def __init__(self, filenames=[], redshifts=[], smooth=6, minWave=3500, maxWave=10000, classifyHost=False, knownZ=True, rlapScores=True, data_files='models_v04'):
""" Takes a list of filenames and corresponding redshifts for supernovae.
Files should contain a single spectrum, and redshifts should be a list of corresponding redshift floats
"""
# download_all_files('v01')
self.filenames = filenames
self.redshifts = redshifts
self.smooth = smooth
self.minWave = minWave
self.maxWave = maxWave
self.classifyHost = classifyHost
self.numSpectra = len(filenames)
self.scriptDirectory = os.path.dirname(os.path.abspath(__file__))
if knownZ and redshifts != []:
self.knownZ = True
else:
self.knownZ = False
self.rlapScores = rlapScores
self.pars = get_training_parameters()
self.nw, w0, w1 = self.pars['nw'], self.pars['w0'], self.pars['w1']
self.dwlog = np.log(w1/w0)/self.nw
self.wave = w0 * np.exp(np.arange(0, self.nw) * self.dwlog)
self.snTemplates, self.galTemplates = load_templates(os.path.join(self.scriptDirectory, data_files, 'models/sn_and_host_templates.npz'))
if self.knownZ:
if classifyHost:
self.modelFilename = os.path.join(self.scriptDirectory, data_files, "models/zeroZ_classifyHost/tensorflow_model.ckpt")
else:
self.modelFilename = os.path.join(self.scriptDirectory, data_files, "models/zeroZ/tensorflow_model.ckpt")
else:
if self.classifyHost:
self.modelFilename = os.path.join(self.scriptDirectory, data_files, "models/agnosticZ_classifyHost/tensorflow_model.ckpt")
else:
self.modelFilename = os.path.join(self.scriptDirectory, data_files, "models/agnosticZ/tensorflow_model.ckpt")
def templates(self):
pars = get_training_parameters()
self.w0, self.w1, self.minAge, self.maxAge, self.ageBinSize, self.typeList, self.nTypes, self.nw, self.hostTypes \
= pars['w0'], pars['w1'], pars['minAge'], pars['maxAge'], pars['ageBinSize'], pars['typeList'], pars['nTypes'], pars['nw'], pars['galTypeList']
self.dwlog = np.log(self.w1/self.w0)/self.nw
self.wave = self.w0 * np.exp(np.arange(0,self.nw) * self.dwlog)
self.snTemplates, self.galTemplates = load_templates('models/sn_and_host_templates.npz')
def __init__(self, inputFlux, templateFluxes, templateNames, wave,
inputMinMaxIndex, templateMinMaxIndexes):
self.templateFluxes = templateFluxes
self.templateNames = templateNames
self.wave = wave
pars = get_training_parameters()
w0, w1, self.nw, = pars['w0'], pars['w1'], pars['nw']
self.inputFlux = mean_zero_spectra(inputFlux, inputMinMaxIndex[0],
inputMinMaxIndex[1], self.nw)
self.templateMinMaxIndexes = templateMinMaxIndexes
self.dwlog = np.log(w1 / w0) / self.nw
def _input_spectrum_single_redshift(self):
trainParams = get_training_parameters()
loadInputSpectraUnRedshifted = LoadInputSpectra(self.inputFilename, 0, 0, self.smooth, trainParams, self.minWave, self.maxWave, self.classifyHost)
inputImageUnRedshifted, inputRedshift, typeNamesList, nw, nBins = loadInputSpectraUnRedshifted.input_spectra()
loadInputSpectra = LoadInputSpectra(self.inputFilename, self.knownZ, self.knownZ, self.smooth, trainParams, self.minWave, self.maxWave, self.classifyHost)
inputImage, inputRedshift, typeNamesList, nw, nBins = loadInputSpectra.input_spectra()
bestTypesList = BestTypesListSingleRedshift(self.modelFilename, inputImage, typeNamesList, nw, nBins)
bestTypes = bestTypesList.bestTypes[0]
softmax = bestTypesList.softmaxOrdered[0]
idx = bestTypesList.idx[0]
return bestTypes, softmax, idx, typeNamesList, inputImageUnRedshifted[0]