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()
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()
t2 = time.time()
chisq_min_c = coeffc[0] - coeffc[1]**2 / 4 / coeffc[2]
result_min[2, tag] = chisq_min_c
result_min[3, tag] = asymc
for m in range(2):
img.axis_sci_ticklabel(0, m, 1)
print(
"%.5f(%.5f). asym: %.3e. %d source + %d contaminations + %d corrections. %.2f sec"
% (ghc, ghc_sig, asymc, num_s, num_non, num_corr, t2 - t1))
img.save_img(data_path + "/result/%d/%s_%.2f_dilute_%d_mg_bins.png" %
(mg_bin_num, data_type, dilute_case[tag], mg_bin_num))
# img.show_img()
img.close_img()
comm.Barrier()
if rank == 0:
numpy.savez(
data_path + "/result/%d/%s_min_change_%d.npz" %
(mg_bin_num, data_type, mg_bin_num), result_min)
img = Image_Plot(xpad=0.25)
img.subplots(1, 2)
img.axs[0][0].plot(dilute_case,
result_min[0],
label="$\chi^2_{min}$",
marker="s")
img.axs[0][0].plot(dilute_case,
result_min[2],
label="$\chi^2_{min}$_corr",
left=left,
right=right,
fit_num=20,
chi_gap=30,
fig_ax=img_check.axs[0][0])[:2]
g2_sym, g2_sig_sym = fq.find_shear_bk(mg2,
mnu2,
mg_bin_num,
left=left,
right=right,
fit_num=20,
chi_gap=30,
fig_ax=img_check.axs[0][1])[:2]
img_check.save_img(img_check_name % ig)
img_check.close_img()
mean_result_array[1, ig] = g1_mean
mean_result_array[2, ig] = g1_sig_mean
mean_result_array[4, ig] = g2_mean
mean_result_array[5, ig] = g2_sig_mean
sym_result_array[1, ig] = g1_sym
sym_result_array[2, ig] = g1_sig_sym
sym_result_array[4, ig] = g2_sym
sym_result_array[5, ig] = g2_sig_sym
comm.Barrier()
if rank == 0: