def __init__(self, w0, w1, nw, snFilename, galFilename=None):
self.w0 = w0
self.w1 = w1
self.nw = nw
self.snFilename = snFilename
if galFilename is None:
self.data = PreProcessing(snFilename, w0, w1, nw)
else:
self.galFilename = galFilename
class ReadSpectra(object):
def __init__(self, w0, w1, nw, snFilename, galFilename=None):
self.w0 = w0
self.w1 = w1
self.nw = nw
self.snFilename = snFilename
if galFilename is None:
self.data = PreProcessing(snFilename, w0, w1, nw)
else:
self.galFilename = galFilename
self.dataCombined = CombineSnAndHost(snFilename, galFilename, w0,
w1, nw)
def sn_plus_gal_template(self, snAgeIdx, snCoeff, galCoeff, z):
wave, flux, minIndex, maxIndex, nCols, ages, tType = self.dataCombined.training_template_data(
snAgeIdx, snCoeff, galCoeff, z)
return wave, flux, nCols, ages, tType, minIndex, maxIndex
def snid_template_data(self, ageIdx, z):
""" lnw template files """
wave, flux, nCols, ages, tType, minIndex, maxIndex = self.data.snid_template_data(
ageIdx, z)
return wave, flux, nCols, ages, tType, minIndex, maxIndex
def sf_age(self):
snName, extension = self.snFilename.strip('.dat').split('.')
ttype, snName = snName.split('/')
if extension == 'max':
age = 0
elif extension[0] == 'p':
age = float(extension[1:])
elif extension[0] == 'm':
age = -float(extension[1:])
else:
print("Invalid Superfit Filename: " + self.snFilename)
return snName, ttype, age
def superfit_template_data(self, z):
""" Returns wavelength and flux after all preprocessing """
wave, flux, minIndex, maxIndex = self.data.two_column_data(
z, minWave=self.w0, maxWave=self.nw)
snName, ttype, age = self.sf_age()
print(snName, ttype, age)
return wave, flux, minIndex, maxIndex, age, snName, ttype
def input_spectrum(self, z, smooth, minWave, maxWave):
wave, flux, minIndex, maxIndex = self.data.two_column_data(
z, smooth, minWave, maxWave)
return wave, flux, int(minIndex), int(maxIndex)
class ReadSpectra(object):
def __init__(self, w0, w1, nw, snFilename, galFilename=None):
self.w0 = w0
self.w1 = w1
self.nw = nw
self.snFilename = snFilename
if galFilename is None:
self.data = PreProcessing(snFilename, w0, w1, nw)
else:
self.galFilename = galFilename
def sn_plus_gal_template(self, snAgeIdx, snCoeff, galCoeff, z):
wave, flux, minIndex, maxIndex, nCols, ages, tType = training_template_data(snAgeIdx, snCoeff, galCoeff, z, self.snFilename, self.galFilename, self.w0, self.w1, self.nw)
return wave, flux, nCols, ages, tType, minIndex, maxIndex
def input_spectrum(self, z, smooth, minWave, maxWave):
wave, flux, minIndex, maxIndex, z = self.data.two_column_data(z, smooth, minWave, maxWave)
return wave, flux, int(minIndex), int(maxIndex), z