def get_sn2010X(colorplt=False):
"""
Kasliwal+10
"""
ebv = 0.1249 # SFD
z = 0.015
D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc
dis_mod = 5 * np.log10(D / 10)
t_max = 55239.2
tb = pd.read_csv("../data/otherSN/Kasliwal2010/photometry.csv")
tb = tb.drop(columns=["source", "event", "instrument"])
tb = tb[tb.upperlimit == "F"]
tb = tb.drop(columns=["upperlimit"])
tb = tb.rename(columns={
'magnitude': 'mag',
'e_magnitude': 'emag',
'band': 'filter',
'time': 'mjd'
})
ixr = tb['filter'].values == "r"
ixg = tb['filter'].values == "g"
ixi = tb['filter'].values == "i"
tb['wave'] = np.zeros(len(tb))
tb['wave'].values[ixg] = 4814
tb['wave'].values[ixr] = 6422
tb['wave'].values[ixi] = 7883
tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1 * ebv,
3.1)
tb['mag0_abs'] = tb['mag0'] - dis_mod
tb['tmax_rf'] = (tb['mjd'] - t_max) / (1 + z)
if colorplt == False:
return tb
else:
tb = add_datecol(tb)
dates = get_date_span(tb)
datesave = []
for i in range(len(dates)):
x = dates[i]
ix = tb["date"].values == x
tbsub = tb[ix]
if len(tbsub) != 0:
flts = tbsub['filter'].values
if "r" in flts and np.sum(np.unique(flts)) != 1:
datesave.append(x)
datesave = np.array(datesave)
mcolor = []
mcolor_unc = []
mjds = []
colorname = []
for i in range(len(datesave)):
x = datesave[i]
ix = tb["date"].values == x
tbsub = tb[ix]
gtb = tbsub[tbsub["filter"].values == "g"]
rtb = tbsub[tbsub["filter"].values == "r"]
itb = tbsub[tbsub["filter"].values == "i"]
if len(gtb) != 0:
gmjds = gtb["mjd"].values
gmags = gtb["mag"].values
gemags = gtb["emag"].values
gwtgs = 1 / gemags**2
gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)
gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)
gemag = 1 / np.sqrt(np.sum(gwtgs))
else:
gmag = 0
if len(rtb) != 0:
rmjds = rtb["mjd"].values
rmags = rtb["mag"].values
remags = rtb["emag"].values
rwtgs = 1 / remags**2
rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)
rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)
remag = 1 / np.sqrt(np.sum(rwtgs))
else:
rmag = 0
if len(itb) != 0:
imjds = itb["mjd"].values
imags = itb["mag"].values
iemags = itb["emag"].values
iwtgs = 1 / iemags**2
imag = np.sum(imags * iwtgs) / np.sum(iwtgs)
imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)
iemag = 1 / np.sqrt(np.sum(iwtgs))
else:
imag = 0
if gmag and rmag:
mcolor.append(gmag - rmag)
mjds.append(0.5 * (gmjd + rmjd))
mcolor_unc.append(np.sqrt(gemag**2 + remag**2))
colorname.append("gmr")
if rmag and imag:
mcolor.append(rmag - imag)
mjds.append(0.5 * (rmjd + imjd))
mcolor_unc.append(np.sqrt(remag**2 + iemag**2))
colorname.append("rmi")
ctb = Table(data=[mjds, mcolor, mcolor_unc, colorname],
names=["mjd", "c", "ec", "cname"])
ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1 + z)
ctb = ctb.to_pandas()
return ctb
def get_ptf10iuv(colorplt=False):
"""
Kasliwal+12, Table 3, not corrected for extinction
"""
z = 0.0251485
ebv = 0.0371 # SFD
D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc
dis_mod = 5 * np.log10(D / 10)
print("adopt g band t_max estimated by myself")
t_max = 55357.387
tb = pd.read_csv('../data/otherSN/Kasliwal2012/PTF10iuv', sep='\t')
tb = tb.drop(columns=["Unnamed: 4"])
tb = tb.rename(columns={'Filter': 'filter', 'MJD': 'mjd'})
tb = tb[~np.array([x[0] == '>' for x in tb['Mag'].values])]
tb['mag'] = np.array(
[float(x.split(" +or-")[0]) for x in tb['Mag'].values])
tb['emag'] = np.array(
[float(x.split(" +or-")[1]) for x in tb['Mag'].values])
tb = tb.drop(columns=["Mag"])
ixg = tb['filter'].values == "g"
ixr = tb['filter'].values == "r"
ixi = tb['filter'].values == "i"
ixz = tb['filter'].values == "z"
ixB = tb['filter'].values == "B"
tb['wave'] = np.zeros(len(tb))
tb['wave'].values[ixB] = 4359
tb['wave'].values[ixg] = 4814
tb['wave'].values[ixr] = 6422
tb['wave'].values[ixi] = 7883
tb['wave'].values[ixz] = 9670
tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1 * ebv,
3.1)
tb['mag0_abs'] = tb['mag0'] - dis_mod
tb['tmax_rf'] = (tb['mjd'] - t_max) / (1 + z)
tb = tb.sort_values(by="mjd")
if colorplt == False:
return tb
else:
tb = add_datecol(tb)
ix = np.in1d(tb["filter"].values, np.array(['g', 'r', 'i']))
tb = tb[ix]
tb = tb[tb.mjd > 55352.5]
tb = tb[tb.mjd < 55593.5]
dates = get_date_span(tb)
datesave = []
for i in range(len(dates)):
x = dates[i]
ix = tb["date"].values == x
tbsub = tb[ix]
if len(tbsub) != 0:
flts = tbsub['filter'].values
if "r" in flts and np.sum(np.unique(flts)) != 1:
datesave.append(x)
datesave = np.array(datesave)
mcolor = []
mcolor_unc = []
mjds = []
colorname = []
for i in range(len(datesave)):
x = datesave[i]
ix = tb["date"].values == x
tbsub = tb[ix]
gtb = tbsub[tbsub["filter"].values == "g"]
rtb = tbsub[tbsub["filter"].values == "r"]
itb = tbsub[tbsub["filter"].values == "i"]
if len(gtb) != 0:
gmjds = gtb["mjd"].values
gmags = gtb["mag0"].values
gemags = gtb["emag"].values
gwtgs = 1 / gemags**2
gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)
gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)
gemag = 1 / np.sqrt(np.sum(gwtgs))
if len(rtb) != 0:
rmjds = rtb["mjd"].values
rmags = rtb["mag0"].values
remags = rtb["emag"].values
rwtgs = 1 / remags**2
rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)
rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)
remag = 1 / np.sqrt(np.sum(rwtgs))
if len(itb) != 0:
imjds = itb["mjd"].values
imags = itb["mag0"].values
iemags = itb["emag"].values
iwtgs = 1 / iemags**2
imag = np.sum(imags * iwtgs) / np.sum(iwtgs)
imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)
iemag = 1 / np.sqrt(np.sum(iwtgs))
if len(gtb) != 0 and len(rtb) != 0:
mcolor.append(gmag - rmag)
mjds.append(0.5 * (gmjd + rmjd))
mcolor_unc.append(np.sqrt(gemag**2 + remag**2))
colorname.append("gmr")
if len(rtb) != 0 and len(itb) != 0:
mcolor.append(rmag - imag)
mjds.append(0.5 * (rmjd + imjd))
mcolor_unc.append(np.sqrt(remag**2 + iemag**2))
colorname.append("rmi")
ctb = Table(data=[mjds, mcolor, mcolor_unc, colorname],
names=["mjd", "c", "ec", "cname"])
ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1 + z)
ctb = ctb.to_pandas()
return ctb
def get_iPTF16hgs(colorplt=False):
"""
De+18, Table 1, already corrected for extinction
"""
z = 0.017
ebv = 0
D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc
dis_mod = 5 * np.log10(D / 10)
tb = pd.read_csv('../data/otherSN/iPTF16hgs/table1.txt', sep="\t")
tb = tb.drop(columns=["Unnamed: 5"])
tb = tb.rename(columns={'Filter': 'filter', 'MJD': 'mjd'})
tb = tb[~np.array([x[0] == '>' for x in tb['Magnitude'].values])]
tb['mag'] = np.array(
[float(x.split(" +or-")[0]) for x in tb['Magnitude'].values])
tb['emag'] = np.array(
[float(x.split(" +or-")[1]) for x in tb['Magnitude'].values])
tb = tb.drop(columns=["Magnitude"])
ixg = tb['filter'].values == "g"
ixr = tb['filter'].values == "r"
ixi = tb['filter'].values == "i"
tb['wave'] = np.zeros(len(tb))
tb['wave'].values[ixg] = 4814
tb['wave'].values[ixr] = 6422
tb['wave'].values[ixi] = 7883
tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1 * ebv,
3.1)
tb['mag0_abs'] = tb['mag0'] - dis_mod
t_max = 57691.59 # from the paper
tb['tmax_of'] = tb['mjd'] - t_max
tb['tmax_rf'] = (tb['mjd'] - t_max) / (1 + z)
"""
plt.errorbar(tb["tmax_rf"].values[ixg], tb["mag"].values[ixg], tb["emag"].values[ixg], fmt=".g")
plt.errorbar(tb["tmax_rf"].values[ixr], tb["mag"].values[ixr], tb["emag"].values[ixr], fmt=".r")
plt.errorbar(tb["tmax_rf"].values[ixi], tb["mag"].values[ixi], tb["emag"].values[ixi], fmt=".y")
"""
tb = add_datecol(tb)
tb = add_physcol(tb)
#tb = tb.drop(columns=["datetime64"])
if colorplt == False:
return tb
else:
#tb = tb[tb.mjd > 55352.5]
#tb = tb[tb.mjd < 55593.5]
dates = get_date_span(tb)
datesave = []
for i in range(len(dates)):
x = dates[i]
ix = tb["date"].values == x
tbsub = tb[ix]
if len(tbsub) != 0:
flts = tbsub['filter'].values
if "r" in flts and np.sum(np.unique(flts)) != 1:
datesave.append(x)
datesave = np.array(datesave)
mcolor = []
mcolor_unc = []
mjds = []
colorname = []
for i in range(len(datesave)):
x = datesave[i]
ix = tb["date"].values == x
tbsub = tb[ix]
gtb = tbsub[tbsub["filter"].values == "g"]
rtb = tbsub[tbsub["filter"].values == "r"]
itb = tbsub[tbsub["filter"].values == "i"]
if len(gtb) != 0:
gmjds = gtb["mjd"].values
gmags = gtb["mag0"].values
gemags = gtb["emag"].values
gwtgs = 1 / gemags**2
gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)
gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)
gemag = 1 / np.sqrt(np.sum(gwtgs))
if len(rtb) != 0:
rmjds = rtb["mjd"].values
rmags = rtb["mag0"].values
remags = rtb["emag"].values
rwtgs = 1 / remags**2
rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)
rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)
remag = 1 / np.sqrt(np.sum(rwtgs))
if len(itb) != 0:
imjds = itb["mjd"].values
imags = itb["mag0"].values
iemags = itb["emag"].values
iwtgs = 1 / iemags**2
imag = np.sum(imags * iwtgs) / np.sum(iwtgs)
imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)
iemag = 1 / np.sqrt(np.sum(iwtgs))
if len(gtb) != 0 and len(rtb) != 0:
mcolor.append(gmag - rmag)
mjds.append(0.5 * (gmjd + rmjd))
mcolor_unc.append(np.sqrt(gemag**2 + remag**2))
colorname.append("gmr")
if len(rtb) != 0 and len(itb) != 0:
mcolor.append(rmag - imag)
mjds.append(0.5 * (rmjd + imjd))
mcolor_unc.append(np.sqrt(remag**2 + iemag**2))
colorname.append("rmi")
ctb = Table(data=[mjds, mcolor, mcolor_unc, colorname],
names=["mjd", "c", "ec", "cname"])
ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1 + z)
ctb = ctb.to_pandas()
return ctb
def get_sn2005ek(colorplt=False):
"""
Drout+13, Table 1, not corrected for extinction
"""
z = 0.016551
ebv = 0.210
D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc
dis_mod = 5 * np.log10(D / 10)
t_max = 53639.9
print("adopt r band t_max from Drout+13")
# tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\t')
# tb = tb.drop(columns=["Unnamed: 6"])
mjds = np.array([
53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3, 53646.5, 53648.0,
53649.2, 53650.4, 53651.3, 53652.5, 53654.2, 53655.2, 53656.2, 53657.2
])
Bmags = np.array([
18.25, 18.38, 18.65, np.nan, 19.10, 19.71, 20.07, np.nan, 20.67, 20.90,
21.05, np.nan, 21.74, np.nan, np.nan, np.nan
])
Bmag_uncs = np.array([
0.02, 0.03, 0.02, np.nan, 0.05, 0.07, 0.07, np.nan, 0.04, 0.04, 0.04,
np.nan, 0.12, np.nan, np.nan, np.nan
])
Vmags = np.array([
17.83, 18.03, 17.92, np.nan, 18.24, 18.66, 18.93, 19.48, 19.63, 19.86,
19.98, 20.35, 20.60, 20.74, 20.88, 21.22
])
Vmag_uncs = np.array([
0.02, 0.03, 0.01, np.nan, 0.02, 0.02, 0.02, 0.06, 0.03, 0.03, 0.04,
0.05, 0.08, 0.10, 0.08, 0.13
])
Rmags = np.array([
17.46, 17.41, 17.60, 17.69, 17.86, 18.18, np.nan, 18.83, 19.03, 19.26,
19.48, 19.75, 20.08, np.nan, 20.47, np.nan
])
Rmag_uncs = np.array([
0.01, 0.02, 0.01, 0.02, 0.01, 0.01, np.nan, 0.03, 0.02, 0.02, 0.02,
0.04, 0.05, np.nan, 0.08, np.nan
])
Imags = np.array([
17.20, 17.13, 17.18, np.nan, 17.47, 17.71, np.nan, 18.13, 18.26, 18.51,
18.61, 18.74, 19.01, np.nan, 19.47, np.nan
])
Imag_uncs = np.array([
0.02, 0.04, 0.02, np.nan, 0.03, 0.02, np.nan, 0.06, 0.02, 0.02, 0.02,
0.03, 0.05, np.nan, 0.06, np.nan
])
mymjds = np.hstack([mjds, mjds, mjds, mjds])
mymags = np.hstack([Bmags, Vmags, Rmags, Imags])
myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])
myfilts = np.hstack([
np.repeat("B", len(Bmags)),
np.repeat("V", len(Bmags)),
np.repeat("R", len(Rmags)),
np.repeat("I", len(Imags))
])
ix = ~np.isnan(mymags)
tb = pd.DataFrame({
'mjd': mymjds[ix],
'mag': mymags[ix],
'emag': myemags[ix],
"filter": myfilts[ix]
})
ixB = tb['filter'].values == "B"
ixV = tb['filter'].values == "V"
ixR = tb['filter'].values == "R"
ixI = tb['filter'].values == "I"
tb['wave'] = np.zeros(len(tb))
tb['wave'].values[ixB] = 4359
tb['wave'].values[ixV] = 5430
tb['wave'].values[ixR] = 6349
tb['wave'].values[ixI] = 8797
tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1 * ebv,
3.1)
tb['mag0_abs'] = tb['mag0'] - dis_mod
tb['tmax_rf'] = (tb['mjd'] - t_max) / (1 + z)
if colorplt == False:
return tb
else:
tb = add_datecol(tb)
ix = np.in1d(tb["filter"].values, np.array(['B', 'R', 'I']))
tb = tb[ix]
dates = get_date_span(tb)
datesave = []
for i in range(len(dates)):
x = dates[i]
ix = tb["date"].values == x
tbsub = tb[ix]
if len(tbsub) != 0:
flts = tbsub['filter'].values
if "R" in flts and np.sum(np.unique(flts)) != 1:
datesave.append(x)
datesave = np.array(datesave)
mcolor = []
mcolor_unc = []
mjds = []
colorname = []
for i in range(len(datesave)):
x = datesave[i]
ix = tb["date"].values == x
tbsub = tb[ix]
gtb = tbsub[tbsub["filter"].values == "B"]
rtb = tbsub[tbsub["filter"].values == "R"]
itb = tbsub[tbsub["filter"].values == "I"]
if len(gtb) != 0:
gmjds = gtb["mjd"].values
gmags = gtb["mag0"].values
gemags = gtb["emag"].values
gwtgs = 1 / gemags**2
gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)
gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)
gemag = 1 / np.sqrt(np.sum(gwtgs))
if len(rtb) != 0:
rmjds = rtb["mjd"].values
rmags = rtb["mag0"].values
remags = rtb["emag"].values
rwtgs = 1 / remags**2
rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)
rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)
remag = 1 / np.sqrt(np.sum(rwtgs))
if len(itb) != 0:
imjds = itb["mjd"].values
imags = itb["mag0"].values
iemags = itb["emag"].values
iwtgs = 1 / iemags**2
imag = np.sum(imags * iwtgs) / np.sum(iwtgs)
imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)
iemag = 1 / np.sqrt(np.sum(iwtgs))
if len(gtb) != 0 and len(rtb) != 0:
mcolor.append(gmag - rmag)
mjds.append(0.5 * (gmjd + rmjd))
mcolor_unc.append(np.sqrt(gemag**2 + remag**2))
colorname.append("BmR")
if len(rtb) != 0 and len(itb) != 0:
mcolor.append(rmag - imag)
mjds.append(0.5 * (rmjd + imjd))
mcolor_unc.append(np.sqrt(remag**2 + iemag**2))
colorname.append("RmI")
ctb = Table(data=[mjds, mcolor, mcolor_unc, colorname],
names=["mjd", "c", "ec", "cname"])
ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1 + z)
ctb = ctb.to_pandas()
return ctb
def get_iPTF14gqr(colorplt=False):
"""
De+18, Table S1, already corrected for extinction
"""
z = 0.063
# ebv = 0.082
D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc
dis_mod = 5 * np.log10(D / 10)
t_exp = 56943.74 #
t_max = 56950.26 # g band max light + 3
tb = Table(fits.open('../data/otherSN/De2018/tables1.fit')[1].data)
tb.rename_column('MJD', 'mjd')
tb['texp_rf'] = (tb['mjd'] - t_exp) / (1 + z)
tb['tmax_rf'] = (tb['mjd'] - t_max) / (1 + z)
# tb = tb[tb["Filt"]=="g "]
tb = tb[~np.isnan(tb['e_mag'])]
tb.rename_column('Filt', 'filter')
tb.rename_column('e_mag', 'emag')
tb.rename_column('mag', 'mag0')
ixg = tb['filter'] == "g "
ixB = tb['filter'] == "B "
ixV = tb['filter'] == "V "
ixr = tb['filter'] == "r "
ixi = tb['filter'] == "i "
ixUVW1 = tb['filter'] == "UVW1"
ixUVW2 = tb['filter'] == "UVW2"
tb['wave'] = np.zeros(len(tb))
tb['wave'][ixUVW2] = 2079
tb['wave'][ixUVW1] = 2614
tb['wave'][ixB] = 4359
tb['wave'][ixg] = 4814
tb['wave'][ixV] = 5430
tb['wave'][ixr] = 6422
tb['wave'][ixi] = 7883
tb['mag0_abs'] = tb['mag0'] - dis_mod
tb = tb.to_pandas()
tb["texp_rf"] = tb["Phase"]
tb = tb.drop(columns=["recno", "Phase", "l_mag"])
"""
ix = np.any([tb['Tel'].values=="P60 ",
tb["filter"].values=='g '], axis=0)
tb = tb[ix]
"""
tb = add_datecol(tb)
tb = add_physcol(tb)
tt = tb["tmax_rf"].values
epochs = [" " for x in range(len(tt))]
epochs = np.array(epochs)
"""
ix = (tt>-5.6)&(tt<-5.55)
epochs[ix] = "epoch 01"
"""
ix = (tt > -5.55) & (tt < -5.50)
epochs[ix] = "epoch 02"
ix = (tt > -5.50) & (tt < -5.45)
epochs[ix] = "epoch 03"
ix = (tt > -5.2) & (tt < -5.0)
epochs[ix] = "epoch 04"
ix = (tt > -5.0) & (tt < -4.7)
epochs[ix] = "epoch 05"
ix = (tt > -4.7) & (tt < -4.5)
epochs[ix] = "epoch 06"
ix = (tt > -4.5) & (tt < -3.5)
epochs[ix] = "epoch 07"
ix = (tt > -3.5) & (tt < -2.5)
epochs[ix] = "epoch 08"
ix = (tt > -1.5) & (tt < -1)
epochs[ix] = "epoch 09"
ix = (tt > -1) & (tt < -0.82)
epochs[ix] = "epoch 10"
ix = (tt > -0.82) & (tt < -0.6)
epochs[ix] = "epoch 11"
ix = (tt > -0.5) & (tt < 0.5)
epochs[ix] = "epoch 12"
ix = (tt > 0.5) & (tt < 1.5)
epochs[ix] = "epoch 13"
ix = (tt > 1.5) & (tt < 2.5)
epochs[ix] = "epoch 14"
ix = (tt > 3.5) & (tt < 4.5)
epochs[ix] = "epoch 15"
ix = (tt > 4.5) & (tt < 5)
epochs[ix] = "epoch 16"
ix = (tt > 5) & (tt < 5.6)
epochs[ix] = "epoch 17"
ix = (tt > 5.6) & (tt < 5.8)
epochs[ix] = "epoch 18"
ix = (tt > 6) & (tt < 7)
epochs[ix] = "epoch 19"
ix = (tt > 7) & (tt < 8)
epochs[ix] = "epoch 20"
ix = (tt > 8) & (tt < 9)
epochs[ix] = "epoch 21"
tb["epoch"] = epochs
if colorplt == False:
return tb
else:
tb = add_datecol(tb)
ix = np.in1d(tb["filter"].values, np.array(['g ', 'r ', 'i ']))
tb = tb[ix]
dates = get_date_span(tb)
datesave = []
for i in range(len(dates)):
x = dates[i]
ix = tb["date"].values == x
tbsub = tb[ix]
if len(tbsub) != 0:
flts = tbsub['filter'].values
if "r " in flts and np.sum(np.unique(flts)) != 1:
datesave.append(x)
datesave = np.array(datesave)
mcolor = []
mcolor_unc = []
mjds = []
colorname = []
for i in range(len(datesave)):
x = datesave[i]
ix = tb["date"].values == x
tbsub = tb[ix]
gtb = tbsub[tbsub["filter"].values == "g "]
rtb = tbsub[tbsub["filter"].values == "r "]
itb = tbsub[tbsub["filter"].values == "i "]
if len(gtb) != 0:
gmjds = gtb["mjd"].values
gmags = gtb["mag0"].values
gemags = gtb["emag"].values
gwtgs = 1 / gemags**2
gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)
gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)
gemag = 1 / np.sqrt(np.sum(gwtgs))
if len(rtb) != 0:
rmjds = rtb["mjd"].values
rmags = rtb["mag0"].values
remags = rtb["emag"].values
rwtgs = 1 / remags**2
rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)
rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)
remag = 1 / np.sqrt(np.sum(rwtgs))
if len(itb) != 0:
imjds = itb["mjd"].values
imags = itb["mag0"].values
iemags = itb["emag"].values
iwtgs = 1 / iemags**2
imag = np.sum(imags * iwtgs) / np.sum(iwtgs)
imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)
iemag = 1 / np.sqrt(np.sum(iwtgs))
if len(gtb) != 0 and len(rtb) != 0:
mcolor.append(gmag - rmag)
mjds.append(0.5 * (gmjd + rmjd))
mcolor_unc.append(np.sqrt(gemag**2 + remag**2))
colorname.append("gmr")
if len(rtb) != 0 and len(itb) != 0:
mcolor.append(rmag - imag)
mjds.append(0.5 * (rmjd + imjd))
mcolor_unc.append(np.sqrt(remag**2 + iemag**2))
colorname.append("rmi")
ctb = Table(data=[mjds, mcolor, mcolor_unc, colorname],
names=["mjd", "c", "ec", "cname"])
ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1 + z)
ctb = ctb.to_pandas()
return ctb
def get_sn2002bj(colorplt=False):
z = 0.012029
D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc
ebv = 0.0787
dis_mod = 5 * np.log10(D / 10)
tb = asci.read('../data/otherSN/Poznanski2010/sn2002bj')
tb.remove_columns(['source', 'upperlimit', 'event', 'instrument'])
tb.rename_column('band', 'filter')
tb.rename_column('magnitude', 'mag')
tb.rename_column('e_magnitude', 'emag')
tb.rename_column('time', 'mjd')
ix = tb['filter'] == 'R'
tb["filter"][ix] = "r"
ix = np.any(
[tb['filter'] == 'r', tb['filter'] == 'B', tb['filter'] == 'I'],
axis=0)
tb = tb[ix]
tb = tb[~tb['emag'].mask]
t_max = 52335.79 - 2 # r band maximum
ixr = tb["filter"] == "r"
ixB = tb["filter"] == "B"
ixI = tb["filter"] == "I"
tb['wave'] = np.ones(len(tb))
tb['wave'][ixr] = 6422
tb['wave'][ixB] = 4450
tb['wave'][ixI] = 8060
tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'], 3.1 * ebv, 3.1)
tb['mag0_abs'] = tb['mag0'] - dis_mod
tb['tmax_rf'] = (tb['mjd'] - t_max) / (1 + z)
tb = tb.to_pandas()
if colorplt == False:
return tb
else:
tb = add_datecol(tb)
dates = get_date_span(tb)
datesave = []
for i in range(len(dates)):
x = dates[i]
ix = tb["date"].values == x
tbsub = tb[ix]
if len(tbsub) != 0:
flts = tbsub['filter'].values
if "r" in flts and np.sum(np.unique(flts)) != 1:
datesave.append(x)
datesave = np.array(datesave)
mcolor = []
mcolor_unc = []
mjds = []
colorname = []
for i in range(len(datesave)):
x = datesave[i]
ix = tb["date"].values == x
tbsub = tb[ix]
gtb = tbsub[tbsub["filter"].values == "B"]
rtb = tbsub[tbsub["filter"].values == "r"]
itb = tbsub[tbsub["filter"].values == "I"]
if len(gtb) != 0:
gmjds = gtb["mjd"].values
gmags = gtb["mag0"].values
gemags = gtb["emag"].values
gwtgs = 1 / gemags**2
gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)
gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)
gemag = 1 / np.sqrt(np.sum(gwtgs))
else:
gmag = 0
if len(rtb) != 0:
rmjds = rtb["mjd"].values
rmags = rtb["mag0"].values
remags = rtb["emag"].values
rwtgs = 1 / remags**2
rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)
rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)
remag = 1 / np.sqrt(np.sum(rwtgs))
else:
rmag = 0
if len(itb) != 0:
imjds = itb["mjd"].values
imags = itb["mag0"].values
iemags = itb["emag"].values
iwtgs = 1 / iemags**2
imag = np.sum(imags * iwtgs) / np.sum(iwtgs)
imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)
iemag = 1 / np.sqrt(np.sum(iwtgs))
else:
imag = 0
if gmag and rmag:
mcolor.append(gmag - rmag)
mjds.append(0.5 * (gmjd + rmjd))
mcolor_unc.append(np.sqrt(gemag**2 + remag**2))
colorname.append("BmR")
if rmag and imag:
mcolor.append(rmag - imag)
mjds.append(0.5 * (rmjd + imjd))
mcolor_unc.append(np.sqrt(remag**2 + iemag**2))
colorname.append("RmI")
ctb = Table(data=[mjds, mcolor, mcolor_unc, colorname],
names=["mjd", "c", "ec", "cname"])
ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1 + z)
ctb = ctb.to_pandas()
return ctb