def generate_lightcurve(model, samples):
t = Table()
for key in samples.keys():
val = samples[key]
t.add_column(Column(data=[val], name=key))
samples = t
model_table = KNTable.model(model, samples)
if len(model_table) == 0:
return [], [], []
else:
t, lbol, mag = model_table["t"][0], model_table["lbol"][
0], model_table["mag"][0]
return t, lbol, mag
def generate_spectra(model, samples):
#kwargs = {'SaveModel':True,'LoadModel':False,'ModelPath':ModelPath}
kwargs = {'SaveModel': False, 'LoadModel': True, 'ModelPath': ModelPath}
kwargs["doAB"] = False
kwargs["doSpec"] = True
t = Table()
for key, val in samples.iteritems():
t.add_column(Column(data=[val], name=key))
samples = t
model_table = KNTable.model(model, samples, **kwargs)
if len(model_table) == 0:
return [], [], []
else:
t, lambdas, spec = model_table["t"][0], model_table["lambda"][
0], model_table["spec"][0]
return t, lambdas, spec
def generate_lightcurve(model, samples):
t = Table()
for key in samples.keys():
val = samples[key]
t.add_column(Column(data=[val], name=key))
samples = t
if Global.n_coeff > 0:
samples["n_coeff"] = Global.n_coeff
try:
samples["gptype"] = Global.gptype
except:
pass
model_table = KNTable.model(model, samples)
if len(model_table) == 0:
return [], [], []
else:
t, lbol, mag = model_table["t"][0], model_table["lbol"][
0], model_table["mag"][0]
return t, lbol, mag
samples['eth'] = eth
samples['flgbct'] = flgbct
samples['beta'] = beta
samples['kappa_r'] = kappa_r
samples['slope_r'] = slope_r
samples['theta_r'] = theta_r
samples['Ye'] = Ye
kwargs = {'SaveModel': False, 'LoadModel': True, 'ModelPath': ModelPath}
kwargs["doAB"] = True
kwargs["doSpec"] = False
# Create dict of tables for the various models, calculating mass ejecta velocity of ejecta and the lightcurve from the model
model_tables = {}
for model in models:
model_tables[model] = KNTable.model(model, samples, **kwargs)
# Now we need to do some interpolation
for model in models:
model_tables[model] = lightcurve_utils.calc_peak_mags(model_tables[model])
#model_tables[model] = lightcurve_utils.interpolate_mags_lbol(model_tables_lbol[model])
baseplotDir = opts.plotDir
plotDir = os.path.join(baseplotDir, "_".join(models))
plotDir = os.path.join(plotDir, "event")
plotDir = os.path.join(plotDir, opts.name)
if opts.analysisType == "cbclist":
plotDir = os.path.join(plotDir, opts.cbc_type)
plotDir = os.path.join(plotDir,
"%d_%d" % (opts.mindistance, opts.maxdistance))
if opts.doSaveModel:
kwargs = {'SaveModel': True, 'LoadModel': False, 'ModelPath': ModelPath}
else:
kwargs = {'SaveModel': False, 'LoadModel': True, 'ModelPath': ModelPath}
kwargs["doAB"] = opts.doAB
kwargs["doSpec"] = opts.doSpec
kwargs["phi"] = phi
t = Table()
for key in samples.keys():
val = samples[key]
t.add_column(Column(data=[val], name=key))
samples = t
if not opts.model in ["SN", "Afterglow"]:
model_table = KNTable.model(opts.model, samples, **kwargs)
if opts.doAB:
t, lbol, mag = model_table["t"][0], model_table["lbol"][
0], model_table["mag"][0]
elif opts.doSpec:
t, lambdas, spec = model_table["t"][0], model_table["lambda"][
0], model_table["spec"][0]
if opts.model == "KaKy2016":
if opts.doEjecta:
name = "KaKy2016_%sM%03dV%02d" % (opts.eos, opts.mej * 1000,
opts.vej * 100)
elif opts.doMasses:
name = "%sQ%.0fa%.0f" % (opts.eos, opts.massratio, opts.chi_eff * 100)
elif opts.model == "DiUj2017":
if opts.doEjecta:
kwargs = {'SaveModel': False, 'LoadModel': True, 'ModelPath': ModelPath}
kwargs["doAB"] = True
kwargs["doSpec"] = False
filts = ["u", "g", "r", "i", "z", "y", "J", "H", "K"]
errorbudget = 0.0
tini, tmax, dt = 0.1, 14.0, 0.1
for grb in data_out.keys():
print('Generating lightcurves for %s' % grb)
if "KN_samples" in data_out[grb]:
samples = data_out[grb]["KN_samples"]
samples['tini'] = tini
samples['tmax'] = tmax
samples['dt'] = dt
data_out[grb]["KN_model"] = KNTable.model('Ka2017', samples, **kwargs)
for ii, row in enumerate(data_out[grb]["KN_model"]):
A = data_out[grb]["KN_model"][ii]["A"]
dm = -2.5 * np.log10(A)
data_out[grb]["KN_model"][ii][
"mag"] = data_out[grb]["KN_model"][ii]["mag"] + dm
data_out[grb]["KN_model"][ii][
"lbol"] = data_out[grb]["KN_model"][ii]["lbol"] * A
data_out[grb]["KN_med"] = lightcurve_utils.get_med(
data_out[grb]["KN_model"], errorbudget=errorbudget)
tt = np.arange(tini, tmax + dt, dt)
#colors = ['coral','cornflowerblue','palegreen','goldenrod']
keys = data_out.keys()
colors = cm.rainbow(np.linspace(0, 1, len(keys)))
colors = ['coral', 'cornflowerblue', 'palegreen', 'goldenrod']
samples['tmax'] = tmax
samples['dt'] = dt
kwargs = {'SaveModel':False,'LoadModel':True,'ModelPath':ModelPath}
kwargs["doAB"] = True
kwargs["doSpec"] = False
# Create dict of tables for the various models, calculating mass ejecta velocity of ejecta and the lightcurve from the model
pcklFile = os.path.join(plotDir,"data.pkl")
if os.path.isfile(pcklFile):
f = open(pcklFile, 'r')
(model_table) = pickle.load(f)
f.close()
else:
if opts.model == "Ka2017_TrPi2018":
model_table = KNTable.model("Ka2017", samples, **kwargs)
elif opts.model == "Bu2019inc_TrPi2018":
model_table = KNTable.model("Bu2019inc", samples, **kwargs)
else:
model_table = KNTable.model(opts.model, samples, **kwargs)
f = open(pcklFile, 'wb')
pickle.dump((model_table), f)
f.close()
if opts.model == "Ka2017_TrPi2018":
samples_gw = KNTable.read_multinest_samples(opts.multinest_samples, "Ka2017")
elif opts.model == "Bu2019inc_TrPi2018":
samples_gw = KNTable.read_multinest_samples(opts.multinest_samples, "Bu2019inc")
else:
samples_gw = KNTable.read_multinest_samples(opts.multinest_samples, opts.model)
t['dt'] = dt
t['vmin'] = vmin
t['th'] = th
t['ph'] = ph
t['kappa'] = kappa
t['eps'] = eps
t['alp'] = alp
t['eth'] = eth
t['flgbct'] = flgbct
t['beta'] = beta
t['kappa_r'] = kappa_r
t['slope_r'] = slope_r
t['theta_r'] = theta_r
t['Ye'] = Ye
distance = 100 #Mpc
t['dist'] = distance
# Create dict of tables for the various models, calculating mass ejecta velocity of ejecta and the lightcurve from the model
models = ["DiUj2017", "Me2017"]
model_tables = {}
for model in models:
model_tables[model] = KNTable.model(model, t)
# Now we need to do some interpolation
for model in models:
model_tables[model] = lightcurve_utils.calc_peak_mags(model_tables[model])
model_tables[model] = lightcurve_utils.interpolate_mags_lbol(
model_tables[model])
plot = KNTable.plot_mag_panels(model_tables, distance=distance)
plt.savefig("test.png")
def calc_lightcurve(self, model_KN):
self.samples = self.samples.downsample(Nsamples=1000)
#add default values from above to table
self.samples['tini'] = tini
self.samples['tmax'] = tmax
self.samples['dt'] = dt
self.samples['vmin'] = vmin
self.samples['th'] = th
self.samples['ph'] = ph
self.samples['kappa'] = kappa
self.samples['eps'] = eps
self.samples['alp'] = alp
self.samples['eth'] = eth
self.samples['flgbct'] = flgbct
self.samples['beta'] = beta
self.samples['kappa_r'] = kappa_r
self.samples['slope_r'] = slope_r
self.samples['theta_r'] = theta_r
self.samples['Ye'] = Ye
kwargs = {
'SaveModel': False,
'LoadModel': True,
'ModelPath': ModelPath
}
kwargs["doAB"] = True
kwargs["doSpec"] = False
model_tables = {}
models = model_KN.split(",")
for model in models:
model_tables[model] = KNTable.model(model, self.samples, **kwargs)
if (model_KN == "Bu2019inc"):
idx = np.where(model_tables[model]['mej'] <= 1e-6)[0]
model_tables[model]['mag'][idx] = 10.
model_tables[model]['lbol'][idx] = 1e30
elif (model_KN == "Ka2017"):
idx = np.where(model_tables[model]['mej'] <= 1e-3)[0]
model_tables[model]['mag'][idx] = 10.
model_tables[model]['lbol'][idx] = 1e30
for model in models:
model_tables[model] = lightcurve_utils.calc_peak_mags(
model_tables[model])
mag_all = {}
app_mag_all_mbta = {}
lbol_all = {}
for model in models:
mag_all[model] = {}
app_mag_all_mbta[model] = {}
lbol_all[model] = {}
lbol_all[model] = np.empty((0, len(tt)), float)
for filt, magidx in zip(filts, magidxs):
mag_all[model][filt] = np.empty((0, len(tt)))
app_mag_all_mbta[model][filt] = np.empty((0, len(tt)))
peak_mags_all = {}
for model in models:
model_tables[model] = lightcurve_utils.calc_peak_mags(
model_tables[model])
for row in model_tables[model]:
t, lbol, mag = row["t"], row["lbol"], row["mag"]
dist_mbta = row['dist_mbta']
if np.sum(lbol) == 0.0:
#print "No luminosity..."
continue
allfilts = True
for filt, magidx in zip(filts, magidxs):
idx = np.where(~np.isnan(mag[magidx]))[0]
if len(idx) == 0:
allfilts = False
break
if not allfilts: continue
for filt, magidx in zip(filts, magidxs):
idx = np.where(~np.isnan(mag[magidx]))[0]
f = interp.interp1d(t[idx],
mag[magidx][idx],
fill_value='extrapolate')
maginterp = f(tt)
app_maginterp_mbta = maginterp + 5 * (np.log10(
(dist_mbta) * 1e6) - 1)
mag_all[model][filt] = np.append(mag_all[model][filt],
[maginterp],
axis=0)
app_mag_all_mbta[model][filt] = np.append(
app_mag_all_mbta[model][filt], [app_maginterp_mbta],
axis=0)
idx = np.where((~np.isnan(np.log10(lbol))) & ~(lbol == 0))[0]
f = interp.interp1d(t[idx],
np.log10(lbol[idx]),
fill_value='extrapolate')
lbolinterp = 10**f(tt)
lbol_all[model] = np.append(lbol_all[model], [lbolinterp],
axis=0)
return (mag_all, app_mag_all_mbta, lbol_all)
