def echelle_2dfit(self, wv_calib, debug=False, skip_QA=False):
"""
Evaluate 2-d wavelength solution for echelle data. Unpacks
wv_calib for slits to be input into arc.fit2darc
Args:
wv_calib (dict): Wavelength calibration
debug (bool, optional): Show debugging info
skip_QA (bool, optional): Skip QA
Returns:
dict: dictionary containing information from 2-d fit
"""
msgs.info('Fitting 2-d wavelength solution for echelle....')
all_wave = np.array([], dtype=float)
all_pixel = np.array([], dtype=float)
all_order = np.array([], dtype=float)
# Obtain a list of good slits
ok_mask = np.where(~self.maskslits)[0]
nspec = self.msarc.shape[0]
for islit in wv_calib.keys():
if int(islit) not in ok_mask:
continue
iorder = self.spectrograph.slit2order(islit)
mask_now = wv_calib[islit]['mask']
all_wave = np.append(all_wave,
wv_calib[islit]['wave_fit'][mask_now])
all_pixel = np.append(all_pixel,
wv_calib[islit]['pixel_fit'][mask_now])
all_order = np.append(
all_order,
np.full_like(wv_calib[islit]['pixel_fit'][mask_now],
float(iorder)))
fit2d_dict = arc.fit2darc(all_wave,
all_pixel,
all_order,
nspec,
nspec_coeff=self.par['ech_nspec_coeff'],
norder_coeff=self.par['ech_norder_coeff'],
sigrej=self.par['ech_sigrej'],
debug=debug)
self.steps.append(inspect.stack()[0][3])
# QA
if not skip_QA:
outfile_global = qa.set_qa_filename(self.master_key,
'arc_fit2d_global_qa',
out_dir=self.qa_path)
arc.fit2darc_global_qa(fit2d_dict, outfile=outfile_global)
outfile_orders = qa.set_qa_filename(self.master_key,
'arc_fit2d_orders_qa',
out_dir=self.qa_path)
arc.fit2darc_orders_qa(fit2d_dict, outfile=outfile_orders)
return fit2d_dict
def echelle_2dfit(self, wv_calib, debug=False, skip_QA=False):
"""
Fit a two-dimensional wavelength solution for echelle data.
Primarily a wrapper for :func:`pypeit.core.arc.fit2darc`,
using data unpacked from the ``wv_calib`` dictionary.
Args:
wv_calib (:obj:`dict`):
Wavelength calibration dictionary. See ??
debug (:obj:`bool`, optional):
Show debugging info
skip_QA (:obj:`bool`, optional):
Flag to skip construction of the nominal QA plots.
Returns:
:obj:`dict`: Dictionary containing information from 2-d
fit.
"""
if self.spectrograph.pypeline != 'Echelle':
msgs.error(
'Cannot execute echelle_2dfit for a non-echelle spectrograph.')
msgs.info('Fitting 2-d wavelength solution for echelle....')
all_wave = np.array([], dtype=float)
all_pixel = np.array([], dtype=float)
all_order = np.array([], dtype=float)
# Obtain a list of good slits
ok_mask_idx = np.where(np.invert(self.wvc_bpm))[0]
ok_mask_order = self.slits.slitord_id[ok_mask_idx]
nspec = self.msarc.image.shape[0]
for iorder in wv_calib.keys(): # Spatial based
if int(iorder) not in ok_mask_order:
continue
#try:
# iorder, iindx = self.spectrograph.slit2order(self.spat_coo[self.slits.spatid_to_zero(int(islit))])
#except:
# embed()
mask_now = wv_calib[iorder]['mask']
all_wave = np.append(all_wave,
wv_calib[iorder]['wave_fit'][mask_now])
all_pixel = np.append(all_pixel,
wv_calib[iorder]['pixel_fit'][mask_now])
all_order = np.append(
all_order,
np.full_like(wv_calib[iorder]['pixel_fit'][mask_now],
float(iorder)))
# Fit
fit2d_dict = arc.fit2darc(all_wave,
all_pixel,
all_order,
nspec,
nspec_coeff=self.par['ech_nspec_coeff'],
norder_coeff=self.par['ech_norder_coeff'],
sigrej=self.par['ech_sigrej'],
debug=debug)
self.steps.append(inspect.stack()[0][3])
# QA
if not skip_QA:
outfile_global = qa.set_qa_filename(self.master_key,
'arc_fit2d_global_qa',
out_dir=self.qa_path)
arc.fit2darc_global_qa(fit2d_dict, outfile=outfile_global)
outfile_orders = qa.set_qa_filename(self.master_key,
'arc_fit2d_orders_qa',
out_dir=self.qa_path)
arc.fit2darc_orders_qa(fit2d_dict, outfile=outfile_orders)
return fit2d_dict
def echelle_2dfit(self, wv_calib, debug=False, skip_QA=False):
"""
Fit a two-dimensional wavelength solution for echelle data.
Primarily a wrapper for :func:`pypeit.core.arc.fit2darc`,
using data unpacked from the ``wv_calib`` dictionary.
Args:
wv_calib (:class:`pypeit.wavecalib.WaveCalib`):
Wavelength calibration object
debug (:obj:`bool`, optional):
Show debugging info
skip_QA (:obj:`bool`, optional):
Flag to skip construction of the nominal QA plots.
Returns:
:class:`pypeit.fitting.PypeItFit`: object containing information from 2-d fit.
"""
if self.spectrograph.pypeline != 'Echelle':
msgs.error(
'Cannot execute echelle_2dfit for a non-echelle spectrograph.')
msgs.info('Fitting 2-d wavelength solution for echelle....')
all_wave = np.array([], dtype=float)
all_pixel = np.array([], dtype=float)
all_order = np.array([], dtype=float)
# Obtain a list of good slits
ok_mask_idx = np.where(np.invert(self.wvc_bpm))[0]
ok_mask_order = self.slits.slitord_id[ok_mask_idx]
nspec = self.msarc.image.shape[0]
# Loop
for ii in range(wv_calib.nslits):
iorder = self.slits.ech_order[ii]
if iorder not in ok_mask_order:
continue
# Slurp
mask_now = wv_calib.wv_fits[ii].pypeitfit.bool_gpm
all_wave = np.append(all_wave,
wv_calib.wv_fits[ii]['wave_fit'][mask_now])
all_pixel = np.append(all_pixel,
wv_calib.wv_fits[ii]['pixel_fit'][mask_now])
all_order = np.append(
all_order,
np.full_like(wv_calib.wv_fits[ii]['pixel_fit'][mask_now],
float(iorder)))
# Fit
# THIS NEEDS TO BE DEVELOPED
fit2d = arc.fit2darc(all_wave,
all_pixel,
all_order,
nspec,
nspec_coeff=self.par['ech_nspec_coeff'],
norder_coeff=self.par['ech_norder_coeff'],
sigrej=self.par['ech_sigrej'],
debug=debug)
self.steps.append(inspect.stack()[0][3])
# QA
# TODO -- TURN QA BACK ON!
#skip_QA = True
if not skip_QA:
outfile_global = qa.set_qa_filename(self.master_key,
'arc_fit2d_global_qa',
out_dir=self.qa_path)
arc.fit2darc_global_qa(fit2d, nspec, outfile=outfile_global)
outfile_orders = qa.set_qa_filename(self.master_key,
'arc_fit2d_orders_qa',
out_dir=self.qa_path)
arc.fit2darc_orders_qa(fit2d, nspec, outfile=outfile_orders)
return fit2d
nspec =int(nires_data[str(0)]['xnorm'])
for ii in nires_data.keys():
if ii != 'arcparam' and ii != 'steps':
all_pix_pypeit = np.concatenate((all_pix_pypeit,
np.array(nires_data[ii]['xfit'])*(np.array(nires_data[ii]['xnorm'])-1.)))
t_tmp = np.full_like(np.array(nires_data[ii]['xfit']), np.float(nires_data[ii]['norder']))
# t_tmp = np.full_like(np.array(nires_data[ii]['xfit']), np.float(ii)+1.)
t_pypeit = np.concatenate((t_pypeit, t_tmp))
all_wv_pypeit = np.concatenate((all_wv_pypeit,
np.array(nires_data[ii]['yfit'])))
###########################################################################
###########################################################################
###########################################################################
# Run the actual fit:
fit_dict = arc.fit2darc(all_wv_pypeit,
all_pix_pypeit,
t_pypeit,
nspec,
debug=debug)
# Plots
arc.fit2darc_global_qa(fit_dict)
arc.fit2darc_orders_qa(fit_dict)
