def classify_lasair_light_curves(object_names=('ZTF18acsovsw', ), plot=True):
light_curve_list = []
for object_name in object_names:
try:
mjd, passband, mag, magerr, photflag, zeropoint, ra, dec, objid, redshift, mwebv = read_lasair_json(
object_name)
except Exception as e:
print(e)
continue
flux = 10.**(-0.4 * (mag - zeropoint))
fluxerr = np.abs(flux * magerr * (np.log(10.) / 2.5))
passband = np.where((passband == 1) | (passband == '1'), 'g', passband)
passband = np.where((passband == 2) | (passband == '2'), 'r', passband)
# Set photflag detections when S/N > 5
photflag2 = np.zeros(len(flux))
photflag2[flux / fluxerr > 5] = 4096
photflag2[np.where(mjd == min(mjd[photflag2 == 4096]))] = 6144
mjd_first_detection = min(mjd[photflag == 4096])
photflag[np.where(mjd == mjd_first_detection)] = 6144
deleteindexes = np.where(((passband == 3) | (passband == '3'))
| (mjd > mjd_first_detection)
& (photflag == 0))
print("Deleting indexes {} at mjd {} and passband {}".format(
deleteindexes, mjd[deleteindexes], passband[deleteindexes]))
mjd, passband, flux, fluxerr, zeropoint, photflag = delete_indexes(
deleteindexes, mjd, passband, flux, fluxerr, zeropoint, photflag)
light_curve_list += [(mjd, flux, fluxerr, passband, zeropoint,
photflag, ra, dec, objid, redshift, mwebv)]
classification = Classify(light_curve_list,
known_redshift=True,
bcut=False,
zcut=None)
predictions, time_steps = classification.get_predictions(
return_predictions_at_obstime=False)
print(predictions)
if plot:
classification.plot_light_curves_and_classifications(
step=True, use_interp_flux=False)
# classification.plot_light_curves_and_classifications(step=False, use_interp_flux=True)
# classification.plot_light_curves_and_classifications(step=False, use_interp_flux=False)
# classification.plot_classification_animation_step()
# classification.plot_classification_animation()
return classification.orig_lc, classification.timesX, classification.y_predict
def main():
mjd = [
57433.4816, 57436.4815, 57439.4817, 57451.4604, 57454.4397, 57459.3963,
57462.418, 57465.4385, 57468.3768, 57473.3606, 57487.3364, 57490.3341,
57493.3154, 57496.3352, 57505.3144, 57513.2542, 57532.2717, 57536.2531,
57543.2545, 57546.2703, 57551.2115, 57555.2669, 57558.2769, 57561.1899,
57573.2133, 57433.5019, 57436.4609, 57439.4587, 57444.4357, 57459.4189,
57468.3142, 57476.355, 57479.3568, 57487.3586, 57490.3562, 57493.3352,
57496.2949, 57505.3557, 57509.2932, 57513.2934, 57518.2735, 57521.2739,
57536.2321, 57539.2115, 57543.2301, 57551.1701, 57555.2107, 57558.191,
57573.1923, 57576.1749, 57586.1854
]
flux = [
2.0357230e+00, -2.0382695e+00, 1.0084588e+02, 5.5482742e+01,
1.4867026e+01, -6.5136810e+01, 1.6740545e+01, -5.7269131e+01,
1.0649184e+02, 1.5505235e+02, 3.2445984e+02, 2.8735449e+02,
2.0898877e+02, 2.8958893e+02, 1.9793906e+02, -1.3370536e+01,
-3.9001358e+01, 7.4040916e+01, -1.7343750e+00, 2.7844931e+01,
6.0861992e+01, 4.2057487e+01, 7.1565346e+01, -2.6085690e-01,
-6.8435440e+01, 17.573107, 41.445435, -110.72664, 111.328964,
-63.48336, 352.44907, 199.59058, 429.83075, 338.5255, 409.94604,
389.71262, 195.63905, 267.13318, 123.92461, 200.3431, 106.994514,
142.96387, 56.491238, 55.17521, 97.556946, -29.263103, 142.57687,
-20.85057, -0.67210346, 63.353024, -40.02601
]
fluxerr = [
42.784702, 43.83665, 99.98704, 45.26248, 43.040398, 44.00679,
41.856007, 49.354336, 105.86439, 114.0044, 45.697918, 44.15781,
60.574158, 93.08788, 66.04482, 44.26264, 91.525085, 42.768955,
43.228336, 44.178196, 62.15593, 109.270035, 174.49638, 72.6023,
48.021034, 44.86118, 48.659588, 100.97703, 148.94061, 44.98218,
139.11194, 71.4585, 47.766987, 45.77923, 45.610615, 60.50458,
105.11658, 71.41217, 43.945534, 45.154167, 43.84058, 52.93122,
44.722775, 44.250145, 43.95989, 68.101326, 127.122025, 124.1893,
49.952255, 54.50728, 114.91599
]
passband = [
'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g',
'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'r', 'r', 'r',
'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r',
'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r'
]
zeropoint = [
27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5,
27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5,
27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5,
27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5,
27.5, 27.5, 27.5
]
photflag = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4096, 4096, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 4096, 6144, 4096, 4096, 4096, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
objid = 'MSIP_01_NONIa-0001_10400862'
ra = 3.75464531293933
dec = 0.205076187109334
redshift = 0.233557
mwebv = 0.0228761
light_curve_list = [(mjd, flux, fluxerr, passband, zeropoint, photflag, ra,
dec, objid, redshift, mwebv)]
classification = Classify(light_curve_list)
predictions = classification.get_predictions()
print(predictions)
classification.plot_light_curves_and_classifications()
classification.plot_classification_animation()
def do(self,debug=False):
# run this under Admin
user = User.objects.get(username='******')
# get New, Watch, FollowupRequested, Following
transients_to_classify = \
Transient.objects.filter(Q(status__name = 'New') |
Q(status__name = 'Watch') |
Q(status__name = 'FollowupRequested') |
Q(status__name = 'Following'))
light_curve_list_z,light_curve_list_noz,transient_list_z,transient_list_noz = [],[],[],[]
for t in transients_to_classify: #.filter(Q(name = '2019np') | Q(name = '2019gf')):
ra, dec, objid, redshift = t.ra, t.dec, t.name,t.z_or_hostz()
if not t.mw_ebv is None:
mwebv = t.mw_ebv
else:
mwebv = 0.0
photdata = get_all_phot_for_transient(user, t.id)
if not photdata: continue
gobs = photdata.filter(band__name = 'g-ZTF')
robs = photdata.filter(band__name = 'r-ZTF')
if not len(gobs) and not len(robs): continue
mjd, passband, flux, fluxerr, mag, magerr, zeropoint, photflag = \
np.array([]),np.array([]),[],[],[],[],[],[]
if redshift: transient_list_z += [t]
else: transient_list_noz += [t]
first_detection_set = False
for obs,filt in zip([gobs.order_by('obs_date'),robs.order_by('obs_date')],['g','r']):
for p in obs:
if p.data_quality: continue
if len(np.where((filt == passband) & (np.abs(mjd - date_to_mjd(p.obs_date)) < 0.001))[0]): continue
mag += [p.mag]
if p.mag_err:
magerr += [p.mag_err]
fluxerr_obs = 0.4*np.log(10)*p.mag_err
else:
magerr += [0.001]
fluxerr_obs = 0.4*np.log(10)*0.001
flux_obs = 10**(-0.4*(p.mag-27.5))
mjd = np.append(mjd,[date_to_mjd(p.obs_date)])
flux += [flux_obs]
fluxerr += [fluxerr_obs]
zeropoint += [27.5]
passband = np.append(passband,[filt])
if flux_obs/fluxerr_obs > 5 and not first_detection_set:
photflag += [6144]
first_detection_set = True
elif flux_obs/fluxerr_obs > 5:
photflag += [4096]
else: photflag += [0]
#except: import pdb; pdb.set_trace()
try:
if redshift:
light_curve_info = (mjd, flux, fluxerr, passband,
zeropoint, photflag, ra, dec, objid, redshift, mwebv)
light_curve_list_z += [light_curve_info,]
else:
light_curve_info = (mjd, flux, fluxerr, passband, zeropoint, photflag, ra, dec, objid, None, mwebv)
light_curve_list_noz += [light_curve_info,]
except:
import pdb; pdb.set_trace()
if len(light_curve_list_noz):
classification_noz = Classify(light_curve_list_noz, known_redshift=False, bcut=False, zcut=None)
predictions_noz = classification_noz.get_predictions()
if len(light_curve_list_z):
classification_z = Classify(light_curve_list_z, known_redshift=True, bcut=False, zcut=None)
predictions_z = classification_z.get_predictions()
if debug:
import matplotlib
matplotlib.use('MacOSX')
import matplotlib.pyplot as plt
plt.ion()
classification_z.plot_light_curves_and_classifications()
for tl in [transient_list_z,transient_list_noz]:
for t,i in zip(tl,range(len(tl))):
best_predictions = predictions_z[0][i][-1,:]
adjusted_best_predictions = np.zeros(10)
idx,outclassnames,PIa = 0,[],0
for j in range(len(classification_z.class_names)):
if classification_z.class_names[j] == 'Pre-explosion': continue
elif classification_z.class_names[j].startswith('SNIa'): PIa += best_predictions[j]
else:
outclassnames += [classification_z.class_names[j]]
adjusted_best_predictions[idx] = best_predictions[j]
idx += 1
outclassnames += ['SN Ia']
outclassnames = np.array(outclassnames)
adjusted_best_predictions[9] = PIa
print(t.name,outclassnames[adjusted_best_predictions == np.max(adjusted_best_predictions)][0])
transient_class = outclassnames[adjusted_best_predictions == np.max(adjusted_best_predictions)][0]
photo_class = TransientClass.objects.filter(name = classdict[transient_class])
if len(photo_class):
t.photo_class = photo_class[0]
t.save()
else:
print('class %s not in DB'%classdict[transient_class])
raise RuntimeError('class %s not in DB'%classdict[transient_class])
def classify_lasair_light_curves(
object_names=('ZTF18acsovsw', ), plot=True, figdir='.'):
light_curve_list = []
peakfluxes_g, peakfluxes_r = [], []
mjds, passbands, mags, magerrs, zeropoints, photflags = [], [], [], [], [], []
obj_names = []
ras, decs, objids, redshifts, mwebvs = [], [], [], [], []
peakmags_g, peakmags_r = [], []
for object_name in object_names:
try:
mjd, passband, mag, magerr, photflag, zeropoint, ra, dec, objid, redshift, mwebv = read_lasair_json(
object_name)
sortidx = np.argsort(mjd)
mjds.append(mjd[sortidx])
passbands.append(passband[sortidx])
mags.append(mag[sortidx])
magerrs.append(magerr[sortidx])
zeropoints.append(zeropoint[sortidx])
photflags.append(photflag[sortidx])
obj_names.append(object_name)
ras.append(ra)
decs.append(dec)
objids.append(objid)
redshifts.append(redshift)
mwebvs.append(mwebv)
peakmags_g.append(min(mag[passband == 1]))
peakmags_r.append(min(mag[passband == 2]))
except Exception as e:
print(e)
continue
flux = 10.**(-0.4 * (mag - zeropoint))
fluxerr = np.abs(flux * magerr * (np.log(10.) / 2.5))
passband = np.where((passband == 1) | (passband == '1'), 'g', passband)
passband = np.where((passband == 2) | (passband == '2'), 'r', passband)
# Set photflag detections when S/N > 5
photflag2 = np.zeros(len(flux))
photflag2[flux / fluxerr > 5] = 4096
photflag2[np.where(mjd == min(mjd[photflag2 == 4096]))] = 6144
mjd_first_detection = min(mjd[photflag == 4096])
photflag[np.where(mjd == mjd_first_detection)] = 6144
deleteindexes = np.where(((passband == 3) | (passband == '3'))
| (mjd > mjd_first_detection)
& (photflag == 0))
if deleteindexes[0].size > 0:
print("Deleting indexes {} at mjd {} and passband {}".format(
deleteindexes, mjd[deleteindexes], passband[deleteindexes]))
mjd, passband, flux, fluxerr, zeropoint, photflag = delete_indexes(
deleteindexes, mjd, passband, flux, fluxerr, zeropoint, photflag)
light_curve_list += [(mjd, flux, fluxerr, passband, photflag, ra, dec,
objid, redshift, mwebv)]
try:
dummy = max(flux[passband == 'g'])
dummy = max(flux[passband == 'r'])
except Exception as e:
print(e)
continue
peakfluxes_g.append(max(flux[passband == 'g']))
peakfluxes_r.append(max(flux[passband == 'r']))
# import sys
# import pickle
# with open('save_real_ZTF_unprocessed_data_snia_osc_12nov2019.npz', 'wb') as f:
# pickle.dump([mjds, passbands, mags, magerrs, photflags, zeropoints, ras, decs, objids, redshifts, mwebvs], f)
# # np.savez('save_real_ZTF_unprocessed_data_snia_osc_12nov2019.npz', mjds=mjds, passbands=passbands, mags=mags, magerrs=magerrs, photflags=photflags, zeropoints=zeropoints, ras=ras, decs=decs, objids=objids, redshifts=redshifts, mwebvs=mwebvs)# , peakflux_g=peakfluxes_g, peakflux_r=peakfluxes_r)
# print("finished")
# # # sys.exit(0)
# # with open('save_real_ZTF_unprocessed_data_snia_osc_12nov2019.npz', 'rb') as f:
# # a = pickle.load(f)
classification = Classify(known_redshift=True, bcut=False, zcut=None)
predictions, time_steps = classification.get_predictions(
light_curve_list, return_predictions_at_obstime=False)
print(predictions)
if plot:
# try:
classification.plot_light_curves_and_classifications(
step=True,
use_interp_flux=False,
figdir=figdir,
plot_matrix_input=True)
# except Exception as e:
# print(e)
# classification.plot_light_curves_and_classifications(step=False, use_interp_flux=True)
# classification.plot_light_curves_and_classifications(step=False, use_interp_flux=False)
# classification.plot_classification_animation_step()
# classification.plot_classification_animation()
return classification.orig_lc, classification.timesX, classification.y_predict
1.9793906e+02, -1.3370536e+01, -3.9001358e+01, 7.4040916e+01, -1.7343750e+00, 2.7844931e+01, 6.0861992e+01,
4.2057487e+01, 7.1565346e+01, -2.6085690e-01, -6.8435440e+01, 17.573107, 41.445435, -110.72664, 111.328964,
-63.48336, 352.44907, 199.59058, 429.83075, 338.5255, 409.94604, 389.71262, 195.63905, 267.13318, 123.92461,
200.3431, 106.994514, 142.96387, 56.491238, 55.17521, 97.556946, -29.263103, 142.57687, -20.85057, -0.67210346,
63.353024, -40.02601]
fluxerr = [42.784702, 43.83665, 99.98704, 45.26248, 43.040398, 44.00679, 41.856007, 49.354336, 105.86439, 114.0044,
45.697918, 44.15781, 60.574158, 93.08788, 66.04482, 44.26264, 91.525085, 42.768955, 43.228336, 44.178196,
62.15593, 109.270035, 174.49638, 72.6023, 48.021034, 44.86118, 48.659588, 100.97703, 148.94061, 44.98218,
139.11194, 71.4585, 47.766987, 45.77923, 45.610615, 60.50458, 105.11658, 71.41217, 43.945534, 45.154167,
43.84058, 52.93122, 44.722775, 44.250145, 43.95989, 68.101326, 127.122025, 124.1893, 49.952255, 54.50728,
114.91599]
passband = ['g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g',
'g', 'g', 'g', 'g', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r',
'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r']
photflag = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4096, 4096, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4096,
6144, 4096, 4096, 4096, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
objid = 'transient_1'
ra = 3.75464531293933
dec = 0.205076187109334
redshift = 0.233557
mwebv = 0.0228761
light_curve_list = [(mjd, flux, fluxerr, passband, photflag, ra, dec, objid, redshift, mwebv)]
classification = Classify()
predictions, time_steps = classification.get_predictions(light_curve_list)
print(predictions)
classification.plot_light_curves_and_classifications()
classification.plot_classification_animation()
