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()
img = Image_Plot(fig_x=5, fig_y=4, xpad=0.1, ypad=0.13) img.subplots(2, 2) for i in range(4): m, n = divmod(i, 2) img.axs[m][n].hist(plt_data[i][0][idx], 20, label=labels[i][0], alpha=alpha, histtype="step", linewidth=ls) img.axs[m][n].hist(plt_data[i][1][idx], 20, label=labels[i][1], alpha=alpha, histtype="step", linewidth=ls) img.axs[m][n].hist(plt_data[i][2][idx], 20, label=labels[i][2], alpha=alpha, histtype="step", linewidth=ls) for i in range(2): for j in range(2): img.axs[i][j].legend() img.axis_sci_ticklabel(i, j, 0) img.axis_sci_ticklabel(i, j, 1) img.show_img()
G1_bin, G1_hist_bin, NU1_hist_bin, chisq_gap=50, max_iters=50, fit_num=20, dg=0.002, ax=img.axs[0][1])[:4] 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)
xs = img.axs[1][i].set_xlim() img.axs[1][i].plot([xs[0], xs[1]], [ total_result[i + dilute_case, 0], total_result[i + dilute_case, 0] ], label="$\chi^2$ before corr") ys = img.axs[0][i].set_ylim() dilute_ratio = dilute_ratio_list[i] num_non = int(num_s * dilute_ratio) img.axs[0][i].plot([num_non, num_non], [ys[0], ys[1]], ls="--", c="gray", label="true dilution") img.set_label(0, i, 0, "Asymmetry") img.set_label(1, i, 0, "$\chi^2$") img.set_label(0, i, 1, "Correction") img.set_label(1, i, 1, "Correction") img.axis_sci_ticklabel(0, i, 0) img.axis_sci_ticklabel(0, i, 1) img.axis_sci_ticklabel(1, i, 1) img.axs[0][i].legend() img.axs[1][i].legend() img.save_img("./%d/asym_%d.pdf" % (mg_bin_num, mg_bin_num)) img.close_img() comm.Barrier()