From b57262406201cea08450180681cd35ef85a81c40 Mon Sep 17 00:00:00 2001 From: Michael Zargham Date: Sun, 26 May 2019 09:56:08 -0700 Subject: [PATCH] plotting galore --- conviction_cadCAD.ipynb | 96 ++++++++++++----------------------------- 1 file changed, 28 insertions(+), 68 deletions(-) diff --git a/conviction_cadCAD.ipynb b/conviction_cadCAD.ipynb index fca9951..31aac40 100644 --- a/conviction_cadCAD.ipynb +++ b/conviction_cadCAD.ipynb @@ -1733,40 +1733,32 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 152, "metadata": {}, "outputs": [], "source": [ "pos = {}\n", "for ind in range(N):\n", " i = last_parts[ind] \n", - " pos[i] = np.array([0, ind-N/2])\n", + " pos[i] = np.array([0, 2*ind-N])\n", "\n", "for ind in range(M):\n", " j = last_props[ind] \n", " pos[j] = np.array([1, 2*N/M *ind-N])\n", "\n", - "for i in last_parts:\n", - " for j in last_props:\n", + "#for i in last_parts:\n", + "#for j in last_props:\n", " " ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 153, "metadata": {}, "outputs": [], "source": [ - "final_supply = df.supply.values[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "edges = last_net.edges\n", + "edges = [e for e in last_net.edges]\n", + "max_tok = np.max([last_net.edges[e]['tokens'] for e in edges])\n", "\n", "E = len(edges)\n", "\n", @@ -1776,13 +1768,13 @@ "edge_color = np.empty((E,4))\n", "cm = plt.get_cmap('Reds')\n", "\n", - "cNorm = colors.Normalize(vmin=0, vmax=final_supply)\n", + "cNorm = colors.Normalize(vmin=0, vmax=max_tok)\n", "scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 172, "metadata": {}, "outputs": [], "source": [ @@ -1800,47 +1792,32 @@ "for i in last_parts: \n", " node_size[i] = last_net.nodes[i]['holdings']*size_scale\n", " node_color[i] = colors.to_rgba('red')\n", - " \n", - "for e in edges:\n", + "\n", + "included_edges = []\n", + "for ind in range(E):\n", + " e = edges[ind]\n", " tokens = last_net.edges[e]['tokens']\n", - " edge_color[i] = scalarMap.to_rgba(tokens)\n", - " \n", - " " + " if tokens >0:\n", + " included_edges.append(e)\n", + " #print(tokens)\n", + " edge_color[ind] = scalarMap.to_rgba(tokens)\n", + "\n", + "iE = len(included_edges)\n", + "included_edge_color = np.empty((iE,4))\n", + "for ind in range(iE):\n", + " e = included_edges[ind]\n", + " tokens = last_net.edges[e]['tokens']\n", + " included_edge_color[ind] = scalarMap.to_rgba(tokens)" ] }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 174, "metadata": {}, "outputs": [ - { - "ename": "ValueError", - "evalue": "RGBA values should be within 0-1 range", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_net\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode_color\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0medge_color\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(G, pos, ax, **kwds)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0mdraw_networkx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_off\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36mdraw_networkx\u001b[0;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0mnode_collection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw_networkx_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m \u001b[0medge_collection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw_networkx_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 279\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwith_labels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0mdraw_networkx_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36mdraw_networkx_edges\u001b[0;34m(G, pos, edgelist, width, edge_color, style, alpha, arrowstyle, arrowsize, edge_cmap, edge_vmin, edge_vmax, ax, arrows, label, node_size, nodelist, node_shape, **kwds)\u001b[0m\n\u001b[1;32m 682\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marrow_color\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 683\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline_width\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 684\u001b[0;31m zorder=1) # arrows go behind nodes\n\u001b[0m\u001b[1;32m 685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 686\u001b[0m \u001b[0;31m# There seems to be a bug in matplotlib to make collections of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, posA, posB, path, arrowstyle, arrow_transmuter, connectionstyle, connector, patchA, patchB, shrinkA, shrinkB, mutation_scale, mutation_aspect, dpi_cor, **kwargs)\u001b[0m\n\u001b[1;32m 4019\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'connector'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4020\u001b[0m obj_type='keyword argument')\n\u001b[0;32m-> 4021\u001b[0;31m \u001b[0mPatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4023\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mposA\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mposB\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, edgecolor, facecolor, color, linewidth, linestyle, antialiased, hatch, fill, capstyle, joinstyle, **kwargs)\u001b[0m\n\u001b[1;32m 75\u001b[0m warnings.warn(\"Setting the 'color' property will override\"\n\u001b[1;32m 76\u001b[0m \"the edgecolor or facecolor properties.\")\n\u001b[0;32m---> 77\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 78\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medgecolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mset_color\u001b[0;34m(self, c)\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \"\"\"\n\u001b[0;32m--> 316\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 317\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mset_facecolor\u001b[0;34m(self, color)\u001b[0m\n\u001b[1;32m 299\u001b[0m \"\"\"\n\u001b[1;32m 300\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_facecolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36m_set_facecolor\u001b[0;34m(self, color)\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'patch.facecolor'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alpha\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fill\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_facecolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 290\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 177\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0melem\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0melem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"RGBA values should be within 0-1 range\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 238\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: RGBA values should be within 0-1 range" - ] - }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAE/CAYAAACevBBvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvVmMLFl63/c7EbnXmln7dre+vUxzJ4cUOdxEUTAoSLApaR5kSIBf9CCSDyIM0BRsCLQFgoYgwhBAQRQE06RpSaBowYBsmLQ1skwJXIbj0azs6eXe23epfcvKrNwzI+L44YsTcSIyqu7tmu65TTL+QFWsGXHixInz/7bzHaW11uTIkSNHjhw5vqFwXnYBcuTIkSNHjj+NyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CcgJOEeOHDly5HgJyAk4R44cOXLkeAnICThHjhw5cuR4CSi87AL8scXP/iwcHsA/+UeAAkW4VNaSG27nyJEjR44/6cgJ+Kb4oR+Ck2MolAENWst+rWU7CJLb6eNXbQNXEvQUyX/Q7Zz0c+TIkePjgpyAb4r/7V/BwQF8+j8lQXZTxEdIns7zz9HhYQCtQOlwnxKCVsg2Nnljkf4LkvwHIf2o/Dcl/eu2c+TIkeNPL5TWiV44x4viy1+Gdhs+9X1JYoyqM4vwwv32+gufYx8zuIbgriL59DmG9ZW1PgUdH9KpfYln/ZBJ/0PT/FP3ypEjR46PAXIN+KbwJuCPYNwnJoqMDj/SflOm4DThfBB8aCQfhOtBhvb8AteJcB3ZW5p/RPzW8oM8vw5IkH60uKLsV5K+Xdac9HPkyPFykBPwTXFwAKcn8AM/nOp0ww4/QVYhwdmEkCCxDFK+jqztfc5LDGRPCAJm+WFo+yTrJ5NYU+cA08IPgAOOknOnyNH6uW3mT9zLfs4rnt3ezrRWfEDCzkk/R44/FchN0DfFj/84HOzDZ/5P0cwCQwZKOvxI63VS69Yys8MNkUXWiXWIOvgryfoFyfyPO64UBIjr68Mg+Q8qCGis+r2mzo3///kPmmEByHh2uwwflMCn2s8HuUaOHC8HWsNgILGvMzN/fJpjTsA3Qb+P3tmBQR/9Hz4DW5sxuVmmVxXtg7hTThEtMN15krhORNoRsTvW9R0yCRxSJtgMMseUK00OzyHur8d8/icZ1wkC11kArj3n6xQE7HW7HU4d/yDPl3pGGzch8Guj9HPSz3E12m341V+FX/xFODqSJjE7Cz/5k/BTPwWbmy+7hNcjJ+CboNNBr6+D56F/+1/BrW2kQwp9lDoArdE26YX7psjQ/k3UmSoxLSsFjguOg1JOuM+Vfa4slVuwtgvR+aKV+yISBrKudbge3TcAFDqhtYfE7rio8B7RNc1SubHpe+qaMSHoKVO7WVVhX6qSfzjT+6YEkQxhIOOY+tPcOX8kgkC4f+oYyXcfFyJVKBUvEwKAto5d+UCpS2ZcO03K11qFSB3j+nNy0v9Y4tEj+IEfgMtL6PeTx8plKBbht34LfvAHX075XgQ5Ad8Q+rVXYf8Anr4nb9pxww/atcjMrLsRmX5YxKCniNwm9LAzDILktk2QU5qw3cmlm0Sqkw7/ojKYDlSRWFeRIBH+OU6SwA15Bx74stSBD74Hvll6aH8SreNPwAuXMP1MNuFfFfx2lRvgucccInJ/noBw7TEn+75TlpMXFUI+xsLJhx0TYJ9j3D7X+t+vEgSs7Sw54Eqyt3+q4h9dSdzWfhUKrSYw8UVJP7p+DoPLS3j9dTg5iVMuZGFmBr7wBXjttW9c2T4I8iCsm+L7vg92n0EpTMThezHZBamltS4KoBMTU5qgXnTd2qecj+Dj1FYnl0niASq1nbmcIgkn6lNEoy6AKj+HHK4SGlIaTHgNHXWMVq9qAqwSRG09ZxaRX3EstmxcIdBkHUtsBxnnx/uD51lInlO+5+6P6usaQeM64eSDCA8fVDi56vwXFk5CV01aCDHPnFia/XFTUZgqukIQCILkftL1bf+e+HdY+6Kb3gRXEXOKwKfI/SoBzT6PeDtx7ZcHv9/BGXYIilXcuXq0/1d/VUj4OvIFGA7h538efv3XP+KC3hC5BnxT/Pe/IJmwfv6/u+KEjMariM2yiU7YdJRMd9SZnX3KnH2lxpMO/lLEZt7U0nESH+TVWtILfpSG7CDZiaWJJ5NcTV2kCOO6jiS9niiHXbAMjeh5HdqV56no0IvjBU7OPOUmnaGa+ln0uV8rPFyzT11H+Npq32Z/0i2T/a5TAkemZUeO6cxjNxFOMo6lhZNMAeS6Y9cLJyhjPXGs78211k2cR/idOlnCiUIl7mO1jYhEU23muU3nxdqWuupbuVaDz7JCGQtA+BxGYLKvGcJ//BWc+ppsBD5BeRZ3Zp6dHdjbe6FiU6mIf3hh4cXO/0YiJ+CboN+HP//nod2Cz3wG1tasg8nqvLJ6r631rIM6+1DUgfipTiVjOeWrTfmD7Y4ok8BtzeiDEn36I3/BZqetlJ5TWmt4nan95tr2OiRJ1OogHHtfVudyA3xdn9VN2swNr/kScMOW8JFDZ7WjxDpX7L9GeMw8l+z9qW19bVmuuOY115s6BzKEV+J1x42EBeVY8SBuKhbEttAphTLbUwKydb/nv424YYSCi3k/g67P/J1NfP/F3uvCgnTT3/3dL3b+NxK5Cfom+P3fR3/hCyKR/8ovSTrKj8Rcc8U1X/hWN9G2bO0xLdmaZZYWmCUFM10vafKM9mUsEyQLUWrO5/r1rnjQhJZhDw8LtYqrhIlI8CAlqKQezXpmdZWAYmIF0q6KIBAhKgjQGfsS50V2TmXVhb1+Q2ReKn2fG1xUJ3//ga+UydgZO1XWfqw2o16sqVx5c6vkdrtOPJCSdpp1DdOGp66bgqNAK+sMW5O1iClt6ckqW9YJV/VVif3Ja8eKhLLafwCeb5VJW8Wzv2Xz/pPfr0p82+G1I/IXAtelMs7MInYle8P+B+5un2eqflnICfgm+N7vhcVF0YT/8n8OG5vEUcdhh4ndicaRyNM+Yj/pZ83wG0cN07FJI2OcseMkz73KNH2dKTeLGBPrzzlmf1S2WS9NullIkDjJDjUtBCTunaGdZJlaAz+pAUT+PI2OvlC7g0sRrYI4oC6lAThuKPnbEeO2hmACz8J3HY0btzsoiUhXyoVCIfnOo6Urp5v2FKTaXRCgtR39HrYxc8/An67DrI732nNe4DfRI6XOS1zGopip+131e22dly7TdWW2750SDqfcDvZ1sn533X2s4zpeANartr8Bc5J+juCTwTjhfZRSV3yD9l/aTJ8SJBMac/I6OtAS74G2+iedvJ71LUnbttt4fH1bdtJY7dp1Yu1aOSK0OQ54nnRLvk/QbUG/SwXF7ExAq+1M10kGRiO4e/eFTv2GIyfgm8B14fRUGtXee1AYMPVRJ9bTH3G47TiAG76FrE7MfFjmA7WJDWKCMB8CRA3fT5PRdR+j2UcGuWeQ/ZQGmd7nxAKAbZ5yY9OUyqybrLqyOtxEx2fVhenUIsK368N0Nun6CqbrJnpnKaElvT0VOGT764ieMfmcNpmnhBGrE1TRPvMvPDfwQXvJejL+wkLJKgvhPa8jlzQyBJuEFcM+lt6f/p0NTVr7TQo49jPa56WFoNTSvoC1T5v2YLfnLPNw5u+vKr/9DZHatkgmM1hRjquwLDrzudL3D+sheoep9mDIztzH99GBZ5EgSTK0yTa6rbbeoeWPtUcqOAVwCzLCoxD+uSVwi1AsWG0sblcKomdGB2jfB+2HoxZ8CDxUWF/afEeGeLUG32MyGtNvt2ift+m1LlnZXmb51gTGYygU2X33fXZ2VvmJv/qM/+Gf32E0uuLVWfiRH4HV1eef9zKQ+4Bvin/0SxKE9d/819ZO6+NJf1OQ4JJoO7mSPJj5ZtKdx1W/x+rwn/d72dBT2vnzl9f/JkPjN52D5V9KanexRmmvK3v/lKZpn28dVyqMEL+KIJKklAiQM/5y4o50unMNn2XKamGdk7BSpPxlU9rtFQKMLQBoLZ1f4Cfvk9ZqsoSsBAFZAsRz3QfWPruuojaWvIcynbx9T5sU7bY5RZAZxJF4J4FYK+z6jt6Nn3y+xHtO7wv3o5OCxJUxDfF3Ej+PKZdlifD8eLhc4KfajfxpzLZdL2F9qNDCYdq0WwjJzxBhIVy3loUiuIXY7THVxlT0tAnhQafq2xYSdRANDcRLDQ+MLHyIEBiWU4flVA7y7CHpBr4v70yBKpTotnuc753SPm/iD4cUHKhVCtQXqtTnKzjjAVRq0FiBUg1KZYbdDoP3H1G/cws96HHkvsabP/oqnQ7XolqFf/fvxGj5cUSuAd8UR0dCwN54mnCvlKizkNYm7I0srdg6x0mdO3We3ZGq7HOsTjaTpp7jbLn+aDZiH6dPthnVt8hb1rXZPxld/Rud/r1GJwg6TdY2YTuphCeG5A0J2kSpwClCwWj0YTXZ2pQOBRpN3MHa5Iy2hq5lEFCGdmz26eiGimyStN5KQrG0tKCIDFMCWaKZqbiNJKwdjliBIq3VEhYVYlqMtCqrDo0wYfu2E8JaWGcRGdmtywEXINSWUEnBIwjCDj8kCi8cO+6NZX0yDjUxDzBj2JN1Ej1LVB+h+8dYd1zXIr+CRYpFsUKUyzBbiraVWwiDl1LEaJ4nkg3l3SVNydZLyRRITL2niTSsh7TG7kmdyJj6mEy19kGH9zbJfIpFKJbQhRLKKUDRCeu4aH1zsVtDE+BPxgSTCapYxq3O0Dpvc/TgCb3zJt5wiENApVJgsTHP6nqdO/MTcAIICjC7CEtrMLMAwz6MBzAcoPs9VKdNvzukUnKpbG2gh32cnddYP3rCb/3Wq/yFvyApKNMBWUpJ9PM/+ScfX/KFXAO+Gb74RfSnPgVo9N/7O/CjP0QiC5WVnUolIgfdxLHI1GP7Cu3xvZHmHPWgqU7eXr/inKvMeVNIk3Oa2K1jGT+9WubQxHMb6/hkU45Ex5c+lvFM9vnP2afT0d3PGZurba0qrc1mje2OzH46SVK2dplp3jWPlUG8NiGZ9YT532hGhtzcOLAr0X6yNNxQyJoSqpLCnZ7al7yGeU8q8b4Cec+R0GGTWYrITUdfCOV/W0uMimRpo+Z7cd3QvWISskwsUgm1s/A6OtLyiQUvV4jTZI9TmZaTtFVCQRjRL7FzlmXBXp/6Dq02nGhrtvZ7jak4Kr8TC/iG+CYj9HgUCxqeCJ86elXh8xTLUCxJroJCKY5BwLpfItDPbn9eWAQl2q1TwPM1nla4pTLFmVlax6ccv/uQi90Dxr0eEFAuFZmvz7K6s059aR63f4lun8NoIGVaWILlDdR8HT3xYNSTZxj0AAftKtRkDKUKLK6ganME5yfQa6FcF7V1H315gXrtO3HcAg8eyDjf3/xNKIWemNEI/tyfg7/7dz/e5As5Ad8Mb72F/uQn5UP/Bz8Hf+a7LJOLn1jX9n7fSx23TDr2PtP5JoJ5TCfiTu9LrStbs5taZmiAblGEBfvabvr69n3UVIelssjmCuJ58QaXFh5SUv0UuQYko4j9aUJLvZ/ovZjO2y73tclSUhpNOoAsgiWdpM3A12oz6Q47XKYD7zJNpVYh0iZcoymaDGMhiUVtzwvbn+NGpk0KohGpQkE6ctdogIYYC9PWhEzBJXxuSzDQKmx3Uy6I0BqReFa7bamIoxIa5HMEn+y2pzNW08SYYeaPzNHGwqGFzLKEWWXVS0SkYyHS0SDW1HUQy6koEeALRSHRYiVclsApoKw2o7Hr2yLZhLXBTz6T40RavCqW0YUynucz8QLcQoFipULn5IyTh+9z9O5DBq022vcolIrMLc6xur3K6s461VoJ+l30xRlcXsizVGoh2a6j5pflXQ+6ciwIYNAFz0dXZlCjnpS3VIaZBdTiigRfVWfR/TbacSAAVXBhMiYolChsv554g5eX8N570ozv3v34+nzTyAn4pvjhH4ajQ/jc75HsVMMlTHe4NrICZax9olTY5JHu0PxpkglJRJvzgxTxm4ANP1tYiAWAgCmSuorQIl8oKe3LITkrVEqLSmtshvQLJcu/ZRGAWZrgkIKLcouxFaEQ5sZ2CmQKGlOR4lj1H69n6/g6sTAbyc20cJASCtJ1alJqehO06XzHQwk2GQ9gNAxNcX0Yhh20qZ9i+BcFx7gxKSaEpVBQMu/G1kTTwkJKK4415RTJJ4SRlGBmhDblxgRtCYfKcS3StMjrKguDvW7Kk2VlSGvnU89nCUGJ57KEpyk/OBnXtMutwYx08H3wDJn25V1NJugofap8f8pxQwIti4ZnloWC5Z+dFiq17T+3fbRBilhtzVaF30WxBMUKqhjet1Ak0DAZDFFoCqUCvWaLs/efcvDWO7T2D/BHY9yCy8ziPCu31lnZXqWxuoTraBiPoHuJPj+C9oW0sdkFWGhAY03I0y3CeIge94n8xf2epKUq19ClAqp3GWrpFSgWUUsbYp3QGiqz6M45FEsE77+DunUfZ/sNgid/hL44pvDJ/4Q/KcgJ+Kb4yZ+QOYF/81+KdAdJydgO4gks006qY54yAWnj78zoiD7Q0o+JMTPYyTLvpY9fESCUHCsbE6u4AlX8rPbzTwXMSIehtBEIggzLQMpakBhyEz+ftq6X8CcmBIcMf7HWqWczZkYsIUhPa/yJADGV0BJVwQqSMUEzhhQjwSEmJpVpycjQsiPCIW47CRJIfb62Vgjxb5VFOE76PTrRPkl4QNxWozqzhLq0r9346TMFNtuXH1jb4buy/cppa0Nau88S4BQYE3H8vCnByvCprd1GUEl+1qF/OBq+ZU6LvxcVWQCMkGEsISpZjixNPG3VsL7ZaPiYbywRlr9Wh0RmyuO6EpVcLKOKlZjQC2Feeu3jDQZoz6NQdOm3Lmk+2+Po7QccvPUOo14PpaA2P8vKnR1Wb2+yurVCtVYM7xmIqfvyAo72oNuWiKbZBZivQ2NVNNtyVeps0JH4DIW0k1FfSNcpoefmUMOeHHdcKFfknPoaam4RNRxAdQbcIvryVEj68bvoxUWc+5/EKVcyOuBUs9eiBfu+jBB1nOf+5GOBnIBvgosLdKMBjoP+n/8hvHqXhHnQjtI1HfV1Ua+ppbrquN0xGdLIyntrffhxYIdt7iRj3zXHE/7YqzqZVDmsjicSThLac/iR+x4EnmgLXip4ZjIJl2PpDKJjk4QPMPaFTeQeNglGkaNGs05piVOdPPFzJkyTKY0t6kBtS4EhRx2fP+XfndaOk1HiaYHDl/KFZmAZFhKaIaPnKsp+K0BImXWb+KPZs9Laa5KEp6wX2HWTzEp0NVIWgQyTu84aupOawWs68ChIvgd7zOmUTzNIvIekkBzCaOYJAckQayGukyvdC8lnVrbARJiX3NpWibq9Rtia+p6y76p1aF0BvPGYfrPF+bNdDt96l4vdfca9PkoHLG6usXrvFiu3NmgsL+AG4zBySQMOejSA8yM4fCYm8Zm5kGwXYXEVtdAQsnXLMBmg+yHbua58f5MRdDsiiNdm0aUKqtuUd+i6UJ2Vc2brqMVVEfYmI5hZlOdonUB1Bn28D94Qbn8T7urOc9qYFOFXfgX+/t+H3V2pqvl5+Omflr+Zmede4qUiJ+CbQGv0pz8tU3H81v8hDTOSstPElbUvg9im9qXPg6hTyyQ9Mvalz4t/q42WmjZFG5OZ5wnhGbKbjFPE54E/RpuZicw+b5wkRCO924ToFqZMzapQQNtaY1YAW0pL1ya61taQNDLuUmcQWWSaS5NPti9dhKErfO1XDIP6sGcairQx847SmlE0NMSaQSpc15HrIXWOiU2w68e/yvVgxytY5cAIgam2ZjR+a8iMsofPuIXkumUVMCSoEkKBmxQGbGuNOQ+SgkzCMmCEhdBCk/axZ1pIUu6VKSEh4zrmnpF2bgt3TuwCsIUdu01ZQmCiBSmFjrRrOScAdKAJAhn7rFwXt1BAFQo4UbR5up8gLq/viSl50BNTceDFlivjbzbfodbQb6MHPblWsSjf+3gIvZ4IyaUqLNTFZdK/lHu4RSFd3xOBsLEOtXnU4FIEyNqcuF/ap6L9djoEB49QO/dR974Nx7hOroHvw4//uAwzSk9HWKnAvXvwB38ghPxxRU7AN8Wnvk9M0F/87AtpgZkmNOvj0KhpjcoMm7AjPl9AC9QJQrziPLB8iHanGWtSKkGaRXQhjiCNo7ozokidWMo3I1VUQju5ptObGh50FTlafsV0R3ZV8NhHQJAvjOssC1cKZtP74o81ZTqVi6fIJeWSMIkRNCnNTmVcz9b4w2sm3BYpy4GJXUi5Gox/VKwYQ/RoKH7tUT/0b/dC8g/vOfUuU+Rra4TmOzF1G5VfxdsZZut4vKxrtX3brGz9OZa7IDNIMW6XWomGqy0hKBErkTY7X+H71kFA4HkEk4n8+T5KKZyCg1so4hTs7ywcIw+JZ4zKFQneTiz0KJMq9QoN/ErzuW2ZkOxxypvIe52MpezKkf5m0EPNzIvGG3jynmcWxXIzGaFbZ0K8QUDw1c+i7r6O2vkEjjXjkUHQPEKVSmg/wJlfit7/L/4i/NzPTZOvQbksBP0bv5F9/OOAnIBvgr099BtviPT3r/4X9BuvJUkubRb1Q+JME6Kf2mdMjWlStDRHld43FZkaBiZlaY3REhSKxPjaLF9dmhwD35LuM0gxIsx43/RQj+u1yZdGkNchkxSx1l+QQAkFralntK5nzNhp07S2IrUz/PCQUW92GWyfa6RxpTtcxxIYwyKruIiiKYdtdzwS7WnYg2FPIlx7l0K0hgRMkFGpjCqWQtN5OGa2VIn+VKGQvFGsvBIVxAgDNrHZZYt+G4S/tcy5lpYamaGDIHR7jGDixXUexU6YexIKwxPr2iKAqISgEGqp0TszZGub0QkN4VKWwNf4vk/g+/iej9Yat1jELZVwikWcUKu1BQDlFCQqOApANN+0TZgktd5EPIX1fPZ8ynbzMe0p8a0WYpeGKcsLEbh14bTFYDyU3Y4LlRn59k3AXuTCc/D3H+A04hmRtHKhtsjGhhghr0OlIqbp5eXrz3tZyBNx3ASPH0f+E/27vwNzTooQi2F0Y0ycjqVFSpSoIZ644SrFlA9QTwW9BClitPZ5Y/AtLSVlHrW3TYIKGeZQulJjTO/7WBLkVfiQtE6BdCaiYKmr72EF0yWj063tq4KMzH3kgvF1tUbGUjvhfRzrGld1fqbMdoWEG0FIZCZBxXiEDiOu9aAnZsT+pURiO04yYrdYtgg1jMauzaJKt6FcFd+z7ce0BRVbm1IWWRmBNfBTRVVMvY9Iq1NWfQdxjRmzshGATX0nqsFowkq+T+VIfWCE3jByuFSNBYViJcw2lUEoU8KqmPgDz6ff7tA6OObk4SP2vvRVHv3e57g8OWXp1jYr9++ydv8OK3d3WL13i/r2Bq4OZGxsvyMEpcegi+hCCapzaKcAnRbsPoSTXWidyTuq1MSsW5uFai3uf9yQNE19DYciRKGJoqSNYGXM3xqp51LZepfGhK4ioVk7KQuf40jbdEwbJW6jrkMy54EbCejKUTAZJtqxtgjckK9SCu24KODtz5/S663wPBSL8Du/A5/+9HNPfSnINeCbwPfRq6vQ66H/4+9Co578CK/TJhPRtUlzapY2qdL7psgxeVylO5s/rnieL/0DaJ1RB6FksgPrJkmCSJsI7feYiNy1Jf8rtFCjQSbKZPkPIeMalskVMjTb8KK2dmOTjUnQMAqJdNATrbTXls68aGueMaGqYimMoLUCukpV8c1HEb5XvB9SmlcimMxYVsxp1nOZ+kBb2qqleSd8uWFkNjp5XbuOIqIATNYy262jNdGQNddyrxirkRsKl4ne0G4fQRw0ZjTtsM14E4/JcMio22PQuqTXvGDQalOq1ajMz1KZm6U8U6M8U6VUreIWXCLhwfapT5mPCU34RkAJ4vaRsG5ZAlhcCalnMO/AEorS56r0b61D6kUC76y7XtX/+ZY53ozSiILp/NS3GzaG+hrKNk0HPl/6bIc/++k7XF5eX465OfjlX4a//tdfuOjfUOQa8E2gVJSEQBWK0nldS6aWqVVdN9n9NxhXflA6sZjan9hM77uJ1mmdn9Y6zTLsPKaLbMg5iK+TGO5lkV+kiaX8nwl/l3Vf21eYNjunA+K0uZb1e0fF/UgKWdZTjFlQT+IOKu0nDCzSSkcvlyqocg3mG2ExMsyAdgkS/bQOH0eFm3rq2FS9R0ud3DQaj1u84jfhNQ0R28ez2hTpKsywEtjPNOW/TBFStBqEJuhReESnTtOJTYWpF4ng1kFA4AegJCtpcX6GucVZ1L0tVKipRTmxI2vICB1Y7ctxoFCWsEhvImO+J6M4TsP0ISZYLVJRbRIzlg2L6JR1fYgT/kR1bgRJN35WZdVrxvPjGxdUGCxnrDuOEuEtfAc69AvjFlClMnrQQzePUPVV0dTL82ExpOzKFiiUQyTghMM39URM1Qz76OYxzM0zHzxjPL7D8+D78Oabzz3tpSEn4JvAcVCf/quwvw9bt60DqY/bD+IPaQpZJJfR0V3RISU6vazrvsi1r8UVQsJ1soN9iyzTZ0LBMObFjAtkkri1bZsxE4Fu6fsZjUtDlEPYIgwdhLdMmdJSZY46cceoZyTfi82iAUSdo7YScNjjk30vHjOLTrkJUv76SLshTMRRBGXSQNpVZ+opIFUoXujdh3WceD8ZBB3Vf5rUpzQXQz6mXqx6SAg+ZFxXWb8JNT/lJMZVq8jlU4r9oYl2QXI9Kn74HvBDbczCzQ+PAAAgAElEQVSLNDHfDxh2enTPzmkfHnH+/lNOHrzP4OKC+Y01FjbWWNxYY251ibmlOrP1BYqVUnjZ8LqTMJ4DxFcZZY0zLhyEVIxwGLYvbVvIXAcKZuyrYioQzTzHJHQh+J4cC90DOCq0gHRkvx/AoI8ONGp+UXIvG6tIpYrq99CX53LtmTkZ1VGuogpldL+DGvdhcRUW11CuE/v6q3NQK8eVG8aIqOpMrIgoBzXbQC1vZgqU2vOZjEaMOh0G5xdcHhzRPTkl6PeZq5VYWl6k8ok3qM6eQX2NkSrQ+79+i53tdf6zHzrif/2365EinYXbt+E7vuPq4y8buQn6pvilX4L9Pfh7/621M0vVmd6noz7KdFBWxx6fZXXyaX0pRVxTxGIR17WmWvteVzSDq8xS2vrNdUFKiWuH+xN1ohKLr09oSBU8Xfb0fU21ZwoLpuMOpXGtY9K0A+wmo1hj8Sehf91O1Sg+ODU1xtRoNE5oNTWda0orT8PUuz0EyOw3pOX7REFbYZniwKv0s8XPFwkC9sswfk3Pj7WqsNoSUc7eOL5G+FyxdSDUvMII3EhzMxH+SoUxFOJXVoWkaThBOun2bAse6edJDxHSlo6rNYEf4I0njPp9Rt0+geehHBen4OKWShTKZUrVCsVqGcfUnWkHkzDrVeCHZINohqUKlGfEf1wswWiEbp2gzw7hdA+ax1In5SrUZmCuDgsNVDFMoFGqhOO9y7FlzSRA6VxIwgtvIm6DuUVUeQaNQvcuoHkkz1oowWVbkiLPzMPWbZTnoXtteZe1eVTrDN25ECFmcVmGDJWqqNqsXMPzUJWq+L7TQmEiGjqObdCh1mr8315/QL95QefohNbeEZeHx4x7fYq1KovrSyyt1FmoFCmOBzAe41TKOLWSWAPQ4hueXWA0URRf/2Ye/9t/x91F2afm5njSXOZ7/vr30Gpld1+1GvybfwPf//3Txz4uyAn4htCvvy4E/PhtsyelFaU0EPtYAs8jivR51yF1rSxT3JTSYZUtTd6Jji7LhGt8lopoonpbi0mX1+4M00MxIg3IsXxbRopOkdOUthN6du1OF6JyatPpG0KaTNCTMAfvqA+DbhzFa2ZjGQ+lAyzXJHOPFZCjTDrBqKM0mpgblyMr0tiU165vUxcKq17t+gpiTdDOkJSOIDevWJPsIBVWEE5aqLC0EatOdeK4Zbq0o32VfUN5viwxUh7Z8h8aF0zWMBhb+MjclxZKUu1TS5NEuUzGE3rNNs3dfQ7ffo+nf/hFnn7xS7R296nfvsXqq6+w9uo9Vl65w+ort1i9u0Ntrhbf1w8kMK3fgW5T2opJ/+kUoDIjs/fU5sXsXyxLisaTXfTREzh6Kokt3IJolTNzsLgCC0uoUlHKbDRW05acUvxM3hgumxJl7k9QlRmYXUSVK2inIIkwzg+knZZrMiXQZVuEls3b0FhG9bvowEeVq1CuofwJ2inE7dcIZmm/fZSZy7SB+Jv1JxO8wZB+s83lwTHnj55ysbdPt9ki0JpKY5HGnR2W72xRn6sy64LqdtCXbSH1gotTr6NqNZQKLYT9S7lvfRXqq+j+gKDdxCkVUJUaujqD3nuCqpbRTgk1N4+anUd94nt59Mjh05+Ghw9jK3u5LNmw/tk/k4zBH2fkBHxD6F/7NRkH/DP/5RXEaxFaguTkTyn7XKY7m6x1rKxSWB1PNKzATiCQChyyh0NMjdtNmT/TRGDfM8o8lL5HuqN2p++TDlhKmSGFvNJ+WxIdsoyX1nEU73gUk+kwJNN+R8YdjgbyNx6EZFoVIi3bZFpJdoKFUpRVShlyhGzTsE1oNhkY0zKEGgNEs0HZ7ywdQavSZGPXvdWmIppLS1PpRqqn3qOO2qFVBlPOKdOyIbV4/LQ2gkzgiRkSYu21WApzDpto6aosP2jcQ1RP9tAZO5ARcFx8rek1WzSf7nH4zgOe/n9fZP9LX+HkvUcUqxVWX7svf6/eZfWVO6zeu0Vjaw3XNvsHvrSh/iV0L2SSAGPaNhppbQ5mFoVoy1U51j5Hn+yhj56GZHsov5uZE22yvgKLy0J2gSd1lGhnZZTjiDYNIgheNtHjASoQLZXZeUkz6ZYIJkMp32SEqsxKGUJtV1XCqGfzTDr9ffpJQk0Tq3LxJz6Tfp9R65Lu0QnN959x+vAxZ8/2GPYHUCgyu7VO484Oja01GmvLNLZXqfgewdkpQaslGndogneqFZyluqSarFTR/gR12ZTxv95E6mfzDlRmCM7P0RdnKFej5hdQvkIvLqL8MPnP4irB0b6MJ67NSp2u3sHdvBM1mS9+UaKdfR++8zvhR37kCj3mY4acgG+KX/2f4PwEfuJvPZc07X2JicrN7ClRkMZzCNSOok74C+1tS2u0zaxArHnq6XvYHysqQwt1k+TjWB22pWGrKa0km0gTZOqHnflkLFrHaACDPgw6ogGM+jGZjqQzoDITBnRUIy1VZZCoybAUzxwTkko6YMrGVb7nF7JqWBVujl9pGVDWdTLqyFgtjCk/SnARpM4j7lxNNjIzb3KhmDTl2mZhrePMU4Tkasa8TkbhBBDFxFAc0Z6q0ew8yv06wkgSAkgqYhZCc77DqDfgYu+Q4/cesvvFr7L/pa9y/O4DOkfHLL9yl5XX7rP22iusvHKX1Vdus3pvh9psLczCFGp5QSBR4oOuaLS9S6mPYplont+qpF9UldnY4uE40Dq7gmznJZlEfQXqq6hyOfbHmikAwzapXCFMHcSEq4c9CAIxudfmUeVKFOAUtRfHifuH1Hh9HQkpKVM78m0p1yXwfCb9AcPzFv3TM7oHxzSfPOPo3UecPt1jMBzi1mrM7WxRf+UOy9sb1FeXaKw1WFipUyqXCFoX+IeH6MtLtC/tT/kTHH+Emq2hSiXU8jpqaU3qpXMBZwfoi1MJcltYgvUdWL0lloWzY4LzU1QwQS01UE5J6mVxCeUNiNxysw0ROhZXZOiVo0ArCALc7/iYq7YviJyAbwj9T/8pHOzB3/nZDMK0iTR1bIpAU5rolL+FuA9WhrStDy9xP2tf+j62RmpHzhrflpXQPm3O1WYYh23ehWwt2czN6k0kO443kY/QnvLOrhPHTjhiz6Bj1UPKnD1NmRaZZgpA5lcqvkD0HMqqi5SJ05jdo6xOARK0YzSoQlxO103WgyHEcAL4xLnRsB6bVL04N69TiN+LDiepN8NRPEmAgVJxApbIx6vicofDpVTkdwWtfSHoMCsVWifG+Coz3tUsi+UPprlm4QVIFsfF9wMuT045efiY/a98jf0v/xEn774Xa7Ovvyp/9++x+uodVu/eprEdarP+JPYZBwF4IwlC6l6IedP35JlMXZVrMu1ddS62iBRC82/rDH28iz7ehaMnIdmWYHZeJiNYXIHGupBlELbnrPHCTjjUCKL6jxLkaFLaaaihJvoQy9IUEbG4U5TjoFFM+gNGzTaD42N6x6d0dvc5f7JH8+CIo/ef0WpeUFmqM7uzzeLdWyxtb4r2ur5KfW2J2dkqjvbBAd1q4R/sozs9IVkl85Erb4ATTKDo4lRrMpfv6pbMftTvoM8P4fQAWqfSLucbsLIJazviWx4NCY4O0Gen4A1xGguomXn0YCIzmC01UIOevJt+B1Z3ZKzyeAiNNTg7Qb3+nThziwS+j37786hPfBfO1yP4fYyQE/ANoe+/Anv7sP8oQ1u0iM507LY2ZfvfrtJCbXPxFGFepVGZ61vScDoYxfblmvPjH8daW0KiNqelTKg2cfhxNGk8ntUEG4V1E6WxtPI9m6AOowUas2laYEhk+0mafyNTsbEmhOdr4xuNHs+q9yhdYDxdXFQ2Owl/Iio37BR9T+4FRMkFEhGqVh3ZQocRLqyya9OJG1L0xrFQMqW5hu8gCLCzBslrCazxwEPRYMOEErH2apFE6evUXtP1mkWwCZJ10Mqlf9nh/PEzDt56h4OvvMXJuw8S2uzqa/dZee0+q6/eC83Gt6nNVUPBzo8zxGktFpNhD3ota6yz8aeGEeO1+dhPGxKlclyprzTZnh3KrO7zDclt3FiXKfYqM+CEZnR7/HA0XtcWpOL3rQMvbnPG9RRGgWu7bWnQKkxI4bj4wxGjdof+8Sn9gyN6xydc7h3QOTmjeXTC4fvPOD8+ozA/w9zGGou3d2jc3qZxa4fG1jqNzTXqG6tUCq64Y/wJFBx0p4N/eIRut9ETX0i2UEC5CqUnqH4b5SjU3AKsbKLqy7C6LTEQ7XM42UUfPoHmqbTTmVkhybUdWdbmUMM+wcEewckxjIc49XlUrSYzHF20UIt19MI8qteRpEWTcJTIyg60TwGNqq8THB/ibN3B2bz34bTRjylyAr4JPA/9xuvQvIDP/y4szFtS6zR5xcjw2SV+Q0yQkCJaksQzFdGrSBB0mqxN9K7viWY6HklHPR7BeCA5eo2Jd9iT86O5S6thIEcVbdYjf1Zs9lWOY3XGliCRqe3bpGJrnTah2tq8/F7bRGf7Mk2HnyBWP9ZUbc06HT1sCz3GhBhFKavYEmG7DezJDRLacChsmQ44MueO5Npm2MwUucbCjYo0ZjcqozamYW8sJnmIybVUCaekK1uEW+ZDTcqSrufIHOrH2l1ISJ7n09o75PDt9zj4o7c5effBlDa79vqrQrT3xWzc2F4XbdabhKbzUKNFyb5hV/y0vXbspy2WYyKszkBtISbaclX8+Coc4tM+R1+coC+bqH44dd7MPMwtoGpz4tIoh1oyWHmm4+fW2kcFFqEaM7D5Ho0lyQiKxJnTlFMg8H0mnS7Dsybd3X16+4f0j04YnJ/TOT2jedrk9PiMi2ab/njCzNoyi9ubLN3eprGzSWNrQ5YrDRZXl3BchR4M0OMhBD7KddDDAcHxCf7pKXrkgeOiShVUtYxTUDiTHpzsg/ZRyxuwsoVaXofldfFZe2OJ2D7ZRR88CaO2kfexsAzLa7AUCiaFkmi4e0/xjw5Roz5qcR5VKctwp3GAvjhHbe6ga1Wpd5D31jqFzbsordCXLSgVUYsrBKdHqGoV5/Xvlv7kTzhyAr4JhkOZjtD34bP/L+xsg8m+M5US0BBGRjXbVa81cSSsNSVhdJ0wCMlESIa5eBn2ZCqxyE/aD7cHcZRvEFi+0phMzbadYlDy9IaBM8YnaJOqTahTWqpFbMSkMkWoZliKZWrXae1Rp0g10lQ9uXZ6AnpbSzTatNGKDVlG4zELMRGbH5gsR9EMQpP4Pub5ABnbG5bJ+FonI6Ko1mgaQCubkbmH74sG5FpaM+G0fIZcx0Mha7eUINQp83A6LeKHgWtJVlv156KVQ++izfG7jzh8y5DsA07ee8DloaXNvv6qBEK9cofVV25Tm5sBE1wTBLH7QTmyb9RH9y6h3xbzMSpOq2jyS9fmQ/NxJapzZd6NDuTb8Cbyet0wz7Q3jlOChu9Dp5/XCM7Rd6hQkXAr37A2y8jYI+1vMhgybrbpHR5x+egJg+MTxt0eg9YFnZNTWqdnXHb6tC+7nJ+3UdUKi5trNO7corGzJZrr1jqN29ssba4zU19ABT560EW3muiJPI8ql9CeR3B6JtrsYAg4UK7ilMs41RKqUoDLc9Te+/L89RVUYxmW11Hrt8WM7LqSy7vdhONn6KfvirndCCCz82JKXt6UoVK1WbGYTMYEe88Idp+ih/1Qw62ISX6uAWcn6MEQdec+lFzod0XLnZ2Hywupx+370G6KMDU7j5proJtnEu39ie/BqX7M5xD8EJET8E3xy78MJ4fwX/1MysybHXAU+VgTQUg6ysWbJM846EjbAUjDvnT8NnmWq3F0ZqUaR56a6N4wmlPZkctpP/V1UdHpoU0RoaaCxKLo6ljDzSTVaIYYL0msWk/NQjNtwrf835FpN2U6ThOx7YuNNOTAuo8h4pTWao/xVSpO1ZggfRXfI/BEOzLXjY5pSfpvpnCbDOX+tik4y/f6UUn/RqhKE2wwTbIol4nncf74GUdfe5djQ7IZ2uzq66+y8uorrN6/K5HGSsdjo3UQR0o7rryDUV/Mo/22+P4UUJmVdl0UK4GqhMN7EvPyEguu5r2GySh04Ml+261gYiHswMSp79PUdSgQhdqrUyjiT8aMWx0GJyd0nu7Sef8p41abyWDAqD+g17qk2+3TG01oty85Pz2j1eowv1KnsbVOfWuDpfUV6ptrLN29TePuHRrbm5SqZSHAQV/6gcBH97owHqNKMg2gf3ZOcHBI0L6Uz6tURc3M4NTrOIsLKH8AT9+D3UcioCxvwMYt1PIGqrECjRUZj6wDMdd32+iTPXj8NTjdl3oG+W1jVbTh+gp6cUXM4aH1ITjYI3j8EN3toJbqOHM16QMWl8CtoJ89lkCyV14HgrCv6srsRzqQ4Vibr6Cqs+jLC7g8Fw26NofuXKIHlzi338BZ2blRkx5MBvzMZ36GX/vSrzEJJvylV/8S//gv/mPWZtdudL1vJHICvgm0ltmeT0/gV/5HKBalT/BG02Q67EdDYfQw1lIZDaSDKlek0ykJgUZkamunkSZUAqeQmtov7TvWSULN0lIhJgi0aLpZAWRRgFJKUzVBTAky9JNaqukADTHa0xgqi7wUKbO1TchGW013wDBFxObeTiFJguZ9BeH8uCaQKYryLcgsPca3lzBThyZh4+M2fsWo/IEMIzETkhvfq4kcNgRrD8kpVSLT6EfaPrMI9gqS1Y5D5/Sco7ffC7XYh5y8816kzS7duyMmYxMIFfpna3MzoWY5SU5xaUzsgR8JMdobo3xP6s64L6L3ZOIALEE2CCAw807HqTlFQArbh6OEROw4gqiNxfUrtBqmMXUkME0Hmkmvz6h5QW/vkM6TZ3SfPsPr9/GHQ0btNoNOh3Hg0wsUncGEZrPN6fEpQaBp3NpiaXuT+voKjY0lljbXadzeoXHnFgtrKzjhtItaKUm4oTV62BNNL/DDiGdF0LzAPzgkOD0Vv2ypgpqdw2k0cBbmcCpFVPMEffQMDp9Jv1BfgmoNVV9G7dyHrVckaYapt2EX3TqXAKn9x3CyFwc4BgHML4pvd74Oi0s4MwuxT9rzCI4PCd79GrrdQjXqEjilfRl1UF+Fbpfg6ROc+hLcexX80OLQuwyHbS3A6a7c8+6bqF4HPRxB6xjWtiV5yHAA7SZqcQl1/9u/ru/hr/zLv8JvP/xthp6krCw4Be7V7/H2T70tCVQ+xsgJ+Cb4/OfRP/gDEGiCn/4v4JOfkI6mVMnWTC3Try5VLNNZwdJMU4RqBxWlx9Sm/alwDaEGyWulEyA4GaRqMhzpDPOvISKbWO3gokQkriEC23yMRci2Kda6v62xmkT0UQBMIb4XRMSijQk37W815FooWsFSpq4CIQVvIvVhzZdqyqHtYT2hry22MBjfayXpe02Ynj8iXEmy4Tu3CNa0m/F4wtnDx1Hg0/Xa7H1WX3tFfLORNhu+E+VYU9OF/vGofYUapQmasiLzTfpFZZu1Tds1bSfqjkJN1LZaOAUohvcNpTdlhrMpmbjecQt4wyHj9iX9oxN6ewd0njyj92wffzKWkVqTCZ7WjIHBeEyn1eai2eTs9IL2WYva4jyN7U3xt25v0Njeigl3c5XawjzKDye/CAKpm+YZwaAn9VKqyOM44g8OLloEx8f4B4foUZgMY3YOt17HmZvBmZ8BfDg7hoMnEuSklJiON++I9jg3h56p4ZSqMi7ZtFPfR1+cwOFj9O5DOD+W918qx211eR21siW+27l6GKxWjH9/doL/1lcIzk9xFhZR66soPIkhWFyC2TrsPSE4OsTZuQu37sjY+sCX4UHKCYm5JT7jrfuoxRVonqDxZbjm5h3Rxn2P4OQIp+SivulTYv35OnDUPeLOP7zDyB8l9s+V5vjXf+1f8yN3f+Truv5HjZyAb4KTE8mENRyif+cz8C3fFpIp2SRqbydMYtdoqZbZNZtQpwOUprTVyA9rR/5mmH8NwSVMwCGxqpBMwktFvlWbVE2S9rS2a0cQG+k8cd9wWrQoqtQOPJK608bEmOVvLdqaq+0Hlnup6B7E5mPLLJ7QiMdGe3WnI4dtki18xNqrjUyStSweCZJ1It/s5fEpx+9crc2uf+J1tr79W9h48xMs379LfXuDcqWUFNQgZZ61yhPWq45MzDqq87hs4VSXdgIWQ9CuSxQzgZaxoTqQphIEYWRsrKlrt4hTLIVBTD2G5+f0D47oPtuj8+QZ/YMjgskYx3HQxSJUKniOy1BDtz+i1WpxfnjM+d4Rw26X+tqSjHXdXKWxvcHS3buhL3ab+tY6xXKJKBAvCCTSutuUQEUl2jS9HkzGonmWyhJhfHKKv7tLcNkB5aBqczjLKzibW7gry6iSC7sP0c8eQKsp/lGtJeK6viLRx6tbqLtvSu5kL8zzrINwakIk6czZIXr3ARzvyoxX5UpkkZFAqXUJsKqvSoCZ74Xjj0MrwsU5/le/THCwh5qfx9naRJVd1Ggg5L60Dij0e29D9xL16jfBxpb45B0H1WlJIGdjTd7Z4VMRdF/5JpQXSMCb9qB1Dtv3xTytFMHeUyiAc/87cOqrH8on8uWjL/ODv/qDdMadxP7Z0iy//Bd/mb/xrX/jQ7nPR4WcgG8I/cnvgsND+NIfpkjUsTrGVGAWEA1FuIpQMwKU7GtFpGoPA8oy/9rjT93UnxN2gBGP6AxStfyrNqlGAUzmp4GMjzWBS4b0zLCbaMq3MBgGUlpraAqejJP+1sjnak08Hj27H2quY9l2S7GGa87xfbRvyHUMk4GULaGxllFWUgkzROUbCptkE8KaRbKJGbbkbzwccfbwfdFm33vI5f4BvfNzRp0uC5trrL/xOsuv3KG+vcncyjKVhXmK5WJ8T0gSZsL060X+W20PL2Laz2/qXCXaqu2rDa9pj38O76k1oQnZxet2GDcvGJ6d0Ts6o7u3L8FMJ6fg+zhKU16YRc3O4ZdKDLWiN/G47PRpHp3SPDjmYv8Qp1hgaXuL+taaRA1vLNNYqdPYWmPp1jZzm5s45QpW4xe4RXShKJpd+1xyK4+GaC0JLSgU0JeX+Kdnocn4TJJdVGo49WXctTWc1WWctTVpgWcHsPc++nhftNLJWLJk1ZclTeTCEnptGzVXR1VrqOp8bDr3JujOhZiQex24bMo1Lk7D6O7Qj16uCNk21iVgqrGGCrS8NztSu93C/9pXCB49QM3M4mxtoxrzqG5L6n9+Scbsdi4JvvYVlHJQb347LK/IEC/HRQ066F5HzNZzDRmydXEi2u7KFly2JInO4FJmdNq6J0FbhRLB04dQKuA0NlB33vxQBdiRN2LlH6xMEXClUOGdn3qH24u3P7R7fRTICfiG0P/8n4sP+Kd+8mof6jUBSjax6khrszWeSTy+1ibXiNgK0+SqrM7PHhubJtXAT/52KjI4VZaIWI0J2okTH0TBT1iatpWEw5iEbX9rlGi+EI4RjjUkZe7njYmiZI1P0a4no4EZ3+s4nDx+amiOpcEWSt847TXRWLRFsBkkmybYsG1orek1m1weHDJoXjDu9QgmE5SjKM/OsLC+xsxyg/JMDd8TzdUpFmQUjHEj+JP4/RlBLUrS4lquh3C+WxW2VbfAVOC+E843bYLyEvEGlnlfKXQQoJRDMB4zbrUYHR8zODykf3hMZ/eA9uNdxhcXaM+jND9LdWOD0lKDoFBgNBnR7/e5HIy4OGvTPDyhuX/M5ekZ8ytL1Lc2adzakujhnU2WNtepb67S2FqnOlONBVhvHLf1MIo6Gs7luFL+0UACgzyxfuh+n+C8iX9wiL+3jx6NoVjGWVrG3doRbXZjHYoO7D9GX5xDpy2m2LMj0WrnF8XE6ziwuiG5mVc2cSozUJmViRRMKsnQiqPbTfT7X4G9h3KNTkvOMQk/fC8k7zXUwpIsF1flPXijkHTjYK7g3a/hf/kLUK3h7OzgrK+hhm2ZVKM6B0urMvTqcJ/g7a+g5us43/JdMD+P7rSg4KLGI0kdWanJuOB+V/JcF8tw91txikX02TG6VISzA6n39TsisBTLBM8eoapVlO+jvvlT8twfAf7FV/8Ff/N//5tM/Am+9qkWq/ztP/O3+YUf/YWP5H4fJnICvin+8o9LLujP/HYmsUYTAGBF/k5pqRY52gkqEuRoaZwK4nGoKVK1I4nT1zLEGvl4LW3XJlewCN2Qq/HxBZFPVtvm4MlIrhWl3cuIFgZRngIvDtqxkyq4YYJ7wvsY7dUENY2H8pti+RrzcPnDSyxxE5igsMxhPEFC4Eq7GXzfx+v3CXwPx3UplkuMuj16IekCuAXZX6pWKFVKFApO6IccS/0nAuxSwp7titAqiroV1wFxmSJrDTL8JrLshIFNcnFAozX4oxHjyy7Ds3P6ewd0Hz+h8+h92o8e411c4PhjKovzVFcaVFcaqMYyk9ocAwp0+gMujs9oPtvlYu+A5sEJ49GYxuaa+F53tsQPu7VO49Y2jVvb1DfXcF03FmoBvLFkvTJtxHUlCKhYlvPCYDs9Dq0saPRoRHB+LiT7bJeg24VCCWd2AXfnFs7yEu6KmI4pFqF1Lgk7nj0UH2evJ6bfxSXUYkOIsVpDd1viirr1KqqxKiktw/HITEaiIbpFqMyh2ufog/fFlHy2L++iOhsmpxgKOS6toRaXJTFIdUamYKzMyDsxWm7gowcDgkfv4X3uD8BxcHduobY3JbdyuykEOidmbgD94B30k0eojR0h3XIxLrvWor27RSFdtyCR060zCfZau42ajIR4Z+ckEVFtBrW6I22nVCE4eCqBh90mzjd9CjXf+Mg/v7dP3+bXvvRr9L0+f+2b/hrff+tjPAWShZyAbwj9E38Ldp/Bv/j1q8nVdGBTvlWjNarYt5omVdtfi8omaGNytQky4d81mk8wTax29GlIyFPEmva3GnK1zb2hBiX+1nEcrGPMz5HvNRQAfIlu1eNQKx6HE5Cb8Z6GTO2o4Q8rLeLXi3SAmB19DmBIy85OFrOmn7IAACAASURBVAbGaSDwA0kf2OkwvLxk2G4z7nbQkwmV2Rq1+Rmqs1XKlRKO66CdAk7BaP5RQ4k1U3vctR29bTpmP7SA2Me1KVSY2SmRylLIN5j4Mq611WZwckZ//4Duk2e033vA5XsPCSZjitWKlLdWoFrSlOsL+LPzDJwinYlHq+fRbHa4OG7S3D/i4uCIcq1KfWOFxsaKBDRtb4Tkeoul2zvMLi3G425NiLzxbWot7bPbRrdOYTxClUpCLrV5OW8yFj8tAUw8glYb//gE/8kTiTLGQdVmcLdv425v46yt41SL0G3L6IVyRdrixSn6/FjMvu0LGd63tCZDfJbWUbMzMN+QUQ6tU1RtHrW0LrMjKQWToViAJmMhtvEY5Xly3aOnYlKuVK14Bz+aCUgtrcmMScoRzdYphnnNC6E878N4iP/kMf7v/3v0aIRz6y7OzhZqfhbVPAQcSTJSX5GhPqMhwZc+D+cnOPfeQL35HaACKVuxKPc6P5CI/rrcn9YpHD+VeYlf+RacSg3daUOnjZ6ZgWfvhuVdD4m3ij47Qo/HqFEHtf0aavvVl//NfsyRE/ANobe3xQe891CGGbgF+Vgc1zLRXaGpRv5Vi6Cviwy2Al+mtFZbWy5YJmGItEm8cUyuZgjOZCwfjj2tnokYjhJPmGE4luZqIoYLxaTv1WjXJtNWFNgUplcslFJDcz6itIg3gT2uNHIf+PG2nboToncjeyym1ZpgMmbcuWTc6eAN+jAZU3ChOj9L4PkMe338QOMUihSqVUozMxSrNemojC/W3M8eMhb5UC3SDaOMowk+/FjbVjaxhpHq2g/wR2Mm3R6jixb9kzP6u/tcPnpM+933uHz4CFUoUdtcp7a5SXVjndriHNVaAWfSZ9g8pnt8xEXfo9kdc3HRpXnaonnWotfqMr+2HGuum6s0VhZprDVo3LlF/dYOlbl5K2Ke+BkjP7eKXRhBIDNbtU5lYnnXRRVLaLcUCxG+R9DuEITBT97uLgRAbQZ3bR13+xbO2hru6jLUqiitCU4P4Z0vQfdStFO0JIW4CE2t84tQKonmeecTcPs1HO1JO3bLMqtPEIhGGc4LrCdD+b68iSSXODmQ4Ye9rvhJZ+YlzsALx4JXa2JCbqzC8hYsrsq42nDqQZCIf23axGRMsL+H9/v/nuDkBOfWHdw7d1HLi6heC7odyQQ2tyhBWMUytFsEn/sP4Hk4b3wr6o1vlRzZnVZE6Kp5gh71xKe7sokaDUJt9xw276G27qGCAH1+Im1MAbsPYH0HtbAi765SQ19eoE9PoFrEcUuoN79XAq9yPBc5Ad8Q+i/8GDx7Br/7/8QEmZUYwo4odkI/bRQV7GeQarhEEWVVSpiE3SQpe2OJjDSkavtbDakWy6hiWYJMjJ9YqdDfaiZMmCQjhu2ct8qJTaveBG2T61jG3mWTqxn/Wvpw0yI+9+VYhGoEg8gEm7G0fqcjDVMnzzEabzjcKZiMUdrHm/gMuj0GrQ69ixaDTpdCpUJlbp7KQvg3N0uxXIqCfqeycGmLcK0EHihiYcy4HkzKU6WiKQBBEYzHeL0e41ab4XmT/vEp3WdCrq2336X7bI/KUoPq5ia1rQ2qGxvMbG1R3dygsrGO5yh6hwc0336L5oP3aD55SnP/mGa7R7PZQWtorC1R31hh6dYO9du3WLp3l/qtLZbWV1hYmsM1Wb5Mwg1bgDDvxSkIGYXtWNKi9qX99rvoQTh5gjHZBwFBt0dw1sTf28N7/7FkfypXQ5Ldwdnewd3aRs3PoPqX6GGY3GI0gOMjeF+iefF92VeuQGNVskMtLIms7CicpQ1Yvw3VWZQXDmsxY8N7l6IxBp4Q4zgULDtt0ZIvTsRvWyoLOTtOONZ/LPcw0xMub6Lml4iGUvUuwR9FApYOBZDg+Bj/c7+H/+BdnM1tnPuviR9XjyWxhVOA2QWZFGFmQcq4/5Tgc78nft9v+S7U/TdlTHCvFQnMqnshft3anCTeKFUk2OtkV97bvW9DzcyiPA99vI+enUMNuuiDRxJwNVcPibcKwwHB44eojXU4O8b5th9GzS58hB/2nzzkBHxD6B/6IXjyGL72FUsLJNae0oRqEgqgU6Zga6lCDUCHUagTm1yv8rda5Gp1eirwk1qrZ8/dWow12CiZvOXfHY8kangcasxRUv/ytO+1JFOofeSJJeAaQrXI0tZWjaaaMNEbf3lq2/bZhkJTgGI8GDLsdBlcdhl2ukwGAwLPpzI/x9zqErONOsVqGd8LUG4Bt2yNAzZ+YeN3T2RJU8STVYQWB+OKsDVDb4I/6DPp9Bi1WgxOJEF/5/FT2o+ecPH2e4wvWtQ2N6htbVHd2mJma5Pq5ga1zU1qmxsUl5bodrs0n+1x8WyP8ydPaT54wPn7j7nYP6B10qRaLdFYXqCxLibixuYq9e0Nll57jcb9V6mtb6Dcgvjxo1mbUv5YpZLWF+VEPlDt+2ECmp64HbzQXBy6SHSnS9Bs4h8d4z1+SnB2hi6WcVdWcbe2RZvduYW7tYOamZUsUp0LdPMA3TyC8QS6XUl3eHIoxKiUENT6Dmp1I8zytCR+zuOnEt27flfI2Fh8lBIrTrcNew/Ev1yqiIbZuYB+T0zI7aZ8V7VZ8d0qJWRam5Wxs4vL8pvZRZyZeRlLawLDRgMRfFHix/U8gvNT/C9+Hv9L/xHVWMZ97RM4O1tQclCtM5meszYj11loCHEGoN/9KsFXv4CzvIb61k+i7rwK/Ut050LyD7iOJMJoHovmu7QpJu7OBfp0T55p9Tbq1huSk2DQR58fopc24GxPop1vvY4zMy9yaaUmAui7X0Pdug37j1Cvftef+EkTPirkBHxD6Fdegf19ePeLEskbeBl+1lSUsBm0741Fi3yevzWtuYZzgyoT2epNmMqra/yuZh3kup4nJig7angylI7yunGvH0VaxIhQU6Sp06SaQahmfHM6IC0h7IzjaGMzWYCxQKTzR6MYDweMOj284QC0plguUp2bYdQb0G9f4k18lOtSrNYoz81SnpulUCzGxGpmvZFCxos0wdo+WhTam+B1u0wumgxPjhkcHtM9PKbzeJfWg/dpvfMQpaC2tkJ1Y43axgbVrS1qOzvUdm5R2xYNdux5XOzu03y6S/PZLs2nu5xb64N2m/rqMvXleRrzVeo1R4j21jaNO6LNFpeWULMNVDVM/2hnpzIZrXSYC9lxAEdMtY6DyaOtjQDoeWHwXJhOdTKS470+wdkp/uER3rM9/N090ApneQV355aQ7PY27uaWTMxuJmQoVmA8IDh+hn72Dhw9g35fTK/tC2n/xXAO6KU11L1PwJ3XURUxO+vAh14HPejimCE3pUpotp9Ewm5wsgfvfF4CnWqzImhcNoXMhz3RGoslef7Al+dsrIpmu7QuGulAch+rxWUx7bqFOOta4GOmltQX5/h/9GW8z/0+qljG+cQ349y9h5qrilm5dS5tZ64B8wswM48qlGWyhS98Fv34Ac7OPdS3fjdq5w5cNtG9dhg5XUaNhujTffkeGjKBggqCcKKFPXmOu9+CWmigAk+iuXsd9OoG7L4HnUu4/TpObUY+v7KktAwevIVa34bzA9TCCur175ao5xw3Qk7AN4T+sR8TH/Bnfy+Smk2yiEhrzfS3xj5XIVdjrnNA+3FmJjMUZypiuGQlsC8QRVlHpmiLXDPTIqaG5nxYSf2zSDNNqLbWCrFvk3B6NmBqQgAjZEQEG+430bt2WsX0+Gu5oHR4Ztyx1gx7A3rNFp3TMzqn5wR+QGV+jurCPNX6IrWFBSpzNQqlovj7lIpJR1nPS7oM5qAi8Hy8/oDxxQXDoyN6u7t033/C5YOHXLz3kO7uIYValdrqCtW1FWpry1RXl0Rr3d6mtrND9dZtnPk67eNTms/2IkJtPtuj+fQZ5093uXi2h1ssUg8jhRurDRqLM9RnXBoln0Zhwtzt2zjrOxKNWpN8y6pSi9NqmjSfvifBcaNeGEhXDPMyu2JpKRTERDoOLTGasN2PJCAp8NHDMbrdFpJ9+gT/0fvo/hA1P4+7vYN7+y7Ozm2JNl5ZlZzDnvk+SlCoCMkfPUM/eRt98Fjmm+205TtaaMhYVN8TrXBpDfXKm6id+0LYYdvTSqHGofXGBEeF0cMMu5gc0sH+Y3j01TAzlCNjWDvtMLhrVr47XyaJoDYngUf1FZm3tr6OGg/Q5weiSdZXpY7dojyTmRzC99GXLYJ33sL77O+hez2cN74Z9/U3UCtLqMGlTPc3DBNhzDdgbiGep/jshOAP/wOcn6Luv4nzbd+NWt1EX56he5dhP1KQtnp+JH7exWVJ6lGqQvsMfborZvilbbj7pjRVrdFHezLGeW4eHr8lQsWt13Eq1Zh4geDRO6i5RdBjVK+D+vY/K0k+Pi5ofgEO/40I3Mt/Btb+HHwj3V43RE7AN8Fkgq5UxHT2f/+GzIaUMgkTkWsBHGWNb7WINRExbBGrGfcapeKbRMEeUcYmkxYxkVQinRaxcnPt9eskVEkNqCzza2ooViJK2wSUZSQwgeR2Im2mRdThb72Jx+CyS7/Vpt9s0W+3GXe6aK2ZX11mfn2VueUG5RnpYFShgGMLQebaEPuCo+nlBP5owqTXY9S6ZHB8Sm9vn87DR7TfeZfW2/8/e28WY0maXoedP+Lu+75l3lyqsqoya+vumoUccWguIiGIhkGbAiyAlG3BNvxgDwwZsiTKA1lPtl8IC5YoPdgCDVoWQPlFJiWBlsEZkjPD7umluqtr6arKrNzz7vu+RcTvh/PHvTezsjnDMtnTw6kfKFR153LjRsSN83/nO985u5jUG3AF/fAmIvClEvBmUvBl0vBm0vBlkvAmY/BlMnCEI5jMJJqVJprlOpqFMpqnBTSPTxTInqJTKiOYStJneG0V8fU1gm1+hdWsR8I7ZZ9PGDMGxkcSEP6geoA7F0Iy3QHp9HCed6ZC60dd3ktur3L6skV8gtfFmC6ShIwpq8mZCavXp8L45ATm3guYtRqEzwc9k4GeXyfQrm1Az6RZVdvVsR1KoemQmhPotSGbNaBagCwcMCSg3wMCpHJFNAkZS/H+ODmgGGtlA2LjBqlYG1jt+0N3ABMGXkiPH3A4IMY9ujjpDljdDgG3eMSLOZ2o3GC/AhvByt002LuNKaVveh0iEOE5mowIaPUiEIywL6o75p8FaRms9l88g/ne27BOT6Bd34F24yb0/BpgjQnY3RaPPRRnlesJQLg9kEKHPD2CfPubgGFA7NyFdvfLENEYZLsGOeySgnZ5IGZTGod0Gtw0xLOsvsdDbgxqKuFo8xZEIgMxo7+2VTwGogkIXUC+eMTNT34LmsujgNdDtqlwCDkzocWikMe70O7+JEeOPi9rVAL+8N8DOk8Bi5seOHyAKwr81L8Gond/0Ef4x67XAPyKS/6lvwRUK5Df+gMIXZyvWo0liljTFo5ODufi37aYShn5y+XK9c/CFvF79U4/DVBhK3AVqM5/l6J4L1paWksK7UsNP5YFSMCCYrZHmpZoT7unODcK4bFYholhu4Nxu4PpoA9zOp3Txv54FLPRGJPhmNNXHg/cwSBcoSC0ZXvMyyhu02DV2u1j3GphVK6hf1pA9+AQnad76B8fY9pswZ2IE1gTUfhiIXgjfv73ygp8uRw82TR0txtSWujXmmhWW2hWm2iWamgWqwRaVdFOB0NWrut5Bqqv5xFfzyO6xr8jKznomgBaVchOnYpgwyDtF4ywYrJsBmRKb2TdSd9xb5BMy3QEOVIgNB6oLF0P76/5bPhCYEazCgvWQJlSnBVg7O3BPDqEcHug5Vah53LQV1agrW1Cz+UgvB4IS/kc2yyPUPOqY9Wbbbcgm3X2I6sFOjsFw4yq83iAWJKJPrEkxGgE7D2GbNQgklmIzW1gdZNV7pK/NSP16gRSf4QAJE1g2IHsNYF+l25UxUOCHkBVsj+4eO/jAfu1kQTFUuEERDwHEYzN3dGkMQPqZ5DtCu9lf4i9fnXu5HQC6+gQxgffhfnwAbTNK9AVrQy3A6LXZI94pCrpcIKbDJcXwuUlQ7P7BNY7fwjh9UO79RbE3S9A+EOQ7QorXZcH0huAmE04jlUvUZcRTXMz4nCxH147o5lHNAtcvc1RNtOA7HeBehkyl4cYDQi8oSiQ3YTmcvNj4HJD6DqsehmyWobY2AIOHkOsXIO49tafXUrXqyxjBPybW8DwFJDGy193hoFfeAj41z77Y/s+12sAfsUlgwGI/gDy+X0gHDnfe9VsQZWhLBbHDM1eBlnTPD/nukwPfz+m/t+vGGn+3/gUQF2mfj/NMGQJVKVcAOo5sw/bBhJLx7Q00mMtgfyctlXqcHsERdo/bwGWxGzMZBoNEg6XE+P+AN1KDa1CGaZpwuHxwh0KwhuNwheLwReNwOF2z53JpJ3oJABpGJj1Bpi2mhiXqxiUyugfn6Gzf0DLw1IZs14f3kxGqYQz8CXj8MYj8EYD8IV98Abd8IQD0AJhwO2DITW0aw00C1U0T8/QKtfRKNf434UyWmdFuPy+edUavwRkg8kEz5o639IeZZmM1G5e9fFHA4KjMm+R1gxCcwAeH4Q/AgQikG4fxHQMjLoq6q/L32GD4VzNbaiIRAtyMILVapEyPjiE8fw5YFnQsivQV1ehr6xCW10jZRwOQkyG/FlbjOXxQfhCgNtHxqNeJn3cqACtOv9uVkkdJ7Ic83G7AR2sHgNhUqjQgNMDyL3HzKvdugktu05gtJkJ0wCmE8hWFbJWgPAGIFa2IGMZ0srFA6B6AtlpUTzU7/L+8qqerq7zPFomgVaBLXx+CIcbIpQAoun5505aFtCuEMgh+RmVciGcKhZgfnwfxjvfgpbMsI+7dQ0iHoXoNhiz2O/w3IcTfE44PczjdnogRwPIhx/A+vC7EKkstNv3OKPr9vB1hz2eG3+Ile54QJOM0RAitQqZyCrDiw6Bt1nmudq4DZHOU8ktNMjSKQWd2VWIVhVy/wnp6VSeo10AJxV0B2SvDWt/F+LGbaB0AAEd4s2fYsvi87b2fwP44L8GzMHlXxdOYOu/AL7065/tcf0J1msAfpUlJeTP/AytKH/v3wBCsv+6DLKavjSK833YIv4pAeocVO3vuzSA4YK15EtBDM7FvOayw5JNic/nm5ednuzXX3JPWqaN1Y/DNoew/aAtE5Ypma3a6qBTrqJbqaFXa0BzOBBIJhHMpBBMpxBMxOEO+KA79LlTkxQarJmBabeLSb2BUYn91t6LA3T39jEoljEsV2BNpvDa860rK/CtUCHszWXhi0fgi/jh8ugQ4z6BazrGyNDQbPZYwZZraBYqaJ2V0CxW0CxW0Ws0Ec6kCKob64itrymQXUVsfQ3R/Ao8gcA54Ft2P5sHTUCygut3OCIym8JO94EGABYf2v4IaVB/BNIXgjCnDK8ftFkZz6aY+wlblvJxlpDjKax2G2apDPPoGMbzZ7CaLVay+TXouVXoqyvQVvIUQLmcjM7s1AjkQoMQkvdsKAbpCTIwoV6GrJwClTMFuDUqgtOrEOk8kMpB+PyQOoBek9SuLwzhckM63UCzARSPmRS2tsUAAa9fnS+eK2mo83K2DxgziOwmkLsKaRoQZ7uQpSOCfrdJ1a+mlMZen6KjhxRVRVMUTIVjpGqHfUAIiMQqRDRD8JGSn99hl0piJZwUkJCmCateh/XkIWZ/+A0Itxva9i2qlVdzzDXut1nhjgY0B4mnOa7jcNEVy+GAbDZgvf8dyGePITa2CLrbd0j9NyuQoz6E00m7SMskcDbLVF0HozyvgTAnFhol0uDjIWnsq29A8/r4HqAz9MEXgIzFIcqnkCe7TFZKpFktS0EjDocTcjyA9ckDiOu3+V6KJ9C+8LMQ8eyf9dP01df/8yWg+cEf/z2OIPAfdj+b43mF9RqAX2VNJpA+H4H4W78LXN162RbRHu/5Xr3TZUCFOA9kS/+WwJI46YKpx6f1U8/ZWjovoYEFmHkrLwD1stDJBtKlPujFY7TvoGVaep7Ww9ecjicYNNvoN5roN1oYtTuYDgcQugPJzXVEVnMIJGJwetywDIu9WacL5nSKaaeLcb2JYbGI/uExOnsvMDg8wrBUxqhUBoSAL5Ni1ZrLwbuSg29lhWImBbbOSARiNiYNOOzC7LfRPTlB6+QEjXoHzXoXrUoTjVIVrWIFzUIZlmEyVH01R2DdUIHqm5uIr+cRzmWhOxwvb3YuOqJZBsHUsviw7DYh6yXIyhnvBY9vIbrRBITLDfjDi8rWF1Izpy0+HCdDNVYm5nPLcjKF1enCqtVhnpzA2N2FcXgEPZninKxdxa6sQl9dhQiGFFNA+lT2WkD5kMfvC7EycrogdBcwHEC2WxT4VNRoikNZFaZWgOw6RHaD/21MSJm2qkC/yZlat48xnFJAdDuAlPy5cJQKaig1smUtTGPadbpGeTivCs0BUS9ClpQoS9Oo7nU4uQGZTTCfsY8kaQ9p08kAYBich9V0gm58hT87myzGo2aKflc507LdhvniOYzv/AFkq0lKefs29CtXSHm2q2Qqeh0eT0RV1E7O4AtPgKBePIH13ncgT4+gXb8JceeLEFs7/Mw0y+zpuj2AN8TXNqbsjVfPSJfHMkAoxk1Yuw7ZKNAaUgJY24bIXaHGxDKByQRW4RAilaU95ukLoHQIrO9AhOOq4qXRh3A6IWcTWI8/hMhfhXBqkC8eQVy7B3Hlzuffxeq3rwCDw+/xTRrwy+b3+J4f3HoNwK+6fue3gUYD+JVfxqVV67KV3kVAhQ2qcjHG8hKgfo9+6rKlpC1eWrbxW/69y7Ou0lrqq14A14ubhOUowWWXqGX62PaaFhpM06TwqdnCZDAELBNOtwvecBDBVAKz8QST/hCmYUJzOKEr5eak3mBu6+ERuvsHGJ4VMCoUMSqXobs98GZT8KVTVAsvV7GrefjW8nBGYuceFnI2xaxZQ+vFczT292kqcXKKZrmJZq2FZqmOdqUGXziIaC6DeC6FaC5NanhjHbGNTcS2rsKXykCzRyxsgDWX2IRlsAXm10cKjSKYVg2oFSAL+wSNYIT2gIEwpMdLMZSuA4EIKyZfkG0M0yDQjgcqc5aVLKYzWP0+TGVKYe7vY/b0GTSffw6s83nZ1TVoqRQdiewNljkj6zDqQ7arFDy5Vb6sZdG5aTRisk2rzmOWkkrjeIrGDdk1uiSFeM7leMC+aLvK36k7gEAEwhukCYth0JXLF2BlCAnp9i6iIu30n+mEgNOsAC4fq7JhD6icKN/lBPuVLg/PS7tO8PQGWNnGMzyPXh89k20dxmgAKU2I2ApEYoX37nRE0J3RRMaOVZStJqyTExjvvQNrfxfa9R3o27egbd8GAh6IRonV8WjIcSNfCIhnKeDSdNK0Li+V5Ae7MN/9FtDrQtu+A+3ul4C1K/w8NUqQgx6EmxutuWf1aABZPeVmI56BDCcIupZF0VetSMAPRICtNyACEYjpiIK2eoVirNVNbssOnpD6v3oHIhBixQtBUZrTDWlMYT17BBGMQCRTkAdPIAJRiDd+8v93Ru9ntr7xs0Dl9//47/GkgV8qfzbH8wrrNQC/6nr+DOi0gbfexAJQ1bKVy6/ST7WDCebOWcC88pybRdgP/6V/2wrQpapzYcwhF+Bp2xXOY+iWAHfZulBzXABobY7Hs9GQNouSvVlN19Ct1NE+K2Lc69OUQlrQJKAZBjAcYVwsYXB6hmGxiFGhhFG5DGc4xNnWbJoVbDoJXyrO0RxVxTpC4YWATafoTEqJYauFxsERmi/2zoFsq1BGs1TDsDdAJJNkoPpKlvaI2QT/rK0hurEOVzhKkHB5CRKfUr0urtWFWWLdwYdat8HKsHpKEGmUFdimIIIKbHWddoO+MEHDnic1DQW0Y16G6RRWrwur0YZZLsM4OILx9Ckwnc4dn/SVPL2M82vQc3kIr2dB+0troT2YWzr2SFe2qlTOTmec+ex1OO/ZUrR3Kkcv4mgCIhoHMmu0SXR7qe4XnF1Gp6aq3ArviUgKmg24umIEaiXS2A4HjR/cLoKiGreThjK7OPyEvVqhEWh7LaXkjs8TjDAZEZhNg/RrMkd6NxCZV74EKnNudCFnE4iIiurTNAW4yt1K0f+y04J1sAfz8SMKp/LrpJV37kCsrALdGtAokm7vtfkZsHvHKqJQ+MKA5uCm5tljWG//PoTTCbF9l+NC2Tz79o0inbQ8Pgh/iJT3RM1J14vAeES1dSytqHQd6DU5y9ttMMUofx0if50fx9kE0F2Qx8/ZrsitQ8zGkLsPSK9fexPC7YVwKarZ4WT1a84gj/Ygx1OIrW2gfAS069C+8HMQf0oZvZ/ZOvsd4O1fAYz+5V/XPMCt/w648/c+2+P6E6zXAPyKS16/BhRLwJP3Aae+VAld1k+9IFiyQxjm9oIXHZnU38B5MD1nnK8vNE/ngGPp9817xWqMZ7lynccX2jSyJECoSleqnzemM4w6XXTLNdSPT1B9/gKjWgO6ZUGzTGjjKbTxGLphwOh0MCqVMKnV4bZtD+0+azrFcZxUnDOv8Qh0l/McuNoKcVMC3XJFGUqccPb14ECB7BmahTI0TSCajiO2kia45lcZUbeaQyybQjjqh+ZU/tMu76I9oOmKrrukmr14fs75c6sou0aZILsMtqGoAtsIpMcHODRmPnv8Sm1MAxUbAKQEZK8Ps16nx+/xCYzdF7BqNWiZzNz5Sc/n5/SxCIVZ5dsbLTvgwh5rm0143UwDGLRhVY6B8hlgSlZsvTZNKyZjVrTpVVLGsRQQibJydHrmGxI7/EJKi0DQrkL227xHgjEIb0AlDknSpUe7jPXzBiDSOYK5onIhNEhjyvnUs32gdMSM5vGQgJpZpxLaMhbCpV6bG5VgBEhmae/o8lCs12tRV+GP8H6dct5ejvoQ0TTFVFDnW2ChN5jOYB3swvzwPRgffwQRDEO/eQfazm3oW9uQxhioiyEyIgAAIABJREFUnXIjMBqQEfAF+fr+EF/L5YHwhUgtd1qQTx5QuZxIqnGhL0HE05CWQdDtdThjHIpTZ2hwtEs2yrSvjGUgYmmyIg4XLS9bFW44JiMyFNfvQUSSFNhJi6TU/lMgEoOMpyCGPchnH/J9Xn8TwuFUwralitcyIStnkMUixN17EO065P4jiDtfhVjf+fzTzZctywS++XNA413AHJ3/mnACvhXgLz8AXJ9fe8zXAPyKS/7kTwLFAvDhe6p3t5Q0c/EBf9EC8SUK94KJhKo0X044sn+fEmXNae0LALv8+6BwWtriJ9sSkSIfczbDsN1Ft1JF/eAY1cdPUf34EVp7+3AaM/i8XnicDuiGAdnvwxgM4E2n537CPtvycCUHbyYNbzoBbzzKNrNtoiHlOZX4ZDxlgPpZ6ZzBROOYf3fLZfijEcRySRpLJCOMqdtYQ2xjA7G1PLzRCADJkQzTIGi4VYKSzspJzHvb6twtp/9cliy1TGObxgJsKyeKHi3TJCGahghFIb1ePuCkdc7fGACkwwU5mUE2mzCLBRgHBzCeP4d5fAItHGaubH6NXsZ51Z9NpklJ29dX05UZhnpP5mzJ3IVgbo2HDH4/2VVmDhNWlKMBEIpCJDJAOg+xcoWVoz8AYUwXFqPnAFcjsIz6kL0GARKSdLLbq1TaBtBrQe4+gtx/yhGhtatA/ip71wZpZTkeAfUiZK1I56WemnlN5IDVLYqtxn1uYFoV3u/RJMVEsQSFXxKQthtWrwn4ghCBKC/QZMgKfzqCCMYhfAFuaJVwyg4qMStlmB+8A/O734aczaDv3FagewfC54OsF/j6psENCgTHoaIpVu9SQvhVf94wIOtVWA/fh/zgbYj8JrSbb7CnG4qqe6YIOerzMxmKUKluco5a9jrcfPhDEImcqqQd3KBUz3gcgy7PYXoDYvMmM3QnQ35fr8P+7srG3BpTPr3Pe+/6WxCQHFXCUsVrmUCnQevIu19kj37vY4jMBsStP7uM3s9smWPg/t8ADv8PFjsAYE6A7M8DP/a/A57ED/b4vsd6DcCvuOTtW8DJKfDiIeZ5rMsV6hxglxTBwAIAL6t45/msS0Ipu1pejiRUrkxzvJgn9ihK+UK+q2laaJ0WUHv0BOWPHqL60cfo7L3ApN6AWxPwulxwqNd3xWPwrawgdGUT/lXOtXpzWaUazsGV4E5+YSSyNPsMQOoO9FpdtEpVNM4qaBVKaJwU0Do9m1e008EQ0bUVxHIZxLIpxFJRxBJhRGM+xHMpRDY24LArHgcrPaHGuZZV5AsHMYtV7ZzSX6aKlyv+y2cY+eAsKbA9XYBtJAGkVkkjuzk3K+wNlJpRlqaENRjDqlZhHB3BePoJzL1dAICWSXOMJ78Ox9r6EmXsVS+8JN7S9EVilMMFO14Pyt1MTsdAu8lc2uIRzfP7fVa3gSAQiQGxNLRMHli5ooLRHcoRTf3RlxgBl4f+x9KiJ/Ooy9fSdNLjyxT2sA+5+xjyyfs0+Vi/RtCNpSCkRSq12yLgNioE1fGQVGoiC2Q3Wel2auzpdptKXJSGiCYggzHS8N0Wr5U/RGFarwl4QxChKO/3cV/ZZRscQXK55+wNjBngcMLqdGF+8gjmt78Bq3gG7eoW9FtvckQok6XYrHLCDchoSAreFwRSOR6jOaNqORDhPTMdQ5aLsD54B/LpxxBbOwTdW/cgfAHmVjeKkKMB1d3eADd+gKLDx1RqQ7IHHU9DePyQQmdroHLC6L+p6mdv3OQMsr3ZcnogTw/IDKxtkQFoVkk1B0PAtbegWRxplBDKuUy1AUY9WA8/gNh+E8Ljoip6NoX2hb/IQIg/T2vWBWrvUBwXuwd4P8fq7aX1GoBfccmvfx0onAH/2/+6JFK6pPKdK4rFEjgvfIjPWRsKQfX0ufEkYD6GZOfNLoGrFBqM4Qijag31J09Rfu8+Wrt7GJycYVyrwur14TAMWv55vXBGo/DmsghduYL4rR1Er23NE3Lc8ficipJzc4bpObA1R0OqhEvVhalEoYTmSYEuTidncHk9iK2vzYPUbZCNJkKIRf0IegWEafLB5wspw372RIVlzKsoOospi0AlBhPaBYBdrmjPjUx9ynW7CLa1AgU18YzKT/VDOjnHbRMRMCxYwzGsdhdmuQJzdw/Gs6ew2k1SxukM9GyGdPHGVehrGxCRqGq/2x8vJXIzDCrPHRcylpXfspyOCGDVAmPgbNVxt8XzpALgRTQBkcwxozYY59iKEBQYTUcKcJ1zsIXDw+MxTYLGdKQ2biAQmDNWkJZJgKsUYD16Hxj0IDauQ+SvAiub3BC1mJUrWzUeW4/UM72LI1QtewMEluopz2IkQYemWIpV7mjA0aJek8rheJrH2WsC3qBKDNIhJn2CFcCvC5VjrABXjicw93YZ1ffsIURmhWrlndvQNq7SHKReZB/VZDXIXm6C19zp5GbDGyS1bFkQ4wGss1NY7/4hUDyFuHEb2q03Ibbv0uDEmC6BrofvVcUjQloE3UaFY0nxDEQiC+EPQ+oOtgnqRfaWBz3+THYTYu0G+9iTId+r5oTc/ZjveXWT7FX5hOKqWAq49ia02RhwedjjdblpKystyOkI1kfvUdmcSvNeP9mDdu9nmGj0w0g3/zldrwH4FZe8fp0AfPjJy/3Zc3m6PL32yAfX8gdg6QFt08hqllYCmHb7GJWr6J+eYVQooH96htbzXfSOTjCuVGB2u5BSYmpJGJoGPRKBN5NGcHMD8Z1tZL7wFjJf+gI8S+B67n3YM5cqknDUatAK8fhUgWsZzTMFsqcF9Kp1hLOZl9ybYmt5xNIxRONBeDST86mjrprDDHDEwusn8OgcFxGmCcBaOIPZamwlRhMvVbP69wTYc+/NBtvKCenkfoubiVAcCKp5VPv3TSeQgyHMTg9WpQHj5ATG3gtYxQK0aIwhAZkstGwaejYHPb8OLbdKCm853tC+jhILMZ7DeR5whUbAa1aA0jFnaRsVyHqZIfBOJ+dHI3EgkaY4JxKjAYhXbVq8yod3OlbJVfbsuep3O11zCl7O+9wWMBlC9pp8fWgQunJq67QhXzyG3H3IsaK1a3SecrkXVHKrSmWt20ubRl0pikNxXsNBh0AXitFGMhynoMofgrAkZK0A+eJj3t8JJaRyuYFuHfAEIcIJUvqTMaSu8borHYOQFuBwkXE4OoTx3tswP3oXIhCAfv0mAXd9HcLtputXvcj3PBqwn+xX4q1wlP1ngBaSLp/Kye1AHh/AevsPgNEQ2vZtiFtvQVy7BaE7Ia0pUCtCjgcESm9gPj9rTxxQ9V4i6EaTnNt1eRhSMexyhKrXouezy0t7yFSe720y4DmcTGHtP4ZIpCGTWQhzBnm8y9zxdB64cgfadKiAF3P7UCHpRWA9+YiU+dXrEO0a5O5HEJt3IHa+/Dqj93O4XgPwKy75T/8pUClD/Lf/zXnR02Wnczl6TmiArsOSwKTVxqhYVurgEhXCxRKGhSL6p6cYV6oQTgek24uplBiOx+j3R3Al4ghurCO2fR2pN99A5o07SN3Ygj8W+/TjlQyL7xWLaBweorVk5t8olNA8K6FZKMGczpbcmtbmHsS2yUQom4Eujfk8rVSmBRj1CC4+JrfYVofCjli0e5r2+bD7my43x0bmrlivZnVnzWYU0DSKFAXNRnwdFYYuLAkpJeRoBKtahVmpwSyUYBwewSoVAaFBX9tQkXcr0NMZaLks9GyO8XeAusZYMmjAQgxl06DAPGyDoOuiBWDxCLJ8RNOKWpFAqzuUMYSHQr5EDiK9Sh9nTVMPejWe5Aly3zZVYDsdEehdVChDUyrr+dyxGu8ZdDkmNB4AHr9SNLvoZ352BPnwu6wqN7eBzCoTjlo1VaVV+L5iKYhIEtLtJghPFNiP+nwP8QzBVgUlCE1XCUZTyNM9yOOnpLyTWchYmhufTo3ex5EUKzdjRtC11IZQCFLkDg/M4hnM++/BvP8O5HAA/eYd6Dd2IK7dgObQadbRU2YkpskkIYCVYiLH6zEhaIlglPfadAzZqkO+eAbru98CXG6OC92+B6xfg3DozCquFxTo+gi6Ther2DEtQeWwv+jrJlf4/h0uGo1YcuEZPeoDkwmQWIG4egfCH5rHMsLlg6wWIUvHpPYDIV67Fw9p2blyBWLzFsS4yzEnCaU74TWXxgzW/jNgOKJ95aALefQJ4PRCu/ezrzN6P8frNQC/yup0gHv3gF4P+PY3gZWVJRGUDsuSGNebGBQWgDosFjEsljBSf48rVTgjEThjUcDrxUxoGI0n6LY7qJeqkC4XIjeuIbV9nX+uX0PqxjXEN9ehOy/fyc4mEwqaDo/4x65kT0kPt0oVeENBGvmvrS7cmzbW5xWtP7aYqZXGlL2nYRdy2Jk7BMGhQsc9AVrhzSl1dSvNFdyKGXC4leetZ9ELf4VlOxXJ0YA0qHJXkoCiEkmjytkMVq8Pq1qHeXQI4/AIZqUG2etDz+XoX7y2QQHUygr0bAYiEKTP7VJ2spyHP0gmzdiZy8shGZpjDrhyZtAdqnoKlE5Y2dZK/N5EhtSxP0BlsUMjNekLsgqEhHD7Cbbe5QpX0cnTEV933iNWSmul3pZqvlV2mzThH3XpnOUNkKoVOkSzCuvpA+B4j5RyKsvz1u8AjRKvdTTFUZhwnMfVrJBGNpVXdCSpADfG8SKvn+dLc5DpGPYhT55DnjwHvH6IhAJdp5Og6wuz0tXYT58zMLZVqdNLQ5GH92F+8A6swgm0rW3oO7eg79wG4kmgXVF2nUP+3FD1jwNKMR1LqSzrKatBf4jHPxlCNuqwnjygiCqRhti+w/D63LpSa0/o+Twa8Nx5/RAOJVSajOjZ3u/Sh9k0INJr3KAoQJS6k0rm4gENQiZjfizWb0KsqRGiyYDv1eUj6zAeAhvXCe7GFPLpB6TK165ToTzsKOCV81EvunPNIEunsI6PoX3xK6TbyydA5Yx93h+xjN76sA7DMpDyp6D9ECQhAa8B+NXWX/krkP/qXwGGgXFuBZ/88n90DmAnjSbcycRcwOQIh2HoOkaTKXqdHhqVGipHx+iUKohtriN94zpSN7YIstvXL61mpZQYttvzOLrG0QmaR0eLDNjTAoatNsKZFOL5FUTzK8q9aQ2x9XXErmwitpaHyxYALf9uy2T1MOySOh731QiHRuWp26dET4uRJfYzVcWnOVQOqYeUnsvzyn0maY/rqF6mHA+BfosjGSC4YtCFnEyUxWIPRrkK8+gIZqEAq96AFg7RmGJtA/rGFrS1dThW8hCJ+KIvaxr8/dMx39eSIlrY1pq66j9L5dQkLcDBkAW0G5B1mlnI8imVvpMxEE/RtCKe4XiS0wnZrRLAw3EqilWWsPB8CuBORwQQTfXBbeZAgippzbEAXNsAo9tQnschboocysHqaBfy4Tus0OJpVtrDPh/wwQjBI5GFDEX5ms0KDToGPR5nLMNec5g5usKhTC6mI24GPEEad5w8hzx9vlD4xlIE/kGLGwFfSCnTLc7FSkvNykvIiQFzfx/m/e/Cev4YWi7P0aDtm6SAp0O2EfpNtfHh+adJSIZCL6+foKw7GKLgVpaMwx5krQrr4/uQjz+EWF1fgG4iy/Gy2QRoFCD7XV4ff5DtD80BzFSecb9LS8h+lxuQ5AoQCDOoQXewYq2dkTUY94HpjF7XW28AkSSTqqZD2Cln8pMPAG8Acu0KWwXTEeTj97ip2NiBWL0O0W8RcCEh3b5Fj9ecQbYbkE8+hnbvK/TWblaobr7xRf75Ecro/ZdP/yW+/s2vY7+1D01oiHqi+Ltf/bv42pe/9rnvd78G4FdZ29vA8+cAANPtxt7//L/AnUxgYlrodftoVquo7e6juruH6vMX0F1OpLevI3V961OrWcs00S6W5lmvdhzdfA725BRC0xBfzSG6kkFsNcNKdn2NVPHGOkKrq9BcHCe5bEk7+WVAlyU5GwPmjFSqWwWx6w5+s33j2n7FpsEKcK6itQ0s/uQfdCklqyY7sN0GHWVMYM/Mym4bVqUIqzOA2erRy7hSg1WtAgC0VAJ6bgXa+hU4Nq9C29hiVetyclMBwb77bMpe3Yi+xovoPZ57IU2KwHSd/LJlAuYU0rSAbouuUI0KZK0EVM54/uIZAlosyVnaRIbirXaVKluHk/aBXv9cZMWKSlHKXpXGY1e4xhSAplTfSqCnOxQQKNOP6YRVZLtCWlnTCW7z2EEJWSlAPnwXaFV5XA6N9Kfby2OOJCDjGdLynTpks0rQHfU5t5zI0iYyluLIj8PF6nE64LXxBgB3gJaIJ88gT/fUuFNOje44ORrkDfA1hRLcWQavyWQIOZrAKpRgfvIY5qMPIfx+6Dt3GNd36w2qkdtV9kxHXdLS4yHHhIIRiGQOMp7mRkBpDBgJqLPnO+zDKpxAfnwfcv85xOY1aDt3Ie58gVS5EHSrahQhBx2ev2BUaQ4UmKrqWpaPeX4iCfoihxPcOAGQkMrC84ibmoliKVauQqxt09N5oqINXR6g14a19xgik4dMsb+LQR/yybu8567ehshegeg1+HmUEtLlA1xOCJVpjWEP5gdvQ7v5RYhEjO5XLx5ChFMQb/305yuj9zNY/+jdf4Rf/cavYjgbnvv/PqcPf/XWX8Vv/OJv/ICO7PtbrwH4VVY+D5ydAQAMIfA/XbmNTrGM2OY6UtevIb197aVqdjocsnKdg+sCZJsnZ+gUS/DHozSUyGc5orOS4fzreh6x9TX4EomXXKEuW1KJbTDqc9dtTBfjSU43KytL/bfGeWBhzFTlOTw/H+pWf18Mj/geS9pV40RVsjbA2j1M1ReX0wlkvQLr5Ajm0QGMapOA22xBDobQUinoyQT0VBLa+ib0K9dY1cYTFJFL9pal0OavJ0c9KlA1jbSo2ztPBBLWbNF/VtWtnIxZQbTrQLNOk4RaiVViPM0KMJ6GiCXZ54zQX1gOlcNU/YwVWDDGh7PuIKh7AgultyeoFLLqetija7ZyW3NwdnPZ+MM0lOtUlYA7GREoPIH591qNKvD0IwKh16+wW7D3mchCxJJM05mMgVaF6tx2nWDl8ZMaT+epqvaq3r1QzlGjHkdhvEw7QrMCefyM1prhOCtIW0lsmoswBIDgokBDdpowT09g7R/A2n0KOehD37kLfeeWGg/KA8YMVrMEVI7UfUI/ZFjgMSay7I0OOmQCAmEKq0yTFeegB+vwBeRH70NWihDXbxJ0b91TPVBBXUC9RNB1eWiZ6XSpTYaKTRQaZKcJnO7y/2fWaJThIrsjwc2ArJ0CnToBfzbj77pymzPiUnl+CwG4vZDHLyDrBYjNHcAfICC3a5zhdThoK5leg+jWea9KMF3K5WZv3KJIy7r/DsTKFYiNK0C/DVk8BLotaF/8+c9XRu9ntBrDBlb/wSrGxvjSr/ucPvz+f/L7+PLKlz/jI/v+12sAfpWVyQCVCgDAcjhQe/wJ3MEAOqXyvFq1TSVssJ30B4jmVxhLl1eOTStpgmwmgUgmBaffvwBYh1NVZY6XgW85Vm46WXqo2+NOqpdpkUqVS0EMwjIIgIrSPQeydlX7J+jTSpuOnI44D7oMtELMRUJywKrE3N+D8fwprEIBZn8Mq9eHFgxBy2Ypekqn+efKFrTVDfrrqh6zUD12W2CEcZ9+waOeSsAJEARcngWVbJmKPnfwQdaqcfyoUQGatUWKTyTOpJnkygJsA2FVHdPpTPbaNMJvVZg9G4ywb6vbzleBhULZ7VOjReOFAtm2BRW6yuR1E3AVYyGlBAZtyJaqcHtNUsq+EFWvAkC9Chw95wiPZVLFHIgAmTxEeg0ylgRmU1a3rfqiug1G2AuNJAgqbi/pVo9ytDJmrDaHHR63Lww4fZD1M8jjZzT0j6bmPc95pW7fm1LS39kyYRkzmC+ewfrkCazTM8hyEdq1HfZxt29BbFyjsrivzmejSCAddBlsEIhyLjeRZa+z32aMXzDGazwZEuD6HVi7T2F9+F1gOIC2fRdi5w7EjbvzSnAZdOHxc37YFslJZdNpe3efvWAfPJ4BMuvQPL6FkE5KUtCVExXeMObnMLvJPq3TrRzJxspwxg35+D1IcwZx9Tbg0LnpKp9AvngM+HzAtbcgEjmITm0BvC4vYyXNqXLvGsF6eB9w+6Hdfouv3ShDHj6hkcbnLaP3M1y//t6v4+/83t95qfq1lyY0/LW7fw2/+e//5md8ZN//eg3Ar7KSSaBeBwCYAP6GJwmXz7s0kqPmX1dzrGBXUghEQtCk6n3pSwBrg622BLSXpOtIU1WopgnY3s52BCKUY5DDA7ho5whp0brOVlo63S9XtX9MFb28pOqXzqvZeSWr6GL7d2oOGiEc7sN4+gTWi+cwy2VYrTYgNGjJJEVQ+TWaU2Qz0HNZiFCMv8PuMdsjSMuV7ah/PlTe7SV4zEVJYBWvOyB1Heh2OD5TKdCXuKlGaAJhmmtk8rRjTGQJvrYhvmWo3t+EgqbaKXvQofiC7tU1Gn947f6t8nZW8XFzNfx8Q+SYn/+LD0s5Hqowgwp7uQ4XAdXlYe5ruw5ZOoasFnitdQdFTCubEPktVretKoVX7QY3E06XcpWKsncbjkNYFivnSJJ9eiF4Hocd/hFCga6HZh/HT4HyMUeSMmsU3dkGD3b1blm0q5xNYNWqMD96D9aLPViFM/Zxb96FfmMH2vXbED4/lcfNCt2nxn32nFsNXt/kKpBaYV7wsMev+UKklp20aMR0DNntQD75CNYDxtBpN9+A2LkLcXWHdL+U7Os3y6SHvQGIQIjsh/qcYdznZ0pKJZQrAP4gkF4DokkKeJwe9nZnE8jCCzU+tKh2kb/OqlVa/FxYJq/ZdAb58G0gFIHcuKGU6QZHiU73uBG68RYtM9tqpAvs9UtvkIldlgU5G8N68RRod6B9+Sf5pGk32OdNr0G88e/MqfAf1fW3/t+/hV9759f+2O/5ifxP4Dv/6Xc+oyP6k6/XAPwq6949yAcPIKSEGY1iuvccHq97YVhhqIzXZYC1rRg1NZrzKek60g5sABYP9MloTo8xW9QP6Q3w9wH80NqAKPRLqtpP7wvbS5rm4ndc7MtKc8my0AM5HMFsNGAeHsF89hjm0QHMYoGUccALPa7mZq9ch755FdrGJvRQiA9wlxe2ny0cDlY2trpYKL9lNdZEwO0RHOyq0sk5YmG7NE3GdAaqlTmnWSvRyMLtZeh4epXVTDoPmcxBOPSFleNsQkDRdG4qOjUqmCdDIJzkqIjbB0CyEvaFCLgeHx/k9nWzr5WpgMnhXLAJFzY458IM2hVe32AMgE6gb9eBGn2CpWkA0CBiaSC/BVzdhjAtVkAqXAGjIeeFw3GCbiRBWn48ZKpQIsf3IgTP17inQLfL+8cXpql/6Yig22sA+WsMMfD6OfpkbyqU25sQAlatAvPpE5hPH8LafwHh888dp/Tt2xBRWgBSla1YAwhWuK0a05Ey67xGmgbZbfKzEYwCgSjFRoMOwatRg3xwH9bjDwF/ANrOXWi33qT9pcc3Byy0KpCdBoWDwejCtEN3KM3BmJvEVhU42yOFHQgDKpjeHp+SmsY4xcILxRaN+RnIXYVYvcbq2iBlLTQdcPuBehnWi0cQ6VXInAJmY8pqt3zMa3OdPWjRrqgeL1hd+8IQMxqjSGMCWT6Dtfsc+k/8RcDlYHzl6QsGcnzx53hNXy/8w3f/IX71934VI2N06dcFBH7lzq/gn/3SP/uMj+z7X68B+FXW4SHknTvAeAz829/mSNKS17HdXxTzKvbydB2pafP5UTkdkSoctPn/bDpT0Y/QNHr42tWnLey4UNUKW0R1yZKWuZghnVzoy9q/z+0FnF7AsGA2W4y9OzokyB7tw6qUofl90II+aF4XrRav34R+4xb0/DqE17cwI1EBAbQ3dC/mKOeUq6WqsB7kqEuwNabsTc6rWycfxt0WK4BmTQmiinRhEhqQyECkVhkEn90AMmvQfIGFonrZP1mFWNijTLJ2xmsSSRFwnWokSErSzHboAKDGqxSNbBpq/Ebj+XJ5FvaOy+dcWvTstQG336aIyZgRjNo1oFljpQUB9HtAPMt+4eoGxGwC2a4pz2RVMUWTdJWKJCGDEYjJSGX0uoBohvmwbu/img8VtTzq8V7xhckUnO1DFvf5fjPrrJh1fTFypAGARrAYj2E+fB/m86ewnj/lPO72Lei334S2cwdado2vP+pDtsqscqes5lAvsacbTQK5dSCc5CZn0OE9EYxygzUZc4MgdMizI1gfvQf57BFEIsWgg1tvMQ7R6QGkqUC3qkA3xF6vPXsOzI1H4HSzfXC2B0BAxLOQIXtEy8VkIt3Bud7CHql/u7frCzJzN722SCFSM8VweiH3HkG2qhBrW3Qpsywaezz9kPR/Igvc+AKEP7wAXqi2UDAGMRnwvRhUNlv3vwv9x34GCKl+d7MMebLHmMArdz/3qt7PctUGNeT/QR4Tc3Lp131OH77xH38DP77645/xkX3/6zUAv+KS/+SfAKUixH//9y4kEb2criPt2LnZhJVsv8NqB1KBbJg2eB4/AWepr2rTji9VtSqt5qXjsqwFyF6ki43pedAWOu0ViyWYx0cwTw5hnRzDPDkCIEkZh4PQvE5o4QD0azvQb74BLZllFehwLoRFsykkBAHW7eN7crkXx2WD4ai76NuO+3yY2mb/DhfkdAJRPWVF26qzR1vjzCVzadNAeoWik9wGRCQxBz1pMwY22BrTOW0qB13IprIA1B2sCu0ZXMvirKe92dH1eWUMIRZMxGy0ZO9INuCy/hvzdhXg2jF6kxmve7vGER9bGFQusGLb2AFSaUBIir+aFVax4Tg9kyMJUuUuD4VrtQLQqpPKXN0Ckvm5Il3a/dxBhzOnHj9BFxpkYR+yXYVwuQgOdl7wcmCIww3pCUAe7ML88F2Yu0/Zx13bgHbrDThuvwmxsUUK1LIoEmsU6fUsdL7RSrtmAAAgAElEQVS/epk950SGlL/LxV6uYbB3HohxYzNUHtS6E/JgF9b734E83IVYWSfo7rwBkcrxPrEM9nTbdW5K/FEFupqyS9cZfDAd0whjMqZgrNem0CwSZ38VANw+Aq9lMhaxsEfaezzkmFNmnfaQwYiKjGT4A9w+MjUfvw2paRAb22z7QFlQPnmP1zm9Aly7x55zuzJvsUih896b9FV61YzK5ne+Be3WF6GtrFCx321BPn/AY7j71R+ejN7PeP3a27+Gv/8Hf/9SFfQv3vhF/PNf+uef603LawB+1fWVrzAN6eP3F2MiQlejEYOFQ5T9gFEWgsIXgvQFKe6QFuljGySXqd7lqtaeA1VL2hFsl9HFxmTR73V7AYcHsj+AWanAPDqGeXqkQPYQVrvNsZ1UikAb8EIL+aGnEtA2rpP69IcXKtfZmBXOZEQQc/uU0UH4pQeENA01W6yq21EPgKKSPX6aZ7Qa7L9Vi+zRNhToJLKcpY2lqEDO5CkAcnrOidKk2tTM/1iGSg0yIfstlXRTJgCFEgRctxsCSg3u8Sm2QiycuCT4oFUhCNB1VeGqAIML1wJQhiXtKqxWhSMp3SbnQHsd/tsXpPhJ6JDFY6BZh7iyDSTSFOf0WgRmj48UcjTFajEYhoCANC1StPuPeM5XtiBWrnIkxj4Xs8minzsdzzd2mBmQNSUc8gU5q20nQ0lV6c6mkN4gZH8M6+EHMB/eh3XwAiKVhn59G9qdt6BtbUMLRnjfDjqQzRJB15gRsBpl3pOJHEE3FKWCd9DhZjEYI/BK0BLUkoDuhPX8CeT734I8O+a40M27EDfu0MrR5VGJR2NF29eAQEy5WWlsCzg9c1Gh1DSCcOWErmPxHOlan58gqjuUb7PgfVw7JSMx7LPadblpmJHZgHAo2no8IMvi8gLjIayP32Hc4Po1KuoleI8/fpfHkclDXLtHarxVXgJeDYikCbymSXvQ8RDme9+GSOWh3XyDn91uE/LoOaDrP5wZvT+A9VuPfwtf/8bXUewXoQkNPqcPf/sv/G38zb/wNz/3hhyvAfhVlmlCfvWrVKT+zv8FqVtKHNRX1Z8CWm9oHlUolqpSzJai4Jar2qVRH9v1ieA8VnTxcOnn3Us/6wGmBsxqDebJCcxTVrHWyRHMYgFaLA59bR3a6hr0aARayA8t7IcWCkF4vATaEHtmUCYJcjLkw2k85IPJH2FCjD/8Um9zPl+8XN3OxoAnAOnwkFpu1ukeZNswDnqsaBNZ+h1Hk4yBs0c+lJWjXWHOx5ou9m8dNNOX3Qbp5G6d1HEozurFrY7V9mHGYuRnbnBhpwXN/ZQXLMGlgGtZQK8Bq3JEj95Ghf6+vTZ/bzQJEUtDOj1ApQD55D6r9mQGcDp4r0zGi55tJEGXLJdHCfCUOKt4SJvDzDpBN3dlMYNq3x8DBbqmocadAgSjboNsuZprhSYWJiqTIeR0Ckgd5mkB1qMPYT55AOF2cxZ3+za0m7dpiekLEzTbZQJut8Hz1++SmQjH1ZiQCr4fDXifBqIESl+Q53XQUZsjCevpI4JurQxx7SZB99pN5c3s4cZqMgQ6qtINRCHCSVa6xpTXxjLVjLAF6fTy+06ekdLObKj7Gbx/bXX8bKICJE5ZiQ8JhohnITZuckNjK9dnY+Xg5oEsHEMePqVdZ3qVrALIVMjH7/F1shsQm3coBGuVFPDSzx3RDISKT5wrmz/+AJAatK/8NP9/t8nefukI4q2f+eHN6P0BLSklir0iDMvAamgV+iWf28/jeg3Ar7KaTchkkiM+v/NbEF/8MkVRuoMPiOXeKsR5kHUvqMuXXJ+W6eLp5LyYx+2FhAar0YR5egbz5Gipmj0ChIC+vqn8jNfZmw35oYUD0ISa+fUFSH3OpvPoQwmw99yp86HnD3Om0R+h2tPtexlsZ5MlkRRVyVJ3kGbtdThPW68QbLstUqfJHAU38QyBNhAg7etwLcBxSZV9af/WjnmcDCHbNYp7+m32FGMZCL9ygbKj/ezMZKeboeRCX4wG2QYYEItNzKf00KWUBPijT4DCPm0bB1TS0roxxarIFwKqJcgHfwRYBkQqS6HZeEAQUPF7CKswCDvlx/aY7jQgj58Dk6GqcreA9Nr8mKSUfPjbPV2AjILTwzGaqaLhhn1W7rb5vhI0yV4XstGBeXQM68lHkN0OK9ydO9Bu3oEWChEkAxGI8WBR5VoW75e6qnLTedpVRhMQkyG9rqUFEWEAARxubsJGiv4e9GE9fgDrwz8Cel1o23cgbt6FuLJNkHe4WRFOhqx0W1VWuiqTV0zH8xALqqdH3NyMR5CnuwT47AZEPAuh83MlJyMaiWg60O9A2vPFw77akOjA6jWI/HWVY2zw3FoGVeK6E/LpfchuA2LnSxCBIOSEwQuyVgCeP+C1Xb3Ka+UL0SLTBl4pIeIrizl8iz1ra/8ZZKkM/ad/ARAmNzL9LuTeR+zx3v4K7+HX60divQbgV1zyzTeBWhV455sAVGyf0/NyVavp3OFeRhdf6O8Kl5dB7t0ezGIR5jEpY/P4CNbpkaKM11jN5tdptZhf4wytJtVsL0gzCk2Fexscy3EwpB6DjvKoHbFi8odVZUvB0UuqXdMgQKvqVg467HGNRkCvy4qiUaVAKBBhVZtaYcWXyCpVrhKjOV3zuUo43eeqy0v7tw7Sw3LUVyYSRX4tvQ4RUo5EDud8ThcAledu5Tttpw4tsw/AEs3vYRDExWtrmZCVEwJu6XBBjQcjQIzVnogmgUCEYrDHH9AqMRondQm5AOZoCgjHuNmwZ7RNZhdLY8YRqaNPOHNsg24kubQRsXj+beWy7qDqVtd5P1lqftacLa67xhEhq1WFPDyAVW3A2n8B6+wI2sZV6Nuq6sytEmidHlKmgy5ks0R9gu4kOFQLHGNKrkLGU6TJ+23+cXkgommKiaSlerwzbgqadVgfvw/rwbuAlATdnTsQ61cJVrpDtRBUT7dZJnMUX6FqfzJYgK694dNpgykrZ0CzRPVyahXweCmNsyxuFrwUuXHGuMTzPRrw72AUYn0HSK5QaKdaRgy28HGM6MG3aYJx+8dIGiinMnl2ABw+Bbw+YO0aEM9B84epHFcezdIyIeKriyraMiFnE8hKAdbjj6H/1C9AeD1kmAZdxgt6/KSbw3/OMnpfr++5XgPwKy6ZywGNBnD8Aogyu9R+0J93fVKjQXY/1+WFdHmAyZTWiifHn0oZ62ub0NbWoefX2atNJFktTQbKYMLJMZR2jW5OQrDicHk5vjAaEGzHfT4Uz4Ft8OWZ1HmF1YM17LI/W2e1J7tdjo+0ahxPSWRoVpFaoYFFIkuQsWnieZ6va/73uUr6sv6tGquSA4YDyF6TFXg8x16zSwU/zCZK5OaY+xELp2vxe23AVaMdy4B7cfZZSkmVcukQ8mQXqBxzFMjpZt82kaU5RygKzeWB1awCe4/oPOVycoTK4wOSOVL50SS9hO1oxTnNPeWGYNiHLB0ApWNWt6qfK7yBxTHNvbk7i5QpB9XZMA1e69FSlSsZFCFNE9buE1gvdmEVy7AO9yHSWeg3dmiAsXWDQNlrcQPkcJHFaJb4wobJ6z1WvcyECjaQFineUZ/3UDTDv6dj9nOFxvGs4imsj96B9fADIBCEtn0b2vYdYGWd1KzmZNiBMSF9Wy9ys5TM836c9HkP6E5ew1GPm0fDhGxVSMdHkgTdcIz+2k43U4tslmDU56ZpOibzYJJdmIuqfCHea1PVcnCqVk69AuvRdznzvP0WNwCWCTkdEyQLh0AgCKxvU0/g8gDTIYSap5fGDCK5qgR3A56z2RToNGC+/YfQfvxnoWVzZLl6LZX3XIZ272dZib+mm38k12sAfpVlmqSghwPIb/8u4HcvXJ8UXcz0EgGrWoN5ejKnjFnNHgMa4+/mlLGqZvVcjh6y9kywHXgw7PMhWFeuQS4P6b5QlMBkq6tHXVZIcxo5zMrisl7mbEJ1cKMIFI8YLNBtA9020KrzNRJZ5Xls08h5CJ8SsizlCNvOTnOwXaJyP7V/63QrwVQTstukIM0fodGEh+MatJccApAEITWeNadlLXt+WYGubYhgg+5FwB0NlEjnmIk11VM+oP1+mlSkVoHkKoRH9RnbTciTXcjSkarKHdzEpPMQyewioABiYbIi9MW5gYDs1OkmNb5ALS/ls0rTIOAOOqyc7OpPZUrLYV+xLE6O9Oga+5PNJswH78Hc34N1fALh8c4NMPQbNwGfjw/9ZoWUqe5gT37Q5TXrd4HKKXvm6VUgloIMRFjxtyqklgMRIJblMU2GBBC3l+NURy9g3f8j5tCmcxDbt6HduAWRXlHWlCoucDrhRkep0EVihV7Zk+GCBZqOeGwApOag9WX5mKNFuaukvAE17udgMIcQbEl0GtxIGAY3pdJitZvdBDKbEJpQ4DhU94iaaT94Ann4DOL6XWDtGpOH7B788w+5IQlGgM0d9qLtqESXl5ue2RQikQdAVy1Ii8zGsA/z29+Ado25wpgM2D/v9yEPHlKodfPHXmf0/oiv1wD8KuvgAHJnh/Tuv/5tyFu3YJ6dqmr2cspYX9uAtraxqGaDwcXoh6WM1i3lIdttQlbP6NIjBJ2iUnn2Dt3uxbzwoEuwWRZI+cOX9zEtk+YNZ/sEn2qRlV5HVTBJVnoiofqUscTCjF/TF8Yhy+DpVHSyw3Vu/vVT+7cOFwVTg64SsoDH7QsuRmFmU24mhEYqUVk72huI+SyzHUJvzpbmcC8I2YwpK43KCS0Ay0fKljHKDN5QBMhskCa23Za6LUa6VQtkMiAokspuANk8+8xLNC8NV9wU4kzZUya1XCC17AsRdFe36BO8vBkwpryOw+4i7AIgiM8mTIISEhj0OOLlckNCwNp9DvPRh7D29yBHYwLuzbvQb2zTI9swIKcjyOoZQReC4K5x1hX1EiP8MuvKzznJcbl+myI2l48CtkiKQe/L/VynB3L3Kaz7fwS5+5iUsg260QQ3SELwvU1HCnRLCwevSIo6Cfv6GjOeA2MK6QkA3QbFZ8MeK8NEVinfoUbntHlbRI77asZ4vKCYARpmZNYgQnHeU8YMmPQVY6J6tB99B7Lbgrjz41TcD7v0eZ6MIB+9A3Sa9P2+chvCF4Qc9yBcHgiXT43LjVm5C8HrJ5Vj3WQI851vQYTi0L76c8qnugNMJpC7D4BIQmX0Rv40n0iv1w/peg3Ar7K+9jXIf/yP+cyNBDH46R+/QBlvQM+TMhb2HKn9R1ocSZhXBGXIwgFQPOToRirPKiySvAC2HT6g52BLwL1sB22NBkBhn0YLdiZtqz6fpUVqhX3aRBYilgBcLvZClRAKgDKamM2rPvZu3fN4vHNA8mn9W90JTCdKvGKSJrYfuioLVthVj9u7cJryBpYA11qIpqYjBbieJcB1L1yemhUFtsf8u1XlpiUQBtwqLjG9BuGPQOpOevB2GmoEqsxRnplBOjm/BeQ2oLmUcYi2NKJkWxrao1mazv54UVHLqVWIlWuklpfSaUjxj4BBi+IciUWOtNA4vmaoeeFhT83j+iBrNZhPH8P85BFkpQxt4wq0O/eYILSap3LXmMGajIHaKTcDlqWug5vjUOUTVpzpPGnlUJQg367x2vlCECGV8WxMWXFbBgFVCsinD2F9+A7k8R7E1k1o27cgrt8iUCu2gpXumBvIZpmboUCYlC2btByNklARkwNIbxCYTmmS0SwBiVXm2Pr8EMaEVbTumjNCslXhZ6HXVP7RrJjhj0Cs3WDik8O1oJlnY2WaoURbH/wBpNMFcetLFICNqWiWkzHkg2/x90UTBF5vALLfhnB76PntdEOOhwRe3QEM20qgxg2P9eg+MBhB+7lfhDAn7PMOB5DFI6DX+pHM6P0sV3vcxtScIulL/tBQ+q8B+FXWb/wG5Ne+xn//V/8lxP/4P5wH2bkHsBpDGPSo7Cwd0VKuU6ci1gbbWErNGaoUnwG9k8+pkf3hl9SRcjoBakVYxUOgeEhxSr3Eh3w4toiWy6zRJcrnZ/Uh5UIQZVe3ah4U0loSSyk6+WKv+NL+rXvhfmUqlanDSXrQkjwfAhCjHh+M82g+js4sxo3UjPNsyTzE4V70cJ1q3rjXBioEWlasZzR+iGfo6+t0sHpU4zDSUgYXrQo9k7tNAAJyOiW1vrnNjYnDwQrXDkkwFjnAEEK9P1pOynad9o2jAcF2ZYtVpR3gDtDic9jmRsq2GJ1X0KbSDPA6SiWMk1MD1tEhzGfs54pYDPrNN6DfvQdx9QY3BVMKg6zxEGiWaUChTDTmvdxBj17OyRwQSxDQhz2eA7ePzl/BGAVMU7uv7CTwDIeQTx7A+ui7kNUiM3Rv3OK4kD84Dw8g6I4Iuo0ye8uhOODxcXNog67uIMMwpHhL6i7Sy4UXDEnIXyd4qg0N3H6wzTGDnIy4oRq0+R5tlyoAWN2CyF6l37MCQ0yVmYYdplA5ZX83nWfIvc9HdkZKyPEY8v7v8z1Ek8DVO9ywdeoQDieFfi4P5GgAkVjlPWgfh90jPtyFdbAP/ef/AwiPi6KtbhPo9yAPH0Ps/NiPXEbvZ7lMy8Rf/7//Ov7Fk38BTWi4l72H3/2V30XYE/5BH9r3XK8B+FXXf/6fUQX9f/7mfKZ0HtRePWUVVj3jQz9mWyWq8Q2XB2LchxwoNam0WNEuAe6ysYU0Zkx0qZxBlk8gS8dUp/Y7pFHDUT5k03SHQjwDDZKAulzBqjBw9ifV12ybSBtwL6QvXdq/BVQAguqr2fOl/TbB2QZfAYjxQNlLLllrevwL9yp7nnUeHLE0I+2kjzUmY/ZtbbCtnPD1FIUqfX7OuQ473Dz4o3xA95pUMHfq8765nBms9LZuU0QmBMFfUxW7PaakO1SoheQDXZmsyOoZ5NFTRv/Z/dx4dkF7W5bqZXYXIjAp5zF9NoUvey2qai0TcAdh1uqwDl7AfP4UQghoW9eg3f0CHLfeoOBICeTkeECgbtf5+wFS4IM+q+9QDCKzVOWaJmSvAfQ6BEYV4ycmQ/6eyYBRid4g0KjBenSfyuVBj0EHN25BXL1BkZjTo3qcNujaPV0n1cseD8MHpOTmyuUlBdtrUjDm8kJ2WpAnz/jz2SuM1xOq1+9SXt/mjFm77TpQPuSGTFrsV0Ny83DlLnv1ur7wYJ8Oee95lODr+UeQJ7sQV25BhpPQ/D7YQRnWsA/c/ybPfywFXLlDtXfjDNBdCni9kKMeRCIP4fFRcKYsZaUxhqyWYL3/DrSf+nehZXKkxLsNfv3ZhxDJFc70/n/svWmMZel5HvZ859x932rfq2vpruru6p7pGYrDbUCLNEVS0RJSlIxAEWLTQhzBgWDDcQw6yKIggWHakm3BCGKAhm0BDiAJEUUOxWU4XLpnpvd936prX27V3fdzzpsfz3fuqe4Zi1Qr4pDm/QCCM1NdVeeec/p7vvd9n+WnLKP3R73++Vv/HF944wtdN6ygGcRnFj6D//DL/+E9vrIfvHoA/DxrawuytMQT7r/710DMR9At7z8NttkhIBiCahwAW7vjtY/dVrI2ixDb1m3UVWB7Dc72GluHpTyJIMkMVDwB9A2zqh0c7xozKKvTPdV32ceGTq+x2x5zuMtK5vz2nS5bz8xvrbZ2ifKDvUPhHLFahNSKEGXysBAMA4aCalY9KUo3vCD6tL7X3cDdFuEBe0dRJtTepje33V5h5dQ/SoZqbgSIRDmfLe3wZ4Ti3KSrRZKNqiVWw6GoBoMYMDnPyk1X/0rpg4cCv9fusLqB4R04TD9JausPaajfN+KxlqM8XXdb5K0625mOtiEV8Q4ohsm57O4GZPkWxBeCNDuwNzbhPLgHKRVhTM/AnJ2HcfJ9MAaGOIfVPs5SK3IeXC10AwXgCNOdqiVgeIoHsHSOLPtmja1aKM5dY0mCVr3CCrjT0ge+JLC6DOfqOThXzwOmQdCdW4AaP0Tmctd3ucWDUHWf7GUBVD9TklSnSWALRvi8Oy1Kcxyb975eg7PxiDag2WG2YRNpErACIa+7YFsQpehPvcffgWZdd2aEgDt1lAdUcbS8r8n3KRAC/IyAlEvfg9QrUHMneAhMpODGJkppH3LpOySxZQagpo8ByRx/nwJUMAIEo5BaGapvhO+xnlG7YQkoF2B/5xswXvwg1JGTUPUimfuWTSZ9q05Z0cD4j2I3+qlfv/gffxF/cvdPnvpvU6kpPPrvH71HV/TDrx4AP8/6ylcgn/0sK6L/9r8G/vbnKVcJhqGaFXrL1orcHFxilAu2LigUdlkhb69pwpVuH8eSNKpIpKCiMZ7OB8dIiNIpPMq2uOkcZB+7AfNWRwOuJrq42afPGF24Sw7GGlq6BW0yEJ7grUGguMNNLRjhYSAUBSBsKR+M5osknjLvIOC2D7iAuYAbgviCbJPurGmwfUIAdXNnByd4kPH7u8H0XSemdpuAW9jldab7ea9NHxCOkNHrVp8RPVP2MV8ZbryiNulglatBRBmQwi5by/UK1LBuLQ9N0szj4EzazWEG9P9rJrTWlEqtDKw+gPPkDsRScIoVOCsrcDbWYExMwZiZY3LQ7GEYvgAkFKN9Y3UfKO5Amg3P5QoKaDR5r+K6ys0N8lkAJFAVdoBoAirOSleZfh78aiUemqKcx+LhHS0XOg8kMzAWlmDMHWa7OhTlOyNC3+V2E6js08XMtqAyQ0BuhHm1VttLVIKw0m01+Husjsc0j6cJZn2jtG905WOG2QVdtFuQ5ZtkEjsO3wulAGVCTR5h/J/77Nxq13H4Huo5t3Ph23zW8ychlSKMTE47gBlw8lvAle+TV5EdgJpYgCSzUJV9oNMiqzmkgTc7zLGPy0h3CPZSr8D+ztdhjM1AfeBjUPUS57wN7d28fAvq6Ad+qjN634v197/x9/Evz/1LtG3+XVRQ+PDEh/Gd3/jOe3thP8TqAfDzrGoVMjpKUsdrfwhk49yMw/Gn5D8SikKVix7Qbq8y13V3nZVBbpAM20SSAd2xBBnP4Ti/7gvqWDM3yUdxs/EFPN9iq+3NJX2BpwD3XaVHrk7WannmFd3QAUdLOvL0Ue60CYaJrA6Ytwi4ImQuu4B7wJqy27I+GCZh6PZjp82qbWdNz21XSbga0NX8wDg1t3YbUtiBFLaA/BrBtqPJSdUSDynpfqhkFhII0985GgcqZV5bLKmJXKqrLe5W9f6QFyXY0uEKnTZkR2fgHvRazg577ObuAaLtVbYu+CrVDbaX/R04j29B7t+AU2vB2d2D8+QJVK4P5uFFGLPzJDAFglC+ICSa4rXtb9BspFtJO/zM5SLBqFvl9kGFwgxkKOaBZhUq2cc5dyTBeW5lX3dbPNCVuzchl87AuXUZamiMQQezhxl0EIwQEB1tQdppssOxu07QjaWA/gmajLTq3ucNBPVct8T3wPTzHV+/D8BgxyI7SO6CY/F9MU2P+W+Y1LCv3tVM5SbvL4R/j6aOsfPhHuJc1zjTR+A1/ZC1x5Brb5I0d+goZGcNKtunmeomnI1l4Obb1K73DUGNzpEF36oDjcoB4C1BZYaZ+1wvabmSwxl0sw7n7e8BvhCMj/8SlNXSHZg9wAHkznnahPYyet+TVWwW8dL//RK2q9sAAL/px1t/8y3MZefe4yv7wasHwM+zSiVINgtAgG//GeSFl9gS3F3XQLvWBV34gyRA5Qa0BWGczj1ia99oPX8LRNjCfTdA7c5b7QOxen++0QWgq9vuz+t0KzwA/H7LYmjB3gbBMBKnb20iw2realNX7IYouC3lQOhdAPcASBkGRBSlHHs68m9rhZvwAAPe1cA4tbThmA4z2IWztw5sPiLIdnSYgTLYWk3mgOwgJBzl3M/qAPltdnrjKbYTTeW13m2LQCKiW8vqAEM7AKmWvNZydsiTCkUTnkmDO5M2fN5s2I0kDIQ06DBL17l1Ac7Ny7BLdTgbm1A+H4wjizAOH4M5f4SjA9vm5030cUa8s8yqvkHjBrRblAft7XB+OzQJlRsE4knOoOtVJjr5glDJHFQ8BQlGoWyLc95aiQAWjgORNOTeTTiX3oTcvwk1OcP28sw8Z9+uTtr1/W5rZ6adNYJuNMHwgGhCO4gJ2cihKO9DpcCOTDgB2d8miFaLnOtmBzh2cCySqYJhTw5k+iCdNmTrMdu+liZVKYP3tW8YavoYDTPg8PO0dRs6EPbi/G5dYEzfkReAkWlgd432nppV7izfAe5fJSFvYBRqYJLXBIE0ygz2CEYh1QIr3mQ/0CyTiKYld9Jqwrl5CbKTh/mJz0BFORNGaY8z5ofXAceGcepjvYze93g1Og1869G30LJbeHXyVeQiuff6kn6o1QPg51mvvQb55V8GbBvOr3wCsjDManRglK3P3BBUMg2JxqFgU1JimpR6hOPUO/r8UO6s0WofAFQSody2F79+oNWsAfcdWl8RzU7W2ljH8b7mujK1Gl5Wa2WfQfDpASbVBEKUBDV0688lTIUT74g+lG6Fq52/bItVWiEPyW8RzKtFzkwHJyj9GZxgK1JLhqS8B9l8CGw8IqGo3aSBQiLDKi+ZIaM5EGYVWipA1h/zoBPPMK4uEgMgXl5vp61NLLSbkpsHqzd3KeyQAFQt0dhh5BDU4BR/RjeysUWggXiJSICuuuJALE2bxwfXYZ99A86923D2ipBGA8bsEZgLR2EcOQbVP0g3JSioWBpOoh+qtE3mdmnX6wzUykBxn599aJKt5XQfST+2xRSk0i4/cyLrseE7LUgpr72RdQKWPwLn0QPIpTOQlYdQc4swjhyHmp7j/XRTsiCcKbugu7vONmw8Q3JZPKPJSo42mAnx/a4VeRiJZ+g9vXKHM/rcCNTQBLsOjqXf9TTnrG19CPIFIIVtuoA1awRdbVSCaIKKgPF5yuEcbe3arHtSqECInYrzb5CRfOx9UJl+yqj8AVbnyoRz/yqwcheIJdhSz2pgDIXJVvcHWfGW80BmBCozqDOpy1ClTCkAACAASURBVB7wdpqQJw/h3LgK42O/BGNoFKgWOFJwHCC/BVl/AHX8Q1CHehm9vfX8qwfAz7O++lXIZz7DNto//HvAf/d3uFE1Gb8H2yUhudVtSLfRWgQJ13bRbScfbGk+a3Shq+Cngt5FWxLana7RO1N09KN0W8/VEqSwSU1mvUIbv/QAQcQXgGrXCLiG76mW8jvkTu7M1A2MqOzTIWp/h3Pr/W06Qh1sJWcHPS2vCKS8D1nR3sqFHQKO6eOMOzPAqjuZg/KZrNwLecijO5BqhZm/47PAwDA7B91ZrqOZxnbXpAFulrJr+rG9StD1B3Si0CF2Itx2pgu4humRwxxt6BAIa8JcGlIrw37zm3AunoH9+BGkVIYxOg5jcQnm4nGoyUMkTWkfZZXshxNLQeXX2AGo7IEzYqEme2+b4RgjhyiPidHdS1oNEqg6Ld1azkD0DFuaDCtgdc85v4gP8mQZcvUsJL8Fdfg4jIVjUJMz1OD6dbIURFe6Db4X+XWg3STYRhMk0rmacsNHYwllevcjkQVgQtbvQ1buUmY0fpjP3Wrzz4f0O98+oAW3LDhbj+jJ3GnzwOFqqvtGGHTfN6r/DugDZ7NGEA/F+RwLu3DOvc44w6MvQ4VCWqdsU8MOA87Nc5yPx+LAyAxUKkfOQirHMAdfAAjF4JR2oBJ9UAMTPJzVSxp4bYjVguS34Hz/DZgf/DjU4RNAdY/vfK0M2AK5fRZq/DDBt5fR21t/ydUD4OdZlgWJRoFOB/KtPwLGx4Fwgo45oSigDJJUOm3dwjSfkvl0TRIs/WdM39POUsYBKZDjELBtm4BrWwBEE4z0Jmn62Eos7QGFLQJup0XXocwgQcQ0yVBuVLgZuS3lSOIp3Srguk1pwC3lqVEt6pD43Q1uvgfBtn/0KdAWEcjeJmeqW8sE3FaTrdTsEME5mWEAgGuiUSpAHtyEPL5L96NpzcQNaA9kv6vD1YQun19nKuvqVWuRpVqCrD3k780MkrE8OKEtHHWUoxnwZpFuhjKg3ZniQDwL8YcgD2/D/t7X6Dq1sQGVStH84thJmPNH2FrVyUwqEAYyw3x++XXI3hqrPNPPz767wU08Nww1Mk3NcTDEc5P2vkYoCpXIcp4bCNO3uVEhC94NIqgUgEYbsrYC5/oloFmDsXAC6vAi71ck7h3cxOGsstNk4s7uKtBusa2czLGV7+YhB0JdiU233R7PAKE4DzFPblGyNDLLeafbNfD5eaAz/HSb0hGRUtyDbD7wEoia9e7sVg1NAH1jXSY5/x7o6E23zWz6IMt3IdffhpqcB2YWaUNptamb9vsJvFdPU30Qi5OolchCdtegcsPkT/g18Ba3oYIxqLEjnOHWit4s2moDlRKsb30V5sJJqPf/LFSjrOVeu4zVvH0e8PnZbu5l9PbW/0+rB8DPs0QgH/oQsLsDOfsWVDDgkXwc+ykTC6gDspaOrhQOukq5Rhci3RZY1wfaBVvXptHRDGVfgBtRcdcDXIDVbXqArFRDkTDVrPL3RbwZ7rPuWa69o9SKnInubuiUoy3O6DTQqsFxtpOf0TVKu0kZ1uo9misUd8kETmQY0N43DMTTMNxMWihIpQS5fxNy7W22pmeOQU3NAbl+srxdU373MGBbXrqUY7N6MUx0W8tP7gDVItTQNM0ZUllvDmwGuoeUbuvcdSjzad1wNAXUqrDOfhf22e/AvncbEAfmzDyMpRdhHj0JI5WGiAOpMcBARZNAaoBAt79JQwylDTYqZc0B8PP+jUxDUjkY2j9YSnnOURNZqGQWKpLUFWOHjOZaka1Xx4bsb0F2toH1DTi3rwJ+P4zFE3SiGh6DCsdJiOreryblMvUyJW3thlfpBiMeUzsY5T3UHsZoN/nuxNNAuQDnyW1gd42HrNFZIBaHqmsSXijKP2u1daqXH7A7cLafAPl1vhjFPU8DnR2iL3PSzT12vCQi2+raXAKAXHsbsv4IavEU28i2lgC1m/r+gq5V7QZbzePzUKk+yOObnMUGApzxhmJwijt8n2ZfhCECqRW6f8dgdyCNGuw3/gwqMwjjY7/w9KHHHwY2H0O2nvQyenvrr2T1APg5l7xwElhfB25f8cDWnTu6utv/lNEFoAHVrTgsaoC7P9z2Qhj8QQJIqw4pbkP2t4GCdhzqAm4ccByoZoUkkkD46ZbyM/NicRxIs8bkn60njNQraE1pbpgGF4MT3HiT2WeMORzaN26v6DADrdMNhVkxuRVxWOtYAZqUNBuQR/cgl88AjSrU7HFg+gjU2DSUz9Rt95Amhx0wVXDnjy75LBACWk1WZat3eT8nFyhz8Qd0S9mvU4LEI1O5shCrwwormoIYfjh3bsK+8H3Yl89CCvswBgdgHH8R5smXoUbGYejPLJUCGdmJvq7MBvubPOCYfla7+7vMP06keQ9Hp3TEowlpVinlgUCl+qFiaUiYDmDSblJe1WnyMCCA7G9A7t2ErG9AHtwG0lkYCydgzB4B+oc8ja7P35VVidUGGjXOZZtVINnHPxeKoPsE/SFW+a2adufS5KZEH8F/9R7vayLDdn1uhDpft9oNJ/j/7YaWCWlHsK3HvNcttrhhGIA/DDU8yTCEcNQ79NgWr88wGOjgC3CmfP7bbF8vvQKVymi/cMXQhXYTcARy9bR2OEsA44ehMsOQB5fZwncNbMJxOEXqwdXMSRjhOA+X7rjGpruWc/Y00GrD+OTnoCJRznnrJY42Wk3I7XMkhB19pZfR21t/JasHwM+55Pd/H9jeAv7H/+Gds92DRhfK0Cduy9t8HJt+0K70x7V27Gp6fbSvLG6zui3ucCPPDAKpfoYTOB0SR5o1bYDgVrjxdwCu49hMUdp4xM05vwEU9ggUA+NQQ5Ns02aH3mGXJ21qT2VnlWEGu+sExnCYWsmhCbZVfT7OuUV0lSqQtWXI9XOQJ/eAkUmoQ8egJueYrORqSM2AV5la7W5kIFxHKcdmAEC1CFl7wEPD4BTU8BRnnGLzz7se1i5YuHaS7mw9nICEonA2NuFcvwj74ptwHt+nzePkJMxTr0AdPg7l90E5QtAt5oHCJg8WblpPcYfP1rZY4e1u8JklM8DwFHOQ/UHA1qBd2AIiSbKWYymIP0jjsEaV5DPT7M7kZXsVcuUtyOoTyPJDqJFJqMUlGC5zORiln7XL8m7XCZqtBjXU9Qp9lxMZOnz5fN7ziKZ4T8r5rn81kmRDy/pDBkdAoEZmqTt3K2PD4DOKxD0msz9IwNxd5SxZGawYHQeA4qHNdQjTrfCu+1qroU0zwnRo292Cc+5bUJl+Aq8/QC6FG7pQpOez3D7PA1YsATV5BOgbgzy+DlhtqOwgRwDhJKS4BSnnoQamWXE/q+XtNOHcugZZfgzfJz8LDIwCFT3nrRSBcBxy4wwQjvcyenvrr3z1APg5l7z4IrCxDty94QGuYZIkZLvVraXbfSY1j+48twvWQS92rlagnKOwxWoolibgJvs4N9MtRbTqbNcdBNxn9L5Orcwwhs3HZCTnt9gK7Rsl0A5PQw2MvYNEQivNPNvJ26uQnRW2SeMpbvyROPWoySxgGLqS5+eD6Yfs7ULuXIHcvQIAULPHoaaPEHxZSmodruFVuQAlWMrQG3S9S0yTvW0SqBwHGJuBSvfzcOHza32vtttsaRlLIMT/1tJB6OE4pFyFffc2nKvnYd+4BBWPwxgahDl/hE5GySS7B9CdgVKela0OjUejyuorGCHTe3ed8qh0P0F3ZJLtXdNHUlRhm/rSZB/by5EEgca2IfUygSpEprGEYsDGYzjnvgN5cAeyuQ41fRjG4hLU9CwtM4MRHTrh87JmrQ7b/tsrBJh4BirV93RIhmiTiliapLmaJhvF0pB4jnPqJ7eB0m7XaAShCFRLz9PdcA497qC1qAHJb/C96LT0fLeCbqjEwDjU6BxnzAdDSFo6HlCHZ8AwIY9uUb87cxRYeJnsaRd4HQeyscJq+MktIByhMc34YWBgim3h/BpU/whb6pEkGdaVPYbbTy95YK87TdJpQlafwD73Jnx/7b+Amj8O1IqQdp3dn1gWWL4J2duE8cJf62X0/gQuRwe8mO/if/DjunoA/Lzra18DdraAX/2cV+EKANNklimAbibss8xmgLFr+1sE3FqJ7UIXcINh2vQ1ytxEQjFvhhuKPQW40m4RMDcecf66s8aNKzfEVvDQJDB8CEb8nfFn0mro6naF+s/dNc4S42kCbjjKnxFNcINVCsrRs2p/iBFrj+/AuX0JWF+Gmpijx/LEHJBI8tDg0zpmlyzVaXn3wU0HsnQbtN2CbD9h1Z/IQPWNkqjldgYAD6QhrAj17BHNKj+TJbAfPoR9+zrtFS0LxuQUjNERmMdOQo3P8HtFg65Ag+6GlugoVn5hbVu5t8O5uD/IWXY6p0lner7frLMK9AW6hhj0IjYIyJUCmbbRFEE3mqQ71lvfhHPrKrCXh5o/BmPxJNTUIS/oIBD2KngXdFsNSoaqBc5SY2l2RjpNz7lMKYKJOEAlz3chFIWk+qEadciT25QC5UagRmdpCekywv1Bfn7XolOEh6NmBbK5zDm34WPlbpg8/ITCBKuhKRLm3EOnY7Mt7/drjbvJrsC1NyHrj2EcexkyfZTSt2ZV+4k7cB7fp4Qtv04NbyoDNTYPDExBVfbhPLkBlRnkexFNUUdd2YfYNozJo7z3zVq36pZOGyjswvrWazBPfRjqfa/qBK4yn3sgAlSKkLsXehm9P6FrrbyGv/u1v4s/vfencMTBK6Ov4F/83L/AyaGT7/Wl/cDVA+DnXV/8IgH4f/1fdHVrH2A2t7zZr5+b0lOEqVaD+tv0IH1o/QGGFjTK/F4XcMPPJAU5NmPztp6wut3SFWoyS+3x4DgwNAXkRmE820p2HNob7qwBO6usnuplIDPoVbjhcFcL6kbkKS15csMcZHMVcvsS5P41akZnjrHKHR5n9e8aX2j/Y1oGatMR4wCg6I1eKiXKdKwW2c+xFKvcYIQX3rUdtLm5aiMGakkrENuGs7oO+95d2DcvQ3a2YM4ehjE2CmNyiuk9ft0GFdFVjYJT2mUr3h/QoCFs09arwOYKiWSZAaB/GMj0s62rtdVSK1MOFEt1W8vw8YAgrYYOf2gDsYwmP6Ugy3fhnPk6nOsXgEoZavEEjKMvQo1Pcj4aCHUD7DkjrXVnlZLfYBWbHmBFHYlTQ+6mbtk2DyPhGKMOGxUeTDSbG6v3ICu3WXmPH6b8x9KdA5eB74gmqtlsD5t+yO4KZ7udNt3I3EhMq0O3samjMNIDbKNbHU+73m547lrKBKolOOdeh3JsqKUP0Me81dAVr34/H90lYaxeJls+nYMxNg8MTAK2Bef+JahojE5TsTS11OV9iGNDpQboS+1W2mJToVArw/76l6Em52F89NN89jWt5+102H6/+v1eRu9P8Co1Szj8+4exW9uFLR6PJhaI4cLnL2A+N/8eXt0PXj0Afs4l//gfAyvLwO/+U69F57bsOk3GD7qA69hAZoikqWSOrcJmlZuNpZ2L3JayTgoSEW4w2yusbDeXCRjRBAFBk6UwMA4Vjr2jDS3NOrC7xu/fWfXsL/tGoGJJSDAEBF22aKQ7M1WAd4AIRumxe/8G28q7G1CHFoFDR6EmZqGi2hXJ1TTjAOnJF/AcqNz/5g+y+CznIRXmrCKVI9AHI167U882CbjaSalDVy6pleBsbsJ5+JBxfQ/vwpieg3loFmpsDMbEJJ2x2q0u81mZPla6hS0ylQ3DC8Uw/GxBrj4kkPSPMCIyrWU6lsWDT6UAtBrc7BMZHpBMksekXmFV6KNJhYpnIMEo5NFtyJlvwLl2Fmi3oRZOwDh+CmpknBVcIOgxu22bMh7rAOiW9zlfj6W00xk80xaBZzUpDqtiKFa70TSwt0npUKMKNXHEa6k2yp53t8tPUCBQ6upRNh5A9jYJ4u6cWkcwIpUDDh2HEUnwRXOlcW6Xx3XYUoBsrkDOvg7VPwy19H62w1t1oF3j9SsF58kD4OF1foZECsgOwBidA/rGKWd6dBWAQ+CNZ1m11op8JgCMiUXdfZLuIViaddhvfB0qFIX5yc/y70xljzro0j6QHoTcvQiU93RG76EfwY7RW38V6/fe/j38o2//o24SkrtMZeJXj/7qj30iUg+An3PJxz8ObG4CF86SeVrQ7eT9bcDno2l9egCIZ9HNwa2XuWG5DlOROBBkUpA0qpy7umHyWyvc+LKDUOkcW8qDE2w7+kNPEa1oXLFNoNUtZdSr9FXuG4WKJyGBAMHVNLnxC7q2jcpqchMNRgFRkNWHkDuXIQ9u0Ghh9jjU1BFgcJiVlzvLNZ8mA8EFUXfu50bMtVv0J3YstrdF6HjkEnvcUINgRINuTM9fK0wCWnsE++EjOA/uwb59AyrbB/PYCzAnJ6HGRjkrbeoM2EAISjms4ByH7eFSHoAmI8UyrOY2l6m9TeXIoB4cZcuz3WL8Y6dN0A2EyFqOpzVYErSkVuKsXs9zEc8AZgDO3WuQs6/Dufw2tdcLx2EcfxlqSM8ru8EYGtT0feqCbilPjWwyxzm7K01zbUQPuqI1tBmFz8/WdqMBWbtH6dDQFDB+hLPTWpG/JxDhIczqeNnEQWbmyu4qZOOB1q5b3c/OSjEA5EZgTB7hPXBHLq7pieuw5QtSknXvCvW7c0tQCy+zDd2sedagSsHZXIVceRMq4AOSaaBvGMbIDJAbZQW+fo+WkYEQkOyjc1WrTolVpwU1PMcDnNvudpnN59+E7Bfg+9SvkGBVKxJ497coGdtehTy8CnXkZZ3R63vH3+3e+slZn/qDT+G1B6+969dGE6NY/e3VH/EV/cVWD4CfZ62vQ2YOASJw/uk/BJaOsZ2cHqSmVbT9ZLfaSHgt5WCEG9fuWjfbVrZWuEHlhpnUksqSQZrKdSuKpwC3UXu6us1vsC3ZP8bqLZ6CmAqq0+7ObiHwQhpc7abeOKWQh9y9Crl3FSgVoGaPcZY7PgMVDHpVrl+TnLrSHpOfRxkeGcowuKE3avz3oN6wBdokIuDNgrURPnT2KmwLqJcg26uwb1yB/fAhnLu3AMeBcfwUzIVjMMfH6adtW5T8GD4gGuNHNHwE0P0NVoSOjiIMRPhnV+/zXuQGGWzQN0IgrFc4K2w3tB1mtttaVqaPjPV2k4eIRqU7z0WMhwm5eRH2ue9Crr4NxOIw5hdhLL0M1T+kW7EHNOEHDifSbnKmW9wl6OZGaFHarPI+O3a3UuwSwRyrO0cX08+Owu46sHaf7OOJBfptt6rUEiuDVbIvqGejNklQ4ThUswHZuM9DiunXHseaLBjQxiejczAGx3kgsjqs1C2dnOV2JwwfbU4vfR+y+YTV7qGjUI6j59dM2RJlAPkdOOe+qbsfZOEbwzNQ2WFIIATk1yCFLbKnU/2Q8p5ugdfIio6mYWQGvYpXM5vl7k3YN67B94n/Emp2kQeyeonmNHq2LVe+pzN6X9Ve0731k77+1pf/Fr505UtwxHnH104MnsDl37z8HlzVD796APw8a38fMjFBks1bp6Gmxrkx13Uaj2YnI5LgxlfY9sB2e4URepkBzm0z/ZzhJlK6Qgp1CTj0TdYZwTurnN3urJLc0j9KwM0N06Kv04JY2qig0wKguDkGI95GqFuUsCzO3O5dgTy6TVcmt8rtG2RFLI6Wivi56bbrHmgG9MzQNbXQBClp1hn/5mbHhqIkcLlmGv4g59uhKPNutQZWCjtwblyAfes67Pt3IXt5mAsnYBw9CXN+ASqljT8K25B6FSoYBZJpGusrg4Sawg4PPYbB3yGKEqHdNTpiZfu79woQpv0065xFKgWVHqDXciTumYU0a3qe23lqnotWA87Vs3AunobcuAD0DVIqtPQSjEyfZyrh10xt18rTtg+A7jZUbpQzzlAUqlbQHt62Jx2ydKVp+jQ3gIcdEVCLvf6QLfaJBWBsnpdd3fd4BG7Skhum4A9BghEe3tbvE9jsDlvdoRifZXqA8+vxIzAyAzwAODr+sq0dw/TzgwJQzMN5+5u8jhc+DDU8pfXANb43oiV3hTzk7Dd5T9JZYGgSxtA0VGYI4g8ybjK/ylFKqp+HnXZbH5BKEGXAGJw+wK7u0NBkcw32d74J8yOfgDr5Ck03qvscC7QaQGoQcv0MHcNOfayX0fuf2bqwcQEf+bcfeUcLOuqP4l998l/hN078xntzYT/k6gHw8ywRSCgE1W5Dzn4LGJtgdRuO8+S9swpxw+R31khY6R8lySjdR0chZWgwcwHX77WiNdDK9ioTY6JJXd2OEbhDEahWlcYL4pCJK+BcMUzGsmrVuMHqtqdsb0LuXmGV22pAzS2xyh07RCOMg1WuW3G50XPBSNcKEa0qKyR/kNWHm9BU2mO7NpZiy/KA/65LnlKGSZvKZhXO7StwrpyDffcWnCfLMCYPwVx6CcaRYzBGR6EciwC59QTSbrMFnB3wWp+1MtuS7aZnS9lsMdwBiklLmT7Oy/1BiGOzmqpV9Nw9BpUeZPvalYK5LlelXf7MeAYqlmEVXSkyQ/fiacj9G1BjU1CHZqEWX4CRSBG4XQ2zS1zrsC0unTarzL1NqOwQ1NA0JNEHVd3zXKiggcXwc0bqkrH0oYx2iTraspxnrN7EERLBagUe/nwBHQ8ZJcHJahGwQ1Hddn8Iya/p2e6e1x0xTKi+EYjVgRqdpb7aldJ1mt4hKxiFCoTZ2l9/BHnr69QML32Q97pZ4/8AMpChCLwX3mAwQzoHjByCMTQFlRogQaySB3ZXeR3Jfki1wPcungH2N3gW6RsjcLv2q502pLQH62tfhnn0RRgf+SQPKZW8to/cAfongJV7kMc3aKQx+0Ivo/c/0/XFN7+IL7zxBYgIHHHgM3z47OJn8aVf+BIM9eP9zHsA/DyrUoHMzQH1OvDaH0N8No0Qtla4kQxOEDCzA9pJSrfLXLD1h7pevdjberq6bTUOVLcjJAMBJMdYWj9cJ/vXZYR2k4yaNQ16Uc4DH9yE3LvK2LbRaWD2ONTUYSCdpXTFcVglmwH+XDcRyB9ihQPouZvOYDW0OYLVAfa3IJYFFU9TOqNnkQhGCbrBSLdt7jgOZOU+zS+uX4J9/y5UOgPz2Iswj70AY2YWylD82aU9yOp9tqwHxqi37TTY4qyV2VoWRwcGKKBUoGSlbxSqTx9wojGSiqwO2bK1Eiu5eIYSlliKhwGtR5ZqQcfrefNcFQjTmP/iGTiXzkBWH0HNHIY6NAc1vwgViUGFYgRnlwBntXT6jwPpdAi6+XX+zoEpSG4UqlEmo9luaxKUTSB0hJ8TINhFkyTi7W/R/Wx3nXPYiQVI/xhbzNUCwT4YIfCKEHi1ZakEIvz+7my3w4NSJMGORmYIKt0PsTowhqY8Rrj7LnTa3WcJH20u5dZFyLW3oA6fBBZfhgrHKCNyOyxiQ2ydGHT9LQAC5PqBkVkCb7IP4gsSePfW2WFP5Gi/2apzjFPYhthtqFiGHRRxug5zUquS2dw3BPMTnyHBqlbgYW1/S8cK1iFXv0dp1ImP9DJ6fwrWamkVf3T7j9C22/jk7CdxtP/oe31JP9TqAfDzrC9/GfK5z/Ek/uu/DPw3v87qNNtPSZEbCei2kwNhAqNb3W6v0hg/v8n2sVvdZgdZmVktoF3XYGcBtRKk1eCGn+yj/Mfu6OB2m5uk4YNsPNFV7jW2VedPQM0sAiNTbE+6vsiBEFvF7ZYn3QhGufFbWiZkd7yQB8cBSvs0mYjE6V3s83nB6EFWuQfn1E5xH/bFM7CvnIVz6xpg2zAWT8A8fgrGwjEYsSjBr9NmS/bxTVb6w9OQZA6qrjfVWkm3iUGQarXpUQ0wSSg7yHa0aUIASLsNaDMMGD4mQGWHocJxtqwNgzaD5T0Cx8F5rqnv4cXTcC6egezvQi0sQU3P0BjDHyTgRJK6eoRnSKHjICW/wYou1Q81MMHUHavDqrpV9xjHHa2RdiVWygQSOSAQogPatmauhyJQE4sMGhCdttSqExTdkYXr/gUQ3Gwb2F5mtesLcsaslOcwNnKI8YSdNufOB+apXfcxl6+g9P06/23I5mOoEx9k58QX4D1uNfguOTbJa1trkLuXIeIwA3v8MIyBCYKsL8BUqL11Xk88Q+Bt1njQsi0+u3CCTHOA76bVhrQasL/3TaBtw/fpX6U8rFFle7q8x2cdy0KufofvWi+jt7d+AlYPgJ9n7e9DTp4EqlXgjW8AI4OsaPwhD3QN852z206LldrAGNA3SgYuwMpH56aKTf0iKpzLqVQ/LR+h2FZuN7VmlBIhuX8dzr2rwMYTqMl5VrnTh4FYUle5Nqtcl53cZjUJX8DbYN3KF25YgkAaNXoXd5qs4CJRbnLhmK5yo0+lKEmrBfvmJdiX3oJ9/SJkZwvG7GFWucdfhBoY5MFCmZB6FbL+AHh8C+gfgxqegkQTUJU8yVu1Mmd/nTZnybUa5TB9I5wxZvqAYIiA6jhsOxZ2uvInlR6kfMdNUALY2i/lWd0dnOcCkMd34Vw6A+fiaaDTpj53ehYYG4NSBg8+sTSvCeDBpV3vHk4kv0HHsVQOqm8Mqn+Cz7+c9+LuTL+XNOQS2QASpBJ9/Nxrd/izWk1g4giMyQXe76o283CfmUuG6+peQXAr79Fkw9LSoMIODwvtBg8iQ9N8bmLznXIDPlzpl6EzqwNhzpmLeTinv8rX4sWPUPKmSWvotLrmGdJuMqv50S1WtLl+YOIwjIEpJjuZGnj3tY1pNA2p7pOpn+qDCseYDR2Ok+WvdGyj1YZ0GnAun4csP4L5qc+xg9Np0be5UeVIYXAKeHAFsnoX6viHexm9vfUTs3oA/JxL5ucoQ9p4ol2c2iS3uLPb/U26OfUznAC5Ic9SstMkuBgmxOrQKL6wTbazK13yB6HadVYHhskqFwqyfJ9t5fvXgEiMUo+ZozTCcGPkulWurrRcSVBIV7n2AemPwbB62DakfzSiWgAAIABJREFUuAsp7EDFEmydmzonOKRB1xfobmxi23CW78O+9Dar3If3YAyPwFg4DnPpFIzpeShoYbzpI9P68U26Sg1NUsPs9/Og0ah6bljVIlCt0O5RhJXf4DiQzEDBZjVuW5R+lfKef3R2BCo7BKNr+2lTKlTKv3Oea9uQe9fYXr78JhCKwDj2ItShWaB/kESeYISkLJ+f96ejiUUinCfvbdOXOpmDyo2wi+EPchZb2ef9NnyenaVh6jAAi9ebGoQEw8DKXbaIS3vA4BSMqUVIdoStajcyLxzTs3nDM3pRBq1D3ap7b4M/t5jX/tlBwOpAjRyidzbAAwQ9QTlLbWuCViCkYymD9MBeewQ58xrnuyc/RF01DHYM7Dbvh57ly8p9YPUBP2c6BzV5BGpwEiqahvj8lGrtb/DrkSTJVdUiD0jRhBdQEUsdCMxoQzotyKN7sN8+Dd/HfxHGsfexJV/ZY8Ti3haBt7ADuX4aauww1PEPQgXDP8ptoLd66y+1egD8PGt5GbKwwM3vi/8TJBti1eHObvvHuBmJeCQWCLWpVoen973NLvsWyX62lS1tqeiaUAQiwP4unHvXSJ7a24KaXgBml6Cm5lmVui3DLjtZhxi0agSAoLZFdHNvXbtCw4TUKpD9bbazM7p9HoiwOtIt6YOVhLO1DvvaBdiX34Z9/RJUIkHXqWMnYS6cgAqFCDB+GnDI9hrk8Q3qbUeYrAODmygaWqKkDIbTlwsE3uwA1NgcJUKhIJRL9Gq3GBNXKfAzhiKU7qT7YUARdK22t8E/O89ttyA3LsK5dBrO1bNQuQGoYy/BmJkHkoluyIBK5jxSVqfJ5yFC0C7sQLaXKVPKDpPFrDOBUS14MYe+oKeR7ZpnmEAiA0kP04d5+SZb1bE01NQxHjTsNq+9WSWhz3X9cmydWay7E7ZFFvf2ilfF7m3xsGS1Gf4wPg9kBvhO+fz6wKcOMLJ1BGAopqtzC3L7IuTKGQbRH31ZO4gpvktu5a4MSK0EeXQL2Fqlg1o8CTW1CDXAgAwxfR7w+vwE3vIeOwJ9o1DhOGVflT1d8brBJO5IYgv2n30Z5vtehfGBj7HyrxWo593bhIpnIYEw5NK3AdPXy+jtrZ/Y1QPg51lvvQX56EfZfvs//mfgN/4mZ3WWBttuJF6wS/KRwhYZ0sk+tpXjGUo3WnVu4P6glgjZkEd3WOU+uEEgd6vcwRFuqB2dv+r6BbtVrtXW7eagl4VrWxpwfcxULe6ySovoNrJhsuKOJmnwcTB6sFyCff0i7KvnYV89B2k1Yc4dYZDBsRdh5Pq4MbvXYtuQtQc0+S/uEnTTfQTlyj7/bCwFNJvA9goJVCLM7x2dIfg6NPqQTptks0aVFSTAQ8rAJFQ8Q8a468Vc3iNIHJjnKp8fUq9RLnTpNOTmReqaj74IY3YeKhziYUiZfB7BkCfhalbeCbrxLDWk6QEejOy2TqOqElyVQbCpl9kWdg1LglEgOwIBgIdX2XoXoSXk1DGokK76a8UuWPG9afF32Npoo9MBGlV6Hxe2+ZyLe9rshBGNauQQXZ0iMZ09rbsFrvOX200Jx7WRigk06nDOvw5srUItvQJMHUH3FXA9tx2HaU3lPXZeXLCPRAm8g9NQkfgzwBsgkUx7nqvcEJ+PGYAUNrQOV2kCmtbzlouwvvrHMKYPw/zZXyTBqln19LwA0DcB3H4bsrkMdeJVqMmFXru5t35iVw+An2eJQE69yDjCC6dZmPiD3NRsm4Bb3CboRJNQqQESbPwBAq4ODkCIrUXZ3iDg3rsKVApQM8eAuSWoyTka3B+scv3a/aejWc/K4GYMrb91N36dUIRGlVUhQI9nx4YKJ8g2DUWfBtx2C86d67CvnId97TycjVXOcQ/Nwlw4BjUxTfmUUp6EqtWCrNwl6Fb2CbrxJMG/VvaMNkp7wOYTtpYTaRrsj0xzE7eaEGWS6V0p8HNZHQ0WMZKZInH+u4Aaz/I757nKMCClApzLb1Iu9OAW1NxRGEdfIHvZZ1JX7ThMD3K9l622JweyLUhhl6z2eJoz3fRAV3blelCToCb6sKOjIV2wMgwgkYMk+oGNB5DlG9TaDkxATS9xll0v07fZ7gCRlG6N60OT1eYYwTB4ACnn2TFxOO/tWpJaJPqpyUWogVE+RMf2gBbwdMD+IEHTpwMXCjtwvvcVIBAk8PYP03XLzVB29bbBCEND7lxklyKWBEJhqMlFYGgKRiRB4HUDLXwBIJaEVIqQ3VVWuH2jUD4fpFri+2aYEGVAQd/vRhX2N74CBCMkWGUGeA8qe5BmlTGBI3PA5jLk5pu9jN7eesdyxMGt3Vvo2B0s9i8iYAZ+8Df9GKweAD/nkp/9WWB9Dbh4jhVYaYfViek/0FbWjOZmjRuha7XYbEEe3CDoPr4NDIzCmD0OzBwF+oZInjpY5botxFadwOvXmbCOzSrJzeHVpCipFLRpRoBzPl8AKj0EpPsJoO5ncJg+Y1+7APvqOTh3b8IYm4IxT9A1Zua9mZoroQqESP5avQNZvk0wGppiKk6rRpCIJQExCBRbT8is7huBmjxMEILtOSNVi5qR3GBFZpLopfrHaUOojUCkWmR15SPblaERMVbCu5uaRHUGsvYY6ugpGIsnoKYOQSmhHtW2+D1PBR6Uu3nMUtyl53Ysxd+dGdJ2kdqExAVdUC/crXY7B9r6gTAkPcRDz+PrwNYyW+ATh4GJRUbuVXVYgmuU4fPzQOb+nGBEW4tu8X3S8YUo7XWTjdCsshMwfoT32rH4OWolPnNbcwEcm9W6KyMSMInp9FcZ77j0fgZBuOYfWjcMx2ZU4u465NY5jgsSDOxQE4tA/ziMWApims8AbwqoleFsLTNRaPwwq2kHfDd8fpK0tMuXNOuw3/oukM/D/PlfgzF2iJ+9uk9i3d4G1MAkxLYhF18HwlGd0Zv7kf5d760f7/Xa/dfw+T/9PErNEgxlwFAGfuejv4Pfevm33utL+4GrB8DPs/b3IYODAATOl/4Z1Isv0Wc2nubct1nVmly/nrEFIGuPIfevUiLUbtIIY3YJamKGRhitg7PcEDdLt8oVR4OrehpwleGFP7guUI0q5TqxNNTgFM05DixnewP21Quwr52Hfe0iVCIJ88hxGHNzMKemSbgxDIK/m2jjCwKlPKPsntwmWPSPEgzb9QMHiyar3PwmAXlokkEA6RwPFb4A57mlPE00Om1+n88PhGMwssP8Z1N/JjfH9tl5rghkbRly6TQ1uoU9qKX3wVhYghobZyVnmJTChGKcF2viEHWyZCOzvbxCAtLABFRWW1Na2payqclTbkiE4eP/1yskMZk+3ut4FhII0ZnqyS16QA9OAVNHSS6q6RazYZJNHYrTztGt9F1L0HoF2H1C3S/A57uzTpC1Lf6nyQWokTkeLByb39+oeBrelj7IhDRr3R+kGcjNc8zfPfo+4MiLrD4dAaDb5S7wRhLA5hPIzbf5sxNJsvHH5oHMEIxUjvP2cp7EL58fiKaBZg3O1mOg3YKaXIQKBPi1WpnAq9naSugG5ty4AufqJfg+9SswFk6yA1IrAq0aq/1oiq5cV78P2duA8cJHgdG5Xru5t55aZ1bO4GP//mNoWI2n/nvEH8HvfuJ38fkXPv8eXdkPt3oA/Dyr3YbMzwOlIuTKJah4mJu1q8k9IBGSe1chqw+gRqe77lNI56DaTQJst8oNeFm37To3Tzfm0HG8lqDp5z83axDH4ly5ViZrN0cC2EHHH6mUvTnutQtAswHj2Auc4x6agZFM8mca2pkrGPXcsArbGnTvsM2e7gegW6yhKDftchHYeOxVSaMzUBOHSeCxOzSDaDeYClUpANBWnYEQQTfJNCSYfpptVPYITM/Ocx2H2cMXtVzItmAs/QzU4nGogSGCrmbxKn+IM123Hdys6vayzeD27RXd2p5ki9kfIMGn3aTTF5QOpnC0ztXwqlTDpzXABiSaYmLVk9s8KOSGocbmqf3ttNhi7rTYYo6l+L2NsucwForRzzmv4yGrBb4/Za31jST459ODzLrNDtJyVBxea6elvS86zPT1854CAHwhoF6Fc/abQH4T6uQHgakFMutF4MYzurm/Eo4D648hN97if4sl2A0YnqHZSm6EGuou8AYIvJ0GnM3HQInuXCo7iO44BOAhQRwoEYjVhqw8hvXtr8H36idhvO9VXnOjovW8+4BlA6OzwPItyJ3zULMnoRZ+ppfR21vvul79t6/iu0+++65f64v0YfPvbcJ8Jinux2n1APg5l+RyUHt7kJU79HJWJmT5njfLNU2Sp+aWgLFptuKerXKhvCrXtrhB67D4bkSdODp1yIZ0tDvR/g7EcRgYMDJDuYx7Xe0WnDs3WOFePQ9nfQXmwhJNMGZmofpyrCJc0Au7rlV+yjz2NuEs3wLW77OCiWknomiC1+EaYWyv8jMkUsDEYaihCa/y9IdpHrK3QWDyB7rMahWKaH9oh+DTrLG1/G7zXMuC3L0G5+JpOJfPAJE4jKX3wThyDMhk2MoMRggkpp8VuWWxAmw3tIOSMKlqe4Uyr8EpVruBEEG3WdN6Wm0+4cqGbIuVYUtbK/rJdJdghFmz209IJEv3Q/WNAiMzbJk3ymxNB6P8zKGYl33rVrvhOKTVBNwgBPd5uNUuwC7J2BzUxCIDMdoaeFsNXqvVIfCK45ly6DGE7G1DvvtlytROfIC2nI0yf657mHPBNxiDrN5nxev3MxEq1cfDieEHRmd4P8vaQMMXYBXfaUG2liF7G2zXj89TBme42cA2s3qVJrPld2B95Q9hLL0P5quf4jy602K7uUmdtxqbB6plOBdfB5JZRgX2Mnp7689Zod8JoWW33vVrEX8EN//OTUymJn+0F/UXWD0Afp61vAw5epQEpN/73+EYdWBrBWpiXle5i5RmuA5FB6tcN5C+pQlU7nLB162ytMsS7Rn3IXtbdGEanIIanu7OZsVx4Cw/INheuwD7znUY49NMDzqyCGNkhO1GlzzlDxFoAmQ8iwiwu0bQ3XoMBIOecUcsRcCvFoHtDS8sPZEBpo7ASGQJcoEwW4ilPGR/k58tFIWK5wgCgVC3ReqSrVDZf/d5bqsJuXmRoHv1rM6SfRnG4WNQsaiuHKMgg/YA0cg0PQKagJXu1mNKiwYmCbzhGEG3USaQudGQQUYmol7SMq46n5M/SIexNufxUtwF1h/QMCI3TGCLZ3l4qBV5TbEU26eCp6vdYBQSjAH7a2RCl/cIzrUSDSniaZpKJNIMoh+aJqBZLT4jN9zAahN4XRcyM8B3xTAhT+5BTr8GNXkYOP5++lxXC7p7orsB7uHOH+Kfv3WW44JIjBV83yhUowlMLfJdeBZ4rQ5kZ5nhF4YJdWiJ1anh62YDs92sK+xqCdZX/ghG/wjMn/sMrS8dW+t5m4xO7BuHRBKQy2/0Mnp76y+0kv9nEuVW+V2/FjSDWPntFfRHf3wlaj0Afp71b/4N5Ld+iwD82Z8Hfud/A0Ym6TD0bJWrDE3iqXqzNihWG9DGCnbHIwY5Nl2odtaAWpkb4sgMNa/aicnZ2YJ99RzJU9cuQsUTtHg89gLnuD694YoQmIK0T3S/XxyHUYaPb9AwJKSzfWNJ3QLdZ2LTzgaZ07G4NtKfhREKd2MIxbJoT1nY6hpGqGQ/7SrdmaRts3XZrL3rPBcApF6Fc+VtznNvXYKanIM6/hKMuQWogF/PNHVGMMDf1ajx35VmBisF2dega/gIukPTULEUjRv0fBGdNmU40SS/r1rQBhMdffgxWb1aHXpv16usnq0OPb5TfWRQ+wL8ee0GQTzKnOYuYcvq8NARitNVav0uZGuZ12+YtJmMJvnPjSoPVePzBHSrRSBzbLpitRuavd3hPQvH9IHKoGzt5jnI9bNQx38GWDjFw1k533Wq6o4y/EFAmXAe3QLuXaJndiQK9I9DZYeBnS0y8JOZA8DrJ/DaNmRnmfdjfxtq5gRUZkC/Zw7vF1jtQgnQqMP+9teAdge+n/8bUMMTJMPVaT0pe1v8HINTwL1LkPuXoQ6/DHW4l9HbWz/8+s2v/Ca+dPlL6Didd3zt1PApnP/8+ffgqn741QPg51k7O5AXXgDqNeAbXwUmRrwq1x/0ZC2dZhcc4NOkFMvq+vZCVwpi25DCLjNdg1Go0RmCbiLLqrBahn39kmYrn4fUazCXTsE89iKMI0dhRMMei1bPQhFJkDzjOlc5DmT9vpYL7XFG6gtQHqV0dF9+iwSj7CA35mw/XZ58vm7gujTrzNvVDlMqFAMyQ9RB+3x0qHK1p83Gu+pzAUCKe1oudAby8DbU4eMwjp1i4IEhXnvcH9Sa3A5QKUBMH5SpDzGGQSLV5mMAgOqfYOUUS3tVab3M5xCMEiQDYVa65V0PdKG6ZDOpl4B6jQzkYh4YmoLKDlGDHI7x/jYqBNtYmt0Ex9K+yDVqggNhzndLu5C1O9REByM8hFXLQDKnmdBRqJFZgq/Pz46Hq93tNGlJqjlHKhTlZ4Cw2qyU4Jx7HSjsQJ38EDCzCGX46DnNO+wBbzACOA6cB9eAh9d50IpEgMFJqMww8OgO1NQiMDr1tHNVLM1RwfYyuword9hqH51l6xuKc373+UCAdhvOhTfhPLwP38//Goy5oxw1NKpAvQipFPn+jy1A7W/CufRtvmO9jN7eeo61Xd3Gyf/rJPYae2jbbQCAqUyE/WF87ze+h5NDJ9/jK/zzVw+An3PJS6eAtTXg0X1WIo2K17qEItgGwgRgN5Qc6FYkUqtANpcZVzg4wVnu8CGoUATSaVOPe/UC7GsX4Kwtwzx8HMbSKZiLJ6AGBkimcewuO5YAk2K7Vy+n0wGWr9MfuFljyzcYYaBDo6oZy9pUITfINmQyA5XMQBk+SDgGBQNSLXKe26hwXhyOAdlhapSVouzJttGNMXyXeS4AyM6GThc6DdlYoRPV0RcpF3Lc+WjM0zrbHba1oaACfogyeV3FHX4mx4HqH6PHcaKPH7peIvmppbWvsbTnh1za5detDtzMX4QTJLPtbxEc9jaZHzw4ST9tw9QysIaWWKXZYjZ8fK5NPS8+4K0tmw8hG8zqhTLJCo8mtKNTke3wkUN0b3Io/xEIr6vTouTKYHiCCsX4fUofOLbXIN//KhBLwjjxAWBUt2qLu6DeBx7whmJAqwXn/mVg5R7n9eEIDxWZEciN87SNPHqKI4H9DR58YikSs7Yes9OxfJsHrJkl7Y4FQGyPha0PDc6923BOvwHzr/8yjBde4aGm3dRz3jpQ2IYanYMoE3LpdaDRy+jtrb/82q3t4p+8+U/wB9f+AJZj4edmfg5f+PAXMJudfa8v7QeuHgA/z7JtyKFDQKEAvPU6kNLknYDOzXUj/Nw5mKtlzW+xAhUh4I7O0LZSGXCePISjmcr2neswxiZhHn8JxrEXYExNU0Oqo+48k4o4Ae4AQ9SplYDHN5iEY+u2TDRNkN3foS63VgEGxmg/GY1BRWJsGxs+SDgOZXW0L/Qmv98F3cwgAxgcG1LKQ2yLFXan/e7zXBHI2mOaYlw6AyntQ514P4yjJ6FGx6Fc/+dupesAlkWJktWCCka0NjjAanLjEWB3WDENTtFMRBkkOFULPFS4blKxNMGvvAuUdrqsXJLPAkAowjlxYQfY3+ZMfPww3bgsnQTlhiZEEgRd1wGrWeUzNv268xEBqkU4G/fZjg+E2CKvFCmladZJyhubp8Y4GCaRyp2Xdih7knqZWc6BMJ+tYWgwdSAPb0Le+jrU9FGo4z8DlRtia7u04402dJAGwgmgWoLcvURtczIDhEJQw9N05bp0hulQH/gEDyQHK14BgTeWYojI/hbUoSXaWjr09ha7w06O2HyvN9dhvfbH8L3yURgf+gRn7bZF4G03gJ01qOwwJDsC3Hob8ug61OIrUHMnecDprd76KV09AH6e1elAslmg1QJuXQXSKYaoWx1v7uoPQaplYP0hW6SJDEF3ZAZI9UHy26xwNeiqaJyB9MdfhDm3ABX06XmyNlnotLRpfpIkID0nYzDAJmT1NtvCbgWeyHHz3lljTnEoAgxPc5YbCrF6DYTIfg7FgUaF89wyjR9UMEJgTGR1e7TNytNqc9O0O2xxPzvPdRzIw9teupAIjJPvh1o4ATWoTUaCEVZnrne1Y7GKrpfZ6jV9jNUr77GS7LTpvTw4AaSHdMgEKyvUywSfSAKIZQjkjSpQ2NB6XccLoAjFCB6bjyDFPOer44cZWGAYTJsytdbXHyToRjQruVnzLCp9wW5Ig+ysQjZpLwkIfa3DcRLZKgVW6KOzvEeu7SJEg1iHTletOn9/OE5ilUCzsWtwrr8N3LkEtfQK1MJL9FqGAorb2mNaA68Cr7WwC+f2BdpVusA7NA3kRiGX3wIqRRgf/xXA6TwNvFDsKiRzQK0CeXAFGD1EaZHjaLmTZoaLTblQuQjrT/4fGDOLMP/6L0ElsrTcrOk57/4Wuxlj8/x7cOU7zOhd+ggNQHqrt37KVw+An2cVizTicBzgz/4YmJ/V7lQmZGedLNfiDl2fXH9eRzjH1SYYUq3APH6KyUELx2GkUl4IukK3HYkQW8sIxb1WbqMC2VnhhtmokMFrMPEH7TYdmCoFslpHpiGpLJShSAqC8iITKwVWua0657OBCBCO6GQaE2g3SWxqN2j7JzYQTb9znmt1IHeuEnQvvQkVS0CdfD+MI8eBTJag61pS+oJQ4rA6rpV07GKIoOsPAZUiQbfdIOj2jQIZRguK1WalWysS8MKMCVTBKA0+Chucb3faHgHOcSgdyq8zErJa5HOZWODhol7U5iYAILzX0XTX4ASNquc+ZvpYVdfLNJ3Y3+Rhol6hdjczqO1AbWqhh6agTD+fp/bklk6LHYSq/r2GD0r7cANga3t/i/PdchHqhQ9DzRyFCkU5az0IvBCdMpQCdtbg3D5HTXgiDYSDUEMzDMC4fRVy6yKMn/91zn6fBd6tRzqBKwS5eYZyLbfdDPAeWtpb2moBrRasr/2/UKEYfJ/+HNTAKMTwaT1vEVItAY0K1Pgi0GnBufAt6rZP/Szzh3urt3oLQA+An2/V65CBAVbAp1+H+DqQtQdsj+oqV7JDkAd3dYV7Hs7qMszDx7psZTU4DGW3PN9dES+lJpJgNaNJVKKDyp38KrC7RoBp1DTZJ85Nd2uZVdngBEE3moAS3Up1HJ1RTM9eKWx1ZSUqGAaCYZJ8dGqSs78FtGo0tDAMBhE8O89tNSDXLxB0r52DGhgh6LpyIavNKjcUoRWm40CUok1jOc+WteljNVmv6MNEFSo3TEZuZgjKHyLo1IraN5kxgewCJHhtlT2guMX74c7dO22IP0Bjj/UHzMWNJqGmjwHDM1DtBqtngB2GUFw7VMW0l3JVu2AZ2pSEDHXZ22BureuqtbfJQ0UkTqvI7BCM8SPMBXbHD6BrmejxgVQK/J2+IA86Ph8ger67cg/y5p8ByRyMkx8ERqbp/OU4QGlbs7QN/lx/EAgngfWHcG6d5YEtngTCYaihQ0BuGFh9DOcbfwjj0/8V1PD408CrDN7zWBpI9QN3L1DTO3eCTHbhPFmstiaFkZltn34Dsr0N3y/8DRjTh5n122kAFW0fmd+AGpmFxNLA9dM6o/dDUNPHnzKI6a3e6q0eAD/fOncO8qEPkVn6D/421K/+GjB8CFKuwrmmXafuXIMxOknAPf4ijOkZ6nHb2qPX1LrJRo0AHE0SWHQrF/UyN/z8ug4ecDhT1Lm8KOXJ0k33QQ1NMdjANNla1Ek+9BUWzgkr+2yDRrWnrwtWANBuwdlbB1p1gq7Pz9SmeLY7zwUAqVU8udDty1BT81AnfgbG3CJUQH+ecFxfox/K0brfTpPZvXB4jYZ2vdpaBuplznSzg0BqgKCrDGpoa0Xer2CEIBlJ0BWrVQcKm6yGAQKnYQKtOhm326s0yhAHGD8C49ASWd/lvE5W0nP5WIb33DA9qZjV9ryTHQEq+3B2Vlh5hmJAtUSwzQyyI9CsQ40foWmGj7F+XW9oW0uZHIcOX45NUlUsBZfFDceB3LoAOf8GMHMMxolXoDKDgD/ItKbyju6KaDp0IAREUpDlW9TwGiZlYuEIgTczBBTycP7jv4bx4U9DHTvFe2X6+HkNg7P0aAIYmATW7kHuXuwmUim9HYht8V5YTGRyrl+Gc+kcfJ/+HIzjLwOBEMSygKrW8+6sQqUHIAPTwJObkGvf58z7+Id6Gb291Vv/idUD4OdZp09DPvxhQAT2P/htWIMJ2FcvQEWiBNyll2AePkoNa6fhpdsoHezeqBBEIwRdFdDReIUtyP4mQdfuAO0ON3s3F3Zvk6Sf3CDU8BT9mG0LAPWiIkIAsyyvVZkeIOj6g6xA/P5uRSZ7m0CrwVZjIEQATGS781wAkEIezuW3SKR6dAfqyBKMpZehZhagDEfrfzXomj6CruviVc5DbBc8FGfnOytAraRBd4jVdSDERJ1mTet169oWM0LQ9Yc4My1pQpVtdaU+aNf/P/beO1iu+zoT/H733s7dr7tfzgkPORAAQTFLIkXKlhgkSrJsyZZU47CuWatqyjW7s1Uur71lq2Zr1vbU7MpWeeVdyvY4iLJkS5ZFU2ISRQJMIBiQwwPw8HLo9zqnG87+8f1uNwBqLfnNmEG+p4oFEmB337790N/vnPMFgkWpQPJQuUA7zqkD1LfWigRen4WeyJChHY4SHK/27Q7roIZ6hYef5cv8b2UAK7NtVnVpg77f47up03aaBGMRdst2jV2r04RU8oDjQHV0tu07tRmJd/T7wPnXGXrv73f9KMLCqh41a+CNxIBYGnLhdQYkxBIcJ8eTBN5ML1S9Dvev/xBqag+M2+4FyjlOPVKd7HiXL3KqMrAFKG3Ae/0ZTj+27m9rbz1Xj8kdfl4zF+E8/h1Yd98P47a7oaJJTuvLG/RtXl+mIcfoTj7n0ScA09QZvX1v0V/IoIJ6d1abPTIOAAAgAElEQVQAwJup3/otyO/+Lld7e3fA+4P/BGPPAe5xbZ0JbJgcr4pLQKmV+CWqQRdmCKhQ3iPrizrph+NYrK/wy7rZpClGVx9NDwYnoDqy/IIUj6NbgM9lN/mlHYlBZfsJNKZBGakeW0qzAawvsmOxQtz5dvZDdXRfw6SW5fkWiUqW5igX2ncT1MQUlKf3pfFUK1lIeS5BV0CHI7tGMPI0qzm3QEJS9zAdpFKd7GStUNuBqlbSiUskaKlwtP1FX1jWIQMhjueVQWBrNiCr88DaPH2Lx3cBE/uY+FRc0c5W2hQl1c1rBjShSnt3+97XHg8t3socQT6R5b54fYke2IYJlAskJY3vIiu8qaVJGiOlWdOysypj9FyHn4UVQiszeGMZ3nOP8rkO3QU1tY/SMytCWVNxTbOafeCN0y7y3KuQMy/TMSsapdNa/wQPMFDw/v7PadP90x8nIdAHXsPUciI9mgYgJw5D1uahdr6HtqBaPy6urbXRTcjKEpxvfwPmgVth3vMADU18L+tKgfv7ch5qdBd3x68/A1m8FGT0BvWWl+u5eOTkI/iTY3+CulPHp/Z8Cr904JeQCL/ziX4BAG+mXnkFcvvt7PL+6s+BD7xX71mj7HSdhjZmqLJLi6cJHJ5LZuj6Is0slMkvyo1VslbDMY5BlUGbw2wP0DcKFQrxy9FzSZyC4uOaNbKAO7o16HZogwQNfqA3NDaWGHBvGNz1dg0RdP19rghkdpqmGMcOQ4p5GAdvg9p7CGp4DMqutpnGkRigDA26umuq5unn67kEVNfhyLmY06A7TH2xUtwZujYPGrViWz8bjrU74VqJh4lKQcu7tH1mvQIprUOKG/RhViDDd+oAjEwPQS+/osMsTB5COnramb++fCgUaXXsqBYhq3pkbWlZ0fIVkEneyeuMxKEmdjOYwNNRf6LtPT2H91aETPJmFYDBAxMMjtyhILMX6M/ckYW66W6Gc4RjHJlXi9xJa5kPAE1Yi0LOHIWcfxXI9ACRMIlj/ePcH5sWvMPfg5x+FeaDnwEsLVtKZWmhuXSJeb8DU1xHXHwDcu4oMLkXRvdg27jFJ1k5DaCQh/Po30H1DcP68M9AdfdDDD3JKW3wkLE6BzWwhbKiC69CThyBmtgDtff2IKM3qLe0RAQffeSjePLik6jY9G2PW3GMZ8fx4i+/iGQ4+TZf4T9dAQBvsuTgAWBxEZg+SyBtVAgodoMjWU2iYpe7yC63ktd+ui71uMV1fuHXyuxEe4YoE0p3MYzAcyhVKayh5X2s4wlVdoC+urEkR6AQdi5+Ek1hjZ0yQMlH9whBt+WM5UIunIanI/0AwDh4O9Seg1B9g1CNsnajYqerlCLYGBa7s3qZ4OU59DJ2HDJ7Szmyl3vHgDTJSKK1rP5jYFq8N6EIk4hMukChlGMHaOokqHia3W5hBcjnODKvFqlJHd8DDG3V3e4q762nR9PpXj6/eO1uF6KTquIcYa/Nc7dbXCMDuFIkwS3dzUNBaZ3GKOO7odI9POw4fvavQfMMT2uyawVIs8GJQkcXX0sfNOTMq5DnvwdM7oC68S4Y3f3sGJXBDr28oQlPTMqlz7UFOfMy5OIJoKsPCFlcJfSOQsVTDFw4fwLeY4/AuO9TUBltDJLK0rN6+TLZ1YNboOIdkLUFeG88A3R0wRjdrnfPWtNr13XEIq0jpWGTYDUyxT20a2tmeYPrg1Q31NBWhnYcfTzI6A3qba3Hpx/HQ4881AJfv2JWDL971+/i39/279+mK/vxKgDgTZY89FFgbhb4xn+l920sTWKLYVFPu77Abjcc5TizWqIbUbXU3hUOTkB19QEdGU1U4ZewNOt8DgE7qFqFRhnZfqhMLyQUZvycH0sHQGx+UfqkHxVLAj0jMNI97Wt2bMjp1+hG9eoRqI4M1IHbYOw5CGSz1MFa4TboAteCbrNK0HVtoJyHNHU2bnmDUqG+UahsX4sBzBCBGoFGGXzecIydtxWik1J5gyDY0tda9E9uVIHly5B8jtOBVIbuVON7uNNuVtklNyrtbjfdy9ex6wTdZq010m51u0sXKUeyQhw9L18hGKU6W9GDanw3CUSmzjt2bPhzZrFr/PcmU6zEbkAlstzJ+g5olSK8Yz8Azr4Gtec9wIE7YSS5hxdP6IdcLWhHKa/lrQ3Hg5x+CTJ3DugaACwT6B6A6tHmHaEwZG0Z3iP/N9QdH4QxPMpDUqoTMC3IygwAxU49kYbUK5CTRyDlPIxtB9j5+5MRp8FDj92E+/wzkOkLsD768zB2HiDBSqS9591YoXnM6C6Oql/9PmR1nhm9I0FGb1BvX/3qP/wqvvzKl3/onx3oP4Bjv3rsLb6if14FALzJkv5+IJcDluYAA4y8W1/k+DTTR7BZmQdmz+nkHYexhcNTHC2Hw9SIas9er1Zhzqoy+F1vawJVph8qreMGnYaWyTDTVRyb+zgd6KBiKaBnFCrd0+50fbnQK8/BO/4yVP8I1MFbYezaT7lQraxHvBocIRyVmxZaQFMramejPP2J61WOhzsHGHrQNcDHOdoX2CeaeZ4eWyc4LjZMbchfBEprfI+RON9bJE4QWZ1jEMXGEq8n0wOM7eQY27T4uPJGe7fb0Us98DXyIbOdy+w5wNocM2uLOe3DXOEEoqOT2ujiBtA3qjN3B+g61tBWn2AOs/ha4XqR3b5t89qUwesyLWB1Ad6Rx7jv3n8HsOsmGNE4O17X1sEPFbRWBKYG3nod3qmXgeUZoHsAMA2gZ5gELyukNck23L/5MtT4Nhg79vD3Up08yCzPECAHp7ir9TzIpeOQyydJsIqnNPDSs1nsBuDY8I4fg3v4B7Du+ySMm+6k1rjFQC9AqiXm/I7uBFKdHIefeSnI6A3qHVOff/Tz+NLLX6KV63X1nsH34MVfefFtuKofvwIA3kzl85ChIX6JPfx7wLZtlH8YFnD5NLB4SYelG0DfKEG3I6PzUg39jwWvXABWZ/nlrXN/VdcQVKaPuay+85DrECg9BjegViT42g12ur2jUJk+jmMBSLl4lVzoNagtO2gBuXMf5UL1Cr/4Y0kgFKM86nrQrZe4y62W2ck39V47S59k1T1MnpDd4DU5DT6vY7eCIGCYegRttCMI62Vts2iy640mIbUSMHuWnZbjMGO3e5D73VSnfmyO1+Xvojt62G1eIx9KkMBlhSDVEmThPLtCf8+8MsfPJcU8WzgO1PhuqLGddP5q1rnr9P9KOE3+tXZ50IFh0n6zo7vtsAUDcvm09mfugLrxvcD4ThjhGMQKtzJv0WyAwOtwTByJQ5UK8E6/zA6/awAwFU1Cuod4by3GIXrf+zoAF8aBWzShTAPvyhUSvQanqNMGILlFeCcP8zPK6hi21p6XXa9cvgDn0W/BvONemHfdB5Xo4JRD66OlWQeWr5Dk1TsGLF2G98oTHGEfvBsqlX0L/7IFFdT/fx2ZPYJ7/+u9qNrVa34/HorjP3/wP+NXD/3q23RlP14FALyZevVVyKFD1AH/4e8DW3q4P3Qd7vCGtwIjU1CWxf2s0i5UyoRXWCVr19+LJtME3XSvzsy1+SXtOtxLug67ylpJx9NVgUgSqmcEqnOgHTG4saadqA5DLp6F2nWAFpBbd0IpHeQeTVBPG6YbFUFXdzHNWosZLI0ax7T1Ch+X7mG0X88IbSidBolHTkP7Xmu/5FgaCIWgXJudbrOuTTTy2noyxvdlmiT2LFygV3G1CHT184u9d4zyFTNE841KntdnWrTXjGd4f66WD/m7XRFgdRbewjQBu6Ob1794mZ2vaVJClO2HMbmXBDffAMX3ihZNSLo6bMHSxKJogsEQoQjDB06+BLz0NDA6BXXj+6B6h6DCcd7TRrndqWu7TZghSDgOtbFK4K0WueM1APRPcpLgW5lGEyTEXToFdfP7eNBKdXH/uzoL2HWoQW1xqRSkUYWcfAEIWVB9Y+09rw61kEaN9qff/BrUtr2wPvxxqEwvn8+19SGhzs89kYEa3kZTlleeBAo6o3coyOgN6p1VIoLPP/p5/Nnrf4aqXYVAkAglcGjwEL73me8hbIbf7kv8JysA4M3UZz4D+Yu/gAIgW4bh/f5vAGO7gMExGE6jbRUYiUFgQHLz7HL8XVmmjx1kRyefo2Xzpzstp8kmTI8rpVahb3PPCAlOFn+oZGmOo+VjhyHL8zD2vQdq/61Qk1vbLluxJEE3FGFesefpThdt0BWP2b7LmhhmN8isHpxkxJ8V1qCrJVaNGh8b0wzvSAzKdag/9hzuN0sb7BDjaY7UGzVaQubXIHNn6U6VytJ0Ipnhr6kudrTl9XZiUSROCVE4ytetl/kaV3e79TKjFhcv8RATiTOBqF4FMt36mqs05Rjfzdg71247jwHt/F2lR+Suw+s3Q2gFaoSj3O++/DQwfQJqxwHgwHtpvRlJ0HSkXiTwaoY0DxwhSCQKLC9Azhwlez7TSZnYIM0zlC89iqUgs9OQl56CuulOqGQH378Vpj68UYEanOLnoxREPOY651foM24YaIWAuDY/s1IRzj98AyoSg/nQZ2EMjnEsLp72bS7TG9tuQo3vBsIxyKkXdEbvTfwnyOgN6h1aIoJnrzyLP3v9z1Cza/jk7k/igW0PwHwXBH0EALyZ+sIXIL/zOwSuj30U6g++wC900wIi9OyV5ct0qwIICp1DMHqGGaTg2Rwh+0lCpfU26DZr9AzWbGHVPUKwjsT4vFem2+lC5SLlQjfcDDU6BtXQph+xlGYZh2ik/0NBV6hCWZll8Lpj065xaCv3uuFoe19YK3Ov26gS3BIZOmR5Lq/b0Xm4vjNVqovg1ahofakHzJ0nwBsWQxWSHdxNptmFoVrgdZkWHagSGe0YhWvlQ34nDSGTef4873O6m2PehYuU7FhhkrsSGaiJvbQINTRDvFnXEhzG6YlhEBQrGwRcK0zAa9agIgm+7uoCvGe/DdQqUHtvBnbdBBWOto0pKppYZVp6deBwp21FgcVLkLPHCOIdWcAkUQqd/QRe16UTWjEP7wf/ALX7AFRHlrGRoQgPcNUiH5Pube/3cwu0euwbbac3iQCeTVJevQL3qccgC3Mwf/aXYU7tBkIxbQlaBKoFhkHklqFGd0AyfVDz55nR2zUIdeAu6oSDCiqof5EKAHgztbDAHbBSwNf/HHjfXRC7CVm+xM7Pl7z0jMLoHqLTlPbUFcMgUBRz7c7XdbS5RIEM5p5hAm88peVCp3SO7mHAMAi6+26G6u+Hqpf5BR7XoGuFoLQd4ptAFwIxQzSvWLrE64inCLr9k1DReNsJqVbUnW6VbOFEBoh1sIl3mtwT1isEHbtB0I2ntaSlyNdeW4DMnSeA945SZqXAUXOykyPsSl4HCyj+mtKOUc1666CAGAlVyrQ4ap0/T09mK8xOOLfI1+ga0PmzeWBgEmrLDTDS3by/zTpfT4fIw3VoBHK1SYoV0fGGFR1UAMjMWcgPvg1kuqD23gJM7OLYP5riZ1fe4ONDEd4Hz+Wu2IoAs+ch518jOz6RZCTh0FZOQDzdgSezgOvBO/wY1NAIVLqTNpdWhKS+8gZ1vNm+NvDWq5BLx6GSaQK0oTi6dm1+xrUyvKMvwH3pMKyHPgN18HYYPsHK3/PaDO1QPSPAwCRQynPPWyszNKFv7K3/exVUUP/KKgDgzdTFi5CdO0nC+vLvA9tHASjuc3vG2T0oIRiJR4JLg4Hw4ug4NzBGTioF2kFq0EUyw67s9Gvc6b56BCrdyS/RvYeAzk6ompYyxVIEJ4MWkIBwtwoQOH3QDcVoxjE/TaCKxKgRHd5OYw4RHgZqJZ2upF2nEhl2xabFa2pqdnOtSJBKZDgeNahplWYNqJbpNZxbpA54cJIRg55L4lQoTIKX3dAxgVq7G89w99moXCsfCkXAbneBebuFVWptm4wVRLqHgJ1f4eFjfBcwthuGFeL99723IXrKQNY5qgUClu+tbTcgjsPDgd2Ad+oo8NJTwOROqH23QfUM8vpiHZwWlNfbXblvzAGDRiMzpyEXjgPZHrpWhSMcNad7eB9qRQKvGYa8fgSIhqn97h4ioG4s0cREezu3gFc8drxCBriYJsVRjs18iHoF3tmTcB/7Fsy774dx130w4h0cgTvNFhlMVmd5oBndyZ/BE0d0Ru+tUNsOBhm9QQX1FlUAwJupr3wF8j/+W6DRhPzKZ4AvfIHSH9dmxJwyCLr1Cg0xnAaJOJYFwIRUC0AlD9U5yA4k3UMv5+MvsdM9/jLU4CjUwTtg7DkIldSRd3pHiFgSUOY/DbqRBFBcp6a0tAGEwjTHGN/NL2WA3W6txL1zswpAtUa/yoq0u2HfKrJaJCh2dPM69GOlXuV+cuEid82DE/Rjrlfa/smeHlOHYwTsZkOnPnW0XaoMg51lJE7JUr0KWbjAvF0rwvH3+nIrUhBOg7vk7iGoyb3ckYvOTrbrfD8eQwVEme2O1bLaTljlPMQMUVtc3oB39Bng4kmo3TcBe26BisdpqhHraGcQO00eJBq11o+EKIvGGZdPM4koHAKiMRiDU5COLl5XeaMly5JLp/ieUxmOkMMxar/zKwTeroEWqx2AtvPMkVdgmARlvTeWRhUyexnON78GY89+mPf9HAw9hm/veStMoapVocZ3Awkd6PDa96EGxqFueH+Q0RtUUG9xBQC8mVpbA+68EyiXgEe/DYxzByfa7AGFXBt0w1Gac/iGE5k+7oKz/UC1Au/VIyRRnXkDastOqBtvp0Y3ZBJ0/a5LG/m3QNcMUZd6FegikoTUypDZs+wIDQOqawiY2AOjQzsVidA2srLRctVCIg0kOkn0gnA8qbWgqBb17rKH42HXASobtIMsrEEWL/MauoeghreSIVwtsJMPRdvAGo5rhrfLUXUowsf5WuBokpm/4tFlaf48PZk7egioi5fY+SazDJpwXZKOJvfCiGnJll1nN2gYrfg8MS3+frWoLS/jgGlA8mtkFkeTkOUrkMOPMQ5x/+3AjgPsLC1NImtUCLwinAw0tTWn53KVPH0cmLtAyZlpAPEkjIEtEM1QRnFVM9CTwOo8ZV2xBFOsInFIfgVYX6Lsqmvomtg+qZaAtVmacEBxjO05ramF5Fbg/t1XgUwnrI//G6j+YahQjHtpP5+3XgVW56FGtgNdQ0B+lS5Wjs3QhJ4gozeooN6OCgB4s/Vb/yuwtAj54heB8gbj5hztCRyJ88u7tM5xYirLnW7XIIk2xw7Td3nmHNSug7SA3L4HCm47KSmWAqKJdqcD1d7pNq4DXbsBmT1HMpUAyPRQ29o9wseLsJMtr/P5XYfPn+S+lY5XLr2USzmClQg7XX9sXCnwPeaW2e1urHBsOjhJ8K3k22Dud6GxDm2fWSaIxzp4zdfJh5Qy2O0uXoAsTLNbj8Qh6ytkZQ9M8HrW5vmao9uBwa0wTKvd7fo/xnaN/+obSvjSsFAMgAfZWKVtomFALp2CPPeP9Kk+cCcwth2qWdehGRl9CNloJyI1a5QnOU2I40Km3wCWrgADY3xfqQyMgUkeEgyTgRBmiCBeLpCYZ1oE2niKFqO5eeptu4evGf2K02QCkwHqrENhTajTsrQSPZtRLMD85C/CGN8GFU2RYNWssdu2G4x87Bokgct1IK8/C5k9E2T0BhXUO6ACAN5Mzc1Btm8HXBfyV18G9u3mSDOSoB1kbp7A0kMGM3Kr7XShlQUYN9wCdeA2qKlt1An7SUA61k8p/BDQrbRdlCJJ6lDnp4GVGXaVPoN5aGtLMtJyX/KJUpGEzsDtaANzo0p3qYpPpuok6MZS/AIvrQOrs5DcEk1D4inqdIe3k1VcXudYORwlEPoxga6t980JApofcXe1fEg8ILcAb+ECkF8le7mhu91khilEhRw7075RYHwXVKYXSsDXshucEGiSleh8XVTzHBtHYjzMuA6kuM7rbtbgnXgJOPYsML4d6uB7oboH+Bn4wRnVAp/DirR0tAy/qEPqdQJvbgkYnADEoUFF/4SOE9Tsa8+hBWmzThB0bG0skoUUc8DqLIHX11b75bk8tNlNoFmDRGIt4hugILUK3B88Du/UG7A++gtQe26ESmZJUHOaGnjrJNpZYdpHRmKQ6Td0Ru+2IKM3qKDeIRUA8GZqehqyezcAgfzNXwP7tpPYYhjc6XYNAyuLmrn8HFAts8s9cCvU6ARUo6rdqGLsCiNxWjm2QFdbVF4Pup4Lb3mG5COfvNQ/DjW8HUaMqR/iugSQyoYmM8W4141nyN4VITAXcwTPeoWgk+4mYCgw5jA3ByxehqwukKCc7aVLU6aHj6mVyLz2w+cTGYJdrdQOpDAMkqCukg8ppehRvDBNJnM4SgvE3BJBeHALQXVllulO/eMMXYgloVydROQ57DBtaq7FsNoJVFEdL2haDKeoFqHSPZB8DvLK08DMOWDnQaj9d3DnWS3yM4ineAjxD0Ouo32aLT2yL3HUXMoDA+OA24Dq7KNkK94BCUWBinb6SmQJ+qtzQLUM1TcOZHsgpQ1gZYYM457Ra7W14nFNUS1xLx9N8OdJM7elWYP3+lG4Tz0G896PwLjtA1DJNMfNV+95S+uMOhzfDdXRzSCGo0+QPX/oHuqtgwrqJ7DOrJ1Bza5hX9++d4UGGAgAeHNVKEB6ewmIf/0lqBtvBboGIQtzEO1GBdOCceMdUPtvgeofpFyoUeUXqx924JtvqH8KdAXe6iywOM0uyAwx+GB4K7tBpbRTljaA8J2bEh0MntdOV+J57eCDWlGnBvUwT1Z3T5JfoSXk6oJ2aRqAynRTQmRabZ1vWLOX/Wxjp8mOUeldr95RIprkiNu0+PrrC/DmL5DJnO0nOC9cpKa4a5A+06Uc0DsMNTAB1TlIgLabBF6l2qYe4gFQvF+OTRANhUiAazb4mGgCsjwLOfIY0KxRRrT7JpKbyhsEyhgTq9CsEbgdbSFqhqjFzudIrmo2gL4RAm/XIJOJYkmai1QLPMzE0vzc1pcgJZLs0DNIpvvSZajeEaBv/DrgFT36X+e+NxKlhacvKbIbkMsX4fztX8K46U6YH/wIDyPJzmv3vI06sHKF7li9o8wkfs3P6H0f1PjuIDQhqJ/Y+rXv/Bq+8tpXYCgDhwYP4YnPPgHLsH70A9/mCgB4MzU7C5maAhwH3v/7xxCP3ssq20XQveFmpgvVym3HKD/swHMJXoZmSl8PutEk4AGyNgdZvKg7yDDHxoNTNOUIhQm6PpnKj/jTe10VihCshJpQFFcIOIbZ2usqnXgjtSIwe46vtbFCH+ZMDyUzHZ0E1/JG2/zCT3cKx/i61YLOQQ6RAR6OtZ23rul2L/DQEU0xVnBjGeifIOCszPLP+obZZSeZZ6t8UpVvbmHXqGX1dJdvhVue0wTeOsHVdeBdOQccfgzI9kDdcCswuQvKcWh6kurkKLya5/OHIvSAtiL0WC5tAGuLkIsnASigdwhw6lwp9IxAReOQaJKHncIKd/7KBIqrkOIGVLIT6B9jJ7t0ifvdvvFrwwtECJLldaBWZmSjaVFiJB7EtiG5NTiPPAxjdBLmg5+GisWB7ICeLOg9r9MkES7TS/tIw4ScPxZk9Ab1r6YubVzCri/tQt1hMlwynMRffuwv8eD2B9/mK/vR9c4/IrwT65FHaH7hecAf/Z9QX/jfYH3oZ6AScYKSrz9NZoHIAEenOlhBDIsEpOtBN5Ik6J5/jfIWnQ6E8T0w+sYYLyfCneL6AselSufH9oxSz+uHMdgNAkMxx2402QkM74CK+mNqB97CBcjlU/SwjiageocZj5jqYhdbyZO9a4WBaJygm8gQ/MobLUkMIjG+bjQORHooH/I8YG0e7oJ2qeoa5OPnLkDCEajeEUgkBqwvsLvecYgj7miK+85mHZA6R9HiAjUNvHaTHssxhjEopUg6atT1+LsC79xrwGuHCbj3fwaqb4Tda26Rh4+uAb3vrmubSZdddTxDUtTiDO9LJAb0DdNzubufft2RGCRG0htWZ3TqUoJB9eUiYEWhtuyHNCrApdfJaN59e8s6lDefaVFS3mAn7zR47QCU51AnXq3A+cZfAFAI/eKvA+k0VEcPjVmcJu+p3YCsLQBKQW0/BBVLQZav0EwjEodxz6eDjN6g/lVU023CUNeSCW3Xfpuu5p9XQQe8mXrmGcgHP8gv0z/8L8ADP8X9bayDXWgoQqmIa7MjMS3d8VwHujAIukuX9S62gwCX7oHRO0qrQsPkF74faiAeATLewfhBw+QI23UIioVVymTiaZKpEhkow2C3W1oHLh4n09hp0i0q0w1lWXSyAvgc+jkR7+AhIhzTVpN5XrsV5nVo+RCs8FXd7gXudiMJqESG+tXVeaB/nHvWFaY/qcEJdm0JJicpz+FO19/d+GNm/765TmvPrCDa1YkkLFmZh7zxPDB7gSPmvTdDpTIE8sIa70M4QuA1LXaQjSrfnxUl6Wn2PGTmLJDKAJkuwG1C9XMMrsIRIJbkAWh9gZ9rKEpmeK0KOC7U6Hb6Li9e4Iqgf+LNnafdgFTyPERVS7wmpZhG5TiQZgPu49+BXDoP8xOfgzE6ATHDZM/7e95GBVLKMyZwbDdUtg9SLV6V0XsXMLI9GDcH9a+mRAQPPfIQnrz0JABgpGMER/+Ho4iH4m/zlf3oCgB4kyXpNFSxCJm/SMcjK3wd6Ib4pXk96CoTsjYPWbpElnCqU+tLLYJuzzAlOI7dBl3XZkcWZbCCP2IW8QiKxVWCS1QHF6Q0qALw7AYwcwpy5TSQX6NBRt8YYJl8nnhapx5pH2YrwmtKZHhdlQ0+txVmp2iGtTWklg95HpCbJ5O5sAZ0D3OsO3eeJKK+8bakpncEqn+UHXcsCYTjUG6zFVhANnOVphlu8ypJFuVMBF6T98NuQuYuQF75AdBsUL+78yDHvI06DyJp7QldK3JMDiHIx9MMI1ibh1w6SeDu7AU6MjrebwvjHUN8r2KYwMZi20ikVoY0G0C1DjW2jWhVJj8AACAASURBVO9v8SLH9/2T9NG+upwmwdquc88dCgFQdEtzXYjdhHvsJXg/+B7MD38Sxo23ArUSVN8k1w+1knYaq3OXPDAJ9E1wVH32KOT0S1BT+6F233Jttx1UUP9KyhMPT116CjW7hnsm70Es9O5g+QcAvNn6xjeA3BrwuV/QO11txNEC3TIAPSJWFiQ3z063WiQwmBbQqEJ19kP1jNJkwt9t6i6JxhExBjiESc7h7rdMgCmtc5ydpHG/CkU1MAtk5Qrk4hvA0mUgmYUanqLDlV1vM5YrOi7PNLWtZBdUKEpLydK63j+H9PtItuRDAGj4sTjNbjqagOrogqwtMlS+d4TXlFsEXBtqZBvjBn2nKysEZTfYZRumNtDQblWNCrvTWAfJWfrHU/x4vUIO3qUzwKvP0ury4J2UEwEE3uIqO15TxwlGYtoNy+E1haK8N+dfAxYuAz2D9GmGDrRP9/A9RpP0ii6ukrgWivKg4rqQahVqeIod7/IMkOmG6t/yZmmP67QsOqVWAQzFoAlD8TDRrMObvgD3W38N87a7YdzzIFBcg+oa1nabdTKrHZsHtmQn1MgOGpYsXGRUYCrLqMAgozeooN51FQDwZuv8OaBWgezc9cNB17AguUV+cZbz3IOGIuxswjEyYruGtI9yUY8XddpQKEpQ88lNijIUFNYIBhAaXqS69Ria+w+vUgAuvEYnLBE6RfWPU8oiXjsAoVbiY0JRHgZi2prSvw7x2n/uk8eUenO32zPCPfjsWYZIDIxDPCEodQ0yPzaeJDjGOHJXTrOd3NOo0t1JvLZBSDJL5rPnklilGK8ni5chZ18Dzr0BjG2FOsj83VbQQnGNnbtvzhGOtby4keyk1ePiRciZV4CVOaB/hD7NyqB2uoNscEQTlBSV14GNJe18VdNs5Qb3180akJvjgWVwC1TkulGX5zKQolEBbBviOZwKWCanBo0qZGUFziN/CmPbLpgPfApolgEzzLAGf89uN+ia5ThQ47vJAyjn4R17CsivwrjxniCjN6ig3sUVAPAmS/7DfwCuXAK++Htog24Isr7ITre0ToeoeAekXqb2t3uYOuFYsuWj3BprRmI07Q9p8wjD5Bi6tEawc7SzlG+kYWlgtpuQK2cgl47zNXtHoEZ2MHChsqE71xhQKbb1s4kMY+1Mi7vjSl4zqbXjk44zbBl6XN/tpnsg68vAwjQ7yI5uanhrZWB0B1T/GAHUMHmgECG719SpUHaNI3q70ba6TGTIfBaP78u3epw+Djn1CrA8xxHzAZ2R2wLenHae0h1yKKrDJCzeq1AUMncOcvolILcMDI7TCzoUhjG0ldMBk9cp4RiDInILHL/75heOB5Xt4d62sMJJwuCWFqmtVR4PElIvMRmwWSOhLhzhHr5ZA4pF2F//C6hUB6yPfgZIxCD5NRjju3mvKgWgUYZUSkBuEWpsF9A5QF3xqRch548FGb1BBfUTUgEAb7Lkhn3A3BwwexmyvsROt7hG5mumB2I3oTaWgVQnu6Z0L1SzqkG3yG44EufuOBQh6JraHaq8wRFzvazDF0iGUqEI5Uvi0b/44nFg+TJfY3gr0D8GVS2yk05kdWeuI/3CMSDTz90rNDiUNgCnTqAMRUlKCkWv7XbnzxPYe0Zoczl7FrAbUP0TBMnlGdpDju0EOrqgGjqAIRxlLKIy24Ycrs2xbq3EcW4syYhDCOj2odgR2w3IiRcgJxler/bdAux+D1TI3xM3SBZLZLiXVqZOWapw15vsZBDBzCnIyRf5PgfH+f/GEjAGt/IQY5paNhXnfnV9Xl+ny+sxLN6T4jqkVqAL2OAUpw7X/DBwLSDVIrv2RgWoVYBIWMupmpB6Fe6j34Rs5GB+5NMwxibhzV8gWat7hJ91tUDC3eIlBmcMbuGBaM7P6B0IMnqDCuonqAIA3kytrkJGRwHXhfel3wFuupl7O3jA2gIAgeoZhXQPsaOrFjSRSfsfh8K0H7TCzH1Viv9PcY3gG4kTdBMZPYa2CE7FHLzLJ4GZU9yj9o2x4zRMPta0+PyNaiuVCcks82f9/XG1SP2rH8sXS3HHrNnHUitrJrM2yMj0QjZWSKrq6mf3W9pgXN7IdjJuTZ2hG47pPGKX71U8jpl9iYBPKEtkyJL2u2SA96BcgPfKU8DZN7hX3X8bMLWXe2BHj3FLaxy/A61JARoVTiCSneysp49DTj7Paxqc0FKsNIzBKb3HtloHIPFcYH2R98Tz2mYi4Rh32g513GpwCip+HfBphnYbeLXDmWHwPngupFGHe+QZeK8dhXX/J6D23wxZvgJlmlDje/malXV2uEuX+VmM7KTkqZiji1WtBOPGe6H6g4zeoIL6SaoAgDdT6+uQkRHu9555HOiMMTSgs5+dYiTGTtQPm4936KQhtJjGYpiUCxXXOEa1tGl/LEUyjz+GbtQgs2fI1q2VuFsd2QqV7ddpRQWCqG/CL3oMmx1odWotiVKzBkCxW0xk22YZ13e7vaMADGD2HFAvQw1OUr+8PEMwGt8N9I1yrN7UmmVl6AShUCsgQSwdUl/Ja6JXlh2/v2MWAq/kliBHHgXmLgFj26D23w41MM5u19EJSsV1IK5HvuGYdsSqtqYDYljA+WOQE8+zgx0YA9wG0NEJY2CKOmlTH3oicQJ+fpkjZU/an5MVgcxfApTDxKLBKUYVXl0ifN/VAjt2p0nwbdahIpoI16jDO/E63O/+Pcx7H4Bx570M7VhdgLFlP72uK+tkc68vAY061PhuqFQn3a+CjN6ggvqJrwCAN1OFAqSri8Scv/tzqFvuZOZrvdLupBLp9v5TKXZcVphRfyVNphJhNxhNtkHXH0MvXoR3+RTlO9k+bZSxBQqKj3cdHXrQIEgJdLfb35IgSb3a6q64202yq/YNO67vdrP9kPwqx8zZPqhsL6RcADaWCETju4FEqm02Eib5SOkOHc0a97a+J3StxHsQT/MeKMXrEIEAlBE9+w9AcQPYeSOD7zNdfD+uzftTytG1Sin+KtoPOp5hpi0AnD0KOfECAbZ/hLaTmV5ec0QT2fQ+WgyTz7m+QGBXhiZvhSGXTtOaxu94kz+EWdysE3jFIzmsUSPJLpbgazTr8GYuwv3GX8F4z50w730QynPgzZ2D6hujVWS9pEfWZcYEju6gfAuAzJyGvPo0VP84LSRjyTdfQ1BBBfUTUQEAb6YcB7JvH5DPAy88C4SEoBGndIa5vXoMa2nbSN+HuVljJxhPAVaEWlMrzDHtxhJD0mfPMoigawAYmCAQ1OgXjHCEXXSzzhGvGaKvciKju1lXG/Pr3a+WKSkNlnSpmiOTubQO9I6xG509z456aIrP6YdLjO8BhrfqUbqOKQzTYEJZYXabjYpObTIobfIJY7EU74PpWzAKRATemVeAI//I///A7VA7D0FF43S6cvVhorx+lctWkoDv2jxkxOnIJadfhJx8gfrnniGgUeGedHCK99UM6SziGEfTtSKzde2mtuXsYsd77g3AUkA8QUa0b0pyzWeutbyurW0va5xcxOKtYAhZXYXztT+DMb4V5oc/ARWLwVubAzwXxuR+ypAqBcqKFqZJytPpVbKxclVG7z1QPcNvwQ9yUEEF9XZWAMCbLOnogCqVIJfPAj39dHdyGawAKwwxQuyGi2sErniaXXE42t7/miF2oTOnITOnaALRO8wQhM4BfrH7AQuRODs20baW8QzQOaBD7LXBRIXSFQDsFlOdP2S3O01g7BqA5HPAzGnmB3cP0CRidR6qbxRqYg8k20/iWLXIa9GApq7K4ZWQlvuU17V7VpqyJZ/1LAJAaH/58lNav9sF3PheqC17+FzNun5v4OEhHGnbPDZrfD/JToK63YScfJ6s5lSa1pKNClT3ENTAJA8FvhlFOAqEIhwPr85wZG1YBN5QDHLuNQhcqHiS++xU15sdpFxbm2g09MGnxqmAZWrbTRcol2D/7VehDAPmRz8No28Akl+F5BboVpXp5WfjupClGf4MjO1iClSzDnnjWciVM1B774TaEmT0BhXUP6ds18ZvPPkb+PIrX0bTa+JjOz6GP7rvj5CJZn70g9/mCgB4kyVf/CKwMA/1279JQLRCEDNMQlApx+4ymgASnUA03t6PWmGI53L8OnMKyK/QD7mzl9aM8TQ7yOIaAcnSQQR+7F+6l2xj/8u/WgLqRY69fYlRNNne7V7d7faNE0Rmz7N7G9lGkFq+wrza8d1QYzu06USZo1IzrElFYebwNqscvfr/TyXPnWxcu1UpRaD2XN6nehXeD74FnD8BDI0DN90NY2CcN7HZ4IECis/jd62RGMlMVojAG0kAjRq8E4eBM6/QeayTsYiqdwSqb4KyrFCE9ykUBsJRyrhWZtoHiHQ3EI5Dpk9CGiUC7+hOmpi8CXgdoFbkiNkM8bBRr7SczriDbsB9/B8gM5dgPvQLUNt3A4U1yOocdcUjO3m/7AZH+5Ui73G6h2Ypfkbv8FaoG94bZPQGFdQm6vOPfh4Pv/owag4P62EzjEODh3D4Fw+/zVf2oysA4M3WV/8a2MgBv/wrWq+b4z+m9lWOpwi6ytAdsQWszXHEvDBNn+eeISCZ5og5TG9hlNdpBwkdt+c0OYLN9mv/aLT2kHAaWvsaY7ervYelVmrn7cY7oDoHGQJ/+RTQ0QnVN8YUpKUZINsPY2IP0DfGkXa1wOe3whylhxhoj0aVEiJltOP7okx5UhDA0HtW1+Z+t7AGeeLr1O9u3Qt1093c74q+fnEpH6rkCXChCP+pa110spOWnJUivDeeA6bfALr6gEwnHcT6xqF6x64CXj2O1ylPWL3CQ4dh0GwkkoDMnoesL0ElklDje3jguR54PY/A6x8A7Ab39rUiu2dTpy29eBjuc0/BfPDTMG66g45ZpTykXoYxeYOWRZXpFb0yAzW0ncQ1ZVyV0atgHLo3yOgNKqhNluM5SP3vqVYSkl8xK4bj//Y4tnS+s41qAgDeZMnP/iwwPws8/EXAVDSjSOjQe/eq/W+lQNCdOc3R48AEkO6kvCiRZsdWWCOx6Oq4OrtJ4Mj0QllhMpnrZRKbPJePi3cAiSxNHvxud/48x9Z943zc3HlG/43uIGCuXKEb1+gOqPE9TNhxbU2ssnkNhskcYbtOAApH2fmV1nltfp6x5xH8lKKMRgQyNw156htAvQrsuRnq4J3c74rQKlJ0p14pkDSlpUuol3l9Ok5RCjkC78xpmn2k0iRX9U+w6zVDfKzntve8ygDW5shsVgaQ0cC7Mg+ZPQuVTAETexiwcD3wikeP51pJAy9dtKS0QetIfY+9c6fhfvOrMO+6D8ZdH4Kyq5BSHrKxrDW9wwRr14HM64PW8DZ+FvUq5PVnIAsXoW54H9REkNEbVFD/LVV36kj+xyRcca/5/VQ4hac/9zRuHLzxbbqyH68CAN5MiUASCaBeBy6dBwYG9f5Xd7uOQ/elmVMcEQ9vJTnI1HvNqHbCKq5pG0ktDWrW2UFn+sjMhWYW10oEQ89lF5bMtsfM1RJkUTOZ4x1Q3UOQgu52E2mooSl2cwvTQDLDbndwC0fYzTo7Ts/T8igmDaFRAyCQUKSdORyKkWBmhtjZh6IELc+FuA7k9KuQI98heB18H9Tum/h+BS0rRyijbYMZibc1vPE0DxJWCLK+BO/1Z4GFi4wDjCXayUTdw+x4wzFes84nFmUC+SUGJkBpe80UfaPPHaMd5tgu7raviy2Dn6tcK3JK4TqAuNT2+mlMAGRpEc5XH4ax+yDM+34GKhKhv3etAigFY3wPpxWeC1m6ApgG1Jg+4Hge5PyrOqN3N9Se25mwFFRQQf031+0P344X5l6AJ17r97pj3Vj8nxZhGe9st7gAgDdbR48ChXXg1ptJpjItYPkKvBktHeofh+ofB2JxMoHjaY5Ii6sahCwGHSiDDGffMCOa4Ei7Xm4zmR2bTF+/O/RcYG2+3e32j0OFYux21+ZpzpHqgqxeAfIrUENTUBN7GZjga1jr5bYsKBSBcmwSjPTomWPmqvaCjkMZOjjBjNDYQjzud195Bnj9MJDOArd8EMaE9sYGuOP1zTb81/N9k50G99WJLA8Dy1fY8a7NA30jQJiRh2pgkh2rpXfDntfSOot/P9fm+ZrpHk4FqmV4J18EohEyoke3/3DgbdZIsDLoNw3X4aHEafDarRCkVILzyFegOrIwP/ZZGL39kOXLBNr8WotMBacJKawDpRztIzN9PCCtzJLdHIkzNCHT81b+lAYV1E98Xc5fxvv/9P1Yr61DKQUDBr7z89/BbSO3vd2X9iMrAODN1tf/BrK+Dnz8QciV05QOpTrJps30kD3sx/01qgQKH4yiSX75OzaQ6dXpPSb/v1pZexCLzsDNspM1THa7CxcgSxepre0ZhhTXgcsnORIe2QFxmzTQCEehxndz/BkKt1OUmjUCrGY1Kz+JKBzTY+YcgTKa5HMINGPb4n5XBMivwnv+u8Dls0DPANTt90P1D7X1xs0GQdpPN/KBVzztStUJxNL8/YVpdrzlPNA7zHG+YRB4s31QZphTA8/TObwRHhLK65RKeS7JVfE0YDfgnXgRMEHQ3nbjmxnFIpwmVArUECtw3AyB1MtAtUwXqqYN59t/A+RWYH7iF2Fs381s41oJUi5ApbhLh90gUWvxEtSgDr/wP6tXvw9ZnYVx8O4gozeooP4FyxMPz88+j7pTxx2jdyBivTsmTAEAb6bOnIHs3w9A4P3mr0E99AnuXOES4HTeLCVIhbaJhGFy1xqOEXiTWYJtvUzwVYpgYJoEqWiSoLU2B2/+gu52J0hOmjvPTnt4G0k8a3OQqyREqnOAjkyeq0G9QXDUel3V5OtxzHwVmzkSJzArAFaU42KnqY0zpiGHH6VxxtAYgbcj275mu3EVCHOMjUiCwKkUkOxiRw0AM6fZ8dpNoHeQXa0VhjE4SVaypYHXl1hZITprVQskWLk2s4+TGVqCnjkKOHWoaAew/70wflhQgd0g8Pp7Y7sBmCakWmBYRTjKc8+zT8E79gLMj/w8CVb1Cl3ADBNSLcKY2KsPEx5k/gLQ0c2YwHAE4ro6o/fFIKM3qKCC+icrAODN1NISZGqKhhxPfRdqYphf6IkMPYsLywRWMwQkqF1FtUh2dKaXnXGjyv2r6xKcGppVnMxChaPtbnfxIjvg3lESgi6d4K52fBc7OD96cHQ71OhOjkMBPcbWhC2oljcxmjVKiJQ2zWhU9YEhwjGtH0PosVsV14WcOQZ58XGC7NhWqJs/CBWNtca0sJvs5g2lGc7Ckblr8x74UiLxmG50/Dn+flcfnbwicXa8qS6yrn22t11jrGMowkPE6gxfJ5nljtzz4F08DlTyUJ4CbroXRjT+5s/Lsele5X8mzRp39dUCd/QAYIVoHfno38K85wEYd99Hg4z5c4BpQfKr3EMn04AnkJVZQFzueZPUGwYZvUEFFdQ/pwIA3mTJwACwtMTOKJkmk7la5B/GUyQt+YH3mT6Cr+fqMXCVQODYBOpERif7KMjqHGTB73YnCajzFyBLl5nv2zsCyS1y5Nk1wH1j7wgNLURoGNGotMhDsELM4HVsSCTO1yznACjuS80QiVcmWdvwXHZ29Srk2DOQ4y9Q+jO1B8a+2wnCzTpB2tY2mAr8d89rA68vJQrHII4NOUcSEpJpINvN+5BIQw1Mwkhm+NrRpJ4C0LOaWuMKsHaFIJ9I8z4CkNlzkPwyUCpD3fxTMDLdb/6QXIfA68uqbE1ya9Q4bq5VmIQ0OwPna38KY//NMB/4OahUhvvzaokSJMNkZ+55kFKe1pyju+jLrdR1Gb0f4OcUVFBBBfUjKgDgzZTrQu68A1hbA/7+rwCLRCYkO/mlX1rj+DTTx86vWSXgAJqQVGWnmcxyJNtyqbpEUOobg5Tz7HaVATW+mxrc2XPUwA5vhRrbCZVIt72VGxUCIzTbWBnc7ypDA1mJY+ZIogV0CkIgNU0CKRRkYwXywvcgM+eAviFg5wEYUxy3o1knsNpNjrSBFvMXEQ28sRSJVaEIXZ7OvAw59SIzbdMZPQnIQg1sgRFPERj9lCFtrymhCCcCa7N8/liSe3IAsjQDWZsDlpegbrwLxsgPATvPBapFOmD5wGvoHXa9DBRyQCIFWV8nwapvCNbHPgvVNwRUipDli3TKKq7RqMO02O0vTJNYN7CF3bFj0w7z3DGo7Yegdr4nyOgNKqigfuwKAHgzdfo0vaCVAr7yx8B99xPcamUSgjp6AGjSk90gOLseQTCaYLdrhSFrfrdb4G433gHMn6dOdGAC6J+AFFYZBZjphjG8jd7Qltbeei4BxbG1lIi2k8quXzVmzrMjTmQ1qUo7aoUiALR+Vylg9jyJVeUiMDgK7DoEY2gLwaxZI3DbjbbVpWvzsBHRWtx4WofbhyC1CuTUC5CzrwAD40AyxY4+3QM1OAkjkiAwxlLtrGDP4Y63WQdyc3ydaILOXwAlP8tXgJlphtHvveWHm2jUSzzQWGFNZtNVL8MrrFKTXG/A+buvAo0GzI9/FsbULh5i5s4yb7mUp343lYEScM+bSHPEH4mTiNbK6O2HOnB3kNEbVFBB/bMrAODNlOtCDhwANtaBf/waASbTx31vo0bA8/2eG7QipNY1wx2s3+0m01B94/Rgvngc8Fyoib0ci86fB4obUEOTUCPbGRCguytxmnxez+U/JvWrynPfPGZOZAHLamfvtva7LgQCOfEi5OjTjPobGIXaeSNUZx+f199La99nMpQ8Zg2HY/zvRJYGJIYJKRcYozf9BjC6HYjFSETL9kMNboERjrY7XkOzpZ0mu3u7CazP87V8khoUZGMZsjwDXDgNNbgF6vYPUZJ0dfkM72qRz+X698VssZaJpArOk49Bzp+G9eCnoA7dToOM1VmtybYgbhNG3wSBeJXXo8Z2Q3Xo0Xcxxz1vpcjQhP7xt/AHL6iggvpJqgCAN1kSjUI1GpDL00BvD0HXsds6V38fnMwC0QRkbZ6dVKUANTDJfeb8BcjcecpZhqY4dr5yhklIQ1NQA+NMMTItjmabdW1q4RFkTAtwmhyHhqLUF1c22K1qly3lWzSaEUAcPa6uQ15+EnLyJQbWD47C2HYDEEtCPBeqUWN3aje5jxXha9rNdlBCspPGH8qg7eTxw5CZM8DEbiAS4k5cJxMZfkCC9ov2O2nR14/1RQ28UXa8SrHzX74MuXAGiCRh3Pszb+4yRXgQqRZoogGPn4FJZy1pVLnntUJwj70M75nvwfzgR2C8/0PcrWsTE4RjkNIGpw6hCBnRa/NQIzuAnhHuee0m5CQPF0FGb1BBBfXfowIA3mTJ538NuHIFePhLOuQ9RqvFmpazJLIc7y5MU7ebzEL1j0OqJeDiCYa3T+4Fkhl6Q+cWyQQe2kKzBiusvZUdRt85zXYog5YGIRJvs5nrFYJ9JAmljTL4HCHAswFlMqHn8KOQ2QvA1n0E3rEdZPmKB9Wsa+BttN2rIG1wNENkH0dTBKXcIuSN5yBLl4Et+wAD1BF3DXLHa4XIko6n+V4cG7BrdK5ybQKv0wQiUUqKNJhjZQYyMw3kizA+/Gmovuui+UTvo6t5CFR7jB2KtA5CUlgDwlF40+fhfvOrMG59P8yf/jhUintzmT/HQ4Bjc/SfykK5LuVdPSOMCbRCtNcMMnqDCiqof4EKAHiTJR/6EDA3C7z0AkG3XmGHF++ArC9zt1stQPVPsltcuAC5cgboGYYa3QGplYGZU5T/DG9lWHs0rqP0GMIgOuCeu0zuOxWkPWYu5QAI9bWhMN2sFHSYg94RmxZw5Ry8I//INJ5dN0EGRmD0j5HVC4GydX6vXb8OeJutZCGkuuiIpRRkaYYa3o1lYOoGdtalHH2QB7bAME0Cb6yDv/rAK+AhYmOpfYBIZqmvLeYIvMtLkDPHYXzoUzC273/zjfdNNMQjsapZI/DqeyWFNR4oFhfgfP0vYEztILO5px8IxYH1ecjaLFORnCZU9xAPMQsXmVo1uqsFsLKxAu+VJwC7GWT0BhXUO7gWS4v4d4/9O5SaJfyne/4T9vXte7sv6ceqAIA3WXLbbcCli8ArzzFfFgqyePGqbneCHswXTzD8YILpO1i8BFmeYTc1tIXRgn53KR67Mp/NrFnM8Dy6WYWiHG23xsyULtGHWu+cRQB4EBiQky9CXnoSSKSgdh2C9A7CyPYCtg0xAGU3eWho1PSoWb85p6G7+gQzcv2EobkL8I4/R+3s1D4NhhsaeCdh+JnBPrvZdYBmncYXnkPAdm197RxHS2mDwFssQo48CeO9D0Ddcs+bx7tOk8Dr6sCIRo2HA19iVaXmWTbW4Xzra1CRGMyHfh7G+FbulJs1kqxMC9JscM8digLry/x8/NxeQGf0PgeZOQ217w6oLTcEGb1BBfUOrlv+n1twdOEoXHHRGe3Eyv+8AvNdsCIKAHiTJXfcASwtQp75LmT5ElAtcreb6uZu98ppoJM6XbHrTPURj4Sq/lGocIydqmGyy7XrJFeJEAyVSUCOJDhiLW9o7WyWUYeuBjXD0vpdh7vTpgN5+QnI8eeBkSmSqrLd3O9qswzlaLlQs8bRrc8m9hOZYh0EXivMIIGZ05A3nuVOeXIPpF4iaHUPUatsGJTfxDvaWuJmnWDpOsw89iVK0SSBt1IAli8Dtg3v8W9B7b0Fxr2f4BTg6rpay+t3uqZF56xmnWYo9TJQLsJ54ruQlUVYH/l5qH2HaOphhrnnLW3wfkeivM56FVi5AjW8DegdY9qRCOTiccjrP+BUYt+db76eoIIK6h1XPf9HD9ZqawAAy7Cw8b9sIBl+56+KAgDeTC0sQMbGAKXg/ckfQP3U/ST8XDwBlPPsdruHgKXLkIVpqO4hekR3dJLB6+937QbBWQcBwG60NLzQBv8o5QjKHV1AKAblNAAIgdewWmxfKWxADn8HMnMW2HUT1PYboBIpbUdJiVILeBtVrY01r7J643BSyQAAIABJREFUtIB4hsBrWnTAmn4dcvwwnarGd0LK64BdY8fbNwalDCjDane8EKDZoBmIawOFVf4aT+v0I4Od6vIlAAa8J74N1dUH44HPQXX1XXuPr9byhrQ8yTB4WKiX2ckWc0CtAvflF+Adexnmhz8B4/a7tY1lEsivQJYuABFt6dnRxfc7d4EEseFt7Qzl3CJDE6Bg3BRk9AYV1Lupfu/I7+G3n/5tGMrAR7Z/BH/58b98uy/px6oAgDdTlQpkeBio1yHfeBji5KlxndhDsLtyGqhXqBsdmGAnZoXYgfljZruhXZ/qBF9obW5Ys5nL63pH2klTDd/L2bD4OBGmAc1d4H63uAF18E7I5C4YkShQr0IMkxKkFvBWCLYGWdVwGtouk9aOyjDJ9j13DHLieSDTDYxsBfLLtF3sGmS3qBRHxLEkx7hAe3TuNsmAdrVlZDjKQ0C1QuC1wvCe/z5Q2ID50C9BTe689t56XmunLqEIDyGi4xLrZcCKcM/brMF74zW4T38Xxvt/GuYHHqA9ZjRJw425MySwWeG2YcnKHGBaUOO7SbwC2hm989MkWE3sCUITggrqXVinVk+h0qzg0OChd83f4QCAN1Mvv8wRtHiQ//ibwGf+DbA6y0SkTA+M0Z1Atk87TXEMCtcmSHkabBtVAotSQDhOzeo1Y+Y0tbt+znDLYUkgyoScOgp58XskDh16PzA0zpFxIUdQDIcIvNGkZgY323tmR4Nwqov7amVQmnTmJciplxgHODDOfF2loLoGgO4R3fHq7jwc4+X4BKtmg1pa1+bz+p1lvUy7zlAEcvYk5NUjMO7/LNSN7712ryoejUxqJUgoTCB2muys62Xexwo7Yu/sSbj/+Pcw9t4I8/5PQqU79Xg5xqjA3DxlWKEIDz6lAlDeoG1ntp9EslZG72Go8d1Qe+8IMnqDCiqot7QCAN5M5fOQ8XGgXof38B8AnYwCVCNbWyNNhCLc4zpa86oUQaZeBqCzdX2tbXkN8ITAFYmz2/UD7A0Tvr2k2Dbk6Pchrz8HDE9CHXwfVGcPO8z1JZ3dq9nQ0QSZ2a5NcHIddqeGSaeuRIZAVCtDTr4AOXcMGN4K9A7RAjIUIfBmB6BMkyfKSJw7aaXawFuvAqV1vk5HD4FSPALvyqx2/FqFPPo1GO+7H+ruh64FOhEeEKpFdvQty8uojjI0NMmqCO/iWbiPfQeqpxfmQ5+BMTTKP491AOU8ZP4sDwZ+dnCzwT3vwBZgYLJF7Gpn9MZg3HhPkNEbVFBBvS0VAPBm6vhxyI0HAQDyx/8X1Kc/ByU6hs+PntNBBS2ziUZVS4SiDCyoFtnxRhhaAMNsj5l9batA7zrXIUceg1w8BbXzIHDgTqgYpUiSW9ASIQ28kTiB13N4CHAcwGvyMJDuZWetFKSUh5w4DLl4guYZnb3AygwJXtl+qGwfc3mV0jGFmgzmM5v9Mbnr8HktDby1ErA6p/2lFby/+TLUjgMw7vsFqExX+x6KkFBVLUD8FKZGjcDrm40oE1Jeh8xfhvvkd4G6to7csY874lgagEAWzvN+JtJ8vGMT/Du6uQYIc0wu1RLkte9DVmahDtwFNbrjXTOqCiqooH7yKgDgzZQI8Ou/DqytAl/8L0A80WYi6zg+MUN656qlRJEEx8jlDfoV+2Nm0aNW0wSg2uEKVgiYm4Z35DGgsAZ1w+3A3ps5tq2W4eUWmfnrj5rD3PtCXI6gHVsbcBhApr+988yvQt44DJk7Rw1vR5a72VQngTfd3QbeUKTFWobnUcpTK+qO16H9pmY9S7UI5BbYHcfS8L7+MBCNcs97fWCCb6Ih4Fi8UdF+2Xbb9KOYg5dbhvv970FmLsN88FMw3vNe3q9wnAeN9QXKvjr0yNuxGbQAQI3vhkoyDjDI6A0qqKDeiRUA8Gbr9dcYXHDzrVqLyr2qKIMkItfmnjWeaptmeC5lStE4IwI9bSYBaUmBuN99BfLS40AkCnXgTmDrXoYo5FfgrS9D9Y1DhbV/su/+JKKTihpakmRwfBxL8XnXFmiesXwFavtBSDwBLF3irjrbx0xbw4IyVFsDrC0wYTdok1m+CnhDEQJvmfF8EAGy/ZDHvwm5cgHGg5+DuuHWaztMu8GO13U5qvbTisTlv4fjQCUPKRfgfv+78F4/BvMDD8D4wP1QlsWDQDwDNCokWfljfM8FqmXueUd3Al1DrdeVxUs000hmOG4OMnqDCiqod0gFALzZeuJxYGUJePA+pvi4LlAvEgws3Tn6Y1rfScq06DplGK1Ol8BrQBwH8srT3O8OTcLYfwdkcJwBCyuzkMIa7RFDlpYNRRhzKNDAW78KeAehYkmaZyzNwHvjWSC/BrXjECRkAitXgO4hAm9Md7i+ljeS5A5VBHAaNMrwO95sP72gPRdSXCc72vOA7mHIay9Cnv0OjA98DOp9D1wbmODYBF7HPzDU9Gsq3qNIgt7NtQq8F56G+4OnYRy6Heb9PweVTPG1E1nKmJYvAflVpk4pReLW+jLQPw41ONWKA5RyQWf0/n/tnXmQXeV55n/fufu9va9Sq9Xa9x0t7GBbYPbFYGxjB7CxM04m4yzlJK44lYmTVKZcKU+lEkNwjI2XAQzBtsxiCIuEwAJLILAkJLSjRupWt3pf7n7POd/88Z7uBntSyHdsrjDv769Wo759dF3l577b8/RpRq+iKGckKsDl8h8PYvv64LZPvqUCTYowZodFWFJ1ko0bVJGEI8gg2L5lvjuM3f4k9uhezOK1mJXnyPmP72K7jmAzozLHDAchBqFIYJ5B0GouynKVCUFDGyZeNRWXt/tnUMhhFq3F4knMX8tsTG2TGEw4IflQYIyI4ERb1ithxwdhbDAQ3unyIcL3sGMDIoC+C80zoes4/qZ7MKvOw7nyZkxV7dR75Lkymy3msNHEVF5xOCJdgkhC5ti5Mfy9r+A98Shm9nzCN9wi2bylgjxXohrGBmTWW10vp0iZUczoIFTViX1kYJghGb0vYQ+9ohm9iqKc0agAl4mdNQt6e+DoPqhrlC3m9ESbuRHiVUGb2XuLRaS81XK/exR/+1Mw3I9ZfT5m6VqZa7pFbOfr2EIOM3s5JuxM3e5O3A5HYtLydoMZb8MMqXh9H9u5D7tnm9wOLzwL6+Yk9GD6PExNEyYamICEInImFUsFG9smEN6hwEAjEN4g79eODQRWki40t0O+iH//HZiGFpzrb8dM75h6c3xPPoDkM9iJpSivJB8Y8uPBOZSF7CheVyfeph+IdeRHP4OzcJl8qDAhuSN2S9iTh+Q1U7WSVJQeBYI5b02TvKfWQvcRiQpsmIZzlmb0KopyZqOlQbksWwYN9bLRPNwj4lLbIsJWyk9VvBPnRwTz3f2vYF96Rua7Z12ImbNUFrCKefz9O8T3ec4KnJAJNqmdIPfXlyq0lJdTJuNAQzsmWY31XPyDr4hrVbIas3QDNjuK7T+GmbFQ2rOhsPxMJIaxnrxWNBEIrxsIb99UqzmWkip8tE9avG4R09SOjVVhf/hN7FAfzkc+i7N07dR7Yn0R3lxahDccLFhNtMiLGfkwkhnBjo3i/vD72MFB2Wxee758ICjkZCs8EoOBE9ihHmk3l4rQf1IMTmYuhpaOqTnv2JDMeTOjOGdfrhm9ivI+IlfK8aVnvsT3dn2Pkl/i6oVX8/Urvk5rVes7/3CF0Qq4TGxtDWZsHHtsP0yfKUtSxcBL2QkH7lbI3NL1ZL67axu0z8NZfQFMaw9mmBn8gzshGsfMWSEj4VLgz+x7QLAU5U44ZjmyZJSoFteqg69g9/1clqnmLMOmB8QOc+ZiidgzwWZ1NC4xhZGEVLVByIMdHwi8ml2omwaJlFS848Oy1ewWMI3t2Jom7OZN2J3P4Vz+ccx5l021dq2VDwXZMXGvAlmqikkIAl7wezPD2FIJ9+EfYA/sJXTZDTgfvAoTCcIVElVy05sbl6o3US1t+qFeTGYMmjvEPjKYL78to3fpOZiFazGhM9+AXVGU3xxX3X8VW45tIe/mAfGC7qjp4OAXDhJ2zuwaUwW4TOyX/wpOnsTc+S/BzW18spqU+W4IOzqCfelp7JE9mKXrMSvPwdTUSyM6PYp/4GVI1WBmL58SXpCfN4iQu8FWszUivMkabCEnc879L0l1O2uhVIqFnAhvqjZYqjYQS4jwhmOBH7OEPNjxIdle9kpQN13Ez3clJKH/hFTfjTOgYRrs2o7/03sx6z+Ec9nHMMnA5NxaEdrsqNw7OyG5QY4mZC5dzEklnR3D+h7ec0/ib30a55yLCF97syQilfJBu7lBXrL3qPzuZDV2uA+THpFzrdnLp2ICJzJ6d23FtHRg1nxAM3oV5X3IseFjLP23pZPiO0F1tJr7b7yfqxdeXaEnOz3O7I8HZzAmnYZs0FK1ERFJECE6cRR/x9Mw3IdZczHObX+JicWxgD86hN2/HepaMMvPx+CLCE04X2EDw4vgJtYiQpisgVwa/+WnsId3QccizNmXY/uPS7xhx2JpP9tgwSuWEuE1jpzuhMJyXzw+KDNhtwQN02Vb2/ew2RHo7QyEt03+W1cn/j1/gWmfS+hP/wnT0ib/eGuncnmNCW6Qg/ciEoXsiLyuBZsZwd+3G+/H92LmLiTy11+TbF7fl5+pahDBHunDDnXL3LdUxHYdEc/puavFFCTAjvSLi1WpgHP+tZrRqyjvY46PHicWiv2KAJf8Ep0jnZV5qF8DrYDLZe9eGOqHdYEjlhPc7768Weas6z6ImbMY4xgsBjt8Cvv6S5jGaTB7OQZvSnhDEREkY0TI3ZJ8v2GGLB6lR7CvvYA9tg/mrYSWdjHPCIUwM5fKXbEN5sSJavl6IlEpWHiymRHxSHZLMuNNVEkLupCF3qNQyENjG6Z+GmQzeA/cCcUCzvW34yxYMfXvLhVkhmv9YKkqLb/DCe6fo8nA+SuD39eH+/07IZEifPPnceYtBoI0o0SVpCSVChIXGI2LYJ86Lqda7Ysw0+ZM+kVrRq+iKL/MUG6IGf97Bnnv7QKcjCR59rZn2TBjQ4We7PRQAS6Xr/8rDPZjv/hF7Ktbsb/4mcx3z7oYWtuCYyODHejGHtwpEX6zl2H8oLLFSkhDkOOL7wW2kZ5UvKk6GOkX0ek+AgvPgsYWOHlU/KI7FkM0aC97nghvsGVNfOqkaFJ4S0Wobw2MK3y5ye0+LEtS9W3i+2zBf+w+7IFXca78FGbDByf9k3GLkB2VreRoIrDWNDLzzo7J0pRFHK6KHu7378QO9RO+4Tac9ReKQE9kD1c1yuJZ/wlssKRlB7ox6VH5EDBr6VRM4NsyeudjVl6kGb2Kokzyla1f4Wsvfo1MKQNAKpJi49yNPPyJhyv8ZO+MCnA57NyJvfBC8H38T34Y84mbMWsuCM5eLNY42J5O7NFdmOaZMGspxi+KCEJwRlRk8ibYLYr4Ns6QFuxAt7hW9XdhFm/A1tSKWFbV4cxcIm1eryTVbLIaMMFJUXCHbIzMcge7ROzrWiFZg/F9fM+Fk4chOw4N0yRiMBzF//nT2Kd/jLngCpxLbsDEgrQjzxXhLeaxsWQQn+jJglVuTOa3oTCkh7BOBHfTfdjXXsHZeBWhy2/ETFT3pbx8qIilpHU91C3Pmx3D9nVh4inM/DUSHRigGb2KopwOjx16jLtevousm+XWlbdyy6pbzvgFLFABLo/HH8d+9KMiRP/rHzB/8PtAcGbUfQT7xmuY1g7oWIrx8sFdqyNBDBOzCmNEkN0iNLTJLLS3U4R3bEhOiRIJEd7aFpyZiyEcDlKI8jITdsJSAceSk0tgNjMWCG/+7cKLlddKj0BdMzS1YyIx7OF9+D/8Jmb+MokJbAiSgXxPKttCVipez5WFsFhSWsjWl83m9BCEIrjbNuM/9Qhm9TrCH/t9TDI15VQVS4n4+i62783JUAfbc0xm1nNWYN5qH5nPYnc/j+0+ohm9iqL8zqICXA7Wwh/+AfT3wbe+gY0nsMcPYTv3YabNgo7FkmzkBvF/kSDhBztlqFHMi/BWN8KJQyK8pYIIbygEJw9jGtsw7YtEvL2SvEaiWmbMXlGWl6LJIPB+DAYC4a1tkU1o38cagz15RFytahqgZTZOJIId7Mf7wZ0QDktgwuxF8m/zg1vefFqE1/rSbo6n5Pe7BbGrzI4ABu/gfrwHvoVpbyf8qf+OaZkRfLgIIhWrGqQdPtwjEYVOCNvXBdlxTOssaTdPxAT6PvbILuxrL2BmL9WMXkVRfqdRAS6Xv/2f2FO92D+5HXv8AGbaHNlMLuVFLEMRaQcXskzaLxbzgfBOh6oGcbzas03sIJduwHol6D2KCe5dschrFbKytDSRFRyJi/A6DjY7LhVvMSd5vFV1IryOg+05BiO9UFUH0+bhhCPYYgF/0z0SmHDNLZg1F0p1+bZbXonvo5CRitcrydfxaqlovSL+4DDud/4FE4sQuul2nMWr5WcmTpNSdbIJnU9jh3vkg0d6RGa9qTpYuBYnNjXLtX1d0m6OxnHWaUavoii/+6gAl8ORI9ilS8GA/fpX4aOfxJRyslAVjknFV8gGbdq4hCbksyK8qXrs0T3iWlVVi1m0DlsYh1NvYqbPxbTNlyrUd+VMJ56CeEqEdyKlyAlJ7u7A/0N4Q2HsqU4x0UhUQ9s8nEhMTqCefRS77QmcD16H+cC1Ul1aK+KaHROLzFBY7CIjspVMblRep1gQMS35uP/nLuxwP6HLriN00RXy9xxHnjcSlzm2tTDYJdvShTy2p1O2luetxnnLLFczehVFeb+iAlwO4+PYOXMgm4WfPQlt00R4nIjYLRJEA+azIkr10yBZiz30KnbfdmhqwyxYLYtLA12YtgWY6XNkzup7UolGE2JA4RZEFIN4QJtLS8VbyEr7uroe41sR3oEu6D8uVev0+TjxhCyE7XkZ/+HvYJavx7nq9zA1gUAWc2KiEVhUkksHTl4haTFHk/IhIjMCTgz34R/g730F5+zzCV13qzhYGScIhHBluzkSg9F+aTd7JejvxuYzmGlzpd08Mef1POyhndjXd8hJ0bJzMRHN6FUU5f2DCnA5ZLOQSsnRz8+3wqJFUkXCZLQeufHAU7kae/Bl7P6dmLa5MG85DPdih09hZi7CtM6SxSrfl58LR6Wa9UoySw3iAX9FeKskZcmGItLi7e2U7ehp8zCpGgzg93bhP3Anproe5yO3Y2bMkWcs5kV4rQ1MNNKTPtFkR6fsNMeHIBLH/dlm/M2P4ixZRuimz2FqG6aMQ/JpWQhL1MjC1mgQ2DA+LE5WqVpYdDbOW2a5kxm9qTqJCqxpeHf/91MURTkDUAEuE7thAwz0w+ZHIZEI8n/TcppT1wqRJHb/DuzhXZjZS2D2Emz/CRgflPi85plBPB8iYo4DVY0Y64q4BfGANp+RO95C4BpV3RAIb1SCEnqOSoXcOgtT04yxHjabwf/h3dj+HpzrPo1ZvkEqT7cImVHZQI4lp9rksYRsPBvestkcxdu3B+9H38N0dBC69macWYvkdztheZ5QJGg3AyO9clucS2P7ToATwsxfg1P/lnZzJsjoHTqFs/YSmDFP282KorxvUQEuh3xeToSMgZ0vQluLCFtdC4Si2H3bsZ2vw/xVmPZ52N5jkEtjZi0Tw4sJQ4pcGrBQ0yR3vJ47GQ9oCxkY6BaRrKqXVrO12HAEOzYkt7yOI+dE9dMxvoe1Fv/JhyQw4cM3YS64QoILvJI8n1vAxqpkU9otynw5n5bfG6+CzDBg8E/24t53F6a2ltDGKyQ8AiutaTc4R5pILBofknZzLg1DvdhiXrabZ694S7vZxb6+QzJ6F67FLD1bM3oVRfmN4Vuf/f37KXpFlrUsIxp6b4yzVIDLxF5wAZzqgUfuk3xcHOy+n2O734DF6zCtMyXRp1TEzF6GqWvFFPNYJ2jbuq7E7Bkj7eZoUhyhfll4q+rEriMcxaZHoPuQPEB9K6apXUTZcbA7f4b/+P2YtRfiXP4JMQXxPFmiKuSwsdTUKVM8EOFiFhK1UrW7RWy2gHvvN6CYxznvQkIXXyOiO3E+lRuXn00G7ebxQZlxjw9jx4clEGHxBpxYSt6jiYzeV7dAfUuQ0Vv7X7yjiqIovz5Fr8jl917OS90v4RiH1qpWXrj9BVpSLZV+tHdEBbhMbHU1ZDKwfxf+iX0wcBKz7GxsfQt0HQTAmb1cqttiVtq2+YwIX00ThCPBSZHEA9pCVlrN+SxU10MqEN5QBLLj2O5D0i6uacS0zMIYIze+nYfxH/oGpqUd5/rPYFrbg1veMchnpNU8cWIUT0m1mxuTmW2pINF/hPE23Yd/7CDOhnMJbbwek6wKKu7olO1kVTD7He3DFvMwPiwhChjM3FU4bwlGkIzezZAZwVl7iSyZKYqi/Ib56rav8vfP/T05NwdIHOG1i67lRx/7UYWf7J3RPmA5HDgApRJEwvj3fgNz86exi87CntgP+XGcuaukci3msF4J65YgPwI1jVDTILfCAKkGbDEHJ4+IOFc3SEoSRparcmls92siqKlqaJmNCUcxWOzoCN5D34DMOM5Nfyh3uNaXWW5uHBuNyzZ0blxmvDHJ4yWWku+P9UMkifv8s/gvbsZZdw6RP/lraZFj5NTJC9ywqurlvCo9jC1mYHQARgaxXgnT1IYzZ9VkS1kyen+OPbo7yOi9QTN6FUX5rbGrd9ek+AK4vsvevr0VfKLTRwW4HDxPKkGAeauwQycgP4KzaD3EqzClvLg6+T6k+6RybKoX4fV9uQUu5qFnQngbobZZqlonjC1ksZ375BY4noLmmZh4lcx53SLeo/dhX9+Jc8XNmHMulfvaYAHMhqPyM7lx2aiOp+SMKBoXI42xAYgl8V7bi/fTB3FWrCH8+T/DmbMEMLKIFQrL60UTssmdz8D4IDY9DKNDUq1HYpjFZ+NU1wNBaMLxA9hfPItp6cC58nbN6FUU5bfO2ra1PHLwkUkRjoairG5dXeGnOj20BV0OPT3Yjg7A4n/3X3GuvFHCC4o5cZEq5kT0krXixewWJuMBbakYtJozUhEnqjDGkRZ1MY89eThYkEpCQxumqg7jeXLP++KT+M9swpx7Kc6lH5VUoGJOwg1CgeVlblyWsyJxueV1QtLmHh+Q0IU3juI++G2c2fNxzrkAZ+laQDrMNhyYhlgfUg1yk5wewuazMNwL2TTW+mKWMX3+1JLVSD/+zmdkdrzuUkyLZvQqivLuUPJKXPfAdWzt3IpjHGbVzeK5Tz9HU7Kp0o/2jqgAl0M6jV0wH9IZzM7t0NqEjSRkO3h8UJyjUnUYN0g/iqekDT0pvE2QSGGMA04Y6xYkcrCUF+GtacLUNsvJjwlhD+7G//G3MbMX4Vx7G6ahRf5uZlR8oKMJOQvyfal4s6MiovEqSA9LHvDIGO79/w6xBM5FGwmtWD8VJxgKgx84YiVrpXLODGNLeejvhsyYLFRV1+PMWzOZlGSLeexr27Cd+zErzsfMX60ZvYqivOtYazk6fJSiV2Rh48L3RBISqACXz00fhZPd2KefAq8ord1oQu50PTe4rw2Ed+ikCG9tM8STU8LrlSQoYcLrOVmNqZs2FUI/cArvP+4C30pgwrylsjiVHQXfk83mQla2mxPVsmhVKoiIZseglMd6Du6D38YO9hHaeCXOirUioJGYzJLDUamaIzFZzMqNB4YafTAyIM/h+zjzVkt0Ib+U0TtjPmaVZvQqiqL8urw3Piacgdhzz4PuExJ2EIpIu9haEcBYUhaY+t6cEt5gxosTxvoutuuAzFmraiHRjKmfBuGoVL35HP5PvoM9dgDn6lsway/G+J6IvFvETpwRZUZEeLHSYk7USiU70guRFO6TP8XfvYPQpdfirFwrFpTGYEIhOWsq5US0qxrEjWv0lLSb+06IWYfvYZpm4sxaKvfEvDWjF5yLbwyWthRFUZRfF62Ay8Q2NGCGh7GDpyAen4wHtL6FoR4RtroWqXgxklzkW2zPUalOq+vFLaquVcIWPBdrDP6WRyQw4aKrMR+6HhMOS8VbzIvwem6QTFQVZPaOSOUazGuJV+Ntexbv6Z/gXHQ5ofXnYJqmYYsFTDwl8+EJE5BEtdz3Zkewngsnj0n1G/hBOwvOwlQFS1b5LHbP89iuI1Lxzl2hLlaKoij/H6gAl8vdd0NPN/zZ/4BIXJaiJ4W3NRBewDjSsu19A9IjgfkGmOoGSNWL8IZC2N0v4T/6Pcyi1VL11tSJUBey2HhKtq5z43JCZExwUpQEE5LqN5bCf30v7o++i7N8Hc4HLsVpmTFZZYuZRyywvQwF1pnjWN8V44/RAfl3FLKYjiWSzGScX8roXRJk9MYr+c4riqL8TqAt6HI59gacOiVbz0M9InR1rcEdrwQVWBAbyvEh+W/1zZh4FdTIghXWSmDCQ/8OiSSh3/9rTPs8iQMc7sXGJjymx2RGm6yWpapwBJJ10pIOR/AHR/Hu/ydobCH8+b/EaWuX5/E9TFVN0G4uyuska8UKMz0kAQ+9x6Qt7oRlQWvpuZggp9f2Bxm9kRjOxk9oRq+iKMpvEK2Ay8Sed55UwD99EKbNglgKgw8YsYbs7RSBDLJvTTgMddOkbWutBCb8+FvYnhOy2bzyHMxELm8kLiKbG5MWcTQ4KQK55U0Pge9j8y7uA3dDJk3ohlsxs+ZgvJLMbqvr5fTJhOR14inAgfy4bDSfOChz30QKm0vjzFstc2xjxADkF1uxfcc1o1dRFOW3hApwmdiWZhgbh2NHMDVViPCGsKc6YaQPGtsgHJKN6Lpgwcr3sBb8px7CvvwszsYbMBddLV7Q2VEIRcTBKjcuv2TipMhzpeLNjsrdbyiOu+k+/EOvEbr+FpzlazCOwY4OYpraZLs5EpMFMJh0xLJOSGILxwYhVYfNjGCa2zEdyzDhCNb3sAdfwb6+XTN6FUVRfsuoAJfLIw9DXy/c/HFxr+o/DkO9IrzRuFSztS3ijOXUtDs8AAAOSUlEQVSVsKEw9qWt+P/5AGbVuRKYEAsqW+ME8YAZCWlIVMvXxSyk6qCQE/GN1+Btfhzv+f8kdNkNOOd/CBOJYPtOYBqmYaIxOSvyXDlPCralrbUyf+45Jr7TjgOehzN/jcyiAdvbKWYaqRrxbtaMXkVRlN8qOgMul4EB7NAwdqgHBntEeGcuwmSGIRKB+rkivPj4x4/i/+huTEMzoT/6B0xzq5hoZAuy2VzIveWkqABjfTKrjVWJA1WiBu/AQbyH78NZdyGRv/1XTCKB7TuOjSYngw5sKGhbR+LSti5kZDmsc5/cJdc3Y4f7MDMWYtrmYxznlzJ6N8KM+dpuVhTlPcW9e+7lz5/6c4pekS9s+AJf+cBX3hP/P6YVcDlkMtjaWpnlPvtTWLEGkx6UoIPaZrnZNQY7Moz/o7uxo4M4130Gs3AFJjsGXgmbqJab4UJWhHfipCheJXPbsT7ZbH6zE/eBb+HMmEXoxk9j6utgpB+bHsO0L8DgiQtXISuvEYnJ5nQkBicOibDXNmOzYxBP4sxdjYmnJKN3/0vYAy9jFq3DLNkweeurKIryXmHnyZ1c/J2LybpZAJKRJP925b9x2+rbKvxk74xWwOWQSMBZZ8HgAGZ6K+THoGmmOFz5Ltb18X96H/a1HTiXfRznnI3Skh4bEOENhWWbOZ4S8U0PScWaapCUIieEzbq43/4n8C2R2/8M0zEb8hn8I3twOhbj1DVjwyGs7wRhCwlwS3LPmx6FU53y2vUt2JEBzNyVmMYZANiuI/ivbpaM3ss/janSjF5FUd6bbO/ajo8/+edsKcuWzi0qwL+zOA7s349Jp7HVTVDfGBhpgH3hafwtmzDrP0jor74um9Hjg9JqjldJlRtJQKpWhNcJiRNVelAWpWwE78Hv4h8/SvjGz2BWrce4BezhX0D9NJyFa6ZyerNjIuahsDhkWQuHX5U74ZZZ2IETmGQNzlmXYMJRyeh9dTOkR3DWf1gzehVFec+zoGEBITMVeZoIJ1jevLyCT3T6aAu6XO65B3pPwp/+sSxYHdyNv+kezIw5ONfciqmqgnxGlqtCIciMyWlRLCEVq+9NbTYXMhCrwX1iE/5LWwld+TGciy/HWA97/AC4HmbuMvlzRFKXcItigekWZXO683W5/W2YJnGBpYIEJ9Q0Yt0idt927JFdmKVnYxau04xeRVF+J7DW8sWnvsgdL92BMYaNczbyk0/8hGjozL/gUAEulwcfgJFh7FUfxvvh3RLFd/1ncNrnSCUbjUuIfW5M/n6iWr4u5iXgPp+VajhZj/fz5/CeeIjQeZcQuuomiISxA93Yk2/gLFyHiUZlwQojrxGOQKkoKUt9J2CgS0w6qhuxpzoxMxZg2hbIHHoyo3cmZvUHMMnqir5tiqIovw3GCmMUveJ7IoZwAhXgcnj2WexVV4Hn4l+2AfNXf4tZdTYmn4ZwVOIBc+PguxKQUEgHUX91ciI0PgDJWvyD+3Efugdn3hLCN94GtXWQGcPfv0PsIJtnBFVvXNrNIJGDkSg2l4Hj+6WF3ToLO9gtjlVzV2MSVZLR+8ozUMgFGb0zK/ueKYqiKG9DZ8DlcOSInPV4Hs7cszCLl8u9bbJWhDY9JGdEpQKM9srXyToY7ZOkpLE87r//C8QTRP7wy7JgVczjv7YNE0/hrPmQbDeHwtiSKwtbjgMWCUo49hoUC+KyZcD2HMXMXoFpngmlIv4rm7Gdr2tGr6IoyhmMVsDlkM/D+vUwPATPP4Nt65AzoGIOkjUizukhcaAKx4PNZgdbtLj/cQ/0nSR002dxVqwFr4h98wC2vwtn+XmYWFwcq4wjVa8Tkqo5nsKePCqhCfEUNLZhTx7G1LViZq+AcAR7bK9k9LbN04xeRVGUMxytgMshHoeLLsR2dUFzK4wPyoZzqkaCF8IRqG6S72fGIJLEfeRB/N3bCV/7KZwLNopID3Thv74ds3AtzvpLZZM6ElhR+p4kIEVi2Mw4HNsr285t87FjA3CqE2fhBkxtE3aoF//lpwGLc9ENmtGrKIryHkAr4DKxF14IJ47DnlfE/CI9BFhI1cuWcz4tC1Zbn8R75mFCH7iS0OU3Qjgk97y7nhP7yPmrprabS0UoZgAjG86eh31zn7xuTSPEq7Anj0iFO2MhlIrY3c9juw5rRq+iKMp7DK2Ay+X886FrJpTyspk8sdk8cAKqGvAPv4G76fs4y84i+pU7MNXVWM/D378D0qM4Ky/CxBNYY7Dh4DQJIy5YkZicH+UzEI1Cy2zJEy7kcFZeDLEU9uhu7J5tmFlLcK7+nGb0KoqivMfQCrhMbEcHdHfD0CmwnkQPJmvwT3ThPfBNqG8kfNNncdrasdbDnjyGPfQqZvEGTGu7tJuDlCLckrSbE9XYU8dh6KS0sZtmQLGAHezGzF6Oae6Age6pjN61l2DqWyr9ViiKolScvJvH8z1S0VSlH+W00Qq4XK65BrpPiGdzNIF1Hdxv/jOMjxD6xH/DWbJCzpTGh7CvbsG0zsK58CMY60psoUViAUGWtTKj2P07IBKF6nqoa8F2HcTUNuOsuURsJrc/ju3tlIzeWUu03awoyvueN4bf4POPfZ6tnVsBWN6ynLuuuotz2s+p7IOdBloBl4lNJjG5HPbwPtynNuEf2E34I7finPMBjO9ifQ9/zzYx6Fh+PiaRAIwYamRHJ+958S32+H6xjzRA8yzsyCnIjuPMWw01jW/J6F2JWXaeZvQqiqIAQ7khFt2xiKHcEL6d8oNORpLs+NwOlrec2ZaUWgGXwzPPyIJUOIT7uU9h/vhPid7yR5gQWCz+mwewnfswi9Zjpndg3BI2HJfFrFxabnrjKWzXYflzOAw1DRCOYY+/jpk2F7NoA/SdwN/2iGT0XvopTE1jpf/liqIoZwx3v3I3mWLmbeIL0o7+u+f+jodueqhCT3Z6qACXwy9+IRWs5xGesQRz+fVY4+AP92H3PI9p6QjazR4WB+uExf0KIF6NHTwJA3vFPjIWh+aZ2J43wHFwVlwEFvwXHoWhXs3oVRRF+S/Y0rmFnJv7le/71ufFEy9W4Il+PVSAy+H22zEPPgjj4/DlL2FdF3/3c+AWZTEqkQIsNpKEzLDc9EaTEs5waCckqkR4a5uhmMd27sV0LIWmduyBl7EHd2IWrsWce5Vm9CqKovwXzKiegWOcX6mAAZqTzRV4ol8PnQGXyx1fx/adwn7scuzxA0G7ebZEBwZiSykv97wmhD1xQNytwuGg5dyMPXEAU92ImbMC+rvwX9kMdS04Z31IM3oVRVHegZ0nd3Lxdy8mW8q+7fupSIo7r7zzjM8EVgEuh2PHsEuXgLXYO76K+fht0m4OR6U1nRuTpapYlbSW0yMy482PQ3MHDPdhMyM4c1dDOCrCmx6W6lkzehVFUU6bf97+z3x585cxGHzr4xiHT674JHdfc/cZP7pTAS6HU6ew8+eD62K2PgNLF2PDMWk3W18cq0b64VQn1E+TNKRUnSxZdR3ETJsN0+fB/pclo3fJ2ZhFmtGrKIpSDifHT7Jp/yYKXoEr5l/BkuYllX6k00IFuFxuvQV6urGPPiLbzaWC5P+6ReyJg+KMFQpJQENTu1TCgJm7GjPUi//qFkzzTMwazehVFEV5P6JLWGViV66CxgYJXDAORJLYrgNggeaZEj3Y0AahMPaNPbJkFa/C7ngCW8jhnHeNZvQqiqK8j9EKuEwmjTiOH8UWRiUmcNosye4NhaG2SZazUnXQvhgO7pTb4OXnYxas0YxeRVGU9zmqAuVgLaxehZ03F/vma+Jo1TITRnpl5ut52Df3Y+ashGgK+/S9cqJ01WdxFq1V8VUURVG0Ai6LkRFsQwMYg938BLQkZeYbictpUUsHVDVgX90CvsVZdwmmqa3ST60oiqKcQegMuBxqamDBAhgahCpH4gJ7joLvYxasg8O7sK8+h1l1IWbuyjN+FV5RFEV599EKuByKRWxNjSQUvfA0liymfTE2MwZ7X8B0LMGsvEAzehVFUd4l0sU0Ja9EfaK+0o9y2mgFXA7FIkTC4HvY8VHM0lXY3c9DJIrzwY9rRq+iKMq7RNEr8ns//j02HdiEg8Oqaat4/FOP05RsqvSjvSO6DVQOxoAvjQMzOoTd8QRmyQacjTer+CqKoryLfOnpL/HYocdwfZeiX2RX7y5ueuimSj/WaaEVcDkcPgyuK1+/2YPzhb/ARGKVfSZFUZT3IY8dfuxtiUglv8S249uw1p7x+zdaAZfD9OmQSEA4gvPh61V8FUVRKkR19FedBOPh+BkvvqACXB6trZjubkxPDyxfXumnURRFed/yjx/6RxLhxOSfk5Ekf3PR31TwiU4f3YJWFEVR3tNsObaFr734NQpugVtX38ptq87sGMIJVIAVRVEUpQJoC1pRFEVRKoAKsKIoiqJUABVgRVEURakAKsCKoiiKUgFUgBVFURSlAqgAK4qiKEoFUAFWFEVRlAqgAqwoiqIoFUAFWFEURVEqgAqwoiiKolQAFWBFURRFqQAqwIqiKIpSAVSAFUVRFKUCqAAriqIoSgVQAVYURVGUCqACrCiKoigVQAVYURRFUSqACrCiKIqiVAAVYEVRFEWpACrAiqIoilIBVIAVRVEUpQKoACuKoihKBVABVhRFUZQKoAKsKIqiKBVABVhRFEVRKoAKsKIoiqJUABVgRVEURakAKsCKoiiKUgFUgBVFURSlAqgAK4qiKEoFUAFWFEVRlAqgAqwoiqIoFUAFWFEURVEqgAqwoiiKolQAFWBFURRFqQAqwIqiKIpSAVSAFUVRFKUCqAAriqIoSgVQAVYURVGUCqACrCiKoigVQAVYURRFUSqACrCiKIqiVAAVYEVRFEWpACrAiqIoilIBVIAVRVEUpQL8X+uqWl1kljhXAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -1850,7 +1827,7 @@ } ], "source": [ - "nx.draw(last_net, pos=pos, node_size = node_size, node_color = node_color, edge_color = edge_color)" + "nx.draw(last_net, pos=pos, node_size = node_size, node_color = node_color, edge_color = included_edge_color, edgelist=included_edges)" ] }, { @@ -1858,24 +1835,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "df['node_count']=df.network.apply(lambda g:len(g.nodes))\n", - "df['net_growth']= df.node_count.diff()\n", - "nets = df[df.net_growth>0].network.values" - ] - }, - { - "cell_type": "raw", - "metadata": { - "scrolled": false - }, - "source": [ - "for i in range(len(nets)-1):\n", - " nx.draw(nets[i],pos=pos, alpha=.5, node_color='b')\n", - " nx.draw(nets[i+1],pos=pos, alpha=.5, node_color='g')\n", - " plt.title(\"Update: $G_{\"+str(i)+\"}$ to $G_{\"+str(i+1)+\"}$\")\n", - " plt.show()" - ] + "source": [] }, { "cell_type": "code",