# idx = detect_num < 1 # detect_num[idx] = 1 # result_stack = result_stack/detect_num # result_stack[idx] = -1000 # print(result_stack) numpy.savez("./imgs/stack_result.npz", shears, result_stack, error_bar_stack, pk_pool, mag_auto_pool, snr_pool, snr_stack) # plot # matplotlib.style.use('default') img = Image_Plot(fig_x=6, fig_y=4, ypad=0.22, xpad=0) img.subplots(2, 2) # img.set_style_default() img.set_style() img.axis_type(0, "major") img.axis_type(1, "major") markers = ['o', 'v', 's', 'h', 'd', 'p', "4", "*", "X", "^", ">", "+"] colors = ["C%d" % i for i in range(10)] plot_data = [ result_stack[:flux_num], result_stack[flux_num:2 * flux_num], result_stack[2 * flux_num:3 * flux_num], result_stack[3 * flux_num:4 * flux_num] ] plot_data_err = [ error_bar_stack[:flux_num], error_bar_stack[flux_num:2 * flux_num], error_bar_stack[2 * flux_num:3 * flux_num], error_bar_stack[3 * flux_num:4 * flux_num] ] labels = ["$\\nu_{F}$", "MAG_AUTO", "SNR", "Resolution factor"]
"/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], linewidth=img.plt_line_width - 0.5,
# total_path = "/mnt/ddnfs/data_users/hkli/selection_bias/paper_data/galsim_dimmer/" total_path = "/mnt/ddnfs/data_users/hkli/selection_bias/paper_data/pts_dimmer/" pic_nm = "chisq_pts.pdf" pic_nm_png = "chisq_pts.png" sex_filter_name = "sex2_1.5" shear_cata = total_path + "parameters/shear.npz" shear = numpy.load(shear_cata) fmt = '%2.f%%' img = Image_Plot(fig_x=5, fig_y=4, xpad=0.45, ypad=0.22) img.subplots(2, 2) img.axis_type(0, "major", tick_len=8, tick_width=2) img.axis_type(1, "major", tick_len=8, tick_width=2) locs = ["upper left", "upper left", "upper left", "upper left"] legend_loc = [(0.02, 0.95), (0.02, 0.95), (0.02, 0.95), (0.02, 0.66)] legend_loc_share_ax = [(0.02, 0.62), (0.02, 0.62), (0.02, 0.62), (0.02, 0.35)] ylims = [(-9, 85), (-24, 285), (-9, 85), (-850, 85)] for m in range(1): source = sources[m] g1 = shear["arr_0"][source] g2 = shear["arr_1"][source] # point source fourier_path = total_path + "result/data/data_%d.hdf5" % source f_h5 = h5py.File(fourier_path, "r") es_data = f_h5["/data"].value