def at2018cow(ax, col, legend):
""" 231.5 GHz light curve and 9 GHz light curve """
d = Planck15.luminosity_distance(z=0.014).cgs.value
# high frequency
a, b, c = sma_lc()
dt, f, ef = b
ef_comb = np.sqrt(ef**2 + (0.15 * f)**2)
nu = 231.5E9
lum = plot_line(ax[0],
d,
dt,
nu * f,
'AT2018cow',
None,
col,
legend,
zorder=10)
ax[0].text(dt[0] / 1.2,
lum[0],
'AT2018cow',
fontsize=11,
verticalalignment='center',
horizontalalignment='right')
# low frequency
nu = 9E9
data_dir = "/Users/annaho/Dropbox/Projects/Research/AT2018cow/data"
dat = Table.read("%s/radio_lc.dat" % data_dir,
delimiter="&",
format='ascii.no_header')
tel = np.array(dat['col2'])
choose = np.logical_or(tel == 'SMA', tel == 'ATCA')
days = np.array(dat['col1'][choose])
freq = np.array(dat['col3'][choose]).astype(float)
flux_raw = np.array(dat['col4'][choose])
flux = np.array([float(val.split("pm")[0][1:]) for val in flux_raw])
eflux_sys = np.array([0.1 * f for f in flux])
eflux_form = np.array(
[float(val.split("pm")[1][0:-1]) for val in flux_raw])
eflux = np.sqrt(eflux_sys**2 + eflux_form**2)
choose = freq == 9
# add the Margutti point
x = np.hstack((days[choose], 83.51))
y = np.hstack((flux[choose], 9.1)) * nu
lum = plot_line(ax[1], d, x, y, 'AT2018cow', None, col, legend, zorder=10)
ax[1].text(x[-1],
lum[-1] * 1.2,
'AT2018cow',
fontsize=11,
verticalalignment='bottom',
horizontalalignment='center')
for col in np.arange(ncol - 1):
rowstr += "%s & "
rowstr += "%s \\\ \n"
outputf = open("paper_table_%s.txt" % label, "w")
outputf.write("\\startlongtable \n")
outputf.write("\\begin{deluxetable}{%s} \n" % colstr)
outputf.write("\\tablecaption{%s\label{tab:%s}} \n" % (caption, label))
outputf.write("\\tablewidth{0pt} \n")
outputf.write("\\tablehead{ %s } \n" % colheadstr)
#outputf.write("\\rotate \n")
outputf.write("\\tabletypesize{\scriptsize} \n")
outputf.write("\startdata \n")
# Get the SMA light curves with fractional dt and scaled flux
a, b, c = sma_lc()
dt, nu, f, ef = a
tvals = np.unique(dt)
for ii, t in enumerate(tvals):
# For each time
choose = dt == t
# For each frequency observed at that time
nu_unique = np.unique(nu[choose])
for jj, nuval in enumerate(nu_unique):
# Select the relevant data points
flux_raw = f[choose][nu[choose] == nuval]
eflux_raw = ef[choose][nu[choose] == nuval]
if len(flux_raw) == 2:
w = 1 / (eflux_raw)**2
fmean, wsum = np.average(flux_raw, weights=w, returned=True)
""" Full SMA light curve breaking data into all frequency tunings,
for the Appendix """
import numpy as np
import matplotlib
from matplotlib import rc
rc("font", family="serif")
rc("text", usetex=True)
import matplotlib.pyplot as plt
import sys
sys.path.append("/Users/annaho/Dropbox/Projects/Research/AT2018cow/code")
from scale_fluxes import sma_lc
from get_radio import get_data_all
out = sma_lc()
t, nu, f, ef = out[0]
# A non-detection will either have a negative flux value
# or an uncertainty that is larger in magnitude than the flux value
det = np.logical_and(f > 0, np.abs(f) > ef)
# Only plot detections
t = t[det]
nu = nu[det]
f = f[det]
ef = ef[det]
npts = len(np.unique(nu))
c = ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c']
markers = ['o', 's', 'v', '^']
f = 0.5 # filling factor t0 = 22 R0 = 7E15 c = 3E10 t = np.arange(1, 30) R = R0 * (t / t0) V = (4 / 3) * f * np.pi * R**3 v = 0.13 * c # Radiation energy density L = np.interp(t, dt, lum) Uph = L / (4 * np.pi * R**2 * c) # Magnetic energy density UB = (6**2 / (8 * np.pi)) # Ratio of energy densities Uratio = Uph / UB # Ratio of X-ray to radio luminosity d = Planck15.luminosity_distance(z=0.014).cgs.value dt_x, f_x, ef_x = get_xrt() l_x = np.interp(t, dt_x, f_x * 4 * np.pi * d**2) a, b = sma_lc() dt_rad, f_rad, ef = b ef_rad = np.sqrt(ef**2 + (0.15 * f)**2) l_rad = np.interp(t, dt_rad, 230E9 * f_rad * 1E-3 * 1E-23 * 4 * np.pi * d**2) Lratio = l_x / l_rad
def sma(ax):
data_dir = "/Users/annaho/Dropbox/Projects/Research/AT2018cow/data"
dat = Table.read("%s/radio_lc.dat" % data_dir,
delimiter="&",
format='ascii.no_header')
tel = np.array(dat['col2'])
choose = np.logical_or(tel == 'SMA', tel == 'ATCA')
days = np.array(dat['col1'][choose])
freq = np.array(dat['col3'][choose]).astype(float)
flux_raw = np.array(dat['col4'][choose])
flux = np.array([float(val.split("pm")[0][1:]) for val in flux_raw])
eflux_sys = np.array([0.1 * f for f in flux])
eflux_form = np.array(
[float(val.split("pm")[1][0:-1]) for val in flux_raw])
eflux = np.sqrt(eflux_sys**2 + eflux_form**2)
a, b, c = sma_lc()
dt, f, ef = b # 230 GHz light curve
ef_comb = np.sqrt(ef**2 + (0.15 * f)**2)
ax.scatter(dt[:-1],
f[:-1],
marker='s',
c='k',
label="SMA: 230.6--234.6 GHz")
ax.errorbar(dt[:-1], f[:-1], ef_comb[:-1], fmt='s', c='#000004',
lw=1.5) # black in inferno
ax.plot(dt[:-1], f[:-1], linestyle='-', c='k', lw=1.5)
#plt.axvline(x=30)
dt, f, ef = c # 345 GHz light curve
ef_comb = np.sqrt(ef**2 + (0.15 * f)**2)
ax.errorbar(dt,
f,
ef_comb,
fmt='*',
c='#f98e09',
lw=0.5,
alpha=0.8,
ms=11,
label="SMA: 341.5--349 GHz")
ax.plot(dt, f, linestyle='-', c='#f98e09', lw=1.5)
# non-detection
# what is the 3-sigma of the non-detection?
# x + 3-sigma
# for 231.5 GHz, it's X=0.62, 3-sig = 3*0.64
val = 0.62
sig = 0.64
ax.scatter(76.27, val + 3 * sig, marker="_", c='k', s=200)
ax.scatter(76.27, val, marker="_", c='k', s=100)
ax.scatter(76.27, val, marker=".", c='k', s=30)
ax.arrow(76.27,
val + 3 * sig,
0,
-3 * sig,
length_includes_head=True,
head_width=5,
head_length=0.1,
fc='k')
# for 351.0, the non-detection is -0.32 +/- 1.76
val = -0.32
sig = 1.76
ax.scatter(76.27, val + 3 * sig, marker="_", c='#f98e09', s=200)
ax.scatter(76.27, val, marker="_", c='#f98e09', s=100)
ax.scatter(76.27, val, marker=".", c='#f98e09', s=30)
ax.arrow(76.27,
val + 3 * sig,
0,
-3 * sig,
length_includes_head=True,
head_width=5,
head_length=0.1,
fc='#f98e09',
ec='#f98e09')
# Do 34 GHz
choose = freq == 34
# dont include the last point
xfit = days[choose][0:-1]
yfit = flux[choose][0:-1]
eyfit = eflux[choose][0:-1]
# fit a nu^2 power law
out = np.polyfit(np.log10(xfit), np.log10(yfit), deg=1)
x = np.linspace(min(xfit), max(xfit))
y = 10**(out[0] * np.log10(x) + out[1])
ax.errorbar(days[choose],
flux[choose],
yerr=eflux[choose],
fmt='o',
mec='#57106e',
lw=1.5,
ms=7,
label="ATCA: 34 GHz",
mfc='white')
ax.plot(x, y, ls='--', c='#57106e')
ax.text(17, 9.5, r'$f_\nu \propto t^2$', fontsize=midfont)
# for SMA proposal
#ax.axvline(x=5, color='#8da0cb', alpha=0.5, lw=7)
#ax.axvline(x=12, color='#8da0cb', alpha=0.5, lw=7)
# cross hatches for the Day 10, Day 13/14, and Day 22 measurements
ax.text(10, 70, 'S', fontsize=smallfont)
ax.text(14, 70, 'S', fontsize=smallfont)
ax.text(22, 70, 'S', fontsize=smallfont)
# optical timeline:
ax.text(3,
100,
r'broad ($>0.1c$) feature',
fontsize=smallfont,
verticalalignment='bottom')
ax.annotate('',
xy=(8.5, 100),
xytext=(8.5, 150),
arrowprops=dict(arrowstyle="-", color='k'))
ax.text(9,
100,
r'HeII ($0.03c$)',
fontsize=smallfont,
verticalalignment='bottom')
ax.text(6,
150,
r'IR excess appears',
fontsize=smallfont,
verticalalignment='bottom')
ax.annotate('',
xy=(16, 100),
xytext=(16, 290),
arrowprops=dict(arrowstyle="-", color='k'))
ax.text(20,
400,
r'Redshifted HeI ($0.01c$) and',
fontsize=smallfont,
verticalalignment='bottom',
horizontalalignment='right')
ax.text(20,
270,
r'Balmer emission ($0.02c$) appear',
fontsize=smallfont,
verticalalignment='bottom',
horizontalalignment='right')
ax.text(17,
150,
r'Lines evolve blueward',
fontsize=smallfont,
verticalalignment='bottom',
horizontalalignment='left')
ax.text(17,
100,
r'and develop wedge shape',
fontsize=smallfont,
verticalalignment='bottom',
horizontalalignment='left')
ax.annotate('',
xy=(30, 100),
xytext=(30, 290),
arrowprops=dict(arrowstyle="-", color='k'))
ax.text(27,
400,
r'Emergence of HeI, CaII, OI,',
fontsize=smallfont,
verticalalignment='bottom',
horizontalalignment='left')
ax.text(27,
270,
r'$z$-band excess',
fontsize=smallfont,
verticalalignment='bottom',
horizontalalignment='left')
ax.set_ylim(0.5, 100)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel("$f_{\\nu}$ [mJy]", fontsize=bigfont)
ax.yaxis.set_tick_params(labelsize=midfont)
ax.legend(fontsize=smallfont, loc='lower left', ncol=1)
ax.set_yticks([0.5, 10, 50])
ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())