def tnsUpdate(**kwargs):
collection = current_collection
projection = {}
radio = 5
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
filter = {}
if "collection" in kwargs.keys() and kwargs["collection"] != "":
collection = kwargs["collection"]
if "filter" in kwargs.keys() and kwargs["filter"] !="":
filter = kwargs["filter"]
db.setCollection(collection)
data = db.getData(filter=filter, projection=projection)
cont=0
for index, row in enumerate(data):
ra = row["ra"]
dec = row["dec"]
tns = tnsxmatch(ra,dec,radio)
if tns!=None:
updated = db.update(filter={"id": row["id"]}, query={"$set": {"crossmatch.tns": tns}})
print("tns update ",row["id"])
cont+=1
print("updated {} of {}".format(cont,len(data)))
def getAll(filter={}, projection=""):
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection("tnssn")
data = db.getData(filter=filter, projection=projection)
return data
def scoreCandidates(collection,filter={}):
lasairarchive = LasairArchive()
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(collection)
data = db.getData(filter=filter)
for indx, row in enumerate(data):
print("ID score",row["id"])
def getLightCurve(ztfid):
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection("lastdetections7")
filter = {"id": ztfid}
projection = {"lightpeak.lightcurve": 1}
data = db.getData(filter=filter, projection=projection)
return data
def getByID(id="", filter="", collection="lastdetections7"):
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(collection)
if filter == "":
filter = {"id": {'$eq': id}}
data = db.getData(filter=filter)
return data
def calcABMagCandidates():
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(current_collection)
data = db.getData()
for index,row in enumerate(data):
try:
if "crossmatch" in row.keys() and len(row["crossmatch"])>0:
ab_mags = calcAbsoluteMagnitud(row)
db.update(filter={"id":row["id"]},query={"$set":{"abmag":ab_mags}})
logger.info("calcABMagCandidates:: updated {1}".format(row["id"]))
except Exception as ex:
logger.error("calcABMagCandidates:: error getting abmags {0} error {1}".format(row["id"],str(ex)))
def calcABMagnitud():
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection("tnssn")
data=db.getData(filter={"Redshift":{'$gt':0},"DiscInternalName":{"$regex":'^ZTF'}},projection={"DiscInternalName":1,"lightcurve.candidates":1,"Redshift":1,"id":1,"Name":1})
for index,row in enumerate(data):
try:
dt=pd.DataFrame(row["lightcurve"]["candidates"])
magsd=dt["magpsf"].tolist()
ab=Convertion.aparentToAbsoluteMagnitud(magsd,z=row["Redshift"])
db.update({"id":row["id"]},query={"$set":{"abmag":ab.tolist()}})
except Exception as err:
logger.error("calcABMagnitud:: Cant calculate ABmagnituds for {0}".format(row["id"]))
def getQuery(collection, filter={}, projection={}):
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(collection)
if filter != {} and filter != "":
filter = json.loads(filter)
else:
filter = {}
if projection != {} and projection != "":
projection = json.loads(projection)
else:
projection = {}
data = db.getData(filter=filter, projection=projection)
return data
def calcRedshiftCandidates(**kwargs):
collection = current_collection
if "collection" in kwargs.keys() and kwargs["collection"] != "":
collection = kwargs["collection"]
filter={}
if "filter" in kwargs.keys() and kwargs["filter"] != "":
filter = kwargs["filter"]
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"] != "":
updatePipeline(kwargs["IDpipeLine"], "calcRedshiftCandidates", STATE_RUNNING)
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(collection)
data = db.getData(filter=filter)
for index,row in enumerate(data):
# try:
print("get redshift to " + row["id"])
if "crossmatch" in row.keys() and len(row["crossmatch"].keys())>0:
good_spec,good_photo,photo_spec,redshift = getRedshifts(row["crossmatch"])
sncos=False
if len(redshift)<=0 and sncos:
if "g" in row["lightpeak"]["lightcurve"] and "r" in row["lightpeak"]["lightcurve"]:
if row["lightpeak"]["lightcurve"]["g"]["detections"]>=2 and row["lightpeak"]["lightcurve"]["r"]["detections"]>=2:
snclasifier = getSNCosmosFit(row["lightcurve"],id=row["id"])
if snclasifier != None:
redshift["sncosmos"]=snclasifier
query={"redshift": redshift, "best_photo_z": good_photo, "best_spec_z": good_spec}
query.update(photo_spec)
up = db.update(filter={"id": row["id"]},query={"$set":query})
print("udpdate " + row["id"])
# except Exception as ex:
# logger.error("calcRedshiftCandidates:: Cant calculate Redshift for {0} error {1}".format(row["id"],str(ex)))
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"] != "":
updatePipeline(kwargs["IDpipeLine"], "calcRedshiftCandidates", STATE_COMPLETED)
def crossMatchCollection(**kwargs):
collection = current_collection
filter = {'crossmatch.check': False}
projection = {}
forcecrossmatch = False
radio = 5
if "collection" in kwargs.keys() and kwargs["collection"] !="":
collection = kwargs["collection"]
if "forcecrossmatch" in kwargs.keys() and kwargs["forcecrossmatch"] !="":
forcecrossmatch = kwargs["forcecrossmatch"]
if "filter" in kwargs.keys() and kwargs["filter"] !="":
filter = kwargs["filter"]
if "radio" in kwargs.keys() and kwargs["radio"] !="":
radio = kwargs["radio"]
if "projection" in kwargs.keys() and kwargs["projection"] !="":
projection = kwargs["projection"]
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"]!="":
updatePipeline(kwargs["IDpipeLine"],"crossMatchCollection",STATE_RUNNING)
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(collection)
if "crossmatch" not in projection:
projection["crossmatch"]=1
if "id" not in projection:
projection["id"] = 1
if "ra" not in projection:
projection["ra"] = 1
if "dec" not in projection:
projection["dec"] = 1
data = db.getData(filter=filter, projection=projection)
print("cross match source to update",len(data))
for index,row in enumerate(data):
if forcecrossmatch or ("crossmatch" not in row.keys() or row["crossmatch"]["check"] == False) :
try:
id = row["id"]
logger.info("try cross match..." + id)
print("try cross match..." + id,index,row["crossmatch"]["check"])
ra = row["ra"]
dec = row["dec"]
#ra = row["ra"]
#ra = row["dec"]
current=row["crossmatch"]
print("cross match",id)
crossdata=crossMatch(ra,dec,radio=radio)
crossdata["lasair"]=current["lasair"]
crossdata["check"] = True
logger.info("check follow up candidates and update ZTF light curves..."+id)
updated=db.update(filter={"id":id}, query={"$set":{"crossmatch":crossdata}})
print("id {0} updated {1}",id,updated)
except Exception as err:
#print("crossmatch error by"+id,err)
print("error cross match",err)
logger.error("crossMatchCollection:: Cant crossmatch {0}".format(row["id"]))
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"]!="":
updatePipeline(kwargs["IDpipeLine"],"crossMatchCollection",STATE_COMPLETED)
def checkLastDetections(**kwargs):
# try:
allrecords=0
collection=current_collection
days_ago=15
if "collection" in kwargs.keys() and kwargs["collection"]!="":
collection = kwargs["collection"]
if "days_ago" in kwargs.keys() and kwargs["days_ago"]!="":
days_ago=kwargs["days_ago"]
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"]!="":
updatePipeline(kwargs["IDpipeLine"],"checkLastDetections",STATE_RUNNING)
logger.info("checkLastDetections:: getting the last ZTF detections from brokers...")
lasairarchive = LasairArchive()
#coneecto to DATABASE
db = MongodbManager()
config=Config()
dbconfig=config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(collection)
#Get last candidates and update previews detection and light curves
bestCandidates = BestCandidates()
table_candidates, alerceDF, lasairDF = bestCandidates.searchCadidates(days_ago)
#check if the new candidates is already in DB
#get all zft id in and array to validate if exist into ddatabase and filter by
listcandidates=table_candidates["id"]
filter={"oid":{"$in":listcandidates.data.tolist()}}
projection={"oid":1 ,"lastmjd":1 ,"last_update":1}
current_data = db.getData(filter=filter, projection=projection)
for remove_data in current_data:
oid=remove_data["oid"]
print("get info for ",oid)
table_candidates.remove_rows(table_candidates["id"] == oid)
#get desi photoz
dataarchive = SussexArchive()
desi_targetsvo, desi_targetstable = dataarchive.getDesiPhotoZfromTable(table_candidates)
alerceTable = QTable.from_pandas(alerceDF)
lasairTable = QTable.from_pandas(lasairDF)
alerceTable.rename_column("oid","id")
lasairTable.rename_column("oid", "id")
alerceTable["id"] = alerceTable["id"].astype(str)
lasairTable["id"] = lasairTable["id"].astype(str)
desi_targetstable["id"] =desi_targetstable["id"].astype(str)
desi_targetstable["desidec"].mask = False
desi_targetstable["desira"].mask = False
#calc separation desi source
ra_ref = desi_targetstable["ramean"].tolist()
dec_ref = desi_targetstable["decmean"].tolist()
cref = SkyCoord(ra_ref, dec_ref, frame='icrs', unit='deg')
ra_desi = desi_targetstable["desira"].tolist()
dec_desi = desi_targetstable["desidec"].tolist()
c1 = SkyCoord(ra_desi, dec_desi, frame='icrs', unit='deg')
desi_distance = cref.separation(c1).arcsec
desi_targetstable["separation"] = desi_distance
#merge all table in one json to save in mongo
desi_targetstable = Table(desi_targetstable, masked=False)
alerceTable = Table(alerceTable, masked=False)
lasairTable = Table(lasairTable, masked=False)
alerceTable["broker"] = "alerce"
lasairTable["broker"] = "lasair"
update_alerce_table = join(alerceTable, lasairTable, join_type='outer', keys='id')
merge_table = join(update_alerce_table, desi_targetstable, join_type='outer', keys='id')
merge_table["desiid"] = merge_table["desiid"].astype(str)
merge_table["field"] = merge_table["field"].astype(str)
lastItems= merge_table.to_pandas()
newItems = lastItems.fillna('', axis=1)
dic_result = newItems.to_dict('records')
newCandidates=0
logger.info("checkLastDetections:: Ingested {0} candidates".format(str(len(dic_result))))
allrecords=len(dic_result)
for index,row in enumerate(dic_result):
id=row["id"]
print("saving candidate",id)
row["comments"]={}
row["snh_score"] = 0.0
if row["broker_1"] != "":
#alerce
#row["pclassearly"]=row["pclassearly_1"]
if row["broker_2"]!="":
row["broker"]=row["broker_1"]+"/"+row["broker_2"]
else:
row["broker"] = row["broker_1"]
row["meanra"]=row["meanra_1"]
row["meandec"]=row["meandec_1"]
row["lastmjd"]=row["lastmjd_1"]
else:
#lasair
#row["pclassearly"] = row["pclassearly_2"]
row["broker"] = row["broker_2"]
row["meanra"] = row["meanra_2"]
row["meandec"] = row["meandec_2"]
row["lastmjd"] = row["lastmjd_2"]
try:
#remove duplicate fields
#del row["pclassearly_1"]
#del row["pclassearly_2"]
del row["broker_1"]
del row["broker_2"]
del row["meanra_1"]
del row["meandec_1"]
del row["meanra_2"]
del row["meandec_2"]
del row["lastmjd_2"]
del row["lastmjd_1"]
except KeyError as er:
print("key error",er,id)
# check if already exist this candidate, if exist update light curve and run check list to alerts
currentdata = db.getData(filter={"id": id}, projection={"nobs": 1, "last_update": 1, "id": 1})
now = datetime.now().timestamp()
rowupdated={}
if len(currentdata) > 0:
currentdata = currentdata[0]
days_from_update = ((now - float(currentdata["last_update"])) / 3600) / 24
if days_from_update < 0.6:
print("last detections is the same, not getting enough to services update classify",id)
logger.info("checkLastDetections:: {0} last detections is the same, not getting enough to services update classify".format(id))
continue
classification = getClassification(id)
#peak = lasairarchive.getPeakLightCurve(classification["light_curve"]["candidates"])
rowupdated["ra"] = row["meanra"]
rowupdated["dec"] = row["meandec"]
rowupdated["lasair_clas"]=classification["lasair_clas"]
rowupdated["alerce_clas"]=classification["alerce_clas"]
rowupdated["alerce_early_class"] = classification["alerce_early_class"]
rowupdated["alerce_late_class"] = classification["alerce_late_class"]
rowupdated["crossmatch"]={"lasair":classification["light_curve"]["crossmatches"],"check":False}
rowupdated["lightcurve"] = classification["light_curve"]["candidates"]
rowupdated["report"] = row
rowupdated["broker"] = row["broker"]
rowupdated["nobs"] = row["nobs"]
rowupdated["lastmjd"] = row["lastmjd"]
rowupdated["sigmara"] = row["sigmara"]
rowupdated["sigmadec"] = row["sigmadec"]
rowupdated["last_magpsf_g"] = row["last_magpsf_g"]
rowupdated["last_magpsf_r"] = row["last_magpsf_r"]
rowupdated["first_magpsf_g"] = row["first_magpsf_g"]
rowupdated["first_magpsf_r"] = row["first_magpsf_r"]
rowupdated["sigma_magpsf_g"] = row["sigma_magpsf_g"]
rowupdated["sigma_magpsf_r"] = row["sigma_magpsf_r"]
rowupdated["max_magpsf_g"] = row["max_magpsf_g"]
rowupdated["max_magpsf_r"] = row["max_magpsf_r"]
rowupdated["id"] = row["id"]
#check if already exist this candidate, if exist update light curve and run check list to alerts
currentdata=db.getData(filter={"id":id},projection={"nobs":1,"last_update":1,"id":1})
now = datetime.now().timestamp()
if len(currentdata)>0 :
#update current data
try:
if currentdata[0]["nobs"] < rowupdated["nobs"]:
peak = lasairarchive.getPeakLightCurve(classification["light_curve"]["candidates"])
rowupdated["lightpeak"] = peak
update_query={"last_update":now,"lightcurve":rowupdated["lightcurve"],"lightpeak":peak,"lasair_clas":rowupdated["lasair_clas"],"alerce_clas":rowupdated["alerce_clas"],"nobs":rowupdated["nobs"],"state":"updated"}
update_id = db.update(filter={"id":id}, query={"$set":update_query})
print("updated source",id,update_id.raw_result)
else:
print("last detections is the same, not getting enough to services update classify",id)
except Exception as err:
print("Error updated",id,currentdata[0]["nobs"],rowupdated["nobs"])
logger.error("checkLastDetections:: {0} Error updated..".format(str(id)))
else:
peak = lasairarchive.getPeakLightCurve(classification["light_curve"]["candidates"])
rowupdated["lightpeak"] = peak
#insert new candidate
print("save new candidate")
rowupdated["state"]="new"
rowupdated["last_update"] = now
db.saveData(rowupdated)
logger.info("checkLastDetections:: {0} Saved candidate with {1} observations".format(id,rowupdated["nobs"]))
newCandidates+=1
logger.info("checkLastDetections:: {0} candidates stored..".format(str(len(dic_result))))
logger.info("checkLastDetections:: alerce table detections {0}".format(str(len(alerceTable))))
logger.info("checkLastDetections:: lasair table detections {0}".format(str(len(lasairTable))))
logger.info("checkLastDetections:: desi detections {0}".format(str(len(desi_targetstable))))
logger.info("checkLastDetections:: new Candidates {0}".format(str(newCandidates)))
db.saveData(data={"date":now,"newcandidates":newCandidates,"allrecords":allrecords,"alerce_records":len(alerceTable),"lasair_records":len(lasairTable),"desi_matchs":len(desi_targetstable),"process":"lastdetections"},collection="tasks")
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"]!="":
updatePipeline(kwargs["IDpipeLine"],"checkLastDetections",STATE_COMPLETED)
def getPeaks(**kwargs):
collection = current_collection
filter = {"lightcurve": {"$exists": True}}
projection = {}
if "collection" in kwargs.keys() and kwargs["collection"] != "":
collection = kwargs["collection"]
if "filter" in kwargs.keys() and kwargs["filter"] != "":
filter = kwargs["filter"]
if "projection" in kwargs.keys() and kwargs["projection"] != "":
projection = kwargs["projection"]
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"] != "":
updatePipeline(kwargs["IDpipeLine"], "getPeaks", STATE_RUNNING)
lasairarchive = LasairArchive()
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(collection)
data = db.getData(filter=filter)
for indx,row in enumerate(data):
print("try to get peak",row["id"])
if len(row["lightcurve"])>0:
peak = lasairarchive.getPeakLightCurve(row["lightcurve"])
query = {}
query["best_photoz_gabmag"] = 999
query["best_photoz_rabmag"] = 999
query["best_specz_gabmag"] = 999
query["best_specz_rabmag"] = 999
if "Redshift" in row or ("redshift" in row and len(row["redshift"].keys())>0):
redshift = []
if "Redshift" in row:
z=row["Redshift"]
redshifts_archives=["tns"]
else:
redshifts_archives=row["redshift"].keys()
for z_key in redshifts_archives:
if z_key == "sncosmos":
if "best"in row["redshift"]["sncosmos"] and "redshift" in row["redshift"]["sncosmos"]["best"]:
z = row["redshift"]["sncosmos"]["best"]["redshift"]
else:
continue
else:
z=row["redshift"][z_key]
if "g" in peak["stats"].keys():
if "magab" not in peak["stats"]["g"]:
peak["stats"]["g"]["magab"]={}
peak["stats"]["g"]["magab"][z_key] = Convertion.aparentToAbsoluteMagnitud(peak["stats"]["g"]["y"],z=z).tolist()
if "r" in peak["stats"].keys():
if "magab" not in peak["stats"]["r"]:
peak["stats"]["r"]["magab"]={}
peak["stats"]["r"]["magab"][z_key] = Convertion.aparentToAbsoluteMagnitud(peak["stats"]["r"]["y"],
z=z).tolist()
if "g" in peak["lightcurve"].keys():
if "magab" not in peak["lightcurve"]["g"]:
peak["lightcurve"]["g"]["magab"]={}
peak["lightcurve"]["g"]["magab"][z_key] = Convertion.aparentToAbsoluteMagnitud(
peak["lightcurve"]["g"]["mag"], z=z).tolist()
if "r" in peak["lightcurve"].keys():
if "magab" not in peak["lightcurve"]["r"]:
peak["lightcurve"]["r"]["magab"]={}
peak["lightcurve"]["r"]["magab"][z_key] = Convertion.aparentToAbsoluteMagnitud(
peak["lightcurve"]["r"]["mag"], z=z).tolist()
peaks=[]
gmag=False
rmag = False
if "g" in peak["stats"].keys():
peak_g = peak["stats"]["g"]["peakmag"]
peaks.append(peak_g)
gmag = True
if "r" in peak["stats"].keys():
peak_r = peak["stats"]["r"]["peakmag"]
peaks.append(peak_r)
rmag=True
if "best_photo_z" in row.keys() and len(row["best_photo_z"])>0:
photoz = row["best_photo_z"]["photo_z"]
best_photomagab=Convertion.aparentToAbsoluteMagnitud(peaks, z=photoz).tolist()
if gmag:
query["best_photoz_gabmag"]=best_photomagab[0]
if rmag:
idxphotorbest = 1 if gmag else 0
query["best_photoz_rabmag"]=best_photomagab[idxphotorbest]
if "best_spec_z" in row.keys() and len(row["best_spec_z"])>0:
specz = row["best_spec_z"]["spec_z"]
best_specmagab=Convertion.aparentToAbsoluteMagnitud(peaks, z=specz).tolist()
if gmag:
query["best_specz_gabmag"]=best_specmagab[0]
if rmag:
idxspecbest = 1 if gmag else 0
query["best_specz_rabmag"]=best_specmagab[idxspecbest]
query["lightpeak"]= peak
if "g" in peak["status"].keys():
query["g_state"]= peak["status"]["g"]
if "r" in peak["status"].keys():
query["r_state"]= peak["status"]["r"]
update=db.update(filter={"id":row["id"]},query={"$set":query})
print("update peak ",row["id"],update)
if "IDpipeLine" in kwargs.keys() and kwargs["IDpipeLine"] != "":
updatePipeline(kwargs["IDpipeLine"], "getPeaks", STATE_COMPLETED)
def classifyCandidate():
#
db = MongodbManager()
config = Config()
dbconfig = config.getDatabase("mongodb")
db.setDatabase(dbconfig["dbname"])
db.setCollection(current_collection)
data=db.getData(filter={"$or":[{"lightpeak.lightcurve.g.magab": {"$exists": True}},{"lightpeak.lightcurve.r.magab": {"$exists": True}}]})
for indx, row in enumerate(data):
print("row",row["id"])
db.setCollection("tnssn")
probabilities={}
filters=[]
if ("g" in row["peak"]["stats"].keys() and "magab" in row["peak"]["stats"]["g"]) or ("r" in row["peak"]["stats"].keys() and "magab" in row["peak"]["stats"]["r"]):
try:
keys = row["peak"]["stats"]["g"]["magab"].keys()
except Exception as err:
keys = row["peak"]["stats"]["r"]["magab"].keys()
for archive in keys:
try:
maxg = min(row["peak"]["stats"]["g"]["magab"][archive])
filters.append({"peak.stats.g.abmag": {"$lte": maxg}})
except Exception:
print("not g band",row["id"])
try:
maxr = min(row["peak"]["stats"]["r"]["magab"][archive])
filters.append({"peak.stats.r.abmag": {"$lte": maxr}})
except Exception:
print("not r band", row["id"])
if len(filters) > 0:
classtypes=db.getData({"$or":filters},projection={"Redshift":1,"ObjType":1,"id":1,"peak":1,"DiscInternalName":1})
if len(classtypes)>0:
classify = []
for idx, classtype in enumerate(classtypes):
if idx >10:
break
data_classifier= {"redshift":classtype["Redshift"],"ObjType":classtype["ObjType"],"id":classtype["id"],"ztfid":classtype["DiscInternalName"]}
if "g" in classtype["peak"]["stats"]:
data_classifier["slope_g"]=classtype["peak"]["stats"]["g"]["slope"]
data_classifier["abmagpeak_g"]= min(classtype["peak"]["stats"]["g"]["abmag"])
data_classifier["magpeak_g"] = min(classtype["peak"]["stats"]["g"]["y"])
if "r" in classtype["peak"]["stats"]:
data_classifier["slope_r"] = classtype["peak"]["stats"]["r"]["slope"]
data_classifier["abmagpeak_r"] = min(classtype["peak"]["stats"]["r"]["abmag"])
data_classifier["magpeak_r"] = min(classtype["peak"]["stats"]["r"]["y"])
classify.append(data_classifier)
probabilities[archive]=classify
if len(probabilities.keys()) > 0:
try:
db.setCollection(current_collection)
upd=db.update(filter={"id":row["id"]},query={"$set":{"probabilities":probabilities}})
logger.error("classifyCandidate:: classifier update {0}".format(row["id"]))
except Exception as err:
logger.error("classifyCandidate:: many candidates try to save for {0}".format(row["id"]))
del probabilities