f_h5.close()
if argv[1] == "e1":
idx = 0
else:
idx = 1
e1 = es_data[:, idx]
ch = numpy.random.choice(range(len(e1)), 10000, replace=False)
# para_path = total_path + "parameters/para_%d.hdf5"%source
# para_h5 = h5py.File(para_path, "r")
# mag_t = para_h5["/mag"].value
# e1 = para_h5["/e1"].value
# # e1 = para_h5["/%s"%argv[2]].value
# para_h5.close()
bins = tool_box.set_bin(e1, bin_num, 1.02)
fourier_path = total_path + "result/data/data_1.5sig/data_%d.hdf5" % source
f_h5 = h5py.File(fourier_path, "r")
f_data = f_h5["/data"].value
f_h5.close()
sex_path = total_path + "result/data/sex2_1.5/sex_%d.npz" % source
s_data = numpy.load(sex_path)["arr_0"]
detect_f = f_data[:, -1] > -1
detect_s = s_data[:, 0] > 0
# P(k=0)
pk0 = f_data[:, 4] / 64 / 60
print(pk0.max(), pk0.min())
h5f = h5py.File(data_path1, "r")
mg1[:num_s] = h5f["/data"][()][:, 0]
mnu1[:num_s] = h5f["/data"][()][:, 2] + h5f["/data"][()][:, 3]
h5f.close()
# non-sheared galaxies
h5f = h5py.File(data_path2, "r")
mg1[num_s:] = h5f["/data"][()][:, 0]
mnu1[num_s:] = h5f["/data"][()][:, 2] + h5f["/data"][()][:, 3]
h5f.close()
h5f = h5py.File(data_path3, "r")
mg1_corr = h5f["/data"][()][:, 0]
mnu1_corr = h5f["/data"][()][:, 2] + h5f["/data"][()][:, 3]
h5f.close()
G1_bin = tool_box.set_bin(mg1[:num_s], mg_bin_num, 1000)
G1_hist_bin = gglensing_tool.set_bin(mg1[:num_s], 2500, 1.001, "log")
NU1_hist_bin = gglensing_tool.set_bin(mnu1[:num_s], 2500, 1.001, "log")
dilute_case = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
dilute_num = len(dilute_case)
task_list = [i for i in range(dilute_num)]
task_list_sub = tool_box.alloc(task_list, numprocs)[rank]
itemsize = MPI.DOUBLE.Get_size()
if rank == 0:
# bytes for 10 double elements
nbytes = 4 * dilute_num * itemsize
else:
nbytes = 0
g2 = h5f["/g2"][()]
h5f.close()
shear_tag = rank
print(shear_tag, g1[shear_tag], g2[shear_tag])
h5f = h5py.File(data_path + "/data_%d_noisy_cpp.hdf5" % shear_tag, "r")
data = h5f["/data"][()]
h5f.close()
G1 = numpy.ascontiguousarray(data[:, 0], dtype=numpy.float64)
G2 = numpy.ascontiguousarray(data[:, 1], dtype=numpy.float64)
NU1 = numpy.ascontiguousarray(data[:, 2] + data[:, 3], dtype=numpy.float64)
NU2 = numpy.ascontiguousarray(data[:, 2] - data[:, 3], dtype=numpy.float64)
G1_bin = tool_box.set_bin(G1, 10, 100)
G2_bin = tool_box.set_bin(G2, 10, 100)
img = Image_Plot(xpad=0.2, ypad=0.2)
img.subplots(2, 5)
bin_type = [bins for i in range(5)]
bins_nums = [[100, 50], [100, 100], [200, 100], [200, 200], [500, 200]]
for j in range(5):
num_g, num_nu = bins_nums[j]
G1_hist_bin = set_bin(G1[:3000], bins_nums[j], 1.001, bin_type[j])
NU1_hist_bin = set_bin(NU1[:3000], bins_nums[j], 1.001, bin_type[j])
gx = data[:, 1]
n = data[:, 2]
u = data[:, 3]
crit = data[:, 4]*3.882833518*100
g1 = data[:, 5]
gt_c = gt*crit
gx_c = gx*crit
nu1 = n + u
nu2 = n - u
print(gt)
print(g1)
print(crit)
gtc_bin = tool_box.set_bin(gt_c,8, 100)
gt_bin = tool_box.set_bin(gt,8, 100)
fq = Fourier_Quad(1, 1)
g_corr_t, corr_sig_t = fq.fmin_g_new(g=gt_c, nu=nu1, bin_num=10, scale=100,
pic_path="%s/%d_t.png"%(data_path,tag), left=-500, right=500)
g_corr_x, corr_sig_x = fq.fmin_g_new(g=gx_c, nu=nu2, bin_num=10, scale=100,
pic_path="%s/%d_x.png"%(data_path,tag), left=-500, right=500)
print(g_corr_t,corr_sig_t)
print(g_corr_x,corr_sig_x)
result[0, rank] = g_corr_t
result[1, rank] = corr_sig_t
dec=dec * units.arcsec,
frame="fk5")
position_theta = len_pos.position_angle(src_pos).rad
cos_2theta = numpy.cos(2 * position_theta)
sin_2theta = numpy.sin(2 * position_theta)
cos_4theta = numpy.cos(4 * position_theta)
sin_4theta = numpy.sin(4 * position_theta)
mg1r = (mg1 * cos_2theta - mg2 * sin_2theta) * crit_coeff
mg2r = (mg1 * sin_2theta + mg2 * cos_2theta) * crit_coeff
mur = mu * cos_4theta - mv * sin_4theta
mnur1 = mn + mur
mnur2 = mn - mur
G1_bin = tool_box.set_bin(mg1r, mg_bin_num, 100)
G1_hist_bin = gglensing_tool.set_bin(mg1r, 4000, 1.001, "log")
NU1_hist_bin = gglensing_tool.set_bin(mnur1, 4000, 1.001, "log")
img = Image_Plot(xpad=0.25, ypad=0.25)
img.subplots(1, 2)
result = gglensing_tool.find_shear_grid(mg1r,
mnur1,
G1_bin,
G1_hist_bin,
NU1_hist_bin,
chisq_gap=50,
dg=10,
fit_num=20,
ax=img.axs[0][0])[:4]
fg1 = field_data[:, 14][idx]
fg2 = field_data[:, 15][idx]
FG1 = field_data[:, 16][idx]
FG2 = field_data[:, 17][idx]
FN = field_data[:, 18][idx]
FU = field_data[:, 19][idx]
idx_thres = fsnr >= thresh
for i in range(g1num):
idx11 = fg1 >= fd_g1[i] - dg1 / 2
idx12 = fg1 <= fd_g1[i] + dg1 / 2
mg1 = FG1[idx11 & idx12 & idx_thres]
mn = FN[idx11 & idx12 & idx_thres]
mu = FU[idx11 & idx12 & idx_thres]
mg1_bins = tool_box.set_bin(mg1, bin_num)
chi_1 = fq.G_bin(mg1, mn, mu, est_g1[i], 1, mg1_bins, 0)
chi_1s.append(chi_1)
for j in range(g2num):
idx21 = fg2 >= fd_g2[j] - dg2 / 2
idx22 = fg2 <= fd_g2[j] + dg2 / 2
mg2 = FG2[idx21 & idx22 & idx_thres]
mn = FN[idx21 & idx22 & idx_thres]
mu = FU[idx21 & idx22 & idx_thres]
mg2_bins = tool_box.set_bin(mg2, bin_num)
chi_2 = fq.G_bin(mg2, mn, mu, est_g2[j], 2, mg2_bins, 0)
chi_2s.append(chi_2)
total_chi_1.append(chi_1s)
total_chi_2.append(chi_2s)
field_name.append(name)
field_name = []
with open(fourier_cata_path + "/cat_inform/exposure_avail.dat", "r") as f:
f_lines = f.readlines()
for ff in f_lines:
field_name.append(ff.split("\n")[0])
# set up bins for G1(or G2) for PDF_SYM
for i in range(20):
h5f_src = h5py.File(field_name[i], "r")
temp = h5f_src["/data"][()][:, mg1_idx:mg2_idx + 1]
if i == 0:
src_data = temp
else:
src_data = numpy.row_stack((src_data, temp))
mg_bin = tool_box.set_bin(src_data[:, 0], mg_bin_num, 100000)
h5f_cor = h5py.File(result_cata_path + "/gg_cor.hdf5", "w")
for i in range(chi_guess_num):
mean = [0, 0]
cov = [[numpy.abs(chi_guess_bin[i] * 2), chi_guess_bin[i]],
[chi_guess_bin[i], numpy.abs(chi_guess_bin[i] * 2)]]
gg = tool_box.rand_gauss2n(cor_gg_len, mean, cov).astype(dtype=numpy.float32)
if i == 0:
gg_1 = gg[0]
gg_2 = gg[1]
