def plot_energy_error(diag_filename, output_filename):
diag_file = open(diag_filename, 'r')
lines = diag_file.readlines()
time = get_column("TIME", diag_filename)
energy = get_column("TOTAL ENERGY", diag_filename)
rot_period = 0.0
dir = os.path.dirname(diag_filename)
inputs_filename = dir + '/' + wdmerger.get_inputs_filename(dir)
rot_period = wdmerger.get_inputs_var(inputs_filename,
"castro.rotational_period")
if (rot_period > 0.0):
time = time / rot_period
xlabel = "Time / Rotational Period"
else:
xlabel = "Time (s)"
ylabel = "Relative Change in Energy"
plt.plot(time, abs((energy - energy[0]) / energy[0]), lw=4.0)
plt.yscale('log')
plt.xlabel(xlabel, fontsize=20)
plt.ylabel(ylabel, fontsize=20)
plt.ylim([1.0e-8, 1.0e0])
plt.tick_params(labelsize=16)
plt.tight_layout()
plt.savefig(output_filename)
wdmerger.insert_commits_into_eps(output_filename, diag_filename, 'diag')
plt.close()
def plot_wd_distance(diag_filenames, output_filename, n_orbits=-1, labels=''):
# If we got only a single diagnostic file,
# create a list out of it for the coming loop.
if (type(diag_filenames) not in [list, tuple]):
diag_filenames = [ diag_filenames ]
labels = [ labels ]
# Cycle through markers for multiple curves on the same plot.
markers = ['o', '+', '.', ',', '*']
j = 0
for diag_filename, label in zip(diag_filenames, labels):
diag_file = open(diag_filename, 'r')
time = get_column("TIME", diag_filename)
dist = get_column("WD DISTANCE",diag_filename)
# Normalize distance by initial distance.
dist = dist / dist[0]
# Normalize time by rotational period.
rot_period = 0.0
dir = os.path.dirname(diag_filename)
inputs_filename = dir + '/' + wdmerger.get_inputs_filename(dir)
rot_period = wdmerger.get_inputs_var(inputs_filename, "castro.rotational_period")
if (rot_period > 0.0):
time = time / rot_period
xlabel = "Time / Rotational Period"
if (n_orbits > 0):
idx = np.where(time < n_orbits)
time = time[idx]
dist = dist[idx]
else:
xlabel = "Time (s)"
ylabel = "WD Distance / Initial Distance"
if (label is not ''):
plot_label = label
if (len(diag_filenames) > 1):
marker = markers[j]
else:
marker = ''
# Use a markevery stride because of how densely packed the star_diag.out data usually is.
plt.plot(time, dist, marker = marker, ms = 12.0, markevery = 500, lw = 4.0, label=plot_label)
j += 1
plt.xlabel(xlabel, fontsize=20)
plt.ylabel(ylabel, fontsize=20)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
# Use the 'best' location for the legend, since for a generic function like this
# it is hard to know ahead of time where the legend ought to go.
# The alpha value controls the transparency, since we may end up covering some data.
if (label is not ''):
plt.legend(loc='best', fancybox=True, framealpha=0.5)
# The padding ensures that the lower-left ticks on the x- and y-axes don't overlap.
plt.tick_params(labelsize=16, pad=10)
# We have now increased the size of both the ticks and the axis labels,
# which may have caused the latter to fall off the plot. Use tight_layout
# to automatically adjust the plot to fix this.
plt.tight_layout()
# Save it into our designated file, which is usually EPS format.
plt.savefig(output_filename)
# Insert git commit hashes into this file from the various code sources.
wdmerger.insert_commits_into_eps(output_filename, diag_filename, 'diag')
plt.close()
def plot_gw_signal(diag_filename,
output_filename,
n_orbits=-1,
do_analytical=0):
diag_file = open(diag_filename, 'r')
time = get_column("TIME", diag_filename)
hplus = get_column("h_+ (axis 3)", diag_filename)
hcross = get_column("h_x (axis 3)", diag_filename)
# Normalize time by rotational period.
rot_period = 0.0
dir = os.path.dirname(diag_filename)
inputs_filename = dir + '/' + wdmerger.get_inputs_filename(dir)
probin_filename = dir + '/probin'
rot_period = wdmerger.get_inputs_var(inputs_filename,
"castro.rotational_period")
if (rot_period > 0.0):
time = time / rot_period
xlabel = "Time / Rotational Period"
if (n_orbits > 0):
idx = np.where(time < n_orbits)
time = time[idx]
hplus = hplus[idx]
hcross = hcross[idx]
else:
xlabel = "Time (s)"
ylabel = "Gravitational Wave Strain"
markers = ['o', '+', '.', ',', '*']
# Now work out the analytical result for a circular binary orbit, if desired.
if (do_analytical == 1):
# Get relevant CGS constants.
G_const = wdmerger.get_castro_const('Gconst')
M_solar = wdmerger.get_castro_const('M_solar')
c_light = wdmerger.get_castro_const('c_light')
parsec = wdmerger.get_castro_const('parsec')
# Relevant variables from the probin file.
mass_P = wdmerger.get_probin_var(probin_filename, 'mass_P') * M_solar
mass_S = wdmerger.get_probin_var(probin_filename, 'mass_S') * M_solar
gw_dist = wdmerger.get_probin_var(probin_filename, 'gw_dist') # In kpc
mu = mass_P * mass_S / (mass_P + mass_S)
M_tot = mass_P + mass_S
dist = gw_dist * 1.e3 * parsec
omega = 2.0 * np.pi / rot_period
coeff = -4 * G_const * mu / (c_light**4 * dist) * (G_const * M_tot *
omega)**(2.0 / 3.0)
hplus_true = coeff * np.cos(2.0 * omega * rot_period * time)
hcross_true = coeff * np.sin(2.0 * omega * rot_period * time)
plt.plot(time, hplus_true, lw=4.0, ls='-')
plt.plot(time, hcross_true, lw=4.0, ls='--')
plt.plot(time,
hplus,
marker=markers[0],
ms=12.0,
markevery=200,
label=r"$\plus$" + " polarization")
plt.plot(time,
hcross,
marker=markers[4],
ms=12.0,
markevery=200,
label=r"$\times$" + " polarization")
else:
plt.plot(time,
hplus,
lw=4.0,
ls='-',
label=r"$\plus$" + " polarization")
plt.plot(time,
hcross,
lw=4.0,
ls='--',
label=r"$\times$" + " polarization")
plt.xlabel(xlabel, fontsize=20)
plt.ylabel(ylabel, fontsize=20)
# Use the 'best' location for the legend, since for a generic function like this
# it is hard to know ahead of time where the legend ought to go.
# The alpha value controls the transparency, since we may end up covering some data.
plt.legend(loc='best', fancybox=True)
# The padding ensures that the lower-left ticks on the x- and y-axes don't overlap.
plt.tick_params(labelsize=16, pad=10)
# We have now increased the size of both the ticks and the axis labels,
# which may have caused the latter to fall off the plot. Use tight_layout
# to automatically adjust the plot to fix this.
plt.tight_layout()
# Save it into our designated file, which is usually EPS format.
plt.savefig(output_filename)
# Insert git commit hashes into this file from the various code sources.
wdmerger.insert_commits_into_eps(output_filename, diag_filename, 'diag')
plt.close()
def plot_wd_location(diag_filenames, output_filename):
# If we got only a single diagnostic file,
# create a list out of it for the coming loop.
if (type(diag_filenames) not in [list, tuple]):
diag_filenames = [ diag_filenames ]
# Cycle through markers for multiple curves on the same plot.
N = len(diag_filenames)
# Determine the number of subplots and their layout.
nRows = 1
nCols = 1
if (N == 2):
nCols = 2
elif (N == 3):
nCols = 3
elif (N == 4):
nRows = 2
nCols = 2
if (N > 1):
fig, ax = plt.subplots(nRows, nCols)
else:
fig = plt.gcf()
ax = plt.gca()
row = 0
col = 0
j = 0
labels = ['(a)', '(b)', '(c)', '(d)']
for diag_filename in diag_filenames:
diag_file = open(diag_filename, 'r')
# x and y locations of each star
xp = get_column("PRIMARY X COM",diag_filename)
yp = get_column("PRIMARY Y COM",diag_filename)
xs = get_column("SECONDARY X COM",diag_filename)
ys = get_column("SECONDARY Y COM",diag_filename)
# Width of domain, from inputs file.
dir = os.path.dirname(diag_filename)
inputs_filename = dir + '/' + wdmerger.get_inputs_filename(dir)
problo = wdmerger.get_inputs_var(inputs_filename, "geometry.prob_lo")
probhi = wdmerger.get_inputs_var(inputs_filename, "geometry.prob_hi")
half_width = (probhi - problo) / 2.0
# If we are in the rotating frame, we want to transform back to the inertial frame.
do_rotation = wdmerger.get_inputs_var(inputs_filename, "castro.do_rotation")
if (do_rotation == 1):
time = get_column("TIME",diag_filename)
rot_period = wdmerger.get_inputs_var(inputs_filename, "castro.rotational_period")
theta = (2.0 * np.pi / rot_period) * time
# Rotate from rotating frame to inertial frame using rotation matrix
xp_old = xp
yp_old = yp
xp = xp_old * np.cos(theta) - yp_old * np.sin(theta)
yp = xp_old * np.sin(theta) + yp_old * np.cos(theta)
xs_old = xs
ys_old = ys
xs = xs_old * np.cos(theta) - ys_old * np.sin(theta)
ys = xs_old * np.sin(theta) + ys_old * np.cos(theta)
# Normalize the locations by this domain size, so that they are in the range [-0.5, 0.5]
xp = xp / half_width[0]
yp = yp / half_width[1]
xs = xs / half_width[0]
ys = ys / half_width[1]
xlabel = "x"
ylabel = "y"
if (N > 1):
curr_ax = ax[row,col]
else:
curr_ax = ax
# Determine the label and position of this subplot based on the number of inputs.
curr_ax.plot(xp, yp, 'b--')
curr_ax.plot(xs, ys, 'r')
curr_ax.set_xlabel(xlabel, fontsize=20)
curr_ax.set_ylabel(ylabel, fontsize=20)
curr_ax.set_xlim([-0.55, 0.55])
curr_ax.set_ylim([-0.55, 0.55])
curr_ax.xaxis.set_ticks([-0.50, 0.0, 0.50])
curr_ax.yaxis.set_ticks([-0.50, 0.0, 0.50])
curr_ax.set_aspect('equal')
# The padding ensures that the lower-left ticks on the x- and y-axes don't overlap.
curr_ax.tick_params(labelsize=16, pad=10)
# Give it a letter label, for multipanel figures.
if (N > 1):
curr_ax.annotate(labels[j], xy=(0.15, 0.85), xycoords='axes fraction', fontsize=16,
horizontalalignment='right', verticalalignment='bottom')
col += 1
if (col > nCols-1):
col = 0
row += 1
j += 1
# Tighten up figure layout.
fig.tight_layout()
# Save it into our designated file, which is usually EPS format.
plt.savefig(output_filename)
# Insert git commit hashes into this file from the various code sources.
wdmerger.insert_commits_into_eps(output_filename, diag_filename, 'diag')
plt.close()