefig(eps_filename)
plt.close()
def get_ni56(results_dir):
"""Return a list of maximum 56Ni production from all completed sub-directories in results_dir."""
import numpy as np
dir_list = sorted(wdmerger.get_parameter_list(results_dir))
diag_filename_list = [
results_dir + '/' + directory + '/output/species_diag.out'
for directory in dir_list
]
ni56_arr = [
np.amax(wdmerger.get_column('Mass ni56', diag_filename))
for diag_filename in diag_filename_list
]
return ni56_arr
def gravitational_wave_signal(eps_filename, results_dir):
"""Plot the gravitational radiation waveform."""
import os
if os.path.isfile(eps_filename):
return
if not os.path.exists(results_dir):
return
from matplotlib import pyplot as plt
print "Generating plot with filename " + eps_filename
diag_file = results_dir + '/output/grid_diag.out'
time = wdmerger.get_column('TIME', diag_file)
strain_p_1 = wdmerger.get_column('h_+ (axis 1)', diag_file)
strain_x_1 = wdmerger.get_column('h_x (axis 1)', diag_file)
strain_p_2 = wdmerger.get_column('h_+ (axis 2)', diag_file)
strain_x_2 = wdmerger.get_column('h_x (axis 2)', diag_file)
strain_p_3 = wdmerger.get_column('h_+ (axis 3)', diag_file)
strain_x_3 = wdmerger.get_column('h_x (axis 3)', diag_file)
plt.plot(time,
strain_p_1 / 1.e-22,
lw=4.0,
color=colors[0],
linestyle=linestyles[0],
marker=markers[0],
markersize=12,
markevery=250,
label=r'$h^x_+$')
plt.plot(time,
strain_x_1 / 1.e-22,
lw=4.0,
color=colors[1],
linestyle=linestyles[1],
marker=markers[1],
markersize=12,
markevery=250,
label=r'$h^x_\times$')
plt.plot(time,
strain_p_2 / 1.e-22,
lw=4.0,
color=colors[2],
linestyle=linestyles[2],
marker=markers[2],
markersize=12,
markevery=250,
label=r'$h^y_+$')
plt.plot(time,
strain_x_2 / 1.e-22,
lw=4.0,
color=colors[3],
linestyle=linestyles[3],
marker=markers[3],
markersize=12,
markevery=250,
label=r'$h^y_\times$')
plt.plot(time,
strain_p_3 / 1.e-22,
lw=4.0,
color=colors[4],
linestyle=linestyles[4],
marker=markers[4],
markersize=12,
markevery=250,
label=r'$h^z_+$')
plt.plot(time,
strain_x_3 / 1.e-22,
lw=4.0,
color=colors[5],
linestyle=linestyles[5],
marker=markers[5],
markersize=12,
markevery=250,
label=r'$h^z_\times$')
plt.tick_params(labelsize=20)
plt.xlabel(r'$t\ \mathrm{(s)}$', fontsize=24)
plt.ylabel(r'$h\, /\, 10^{-22}$', fontsize=24)
plt.legend(loc='best', prop={'size': 20})
plt.tight_layout()
plt.savefig(eps_filename)
wdmerger.insert_commits_into_eps(eps_filename, diag_file, 'diag')
plt.close()
def gravitational_wave_signal(eps_filename, results_dir, max_time = 1.e20, markevery = None,
scale_exp = -22, vary_lines = False):
"""Plot the gravitational radiation waveform."""
import os
if os.path.isfile(eps_filename):
return
if not os.path.exists(results_dir):
return
from matplotlib import pyplot as plt
import numpy as np
print "Generating plot with filename " + eps_filename
scale = 10.0**scale_exp
if vary_lines:
linestyles = ['-', '--', ':', '-', '--', ':']
markers = ['', '', '', 'o', 's', 'D']
colors = ['b', 'g', 'r', 'c', 'm', 'k']
else:
linestyles = ['-', '--', '-', '--', '-', '--']
markers = ['', '', '', '', '', '']
colors = ['b', 'g', 'b', 'g', 'b', 'g']
diag_file = results_dir + '/output/grid_diag.out'
time = np.array(wdmerger.get_column('TIME', diag_file))
strain_p_1 = np.array(wdmerger.get_column('h_+ (axis 1)', diag_file)) / scale
strain_p_2 = np.array(wdmerger.get_column('h_+ (axis 2)', diag_file)) / scale
strain_p_3 = np.array(wdmerger.get_column('h_+ (axis 3)', diag_file)) / scale
strain_x_1 = np.array(wdmerger.get_column('h_x (axis 1)', diag_file)) / scale
strain_x_2 = np.array(wdmerger.get_column('h_x (axis 2)', diag_file)) / scale
strain_x_3 = np.array(wdmerger.get_column('h_x (axis 3)', diag_file)) / scale
# Generate an offset for the x- and y- strains. Then round this to the nearest
# integer for the convenience of the plot legend.
offset = max( int(round(2.5 * (max(np.max(strain_p_3), np.max(strain_x_3)) - min(np.min(strain_p_2), np.min(strain_x_2))))),
int(round(2.5 * (max(np.max(strain_p_2), np.max(strain_x_2)) - min(np.min(strain_p_1), np.min(strain_x_1))))) )
z_offset = 0
y_offset = offset
x_offset = 2 * offset
strain_p_1 += x_offset
strain_p_2 += y_offset
strain_p_3 += z_offset
strain_x_1 += x_offset
strain_x_2 += y_offset
strain_x_3 += z_offset
z_label_offset = ''
y_label_offset = '+ {:d}'.format(y_offset)
x_label_offset = '+ {:d}'.format(x_offset)
label_p_1 = r'$h^x_+{}$'.format(x_label_offset)
label_p_2 = r'$h^y_+{}$'.format(y_label_offset)
label_p_3 = r'$h^z_+{}$'.format(z_label_offset)
label_x_1 = r'$h^x_\times{}$'.format(x_label_offset)
label_x_2 = r'$h^y_\times{}$'.format(y_label_offset)
label_x_3 = r'$h^z_\times{}$'.format(z_label_offset)
markersize = 12
mask = np.where(time <= max_time)
plot_p_1, = plt.plot(time[mask], strain_p_1[mask], lw = 4.0, color = colors[0], linestyle = linestyles[0],
marker = markers[0], markersize = markersize, markevery = markevery, label = label_p_1)
plot_p_2, = plt.plot(time[mask], strain_p_2[mask], lw = 4.0, color = colors[2], linestyle = linestyles[2],
marker = markers[2], markersize = markersize, markevery = markevery, label = label_p_2)
plot_p_3, = plt.plot(time[mask], strain_p_3[mask], lw = 4.0, color = colors[4], linestyle = linestyles[4],
marker = markers[4], markersize = markersize, markevery = markevery, label = label_p_3)
plot_x_1, = plt.plot(time[mask], strain_x_1[mask], lw = 4.0, color = colors[1], linestyle = linestyles[1],
marker = markers[1], markersize = markersize, markevery = markevery, label = label_x_1)
plot_x_2, = plt.plot(time[mask], strain_x_2[mask], lw = 4.0, color = colors[3], linestyle = linestyles[3],
marker = markers[3], markersize = markersize, markevery = markevery, label = label_x_2)
plot_x_3, = plt.plot(time[mask], strain_x_3[mask], lw = 4.0, color = colors[5], linestyle = linestyles[5],
marker = markers[5], markersize = markersize, markevery = markevery, label = label_x_3)
plt.tick_params(labelsize=20)
plt.xlabel(r'$t\ \mathrm{(s)}$', fontsize = 24)
plt.ylabel(r'$h\, /\, 10^{}$'.format('{' + str(scale_exp) + '}'), fontsize = 24)
xlim = [time[mask][0], time[mask][-1]]
plt.xlim(xlim)
ylim = [min(np.min(strain_p_3), np.min(strain_x_3)) - 0.05 * offset, max(np.max(strain_p_1), np.max(strain_x_1)) + 0.5 * offset]
plt.ylim(ylim)
# Taking a trick from http://matplotlib.org/users/legend_guide.html#multiple-legends-on-the-same-axes
# to split the legend into three parts.
legend_1 = plt.legend(handles = [plot_p_1, plot_x_1], loc = [0.1, 0.85], prop = {'size':20}, ncol = 2, fancybox = True)
legend_2 = plt.legend(handles = [plot_p_2, plot_x_2], loc = [0.1, 0.55], prop = {'size':20}, ncol = 2, fancybox = True)
legend_3 = plt.legend(handles = [plot_p_3, plot_x_3], loc = [0.1, 0.25], prop = {'size':20}, ncol = 2, fancybox = True)
plt.gca().add_artist(legend_1)
plt.gca().add_artist(legend_2)
plt.gca().add_artist(legend_3)
plt.tight_layout()
plt.savefig(eps_filename)
wdmerger.insert_commits_into_eps(eps_filename, diag_file, 'diag')
plt.close()
def angular_momentum_comparison(eps_filename, results_base, ncell, subtract_grid_losses = False):
"""Plot the system angular momentum as a function of evolution method."""
if not is_root():
return
import os
if os.path.isfile(eps_filename):
return
if not os.path.exists(results_base):
return
import matplotlib.pyplot as plt
import numpy as np
idx = 0
for rot in ['0', '1']:
for hybrid in ['0', '1']:
results_dir = results_base + '/rot' + rot + '/hybrid' + hybrid + '/n' + ncell
diag = results_dir + '/output/grid_diag.out'
time = np.array(wdmerger.get_column('TIME', diag))
lerr = np.array(wdmerger.get_column('ANG. MOM. Z', diag))
if subtract_grid_losses:
bndy_diag = results_dir + '/output/bndy_diag.out'
bndy = np.array(wdmerger.get_column('ANG. MOM. Z LOST', bndy_diag))
lerr = lerr + bndy
mask = np.where(time <= 100.0)
title = ''
if rot == '0':
title += 'Inertial Frame'
else:
title += 'Rotating Frame'
if hybrid == '0':
title += ', Standard Equations'
else:
title += ', Hybrid Equations'
plt.plot(time[mask], abs((lerr[mask] - lerr[0]) / lerr[0]), lw = 4.0, linestyle = linestyles[idx], marker = markers[idx], markersize = 12.0, markevery = 5000, label = title)
idx += 1
plt.tick_params(labelsize = 20)
plt.xlabel('Time (s)', fontsize = 24)
plt.ylabel(r'Relative Change in $L_z$', fontsize = 24)
plt.legend(loc = 'best', prop = {'size':16}, fancybox = True, framealpha = 0.5)
plt.ylim([-5e-5, 1.e-3])
plt.tight_layout()
plt.savefig(eps_filename)
plt.close()