def get_arcs(image, skypol=2, clip=3, fferr=0.015):
"""Fit arc image and create table with one-dimensional arc spectra.
Parameters
----------
image: ~fits.HDUList
prepared image set with one extension for each chip
skypol: ~int, optional
degree with which image is fit along spatial direction (default: 2)
clip: ~float, optional
maximum sigma with which points are allowed to deviate from fit
(default: 3)
fferr: ~float, optional
Flat field uncertainty, used for estimating uncertainties
(default: 0.015)
Returns
-------
Table with columns 'x' (positions on exposed part of CCD) and
extracted fluxes 'f' (with dimension equal to number of extensions)
"""
data = multiext_data(image)
ron = multiext_header_value(image, 'RDNOISE')
arc, chi2, _, ntbadl, ntbadh = extract.fitsky(data, ron=ron, skypol=skypol,
clip=clip, fferr=0.015,
ibadlimit=7)
x = multiext_x_coords(image, 'CCDSEC').squeeze()
arctab = Table([x.T, arc.T, chi2.T], names=('x', 'f', 'chi2'))
arctab.meta['grwlen'] = image[0].header['GRWLEN']
return arctab
def extract_arc(gmos_slice, extracted_spectrum):
arc = np.array(gmos_slice.get_prepared_arc_data())
if extracted_spectrum.meta['nstars'] > 1:
raise ValueError('cannot yet extract arcs for more than one star')
psf_offsets = (extracted_spectrum.meta['tracepos'] +
extracted_spectrum.meta['psfpar'][0])
ix = np.round(psf_offsets).astype(np.int)
ix = np.minimum(np.maximum(ix, 0), arc.shape[1]-1)
if ix.ndim == 1:
ix = ix[np.newaxis,:]
(_, chi2, arcfit, ntbadl, ntbadh) = extract.fitsky(arc, ibadlimit=99)
arc = arcfit[(0,1,2),ix[:,(0,1,2)],:].transpose(2,0,1)
arctab = Table([extracted_spectrum['x'], arc, chi2.T],
names=['x', 'arc', 'chi2'])
arctab.meta['nstars'] = 1
arctab.meta['nbad'] = [ntbadl, ntbadh]
return arctab
def get_arcs(image, skypol=2, clip=3, fferr=0.015, fitrange=None):
"""Fit arc image and create table with one-dimensional arc spectra.
Parameters
----------
image: ~fits.HDUList
prepared image set with one extension for each chip
skypol: ~int, optional
degree with which image is fit along spatial direction (default: 2)
clip: ~float, optional
maximum sigma with which points are allowed to deviate from fit
(default: 3)
fferr: ~float, optional
Flat field uncertainty, used for estimating uncertainties
(default: 0.015)
fitrange: slice, or list of two ~int
slice along slit, or offsets relative to centre, to extract arc from
Returns
-------
Table with columns 'x' (positions on exposed part of CCD) and
extracted fluxes 'f' (with dimension equal to number of extensions)
"""
data = multiext_data(image)
ron = multiext_header_value(image, 'RDNOISE')
arc, chi2, _, ntbadl, ntbadh = extract.fitsky(data,
ron=ron,
skypol=skypol,
clip=clip,
fferr=0.015,
fitrange=fitrange,
ibadlimit=7)
x = multiext_x_coords(image, 'CCDSEC').squeeze()
arctab = Table([x.T, arc.T, chi2.T], names=('x', 'f', 'chi2'))
arctab.meta['grwlen'] = image[0].header['GRWLEN']
return arctab
plt.ion()
plt.clf()
plt.plot(scitab['x'], scitab['f'][:,0,:])
plt.draw()
if __name__ != '__main__':
# get corresponding arc
prep_arc_fits = raw_sci.mask_arc.prepared_fits.fits.fits_data
arcdata = np.array([prep_arc_fits[chip].data[sci_slice, :]
for chip in range(1,4)])
ix = np.round(psf.offsets()+tracepos[0]).astype(np.int)
if len(tracepos) == 1:
ix = ix[np.newaxis,:]
(_, arcchi2, arctest, ntbadl, ntbadh) = extract.fitsky(arcdata)
scitab['a'] = arctest[(0,1,2),ix[:,(0,1,2)],:].transpose(2,0,1)
# plt.plot(dayarc['w'], dayarc['f']-5000.)
# plt.plot(model_wide_slit_arc['w'], model_wide_slit_arc['f']-3500.)
# scitab['w'] = lines.fit(np.arange(3)[np.newaxis,:],
# scitab['x'][:,np.newaxis])[:,np.newaxis,:]
# plt.plot(scitab['w'][:,0,:], scitab['a'][:,0,:])
# **** AM HERE ***
specpos_x = raw_sci.raw_fits.mask.table[slitno]['specpos_x']
# print slitno, specpos_x
# specpos_x xrefbest
# 18: -56. -> 624.
# 19: 124. -> 466.
# 20: -119 -> 679.
