def koala(ax):
z = 0.2714
dat = ascii.read("../../data/koala_lc/ZTF18abvkwla_lct.csv")
filts = np.array([val[2:3] for val in dat['filter']])
# Plot the r-band light curve (rest-frame g)
choose = np.logical_and(filts=='r', dat['mag'] > 0)
jd = dat['jdobs'][choose]
t0 = jd[0]
dt = (jd-t0)/(1+z)
gmag = dat['mag'][choose] - 0.115 #extinction in SDSS r
egmag = dat['mag_unc'][choose]
Gmag = gmag-Planck15.distmod(z=z).value+2.5*np.log10(1+z)
ax.errorbar(
dt, Gmag, yerr=egmag, fmt='s', c='k', lw=1, ms=5, zorder=5)
ax.plot(dt, Gmag, c='k', lw=3, zorder=5)
choose = np.logical_and(filts=='g', dat['mag'] == 0)
jd = dat['jdobs'][choose]
dt = (jd-t0)/(1+z)
lims = dat['limmag'][choose] - 0.167 # extinction in SDSS g
Lims = lims - Planck15.distmod(z=z).value + 2.5*np.log10(1+z)
ax.scatter(
dt, Lims, edgecolor='k', facecolor='white', marker='s', label=None,
zorder=5)
for ii,dt_val in enumerate(dt):
ax.arrow(dt_val, Lims[ii], 0, +0.1, color='k',
length_includes_head=True,
head_width=0.2, head_length=0.05, label=None, zorder=5)
def slsne(ax):
""" Data points from Dan Perley """
dat = Table.read(
"../data/from_dan_perley.txt", delimiter='|',
format='ascii.fast_no_header')
cl = np.array([val.split(" ")[0] for val in dat['col7']])
z = np.array([val[13:20] for val in dat['col7']])
maxcol = dat['col3']
filt = np.array([val.split(" ")[1] for val in maxcol])
maxmag = np.array([val.split(" ")[2] for val in maxcol]).astype(float)
timecol = dat['col6']
rise = np.array([val[0:6] for val in timecol])
fade = np.array([val[6:] for val in timecol])
slind = np.where(['SLSN' in val for val in cl])[0]
leg = True
for ii in slind:
bad = np.logical_or('+' in rise[ii], '+' in fade[ii])
if bad==False:
timescale = float(rise[ii]) + float(fade[ii])
if leg:
ax.scatter(
timescale, maxmag[ii]-Planck15.distmod(float(z[ii])).value,
marker='v', color='#7570b3', label="43 SLSNe")
leg = False
else:
ax.scatter(
timescale, maxmag[ii]-Planck15.distmod(float(z[ii])).value,
marker='v', color='#7570b3', label="_nolegend_")
# Background
x = [32+61, 29+45, 21+54, 29+25, 33+25, 13+27, 15+24, 29+49, 28+36, 21+19]
y = [-21.78, -22.09, -22.03, -20.31, -21.94, -21.39, -20.88, -21.61, -19.90, -22.42]
ax.scatter(x, y, marker='v', color='lightgrey', label="_nolegend_", zorder=0)
def plot_98bw(ax):
offset = 0.5
dat = ascii.read(data_dir + "/sn1998bw.dat", delimiter=';')
jd = dat['JD']
rband = dat['Rcmag']
erband = dat['e_Rcmag']
# Extinction is 0.127 in R-band in this direction
dm = Planck15.distmod(z=z).value - Planck15.distmod(z=0.0085).value
ax.plot(jd - jd[0],
rband + dm - 0.127,
color='#e55c30',
lw=1.5,
label="98bw $Rc$")
ax.plot((jd - jd[0]) / (1.0085), (rband + dm) + offset - 0.127,
color='#e55c30',
lw=0.5,
ls='--',
label="98bw $Rc$+%s mag" % offset)
gband = dat['Bmag']
egband = dat['e_Bmag']
# Extinction is 0.212 in B-band in this direction
ax.plot(jd - jd[0],
gband + dm - 0.212,
color='#140b34',
lw=1.5,
label="98bw $B$")
ax.plot((jd - jd[0]) / (1.0085), (gband + dm) + offset - 0.212,
color='#140b34',
lw=0.5,
ls='--',
label="98bw $B$+%s mag" % offset)
ax.axvline(x=-0.1, c='k', lw=0.5)
ax.text(0, 18.7, 'GRB 980425', fontsize=10, rotation=270)
def random_parameters(redshifts, model, r_v=2., ebv_rate=0.11, **kwargs):
# Amplitude
amp = []
for z in redshifts:
amp.append(10**(-0.4 * Planck15.distmod(z).value))
if modeltype == 'DES' or viewingangle_dependece == False:
return {
'amplitude': np.array(amp),
'hostr_v': r_v * np.ones(len(redshifts)),
'hostebv': np.random.exponential(ebv_rate, len(redshifts))
}
elif modeltype == 'Bulla' and fixedtheta:
return {
'amplitude': np.array(amp),
'theta': np.array([theta] * len(redshifts)),
'hostr_v': r_v * np.ones(len(redshifts)),
'hostebv': np.random.exponential(ebv_rate, len(redshifts))
}
elif modeltype == 'Bulla' and fixedtheta == False:
return {
'amplitude': np.array(amp),
#'theta': np.random.uniform(0,90,size=len(redshifts)), # distribution of angles.Uniform distribution of theta (viewing angle) ($\^$
'theta': np.arccos(np.random.random(len(redshifts))) / np.pi *
180, # distribution of angles. Sine distribution of theta (viewing angle) ($\^{circ}$)
'hostr_v': r_v * np.ones(len(redshifts)),
'hostebv': np.random.exponential(ebv_rate, len(redshifts))
}
def sn2009bb(ax):
""" got this from the open SN catalog """
ddir = "/Users/annaho/Dropbox/Projects/Research/ZTF18abukavn/data/lc"
dat = np.loadtxt(ddir + "/sn2009bb.txt", delimiter=',', dtype='str')
mjd = dat[:, 1].astype(float)
mag = dat[:, 2].astype(float)
#emag = dat[:,3].astype(float)
band = dat[:, 5]
dt = mjd - mjd[0]
choose = band == 'g'
g = mag[choose]
gdt = dt[choose]
choose = np.logical_or(band == 'r', band == 'R')
r = mag[choose]
rdt = dt[choose]
distmod = Planck15.distmod(z=0.0104).value
dt_grid = np.arange(0, 50, 1)
gplt = np.interp(dt_grid, gdt, g)
rplt = np.interp(dt_grid, rdt, r)
gr = gplt - rplt
ax.plot(gr,
gplt - distmod,
c='orange',
lw=3,
alpha=0.3,
label="Radio, no GRB")
ax.text(gr[0], gplt[0] - distmod, "SN2009bb")
def at2018cow(ax,c):
z = 0.0141
dat = pd.read_fwf("../../data/fbot_lc/at2018cow_photometry_table.dat")
mjd = dat['MJD']
filt = dat['Filt']
mag = dat['ABMag']
emag = dat['Emag']
choose = filt == 'g'
xall = mjd[choose][1:].values
yall = mag[choose][1:].values.astype(float)-0.287
yerrall = emag[choose][1:].values.astype(float)
# Add the i-band detection prior to peak
xall = np.insert(xall, 0, 58286.1950)
yall = np.insert(yall, 0, 14.32-0.147-0.7)
yerrall = np.insert(yerrall, 0, 0.03)
# Add the o-band detection prior to peak
xall = np.insert(xall, 0, 58285.44)
yall = np.insert(yall, 0, 14.70-0.198-0.4)
yerrall = np.insert(yerrall, 0, 0.10)
dt = xall-xall[0]
Yall = yall-Planck15.distmod(z=0.0141).value
ax.plot(dt, Yall, c=c)
def main():
x1 = input('Redshift:')
z = float(x1)
mu = Planck15.distmod(z).value
d = 10**(mu / 5 + 1)
x2 = input('Convert from [arsec]/coords/dist:')
if (x2 == '') or (x2 == 'arsec'):
x3 = input('Arcsec:')
theta = Angle(f'0d0m{x3}s').degree
a = d * np.tan(theta)
print('-------------------------------------')
print('Distance:', a, '[parsec]')
elif x2 == 'coords': # not ready
ra, dec = float(x1.split()[0]), float(x1.split()[1])
c = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame=FK5)
print('-------------------------------------')
print('RA DEC (2000):', c.to_string('hmsdms'))
elif x2 == 'dist':
x3 = input('Distance [parsec]:')
a = float(x3)
theta = np.arctan(a / d)
ang = Angle(np.arctan(a / d), unit=u.deg)
dms = ang.to_string(unit=u.degree, sep=('deg', 'm', 's'))
arsec = dms.split('m')[-1]
print('-------------------------------------')
print('Arsec:', arsec)
def iptf17cw(ax):
# R-band
g = np.array([19.041, 19.235, 19.294, 20.93])
r = np.array([18.703, 18.721, 18.793, 19.998])
distmod = Planck15.distmod(z=0.093).value
gr = g - r
ax.plot(gr, g - distmod, c='orange', lw=3, alpha=0.3)
ax.text(gr[0], g[0] - distmod, "iPTF17cw")
def at2018gep(ax, shift=3):
""" LC of AT2018gep
shifted to the left by some number of days.
for example, to align with g-band max,
you would shift the whole thing left by 3 days
"""
DATA_DIR = "/Users/annaho/Dropbox/Projects/Research/ZTF18abukavn/data/phot"
f = DATA_DIR + "/ZTF18abukavn_opt_phot.dat"
dat = np.loadtxt(f, dtype=str, delimiter=' ')
instr = dat[:, 0]
jd = dat[:, 1].astype(float)
zp = 2458370.6473
dt = jd - zp - shift
filt = dat[:, 2]
mag = dat[:, 3].astype(float)
emag = dat[:, 4].astype(float)
distmod = Planck15.distmod(z=0.033).value
choose = np.logical_and(mag < 99, filt == 'u')
order = np.argsort(dt[choose])
ls = ':'
lw = 1.0
ax.plot(dt[choose][order],
mag[choose][order] - distmod,
ls=ls,
c='cyan',
lw=lw)
choose = np.logical_and(mag < 99, filt == 'g')
order = np.argsort(dt[choose])
ax.plot(dt[choose][order],
mag[choose][order] - distmod,
ls=ls,
c='g',
lw=lw)
choose = np.logical_and(mag < 99, filt == 'r')
order = np.argsort(dt[choose])
ax.plot(dt[choose][order],
mag[choose][order] - distmod,
ls=ls,
c='r',
lw=lw)
choose = np.logical_and(mag < 99, filt == 'i')
order = np.argsort(dt[choose])
ax.plot(dt[choose][order],
mag[choose][order] - distmod,
ls=ls,
c='pink',
lw=lw)
choose = np.logical_and(mag < 99, filt == 'z')
order = np.argsort(dt[choose])
ax.plot(dt[choose][order],
mag[choose][order] - distmod,
ls=ls,
c='grey',
lw=lw)
def ps1_10ah(ax):
""" the only Drout gold transient with z < 0.1 """
gr = np.array([0, -0.2, 0.05])
phase = np.array([-3, 0, 14])
g = np.array([22.18, 19.95, 21.64])
distmod = Planck15.distmod(z=0.074).value
G = g - distmod
ax.plot(gr, G, ls='-', c='grey', zorder=0, lw=3, alpha=0.5)
ax.text(gr[0], G[0], 'PS1-10ah', fontsize=12)
def test_template_spectra():
from astropy import units
from skypy.utils.photometry import mag_ab, SpectrumTemplates
from astropy.cosmology import Planck15
from speclite.filters import FilterResponse
class TestTemplates(SpectrumTemplates):
'''Three flat templates'''
def __init__(self):
self.wavelength = np.logspace(-1, 4, 1000) * units.AA
A = np.array([[2], [3], [4]]) * 0.10885464149979998
self.templates = A * units.Unit(
'erg s-1 cm-2 AA') / self.wavelength**2
test_templates = TestTemplates()
lam, flam = test_templates.wavelength, test_templates.templates
# Gaussian bandpass
filt_lam = np.logspace(0, 4, 1000) * units.AA
filt_tx = np.exp(-((filt_lam - 1000 * units.AA) / (100 * units.AA))**2)
filt_tx[[0, -1]] = 0
FilterResponse(wavelength=filt_lam,
response=filt_tx,
meta=dict(group_name='test', band_name='filt'))
# Each test galaxy is exactly one of the templates
coefficients = np.eye(3)
# Test absolute magnitudes
mt = test_templates.absolute_magnitudes(coefficients, 'test-filt')
m = mag_ab(lam, flam, 'test-filt')
np.testing.assert_allclose(mt, m)
# Test apparent magnitudes
redshift = np.array([1, 2, 3])
dm = Planck15.distmod(redshift).value
mt = test_templates.apparent_magnitudes(coefficients, redshift,
'test-filt', Planck15)
np.testing.assert_allclose(mt, m - 2.5 * np.log10(1 + redshift) + dm)
# Redshift interpolation test; linear interpolation sufficient over a small
# redshift range at low relative tolerance
z = np.linspace(0.1, 0.2, 3)
m_true = test_templates.apparent_magnitudes(coefficients,
z,
'test-filt',
Planck15,
resolution=4)
m_interp = test_templates.apparent_magnitudes(coefficients,
z,
'test-filt',
Planck15,
resolution=2)
np.testing.assert_allclose(m_true, m_interp, rtol=1e-5)
assert not np.all(m_true == m_interp)
def d2bk(ax):
ax = axarr[1,0]
name = 'SNLS05D2bk'
z = 0.699
dm = Planck15.distmod(z=z).value
choose = np.logical_and.reduce((names == name, ~islim, filt=='i'))
# R-band light curve
ax.errorbar(
(jd[choose]-jd[choose][0])/(1+z), mag[choose]-dm,
yerr=emag[choose].astype(float), c='k', fmt='s')
plot_18gep(ax, 'g')
def iptf15ul(ax,c):
z = 0.066
dat = ascii.read("../../data/fbot_lc/iptf15ul_corr.txt")
mjd = dat['col2']
filt = dat['col3']
dm = Planck15.distmod(z=z).value
mag = dat['col4']
emag = dat['col5']
choose = np.logical_and(filt == 'g', mag != '>')
dt = (mjd[choose]-mjd[choose][0])/(1+z)
dm = -21.1-min(mag[choose].astype(float))
ax.plot(
dt-1.2, mag[choose].astype(float)+dm+2.5*np.log10(1+z), c=c)
def test_template_spectra():
from astropy import units
from skypy.galaxy.spectrum import mag_ab, magnitudes_from_templates
from astropy.cosmology import Planck15
# 3 Flat Templates
lam = np.logspace(0, 4, 1000) * units.AA
A = np.array([[2], [3], [4]])
flam = A * 0.10884806248538730623 * units.Unit('erg s-1 cm-2 AA') / lam**2
spec = specutils.Spectrum1D(spectral_axis=lam, flux=flam)
# Gaussian bandpass
bp_lam = np.logspace(0, 4, 1000) * units.AA
bp_tx = np.exp(-((bp_lam - 1000 * units.AA) /
(100 * units.AA))**2) * units.dimensionless_unscaled
bp = specutils.Spectrum1D(spectral_axis=bp_lam, flux=bp_tx)
# Each test galaxy is exactly one of the templates
coefficients = np.diag(np.ones(3))
mt = magnitudes_from_templates(coefficients, spec, bp)
m = mag_ab(spec, bp)
np.testing.assert_allclose(mt, m)
# Test distance modulus
redshift = np.array([0, 1, 2])
dm = Planck15.distmod(redshift).value
mt = magnitudes_from_templates(coefficients, spec, bp, distance_modulus=dm)
np.testing.assert_allclose(mt, m + dm)
# Test stellar mass
sm = np.array([1, 2, 3])
mt = magnitudes_from_templates(coefficients, spec, bp, stellar_mass=sm)
np.testing.assert_allclose(mt, m - 2.5 * np.log10(sm))
# Redshift interpolation test; linear interpolation sufficient over a small
# redshift range at low relative tolerance
z = np.linspace(0, 0.1, 3)
m_true = magnitudes_from_templates(coefficients,
spec,
bp,
redshift=z,
resolution=4)
m_interp = magnitudes_from_templates(coefficients,
spec,
bp,
redshift=z,
resolution=2)
np.testing.assert_allclose(m_true, m_interp, rtol=1e-2)
with pytest.raises(AssertionError):
np.testing.assert_allclose(m_true, m_interp, rtol=1e-5)
def gap(ax):
""" Data points from Dan Perley """
dat = Table.read("../data/from_dan_perley.txt",
delimiter='|',
format='ascii.fast_no_header')
cl = np.array([val.split(" ")[0] for val in dat['col7']])
z = np.array([val[13:20] for val in dat['col7']])
maxcol = dat['col3']
filt = np.array([val.split(" ")[1] for val in maxcol])
maxmag = np.array([val.split(" ")[2] for val in maxcol]).astype(float)
timecol = dat['col6']
rise = np.array([val[0:6] for val in timecol])
fade = np.array([val[6:] for val in timecol])
useind = np.where(['Gap' in val for val in cl])[0]
leg = 'Ca-rich Gap'
for ii in useind:
bad = np.logical_or('+' in rise[ii], '+' in fade[ii])
if bad == False:
timescale = float(rise[ii]) + float(fade[ii])
ax.scatter(timescale,
maxmag[ii] - Planck15.distmod(float(z[ii])).value,
marker='^',
c='k',
label=leg)
leg = '_nolegend_'
""" Data from KDE """
cadata = ascii.read('../data/carich_gap.txt')
name = cadata['Object']
risetime = cadata['Rise']
falltime = cadata['Fade']
peakmag = cadata['PeakMag']
for i in range(len(name)):
if risetime[i] == -99 or falltime[i] == -99:
continue
timescale = risetime[i] + falltime[i]
if ('ZTF' in name[i]):
ax.scatter(timescale,
peakmag[i],
marker='^',
c='k',
label='_nolegend_',
zorder=5)
else:
ax.scatter(timescale,
peakmag[i],
marker='^',
c='lightgrey',
label='_nolegend_')
def sn2002bj(ax):
distmod = Planck15.distmod(z=0.012108).value
dt = np.array([1, 3, 4, 5, 9, 11, 15])
B = np.array([14.79, 14.97, 15.03, 15.15, 15.75, 16.27, 17.92])
V = np.array([14.91, 15.03, 15.10, 15.20, 15.71, 16.04, 17.93])
R = np.array([14.97, 15.06, 15.08, 15.21, 15.65, 16.06, 17.32])
ax.plot(B - R,
B - distmod,
ls='-',
c='purple',
zorder=0,
lw=3,
alpha=0.3,
label="SN2002bj")
def drout(axarr):
""" Comparison to the transients from Drout+14 """
# redshift key
z = {
'10ah': 0.074,
'10bjp': 0.113,
'11qr': 0.324,
'12bb': 0.101,
'12bv': 0.405,
'12brf': 0.275,
'11bbq': 0.646,
'13duy': 0.270,
'13dwm': 0.245,
'13ess': 0.296
}
inputf = ddir + "/drout14.txt"
dat = np.loadtxt(inputf, delimiter=';', dtype=str)
names = np.array([val.strip() for val in dat[:, 0]])
unames = np.unique(names)
for ii, ax in enumerate(axarr.reshape(-1)):
name = unames[ii]
choose = names == name
redshift = z[name]
# only do this if there is a known redshift
filt = np.array(dat[:, 1][choose]).astype(str)
dt = dat[:, 2][choose].astype(float)
islim = dat[:, 3][choose]
mag = dat[:, 4][choose].astype(float)
distmod = Planck15.distmod(z=redshift).value
isdet = np.array([val == " " for val in islim])
isfilt = np.array(['g' in val for val in filt])
c = np.logical_and(isdet, isfilt)
ax.plot(dt[c], mag[c] - distmod, ls='-', c='g')
isfilt = np.array(['r' in val for val in filt])
c = np.logical_and(isdet, isfilt)
ax.plot(dt[c], mag[c] - distmod, ls='-', c='r')
isfilt = np.array(['i' in val for val in filt])
c = np.logical_and(isdet, isfilt)
ax.plot(dt[c], mag[c] - distmod, ls='-', c='pink')
isfilt = np.array(['z' in val for val in filt])
c = np.logical_and(isdet, isfilt)
ax.plot(dt[c], mag[c] - distmod, ls='-', c='grey')
ax.text(0.9,
0.9,
'PS1-%s' % name,
fontsize=12,
transform=ax.transAxes,
horizontalalignment='right',
verticalalignment='top')
def core_collapse(ax):
""" Light curves of CC SNe from RCF provided by Yashvi Sharma """
# Load the table of events
df = pd.read_csv("../data/ccsne/ccsne_final_new.csv")
keep = df['redshift'] != 'None'
nearby = df['redshift'][keep].astype(float) < 0.05
sampled = df['numpoints_lc'][keep] > 50
choose = np.logical_and(nearby, sampled)
z = df['redshift'][keep].values[choose].astype(float)
# Load the array of light curves
lcs = np.load("../data/ccsne/ccsne_lcs_new.npy",
allow_pickle=True,
encoding='latin1')
lcs = lcs[keep][choose]
# Plot one of them
leg = True
for ii in np.arange(sum(choose)):
lc = lcs[ii]
filt = lc['filter']
jd = lc['jd']
mag = lc['mag']
gband = filt == 'g'
if sum(gband) > 30:
x = jd[gband] - jd[gband].values[0]
y = mag[gband]
peakmag = np.min(mag)
thalf = np.interp(peakmag + 0.75, y, x)
absmag = peakmag - Planck15.distmod(z=z[ii]).value
if leg:
ax.scatter(thalf,
absmag,
c='cornflowerblue',
marker='s',
zorder=0,
s=12,
label="871 CC SNe")
leg = False
else:
ax.scatter(thalf,
absmag,
c='cornflowerblue',
marker='s',
zorder=0,
s=12,
label="_nolegend_")
leg = False
def iptf16asu(ax,c):
z = 0.187
dat = pd.read_fwf("../../data/fbot_lc/iptf16asu.txt")
filt = dat['filt']
# choose = filt == 'r'
# dt = dat['dt'][choose] / (1+z)
# mag = dat['mag'][choose]
# Mag = mag[0:10].astype(float) - Planck15.distmod(z=z).value + 2.5*np.log10(1+z)
# ax.plot(dt[0:10], Mag, c=c)
choose = filt == 'g'
dt = dat['dt'][choose] / (1+z)
mag = dat['mag'][choose][16:30].astype(float).values
Mag = mag - Planck15.distmod(z=z).value + 2.5*np.log10(1+z)
ax.plot(dt[16:30]+3, Mag, c=c)
def plot_18gep(ax, band, c='k'):
""" Plot 18gep lc for a given band """
z = 0.03154
dm = Planck15.distmod(z=z).value
dt_gep, filt_gep, mag_gep, emag_gep = get_lc()
isdet_gep = np.logical_and(mag_gep<99, ~np.isnan(mag_gep))
choose = np.logical_and(filt_gep == band, isdet_gep)
x = dt_gep[choose]/(1+z)
y = mag_gep[choose]-dm
# ax.errorbar(x, y,
# yerr=emag_gep[choose],
# c='k', fmt='s')
order = np.argsort(x)
ax.plot(
x[order], y[order], c=c,
lw=0.5, label="18gep, $%s$" %band, ls='--')
def short_log_likelihood(params):
alpha = params[0]
beta = params[1]
MB = params[2]
sigma_int = params[3]
omega_m = params[4]
if omega_m < 0:
return -np.inf
if sigma_int < 0:
return -np.inf
cosmo = FlatLambdaCDM(H0=70, Om0=omega_m)
mu_cosmo = cosmo.distmod(z).value
dmu = mb_obs - mu_cosmo - MB + alpha * x1_obs - beta * c_obs
dmue = np.sqrt(mbe**2 + (alpha * x1e)**2 + (beta * ce)**2 + sigma_int**2)
return -0.5 * np.sum((dmu / dmue)**2) - 0.5 * np.sum(np.log(dmue**2))
def at2018cow(ax):
distmod = Planck15.distmod(z=0.014).value
DATA_DIR = "/Users/annaho/Dropbox/Projects/Research/ZTF18abukavn/data/lc"
f = DATA_DIR + "/18cow.txt"
dat = np.loadtxt(f, dtype=str)
jd = dat[:, 0].astype(float)
filt = dat[:, 2]
mag = dat[:, 4].astype(float)
emag = dat[:, 5].astype(float)
zp = 58285
dt = jd - zp
print(dt)
tgrid = np.arange(dt[0], 30, 1)
band = filt == 'g'
choose = band
order = np.argsort(dt[choose])
g = np.interp(tgrid, dt[choose][order], mag[choose][order])
band = filt == 'r'
choose = band
order = np.argsort(dt[choose])
r = np.interp(tgrid, dt[choose][order], mag[choose][order])
gr = g - r
xdata = gr
ydata = g - distmod
ax.plot(xdata, ydata, c='k', ls='--', lw=2, zorder=2)
markt = np.array([1.8, 10, 20])
labs = np.array(["1 day", "10 days", "20 days"])
cols = np.array(['#e55c30', '#84206b', '#140b34'])
for ii, t in enumerate(markt):
prevptx = np.interp(t - 0.1, tgrid, xdata) #xdata[tgrid<t][-1]
prevpty = np.interp(t - 0.1, tgrid, ydata) #ydata[tgrid<t][-1]
newptx = np.interp(t, tgrid, xdata)
newpty = np.interp(t, tgrid, ydata)
ax.annotate('',
xytext=(prevptx, prevpty),
xy=(newptx, newpty),
arrowprops=dict(color=cols[ii], width=1, headlength=10))
# ax.text(newptx, newpty, "$\Delta t$= %s" %labs[ii], fontsize=12,
# horizontalalignment='left', verticalalignment='top')
ax.text(-0.37, -20.4, "AT2018cow", fontsize=14)
def snls05d2bk(ax, c):
z = 0.699
mw_ext = 0.029
dat = pd.read_fwf("../../data/fbot_lc/SNLS05D2bk.txt")
jd = dat['JD']
filt = dat['F']
mag = dat['mag']
emag = dat['emag']
choose = filt == 'i'
xall = jd[choose][0:-2].values.astype(float)
yall = mag[choose][0:-2].values.astype(float)
yerrall = emag[choose][0:-2].values.astype(float)
ax.plot(
(xall-xall[0]-4)/(1+z),
yall-mw_ext-Planck15.distmod(z=z).value+2.5*np.log10(1+z),
c=c)
def d4ec(ax):
ax = axarr[0,1]
name = 'SNLS04D4ec'
z = 0.593
offset = 3
dm = Planck15.distmod(z=z).value
choose = np.logical_and.reduce((names == name, ~islim, filt=='i'))
ax.errorbar(
(jd[choose]-jd[choose][0])/(1+z)+offset, mag[choose]-dm,
yerr=emag[choose].astype(float), mec='k', mfc='white', fmt='s',
label="D4ec, $i$", c='k')
choose = np.logical_and.reduce((names == name, ~islim, filt=='g'))
ax.errorbar(
(jd[choose]-jd[choose][0])/(1+z)+offset, mag[choose]-dm,
yerr=emag[choose].astype(float), mec='k', mfc='white', fmt='D',
label="D4ec, $i$", c='k')
plot_18gep(ax, 'g')
plot_18gep(ax, 'UVW1', c='grey')
def ksn2015k(ax):
diff = Planck15.distmod(z=0.09).value
gr = -0.17
G = 20.01 - diff
ax.errorbar(gr,
G,
xerr=0.20,
yerr=0.12,
fmt='v',
c='#84206b',
mfc='#84206b',
ms=12,
label=None)
ax.text(gr,
G,
"KSN2015K",
fontsize=14,
horizontalalignment='right',
verticalalignment='bottom')
def full_log_likelihood(params):
alpha = params[0]
beta = params[1]
MB = params[2]
sigma_int = params[3]
meanx = params[4]
meanc = params[5]
sigpriorx = params[6]
sigpriorc = params[7]
if sigma_int < 0:
return -np.inf
if sigpriorx < 0:
return -np.inf
if sigpriorc < 0:
return -np.inf
cosmomu = cosmo.distmod(z).value
return np.sum(
negtwoLL(alpha, beta, cosmomu, MB, mb_obs, x1_obs, c_obs, mbe**2,
sigma_int**2, x1e**2, ce**2, 0.0, 0.0, 0.0, meanx, meanc,
sigpriorx**2, sigpriorc**2)) / (-2.0)
def sn2006aj(ax):
t0 = Time('2006-02-18').mjd + 0.149
t = np.array([
Time('2006-02-20').mjd + 0.162,
Time('2006-02-21').mjd + 0.196,
Time('2006-02-23').mjd + 0.196,
Time('2006-02-25').mjd + 0.112,
Time('2006-02-27').mjd + 0.167,
Time('2006-03-04').mjd + 0.097
])
dt = t - t0
V = np.array([17.84, 17.73, 17.30, 17.10, 17.0, 17.09])
R = np.array([17.76, 17.22, 17.22, 16.96, 16.84, 16.88])
gr = V - R
distmod = Planck15.distmod(z=0.033023).value
plt.scatter(gr,
V - distmod,
marker='o',
edgecolor='orange',
facecolor='white',
label='Ic-BL')
plt.plot(gr, V - distmod, c='orange')
def sn2007ru(ax):
""" got this from the open SN catalog """
ddir = "/Users/annaho/Dropbox/Projects/Research/ZTF18abukavn/data/lc"
dat = np.loadtxt(ddir + "/sn2007ru.txt", delimiter=',', dtype='str')
mjd = dat[:, 1].astype(float)
mag = dat[:, 2].astype(float)
band = dat[:, 5]
dt = mjd - mjd[0]
choose = band == 'V'
g = mag[choose]
gdt = dt[choose]
choose = np.logical_or(band == 'R_c', band == "r'")
r = mag[choose]
rdt = dt[choose]
distmod = Planck15.distmod(z=0.0593).value
dt_grid = np.arange(5, 34, 1)
gplt = np.interp(dt_grid, gdt, g)
rplt = np.interp(dt_grid, rdt, r)
gr = gplt - rplt
ax.plot(gr, gplt - distmod, c='purple', lw=3, alpha=0.3)
ax.text(gr[1], gplt[1] - distmod, "SN2010bh", horizontalalignment='right')
def novae(ax):
""" Data points from Dan Perley """
dat = Table.read("../data/from_dan_perley.txt",
delimiter='|',
format='ascii.fast_no_header')
cl = np.array([val.split(" ")[0] for val in dat['col7']])
maxcol = dat['col3']
filt = np.array([val.split(" ")[1] for val in maxcol])
maxmag = np.array([val.split(" ")[2] for val in maxcol]).astype(float)
timecol = dat['col6']
rise = np.array([val[0:6] for val in timecol])
fade = np.array([val[6:] for val in timecol])
useind = np.where(
[np.logical_or('nova' in val, 'Nova' in val) for val in cl])[0]
leg = '6 Novae'
for ii in useind:
bad = np.logical_or('+' in rise[ii], '+' in fade[ii])
if bad == False:
timescale = float(rise[ii]) + float(fade[ii])
lum = maxmag[ii] - Planck15.distmod(z=0.000177).value
if timescale > 3:
ax.scatter(timescale, lum, marker='*', c='k', label=leg)
leg = '_nolegend_'
if __name__ == "__main__":
fig, ax = plt.subplots(1, 1, figsize=(7, 6))
load_lc(ax)
plot_98bw(ax)
ax.set_xlabel("Days (Rest-frame) Since Last Non-Detection of ZTF18aaqjovh",
fontsize=16)
ax.set_ylabel("Apparent Mag (AB)", fontsize=16)
ax.set_ylim(17.5, 21.5)
ax.set_xlim(-1, 52)
ax.tick_params(axis='both', labelsize=14)
ax.invert_yaxis()
#ax2.invert_yaxis()
# Put a twin axis with the absolute magnitude
ax2 = ax.twinx()
y_f = lambda y_i: y_i - Planck15.distmod(z=z).value
ymin, ymax = ax.get_ylim()
ax2.set_ylim((y_f(ymin), y_f(ymax)))
ax2.plot([], [])
ax2.tick_params(axis='both', labelsize=14)
ax2.set_ylabel("Absolute Mag (AB)", fontsize=16, rotation=270, va='bottom')
ax2.set_xlim(-1, 52)
ax.legend(loc='lower center', fontsize=12, ncol=2)
fig.tight_layout()
#plt.show()
plt.savefig('lc.eps', dpi=300)