def test_wavelengths(self):
if self.data_unavailable:
self.skipTest("Failed to download test data.")
# Read flat
flat_hdu = fits.open(self.testflat)
header = flat_hdu[0].header
flat = flat_hdu[0].data
ny = flat.shape[0]
# Find fibers (necessary)
xpk, ypos, cut = desiboot.find_fiber_peaks(flat)
# Trace
xset, xerr = desiboot.trace_crude_init(flat, xpk, ypos)
xfit, fdicts = desiboot.fit_traces(xset, xerr)
# Test fiber_gauss_old for coverage
gauss = desiboot.fiber_gauss_old(flat, xfit, xerr)
# Gaussian
gauss = desiboot.fiber_gauss(flat, xfit, xerr)
# Read arc
arc_hdu = fits.open(self.testarc)
arc = arc_hdu[0].data
arc_ivar = np.ones(arc.shape)
# Extract arc spectra (one per fiber)
all_spec = desiboot.extract_sngfibers_gaussianpsf(arc, arc_ivar, xfit, gauss)
# Line list
camera = header['CAMERA']
llist = desiboot.load_arcline_list(camera)
dlamb, gd_lines = desiboot.load_gdarc_lines(camera,llist)
#
all_wv_soln = []
for ii in range(1):
spec = all_spec[:,ii]
# Find Lines
pixpk, flux = desiboot.find_arc_lines(spec)
id_dict = {"pixpk":pixpk,"flux":flux}
# Match a set of 5 gd_lines to detected lines
desiboot.id_arc_lines_using_triplets(id_dict,gd_lines,dlamb)
# Now the rest
desiboot.id_remainder(id_dict, llist, deg=3)
# Final fit wave vs. pix too
final_fit, mask = dufits.iter_fit(np.array(id_dict['id_wave']),
np.array(id_dict['id_pix']),
'polynomial', 3, xmin=0., xmax=1.)
rms = np.sqrt(np.mean((dufits.func_val(
np.array(id_dict['id_wave'])[mask==0], final_fit)-
np.array(id_dict['id_pix'])[mask==0])**2))
final_fit_pix,mask2 = dufits.iter_fit(np.array(id_dict['id_pix']),
np.array(id_dict['id_wave']),
'legendre',4, niter=5)
# Save
id_dict['final_fit'] = final_fit
id_dict['rms'] = rms
id_dict['final_fit_pix'] = final_fit_pix
id_dict['wave_min'] = dufits.func_val(0, final_fit_pix)
id_dict['wave_max'] = dufits.func_val(ny-1, final_fit_pix)
id_dict['mask'] = mask
all_wv_soln.append(id_dict)
self.assertLess(all_wv_soln[0]['rms'], 0.25)
def test_wavelengths(self):
if self.data_unavailable:
self.skipTest("Failed to download test data.")
# Read flat
flat_hdu = fits.open(self.testflat)
header = flat_hdu[0].header
flat = flat_hdu[0].data
ny = flat.shape[0]
# Find fibers (necessary)
xpk, ypos, cut = desiboot.find_fiber_peaks(flat)
# Trace
xset, xerr = desiboot.trace_crude_init(flat, xpk, ypos)
xfit, fdicts = desiboot.fit_traces(xset, xerr)
# Test fiber_gauss_old for coverage
gauss = desiboot.fiber_gauss_old(flat, xfit, xerr)
# Gaussian
gauss = desiboot.fiber_gauss(flat, xfit, xerr)
# Read arc
arc_hdu = fits.open(self.testarc)
arc = arc_hdu[0].data
arc_ivar = np.ones(arc.shape)
# Extract arc spectra (one per fiber)
all_spec = desiboot.extract_sngfibers_gaussianpsf(
arc, arc_ivar, xfit, gauss)
# Line list
camera = header['CAMERA']
llist = desiboot.load_arcline_list(camera)
dlamb, gd_lines = desiboot.load_gdarc_lines(camera, llist)
#
all_wv_soln = []
for ii in range(1):
spec = all_spec[:, ii]
# Find Lines
pixpk, flux = desiboot.find_arc_lines(spec)
id_dict = {"pixpk": pixpk, "flux": flux}
# Match a set of 5 gd_lines to detected lines
desiboot.id_arc_lines_using_triplets(id_dict, gd_lines, dlamb)
# Now the rest
desiboot.id_remainder(id_dict, llist, deg=3)
# Final fit wave vs. pix too
final_fit, mask = dufits.iter_fit(np.array(id_dict['id_wave']),
np.array(id_dict['id_pix']),
'polynomial',
3,
xmin=0.,
xmax=1.)
rms = np.sqrt(
np.mean((dufits.func_val(
np.array(id_dict['id_wave'])[mask == 0], final_fit) -
np.array(id_dict['id_pix'])[mask == 0])**2))
final_fit_pix, mask2 = dufits.iter_fit(np.array(id_dict['id_pix']),
np.array(
id_dict['id_wave']),
'legendre',
4,
niter=5)
# Save
id_dict['final_fit'] = final_fit
id_dict['rms'] = rms
id_dict['final_fit_pix'] = final_fit_pix
id_dict['wave_min'] = dufits.func_val(0, final_fit_pix)
id_dict['wave_max'] = dufits.func_val(ny - 1, final_fit_pix)
id_dict['mask'] = mask
all_wv_soln.append(id_dict)
self.assertLess(all_wv_soln[0]['rms'], 0.25)