def run_sky_sub(metadata, prev_suffix, curr_suffix, ns=False):
if ns:
run_sky_sub_ns(metadata, prev_suffix, curr_suffix)
else:
# subtract sky frames from science objects
for obs in metadata['sci']:
if len(obs['sky']) > 0:
sky_fn = obs['sky'][0]
sky_proc_fn = os.path.join(out_dir, '%s.p%s.fits' % (sky_fn, prev_suffix))
for fn in obs['sci']:
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))
print('Subtracting sky frame for %s' % in_fn.split('/')[-1])
# subtract scaled sky frame!
pywifes.scaled_imarith_mef(
in_fn, '-', sky_proc_fn, out_fn,
scale='exptime')
else:
for fn in obs['sci']:
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))
print('Copying image %s' % in_fn.split('/')[-1])
# subtract scaled sky frame!
pywifes.imcopy(in_fn, out_fn)
# copy stdstar frames
std_obs_list = get_std_obs_list(metadata)
for fn in std_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
print('Copying standard star image %s' % in_fn.split('/')[-1])
pywifes.imcopy(in_fn, out_fn)
return
def run_obs_coadd(metadata,
prev_suffix,
curr_suffix,
method='sum',
scale=None):
for obs in (metadata['sci'] + metadata['std']):
# if just one, then copy it
if len(obs['sci']) == 1:
fn = obs['sci'][0]
in_fn = '%s%s.p%s.fits' % (out_dir, fn, prev_suffix)
out_fn = '%s%s.p%s.fits' % (out_dir, fn, curr_suffix)
print 'Copying image %s' % in_fn.split('/')[-1]
pywifes.imcopy(in_fn, out_fn)
# coadd sci frames!
else:
in_fn_list = [
'%s%s.p%s.fits' % (out_dir, fn, prev_suffix)
for fn in obs['sci']
]
out_fn = '%s%s.p%s.fits' % (out_dir, obs['sci'][0], curr_suffix)
print 'Coadding images for %s' % in_fn_list[0].split('/')[-1]
pywifes.imcombine_mef(in_fn_list,
out_fn,
scale=scale,
method=method)
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
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_sky_sub(metadata, prev_suffix, curr_suffix, ns=False):
if ns:
run_sky_sub_ns(metadata, prev_suffix, curr_suffix)
else:
# subtract sky frames from science objects
for obs in metadata['sci']:
if len(obs['sky']) > 0:
sky_fn = obs['sky'][0]
sky_proc_fn = '%s%s.p%s.fits' % (out_dir, sky_fn, prev_suffix)
for fn in obs['sci']:
in_fn = '%s%s.p%s.fits' % (out_dir, fn, prev_suffix)
out_fn = '%s%s.p%s.fits' % (out_dir, fn, curr_suffix)
print 'Subtracting sky frame for %s' % in_fn.split('/')[-1]
# subtract scaled sky frame!
pywifes.scaled_imarith_mef(
in_fn, '-', sky_proc_fn, out_fn,
scale='exptime')
else:
for fn in obs['sci']:
in_fn = '%s%s.p%s.fits' % (out_dir, fn, prev_suffix)
out_fn = '%s%s.p%s.fits' % (out_dir, fn, curr_suffix)
print 'Copying image %s' % in_fn.split('/')[-1]
# subtract scaled sky frame!
pywifes.imcopy(in_fn, out_fn)
# copy stdstar frames
std_obs_list = get_std_obs_list(metadata)
for fn in std_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
print 'Copying standard star image %s' % in_fn.split('/')[-1]
pywifes.imcopy(in_fn, out_fn)
return
def run_obs_coadd(metadata,
prev_suffix,
curr_suffix,
method='sum',
scale=None):
for obs in (metadata['sci'] + metadata['std']):
# if just one, then copy it
if len(obs['sci']) == 1:
fn = obs['sci'][0]
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
print('Copying image %s' % in_fn.split('/')[-1])
pywifes.imcopy(in_fn, out_fn)
# coadd sci frames!
else:
in_fn_list = [
os.path.join(out_dir, '%s.p%s.fits' % (fn, prev_suffix))
for fn in obs['sci']
]
out_fn = os.path.join(out_dir,
'%s.p%s.fits' % (obs['sci'][0], curr_suffix))
print('Coadding images for %s' % in_fn_list[0].split('/')[-1])
pywifes.imcombine_mef(in_fn_list,
out_fn,
scale=scale,
method=method)
return
def run_superbias(metadata,
prev_suffix,
curr_suffix,
method='row_med',
**args):
bias_list = [
os.path.join(out_dir, '%s.p%s.fits' % (x, prev_suffix))
for x in metadata['bias']
]
print('Calculating Global Superbias')
pywifes.imcombine(bias_list, superbias_fn, data_hdu=my_data_hdu)
# decide what bias model you will actually subtract - could be just data
if method == 'fit' or method == 'row_med':
# Fit a smart surface to the bias or take the median
# A bit experimental so far ... but you know what you are doing, right ?
pywifes.generate_wifes_bias_fit(superbias_fn,
superbias_fit_fn,
data_hdu=my_data_hdu,
method=method,
**args)
else:
pywifes.imcopy(superbias_fn, superbias_fit_fn)
# generate local superbiases for any science frames
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):
# Find if there is an associated bias (or more ?)
local_biases = get_associated_calib(metadata, fn, 'bias')
if local_biases:
local_bias_fn = get_associated_calib(metadata, fn, 'bias')[0]
print('Calculating Local Superbias for %s' % local_bias_fn)
local_superbias = os.path.join(
out_dir, '%s.fits' % (local_bias_fn + '.lsb'))
local_superbias_fit = os.path.join(
out_dir, '%s.fits' % (local_bias_fn + '.lsb_fit'))
if os.path.isfile(local_superbias_fit):
continue
# step 1 - coadd biases
local_biases_filename = [
os.path.join(out_dir, '%s.p%s.fits' % (x, prev_suffix))
for x in local_biases
]
pywifes.imcombine(local_biases_filename,
local_superbias,
data_hdu=my_data_hdu)
# step 2 - generate fit!
if method == 'fit' or method == 'row_med':
pywifes.generate_wifes_bias_fit(local_superbias,
local_superbias_fit,
data_hdu=my_data_hdu,
method=method,
**args)
else:
pywifes.imcopy(local_superbias, local_superbias_fit)
return
def run_superbias(metadata, prev_suffix, curr_suffix,
method='row_med', **args):
bias_list = [
'%s%s.p%s.fits' % (out_dir, x, prev_suffix)
for x in metadata['bias']]
print 'Calculating Global Superbias'
pywifes.imcombine(bias_list, superbias_fn, data_hdu=my_data_hdu)
# decide what bias model you will actually subtract - could be just data
if method == 'fit' or method == 'row_med':
# Fit a smart surface to the bias or take the median
# A bit experimental so far ... but you know what you are doing, right ?
pywifes.generate_wifes_bias_fit(
superbias_fn, superbias_fit_fn,
data_hdu=my_data_hdu, method=method, **args)
else:
pywifes.imcopy(superbias_fn, superbias_fit_fn)
# generate local superbiases for any science frames
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):
# Find if there is an associated bias (or more ?)
local_biases = get_associated_calib(metadata,fn, 'bias')
if local_biases:
local_bias_fn = get_associated_calib(metadata,fn,'bias')[0]
print 'Calculating Local Superbias for %s' % local_bias_fn
local_superbias = '%s%s.fits' % (out_dir, local_bias_fn+'.lsb')
local_superbias_fit = '%s%s.fits' % (out_dir, local_bias_fn+'.lsb_fit')
if os.path.isfile(local_superbias_fit):
continue
# step 1 - coadd biases
local_biases_filename = [
'%s%s.p%s.fits' % (out_dir, x, prev_suffix)
for x in local_biases]
pywifes.imcombine(local_biases_filename,local_superbias, data_hdu=my_data_hdu)
# step 2 - generate fit!
if method == 'fit' or method == 'row_med':
pywifes.generate_wifes_bias_fit(local_superbias,
local_superbias_fit,
data_hdu=my_data_hdu,
method=method, **args)
else:
pywifes.imcopy(local_superbias, local_superbias_fit)
return
def run_obs_coadd(metadata, prev_suffix, curr_suffix,
method='sum', scale=None):
for obs in (metadata['sci']+metadata['std']):
# if just one, then copy it
if len(obs['sci']) == 1:
fn = obs['sci'][0]
in_fn = '%s%s.p%s.fits' % (out_dir, fn, prev_suffix)
out_fn = '%s%s.p%s.fits' % (out_dir, fn, curr_suffix)
print 'Copying image %s' % in_fn.split('/')[-1]
pywifes.imcopy(in_fn, out_fn)
# coadd sci frames!
else:
in_fn_list = ['%s%s.p%s.fits' % (out_dir, fn, prev_suffix)
for fn in obs['sci']]
out_fn = '%s%s.p%s.fits' % (out_dir, obs['sci'][0], curr_suffix)
print 'Coadding images for %s' % in_fn_list[0].split('/')[-1]
pywifes.imcombine_mef(in_fn_list, out_fn,
scale=scale,
method=method)
return
def run_superbias(metadata, prev_suffix, curr_suffix,
method='row_med', **args):
bias_list = [
os.path.join(out_dir, '%s.p%s.fits' % (x, prev_suffix))
for x in metadata['bias']]
print('Calculating Global Superbias')
pywifes.imcombine(bias_list, superbias_fn, data_hdu=my_data_hdu)
# decide what bias model you will actually subtract - could be just data
if method == 'fit' or method == 'row_med':
# Fit a smart surface to the bias or take the median
# A bit experimental so far ... but you know what you are doing, right ?
pywifes.generate_wifes_bias_fit(
superbias_fn, superbias_fit_fn,
data_hdu=my_data_hdu, method=method, **args)
else:
pywifes.imcopy(superbias_fn, superbias_fit_fn)
# generate local superbiases for any science frames
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):
# Find if there is an associated bias (or more ?)
local_biases = get_associated_calib(metadata,fn, 'bias')
if local_biases:
local_bias_fn = get_associated_calib(metadata,fn,'bias')[0]
print('Calculating Local Superbias for %s' % local_bias_fn)
local_superbias = os.path.join(out_dir, '%s.fits' % (local_bias_fn+'.lsb'))
local_superbias_fit = os.path.join(out_dir, '%s.fits' % (local_bias_fn+'.lsb_fit'))
if os.path.isfile(local_superbias_fit):
continue
# step 1 - coadd biases
local_biases_filename = [