def do_knfit(df,
outputDir='./knfit',
errorbudget=1.0,
n_live_points=100,
evidence_tolerance=0.5):
if len(df.index) < 2:
print('Not enough data for candidate %s... continuing.',
df.candname.values[0])
return
if not os.path.isdir(outputDir):
os.makedirs(outputDir)
candname = df.name.values[0].decode()
filters = list(set(df.filtname))
mint = 0.0
maxt = 7.0
dt = 0.05
tt = np.arange(mint, maxt, dt)
doWaveformExtrapolate = True
ZPRange = 5.0
T0Range = 2.0
ModelPath = "../gwemlightcurves/output/svdmodels/"
T0 = np.inf
mag_min = np.inf
data_out = {}
for index, row in df.iterrows():
filt = row.filtname
mag = row.magpsf
dmag = row.sigmapsf
mjd = Time(row.jd, format='jd').mjd
magzp = row.magzpsci
if 99.0 == mag:
mag = magzp
dmag = np.inf
else:
T0 = np.min([T0, mjd])
mag_min = np.min([mag_min, mag])
if not filt in data_out:
data_out[filt] = np.empty((0, 3), float)
data_out[filt] = np.append(data_out[filt],
np.array([[mjd, mag, dmag]]),
axis=0)
distmod = mag_min - -16
distance = 10**((distmod / 5.0) + 1.0) / 1e6
for ii, key in enumerate(data_out.keys()):
if key == "t":
continue
else:
data_out[key][:, 0] = data_out[key][:, 0] - T0
data_out[key][:, 1] = data_out[key][:, 1] - 5 * (
np.log10(distance * 1e6) - 1)
for ii, key in enumerate(data_out.keys()):
if key == "t":
continue
else:
idxs = np.intersect1d(
np.where(data_out[key][:, 0] >= mint)[0],
np.where(data_out[key][:, 0] <= maxt)[0])
data_out[key] = data_out[key][idxs, :]
for ii, key in enumerate(data_out.keys()):
idxs = np.where(~np.isnan(data_out[key][:, 2]))[0]
if key == "t":
continue
else:
data_out[key] = data_out[key][idxs, :]
for ii, key in enumerate(data_out.keys()):
if not key in filters:
del data_out[key]
for ii, key in enumerate(data_out.keys()):
if ii == 0:
samples = data_out[key].copy()
else:
samples = np.vstack((samples, data_out[key].copy()))
idx = np.argmin(samples[:, 0])
samples = samples[idx, :]
Global.data_out = data_out
Global.errorbudget = errorbudget
Global.ZPRange = ZPRange
Global.T0Range = T0Range
Global.doLightcurves = 1
Global.filters = filters
Global.doWaveformExtrapolate = doWaveformExtrapolate
modelfile = os.path.join(ModelPath, 'Bu2019inc_mag.pkl')
with open(modelfile, 'rb') as handle:
svd_mag_model = pickle.load(handle)
Global.svd_mag_model = svd_mag_model
modelfile = os.path.join(ModelPath, 'Bu2019inc_lbol.pkl')
with open(modelfile, 'rb') as handle:
svd_lbol_model = pickle.load(handle)
Global.svd_lbol_model = svd_lbol_model
plotDir = os.path.join(outputDir, candname)
if not os.path.isdir(plotDir):
os.makedirs(plotDir)
max_iter = -1
best = []
parameters = ["t0", "mej", "phi", "theta", "zp"]
labels = [
r"$T_0$", r"${\rm log}_{10} (M_{\rm ej})$", r"$\Phi$", r"$\Theta$",
"ZP"
]
n_params = len(parameters)
pymultinest.run(myloglike_Bu2019inc_ejecta,
myprior_Bu2019inc_ejecta,
n_params,
importance_nested_sampling=False,
resume=True,
verbose=True,
sampling_efficiency='parameter',
n_live_points=n_live_points,
outputfiles_basename='%s/2-' % plotDir,
evidence_tolerance=evidence_tolerance,
multimodal=False,
max_iter=max_iter)
multifile = lightcurve_utils.get_post_file(plotDir)
data = np.loadtxt(multifile)
t0, mej, phi, theta, zp, loglikelihood = data[:,
0], 10**data[:,
1], data[:,
2], data[:,
3], data[:,
4], data[:,
5]
idx = np.argmax(loglikelihood)
t0_best, mej_best, phi_best, theta_best, zp_best = data[idx, 0], 10**data[
idx, 1], data[idx, 2], data[idx, 3], data[idx, 4]
zp_mu, zp_std = 0.0, Global.ZPRange
zp_best = scipy.stats.norm(zp_mu, zp_std).ppf(zp_best)
tmag, lbol, mag = Bu2019inc_model_ejecta(mej_best, phi_best, theta_best)
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), f)
f.close()
title_fontsize = 30
label_fontsize = 30
plotName = "%s/corner.pdf" % (plotDir)
figure = corner.corner(data[:, :-1],
labels=labels,
quantiles=[0.16, 0.5, 0.84],
show_titles=True,
title_kwargs={"fontsize": title_fontsize},
label_kwargs={"fontsize": label_fontsize},
title_fmt=".2f",
smooth=3,
color="coral")
figure.set_size_inches(14.0, 14.0)
plt.savefig(plotName)
plt.close()
tmag = tmag + t0_best
#colors=cm.rainbow(np.linspace(0,1,len(filters)))
colors = cm.Spectral(np.linspace(0, 1, len(filters)))[::-1]
color2 = 'coral'
color1 = 'cornflowerblue'
plotName = "%s/models_panels.pdf" % (plotDir)
#plt.figure(figsize=(20,18))
plt.figure(figsize=(20, 28))
cnt = 0
for filt, color in zip(filters, colors):
cnt = cnt + 1
if cnt == 1:
ax1 = plt.subplot(len(filters), 1, cnt)
else:
ax2 = plt.subplot(len(filters), 1, cnt, sharex=ax1, sharey=ax1)
if not filt in data_out: continue
samples = data_out[filt]
t, y, sigma_y = samples[:, 0], samples[:, 1], samples[:, 2]
idx = np.where(~np.isnan(y))[0]
t, y, sigma_y = t[idx], y[idx], sigma_y[idx]
if len(t) == 0: continue
idx = np.where(np.isfinite(sigma_y))[0]
plt.errorbar(t[idx],
y[idx],
sigma_y[idx],
fmt='o',
c=color,
markersize=16,
label='%s-band' % filt)
idx = np.where(~np.isfinite(sigma_y))[0]
plt.errorbar(t[idx],
y[idx],
sigma_y[idx],
fmt='v',
c=color,
markersize=16)
magave = lightcurve_utils.get_mag(mag, filt)
ii = np.where(~np.isnan(magave))[0]
f = interp.interp1d(tmag[ii], magave[ii], fill_value='extrapolate')
maginterp = f(tt)
#plt.plot(tt,maginterp+zp_best,'--',c=color,linewidth=3)
#plt.fill_between(tt,maginterp+zp_best-errorbudget,maginterp+zp_best+errorbudget,facecolor=color,alpha=0.2)
plt.plot(tt, maginterp + zp_best, '--', c=color2, linewidth=3)
plt.fill_between(tt,
maginterp + zp_best - errorbudget,
maginterp + zp_best + errorbudget,
facecolor=color2,
alpha=0.2)
plt.ylabel('%s' % filt, fontsize=48, rotation=0, labelpad=40)
plt.xlim([0.0, 10.0])
plt.ylim([-22.0, -8.0])
plt.gca().invert_yaxis()
plt.grid()
if cnt == 1:
ax1.set_yticks([-22, -18, -14, -10])
plt.setp(ax1.get_xticklabels(), visible=False)
#l = plt.legend(loc="upper right",prop={'size':36},numpoints=1,shadow=True, fancybox=True)
elif not cnt == len(filters):
plt.setp(ax2.get_xticklabels(), visible=False)
plt.xticks(fontsize=36)
plt.yticks(fontsize=36)
ax1.set_zorder(1)
plt.xlabel('Time [days]', fontsize=48)
plt.tight_layout()
plt.savefig(plotName)
plt.close()
return
parameters = ["q", "lambdatilde"]
n_params = len(parameters)
pymultinest.run(myloglike_combined,
myprior_combined,
n_params,
importance_nested_sampling=False,
resume=True,
verbose=True,
sampling_efficiency='parameter',
n_live_points=n_live_points,
outputfiles_basename='%s/2-' % plotDir,
evidence_tolerance=evidence_tolerance,
multimodal=False)
labels = [r"q", r"$\tilde{\Lambda}$"]
multifile = lightcurve_utils.get_post_file(plotDir)
data_combined = np.loadtxt(multifile)
q_combined = data_combined[:, 0]
lambdatilde_combined = data_combined[:, 1]
data_combined = np.vstack((q_combined, lambdatilde_combined)).T
plotName = "%s/corner_combined.pdf" % (plotDir)
figure = corner.corner(data_combined,
labels=labels,
quantiles=[0.16, 0.5, 0.84],
show_titles=True,
title_kwargs={"fontsize": 24},
label_kwargs={"fontsize": 28},
title_fmt=".2f")
figure.set_size_inches(14.0, 14.0)
plt.savefig(plotName)
def plot_results(plotDir):
multifile = lightcurve_utils.get_post_file(plotDir)
data = np.loadtxt(multifile)
a = pymultinest.Analyzer(n_params=n_params,
outputfiles_basename='%s/2-' % plotDir)
s = a.get_stats()
if opts.model == "BlackBody":
T = data[:, 0]
F = data[:, 1]
loglikelihood = data[:, 2]
idx = np.argmax(loglikelihood)
T_best = data[idx, 0]
F_best = data[idx, 1]
truths = [np.nan, np.nan]
wav, flux = spec_model(T_best, 10**F_best)
spec_best = {}
spec_best["lambda"] = wav
spec_best["data"] = flux
elif opts.model == "BlackBodyx2":
T1 = data[:, 0]
F1 = data[:, 1]
T2 = data[:, 2]
F2 = data[:, 3]
loglikelihood = data[:, 4]
idx = np.argmax(loglikelihood)
T1_best = data[idx, 0]
F1_best = data[idx, 1]
T2_best = data[idx, 2]
F2_best = data[idx, 3]
truths = [np.nan, np.nan, np.nan, np.nan]
wav1, flux1 = spec_model(T1_best, 10**F1_best)
wav2, flux2 = spec_model(T2_best, 10**F2_best)
spec_best = {}
spec_best["lambda"] = wav1
spec_best["data"] = flux1 + flux2
if n_params >= 8:
title_fontsize = 26
label_fontsize = 30
else:
title_fontsize = 24
label_fontsize = 28
plotName = "%s/corner.pdf" % (plotDir)
figure = corner.corner(data[:, :-1],
labels=labels,
quantiles=[0.16, 0.5, 0.84],
show_titles=True,
title_kwargs={"fontsize": title_fontsize},
label_kwargs={"fontsize": label_fontsize},
title_fmt=".2f",
truths=truths)
if n_params >= 8:
figure.set_size_inches(18.0, 18.0)
else:
figure.set_size_inches(14.0, 14.0)
plt.savefig(plotName)
plt.close()
plotName = "%s/spec.pdf" % (plotDir)
plt.figure(figsize=(14, 12))
plt.loglog(spec_best["lambda"], spec_best["data"], 'k--', linewidth=2)
plt.errorbar(data_out["lambda"],
np.abs(data_out["data"]),
yerr=data_out["error"],
fmt='ro',
linewidth=2)
plt.xlabel(r'$\lambda [\AA]$', fontsize=24)
plt.ylabel('Fluence [erg/s/cm2/A]', fontsize=24)
#plt.legend(loc="best",prop={'size':16},numpoints=1)
ymin = np.min(np.abs(data_out["data"]) - 2 * data_out["error"])
ymax = np.max(np.abs(data_out["data"]) + 2 * data_out["error"])
plt.ylim([ymin, ymax])
plt.grid()
plt.savefig(plotName)
plt.close()
filename = os.path.join(plotDir, 'evidence.dat')
fid = open(filename, 'w+')
fid.write("%.15e %.15e" % (s['nested sampling global log-evidence'],
s['nested sampling global log-evidence error']))
fid.close()
if opts.model == "BlackBody":
filename = os.path.join(plotDir, 'samples.dat')
fid = open(filename, 'w+')
for i, j in zip(T, F):
fid.write('%.5f %.5f\n' % (i, j))
fid.close()
filename = os.path.join(plotDir, 'best.dat')
fid = open(filename, 'w')
fid.write('%.5f %.5f\n' % (T_best, F_best))
fid.close()
elif opts.model == "BlackBodyx2":
filename = os.path.join(plotDir, 'samples.dat')
fid = open(filename, 'w+')
for i, j, k, l in zip(T1, F1, T2, F2):
fid.write('%.5f %.5f %.5f %.5f\n' % (i, j, k, l))
fid.close()
filename = os.path.join(plotDir, 'best.dat')
fid = open(filename, 'w')
fid.write('%.5f %.5f %.5f %.5f\n' %
(T1_best, F1_best, T2_best, F2_best))
fid.close()
grb = grbdir.split("/")[-1]
grbdir = os.path.join(grbdir, '1.00')
dataDirs.append(grbdir)
grbs.append(grb)
#grbs_skip = ["GW170817","GRB060614","GRB050709","GRB130603B"]
grbs_include = ["GW170817", "GRB060614", "GRB050709", "GRB130603B"]
nsamples = 100
data_out = {}
for grb, dataDir in zip(grbs, dataDirs):
if not grb in grbs_include: continue
if not grb in data_out:
data_out[grb] = {}
multifile = lightcurve_utils.get_post_file(dataDir)
data = np.loadtxt(multifile)
#if (data.size > 0) and (not grb in grbs_skip):
if data.size > 0:
data_out[grb]["KN"] = data
data_out[grb]["KN_samples"] = KNTable.read_multinest_samples(
multifile, 'Ka2017_A')
data_out[grb]["KN_samples"] = data_out[grb]["KN_samples"].downsample(
Nsamples=nsamples)
ModelPath = '%s/svdmodels' % ('../output')
kwargs = {'SaveModel': False, 'LoadModel': True, 'ModelPath': ModelPath}
kwargs["doAB"] = True
kwargs["doSpec"] = False
filts = ["u", "g", "r", "i", "z", "y", "J", "H", "K"]
basedataDir = dataDir
post = {}
for name in names:
post[name] = {}
for errorbudget in errorbudgets:
if opts.doModels:
plotDir = os.path.join(baseplotDir,"%.2f"%float(errorbudget))
dataDir = plotDir.replace("fitting_models","models").replace("_EOSFit","")
elif opts.doSimulation:
plotDir = os.path.join(baseplotDir,"%.3f"%(float(errorbudget)*100.0))
elif opts.doGoingTheDistance or opts.doMassGap:
dataDir = os.path.join(basedataDir,"%.2f"%float(errorbudget))
plotDir = os.path.join(baseplotDir,"%.2f"%float(errorbudget))
multifile = lightcurve_utils.get_post_file(dataDir)
data = np.loadtxt(multifile)
filename = os.path.join(dataDir,"truth_mej_vej.dat")
truths_mej_vej = np.loadtxt(filename)
truths_mej_vej[0] = np.log10(truths_mej_vej[0])
filename = os.path.join(dataDir,"truth.dat")
truths = np.loadtxt(filename)
if opts.doEjecta:
mej_em = data[:,1]
vej_em = data[:,2]
mej_true = truths_mej_vej[0]
vej_true = truths_mej_vej[1]
plt.close()
H0Dir = os.path.join(plotDir,'H0')
if not os.path.isdir(H0Dir):
os.makedirs(H0Dir)
rm_command = "rm %s/*" % H0Dir
#os.system(rm_command)
parameters = ["H0","Om0","0de"]
labels = [r'$H_0$',r'$\Omega_m$',r'$\Omega_\Lambda$']
n_params = len(parameters)
pymultinest.run(myloglike_H0, myprior_H0, n_params, importance_nested_sampling = False, resume = True, verbose = True, sampling_efficiency = 'parameter', n_live_points = n_live_points, outputfiles_basename='%s/2-'%H0Dir, evidence_tolerance = evidence_tolerance, multimodal = False, max_iter = max_iter)
multifile = lightcurve_utils.get_post_file(H0Dir)
data = np.loadtxt(multifile)
H0_EM, Om0, Ode0, loglikelihood = data[:,0], data[:,1], data[:,2], data[:,3]
idx = np.argmax(loglikelihood)
H0_best, Om0_best, Ode0_best = data[idx,0:-1]
plotName = "%s/corner.pdf"%(H0Dir)
figure = corner.corner(data[:,:-1], labels=labels,
quantiles=[0.16, 0.5, 0.84],
show_titles=True, title_kwargs={"fontsize": title_fontsize},
label_kwargs={"fontsize": label_fontsize}, title_fmt=".3f",
smooth=3)
figure.set_size_inches(18.0,18.0)
plt.savefig(plotName)
plt.close()
