plt.ylabel(r'Peak i-band magnitude')
plt.gca().invert_yaxis()
cbar = plt.colorbar()
cbar.set_label(r'Ejecta mass log10($M_{\odot}$)')
plt.savefig(plotName)
plt.close()
bounds = [15, 35]
xlims = [15.0, 35.0]
ylims = [1e-2, 1]
plotName = "%s/appi.pdf" % (plotDir)
plt.figure(figsize=(10, 8))
for ii, model in enumerate(models):
legend_name = get_legend(model)
bins, hist1 = lightcurve_utils.hist_results(
model_tables[model]["peak_appmag_i"], Nbins=25, bounds=bounds)
plt.semilogy(bins,
hist1,
'-',
color=colors_names[ii],
linewidth=3,
label=legend_name)
plt.xlabel(r"Apparent Magnitude [mag]", fontsize=24)
plt.ylabel('Probability Density Function', fontsize=24)
plt.legend(loc="best", prop={'size': 24})
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.xlim(xlims)
plt.ylim(ylims)
plt.savefig(plotName)
plt.close()
linestyle='--',
linewidth=2)
g.ax_marg_y.plot([0., 1500], [197., 197.],
color=color2,
linestyle='--',
linewidth=2)
g.ax_marg_x.text(1.42, 3500, '$q < 1.38$', color=color2, fontsize=14)
g.ax_marg_y.text(1100,
150,
'$\\tilde{\\Lambda} > 197$',
color=color2,
fontsize=14,
rotation=-90)
bounds = [1.0, 1.7]
bins, hist1 = lightcurve_utils.hist_results(q_gw, Nbins=15, bounds=bounds)
hist1 = 4800.0 * hist1 / np.max(hist1)
for ii in range(len(bins) - 1):
bin_start, bin_end = bins[ii], bins[ii + 1]
val = hist1[ii]
#g.ax_marg_x.fill_between([bin_start, bin_end],[0,0],[val,val],facecolor=color1,alpha=1.0)
#g.ax_marg_x.plot([bin_start, bin_end],[val,val],color=color1,alpha=1.0)
bounds = [0, 600]
bins, hist1 = lightcurve_utils.hist_results(lambdatilde_gw,
Nbins=25,
bounds=bounds)
hist1 = 1500.0 * hist1 / np.max(hist1)
for ii in range(len(bins) - 1):
bin_start, bin_end = bins[ii], bins[ii + 1]
val = hist1[ii]
elif opts.labelType == "model":
color = colors[ii]
colortrue = colors[ii]
linestyle = linestyles[jj]
else:
color = 'b'
colortrue = 'k'
linestyle = '-'
samples = np.log10(post[name][errorbudget]["mej"])
if (opts.labelType == "errorbar") and (float(errorbudget) < 1.0):
bounds = [-2.8, -1.8]
else:
bounds = [-3.5, 0.0]
bins, hist1 = lightcurve_utils.hist_results(samples,
Nbins=25,
bounds=bounds)
if (opts.labelType == "name" or opts.labelType == "model") and jj > 0:
plt.semilogy(bins, hist1, '%s%s' % (color, linestyle), linewidth=3)
else:
plt.semilogy(bins,
hist1,
'%s%s' % (color, linestyle),
label=label,
linewidth=3)
plt.semilogy([
post[name][errorbudget]["truths"][1],
post[name][errorbudget]["truths"][1]
], [1e-3, 100.0],
'%s--' % colortrue,
color2 = 'coral'
color1 = 'cornflowerblue'
color3 = 'palegreen'
color4 = 'darkmagenta'
colors_names = [color1, color2, color3, color4]
bounds = [16, 34]
xlims = [15.0, 35.0]
ylims = [1e-2, 1]
plotName = "%s/appi.pdf" % (baseoutputDir)
plt.figure(figsize=(12, 8))
for ii, distance_set in enumerate(distance_sets):
distance_underscore = "%d_%d" % (distance_set[0], distance_set[1])
legend_name = "%d-%d Mpc" % (distance_set[0], distance_set[1])
bins, hist1 = lightcurve_utils.hist_results(
data[distance_underscore]["K"][:, 4], Nbins=25, bounds=bounds)
plt.semilogy(bins,
hist1,
'-',
color=colors_names[ii],
linewidth=3,
label=legend_name)
bins, hist1 = lightcurve_utils.hist_results(
data[distance_underscore]["g"][:, 4], Nbins=25, bounds=bounds)
plt.semilogy(bins, hist1, '--', color=colors_names[ii], linewidth=3)
plt.xlabel(r"Apparent Magnitude [mag]", fontsize=24)
plt.ylabel('Probability Density Function', fontsize=24)
plt.legend(loc="best", prop={'size': 24})
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.xlim(xlims)
opts.analysisType) + "_m1_" + str(
np.round(m1m, decimals=1)) + "_m2_" + str(
np.round(
m2m,
decimals=1)) + '_chi_' + str(chi) + ".pdf"
if twixie_tf:
plotName = "/home/andrew.toivonen/gwemlightcurves/mass_plots/mej_" + str(
opts.analysisType) + "_m1_" + str(
np.round(m1m, decimals=1)) + "_m2_" + str(
np.round(m2m, decimals=1)) + "twixie.pdf"
plt.figure(figsize=(15, 10))
ax = plt.gca()
for ii, model in enumerate(models):
legend_name = get_legend(model) + ' EOS: ' + runType
bins, hist1 = lightcurve_utils.hist_results(
np.log10(samples_gp["mej"]),
Nbins=20,
bounds=bounds)
plt.step(bins,
hist1,
'-',
color='b',
linewidth=3,
label=legend_name,
where='mid')
lim = np.percentile(np.log10(samples_gp["mej"]), 90)
plt.plot([lim, lim], ylims, 'k--')
plt.xlabel(r"${\rm log}_{10} (M_{\rm ej})$", fontsize=24)
plt.ylabel('Probability Density Function', fontsize=24)
#plt.legend(loc="best",prop={'size':24})
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
samples['mej'] = samples['mej'] * 10.0
idx = np.where(samples['mej'] > 0.1)[0]
samples['mej'][idx] = 0.1
bounds = [-3.0, -1.0]
xlims = [-2.8, -1.0]
ylims = [1e-1, 2]
plotName = "%s/mej.pdf" % (plotDir)
plt.figure(figsize=(15, 10))
ax = plt.gca()
for ii, model in enumerate(models):
legend_name = get_legend(model)
bins, hist1 = lightcurve_utils.hist_results(np.log10(samples["mej"]),
Nbins=20,
bounds=bounds)
plt.step(bins,
hist1,
'-',
color='k',
linewidth=3,
label=legend_name,
where='mid')
lim = np.percentile(np.log10(samples["mej"]), 90)
plt.plot([lim, lim], ylims, 'k--')
plt.xlabel(r"${\rm log}_{10} (M_{\rm ej})$", fontsize=24)
plt.ylabel('Probability Density Function', fontsize=24)
#plt.legend(loc="best",prop={'size':24})
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)