8]
idx = np.argmax(loglikelihood)
t0_best, mej_1_best, vej_1_best, Xlan_1_best, mej_2_best, vej_2_best, Xlan_2_best, zp_best = data[
idx, 0], 10**data[idx, 1], data[idx, 2], 10**data[idx, 3], 10**data[
idx, 4], data[idx, 5], 10**data[idx, 6], data[idx, 7]
if opts.doAbsorption:
t_best, lambdas_best, spec_best = Ka2017x2_model_spec_ejecta_absorption(
mej_1_best, vej_1_best, Xlan_1_best, mej_2_best, vej_2_best,
Xlan_2_best)
else:
t_best, lambdas_best, spec_best = Ka2017x2_model_spec_ejecta(
mej_1_best, vej_1_best, Xlan_1_best, mej_2_best, vej_2_best,
Xlan_2_best)
truths = lightcurve_utils.get_truths(opts.name, opts.model, n_params, True)
pcklFile = os.path.join(plotDir, "data.pkl")
f = open(pcklFile, 'wb')
pickle.dump((data_out, data, t_best, lambdas_best, spec_best, t0_best, zp_best,
n_params, labels, truths), f)
f.close()
if n_params >= 8:
title_fontsize = 26
label_fontsize = 30
else:
title_fontsize = 24
label_fontsize = 28
plotName = "%s/corner.pdf" % (plotDir)
svd_mag_color_model = pickle.load(handle)
Global.svd_mag_color_model = svd_mag_color_model
elif opts.model in ["Ka2017x2inc", "Ka2017x3inc"]:
Global.svd_mag_color_models = []
for colorm in colormodel:
if colorm == "a1.0":
Global.svd_mag_color_models.append("a1.0")
else:
modelfile = os.path.join(ModelPath, '%s.pkl' % colorm)
with open(modelfile, 'rb') as handle:
svd_mag_color_model = pickle.load(handle)
Global.svd_mag_color_models.append(svd_mag_color_model)
data, tmag, lbol, mag, t0_best, zp_best, n_params, labels, best = run.multinest(
opts, plotDir)
truths = lightcurve_utils.get_truths(opts.name, opts.model, n_params,
opts.doEjecta)
pcklFile = os.path.join(plotDir, "data.pkl")
f = open(pcklFile, 'wb')
pickle.dump((data_out, data, tmag, lbol, mag, t0_best, zp_best, n_params,
labels, best, truths), f)
f.close()
if n_params >= 6:
title_fontsize = 36
label_fontsize = 36
else:
title_fontsize = 30
label_fontsize = 30
plotName = "%s/corner.pdf" % (plotDir)