def run_superflat(metadata,
prev_suffix,
curr_suffix,
source,
scale=None,
method='median'):
if source == 'dome':
flat_list = [
'%s%s.p%s.fits' % (out_dir, x, prev_suffix)
for x in metadata['domeflat']
]
out_fn = super_dflat_raw
elif source == 'twi':
flat_list = [
'%s%s.p%s.fits' % (out_dir, x, prev_suffix)
for x in metadata['twiflat']
]
out_fn = super_tflat_raw
else:
raise ValueError, 'Flatfield type not recognized'
print 'Generating co-add %sflat' % source
pywifes.imcombine(flat_list,
out_fn,
data_hdu=my_data_hdu,
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_superflat(metadata, prev_suffix, curr_suffix,
source, scale=None, method='median'):
if source == 'dome':
flat_list = [
'%s%s.p%s.fits' % (out_dir, x, prev_suffix)
for x in metadata['domeflat']]
out_fn = super_dflat_raw
elif source == 'twi':
flat_list = [
'%s%s.p%s.fits' % (out_dir, x, prev_suffix)
for x in metadata['twiflat']]
out_fn = super_tflat_raw
else:
raise ValueError, 'Flatfield type not recognized'
print 'Generating co-add %sflat' % source
pywifes.imcombine(flat_list, out_fn,
data_hdu=my_data_hdu,
scale=scale,
method=method)
return
print('Repairing blue bad pixels for %s' % in_fn.split('/')[-1])
pywifes.repair_blue_bad_pix(in_fn, out_fn, data_hdu=my_data_hdu)
return
#------------------------------------------------------
# Generate super-bias
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'))
