def augment_data(self, flux, stdDevMean=0.05, stdDevStdDev=0.05):
minIndex, maxIndex = ProcessingTools().min_max_index(flux, outerVal=0.5)
noise = np.zeros(self.nw)
stdDev = abs(np.random.normal(stdDevMean, stdDevStdDev)) # randomised standard deviation
noise[minIndex:maxIndex] = np.random.normal(0, stdDev, maxIndex - minIndex)
# # Add white noise to regions outside minIndex to maxIndex
# noise[0:minIndex] = np.random.uniform(0.0, 1.0, minIndex)
# noise[maxIndex:] = np.random.uniform(0.0, 1.0, self.nw-maxIndex)
augmentedFlux = flux + noise
augmentedFlux = normalise_spectrum(augmentedFlux)
augmentedFlux = zero_non_overlap_part(augmentedFlux, minIndex, maxIndex, outerVal=0.5)
return augmentedFlux
def __init__(self, filename, w0, w1, nw):
self.filename = filename
self.w0 = w0
self.w1 = w1
self.nw = nw
self.numSplinePoints = 13
self.processingTools = ProcessingTools()
self.readSpectrumFile = ReadSpectrumFile(filename, w0, w1, nw)
self.preProcess = PreProcessSpectrum(w0, w1, nw)
self.spectrum = self.readSpectrumFile.file_extension()
if len(self.spectrum) == 3:
self.redshiftFromFile = True
else:
self.redshiftFromFile = False
class CombineSnAndHost(object):
def __init__(self, snInfo, galInfo, w0, w1, nw):
self.snInfo = snInfo
self.galInfo = galInfo
self.processingTools = ProcessingTools()
self.numSplinePoints = 13
self.preProcess = PreProcessSpectrum(w0, w1, nw)
def overlapped_spectra(self):
snWave, snFlux, snMinIndex, snMaxIndex = self.snInfo
galWave, galFlux, galMinIndex, galMaxIndex = self.galInfo
minIndex = max(snMinIndex, galMinIndex)
maxIndex = min(snMaxIndex, galMaxIndex)
snFlux = zero_non_overlap_part(snFlux, minIndex, maxIndex)
galFlux = zero_non_overlap_part(galFlux, minIndex, maxIndex)
return snWave, snFlux, galWave, galFlux, minIndex, maxIndex
def sn_plus_gal(self, snCoeff, galCoeff):
snWave, snFlux, galWave, galFlux, minIndex, maxIndex = self.overlapped_spectra(
)
combinedFlux = (snCoeff * snFlux) + (galCoeff * galFlux)
return snWave, combinedFlux, minIndex, maxIndex
def template_data(self, snCoeff, galCoeff, z):
wave, flux, minIndex, maxIndex = self.sn_plus_gal(snCoeff, galCoeff)
wave, flux = self.processingTools.redshift_spectrum(wave, flux, z)
flux = zero_non_overlap_part(flux, minIndex, maxIndex, outerVal=0)
binnedWave, binnedFlux, minIndex, maxIndex = self.preProcess.log_wavelength(
wave[minIndex:maxIndex + 1], flux[minIndex:maxIndex + 1])
newFlux, continuum = self.preProcess.continuum_removal(
binnedWave, binnedFlux, self.numSplinePoints, minIndex, maxIndex)
meanZero = self.preProcess.mean_zero(newFlux, minIndex, maxIndex)
apodized = self.preProcess.apodize(meanZero, minIndex, maxIndex)
fluxNorm = normalise_spectrum(apodized)
fluxNorm = zero_non_overlap_part(fluxNorm,
minIndex,
maxIndex,
outerVal=0.5)
# Could median filter here, but trying without it now
return binnedWave, fluxNorm, (minIndex, maxIndex)
def __init__(self, snInfo, galInfo, w0, w1, nw):
self.snInfo = snInfo
self.galInfo = galInfo
self.processingTools = ProcessingTools()
self.numSplinePoints = 13
self.preProcess = PreProcessSpectrum(w0, w1, nw)