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 best_redshifts(self):
redshifts = []
for i in range(20):
host, name, age = classification_split(self.bestTypes[i])
redshift, crossCorr, medianName = self.calc_redshift(name, age)
redshifts.append(redshift)
return redshifts
def best_broad_type(self):
bestMatchList = []
for i in range(10):
host, name, age = classification_split(self.bestTypes[i])
bestMatchList.append([host, name, age, self.softmax[i]])
host, prevName, bestAge, probTotal, reliableFlag = combined_prob(
bestMatchList)
self.labelBestSnType.setText(prevName)
self.labelBestAgeRange.setText(bestAge)
self.labelBestHostType.setText(host)
if host == "":
self.labelBestHostType.setFixedWidth(0)
if self.bestRedshiftErr is None:
self.labelBestRedshift.setText(str(self.bestRedshift))
else:
self.labelBestRedshift.setText("{} {} {}".format(
str(self.bestRedshift), "±", self.bestRedshiftErr))
self.labelBestRelProb.setText("%s%%" % str(round(100 * probTotal, 2)))
if host == "":
self.labelBestHostType.setFixedWidth(0)
if reliableFlag:
self.labelInconsistentWarning.setText("Reliable matches")
self.labelInconsistentWarning.setStyleSheet('color: green')
else:
self.labelInconsistentWarning.setText("Unreliable matches")
self.labelInconsistentWarning.setStyleSheet('color: red')
def list_best_matches_single_redshift(self):
print("listing best matches...")
redshifts, redshiftErrs = self.best_redshifts()
self.listWidget.clear()
header = ['No.', 'Type', 'Age', 'Softmax Prob.']
if self.classifyHost:
header.insert(1, 'Host')
if not self.knownRedshift:
header.insert(3, 'Redshift')
if self.getRlapScores:
header.insert(5, 'rlap')
self.listWidget.addItem("".join(word.ljust(25) for word in header))
for i in range(20):
host, name, age = classification_split(self.bestTypes[i])
prob = self.softmax[i]
redshift = redshifts[i]
line = [str(i + 1), name, age, str(prob)]
if self.classifyHost:
line.insert(1, host)
if not self.knownRedshift:
line.insert(3, str(redshift))
if self.getRlapScores:
fluxes, snNames, templateMinMaxIndexes = self.get_smoothed_templates(
name, age, host)
rlapCalc = RlapCalc(self.inputImageUnRedshifted, fluxes,
snNames, self.wave, self.inputMinMaxIndex,
templateMinMaxIndexes)
rlap = rlapCalc.rlap_label()[0]
line.insert(5, str(rlap))
self.listWidget.addItem("".join(word.ljust(25) for word in line))
if i == 0:
SNTypeComboBoxIndex = self.comboBoxSNType.findText(name)
self.comboBoxSNType.setCurrentIndex(SNTypeComboBoxIndex)
AgeComboBoxIndex = self.comboBoxAge.findText(age)
self.comboBoxAge.setCurrentIndex(AgeComboBoxIndex)
hostComboBoxIndex = self.comboBoxHost.findText(host)
self.comboBoxHost.setCurrentIndex(hostComboBoxIndex)
rlap, rlapWarning = self.low_rlap_warning_label(
name, age, host)
if rlapWarning:
self.labelRlapWarning.setText("Low rlap: {0}".format(rlap))
self.labelRlapWarning.setStyleSheet('color: red')
else:
self.labelRlapWarning.setText(
"Good rlap: {0}".format(rlap))
self.labelRlapWarning.setStyleSheet('color: green')
if not self.knownRedshift:
self.bestRedshift = redshifts[0]
self.bestRedshiftErr = redshiftErrs[0]
self.best_broad_type()
def list_best_matches(self, n=5, saveFilename='DASH_matches.txt'):
"""Returns a list of lists of the the top n best matches for each spectrum"""
bestTypes, softmaxes, bestLabels, inputImages, inputMinMaxIndexes = self._input_spectra_info(
)
bestMatchLists = []
bestBroadTypes = []
rlapLabels = []
matchesReliableLabels = []
redshifts = []
redshiftErrs = []
for specNum in range(self.numSpectra):
bestMatchList = []
for i in range(20):
host, name, age = classification_split(bestTypes[specNum][i])
if not self.knownZ:
redshift, _, redshiftErr = self.calc_redshift(
inputImages[specNum], name, age,
inputMinMaxIndexes[specNum])
redshifts.append(redshift)
redshiftErrs.append(redshiftErr)
prob = softmaxes[specNum][i]
bestMatchList.append((host, name, age, prob))
bestMatchList = np.array(bestMatchList)
bestMatchLists.append(bestMatchList[0:n])
bestBroadType, matchesReliableFlag = self.best_broad_type(
bestMatchList)
bestBroadTypes.append(bestBroadType)
rlapLabel, rlapWarningBool = self.rlap_warning_label(
bestTypes[specNum][0], inputImages[specNum],
inputMinMaxIndexes[specNum])
rlapLabels.append(rlapLabel)
if matchesReliableFlag:
matchesReliableLabels.append("Reliable matches")
else:
matchesReliableLabels.append("Unreliable matches")
bestMatchLists = np.array(bestMatchLists)
if not redshifts:
redshifts = self.redshifts
redshiftErrs = [None] * len(self.redshifts)
else:
redshifts = np.array(redshifts)
redshiftErrs = np.array(redshiftErrs)
if saveFilename:
self.save_best_matches(bestMatchLists, redshifts, bestBroadTypes,
rlapLabels, matchesReliableLabels,
saveFilename)
return bestMatchLists, redshifts, bestBroadTypes, rlapLabels, matchesReliableLabels, redshiftErrs