def test_filter_scale():
# Test scale_in_filter() method which is called in coadding
wave = np.arange(3000., 10000.)
flux = np.ones_like(wave)
gdm = np.ones_like(wave, dtype=bool)
#
par = Coadd1DPar()
par['filter'] = 'DECAM-R'
par['filter_mag'] = 17.
# Run
scale = flux_calib.scale_in_filter(wave, flux, gdm, par)
assert np.isclose(scale, 41.698475048180406, rtol=1e-3)
def run(self):
"""
Runs the coadding
"""
# Load the data
self.waves, self.fluxes, self.ivars, self.gpms, self.header = self.load_arrays()
# Coadd the data
self.wave_grid_mid, self.wave_coadd, self.flux_coadd, self.ivar_coadd, self.gpm_coadd = self.coadd()
# Scale to a filter magnitude?
if self.par['filter'] != 'none':
scale = flux_calib.scale_in_filter(self.wave_coadd, self.flux_coadd, self.gpm_coadd, self.par)
self.flux_coadd *= scale
self.ivar_coadd = self.ivar_coadd / scale**2
def order_phot_scale(spectra,
phot_scale_dicts,
nsig=3.0,
niter=5,
debug=False):
'''
Scale coadded spectra with photometric data.
Parameters:
spectra: XSpectrum1D spectra (longslit) or spectra list (echelle)
phot_scale_dicts: A dict contains photometric information of each orders (if echelle).
An example is given below.
phot_scale_dicts = {0: {'filter': None, 'mag': None, 'mag_type': None, 'masks': None},
1: {'filter': 'UKIRT-Y', 'mag': 20.33, 'mag_type': 'AB', 'masks': None},
2: {'filter': 'UKIRT-J', 'mag': 20.19, 'mag_type': 'AB', 'masks': None},
3: {'filter': 'UKIRT-H', 'mag': 20.02, 'mag_type': 'AB', 'masks': None},
4: {'filter': 'UKIRT-K', 'mag': 19.92, 'mag_type': 'AB', 'masks': None}}
Show QA plot if debug=True
Return a new scaled XSpectrum1D spectra
'''
from pypeit.core.flux_calib import scale_in_filter
norder = spectra.nspec
# scaling spectrum order by order.
spectra_list_new = []
for iord in range(norder):
phot_scale_dict = phot_scale_dicts[iord]
if (phot_scale_dict['filter'] is not None) & (phot_scale_dict['mag']
is not None):
speci = scale_in_filter(spectra[iord], phot_scale_dict)
else:
#ToDo: Think a better way to do the following
try:
spec0 = scale_in_filter(spectra[iord - 1],
phot_scale_dicts[iord - 1])
speci = spectra[iord]
med_flux = spec0.data['flux'] / speci.data['flux']
mn_scale, med_scale, std_scale = stats.sigma_clipped_stats(
med_flux, sigma=nsig, iters=niter)
med_scale = np.minimum(med_scale, 5.0)
spectra.data['flux'] *= med_scale
spectra.data['sig'] *= med_scale
msgs.warn(
"Not enough photometric information given. Scaled order {:d} to order {:d}"
.format(iord, iord - 1))
except KeyError:
msgs.warn(
"Not enough photometric information given. Scale order {:d} to order {:d} failed"
.format(iord, iord - 1))
try:
spec0 = scale_in_filter(spectra[iord + 1],
phot_scale_dicts[iord + 1])
speci = spectra[iord]
med_flux = spec0.data['flux'] / speci.data['flux']
mn_scale, med_scale, std_scale = stats.sigma_clipped_stats(
med_flux, sigma=nsig, iters=niter)
med_scale = np.minimum(med_scale, 5.0)
speci.data['flux'] *= med_scale
speci.data['sig'] *= med_scale
msgs.warn(
"Not enough photometric information given. Scaled order {:d} to order {:d}"
.format(iord, iord + 1))
except:
msgs.warn(
"Not enough photometric information given. No scaling on order {:d}"
.format(iord))
speci = spectra[iord]
spectra_list_new.append(speci)
if debug:
gdp = speci.sig > 0
plt.plot(spectra[iord].wavelength[gdp],
spectra[iord].flux[gdp],
'k-',
label='raw spectrum')
plt.plot(speci.wavelength[gdp],
speci.flux[gdp],
'b-',
label='scaled spectrum')
mny, medy, stdy = stats.sigma_clipped_stats(speci.flux[gdp],
sigma=3,
iters=5)
plt.ylim([0.1 * medy, 4.0 * medy])
plt.legend()
plt.xlabel('wavelength')
plt.ylabel('Flux')
plt.show()
return collate(spectra_list_new)