def rlap_warning_label(self, bestType, inputImage, inputMinMaxIndex):
host, name, age = classification_split(bestType)
snInfos, snNames, hostInfos, hostNames = get_templates(
name, age, host, self.snTemplates, self.galTemplates, self.nw)
if snInfos != []:
if self.rlapScores:
templateImages = snInfos[:, 1]
templateMinMaxIndexes = list(zip(snInfos[:, 2], snInfos[:, 3]))
rlapCalc = RlapCalc(inputImage, templateImages, snNames,
self.wave, inputMinMaxIndex,
templateMinMaxIndexes)
rlap, rlapWarningBool = rlapCalc.rlap_label()
if rlapWarningBool:
rlapLabel = "Low rlap: {0}".format(rlap)
else:
rlapLabel = "Good rlap: {0}".format(rlap)
else:
rlapLabel = "No rlap"
rlapWarningBool = "None"
else:
rlapLabel = "(NO_TEMPLATES)"
rlapWarningBool = "None"
# import matplotlib
# matplotlib.use('TkAgg')
# import matplotlib.pyplot as plt
# plt.plot(inputImage)
# plt.plot(templateImage)
# plt.show()
return rlapLabel, rlapWarningBool
def calc_redshift(self, inputFlux, snName, snAge, inputMinMaxIndex):
host = "No Host"
snInfos, snNames, hostInfos, hostNames = get_templates(
snName, snAge, host, self.snTemplates, self.galTemplates, self.nw)
numOfSubTemplates = len(snNames)
templateNames = snNames
templateFluxes = []
templateMinMaxIndexes = []
for i in range(numOfSubTemplates):
templateFluxes.append(snInfos[i][1])
templateMinMaxIndexes.append((snInfos[i][2], snInfos[i][3]))
redshift, crossCorr, medianName = get_median_redshift(
inputFlux,
templateFluxes,
self.nw,
self.dwlog,
inputMinMaxIndex,
templateMinMaxIndexes,
templateNames,
outerVal=0.5)
print(redshift)
if redshift is None:
return 0, np.zeros(1024)
return round(redshift, 4), crossCorr
def get_sn_and_host_templates(self, snName, snAge, hostName):
snInfos, snNames, hostInfos, hostNames = get_templates(
snName, snAge, hostName, self.snTemplates, self.galTemplates,
self.nw)
return snInfos, snNames, hostInfos, hostNames
