def at2018gep(ax):
""" Bolometric LC of AT2018gep """
dt, lum, llum, ulum = load_lc()
ax.errorbar(dt,
lum,
yerr=[llum, ulum],
fmt='s',
c='k',
zorder=10,
label="SN2018gep")
def plot_18gep():
""" Plot the bolometric LC of 2018gep """
dt, lum, llum, ulum = load_lc()
plt.errorbar(dt,
lum,
yerr=[llum, ulum],
fmt='o',
c='#140b34',
mfc='#140b34',
mec='#140b34',
lw=0.5,
label="SN2018gep")
def lum_panel(ax, lines=True):
""" Panel showing the luminosity evolution """
dt, lum, llum, ulum = load_lc()
# choose just the optical points
opt = np.logical_or(dt < 0.1, np.logical_and(dt > 0.5, dt < 3.22))
ax.errorbar(dt[opt],
lum[opt],
yerr=[llum[opt], ulum[opt]],
fmt='o',
c='lightgrey',
lw=0.5)
# UV + opt points
ax.errorbar(dt[~opt],
lum[~opt],
yerr=[llum[~opt], ulum[~opt]],
ms=8,
fmt='o',
mec='k',
mfc='lightgrey',
lw=0.5,
c='k',
label="SN2018gep")
if lines:
ax.plot(dt, lum, lw=1, c='k')
ax.set_ylabel(r'$L_\mathrm{bol}$ (erg/s)', fontsize=16)
ax.set_yscale('log')
ax2 = ax.twinx()
ax2.set_ylabel(r"$(L_\odot$)", fontsize=16, rotation=270, labelpad=15.0)
y_f = lambda y_i: y_i / 3.839E33
ymin, ymax = ax.get_ylim()
ax2.set_ylim((y_f(ymin), y_f(ymax)))
ax2.plot([], [])
ax2.set_yscale('log')
ax.tick_params(axis='both', labelsize=16)
ax2.tick_params(axis='both', labelsize=16)
def print_table():
# Load the bolometric light curve
dt, lum, llum, ulum = load_lc()
# Load the radius
dt, rad, lrad, urad = load_radius()
# Load the temperature
dt, temp, ltemp, utemp = load_temp()
# Table of measurements
dtprint = np.array([round_sig(val, 2) for val in dt])
lprint = np.array([round_sig(val, 2) for val in lum / 3.839E43])
lprint = np.array([round_sig(val, 2) for val in lum / 3.839E43])
ulprint = np.array(
[np.round(val,ndec(lprint[ii])) \
for ii,val in enumerate(ulum/3.839E43)])
llprint = np.array(
[np.round(val,ndec(lprint[ii])) \
for ii,val in enumerate(llum/3.839E43)])
rprint = np.array([round_sig(val, 2) for val in rad / 1.496E13])
urprint = np.array(
[np.round(val,ndec(rprint[ii])) \
for ii,val in enumerate(urad/1.496E13)])
lrprint = np.array(
[np.round(val,ndec(rprint[ii])) \
for ii,val in enumerate(lrad/1.496E13)])
tprint = np.array([round_sig(val, 2) for val in temp / 1E3])
utprint = np.array(
[np.round(val,ndec(tprint[ii])) \
for ii,val in enumerate(utemp/1E3)])
ltprint = np.array(
[np.round(val,ndec(tprint[ii])) \
for ii,val in enumerate(ltemp/1E3)])
outputf = open("physevol_tab.txt", "w")
outputf.write("\\begin{table}[] \n")
outputf.write("\centering \n")
outputf.write(
"\caption{Physical evolution of AT2018gep from blackbody fits.\
Uncertainties represent the 16-to-84 percentile range from a\
Monte Carlo simulation with 600 trials.} \n")
outputf.write("\\begin{tabular}{lrrr} \n")
outputf.write("\hline \n")
outputf.write(
"$\Delta t$ & $L (10^{10} L_\odot)$ & $R$ (AU) & $T$ (kK) \\\ \n")
outputf.write("\hline")
for ii, l in enumerate(lprint):
t = tprint[ii]
ut = utprint[ii]
lt = ltprint[ii]
if tprint[ii] >= 10:
t = int(t)
ut = int(ut)
lt = int(lt)
linestr = "$%s$ & $%s^{%s}_{%s}$ & $%s^{%s}_{%s}$ & $%s^{%s}_{%s}$ \\\ \n" % (
dtprint[ii], l, "+%s" % ulprint[ii], "-%s" % llprint[ii],
int(rprint[ii]), "+%s" % int(urprint[ii]),
"-%s" % int(lrprint[ii]), t, "+%s" % ut, "-%s" % lt)
outputf.write(linestr)
outputf.write("\hline \n")
outputf.write("\end{tabular} \n")
outputf.write("\label{tab:physevol} \n")
outputf.write("\end{table} \n")
outputf.close()
# integrate A(z)dz from 0 to x
a = np.array([quad(az, 0, xval, args=(y,))[0] for xval in x])
aerr = np.array([quad(az, 0, xval, args=(y,))[1] for xval in x])
# integrate B(z)dz from 0 to x
b = np.array([quad(bz, 0, xval, args=(s, y))[0] for xval in x])
berr = np.array([quad(bz, 0, xval, args=(s, y))[1] for xval in x])
lum = mni_g * np.exp(-x**2) * ((eps_ni-eps_co)*a + eps_co*b)
return lum
if __name__=="__main__":
# Load the bolometric light curve
dt, lum, llum, ulum = load_lc()
# Plot a sample Ni light curve, say M_Ni = 0.1 solar masses
mni0 = 0.3 # solar masses
tdiff0 = 10 # days
#kappa = 0.1 # cm2/g
# Plot
fig,ax = plt.subplots(1,1,figsize=(7,5))
ax.errorbar(
dt, lum, yerr=[llum, ulum], fmt='o',
mec='k', mfc='k', ms=5, c='k')
# Various models
dt_plot = np.linspace(0.1, 40, 1000)
def func(t, Ee, te, alpha):
""" Takes t in sec, Ee in erg, te in sec """
lum = Ee * (alpha - 1) / (te * (1 + t / te)**alpha)
return lum
def logfunc(t, Ee, te, alpha):
""" Takes t in 10^4 sec, Ee in 10^51 erg, te in 10^4 sec """
loglum = 51 + np.log10(Ee) + np.log10(alpha-1) \
- 4 - np.log10(te) - alpha*np.log10(1+t/te)
return loglum
if __name__ == "__main__":
dt_all, lum_all, llum, ulum = load_lc()
# Ignore the first point
dt = dt_all[1:]
lum = lum_all[1:]
dt_sec = dt * 86400
# sample lum from its distribution where sigma = 20% of lum
npts = len(dt)
ndraw = 10
# Make ndraw new versions of a new lum array, repopulated with the lum
# from the Gaussian dist with STD of ulum
lum_new = np.zeros((npts, ndraw))