From 33886a860cc153b596876491542c6b765adfc838 Mon Sep 17 00:00:00 2001 From: wssuzb Date: Sun, 24 Dec 2023 16:49:35 +0800 Subject: [PATCH] coding --- src/run.jl | 2 +- test/data/plot_color_variation.py | 305 ++++++++++++ test/data/run_i_z.h5 | Bin 0 -> 493440 bytes test/fig/lc_check_i_z.svg | 278 +++++------ test/fig/plot_cv.svg | 313 ++++++------ test/fig/plot_sf.svg | 768 +++++++++++++++--------------- test/run_demo.ipynb | 349 +++++++------- 7 files changed, 1170 insertions(+), 845 deletions(-) create mode 100644 test/data/plot_color_variation.py create mode 100644 test/data/run_i_z.h5 diff --git a/src/run.jl b/src/run.jl index 8e4b281..59c195f 100644 --- a/src/run.jl +++ b/src/run.jl @@ -100,7 +100,7 @@ function runall(lc1::lightcurve, lc2::lightcurve; sf_bin_edges=1:0.1:5, cv_bin_e end result = ( - sf = sf, cv = cv, fit = fit, par = par, sf_min = [sf_min_1, sf_min_2], t_min = [t_min_1, t_min_2], t_max = [t_break_1, t_break_2], num_all = num_all, num_cut = num_cut, num_pos = num_pos + sf = _sf, cv = _cv, fit = _fit, par = _par, sf_min = [sf_min_1, sf_min_2], t_min = [t_min_1, t_min_2], t_max = [t_break_1, t_break_2], num_all = num_all, num_cut = num_cut, num_pos = num_pos ) return result diff --git a/test/data/plot_color_variation.py b/test/data/plot_color_variation.py new file mode 100644 index 0000000..f07875c --- /dev/null +++ b/test/data/plot_color_variation.py @@ -0,0 +1,305 @@ +import sys +import warnings +from scipy.signal import argrelextrema +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from scipy.stats import binned_statistic +from matplotlib.ticker import ScalarFormatter +import matplotlib.ticker as ticker +from scipy.optimize import curve_fit +from scipy import interpolate +from scipy import stats + +import h5py +warnings.filterwarnings("ignore") +# matplotlib.use('agg') +plt.rcParams.update({ + 'font.size': 12, + 'font.family': 'Times',#sans-serif + 'axes.linewidth': 0.5, + 'axes.spines.bottom': True, + 'axes.spines.left': True, + 'axes.spines.right': True, + 'axes.spines.top': True, + 'xtick.direction': 'in', + 'xtick.top': True, + 'ytick.right': True, + 'xtick.bottom': True, + 'ytick.left': True, + 'ytick.direction': 'in', + 'xtick.major.size': 3, + 'xtick.major.width': 0.8, + 'ytick.major.size': 3, + 'ytick.major.width': 0.8, + 'xtick.minor.size': 1.5, + 'xtick.minor.width': 0.6, + 'ytick.minor.size': 1.5, + 'ytick.minor.width': 0.6, + "text.usetex": True, + "axes.titlesize":"medium", + "figure.dpi" : 500 +}) +def geterror(x,perclim=84.1): + """ + x: tau list. + return: + tau, tau_lerr, tau_uperr + """ + tau = stats.scoreatpercentile(x, 50) + centau_uperr = (stats.scoreatpercentile(x, perclim))-tau + centau_loerr = tau-(stats.scoreatpercentile(x, (100.-perclim))) + return [round(tau,3), round(centau_loerr,3), round(centau_uperr,3)] + +# f = h5py.File('./run_result/run_bin_103.68_montano22_n1_nsim_1000_mode_both_nsigma_3_band_i_z_sun14.h5', 'r') +f = h5py.File('./run_i_z.h5', 'r') + +fit = f['fit'] +band1, band2 = f['band'].value.flatten() + +num_all = f['num_all'].value +num_cut = f['num_cut'].value +num_pos = f['num_pos'].value + +err1, err2 = f['par'].value[0][6], f['par'].value[1][6] + +t1_min, t2_min = f['t_min'] +t1_min, t2_min = 10 ** t1_min, 10 ** t2_min + +sf1_min, sf2_min = f['sf_min'] + +t1_max, t2_max = f['t_max'] +t1_max, t2_max = 10 ** t1_max, 10 ** t2_max + +t_used_min = max(t1_min, t2_min) +t_used_max = min(t1_max, t2_max) + +flux_ratio = f['flux_ratio'].value.flatten() + + +sf = f['sf'].value +bin_sf_1_t = 10 ** sf[0][0] +bin_sf_1_t_err = 10 ** sf[0][1] +bin_sf_1_sf = sf[0][2] +bin_sf_1_sf_err = sf[0][3] +# bin_sf_1_sf_err_hig = sf[0][4] + +idx_br_1 = np.where(bin_sf_1_t <= t1_max, True, False) + +bin_sf_2_t = 10 ** sf[1][0] +bin_sf_2_t_err = 10 ** sf[1][1] +bin_sf_2_sf = sf[1][2] +bin_sf_2_sf_err = sf[1][3] +# bin_sf_2_sf_err_hig = sf[1][4] + +idx_br_2 = np.where(bin_sf_2_t <= t2_max, True, False) + + +# flux-flux + +cv_1_t = 10 ** f['cv'][0][0] + +cv_1_t_err = 10 ** f['cv'][0][1] +# cv_1_t_err = [(cv_1_t[i+1] - cv_1_t[i]) / 2 for i in range(len(cv_1_t)- 1)] +cv_1_y = f['cv'][0][2] +cv_1_y_err = f['cv'][0][3] + +idx_cv_1 = np.where((cv_1_t>=t_used_min) & (cv_1_t<=t_used_max), True, False) + + +# mag-mag + +cv_2_t = 10 ** f['cv'][1][0] +cv_2_t_err = 10 ** f['cv'][1][1] +cv_2_y = f['cv'][1][2] +cv_2_y_err = f['cv'][1][3] + +idx_cv_2 = np.where((cv_2_t>=t_used_min) & (cv_2_t<=t_used_max), True, False) + + + +cv_use_bin= 10 ** np.arange(0., 5.1, 0.1) +# cv_use_bin= 10 ** np.arange(0., 5.2, 0.2) +bin_width_all = [(cv_use_bin[i+1] - cv_use_bin[i]) / 2 for i in range(len(cv_use_bin)- 1)] +xerr_all = np.vstack((bin_width_all, bin_width_all)) +bin_width_all = np.array(bin_width_all) +bin_centers_all = cv_use_bin[1:] - bin_width_all + + +""" +TODO ERROR. +""" + +# For plotting --------------------------------------------------------------> +mosaic = ['A', 'B'] +figure_mosaic = """ +AC +BD +""" +colors = ['xkcd:mid blue', 'darkred'] + +fig, axes = plt.subplot_mosaic(mosaic = figure_mosaic, figsize=(4.2, 3.5)) + + +axes['A'].axhline(np.sqrt(2) * err1, lw=0.2, ls='-', color=colors[0]) +axes['A'].axhline(np.sqrt(2) * err2, lw=0.2, ls='--', color=colors[1]) + +axes['A'].scatter(t1_min, sf1_min, marker='x', zorder=3, s=8, linewidth=1.2, color='blue') +axes['A'].scatter(t2_min, sf2_min, marker='x', zorder=3, s=8, linewidth=1.2, color='red') + +axes['A'].scatter(bin_sf_1_t[idx_br_1], bin_sf_1_sf[idx_br_1], + marker='^', s=5, color='black', linewidth=0.2,facecolors='none',) +axes['A'].scatter(bin_sf_1_t[~idx_br_1], bin_sf_1_sf[~idx_br_1], + marker='^', s=5, linewidth=0.2, facecolors='none', color='gray') + +axes['A'].errorbar(bin_sf_1_t[idx_br_1], bin_sf_1_sf[idx_br_1], + yerr=np.vstack((bin_sf_1_sf_err[idx_br_1], bin_sf_1_sf_err[idx_br_1])), + ms=1, ls='none',mew=0.2, elinewidth=0.1, mec='black',ecolor='black', capsize=0.,mfc='none') + +axes['A'].errorbar(bin_sf_1_t[~idx_br_1], bin_sf_1_sf[~idx_br_1], + yerr=np.vstack((bin_sf_1_sf_err[~idx_br_1], bin_sf_1_sf_err[~idx_br_1])), + ms=2, ls='none', mew=0.2, elinewidth=0.1, mec='gray', ecolor='gray',mfc='none', capsize=0.) + +axes['A'].scatter(bin_sf_2_t[idx_br_2], bin_sf_2_sf[idx_br_2], + marker='s', s=5, color='black', linewidth=0.2,facecolors='none',) +axes['A'].scatter(bin_sf_2_t[~idx_br_2], bin_sf_2_sf[~idx_br_2], + marker='s', s=5, linewidth=0.2, facecolors='none', color='gray') + +axes['A'].errorbar(bin_sf_2_t[idx_br_2], bin_sf_2_sf[idx_br_2], + yerr=np.vstack((bin_sf_2_sf_err[idx_br_2], bin_sf_2_sf_err[idx_br_2])), + ms=1, ls='none',mew=0.2, elinewidth=0.1, mec='black',ecolor='black', capsize=0.,mfc='none') +axes['A'].errorbar(bin_sf_2_t[~idx_br_2], bin_sf_2_sf[~idx_br_2], + yerr=np.vstack((bin_sf_2_sf_err[~idx_br_2], bin_sf_2_sf_err[~idx_br_2])), + ms=2, ls='none', mew=0.2, elinewidth=0.1, mec='gray', ecolor='gray',mfc='none', capsize=0.) + +axes['A'].plot(fit[0][0], fit[0][1], lw=0.6, color=colors[0])#, label=r'$%s$-band'%str(band_pair[0])) +axes['A'].plot(fit[1][0], fit[1][1], lw=0.6, color=colors[1], ls='--')#, label=r'$%s$-band'%str(band_pair[0])) + + +axes['A'].set_yscale('log') +axes['A'].set_ylabel('SF [mag]', fontsize=10) + +axes['A'].set_ylim(6e-3, 1e-1) + +axes['A'].yaxis.set_major_formatter(ScalarFormatter()) +axes['A'].set_yticklabels(['','', 0.01, 0.1]) + + +axes['B'].errorbar(cv_1_t[~idx_cv_1], cv_1_y[~idx_cv_1], + xerr= bin_width_all[~idx_cv_1], + yerr=cv_1_y_err[~idx_cv_1], fmt='o', mfc='none',mew=0.2,elinewidth=0.2, ms=2, lw=0.5,ls='none', + color='black', zorder=2, alpha=0.5,capsize=0.5) + +axes['B'].scatter(cv_1_t[idx_cv_1], cv_1_y[idx_cv_1], + marker='o', s=5, linewidth=0.2, color='magenta')#facecolors='none', + +axes['B'].errorbar(cv_1_t[idx_cv_1], cv_1_y[idx_cv_1], + xerr= np.vstack((bin_width_all[idx_cv_1], bin_width_all[idx_cv_1])), + yerr=np.vstack((cv_1_y_err[idx_cv_1], cv_1_y_err[idx_cv_1])), color='black', + mew=0.2, elinewidth=0.2, lw=0.3,ls='-', capsize=0.5)#, label='%s vs. %s'%(band_pair[0], band_pair[1]), color='black', ecolor='black', zorder=2) + +axes['B'].axhline(y=1 / flux_ratio[0], xmin=0, xmax=1e4, color='gray', ls='--', zorder=0, lw=0.5) # ewidth + + +axes['B'].set_ylabel(r'$C_f(\tau)$', fontsize=10) + +axes['B'].set_yticks(np.arange(0.30, 1.5, 0.1), major=True) +axes['B'].set_yticks(np.arange(0.30, 1.52, 0.02), minor=True) +axes['B'].set_ylim(0.5, 1.1) + + +axes['B'].text(0.05, 0.05, 'BWB', transform=axes['B'].transAxes, color='blue', fontsize=7) +axes['B'].text(0.05, 0.9, 'RWB', transform=axes['B'].transAxes, color='red', fontsize=7) + + +axes['B'].set_xlabel(r'$\tau$ [sec]', fontsize=10) +axes['A'].text(0.9, 0.9, '(a)', transform=axes['A'].transAxes, color='black', fontsize=7) +axes['B'].text(0.9, 0.9, '(c)', transform=axes['B'].transAxes, color='black', fontsize=7) + +axes['A'].text(0.8, 0.1, r'$\sqrt{2}\sigma_n^i$', transform=axes['A'].transAxes, color=colors[0], fontsize=5) +axes['A'].text(0.8, 0.2, r'$\sqrt{2}\sigma_n^z$', transform=axes['A'].transAxes, color=colors[1], fontsize=5) + +axes['A'].scatter(150, 0.047, marker='s', s=8, color='black', + facecolors='none', lw=0.3) +axes['A'].plot([130, 170], [0.047, 0.047], ls='--', color=colors[1], lw=0.5) + +axes['A'].scatter(150, 0.063, marker='^', s=8, color='black', + facecolors='none', lw=0.3) + +axes['A'].plot([130, 170], [0.063, 0.063], ls='-', color=colors[0], lw=0.5) +axes['A'].text(200, 0.06, r'$%s$-band'%str(band1), color=colors[0], fontsize=6) +axes['A'].text(195, 0.045, r'$%s$-band'%str(band2),color=colors[1], fontsize=6) + +plt.setp(axes['A'].get_xticklabels(), visible=False) + + +use_bin = 10 ** np.arange(1, 5.2, 0.1) +axes['C'].hist(num_all, bins=use_bin, histtype='step', color='black', ls=(5, (11, 3)),lw=0.5, label='All pairs') +axes['C'].hist(num_cut, bins=use_bin, histtype='step',ls=(0, (1, 0.5)), lw=0.5, color='blue', label=r'$3\sigma$-cut', zorder=4) +axes['C'].hist(num_pos, bins=use_bin, histtype='step',lw=0.7, color='magenta', label=r'$3\sigma$-${\rm cut}~\&~C_{f/m}>0$', zorder=3) +# axes['A'].legend(loc='upper left', frameon=False, fontsize=5) +axes['C'].set_ylabel('number of pairs', fontsize=10) +axes['C'].set_yscale('log') +axes['C'].set_ylim(2, 3e4) + +axes['D'].set_xlabel(r'$\tau$ [sec]', fontsize=10) +axes['C'].text(0.9, 0.9, '(b)', transform=axes['C'].transAxes, color='black', fontsize=7) +axes['D'].text(0.9, 0.9, '(d)', transform=axes['D'].transAxes, color='black', fontsize=7) + +axes['D'].axhline(y=1.0, xmin=0, xmax=1e4, color='gray', ls='--', zorder=0, lw=0.5) # ewidth + + +axes['D'].errorbar(cv_2_t[~idx_cv_2], cv_2_y[~idx_cv_2], + xerr= bin_width_all[~idx_cv_2], + yerr=cv_2_y_err[~idx_cv_2], fmt='o', mfc='none', + mew=0.2,elinewidth=0.2, ms=2, lw=0.5,ls='none', + color='black', zorder=2, alpha=0.5,capsize=0.5) + +axes['D'].scatter(cv_2_t[idx_cv_2], cv_2_y[idx_cv_2], + marker='o', s=5, linewidth=0.2, color='blue')#facecolors='none', + +axes['D'].errorbar(cv_2_t[idx_cv_2], cv_2_y[idx_cv_2], + xerr= np.vstack((bin_width_all[idx_cv_2], bin_width_all[idx_cv_2])), + yerr=np.vstack((cv_2_y_err[idx_cv_2], cv_2_y_err[idx_cv_2])), color='black', + mew=0.2, elinewidth=0.2, lw=0.3,ls='-', capsize=0.5)#, label='%s vs. %s'%(band_pair[0], band_pair[1]), color='black', ecolor='black', zorder=2) + + +axes['D'].text(0.05, 0.05, 'BWB', transform=axes['D'].transAxes, color='blue', fontsize=7) +axes['D'].text(0.05, 0.9, 'RWB', transform=axes['D'].transAxes, color='red', fontsize=7) + +axes['D'].set_yticks(np.arange(0.5, 1.5, 0.1), major=True) +axes['D'].set_yticks(np.arange(0.5, 1.5, 0.02), minor=True) +axes['D'].set_ylim(0.7,1.3) +# axes['B'].set_ylabel(r'$\bar{\vartheta}(\tau)$ [deg]', fontsize=10) +axes['D'].set_ylabel(r'$C_m(\tau)$', fontsize=10) + +plt.setp(axes['A'].get_xticklabels(), visible=False) +plt.setp(axes['C'].get_xticklabels(), visible=False) +# plt.setp(axes['B'].get_xticklabels(), visible=False) +for ax in ['A', 'B', 'C', 'D']:#, 'C' + axes[ax].set_xscale('log') + axes[ax].set_xlim(80, 3e4) + axes[ax].tick_params(axis='x', labelsize=8) + axes[ax].tick_params(axis='y', labelsize=8) + +axes['A'].fill_between(np.arange(t_used_min, t_used_max, 0.1), 2e-3, 1e-1, + facecolor='palevioletred', alpha=0.15,zorder=0) +axes['B'].fill_between(np.arange(t_used_min, t_used_max, 0.1), 0., 2, + facecolor='palevioletred', alpha=0.15, zorder=0) +axes['C'].fill_between(np.arange(t_used_min, t_used_max, 0.1), 1, 3e4, + facecolor='palevioletred', alpha=0.15, zorder=0) +axes['D'].fill_between(np.arange(t_used_min, t_used_max, 0.1), 0, 2, + facecolor='palevioletred', alpha=0.15, zorder=0) + +# ========================================================================== + + +axes['C'].legend(loc='upper left', frameon=False, fontsize=4) + + +# ========================================================================== +plt.tight_layout() +plt.subplots_adjust(wspace=0.3, hspace=0.03) +# plt.savefig('./figure/mag_flux_new_sun14_test.png', dpi=500, 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b/test/run_demo.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -38,48 +38,37 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 44, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading lightcurve, band used: i-band\n", - " \n", - "\n", - "\t Time: \n", - "\t\t min time: 0.0\n", - "\t\t max time: 23872.32\n", - "\t\t mean cadence: 105.16\n", - "\t\t median cadence: 103.68\n", - " \n", - "\t Flux: \n", - "\t\t mean flux: 0.6\n", - "\t\t median flux: 0.6\n", - " \n", - "\t Error: \n", - "\t\t mean error: 0.002149122807017544\n", - "\t\t median error: 0.002\n", - "\n", - "Loading lightcurve, band used: z-band\n", - " \n", - "\n", - "\t Time: \n", - "\t\t min time: 0.0\n", - "\t\t max time: 23880.96\n", - "\t\t mean cadence: 104.28\n", - "\t\t median cadence: 103.68\n", - " \n", - "\t Flux: \n", - "\t\t mean flux: 0.47\n", - "\t\t median flux: 0.47\n", - " \n", - "\t Error: \n", - "\t\t mean error: 0.0025826086956521744\n", - "\t\t median error: 0.003\n", - "\n" - ] + "data": { + "text/plain": [ + "230-element Vector{Float64}:\n", + " 0.0\n", + " 103.68\n", + " 207.36\n", + " 311.04\n", + " 414.72\n", + " 518.4\n", + " 622.08\n", + " 725.76\n", + " 829.44\n", + " 933.12\n", + " ⋮\n", + " 23060.16\n", + " 23163.84\n", + " 23267.52\n", + " 23371.2\n", + " 23474.88\n", + " 23578.56\n", + " 23673.6\n", + " 23777.28\n", + " 23880.96" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -87,13 +76,12 @@ "z_lc = load_data(\"./data/z_4395.txt\", [1, 2, 3]; band=\"z\")\n", "\n", "# convert time to second, and start it from 0 second.\n", - "i_lc.time = round.(i_lc.time * 24 * 3600, digits=2)\n", "i_lc.time = i_lc.time .- i_lc.time[1]\n", - "z_lc.time = round.(z_lc.time * 24 * 3600, digits=2)\n", - "z_lc.time = z_lc.time .- z_lc.time[1]\n", + "i_lc.time = round.(i_lc.time * 24 * 3600, digits=2)\n", "\n", - "println(i_lc)\n", - "println(z_lc)" + "z_lc.time = z_lc.time .- z_lc.time[1]\n", + "z_lc.time = round.(z_lc.time * 24 * 3600, digits=2)\n", + "\n" ] }, { @@ -106,68 +94,7 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-51.84:103.68:25038.72" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t_binsize = 103.68\n", - "lc_edges = bin_lc_edges(t_binsize, 0, 25000)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Loading lightcurve, band used: z-band\n", - "\n", - "\t Time: \n", - "\t\t min time: 0.0\n", - "\t\t max time: 24986.88\n", - "\t\t mean cadence: 103.68\n", - "\t\t median cadence: 103.68\n", - "\t Flux: \n", - "\t\t mean flux: 0.47\n", - "\t\t median flux: 0.47\n", - "\t Error: \n", - "\t\t mean error: 0.0025712396521364884\n", - "\t\t median error: 0.003\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - " \n", - " \n" - ] - } - ], - "source": [ - "i_lc_bin = bin_light_curve(i_lc; lc_edges = lc_edges)\n", - "z_lc_bin = bin_light_curve(z_lc; lc_edges = lc_edges)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -178,26 +105,26 @@ "\t Time: \n", "\t\t min time: 0.0\n", "\t\t max time: 23846.4\n", - "\t\t mean cadence: 106.46\n", + "\t\t mean cadence: 105.98\n", "\t\t median cadence: 103.68\n", "\t Flux: \n", "\t\t mean flux: 0.6\n", "\t\t median flux: 0.6\n", "\t Error: \n", - "\t\t mean error: 0.002151111111111112\n", + "\t\t mean error: 0.0021504424778761065\n", "\t\t median error: 0.002\n", ", Loading lightcurve, band used: z-band\n", "\n", "\t Time: \n", "\t\t min time: 0.0\n", "\t\t max time: 23846.4\n", - "\t\t mean cadence: 106.46\n", + "\t\t mean cadence: 105.98\n", "\t\t median cadence: 103.68\n", "\t Flux: \n", "\t\t mean flux: 0.47\n", "\t\t median flux: 0.47\n", "\t Error: \n", - "\t\t mean error: 0.002574411736387197\n", + "\t\t mean error: 0.0025801828333785826\n", "\t\t median error: 0.003\n", ")" ] @@ -219,14 +146,17 @@ } ], "source": [ - "lc1_bin, lc2_bin = get_common_lc(i_lc_bin, z_lc_bin)\n", - "# lc1_bin.band = \"i_bin\"\n", - "# lc2_bin.band = \"z_bin\"" + "t_binsize = 103.68\n", + "lc_edges = bin_lc_edges(t_binsize, 0, 25000)\n", + "\n", + "i_lc_bin = bin_light_curve(i_lc; lc_edges = lc_edges)\n", + "z_lc_bin = bin_light_curve(z_lc; lc_edges = lc_edges)\n", + "lc1_bin, lc2_bin = get_common_lc(i_lc_bin, z_lc_bin)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -238,7 +168,7 @@ }, { "data": { - "image/png": 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6Mhas+jyGh4fr6+tpOkCg8tHIPL2pKi5BQ+OnJB0OhYeamn5tkHS1WU/EJTjbuIdi4hteu3fvnvq4iWbmceea9g2P2mZ6CtA15vf7T58+HQwGp5O4tbW1tbV1sr0UQ9Wf3U+EXQn3jvp6k0yLKyoqDIqkYZJraT7b1tZGs3SKTGUPlaZl3z9ZsS2t20zSzsHhY2N1Do1opBe67VEExUItUnCaosMAgF27dnn9fUBqUYqVFRUVseWIRRqdMqv+7AckPXnsQgY9ee6TJFVq4p20lwu2mxWD0fiMvOTk5JSUFAAATrtEkdNe7xFUkp2Z/3gI3LIseyntPeMY2mGUOqcI9146t7IgvyDxPo5zDH5EeY6F6RnMZ5Iq8jtbayPe+lNtXSiSeMRLQiVllRgmzc2rGB3+mHEeHg0Mdzjb+V3t7e0AgJbW1lStPDltcUlZJRhfctGcwvlFi1mx2OPqt9lsEz8qbW1tU3x+IAiCAADNzc133nkngiA//OEPZ7suY2ZUK4qiXnnllZdeeumFF1549NFHz5270N1z/vz522+/XSgU/ud//ie/pba29pZbbkEQ5Fe/+tWhQ4deeumlF1988fbbbz969OjnrDDfCk1KSvrFL34x07w33VCZ5cuXL1++/Pnnn7/2hx4Y6Dx7/rTGWJljnr9wwW0ogrFsBAAZv5cg7DpRY/3p51KzHi0p/cFf/t6oV2bFZrcPfGKQnG3t25Wfuil2+/Fje9auWzv5Ybnjx/auXrMaRVDAcQwTpAjbsPUIFj5kcxUWZOdf+fO8jvX29qakpAwMDOTm5s5iNfr6+nAcz8zMvGRKr8/beK42OUmv1hgvmfh863mU8WdnZ8du7O3tBQCkpKSctTbqxI397VuzCp+Jy8hyLMeSyMVQJ5nanJ0n3mJZmUiSnZK0YnHVaP/ImWT1gtgsNTU1S5cu9Ycden3FSOgYLlREdzG0F0HGegQMGV87c377sHdAS3DHj++dV5ltkKUlrL/T+k+jpGfUP2hW5SVMYO95N1k2zLCJ+ycEIqMx/S53WBUXyGWsJqm3I/IarXq1QiQ3mXOKi3CxUK5JudWc2eYg1Kmq5IS5AAA5OTkkGf/46wIEScp6QJ+UnBzoniz7RDTlcgx87HZGnCFWI50w4XVyFouFZdns7GwM+Za53YDIjLlKYedIvUmiPN923mgxjXS3lllMSpVZJBLF5c3OzhaLxa6Qu33YhhGHBwaJvLyx95nl2Oa2Foz15efn8yHk/X5/cvK496S3txdBEP4uCIKgm1NhYeH27dsXLVo02xUZh69VVVV8f2hC77333v79+w0GA0mSnZ2d69ev/9Of/lRVVVVUVPTJJ5+UlY0tG7J48eJdu3YVFhZ2d3cXFBQ888yF/56PPPLI5+zm4EcZhcPhy+hBvul63GeRbfANJLTTrJ0LALJ582azbs5ocGzpR19w2B5KzSt6UiU1IwheWnQriAzFZhfL0hyhJImiJHaj33VGKTh6uvmtyQ462PmWQnDsTP/BoKeRpgM0FRCIk/SZ9waRXESYPq1q22w22xekb/5sw0mGYWZ9zfmmpqazDSenSBB9zxtt50nfx/2tvwaTXIhwOMx3tTpDLiG3s79jG0OP655vamo6U39swNGQotDYw2lCRVFcCT09PV0D50QxA7IxFJfgdfbed02GSgDA5s2bc1JXO7ydsbn27dsVCg74Iv5Uc2Vx4UYEFXnD3oGBgUHbgFw+7qYoWZ9XmL86XZfnJ3wq7Kh1cO9kZ52R/YAjnJWbsmyyBCmWr9pD+VKhbLIEeQUrS/PKJ9sLAFJUtCHdlAoAMJvNVVVV/DD0vILV8/LmTp7rEpJTy5KMBQWZM7gHxgVaXcqmjJyvGWW6yeaYTk0g0mRaKi2puWbjwhxTeavX5yHa3L1/Gep8C8UmnTkDANBKNZ3unn5P32DHH1z2Q9HtDcNNPaMnhjresvX8zeUatVqtEz+il/zcQhB0w6kfamx1dMw019RTPD8nliG6zj43Orx/phmnWat169YZDAb+dU5Ozj333PPwww9Ho0BOLEQikZw5c2bt2gs9pIWFhR0dHbPYLoIN92tHoFxC4XOjz68RBCdDvYGIr6+vLxIZRUVJ2ZkbNPILvVkPPPCAQVtkd1/4OrFM0M5I09NvV7DDrtDYaAcCkfpIjUSaBSYh1xQ7QmYMN4hVxbhAEY6IAQD9Xntu9iq9LPHkuTinTp2oq6u7vFO+bB6PBwDg9SZeGT4WwzB+v9/r9XIcN3VKp3XHQMdrp3v21lvjh0Hzx6IoKnY25DTrOZ1Kxur3DHa7Tve1/m6k//3J0tTV1dUer3EGXRrcGUbzBboVAU/96dOnT52KHz3ldDr37t3tDnvCvnMjRA6iKCHDwwCAvr4+AADDMq7Ajr7W36mFWJI625KxVp+ywe6qjyYAAByorh4csqmUObHF+qkSpX4RejGuC4ZiCIKHw2G73Q4AYBlCBA40nn89RZ0NAHjggQdMmtyQv/mzmkMN9R8pJJrYovR6/a233goASDMvckdSMrLumOysUUyckb5GKpj0l9dsmmvJmLRZDwCoqKiIG4ETZ926damp8SNtLplrarm5uVqttri4eEa50rNXahVpBWmX+cgLRdHy8vK8vDy5XA4AKEkqJBmdJed+jXGpQGS4xKHVaRSaJVGXyRUFHo+bYRirc1gmkKqlaSJ1MZAXnR48MjK81dr5Fk35PR4P/2VstDWP+ncPtG8lgv0Ji+UXnZ3pN2gyNpttYjRll8vl88EB+hAEfS78A8nR0VG/3w8AWLp0aW9v79mzUy08UlIy1mfK/+rO4sTcWW6422y2b3/72y+++OLzzz//3e9+dzrTJUdGRp544omf/vSnzz777JYtW/j3/bJLu5bSzEuSTEtitySbFnt9fW+8+cZoYFAvNz7++OOxe3FcTgPEG/QRBOHwdGXrcx5//HGTaQlNumiaDgaDkfAQh0oVqg0Wc7En7AQTcVwEMxn1y8tTSj1hryvkrqmpcYZcFl3mvfc9kpKSG7uoTUKR0GBf61Zv6OiQ75qGf66pqQEA1NbWXjJlMBisr68/deoURVH8Fynu63RxY1goTpHKc4VsWCKQROj4kQ+1tbV2u72trQ3MRG1tbW1tLR8BkJ9oEpcgupEkSZZlI5FIiAxLcZFAblFqK0LexmhKkiT5ufBDvmFX4LP+1q1iasCStC7VvCojdfkoMIz4Dva2/CbgaYotv8N2LOx532PdmWZekWWsLCr4sp8TBYLWbdu2hciQ1XGKYlOlygKFshgA8G//9m8auRoVmAIhx7Zt2wAADMvQ1A53/ztx1TZr52dlj4s9olKkN5z9YMeOHQwT6nd1EKzGmLwmNkGSsSrg/jsRbJji7VJrl2kVicfJ8OK+AnEMBsO99947RYIbyJIlSwAA03ywe0liXJRvzpXK0pIy1l0ycZo6uTi52JCxiQDCuhN7fH5v3Zm6bKUy25hmybvHbCg1KfMieDmQZYSDvbW1tZ/VfGYPOHHaEWZ1UkWeQJQ4UFptbe3IyEhHx4y77hL68MMPrdb4JQUOHjw4zU4ElmVhEx+CpqM8pbTAMJtjRydCMbGl7Ce6pCnGAF8B1dXVDQ0NAAA+klhnZ+cUieOijQEALtlXePXM5hj3UCi0bNmyv/71rwsWLAAAHDlyZNWqVadOnZo4OjOqvr7+0Ucf3bZtW0lJic/n27hx48svv/zTn/708kq7xoqLi4uLi2PbdghAbEP7RMhBo+oHCbNoRMKdO99TS5jiqo3oxXHDKpH4xNkTLfVnF61fVJBSumXLFoahHb4WL+FTiZWx2T3u02Jd6ZYtW0iS1Ek1ba7OhqFz+UwhAAAgiIi0BfFk7cSjxsAEahSXeyNU8FJN/CuI42hb799b20dbnVaapQWYYIrEI33vW3tO2pGS1NHuwca+W2+99eDBg3fddVc0wf79+2+77bbjn/117a2PItolpTl3OHv+b9Bx3pI0NqbCE/Z2u/v07c0hj3/OnOenWU+KoZodrbpI40hBQG26tbq6GkGQjRs3xqaprq5GUfSWW25paGjIyc05UX9q/YplA+osvywngumkKiMZHuE4muUkdXWNNptt47/cQtCEEGMksmSJPFcsF/LnggdwP6XBBRqGGlsCMxAJRqhRDlWZjZUcAGvWrAEAEBHiRMceHBwY7s5ISd5sVkWAqhwgCLj4K6NXJDd0vCME+wcGFgKBkWKlEnkeGD9gg38mGNvlKcSEA+7OUdtnDs/cTH0xilVmp64eP2sWAVgKLkia4h3bsmXLLP7YfbGtXLkSALBmzZrJAgdFmc1moVBYXFxs9zuP9nerG/4rNGTHBStXrrodx3GO49JUybTMSAfNo5za6v2AdDSW5A0Vpj4w0sG6RO5QsEcqTzBVt2u0R9F8hiXI/PyffM5zGfINM/SBgS6X2fz96MbRkDtM7PN3hhYvXnTJLieXy/XnP/8ZrpUBQdAl8R2FsXN+rnOz2XB/5ZVXjEYj384GACxfvlwkEr3++uuT/doyDHP//ff/8Ic/5J9ZsCw7MDAQnSk109KuExKxjgViDEt8ISTSNJetOqjWrJQ9GN0okqQ6en8dJoZTtRdifSAIqhFL3e6eV9/65Ac/+AEAYMeOHTgymlm+KFU21jLXSbUykUx9sXEvUWQHg5NH8AAAAMDSPlXKHSqJ3IgHnUFX9Y59BoNh6dKln+eUp4Nj6UjE7Y8EGJaZuuHOciTHRIKM1etpG+z+4HyvsH20O0SG+NVtOI61D/yjv8tZPxywuAe/fu/XaJpetfZusVjcN3TAaVcpZIrWtrY5VUXesIuhQ3GhRXbv3n3XXXft2LFj3boEHZkIgoRIQkmHfYHeQa7BFzpEOEZosgogFwZhjwQcfa4Wkf+s35vb4+4OddQQI4MIN3/DpnsEAoEr5CYZ5tChU7fddpvP3Tbg6Rrp/4d7iMzUreuTrFRkIRgujQaKEmLCHN1c1DAvJMhGwoMffngyJSMl26IuSt3QqwQSRU50iAKKoHpNNgJwjSYTRUX8MJW4xpxGbUEAbtQXI5hRLFqdkbV4OhdFJpShqMigsgAESTi9W6Vek5KcPXE7dH2SCMR6qVYqw3BhBEHGfoLS0tJkMllxcbHL7w7TWgkuVSgsACC33nprhCFHA64Tn74hU5fOnVuO42O5AmQoSIUEFAVmMmq/sbGxubn5wQcfjN0YosIABBFu/CxkhCNpiqZD05kV0DNSS4b2M/QjU6w/AEHQzYllWY/j6PDghwF0wzIAGhsbk5KSSktLZ7te0zWbQ2X++c9/xg0tLSsr27lz52Tpt23b1tvbG31WrlarBwYGHnvsscsr7TpRWPA1Flka+18zFsfSCBtWSeIC1XEIgogxFEXGBqkLxUkCoi8cOWEPOjmOHez7sM/VlqocFxQiWWnO1WdHxxAjCMbSfg5M2gPKMhGaCa9et3btuk1qdSnh2Ns38NGgN/759eWprq4+cOAAAGD//v0AgIMHD0Z3RUIDxvQ7kjLvXZaRHCRD23f84+jRowRBNDc3W61WPhePoQNa02p56l1F5kVlqeUc5RaQdoYNDPqG9+/f39vb29F2kCQcHiIEAJAIxj17MZmWtI2c6hw5MtTzXrIYXZCaLU+9X5+8kePGVl7ocfcNWA+7bAdBIp6wb37K3KycB3IL/x+Oy0fDVDhsD/tbDxw40NnZ2dfX1+3qBchIJDzi9jaFSbeCG2UoHy64cCullWp63YONw832gDOIqN1hL8ZRElyICZTr1q2beKuwdu3adevWqaTqdrfVF67x2Q9rJNrU1MxvfOMbcSlzDQUUWK41rZ7szc9PWx0BK0WSVADA/fffP82ehnTjJo3xbgxPsBIqb/PmzeXlU8wNha4vMqE0W5uZk/uQOeNekCgqpVwks+hy03O+pku68GkU4yKtRH1mODjY95HXeTw2cZY2Iy99jTz1bq+7rrq6mv9eAwD2798ffT3RgHfI6Tzoc40bAKMRiBBsrUg7fscWyXUAACAASURBVOIBh+iVxYbMr1rtNR6P+/e///0Up+b19+HAS5Puqc4fgqDLdRlTwq6BhLVyu93vv//+8PAwAGD//v0EQezdu7en/SAZsTN0z9ETO48ePfTQo7f4L8Z1mFgIRVGx0db515d8tnn1zFqPu8/nO3Xq1KpVq2I3mkymv/zlLyRJRtchj/XHP/5x7ty5CYe+XEZp14+HH354sl0IKsjOf4wk4z4fSP6c7zhGa3B03OUL+tqUIvfA8FFMnxv2tWu1womhqZctG5vbh+EyOY64Qx6tVAMS4ThmlBZm6ZP4JSpRmhDQVkdwKGHi6aipqWFZdv78+UFfq33wMxujXUAsHO7/1Dqa0u3o5dMwdBAXqIorFxoVSRWVX0aCze6h/Q5VmTHbPGo7EYmo2kYG1rL0sWPHKsqzTp2sLalaVTlfA2gglatT876dl3tbWfP/JqHBw927KMwSAsmKtHsL59zb3vJRunrcrEQxLmIRFUkO0uE+KuKsXHSLWCHxG5JCvvNK7VwAgCvsGQk47UOfBj3nWIZA0XENVpZjAQAVZUq1fgGGYQX6nCHdOkq9IiLOG/b8mu7q1MpUpZaCgGYFol2Znn5bYe+R7Nz7CPo8EnPVUARhWUaMi8RCUWnSvGFgNmfcFwwGRSIRgiBx6zKIRCIMw3AGRzGlXGAn/F6ByICiqEwmmzj+ZIoPVVwCfr2h6awBkZOTYzYnWBopih8FOIs/Z9BMLVmyRKFQzJ07aVCdJUuWTPz9DJKBEN3pHT2hM194+Eaz9KKqBRlmczor6BjtHnQewoLD9IqFrrBvaPQUToQBWHb06FEcx/nndfyz6YWVCwc9zWyk2207Yswqulh4CAWRu+/6Co1TJGETXgx+xdCBpcvvFwilGI6daN7tHt1PRu4BiRafdviHMo0lNUgPgl3Xv/wQdCOqq6v7zW9+097ebrfbSZJ89dVXZ7tGAABQV1f329/+lq9VJBLZunVrdBfLsgRBRKeimUymd9/51Z//vre3y4Q39xwX9Dz1eHl+IeYOe2ynrK+++mpHRwcfhuF3v/vd8ePH33rrrb6+PrfbTRDEyy+//Nprr33yySdSqfShhx66//77L/mv9mqYtYa7zWZjWTZukUKJRBKJRFwuV8L2QXt7+7Jly/bt23fkyBGFQtHd3f3MM8/wwbAvo7TrR1pa2sT4CVEVFfMnrr6bmpa2Zm38vI2M/CeYT4IVOZtRFLXMeQrFxRgaHzdGpVLFjksWiXQeOsHMaL/ff+rkSaOZSM4cG0SRnPN1Uyql0GlHQ+4k4djq7nV1dTRNT9bVOjIyQpLkwMDAwoUL+zr3BoWpqZl0xHXEYT8MtIta+na77Ue7usSDXooviqFcmYVlapUKAKBUKoMed8h9RquV9gxjIeunXg8SpIqarKfbWz6cU/q9Uz19C5dJ5HIZQRAIgixcWAkQpGTuRpf944i33kEWckh447K1OIonDBtSbJojlVag/iyFplxEkhKJhOJoIVdwrvEYxYizC3MWp5eas8qSBosm9kf2uPrNQhxPWSISXWg6zJ8/H0VRnUzb5xaqQW2EkMsq7p0/330h5mBenlarLS+fF1tIji5rbkqJUqwgSbKgoCAnJwdMQ7GpoB3/l7SszIS9pLy0tKnmgPIJ+B+y6ZNIJBKJBLbLv0j4pVXlcvlkERJUKlVcwx1H8Xkp5QJJudY09kBv2Ddi1shwHBUL5IgLiTBBavRMZ9tr3UyuVlTf0+f0+7s7h8+j4aGqypIwRTX1nRaEh7PyTYvTSho8GkXaRjJsFYuzAQB2T7dBJFHrzM6gC6UvLF3OcixgfFpzHgBIJBKJMCERMugc2qUyfTm2bu+8886mL2+UYkCVuvjOO/SeCKmd9PkQBEGXY/78+W+99RYfM3EWI6vEmT9//rZt2/jOIz7MQ11dHcuyxcXF4UCfL3DeEcjNBJle54mnfvRQRCb92U9+tv2D7TqdbtmyZXt2/RPFxTm6LGma9J133qEoimVZvo+4qqpq5cqVKIoyDEOSpEQiefbZZ59++mkcx3Ec528Jrv3JzmaPO7jY2xfFN3G8Xu/EprbH43E6nXz0vRdeeAEAcPr06Xnz5p0+fTorK2umpd1AjEYjACDuwY1cLpfL5fynM0ogFK5ee2FmZFn5uOVyJsOyETbiomTxYSL83oH2jt2c+tYCkSJ2+8Kq1RhwCjiiruG0GBdlZmZyHN3VfnCUVpTNLQOJuBwd7oC7tbcvNyPoHNqjya5o9WduKP1RW09OhBFW5d/haGWWzr9/dPB3Dp/17NkPUFmSNtuSq7Lw2Q3Jt6XmuMRyQ2VOZR+Sp9Ubg3UNKYj9qONIa08OzYybumoymTiOMxqNMumTKee1OSnlLMuaTKbo2xjHZDJJJBKRaOy90su0J7vPdPedDXhG5xY9XpCfnaQryJpDE4EuufpCvL/m5uaCsjmegVFztiF2OK/JZEJRlKbpYlNhQf6tQpGI34hhGMuyarVaLBaLxeK4m7RokCmtVjv9R0MLFlSq1TNYuAeCrqDS4hIKY3TGNPrivR+CIBwTwgXpAIACQ26nvALNLSgoejA9FNrby6VZQojQRNO9YVtN13lxv6AiVX62u79VJXoMTZ8vVVg4TBUivf3nTntJckFRAUBlAACNRNXb35Ah1QEMc/gdCoEg+nUrS197AjTok9ZTMX0agUjw3PlP12wqU4rThUJhaVFJ/+ARIDXF1x6CoJuAzWbjOK64qMjW+y5Onu52FZr6rA7r3uw5i1lEhKFYRUWFQCAAAKD4VGtfXIdmreHOP9xH0XGD7Pl7l4kdzACAkZERAEBLS8uXv3yhi6WiosJsNm/ZsmX79u3TL+3IkSOHDx9+8cUXo1ueffbZ6PK2NzQURauqqvh2YUZGBgAgELjE3FOByMj5qGOnTywoGdchbe18E6UaveH4Sag5OTkkmU5F7OcadqGS1LQ0/UDb28O976syN5+3txk4jU6ns9lssYt3Wrv/7PKOOIkFjKzUnP3VCKpZkl6FocLCooV8VYtKqhBUkJG1Mjjwl5Cjxk8v9UX8sQctKqlCUVQkFCWn5UokkoK8AkNKcVL2wMKiR1rOvSPCEzV2EaSwuFKpVE4dw0SlUonFYrVaHdv37Cf8gYgzaNvldhRmZN2GIqg5NYnj8PaWapE0Jycnp6WzNaMgs71xZ3HB9xIWW5BXYEpKxrBphcnnr9RM8aPSr5/eDuimkp6ezmGAYknC3yGRlIeosEaiRsixn9/CgsLo57+ooBgAIBfJJIISdVZaXtm3kwLBf5x3JmfPE0lSBSI2KSkJANAd8LZ1nQgGiXn5SZhQBgDAUIwSpbIsyQERQYcVkrEF42RS2Yqltw/4HRrEKZdfuKPudDQbpG3kyC4k8wl+i1BRELnYkQ9B0M2joqLC6x0FAJXKxCzLBQOBtesP3fv6H1IsQy5SsVpvAQBYLBZ+SexrvJJ63AJPLMvOtAk6aw13vr8wbmQtH5RdpVJNTK9QKAAApaWlsa1zi8Wyd+9ejuOmX9qyZcuWLl0a+zZNs4H1RdXVdqSlL8A33AcHBxmG0evUuGpBCBOmquKXquEhqCA4si8rP/3kYPK89LsNaV4fI9civlP1zZUVi3v7+rKzs/miVHJCl3pbROlWDUT0cl1B4QqWZfk+8ry8PL7hnp+fDwDIzc2ViucYG4AQaDLV45Z0zc/Pp2mapmn+yQnfZs2bsxxDsZLc+HVAY3NNukz9lHK0WcOiAlma2Jh+VzhMAABMckOfc6Cj28MxtamZaW3Orjmt/+O0HqWpr+HCBA9z8vLy+G9m3CMRCPrCkAjEI36fSKgFAATJkJiyo/jYQ628vDw+siS4+AWPRCLFuUU4jiMIhiLo3Pz4UfVhOkJQbZ6B41RkNSa8MAgnWWHu6jsRILSWJAbFxr5rEolk5cqVETpitXeG+rsQVITLBQrgHiXyU3O/Gbk4mEsv01odw7H/AE6dOjVv3rjharH4VZ/4GwkIgmYRy7L19fVTLGw3ODgIANBoEs/QO7Dnt+9s22K0zF+wYotGoNq1a9eiRYsAABJZlpjj4iJT8b9R46MbX0U+n4+m6eionl/+8pczLWHWGu5GoxFBkNjlkwAAPp8Px/GEV8JoNIrFYpls3GrnAoEgGAza7fbpl4YgCIIg/PMRCHDcqHW/Vug72l+6Kmdp/ZmTGOKfM2+uLmV5YY4uRZv4Hxgu0GiTVg+HlbclzdXKjTl562iaTtaXHR74ZbP4cP1I7kpuRf2ZkwLUXrzodrEmv1gvUWB9AICysrIpxoShuDgzZwVN03LRpGvaR/FFxS5mdqVoZZr8tFyWtSDI2C2iLWA/PXTS6D9l7XBnq+XpuQ92NAuF4uSET4cg6KaAICTLsGwEAYhYmk4zl4hRVlpaGv3hLSsrA+PnMaerUg5GcrVGqVxTQtMXnoAJMIF1hGs591H5oz8myfjvmhATIpj8fH1tiEIXLMyypK7Ly7HjQlWEGvsHTNCR9rZTWdkX7hN27PhoYsO9vb2d73Jrbm52jY58afOdM30nIAi6smia3rVrF99wdzqdYMISSPzaSXxIkv7+fq1WGxudVqGrFCmLhkN5SUoTy7Amk4nPbjAYZn0tEYFAEG2F0jR9GX3HsxYOUqVSVVZW8gNgovx+f1VVVdxzBB6O4/PmzXM4HLEbSZJUKBRGo3GmpUEXIIjKUOWNFKRJ8P6BHV1N/9PlqheKDWnGlPXr1+t0usnypWSsyk2ukAtlAIDKysqFCxciqEBtWKwxrLGo6cHBXT3Nr3SNdvR5Bgqy8tPT06Px9afGF3XFzu6yCASC8vLyysrK2I0pyiSj3KIyLExO/3KqscSssaRmXd1F3SDoOqeX6UKc0GFrCAaD4cBUiw5OB4ZiRWklGbm3xIWBH3KfJF3VftfphLkkAokzsKe/7TW1zAQAMjE0qkGd938f7vB6vQCA061/k6HVflf84r7vvvsu/8Lnf2+w+22Wvu6C3EHQF09cg22i6A/B8ePHjx8/Hr87pv39ySef2Gy22J1UxKkwr8/VWxCAKBSK9evX6/V6AEBlZWXcP/cb0WzGcd+8eTN/zxTV1NTErxfDi5vG95WvfKWxsTH2bqm9vX3Tpk0IgkynNCghvXmpRrkg3TAv2bRUorCgSIpCJL9krhUrVqxYsSK+qORl6cnLTerFZmOlTGlhkeS0SQbb3HAMMl22PtOUul4sS+Pj2U08fQi6qWAIiiHYp7XNdUeqpcrCz19gwl8VmdSCiJIkisQBl8QCMcVlSuQWkSRxBAKOYxXCmoaG/+p31EvFGoIWiSTjVrfoGjktBNXtbduc/mEGqBCBGp18pQIIgqaJoqipA5dFl2JgWXbiuFZrz7soV9092p0wL8sSve1vBondcTPixnCsUCD4oo6tmM2G+ze/+U2Hw1FbW8v/WV1dTZLkd7/7Xf7PwcFBo9H4+OOPR9M/8sgjBQUFf/jDH/g/9+/f7/F4fvWrX02nNGgy5eXl99xzDwAAF6pTsu5JU+fHhYefvmXLlpnN5vnz5wtFBlP6nRnqXINs0j77G8769es3bNgw27WAoOuIh/AO+87b+94jI7ZLp74sGepcnWmzUGRIuBdDUIN8QUrGHZMt1yrABBFGqZRnpxvK52RuDLKVwfEjbmgOB4Azm6r0iiSNYqNEtyxhORAEzci777577ty56aTs7Ox8++234zYKhGqKRYPBIavjbFfbH4Z9n8UuFokgQgSTUAw6Onqmc2Cf3/1hr7M5upcI9vgYcPutt69fv/6KnMv1ZtbGuAMAVCrVkSNHfvrTnx48eJBhmIGBgZqammhIR5FIpNPp+KcbPIFAcOjQoaeffvrJJ5/Ecdzj8TQ0NCQnJ0+nNGg6Nm3axDDMdNbimWZRM40UDkHQDQRDMa1YwfoFLEVgCVbGuwLKysqmWB8KALBp0yYAwGQ/NQJMgGFLs3Pu4/984bmfWUc+k+nHlqeQ4BgJ1irVhaFQaOXKlelWEU35cYEiYWkJHT58OBKJxK5tB0FQQtXV1QRBrFq1igzbUK520N1llCVeckQgMke4RSXpyxjKi7IRlg2xMaMtODYi1y1Qy/KzkldQ4SYM0J6IIyavyR3xm3HRF3US2mw23AEAqamp0R70OAaDobs7/imJRCKZYpmuKUqDpoOfJHFFGu58UbDhDkFfYJnqtC7VfEQ9X6LIvrwgTpeEoii/EsJkCS75U/PQQw9pNBp+TrxQKJRr5kbCNokkCwDgJXx6sfzb3/529FjJxrnhcEAxkwfsw/bDFEkBABvuEDTGPXrA5kVzwLhBbjRNUxQFANc/sFOE+YaGPyPkWf3954KRFgAejE1JCU0SXAwAEIg0WXO+g4P+EBVSASW/l6GDgotj3lS6hVLN3XM0uiHfcLgz2NXVUTHfaNZPGjwKAPDpp5+++uqrBw8e/J//+Z/vfOc7kyWbYmXM2TWbQ2UgCIKgG1pVVdWiRYsvnW726HS62CDCGonaS3j410Ffk1SeGbs6m1QoC/jbZnaA8GmWaLwSNYWgG96bb74JACDDVoZop4In4/baRo6FI2e6nE1Zlq+G2XWVhQ+lmRb6R+s0oh6KGYsxxVA+DCHvv//+ixsQGqQHw65oAoffunblWj7CI59AJC1WcgGb2x7y2zhxZuIFXi5av379gQMHpgi/AQDo7u42Go1PPvnkdM/8GoINdwiCIOgy6XS6qf//XYc4XHXowF9OnzspUxRM3IsJ41eSfu+996YqTXa7znjPgLOBA9zUKSHoOnfJD/AlEwwMDJCkz0sSatP9YsXS2IHpHEv57IckWJdFX4JhQgAwAACCYHNKf+All+PY2AAQkvRgYoPFcmEB9fLy8vLyeSLOaw8433vvvaD3PI1rjXpD9JdnzZo1BoNBKjVFAl3ecI+PmGTG6nhThxyUSCR6vf76/HGDDXcIgiDoJqKXGU+3t/R2HFRJEq7ewnHsWOcfxZANZ/fwr/mYB7FJHb7BsjkFcwqrdKq8IeuR+rO7WO6LOawWuhk0Nzd/ngSBsJuirYOuFq0iHceVBpnJ4WmP7kUQXJV8O4ncwv+5cuVK/gWGiRUisdXVEU05GgloJeronykpKSkpKWpVERXqb25uCmPaNHVK7HELCgpkMhmGK0K2XWpRh5fwTetsp5SUlNTV1fXss89+/qKuuJuu4X706NFf/OIXWq1Wq9X+4he/mO3qQBAEQdcUiqJp8qaQuwYkWolFJc9wuM5H/zzf9Fu95Fyf7SjLsSdO7A8GPbGJWcpVWLjUYrFIBVKSJU3Sc02t8fExIOhGwXIeloufLtLY2OjzXWgKc5ybf+F0OltbW+NStp1/WSM8m2koxVB8zpw5BlMegsk4wNXU1AAAhkdqTbpiIXZhgUV+7SQAgEajyS+sBGyEoCMAgEhoCEeEGBq/LBGCoCaNhWZ6WA4gCaNIIUiS5SE3WZmry77cN+Da4VuhycnJN9LKqbOlsrJy3rx5//7v/w4u9aAEgiAI+uJBAOIIzbWkpwMkwb9/ES4kkLHFmyWKQmc426AtH7QeFHE1zU3B8oqX+F00SwtE2mjKrNS19tAftMaVcQV2dXWZzYnDzMfxeDxerzd2zD0AgKKozs7OrKys6Z0cBF0mhgrKseMNp1/In/OD2O3Hjh2TyWRJSUktHX9XCWrbe/5RUnDf3LlzU1NTq6qqUBR96aWXcnJyDh48KNUuGY30opgEAMCvc+R0tPQ42vft25ebpxIqC83mkYmz2A0Gg8Fg8Lh7A5Fgd3tXdk6qXCYGiQz3/FUtaO92tBoVCZZ1l8vlZWVlaWlpHgeHYRhfq0OHDk1xyhRFvfrqqxRFEQTR3d29ZcuW0tJSAEBzc/Ozzz67Y8eO73//+3zD+ujRoz/72c/27dv33//930VFRWfOnOE47vDhw08//fTatZezGmNnZycAgCCIV155ZaZ5b7oedxzHJRIJf68DG+4QBEE3IUv6ooyMyefURob4dRndgSG9qTIjfbVUKE1PWRNgslD5fF/YCQDo6+sbGj6ilY+tMYcAJCN9PcqOMuNHy+zf96nVap1Orbq6Og8fPhS3MRAI7NixY7onBkEAAACGh4enDvTU19cXvwlFfaSWkeVPSMACwPlCI6hI5yHMFC1fsqzqzTffvOuuO5966imRSBQMBl9//XUAgFZTIBLkxhYplqTJQJBmIiGA6KSasrKyyZZRF4lNhK9p+/btztFGflH2iUxpd3jJtGxj8cRdfr9/1apV77777mOPPRat1WuvvTbFOwAA+Nvf/nbfffc988wzzz///JYtW2655Zbq6moAQGFh4SeffFJWVhZNuWTJko8//jg/P7+rq4um6aeffvrHP/7x6tWrv/Wtb11e0Em+FarRaC6jIXrTNdwhCIKgm9zGjRvnzJkz2V6TcXH/SBcAgAjbdTJddB1Ag76qJPfLoWBfIBL83zdelSqL4jI+/vjjyYaFPdbDLMd6PB4AABWxB5x/7xl4n0/Ab0yM4zqaf8fSu6c5Sj5aFEEQfLBL6PPg38Nr807SNB0MBmOXgb/itm/fPjw8PEWCbdu28S/CbV3Bzh4AgMM/jOMLyvPuHLYfjybgODYS/Gdby69ZNpyfvg7FKxSi/NVrlnR1/zbk/aClufmZZ5554YUXysvLff4ukUCgUMSvgUD4OxT4wQgVumSdzcZKknELZTkEST/356P7z8TfWgglZgwvNsoTLMfmdrs3btxYUFCwadOmlpaWaK2mPuL69esNhgulWSyWe+655+GHH45GgZzYpJZIJGfOnIl2sRcXF3d0dNhsV2v5ucnAhjsEQRAEjRHhwv999eUhW02SYdzaT9/61rfkcrlZX9Hfu12OH+VoV8LsmaaqEc/gb37zG5ajHK42CpWRQGJzNQIAfv3rX096VAShABeicBQZ9395uOfvgDnc7x6ISx4tav/+/fv375/5WULj8GMqrs072dfXt3379mAweJXKp1kKMAc72t6cTuL29vbq6mq7b0AASARBcUwoVRY6XU0AAMBxVncry4ol8my1PBMAgCCIWq2+966vi8XZIQp76OtfX7hw4ZYtWwAABCdSy9P517GU6vwIo1CIEwxuicNE7ErsRO/I0SnSIImGtwEA1Gr1bbfdBgAgSfLrMbWamlg8bkzOkiVLent7GxoapshSUlISfS2TycC1utmLBRvuEARBEDSG42gcbbb7h0DCOXAAKFR5HCdEJ8yf4+G4xNH3AcZ+1m5r1hsWanSb89Nuk0uM7Z1/Q9jPnEF7XPrnnnsuQvpszrqioifFgvmusDt2L0V7GEDZRk8DAHbs2MEPm/E4alH2WJezIxLxDnT/fTRwNDYMNgDg8OHD77///mW/A1cJX/ndu3fPdkUS6Hb1dfbvsfdt57grsAThbEMYLhQhRgHgAADPPfdc3G6bbxDlajyuswAAwqj1SgWAduvUBc8//zwAQCVWOjzdKHdgcPScXpEqUWxMT/sXPuPzzz+vVCozMjJSkm/buzfY29v99ttvoyhKMiRJDCWsikY7l0WXGJXTmOaBYiwnUgq1YiH+kweXrJ2XMTEJX8OJ+FoBAH784x/39fX98Y9/jF29YZqUSiUAoKOjY4o0cW19AMBVfXKSEGy4QxAEQdAYmvILMVbITNohmmauIsFKvSpvsgQYhiEIY1KmCHHRV7/61ezsbLnELERQIUb2DFRzgNuzZ8/hw4cBAGTYhnFHW2xnDbp5paWl9933b5GYFaA4lpIY1tLcsoU5m4ftx7o733UGjts87Q7HaRwNeIL9Lve5SMSOISMUO6656R455A/XWf3X+iH+1FzD+weGj/S4+ydLwHd4HzhwYOLGgwcPXr2KcYCzBUaC3uYIYaMJ59U7EK/PO+jxHPY6j1+l8t1hD4avLS779wHnWYYe+xjv3LmztrYWAGB394jQoMPVMmyvGRyttQ3vNKjHjRxDASNCWYVQLhIoEx7i1KlTe/fue/Hln6WmGH/5y1/+9KdPJ+nnf85qi8TJJLIqP33lZZfAL4b6m9/8Jj09HQDw8ssvzyg7HzwnPz//sitwbdx0UWUgCIIgaAoCoYbkVhcVfCMQCEyW5uGHH56ihPw536YRt0amBwBIpVJ+Y6bl3gjXVJ6z2eFqbW/fbkq1jPiTydFPRZhPjDIYguI4juMKtSrfHwmopSoAAMMEKCAW4SIAEI0siSOsCFDoVRZVSjkJ7BUZawAAloLvROgAHtP9z3F0wNOkULBW33C2ITNhDWtqaliWraiomPG7c7kYKhj0tbis+4b9EX8k0Fh3lmEYPvYIj2NJa88nowuKOu3dY9k4ztq70x0saB3uuIVj48YRXVJtbW1eXh7LsnGxeuK4Q57KtHlKU3FqnoVDOHCV+1BHAsMc2eO2f6YxLq6pqUFRNGYR0M8rTBGEr/nuu7+i0Wh9YVm7vQVh61iGAACEw2GZTDYachklWJhbl5tzXygUamnegdI2hg7jQkG0kPzsO8JcvUqZTdP0HXfcoVaraXrsztDj8fzkJz+58847/+2rjww4zgaDQduwa2IAx6ipvyxXisfjefDBB++///577rmH3zIwED/ALA7DjIt9efDgQYvFEjsn9foEe9whCIIgaJy777536gRpaWlT7MUw7O6770lYLI5L9apMJtQhw89ppLr8wm+F2bUFqSujaQQI6w8OAgBYNuL0tOelFmzevBkAIJZlZhU8oZPfjiNYbA0XLqxcWGr2R8Y6VhEE1yVtCLEbMtXJsVNdnU7ngQMH+LuRnvbd53rPMuy4hktdXV1dXd3UJ37ZOI40pH5ZlvmN1dnpNOlta/3nub7m0dDYuCCv86THcaK7f8+w305ffIDg9533Ok4MDOx2hl2xieOqHQ6Ho2sD9fT0uFyuuro6hmUa+87bRxpc9sZgMNjS0pLw7MIUQbNMRZlFItIsXFglFJmDvnNX+uzHyVRnqtRrfZFBSwAAIABJREFUEdO9Ac9Zr9fLzzNmWba6ujpBsJcZ8gUHUowLTSazUCiUCWVkqE8hGGnv+ks4bPOE27v79woYj9m4ONr8M+ofMKU+hAsn9qxfTGA0CoXC2B3f/e53cRx/8803EQTVKTM9rn3nGs9PyD5m6i9LrLvvvvsyhrjwnnjiCQzDtm7dyv/Z399fX18/dZY9e/Y4HA7+dVtb20cfffTnP/85erIURYXD4dj0FEXF3sDw7X6KGjdK7RqAPe4QBEEQNE5xcYKQc9OHIEhhYWE0PEVssSRJopgkNetBVGgSYiIAwPoNt8WmwYU6nBw9fPyIXiFSmvIEAkFeXh4/Aa6sfEF0QC1fFADAZDIBk6lj4DTDJfFt+kio35hXkYcpBPTokE+QobkQs9Lt7GrrqEnOSiL9B0dte5Pzlvd5BkoUY7Fx+PgYBQUFAICmpiaapieLvTM8PCwSiXp6eubOHZu/S5Jkb29vIBAoKSlpamoqKipqbm7mw/9xLMXQfktplUWXSeWviTgPBe2HuSS0192for0wbREXKFLzv5lmLl+QwjX2ntfhaudIX2FhnjLtywbd/BwtppMmWOmWY6mutkOaJG3nYBcfRaRnuJcVsh2t+8MaAkPC/R1vi0VKoQztHGIIZ+u8eeMijZw/f95JuJaVFQq0GpUyySPwAgSRqUr9rgaFdu7Ew31+HOBYlkMEhmR1UoBSeUIHcRIFYBPHcYODg0aj0WQyXV7JTU1NAAGGZCGKjY3DLsu58yP6WEHewyNeh1FSZ7OGFPL/AABs2LAhmgbBRBNLi00Qa+/evR988MGtt976yiuvRCKR7p7GTz4+VV5xZYaXFBUVXV50xbha2Wy2jz/+eOHChVNkycrK+tvf/vbGG2+IxeJQKDQ0NHTo0KGcnByKourq6l5//fX29vaRkREAwNatW48dO7Z169a+vj632x0Oh3/+85+/8cYbO3fulEqlDz300Fe+8pVHH330Ms955mDDHYIgCIKuqaKSqmjH3uLFi2O78QAAGnlya/MOmVq8OfOx2O0ZGRkcx4VCCSLrKYQiZ8BpVpoAACSLZaZl6KTaQMAdjgyea/UIEUF6erq1620p19XuKF1csCopy0owUpVYFVtIJDQUQZUAAMBxbc1Hg5x0soa7zdohkkqbO1pjG+5E2NPTdcbmJUtKSlqba/IKcls6WxcsWNDZ2UlFfJmWvLR0EgFIenq6VJqfbPEgeMQkNwQiQblIxtA+mbpEnxk0qAssFlJJe5s7Gxyjwcw5xXNKVhjUlvnFiCvk0cu0cTWxdv95ZOCDIZvO6rVb7SfFsixfqHHYdnqk759rV1V0CJX5xU8HIv7OQCgSqhvp2+0YyrV7coqKinp7e81J5lPn65R4BC0vE4jNKIKyF++LFJqySNgKgApcaa6QO1PLqIvLZBKZM+wSYq7hvoPOwQKBenmEclGRxKGKplBdXb1mzRoAwPn2Zo+78Wv/+u24BAiiBACoRAoXuT7FIkcQHACwePFi/iYwJSVl4oRLkOhjyduwYUMoFOJDJRIEgeP4fzz36IsvvEGEZzMm6YYNG8LhMH8i4XBYKBRu27aNZdkpQr4cOXKEJMkf/ehH/DcxGAxGR7XNnz//nXfeAQCEQiG+zEWLFlVUVMSe9bPPPvsf//EfBEHwuSbepV89N13D/fjx48eOHeMDlH7ve9/73ve+N9s1giAIgm4uhYWFAIDJAgKGvG3h4X+I5auwaQ/plghVIcoNgCns74iw4hSpFgCAoiK1PGP33ndVMoVOJ+B0ywKD5gX6fLVUPadoFUv2cwwRosJSgQQAQBIjQ11/SisoPd3NJGH2/s7/E6Vu7nH3yyixTqdzu91JSdGIfpy16y0Opztc4/pZ+1t/M9h5mtYsOtW53dr5xzMNrl435Y/4GxuOCHAaaGVzkqLpkbw5yxmaNEnF/dZ+S3L2YH+zMWvu/JJ5GIrl5xc1HH06MGTtCJY021qXlS0Oettzc3PDIEHbyJR2hzHFIVKUmFU9ycaFAACFtMRkSnel/3/27js8qutOHP65d3rvVaM66hUhQAKBBIhmcMMl9jqvndjrZO14n/Tn2c0v+T0hxVm/fpMn3k02a3uzqY43sR0H28HYYMAIEEVCXahLSKPRjKZqervl/ePi0SAkIdFkWd/PX6Nzzzn33BkhvvfOOd/DVuq3l+XL1bpMJUWJQt5LrgRfk+AJzP0XOvPzs7u6zg8l9AR5yTF+3mHhy/SPIIRoRJM0xcJwhGE4i08REYvFjWFYyrXPy2KxsNlsk8m0cDWaRnpjeU6eBiGUJjUciZg1GTKkqL049KEQP24dvFhcetVzYmai9qw5+kwhsyNv48kPtm/f5osFcOpYzNkRjz3EZ6tSKyczGIq5apFIPWs8JpPJZDItvFvTwnjs9KWuPQDM8lmapv1+/1ID0VUXuFdWVhYVFTFvk0x262+mAQAAgJshU9fwFRtxdvnim7C5SiHNvzTQm65Xpqa69nuaPZd/T2RWeoiaEvOuRECrkagRQuXl5RRVOu3udwfd3jDG5XJ53AhfvdEVTt+cvlsh4qiMboKTELLwxsYT9Zu2T0xMzASvNJIoKyMsTOImaTSzjlNjvFuikw9NUyWZe0eNPRm5j2QO/Nk5/D8TQ2+7pDVpierUAZeXlxMEEfQNeSedPcH+0QFClUkJuUJm3rAu43MxNGKcjOZqzAghoTRv2t1P8dUxIp6aoZOi4on4tDy90iQ3ctKuxI5ZukyD0hAr2IEQKi0tZZ6SspwsKr0gos7ws1U99kHDhX+xDQ/t2/hTCytDahDpMh+KxhBCSClUjLktJqkBIcThKkOB0e7OJpISGAz3pg7e6XSyWCyV6qr4uLOzE8fI6wfuiI5HHULRlX1/MuQmDEtXi5S5aZu6zjZnZJpnJSHt6Oigqfiu3ftomh4YGMjPz08W7rnrnmbLBQHe2NP+Q4X+nkCiSqKRimXls56UP/TQQ8z8E71ez2Q8vLWysrJueZ+3EDOBiqIoDMOYNPDbt2//2c9+tryjOn36NEIoGo3+6le/WmrbVRe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" 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}, "metadata": {}, "output_type": "display_data" @@ -250,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -287,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -300,9 +230,9 @@ " \n", " \n", "sigma: 0.52 +/- 0.03 \n", - "beta: 1.67 +/- 0.09 \n", - "tau: 9.21 +/- 1.38 \n", - "SF: 5.33 +/- 0.39 \n", + "beta: 1.68 +/- 0.09 \n", + "tau: 9.19 +/- 1.32 \n", + "SF: 5.35 +/- 0.38 \n", " \n" ] }, @@ -315,10 +245,10 @@ "[1.0, 1000.0, 1.0, 0.005]\n", " \n", " \n", - "sigma: 0.69 +/- 0.03 \n", - "beta: 1.77 +/- 0.12 \n", - "tau: 13.18 +/- 2.85 \n", - "SF: 6.39 +/- 0.77 \n", + "sigma: 0.68 +/- 0.03 \n", + "beta: 1.78 +/- 0.12 \n", + "tau: 13.08 +/- 2.75 \n", + "SF: 6.30 +/- 0.74 \n", " \n" ] }, @@ -326,7 +256,7 @@ "data": { "text/plain": [ "2-element Vector{Float64}:\n", - " 3.4844129256093717\n", + " 3.481640891036401\n", " 4.12" ] }, @@ -362,12 +292,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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zMJwT4EGjtmzdY/kRWFGU/PLnrKn7X1E6wAMKCOqstNzy9D+Wh1UYlUvQRJWLjTy1OvaU9l28JoeR/PMFyuy25IIGj8P097CdOdrd29VSmwtKZdOFT1jhPdXDk9T+M2j9Z8gufkqq5c/fUsfBaICgDqqrbwQAFJQJiLtb0tV2DpRyU7Sx0DCI4jwef3xOmHQGPOl9YAZuEQNuJz9u586dQJMLicdY035CUDrVdQLdI0R+aemrSAd4QAFB7VNRKz53N6O2XvQ49rr3qKnBqr9SFLblDLepnGSKvqWLq0s6pa+zHaZKPUOKKlXRu5lB20olNONnOgvaTqXGM4trc0pqsqMvWpVf1JtxAKFzNG9RXScAAETnP9Wf+TNCa+VsaIfBVgMEtUNuWf3Ra49mjHBbO98/cIjT+8zziVK7R8jgz9+bxJl9DNEzGTnQLiq1pNF5djZwN2YomUHbatSsE6EpU/36duwTk/OqVh+OSM6rYivKGemnttXMiM1uaL4A1XXChSJ+3rEPAaZ83kY6ALYaIKgdzt3N+HTGECN9lqS2LC/9UbyraR9mtcLUoF4k0zfnAQCYdOqyWUP/CkvyUZQzzfruu54nVajfDfKwNWvl2seXKqsRnbqVtmnhKC6bkX+VYWbC3TBh3La/Hhjq/7eBQKUrlQqSUCNAZ12SMBog6OVIkkzKrkzJKS4sLMgqsfO0opee/VJcmm8TMJ9QSoKJq+n5fHvzpguc+XrUGeB8scVQfZuaBRMG8bjMDn/utbjcdwM9uGwGAIBi7ELrP0x1/fMPJ3z3190s7TJY4T28KJb52UHtUYZO9JJokEqlUqk0LS0NAGBmZmZqatrdFUG9R6NY8fOlxD7WhhxEAWS1dZVlxff2/V0/pApI3YXXRHyv1BqaPXYc+H4HAAC4uvHauvhGq4A5C5T8+M7kAgCguLrx/QkDtZOangWTuLWNjTM029X0StK85iO0VvoyNHsEAEAgELR3p+gl0ZCZmZmQkBAVFQUA+PTTTz/55JPurgjqPY5ef/jWKDcXG6O0NDzirsyt4KeLylEiPTOO/E8EseLTFGwT5yJBEXZydW2jJPPP5YWUPj7TP9DXY4waNaqTH02lUHDiP4MyUF0nkCQ5t+zwv0T/p2crzlxodfW5c+dqXggEgvbuFL0kGgYPHjx48GA4yhOkc7VCGQWhuNgYAQDk4kYfPPK8dMaMt94Ku3i6QIUzAjfL9F30z37l5T9FVV9mhl0xH/Sp26Bpuvr0vnbGSdmVIwfaNZ+ZQfMqNx6NRR1RJZSxph9A6Bws9xZuBcAzQ7xpWw1wlCcI0g2CIMtrhCKpsqZBamWiDwAQ11c3hO9MULuRZgMik4sAgkjoFuEVBnv+TdIbt5FafJdr7rj/19/5ussFAMDk4S4XorJyy+q0c0oFoj9CU7yCP0Cth2hyQRmzj1SKVRkXsbwIHX50L2k1QJCuqDH8enxuUk5lZUaMo7uPBEMNuSyZUn3gUqovKQ3Qz3xrtmd2hfyMoEFNuc2mU9fM86erhYpHYoq+hfkLH0vZAfp6jK/n+v129ZFSjT3Krth0PBLHiWVv+Zgbcih8O4TOUcb+RMrqaf1nMH28Vcl7AK6iuk7UyUfDVgME/ceRqw+ZdNrGBaP93K0XT/b86m3f3LK6zb/fHiU7W8galF2tlp9f4mxKxwhk2sQxIwfZ0zGx4uY6+rCPX/y42g4zN+R8896Ir+f6OVrwl4cM2/zBGDszAwCAr6+vMvYnUlrLHLcBIAhCZbKmHVA9/hfLvqGTz4XRAEFPFVY2AAQZP9SJ8vSxkVQjDmMKfo7nPIxnNwCx98epHMHpRf6uhiwGjZQ3KELX0Id9jFoMeqWFcVh0HofRfByHUZzsJ7nQtBcjNBZr6k+6SgcYDRD0VHZpnaezuXZSjeH7z8ZOws4KGH0uCZwv3Hkcm1zwq2IOg8MfXLKLlDd2TS5ozJs372lhqWeJ6nRm4MYWI8ciNBZryj5l9F68OqOTHwejAYKewjCCTkU1r3GCPHL5gW/jCZO+vtVm4+byYz+zvL9khv+X744ymnWQweK61F+neS/smlwAALi4PB39heo2hSRwLO/2s4upEo+htj6oaQevy9aC0QBBTUgS6DGQ3LJ6AACGE5HJxUpBTl99udOIkPKSYl5FOGPQO3QTRwAAoNI50/b6efUFmKxbSkVoLNbU/aqUv1uclVDGHiClAua4lq2JDoBnKCAIKFTYpejs9CJBVuxVQ7cxeWW1KIo21gvmvjXFwN6v6uwyL7PxTItJVP5oUFoKAAAEpri7ndY3mOo4prtq1qSD/PLn2jlPeiV1kAsAthogiCDIvefirYy5mxaO8XGz3LhwdEWdVFiZO5qdZpV7ZH+sKp0bNJ5xjz7kyVPkCEwRsQm19KT1n9mthT9tO+DV6aqHJ1r0SnYSjAboTZeQXeFkZeg/wFYzXOON+3lcrPoz44h7jfZC/sAP2VcCJ82gD5qnuLmGVMkAifeQXNBAaCzWlB/xoihSIXy2V7Iz4AEF9KbLKqkd4WGrfW2IP1pudsds6lb6/SN9+nuzgL3i1npm4GZAYLTbv9JFJBo0hdZvevfW3BxC59AGf8DwHQlA60PRdgyMBuhNh6lxOg0FAMSll5WVlq0fVGI6ZRuFbw+UEvmNr9EFfwAANOngNBY4yupo/XR5KXSHyWQyNrvpbksEpek2FwA8oIDecARBWBhzi6oaM4tqroaGe7PyzaduQw3tJVm3SLWM0Xc8hWdLc52IOoxQhG+g2vj0kFwAAHz//fevdPuw1QC9iZRq7HJMTkZhTWrM9X7DJzaIZRiGj+EJCzgcGs8Kr807F3rfiSuleS7QLI+ae6jTzhHSWoq+RbcW/hTynCdZ6ApsNUBvHIUK+/5UrIURZ/2CUb5upp/PGoLhhAIjCfsAGWpU8c/KPVdy9bhcV68RAKUCAAhhqSJiC3Pcxp6TC10ARgP0xglLyB850NbfwxZBgEqN/3g2HqVQpgzvw2zIcKSWUgC+xOj27Pc/0QzQ3JQLY9dR+PbdXXiXavcBxcKFC9u1/LFjx9r7ERD0Sj0urFkxexgAQI3h9zPLTd3dvnzH1waUVuVe+ts0wNDRBq9KV9xaD8g+hKhC8einHpgLVfWSilpxTmmdnTmPQUOZTKa2S1JX2h0NKpXqxIkTbVx40aJF7d0+BL1qajXOpFNJkjx85WGdSP7NFG8nWoUq7tB58BadmU8f8qEq4Te86rGz+B4tIYY5fiOFZ9vdJT9VK5T9ejkJAFAvlt95VJRdUjvFzzUkJETnH9TuaAgJCUFRtI0Lz5o1q73bh6BXzdCAVd0giUgsTMiq8HAw8eTVqeIOivotQq/HqCoeAQA06eCNpDP8l/eoXFBjxI5T0YuCPfvZm2zMj1gy1Vuhwvb9c5+GUvwH6LjOdvc1TJ/+9GKPioqK995779llIiMjd+zYERsbO3Xq1E5VB0G60CBWHL36cNOxyDn/27z1j3vG+uwDFxLCEgsm+jg76qtUcQfpAev/Dkv05RRQTJpuWKQP+RA1dsHSLwFAdm/xzcU8LhnsatnP3kQ7h0mnLp055EJU1gvW6phOdUPm5uZ+9dVXBEEIBALtzFOnTn3xxRempqaHDx8ODQ3tdIUQ1CmVdeK9/8QPd7fZsHDUYHu9r+b6VjdIS6tF+np0RwueQpBbR7Xc+WeELVnsPflDhP70iB2198UqU0iVtBuLbyGvrH6Ak1mLmXpMOp2GKtW4bj+rU9EwatSo06dPW1lZmZmZubm5ZWRkAAAOHTp04MCBRYsWHT9+PCEhQUd1vkRCQsLBgwfd3Nzc3NwOHjzYNR8KvRbO3M7QtMA1k5V1ksxigb4ePWR0P2redT5WSYirFvPCJ48dglo8feIDXpWmivuFFbxTt8996aT2Xsvg9kQHdorOXvJUWlp66dIlW1vbnJycn3/++cCBA4WFhU5PBr22te2i47T+/fs7ODh89tlnAAATE5OXLg+9IUiSbJTINYMpAgAUKmzfvw+4bObsMf2InOtujIJQuymmJkpSzSYairXHDnhVmiruIHP8NoRt1H21t8LZyjA1v7r5AQUAQCJXqTGcQWulB/D8+fOaFx2Ihs5e1zB8+PChQ4eam5uPHDmyf//+AACFQtH1OyebzTY2NtYEpLGxcRd/OtRjqTGCQW/6+6dW4/EZ5XKFavlbPnaiGGNhMjNwM53JYgZsQGgsQlypjNoDANljcwEA4Odhk5hdmV5Yo52jUGEHzj+YMcKt1eW1rYYO7BSdbTWkp6f/888/Li4upaWlKSkpcrlcJpPV1NSYmZkBAEo1415AUDeh01C5Uk2SJADIb9cfNUoUW6YNNqu7V1sSl+f0sZOmH4FCZQZsUERsIoTl5qJKVXw+c8J2hMXv7tpbQUUpa971O3zl4YWozMeFgl8vJ2WX1E71d/XzsNH9Z3Vy/a+++mrevHkPHjwYPHjw559/vmHDhgMHDqxcuTI4OPjevXuTJ0/WSZUQ1F61QtmFe5mFpVV5WWnrfiOcLPj3M8rdHUz6qx6oS+4flUz5UBYmv/CAJDwBAIBCZY5dr4jYPMu/D8P/i56ZCxpGBuw18/wFDdJvHlwa62W/cOIgemuHEp3X2WhwcHCIiYkhCIJCoQAA5syZAwAYPXr0mTNnQkJCAgICdFAjBLVTbln96Vtpc8a5T/Q0i+SLchRodFqppRGbLsEE6feSxCbjbSWcgqs07/eRyicPhkJpqJUnXv4QYfG6tfY2MeXrWRpz+1i/wkMe3dx5qcmFqqqqK1euWFlZBQcHr1mzRidbhqAO+Cv88edv+fC5zLKyskaRokostjLhTlBfTBQklhiuG00PoxSFUW2H0bzeA3d/1KyizryKlyYwAzfrfOCD15QObq9SKBQFBQXZ2dlCodDf3//ff//t/DYhqMNqhDIel8HnMgEAIqnyVlK+AZvxtX2ckbVjEdd/kPgq09IDQZkU8wHa4dLUmVfx4hhm4GaA0rq19h6ks9GQkJDg4ODg7u4+cOBALy+vIUOGDBkyRCeVQVDHSBVqfT0GAECtxo/dSMbUxGrH+0y+tcg5REHSmOO34+VJzMCNRE2GOv08gLnwHJ2NhiNHjkRHR8tksl9//VUqlaakpNjb2+uiMAjqIEMuS9AgJUlw5Nqj8hrRFPMCroktzfv9qjoxm0lDWDzm+O2qxN9pbtPw8kfWsmS8NP51zAUMw17p9jsbDUOGDNFc4CQSiQAATk5OmhcQ1MVEUmVMck52SS2dSqFTqUeuJiVkli11SOXwzWje74ukyqSscjO+HgAAYfGYQdtUCb/R3GfMnDqJGbDhtcsFAMD//ve/V7r9znZD2tjYhIaGenl51dTUZGVl9e3bNz8/XyeVQVAbKdXYmdvp5bXihoIkS7s+QlzPgMOMfVzW35zFV1ZmMgdGp5bcup85kzh3S8jUrIKweBQDG6I6k+b5bvcW32GaS4c0rl275uPjo9uL/TrbaqDT6QsWLLh9+/aCBQtGjRrVv3//rCzd3wQGQS/w6+UkJyvDNe/6+w2wn+zrMjfQPS2/2sSAPXSQ60P9GdyK26Aq6X96p/twRQhX86hbUhmzl2Jg9frmQgv19fVCoVC32+xsq2Hs2LFVVVWa11FRUREREe+8806nq4KgtiquFlJRip970+WAYpnyRGgiX59Fp1OH2dMaM0JTuRzP8sMkgTGn/oScuAYAqYz+kcIypHkv6NbCezpdjg3p4uLyySef/PjjjzrcJgS9WFFlQz+7pnt2MJw4EZYmV6qXh/gMMEcUYesVHgvrLANJgqTQuQBTAJJU3v2ewjKCufBSOriH4vvvvy8rKyPJprvWMjIyNm/e3OnCIKjdolKLK+TsNR+OtmTJ/Wp/F/ksQ7lmCLtSb8pfirC1ynu7ONUSCncGzfv97q70NdDZaNi0adPQoUODg4M1d5ITBHH06FFdFAZBbWJnwb8Rnzva0/5aXG5Wce2s4NEDLRBl2PrLxIQPnAfWVFcBAEzyhCIAACAASURBVBA2nxm8S3H9y4XTvGAutFFno2Hy5MktxoDj8V6DS9ChXqC8RhT+sDD8+iUjpyG7/o7NKK51sOSP7MNR3PwmmhNiYWTJoD49XkboeszgHxShq7HS+1Qbn24s+3XR2b4GlUrVYg6VCp+IBb1y4UmFp8LThvezHmij937woMziWgAAh0W//bi6rF6lR4omKP9ShH3bfGRHUiEhMRWl543C0DN1djeeNWvWunXrnJ2dDQ0NAQAEQRw+fDgwMFAXtUFQ66oaJA8yy7+e44uiFIIkz91O57DowcP6PL6d6udqbOW6z/raElwsYPh9rr1XipBUK8M3MEaspBg5d2/xr4vORsO3337777//WllZafsaCgoKdFEYBD1XUlblWC97FKWQJJmQVcF1cVk1x9eZyK5pjONn5NKZU5WSWoTFR/RMNcsTkmpl2Hr6iBWoiWv3Vv6KaE8C6JAOxmuorKxsPgeenoBetUaJwsXaEABw5nZ6Vb3k03Eezki+OvWvGNLXw22k4aM9qKEDM3iX4tZ6zDSAVIh6cS7EZ5Rdis5OjU2PLCTt7cvfDfRwtNTNODSd7Wtwd3dvMUczdisEvTr6eowGsTzmcWnog3wnS0N/4yr1oz+Z47dLVBQmz5w14xeAoKS8gTnhO0bBdXb+Jcaor3plLvwV8Tg+vWztPP93AtzXzvNfMHHQ4StJD3MqX75mG3Q2GrhcbkpKSvM5+/fv7+Q2IahVQokiLCF/6bof1BhxNS7nj9BkFxuj/sYqVfJJ5vjtWZUKAz0GSkEoXHNG4CZl5PekpMb8rd2Tl//UK/sXahplqfnVy94axmUzNHNsTPXXzhvxZ1iqTg4vOhsNX3/99dSpU01NTfv06dOnTx9HR8fdu3froC4I+q87j4o2/3EPwwlcLGAzqGUCMUGQ77rJ1IXRYOCie5l1Z2+n+T4ZPZXCNWcEfKu4vYWU1VEMHbq38lckNb/a192mxYMp9PUYlkbcyjpx57ff2b4GFEWPHDnCYDTlFo7j+/bt63RVEPQf+eX14UkFO5aMo1EpcdfZ9zPL6TSKtYl+ffLZhAJhbdj3GC/gc9rtSBCsXYWoL0LoXITda588IFeqOSz6s/M5bLpUoe789tsdDdoRYjU2bNgwZsyY5gvQaLTnLQxBHXMzIf/dcR40KgUA8CCz3NLK8vOZQ5zKTqWWGA2b8LYRN9eo8QxqMRDBmx5Fg5XeV6f+xRy/HaHrdWvhr5C5ESejqObZ+aUCobmhDr51u/fbpUuXNp9skQsAgBEjRmhfL1++vGNlQVBz5bViTcf7v5GZxVWNb41y61f9N1NPP4IYS6VQSIUYQWlAVk+IKoAmFx79yRy/HWFwu7vwV2igk3lybpWg4T+P5EzMruBxmNreh85od6vh/v37be9NiIuLa+/2IehZTBpVplBnFNWcvJVqY6o/Dr+G0PWogxeD9HtAKUQYeoyAb9RR+9SZl9UmQK3O6PW5AACgUSlLZw7dcTrG38Omsl6SWVJ7J1NYUi1cNddPJ9tvdzT8/vvvbV943Lhx7d0+BDWX+PDRYC/Pgc5m1+Nzr8XnOpjzWBUVgDCkD/0oOa/KyZovaLBhh+yg0BiUoE0GGUtYOf8y3j/W63NBw9GSv2NJwL3k4qziWkS/ZvRwzw8mebXzibnP1e5oGDhw4MsXgqDOqWmUnbiZUlErfnDj5JAJ73r2sbgUk02noevmj9hfcYuozclLibvwULkiZNjRtHDNyI4UfctRI4YjTD6F10UPYe4J6FR03GDHykx7X183JycrHW4Z9hFCPU5Fnfi7k1EThjrv/iwoYLDjjo8D49JLcRz3dDb74cSt7LjrJxpGMR4eWDGGb8BhPlmJVEbvpeiZMIZ/2p2l9yIwGqAe59SttCVTvfs7NI3ddPzGo/Ja8SBnC39Xo/Vm51GWwXy9G3xCQIv/HhdkAgA0uYDQ9ehDP+rGsnuZXhIN0dHR27dv53K5XC53+/bt3V0O1CnlNeK+tk3XIxRWNl6Ly5050m3+GEeT+LV07/ec3YcQslqAUOh+K1TRe0lZrTJ2H8yFVnGf6MBO0UvGVhg+fPjQoUM1D9rUXn8FvaZQSlNPWmZxbUJW+ewF5vNGOYovfBLODnAy88CyPkJGWdD9v1QlHKb7/s8u4UsK8x364IXdW3PPVFFRoXnx5kYDiqIoinK5b0S/dK+HIIhSjQuliu2novWY9K9mDlBeWVphMVWJuZGEGtD1WBN3UvgOFLah8t4PM778BTVy7O6SeyjtHkGnt3Ld5Iv1kgMKqNeoaRAz5WV/3kzddiIKwdUj7ElKxBrgPPH3TH6AtwNF34rmPpNi6AgAQI37MHyWKCM2AeLVPuLtzdSRVsMff/xRVFTE4XDGjBnj5eWl85qgN1NSdsVfEY9ZVDIt5VGmkINgqu8cwz7861ZRwCfoveNz/LeZG3KaL0+qpKqkE/ShiwGllzR+e5SO/EyXLFkSHh7u7++v82qgN1bs49LwpMJv3xtJIdWLH4SpMdU6i+sJYocqxJolr+DbOVtlbyf67KMYNJ26J1VSReha2oAQqj38PXwlOnJAMXPmTE0uSCQS4RNKpVLXtUFvkH/uZqx8e5gBh3nnUVFGYdU2+zD3kZNrbaaMHj3K0mMkpeguwjKUX11OCMsBzIUu0ZFosLOz07yIjo7++OOPHR0djx8/npeXp9PCoDdIg1jB4zD1mPTHhYI/riWOM8h0GDqeNnDOkL6WDbUC5b1dVKfRCI2F2g6TX11OyuoUoWtgLrxqHYkGJrPpErQJEyb88ssv8+fPX7ZsWf/+/TUzr1y5orPqoF5NqlCdOHv50s1IqUJFo6FlNaIfTkYuM7zEMzZnDJoLAKBJK9S1+ajlIFbwbtak3WRjCdVuGLs4lDbgbZgLzT37zIfO60hfA47j2tc8Ho/P/88wlSkpKVOmTOlsXVCvhuHEXxGPU/OrsZpSBqsuMk9ZJ5RuOx6lT1VZkhX3HxRiRVFU+xHZQgbf2Iw19SdAZQBcDUiCom+z6mQKUicCr2AM5dfXlClTjI11PGhNR6Lh9OnTAoEAeXKHV2JiovZh2RiG3b1795tvvtFZgVBv9MvFRBsz/Z0fjwsLI/T19b0HD12441KjrO6Aa6jMaSU4v1pxZ5twwP8e5dJsXD0RpgHA1fJrX1LtfGkDZgMAABB18xfoYUxNTXW+zY5EQ3Fx8b1791AU1c6Jjo7WvMBxvLa2VjelQb2UoFEqaJT+b9ZQzSRJgp8vJpBK8Sr+mUviIEqtVQPTsVRlzIneuXr0moMXkCe5MIw2cE73Vv5G6Ug0LFu2bNeuXc97d8OGDZ2oB+r9MvIrBziZaScjHhZmVKq/t7+SxQ9xdB7bUFmMiypJ/5+Mcn8mHu4l6s1gLnSLjnRDTp069QXvjh8/vqPFQL1cg1h+Ojxty869dx5kRSQVEgSZXlQTnZSxxeqC5bjPq/W99akq39LvJ9tLXPkq9tS9KN9JnRtO4ZrBXOh6HWk1jBw58gXv+vr6drQYqDeLzyj7527GzJFuw/tZSWhIrVD2xYGb6bfD7CxNLQw5AEHS8yvGlR8nRRXMATNRy0EAAOb4rSFZmSg8GdEdOhINe/bs0TzMbvTo0ZMmTaqrq1uxYsX169f5fP5HH320YsUKOIo01IJQqjx7O33b4gAWg5p0h8NgsWhUCkVc0l8Z66w0YIw7U3lxjS/NiystwCbvo1xLAACQSon8yrIBId9SHV70pwh6RTqyD48ZMyYzM3Px4sWTJk0CAEyfPj0sLOz06dORkZFMJnPPnj26LhJ67T3ILB/tac9iNP0pemecR1RU9AeMsymEexZzWOGppQ+M5gVwMxkB36JWnkCTC5c+pXsvpDq1HLIc6hodiYabN2+eP3/excUFAJCYmBgdHf3jjz8GBgZaWFgsXbpUKBTqukjotVcrlFkYNd0gjOPE6fM3PmCcVfmvJ1l8qoGF8dTvJ8hOMsd+o047hxVFkZgS5kK360g0oCiqvf07KioKQZCgoCDtu/r6+ropDepF9NmMRokCAEAQZFxS2ljxnyr/9V7DRppa2Aa6G/EdBjIDNyvC1jFGr1Mn/aFOPUMf8iHMhe7VkWggm12IFhYW5ubmZmhoqJ2DYfDmeaglb1eLu4+KSJI8c/G6oziq3mOZ17ARJaUVqLyG/eggqRSjpm7McRuVt77lTdwUOP8r2L/Q7ToSDWKxOCEhAcOwo0ePhoaGaoZd04iPj3d17YVPK4c6TKpQ7f3lKI2KerlYLN17435yDoNGHe7l+iCtYNexqwGM+/Rhn2oeG4EwOCRJUKnUwRPmdXfVUIfOUKxevXrJkiWXL1+Wy+UrVqyYN28eACAuLu7mzZv79u1buXLlzJkzdV0n9PqpE8l/v/aoUaJIuXdfznUtqmqsqpcY6js9ylWMOfphkVHwKv2wVPu5qKUnAIBoLJZf+5I1YXuvfOD966gj0aCnp3fy5EmFQgGa3YXp7u5ua2u7ePFiOGorBABQqLBtf0YtmjjI3dF0Y/m9cYMdtp+M5jDp33nmbI0ztDLmenOiGMO+RMuYQJsL47+jGPfp7sKhJh0fOUsbChqaMa07XQ/US4QnFozxtHd3NAUACGrrf/gr1tHScKVTSnF+Tt+A+bzpQcidVcqEI6T+XIIik1/bA3Ohp4HXJkE6phk7IKuk1tvFAgBQkZWYcvOPYIPH690zefKCc8RMgCCIngl71hEEoShj9ytjfoK50APBaIB0RqHCUvKrP12xNqukFsNJKkppKEoRXl9bSpjP4CWi2ZewwihCKdYsjHDM2LOOUFEKp/8UmAs9EIwGSDdi0krXHo5Izq2SKZSRycU5ZXXx924LLq76WTbbmgtQUk1IqpUmg9j6T89zIxyzid+c7y8KJRpLurFyqFUwGiAdSCsQ3ErM37Y44P0JA12sjZZM9f4m2NQhZ9+PDbM+fncaDahJUQXiNrNcSCyy+88YourH/6I2QykG1t1VOfQ8MBogHbgQlbVk6mDNLRLZWenKygzizqb94hAx1TjmcUkDxfim+eqK7AcqtzmWinS8/KFmLWXcQVJcxQzcBBD4e9jjwP8lkA40ShQWRhwAgDrjctnDW7n/bogWO80IDuhjY+TvYUuSpKN3gNW83/pXnaA6jsZL40m1AuZCDwcf+wPpAIFjAAAs+4Y663oVxZqQN04yEtHw20lI3372JlMmBnq5WAAAcL/l8iufo05j1cmnSIoY5kJPBv/HQDoQf/lwedw5Vfql/eLZIjlmYGLDlJdLk/8JYieBJ89iJWqyFdE/smYewQujSGkNzIUeDrYaoM4ilWJUkCJ4QDvG/aix5JEzo9pQmFTutBgpCO2HJalT/gYAEDXZ8rBvWJP2UHg2Bn4fcsa9B3Ohh4PRAHVcdYP0bOj9KdVbgazugcw5obrOjW9cgxv8QN9Kb+Qtnj0TvfuVOusKXlovD0tnTd6reWLlhx9/3t2FQy8HowHqoKKqxl/PRa40+JvJQRupFn0p2aY27AzuaH0j85XzAw04TACAqm8wXpZAI6u0uQC9LmA0QO2TnfZQUFU+InDKX9fjVxme4/gs/ieuhK1eAfq/PYGIZtfG5A0arMkFLPsGXhzHmrJ3zUQKhUbr7sKh9oHHe9DLZWdna56ETipEgivrasJ2qmvyp4h+1vN8JyP8xMTaPWq2xRDxRfqguQOZ+Uh9LtCcrci4zJqyF1AZNJgLryEYDRCIiIh48QIPHjwoLS0llWL55aVK+/Eii7GKs3PS0UH14TvtZA/zLefwjExY0w4qY/arXGfpKSuVEZu1udA1XwHSORgNEIiKinrJEtIaQiyQXfwsTX/iH7H1Z2NLhHSb4epbEhW43/c777fXAAAQtiFr6k+Mhz+zCBFelQZz4XUHowECpLzhBe8Stbnyh6dkFz85J+hXyfEc5GLhbGv2g2SBAOezqWTQyKHaJWUYpV6Ko3pG7LdPwlx43cFo6J1qamqkUmlbllQln1ZnXFQ/Pt/qu0R9oSJ0TWKh8Iao31TG7SkuBJtBS8gqL62TFXptr8b1q46GpKQ+liuxsJjUwmMLKN6L/N7bBHOhF4BnKHqn+Ph4U1NTHx+fFy+mTjuHF8fQBs3DCu4AXE0b+Hbzd4n6QkXo6irHdxXy+MxGTqOohHrxf5ci3cRy5YH5I7xdLY83rrCvWIsk/yEUS/tlbuWNX8pxC4KjBvcOsNXQO6EoiiDIi5dRp53DCu6yJv+IoDTWpD1YcYzmykUNor5Qcm3VIemsq4kV1WrOOOaDs4zFe6oD/cl7YzzMvV0tSaV4XMOhEtdlffSlpnip+dhPOW5BL/g46PUCo6F3wrKuqXNuvmCBxHM7NbkAUDoAAKA01qTdWHFs03XNtbnyG6t+lsyaPCHg04kuhuY2KXoTJqtO1VIsK7iD9SvvEsJy+aXPMvmTMLvRrMk/Og30ozqO7oovBnUVGA29kDLuZ0JWT1Snq5JPt7qAKvn0xQvnWVP2NeWCBkpjTdqFFUQqb2++fe2cHENMzSxcbIyofQKldLP4CnoY/a3tdpclcozE1IprKyieC65VWPSzNwVUhl1fzy76blBXebOiQSaT3bp1q7ureCVu375NEAQAQBn3MymupHvOY/gtw0sfqB6darkoSeBF0YieKaD8p6eJlDeoHp7AKpNVKWc37T2WYxYyseEnUtF4JTYnraDa3FCviuoodn5nOHGPIIHY/YNt95AZI/pqhm/x8/Prqi8KdZE3KxokEklubm53V9FBp049s5M3Ex8fj2GYJheYgZsAggAKypq0Cy9LaJkOCIVi3BcvT1Te2918tuzvucrovSSFltBvRy3Bi22wTOXPKDq24O/rMZZG3I+neS8bZ6ZIOBqt9kxV2B1JZi2YMHDUIDvNuoGBgbr+ulA3g2coXhsvDTVl/CGa4r/jIFCorEm75Ne+VAFA93y3abG4g6SshjZwjvrxP4DEGaNWaeZTHUY2lKTvEc3xZTnw8Gp3nvhILK0vOvQ760s7qo2AVMC7/yv69n55zvlpAfwl7414ZV8U6hHerFYDIa4iarO7u4ombbzuoE0wpTrlLywntJXxUShU1qRd6tQzeEkc0OSCsJzqNAYvfQBIRJ1+Xhm5EwCgTjunrCveI5qzfuHY8eKjFJRmn/WjJaVawBlQZP02VnRP9eAwEbB9763qzxbOXrLkI51VDvVUr1M0CASCW7dunT179quvvkpJSWnv6oSoQhH2DVYcj+WEvory2uvo0aPV1dVtXJhoLMFyQkmlpJX3MKX86hcUk76kQqQI3/Ts+8r7h1GLgaiNjzLuIFH1WJ0TKr/8OSEspbqOZ888rE6/EHvpKFYUfdt46UTfvtTIDUWYRa6QeUgxZ71dREh/5EACtRC3uqEes+lC4ehB9gFeDp351tDr4nWKhsuXL8fHx8+ePdvHx2fr1q3tWpcQVyquLGeM/JLmOVeV9q866+orKrLt6HQ6SZJtWZJoLJFfW4lwLeSXPmuZDrhafm0l1XE0aump9/4lLP+O4taG5u8r7mwn6vOZgZuUcT+Tkmpm8E6657vst0/Svd5njttAMR+gN/ecQiahe71XXid1L/gpsZaz+r41i0HbsPRtw7d+9q0/7msqtbRzChzls+vToGH94ajwb4rXqa9h4cKFOI4DAEpKSpyd2/E8ZUJcqbi8jDFuPYqYIpTHrGk/yS/9DwBA6zv5VdWqO5pcYI3/Ds09RR8aIL/4MWvazwhTHwAAcLX86hdUh5G0AbPB1e8QBldv4TXpsUkKALBiTJVXIUlaQSoa6YPmkGoZlhvGDNyEMLiMESsBADjxL4YTV2Jz4tPLhAT73oWjXrgkAjf9u85l1ig3YSyPz2UBwGJN2Tfp94WpCj19PeZLCoV6l+5pNUgkkqtX//N3W6VSbd269ezZs4cOHdq2bRuGYc+uhaIonU6XyWR37txZvXp1Gz/raS6YuWvmIFQma9pP6vRLum07nD17th1LE5j68b/q7BsvWepJLmge/Ua1H0H3WSK/9CmpEDXPBVIp0dwipUkHLP+O+vEFrPQBQmczRq+mDV6E0DnskGPKu9/jZYmaLUdFR3979A6CgI0eWQZsmoOZQamU8XfdkLXzR743fqC2gFIZO8zgwy8HVI4YOqC9PxDotdbV0RAWFrZjx46ZM2f++uuvzeevW7fO3Nx89uzZH3/8sYGBgfZ4YdWqVYsXL165cqV2yf379x86dMjAwKAtH0eKq+SXljICN2pzQQOhMllT96vT/sVywzr9nQAAgCTJzMzMti5NYPLrqxCuJV4YqU6/8Nyl/psLGlT7EfShH8kvfaq8t5vElXj1Y+mJGZJffLVfBGFw9eb/CzAF1dJTb1Eo3XMehWsBAEDYRqzpB5X3dmnSobZR6udhMxGNAJJqNb/vllT7QexSL17D7aQCnGg6zMkvrz/w7/05BrF9xn1I1TPq0A8Gel119QFFUFBQUFAQjuPx8fHamRiGHT58ODu76dxBcHDw8OHDN2zYgCDIzp07m69+4cKF+fPnW1lZhYWFBQW14Yp9pgHC5JGNJcC0X4t3SHkDqZIgXf9INZJQ3FyHWnlR7STMwElY0k6AqVrc1wQAALhKfv4j5oTtFJ4tUZNF1BfhZQnKyJ2MUauoDiMJYRlWeI+oKyCqHqOWnjTXCWhl6pMV1YrwTbQBs+mDp7c4W4GwjZjTDghOL75Fm1bTKNPPOF6jrzxYP6EBz/T29rIfNWnZlWU/VVFWHpQWVTV+9cstQy5jrWWonrU7fcgHXfKjgXqQHtENmZCQIBKJjIya/i7xeDyBQJCcnNxisXv37n3xxReTJ08eOHBgQkJCW7aM0Fis6Qef7XfU9Eoyx21An4mMjsHLErC8cEC0chz0HyShuLGaYu5B95wHgOba5D1YcbT60SmioQgvS1BnXlE9PAEAACidMWatMmq3InKn9ORb8utf4mWJWFE0ACQhKscyLjNHrNR77yLFzJ3mPovus6RpUNYnRxlUh5HPjuYuU6rX/5Uaa/3FNPSaJbXOhC5enjo0r7zRz8P20+lD6HxrzrSfZlPP755tY2Nq8P1HY78wvApz4Y3VI7ohy8rKaDSadgRBfX19AEB5ebmn53+uzB85cmRRUVGrW0hNTU1JSYmNjW0xf/78+fPnz9f2OwLDoaC13odOwssSlFE/Ikye/NqXrEm7WlyArEXU5SnC1iN6xqS0RnFrPSAtAGhKB+nJmcTd7U8WROgD3gZUBtVhJABAGbOXGbQVNetHb/xT7/0thKhccXUlM2iL5iiDNXW//PKTsdtxtTx0tab3gSi5+uzpjzMR6WO9HMZ6OQjKbZUb/dZk+jpZ8Rl0qkTS9JOvxri3+J9YhW9AMZnq5hqKiSvMhddOq63p/Pz8+fPnt2s7PSIapFIpi8XSTlKpVCqVKpG0dg7/OWxsbPT09J798k5OTuBJv6P80v/U5kJSIXxuLuBqgFIBaLqXmVRJSHE1iSkBAEAtRS09tfs8UZuLFUUDkiBVElJUSdQXsKYdQHN/odr3k/01h8QUQK0g1VJSIeZ8GocwOJq1lNF78apUAAAoZiBMAxKb2vS5KI0ZtFV+fglj9GrUchDCNdcOhaJJB9WDw1SnnwGCtsgFoGkWTflRfnEpUQvk11ay3cbT+s8AAAwYMODZ7piU/Or54wdcic05GZZag/Mm25rs+CggtaB6492mDtSzt9OHDx7MsvSy/WUYxdyD7tW+XyaoJ/jyyy+fnfnnn3+2dzs9Iho4HI7m1iANuVyOYZienl7bt8Dn8w2A0F98rmlaKWLNPIwwm/YNLDdMGf8LqZIps3cqH9XT95zT5oI6/YIifCMg8KZKltxD2IZNa2VeU9ze8rTIJZEIu+mQB694pIxqugEBQSjsef8ieiYAAJpHCFGXp84No9r5IXQOoLEABdVugTluk+LmGtRhlOaaZeTQoaY3SEKdfJruu5TWf/qzX02TDvJLn5JSXotcaCqAzmFNP4BfGEF1XqvJBQBASkqKmZnZ0KFDmy+J4cSqQ+H55fVD+lqWWRs5WfJ/+DvWt7+NWK56XCi4FJ1tZcz16WcFAHAYswDmwmuq1VbDsw3ql+oR0WBpaSmTyXAcR1EUACASiQAA5ubm7dxMs/YzQ7/5GwiDSzF0BACgTAe0LBc1fdpeoBg5070XAAAQOgcgFEB7evYetfdjTdoNANBcRIAwuNq3aP2nU10mELU5yns/sKYdQPRMtK13xug1FCNnrOAuM2D9f255BgDRM2JNOyi/tkJNoT7tdyQJReiap70PraE6jAQEpj71IfPrmy1yoWnLdE4m4UJxfXqZhuYnqSVTqE+Fp5XWCA05rK/n+vm622SF2S6Z6p1RVHM5JkcoUdzPKJ89pn8f66ZYRFA4PPybrkdEg4+PD5/Pr6ystLa2BgDU19fzeDwvL692bYTCd2DP2tjqW6jtcJbtcAAASyCgCf/RtvABAKi5B2ru0foGDawpzzt/gdKBvFF+9Qu9J+2F5mgeIURtrjJmP2PkM007lMaatEd+bUXTZBtyQYPqNBbpP7vVXNCob2jQXA+mUGG3EguuRmfrG1Q3AKMAb4dHuZW/XEqqF8ntTA1mjHDzdbcBAGiuGetnb3I5Jtunn/UHk/7TrdPqdSXQG6V7zlDgOK75PdZAUXThwoVXrlzRTN64cWPJkiUt/u71NAjHlOH3uSJiE8BVLd7Cyx/ilSl07/dbXxOlsYJ3YYWReHmSInxTW3JBIzo2rtX5BEE+yq2saZRFJhcnZJavPRyBUhBfd2s/D9uqesn8bRe2nojicZi7Pwv67qOA0Ad5l2OypQoVAKCiVrzr71hzQw5Pr2Ub4ZNPPmlLSVAv1tWthpiYmMjIyPPnzwsEgs2bN7u5uYWEhAAAtm7dunXr1l9++YUkSaFQuHnz5ldUQBtvW2gLmkcIAEB+9QvW5B8BpWnvEDzFVQAAFPVJREFUwssfKiO/1xxlPHdNKoM1+UciNAgdNbuNuQAAaHWsx6Kqxl8uJrraGtna2qlwsPtM3MiBdhN9nE8Xp8akleRL6mgoYm7I2fVJIIWCAAC2LR57NS7n+1Mx4Qn5rLDUCT7OA53M6n1sW2zWygo+n/JN19XR4Ofn5+fnt3bt2hbzGQzGli1bWl1FhwwNDUeM0OVAA9p0YE7aA9qYCxpUBm3gOzT3GW35FJwgK2vFGEERNEhN+U97ZxUqbO+5+1/P9bMw4lQl93ew4I3xciBJcuXBsPjoBBbHYM708bNGuR2+8rCiTmxtog8AoKKU6f59p/v3pZRGfj23aWgmQ0PDDn17qDfrEX0NnSeVSiUSSWpqKgDAzMzMzMys1cWoVOqAATq+F+BJOqwghKq25oLGy0Z81kgrEPwRmmJtwjWxcz167ZFSjS2e4m1lzAUAxDwuHTHA1sKoqeskr7yhoLIht7SOIMlBfcxnjvUeN8YLAGBtoi9okGqiAXqjaPYIAEB1dfXzdorn6SXRkJmZmZCQEBMTAwD45JNPuvhQmeYRQmBKrGgba2uM9tynTuSW1Z0OT1s335/PZTWmh66Z519Y2fDDX7FbPhjNZTNKq4XerpYAgKp6yf2MsvviFJRKnTisT0m1cACPqa/XdHFEo0TBZdNbbLl///46rBPqmebNazpcra6ubu9O0UuiYfDgwYMHD964cWN3FUAf9G41L6JduaBStey/fNa5uxkfTxvM5z69HszBgj/F1yX0fn7ImH40KlpWI7z5IC86rbSwsnHp7D7pRfXvTxi46XgkldrUwSyRq3LL6j6eNrjFljVdPFDvpm01dGDX6CXR0HYymYzNZut8syRJ5hcWtWuVefPmvfQgv7ZRZmfW8qLGQX3MD154cCshPz6jtKRaxGJQ3xrllkvPXjBhUPTj8g3H7iqUGINGBQAUVjYcupQ0L2gASmnTwQsEafWI26u6TG1t7dGjR7u7iiZt6fxrcVaCJMnU/OqjV5Me5VYduJBAEsDKRP+dAPd3Az00WTDAyUypwlGU8tvVh3v/uX/mdvpnM4YMdrV8Vd8B6r3erFYDQRCv6HKJepGcSmeqMJxO1eX2eRxmRZ3Y0ohbK5RlFtd+vPtaVb2EQUNtzQwWTfQc4GSmwvBzd9K/OBCWnVoiPR6Jk8ii4EHuDqbXr6uNjY1bXCgNQW33ZkUDhhMqNf7y5drjUW7lybA0I30WjWe9+tcIN1vjdwM92EzdXGg8abjz9j+j9Nj0rOLarPzqd0ZyJg3vE5FUuGnhaB6HCQBg0NB5QQPmjvNYVx238u3hhgacl24TgtriTYmGrJLaU7fSGuvr8lPySw6GBQ12DBriROn0EXhCVsW1uJxv3x9poMfYXHB7w2dBd5OLtp+K3rxo9EufRvs8JEnmVzQ8yql6mFuZXVKLEyQNpbhaG1PsTQy5rLj0suUhPppc0KJQEC6bzmE9PQ2Boqj2JncI6oA3IhoSsysuRGX9b+ZQCib75x/BB4sD/gpPO3Q56dPpLfvt2+vsnfRv3htpoMfQXmQ5epB9bll9YnbFkL7tu6BQKFE8yqt6mFOZnFsllCoRBHGy5M8a1c/b1cLBnF9Y1bAz7dpkXxcHC16rocNkMpvPDwwM7HA2QRB4Q6Lh9K20TYtGc9kMgUAGAGDQ0AUTB209ca+4qtHOnNfhzSpUGI1KMXhy+YDWkL6WqfnVbYkGmVKdX96Qkl/1MKeqoKKBJEkDDtOzj4WXi8WgPubNt9zX1tjW1MDRkv+8TX3++edU6tP/mxTKm9XBDOlcL4mGrKysrKwszRhQ06dPnz796cAHtUKZkQGby265A/u626QWCDoTDRhO0Frr1KShqBojnp0PAFCosIKKhrzy+rzy+pxiQVWjgiRJlIK42hq/O87Dy8Xc0ZLfsb/2zXMBgjQWLFigeZGcnNx8p2iLXvL7xOPx7O3tR48eDQCwt7dv/pZChWme5twCm0GrE8o786F6TLpQqlRjBI36nz/RmcU1DhZNiSORq8pqRAUVDXll9Xnl9aU1IoIgAQBGBuzG7JipU6d59nd0tTFuS7dley90hSDNHgEAaGxsbO+6vSQazM3Nzc3NtRnZnClPr6JW/Oz8/IoG7Q7cMQgCArwdjocmfzjJEwCA4URBRUNqQfW1uFwPJ7ObD/Ir6sQiqVKzMJ/LdLYy9HW3cbY2dLYy5HGYhw5VBA9zavuINfBGaai9tHvE8wZVfYFeEg0vQKehzlaGtxILAgc7amdW1kmSsitCRrd7OGkMJxrEijqRrF4k1/xLLxTM3XKexMnkuxmp2E0AAIKA9EKBpTF3eH8bS2OOlbG+owXPyED3l2BC0KvT+6MBALAo2HPfv/eT86r6WrBKBMKTt1KTc6uXveVDp/2np0ChwkQypUSmEstV4icvJDKVWK5slCjrhLIGiUIoUTRfhUal8Dksa2N9Bg2tNdF/d5zHEDcrK2Nuiy23oMLwmLTSuMdlwCjLz5vi4Wj6Sr42BHVC74kGFYYLJQq5CsNwQqnC1DihVP2/vbsPauLM4wD+bN5BCAF5C6DAlVjfELQqOqct2GnNFGZ8YWqnOHjSk+owN047TqedUSscCNZRqdbjxuHFEdIWQauCQBUHQitK5WgJzI1nlbeQEAVEhRDyBnt/rE1jWAMJyIbN7zP+wT55dvPLg/my2ezuM2oYHdXpR/XG0WV/8et4+FT6W2fLPRV3/mNfgWvhtRat3jiiNw6P6HUG45BGbxwlOXbIZTPdXLle7jxfzzmLgr093V3m8l083XneHq4CdxfTlwg4jv+z5+dtGya+nPG+YuDfV/7z16Xzgv09QoSC2t86yurv7ftgLY9Dn98FoAH6/Hf8oe7uf8cuW++j0ww+GRqR9w7y2CwXHnsOj+3l7hIw182VxyFOGXJ34bi5ctxduG6uz3+e3hOftXrjvy41Htix3tvDte9uXWSYv3idv7S5M6/it39sWTWNTwTAFNEnGiJF/kmbV7GYDB6HxWRiLhw2i8ngcZgsFpPLZnLZLB6HNfRs4MIFY0rKJqqK/M+9njWLA71fPO4QHRly9dZ9nWGUa/VjCAAziT7RsHC+98bVr1nvM/TK7pWMYZhYLJ6wm+qxmvRMikBv976ncCMm4ECc65w5b2/v+Pj4V7TxqKioCfu4cFkarWF8+7BW78KFSx6AA3GuaGAwGNTeKznyNf+brXKLxidD2qdq3Vy+C+kqAFCCJh8ofv3116ampoqKCoTQxx9/nJycTHVF5IJ8+YHe7mfKmnZsjCBaunsHT/9w52/iCGoLA7S0atXzY9s9PT22viloEg0ikcjf35948Q4+h8LfY1dImzsP5te21v9P9rROKBTu2bRyiudlAkAqJyeH+CE3N9fWdWkSDe7u7u7u7qaMdGQYhmKWh8QsDzmN7sdveVMoFFJdEaAt0zuC2KG2iXMda3AoLAYDbqkAHBZEAwCABEQDZcwnBAbA0UA0UCY2NtbHZ3JT4AEw42hyGHI2srjlDAAOBfYaAAAkIBoAACQgGgAAJCAaAAAkaHIYcmBg4PHjx5WVlQghkUgkEomorggA6hHvCITQ/fv3bX1T0CQalEplS0vLkydPEELbt2+HaAAAIXT69GniB+eNhvDw8PDw8NTUVKoLAcCBmPYa7HhrwLEGAAAJiAYAAAmIBgAACYgGAAAJiAYAAAmniIbbt29fv36d6ipmH4lE8uDBA6qrmH3Sjh/HcZzqKqbKKaLh1q1b165do7qK2QeiwT5pJ05ANAAA6AmiAQBAgibR0NnZ2dnZaVpUqVRnzpyZmadWKBT5+fl2rJiZmWkwkMxkZSEtLc3Ko1lZWTqdbnx7UVFRe3u7RWNeXp5SqTRvyc/P7+7uNi3W19ffuHFjwpKmRV1dXW1tra1rdXd3T2a027u6ii5ceNmjyocP8777jvShtOPHLVp0ev2RP043JhgMhsxTp8xbCktLO+SWMw+9IgUFBXLbn8viDTIZEA1T1d3dnZeXZ8eKhw8f1uv1E3ZLTU218sH1ZdFQWFjY1tZm0Zibm6tQKMxbLKLh5s2b1dXVE5Y0LV5pNLR1dRWWlr7sUUVPT+63345vx3E8dVw0aLXarG++MW/RGwyHT540bzlXWtoO0QAAcAY0ubxKoVD09/dHR0cjhEJCQrhcrkqlMl1Scvv27aGhoVd08ZVCoVAoFHZs3GAwZGZmstkTz4Kbmpr6shkrdDpdVlYWl8u1aG9vby8qKqqvrzdvVCqVeXl5VVVVphbij7Dpm936+vqRkRHTa3nw4IFEImloaJj0a7JBXV3d2NiYrePW3d3d3d2deuyY9W7tXV3tXV3jdwEISpVKqVKNf5TYO7No1+l0Op3OvNFgMBgMBvOWDrm8sLT0519+MW0n7cSJiScZcXOboAMZuVxeUFAwyc99UqmU+KG5udnWe5Ey6XG1Ym1trVqt5vP5CCGBQODp6cnlcgMCAohHMQzj8/leXl6v6Nl5PJ59k1AFBwdP+B8IwzArv1QMw4KDgxkMy70/DMP8/PxcXCyn2A0ICDDPEQzDhEKhqQXDMA8PD09PT1MH0o1MCwzDBAKBQGDbjH4YhvF4PGFwMOJwrP1js13mzPGbN4/0UYzD4ZJtBONyMQwLWbDAopHBYMwPC3uhG4MRLBL92Y3B8J83j8fnP++AYcEiEcblTlCkvYRCIY/Hm0xP04cIo9G4YcOG9957b/LPgtHgC1gAwLSDYw0AABIQDQAAEhANAMycWfT53Xmjobe3t7q6uqSk5LPPPpPJZFSXM2vodLqrV69KpdJPPvnEji/YnRmO44mJiVRXMVnOGw1lZWUNDQ3btm2LiorKyMigupxZ4+7du8XFxdHR0V5eXhcvXqS6nNlEKpWOPw/NYdHkvAYTtVotlUrj4uJMLXq9/ujRowsWLCBuSP/555+zWCyEUFJSEjFXtVwuDwsLo6xixzD5cYuMjJRIJGq1WqFQHDx4kLqSqTf5QUMIKRQKHx+fSX7p6Ajos9dw/fr1I0eObN261eIU6f379/v7+2/btm3Pnj0eHh6mHQQmk8nhcDQaTW1t7RdffEFFyQ7B1nFDCGm12vLy8qCgoPGnWjkJOwattbV16dKlM17pFOD0kpGRERcXZ1o0GAx8Pl+lUhGLbW1tvr6+Y2Njpg5ZWVkKhWKmq3Q8kx83jUajVqtxHD9z5syePXsoqdZBTH7QampqhoeHcRyPjo6mpFQ70GevgVRjY+Pg4ODcuXOJRYFA0Nvb29zcTCxeunQpMTExMDAQ7gFlwcq4ZWdnZ2dnI4Q8PT0tLtZyclYGjc1mV1VVXbx4sbOz89KlS5SWOVk0jwaFQsFms03XKRBnUhMXJv/000+ffvppXFxcREREY2MjlVU6HivjtmPHjhUrVlRWVtbW1h6b6FoGp2Jl0NatWxcfH89gMLRaLT5Lvr+k22FIC8PDw+aXALBYLBaLpVarEUJvvvmmrZepOg8r4xYUFBQUFIQQsumEfGdgZdAIW7Zs2bJlCxWl2YPmew1ubm5jY2OmxZGREaPROGfOHApLmhVg3OxAs0GjeTQEBARoNBriS0qE0ODgIELI39+f0qJmARg3O9Bs0GgeDVFRUZ6eniqVilgcGBgQCAQrVqygtirHB+NmB5oNGt2iYXR01BTbCCEmk5mUlFReXk4sVlVV7d69m8lkUlSd44JxswO9B40mt3JBCNXX10skkgsXLrS1tQ0PD/f29i5ZsgQh9NZbb1VUVNy7d6+xsfHx48eHDh2avb+tVwHGzQ7OMGhwKxcAAAm6faAAAEwLiAYAAAmIBgAACYgGAAAJiAYAAAmIBgAACYgGMP1kMtkbb7yRkpJifk2BHS5fvrx9+/akpKTpKgxMHkQDIGc0GteuXdva2mrf6gkJCTk5OeOn1bLJ5s2bCwsLp7IFYDeIBkDu3Llzcrn8wIEDL+vw8OHD1atXz2RJYCZBNAASOp2uvb396NGjZWVlv/wxy6uF6urq+fPnz3BhYMZANAAS+fn5u3bt+vDDD5csWbJ//37SPjU1NW+//fYMFwZmDM3v8gTsMDw83NfXFxoaihBKT0/funVrTU3Nhg0bTB0aGxsrKyuLi4u9vb1zcnJSUlJetimdTnfy5MnXX39do9G0tLSsWbNm06ZNCCGlUpmdnR0REdHR0eHn57d7926EkEajSUtLCwwM9PX17e/vT05Odto7VjsEqu9bCxxOdnZ2T0+PaXHlypVr1qyx6NPR0SEUCl+2hebm5mPHjuE4npubW1BQQDQ2NDRIJBIcx0dGRkQiUUtLC9EeExNTU1OD47hYLD579izRuGjRoqKiIhzHjUbjzp07p+mVARvAXgN4wdOnT3U6nVAoNLVkZGSIxeKrV6+az8UilUqjo6Mn3FpQUFBycrJCoYiJiVm5cuWiRYsQQqWlpXq9Pjw8nOjz7rvvlpWVsdns6urqK1euEI1paWnr16+fxtcFbAXHGsALcnNzk5OTzVs2bty4fv36AwcO4GbX70ul0piYmAm3JhaLT506defOnc2bNy9evLirqwsh9Pvvv7PZ7PN/CA0NTUxMbGlp8fPz43A4xIrvv//+7L11Gj1ANIA/9fX1sdlsLy8vi/bDhw/LZLKSkhJTiykaqqurrWzwxx9/FIvF5eXljx492rt371dffYUQWrhwoV6v/8DM0qVLQ0NDe3t7DQaDaV3zGyiBmQfRAP709ddfR0ZGNo3j6uq6bNmyL7/8kni7qtVqjUYTFhZWUVFh+jtPqqmp6dy5cwghJpMZGxtLzPgYHx/P5/NNmaJUKr///vt33nln+fLlxcXFRGN/fz+xIqAKHGsAz/X39584cSIzM9NKn/PnzyckJLi5uSUkJJSUlLBYrNjYWCv9PTw8nj17JpFItFqtXC5PT09HCPF4vJqamvT0dJlMFhgYODIysnPnTgaDUVVVdejQoUePHvn4+IyNje3YsWOaXyGwBdwADkw/mUx248aNffv2TX1To6Oju3btOnv27NQ3BWwCHygAACQgGgAAJCAawCtRXFy8d+/eKV6UXVZW9tFHH01XScAmcKwBAEAC9hoAACQgGgAAJCAaAAAkIBoAACQgGgAAJCAaAAAkIBoAACQgGgAAJCAaAAAkIBoAACQgGgAAJCAaAAAkIBoAACQgGgAAJP4PVdaE860dQWgAAAAASUVORK5CYII=" 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" }, "metadata": {}, "output_type": "display_data" @@ -380,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -400,7 +330,7 @@ { "data": { "text/plain": [ - "binned_result([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95 … 4.05, 4.15, 4.25, 4.35, 4.45, 4.55, 4.65, 4.75, 4.85, 4.95], [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05 … 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN … 1.0063432989227807, 1.0446850849001021, 1.1512621944044665, 1.2836195587704153, NaN, NaN, NaN, NaN, NaN, NaN], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.004768467267551926, 0.006746507565954501, 0.009756967002478032, 0.021616733968476583, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float64[])" + "binned_result([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95 … 4.05, 4.15, 4.25, 4.35, 4.45, 4.55, 4.65, 4.75, 4.85, 4.95], [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05 … 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN … 1.0058952800258125, 1.0418722520378003, 1.1456921168637495, 1.2836195587704153, NaN, NaN, NaN, NaN, NaN, NaN], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0051803154346720375, 0.0068836854267073765, 0.009789991536768902, 0.021942340317344947, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float64[])" ] }, "metadata": {}, @@ -426,73 +356,156 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "21-element Vector{Float64}:\n", - " 2.4444444444444446\n", - " 1.0\n", - " 0.7708333333333299\n", - " 0.6306818181818181\n", - " 0.8888888888888888\n", - " 0.7777777777777778\n", - " 0.6666666666666666\n", - " 0.6999999999999944\n", - " 0.6923076923076923\n", - " 0.6363636363636364\n", - " ⋮\n", - " 0.6842105263157866\n", - " 0.7\n", - " 0.6909814323607406\n", - " 0.7272727272727256\n", - " 0.7564102564102549\n", - " 0.7916666666666644\n", - " 0.8214285714285694\n", - " 0.9047619047619021\n", - " 0.9999999999999969" - ] + "image/png": 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7RZejhqysrC1bthQXFz9//ry72+bl5fH5fHt7e+Uii8XicrkFBQUaq1lZWU2fPl25/ytXrkREwISOwKREDvfe8kGkhxOzRSQZ7OWwc8XkYD8XvIt6jS5HDVQq9W9/+1tbW9vRo0fr6upWrFihfpt2xzgcDo1Go9FeXrxlMpkIoaqqquHDX5u74d///veUKVOGDh26fPnyQYMG6XB+FQAjx7AwM+aZzXU/DUmn01evXi0UCg8fPtza2rpixYquDAkpFArVx4ChUqlUKlUgEGisNnjw4E2bNqWkpOzZsyciImL69Omenp46lwoA6K6eXry0srL66KOPVq5cefTo0b1797a0tHS8PoPBaG9/dYVGLBbLZDIrKyuN1T755JOQkJDU1NTLly9XVVXNnj1bLn+tn7l6lyd10McBGAm5vP32o6pfrz1KySl5UtFgsJ+r/aHAs8sTi8Vat27dwoUL09PTO+715ObmJhKJVJ9zPp+PEHJxee1bVnFx8bNnz0JDQxFCU6dOvXPnDpfLvXXrlvo66l2e1EE0AGNQyeVvO3mjuqFlcD97V3vrG4XPvz17q01qiKFMtD8U+HR5Uufo6Lht2zblwPNvEhoaamtrW1NTo1zk8XgsFiskJER9HS6X6+/vr1q0t7efPXu2paWlHksFoPdIZfIjqXdXzhk5Z+ygNh5niBdrWUzIqEHuSZcL8S6tG7odDdXVncza5uDg0MHKFAolNjY2OTlZuZiWlhYXF6ccJy4rK2vTpk0IodGjR+fn5zc1vZw5UvmlIzAwsLulAoCLB+y6oT5OTiwrhFBJSQmPx0MIhQd41NQLWiWEGQOt26chb9++7ezsHB4e3umat27dqqqqmjt3rkZ7QkJCQkLCgQMHFApFc3Pzli1blO0FBQXnz5/fsmWLpaXlkSNHEhISXF1d6XS6UCjctm1bdztQAICX2kaBF1a/RjcH67omkafWPKnGqdvRMGfOnD/++OOzzz6bP3++xhVHlXv37v3yyy/jxo3TzgWEEJ1O37p1q3b7mjVr1qxZo3zs4+Oze/fu7tYGgDGgU6niVql2u1gio5sRZiBlXS5eRkdHjx8//sCBA+vXr7ezs/Pw8FB2YWpoaOBwODweLyoqauPGjdrXHQDoC4Z4OySlF0YO90YIkUgk5S1CArGkrknoaEOYD4WO/RosLS3Xrl27du3aZ8+epaenK7swhYSEDBgwoH///nqtEACCcbZjuNkzf0wveidy8Ny5c2k0GrdJ+EPqvbnjB2vfcGm0ejrKU//+/Wtra7/44ouioqJhw4bppSaToVCg6/efX8x9mptTUkn909PVYVFUoAvMo90H/GXy0OwHFTuTsul0ikymoNPI704M8HWzxbuubtDDAHDDhw9fvnw5h8OJjo4OCgoKDAw0wEzZmZmZ165dU3bkiI+PN87uDAcv3CGRSPGxE/a33Fu9YjKH17rrp+z/e3uUr7vpzyTex5FIaOwwr7HDvNqkMiqZTKHgcxJd/a7F7nZt0CUaVq1a1djYOGXKlMmTJ/v4+MyaNWvWrFlZWVkMBqOgoOD3339vampisVjLli0LCAjQYf9dERkZGRkZaZyJoFRW3chrEX/+13GqloGe9uvej/jut9vblk3CsTBgSHQaniMzKxQK5QMdPim61L1z584JEyY4OTlVV1fHxsZ6enoqFIrAwEAmkxkcHKxcJzU1NT09vfeiwfjdK6kZH9hPo9HVnoEUSNQmtaQby+igAGDSJRr27t2bkZGh/q1BJpPt2LHDycnpH//4h7IDQkBAQB/vpCRqk1pbYkyczbA0E7VCNABjp8tXIAqFonE2gUaj7dixY86cOTt27FC2eHt79/F7JV3tGJXcZu12bqPQlmGgwXVBr5LK5OUvmgqf1dY3i/CuRf90iQblPVHafHx83nnnnZ9//rlnJZmIsACPP++yW0Rt6o1/3mUP8nLA66QU0KPbj6q2nbyRWfC8rIqXdLlo7y+36vkmFRC6/I5yuVyRCPtdGDhwIJvN7llJJsLakr40Ojj+h8xLt0t5fHERm7v/t7ybDyqXRAfhXRroqbslL24+5KxfGLE0Oqiflfijd0NjxgzYfzZPapRT1OlGl2j461//2sE4rurDMfRxQX7OW5dNlMrbH1fUF5bWhgd4bFo83tzM1CYTPHv2rOpMeB9xMfdp7IzhFmY0hNDVq1cRQn7udiMHud0s5uBdmt7oEg1Tpkyh0+kffvihWCzWfra+vr7HVZkOK3OzmHD/MUM9F04dFuLf1WHyiKW4uLhP/T1ob1e0t7czLc002gN8HJ/VNOFSUm/Q8Uvvzp07nz17Nnjw4G+++YbDeZmUCoViz549hhlYASa264rntc07fsz65vTNY2kFm49fe1wBqa0fGDPMGKWejPKk48GtmZlZampqYmLiunXr1q5d6+7u7ujo+OzZs9GjR6vGYuhVxt/lCXd5j6vPXiteOXtkUwCjvLw8bPyI73+/EzHUc+ooX7xLIzYymUQikVpEEuvXDxwesuv6uxjRHDOoZ12eenSqfPXq1c+fPz98+PD8+fNjYmKSkpI6HQAOGEZ7u+LH9MJ/LRmvnHMVIeRix9i4aHzarVJhqwTf2kzA9DC/42kFygHdJkyYgBB6Vt1453F1WIDpXLDv6SkxBweHZcuW6aUUoDPlmP3q2C+a+rvZWpm/9meNRiWPGuz+4Fld6BD3bu1fIpGoZm0FCKER/q5SmXxHUpafhx3TwnrfudtSWfv/zR1lRiPMcAydMrWz5Sbp7t27/fr1U83ro+3jjz/WaBGIJTZWGIdvNlZ0gbh7Rw0CgeDo0aP/+Mc/urWVyQsb4hEywLWyjt8saAsN8HC2JcxADF0E0UAAbDabRqN1EA3aZ8Wcba2q6zEG/ufU8cd086C3vb1dY6R/oGRGoxDrPutugW55psnFjiFqk5ZV8dQbaxuFjysaBns74lUVIBA4ajBZH84dvevnnNAh7hZSfl2TKOVmScbd8g/njqKQiXHhzRhUcvnVDS3WFmY+riyLPnZHHBw1mCwnW6uv4iazGObZRZW3izkkRNqxYrKPq8keAOtXbaPw61PZF3OfNraIH5bX7fopJ+NuOd5FGRRRowG6PHUFhUKeOrL/e5MCZo7xnxk+gG5C5897lUQqP3A+b/7EoXFvjehnLX03csjni8Y+5TRkP+ho+iUjhP/EdoanPrEdRAPQr1vFnFGD3Ps52yCEUlNTEUI0KmVxdOCVvGd4l9Y9xjKxHQCmoYLLH+j18nqQ6uqPhRmNRqOY0r2VHYNoAD3VKpHJ5CZ1exUJofZ2jHtJ29sViCB3T/QcXKEAOpLK2i9kP7lRWFH0kPOvI1ctLcwWTQsyjev8vh52D8u5/p72CKFXs7oL25BCQaP2lb+mfeV1Ar37+lQ2mUz69/9FRQ733hE3eVlMyKEL+Y+em8LNnSP9XR+y6x+waxFCCxcuRAgJW6VHUu/NGjsQ79IMB44agC7ul9UyLelvjxukavFwZP7zL2P2/HJz+/LJOBamFxQK+e/zRv/054PUm0/7ObNa7r6oqhe8FTEwyNcZ79IMB6IB6KLoWW14gIdGoyPLsr1d0SaVm8BVUqYVfeVbI/jCtpoGgbWlmbMdo691FYNoALpok8gxeweam1HbJDITiAYlphWdaYUxY0BfQNRzDdDlyTDEbbInlQ0cLr+u6bWBgj0cmc+qGzVWlrcrmgStmLNv4EKhQDX1LQWlL8prm+WmdQ2li3AY5Ql3MMqTAaTefPrnXbaPowW3SZh4Ps/KnLY8JsSGYY4QGhvoufHw1fChnvZMC9X6v2YWhwd4GsnVPQ6XfzK90NbaoiTvyrAx0ypeNM+LHDTUpw+dLECGn9gO9AUXc5+yXzR9FTdFLBJIOXmfLJ2Q/6R650/Z25ZNIpNJVuZmq+eM3H7yxqhBblX1LVfvsXMeVDnbMZbHhOBdOEIINQvaDqXcXT1npIsd499PM2OnB/NFkv2/3bagm5nG5VUDIOoXCtCrFArFpdtlK2aFqF/GHzHQrb+b7b2nNcpFf0/7nXFTPJ1seHyxqFW6JDoo7q0RZOM4V5dx79mMMD8XO4aqhWlptmRaUEpOicaa9XxRdX1L3/y60TE4agAYeHyxk62VGVXzbGKQr3NpVeOIgW7KRRqVHDHMM6O/08xwfwrFiE49ltc0TxreX6PRzcG6saVVtXjncXXyzRKWJbWG/cjcaUCQr/NbY/xppnICtefgqAFgIRFlOHUdZT+ovPWo6tP3IxZNHjTEhbY5dgKTQT+YnI93XUYEogFgsLM25zYKJVq3Et0vrfXzsMPcBC9sNvv+/fsajT5urEcVdRqNnDq+HdMcvfy6VLosZriV+cvrryQSaeqI/lQqRfuyS58F0QAwkEikaaP9Dl7Il8hepcOdJ9XlL5pCBrjgWJi6moaWA7/f+frkn/tOX9v/Wx6H+2qW5knDfS7mltaojY7JF7b991LhrDH+CKF6vtiJZUWnaX6bDvZzLuE0GKZ44wfnGgC26aF+aah0w/dXfJws75bUxP+QybSif7ogwki+aRSU1qbklPw1athoDxKfz/cdMvC/lwunjPAZPdgdIcS0oq+YFXLi8n0bK/OqJtmxi/cqavnvTBysHOSqXd5O1TqNghCikskyGZyPfImo0ZCZmXnt2jVlR474+Hjo4NAbpof6TRzu/aC0kn3P6sN5ox1sDDFlYVfI5e3nrhdvWDjWkk5rqEIIIS9n5tr3wradvBHs56KcDMLDkbn+/bEveC21AR621hYeDtYUystjZHsby5qGFoVCoRFzTzm8IT4mNaau+gvs7mguRP1CAaM8GYa5GdXfw97DkWk8uYBeTsBjZ/l6T206jRrg7fS06tU3AhIJudpbB/u59HO2UeUCQohKIQf6Oidnl6jP711W3VhWxRvq49T75RtOT0Z5IupRA+jLhK1SFgOjOzaLYd4i6tIEPG+PHfTL1eJdP2f5uTAq6kWHk/ObhW2r3obhtl+BaADEY29tkfuQo91ewxP4unepsyOZTHpvckB9s6isutF+8gwvZ5v+0EvydRANgHg8nJh1TaKahhZXe2tVY12zqLymaXFUYNf342BjaVRflIwKRAMgpA9mBB+8kB8R6IVaxEJBa+a98syC8tgZw42kp7YJIOppSNB3fPvtt9qNbg7Wn/91HFKgawUVNworWqWyz/46Vjk8PNALOGoAxo7H42G2m9EoU0f2D/S0EgqF/ftr3jEBegiiAfSUdgcBDa2trebm5m96ViKRmJmZabfL2xV/3Cq9+bAyI6+s/ejVCcO9Jw111/4xzs59awgGgyHqFwoY5cl4/POf/ySTO/pF+vrrrzt49syZM+Xl5RqNEqk8/oerojbpxkXjo0b5frogoqqOv+PHbMzpIcCbwMR2X+JdDrHt3bu3J5tbWnZykl81lQMmEokklUo1GpNzSkKHeLwbOcSCTkUIMSzMFk8L8na2yS6q6EmpfQ1MbAd6pKmpCcefLpO3a4+kkve4aupIzdMHU0f55pe8MFRdfR2cawD4UCjQ9fvPk3NKSvIfZbHlLh7V708eOsT75S0MUlm7uZnmL6cd06JF1GbwSvsoiAaAj5OX7wvE0i1/i/zNhjt69GgbB7d9527PCBugnN7Cgk5tEbVpDE5dyxPYqY1SC3oVfKEAnSCTyXof3I3bKHxS0bB6zkjVLVJOtlafLRx7OuOBchDk8UH9frvxWGOrczcehWnNiwN6CUQD6ASDwVi1apV+91nE5mp/yC3NaV7ONpy6FoTQlBH9G5rF3569xa5plMran3J4X/2UbWFGG+Hvqt9KwJvAFwrQOcx+B52SyOTJ2SX3ntb8mVdmlnRjeuiA4f8bIapNIrM0x5j8ysrcrFUiRQiRyaSP54fll9Sk3nyaVVThfqds1hj/Ie4sVG8K0+0SAhw1gF7BF7ZtPJxBN6NsXDR+2ijfD2YMv3qPfSTlrvJZV3vr8hqMyyLlL5rU75ga4e+6Zu7oicO9V88ZpTpDCTXT5gUAABEWSURBVAyDqNEAXZ6M3I9XiuaNHxwT7q/smOBix/hkfngDX1xcXocQCuzvVFxex6njq2+S86DSwcaSYaF5hAKdoHUGE9sBo/Okon7lWyM1GmeEDbj5kDPE25FCIX/8bth/fr01fIBLRW2zeVntpQdNjXzx2vfCtXe1ePFig5RsgmBiO2BcFApEIZO176uwt7FoErycJMbDibkzbkruQ86N2mZLR/7EMSMCfeFuCCMC0QD0j0RC7QqF9m1XtTyB+vS5FDIpYphneaDX6NEDBkAuGBmIBqP2pLLhdMbDO1n3be/WD3vYsmDKMDcH6843MwJBvs4Zd8snj/BRtSgUit+znizqzihMAEcQDcbr0u2ymw8r494aGcCo8/f3N7d1++ZM7qKowCA/AvyBfW/S0B1JN2oaWqaM7C9vVzyuqD+d8XBYf2dfd83Jr/z8/FxcjGXaG6BC1CsUJk8gllzKK9u0eLyr/cvpngd42P9r8bjjfxQoFAS4MdmCTt38QaSbg/XJS4WZBeUZd8sXRQXOmzBYe83Q0FBra2IcCvUpcNRgCGPHju1gLBNMhWW1oYPdqZTXstuGYe7lZFPB5RNipDMSiTQpxGdSiI/wyeXVczSvVgAjB9FgCOHhGNfkOiYQS9481QLcfQh6HVG/UJh8lycnW6vqeoF2e1U938nWyvD1ACKCLk8maKiP04k/7jfwxepX+55yGiQyuRMLogF0CXR5MkFUCnnV7JHb/nt92mg/Xou4ur6l5Nqjmw85ny4Yg3dpoE/AIRokEsmuXbv8/f15PF5DQ8P69eupVIwyGhsbv/rqKz8/PxaLxWQyo6KiDF8qvvw97bctn3wprzSrsNK1kTxprPNXcZMpFKJ+BwTEgsPv2caNG11cXObPn79y5UobG5uEhATtdXg83qRJk9asWbNs2bLm5ua1a9cavk5jYEGnzhk76K0I/4VThkWN8iVoLnQ6riwwQoY+apDJZIcOHXry5IlyccaMGeHh4fHx8Ro9anfu3BkdHe3h4YEQmjRpkvIBIKg+m+yEZui/Qnl5eXw+397eXrnIYrG4XG5BQYH6OjKZ7Lvvvps2bZpy0cfHR/UYEJHex48DBmDoaOBwODQajUZ7OcIPk8lECFVVVamvU15eLhaLGxsbz549e+zYsc8++0wgwLiMB3pOKmu/9/TFUw4v9yEHuksAdYaOBqFQaGHx6moclUqlUqkan/ynT58ihEpLS+fNmxcbG+vl5bVkyRID19kXFJfXbTh45X7pCyqFXMnlf3ns2qXbpXgXBYyFoaOBwWC0t7+aj0QsFstkMisrjAv1AQEBygdjxow5d+6cxtxn6l2e1JlwTwf9qm8WHUm5u2nx+KXTg31cWe9OHLIzbsq9py9uFVd1vjEwYtofCmJMbOfm5iYSiVQznfH5fISQxo137u7uCCE/Pz/lovIoo6ysTH0d9Ynt1EE0dNGl22XzJgyxtX51ZweNSl4xa0RyzhMcqwI9p/2hIMbEdqGhoba2tjU1NcpFHo/HYrFCQkLU1xkyZIirq2t1dbVyUSgUIoT8/f0NXKppq6htHuhlr9Fox7QQt8lwqQcYG0NHA4VCiY2NTU5OVi6mpaXFxcUpz2BnZWVt2rQJIUSlUjds2HDp0iXlOlevXn3//fc9PT0NXKppo1LIUpnmTJNIrWst6ONw6EKTkJBQXV194MCBxMTE5ubmLVu2KNsLCgrOnz+vPBPx4YcfslisTz75ZPfu3XV1dUeOHDF8naZtsLfDnSfVGo1l1Y3Odgxc6gHGBoeO0nQ6fevWrdrta9asWbNmjfIxiURav369YevqW6aO9P380J8ejtbDB7ycD6qqvuXA+TsfzhuNb2HASMDtVX0UnUbZtHj8iT8KTv35sPBR1ZfHMmXy9tVzRhJikBhgABANfZettflH74a1SeXrS6/88y9jtOeGAX0ZIW/XAXpEp1FYDHPIBaCBqNFg8qM8AdBzMMoTAABDT0Z5IupRA+g6Fotla2uLdxWAYIh61AC6ztvb29vbG+8qAMHAUQMAAANEAwAAA0QDAAADRAMAAANEAwAAA1GjAbo8AdAp6PIEAMAAXZ4AAHoG0QAAwADRAADAANEAAMAA0QAAwADRAADAANEAAMBA1GiALk8AdAq6PAEAMECXJwCAnkE0AAAwQDQANHToULxLAEYHogGgd955B+8SgNExzWhgs9knTpzAuwqiSk1NvXPnDt5VENXXiYkisRjvKvQAogFoSklJycvLw7sKotqVmCgUifCuQg9MMxoAAD1E1GiALk8AdKonXZ6IGg3e3t5LlixRKBQKheLLL78UCAS7d+822E+/efPm5cuXddhw//799fX1na527ty5wsLCNz2bn5+fnJys3S6TyRISErTbt2/fLpVK1VsSExO5XK5q8fbt22lpaZ1WpS9Hjx7lcDjd3YrL5SZ+/31X1kzYu1cmk73p2ZO//lpWXq7dXvjo0bmLFzUaS8vLfzx3Tr2lnsfbf+yYesvx06efd//l6IbP53/zzTddX1/xP0uWLCnHetUdIGo0lJeXq79UA0dDTk7OpUuXdNjwu+++q6ur63Q1jWiQy+Xqz3Y3GrZt2yaRSNRbNKLh1q1bF7U+Fb3nyJEjlZWV3d2qe9Hw+jum7uSvv5Y9f67dXlhcrB0NT589Szp7Vr2lrqHhu9ej4djp0+Xdfzm66W40qGh8XrqCqB2l+5Rx48bZ2dnhXQXoW4gaDU1NTU1NTaqzDAKBQCAQqBbZbDabze69cxA3b95saWlR7V8ul+fk5Gj8ZcZUX1+/f/9+BweHjlcrLCwUiUSlpaWYz+bn51dXV2u/OplMJpPJtNulUun27dtpNJqqhcvlJiYmOjk5KRdv377d0NCg2vDOnTuVlZW1tbWdvhzdcDico0ePdvewi8vlcuvqvvz3vztdUyaXJ+zdS6Vi/26XPX9+8tdfc7SuzhYWF5eWl2vsv5TNLmWz1RvrGxrqeTz1lucczvHTp6/m5CgXRWLx14mJlpaWnVRpYYEolE5fiwY+n8/n83X4xS4vL2exWN3ahELQc3gPHjwgk8keHh6qFgqF4unpqXxMIpEsLCxcXFx66aeTSCQmk6n6S04mk/v169fFDd3d3dU/pW/i6OjIYDDetBNra2t7e3vMpzAr6devH4lEUm9xd3c3MzNTLaq/HBKJZG9vb21t3WmRuiGRSK6urnQ6vbsbmpmZufv4IDOzTv4h1G/AABKdjvksiUx28fCwsLHRfIpKZTCZju7urzXSaJYMhrOnp3qjGZ3+WhlksquXlzmT+XL/JJKnry/FwqKTIsk6fpenUqmq3/Oua21tDQ4OnjFjRtc3IanuzQIAABWinoYEAPQqiAYAAAaIBgAMhFhf3vtQNHC53PT09DNnzqxbt+7+/ft4l0MwbW1tKSkpmZmZH330UUVFBd7lEI9CoVi0aBHeVXRDH4qGCxcu5Obmzp8/PzQ0FLNrEOjAo0ePTp06FRkZaWdnd/b1XkCgKzIzM8vKyvCuohuI2q9BRSAQZGZmxsTEqFokEsmuXbv8/f15PF5DQ8P69euVl7hjY2OV3QorKir8/Pxwq9iYdP3dCw4OTkpKEggEHA7nX//6F34lG4uuv3UIIQ6H4+joaG5ujlOxuiDwUcPly5d37tw5d+7cgwcPqrdv3LjRxcVl/vz5K1eutLGxUR0gUCgUMzMzkUh09erVDRs24FGyEenuu4cQam1tTU5O9vDw0KFLginR4a0rKioi3lBaCoJLSEiIiYlRLUqlUiaTWVNTo1wsKytzcnJqb29XrbBjxw4Oh2PoKo1V1989kUgkEAgUCsXBgwdXrlyJS7VGpetvXUZGhlAoVCgUkZGRuJSqGwIfNWDKy8vj8/mqnoIsFovL5RYUFCgXf/vtt0WLFrm7u+t236TJ6+Dd27Nnz549exBCtra2Otw3afI6eOtoNFpaWtrZs2fLy8t/++03XMvsBlOLBg6HQ6PRVD2RmUwmQqiqqgohdP369Y8//jgmJiYoKAhGMcLUwbu3ePHikJCQixcvXr161ZA3uRJFB2/d2LFj582bRyaTW1tbFcS5fkn405AahEKhhYWFapFKpVKpVIFAgBAaP358d+9L7Ws6ePc8PDyUd6x0qx9+39HBW6f09ttvv/3223iUpiNTO2pgMBjt7e2qRbFYLJPJrKyscCyJQODd05npvXWmFg1ubm4ikUg19gmfz0cI9d4tmCYG3j2dmd5bZ2rREBoaamtrW1NTo1zk8XgsFiskJATfqogC3j2dmd5bR/hokMvl6uOjUSiU2NhY1fhoaWlpcXFxlO6PmdFHwLunM5N/64g6lAtCKDs7Oykp6ddffy0rKxMKhVwuNyAgACE0YcKE1NTUJ0+e5OXlNTQ0xMfHE/p/qJfAu6ezPvLWwVAuAAAMhP9CAQDoDRANAAAMEA0AAAwQDQAADBANAAAMEA0AAAwQDUBv7t+/P2LEiNWrV6vfTaCD8+fPL1y4MDY2Vl+FAR1ANIDXyGSy8PDwoqIi3TZfsGBBYmIiWdepmZTmzJnz3//+tyd7AD0H0QBec+LEiYqKik2bNnWwzosXL0aPHm2wkgAuIBrAK21tbc+ePdu1a9eFCxdu3br1ptXS09O9vLwMWRgwPIgG8MrRo0eXLVv2/vvvBwQEbNy48U2rZWRkTJ482ZCFAcMztVGegM6EQmFdXZ2Pjw9CaOvWrXPnzs3IyJg0aZL6Onl5eRcvXjx16pSDg0NiYuLq1avftLe2trb//Oc/AwcOFIlEhYWFYWFhs2fPrqqq2rNnT1BQEJvNdnZ2jouLQwiJRKLNmze7u7s7OTnV19cvX768j49YbSzwHrcWGIs9e/ZUV1erFkeOHBkWFqa9GpvNdnV1xdxDQUHB7t27lY8PHz78ww8/KB/n5uYmJSWJxeIBAwYUFhYqGydOnJiRkaFQKKKjo48dO6ZsHDx48MmTJxUKhUwmW7p0qV5eF9ANHDUAhBBqampqa2tzdXVVtSQkJERHR6ekpKjPwoIQyszMjIyM7HSHHh4ey5cv53A4EydOHDly5ODBg3/55ReJRDJs2DDlClFRURcuXKDRaOnp6b///ruycfPmzePGjdPbqwI9ANEAEELo8OHDy5cvV2+ZNm3auHHjNm3aNHPmTBKJpGrPzMycOHFipzuMjo7+9ttvf/jhh//85z9MJvP3338vKSmh0WinT59WruDj4xMVFZWbm+vs7GxmZqZsfPfdd/X3mkCPQDQAVFdXR6PR7OzsNNq3bds2fvz4M2fOvPfee6rGzMxM5aXN9PT0qVOnvmmff/zxR3R09Ntvvy2Xy/ft2/fVV19Nnz5dIpGo70oikdTW1nK5XKlUqhqmXS6XE3oEFJMBVygA2rt3b3BwcL4WS0vLwMDAL774QjXSmUAgEIlEfn5+qampqj/1mPLz80+cOIEQolAoM2fONDc3nzdvHpPJTE9PV65QVVX1888/T506dfjw4adOnVI21tfXK7cCuIOjhr6uvr7+m2++2b59ewfrnD59esGCBQghBoOxYMGCM2fOUKnUmTNndrCJjY1Nc3NzUlJSa2trRUXF1q1bzc3NMzIytm7dev/+fXd3d7FYvHTpUjKZnJaWFh8fX1tb6+jo2N7evnjxYj2/QqATGAAO6M39+/evXLmydu3anu9KLpcvW7bs2LFjPd8V0A18oQAAYIBoAABggGgA+nTq1Km///3vPbwp+8KFCx988IG+SgK6gXMNAAAMcNQAAMAA0QAAwADRAADAANEAAMAA0QAAwADRAADAANEAAMAA0QAAwADRAADAANEAAMAA0QAAwADRAADAANEAAMAA0QAAwPD/PLws1mtbPbgAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "filter(row -> all(x -> !(x isa Number && isnan(x)), row), bincv_flux.y)" + "plotcv(bincv_flux; proper_time = proper_time)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "[10.0, 13182.567385564074, 2.0, 0.1]\n", + "[1.0, 1000.0, 1.0, 0.005]\n", + " \n", + " \n", + "sigma: 0.52 +/- 0.03 \n", + "beta: 1.67 +/- 0.09 \n", + "tau: 9.21 +/- 1.38 \n", + "SF: 5.33 +/- 0.39 \n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "[10.0, 13182.567385564074, 2.0, 0.1]\n", + "[1.0, 1000.0, 1.0, 0.005]\n", + " \n", + " \n", + "sigma: 0.69 +/- 0.03 \n", + "beta: 1.77 +/- 0.12 \n", + "tau: 13.18 +/- 2.85 \n", + "SF: 6.39 +/- 0.77 \n", + " \n", + " \n", + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, { "data": { - "image/png": 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" + "text/plain": [ + "(sf = [0.03 0.02 NaN NaN; 0.08 0.02 NaN NaN; … ; 4.93 0.02 NaN NaN; 4.98 0.02 NaN NaN;;; 0.03 0.02 NaN NaN; 0.08 0.02 NaN NaN; … ; 4.93 0.02 NaN NaN; 4.98 0.02 NaN NaN], cv = [0.05 0.05 NaN 0.0; 0.15 0.05 NaN 0.0; … ; 4.85 0.05 NaN 0.0; 4.95 0.05 NaN 0.0;;; 0.05 0.05 NaN 0.0; 0.15 0.05 NaN 0.0; … ; 4.85 0.05 NaN 0.0; 4.95 0.05 NaN 0.0], fit = [1.0 0.007373881570092869; 1.2589254117941673 0.007373904144765531; … ; 39810.71705534977 0.053792576332625075; 50118.72336272725 0.053792857102606215;;; 1.0 0.0097008312984255; 1.2589254117941673 0.009700836858738297; … ; 39810.71705534977 0.06459179788439393; 50118.72336272725 0.06461848733292105], par = [0.05328506547586806 0.003931252435192943 … 0.005214087497955578 0.00030182906638481473;;; 0.0638869808246581 0.00774853169438437 … 0.006859515761817004 0.0003333099709858289], sf_min = [0.014747666510017552, 0.019401640443347242], t_min = [3.22784063639378, 3.4844129256093717], t_max = [4.12, 4.18], num_all = [103.68, 207.36, 103.68, 311.04, 207.36, 103.68, 414.72, 311.04, 207.36, 103.68 … 1036.800000000003, 933.1200000000026, 829.4400000000023, 725.760000000002, 622.0800000000017, 518.4000000000015, 414.72000000000116, 311.0400000000009, 207.36000000000058, 103.68000000000029], num_cut = [103.67999999999847, 103.67999999999984, 103.68000000000006, 103.68000000000029, 103.68000000000029, 103.68000000000029, 103.68000000000029, 103.68000000000029, 103.68000000000029, 103.68000000000029 … 23431.68, 23431.68, 23431.68, 23535.36, 23535.36, 23535.36, 23639.04, 23639.04, 23742.72, 23742.72], num_pos = [103.68000000000006, 103.68000000000029, 103.68000000000029, 103.68000000000029, 103.68000000000029, 103.68000000000029, 207.35999999999876, 207.35999999999876, 207.35999999999876, 207.35999999999967 … 23431.68, 23431.68, 23431.68, 23535.36, 23535.36, 23535.36, 23639.04, 23639.04, 23742.72, 23742.72])" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "plotcv(bincv_flux; proper_time = proper_time)" + "res = runall(lc1_bin, lc2_bin; sf_bin_edges=sf_bin_edges, cv_bin_edges=cv_bin_edges, nsigma=nsigma, erron=erron, nsim=nsim, fi_np = fi_np , lower_bounds=lower_bounds, upper_bounds=upper_bounds, p0=[1, 1e3, 1, 0.005], mode=mode)\n" ] }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "50×4×2 Array{Float64, 3}:\n", + "[:, :, 1] =\n", + " 0.05 0.05 NaN 0.0\n", + " 0.15 0.05 NaN 0.0\n", + " 0.25 0.05 NaN 0.0\n", + " 0.35 0.05 NaN 0.0\n", + " 0.45 0.05 NaN 0.0\n", + " 0.55 0.05 NaN 0.0\n", + " 0.65 0.05 NaN 0.0\n", + " 0.75 0.05 NaN 0.0\n", + " 0.85 0.05 NaN 0.0\n", + " 0.95 0.05 NaN 0.0\n", + " ⋮ \n", + " 4.15 0.05 0.821429 0.00485414\n", + " 4.25 0.05 0.904762 0.00784384\n", + " 4.35 0.05 1.0 0.0168877\n", + " 4.45 0.05 NaN 0.0\n", + " 4.55 0.05 NaN 0.0\n", + " 4.65 0.05 NaN 0.0\n", + " 4.75 0.05 NaN 0.0\n", + " 4.85 0.05 NaN 0.0\n", + " 4.95 0.05 NaN 0.0\n", + "\n", + "[:, :, 2] =\n", + " 0.05 0.05 NaN 0.0\n", + " 0.15 0.05 NaN 0.0\n", + " 0.25 0.05 NaN 0.0\n", + " 0.35 0.05 NaN 0.0\n", + " 0.45 0.05 NaN 0.0\n", + " 0.55 0.05 NaN 0.0\n", + " 0.65 0.05 NaN 0.0\n", + " 0.75 0.05 NaN 0.0\n", + " 0.85 0.05 NaN 0.0\n", + " 0.95 0.05 NaN 0.0\n", + " ⋮ \n", + " 4.15 0.05 1.04469 0.00697745\n", + " 4.25 0.05 1.15126 0.00955202\n", + " 4.35 0.05 1.28362 0.0205848\n", + " 4.45 0.05 NaN 0.0\n", + " 4.55 0.05 NaN 0.0\n", + " 4.65 0.05 NaN 0.0\n", + " 4.75 0.05 NaN 0.0\n", + " 4.85 0.05 NaN 0.0\n", + " 4.95 0.05 NaN 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res.cv" + ] }, { "cell_type": "code",