def run_bias_sub(metadata, prev_suffix, curr_suffix,
method='sub', **args):
full_obs_list = get_full_obs_list(metadata)
sci_obs_list = get_sci_obs_list(metadata)
std_obs_list = get_std_obs_list(metadata)
sky_obs_list = get_sky_obs_list(metadata)
for fn in full_obs_list:
in_fn = '%s%s.p%s.fits' % (out_dir, fn, prev_suffix)
out_fn = '%s%s.p%s.fits' % (out_dir, fn, curr_suffix)
if skip_done and os.path.isfile(out_fn):
continue
# figure out which bias to subtract
local_biases = get_associated_calib(metadata,fn, 'bias')
if local_biases:
local_bias_fn = get_associated_calib(metadata,fn,'bias')[0]
local_superbias = '%s%s.fits' % (out_dir, local_bias_fn+'.lsb')
bias_fit_fn = '%s%s.fits' % (out_dir, local_bias_fn+'.lsb_fit')
bias_type = 'local'
else:
bias_fit_fn = superbias_fit_fn
bias_type = 'global'
# subtract it!
print 'Subtracting %s superbias for %s' % (
bias_type, in_fn.split('/')[-1])
if method == 'copy':
pywifes.imcopy(in_fn, out_fn)
else:
pywifes.imarith(in_fn, '-', bias_fit_fn, out_fn,
data_hdu=my_data_hdu)
return
def run_bias_sub(metadata, prev_suffix, curr_suffix,
method='sub', **args):
full_obs_list = get_full_obs_list(metadata)
sci_obs_list = get_sci_obs_list(metadata)
std_obs_list = get_std_obs_list(metadata)
sky_obs_list = get_sky_obs_list(metadata)
for fn in full_obs_list:
in_fn = os.path.join(out_dir, '%s.p%s.fits' % (fn, prev_suffix))
out_fn = os.path.join(out_dir, '%s.p%s.fits' % (fn, curr_suffix))
if skip_done and os.path.isfile(out_fn):
continue
# figure out which bias to subtract
local_biases = get_associated_calib(metadata,fn, 'bias')
if local_biases:
local_bias_fn = get_associated_calib(metadata,fn,'bias')[0]
local_superbias = os.path.join(out_dir, '%s.fits' % (local_bias_fn+'.lsb'))
bias_fit_fn = os.path.join(out_dir, '%s.fits' % (local_bias_fn+'.lsb_fit'))
bias_type = 'local'
else:
bias_fit_fn = superbias_fit_fn
bias_type = 'global'
# subtract it!
print('Subtracting %s superbias for %s'%(bias_type, in_fn.split('/')[-1]))
if method == 'copy':
pywifes.imcopy(in_fn, out_fn)
else:
pywifes.imarith(in_fn, '-', bias_fit_fn, out_fn,
data_hdu=my_data_hdu)
return
# figure out which bias to subtract
local_biases = get_associated_calib(metadata,fn, 'bias')
if local_biases:
local_bias_fn = get_associated_calib(metadata,fn,'bias')[0]
local_superbias = os.path.join(out_dir, '%s.fits' % (local_bias_fn+'.lsb'))
bias_fit_fn = os.path.join(out_dir, '%s.fits' % (local_bias_fn+'.lsb_fit'))
bias_type = 'local'
else:
bias_fit_fn = superbias_fit_fn
bias_type = 'global'
# subtract it!
print('Subtracting %s superbias for %s'%(bias_type, in_fn.split('/')[-1]))
if method == 'copy':
pywifes.imcopy(in_fn, out_fn)
else:
pywifes.imarith(in_fn, '-', bias_fit_fn, out_fn,
data_hdu=my_data_hdu)
return
#------------------------------------------------------
# Generate super-flat
def run_superflat(metadata, prev_suffix, curr_suffix,
source, scale=None, method='median'):
if source == 'dome':
flat_list = [
os.path.join(out_dir, '%s.p%s.fits' % (x, prev_suffix))
for x in metadata['domeflat']]
out_fn = super_dflat_raw
elif source == 'twi':
flat_list = [
os.path.join(out_dir, '%s.p%s.fits' % (x, prev_suffix))
for x in metadata['twiflat']]