def get_lightcurve_alerts(username, password, list_names):
"""Query the light curve for a list of candidates"""
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {
'objectId': {
'$in': list(list_names)
}
},
"projection": {
"objectId": 1,
"candidate.jd": 1,
"candidate.ra": 1,
"candidate.dec": 1,
"candidate.magpsf": 1,
"candidate.isdiffpos": 1,
"candidate.fid": 1,
"candidate.sigmapsf": 1,
"candidate.programid": 1,
"candidate.magzpsci": 1,
"candidate.magzpsciunc": 1,
"candidate.sgscore1": 1,
"candidate.sgscore2": 1,
"candidate.sgscore3": 1,
"candidate.distpsnr1": 1,
"candidate.distpsnr2": 1,
"candidate.distpsnr3": 1,
"candidate.field": 1,
"candidate.rcid": 1,
"candidate.pid": 1
}
},
"kwargs": {
"hint": "objectId_1"
}
}
r = k.query(query=q)
try:
if r['data'] == []:
print("No candidates to be checked?")
return None
except KeyError:
#Try the query one more time
r = k.query(query=q)
try:
if r['data'] == []:
print("No candidates to be checked?")
return None
except KeyError:
return None
return r['data']
def check_history(list_sources, radius=1.):
'''Query ZTF and ZUDS alerts and select
only those sources without a negative detection.
'''
# Get the coordinates of the candidates
sources = query_kowalski_coords(username,
password,
list_sources,
catalog='ZUDS_alerts')
coords_arr = list((c['ra'], c['dec']) for c in sources)
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "cone_search",
"object_coordinates": {
"radec": f"{coords_arr}",
"cone_search_radius": f"{radius}",
"cone_search_unit": "arcsec"
},
"catalogs": {
"ZTF_alerts": {
"filter": {
'candidate.isdiffpos': {
'$in': ['f', 0]
}
},
"projection": {
"objectId": 1,
"candidate.ra": 1,
"candidate.dec": 1
}
},
"ZUDS_alerts": {
"filter": {
'candidate.isdiffpos': {
'$in': ['f', 0]
}
},
"projection": {
"objectId": 1,
"candidate.ra": 1,
"candidate.dec": 1
}
}
}
}
r = k.query(query=q)
with_neg_sub = []
for s, i in zip(sources, r['result_data']['ZTF_alerts'].keys()):
if len(r['result_data']['ZTF_alerts'][i]) != 0 or len(
r['result_data']['ZUDS_alerts'][i]) != 0:
with_neg_sub.append(s['name'])
selected_list = list(s['name'] for s in sources
if not (s['name'] in with_neg_sub))
return selected_list
def get_jdstarthist_kowalski(source_names, username, password):
'''Query kowalski, look for one alert and return a list of candidate.jdstarthist '''
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {
"objectId": {
"$in": list(source_names)
}
},
"projection": {
"_id": 0,
"objectId": 1,
"candidate.jdstarthist": 1
}
}
}
r = k.query(query=q)
jdstarthist_tuples = set(
list((s['objectId'], s['candidate']['jdstarthist'])
for s in r['result_data']['query_result']))
#re-output both lists to avoid mixed sorting
names_out = list(x[0] for x in jdstarthist_tuples)
jdstarthist_list = list(x[1] for x in jdstarthist_tuples)
return names_out, jdstarthist_list
def get_lightcurve_alerts_aux(username, password, list_names):
"""Query the light curve for a list of candidates"""
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts_aux",
"filter": {
'_id': {
'$in': list(list_names)
}
},
"projection": {}
},
"kwargs": {
"hint": "_id_"
}
}
r = k.query(query=q)
if r['result_data']['query_result'] == []:
print("No candidates to be checked?")
return None
out = []
for l in r['result_data']['query_result']:
with_det = list({
'objectId': l['_id'],
'candidate': s
} for s in l['prv_candidates'] if 'magpsf' in s.keys())
out = out + with_det
return out
def query_kowalski_coords(username, password, names):
'''Query kowalski to get the coordinates of given ZTF sources.'''
names = list(names)
k = Kowalski(username=username, password=password, verbose=False)
q = {"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {"objectId": {"$in": names}},
"projection": {"_id": 0,
"candid": 1,
"objectId": 1,
"candidate.ra": 1,
"candidate.dec": 1
},
}
}
results_all = k.query(query=q)
results = results_all.get('data')
sources = []
for n in names:
source = {}
source["name"] = n
source["ra"] = list(r["candidate"]["ra"] for r in results if
r["objectId"] == n)[0]
source["dec"] = list(r["candidate"]["dec"] for r in results if
r["objectId"] == n)[0]
source["candid"] = list(r["candid"] for r in results if
r["objectId"] == n)[0]
sources.append(source)
return sources
def match_kowalski_clu(username, password, list_in, catalog='ZUDS_alerts_aux'):
'''Query kowalski and apply the CLU filter'''
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "find",
"query": {
"catalog": catalog,
"filter": {
"_id": {
'$in': list(list_in)
}
},
"projection": {
"objectId": 1,
"cross_matches.CLU_20190625": 1
}
}
}
r = k.query(query=q)
query_results = r['result_data']['query_result']
list_out = list(t['_id'] for t in query_results
if len(t['cross_matches']['CLU_20190625']) != 0)
return list_out
def check_lightcurve_alerts(username, password, list_names, min_days,
max_days):
"""Re-query light curve info for a list of candidates\
and check that their full/updated duration is consistent\
with the time limits provided"""
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {
'objectId': {
'$in': list(list_names)
}
},
"projection": {
"objectId": 1,
"candidate.jd": 1,
"candidate.ndethist": 1,
"candidate.jdstarthist": 1,
"candidate.jdendhist": 1,
"candidate.jdendhist": 1,
"candidate.magpsf": 1,
"candidate.sigmapsf": 1,
"candidate.programid": 1,
}
},
"kwargs": {
"hint": "objectId_1"
}
}
r = k.query(query=q)
if r['data'] == []:
print("No candidates to be checked?")
return None
old = []
objectid_list = []
for info in r['data']:
if info['objectId'] in old:
continue
if (info['candidate']['jdendhist'] -
info['candidate']['jdstarthist']) < min_days:
continue
if (info['candidate']['jdendhist'] -
info['candidate']['jdstarthist']) > max_days:
old.append(info['objectId'])
objectid_list.append(info['objectId'])
clean_set = set(objectid_list)
#Remove those objects considered old
for n in set(old):
try:
clean_set.remove(n)
except:
pass
return clean_set
def query_kowalski_clu(username, password, clu):
'''Query kowalski to get a table of CLU galaxies.'''
k = Kowalski(username=username, password=password, verbose=False)
q = {"query_type": "general_search",
"query": "db['CLU_20180513'].find({},{'distmpc': 1})"
}
r = k.query(query=q)
return r
def xmatch(_radecs, batch_size: int = 100, verbose: int = 0):
k = Kowalski(username='******', password='******', verbose=False)
num_obj = len(_radecs)
if verbose:
print(f'Total entries: {num_obj}')
num_batches = int(np.ceil(num_obj / batch_size))
times = []
# ids = set()
for nb in range(num_batches):
# print(_radecs[nb * batch_size: (nb + 1) * batch_size])
q = {
"query_type": "cone_search",
"object_coordinates": {
"radec": f"{_radecs[nb * batch_size: (nb + 1) * batch_size]}",
"cone_search_radius": "1",
"cone_search_unit": "arcsec"
},
"catalogs": {
"Gaia_DR2": {
"filter": {},
"projection": {
"_id": 1
}
}
}
}
tic = time()
r = k.query(query=q)
toc = time()
times.append(toc - tic)
if verbose:
print(
f'Fetching batch {nb + 1}/{num_batches} with {batch_size} sources/LCs took: {toc - tic:.3f} seconds'
)
# Data are here:
data = r['result_data']
# TODO: your magic here
if verbose == 2:
print(data)
# for sc, sources in data['Gaia_DR2'].items():
# ids = ids.union([s['_id'] for s in sources])
# print(len(ids))
if verbose:
print(f'min: {np.min(times)}')
print(f'median: {np.median(times)}')
print(f'max: {np.max(times)}')
def query_db(coords, r=3.):
""" given a set of coordinates, get the matchIDs from the database
Note that everything that is in the aperture is returned.
Parameters
----------
coords : 2d-array
2d-array with Ra,dec in degrees
r : float
matching radius
coords : str
catalog name
Returns
-------
matchIDs : 1d-array
an array with matchIDs (int64)
"""
k = Kowalski(username='******',
password='******',
verbose=False)
# cone search radius must be in radians:
cone_search_radius = r * np.pi / 180.0 / 3600.
# construct query: RA and Dec's must be in degrees; RA must c (-180, 180]
query = {'$or': []}
for ra, dec in coords:
# convert
#_ra, _dec = radec_str2geojson(*obj_crd)
obj_query = {
'coordinates.radec_geojson': {
'$geoWithin': {
'$centerSphere': [[ra - 180., dec], cone_search_radius]
}
}
}
query['$or'].append(obj_query)
q = {
"query_type": "general_search",
"query": "db['ZTF_20181220'].find(%s)" % (query)
}
# execute query
output = k.query(query=q)
# get matchids
matchIDs = [str(l['_id']) for l in output['result_data']['query_result']]
return (matchIDs)
def query_kowalski_coords(username, password, names):
'''Query kowalski to get the coordinates of given ZTF sources. '''
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "general_search",
"query": "db['CLU_20180513'].find({},{'distmpc': 1})"
}
r = k.query(query=q)
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {
"objectId": {
"$in": names
}
},
"projection": {
"_id": 0,
"candid": 1,
"objectId": 1,
"candidate.ra": 1,
"candidate.dec": 1
},
}
}
results_all = k.query(query=q)
results = results_all['result_data']['query_result']
sources = []
for n in names:
source = {}
source["name"] = n
source["ra"] = list(r["candidate"]["ra"] for r in results
if r["objectId"] == n)[0]
source["dec"] = list(r["candidate"]["dec"] for r in results
if r["objectId"] == n)[0]
sources.append(source)
return sources
def get_cutouts(name, username, password):
"""Query kowalski to get the candidate stamps"""
from penquins import Kowalski
k = Kowalski(username=username, password=password, verbose=False)
if type(name) == str:
list_names = [name]
elif type(name) == list:
list_names = name
else:
print(f"{name} must be a list or a string")
return None
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {
'objectId': {
'$in': list(list_names)
}
},
"projection": {
"objectId": 1,
"candidate.jd": 1,
"candidate.ra": 1,
"candidate.dec": 1,
"candidate.magpsf": 1,
"candidate.fid": 1,
"candidate.sigmapsf": 1,
"candidate.programid": 1,
"candidate.field": 1,
"candidate.rcid": 1,
"cutoutScience": 1,
"cutoutTemplate": 1,
"cutoutDifference": 1,
}
},
"kwargs": {
"hint": "objectId_1"
}
}
r = k.query(query=q)
if r['data'] == []:
print("No candidates to be checked?")
return None
else:
alerts = r['data']
return alerts
def get_index_info(catalog):
"""List which indexes are available on Kowalski to query a catalog
more quickly"""
q = {
"query_type": "info",
"query": {
"command": "index_info",
"catalog": catalog
}
}
k = Kowalski(username=username, password=password, verbose=False)
r = k.query(query=q)
indexes = r['result_data']['query_result']
for ii, (kk, vv) in enumerate(indexes.items()):
print(f'index #{ii+1}: "{kk}"\n{vv["key"]}\n')
def get_ztf(filename, name, username, password):
k = Kowalski(username=username, password=password, verbose=True)
q = {"query_type": "general_search",
"query": "db['ZTF_alerts'].find({'objectId': {'$eq': '"+name+"'}})"
}
r = k.query(query=q,timeout=10)
if len(r['result_data']['query_result']) >0:
candidate = r['result_data']['query_result'][0]
prevcandidates= r['result_data']['query_result'][0]['prv_candidates']
jd = [candidate['candidate']['jd']]
mag = [candidate['candidate']['magpsf']]
magerr = [candidate['candidate']['sigmapsf']]
filt = [candidate['candidate']['fid']]
for candidate in prevcandidates:
jd.append(candidate['jd'])
if not candidate['magpsf'] == None:
mag.append(candidate['magpsf'])
else:
mag.append(candidate['diffmaglim'])
if not candidate['sigmapsf'] == None:
magerr.append(candidate['sigmapsf'])
else:
magerr.append(np.inf)
filt.append(candidate['fid'])
filtname = []
for f in filt:
if f == 1:
filtname.append('g')
elif f == 2:
filtname.append('r')
elif f == 3:
filtname.append('i')
idx = np.argsort(jd)
fid = open(filename,'w')
for ii in idx:
t = Time(jd[ii], format='jd').isot
fid.write('%s %s %.5f %.5f\n'%(t,filtname[ii],mag[ii],magerr[ii]))
fid.close()
def get_lightcurve_alerts_aux(username, password, list_names):
"""Query the light curve for a list of candidates"""
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts_aux",
"filter": {
'_id': {
'$in': list(list_names)
}
},
"projection": {}
},
"kwargs": {
"hint": "_id_"
}
}
'''
q = {"query_type": "info",
"query": {
"command": "index_info",
"catalog": "ZTF_alerts"
}
}
# ZTF19acdpipl
'''
r = k.query(query=q)
#indexes = r['result_data']['query_result']
#for ii, (kk, vv) in enumerate(indexes.items()):
# print(f'index #{ii+1}: "{kk}"\n{vv["key"]}\n')
if r['result_data']['query_result'] == []:
print("No candidates to be checked?")
return None
out = []
for l in r['result_data']['query_result']:
with_det = list({
'objectId': l['_id'],
'candidate': s
} for s in l['prv_candidates'] if 'magpsf' in s.keys())
out = out + with_det
return out
ra_header_.append(hdu[0].header['CRVAL1'])
dec_header_.append(hdu[0].header['CRVAL2'])
#filt_.append(hdu[0].header['filter'])
filt_.append(opts.filter)
date = Time(hdu[0].header['DATE-OBS'], format='isot', scale='utc')
date_.append(date)
date_jd_.append(date.jd)
name_.append(opts.transient)
if opts.doKowalski:
q = {
"query_type": "general_search",
"query":
"db['ZTF_alerts'].find({'objectId': {'$eq': '" + ID + "'}})"
}
r = ko.query(query=q, timeout=30)
if len(r['result_data']['query_result']) > 0:
# getting metadata
candidate = r['result_data']['query_result'][0]
ra, dec = candidate['candidate']['ra'], candidate['candidate'][
'dec']
ra_all_tran.append(ra)
dec_all_tran.append(dec)
# print("query worked")
# print('solve-field '+files[i][:-5]+'_red.fits --ra '+str(ra)+' --dec '+str(dec)+' --dir /home/roboao/Tomas/output --scale-units arcsecperpix --scale-low 0.255 --scale-high 0.26 --radius 0.04 --overwrite')
else:
ra_all_tran.append(np.nan)
dec_all_tran.append(np.nan)
else:
idx = np.where(opts.transient == transients["name"])[0]
def query_kowalski_frb(args, t):
"""Query kowalski with cone searches centered at given locations"""
# Prepare a dictionary for each source
dict_sources = {}
for s in t:
if args.frb_names is not None and not (s['frb_name']
in args.frb_names):
continue
try:
coords = SkyCoord(ra=s["rop_raj"],
dec=s["rop_decj"],
unit=(u.hourangle, u.deg),
frame='icrs')
except ValueError:
pdb.set_trace()
id_ra = f"{str(coords.ra.deg).replace('.','_')}"
id_dec = f"{str(coords.dec.deg).replace('.','_')}"
id_coords = f"({id_ra}, {id_dec})"
date = Time(s['utc'].replace('/', '-'), format='iso')
dict_sources[s['frb_name']] = {
'ra': coords.ra.deg,
'dec': coords.dec.deg,
'id_coords': id_coords,
'jd': date.jd,
'candidates': []
}
# Check that there is at least one source
if len(dict_sources.keys()) == 0:
print("No FRBs correspond to the given input.")
if args.frb_names is not None:
print(
f"No FRB among {args.frb_names} are present in {args.cat_name}"
)
return None
# coords_arr.append((coords.ra.deg,coords.dec.deg))
coords_arr = list((dict_sources[k]['ra'], dict_sources[k]['dec'])
for k in dict_sources.keys())
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "cone_search",
"object_coordinates": {
"radec": f"{coords_arr}",
"cone_search_radius": args.search_radius,
"cone_search_unit": "arcmin"
},
"catalogs": {
"ZTF_alerts": {
"filter": {
"candidate.drb": {
'$gt': 0.5
},
"candidate.ndethist": {
'$gte': args.ndethist
},
"classifications.braai": {
'$gt': 0.5
},
"candidate.ssdistnr": {
'$gt': 10
},
"candidate.magpsf": {
'$gt': 10
}
},
"projection": {
"objectId": 1,
"candidate.rcid": 1,
"candidate.drb": 1,
"candidate.ra": 1,
"candidate.dec": 1,
"candidate.jd": 1,
"candidate.magpsf": 1,
"candidate.sigmapsf": 1,
"candidate.fid": 1,
"candidate.sgscore1": 1,
"candidate.distpsnr1": 1,
"candidate.sgscore2": 1,
"candidate.distpsnr2": 1,
"candidate.sgscore3": 1,
"candidate.distpsnr3": 1,
"candidate.ssdistnr": 1,
"candidate.isdiffpos": 1
}
}
},
"kwargs": {
"hint": "gw01"
}
}
r = k.query(query=q)
for idcoords in r['result_data']['ZTF_alerts'].keys():
#Identify 'candid' for all relevant candidates
objectId_list = []
with_neg_sub = []
stellar_list = []
# No sources
if len(r['result_data']['ZTF_alerts'][idcoords]) == 0:
key = list(k for k in dict_sources.keys()
if dict_sources[k]['id_coords'] == idcoords)[0]
dict_sources[key]['candidates'] = []
print(f"No candidates for {key}")
continue
for i in np.arange(len(r['result_data']['ZTF_alerts'][idcoords])):
info = r['result_data']['ZTF_alerts'][idcoords][i]
if info['objectId'] in stellar_list or (info['objectId']
in with_neg_sub):
continue
if info['candidate']['isdiffpos'] in ['f', 0]:
with_neg_sub.append(info['objectId'])
try:
if (np.abs(info['candidate']['distpsnr1']) < 2.
and info['candidate']['sgscore1'] >= 0.5):
stellar_list.append(info['objectId'])
except:
pass
try:
if (np.abs(info['candidate']['distpsnr1']) < 15.
and info['candidate']['srmag1'] < 15.
and info['candidate']['srmag1'] > 0.
and info['candidate']['sgscore1'] >= 0.5):
continue
except:
pass
try:
if (np.abs(info['candidate']['distpsnr2']) < 15.
and info['candidate']['srmag2'] < 15.
and info['candidate']['srmag2'] > 0.
and info['candidate']['sgscore2'] >= 0.5):
continue
except:
pass
try:
if (np.abs(info['candidate']['distpsnr3']) < 15.
and info['candidate']['srmag3'] < 15.
and info['candidate']['srmag3'] > 0.
and info['candidate']['sgscore3'] >= 0.5):
continue
except:
pass
objectId_list.append(info['objectId'])
set_objectId = set(objectId_list)
# Remove objects with negative subtraction
if args.reject_neg:
for n in set(with_neg_sub):
try:
set_objectId.remove(n)
except:
pass
# Remove stellar objects
for n in set(stellar_list):
try:
set_objectId.remove(n)
except:
pass
# Add the list of ZTF candidates to the FRB list
key = list(k for k in dict_sources.keys()
if dict_sources[k]['id_coords'] == idcoords)[0]
dict_sources[key]['candidates'] = list(set(set_objectId))
tot_sources = len(r['result_data']['ZTF_alerts'][idcoords])
print(
f"{len(set_objectId)}/{tot_sources} candidates selected for {key}")
return dict_sources
# num_batches = 100
times = []
for nb in range(num_batches):
qu = {
"query_type":
"general_search",
"query":
"db['ZTF_sources_20190412'].find({}, " +
"{'_id': 1, 'data.programid': 1, 'data.hjd': 1, " +
f"'data.mag': 1, 'data.magerr': 1}}).skip({nb*batch_size}).limit({batch_size})"
}
# print(qu)
tic = time()
r = k.query(query=qu)
toc = time()
times.append(toc - tic)
print(
f'Fetching batch {nb+1}/{num_batches} with {batch_size} sources/LCs took: {toc-tic:.3f} seconds'
)
# Light curves are here:
# print(r['result_data']['query_result'])
# Must filter out data.programid == 1 data
print(f'min: {np.min(times)}')
print(f'median: {np.median(times)}')
print(f'max: {np.max(times)}')
def query_kowalski(username, password, list_fields, min_days, max_days,
ndethist_min, jd, jd_gap=50.):
'''Query kowalski and apply the selection criteria'''
k = Kowalski(username=username, password=password, verbose=False)
# Correct the minimum number of detections
ndethist_min_corrected = int(ndethist_min - 1)
jd_start = jd
jd_end = jd + jd_gap
#Initialize a set for the results
set_objectId_all = set([])
for field in list_fields:
set_objectId_field = set([])
q = {"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {
'candidate.jd': {'$gt': jd_start, '$lt': jd_end},
'candidate.field': int(field),
'candidate.drb': {'$gt': 0.9},
'classifications.braai': {'$gt': 0.8},
'candidate.ndethist': {'$gt': ndethist_min_corrected},
'candidate.magpsf': {'$gt': 12}
#'candidate.isdiffpos': 't'
},
"projection": {
"objectId": 1,
"candidate.rcid": 1,
"candidate.ra": 1,
"candidate.dec": 1,
"candidate.jd": 1,
"candidate.ndethist": 1,
"candidate.jdstarthist": 1,
"candidate.jdendhist": 1,
"candidate.jdendhist": 1,
"candidate.magpsf": 1,
"candidate.sigmapsf": 1,
"candidate.fid": 1,
"candidate.programid": 1,
"candidate.isdiffpos": 1,
"candidate.ndethist": 1,
"candidate.ssdistnr": 1,
"candidate.rb": 1,
"candidate.drb": 1,
"candidate.distpsnr1": 1,
"candidate.sgscore1": 1,
"candidate.srmag1": 1,
"candidate.distpsnr2": 1,
"candidate.sgscore2": 1,
"candidate.srmag2": 1,
"candidate.distpsnr3": 1,
"candidate.sgscore3": 1,
"candidate.srmag3": 1
}
},
"kwargs": {"hint": "jd_field_rb_drb_braai_ndethhist_magpsf_isdiffpos"}
}
#Perform the query
r = k.query(query=q)
print(f"Search completed for field {field}, \
{Time(jd, format='jd').iso} + {jd_gap:.1f} days.")
#Identify 'candid' for all relevant candidates
objectId_list = []
with_neg_sub = []
old = []
out_of_time_window = []
stellar_list = []
try:
if r['result_data']['query_result'] == []:
print("No candidates")
continue
except KeyError:
print(f"ERROR! jd={jd}, field={field}" )
#pdb.set_trace()
continue
for info in r['result_data']['query_result']:
#if info['objectId'] == 'ZTF19abyfbii':
# pdb.set_trace()
if info['objectId'] in old:
continue
if info['objectId'] in stellar_list:
continue
if np.abs(info['candidate']['ssdistnr']) < 10:
continue
try:
if info['candidate']['drb'] < 0.5:
continue
except KeyError:
pass
if info['candidate']['isdiffpos'] in ['f',0]:
with_neg_sub.append(info['objectId'])
if (info['candidate']['jdendhist'] - info['candidate']['jdstarthist']) < min_days:
continue
if (info['candidate']['jdendhist'] - info['candidate']['jdstarthist']) > max_days:
old.append(info['objectId'])
try:
if (np.abs(info['candidate']['distpsnr1']) < 2. and info['candidate']['sgscore1'] >= 0.5):
stellar_list.append(info['objectId'])
except:
pass
try:
if (np.abs(info['candidate']['distpsnr1']) < 15. and
info['candidate']['srmag1'] < 15. and
info['candidate']['srmag1'] > 0. and
info['candidate']['sgscore1'] >= 0.5):
continue
except:
pass
try:
if (np.abs(info['candidate']['distpsnr2']) < 15. and
info['candidate']['srmag2'] < 15. and
info['candidate']['srmag2'] > 0. and
info['candidate']['sgscore2'] >= 0.5):
continue
except:
pass
try:
if (np.abs(info['candidate']['distpsnr3']) < 15. and
info['candidate']['srmag3'] < 15. and
info['candidate']['srmag3'] > 0. and
info['candidate']['sgscore3'] >= 0.5):
continue
except:
pass
objectId_list.append(info['objectId'])
set_objectId = set(objectId_list)
#Remove those objects with negative subtraction
for n in set(with_neg_sub):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
#Remove stellar objects
for n in set(stellar_list):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
#Remove those objects considered old
for n in set(old):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
#Remove those objects whole alerts go bejond jd_trigger+max_days
for n in set(out_of_time_window):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
#print(set_objectId)
set_objectId_all = set_objectId_all | set_objectId
#print("Cumulative:", set_objectId_all)
print("Field", field, len(set_objectId_all))
return set_objectId_all
def run(
cleanup: str = "none",
checkpoint: str = "../models/tails-20210107/tails",
config: str = "../config.yaml",
date: Optional[str] = None,
nthreads: int = N_CPU,
output_base_path: str = "./",
score_threshold: float = 0.6,
twilight: bool = False,
single_image: Optional[str] = None,
):
"""🚀 Run Tails on ZTF data
:param cleanup: Delete raw data: ref|sci|all|none
:param checkpoint: Pre-trained model weights
:param config: Path to yaml file with configs and secrets
:param date: UTC date string YYYYMMDD
:param nthreads: Number of threads for image re-projecting
:param output_base_path: Base path for output
:param score_threshold: score threshold for declaring a candidate plausible (0 <= score_threshold <= 1)
:param single_image: Run on single ccd-quad image, feed id in format ztf_20200810193681_000635_zr_c09_o_q2
:param twilight: Run on the Twilight survey data only
:return:
"""
p_base = pathlib.Path(output_base_path)
config = load_config(config)
# build model and load weights
model = Tails()
model.load_weights(checkpoint).expect_partial()
if not (0 <= score_threshold <= 1):
raise ValueError("score_threshold must be (0 <= score_threshold <=1)")
if not (1 <= nthreads <= N_CPU):
raise ValueError(f"nthreads must be (1 <= nthreads <={N_CPU})")
cleanup = cleanup.lower()
if cleanup not in ("all", "none", "ref", "sci"):
raise ValueError("cleanup value not in ('all', 'none', 'ref', 'sci')")
if single_image:
datestr = single_image[4:12]
date = datetime.datetime.strptime(datestr, "%Y%m%d")
print(date)
p_date = p_base / "runs" / datestr
if not p_date.exists():
p_date.mkdir(parents=True, exist_ok=True)
names = [single_image]
else:
if date:
datestr = date
else:
datestr = datetime.datetime.utcnow().strftime("%Y%m%d")
date = datetime.datetime.strptime(datestr, "%Y%m%d")
print(date)
p_date = p_base / "runs" / datestr
if not p_date.exists():
p_date.mkdir(parents=True, exist_ok=True)
# setup
kowalski = Kowalski(
username=config["kowalski"]["username"],
password=config["kowalski"]["password"],
)
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_ops",
"filter": {
"jd_start": {"$gt": Time(date).jd, "$lt": Time(date).jd + 1}
},
"projection": {"_id": 0, "fileroot": 1},
},
}
if twilight:
q["query"]["filter"]["qcomment"] = {"$regex": "Twilight"}
r = kowalski.query(q).get("data", dict())
fileroots = sorted([e["fileroot"] for e in r])
names = [
f"{fileroot}_c{ccd:02d}_o_q{quad:1d}"
for fileroot in fileroots
for ccd in range(1, 17)
for quad in range(1, 5)
]
# fetch data first
nsp = [(name, config, p_base) for name in names]
with mp.Pool(processes=N_CPU) as pool:
list(tqdm(pool.imap(fetch_data, nsp), total=len(nsp)))
for name in tqdm(names):
process_ccd_quad(
name=name,
p_date=p_date,
checkpoint=checkpoint,
model=model,
config=config,
nthreads=nthreads,
score_threshold=score_threshold,
cleanup=cleanup,
)
# fields = k.query(q_get_fields)['result_data']['query_result']
fields = [245, 246, 744]
rcids = list(range(0, 64))
# print(rcids)
# field, rcid = 245, 7
for field in fields:
for rcid in rcids:
q_count = {
"query_type":
"general_search",
"query":
f"db['ZTF_alerts'].count_documents({{'candidate.field': {field}, 'candidate.rcid': {rcid}}})"
}
num_alerts = k.query(q_count)['result_data']['query_result']
print(f'field: {field}, rcid: {rcid}, num_alerts: {num_alerts}')
q = {
"query_type":
"general_search",
"query":
f"db['ZTF_alerts'].find({{'candidate.field': {field}, 'candidate.rcid': {rcid}}}, "
"{'candidate.jd': 1, 'candidate.magpsf': 1, 'candidate.sigmapsf': 1, '_id': 0})"
}
# print(q)
data = k.query(q)['result_data']['query_result']
data = [d['candidate'] for d in data]
df = pd.DataFrame(data)
print(df)
class Scope:
def __init__(self):
# check configuration
with status("Checking configuration"):
check_configs(config_wildcards=["config.*yaml"])
self.config = load_config(
pathlib.Path(__file__).parent.absolute() / "config.yaml")
# use token specified as env var (if exists)
kowalski_token_env = os.environ.get("KOWALSKI_TOKEN")
if kowalski_token_env is not None:
self.config["kowalski"]["token"] = kowalski_token_env
# try setting up K connection if token is available
if self.config["kowalski"]["token"] is not None:
with status("Setting up Kowalski connection"):
self.kowalski = Kowalski(
token=self.config["kowalski"]["token"],
protocol=self.config["kowalski"]["protocol"],
host=self.config["kowalski"]["host"],
port=self.config["kowalski"]["port"],
)
else:
self.kowalski = None
# raise ConnectionError("Could not connect to Kowalski.")
print("Kowalski not available")
def _get_features(
self,
positions: Sequence[Sequence[float]],
catalog: str = "ZTF_source_features_20210401",
max_distance: Union[float, int] = 5.0,
distance_units: str = "arcsec",
) -> pd.DataFrame:
"""Get nearest source in feature set for a set of given positions
:param positions: R.A./Decl. [deg]
:param catalog: feature catalog to query
:param max_distance:
:param distance_units: arcsec | arcmin | deg | rad
:return:
"""
if self.kowalski is None:
raise ConnectionError("Kowalski connection not established.")
if catalog is None:
catalog = self.config["kowalski"]["collections"]["features"]
query = {
"query_type": "near",
"query": {
"max_distance": max_distance,
"distance_units": distance_units,
"radec": positions,
"catalogs": {
catalog: {
"filter": {},
"projection": {
"period": 1,
"ra": 1,
"dec": 1,
},
}
},
},
}
response = self.kowalski.query(query=query)
features_nearest = [
v[0] for k, v in response.get("data").get(catalog).items()
if len(v) > 0
]
df = pd.DataFrame.from_records(features_nearest)
return df
def _get_nearest_gaia(
self,
positions: Sequence[Sequence[float]],
catalog: str = None,
max_distance: Union[float, int] = 5.0,
distance_units: str = "arcsec",
) -> pd.DataFrame:
"""Get nearest Gaia source for a set of given positions
:param positions: R.A./Decl. [deg]
:param catalog: Gaia catalog to query
:param max_distance:
:param distance_units: arcsec | arcmin | deg | rad
:return:
"""
if self.kowalski is None:
raise ConnectionError("Kowalski connection not established.")
if catalog is None:
catalog = self.config["kowalski"]["collections"]["gaia"]
query = {
"query_type": "near",
"query": {
"max_distance": max_distance,
"distance_units": distance_units,
"radec": positions,
"catalogs": {
catalog: {
"filter": {},
"projection": {
"parallax": 1,
"parallax_error": 1,
"pmra": 1,
"pmra_error": 1,
"pmdec": 1,
"pmdec_error": 1,
"phot_g_mean_mag": 1,
"phot_bp_mean_mag": 1,
"phot_rp_mean_mag": 1,
"ra": 1,
"dec": 1,
},
}
},
},
"kwargs": {
"limit": 1
},
}
response = self.kowalski.query(query=query)
gaia_nearest = [
v[0] for k, v in response.get("data").get(catalog).items()
if len(v) > 0
]
df = pd.DataFrame.from_records(gaia_nearest)
df["M"] = df["phot_g_mean_mag"] + 5 * np.log10(
df["parallax"] * 0.001) + 5
df["Ml"] = (df["phot_g_mean_mag"] + 5 * np.log10(
(df["parallax"] + df["parallax_error"]) * 0.001) + 5)
df["BP-RP"] = df["phot_bp_mean_mag"] - df["phot_rp_mean_mag"]
return df
def _get_light_curve_data(
self,
ra: float,
dec: float,
catalog: str = "ZTF_sources_20201201",
cone_search_radius: Union[float, int] = 2,
cone_search_unit: str = "arcsec",
filter_flagged_data: bool = True,
) -> pd.DataFrame:
"""Get light curve data from Kowalski
:param ra: R.A. in deg
:param dec: Decl. in deg
:param catalog: collection name on Kowalski
:param cone_search_radius:
:param cone_search_unit: arcsec | arcmin | deg | rad
:param filter_flagged_data: remove flagged/bad data?
:return: flattened light curve data as pd.DataFrame
"""
if self.kowalski is None:
raise ConnectionError("Kowalski connection not established.")
query = {
"query_type": "cone_search",
"query": {
"object_coordinates": {
"cone_search_radius": cone_search_radius,
"cone_search_unit": cone_search_unit,
"radec": {
"target": [ra, dec]
},
},
"catalogs": {
catalog: {
"filter": {},
"projection": {
"_id": 1,
"filter": 1,
"field": 1,
"data.hjd": 1,
"data.fid": 1,
"data.mag": 1,
"data.magerr": 1,
"data.ra": 1,
"data.dec": 1,
"data.programid": 1,
"data.catflags": 1,
},
}
},
},
}
response = self.kowalski.query(query=query)
light_curves_raw = response.get("data").get(catalog).get("target")
light_curves = []
for light_curve in light_curves_raw:
df = pd.DataFrame.from_records(light_curve["data"])
# broadcast to all data points:
df["_id"] = light_curve["_id"]
df["filter"] = light_curve["filter"]
df["field"] = light_curve["field"]
light_curves.append(df)
df = pd.concat(light_curves, ignore_index=True)
if filter_flagged_data:
mask_flagged_data = df["catflags"] != 0
df = df.loc[~mask_flagged_data]
return df
@staticmethod
def develop():
"""Install developer tools"""
subprocess.run(["pre-commit", "install"])
@classmethod
def lint(cls):
"""Lint sources"""
try:
import pre_commit # noqa: F401
except ImportError:
cls.develop()
try:
subprocess.run(["pre-commit", "run", "--all-files"], check=True)
except subprocess.CalledProcessError:
sys.exit(1)
def doc(self):
"""Build docs"""
from scope.utils import (
make_tdtax_taxonomy,
plot_gaia_density,
plot_gaia_hr,
plot_light_curve_data,
plot_periods,
)
# generate taxonomy.html
with status("Generating taxonomy visualization"):
path_static = pathlib.Path(
__file__).parent.absolute() / "doc" / "_static"
if not path_static.exists():
path_static.mkdir(parents=True, exist_ok=True)
tdtax.write_viz(
make_tdtax_taxonomy(self.config["taxonomy"]),
outname=path_static / "taxonomy.html",
)
# generate images for the Field Guide
if (self.kowalski is None) or (not self.kowalski.ping()):
print("Kowalski connection not established, cannot generate docs.")
return
period_limits = {
"cepheid": [1.0, 100.0],
"delta_scuti": [0.03, 0.3],
"beta_lyr": [0.3, 25],
"rr_lyr": [0.2, 1.0],
"w_uma": [0.2, 0.8],
}
period_loglimits = {
"cepheid": True,
"delta_scuti": False,
"beta_lyr": True,
"rr_lyr": False,
"w_uma": False,
}
# example periods
with status("Generating example period histograms"):
path_doc_data = pathlib.Path(
__file__).parent.absolute() / "doc" / "data"
# stored as ra/decs in csv format under /data/golden
golden_sets = pathlib.Path(
__file__).parent.absolute() / "data" / "golden"
for golden_set in golden_sets.glob("*.csv"):
golden_set_name = golden_set.stem
positions = pd.read_csv(golden_set).to_numpy().tolist()
features = self._get_features(positions=positions)
if len(features) == 0:
print(f"No features for {golden_set_name}")
continue
limits = period_limits.get(golden_set_name)
loglimits = period_loglimits.get(golden_set_name)
plot_periods(
features=features,
limits=limits,
loglimits=loglimits,
save=path_doc_data / f"period__{golden_set_name}",
)
# example skymaps for all Golden sets
with status("Generating skymaps diagrams for Golden sets"):
path_doc_data = pathlib.Path(
__file__).parent.absolute() / "doc" / "data"
path_gaia_density = (pathlib.Path(__file__).parent.absolute() /
"data" / "Gaia_hp8_densitymap.fits")
# stored as ra/decs in csv format under /data/golden
golden_sets = pathlib.Path(
__file__).parent.absolute() / "data" / "golden"
for golden_set in golden_sets.glob("*.csv"):
golden_set_name = golden_set.stem
positions = pd.read_csv(golden_set).to_numpy().tolist()
plot_gaia_density(
positions=positions,
path_gaia_density=path_gaia_density,
save=path_doc_data / f"radec__{golden_set_name}",
)
# example light curves
with status("Generating example light curves"):
path_doc_data = pathlib.Path(
__file__).parent.absolute() / "doc" / "data"
for sample_object_name, sample_object in self.config["docs"][
"field_guide"].items():
sample_light_curves = self._get_light_curve_data(
ra=sample_object["coordinates"][0],
dec=sample_object["coordinates"][1],
catalog=self.config["kowalski"]["collections"]["sources"],
)
plot_light_curve_data(
light_curve_data=sample_light_curves,
period=sample_object.get("period"),
title=sample_object.get("title"),
save=path_doc_data / sample_object_name,
)
# example HR diagrams for all Golden sets
with status("Generating HR diagrams for Golden sets"):
path_gaia_hr_histogram = (
pathlib.Path(__file__).parent.absolute() / "doc" / "data" /
"gaia_hr_histogram.dat")
# stored as ra/decs in csv format under /data/golden
golden_sets = pathlib.Path(
__file__).parent.absolute() / "data" / "golden"
for golden_set in golden_sets.glob("*.csv"):
golden_set_name = golden_set.stem
positions = pd.read_csv(golden_set).to_numpy().tolist()
gaia_sources = self._get_nearest_gaia(positions=positions)
plot_gaia_hr(
gaia_data=gaia_sources,
path_gaia_hr_histogram=path_gaia_hr_histogram,
save=path_doc_data / f"hr__{golden_set_name}",
)
# build docs
subprocess.run(["make", "html"], cwd="doc", check=True)
@staticmethod
def fetch_models(gcs_path: str = "gs://ztf-scope/models"):
"""
Fetch SCoPe models from GCP
:return:
"""
path_models = pathlib.Path(__file__).parent / "models"
if not path_models.exists():
path_models.mkdir(parents=True, exist_ok=True)
command = [
"gsutil",
"-m",
"cp",
"-n",
"-r",
os.path.join(gcs_path, "*.csv"),
str(path_models),
]
p = subprocess.run(command, check=True)
if p.returncode != 0:
raise RuntimeError("Failed to fetch SCoPe models")
@staticmethod
def fetch_datasets(gcs_path: str = "gs://ztf-scope/datasets"):
"""
Fetch SCoPe datasets from GCP
:return:
"""
path_datasets = pathlib.Path(__file__).parent / "data" / "training"
if not path_datasets.exists():
path_datasets.mkdir(parents=True, exist_ok=True)
command = [
"gsutil",
"-m",
"cp",
"-n",
"-r",
os.path.join(gcs_path, "*.csv"),
str(path_datasets),
]
p = subprocess.run(command, check=True)
if p.returncode != 0:
raise RuntimeError("Failed to fetch SCoPe datasets")
def train(
self,
tag: str,
path_dataset: Union[str, pathlib.Path],
gpu: Optional[int] = None,
verbose: bool = False,
**kwargs,
):
"""Train classifier
:param tag: classifier designation, refers to "class" in config.taxonomy
:param path_dataset: local path to csv file with the dataset
:param gpu: GPU id to use, zero-based. check tf.config.list_physical_devices('GPU') for available devices
:param verbose:
:param kwargs: refer to utils.DNN.setup and utils.Dataset.make
:return:
"""
import tensorflow as tf
if gpu is not None:
# specified a GPU to run on?
gpus = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_visible_devices(gpus[gpu], "GPU")
else:
# otherwise run on CPU
tf.config.experimental.set_visible_devices([], "GPU")
import wandb
from wandb.keras import WandbCallback
from scope.nn import DNN
from scope.utils import Dataset
train_config = self.config["training"]["classes"][tag]
features = self.config["features"][train_config["features"]]
ds = Dataset(
tag=tag,
path_dataset=path_dataset,
features=features,
verbose=verbose,
**kwargs,
)
label = train_config["label"]
# values from kwargs override those defined in config. if latter is absent, use reasonable default
threshold = kwargs.get("threshold", train_config.get("threshold", 0.5))
balance = kwargs.get("balance", train_config.get("balance", None))
weight_per_class = kwargs.get(
"weight_per_class", train_config.get("weight_per_class", False))
scale_features = kwargs.get("scale_features", "min_max")
test_size = kwargs.get("test_size", train_config.get("test_size", 0.1))
val_size = kwargs.get("val_size", train_config.get("val_size", 0.1))
random_state = kwargs.get("random_state",
train_config.get("random_state", 42))
feature_stats = self.config.get("feature_stats", None)
batch_size = kwargs.get("batch_size",
train_config.get("batch_size", 64))
shuffle_buffer_size = kwargs.get(
"shuffle_buffer_size", train_config.get("shuffle_buffer_size",
512))
epochs = kwargs.get("epochs", train_config.get("epochs", 100))
datasets, indexes, steps_per_epoch, class_weight = ds.make(
target_label=label,
threshold=threshold,
balance=balance,
weight_per_class=weight_per_class,
scale_features=scale_features,
test_size=test_size,
val_size=val_size,
random_state=random_state,
feature_stats=feature_stats,
batch_size=batch_size,
shuffle_buffer_size=shuffle_buffer_size,
epochs=epochs,
)
# set up and train model
dense_branch = kwargs.get("dense_branch", True)
conv_branch = kwargs.get("conv_branch", True)
loss = kwargs.get("loss", "binary_crossentropy")
optimizer = kwargs.get("optimizer", "adam")
lr = float(kwargs.get("lr", 3e-4))
momentum = float(kwargs.get("momentum", 0.9))
monitor = kwargs.get("monitor", "val_loss")
patience = int(kwargs.get("patience", 20))
callbacks = kwargs.get("callbacks",
("reduce_lr_on_plateau", "early_stopping"))
run_eagerly = kwargs.get("run_eagerly", False)
pre_trained_model = kwargs.get("pre_trained_model")
save = kwargs.get("save", False)
# parse boolean args
dense_branch = forgiving_true(dense_branch)
conv_branch = forgiving_true(conv_branch)
run_eagerly = forgiving_true(run_eagerly)
save = forgiving_true(save)
classifier = DNN(name=tag)
classifier.setup(
dense_branch=dense_branch,
features_input_shape=(len(features), ),
conv_branch=conv_branch,
dmdt_input_shape=(26, 26, 1),
loss=loss,
optimizer=optimizer,
learning_rate=lr,
momentum=momentum,
monitor=monitor,
patience=patience,
callbacks=callbacks,
run_eagerly=run_eagerly,
)
if verbose:
print(classifier.model.summary())
if pre_trained_model is not None:
classifier.load(pre_trained_model)
time_tag = datetime.datetime.utcnow().strftime("%Y%m%d_%H%M%S")
if not kwargs.get("test", False):
wandb.login(key=self.config["wandb"]["token"])
wandb.init(
project=self.config["wandb"]["project"],
tags=[tag],
name=f"{tag}-{time_tag}",
config={
"tag": tag,
"label": label,
"dataset": pathlib.Path(path_dataset).name,
"scale_features": scale_features,
"learning_rate": lr,
"epochs": epochs,
"patience": patience,
"random_state": random_state,
"batch_size": batch_size,
"architecture": "scope-net",
"dense_branch": dense_branch,
"conv_branch": conv_branch,
},
)
classifier.meta["callbacks"].append(WandbCallback())
classifier.train(
datasets["train"],
datasets["val"],
steps_per_epoch["train"],
steps_per_epoch["val"],
epochs=epochs,
class_weight=class_weight,
verbose=verbose,
)
if verbose:
print("Evaluating on test set:")
stats = classifier.evaluate(datasets["test"], verbose=verbose)
if verbose:
print(stats)
param_names = (
"loss",
"tp",
"fp",
"tn",
"fn",
"accuracy",
"precision",
"recall",
"auc",
)
if not kwargs.get("test", False):
# log model performance on the test set
for param, value in zip(param_names, stats):
wandb.run.summary[f"test_{param}"] = value
p, r = wandb.run.summary["test_precision"], wandb.run.summary[
"test_recall"]
wandb.run.summary["test_f1"] = 2 * p * r / (p + r)
if datasets["dropped_samples"] is not None:
# log model performance on the dropped samples
if verbose:
print("Evaluating on samples dropped from the training set:")
stats = classifier.evaluate(datasets["dropped_samples"],
verbose=verbose)
if verbose:
print(stats)
if not kwargs.get("test", False):
for param, value in zip(param_names, stats):
wandb.run.summary[f"dropped_samples_{param}"] = value
p, r = (
wandb.run.summary["dropped_samples_precision"],
wandb.run.summary["dropped_samples_recall"],
)
wandb.run.summary["dropped_samples_f1"] = 2 * p * r / (p + r)
if save:
output_path = str(
pathlib.Path(__file__).parent.absolute() / "models" / tag)
if verbose:
print(f"Saving model to {output_path}")
classifier.save(
output_path=output_path,
output_format="tf",
tag=time_tag,
)
return time_tag
def test(self):
"""Test different workflows
:return:
"""
import uuid
import shutil
# create a mock dataset and check that the training pipeline works
dataset = f"{uuid.uuid4().hex}.csv"
path_mock = pathlib.Path(
__file__).parent.absolute() / "data" / "training"
try:
if not path_mock.exists():
path_mock.mkdir(parents=True, exist_ok=True)
feature_names = self.config["features"]["ontological"]
class_names = [
self.config["training"]["classes"][class_name]["label"]
for class_name in self.config["training"]["classes"]
]
entries = []
for i in range(1000):
entry = {
**{
feature_name: np.random.normal(0, 0.1)
for feature_name in feature_names
},
**{
class_name: np.random.choice([0, 1])
for class_name in class_names
},
**{
"non-variable": np.random.choice([0, 1])
},
**{
"dmdt": np.abs(np.random.random((26, 26))).tolist()
},
}
entries.append(entry)
df_mock = pd.DataFrame.from_records(entries)
df_mock.to_csv(path_mock / dataset, index=False)
tag = "vnv"
time_tag = self.train(
tag=tag,
path_dataset=path_mock / dataset,
batch_size=32,
epochs=3,
verbose=True,
save=True,
test=True,
)
path_model = (pathlib.Path(__file__).parent.absolute() / "models" /
tag / time_tag)
shutil.rmtree(path_model)
finally:
# clean up after thyself
(path_mock / dataset).unlink()
def query_kowalski(username,
password,
ra_center,
dec_center,
radius,
jd_trigger,
min_days,
max_days,
slices,
ndethist_min,
within_days,
after_trigger=True,
verbose=True):
'''Query kowalski and apply the selection criteria'''
k = Kowalski(username=username, password=password, verbose=False)
# Initialize a set for the results
set_objectId_all = set([])
slices = slices + 1
for slice_lim, i in zip(
np.linspace(0, len(ra_center), slices)[:-1],
np.arange(len(np.linspace(0, len(ra_center), slices)[:-1]))):
try:
ra_center_slice = ra_center[int(slice_lim):int(
np.linspace(0, len(ra_center), slices)[:-1][i + 1])]
dec_center_slice = dec_center[int(slice_lim):int(
np.linspace(0, len(dec_center), slices)[:-1][i + 1])]
except IndexError:
ra_center_slice = ra_center[int(slice_lim):]
dec_center_slice = dec_center[int(slice_lim):]
coords_arr = []
for ra, dec in zip(ra_center_slice, dec_center_slice):
try:
# Remove points too far south for ZTF.
# Say, keep only Dec>-40 deg to be conservative
if dec < -40.:
continue
coords = SkyCoord(ra=float(ra) * u.deg, dec=float(dec) * u.deg)
coords_arr.append((coords.ra.deg, coords.dec.deg))
except ValueError:
print("Problems with the galaxy coordinates?")
continue
# Correct the minimum number of detections
ndethist_min_corrected = int(ndethist_min - 1)
# Correct the jd_trigger if the user specifies to query
# also before the trigger
if after_trigger is False:
jd_trigger = 0
try:
print(
f"slice: {int(slice_lim)}:{int(np.linspace(0,len(ra_center),slices)[:-1][i+1])}"
)
except:
print(f"slice: {int(slice_lim)}:{int(len(ra_center))}")
q = {
"query_type": "cone_search",
"query": {
"object_coordinates": {
"radec": f"{coords_arr}",
"cone_search_radius": f"{radius}",
"cone_search_unit": "arcmin"
},
"catalogs": {
"ZTF_alerts": {
"filter": {
"candidate.jd": {
'$gt': jd_trigger
},
"candidate.drb": {
'$gt': 0.8
},
"candidate.ndethist": {
'$gt': ndethist_min_corrected
},
"candidate.jdstarthist": {
'$gt': jd_trigger,
'$lt': jd_trigger + within_days
}
},
"projection": {
"objectId": 1,
"candidate.rcid": 1,
"candidate.ra": 1,
"candidate.dec": 1,
"candidate.jd": 1,
"candidate.ndethist": 1,
"candidate.jdstarthist": 1,
"candidate.jdendhist": 1,
"candidate.jdendhist": 1,
"candidate.magpsf": 1,
"candidate.sigmapsf": 1,
"candidate.fid": 1,
"candidate.programid": 1,
"candidate.isdiffpos": 1,
"candidate.ndethist": 1,
"candidate.ssdistnr": 1,
"candidate.rb": 1,
"candidate.drb": 1,
"candidate.distpsnr1": 1,
"candidate.sgscore1": 1,
"candidate.srmag1": 1,
"candidate.distpsnr2": 1,
"candidate.sgscore2": 1,
"candidate.srmag2": 1,
"candidate.distpsnr3": 1,
"candidate.sgscore3": 1,
"candidate.srmag3": 1
}
}
},
"kwargs": {
"hint": "gw01"
}
}
}
# Perform the query
r = k.query(query=q)
print('Search completed for this slice.')
objectId_list = []
with_neg_sub = []
old = []
out_of_time_window = []
stellar_list = []
# Try to query kowalski up to 5 times
i = 1
no_candidates = False
while i <= 5:
try:
if r['data'] == []:
no_candidates = True
keys_list = list(r['data']['ZTF_alerts'].keys())
break
except (AttributeError, KeyError, TypeError):
print(f"failed attempt {i}")
i += 1
if i > 5:
print(f"SKIPPING jd={jd}, field={field} after 5 attempts")
continue
if no_candidates is True:
if verbose is True:
print(f"No candidates")
continue
for key in keys_list:
all_info = r['data']['ZTF_alerts'][key]
for info in all_info:
if info['objectId'] in old:
continue
if info['objectId'] in stellar_list:
continue
if np.abs(info['candidate']['ssdistnr']) < 10:
continue
if info['candidate']['isdiffpos'] in ['f', 0]:
with_neg_sub.append(info['objectId'])
if (info['candidate']['jdendhist'] -
info['candidate']['jdstarthist']) < min_days:
continue
if (info['candidate']['jdendhist'] -
info['candidate']['jdstarthist']) > max_days:
old.append(info['objectId'])
if (info['candidate']['jdstarthist'] -
jd_trigger) > within_days:
old.append(info['objectId'])
# REMOVE! Only for O3a paper
#if (info['candidate']['jdendhist'] -
#info['candidate']['jdstarthist']) >= 72./24. and info['candidate']['ndethist'] <= 2.:
# out_of_time_window.append(info['objectId'])
if after_trigger is True:
if (info['candidate']['jdendhist'] -
jd_trigger) > max_days:
out_of_time_window.append(info['objectId'])
else:
if (info['candidate']['jdendhist'] -
info['candidate']['jdstarthist']) > max_days:
out_of_time_window.append(info['objectId'])
try:
if (np.abs(info['candidate']['distpsnr1']) < 1.5
and info['candidate']['sgscore1'] > 0.50):
stellar_list.append(info['objectId'])
except (KeyError, ValueError):
pass
try:
if (np.abs(info['candidate']['distpsnr1']) < 15.
and info['candidate']['srmag1'] < 15.
and info['candidate']['srmag1'] > 0.
and info['candidate']['sgscore1'] >= 0.5):
continue
except (KeyError, ValueError):
pass
try:
if (np.abs(info['candidate']['distpsnr2']) < 15.
and info['candidate']['srmag2'] < 15.
and info['candidate']['srmag2'] > 0.
and info['candidate']['sgscore2'] >= 0.5):
continue
except (KeyError, ValueError):
pass
try:
if (np.abs(info['candidate']['distpsnr3']) < 15.
and info['candidate']['srmag3'] < 15.
and info['candidate']['srmag3'] > 0.
and info['candidate']['sgscore3'] >= 0.5):
continue
except (KeyError, ValueError):
pass
objectId_list.append(info['objectId'])
set_objectId = set(objectId_list)
# Remove those objects with negative subtraction
for n in set(with_neg_sub):
try:
set_objectId.remove(n)
except (ValueError, KeyError):
pass
# Remove stellar objects
for n in set(stellar_list):
try:
set_objectId.remove(n)
except (ValueError, KeyError):
pass
# Remove those objects considered old
for n in set(old):
try:
set_objectId.remove(n)
except (ValueError, KeyError):
pass
# Remove those objects whole alerts go bejond jd_trigger+max_days
for n in set(out_of_time_window):
try:
set_objectId.remove(n)
except (ValueError, KeyError):
pass
print(set_objectId)
set_objectId_all = set_objectId_all | set_objectId
print("Cumulative:", set_objectId_all)
return set_objectId_all
def fetch_lc_radecs(_radecs):
k = Kowalski(username='******', password='******', verbose=False)
num_obj = len(_radecs)
print(f'Total entries: {num_obj}')
batch_size = 100
num_batches = int(np.ceil(num_obj / batch_size))
times = []
ids = set()
for nb in range(num_batches):
# print(_radecs[nb * batch_size: (nb + 1) * batch_size])
q = {
"query_type": "cone_search",
"object_coordinates": {
"radec": f"{_radecs[nb * batch_size: (nb + 1) * batch_size]}",
"cone_search_radius": "2",
"cone_search_unit": "arcsec"
},
"catalogs": {
"ZTF_sources_20190412": {
"filter": {},
"projection": {
"_id": 1,
"filter": 1,
"data.expid": 1,
"data.ra": 1,
"data.dec": 1,
"data.programid": 1,
"data.hjd": 1,
"data.mag": 1,
"data.magerr": 1
}
}
}
}
tic = time()
r = k.query(query=q)
toc = time()
times.append(toc - tic)
print(
f'Fetching batch {nb + 1}/{num_batches} with {batch_size} sources/LCs took: {toc - tic:.3f} seconds'
)
# Light curves are here:
data = r['result_data']
# TODO: your magic here
# print(data)
for sc, sources in data['ZTF_sources_20190412'].items():
ids = ids.union([s['_id'] for s in sources])
print(len(ids))
# FIXME: Must filter out data.programid == 1 data
print(f'min: {np.min(times)}')
print(f'median: {np.median(times)}')
print(f'max: {np.max(times)}')
def query_kowalski(username,
password,
list_fields,
min_days,
max_days,
ndethist_min,
jd_gap=50.):
'''Query kowalski and apply the selection criteria'''
k = Kowalski(username=username, password=password, verbose=False)
#Initialize a set for the results
set_objectId_all = set([])
for field in list_fields:
#Correct the minimum number of detections
ndethist_min_corrected = int(ndethist_min - 1)
jd = 2458650.0
jd_start = jd
jd_end = jd + jd_gap
q = {
"query_type": "find",
"query": {
"catalog": "ZTF_alerts",
"filter": {
'candidate.jd': {
'$gt': jd_start,
'$lt': jd_end
},
'candidate.field': field,
'candidate.rb': {
'$gt': 0.5
},
'candidate.drb': {
'$gt': 0.5
},
'candidate.ndethist': {
'$gt': ndethist_min_corrected
},
'candidate.magpsf': {
'$gt': 16
}
#'candidate.isdiffpos': 't'
#'objectId'
},
"projection": {
"objectId": 1,
"candidate.rcid": 1,
"candidate.ra": 1,
"candidate.dec": 1,
"candidate.jd": 1,
"candidate.ndethist": 1,
"candidate.jdstarthist": 1,
"candidate.jdendhist": 1,
"candidate.jdendhist": 1,
"candidate.magpsf": 1,
"candidate.sigmapsf": 1,
"candidate.fid": 1,
"candidate.programid": 1,
"candidate.isdiffpos": 1,
"candidate.ndethist": 1,
"candidate.ssdistnr": 1,
"candidate.rb": 1,
"candidate.drb": 1,
"candidate.distpsnr1": 1,
"candidate.sgscore1": 1,
"candidate.srmag1": 1,
"candidate.distpsnr2": 1,
"candidate.sgscore2": 1,
"candidate.srmag2": 1,
"candidate.distpsnr3": 1,
"candidate.sgscore3": 1,
"candidate.srmag3": 1
}
},
"kwargs": {
"hint": "jd_field_rb_drb_braai_ndethhist_magpsf_isdiffpos"
}
}
#Perform the query
r = k.query(query=q)
print(f"Search completed for field {field}.")
# #Dump the results in a json file
# with open(f'results_clu25Mpc_1week_{i+1}.json', 'w') as j:
# json.dump(r, j)
#Identify 'candid' for all relevant candidates
objectId_list = []
with_neg_sub = []
old = []
out_of_time_window = []
stellar_list = []
if r['result_data']['query_result'] == []:
print("No candidates")
continue
for info in r['result_data']['query_result']:
#if info['objectId'] == 'ZTF19abyfbii':
# pdb.set_trace()
if info['objectId'] in old:
continue
if info['objectId'] in stellar_list:
continue
if np.abs(info['candidate']['ssdistnr']) < 10:
continue
if info['candidate']['isdiffpos'] in ['f', 0]:
with_neg_sub.append(info['objectId'])
if (info['candidate']['jdendhist'] -
info['candidate']['jdstarthist']) < min_days:
continue
if (info['candidate']['jdendhist'] -
info['candidate']['jdstarthist']) > max_days:
old.append(info['objectId'])
try:
if (np.abs(info['candidate']['distpsnr1']) < 2.
and info['candidate']['sgscore1'] >= 0.76):
stellar_list.append(info['objectId'])
except:
do = 'do nothing.'
try:
if (np.abs(info['candidate']['distpsnr1']) < 15.
and info['candidate']['srmag1'] < 15.
and info['candidate']['sgscore1'] >= 0.5):
continue
except:
do = 'do nothing.'
try:
if (np.abs(info['candidate']['distpsnr2']) < 15.
and info['candidate']['srmag2'] < 15.
and info['candidate']['sgscore2'] >= 0.5):
continue
except:
do = 'do nothing.'
try:
if (np.abs(info['candidate']['distpsnr3']) < 15.
and info['candidate']['srmag3'] < 15.
and info['candidate']['sgscore3'] >= 0.5):
continue
except:
do = 'do nothing.'
objectId_list.append(info['objectId'])
set_objectId = set(objectId_list)
#Remove those objects with negative subtraction
for n in set(with_neg_sub):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
#Remove stellar objects
for n in set(stellar_list):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
#Remove those objects considered old
for n in set(old):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
#Remove those objects whole alerts go bejond jd_trigger+max_days
for n in set(out_of_time_window):
try:
set_objectId.remove(n)
except:
do = 'do nothing'
print(set_objectId)
set_objectId_all = set_objectId_all | set_objectId
print("Cumulative:", set_objectId_all)
'''
print('----stats-----')
print('Number of sources with negative sub: ', len(set(with_neg_sub)))
print('Number of sources with only pos subtraction: ', len(set_objectId))
print(f"Number of sources older than {max_days} days: {len(set(old))}, specifically {set(old)}")
'''
return set_objectId_all
def get_ztf(filename,
name,
username,
password,
filetype="default",
z=0.0,
zerr=0.0001,
SN_Type="Ia"):
k = Kowalski(username=username, password=password, verbose=True)
q = {
"query_type": "general_search",
"query": "db['ZTF_alerts'].find({'objectId': {'$eq': '" + name + "'}})"
}
r = k.query(query=q, timeout=10)
if len(r['result_data']['query_result']) > 0:
candidate = r['result_data']['query_result'][0]
prevcandidates = r['result_data']['query_result'][0]['prv_candidates']
print(candidate, prevcandidates)
jd = [candidate['candidate']['jd']]
mag = [candidate['candidate']['magpsf']]
magerr = [candidate['candidate']['sigmapsf']]
filt = [candidate['candidate']['fid']]
for candidate in prevcandidates:
jd.append(candidate['jd'])
if not candidate['magpsf'] == None:
mag.append(candidate['magpsf'])
else:
mag.append(candidate['diffmaglim'])
if not candidate['sigmapsf'] == None:
magerr.append(candidate['sigmapsf'])
else:
magerr.append(np.inf)
filt.append(candidate['fid'])
filtname = []
for f in filt:
if f == 1:
filtname.append('g')
elif f == 2:
filtname.append('r')
elif f == 3:
filtname.append('i')
idx = np.argsort(jd)
if filetype == "lc":
mjds, fluxs, fluxerrs, passband = [], [], [], []
for ii in idx:
t = Time(jd[ii], format='jd').mjd
flux = 10**((mag[ii] + 48.60) / (-2.5))
fluxerr = magerr[ii] * flux
mjds.append(t)
fluxs.append(flux)
fluxerrs.append(fluxerr)
passband.append(filtname[ii])
return mjds, fluxs, fluxerrs, passband
fid = open(filename, 'w')
if filetype == "default":
for ii in idx:
t = Time(jd[ii], format='jd').isot
fid.write('%s %s %.5f %.5f\n' %
(t, filtname[ii], mag[ii], magerr[ii]))
elif filetype == "snmachine":
fid.write('HOST_GALAXY_PHOTO-Z: %.4f +- %.4f\n' % (z, zerr))
fid.write('SIM_COMMENT: SN Type = %s\n' % SN_Type)
for ii in idx:
t = Time(jd[ii], format='jd').mjd
flux = 10**((mag[ii] + 48.60) / (-2.5))
fluxerr = magerr[ii] * flux
fid.write('OBS: %.3f %s NULL %.3e %.3e %.2f %.5f %.5f\n' %
(t, filtname[ii], flux, fluxerr, flux / fluxerr, mag[ii],
magerr[ii]))
fid.close()
def agn_b_scores(name, username, password, colors=False):
k = Kowalski(username=username, password=password, verbose=False)
q = {
"query_type": "find",
"query": {
"catalog": 'ZTF_alerts',
"filter": {
"objectId": name
},
"projection": {
"_id": 0,
"cutoutScience": 0,
"cutoutTemplate": 0,
"cutoutDifference": 0
},
}
}
r = k.query(query=q)
alerts = r['data']
ra, dec = alerts[0]['candidate']['ra'], alerts[0]['candidate']['dec']
cc = SkyCoord(ra, dec, unit=(u.deg, u.deg))
table = Irsa.query_region(coordinates=cc,
catalog="allwise_p3as_psd",
spatial="Cone",
radius=2 * u.arcsec)
# AGN WISE
if len(table['w1mpro']) == 0:
agn = False
temp_points = 6
else:
w1, w1_err, w2, w2_err, w3, w3_err = table['w1mpro'], table[
'w1sigmpro'], table['w2mpro'], table['w2sigmpro'], table[
'w3mpro'], table['w3sigmpro']
if w1 - w2 > 0.8 + 0.1 and w2_err < 0.5 and w1_err < 0.5:
agn = True
temp_points = -2
elif w2 - w3 > 2.5 + 0.1 and w2_err < 0.5 and w3_err < 0.5:
agn = True
temp_points = -2
elif w1 - w2 > 0.8 and w2_err < 0.5 and w1_err < 0.5:
agn = True
temp_points = 0
elif w2 - w3 > 2.5 and w2_err < 0.5 and w3_err < 0.5:
agn = True
temp_points = 0
elif w1 - w2 > 0.8 - 0.2 and w2_err < 0.5 and w1_err < 0.5:
agn = False
temp_points = 2
elif w2 - w3 > 2.5 - 0.3 and w2_err < 0.5 and w3_err < 0.5:
agn = False
temp_points = 2
elif w1 - w2 > 0.8 - 0.5 and w2_err < 0.5 and w1_err < 0.5:
agn = False
temp_points = 4
elif w2 - w3 > 2.5 - 0.5 and w2_err < 0.5 and w3_err < 0.5:
agn = False
temp_points = 4
else:
agn = False
temp_points = 6
# low b
if np.abs(cc.galactic.b.value) < 15:
b_temp_points = -10
else:
b_temp_points = 0
if colors:
return temp_points, agn, [w1 - w2, w2 - w3]
else:
return temp_points, b_temp_points
class WhiteDwarf(object):
def __init__(self, config_file: str):
try:
''' load config data '''
self.config = self.get_config(_config_file=config_file)
''' set up logging at init '''
self.logger, self.logger_utc_date = self.set_up_logging(
_name='archive', _mode='a')
# make dirs if necessary:
for _pp in ('app', 'alerts', 'tmp', 'logs'):
_path = self.config['path']['path_{:s}'.format(_pp)]
if not os.path.exists(_path):
os.makedirs(_path)
self.logger.debug('Created {:s}'.format(_path))
''' init connection to Kowalski '''
self.kowalski = Kowalski(username=secrets['kowalski']['user'],
password=secrets['kowalski']['password'])
# host='localhost', port=8082, protocol='http'
''' init db if necessary '''
self.init_db()
''' connect to db: '''
self.db = None
# will exit if this fails
self.connect_to_db()
except Exception as e:
print(e)
traceback.print_exc()
sys.exit()
@staticmethod
def get_config(_config_file):
"""
Load config JSON file
"""
''' script absolute location '''
abs_path = os.path.dirname(inspect.getfile(inspect.currentframe()))
if _config_file[0] not in ('/', '~'):
if os.path.isfile(os.path.join(abs_path, _config_file)):
config_path = os.path.join(abs_path, _config_file)
else:
raise IOError('Failed to find config file')
else:
if os.path.isfile(_config_file):
config_path = _config_file
else:
raise IOError('Failed to find config file')
with open(config_path) as cjson:
config_data = json.load(cjson)
# config must not be empty:
if len(config_data) > 0:
return config_data
else:
raise Exception('Failed to load config file')
def set_up_logging(self, _name='ztf_wd', _mode='w'):
""" Set up logging
:param _name:
:param _level: DEBUG, INFO, etc.
:param _mode: overwrite log-file or append: w or a
:return: logger instance
"""
# 'debug', 'info', 'warning', 'error', or 'critical'
if self.config['misc']['logging_level'] == 'debug':
_level = logging.DEBUG
elif self.config['misc']['logging_level'] == 'info':
_level = logging.INFO
elif self.config['misc']['logging_level'] == 'warning':
_level = logging.WARNING
elif self.config['misc']['logging_level'] == 'error':
_level = logging.ERROR
elif self.config['misc']['logging_level'] == 'critical':
_level = logging.CRITICAL
else:
raise ValueError(
'Config file error: logging level must be ' +
'\'debug\', \'info\', \'warning\', \'error\', or \'critical\'')
# get path to logs from config:
_path = self.config['path']['path_logs']
if not os.path.exists(_path):
os.makedirs(_path)
utc_now = datetime.datetime.utcnow()
# http://www.blog.pythonlibrary.org/2012/08/02/python-101-an-intro-to-logging/
_logger = logging.getLogger(_name)
_logger.setLevel(_level)
# create the logging file handler
fh = logging.FileHandler(os.path.join(
_path, '{:s}.{:s}.log'.format(_name, utc_now.strftime('%Y%m%d'))),
mode=_mode)
logging.Formatter.converter = time.gmtime
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# formatter = logging.Formatter('%(asctime)s %(message)s')
fh.setFormatter(formatter)
# add handler to logger object
_logger.addHandler(fh)
return _logger, utc_now.strftime('%Y%m%d')
def shut_down_logger(self):
"""
Prevent writing to multiple log-files after 'manual rollover'
:return:
"""
handlers = self.logger.handlers[:]
for handler in handlers:
handler.close()
self.logger.removeHandler(handler)
def check_logging(self):
"""
Check if a new log file needs to be started and start it if necessary
"""
if datetime.datetime.utcnow().strftime(
'%Y%m%d') != self.logger_utc_date:
# reset
self.shut_down_logger()
self.logger, self.logger_utc_date = self.set_up_logging(
_name='ztf_wd', _mode='a')
def init_db(self):
"""
Initialize db if new Mongo instance
:return:
"""
_client = pymongo.MongoClient(
username=self.config['database']['admin'],
password=self.config['database']['admin_pwd'],
host=self.config['database']['host'],
port=self.config['database']['port'])
# _id: db_name.user_name
user_ids = [
_u['_id'] for _u in _client.admin.system.users.find({}, {'_id': 1})
]
db_name = self.config['database']['db']
username = self.config['database']['user']
# print(f'{db_name}.{username}')
# print(user_ids)
if f'{db_name}.{username}' not in user_ids:
_client[db_name].command('createUser',
self.config['database']['user'],
pwd=self.config['database']['pwd'],
roles=['readWrite'])
self.logger.info('Successfully initialized db')
def connect_to_db(self):
"""
Connect to MongoDB-powered database
:return:
"""
_config = self.config
try:
if self.logger is not None:
self.logger.debug(
'Connecting to the database at {:s}:{:d}'.format(
_config['database']['host'],
_config['database']['port']))
_client = pymongo.MongoClient(host=_config['database']['host'],
port=_config['database']['port'])
# grab main database:
_db = _client[_config['database']['db']]
except Exception as _e:
if self.logger is not None:
self.logger.error(_e)
self.logger.error(
'Failed to connect to the database at {:s}:{:d}'.format(
_config['database']['host'],
_config['database']['port']))
# raise error
raise ConnectionRefusedError
try:
# authenticate
_db.authenticate(_config['database']['user'],
_config['database']['pwd'])
if self.logger is not None:
self.logger.debug(
'Successfully authenticated with the database at {:s}:{:d}'
.format(_config['database']['host'],
_config['database']['port']))
except Exception as _e:
if self.logger is not None:
self.logger.error(_e)
self.logger.error(
'Authentication failed for the database at {:s}:{:d}'.
format(_config['database']['host'],
_config['database']['port']))
raise ConnectionRefusedError
if self.logger is not None:
self.logger.debug(
'Successfully connected to database at {:s}:{:d}'.format(
_config['database']['host'], _config['database']['port']))
# (re)define self.db
self.db = dict()
self.db['client'] = _client
self.db['db'] = _db
# @timeout(seconds_before_timeout=120)
def disconnect_from_db(self):
"""
Disconnect from MongoDB database.
:return:
"""
self.logger.debug('Disconnecting from the database.')
if self.db is not None:
try:
self.db['client'].close()
self.logger.debug(
'Successfully disconnected from the database.')
except Exception as e:
self.logger.error('Failed to disconnect from the database.')
self.logger.error(e)
finally:
# reset
self.db = None
else:
self.logger.debug('No connection found.')
# @timeout(seconds_before_timeout=120)
def check_db_connection(self):
"""
Check if DB connection is alive/established.
:return: True if connection is OK
"""
self.logger.debug('Checking database connection.')
if self.db is None:
try:
self.connect_to_db()
except Exception as e:
self.logger.error('Lost database connection.')
self.logger.error(e)
return False
else:
try:
# force connection on a request as the connect=True parameter of MongoClient seems
# to be useless here
self.db['client'].server_info()
except pymongo.errors.ServerSelectionTimeoutError as e:
self.logger.error('Lost database connection.')
self.logger.error(e)
return False
return True
def insert_db_entry(self, _collection=None, _db_entry=None):
"""
Insert a document _doc to collection _collection in DB.
It is monitored for timeout in case DB connection hangs for some reason
:param _collection:
:param _db_entry:
:return:
"""
assert _collection is not None, 'Must specify collection'
assert _db_entry is not None, 'Must specify document'
try:
self.db['db'][_collection].insert_one(_db_entry)
except Exception as _e:
self.logger.info('Error inserting {:s} into {:s}'.format(
str(_db_entry['_id']), _collection))
traceback.print_exc()
self.logger.error(_e)
def insert_multiple_db_entries(self, _collection=None, _db_entries=None):
"""
Insert a document _doc to collection _collection in DB.
It is monitored for timeout in case DB connection hangs for some reason
:param _db:
:param _collection:
:param _db_entries:
:return:
"""
assert _collection is not None, 'Must specify collection'
assert _db_entries is not None, 'Must specify documents'
try:
# ordered=False ensures that every insert operation will be attempted
# so that if, e.g., a document already exists, it will be simply skipped
self.db['db'][_collection].insert_many(_db_entries, ordered=False)
except pymongo.errors.BulkWriteError as bwe:
self.logger.info(bwe.details)
except Exception as _e:
traceback.print_exc()
self.logger.error(_e)
def replace_db_entry(self, _collection=None, _filter=None, _db_entry=None):
"""
Insert a document _doc to collection _collection in DB.
It is monitored for timeout in case DB connection hangs for some reason
:param _collection:
:param _filter:
:param _db_entry:
:return:
"""
assert _collection is not None, 'Must specify collection'
assert _db_entry is not None, 'Must specify document'
try:
self.db['db'][_collection].replace_one(_filter,
_db_entry,
upsert=True)
except Exception as _e:
self.logger.info('Error replacing {:s} in {:s}'.format(
str(_db_entry['_id']), _collection))
traceback.print_exc()
self.logger.error(_e)
def cross_match(self,
_jd_start,
_jd_end,
_stars: dict,
_fov_size_ref_arcsec=2,
retries=3) -> dict:
for ir in range(retries):
try:
self.logger.debug(f'Querying Kowalski, attempt {ir+1}')
# query Kowalski:
# if False:
q = {
"query_type": "cone_search",
"object_coordinates": {
"radec": str(_stars),
"cone_search_radius": str(_fov_size_ref_arcsec),
"cone_search_unit": "arcsec"
},
"catalogs": {
"ZTF_alerts": {
"filter":
f'{{"candidate.jd": {{"$gt": {_jd_start}, "$lt": {_jd_end}}}}}',
"projection": "{}"
}
},
"kwargs": {
"save": False
}
}
# {"candidate.jd": {"$gt": _jd, "$lt": _jd + 1}}
# {"_id": 1, "objectId": 1,
# "candid": 1,
# "candidate.jd": 1,
# "candidate.programid": 1,
# "candidate.rb": 1,
# "candidate.magpsf": 1,
# "candidate.sigmapsf": 1}
# ,
# "Gaia_DR2_WD": {"filter": '{}',
# "projection": '{"_id": 1, "coordinates": 0}'}
# print(q)
r = self.kowalski.query(query=q, timeout=300)
# print(r)
matches = r['result_data']['ZTF_alerts']
# only return non-empty matches:
non_empty_matches = {
m: v
for m, v in matches.items()
if ((v is not None) and (len(v) > 0))
}
return non_empty_matches
except Exception as _e:
self.logger.error(_e)
continue
return {}
def get_doc_by_id(self, _coll: str, _ids: list, retries=3) -> dict:
for ir in range(retries):
try:
self.logger.debug(f'Querying Kowalski, attempt {ir+1}')
q = {
"query_type": "general_search",
"query":
f"db['{_coll}'].find({{'_id': {{'$in': {_ids}}}}})",
"kwargs": {
"save": False
}
}
# print(q)
r = self.kowalski.query(query=q, timeout=300)
# print(r)
result = r['result_data']['query_result']
# convert to dict id -> result
matches = {obj['_id']: obj for obj in result}
return matches
except Exception as _e:
self.logger.error(_e)
continue
return {}
def dump_lightcurve(self, alert, time_label='days_ago'):
path_out = os.path.join(self.config['path']['path_alerts'],
alert['_id'])
if not os.path.exists(path_out):
os.makedirs(path_out)
dflc = make_dataframe(alert)
filter_color = {1: 'green', 2: 'red', 3: 'pink'}
if time_label == 'days_ago':
now = Time.now().jd
t = dflc.jd - now
xlabel = f'Days Before {str(datetime.datetime.utcnow())} UTC'
elif time_label == 'jd':
t = dflc.jd
xlabel = 'Date (JD)'
elif time_label == 'datetime':
t = Time(dflc.jd, format='jd').datetime
xlabel = 'Date (UTC)'
plt.close('all')
fig = plt.figure()
ax = fig.add_subplot(111)
for fid, color in filter_color.items():
ref_flux = None
# plot detections in this filter:
w = (dflc.fid == fid) & ~dflc.magpsf.isnull() & (dflc.distnr <= 5)
if np.sum(w):
# we want to plot (reference_flux + sign*difference_flux) -> mag
sign = 2 * (dflc.loc[w, 'isdiffpos'].values == 't') - 1
ref_mag = np.float64(dflc.loc[w].iloc[0]['magnr'])
ref_flux = np.float64(10**(0.4 * (27 - ref_mag)))
ref_sigflux = np.float64(dflc.iloc[0]['sigmagnr'] / 1.0857 *
ref_flux)
difference_flux = np.float64(
10**(0.4 * (27 - dflc.loc[w, 'magpsf'].values)))
difference_sigflux = np.float64(
dflc.loc[w, 'sigmapsf'].values / 1.0857 * difference_flux)
if not isinstance(difference_flux, np.ndarray):
difference_flux = np.array([difference_flux])
if not isinstance(difference_sigflux, np.ndarray):
difference_sigflux = np.array([difference_sigflux])
dc_flux = ref_flux + sign * difference_flux
dc_sigflux = np.sqrt(difference_sigflux**2 + ref_sigflux**2)
if not isinstance(dc_flux, np.ndarray):
dc_flux = np.array([dc_flux])
if not isinstance(dc_sigflux, np.ndarray):
dc_sigflux = np.array([dc_sigflux])
# mask bad values:
w_good = dc_flux > 0
# print(dc_flux)
# print(dc_sigflux)
# print(w_good)
dc_mag = 27 - 2.5 * np.log10(dc_flux[w_good])
dc_sigmag = dc_sigflux[w_good] / dc_flux[w_good] * 1.0857
ax.errorbar(t[w][w_good],
dc_mag,
dc_sigmag,
fmt='.',
color=color)
wnodet = (dflc.fid
== fid) & dflc.magpsf.isnull() & (dflc.diffmaglim > 0)
if np.sum(wnodet) and (ref_flux is not None):
# if we have a non-detection that means that there's no flux +/- 5 sigma from
# the ref flux (unless it's a bad subtraction)
difference_fluxlim = np.float64(
10**(0.4 * (27 - dflc.loc[wnodet, 'diffmaglim'].values)))
dc_flux_ulim = ref_flux + difference_fluxlim
dc_flux_llim = ref_flux - difference_fluxlim
if not isinstance(dc_flux_ulim, np.ndarray):
dc_flux_ulim = np.array([dc_flux_ulim])
if not isinstance(dc_flux_llim, np.ndarray):
dc_flux_llim = np.array([dc_flux_llim])
# mask bad values:
w_u_good = dc_flux_ulim > 0
w_l_good = dc_flux_llim > 0
dc_mag_ulim = 27 - 2.5 * np.log10(dc_flux_ulim[w_u_good])
dc_mag_llim = 27 - 2.5 * np.log10(dc_flux_llim[w_l_good])
ax.scatter(t[wnodet][w_u_good],
dc_mag_ulim,
marker='v',
color=color,
alpha=0.25)
ax.scatter(t[wnodet][w_l_good],
dc_mag_llim,
marker='^',
color=color,
alpha=0.25)
plt.gca().invert_yaxis()
ax.set_xlabel(xlabel)
ax.set_ylabel('Magnitude')
plt.savefig(os.path.join(path_out, 'lightcurve.jpg'),
bbox_inches="tight",
pad_inches=0,
dpi=200)
def dump_cutout(self, alert, save_fits=False):
path_out = os.path.join(self.config['path']['path_alerts'],
alert['_id'])
if not os.path.exists(path_out):
os.makedirs(path_out)
for tag in ('science', 'template', 'difference'):
data = alert[f'cutout{tag.capitalize()}']['stampData']
tmp = io.BytesIO()
tmp.write(data)
tmp.seek(0)
# new format? try to decompress loss-less fits:
try:
decompressed_file = gzip.GzipFile(fileobj=tmp, mode='rb')
with fits.open(decompressed_file) as dff:
if save_fits:
dff.writeto(os.path.join(path_out, f'{tag}.fits'),
overwrite=True)
# print(dff[0].data)
img = dff[0].data
plt.close('all')
fig = plt.figure()
fig.set_size_inches(4, 4, forward=False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
# remove nans:
img = np.array(img)
img = np.nan_to_num(img)
if tag != 'difference':
# img += np.min(img)
img[img <= 0] = np.median(img)
plt.imshow(img,
cmap='gray',
norm=LogNorm(),
origin='lower')
else:
plt.imshow(img, cmap='gray', origin='lower')
plt.savefig(os.path.join(path_out, f'{tag}.jpg'), dpi=50)
# failed? try old jpg format
except Exception as _e:
traceback.print_exc()
self.logger.error(str(_e))
try:
tmp.seek(0)
Image.open(tmp).save(os.path.join(path_out, f'{tag}.jpg'))
except Exception as _e:
traceback.print_exc()
self.logger.error(str(_e))
self.logger.error(
f'Failed to save stamp: {alert["_id"]} {tag}')
def get_ps1_image(self, alert):
"""
:param alert:
:return:
"""
# TODO: get PanSTARRS image
pass
def run(self, _all=False):
# compute current UTC. the script is run everyday at 19:00 UTC (~noon in LA)
utc_date = datetime.datetime.utcnow()
utc_date = datetime.datetime(utc_date.year, utc_date.month,
utc_date.day)
# convert to jd
jd_date = Time(utc_date).jd
self.logger.info('Starting cycle: {} {}'.format(
str(utc_date), str(jd_date)))
if not _all:
# grab last night only
jd_start = jd_date
jd_end = jd_date + 1
else:
# grab everything:
utc_date_survey_start = datetime.datetime(2017, 9, 1)
jd_date_survey_start = Time(utc_date_survey_start).jd
jd_start = jd_date_survey_start
jd_end = jd_date + 1
# with open('/Users/dmitryduev/_caltech/python/ztf-wd/code/wds.20180811.json') as wdjson:
with open(self.config['path']['path_wd_db']) as wdjson:
wds = json.load(wdjson)['query_result']
total_detected = 0
matches_to_ingest = []
# for batch_size run a cross match with ZTF_alerts for current UTC
for ic, chunk in enumerate(chunks(wds, 1000)):
self.logger.info(f'Chunk #{ic}')
# print(chunk[0]['_id'])
# {name: (ra, dec)}
stars = {c['_id']: (c['ra'], c['dec']) for c in chunk}
# print(stars)
# run cone search on the batch
matches = self.cross_match(_jd_start=jd_start,
_jd_end=jd_end,
_stars=stars,
_fov_size_ref_arcsec=2,
retries=3)
self.logger.debug(list(matches.keys()))
total_detected += len(matches)
self.logger.info(
f'total # of white dwarfs detected so far: {total_detected}')
if len(matches) > 0:
# get full WD info for matched objects:
wds = self.get_doc_by_id(_coll='Gaia_DR2_WD',
_ids=list(map(int, matches.keys())),
retries=3)
# append to corresponding matches
self.logger.debug(list(matches.keys()))
for match in matches.keys():
for alert in matches[match]:
alert['xmatch'] = dict()
alert['xmatch']['nearest_within_5_arcsec'] = {
'Gaia_DR2_WD': wds[int(match)]
}
self.logger.debug('{} {}'.format(
alert['_id'], alert['xmatch']
['nearest_within_5_arcsec']['Gaia_DR2_WD']['_id']))
matches_to_ingest.append(alert)
# generate previews for the endpoint
self.dump_cutout(alert, save_fits=False)
self.dump_lightcurve(alert)
# raise Exception('HALT!!')
# collection_obs
# ingest every matched object into own db. It's not that many, so just dump everything
if len(matches_to_ingest) > 0:
self.insert_multiple_db_entries(
_collection=self.config['database']['collection_obs'],
_db_entries=matches_to_ingest)
self.logger.info(f'total # of white dwarfs detected: {total_detected}')
self.logger.info('Creating indices')
self.db['db'][self.config['database']['collection_obs']].create_index(
[('coordinates.radec_geojson', '2dsphere')], background=True)
self.db['db'][self.config['database']['collection_obs']].create_index(
[('objectId', pymongo.ASCENDING)], background=True)
self.db['db'][self.config['database']['collection_obs']].create_index(
[('candid', pymongo.ASCENDING)], background=True)
self.db['db'][self.config['database']['collection_obs']].create_index(
[('candidate.programid', pymongo.ASCENDING)], background=True)
self.db['db'][self.config['database']['collection_obs']].create_index(
[('candidate.jd', pymongo.ASCENDING)], background=True)
self.logger.info('All done')
def shutdown(self):
self.kowalski.close()
def sentinel(
utc_start: Optional[str] = None,
utc_stop: Optional[str] = None,
twilight: Optional[bool] = False,
test: Optional[bool] = False,
verbose: Optional[bool] = False,
):
"""
ZTF Sentinel service
- Monitors the ZTF_ops collection on Kowalski for new ZTF data (Twilight only by default).
- Uses dask.distributed to process individual ZTF image frames (ccd-quads).
Each worker is initialized with a TailsWorker instance that maintains a Fritz connection and preloads Tails.
The candidate comet detections, if any, are posted to Fritz together with auto-annotations
(cross-matches from the MPC and SkyBot) and auxiliary data.
:param utc_start: UTC start date/time in arrow-parsable format. If not set, defaults to (now - 1h)
:param utc_stop: UTC stop date/time in arrow-parsable format. If not set, defaults to (now + 1h).
If set, program runs once
:param twilight: process only the data of the ZTF Twilight survey
:param test: run in test mode
:param verbose: verbose?
:return:
"""
if verbose:
log("Setting up MongoDB connection")
init_db(config=config, verbose=verbose)
mongo = Mongo(
host=config["sentinel"]["database"]["host"],
port=config["sentinel"]["database"]["port"],
username=config["sentinel"]["database"]["username"],
password=config["sentinel"]["database"]["password"],
db=config["sentinel"]["database"]["db"],
verbose=verbose,
)
if verbose:
log("Set up MongoDB connection")
collection = config["sentinel"]["database"]["collection"]
# remove dangling entries in the db at startup
mongo.db[collection].delete_many({"status": "processing"})
# Configure dask client
if verbose:
log("Initializing dask.distributed client")
dask_client = dask.distributed.Client(
address=
f"{config['sentinel']['dask']['host']}:{config['sentinel']['dask']['scheduler_port']}"
)
# init each worker with Worker instance
if verbose:
log("Initializing dask.distributed workers")
worker_initializer = WorkerInitializer()
dask_client.register_worker_plugin(worker_initializer, name="worker-init")
if test:
frame = "ztf_20191014495961_000570_zr_c05_o_q3"
with timer(f"Submitting frame {frame} for processing", verbose):
mongo.db[collection].update_one({"_id": frame},
{"$set": {
"status": "processing"
}},
upsert=True)
future = dask_client.submit(process_frame, frame, pure=True)
dask.distributed.fire_and_forget(future)
future.release()
del future
return True
if verbose:
log("Setting up Kowalski connection")
kowalski = Kowalski(
token=config["kowalski"]["token"],
protocol=config["kowalski"]["protocol"],
host=config["kowalski"]["host"],
port=config["kowalski"]["port"],
verbose=verbose,
)
if verbose:
log(f"Kowalski connection OK: {kowalski.ping()}")
while True:
try:
# monitor the past 24 hours as sometimes there are data processing/posting delays at IPAC
start = (arrow.get(utc_start) if utc_start is not None else
arrow.utcnow().shift(hours=-24))
stop = (arrow.get(utc_stop)
if utc_stop is not None else arrow.utcnow().shift(hours=1))
if (stop - start).total_seconds() < 0:
raise ValueError("utc_stop must be greater than utc_start")
if verbose:
log(f"Looking for ZTF exposures between {start} and {stop}")
kowalski_query = {
"query_type": "find",
"query": {
"catalog": "ZTF_ops",
"filter": {
"jd_start": {
"$gt": Time(start.datetime).jd,
"$lt": Time(stop.datetime).jd,
}
},
"projection": {
"_id": 0,
"fileroot": 1
},
},
}
if twilight:
kowalski_query["query"]["filter"]["qcomment"] = {
"$regex": "Twilight"
}
response = kowalski.query(query=kowalski_query).get("data", dict())
file_roots = sorted([entry["fileroot"] for entry in response])
frame_names = [
f"{file_root}_c{ccd:02d}_o_q{quad:1d}"
for file_root in file_roots for ccd in range(1, 17)
for quad in range(1, 5)
]
if verbose:
log(f"Found {len(frame_names)} ccd-quad frames")
log(frame_names)
processed_frames = [
frame["_id"] for frame in mongo.db[collection].find(
{
"_id": {
"$in": frame_names
},
"status": {
"$in": ["processing", "success"]
},
},
{"_id": 1},
)
]
if verbose:
log(processed_frames)
unprocessed_frames = set(frame_names) - set(processed_frames)
for frame in unprocessed_frames:
with timer(f"Submitting frame {frame} for processing",
verbose):
mongo.db[collection].update_one(
{"_id": frame}, {"$set": {
"status": "processing"
}},
upsert=True)
future = dask_client.submit(process_frame,
frame,
pure=True)
dask.distributed.fire_and_forget(future)
future.release()
del future
except Exception as e:
log(e)
# run once if utc_stop is set
if utc_stop is not None:
break
else:
log("Heartbeat")
time.sleep(60)
def query_kowalski_alerts(username,
password,
date_start,
date_end,
catalog='ZUDS_alerts',
min_days=None,
starthist=None):
'''Query alerts with kowalski and apply the selection criteria'''
k = Kowalski(username=username, password=password, verbose=False)
# Initialize a set for the results
set_objectId_all = set([])
print(date_start.jd, date_end.jd, starthist.jd)
q = {
"query_type": "find",
"query": {
"catalog": catalog,
"filter": {
"candidate.jd": {
'$gt': date_start.jd,
'$lt': date_end.jd
},
"candidate.drb": {
'$gt': 0.6
},
"classifications.braai": {
'$gt': 0.6
},
"candidate.jdstarthist_single": {
'$gt': starthist.jd
},
"candidate.fwhm": {
'$gt': 0.5,
'$lt': 8
},
},
"projection": {
"objectId": 1,
"candid": 1,
"candidate.rcid": 1,
"candidate.ra": 1,
"candidate.dec": 1,
"candidate.jd": 1,
"candidate.ndethist": 1,
"candidate.jdstarthist_single": 1,
"candidate.jdstarthist_stack": 1,
"candidate.jdendhist_single": 1,
"candidate.jdendhist_stack": 1,
"candidate.magpsf": 1,
"candidate.sigmapsf": 1,
"candidate.fid": 1,
"candidate.programid": 1,
"candidate.isdiffpos": 1,
"candidate.ndethist": 1,
"candidate.ssdistnr": 1,
"candidate.rb": 1,
"candidate.drb": 1,
"candidate.distpsnr1": 1,
"candidate.sgscore1": 1,
"candidate.srmag1": 1,
"candidate.distpsnr2": 1,
"candidate.sgscore2": 1,
"candidate.srmag2": 1,
"candidate.distpsnr3": 1,
"candidate.sgscore3": 1,
"candidate.srmag3": 1,
"candidate.fwhm": 1,
"candidate.lstype1": 1,
"candidate.lszspec1": 1,
"candidate.lsz1": 1,
"candidate.lszphotl681": 1,
"candidate.alert_type": 1
}
},
"kwargs": {} # {"limit": 3}
}
# Perform the query
r = k.query(query=q)
result = r['result_data']['query_result']
set_names = set(
list(c['objectId'] for c in r['result_data']['query_result']))
print(f"There are {len(set_names)} sources found")
# Match with CLU
list_clu = match_kowalski_clu(username, password, set_names)
print(f"{len(list_clu)} sources were found matched with CLU galaxies")
# Stricted selection
reject = []
done = []
done_names = []
for info in r['result_data']['query_result']:
if info['objectId'] == 'ZUDS20esmwf':
import pdb
pdb.set_trace()
if not (info['objectId'] in list_clu) or info[
'objectId'] in done_names or info['objectId'] in reject:
continue
# If single alerts (not stack), check that there is enough
# time separation between first and last observation
try:
if info['candidate']['alert_type'] == 'single':
if info['candidate']['jdendhist_single'] - info['candidate'][
'jdstarthist_single'] > min_days:
pass
else:
reject.append(info['objectId'])
continue
else:
pass
except (KeyError, ValueError, TypeError):
pass
try:
if (np.abs(info['candidate']['distpsnr1']) < 1.
and info['candidate']['sgscore1'] >= 0.60):
reject.append(info['objectId'])
continue
except (KeyError, ValueError, TypeError):
pass
#try:
# if (np.abs(info['candidate']['distpsnr1']) < 1. and
# info['candidate']['lstype1'] == 'PSF'):
# reject.append(info['objectId'])
# continue
#except (KeyError, ValueError, TypeError):
# pass
try:
if (np.abs(info['candidate']['distpsnr1']) < 1.
and info['candidate']['lsz1'] < 999.
and info['candidate']['lszspec1'] > 0.1):
reject.append(info['objectId'])
continue
except (KeyError, ValueError, TypeError):
pass
try:
if (np.abs(info['candidate']['distpsnr1']) < 1.
and info['candidate']['lsz1'] < 21.
and info['candidate']['lszphotl681'] > 0.1):
reject.append(info['objectId'])
continue
except (KeyError, ValueError, TypeError):
pass
try:
if (np.abs(info['candidate']['distpsnr1']) < 15.
and info['candidate']['srmag1'] < 15.
and info['candidate']['srmag1'] > 0.
and info['candidate']['sgscore1'] >= 0.49):
reject.append(info['objectId'])
continue
except (KeyError, ValueError, TypeError):
pass
try:
if (np.abs(info['candidate']['distpsnr2']) < 15.
and info['candidate']['srmag2'] < 15.
and info['candidate']['srmag2'] > 0.
and info['candidate']['sgscore2'] >= 0.49):
reject.append(info['objectId'])
continue
except (KeyError, ValueError, TypeError):
pass
try:
if (np.abs(info['candidate']['distpsnr3']) < 15.
and info['candidate']['srmag3'] < 15.
and info['candidate']['srmag3'] > 0.
and info['candidate']['sgscore3'] >= 0.49):
reject.append(info['objectId'])
continue
except (KeyError, ValueError, TypeError):
pass
done_names.append(info['objectId'])
done.append((info['objectId'], info['candid']))
# Check that no source was kept among the rejected ones
checked = list(d for d in done if not (d[0] in reject))
checked_names = list(c[0] for c in checked)
print(f"{len(done)} sources survived stellarity and bright sources cuts")
# Check history for negative subtractions in ZUDS and ZTF alerts
list_selected = check_history(checked_names)
print(
f"{len(list_selected)} sources have no historical negative detections")
sources = list({
'name': c[0],
'candid': c[1]
} for c in checked if c[0] in list_selected)
return sources
