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
output = Lambda(lambda x: K.mean(x, axis=1))(output)
output = Dense(len(classes),
activation='softmax',
kernel_initializer='normal',
kernel_regularizer=regularizers.l2(l=0.01),
bias_regularizer=regularizers.l2(l=0.01),
use_bias=True)(output)
model = Model(inputs=input, outputs=output)
return model
t = fits.open('table1.fits', memmap=True)
train = Table(t[1].data).to_pandas()
train = shuffle(train)
classes = train['class'].to_frame()
train = train.drop(['class', 'Name'], axis=1)
metadata = pd.read_csv('metadata.csv')
print('Read training set and metadata...')
num_columns = len(train.columns)
X_train = np.array([train.values])
train = train.reset_index()
class_map = dict()
for i, val in enumerate(list(classes['class'].values)):
class_map[i] = val
targets = classes
target_map = np.zeros((targets.shape[0], ))
target_map = np.array(
[class_map[i] for i, val in enumerate(list(classes['class'].values))])
Y = to_categorical(target_map)
def fitting(directory, groupid, sky_coord, ja200_coord, cutoff=None):
results_table = Table({
'MJD': [],
'Filter': [],
'Mag': [],
'Error': [],
'ZP': [],
'SUB': []
})
for filename in os.listdir(directory):
if filename != '.DS_Store':
MJD, mg, zp, err, filter_, sub = CP.Return(
directory + '/' + filename, groupid, sky_coord,
(ja200_coord[0], ja200_coord[1]), cutoff)
if isinstance(mg, float) and mg != 0.0:
pass
else:
continue
results_table.add_row([MJD, filter_, mg, err, zp, sub])
results_table = results_table.group_by(['MJD', 'Filter', 'SUB'])
results_table = results_table.groups.aggregate(np.mean)
df = results_table.to_pandas()
indices = (df[df['ZP'] == 0].index.to_numpy())
for i in indices:
zp = np.array(df.loc[(df['MJD'] == df['MJD'][i])
& (df['Filter'] == df['Filter'][i]) &
(df['ZP'] != 0)]['ZP'])
zp_idx = (df[(df['MJD'] == df['MJD'][i])
& (df['Filter'] == df['Filter'][i]) &
(df['ZP'] != 0)].index)
if len(zp_idx) == 0:
df = df.drop([i])
else:
df = df.drop([zp_idx[0]])
df['ZP'][i] = zp
if cutoff is not '':
df = df[(df['MJD'] < int(cutoff)) | (df['SUB'] == 1)]
df['Mag'] = df['ZP'] - df['Mag']
results_table = df
results_table = results_table.drop(columns=['SUB'])
results_table.to_excel("Photometry_data.xlsx", sheet_name='Sheet_name_1')
plt.figure()
B = results_table[results_table['Filter'] == 1]
plt.errorbar(B['MJD'], B['Mag'], yerr=B['Error'], fmt='o')
scatter = plt.scatter(B['MJD'], B['Mag'], label='B')
V = results_table[results_table['Filter'] == 2]
plt.errorbar(V['MJD'], V['Mag'], yerr=V['Error'], fmt='o')
plt.scatter(V['MJD'], V['Mag'], label='V')
gp = results_table[results_table['Filter'] == 3]
plt.errorbar(gp['MJD'], gp['Mag'], yerr=gp['Error'], fmt='o')
plt.scatter(gp['MJD'], gp['Mag'], label='gp')
ip = results_table[results_table['Filter'] == 4]
plt.errorbar(ip['MJD'], ip['Mag'], yerr=ip['Error'], fmt='o')
plt.scatter(ip['MJD'], ip['Mag'], label='ip')
rp = results_table[results_table['Filter'] == 5]
plt.errorbar(rp['MJD'], rp['Mag'], yerr=rp['Error'], fmt='o')
plt.scatter(rp['MJD'], rp['Mag'], label='rp')
I = results_table[results_table['Filter'] == 6]
plt.errorbar(I['MJD'], I['Mag'], yerr=I['Error'], fmt='o')
plt.scatter(I['MJD'], I['Mag'], label='SDSS-I')
R = results_table[results_table['Filter'] == 7]
plt.errorbar(R['MJD'], R['Mag'], yerr=R['Error'], fmt='o')
plt.scatter(R['MJD'], R['Mag'], label='SDSS-R')
Z = results_table[results_table['Filter'] == 8]
plt.errorbar(Z['MJD'], Z['Mag'], yerr=Z['Error'], fmt='o')
plt.scatter(Z['MJD'], Z['Mag'], label='SDSS-Z')
G = results_table[results_table['Filter'] == 9]
plt.errorbar(G['MJD'], G['Mag'], yerr=G['Error'], fmt='o')
plt.scatter(G['MJD'], G['Mag'], label='SDSS-G')
ax = scatter.axes
ax.invert_yaxis()
plt.legend()
plt.savefig('Light_curve.png')
plt.show()
