{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ei_net import * # import the .py file but you can find all the functions at the bottom of this notebook\n",
    "from utilities import show_values\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################\n",
    "############ PLOTTING SETUP ##############\n",
    "EI_cmap = \"Greys\"\n",
    "where_to_save_pngs = \"../figs/pngs/\"\n",
    "where_to_save_pdfs = \"../figs/pdfs/\"\n",
    "save = True\n",
    "##########################################\n",
    "##########################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The emergence of informative higher scales in complex networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 01: Effective Information in Networks\n",
    "\n",
    "## Networks and Causal Structure\n",
    "\n",
    "Networks provide a powerful syntax for representing a wide range of systems, from the trivially simple to the highly complex. It is common to characterize networks based on structural properties like their degree distribution or whether they show community structure. While our understanding of these structural properties of networks has been crucial for the rapid rise of network science as a discipline, there is a distinct gap in our treatment of both dependencies between nodes and also higher scales in networks. This gap is especially pressing because networks often have an interpretation where links represent dependencies, such as contact networks in epidemiology, neuronal and functional networks in the brain, or interaction networks among cells, genes, or drugs, and these networks can often be analyzed at multiple different scales.\n",
    "\n",
    "Previously, others have used directed acyclic graphs known as \"causal diagrams\" to represent causal relationships as dependencies in networks. But there has been little research on quantifying or broadly classifying such causation in networks, particularly those that have both weighted connections and feedback, which are hallmarks of complex systems across domains. Here we introduce information-theoretic measures designed to capture the information contained in the dependencies of networks and which can be used to identify when these networks possess informative higher scales.\n",
    "\n",
    "## Effective Information\n",
    "\n",
    "Describing cause and effect implicitly invokes the idea of a network. For example, if a system in a particular state, *A*, always transitions to state *B*, the causal relationship between *A* and *B* can be represented by a node-link diagram wherein the two nodes---*A* and *B*---are connected by a directed arrow, indicating that *B* depends on *A*. In such a network, the out-weight vector, $W^{out}_{i}$, of a node, $v_i$, represents the possible transitions and their probabilities from that node. Specifically, $W^{out}_{i}$ consists of weights $w_{ij}$ between node $v_i$ and its neighbors $v_j$, where $w_{ij}=0.0$ if there is no edge from $v_i$ to $v_j$. This means the edge weights $w_{ij}$ can be interpreted as the probability $p_{ij}$ that a random walker on $v_i$ will transition to $v_j$ in the next time step. We will refer to such a network as having a *causal structure*. \n",
    "\n",
    "In the cases where links between nodes represent dependency in general, such as influence, strength, or potential causal interactions, but not explicitly transitions (or where details about transitions is lacking), for our analysis we create $W^{out}_{i}$ by normalizing each node's out-weight vector to sum to $1.0$. This generalizes our results to multiple types of representations (although what sort of dependencies the links in the network represent should be kept in mind when interpreting the values of the measures we introduce below).\n",
    "\n",
    "A network's causal structure can be characterized by the uncertainty in the relationships among the nodes' out-weights (possible effects) and in-weights (possible causes). The total information in the dependencies between nodes is a function of this uncertainty and can be derived from two fundamental properties. The first is the uncertainty of a node's effects, which can be quantified by the Shannon entropy of its out-weights, $H(W^{out}_{i})$. The average of this entropy, $\\langle H(W^{out}_{i} )\\rangle $, across all nodes is the amount of noise present in the network's causal structure. Only if $\\langle H(W^{out}_{i} )\\rangle $ is zero is the network is *deterministic*.\n",
    "\n",
    "The second fundamental causal property is how weight is distributed across the whole network, $\\langle W^{out}_{i}\\rangle$. This vector $\\langle W^{out}_{i}\\rangle$ consists of elements that are the sum of the in-weights $w_{ji}$ to each node $v_i$ from each of its incoming neighbors, $v_j$ (then normalized by total weight of the network). Its entropy, $H (\\langle W^{out}_{i}\\rangle)$, reflects how certainty is distributed across the network. If all nodes link only to the same node, then $H(\\langle W^{out}_{i}\\rangle)$ is zero, and the network is totally *degenerate* since all causes lead to the same effect.\n",
    "\n",
    "From these two properties we can derive the amount of information in a network's causal structure, the *effective information* ($EI$), as:\n",
    "\n",
    "$$ EI = H(\\langle W^{out}_{i} \\rangle) - \\langle {H}(W^{out}_{i}) \\rangle $$ \n",
    "\n",
    "Here, we use this measure to develop a general classification of networks. Networks with high $EI$ contain more certainty in the relationships between nodes in the network (since the links represent greater dependencies), whereas networks with low $EI$ contain less certainty.\n",
    "\n",
    "In this work, we show how the connectivity and different growth rules of a network have a deep relationship to that network's $EI$. This also provides a principled means of quantifying the amount of information among the micro-, meso-, and macroscale dependencies in a network. We introduce a formalism for finding and assessing the most informative scale of a network: the scale that minimizes the uncertainty in the dependencies between nodes. For some networks, a macroscale description of the network can be more informative in this manner, demonstrating a phenomenon known as *causal emergence*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.0 Create a Few Example Transition-Probability Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Copy_Copy = np.array([[1.0, 0.0, 0.0, 0.0],\n",
    "                      [0.0, 0.0, 1.0, 0.0],\n",
    "                      [0.0, 1.0, 0.0, 0.0],\n",
    "                      [0.0, 0.0, 0.0, 1.0]])\n",
    "\n",
    "And_And   = np.array([[1.0, 0.0, 0.0, 0.0],\n",
    "                      [1.0, 0.0, 0.0, 0.0],\n",
    "                      [1.0, 0.0, 0.0, 0.0],\n",
    "                      [0.0, 0.0, 0.0, 1.0]])\n",
    "\n",
    "Or_Or     = np.array([[1.0, 0.0, 0.0, 0.0],\n",
    "                      [0.0, 0.0, 0.0, 1.0],\n",
    "                      [0.0, 0.0, 0.0, 1.0],\n",
    "                      [0.0, 0.0, 0.0, 1.0]])\n",
    "\n",
    "Or_Copy   = np.array([[1.0, 0.0, 0.0, 0.0],\n",
    "                      [0.0, 0.0, 1.0, 0.0],\n",
    "                      [0.0, 0.0, 0.0, 1.0],\n",
    "                      [0.0, 0.0, 0.0, 1.0]])\n",
    "\n",
    "Star      = np.array([[1.0, 0.0, 0.0, 0.0],\n",
    "                      [1.0, 0.0, 0.0, 0.0],\n",
    "                      [1.0, 0.0, 0.0, 0.0],\n",
    "                      [1.0, 0.0, 0.0, 0.0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.0.1 Plot these TPMs, showing their $EI$ values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1584x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(1, 5, figsize=(22,4))\n",
    "\n",
    "c0 = ax0.pcolor(\n",
    "    np.arange(-0.5, Copy_Copy.shape[0], 1), \n",
    "    np.arange(-0.5, Copy_Copy.shape[0], 1), \n",
    "    Copy_Copy, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c1 = ax1.pcolor(\n",
    "    np.arange(-0.5, And_And.shape[0], 1), \n",
    "    np.arange(-0.5, And_And.shape[0], 1), \n",
    "    And_And, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c2 = ax2.pcolor(\n",
    "    np.arange(-0.5, Or_Or.shape[0], 1),\n",
    "    np.arange(-0.5, Or_Or.shape[0], 1),\n",
    "    Or_Or, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c3 = ax3.pcolor(\n",
    "    np.arange(-0.5, Or_Copy.shape[0], 1),\n",
    "    np.arange(-0.5, Or_Copy.shape[0], 1),\n",
    "    Or_Copy, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c4 = ax4.pcolor(\n",
    "    np.arange(-0.5, Star.shape[0], 1),\n",
    "    np.arange(-0.5, Star.shape[0], 1),\n",
    "    Star, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "\n",
    "show_values(c0, ax=ax0, fmt=\"%.1f\", fontsize=16)\n",
    "show_values(c1, ax=ax1, fmt=\"%.1f\", fontsize=16)\n",
    "show_values(c2, ax=ax2, fmt=\"%.1f\", fontsize=16)\n",
    "show_values(c3, ax=ax3, fmt=\"%.1f\", fontsize=16)\n",
    "show_values(c4, ax=ax4, fmt=\"%.1f\", fontsize=16)\n",
    "\n",
    "ax0.invert_yaxis()\n",
    "ax1.invert_yaxis()\n",
    "ax2.invert_yaxis()\n",
    "ax3.invert_yaxis()\n",
    "ax4.invert_yaxis()\n",
    "\n",
    "xlabs = ylabs = ['0|0','0|1', '1|0', '1|1']\n",
    "\n",
    "ax0.set_xticks(np.arange(0, Copy_Copy.shape[0], 1))\n",
    "ax0.set_yticks(np.arange(0, Copy_Copy.shape[1], 1))\n",
    "ax0.set_xticklabels(xlabs, fontsize=14)\n",
    "ax0.set_yticklabels(ylabs, fontsize=14)\n",
    "ax0.set_xticks(np.arange(-0.5, Copy_Copy.shape[0]-0.5, 1), minor=True)\n",
    "ax0.set_yticks(np.arange(-0.5, Copy_Copy.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax1.set_xticks(np.arange(0, And_And.shape[0], 1))\n",
    "ax1.set_yticks(np.arange(0, And_And.shape[1], 1))\n",
    "ax1.set_xticklabels(xlabs, fontsize=14)\n",
    "ax1.set_yticklabels(ylabs, fontsize=14)\n",
    "ax1.set_xticks(np.arange(-0.5, And_And.shape[0]-0.5, 1), minor=True)\n",
    "ax1.set_yticks(np.arange(-0.5, And_And.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax2.set_xticks(np.arange(0, Or_Or.shape[0], 1))\n",
    "ax2.set_yticks(np.arange(0, Or_Or.shape[1], 1))\n",
    "ax2.set_xticklabels(xlabs, fontsize=14)\n",
    "ax2.set_yticklabels(ylabs, fontsize=14)\n",
    "ax2.set_xticks(np.arange(-0.5, Or_Or.shape[0]-0.5, 1), minor=True)\n",
    "ax2.set_yticks(np.arange(-0.5, Or_Or.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax3.set_xticks(np.arange(0, Or_Copy.shape[0], 1))\n",
    "ax3.set_yticks(np.arange(0, Or_Copy.shape[1], 1))\n",
    "ax3.set_xticklabels(xlabs, fontsize=14)\n",
    "ax3.set_yticklabels(ylabs, fontsize=14)\n",
    "ax3.set_xticks(np.arange(-0.5, Or_Copy.shape[0]-0.5, 1), minor=True)\n",
    "ax3.set_yticks(np.arange(-0.5, Or_Copy.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax4.set_xticks(np.arange(0, Star.shape[0], 1))\n",
    "ax4.set_yticks(np.arange(0, Star.shape[1], 1))\n",
    "ax4.set_xticklabels(xlabs, fontsize=14)\n",
    "ax4.set_yticklabels(ylabs, fontsize=14)\n",
    "ax4.set_xticks(np.arange(-0.5, Star.shape[0]-0.5, 1), minor=True)\n",
    "ax4.set_yticks(np.arange(-0.5, Star.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax0.xaxis.tick_top()\n",
    "ax1.xaxis.tick_top()\n",
    "ax2.xaxis.tick_top()\n",
    "ax3.xaxis.tick_top()\n",
    "ax4.xaxis.tick_top()\n",
    "\n",
    "ax0.set_title('Copy-Copy logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Copy_Copy), fontsize=20, pad=10)\n",
    "ax1.set_title('And-And logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(And_And),   fontsize=20, pad=10)\n",
    "ax2.set_title('Or-Or logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Or_Or),     fontsize=20, pad=10)\n",
    "ax3.set_title('Or-Copy logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Or_Copy),   fontsize=20, pad=10)\n",
    "ax4.set_title('Star-like logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Star),      fontsize=20, pad=10)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(where_to_save_pngs+\"Example1_LogicGates.png\", bbox_inches='tight', dpi=425)\n",
    "    plt.savefig(where_to_save_pdfs+\"Example1_LogicGates.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Add noise to the transition probability matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = np.random.uniform(0.0,0.1,size=Copy_Copy.shape)\n",
    "Copy_Copy_noise = Copy_Copy + noise\n",
    "Copy_Copy_noise = Copy_Copy_noise / Copy_Copy_noise.sum(axis=1)\n",
    "\n",
    "noise = np.random.uniform(0.0,0.1,size=And_And.shape)\n",
    "And_And_noise = And_And + noise\n",
    "And_And_noise = And_And_noise / And_And_noise.sum(axis=1)\n",
    "\n",
    "noise = np.random.uniform(0.0,0.1,size=Or_Or.shape)\n",
    "Or_Or_noise = Or_Or + noise\n",
    "Or_Or_noise = Or_Or_noise / Or_Or_noise.sum(axis=1)\n",
    "\n",
    "noise = np.random.uniform(0.0,0.1,size=Or_Copy.shape)\n",
    "Or_Copy_noise = Or_Copy + noise\n",
    "Or_Copy_noise = Or_Copy_noise / Or_Copy_noise.sum(axis=1)\n",
    "\n",
    "noise = np.random.uniform(0.0,0.1,size=Star.shape)\n",
    "Star_noise = Star + noise\n",
    "Star_noise = Star_noise / Star_noise.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1 Plot these TPMs, showing their $EI$ values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CXxfD/ynLrVPUjUBiTER9Pyd5j1wznC6J7ncndSnq7gYF5GmvmMYUsmYfxvhPZuqjRJz1gD4paQ3CPxK8knDPOSMx+g+Nz3Aaidl71I8Dbm9tnWrrF/D5+NzW4Eajw4FBTcTJ6PnJOfw/A6yT4r49bvR7KOFem1VmL2hGHpo9oy+WjcVRt5NtXcnaIHLM6KO+zv1pwn23WGbnAz2y3H8Vw99KwzptEPWrGJuc0Qd8Fh8jfQgMTZE3dfZyIkzR9VD0S+sn1FDfpuzYlPwJ/4mx/B6ZcO+NrwT9BNiwbDpUroSbLZBXxIHiG+HrY7zTUvyGxAL5RsJ9VoyzW0qcTEVxU5ZbZ3za6Ac0NAyuh3/lm5ldsJuQ98GY/hlF5vMcclQ2sdAsikqfLd8EEsa8LL89o9/4hPuz+KA122jaCZ81s4gsA10T8k6g8SD90XjPRkuWcat5AP6d5faL6HZeSvhBsTJI3iOTr1FZbjtGtxfJsTy4wDzNjOWpNs/7GVNEei+nlM1G8if8X4zvZ70Uv06xjP6vuXlsDxc+KMy8i42z3DMV+MUpcUaRw8BOfWfwsiy3EeQYoMfnPJPmGfoyxuJsA+X61C8pSXbWa2P4WUm/6P8W8EHCbUZ8NmmNVuY5FFROc+hT5tn8N0ecJ8j6WIIbw5big4W0Ttb1yXtE9wkp+p35ePOVFpSf42MaN6b49cSNgCFNz3Okd3gMf2JT8mf5HRbjXJrDP2NMPrCl+tLRL4roW1Bv6M0evJ4c3e5uxr0zRvdbCwibWca/KMstMyhYRsrgs4n00gx9e0S3x3PEyXwAHB7/Xws3OiwgZXsL3PjY4B7RPc3Q91AMe0gL3mXmXVyX4rcucTkmBRj8Y5yjYvjkEt58hr6vxjgX5vD/ITn6Obqa/d7bhQ4n4hWla9EtM9i/NiV8pp//CdAly/2KGCf5QfL2ZPpNyJtmlPhldGu0ZBmf0b4kXl2i254x/CMp4bP7TXkNH7ghY1FMu28h8ufI05g0/U68n5lFpJeZVNE/n/yJOBkD7X45/MfhffqCtg/qSFesn+dRb5gJuAHoblLaEZow9DVxr/vwti5bt2pjeu/SjI+uOXSqSUNfzMcq/ENA2rLWkrVBpBj6svL9BtA5JU5mok32xJxZ+Ji8kSEuqx4pxNB3TXT7vxaUm6LroSbS2zmGT04ay7f0OGN3+GuONDPvsJHtqlRXW1y621x2iL+NlnyEEKab2RxgUzPrFRpu5rkan+WVZEL8/XxWOqvN7Hp8ScBX8RlA4NPvu+OdzgBgZuvha9KTXBnSl9UWSr58fmRmL+LTkrei8dKz/6SkNxE3Bnw+4f4nfB+gb+BLn8G/xG+Czz5b0izpnR1w6/aEFL//pMiT+XtiMnAI4S0zm41XSE0xLP4+HEJYU6iw2ZjZuvj07dkhhFkpQRrJGOMZ/vX2ZPyrUW+8oslQ8MERcWr09rgx7yxPuhEr8C+I1cze+Lt4ODRc7vAX4HfAyWb2ixDCqpS4acvOZ8ff3lluGX1rpDshhE/NbGKUoQ4zOws3/mdzTwhhUvSvwZffrCFriUcIYYGZjcPrloNIP0VsUgjh0xyy75olwzp4wz07hJC2rGUCiSU+zSBnXZTl/kVcfx/Hl1F1B54LISxOCT8RX3pUCMPwxvGfBUvbmHz1yhIzm4R3FBoQD034Kf5RYiCNTxXduAgZMu9skJmNSvEfHH+H4h+HROX5X5qjmf2Axhvw3xVKe/LmG6F+uV5LKER3h+E68l+8/HXDjfppW1tMxNu2QhiG131FLfdNkE93F5nZy3jd0wAzq8X33foSPgO/FLq7aQ7d3TL+DqXIbQlE2WlNHS5W17JJ63d8FMv3CLyMZbY4ugb/MHQ6vmQxs1XGYfjss7RxTovzEEL40Mxewre8GIIvm8+nn5+a2VMk+k052Bqfef9kCKElJ3LmlAd/5qnjATPbDd+vfRg+C6xrIsjG+OSPQsjUF3uZ2a4p/hvgxp8tyL1tSIckhPAPM7sb/xif6VN+Ed/e5MtmdjNu1AuFpmlmB+HLP3ei/tlnswGND4h4KSS20oppHY7PQMvmhRBCS04C/iGev/8AX85hNyh3G5TRm8dD+jZQ/waOieH+Yma98ck3b4YQZqeETx0f5yAzXn8ob6j8NKseMrMN8G0TDsRXQK2dCNKcfkLvHO9oo/hbtvF6WzT0zcMzXMyDBF/KkYmfK92B+AA829D3QY6Bc6ZR6ZVwvw6ftXU69Ya+03BDzU1Z4dYjfSA9Bp/RkpGzHPnM3D/Je0mHaLz8AG/Esvkbbiw51cwuioaxzJ5i1xYnciN6AQtCyqmoOeTJ5LmR/FnutQXcN/NMWrIHRqYTmE+WNC7HDb/z8AHOXHx2BtTv4VUovfHZbBvScmNNe6Zuj7tsx4TB7DDS93FIazQzDVm2AbapspfW+TyLxu9zFvV7hewX/ZMGSvC8fBXPW1onIddHgtX41PIMzZG7WIqtiwrR40JZD/gohLCsyZC5KVqeeKLi/3AdfALvPH1MnOGL76vZrQgZ+sTfI5sI17OINEU67+J9iwEFhM2ESRvE5dKdH9C4PZ+Jz9jOxCnXvYulkrrbC5if1v4XmUZR8pjZFrju9sI/PDxMve5uhn+wbY7uHt1EOOlu6aikDreJPnuk0fgkTmj4F74f+aDg+/xm9jwvRZ89W9Yk5aovStFnhzzyhBBWmdlHSXczOxIfBy3DVyG9gc+iXIN/ZN6N5tUXP20inOqLFOIH+0fihZl1wvvKNwIn4rP77smZQBZm9j38MMWP8Hf7Nr7aJODGte1Jf7e56o3DSewrj29T0xJD3+7x97E8k4PK3QZVuo8PLdP95vQT1scnggzC92O8GV/JsBpfdfV/NE/v94tXLsqm923R0DcRr0S/hCtKoWSMd31J35i1XyJchg3MrFOKsa9vWvjgm2XfB3zFzLbCX/y2wN9DCPOzws3CjTG5mIjPlvsSvoyvULLzmbbhdK58gluO3852MLPO+JeLBhuKhhCWmdkYfFPwkWY2Gd876JmQvklpMXwMrG9mXZKzrXLIk/l7I9LzvFGKWxqZyrLYjlo22bKk0cg9flX9Lv7ldXhyNpOZHVukDJl3+2IIYYe8IasUM9sQb5AB/mpmf80R9DQK2LA1D5lnnet99006hBBqm0gzY6Dcz8xyfYHc38wG5PgqVghFy92Ce+RKK1kXFa07eVgI9DGz7i0w9jVHnh/gDffXQwhjsj2iHp9UpAyZZ3NYC7/+iqaZiM8I2AdfJp5KHEDsGf99MiVIqs6GEDZp4t4AXzCzdUMI+Tbw3qfYezeDSurux3i/q2sLjH3NkedHuIH+hBDCrdkeZnYCbugrhsyzOSjosJzWoi3o8M5mtk6OWelpFKtr2TTVfifjXIM/m1PN7Fz8kIel+NKylpCdh2kp/uWqL0rRZ4eG8iTHQF3weiFpTPkNvoRzxxDCtEScAeQ+eC8XmWezdghhaZFxRYI4Zv+HmW2Hb++0NwUY+uIYcxRutNshJA7PzDHbsu62OWQ5Ht8KppScjNsGfmNmNSGEX6eEKXcbVOk+PrjuzywiXjbNkec03Mh3bgjht9kecYbv/xUpQ+bZfDuE8Kci45aEmqaDtDo34WvSv2pmW+cLGE9jyvBi/N0zJdwW+JLTN1Ms453xKedJMum8mOKXeVmn0/xZbnfgVuJdzWyffAGLyOd6+Ek3meOsk+yR4vZFfAZTWj4za+RPx5cadqLlXwaJ96qh/otFNrvH+7yQCJ+RtQFmNojCvu6Cn6ADbmBpVtmPA7Q3gI3jMqAkjWTEZwvU4PsEJI18m0T/JBnDc6ekR1w2PRnYJn596IichC+jeB7/IJB2zQf2iSceNZdMOWykO3Ewkfa+c2J+cuXBeAOUS+4n8ff+jeYKHcvZTLycpi2R2bO5aWeRsy6K7BV/M89wKv51/LNxaXGSYp7l0/iHlP2LiJMkX73SE69Lk2wRf+9M8UurXyHqciwvSTJ1UrGDBlE8Y/B38RUz2yZPuG/gB6FMI327i6IJIUzHZ4B2x5fkpGJm/ajX++tKce8cFKu7U/DtID5nZsllLFC87taQ/+t2U+TT3XVpvIwKmq+7Zun7Y0h3W58xVE6HZ+BbXvQgjw5nyOq3F6tr2aT1O3rj5XspjY1u9+EzYL6B69fmwN8S2xU1h3zjjvVT5Mmnn53I2makCabgBxF8LvadmktOefDxX9p4YHPg1RQjXyd82XSSzPLftHYeVF+Ui8yYKruOzjl+wieSrIdvQ5E08vWkfpl6pfkIN9r/F/iVmV2QEqbcZSqjN7vl6L82qLtCCAtwQ/rAaAxPUmw/AXyCUXNpTj3U7D4+6eWt8npfrs3/WnJRv+ntm8BOOcLsT8MDG4Znxdkwy70TbuUPwDmJNGZF97RTdzObNO6ecm/DG7QFeOM2tZn5zJy49SG5N2gdhq/1z/xfS/3pV1skwmY2e70+4T4huuc7dffEHPd/FDe8votXPN2LzOMEGm+k/7V4z//R8LSeHtEtkHWKJ97g5jp1N3NSavIee5K+sX/mhK20U3f7UNipu5kNRQs6dRf/GhLwacCdstx7Ur8xeVL+raN76klpeEcuxLKddiBHb/xrVcX1uRxX1L8A7JwnzG9imPOz3EZFtz1TwtemvLuSnrpLfd32pzxhtsA7jW8TD41Jky0RJ03PMve6g/Kdupt5NkckwmdOKJ+WuPdN0b3cp+4WcnLnung9WvCpu8Cfo9shifD7EQ8FSpH/H9F90xQZuuBtzVJyHLiBd0Z6NJUfXQWV41/Hd/E6sHWK/5fju1gN7JXwO5lmbvAd42+flXbaoWED8A8XgcQhEzSx0XsT98116u6M6P7lRPhjovsUGrZvt5CoT6N75lTEBveIfvlO3Z1B4vTJ6F+I7q6HfyxZDmyb8Es9dRcYHd0OSJHn0xzy3xXd0zYW74b3Nz8hd/9tOAX0KXQVVZ4rqcPb4IaF1fgWHWkHYw3CD8DInJrZHF0r5NTdRgfRRP9M/3RO/P1CkXlMOzgg0wdfQKIdo35CwDVZbp2ibgRafuruxdG9JafuZg75K+bU3Zl4/yD7QEIDzk+TP/ovAF7PU3ZW4X2mLVL8uybT0xUAjgX2JeUARXxsldGtI7PcM23Mr1Li1OB19iyyDpXE+2I3ZL3b2iy/WoroL6fcsyWn7q6N74UXgMsTYUvWBpH71N3Mvc9KuI/A280PyDpAhvpx1y00rNMGUtypu9tRf+ruVinyFtJPaE49lDn881uJ8DvhdX+a/JdH90YHu0b//8ZndVIO/+0poB5rtg6VK+EWC+YHXmQ6X08Cv8cr2NHUH1X/bCJOpkF4D7gaP5b9lej2BI0biVn4/h3P4pXF74A/RLcAXJ1Hvu9nFZIftCCfp+CNecCtz1dHRb8G39Mr4PvZZMc5M7ovis/jwliQAj6Tb/1E+AnR796Ytz/EvGaMmfdnK2Qi7ley8vmHZuRvAolBenT/O/WG2SuiomSOwf5bSvifR78F8dlchA+K3ozPaU0i/J6kD7o3xU8oDfg6/Mvis8ictFRbQJ66x3cV8C8ZF0WZMqdABRIneVJ/OtEr8X6joxzTMmklwnfCO2orYthz8QpoUFaYq6k3FP8lynEdbpxdAfy50npcjivr3b7cRLha3GD2DvHEKIo09EX3EdQPHP6BH07zYHzGD+VKLyV9yyrjeY2wwPgY7qB8suXTM7wTkDGcT8Lrx2txg/29+dLL88yT+rQLXg99ig+KL8C/hH0a3XdJhN8oS/8mxPA34R2VjO6c11TeonumQ7EI30cj0z5MLSJfx8Uy8kmU44J4v4+orzcHZoX/bHzvy/HOySWxLKzB9/NJe0anU1+/n4/r8QmJNDMnyj2J6/WlMb3Xo3uzTxzU1eBd1OD1b8AHXfdHvbgc//IacF0/MiXuybTASBDT2Id6I/sreFt8IW4Y+CS6j6VxX6Wkhr7oPhw/zfLTqLMXRB3+FF9uslMifD/80J+A108Z3V2apbvJ0+hST62NeQ7xPmOjXtyAt4ejC8zXiVm6e2OU53G8PXw8pp99KmbGILkcH4Rcitff2bqbfEbfju7PZ+nucYk034thJgJ/jOn+nfq6vmwd+I54tQEdHk4ZQXE+AAAgAElEQVT9GOGtWPbOx/f8mhBlWgF8NhGnGF3LDPbvpb5tupB6o9TrucpV1NNVMdwLzchfI6NEdP9uls5eH+XJPO/JJD4448a1FVGWv8ZndH/Uv0y/Kfuk4VyGj7Wo7w/Nijp2EXAbbmQ4vsB8ZYw4s+O7ugwf9z2FT2SYngif0f138Tb593h/fwl+Qm7aM8qccHwv3tf8BQ2NCCfhddDKGOYy3HB7L94uvFpp/WprV3xXAe8j3Y73uS6Jf2f21buHhkal3lFvPo7l5Rfx6hX9M+3PmzH9P+Fj5/eoN2zVZqVXS4UMfdGtO37wXIhlMTuvJWmDyG3oG5yV/sN43XUrrtsrgIMT4dfG9zQN+Bj7IvwD+QLq+wnJD5mpp9YC38LryOUxPxfgY5gXks8tT76KrYc2wfv/q/G6+uIo9yrq+wnJZ3RQdJ8R8/sL4Mws/4HUG6RfjHm4GK/DJkf31EltJdGhSitxEy9oKF4JvooP5lbiyv4QvpS00THX+BeyibjldXl8iOeQYtHGG41Z+IaNV+NT3lfgCv9dchi/YtzesQAuA/q0MJ8D4kt/Af+ClJm9Nh7/arhuSpyR+KakH0WZZ+KVX9rsrgmxIHXDK5E3Y5w38K9/OY8Lxw1OmVlq2zQjbxNIH6TX4AbL5/DKeinemf42KV9uYpwTopIsjzLdii/PeBVYmAi7JymD7ujXJz7vaTGthbgh5HwKnDmDzyjIGIVX4MaFH1J//PaVifA9Yvoz4z1nxzLXJ88z+gLwL7yxWkOKQQlfBno/8D6uH+/ixp3fkvIVpBouvHIMwHcLCPtIDPuV+P+otOcY/WrJ0ZjjR6T/E69XFgOP4TOtcqaXksa+FNj5pn7W671NyRby69m6+ABobix3mXK6Wb70UtLJp09b4gPneXjdNS/q5pY50toYH9zPx+vPSXgHODMLMPnlMDVv0e/A+F4WRD2cjTfKexdRng7AP5Qspd4IulXUq0DjAcxwvDP4USwLE/FZJKnPCK9DL8Dr28wAbEIizGfwDsKrUY4leMfgDnzvl86V1rtquvB6eizeFi6Lz/tVfOC1SY44J9NCI0FMZwPgV3h793Est3Pwjwj75IhTckNf9BuK16fZunsLMDhHWpvgRvUP4nN7EW+XMzOTvpMIn2roi34H4wOHbN29kwJnR2el8VTUmQX4gG8I9YOinonwX8T7VR/hfcongENzPaP43C+K5SSju8mB2UZ4f2JyQndvxz8kNJr1pavlV4V1eB18v9YJeDu2Kuryc3hdPyglTsG6RtZgH1+98VLM4/u4wWqjJuTLGKJOb0beUg190W9//ENyZiZ8ZmDbK0dau+J9pSVR3x6N7y0zM37brLA56zh8ttV38UkZn8RrOj5g3qzAfNXgfZ9pUfa5+PiyV9Tb51LiZJ79UrzOuwufmZfLGNoXNya8T+5ZwtvHcvtWlGNBLLfXUETd11EufHz8bbxfN42GtoAH8f5R2my//fG2YQn1k1Vqs8raD/AZnsvwcdMt+GzcMdlhY/haKmjoi+7dqP9AP5qGK2Va3AaRw9AX/TaJOvt2fPYZXci14rI3bnDMjI9fw20Zu8Z7XJYIn2roi34j4rvPjHHfwW1AXyni+RdcD8Xw2+L9//l4XfMcXhfke0Y/xsdXmZVJyRnC6+IGwMzHgqX4mOB+fC/Vsq3asShAh8TMZgGEpjfPT4u7J95hvDWEUOwmzq2KmU0A9ggh5DscJFfczXDj1JMhhDa3t0Tcj+c9YFIIodB9P8opz6n4rLozQgil2M9QiA6BmZ2PLzfeP4TwcIVl6YQ3wl1DCP2aCi9ER8bMLgZ+ghsq/1VhWToTt2UJIRS6f68QbQYz+y0+QWG3EMLEpsIn4mbarvXxpfFLyiBiizCzZ/C90NYJISyvsCxDcYNPmx/LCdGeMbNv4bMnTwkhFHPYarnkaTP1UDlpi4dxtBd+En//WFEpys+P8CWHFc2nmW0YT8fKduuML+FYC7f4t6Y8/VPcBuJLbFfjX1SFEAly6M52+Bf7BZRo8/QCZVnPzHok3Az/8jaQVq5XhGjL5NDd7fEZFx9QfzJpa8jS28y6J9wMX6WwMdJd0TE5Gm+7xlTSyGdmPcysV4r7Kfhsmodac3BtZn2Th+rEg4WuiP+qvhCiBOToJwzCP16sAh5oRVnaVD1UCTpXWoD2RByMHowv4zsAuD+E8ExlpSo90WD1NXxt/tfxqeu3V1Qo+CrwazN7DF/isz5+Qu8QfOnfVa0sz53R8Pg8voyhFi8bPfCDPt5pZXmEaC88Z2Yz8eUqn+D1zEH4h6fTW7nRHQb83cweIW7OHN0+h9czo1pRFiHaOpPM7DXql5gPwZfP1wDfDCGsaEVZRgC3ZunuOvgSne3xZXG/bkVZhKgY0YD1U3wrmNPwpWEXV1Qo3x7kf2b2KL6nYBd89sxw/IPej1pZnh8BR5jZf/Bln33xJfsb48vnZOgTojTcG23qme3INsXHx92BH4cQ3m1FWdpaPdTqyNBXHDvi+28swg1fZ1ZWnLKxGb5Z6VJ8Lfu3Qghr8kcpO8/gswV2xzsz4HuznI+f4LmsleW5Bd+b6Kv4Hh9Loox/DCHc1cqyCNGeuBbf0+5YfHC+EN+r67IQwoRWlmUa3skfgRssOuP7pf0BuCCE8H4ryyNEW+bP+J52X8ON4gvx/fAuDSE83sqyvIbv0TQCH0R0wo3zv8d194NWlkeIStEJ77Ovwvfp+lEIYU5lRWIevl/dHsDe+B5j7+KHl/w2hPBmK8vzCL731kh8osBqvP2/Aj9osOPuYyVEabkZ3xvwCHxvuiX4folXhRDuaWVZ2lo91Op06D36hBBCCCGEEEIIIYSoFrRHnxBCCCGEEEIIIYQQVYAMfUIIIYQQQgghhBBCVAEy9AkhhBBCCCGEEEIIUQXI0NdGMLNHzCw0cZ2bEn7vVpbzCDO7ysyeMLNFUYZbm5FOHzM7xczuNrOZZrbMzD42s4lm9k0zy1s2zexLMe67ZrbCzN4xs4fN7MBShBeiNWgveh/vvYmZ3Rh1Z4WZzTKzK82sdwvTPT4rr6eUIo45p5rZM2a2xMw+MbPnzOyMpuoWIcpNR9T7GC9XXlNP4TOzi83sX2Y2O/YRFpjZi2b2SzPrkxK+JP0TIcpBR9T7mNZBMS9zoh6/YWa3m9muKWFPLuAZfdrc8EK0Nh1Y74tKqzntdzF1S0dFh3G0EczsQ/x0mvPzBPtHCGFKVvjeQO8QwsetICLxvpOA7fFTdOYAWwG3hRCOLzKdM4Br8BNxxgNvAxsBh+On2N4JHJl2EpaZXQL8ON7/IeADYEP8VOTHQgg/aUl4IVqLdqT3mwP/BT4D3AtMBXYG9sJPrhsRQviwGekOAF7BTw3sCZwaQhjd0jhmdht+Muj7wH34CeL7AkOBW0IIJxYrqxCloiPqvZnNAtYDrkzxXhJCuCwlzkrgBWAKrstrA8OAnYB3gGEhhNlZ4UvSPxGiHHRQvb8Y+AnwIXAP3v/eAj+9uzNwYgjh1qzwnwO+nCO53fCTMx8IIRzcnPBCtDYdVO+LTqvY9rvYuqXDEkLQVeEL2AwIwAtFhp9eAVn3AgYDBuwZ5bi1GensDRwC1CTc++JGvwB8NSXeqdFvDNA1xb9LS8Lr0tVaVzvT+4fjvf8v4X55dP9zM9I04DHgdeDSmM4pLY0DfCW6vwFskOXeFRgX/Q6v9PvX1TGvjqr3wCxgVpH3XyuH+/nx/n9KuJekf6JLV6mvjqj3sT//KfAu8JmE316ZdroIuZ6KcQ4tR3hdukp9dUS9b25axbTfpa5bqvnSEqa2wU7x99kiwz9XBlnyEkIYH0KYEaI2tSCdf4cQxoUQ1iTc3wX+HP/dM9vPzLrhHfy3gdNCCCtT0l3V3PBCtDLtQu/jl7mR+ED96oT3L4FPgBPMbO0ik/4ubvD/ekyjVHG+En9/F0L4IOMY9T+zPOI7RcoqRKno6HpfMCGE5Tm8/hF/ByfCl6R/IkQZ6Ih6PwjfIuqZEML72R4hhPHAYnx1TSFybYfP5p0LPFDq8EKUiQ6n981Nq8j2u2R1S7UjQ1/boF1UBK1Ixvi2OuG+L664dwFr4tr8n5rZ93Ksxy82vBCtSXvR+73i7yMphvnFwJNAD7xTXRBmNhS4CPh9COHxEsfpG3/fSPHLuO1mZl0LlVeIEtJh9R7oZr6/5tmxHd7LzDo1Q7ZD4u/LzYgrRCXoiHo/A1gJ7GxmG2R7mNnuwDr4DP1COC3+3hBCKGTPvWLDC1EOOqLel6PvkKSUdUtV07nSAgigXrGHm9kmOcJcGkL4JBE+b0VgZmfhe+IUyqQQwj1FhC85ZtYZyOyf9c+E9xfi73LgRWDbRNzHgSNCCPObGV6I1qS96P2W8Xd6Dv8Z+Ne7IcC/mrpZ1PFb8Jm2ZxciYJFxMrP4Nk3x2yz+do5/Ty3k/kKUkA6p95G+uB5n86aZfT2E8J9ckczsR/h+nL3w5/FF3Mh3UYH3FaLSdDi9DyEsMLOf4sv1ppjZPfh+Wpvj+2g9CpzelMBm1h04Hl+ql3cP3+aEF6KMdDi9L3FaqZSqbukIyNBXYczMgB3iv1/PEWxBCGFUIvwafJPqfJyFT28tlLH4hpaV5CLcIPdgCOHhhN9n4u+P8c25dwMm4QP6y/CK43bql/wWG16IVqGd6X2v+JtrU+CMe6GdjvOAzwNfDCEsK0OcB4BjgR+Y2d9CCAsAzKwL8KuscC06LViIYungen8T8AQwGV9Wsxm+hP404CEz2zWE8FKOuD/CD+vK8E/gZH2kE+2Bjqz3IYQr40E8N+J7ZmeYCYxJLrvLwVHxfg+ErMN3ShheiJLTgfW+1H2HVEpUt1Q9WrpbeQbjSvFkCMFyXH1Swk8LISzJl3AIoTZPmmnXyeXLZtOY2XeBH+KzbE5ICZIpr6vxzXUnhhCWhBBewfflmgPskbUst9jwQrQWHVLvzWwXfEbe70IIT5Upzt/wjYA3x7/0XWtmv8eN/LvhswLBO1NCtCYdUu+jfL8KvjfveyGEpSGEV0MIZ+Bf5LsDo/LE7RtCMHxG4OG4kfBFM9shVxwh2hAdVu/N7CfAHfiBeJvjJ2fviG+jcZuZXVJAMplluNcWeNtiwwtRDjqs3rcGJapbqh4Z+ipPZprupCLDV9X+fGb2HeD3+My7vUKchZNgYfx9MYQwK9sjhLAUH9yDH+HdnPBCtBbtSe8zX9965fDPuC/M4Q/ULb+9GZ/Of26+sC2JE3w/nkOAnwHzgZPiNQMYjs8mAtDXPtHadDi9L4DM4Vu7NxUwGgnvxmfj98HrBiHaOh1S781sT+Bi4L4Qwg9CCG9EI/8L+Mf2ucAPzWyzPGlsg7fbc4AHC7hnUeGFKCMdUu9LnFYqpahbOgpault5Moqda8lKrvBNVgTtZY++KOcVwKvAl0Lu6bbT4m+uyuGj+Nu9meGFaC3ak95n9GhIDv/MyZe59uPI0DMrjeW+SqER15vZ9fiBG2c1M07mNO2L41WHma0V5f0ghPBmE/IKUWo6ot43RWb5bcGn94YQ3jKzKcDnzGyDkHW6thBtkI6q9wfH3/FJjxDCUjP7Hz4o/zzph2eBDuEQ7ZeOqvet0XcoRd3SIZChr/KUrSKgHezRFzfTvAj/4rFvEx32fwEB2NrMakLiNB/qD9t4s5nhhWgt2pPeZxrSkUk9MrN1gBHAUuDpJu6zArghh98OeIM8Ee8kPNWCOPk4BugK/LWAsEKUmo6o902ROXmv2M54//irwbxo63RUve8WfzfM4Z9xX5nmGT/MnYDreK5+QLPDC1FmOqret0bfoUV1S4cihKCrQhe+dHoxvodc91KHbwX598QNabc2EW5zYCugS8L93Bj/OWD9Au95b4zz/YT7SHzPrY+AXs0Nr0tXua/2qPf4MvcA/F/C/fLo/ueUOKl6nyP9UTGdU4qQKWccYN0Ut8/hs4cWAP0rXQ50dayrI+s9MBRYOyVsLb6kPgBnJ/yGpLXN8bmcH+M8mUf2gvonunSV8+rgen9UDP8usHEi/AGxD74M6JNDjhNi/HEFyl1UeF26ynV1ZL1vblqJcHnb75bWLR3p0oy+yrIVvjRtEfDTHMvSAK4PIczNCv9KKPy0ypJiZl8Gvhz/7Rt/dzWzMfHvD0IIP0pE+xf+5WFTYFZM5yTg1/iXtyeA76bkf1YIYUzC7dv4LJ7Lzewg4MWY7pdjWqeEED5uQXghyk2703vgTOC/wB/M7EvAa8AuwF749PtzUuI00vtW5FEzW4ZvB7AYNzQchDf8h4QQ3mlleYToyHp/NL5fzuPAW7hObo7r5Fr4XlqXJdI5ELjQzCbis+4/xE/e3QM/jONdGp6019z+iRDlpCPr/R3AY8A+wGtmdjeut0PxpXcG/CyE8GEOOTLLcK8rUO5iwwtRLjqy3jcrrSLb75bWLR2HSlsaO/IFnIhbpPNdnxJnp2SFv7GCMo9qQt5ZKXFmRb/aItIJwIQcMmwIXIUPGFYCHwB3AzuXIrwuXeW82qPeRzkGADcB86IevQVcCfTOEb6R3udJO1MflGpG34+B5/H9OVfgywKvBjap9PvX1TGvjqz3uHHur8DUqJOr8Nm1j8Z8Wko62wJ/xLf1+ACf6fAx8GzU/UarAAroV8yqdDnQ1bGujqz30b0LvszwadzosRo/COt+YGSe+w+N6c0GOhUgb1Hhdekq59XR9b6ZaRXVfje3bulol8WHJYQQQgghhBBCCCGEaMfUVFoAIYQQQgghhBBCCCFEy5GhTwghhBBCCCGEEEKIKkCGPiGEEEIIIYQQQgghqgAZ+oQQQgghhBBCCCGEqAJk6BNCCCGEEEIIIYQQogqQoU8IIYQQQgghhBBCiCpAhj4hhBBCCCGEEEIIIaoAGfqEEEIIIYQQQgghhKgCZOgTQgghhBBCCCGEEKIKkKFPCCGEEEIIIYQQQogqQIY+IYQQQgghhBBCCCGqABn6hBBCCCGEEEIIIYSoAmToE0IIIYQQQgghhBCiCpChTwghhBBCCCGEEEKIKkCGPiGEEEIIIYQQQgghqgAZ+oQQQgghhBBCCCGEqAJk6BNCCCGEEEIIIYQQogqQoU8IIYQQQgghhBBCiCpAhj4hhBBCCCGEEEIIIaoAGfqEEEIIIYQQQgghhKgCZOgTQgghhBBCCCGEEKIKkKFPCCGEEEIIIYQQQogqQIY+IYQQQgghhBBCCCGqABn6hBBCCCGEEEIIIYSoAmToE0IIIYQQQgghhBCiCpChTwghhBBCCCGEEEKIKkCGPiGEEEIIIYQQQgghqgAZ+oQQQgghhBBCCCGEqAJk6BNCCCGEEEIIIYQQogqQoU8IIYQQQgghhBBCiCpAhj4hhBBCCCGEEEIIIaoAGfqEEEIIIYQQQgghhKgCOqyhz8xGmdmYSstRSqotT2Z2splNqLQcpaQa89SeqDYdgerLUzXqSDXmqT1RbToC1ZenatSRasxTe6LadASqL0/VqCPVmKf2RLXpCFRfnqpRR9pqnqra0GdmZ5rZm2a23MyeN7Pdmgi/nZn9x8yWmdlcMzvPzKy15C2EYvJkZmuZ2Rgze9nMVrW1Amhmu5vZffFZBzM7uYA4A81snJl9YmYfmNkfzKxrK4hbEM3M0+/N7Ln4TmeVX8rqRnovvW9tpPeVR3ovvW9tpPeVR3ovvW9tpPeVR3ovvW9t2qveV62hz8yOBn4PXAB8Hvgv8JCZDcwRfl3gUeA94AvA94AfAz9oFYELoNg8AZ2A5cAfgQdaRcji6Am8ij/rZU0FNrNOeD7WAXYDjgWOAH5XRhmLpag8RWqAscDN5RKqoyC9B6T3lUB6X0Gk94D0vhJI7yuI9B6Q3lcC6X0Fkd4D0vtK0D71PoRQlRfwDHB9wm0GcGH8exQwJsvvW8AioHuW2y+AuYBVOj/NyVMi3B+BCZXOQ568LQFOTridnC0zcACwBhiQ5XY8XtmtW+k8NCdPCb8fAbMqLXd7vqT3jeJK79tgnhJ+0vuWP3PpfcNw0vs2mKeEn/S+5c9cet8wnPS+DeYp4Se9b/kzl943DCe9b4N5SvhVTO+rckZfnOq5I/BIwusRYHiOaLsCT4QQsq20DwP9gdpSy1gszcxTtbEr8FoIYXaW28NAN/zZiA6M9L5qkd6LnEjvqxbpvciJ9L5qkd6LnEjvqxbpfZmoSkMfsAE+rfW9hPt7QN8ccfrmCJ/xqzTNyVO1kfaOPgA+peM8A5Eb6X11Ir0X+ZDeVyfSe5EP6X11Ir0X+ZDeVyfS+zJRrYY+IYQQQgghhBBCCCE6FNVq6MtYgTdKuG8EvJsjzrs5wmf8Kk1z8lRtpL2jzJeQjvIMRG6k99WJ9F7kQ3pfnUjvRT6k99WJ9F7kQ3pfnUjvy0RVGvpCCCuB54F9E1774ifZpPEUsJuZrZUI/w4wq9QyFksz81RtPAUMNbNNstz2BVbgz0Z0YKT3VYv0XuREel+1SO9FTqT3VYv0XuREel+1SO/LROdKC1BGLgduMbP/AU8CZ+Abb/45R/i/AL8ExpjZb4EhwM+AX4V4ZEoboNg8YWZbA11xy3hPM/scQAhhUvnFzY+Z9QS2iP/WAAOjfAtCCG+nRHkEmAzcbGY/BPoAl+InFS1qDZmbohl5wsy2wI/t7g90zbwjYEpsAEThSO+R3rc20vuKI71Het/aSO8rjvQe6X1rI72vONJ7pPetTbvV+0ofUVzOCzgTt9ZnLMK7Z/mNInFUNbAd8Dh+nPM8vGJoE0dvtyBPs4CQvCqdjyjbnmmyZfJAylHVwEDgfmAp8CHwB6BbpfPSwjxNyBGnttL5aY+X9F56307yJL0v7TuQ3kvv20OepPelfQfSe+l9e8iT9L6070B6L71vD3mquN5X84w+Qgh/Av5URPhXgN3LJ1HLaUaeassnTcsIIUwArMg4bwMHl0WgEtDMPO1ZFmE6KNJ76X1rI72vPNJ76X1rI72vPNJ76X1rI72vPNJ76X1r0171vir36BNCCCGEEEIIIYQQoqMhQ58QQgghhBBCCCGEEFVAVS/dbYIJwHqVFqLETKC68jQJGFNpIUpMNeapPTGB6tIRqL48VaOOVGOe2hMTqC4dgerLUzXqSDXmqT0xgerSEai+PFWjjlRjntoTE6guHYHqy1M16kibzJPFzQKFEEIIIYQQQgghhBDtGC3dFUIIIYQQQgghhBCiCujQhj4zO63SMpQa5al9UI15ai9U47NXntoH1Zin9kI1PnvlqX1QjXlqL1Tjs1ee2gfVmKf2QjU+e+WpfdDW8tQmDX1mdkgr3apVXkYr5geUp2ZTjXlqT0jvW4Ty1EyqMU/tCel9i1Cemkk15qk9Ib1vEcpTM6nGPLUnpPctQnlqJtWYp0Jpk4Y+oDVfSGtQbfkB5UmUnmp7/tWWH1CeROmptudfbfkB5UmUnmp7/tWWH1CeROmptudfbfkB5amqaFOHcUSL6yFdunQ5tV+/fmW/35IlS+jZs2fZ77Ns2TK6d+9e9vuA8tQSqjFPb7/99gchhA3LfqMWIL1vOcpT86nGPEnvG6Py1HyUp5Yhva9Het9ylKfmU415kt43RuWp+ShPLaOt6X2bMvRlGDRoUKitrWXFihWVFqVF1NTUcPLJJ9f9/8UvfrFywpSIxYsX89JLLwHQtWtXhg8fXmGJWs7777/PlClTAFh77bXZeeedKyxRy1mwYAEvvfQSp59++vMhhJ0qLU8hDBo0KAwaNIiVK1dWWpQW0blzZ0488UQAzIzddtutwhK1nEWLFjFp0iTA9X7EiBEVlqjlvPvuu7z22msA9OzZk512ahdqkpcFCxbw8ssvtzu9HzBgAKtXr660KC2iW7duHHfccYC3/bvvvnuFJWo5CxcurNP7bt26VUV7P2/ePKZOnQrAOuusw4477lhhiVrOhx9+yCuvvNLu9H7LLbds93rfvXt3DjvsMAA6depUFe39Rx99VNfPX2uttaqibZw3bx6vv/464Hq/3XbbVViilrNgwQKmTp3a7vR+4403Zs2aNZUWpUX07NmTo446CnC9r4bx/Ycffsirr74KuN5XQ9v4zjvv8OabbwLQq1cvPv/5z1dYopYzf/58Jk+eXLDed24NoZrD888/zyeffFJpMVpEtqHPzNhss80qK1AJ+PDDD+s6AJ06dWLw4MEVlqjl1NTU1Bn6unXrVhV5mjNnTt17ak8899xzLFu2rNJitIiuXbs2MPRtvvnmFZao5cyfP79uwN+lS5eq0JEQQp2hb6211qqKPM2ePZuXX3650mIUzXPPPdfuP+z16NGjztBnZlVRnubNm1d1er9mzZo6Q1+16H3Xrl155ZVXKi1G0UyaNKndG/p69uxZZ+irqampivKU3X/s0qVLVYxdVq1aVWfo69atW1XkqUuXLnV1WXvimWeeafeGvj59+jQw9G2xxRYVlqjldOvWrc7Q17Vr16rQkZUrV9YZ+qqlve/cuTOTJ08uOHxb3aNPCCGEEEIIIYQQQghRBDL0CSGEEEIIIYQQQghRBcjQJ4QQQgghhBBCCCFEFSBDnxBCCCGEEEIIIYQQVYAMfUIIIYQQQgghhBBCVAEy9AkhhBBCCCGEEEIIUQXI0CeEEEIIIYQQQgghRBUgQ58QQgghhBBCCCGEEFWADH1CCCGEEEIIIYQQQlQBMvQJIYQQQgghhBBCCFEFyNAnhBBCCCGEEEIIIUQVIEOfEEIIIYQQQgghhBBVgAx9QgghhBBCCCGEEEJUATL0CSGEEEIIIYQQQghRBcjQJ4QQQgghhBBCCCFEFSBDnxBCCCGEEEIIIYQQVYAMfUIIIYQQQgghhBBCVAHt2tC3ySabcPvtt7Nw4UI+/vhj7rzzTgYMGFBQ3NraWm6//XY++ugjlixZwr///W923HHHBmEGDx7MlVdeyUsvvcTixYt55513uPfee/nsZz9bjuwAMHv2bI499lg22mgjPnj9RDUAACAASURBVPOZz3D00Ufz9ttvFxR3+fLl/PznP2fTTTeld+/e7LHHHkycODE17Ny5czn99NOpra2lV69ebLXVVpx77rmlzEods2fP5sgjj2S99dajV69efPWrXy0qTz/+8Y/p378/PXr0YPjw4Tz++OONwl1++eUceuih9O/fn5qaGkaNGlXiXDRk9uzZHHHEEfTq1Yt1112Xww8/vOg89evXj+7du7Prrrum5mnNmjVceOGF1NbWstZaa7H99ttz5513ljor7Y6W6P2AAQMYM2YMb731FkuXLmXatGn85je/oUePHnVh9thjD0IIOa9ddtml5HmaPXs2Rx99NBtssAF9+vThyCOPLKo8/exnP2PgwIGsu+667LbbbjzxxBOpYefOncupp57KgAED6NmzJ0OGDOGcc84pZVbqKLeOLF68mKOOOootttiCtddem/XWW4+dd96ZW2+9tRzZAVqnLsvmb3/7GzU1NQWX72qmWtv7cpanxYsXc/TRRzN48GB69uxJ79692WWXXcquI+XU++nTp/O9732Pz372s/Ts2ZN+/fpx6KGH8tJLL5UjO0B1vqf2wsYbb8yYMWOYNWsWb731FmPHjmXjjTcuOO7VV1/Nyy+/zNy5c/nf//7H2Wef3aC9z9CrVy8uuOACXn75ZebNm8err77KH//4x1JnB6jePnG1jV3mzJnDcccdR79+/ejbty/HHnsss2fPLiju8uXLOfvss9lss83o06cPe+21V2qePvzwQ370ox+xzTbb0KdPH7beemt+8IMfMH/+/FJnp11R7n4+QPfu3bn88suZM2cOy5cv5+WXX+ZrX/taObIDuI4cddRRrL/++vTu3ZsjjjiiKB35yU9+wiabbELPnj0ZMWJEajtyzDHHsOWWW7LuuuvSp08fhg0bxm233VaO7ACuI1/72tfo27cvG220Ecccc0zROrLpppuy/vrrs+eeezbSkVtuuYUePXrkvN59992S56nc9XOmD7P99tuzzjrr0L9/fw477LCy9WHaraGve/fu/Pvf/2arrbbipJNO4oQTTmDw4MGMHz8+tRHPZv3112fixIlsu+22nH766RxzzDEAjB8/nq222qou3MiRI9lrr70YO3YshxxyCGeeeSYbbrghTz/9NDvssEPJ87R06VIOOOAApk+fzvXXX88NN9zA66+/zv77788nn3zSZPwzzjiDm266iXPPPZe77rqLvn37csghhzQqPG+99Ra77bYbM2bM4LLLLuP+++/nF7/4BZ07dy5Lnr70pS8xdepUxowZw80338yMGTPYe++9C8rTKaecwujRo/nVr37FuHHj6NevH/vvvz+TJk1qEG706NG8//77HHbYYSXPQ5KlS5ey9957M3XqVMaOHcstt9zCjBkz2GuvvQrK0ze/+U2uv/56fv3rX3P//ffTr18/9ttvv0Z5Ovfccxk1ahTf+c53eOihhxg2bBhHHnkkDz74YLmy1uZpid736NGDxx57jN13351zzz2XAw88kNGjR/PDH/6QG2+8sS7cCy+8wLBhwxpdkydPZt68eTz77LMlzdPSpUvZb7/9mDZtGjfccAM33XQTM2fOZOTIkQWVp9NOO40bbriBX/7yl9xzzz307duXgw46qFF5mjVrFiNGjGDGjBlcfvnlPPjgg2XV+3LryMqVK+ncuTM///nPue+++/jLX/7C0KFDOeGEE7jiiivKkqfWqMsyLFy4kO9///v07du31Flpd1Rre1/u8pTRkZ/97Gfce++93HbbbQwdOpQTTzyxbDpSbr1/5JFHGD9+PCeddBLjxo3jT3/6E/Pnz2fYsGE8//zzZclTtb2n9kL37t255557GDx4MGeeeSZnnHEGm2++Offdd19B7f3dd9/N8OHDueCCCzj66KO55ZZb+Pa3v81VV13VIGyvXr146KGH2GOPPTj//PM5/PDDOe+881iyZEnJ81StfeJqHLsceOCBTJs2jeuuu47Ro0czc+ZMDjjggILy9K1vfYsxY8Zw7rnncscdd9C3b99Gg/kQAkcddRS33347Z511FnfffTdnnXUWd9xxB0ceeSQhhJLnqz3QGv18gLvuuotvfOMbXHTRRRxyyCE8+eST3HbbbRx33HElz9PSpUvZd999mTZtGjfddBNjx45l5syZ7LPPPgWVp1NPPZUbbriBUaNGce+999KvXz8OPPDA1Hbkpz/9KXfffTe33HILQ4cO5aSTTuLKK68sS54yep/RkWL0/lvf+lad3t9555307du30Ue7/fffnwkTJjS4xo8fT58+fdhxxx1L3j9ujfr5kUceYcKECZx44oncd999XH311cyfP59dd921LH2YvDNXKnUNHDgwrL322gHIeX33u98Nq1evDptvvnmdW21tbVi1alX4/ve/nzfuOeecE1atWhU222yzOrcePXqEd999N/z973+vc+vTp0+juOuuu25YsGBBGDt2bN57AKGmpiZce+214dprrw3XXXddWLZsWd7r0ksvDTU1NeHVV1+tc3vttddCp06dwkUXXZQ37jPPPBOAcO2119a5LV68OAwePDgcdNBBDcLuu+++YccddwyLFi1qUqbkNWfOnLo83XzzzWHNmjV5ryuuuCLU1NSE6dOn17m9/vrroVOnTuGyyy7LG/fFF18MQLjhhhvq3FauXBmGDBkSDjnkkAZhV69eXecPhPPOO69J2TLXjBkz6vJ0++23h6a48sorQ01NTZgxY0ad2xtvvBE6deoUfve73+WNO2nSpACEG2+8sc5t1apVdXnK8N5774WuXbuG8847r0H8vffeO2y33XZNyjh79uxw7bXXBuC5fLrWlq6BAweG7t27l03v99133xBCCPvuu28D9wsvvDCsWrUq770HDhwYPv3003DJJZc0qfddu3atK0/XX399WLlyZd7rd7/7XaipqQlTpkypc5s2bVro1KlTuOSSS/LGffbZZwPQ4D5Lly6t0/vssCNHjgw77bRT+OSTT5qUKXnNnTu3Lk+33nprk+WvNXQkF8OGDQvbbrttk+GmTZtWl6c777yzzdRlmeuUU04JI0eODCeddFLYeOONC6rL3nrrrXap9926dWv37X2PHj3qytPo0aPbXHnKvjI60lS4bL2/7bbbmtSp1tD7+fPnhzVr1jSIu3DhwrDeeuuFE044oUkZp06dWpenu+66qyre05tvvtku9X7DDTcMvXv3znn97Gc/C6tXrw477LBDndv2228fVq1aFc4555y8cQ8//PAQQgiHH354A/crrrgirFq1KvTv37/O7cYbbwxvv/12GDhwYN40064BAwbUlacbb7yxzZSnlvSJ33777bo8/eUvf6mKscukSZMatPeffPJJ3uuSSy4JNTU14eWXX65zmzx5cujUqVO48MIL88Z96qmnAhCuueaaOrePP/44DB48OBx44IF1bpk676qrrmoQ//e//30Awosvvpj3Ppm6rL3pfU1NTcX7+SNGjAghhHDSSSc1CDdu3Lgwd+7cJmXs06dPXXm66aabwurVq/Nel19+eaipqQlTp06tc5sxY0bo1KlTuPTSS/PGff755wMQRo8eXee2fPnyMGTIkHDwwQc3ee9ddtklbLvttk2Gy7Qj1157bfjrX/8ali5dmvfK6P0rr7xS5zZlypQ6vc8X9+mnnw5A+POf/1zntmjRojodyRf30UcfDUC4/PLLm5TxhRdeqMvTPffc0ybq5/fffz98+umnDeJ+9NFHdX2YpmR8/fXXi9L7kszoM7NRZjamFGkVyqGHHsrTTz/N66+/Xuc2a9YsnnzyySa/YA0bNowZM2bwxhtv1LktXbqUJ554goMPPphOnToBPqU6yaJFi5g+fXrBSweK4YEHHmDnnXdm8803r3Orra1l11135f77728ybpcuXTjiiCPq3Dp37syRRx7Jo48+yooVKwB44403ePTRRznzzDPp0qVLyfOQZNy4cQwbNowtttiizm3TTTdlxIgR3HfffXnj3nfffXTp0oWjjz66zq1z584cffTRPPzww3V5Aqipab3Jqffdd1/OPN17771Nxk3L0zHHHNMgTw8//DArV67k+OOPbxD/+OOP55VXXuHNN98sYY6aR3vT+65duwKuw9ksXLiQmpoazCxn3BNOOIGamhrGjh3bAunTuf/++9lll10alafhw4czbty4JuN26dKFI488ss6tc+fOHHXUUQ30/vXXX+eRRx5pNb1vDR3JRZ8+fcryhb+16jKg7styuZaOtYT2pvdttb1vzfKUpFw60hp6v8EGGzSqq3v16sWQIUOYO3duCXPjVON7ag6V0Pv999+f5557rkF/5+233+aZZ57hgAMOyBs3084tXry4gfvHH3/coL3v0aNH3Wy/ZNhyUI194mocu+TL0wMPPJA37oMPPpiapyOOOILHHnusLk8rV64EYJ111mkQv1evXoBv31Np2lt7X2g/f9iwYQA89NBDDcL985//pH///nX+pWLcuHE5+/lN6f24cePo0qULRx11VJ1bpp//yCOPVKwdKYfeJ3UkjVtvvZWuXbs2eB6lojXq59buwxTcEpjZmWb2ppktN7PnzWy3JsJvZ2b/MbNlZjbXzM6zfCPpItlmm2149dVXG7lPnjyZrbfeOm/cTz/9tK6CzWbFihX06NGjQaFN0rt3b7bddltee+214oVugtdee41tttmmkfvWW2/N1KlT88adMmUKtbW1jaY1Dx06lJUrV9ZVmE899RQAa621FgcddBC9evWiX79+fPOb30wd6LSUyZMn58zTlClT8sadMmUKm266aaM8bbPNNqxcuZKZM2eWVNZCmTx5Mttuu20j92222abJPE2ePLmgPE2ePJlu3bo1qGwy4YAm71MqqknvH3vsMaZPn87FF1/M/7N353E1Zn8cwD+3VQptKKJiLEXG0mD8ZLJFtIylGvsSwlhiGEbWsYz5zQzGDBPZRmTL0iYiJbKNsleUNk2MlITS5vz+aO7z69Zdyt3q+r5fr/ua5vGc7vl2z/c55zn3ec5jZWUFXV1d9O/fH/Pnz4evry8KCwtFlp04cSLi4uLw4MEDqWOoKiEhQWSOSDrOiMp7a2trgfZ05coVABW3RDg6OkJPTw/NmjXDlClT5Jb38s4RPsYYysrKkJubi507d+Ls2bNYsGCB9EEIqZcijmWlpaXw8vLCokWLquW/oqhS3tfV/l6RfaOwHPH29pY+iCoUmfeV5eXl4f79+7Cysvqwikuol6p9TqLUtbzv2LGj0NxLSkpChw4dxJa9ePEiUlJSsGrVKnTo0AG6urqws7ODl5cX9u7dy/X3n376KRo2bIicnBzs27cPf//9NzIzM+Hv74/WrVvLKhSOKo6JVfHcJTExUWjfYmVlJTGmxMTEGsVkbW2Nvn374scff0R8fDzevHmDmzdvYuPGjXBwcBBYWkKe6lreK2KcX15eDgDVxgb8yRhh/Zg0EhISRPaNNRnnf2g/4ufnh4iICMyfP1/6IITUS9Y5wj93qTzJW1lRURFOnjwJR0dHGBoafnjlRVDW8Zk/hpFHztdooo/H43kA+BXABgDdAFwBEM7j8YT2gjwerzGAcwD+AfAZgPkAFgNYKIM6A6hYd+fly5fVtufl5cHAwEBs2YcPH6Jdu3YCjYTH46Fnz57c7xblt99+A4/Hk8v97nl5edDX16+23cDAQGislb18+VJoWX4seXl5AIDs7GwAFWtitGvXDkFBQVi3bh3OnDkDFxcXmX+DJOrzEPX51bQs/9+VQREx8dtC1b5TkbGrWt4XFxejb9++UFNTQ0JCArcof2hoKObMmSOyXO/evdG+fXu5XM0HSJ/34toTv/zTp08BVKzn165dO4SEhGDDhg0IDw/H8OHD63Xeb9u2DZqamjA2NsacOXPw66+/YuLEiVLUvvb1kmVMP/74I4qLi/Hdd99JWeMPo2p5X5f7e0XmiJaWFpo2bYq5c+diy5Yt9TpHqpo7dy4YY3KZFFPFz0mYupj3BgYGyM/Pr7Zd1Hi3suLiYgwbNgxqamq4evUqnjx5gqCgIERERODbb7/l9jM1NQUAfP/99ygvL8e4ceOwYMECdOnSBSEhIdDT05NVOABUd0ysaucuoupVk5jE/T34vxuo6IdOnDiBdu3awc7ODs2bN8cXX3wBCwsLBAQEyCAKyepi3itinP/w4UMAqHbl3ueff87VQZakyRFJ7alq3m/fvh0NGjRA8+bNMW/ePGzevBkTJkyQovbCiTr/kCbvq+ZIVSEhISgoKJDLOoqA8o7P8+bNk9sYpqbXci4EsI8x5vfv/8/l8XhDAcwCIOxsZByAhgAmMcaKANzn8XgdASzk8XibGGNM2opLw9fXF/PmzcP+/fsxb948FBYWwsfHB5aWlgBEXy69dOlSjBs3DlOnThU521zX8f/0/fr1405e7O3t0bhxY0ycOBHnzp3DkCFDlFlFUneoVN5ra2vjyJEjaNasGcaPH4/MzEz07NkTK1euRFlZGWbPni203KRJk1BSUqKwgZc88I9p/fr1w9atWwEA/fv3R+PGjTF+/HhERERg6NChyqziB/Pw8EDv3r3x4sULBAcHY+7cuVBXV4eXl5eyq1ZrKSkp2LBhA06cOIEGDRooqxoqlfcfc3/PVzVH5s2bV29zpKoffvgBAQEB2L17t9KugJUVJX9OKpX32tra2L17N4yNjeHl5YWsrCz06NEDixcvRllZGRYtWgQA3Jep6enp8PT05Mqnp6fj3LlzcHNzw969e5USA/k/VT13+frrr3Hjxg1s3boVHTp0wMOHD7Fu3TqMGzcOgYGBirgFW+Xyvibj/IiICCQkJGDr1q2YOHEikpKSMHLkSIwZMwZA3bht+kO5u7ujV69eePHiBUJDQzF//nyoq6tjxowZyq6a1A4cOIBmzZrV2/MVYfhjmF27dsllDCNxoo/H42kB6AHg5yr/FAGgj4hinwO49O9BgO8sgLUALABUW2CMx+PNADADqNlMurgrWSTNuqalpWHcuHHYtm0bN4CPi4vD5s2bsXjxYu7ql8q8vLzwww8/wMfHR26dvrhvLyV9i6Gvry/08c/8GWT+35T/3wEDBgjsN2jQIADAnTt3ZNpZiprZr8k3MwYGBsjIyBBaFpD9Ny41pYiY+G2BMSZwVZ+iYlfFvPf09ET//v3Rtm1bbr2uS5cu4dWrV/Dz84Ovry/u3r0rUIa/DkRYWJhcbg8BpM97ce2JX97IyAjA//Ocb/DgwQCA27dvy7TjVGTeN23aFE2bNgVQsaZTYWEhFi1ahKlTp8p0LR9FxDR//nwMGDAAvXv35tpESUkJGGPIz8+HtrY2dHR0pA1FJFXM+7rc3ysrR4qKirB48eJ6mSOV+fr6YtmyZVi3bh2mTp36gbUWTxU/p6qUlff89TFFyc/PF3nVh7A+s7Lx48fDzs4O3bt3R3p6OoCK2z8LCgqwZcsW7N27Fw8ePOA+25iYGIHycXFxKCgoQJcuXcS+T22p6phY1c5d9PX1PzgmAwMDPHnyRGhZ/r8DFevBHTt2DKGhoejfvz8AoG/fvrCwsICLiwtOnz4NJycnaUMRSRX7+5qO88vLyzF69GgEBARwt4U/e/YM3333HbZs2SJ0XCANaXLEwMBAaI7w/xY1GRN/++23mDJlikL6e2nyvmqOVPb06VNERUVh1qxZclu7VhljGB8fH6xdu1ZuY5iafFVgDEAdFZfpVvYPAFHPNTYRsT//36phjO1kjNkyxmxrcqm8NPdRAxWP1W7ZsiWsrKzQtm1b2NraQk9PD5mZmdUO0OPHj8f27dvx888/Y8OGDRJ/94eysrISWvfExESJ921bW1sjPT292jpjSUlJ0NLS4tYhkrSGjQyXWQAgem0eUetfVGZtbY20tLRqMSUkJEBLS0tp39536tRJ6FptotYrqFq2JjF16tQJxcXF1a4k4f8tJb2PDKhc3tvY2CAvL09gUX4AuHHjBgDhueHi4gJDQ0O53bYLiK57YmKixHwVlfeJiYkC7UlSe5H1t8aKyBFRbG1t8ebNG/zzT9WmKB1FHMsSEhJw+vRpGBoacq9Dhw4hOzsbhoaGiridV+XyHqib/b0y+8YePXrILUcUlff+/v6YPXs2vvnmG/j4+EhfeTH1UrXPSYg6mfdJSUlCx778K5/Esba2xsuXL7lJPr64uDjud/DfQxxZX9mjimNiVTx3sbKyErk+pKSYrKysahQTfx26Hj16COxna2vL7S9ndTLvFTXOT0xMRLdu3WBhYYFOnTqhVatW3ARfbGysxHrWhrW1tci+sSbj/LrYj8gjR/jnLsLWTj58+DC3vIK8KPL47O/vj6+//hoLFy6U6xhGcY9lkjH+0934t98AgLm5eY2ejML3/v17JCUlITU1FaampvDw8MAff/whsM+XX36JvXv3YteuXVi8eLFMY6hq+PDhuHHjhsATxjIyMnD16lUMHz5cbNlhw4ahtLQUJ06c4LaVlZUhMDAQgwYNgra2NgCgV69eMDExwfnz5wXKR0REAPh/ByMrzs7OuHbtmsBBl//0JGdnZ4llS0tLcezYMW5bWVkZjh49CgcHBy4mReM/EUpYTC4uLmLLiorpyJEjAjENHToUmpqaOHjwoED5AwcOoHPnzgLt/mMiTd4/e/YMhoaG1TqQXr16AYDQpx1NmjQJOTk5Ep+yJg0nJydcv369Wnu6cuWKxG9zhw8fjtLSUgQGBnLbysrKcOzYMaF5f+7cOYHyZ8+eBSD7vFdEjohy8eJF7mEjsqSIY9mhQ4dw4cIFgdeQIUNgbGyMCxcuiF1LUpWpYn+vzL4xJiZGLjmiqLw/efIkpkyZgmnTpuHnn6tejCJbqvg51RdnzpyBra0tzM3NuW2tWrVCr169cObMGbFlnz9/DgMDg2pjJX5fx1/zLTs7G/Hx8bC3txfY77PPPkPjxo1x69YtGUTyf6o4JlbFcxdxMQ0bNkxsWUdHR6ExHT9+HAMHDuRiat68OQDg5s2bAuX/+usvAECLFi1kEkt9o+hxfkZGBje5M2fOHJw9e7baRKG0nJ2dRY7zJeW9k5OTyHH+4MGDldaPyCPvq+ZIZQEBAbCxscGnn34quyCqUNTx+eTJk5g6dSo8PT3lPoYBY0zsC4AWgDIAblW2bwNw8d+fV6PiHn/+v+0HEFZl/88AMACWkt6zdevWTFdXl/27v9BXw4YNWXJyMrt79y5zcXFhzs7O7Pbt2+zx48cCZVu3bs1KS0vZihUruG0aGhps06ZNzNXVlfXv35/NmTOH/f333ywmJoZpampy+9nZ2bGioiJ28+ZN9vnnn7NevXpxr65du4qtHwCmpqbGduzYwXbs2MF27tzJioqKxL5evHjB2rRpwzp16sSOHj3Kjh07xmxsbJiFhQXLycnh9ktKSmLq6upsxYoVAuVHjx7N9PX12fbt29np06fZl19+ybS1tdmVK1cE9vPz82MA2LRp01hISAjbsmUL09fXZ/369WOFhYVi65iVlcXFtH//fvb+/Xuxr9evX7O2bduyzp07s5MnT7JTp06xLl26MEtLS1ZQUMDtl5aWxtTV1dnq1asFynt4eDB9fX22c+dOdu7cOTZq1Cimra3Nbt68KbDfjRs32NGjR9nhw4cZAObm5saOHj3Kjh49yt68eSO2jsnJyVxMx44dY5K8efOGi+nUqVMsKCiIi+n169fcfunp6UxdXZ2tWbNGoDw/Jj8/P3b+/Hkupri4OIH9lixZwrS1tdkvv/zCoqKi2MyZMxmPx2MhISES6/jkyRO2Y8cOBuCmpHyrS3mvo6Mjt7w3Nzdnr169Yg8fPmQTJ05k9vb2bNGiRezVq1fsr7/+YjweT+C9mjZtykpKStivv/4qMdcrv7S0tLj25Ofnx0pKSsS+Xr58ydq2bcs6derEAgMD2fHjx5mNjQ2ztLRkeXl53H7JyclMXV2drVy5UqC8m5sb09fXZ76+vuzMmTNsxIgRTFtbm12/fl1gv127djEAbPr06SwsLIxt3bqV6evrsy+++IIVFxeLrePff//NxXTgwIE6kSO+vr5s8uTJ7MCBAyw6OpodP36ceXh4MABs48aNEuv48OFDLqbjx4/XmWNZ1dekSZNYy5YtJdbv/fv3LCMjo17mvba2dr3v7xs2bMi1p127dtWJ9vTHH3+wyZMnM39/fxYVFcUCAwO5HPnhhx8k1rFy3h88eLBO5P3FixeZtrY26969O4uNjWVXr17lXvHx8RLrmJSUxMV04sQJlfic0tLS6mXeN23alBkYGIh8tWzZkj1+/Jg9ePCAjR07lo0ZM4bdu3ePpaWlMTMzM24/GxsbVlpayjZs2MBt69KlCysoKGDJycls1qxZzNnZma1cuZIVFBSw+Ph4ZmhoyO3r6urKSktLWXBwMHNzc2MzZ85kWVlZ7OHDh8zU1FRsHVu1asW1pz179tSJ9iTtmDgzM5OLKSAgQCXOXW7fvi3Q3799+1bs6/nz56xNmzbM2tqaHTlyhB09epR17tyZWVhYsH/++YfbLzExkamrq7Ply5cLlOfHtG3bNhYaGsrFdPnyZW6fp0+fMlNTU2ZiYsK2bNnCTp8+zbZs2cKaNWvGzMzMBN5H2It/LKtvea+mplYnxvlLly5lY8aMYV988QWbMGECu3btGsvOzmaWlpYS+3sjIyOuPe3du5eVlZWJfb169YrL+xMnTrCTJ09yeZ+fn8/t9/jxY6aurs5WrVolUN7d3Z3p6+uzHTt2sLNnz7KRI0cybW1tduPGDW6f7du3s0mTJrH9+/ezyMhIduzYMebu7s4AsA0bNkisI78f2bFjBzt06BArLCwU+8rJyeHynp8j/Lx//vw5tx8/75cvXy5QvnKOhIWFcTkSGxtb7b1iY2O5sb2kelV+xcfHczGdOnWqThyfo6OjuTHM5cuX2ZUrV7hXXFycxDo+fvy4Vnlf04PBdQA738ZnAQAAIABJREFUq2x7BOAHEQeCWQAKADSotG0ZgL8B8GpyIJA00QeAtWrVigUGBrJXr16xgoICdvLkSWZubi6wj7m5OWOMsVWrVnHb1NXVWUhICHv27Bl79+4dS0lJYWvXrq02ybBq1SqRg8W0tDSJ9avtRF9RURF7+PAhc3V1ZY0aNWJ6enrMycmJJSUlCeyTlJTEADAfHx+B7Xl5eWzu3LmsefPmTFtbm9na2rKzZ88KfZ/du3cza2trpqWlxUxMTNjMmTMFOmRZTfS9f/+epaens5EjR3Ixubq6stTUVIF9UlNTGQC2cuVKge1v375l3t7eXEw9e/ZkFy5cEHoyLOpzqPpeVV+1nehjjLGMjIxqMaWlpQltI1XbUWFhIVuwYIFATFFRUdXeo6ysjK1du5a1bt2aaWlpMRsbmxrXT9qJPqakvJc00SdN3gNgVlZW7MiRIywzM5MVFhayhw8fsp9++onp6+tXex9vb2/GGGPdu3eXWKfKr9pO9JWUlLCUlBT25Zdfcu3J2dmZPXr0SGCfR48eMQBs+fLlAttfvXrF5s+fz7Wnzz77jJ07d07o++zZs0cg72fPni0wmSiriT7G5J8jsbGxzNHRkZmYmDAtLS3WokULNnDgQBYaGlqj+tV2ok9RxzJhxzZFTfQxJeW9pIm++tDf13aiTxHt6fLly0JzJCQkpEb1q+1EH2Pyz/uqx3Rhn784tZ3oqw+fU5qUE31MSXkvaaLPwMCAde7cmQUHB7OCggJWUFDAQkNDWZcuXQT26dKlC2OMsY0bNwps7927Nzt58iTLyspihYWFLDk5mf3222/MwsKi2vu4ubmxuLg4VlRUxHJzc9mhQ4dY+/btJdavthN99WFMXNuJvvpw7lLbiT7+RFrVmBISEgT2SUhIYADYsmXLBLa/ePGCzZkzhzVr1oyLKTw8vNp78CekzM3Nmba2NjM3N2eTJ09mycnJNapffcx7SRN90vT3qMU4f+3atSw1NZW9e/eOPXv2jO3bt4+ZmZnVaJxf24m+srIylpqaykaMGMG1JxcXF5aSkiKwT0pKCgPAVqxYIbD99evX1cb558+fF9gnJiaGDR06VKAfGTBgAAsKCqpR/dJqOdHH/9tWzZHExESBfRITE7kcqbw9NzeXzZ07VyBHzpw5I/R9Zs+ezTQ0NFhaWppcJ/oUcXxeuXKl2DGMpPrVdqKP92+SivXv47f9AcwGEAtgJgBPAJ0YYxk8Hm81AAvG2OR/928C4CGAaADrALQHsA/AGsbYL5Lez9zcnOXm5uLt27cS61aXqampcbcG8Xg8uTzeWtFyc3O52xh1dHQwfvx4JddIeo8fP8aFCxcAVCyWOXr0aCXXSHpZWVk4ffo0vLy84hhjH3RPgzLyPicnB0VFRZJ2rdO0tLTw22+/Aag4BkyaNEnJNZJeTk4OQkNDAQC6urpyXSNDUR49eoTo6GgAgLGxMUaMGKHcCsnAkydPcObMmXqX9//88w+Ki4s/pLp1RsOGDbF582YAgLq6utwWVlakp0+fcnmvp6eHsWPHKrlG0nv48CEuXrwIoGLR8i+//FLJNZJeRkYGIiIi6l3eFxUVoays7EOqW2fo6elh+fLlAABNTU1MnjxZuRWSgaysLISHhwMAGjVqpBJ948OHD3H9+nUAFf29Kjy188mTJ7h48WK9y/usrKx6/VRboOIBd/w1fLW0tFTi/P7JkyfcrfCNGzeGq6urkmskvaSkJO5W+ObNm0tcQqQ+SEtLw/nz52uc9zV6bAlj7AiPxzMCsByAKYD7AIYxxqo/XqRi/1c8Hm8wKi7/vQngJYBfAGyqYRyEECWjvCfk40N5T8jHh/KekI8P5T0hqq3GzydmjG0HsL0W+98D0O9DKkUIqRso7wn5+FDeE/Lxobwn5ONDeU+I6qq3T90lhBBCCCGEEEIIIYT8H030EUIIIYQQQgghhBCiAmp8664E0QD0ZfS7CCH1QzQo7wn52ESD8p6Qj000KO8J+dhEg/KekHpLJhN9jLFoWfweQkj9QXlPyMeH8p6Qjw/lPSEfH8p7Quo3unWXEEIIIYQQQgghhBAVQBN9hBBCCCGEEEIIIYSoAJroI4QQQgghhBBCCCFEBdBEHyGEEEIIIYQQQgghKoAm+gghhBBCCCGEEEIIUQE00UcIIYQQQgghhBBCiAqgiT5CCCGEEEIIIYQQQlQATfQRQgghhBBCCCGEEKICaKKPEEIIIYQQQgghhBAVQBN9hBBCCCGEEEIIIYSoAJroI4QQQgghhBBCCCFEBdBEHyGEEEIIIYQQQgghKoAm+gghhBBCCCGEEEIIUQEayq6AKIMGDUJpaamyqyEVHo/H/cwYw927d5VYG9koLCzkfi4pKUF8fLwSayMbL1++5H4uKipSiZgKCgqUXYUP4uDgUO/zXl1dnfv5/fv3uH37thJrIxtv377lfi4uLlaJHMnNzeV+LiwsVImYXr16pewqfBAHBweUl5cruxpS0dTU5H4uLy9Xifb05s0b7mdVyfucnBzu57dv36pETPn5+cquwgext7ev93mvra3N/VxWVqYS7en169fcz+/evVOJc5cXL15wPxcWFuLevXtKrI1s1Nf+3tHREYwxZVdDKjo6OtzPZWVluHXrlhJrIxuV25Mq5v2bN29U4vhcec6iJursRN+wYcOUXQWZU4UT/srKy8sRFxen7GrIVFFREW7evKnsany0VDHvVWEAUFlZWZnK5UhhYaHKHcvqEycnJ2VXQeZUrT2VlpZS3hOZGjRokLKrIFOMMZVrT6WlpSp37lJYWIg7d+4ouxofLRcXF2VXQabev3+vEhNIlZWUlKhcjrx9+1bljs81Uadu3eXxeM48Hm9nUVGRsqtCCFEQyntCPj6U94R8fCjvCfn4UN4Tohx16oo+xlgIgBBzc/PpzZs3h5panZqHrDXGGJ49e8b9f4sWLZRYG9koKSnhLoUtKytDRkaGkmskPT09PTRv3hwAoKGhgWbNmim5RtIrLi4WuDWxLquc9ykpKfX+Vh4ej4f27dtz/29mZqbE2shGcXExd8ubmpoaTE1NlVwj6b19+5a75U1TUxNNmzZVco2kV1/z3tTUtN739+Xl5QL9vSrk/bt377j+Xl1dXSXy/s2bN1zeFxcXC3xm9ZWOjk69GbdUznsTExOBJW7qo/Lycjx//hxARd+vCuP8d+/ecf1ISUkJkpOTlVwj6RkYGHCfjar090VFRbW+jU9ZKud9YmJivb91V1NTE+3atQNQkfeq0De+e/cOeXl5AFSnv3/9+jV3S7KWlla96SfFKSws5D6nmqhTE32V9evXT2Dti/qorKwMhw8fBlBxIFCF2xRyc3MRFhYGoOKgcOTIESXXSHodO3bEyJEjAQCNGjWCg4ODkmskvezsbERERCi7GrXm7++Pd+/eKbsaUtHS0sLatWsBVEyKDR06VMk1kl5OTg6CgoIAAA0aNFCJHElJScGlS5cAAI0bN1aJmLKysnD+/HllV6PWBg0aJLDGXX307t07BAQEAKgYJDs6Oiq5RtJ7+vQpQkNDAVRMJqnCsezhw4dc3r98+ZI7rtVnFhYW9fJ2OHt7e2ho1NnTkBopLCzEiRMnAFTk/ZAhQ5RcI+llZ2fjzJkzACrWf9y6dauSayQ9Ozs7jB8/HgCgr6+PgQMHKrlG0svMzMTFixeVXY1a8/X1xfv375VdDakYGhpizZo1ACom/VRt/Kinp6cSY5j79+/j6tWrACom+1VhDJOWlobIyMga71+/v0InhBBCCCGEEEIIIYQAoIk+QgghhBBCCCGEEEJUAk30EUIIIYQQQgghhBCiAmiijxBCCCGEEEIIIYQQFUATfYQQQgghhBBCCCGEqACa6COEEEIIIYQQQgghRAXQRB8hhBBCCCGEEEIIISqAJvoIIYQQQgghhBBCCFEBNNFHCCGEEEIIIYQQQogKoIk+QgghhBBCCCGEEEJUAE30EUIIIYQQQgghhBCiAmiijxBCCCGEEEIIIYQQFUATfYQQQgghhBBCCCGEqACa6COEEEIIIYQQQgghRAXQRB8hhBBCCCGEEEIIISqAJvoIIYQQQgghhBBCCFEBNNFHCCGEEEIIIYQQQogKoIk+QgghhBBCCCGEEEJUQL2e6MvKysK4ceNgamoKExMTjBkzBk+ePKlR2Xfv3mHZsmVo06YNjIyM0L9/f1y+fLnafrm5uVi0aBE6deoEIyMjWFtbY+HChcjJyZF1OACAJ0+eYMyYMWjevDmaNWsGDw8PZGZm1qjsu3fv8N1338HS0hIGBgb44osvhMYEAH///Te8vLxgYWGBJk2aoGPHjlixYoUsQ+GYmppi+/btuHv3Lu7duwdfX1+0aNGiRmVbtGiBX375BbGxsUhKSsKFCxfwzTffQEdHh9unadOm+PbbbxEcHIy7d+8iLi4OBw8eRM+ePeUSD1DxOX311Vdo2rQpjI2N4e7uXqvPaenSpTA3N0eTJk3Qr18/XLp0qdp+7du3h7a2drVXUFCQrMOpV1q2bIl9+/YhIyMDGRkZ2L9/P8zMzGpU1szMDNu3b8e9e/eQnZ2Nv/76Cz4+PmjYsCG3T/PmzbFy5UpcuHAB6enpSE5OxqlTp9CnTx95hYQnT57Azc0NBgYG0NfXx6hRo2rVnr799lu0bNkSurq6+M9//oOYmJhq+7Vp0wbq6urVXqdOnZJ1OAAqYvLw8ICxsTGMjIzg5uZW6xxp3bo1GjduDDs7u2o5sn//fmhpaYl8PXv2TC4xyTPv9+/fLzTn+S95xFRf8NuTkZERDA0Na92elixZglatWqFRo0bo27ev0GPuJ598Ak1NzWoveR1z+Xmvr6+PJk2a1DrvFy9ejBYtWqBhw4bo06dPtbx//fo1PDw80K5dO+jp6cHAwAC9evXCgQMH5BEOAMUcyyo7fPgw1NXV0bp1a1lUXygTExNs2rQJV69exbVr17BlyxaYmJhILDd79mzcv39f6CsuLk5gX319faxduxYxMTG4efMmAgIC5Nrn1BeqOs6X57Hs9evXGDNmDDp27IgmTZrA2NgYn3/+OQ4ePCiPcABUjK2OHTuG/Px8vHr1CsePH0erVq1qVNbCwgLHjh3Dy5cv8ebNG1y4cAE9evSott+CBQsQHByM7OxsMMawatUqWYchICsrC2PHjoWJiQmaN2+Or776qtZtz9LSEoaGhrC3t6/W9vz9/dGwYUORr4+5vzczM8OhQ4fw/Plz5OTk4MiRIzVuT61atcKuXbuQnJyM/Px83L9/H6tXrxYY5/fr1w/FxcUiX/I4f1TF80Z5j2EAYNOmTXBxcUGLFi2gpqaG1atXyzgKQfIewzx69Aje3t7o2rUrGjdujJYtW8LV1RV37tyRRzjQkMtvVYDCwkIMGzYMWlpa2LlzJ3g8HtasWQNHR0dcv34durq6YsvPmjULZ8+exfr162FhYYGdO3fC1dUVFy5cwKeffgoAYIzB3d0dKSkpWL58OTp06ICkpCSsW7cO8fHxiIqKAo/Hk2lMjo6O0NbWhp+fHxfT0KFD8ddff0mMaebMmThz5gw2bNgAS0tL+Pr6wtnZGdHR0VxMAJCRkYH+/fvDwsICP//8M5o3b46MjAw8fvxYZrHwNWjQAAEBASgpKcGiRYvAGMM333yDQ4cOYejQoSgqKhJZVkdHBwcOHICmpiY2bdqE7OxsdOnSBQsWLIClpSXmzJkDALCxsYGTkxOOHTuGW7duQVNTExMmTMDhw4cxbdo0XLhwQaYxFRYWYsiQIdDW1sauXbvA4/GwevVqODg4IC4uTuLn5OXlhfDwcPzwww/c5+Tk5ISYmBiBzwkABg8eXG0Ctn379jKNpz7R0dFBUFAQSkpKMGvWLDDGsHz5cgQHB6Nv374oLCwUWbZhw4Y4efIkNDU1sX79emRlZaF79+5YunQp2rRpA09PTwBA165dMWLECBw8eBA3b96ElpYWPD09ERISgrFjx+Ls2bMyjamwsBCDBg2CtrY29u7dCx6Ph5UrV2LgwIG4ffu2xPY0bdo0nD59Gj/++CPatGmD7du3w9HREbGxsejatavAvg4ODtUGxx06dJBpPPyYhgwZAi0tLezevRs8Hg+rVq2qcY7MmDED4eHh2LhxIywtLfHHH39g+PDhiImJ4WJydHSsNtBhjGHEiBGwtLSs0Yn4h8Qkz7x3dHSsNihgjGHkyJFyiam+KCwshIODA7S1tbFnzx4uRwYPHoz4+HiJf/vp06dz7alNmzb4448/MGzYMFy6dElojlQ95sorRwYOHAhtbW3s27cPPB4PK1aswIABA3Dnzp0a5X1YWBj++9//cnk/dOhQXLlyhYuppKQEGhoaWLp0KSwsLFBcXIyjR49i4sSJyMnJwYIFC2Qek6KOZQCQn5+PhQsXyjUvGjRogN27d6OkpAQ+Pj5gjGHu3LnYu3cvRo4cKXYMc/z48Won9zo6OvD19UVUVBS3TVNTE3v27IG+vj42bdqEFy9eYOTIkdi2bRtmzJiBv/76S27x1WWqOs6X97GMn/fffvutQN5PnjwZOTk58Pb2llk8QEWbvnDhAoqLizFp0iQwxrBu3TpERUWhS5cuYsdlhoaGuHz5Ml6/fg0vLy8UFhZi4cKFiIqKQs+ePZGUlCQQe0FBAU6dOoVZs2bJNIaqKp+P8dve999/j6FDh+LGjRs1antnzpzB+vXrYWlpiR07dsDFxQVRUVFc2xs6dCiio6MFyjHGMHr0aFhYWHy0/b2Ojg7Onj2L4uJiTJs2DYwxrF69GhEREejRo4fEcX54eDg0NTWxZs0aPHnyBLa2tlixYgU++eQTjB8/HgBw69Yt2NnZVSu/Y8cOGBgY4ObNmzKNSRXPGxUxhgGAXbt2oXHjxnB1dcWOHTtkHkfVmOQ9hjl37hyio6MxceJEdOvWDfn5+fj555+5iU5hX3JIQyYTfTwebzUAC8bYZFn8vprYu3cv0tLScPv2bbRt2xYA0LlzZ3Tp0gW7d+/GvHnzRJa9e/cujh49ij/++AMTJ04EANjZ2cHW1hbr1q3DsWPHAAApKSm4du0afvvtN0ydOhVAxbcAampqmD9/PpKTk2WaPHv27EFaWhru3r3LxWRjY4POnTtj165dmD9/vtiYjhw5gh07dgjE1L17d6xduxaBgYHcvnPnzkWLFi1w9uxZaGpqcvvKw5gxY9C6dWsMGDAAGRkZAIDExERER0dj7Nix2L17t8iytra2aNOmDSZMmMCd0F+9ehX6+vqYPn06GjRogHfv3uHmzZvo378/ysvLubIxMTGIiIiAl5eXzCf6du/ejbS0NNy7dw+ffPIJgIrPqVOnTvDz8xM7iLp79y4OHz6MnTt3YtKkSQAq2lTXrl2xZs0anDhxQmB/Y2Nj9OrVS6b1lxVl5P3EiRNhYWGBzz77DGlpaQCABw8eIC4uDpMnT8b27dtFlu3Vqxc++eQTjBw5kjvJunz5MgwMDDBnzhzo6OigqKgIV69eha2trUB7ioyMxNWrVzFv3jyZT/Tt2rULqampSExM5NpTly5d0KFDB+zcuVPsyfidO3dw6NAh7Nq1C1OmTAEAfPHFF7CxscGqVauqfYtnbGyM3r17y7T+wuzevRupqam4f/++QI5YW1tLzJE7d+7g8OHD8PPzE8iRTz/9FGvWrMHJkycBVFzJ27RpU4Gyly9fRm5uLlauXCmXmOSd9+JiktcV17WljLzn58iDBw8E/vZWVlY1yhF+e5o8eTIA4e2Jz8jISCE54ufnh9TUVCQlJQnkffv27bFjxw4sXLhQZNk7d+4gICAAu3fvFsj7zp07C+S9kZFRtat4hg0bhkePHmHv3r0yn+hT5LEMAJYsWYIuXbrA1NQUkZGRMo2Fb/To0TAzM4OTkxN3Nc+jR48QFhYGNzc37N+/X2TZf/75B//884/ANmdnZ2hqaiI4OJjbNmTIELRv3x5TpkzhJvUuX76MEydOYOHChRgzZowcIqsdGufLhiKOZUZGRvD39xco6+joiOTkZOzbt0/mE33Tp09HmzZt0KFDB+6Cgbt37yI5ORleXl7YvHmzyLKzZs1C8+bN0a9fP6SmpgIALly4gNTUVKxZswYeHh7cvp06dQJjDOrq6nKf6OO3vTt37gicj9nY2NSo7R05cgS+vr4Cba9Hjx4C52PC+vvY2Fjk5ubCx8dHTpHVjjLy3tPTE5aWlrCxseHa07179/DgwQNMnz4dv/76q8iyffr0Qbt27TB8+HCcP38eAHDx4kUYGBhgwYIF3Dj/9evXuHHjhkDZ1q1bo2PHjtiyZQvev38v05hU8bxREWMYALh//z7U1NRQVlYm94k+RYxhPDw8MHv2bIEvkAYMGIA2bdpg69at+PPPP2UaU41v3eXxeLN5PF4aj8d7x+Px4ng8nsiZIR6P14DH4+3j8Xh3eTxeKY/Hi5ZJbSsJCwtDz549uQMwUHH59+eff46wsDCxZU+fPg1NTU2MHj2a26ahoYHRo0fj/PnzKC4uBlDxrRgANGrUSKB8kyZNAEDmBwJxMYWGhkosKywmNzc3nDt3jospNTUV586dw+zZs7lJPnkaNGgQbt26xU3yARWXw8fFxcHBwUFsWX793rx5I7C9oKAAampqXJIUFBQITMoAQHl5ORITE+XyjVhoaCg3acRnaWmJPn36SPycQkNDoampCTc3N26bhoYG3N3dBT6nuqKu5b2joyNu3rzJTfIBQGZmJq5fv45hw4aJLaulpQWg4raWyl69elWj9nTv3j2YmprKIgwBISEh6N27d7X29J///EfgZFBUWU1NTYHBsIaGBjw8PBAREaG09iQuR0JCQiSW/dAc8ff3h5aWlsDfQ1aUlfcHDhyQW0yi1LW8l0V7cnd357bx//bKzBFp8j44OFhk3vOvhBDHyMgIGhqyv6FDkcey2NhYHDx4EL///rtsg6jC3t4ed+/eFbhl7++//8atW7fQv3//Wv8+FxcXvHjxArGxsdy2Ll26oKioqNqVe1euXIGNjQ2aNWv24QHUQl3Le1Uc5yvzWCavvHdxccG1a9cE7gpKT09HbGwsXF1dxZbt3bs3kpOTuUk+oOKqmkuXLsHJyQnq6urcdsaYzOsuijzOx6q2PWH4/X3lz1je6lreOzk54fr169Xa05UrV+Dk5CS2LH+cX1BQILA9Pz9fYJwvzNixY6GmplZtklwWVPG8UVFjGDU1xa0yp4gxjLGxcbV22KRJE7Rv3x7Z2dkyjKZCjf56PB7PA8CvADYA6AbgCoBwHo8nalEUdQDvAPwOQHxv/IESExNhbW1dbbuVlZXApd6iylpYWAjcr88vW1JSwh1crK2t0bdvX/z444+Ij4/HmzdvcPPmTWzcuBEODg7o2LGj7AL6t16dOnWqtt3a2lpiTAkJCTWK6erVqwAqbkcZPnw4mjRpAlNTU3h6eiI3N1dGkfxf+/bt8ejRo2rbHz16JJBIwsTGxiI1NRVLly7FJ598goYNG+Lzzz/HlClTcPDgQbG3zGhqaqJbt25ISUmROoaqxLW9xMREsWVFfU7W1tYCnxNfWFgY9PX10ahRI9jZ2Sl0fb66mPcdO3YU+jdOSkqSeHtddHQ0UlJSsHr1anTo0AG6urqws7ODl5cX9u7dK/Z2AE1NTfTs2VNoW5bWgwcPROZ9QkKCxLKWlpYi21PV9h8aGgo9PT3o6OigT58+clufLyEhQWRM0uaIqJwuKirC8ePHMWzYMBgaGn545UVQZN7zyTsmYepi3kvbnmqTI2FhYWjcuDG3voq8jrnS5L2omDp16iQ0JsYYysrKkJubi507d+Ls2bMyv6oHUNyxrLS0FDNnzsSiRYskjiOk9cknnyA5Obna9sePHwtMAtSEiYkJevbsidDQUIEvk96/f4+ysrJq+/MnoeQdI1A3814Vx/mKPJZVzns/Pz9ERESIvTPoQ3Xq1An379+vtv3BgwdCP7/KysvLuXZeWXFxMRo2bFjrHJOVhIQEmbe9mvT3J0+ehKOj40fd31tZWQntLxITE2FlZSW2bGRkJJKTk7F+/Xp07NgRurq6sLe3x5w5c+Dn5yd2nD9+/HjEx8dL7Ks+hCqeNypyDKMoijwfqywvLw/379+XeX8D1PyKvoUA9jHG/BhjiYyxuQCeAhB67TRj7C1jbCZjbCeALBnVVcDLly+hr69fbbuBgQFevnwptmxeXp7IsvzfDQA8Hg8nTpxAu3btYGdnh+bNm+OLL76AhYUFAgICZBBFzeslKSZRfw9+Z5GXlwcA3GzxzJkz0a5dOwQFBWHdunU4c+YMXFxcZP7tZZMmTfDq1atq2/Pz87lvTEUpLi6Gm5sbeDwezp8/j4SEBBw6dAiRkZESb83z9vaGqakpfH19paq/MHl5eVxbqczQ0LBGbU9YWf42/ucEVNxitXnzZoSGhuLPP/9EgwYN4O7uLpe2J0Kdy3sDAwPk5+dX2y6q/VdWXFwMR0dHqKmp4dq1a8jKykJwcDDOnj2LxYsXiy27dOlStGjRQuwtAx9KHu2pat4DFd+S/vrrrwgPD4e/vz8aNGiAUaNGyWVhfmmPZeJiElU+KCgIBQUFmDBhwgfUWDJF5X1lwcHBKCgo4NaVUZA6l/fS/u3F9Y2Vyw8fPhxbtmzB6dOnsX//fjRo0ACjR4+WyyL2isp7ANi2bRu0tLTQtGlTzJ07F1u2bOFuKZMlRcX03//+F8XFxVi6dKmUNZasSZMm1a4OASquBG/cuHGtfhf/CqWqVwakpaWhUaNGaNOmjcB2/tpLksZKMlLn8l5Vx/mKOJYBwPbt26GjowMTExPMnz8fmzZtkkv/KKruomKt7OHDh2jXrp3AxBaPx+MehqCoCa+qRI1DpDkfq9r2qgoJCUFBQQHGjRv3ATX+YHUu76VpT8XFxejfvz/U1NRw584d5OXl4ezZszh9+rTYSe5evXqhXbt2cntQlSqeNyohjyVjAAAgAElEQVRyDKMoyopp3rx5YIzJ5YsYiddw83g8LQA9APxc5Z8iAMjskWA8Hm8GgBmA8g7swnz99de4ceMGtm7dig4dOuDhw4dYt24dxo0bh8DAQIVeUioL/Evf+/Xrhy1btgCouDWlcePGmDhxIs6dO4chQ4Yos4ocbW1t/P777zAyMoK3tzeys7Px6aefYt68eSgvL8fy5cuFlnNxccGsWbPw22+/1etFrPmfD5+rqyvs7OywYsUKjB07Vq7vray85192Lw/8xa+NjY3h5eXFPYxj8eLFKC8vxzfffCO03OjRo+Ht7Y2ffvqJuyK2Ptq6davA/48YMQJ9+vSBj4+PoieS5OLAgQNo1qwZHB0dlV0VmfH391doTB97f191Iv/LL7/Ef/7zHyxfvlzRJ18y5eHhgd69e+PFixcIDg7GvHnzoK6uDi8vL2VXrdZSUlKwYcMGHD9+HA0aNFB2dWrFxcUFCQkJ1a4MP336NL7++musX78eK1euRE5ODtzc3LhFueV9y+LHnveqNs7nc3d3R69evZCbm4uQkBB4e3tDXV0dM2bMUHbVOL6+vpg3bx7279+PefPmobCwED4+PrC0tAQg+1un6zL+GGbo0KEKeT9VzHttbW3u7zh58mQ8efIEn332GZYtW4by8nLMnTtXaLkJEyagpKQEhw8flmv95E2Z543kw2zcuBGHDh2Cn5+fXK7er0nvZYyKS3X/qbL9HwAyWwCNMbaTMWbLGLPV09OTuL++vr7IK3skzfiLuyqI/+8AcObMGRw7dgy7du2Cp6cn+vbtC09PT+zatYv7hkCWxNVLUkyi/h78GWT+wZX/3wEDBgjsN2jQIACQ+eOdX716JfTbaH19faFX+lXm7u7O3ap76tQp3LhxA35+fli/fj3Gjx8v9BLugQMH4ueff8aRI0fELgAsDVHf6NXk2yZRZfnbxHWC6urqGDlyJLKysvD06dNa1rrW6mTe5+fni/ymVFj7r2zChAmws7ODu7s7jh49iitXruD333/HihUrMHXqVHTu3LlamaFDh2Lbtm3w9/fHxo0bax5YLcijPVXNe2HU1dUxevRoubQnaY9l4mISVv7p06eIjIzEV199JZc1iPjvq8i8f/r0KS5cuAAPDw+5xSREncx7af/24vpGceXlnSOKyvumTZvC1tYWQ4cOxfbt2zFhwgQsXrwYpaWlUkRQu3rJKqb58+ejf//+6N27N/Lz85Gfn4+SkhIwxpCfny92SY8PUVBQIPTKPVFX+onSuXNntGnTRug6P69fv4a3tzf09fVx8uRJXL58GSNGjOAeLpWTk/PhAdRMncx7VR3nK+pYxs/7IUOG4Pfff8e4ceOwZMkSmee9uKvwJV0Fk5aWhnHjxqFHjx54/Pgxnj59is8//5wbvytgrCuUuD77Q8/Hqra9yp4+fYqoqCi4u7t/9P29NO1pypQpsLe3h6urKw4dOoTLly9j8+bNWLJkCWbMmAEbG5tqZbS0tDBq1CiEh4fLZQkrQDXPG5V17iJPio7J19cXPj4+WLt2LfcwKFmrn19TQfR97UlJSRLvcbayskJ6enq1e/WTkpKgpaXFrQnBX3Oi6qOObW1tuf1lSdy6BJJisra2rlFMktY3ELdQ6YdITk5Gu3btqm1v166dxHvwO3bsiPz8fGRmZgps509GVp357tOnD7Zv346IiAgsW7ZMypqLJmotlaSkJIl/X1GfU2JiosDnJImsP6f6QlR+87+FF8fa2hovX75Eenq6wPb4+HgA1R8/369fP+zduxehoaEyfzplZZ06dRKZ95LWt+nUqRPS0tJEtqeafjsk6/Ykaj2LmqyxIilHhMUUEBCA8vJyuV6ZqOi858ckr1uR6xNp21NdzBFp8l5UTAkJCTWKqUePHnjz5k21J8JKSxHHssTERISHh8PIyIh7HT58GNnZ2TAyMpJ535+SkiL079m2bVuRa20J4+rqitLSUpEPkYiPj4ejoyOGDRsGZ2dnDB8+HGVlZSgqKpLLmlH1gSqO85V5LJNX3kuzrhUAnDhxAi1btoSVlRXatm0LW1tb6OnpITMzU+AhOIokj7Ynrr8/fPgwysvL6/WV47KSkJAgNBdErdFdWefOnZGXlyfwcBcA3N1dwj47JycnGBoayu22XUA1zxuVOYaRF0Wej/n7+2POnDlYuHChXOcsajLR9wJAOYDmVbY3B/BM5jWqoeHDh+PGjRsCT9/MyMjA1atXJT5909HREaWlpQKPpC4rK8Px48cxcOBAaGtrAwCaN68I+ebNmwLl+QeMFi1ayCQWPnExDR8+XGzZYcOGCY0pMDAQgwYN4mLq1asXTExMuMeO80VERAD4/+BGVs6fP49u3bqhVatW3DYzMzP06NED586dE1s2JycH+vr6MDc3F9jetWtXAMCzZ/9vft27d4efnx9iY2Ph7e0t11tdhg8fjuvXrwt0JPwnQkn6nIYPH47S0lIcP36c21ZWVoZjx44JfE7C8D/P1q1by+VpwlXUybwPDw+Hra2tQJto1aoVevXqhfDwcLFl//nnHxgYGHC3hPDxB/iVv+367LPPcPDgQcTExMDLy0uu7cnZ2RnXrl2r1p5iY2Ph7OwstqyTkxNKS0tx7NgxbltZWRmOHj2KwYMHS2xPR48elUt74j81TViOSHpqGj9HAgMDBeoqLkcOHDgAGxsb7tggD4rO+4MHD8LGxoZbp0tB6mTei2tPknJEXHtSZo5Ik/fOzs4i897BwUFsTAAQExMDPT09mT/NVRHHsoCAAERGRgq8HBwcYGxsjMjISHz99dcyjSkqKgpdunSBmZkZt61Fixbo2rUroqOja/Q7NDQ04OjoiEuXLkm8IiUzMxNpaWnc+pAhISEyv0pRiDqZ96o4zlfWsQwALl26JJe8Dw4ORu/evQXGVubm5jV6UiXf+/fvkZSUhNTUVJiamsLDwwN//PGHTOtZG/I4H6va9ioLCAig/v5fYWFh6NWrV7X21KdPH4lP23727BkMDQ2rTX7x13wU9lTTCRMmICcnR+ZX71amiueNyhzDyIuizsdOnjwJT09PeHp64qeffpJ9IJVInOhjjJUAiAMwuMo/DUbF03mUYsqUKTA3N4e7uztCQ0MRFhYGd3d3mJmZwdPTk9svMzMTjRs3xg8//MBt69q1K0aPHo0lS5Zg3759iIqKwqRJk5Ceng4fHx9uP1dXV5iammL69Onw8/PDxYsX4efnh+nTp8PMzAwuLi4yjWnq1KkwNzeHm5sbQkJCEBoaCjc3N5iZmWHatGncfhkZGdDT08OGDRuqxbR48WLs3bsXUVFRmDBhAtLT0wXWstPQ0MDatWsRHh6OuXPn4vz589ixYwe8vb3Rr18/2NvbyzSmQ4cOISsrC35+fhg8eDAGDRoEPz8/PH36VGBx0JYtWyIlJQXz5s3jtgUGBuL169fYu3cvRo0ahc8//xwzZszAsmXLcPfuXW5g1rZtW+zZswcvX77Ezp07YWNjg27dunEvWfP09IS5uTlGjx6N4OBghISEYPTo0TAzM8P06dO5/TIyMtCwYUOsX7+e29a1a1e4ublh0aJF2LNnDy5cuIDx48cjPT1d4AEjR44cwfjx43HgwAFER0dzB79bt24J/D55qat5v3//fmRmZiIgIACOjo5wdHREQEAA/v77b+zbt4/br1WrVsjJyRF4yEZAQAAKCgpw9OhRfPXVV+jbty/mzp2L77//Hrdu3cK1a9cAVFxteuTIEeTl5WHr1q3o2rUrbG1tuZesTZs2DRYWFhgxYgSCgoIQHByMESNGoFWrVgJr6WRkZEBLSwtr167ltnXr1g3u7u5YuHAhdu3ahcjISIwZMwZpaWlYvXo1t9+hQ4cwZswY7N+/H1FRUTh8+DAGDhyI+Ph4gWOjrHh6esLCwgKjRo3icmTUqFFo1apVtRzR0dHBunXrBGKqmiPjxo1Deno6Vq1aVe29bt26hQcPHsj9yjdF5H3VmBS9dmJdzXt+jowcOZL7248cOVJoe2rQoEG19uTu7o5vvvkGu3fv5tpTWlqawN/+8OHDGDduHPz9/REdHY0jR45g0KBBuHXrlkBfKyvTp0+HhYUFvvzySy7vv/zyS7Rq1Upg7byMjAxoamri+++/F4jJw8MDCxYsEJv3O3bswNSpU3Hw4EFcvHgRJ06cwJgxYxAYGAgfHx+Zr4mqiGNZ7969YW9vL/AyMTGBtrY27O3tZX4lwPHjx5GdnY2tW7eif//+sLe3x2+//YZnz57h6NGj3H6mpqa4ffs2Zs6cWe132NvbQ19fX+ykh7e3NwYPHozPPvsMo0aNwtGjR1FWVlZtzSV5qKt5r4rjfEUcy3bu3AlPT08EBAQgJiYGJ0+exLhx43D8+HEsW7ZM5nnv5+eH9PR0BAUFwcXFBc7OzggKCsKTJ0+wY8cObr/WrVujtLQUK1as4LZpaGhg06ZNcHV1Rf/+/TFnzhzcvHkTDx48wC+//CLwPj169MCoUaMwcuRIABVXBY0aNQqjRo2Cjo6OTGOq3Pb452Oi2l6jRo2Eno99++233PnYxIkTq52P8fH7e0VfzVdX83737t3IyMhAYGAgnJ2d4eTkhMDAQO5ckq9169Z4+/atwNVQ/v7+KCgoQFBQEMaPH48vvvgCCxcuxMaNGxEXF4crVwTDatq0KQYPHswdb+VFFc8bFTGGASq+hAkMDOQmzhMTExEYGIjAwECxT1H+EIoYw8TExGDcuHH49NNPMWnSJFy7do173bp1S6bxADV4GMe/NgHw5/F4NwDEApgJoAUAkY805fF41gC0ULEGgB6Px+sKAIyx21LV+F+6uro4ffo0lixZgmnTpoExBnt7e/z3v/9F5TUAGGMoLy+vtqCrr68vVq9ejTVr1uDVq1ewsbHBqVOnBCaGGjdujOjoaKxfvx6bN2/Gs2fPYGJigmHDhsHHxwc1WWugtjGFh4fj22+/haenJxfTzz//XO29hMW0c+dOrFq1CmvWrEF+fj5sbGwQHBxcbbJr/PjxUFNTwy+//IL9+/fD0NAQX331FdauXSvzS3uLioowduxYrFixAps2bQKPx8OVK1fw/fffCyQoj8eDhoaGwKLHWVlZGDFiBLy9vfHNN9/A0NAQ2dnZOHToEH7//XfuKqtu3bpBX18f+vr6QhdStbCwkGlMurq63JNap06dCsYY+vfvX+1zEtX2/Pz8sHLlSqxevRr5+fno0qULQkJCBD4nCwsL5OTk4LvvvkNeXh50dXXRvXt3hISEwMHBQabxiFHn8r6wsBCurq5Yv349fH19wePxEBMTg++++w5v374V2Ldqe3ry5AkcHBywZMkSLF++HIaGhvj777/x559/4pdffuHak62tLQwMDGBgYIDQ0NBqdZC0TkNt6erq4vz581i4cCEmTZoExhgGDBiAzZs316g97dmzB8uXL8fKlSuRn5+PTz/9FKdPn0b37t25fSwtLZGTk4MlS5Zw7cnW1hanT5+Wy8N3+DmyaNEiTJkyhcuRX375pUYx7dq1CytXrsSqVau4HAkNDRU6ce/v7w8NDQ2MGTNG5nEIi0meea/omESoc3mvq6uLiIgILFq0CJMnT+ZypDbtacWKFQLtKSwsTCBHLCws8Pz5cyxdupTLkR49eiAsLEwux1xdXV1ERkZi4cKFmDhxIhhjGDhwYK3y3sfHBytWrODyPjw8XCAm/hhg8eLFyMvLg7GxMaysrBASEiLxKoIPjUnexzJFKyoqwtSpU7FkyRL88MMP4PF4uHbtGn788UeBK+2EjWH4XFxckJ+fL/YKQCMjIyxZsgRGRkbIzc1FZGQktm3bVqt1AKVUJ/NeFcf58j6Wde7cGSEhIVx/b2xsjI4dOyIoKEjilZAforCwkMtzf39/8Hg8REZGwtvbW2BcJixHGGNo164dxo4dC319fWRlZWHPnj3YsGFDtbUE58yZg8mTJ3P/7+7uDnd3dwAVx++MjAyZxVT5fKxy2/vpp59q9Dnt2LGjWtsLCgoS2t8fPHgQGhoa8PDwkFn9a6HO5X1hYSGGDBmCn376CXv27AGPx0NUVBQWLVoksT1lZGRwD59YvXo1jI2NkZWVhd27d2Pjxo3V7s4ZM2YMNDU14e/vL4uqi6SK542KGMMAwLZt2/Dnn39y/3/s2DHuqrnU1FSZnuMrYgwTFRWF4uJixMfHw87OTqC8ubl5tdvOpcWr6S1pPB5vNoBvAZgCuA9gAWMs5t9/Ww3AgjE2udL+6QDMq/4expjEmSRzc3N2+/ZtpV26KStlZWXcxBOPx1OJtZZyc3O5S6ffvHlT7Ume9VHHjh25bwgNDAzg6uqq5BpJLzs7GxEREfDy8opjjH3wZWiKzvuSkhK8e/fuQ6tbJ1T+lkdNTU1uC6wqUk5ODoKCggAADRs2VNaAVKZSUlJw6dIlABUn2ZIuy68PsrKycP78+XqX90lJSdDU1PzQ6tYJ7969465SV1dXV4m8f/r0Kfdlh56eHr766isl10h6Dx8+5PK+6lV59ZWFhQVcXFzqXd7fu3dPkQ8ekIvCwkLuShMNDQ1MnDhRyTWSXnZ2Ns6cOQMAeP78ucAVePWVnZ0dd4V806ZNFfZkW3nKzMzExYsX613eP3/+vN4/TdnQ0BBr1qwBUDHmV4Un2vLHj0DFw6b4k+j12f3793H16lUAFcs0qMI4Py0tDZGRkTXO+xr3sIyx7QC212J/i5ruSwipmyjvCfn4UN4T8vGhvCfk40N5T4jqqrdP3SWEEEIIIYQQQgghhPwfTfQRQgghhBBCCCGEEKICZLU4RjQAfRn9LkJI/RANyntCPjbRoLwn5GMTDcp7Qj420aC8J6TekslEH2MsWha/hxBSf1DeE/Lxobwn5ONDeU/Ix4fynpD6jW7dJYQQQgghhBBCCCFEBdBEHyGEEEIIIYQQQgghKoAm+gghhBBCCCGEEEIIUQE00UcIIYQQQgghhBBCiAqgiT5CCCGEEEIIIYQQQlQATfQRQgghhBBCCCGEEKICaKKPEEIIIYQQQgghhBAVQBN9hBBCCCGEEEIIIYSoAJroI4QQQgghhBBCCCFEBdBEHyGEEEIIIYQQQgghKoAm+gghhBBCCCGEEEIIUQE00UcIIYQQQgghhBBCiAqgiT5CCCGEEEIIIYQQQlQATfQRQgghhBBCCCGEEKICeIwxZdehGnNzc+bj46PsapAaqIvt50PweDxlV0EuvLy84hhjtsquR02Ym5uzZcuWKbsaMqGq7YnUD/Ut76m/J8qgKuMXoKLPobwnsqZqOaKK6lve0zifEOnVNO81FFEZorroQEdkidoTIYQQRaD+hhDxKEeIrFGbIkRx6tStuzwez5nH4+0sKipSdlUIIQpCeU/Ix4fynpCPD+U9IR8fyntClKPO3robHx8PbW1tZVdFKuXl5Thy5AgA4P3791i5cqWSayQ9ExMTzJkzBwDQoEEDODs7K7lG0nvy5AmuXbsGANDX11eJmLKzsxEZGVnvLukvLS1FfR8IaGtr4/vvvwdQ8c3lhAkTlFwj6eXk5CA8PBwAoKOjg9GjRyu5RtJLTU1FbGwsAMDAwAAODg5KrpH0nj59ipiYmHqX93FxcdDQqN83GLx79w7BwcEAAHV1dbi7uyu5RtJ7/vw5IiMjAQC6uroYNWqUkmskvZSUFFy5cgUA8OLFC4SFhSm5RtIzMzPDwIED613e3759u97nfWFhIYKCggAAGhoaKpEjz549w8WLFwEAenp6cHJyUnKNpJeSkoKbN28CADIzM7Fz504l10h6VlZWGDduXL3L+9LSUrx//17ZVZGKvr4+Fi5cCADQ1NTEiBEjlFwj6T19+hSXLl0CUJH3w4cP/x979x0WxdX2Afg3dATpYBfWgjSxYI2CYmKjmDcqYBJ7I/LFGGtUxF6jiZpEIygae0FREQTFhr2BilKUqqDGRrGgNM/3B+/Oy7osdQusz31de4Wsc+A8M/Occ+ZMU3CNai4pKQkxMTEAAFNTU6UY5z969AgXLlyo+7fuampqQktLS9HVqJGioiKR/3/37p2CaiI9pSdhOI6r89sIKGmkhVRUVJQiJg0NDUVXoVrevXtX5yf6CgsL+Z+VJUdKn3RRlhwpnffKsp1Kx1SXaGpq1tm6S6IM+1PpfkQZc+Tjx4/48OGDAmsjHQUFBYquQrVoaWnV+Ym+TycslCFHlD3vi4uLleJ4rK62Xe/evavzE33KniPKMs4v3b8oS0xVHSvXqlt3CSGEEEIIIYQQQggh1UMTfYQQQgghhBBCCCGEKAGa6COEEEIIIYQQQgghRAnQRB8hhBBCCCGEEEIIIUqAJvoIIYQQQgghhBBCCFECNNFHCCGEEEIIIYQQQogSoIk+QgghhBBCCCGEEEKUAE30EUIIIYQQQgghhBCiBGiijxBCCCGEEEIIIYQQJUATfYQQQgghhBBCCCGEKAGa6COEEEIIIYQQQgghRAnQRB8hhBBCCCGEEEIIIUqAJvoIIYQQQgghhBBCCFECNNFHCCGEEEIIIYQQQogSoIk+QgghhBBCCCGEEEKUAE30EUIIIYQQQgghhBCiBGiijxBCCCGEEEIIIYQQJVCnJ/oyMjLg5eUFY2NjGBkZwcPDA48ePapU2Q8fPuCXX35Bs2bNUL9+ffTs2RMXLlwot8z+/fuhrq4OCwsLKdS+bE2aNMGuXbvw5MkTPH36FHv27EHTpk0rVbZp06YICAhAYmIiXr58idu3b2P+/PmoV6+eyHLa2trw9fXF7du38fLlS9y/fx+bN29G8+bNZRESMjMz8d1336Fhw4Zo0KABhg0bhoyMjEqV/fDhA+bOnQuBQAAjIyP07t0bFy9eFFvu1atXmDFjBmxsbGBkZARra2tMnToVL168kHY4AEr2PQ8PDxgYGEBfXx9Dhgyp0r43c+ZMNG7cGPXq1cMXX3yB8+fPiy33+++/Y9CgQWjcuDFUVFSwcOFCKUdRNzVt2hT79u3Dixcv8PLlSxw4cADNmjWrVNlmzZohMDAQycnJyM3NRVxcHBYtWiSWIwBgYGCANWvWIDk5GW/evEFqaiq2bNki7XAAlOxPQ4cOhb6+PvT09DB48OAq70+NGjWCtrY2unfvLrY/vXnzBp6enmjVqhV0dHRgYGCALl26YNeuXbIIB4B8cqS0ffv2QUVFpdL7QnVkZGTg22+/hZmZGUxNTeHl5VWlmObMmQMLCwsYGBigV69eYn3Ojh07oKWlJfHz77//yiKsOkG47hs0aAAzM7NqrXuBQABDQ0P06tVLYj8yffp0WFtbw9DQEFZWVvj5559l2o94eXnBxMQExsbGVR7DzJ49G82bN4eenh4cHR3LHMO0bt0aGhoaYp+jR49KOxwAsm/LHjx4gClTpsDe3h66urpo1KgRBg0ahDt37sgiHABAgwYNsGrVKkRFRSEqKgqrV69Gw4YNKyw3ceJEREdHl/m5fPmyyLL6+vqYMWMGjh49ikuXLiEkJASzZs2CgYGBrMKqE+SRI6Xt378fGhoaEAgE0qh+mWTdjwg9fvwYEydOhLm5OfT09NCmTRvMmzdPmqHwMjIyMGzYMJiamsLExASenp5V3k7m5ubQ19eHk5NTmTFZWlpCU1NT7COrtkwZj8fqiiZNmmDnzp3IzMzE48ePsXv37iqte39/f8THx+P58+e4desW/Pz8xNb98ePH8ebNG7GPj4+PLEKSyxgGKMl7b29vWFhYQF9fH1ZWVvDz85NmKDxlbcs8PT1hZGQEQ0NDDB06tEoxzZo1C02bNoWuri569OhR5vHYsGHD0KZNG+jp6cHY2BjdunXD7t27ZREO1GTyW+UgLy8P/fr1g6amJrZu3QqO4zB//nz07dsXMTEx0NHRKbf8hAkTEB4ejpUrV6JFixb4+++/4eLiggsXLqB9+/Ziy+fk5GD69OmVGtxVl7a2No4fP46CggJMnDgRjDEsWLAA4eHh6Nq1K/Ly8iSWrVevHkJDQ6Guro4lS5YgIyMDDg4O8PX1RcuWLTFq1Ch+2Y0bN8LNzQ3Lli1DTEwMmjVrBl9fX4SFhaFbt2549+6d1GLKy8vDwIEDoampiYCAAHAch8WLF2PAgAG4fv16hdtp0qRJiIiIwLJlyyAQCODv749Bgwbh7NmzaNeuHQCAMQYPDw8kJydj3rx5sLKyQkJCApYuXYqYmBicO3cOHMdJNaYvv/wSmpqa+Oeff8BxHPz8/NCnTx/cuXOnwpjGjx+PsLAw/Prrr2jRogU2btyIAQMG4PLlyyL73pYtW6Cnp4evv/4a/v7+Uqt/XaatrY0TJ06goKAA48aNA2MMixYtwsmTJ+Hg4FBhjkREREBNTQ2LFi3Co0eP0KlTJ8yfPx+tWrXC999/zy9rYGCAc+fOgTGGhQsXIj09HY0bN0b37t2lHlNeXh769OkDTU1NbN++HRzHYd68eXB2dkZsbGyF+9O4ceMQFhaG1atXo0WLFtiwYQP69++PK1eu8PtTQUEB1NTU+E41Pz8f+/fvx4gRI/DixQtMnTpV6jHJI0eEcnJyMHXqVJm2z3l5eRgwYAA0NTWxZcsWcByHhQsXon///rh582aFMXl7eyMiIgLLly/n2zJ3d3dERUXxbdnAgQMRFRUlUo4xhiFDhkAgEMg0vtqsdD+yefNmcByHRYsWYcCAAbhx40aF6/6HH34QWfebNm2Cu7s7zp07J9KPDB06FMnJyfDz8+P7kSVLliAmJgZRUVFS70f69+8PDQ0NBAYGguM4LFiwAP369UN0dHSFMU2cOJEfwwgEAvz9999wdXXF+fPnxXKkX79+YgN9S0tLqcVSOiZZt2UnT57E2bNnMWrUKHTs2BE5OTn49ddf0a1bN1y8eBEODg5SjUlLSwubNm1CQUEBFixYAMYYfHx84O/vDy8vL3z48EFi2SNHjohN6Glra+Ovv/4SG/z//vvvMDc3x6ZNm5CWloYWLZ50H5MAACAASURBVFrghx9+gI2NDUaPHi3VmOoKeeYIUNKPzJgxo873IwCQnp6OPn36wNzcHL/99hvMzMzw8OFDpKSkyCSm/v37i8VU2e3k7e2N8PBwrFixgm+f3dzccP78eZGYAKBv375yacuU8XisrtDW1kZoaCgKCgrwww8/gDEGPz8/hIWFoXv37hWu+2PHjkFNTQ1Lly7l1/3cuXPRsmVLsbb07t27mDJlish3Dx8+lHpM8hjDCOvu7OwMCwsLrFmzBg0aNJBp3itjW9a3b19oampi27Zt/NzSV199hVu3blVqbun48eNYtWoV3+e4uLjg4sWLYsdjv/zyC8zNzZGfn4+goCCMGjUKL168wM8//yzVmOrsRN+WLVuQmpqKuLg4tGrVCgDQtm1bWFtbIyAgoNwD1zt37mDfvn3YvHkzn/ROTk5o164dFi1ahMOHD4uVmT17Nuzt7dGwYUOcOXNGJjGNGTMGAoEA7du3R2pqKgDg3r17iI2Nxbhx4/Dnn39KLNu9e3e0bt0agwYNwunTpwEA58+fh6GhIaZMmQJtbW28f/8e2traGDx4MNauXYt169bx5Z8/f44jR46ge/fuOHXqlNRi2rZtG9LS0nDnzh20bNkSQMl2atu2LQIDA/HTTz9JLBsbG4v9+/dj06ZNGDlyJADA0dERDg4OWLJkCQ4ePAgASE5OxtWrV/Hnn39i3LhxAEq2p4qKCqZMmYKkpCSpDgQ2b96M1NRUJCYm8vuevb09LC0t4e/vj2nTpkkse+fOHezZsweBgYEYM2YMAKBXr16ws7PDggULRM5M3rt3DyoqKigqKqKJvv8aN24cWrRoATs7O76Rv3v3LuLj4zFhwgSsX79eYtkvvvgCrVu3houLC7+PR0VFwcjICFOnTuVzBACWLl0KHR0ddOzYEW/evOF/x4EDB6Qek3B/un//vsj+1Lp160rvT1u3bhXZn2xtbTF//nyEhIQAAIyNjbFnzx6Rsi4uLnjw4AG2bt0q9Yk+eeWI0KxZs9CuXTs0atRIqu1XaVu3bkVaWhru3r3Lt2V2dnaws7PDli1bxAaLpQnbMn9/f36Q7+TkhA4dOmDx4sU4dOgQAMDU1BSmpqYiZS9evIhXr17J7IxsXSBc97GxsSL9SFXXfel+pGPHjmX2I3/99ZdYP/LTTz9JvR8JDAxEamoq7t27JzKGsbGxwebNm8sd7JUew5TenySNYYyNjdG1a1ep1V0SebRlw4YNw//93/+JTLr26dMHFhYWWL9+PXbs2CHVmL755hs0adIEgwcPRmZmJgAgKSkJhw8fxpAhQ8o9C//8+XM8f/5c5DsXFxeoqakhNDSU/6558+Zo3749li5dym+76OhofPz4EXPnzoW5ublMDj5rO3nmCADMmTNH5uN8efQjADB58mQ0btwYJ0+ehLq6ukxiEQoMDORjKr2dbG1tK9xOsbGx2LdvHwICAkRiat++PRYtWoTg4GCR5U1MTOTSlinj8VhdMXr0aAgEAnTs2FFk3d++fRtjx47FX3/9JbFst27d0KpVK3z99dd8Dl+4cAGGhob46aefRMb5APD27VvcuHFDtgFBPmMY4H95f+LECT7vHR0dZRqTMrVlwrml+Ph4kbbMysqqUnNLe/fuxZYtW/i5pV69esHe3h4LFy7EkSNHAJSMxz69m0p4PPbPP/9IfaJPKrfuchw3muO4c9L4XZUVGhqKrl278hsCAAQCAb744gscO3aswrLq6urw9PTkv1NTU4OnpydOnjyJ/Px8keUvXbqEPXv24I8//pBuEJ9wdXXF9evX+YYNKJmdv3LlClxdXcstK9z5X79+LfJ9bm4uVFRU+EGxqqoq1NTURCYvgJIzmQCgoiLdu7nDwsLQpUsXvhEAAAsLC3Tv3l1koCuprLq6OoYOHcp/p6amhqFDh+LUqVP8diooKAAA6OnpiZQX3vLy8eNHqcQidOzYMb4zERIIBOjRowd/MCJJSEgI1NXV4eXlxX+npqYGLy8vnDhxQmTfk/a2kDZF5L2bmxuuXbsmciYnPT0dly9fhru7e7llNTQ0AIjnSE5OjkiO1KtXD8OHD8e2bdvE8kQWQkJCJO5PFd2SIml/GjZsmNj+VBZjY2OoqUn/fI+8cgQoaZ93795d7uBPGkJDQ8XaMoFAgO7du1e6z/Hw8OC/U1NTg4eHByIjI8vdTrt27YKGhoZIf6VIish7WfQjn657YT9Sv359kfL6+voApN+PSGMM8+n+5OnpWeH+JEvyaMtMTEzErqzU19eHpaUlHj9+LMVoSjg5OeHu3bv8JB8APHnyBHfu3EGvXr2q/Pvc3Nzw8uVLXLlyhf9OOH779OodYf8jzStJq6uujvMrmyOXL1/Gnj17yj1ZKA3y6EdSUlIQGRmJSZMmyfzAWFgvSdupova5trZlyng8Vh2KyHsXFxfcuHFDbN1fvXq1wnUvHOd/uk4/XffyJo8xTGpqKiIjI+Hj4yO3vFe2tuzYsWMS27KKjl2OHTtWpbmlT8nqeKxSrQjHcU4cx4VwHPeY4zjGcdzoSpRpznHcMY7j3nEc95LjuD84jtOocY3/Kz4+Hra2tmLf29jYICEhocKyAoFA7H59GxsbFBQUIDk5mf+usLAQPj4+mD59usiGlwVra2vEx8eLfZ+QkAArK6tyy549exZJSUlYsmQJrKysoKOjg169esHHxwdbtmzhL3V++/Yt9uzZg0mTJsHJyQk6OjqwtrbGsmXLEBsbi7Nnz0o1pvj4eNjY2Ih9b21tjcTExHLLJiQkwMLCQuJ2Ek722NjYoGfPnli5ciWio6P5MzQrVqxAv379Klx3VRUXFydx3ytr+5Umad+ztbUV2/cUrTbmvY2NDeLi4sS+j4+Ph7W1dbllT58+jaSkJCxfvhzW1tbQ0dFB79698eOPPyIgIIDPkY4dO6JevXp49uwZ9u3bh9zcXGRlZeHgwYMyeT5nXFwc7OzsxL63tbWtcH+Ki4ur0v7EGENRURFevXqFgIAAnDhxQupX8wnrJY8cKSwshLe3N2bMmCHz9jkhIUFiTNJqyz71/v17BAcHw8XFBUZGRtWvfBXUxryvybqPj48vc91bW1tXuh/p37+/1PuRmo5hytufPs37sLAw6OvrQ1dXFz179pTZM63k2ZaVlpWVhXv37lXYB1RHixYtyszP1NRUtGjRokq/q0GDBujUqRMiIiJQXFzMf5+SkoLo6GiMHz8e1tbW0NbWhq2tLSZMmICLFy8iPT29pmFUqDbmvbxypLCwEJMmTcK0adOUoh8RTiJra2vDxcUFenp6aNiwIcaOHYtXr15JKRLRekka59d0O32ae2FhYTAwMED9+vXh6Ogos7ZMGY/HylIb876m6z45ORmLFy9GmzZtoKOjAycnJ0yaNAmBgYFit/3a29sjMzMTWVlZuHLlCn/FnLTJYwwjzHstLS24urpCX18fjRo1wrhx42SW98rWlsXHx0scw1R3bqkyx2ObN2/GyZMny70Ksroqe7pAF8A9AFMAvK9gWXAcpwogDEB9AI4AvgUwFMBv1aumuKysLBgaGop9b2RkhOzs7ArLlvWAY+GBVOnyq1evRn5+Pn755Zca1rhihoaG/Jmc0rKzs8uMtbT8/Hz07dsXKioqiI6OxvPnz3H8+HGEh4eL3S7j7e2NkJAQhIeH4/nz57h58ybU1dXh7u6OwsJCqcYkqe6GhoYVbqfs7Owyt5Pw9wnLcxyHw4cPo3Xr1nB0dISZmRl69eoFCwsL7N27VwpRiKrpvieprPDfa5Fal/dGRkY1ypHevXtDRUUFd+7cQXZ2Nk6ePInjx4+LNK6NGzcGAKxatQrFxcUYPHgwfHx80K5dO0RGRkJXV1da4QCQ7/60YcMGqKurw8TEBD/++CPWr18vk4GNvGJatWoV8vPzMWfOnBrWuGKS6lWZtqy8ssJ/L0tISAhev36N4cOHV6PG1Vbr8l5Sn12TfuTT/YnjOBw5cgStW7dGz549YWpqCicnJwgEApn1IzWJqbwcKV3e1dUVa9euRVhYGLZv3w4tLS14eHjI5MHPiuobJ0+eDMaY1G95AUquFizryu7c3Fyxqz8r4uLiAlVV1TKvdvjpp5/w8OFD7Nq1CxcvXsSOHTvw+PFjzJo1q9p1ryKly/vK5sjq1atRUFAgl3G+PPqRp0+fAigZ67du3RpHjx7F0qVLERERAXd3d6lfnSyLvC+rb3RxccHatWsRGhrKt2Wenp5ijyWRBmU8HpOg1uV9eeu+opcTlV73N2/exL///ouwsDBERERg+vTpIsteunQJs2fPhpeXF0aMGIGUlBRs2LABM2fOlFYoPHmMYZ48eQKg5Hl+n+b9oEGD5Jb3db0tq+52Kq+s8N9L27hxI7S0tNCgQQP89NNPWLt2LUaMGFGD2petUtcIMsaOAzgOABzH/VOJIv0A2AIwZ4xl/LfcLABbOI7zZYy9Lrd0LZGcnIwVK1bg4MGD0NLSUnR1yiV8+LWpqSnGjRuHjIwMdOrUCbNnz0ZRUZHIAHjBggUYNmwY5syZg+joaDRr1gxz5szB4cOH0b9//3IfdFpb/d///R+uX7+OP/74A1ZWVkhMTMTSpUvx3Xff4dChQ7XiEvi6RtnyXlNTE7t374aZmRlGjx6NR48eoXPnzvD19UVRUREmT54M4H+3S6SlpYm8oCMlJQWXLl3Cd999h4CAAIXEUFNeXl7o1q0bXr58iZCQEEyePBmqqqrw9vZWdNWqLDk5GcuXL0dwcHCtb5+ra9euXTAzM8OAAQPk9jeVLe+rwsfHB9evX8eff/6pNP1I6Wc/AcB//vMf9OzZE35+fiLtW121YsUK/pmesr4aq6ZcXV2RmJhY5tWJfn5+aNu2LZYtW4a0tDQIBAJ4e3vj119/xc8//wzGmEzr9rnmfXJyMlauXImgoCCl6UeEB79OTk78rcjOzs7Q19fHiBEjEBkZif79+yuyitXyaVv29ddfw9HREX5+fvjuu+8UVCtxdel4TNnyvvS6Hz9+PDIzM+Hg4IBffvkFRUVFInewLFu2TKRsWFgY9uzZg5kzZ2Ljxo117kUowj7CycmJz5XevXtDT08PI0eOrJN5r6xtGQB4enqia9euePnyJUJDQzFlyhSoqqpi4sSJUv07snoZR3cACcJG4L9OANAE4ABA7HpkjuMmApgIoFK3KEmaXZU0S/xp2bJelSycbRWWnzp1KpydndG1a1f+7EJhYSEYY8jJyYGmpia0tbUrrGtl5eTkVHsmedSoUfwD69PS0gCUnK3Izc3Fhg0bEBgYiLt378La2hozZszApEmTRB5afePGDcTGxmL06NHYuHGj1GKSVPfKnBUzMDAoczsJf5+wfHh4OA4cOICwsDA4OzsDAHr27AmBQAB3d3eEhYVV+Py2qqjpvlfWQ7WF+568bs+TEZnnfXlXeVaUI2PGjEHv3r1hZWXFP/vj4sWLyM3NxaZNm7B582bExsbyl4N/+jDuGzduIDc3t8y39dWEPPen0i97GDBgAPLy8jBjxgyMHTtWqs+/kEdMU6ZMQZ8+fdCtWze+fS4oKJBZ+1yTtkxSnyP8fWXt+0+fPsWZM2fg4+Mjk+d2SJFc+vvqXl0hqR/5dH8S9iPHjx8X60fc3Nxk0o/UJKbycqS88qqqqhgyZAjmzp2Lp0+folGjRlWsuWTy7hs3bdqEuXPnYunSpRg7dmw1a12+169fl3nlnqQr/SSxtbWFQCDAmjVrxP6tZ8+eGDBgAH744Qf+wfC3bt3C48ePsXHjRjg5OYm9jbsWqPV5X5kcmTZtmtg4v673I8L/fvnllyLLffXVVwCA27dvS/XgWBbHY+X1jUKqqqoYPHgwfH19pd6WKePxmJTIPO/LW/dltQeljRw5Ek5OTrC3txdZ969fv8aff/6JwMBA3Lt3T2L5oKAguLu7w9bWFtevX6+wrpUljzGM8L99+vQRWU6Y93fu3JFL3tf1tqy626mqbVlZx2OzZs3CmDFjpHo8JqvT0w0BPPvku5cAiv/7b2IYYwGMsU6MsU6VuTVO0rOeEhISKnxOi42NDdLS0sTOlCQkJEBDQ4M/K5yQkIDw8HB+Y5iammLfvn148uQJTE1N4evrW2E9q0JS3YVXFpTH1tYWWVlZfMMmFB0dDQBo06YNvxwAxMTEiCyXkpKC7OxsfjlpkfSMjsTExAqftWBtbY309HSJ20n4AFDhM9scHBxEluvUqRMA4P79+9Wuf1kkPW9I0nNKSpO078XHx4vse3WUzPO+vGc+VvT8BDs7O2RlZYk84BcAbt68CQD8/ljRs6Skfam4ra2txOcOVrQ/2dra1mh/6tSpE96+fYtnzz7dbDUjjxyJj4/H8ePHYWRkxH/27t2LJ0+ewMjISOq389bkuTGVbctK27t3L4qLi+V92251yDzva7LubWxsylz3iYmJIuteOPiX1I9U1AdXVU3HMOXtT5XtR6T9YHJ5tmU7d+7kn58s7bFYaampqWXmp0AgEOtLyuPm5obCwkKEh4eL/VvpNq004boUCARVqbK81PpxfmVyRDjONzMz4z/79+/HkydPYGZmhnnz5lVYz6qQRz9SUa5J+8pkSc9MTExMrPF2Kiv3yiLttkwZj8ekROZ5L4t1LxznV3adSvsKanmMYSrKNWnniLK2ZdV9DnxNj+8dHBxkcjxW9+5D+S/h2zdLD7Qq+/ZNV1dXFBYWirySuqioCEFBQejbty80NTUBlNw2derUKZFPv379YGJiglOnTsHHx0eqMQnfylP6gf/NmzdH9+7dcfz48XLLPnv2DEZGRmIPhxYepAjv3RfuQJ8ezLRq1QqGhob8ctIifHNV6Ua3sm+ucnFxQWFhIYKDg/nvioqKcOjQIXz55Zf8dmrQoAGA/zXkQsIz48JnrkmLu7s7rl69KrbvXbp0qcJ9T/jcjaCgIP67oqIiHDhwAP369eNjImUTvt2t9IGPubl5pd7uJsyRTweOnTt3BgD+jY2PHz/GzZs3+TNGQl27doW+vr7YflZTgwYNkrg/DRo0qNyykvan/fv3V2p/ioqKgq6uLszMzGoWRBn1knWO7N27F2fOnBH59O/fHyYmJjhz5gx+/PFHqcbk5uYm9ha+9PR0XLlyBW5ubuWWFfY5hw4dEonp4MGD+Oqrr8rcTrt370bbtm3Rrl076QVRR8miH/l03TdsWHKMIqkfadKkiVRiESpvDFPZ/amsMYyk/enT5Zo3b87HLC3yassOHz6MMWPGYPz48WVeISdNUVFRsLOzE9n+jRo1Qvv27XH+/PlK/Q41NTX069cPly5dKvNqAeFV5J8+2Fz4UPDnz59Xt/p1mjxyZNeuXYiMjBT5CMf5wrc9SjsmWfcjXbt2RcOGDREZGSlS/uTJkwDEx/815erqKnE7VdQ+S4qpsm3ZwYMHZdKWKePxWF1x/PhxdO7cWWzdd+vWrdrrXjjOFz7zTRIvLy/k5eWVOdlTE/IYwwjz/tSpUyLlhXkv3P+kRRnbMnd392rPLQlP5lU0tyTJ+fPnZXI8BsZYlT4A3gIY/cl3owGcK/X/iwHEfbKMKQAGwLmiv9G8eXP25s0bVlhYKPGTk5PDWrZsyWxtbdmhQ4dYcHAwa9u2LRMIBCw7O5tfLjk5mamqqrIFCxaIlPf09GQGBgZs06ZN7MSJE2zw4MFMU1OTXbt2rdy/O2LECNakSZNylxF+3r9/z/z9/Zm/vz/7+++/Wb169cr9mJiYsOTkZHbv3j3m4eHBhg4dymJjY1lqaiozNTXll2vTpg0rLCxkixcv5r+zsrJiubm57MGDB2zChAlswIABbO7cuSw3N5dFR0czHR0dVq9ePaarq8vu3LnDcnJy2Ny5c9mAAQPYhAkTWFJSEsvOzmaWlpbl1rFVq1Z8TDt27GB5eXnlfl68eMFatGjBbG1t2f79+9mBAwdY27ZtmYWFBXv+/Dm/XGJiIlNVVWXz5s0TKT906FBmYGDANmzYwMLCwth//vMfpqmpyS5dusQv8++//7JGjRqxhg0bsnXr1rHw8HC2bt06ZmZmxpo2bSryd8r6xMfH8zEFBQWxjx8/lvt58+YNa9myJbOzs2OHDx9mR44cYfb29kwgELDXr1/zy6WlpTFVVVW2cOFCkfJeXl7MwMCABQQEsMjISDZkyBCmqanJbt68KbLc9evX2YEDB9i+ffsYAObh4cEOHDjADhw4wN6+fVtuHR89esT8/f0ZgJtVzXFF5r2enh5TV1eX+NHX12dJSUns7t27bPDgweybb75hd+7cYSkpKczAwIBfrmXLlqywsJAtXLiQ/65Vq1Z8jowdO5Z99dVX7JdffmG5ubns5s2bTENDg1+2X79+rLCwkAUHBzM3Nzc2ZswY9ujRI5aQkMDq169fbh11dHT4/Wnz5s2sIm/fvuX3pyNHjrCjR4/y+9ObN2/45dLT05mqqipbtGiRSHnh/rR582Z26tQpfn+Kjo7ml9m0aRMbPXo027VrFzt37hw7dOgQ8/LyYgDYypUrK6zjs2fP+Jh27dpVa3Lk08+oUaNYkyZNKqzfx48f2f3790Xy/sOHD+V+Xr16xbdlQUFB7ODBg3xb9vLlS365+/fvM1VVVebn5ydS3sPDgxkYGLCNGzey48ePs2+++YZpamqyK1euiP2tK1euMABs1apVFdar9Cc5OblO5v3r16/Z+/fvJX5evnzJr/sDBw6woKAgft2/ePGCX07Yj/j5+YmUF/YjwnUv7EcuX77ML/Ps2TO+H1m/fj2LiIhg69evZw0aNGBNmzYV+TtlfbKzs/n9acuWLaygoKDcT3Z2Nj+GOXjwIDt06BA/hsnKyuKXS0pKYqqqqmz+/Pki5YX706ZNm1hERAS/P127do1fZseOHczDw4MFBgaykydPsp07d7IePXowAGznzp0V1lHYj/j7+7Pdu3fXirYsKiqKaWpqso4dO7JLly6xK1eu8J+YmJgK65iYmMjHtGTJEtaxY8dyP1988QV79OgRS0pKYlOnTmU///wzu3//PsvIyGA9evTgl3NxcWGFhYXs77//Fvsd06dPZ4wxNn369DL/hqOjI3v27Bl7/vw5W758OZs4cSJbvnw5e/HiBXv69KnI3ynr85///KdO5n1eXp7Cc6Ssj3CcX1F+FBQUsJycHH5/CgwMrDX9yObNmxkANn78eHbs2DG2fv16ZmBgwJycnNj79+/LrWNqaiof0549e1h+fn65n6ysLIkxvXr1il/uwYMH/HYqXV4Y099//83Cw8P5mK5evcovU7otO3HihFhbVlEdY2Nj+Zhmz56tFMdjXbp0qZN5r6enx3R1dSV+zMzM+HXv5eXFPD09+XXfoEEDfjlra2tWWFjIlixZwn9nY2PDcnNzWVJSEps4cSIbOHAg8/X15dd9/fr1ma6uLuvXrx+LiIhgPj4+zNXVlX333XcsNDSUMcaYn59fufXT1dVlzZo14/enbdu2lTs2kNcY5v3792J5v27dOj7v8/Lyyq1jUlISH9PevXuVoi27desWH9ORI0dYUVFRuZ/c3Fx+DBMcHMwOHz7Mj2FycnL45VJSUvi5pdLlhXNL/v7+InNL169f55fZuHEjGzVqFNuxYwc7ffo0CwoKYp6engwAW758eYV1FG6nyua9rB78cwXAPI7jmjLGMv/7XV8A+QCipfEHdHR0cPLkScyYMQOjR48GYwx9+vTBb7/9JvJWTMYYiouLxW6327JlC/z8/LBgwQLk5OTA3t4eYWFh6NixozSqVy15eXlwcXHBqlWrsGXLFnAch3PnzmHWrFkiDwXlOA5qamoil6w+evQIzs7OmDt3LubPnw9jY2NkZmZi27Zt+PXXX4WNMT5+/AhXV1fMnDkTY8aMwbx58/Dq1StcvXoVS5cuRWZmpli9akJHRwfh4eGYNWsWxo8fD8YYevfujdWrV1dqO/n7+2PhwoVYtGgRcnNz0bZtWxw9ehQdOnTgl9HT00NUVBSWLl2KtWvX4t9//0XDhg3h4uICX19fqb8lVUdHB6dPn8a0adMwcuRIMMbw5ZdfYu3atZWKaevWrfD19YWfnx9ycnLQrl07hIeHi+17GzZswPbt2/n/DwoK4q92SE1NFTnbVUvIPO/z8vLQv39/rFmzBtu2bQPHcTh79iymT59eYY48fPiQf3DzwoULYWJigszMTGzZsgUrV67kcwQAzp49i2+++QYLFixAUFAQ3r17h/DwcMyePRsfPnyQRig8HR0dnDlzBlOnTsWIESP4/WndunWV2p+2bdsGX19fzJs3j9+fIiIiRPYnYd7MmDEDWVlZMDExgbW1NUJDQys8m1jdmOSRI/Kko6ODiIgIzJo1C2PHjgVjDM7OzpVuywICArBgwQIsWrSI73NCQkJE2jKhXbt2QU1NDcOGDZN5XFIgl/5e2I+MGzeO70fWrFkj1r5XZt23bdtWbN0L+5Fly5bh999/l0s/cuLECcyYMQNjxozh96eqjGHmz58vMoYJDQ0ViUkgEODFixeYM2cOsrKyoKOjAwcHB4SGhqJfv35SjUcYk6zbsjNnziA/Px8xMTHo0aOHSHlzc3Okp6dLNaYPHz7ghx9+wLRp07B48WJwHIcbN25gzZo1eP/+fy+pFPY5Zd0e5ebmhpycHFy4cKHMv/Hu3TuMHj0aEydOxMiRI2FiYoKXL1/iwoUL8Pf3F/k7tYhc8l7WOSJv8upHRowYARUVFfz222/YsWMHjIyM8O2332LJkiVSv4VPuJ1mzpwpEtOn7bOkmDZv3oz58+dj4cKFfEzHjh0TicnCwkKsLevYsSOOHTsmk7ZMGY/HpEQu43w3NzesXLkSAQEB4DgOUVFR+OWXXyq17vv06YO5c+fCz88PxsbGePz4Mf755x+sXr2aX/f//vsvOI6Dr68vjI2NUVhYiLi4OIwZM0bkiixpkccYBgCGDx8ulvfDhg2TWd4rY1sWGRmJ6s+sCwAAIABJREFU6dOnY9SoUfzc0u+//16pmAIDAzFv3jzMnz9f4tySnZ0dQkJCMGvWLP54zMrKCkePHpXJ8RhX+uBW4kIcpwtAeHPxZQArAYQAyGKMPeI4bjRKzgL0/u/yqgBuA3gBYDoAYwDbAQQzxiZX9PfMzc1ZXFxcnX8DVlFREf+A1Y8fP4q92rsuaty4Mf/qcW1tbQwdOlTBNaq59PR0fgBuZGSEIUOGKLhGNZeZmYnw8HB4e3tHM8aqdb22IvI+Jyenth7UVJqGhgZ+//13ACXPjxg/fryCa1Rzz58/x5EjRwCUdIS16Q131ZWUlIRz584BKMl7ab5oQVEyMzNx+vTpOpf39+7dk+rDhxXhw4cPOHDgAICSh8SPHDlSwTWquX///Zd/ppyurq5S5P39+/f5F1s8f/4chw8fVnCNaq558+YYOHBgncv7xMTE2v6ioQrl5eVh//79AEpu0VaGN1k/efKEv1Wufv36SjEmvn//Pq5cuQIASEtLwx9//KHgGtWcnZ0dxo0bV+fyPicnR+rPupY3Q0ND/vmdGhoadeWkbLkyMzP5lxDq6enhm2++UXCNai4hIYF/qUqDBg0qvKW4LkhLS8OZM2cqnfeVfUZfJwC3/vvRBrDovz8vLmthxlgxAFcAeQAuAdgP4BCAGZX8e4QQxaO8J+TzQ3lPyOeH8p6Qzw/lPSFKrFKn0hhj5wBU6fpIxtgjAHV/6pSQzxTlPSGfH8p7Qj4/lPeEfH4o7wlRbnX2rbuEEEIIIYQQQgghhJD/oYk+QgghhBBCCCGEEEKUgLQm+m4D+EdKv4sQUjdQ3hPy+aG8J+TzQ3lPyOeH8p6QOkwqr7tijN1GSWNACPlMUN4T8vmhvCfk80N5T8jnh/KekLqNbt0lhBBCCCGEEEIIIUQJ0EQfIYQQQgghhBBCCCFKgCb6CCGEEEIIIYQQQghRAjTRRwghhBBCCCGEEEKIEqCJPkIIIYQQQgghhBBClABN9BFCCCGEEEIIIYQQogRooo8QQgghhBBCCCGEECVAE32EEEIIIYQQQgghhCgBmugjhBBCCCGEEEIIIUQJ0EQfIYQQQgghhBBCCCFKgCb6CCGEEEIIIYQQQghRAjTRRwghhBBCCCGEEEKIEqCJPkIIIYQQQgghhBBClICaoisgSXh4ODiOU3Q1pEZFRQXTp09XdDVqTF1dnf/5/fv3CA0NVWBtpKOgoID/OScnB8HBwQqsjXQUFhYqugrV8vPPP6O4uFjR1agRFZX/nT/5+PEjDh06pMDaSEfp/SkvL08pciQ/P5//OTc3F8eOHVNgbaSjdFtWl0RERNT5/v7jx4/8z8XFxTh69KgCayMdRUVF/M/v3r1Tirbsw4cP/M+GhoYYPHiwAmsjHRoaGoquQrWEhYUpugo1Vjrvi4qKlKIfKd3fv337FiEhIQqsjXSUzvvGjRsrxfGYtra2oqtQLT///DMYY4quRo2oqqryPxcUFCjdsfDbt2+Voi0rnfcvX77E4cOHFVgb6ajqOL/WTvRlZ2crugpS17RpU0VXQeqUbTt9/PgRr169UnQ1PltNmjRRdBWkTtn2J8aY0sVUXFyMrKwsRVfjs6Vs/QgApduflDHv1dXVYWpqquhqfLaULUcA5YuJMaZ0MWlqairl8VhdoYzjfGXLkY8fPypdTJ/rOL9W3brLcZw7x3EB79+/V3RVCCFyQnlPyOeH8p6Qzw/lPSGfH8p7QhSjVl3Rxxg7BuCYubn5hA4dOkBNrVZVr8o+fvyI6Oho/v87d+6swNpIR15eHuLi4gAAampq6Nixo4JrVHOvXr1CSkoKAOD169c4f/68gmtUcyYmJujWrZuiq1EppfO+Y8eOdT7vi4qKEBMTAwDgOA6dOnVScI1q7u3bt0hISABQchVM+/btFVyjmnv58iXS0tIAlNy6e+nSJQXXqOZMTU3rTD/zad6XvhWmLioqKsKtW7cAlOS9g4ODgmtUc2/fvkViYiKAkttD7e3tFVyjmnv58iXS09MBlPT3N2/eVGyFpMDExKTObJvSee/g4FDn876goAC3b98GUPLYji5duii4RjWXm5vL9/eamppo166dgmtUc8+ePcPDhw8BlIz5z5w5o+Aa1Vzjxo3Ro0cPRVejUkrnfXBwsMgt73WRjo4OBg4cCKDkNt4OHToouEY1l5ubi6SkJAAleW9nZ6fgGtXcs2fPkJmZCQCoX78+bG1tFVyjmsvKysKDBw8qvXytPaJu06YNtLS0FF2NGikqKuIn+jiOU4od7NWrV/xEn7q6ulLElJaWxk/0vXv3DhcvXlRwjWquVatWdWairzQrKytoamoquho1UlBQIDLRpww58uLFC5GJPmWIKTk5mZ/oe/v2LS5fvqzgGtVc69at68xEX2nW1tYiz3+tiz58+MBP9KmoqChFjjx79oyf6FOWvE9KSuIn+t69e6cUE30tW7asMxN9pdnY2NT5E3t5eXn8RJ+qqmqd3A6fyszM5Pt7DQ0Npch7VVVVfqIvJycHp0+fVnCNaq5du3Z1ZqKvtDNnztT5iT4jIyORiT5lyJHMzEx+ok9DQwPW1tYKrlHNqaio8BN9Ojo6StE+p6amVmmir1bduksIIYQQQgghhBBCCKkemugjhBBCCCGEEEIIIUQJ0EQfIYQQQgghhBBCCCFKgCb6CCGEEEIIIYQQQghRAjTRRwghhBBCCCGEEEKIEqCJPkIIIYQQQgghhBBClABN9BFCCCGEEEIIIYQQogRooo8QQgghhBBCCCGEECVAE32EEEIIIYQQQgghhCgBmugjhBBCCCGEEEIIIUQJ0EQfIYQQQgghhBBCCCFKgCb6CCGEEEIIIYQQQghRAjTRRwghhBBCCCGEEEKIEqCJPkIIIYQQQgghhBBClABN9BFCCCGEEEIIIYQQogRooo8QQgghhBBCCCGEECVAE32EEEIIIYQQQgghhCiBOj3Rl5GRgWHDhsHU1BQmJibw9PTEo0ePKlX2w4cPmD17NszNzaGvrw8nJydcuHBBbDlLS0toamqKfY4ePSrtcAAob0xeXl4wMTGBsbExPDw8qhxT8+bNoaenB0dHxzJjKm3//v3Q0NCAQCCQRvXL1KRJE/zzzz9IT0/Hw4cPsX37djRp0qTSZTds2IDY2Fg8fvwY169fx9y5c1GvXj2xZfX19bF8+XLExsbi6dOnuHfvHv766y9ph1OnyGt/evz4MSZMmIBmzZpBV1cXlpaW8PX1lWYoPFnn/Y4dO8rMeeHn33//pZgqoXHjxggMDERycjJSUlKwbdu2KuX9n3/+iZiYGDx8+BBXrlzB7NmzxfLey8sLW7duRXR0NJ4/f44//vhD6nHURfLoG4GSvJ84cSKaN2+O+vXrw9LSEvPmzZNmKLyMjAx8++23MDMzg6mpKby8vKoU05w5c2BhYQEDAwP06tWrzBzR0tKS+JFV3itbTI0aNcJff/2FW7du4fbt29iwYQMaNWpU6bK//vorzp8/j3v37iEyMhJTp06FtrY2v4ypqSlmzJiBw4cPIyYmBtevX8eOHTvQuXNnqcdS1yjj+DEjIwNDhw6Fvr4+9PT0MHjw4CrFNHPmTDRq1Aja2tro3r07zp8/L7bcx48fsWLFClhYWEBLSwvt2rXDoUOHpB0KTxm3U9OmTXHgwAFkZ2cjJycHBw8eRLNmzSpV1sLCAgcOHEBWVhbevHmD06dPw8HBQWw5juMwe/ZspKamIi8vD7du3cLgwYOlHUqd07RpU+zfvx+vXr1CVlYWgoKCKr3umzVrhq1btyI1NRVv3rxBfHw8Fi9eLDLW6tWrF4qKiiR+unbtKvWYZN03CgnHMObm5tDT00ObNm1kNobJzMzE999/j0aNGqFhw4b49ttvkZGRUamyHz58wNy5c9GiRQsYGxvD2dkZFy9eFFvu1atXmDFjBmxtbWFsbAwbGxtMmzYNL168kHY4AJSvfVaTyW+Vg7y8PPTv3x+amprYsmULOI7DwoUL0a9fP0RHR0NHR6fc8t7e3ggPD8eKFSsgEAiwadMmuLm54fz582jXrp3Isn379oWfn5/Id5aWlhRTFWLS0NBAYGAgOI7DggULKh3TxIkTER4ejpUrV0IgEODvv/+Gq6srzp8/j/bt24stn5OTgxkzZqBhw4ZSj0VIW1sbR44cQUFBAXx8fMAYg6+vL0JCQuDo6Ii8vDyJZevVq4fDhw9DXV0dy5cvR2ZmJjp06IDZs2ejZcuWGDduHL+svr4+wsPDwRjDsmXL8OjRIzRq1AhdunSRWWy1nbz2p/T0dPTu3RsWFhb4/fff0aBBA6SnpyMlJUVmMcky7wcOHCjW2TDGMHjwYAgEAqnnizLGpK2tjeDgYOTn52Py5MlgjGH27Nk4fPgwevfuXWHeHzx4EGpqali1ahWf9zNnzkSLFi0wceJEftmhQ4fC2NgYUVFRGDRokFRjqKvk1Temp6fD2dmZz3szMzM8fPhQZnk/YMAAsZj69++PmzdvViqmiIgILF++HAKBAP7+/nB3d0dUVJRIjkRFRYmUY4xhyJAhMst7ZYtJS0sLO3fuREFBAWbOnAkAmDp1Knbv3g1XV1e8f/9eYlltbW3s2LEDampqWLt2LZ4+fYq2bdtiypQpsLCwwJQpUwAAdnZ2cHFxwaFDh3D79m2oq6vj+++/x+7du+Ht7Y2zZ89KNaa6QhnHj3l5eejTpw80NTWxfft2cByHefPmwdnZGbGxsRXGNG7cOISFhWH16tVo0aIFNmzYgP79++PKlSsiMfn5+WHNmjVYtmwZHBwcsG/fPnh4eCA0NBQuLi5Sj0nZtpO2tjZOnz6N/Px8jB49GowxLFmyBGfOnEG7du3K7e+NjIxw4cIFvHnzBj/88APy8vIwdepUnDlzBl27dkViYiK/7JIlSzB9+nTMmzcP0dHRGDZsGA4cOAB3d3eEh4fLLL7aTFtbG5GRkcjPz8eYMWPAGMPixYtx6tQpdOjQocKx1okTJ6Curo4FCxYgIyMDnTp1woIFC9CqVSt89913AICYmBj06NFDrHxAQACMjIxw48YNqcYkj74RKBnD9OnTB+bm5vjtt99kPoZxcXGBhoYGAgICwHEcFi1ahIEDB+LatWsVxjRp0iScOHECy5Ytg4WFBQICAvD111/zOQaU9O2enp5ITk7GvHnz0KZNGyQmJmLp0qWIiYnB2bNnwXGcVGNStva5zk70BQYGIi0tDXfv3kWrVq0AAG3btoWtrS02b96Mn3/+WWLZ2NhY7Nu3DwEBARg1ahQAwMnJCe3bt8eiRYsQHBwssryJiYlMZvc/pawxpaam4t69eyIx2djYVBjTnTt3sG/fPmzevFkkpnbt2mHRokU4fPiwWJk5c+bA3t4eDRs2xJkzZ2QS08iRI2FhYYEuXbogLS0NABAXF4ebN29i9OjR2Lhxo8SyXbt2RatWrTBkyBB+8H7x4kUYGhrixx9/hLa2Nn/gMH/+fOjo6KBnz5548+YN/zs+3ZafE3ntTz/++CMaN26MyMhIqKur88vKKiZZ572pqSlMTU1Fyl68eBGvXr0Sm/CnmMo2fPhwmJub44svvuDzPj4+HlevXsXIkSOxadMmiWW7dOmCli1bwtPTE+fOnQMAXLp0CQYGBvDx8RHJe09PTzDGAAB9+vSRehx1kbz6RmHenzx5ks97Wdm6dSsfU8uWLQGUTPjY2dlhy5Yt/CSQpJj2798Pf39/kZg6dOiAxYsX82eG5Z0jyhiTl5cXmjVrhn79+uHhw4cAgMTERJw6dQrffvsttm7dKrGsg4MDBAIBRo8ezV+pcPXqVRgYGGDcuHHQ0tLChw8fcPPmTfTt2xfFxcV82QsXLiA8PBwTJkz4bCf6lHH8uHnzZqSmpuL+/ft8TPb29mjdujX8/f0xbdq0cmPas2cPtm7dijFjxgAouTLJ1tYW8+fPR0hICADg+fPnWLNmDWbPno0ZM2YAAJydnZGcnIzZs2dL/UBSGbfThAkT0KJFC1hZWfGTJLGxsXjw4AG8vb2xdu1aiWUnTZqEBg0aoFevXkhNTQUAnDlzBikpKVi4cCGGDRsGoKQtmz59OlatWoXffvsNAHDu3Dm0bNkSK1as+Gwn+saPH48WLVrAxsaGX/d3795FYmIiJk6ciHXr1kks26NHD1haWmLgwIGIjIwEULJOjYyMMG3aNH6s9ebNG1y7dk2kbPPmzWFtbY21a9fi48ePUo1JHn0jAEyePFluY5ht27YhLS0Nt2/fFonJ3t4egYGB+Omnn8qN6cCBA/j7778xcuRIAICjoyM6deqEpUuXIigoCACQnJyMq1ev4s8//8TYsWMBlMSuoqKCKVOmICkpSaoXKSlj+yyVW3c5jhvNcdw5afyuygoNDeUnTYQEAgG++OILhIaGVlhWXV0dHh4e/Hdqamrw9PTkzyIowucW07FjxyosW5WYLl++jD179mD9+vXSDeITAwYMwM2bN/mDfQB49OgRrl27hoEDB5ZbVtjwlp64A4Dc3FyoqKjwZybq1asHLy8v7Ny5U2zZ2qK25b209qeUlBScPHkSPj4+Mu8ohfVSRN7v2rULGhoa8PLyqnkQZdRL2WLq378/oqOjxfL++vXrGDBgQLllK5v3APhJvtqqtuW9tPanlJQUREZGyjXvhRPAQgKBAN27d692W+bh4VHpHPH09Kx5EGXUS9li+vLLL3H79m1+kg8ouV0pJiYGX331VbllhfvR27dvRb5//fq1SN6/efNGZJIPAIqLi5GQkCDTq5aqorblfV0dP4aEhKBbt25iMfXo0aPCx+eEhIRAXV1dpH9TU1PDsGHDcOLECT6mEydOoKCgAMOHDxcpP3z4cNy9e1ekD5MGZdxO7u7uuHr1qsiVUOnp6bh06VKFV9p37doVSUlJ/CQfUHKl0IULF+Dm5gZVVVUA4K9S37Vrl0j53bt3w97eHhYWFtILqJoUkffu7u64du2a2Lq/fPlyheteQ0MDQEkbW1pOTo7YWOtTw4cPh4qKCnbs2FGD2pdNHn2jcAwzadIkuYxhwsLCxGKysLBA9+7dERYWVm7Z48ePQ11dHUOHDuW/U1NTw9ChQ3Hq1Ck+poKCAgBA/fr1Rcrr6+sDgNQnZJWxfa7URB/HcU4cx4VwHPeY4zjGcdzoSpRZz3HcTY7jPnAcl17Tin4qISEBNjY2Yt9bW1sjISGh3LLx8fGwsLAQezaSjY0NCgoKxC5xDQsLg4GBAerXrw9HR0eZPctOGWOKj4+Hra2t2Pc2NjY1jik5OZn/rrCwEJMmTcK0adNEElQWrKysyqx7YmIi2rRpU27ZqKgoJCcnY8GCBWjTpg10dHTg6OgIb29vbNu2jb8kvV27dqhXrx5evHiBf/75B48fP8ajR4+wc+dONG/eXCZxfao25r089qfLly8DKLl9YODAgdDV1YWZmRnGjBmDV69eSSmS/5Fn3gu9f/8ehw4dgouLC4yMjKpfeQmUMSYrKyuRW26E7t+/X+EZxfPnzyMlJQV+fn6wtLTkr9SdOHEitm/fXu6tKPJWG/NeHvvTlStXAPwv7+vXr48GDRpg7NixMst7SW1ZWfvZp2WrmyPBwcEyzXtli6l169Z48OCB2PdJSUkVjjUuXbqEtLQ0zJo1C61atUK9evXQrVs3jBo1Cnv37i33tl91dXV06NBBZJwjS7Ux75Vx/BgXFwc7Ozux721tbREfH19hWYFAIBaTra2tSExxcXHQ1NQUi0W4Liv6O1WljNvJ1tYWcXFxZda3rL6otOLiYn6CorT8/HzUq1ePnxixtbXFhw8fxHJc+Hcr+jvSUBvz3sbGBvfu3RP7Pi4uDtbW1uWWPXXqFB48eIAVK1bA2toaOjo6cHZ2xuTJk+Hv71/uWGv48OGIjo4uc7vXlDz6xtJjGBcXF+jp6aFhw4YyHcNIGpdVNyZra2uRmGxsbNCzZ0+sWrUKMTExePv2LW7evImVK1eiX79+sLKykl5AUM72ubJX9OkCuAdgCgDJIxPx370dgPSnxgFkZWXB0NBQ7HsjIyNkZ2dXq6zwu6ysLP47FxcXrF27FqGhodi+fTu0tLTg6emJPXv21DCCyterrsdkYGBQZr0qiik7O1vi+hD+u9Dq1atRUFCAX375pYY1rpihoSFycnLEvs/Ozi4z1tLy8/Ph4uICFRUVXLlyBRkZGTh69ChOnjyJWbNm8csJH/S9ePFiFBcX4/vvv8fUqVNhb2+PY8eOQVdXV7pBla1W5r2s96enT58CKHluTOvWrXHs2DEsX74c4eHhcHV1lfoZJHnlfWkhISF4/fq12BklaVHGmAwMDGqU9+7u7lBRUcHFixeRlpaG4OBgnDx5ErNnz5ZJfWugVua9rPenT/M+JCQEy5YtQ3h4ONzc3OSW95Vpy2prjihjTPr6+mJXhwAlV4jo6emVW7agoABeXl7gOA4RERGIjY3Frl27cPbsWSxcuLDcsj/99BMaNmyIgICAmlS/Kmpl3ivb+FEWbZkwJmGOCNfbp1cvfbqctCjjdpK0PSRtg9IePHiA1q1bi5x44DiOf7628HsjI6MyxxTC7SOLExdlqHV5L2m9SNpXSsvPz0evXr2goqKCu3fvIjc3F5GRkQgLCyv3VtJu3brB0tISO3furHH9yyKPvlE4hvH29kbr1q1x9OhRLF26FBEREXB3d5f6GEbS2LeyMUkqK/zdQEneBAcHo3Xr1nB0dORvibewsKgz8zCKbp8r9Yw+xthxAMcBgOO4fypZZvJ/l58BoF8166dwnz4L4Ouvv4ajoyP8/Pz4h3rWNcoWU3JyMlauXImgoCBoaWkpujrl0tTURGBgIExMTODt7Y3MzEw4ODhg5syZKCoq4u/XFzYA6enpIi/oSE9PR2RkJDw8PLBt2zaZ1vVzzXthZ+jk5MS/8dTZ2Rl6enoYPnw4Tp48WeGtmrXdzp07YWZmVuGt5nVJbY5JU1MTAQEBMDExgY+PDzIzM9GxY0dMnz4dxcXFIpP8ikZ5L5r3+vr6SpP3u3btgpmZWZ2Po7TaHJOGhgb++OMPGBsbY/r06Xjy5Ans7e0xefJkFBUVYcGCBWWWc3d3h7e3NzZs2ICbN2/Kpa6fa97XpfHj56wubadNmzZh8uTJ2L59O6ZMmYK8vDz4+vrybwiW9oRLTShb3mtqamLv3r0wNTXFyJEjkZGRgc6dO2PevHkoKirCjz/+WGa5ESNGoKCgAHv37pVzjaWn9BhGeGu7cAwzYsQIREZGon///oqsYrX83//9H65fv44//vgDbdq0wf3797F06VJ8//33OHjwIFRUpPIUOqVVa17GwXHcRAATgcqdxZA0Y1yZsy2GhoZlvipZ+PvK+/uqqqoYPHgwfH198fTpU/7KK2lQ1piqe2bGwMBA5Lk4QsLZbmH5adOmwdnZGV27duX/VkFBARhjyMnJgaamJrS1tWsaCi8nJ0fimYiyYi1t+PDhcHR0RMeOHZGeng6g5HLr169fY926ddi2bRvi4uL47fbpW0Wjo6Px+vVr2NvbSycYBatO3st6fzI2NgYAsecv9e3bFwBw+/ZtqR5Uyjvvnz59ijNnzsDHxwdqarLpApQxptzc3Grn/ffff4+ePXuiS5cufN5fvXoVr1+/xu+//47t27fL5HaR2qo29vfC/3755ZciywnbgTt37sgl7yvTltXWHFHGmF6/fl3mlXsGBgZlXulXmqenJ7p164Y+ffrwsd24cQNv377FsmXLsHfvXrFbnPr06YNVq1YhKChI5s8hk7fa2N/Le/xY07asvJiE61S43hhjIleNyOpKMWXcTuVdaVjRlT1paWkYPnw4/vrrL/52vejoaKxbtw4zZszgr7ySdEWUrK7sUZSq5n1NrhQbO3YsevfuDUtLS/4ZiRcuXEBubi78/f0REBCA2NhYkTIaGhrw8PDA8ePHZXKLa3l1l2bfWNEY5vbt21Kd6CvvLpfKxJSRkVFmWeG/A0BERASCgoIQGhoKZ2dnAEDPnj1hYWGBQYMG4fjx43Bzc6tpKCL1Urb2udZMgzLGAhhjnRhjnSpzW6KkZz8kJiZWeA+/jY0N0tPTxe7VT0hIgIaGhsiDJcsjzVc6C+uljDGVdb95QkJCjWMS3t+ekJCA8PBwmJmZ8Z/9+/fjyZMnMDMzw7x586QXEEq2R1nPBRCeaSiPjY0NsrOz+YN9oejoaP53CP9GeWrTGcGaqE7ey3p/qui5KNI+eyTvvN+zZw+Ki4sxYsSImlW8gnopW0ySnsFpaWlZ5jO8SrO2ti4z72/dugWg5Dlgn5Pa2N/LO++tra0ltmUVPXfG2tq6yjmyd+9eFBcXy+wWV2G9lC2mpKSkMvOzVatWFT4/z9LSEjk5OWIHaXfu3AEAsZi6d++OP//8E5GRkVIft9QGtbG/l/f4sSbPfrO1tUVaWppYTPHx8SIx2draIj8/X+y5lsJ1Ke1nvynjdoqLi5P4/LHKPEMrODgYTZs2hY2NDVq1aoXOnTtDV1cXjx494ic44uLioKWlJdYOCP+utJ/VpShVzfuaPPPRzs4OWVlZIi9CAUpOsAAosx9yd3eHkZGRTF7CISSPvlERYxhJ47LqxpSYmCgSk/BZjQ4ODiLLderUiV9empSxfa41E31V5erqimvXrokks/CtPK6urhWWLSwsFHkldVFREYKCgvDVV19BU1NTYtmioiIcPHgQzZs3l/rb0JQxJjc3N4kxVTQLL4zp4MGDInX9NKZdu3YhMjJS5NOvXz+YmJjwbyCSpoiICHTq1Anm5ub8d82aNUPXrl0RERFRbtnnz5/D0NCQv4RfSNhoPXnyhP9vTEwMevfuLbJc586doaenx08QfG7ksT917doVDRs2RGRkpEj5EydOAPjftpIWeef97t270bZtW7Rr1056QZRRL2WL6cSJE3BwcBDL+y5duvD7hiSS8r5jx44A/r+9ew+Kqu7/AP5eYWWzBaZGAAAPBElEQVQKddbNC4oJiE1Nird5mpowsxiZNBXKW6CoNJY6U81o3gpbIJUsJdQZFYXyJ1DjtVFRQkfNUUfMfpojYRCC8IAiISgXV2SFz/OHD9+HDQWUy8LZ92vmzJw9fg/n81nP55yz391zvv97tgo9XFvsT4+q+yNHjgCof6HZXOPHj8e5c+fq5ZSSktLkY9k/c9qzZ49Na0SLOR07dgzDhg3Ds88+q5a5urpixIgROHbsWIPr3rx5EwaDweqYAUDFW1hYqJYNHz4c0dHROHPmDD799NN2P/p2W9Di9ePEiRNx9uzZejk1ZTTXCRMmwGKxYPfu3VY57dy5E76+viqnt956C3q9Hj/88IPV+gkJCRg8eHC981BzafH/KTExEa+88orVe+Xm5gZvb+9GR0mtVVNTg/T0dGRnZ6NPnz6YOnUqoqOj1b8nJyejqqoK06dPt1pv+vTpSE1NrffFoL1ITEzEyy+/XO+9b8oozoWFhTAajfU6T2ufj1j7GauumTNnoqioCElJSS0Q/cO1xbmxra9h3n77bZw7d85qlNjc3FykpKRg3LhxDa47duxYWCwW/PTTT2rZ/fv3sXfvXvj4+KicevfuDQD1HmFR23Hbt2/fFsmllhaPzxCRx5oAVACY/Y9lswGceET7RQByHmcb/fv3l9LSUrl3794jp5KSEhkwYIAMGjRIdu/eLXv27BEvLy9xd3eX4uJi1e6vv/4SBwcHMZlMVutPmTJFDAaDbN68WX7++Wd55513xMnJSc6ePavaxMXFyZQpU+S7776Tw4cPS3x8vHh7ewsAiY+PbzC+e/fuyZ07d2TLli2yZcsW2bp1a6PtO0JO169fVznFxcVJVVVVg9OtW7fE09NTBg0aJHv27JG9e/eKl5eXeHh4SElJiWqXmZmpcqq7fm1O0dHRkpycrHL69ddfG9xuUFCQuLq6NhpfVVWVZGRkqJzCwsKke/fuDU6urq6SlZUlaWlpEhgYKAEBAZKamipXr16Vfv36qXZeXl5isVgkIiJCLRsyZIiUlZVJZmamzJ8/XyZMmCAmk0nKysrkwoULYjQaVVs/Pz+xWCxy4MABmTJlisybN0/y8/MlIyND+vTp02CML730kmzZskUA/P/j1rgt6768vLxd7E+xsbECQD744AM5dOiQbNiwQQwGg7z++uty7969BmOsqKhQ+1NMTEy7qPva6ezZswJAvv7660bjqjvl5+ernJpynOgIOaWlpamcTCaT9OzZs8HJzc1NsrOzJS0tTYKCgmTGjBnyxx9/SE5Ojri7u6t2w4cPF4vFIqtXr1bLRowYIWVlZXLlyhX56KOPxN/fX8LCwqSsrEx+//136dWrl2rr7e0twcHBEhwcLMXFxXLq1Cn1+oUXXmgwxldffbVD1n1FRUW72J9q637OnDly8OBBWb9+var7ysrKBmMsLS1V+1NsbKxUVlY2OBUXFz8yp5s3b6p2GRkZ4uDgIF988YXV+rU5bdq0SZKSklROKSkp9baVkpKiaqSxuOpOubm5KqeEhARN5JSammp1vvf09GxwGjx4sOTk5Eh6errMnTtXPvzwQ7l8+bLk5uaKl5eXavfaa6+JxWKRqKgotWzUqFFSXl4u2dnZsmjRIpk+fbp89dVXUl5eLpcuXZKBAweKp6enjBkzRm7duiV5eXkSGBgokyZNspoai9HX17dD1r3ZbG4X5/vmXD/evn1b7U/ff/+9NKaiokLtV/v27ZP9+/fLkCFDxMPDQ8rLy1W7nJwccXBwkPDwcKv1p02bJgaDQWJiYuTo0aMyadIkcXJykvPnz1u1W7p0qTg5OUlkZKT88ssvMm/ePNHpdJKYmNhojHl5eSqnH3/8URPX+ZcuXVI5LVmyRP47wuwjJ2dnZ8nMzJRLly6Jn5+fTJw4US5evChZWVnSpUsX1c7NzU0sFouYTCa1TK/XS1RUlPj7+8ubb74pH3/8sVy7dk1OnjwpnTt3ttrO6tWr5e7du7Jw4UIZPXq0bN68Waqrq2X8+PGNxjhs2LAOWfd6vV4cHBweOXXt2lW99/7+/uLn56fe+27duql2Hh4eYrFYJDQ0VC0bMGCAlJaWSkZGhsyePVt8fHxkyZIlUlpaKr/99ps4OjpabcvFxUWqqqpkw4YNDcb0z6lnz55qf9q2bVu7OTfGxMSoa5jExER1DTNq1Ci5e/dugzFeuXLFqu7v3LnT4PT333/LgAED5MUXX5SdO3fKrl27ZPDgweLu7i6FhYWq3Z9//ikODg6yfPlyq/UnT54sBoNBNm7cKAcPHhR/f39xcnKS06dPqzYFBQXSp08fcXFxkXXr1klSUpKsW7dOevXqJf369bPazsOmCxcuqJz279+vieNzVlbWY9V9u3lG3+NydnbG4cOHsXjxYrz//vsQEbzxxhtYu3at1YikIoLq6up6tzrGxMTAZDIhLCwMt2/fVqOZDh8+XLVxd3dHUVERPvvsM5SUlMDZ2RkjRoxAYmIifH1b/vmjWs5p0aJFCA4OVjlFRkY2KafY2FiYTCaEhoaqnA4ePGiVU1szm83w8/NDRESE+nbu5MmT+Pzzz3Hnzh3VTqfTwdHR0ern0nl5efD19cXSpUsREhICo9GIa9euYfv27YiMjKw9eaq/GRgYiGXLliEuLg5msxlHjhxBaGgoKisr2y7hdqSt9qeZM2eiU6dOWLt2LbZv3w6j0YjAwECsXLmyxW9vb4u6rxUfHw9HR0cEBAS0aA72kJPZbMa7776LFStWYOPGjdDpdDh16hSWL1/epLofO3YsFi9ejGXLlsFoNOL69euIj49HVFSUVd37+flh8eLF6vXIkSMxcuRIAIC/vz/OnDnTqnm2R221PwUFBam6j4uLg9FoREBAQKvVfXJyMpYsWWKV05o1a5qU09atWxEaGorw8HCV04EDBx5aIwkJCXB0dMR7773XojnYQ053797FjBkzEBISgrVr1wJ48FzdlStXWt2i87C6v3btGiZPnoxPPvkECxcuRPfu3VFQUIAdO3Zg06ZNqu6HDRsGg8EAg8FQ71t+AOqWH3ujxetHZ2dnHD9+HAsWLEBQUBBEBD4+Pli3bl2Tctq2bRtCQkKwfPly3L59G0OHDkVycrL6dXitVatWoUuXLli/fj1u3LiB559/Hrt27WrR51nVzUlr/09msxk+Pj749ttvERcXB51Oh2PHjmHBggWNnu9FBAMHDkRAQAAMBgPy8/Oxbds2REREwGKxWG0nJCQEFRUVapTtjIwMTJs2DYcOHWqzXNsbs9mMMWPGIDIyEtu3b4dOp8Px48excOHCRt/73NxceHt7w2Qy4csvv0SPHj2Ql5eH2NhYREREWF1rAUBgYCD0en2rjbZbq63OjbXXMJGRkVbXMCtWrGiVa5ikpCQsXboUc+bMgYhg9OjR+Oabb5qUU3R0NMLCwhAeHo7S0lJ4eXlh3759Vjl169YNJ06cwKpVqxAVFYUbN27AxcUF48aNQ0hICJpyK/jj5qS147Punzv9QxvpdF0A1F5pnAGwGsABACUi8m+dTjcbD74FGF1nnYF4MGz3TADvAaj9HedlEalqaHtubm6Smpra7kdWasz9+/eRkJAA4MEBadasWTaOqPmKi4vVT6efeuqpVr/IbgtXr17FiRMnADy4hU4LD8AeOHAg5syZg7lz554XkSe619QWdZ+WltbgbeYdQVVVlfqw1qlTJ8ycOdPGETVfUVGRuvB8+umnMXXqVBtH1HxXrlzB6dOnATz4QL5582YbR9R8zz33HGbNmtXh6v7y5cvQ6/VPEm67UVlZiR07dgB4MMBVaz43rq0UFhaqx1E4Oztj8uTJNo6o+TIzM1VneUFBQat/wGsLnp6emDRpUoer+/T09FYbRKWtmM1m7Ny5EwCg1+sRHBxs44iaLz8/X93G2LVrV03UfXp6OlJSUgAAWVlZWLNmjY0jar6hQ4di/vz5Ha7uCwoKOvxzxo1GI1auXAngwWAerf1Fc1vIz89Xj6Po2rUr/Pz8bBxR82VkZKjbfF1cXBq9/bYjyM7OxtGjR5tc9009w/4LwC91Xof/d9qOBz/rfZhYAK/XeV37UDEPADlN3C4R2Q7rnsj+sO6J7A/rnsj+sO6JNKxJHX0icgLAY/3ms27vPxF1PKx7IvvDuieyP6x7IvvDuifStg476i4RERERERERERH9Dzv6iIiIiIiIiIiINKClOvouAvi/FvpbRNQxsO6J7A/rnsj+sO6J7A/rnqgDa5HhrkTkIh4cDIjITrDuiewP657I/rDuiewP656oY+Otu0RERERERERERBrAjj4iIiIiIiIiIiINYEcfERERERERERGRBrCjj4iIiIiIiIiISAPY0UdERERERERERKQB7OgjIiIiIiIiIiLSAHb0ERERERERERERaQA7+oiIiIiIiIiIiDSAHX1EREREREREREQawI4+IiIiIiIiIiIiDWBHHxERERERERERkQawo4+IiIiIiIiIiEgD2NFHRERERERERESkAY62DuBRSkpK0LlzZ1uH0Sw1NTVqXkRw8+ZNG0bTMkpLS9V8TU2NJnIqLy9X83q9Hq6urjaMpmX06NHD1iE8kZKSEuj1eluH0Sz3799X86z79quiokLNd+7cGX379rVhNC3jmWeesXUIT6SkpAQODg62DqNZqqqq1LyIoLi42IbRtIx/1r0Wcqpb93q9Hr1797ZhNC3DYDDYOoQnUlxc3OHrvrKyUs3X1NSgqKjIhtG0jLKyMjVfXV2tufO9k5MT+vfvb8NoWkavXr1sHcIT6d+/P6qrq20dRrPUPeZq5dxY97OwVnKqW/cWi0Vzx+em0IlIK4Xy5Nzc3CQkJMTWYRB1eHPnzj0vIv+ydRxNwbonahmseyL7w7onsj+seyL709S6b5cdfTqdrghArq3jINIANxHpaesgmoJ1T9RiWPdE9od1T2R/WPdE9qdJdd8uO/qIiIiIiIiIiIjo8XAwDiIiIiIiIiIiIg1gRx8REREREREREZEGsKOPiIiIiIiIiIhIA9jRR0REREREREREpAHs6CMiIiIiIiIiItIAdvQRERERERERERFpADv6iIiIiIiIiIiINIAdfURERERERERERBrAjj4iIiIiIiIiIiIN+A/ujadNxDd69gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1584x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(1, 5, figsize=(22,4))\n",
    "\n",
    "c0 = ax0.pcolor(\n",
    "    np.arange(-0.5, Copy_Copy_noise.shape[0], 1),\n",
    "    np.arange(-0.5, Copy_Copy_noise.shape[0], 1),\n",
    "    Copy_Copy_noise, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c1 = ax1.pcolor(\n",
    "    np.arange(-0.5, And_And_noise.shape[0], 1),\n",
    "    np.arange(-0.5, And_And_noise.shape[0], 1),\n",
    "    And_And_noise, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c2 = ax2.pcolor(\n",
    "    np.arange(-0.5, Or_Or_noise.shape[0], 1),\n",
    "    np.arange(-0.5, Or_Or_noise.shape[0], 1),\n",
    "    Or_Or_noise, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c3 = ax3.pcolor(\n",
    "    np.arange(-0.5, Or_Copy_noise.shape[0], 1),\n",
    "    np.arange(-0.5, Or_Copy_noise.shape[0], 1),\n",
    "    Or_Copy_noise, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c4 = ax4.pcolor(\n",
    "    np.arange(-0.5, Star_noise.shape[0], 1),\n",
    "    np.arange(-0.5, Star_noise.shape[0], 1),\n",
    "    Star_noise, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "\n",
    "show_values(c0, ax=ax0, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c1, ax=ax1, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c2, ax=ax2, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c3, ax=ax3, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c4, ax=ax4, fmt=\"%.2f\", fontsize=16)\n",
    "\n",
    "ax0.invert_yaxis()\n",
    "ax1.invert_yaxis()\n",
    "ax2.invert_yaxis()\n",
    "ax3.invert_yaxis()\n",
    "ax4.invert_yaxis()\n",
    "\n",
    "xlabs = ylabs = ['0|0','0|1', '1|0', '1|1']\n",
    "\n",
    "ax0.set_xticks(np.arange(0, Copy_Copy_noise.shape[0], 1))\n",
    "ax0.set_yticks(np.arange(0, Copy_Copy_noise.shape[1], 1))\n",
    "ax0.set_xticklabels(xlabs, fontsize=14)\n",
    "ax0.set_yticklabels(ylabs, fontsize=14)\n",
    "ax0.set_xticks(np.arange(-0.5, Copy_Copy_noise.shape[0]-0.5, 1), minor=True)\n",
    "ax0.set_yticks(np.arange(-0.5, Copy_Copy_noise.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax1.set_xticks(np.arange(0, And_And_noise.shape[0], 1))\n",
    "ax1.set_yticks(np.arange(0, And_And_noise.shape[1], 1))\n",
    "ax1.set_xticklabels(xlabs, fontsize=14)\n",
    "ax1.set_yticklabels(ylabs, fontsize=14)\n",
    "ax1.set_xticks(np.arange(-0.5, And_And_noise.shape[0]-0.5, 1), minor=True)\n",
    "ax1.set_yticks(np.arange(-0.5, And_And_noise.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax2.set_xticks(np.arange(0, Or_Or_noise.shape[0], 1))\n",
    "ax2.set_yticks(np.arange(0, Or_Or_noise.shape[1], 1))\n",
    "ax2.set_xticklabels(xlabs, fontsize=14)\n",
    "ax2.set_yticklabels(ylabs, fontsize=14)\n",
    "ax2.set_xticks(np.arange(-0.5, Or_Or_noise.shape[0]-0.5, 1), minor=True)\n",
    "ax2.set_yticks(np.arange(-0.5, Or_Or_noise.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax3.set_xticks(np.arange(0, Or_Copy_noise.shape[0], 1))\n",
    "ax3.set_yticks(np.arange(0, Or_Copy_noise.shape[1], 1))\n",
    "ax3.set_xticklabels(xlabs, fontsize=14)\n",
    "ax3.set_yticklabels(ylabs, fontsize=14)\n",
    "ax3.set_xticks(np.arange(-0.5, Or_Copy_noise.shape[0]-0.5, 1), minor=True)\n",
    "ax3.set_yticks(np.arange(-0.5, Or_Copy_noise.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax4.set_xticks(np.arange(0, Star_noise.shape[0], 1))\n",
    "ax4.set_yticks(np.arange(0, Star_noise.shape[1], 1))\n",
    "ax4.set_xticklabels(xlabs, fontsize=14)\n",
    "ax4.set_yticklabels(ylabs, fontsize=14)\n",
    "ax4.set_xticks(np.arange(-0.5, Star_noise.shape[0]-0.5, 1), minor=True)\n",
    "ax4.set_yticks(np.arange(-0.5, Star_noise.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax0.xaxis.tick_top()\n",
    "ax1.xaxis.tick_top()\n",
    "ax2.xaxis.tick_top()\n",
    "ax3.xaxis.tick_top()\n",
    "ax4.xaxis.tick_top()\n",
    "\n",
    "ax0.set_title('Copy-Copy logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Copy_Copy_noise), fontsize=20, pad=10)\n",
    "ax1.set_title('And-And logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(And_And_noise),   fontsize=20, pad=10)\n",
    "ax2.set_title('Or-Or logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Or_Or_noise),     fontsize=20, pad=10)\n",
    "ax3.set_title('Or-Copy logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Or_Copy_noise),   fontsize=20, pad=10)\n",
    "ax4.set_title('Star-like logic gate\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(Star_noise),      fontsize=20, pad=10)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(where_to_save_pngs+\"Example2_LogicGates.png\", bbox_inches='tight', dpi=425)\n",
    "    plt.savefig(where_to_save_pdfs+\"Example2_LogicGates.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Random Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand0 = np.random.rand(4,4)\n",
    "rand0 = np.array([rand0[i]/sum(rand0[i])\n",
    "                  for i in range(rand0.shape[0])])\n",
    "\n",
    "rand1 = np.random.rand(4,4)\n",
    "rand1 = np.array([rand1[i]/sum(rand1[i])\n",
    "                  for i in range(rand1.shape[0])])\n",
    "\n",
    "rand2 = np.random.rand(4,4)\n",
    "rand2 = np.array([rand2[i]/sum(rand2[i])\n",
    "                  for i in range(rand2.shape[0])])\n",
    "\n",
    "rand3 = np.random.rand(4,4)\n",
    "rand3 = np.array([rand3[i]/sum(rand3[i])\n",
    "                  for i in range(rand3.shape[0])])\n",
    "\n",
    "rand4 = np.random.rand(4,4)\n",
    "rand4 = np.array([rand4[i]/sum(rand4[i])\n",
    "                  for i in range(rand4.shape[0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.1 Plot these TPMs, showing their $EI$ values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(1, 5, figsize=(22,4))\n",
    "\n",
    "c0 = ax0.pcolor(\n",
    "    np.arange(-0.5, rand0.shape[0], 1),\n",
    "    np.arange(-0.5, rand0.shape[0], 1),\n",
    "    rand0, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c1 = ax1.pcolor(\n",
    "    np.arange(-0.5, rand1.shape[0], 1),\n",
    "    np.arange(-0.5, rand1.shape[0], 1),\n",
    "    rand1, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c2 = ax2.pcolor(\n",
    "    np.arange(-0.5, rand2.shape[0], 1),\n",
    "    np.arange(-0.5, rand2.shape[0], 1),\n",
    "    rand2, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c3 = ax3.pcolor(\n",
    "    np.arange(-0.5, rand3.shape[0], 1),\n",
    "    np.arange(-0.5, rand3.shape[0], 1),\n",
    "    rand3, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "c4 = ax4.pcolor(\n",
    "    np.arange(-0.5, rand4.shape[0], 1),\n",
    "    np.arange(-0.5, rand4.shape[0], 1),\n",
    "    rand4, edgecolors='#999999', linewidths=3.0, cmap=EI_cmap)\n",
    "\n",
    "show_values(c0, ax=ax0, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c1, ax=ax1, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c2, ax=ax2, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c3, ax=ax3, fmt=\"%.2f\", fontsize=16)\n",
    "show_values(c4, ax=ax4, fmt=\"%.2f\", fontsize=16)\n",
    "\n",
    "ax0.invert_yaxis()\n",
    "ax1.invert_yaxis()\n",
    "ax2.invert_yaxis()\n",
    "ax3.invert_yaxis()\n",
    "ax4.invert_yaxis()\n",
    "\n",
    "xlabs = ylabs = ['0|0','0|1', '1|0', '1|1']\n",
    "\n",
    "ax0.set_xticks(np.arange(0, rand0.shape[0], 1))\n",
    "ax0.set_yticks(np.arange(0, rand0.shape[1], 1))\n",
    "ax0.set_xticklabels(xlabs, fontsize=14)\n",
    "ax0.set_yticklabels(ylabs, fontsize=14)\n",
    "ax0.set_xticks(np.arange(-0.5, rand0.shape[0]-0.5, 1), minor=True)\n",
    "ax0.set_yticks(np.arange(-0.5, rand0.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax1.set_xticks(np.arange(0, rand1.shape[0], 1))\n",
    "ax1.set_yticks(np.arange(0, rand1.shape[1], 1))\n",
    "ax1.set_xticklabels(xlabs, fontsize=14)\n",
    "ax1.set_yticklabels(ylabs, fontsize=14)\n",
    "ax1.set_xticks(np.arange(-0.5, rand1.shape[0]-0.5, 1), minor=True)\n",
    "ax1.set_yticks(np.arange(-0.5, rand1.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax2.set_xticks(np.arange(0, rand2.shape[0], 1))\n",
    "ax2.set_yticks(np.arange(0, rand2.shape[1], 1))\n",
    "ax2.set_xticklabels(xlabs, fontsize=14)\n",
    "ax2.set_yticklabels(ylabs, fontsize=14)\n",
    "ax2.set_xticks(np.arange(-0.5, rand2.shape[0]-0.5, 1), minor=True)\n",
    "ax2.set_yticks(np.arange(-0.5, rand2.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax3.set_xticks(np.arange(0, rand3.shape[0], 1))\n",
    "ax3.set_yticks(np.arange(0, rand3.shape[1], 1))\n",
    "ax3.set_xticklabels(xlabs, fontsize=14)\n",
    "ax3.set_yticklabels(ylabs, fontsize=14)\n",
    "ax3.set_xticks(np.arange(-0.5, rand3.shape[0]-0.5, 1), minor=True)\n",
    "ax3.set_yticks(np.arange(-0.5, rand3.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax4.set_xticks(np.arange(0, rand4.shape[0], 1))\n",
    "ax4.set_yticks(np.arange(0, rand4.shape[1], 1))\n",
    "ax4.set_xticklabels(xlabs, fontsize=14)\n",
    "ax4.set_yticklabels(ylabs, fontsize=14)\n",
    "ax4.set_xticks(np.arange(-0.5, rand4.shape[0]-0.5, 1), minor=True)\n",
    "ax4.set_yticks(np.arange(-0.5, rand4.shape[1]-0.5, 1), minor=True)\n",
    "\n",
    "ax0.xaxis.tick_top()\n",
    "ax1.xaxis.tick_top()\n",
    "ax2.xaxis.tick_top()\n",
    "ax3.xaxis.tick_top()\n",
    "ax4.xaxis.tick_top()\n",
    "\n",
    "ax0.set_title('Random TPM 0\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(rand0), fontsize=20, pad=10)\n",
    "ax1.set_title('Random TPM 1\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(rand1), fontsize=20, pad=10)\n",
    "ax2.set_title('Random TPM 2\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(rand2), fontsize=20, pad=10)\n",
    "ax3.set_title('Random TPM 3\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(rand3), fontsize=20, pad=10)\n",
    "ax4.set_title('Random TPM 4\\n $EI = %.3f$ \\n'%\n",
    "              effective_information(rand4), fontsize=20, pad=10)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(where_to_save_pngs+\"Example3_RandomTPMs.png\", bbox_inches='tight', dpi=425)\n",
    "    plt.savefig(where_to_save_pdfs+\"Example3_RandomTPMs.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Example calculation figure (Supplemental Information, Figure 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "############ PLOTTING SETUP ##############\n",
    "from matplotlib import gridspec\n",
    "\n",
    "plt.rc('axes', linewidth=3)\n",
    "font = {'family': 'serif',\n",
    "        'color':  'k',\n",
    "        'weight': 'normal',\n",
    "        'size': 28}\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "##########################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "TPM = np.array([[0.0, 0.0, 0.0, 0.5, 0.5],\n",
    "                [1/3, 0.0, 1/3, 1/3, 0.0],\n",
    "                [0.0, 0.5, 0.0, 0.5, 0.0],\n",
    "                [0.0, 0.0, 0.0, 0.0, 1.0],\n",
    "                [0.5, 0.0, 0.0, 0.5, 0.0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n",
      "/usr/local/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n",
      "/usr/local/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 525.6x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7.3, 16))\n",
    "gs  = gridspec.GridSpec(4, 1, height_ratios=[7, 10, 1.2, 1.2]) \n",
    "\n",
    "xlabs = ylabs = ['$A$', '$B$', '$C$', '$D$', '$E$']\n",
    "\n",
    "ax0 = plt.subplot(gs[1])\n",
    "ax0.set_xticks(np.arange(0, TPM.shape[0], 1))\n",
    "ax0.set_yticks(np.arange(0, TPM.shape[0], 1))\n",
    "ax0.set_xticklabels(xlabs, fontsize=32)\n",
    "ax0.set_yticklabels(ylabs, fontsize=32)\n",
    "ax0.set_xticks(np.arange(-0.5, TPM.shape[0]-0.5, 1), minor=True)\n",
    "ax0.set_yticks(np.arange(-0.5, TPM.shape[0]-0.5, 1), minor=True)\n",
    "ax0.tick_params(axis='y', which='major', pad=7)\n",
    "\n",
    "c0 = plt.pcolor(\n",
    "    np.arange(-.5, TPM.shape[0], 1),\n",
    "    np.arange(-.5, TPM.shape[1], 1),\n",
    "    TPM, edgecolors='k', linewidths=3.0, cmap='Blues', vmin=-.05, vmax=1.2)\n",
    "\n",
    "show_values(c0, ax=ax0, fmt=\"%.2f\", fontsize=26)\n",
    "\n",
    "ax0.invert_yaxis()\n",
    "ax0.xaxis.set_label_position(\"top\")\n",
    "ax0.xaxis.tick_top()\n",
    "ax0.set_xlabel(r'$t + 1$', size=28, labelpad=8.0)\n",
    "ax0.set_ylabel(r'$t$', size=28, rotation=0, labelpad=27.0)\n",
    "ax0.xaxis.label.set_position((0.5,5.0))\n",
    "\n",
    "ax0.text(4.65, 0.20, '$=W_{A}^{out}$', ha='left',\n",
    "         rotation=0, wrap=True, size=32)\n",
    "ax0.text(4.65, 1.20, '$=W_{B}^{out}$', ha='left',\n",
    "         rotation=0, wrap=True, size=32)\n",
    "ax0.text(4.65, 2.20, '$=W_{C}^{out}$', ha='left',\n",
    "         rotation=0, wrap=True, size=32)\n",
    "ax0.text(4.65, 3.20, '$=W_{D}^{out}$', ha='left',\n",
    "         rotation=0, wrap=True, size=32)\n",
    "ax0.text(4.65, 4.20, '$=W_{E}^{out}$', ha='left',\n",
    "         rotation=0, wrap=True, size=32)\n",
    "\n",
    "\n",
    "ms = 78\n",
    "ax1 = plt.subplot(gs[2])\n",
    "Win_j = TPM.sum(axis=0).reshape(1,TPM.shape[0])\n",
    "Win   = W_in(TPM).reshape(1,TPM.shape[0])\n",
    "\n",
    "c1 = plt.pcolor(\n",
    "    np.arange(-.5, Win_j.shape[1], 1),\n",
    "    np.arange(0.0, 1.5, 1),\n",
    "    Win_j, edgecolors='k', linewidths=3.0, cmap='Oranges', vmin=0, vmax=3.0)\n",
    "\n",
    "show_values(c1, ax=ax1, fmt=\"%.2f\", fontsize=26)\n",
    "ax1.set_xlabel(\"\")\n",
    "ax1.set_ylabel(\"\")\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "ax1.set_xticklabels([''])\n",
    "ax1.set_yticklabels([''])\n",
    "\n",
    "ax2 = plt.subplot(gs[3])\n",
    "c2 = plt.pcolor(\n",
    "    np.arange(-.5, Win.shape[1], 1),\n",
    "    np.arange(0.0, 1.5, 1),\n",
    "    Win, edgecolors='k', linewidths=3.0, cmap='Oranges', vmin=0, vmax=0.75)\n",
    "\n",
    "show_values(c2, ax=ax2, fmt=\"%.2f\", fontsize=26)\n",
    "ax2.set_xlabel(\"\")\n",
    "ax2.set_ylabel(\"\")\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "ax2.set_xticklabels([''])\n",
    "ax2.set_yticklabels([''])\n",
    "\n",
    "string10 = r'$= \\displaystyle\\sum_{i=1}^N w_{ij}$'\n",
    "string20 = r'$= \\langle W_{i}^{out} \\rangle$'\n",
    "\n",
    "ax1.text(4.65, -.15, string10, ha='left', rotation=0, wrap=True, size=28)\n",
    "ax2.text(4.65, 0.15, string20, ha='left', rotation=0, wrap=True, size=32)\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0.05)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(where_to_save_pngs+\"Example4_ExampleTPM.png\", bbox_inches='tight', dpi=425)\n",
    "    plt.savefig(where_to_save_pdfs+\"Example4_ExampleTPM.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The adjacency matrix of a network with 1.158 bits of effective information. The rows correspond to $W^{out}_{i}$, a vector of probabilities that a random walker on node $v_i$ at time $t$ transitions to $v_j$ in the following time step, $t+1$. $\\langle W^{out}_{i}\\rangle$ represents the (normalized) input weight distribution of the network, that is, the probabilities that a random walker will arrive at a given node $v_j$ at $t+1$, after a uniform introduction of random walkers into the network at $t$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Win = W_in(TPM)\n",
    "vals = Win\n",
    "Win_cols   = plt.cm.Oranges(Win+0.15)\n",
    "WoutA_cols = plt.cm.Blues(max(TPM[0]))\n",
    "WoutB_cols = plt.cm.Blues(max(TPM[1]))\n",
    "WoutC_cols = plt.cm.Blues(max(TPM[2]))\n",
    "WoutD_cols = plt.cm.Blues(max(TPM[3])*0.66)\n",
    "WoutE_cols = plt.cm.Blues(max(TPM[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n",
      "/usr/local/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1497.6x842.4 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpm0 = TPM\n",
    "Gtpm0 = check_network(tpm0)\n",
    "fig, ((ax00, ax01), (ax02, ax03), (ax04, ax05)) = plt.subplots(\n",
    "                                                    3, 2, figsize=(16*1.3,9*1.3))\n",
    "plt.subplots_adjust(left=None, bottom=0.1, right=None, \n",
    "                    top=None, wspace=0.17, hspace=0.3)\n",
    "\n",
    "ax00.bar(\n",
    "    np.linspace(0.0, 5.5, 5), \n",
    "    TPM[0]+0.01, color=WoutA_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$W_{A}^{out}$')\n",
    "x_wij = np.linspace(0.0, 5.5, 5)\n",
    "y_wij = tpm0[0]\n",
    "for i in range(len(x_wij)):\n",
    "    ax00.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center',\n",
    "              fontsize=21, color='#262626')\n",
    "    ax00.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center',\n",
    "              fontsize=21, color='royalblue', alpha=0.6)\n",
    "    \n",
    "ax00.bar(\n",
    "    np.linspace(-.6, 4.9, 5),\n",
    "    vals, color=Win_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$\\langle W_{i}^{out} \\rangle$')\n",
    "\n",
    "x_wij = np.linspace(-.6, 4.9, 5)\n",
    "y_wij = vals\n",
    "for i in range(len(x_wij)):\n",
    "    ax00.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center',\n",
    "              fontsize=21, color='#262626')\n",
    "    ax00.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center',\n",
    "              fontsize=21, color='#e56c13', alpha=0.9)\n",
    "\n",
    "ax00.set_ylim(0.0,1.19)\n",
    "ax00.set_xlim(-0.95,5.85)\n",
    "ax00.set_xticks(np.linspace(-.3, 5.2, 5))\n",
    "ax00.set_yticks(np.linspace(0, 1, 6))\n",
    "ax00.set_yticklabels(np.round(np.linspace(0, 1, 6), 2), size=16)\n",
    "ax00.set_xticklabels(xlabs, size=22)\n",
    "ax00.set_ylabel(r'$w_{ij}$', fontsize=30, rotation='horizontal', labelpad=25)\n",
    "ax00.set_xlabel('out-neighbor', fontsize=20, labelpad=0)\n",
    "ax00.set_axisbelow(True)\n",
    "ax00.grid(which='major', linestyle='-', \n",
    "          color='#999999', linewidth=2.5, alpha=0.3)\n",
    "ax00.legend(loc=2, fontsize=22, framealpha=0.7)\n",
    "strax1 = r'$D_{KL}[W^{out}_{A}||\\langle W_{i}^{out} \\rangle] = %.3f $'%\\\n",
    "        effect_information_i(Gtpm0, node_i=0)\n",
    "ax00.text(3.12, 0.802, strax1, ha='center', fontsize=28, color='k')\n",
    "\n",
    "ax02.bar(\n",
    "    np.linspace(0.0, 5.5, 5),\n",
    "    tpm0[1]+0.01, color=WoutB_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$W_{B}^{out}$')\n",
    "x_wij = np.linspace(0.0, 5.5, 5)\n",
    "y_wij = tpm0[1]\n",
    "for i in range(len(x_wij)):\n",
    "    ax02.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#262626')\n",
    "    ax02.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='royalblue', alpha=0.6)\n",
    "\n",
    "ax02.set_xticklabels([\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
    "ax02.bar(\n",
    "    np.linspace(-.6, 4.9, 5), \n",
    "    vals, color=Win_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$\\langle W_{i}^{out} \\rangle$')\n",
    "x_wij = np.linspace(-.6, 4.9, 5)\n",
    "y_wij = vals\n",
    "for i in range(len(x_wij)):\n",
    "    ax02.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#262626')\n",
    "    ax02.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#e56c13', alpha=0.9)\n",
    "\n",
    "ax02.set_ylim(0.0,1.19)\n",
    "ax02.set_xlim(-0.95,5.85)\n",
    "ax02.set_xticks(np.linspace(-.3, 5.2, 5))\n",
    "ax02.set_yticks(np.linspace(0, 1, 6))\n",
    "ax02.set_yticklabels(np.round(np.linspace(0, 1, 6), 2), size=16)\n",
    "ax02.set_xticklabels(xlabs, size=22)\n",
    "ax02.set_ylabel(r'$w_{ij}$', fontsize=30, rotation='horizontal', labelpad=25)\n",
    "ax02.set_xlabel('out-neighbor', fontsize=20, labelpad=0)\n",
    "ax02.set_axisbelow(True)\n",
    "ax02.grid(which='major', linestyle='-', color='#999999', linewidth=2.5, alpha=0.3)\n",
    "ax02.legend(loc=2, fontsize=22, framealpha=0.7)\n",
    "strax1 = r'$D_{KL}[W^{out}_{B}||\\langle W_{i}^{out} \\rangle] = %.3f $'%\\\n",
    "        effect_information_i(Gtpm0, node_i=1)\n",
    "ax02.text(3.12, 0.802, strax1, ha='center', fontsize=28, color='k')\n",
    "\n",
    "ax04.bar(\n",
    "    np.linspace(0.0, 5.5, 5),\n",
    "    tpm0[2]+0.01, color=WoutC_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$W_{C}^{out}$')\n",
    "x_wij = np.linspace(0.0, 5.5, 5)\n",
    "y_wij = tpm0[2]\n",
    "for i in range(len(x_wij)):\n",
    "    ax04.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#262626')\n",
    "    ax04.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='royalblue', alpha=0.6)\n",
    "\n",
    "ax04.set_xticklabels([\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
    "ax04.bar(\n",
    "    np.linspace(-.6, 4.9, 5),\n",
    "    vals, color=Win_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$\\langle W_{i}^{out} \\rangle$')\n",
    "\n",
    "x_wij = np.linspace(-.6, 4.9, 5)\n",
    "y_wij = vals\n",
    "for i in range(len(x_wij)):\n",
    "    ax04.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center',\n",
    "              fontsize=21, color='#262626')\n",
    "    ax04.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#e56c13', alpha=0.9)\n",
    "\n",
    "ax04.set_ylim(0.0,1.19)\n",
    "ax04.set_xlim(-0.95,5.85)\n",
    "ax04.set_xticks(np.linspace(-.3, 5.2, 5))\n",
    "ax04.set_yticks(np.linspace(0, 1, 6))\n",
    "ax04.set_yticklabels(np.round(np.linspace(0, 1, 6), 2), size=16)\n",
    "ax04.set_xticklabels(xlabs, size=22)\n",
    "ax04.set_ylabel(r'$w_{ij}$', fontsize=30, rotation='horizontal', labelpad=25)\n",
    "ax04.set_xlabel('out-neighbor', fontsize=20, labelpad=0)\n",
    "ax04.set_axisbelow(True)\n",
    "ax04.grid(which='major', linestyle='-', color='#999999', linewidth=2.5, alpha=0.3)\n",
    "ax04.legend(loc=2, fontsize=22, framealpha=0.7)\n",
    "strax1 = r'$D_{KL}[W^{out}_{C}||\\langle W_{i}^{out} \\rangle] = %.3f $'%\\\n",
    "        effect_information_i(Gtpm0, node_i=2)\n",
    "ax04.text(3.12, 0.802, strax1, ha='center', fontsize=28, color='k')\n",
    "\n",
    "\n",
    "ax01.bar(\n",
    "    np.linspace(0.0, 5.5, 5), \n",
    "    tpm0[3]+0.01, color=WoutD_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$W_{D}^{out}$')\n",
    "x_wij = np.linspace(0.0, 5.5, 5)\n",
    "y_wij = tpm0[3]\n",
    "for i in range(len(x_wij)):\n",
    "    ax01.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#262626')\n",
    "    ax01.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='royalblue', alpha=0.6)\n",
    "\n",
    "ax01.set_xticklabels([\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
    "ax01.bar(\n",
    "    np.linspace(-.6, 4.9, 5),\n",
    "    vals, color=Win_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$\\langle W_{i}^{out} \\rangle$')\n",
    "\n",
    "x_wij = np.linspace(-.6, 4.9, 5)\n",
    "y_wij = vals\n",
    "for i in range(len(x_wij)):\n",
    "    ax01.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#262626')\n",
    "    ax01.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#e56c13', alpha=0.9)\n",
    "\n",
    "ax01.set_ylim(0.0,1.19)\n",
    "ax01.set_xlim(-0.95,5.85)\n",
    "ax01.set_xticks(np.linspace(-.3, 5.2, 5))\n",
    "ax01.set_yticks(np.linspace(0, 1, 6))\n",
    "ax01.set_yticklabels(np.round(np.linspace(0, 1, 6), 2), size=16)\n",
    "ax01.set_xticklabels(xlabs, size=22)\n",
    "ax01.set_ylabel(r'$w_{ij}$', fontsize=30, rotation='horizontal', labelpad=25)\n",
    "ax01.set_xlabel('out-neighbor', fontsize=20, labelpad=0)\n",
    "ax01.set_axisbelow(True)\n",
    "ax01.grid(which='major', linestyle='-', color='#999999', linewidth=2.5, alpha=0.3)\n",
    "ax01.legend(loc=2, fontsize=22, framealpha=0.7)\n",
    "strax1 = r'$D_{KL}[W^{out}_{D}||\\langle W_{i}^{out} \\rangle] = %.3f $'%\\\n",
    "        effect_information_i(Gtpm0, node_i=3)\n",
    "ax01.text(3.12, 0.802, strax1, ha='center', fontsize=28, color='k')\n",
    "\n",
    "ax03.bar(\n",
    "    np.linspace(0.0, 5.5, 5), \n",
    "    tpm0[4]+0.01, color=WoutE_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$W_{E}^{out}$')\n",
    "x_wij = np.linspace(0.0, 5.5, 5)\n",
    "y_wij = tpm0[4]\n",
    "for i in range(len(x_wij)):\n",
    "    ax03.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#262626')\n",
    "    ax03.text(x_wij[i], y_wij[i]+0.040, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='royalblue', alpha=0.6)\n",
    "\n",
    "ax03.set_xticklabels([\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
    "ax03.bar(\n",
    "    np.linspace(-.6, 4.9, 5), \n",
    "    vals, color=Win_cols, linewidth=2.0, edgecolor='k',\n",
    "    width=0.6, label=r'$\\langle W_{i}^{out} \\rangle$')\n",
    "\n",
    "x_wij = np.linspace(-.6, 4.9, 5)\n",
    "y_wij = vals\n",
    "for i in range(len(x_wij)):\n",
    "    ax03.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#262626')\n",
    "    ax03.text(x_wij[i], y_wij[i]+0.035, \"%.2f\"%y_wij[i], ha='center', \n",
    "              fontsize=21, color='#e56c13', alpha=0.9)\n",
    "\n",
    "ax03.set_ylim(0.0,1.19)\n",
    "ax03.set_xlim(-0.95,5.85)\n",
    "ax03.set_xticks(np.linspace(-.3, 5.2, 5))\n",
    "ax03.set_yticks(np.linspace(0, 1, 6))\n",
    "ax03.set_yticklabels(np.round(np.linspace(0, 1, 6), 2), size=16)\n",
    "ax03.set_xticklabels(xlabs, size=22)\n",
    "ax03.set_ylabel(r'$w_{ij}$', fontsize=30, rotation='horizontal', labelpad=25)\n",
    "ax03.set_xlabel('out-neighbor', fontsize=20, labelpad=0)\n",
    "ax03.set_axisbelow(True)\n",
    "ax03.grid(which='major', linestyle='-', color='#999999', linewidth=2.5, alpha=0.3)\n",
    "ax03.legend(loc=2,fontsize=22, framealpha=0.7)\n",
    "strax1 = r'$D_{KL}[W^{out}_{E}||\\langle W_{i}^{out} \\rangle] = %.3f $'%\\\n",
    "        effect_information_i(Gtpm0, node_i=4)\n",
    "ax03.text(3.12, 0.802, strax1, ha='center', fontsize=28, color='k')\n",
    "\n",
    "string1 = r'$EI = \\displaystyle\\frac{1}{N}$'+\\\n",
    "          r'$\\displaystyle\\sum_{i=1}^N D_{KL}[W_{i}^{out}||$'+\\\n",
    "          r'$\\langle W_{i}^{out} \\rangle]$'\n",
    "string2 = r'$EI = \\displaystyle\\frac{1}{5}$'+\\\n",
    "          r'$\\hspace{0.5cm} [0.592 + 1.061 + 1.385 + 1.737 + 1.016]$'\n",
    "string3 = r'$EI = 1.158 \\hspace{0.5cm}$' r'$\\rm bits$'\n",
    "\n",
    "ax05.text(-.02, 0.590, string1, ha='left', rotation=0, wrap=False, size=28)\n",
    "ax05.text(-.02, 0.250, string2, ha='left', rotation=0, wrap=False, size=28)\n",
    "ax05.text(-.02, -0.01, string3, ha='left', rotation=0, wrap=False, size=28)\n",
    "\n",
    "ax05.axis('off')\n",
    "\n",
    "if save:\n",
    "    plt.savefig(where_to_save_pngs+\\\n",
    "                \"Example4_ExampleCalculation.png\", bbox_inches='tight', dpi=425)\n",
    "    plt.savefig(where_to_save_pdfs+\\\n",
    "                \"Example4_ExampleCalculation.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each node's contribution to the $EI$ ($EI_i$) is the KL divergence of its $W^{out}_{i}$ vector from the network's $\\langle W^{out}_{i}\\rangle$, known as the *effect information*.\n",
    "\n",
    "$$ EI = \\dfrac{1}{N} \\displaystyle\\sum_{i=1}^N \\text{D}_{_{KL}}[W^{out}_{i} || \\langle W^{out}_{i} \\rangle] $$\n",
    "\n",
    "where $EI$ is the average of the *effect information*, $EI_i$, of each node. This is equivalent to our derivation of $EI$ from first principles above since\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    EI &= \\dfrac{1}{N} \\displaystyle\\sum_{i=1}^N \\text{D}_{_{KL}}[W^{out}_{i} || {\\langle W^{out}_{i}\\rangle}]\\\\\n",
    "    &= \\dfrac{1}{N} \\displaystyle\\sum_{i=1}^{N} \\displaystyle\\sum_{j=1}^{N} w_{ij}\\log_2\\bigg(\\dfrac{w_{ij}}{W_{j}}\\bigg)\\\\\n",
    "    &= \\dfrac{1}{N}  \\displaystyle\\sum_{i=1}^{N}\\bigg( \\displaystyle\\sum_{j=1}^{N} w_{ij}\\log_2(w_{ij}) - \\sum_{j=1}^{N} w_{ij}\\log_2(W_{j})\\bigg)\\\\\n",
    "    &= \\dfrac{1}{N}  \\displaystyle\\sum_{i=1}^{N} \\displaystyle\\sum_{j=1}^{N} w_{ij}\\log_2\\big(w_{ij}\\big) - \\dfrac{1}{N} \\displaystyle\\sum_{i=1}^{N} \\displaystyle\\sum_{j=1}^{N} w_{ij}\\log_2\\big(W_{j}\\big)       \n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "- Note that for a given node, $v_i$, the term in the first summation above, $\\sum_{j=1}^{N} w_{ij}\\log_2\\big(w_{ij}\\big)$, is equivalent to the negative entropy of the out-weights from $v_i$, $-H(W_i^{out})$. Also note that $W_j$, the *j*th element in the $\\langle W^{out}_{i}\\rangle$ vector, is the normalized sum of the incoming weights to $v_j$ from its neighbors, $v_i$, such that $W_j=\\frac{1}{N} \\sum_{i=1}^N w_{ij}$. We substitute these two terms into the equation above such that: \n",
    "\n",
    "$$ EI = \\dfrac{1}{N}   \\sum_{i=1}^{N}-H(W_i^{out}) -  \\sum_{j=1}^{N} W_j\\log_2\\big(W_{j}\\big) $$\n",
    "\n",
    "This is equivalent to the formulation of $EI$ above, since $H(\\langle W^{out}_{i}\\rangle) = -\\sum_{j=1}^{N} W_j\\log_2(W_{j})$:\n",
    "\n",
    "$$ EI = H(\\langle W^{out}_{i}\\rangle) -\\langle H(W_i^{out}) \\rangle $$\n",
    "\n",
    "In this figure, we adopt the relative entropy formulation of $EI$ for ease of derivation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Network motifs and effective information (N = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "G01 = nx.DiGraph()\n",
    "G02 = nx.DiGraph()\n",
    "G03 = nx.DiGraph()\n",
    "G04 = nx.DiGraph()\n",
    "G05 = nx.DiGraph()\n",
    "G06 = nx.DiGraph()\n",
    "G07 = nx.DiGraph()\n",
    "G08 = nx.DiGraph()\n",
    "G09 = nx.DiGraph()\n",
    "G10 = nx.DiGraph()\n",
    "G11 = nx.DiGraph()\n",
    "G12 = nx.DiGraph()\n",
    "G13 = nx.DiGraph()\n",
    "\n",
    "G01.add_nodes_from([0,1,2])\n",
    "G02.add_nodes_from([0,1,2])\n",
    "G03.add_nodes_from([0,1,2])\n",
    "G04.add_nodes_from([0,1,2])\n",
    "G05.add_nodes_from([0,1,2])\n",
    "G06.add_nodes_from([0,1,2])\n",
    "G07.add_nodes_from([0,1,2])\n",
    "G08.add_nodes_from([0,1,2])\n",
    "G09.add_nodes_from([0,1,2])\n",
    "G10.add_nodes_from([0,1,2])\n",
    "G11.add_nodes_from([0,1,2])\n",
    "G12.add_nodes_from([0,1,2])\n",
    "G13.add_nodes_from([0,1,2])\n",
    "\n",
    "G01.add_edges_from([(0,1),(0,2)])\n",
    "G02.add_edges_from([(0,1),(2,0)])\n",
    "G03.add_edges_from([(0,1),(0,2),(2,0)])\n",
    "G04.add_edges_from([(1,0),(2,0)])\n",
    "G05.add_edges_from([(1,0),(1,2),(0,2)]) # e. coli \n",
    "G06.add_edges_from([(1,0),(1,2),(0,2),(0,1)])\n",
    "G07.add_edges_from([(1,0),(0,2),(2,0)])\n",
    "G08.add_edges_from([(1,0),(0,1),(0,2),(2,0)])\n",
    "G09.add_edges_from([(1,0),(0,2),(2,1)])\n",
    "G10.add_edges_from([(1,0),(2,0),(1,2),(0,1)])\n",
    "G11.add_edges_from([(1,0),(2,0),(2,1),(0,1)])\n",
    "G12.add_edges_from([(0,1),(1,0),(1,2),(2,1),(0,2)])\n",
    "G13.add_edges_from([(0,1),(1,0),(1,2),(2,1),(0,2),(2,0)])\n",
    "\n",
    "motif_dict = {\"Motif 01\": {\"G\":G01, \"edges\":str(list(G01.edges())), \"EI\":effective_information(G01)},\n",
    "              \"Motif 02\": {\"G\":G02, \"edges\":str(list(G02.edges())), \"EI\":effective_information(G02)},\n",
    "              \"Motif 03\": {\"G\":G03, \"edges\":str(list(G03.edges())), \"EI\":effective_information(G03)},\n",
    "              \"Motif 04\": {\"G\":G04, \"edges\":str(list(G04.edges())), \"EI\":effective_information(G04)},\n",
    "              \"Motif 05\": {\"G\":G05, \"edges\":str(list(G05.edges())), \"EI\":effective_information(G05)},\n",
    "              \"Motif 06\": {\"G\":G06, \"edges\":str(list(G06.edges())), \"EI\":effective_information(G06)},\n",
    "              \"Motif 07\": {\"G\":G07, \"edges\":str(list(G07.edges())), \"EI\":effective_information(G07)},\n",
    "              \"Motif 08\": {\"G\":G08, \"edges\":str(list(G08.edges())), \"EI\":effective_information(G08)},\n",
    "              \"Motif 09\": {\"G\":G09, \"edges\":str(list(G09.edges())), \"EI\":effective_information(G09)},\n",
    "              \"Motif 10\": {\"G\":G10, \"edges\":str(list(G10.edges())), \"EI\":effective_information(G10)},\n",
    "              \"Motif 11\": {\"G\":G11, \"edges\":str(list(G11.edges())), \"EI\":effective_information(G11)},\n",
    "              \"Motif 12\": {\"G\":G12, \"edges\":str(list(G12.edges())), \"EI\":effective_information(G12)},\n",
    "              \"Motif 13\": {\"G\":G13, \"edges\":str(list(G13.edges())), \"EI\":effective_information(G13)}}\n",
    "\n",
    "ei_heights = np.array([list(motif_dict.values())[i]['EI'] \n",
    "                       for i in range(len(list(motif_dict.values())))]) + 0.005\n",
    "ei_bars = np.array(range(len(list(motif_dict.values()))))\n",
    "colors = [\"#486164\",\"#9094c9\",\"#ab4e53\",\"#fa8d11\",\"#74d76c\",\n",
    "          \"#bc7dc6\",\"#db453b\",\"#cad24b\",\"#8f52d2\",\"#00aaff\",\n",
    "          \"#c2843a\",\"#4f5435\",\"#d05185\"]\n",
    "\n",
    "bar_labels = list(motif_dict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "from matplotlib import gridspec\n",
    "\n",
    "colors = [\"#45af9c\",\"#5b91cb\",\"#9f8448\",\"#bf6d8c\",\"#9876c0\",\"#bb6eac\",\"#5ea05c\",\n",
    "          \"#cf5c57\",\"#c24864\",\"#c69932\",\"#d3468f\",\"#ce5c2f\",\"#6b6cd9\",\"#78b43d\",\"#ba58c2\"]\n",
    "\n",
    "i = 10 \n",
    "ns = 250\n",
    "ew = 3.5\n",
    "nc = 'w'\n",
    "ec = '#333333'\n",
    "oc = '#e4c600'\n",
    "nc_o = '#333333'\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(17,8))\n",
    "plt.subplots_adjust(wspace=0.10, hspace=0.1)\n",
    "plt.rc('axes', axisbelow=True)\n",
    "plt.rc('axes', linewidth=1.5)\n",
    "\n",
    "gs = gridspec.GridSpec(2, len(ei_bars), height_ratios=[7,1.7])\n",
    "ax0 = plt.subplot(gs[0, :])\n",
    "cols_i = 'grey'\n",
    "ax0.bar(ei_bars, ei_heights, color=colors, width=0.75, \n",
    "        edgecolor='#333333', linewidth=3, alpha=1)\n",
    "ax0.set_xlim(min(ei_bars)-0.5,max(ei_bars)+0.5)\n",
    "ax0.set_xticks(ei_bars)\n",
    "ax0.set_xticklabels([\"\"]*13)\n",
    "\n",
    "ax0.set_yticklabels(np.round(np.linspace(0,1.6,num=9),2), size=18)\n",
    "ax0.set_ylim(0, max(ei_heights)+0.04)\n",
    "ax0.set_xlim(-0.5,12.5)\n",
    "ax0.set_ylabel(\"$EI$\", size=28)\n",
    "ax0.grid(linestyle='-', color='#999999', linewidth=2.5, alpha=0.35)\n",
    "\n",
    "for q, Q in enumerate(list(motif_dict.values())):\n",
    "    g = Q['G']\n",
    "    ax0 = plt.subplot(gs[-1, q])\n",
    "    pos = nx.circular_layout(g)\n",
    "    nx.draw_networkx_nodes(g, pos, node_size=ns, node_color=colors[q], \n",
    "                           linewidths=3, edgecolors=\"#333333\", ax=ax0)\n",
    "    nx.draw_networkx_edges(g, pos, width=ew*0.9, edge_color=\"#3F3F3F\",#colors[q], \n",
    "                           arrowsize=19, alpha=1, ax=ax0)\n",
    "    ax0.set_axis_off()\n",
    "    posy = np.array(list(zip(*list(pos.values())))[1])\n",
    "    posx = np.array(list(zip(*list(pos.values())))[0])\n",
    "    ax0.set_ylim(min(posy)*1.35, max(posy)*1.5)\n",
    "    ax0.set_xlim(min(posx)*1.69, max(posx)*1.6)\n",
    "    title = list(motif_dict.keys())[q]\n",
    "    ax0.set_title(title, fontsize=16, pad=-0.35)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(\"../figs/pngs/EffectiveInformation_NetworkMotifs.png\", bbox_inches='tight', dpi=425)\n",
    "    plt.savefig(\"../figs/pdfs/EffectiveInformation_NetworkMotifs.pdf\", bbox_inches='tight', dpi=425)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of Chapter 01. In [Chapter 02](https://nbviewer.jupyter.org/github/jkbren/einet/blob/master/code/Chapter%2002%20-%20Network%20Size%20and%20Effective%20Information.ipynb), we will look at the $EI$ of common networks\n",
    "_______________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References:\n",
    "- __[Hoel, E. P. (2017). When the Map Is Better Than the Territory. Entropy, 19(5), 188. doi: 10.3390/e19050188](http://www.mdpi.com/1099-4300/19/5/188)__\n",
    "- __[Hoel, E. P., Albantakis, L., & Tononi, G. (2013). Quantifying causal emergence shows that macro can beat micro. Proceedings of the National Academy of Sciences, 110(49), 19790–5. doi: 10.1073/pnas.1314922110](http://www.pnas.org/content/110/49/19790)__\n",
    "- __[Tononi, G. (2001). Information measures for conscious experience. Archives Italiennes de Biologie,139(4), 367–371.  doi: 10.4449/aib.v139i4.51](https://www.ncbi.nlm.nih.gov/pubmed/11603079)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______________________"
   ]
  }
 ],
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