def jack_main_func(id_cen, N_bin, n_rbins, cat_ra, cat_dec, cat_z, img_x, img_y, img_file, band_str, sub_img,
sub_pix_cont, sub_sb, J_sub_img, J_sub_pix_cont, J_sub_sb, jack_SB_file, jack_img, jack_cont_arr,
id_cut = False, N_edg = None, id_Z0 = True, z_ref = None, id_S2N = False, S2N = None, id_sub = True, edg_bins = None,
sub_rms = None, J_sub_rms = None, jack_rms_arr = None, N_weit = None):
"""
combining jackknife stacking process, and
save : sub-sample (sub-jack-sample) stacking image, pixel conunt array, surface brightness profiles
id_cen : 0 - stacking by centering on BCGs, 1 - stacking by centering on img center
N_bin : number of jackknife sample
n_rbins : the number of radius bins (int type)
cat_ra, cat_dec, cat_z : catalog information about the stacking sample, ra, dec, z
img_x, img_y : BCG position (in image coordinate)
img_file : img-data name (include file-name structure:'/xxx/xxx/xxx.xxx')
band_str : the band of imgs, 'str' type
sub_img, sub_pix_cont, sub_sb (stacking img, pixel counts array, SB profile):
file name (including patch and file name: '/xxx/xxx/xxx.xxx') of individual sub-sample img stacking result
J_sub_img, J_sub_pix_cont, J_sub_sb (stacking img, pixel counts array, SB profile):
file name (including patch and file name: '/xxx/xxx/xxx.xxx') of jackknife sub-sample img stacking result
jack_SB_file : file name of the final jackknife stacking SB profile ('/xxx/xxx/xxx.xxx')
jack_img : mean of the jackknife stacking img ('/xxx/xxx/xxx.xxx')
jack_cont_arr : mean of the pixel count array ('/xxx/xxx/xxx.xxx')
id_cut : id_cut == True, cut img edge pixels before stacking, id_cut == False, just stacking original size imgs
N_edg : the cut region width, in unit of pixel, only applied when id_cut == True, pixels in this region will be set as
'no flux' contribution pixels (ie. set as np.nan)
id_Z0 : stacking imgs on observation coordinate (id_Z0 = True, and reference redshift is z_ref)
or not (id_Z0 = False, give radius in physical unit, kpc), default is True
id_S2N, S2N : if set S/N limitation for SB profile measure or not, Default is False (no limitation applied).
if id_S2N = True, then measure the SB profile, and in region where S/N is lower than S2N with be
treated as only one radius bins (edg_bins = None), (or measured according edg_bins.)
id_sub : measure and save the SB profiles for sub-samples of not, default is True
sub_rms, J_sub_rms : pixel standard deviation of stacking images (for sub-sample and jackknife sub-sample)
jack_rms_file : the final rms_file (total sample imgs stacking result)
N_weit : if weight the cluster image with satellite number or not ('None')
if N_weit != None, is has the same length as the cat_ra array
"""
print( 'id_Z0', id_Z0 )
print( 'id_cut', id_cut )
print( 'N_edg', N_edg )
print( 'z_ref', z_ref )
zN = len( cat_z )
if N_weit is None:
wit_n_arr = np.ones( zN,)
else:
wit_n_arr = N_weit + 0.
id_arr = np.arange(0, zN, 1)
id_group = id_arr % N_bin
lis_ra, lis_dec, lis_z = [], [], []
lis_x, lis_y = [], []
lis_wn = []
for nn in range( N_bin ):
id_xbin = np.where( id_group == nn )[0]
lis_ra.append( cat_ra[ id_xbin ] )
lis_dec.append( cat_dec[ id_xbin ] )
lis_z.append( cat_z[ id_xbin ] )
lis_x.append( img_x[ id_xbin ] )
lis_y.append( img_y[ id_xbin ] )
lis_wn.append( wit_n_arr[ id_xbin ] )
band_id = band.index( band_str )
## img stacking
for nn in range(N_bin):
set_z = lis_z[ nn ]
set_ra = lis_ra[ nn ]
set_dec = lis_dec[ nn ]
set_x = lis_x[ nn ]
set_y = lis_y[ nn ]
set_wn = lis_wn[ nn ]
sub_img_file = sub_img % nn
sub_cont_file = sub_pix_cont % nn
if sub_rms is not None:
sub_rms_file = sub_rms % nn
else:
sub_rms_file = None
if id_cut == False:
stack_func(img_file, sub_img_file, set_z, set_ra, set_dec, band[ band_id ], set_x, set_y, id_cen,
rms_file = sub_rms_file, pix_con_file = sub_cont_file, N_weit = set_wn )
if id_cut == True:
cut_stack_func(img_file, sub_img_file, set_z, set_ra, set_dec, band[ band_id ], set_x, set_y, id_cen, N_edg,
rms_file = sub_rms_file, pix_con_file = sub_cont_file, N_weit = set_wn )
for nn in range( N_bin ):
id_arry = np.linspace(0, N_bin -1, N_bin)
id_arry = id_arry.astype(int)
jack_id = list(id_arry)
jack_id.remove(jack_id[nn])
jack_id = np.array(jack_id)
jack_img_file = J_sub_img % nn
jack_cont_file = J_sub_pix_cont % nn
weit_aveg_img(jack_id, sub_img, sub_pix_cont, jack_img_file, sum_weit_file = jack_cont_file,)
if sub_rms is not None:
jack_rms_file = J_sub_rms % nn
weit_aveg_img(jack_id, sub_rms, sub_pix_cont, jack_rms_file,)
## SB measurement
if id_sub == True:
## sub-samples
SB_pros_func(sub_img, sub_pix_cont, sub_sb, N_bin, n_rbins, id_Z0, z_ref)
if id_S2N == False:
## jackknife sub-samples
SB_pros_func(J_sub_img, J_sub_pix_cont, J_sub_sb, N_bin, n_rbins, id_Z0, z_ref)
## final jackknife SB profile
tmp_sb = []
tmp_r = []
for nn in range( N_bin ):
with h5py.File(J_sub_sb % nn, 'r') as f:
r_arr = np.array(f['r'])[:-1]
sb_arr = np.array(f['sb'])[:-1]
sb_err = np.array(f['sb_err'])[:-1]
npix = np.array(f['npix'])[:-1]
nratio = np.array(f['nratio'])[:-1]
idvx = npix < 1.
sb_arr[idvx] = np.nan
r_arr[idvx] = np.nan
tmp_sb.append(sb_arr)
tmp_r.append(r_arr)
## only save the sb result in unit " nanomaggies / arcsec^2 "
tt_jk_R, tt_jk_SB, tt_jk_err, lim_R = jack_SB_func(tmp_sb, tmp_r, band[ band_id ], N_bin)[4:]
sb_lim_r = np.ones( len(tt_jk_R) ) * lim_R
with h5py.File(jack_SB_file, 'w') as f:
f['r'] = np.array(tt_jk_R)
f['sb'] = np.array(tt_jk_SB)
f['sb_err'] = np.array(tt_jk_err)
f['lim_r'] = np.array(sb_lim_r)
else:
if id_Z0 == True:
lim_SB_pros_func(J_sub_img, J_sub_pix_cont, J_sub_sb, jack_SB_file, n_rbins, N_bin, S2N, band_id, edg_bins,)
else:
zref_lim_SB_adjust_func(J_sub_img, J_sub_pix_cont, J_sub_sb, jack_SB_file, n_rbins, N_bin, S2N, z_ref, band_id, edg_bins,)
# calculate the directly stacking result( 2D_img, pixel_count array, and rms file [if sub_rms is not None] )
order_id = np.arange(0, N_bin, 1)
order_id = order_id.astype( np.int32 )
weit_aveg_img(order_id, sub_img, sub_pix_cont, jack_img, sum_weit_file = jack_cont_arr,)
if sub_rms is not None:
weit_aveg_img(order_id, sub_rms, sub_pix_cont, jack_rms_arr,)
return
def sat_BG_fast_stack_func(bcg_ra,
bcg_dec,
bcg_z,
sat_ra,
sat_dec,
img_x,
img_y,
img_file,
band_str,
id_cen,
N_bin,
n_rbins,
sub_img,
sub_pix_cont,
sub_sb,
J_sub_img,
J_sub_pix_cont,
J_sub_sb,
jack_SB_file,
jack_img,
jack_cont_arr,
rank,
id_cut=False,
N_edg=None,
id_Z0=True,
z_ref=None,
id_S2N=False,
S2N=None,
edg_bins=None,
id_sub=True,
sub_rms=None,
J_sub_rms=None,
jack_rms_arr=None,
weit_img=None):
"""
combining jackknife stacking process, and
save : sub-sample (sub-jack-sample) stacking image, pixel conunt array, surface brightness profiles
id_cen : 0 - stacking by centering on BCGs, 1 - stacking by centering on img center
N_bin : number of jackknife sample
n_rbins : the number of radius bins (int type)
cat_ra, cat_dec, cat_z : catalog information about the stacking sample, ra, dec, z
sat_ra, sat_dec : satellites' location
img_x, img_y : satellites position (in image coordinate)
img_file : img-data name (include file-name structure:'/xxx/xxx/xxx.xxx')
band_str : the band of imgs, 'str' type
sub_img, sub_pix_cont, sub_sb (stacking img, pixel counts array, SB profile):
file name (including patch and file name: '/xxx/xxx/xxx.xxx') of individual sub-sample img stacking result
J_sub_img, J_sub_pix_cont, J_sub_sb (stacking img, pixel counts array, SB profile):
file name (including patch and file name: '/xxx/xxx/xxx.xxx') of jackknife sub-sample img stacking result
jack_SB_file : file name of the final jackknife stacking SB profile ('/xxx/xxx/xxx.xxx')
jack_img : mean of the jackknife stacking img ('/xxx/xxx/xxx.xxx')
jack_cont_arr : mean of the pixel count array ('/xxx/xxx/xxx.xxx')
id_cut : id_cut == True, cut img edge pixels before stacking, id_cut == False, just stacking original size imgs
N_edg : the cut region width, in unit of pixel, only applied when id_cut == True, pixels in this region will be set as
'no flux' contribution pixels (ie. set as np.nan)
id_Z0 : stacking imgs on observation coordinate (id_Z0 = True, and reference redshift is z_ref)
or not (id_Z0 = False, give radius in physical unit, kpc), default is True
id_S2N, S2N : if set S/N limitation for SB profile measure or not, Default is False (no limitation applied).
if id_S2N = True, then measure the SB profile, and in region where S/N is lower than S2N with be
treated as only one radius bins (edg_bins = None), (or measured according edg_bins.)
id_sub : measure and save the SB profiles for sub-samples of not, default is True
sub_rms, J_sub_rms : pixel standard deviation of stacking images (for sub-sample and jackknife sub-sample)
jack_rms_file : the final rms_file (total sample imgs stacking result)
---------------------------
weit_img : array use to apply weight to each stacked image (can be the masekd image after resampling), default is array
has value of 1 only.
"""
from img_sat_BG_jack_stack import weit_aveg_img
from img_sat_BG_jack_stack import SB_pros_func
from img_sat_BG_jack_stack import aveg_stack_img
from img_sat_BG_stack import cut_stack_func, stack_func
zN = len(bcg_z)
id_arr = np.arange(0, zN, 1)
id_group = id_arr % N_bin
for nn in range(rank, rank + 1):
id_xbin = np.where(id_group == nn)[0]
set_z = bcg_z[id_xbin]
set_ra = bcg_ra[id_xbin]
set_dec = bcg_dec[id_xbin]
set_s_ra = sat_ra[id_xbin]
set_s_dec = sat_dec[id_xbin]
set_x = img_x[id_xbin]
set_y = img_y[id_xbin]
sub_img_file = sub_img % nn
sub_cont_file = sub_pix_cont % nn
if sub_rms is not None:
sub_rms_file = sub_rms % nn
else:
sub_rms_file = None
if id_cut == False:
stack_func(img_file,
sub_img_file,
set_z,
set_ra,
set_dec,
band_str,
set_s_ra,
set_s_dec,
set_x,
set_y,
id_cen,
rms_file=sub_rms_file,
pix_con_file=sub_cont_file,
weit_img=weit_img)
if id_cut == True:
cut_stack_func(img_file,
sub_img_file,
set_z,
set_ra,
set_dec,
band_str,
set_s_ra,
set_s_dec,
set_x,
set_y,
id_cen,
N_edg,
rms_file=sub_rms_file,
pix_con_file=sub_cont_file,
weit_img=weit_img)
commd.Barrier()
for nn in range(rank, rank + 1):
id_arry = np.linspace(0, N_bin - 1, N_bin)
id_arry = id_arry.astype(int)
jack_id = list(id_arry)
jack_id.remove(jack_id[nn])
jack_id = np.array(jack_id)
jack_img_file = J_sub_img % nn
jack_cont_file = J_sub_pix_cont % nn
weit_aveg_img(
jack_id,
sub_img,
sub_pix_cont,
jack_img_file,
sum_weit_file=jack_cont_file,
)
if sub_rms is not None:
jack_rms_file = J_sub_rms % nn
weit_aveg_img(
jack_id,
sub_rms,
sub_pix_cont,
jack_rms_file,
)
commd.Barrier()
if rank == 0:
## SB measurement
if id_sub == True:
## sub-samples
SB_pros_func(sub_img, sub_pix_cont, sub_sb, N_bin, n_rbins, id_Z0,
z_ref)
if id_S2N == False:
## jackknife sub-samples
SB_pros_func(J_sub_img, J_sub_pix_cont, J_sub_sb, N_bin, n_rbins,
id_Z0, z_ref)
## final jackknife SB profile
tmp_sb = []
tmp_r = []
for nn in range(N_bin):
with h5py.File(J_sub_sb % nn, 'r') as f:
r_arr = np.array(f['r'])[:-1]
sb_arr = np.array(f['sb'])[:-1]
sb_err = np.array(f['sb_err'])[:-1]
npix = np.array(f['npix'])[:-1]
nratio = np.array(f['nratio'])[:-1]
idvx = npix < 1.
sb_arr[idvx] = np.nan
r_arr[idvx] = np.nan
tmp_sb.append(sb_arr)
tmp_r.append(r_arr)
## only save the sb result in unit " nanomaggies / arcsec^2 "
tt_jk_R, tt_jk_SB, tt_jk_err, lim_R = jack_SB_func(
tmp_sb, tmp_r, band_str, N_bin)[4:]
sb_lim_r = np.ones(len(tt_jk_R)) * lim_R
with h5py.File(jack_SB_file, 'w') as f:
f['r'] = np.array(tt_jk_R)
f['sb'] = np.array(tt_jk_SB)
f['sb_err'] = np.array(tt_jk_err)
f['lim_r'] = np.array(sb_lim_r)
else:
if id_Z0 == True:
lim_SB_pros_func(
J_sub_img,
J_sub_pix_cont,
J_sub_sb,
jack_SB_file,
n_rbins,
N_bin,
S2N,
band_str,
edg_bins,
)
else:
zref_lim_SB_adjust_func(
J_sub_img,
J_sub_pix_cont,
J_sub_sb,
jack_SB_file,
n_rbins,
N_bin,
S2N,
z_ref,
band_str,
edg_bins,
)
# calculate the directly stacking result( 2D_img, pixel_count array, and rms file [if sub_rms is not None] )
order_id = np.arange(0, N_bin, 1)
order_id = order_id.astype(np.int32)
weit_aveg_img(
order_id,
sub_img,
sub_pix_cont,
jack_img,
sum_weit_file=jack_cont_arr,
)
if sub_rms is not None:
weit_aveg_img(order_id, sub_rms, sub_pix_cont, jack_rms_arr)
print('%d-rank, Done!' % rank)
return