def run_wave_soln(metadata, prev_suffix, curr_suffix, **args):
# First, generate the master arc solution, based on generic arcs
wsol_in_fn = '%s%s.p%s.fits' % (out_dir, metadata['arc'][0],
prev_suffix)
print 'Deriving master wavelength solution from %s' % wsol_in_fn.split('/')[-1]
pywifes.derive_wifes_wave_solution(wsol_in_fn, wsol_out_fn,
**args)
# local wave solutions for science or standards
sci_obs_list = get_sci_obs_list(metadata)
std_obs_list = get_std_obs_list(metadata)
for fn in sci_obs_list + std_obs_list:
# Check if the file has a dedicated arc associated with it ...
# Only for Science and Std stars for now (sky not required at this stage)
# (less critical for the rest anyway ...)
# As per Mike I. pull request: if two arcs are present, find a solution
# for both to later interpolate between them.
# Restrict it to the first two arcs in the list (in case the feature is
# being unknowingly used, avoid too much lost time).
local_arcs = get_associated_calib(metadata,fn, 'arc')
if local_arcs :
for i in range(np.min([2,np.size(local_arcs)])):
local_arc_fn = '%s%s.p%s.fits' % (out_dir, local_arcs[i], prev_suffix)
local_wsol_out_fn = '%s%s.wsol.fits' % (out_dir, local_arcs[i])
if os.path.isfile(local_wsol_out_fn):
continue
print 'Deriving local wavelength solution for %s' % local_arcs[i]
pywifes.derive_wifes_wave_solution(local_arc_fn, local_wsol_out_fn,
**args)
return
def run_wave_soln(metadata, prev_suffix, curr_suffix, **args):
# First, generate the master arc solution, based on generic arcs
wsol_in_fn = os.path.join(out_dir, '%s.p%s.fits' % ( metadata['arc'][0],
prev_suffix))
print('Deriving master wavelength solution from %s' % wsol_in_fn.split('/')[-1])
pywifes.derive_wifes_wave_solution(wsol_in_fn, wsol_out_fn,
**args)
# local wave solutions for science or standards
sci_obs_list = get_sci_obs_list(metadata)
std_obs_list = get_std_obs_list(metadata)
for fn in sci_obs_list + std_obs_list:
# Check if the file has a dedicated arc associated with it ...
# Only for Science and Std stars for now (sky not required at this stage)
# (less critical for the rest anyway ...)
# As per Mike I. pull request: if two arcs are present, find a solution
# for both to later interpolate between them.
# Restrict it to the first two arcs in the list (in case the feature is
# being unknowingly used, avoid too much lost time).
local_arcs = get_associated_calib(metadata,fn, 'arc')
if local_arcs :
for i in range(np.min([2,np.size(local_arcs)])):
local_arc_fn = os.path.join(out_dir, '%s.p%s.fits' % (local_arcs[i], prev_suffix))
local_wsol_out_fn = os.path.join(out_dir, '%s.wsol.fits' % (local_arcs[i]))
if os.path.isfile(local_wsol_out_fn):
continue
print('Deriving local wavelength solution for %s' % local_arcs[i])
pywifes.derive_wifes_wave_solution(local_arc_fn, local_wsol_out_fn,
**args)
return
data_hdu=my_data_hdu,
slitlet_def_file=slitlet_fn)
else:
pywifes.wifes_slitlet_mef(in_fn, out_fn, data_hdu=my_data_hdu,
slitlet_def_file=slitlet_fn)
gc.collect()
return
#------------------------------------------------------
# Wavelength solution
def run_wave_soln(metadata, prev_suffix, curr_suffix, **args):
# First, generate the master arc solution, based on generic arcs
wsol_in_fn = os.path.join(out_dir, '%s.p%s.fits' % ( metadata['arc'][0],
prev_suffix))
print('Deriving master wavelength solution from %s' % wsol_in_fn.split('/')[-1])
pywifes.derive_wifes_wave_solution(wsol_in_fn, wsol_out_fn,
**args)
# local wave solutions for science or standards
sci_obs_list = get_sci_obs_list(metadata)
std_obs_list = get_std_obs_list(metadata)
for fn in sci_obs_list + std_obs_list:
# Check if the file has a dedicated arc associated with it ...
# Only for Science and Std stars for now (sky not required at this stage)
# (less critical for the rest anyway ...)
# As per Mike I. pull request: if two arcs are present, find a solution
# for both to later interpolate between them.
# Restrict it to the first two arcs in the list (in case the feature is
# being unknowingly used, avoid too much lost time).
local_arcs = get_associated_calib(metadata,fn, 'arc')
if local_arcs :
for i in range(np.min([2,np.size(local_arcs)])):
local_arc_fn = os.path.join(out_dir, '%s.p%s.fits' % (local_arcs[i], prev_suffix))
