def getLastLasair(request):
lasair = LasairArchive()
lastItems = lasair.getLastDetections(days_ago=4)
if request.GET.__contains__('jobid'):
jobid = request.GET['jobid']
else:
status = {"error": "job id is required"}
newItems = lastItems.fillna('', axis=1)
# lastItems.replace(np.nan, '', regex=True)
dic_result = newItems.to_dict('records')
# for index,row in enumerate(dic_result):
# for key in row.keys():
# if math.isnan(row[key]):
# row[key]="nan"
return JsonResponse(dic_result, safe=False)
def searchCadidates(self, days=15):
alerce = AlerceArchive()
alerceGoodCandidates = alerce.getCandidates(days)
lasairbroker = LasairArchive()
lasairGoodCandidates = lasairbroker.getLastDetections(days)
# alerceGoodCandidates = [r["result"][target] for target in r["result"]]
alerceTable = QTable.from_pandas(alerceGoodCandidates)
lasairTable = QTable.from_pandas(lasairGoodCandidates)
print("candidates found lasair{0} alerce{1} ".format(
len(lasairGoodCandidates), len(alerceGoodCandidates)))
meanaler_val = {
"ramean":
np.nan_to_num(
np.concatenate((alerceGoodCandidates["meanra"].values,
lasairGoodCandidates["meanra"].values),
axis=0)),
"decmean":
np.nan_to_num(
np.concatenate((alerceGoodCandidates["meandec"].values,
lasairGoodCandidates["meandec"].values),
axis=0)),
"maggmax":
np.nan_to_num(
np.concatenate((alerceGoodCandidates["max_magap_g"].values,
lasairGoodCandidates["maggmax"].values),
axis=0)),
"maggmin":
np.nan_to_num(
np.concatenate((alerceGoodCandidates["min_magap_g"].values,
lasairGoodCandidates["maggmin"].values),
axis=0)),
"magrmax":
np.nan_to_num(
np.concatenate((alerceGoodCandidates["max_magap_r"].values,
lasairGoodCandidates["magrmax"].values),
axis=0)),
"magrmin":
np.nan_to_num(
np.concatenate((alerceGoodCandidates["min_magap_r"].values,
lasairGoodCandidates["magrmin"].values),
axis=0)),
"id":
np.concatenate((alerceGoodCandidates["oid"].values,
lasairGoodCandidates["oid"].values),
axis=0)
}
bigdata = pd.DataFrame(meanaler_val)
bigdata_drop = bigdata.drop_duplicates(subset="id", keep=False)
bigtable = bigdata
if bigdata_drop.size > 0:
bigtable = bigdata_drop
table_candidates = QTable(QTable.from_pandas(bigtable), masked=False)
return table_candidates, alerceGoodCandidates, lasairGoodCandidates