def mc_plot(result_arr, mc_arr, img_name):
img = Image_Plot(fig_x=6, fig_y=4, xpad=0.25, ypad=0.2)
img.subplots(1, 2)
scale = 1000
img.axs[0][0].errorbar(result_arr[0],
result_arr[1],
result_arr[2],
marker="s",
fmt=" ",
label="g1",
capsize=img.cap_size)
img.axs[0][0].errorbar(result_arr[3],
result_arr[4],
result_arr[5],
marker="s",
fmt=" ",
label="g2",
capsize=img.cap_size)
img.axs[0][1].errorbar(result_arr[0],
scale * (result_arr[1] - result_arr[0]),
scale * result_arr[2],
marker="s",
fmt=" ",
label="g1",
capsize=img.cap_size)
img.axs[0][1].errorbar(result_arr[3],
scale * (result_arr[4] - result_arr[3]),
scale * result_arr[5],
marker="s",
fmt=" ",
label="g2",
capsize=img.cap_size)
mc_str = "Mean\n$m_1 = %.5f (%.5f)$\n$c_1 = %.5f (%.5f)$\n$m_2 = %.5f (%.5f)$\n$c_2 = %.5f (%.5f)$" \
% (mc_arr[0, 0], mc_arr[0, 1], mc_arr[0, 2], mc_arr[0, 3],
mc_arr[1, 0], mc_arr[1, 1], mc_arr[1, 2], mc_arr[1, 3])
img.axs_text(0, 0, 0.8, 0.05, mc_str, text_fontsize=img.legend_size)
img.set_label(0, 0, 0, "EST g")
img.set_label(0, 1, 1, "$10^3 (\hat{g} - g_{true})$")
img.axs[0][0].plot([-0.1, 0.1], [-0.1, 0.1],
ls="dashed",
c="grey",
alpha=0.5)
img.axs[0][0].set_ylim(-0.05, 0.05)
img.axs[0][1].set_ylim(-0.1, 0.1)
for i in range(2):
img.set_label(0, i, 1, "TRUE g")
img.axs[0][i].legend(fontsize=img.legend_size, loc="lower right")
img.axs[0][i].set_xlim(-0.05, 0.05)
img.save_img(img_name)
img.close_img()
def plot_gf_pix(x, y, ichip, gf1, gf2, gf, gf1_scale, gf2_scale,gf_scale,dot_size=1,pic_path=None):
chip_row, chip_col = numpy.divmod(ichip, 9)
x = x + chip_col * 2112
y = y + chip_row * 4644
img = Image_Plot(xpad=0.2,ypad=0.1)
img.subplots(1, 3)
color_cm = 'bwr'
norm = plt.Normalize(vmin=numpy.min(gf1_scale[0]), vmax=numpy.max(gf1_scale[1]))
cmap = plt.get_cmap(color_cm)
cl = cmap(norm(gf1))
fig = img.axs[0][0].scatter(x, y, color=cl, s=dot_size)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][0])
norm = plt.Normalize(vmin=numpy.min(gf2_scale[0]), vmax=numpy.max(gf2_scale[1]))
cmap = plt.get_cmap(color_cm)
cl = cmap(norm(gf2))
fig = img.axs[0][1].scatter(x, y, color=cl, s=dot_size)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][1])
norm = plt.Normalize(vmin=numpy.min(gf_scale[0]), vmax=numpy.max(gf_scale[1]))
cmap = plt.get_cmap(color_cm)
cl = cmap(norm(gf))
fig = img.axs[0][2].scatter(x, y, color=cl, s=dot_size)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][2])
for i in range(3):
img.axis_sci_ticklabel(0,i,0)
img.axis_sci_ticklabel(0,i,1)
img.set_label(0,i,0,"y")
img.set_label(0,i,1,"x")
if pic_path:
img.save_img(pic_path)
# img.show_img()
img.close_img()
def plot_label(x, y, cent_xy, cent_label, title, show=None, pic_path=None):
ncent = cent_label.shape[0]
img = Image_Plot()
img.subplots(1, 1)
for i in range(ncent):
img.axs[0][0].scatter(cent_xy[0][i],
cent_xy[1][i],
color="C%d" % i,
marker="*",
s=80)
idx = cent_label[i] == 1
img.axs[0][0].scatter(x[idx],
y[idx],
edgecolors="C%d" % i,
color="none",
label="%d, %d" % (i, idx.sum()),
s=5)
img.axs[0][0].set_title(title)
img.axs[0][0].legend()
if pic_path:
img.save_img(pic_path)
if show:
img.show_img()
img.close_img()
'%d' % ny],
['param', "RA", '%.2f' % delta_ra],
['param', "DEC", '%.2f' % delta_dec],
['param', "pixel_scale",
'%.6f' % pixel_scale], ['param', "sigma",
'%.4f' % sigma],
['param', "amplitude", '%.2f' % amplitude],
['param', "dx", '%.4f' % dx], ['param', "dy",
'%.2f' % dy],
['param', "density/arcmin^2",
'%d' % dens_num]]
path_items = tool_box.config(envs_path, ['add' for i in range(len(contents))],
contents, True)
img = Image_Plot()
img.subplots(2, 2)
inverse = range(nx - 1, -1, -1)
for i in range(2):
for j in range(2):
tag = i * 2 + j
if tag < 3:
fig = img.axs[i][j].imshow(g[tag][inverse], cmap="jet")
img.figure.colorbar(fig, ax=img.axs[i][j])
else:
num_dens = numpy.histogram2d(gal_coord[0], gal_coord[1],
[ra_bin, dec_bin])[0]
fig = img.axs[i][j].imshow(num_dens, cmap="jet")
img.figure.colorbar(fig, ax=img.axs[i][j])
img.set_label(i, j, 0, "DEC. [arcmin]")
img.set_label(i, j, 1, "R.A. [arcmin]")
h5f.close()
# true, mean, measured, sigma, diff_percent, number
plt_data_com = [
shear_field_grid_true, shear_field_grid_mean, shear_field_grid,
shear_field_grid_mean - shear_field_grid_true,
shear_field_grid - shear_field_grid_true
]
plt_data_m = [shear_field_grid_err, num_in_grid]
plt_data_coms.append(plt_data_com)
plt_data_ms.append(plt_data_m)
img = Image_Plot(fig_x=5, fig_y=5 / 4 * 3)
img.subplots(len(grid_nx), 5)
titles = [
"True", "Mean in grid", "Measured", "Mean - True", "Measured - True"
]
for tag in range(len(grid_nx)):
nx = grid_nx[tag]
inverse = range(nx - 1, -1, -1)
grid_min = min([plt_data_coms[tag][i].min() for i in range(3)])
grid_max = min([plt_data_coms[tag][i].max() for i in range(3)])
diff_min = min([plt_data_coms[tag][i].min() for i in range(3, 5)])
diff_max = min([plt_data_coms[tag][i].max() for i in range(3, 5)])
from Fourier_Quad import Fourier_Quad
from plot_tool import Image_Plot
h5f = h5py.File("test.hdf5", "r")
img = h5f["/img"][()]
img_pow = h5f["/img_pow"][()]
img_pow_real = h5f["/img_pow_real"][()]
img_pow_imag = h5f["/img_pow_imag"][()]
img_pow_arg = h5f["/img_arg"][()]
h5f.close()
fq = Fourier_Quad(img.shape[0], 12)
img_pow_fq = fq.pow_spec(img)
img_pow_arg_fq = fq.pow_arg(img)
img_pow_real_fq, img_pow_imag_fq = fq.pow_real_imag(img)
datas = [[img, img_pow, img_pow_arg, img_pow_real, img_pow_imag],
[img, img_pow_fq, img_pow_arg_fq, img_pow_real_fq, img_pow_imag_fq],
[
img - img, img_pow - img_pow_fq, img_pow_arg - img_pow_arg_fq,
img_pow_real - img_pow_real_fq, img_pow_imag - img_pow_imag_fq
]]
img = Image_Plot()
img.subplots(3, 5)
for i in range(3):
for j in range(5):
fig = img.axs[i][j].imshow(datas[i][j])
img.figure.colorbar(fig, ax=img.axs[i][j])
img.del_ticks(i, j, [0, 1])
img.show_img()
import matplotlib
matplotlib.use("Agg")
from sys import path, argv
path.append("/home/hklee/work/mylib")
path.append("/home/hkli/work/mylib")
path.append("D:/GitHub/astrophy-research/mylib")
from plot_tool import Image_Plot
import numpy
import h5py
import os
parent_path = "/mnt/ddnfs/data_users/hkli/bias_check/data_from_pi/psf_scale_test"
img = Image_Plot(fig_x=7, fig_y=2, ypad=0, xpad=0.2)
img.subplots(4, 1)
m_scale = 100
c_scale = 10000
x_tick_pos = []
for i in range(7):
noise_free_path = parent_path + "/data_%d/shear_result_noise_free_epsf_ori_pdf.hdf5" % i
noisy_path = parent_path + "/data_%d/shear_result_noisy_cpp_epsf_ori_pdf.hdf5" % i
print(i, os.path.exists(noise_free_path))
print(os.path.exists(noisy_path))
#
# continue
h5f = h5py.File(noise_free_path, "r")
sym_mc_nf = h5f["/sym_mc"][()]
mean_mc_nf = h5f["/mean_mc"][()]
h5f.close()
import os
my_home = os.popen("echo $MYWORK_DIR").readlines()[0][:-1]
from sys import path, argv
path.append('%s/work/mylib/' % my_home)
import h5py
import numpy
from plot_tool import Image_Plot
src_path = argv[1]
ra_bin = [1000, 5000, 10000, 15000, 30000]
labels = []
sub_num = numpy.array([75, 34, 56, 35], dtype=numpy.intc)
img = Image_Plot(fig_x=8, fig_y=6)
img.subplots(2, 2)
for i in range(2):
for j in range(2):
tag = int(i * 2 + j)
h5f = h5py.File(src_path + "/group_predict_%d.hdf5" % tag, "r")
group_label = h5f["/data"][()]
ra_dec = h5f["/ra_dec"][()]
h5f.close()
for k in range(sub_num[tag]):
idx = group_label == k
print(k, idx.sum())
# group_label += sub_num[:tag].sum()
print(tag, group_label.min(), group_label.max(), sub_num[:tag].sum())
print("\n")
img.pts_scatter(i, j, ra_dec[:, 0], ra_dec[:, 1], group_label,
h5f.close()
print(chisqs.shape)
print(chisqs.min(), chisqs.max())
omega_cm_num = omega_cm_bin.shape[0]
omega_bm_num = omega_bm_bin.shape[0]
sigma8_num = sigma8_bin.shape[0]
my, mx = numpy.zeros((omega_cm_num, sigma8_num)), numpy.zeros(
(omega_cm_num, sigma8_num))
for i in range(omega_cm_num):
my[i] = omega_cm_bin[i]
for j in range(sigma8_num):
mx[:, j] = sigma8_bin[j]
img = Image_Plot(fig_x=4, fig_y=3, xpad=0.25, ypad=0.28)
img.subplots(2, 5)
for i in range(2):
for j in range(5):
tag = i * 2 + j
idx = chisqs[tag] > -200
print(idx.sum())
x = mx[idx].flatten()
y = my[idx].flatten()
z = chisqs[tag][idx].flatten()
img.scatter_pts(i, j, x, y, z, color_map="jet", pts_size=18)
img.axs[i][j].set_title("$\Omega_b=%.3f$" % omega_bm_bin[tag])
img.set_label(i, j, 0, "$\Omega_m$")
img.set_label(i, j, 1, "$\sigma_8$")
# the galactic parameters
h5f = h5py.File(parent_path + "param_slope.hdf5", "w")
h5f["/FIELD_g"] = shear_field
h5f["/x"] = x
mag_i = tool_box.mag_generator(total_num, 20, 23.5)
flux_i = tool_box.mag_to_flux(mag_i)
h5f["/mag"] = mag_i
h5f["/flux"] = flux_i
print(flux_i.min(), flux_i.max())
# Plot
img = Image_Plot()
img.subplots(1, 3)
img.set_style()
inverse = range(nx - 1, -1, -1)
norm_g = plt.Normalize(vmin=numpy.min(shear_field_ch),
vmax=numpy.max(shear_field_ch))
cmap_g = plt.get_cmap('jet')
for i in range(3):
if i == 0:
fig = img.axs[0][i].imshow(shear_field[inverse], cmap="jet")
img.figure.colorbar(fig, ax=img.axs[0][i])
elif i == 1:
num_dens = numpy.histogram2d(gal_coord[0], gal_coord[1],
[ra_bin, dec_bin])[0]
fig = img.axs[0][i].imshow(num_dens, cmap="jet")
print("E-PSF")
gal_imgs = numpy.zeros((size, 4*size))
# gal_imgs_cpp = numpy.zeros((size, 4*size))
psf_imgs = numpy.zeros((size, 4*size))
# psf_imgs_cpp = numpy.zeros((size, 4*size))
for i in range(4):
psf = fq.cre_psf(psf_scale,ellip_theta=(psf_e, numpy.pi/4*i + theta))
gal = fq.convolve_psf(pts, psf_scale, 1, ellip_theta=(psf_e, numpy.pi/4*i + theta))
gal_imgs[:,int(i*size):int((i+1)*size)] = gal
psf_imgs[:,int(i*size):int((i+1)*size)] = psf
diff_psf = psf_imgs - epsf_cpp
print(diff_psf.min(), diff_psf.max())
img = Image_Plot(fig_x=4,fig_y=3)
img.subplots(3, 1)
fig = img.axs[0][0].imshow(psf_imgs)
img.figure.colorbar(fig,ax=img.axs[0][0])
fig = img.axs[1][0].imshow(epsf_cpp)
img.figure.colorbar(fig,ax=img.axs[1][0])
fig = img.axs[2][0].imshow(diff_psf)
img.figure.colorbar(fig,ax=img.axs[2][0])
img.show_img()
diff_gal = gal_imgs - gal_epsf_cpp
print(diff_gal.min(), diff_gal.max())
img = Image_Plot(fig_x=4,fig_y=3)
img.subplots(3, 1)
fig = img.axs[0][0].imshow(gal_imgs)
img.figure.colorbar(fig,ax=img.axs[0][0])
xylim = 0
xy_lock = -1
move_steps = 200
show_num = 150
gh = numpy.linspace(-0.1, 0.1, move_steps)
m, n = divmod(move_steps, numprocs)
st, ed = int(m * rank), int(m * (rank + 1))
img_col = 6
img_row = 3
for k in range(st, ed):
img = Image_Plot(fig_x=3, fig_y=3, xpad=0.4, ypad=0.4, xy_lb_size=12)
img.subplots(img_row, img_col)
for i in range(img_row):
for j in range(img_col):
if i < img_row - 1:
tag = i * img_col + j
if component_tag == 0:
G1_hat = datas[tag][:show_num, 0] - gh[k] * (
datas[tag][:show_num, 2] +
datas[tag][:show_num, 3])
img.axs[i][j].scatter(G1_hat,
datas[tag][:show_num, 1],
s=6,
label="g1=%.3f" % gh[k])
else:
comm.Barrier()
t2 = time.time()
if rank == 0:
for j in range(shear_num):
h5f = h5py.File(parent_path + "shear_%d.hdf5" % j, "r")
temp = h5f["/data"].value
h5f.close()
if j == 0:
data = temp
else:
data = numpy.row_stack((data, temp))
img = Image_Plot()
img.subplots(2, len(ch_num))
img.set_style()
for i in range(len(ch_num)):
idx = [n + m*gal_each_shear for m in range(shear_num) for n in range(ch_num[i])]
mg1 = data[:, 0][idx]
mg2 = data[:, 1][idx]
mnu1 = data[:, 2][idx] + data[:, 3][idx]
mnu2 = data[:, 2][idx] - data[:, 3][idx]
gh1, gh1_sig = fq.fmin_g_new(g=mg1, nu=mnu1, bin_num=8, scale=100,fig_ax=img.axs[0][i])[:2]
gh2, gh2_sig = fq.fmin_g_new(g=mg2, nu=mnu2, bin_num=8, scale=100,fig_ax=img.axs[1][i])[:2]
result_1[i, shear_num*2] = gh1
scale = 100
xy_bin_num, radius_bin_num = 200, 60
h5f = h5py.File(data_path + "/data_%s_%d.hdf5" % (data_nm, rank), "r")
print("g1:", g1, "g2:", g2)
print(data_path + "/data_%s_%d.hdf5" % (data_nm, rank))
mg1 = h5f["/mg1"][()] / scale
mg2 = h5f["/mg2"][()] / scale
mn = h5f["/mn"][()] / scale
mu = h5f["/mu"][()] / scale
h5f.close()
img = Image_Plot(xpad=0.15, ypad=0.2, fig_x=4, fig_y=3)
img.subplots(6, 6)
xy_bin = component_fit.get_bin(mg1, mg2, xy_bin_num)
num, xgrid, ygrid, radius_grid = component_fit.get_2dhist(mg1, mg2, xy_bin)[:4]
dpl, radius_bin, radius_mask, mean_of_annuli = component_fit.get_dipole(
num, radius_grid, radius_bin_num)
qpl, dpl_fit, sin_theta, cos_theta = component_fit.get_quadrupole(
dpl, xgrid, ygrid, radius_bin, radius_bin_num)
qpl_fit, sin_2theta, cos_2theta = component_fit.fit_quadrupole(
qpl, xgrid, ygrid, radius_bin, radius_bin_num)
dpl = numpy.nan_to_num(dpl)
my_g2 = tool_box.allot([i for i in range(fg2_bin_num)], cpus)[rank]
for i in range(fg2_bin_num):
idx_1 = fg2 >= fg2_bin[i]
idx_2 = fg2 < fg2_bin[i + 1]
idx_t = idx_1 & idx_2
g2, g2_sig = fq.fmin_g_new(mg2[idx_t], mnu2[idx_t], 8, fit_num=20)[:2]
result[2, i] = g2
result[3, i] = g2_sig
comm.Barrier()
t2 = time.time()
if rank == 0:
img = Image_Plot()
img.subplots(1, 2)
img.axs[0][0].errorbar(fg1_bin[:fg1_bin_num],
result[0, :fg1_bin_num],
result[1, :fg1_bin_num],
capsize=3,
c="C1",
label="g1")
img.axs[0][0].scatter(fg1_bin[:fg1_bin_num],
result[0, :fg1_bin_num],
c="C1",
marker="s")
xs = img.axs[0][0].set_xlim()
ys = img.axs[0][0].set_ylim()
max_x = max([abs(xs[0]), abs(xs[1])])
shear_num = g1.shape[0]
sub_num = 4
data_col = 4
result_g1 = numpy.zeros((5, shear_num))
result_g1[4] = g1
result_g2 = numpy.zeros((5, shear_num))
result_g2[4] = g2
parent_path = "E:/test/data_1"
if mode == "all":
for ir in range(1):
img = Image_Plot(fig_x=5, fig_y=4, cap_size=2.5)
img.subplots(2, shear_num + 2)
pic_nm = parent_path + "/data_total_noise-free.png" #%ir
result_path = parent_path + "/data_total_noise-free.txt" #%ir
for tag in range(shear_num):
# h5f = h5py.File(parent_path + "/data_rotation_%d/data_%d.hdf5"%(ir,tag),"r")
h5f = h5py.File(parent_path + "/data_total/data_%d.hdf5" % tag,
"r")
data = h5f["/data"].value
print(data.shape)
h5f.close()
data_nms = [
"Noise-free", "Noise_power", "Noise_residual", "Normal"
]
# my_home = os.popen("echo $MYWORK_DIR").readlines()[0][:-1]
from sys import path, argv
# path.append('%s/work/mylib/' % my_home)
path.append("D:/GitHub/astrophy-research/mylib")
from plot_tool import Image_Plot
import h5py
import numpy
import matplotlib.pyplot as plt
import tool_box
data_path = "E:/works/correlation/CFHT/2021_6_15/cut_2.5_new/unsmooth/ODDS_0.4"
h5f = h5py.File(data_path + "/bias_test.hdf5", "r")
img = Image_Plot(fig_x=4, fig_y=3, xpad=0.14, ypad=0.2)
img.subplots(2, 4)
for i in range(2):
for j in range(2):
tag = int(2 * i + j)
data = numpy.abs(h5f["/%d" % tag][()])
scale = 1
# img.axs[0][tag].errorbar(data[:,0], data[:,1]*scale, data[:,2]*scale,fmt=" ",capsize=3,marker="s", label="$10^3 c_1$")
img.axs[0][tag].scatter(data[:, 0],
data[:, 1] * scale / data[:, 2] * scale,
marker="s",
label="$|c_1|/\delta_{c_1}$")
# img.axs[1][tag].errorbar(data[:,0], data[:,3]*scale, data[:,4]*scale,fmt=" ",capsize=3,marker="s", label="$10^3 c_2$")
img.axs[1][tag].scatter(data[:, 0],
data[:, 3] * scale / data[:, 4] * scale,
marker="s",
file_tag = 1
matplotlib.style.use('default')
plt.rcParams['font.family'] = 'serif'
total_path = "./imgs/"
# read the data
file_npz = numpy.load(data_path)
shears = file_npz["arr_0"]
# only the 0 & 1'th column is needed
ori_data, snr = file_npz["arr_1"][:, 0], file_npz["arr_1"][:, 1]
shear_data = file_npz["arr_2"]
# data_relative_err = file_npz["arr_4"]
# plot
img = Image_Plot()
img.subplots(1, img_num)
markers = ['o', 'v', 's', 'h', 'd', 'p', "4", "*", "X", "^", ">", "+"]
colors = ["C%d" % i for i in range(10)]
labels = ["P$_{k0}$", "SNR$_S$", "SNR$_A$", "MAG_AUTO", "Resolution"]
fmt = '%2.f%%'
for i in range(img_num):
if i == 0:
img.axs[0][i].set_ylabel("relative error", fontsize=img.xy_lb_size)
# img.axs[1][i].set_ylabel("relative error",fontsize=img.xy_lb_size)
# else:
# img.axs[0][i].set_yticklabels([])
path.append("D:/Github/astrophy-research/mylib")
path.append("D:/Github/astrophy-research/multi_shear_detect")
from Fourier_Quad import Fourier_Quad
from plot_tool import Image_Plot
import matplotlib.pyplot as plt
from matplotlib import cm
psf_type = "Moffat"
psf_flux = 1
psf_scale = 4
stamp_size = 48
seed = 56525
fq = Fourier_Quad(stamp_size, seed)
img = Image_Plot()
img.subplots(1, 1)
fig = img.axs[0][0].imshow(fq.kx2 + fq.ky2)
img.figure.colorbar(fig, ax=img.axs[0][0])
img.axs[0][0].set_title("$k^2$")
img.del_ticks(0, 0, [0, 1])
img.save_img("E:/kxy.png")
img.show_img()
pst_num = 50
gal_fluxs = [4000, 16000, 32000, 100000]
steps = [0.6, 1, 2, 4]
fq = Fourier_Quad(stamp_size, seed)
max_radius = 7
pts = fq.ran_pts(pst_num, max_radius, step=1)
matplotlib.use("Agg")
import os
my_home = os.popen("echo $MYWORK_DIR").readlines()[0][:-1]
from sys import path, argv
path.append('%s/work/mylib/' % my_home)
from subprocess import Popen
import numpy
from plot_tool import Image_Plot
# plot
# matplotlib.style.use('default')
img = Image_Plot(fig_x=6, fig_y=4, ypad=0,xpad=0.2)
img.subplots(2, 1)
# img.set_style_default()
img.set_style()
img.axis_type(0, "major")
img.axis_type(1, "major")
for i in range(2):
img.axs[i][0].set_ylabel("Relative error", fontsize=img.xy_lb_size + 2)
img.axs[0][0].set_xticklabels([])
img.axs[1][0].set_xlabel("$g_1$", fontsize=img.xy_lb_size + 2)
markers = ['o', 'v', 's', 'h', 'd', 'p', "4", "*", "X", "^", ">", "+"]
colors = ["C%d" % i for i in range(10)]
for tag, z in enumerate(src_z):
print("Source plane 1 at z = %.2f, %.5f Mpc/h" % (z, src_com_dist[tag]))
# show the shear/kappa map
bin_num = 100
Rmax = 1000
print("Max to %.3f Mpc/h" % (com_dist_len * Rmax / 3600 / 180 * numpy.pi))
if rank == 0:
ra_bin = numpy.linspace(-Rmax, Rmax, bin_num + 1)
dec_bin = numpy.linspace(-Rmax, Rmax, bin_num + 1)
ra, dec = GGLensing_tool.get_pos(ra_bin, dec_bin, bin_num)
shear_data = [GGLensing_tool.cal_shear(ra, dec, nfw, z) for z in src_z]
img = Image_Plot(xpad=0.25, ypad=0.25)
img.subplots(4, src_plane_num)
for i in range(src_plane_num):
kappa, g1, g2 = shear_data[i][0], shear_data[i][1], shear_data[i][2]
g = numpy.sqrt(g1**2 + g2**2)
img.scatter_pts(0, i, ra, dec, kappa)
img.scatter_pts(1, i, ra, dec, g)
img.scatter_pts(2, i, ra, dec, g1)
img.scatter_pts(3, i, ra, dec, g2)
img.axs[0][i].set_title("kappa from source plane at Z=%.3f" % src_z[i])
img.axs[1][i].set_title("g from source plane at Z=%.3f" % src_z[i])
img.axs[2][i].set_title("g1 from source plane at Z=%.3f" % src_z[i])
img.axs[3][i].set_title("g2 from source plane at Z=%.3f" % src_z[i])
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]
gh, gh_sig, coeff, asym = result
# chisqs_min = coeff[0] - coeff[1] ** 2 / 4 / coeff[2]
# if ij[1] == 0:
# total_result[ij[0], 0] = asym
# total_result[ij[0]+dilute_case, 0] = chisqs_min
data_noisy_e = h5f["/data"][()]
h5f.close()
scale = 10000
diff = (data_noise_free_e - data_noisy_e)
estimator = [diff[:,0],diff[:,2],diff[:,2]+diff[:,3],
diff[:,1],diff[:,3],diff[:,2]-diff[:,3]]
est = ["G1","N","N+U",
"G2","U","N-U"]
bin_num = 5000
img = Image_Plot()
img.subplots(2,3)
for i in range(6):
m,n = divmod(i,3)
img.axs[m][n].hist(estimator[i]/scale,bin_num)
fit = tool_box.gaussnosie_fit(estimator[i]/scale,bin_num)
img.axs[m][n].plot(fit[0],fit[1],c="C1",ls="--",linewidth=1.5)
print("plot %s"%est[i])
ys = img.axs[m][n].set_ylim()
img.axs[m][n].plot([0,0],[ys[0],ys[1]],ls="--",alpha=0.5,c="k",linewidth=1)
print(fit[4])
text_str = "%s\n%.4f\n%.4f\n%.4f"%(est[i],fit[4][0],fit[4][1],fit[4][2])
img.axs_text(m,n,0.8,0.1,text_str)
def plot_gf(ra, dec, gf1, gf2, pic_path, gf1_bin_num = 15, gf2_bin_num = 18,gf_bin_num = 18):
gf = numpy.sqrt(gf1**2 + gf2**2)
label_1 = numpy.zeros_like(gf1)
label_2 = numpy.zeros_like(gf2)
label_3 = numpy.zeros_like(gf)
gf1_bin = numpy.linspace(gf1.min(), gf1.max(), gf1_bin_num+1)
gf2_bin = numpy.linspace(gf2.min(), gf2.max(), gf2_bin_num+1)
gf_bin = numpy.linspace(gf.min(), gf.max(), gf_bin_num+1)
for i in range(gf1_bin_num):
idx1 = gf1 >= gf1_bin[i]
idx2 = gf1 < gf1_bin[i+1]
idx = idx1 & idx2
label_1[idx] = (gf1_bin[i]+ gf1_bin[i+1])/2
for i in range(gf2_bin_num):
idx1 = gf2 >= gf2_bin[i]
idx2 = gf2 < gf2_bin[i+1]
idx = idx1 & idx2
label_2[idx] = (gf2_bin[i]+ gf2_bin[i+1])/2
for i in range(gf_bin_num):
idx1 = gf >= gf_bin[i]
idx2 = gf < gf_bin[i+1]
idx = idx1 & idx2
label_3[idx] = (gf_bin[i]+ gf_bin[i+1])/2
img = Image_Plot()
img.subplots(1,3)
norm = plt.Normalize(vmin=numpy.min(label_1), vmax=numpy.max(label_1))
cmap = plt.get_cmap('YlOrRd',gf1_bin_num)
cl = cmap(norm(label_1))
fig = img.axs[0][0].scatter(ra, dec, color=cl, s=3)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][0])
norm = plt.Normalize(vmin=numpy.min(label_2), vmax=numpy.max(label_2))
cmap = plt.get_cmap('YlOrRd',gf2_bin_num)
cl = cmap(norm(label_2))
fig = img.axs[0][1].scatter(ra, dec, color=cl, s=3)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][1])
norm = plt.Normalize(vmin=numpy.min(label_3), vmax=numpy.max(label_3))
cmap = plt.get_cmap('YlOrRd',gf_bin_num)
cl = cmap(norm(label_3))
fig = img.axs[0][2].scatter(ra, dec, color=cl, s=3)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][2])
img.save_img(pic_path)
# img.show_img()
img.close_img()
h5f.close()
h5f = h5py.File(data_path + "/new_pdf_noise_free.hdf5", "r")
ave_result = h5f["/average"][()].T
pdf_result = h5f["/PDF"][()].T
new_pdf_result1 = h5f["/new_PDF/g1"][()]
new_pdf_result2 = h5f["/new_PDF/g2"][()]
h5f.close()
shear_num = ave_result.shape[1]
img = Image_Plot(cap_size=4, xpad=0.2, ypad=0.2)
img.subplots(1, 2)
print(ave_result)
mc1 = numpy.array(tool_box.data_fit(g1, ave_result[0], ave_result[1]))
mc2 = numpy.array(tool_box.data_fit(g2, ave_result[2], ave_result[3]))
mc1[0] = mc1[0] - 1
mc2[0] = mc2[0] - 1
print(mc1)
print(mc2)
img.axs[0][0].errorbar(0,
mc1[0],
mc1[1],
label="ave m1",
marker="s",
capsize=img.cap_size)
bin_num = 8
bin_num2 = int(bin_num / 2)
x = numpy.linspace(-1, 1, signal_num).reshape((signal_num, 1))
x2 = x * x
x3 = x * x * x
x4 = x * x * x * x
signals = a1 + a2 * x + a3 * x2 + a4 * x3 + a5 * x4
parameters = [a1, a2, a3, a4, a5]
print(
"Signals: ", signals[:, 0],
".\n%.4f + %.4f*x + %.4f*x^2 + %.4f*x^3 + %.4f*x^4" % (a1, a2, a3, a4, a5))
ellip = numpy.zeros((signal_num, num))
img = Image_Plot()
img.subplots(1, 1)
fq_shear = numpy.zeros((2, signal_num))
for i in range(signal_num):
# rng = numpy.random.RandomState(i+1)
# ellip[i] = rng.normal(signals[i,0], 0.3, num)
ellip[i] = numpy.random.normal(signals[i, 0], 0.3, num)
# noise = rng.normal(0, numpy.abs(ellip[i])/5)
# ellip[i] += noise
t1 = time.time()
gh, gh_sig = fq.fmin_g_new(ellip[i], numpy.ones_like(ellip[i]), 8)[:2]
fq_shear[0, i] = gh
fq_shear[1, i] = gh_sig
t2 = time.time()
print("signal:[%.4f at %.4f] %.4f (%.4f) [%d gal], Time: %.2f sec" %
(signals[i, 0], x[i, 0], gh, gh_sig, num, t2 - t1))
sub_task = tool_box.allot(task_list, cpus)[rank]
for i in sub_task:
m, n = divmod(i, nx)
gh = a1_range[n] + a2_range[m]*x
# y(a2)-axis, x(a1)-axis
chisq[m,n] = fq.get_chisq(mg1, mnu1, gh, mg_bins, bin_num2,inverse, 0)
chisq_new[m,n] = fq.get_chisq_new(mg1, mnu1, gh, mg_bins, bin_num2, inverse, 0, num_exp)
comm.Barrier()
if rank == 0:
numpy.savez(cache_nm, chisq, chisq_new)
img = Image_Plot(fig_x=12, fig_y=9)
img.subplots(1, 1)
chi_temp = fq.get_chisq(mg1, 0, 0, mg_bins, bin_num2, inverse, 0)
img.axs[0][0].hist(mg1, hist_bin, histtype='step',linewidth=img.plt_line_width, label="Before PDF, $\chi^2$=%.2f"%chi_temp)
# num_null = numpy.histogram(mg, mg_bins_test)[0]
# n1 = num_null[0:bin_num2_test][inverse_test]
# n2 = num_null[bin_num2_test:]
# chi_null = (n1-n2)/numpy.abs(n1-n2) * numpy.sum((n1-n2)**2/(n1+n2))*0.5
# img.axs[0][1].scatter(range(bin_num2_test), chi_null,marker="p", s=140, label="Before PDF, $\chi^2$=%.2f"%chi_temp)
a2_test = numpy.linspace(a2-0.1, a2+0.1, 5)
for i in range(5):
gh = a2_test[i]*x
chi_temp = fq.get_chisq(mg1, mnu1, gh, mg_bins, bin_num2, inverse, 0)
img.axs[0][0].hist(mg1-gh*mnu1, hist_bin, histtype='step',linewidth=img.plt_line_width, label="$a_2$=%.2f, $\chi^2$=%.2f"%(a2_test[i],chi_temp))
print(0)
idx = err_2s <= 10
if idx.sum() > 0:
print(idx.sum(), err_2s[idx].min(), err_2s[idx].max())
else:
print(0)
#
# idx = err_1s > 0.34
# print(idx.sum(), err_1s[idx].min())
# idx = err_2s > 0.34
# print(idx.sum(), err_2s[idx].min())
#
#
# histogram of \chi^2
img = Image_Plot()
img.subplots(1, 2)
img.axs[0][0].hist(err_1s, 100)
img.axs[0][1].hist(err_2s, 100)
img.set_label(0, 0, 1, "$\chi^2$")
img.set_label(0, 1, 1, "$\chi^2$")
img.set_label(0, 0, 0, "Num")
img.set_label(0, 1, 0, "Num")
img.axs[0][0].set_yscale("log")
img.axs[0][1].set_yscale("log")
img.axs[0][0].set_xscale("log")
img.axs[0][1].set_xscale("log")
img.save_img(total_path + "chisq_%d.png" % i)
img.close_img()
# img.show_img()
#
#
data_path +
"/cuts_pi_all_sample_w_maxpkfit_sq/sym/sex2_1.5/flux2_ex3/total.hdf5", "r")
mc1_mpk_pk_fit = h5f["/mc1"][()][:, ch]
mc2_mpk_pk_fit = h5f["/mc2"][()][:, ch]
h5f.close()
h5f = h5py.File(
data_path +
"/cuts_pi_all_sample_w_maxpkfit_sq/sym/sex2_1.5/mag_true/total.hdf5", "r")
mc1_mpk_tmag = h5f["/mc1"][()][:, ch]
mc2_mpk_tmag = h5f["/mc2"][()][:, ch]
h5f.close()
matplotlib.rcParams["font.family"] = "serif"
img = Image_Plot(fig_x=6, fig_y=4, xpad=0.2, ypad=0.2, legend_size=13)
img.subplots(1, 1)
img.axis_type(0, "major", tick_len=6, tick_width=1.5)
img.axis_type(1, "major", tick_len=6, tick_width=1.5)
img.axs[0][0].errorbar(x_coord,
100 * mc1_tf_pk_fit[0],
100 * mc1_tf_pk_fit[1],
linewidth=img.plt_line_width - 0.5,
capsize=img.cap_size,
marker="o",
fillstyle="none",
c="C2",
label="$m_1$, weight=$F^{-2}$")
img.axs[0][0].errorbar(x_coord,
100 * mc2_tf_pk_fit[0],
100 * mc2_tf_pk_fit[1],
exit()
t1 = time.time()
with Pool(ncpus) as pool:
sampler = emcee.EnsembleSampler(
nwalkers,
ndim,
MCMC_program.ln_prob_g,
args=[MG, NU, mg_bins, bin_num2, inverse, ra, dec, shear_tag],
pool=pool)
pos, prob, state = sampler.run_mcmc(p0, step)
t2 = time.time()
print("Time: %2.f sec" % (t2 - t1))
# Plot the walkers
img = Image_Plot(fig_x=16, fig_y=4)
img.subplots(ndim, 1)
for i in range(nwalkers):
for j in range(ndim):
img.axs[j][0].plot(range(step),
sampler.chain[i, :, j],
color='grey',
alpha=0.6)
img.axs[j][0].plot([0, step], [parameters[j], parameters[j]])
img.save_img(parent_path + "pic/%s_mcmc_walkers_nw_%d_stp_%d_expo_%d.png" %
(shear_cmd, nwalkers, step, expo_num))
img.close_img()
# Plot the panels
samples = sampler.chain[:, 150:, :].reshape((-1, ndim))
print(samples.shape)
corner_fig = plt.figure(figsize=(10, 10))
