{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Copyright (C) 2024 Richard Stiskalek\n", "# This program is free software; you can redistribute it and/or modify it\n", "# under the terms of the GNU General Public License as published by the\n", "# Free Software Foundation; either version 3 of the License, or (at your\n", "# option) any later version.\n", "#\n", "# This program is distributed in the hope that it will be useful, but\n", "# WITHOUT ANY WARRANTY; without even the implied warranty of\n", "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\n", "# Public License for more details.\n", "#\n", "# You should have received a copy of the GNU General Public License along\n", "# with this program; if not, write to the Free Software Foundation, Inc.,\n", "# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n", "from os.path import exists\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from corner import corner\n", "from getdist import plots\n", "from astropy.coordinates import angular_separation\n", "import scienceplots\n", "from os.path import exists\n", "import seaborn as sns\n", "\n", "\n", "from reconstruction_comparison import *\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "\n", "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n", "fdir = \"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quick checks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogue = \"CF4_TFR_i\"\n", "simname = \"Carrick2015\"\n", "zcmb_max=0.05\n", "sample_beta = None\n", "sample_alpha = True\n", "\n", "fname_bayes = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"bayes\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "fname_mike = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "\n", "X = []\n", "labels = [\"Full posterior\", \"Delta posterior\"]\n", "for i, fname in enumerate([fname_bayes, fname_mike]):\n", " samples = get_samples(fname)\n", " if i == 1:\n", " print(samples.keys())\n", "\n", " X.append(samples_to_getdist(samples, labels[i]))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = [f\"a_{catalogue}\", f\"b_{catalogue}\", f\"c_{catalogue}\", f\"e_mu_{catalogue}\",\n", " \"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"alpha_{catalogue}\"]\n", "# params = [\"beta\", f\"a_{catalogue}\", f\"b_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "# params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"mag_cal_{catalogue}\", f\"alpha_cal_{catalogue}\", f\"beta_cal_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "\n", "\n", "g = plots.get_subplot_plotter()\n", "g.settings.figure_legend_frame = False\n", "g.settings.alpha_filled_add = 0.75\n", "\n", "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", "plt.gcf().suptitle(catalogue_to_pretty(catalogue), y=1.025)\n", "plt.gcf().tight_layout()\n", "# plt.gcf().savefig(f\"../../plots/method_comparison_{simname}_{catalogue}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# catalogue = [\"LOSS\", \"Foundation\"]\n", "catalogue = \"CF4_TFR_i\"\n", "simname = \"IndranilVoid_exp\"\n", "zcmb_max = 0.05\n", "sample_alpha = False\n", "\n", "fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_mag_dipole=True,\n", " sample_beta=False,\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max)\n", "\n", "\n", "samples = get_samples(fname, convert_Vext_to_galactic=True)\n", "\n", "samples, labels, keys = samples_for_corner(samples)\n", "fig = corner(samples, labels=labels, show_titles=True,\n", " title_kwargs={\"fontsize\": 12}, smooth=1)\n", "# fig.savefig(\"../../plots/test.png\", dpi=250)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Paper plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 0. LOS velocity example" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fpath = \"/mnt/extraspace/rstiskalek/catalogs/PV/CF4/CF4_TF-distances.hdf5\"\n", "\n", "loader_carrick = csiborgtools.flow.DataLoader(\"Carrick2015\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_lilow = csiborgtools.flow.DataLoader(\"Lilow2024\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_cb2 = csiborgtools.flow.DataLoader(\"csiborg2_main\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_cb2X = csiborgtools.flow.DataLoader(\"csiborg2X\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_CF4 = csiborgtools.flow.DataLoader(\"CF4\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_CLONES = csiborgtools.flow.DataLoader(\"CLONES\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "angdist = angular_separation(\n", " np.deg2rad(loader_carrick.cat[\"RA\"]), np.deg2rad(loader_carrick.cat[\"DEC\"]),\n", " np.deg2rad(csiborgtools.clusters[\"Virgo\"].spherical_pos[1]),\n", " np.deg2rad(csiborgtools.clusters[\"Virgo\"].spherical_pos[2]))\n", "k = np.argmin(angdist)\n", "print([loader_carrick.cat[\"RA\"][k], loader_carrick.cat[\"DEC\"][k]])\n", "print(csiborgtools.clusters[\"Virgo\"].spherical_pos[1:])\n", "print(csiborgtools.clusters[\"Virgo\"].spherical_pos[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "loaders = [loader_carrick, loader_lilow, loader_CF4, loader_cb2, loader_cb2X, loader_CLONES]\n", "simnames = [\"Carrick2015\", \"Lilow2024\", \"CF4\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\"]\n", "\n", "\n", "with plt.style.context(\"science\"):\n", " plt.rcParams.update({'font.size': 9})\n", " plt.figure()\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "\n", " for i, (simname, loader) in enumerate(zip(simnames, loaders)):\n", " r = loader.rdist\n", " vrad = loader.los_radial_velocity[:, k, :]\n", "\n", " if simname == \"Carrick2015\":\n", " vrad *= 0.43\n", "\n", " if len(vrad) > 1:\n", " ylow, yhigh = np.percentile(vrad, [16, 84], axis=0)\n", " plt.fill_between(r, ylow, yhigh, alpha=0.66, color=cols[i],\n", " label=simname_to_pretty(simname))\n", " else:\n", " plt.plot(r, vrad[0], label=simname_to_pretty(simname), c=cols[i])\n", "\n", " plt.xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n", " plt.ylabel(r\"$V_{\\rm rad} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "\n", " plt.xlim(0, 90)\n", " plt.ylim(-1000, 1000)\n", " plt.legend(ncols=2, fontsize=\"small\")\n", " plt.axvline(12.045, zorder=0, c=\"k\", ls=\"--\", alpha=0.75)\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/LOS_example.pdf\", dpi=450, bbox_inches='tight')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Evidence comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "\n", "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\",]\n", "catalogues = [\"LOSS\", \"Foundation\", \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "y_BIC = np.full((len(catalogues), len(sims)), np.nan)\n", "y_lnZ = np.full_like(y_BIC, np.nan)\n", "\n", "for i, catalogue in enumerate(catalogues):\n", " for j, simname in enumerate(sims):\n", " fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=simname != \"IndranilVoid_exp\",\n", " zcmb_max=zcmb_max)\n", "\n", " # y_BIC[i, j] = get_gof(\"BIC\", fname)z\n", " y_lnZ[i, j] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " y_lnZ[i] -= y_lnZ[i].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " figwidth = 8.3\n", " fig, axs = plt.subplots(2, 3, figsize=(figwidth, 0.5 * figwidth))\n", " fig.subplots_adjust(hspace=0)\n", "\n", " x = np.arange(len(sims))\n", " y = y_lnZ\n", " for n in range(len(catalogues)):\n", " i, j = n // 3, n % 3\n", " ax = axs[i, j]\n", " ax.text(0.1, 0.875, catalogue_to_pretty(catalogues[n]),\n", " transform=ax.transAxes, #fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='left',\n", " bbox=dict(facecolor='white', alpha=0.5),\n", " )\n", " ax.scatter(x, y[n], c=\"k\", s=7.5)\n", "\n", " y_min, y_max = ax.get_ylim()\n", " y_offset = (y_max - y_min) * 0.075 # Adjust the fraction (0.05) as needed\n", "\n", " for k, txt in enumerate(y[n]):\n", " ax.text(x[k], y[n, k] + y_offset, f\"({y[n, k]:.1f})\",\n", " ha='center', fontsize=\"small\")\n", "\n", " ax.set_ylim(y_min, y_max + 2 * y_offset)\n", "\n", " for i in range(3):\n", " axs[1, i].set_xticks(\n", " np.arange(len(sims)),\n", " [simname_to_pretty(sim) for sim in sims], rotation=35)\n", " axs[0, i].set_xticks([], [])\n", "\n", " for i in range(2):\n", " for j in range(3):\n", " axs[i, j].set_xlim(-0.75, len(sims) - 0.25)\n", "\n", " axs[i, j].tick_params(axis='x', which='major', top=False)\n", " axs[i, j].tick_params(axis='x', which='minor', top=False, length=0)\n", " axs[i, j].tick_params(axis='y', which='minor', length=0)\n", "\n", " axs[i, 0].set_ylabel(r\"$-\\Delta \\ln \\mathcal{Z}$\")\n", "\n", " fig.tight_layout()\n", " fig.savefig(f\"../../plots/lnZ_comparison.pdf\", dpi=500, bbox_inches='tight')\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Dependence of the evidence on smoothing scale" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "\n", "ksmooth = [0, 1, 2, 3, 4]\n", "scales = [0, 2, 4, 6, 8]\n", "sims = [\"Carrick2015\", \"csiborg2_main\"]\n", "catalogues = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\"]\n", "\n", "y = np.full((len(sims), len(catalogues), len(ksmooth)), np.nan)\n", "for i, simname in enumerate(sims):\n", " for j, catalogue in enumerate(catalogues):\n", " for n, k in enumerate(ksmooth):\n", " fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=True, smooth=k,\n", " zcmb_max=zcmb_max)\n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " y[i, j, n] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " y[i, j, :] -= y[i, j, :].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for i, simname in enumerate(sims):\n", " for j, catalogue in enumerate(catalogues):\n", " print(simname, catalogue, y[i, j, -1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", " plt.figure()\n", "\n", " ls = [\"-\", \"--\", \"-.\", \":\"]\n", " for i, simname in enumerate(sims):\n", " for j, catalogue in enumerate(catalogues):\n", " plt.plot(scales, y[i, j], marker='o', ms=2.5, ls=ls[i],\n", " label=catalogue_to_pretty(catalogue) if i == 0 else None, c=cols[j],)\n", "\n", " plt.xlabel(r\"$R_{\\rm smooth} ~ [\\mathrm{Mpc} / h]$\")\n", " plt.ylabel(r\"$-\\Delta \\ln \\mathcal{Z}$\")\n", " plt.legend()\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/smoothing_comparison.pdf\", dpi=450)\n", " plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. External flow consistency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CF4\", \"CLONES\"]\n", "# sims = [\"Carrick2015\", \"Lilow2024\", \"CF4\", \"csiborg2_main\", \"csiborg2X\"]\n", "# cats = [[\"LOSS\", \"Foundation\"], \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "# cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_not2MTForSFI_i\"]\n", "\n", "X = {}\n", "\n", "for sim in sims:\n", " for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"bayes\",\n", " sample_alpha=True, zcmb_max=0.05)\n", "\n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " with File(fname, 'r') as f:\n", " X[f\"{sim}_{cat}\"] = np.linalg.norm(f[f\"samples/Vext\"][...], axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", "\n", " fig, axs = plt.subplots(2, 2, figsize=(3.5, 2.65 * 1.1))\n", " fig.subplots_adjust(hspace=0, wspace=0)\n", "\n", " for k, cat in enumerate(cats):\n", " i, j = k // 2, k % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " sns.kdeplot(X[f\"{sim}_{cat}\"], fill=True, bw_adjust=0.75, ax=ax,\n", " label=simname_to_pretty(sim) if i == 0 else None)\n", "\n", " ax.text(0.725, 0.85, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='center',\n", " bbox=dict(facecolor='white', alpha=0.5, edgecolor='none'))\n", "\n", " ax.set_ylabel(None)\n", " ax.set_yticklabels([])\n", " ax.set_xlim(0)\n", "\n", " handles, labels = axs[0, 0].get_legend_handles_labels()\n", " fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.1),\n", " ncol=3)\n", "\n", " for i in range(2):\n", " axs[-1, i].set_xlabel(r\"$|\\mathbf{V}_{\\rm ext}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[i, 0].set_ylabel(\"Normalised PDF\")\n", "\n", " fig.tight_layout()\n", " fig.savefig(f\"../../plots/Vext_comparison.pdf\", dpi=450)\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. What $\\beta$ is preferred by the data? " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CF4\", \"CLONES\"]\n", "cats = [\"LOSS\", \"Foundation\", \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "# cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_not2MTForSFI_i\"]\n", "\n", "X = {}\n", "for sim in sims:\n", " for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"bayes\",\n", " sample_alpha=True, zcmb_max=0.05, sample_beta=True)\n", "\n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " with File(fname, 'r') as f:\n", " X[f\"{sim}_{cat}\"] = f[f\"samples/beta\"][...]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", "\n", " fig, axs = plt.subplots(3, 2, figsize=(3.5, 2.65 * 1.8))\n", " fig.subplots_adjust(hspace=0, wspace=0)\n", "\n", " for k, cat in enumerate(cats):\n", " i, j = k // 2, k % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " sns.kdeplot(X[f\"{sim}_{cat}\"], fill=True, bw_adjust=0.75, ax=ax,\n", " label=simname_to_pretty(sim) if i == 0 else None)\n", "\n", " ax.text(0.1, 0.85, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='left',\n", " bbox=dict(facecolor='white', alpha=0.5, edgecolor='k')\n", " )\n", "\n", " ax.axvline(1, c=\"k\", ls=\"--\", alpha=0.75)\n", " ax.set_ylabel(None)\n", " ax.set_yticklabels([])\n", "\n", " handles, labels = axs[0, 0].get_legend_handles_labels()\n", " fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.075),\n", " ncol=3)\n", "\n", " # for i in range(3):\n", " for j in range(2):\n", " axs[-1, j].set_xlabel(r\"$\\beta$\")\n", "\n", " for i in range(3):\n", " axs[i, 0].set_ylabel(\"Normalised PDF\")\n", "\n", " fig.tight_layout()\n", " fig.savefig(f\"../../plots/beta_comparison.pdf\", dpi=450)\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Bulk flow in the simulation rest frame " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\"]\n", "\n", "\n", "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "\n", " plt.figure()\n", " for i, sim in enumerate(sims):\n", " r, B = get_bulkflow_simulation(sim, convert_to_galactic=True)\n", " B = B[..., 0]\n", "\n", " if sim == \"Carrick2015\":\n", " B *= 0.43\n", "\n", " if sim in [\"Carrick2015\", \"Lilow2024\", \"CLONES\"]:\n", " plt.plot(r, B[0], label=simname_to_pretty(sim), color=cols[i])\n", " else:\n", " ylow, yhigh = np.percentile(B, [16, 84], axis=0)\n", " plt.fill_between(r, ylow, yhigh, alpha=0.5,\n", " label=simname_to_pretty(sim), color=cols[i])\n", "\n", " plt.xlabel(r\"$R ~ [\\mathrm{Mpc} / h]$\")\n", " plt.ylabel(r\"$|\\mathbf{B}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " plt.xlim(5, 200)\n", " plt.legend(ncols=2)\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/bulkflow_simulations_restframe.pdf\", dpi=450)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Bulk flow in the CMB frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\"]\n", "# cats = [[\"LOSS\", \"Foundation\"], \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "\n", "data = {}\n", "for sim in sims:\n", " for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"bayes\",\n", " sample_alpha=True, zcmb_max=0.05)\n", " data[f\"{sim}_{cat}\"] = get_bulkflow(fname, sim)\n", "\n", "def get_ax_centre(ax):\n", " # Get the bounding box of the specific axis in relative figure coordinates\n", " bbox = ax.get_position()\n", "\n", " # Extract the position and size of the axis\n", " x0, y0, width, height = bbox.x0, bbox.y0, bbox.width, bbox.height\n", "\n", " # Calculate the center of the axis\n", " center_x = x0 + width / 2\n", " center_y = y0 + height / 2\n", " return center_x, center_y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " nrows = len(sims)\n", " ncols = 3\n", "\n", " figwidth = 8.3\n", " fig, axs = plt.subplots(nrows, ncols, figsize=(figwidth, 1.25 * figwidth), sharex=True, )\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", " # fig.suptitle(f\"Calibrated against {catalogue}\")\n", "\n", " for i, sim in enumerate(sims):\n", " for j, catalogue in enumerate(cats):\n", " r, B = data[f\"{sim}_{catalogue}\"]\n", " c = cols[j]\n", " for n in range(3):\n", " ylow, ymed, yhigh = np.percentile(B[..., n], [16, 50, 84], axis=-1)\n", " axs[i, n].fill_between(\n", " r, ylow, yhigh, alpha=0.5, color=c, edgecolor=c,\n", " label=catalogue_to_pretty(catalogue) if i == 1 else None)\n", "\n", "\n", " # CMB-LG velocity\n", " kwargs = {\"color\": \"mediumblue\", \"alpha\": 0.5, \"zorder\": 10, \"hatch\": \"x\"}\n", " for n in range(len(sims)):\n", " axs[n, 0].fill_between([r.min(), 15.], [627 - 22, 627 - 22], [627 + 22, 627 + 22], label=\"CMB-LG\" if n == 0 else None, **kwargs)\n", " axs[n, 1].fill_between([r.min(), 15.], [276 - 3, 276 - 3], [276 + 3, 276 + 3], **kwargs)\n", " axs[n, 2].fill_between([r.min(), 15.], [30 - 3, 30 - 3], [30 + 3, 30 + 3], **kwargs)\n", "\n", " # LCDM expectation\n", " Rs,mean,std,mode,p05,p16,p84,p95 = np.load(\"/mnt/users/rstiskalek/csiborgtools/data/BulkFlowPlot.npy\")\n", " m = Rs < 175\n", " kwargs = {\"color\": \"black\", \"zorder\": 0, \"hatch\": \"//\", \"alpha\": 0.25}\n", " for n in range(len(sims)):\n", " axs[n, 0].fill_between(\n", " Rs[m], p16[m], p84[m],\n", " label=r\"$\\Lambda\\mathrm{CDM}$\" if n == 0 else None, **kwargs)\n", "\n", " for n in range(3):\n", " axs[-1, n].set_xlabel(r\"$R ~ [\\mathrm{Mpc} / h]$\")\n", "\n", " for n in range(len(sims)):\n", " axs[n, 0].set_ylabel(r\"$|\\mathbf{B}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[n, 1].set_ylabel(r\"$\\ell ~ [\\mathrm{deg}]$\")\n", " axs[n, 2].set_ylabel(r\"$b ~ [\\mathrm{deg}]$\")\n", "\n", " for i, sim in enumerate(sims):\n", " ax = axs[i, -1].twinx()\n", " ax.set_ylabel(simname_to_pretty(sim), rotation=270, labelpad=7.5)\n", " ax.set_yticklabels([])\n", "\n", " axs[0, 0].set_xlim(r.min(), r.max())\n", "\n", " axs[0, 0].legend()\n", " handles, labels = axs[1, 0].get_legend_handles_labels() # get the labels from the first axis\n", " fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.975), ncol=len(cats) + 2)\n", "\n", " fig.tight_layout(rect=[0, 0, 0.95, 0.95])\n", " fig.savefig(f\"../../plots/bulkflow_CMB.pdf\", dpi=450)\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8. Full vs Delta comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogue = \"CF4_TFR_i\"\n", "simname = \"csiborg2X\"\n", "zcmb_max=0.05\n", "sample_beta = True\n", "sample_alpha = True\n", "\n", "fname_bayes = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"bayes\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "fname_mike = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "\n", "X = []\n", "labels = [\"Full posterior\", \"Delta posterior\"]\n", "for i, fname in enumerate([fname_bayes, fname_mike]):\n", " samples = get_samples(fname)\n", " if i == 1:\n", " print(samples.keys())\n", "\n", " X.append(samples_to_getdist(samples, labels[i]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = [f\"a_{catalogue}\", f\"b_{catalogue}\", f\"c_{catalogue}\", f\"e_mu_{catalogue}\",\n", " \"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"alpha_{catalogue}\"]\n", "# params = [\"beta\", f\"a_{catalogue}\", f\"b_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "# params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"mag_cal_{catalogue}\", f\"alpha_cal_{catalogue}\", f\"beta_cal_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "\n", "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 11})\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", " g.settings.fontsize = 12\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " # plt.gcf().suptitle(catalogue_to_pretty(catalogue), y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/method_comparison_{simname}_{catalogue}.pdf\", dpi=300, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Guilhem plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Manticore vs linear comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "\n", "sims = [\"Carrick2015\", \"csiborg2X\"]\n", "catalogues = [\"LOSS\", \"Foundation\", \"2MTF\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "y_lnZ = np.full((len(catalogues), len(sims)), np.nan)\n", "\n", "for i, catalogue in enumerate(catalogues):\n", " for j, simname in enumerate(sims):\n", " fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=simname != \"IndranilVoid_exp\",\n", " zcmb_max=zcmb_max)\n", "\n", " y_lnZ[i, j] = - get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " # y_lnZ[i] -= y_lnZ[i].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bayes_factor = y_lnZ[:, 1] - y_lnZ[:, 0]\n", "\n", "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", " plt.figure()\n", "\n", " sns.barplot(x=np.arange(len(catalogues)), y=bayes_factor / np.log(10), color=\"#21456D\")\n", " plt.xticks(\n", " np.arange(len(catalogues)),\n", " [catalogue_to_pretty(cat) for cat in catalogues],\n", " rotation=35, fontsize=\"small\", minor=False)\n", " plt.ylabel(r\"$\\log \\left(\\mathcal{Z}_{\\rm Manticore} / \\mathcal{Z}_{\\rm linear}\\right)$\")\n", " plt.tick_params(axis='x', which='both', bottom=False, top=False)\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/manticore_vs_carrick.png\", dpi=450)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All possible things" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dipole magnitude" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "sim = \"IndranilVoid_gauss\"\n", "\n", "X = []\n", "for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_mag_dipole=False,\n", " sample_alpha=False, zcmb_max=0.05)\n", " \n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " samples = get_samples(fname, convert_Vext_to_galactic=False)\n", "\n", " # keys = list(samples.keys())\n", " # for key in keys:\n", " # if cat in key:\n", " # value = samples.pop(key)\n", " # samples[key.replace(f\"_{cat}\",'')] = value\n", " \n", " samples = samples_to_getdist(samples, catalogue_to_pretty(cat))\n", " X.append(samples)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# params = [\"Vmag\", \"l\", \"b\", \"a_dipole_mag\", \"a_dipole_l\", \"a_dipole_b\"]\n", "params = [\"Vx\", \"Vy\", \"Vz\"]\n", "# params = [\"Vmag\", \"l\", \"b\"]\n", "\n", "with plt.style.context('science'):\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " # plt.gcf().suptitle(catalogue_to_pretty(cat), y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/vext_{sim}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flow | catalogue" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "params = [\"Vmag\", \"beta\", \"sigma_v\"]\n", "\n", "for catalogue in catalogues:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim)\n", " for sim in sims]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{catalogue}', y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/calibration_{catalogue}.png\", dpi=500, bbox_inches='tight')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flow | simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogues = [\"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "params = [\"Vmag\", \"l\", \"b\", \"beta\", \"sigma_v\"]\n", "\n", "for sim in sims:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim, catalogue)\n", " for catalogue in catalogues]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{sim}', y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/calibration_{sim}.png\", dpi=500, bbox_inches='tight')\n", " plt.gcf().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stacking vs marginalising CB boxes\n", "\n", "#### $V_{\\rm ext}$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"csiborg2X\"\n", "catalogue = \"2MTF\"\n", "key = \"Vext\"\n", "\n", "X = [get_samples(sim, catalogue, nsim=nsim, convert_Vext_to_galactic=False)[key] for nsim in range(20)]\n", "Xmarg = get_samples(sim, catalogue, convert_Vext_to_galactic=False)[key]\n", "\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharey=True)\n", "fig.suptitle(f\"{simname_to_pretty(sim)}, {catalogue}\")\n", "fig.subplots_adjust(wspace=0.0, hspace=0)\n", "\n", "for i in range(3):\n", " for n in range(20):\n", " axs[i].hist(X[n][:, i], bins=\"auto\", alpha=0.25, histtype='step',\n", " color='black', linewidth=0.5, density=1, zorder=0,\n", " label=\"Individual box\" if (n == 0 and i == 0) else None)\n", "\n", "axs[i].hist(np.hstack([X[n][:, i] for n in range(20)]), bins=\"auto\",\n", " histtype='step', color='blue', density=1,\n", " label=\"Stacked individual boxes\" if i == 0 else None)\n", "axs[i].hist(Xmarg[:, i], bins=\"auto\", histtype='step', color='red',\n", " density=1, label=\"Marginalised boxes\" if i == 0 else None)\n", " \n", "axs[0].legend(fontsize=\"small\", loc='upper left', frameon=False)\n", "\n", "axs[0].set_xlabel(r\"$V_{\\mathrm{ext}, x} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[1].set_xlabel(r\"$V_{\\mathrm{ext}, y} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[2].set_xlabel(r\"$V_{\\mathrm{ext}, z} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[0].set_ylabel(\"Normalized PDF\")\n", "fig.tight_layout()\n", "fig.savefig(f\"../../plots/consistency_{sim}_{catalogue}_{key}.png\", dpi=450)\n", "fig.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $\\beta$ and others" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"csiborg2_main\"\n", "catalogue = \"Pantheon+\"\n", "key = \"alpha\"\n", "\n", "X = [get_samples(sim, catalogue, nsim=nsim, convert_Vext_to_galactic=False)[key] for nsim in range(20)]\n", "Xmarg = get_samples(sim, catalogue, convert_Vext_to_galactic=False)[key]\n", "\n", "\n", "plt.figure()\n", "plt.title(f\"{simname_to_pretty(sim)}, {catalogue}\")\n", "for n in range(20):\n", " plt.hist(X[n], bins=\"auto\", alpha=0.25, histtype='step',\n", " color='black', linewidth=0.5, density=1, zorder=0,\n", " label=\"Individual box\" if n == 0 else None)\n", "\n", "plt.hist(np.hstack([X[n] for n in range(20)]), bins=\"auto\",\n", " histtype='step', color='blue', density=1,\n", " label=\"Stacked individual boxes\")\n", "plt.hist(Xmarg, bins=\"auto\", histtype='step', color='red',\n", " density=1, label=\"Marginalised boxes\")\n", "\n", "plt.legend(fontsize=\"small\", frameon=False, loc='upper left', ncols=3)\n", "plt.xlabel(names_to_latex([key], True)[0])\n", "plt.ylabel(\"Normalized PDF\")\n", "\n", "plt.tight_layout()\n", "plt.savefig(f\"../../plots/consistency_{sim}_{catalogue}_{key}.png\", dpi=450)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SN/TFR Calibration consistency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "catalogues = [\"Pantheon+\"]\n", "sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "\n", "for catalogue in catalogues:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim)\n", " for sim in sims]\n", "\n", " if \"Pantheon+\" in catalogue or catalogue in [\"Foundation\", \"LOSS\"]:\n", " params = [\"alpha_cal\", \"beta_cal\", \"mag_cal\", \"e_mu\"]\n", " else:\n", " params = [\"aTF\", \"bTF\", \"e_mu\"]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{catalogue}', y=1.025)\n", " plt.gcf().tight_layout()\n", " # plt.gcf().savefig(f\"../../plots/calibration_{catalogue}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V_{\\rm ext}$ comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogues = [\"LOSS\"]\n", "# sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "sims = [\"Carrick2015\"]\n", "params = [\"Vmag\", \"l\", \"b\"]\n", "\n", "for sim in sims:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim, catalogue)\n", " for catalogue in catalogues]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{simname_to_pretty(sim)}', y=1.025)\n", " plt.gcf().tight_layout()\n", " # plt.gcf().savefig(f\"../../plots/calibration_{sim}.png\", dpi=500, bbox_inches='tight')\n", " plt.gcf().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bulk flow in the simulation rest frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"csiborg1\", \"csiborg2_main\", \"csiborg2X\"]\n", "convert_to_galactic = False\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", "cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "\n", "for i, sim in enumerate(sims):\n", " r, B = get_bulkflow_simulation(sim, convert_to_galactic=convert_to_galactic)\n", " if sim == \"Carrick2015\":\n", " if convert_to_galactic:\n", " B[..., 0] *= 0.43\n", " else:\n", " B *= 0.43\n", "\n", " for n in range(3):\n", " ylow, ymed, yhigh = np.percentile(B[..., n], [16, 50, 84], axis=0)\n", " axs[n].fill_between(r, ylow, yhigh, color=cols[i], alpha=0.5, label=simname_to_pretty(sim) if n == 0 else None)\n", "\n", "axs[0].legend()\n", "if convert_to_galactic:\n", " axs[0].set_ylabel(r\"$B ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[1].set_ylabel(r\"$\\ell_B ~ [\\degree]$\")\n", " axs[2].set_ylabel(r\"$b_B ~ [\\degree]$\")\n", "else:\n", " axs[0].set_ylabel(r\"$B_{x} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[1].set_ylabel(r\"$B_{y} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[2].set_ylabel(r\"$B_{z} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "\n", "for n in range(3):\n", " axs[n].set_xlabel(r\"$R ~ [\\mathrm{Mpc}]$\")\n", "\n", "\n", "fig.tight_layout()\n", "fig.savefig(\"../../plots/bulkflow_simulations_restframe.png\", dpi=450)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bulk flow in the CMB rest frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"csiborg2_main\"\n", "catalogues = [\"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True)\n", "cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "# fig.suptitle(f\"Calibrated against {catalogue}\")\n", "\n", "for i, catalogue in enumerate(catalogues):\n", " r, B = get_bulkflow(sim, catalogue, sample_beta=True, convert_to_galactic=True,\n", " weight_simulations=True, downsample=3)\n", " c = cols[i]\n", " for n in range(3):\n", " ylow, ymed, yhigh = np.percentile(B[..., n], [16, 50, 84], axis=-1)\n", " axs[n].plot(r, ymed, color=c)\n", " axs[n].fill_between(r, ylow, yhigh, alpha=0.5, color=c, label=catalogue)\n", "\n", "\n", "# CMB-LG velocity\n", "axs[0].fill_between([r.min(), 10.], [627 - 22, 627 - 22], [627 + 22, 627 + 22], color='black', alpha=0.5, zorder=0.5, label=\"CMB-LG\", hatch=\"x\")\n", "axs[1].fill_between([r.min(), 10.], [276 - 3, 276 - 3], [276 + 3, 276 + 3], color='black', alpha=0.5, zorder=0.5, hatch=\"x\")\n", "axs[2].fill_between([r.min(), 10.], [30 - 3, 30 - 3], [30 + 3, 30 + 3], color='black', alpha=0.5, zorder=0.5, hatch=\"x\")\n", "\n", "# LCDM expectation\n", "Rs,mean,std,mode,p05,p16,p84,p95 = np.load(\"/mnt/users/rstiskalek/csiborgtools/data/BulkFlowPlot.npy\")\n", "m = Rs < 175\n", "axs[0].plot(Rs[m], mode[m], color=\"violet\", zorder=0)\n", "axs[0].fill_between(Rs[m], p16[m], p84[m], alpha=0.25, color=\"violet\",\n", " zorder=0, hatch='//', label=r\"$\\Lambda\\mathrm{CDM}$\")\n", "\n", "for n in range(3):\n", " axs[n].set_xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n", "\n", "axs[0].legend()\n", "axs[0].set_ylabel(r\"$B ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[1].set_ylabel(r\"$\\ell_B ~ [\\mathrm{deg}]$\")\n", "axs[2].set_ylabel(r\"$b_B ~ [\\mathrm{deg}]$\")\n", "\n", "axs[0].set_xlim(r.min(), r.max())\n", "\n", "fig.tight_layout()\n", "fig.savefig(f\"../../plots/bulkflow_{sim}_{catalogue}.png\", dpi=450)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Smoothing scale dependence" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "simname = \"Carrick2015\"\n", "catalogue = \"Pantheon+\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Goodness-of-fit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "scales = [0, 4, 8, 16, 32]\n", "\n", "y = np.asarray([get_gof(\"BIC\", simname, catalogue, ksmooth=i)\n", " for i in range(len(scales))])\n", "ymin = y.min()\n", "\n", "y -= ymin\n", "y_CF4 = get_gof(\"BIC\", \"CF4\", catalogue) - ymin\n", "y_CF4gp = get_gof(\"BIC\", \"CF4gp\", catalogue) - ymin\n", "\n", "plt.figure()\n", "plt.axhline(y[0], color='blue', label=\"Carrick+2015, no smoothing\")\n", "plt.plot(scales[1:], y[1:], marker=\"o\", label=\"Carrick+2015, smoothed\")\n", "\n", "plt.axhline(y_CF4, color='red', label=\"CF4, no smoothing\")\n", "\n", "plt.xlabel(r\"$R_{\\rm smooth} ~ [\\mathrm{Mpc}]$\")\n", "plt.ylabel(r\"$\\Delta \\mathrm{BIC}$\")\n", "plt.legend(ncols=1)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"../../plots/test_smooth.png\", dpi=450)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"Carrick2015\"\n", "catalogue = \"Pantheon+\"\n", "\n", "\n", "X = [samples_to_getdist(get_samples(sim, catalogue, ksmooth=ksmooth), ksmooth)\n", " for ksmooth in [0, 1, 2]]\n", "\n", "params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\"]\n", "# if \"Pantheon+\" in catalogue or catalogue in [\"Foundation\", \"LOSS\"]:\n", "# params += [\"alpha_cal\", \"beta_cal\", \"mag_cal\", \"e_mu\"]\n", "# else:\n", "# params += [\"aTF\", \"bTF\", \"e_mu\"]\n", "\n", "\n", "\n", "g = plots.get_subplot_plotter()\n", "g.settings.figure_legend_frame = False\n", "g.settings.alpha_filled_add = 0.75\n", "\n", "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", "plt.gcf().suptitle(f'{catalogue}', y=1.025)\n", "plt.gcf().tight_layout()\n", "plt.gcf().savefig(f\"../../plots/calibration_{catalogue}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Void testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evidence comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "\n", "sims = [\"no_field\", \"IndranilVoid_exp\"]\n", "cats = [\"LOSS\", \"Foundation\", \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "neglnZ = {}\n", "kfound = []\n", "for sim in sims:\n", " for cat in cats:\n", " sample_alpha = sim not in [\"IndranilVoid_exp\", \"no_field\"]\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max)\n", " \n", "\n", " neglnZ[f\"{sim}_{cat}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "simA = sims[0]\n", "simB = sims[1]\n", "\n", "print(f\"lnZ_({simA}) - lnZ_({simB})\\n\")\n", "for cat in cats:\n", " lnZ_A = - neglnZ[f\"{simA}_{cat}\"]\n", " lnZ_B = - neglnZ[f\"{simB}_{cat}\"]\n", " print(f\"{cat:15s} {lnZ_A - lnZ_B:.1f}\")\n", "\n", "\n", "print(f\"\\n(Positive -> preference for {simA})\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Goodness-of-fit comparison" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "no_Vext = True\n", "\n", "sims = [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "neglnZ = {}\n", "kfound = {}\n", "for sim in sims:\n", " for cat in cats:\n", " kfound[f\"{sim}_{cat}\"] = []\n", " for ksim in range(500):\n", " sample_alpha = False\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\", nsim=ksim,\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " no_Vext=no_Vext, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " kfound[f\"{sim}_{cat}\"].append(ksim)\n", " neglnZ[f\"{sim}_{cat}_{ksim}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", "\n", "neglnZ_no_field = {}\n", "neglnZ_dipole = {}\n", "sim = \"no_field\"\n", "for cat in cats:\n", " sample_alpha = False\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " no_Vext=True, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " neglnZ_no_field[f\"{cat}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " no_Vext=None, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " neglnZ_dipole[f\"{cat}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving to `../../plots/void_goodness_of_fit_observer_no_Vext.png`.\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", " figwidth = 8.3 \n", " fig, axs = plt.subplots(2, 2, figsize=(figwidth, 0.65 * figwidth))\n", "\n", " for n, cat in enumerate(cats):\n", " i, j = n // 2, n % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " x = kfound[f\"{sim}_{cat}\"]\n", " y = [neglnZ[f\"{sim}_{cat}_{ksim}\"] / np.log(10) for ksim in x]\n", " x = np.array(x) * 0.674\n", " ax.plot(x, y, label=simname_to_pretty(sim))\n", " \n", " # if no_Vext is None:\n", " # y_no_field = neglnZ_no_field[cat] / np.log(10)\n", " # if cat != \"CF4_TFR_w1\":\n", " # ax.axhline(y_no_field, color=\"black\", ls=\"--\", label=\"No peculiar velocity\")\n", " y_no_field = neglnZ_no_field[cat] / np.log(10)\n", " ax.axhline(y_no_field, color=\"black\", ls=\"--\", label=\"No peculiar velocity\")\n", "\n", " y_dipole = neglnZ_dipole[cat] / np.log(10)\n", " ax.axhline(y_dipole, color=\"black\", ls=\":\", label=\"Constant dipole\")\n", "\n", " ax.text(0.5, 0.9, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, #fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='center',\n", " bbox=dict(facecolor='white', alpha=0.5),\n", " )\n", "\n", " if n == 0:\n", " ax.legend(fontsize=\"small\", loc=\"upper left\")\n", "\n", " ax.set_ylabel(r\"$-\\Delta \\log \\mathcal{Z}$\")\n", " ax.set_xlabel(r\"$R_{\\rm offset} ~ [\\mathrm{Mpc} / h]$\")\n", " ax.set_xlim(0)\n", "\n", " fig.tight_layout()\n", " fname = f\"../../plots/void_goodness_of_fit_observer.png\"\n", " if no_Vext:\n", " fname = fname.replace(\".png\", \"_no_Vext.png\")\n", " print(f\"Saving to `{fname}`.\")\n", " fig.savefig(fname, dpi=450)\n", " fig.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Single parameter radial dependence" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "key = \"Vext\"\n", "# key_label = r\"$\\sigma_v ~ [\\mathrm{km} / \\mathrm{s}]$\"\n", "key_label = r\"$|\\mathbf{V}_{\\rm ext}| ~ [\\mathrm{km} / \\mathrm{s}]$\"\n", "no_Vext = None\n", "\n", "sims = [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "data_mean = {}\n", "data_std = {}\n", "kfound = {}\n", "for sim in sims:\n", " for cat in cats:\n", " kfound[f\"{sim}_{cat}\"] = []\n", " for ksim in range(500):\n", " sample_alpha = False\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\", nsim=ksim,\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " no_Vext=no_Vext, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " kfound[f\"{sim}_{cat}\"].append(ksim)\n", " with File(fname, 'r') as f:\n", " x = f[f\"samples/{key}\"][...]\n", " if key == \"Vext\":\n", " x = np.linalg.norm(x, axis=-1)\n", "\n", " data_mean[f\"{sim}_{cat}_{ksim}\"] = x.mean()\n", " data_std[f\"{sim}_{cat}_{ksim}\"] = x.std()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving to `../../plots/void_Vext_per_observer.png`.\n" ] }, { "data": { "image/png": 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4O1lqOiRBEAStIxKgl5jWvxGbTlzn098jCf2kPQqF6A0kCK9jrFvutXdp1C0wMBB/f///7K0KCgoiKiqKwMDA59a15yUnz97VsbCwICwsLH9Z3MCBA5/7/tkE6FXjWlhYqOslCmXIRz3qsTniBu+vOMahWd0pZ6Cr6ZAEQRC0ikiAXqJ8OX2+HObG4MUH2Bxxg77Nqms6JEEQXsPPzw8LCwtcXV3z775MmTIFR0dHli9fnr8szt3dPf8ujb+/P2ZmZvlJS1BQUH4C5e/v/5/vn+Xm5vafcQWhoPR0dfjRrwVtP9/B7PWnmDtYLIUTBEEoTiIBeoVernb0dLFj0pooOjWyoaKRvqZDEkqwe/fuaTqEAitNsRaGt7f3S/fcvKxy28uOTZo0iUmTJv3n2LPyEqJXVYTL+7mZmZmoGCe8Vn07Mz4b4MyMdbH0crUTlUeFIivO9/SyOn8I2kckQK/x5VBX3CZvZe76UwQOFf0/hP8yNjZGX1+fDRs2aDqUQtHX18fY2FjTYQiCVvuwe122Rt1kdNBRjsztgYmhmJKFgtPU/CPmD6EsEO+2r1HN0oSp/Z35fG0sg9s40MRerO8XnmdqasrYsWNJS0vTdCiFYmxsjKmpqabDEAStpqujw4+jWtB6+nZmrI1l0XA3TYcklCKamn/E/CGUBSIBeoMxXeoQfPgK4389we7Pu6CrozWtk4QCMjU1FZOBIAhFUsumIjMHNmHS6ih6u9nRvn4VTYcklCJi/hGEohGf5t9AX0+Hb99xJyo+iZ/2XNZ0OIIgCEIZ4+9RmzZ1K/PByuM8Ts/SdDiCIAhlntYkQAXp0v0qLWpZ8U4HJ2aGnOR2cula6iQIglAc1NGpuzRRZY7R0VHw/cgW3HuYzvQ/Y+UPThAEoRRTx/yiUCqVStlGK8HadO/Ioe17i/z85CeZuAZspXUdK1aNaytjZIIgCGWHl5cXW7Zs0XQYxa59j87s/3u3SmMsD/uHCaui2Dq5k1gKJwiC8AI55xetuQN0OOUM2++dKPLzzU0MCBziwqaIG2yP0c4rnIJQEpmbmxMQEPCf4wqFgoULF6r13PHx8Xh6ev7n/19GoVDg7++Pj48Prq6uBAUFFXhsoeSLfRRHVm62SmOM6lybtvUqM2blcR49FUvhBEEQ1EVrEiBLfVM+uPAdT3MyijyGd4saeDSy4dPfI8Q6bUEoISwsLIiOjn7uWGhoKC4uLhqK6NWWL19OSEgIUVFRhISE/CfuV4mPj1dzZIKqHmansfDqOpXG0NFRsOy9Fjx4lMFnf8bIFJkgCILwIq1JgBpVcCAh/QFz4/8o8hgKhYJv3nHn/qMM5qw/JWN0giCowsXF5blkYvny5fj6+mowojdLSkrC0dGxQI/18fFRczSCqmoaVWVW3BrOP76u0jgOlcszZ1BTft57mT1nbssUnSAIgvAsrUmAyusaMcXBl4VXQ1SaoOytyjOlXyN+2HWR2KtJMkYoCKVYVhrci1bPV9abC4/4+vqyfPlyAFJSUjAzM8PMzCz/5ykpKfj4+ODp6ZmfTAQEBOQvQ1u4cCFBQUF4enrm321xcnLKT6p8fHyIj4/H39//uTEKKy8GJycnVqxYkR9j3riurq7/uSsUEBBAfHw8Pj4+hIeHExoaio+PDz4+PigUCqKjo4mPj8fV1RV/f3+cnJxYuHAh/v7+zy21e9nv4MXnhYaGvva48Gq1TeywN7LmvbNfk6PMUWmsdzvWpEN9az5YeZyHYimcIAiC7LSqD9Bkh0GsubOH988vYa/blygUiiKNM7ZrXdYducpHv5xgzwzRG0gQSLkAIa7qGdsnCqxev5zNxcWFyMhIAIKCgvD19SUp6f8XKMzMzAgJCZGG+zeRCAwMxMfHh5SUFOLi4vITqPDwcDw8PHBxcWHt2rW4uLgQHx9PeHg4ZmZmhIWF5ScihV1mlxdDdHQ0AQEBLF++/LlxU1JScHV1JS4uLv85gYGBhIeH5z8XwNvbm4ULF2JhYZEfX3x8PFFRUcTHx+Pk5IRSqcxPnPz8/F76O3B0dHzuef7+/nh7ewO88rjwcroKHX5q8AntIj7lu+tb+KhGvyKPpaOjYNnIFjSfuo2pf0Tz3XvNZYxUEARB0KoEqJyuAd/XG0eXqCn8fiuMt227FGkcfT0dFo9ohsfsXawIv8ToLnVkjlQQShmzulKioq6xC8DX15fw8HDCwsIICwv7T5GB6OhoIiMjiY+PJyUlBYAVK1Zgbm5OcnIyAAMHDsy/O+Lv709gYCDR0dF4eHg8lwzA65el+fv75ydgzyYueVxcXPLHT0pKyh8z745QXnyvEh0dzdq1a4mK+v/vPG85XV5SlPf/b/odPPu8Z5PGVx0XXq2NeUM+qObF1Es/09uqBY7GNkUeq7qlCfMGu/DhLyfo414NT+eqMkYqCIKg3bQqAQLwrOTK4CodmXBxBb0rt8BCv2KRxmlW05L3OtZiVuhJertVw9bCWOZIBaEU0Td+410adfPz88PHx+el+2qCgoKIiooiMDDwubsro0aNIioqilGjRhESEoKZmRlJSUmEhYUREhJCSEgIy5cvx9/fn8jISMzMzAgMDMx//quKE+TdTXqdsLAwnJyccHJyIiQkBA8PD+Lj40lKSsqPI8+zCVHeUrawsLDnxnsx2Sno7+BVz3vTeMLLzas1gi33juJ37lvCXBcUeaUBwDsdnNgccZ2xPx3n+LyemJkYyBipIAiC9tLKtVtf1/EnS5lNwMWfVBpnhk9jjA31mLRaTVe+BUEosLx9P3l3U57l5uZGZGQkAQEB+Xts/P398ff3z78bk/e8Z4sn+Pj4sG7dOlxcXPDz8yMlJQVPT0/8/f0JDw8vdIx553F1lZYLTpo0iUmTJgHg6uqKj48Pu3f/t5eMh4dH/n6egIAAkpKSCAgIyF/KVhAv+x0I8qugZ8yK+uPZnRTDzwk7VBpLoVDw3XvNeZyeTcAaMc8IgiDIRWsaobq6umJra8vgwYMZPHgw31/fwgcXvuOQ+ze0Nm9Q5HHXH7vGO98fZu3H7ejR1E7GiAVBEEqP4OBggoODSUhIeG5pnrZ4cY4ZcWYRG+8e5lyrlVQtV0mlsVcdiGPMyuNinhEEQSupY37RmgToxe6xOcocWh4fz9PcDKJbfI++TtFWAyqVSgZ8tY9zN1OJmN+TCkb6coUsCIJQ6sjZqbs0efF1J2c9ov7hUTQzrcOmJl+otBROqVTi8/V+Yq8mcXxeTypVMJQjZEEQhFJFzvlFK5fAAegqdFle/yPOPb7ON9c2FHkchULBN2+7k/w4g1mhJ2WMUBAEoZhpx/WwYmGuX4Fl9cay5d5R1t7Zp9JYCoWCpe82IzM7l4mrIuUJUBAEQYtpbQIE0LRiTT6s3oeZcau4/vRukcepYVWezwY4szz8Iicu35cxQkEQBDVIuws398CpJXBgLGztDmtqw2+i0pic+lu3YaB1e8ZeWMbdjGSVxrIxN+bLYa6EHLvG5gjVmq0KgiBoO61OgABm1hyOmX55xv/zg0rjvN+lDk1qWDDu5+NkZqvWBE8QBEEWGalSohP7Nezzg43t4Gcr+NUatnSGo5Pg1n7QNQT7XuD6GShzNR11mfJdvQ9QoOCDC9+pPNbAlvZ4uVXjw18iuJv6VIboBEEQ1CQnAx6chsvrIGIW7BoMj29qOqp8WlcG+0UV9Uz4ps5ofE/NZdu94/S0KlrDOT1dHb57rzntZuxg8d/nmejVUOZIBUEQXiPrMdyLgXuRcDdS+m/KRelnekZSPyXzumDnARYNoVJDqOgEOrqajbuMszIw47t6HzDo1DxC7xzAu0q7Io+lUCj49h13mk3ZxrifT/Dn+HYq7S0SBEFQWU6GNNckn5e+ks5C0hnpmPLfGwJGVmBeDzJTgZJRyEXrEyAAH+t2rKy0nXEXltHJoglGukXbYOpcw5wPu9cjcPMZ+rhXp7ZN0XoMCYIgvFZuNtyPhTtH/k12oqSJByXolgPLplCtC7hMg8ruYFZbJDoaNNC6PWsr72fM+aV0sGiMpYFpkceyqliOJe82463FB/nj0BWGtP1v3ytBEAS1yHoM96Kleed+jPSVfOGZRKcyWDSQLrQ5jweL+lLio2IlTHUQCRD/9lqoO5ZGR/yZdyWY2TXfKfJYU/o1ZEvkdcb9fJztUzzQ0RFX5wRBUFFOBtyNgjuHIGG/9N/Mh9LStUqNoWp7aPyJlOxY1IciVrUU1EOhUPB9vXE0ODKKcReWEew8VaXxertWY3BrByatjqJdPWuqWZrIFKkgCMK/lLlScnPnKCQeg8TjkHxWOq5nJM09Nm2h0ThpVYF5PShXehpoi1nyX7VN7AhwGEjglXUMtelMHZNqRRrHyECPpe82p8f83fyy7zLvdaolc6SCIJR52enShJOwF27tkyaenAzQM4YqraDJJLBtLyU8RbxjLRSvKoYWLK37AUNOL2BA5TYqLYUDWDjUlQPnExmz8hibJ3USF9sEQVBNTpa0muD2gX8vtB2WlqwpdMC8AVg3B+cPoXKzMnGhrXRHL7MpDoNYc3sPY84vJdw1sMhrq9vWs+adDk5M/zOG7k1sqWphLHOkgiCUKUolpF6Ca9vh+napMEFOOhhaSHd3WiwAmzbSFTdd0WustBpcpSOhiQd5//xS2pk3orKheZHHMjMx4PuRzemzcC9B4RcZ3aWOjJEKglDm5eZIy9kS9kgX2m4fkpa46Zn8e6FtgvTfyu5gUEHT0cpOaxqhvtil+1V23I+ge/Q0VjcKYIhN5yKfL+VJJu5TtuHiYCE2qgqC8F9ZTyBhn5TwXN8OD+NBx0BKeKp3A7tOUMlZuvpWCqijU3dpUtA5JjEjmQZHRtHB3JmQxtNVnhsm/B7J7wfiODS7u9h3KgjC6z2+Cdd3wI1dcHM3ZCRJCY9NG7DtCLYdwNKlxF1oU8f8ojUJUGG6x/qenMu+5JNcaP0T5vpFz3q3RN5gyJKD/DqmNQNa1CjyOIIglBFPbsPVv+DqFrgZLi1rq+gA1btLX7YdQb907+eQs1N3aVKY173uzn58T80luNEUBtl0VOm8aRnZtPpsO+YmBoRN90RPt3QkzIIgFIPcbGkJ9bW/4do2eHBSuqhWuZlUKKeaJ1RuXuISnleRc34RS+Be4ps6o6l35D2mXPqZH+t/VORxvNyq0de9GhNWRdKhQRUqVRBr9QVB6zy6BnHrIS4UEo9Kk49NW2g+D2r0lCq0iTvEWmVglfaEJh5k7IVldLRogrUKS+GMDfUI8m+J5+wwvvrrLAF9G8kYqSAIpU5GClzdKiU8N3ZCRrJUha16d3CZDNW6Qrmiv+eUFeJS0UtULVeJuTVHsPzmNo6mnFNprEXD3MjJVTLlD+1bEiIIWivlIkTNhxB3WGUPx6dKfRA6/Qoj7kLffdDkEzCvI5IfLbWs3lgUKBhzfimqLsRoVtOST3vXZ8HmM0TFP5ApQkEQSo2n9+HcCvirG/xiBbuHSftKG42DAcfgnUTwWAW1Bonk518iAXqF96v1wq1ibUafW0JWbnaRx7E2M2LeWy4EH75K2KlbMkYoCEKJ8ugGRC+AP53hjzoQNQcq2oNnMIy4Bz02Q923S2Q/BKH4WRmY8X29cWy4e4h1iftVHm9K30Y4Vzdn5I9HeJJR9DlLEIRSIiMVLvwKW7vDbzawfzTkZkLrb2D4TfCJhGYzpeptog/cf4gE6BV0Fbosr/8RZx5fZfH1jSqNNaSNA50aVuGjX07w6GmWTBEKgqBxmQ/h/C+wuROsqgERM6XyoN02SElP1xDpilsZrKAjqM6nSju8rdvywfnvuJuRrNJY+no6rBjdioSkND4LjpEpQkEQSpSsNLi8Drb3g18qw553ITsN2iyBt29Dnz3QaCyUt9V0pCWexvYApaSkYGZmlv99dHQ0ZmZmWFhYEBkZiZubG2ZmZoSHhxMfH4+FhQXx8fFMmjQJ4JXH5eRSsRbjqvdhxuXf8bFuRw0j6yKNo1AoWDKiGc2mbGPGuli+fttd5kgFQSg2yly4EQ7//AZXNko9e2w7QqefwbE/GIhKXJpWGuaXPMvqjqXBET/GnF+qclW42jYVmTfYhY9/i6Brk6p0ayI+BAlCqZeTJRXNufQHXNkklaqu7A4t5kNNX5HsFFGx3wEKDQ0lICCAzp2fLzE9f/58nJyccHBwyJ+sAAIDA/Hz88Pb2zv/+a87LrfZNd/GXL8C4y4sU2mddg2r8nwxsAkrdl/i0IVEGSMUBKFYPE6AyDmw2gm2dpX6J7h9DsOuQZ/dUPcdkfxoWGmbXwAqG5qzrN5Y1t89xJ939qk83nudatKlcVXGrDzO3dSnqgcoCIJmPDgDhz+B321hWw+4GwFNJ8GQS+B9QtpHKpKfIiv2BMjb2xt/f///HPf09ESpVJKcnPzcVbhnr+K5uLiwdu3aVx5Xhwp6xiypO4a/7h1j093DKo3l71GblrWt+GDlcdLEGm1BKPlys+HKFvjbC1ZVh+j50t2e/kdg8Dmpok6FapqOUvhXaZtf8gys0h7fKu354Px33EpXrYiBQqHgh5HNUSqVfPDTcZULLAiCUIwyH8LZIAhtBmsbwcXVUHso+ETD4PPgNh1Ma2o6yjKhRO0Bio+PJzo6Ov/76OhoLCws8r+3sLAgOjr6lcfVpV/l1vSybM64C9/zKDutyOPo6Cj4fmRzbiU/ZVboSRkjFARBVmmJcOIL+L06bO8DT25B22Xwzm1pqVuVlqJ6WylTUueXPMvqjsNQR5+R575WOWmpbGrEspHN2RF7i1/2XpYpQkEQ1EKphNtHpP08v9rAgffBqLK0l3T4TWj9NVg1FXOOzEpUH6CUlBQcHR3x8fFhxYoVPHjw8ithrzr+OgkJCXh5eeV//6Zu3c9SKBQsrfcBDQ6P4vPLv/FN3fcLff48NatUZLq3M5/9GUMf9+q0rG1V5LEEQZDZvRg4tRguBYOOHtQZDvX9pMlHeKm8Dt15EhISNBjNq6lzfgHV5hiASgYVWdngY3rFTGdlwnZG2fUoUhx5ejS1492ONZn8RzRt6llT20YszxSEEiX9AfzzO5xbCcnnpKbYLlOl5dRiaRug3vmlxCRAfn5++f/v6elJQEAATk5OpKSk/OexlSpVeunx17G1tVWpe6y9URW+cBrG5Es/M6yqBy4VaxV5rA+61mHTieuMWXmMI3O6Y2RQYv4YBEH75GbDlc1S4nP7IJSvBs3nQL2Rol9CAbz4Qf/ZJKCkUPf8AqrPMQA9rZrznm03PvlnOR4WTXEwtlFpvHlvuXDgfCKjfjxC+PQu6OuVqEUfgqCd7kXD6aXShTZlLjj0hTbfgl1nqVG2kE+d80uJ+E2Hh4fj6en53LGkpCRcXFxISkr6z7FXHVe38TX606B8DfzPLSZHmVPkcXR1dPhhVAtuPHjCrNBTMkYoCEKBZT2Gk9/CmpqwU9rsTtcQGBoPTSeK5KeMKC3zS56v6/hTSb8i7579mlxlrkpjmRjq8dPoVpy6nsycDWKuEQSNyc2GuFDY2BZCXOHmHnCfCW8nQNd1UM1TJD/FrET8th0dHZ/buBoVFYWvry8eHh7Ex8fnH4+Ojn7tcXXT19Fjef2PiHp4iR9ubFVprDpVTZk+oDHLdl7gyD93ZYpQEIQ3SkuEY9Pg92pwdCLYtAWfKOh3AJy8paVvQplRWuaXPBX1TPipwSfsSz6p8jwD4OJYic/6O/PNtnPsO3tHhggFQSiwzIf/XmirBTt9pCSn63oYGgcuAWAktkFoSrHP9OHh4YSFhREfH09QUBAeHh44OjoSHR1NUFAQAE5OTvnlRwMDA1m4cCGOjo4Abzyubi3N6uNn14Opl36hX+XW2JazLPJYY7vVYUvkjX+XwvXA2FB88BIEtXl4FWIXwfmfpCSnvh84jxdV3MqQ0j6/5OlcqSmj7Xox6eIKulu646jiUriPe9Zn/7lERv54hKNze2BVsZxMkQqC8FJPbkvLqs/+IDUqrTkIuq0Hq+K7myy8nkKpJTUyvby8VF6fnScl6zH1Do+khVldNjb5QqWxLt1+SKvPtvNux5oEDnWVJT5BEJ6RGgeRs6Vyoobm4PwRNPxALHFTEznfa0sTuV/3o+w0nI/4U8PImj1uC9FRcXnMnZSntJz2Ny4OFoR80gEdHVFRShBkl3oZogOl4ga6htBgNDh/COXtNB1ZmSDn+2yJWAJX2pjpl2dJ3TFsunuEDYmHVBqrlk1FZvg05vtd/3DwvGiQKgiyeXgV9o6EP+rAjV3QahEMuwpun4nkRyjxKugZ83PDT9mffIplN1Sf8KuYGbHcryW7Tt1m2c4LMkQoCEK+pHMQPkyab67+Bc1mwfAb0GqhSH5KKJEAFZG3dVt6W7Vg7IXvSMl6rNJY73epTes6lRm94hgPn2bJFKEgaKlHN2DfaPijljQRtVokrbduPB70TTQdnSAUWEeLJoyp1puAiz9x6Ynq5V+7NK7K2G51mbHuJLFXk978BEEQXi/pLOz0hT8bQsI+aLMYhl2R9vcYmmo6OuE1CrTpZMqUKVSqVCn/+7xVcwqF4rmGbXnfr1u3joiICJlDLVkUCgXL6o2l/uFRTL70Ez/W/6jIY+nq6PCjXwtaTvubyWui+H5kCxkjFQQt8eQ2RM+Hs8vBoAI0nwcNx4ikpxQQc8yrBdYayc77Ubxz9ksOuH+FrkJXpfG+8GnMwfOJjPj+MAdndaN8OX2ZIhUELZJ0HiJnwuV1UKE6tP9R6t+ja6DpyIQCKlAC5OjoyKhRowo8qJmZWVHjKVWqlavM/FrvMu7CMobYdKKteaMij2VvVZ75b7kw7ucT9HK1o0dTcctUEAokPQliFsLpJdKaa7fPpTXXBhU0HZlQQGKOebXyekb82nAC7SI+5eur65noMFCl8Qz1dfl5TGvaTt/OpNXigpsgFEpqPER8AZfWgImdSHxKsQItgXtxYoqNjSU2NhaAlStXMmXKFB4+fPjKx5dl71frRQvTevid+5aM3EyVxnq7vRNdG1dl3M8nuP8oXaYIBaGMyn4KkXNhtQOc/g4afwpDr4DbNJH8lDJlYY5JSEjAy8vrua7lcmlj3pBPawzgs8u/cfbxVZXHq21TkS+HubHqQDwbjl9TPUBBKOseJ8A+fwiuAzfDoM0SGHIRGviJ5KcYBAcH4+XlRUKC6kuB8xRpD1BYWBimpqasWLGCkJAQJk+ezLp162QLSh3UNTnpKnQJqj+ey2m3CLyi2u9AoVDw3XvNycrOZfyvEWhJgT5BKBxlLlxcI202jZwJdUdIzUubzwZDM01Hp7XknKBK4xxja2vLli1bnutaLqfZNd/BydiG4acXkpWbrfJ4w9o50r9ZdT785QRX76m2j1UQyqz0JDgaIDXMjl8PzefDkDho9IG04kAoFoMHD2bLli3Y2trKNmaREiBXV1ccHBwIDQ3F398fU1NTHBwcZAtKHdQ5OTWq4MAEe2/mxgfzz5MbKo1VxcyIxSOasTniBmuPXJUnQEEoK24fgfUtIHwoWLnBoLPQ5lswrqzpyLSenBNUaZxj1K2crgG/N5zEycfxzIn/Q+XxFAoFi0c0w9zEgBHLDpOZnSNDlIJQRuRkQMyXsNoRTi+DppOkC21NJ4C+saajE2RQpAQoPj6emJgYIiIi8PDwAODKlSuyBlbafO44lGrlrBh9bonKd276NauObyt7JqyK5OaDJzJFKAil2KNrsGsQbGwNudnQZx903wBmtTQdmaAGYo55OTfT2nzm8BZzr/xBZOpFlcczMzHg1w/acPJaMl+EnJQhQkEo5ZRKiNsAwfXh2BSoPVSqItpsJhhU1HR0goyKlAB17tyZtWvXEhUVhVKpZPLkyaSkpMgcWulipGvIj/U/ZF/ySX69tUvl8RYNc8PEUI/RK46RmyuWwglaKisNTsyAP+rCrf3Q6RfwiQTb9pqOTFAjMce82jTHt2hc3pHhZxbyNCdD5fFcHSsxy7cJS7dfYHuMfOvrBaHUuX8SNneEnQPArA74noZ234GxtaYjE9RAoSzA7YrU1FRWrFiBt7c39vb2xRCW/IqrO/mw04Fsu3eC861XYm2oWrPFvWfu4LVwD4FDXBjTta5MEQpCKaBUQvxGOPKJVN66yQRwnQL65TUdmfAGRXmvFXNM4Zx9fBXXYx/wQTUvvqrjr/J4SqWSQd8e4OjFexyZ0x27SqJ0vKBFnt6HE9PhXBCY1oY230D1bpqOSngJOd9nC3QHyNTUlFGjRhEWFsaUKVPYsGGDLCcvi76pMxpdhQ7jLixTeayODasw2rM2n6+L5fzNFNWDE4TSIDUOtnaXrsKZN4DBZ6HFXJH8lGFlYo55dLXYTtWgvD1zar7DN9c2sD/plMrjKRQKfhjVAhNDPUZ8f4Ss7FwZohSEEi43W9rf80ctuBQMrb4G31Mi+dESBV4ClzdBzZ8/HwcHByZPnsyUKVPyS5WWeOnF0/Xa0sCUpXXHEJJ4gI2Jh1Qeb5ZvExwqV+DdH46QkSU2qQplWE6mVNb6z4aQcgF6bIFe28C0pqYjE4pBqZ9jHpyGmEXFdrqPa/SnjVkD3jnzJQ+zVd8ralHekF8+aE1E3H3mbFA9qRKEEu3WQQhxhYPjwMkHhlyCxh+BrmgMrC2KtAeoadOmLFiwgPnz5xMXF8fkyZNZtGjRc30aSpw7hyFsKKQnq/1UvlU60NuqBWPOf0dy1iOVxjIy0OPn91tx8fZDsUlVKLtuH4Z1TSDyC6mJ6aCzYN9b01EJGlIq5xjTWnB0IkTMlJZwqpmuQpdfG07kftZDPvlnuSxjtqhlxQyfxny99Rzhp27JMqYglChpd2H327CpHegZgfcJ6BAERlaajkwoZgXaA1QQqampBAUFkZSUhLu7O/3795djWNl4dXJhy5AroGsEHVdCjR5qPV9C+n3qHxmJj3U7Vjb4ROXxlmw/z7TgGLZM6kTHhlVkiFAQSoDMh1KlnTPfg3Vz6LACKjXSdFSCCtS1F6bEzzFeXmyZ0RKOT4UmE6FlICgUaj/vypvbGXXuG7Y0mUnvyi1VHi83V4nP1/uIvpLEkTndsTEXJX+FMkCZC+dWwrHJgEL691nvXVAU6T6AoCHFvgeoIJKTk5k4cSLz58+nadOmcg0rn/J24HsGLBvDtp6wd6T04UtNbMtZsqi2Hz8l7CDsQZTK443tWpcO9a3xX3GUB49Ur/wjCBp3dSsEN4ALv0ldtfsdFsmP8EolfY5JSEjAa+ZRIo3ehtgv4cAY6UOXmr1n242els0Zde5b7memqjyejo6C5f4tMdDTYcT3R8jOEfuBhFLuwRnY0Ab2+4NDP3jrH6g/UiQ/pYicjbbzFPkO0IYNG0hK+v++mrCwMNauXStbYHJzdXXF1taWwYMGMbhJGhz+GMpZQqdf1VZSV6lU0jlyEvFP73CmVRDl9YxUGu9WUhotpv1NqzqVCf6oLYpiuLooCLJLS4RDH8HltdJm0/Y/QoUamo5KUFFwcDDBwcEkJCQQFaX6RZ/SNsc8d2Xy/M+wbxTUeksq3a6jp9Zz38lIouERP9qbOxPaeLosc8ORf+7SY/5uxnWvy2zfkpdwCsIbZadD1ByICYSKNaHDcqjaTtNRCSqQ8w5Qkd6VBw4ciIWFBWZmZvnH4uLiZAlIXWxtbZ//pdl1hj3vSDXfG4+H5vNAr5ys51QoFKxo8DHOR/yZevlnltT9QKXxqloY8/3IFgxefICf9lxmZGfRBFIoRZRK+GcVHB4PCl3wWAO1BhfLMiFB/QYPHszgwYPx8vJSeazSOMc8p967UtXC8CGQ9QS6BIOuodpOV8XQgh/rfYjPqTmsuh3O8KqeKo/Zqk5lZvk2YVpwDM1rWtHL1U6GSAWhmNw5Kn3Ge3gFXD8Dl8lq/TcolD5FSoB8fX0ZMGDAc8diYmJkCajYVHSAPnvh5LfSmu0bYeCxWloiJyMn46rMrTWCT/5Zjq91B1qbN1BpvF6udozqXIspf0TTqo4V9e3M5AlUENTp6T3Y5w9XNkKtIdDmWzCy1HRUQglVJuaYmgNBzxh2esP2ftBtvbTpWk28q7Rj2D0Pxl1YRntzZ2oYqd68cVy3uhy7eI/RK45ywK4bjtYVZIhUENQoJ0MqRBITCJXdYeAmsKin6aiEEqhICyDNzc3/U43nypUrsgRUrBQ60OQT8I4AFBDaDGK+hFx5y02Pq96HFqb1ePfsV7J07p47uCmO1uV5Z9lhnmZmyxChIKjR1a1SaevbB6BrKHiuFsmP8FplZo6x7wU9/oJb++BvL+lukBotrfsBZnrlefvMl+TKsP8orz9QpfKGDFt6SMw3Qsl2/5T0OS52ETSbDf0OieRHeKUiJUDJycnY29tTq1YtatWqRc2aNRk1apTcsRWfSo3AJ0Iqv3s0QCqPmHJRtuF1Fbr81OATrj5N5Iu4VSqPZ2Sgxy9jWnPl7mOm/BEtQ4SCoAZZT2DfaPi7N1RuBoPOgNOANz9P0Hplao6p5gk9t0tLcrb1gEzVWiO8jqm+Cb81nMiB5NN8c02eZrKmxgas/rAtF28/ZOIq1fd2CYLscrMhcg6EukmFR7xPgOtUte+9E0q3IiVAERERREVFERkZmf9VaienPLqG0OpL6Lsfnt6FtY0h9mvZ7gbVK1+dL5yGsuhqKCdSL6g8Xn07MxYMceGnPZfZHHFdhggFQUaJEbCuKVxcBe2XS01NjUX5dqFgytwcY9seeu+C+7HwVxfISFHbqTpYNOaTGgOYeukXTj6SZ99Uo+rmfDXcjd/2x7H6YLwsYwqCLJLOwfqWEDFDKj/vEwmWTTQdlVAKFCkB8vT0xMHBAVNTU0xNTTEzM2Pq1Klyx6YZVdvCwJPQYDQcmQCb2kOqPJPIRPuBNK3oxIgzX5GRm6nyeO92rElf92p88NNxrt17LEOEgqAiZS5EL4CNrcDQDAbGQAM/UehAKJQyOcfYtAKv3dLqgs0dpX1xajKn5jvUMbFjyKkFsiy7Bhje3olh7Rz5+NcIzlxXf0NxQXgtZS6cWgIhLpD1GPofhRZzRaEDocCKlABduXKFRYsWsWHDBvbs2cOGDRtK99W5F+kbQ5tvpLtBabeku0Fng1Tu7q2no8uvDSZwKS2BWXFrVA5ToVCw9N3mmBkbMOL7w2Rli34NggY9uS1d3T42FZpMkvr6mNXWdFRCKVRm55jKbtB3Hzy5BZs6SP9Vg3K6BvzRaDKXn94i4NJK2cb9argbtWwqMHTpQVLTVL+IJwhF8uQ2bO0htVOo7wcDo8G6maajEkqZQidAqampLFiwgMuXL7Nr1y7WrVvHrl27ZOn7UOLk3Q2qPURqoLWtJ6TdUWnIhhUc+NxpCIFX1xL1UPV9RmYmBvw8pjUxV5OYvf6UyuMJQpFc3wHrGkPSWfAK//dKnL6moxJKoTI/x1RqBP0OSo24N7aDR9fUcpqGFRz4svYoll7fzPZ7J2QZ08hAj1Xj2nL/UQZ+y4+Sm6vaRUFBKLQrW2BtI2k5aa/t0HaJWqsrCmVXkRqh7t69m86dOz93LCYmpkR2586jcvOkq9tg73ugzIEOK8Cxb5GHysrNpvnxD8lSZhPZ4jsMdQyKHte/vt12julrY9kwoQOezlVVHk8QCiQnC05Ml0qOVu8uNRY2rqzpqAQNkqNRXWmcY/Kbbf/bD+mNHl6FLZ2kfaZ99oCpk+wxKZVKekRPI+ZRHKda/khlQ3NZxt0Rm8DAb/YzpW8jpvRrJMuYgvBa2U+lbQlnvgf73tDxJzCy0nRUQjGRu9E2FHEJnLu7+3+OydF5Wp0SEhLw8vIiODi4aAPY94RBp8GmNezoB3tHSetOi0BfR49fG07gnyc3ZVkKB/Bh93p0aVyVUcuPkpCUJsuYgvBaj27A5g5SydGWC6HnVpH8aLHg4GC8vLxISEhQeazSOMfkNdsuUPIDUNEe+h6QGnBvbAtJ52WPSaFQ8EvDCeQqc3nv7NcU4XrnS3VrYstn/Z2Zt/E026JvyjKmILzSgzMQ6g7nf4Z230P3zSL50TKDBw9my5Yt2NrayjZmkRKgF9diX7lypcSvzy705PQyRlbQbSN0WAmXgmGdC9yNLNJQzhUcmeE0lAVX1spSFU5HR8FyvxaU09dlxPeHyc4R+4EENbq+E9Y1gcc3pOU8TSdKfbUErSXnBFUa55giKW8nJUHlLGFze7h/UvZTVDG04OeGn7L1/nG+v/GXbONO6N2A3q52jPrxCP/cSpVtXEHIp1RK+69D3QGFVOGt4fuiqI4giyJ9YgkLC2PDBqnHwIoVK/D09MTcXJ5b6yWeQgH135OqWxmYwoaWUtWrIpTLDrD3xaViTd4+86UslXosK5TjlzGtOXH5PnM3nFZ5PEH4D2UuRM6Grd3Burn076BKS01HJZQxWjXHGFtD371QvrpUHS4xQvZT9LJqwQfVvJhwMYizj6/KMqZ00a0ltpVMGLxYFEUQZJaRCrsGSfuv674j9faxaKDpqIQypEgJUFJSEqampri7uxMdHZ2/WVWrmNWC/kekuvPHpsLWroUukKCno8tvDSdy5ekdpl/+TZawWta24nPvxiz66yw7T6q+FEUQ8qUnS93sT8wA9xnSkrdylTQdlVAGad0cU66SVCLbrC785QG3D8t+ii9rj8LJyIbBp+aTniNPslLBSJ8/PmzL3dSnjPzxiCiKIMjjXrRU3vrGDuiyDtr/IAodCLIrcAIUGxv73FelSpUYOHAgnp6eXL16lffff1+dcZZMuvrQYp5U9SrprFQu+0Z4oYaoX74Gs53e5utr6zmUfEaWsMb3qEf3praM+vEoV0V/IEEO909KXbbvHIGe26QESCx5E2Sk9XOMoSl47QJLF6mc/M09sg5vpGvIH85TuJh2U9bS2LVsKvLT+63YefIW8zaKlQeCCpRKOPOj1NjU0Bx8YqCmj6ajEsqoAleBq1mzJq6urq/cRBkTE8OlS5dkDU5OclQmeq20RAgfBjfDwXUauH8BOroFemqOMoe2Jz7lXlYKsS1+wESGKx0pTzJpN2MHFY30CZvuiZGBnspjClrq4hrYNwrM6kC3DVDRQdMRCSVYUd9rxRzzr6w02NEfbu2DriFSxSsZLbm2kY/++YHNTWbiVVm+5auL/jrLzJCTrB7Xlj7u1WQbV9ASWY9hn5+0v7rRWGi1SDQ1Ff5Dzs/yBf5UvHz58v+UJX3W7t27ZQmo1DK2ht47pP1AJ6ZD4lHwDC5QpRJdhS6/NpxAk6PvM+Xyzyyp+4HK4ZiZGLB6XFs6z9rFhFVRLHuvucpjClomNxuOTIRT30LtYdD+R6lJsCCogZhj/qVvDD02Q9gQ2N4POv8Otd+Sbfhx1fsSnhTDiLOLOFnxR+zKyVNN69Ne9Tl1LRn/oKPUrFKBBtXMZBlX0AKpcbC9Lzy8Ap5/Qi1fTUckaIECrWG5evXqaycm4LmfX716VaWgSi2FDrhOlZbEPTgN65rCnaMFemptEzvm13qXpdc3sy9JnkpAzjXM+eYdd37fH8ev+y7LMqagJdKTpU7bp5dC26XQ+TeR/AhqI+aYF+gaQpc/oc4wCB8KZ5fLNrRCoeCXBhMw1jHkrVPzyS5CAZ9XjfvDqBY4WpfH95v93H+ULsu4Qhl3fYe0vDonA7yPi+RHKDYFSoCWLy/cm29hH1/m2HYEn2ioYA+b2sGZH6S1rW8wrnof2pk3YsSZRTzKlqeXz9C2jrzbsSaf/h5JVPwDWcYUyrjkC7C+GdyLgt67pOUIouyooEZijnkJHT2p2WOjsbB/NMR+JdvQlQwq8ofzFA6nnGNOvDy96ABMDPX4c3x7nmRkM2zpITKz5UmuhDJIqYToQOlCm3UrUeVNKHYFWgLn4eHB5MmTC9SITq5Ga6VeeVvosxeOfAoHxkgfJtste+2aVh2FDr80mIDzUX8+/Wc5QQ0+liWUhUNdOXU9maFLDnJgVjesKpaTZVyhDLq+E3YNhPLVwDsCTB01HZGgBcrCHJPXbHvw4MGq9Zt7lkIH2iwG/fJwZIK0T8Ltc1kuSLQ1b8QXTkOZEbeKDhaN6WDRWIaAobqlCWs+bEuvBXuYtDqKb99pJsu4QhmSlQb7Rkr7fVynQbNZoqiO8FrBwcEEBwfL0mg7T4GLIJR2rq6u2Nrayjs5FdSFX6UreJZNoOt6KTl6jRU3/8bv3LeyblJNSEqjzfTtNKhmxqaJHdHTFW82wgvOfA8HP4RqXaFLMBhU1HREQiny7AQVFRWl6XCKndoL7UTNh+NTofGn0OpLWZKgHGUOnpGTuZB2g5Mtf8TKwEz1OP/12/44xv50nK+Gu+HnUVu2cYVS7tEN2NFXWmnQ6VdR5U0oFDnfZ7XmU7CtrS1btmwp/uQHpCZefQ/C44R/lxZFv/bhI22742XVkpFnvyYxI1mWEGwtjPl9bBsOXbjLjHXydxsXSrHcbCnxOfABNBoHPbaI5EcotMGDB7NlyxZsbV9/gUcoItcp0GYJnPxK+reqzFV5SF2FLmucJ5Odm8Pw0wvJlWHMPG+3d+KDrnWYtDqK3advyzauUIrdPizt93l6H/ofFsmPoFFakwBpnLW7tMbVuCpsbAvxm175UIVCwYr641EoFLx39mvZlny0rWfNnEFNWbL9PCFHr8oyplDKZT2Gv/tId3/a/QBtvilw+XZBEIqZ8zjosBLO/gh73wMZChjYGFZiVaMAdjyIZNHVUBmC/L+5g5vSuZENby87xIWEVFnHFkqZ87/A5o5SOwXvCGlFjCBokEiAipOJDfTdD9W7S30eYha9sjhCZUNzfm7wKdvuH2f5zW2yhfBB1zoMbm3PmJXHibmSJNu4Qin05BZsbAe3D0rNTRuO1nREgiC8Sf33wGM1/LMKwt6CnCyVh+xq6cZkB1+mXv6ZoynnZAhSoqujwy9jWlPV3Bjfb/bz4FGGbGMLpURuDhz+BPa+K62G8QoH48qajkoQRAJU7PSNoes6cJkMRyfCwbHSEqSX6GnVnPftevHJP8v558kNWU6vUChYPKIZDaqZMnjxAe6mPpVlXKGUeXAaQpvD03vQ7xBU76rpiARBKKjab0lNUq9shJ0DIFv1ktOznN6muWldfE/NJSnroQxBSioa6bPuk/akPs3irSUHyMgSleG0RtYT2NEPTi2Wlm+2Xw66BpqOShAAkQBphkIHWsyDDkFSf4cd/aU3ipdYVMeP6uWsGHJ6AVmvSJQKy8hAjz8+bEd2Ti5DRalS7ZOwHza2gXKVYMAxsHTWdESCIKuUlJTnvg8PDycoKIjQ0FAWLlxY5OMlimM/ab/ejTD4u9cr55CC0tfR40/nqTzJSeft01/Kuh/I3qo8f45vR1T8A8asPFZiK/kJMkq7A5vaw8090OMvafmmaKcglCAaS4C0YoJ6k/qjpDeGhL2wqYN0Nf4FxrrlWN1oMicfxTMzbpVsp65qYcyaD9sSFf+AT3+PFBOStohbD1u7QmV36HfgjRUJBaE0CQ0NJSAg4D9NVQMDA/Hz88Pb2zv/cUU5XuJU7wa9dkDicfirK2Sots+mWrnK/N5wElvvH+fra+tlClLSopYVQX4tWXf0GnM3nJZ1bKGESToH61tA2m3odxBq9NB0RILwHwXqA5SamkpQUFCBezQoFAomTJjw0p+HhoYSERFBeHj4c6VSAwMDCQsLA2DhwoWEhobi7e1d6OOlTo3u0PcAbO0u7cfwCv/Ph1I309rMdBrO9Mu/0c3SnTbmDWU5dfNaVnz7jjtjVh6nUXVzUaq0rDvzg1Q9qtYgqfyoWIoglBByzTHe3t64uLgQHh6efyw8PBwzM7P8711cXFi+fDlmZmaFOl5i5xfb9tK8sbUbbPGQmheXMy/ycD2tmjPJfiCTL/1EK7P6tDKTrzll/+Y1uHL3MV+EnKSGlQnD2jnJNrZQQtw6CNu9wKSatLe0QjVNRyQIL1WgBMjU1JSJEyfKckKtnKDexKqpdDV+iwdsagu9w//TgDLAYSB/3z/BsNOBxLb8EVN9E1lOPaydE2eupzBpdRR1qlakff0qsowrlCBKJUR8AZGzwHk8tP5KNJ0TShQ555gXRUdHY2Fhkf+9hYUF0dHRhT5eolk3lxpvb+4Mf3lC7zCVkqA5Nd/hcMpZfE/OJablD1gamMoW6ie96nP13mM+/OUEVc2N6dzIRraxBQ2LWw/hQ6BKK+i2EQzl+3sjCHKT9VNQTEwMDx8WfvOkVkxQb2JWW9qMrtCT9mckPV+JR1ehy6qGk0jKfsTo84tlXbI2d3BT2tWzZtjSQ8QnPpJtXKEEyM2B/e9LyU/LQGj9tUh+hFKrKHPMgwcPZDn+JgkJCXh5eeV/BQcHF2mcIrNsAn12w8MrUhKUXvQecnn7gZ7mZjL8jLz9gRQKBV8Pd6dTgyoMXXqQk1dFNdIy4fQy2OkDDv2g13aR/AiyCA4Ofu59NSEhQbaxZfkktGHDBgCaNm363J2dgiqOCUrjk1NBVKgurZctZyltHrz/fMNSB2MbguqP5887+/jtVphsp9XT1eHXD1pjUd4An6/3k/wkU7axBQ3KToddA+H8Suj4MzSdJDahCrJT5wSVR5U5plKlSrIcf5O8Ztt5Xxppum3ZBPrs+X8SlJFS5KHsylmxulEA2+9HsPDqOtlCBNDX0+G3sW2obVORAV/t49q9x7KOLxQjpRJOfC5VtG08HjzXgK6hpqMSyoi8Btt5X3I22lY5ARo9ejTz5s3D19cXX19f4uPjCz1GcUxQJWJyKghja+i7V0qGNneEu5HP/di3Sgfete3K2AvfyVYaG8CivCEhn3Tg3sN0hi45KCrDlXZZj6XKUNf+hu4bod4ITUcklFHqnKBA9TnGxcWFpKT/32VISkrCxcWl0MdLDcvGzyRBXSGz6CWtu1m6M81hMNMu/cr+pFMyBgnly+kT+mkHTAz16Ldon+gRVBrlrzCYDS0XihUGQqmi8t/UwMBAVqxYwdq1a1m7du0rix+8jtZNUG9SrhJ47ZaWxW3pDHeOPvfjJXXGYGdoyeBT88nIle9uTS2bigSPb8exS/cY9/MJURmutEpPhi2ekHhCqhBl31vTEQlCkak6x3h4eDyXNEVHR+Pr61vo46WKZWPwCoPUi1KBnayi32H5wmk47cwbMfj0PO5kyLtczapiOTZM7Ejy4wx8vt7Hkwx5Wj0IxSAnA8IGwfkV/64wUM8ePkFQF5UTIFNTU65cuZL//cqVKws9hlZOUG9iaCZtZK3UWLqK98ydIBM9I4Kdp3L28TUmX/xJ1tO2rlOZH0a24I9DV1i4+YysYwvFIC1RunOYclG6CmzbXtMRCYJKCjPHhIeHs3z5cuLj4wkKCsqfJwIDA/OrhQL5BXMKe7xUsXKBXjulpsfbeha5T5Ceji7BzlMAGHhyjmz96PI4WVdg/acdOHczlbe/O0RWtnz7jQQ1yUqDv/vA1b+g2waxwkAolRRKGS7zjx49mtGjRxMWFkZYWBi7du165WPDw8MJCwsjKCiIwMBAPDw8cHR0JDw8nOjoaBwdHYmPj2fSpEn5jy/M8Vfx8vJiy5Ytqr7U4pf5CP7qIn2g7bsPKjXK/9HiaxsZ/88PbG06m55WzWU97cLNZ5i9/hS/jGmFdwt7WccW1ORxgnTHMPOhdPXXQr7ytYJQUOp4ry3MHKMpJXaOuX1EmkOsm0t95/SNizTM4eSzdIicwLjqffi6zmiZg4Q9Z27j/dV+fFrW4MdRLQpUEl3QgIxUaXn1vRipEa9dJ01HJGgROd9nZUmAUlNT8fHxwdXVlfnz58sRl+xK7ORUEBkp0lX9J7elctlmUr8epVKJV8znHEu9QGzLH7AtZynbKZVKJf5BR9lw4jrbJnemeS0r2cYW1ODRNdjcCXKzpUpQpjU1HZGgpdTxXivmGBXd+rfXXJXW0odWvXJFGmbp9U18eOF7ghtNYZBNR5mDhJCjV3n3hyOM71mP2b5NZR9fUNHT+1Ij7Yfx0HM7VGmh6YgELSPn+6wsu9Xc3NwICgrCz8+v7C1FKwkMzf5tbmch9Xl4dA2Qyon+0nACBjp6DD0dSI5SvsIFCoWCpe82x8WhEoO+PSCq9JRkqXFSE12QEmSR/AhljJhjVFS1ndSU8s4h2NFP2r9RBGOr9WGITSfeO/s1Zx5defMTCsmnpT0L3nLh223nWbL9vOzjCypIS4TNHeDxTeizXyQ/QqknSwK0bt067O3tcXBwwM/PT44hhRcZWUndvnX1peUMaXcBsDQw5Y9GUziQfJp58fKW9jbU1+WPj9pSwUgfn6/3kyLKY5c8KZdgUzvpim7f/VChhqYjEgTZiTlGBrYdpCVwCftghzfkFP79XKFQEFR/PE7GVRlwcjapRdxX9DofdKvLp73rMy04hj8OFb6qrKAGjxOk1hwZydI8Y+ms6YgEQWWyJEBNm/7/VnXnzp3lGFJ4GZOqUmGEjFTY1j2/vGl7C2emO77FF3Gr2ZsUK+spLSuUI+ST9txJecrgxQfIyBLlsUuM1DhpaaR+RWlSKm+n6YgEQS3EHCMTu87QfRPc2AXhQ6Uls4VkrFuODU0+JzEzmRFnF6mlWugM78YMb+/EmJXH2RErf18poRAeXZMusmU/hb4HwLyupiMSBFnIkgCtWLEif1lCUarACYVg6iQth0uNk6qwZKcDMN1pCB0snBl8ar7spUrrVDXlz/HtiIx7gN/yo+TmivLYGpca/2/yYyJVezOuoumIBEFtSsMck9dsu0Q22X5W9a7QdR3Eb4A974Ky8FXXahrb8nvDSWy8e5gvr4bIHqJCoWDxO+50a2LL8O8OcfTiPdnPIRRAavwLy6udNBuPoLXyGm7L2WhblgTI0dGRyZMnA+Dg4CDHkLIrNZNTQVg6S+u57x6H3cNAmYuuQpc/Gk1BgYK3Ts2XdT8QQKs6lfnp/VZsirjBlD+iRY8gTXp4VUp+dA3Baw+Y2Gg6IkFQywSVpzTMMXnNtktsk+1nOfQBj9VwcbXUyLII7+delVsy1WEwUy79zJ4HMbKHqKerwy9jWuHiUImBX+/j7I0U2c8hvEbe8mpdQ7G8WtC4vIbbcjbaliUBio6OZvfu3cTGxhITI/8boRxK1eRUEDatweMPiFsPx6YBYG1oTrDzFPYnn2Zm3GrZT+nlVo2vhrvx/a5/+PZvsUFVIx7dkJIfHT3osxfKy/dmIAiqUMcElac0zDGlTq1B0PEnOBcERycVKQmaVXM4nSwa43tqHteeJsoeopGBHms/bkd1y/L0/XIvV0UxnuKRfEHa86NfQSyvFsosWRKgiRMnolQqWbt2rdigWpwc+0KrRRCzAM5Jy0I6WDRmds23mRP/BzvuR8h+ypGdaxHQpyGfr43lt/1xso8vvMbjBCn5QSktexOTkqAlxByjJvVGQJslELsIouYW+um6Cl3+dJ5GBT0j+sZ+QVpOuuwhmhobsHFiB0wMdekTuIfElKeyn0N4RtJ52NRBqjrbd59YYSCUWQVOgBYtWvTan0+cOJH58+dTsWJFlYMSCqHxx9DgfTjwPtwIB2Cygy/dLN0YejqQ60/vyn7Kaf0bMbJTLT78+QRbIm/IPr7wEk9uw5ZOkJsp3fkRyxEEGaXnZLL93gkWXlmnsRjEHKMhzuOg2Rw4MR1OLi700ysZVGRzk5lcSkvg3bNfqWV5dGVTIzZN6kRaZg59v9xLsqhIqh7J/0jzjJGVNM8YW2s6IqEMuJ+Zyk83t9Mz+jPuZiRrOpx8BU6A1q5dy8mTJ9UZi1AUCgW0XQJ2HrDTG5IvoKPQYVXDAEx0yzHw1Bwyc7NkPqWCRcNd6desGiO+P8z+c3dkHV94wdP7sMUDsh5Ld34qlsw9EELpcjcjmd8SdjEgdhaW+7zpEfMZKxL+Jr0I5ZHlIOYYDXKdCk0mwuHxcP6XQj+9UQUHfms4kbV39hN4da388QH2VuXZMqkjt5Kf4v3VPh6nyzuvab3Uy1LyY2gBXrulJEgQiuhK2m2WXNtIp4iJWO/zxe/cYp7kpHMvK1XToeUrcAKkVCr54Ycf6Nq1Kxs2bFBnTEJh6eiB559gYgt/94b0JCoZVCSk8WdEP7zMxIsrZD+lro4OQf4taVfPmkHfHiA6/oHs5xCAjBSp71P6PWlSEk1OBRWce3yNOfFraHH8Q6rsH8SIs1+RkHGfaQ6DOdMqiIutf6GcroFGYhNzjAYpFNAyEBqMhn0j4VLhk5gB1m35zPEtpl76hb/vnVBDkFDPzoyNEzpw/mYKby0+SHqmaMsgi9R42NRR2vPTZzcYV9Z0REIpo1QqiXl4mamXfqbB4VE4HnqbiRdXYqCjzw/1x3G7fTD73BfRoLy9pkPNp1fQB65YsYKmTZuSmppKUFAQ7u7u+Pr64ufnJ5YklASGplKTu/XNpDtBvXbSzLQu39QZzdgL39HKrD6+VTrIekoDPV1Wf9iW3gt20/+rfeyc5kGdqqaynkOrZT2GrT3g0VXos0/0XxAKTalUcupxPBsSDxOSeIDzT65TQdeYrpauvG/Xi+6W7lQ2NNd0mICYYzROoYB2yyDrCeweCvrGYN+7UEPMdBrOyUfxDD49jxPNl1LHpJrsYbo4VmLdJ+3p9+U+3vn+MKvGtkFfT5btzNrp0TXpzo+ekWipIBSKUqkk+tElNiQeZl3ifi6n3cJCvwJeVi2ZXfNtPCu5UEHPWNNhvpJCqcKC3fXr17NgwQLc3d0JCAigRo2Suy/By8uLLVu2aDoM9bt1QFouVXcEtP8RJTD09AI23T3CseZLaFRB/uVTSY8z6D4vnNS0LMI+86SapYns59A62emwrSfcjZDu/Fi7azoioZTIUeZwKPksG+8eZtPdI1xLT6SinjF9rFrhY90Wz0quar3LI+d7rZhjNCA3G3b5wrVtUrsFu8I1nn2Y/YQWxz8iV5nL8eZLMdVXz3yw82QCg749wIDmNQjya4mOjkIt5ynTHidIpa5RSk1ORWEd4Q3y5pf1dw+y6e4RbqTfw1yvAv2tWzPQuh0dLZqgr1PgeyuFJuf7bJGjfPjwIZGRkcTFxREVFcXatWt58EAsg9K4qu2g/Y+w9z2wbIKi4fusaPAxZx5fo1/sTCJaLMVcv4Ksp7Qob8imiR3pMieMPl/uZec0D6wqlpP1HFolNxvCBsOdI9B7p0h+hDfKzs3hYMppQu4cYMPdwyRmJlPVsBJ9rFrSt3IrOlg0xkBHX9NhFoqYYzRERw88/4DtfaWvPnuhsluBn15Rz4TNTWbS7Pg4hpxewOamX6Cr0JU9zK6Nbfn5/da8s+wwJoZ6fPuOOwqFSIIKLO3Ov4V1skWpa+G1snKz2Zd8kvWJh9h49zB3M1OwNbSkb+VW9K/cmrbmjdSa9KhLge8b563Jjo2NxdfXF3NzcwIDAwGYNGkS8fHx6olQKLx670KjsXDoI7h9BGPdcmxsMoOkrEcMOb1A9iapADbmxmye1ImHaZn0WbhHVOkpKqUS9o+Gq39B1xApoRWEl8jIzWTbveO8d/Yrquz3pVPkJLbdP8EQm04cbbaYG+3W8H39D+li6VYqkp/SPseUqWbbuobQNRQsGsC2HlJTzEKoZWLLn85T2X4/gqmXCl9UoaD6NavOspHN+XnvZab9GSMadBdU+oNnCuvshor2mo5IKGEyc7P4+94J3j0jzS9doqaw60EUw208ONZsMdfbrea7emPpVKlpsSQ/6mi0XeAlcG5ubigUCqKjo1EqlTg6OhIQEMCoUaNkC0adXF1dsbW1ZfDgwWWnGerr5GTBls6Qegl8osCkKjvvR9I9ehpTHQYxp9YItZz2/M0Uus3bjaN1ebZM6kQFo5L/watEOToZYgKh8+9QZ5imoxFKmMzcLMIeRLPuzn423TvCw+w0ahvb0d+6Nf0qt8a9Yh2NXQUPDg4mODiYhIQEoqKiCv380j7HlJklcM9KfwAb2kBuBvQ7XOieMN9e28DH//zIyvof855ddzUFCcvD/mHCqigm9G7A597O4k7Q62Q+hM2dpb2lfQ+ART1NRySUEFm52exJimXtnf1svHuYlOzH1DK2xce6Hd7WbWlSwUnj/7bkfJ8tcAKkoyPdLPLw8CAgIIDOnQu3LljTyuTk9CZpiRDiKvWM6bMXdA1YcOVPplz6mfWNP6e/dRu1nDbmShK9FuzGuYY5GyZ0wMig9N0a1YjYr+DIBGj9DTQer+lohBLiaU4Gux5EEZp4kL/uHSM1+wn1TKoz8N9JqUF5e41PSs8q6nutmGNKqEfXYUMrKGcpLZUyLHihG6VSyfvnl/BTwg52uc6no0UTtYW5ZPt5pgXHMKVvQ6b2d1bbeUq1rDTY1h3un5Q+E1g11XREgoZl5+awNzmWdXcOsPHuYR5kPaSmcVV8rdszsEp7GpV3KBPzy8sU+JOph4cHy5cvx8FB9CApNYytodt62NgOjnwKbZcSYO9L9MPLDD+zkDomdmopSdjUwYKQT9rT78u9DP72AH+Ob085A/nXgJcp/6yWkp+mk0XyI5Cdm8PupBjW3N7DxruHeZzzlIbl7RlfvV+JTHrkIOaYEqpCdWkv4sY2sKMf9NouLZErAIVCwdK6HxD/9DYDYmdzrPliapuoZ6/Jh93rkZ2jZMa6WHR1FAT0baSW85RaOZlShdi7UeAVJpIfLZajzGF/0inWJR5gfeIh7mel4mhkwyi77vhYt6NphZplbn55mQInQD4+PmJiKo2sm0Obb+HAGLBpi6LmQH5u8CmtToynb+wXnGguf1EEgFZ1KrPuk/Z4f7WfIUsO8MdH7TDUF0nQS13fCXtHSJX7WszTdDSChuQoczicfJZ1iQcISTzA3cwU6hjbMdHeh4FV2lHXpLqmQ1QrMceUYBYNoPsW+MsTwodDl2BQFGwLsb6OHuucP6PVifH0jPmMo80WY2mgnnYJn/SqT05uLrNCT6Gjo8NErwZqOU+pk5sDu4fDzd1SZb8qLTUdkVDMcpW5HE05T/Cdvfnzi305a9617YqPdTtcK9bSiqTnWQVOgErLOmzhJRqMlspj7x0Jlk0pb1aLTU2+wO3YWN46NZ+tLrPVUqWnff0qrPu4PQO/2c+QJQdZ82FbkQS96G4k7BgA1bpChyCpF4egNZRKJcdTL/DHnT2EJh7kdkYSdoaWDLXpzFs2HXGpoD2TkphjSriqbcEzWLqLcMga2iwu8PuVmX55tjadTYvjH9Ivdibhbgsw1FFPKfaJXg3JyVUyK/QkCgVM6K3lSZBSCQfHQlyIVNiimoemIxKKiVKpJOrhJdbe2ce6xANcT7+LraElQ2w6MahKB43uGS0JxOYMbaBQSB+uQ91gpw8MOIqjsQ1rG0+lW9Q0plz6mYW11fPho2PDKvw5vh2+3+5n2HeHWD2uDQZ6IgkC4OEVqddPpUbQZZ1UflbQCuceX2PN7T0E39nLlad3sDG0YKB1e3yrtKe5aV10Cnh1XRCKlWM/aLsMDrwvlU1uOqngTzW2YXPTmXSMnMi7Z75idaPJavvwNblvI5RKmBlyEtDyJOjEdDj7I3T8SfrzE8q8C0+us+rWbv68s4/4p7ex1DfF27otg6t0oI15QzG//KtAn7jyOnMX5M1KqVSiUCiYMGGCysEJMjKoAF1CYH1zqTx2hyA8K7nydR1/xv/zAw3L2zO8qqdaTt25kQ3BH7Vj8OIDDFt6iFUiCYL0JNjaHQwqQo+/pK7rQpmWkH6fP27vYc2dPZx8FI+ZXvn8Sam9hbNa7sKWFmKOKUUajoYnN+FoAJSvBrUKXlW1pVl9fm84Cd9Tc6lpbMvMmsPVFuaUfo1QKpXMDDmJUol2Loc7tQSi5kLLL6X2GEKZdScjiXV39rPq9m4iH17EVM8Eb+u2/FjlQzqaN0FPR3vnl1cpUAJkamrKxIkT1R2LoG6WztB2KewbBXYeUHMgH1bvy6nH8Yw6+y21je1oYaaekpiezlX540MpCRr+3WF+H9tae5OgnAxpM3H6feh/FIwsNR2RoCaPstPYkHiIVbd3sycpFgMdPXpbteALp2F0t3RX2zKg0kbMMaVMs9lSdbjd74BxVbBtX+CnDqzSnri0W0y9/AsORlV4x7aL2sKc2t8ZHR0Fs0JPkpaRrV0lsuNC4dB4aDIRmoqLBWVRStZj1iceJPjOPvYmnURXoUMPS3cmN55OT8vmlNMV88vriDU32qbee3AjDPb5QeVmKCra8329cfzz5CZ9Y78gosVSqpWrrJZTd2lclT8+astbiw/y9rLD/PaBFiZBSiXseRcSj4PXHjCrpemIBJll5+YQnhTNqlvhbLp7hLTcDDqYN2ZF/fF4W7fDVN9E0yEKgmoUCui4EtJuwY6+Uo8gi/oFfvpkh0FceXqHUee+oaqhBV0s3dQW6uS+jTAy0OOzP2NIy8hmwRCXsp8E3ToI4UOlu3MtF2g6GkFG2bk57HwQye+3wtl87wiZudl0tGjM8vof0d+6NRb6FTUdYqlR4D5ApV2Z7dFQFBkpsK4JmNhKfR109EjMSKbZ8XFY6FfgoPvXlNczUtvpd8QmMGTJQTrUt2bVuLYYG2pRHn7ic4icLe35qemj6WgEmSiVSmIeXWb17d0E397Hncwk6plUZ5hNZ96y6UQNI2tNh1hstPW9VuuabQNkpMLGtpD1EAacAOOCXzzLzs2hT+wMDiSf5qD71zSp6KTGQGFF+EU++T2St9s7sXiEO7o6ZXQfRNI52NgaLJsWqmS5UHLlzS+rboXzx5293M1MoWF5e96u6skQm07YGFbSdIhqp2qj7ZfRmgRIKyen17l9BDa1A9ep0GwWAKcexdP6xMd0smjChiafq3VPwt4zdxj07X6aOlRi3SftqWikr7ZzlRgXfoc9b0OLBeASoOloBBncSn/Aqtvh/H4rnHNPrlHZwIzBVToyrGpnrargBuqZoEoTbU38eHQdQpuBqRN47Qa9cgV+6uPsp3SInMCtjAcca7aE6kbqWX2QZ83BeMasPE6/ZtUI8m9Z9lYgPLkF61uCgSn0O1ioprVCyXP16R2Cb+9lze09nP13fnmrSkeGV/WkSQUnrZpf8sj5Pqs1CZDWTk6vEzlHuiPRd79U4hT4+94Jesd8zsc1+rOojp9aT3/s0j28v9pHzSoVWP9pRypVKMNXqhL2Sz006gyHDitEuetSLCM3ky13j/HLrZ3svB+FgY4efSu3YriNB56VXLV+s6m2vtdq6+sGpCW9m9qDozd4rCrU+9udjCRaHv+IcroGHHL/hkoG6l3CszniBiO+P0zHhlVYNbZN2VmBkPlQanqe/gAGHJWq9AmlTmrWE9Ym7uP3W+EcTjmLsY4hfSq3YphNZzG/IO/7bBm9BywUiMsUsGkNu4dJSxmAHlbN+KaOP19dCyXo5ja1nr5FLSu2Te7M1XtP6DE/nMSUp2o9n8akXJSKHlRtB+1+EMlPKST1U7jI2PPfYbNvMANPzSEp6xHf1xvL7fZ/Euw8le5WzbR+chK0lHVz6PQrXFoDUYVr5lzF0IKdrvO5n/mQ3jGfk5aTrp4Y/9XHvRohn7Tn0PlE+i/aR2paplrPVyxyMqV+co+uSsveRPJTquQocwh/EM2QU/Opst+X988tpYKuEasbBZDYYR1/OE8R84saiARIm+noQudVkJEsNUr717jqfRlTrTdjzi9lx/0ItYbQ2N6CHVM9SHqcSbd54dx88ESt5yt26cnwd28wspaa0OlqwVK/MuReZgrfXFtP46OjcTs2lg13DzHKrjvnWq3kWPMl+FfrhZl+eU2HKQiaV2sQuM2AE5/Blc2FemptEzu2uczm5KM4Bp2aR3ZujpqClHRuZMPmgE6cvZFMz/m7uZtaii++KZVSZddbB6D7JqjUUNMRCQV04cl1plz6CfsDw/CMmkzUw0t84TSM6+1Ws911HkNsOqt1P7a2EwmQtqtoLzW2u7gaLv0JgEKhYHGdMXSv5I7PyTnEPLys1hDq2pqyc5oHmdm5dJ0bTnziI7Wer9jkZMGugfD0PvT8CwzNNB2RUAC5ylzCH0Tjc3I2tvvfIuDiT9QxsWNb0zlcb7uGwNojqVe+uqbDFISSx/1zcOwvVSBLOleopzYzrcv6Jp+z/X4E/ue/Rd2r81vUsmLHNE8SU9PxnB3GlbuP1Xo+tYmcBf/8Dp1/A9sOmo5GeIO0nHR+TdhFmxMfU+/wSJbf+JveVi041mwx51v/RICDL7blRGuM4iASIAFqD4GavlJ370c3ANDT0eVP56nUMbGjZ8xnXH96V60hOFpXYOc0Dwz0dOg6N5xzN1PUer5icfhjuLUPuoWCaU1NRyO8wd2MZBZeWUedw+/iGTWZc4+v82XtUdxqH0xI4+n0EEsQBOH1FDrSUrgKNWB7X6niaCF0s3Tnlwaf8nPCTiZeXKH2JKhBNTPCp3uiUCjwnL2L09eT1Xo+2V1cAxFfQPO50h04oURSKpUcTD6N39lvsNk/iBFnF2GkY8Ba52nc7hDM9/U/pLlZPa0saqBJIgESpD0p7X4AvfKwdwQocwEw0TNia9PZGCr06RE9jZQs9V4hs6tkws5pHlSuWI5uc8M5cfm+Ws+nVmd+gDPLoO13YNtR09EIr5CrzGXX/Uh8Ts7G7sAQPo/7jRam9Tjo/jVnWgXxUY1+WBqISkqCUGAGFaSlWOn3IOwtKORytqFVPVhSdwxfXQtl/pU/1RPjM2pYlSdsuidVzY3pNjecg+cT1X5OWdw+JPWUq/O2tJ9XKHFupN/li8u/43TobdpFfMrOB1GMq9aH+Da/EeYWyMAq7UUzbA0SCZAgKWcOnX6Gm7vhzPf5h6sYWrDddS63MpLoG/sFGbnq3TBa2dSIv6d2pp6dKb0X7GbPmdtqPZ9aJOyFg+Og0Tho4K/paISXuP70LjPjVuF48G26Rk/l/OPrLPr3bs+qRgG0MW8orsYJQlGZ1gTPP+HGToiYUeinj6vel5lOw5l2+Re+v67+ynpWFcuxbUpn3Jwq0ffLvWw8cV3t51RJapx0h61KS+gQJArrlCA5yhz+vneCPjEzsD8wnK+uraeTRRP2uS3iStvfmVNrBA7GNpoOUwDKSP1HQRbVPKHhGDg6Cap1AbPaANQ1qc5fTWfROWoSb5/5kj8aTUFHob7c2dTYgI0TOjL8u0N4f7WfH0e1YGAre7WdT1apcbDDW7rr0/prTUcjPCM7N4dt94+z/OY2dtyPxFjXkEFVOvCebTdamIrlB4Igq+pdpaVZx6aAlRs49i3U06c7DiE56xFjLyzDQr8Cg2zUeye9gpE+IZ+05/0Vx3h72SESU1wZ3aWOWs9ZJBmpsK0XGFpAtw2gK+4glAQ30u/yc8JOfkrYwY30ezSp4MT39cbylk0nKugZazo84SVEAiQ8r+VCuLELdr8tNVLTkf6KtDZvwB+NpuB9cjZ2hlZq7xFkbKhH8EftGPfLCd778Qg3Hjzhk171S/aH1MyHUsW3cpWgy7r8352gWXcyklh+cxsrbm4nIeM+7hXrEFR/PL5V2ouJSZBNQkICXl5eotn2s5oGwN0I2D0czCPAvOAJhUKh4Ks6/jzIesTwM19ioV+BLpZuagwWDPR0WeHfCmszIyaujuLa/SfMHdQUHZ0SMu/kZkPYIHh6B/ofg3IWmo5IqymVSvYmxbLk+ib+unccI10DBlfpyCi77rhXrFOyP6+UMs822paL+IQmPE/fBDr9BpvaQsyX4Pr/tcX9rduwuO77fHjhe2zLVeLjGgPUG4qeDj+MbE71SsZ8EXKSmw/S+HKYK3q6JXDlZm4OhA2ROnEPOCYtKRQ0RqlUcjz1AkuvbyIk8SAGOnoMtemMv11PmlYUBSkE+dna2mpvI9RXUSikogihzaReaAOOS3uECkhHocNPDT4hKesR/WJnssdtIc3N6qkvXkBHR8G8wS5Uq2RCwJoort9/wgr/liWjYeqRiXAjTOr1U4hkUpDX05wM1tzew5Lrmzj9+AoNTGqIuz1qlndhycvLS7YxS+AnSfXIuzoXHBys6VBKPptW0GSCVF0m6exzPxpXvS8B9r588s9yVt8KV3soCoWCqf2d+e695vyy7zJvLTlIWka22s9baBFfwPW/ocufYF5X09ForbScdH66uR23Yx/Q8sRHHE/9h4W1R3Kz3R/8WP8jkfyoUXBwMF5eXrJeoRPKAIMK0H0jPL4Je0ZIfWsKQV9Hj3WNp+FSsRY9Yj7j3ONragr0ee93qUPwR+0IP3WLnvN3c++hehu0vtG5lXDqW2jzrbRcXSh2dzKS+Pzyb1Q/MBS/c99ib2RNuGsgp1sF4V+tl0h+ShmFUt11JksILy8vcXWuMLLTYV1TafLqf+S55VxKpZKR577m91vhbG4ykx5WzYolpF0nbzH8u0PUszNl3cftsapYrljO+0ZxG2DnAGixAFwCNB2NVvrnyQ1+uLGVX2/t4mF2Gj0tm/F+td50s3RT63414b+09b1WW193gcVvhB39pWXWTScW+ukpWY9pHzGB+1mpHHL/utg2kkfHP8Dnm/0Y6esS+mkH6tpqoCrk7UOwuRPUfRfa/yCKHhSzM4+u8PW1Day5vQd9HV1GVO3Kh9X7UsvEVtOhaR0532fFJwPh5fTKQadf4F4UxH713I8UCgXL642nh2UzvE/O5kjK2VcMIq8ujauyfaoH1+8/wXP2LuJKQsPUpHOw521w8oGmkzQdjVbJUeawMfEQnpEB1D38Hmtu78HfridxbX7lL5fZ9LBqJpIfQSgpHPtB08lwbDLc3FPop5vpl2en6zyMdQ3xiJrMrfQHagjyv1wcK7F3RleMDfXwnL2LA+eKuUz2o+uwY4BU8a3tEpH8FBOlUsmeBzF0jZpCo6P+7HoQxayaw7nRbg1L630gkp8yQHw6EF6tSgto/KlUxjTp/HM/ymuU6m5am57R0zn1KL5YQmrqYMHuz7ugUCjoNHMXR/5Rb4PW18pIkUqRVnCQSoiLialYJGU9ZOGVdTgdfIf+J2fxOCedVQ0ncaPdGgJrjxQlRgWhpGo+B2w7SRv5/226XRhVDC0Idw0kMzeLLlGTeZD5UA1B/ld1SxPCpnvi4liJPl/uYdWBuGI5L1lp0hyjZwRdQ0XFt2KgVCrZcT+CNhEf0zkqgLuZKaxuFMCVtr8T4OCLuX7B97AJJZtIgITXazZT6uq9d8R/GtoZ6RqypcksHIyq0CVqCpeeFM/af3ur8oRP70I9W1N6B+7hz8NXiuW8z1HmSpWN0u9J69v1yxd/DFrm9KMr+J39Brv9Q5h++Tc6WDgT2eI7jjZfzNCqHpQTHw6EN4iOjiY+Pp6UlBTCw8NJSUkBIDw8nKCgIEJDQ1m4cGH+4191XCgiHV3wDAZdI9jpDTkZhR6ihpE1YW4LuJuZQrfoqaRmPVFDoP9lamxA6CcdGNbOiTErjzMtOIac3Fz1nVCphL3vQco/0H0zGFmp71wCucpcNiYeotnxcXSPnkaOMpetTWcT3eJ7hth0Rl9UdS1zRAIkvJ6eEXT8BRJPwKnF//mxqb4JO13nYa5fHs+oydxMv1csYVWqYMiWgI74tLRn1PKjzFl/itzcYtzOFjUfrv4FHmvA1Kn4zqtlcpW5bL13jM6Rk3A+6s/W+8eZ6jiIG+1W82vDibhWrK3pEIVSZP78+Tg5OeHg4EB0dDRmZmYABAYG4ufnh7e3NwChoaGvPS6owMgSuq2H+7FwaHyRhqhrUp1drgu4nHaLnjGf8ST7qawhvoq+ng6L33FnwVsufLfjAoO+PcDDp1nqOVnMQrj8p1RFz7Kxes4hkJWbzapb4TQ84kf/k7Mw0S3HLtf5HG22mJ5WzUUp6zKsRKW0eROShYUFkZGRuLm5YWZmRnh4OPHx8VhYWBAfH8+kSdJei1cdF2Rm0wqcP4Lj08C+N5jVeu7HVgZmhLkuoM2JT/CMmsx+t0VUNlR/GWgDPV1+GNmcWlUq8EXISS7efsiPo1qov1zp9V1wYjq4zYAaPdR7Li2VnpPJ6tu7+epaKBee3KBZxTr80WgKA6zbYKCjr+nwhFLK09OTkJCQ546Fh4fnJ0IALi4uLF++HDMzs5cez0uGBBVUdoN238E+P7BuCXWHF3qIJhWd2O4yF8+oyfSN/YK/ms4ulrvACoWCD7rVpZZNRUZ8fxiPWbsIHt8OJ2sZl0Zd+1tqIOs6DWr6yDeukC89J5Nfbu1k4ZV1XE1PpJdlc1Y2+JhWZg00HZpQTErUHSBxda4Eaz4HTKrCvpHS8q8X2JWzIsx1ASlZT/AoxrXZCoWCT3s3YM2HbdkZm0DXuWEkJKWp74SPrkH4W1C9G7h/rr7zaKn7manMjltNjYNSmdE6xnYcdP+aY82XMNimo0h+BJXFx8cTHR2d/310dDQWFv9vKGlhYUF0dPQrjwsyqTdSqmq231+6G1QELczqsbXpbA6lnMXn5Gwyc9V0N+YlujSuyu7Pu5CZnUuHGTsIO3VLnoGT/4Gwt8C+FzSbJc+YQr7krEcsuPInDgeHM/b8MlqY1SO25Q/85TJbJD9qduP+E/UuGy2kEpUAeXp6olQqSU5Ofu4uz4tX4dauXfvK44Ka6JtAx5/g1gE488NLH1LLxJbdboHcyUjCM2oyyVnFV6XNy60aYdO7cP9hBh2+2ElE3H35T5KTATu8Qb8CeKwGUWFMNhef3OT9c0uofmAo8678Sf/KbTjfeiWbms6kjXlDsQxBzTKycjh04S6/7rus6VDULiUlBUdHR3x8fEhJSeHBg5dXE3vV8VfJ6zWX9yV6zr2BQiHdBTKvJ1U5S08u0jDtLZzZ2GQGux5EM+jUPLJyi69PXF1bU/Z90ZVmtawY8NU+Fv11FpU6i2SkwvY+0sVGMcfI6urTO4y/8APVDgxhxuVV9LRqxoXWPxHsPJXGFcQydnV4+DSLrVE3+fT3CJpO+ov6n2zm5NXC/TvP6y+X9yVnn7kStQQOyN+g6uLiAoircyWKbQdo8D4cDYAaPaGi/X8eUr98DcLdAukYOZFuUVMJc1tART2TYgnPuYY5+2d2ZfDig3SbG86SEc0Y0tZRvhMc/AiSTkt9kcpZvPnxwmsplUoOJJ/mq2uh/HXvGNYG5kx1HMRou15YGmig14YWycrOJTL+AfvP3eHA+UQiLj8gPSuHShUMeauNAwZ6upoOUS38/Pzy/9/T05OAgACcnJzyiyE8q1KlSi89/iq2traiD1Bh6RlJ+4FCXKVCO902FqmaZjdLd0Ibf8aAk7MZejqQNY0mo6dTPH+HzUwMCPm4PfM2nmZmyEmi4x/ww6gWmBoXcjleXmGdtDvgfQIMKqonYC0T9fAiX14NIeTOQcz0Tfi4Rn8+qOZFFUMxh8stN1fJqevJhJ26ze7Ttzl++R7ZOUrsrUzo2NCGGd6NqWVTuL/XgwcPZvDgwfnfe3l5yRZviUuAnr06t2LFCtmvzuV58ZcqFFDLQLi2VVq20GvHSycr5wqOhLkuoHNkAN2iprHTdV6xdUiubGrE31M688nvkYxecYzT15OZM6gperoqXkm78BucWw4dVoKVizzBaqlcZS5/3TvGvPhgTjz8h4bl7fm5wae8ZdMRQx1RyU0dlEolF28/ZM+ZO+w+fZvD/9zlcXo2Zsb6tK5rzRc+jWlTtzINq5uhq1O4fyvBwcHP3e2Q8wqdnMLDwwkMDCQsLCz/WFJSEj4+PkRERDx3zMXFBRcXl5ceF2RW0QE6/wZ/e0mFdhqPL9IwvSu3ZK3zVHxOzUHvjA6/N5qErqJ4kiAdHQWfDXCmib0Fo1ccpf2MHawe15aG1QuxFzZqHlzdAj3+AjNR3EUVSqWSXQ+iCLyylr3JJ3E0smFJ3TG8U9UTEz0jTYdXptx7mE7YqVvsPn2bPWfucP9RBuXL6dG+fhW+HOpGp4ZVcJRzf5yMSlQCJK7OlQIGFaDdD/B3L7i4GuoMe+nDXCrWYpfrfDyiAugePY3tLnOLLQky1Nflu3eb0biGOZNWR3HmRgq/jGmNVcVyRRvw/knYP1par17/PXmD1SKZuVmsvbOfwCtrOfvkGu3NndnhMo8ulVzFEjc1ePAog/3n7rD7zB32nL7NzaQ0DPR0aFnbigm9G9ChQRWa2JsXOuF5kTqv0MnJ0dERf3///O+joqLw9fXFw8ODgICA/OPR0dGvPS6ogX1vqefc0YlSw0/r5kUapp91G4IbTWXw6XkA/NZwUrHdCQLo5WrHfrtuDF1ykE6zdrF4RDMGt3Z48xOv74ATn0uFdex7qT/QMipHmcOGxMMsuPIn0Y8u41axNiHOn9HPunWxJcNlXd5dnl0nb7E9NoGoeOlmRJMaFrzToSadG1WhWU3LUrGKoMQkQOLqXCli3xNqDZZKmFbrCsaVX/owd9M67HKZT5foKfSI/oztLnMpX0xXXxQKBX4etalna8rw7w7lX5FzcaxUuIEyUqV+FeZ1pfXqQqGlZj0h6OY2Fl/fRELGfXpYNmN5/fG0NhcbTuWUkZXD8Uv32XP2NvvO3iH6ShJKpbRPwcutGp0b2dCmbmX1V0ksoRwdHYmOjiYoKAgAJyen/AI6gYGBLFy4EEdHacnsm44LatBiPtw5DLt8wSe6yMuMfaq0A2Dw6XlkKXNY02hysfZwcbKuwO7Pu/DxbxH4LT/KsYv3CBziSjmDV3wgTI2Xih5U7y4K6xRRek4mv98OY9HVUC6lJdDZoinhroF0smgiLq7JIPlJJrtP32LXydvsPnObu6npVCinR+dGNozsVIsujasW/QKzBimUKu3Yk09eZZ68Ccbf3x9PT0+8vb1xdXUlKioKIH8yet3xl/Hy8hJ3gOSUdheC60nV0DzXvPahx1LO0yVqCk0qOLLNZU6x3QnKc/PBE4YuPciZGyl887Y7w9oVcMOjUilNxjd2ShOy6PdTKHczkvnm+gaWXf+LjNwshlbtxCc1BtCgvL2mQysT8pa17T4trbc+dOEuaZk5WFYwpGODKnRoUIXOjWywtSjef2/a+l6rra9bVo+uwbqmULVdkfcD5dl09zADT86lp1Uz/nSeWuzLa5VKJb/tj2PCqkjq2Zqyalxb7K1eaJid/RQ2tILMh+AdCeXU3z6iLHmUncYPN7byzbUNJGYm079yawIcfHE3raPp0Eq1vLlle0wCO2JvcezSPXJylTSsZoanc1U8nW1oXkszd3nkfJ8tMQkQSGWsk5KSAGkv0LOV4KKjo3F0dPxPH6CXHX8ZMTmpwT+rpE2bPbe9sR/O0ZRzdIueSsPy9mx3mVtshRHypGfmMGFVJL/tj2NIW0e+Gu6GyZuuhJ9eBgfHQtdQcBpQPIGWATfT7/Hl1RBW3NyOrkKHMdV6M75GP2wMC3n3TfiP9MwcDl5IZEdsAjtjb3Ht/hMM9HRoVduKzo1s6NTQhobVzNDR0dxVT219r9XW1y27K1ukSmhtl0KjsSoNte3ecQacnEVni6asb/x5sfQJetHJq0kM++4QyY8z+GFUS3q52kk/UCph73twKRgGHBPNTgshOesRS69v5ttrG3mc85S3q3oy0d6H2iZ2mg6t1MrMzuHoxXv/Jj0JxCU+xshAl/b1renexJauTWyL/WLay5TZBEidxOSkBkolbO0Gyedh0Flpf9BrnEi9QNeoqdQ2tmWn63zM9Mu/9vHqsOZgPJ/8FkE1SxN++6ANDaqZvfyBd6OkK3MN/KHtkmKNsbS6nJZA4JV1/HYrjPK6RnxUoy/jqvfBQl9UM1JFXOIjwk/dYtep2xw8n8jTzBxqWJrQtUlVujSuSpu61m9O5ouRtr7XauvrVouDH8LZ5eB9HCybqDRU2IMo+sR8QSuz+mxu8oVGNsGnPMlkzMpj/BV1kw+61mGWbxMMLv4C+0ZBp9+K1AhWG93LTOHbaxv57sZmMnOz8bPrwUR7H+zKWWk6tFLpbupTtsfeYmdsAnvP3uFxejY25kZ0a2JL9ya2tK9vXeKWTIsEqAjE5KQmD6/Cnw2h7jsF2iMT/fASHpGTcTSuwk6X+VTSQKnPCwmpvPP9YeLuPCJwqCsjOjg9v044IxVCXMDQAvofAl3DYo+xNDn1KJ4FV9ay9s5+KhuY8an9APztehb7UseyIi0jm4MXEgk/dZuwU7eIS3yMvq4OrepY4elcla6Nq1KnasUSu7ZdW99rtfV1q0V2OmxoKS0R84mS+tCpYH/SKXrFTKdpBSe2uswu9hUIIC0r+mHXP3z2Zyw+Ne/zY6UJKOq+Ax1+LPZYSpvbGQ9YdDWUH29sRaFQMKZabz6t4Y21oVgyWBhKpZIzN1LYEXuL7TE3iYx/gAIF7jUr0bWxLV0bV6VRdbMSO7eAvO+zJSu1E0qfivbQfC4cHg+1BoFNm9c+3KViLfa4LaRL1GTaRXzKLtf52JazLJZQ89S1NWXvjC5MXhPNR7+cYP/ZOyx5t5nUtyFvWUL6A+gdJpKf1ziWcp55V4L5694xapSzZmndMbxr200jy0xKu7jER+yMTSDslLSXJz0rh+qWJng62zBnUFXa17emgpG+psMUhOKhVw48/5T6Ax0cC51+UWm49hbO7HKdT7foqXhGTma769xivzOtUCgY07UuLavrUHlXG04+tuWi7ngGFmsUpcuN9LssvBLCioS/KadjwKf23nxUvZ9GLpyWVk8zs9l79g7bYxLYdfIWt5KfYmKoh4ezDT92lgoYWFYofQUM5CDuAAmqy82BjW0gIxkGxkqT1xv88+QGnlGT0VPoEu4aiKOxjfrjfImNJ64z9qfjWJQ34JcxrXF7EgyHPoRuG8Cxn0ZiKumOppzji7hV7HoQRT2T6kxxGMSgKh2KtdJSaZebqyTmahLbom+yNeom5xNSMdDToU3dyng6V8WjkU2JvsvzOtr6Xuvq6oqtra3oMSenC7/CnhHgsQZqv6XycFEPL9I1aipVDMzZ5bqAquWKeV9ibg5s60Hu3SimZAbx/dF0fFrU4Ou33TEzEReO8lx/epf5V/7kp4QdVNAz4uMa/RlXrS+mKt4J1Bb3Hqaz6+QttkXfZPfp26Rl5uBkXYFuTarStbEtrepYYahf8stUPyuv31xCQkJ+8TNViQRIkEfSOal6T5MJ0GJugZ5y/eldPKMm8ygnjV0u82lYoQD9EtTg6r3HjFh2GO5FEt7gC3QajUHRdrFGYinJjqScZVbcGnY+iKSBSQ1mOA1jgHUbdBQqNpnVEo/Ts9h79g47/l1znZiajrmJAd2b2tLL1Y5ODW1K1F6eotLW91ptfd1qpVRC+FC4+hcMjJGlEueFJ9fxjJQuvoW5LaCmsa0MgRbQ8c8ger7URLyaJ+uOXOXj3yKoaKRPkH9L2tazLr5YSqBnE5+KesZMsPfmg2peYjl1AfxzK5Vt0Qlsj0ng+OV7ALg5VqKXazV6uthSp6qphiOUh9gDVATi6lwxiJgFkbPAJ7LAG1fvZiTTNXoqV58msrXpbI31hsl88oDHqxoT/9CQL3V+4Du/dqWyrr3clEol+5NPMTt+DXuSYqlvUoMZTkPxtm4rEp8CuHH/CdtjpUnpwPlEMrNzqVO1Il0b29KtSVVa1rZCT7ds/B7VcYWuNBEJkJpkPpQurpWrBP0OgQxLbPMuvqVmP2Gn6zwaVyiGFgdXNsP2vtB8HrhO+X8s95/gH3SUw//c5cPu9Zg+wLnUXZ1X1c30e8yLD2blv4nPRHsfPqjmVWx9A0uj3FwlkfEP2BJ5g23RN7l85xFGBrp0amhD96a2dGtcFWuzsvf7EwlQEYjJqRjkZEprtnUNYMBxKOCSqNSsJ/SJncGJ1H8IafwZPa2K1gW8yJRK2DEAEvZwsNEOhv2egL6uDj+OakHnRppZmlcS7E86xedxv3Eg+TSNKzgy3XEI/Sq3FonPG1y795hNETfYdOI6kfEP0NNV0KZOZWlSamKLo/XrqyWWdtr6Xqutr7tYJJ6Aja2h8SfQMlCWIe9lptA9ehqX0hLY0mQW7S2cZRn3pVIuQagb2HaSlle/sLQ1JzeXpdsvMCv0FHWqVmSFf0saVi/7G/xvpT9g3pVgVtzcTnm9cky092FstT4i8XmFnNxcjl+6z5bIG2yKuEFCUhpWFcvRo6ktPVxs6digCkYGpX8VweuIIghCyaRrAB1/kqr3nPwamr66L9OzTPVN2OEyj0Gn5tIndgY/N/iU4VU91RzsM04thisbodsG2jq24GjtNEavOEbfL/fi71GbWb5NSlwpSHU6lHyGzy//xt7kkzStUJPNTWbS26pFqdyPUhzyKutsjZL285y6nkw5fV08nW34aXQrujapKhXYEAShaKybSXdOjk4C285QvYvKQ1oZmLHX7Uv6xc6ka/QUghtNoZ/164v4FElWmnSBzcgaOv360uauujo6jO9Zn04NbRi1/AjtZuxkWv9GfNSjXpm5Q/ys+5mpLLiylmU3tmCkY8jnTkMYV72PRqrzlXRPM7MJO3Wbv//tz/PgUQZVzIzo41aNvs2q07K2Jbo6Ze/vSHHQnk91QvGwbgbOH8OJGeDQF8xqF+hp5XQNCG38Of7nv+XtM1+SmJHMBHsf9X/oTjwhTarO4/OLHtiYG7NxQkeCwi8yfW0se8/eIci/Ja6OZbuR55GUs8y4/DvhSTE4l3dkY5MZ9LFqJRKfl8jOyeXoxXtsjbrJtuibXLv/BFNjfbo2rsqnvevj6VxVVG0TBDk1+RRuhkvNt31PgXFllYesoGfMNpfZvH3mS7xPzmFZvbGMrtZLhmD/pVTCgTHw8LK0KsLw9fswnGuYc2BmN+ZuOM2s0FNsi75JkH9LalYpG1XPUrOe8NW1UL65tgGASfYD+aTGAFHc4AVpGVLSs/HENXbE3uJJRjZ1bU15u70TPV3scHOspNFm12WFSIAE+TWbJd1R2TcK+uyFAi6Z0tPRZWX9T7AxsGDSpZXcynjAV3X81bfkKj0Jdg0Ey6b/WVaho6NgdJc6dGhQBb/lR+k8axfje9ZjSt9GZW599onUC3x++Xd2PoikYXl7Qpw/o78obvAf6Zk57D17J3/NdfKTTKqaG9HTxY6eLna0rVcZA72y9XdDEEoMhQ50/g3WOkuV4XpufendlMIy1DHgj0ZTsDYw5/3zS7j6NJF5tUbI8/53/mf45zfo/DtUalSwePR1meXbhB4utvgvP0qrz7YzZ1BTRnaqVWo/9D7JfsqS65v48moo6bmZfFCtNwEOvlgalI2N+XK4k/KUv2MS2B5zk31nE0nPyqFRdTM+7V2fvu7VqWVTNpLgkkQkQIL89I2h40rY3Enq5t3w/QI/VaFQMKfWCKoaVmLshWXczkzi1wYT5e8to1RKk2jmQ+i7/5Uba+vamrL78y58+/c55m88w7boBH4c1aJM3A2KfRjH53G/8de9Y9Qzqc5a52miuMELMrJy2H3mNhuOX+fv6Js8Ss+mlk1F3utUi16udrg4WIg7ZIJQXIyrSMvItvWE00vB+UNZhtVR6PBtnfexN7Lm03+CuJaeyC8NJqg279yLgYMfQH0/qDOs0E9vUcuKw3O6M/3PWD79PZKtUTf5fmRz7CqVnrsl6TmZLL+5jXlXgknOeoyfXQ+mOQ7GxrD0z59yePAog82RN1h/7BoHLySiQEHL2lZM93amR1PbMnPnr6QSCZCgHrYdpTf+o5OgRg+oUKNQTx9T3QtrQ3OGnF5Ap6eT2NRkBpXl7Pp88hu4ugV6bHljbPp6Okz0akiPpnaMXnGUTjN38WGPukzt16hUbjg89SieWXGrWX/3EDWNq7K6UQCDqnRAVyHuXoC0/GD3mdtsibjB9tgEUtOyqG9nyoc96tHXvTp1bcVVS0HQmBo9wPkjODIRqrYHy8ayDKtQKPi4xgCql6vM0NOB3EyfzMYmM4p2lyI9CXYOAIsG0KboLRXKl9Pnm3fc6eVqx5iVx2g2ZRtzBjXlnQ41S/TdoKzcbH65tZPZcWu4lZHE21U9+dxpCPZGVTQdmsYlP8lkW/RNNhy/xt6zd8jNhfb1rVn6bnN6udhRqYJovl5cRBU4QX0yH8KfDcGsLvTeWaTlCsdSztM39guMdAz4q+kseXoFJR6XGrc2+ghaLyrUU7Oyc1m8/TzzN56muqUJy95rTqs6qq9FLw6xD+OYFb+ajXcP42BUhc8c32K4jSd6OiLxeZKRzc7YBDZF3GBnbAJpmTnUtzOlj1s1+jWrTj07M02HWGpo63uttr5ujchOh/UtICdDarsg8x6Soynn6BMzg4p6Jmx1mUVdk+oFf/K/zU65FwXekVDRXpaYUp5k8tmfMfy2P4629Srz3bvNS1xFyVxlLiGJB/js8q9cTrvFoCodmOk0nNomdpoOTaPuPUzn75gEtkTeYO+ZO2Tn5tKqdmX6N69OX/dqVDYVVe8KSpTBLgIxOWnI9Z2wtRt0WAH1RxZpiGtPE+kd8zlXnyay1nkq3a2aFT2e9GQIaSotpeh7EHSLtlH9QkIqH/x0nIi4+4zqXIsZPk2oWEI3vV9Ju820y78SfGcvTkZV+czxLYbYdEK/gGXKy6q0jGx2nrzF+uPX2HXyFk8zc2hib04f9+r0casm1lwXkba+14pec8Us+YLUdqHmIOj0k+zDX0m7Ta+Yz7mV8YDQxtPpXKlpwZ54dArELoTeu8Cus+xx7Tt7h3E/HycxNZ2ZA5vg71G7RNwNCnsQxeSLPxH96DI9LZszt9Y7xdNfqYRKfpLJ5ojrhBy9xqELdwFoVccKL7dq9HWvho25aO5aGOroMycSIEH99rwL8eth0FkoX7QrQY+y03jr1Hz+vh/BojqjGF+9f+H3XiiVsKM/3NoHPjEqX5nLyc1lRfglvgg5iamxPl+/7U5Pl5JzpeteZgpz44P5/sZfWBmYMtNpOO9U7aLVd3wysnIIO3Wb9cevsT0mgScZ2TS1t6Bf8+r0da+OQ+Xymg6x1NPW91ptfd0adf4X2PsueP4BteRPOlOznuB7ai7hSdF8V7cAFeIuh0iFdVotkqrWqcmTjGy+WBfLj2EXaV2nMt+P1NzdoNiHcUy6tIKwB9G0MqvPglrv0da8YAUfypqHT7PYEZPAxojr7Dp5i+wcJe3rW9OvWXV6udqJ5uoyEHeAikBcndOgjBQIri+t1e75d5Er9+Qoc5h26VcCr67lXduufF9vHIY6hdikemoJHPoIum8Chz5FiuFlrt9/wse/RbDr5C283KoROMRFoxtVU7Oe8PW19Xx9bT0KFEx28GV8jX4Y62rnm292Ti77zyUSeuwaf0XdIDUtiwbVzBjQvAb9m1fHqYQtIymt1HGFrjQRCZAGKJUQPhSu/gUDY8BU/jsO2bk5fHLxR5Ze38z7dr1YXHfMy++eJ52F0OZg31tKyIqhOMrB84mMWXmMu6npTBvgzJgudYqtb9DVp3f4/PLvrL69m9rGtgTWHomXVUutKwqT9DiDv2MS2BxxnT1n7pCZnYubYyW8W9RgQIsaVDETy9vkJBKgIhCTk4Zd+1uq3NN+OTTwU2moVbfCGXn2G9xMaxHiPJ2q5QpQUSbxxL/7fsZC669VOv/LKJVK1h+/xuQ10TxOz2ZKv0aM6VIHfb3iq6iWlpPOsutbWHB1LWk5GYyr3ocAe18qGWjfUq7snFyO/HOP9cevsTnyBg8eZeBkXR7vFvZ4t6ghChmokba+12rr69a4zIewzgUMzaH/4VdW9FTVipt/88H572ht1oCQxp89XxwhIxVC3UG3HAw4KvuepNd5nJ7FrNCTLA+7RINqpnz7TjOa1bRU2/nyVhb8cGMr5vrl+cJpGCNtu2vVyoJ7D9P5K+ommyOuc+B8Itk5SprXsqSfe3W83KpRzbL0VOorbUQCVARicioB9o6CS8FSEztTR5WGOp5yngEnZ5OjzGVd42mvv+Wet+/HyBr6HVTbBAmQmpbJ3A2nWB52idpVK7JwiCsdG6q38k1mbhYrbm5nTvwf3M9K5T3bbkx3/B979x0WxdUFcPi3VEWUJqJiA+xdEHsXLFGJMaAhTWMimmaqLcVUYyDNVAOmJ4aoqIktKti7wNq7rBU7TWzU+f7YwBdiQ5hl23mfhydhmL33jKNzOTN3zn0E70qGGwRNUU5eAav3nmNx8imWalNJv5JDvepVGNaxHg92rE+b+m5Wd3fSGKz1Wmutx20SLiTBgi7Q4mnoXvaqa3ezIWMPw3a+SxXbSsS1eZP2Lo1BKYS/h8LZDfqiBwZ4ClUaO46l88KP29l5Ip2RPf2YGtpG1SlXV/Ov8+mJ+UQdn4eNRsOkBiN4od5QqthZxxOOzKu5/JV0igXbTrBu/3kAujetwf2B+ult8qSnYkgCVAYyOJmA3Gz9InbOdeD+tVDOO0bnczIYsXsamzL38UnjCJ6vN/TmX3AVBZY/AGfW66dI3GM57rLadTydCb8ls+XwRe5r580HD/urPtUqrzCfX87E857ud07duMijtfrwlt9j+DrVUrUfU1ZQWMj6/ReYt/U4i5L009sa1qxKSPu6DAmoQ4CvhyQ9Fcxar7XWetwmY89XsOF56D8P/EIN1s3J6xcI3fUeu7J1fN3sOZ46lwTb39IvzFr/PoP1WxoFhYV8t+oI78/fjQJMGdqKiKDG5ZqJUKAU8GPqSqam/ExabjbP1QvhNZ9wq5hZkJNXwPKdZ5i75TjLd6aSV1BI96ZePNixHkPa15V3eoxAzeusdZeBEhXLoap+Je8/e+nX4Wn3arma83J0Iz7gQyYf+Z4XDs1kbcZuvm/xMm72/0o0dn0Gx/4q1Xo/amrTwJ0VrwexYNtJ3vhjB4GTlxIR1IgJIS3LXec/v7CA2WdX8a5uNrrrZwnz6sEy//dp7lxxx2dMhYUK245eYsG2EyzYfpILWTfwqeFMRFBjQjvVp5m3iyQ9Qlibls/qb3SteRKqtwWXhgbppl7lGmzo8AkvHJzJwsTXGH12F/nt38DByMkPgK2NDWODmzCsY33en7+bKbFaflx7lA/C/enXpvY9taUoCosubuH1Iz+y7+oJwmv2ZlrDUfhYwQ22XcfT+W2Djjmbj5NxNZe2Ddx4Z3hbQuWdHosiCZCoWLV7QJuXYdvrUDdIP1CVg72NHZ80GUt3t5aM3vspbbc8ze+tptDVrQWkrtEvxNp2gv7F1Aqm0Wh4sFN9Brbz5usVB/lsyX5+26Dj5cEteLpf43teRLVQKSTu/AamHv2ZQ9dO80CNrixs+xatq5ZvOqE5UBSFPSczmbf1OPO3nuBU2jVquVUmrFN9QjvVlyc9Qlg7jQZ6f6cvjb0iDIZtATvD3KF3tHHg25rB5G58jWVVajAl9wxzrpwwmZtQntUq8fkTHRjduyGTf9fy4CdrCWpVi2nh7WheijXN1qfvZvKRH9iStZ8+7m35seWrBLo0MXzgRnQh6zpzt5zg94069pzMxMulEiN7+fFIN195Z9RCyRQ4UfHyb+jna+ddgbBk/ZMhFZy8foFH9nzIlqz9fFJrAOO3T0NTvS0M/htMYM2bi5dvEPXXXr5bfYQa1SoxeWgrHuvhe9eqPYqisOzSdt44+hM7s1MYWD2Q9xuOwr9aowqK3HiOnL3M/G0niNt6gkNnLuPu7MgDHeoS1rkBnRt5msT6F6Ika73WWutxm5xLO/WLpDZ8CPr8aJhqbDfSIK4D2DlxoH8sofs/59j1c3zV7FmeqN3fpG7GKIrCUm0qb/yh5diFqzzczYcpD7Si3i1e1D909RQTDs9i8cWtBFRrxIeNniTIw98IUVeMnLwC/t6RyuyNOuJ3n8XWRsPAdt480s2X4Na1Kqyinig9eQeoDGRwMjGZR2Cev/7JTNBs1Qap/MICph/5kUEbX6amoiFv2Gbqu7dQpW21pJzPZtr83czbeoKGNavyxrDWDO1QF1ubkhdbRVFYlb6DN4/+zNasA3R3bcm0Rk9Y/BoLxy5c4c/Ek8zfeoJdJzJwrmTHYP86hHVuQO8WNSu0sp64d9Z6rbXW4zZJh36FVY9D1xnQ5gV12y7IgyX9IW0PhG6Haj5cK7jBCwdn8l3q34R6dWdms/Elq8SZgNz8An5YfZSoRfvIvJrL6N4NeTWkBTVdK3MxN5N3U2bz7ekl1HGszvRGoxlRs5dJJXJqURSFnccz+G1DCvO2nCDjai7tfT14pLsvwzrWw925fFPUhWFJAlQGMjiZoCN/QHw49IqB5mPUaVNRYNXjFKTEMcSnLxtt7ZjRdJzJ3ZUD/Tzjd+J2Eb/7LE1qV2PS/S0Z1rEetjY2rE3fxdspv7IuYzcdqjXhvYYjCfYIMLljUEvK+WwWbj/Jn9tPsutEBpUdbOnXpjZhnerTr03te54uKIzHWq+1stacidn0KuyeAYOX66dbq0FRYN04OPgDhKzST+n+l3nn1jPuwOc4aOz5rsVLDPLsqE6/Krqak8+3Kw8xY+l+rmtu0LT/GXY6b8bWRsMbPg/zfL2hVDJgpVRjSb+Sw9zNx/llfQp7TmZS07Uy4V19eLibj0xxMwOGWGdOEiBhXGvHwaGf4MGt5X4fCNBX40l6F4JjuewzhBcOzuSnMyvp6daamc3G08y5Xvn7UNn2o5eI/GsvK3alUqv5ZWwC9nBYOUq7qg152+8xhnh2ssjER3c+mz8TT7Fw+wl2Hs/AycGWAW29eaBDPYLb1KaKoyQ95shar7XWetwmqzBfv/bchUQITVSnPPX2tyHpHej9AzR74pa7nM1J46l9n7Hs0nZGe/fno8ZjcLc3rYpp1wty+OjoAj48PofrhTk4HWnOWK8HmNDPn1puTsYOTzWFhQpr95/j1/U6FiefoqBQYUBbb0b29COolUxxM0fyBKgM5O6cicq/Dgu6wvUL+iTIuU7Z29r/HawdA52mg//k4s0JaVqePvAFJ65fYGKDMKb4PGRSaxcUKAX8dWELbx38nb05R7FJ88DjSHvGtwxiTJ/GeFlI1RlFUTiYmsWipFP8lXSKPSczqfxP0jOsQz36tamNkyQ9ZssQd+jMiSRAJuhGBszvABpb/RpwlT3L3tbembD+Gej4AQRMueOuiqLwXerfTDg8CweNHZ82Gcsjtfoa/UZWfmEBP55ZwTspv3E+N4OnvAfyXM1Q5q26QEz8YW7kFTKiSwPGD2xKs1IUSzBFiqKQrEtj4Xb9zbVTaddoXKsaj/f0I7xrA2q4WMZ4aq0kASoDGZxM2NUzML8zOLrqB6myrC9wYhksC4HmEdDj65veKbpRkMv0Y3/w4bE5VHeoxrSGT/B47SBsNMa7A3St4Aa/nkngkxPzOXItlV5ubZjYIIzGBU35Nv4wv67XkZtfyAMd6jI2uAmBfuZX6Swvv5CtRy6yVHuaZTtSOXbhClUr2TGgrTf3B9YlqLU86bE01nqttdbjNnlZR/U32Zzrwv2ryza+pMTBiuHQejx0/azU76yey0nnpUPf8se5tfRxb8unTcbSpmrFL5RaqBQy7/x6ph79hcPXThNeszfvNRyJn9P/S2NnXcvlp7UpfLPiIGcyrtO7RU0ighozsF3tm95PNTUFhYVsO3KJxcmnWZx0ihOXrlK9qiMh7evycDcfOjSsbnZjp7g1SYDKQAYnE5e+Tz9I1QiEQcvA1r70n01dq5/qUDcY+s+/4wKrx66dZcrRH5hzbh3tqjbk/YajGFg9sEIvjmdupPH1qUV8e3oJmXlXeaBGFyY0CKOja7MS+2VczeW39SnMWnWEYxeu0LqeGw9382F4lwYmvQBb+pUcVu89y987Ulm56wyZ1/Ko5VaZ+9p5c1+7OvRo5kUlh/ItgitMl7Vea631uM3CpZ3wZ0/wDNCPL/dSHvvIHFj1GPiFQdCvUIabZssvJfLCwZkcuZbKqNr9eK/hSLwrVb/ndu6VoigsvriVN4/+zO4rOgZWD+SDhqNpW+32SVhufgELt58kOv4wiSlp1KtehZE9/Xi4mw91PG6uHGcsN3ILWLv/HEuS9TfXLl6+gZdLJQYH1OGBDvXo2qSGTHGzQJIAlYEMTmYgdQ0s7g8+D+jLl9qXYi7ywZ9gbQTU7gkD/yrdZ4DNmfuYcHgWmzP3065qQ17zeYhhXt0M9kSoQClgxaVkvkv9m8UXt1LJxoEnvQcwvt5QfO+ysFxBYSErd53l1/UpLN95BgWFoFa1GBpYj/v86+BWxbgvrObmF5CsS2ftvnPE7z5Dsi6dQkWhdT037vP3ZmBbb9o2cJeS1VbCWq+11nrcZuPMBljcD+r2g+Dfwb4Uv8zvmgGbXoLGj0Lv76EcxQHyCvOJOb2Mt1N+5WrBDcbVHcTL9R+kTqVyTMu7jesFOfx+djUzTi5k75Xj9HJrw/sNR+nXx7sHWl0aMauOsGDbCW7kFdC7RU0e7ubDgLbeuDhV/Lhz/OIVVu89x+q9Z0nYfZarOfn4eTkzOKAuQwLqEOhXXcYZCycJUBnI4GQmUhbAqkfBpREMmH/71byVQtj2Jmg/0FeQ6/71vT01Qn93bE36TqYdi2V1+k78KtfmSe/+jPLuRy1Hj3IfSqFSyNasA8w/v5E559aRmnOJNlV9edJ7AI/XCsalNAPwf6Rl57Bg2wnmbDnO9qOXsLXR0KOZF8Gta9OruRfN67gafAC4mpNPUsolNh+6yKZDF9h+9BLXcwtwdbKnV4ua9G1Vi6BWtUzqbqGoONZ6rbXW4zYrJ5bpF0l1rgvBseDZ7tb7KYWwZTLs/AjaTdS/V6rSzbGsvKt8fGIeX51cxNWCGzxSqw/P17ufdlUblnsmwsGrJ/n5TDzfnV5OWt5lhnh24qX6w+jp1rpcbV++nsfC7Sf5bb2OrUcuYm9rQ49mNRgcUJfeLWviW8PZILMozqRfY+PBC2w4eJ71+8+ju3AFG42GAF93Brarw5CAOjSpXU2mt1kRSYDKQAYnM5K2B5Y/CNfPQ6/vwOf+/995K8gF3ULY8wWc2wKdo6DtK+VeR2hb5gG+PrWYeefXk6fkM8AjkH4e/vR0b00rZ59SPRlSFIVj18+xMXMvGzL2suzSds7kpOHl4MawGl0Z7T2AgGqNVLtYn8u8zpLk0yxOPsXmQxe5kVdA9aqOdGlSg7YN3GnbwI029d3wrFapTH0WFBaSmn6dI2cvcyA1i4OpWew5mcGuExkUFCq4OtnTuUkNujWtQbcmNWhd302mHAirvdZa63GbnYxDEP8QpO+HzpH6G2hFN6Pyb8Dh2bDrU8g4AN1m6N/7MYDs/GvEnF7GpyfmcyYnDd/KtQj16s79NTrTrmpDKtvefT2a/MIC9l45zobMPfx2ZhXbLx/C1c6Zx2r3ZXy9oTR08lY97lOXrrJUe5ol2tNsPHiBgkIFb3cnujetQXs/D1rUdaV5Hdd7Wk8nL7+QU2lXSTmfza4TGew8ns7OY+mcuHQVgKbeLnRvWoNeLWrSo5kXrkae9SCMRxKgMpDByczkZMHqJ+DYQrCxB49W4NoUTq/SJ0a1e4L/a1Cvn6rdZuZd4fezq/nj3Fq2ZR0iV8nD1c6Z5s718KlcE5/KNXG3r0qholCoFHKtMIdj189x9NoZjlxL5UJuJgAtnRvQx70toV7d6eLaHFuNYd95uZFbwLajF1m77zyJKZfYeTydrGt5AFRxtKO+ZxXqezrj7uyIS2V7qjnZ42BnQ15+IfmFCjl5hWRdyyXzWi4ZV3I5nXaVk2lXyS/QXx4q2dvSpHY1mtdxoVMjTzo39qRJbReZbiBuYq3XWms9brNUkKN/wrN7hv5753rg2gTSdsH1i+ATAm0nQq0uBg8lrzCfNek7iTu/gYUXNnMpLwtbjQ3NqtSjbVU/aji44mxbGWfbSuQq+VzMzeJibiYnblxAe/ko1wtzsNPYMsCjPSNrBzPYs1OFreOTdS2XLYcvsuHABTYePM+ek5nkFRQCUL2qI16ulalRrRKe1RxxsLPF1kaDna2GazkFpF/JIf1KDheybnAq7RqF//wqWq2yPW0auNGmvjsdGlanW9MaJv3Oq6hYkgCVgQxOZkhR4Pw2uJgMF7WQvgdqdICWT4P7vc1lLovrBTlszzrEhsw9HL6ayrHr5zh2/RyZ+Vew1dhggw2ONvb4VK6Jn1Mt/CrXJtClMV1cmxt93QdFUdBduMLek5mcuHSFExevcOLiVTKu5nL5eh6Xr+WSX6hgZ6PBztYGe1sbXKvY4+rkgIuTA3U8quBTw5kGnlXw9apKfc8qJl8JSJgGa73WWutxm7W0vfrxJeMAZB6EKnX0T3xcGxslnPzCAnZkH2VH9lG0l4+yO/sYGfnZXMm/TnbBdRw0dng6uOLp4EJtRw8CXRrToVpT2lXzw8nW+ElCXn4hR89dZt/pTFLOZXPh8g0uZN3gUnYOufmFFBQWkl+gUNnBDjdnB9ydHalRrRINajjj4+mMj5cz9TyqyI01cVuSAJWBDE5CCGF41nqtlbXmhBDCMAyxzpwswCGEEEKUk7e3t1UmfkIIYWhFN5ZCQkJUa9Nq5rSkpqYSEhJCbGyssUMRQgiLExsbS0hICKmpqcYORQghhLgjq3kCJHfnhBDCcAxxh04IIYQwBKt5AiSEEEIIIYQQVpMAWcq0DEuYwmcJxwCWcRyWcAxgGcchx2DeLGGMsZTzZwnHIcdgOizhOCzhGEDd66wkQGbGEv4SW8IxgGUchyUcA1jGccgxmDdLGGMs5fxZwnHIMZgOSzgOSzgGkATIoEr7l8RY+5WWMeKzhGO4l/1Kyxh/xqZ8DIbYzxh9yrkQZWGMf+fyd9Dwfcq5KDtLOAa1+7WEYzDEfmqSBOg/LOXkSwJk+P1KSy6Kht/PGH3KuRBlIQmQ4fczRp9yLsrOEo5B7X4t4RgMsZ+arGYh1Dp16uDv73/X/VJTU/H29pb9yrCfKcdmKfuZcmymvp8px2Yp+6WmpnLjxg327dt31/YsjZpjjCmfY0vZz5Rjs5T9TDk2U9/PlGMz5n5arZbTp0/fdb/SsJoESAghhBBCCCFkCpwQQgghhBDCakgCJIQQQgghhLAakgAJIYQQQgghrIadsQMwtISEBHQ6He7u7uh0OiZOnGjskEpFq9Xi6uqKu7s7SUlJtG/fHldXV7M5nszMTFxdXYu/v13cpnw8/z0GczwncXFxpKenk5KSQmZmJtHR0YB5nY/bHYM5nY+EhATc3d1JT09n3rx5Znke4PbHYU7nQm3meozmfM5kfDENMr6YxjGAZYwxFT6+KBYuKCio+P8jIyOVefPmGTGa0gsNDVUAxdXVVYmMjCzeburHM2/ePGXixImKv79/ie23i9sUj+d2x2Bu5yQlJaVEnKGhocXfm8v5uNMxmNP5+PelNigoSImOji7+/yKmfB6K3O44zOlcqM1cj9Ecz5mML6ZxDIoi44uimMYxFLGEMaaixxeLngKXkJBQ4g6Lv78/c+bMMV5A9yA4OBhFUcjIyCiRsZv68YSGhjJ27NgS224Xt6kez62OAczvnGRmZpaIJTAwkPj4eLM6H7c7BjCv85GSklL8/+np6bRv396szkORWx0HmNe5UJM5H6M5njMZX0zjGEDGFzCNYyhiCWNMRY8vFp0AabVa3N3di793d3dHq9UaMaJ7o9PpSsRrrsdzu7jN8XjM6Zz4+/uTnJxc/H1KSgr+/v5mdT5udwxFzOV8+Pr6AvrpFkFBQWZ3Horc6jiKmMu5UJO5H6MlnDNz/Hd0O+Z0PmR8MY1jKGIJY0xFjy8WnQClpaUZO4RyyczMxNfXl7CwMDIzM832eG4Xtzkej7mek8zMTBISEpgyZYrZno9/H8O/t5nL+dBqtaSnp+Ph4QGY77+L/x5HEXM6F2ox92O0hHNmrv+ObsVcz4eML6bBEsaYihxfLDoB+u8foDmJiIjA398fV1dXgoODmTRpktkez+3iNrfjMedzMmbMGOLj43F1dTXb8/HvYwDzOx/+/v5ERESQmJjI2LFjzfY8/Pc4wPzOhVrM+Rgt5ZyZ67+j/zLn8yHji2mwhDGmIscXi06A/P39SU9PL/4+PT29xCM1U5WQkEBwcHCJbUWxm+Px3C5uczoecz4nUVFRREZG4uvri06nM8vz8d9jMKfzERcXVyLWwMBAkpKSzO483O44zOlcqM1cj9GSzpm5/Tu6FXM+HzK+GP8YLGGMMcb4YtEJUFBQEDqdrvh7rVbLiBEjjBhR6fj6+pZ4STI5OZkRI0aY7fHcLm5zOh5zPSdFc2mL5tYmJCSY3fm41TGY0/lwdXUlLCys+PvExMQ7xmqKxwC3Pw5zOhdqM9djtKRzZm7/jm7FXM+HjC+mcQyWMMYYY3zR/FN6zmIlJCSg1WqLM3tTqHVeGkW16UE/9/HflS9M+XgSEhKIj48nJiaGyMjI4gvL7eI2xeO53TGY2znR6XT4+fmV2BYZGcnEiRPN5nzc6RjM6XzExcUBFK83ERkZCdw+VlM8Brj9cZjTuVCbuR6jOZ4zGV9M4xhAxhdTOYYiljDGVPT4YvEJkBBCCCGEEEIUsegpcEIIIYQQQgjxb5IACSGEEEIIIayGJEBCCCGEEEIIqyEJkBBCCCGEEMJqSAIkhBBCCCGEsBqSAAkhhBBCCCGshiRAQpiYhIQEJk2aVGKRr4qWmZl5x5/HxMQwadKkiglGCCGEKmR8EUJPEiBh1TIzMxk7dixubm7ExMQQFRXFpEmTiIqKMlpM8fHxREZGFi/uNXbsWDQazS0HrLi4ODQaDTExMar1n5mZWdxeZmYmwcHBNw1GEREReHh4qNanEEJYGhlfbibjizAVdsYOQAhjcnV1xc/Pj+HDhxMREVG83c/PD39/f4KCgowYHfj6+jJ27FjS09OJjo4uXhm5SHp6Oq6uriViL6+5c+cSGhoK6P98fH19CQ4OVq19IYSwBjK+3EzGF2Eq5AmQsHrx8fGEhYUVf5+ZmYlOp8PX19eIUZU0duxY4uLiSmzTarW0b9/+pn3vNr3gblJSUkoce0JCgtEHaiGEMEcyvpQk44swFfIESFi9hIQE5s2bB+gv+tHR0cybN8+kBihfX198fX1LDBbp6ek3DVAJCQmEhYURERFBYGAgiYmJ+Pn5Fd/BS0hIQKvVFk9/mDhxYonPa7VaAgMDi78vGuyKBsfExMSb7hIKIYS4NRlf/k/GF2FKJAESVq3oYp2UlERmZmbxNAB/f39jh3aTsWPHEh0dfce7ZUFBQQQFBREYGEhoaCihoaEEBATQvn17XF1dmTRpEsnJyQAEBAQQERGBq6tr8efnzJlTYgBKSEjA19e3eMrCpEmTZIASQohSkPFFxhdhuiQBElat6I5X0UW/6CIeHx9/x8/FxcUVX9ijo6MJDg4mKCjIoHf1QkNDGTNmTIm4bzcd4d9xBAUFER0djZ+fX4nBrWigupP4+PjiF1QzMzNLDGZCCCFuT8aXO5PxRRiTvAMkrFp8fHyJFzB1Oh3p6eml+ty/B7aIiIhSDU5FVYHKavjw4fdckcfDw6NUA0tcXBwjRowosS0hIaF4GoTM1RZCiNKT8eX/ZHwRpkYSIGHV/nvR/fcLmlqtFoCoqCgSEhKIiYkhMzMTrVZLUlIScXFxxMXFodPpiuc+JyQkFG//72dBPwAWffZeFN2JGzt2LJGRkbi7u99x/3+XNJ0zZw4jRowgKCiIhISEEvv8e7/ExMQSUzOK+iwa3IraUbMkqhBCWCoZX2R8EaZLEiBhlXQ6HVFRUQQFBTF37tzi7SNGjChep8DV1bV4n6Kv6dOn4+/vXzxvOTQ0FF9fX4KCgtDpdERHR+Pq6kpQUFDxWg/u7u7FUx7+/dnS0Gq1TJ8+nenTp6PT6fD39y/+0ul0TJ8+vcS6CkUSExNJSEggKiqKKVOmFH9mypQpREVFERcXVzw/HfSD0X/XXdDpdCXiDAwMJCkp6ZaVgYQQQujJ+CLjizADihDitiZOnKjEx8criqIoycnJysSJExVFUZTQ0NDifYr+PyUlRcnIyFCio6OV6OhoZeLEiUpycvJNbRbtf6ufFfVZHqGhobdt+3YiIyOVjIyMe/6MEEKIspHx5c6fEcKQpAiCEHcwZcqU4rtfWq2WKVOmFD/a1+l0xWs66HQ64uLi8PX1xd3dHV9f3+L51Onp6eh0uuJSoYGBgcTFxRl0vnNp5pn/W1pamryAKoQQFUjGFyGMR6MoimLsIIQQ/1dUFWfs2LH3XPUnISGBsWPHEhQURGRkZKkGHZ1Oh1arLfW0CYCYmBiio6NLVelHCCGEaZDxRQg9SYCEEEIIIYQQVkOKIAghhBBCCCGshiRAQgghhBBCCKshCZAQQgghhBDCakgCJIQQQgghhLAakgAJIYQQQgghrIYkQEIIIYQQQgirIQmQEEIIIYQQwmpIAiSEEEIIIYSwGpIACSGEEEIIIayGJEBCCCGEEEIIqyEJkBBCCCGEEMJqSAIkhBBCCCGEsBpWkwA9/fTTxg5BCCEsnlxrhRBCmDo7YwdQUeLi4khNTb3rfqmpqXh7e8t+ZdjPlGOzlP1MOTZT38+UY7OU/VJTU0lLS7trW5bIw8MDe3t7vL297/jnJH9XTWM/U47NUvYz5dhMfT9Tjs0Y+6WmppKamkpeXp56Y4xiJby8vEq135AhQ2S/Mu5nyrFZyn6mHJup72fKsVnKfkOGDCl1e5ZGzTHGlM+xpexnyrFZyn6mHJup72fKsRlzv9JeZ0vDaqbAlVZ4eLhJ71daxojPEo7hXvYrLWP8GZvyMRhiP2P0KedClIUx/p3L30HD9ynnouws4RjU7tcSjsEQ+6lJoyiKUuG9GkHNmjU5d+6cscMot5CQEBYtWmTsMMrFEo4BLOM4LOEYwDKOw1KOATD74ygLSxhjLOHvIFjGccgxmA5LOA5LOAZQ9zprNU+ASjMH0RxYwl1YSzgGsIzjsIRjAMs4DjkG82YJY4ylnD9LOA45BtNhCcdhCccA6l5nreYJUEBAAN7e3oSHh1vMXwQhhDAVsbGxxMbGkpqaSnJysrHDqXCWcodVCCFMlZrXWaupAuft7S2DkxBCGEjRzaWiaXDGEhcXR3p6OikpKWRmZhIdHQ1AQkICOp0Od3d3dDodEydOLNN2IYQQ5s9qpsAJIYSwbDqdDp1OR0REBJGRkaSnpxMVFQVAZGQkERERhIaGAvpEqSzbhRBCmD+rSYCOXbhCQWGhscMQQghhIJmZmcyZM6f4+8DAQOLj40lISMDV1bV4u7+/P3PmzLnn7XdyI69ArcMQQghhYFYzBe5AahbfrDjE8wObGTsUYeKysrK4du2ascMwOU5OTri4uBg7DCFuy9/fv8T7RykpKfj7+6PVanF3dy/e7u7ujlarveftd3LgdJaKRyLMmYwh6pKxRxiC1SRAPp5VeCduF8Gta9PUW/4hiVvLysriq6++Ii8vz9ihmBx7e3uee+45GYiEWcjMzCQhIYHk5GSmT59+y31ut6J4WVYaP5t5na7DIvDI15dolYI71knGEPXJ2GO9iorrFElNTVWtbatJgBrXduFsdWfGxmxh1dR+2Nlazew/cQ+uXbtGXl4ew4YNw9PT09jhmIyLFy+yYMECrl27JoOQMAtjxowhPj4eV1dXPDw8yMzMvGmfe91+J9WrOnLZZxDxHwzCydFqhlbxHzKGqEvGHuv23xtJahbZsZqrtK2NhpiITvR9N55Pl+xn4v0tjR2SMGGenp7UqlXL2GEIIcogKiqKyMhIfH190el0+Pv7k5iYWPzz9PR0/P3973n7nbSo48rOjOt8tGgfb4W1Uf+ghFmRMUQI02ZVj0Ha+1XnlSHNmf7nHnafyDB2OEIIIVQWFxdHUFAQvr6+gL6cdVBQEDqdrngfrVbLiBEj7nn7nVSpZMerQ1rw+bIDHEyV94GEEMKUWVUCBDB5aEuaebsQEbOFHKnaI4QQFkOn0xEWFkZAQAAajQaNRlM8lS0yMpKoqKjictZF5a3vdfudvDy4OfU9q/DCT9uxkjXGhRDCLFnNFLjU1FRCQkIIDw8nOmIAPd9awfQ/9/B2WFtjhyaEEGav6GVVNV9SvVe+vr63TTyCgoIICgoq9/Y7cbS3ZcaoQAZ/uJrZG4/xaHffe/q8EEKIimE1T4C8vb1ZtGgR4eHhtKrnxpQHWvHZkgNsP3rJ2KEJM5KZmUlYWBh+fn74+fkRHBxcXB43ICAANze34p/5+fkRFhZW4vPBwcEEBATctv2oqKgSn/9ve6XpKyAgoDi2ov5Ks4hjXFwcMTExZf2jEVYuPDycRYsW4e3tbexQjKpn85oM71yfN/7YQVp2jrHDESbGEseQsLCw4gWHi4wdO5bg4OAS27RabYltCQkJNx2fEBXFap4A/ddLg5qxTHuasTFb2PTeQKnaI+5Kp9MREBBAZGQk8+bNA/QX9KJ3DTIzM5k1a9Ztp8potVrS09Pv2MfEiROZOHFi8f4BAQFkZNz8vtrd+oqMjCz+WWZmJgEBAfj6+t7xRe7STPERQtzdB+H+BExewltzd/LVkx2NHY4wEZY6hgQHBzNv3rzifgGSkpJKvEcH+oSnKAEKCwsjMzPzpn2EqChW8wTov+xsbfg2ohOn067x9rydxg5HmIGxY8cSERFBRERE8TZ/f/8SK8bfyZgxY5gyZYqBors9V1dXgoKCSEhIqPC+hbBGXq6VeSu0DT+vS2HrkYvGDkeYCEsdQ4YPH17iZzqdDl9fX4KCgko8OZozZ05xUjVv3jwiIyMNG7gQd2C1CRBAk9ouvDO8DTNXHmbtvnPGDkeYuISEBMaOHVvmz/r6+hbf6atICQkJzJ07945PeGJiYsp8bEKIm43u0xB/H3de/CmRvPxCY4cjTICljiGurq74+voWJ0FxcXHF0+fi4+MB/VOkzMxMo8QvxK0YJQEqetdg0qRJJS4GCQkJxMTEEBcXV2I+6b1uvxfjgpvQs7kX42ZtJfNqbtkPSli0osf0d7t4T5o0iYCAgOKvos+NHTtW9btdt+sL9HcKi+Z4R0ZGcuzYsdvGHhcXR0REhDwhEqIcigrtFK1abmtjw+dPdODA6Sy+WXnIyNEJY7PkMQT0U6iLpvXFx8czfPhwgoKCmDt3LvD/cvRClEVsbCwhISGqFtmp8ARIp9Oh0+mIiIggMjKS9PT04uQlMjKSiIiI4rsMRY9O73X7vbCx0TDzqU5kX89jwq9J5T4+YZmKLvx3m68cGRlJcnJy8Zevry9xcXH4+/urfufrVn0VmTVrFsnJycybNw+dTnfHKRahoaEl5qELIe7dvwvtFGnbwJ2xwY34YMFuTl66asTohLFZ8hgCMGLEiOKbaOnp6cVPhdzd3dHpdMTHx0vBA1FmhiiyU+EJUGZmJnPmzCn+PjAwkPj4eBISEkr8A/P392fOnDn3vL0s6lavwsePt+ePzcf5c/vJMrUhLF9QUBDR0dH3/LnExMTil1/DwsKKX0ytCEULQt7tCWl0dLRMgRPCAN54sA0uVRx49dckWRvIylnyGOLv7096enrxQsT//nxcXJw8ARImp8ITIH9/f5KTk4u/T0lJwd/fH61Wi7u7e/F2d3d3tFrtPW8vq4e6NOD+wLqM/3E75zKvl7kdYbmio6OJiYkpUSpap9Pd9e/dv++yRUdH3/RvwNCio6OZPn168YKQt5KQkEBoaKiUwRZCZdUq2/PRowH8vSOVxcmnjR2OMCJLHkNAn+xMnz69RKnrsLAw5syZIzMMhMkxahGEzMxMEhISmDJlCmlpabfc5163307R/Oyir6J52kU0Gg0zRgXiYGfDuJgtFBbKnTpRkq+vL8eOHSM+Pv62azSYIl9fX4YPH86kSZNuu0/RXbrhw4dXYGTCEhTNzS76MuZCqKYqpH1d+repzYRfk8i+nmfscISRWPIYAvppcFqt9qYnQFqt9qbjHDt2LGFhYcVPtsryCoMQ5WHUxW/GjBlDfHw8rq6ueHh43PLuwr1uv52i+dl3Ur1qJaIjOjP0ozXMXHmIZwc0LXX7wjq4uroWv+j5XykpKXf9fFBQUKnv3Pn7+992ysyd+rpV+3ebdlGWaRlCgH5u9r/fewkJCTFiNKZJo9HwyePt6TBlKe/P303koxUzfUmYHksdQ0D/Pumt+rvVNhlzhLEZ7QlQVFQUkZGR+Pr6otPpiuePFklPT8ff3/+et5dX31a1eLZ/E6bO3cnekzcvHiaEEELcq/qezkx5oBXfxh9m5/E7L2YphBDCsIxWBrvoxTr4f3nEf1dH0Wq1jBgx4p63q+HtsLY0qlWNJ7/dzPXcfFXaFEIIYd2e7d+U5nVcGP/DdgoKZW0gIYQwFqOUwQ4LCyMgIACNRoNGoymeyhYZGUlUVFTxXNCi8tb3ur28KjnY8sPTXUg5n83UOTtVaVMIIYR1s7fTrw2080Q6MQlHjB2OEEJYrQp/B8jX1/e2c1KDgoJuWSbxXreroXkdV94b0Y6JvyXTr01tglvXNkg/QgghrEeHhtV5sncj3o3bRUj7uni7Oxk7JCGEsDpGrQJn6sYFNya4dS3GzdrKxcs3jB2OEEIIE1VUafS/FUZv5a2wNlRxtJPFt4UQohSKqo2qWWXUqFXgTJ1Go2HmU53o+Poynv1+G3Ne7IFGozF2WKICXLx40dghmBT58xDizkpTabSIaxUHoh4NYOTXm1iSfJrBAXUMHJ2oaHLNVIf8OQr4f7VRNauMSgJ0F16ulfn6yY48NGM9P6w5ypN9Ghk7JGFATk5O2Nvbs2DBAmOHYnLs7e1xcpLpOkKo4YEO9Zi9QcervybRs7kXVSvbGzskoQIZQ9QnY48wBKtJgIqmJ/x3zYrSGORfhyf7NGTK71q6Na1Bk9ouBopSGJuLiwvPPfcc165dM3YoJsfJyQkXF/m7L24tNjaW2NhYWQi1lDQaDZ+ODJS1gSyMjCHqk7FHGILVJED3Mj3hVj4I92f9gQs8OXMzq9/qh4OdrYrRCVPi4uIiF1sh7pEhpihYuvqezrw2rDVT5+xkRJcG+Pt6GDskoQIZQ4QwfVZTBGFjxl4u5maW+fNOjnb88HQX9p/O4r35u9ULTAghhNV6pl8TWtR1YfyP28kvkLWBhBCiIlhNAnSl4AZP7//itiW4S6NtA3emhrbm82UHWLf/nIrRCSGEsEb2djZ88UQHdp/MYObKQ8YORwghrILVJECtq/ow/8JG/ji3tlztjB/YjB7NvIiI3kJado46wQkhhDBrl3KzyvzZ9n7VGRfcmPfn7+bExSsqRiWEEOJWrCYBqu3owYiaPXn2wFeczUkrczs2NhpiIjpzI6+QcbO2lOuJkhBCCMuw+8oxcgpzy/z5Nx9sg5uzIy//kiTjihBCGJjVJEAAXzd9HgcbO8bsm1GuAaa2uxPREZ1YvvMMXy0/qGKEQgghzNG1ghtEHptb5s9XrWzPx4+1Z+WuMyzcflLFyIQQQvyXVSVAHg7ViGn+IksvbeOnMyvL1daAtt6MH9iMqXN3kpRySaUIhRBCmKMaBVV559AvfDrn2zK3MTigDiHt6zLht2Qyrpb9aZIQQliS2NhYQkJCVF1mwaoSIICQGp15vFYQLx6ayakbF8rV1tthbWjXwJ1RX28iUwYrIYSwWgFezahftSbL/I6Va4bBR48GcD0nnzf/2KFidEIIYWQFeVBYUKaPhoeHs2jRIry9vVULx+oSIIDPmz6Ds21lntr3WbkGKns7G358pitZ13J59vttMm9bCCGslK3Ghq+aPseq9B38fm51mdup7e7EuyPa8fO6FDYePK9ihEIIYWCFBZClg5PLYfcXsOF5WDwAfmsIMZUhfa+xIyxmNQlQamoqISEhxMbG4mrvzPctXmZlWjKzUpeVq936ns589WQnFiWd4oc1R1WKVgghzIshpiiYm/s8OxDq1Z2XD0WTkZdd5nZG925Ip0aePPf9dm7klu2OqRBCGExhAWQcgpQ42P42rBgOf7SCGCeY7QdLBsKWiXB6NdhVAp+h0ONrcKpl7MiL2Rk7gIri7e3NokWLir8fUD2Qp7wH8sqhGPp5BNCgcs0yt31/YF3G9G3EpNnJdGxYnZb13NQIWQghzEZ4eDjh4eGEhIQYOxSjmtHkaZpuepLXj/zIN83Hl6kNGxsNX47uQNc3/ybyr728FdZG5SiFEKKU8q7BpZ1wacc//92pf5JTcEP/88o1wK051OoOzSPArSm4NoYqdcDG1oiB35nVJEC38kmTCFakJTF67ycktI/ERlP2B2IfhPuz5fBFRn2ziXXvDKCKo1X/0QohhFXyrlSd9xuO4qVD3zLKux8dXJqWqZ2m3i5MCGlB5F97ebBjPbmxJoQwvMJ8fXJzfhuc3w4XEiFjHyiFYGMP7i2gelto/DB4tAb3VuBUw9hRl4nVTIG7lWp2VfipxausydjFjBMLytVWJQdbfnymK6cuXeXVX5JUilAIIYS5ebZuCG2r+jFu/xfkl/GlX4CXBzenYc1qPPfDNgoKC1WMUAghgJws/fs6296EP3vDdy4wtx2sfxYuaaFmJ+gxE8KSYcwVGL4D+vwIbV6COn3NNvkBK0+AAPp4tOPl+g8y5ciP7M7Wlautpt4ufDIykN826Ji9oXxtCSGEME92NrZ823w8O7NT+PrUort/4DYc7Gz5anQHtMfS+WbFIRUjFEJYpeuXIGU+bHgB5vrDD+7693X2R4OjGwS+A0PXw1OX9clOrxhoEQGe/mDrYOzoVSXztIBpDZ8gPk3LI3s+JLHjV1Qqx0l+tLsvmw5e4KWfE2nn407zOq7qBSqEEMIsdHBpyrg6g3jj6E+EenXHu1L1MrXTsZEn44Ib89783QwOqItPDWeVIxVCWKy8a3B2A5yKh9RVcGkXoEA1H6jVA1o+C7W66d/Z0WiMHW2FsvonQACVbB2Y3Woyh6+m8vrRH8vd3iePt8fXqyqPfrmRKzfyVIhQCCGEufmg0Wiq2FbixUMzy9XO1NA2eFarxPgfZLkFIcQdKAqk7YUdUbAoCL53gyUD4Ogf+nd2+vwEj52ER3XQ9ydo/iS4NbG65AckASrWqqoP0xs9wacn5rM6rXwL0Dk52vHLc904m3GN8T9slwFLCCEs3L+XWijiau/MZ03GEXd+A8subi9z286V7Pl8VCBr95/n1/UyvVoI8S/5N+D4Ulg7Dn6tD3NaQeLbYOMIXT6Ch/bD46eg78/Q9HGoWtfYEd8zQyyzIAnQv7xYfxi93dowcu9HZOZdKVdbjWtV46vRHZm39YSsDySEEBauaKmF8PDwEtsfqtmLIPd2PHvgS64VlY0tg6DWtQnv6sPrsVrOZV4vb7hCCHN2IwMO/gJ/D4MfPGDZYDidAL7DYPAKGJ0Og5dC6/Hg3szsn/CEh4ezaNEivL29VWvTahKg1NTTN92d+y8bjQ0/tZzA5YJrPH/w63L3+WCn+kQENWLib8nsOJZe7vaEEMJUyUKot6bRaPim2XjO5qbzvu73crU1/WF/7O1seennRJlZIIS1uXYB9n4Li4LhpxqweiRcOwvt34SH9sEjR6DbDKjXT7/4qLgjq0mAvJXDLJr50k135/6rXuUafN30OX47u4p559aXu98Pwv1pWdeVx7/aQMbV3HK3J4QQpsgQd+gsRaMq3rzmE85Hx+ex78rxMrfjUdWRz0a2Z0nyaRZuP6legEII03QjDfbF6N/n+bkWbHgOUKDb5zAyFR7cAv6Twb252T/lqWhWkwBhVwn+6gtbJkPBnRORR2r1JcyrB+MOfM6ZG2nl6tbR3pafn+tG5tVcnp61Ve7aCSGEFZrkMxzfyrUYt/8LCpWyr+lzf2A9hgbW5ZVfkrh4uexT6oQQJirvKhz+HZYOgZ9qwvqnAQ30/BZGnYOQBGj5DFSpbexIzZr1JEBenaHTdNj1CSzoClm3fy9Ho9Ews9l4HG3sGbXvo3INVgANPJ35NqIzS7Wn+XzZgXK1JYQQwvw42jjwbfPxbMzcy/epy8vV1iePt6egUGHSb8kqRSeEMCqlEE6vhlWj4EcvSHhE//Sn62cw8gyExEPzMVC5bOX0xc2sJwHSaMB/EjywGXIz9QtAHZ132909HKrxc8sJxKdpmXFiQbm7H+Rfh5cHN+etubtYt/9cudsTQghxa5mZmSW+12q16HQ6MjMzSUhIKP55QkICMTExxMXFERUVVbz/7baXV2/3toyq3Y+Jh7/jfE5Gmdup4VKZqEcDmLf1BEuST6sWnxCigmWfgO1vw68+sKgvnNuk/131kRR4cDO0eg6cvIwdpUWyngSoiFcghCVD/ftg5XDY8DwU5Nxy12CPAF6pH8qUIz+y83JKubueGtqans29GPn1Jk5dulru9oQQQvxfXFwckyZNom/fviW2T58+HT8/P3x8fNBqtbi6ugIQGRlJREQEoaGhxZ+/03Y1fNR4DLYaG14+9G252hnRpQED2tbmxZ+2k37l1mOYEMIEFeRBynxY3F+f+Oz6BOr1h2Gb4eHD+qIGLr7GjtLiWV8CBOBQDYJjocc3+pfLFnaHK7euXDSt0SiaOdfl4T3Ty1XCFMDWxoYfnulCFUc7Hv1yAzdyC8rVnhBCiP8LDQ1l7NixN20PDg5GURQyMjKYOHEioH/KU5QIAfj7+zNnzpzbbldLdQcXPmkcwe/n1rDiUlKZ29FoNHzxRAdy8gqYKFPhhDB92Sdg6+vwaz1YEQq5l6H39zDqLPSKgZqdpZBBBbLOBAj0f8laPq3PuK+dg7j2cG7rTbs52jjwe6spHLt+jlcPxZS72+pVKzF7fHf2n87i5V+klKkQQlQEnU6HVqst/l6r1eLu7l78vbu7O1qt9rbb1fR47WB6u7Xh6QNflOvGWi03Jz56rD1zNh9ncfIpFSMUQqhCUeBUAvw9FH7zhb1fge+DMGK3voJbsyfA3tnYUVol602AitQIgNBEqOYHf/aEgz/dtEtz5/p82mQsM08vYcnFm5Oke9W2gTufPxHIr+t1RMcfLnd7Qggh7iwzMxNfX1/CwsLIzMwkLe3WFT5vt/1uUlNTCQkJKf6605pzGo2Gb5u/wJmcNN7TzS5Tf0VGdGnAfe28efGnRNKyZSqcECYh7yrsnQmxzWFxMGTpoMdMfUGDHl+BRytjR2gWitaXK/pSc505O9VaMnFFg1N4ePjNawE5ecH9q2H9s7D6Ccg8Ah3fL/EoclydwSy7uJ3Rez9hd5doajq6Ux4Pd/Nl94kMJv+upam3C71a1CxXe0IIYUyxsbHExsaa5EKoERERxf8fHBzMpEmT8PPzu6lYAoCHh8ctt9+Nt7c3ixYtKvX+javU4XWfcN7Vzebhmn1oVdXnnvsEfTL1+RMd6DBlKa/+msSPz3QtUztCCBVkn4I9X8KBWfopbr7DoFc01Oou09vK4L+/s4eEhKjWttU8ASoanG67EKqtg34OZuePQPsBrHq8xHpBGo2G71u8jEaj4Ym9H6syde39h9rRs7kXj3+1kWMXrpS7PSGEMBZTXQg1ISGB4ODgEtvS09Px9/cnPT39pm23224IE32G08jJm4j9M8q13EJN18p8/Fh74rae4K9EWSBViAp3IQniH4bffGB/DDQbA4/qoP88qN1Dkh8TZDUJUKloNNDuVQj+A47OhSUDISer+Mc1HN34qcWrLE9L4qtTf5W7OztbG356thvuzg6M+Gwd2dfzyt2mEEKI//P19S1RGCE5OZkRI0YQFBSETqcr3q7Vau+43RAcbRyIbv4CW7MOEH16abnaCutcnyEBdXjhp0RZIFWIiqAocHIF/Nkb4gLh/LZ/1u05DV2ioGp9Y0co7kASoFtpNEK/6NQlrf69oGv/X7dnoGcHnq93PxMOz2JP9rFyd+VWxYE/XuzJ6bSrPP3dVimKIIQQZZSQkEB0dDQ6nY6YmBh0Oh2+vvpysjExMcTExODn51dc3joyMpKoqKjiMtd3224I3d1a8ZT3QCYf+Z4zN8r2/hH8fyocwEs/S4EdIQxGKdSXsY5rD0sGQP5V6D9fX8K69fNS1MBMaBQruUqGhITc0/xsANL2wpL+YFsJhqwEFz8Arhfk0GHb8yiKwvZOX+JkW6nc8S1OPsXDn2/g7bA2vDKkRbnbE0IIYyjTtdYClOe4M/KyabbpKbq5tiCu7dRyxbFw+0ke/2ojPz7ThdBODcrVlhDiX5RC0P0JSe9A2m7w7g3+r0GdvjLFrYKoOb4Y7QmQqa7UXYJHS32ZbBs7WNAVLu0EoLKtI3+0fg3d9XO8fChala6GBNRlYkgL3onbRfzuM6q0KYQQwvS52Vfl86ZPM//CRhZd2FKuth7oUI9hHerx8s9JnMu8rlKEQlgxRdEnPnP9YcWDULkGPLBBXzyrbpAkP2aqwhMgc1ipu4Sq9eGBjeBcF/7qDecTAWjh3IAZTccRfXop889vUKWr14a1Irh1bUZ/swnd+WxV2hRCCGH6hnv15L7qHXj2wJdk518rV1ufjGyPg50Nz8q0aiHKTlHg+GKYFwDLH4BK7jB0vf4ViVrdjB2dKKcKT4DMYaXum1T2hPtXgWszfT3389sAGON9Hw/W6MZT+z7j5PUL5e7G1saG78d1waOqI8M/W0fWtdy7f0gIIYTZ02g0fN3sOdLzsnnj6E/laqt61Up89WRHVu4+y49rU9QJUAhrkroOFnSBZSH6d3ruX6N/4lO7u7EjEyoxqSIIprRS900cqsGQFeDeEhb3g3Nb0Gg0zGrxEtXsnAjf8wF5hfnl7sa1igNzXurJ+czrjPp6E/kFZS+NKoQQwnw0qFyT9xqO5MuTf7E962C52hrQ1ptRvfx47XetzCgQorQu7YYl98FfvaAwT//+99B14N3LyIEJtZlUAmTolbrLzaEqDP4bPNrA4v5wfjtu9lX5o/VrbMs6yNSUn1XppkltF355rjtr9p1jyu8GTuyEEEKUW9Fi27GxseVqZ3y9B/Cv1pCn9n1W7ptq0x/2p4ZLJSJitlBQKDfThLit7BOQ8DjMbQtZR6HfXAjdDnWD5R0fExAbG0tISIiqC22bTAIUERGBv78/rq6uxSt1e3h43HLf222/k6LBqeirzIOUQ1UYvAw8WuvLH17aTWfX5nzQcDQfHpvD8kuJZWv3P3q3rMknj7fn2/jDzEo4rEqbQgihtqKBqehLzQHKnNx1se1SsrOxZVbzl9h/9QQfH59XrracK9nz7ZhObD96ic+WHihXW0JYpBsZsOlVmN0YTq2EHt/AQ/ugYRhoTOZXZKtniIW2TeLsVsRK3UWDU9FXuQYpe2cYtASqNtBPh8s8zKsNQhlYPZDH9kSVay2Hf3uyTyOe6deECb8lkyCV4YQQJqhoYCr6UnOAslbtqjXk5foP8o7uN45cLV9C2aVJDV4a1JxpC3az83j63T8ghDUoyIM9X8HvjWDftxDwOjx6FFqOA1t7Y0cnKoBJJECmvFL3bTm66t8JquQOi4KwuXKKn1tOwMHGjof3TCe/sECVbj54uB1BrWox8utNHDidqUqbQgghTNvbfo9R29GDsftnlLuS2+vDWtGijitPztzMtZzyv6sqhFk7uQLmtoEN46HB/fDIEQicKguYWhm70uw0ZcqUEtPOii7GGo2mxIW56Pu5c+eSmHjrqWAJCQnEx8cXr9QdFBSEr68vWq2WmJgYgFuu1F20mvfdtleoyp4QkgALu8Hi/ng+sJHYVlPonTSRd3S/8l7DUeXuwtbGhh+f6Uq/9+MJ/XQda97qRw2XyuWPXQghTISaY4ylcLKtRHTzF+iXPIUfz6xgtPeAMrflYGfLd+O60H3qcqbO2cnHj7dXMVIhzETmEdj0MpxYArV7QNBs8Gxn7KiEkZQqAfL19WXMmDGlbvTfJar/KygoiKCgICIjI0tsv10CU7R/abdXuCq19VVCFnSFpYPocf9q3ms4kjeO/kR315b0q17+gaZqZXvmvdyTXm+v4KEZ61k6pS+VHUp16oQQwuSpOcZYkmCPAB6rFcQrh2IYWD2QWo73/v5rkabeLrz/UFte/TWZ/m1rE9y6toqRCmHC8q5C0nuw61NwqqUvcOAXKsUNrFyppsD9d2DauXMnO3fuBOC7775jypQpXL58+bb7WzyXhvrqcBn7YUUok+sNo59HAI/uiVTtfaA6HlWY81JP9p7KZGzMVgoLZXE7IYRlkDHm9j5rMg4HGzuePfBVuafCRQQ1Jrh1LZ6etZWLl2+oFKEQJuz4YohtDrtn6N/zefjgPwUOJPmxdmV6Byg+Ph4XFxdmzZrFvHnzmDx5MnPnzlU7NvPi6Q8D/4TTq7BZO4ZfW07A3saO8D0fqPY+UICvB9+N68KfiSd5J26XKm0KIYSpkTHm/zwcqvFV0+dYeGETcec3lKstjUbDN091Ir9Q4bnvt5U7oRLCZGWfgr8f0C9k6tZMX9kt8C2wk1cIhF6ZEqCAgAB8fHyIi4tj7NixuLi44OPjo3Zs5qdOX+j7Cxz+Fc/dX/BH69fYlLmPN1N+Uq2LkPZ1mfZQOz5dsp+f1h5VrV0hhDAVMsaUFOrVnQdqdOW5g1+Rlnv57h+4g5qulfn6yY4s25HKD2tkDBEWprAAdn0OfzSH89v0090G/w0ufsaOTJiYMiVAOp2OHTt2kJiYWPwezrFjx1QNzGw1egg6ToPEt+l+YRfT/1kfaNGFLap18dyApjzVpxEv/pTIqj1nVWtXCCFMgYwxJWk0Gr5u9hy5hfm8eGhmudsb5F+Hp/o0YsrvWg6mZqkQoRAm4NJOmN8RNr0ETR6H8AMy3U3cVpkSoL59+zJnzhySk5NRFIXJkyeTmZmpcmjqUmuV7lLxnwJNn4A1o3nVwYuhNbrw+N4odNfUSVY0Gg0fPRZA31a1eOzLDew+kaFKu0IIK6MocP0SXNoNJ5fDkT/K3JSaK3XLGHOzWo4efNZkHL+dXcWyi9vL3d608HbU9ajCkzM3k5OnzjRtIYwi/wZsfR3mtYeCHBi2CXp8DY4uxo5MqETN8aWIRinFJOCsrCxmzZpFaGgoDRo0UK3zihQSEsKiRYsqrsOCXFgyEC7t4PL9q/E/MINqdk5s6vAZlW0dVeniyo087vtgFWcyrrH6rf7Uq15FlXaFEBYkNxuyjkD2SbhyErJPwOUUyErR/zf/+v/3ta0EEdfKdce0LNdaixhjhgxi0eKlBu1DURQGal9n35Xj7O0yCxf78l3zd5/IoPc7KxjTtxEfPhKgUpRCVKBzW2D1aP21LOAN8J8Mtg7GjkoYiJq/y5cqAQL9ADV37lx0Oh2BgYEMGzZMlQAqSoUnQAA5mfrHsRpb9vT7nQ473iC8Zm++b/EyGpUeyV7Iuk7fd1fiaG/LyjeCcXdWJ7kSQpiRwgLIPqZf5yLrqD7hyTwE6fvh6un/72frCM71oJqvvnqli5/++yre+i8nr3L/8lDWa63ZjzH+TiyKmw0+Qw065ebk9Qu02DyGh2v1Jrr5i+Vu75sVB5k0W8u8l3syoK13+QMUoiLkX4ftU/WlrT3bQ58fwL2FsaMSBmaUBOjfduzYwZw5c9BoNIwYMYK2bduqEowhGSUBAsg4pE+CanXjlzbPMnL/Z3zbbDxj6w5WrYsjZy8T9F48TWpX488JvXFylDWChLBIigLXzkL6Pv1X2h5I3wPpe///JMfGQZ/YuDbRVz9yaw6ujaFqA/3izQaeD6/GtdYsx5iOXiwaeQHqBEG3z8G9ucH6mnlqMc8c+JJVAZH08SjfQo6KohD26TqSdGlsnXYfNV2lSpYwcee2wupRkH0cOrwLbV4BG1tjRyUqgNEToH+bP38+iYmJVK9enYiICKpVq6ZKYGozWgIEcOJvWDYY2k7gOdc6xJxexvrAT+jk2ky1LrYfvcSQD1fRo7kXv4/vgb1dmV7vEkKYCkWBq6lwIQkuJv3z32S4cUn/c9tK+gTHozV4tAL3VuDWBKrUMeovA2pfa81qjPliLGx6Uf+LWavnof1bBnkPoVAppE/SRE7euMCeztFUKWdp34uXb9Dljb9pWrsaf03sg42NvDQuTFBBLiS+Azs+/Oepz0/grt7vUcL0mVQCVCQrK4uYmBjS09NNcvqCURMggJ2fwOZXye/7Mz2ztJy4foHkTl/j5eimWhcJu88w/LP1hHaqx7djOssgJoS5UArhsg4u7YK0XfpqRhcS4do5/c+daoJngH7Qr94G3Fvqp7GZ4F1PQ11rzWaMKciBnZ9C8vtg7wydPoSmI0Gj7k2po9dSab15HGPqDOTzps+Uu711+88xJHI1bz7YhgkhMpVImJi0vZDwKGTsg/Zvg/8ksJHZLtZGzfFFtb89GRkZTJgwAbDucqW31eZluLQTu3XjWDh4OW0Of8Pw3e+TEBCJvUr/iINa1yZmbCdGz9yMu7Mj0x/2V+1dIyGEihRF/45O6mo4vRpS10BOuv5nlb30SU6zJ/UJT41AqFLb6ku5ms0YY+sIAVOgyWOwZSKsGQ37o6Hbl+AVqFo3DZ28+aDRE7x06FuG1ehGT/fW5WqvZ/OaTBjSgvfn76ZLE0+6NqmhUqRClINSCLs/hy2T9e8tPrgdPMs37VMIKEcCtGDBAtLT04u/j4+PZ86cOQBWvWDdbWk00DMa0nZTY82TLAj6mR6732Pyke/5pMlY1boJ7dSA9Cu5vPJLEh5VK8mdPCFMgVIIabv1yc7ZjXBuI1y/qL+D6dUJWj0HNbvqEx8nL2NHaxLMfoxxrgPBv0PzsbDhef27oM1GQ6fp+nexVDC+3lDmn9/I6H2fsLvzt+WeCjflgVZsPHSB0d9sYtP7A6letZIqcQpRJlfP6t/1ObUSWr+o/7djJ38nhTrKlAANHz4cd3d3XF1di7elpKSoFZPlsneCAQtgXgCdkz/kk+ZP8cLhaAKrNeahWr1V6yYiqDHpV3J4N24XblUceKpvI9XaFkKUUvYJOL0KTsVD6ip9wmNXGWp01P9SXKsb1OqqnyYlSrCoMca7JwzXwr5o2P4G6BZAxw+g+ZhyT2G00djwY8tXaL15HJOP/MCXzZ4tV3t2tjb8+HRXurz5N2NjtjLvpZ4ylVoYx/ElsPoJ/U2iwSugXj9jRyQsTJkSoBEjRvDggw+W2LZjxw5VAjKUokXqwsPDCQ8PN14gLn4Q9BssG8LzNTqyrWZvntz3KS2dG9Cyqnp3NSfd35KMK7m8/EsirlXsCe3UQLW2hRC3kJP1T8KzEk4n6NelQKN/d6fZGKgbDDU766dIWaDY2FhiY2NVWajOHMeYO7Kxg1bPQsPh+qk865+GA99Dz5lQo325mm7o5M30RqN58dBMHvTqRi/3NuVqr7a7E7PGdmbYx2v54u8DvDjIcNXshLhJQS5snaIvb11/sL68tUpPTIX4tzK9lenm5sbly5dLbDPpOdmAt7c3ixYtMm7yU6TBYGj/JprEqXzn0hY/p9o8sOsdMvOuqNaFRqNh+sP+PNSlAWOit7Bil3qr5woh0L/Hk7YXtB/Cwh7wgweseBDOrIF6A/RPe0enQVgidJoG3r0sNvkBCA8PZ9GiRXh7l38tGXMcY4pussXGxt5+p8qe0Od7eGATFObpp8VtfFG/WG05PF/vfrq7tmT0vk+48u+FbcsouHVtXhrUnLfn7WLzoQvlbk+IUsk+AX/2gD1fQtfP4L5FkvwIQH+DLSQkRJUbbEXKVAVu/vz5jBkzBg8PD0C/jkBGRgZpaWmqBaY2o1eB+6/CAlgyANJ2c2zIMvx3T6OzazMWt3sXW416lZ3y8gt59MsNrN57joUTetOtqbzYKkSZFRbo3+HRLYDji/Tlju2c9Gu/1BsI9fpDNTN4P8WA1LjWWsUYU5ivf7l7+1RwdIceX4NPSJn7T7l2htabxzLKux9fN3u+zO0UyS8oZNCHqzh24Qqb3huIZzV590IY0PHFsGokOLhAvzng1cHYEQkTpObv8mV6ApSYmEhycjJJSUnFX2PGjFElIKthYwtBs8HGDp+NLzOn5URWXEpm6tFfVO3G3s6Gn5/tRsdG1Rn+6Vp2HEu/+4eEEP9XkAcnV8KaMfBzLfirF+jm6xOeQcv0T3nu+wtajrP65EctVjHG2NhB21fgoX36dZz+vh9WPqR/V6wM/JxqE9V4DN+cWsyqtPJPF7SzteGnZ7qSl1/IkzM3U1BYWO42hbhJYT5smQTLQqB2DwjTSvIjKkSZEqDg4GB8fHxwcXHBxcUFV1dXXnvtNbVjs3xONfR3Os5uol/Kn0xvNJoPjsUy79x6Vbup5GDLHy/2oIm3C0M/WsPB1CxV2xfC4hTkwMnlsDZCn/Qs6a8vWd30CXhwKzx+Enp+A/UHSlUiA7CqMaZaAxi0VH9D7FQ8xDaHI3/op1jeo6frDqa3WxtG7/uEy/lXyx1aLTcnfni6K2v3nyPqr33lbk+IEq6ehUV99eskdv4IBiyESuqtjSjEnZQpATp27Bgff/wxCxYsYPXq1SxYsMDy7s5VlFrd9Avl7YhkgsaZh2r2YtTej9iVrW7FI+dK9sx/pRe13CozJHI1uvPlm3MuhMXJvwFH58KKEfCDJywZqC9q0GwMhCXDI0ehcyR4dVR9UUtRktWNMRoNNH4YwveDd2+ID4cVoff8NMhGY8MPLV8hPS+bCYdnqRJa75Y1ee2BVkz/cw+r9pxVpU0hOLMB5vlD1lEYuhbavWr1a52JinXPVeCysrL48MMPCQoK4ujRo8Xbk5OTVQ3MqrR9BVLXoFkziu9Dt9Pt6mlCdrzFto5fUNPRXbVu3J0dWTSxN/2nJTAkcjUrXg+ijkcV1doXwuwoCpzbDId+gaNzIDdLX7Wt3UTwGQruLWRQrmBWPcY4eUH/uZASB+vGwR8todese3o3qEHlmnzceAzjDnzBUM8uDPQs/3SiCSEt2HbkEqNnbmbTewNk3BBlpyj6d982vwq1ukO/P2TtM2EUZSqCsGrVKvr27Vti244dO2jXznRX5zW5Igj/de0CzGkN1dtwOugXOmx/gXqVarCm/UdUVrly1Om0q/SfloC9rQ0rXg/Cy7V8i+cJYXauntUnPQd/gMzD4FwPmjwOTR4D18bGjs6sqXGtlTEGuHZOPwXz+GJoOhq6fwH2pUs8FEVhoPZ1dl/RsbdLDO721codzqXsG3R/czk13Sqz4vUgHOzUK9YjrETeNVj7FByJhbav6hc2tSnTaizCShm9CEJgYOBN2zRyl7R8nGpA0K9waiV1Dv7Korbvsjv7GKP3fUIZctQ7quNRhcWT+nAtN5+QqNVcyr6havtCmKSCPDj2l/5l21/qQuLb4BkIIQnw2DHo+J4kPyZCxhjAqSYM/At6fw9H/4B57eHSrlJ9VKPR8H2Ll7lRkMezB75SJZzqVSvx6/Pd2HU8g9d+N+M1mYRxZJ+Ehd301+B+c6HLR5L8CKMqUwL037nYx44ds+z52RWlbjC0nQDbXqN9zmV+aTmBP86t5Z2UX1XvyterKosm9uHi5RxCIleTfiVH9T6EMAmZR/SLT/5aD/4eClfPQPcvYdRZCP4N6vSVd3pMjIwx/9BooNlofWUsW0f9ukF7vipVgQTvStX5utlz/HFuLX+cXaNKOO39qhP5iD/RCYeZu/m4Km0KK3BmA8S1h5wMGLYZGoYZOyIhypYAxcfHs2DBAgBmzZpFcHAwbm6mXbmjVIvUmYKO74NHa0h4lFCP9rzfcBTv6H4jVqUB7N+aeruwZHIfzmRc5/6o1WRczVW9DyGMIv8GHPoV/uwJvzeG/THgFwrDd0JYErR8GhxdjR2lRVFzoTpzHGMMyq2Jvvpg8wjY8DysHAG5l+/6sYdq9mK4V0+eOfAVZ26os4bSU30bEd61Ac/9sI3dJzJUaVNYsP3f6yu9ubWA0ESo3sbYEQkBlDEBSk9Px8XFhcDAQLRaLUePHmXlypVqx6Yqb29vFi1aRHh4uLFDuTNbBwj6Tb8i8tbJvOYTzmO1gnhi38dsydyvenfN67iyZFIfTly8ytCo1WRKEiTM2dWz+oUlf60Hqx4HjR0E/w4j/3nqI4OvwYSHh7No0SK8vb3L3ZY5jjEGv8lmV0n/HlD/ODi1Qj8lLm3PHT+i0Wj4ptnzVLK154l9H1OolH8tH41Gw+dPdKBJ7WqEf75eplCLWyssgE0v69/5afYkDFkJlasbOyphptS8wVak1AnQzp07S3x5eHgwfPhwgoODOX78OE8//bRqQVk9t6b6+bF7vkRzKp5ZLV4ksFoThu58m+PXz6neXct6biye1IdjF67IkyBhns5vh/hH4Nf6sOszaDgCHj4E96+CRuGyVo8ZMPcxpsJusvk9CKFJYFdZPyXu8Ow77u7hUI0fW7zKyrRkvj6lzsvDlR3s+H18D67m5PPE15vIL5BFUsW/5F7Wv2u5+3P9jace34CtvbGjEmZMzRtsRUpdBa5hw4YEBATc9oX8HTt2cOTIEdUCU5vJV4H7L6UQFg+AjH0wYg+XbGzpuG08lW0c2NRhBi6lrAZ0L3YdTyckag113J1YNKkPHlXVrT4nhKoK80G3AHbNgPNboJoPtHpeXzHL0cXY0Vmtsl5rZYy5R/nXYd3TcOhnfdn2jh+Aze0rs71w8BtiTi8jqdNXtHBuoEoIGw6cZ0jkap7p34QPwv1VaVOYuewTsHQwXDmpL3ZQr7+xIxIWRM3rbKlLcERHR99UlvTfVq1apUpA4h8aG+jzI8xpBeufpXq/WJa0e4/O218gbPd7LG33PvYqV1Bp08CdZVP6MvjDVQz6cBWLJ/XBs5rcORcmJu8qHPwRdn0Kl49B7V4w8E+oP/iOvwAK0yZjzD2yq6wfI6q30a+pkrZXP93zNsn/h42eJCFtB4/uiWRrx89xtHEodwjdm3kx/WF/Jv6WTOt6bjzU1afcbQozdj4Rlg3R/90ctgXcmxs7IiFuq1RT4I4fP37HgQko8fPjx4+XKyjxD2dv/aPjo3/AkTk0c67HgjZTWZO+i2cPfKl6eWyAFnVd+fu1IC5evsF901dxIeu66n0IUSZXz8K2N+CXerDxRfDqBGHJMHQN+NwvyY8ZkzGmjDQaaPMSDFoG5zbBgi5w+fgtd61s68jsVpPZd+UEbx79WbUQxgU35tHuvjz3wza0OnUKLQgzlLIA/uqpfxL/4DZJfoTJK1UCFB0dfU+N3uv+4g4aPQR+w2H9M3D1LH082vFd85eYlfo3UcfnGqTLpt4u/P1aEJlXcxn04WpJgoRxpR+A1U/Arw30c8qbPAaPHNXf7faUaTeWQMaYcqrXX/9LZ8ENmN8JLiTdcre21fyY1nAUHx+PY3WaOmv5aDQaZowKpHU9N8I/X8/5TBkvrM7uL2BFKNQfAvev1q9rKISJK9UcqqCgICZPnlyqhegM8VTC6vX8BmJbwJqnYNASRnr3I+X6WSYf+Z4Glb0YUbOX6l02rlWNpVP6Mmj6Ku6bvoqlk/vi5VpZ9X6EuK30/ZD0HhydA1Vq60vENx8j5astkNpjTGZmJq6ursXfJyQkoNPpcHd3R6fTMXHixDJtN2luTfTTjv4O0Zd/D44Fn5CbdnulQSgr0pJ5bG8UuztH4+FQrdxdO9rb8vsLPejx1nIe/mIDy6b0xdFenshaPKUQtkyCnR9D21ehc6SsqSbMh2IlhgwZYuwQyufYYkX5GkXZN0tRFEUpLCxUHtsdqTisvE9Zk7bTYN0ePpOlNBq/QPGfuFg5k37VYP0IUSzjkKKseEhRvtYoys/1FGXPTEXJv2HsqEQpGfNaO2/ePGXixImKv79/ie1BQUHF/x8ZGanMmzevTNvvxGTGmLxrivL3g4ryjY2i7Iu55S6nr19U3FcPUx7Y8bZSWFioWteJRy8qHqNjlTHfbla1XWGC8nMUZWW4/jq9c4axoxFWQs3rrNWk6mazEOrtNBisr2616SW4fByNRsN3LV6ih1tLhu58m73ZxwzSbaNa1Vg2pS9Xc/LpPy2BExevGKQfIcg+qX/KGdsczm6Ent/CI0eg5TiwlYqEps4Q6zTcq9DQUMaOHVtiW0JCQomnQf7+/syZM+eet5sNu8rQfy60eBrWRoA2Ev7z1My7UnW+a/4SCy9s4vvU5ap13d6vOt882YnYTceYseyAau0KE5Obra/0ljIf+s2BNi8YOyIh7pnVJEBmsxDqnXT7DBzd9AuLKQoONvbMbzsVn8o1GaB9ndM3Lhqk24Y1q7Hi9SAA+k9L4MjZu69ALkSpZZ/Ul/Od3QiOL4Iun+gTnxYR+oWBhVkwxDoNatBqtbi7uxd/7+7ujlarveftZkVjo19/pf1bsHUybJ5wUxL0gFc3xngP5IWD33Do6inVuh7epQETQlrw1tydLNWeVq1dYSKuX4RFfeH8VhiyHBqGGTsiIcrEahIgi+BQDXp9B6dXwf5ZAFSzq8LSdu9jq7FhQPJrZORlG6Tr+p7OrHg9COdKdgz4IIF9pzIN0o+wItkn9XeoZzeElHkQ+DY8qtPfTZSFS4VK0tJuXZnsXrffTdEsg6Ivo8820Gigw9v6RGjXJ7BunP6djX/5rMk46lWuwUO7PyCnUL0FsN8Y1prB/nV4cuZm9pzMUK1dYWTZJ2Fhd/1aP0PXgXdvY0ckLFzRzIKiLzVnGEgCZG7q9YNmT8HmV/QXIaB2JQ9WBHzA2dx0Qna8xfWCHIN0XcvNib9fC6KWa2UGTItn6xHDPHESFu5GGmx6FX5vDMf+hI7T4LHjEDAF7J2NHZ2wMB4eHqpsv5uiWQZFXyYz26DVc9DnJ/1NszVjSiRBVewqE9vqNfZfOcnkw9+r1qWNjYZZ47rQsGZVwj5dxzmpDGf+Mg7Cgq5QmAvDNoFnO2NHJKxA0cyCoi81ZxhIAmSOunwMDq7/DGb6aQ1Nq9Rjabv3Sb58hIf3TCe/sMAgXXtWq8TSKX1pUdeNkMjVrNx1xiD9CAuUfwO0H8JvvrA/Gvxfg0dSoN0ESXyEwfj7+5Oenl78fXp6Ov7+/ve83aw1HQl9f4FDP8Hq0fCv8aFtNT+iGj/FjJMLWXpxm2pdVnG0Y+5LPSlUFEZ8to5rOfmqtS0q2IUk/ZMfR1d4YBO4NDR2REKUm9ESoMzMzBLfJyQkEBMTQ1xcHFFRUWXebhUcXaD3LDgdDwf+f9euk2sz5rV5g8UXt/LsQcMslArg4uTAwgm96NWiJiNmrGPeluMG6UdYCEWBlDiIbQbb34QmI/WJT+BUcKhq7OiEhQsKCkKn0xV/r9VqGTFixD1vN3tNHoW+v8LhX2HN6BJPgsbXG8qg6h0ZtfdjztxQbzHT2u5OzHupJwdTsxgTvYXCQlkmw+ykroW/+uiTnqHroEotY0ckhCpKtQ5QVlYWMTExpV6jQaPR8Oqrr97y53FxcSQmJpKQkEBycnLx9sjISOLj4wGIiooiLi6O0NDQe95uNeoNgKZP6KfC1RsIzvrHgoM8OzKr+UuM3vcJNR3ceafh4wbpvrKDHbOf786z329j9MzNnMu8zvMDmxmkL2HGLmr1lQvPrIf6g2Dwcv16JUL8i1pjTEJCAvHx8eh0OmJiYggKCsLX15fIyEiioqLw9fUFKB4r7nW72Wv8sL5AQsIjYFtJX2lRo0Gj0fBjy1dos2Ucj+75kPj2H2KrUWcdnzYN3Pnhma6Ef76et+bt5L0RMnXKbJz4G5YPg1rdYOBCeVIvLItqBbXvQUpKSol1GuLj45XQ0NCbvr/X7XdiMms0qOl6uqL8WFNRlgxWlP+suTBdF6uwIliZeXKxQUMoLCxUps7ZoTg/NluZ9FuSUlAgaz8IRVGunFGUVU/o14j4vbminFhu7IhEBbHIa20pmNVxH/hRv67chhdKjB2r03YomhX9lHeP/qp6l1/+fUBxfmy28v2qw6q3LQzg6HxFmWmvKEtDZB02YTLUvM6W6glQae3YsQM/Pz+qVbu3laWtskypGiq56e/g/T0UjvwOjR8p/tGkBiM4m5POMwe+pIaDK8O8uhkkBI1GwzvD2+Lt7sSrvyZxNuM6MWM7yyrg1qogB3Z9Bknv6yu59fgamo8BG1UvNcJKlXWMEf/RdBTkX4f1z4CdE3T6AIDe7m150/cR3k75jR5urenp3lq1Lp/t34TjF67w8i9J1PGoQr82tVVrW6js8O+w6nHwC9VPm7S1N3ZEQqhOlXeAFixYAEC7du1ISEi4589XRJlSkytRqhaf+6HhQ7BhPFw7X7xZo9HwWZNxDK/Zg4f3TGdt+i6DhhER1JjZ47uzbEcqD36ylsvX8wzanzBBJ5fDH61g2xv6pOeRo9DyaUl+LJwhy5QWKe8YI26h5dP6Nbe00/U3Lf4x1e8Ruru15OE907mYm6ladxqNhshH/enXpjYjv97I7hNSHtskHfwJEh6FJo9B0GxJfoTFKncCNG7cOD744ANGjBjBiBEjSrw8WloVUabUZEuUqqH7F/p53RueL7HZRmPDzy0n0N21JSE73iL58mGDhjEkoC5/TuzNruPpDJyWwHkpfWodsk/C3w/AkoHgXAdG7Ppn0V5XY0cmKoAhy5SCOmOMuI22L0O7SbDpZTjyBwC2Gltmt5pMbmE+I/d+ROF/1g4qD1sbG358piuNalYj9NO1nLp0VbW2hQoO/KCvEth8DPT+HmxkJoewXOVOgCIjI5k1axZz5sxhzpw5ty1+cCdSprScKnvqF7tLmQe6P0v8yNHGgYVt36a5cz0GJL/OwasnDRpK1yY1WP56MBezbxD03kpSzhtmYVZhAgryYMdH+upu57dB8B8QsgrcWxg7MmFB1BhjxB10mg5NHtdPeTq9CgDvStX5tdVE/r6USOSxOap2V8XRjnkv98TB1oYHPl5D+hXDrFsn7tH+WbDmSWgxDnrO1N9UFcKClftvuIuLC8eOHSv+/rvvvrvnNqRMqQoajoD6g/VzunMyS/zI2a4yS9u9j5eDK/2Sp3Dy+gWDhtKirisJb/bD3s6WoHdXkpRyyaD9CSM4uxnm+cPWyfq7hQ8fhEYj9KvPC6EiNcaYilA0zdrspldrNNDrO/Duo3+Se0k/XXpA9UBe9wnnjaM/qz6F2su1Mgsm9Obi5RwemrGe67myRpBR7Z8FayP0i+b2+FqSH2FyiqZaqznFWpW/5StXrmTnzp189NFHzJ079477JiQkEB0dXVymtCiRKSo7GhcXB9xcjrS0262WRqO/a5N3BTZPuOnHHg7VWBnwIbbYEJw8mfM5hp1/Xa96FVa+EYRPDWcGTV/Fil3qvxcgjOBGBqwdCwu76l+eDk2CbjPAQV5KF4ZzL2OMsRRNszbL6dW29jAgDlwbwdJBcOU0AO80fJyebq0I3z2dcznpd2nk3jSuVY15L/dk5/F0nvp2CwWF6k21E/fgwA//T366fSE3sYTqMvKyiT27hod3T+dSblaZ2iiaaq3mFGuNopR/tcysrCzCwsIICAhg+vTpasSlupCQEBYtWmTsMAxvXzSsG6efilSnz00/Trl2hu6JL1Pd3oU17T/Cw8C/uF7LyeeJbzaxYtcZvniiA4/39DNof8JAFAWOzoWN46HgBnT8QD9VQuaIi/8wxLVWxpgKcvUszO8IlTxg6HpwqMq5nHTabXmGZlXqqro+UJFlO04TPmMDT/T247ORgaVaC0qo5OAvsHoUtBgLPb6R5Eeo5ti1s/x1cQuLLmxhfeYeCpRC/Ks25MeWr9K6qm+Z21XzOqvKE6D27dsTExNDRESETEUztuZjoHYPWDsG8q7d9GM/p9okBERyNiedAdrXyMoz7EuoTo52zB7fnZE9/Xj2+228NXenrAZubq6chr/vh/iHoFZ3CD8ArZ6V5EdUGBljKkiVWjBoKWSlwMqHoDCfmo7uxLaewrqMPUw9+ovqXd7Xrg5fjO7A96uP8sHCPaq3L27jSCyseQKaPfnPtDdJfkTZFSqFJGUd5q2jv9B2yzh8N45k0uHvqWTrwFdNn+NUj9kkd/6mXMmP2lSpTzt37lwaNGgAQEREhBpNirLS2Ojnc89pBYlvQ5eom3Zp7lyf+IAP6Z00gcE73mS5/zSq2FU2WEh2tjbMGBWIX82qvPHHDlLOZxMT0RknRymPbNIURT83fMsE/XS3AQvA9wFjRyWskIwxFcijFfSPg6X3waaXoPuX9HJvw7RGo5hy5Ac6uzZjsGcnVbsc2dOPtOwc3pq7k+pVHRkb3ETV9sV/6BZCwmPQ+DHoFS3v/IgyyS3MY1XaDhZc2MSSi9s4l5uOq50zA6sH8rrPwwyo3p6qdk7GDvO2VPkNtF27dsX/37dvXzWaFOXh2gjavwXb34RGD4HnzRXy2lbzY7n/NIKTpzB4x1SWtHvXoEmQRqNh/MBm+NRw5qmZm7lvegJ/vNiTmq6G61OUQ/YpfUWg0/HQdDR0+Vi/8K4QRiBjTAWr10//VGDdOPBoA82fYmKD4WzO3M9je6LQdvoaH6daqnb50qBmXLx8gwm/JePu7EhY5waqti/+cXI5rBwBvg/qS11L8iPuwfWCHOLTtCy4sJG/LmwhM/8KjZy8ebRWHwZ7dqKLa3PszWTtP1X+5s+aNat4WoKpVuixOm1f1ZcjXvMUFN66wk5H12b87T+NxMuHGLJjKtcKbhg8rCEBdVn+ejBnMq7T++0V7Dkpi+GZFEWBAz/CnJaQsR8G/w19vpfkRxiVjDFG0GKs/j2/9c/A2c3F68q52TsTuus9bhTkqtqdRqNh2kPteKiLDxExW6RwjiGkrtNX+qs3AIJ+k2nMolQy867w65kEHtz5LtXXhHL/zrfYnnWI5+vdz+7O0Rzq+gMfNYmgp3trs0l+QKUEyNfXl8mTJwPg4+OjRpOqM9sSpWVlaw+9ZkHaLtg147a7dXVrwd/+09h++RAhO97ieoHh12Ro5+PO2rf641HVkeD34lm247TB+xSlkH1KP+1lzWjweQAe2qsfKIUoBUOUKS1iDmOMRer2OXh1guXD4Mpp3OyrMr/NVPZdPcFzB79SvTsbGw3fPNWRfq1r8+gXG9l0yLBLNliV89th2WCo1Q36zdX/jiDEbaTlXuaH1OXcp32dGmuH8/jeKFJzLjHV71EOdv2e/V2/492GI2lV1cdsC5eokgBptVpWrVrFzp072bFjhxpNqs6sS5SWlVcHaDUeEqfqX2q9je5urVja7n22ZO4nZMfUCkmCars7seKNYHq3rMlDM9YzY+l+VChIKMpCUWD/9/qnPmm74b4l0PcncHQ1dmTCjBiiTGkRcxhjLJKtg/59IFsHfRKUf4N21RrybbMX+D51OTGnl6repZ2tDT8/240ODasz/NN17Dqubvltq5S+H5YMBPdWMPBPsKtk7IiECUrLvcz3p/9mQPJr1Fw3gqf2fcbVght80iSCUz1ms7XjF0zyGUGTKnWNHaoqVEmAJkyYgKIozJkzR15QNTUd34PKNfRzue+QYPR0b81S//fZ/E8SVBHT4ao42jH7+e68PLg5b87ZyVPfbpYF8Sra9Uv6Cm9rnwLfYfDQPmgwyNhRCVGCjDFG5FRD/0tz2m7Y+AIAo7z78XSdwTx34Gu2Zh5QvctKDrb88WIPGtWqytCP1nAwtWxrhwjg8jFYHAxVvPUV/uyrGDsiYUIu5mYy6/Qy+iVPxmvdcCL2f05OYR6fN3maMz1jWRf4Cc/XG0qdSp7GDlV1pV4H6OOPP+bVV181dDwGYxFrNJTVyeX6uz99foamj99x13Xpu7lP+zqdXZuzqN07ONlWzJ2i+VtP8PR3W2la24XfX+hOHQ+5SBvcqQRY9TgU5kLvH8AnxNgRCQtQ1mutuY8xAQEBeHt7Ex4ebpkzDfZ/r79R0ucnaDqS3MI8eiVO4OSNCyR3+hovR/XfE0zLzuG+6QlkXM1l+WtB+HpVVb0Pi3btHCzopv//YRvBqaZx4xEm4VJuFgsvbGLuuXWsydiFokBv9zaEenVnaI0u1HR0N3aIN4mNjSU2NpbU1FSSk5NVabPUT4DmzJnDrl27VOlUVLB6A6DRw/qSptfuPKe6p3trlvlPY0vm/gorjADwYKf6xL8RzKXsG3Sfupy1+85VSL9WqSAPtkyGxf3AvTmM2C3JjzA6cx9jLH6adfMn9RUh142DS7twsLEnrs2bFCiFhO16j9zCPNW79KjqyKKJfajiaMfgD1dx6pJh162zKDlZsHgAFFyHkHhJfqxcdv41fjuTwH3a16m17iHG7f+CQhS+bvoc53r9QUL7SMbVHWySyQ8YZop1qRMgRVGYOXMm/fv3Z8GCBaoFICpItxn6hc42vXTXXXu6t+Zv/2lsyzpYoUlQmwburHtnAK3ru3F/1Bo+WrRXFk1VW/YJ+LMH7PoEOk2HISuhSm1jRyWEjDHmoMdX4NYUlj8IOVnUruRBXJs32Zp1kJcOfWuQLr1cK7Nkcl9sbTQM/nAVZzNuXuBb/Ef+Dfg7BK6cgMEroJoUDrFGNwpy+fPCJkbsmobX2hE8tjeKy/nXmPHP9LZV7aMYW3cwng6uxg7VKEqdAM2aNYtvv/2WuXPnkpKSQmBgIB9//DGXL182ZHxCLZU9ocuncOR3OPH3XXfv8a8kaLD2zQpLgjyrVWLBq72YENKCd+N2M/yzdWRcVbfcqtXS/Qlz28K1szB0A/hPkjUghMmQMcYM2FXWF0W4cUk/HU5R6OrWgq+aPcs3pxbz3em7jy1l4e3uxOLJfcnJL2Twh6s5n3ndIP1YhMJ8iA+HC4lw31LwaGnsiEQFyivMZ8WlJEbv/YSa60bwwM53OHztNG/7Pcbx7r+yscNnPFsvxCBTVs1NqX/7KVqIzsXFhQkTJpCYmIiPjw99+/blmWee4cSJEwYLUqikyWNQJ1g/hSHvyl137+7WqrhE9mDtm1zJr5hBx9bGhjcebM38V3qx/egler21nP2nMyukb4uUfwPWPwfLHwDvPhC2A2qqu5K7EOUlY4yZcPHTvzOYEgf7ZgIQUWcQ4+oM5pkDX7I5c59Bum3g6cySyX3IvpHH4MjVXLxcMTflzIqiwLqn4fhifaJaq4uxIxIVQFEUNmXs4+n9X1B7XTgDtK+xMXMv4+sNZX+X79jReSYTfYZTv7KXsUM1KWW+/Xv58mWSkpJISUnh22+/xd/fX824hCFoNNDzW7hxEba/VaqPFCVBiZcP0187hcxSJE5q6demNuveGYCTox29317Bgm3yC9A9Sz8A8zvCge+gxzf6QVEWNRVmQMYYE+Y3DFo9Bxtfgks7Afi86dN0dGnKgzvf4/SNiwbptmHNaiyZ3JeMKzkM/nAVl7IlCSoh6T39tb73D1D/PmNHIwzs6LVU3j76Cw03jqJb4kssvbSN0d790Xb6hkNdf+DdhiNp5lzP2GGarFInQEVzsnfu3MmIESNwc3MjMjISgIkTJ6LT6QwToUqsbiHU23HxhcC3YfcMuFC6Shrd3Vqxqn0kB66cok/SRC7lVlxJUp8aziRM7ccg/zqM/HoTr8VqycsvrLD+zdrh2RDXHgrz4MHt0PJpfRIshAGUdyFUcx9jrE7nj8C9BawYDrnZxUURHGzsuH/HW1w10IyBxrX0SdDFyzkM+VCeBBU78CMkvgUdp9212qswXxdzM/nq5F902jaeRhuf4NMTC+jl3po17T/iePdfiWz8FO2qNTTbxUkrUqnLYLdv3x6NRoNWq0VRFHx9fZk0aRJjxowxdIyqsOoy2P9VkAdxgfr3P0K3g41dqT62O1tHcPJkqtu7EB/wIbUreRg40P9TFIWZKw/x+h87aO9bnZ+f7Uptd6cK69+sFOTB5ldgz5fQ5HH9kx9Z+0FUkLJea2WMMUOZR2CePzS4H4J/A2BXdgpdt7/EAI/2zG3zBjYGes/wYGoWgz5cRfWqjiyZ3BfPala8uOfJFbB0EDR7Uj/LQ375tSg5hbksvbidn8/Es+zSdgAGVg/k0Vp9GeLZicq2jkaOsOKoeZ0t9ZVJq9WSnJxM3759iY+P5+jRo2YzMIn/sLWHXrMgbRfs/rzUH2td1Zf1gZ9wOf8q3RNf5ti1swYMsiSNRsMz/Zvy92tBnLx0hW5SKvvWrp2DRX30c/N7fK1fs0OSH2EGZIwxQ66NoGc0HJmtf+IMtKnqx2+tJjH/wkbeTvnVYF039XZh6eS+/6wVtIoLWVZaGOHSTlgRql/uosfXkvxYCEVRSMo6zHMHvqL2unAe3PUuZ3LS+KzJOM72/INF7d5leM2eVpX8qK3UCVBQUBApKSmsXLmSvn37GjImURG8AqHVeNj+pn6l6FJqUqUuGzt8ho1GQ7fEl9l/pWLfy+nUyJON7w2kVT1X7o9aQ9RfUiq72LmtMC8AslLg/nXQ8hkZDIXZkDHGTDV+GBo9AuufgcvHARhaoysfNHyC93SziT27xmBdN/V2YdlrQWRezeW+6as4Z23V4a6c1j/5cW0C/eaUejaHMF3nctL56NhcWm6OIHDbcyy4sJEnvQewt0sMiZ2+4rl691PdwcXYYVqEUidAYWFh+PhILXmL0vE9qFRdXyGsdDMhAahf2YsNgZ9S3d6FHomvkJR12IBB3qyoVPak+1vw/oLdhH26lrTsnAqNweTsn6Vf36dqAwhLluo/wuyY+xhj1e+Z9vgaHN0g4VF9GWZgss9DPFYriCf2fcyWzP0G67pxrWosm9KX7Ot5DJiWQGq6lawTlHtZn/xo7OC+xfKk34zlFuax8PxGQnZMpc76h3kz5WdaVW3A3/7TONl9NlGNx9DCuYGxwzSq8r5jeiulfgfI3Fnl/OzSOPYX/D0U+s2FhmH39NGMvGwGad9kz5Vj/NX2bfp4tDNMjHewas9Znvx2M5Xtbfnx2a50auRZ4TEYVUEubBgP+6OhxdP6BW9tHYwdlbBi1nqttdbjLnZ2I/zZEwLfgfZvAPp3F4KSJnP42mm2dfyCBpVrGqx73flsBn+4CjtbG5ZM7ku96hacEBTkwbIhcH4LDNusL0YhzM6e7GP8kLqc386u5lJeFgHVGjG6dn/Ca/XGzb6qscMzSUZ5B0hYKJ/7wWcobHwBcu6tupubfVXiA6bT1bUFA7VvMP/8BsPEeAd9W9Vi03sDqeNRhQHTEvhk8T7rmRJ3/SIsCoKDP+jf6er5jSQ/QgjjqNUNAl6HxLfhvP5FbUcbBxa2fQtn28oM2TGVy/lXDda9r1dVlr8ejKIoDJgWj+58tsH6MipFgY3jIXUVDFggyY+ZuZx/lehTS+iw9XlabxnL7LOrebx2ELs7R5PU6WueqRciyU8FKdUToKysLGJiYkpVVk9RFDQaDa+++qoqAarF6u/O3Un2KfijOTQZCT2+uueP5xbmMWrvx8w5t46ZzZ8nos4gAwR5Z/kFhUxbsIdPluyjT4uazBrXxbKrAl3aBX/fD/nXYcBCmfImTEZZrrUyxliIgjxY0AXyLusXXLbXV+o8cOUknbe/QGeXZixu9x52NrYGCyE1/RqDP1zFlRv5LJrYm2Z1XA3Wl1Hs/lKfAPWaBc2fMnY0ohQURWFr1gFiTi9j7rl13CjMY2D1QJ707s9gz07Yy7tbpabmdVamwAm9XTNg08vw4Fbw6nDPHy9UCnnh4Ey+OvUX7/o9zhu+jxilDv3qvWd56tst2Ghg1tgu9G5puCkXRpOyAFY9pn/xdeCfUFUWOhOmw1qvtdZ63DfJOAhz2+l/Oe/+ZfHmhDQtA7WvM9q7P982e8Gg48OFrOvcH7WGMxnX+XNCb9r5uBusrwp1crn+vZ/WL0LXT4wdjbiLjLxsfj2TwKzUv9l75TgNKnnxZJ0BPFG7P96Vqhs7PLMkU+DKwKpfUC2NVs+BZztY85T+vZJ7ZKOx4Yumz/B+w1FMTfmFZw58SYFSYIBA76xPy1pseX8gzeu4cv9Hq3lr7k7LWThVKYTEd2DFg1B/EDywQZIfYTIM8ZKqMENuTaFzFOz5Ck7FF28O8vAnuvkLxJxexkfH5xk0hBoulVn2WhC+Xs4Mmp7AxoMXDNpfhUjfDytHQL2B+j9fYZIURWFDxh4e3xNF7XXhvHI4hiZOdVjh/wEp3X/mDd9HJPkxEfIESPzfpZ0wrz0EvgXt3yxzMz+kLidi/wyGeHbi91ZTjFKnvrBQYcayA7w3fxdt67sTPbYzjWtVq/A4VJN3BVaNAt186PA+BLwmJa6FSbLWa621HvctKYWwuJ/+adCIPVDJrfhHbx79ifd1vzOn9esMr9nToGFkX8/joRnr2X70Er88142B7bwN2p/B3EiDuI5gVxmGbQIHMx7LLFRW3lV+PZvAt6eWsO/qCfwq1yaizn2MrB2Ml6Pb3RsQpSJPgIRhVG8L7SZC0vv6u01lNNp7AH+1fYeVl5IJSp5EWu5l9WIsJRsbDS8Pbs7KN4LJuJpL1zf+5psVB82zQMLVM7Cwh376w4CF0P51SX6EEKZLYwO9f9TfuNn0Yokfves3kodr9ubxvVFszNhr0DCqVrZn/iu9CGpdi/DP1xO7qfRr3pmMgjxYMRxys/TlriX5MSlJWYcZs+8zaq97iBcPzaRplbrEB3zI4W4/MNFnuCQ/JkwSIFFS+6lQrYF+Klxh2aewDfLsyJrAjzhyNZUu21/k2LWz6sV4DwL9qrP5/YGM6uXHpNlahkSu5nSa4SoRqS5tj/7O3/WL+nKnvkONHZEQQtxd1br6svyHfoHji4s3azQafmj5Cp1dmhOy4y0OXj1p0DAqOdjy63PdeLibDxHRW/hmxUGD9qe6zS/D2fXQP04/Nguju5p/nVmnlxGw5RkCtz3H8kuJTPIZwcnus4lrO5UgD39sNPLrtamTMyRKsqsEvb7Try+w95tyNdXBpSmbO86gkEI6bX+hwhdMLeLkaMdHj7Vn8aQ+pJzPpvPry1iw7YRRYrknpxJgYTeo5KEvTlG9tbEjEkKI0msyEurdB2vHwo2M4s1F5bFrO7ozIPl1zuWkGzQMO1sbvn6yIy8Oasak2VrembcLs5j9vy9G/y5V96/A27DTBcXd6a6d5dVDMdRZ/whj939OLUcPFrd7l+M9fmWq36PUruRh7BDFPZAESNysdnf9oppbp0B2+e7ONXTyZnOHGfhUrkmvpFdZcSlJCNGolgAAR61JREFUpSDvXa8WNdky7T56t6zFyK83MW7WVrKv5xktnjs68CMsHQg1u+iLHTib6dx1IayEFNq5BY0GesVA/jXY9FKJH7naO7PMfxp5Sj6DtG9wJf+6gUPR8N6IdkwLb8fHi/fx7PfbyC8w4QI5ZzfChmeh5bPQYqyxo7FahUohKy4lMUT7Jg03juKH1BWMqTOQlG4/scT/PQZ7dsJWY7iy7kLPEEV2JAESt9ZpOji6wPpn9AuvlYOngyurAiLp7daGwTve5Jcz8Xf/kIG4VXHg52e78u2YTvyVeJKOry1lzd5zRovnJooC29+GNaOh6RP/zPmWRdGE4RQqhSRlHebto78wYtc0Y4djtry9vVm0aBHh4eHGDsW0OHtD18/g0M9wfGmJH9WrXIOl7d7nyLUzPLjrXXILDX9DavzAZswa25nYTcd4+IsNXMvJN3if9+xKKiwP1d8A6/qZsaOxSpfzr/LlyT9ptukpBmhf41TORWY1f5HTPWcT1XgMPk61jB2iVQkPD2fRokV4e6t3M1gSIHFrji76x+4nlkJK+UuWVrGrzMK2bzOydjAj937EdF2s0aYgaDQaHunuy5Zp9+FToyohUasZ/+N2Lhv7aVBBLqx+ApLegY4fQM9okAXShAFcyb/OwvMbeXLfJ9ReF07gtuf4/OSfaDSQU3jvZfCFuKOmo/Tlm9eNhZysEj9qW82PhW3fYm36bp7Y+zGFiuGfyjzU1Ye5L/Vk3b5zDP5wFZeybxi8z1IryNEvdWBjD/3mga29sSOyKkeupvLCwW+os+4RXjr0LW2q+rI+8BN2dJrJk3UG4mRrwQusWxn57Urcnu8D4PMAbBgPdYJLlDItCzsbW2Y1fwlvx+q8dvRHjl4/w8xm43GwMc4FvoGnM4sn9eGHNUd5448dJOw+w5ejO9K3lRHu7ORd0d/xS10NQb9B40cqPgZh0Y5eS+XvS4ksu7id1em7yFXyaFqlLo/V6stgz050cW0uK5ILw9Bo9Dd0/mgOWyZCr+gSP+7r0Y7fWk1ixO5p1HBw5dMm4wy+kHZw69osey2I0E/WEvRuPAsn9ManhrNB+7wrRYH1z+qXpHhgIzjVMG48VkJRFNak72TGyYUsubgND/tqPF/vfp6uO5g6lTyNHZ4wEBntxJ11//L/g1bvWeVuTqPR8E7Dx/FzqsWYfTNIuXaW+W2m4mGk0p42Nhqe6tuI4Na1eP6H7Qz9aA2P9fDlg3B/XKs4VEwQ1y/pV/fO2A+D/4Y6fSumX2HRCpQCNmXs46+LW1h8cStHrqVir7Gjh1srIhs/yWDPjjR0knfLRAWpWle/gOf6Z6DRQ+Ddu8SPw2r24GJuJs8e/IoaDq5M8TX8VMIAXw9WTe3HsI/X0vfdlcx7uScBvkZ8kX1/DBz4Hvr8CDXaGy8OK3GjIJc/zq1lxskF7MrW0crZh+9avMTDNftQybaCxn9hNFazEGpAQADe3t6Eh4fLHO17tXemftC6fw1491Kt2Y0Ze3lg5zu42FVhif+7NK1ST7W2y0JRFH5am8LrsVqqVrbn8yc6MKCtgX9BzD4Bi/tDToY++fH0N2x/wqJl519jZVoyiy9uZcnFbaTlXaamgztDPDsyyLMjfdzbUtXOySB9x8bGEhsbS2pqKsnJyQbpo7y0Wi2urq64u7uTlJRE+/btcXV1JSEhAZ1Oh7u7OzqdjokTJwLcdvutyEKopaAUwl+99e+4jNgN9jf/XXz76C+8o/uNmc3GM67u4AoJ61L2DYZ/uo59pzL5/umuDA6oUyH9lnB+GyzsDs3HQI+vK75/K3IhJ4OZp5fwzanFXMjNZFD1jrxUfxh93Nsa/MmjKB81r7NWkwDJ4FQOSiH82ROundMPWnaVVWv62LWzDNkxlVM5F/mj1WsM9OygWttldTrtKuN/3E787rM83M2HDx8JwM0QT4PS9+lXS7d1hCErwaWh+n0Ii3cuJ52/Lmxm4YXNrPlnaluLKvUJqdGZ+z07E+jSpELXpDDla21YWBhxcXG4uroyZcqU4oQmODiY+Hh9cZaoqCh8fX0JDQ297fZbMeXjNimZh2FOG311s64f3/RjRVF44dA3fHVyEb+3msxDtXrfohH1Xc/NZ0z0FhYlneLDh/15pn/TCulX3/lFmOsPznVg6DqQpw8GcejqKT49MZ+fz8Rjiw2jvPvxQr0HaFzFCAmvKBM1r7MyBU7cncZGvzbQ3DaQ+DZ0jlStaR+nWmzuOINH90QyeMdUoho/xcv1HzTqXZg6HlWY/0ovZm88xuTZyazee47PRgaqe1fw3Bb9tDfnujB4OVSRijKi9FKunSlOejZl7sNGo6GnW2s+avwUQzw7SYWi2wgODmbevJJFXRISEnB1dS3+3t/fn+joaFxdXW+5/XYJkCgl18bQ4R39MgsNR4BXYIkfazQaZjR5mqy8qzy2N4pqdlW4rwJujFV2sOOXZ7vx5pydTJqt5diFK3z4iD+2Nga+eVBYAPHhUJgD/edJ8qMyRVHYlLmPj4/PY9HFrXg5uDHV91HG1R2Eu71xpt5bmys38th06AJr9p5j0tBWhrmhXAaSAInScWsC7afC9qn6QUvFqVrV7KqwsO1bvHH0J149HMPubB3fNn+ByraOqvVxrzQaDY9296VPi5qM/3E74Z+v5/7Aunz8WHtqupbzCdjJ5bD8Qf2f4X2LwdFVlZiF5VIUhV3ZOhZc2MjCC5vYe+U4jjb2BLn7832Llxni2YnqDi7GDtMs6HQ6MjMz8ffXX8O0Wi3u7u7FP3d3d0er1d52u1BBm5fhyB+w9ikITbqp0pmNxobvW7xCVv5VHtz1Lsv9P6Cnu+EXgrax0TAtvB0+NZx59dckdOez+fHZblSrbMBCPdvfhNQ1MCRe/wRIqKJAKeCvC1v46Pg8tmYdoFmVenzX4iUeqdUHRxvT+AXcUuXmF5CUksa6/edZu/8c249eIr9AoY67Ew919cGtivvdG6kAkgCJ0ms7AY7OhTVPwoPbVS3PaauxZXqjJ2nl7MOT+z5l/9WTLGg7lbqVjFsFp7a7E/Ne7smCbSeZ8Fsy7Scv4d0R7RjV0w8bmzI8pUpZAPEPQd3+0G/OLefACwH6pGd71kHmnd/AggsbOXb9HK52zgzy7MDbfo/R36M9zipOR7UWmZmZ+Pr6EhYWxqxZs0hLS7vlfrfbLlRgYwe9v4O4DrDrE/CffNMudja2/NH6dYbseJPBO94kIeBDOro2q5DwnurbCJ8azoz8eiNB765k7ss9aeBpgApxx5eAdrp+3b06fdRv3wrdKMjll7PxfHw8jiPXUunp1pol7d5jYPXACp0KbE0UReHw2cus2nOWVXvOsunQRa7m5OPqZE/3Zl5EPRJA75Y18fOqalLvWJlUAmTIF1SFCmztoff3ML8D7PwYAqao3sXDtfrQtEpdHtj5DgFbnmVemzcr5M7fnWg0Gh7sVJ/eLWvyeuwOXvhxO7Ebj/HFE4E0q+Na+oYO/w6rHge/MOj7i6zvIG5SqBSyLesg889vZN759Zy8cYEaDq48UKMrw2p0pZd7G6OVjbcEERERxf8fHBzMpEmT8PPzIzMz86Z9PTw8brn9dlJTUwkJCSn+Xgru3IWnP7R5CRLfAd8HwbXRTbtUsnXgz7Zv01/7GgO0r7M28CPaVPWrkPD6tqpFwpv9GPHZOnq9vYLZ47vTtYmKN+QuH9ePBw2GQDv53aW8MvOu8M2pxXx+ciEXc7N40Ksbv7WaRAeXCnyXy4pkX89j7f5zxO8+S8LuM5xKu4aDnQ1dm9Rg4v0t6dXcizYN3Mo9hbSouE6R1NTU8ob+f4oJCQ0NVQDF1dVViYyMLN4eFBRU/P+RkZHKvHnz7rj9VoYMGWKAiK3U5omK8q2joqQfMFgXF3IylN7bX1XsVg5QvjixUCksLDRYX/dq/f5zStsJixS3UbHKO/N2Ktdy8u7+of0/KMrXGkVJGKkoBfkGj1GYj7yCfCXhklZ5et/nSq21IxRWBCs11oQpT+/7XFmTtlPJLzSvvy+meq2Nj48vMWZER0croaGhSnx8vBIaGlpivzttvx1TPW6TlntVUX71UZQ/eyvKHa7xmblXlIAtzyieq0OV/dknKjBARbl0+YYyYFq84jYqVvlh9RF1Gs2/oShz2yvKLw0U5Xq6Om1aqTM3LikTD81SqibcrzjG36dE7PtMOXzltLHDsjj5BQVKUsolJeqvvcX/Hpwfm620eXWRMuHXRGXFzlTl6o1S/C5UTmpeZ03qCZC8oGomAt8G3UJY8xQ8sF5fJEFlng6urAz4kIlHZjH+4DckXz7Ct81eMIna/N2bebF12n18umQ/Hy/ex7wtx/n48fb0b3ObktlFZcSbj4We3xjkz0uYl/zCAtZn7Gbe+Q3MP7+Bi3lZ1K/kxQivngzz6kYX1+bYamyNHaZF8fX1ZezYscXfJycnM2LECIKCgpg0aVLxdq1We8ftQkX2TvoFUhf3g4M/QrPRt9zNxb4KK/yn0yvpVfomT2Rd+09oVKVi1rDyqOrIool9mPhbMuN/3M6+U5lMf9gfe7tyXMc3vQJpu2HYpnIvMG6tTl6/QNTxuXyX+jeONvY8W28IL9R7gJqOpvF+iSW4lH2DVXvOEr9b/5V+JQfnSnb0aObF9IfbEdy6Nr5eVY0dZpmZVAIE8oKqWbCrrJ+//WdP2PsNtHrOMN3Y2PJpk3EEVGvEU/s+Y9+VEybxXhCAo70tUx5oRVjn+rzySxKhn6xjSEAdIh8JoG71Kv/fcddnsOllaDUeus3Qr4gurFJuYR4JaTtYcGEjf13YwqV/kp5R3v0I8+pB+2qNTWp+tKXx9fVFq9USExMDgJ+fX/ENs8jIyOIy18BdtwsV1Q2Gxo/B5gn66WCVPW+5m4dDNRICIumV9Cp9kiawPvCTCqt2aG9nw2ejAmlR15UJvyVx8EwWPz3blepVK917Y0fnwt6vocc3sthpGaRcO8P0Y3/w85l4XOyq8KbvIzxbNwRXewO8o2VlCgsVdp3IYOWuVFbsOkOSLg1FgbYN3HiyT0P6tqpFB7/q5Uv+TYjJJUCGekFV5merrHYPaPE0bJkM9QdDtQYG6+qRWn1pXqV+8XtBcW3epIeR3wsq0rBmNf6c0Js/E08xaXYyAZOXMCGkBeMHNsNxdxRsew3aTdK/5Cq/3FqdoqRn7vl1/HlhM1n5V2nk5M1TdQbwQI2uBFZrYvZJj0HnaKvsdglMUFAQQUFBpd4uVNb1EzixVH+zKOjX2+7m5ejGqoAoeia9Qp+kiawL/IR6lSvuhthTfRvR1Lsaj325kZ5vreD38d1p0+AenjhkpcDaMeA3HFqMM1ygFujg1ZN8oPuD38+tprq9Cx82epKxdQZJIZhyyr6ex+q951ixK5WVu85wPusG1Srb06dlTZ7o3ZB+rWvjVd7KtybKpBIgQ76g6u3tLYvUqa3zh3BiCayNgCErDPoLfrtqDUns9CXDd02jb/IkPm/yNE/XHWISvzxqNBoe6FCPoFa1+PDPvXywcA/Oez7gabdY/XTB9lMl+bEiiqKQePkQv5xJ4I9za0nLu0wTpzqMrzeUMK8etHRuYBJ/b9Xy35tJ/77RJESpVPaELh/DmtHQZCTUvX3SWbuSB6vbR9Fjuz4JWhMYVaGzAro19WL9OwN45IsNBL8fz1ejOzK8S4O7f7AgB1aOgEqe0HvW/9q776gorvaB4186iii9CiqoYFfEbqxgi5qo2NITI5iYXjSm/ZI3RSHNNzEF04saFY3RaEzY2I0FWCuKKKuCVGFZel3m98cGXknEUBa23c85nuOuszP34co8e2fuPFfkhEY6V3yVNxTr2Ji1Hy8bZz4IWMLD3lN1ukyGobucU8zuk+nsPpnOwfM5VKlrCPDqyPyR3Zgy0IvhPVyN5i7PrejNAEgmkxEZGVm36jaAUqlk7ty5xMXF1XsvKCiIoKCgm74vtCHrjjBuLfwyFc5/Bb0XterhNM8FreT55M9ZmrSG+MKLrOm1lPYWzZiG0Ars21nx1sJBPOW+BdekDbyeOo/zleNZ1bWkdUqoCnpFUZrJ+qw9rMvcQ1JJGl42zizynsLdnhPo16GbUQ16BEHrAh+AC9/C/iWw4IxmqnUDfGzd2DvkHcbFPc/4uLYfBPm42PHbyyE8+fVxFn32J/LLebwxf9CtvzT+uQzyzsCcI5rcKdzS2aLLvKFYz+bsA/jYuvJJr8d50HuSWMOnGdQ1NRy/lMevJ9LZdeIaFzIKsbY057ZAN95eOIjJA73p5mZ631H0ZgAkHlA1UL5TNInrz2fAd3KrL+RmZW7J6sBHCOrYnSXnPiSu8AKbB7xMoJ1vqx630eSrcE16C2nof+g3+D7Wr5cz5IWdPDO9N09O60V7G735lRO04Hqlik1ZB1iXuYcjBeews7BlltsoPgx8lAlOA0UhA0FoLDMzGPsZbBwA8W/C8LduuXnXdh7s0+EgqJ21JdHhIxjs58wL6+WcvJLPt0tH3Xy6kGIbnPkQbvtIq4uIG6MzRZf5j+IHYrIP0tXWnejeT3K/V6go/99EZZXV7E3MYqc8nV3ya+QWVeDa0ZbJA7x4NWwAE/p60MHWtH+mZpIkSbpuRK2YmBiUSiWgeRboxvV+5HI5fn5+/1gH6Gbv38zMmTPFFLjWUp4PP/YBl0Fw+y9tdmv/bNFl5p5+k7Ty63za6wnu9dLxXP0T78KR5yH4/2DoawAUl1fxzvZEPvo1CQ8HW96YP4jZw3zF3QADVlRdyvbrR1iXuYff8xIww4zJzsHc7TmBma7DsTPxOemmeq411bi17vhrIH8b5p0Ep97/uvmVsizGxT2PhZk5e4PfadNngmodSb7OvR8dxNzcjG+XjmZEzxsKORSlwqaB4D0eJseIqW8N+PvA52W/u7jPKxQrc3HRsLFyi8rZfTKDnfJr7DmTSWmlmh6eHZke1Jnbg7wZ4u/SvAXc9Yg2z7N6NQBqTSI5tbIrO2DXTJjwLQTe12aHLa4uY+n5j/guU8aDXpP5KPBR3XwBra32FvQiDHvzH0nuUlYhL204wa4T6Yzo6Urk3YMZ1E2U6zQUlTVV7Mo9zobMfey4fpSymgpGOvTmbo8JzPMYi4t1J103UW+Y6rnWVOPWuupy2Ngf2nvAnfsbNWC4UpbFhPhlSJLEnuCoNqsOd6MsVRn3rTlEXEoub8wfxNLJAZhJavh5vGYQNO+kKHl9E+eKr/J6yg9syt4vBj7NkJpbwi8JaexIuMafF64jITHE34Xb/xr0BHgZV24SA6BmEMmpDcTeA6k7YUEi2Hm16aG/Tf+dR89/RJd27mzq/xJ97bu13cFrBz+DXoDhb98yYe85m8nydXIuZBRwz21+/F/YAKOtsGLoJEkivjCZbzNi2ZC1F2VVEQPs/VjoMZ75HmPp2s5D103US6Z6rh08eDDe3t6iwqg2pMlgRyiM/wp6Pdi4j5TnMD5uGZVSFXuCo+jevm3WCbpRVXUNr20+xYe/nueOIT58GbQbm9ORmoGc56g2b48+SynN4P9SvmN95l58bF15xe9u7hcDn0ZJzixke3wa2+PSOHFFibWlOeP6eNTd6XHrZHzfKWqrjaanp5OQkKCVfYoBkKA95XnwY9+/psLtbPNb/eeLU5l3+k0ulWawJnApD3lPaf2pZqdWw+Gnm1Tqulpdw1d7L/HmltNUq2t4ZkYflk4OoJ21OPHrg9SyHH7I/IPvM2V1xQzu8ZzIvZ4T23ZgbaBM9VxrqnG3mth7IG03LEyCdi6N+kh6eS4T45dRpC7jj+BInT0buj0+je/Wf8km//+QHfACniFv66Qd+iijPI83FOv4Iv1XXK078Yrf3SzyniKe8bkFSZI4n17AtuOp/ByfxrlrBdjZWDJpgBczgzszaYA3HduZxs9Pm+dZ8Y1L0B5bZxj/Jey8Hc59Dn3C//0zWtSrgy/Hh33Ek0mf8PC5DzioOsvHgY+13pS4Mx//NfhZ1qR1fiwtzAkP6UnY8C6s2naGt7ae5nNZMi/P6c9do7thYW785Sf1TZm6gq05h/gq/Tf2Kk9ha27NbPdRrA54hBDnQaKYgSC0tVHvwYadmucqJ3zdqI9427qwb8i7hMQvZ8zxZ4kNXsUAe/9Wbug/zezbnmn9PueEqh9T1vflbZJ5eGIPk372M6+ykMgrG/ko9WfaW9jwdvcHecz3DlHOugGSJHEmVcW2uFS2xaVxMbOQju2smDbIm5fn9Cekn6e4aNpCJnMHSExPaEP7wiF5Pcw/DZ38dNKEHzJkRJz7L13beRAz4BV6ddDylcBzX8K+h2HA0zDyvRbd7UrJLuI/m0+x9XgqvTt34q0Fgwjp37ZTCE2RJEkcLTjPdxkyNmTtpaC6hLGO/XnAaxJz3Edjb9le1000KK0xRcGQiDtArSAxWlMW+879msW3Gym3soDJCStQlGWxO+gthjn0asVG/o0kwW9hkL6PijkneGl7DtGyZO4c4sNHDw3Dwc60yjgXV5fxwdUtvHs1BrVUwzNd5vBslzA6Wdnpuml6p3bQs+XYVbYdT0WRU4yjnTXTgjoza6gP43p7YGNl2hfjxDNAzSCSUxuqLIJNA8CuM9yxF8x18wt7rvgqc0+9yeWyLN4PiCCi8+3auQKXvB5k90CfCBjzidam+sWn5PLSjyf488J1Qvp58uaCQfTxcdDKvoX/uVqWzbcZsXyfKeNSaQbeNi7c7xXKg96TdPLcgLEx1XOtqcbdqqQa2DoKKgth3gmwaPzgoaCqhGknXuJ00WV2DPoP45wGtGJDb3DuC9i3GKZsBb9ZAPwcl8rSL4/Rqb01Xz0ykmE9XP9lJ4avsqaKtdd28YZiHaqqEh71mc6KbgtwsxGFIP4uKb2AmKNX2XLsKpeyinC0s2ZmsA+zhvoyppe7SSxK2lhiANQMIjm1sYwDsG0cjHwHBj6rs2aUqst57sJaPr32CzNch/NF76dbdgJO2Qq/z4OAezXT/cy0e2KSJIkdCdd4deMJLueUcNfobrw4qx8+LuJqWUtU1lSx4/pRPr/2K7/nJWBnYUuY+23c6zmRsU79xRQ3LTLVc62pxt3qck/B5sGa6ppBLzTpoyXVZdxx8jUOqxKJGfAKt7sOa6VG/kWVDJsGQY+7YPzn9f7p6vViHvr0TxIUebw8pz/P3N7b4EsS30yNVMPGrP28dOlrrpblcJ9XCK/530uXdu66bppeuZxTzJZjV4k5epXENBUd21kxfXBnwoZ3YVxvDzHoaYAYADWDSE46cPgZOPuppvynY4BOm7Ij5wiLEt/HzMyMb/s+zxSXIU3fServsGs6+M2GkHWtemerslrNV3suEbU9kYLSShZP7MGzM/rg2tG21Y5pbGqruH2fKWND5j5yqwoY0ak3D3eewjz3sXQw8fV6WoupnmtNNe42cfg5SPxEU2G0Y9MKkZSrK1lw+i125h7nh37Lme8xrnXaqK7U3K2qKoS5crjJFK+q6hre/ukM7/2SyJhe7qwNH4GXk/FMtd2vPM1zyWuJL0xmpusI3u7xIH06dNV1s/RGtqqMLceusvnoVeJT8mhvbcG0oM7MGdaF0P6eJj+9rTHEAKgZRHLSgapS2DwIbJxg1iGdTYWrlVWh5MGz77I7L56nfGexqucibMwbOaUi809NWVavcTD1pyZNxWiJ4vIqPvntAv/ddZ4aSWLp5EAenxpIp/amNY+8KQqqSvg243c+u7aT8yWpeNo4cbfHBO73ChVV3NqAqZ5rTTXuNlFVDOt7gXP/Zi22XVVTzUOJ77Eucw/RvZ9kcedp2m/j0ZfgZBTMPgJuwbfcdP+5LBZHH6G8Us3HDw9jxmAf7benDV0oSWN58hf8fP0IwR178l7PcMY49dd1s/RCUVkVOxLS2PjnFfYlZmNpYUZofy/ChvsydVBn7GxEIYOmEFXgBMNg1R7Gfw0/jYZT78Og53XaHA8bJ3YGvcmHqdtYnvwle/NPsb7fCnp36HLrD+aegp3TNElt8uY2G/wAdLC1YtkdfVk0oQcf7DzHf3edZ60smWem9yY8pCftxcmzzukiBZ+k7eCHzD+oqKlitttoVgcsYaKo4iYIhs2qA9z2EeyeBYqt4D+naR83t+Tbvs/TydKO8HOrya0s4IVuC7RXlS3zEJxYBUPf+NfBD8DY3h4ceXMaj311jLv+e5AHx3fn7YWD6GBrWKWMcyryeV3xA9HXdtLZxpV1/V5ggcc4zLU8NdzQVFarkZ3JZNOfV9h1Ip2ySjWjA91Y/cAQ7hzqi6OJFcLQV+Lbk9C6PEfCgGfg+CvQZTo4tWE1npswNzPnqS6zGec4gLvOrCTo6KOs7PEQT/rOuvlJu+AS7JgMnbrDtB2aQZ0OONvb8OYCzeriUT8n8nrMKdbsTuL5mX14YFx3k711XlFTyZbsQ3yStoPDqkQ8rJ14rksY4Z1vx8vWWdfNEwRBW7rdAV1nwKEnwWcSWNs36ePmZuZ8FLgUV+tOvHjpa7IrVbwfENHyL+uVhSC7F9xHaNaDayRnexvWP3EbX+9LYcW6BA6cy2JtxEiGdm/cmke6VKauYPXVray8vBELM3NW9VjEYz53YNuGFwf1jSRJHL+Uy4+Hr7Dl2FXySyrp4+PAC3f2Y+7wLuI5Xj0kpsAJra+6TPNgqHVHmHUYLPTjKleZuoIXL37F6tSfGOc4gG/6Plf/Qc2SDM2cbgsbmHUQ2ulP5Z7LOcWs2naGHw9fwdupHc/N6MPdt/mZzEDofHEqX6T/yncZMnKrChjvOIBHfWZwh9tIsZK4jpnquVYstdAGiq7Cht6aCpyj3m/2bj5N28HS82tY4DGOb/o+17JFOP94QHNXav6pJj+fVOtSViGLo49w4rKS52f2YdnMvnr5EHyNVMP6zD28eOlrsiryedRnBq/43Y2zdUddN01nUnNLWH9IwYZDl1HkFOPl2I55I7uyYGQ3UcVVi1pjmQUxABLaRvZx2DoSglbAsDd03Zp69ipP8sDZd8mvKmZ14BIe9JqMWYUKto2BChXMPgz2ullR/N8kpRewatsZth5PxcuxPU9N68X94/yNcoG0v9/tcbbqyP1eoSzuPFVnK74L/2Sq51pTjbvNySPh2EswNx5cBjZ7NzFZB7j7TCRjnfqxZcCrzVv3K2WLZs2fCd9A4P3NbgtAtbqGd3cksmrbWfr7OrI2YgSB3p1atE9tOqY6zxNJn3C88AKz3UYT2XORyS4bUFpRzfb4NNYdVLD/fDbtrS25c6gvC0d1ZXSgm1jMvBWJIgjNIJKTHoh/C+JehTv2gddtum5NPQVVJTx14VO+yfidOY5BrL+6F+vCFLjzoM6n7TVGUnoB7+1IZNORq7h1suWZ6b15wEgGQqllOURf28kX6b+SU6livOMAlvhM5w63EY0vYiG0GVM915pq3G1OXaUprmNlr7k41YIpbHuVJ7nz5Gt0b+fFrqC3cG/KEgklmbCxH3iNhckxWlsPTq7IY3H0Ea7mFvPa3IE8OilAp+WyMyvyeCH5S77LlDHQ3p//BjxikgUOJEni6MVc1h1UsPXYVYrKqxkV4MY9Y/y4c4iPwT2/ZajEAKgZxPQEPVCjhp/HQVGqZrqAjYOOG/RPO7IOYvP7fEaW5HB4zIdM6v2I9h6UbQMp2UW8sz2RHw9fxrWj4Q6E1JKa3bnxfJb2C7ty47CzsOV+r1Ae9ZlBrw7ibo8+ao0pCoZEDIDaUMZBzR36sZ9ppsO1wKmiFKbKX8LW3JrfglbSw64RdzUkCXZOh+sJsOAstNPucztlldW8vvkUH/92gZEBrnzy8HD83Zv2zFNLlasrWZ26lbcUG7A1t+atHg+wyHuKyRWUyVaVsf7wZb4/oOBiZiG+LnbcNbobC0d1w6+N+0QQA6BmEclJTxRdhY39ocvtELpe162pT5JgXzhS0jes6n0XL1ZkMsN1OJ/1etLgHqi/cSDkYm/D07f35qEJ3fV+IHS9UsVX6b/xWdovXCnPJsi+O0t8prPQY7xYt8dAmOq51lTj1pk9i+DyVliYBO1btsjmlbIspiS8SG5VIdsHvc5Ihz63/sC5z2FfuKYwTtfpLTr2rRxKyubRL46RpSrj9XkDiQjp2ep3gyRJ4ufrf/LshbWklufwmM9MXvW/B0cr0/myX62u4bdTGXx/QMHuk+lYWphxR7AP947xZ0wvd6NcwNZQiAFQM4jkpEcuboDYu2DidxBwr65b8z/HX4P41+vmc/+UfYhHzn9EeU0lHwQs4QGvSQZ1NwjqD4Sc7W14bEogD43vrlfrCEmSxPGCJD5O28HGrP2YmcFCj/E86jODIZ10u4CuKSirrOZoci57E7O4nFPE94+3bHqqqZ5rTTVunSnLhQ2B4DsVQr5v8e7yKguZdfI1jhde4Pu+y5nrMebmGxakwMYB0GMhjP+8xcf9NyUV1by26SSfxSYzoqcraxYNo6dn6xQdSCpJ5YmkT4jNkzPZOZgPApaY1B13RXYR3+xLYf0hBdkF5Qzs6si9Y/yZO6KrKF2tJ8QAqBlEctIzsvvg8k8w7yR08td1ayBxLeyPgGFvw+AVdW8rqwp5OukzvsuUMdk5mLW9n8K3nZsOG9o8iuwi3vvlHBsOXaadtQUPTejOo5MC8HTU3SrkZeoKNmTt5ePU7ciLLtGtnQePdJ7OQ95TTLqqUGurqq5BfjmP/eey2X8um2OXrlNRVYNbJ1vG9nLn08XDW1RN0FTPtaYat04lfQN7HoSZMug8scW7q6ip5KGz77E+ay+RPR7m+a5z61/0qlHDtrFQmgHzTjW5FHdLHErK4bEvj3JNWcqLs/rzxNRALC2087B9YXUJ/0lZx39Tf8LX1o3VAUuY7jrc4C74NUdltZpd8nS+2nuJvYlZOLS3Yv7Ibtw31p/+XZrwTJjQJsQAqBlEctIzlUWa0ti2zjDrkG5LYyu2wW9zoM8jmsX2bnLS33n9GBHn/kthdSnv9HyYxZ2nGeRib5n5pXzy+wW+2nOJ8io1d43uxlO3927T+eU5Ffl8kraDT9J2kFtVyDSXoTzqM4MpLsEG+TPVd5XVahIUSg4l5XA4KZtjl3IpLq+mYzsrRge6MaaXO+P7etDLu5NWvvCY6rnWVOPWKUnSPFdakgnzT4OlrRZ2KfHKpW946/IGFnlP4ZNej/+vTPaJd+DIcrhzv04K+ZRWVPPW1jOs2Z1E/y4OrHloGAO6OjV7f5Ik8WPWPp5NjqagqoQX/RbybJcwk1jP58r1Yr7Zd4nvDyjIKShneA9XHhzvz6yhvno/VdyUiQFQM4jkpIeyj8NPo2Dg8zD8bd20IeMg7AjVLLAX+iOYN3zlu6CqhGeTo/kyfTfjHAfweZ+nDLYMaEFpJV/uucTHvyWRW1jBrKE+PDO9T6te8UoqSeWDq1v5LkOGOWY85D2Fp7rMwr+9V6sd0xRJkkRSegF7E7PYczaLQ0k5lFRUY29ryYierowKdGdsb3cGdHHU2hXkG5nqudZU49Y55XnYNACCXoShr2ltt99lxLI4cTUjHHqxZcCrOBelwubB0P8JGPmO1o7THAmKPJZ+eYyk9AIenxrIijv70d6maV/azxVf5bHza9ibf4o5bqP5IHAJPraGN7uhKarVNew+mcFXey8iO5NJx3ZWLBjZjYcmdKd3ZwddN09oBDEAagaRnPSUfBUcffGvKQwT2vbYeWdh223gMgim/6pZ8LQR/sg7Qfi51WRU5PG6/30802UOlrcYOOmzsspq1h+6zOqd57hyvYRJ/T15ZkYfRgVoJxFKksRe5Unev7qVnbnH8LB24nHfO1jicztOVmKam7ZkKEuRnclkX2IW+89nk1NQjrWlOSN6ujKujwfj+3i02oDn70z1XCsqjerQ0Zfg5Luau0CO2ntu8FD+WWadfB0Xc1tOZJ7AVpJgboJW7jS1VFV1Dat3nSfy5zN4Obbn/fuCCen/7xeTSqrLeEOxnveuxtC1nTtrAh9jsktwG7RYdzLzS/l67yW+3Z9CRn4ZwX7OPDi+O3OGd8GuiQNHQTfEQqgtYKpJWe9JNbBjEigTNXOq27fRFaiiNNg6AmxdNNMZbJq24FxJdRmvpnzH6qs/0c++K9G9nmSYg/6vF9SQanUNW4+l8t4viZy7VsBgPyeWhAYwa6hvs54HKVdXsiFrL6uv/sTpYgX9OnTj2S5zWOA5TqzdowXllWqOXbrOnrNZxJ7O4EyqCnMzMwZ1c2RMLw/G9nZnRE/XJl8V1gZTPdeaatx6oboMfuwLHXzhjj1aW5MH4HJpJrt/vZ2Hs09yLORrRvds2YKn2nYxs5CnvonjwPlsZg31ZdVdQXg53fzZzu05R3gi6ROyKpW82G0hy7rOM9rpbpIkcfhCDmtlF9mRkIaNpQXzRnZl0fjuLZo2KOiWuAPUDCI56bGSTM0UBpcgmL6rRQvbNUqFCn4aDVXFMPsI2Hk2e1fxBclEnFvNiaIUHvWZwVvdH6STlZ322trGamokfjuVwWexF9hzNgvXjrY8PKE7i0N64trx3696ZlUo+TTtFz5N28H1qgKmuwzjyS6zmOg0yCQeqG0tNTUSJ68o+eNsFvvPZXH0oqZwgYu9DaH9PZk0wIvxfTxxtm/cXczWZKrnWlONW2+k/g6/TK6r4qk1OfFIW4bzo/co7ra1JarnwzzbJUyvzmeSJLHpyBVWrD9BeWU1L83uT0Roz7o7vqllOTyR9DE/Xz/CZOdg1vRaarDTt/9NcXkVPx6+wud/JHPuWgE9PDsSPrEHC0d306vqp0LziAFQM4jpCXou9Tf4ZQqMiIRBy1rvOOoK+GUq5J6EWYfBqeV3bapr1Hyctp2XL32DvWU7PghYwjz3sXqVIJvjQkYB0bHJrDuoQC1J3DXaj6WTAwjw+ufdstNFCt6/uoUNmfuwMrfgAa9JPOk7q3GLCgo3la4s5Y8zmew5m8nexGyUxRXY21oyupc7Y3tpnuPp3dlBb9akEAuhigGQzsXeDWm/adYG0sbipNXlsDkILNuhnnWYly+vY9XljdzrGUJ07ydp18hp020lv6SS/2w+xZd7L9KnswNR9w3imM0h/u/Sd3SysmN1wCPMdR9j8LnpZpIzC/nij2TWHbxMcXk1twd5Ex7Sk7G93Y0yXlMlBkDNIJKTATjyApx6D+48CB7Dtb9/qUZTflsRAzNitV7FJ608h6eSPmNrziEmOQ9mTeBjRjEAUBZX8OWeS0THXiC7oJyQfp5EhPYktJ8nBwvOEHl5I7vz4vGxdeVxnzt4uPNUk1o0T1vKKqs5nJSD7Ewmf5zNIim9ADMzCOrmxMS+nkzo58lQfxesLPW7Up6pnmtNNW69UpqtWRuo250w4euW7+/PZXD6v5rnfpz7ArA+cw+LEt+nT4cu/DTw//SycIBckcfDW7dzymc3koOKRR63816fRXS0NNzZCTdTO2MhOvYCf5zNwsXehgfGdWfRhO50djauWAUNbZ5nxdNfgv4Y+gZk7Iff52kSTjtX7e1bkjTJ7OI6mLSpVUqY+ti6sWXgq/xy/SiPnf+Yvn+G82zXObzYbSEdLNtp/XhtxamDDc/P7MMTUwOJOXqVT2XnuXPr15hfvEBZp2x6t+/KD/2WM899LFbm4pTSWJIkkZxZiOx0JrGnMzh84TrlVWq8HNsxoa8nL9zRl3F9PPRiWpsgGIT27jAiCvaFQ897W1ZYJ/NPTWGF4SvrBj8Ad3lOINDOh1knX2fwkaXEDHiFMU79tdB47SioKuGrig2c7L8DXzpTvnccu5QuDJqdRnhIzzYphNLaCkor+eGAgrWyZBQ5xQz2c2JtxAhmN/OZVcE0iW8rgv6wsNIMTmIGw+8LYMZvoK0v1CeiNHeXRn8I3edqZ58NmO46nAlOA4m6vInIK5v4LkPGOz0Xs8BjnEHfii+SirnifZTLE3ZQUZGHZ2k3LPaFkp3tw8HhdgROLGCwn7Oum6nXyivVHErKZvfJDHafTOdqbgk2VuaMDnDj/+YOIKSfJwFeHQ36/4kg6FSvRZD8A+xbrKkK15xnMqtKYM/94D4MBj73j38O6tiD+OFrmH/qLSYkLCOyx8M802WOTn9vJUlia84hHk/6mKLqMj4IWMJSn5kUjKjmjS2neGG9nG/3p7DqrsGM7+uhs3a2xIWMAtbKkll/6DLlVWpmDfHli0dGMsRfC9MdBZMjBkCCfrH3gUmbYftEzZS4Ue+2fJ/nPoejL0Dwq9D/8ZbvrxHaW9jyWvf7eMB7Es9eWMtdZ1byYeo23u0ZzijHPm3SBm25UJLGB1e38m1GLAD3ek7kCd876WvfjesTyvl2fwpf773EuoMKgro58fDEHswZ1kUnFcj0jSRJXMgoZM/ZzHpr8vi62DFloBeTBnhxW6C7+FkJgraYmcO4L2Bjfzj+Kox6r+n7OLIcStLh9p0Nrg3nau3A74NX8fKlb3gueS0H88/wdd/ndDL9N7Ush8eS1rDj+lHucB3BR72W1k3Nc7a3YPUDQ3lgXHee/z6BmVF7mDzAizcXDCLQu2nVT3VBkiRkZzL55LcLyM5k4mJvw6OTAlg0oUeD1e4EoTHEM0CCfjq1Gg4/DaEboMeC5u8nJQZ+nw99lsBta7RaHrUp9ilP8eyFaORFl5jjNpqVPRbp/fNBh/MTibqyie3Xj+Bu7chjvjNZ0nk6Ltb/TJrqmhp+P5XJF38kE3smk07trJgzvAv33ObHYD9nk7qjUV6p5sD5bH49kc7uk+lcU5bWrckzvo8HUwd508u7k9H+TEz1XGuqceutE+9oLnzN/lNzJ6exrv4KO6dp8kW/pY36yI6cI9x/9l0crOzY2P8lhnTS3lpEt1Jdo2ZN2s+8fOkbOlnasSZwKbPcRze4vSRJ/ByXxisbT5CWV8qD47qzYlZf3Drp3xTt8ko1G49cYc3uJJLSCxjY1ZFHJgUwe2gXbK3FNDdTJYogNINITgZGkkB2L1zeCjP/AI8RTd9HyhaIXQD+cyHkh9Yvr/0vaqQa1mfu4cVLX5NZoSS88zRe9bsHdxtHnbbrRmpJzY7rR3nnymb+VJ2jl50vz3UN4y6PCY1eL0KRXcR3B1LYcOgyGfllBHp3YuGobswf2RVvI7xiV1MjkXhNxd7ELPYlZnE4KYfSSjVdXe2YOsib0P5ejApwM5m7PKZ6rjXVuPVWTTVsGa5ZI2ievHELXZflwsZ+4DIQbt/VpAtmV8qymH/qLeRFl1jVYxFPd5mNeSvmnITCZMIT/7cEw9s9Hmx0kYOKKjWf/n6B93YkUqWWeGJqII9P7YV9O6tWa29jZanK+OKPi3y55yJ5xRVMG9SZx6YEMirA1WgvGgmNJwZAzSCSkwGqLoMdk0F5Bu7YDy5NeND00maIXfjX4Od77T1LpAVl6grWpP7M25d/pEqq5pkuc3i6y2ydVk5TVhXy5bXdfJK2gyvl2Yx26MuyrnO53XVYs5O4uqaGvWez+OGggp3ydCqq1Yzp5c6CUd2YMbizQa/JUFRWxd7ELH47lcHvpzLIUpVha2XBqABXxvXxYPIALwKN+C7PrZjquVYstaCHck9rnikduAyGv3XrbSUJds+BzAMw/0yz1oerrKni5Uvf8M6VzUxxDubbvs/jpuULXAVVJbyS8g0fp+6gb4eurO3d/EW4lcUVvLfjHNGyC9jbWvHM9N48PLEH7azbPl/Gp+QSHZvMlmOpWFuac89tfiyZ1JPuHh3bvC2C/mmNZRbEAEjQbxUF8PN4KMmA2YegU/d//8zFjSC7G7rPh4nf6tXg50bKqkJWXd7ImtTtWJtb8nSX2TzlO7tNF1KNL0jm02s72JC5D7VUwwKPsTzme4fWp3AUlFayPT6NHw9f4WBSNtaW5kzq70XY8C5MGuBFB1vdX3m8lcpqNccv5bIvMZt957JIUORRrZbo4dmRyQO8mDzAi+E9XMXUDEz3XGuqceu9hLfh2MuaRbZ9pzS83fmvYO8imLwF/Ge36JC7c+O470wUAGt6PaaVtXckSWJd5h88l/w5xeoyXvO/lyd9Z2ml8mZabgmrfj7LuoMKXDva8tyM3tw/tnurn88qqtT8dDyV6Nhk4hV5dHW1IyI0gHvH+Bn0BTKh9Yg7QM0grs4ZsNIc2HabZhHTGbHg0OPm21WXw7EX4dQH0PMezYrgDTzAqk+yKpREXt7IZ9d2Ym1uyYNek3nUZwY97Tq3yvFUVcVszj7A2mu7iC9MxtfWjfDO01jsPVXrVytvJkNZypZjV9l85ConriixsTJnfB8PZgz2Yeogb1w72rZ6G/5N7bS2A+ey2fPXtLaSimoc7awZ29udcX08GN/HAz93sd5RLbEQqhgA6SWpBnbOgOyjmqlw9l3+uU36XtgxBQLuhfFfaOWw2RX5LD3/EVtyDjHbbTSf9Hq82dOdj6rO83zy5xxSnWWu+xjeD4igs60Wl4n4S0p2EZHbzrDxz6u4dLThkb+KDTjaaXcwcjGzkG/2pbDukIK8ogrG9/FgyaSeTB7ghYW54ZfpFlqPGAA1g0hOBq4oFX4eB8XpMOApGPwSWN9wazz3FMjuAVUyjFgF/Z/U+TM/TZVZkceHqdv44tpucqsKCHUO4iGvycx0G0F7i5YNCoqry/g9L4H1mXvYcf0Y1ZKaSc5BPOozg2muQ7Ew081AUZFdxC/ya/yScI2jF68DMMTfhWmDvJnYz5O+Pg5tsm5FYVkVJy7nEZ+Sx9GL1zmafB1VaRU2VuaM6OHK+L6eTOjrQX9fR8zNTW9aW1OY6rnWVOM2COVK2DwYbF1g1kGwvOF8ev0EbBurec502g5o5LOOjbU56wBLz39ElaRmWde5POF7J3aNXBfuQkkaL178mq05h+jfwY93AxYT6jxYq+27mUtZhXz4axLrDymwsjDnrtHduOc2fwZ2dWz2naz8kkq2HU9l459XOHwhB0c7a+4a3Y0HxnU3iGp0gn4QA6BmEMnJCFSVaCr7nIwCq47QbaZmalxRKqjOg0MvCF0Hzv103dIWKVdXEpN9gE+v/cKfqnN0sGjHbLdR3Ok2kqGdAvG2/fc1D6pqqjlXcpWD+WfZef0Ye/NPUVFTxSD77tzjOYEFHuPxstWvNXuuF5az+2Q6u06ks+dMJqWVauxsLBns50ywvzN9fRzo4+NAD4+OWFk2b1BUra4hNbeE5MxCzqapSExTcSY1n+TMQiQJOthaEuznzOhAN0YFujHYz1kn8+ENmamea001boNxXQ5bR4LfHOj3uCZPlGZr3uvgA3fsAevWuaObW1nA6yk/EH1tJ45WHXix20IWeIy76R2h6ho1O3OPsfbaLn7NjaOzrQtvdn+Auz0ntPmFqmxVGdGyZL4/oCBLVUbvzp2YP7Ib43q707+L4y0vTtUuAXDwfDZ/nM0i9nQG1WqJ8X3cuWu0HzODfcSUYaHJxACoGURyMiJFaXDsJcg7Dfa+0MEXHHtpFsCz1P30KW1KKc1gXeYe1mXuIbn0GgCeNk4MtPfHw9oJF+uOOFnZU66uJL+6mPyqYi6WpnOyKIXymkoszSwY69if6a7DuN1lmN6X3q5VXqlGfjmP45dyOXYpF7kij4z8MgCsLMzp7Nxe88epPc72trS3tqC9jSU2VhZUq2uoUktUq2tQlVSSW1ROXlEFaXmlXM4ppkpdA0Cn9lb08XGgr48Dg7o5E+znTA9PezEFo4VM9VxrqnEblKTvYN/DUFMFmIFlO7DzhlmHoL1bqx/+SlkW/0lZx7cZsdRQQ6CdD2Mc++Fq1YmsynyyKvKRF10ks0LJkI4BLO48lXs9QxpdgbO1VKtr2HM2i3UHFfx6Mp2ySjUd21kxrIcL3k7tcbSzwcHOmrLKatLySrmWV8L59AJyCsqxtDBjsJ8zdw7xJWx4Fzwc9K/ktmA4xACoGURyEgxdenkucYUXiCtI5mzxFa5XFnC9SkVeZRHtLKxxtOyAo5U9Xdq5EdyxJ8EdezLQ3p8OjZxuoe+UxRWcu1bA+WsqUvNKuJZXSmpuCfkllZRVVFNaqaayWo2luRkWFuZYWZjjYGeNi70NzvY2eDu2x9/Dnu4e9vTw6Ehn5/YmWaWttRnTuVYmk6FQKHByckKhULBs2bIGtzWmuI1adTkoEyHvFBRd0Vw4u9lzQa0osyKP/crT7M8/w4H8MxSry/CwdsTdxhH/dp7c5xXKoI6NKPijA5XVahIUSg4lZXP0Yi65heXkl1SiKqnExsoCH+f2+Djb4eduz+hAN4b3dMXORJYAEFqfNs+zBv+/sikJShAMmbetC962LtzpNkrXTdEJpw42jA50Y3Rg61+pFQSAyMhIYmNjAYiKiiImJoawsDAdt0poEUtbcBus+aMjnjbOLPAczwLP8TprQ3NZW1owoqcrI3pqvwiDILQlg5/rERkZSXh4eF1SiomJ0XGLBEEQBEMnk8lwcHCoex0UFMTGjRt11yBBEARBawx6ACQSlCAIgtAa5HI5Tk5Oda+dnJyQy+U6bJEgCIKgLQY9BU4kKEEQBKE15OXlNWn79PR0Zs6cWfdarDknCILQMrXry9VKT0/X2r4NegDUlAQlkpMgCIL2tWaC0iVnZ2dUKlWjt/f29hZFEARBELTo79/Vb/we31IGPQXO2blp65hs37697o+hDn5u/KJhqIwhBjCOOIwhBjCOOAw1hoULF9Y7r3p7G0ap9X8TFBSEUqmse61UKgkKCmpwe2MY+Bnq/8G/M4Y4RAz6wxjiMIYYQLvnWYMeADUlQRlDcgLj+E9sDDGAccRhDDGAccQhYtAvISEhKBSKutdyuZz58+c3uL0x5Bhj6T9jiEPEoD+MIQ5jiAHEAKhOUxNUYzT2P4mutmssXbTPGGJoynaNpYufsT7H0Brb6eKYoi+MX2RkZF35a0ArJbB18Xsu/g+2/jFFXzSfMcSg7eMaQwytsZ02GfQACLSfoIyl88UAqPW3ayxxUmz97XRxTNEXxi8kJIRly5YRFhamtTXmxACo9bfTxTFFXzSfMcSg7eMaQwytsZ02mUmSJLX5UXWgc+fOt5y/XSs9Pb1Rc9jFdvpxTFPbTp/bpu/b6XPbjGW79PR0ysvLSUxM/Nf9GRtt5hh97mNj2U6f22Ys2+lz2/R9O31umy63k8vlXLt27V+3awyTGQAJgiAIgiAIgiAY/BQ4QRAEQRAEQRCExhIDIEEQBEEQBEEQTIYYAAmCIAiCIAiCYDIsdd2A1iaTyVAoFDg5OaFQKLRWyae1yeVyHBwccHJyIj4+nuDgYBwcHAwmHpVKhYODQ93rhtqtz/H8PQZD7JOYmBiUSiUpKSmoVCqio6MBw+qPhmIwpP6QyWQ4OTmhVCrZvHmzQfYDNByHIfWFthlqjIbcZyK/6AeRX/QjBjCOHNPm+UUyciEhIXV/j4yMlDZv3qzD1jReWFiYBEgODg5SZGRk3fv6Hs/mzZulZcuWSUFBQfXeb6jd+hhPQzEYWp+kpKTUa2dYWFjda0Ppj1vFYEj9ceOpNiQkRIqOjq77ey197odaDcVhSH2hbYYaoyH2mcgv+hGDJIn8Ikn6EUMtY8gxbZ1fjHoKnEwmq3eFJSgoiI0bN+quQU0QGhqKJEnk5+fXG7HrezxhYWFERETUe6+hdutrPDeLAQyvT1QqVb22DBkyhNjYWIPqj4ZiAMPqj5SUlLq/K5VKgoODDaofat0sDjCsvtAmQ47REPtM5Bf9iAFEfgH9iKGWMeSYts4vRj0AksvlODk51b12cnJCLpfrsEVNo1Ao6rXXUONpqN2GGI8h9UlQUBAJCQl1r1NSUggKCjKo/mgohlqG0h9+fn6AZrpFSEiIwfVDrZvFUctQ+kKbDD1GY+gzQ/w9aogh9YfIL/oRQy1jyDFtnV+MegCUl5en6ya0iEqlws/Pj7lz56JSqQw2nobabYjxGGqfqFQqZDIZK1asMNj+uDGGG98zlP6Qy+UolUqcnZ0Bw/29+HsctQypL7TF0GM0hj4z1N+jmzHU/hD5RT8YQ45py/xi1AOgv/8ADUl4eDhBQUE4ODgQGhrK8uXLDTaehtptaPEYcp8sXryY2NhYHBwcDLY/bowBDK8/goKCCA8PJy4ujoiICIPth7/HAYbXF9piyDEaS58Z6u/R3xlyf4j8oh+MIce0ZX4x6gFQUFAQSqWy7rVSqax3S01fyWQyQkND671X23ZDjKehdhtSPIbcJ1FRUURGRuLn54dCoTDI/vh7DIbUHzExMfXaOmTIEOLj4w2uHxqKw5D6QtsMNUZj6jND+z26GUPuD5FfdB+DMeQYXeQXox4AhYSEoFAo6l7L5XLmz5+vwxY1jp+fX72HJBMSEpg/f77BxtNQuw0pHkPtk9q5tLVza2UymcH1x81iMKT+cHBwYO7cuXWv4+LibtlWfYwBGo7DkPpC2ww1RmPqM0P7PboZQ+0PkV/0IwZjyDG6yC9mf5WeM1oymQy5XF43steHWueNUVubHjRzH2+sfKHP8chkMmJjY1m7di2RkZF1J5aG2q2P8TQUg6H1iUKhwN/fv957kZGRLFu2zGD641YxGFJ/xMTEANStNxEZGQk03FZ9jAEajsOQ+kLbDDVGQ+wzkV/0IwYQ+UVfYqhlDDmmrfOL0Q+ABEEQBEEQBEEQahn1FDhBEARBEARBEIQbiQGQIAiCIAiCIAgmQwyABEEQBEEQBEEwGWIAJAiCIAiCIAiCyRADIEEQBEEQBEEQTIYYAAmCIAiCIAiCYDLEAEgQ9IxMJmP58uX1FvlqayqV6pb/vnbtWpYvX942jREEQRC0QuQXQdAQAyDBpKlUKiIiInB0dGTt2rVERUWxfPlyoqKidNam2NhYIiMj6xb3ioiIwMzM7KYJKyYmBjMzM9auXau146tUqrr9qVQqQkND/5GMwsPDcXZ21toxBUEQjI3IL/8k8ougLyx13QBB0CUHBwf8/f2ZN28e4eHhde/7+/sTFBRESEiIDlsHfn5+REREoFQqiY6OrlsZuZZSqcTBwaFe21tq06ZNhIWFAZqfj5+fH6GhoVrbvyAIgikQ+eWfRH4R9IW4AySYvNjYWObOnVv3WqVSoVAo8PPz02Gr6ouIiCAmJqbee3K5nODg4H9s+2/TC/5NSkpKvdhlMpnOE7UgCIIhEvmlPpFfBH0h7gAJJk8mk7F582ZAc9KPjo5m8+bNepWg/Pz88PPzq5cslErlPxKUTCZj7ty5hIeHM2TIEOLi4vD396+7gieTyZDL5XXTH5YtW1bv83K5nCFDhtS9rk12tckxLi7uH1cJBUEQhJsT+eV/RH4R9IkYAAkmrfZkHR8fj0qlqpsGEBQUpOum/UNERATR0dG3vFoWEhJCSEgIQ4YMISwsjLCwMAYPHkxwcDAODg4sX76chIQEAAYPHkx4eDgODg51n9+4cWO9BCSTyfDz86ubsrB8+XKRoARBEBpB5BeRXwT9JQZAgkmrveJVe9KvPYnHxsbe8nMxMTF1J/bo6GhCQ0MJCQlp1at6YWFhLF68uF67G5qOcGM7QkJCiI6Oxt/fv15yq01UtxIbG1v3gKpKpaqXzARBEISGifxyayK/CLokngESTFpsbGy9BzAVCgVKpbJRn7sxsYWHhzcqOdVWBWquefPmNbkij7Ozc6MSS0xMDPPnz6/3nkwmq5sGIeZqC4IgNJ7IL/8j8ougb8QASDBpfz/p3viAplwuByAqKgqZTMbatWtRqVTI5XLi4+OJiYkhJiYGhUJRN/dZJpPVvf/3z4ImAdZ+tilqr8RFREQQGRmJk5PTLbe/saTpxo0bmT9/PiEhIchksnrb3LhdXFxcvakZtcesTW61+9FmSVRBEARjJfKLyC+C/hIDIMEkKRQKoqKiCAkJYdOmTXXvz58/v26dAgcHh7ptav+sXLmSoKCgunnLYWFh+Pn5ERISgkKhIDo6GgcHB0JCQurWenBycqqb8nDjZxtDLpezcuVKVq5ciUKhICgoqO6PQqFg5cqV9dZVqBUXF4dMJiMqKooVK1bUfWbFihVERUURExNTNz8dNMno7+suKBSKeu0cMmQI8fHxN60MJAiCIGiI/CLyi2AAJEEQGrRs2TIpNjZWkiRJSkhIkJYtWyZJkiSFhYXVbVP795SUFCk/P1+Kjo6WoqOjpWXLlkkJCQn/2Gft9jf7t9pjtkRYWFiD+25IZGSklJ+f3+TPCIIgCM0j8sutPyMIrUkUQRCEW1ixYkXd1S+5XM6KFSvqbu0rFIq6NR0UCgUxMTH4+fnh5OSEn59f3XxqpVKJQqGoKxU6ZMgQYmJiWnW+c2Pmmd8oLy9PPIAqCILQhkR+EQTdMZMkSdJ1IwRB+J/aqjgRERFNrvojk8mIiIggJCSEyMjIRiUdhUKBXC5v9LQJgLVr1xIdHd2oSj+CIAiCfhD5RRA0xABIEARBEARBEASTIYogCIIgCIIgCIJgMsQASBAEQRAEQRAEkyEGQIIgCIIgCIIgmAwxABIEQRAEQRAEwWSIAZAgCIIgCIIgCCZDDIAEQRAEQRAEQTAZYgAkCIIgCIIgCILJEAMgQRAEQRAEQRBMhhgACYIgCIIgCIJgMv4fMUK1y8fvVVAAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", " figwidth = 8.3\n", " fig, axs = plt.subplots(2, 2, figsize=(figwidth, 0.65 * figwidth))\n", "\n", " for n, cat in enumerate(cats):\n", " i, j = n // 2, n % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " x = kfound[f\"{sim}_{cat}\"]\n", " y = [data_mean[f\"{sim}_{cat}_{ksim}\"] for ksim in x]\n", " yerr = [data_std[f\"{sim}_{cat}_{ksim}\"] for ksim in x]\n", " x = np.array(x) * 0.674\n", "\n", " ax.plot(x, y, label=simname_to_pretty(sim))\n", " # ax.fill_between(x, np.array(y) - np.array(yerr), np.array(y) + np.array(yerr), alpha=0.5)\n", "\n", " ax.text(0.5, 0.9, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, #fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='center',\n", " bbox=dict(facecolor='white', alpha=0.5),\n", " )\n", "\n", " if n == 0:\n", " ax.legend(fontsize=\"small\", loc='upper right')\n", "\n", " ax.set_ylabel(key_label)\n", " ax.set_xlabel(r\"$R_{\\rm offset} ~ [\\mathrm{Mpc} / h]$\")\n", " ax.set_xlim(0),\n", "\n", " fig.tight_layout()\n", " fname = f\"../../plots/void_{key}_per_observer.png\"\n", " if no_Vext:\n", " fname = fname.replace(\".png\", \"_no_Vext.png\")\n", " print(f\"Saving to `{fname}`.\")\n", " fig.savefig(fname, dpi=450)\n", " fig.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "venv_csiborg", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 2 }