class CleanSpectra(object):
def __init__(self,
min_wavelength=3500,
max_wavelength=8300,
max_masked_fraction=1.0):
self.min_wavelength = min_wavelength
self.max_wavelength = max_wavelength
self.max_masked_fraction = max_masked_fraction
def load_data(self, h5file, selection=None):
if not isinstance(selection, slice):
selection = slice(selection)
datafile = h5py.File(h5file, 'r')
wavelengths = 10**datafile['log_wavelengths'][:]
mask = ((wavelengths >= self.min_wavelength) &
(wavelengths <= self.max_wavelength))
self.wavelengths = wavelengths[mask]
self.spectra = datafile['spectra'][selection, mask]
self.weights = datafile['ivars'][selection, mask]
datafile.close()
# remove rows with excessive missing data
good_rows = (self.weights == 0).mean(1) < self.max_masked_fraction
self.spectra = self.spectra[good_rows]
self.weights = self.weights[good_rows]
self.weights **= 0.5
return self
def fit_wpca(self, n_components=200, regularization=False):
self.wpca = WPCA(n_components=n_components,
regularization=regularization)
self.wpca.fit(self.spectra, weights=self.weights)
return self
def reconstruct(self, spectra=None, weights=None, p=2):
if spectra is None:
spectra = self.spectra
if weights is None:
weights = self.weights
new_spectra = self.wpca.reconstruct(spectra, weights=weights)
SN = abs(spectra * weights)**(1. / p)
SN /= SN.max(1, keepdims=True)
return SN * spectra + (1 - SN) * new_spectra
class CleanSpectra(object):
def __init__(self, min_wavelength=3500, max_wavelength=8300,
max_masked_fraction=1.0):
self.min_wavelength = min_wavelength
self.max_wavelength = max_wavelength
self.max_masked_fraction = max_masked_fraction
def load_data(self, h5file, selection=None):
if not isinstance(selection, slice):
selection = slice(selection)
datafile = h5py.File(h5file, 'r')
wavelengths = 10 ** datafile['log_wavelengths'][:]
mask = ((wavelengths >= self.min_wavelength) &
(wavelengths <= self.max_wavelength))
self.wavelengths = wavelengths[mask]
self.spectra = datafile['spectra'][selection, mask]
self.weights = datafile['ivars'][selection, mask]
datafile.close()
# remove rows with excessive missing data
good_rows = (self.weights == 0).mean(1) < self.max_masked_fraction
self.spectra = self.spectra[good_rows]
self.weights = self.weights[good_rows]
self.weights **= 0.5
return self
def fit_wpca(self, n_components=200, regularization=False):
self.wpca = WPCA(n_components=n_components,
regularization=regularization)
self.wpca.fit(self.spectra, weights=self.weights)
return self
def reconstruct(self, spectra=None, weights=None, p=2):
if spectra is None:
spectra = self.spectra
if weights is None:
weights = self.weights
new_spectra = self.wpca.reconstruct(spectra, weights=weights)
SN = abs(spectra * weights) ** (1. / p)
SN /= SN.max(1, keepdims=True)
return SN * spectra + (1 - SN) * new_spectra
