def calc_star_stats():
util.mkdir(stats_dir)
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Catalog: ', img)
img_files = [
out_dir +
'sta{img:03d}{suf:s}_scan_clean.fits'.format(img=ii, suf=suf)
for ii in img
]
stats_file = stats_dir + 'stats_' + key + '.fits'
redu.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file, flux_percent=0.2)
# stats1 = table.Table.read(stats_file)
# redu.add_frame_number_column(stats1)
# stats1.write(stats_file, overwrite=True)
# stats_file_mdp = stats_file.replace('.fits', '_mdp.fits')
# stats2 = table.Table.read(stats_file_mdp)
# redu.add_frame_number_column(stats1)
# stats2.write(stats_file_mdp, overwrite=True)
return
def calc_star_stats():
util.mkdir(stats_dir)
## Loop through all the different data sets
#for key in ['set_name']: ## Single key setup
#for key in ['open_IVBR', 'LS_IVBR', 'docz_IVBR']:
#for key in []:
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Catalog: ', img)
img_files = [out_dir + 'sta{img:03d}{suf:s}_scan_clean.fits'.format(img=ii, suf=suf) for ii in img]
stats_file = stats_dir + 'stats_' + key + '.fits'
redu.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file, flux_percent=0.2)
## DEBUG - single threaded
#key_i = 'open_BRIV'
#fmt = '{dir}sta{img:03d}{suf:s}_scan_clean.fits'
#image_file = fmt.format(dir=out_dir, img=dict_images[key_i][0], suf=dict_suffix[key_i])
#stats_file = f'{stats_dir}stats_{key_i}.fits'
#redu.calc_star_stats([image_file], output_stats=stats_file)
#moffat.fit_moffat_single(image_file,image_file.replace('.fits', '_stars_stats.fits'), 0.2)
return
def analyze_4F_stacks():
## Loop through all the different data sets
#for key in ['set_name']: ## Single key setup
all_images = []
all_starlists = []
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
odr = dict_filt[key]
if odr != 'I':
all_images += [
stacks_dir + f'fld2_stack_{suf}_{odr}.fits' for f in filters
]
all_starlists += [
stacks_dir + f'4F/fld2_stack_{suf}_{odr}_{f}_{odr}_stars.txt'
for f in filters
] # unfortunate naming error
stats_file = stats_dir + 'stats_stacks.fits'
starlist_stats = [
strlst.replace('_stars.txt', '_stars_stats.fits')
for strlst in all_starlists
]
## Calc stats on all the stacked images
#redu.calc_star_stats(all_images, output_stats=stats_file, starlists=all_starlists, fourfilt=True)
print("Starting moffat fitting")
moffat.fit_moffat(all_images,
stats_file,
starlists=starlist_stats,
flux_percent=0.2)
return
def analyze_stacks():
# Loop through all the different data sets and reduce them.
all_images = []
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
fwhm = dict_fwhm[key]
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Images: ', img)
print(' Fwhm: ', str(fwhm))
image_file = [stacks_dir + 'beehive_stack_' + suf + '.fits']
all_images.append(image_file[0])
reduce_fli.find_stars(image_file, fwhm=fwhm, threshold=3, N_passes=2, plot_psf_compare=False,
mask_file=calib_dir + 'mask.fits')
# Calc stats on all the stacked images
out_stats_file = stats_dir + 'stats_stacks.fits'
reduce_fli.calc_star_stats(all_images, output_stats=out_stats_file)
moffat.fit_moffat(all_images, out_stats_file, flux_percent=0.2)
# DEBUG - single threaded
# image_file = stacks_dir + 'beehive_stack_' + dict_suffix['open'] + '.fits'
# redu.find_stars_single(image_file, dict_fwhm['open'], 3, 2, False, calib_dir + 'mask.fits')
return
def calc_star_stats():
util.mkdir(stats_dir)
## Loop through all the different data sets
#for key in ['set_name']: ## Single key setup
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Catalog: ', img)
img_files = [
out_dir +
'sta{img:03d}{suf:s}_scan_clean.fits'.format(img=ii, suf=suf)
for ii in img
]
stats_file = stats_dir + 'stats_' + key + '.fits'
redu.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file, flux_percent=0.2)
## DEBUG - single threaded
# fmt = '{dir}sta{img:03d}{suf:s}_scan_clean.fits'
# image_file = fmt.format(dir=out_dir, img=dict_images['LS_c'][0], suf=dict_suffix['LS_c'][0])
# stats_file = stats_dir + 'stats_LS_c.fits'
# redu.calc_star_stats(image_file, stats_file, flux_percent=0.2)
return
def calc_fourfilt_stats():
# Getting unique suffixes (loop states):
suffixes = list(set(dict_suffix_rot.values()))
# Grouping by suffixes
for suf in suffixes:
# keys with given suffix
keys = [key for key in dict_suffix_rot.keys() if dict_suffix_rot[key] == suf]
# Iterating through filters
for f in filters:
stats_file = stats_dir + f'stats_{suf}_{f}.fits'
img_files = []
starlists = []
for key in keys:
img = dict_images_rot[key]
odr = dict_orders_rot[key]
img_files += [out_dir + 'sta{img:03d}{suf:s}_scan_clean.fits'.format(img=ii, suf=suf) for ii in img]
starlists += [out_dir + 'sta{img:03d}{suf:s}_scan_clean_{f:s}_{odr:s}_stars.txt'.format(img=ii, suf=suf, f=f, odr=odr) for ii in img]
reduce_fli.calc_star_stats(img_files, output_stats=stats_file, starlists=starlists, fourfilt=True)
print("Starting moffat fitting")
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
## SINGLE THREAD
# reduce_fli.calc_star_stats_single(img_files[0], starlists[0], True)
return
def calc_4F_stats():
# Getting unique suffixes (loop states):
suffixes = list(set(dict_suffix_rot.values()))
# Grouping by suffixes
for suf in suffixes:
# keys with given suffix
keys = [key for key in dict_suffix_rot.keys() if dict_suffix_rot[key] == suf]
# Iterating through filters
for f in filters:
stats_file = stats_dir + f'stats_{suf}_{f}.fits'
img_files = []
starlists = []
for key in keys:
img = dict_images_rot[key]
odr = dict_orders_rot[key]
img_files += [out_dir + 'sta{img:03d}{suf:s}_scan_clean.fits'.format(img=ii, suf=suf) for ii in img]
starlists += [out_dir + '4F/sta{img:03d}{suf:s}_scan_clean_{f:s}_{odr:s}_stars.txt'.format(img=ii, suf=suf, f=f, odr=odr) for ii in img]
starlist_stats = [strlst.replace('_stars.txt', '_stars_stats.fits') for strlst in starlists]
print(f"Calc Star_Stats: {suf} \n Filter: {f}")
redu.calc_star_stats(img_files, output_stats=stats_file, starlists=starlists, fourfilt=True)
print("Starting moffat fitting")
moffat.fit_moffat(img_files, stats_file, starlists=starlist_stats)
## DEBBUG: SINGLE THREAD
#print("stars: ", starlists[0])
#print("stats: ", stats_file)
#redu.calc_star_stats_single(img_files[0], starlists[0], True)
#moffat.fit_moffat_single(img_files[0], starlist_stats[0], 0.2)
#break
#break
return
def analyze_stacks():
## Loop through all the different data sets
#for key in ['set_name']: ## Single key setup
all_images = []
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
# o/c loop distinction
fwhm = 15 if re.search('open', key) else 8
thrsh = 5 if re.search('open', key) else 6
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Images: ', img)
print(' Fwhm: ', str(fwhm))
print(' thrsh: ', str(thrsh))
image_file = [stacks_dir + 'beehive_stack_' + suf + '.fits']
all_images.append(image_file[0])
redu.find_stars(image_file, fwhm=fwhm, threshold=thrsh, N_passes=2, plot_psf_compare=False,
mask_file=calib_dir + 'domemask.fits')
## Calc stats on all the stacked images
out_stats_file = stats_dir + 'stats_stacks.fits'
redu.calc_star_stats(all_images, output_stats=out_stats_file)
moffat.fit_moffat(all_images, out_stats_file, flux_percent=0.2)
## DEBUG - single threaded
# image_file = stacks_dir + 'fld2_stack_' + dict_suffix['open'] + '.fits'
# redu.find_stars_single(image_file, dict_fwhm['open'], 3, 2, False, calib_dir + 'mask.fits')
return
def calc_star_stats():
util.mkdir(stats_dir)
## Loop through all datasets
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
bin = 'bin1' if 'bin1' in key else 'bin2' # key should contain bin info
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Catalog: ', img)
img_files = [
out_dir +
'sta{img:03d}{suf:s}_scan_clean.fits'.format(img=ii, suf=suf)
for ii in img
]
stats_file = stats_dir + 'stats_' + key + '.fits'
redu.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file, flux_percent=0.2)
# DEBUG - single threaded
# fmt = '{dir}sta{img:03d}{suf:s}_scan_clean.fits'
# image_file = fmt.format(dir=out_dir, img=dict_images['LS_c'][0], suf=dict_suffix['LS_c'][0])
# stats_file = stats_dir + 'stats_LS_c.fits'
# redu.calc_star_stats(image_file, stats_file, flux_percent=0.2)
return
def calc_mof_stats():
# Open Loop
stats_file = stats_dir + 'stats_open.fits'
img_files = [
out_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum_o
]
moffat.fit_moffat(img_files, stats_file)
# Closed Loop - 3S
stats_file = stats_dir + 'stats_closed_threeWFS_LS.fits'
img_files = [
out_dir + 'obj{0:04d}_threeWFS_LS_clean.fits'.format(ii)
for ii in fnum_threeWFS_LS
]
moffat.fit_moffat(img_files, stats_file)
# Closed Loop - 3L
stats_file = stats_dir + 'stats_closed_threeWFSLS_B2_c.fits'
img_files = [
out_dir + 'obj{0:04d}_threeWFSLS_B2_c_clean.fits'.format(ii)
for ii in fnum_threeWFSLS_B2_c
]
moffat.fit_moffat(img_files, stats_file)
# Closed Loop - 4
stats_file = stats_dir + 'stats_closed_threeWFSMean_B2_c.fits'
img_files = [
out_dir + 'obj{0:04d}_threeWFSMean_B2_c_clean.fits'.format(ii)
for ii in fnum_threeWFSMean_B2_c
]
moffat.fit_moffat(img_files, stats_file)
return
def calc_mof_stats():
# Open Loop
stats_file = stats_dir + 'stats_open.fits'
img_files = [
out_dir + 'obj{0:04d}_o_clean.fits'.format(ii)
for ii in fnum_o_30 + fnum_o_60
]
moffat.fit_moffat(img_files, stats_file)
# Closed Loop - 3S
stats_file = stats_dir + 'stats_closed_3WFS_S.fits'
img_files = [
out_dir + 'obj{0:04d}_threewfs_small_c_clean.fits'.format(ii)
for ii in fnum_c_3S_30 + fnum_c_3S_60
]
moffat.fit_moffat(img_files, stats_file)
# Closed Loop - 3L
stats_file = stats_dir + 'stats_closed_3WFS_L.fits'
img_files = [
out_dir + 'obj{0:04d}_threeWFS_big_c_clean.fits'.format(ii)
for ii in fnum_c_3L_30 + fnum_c_3L_60
]
moffat.fit_moffat(img_files, stats_file)
# Closed Loop - 4
stats_file = stats_dir + 'stats_closed_4WFS.fits'
img_files = [
out_dir + 'obj{0:04d}_fourWFS_c_clean.fits'.format(ii)
for ii in fnum_c_4_30 + fnum_c_4_60
]
moffat.fit_moffat(img_files, stats_file)
return
def calc_mof_stats():
# Open Loop
stats_file = stats_dir + 'stats_open.fits'
img_files = [
data_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum_o
]
moffat.fit_moffat(img_files, stats_file)
# Closed Loop
stats_file = stats_dir + 'stats_closed.fits'
img_files = [
data_dir + 'obj{0:04d}_c_clean.fits'.format(ii) for ii in fnum_c
]
moffat.fit_moffat(img_files, stats_file)
def calc_moffat():
reduce_dir = root_dir + 'reduce/pleiades/'
stats_dir = root_dir + 'reduce/stats/'
#open loop
fnum = np.arange(57, 67)
img_files = [reduce_dir + 'obj{0:03d}_clean.fits'.format(ii) for ii in fnum]
moffat.fit_moffat(img_files, stats_dir + 'stats_open_mdp_alt.fits', x_guess=6, y_guess=8, flux_percent=0.5)
#closed loop
fnum = np.arange(47, 57)
img_files = [reduce_dir + 'obj{0:03d}_clean.fits'.format(ii) for ii in fnum]
moffat.fit_moffat(img_files, stats_dir + 'stats_closed_mdp_alt.fits', x_guess=3.3, y_guess=3.3, flux_percent=0.5)
return
def calc_star_stats():
util.mkdir(stats_dir)
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Catalog: ', img)
img_files = [out_dir + 'sta{img:03d}{suf:s}_scan_clean.fits'.format(img=ii, suf=suf) for ii in img]
stats_file = stats_dir + 'stats_' + key + '.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file, flux_percent=0.2)
return
def calc_star_stats():
util.mkdir(stats_dir)
# Open Loop
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in fnum_o
]
stats_file = stats_dir + 'stats_open.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - B2
img_files = [
out_dir + 'obj{0:03d}LS4WFS_B2_c_scan_clean.fits'.format(ii)
for ii in fnum_c_B2
]
stats_file = stats_dir + 'stats_closed_B2.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - 4W
img_files = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii)
for ii in fnum_c_4W
]
stats_file = stats_dir + 'stats_closed_4W.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - n1
img_files = [
out_dir + 'obj{0:03d}LS4WFS_zc11_c_scan_clean.fits'.format(ii)
for ii in fnum_c_zc
]
stats_file = stats_dir + 'stats_closed_zc.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#tip tilt
img_files = [out_dir + 'obj{0:03d}tip_tilt_scan_clean.fits'.format(ii)\
for ii in fnum_tt]
stats_file = stats_dir + 'stats_closed_tt.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
return
def analyze_stacks():
## EDITED FOR 4F DATA
## Loop through all the different data sets
#for key in ['set_name']: ## Single key setup
all_images = []
for key in dict_suffix.keys():
img = dict_images[key]
suf = dict_suffix[key]
fwhm = dict_fwhm[key]
filt = dict_filt[key]
thrsh = 10
peak_max = 30000
sharp_lim = 0.9
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Images: ', img)
print(' Fwhm: ', str(fwhm))
image_file = [stacks_dir + 'fld2_stack_' + suf + '_' + filt + '.fits'
] ## EDITED LINE
all_images.append(image_file[0])
#redu.find_stars(image_file, fwhm=fwhm, threshold=6, N_passes=2, plot_psf_compare=False, mask_file=calib_dir + f'mask_{filt}.fits')
redu.find_stars(image_file,
fwhm=fwhm,
threshold=thrsh,
N_passes=2,
plot_psf_compare=False,
mask_file=calib_dir + f'mask_{filt}.fits',
peak_max=peak_max,
sharp_lim=sharp_lim)
## Calc stats on all the stacked images
out_stats_file = stats_dir + 'stats_stacks.fits'
redu.calc_star_stats(all_images, output_stats=out_stats_file)
moffat.fit_moffat(all_images, out_stats_file, flux_percent=0.2)
## DEBUG - single threaded
# image_file = stacks_dir + 'fld2_stack_' + dict_suffix['open'] + '.fits'
# redu.find_stars_single(image_file, dict_fwhm['open'], 3, 2, False, calib_dir + 'mask.fits')
return
def calc_star_stats():
util.mkdir(stats_dir)
# Open Loop
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in fnum_o
]
stats_file = stats_dir + 'stats_open.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - threeWFS_LS
img_files = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean.fits'.format(ii)
for ii in fnum_c
]
stats_file = stats_dir + 'stats_closed_threeWFS_LS_c.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
return
def analyze_stacks():
## Loop through all the different data sets
#for key in ['set_name']: ## Single key setup
all_images = []
#for key in dict_suffix.keys():
for key in ['LS_3wfs_r2', 'LS_5wfs_r2', 'open_r2']:
img = dict_images[key]
suf = dict_suffix[key]
fwhm = dict_fwhm[key]
thrsh = 10
peak_max = 30000
sharp_lim = dict_sharp[key]
print('Working on: {1:s} {0:s}'.format(key, suf))
print(' Images: ', img)
print(' Fwhm: ', str(fwhm))
print(' Thresh: ', str(thrsh))
image_file = [stacks_dir + 'beehive_stack_' + suf + '.fits']
all_images.append(image_file[0])
redu.find_stars(image_file,
fwhm=fwhm,
threshold=thrsh,
plot_psf_compare=False,
mask_file=calib_dir + 'domemask.fits',
peak_max=peak_max,
sharp_lim=sharp_lim)
## Calc stats on all the stacked images
out_stats_file = stats_dir + 'stats_stacks.fits'
redu.calc_star_stats(all_images, output_stats=out_stats_file)
moffat.fit_moffat(all_images, out_stats_file, flux_percent=0.2)
## DEBUG - single threaded
# image_file = stacks_dir + 'fld2_stack_' + dict_suffix['open'] + '.fits'
# redu.find_stars_single(image_file, dict_fwhm['open'], 3, 2, False, calib_dir + 'mask.fits')
return
def calc_star_stats():
util.mkdir(stats_dir)
# Open Loop
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in fnum_o
]
stats_file = stats_dir + 'stats_open.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - B2
img_files = [
out_dir + 'obj{0:03d}LS_B2_c_scan_clean.fits'.format(ii)
for ii in fnum_c_B2
]
stats_file = stats_dir + 'stats_closed_B2.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - n7
img_files = [
out_dir + 'obj{0:03d}LSnrej7_zc21_c_scan_clean.fits'.format(ii)
for ii in fnum_c_n7
]
stats_file = stats_dir + 'stats_closed_n7.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - n1
img_files = [
out_dir + 'obj{0:03d}LSnrej1_zc21_c_scan_clean.fits'.format(ii)
for ii in fnum_c_n1
]
stats_file = stats_dir + 'stats_closed_n1.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
return
def calc_star_stats():
util.mkdir(stats_dir)
# Open Loop
img_files = [out_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum_o]
stats_file = stats_dir + 'stats_open.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - threeWFS_LS
img_files = [out_dir + 'obj{0:04d}_threeWFS_LS_clean.fits'.format(ii) for ii in fnum_threeWFS_LS]
stats_file = stats_dir + 'stats_closed_threeWFS_LS.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - threeWFSLS_B2_c
img_files = [out_dir + 'obj{0:04d}_threeWFSLS_B2_c_clean.fits'.format(ii) for ii in fnum_threeWFSLS_B2_c]
stats_file = stats_dir + 'stats_closed_threeWFSLS_B2_c.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - threeWFSMean_B2_c
img_files = [out_dir + 'obj{0:04d}_threeWFSMean_B2_c_clean.fits'.format(ii) for ii in fnum_threeWFSMean_B2_c]
stats_file = stats_dir + 'stats_closed_threeWFSMean_B2_c.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
# Open Loop - 2 filter
img_files = [out_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum_o_2filt]
stats_file = stats_dir + 'stats_open_2filt.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - threeWFS_LS - 2 filter
img_files = [out_dir + 'obj{0:04d}_threeWFS_LS_clean.fits'.format(ii) for ii in fnum_threeWFS_LS_2filt]
stats_file = stats_dir + 'stats_closed_threeWFS_LS_2filt.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - threeWFSLS_B2_c - 2 filter
img_files = [out_dir + 'obj{0:04d}_threeWFSLS_B2_c_clean.fits'.format(ii) for ii in fnum_threeWFSLS_B2_c_2filt]
stats_file = stats_dir + 'stats_closed_threeWFSLS_B2_c_2filt.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - threeWFSMean_B2_c - 2 filter
img_files = [out_dir + 'obj{0:04d}_threeWFSMean_B2_c_clean.fits'.format(ii) for ii in fnum_threeWFSMean_B2_c_2filt]
stats_file = stats_dir + 'stats_closed_threeWFSMean_B2_c_2filt.fits'
reduce_fli.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
return
def calc_star_stats_fourfilt():
util.mkdir(stats_dir)
# Open Loop
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in fnum_o
]
# R
stats_file = stats_dir + 'stats_open_R.fits'
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_R_stars.txt'.format(ii)
for ii in fnum_o
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='R')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# V
stats_file = stats_dir + 'stats_open_V.fits'
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_V_stars.txt'.format(ii)
for ii in fnum_o
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='V')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# B
stats_file = stats_dir + 'stats_open_B.fits'
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_B_stars.txt'.format(ii)
for ii in fnum_o
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='B')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# I
stats_file = stats_dir + 'stats_open_I.fits'
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_I_stars.txt'.format(ii)
for ii in fnum_o
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='I')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
#Closed Loop - threeWFS_LS
img_files = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean.fits'.format(ii)
for ii in fnum_c
]
# R
stats_file = stats_dir + 'stats_closed_R.fits'
starlists = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean_R_stars.txt'.format(ii)
for ii in fnum_c
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='R')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# V
stats_file = stats_dir + 'stats_closed_V.fits'
starlists = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean_V_stars.txt'.format(ii)
for ii in fnum_c
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='V')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# B
stats_file = stats_dir + 'stats_closed_B.fits'
starlists = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean_B_stars.txt'.format(ii)
for ii in fnum_c
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='B')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# I
stats_file = stats_dir + 'stats_closed_I.fits'
starlists = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean_I_stars.txt'.format(ii)
for ii in fnum_c
]
reduce_STA.calc_star_stats(img_files, output_stats=stats_file, filt='I')
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
return
reduce_fli.find_stars(img_files, fwhm=7, threshold=15, N_passes=2, plot_psf_compare=False, \
mask_flat=flat_dir+"flat.fits", mask_min=0.8, mask_max=1.4, \
left_slice =20, right_slice=20, top_slice=25, bottom_slice=25)
return
def calc_star_stats():
util.mkdir(stats_dir)
# Open Loop
img_files = [out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in fnum_o]
stats_file = stats_dir + 'stats_open.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - 4W
<<<<<<< HEAD
img_files = [out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii) for ii in fnum_c_4W]
=======
<<<<<<< HEAD
img_files = [out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii) for ii in fnum_c_4W]
=======
img_files = [out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii) for ii in fnum_c_4@]
>>>>>>> 9d0fe2dfc407d195227cb24b248c5a5cff886914
>>>>>>> 89ee2c675e89b7f849298b2cbbd5d3a37068a09f
stats_file = stats_dir + 'stats_closed_4W.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
#Closed Loop - B2
img_files = [out_dir + 'obj{0:03d}LS4WFS_B2_c_scan_clean.fits'.format(ii) for ii in fnum_c_B2]
def calc_fourfilt_stats():
# Open Loop - B
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in rot_o_4
]
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_B_BVIR_stars.txt'.format(ii)
for ii in rot_1_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_B_VIRB_stars.txt'.format(ii)
for ii in rot_2_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_B_IRBV_stars.txt'.format(ii)
for ii in rot_3_o
]
stats_file = stats_dir + 'stats_open_B.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# Open Loop - V
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in rot_o_4
]
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_V_BVIR_stars.txt'.format(ii)
for ii in rot_1_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_V_VIRB_stars.txt'.format(ii)
for ii in rot_2_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_V_IRBV_stars.txt'.format(ii)
for ii in rot_3_o
]
stats_file = stats_dir + 'stats_open_V.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# Open Loop - R
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in rot_o_4
]
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_R_BVIR_stars.txt'.format(ii)
for ii in rot_1_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_R_VIRB_stars.txt'.format(ii)
for ii in rot_2_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_R_IRBV_stars.txt'.format(ii)
for ii in rot_3_o
]
stats_file = stats_dir + 'stats_open_R.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# Open Loop - I
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in rot_o_4
]
starlists = [
out_dir + 'obj{0:03d}_o_scan_clean_I_BVIR_stars.txt'.format(ii)
for ii in rot_1_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_I_VIRB_stars.txt'.format(ii)
for ii in rot_2_o
]
starlists += [
out_dir + 'obj{0:03d}_o_scan_clean_I_IRBV_stars.txt'.format(ii)
for ii in rot_3_o
]
stats_file = stats_dir + 'stats_open_I.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# Closed Loop - B
img_files = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii)
for ii in rot_c_4
]
starlists = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_B_BVIR_stars.txt'.format(ii)
for ii in rot_1_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_B_VIRB_stars.txt'.format(ii)
for ii in rot_2_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_B_IRBV_stars.txt'.format(ii)
for ii in rot_3_c
]
stats_file = stats_dir + 'stats_closed_B.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# Closed Loop - V
img_files = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii)
for ii in rot_c_4
]
starlists = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_V_BVIR_stars.txt'.format(ii)
for ii in rot_1_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_V_VIRB_stars.txt'.format(ii)
for ii in rot_2_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_V_IRBV_stars.txt'.format(ii)
for ii in rot_3_c
]
stats_file = stats_dir + 'stats_closed_V.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# Closed Loop - R
img_files = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii)
for ii in rot_c_4
]
starlists = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_R_BVIR_stars.txt'.format(ii)
for ii in rot_1_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_R_VIRB_stars.txt'.format(ii)
for ii in rot_2_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_R_IRBV_stars.txt'.format(ii)
for ii in rot_3_c
]
stats_file = stats_dir + 'stats_closed_R.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
# Closed Loop - I
img_files = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean.fits'.format(ii)
for ii in rot_c_4
]
starlists = [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_I_BVIR_stars.txt'.format(ii)
for ii in rot_1_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_I_VIRB_stars.txt'.format(ii)
for ii in rot_2_c
]
starlists += [
out_dir + 'obj{0:03d}LS4WFS_c_scan_clean_I_IRBV_stars.txt'.format(ii)
for ii in rot_3_c
]
stats_file = stats_dir + 'stats_closed_I.fits'
reduce_STA.calc_star_stats(img_files,
output_stats=stats_file,
starlists=starlists,
fourfilt=True)
moffat.fit_moffat(img_files, stats_file, starlists=starlists)
return
def calc_star_stats():
util.mkdir(stats_dir)
# Open Loop
img_files = [
out_dir + 'obj{0:03d}_o_scan_clean.fits'.format(ii) for ii in fnum_o
]
stats_file = stats_dir + 'stats_open.fits'
#reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
#moffat.fit_moffat(img_files, stats_file)
# EXPERIMENT 1:
# Closed - LS_c
img_files = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean.fits'.format(ii)
for ii in fnum_LS_c
]
stats_file = stats_dir + 'stats_closed_LS.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
# Closed - LS_B2_c
img_files = [
out_dir + 'obj{0:03d}threeWFSLS_B2_c_scan_clean.fits'.format(ii)
for ii in fnum_LS_B2_c
]
stats_file = stats_dir + 'stats_closed_LS_B2.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
# Closed - Mean_B2_c
img_files = [
out_dir + 'obj{0:03d}threeWFSMean_B2_c_scan_clean.fits'.format(ii)
for ii in fnum_Mean_B2_c
]
stats_file = stats_dir + 'stats_closed_Mean_B2.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
# EXPERIMENT 2
# Closed - nrej1
img_files = [
out_dir + 'obj{0:03d}threeWFSLS_nrej1_c_scan_clean.fits'.format(ii)
for ii in fnum_nrej1
]
stats_file = stats_dir + 'stats_closed_nrej1.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
# Closed - nrej4
img_files = [
out_dir + 'obj{0:03d}threeWFSLS_nrej4_c_scan_clean.fits'.format(ii)
for ii in fnum_nrej4
]
stats_file = stats_dir + 'stats_closed_nrej4.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
# Closed - nrej7
img_files = [
out_dir + 'obj{0:03d}threeWFS_LS_c_scan_clean.fits'.format(ii)
for ii in fnum_nrej7
]
stats_file = stats_dir + 'stats_closed_nrej7.fits'
reduce_STA.calc_star_stats(img_files, output_stats=stats_file)
moffat.fit_moffat(img_files, stats_file)
return