def extinction_correct(band_id, sub_z, sub_ra, sub_dec): tot_N = len(sub_z) ii = np.int(band_id) for jj in range(tot_N): ra_g = sub_ra[jj] dec_g = sub_dec[jj] z_g = sub_z[jj] file = dfile + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (band[ii], ra_g, dec_g, z_g) data = fits.open(file) img = data[0].data head_inf = data[0].header wcs = awc.WCS(head_inf) x0 = np.linspace(0, img.shape[1] - 1, img.shape[1]) y0 = np.linspace(0, img.shape[0] - 1, img.shape[0]) img_grid = np.array(np.meshgrid(x0, y0)) ra_img, dec_img = wcs.all_pix2world(img_grid[0,:], img_grid[1,:], 1) pos = SkyCoord(ra_img, dec_img, frame = 'fk5', unit = 'deg') BEV = sfd(pos) Av = Rv * BEV Al = A_wave(l_wave[ii], Rv) * Av correct_img = img * 10**(Al / 2.5) ## save the extinction correct data hdu = fits.PrimaryHDU() hdu.data = correct_img hdu.header = head_inf hdu.writeto(tmp + 'test/Extinction_correct_%s_ra%.3f_dec%.3f_z%.3f.fits'%(band[ii], ra_g, dec_g, z_g), overwrite = True)
def single_map_func(band_str, ra_lis, dec_lis, z_lis, img_file, out_file):
l_x0 = np.linspace(0, 2047, 2048)
l_y0 = np.linspace(0, 1488, 1489)
img_grid = np.array(np.meshgrid(l_x0, l_y0))
Ns = len(z_lis)
for jj in range(Ns):
ra_g, dec_g, z_g = ra_lis[jj], dec_lis[jj], z_lis[jj]
img_data = fits.open(img_file % (band_str, ra_g, dec_g, z_g), )
img_arr = img_data[0].data
header = img_data[0].header
wcs_lis = awc.WCS(header)
ra_img, dec_img = wcs_lis.all_pix2world(img_grid[0, :], img_grid[1, :],
0)
pos_img = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
p_EBV = E_map.ebv(pos_img)
A_v = Rv * p_EBV
A_l = A_wave(l_wave[kk], Rv) * A_v
## save the extinction correct data
hdu = fits.PrimaryHDU()
hdu.data = A_l
hdu.header = header
hdu.writeto(
out_file % (band_str, ra_g, dec_g, z_g),
overwrite=True,
)
return
def mask_A(band_id, z_set, ra_set, dec_set):
kk = np.int(band_id)
Nz = len(z_set)
param_A = 'default_mask_A.sex'
out_cat = 'default_mask_A.param'
out_load_A = '/mnt/ddnfs/data_users/cxkttwl/PC/A_mask_%d_cpus.cat' % rank
## size test
r_res = 1. # 2.8 for larger R setting
for q in range(Nz):
z_g = z_set[q]
ra_g = ra_set[q]
dec_g = dec_set[q]
print('Now band is', band[kk])
print('*' * 20)
pro_f = d_file + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (
band[kk], ra_g, dec_g, z_g)
data_f = fits.open(pro_f)
img = data_f[0].data
head_inf = data_f[0].header
wcs = awc.WCS(head_inf)
cx_BCG, cy_BCG = wcs.all_world2pix(ra_g * U.deg, dec_g * U.deg, 1)
R_ph = rad2asec / (Test_model.angular_diameter_distance(z_g).value)
R_p = R_ph / pixel
x0 = np.linspace(0, img.shape[1] - 1, img.shape[1])
y0 = np.linspace(0, img.shape[0] - 1, img.shape[0])
img_grid = np.array(np.meshgrid(x0, y0))
ra_img, dec_img = wcs.all_pix2world(img_grid[0, :], img_grid[1, :], 1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
BEV = sfd(pos)
Av = Rv * BEV * 0.86
Al = A_wave(l_wave[kk], Rv) * Av
img = img * 10**(Al / 2.5)
hdu = fits.PrimaryHDU()
hdu.data = img
hdu.header = head_inf
hdu.writeto('/mnt/ddnfs/data_users/cxkttwl/PC/source_data_%d.fits' %
rank,
overwrite=True)
file_source = '/mnt/ddnfs/data_users/cxkttwl/PC/source_data_%d.fits' % rank
cmd = 'sex ' + file_source + ' -c %s -CATALOG_NAME %s -PARAMETERS_NAME %s' % (
param_A, out_load_A, out_cat)
a = subpro.Popen(cmd, shell=True)
a.wait()
source = asc.read(out_load_A)
Numb = np.array(source['NUMBER'][-1])
A = np.array(source['A_IMAGE'])
B = np.array(source['B_IMAGE'])
theta = np.array(source['THETA_IMAGE'])
cx = np.array(source['X_IMAGE']) - 1
cy = np.array(source['Y_IMAGE']) - 1
p_type = np.array(source['CLASS_STAR'])
Kron = 6 * r_res # iso_radius set as 3 times rms (2.8 from size test)
a = Kron * A
b = Kron * B
mask = load + 'bright_star_dr12/source_SQL_Z%.3f_ra%.3f_dec%.3f.txt' % (
z_g, ra_g, dec_g)
cat = pds.read_csv(mask, skiprows=1)
set_ra = np.array(cat['ra'])
set_dec = np.array(cat['dec'])
set_mag = np.array(cat['r'])
OBJ = np.array(cat['type'])
xt = cat['Column1']
tau = 10 * r_res # the mask size set as 10 * FWHM from dr12
set_A = np.array([
cat['psffwhm_r'], cat['psffwhm_g'], cat['psffwhm_i']
]) * tau / pixel
set_B = np.array([
cat['psffwhm_r'], cat['psffwhm_g'], cat['psffwhm_i']
]) * tau / pixel
set_chi = np.zeros(set_A.shape[1], dtype=np.float)
lln = np.array([
len(set_A[:, ll][set_A[:, ll] > 0]) for ll in range(set_A.shape[1])
])
lr_iso = np.array(
[np.max(set_A[:, ll]) for ll in range(set_A.shape[1])])
sr_iso = np.array(
[np.max(set_B[:, ll]) for ll in range(set_B.shape[1])])
# bright stars
x, y = wcs.all_world2pix(set_ra * U.deg, set_dec * U.deg, 1)
ia = (x >= 0) & (x <= img.shape[1])
ib = (y >= 0) & (y <= img.shape[0])
ie = (set_mag <= 20)
iq = lln >= 2
ig = OBJ == 6
ic = (ia & ib & ie & ig & iq)
sub_x0 = x[ic]
sub_y0 = y[ic]
sub_A0 = lr_iso[ic]
sub_B0 = sr_iso[ic]
sub_chi0 = set_chi[ic]
# saturated source(may not stars)
xa = ['SATURATED' in qq for qq in xt]
xv = np.array(xa)
idx = xv == True
ipx = (idx & ia & ib)
sub_x2 = x[ipx]
sub_y2 = y[ipx]
sub_A2 = 3 * lr_iso[ipx]
sub_B2 = 3 * sr_iso[ipx]
sub_chi2 = set_chi[ipx]
comx = np.r_[sub_x0[sub_A0 > 0], sub_x2[sub_A2 > 0]]
comy = np.r_[sub_y0[sub_A0 > 0], sub_y2[sub_A2 > 0]]
Lr = np.r_[sub_A0[sub_A0 > 0], sub_A2[sub_A2 > 0]]
Sr = np.r_[sub_B0[sub_A0 > 0], sub_B2[sub_A2 > 0]]
phi = np.r_[sub_chi0[sub_A0 > 0], sub_chi2[sub_A2 > 0]]
cx = np.r_[cx, comx]
cy = np.r_[cy, comy]
a = np.r_[a, Lr]
b = np.r_[b, Sr]
theta = np.r_[theta, phi]
Numb = Numb + len(comx)
mask_A = np.ones((img.shape[0], img.shape[1]), dtype=np.float)
ox = np.linspace(0, img.shape[1] - 1, img.shape[1])
oy = np.linspace(0, img.shape[0] - 1, img.shape[0])
basic_coord = np.array(np.meshgrid(ox, oy))
major = a / 2
minor = b / 2 # set the star mask based on the major and minor radius
senior = np.sqrt(major**2 - minor**2)
tdr = np.sqrt((cx - cx_BCG)**2 + (cy - cy_BCG)**2)
dr00 = np.where(tdr == np.min(tdr))[0]
for k in range(Numb):
xc = cx[k]
yc = cy[k]
lr = major[k]
sr = minor[k]
cr = senior[k]
chi = theta[k] * np.pi / 180
set_r = np.int(np.ceil(1.2 * lr))
la0 = np.max([np.int(xc - set_r), 0])
la1 = np.min([np.int(xc + set_r + 1), img.shape[1] - 1])
lb0 = np.max([np.int(yc - set_r), 0])
lb1 = np.min([np.int(yc + set_r + 1), img.shape[0] - 1])
if k == dr00[0]:
continue
else:
df1 = (basic_coord[0, :][lb0:lb1, la0:la1] -
xc) * np.cos(chi) + (basic_coord[1, :][lb0:lb1, la0:la1]
- yc) * np.sin(chi)
df2 = (basic_coord[1, :][lb0:lb1, la0:la1] -
yc) * np.cos(chi) - (basic_coord[0, :][lb0:lb1, la0:la1]
- xc) * np.sin(chi)
fr = df1**2 / lr**2 + df2**2 / sr**2
jx = fr <= 1
iu = np.where(jx == True)
iv = np.ones((jx.shape[0], jx.shape[1]), dtype=np.float)
iv[iu] = np.nan
mask_A[lb0:lb1, la0:la1] = mask_A[lb0:lb1, la0:la1] * iv
mirro_A = mask_A * img
hdu = fits.PrimaryHDU()
hdu.data = mirro_A
hdu.header = head_inf
hdu.writeto(
load +
'mask_data/A_plane/1.5sigma/A_mask_data_%s_ra%.3f_dec%.3f_z%.3f.fits'
% (band[kk], ra_g, dec_g, z_g),
overwrite=True)
'''
plt.figure()
ax = plt.imshow(mirro_A, cmap = 'Greys', origin = 'lower', vmin = 1e-3, norm = mpl.colors.LogNorm())
plt.colorbar(ax, fraction = 0.035, pad = 0.01, label = '$flux[nmaggy]$')
hsc.circles(cx_BCG, cy_BCG, s = R_p, fc = '', ec = 'b', )
hsc.circles(cx_BCG, cy_BCG, s = 1.1 * R_p, fc = '', ec = 'b', ls = '--')
plt.scatter(cx_BCG, cy_BCG, s = 10, marker = 'X', facecolors = '', edgecolors = 'r', linewidth = 0.5, alpha = 0.5)
plt.title('A mask img ra%.3f dec%.3f z%.3f in %s band' % (ra_g, dec_g, z_g, band[kk] ) )
plt.xlim(0, mirro_A.shape[1])
plt.ylim(0, mirro_A.shape[0])
plt.savefig(
'/mnt/ddnfs/data_users/cxkttwl/ICL/fig_class/A_mask/A_mask_%s_ra%.3f_dec%.3f_z%.3f.png'%(band[kk], ra_g, dec_g, z_g), dpi = 300)
plt.close()
'''
return
def source_compa():
bins = 90
kd = -20
zg = z[kd]
rag = ra[kd]
decg = dec[kd]
x0 = np.linspace(0, 2047, 2048)
y0 = np.linspace(0, 1488, 1489)
grd = np.array(np.meshgrid(x0, y0))
param_A = '/home/xkchen/mywork/ICL/data/SEX/default_mask_A.sex'
out_cat = '/home/xkchen/mywork/ICL/data/SEX/default_mask_A.param'
out_load_A = '/home/xkchen/mywork/ICL/data/SEX/result/mask_A_test.cat'
tmp_load = '/home/xkchen/mywork/ICL/data/test_data/'
load = '/home/xkchen/mywork/ICL/data/total_data/sample_02_03/'
file = 'frame-r-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (rag, decg, zg)
data = fits.open(load + file)
img = data[0].data
Head = data[0].header
wcs = awc.WCS(Head)
Da = Test_model.angular_diameter_distance(zg).value
Ar = rad2asec / Da
Rp = Ar / pixel
cx_BCG, cy_BCG = wcs.all_world2pix(rag * U.deg, decg * U.deg, 1)
ra_img, dec_img = wcs.all_pix2world(grd[0, :], grd[1, :], 1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
EBV = sfd(pos)
Av = Rv * EBV * 0.86
Al = A_wave(l_wave[2], Rv) * Av
img1 = img * 10**(Al / 2.5)
## threshold 1.3sigma
hdu = fits.PrimaryHDU()
hdu.data = img1
hdu.header = Head
hdu.writeto(tmp_load + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits' %
('r', rag, decg, zg),
overwrite=True)
file_source = tmp_load + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits' % (
'r', rag, decg, zg)
cmd = 'sex ' + file_source + ' -c %s -CATALOG_NAME %s -PARAMETERS_NAME %s -DETECT_THRESH %s -ANALYSIS_THRESH %s' % (
param_A, out_load_A, out_cat, '1.5', '1.5')
print(cmd)
a = subpro.Popen(cmd, shell=True)
a.wait()
source = asc.read(out_load_A)
Numb = np.array(source['NUMBER'][-1])
A = np.array(source['A_IMAGE'])
B = np.array(source['B_IMAGE'])
chi1 = np.array(source['THETA_IMAGE'])
cx1 = np.array(source['X_IMAGE']) - 1
cy1 = np.array(source['Y_IMAGE']) - 1
Kron = 5
Lr1 = Kron * A
Sr1 = Kron * B
file_source = tmp_load + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits' % (
'r', rag, decg, zg)
cmd = 'sex ' + file_source + ' -c %s -CATALOG_NAME %s -PARAMETERS_NAME %s -DETECT_THRESH %s -ANALYSIS_THRESH %s' % (
param_A, out_load_A, out_cat, '1.3', '1.3')
print(cmd)
a = subpro.Popen(cmd, shell=True)
a.wait()
source = asc.read(out_load_A)
Numb = np.array(source['NUMBER'][-1])
A = np.array(source['A_IMAGE'])
B = np.array(source['B_IMAGE'])
chi2 = np.array(source['THETA_IMAGE'])
cx2 = np.array(source['X_IMAGE']) - 1
cy2 = np.array(source['Y_IMAGE']) - 1
Kron = 7
Lr2 = Kron * A
Sr2 = Kron * B
a0 = np.max([cx_BCG - 1.1 * Rp, 0])
a1 = np.min([cx_BCG + 1.1 * Rp, 2047])
b0 = np.max([cy_BCG - 1.1 * Rp, 0])
b1 = np.min([cy_BCG + 1.1 * Rp, 1488])
plt.figure()
plt.title(r'$Cluster \; ra%.3f \; dec%.3f \; z%.3f$' % (rag, decg, zg))
plt.imshow(img1,
cmap='Greys',
vmin=1e-3,
origin='lower',
norm=mpl.colors.LogNorm())
hsc.circles(cx_BCG, cy_BCG, s=Rp, fc='', ec='b', alpha=0.5)
hsc.ellipses(cx1,
cy1,
w=Lr1,
h=Sr1,
rot=chi1,
fc='',
ec='g',
ls='-',
linewidth=0.5,
label=r'$1.5 \sigma$',
alpha=0.5)
hsc.ellipses(cx2,
cy2,
w=Lr2,
h=Sr2,
rot=chi2,
fc='',
ec='r',
ls='-',
linewidth=0.5,
label=r'$1.3\sigma$',
alpha=0.5)
plt.xlim(a0, a1)
plt.ylim(b0, b1)
plt.savefig('/home/xkchen/mywork/ICL/code/source_C_ra%.3f_dec%.3f.png' %
(rag, decg),
dpi=600)
plt.show()
raise
return
def stack_test():
x0 = 2427
y0 = 1765
Nx = np.linspace(0, 4854, 4855)
Ny = np.linspace(0, 3530, 3531)
sum_grid = np.array(np.meshgrid(Nx, Ny))
sum_array_0 = np.zeros((len(Ny), len(Nx)), dtype=np.float)
count_array_0 = np.ones((len(Ny), len(Nx)), dtype=np.float) * np.nan
p_count_0 = np.zeros((len(Ny), len(Nx)), dtype=np.float)
SB_ref = []
Ar_ref = []
bins = 60
for kd in range(stack_N):
zg = z[kd]
rag = ra[kd]
decg = dec[kd]
param_A = '/home/xkchen/mywork/ICL/data/SEX/default_mask_A.sex'
out_cat = '/home/xkchen/mywork/ICL/data/SEX/default_mask_A.param'
out_load_A = '/home/xkchen/mywork/ICL/data/SEX/result/mask_A_test.cat'
tmp_load = '/home/xkchen/mywork/ICL/data/test_data/'
load = '/home/xkchen/mywork/ICL/data/total_data/sample_02_03/'
file = 'frame-r-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (rag, decg, zg)
data = fits.getdata(load + file, header=True)
img = data[0]
Head = data[1]
wcs = awc.WCS(Head)
Da = Test_model.angular_diameter_distance(zg).value
Ar = rad2asec / Da
Rp = Ar / pixel
xt = np.linspace(0, img.shape[1] - 1, img.shape[1])
yt = np.linspace(0, img.shape[0] - 1, img.shape[0])
grd = np.array(np.meshgrid(xt, yt))
cx_BCG, cy_BCG = wcs.all_world2pix(rag * U.deg, decg * U.deg, 1)
ra_img, dec_img = wcs.all_pix2world(grd[0, :], grd[1, :], 1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
EBV = sfd(pos)
Av = Rv * EBV * 0.86
Al = A_wave(l_wave[2], Rv) * Av
img1 = img * 10**(Al / 2.5)
imgt = flux_recal(img1, zg, z_ref)
eta = Da_ref / Da
mu = 1 / eta
xn, yn, resam = gen(imgt, 1, eta, cx_BCG, cy_BCG)
xn = np.int(xn)
yn = np.int(yn)
if eta > 1:
resam = resam[1:, 1:]
elif eta == 1:
resam = resam[1:-1, 1:-1]
else:
resam = resam
ix0 = np.int(Head['CRPIX1'] * mu)
iy0 = np.int(Head['CRPIX2'] * mu)
RA0 = Head['CRVAL1']
DEC0 = Head['CRVAL2']
keys = [
'SIMPLE', 'BITPIX', 'NAXIS', 'NAXIS1', 'NAXIS2', 'CRPIX1',
'CRPIX2', 'CENTER_X', 'CENTER_Y', 'CRVAL1', 'CRVAL2', 'CENTER_RA',
'CENTER_DEC', 'ORIGN_Z'
]
value = [
'T', 32, 2, resam.shape[1], resam.shape[0], ix0, iy0, xn, yn, RA0,
DEC0, rag, decg, zg
]
ff = dict(zip(keys, value))
fil = fits.Header(ff)
fits.writeto(tmp_load + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits' %
('r', rag, decg, zg),
resam,
header=fil,
overwrite=True)
file_source = tmp_load + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits' % (
'r', rag, decg, zg)
cmd = 'sex ' + file_source + ' -c %s -CATALOG_NAME %s -PARAMETERS_NAME %s' % (
param_A, out_load_A, out_cat)
print(cmd)
A = subpro.Popen(cmd, shell=True)
A.wait()
source = asc.read(out_load_A)
Numb = np.array(source['NUMBER'][-1])
A = np.array(source['A_IMAGE'])
B = np.array(source['B_IMAGE'])
chi = np.array(source['THETA_IMAGE'])
cx = np.array(source['X_IMAGE']) - 1
cy = np.array(source['Y_IMAGE']) - 1
Kron = 6
Lr = Kron * A
Sr = Kron * B
CX = cx * 1
CY = cy * 1
a = Lr * 1
b = Sr * 1
theta = chi * 1
mask_A = np.ones((resam.shape[0], resam.shape[1]), dtype=np.float)
ox = np.linspace(0, resam.shape[1] - 1, resam.shape[1])
oy = np.linspace(0, resam.shape[0] - 1, resam.shape[0])
basic_coord = np.array(np.meshgrid(ox, oy))
major = a / 2
minor = b / 2
senior = np.sqrt(major**2 - minor**2)
tdr = np.sqrt((CX - xn)**2 + (CY - yn)**2)
dr00 = np.where(tdr == np.min(tdr))[0]
for k in range(Numb):
xc = CX[k]
yc = CY[k]
lr = major[k]
sr = minor[k]
cr = senior[k]
set_r = np.int(np.ceil(1.2 * lr))
la0 = np.int(xc - set_r)
la1 = np.int(xc + set_r + 1)
lb0 = np.int(yc - set_r)
lb1 = np.int(yc + set_r + 1)
if k == dr00[0]:
continue
else:
phi = theta[k] * np.pi / 180
df1 = lr**2 - cr**2 * np.cos(phi)**2
df2 = lr**2 - cr**2 * np.sin(phi)**2
fr = ((basic_coord[0, :][lb0:lb1, la0:la1] - xc)**2 * df1 +
(basic_coord[1, :][lb0:lb1, la0:la1] - yc)**2 * df2 -
cr**2 * np.sin(2 * phi) *
(basic_coord[0, :][lb0:lb1, la0:la1] - xc) *
(basic_coord[1, :][lb0:lb1, la0:la1] - yc))
idr = fr / (lr**2 * sr**2)
jx = idr < 1
iu = np.where(jx == True)
iv = np.ones((jx.shape[0], jx.shape[1]), dtype=np.float)
iv[iu] = np.nan
mask_A[lb0:lb1, la0:la1] = mask_A[lb0:lb1, la0:la1] * iv
mirro_A = mask_A * resam
plt.figure()
ax = plt.subplot(111)
ax.set_title('source mask after resample %d' % kd)
ax.imshow(resam,
cmap='Greys',
vmin=1e-3,
origin='lower',
norm=mpl.colors.LogNorm())
hsc.ellipses(CX, CY, w=a, h=b, rot=theta, fc='', ec='r', alpha=0.5)
plt.xlim(0, resam.shape[1])
plt.ylim(0, resam.shape[0])
plt.savefig('/home/xkchen/mywork/ICL/code/source_%d.png' % kd, dpi=300)
plt.close()
ox = np.linspace(0, resam.shape[1] - 1, resam.shape[1])
oy = np.linspace(0, resam.shape[0] - 1, resam.shape[0])
oo_grd = np.array(np.meshgrid(ox, oy))
cdr = np.sqrt((oo_grd[0, :] - xn)**2 + (oo_grd[1, :] - yn)**2)
idd = (cdr > Rpp) & (cdr < 1.1 * Rpp)
cut_res = mirro_A[idd]
id_nan = np.isnan(cut_res)
idx = np.where(id_nan == False)
bl_array = cut_res[idx]
back_cc = np.mean(bl_array)
mirroA = mirro_A - back_cc
SB2, R2, Anr2, err2 = light_measure(mirroA, bins, 1, Rpp, xn, yn,
pixel, z_ref)
SB_ = SB2[1:]
Ar_ = Anr2[1:]
R_ = R2[1:]
err_ = err2[1:]
SB_ref.append(SB_)
Ar_ref.append(Ar_)
la0 = np.int(y0 - yn)
la1 = np.int(y0 - yn + resam.shape[0])
lb0 = np.int(x0 - xn)
lb1 = np.int(x0 - xn + resam.shape[1])
idx = np.isnan(resam)
idv = np.where(idx == False)
sum_array_0[la0:la1,
lb0:lb1][idv] = sum_array_0[la0:la1,
lb0:lb1][idv] + mirroA[idv]
count_array_0[la0:la1, lb0:lb1][idv] = resam[idv]
id_nan = np.isnan(count_array_0)
id_fals = np.where(id_nan == False)
p_count_0[id_fals] = p_count_0[id_fals] + 1
count_array_0[la0:la1, lb0:lb1][idv] = np.nan
ll0 = [np.min(kk / Angu_ref) for kk in Ar_ref]
tar0 = np.min(ll0)
ll1 = [np.max(kk / Angu_ref) for kk in Ar_ref]
tar1 = np.max(ll1)
tar_down = tar0 * Angu_ref
tar_up = tar1 * Angu_ref
inter_frac = np.logspace(np.log10(tar0), np.log10(tar1), bins)
inter_ar = inter_frac * Angu_ref
m_flux = np.ones((stack_N, bins), dtype=np.float) * np.nan
for pp in range(len(SB_ref)):
id_count = np.zeros(bins, dtype=np.float)
tsb = SB_ref[pp]
tar = Ar_ref[pp] / Angu_ref
t_flux = 10**((22.5 + 2.5 * np.log10(pixel**2) - tsb) / 2.5)
for kk in range(len(tar)):
sub_ar = np.abs(inter_frac - tar[kk])
id_min = np.where(sub_ar == np.min(sub_ar))[0]
id_count[id_min[0]] = id_count[id_min[0]] + 1
id_nuzero = id_count != 0
id_g = np.where(id_nuzero == True)[0]
m_flux[pp, id_g] = t_flux
m_count = np.zeros(bins, dtype=np.float)
inter_flux = np.zeros(bins, dtype=np.float)
for pp in range(bins):
sub_flux = m_flux[:, pp]
iy = np.isnan(sub_flux)
iv = np.where(iy == False)[0]
m_count[pp] = len(iv)
inter_flux[pp] = inter_flux[pp] + np.sum(sub_flux[iv])
inter_flux = inter_flux / m_count
id_nan = np.isnan(inter_flux)
id_x = id_nan == False
id_inf = np.isinf(inter_flux)
id_y = id_inf == False
id_zero = inter_flux == 0
id_z = id_zero == False
id_set = id_x & id_y & id_z
ref_ar = inter_ar[id_set]
ref_flux = inter_flux[id_set]
ref_SB = 22.5 - 2.5 * np.log10(ref_flux) + 2.5 * np.log10(pixel**2)
f_SB = interp(ref_ar, ref_SB, kind='cubic')
mean_array_0 = sum_array_0 / p_count_0
where_are_inf = np.isinf(mean_array_0)
mean_array_0[where_are_inf] = np.nan
id_zeros = np.where(p_count_0 == 0)
mean_array_0[id_zeros] = np.nan
SB, R, Ar, error = light_measure(mean_array_0, bins, 1, Rpp, x0, y0, pixel,
z_ref)
SB_0 = SB[1:] + 0
R_0 = R[1:]
Ar_0 = Ar[1:]
err_0 = error[1:]
Ar0 = (Ar_0 / Angu_ref) * Angu_ref
plt.figure(figsize=(16, 8))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1])
ax = plt.subplot(gs[0])
bx = plt.subplot(gs[1])
ax.plot(ref_ar, ref_SB, 'r-', label='$stacking \, profile$', alpha=0.5)
ax.plot(Ar0, SB_0, 'g--', label='$stacking \, img$', alpha=0.5)
bx.plot(Ar0[(Ar0 > tar_down * 1.01) & (Ar0 < tar_up / 1.01)],
SB_0[(Ar0 > tar_down * 1.01) & (Ar0 < tar_up / 1.01)] -
f_SB(Ar0[(Ar0 > tar_down * 1.01) & (Ar0 < tar_up / 1.01)]),
'b*',
alpha=0.5)
ax.set_xscale('log')
ax.set_xlabel('$R[arcsec]$')
ax.set_ylabel('$SB[mag/arcsec^2]$')
ax.tick_params(axis='both', which='both', direction='in')
ax.legend(loc=1, fontsize=12)
ax.invert_yaxis()
bx.set_xlabel('$R[arcsec]$')
bx.set_xscale('log')
bx.set_ylabel('$ \Delta{SB}[mag/arcsec^2] $')
bx.tick_params(axis='both', which='both', direction='in')
plt.subplots_adjust(hspace=0)
plt.tight_layout()
plt.savefig('/home/xkchen/mywork/ICL/code/stack_test.png', dpi=300)
plt.close()
raise
return
def resamp_test():
for kd in range(10):
z_ref = 0.25
Da_ref = Test_model.angular_diameter_distance(z_ref).value
Angu_ref = (R0 / Da_ref) * rad2asec
Rpp = Angu_ref / pixel
bins = 50
x0 = np.linspace(0, 2047, 2048)
y0 = np.linspace(0, 1488, 1489)
grd = np.array(np.meshgrid(x0, y0))
r_star = 2 * 1.5 / pixel
zg = z[kd]
rag = ra[kd]
decg = dec[kd]
param_A = '/home/xkchen/mywork/ICL/data/SEX/default_mask_A.sex'
out_cat = '/home/xkchen/mywork/ICL/data/SEX/default_mask_A.param'
out_load_A = '/home/xkchen/mywork/ICL/data/SEX/result/mask_A_test.cat'
tmp_load = '/home/xkchen/mywork/ICL/data/test_data/'
load = '/home/xkchen/mywork/ICL/data/total_data/sample_02_03/'
file = 'frame-r-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (rag, decg, zg)
data = fits.open(load + file)
img = data[0].data
Head = data[0].header
wcs = awc.WCS(Head)
Da = Test_model.angular_diameter_distance(zg).value
Ar = rad2asec / Da
Rp = Ar / pixel
cx_BCG, cy_BCG = wcs.all_world2pix(rag * U.deg, decg * U.deg, 1)
ra_img, dec_img = wcs.all_pix2world(grd[0, :], grd[1, :], 1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
EBV = sfd(pos)
Av = Rv * EBV * 0.86
Al = A_wave(l_wave[2], Rv) * Av
img1 = img * 10**(Al / 2.5)
## part1: find the source and mask in original image
hdu = fits.PrimaryHDU()
hdu.data = img1
hdu.header = Head
hdu.writeto(tmp_load + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits' %
('r', rag, decg, zg),
overwrite=True)
file_source = tmp_load + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits' % (
'r', rag, decg, zg)
cmd = 'sex ' + file_source + ' -c %s -CATALOG_NAME %s -PARAMETERS_NAME %s' % (
param_A, out_load_A, out_cat)
print(cmd)
A = subpro.Popen(cmd, shell=True)
A.wait()
source = asc.read(out_load_A)
Numb = np.array(source['NUMBER'][-1])
A = np.array(source['A_IMAGE'])
B = np.array(source['B_IMAGE'])
chi = np.array(source['THETA_IMAGE'])
cx = np.array(source['X_IMAGE']) - 1
cy = np.array(source['Y_IMAGE']) - 1
Kron = 6
Lr = Kron * A
Sr = Kron * B
cat = pd.read_csv(
'/home/xkchen/mywork/ICL/data/star_catalog/source_SQL_Z%.3f_ra%.3f_dec%.3f.txt'
% (zg, rag, decg),
skiprows=1)
ra_s = np.array(cat['ra'])
dec_s = np.array(cat['dec'])
mag = np.array(cat['r'])
x_side = img.shape[1]
y_side = img.shape[0]
x, y = wcs.all_world2pix(ra_s * U.deg, dec_s * U.deg, 1)
ia = (x >= 0) & (x <= x_side)
ib = (y >= 0) & (y <= y_side)
ie = (mag <= 20)
ic = ia & ib & ie
comx = x[ic]
comy = y[ic]
comr = np.ones(len(comx), dtype=np.float) * r_star
com_chi = np.zeros(len(comx), dtype=np.float)
CX = np.r_[cx, comx]
CY = np.r_[cy, comy]
a = np.r_[Lr, 2 * comr]
b = np.r_[Sr, 2 * comr]
theta = np.r_[chi, com_chi]
Numb = Numb + len(comx)
mask_A = np.ones((img.shape[0], img.shape[1]), dtype=np.float)
ox = np.linspace(0, img.shape[1] - 1, img.shape[1])
oy = np.linspace(0, img.shape[0] - 1, img.shape[0])
basic_coord = np.array(np.meshgrid(ox, oy))
major = a / 2
minor = b / 2
senior = np.sqrt(major**2 - minor**2)
tdr = np.sqrt((CX - cx_BCG)**2 + (CY - cy_BCG)**2)
dr00 = np.where(tdr == np.min(tdr))[0]
for k in range(Numb):
xc = CX[k]
yc = CY[k]
lr = major[k]
sr = minor[k]
cr = senior[k]
set_r = np.int(np.ceil(1.2 * lr))
la0 = np.int(xc - set_r)
la1 = np.int(xc + set_r + 1)
lb0 = np.int(yc - set_r)
lb1 = np.int(yc + set_r + 1)
if k == dr00[0]:
continue
else:
phi = theta[k] * np.pi / 180
df1 = lr**2 - cr**2 * np.cos(phi)**2
df2 = lr**2 - cr**2 * np.sin(phi)**2
fr = ((basic_coord[0, :][lb0:lb1, la0:la1] - xc)**2 * df1 +
(basic_coord[1, :][lb0:lb1, la0:la1] - yc)**2 * df2 -
cr**2 * np.sin(2 * phi) *
(basic_coord[0, :][lb0:lb1, la0:la1] - xc) *
(basic_coord[1, :][lb0:lb1, la0:la1] - yc))
idr = fr / (lr**2 * sr**2)
jx = idr < 1
#jx = (-1)*jx+1
#mask_A[lb0: lb1, la0: la1] = mask_A[lb0: lb1, la0: la1] * jx
iu = np.where(jx == True)
iv = np.ones((jx.shape[0], jx.shape[1]), dtype=np.float)
iv[iu] = np.nan
mask_A[lb0:lb1, la0:la1] = mask_A[lb0:lb1, la0:la1] * iv
mirro_A = mask_A * img1
SB2, R2, Anr2, err2 = light_measure(mirro_A, bins, 1, Rp, cx_BCG,
cy_BCG, pixel, zg)
'''
## test the flux distribution in each bins
sub_R = np.logspace(0, np.log10(Rp), bins)
cdr = np.sqrt((grd[0,:] - cx_BCG)**2 + (grd[1,:] - cy_BCG)**2)
Nr = len(sub_R)
for xx in range(Nr - 1):
idr = (cdr >= sub_R[xx]) & (cdr < sub_R[xx + 1])
sub_f = mirro_A[idr]
id_nan = np.isnan(sub_f)
iv = np.where(id_nan == False)[0]
bl_array = sub_f[iv]
plt.figure()
plt.title('$flux \; distribution \; in \; %d \; bins$' % (xx))
plt.hist(bl_array, histtype = 'step', color = 'b', normed = True)
plt.xlabel('$flux[nmagy]$')
plt.savefig('/home/xkchen/mywork/ICL/code/flux_%dbins_DF_%.3fra_%.3fdec_%.3fz.png' % (xx, rag, decg, zg), dpi = 300)
plt.close()
'''
# flux scale and pixel resample
eta = Da_ref / Da
mu = 1 / eta
SB_ref = SB2 - 10 * np.log10((1 + zg) / (1 + z_ref))
Ar_ref = Anr2 * mu
f_SB = interp(Ar_ref, SB_ref, kind='cubic')
imgt = flux_recal(img1, zg, z_ref)
mirroA = flux_recal(mirro_A, zg, z_ref)
xn, yn, resam = gen(mirroA, 1, eta, cx_BCG,
cy_BCG) #############???????
xn = np.int(xn)
yn = np.int(yn)
if eta > 1:
resam = resam[1:, 1:]
elif eta == 1:
resam = resam[1:-1, 1:-1]
else:
resam = resam
SBn, Rn, Anrn, errn = light_measure(resam, bins, 1, Rpp, xn, yn, pixel,
z_ref)
arn = Anrn[(Anrn >= np.min(Ar_ref)) & (Anrn <= np.max(Ar_ref))]
plt.figure(figsize=(16, 8))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1])
ax = plt.subplot(gs[0])
bx = plt.subplot(gs[1])
ax.plot(Anrn, SBn, 'b-', label='$SB_{resample}$', alpha=0.5)
ax.plot(Ar_ref, SB_ref, 'g--', label='$SB_{ref}$', alpha=0.5)
bx.plot(
arn, SBn[(Anrn >= np.min(Ar_ref)) & (Anrn <= np.max(Ar_ref))] -
f_SB(arn), 'g*')
bx.axhline(y=np.mean(SBn[(Anrn >= np.min(Ar_ref))
& (Anrn <= np.max(Ar_ref))] - f_SB(arn)),
ls='--',
color='b')
ax.set_ylabel('$SB[mag/arcsec^2]$')
ax.set_xlabel('$R[arcsec]$')
ax.set_xscale('log')
ax.tick_params(axis='both', which='both', direction='in')
ax.invert_yaxis()
ax.legend(loc=1, fontsize=15)
bx.set_xlabel('$R[arcsec]$')
bx.set_xscale('log')
bx.set_ylabel('$ \Delta{SB}[mag/arcsec^2] $')
bx.tick_params(axis='both', which='both', direction='in')
bx.legend(loc=3, fontsize=12)
plt.subplots_adjust(hspace=0)
plt.tight_layout()
plt.savefig(
'/home/xkchen/mywork/ICL/code/resamp_err_SB_%d_ra%.3f_dec%.3f_z%.3f.png'
% (bins, rag, decg, zg),
dpi=300)
plt.close() #############???????????????
'''
xn, yn, resam = gen(imgt, 1, eta, cx_BCG, cy_BCG)
xn = np.int(xn)
yn = np.int(yn)
if eta > 1:
resam = resam[1:, 1:]
elif eta == 1:
resam = resam[1:-1, 1:-1]
else:
resam = resam
## handle with these pixels around 0
# from mask
id_zero = np.where(mask_A == 0)
tt_mask = mask_A * 1
tt_mask[id_zero] = np.nan
ttx, tty, ttmask = gen(tt_mask, 1, eta, cx_BCG, cy_BCG)
if eta > 1:
ttmask = ttmask[1:, 1:]
elif eta == 1:
ttmask = ttmask[1:-1, 1:-1]
else:
ttmask = ttmask
id_nan = np.isnan(ttmask)
ttmask[id_nan] = 0
idnzeo = np.where(ttmask != 0)
ttmask[idnzeo] = 1
tt_img = ttmask * resam
SB3, R3, Anr3, err3 = light_measure(tt_img, bins, 1, Rpp, xn, yn, pixel, z_ref)
# from masked img
id_zero = np.where(mirroA == 0)
tt_mirro = mirroA * 1
tt_mirro[id_zero] = np.nan
x2, y2, resam2 = gen(tt_mirro, 1, eta, cx_BCG, cy_BCG)
x2 = np.int(x2)
y2 = np.int(y2)
if eta > 1:
resam2 = resam2[1:, 1:]
elif eta == 1:
resam2 = resam2[1:-1, 1:-1]
else:
resam2 = resam2
id_nan = np.isnan(resam2)
resam2[id_nan] = 0
SB5, R5, Anr5, err5 = light_measure(resam2, bins, 1, Rpp, x2, y2, pixel, z_ref)
### part2: mask after resample (mask with the same source)
CX_ = CX * mu
CY_ = CY * mu
a_ = a * mu
b_ = b * mu
res_mask_1 = np.ones((resam.shape[0], resam.shape[1]), dtype = np.float)
ox_ = np.linspace(0, resam.shape[1] - 1, resam.shape[1])
oy_ = np.linspace(0, resam.shape[0] - 1, resam.shape[0])
basic_coord = np.array(np.meshgrid(ox_, oy_))
major = a_ / 2
minor = b_ / 2
senior = np.sqrt(major**2 - minor**2)
tdr = np.sqrt((CX_ - xn)**2 + (CY_ - yn)**2)
dr00 = np.where(tdr == np.min(tdr))[0]
for k in range(Numb):
xc = CX_[k]
yc = CY_[k]
lr = major[k]
sr = minor[k]
cr = senior[k]
set_r = np.int(np.ceil(1.2 * lr))
la0 = np.int(xc - set_r)
la1 = np.int(xc + set_r +1)
lb0 = np.int(yc - set_r)
lb1 = np.int(yc + set_r +1)
if k == dr00[0] :
continue
else:
phi = theta[k]*np.pi/180
df1 = lr**2 - cr**2*np.cos(phi)**2
df2 = lr**2 - cr**2*np.sin(phi)**2
fr = ((basic_coord[0,:][lb0: lb1, la0: la1] - xc)**2*df1 + (basic_coord[1,:][lb0: lb1, la0: la1] - yc)**2*df2
- cr**2*np.sin(2*phi)*(basic_coord[0,:][lb0: lb1, la0: la1] - xc)*(basic_coord[1,:][lb0: lb1, la0: la1] - yc))
idr = fr/(lr**2*sr**2)
jx = idr<=1
jx = (-1)*jx+1
res_mask_1[lb0: lb1, la0: la1] = res_mask_1[lb0: lb1, la0: la1]*jx
resam1 = res_mask_1 * resam
SBt, Rt, Anrt, errt = light_measure(resam1, bins, 1, Rpp, xn, yn, pixel, z_ref)
# cut these pixel which is 0 before resamp
xm, ym, res_mask_2 = gen(mask_A, 1, eta, cx_BCG, cy_BCG)
if eta > 1:
res_mask_2 = res_mask_2[1:, 1:]
elif eta == 1:
res_mask_2 = res_mask_2[1:-1, 1:-1]
else:
res_mask_2 = res_mask_2
mix, miy, mirro_img = gen(mirroA, 1, eta, cx_BCG, cy_BCG)
mix = np.int(mix)
miy = np.int(miy)
if eta > 1:
mirro_img = mirro_img[1:, 1:]
elif eta == 1:
mirro_img = mirro_img[1:-1, 1:-1]
else:
mirro_img = mirro_img
val, cont = sts.find_repeats(res_mask_2)
ids = np.where(cont == np.max(cont))[0]
res_mask2 = res_mask_2 / val[ids[0]]
print('scale factor = ', val[ids[0]])
SB4, R4, Anr4, err4 = weit_l_measure(mirro_img, res_mask2, bins, 1, Rpp, mix, miy, pixel, z_ref)
ar4 = Anr4[(Anr4 >= np.min(Ar_ref)) & (Anr4 <= np.max(Ar_ref))]
ar3 = Anr3[(Anr3 >= np.min(Ar_ref)) & (Anr3 <= np.max(Ar_ref))]
art = Anrt[(Anrt >= np.min(Ar_ref)) & (Anrt <= np.max(Ar_ref))]
plt.figure(figsize = (16, 8))
gs = gridspec.GridSpec(1, 2, width_ratios = [1,1])
ax = plt.subplot(gs[0])
bx = plt.subplot(gs[1])
ax.plot(Anrt, SBt, 'g-.', label = '$SB_{re-load \, source \, at \, z_{0}}$', alpha = 0.5)
ax.plot(Anr4, SB4, 'b-', label = '$SB_{resample \, mask \, Metrix}$', alpha = 0.5)
ax.plot(Anr3, SB3, 'r:', label = '$SB_{correct \, resample}$', alpha = 0.5)
ax.plot(Ar_ref, SB_ref, 'k--', label = '$SB_{ref}$', alpha = 0.5)
bx.plot(ar4, SB4[(Anr4 >= np.min(Ar_ref)) & (Anr4 <= np.max(Ar_ref))] - f_SB(ar4),
'b*', label = '$ [SB_{resample \, mask} - SB_{ref}] $', alpha = 0.5)
bx.plot(ar3, SB3[(Anr3 >= np.min(Ar_ref)) & (Anr3 <= np.max(Ar_ref))] - f_SB(ar3),
'r*', label = '$ [SB_{correct \, resample} - SB_{ref}] $', alpha = 0.5)
bx.plot(art, SBt[(Anrt >= np.min(Ar_ref)) & (Anrt <= np.max(Ar_ref))] - f_SB(art),
'g*', label = '$ [SB_{re-load \, source} - SB_{ref}] $', alpha = 0.5)
bx.axhline(y = np.mean(SB4[(Anr4 >= np.min(Ar_ref)) & (Anr4 <= np.max(Ar_ref))] - f_SB(ar4)), color = 'b', ls = '--', alpha = 0.5)
bx.axhline(y = np.mean(SB3[(Anr3 >= np.min(Ar_ref)) & (Anr3 <= np.max(Ar_ref))] - f_SB(ar3)), color = 'r', ls = '--', alpha = 0.5)
bx.axhline(y = np.mean(SBt[(Anrt >= np.min(Ar_ref)) & (Anrt <= np.max(Ar_ref))] - f_SB(art)), color = 'g', ls = '--', alpha = 0.5)
ax.set_title('resample SB profile comparation')
ax.set_ylabel('$SB[mag/arcsec^2]$')
ax.set_xlabel('$R[arcsec]$')
ax.set_xscale('log')
ax.tick_params(axis = 'both', which = 'both', direction = 'in')
ax.invert_yaxis()
ax.legend(loc = 1, fontsize = 15)
bx.set_xlabel('$R[arcsec]$')
bx.set_xscale('log')
bx.set_ylabel('$ \Delta{SB}[mag/arcsec^2] $')
bx.tick_params(axis = 'both', which = 'both', direction = 'in')
bx.legend(loc = 3, fontsize = 12)
plt.subplots_adjust(hspace = 0)
plt.tight_layout()
plt.savefig('/home/xkchen/mywork/ICL/code/resample_test_SB_%d_ra%.3f_dec%.3f_z%.3f.png' % (bins, rag, decg, zg), dpi = 300)
plt.close()
'''
raise
return
def color():
z_ref = 0.25
Da_ref = Test_model.angular_diameter_distance(z_ref).value
Angu_ref = (R0 / Da_ref) * rad2asec
Rpp = Angu_ref / pixel
bins = 90
bint = 90
kd = 0
#kd = 19
x0 = np.linspace(0, 2047, 2048)
y0 = np.linspace(0, 1488, 1489)
grd = np.array(np.meshgrid(x0, y0))
r_star = 2 * 1.5 / pixel
zg = z[kd]
rag = ra[kd]
decg = dec[kd]
load = '/home/xkchen/mywork/ICL/data/total_data/sample_02_03/'
file1 = 'frame-g-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (rag, decg, zg)
data = fits.open(load + file1)
img01 = data[0].data
Head = data[0].header
wcs = awc.WCS(Head)
Da = Test_model.angular_diameter_distance(zg).value
Ar = rad2asec / Da
Rp = Ar / pixel
cx_BCG, cy_BCG = wcs.all_world2pix(rag * U.deg, decg * U.deg, 1)
SB01, R01, Anr01, err01 = light_measure(img01, bins, 1, Rp, cx_BCG, cy_BCG,
pixel, zg)
file2 = 'frame-r-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (rag, decg, zg)
data = fits.open(load + file2)
img02 = data[0].data
SB02, R02, Anr02, err02 = light_measure(img02, bins, 1, Rp, cx_BCG, cy_BCG,
pixel, zg)
## redden
ra_img, dec_img = wcs.all_pix2world(grd[0, :], grd[1, :], 1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
EBV = sfd(pos)
Av = Rv * EBV * 0.86
Al1 = A_wave(l_wave[1], Rv) * Av
img1 = img01 * 10**(Al1 / 2.5)
SB1, R1, Anr1, err1 = light_measure(img1, bins, 1, Rp, cx_BCG, cy_BCG,
pixel, zg)
Al2 = A_wave(l_wave[2], Rv) * Av
img2 = img02 * 10**(Al2 / 2.5)
SB2, R2, Anr2, err2 = light_measure(img2, bins, 1, Rp, cx_BCG, cy_BCG,
pixel, zg)
DM1 = SB01 - SB02
DM2 = SB1 - SB2
plt.figure(figsize=(16, 9))
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax = plt.subplot(gs[0])
bx = plt.subplot(gs[1])
ax.plot(R01, DM1, 'g-', label=r'$g-r_{before \, correct}$', alpha=0.5)
ax.plot(R1, DM2, 'r-', label=r'$g-r_{after \, correct}$', alpha=0.5)
ax.set_xlabel('$R[arcsec]$')
ax.set_ylabel('$g-r$')
ax.set_xscale('log')
ax.legend(loc=1)
bx.plot(R01, DM1 - DM2, 'b-', label=r'$\Delta_{c_{before} - c_{after}}$')
bx.set_xlabel('$R[arcsec]$')
bx.set_xscale('log')
bx.set_ylabel(r'$\delta_{g-r}$')
bx.set_yscale('log')
bx.legend(loc=1, fontsize=15)
plt.savefig('/home/xkchen/mywork/ICL/code/color_test.png', dpi=300)
plt.close()
raise
return
def pho_mask(band_id, z_set, ra_set, dec_set):
kk = np.int(band_id)
Nz = len(z_set)
param_A = 'default_mask_A.sex'
out_cat = 'default_mask_A.param'
out_load_A = tmp + 'A_mask_%d_cpus.cat' % rank
## size test
r_res = 2.8 # 2.8 for larger R setting
for q in range(Nz):
z_g = z_set[q]
ra_g = ra_set[q]
dec_g = dec_set[q]
try:
pro_f = d_file + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (
band[kk], ra_g, dec_g, z_g)
data_f = fits.open(pro_f)
img = data_f[0].data
head_inf = data_f[0].header
wcs = awc.WCS(head_inf)
cx_BCG, cy_BCG = wcs.all_world2pix(ra_g * U.deg, dec_g * U.deg, 1)
R_ph = rad2asec / (Test_model.angular_diameter_distance(z_g).value)
R_p = R_ph / pixel
x0 = np.linspace(0, img.shape[1] - 1, img.shape[1])
y0 = np.linspace(0, img.shape[0] - 1, img.shape[0])
img_grid = np.array(np.meshgrid(x0, y0))
ra_img, dec_img = wcs.all_pix2world(img_grid[0, :], img_grid[1, :],
1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
BEV = sfd(pos)
Av = Rv * BEV * 0.86
Al = A_wave(l_wave[kk], Rv) * Av
img = img * 10**(Al / 2.5)
hdu = fits.PrimaryHDU()
hdu.data = img
hdu.header = head_inf
hdu.writeto(tmp + 'source_data_%d.fits' % rank, overwrite=True)
file_source = tmp + 'source_data_%d.fits' % rank
cmd = 'sex ' + file_source + ' -c %s -CATALOG_NAME %s -PARAMETERS_NAME %s' % (
param_A, out_load_A, out_cat)
a = subpro.Popen(cmd, shell=True)
a.wait()
source = asc.read(out_load_A)
Numb = np.array(source['NUMBER'][-1])
A = np.array(source['A_IMAGE'])
B = np.array(source['B_IMAGE'])
theta = np.array(source['THETA_IMAGE'])
cx = np.array(source['X_IMAGE']) - 1
cy = np.array(source['Y_IMAGE']) - 1
p_type = np.array(source['CLASS_STAR'])
Kron = 6 * r_res # iso_radius set as 3 times rms (2.8 from size test)
a = Kron * A
b = Kron * B
mask = load + 'photo_z/star_cat/source_SQL_Z%.3f_ra%.3f_dec%.3f.txt' % (
z_g, ra_g, dec_g)
cat = pds.read_csv(mask, skiprows=1)
set_ra = np.array(cat['ra'])
set_dec = np.array(cat['dec'])
set_mag = np.array(cat['r'])
OBJ = np.array(cat['type'])
xt = cat['Column1']
tau = 10 * r_res # the mask size set as 10 * FWHM from dr12
set_A = np.array([
cat['psffwhm_r'], cat['psffwhm_g'], cat['psffwhm_i']
]) * tau / pixel
set_B = np.array([
cat['psffwhm_r'], cat['psffwhm_g'], cat['psffwhm_i']
]) * tau / pixel
set_chi = np.zeros(set_A.shape[1], dtype=np.float)
lln = np.array([
len(set_A[:, ll][set_A[:, ll] > 0])
for ll in range(set_A.shape[1])
])
lr_iso = np.array(
[np.max(set_A[:, ll]) for ll in range(set_A.shape[1])])
sr_iso = np.array(
[np.max(set_B[:, ll]) for ll in range(set_B.shape[1])])
# bright stars
x, y = wcs.all_world2pix(set_ra * U.deg, set_dec * U.deg, 1)
ia = (x >= 0) & (x <= img.shape[1])
ib = (y >= 0) & (y <= img.shape[0])
ie = (set_mag <= 20)
iq = lln >= 2
ig = OBJ == 6
ic = (ia & ib & ie & ig & iq)
sub_x0 = x[ic]
sub_y0 = y[ic]
sub_A0 = lr_iso[ic]
sub_B0 = sr_iso[ic]
sub_chi0 = set_chi[ic]
# saturated source(may not stars)
xa = ['SATURATED' in qq for qq in xt]
xv = np.array(xa)
idx = xv == True
ipx = (idx & ia & ib)
sub_x2 = x[ipx]
sub_y2 = y[ipx]
sub_A2 = 3 * lr_iso[ipx]
sub_B2 = 3 * sr_iso[ipx]
sub_chi2 = set_chi[ipx]
comx = np.r_[sub_x0[sub_A0 > 0], sub_x2[sub_A2 > 0]]
comy = np.r_[sub_y0[sub_A0 > 0], sub_y2[sub_A2 > 0]]
Lr = np.r_[sub_A0[sub_A0 > 0], sub_A2[sub_A2 > 0]]
Sr = np.r_[sub_B0[sub_A0 > 0], sub_B2[sub_A2 > 0]]
phi = np.r_[sub_chi0[sub_A0 > 0], sub_chi2[sub_A2 > 0]]
cx = np.r_[cx, comx]
cy = np.r_[cy, comy]
a = np.r_[a, Lr]
b = np.r_[b, Sr]
theta = np.r_[theta, phi]
Numb = Numb + len(comx)
mask_A = np.ones((img.shape[0], img.shape[1]), dtype=np.float)
ox = np.linspace(0, img.shape[1] - 1, img.shape[1])
oy = np.linspace(0, img.shape[0] - 1, img.shape[0])
basic_coord = np.array(np.meshgrid(ox, oy))
major = a / 2
minor = b / 2 # set the star mask based on the major and minor radius
senior = np.sqrt(major**2 - minor**2)
tdr = np.sqrt((cx - cx_BCG)**2 + (cy - cy_BCG)**2)
dr00 = np.where(tdr == np.min(tdr))[0]
for k in range(Numb):
xc = cx[k]
yc = cy[k]
lr = major[k]
sr = minor[k]
cr = senior[k]
chi = theta[k] * np.pi / 180
set_r = np.int(np.ceil(1.2 * lr))
la0 = np.max([np.int(xc - set_r), 0])
la1 = np.min([np.int(xc + set_r + 1), img.shape[1] - 1])
lb0 = np.max([np.int(yc - set_r), 0])
lb1 = np.min([np.int(yc + set_r + 1), img.shape[0] - 1])
if k == dr00[0]:
continue
else:
df1 = (basic_coord[0, :][lb0:lb1, la0:la1] - xc) * np.cos(
chi) + (basic_coord[1, :][lb0:lb1, la0:la1] -
yc) * np.sin(chi)
df2 = (basic_coord[1, :][lb0:lb1, la0:la1] - yc) * np.cos(
chi) - (basic_coord[0, :][lb0:lb1, la0:la1] -
xc) * np.sin(chi)
fr = df1**2 / lr**2 + df2**2 / sr**2
jx = fr <= 1
iu = np.where(jx == True)
iv = np.ones((jx.shape[0], jx.shape[1]), dtype=np.float)
iv[iu] = np.nan
mask_A[lb0:lb1, la0:la1] = mask_A[lb0:lb1, la0:la1] * iv
mirro_A = mask_A * img
hdu = fits.PrimaryHDU()
hdu.data = mirro_A
hdu.header = head_inf
hdu.writeto(load +
'photo_z/mask/A_mask_%s_ra%.3f_dec%.3f_z%.3f.fits' %
(band[kk], ra_g, dec_g, z_g),
overwrite=True)
except FileNotFoundError:
continue
return
def sky_light():
band_add = ['r', 'i', 'z']
band_fil = ['u', 'g', 'r', 'i', 'z']
load = '/mnt/ddnfs/data_users/cxkttwl/ICL/data/'
param_sky = 'default_sky_mask.sex'
out_cat = 'default_mask_A.param'
out_load_sky = './result/sky_mask_test.cat'
x0 = np.linspace(0, 2047, 2048)
y0 = np.linspace(0, 1488, 1489)
img_grid = np.array(np.meshgrid(x0, y0))
for q in range(len(z)):
for l in range(len(band_fil)):
pro_f = d_file + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (
band_fil[l], ra[q], dec[q], z[q])
ra_g = ra[q]
dec_g = dec[q]
z_g = z[q]
data_f = fits.open(pro_f)
img = data_f[0].data
head_inf = data_f[0].header
wcs = awc.WCS(head_inf)
cx_BCG, cy_BCG = wcs.all_world2pix(ra_g * U.deg, dec_g * U.deg, 1)
R_ph = rad2asec / (Test_model.angular_diameter_distance(
z[q]).value)
R_p = R_ph / pixel
ra_img, dec_img = wcs.all_pix2world(img_grid[0, :], img_grid[1, :],
1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
BEV = sfd(pos)
Av = Rv * BEV * 0.86
Al = A_wave(l_wave[l], Rv) * Av
img = img * 10**(Al / 2.5)
# mask in sky only
hdu = fits.PrimaryHDU()
hdu.data = data_f[0].data
hdu.header = head_inf
hdu.writeto('source_data.fits', overwrite=True)
file_source = './source_data.fits'
cmd = 'sex ' + file_source + ' -c %s -CATALOG_NAME %s -PARAMETERS_NAME %s' % (
param_sky, out_load_sky, out_cat)
print(cmd)
a = subpro.Popen(cmd, shell=True)
a.wait()
source = asc.read(out_load_sky)
Numb = np.array(source['NUMBER'][-1])
A = np.array(source['A_IMAGE'])
B = np.array(source['B_IMAGE'])
theta = np.array(source['THETA_IMAGE'])
cx = np.array(source['X_IMAGE']) - 1
cy = np.array(source['Y_IMAGE']) - 1
p_type = np.array(source['CLASS_STAR'])
Kron = 6
a = Kron * A
b = Kron * B
ddr = np.sqrt((cx - cx_BCG)**2 + (cy - cy_BCG)**2)
ix = ddr >= 0.95 * R_p
iy = ddr <= 1.15 * R_p
iz = ix & iy
s_cx = cx[iz]
s_cy = cy[iz]
s_a = a[iz]
s_b = b[iz]
s_phi = theta[iz]
s_Num = len(s_b)
mask_sky = np.ones((img.shape[0], img.shape[1]), dtype=np.float)
ox = np.linspace(0, 2047, 2048)
oy = np.linspace(0, 1488, 1489)
basic_coord = np.array(np.meshgrid(ox, oy))
major = s_a / 2
minor = s_b / 2
senior = np.sqrt(major**2 - minor**2)
for k in range(s_Num):
xc = s_cx[k]
yc = s_cy[k]
lr = major[k]
sr = minor[k]
cr = senior[k]
chi = s_phi[k] * np.pi / 180
set_r = np.int(np.ceil(1.2 * lr))
la0 = np.max([np.int(xc - set_r), 0])
la1 = np.min([np.int(xc + set_r + 1), img.shape[1] - 1])
lb0 = np.max([np.int(yc - set_r), 0])
lb1 = np.min([np.int(yc + set_r + 1), img.shape[0] - 1])
df1 = lr**2 - cr**2 * np.cos(chi)**2
df2 = lr**2 - cr**2 * np.sin(chi)**2
fr = (basic_coord[0,:][lb0: lb1, la0: la1] - xc)**2*df1 +(basic_coord[1,:][lb0: lb1, la0: la1] - yc)**2*df2\
- cr**2*np.sin(2*chi)*(basic_coord[0,:][lb0: lb1, la0: la1] - xc)*(basic_coord[1,:][lb0: lb1, la0: la1] - yc)
idr = fr / (lr**2 * sr**2)
jx = idr <= 1
iu = np.where(jx == True)
iv = np.ones((jx.shape[0], jx.shape[1]), dtype=np.float)
iv[iu] = np.nan
mask_sky[lb0:lb1, la0:la1] = mask_sky[lb0:lb1, la0:la1] * iv
mirro_sky = img * mask_sky
hdu = fits.PrimaryHDU()
hdu.data = mirro_sky
hdu.header = head_inf
hdu.writeto(
load +
'mask_data/sky_plane/0.3sigma/sky_mask_data_%s_ra%.3f_dec%.3f_z%.3f.fits'
% (band_fil[l], ra_g, dec_g, z[q]),
overwrite=True)
plt.figure()
ax = plt.imshow(mirro_sky,
cmap='Greys',
origin='lower',
vmin=1e-3,
norm=mpl.colors.LogNorm())
plt.colorbar(ax, fraction=0.035, pad=0.01, label='$flux[nmaggy]$')
hsc.ellipses(s_cx,
s_cy,
w=s_a,
h=s_b,
rot=s_phi,
fc='',
ec='r',
ls='--',
lw=0.5)
hsc.circles(comx, comy, s=comr, fc='', ec='b', ls='-', lw=0.5)
hsc.circles(
cx_BCG,
cy_BCG,
s=R_p,
fc='',
ec='b',
)
hsc.circles(cx_BCG, cy_BCG, s=1.1 * R_p, fc='', ec='b', ls='--')
plt.scatter(cx_BCG,
cy_BCG,
s=10,
marker='X',
facecolors='',
edgecolors='r',
linewidth=0.5,
alpha=0.5)
plt.title('BL_03 mask img ra%.3f dec%.3f z%.3f in %s band' %
(ra_g, dec_g, z_g, band_fil[l]))
plt.xlim(0, mirro_sky.shape[1])
plt.ylim(0, mirro_sky.shape[0])
plt.savefig(
'/mnt/ddnfs/data_users/cxkttwl/ICL/fig_class/sky_03_mask/BL_mask_%s_ra%.3f_dec%.3f_z%.3f.png'
% (band_fil[l], ra[q], dec[q], z[q]),
dpi=300)
plt.close()
print(q)
return
def spec_mask_B(band_id, z_set, ra_set, dec_set):
Nz = len(z_set)
kk = np.int(band_id)
for q in range(Nz):
ra_g, dec_g, z_g = ra_set[q], dec_set[q], z_set[q]
file = d_file + 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (band[kk], ra_g, dec_g, z_g)
data = fits.open(file)
img = data[0].data
head_inf = data[0].header
wcs = awc.WCS(head_inf)
x_side = data[0].data.shape[1]
y_side = data[0].data.shape[0]
x0 = np.linspace(0, img.shape[1] - 1, img.shape[1])
y0 = np.linspace(0, img.shape[0] - 1, img.shape[0])
img_grid = np.array(np.meshgrid(x0, y0))
ra_img, dec_img = wcs.all_pix2world(img_grid[0,:], img_grid[1,:], 1)
pos = SkyCoord(ra_img, dec_img, frame = 'fk5', unit = 'deg')
BEV = sfd(pos)
Av = Rv * BEV * 0.86
Al = A_wave(l_wave[kk], Rv) * Av
img = img * 10**(Al / 2.5)
R_ph = rad2asec/(Test_model.angular_diameter_distance(z_g).value)
R_p = R_ph/pixel
cenx, ceny = wcs.all_world2pix(ra_g*U.deg, dec_g*U.deg, 1)
mask = load + 'bright_star_dr12/source_SQL_Z%.3f_ra%.3f_dec%.3f.txt'%(z_g, ra_g, dec_g)
cat = pds.read_csv(mask, skiprows = 1)
set_ra = np.array(cat['ra'])
set_dec = np.array(cat['dec'])
set_mag = np.array(cat['r'])
OBJ = np.array(cat['type'])
xt = cat['Column1']
tau = 10 * 3 # the mask size set as tau * FWHM from dr12
set_A = np.array( [ cat['psffwhm_r'] , cat['psffwhm_g'], cat['psffwhm_i']]) * tau / pixel
set_B = np.array( [ cat['psffwhm_r'] , cat['psffwhm_g'], cat['psffwhm_i']]) * tau / pixel
set_chi = np.zeros(set_A.shape[1], dtype = np.float)
lln = np.array([len(set_A[:,ll][set_A[:,ll] > 0 ]) for ll in range(set_A.shape[1]) ])
lr_iso = np.array([np.max(set_A[:,ll]) for ll in range(set_A.shape[1]) ])
sr_iso = np.array([np.max(set_B[:,ll]) for ll in range(set_B.shape[1]) ])
# bright stars
x, y = wcs.all_world2pix(set_ra * U.deg, set_dec * U.deg, 1)
ia = (x >= 0) & (x <= img.shape[1])
ib = (y >= 0) & (y <= img.shape[0])
ie = (set_mag <= 20)
iq = lln >= 2
ig = OBJ == 6
ic = (ia & ib & ie & ig & iq)
sub_x0 = x[ic]
sub_y0 = y[ic]
sub_A0 = lr_iso[ic]
sub_B0 = sr_iso[ic]
sub_chi0 = set_chi[ic]
# saturated source(may not stars)
xa = ['SATURATED' in pp for pp in xt]
xv = np.array(xa)
idx = xv == True
ipx = (idx & ia & ib)
sub_x2 = x[ipx]
sub_y2 = y[ipx]
sub_A2 = 3 * lr_iso[ipx]
sub_B2 = 3 * sr_iso[ipx]
sub_chi2 = set_chi[ipx]
comx = np.r_[sub_x0[sub_A0 > 0], sub_x2[sub_A2 > 0]]
comy = np.r_[sub_y0[sub_A0 > 0], sub_y2[sub_A2 > 0]]
Lr = np.r_[sub_A0[sub_A0 > 0], sub_A2[sub_A2 > 0]]
Sr = np.r_[sub_B0[sub_A0 > 0], sub_B2[sub_A2 > 0]]
phi = np.r_[sub_chi0[sub_A0 > 0], sub_chi2[sub_A2 > 0]]
Numb = len(comx)
mask_B = np.ones((img.shape[0], img.shape[1]), dtype = np.float)
ox = np.linspace(0, img.shape[1] - 1, img.shape[1])
oy = np.linspace(0, img.shape[0] - 1, img.shape[0])
basic_coord = np.array(np.meshgrid(ox,oy))
major = Lr / 2
minor = Sr / 2
senior = np.sqrt(major**2 - minor**2)
## mask B
for k in range(Numb):
xc = comx[k]
yc = comy[k]
lr = major[k]
sr = minor[k]
cr = senior[k]
theta = phi[k] * np.pi / 180
set_r = np.int(np.ceil(1.2 * lr))
la0 = np.max( [np.int(xc - set_r), 0])
la1 = np.min( [np.int(xc + set_r +1), img.shape[1] - 1] )
lb0 = np.max( [np.int(yc - set_r), 0] )
lb1 = np.min( [np.int(yc + set_r +1), img.shape[0] - 1] )
df1 = (basic_coord[0,:][lb0: lb1, la0: la1] - xc)* np.cos(theta) + (basic_coord[1,:][lb0: lb1, la0: la1] - yc)* np.sin(theta)
df2 = (basic_coord[1,:][lb0: lb1, la0: la1] - yc)* np.cos(theta) - (basic_coord[0,:][lb0: lb1, la0: la1] - xc)* np.sin(theta)
fr = df1**2 / lr**2 + df2**2 / sr**2
jx = fr <= 1
iu = np.where(jx == True)
iv = np.ones((jx.shape[0], jx.shape[1]), dtype = np.float)
iv[iu] = np.nan
mask_B[lb0: lb1, la0: la1] = mask_B[lb0: lb1, la0: la1] * iv
mirro_B = mask_B * img
hdu = fits.PrimaryHDU()
hdu.data = mirro_B
hdu.header = head_inf
hdu.writeto(load + 'mask_data/B_plane/1.5sigma/B_mask_data_%s_ra%.3f_dec%.3f_z%.3f.fits'%(band[kk], ra_g, dec_g, z_g),overwrite = True)
plt.figure()
ax = plt.imshow(mirro_B, cmap = 'Greys', origin = 'lower', vmin = 1e-7, vmax = 1e2, norm = mpl.colors.LogNorm())
plt.colorbar(ax, fraction = 0.035, pad = 0.01, label = '$flux[nmaggy]$')
hsc.circles(cenx, ceny, s = R_p, fc = '', ec = 'b', ls = '-')
hsc.circles(cenx, ceny, s = 1.1 * R_p, fc = '', ec = 'b', ls = '--')
plt.scatter(cenx, ceny, s = 10, marker = 'X', facecolors = '', edgecolors = 'r', linewidth = 0.5, alpha = 0.5)
plt.title('B mask img ra%.3f dec%.3f z%.3f in %s band' % (ra_g, dec_g, z_g, band[kk]))
plt.xlim(0, mirro_B.shape[1])
plt.ylim(0, mirro_B.shape[0])
plt.savefig(
'/mnt/ddnfs/data_users/cxkttwl/ICL/fig_class/spec_mask/B_mask_%s_ra%.3f_dec%.3f_z%.3f.png'%(band[kk], ra_g, dec_g, z_g), dpi = 300)
plt.close()
return
def extin_pro():
load = '/home/xkchen/mywork/ICL/data/total_data/'
idu = (com_Mag >= 17) & (com_Mag <= 18)
x0 = np.linspace(0, 2047, 2048)
y0 = np.linspace(0, 1488, 1489)
img_grid = np.array(np.meshgrid(x0, y0))
sub_ra = ra[idu]
sub_dec = dec[idu]
sub_z = z[idu]
zN = 20
cat_Rii = np.array([
0.23, 0.68, 1.03, 1.76, 3.00, 4.63, 7.43, 11.42, 18.20, 28.20, 44.21,
69.00, 107.81, 168.20, 263.00
])
R_smal, R_max, bins = 1, 200, 55
plt.figure(figsize=(20, 20))
gs = gridspec.GridSpec(zN // 5, 5)
for kk in range(zN):
ra_g = sub_ra[kk]
dec_g = sub_dec[kk]
z_g = sub_z[kk]
Da_g = Test_model.angular_diameter_distance(z_g).value
## original image
file = 'frame-%s-ra%.3f-dec%.3f-redshift%.3f.fits.bz2' % (
'g', ra_g, dec_g, z_g) ## data
data = fits.open(load + file)
img = data[0].data
wcs = awc.WCS(data[0].header)
cx, cy = wcs.all_world2pix(ra_g * U.deg, dec_g * U.deg, 1)
Intns, Intns_r, Intns_err, Npix = light_measure(
img, bins, R_smal, R_max, cx, cy, pixel, z_g)
id_nan = np.isnan(Intns)
flux = Intns[id_nan == False]
flux_err = Intns_err[id_nan == False]
sub_R0 = Intns_r[id_nan == False]
flux0 = flux + flux_err
flux1 = flux - flux_err
sub_SB0 = 22.5 - 2.5 * np.log10(flux) + 2.5 * np.log10(pixel**2)
SB0 = 22.5 - 2.5 * np.log10(flux0) + 2.5 * np.log10(pixel**2)
SB1 = 22.5 - 2.5 * np.log10(flux1) + 2.5 * np.log10(pixel**2)
err0 = sub_SB0 - SB0
err1 = SB1 - sub_SB0
id_nan = np.isnan(SB1)
err1[id_nan] = 100.
sub0_err0 = err0 * 1
sub0_err1 = err1 * 1
## SFD 1998
ra_img, dec_img = wcs.all_pix2world(img_grid[0, :], img_grid[1, :], 1)
pos = SkyCoord(ra_img, dec_img, frame='fk5', unit='deg')
BEV_98 = sfd(pos)
Av_98 = Rv * BEV_98
Al = A_wave(l_wave[1], Rv) * Av_98
c0_img = img * 10**(Al / 2.5)
Intns, Intns_r, Intns_err, Npix = light_measure(
c0_img, bins, R_smal, R_max, cx, cy, pixel, z_g)
id_nan = np.isnan(Intns)
flux = Intns[id_nan == False]
flux_err = Intns_err[id_nan == False]
sub_R1 = Intns_r[id_nan == False]
flux0 = flux + flux_err
flux1 = flux - flux_err
sub_SB1 = 22.5 - 2.5 * np.log10(flux) + 2.5 * np.log10(pixel**2)
SB0 = 22.5 - 2.5 * np.log10(flux0) + 2.5 * np.log10(pixel**2)
SB1 = 22.5 - 2.5 * np.log10(flux1) + 2.5 * np.log10(pixel**2)
err0 = sub_SB1 - SB0
err1 = SB1 - sub_SB1
id_nan = np.isnan(SB1)
err1[id_nan] = 100.
sub1_err0 = err0 * 1
sub1_err1 = err1 * 1
## SFD 2011
BEV_11 = sfd(pos) * 0.86
Av_11 = Rv * BEV_11
Al = A_wave(l_wave[1], Rv) * Av_11
c1_img = img * 10**(Al / 2.5)
Intns, Intns_r, Intns_err, Npix = light_measure(
c1_img, bins, R_smal, R_max, cx, cy, pixel, z_g)
id_nan = np.isnan(Intns)
flux = Intns[id_nan == False]
flux_err = Intns_err[id_nan == False]
sub_R2 = Intns_r[id_nan == False]
flux0 = flux + flux_err
flux1 = flux - flux_err
sub_SB2 = 22.5 - 2.5 * np.log10(flux) + 2.5 * np.log10(pixel**2)
SB0 = 22.5 - 2.5 * np.log10(flux0) + 2.5 * np.log10(pixel**2)
SB1 = 22.5 - 2.5 * np.log10(flux1) + 2.5 * np.log10(pixel**2)
err0 = sub_SB2 - SB0
err1 = SB1 - sub_SB2
id_nan = np.isnan(SB1)
err1[id_nan] = 100.
sub2_err0 = err0 * 1
sub2_err1 = err1 * 1
## SDSS catalogue
cat_pro = pds.read_csv(
'/home/xkchen/mywork/ICL/data/BCG_pros/BCG_prof_Z%.3f_ra%.3f_dec%.3f.txt'
% (z_g, ra_g, dec_g),
skiprows=1)
dat_band = np.array(cat_pro.band)
dat_bins = np.array(cat_pro.bin)
dat_pro = np.array(cat_pro.profMean) # in unit of nmaggy / arcsec^2
dat_pro_err = np.array(cat_pro.profErr)
idx = dat_band == 1
tt_pro = dat_pro[idx]
tt_bin = dat_bins[idx]
tt_proErr = dat_pro_err[idx]
tt_r = (cat_Rii[tt_bin] * Da_g / rad2asec) * 1e3 # arcsec --> kpc
cat_SB = 22.5 - 2.5 * np.log10(tt_pro)
SB0 = 22.5 - 2.5 * np.log10(tt_pro + tt_proErr)
SB1 = 22.5 - 2.5 * np.log10(tt_pro - tt_proErr)
err0 = cat_SB - SB0
err1 = SB1 - cat_SB
id_nan = np.isnan(cat_SB)
cc_SB, SB0, SB1 = cat_SB[id_nan == False], SB0[id_nan == False], SB1[
id_nan == False]
cc_r, err0, err1 = tt_r[id_nan == False], err0[id_nan == False], err1[
id_nan == False]
id_nan = np.isnan(SB1)
err1[id_nan] = 100.
cc_err0 = err0 * 1
cc_err1 = err1 * 1
ax = plt.subplot(gs[kk // 5, kk % 5])
ax.errorbar(sub_R0,
sub_SB0,
yerr=[sub0_err0, sub0_err1],
xerr=None,
ls='-',
fmt='r.',
label='No correction',
alpha=0.5)
ax.errorbar(sub_R1,
sub_SB1,
yerr=[sub1_err0, sub1_err1],
xerr=None,
ls='-',
fmt='g.',
label='SFD 1998',
alpha=0.5)
ax.errorbar(sub_R2,
sub_SB2,
yerr=[sub2_err0, sub2_err1],
xerr=None,
ls='-',
fmt='b.',
label='SFD 2011',
alpha=0.5)
ax.errorbar(cc_r,
cc_SB,
yerr=[cc_err0, cc_err1],
xerr=None,
ls='--',
fmt='k.',
label='SDSS cat.',
alpha=0.5)
ax.set_xscale('log')
ax.set_xlabel('k[kpc]')
ax.set_ylabel('SB[mag/arcsec^2]')
ax.set_ylim(20, 28)
ax.set_xlim(1, 200)
ax.legend(loc=1)
ax.invert_yaxis()
plt.tight_layout()
plt.savefig('Extinct_BCG_pros_g_band.png', dpi=300)
plt.close()
raise
def main():
x0 = np.linspace(0, 2047, 2048)
y0 = np.linspace(0, 1488, 1489)
"""
# total catalogue with spec-redshift
with h5py.File('/mnt/ddnfs/data_users/cxkttwl/ICL/data/mpi_h5/sample_catalog.h5', 'r') as f:
catalogue = np.array(f['a'])
z = catalogue[0]
ra = catalogue[1]
dec = catalogue[2]
"""
for kk in range(3):
with h5py.File(
load + 'random_cat/cat_select/rand_%s_band_catalog.h5' %
(band[kk]), 'r') as f:
tmp_array = np.array(f['a'])
ra, dec, z, rich = np.array(tmp_array[0]), np.array(
tmp_array[1]), np.array(tmp_array[2]), np.array(tmp_array[3])
zN = len(z)
set_z, set_ra, set_dec = z, ra, dec
DN = len(set_z)
m, n = divmod(DN, cpus)
N_sub0, N_sub1 = m * rank, (rank + 1) * m
if rank == cpus - 1:
N_sub1 += n
map_stack(kk, set_ra[N_sub0:N_sub1], set_dec[N_sub0:N_sub1],
set_z[N_sub0:N_sub1])
commd.Barrier()
if rank == 0:
mean_map11 = np.zeros((len(y0), len(x0)), dtype=np.float)
for pp in range(cpus):
with h5py.File(
tmp + 'stack_dust-map-11_%d_in_%s_band.h5' %
(pp, band[kk]), 'r') as f:
map_11 = np.array(f['a'])
mean_map11 = mean_map11 + map_11
mean_map11 = mean_map11 / DN
with h5py.File(
load + 'random_cat/angle_stack/map_11_stack_%s_band.h5' %
(band[kk], ), 'w') as f:
f['a'] = np.array(mean_map11)
plt.figure(figsize=(7.5, 4.5))
gf1 = plt.imshow(mean_map11, cmap='rainbow', origin='lower')
plt.title('$map_{2011} \; stack \; in \; %s \; band$' %
(band[kk], ))
plt.colorbar(gf1, fraction=0.035, pad=0.01, label='$ E_{B-V} $')
plt.subplots_adjust(left=0.01, right=0.85)
plt.savefig(
load +
'random_cat/angle_stack/map_11_stack_%s_band.png' % band[kk],
dpi=600)
plt.close()
Av = Rv * mean_map11
Al = A_wave(l_wave[kk], Rv) * Av
devi_flux = 10**(Al / 2.5) - 1
plt.figure(figsize=(7.5, 4.5))
plt.title(
'$ A_{\\lambda} \,[based \; on \; map_{2011} \; %s \; band]$' %
(band[kk], ))
gf1 = plt.imshow(Al, cmap='rainbow', origin='lower')
plt.colorbar(
gf1,
fraction=0.035,
pad=0.01,
)
plt.subplots_adjust(left=0.01, right=0.85)
plt.savefig(load +
'random_cat/angle_stack/Al_map-11_stack_%s_band.png' %
band[kk],
dpi=300)
plt.close()
plt.figure(figsize=(7.5, 4.5))
plt.title(
'$ 10^{ A_{\\lambda} / 2.5} - 1 \,[based \; on \; map_{2011} \; %s \; band]$'
% (band[kk], ))
gf1 = plt.imshow(devi_flux, cmap='rainbow', origin='lower')
plt.colorbar(
gf1,
fraction=0.035,
pad=0.01,
)
plt.subplots_adjust(left=0.01, right=0.85)
plt.savefig(
load +
'random_cat/angle_stack/flux_devi_map-11_stack_%s_band.png' %
band[kk],
dpi=300)
plt.close()
commd.Barrier()