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_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 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_open():
reduce_dir = root_dir + 'reduce/'
stats_dir = root_dir + 'stats/'
os.chdir(data_dir)
fnum1 = [
9, 10, 13, 14, 17, 18, 21, 22, 28, 29, 32, 33, 36, 37, 40, 41, 46, 47,
50, 51
]
fnum2 = [
54, 55, 58, 59, 64, 65, 68, 69, 72, 73, 76, 77, 82, 83, 86, 87, 90, 91,
94, 95
]
fnum3 = [
100, 101, 104, 105, 108, 109, 112, 113, 118, 119, 122, 123, 126, 127,
130, 131
]
fnum4 = [
141, 142, 149, 150, 179, 180, 185, 186, 193, 194, 200, 201, 206, 207,
212, 213, 218, 219
]
fnum = fnum1 + fnum2 + fnum3 + fum4
img_files = ['obj_o{0:03d}_clean.fits'.format(ii) for ii in fnum]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_open.fits')
return
def analyze_stacks():
open_img_files = [
stacks_dir + 'FLD2_stack_open_30.fits',
stacks_dir + 'FLD2_stack_open_60.fits'
]
closed_img_files = [
stacks_dir + 'FLD2_stack_closed_3S_30.fits',
stacks_dir + 'FLD2_stack_closed_3L_30.fits',
stacks_dir + 'FLD2_stack_closed_4_30.fits',
stacks_dir + 'FLD2_stack_closed_3S_60.fits',
stacks_dir + 'FLD2_stack_closed_3L_60.fits',
stacks_dir + 'FLD2_stack_closed_4_60.fits'
]
#Find stars in image
reduce_fli.find_stars(open_img_files,
fwhm=8,
threshold=10,
N_passes=2,
plot_psf_compare=False)
reduce_fli.find_stars(closed_img_files,
fwhm=4,
threshold=10,
N_passes=2,
plot_psf_compare=False)
# Calc stats on all the stacked images
reduce_fli.calc_star_stats(open_img_files + closed_img_files,
output_stats=stats_dir + 'stats_stacks.fits')
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 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_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 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_star_stats_closed():
reduce_dir = '/Volumes/g/lu/data/imaka/2017_01_11/fli/reduce/'
stats_dir = '/Volumes/g/lu/data/imaka/2017_01_11/fli/reduce/stats/'
os.chdir(data_dir)
fnum1 = [
7, 8, 12, 15, 16, 19, 20, 26, 27, 30, 31, 34, 35, 38, 39, 44, 45, 48,
49, 52
]
fnum2 = [
53, 56, 57, 62, 63, 66, 67, 70, 71, 74, 75, 80, 81, 84, 85, 88, 89, 92,
93
]
fnum3 = [
98, 99, 102, 103, 106, 107, 110, 111, 116, 117, 120, 121, 124, 125,
128, 129
]
fnum4 = [
155, 156, 163, 164, 167, 168, 171, 172, 177, 178, 183, 184, 191, 192,
198, 199
]
fnum5 = [204, 205, 210, 211, 216, 217, 222, 223, 226, 227, 230, 231]
fnum = fnum1 + fnum2 + fnum3 + fnum4 + fnum5
img_files = ['obj_c{0:03d}_clean.fits'.format(ii) for ii in fnum]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closed.fits')
return
def calc_star_stats_tt():
reduce_dir = '/Volumes/g/lu/data/imaka/2017_01_11/fli/reduce/'
stats_dir = '/Volumes/g/lu/data/imaka/2017_01_11/fli/reduce/stats/'
os.chdir(data_dir)
fnum = [134, 135, 136, 137, 138, 139, 140]
img_files = ['obj_tt{0:03d}_clean.fits'.format(ii) for ii in fnum]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_tt.fits')
return
def calc_star_stats():
data_dir = root_dir + 'reduce/FLD2/'
stats_dir = root_dir + 'reduce/stats/'
# Open Loop
fnum = [63, 67, 71, 75, 80, 84, 88, 92, 96, 100, 104, 108, 112, 124]
img_files = [
data_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_open.fits')
# Closed Loop
fnum = [64, 68, 72, 76, 77, 81, 85, 89, 93, 97, 101, 105, 109, 113]
img_files = [
data_dir + 'obj{0:04d}_c_clean.fits'.format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closed.fits')
# Closed A
fnum = [65, 69, 73, 78, 82, 86, 90, 94, 98, 102, 106, 110, 114]
img_files = [data_dir + "obj{0:04d}_cA.fits".format(ii) for ii in fnum]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closedA.fits')
# Closed B
fnum = [66, 70, 74, 79, 83, 87, 91, 95, 99, 103, 107, 111, 115]
img_files = [data_dir + "obj{0:04d}_cB.fits".format(ii) for ii in fnum]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closedB.fits')
return
def analyze_stacks():
img_files = [
'west_stack_open.fits', 'west_stack_ttf.fits', 'west_stack_closed.fits'
]
find_stars(img_files,
fwhm=5,
threshold=4,
N_passes=2,
plot_psf_compare=True)
# Calc stats on all the stacked images
reduce_fli.calc_star_stats(img_files, output_stats='stats_stacks.fits')
return
def run_find_stars():
work_dir = '/Users/jlu/work/imaka/pleiades/press_release/'
img_files = [
'west_stack_open.fits', 'west_stack_ttf.fits', 'west_stack_closed.fits'
]
for ii in range(len(img_files)):
img_files[ii] = work_dir + img_files[ii]
reduce_fli.find_stars(img_files, fwhm=5, threshold=4, N_passes=2)
# Calc stats on all the stacked images
reduce_fli.calc_star_stats(img_files,
output_stats=work_dir + 'stats_stacks.fits')
return
def calc_star_stats():
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]
reduce_fli.calc_star_stats(img_files, output_stats='stats_open.fits')
#closed loop
fnum = np.arange(47, 57)
img_files = [reduce_dir + 'obj{0:03d}_clean.fits'.format(ii) for ii in fnum]
reduce_fli.calc_star_stats(img_files, output_stats='stats_closed.fits')
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_open():
reduce_dir = '/Volumes/g/lu/data/imaka/2017_01_10/fli/reduce/pleiades/'
stats_dir = '/Volumes/g/lu/data/imaka/2017_01_10/fli/reduce/stats/'
# Old Naming Scheme
fnum1 = [
10, 11, 14, 15, 18, 19, 24, 25, 28, 29, 34, 35, 38, 39, 40, 41, 44, 45,
50, 51, 55
]
fnum2 = [
60, 61, 64, 65, 68, 69, 74, 75, 78, 79, 82, 83, 88, 89, 92, 93, 96, 97,
102, 103
]
fnum = fnum1 + fnum2
img_files_old_name = [
reduce_dir + 'obj{0:03d}_clean.fits'.format(ii) for ii in fnum
]
#New Naming Scheme
fnum1 = [
106, 107, 110, 111, 116, 117, 120, 121, 124, 125, 130, 131, 134, 135,
138, 139, 144
]
fnum2 = [
145, 148, 149, 152, 153, 158, 159, 162, 163, 166, 167, 172, 173, 176,
177, 180, 181
]
fnum3 = [
184, 185, 190, 191, 194, 195, 198, 199, 202, 203, 208, 209, 212, 213,
216, 217
]
fnum4 = [
220, 221, 226, 227, 230, 231, 235, 238, 239, 244, 245, 248, 249, 252,
253
]
fnum = fnum1 + fnum2 + fnum3 + fnum4
img_files_new_name = [
reduce_dir + 'obj_o{0:03d}_clean.fits'.format(ii) for ii in fnum
]
img_files = img_files_old_name + img_files_new_name
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_open.fits')
return
def calc_star_stats():
# Open Loop
img_files = [
data_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum_o
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_open.fits')
# Closed Loop
img_files = [
data_dir + "obj{0:04d}_c_clean.fits".format(ii) for ii in fnum_C
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closed.fits')
return
def analyze_stacks():
data_dir = root_dir + 'reduce/stacks/'
stats_dir = root_dir + 'reduce/stats/'
img_files = [data_dir + 'FLD2_stack_open.fits',
data_dir + 'FLD2_stack_closed.fits',
data_dir + 'FLD2_stack_closedA.fits',
data_dir + 'FLD2_stack_closedB.fits',
data_dir + 'FLD2_stack_closedC.fits',
data_dir + 'FLD2_stack_closedD.fits']
#Find stars in image
reduce_fli.find_stars(img_files, fwhm=5, threshold=10, N_passes=2, plot_psf_compare=False)
# Calc stats on all the stacked images
reduce_fli.calc_star_stats(img_files, output_stats= stats_dir + 'stats_stacks.fits')
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():
data_dir = root_dir + 'reduce/FLD2_2/'
stats_dir = root_dir + 'reduce/stats/'
util.mkdir(stats_dir)
# Open Loop
fnum = [4, 5, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45]
fnum += [51, 54, 57, 60, 63, 66, 69, 72, 75, 78, 81, 85, 88, 89]
fnum += [92, 95, 99, 103, 107, 110, 113, 116, 119, 122, 125, 128]
fnum += [
131, 134, 137, 140, 143, 146, 149, 152, 155, 158, 161, 164, 167, 170
]
img_files = [
data_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_open.fits')
# Closed Loop
fnum = [96, 100, 104]
img_files = [
data_dir + 'obj{0:04d}_c_clean.fits'.format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closed.fits')
# Closed A
fnum = [6, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47, 50, 53]
fnum += [56, 59, 62, 65, 68, 71, 74, 77, 80, 83, 87, 91, 94, 98, 102, 106]
fnum += [109, 112, 115, 118, 121, 124, 127, 130, 133, 136, 139, 142, 145]
fnum += [148, 151, 154, 157, 160, 163, 166, 169]
img_files = [
data_dir + "obj{0:04d}_cA_clean.fits".format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closedA.fits')
# Closed B
fnum = [10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49, 52, 55, 58]
fnum += [
61, 64, 67, 70, 73, 76, 79, 82, 86, 90, 93, 97, 101, 105, 108, 111
]
fnum += [
114, 117, 120, 123, 126, 129, 132, 135, 138, 141, 144, 147, 150, 153
]
fnum += [156, 159, 162, 165, 168, 171]
img_files = [
data_dir + "obj{0:04d}_cB_clean.fits".format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closedB.fits')
return
def calc_star_stats_closed():
reduce_dir = '/Volumes/g/lu/data/imaka/2017_01_10/fli/reduce/pleiades/'
stats_dir = '/Volumes/g/lu/data/imaka/2017_01_10/fli/reduce/stats/'
# Old Naming Scheme
fnum1 = [
8, 9, 12, 13, 16, 17, 22, 23, 26, 27, 32, 33, 36, 37, 42, 43, 48, 49,
52, 53, 58, 59
]
fnum2 = [
62, 63, 66, 67, 72, 73, 76, 77, 80, 81, 86, 87, 90, 91, 94, 95, 100,
101, 104, 105
]
fnum = fnum1 + fnum2
img_files_old_name = [
reduce_dir + 'obj{0:03d}_clean.fits'.format(ii) for ii in fnum
]
#New Naming Scheme
fnum1 = [
108, 109, 114, 115, 118, 119, 122, 123, 128, 129, 132, 133, 136, 137,
142, 143, 146
]
fnum2 = [
147, 150, 151, 156, 157, 160, 161, 164, 165, 170, 171, 174, 175, 178,
179, 182, 183
]
fnum3 = [
188, 189, 192, 193, 196, 197, 200, 201, 206, 207, 210, 211, 214, 215,
218, 219, 224
]
fnum4 = [225, 228, 229, 232, 233, 236, 237, 242, 243, 246, 247, 250, 251]
fnum = fnum1 + fnum2 + fnum3 + fnum4
img_files_new_name = [
reduce_dir + 'obj_c{0:03d}_clean.fits'.format(ii) for ii in fnum
]
img_files = img_files_old_name + img_files_new_name
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closed.fits')
return
def calc_star_stats_ttf():
reduce_dir = '/Volumes/g/lu/data/imaka/2017_01_11/fli/reduce/'
stats_dir = '/Volumes/g/lu/data/imaka/2017_01_11/fli/reduce/stats/'
os.chdir(data_dir)
fnum1 = [
143, 144, 145, 146, 147, 148, 151, 152, 153, 154, 157, 157, 158, 161,
162, 165, 166
]
fnum2 = [
169, 170, 173, 174, 175, 176, 181, 182, 187, 188, 189, 190, 202, 203,
208, 209, 214
]
fnum3 = [215, 220, 221, 224, 225, 228, 229]
fnum = fnum1 + fnum2 + fnum3
img_files = ['obj_ttf{0:03d}_clean.fits'.format(ii) for ii in fnum]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_ttf.fits')
return
def analyze_stacks():
open_img_files = [stacks_dir + 'orion_stack_open.fits']
closed_img_files = [stacks_dir + 'orion_stack_closed_LS.fits', \
stacks_dir + 'orion_stack_closed_B2.fits']
#Find stars in image
#reduce_fli.find_stars(open_img_files, fwhm=10, threshold=3, 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)
reduce_fli.find_stars(closed_img_files, fwhm=7, threshold=3, 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)
# Calc stats on all the stacked images
reduce_fli.calc_star_stats(open_img_files + closed_img_files,
output_stats=stats_dir + 'stats_stacks.fits')
return
def calc_star_stats():
data_dir = root_dir + 'reduce/FLD2_2/'
stats_dir = root_dir + 'reduce/stats/'
# Open Loop
fnum = [14, 17, 29, 23, 26, 29, 32, 35, 38, 41, 44, 47, 50, 53, 56, 59]
fnum += [62, 65, 68, 71, 74, 77, 80, 83, 86, 89, 92]
img_files = [
data_dir + 'obj{0:04d}_o_clean.fits'.format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_open.fits')
# Closed A
fnum = [12, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49, 52, 55]
fnum += [58, 61, 64, 67, 70, 73, 76, 79, 82, 85, 88, 91]
img_files = [
data_dir + "obj{0:04d}_cA_clean.fits".format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closedA.fits')
# Closed D
fnum = [18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 63]
fnum += [66, 69, 72, 75, 78, 81, 84, 87, 90, 93]
img_files = [
data_dir + "obj{0:04d}_cD_clean.fits".format(ii) for ii in fnum
]
reduce_fli.calc_star_stats(img_files,
output_stats=stats_dir + 'stats_closedD.fits')
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 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]
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():
data_dir = root_dir + 'reduce/pleiades/'
img_files = [
data_dir + 'pleiades_stack_open_r.fits',
data_dir + 'pleiades_stack_tt_r.fits',
data_dir + 'pleiades_stack_closed_r.fits',
data_dir + 'pleiades_stack_open_i.fits',
data_dir + 'pleiades_stack_tt_i.fits',
data_dir + 'pleiades_stack_closed_i.fits'
]
reduce_fli.find_stars(img_files,
fwhm=5,
threshold=10,
N_passes=2,
plot_psf_compare=False)
# Calc stats on all the stacked images
reduce_fli.calc_star_stats(img_files, output_stats='stats_stacks.fits')
return
def analyze_stacks():
open_img_files = [stacks_dir + 'FLD2_stack_open.fits']
closed_img_files = [
stacks_dir + 'FLD2_stack_threeWFSLS_B2_c.fits',
stacks_dir + 'FLD2_stack_threeWFSMean_B2_c.fits',
stacks_dir + 'FLD2_stack_threeWFS_LS.fits'
]
#Find stars in image
reduce_fli.find_stars(open_img_files, fwhm=9, threshold=20, N_passes=2, plot_psf_compare=False, \
mask_flat=flat_dir+"flat.fits", mask_min=0.7, mask_max=1.4, \
left_slice =25, right_slice=0, top_slice=20, bottom_slice=0)
reduce_fli.find_stars(closed_img_files, fwhm=4, threshold=20, N_passes=2, plot_psf_compare=False, \
mask_flat=flat_dir+"flat.fits", mask_min=0.7, mask_max=1.4, \
left_slice =25, right_slice=0, top_slice=20, bottom_slice=0)
# Calc stats on all the stacked images
reduce_fli.calc_star_stats(open_img_files + closed_img_files,
output_stats=stats_dir + 'stats_stacks.fits')
return
