def time_series_plot(eps_filename, results_dir, times, field = 'density',
zlim = [1e-4, 1e8], nrows = 3, ncols = 3, smallplt = False,
colormap = 'hot', zoom = 1.0, xticks = None, yticks = None):
"""Make a time series plot of the density."""
if not is_root():
return
import os
if os.path.isfile(eps_filename):
return
if not os.path.exists(results_dir):
return
print "Generating plot with filename " + eps_filename
directory = results_dir + '/output/'
pltfiles = [directory + plt for plt in wdmerger.get_plotfiles(directory)]
wdmerger.multipanel_slice_plot(field, eps_filename,
[wdmerger.get_nearest_plotfile(pltfiles, time) for time in times],
nrows = nrows, ncols = ncols,
annotate_time = True,
zlim = zlim,
zoom = zoom,
xticks = xticks,
yticks = yticks,
colormap = colormap
)
def collision_time(results_dir):
"""Calculate the time of the collision."""
out_file = results_dir + '/collision_time.txt'
if os.path.isfile(out_file):
return
print("Calculating collision time in directory " + results_dir)
import yt
# Load all the output files in the output directory.
# Search them one by one (in reverse) until we find
# the time where the density threshold is crossed
# at the center of the simulation.
density_threshold = 1.0e2
plots = wdmerger.get_plotfiles(results_dir, prefix='smallplt')
plots.reverse()
if (plots == []) or (plots == None):
return
for i, plot in enumerate(plots):
ds = yt.load(results_dir + '/output/' + plot)
# Get a region that is only one coarse zone-width wide
dx = ds.domain_width / ds.domain_dimensions
sph = ds.sphere([0.0, 0.0, 0.0], max(dx))
# Find the maximum density in that region
rho_max = sph.max("density")
if rho_max < density_threshold:
idx = i - 1
break
ds = yt.load(results_dir + '/output/' + plots[idx])
file = open(out_file, 'w')
file.write(str(ds.current_time.v))
file.close()
def vol_renders():
"""Volume render the simulations."""
import os
import yt
yt.enable_parallelism()
results_base = 'results/approximate/'
plots_base = 'plots/'
for mass_P in ['0.90']:
for mass_S in ['0.60', '0.90']:
for roche in ['0.90', '1.00']:
for rot in ['0', '1']:
for hybrid in ['0', '1']:
for ncell in ['256', '512', '1024']:
mass_string = "_m_P_" + mass_P + "_m_S_" + mass_S
results_dir = 'mass_P_' + mass_P + '/mass_S_' + mass_S + '/roche' + roche + '/rot' + rot + '/hybrid' + hybrid + '/n' + ncell + '/'
output_dir = results_base + results_dir + '/output/'
if not os.path.exists(output_dir):
continue
plot_list = wdmerger.get_plotfiles(output_dir, prefix='smallplt')
plot_list = [output_dir + plot for plot in plot_list]
plot_dir = plots_base + results_dir
if not os.path.exists(plot_dir) and is_root():
os.makedirs(plot_dir)
ts = yt.DatasetSeries(plot_list)
for ds in ts.piter():
pltname = ds.basename
outfile_name = plot_dir + 'approximate' + mass_string + '_roche_' + roche + '_rot_' + rot + '_hybrid_' + hybrid + '_n_' + ncell + '_' + pltname + '.png'
if os.path.isfile(outfile_name):
continue
wdmerger.vol_render_density(outfile_name, ds)
def amr_ignition(file_base, results_base, do_amr=True):
"""Plot the effect of the resolution on the detonation conditions."""
if not os.path.isdir(results_base):
return
size = 1.6384e9
ncell_list = sorted([int(n[1:]) for n in os.listdir(results_base)])
res_lists = [[]] * len(ncell_list)
dist_lists = [[]] * len(ncell_list)
time_lists = [[]] * len(ncell_list)
base_res_list = []
for i, ncell in enumerate(ncell_list):
base_res_list.append(size / ncell / 1.e5)
dir = results_base + '/n' + str(ncell)
if not os.path.isdir(dir):
continue
# Generate a plot of the distance from the center of the ignition point,
# and also the time of the ignition.
# Get the list of refinement values we have tried.
r_list = wdmerger.get_parameter_list(dir)
if (r_list == []):
continue
# Sort them numerically.
r_list = ['r' + str(r) for r in sorted([int(r[1:]) for r in r_list])]
# Cycle through the plot files.
# Get the distance from the origin of the point where T > 4e9 K, if it exists.
zonesPerDim = []
res_list = []
dist_list = []
time_list = []
for r in r_list:
results_dir = results_base + '/n' + str(ncell) + '/' + r
if not os.path.isdir(results_dir):
continue
print('Searching in directory ' + results_dir)
prefix = 'det_x_plt'
plot_list = [
results_dir + '/output/' + plot
for plot in wdmerger.get_plotfiles(results_dir, prefix=prefix)
]
# Since we'll have stopped after the ignition has occurred,
# we'll search through the plotfiles backward.
T_max = -1.0
T_last = -1.0
for plot in reversed(plot_list):
[T_max, x, y, z] = wdmerger.get_maxloc(plot, 'Temp')
if (T_max < 4.0e9 and T_last >= 4.0e9) or (T_max >= 4.0e9
and T_last < 0.0):
dist = abs(x.v) / 1.0e5
time = wdmerger.get_time_from_plotfile(plot)
zonesPerDim.append(ncell * int(r[1:]))
ds = yt.load(plot)
problo = ds.domain_left_edge.v
probhi = ds.domain_right_edge.v
size = probhi[0] - problo[0]
res_list.append(size / (ncell * int(r[1:])) / 1.0e5)
dist_list.append(dist)
time_list.append(time)
break
else:
T_last = T_max
res_lists[i] = res_list
dist_lists[i] = dist_list
time_lists[i] = time_list
# Plot location of the ignition and time on the same axis.
# We want to combine data at low resolution using their coarse
# grid runs, with the AMR data at the highest resolution.
res_list = []
dist_list = []
time_list = []
for i in range(len(ncell_list) - 1):
res_list.append(res_lists[i][0])
dist_list.append(dist_lists[i][0])
time_list.append(time_lists[i][0])
for res in res_lists[-1]:
res_list.append(res)
for dist in dist_lists[-1]:
dist_list.append(dist)
for time in time_lists[-1]:
time_list.append(time)
fig, ax1 = plt.subplots()
ax1.plot(res_list,
dist_list,
linestyle=linestyles[0],
marker=markers[0],
color=colors[0],
lw=2.0,
label='Ignition location')
ax1.set_xscale('log', basex=10)
ax1.set_xlabel(r"Finest resolution (km)", fontsize=24)
ax1.set_ylabel(r"Ignition location (km)", fontsize=24, color=colors[0])
ax1.tick_params(axis='y', labelcolor=colors[0], labelsize=16)
ax1.tick_params(axis='x', labelsize=16)
ax1.set_ylim(bottom=0.0)
ax2 = ax1.twinx()
ax2.plot(res_list,
time_list,
linestyle=linestyles[1],
marker=markers[1],
color=colors[1],
lw=2.0,
label='Ignition time')
ax2.set_ylabel(r"Ignition time (s)", fontsize=24, color=colors[1])
ax2.tick_params(axis='y', labelcolor=colors[1], labelsize=16)
ax2.set_ylim(bottom=0.0)
fig.set_size_inches(11, 8.5)
fig.tight_layout()
plt.savefig(file_base + '.eps')
plt.savefig(file_base + '.png')
plt.close()
def rho_T_sliceplots(output_base,
results_base,
smallplt=True,
prefix="",
domain_frac=1.0,
x_ticks=[2.0e9, 4.0e9],
y_ticks=[2.0e9, 4.0e9],
scale_exp=9):
"""Create a rho/T sliceplot for every plotfile in a given directory."""
import os
if not os.path.isdir(output_base):
os.makedirs(output_base)
from PIL import Image
if (smallplt):
plt_prefix = 'smallplt'
r_list = wdmerger.get_parameter_list(results_base)
# Strip out the non-refinement directories
r_list = [r for r in r_list if r[0] == 'r']
if (r_list == []):
return
dir_list = [results_base + r + '/output/' for r in r_list]
for param, directory in zip(r_list, dir_list):
output_dir = output_base + param
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
plt_list = wdmerger.get_plotfiles(directory, plt_prefix)
eps_list = [
output_dir + "/rho_T_slice" + prefix + "_" + param + "_t_" +
str("%.2f" % wdmerger.get_time_from_plotfile(directory + pltfile))
+ '.eps' for pltfile in plt_list
]
# Generate the plot
for eps_file, pltfile in zip(eps_list, plt_list):
if os.path.isfile(eps_file):
continue
print("Generating plot with filename " + eps_file)
wdmerger.rho_T_sliceplot(eps_file,
directory + pltfile,
domain_frac=domain_frac,
x_ticks=x_ticks,
y_ticks=y_ticks,
scale_exp=scale_exp)
jpg_list = [eps.replace('eps', 'jpg') for eps in eps_list]
mpg_filename = output_dir + "/rho_T_slice" + prefix + "_" + param + ".mpg"
if not os.path.isfile(mpg_filename):
print("Generating file %s" % mpg_filename)
wdmerger.make_movie(output_dir, jpg_list, mpg_filename)
def amr_detonation(eps_filename, results_base):
"""Plot the effect of the refinement based on the nuclear burning rate on the detonation conditions."""
import os
if os.path.isfile(eps_filename):
return
else:
print("Generating file %s" % eps_filename)
import math
import numpy as np
from matplotlib import pyplot as plt
if not os.path.isdir(results_base):
return
diag_file = None
# First, generate a plot of nickel production.
ncell_list = sorted([int(n[1:]) for n in os.listdir(results_base)])
# Now, generate a plot of the distance from the center of the ignition point.
i = 0
for ncell in ncell_list:
results_dir = results_base + 'n' + str(ncell) + '/self-heat/dxnuc/'
if not os.path.isdir(results_dir):
continue
# Get the list of parameter values we have tried
r_list = wdmerger.get_parameter_list(results_dir)
# Strip out the non-refinement directories
r_list = [r for r in r_list if r[0] == 'r']
if (r_list == []):
continue
# Sort them numerically.
r_list = ['r' + str(r) for r in sorted([int(r[1:]) for r in r_list])]
# Cycle through the plot files.
# Get the maximum value of t_sound_t_enuc, and its distance from the origin.
# Stop if we hit a detonation (t_sound_t_enuc > 1).
for r in r_list:
results_dir = results_base + 'n' + str(
ncell) + '/self-heat/dxnuc/' + r
prefix = 'smallplt'
plt_list = [
results_dir + '/output/' + plt
for plt in wdmerger.get_plotfiles(results_dir, prefix=prefix)
]
for plt in plt_list:
[ts_te_max, x, y,
z] = wdmerger.get_maxloc(plt, 't_sound_t_enuc')
if ts_te_max > 1.0:
[rho_max, x, y, z] = wdmerger.get_maxloc(plt, 'density')
dist = np.sqrt(x**2 + y**2)
time = wdmerger.get_time_from_plotfile(plt)
print(ncell, r, time, rho_max, ts_te_max, dist.v / 1.e5)
break
i += 1
# Generate the plot name, and skip this iteration if it already exists.
eps_filename = plots_dir + 'kh_t' + str(t) + '_p' + str(problem) + '_v' + str(v) + '_n' + str(ncell) + '.eps'
if (os.path.isfile(eps_filename)):
print "Plot with filename " + eps_filename + " already exists; skipping."
continue
print "Generating plot with filename " + eps_filename
# First we load in the numerical data.
# Get the list of plotfiles in the directory.
plotfiles = wdmerger.get_plotfiles(dir)
# Figure out which one in the list we want by searching through
# and checking the Header file. We'll take the first plotfile
# after the desired time.
plotfile_times = np.zeros(len(plotfiles))
for n in range(len(plotfiles)):
plotfile_times[n] = wdmerger.get_time_from_plotfile(dir + '/' + plotfiles[n])
for n in range(len(plotfile_times)):
index = n
time_curr = plotfile_times[n]